diff --git a/Experiments/Visualization.ipynb b/Experiments/Visualization.ipynb
index 1cff0ff4cb8040e20b1834b6f56b49e493a1b231..d18f50f261a155f1848b9d6c1c5f38195a4f8ed7 100644
--- a/Experiments/Visualization.ipynb
+++ b/Experiments/Visualization.ipynb
@@ -1 +1,2752 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Visualization.ipynb","provenance":[],"collapsed_sections":["3bATAS0GMTqT"]},"kernelspec":{"name":"python3","language":"python","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"WVOZHtI_Lgsj","colab_type":"text"},"source":["# Mount Drive:"]},{"cell_type":"code","metadata":{"id":"vt-0rCaRK8qU","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":124},"executionInfo":{"status":"ok","timestamp":1592990829121,"user_tz":-120,"elapsed":27824,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"fcea2ae8-e68e-4cf6-d2a8-def22e6911e3"},"source":["#Import drive\n","from google.colab import drive\n","#Mount Google Drive\n","drive.mount(\"/content/drive\")"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vGK5XnB_LrNq","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":104},"executionInfo":{"status":"ok","timestamp":1592990834974,"user_tz":-120,"elapsed":2845,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"09d57299-9168-4a2c-d8f5-b7283e51d8cc"},"source":["import os\n","os.chdir('drive/My Drive/Work/Thesis_Julien_Dejasmin/Work/code/Pytorch_CNN_mixt_representation')\n","!ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["data\t     OAR.2066986.stderr  OAR.2066988.stdout\t  reconstruction_im\n","dataloader   OAR.2066986.stdout  OAR.2066989.stderr\t  trained_models\n","Experiments  OAR.2066987.stderr  OAR.2066989.stdout\t  utils\n","img_gif      OAR.2066987.stdout  parameters_combinations  VAE_model\n","main.py      OAR.2066988.stderr  README.md\t\t  viz\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"EspLyKYzLv8K","colab_type":"text"},"source":["# Import:"]},{"cell_type":"code","metadata":{"id":"FXB9r3fxLww2","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592990847163,"user_tz":-120,"elapsed":10085,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}}},"source":["from utils.load_model import load\n","from viz.visualize import Visualizer as Viz\n","import matplotlib.pyplot as plt\n","from viz.visualize import reorder_img\n","from dataloader.dataloaders import *\n","from torch.autograd import Variable"],"execution_count":3,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Z7qeqVmGL8xs","colab_type":"text"},"source":["# Load model:"]},{"cell_type":"code","metadata":{"id":"CI7NJ2aDL77V","colab_type":"code","colab":{}},"source":["import os\n","# os.chdir('../')\n","# !ls\n","path_to_model_folder_mnist = 'trained_models/mnist/'\n","path_to_model_folder_fashion = 'trained_models/fashion_data/'\n","path_to_model_folder_dsprites = 'trained_models/dSprites/'\n","path_to_model_folder_celeba = 'trained_models/celeba_64/'\n","path_to_model_folder_chairs = 'trained_models/rendered_chairs/'\n","\n","model_mnist = load(path_to_model_folder_mnist)\n","model_fashion = load(path_to_model_folder_fashion)\n","model_dpsrites = load(path_to_model_folder_dsprites)\n","model_celeba = load(path_to_model_folder_celeba)\n","model_chairs = load(path_to_model_folder_chairs)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yD8i2kDiaw7W","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":104},"executionInfo":{"status":"ok","timestamp":1592308168217,"user_tz":-120,"elapsed":601,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"0a17e674-8a93-4e88-89a3-124b717ae680"},"source":["# Print the latent distribution info\n","print(model_mnist.latent_spec)\n","print(model_fashion.latent_spec)\n","print(model_dpsrites.latent_spec)\n","print(model_celeba.latent_spec)\n","print(model_chairs.latent_spec)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["{'cont': 10}\n","{'cont': 10}\n","{'cont': 6}\n","{'cont': 32}\n","{'cont': 32}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"32FiS-PBMCGa","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1592233570230,"user_tz":-120,"elapsed":842,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"e28351f0-d604-4e8f-acd8-4d13e2f8e701"},"source":["# Print model architecture\n","print(model_mnist)\n","print(model_fashion)\n","print(model_dpsrites)\n","print(model_celeba)\n","print(model_chairs)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["VAE(\n","  (img_to_last_conv): Sequential(\n","    (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (3): ReLU()\n","  )\n","  (last_conv_to_continuous_features): Sequential(\n","    (0): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","  )\n","  (features_to_hidden_continue): Sequential(\n","    (0): Linear(in_features=512, out_features=256, bias=True)\n","    (1): ReLU()\n","  )\n","  (fc_mean): Linear(in_features=256, out_features=10, bias=True)\n","  (fc_log_var): Linear(in_features=256, out_features=10, bias=True)\n","  (latent_to_features): Sequential(\n","    (0): Linear(in_features=10, out_features=256, bias=True)\n","    (1): ReLU()\n","    (2): Linear(in_features=256, out_features=512, bias=True)\n","    (3): ReLU()\n","  )\n","  (features_to_img): Sequential(\n","    (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","    (2): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (3): ReLU()\n","    (4): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (5): Sigmoid()\n","  )\n",")\n","VAE(\n","  (img_to_last_conv): Sequential(\n","    (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (3): ReLU()\n","  )\n","  (last_conv_to_continuous_features): Sequential(\n","    (0): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","  )\n","  (features_to_hidden_continue): Sequential(\n","    (0): Linear(in_features=512, out_features=256, bias=True)\n","    (1): ReLU()\n","  )\n","  (fc_mean): Linear(in_features=256, out_features=10, bias=True)\n","  (fc_log_var): Linear(in_features=256, out_features=10, bias=True)\n","  (latent_to_features): Sequential(\n","    (0): Linear(in_features=10, out_features=256, bias=True)\n","    (1): ReLU()\n","    (2): Linear(in_features=256, out_features=512, bias=True)\n","    (3): ReLU()\n","  )\n","  (features_to_img): Sequential(\n","    (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","    (2): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (3): ReLU()\n","    (4): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (5): Sigmoid()\n","  )\n",")\n","VAE(\n","  (img_to_last_conv): Sequential(\n","    (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (3): ReLU()\n","    (4): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (5): ReLU()\n","  )\n","  (last_conv_to_continuous_features): Sequential(\n","    (0): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","  )\n","  (features_to_hidden_continue): Sequential(\n","    (0): Linear(in_features=512, out_features=256, bias=True)\n","    (1): ReLU()\n","  )\n","  (fc_mean): Linear(in_features=256, out_features=6, bias=True)\n","  (fc_log_var): Linear(in_features=256, out_features=6, bias=True)\n","  (latent_to_features): Sequential(\n","    (0): Linear(in_features=6, out_features=256, bias=True)\n","    (1): ReLU()\n","    (2): Linear(in_features=256, out_features=512, bias=True)\n","    (3): ReLU()\n","  )\n","  (features_to_img): Sequential(\n","    (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","    (2): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (3): ReLU()\n","    (4): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (5): ReLU()\n","    (6): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (7): Sigmoid()\n","  )\n",")\n","VAE(\n","  (img_to_last_conv): Sequential(\n","    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (3): ReLU()\n","    (4): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (5): ReLU()\n","  )\n","  (last_conv_to_continuous_features): Sequential(\n","    (0): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","  )\n","  (features_to_hidden_continue): Sequential(\n","    (0): Linear(in_features=512, out_features=256, bias=True)\n","    (1): ReLU()\n","  )\n","  (fc_mean): Linear(in_features=256, out_features=32, bias=True)\n","  (fc_log_var): Linear(in_features=256, out_features=32, bias=True)\n","  (latent_to_features): Sequential(\n","    (0): Linear(in_features=32, out_features=256, bias=True)\n","    (1): ReLU()\n","    (2): Linear(in_features=256, out_features=512, bias=True)\n","    (3): ReLU()\n","  )\n","  (features_to_img): Sequential(\n","    (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","    (2): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (3): ReLU()\n","    (4): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (5): ReLU()\n","    (6): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (7): Sigmoid()\n","  )\n",")\n","VAE(\n","  (img_to_last_conv): Sequential(\n","    (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (3): ReLU()\n","    (4): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (5): ReLU()\n","  )\n","  (last_conv_to_continuous_features): Sequential(\n","    (0): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","  )\n","  (features_to_hidden_continue): Sequential(\n","    (0): Linear(in_features=512, out_features=256, bias=True)\n","    (1): ReLU()\n","  )\n","  (fc_mean): Linear(in_features=256, out_features=32, bias=True)\n","  (fc_log_var): Linear(in_features=256, out_features=32, bias=True)\n","  (latent_to_features): Sequential(\n","    (0): Linear(in_features=32, out_features=256, bias=True)\n","    (1): ReLU()\n","    (2): Linear(in_features=256, out_features=512, bias=True)\n","    (3): ReLU()\n","  )\n","  (features_to_img): Sequential(\n","    (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (1): ReLU()\n","    (2): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (3): ReLU()\n","    (4): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (5): ReLU()\n","    (6): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n","    (7): Sigmoid()\n","  )\n",")\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0e28tlr5MEcE","colab_type":"text"},"source":["# Visualize various aspects of the model:"]},{"cell_type":"markdown","metadata":{"id":"-nvQHSM2MJ5s","colab_type":"text"},"source":["## Create a Visualizer for the model"]},{"cell_type":"code","metadata":{"id":"VRdpRR7uMC9N","colab_type":"code","colab":{}},"source":["viz_mnist = Viz(model_mnist)\n","viz_mnist.save_images = False  # Return tensors instead of saving images\n","\n","viz_fashion = Viz(model_fashion)\n","viz_fashion.save_images = False \n","\n","viz_dsprites = Viz(model_dpsrites)\n","viz_dsprites.save_images = False \n","\n","viz_celeba = Viz(model_celeba)\n","viz_celeba.save_images = False\n","\n","viz_chairs = Viz(model_chairs)\n","viz_chairs.save_images = False"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"QJrHRrt1MNbh","colab_type":"text"},"source":["## Samples"]},{"cell_type":"code","metadata":{"id":"PlR-LCwChJjF","colab_type":"code","colab":{}},"source":["size=(8,8)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mdHOjdtCML-T","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":612},"executionInfo":{"status":"ok","timestamp":1592308182350,"user_tz":-120,"elapsed":1312,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"52286d6f-b636-4ade-e7a0-6ef54466a946"},"source":["samples = viz_mnist.samples(size=size)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(samples.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f4eca579048>"]},"metadata":{"tags":[]},"execution_count":9},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"v2Xz5Q66hO-K","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":612},"executionInfo":{"status":"ok","timestamp":1592308187714,"user_tz":-120,"elapsed":1111,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"c08e01d8-a0ce-4e84-d3a5-c656e74564f3"},"source":["samples = viz_fashion.samples(size=size)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(samples.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f4ec8cd66a0>"]},"metadata":{"tags":[]},"execution_count":10},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"YKxu13rwhPEZ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":612},"executionInfo":{"status":"ok","timestamp":1592233780109,"user_tz":-120,"elapsed":1588,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"1cac68a7-033e-4f3d-dbc9-8f1fdf6100ea"},"source":["samples = viz_dsprites.samples(size=size)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(samples.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f72db4fc470>"]},"metadata":{"tags":[]},"execution_count":32},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"QLWsKX-zrB0K","colab_type":"code","colab":{},"outputId":"93ae459d-0f92-426c-fcfc-57f5ec108ea2"},"source":["samples = viz_celeba.samples(size=size)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(samples.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fbf980fde50>"]},"metadata":{"tags":[]},"execution_count":16},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"5uuFdjXrrCLj","colab_type":"code","colab":{},"outputId":"77dac9f0-52c3-4f44-8f51-f5e3a64c177e"},"source":["samples = viz_chairs.samples(size=size)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(samples.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fbf98072590>"]},"metadata":{"tags":[]},"execution_count":17},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"rJAV3HhbMRI8","colab_type":"text"},"source":["## All latent traversals:"]},{"cell_type":"code","metadata":{"id":"9c0z-HzQMRwV","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":534},"executionInfo":{"status":"ok","timestamp":1592309864510,"user_tz":-120,"elapsed":1216,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"7dfbb957-ca6e-48aa-ab01-47c6c8f17d6e"},"source":["traversals = viz_mnist.all_latent_traversals(size=12)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"gAf8_C_Wh7It","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":612},"executionInfo":{"status":"ok","timestamp":1592234363124,"user_tz":-120,"elapsed":1970,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"4c197b12-9491-4696-d5c6-27b21e853f77"},"source":["traversals = viz_fashion.all_latent_traversals()\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f72db2dcac8>"]},"metadata":{"tags":[]},"execution_count":44},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"KLOEa2xhh7Tc","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":488},"executionInfo":{"status":"ok","timestamp":1592234380887,"user_tz":-120,"elapsed":1688,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"8fcaa188-30b9-44d7-a415-b79ebe996efc"},"source":["traversals = viz_dsprites.all_latent_traversals()\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f72db247278>"]},"metadata":{"tags":[]},"execution_count":45},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"9GEF-hOErHiF","colab_type":"code","colab":{},"outputId":"6c0d1343-4b2d-4df5-da48-5f0ca7208989"},"source":["traversals = viz_celeba.all_latent_traversals()\n","\n","fig = plt.figure(figsize=(30, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fbf9b0ce4d0>"]},"metadata":{"tags":[]},"execution_count":21},{"output_type":"display_data","data":{"text/plain":["<Figure size 2160x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"JoJnXMmurHoz","colab_type":"code","colab":{},"outputId":"800efecc-b99c-4e65-b06c-d973554a9aff"},"source":["traversals = viz_chairs.all_latent_traversals()\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fbf8a637490>"]},"metadata":{"tags":[]},"execution_count":22},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"3bATAS0GMTqT","colab_type":"text"},"source":["## Plot a grid of two interesting traversals:"]},{"cell_type":"code","metadata":{"id":"J3wQmCNnMVQR","colab_type":"code","colab":{}},"source":["# Traverse 3rd continuous latent dimension across columns:\n","traversals = viz_mnist.latent_traversal_grid(cont_idx=2, cont_axis=1, disc_idx=0, disc_axis=0, size=(10, 10))\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"J8axyQZansSH","colab_type":"code","colab":{}},"source":["traversals = viz_fashion.latent_traversal_grid(cont_idx=1, cont_axis=0, size=(10, 10))\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"a73u9EMTnsiB","colab_type":"code","colab":{}},"source":["traversals = viz_dsprites.latent_traversal_grid(cont_idx=1, cont_axis=0, size=(6, 10))\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Qj3lRa5erQU1","colab_type":"code","colab":{},"outputId":"0bd62915-aba5-4468-d437-612e8df405bb"},"source":["traversals = viz_celeba.latent_traversal_grid(cont_idx=1, cont_axis=0, size=(6, 10))\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fbf8813b750>"]},"metadata":{"tags":[]},"execution_count":23},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"YqTzdX_4rQZq","colab_type":"code","colab":{}},"source":["traversals = viz_chairs.latent_traversal_grid(cont_idx=1, cont_axis=0, size=(6, 10))\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SLbxdxkaMZAD","colab_type":"code","colab":{}},"source":["# Reorder discrete latent to match order of digits\n","ordering = [0, 1, 7, 2, 4, 5, 3, 9, 6, 8]  # The 9th dimension corresponds to 0, the 3rd to 1 etc...\n","traversals = reorder_img(traversals, ordering, by_row=True)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"NKa9G7gbMWjI","colab_type":"text"},"source":["## Plot traversal of single dimension"]},{"cell_type":"code","metadata":{"id":"V2a3xwUiMcx2","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":117},"executionInfo":{"status":"ok","timestamp":1592235503133,"user_tz":-120,"elapsed":1214,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"d60225d3-ebfa-4eb5-a809-1b95c31eb7cd"},"source":["traversal = viz_mnist.latent_traversal_line(cont_idx=0, size=12)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversal.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f72dab0c0b8>"]},"metadata":{"tags":[]},"execution_count":66},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"WZO5C_strW4F","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":117},"executionInfo":{"status":"ok","timestamp":1592236309371,"user_tz":-120,"elapsed":1255,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"f31ac497-212a-44f2-c569-51dae48513ff"},"source":["traversal = viz_fashion.latent_traversal_line(cont_idx=0, size=12)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversal.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f72da651b00>"]},"metadata":{"tags":[]},"execution_count":79},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"6ZK3hBTlrXBq","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":116},"executionInfo":{"status":"ok","timestamp":1592236313963,"user_tz":-120,"elapsed":764,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"0244e8b9-f0ec-44f4-9398-110cb89040c2"},"source":["traversal = viz_dsprites.latent_traversal_line(cont_idx=0, size=12)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversal.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f72da636208>"]},"metadata":{"tags":[]},"execution_count":80},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAlAAAABSCAYAAACbtepnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO19W4x02VXet6u6quva3dWX/9eIsWJbWCA/BGMsYytWBEQggyJ4QQgLCT9YmhcigRQpGSsSEnkiLxAiIcSIcHkggYTEwfIDxjHkJQ/GNhjwhYEhGeQZ2Z75u7vut66uzUOdb/c6u/fp7vr/s09VT69PKlVXVXfXV1+ttfbaa6+9j7HWQqFQKBQKhUJxd5Q2TUChUCgUCoXivkETKIVCoVAoFIo1oQmUQqFQKBQKxZrQBEqhUCgUCoViTWgCpVAoFAqFQrEmNIFSKBQKhUKhWBPPlEAZYz5sjHnZGPOKMebFvEgpFAqFQqFQbDPM054DZYwpA/hbAD8I4DUAnwfwEWvtV/Ojp1AoFAqFQrF9eJYK1PsBvGKt/X/W2jmA3wPwY/nQUigUCoVCodhe7DzD334bgK+Lx68B+F7/l4wxLwB4IXn4Pc/wfgqFQqFQKBRF4om19iT0wrMkUHeCtfYlAC8BgDFGrxujUCgUCoXivuAfsl54liW81wG8TTx+PnlOoVAoFAqF4i2NZ0mgPg/gXcaYdxhjqgB+EsAn86GlUCgUCoVCsb146iU8a+3CGPOvAHwaQBnAb1prv5IbM4VCoVAoFIotxVMfY/BUb6Y9UAqFQqFQKO4PvmitfV/oBT2JXKFQKBQKhWJNaAKlUCgUCoVCsSaiH2OgUCgUCoVCEQvGGPdzkW1J9zKBkmKFHhNSyCJFvQkhrrd9+dvAPUtjPr+tvIG3juahz+HzvC+8ge3zz9vsRGLbub/VeAP3l/t95Q1sF3fJM+tna+21MSkm73uTQPmC8XFISF84imqt3YgR+Fxv4571xRfNPUtz+dpNmhOb5n3T/U28Q49j41ntnLhvvDfln+vYCnC/NPcHRj+2bIvmWbbi89smze9iK0B2PN82zeW95Ltp7iHekmsonvu8/efyxNYnUKEvOOsmYa3Fcrl0oi2Xy2uvb5p7qVS69rv+Fx4yiE3zvs1ws4w2NvesIFEqlTJ5Z3GX9rINmtNWbuO9rbbiB+ib9C6Ce9YA4ttKyM4ZV7ZRc/KXz5PXNmp+l3i+zZrfFFtCem8L71Bc8ZNWn3ORMfEuNh7S3ufLx7G4b3UCddOXHrrxdy8vL1MCLpdLGGMKM+CbgnKpVEK5XE59Dr7mf+H+jYhpvKEM/zbeAFKah/jLGW8s3j5n3srlsuPLz8AbeVtrU58BKGbWFQoUMkD4NxnkQlr7nyE296wBhTpL7X3Nfd6b8M8sW5H2Lv2TvOVnuMluYvIGELQVX3MAt2rOGFmkrWTxltx9nYuOiXeN5VLzUql0LZbzcywWi436p897Z2cnxRvIthX6QFG8QzGxXC47rX3uIdu+vLzEYrFwfPO2861NoG5ztp2dHScmBeXvLxYLJx5vMkMFismgQ9zL5bL74sldzlj8L56vSe6x+Mv3CgUK/+YHaHKnwZKn/HwxcJOtMEj4tiIDNPUulUrOVvwBsYjB5a62Qi5ZtiLtPbbmQNhWyFvy599Jv+SN2ITm/iAuuYf8kzdqLgMzsUlbIW9ZjZIxcbFYXNO8iMmN5J8Vy/kzAOePUm9pLzLR3qR/Su6+f8rxhwnVJvyT91m8Q/7JWO7Hc97H0DxkK/4YVKlUUvf8XOS7XC5xcXGR8k8gjp1vbQIFhGe1vFWrVezs7KBSqTghl8slSqWS+9IXiwXm83lmwADyN+CbnI3GSu680cClwV5cXLjPQvhLYjEg9ZazQsm7UqmknM433CzN5eeIwTvEnbZCzpVKJVWxJOfLy0vM53P3c0yn8zlnDeS0bXKXmnM2S1uZz+cpvck95uByF1uRtk5I3rz5E4UiNPe5y3hSqVQcd8Ja6+xjNps5/5Q8ZdtA3rz9z+APKj5vcvfjitQ8VO0rwj9lLN/d3b1mK7IaIm2cAyMhJzyxON8Wy6k5YwtwXXPGRIki/VNWmyRvaS8y2WY8nM/nKJVKuLi4ABCnihPiLW++rZBvrVZLTXLon+RNe8my7Tz4b2UC5Zfu5EyFX36tVkOlUkGtVsPu7q4L3tZazGYzXFxcuCBHUeXabkwD8LkzwO3s7GB3d9dxrtVq7nMBcAnffD7HZDJxfx9a9wfyDXShYMEgITWvVquo1+vuec6syHs6nWZqHnNgCQU5ak69qbmsWNJOmLCWy2XMZrNrlRAiZqDzB3KpOe/5exzELy4unK0wWMS287vayu7urrOVarXquJD3dDp19sLXivDPLFupVCopO2GApn9KW2Fgp40DV7YRe3Lm24qMK7VaDfV63dl+yFaoOXA1sYmd+IViOTnW63VnK3JQ5OSGtjIej1P+6Vf+YtpLyD99zWXyJ2P5eDxGqVTCfD6/1stVlH/KSp8cg6g9ixDSP8mbtlIEb96Hxv1KpeJspF6vo9FopCY51Hw2m2E0Grm4EnOZeisTKCBcvpODSrPZdCIymWL5cTqdYjKZuBnBdDq9tqbL9wDiBzrpcI1Gw93q9bqbCTBYTCYTZ7j8P3Ltv6jET85sq9Wq41ur1dBqtZzTGbNaMmWAo+Y0XC6NFVFVkE5H3lmaM1jQVqbTKYwx12xFJrAxELKVarXqEtVWq+XsnXZurXXJ6mQySfUCSFvh/489sPi2Iv2y3W6nkj/6p28rRfgntQZwLUBLW2FsoeY7OztukjAejzGZTGCMwWQyAQCnOe28iIHcj4mSd6vVcnZujHGJ33A4TNmK7BWR2sQc0GUSQs3Jl/fUnHFlOp06+5lMJqnla+mfMZORLFvhjX5arVZdJYR2zr+V/imThSITP46frVbL2Qx9lYkfNWeCOx6PU0uSMblnVfyoOfm2Wi202203WfDHT/49cFVRizEObX0CJQMFM+ZGo4GDgwO02220221nAFzWGI/HGI1GGAwGLmD4AZqPY1Vx5EDOjJ9ffKfTcQbAiggH8+FwiOFwiMFg4AKGbHSOlYhkJawyadrf33eac+YFwA0sw+EQ/X4/VQ30tY4ZpGWgoObNZhPtdttxZ6DjADKZTFK2MhwOAeDaBoS8NQ/NEmWAazQaKd4MFgzQnGUNBgMMh0P3P0KbD2Jyl7ZCm6Ct7O3tOf9sNBoA4PyTtsKZL4BrjcJFJK0yrtBWDg4OnH82Gg2XQNE/B4MBqtUq+v1+cIITS2/+LG2lXq+jXq+j3W5jb2/P6c7JAm1lMplgMBig3++nlvb8zR8xEpEsW/Fj+f7+PlqtFprNptOV/knejJV+Q/mm/JPcqfnu7q6b4JA3lyiZoMT2T8k/yz+lrbRaLbRaLQAr/8yyFZ97bFvx/bPRaGBvbw+dTgd7e3s4ODhwVajlconZbObiIZM/yZvJX54+upUJVChYUEh++YeHhzg4OHBO12g0UkGOiQlw1efC9XM6XyzuvMmsv9FoYH9/H/v7+zg6OsLe3p4z3p2dHRfkuIQgA/N0OsVisUgt6eVpuKHSqT9b6XQ6ODg4cJpzJsAZy2AwcEupAK6toedtuD5/qblvK51OB51OxwXpZrMJYLUkMxqN3MwRuLKV+XyOnZ2dlK3EHljkoMjgTDunrezu7rqZFsvvnK3L8rtsBM2Te5atMEC3Wi3He39/H51OB81mE61WywU5+qcsvc9mM1fJkoEu9sAi/bPdbqPT6eDo6CiVANI/x+PxNf+k/fM7iOGfWdylnR8cHKDT6TjdmfyR33A4TFVeGQ/ZmxOrSiztJSsm0j9lPDfGOM1ZeeVgOJvNnL0wtsTAXfzz6OjI8ebEktUnxpVyuez6oSaTCS4uLtxz8r3y8k//JlsCmISQ9+HhoZugsco6HA5dXAHgbGU2mzlbieWf8jPc5J+0mb29PVQqFdf2Qv8kb1bT/GZzvs+z8t+6BMoP0DJQcIbY6XRwcnKCo6MjHB4eugDNKk6323WzRCDd0Cd3csQM0n7JtNlsuuTp8ePHLmC0Wi1UKhVX7m21Wuh2u6lqwmw2S+1CiTGghwZEas5sX2rOpJUBut/vo9vtpgZFX++8k9asSohc/trf38fx8TGOj49dAthqtVAqldyMpd/vo16vA7jaacVkW2oea8YVmt3u7+/j8PAQjx49Sg3oMoEaDAY4OztzMy0ALsjJXWKxbDxUHebAcnx8jKOjIxwdHbkZLhO8fr/vqk/GmKCtsDKSt43fZCscEE9OTlKJa6VScYnfcDjE+fm5q2BySXKT/ilj4snJiZsstFotN4gMh0M0Gg23BMx+ESbb/o7fvDhL7iHNOZjTXuifTFoHgwEGg4FbYSD3kH8WZedyMGdM7HQ62N/fdwkU/fP8/NxNCjg+kTf7vGLzlkmInOCcnJzg+PjYTebpn4yJlUoFAJzWjC285WkvftLn95ox8eM4dHh46OLi7u6uWwXp9/toNBopO5lOpy4JzHvc37oECrh9MOfAQqfb29tzVQXOzJvNpsvy2UTJ2bmccfH9nlXMmwZzWe49Pj7Go0ePcHh46LJ/ZtAcyFmSlE2IvuHGdDq/+sQk5PHjxzg+Pna8m80mlsslRqNRah1d6s2AwSbtvAOGX2IPORy5c1BstVool8uYTqeuklatVp2+rAbS6WTlL+9ZYmhgYUA7Ojpyds7ZVq1Ww2KxwHA4RLPZdL0h5DqZTFxVgdW/mJUQObvlslen08Hx8XEqQLfbbQDAeDx2S3rSVtjTJTXP2z95n1V9YgJFW6GdV6tVNzPv9XrOP1nBoX/K3W2xqpWhXhYm20ygjo6OUlUFLvWyckYb5+xc7uKM7Z9cXvf900/+mEA1m000m01X4ZNVBbmqkGflL8s/2dLgJyGHh4duglav17FYLFxM5CSezc3NZjO1E7Io/5Say2T75OTErYwYs+rrY0wE4CYInOCTNyuZ9Ku8JwqhxM9Ptqk5J5XD4dD1RDGmTCYTDIfD1K48GVueFVubQGVVcZhAcab46NEjtw69XC5dqXp3dzfVEDcYDFwDqF/Oi1FR8GcszWbTlR9lEsUMWiYhxhj35XMmw50zfhkyL87+EphsBqbmTFxPTk7coHhxcYHBYOB2oXDgptbj8diVUOWsJcbsXO7UoOa0FQbpw8ND7O3toVwuu8G8Xq/DmFUDOQfK0Wjkdv3EWibIKlWzL4QzW1mFajabruInkxA2lA8GA4xGo5S95Dko+oMLNaeODNDSPxmgrbUYjUaur44DIvtcQprHrkCx4kfNZWyh5rVazdkKWwOYZLPPJWuZIC/O/DmrF4dLGkxaWT3jMjV7uZg80caHw6HbrSztPIZ/+po3m0239Ej/pOblchmTycT17bB6w1g+HA4L809qziqrtPNOp+Mmlp1OB41Gw/Un1ut1F/cYD+mf7Imi/xZl577mHD87nQ6stRiPx641gP7JyutwOHQ7fqVd5oVQbGEvKzX37aXT6aBWq2E6nTrenDRMJhP0+33UarXUuJ8n961LoPxgIYVkMiJ7W2i47XYbl5eXqf4E2azK9Wi5C0W+X17cQ8Yrd2zIGePx8bFLoPwdVqPRCN1u1wVtuUU2zyAX0ttfluESqUxeWcm5uLhIrfNPp1MXsFlNk2d1xHK6rH4cVkWYkLAiUiqV3K4kAC5xYmVHnjcSI2D4vP0eCzbT0laYTNXrdUwmE8eNM/J+v+92YUnN8+yzkJ9fJvNScwbpUI/IcrlM9clxUOFysNxOHcNW+Bn8WS6Tbtq6jC37+/upYyQAuEFlMBig1+s53/UH81hLeP4uNmouKyOsnkn/ZFP2cDh0O95oJ7H9M6S53Fjjx5ZSqYTxeOyOSpnP5245T+6OlLyLiC08FsW3c2reaDRc4sem8clkgl6vl1phiDGYkzPvpd6y8kfujC1MBOmfjIncjEX/lHElZh+UtHPqzkRaVou5osAEieMn2wQGg0Fq/PR558F96xIoIBzkpOFygOHuBw7mLOWy4Y1r5/5g6M+28ubuDy4MwDJgyN14dCjgakcbv3gatD+QF5mIsKJD3rLBdjabuYSVmsujJeR5UXk7nQwWrOT43Fm5lIlUu92GMSbVJDmdTlO8efP1jjlT9HlTcwa7vb29VACWDfxy+7c8HiCGnWfxlrNFBmr66N7eHi4vL1Pn+wyHQ3S73ZTm0kdjDub+YYhScw4wtHF50CCXu6R/krvknbe9+P5ZKl3tUJI7Tlkx5hKeXCaazWapowL49/xssQdzfxlPnivna26MSfVrTSYTp7k8BDKmrUjutBcmrbICKJfd5dE03EjDxEn6ZyiBymuC4yetchySSbfcecoVHNnD6sfymBPKm3jzvcmdfkfejBsA3DKp3GBzk608q+al239ls+CH44cOnWgtl+HkcQX8O7+cHiNxygK/IJ8fgGsn0wJX2+dLpavDy/z/FwNZRiR5h3hIg7TWOiP1ucbWPOQIoc8k7UImcxxUgfRnipVoS96E/51nbSv3g5c8GFTex5gd+vC5SvuWr4d4yySWvPl8TPi6SM6h0/Mlb/6dTDr4fIh3Xvr7OkvevCd3GQ99TkwGboorMWNMKA7KeM37UHJBWwlxjWnnkneWzqG4QjDp4Ofyucfg6//s24rfuiJ5hzTfBG8+lpuQ5CYNeRUAGX+stS6Wh/73XZ6/K7ayAuVDBmY2V7MJkmc+sAGYZUeukbNxPOa5MlmcJXfudJEnpbKHhYeVsS+BBzty10DW/47JXWrO5jtqzjV8fib2rrDfiQ2pvoPGhu+AnAGSNzkCcD0WfI7N1zftFow9sMgAx0qH7LUpl8upxmXyZ8Okf42wPGdat3Enb/ZNSFvh0hf7WOij0+nU7ewpQvOs5IMBmnYuOZZKJVSr1dRzvp3fhBi6+/FQXr6CtsLqDVsZGBOpubw0B3nmjdCAKJM9edRJKCbSfkKx3P//sSHthW0W3PhA2+j3+y7Gy76+yWTidlLH5ii/R19z+if1puZyWV1qzt9hohLTVvzP4Z8fyJ3RMh4Oh0N3sjrbXmQPqDyxPstWntU/tzqBkgYgDZPLFiznLhYL1Go11zzW7XZxfn6OXq+HwWDgDJjO51esYvDmvbwWkuzJotHyxFcO5N1uF91uF/1+3/GWlwCIdX4V+YYSPjYoc8mFCWu9XndNwL1eD91uF71eL2jAMXjL704GVX+3zmg0Qq/Xc5qzoZB2RO60Fbm9O6atSF3Im02+TJCoORuAa7Wa8wPaSq/XcwMMNZez+pjcfVuhf3LHGhuAZ7OZS6B45IXUXB6/EJM3gFRQ5XvKBIk71rg7tlqtuu9ExhbauZwwFFEJCWkubYW7BLkDlj0h5+fnOD8/d4kUj18oKq4ASCXZ5MCY2Gg0XIP+zs6OW/6i5v1+3yWvMpbH4iy5+/7JyctgMHCaM1nlZyPvbrfreBetechWmDixxxaAS6jZa0ZboX/SlvyLf8fiDSA1eecYxE0bXFacz+epYwy63S7Ozs6uaR6quuWBrUug/IxZVpyYZPR6PdcAyWZD2X3f7/dxdnaGJ0+euCAtZ+ryRNUY/DnDYqCQM0AZKBaLhTtugQN9t9vFkydPcHZ2hn6/n0pGZCUt5qyW3H3NmbAaY1yj5OXlpRt0yFsOjHKAicHd581klQGOAznXwy8vL90sl79zdnaGs7MzF6TlwOjbSl7cQwGOvR6cubI/iAMiD4pjsKDmHNC5S0YOMHnqzRmu9E9pK6wg9Ho9d+aQ3I5urXWfj5pzsiAHRnkOVN7I8s96ve4SbfqntdYdFcEAzgB9enqamu36g3reSbdfuZEbNmq1GrrdbuqQT/b0ydO86Z9MojbhnxzIaSt+I/5isUgdMzIej3F+fu7shf4pB/VYqwu+jdM/Gc8Zy3lw42w2Sx2k2e/3M2NiLFvxx0+pOQ8l5fjJ2MKEkJ9hNBrh9PTUaS4nC6GJZV4I2QpXlWq1mtObuxuttW7s5+SRCSs1l5MF/0w/vuezYOsSKAnOVljq5YDIRltjjAvO8tgCHnZHA5AlYDkgxjIAuZzBCg7PqJAnR7NZvFQqud/jF396euoqaHKJRga5vBxO9hHIgcUP0Eye2IzKrehyUKTjcakjtubk7c+0aCvygrbkysFcan5+fo4nT56g3++78rUMFnyfvOEP5hw05AYIAC5wc5BhgJaJCI+7yJok5BmgpY3LJTupuTGrM4d4mYvlcpmq5Lz55ptuMGcCRX+POUmQSwK0AXnZDalxu91ObUzxAzQ1jxGc+T+kf/oVHA4uTKBYweHzrETJpPXs7Cw1qQwN5nnC5y0159Ed3LHG54GrzR2M5U+ePHETBVZyZLJdBHfyY5WSy9O0f66IzOdz9Ho9nJ+f4/T01E3OfP8sgre/rCgLEDIpZExk0spYTlsJJX6xKtuhmMjGfTboc/LDYsR8PneV7TfffDPlnzwvLG//3MoEys+ieSAgy4vcPUNj5s4HWa5mtUcuccgMOs8kJMRdDuY03n6/73bEyOSkXC6nqgostzNYjEajQjJ/yZ2aT6dT9Ho9t4tBfhYmULIML5cgx+OxCxZ+RSHvWa6sQM1mM1SrVderxd4hLnVwizGPLuD2aKk5S/Gh5bA8ectAN5vN3HIurykIwM0QecaZPDuJQZqa89A4f2CJmfzRVnhhY95kUsXTgenDXG7yl5V8O48VoKXm7EHs9XquwsZZK68HxmBNOz89PXUBejQauR6XmFUcf2LGGCivPykH+Xq97nbH+v7J2blcOpW8Y1Ra2a/FHif29TFhJfdms+n8k8mK1JwxMdbSqdTA15xXjeA4xMSP8ZsTed8/mUD5mseYnEn+0j9pB6wMy2q9nFRSc8YVxhaZiMTQXG64Cvknx30ArqjAa5jK/ufBYIDT01N3xQNOFGL451YnUH5FhFe25s46BgrOBHhKqjyUj8E61GeR90xR/kwDYBLCgZyDDgMaZ+pMtBiQ5WFxsgLF/x171sLdOtT8/Pw8lSzJw9b8Bj4GCr8CFYN71hIBz5Dh78jzWBgs+Bx5h5bBYiQhWQGatsIt07R/at5sNlGpVJyd04ak5v5yb+yE2++vYNJn7dVFbLldmkkdl7O5vHpbNSRP//R5S//k5Mbaq6VGDuYcbKSt0Ef9GW7sZFsum8pdUtZaF/9YISYvGVP82OJXiPO0c7nky++fkxxWmgCkjuTgdROXy2Vqc420lVCfYsyYKDX3L5bOxISX/ZEHUMrJPJce/cQvJm85yZlMJu6IAODqGpQ8W+sm/5Tjp28rMezl8vLSjfWMiXLXNJfU2QLDOMlxn7r7sTzvmLiVCRRw3XCBq+5/Gih3m8iD4vxGPn7xWeu3sWa4xhgXLGiwsp+Lpzfv7Ow4I6SRy11W8vIzsSpQPm+Z/fuPeSHY3d1d97lYEeGAKXeH+YabN2fey0QEuLIVVsBYXudlIciFlQby5i1L85jJHy8Dwtdo51zOY+WMn5PBgQmW3NUWsxpCm2BAo+bkRs2ZaPPyCrQBLmPIHW1yYClqcJGa8zMxUapUKu6MNoJak7vsIYpZUZC8ObjILed8X1ZUqTmTWdq/7GmU/hmzj8hPoqR/0nbYp8VEm9dgYxJO+/B3+hbtn+TN1zg2MenjNRIBpPyTyTh5+5oXUT0DVvYLIBU/2FdEO6etSP/07Tx2wirtHEDKP+mzvHoH20oAuKVhaeey7znGBpU7JVDGmFcBDABcAlhYa99njDkE8PsA3g7gVQA/Ya09f1ZC0gBkIgKsBOLAslgsXPlXnvnAbFsun3EgkiLG7seRywQygDBAl0olDAaD1PkarCzI6/bwc3BgkQE6RkVEBgsAqQSKywbytFoGQXKnwfJz8LuKPbD4tkKnomasMgwGg9Q2XGkrTLIld7n0GIM7dZG2Ih+zysBD+AC45Q3ylppnVVnzBP+nr7m/A5B9C7xeH8GeIXL3baWInhZp19JWpOZcYuLr0jbkZ8jqT4zlnzJpBZCKNVJzOfCQp3+j3vwOi/ZPLgfJ3j+puYyF0s5j9bRIzj534Gq3mhzMuezOg3efxj9jTuQJmYQwJtZqNTcOcQJE22Y8DNlKDP/M0lzGRNo5l/XkwZ7UlsUT2okc+/P2zXUqUN9vrX0iHr8I4LPW2l80xryYPP63ubDC9eyfxiaX9PwDBDkYyZ08dDL5c4zyIznz3uft9+jIE4UJvh7iHbtUTd7kKh/LHRHymH05G5NGKh0tlLTGcLwQbzrbYrFwPVzkLSs5cmlB8vbtJW/O8mfp3HK5Yz6fo1wuu8vOkDc19/X2d7EVUcnhz37VmEuRvp37vP37mHwJmbiGNC+V0pf6ARDU2behWEmr5E/uTD4kJ3nCepZ/ZmkesyIieYfsXF6xwNc8ZCdFxkRyD8V3X3P5d9Q8y25ixkMipDm5yyNdyF3GzCz/jJn4yf/pj9PkTs3ZcymTcXJkK0CW5nnhWZbwfgzA9yU//w6A/4NICRQA16wnv2D/0iAUhgHBFy1m5cnnDlzN0PkcuXNZj+vohJy9+8lXzMRP8g6VOH3N/dO5pdZSc593DFibXiKQvOXgwkHRP6Fb6i0HklCQKyoJybIVOSOTHP0JRhGaEzcNiqFJAoBbbaVIzf0lGbnsLm3lNs2LSFaJrIGFSatvKyH/LMpWJP+bNJeJtowtkuNNsaWIRMSP5dLO5YQyy1ayeMeCtA+f+3K5dLz9q0nQnuUEskjfJCRvyUVqTqwTE/PkftcEygL4Y2OMBfDr1tqXADy21n4jef2bAB7nwgjpQRFIBwyKynt/IAcQDGhZt7wRMgDJvVQquTJ26NId/mf1P3+RiQifl4375O2f4MrPFDLUTWkuebPqJAMFlxSk3r72RWnu32grvuaSU8g+srSPwZ0I2XmW5jfx3YSdh3jL6wj6fysnNVnfg69P3vyJ0ASLyZ9/WvSm/fMmzTlYh3hv0lYkf5+P1FzaOT8rX+Pf3BTLY2ruj5O+f8rEj7gpJt4HzYuM5XdNoD5krX3dGPMIwGeMMX8jX7TW2iS5ugZjzAsAXliXmDQuuQYqGyeT/+/uQ4HgtvsYuI275OxXz7K4F8U7VM0hd6m5Pyje9IUAXssAAAeNSURBVBk2pTmfu7y8vKa95HYXvWMP6HJ2zudkb8pdNd+kncuAHdJ8m2xF+p2vubyWo5+0btpW/M9Anpx1b7PmPnd/kOdnkZOzbeQtY/q6mm/CP+X7+34qewAJP+G4D5r7fIvyzzslUNba15P7N4wxnwDwfgDfMsY8Z639hjHmOQBvZPztSwBeAoCsJOuG9818TgoXMtys/xHzi7/tfXzuoerZprmH3tPn6ztciPOmuPuVS+DmC6Xexr1Izf1B3X8t9Dd3/TkWbhoYiSzu28Kbj59G86J5h97nNh/dFlsJveddY8u28QbSCfa6/lkU79D7PyTNY/O+NYEyxjQBlKy1g+TnHwLw7wF8EsBHAfxicv+HubNL4IshB8m7/N0m8TTct4k3f74vmmc53bp/uwk8reb+324CITtf5+82Ccn9vti5z+G+cr+vvLMe3/XvNgHVPH/cpQL1GMAnkoC4A+C/WGv/yBjzeQD/zRjzMQD/AOAn4tFMYxu+1KfFfeV+X3kD95f7feUN3F/u95U3cH+5K+/icV+5bxtvUyShdZfwFAqFQqFQKDaIL1pr3xd6oRR6UqFQKBQKhUKRDU2gFAqFQqFQKNaEJlAKhUKhUCgUa0ITKIVCoVAoFIo1oQmUQqFQKBQKxZrQBEqhUCgUCoViTWgCpVAoFAqFQrEm7notvLwwBPBywe95H3AM4MmmSWwpVJswVJcwVJdsqDZhqC5hqC4r/JOsF4pOoF7OOpDqIcMY8wXVJQzVJgzVJQzVJRuqTRiqSxiqy+3QJTyFQqFQKBSKNaEJlEKhUCgUCsWaKDqBeqng97svUF2yodqEobqEobpkQ7UJQ3UJQ3W5BYVeTFihUCgUCoXirQBdwlMoFAqFQqFYE5pAKRQKhUKhUKyJwhIoY8yHjTEvG2NeMca8WNT7bgOMMb9pjHnDGPNl8dyhMeYzxpi/S+47yfPGGPOfEp3+yhjz3s0xjwtjzNuMMX9qjPmqMeYrxpifTZ5/0NoYY2rGmD8zxvxlossvJM+/wxjzueTz/74xppo8v5s8fiV5/e2b5B8bxpiyMeYvjDGfSh6rLgCMMa8aY/7aGPMlY8wXkucetC8BgDHmwBjzB8aYvzHGfM0Y88GHrosx5jsSO+Gtb4z5uYeuy7ooJIEyxpQB/CqAHwbwbgAfMca8u4j33hL8NoAPe8+9COCz1tp3Afhs8hhYafSu5PYCgF8riOMmsADwr6217wbwAQA/k9jFQ9dmBuAHrLXfBeA9AD5sjPkAgP8A4Jettd8O4BzAx5Lf/xiA8+T5X05+762MnwXwNfFYdbnC91tr3yPO73novgQAvwLgj6y13wngu7CynQeti7X25cRO3gPgewCMAXwCD1yXtWGtjX4D8EEAnxaPPw7g40W897bcALwdwJfF45cBPJf8/BxWh4wCwK8D+Ejo997qNwB/COAHVZuUJg0Afw7ge7E6FXgned75FIBPA/hg8vNO8ntm09wj6fE8VoH9BwB8CoBRXZw2rwI49p570L4EYB/A//e/94eui6fFDwH4v6rL+reilvC+DcDXxePXkuceMh5ba7+R/PxNAI+Tnx+kVsnyyncD+BxUGy5TfQnAGwA+A+DvAXSttYvkV+Rnd7okr/cAHBXLuDD8RwD/BsAyeXwE1YWwAP7YGPNFY8wLyXMP3ZfeAeBNAL+VLPv+hjGmCdVF4icB/NfkZ9VlDWgT+RbArlL6B3uehDGmBeB/APg5a21fvvZQtbHWXtpVef15AO8H8J0bprRxGGP+JYA3rLVf3DSXLcWHrLXvxWq55WeMMf9cvvhAfWkHwHsB/Jq19rsBjHC1LAXgweoCAEj6BX8UwH/3X3vIutwVRSVQrwN4m3j8fPLcQ8a3jDHPAUBy/0by/IPSyhhTwSp5+l1r7f9MnlZtElhruwD+FKulqQNjDK9fKT+70yV5fR/AacFUi8A/A/CjxphXAfweVst4vwLVBQBgrX09uX8Dq36W90N96TUAr1lrP5c8/gOsEqqHrgvxwwD+3Fr7reSx6rIGikqgPg/gXclumSpWJcNPFvTe24pPAvho8vNHser/4fM/nex6+ACAniipvqVgjDEA/jOAr1lrf0m89KC1McacGGMOkp/rWPWFfQ2rROrHk1/zdaFePw7gT5LZ41sK1tqPW2uft9a+HasY8ifW2p/CA9cFAIwxTWNMmz9j1dfyZTxwX7LWfhPA140x35E89S8AfBUPXBeBj+Bq+Q5QXdZDUc1WAH4EwN9i1cvx7zbd/FXkDSsD/QaAC6xmRB/DqhfjswD+DsD/BnCY/K7Basfi3wP4awDv2zT/iLp8CKsS8V8B+FJy+5GHrg2AfwrgLxJdvgzg55Pn3wngzwC8glXJfTd5vpY8fiV5/Z2b/gwFaPR9AD6lujg93gngL5PbVxhjH7ovJZ/1PQC+kPjT/wLQUV0sADSxqsjui+cevC7r3PRSLgqFQqFQKBRrQpvIFQqFQqFQKNaEJlAKhUKhUCgUa0ITKIVCoVAoFIo1oQmUQqFQKBQKxZrQBEqhUCgUCoViTWgCpVAoFAqFQrEmNIFSKBQKhUKhWBP/CApBXuB+VMJqAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"GRLSUXERrXId","colab_type":"code","colab":{},"outputId":"c8cf4446-8f29-4737-f734-9b9d99567c09"},"source":["traversal = viz_celeba.latent_traversal_line(cont_idx=0, size=12)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversal.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fbf88127a90>"]},"metadata":{"tags":[]},"execution_count":24},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"qEYzXVJtrXU-","colab_type":"code","colab":{},"outputId":"1633c72d-4c5e-4ef8-a9c4-2157079299f9"},"source":["traversal = viz_chairs.latent_traversal_line(cont_idx=0, size=12)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversal.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fbf88099890>"]},"metadata":{"tags":[]},"execution_count":25},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"V3_-nCdAMeF8","colab_type":"text"},"source":["## Plot reconstructions"]},{"cell_type":"code","metadata":{"id":"sil-OQR8MiZh","colab_type":"code","colab":{}},"source":["# Get MNIST test data\n","_, dataloader_mnist = get_mnist_dataloaders(batch_size=32)\n","_, dataloader_fashion = get_fashion_mnist_dataloaders(batch_size=32)\n","_, dataloader_dsprites = get_dsprites_dataloader(batch_size=32)\n","dataloader_celeba = get_celeba_dataloader(batch_size=32)\n","_, dataloader_chairs = get_chairs_dataloader(batch_size=32)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"crW-qFSCMfw-","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":612},"executionInfo":{"status":"ok","timestamp":1592235831365,"user_tz":-120,"elapsed":1731,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"73cd1478-7064-4c18-a28d-b29a5150a827"},"source":["# Extract a batch of data\n","for batch_mnist, labels_mnist in dataloader_mnist:\n","    break\n","\n","recon = viz_mnist.reconstructions(batch_mnist, size=(8, 8))\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(recon.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f72da788208>"]},"metadata":{"tags":[]},"execution_count":73},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Nrwn4BsupMXC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":612},"executionInfo":{"status":"ok","timestamp":1592235849675,"user_tz":-120,"elapsed":1550,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"4ea633ef-2701-46ef-a4c0-0dd246700d8b"},"source":["# Extract a batch of data\n","for batch_fashion, labels_fashion in dataloader_fashion:\n","    break\n","\n","recon = viz_fashion.reconstructions(batch_fashion, size=(8, 8))\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(recon.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f72da75bda0>"]},"metadata":{"tags":[]},"execution_count":74},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"nE4_xqIxpMlw","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":612},"executionInfo":{"status":"ok","timestamp":1592235864186,"user_tz":-120,"elapsed":1979,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"7fffb246-a927-484c-ff89-ca98620c1cb4"},"source":["# Extract a batch of data\n","for batch_dsprites, labels_dsprites in dataloader_dsprites:\n","    break\n","\n","recon = viz_dsprites.reconstructions(batch_dsprites, size=(8, 8))\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(recon.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f72da73f9b0>"]},"metadata":{"tags":[]},"execution_count":75},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"9GJbmVyorjlb","colab_type":"code","colab":{},"outputId":"665362f9-785a-47a7-8fe2-2fee8b195c7b"},"source":["# Extract a batch of data\n","for batch_celeba, labels_celeba in dataloader_celeba:\n","    break\n","\n","recon = viz_celeba.reconstructions(batch_celeba, size=(8, 8))\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(recon.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f2443943b50>"]},"metadata":{"tags":[]},"execution_count":12},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"zYl0tnrMrjxE","colab_type":"code","colab":{},"outputId":"0bb5bb9f-30df-4913-a18c-cb7f21d699e5"},"source":["# Extract a batch of data\n","for batch_chairs, labels_chairs in dataloader_chairs:\n","    break\n","\n","recon = viz_chairs.reconstructions(batch_chairs, size=(8, 8))\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(recon.numpy()[0, :, :], cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f24439a6590>"]},"metadata":{"tags":[]},"execution_count":13},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"cC8TGUi9MkPd","colab_type":"text"},"source":["## Encode data:"]},{"cell_type":"code","metadata":{"id":"TZQzcDimMlsB","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":208},"executionInfo":{"status":"ok","timestamp":1592235871522,"user_tz":-120,"elapsed":1072,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"2f662a11-4988-4b77-a1af-11abd53316ca"},"source":["encodings = model_mnist.encode(Variable(batch_mnist))\n","\n","# Continuous encodings for the first 5 examples\n","encodings['cont'][0][:5]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[-1.4983e+00,  8.4277e-01, -5.2083e-03, -4.4545e-03,  1.4016e+00,\n","         -5.5452e-01, -5.7275e-03,  8.8822e-05, -1.3115e+00, -6.0071e-03],\n","        [-2.9452e-01, -2.4889e-01, -1.2956e-02, -2.1580e-02,  7.8090e-01,\n","          8.9275e-01,  2.8407e-03, -1.3607e-02,  1.0150e+00,  6.6306e-03],\n","        [-5.4490e-01,  1.5352e+00,  3.2081e-02, -2.1006e-02, -1.4876e+00,\n","          1.1628e+00, -5.9816e-03,  5.4252e-03, -6.0219e-01, -7.6609e-03],\n","        [-1.0434e+00,  7.5063e-01,  1.2917e-02, -5.3259e-03, -1.5980e-01,\n","         -3.9018e-01, -2.5563e-03,  9.1539e-03,  6.2317e-01, -1.2106e-02],\n","        [ 1.5325e+00,  1.6092e-02,  1.5755e-02, -1.1477e-03, -1.3514e+00,\n","          1.2069e+00, -5.9879e-03,  8.6866e-03,  5.2680e-02,  2.2610e-03]],\n","       grad_fn=<SliceBackward>)"]},"metadata":{"tags":[]},"execution_count":76}]},{"cell_type":"code","metadata":{"id":"W4zXagbHp43K","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":208},"executionInfo":{"status":"ok","timestamp":1592235912354,"user_tz":-120,"elapsed":1217,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"1fc42674-d3fb-41c9-a038-1c79cff14f5c"},"source":["encodings = model_fashion.encode(Variable(batch_fashion))\n","\n","# Continuous encodings for the first 5 examples\n","encodings['cont'][0][:5]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[ 1.7015e+00,  3.6314e-03,  5.7292e-02,  4.7376e-02,  7.4806e-01,\n","         -6.0539e-02, -1.3251e-02, -3.5400e-02,  5.7073e-02,  3.8185e-02],\n","        [-9.8607e-01,  2.6065e-02, -1.2025e-01, -1.8813e-02,  3.1769e-01,\n","          4.1996e-02,  4.5641e-01,  1.8468e-02, -7.6687e-02, -4.6500e-02],\n","        [ 8.0841e-02,  2.7969e-02, -8.1034e-02, -1.3912e-03,  8.6376e-01,\n","          7.3281e-03,  6.2990e-01, -1.4536e-02, -4.4928e-02, -2.3681e-02],\n","        [ 6.6009e-02,  3.0442e-02,  8.0643e-02,  4.6564e-02,  1.8354e+00,\n","         -4.7582e-02,  2.9833e-01, -2.8216e-02,  5.7306e-02,  2.8040e-02],\n","        [-3.5284e-01,  7.3428e-03,  2.4478e-03,  2.6899e-03, -8.7546e-01,\n","         -1.7018e-02,  1.3330e-01, -1.7264e-02, -2.2724e-02, -1.2185e-02]],\n","       grad_fn=<SliceBackward>)"]},"metadata":{"tags":[]},"execution_count":77}]},{"cell_type":"code","metadata":{"id":"Os99CU8Ip5A9","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":190},"executionInfo":{"status":"ok","timestamp":1592235914478,"user_tz":-120,"elapsed":760,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"d1353b70-8fde-4914-a524-7394b9b2a31f"},"source":["encodings = model_dpsrites.encode(Variable(batch_dsprites))\n","\n","# Continuous encodings for the first 5 examples\n","encodings['cont'][0][:5]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[ 1.4230e-02, -9.6438e-03,  1.3452e+00, -3.8103e-03,  4.7964e-02,\n","          2.8417e-02],\n","        [ 1.0025e-02, -3.8424e-02, -9.8186e-01,  5.4055e-02, -2.1191e-02,\n","         -1.3381e+00],\n","        [-6.9912e-03,  6.3463e-03,  2.7954e-01, -9.3445e-05, -1.5486e-02,\n","          5.7476e-01],\n","        [-6.5360e-03,  1.5082e-02,  2.4653e-01, -8.7794e-03, -1.5777e-02,\n","          2.9785e-01],\n","        [ 1.7069e-02, -3.0237e-02, -9.6103e-01,  2.3664e-02, -1.6470e-02,\n","         -9.7090e-01]], grad_fn=<SliceBackward>)"]},"metadata":{"tags":[]},"execution_count":78}]},{"cell_type":"code","metadata":{"id":"hxv-YVxurwLL","colab_type":"code","colab":{},"outputId":"c851736d-a9ad-4bb5-d574-f4305fb8c76a"},"source":["encodings = model_celeba.encode(Variable(batch_celeba))\n","\n","# Continuous encodings for the first 5 examples\n","encodings['cont'][0][:5]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[-1.3779e-01, -5.1264e-01, -5.0707e-01, -6.1149e-01, -3.5573e-02,\n","          4.4275e-01, -2.9825e-03, -2.6522e-02, -6.4544e-02, -1.3954e+00,\n","          2.9860e-02,  6.5649e-01, -3.3539e-01, -9.7458e-04,  1.5257e-02,\n","          4.7139e-02,  9.4665e-01, -2.5833e-03,  1.2064e+00,  3.8720e-02,\n","          5.2554e-01,  7.7422e-02, -2.1598e-01,  1.7352e-01,  4.1477e-02,\n","          2.4813e-02,  3.8626e-02, -6.2475e-02,  2.5990e-01, -4.6569e-02,\n","          6.2493e-03, -4.6588e-02],\n","        [-7.0240e-01, -2.2950e-01,  1.9378e-01,  5.2681e-01, -1.5962e-01,\n","          2.3552e-01,  1.1604e-01, -1.9731e-02, -3.6130e-02,  4.1161e-01,\n","          2.8686e-01,  4.9956e-02,  6.7169e-02,  2.7666e-01, -4.5861e-02,\n","          8.0235e-03, -1.0799e+00, -1.2274e-01,  1.0940e+00,  7.1488e-02,\n","         -1.1883e+00, -1.1481e-01, -1.4655e-01,  5.9016e-02, -9.7642e-02,\n","          3.5401e-01, -4.0041e-03,  5.4516e-01,  1.3173e+00,  2.9270e-01,\n","          4.7857e-01,  3.8227e-02],\n","        [ 3.7233e-01,  3.3370e-01, -2.8152e-02,  5.8523e-01, -1.9884e-01,\n","         -4.1059e-01,  6.5947e-02, -2.9415e-02, -5.7276e-02, -3.9407e-01,\n","          1.6813e-01,  1.0582e-01, -4.0877e-01,  6.4707e-02, -4.2782e-03,\n","          1.5508e-02,  3.7352e-01,  5.1221e-02,  2.3878e+00,  1.0846e+00,\n","         -4.8414e-01,  5.2485e-02, -8.7318e-02, -8.0938e-01, -9.2104e-02,\n","          2.2187e-01,  1.0531e-02,  2.8334e-01, -5.6260e-01, -7.8051e-03,\n","          5.6553e-02,  9.9819e-06],\n","        [ 3.1733e-01, -4.7560e-01, -2.3807e-01,  2.9907e-01, -1.4559e-01,\n","         -7.3912e-01, -1.1918e-02, -1.0029e-02,  2.3902e-02, -1.0889e-02,\n","          1.2000e-01, -1.5203e-01, -1.7572e-01,  8.7495e-02, -2.7238e-02,\n","         -1.6006e-02, -7.6745e-01, -1.0538e-01,  2.9323e+00, -1.5575e-01,\n","         -1.5312e+00,  1.3383e-01,  4.8844e-02,  5.9153e-01, -4.8734e-02,\n","          1.8024e-01, -3.2823e-02, -3.3392e-01, -3.4411e-01, -1.1318e-02,\n","          9.4986e-02, -2.8805e-02],\n","        [ 1.7596e-01, -4.1956e-01, -3.2831e-01, -2.8357e-01, -7.9338e-02,\n","          4.2089e-01,  2.1436e-02, -2.5265e-03,  8.1027e-03, -5.1026e-01,\n","          1.7318e-02, -2.5700e-01, -5.3574e-01,  6.9628e-02, -1.9916e-02,\n","          7.2769e-03,  6.3007e-01, -3.8968e-02, -1.7731e+00,  4.4104e-02,\n","         -1.5544e-01, -6.1640e-02, -3.3437e-01, -9.6973e-02, -3.1370e-02,\n","          1.2929e-01,  5.9590e-03,  6.2087e-02,  2.2925e-01,  1.4430e-01,\n","          1.0176e-01, -5.0824e-02]], grad_fn=<SliceBackward>)"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"T7AaGReRrwQY","colab_type":"code","colab":{},"outputId":"23c595a7-ad88-4462-f1f1-c4008256245a"},"source":["encodings = model_chairs.encode(Variable(batch_chairs))\n","\n","# Continuous encodings for the first 5 examples\n","encodings['cont'][0][:5]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[ 0.0930,  0.1647,  0.0540,  0.1675,  0.0646,  0.0592, -0.0892,  0.1839,\n","          0.0183,  0.0508, -0.0224,  0.0921, -0.1121, -0.2782,  0.0785,  0.1275,\n","         -0.0297,  0.2134,  0.1295, -0.2447, -0.0822, -0.0591,  0.2212,  0.0884,\n","          0.1223, -0.0608,  0.0187, -0.0394, -0.1994, -0.1010, -0.1117, -0.2373],\n","        [ 0.0411, -0.0240,  0.0453, -0.0978,  0.0245,  0.0384,  0.0234, -0.0093,\n","          0.0683, -0.0912, -0.0102,  0.0345,  0.0352,  0.2328,  0.0047, -0.0060,\n","          0.0588, -0.0748,  0.0128,  0.0695, -0.0492,  0.1295, -0.0582, -0.0634,\n","         -0.0390,  0.1097,  0.0667,  0.0088,  0.0924,  0.0795, -0.0166,  0.0845],\n","        [ 0.0943,  0.1567,  0.0597,  0.1523,  0.0649,  0.0605, -0.0848,  0.1767,\n","          0.0226,  0.0392, -0.0252,  0.0935, -0.1038, -0.2513,  0.0754,  0.1249,\n","         -0.0217,  0.1986,  0.1264, -0.2326, -0.0810, -0.0435,  0.2084,  0.0799,\n","          0.1151, -0.0492,  0.0249, -0.0362, -0.1829, -0.0893, -0.1083, -0.2236],\n","        [ 0.0865,  0.1053,  0.0678,  0.0735,  0.0532,  0.0713, -0.0591,  0.1232,\n","          0.0489, -0.0129, -0.0267,  0.0796, -0.0626, -0.0902,  0.0566,  0.0956,\n","          0.0064,  0.1254,  0.1038, -0.1497, -0.0826,  0.0296,  0.1399,  0.0337,\n","          0.0650,  0.0135,  0.0425, -0.0220, -0.1069, -0.0339, -0.0899, -0.1414],\n","        [ 0.0870,  0.1220,  0.0613,  0.0875,  0.0569,  0.0649, -0.0548,  0.1284,\n","          0.0394, -0.0006, -0.0170,  0.0764, -0.0661, -0.1238,  0.0609,  0.0921,\n","          0.0013,  0.1348,  0.1052, -0.1609, -0.0756,  0.0089,  0.1501,  0.0419,\n","          0.0778, -0.0067,  0.0343, -0.0220, -0.1224, -0.0423, -0.0914, -0.1507]],\n","       grad_fn=<SliceBackward>)"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"c_uuSyI8jne1","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kA4emg8rMoNZ","colab_type":"text"},"source":["# Chairs3D:"]},{"cell_type":"code","metadata":{"id":"-49ZeHw7tB26","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592991732861,"user_tz":-120,"elapsed":541,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}}},"source":["import torch\n","from VAE_model.models import VAE"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"dWqj-Sd8sTbv","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592991778452,"user_tz":-120,"elapsed":585,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}}},"source":["path_to_model_folder_chairs = 'trained_models/rendered_chairs/'\n","expe_name_1 = 'VAE_bs_64'\n","expe_name_2 = 'VAE_bs_256'\n","expe_name_3 = 'beta_VAE_bs_64'\n","expe_name_4 = 'beta_VAE_bs_256'\n","\n","img_size = (3, 64, 64)\n","latent_spec = {\"cont\": 10}\n","model_chairs = VAE(img_size, latent_spec=latent_spec)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"id":"qTRfs1OFsTfE","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592991779260,"user_tz":-120,"elapsed":519,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}}},"source":["file_path = os.path.join(path_to_model_folder_chairs, expe_name_1, 'checkpoints', 'last')\n","checkpoint = torch.load(file_path, map_location=torch.device('cpu'))\n","model_chairs.load_state_dict(checkpoint['model_states']['model'])\n","\n","viz_chairs = Viz(model)\n","viz_chairs.save_images = False"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"id":"h49JC8-8jyoZ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1592991780840,"user_tz":-120,"elapsed":642,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"305d346e-9ccd-432e-f5c0-01218076719c"},"source":["print(model_chairs.latent_spec)"],"execution_count":19,"outputs":[{"output_type":"stream","text":["{'cont': 10}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MnHv4uY5j2UT","colab_type":"code","colab":{}},"source":["print(model_chairs)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yy2QEQ0oj3PY","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592991782739,"user_tz":-120,"elapsed":571,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}}},"source":["viz_chairs = Viz(model_chairs)\n","viz_chairs.save_images = False"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kBUU9Gjij7LJ","colab_type":"text"},"source":["## Samples:"]},{"cell_type":"code","metadata":{"id":"VBy86xYYj6gI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":594},"executionInfo":{"status":"ok","timestamp":1592991785393,"user_tz":-120,"elapsed":1582,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"2bd27ea9-8a2d-4564-c5c3-320884f08725"},"source":["size = (8, 8)\n","samples = viz_chairs.samples(size=size)\n","\n","fig = plt.figure(figsize=(10, 10))\n","\n","samples = samples.permute(1, 2, 0)\n","plt.imshow(samples.numpy())\n","plt.show()"],"execution_count":21,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"QyAFLLm5kHPY","colab_type":"text"},"source":["## All latent traversal:"]},{"cell_type":"code","metadata":{"id":"OI50-KsbkFjA","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":594},"executionInfo":{"status":"ok","timestamp":1592991791418,"user_tz":-120,"elapsed":1114,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"4d09a1d0-a6a1-407f-88e8-220943b6fb57"},"source":["traversals = viz_chairs.all_latent_traversals(size=8)\n","fig = plt.figure(figsize=(10, 10))\n","traversals = traversals.permute(1, 2, 0)\n","\n","plt.imshow(traversals.numpy())\n","plt.show()"],"execution_count":22,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAdsAAAJCCAYAAACI+20iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9W4w0SXbf94tL3qr68vV3ndteZndWpGiKBAWKlycaWFo2DUuCwIVgQQ+2tQD5YOlN4MqAAFt6oQAKAghYMKAVBFt6sLwE1rAIEjBWhrx+kAws1xSH2uXu7Fx2Z2d25rt/fatLZkaEHyIyq7q7uru6O6sqs1T/Qc9XXV2VGf+ME+ecOHHihHDOscEGG2ywwQYbLA5y1Q3YYIMNNthgg3XHxthusMEGG2ywwYKxMbYbbLDBBhtssGBsjO0GG2ywwQYbLBgbY7vBBhtssMEGC8bG2G6wwQYbbLDBgrEQYyuE+M+EEN8VQrwthPjbi7jHBhtssMEGG3QFoul9tkIIBbwF/CfAB8A3gL/qnPt2ozfaYIMNNthgg45gETPbnwPeds6965zLgX8B/KUF3GeDDTbYYIMNOgG9gGu+Cvxw6vcPgJ+/6AtCiE0Zqw022GCDDToP55yY9f4ijO1cEEL8GvBr1e+vvfYav/iLv7iq5iwEX//613n27Bl/+S//5VU3pVG8++67fPOb3+SXf/mX2dvbW3VzGsNoNOJ3f/d3+bEf+zF+6qd+atXNaRS///u/jxCCX/mVX1l1UxrFm2++yXe/+13+4l/8iyRJsurmNIbnz5/zr/7Vv+Jnf/Znef3111fdnEbx1a9+lTt37vBLv/RLq25Ko/g3/+bf8OGHH57790Ws2f4i8D845/7T8Pt/B+Cc+80LvuO+8IUv8JWvfKXRtqwan//85/nGN77BwcHBqpvSKL785S/z67/+63zzm9/kZ37mZ1bdnMbw8OFDXn75Zb70pS/xm795rrh2Em+88QZSSt56661VN6VRfOlLX+K3fuu3ePjwIffu3Vt1cxrDN7/5Tf7cn/tzfPnLX+aLX/ziqpvTGJxz7Ozs8Au/8At87WtfW3VzGsUXvvAFvvrVr547s13Emu03gM8JIV4XQsTAfwn8ywXcZ4MNGoUQM8fIWmCdua0z1q3fKj7rxmseNB5Gds6VQoi/AfyfgAL+qXPuW03fZ4MNNthggw26goWs2Trnfh/4/UVcGwSwyafaYIMNNtigO+hUBSkhBELMCkH8hxeS2GCDDTbYoDtYWTbyVXHawJ7+3a9JnzPjFcLPhx3nf2aVCO2r4JPWBFONvujLtJJTDe8g+Ucf2ln1XcPJee1A2/tjgw02mB/NjefOGNvLccEDcc4bNAHVLHhewyuEoOmM7TP3mHHPCpea2ilO9Xeu2l4hFmP4ppwIUd1nChffUZz++DV5XX6n6+CiBI/LmzlxQLw/1R5e1fUrdpM2zne/m4+XxTorVb9dvY03bdcynLCNo9cspp5nAzqyE8b25plrZ79/lWd3wvjVX2pGsFeVlXfmvrWSbG7A3oTZdR/LWV6Vr9Wc03TTPqu+ftWrnLjvCV7QSJ+dirCE2wTMf/3rGLSTz7R5WTx9j9lj+qLvwsRRvxmvph340/eAq7fxqu3x/l4lL66Wxavee562VVj0pGfWfatdPNVk4SZtaP+a7ZyK7XwFeHaGdPLaF11/lpEWJ9eOb6J45/juRYq9aUPdGC/mbNsV73Fdvie3GyyB19Wver1v1byaadeFI+Easjpvm2Z9rklZ9Drg+tc444LckFf97wJ5zc/3ultxpp/KyXY0IoviLLdZ781znevce/L6ZDfdhFvrje3Nu82d7404x0We82XP1X/9+p7OTbmdx+sygTg7fzl9XW7Ga16BvOI9mvFs2xhma6BNLaBV9Y9zrv65+TW5kSzCzW2am3q4zrlGllzawAtAMOmzJtAArYuvvxKd0cz1OhFGngeXPYTrPKQqkHXT69wEi+HlZhrcZXO7CE22ZZm85rlXY4qtZbyu8rllXaep+1yrPSFX5MbXuQbm5uU/fL17UAX6XSucvaZQhfqhWW6tN7bTxM/9zOJu3oz72EqcdCXa5kTc5LqrWudxC9Y4J5RAixyjm8LzguusN970vgu9PiDcoqVixn2X8PzqKMYiLn6Oym2jc3kVtN7YwgUGdwmCvMgOvsiRWLgiWGA26yp5Lfoe53FbFwOxqvstdFvejBDV0ngt8tozZHEtnDAXom9CTLpuDXh1wtjCmgjRDGx4dQ/rym1tea1ZmHMa69pnsODZ8wrQGWN7c7iTL6fCVt3GRJHUm5JqWl3mN4NXeD1rb3G3UCWlnHy32l7QXUw6bLrP/Isu84LTfbYeY2wabmpwzcpW2eCmWF9j6xw2ZA46HM5OQs7VGlG1FWSynedmWwSWBedsyPYEZ229Vlh5gkJIzw+BkLJDvFzgNsn6rBI4qrCSwOfiSyEQcvJ7m3EyOzf8G0JlzvlktWorSNdk0Vpb91PFD8DZSha7yavur6rvpt4HN1t3QGdksXpdGdk6UUpMiv+IU6/bDq8L69/qf07PjKf7apm81tbYWmcp8oKyzCmLktIY3xnOUpoSGQyRlAodRSRJQhQnKKVaLVjOWYqipCxyyrLAlAZjLc4ajClx1qG0RgiJjiLiOCZOEnR4r61wzmGMocjHlGWJMQZrDNZZ/68xyNA3SnteUZwQRRGyxf0F3iAVRU5ZFJ5XkENrLMaUwSkSSKUDr9jzku2WRWstZVF4OTQl1jisszjreeFAKlnLoucVd2CMOcqyDP1V+v6yDofFGIOzruallCaKInQUoZTqiCwW2MDLWm9wrfVyKUVwzqXyvLRGaYUQatVNvxDWWsqyDLzcZOnAOYw1CEBIr/+U0iitfX+FycgysJbG1g8Ww/HxEcdHBxSFV95laXC2pCjLEA/yBjeOIvrbO9y+c4cs67VaEVhjGQ2HHB7uk4/HQeGVWGu8sFmDkAKQRFFE1utx+/Zdev0+Wrfb2Ob5mMP9fUajIdZaTGkojcEa339V6F9rTZKm7O7usb2zQxRFre0z5xymLDk6PGRwfByUmsOUBmuDE+isN7hKkSQJW1vb7OzeIkmWpwiuA2sMg8GAo6MDyqIECMbI1M4two+xKIrIsj67t3ZJ0wyl2qu8nXPk4zGHB/uM8zE4vAK3BhOMUjUr8rKYsb29Ta/XR8p2j7GyKDg8OGA0GtazXGstWIuxFkJETCpFEsf0+lv0+n2iqL2yWDnqg+NjBoNjrLXUVbGCsYVQX0AKIqVJsox+r08Uxxtje1NYaxmPxxwdDSjLShGUmNJQFAXgw3jWGITwM+Gt7W3SJIUWDxjrHEWRMzgeMB6PEEJgjffqirLEBcGqPNbxeESv1yfLsrm2Ua0SpiwZDoccHx/X75VliQkzXQdYa8A54jhG6ygogmh1jZ4DxlpGoxFHR0e+D6TEGh+JKEvjlZ61WGcZSImzjqzXJ4njVTf9QthglI4OjyiKEqWVV27GeGciLHdYaxFAvjUmTVOSJF110y+Ec468yDk6OmI4HPqZeOgzPxuc8AJHkgxQSpGm7R5jzkFpDMfHxxwdHoAQKKUm8mctEBwLZ9FaY50jTpL2jzFjGAwHvHjxAmMsUaR9P4R+qpLdbdCPW9s7RFGMXiKvtTW2YeHPDw5nfeze+VRyL1J+HddYCy544c51Ii+g9kin1jVd8OSqtTNjLdYUxCbCOc+47dSqtVob1m2r1H8bflwIe5myQAhqx6L1CP1ljMHikP4trPMGq1LcZVlgBCEE24U93i4syxhKU9br6tY5zJShLcsCZwxJGvux2AE45yiNIS8KpDVopYO+MDUv7wSWweFtvyz63CeHMSXjcY4TjiiKEQicNRNZND5KprXClNt0JR/YGsNwOKI0JbHxyxW4kK8j/L9lWYKzpGla68VlYW2NbbXuoKPIz1QFSCGxxqKL3M+YrMEVDuEEUZyio6jV65oQanVKidIabf3MRwqBdaAjTZH7ULITBWDRUYKOYr9e0XLlLaRAak0UxVjnkFIQuZjSlBR5gbGGsiyw1qB0hI4TlGp3f0HgpRRRHGGsQ0qJFBJjNaoo/PqgAKxX3DqKUbr9Q1MIH/rWWvsZnVJoKXEOlFI+V0KUCCOwAt9nLQ75VxCAlH49VmvtZ4BaIZBI62ftUGKEN0NSSlQULXX977oQUiKVRio5abtUWCcRxtQRJGMNWiik1kjZ3pA/TGqDC6lOzCikEDgESIOzPtpZOUdSqaXnRLR/RF8DQgiklMRJTJKk6PCAhZQ454jKmLLIyfMC4QRKCbJeD607oAiEX/+Kp0JxUimcA2MitPaJOAKBk4os6xHHcavXksD3mVKaOEnCeqYNhlSgbYRWmrIsGDsQEaRpRpIkrU8i8rLo12LHcYoxBqkEUiisc2ilKYockQucsURRRJJmaN1uXuBlMY5jkjQN48v3IYA1GikLRCGwpUVJRZJmRB0YYwiB1hFJmlCWfslJawVCopyilAZRgikNWkGcpMRR3PrkKPBOUJImxHGCtQatFVIoJBIrvENujEEKidY+oa39TkTQ93FMHMcQHPXwJwQShK3XcKXUdULbxtg2ACElWmkfCjE+3OPKiXdTFAXWOoSSRHFMHCetN0jgHTelFForilzUa5q2yuYtcr/2IkFp72xUCrDtUFISKcVYCsrSYMoChw/RFSHj1QmQWocM6w4obrzB1UqjlKQsC4rcAKLOoCzL0odXpUTHMXHc/ggLVA6SQitFDj77nyKsrftMZWMtToDWuh5jbe8zz0uitUYKQV7kFEUOCJ9pbf0M0LqgP6LYj7EO8PKyqNBKMipzRsOQv2JdnflvrKv7rO27GCr4ZDVFpBX5yDEaDj0na6i224XdaOiQ77Fsfd8NLXxlhPVaZxkMjnj+9CmDo0OKPPdKuyxx4LeOxAl37t6r96O2HQ4/MPLRkOdPn3C4/4K82i5TeuWmo4g4ydje2UXIu0jZkX1yOIqi4ODFc/afP2M4HGCCMTLWeO81zUizPjvb251wjqo9jdYajo8Oef70CYPjIx9iLX1oHCFqWYyjl1o/W5+Gs47xaMizx484PNinyMdhe5Nfn9ZRTByn7N7a8+HzDvRZtcZe5Dn7L57x4tkzRqNB2KplcDiUUkRpRn9rm72925OZVItRy6IxHB0d8uzJ41oWjSkBh5B+SS3NevR6vaA7VtvueeGsN7JPHz/i6PCAfDyiKAvA+Zl6HJOmPe4+eGkl7VtLY+tDqobh0SE/ev893nvrOzx59JDh8Nh726VBKk1/e4fdvduIP/2TvPLqq50QKmct49GAxw9/xPf+5Fs8/PCHHB8dUgReTviQ+M7eHT7xqc9w7/79VTd5LjhnKfKcF88e8973vs2HP/g+L54/DbxKnIMojtm+tce9B6+yu7MDzrY+6Qt8lvXRwT4f/uAdfvDO2zx78ojxeEhZhHC5juhvb7O7d5ckjrGvvtp6XlUizWhwzKOPPuDt73ybxx9/xGBwVC8DCKnIen22b+3xqc98jldefW3VzZ4LVZb1i6eP+MHb3+VHH7zP4f5zjKkydgVRErO1s8dLr7zG3Tv3zpYDayV8cuHRwT4fffB9Pvj+e7x49oQ8L3DWr9UqpUl7fW7fvc/W1rbfctdy1LI4HPD08UM++P47PH/2mPFoiDUm5BpKoiRhe3ePKIopP5EvvdTlmhpbR1kW7D9/yrvf+Tbf+fabHO/vk+fjSUahUERJEvZq7vLGn/rxOvW9ragyWo8OD/jg3Xf4zr//Q549fkIxHtZbEowT6Ciit/UxAnj9s28EgWvvlgTw2w1GwwGPPvyA7/3Jv+dHP3yf0fGRDx07fGKRVkQf9xgeD3j1tU9QfPr11teG9UVIcp4/fcx7b/0J7779FscH+5RFHrKRLQiFjhNu3X7KnXv3yD/7OayztDstxW/LOjx4wQfff5e33/o2B8+eUYxHk6xWJFEc0Xu6S5KkvPGnfqwjsmgZDQc8/vgjvv/u93j00Y8YD47qbH/rQGnNs6fPcNbyyU+/7jOu2y6L1m8b3H/+jI9++D4f/vAHDA8PMNYXVrHOgZBIFTEej3nw8qv1Xv5295nPsB4Mjnn+5AlPHn/M4f4LTJH7vzrrDxU91uR5wa3bdxgNh0uXxbU1tkWes3/wgidPn7C/f8DweADOL/yX1mFsTmEcUh+zv/+C0WjoQ0QtF6qyLDg+OuTp8ye8eLHP0dEx1hRIKbHOURpLXnpnYn9/n+PjI8qybLUi8FspDMPhgOcvnvPixXMOj44oxqOQUeiNrS1LSiM5ODrk8OiQPM991nKL+8xav9f54GCf5y9ecHh4xGg48oUshPC8nKUwEB0PfMGB8cjvFdTt3f5TObTHg2MODg44PjpmOBphyjLsW/eOhLEOof0e4+Fo6LdltF0WjWE8HnE8OGY4HDIej8nLyQzPOihtCbJkOB4zGo0oisrYtreusN+jX5IXOXnIgi9CJSkRdjSARUsorSMvipDb0vZJiN/Lbq1FKBWykn0mssP5TOuQvKeiCITEhCIey9SLa2lsIezLFJq4v0OydYtx6bCmQCmNcA6NX5vo7ewi4sx/vr06oIZDYIVCp1tkO7cZjEqKfOQTVYRAW5BRRL+/TdTbxglvhLsAh0DGKdn2bZKDAcZJlPRJONYCUpJmfbLtWwgd1dmFbYdDgIpIt2/R271N6fy+Rh2yyJGKKE7Y2r2FjNOpetBtVdtAVZFbapKtXbZv36NwgmI8RlX5D0Kio5hsa5u4vwVCdqG7wt4fRdrf4dbdlxiXlsHREbLaYhK2xKRZj+1bt1FxMnGK2t1pPj8gzbh19wGDcU70oo+1JmRSh5KhScr23h5ZfwvZ4kpf0xAIVBSzs3eH+698kqS3RZ6P6z3tvsRrQra1xa3bd4jjONSPXx7W0thW2Z/9rW1efvWToDRHh4c4WxKpqFYEcRyRZH3uPXiZKE47kAwgUFKSZT3u3X+Z4edybt19QFmMQ7aroi5nmGXs3LpN1tvyCTerbvoF8ApMEscJt/bu8NqnP0u2s8tocIySMmQd+72pUZrQ62+zvbMXakC3O/mrksWtrR1efu1T6CTj+OgQrJ2qfyxRkSZOM/bu3Kv3RbeZl99uociyjLv3X6K0jtsP9jFF4evpSj/DkFKi44Sd3VvESdqJZD2/vS5mZ3ePVz/1Ov1bexR5GGOy2p8JQmmyXp9eNcZEuw/FqLLHs7TH/ZdeIen1GY989EiGus5CSJACHSVs79wK2cjt5VShyrLe3tnl1U8q7r70MsLh5U36rXaEfeFZmqGjCCmWW3tgbY2t0pos63Hn7j2iJEUAcRqTxBlSilBhyddLTtLpQv3tFiyplK8LvLfHK87x4MFL4SCF1BcXwGFKG/ZzSuIk8ZmSLR8wQgiiKGZre4f7L73M7p7PXk2S1NcvBb8dyPgi/r1+zzsXLc8CrWWxl3Hnzl2yXh9wRHFCErbC+GIdvmpPv9/rhIKr97LHCTs7uyAE9x88II4Tvy1LqTpsWRYFUkmiFWy3uCom22M0Wa/H3u27bG3vhAM9UqJQ5KIoCoq8wDpLnMSoUDin7ZDCb3Xc2t4hTtKw79brDoEPxxZ5QV4UpGkaMsjb7yAR9rOnaRoKVnjZ1Fojpa+uV4XQcVURjOU6tGtpbGEyW4qShNtxTJZlbPW3iOMEIQSjPKcYjxgOB0CoNtLyGFelCKoN57u7u8RRTNbrkSYpSivyoqTIx+Qjn/ZehypbjumiCP1QiD9LU7Ksh440xjrG4zHFeMhoNCaJfU1T0RFuMhR1SLMeSZL4YiNJDAjGQRYHwwFRBwxtBT+R88p7Z3eXKPLjzCtp5ZXbeMRoNMAYiwrVpdrPz5dtVUr57S9qmyzLyNIMpRXG+EMzxqMheZ4TaR2ix23nRSip6Z3wOEmI45he1kdrhQXKoiAfjxiORsShlGMn4KptS77esdbaO+rBOTLWUuY5aiwwpVsJq/U2tlKQxAlxFLO9s02aZujIz2CjPGcc+U3rRVl0QAFMQQiiWKP1Fv1+nzTNfEhSKcqyZDyOGCnlQ0RS0pXapj4EruhlPbIsratfKa2x1hJHEXkUQpOhnGFnmAkRivBXhjYhijQgiPOcPMhiVV+4G8xEiEhEgVfmq0SF6jxlWTKOfDGPosiRystiu5MQwU9R/XJMr9cjiROSLPOyGAr3j8caHQpf+FOnaL2zDtTHHqahKEySJCRJEg6REBRR4atKhT3RQopOOOv+3HtfRcpPRnR9tKisjK1WSCkoyjJU3XNLXWNfW2MrhSBJUra2ttA6Is2q8n6yPrNRCe+3DYejULpr1a2+HJVy6/f6OOfIwkypOptRTQ0UEK0/O3QaSinSLAvhoCSULdR+RoSv4+pn6gIp/Qy/C6jC4f1+SRzFpFk6OddVTqowga9WpFoeaq1QyWKv30cKSZqmxEmMDrV3fQjPy6I/PUd3ZqaktSbNvOMQx6HsazSpE6yU9GMu9F1XeEmlSJK0XuOsTvSp1px15PlIKbHWoeq6yO3O/KrK2KZpWhvb6TOGNQR5VOg8D2VDw0x/SbzW09iGeHyapvT7WygliaO4XjOqQ5YhIUqG8za7YJQqBZf1+jjr14v8TCIcZu0cSvgsXoA4bv86GVTl1vw6u1aaKPI/lcIGn+yglKy3WHTiEIKwtpmkKc45X1IuDrKoZP33qgKRylUn1mzBl0SN4pheOAM6iqJ6ViuFxEkXkm48lyjW4azldqOSxTTNsKZEhdlr5ciCOMFLCsKsvd3wBfglSRJ7ZzXovWqMCSFwVtblGY0x4eB4P9NvKypeUex3KPj+UGGGruqa1VV/CagjnJs12xuiOrUjDmu1QlCHRqqZXrX2iZgUwJ/HKJ3unGWHjioPLstSf4KRVsHL9grahbULIf2atdLKZ4Zeet1TvGCp22omCs6f5KNCXymp6n4RhD7r+e/4jMLLB8s0t1WE+lQ4FB5cfcpKFYmoZLFqo9KaKITC2qveQuJXGGP+jGHq/vKZyMEpEpAQB2McspRb7khUyxRJkoQDFSayKGpZDEgnstt2XlXiV12jGlFHHqodC046bxRif/KWn6C0mxf4uupxFNc5HJV+l0EP+gMIfGRMJL42slrytqa1NLYAUiriOMb1el4ZhLT36ugyIf0sUCpFHBmi+PITZNowlqQUnpdzoVC/51ptg3HOocJpMlrrCeerNn7JNml6NqGVDoraZ/LKkFgjpavr0lbZy2KuWbtb2bpTdZpRldmJ8+X+pFL1YBdOoqTzSVSxq9ffWyFwF6DK+KzPcxWTo+kqWaycC62178sOLGtUs3SozhbGZ7sGB8nvjZZ1H4p6e1r7eVVORLX2TBhPVXjcBZ0ow3F1UbVFpsWotjRFUeSdPFsVJ5IhSzxEIJzDSolG+wIXm1N/moGUog4NV5WhVBgg09BBC1czjYvgP+o/L1jyjuiAasAAmOocUcGUo+DXVhyOyHFiNn8R2pDcoZSqk1CqqjVVeHwaVYLNvINl1dSkkkREYR3MhszJs0X5p2Wx7YobquUXv7ViuqTfrHFUG94OFEmYnqlaq+uxMZNXiK504TQjmCyZVca26rMTbReADpEkJed0aFeLKhrhnaFJn9Wh4/BvtfK8iv5aW2MLU4KltVdwp4VqCld58NPJokKIlYVcqwFTramcR6ELSgAmHupppdZEn60SgokzN+3UdJ0XTJTWPLwu+1ubUMniZAa4XrymZXEdeMHVDOgqeK2Nsa09l1NGr8o6bvIe/pdTL0/co7mw5UW8mhSYea+16Blw07yqa16GZczsG+c2lfBxHjrJa8b1K7hqarIkXqfv3+wN/P88HTf1Zvf7bFVoM6e1MLaLGwyiHtdXu8UCBowQC1MwqxFQESgtUrHMX36zzYN0FuZvb1f27E6jMnIz/iImL9qw9HE9zJDLKceps7SAWdyq/IuOE5vgmlzWwthO43RI6ybXmby+8eVu3gaaU5ltMCxVE5rqr8l1V8ttcZnPQQLE1XYFNq7j1s7pWxIu6bfOM7+A3/R6aXdxyom4xjjvvLFtfoC24zCCWbyaMExtUGin27AuvE6jeV4r5DitTBfg0K4SE2PgGrMI7eA20WWLcGivWuaiace6eUxPaa4WFZuHV2eM7ax9oBd5UpeSP8dDX8UYOTsbukkjZs+BVzH4Z87yFtCOVXKbNzHoJvdYOWbOWm4Wa2kLt5PRIwE3XNoQYR125bhiBGS+S56OD7MSqouIHk3Gc/V7I5c9gfbndJ+Di5/FxR0gFiCI18WsWd51O7r67ixFtnKPMmRMX//rbUroaG622S5es9szu3VzjLELeC1fHi9aAJ5gnnZdxG3Vw+xmEFezMis+BaTJZ70MeeyEsb2qMrrouV22frBsJTB9Pzf109Q1T7y/ylUTd31mp73Os5deNi935v8zPzVns9rDa777ztuudhmeGY2ZHnvuCry4+LOt4O0m0jm/HLkLG+/cSQ1y5SlLQ8+l2h/c9CpwVUFhUehMGLnC6T2tVzfE9d6BCz+zrNnGeVt73Kk1g3n28l46qJYc9qlbM90u50t/z/jU+ddxl5+Es+yTZGY966oNVzGS8xi2NvC6ath4nmfQhtN/ruXQXPodh3Orz/1YiLMWxm+1k+Aq/edEc2viC8FUDHlaVc6S/2qcX4V/J2a2J9BIZ82jCJq4z03ggqMZvLgGvcKlwbvV5/2BRSjvVWMRbWgDr3ZryTbi5Kx5vTDDIbve19qJExGP6vXFjv48aPfMVpwNVcyXgHCRFz6fh74Mr/Tk9qLq3Miptp3if3GfLmPf6nw4nUF7eZsu65MmNz7dENXea078b03Qoue8Rlj1DLd5eEKntyZe/i2x2qWsOXFWL7sz78/6/TK0emZ7/fX3878oRCtyBc/Z2kM9Mmfmf17Yua4VhhbOPv3Z7b5KKLkdvGDS6jbIUOM4J7luoWhP124wN67ZaStOqJoHMzXVJcl+846ZVs9sJ+uUp9Yvp9dsr3ggwPzJHTdZT7payHPWOl+Te/5m3XuhSjUkUly8frkYctfldhW5WMUMcJ46tg3cZOm2r5LzK+dedMBKXyaLbVizvi6ccyd073nrmnV0BmkAACAASURBVCe/s7z2XReVzTkvlwZm8VqHMHKNkFQzK3ljwUapwomMWBFenNpCNClLdv37LAvT+17FjDZcV6Bu+p0mMG2YzrThTGj+Om1cndZwcyqCtkQ55kWdXOZ/OfP3LvNaxGfbAFelEV2wrnlmaawjuDDb3H/gytfsiLFl5W7RiYdfy9YM/7pjA+a82UzXBv4szOTgzp8VdYXzxdtOzufW9lnUdXh1HU1ts1oZrthn1dhrPa+LcM22izaQFkK4W7du8frrr6+6KY3ie9/7HoPBgJ/+6Z9edVMaxdOnT3n//ff58R//cbIsW3VzGkNZlvzxH/8xDx484JVXXll1cxrFt771LYQQ/MRP/MSqm9IoPvzwQx49esSf+TN/pj7neR0wGAz47ne/yyc/+Unu3Lmz6uY0ij/6oz+i3+/zxhtvrLopjeK9997jxYsXOOdmerWtkc7RaMRHH33U6DXPWzO8bo3Oq34vz3Occ43waprLTTAcDgF48uRJowpu1bVTqwPrj46Obtxnl3FZxAEMF13PGAPQ+Bi7+N7XW9u+yrM5Pj4G4OHDhzMPd2/q3suWzaIoANjf3yfP80avvepx5pxjPB4vRBbPoun8ivOvNxqNLv5mW2a2X/jCF/jKV76y6qY0is9//vN84xvfYH9/v/UhvKvgy1/+Mr/+67/OH/zBN/mzf/ZnVt2cxvDw4UNefvllvvSlL/Gbv/mbq25Oo3jjjTeQUvLd7353rWTxN37jN/gH/+Af8PHHH3P//v1VN6cx/MEf/AE/93M/x5e//GW++MUvrro5jcE5x87ODr/wC7/A1772tVU3p1F84Qu/yle/+r+fO7Nt9dafdcE6KbdprButde0nmHBbN44bXt3CuvLyuJjTxthusMEGG2ywwYKxMbYbbLDBBhtssGCsibFdx5DEBhtssMEG64LWZCPPjalCElV9CWjT9tbrZ7+drI/cnlrHN8epZ1Kt16wFt9PY1BfeYIMNzqJTxna+Q60vvMB0CaiGWtUAphyI+Q5aOPdCtIoXTHE7y2mulraQUoVpeTxZAWllTWoMZ6s5tbgjroR14bHB/BCTqn8rRGeMbRPZa96OTZVdnOs7/qSK04fOzypxyIlPXLFdN8CJZ+PmO1lj+lnUX72kFOCN2nWT7wf9OG893NP1sq9zzuxc7Zrx3lyqfFaZz3nuO4NXde9FcDstI/PcQpz5/JzcQlTnZDSnScM4XV/96qVIr1e+9PSZtq5xfX9Z7eWrfn9uWTzlaC4Cs7hdp331d69436ZLTXZgzfbiExcmH7v4M2evMachEGcV0GQWerptc7b1Kp+/Kq95b3+BwZi8vr6xvLFzdN3umnGs0/R2g8YcgJtco6EvnuDTRLsafjbzXm7SP5Pf/U9Dz1tcdpD7VXXH5d/x3zv7nbrPltBf13l21/3O9E8TOO86N7nHPDrtpA681m3OReuN7fyEr+aBNPkgJwLgrhSqmK8Nqwt9zLBb832viYS1GfvCFz2QV4WVGvANLh2Hs4f0zcbluvZXGyJiF+OK7WuwLZ0JI1+EeWb7TYY6JifmnL1v46ZxFbxuijnk8/J7NRdyW2aS2Tz36mqfXYZW8mrkXt2TxXlDoNdrz/XDu01gkWPszIEdDfJq/cx2roe2yI4+de2Zp/8s6tYLvMGZ83NbNliauva68Fr4/Zbo1F127aUZpAXfa/raDtc6Q3uDO5y633qNMee8c9X0vTo/s130wz+ZkdnwtS849mxZQrWo664jr1Vj1ecDd/Ye03sEl3G/Jd3n/HHW7f46j9dy5b/5e3XC2Lbj4S8Gs7hteLUX68oL1pebw4UcgGaz0tuAdeIyjXXk1QljC+v58CusK7cNr+5hfbl5XuvLbwMPNxXJaFcaWmeM7XXh4+9hvcRNVkHF1P8EIKRPC29bpupMBB41ryBgFbvJfszmU/IXiile/nWlIAFcd3kBztmax4l/w+ZScZoXNL/3YAGYxcu/7zXeSV6T122H5+Vq3c0JWeREH3XpJBtnLbZ2OE5ow8mrU3y6wMtai7VmIndU2wDDFjLEib5aBdba2FprMMZQliVlUfrOsBZjDcJ5AyuEREcRURSjowgpZeuFy1iLNQZjSozxHCteztrAQaK0JooidBSjlGo9L+scpgycrMEaMxlExiJC30il6v5SSjVyaPgiYa2lLEusKUPfWa/MK1lEIKRASoWOIrTWaK2RQq266RfCWhv6y/eZrZykwAsHUnlZ1NrzUlq3fow55yjLkqIosMbURhfhsNY7Fyrwkkp5XlIhWz7GKl7jfIQ1JnA6aYCqvpFS+bGlJFK2nZelKHIGx8cU+RiEQCldy5uS0us/qZBB56/CUV9bY+uspShKhsNjxqMR+ThnPB5jyoLSlLigyJGSJEnY3rnF3t5tkjRtt2BZS1kUDIcDxvmYMi/I8wJT5pSmxJRl7d3FSUKvv8WtvT2yrIdS7VXeXhEUDI8HjMZDTGkoioKyLDGmwJQmzHQFOorIsh7bOzv0+1utNrbOOYwxDAfHDIcDytI7EWVpcLb0suj8rF1qTZqk9La22NraJo7bbZSsMQwGA46PDynLEhwYY8EZymCkpPQGKY5jkjRja2ubJEnazcsaBscDDg9ekBcFUgT5CtEW5xxSKaRURFFEHCdkvYwkSVs9xqy1DIYDnj5+xHg8Quu4dlZr50FKhFJo5R31OEmI4xit22sqrLEcHR7ywfvfZ//Fc5K0R39riyzNyHoZcZKiI41WEUorlPIOX/WzLLT3Cd4QzuGN0mDEaDSkyIvwb06Re8NUlgVlUSCFYO/ugDRNiZNk1U2/EA4oy5LhcMRoOMQYQ17kFONxME5FPZsXOHpbW0RR1HpFAFCWhsFwwOB44GdN1lCMc99PZYl1fiYFjiRJcc6Rpila61YrbxOM0sHBAc5aEKKeEZaFwTkbZkwWpTW3ipw4jomiqLW8nHMYaxkMjnn+7BnGWKIowjk/+7PVTDd8VkpB1usR6YgoRJDaCmsdg8Exjx89pigL0jRDaY2o+IRIBPhQZRzHICCK4lbP2p1zDI+P+dGHHzIej9je3iXrZfUsUErpo0eBn9aavt1Ca33hDoNVwzrH0cEB777zDk8eP+buvfvcu38fs2NAeFtgTYzRFm00ShmU1sRxvFRe62tsw3/WWozxittaryAKazHGUJSGoiixJmdrvI0xhmWkzd8ItXdtKa0PJXvDZCmMwVhLWXpja8oCHWmM8Qaq9Qhr0CaE/621GGcpAz9r/IypDLP3siy6kfBShVZLH0YWQpyUS+fCbLdEFDn9ft9HXVrcZ75gmh9fRVFQlAaHD0PaML6Ampu1Bq0qWWw/rDHk+ZjROAchSU+XMjXUjoXA+chLB2SxNIbRcMTx4BipI1SkiZEgDNI5ZKDgSoezDpO1n5cQAmMdR8cDnj/fR8cJva0+SZr6yITSCClDSTyfjS6CnG5mtg1CSr/GJ60iiuP6gRfSn2pirAEUUZISxTFCtNfjnsCvNyilcM6hgAhACPLcKwXrLNaWaB2jo47wEtTrYA7qASKEpBCCUgokDmFKv1YWRX49adXtvgzCc5FK4UTVdxopFUgxCZGXDiHw60xK0bZsypPw48iHIDUWgZA+T0A5z9cag7MGZyahV9XyKAR4FSG1RkURKuQKyBBunU649D/Wy6xWrZ7VgjdKOoqIkhg19mubQvo+kUoip+uzOoeQtJ4TeF5JmtDf6qMjHZx0hxPVkaXe6asT3So6S6a11sZWSoXSEVFkkSEsEsURcRyR5zlKDnHWgdb0en2iKG69YNUKTkdE2oTkE+vDczpC64g8H+EsKCHIen2SJG112K5ClSBUhRkFIWkqMuhcU5Q5IwfOOpI0I0kyb5Ra3GdVsomOY+I4wRhTJ2lYZ9GlpigKxHiMLQ1aa5I0Q+v2hpArSCmJ4oQkTZFF4RPytAYhiExEUZaURY4zDikkcZJ0gpcQMqzD9gFBFMXEcYSSGsck87UsSiQCHcWtX8qAYJSSlK2dXcqyJI5i4igijmNkvcQUssqlRamo9clR4HmlWY+923d58vgJURRN9GEUhSQpdWK9dhUnLa6tsa2yzZSUCBFS3sMPgAxeXpwkCClIe/3Wr2lWqBIapCeGNVXauw89SimJkgjnIrJevxOKADwvrRVKKh96NAbrqLN3cQ4VlHma9YjjDjhHeFmLtSaKdL0+W2W2WmNx1vhZYRSRJAlxnHTEOZLEoc3VTM8YP748t9IfT6kkWmrikDfQ9j7z67ARvSzDliVO4KMPyi8AGhPGmgCpfZKU6ohRiuOYne0tRoMBUkqf32FKhK2yk10wsDJkIkvaHWGpnIiEvdu36W9tAZPExCLPfVQv5A1IIcMSyPJ1/aXGVgjxT4H/AnjknPvJ8N5t4H8DPg18H/grzrnnwkvbbwP/OTAA/mvn3P+3mKZfDBfWioaDI148f87R4QH52CdGWevXlxASB2xtbfsBo9ofMgHvOOTjMfv7zzk82A9Z1qXfDhTCWkIp0qzn09874kQ45yiLguOjAw739xmOhtjSKwMf7heoSKOjGClvdUJxV7DWMRoOOdh/wXAwCFu2/JYZh/PrZ0qRJDFaq07tIS7LgsMDz8s7SRZj/fq0UhoVxWxtb3sHsQNOBIAUPmQ8HA5qXkDIkTAIqUjSlKzXZ6vlGfHTUEqiVIQpS46PDjk+3EdJFRwI7/SlWY80y7h95x7Q6sBRDa01vaxHFEUcHx6y/+wpxXiEDMttcRyxtbXN1vYOWzu7bG1vLz3xa56Z7f8M/I/AP5t6728D/5dz7u8LIf52+P1LwK8Anws/Pw/8T+HfpcKF/ZrHRwd88P57vP/u2zx7/JDhcOiTa6xDRRFpr0/W3+ZTn/lcJ9YmwM8YxqMhTx7+iPfefouHP/qQ4+MDv484rC8lWY/+9i4vvfIar7zyaid4eUObc/DiGT949y0++uH77L94RpEXdVJNFKf0tne4ffc+e7t7Lfe3PSZbf4746MMf8MH33+P508eMxyPvIFk/W+9tbbO9s0ccVRmS1AUv2omQHJWPeP7kEe+9/RZPHz1kODgOGfFeFrNen+3dPV75xKd58OClVTd6TjiMKRkeH/KjH36fxx/9iKPD/Trp0DlI0pTtW7e5/9IrbPW3cHfvrLrR88GBNQUvnj/m4w8+YHB8QFkUFEWOs44oitjevcXenfvwY3+ane1tXK+36lZfCJ+GI9Ba4ozh6eOPePThGGcNRZGDA60Vu7ducfv2fV751Kf55OtvoO/rpW5puvROzrn/Rwjx6VNv/yXgPw6v/xfg/8Yb278E/DPn09f+XyHELSHEy865j5pq8DxwzpHnY54++pjvfetN3nnrTzh88Zw8H+Os81sSpCZKUnZu3WZv7zauCqO0GJUTcbj/gvff+R5/8uY3efLoEflwgLG+4o3Fr431tnYQwGc/96dCeLndsNYyHBzz8Qfv871vvcmHP/wBg8MDvw/VOqzzYf+k1+fVT3yKV159DWOqPcXthXOOosi9Qfrut3n37bc4fPGMIs990oZ1CKWJ05Rbt+9xa2+P4vXP4KzDqfYG8Jzzs7yjwwM+/MF7vPXtN3n+5An5KMiitTgkcZqytXMLAXz69dex9+61ehsJ+JyAPB/z7PEjvv/2d/johz9keHQQdjT4ohY6jun1txkeHXLv3n1eevllXNZuowR+D/Hw+JiHH37Ae2//Ccf7LyhNgTNeLyqlSHt9bt+9R5om3L13r54Fth3OOoZHBzz88Icc77/AlHldjU4IQZKlbG/v8uzpIwTQW/Le6Os+wQdTBvRj4EF4/Srww6nPfRDeW6qxtdYwGg159vQxH/3oA548eUw+GoE1OASlsVjGyNEYhOL4+MjPMsK6U1sVQaW4Dw5e8OjRRzx+9Iijg31s6Wd+xjmMhdF4TF5a9vdfMBoNQ4io3bx8gYQjnj55xKNHH/Pi+XOK8Qi/icun9rvxiOG4oLe1w/HRIUXR/q0/1lrG4xEvnj/l0aOPef70CaPhwDt3gZdlzHCUg1Dsv3juZ73WtjqhYhLyP+Tpk0c8ffKYo4MDbFkAwm/5cY5RnlMax/NnTxkMjimLkjhueZ85y3g0Yn//Oc+ePObF82cU+ajOqbHWQZ6TF4as/5T9/ReMRyPstm11hKyOsgyP2X/xjOfPnjEaHCOCbqgydsd5iUPw5NFDjg4PKYuCOG5vIRJvTy2l8UWMDg/2OTo8AGdRIbzvnCMvfAEgi+Tug1d4+ROfZGf31tJyWm48np1zTghx5dEjhPg14Nduev9ZcM6XNCyRqKRHlG1TlGBtiRQSrR0ISRQlZFs7OBkFQWu3EoCQneuAKCXqbSOGOdaNkVKgHEgEUkdk/R2ETrwyb7lBAs+rNGBVhE63kHGGLUxIchMIJ0BK0rSPTjOME/Vsvs3h1qr4QyWLKu1jxyUOg1YSHKggi3GvjxGK0k4SwtrKC0KfOSBKiNItOB5hLb48HoSxFpH0tnA69k5u66MsPhvXOnAyIsq8LJq8rJMSfbUvRZr2UUmKcYQ+68Y4s0Kh0y2ibIvBMAdhiZRC4rdsxXGKzvpYFfm9/HZ5Z/FeH8Lr9GyL3vYeg8GYvBgixSSvQ8UxKukh0wyDr3Zm7fJ4XdfYPqzCw0KIl4FH4f0PgU9Mfe618N4ZOOf+MfCPAa5jrC+Cj99HbG3v8sonXkenPY6PDnHG+KQaqv1lEdnWNrdu30XpCNmBIulS+sSne/dfYTQu2L79jDIf+dR2WdX/VMS9HnfvPSBOs0m5uRZDSkkcx9zau8Nrn/4sSX+b4fGhzxpXGiE8L52m7Ozu0d/emSRItbjPqj21/a0dXnr1Uwgdc3iwjzOGSGvfX0Kh4oi0t8Wt23eJdOQzKFvMC7wsJknG3XsvM3wjZ/vOPcrxCB1KGUrpa9PGWcbtu/dIOiGLIugPzc7uLV799Ge9UTo+QAlfY1cKv4UkznyIvL+9i+5Isp6UiizNeOnV17BCsP/8GdaURMpnIFe8st4Wd+498GHWFs/WYbLzJNKae/fv89mf+I/YuXOXwdEBSgokfp90lmVk/W3629vs3blPnKRI1f6iFv8S+K+Avx/+/T+m3v8bQoh/gU+M2l/2ei34hx9FEVv9Le49eIlsaxvnLFEcE0cJUopQHN5nJfe3QjZhiwUKKsWtSNOMvdu3scD9By+jtCJOUqJQDMKUvipRdcCCbHlmazVY4jhhZ+cWD15+lZ1bewDEcVKXLbQmnFgiBb1+vzPbLbSO6Pf63Lv/gKy/hTFlXVO3qrjkf5zP2u3EXlSBUpIkTbm1t4cTggcvvxIyPxN0JYvGc9NR5IvGtFxxw1SfbW3x4KVX2d3dAynC3k2/9aw+PUcI+v0+SunWZ5D7Pd++ZvqdO/dIsz7Gln6roI5CUh51laVer0cUJZ3oMwClFDs7u7yepLz8yqu+lsLUoQNR2HOLc2zv7BLFyy2vOc/Wn/8Vnwx1VwjxAfDf443sV4QQXwR+APyV8PHfx2/7eRu/9ee/WUCbL4WoCj9EEb3+FkmWEccJvV6vLoI+HheMR0NGo6GP2a+iodeAFAIVCh/c2ttDSUXW65GmGVoritIwHo0Yj4deySnZ3iybKfjTfCRRHLO9s8P29g5JmpCFdH5rHeM8pxiPyPOcOIpa7xzBRMEprX2BkTQjiv1BCkmcgIA8L3xpwOHQKwAhukDNh1OlT8jbvbWH1oos65GGpJO8LCnGY0aVLHbAOaoghECriK2tLba3t0mzlDTNiHSEsZY8zynyIIthv3dXuEkhSVJfjMQfEJGGutbUGdej0ciHzTuyHRL8ko3Smt0k5fatPYSU9XqssRYpBMYYRuPR1AlvLZrZOuf+6jl/+vyMzzrgv71po26McG6mlLIWpCzL/GAJs6RxnjOKFUqKEO5vv0DV5zOGk2+21TZZlvlqSrE/waMoS0ZxxHCgKIrclwhs+3JLDV9as5d5pyjNfLag1r4E22g8ZjzUjEZDoijqhBMB4QzUsPE+juOa10QWx4xHQ7SU/thHKTrRZ1UTtY7YjmLfb5lX4Ep6YzseDYkGiqIskEp2Ii8CCPVzBWmW0UtT0p53jrTWGGsZj8fko4jhaISOdCh4335uzjkcliiKSdPUO31J4jk4yIuCssjRSmOd6ZShtdYnRHldn/oDFiKNFJIy1L0viwIVQsdiyQU72pzweG1UHncSJ/R7vrBDpeD8mosPxyrpDVdRH6PV/sFS1Tft9Xo460+9ScLJN0rK2mOTUjIcDidp7S1PtpmEyP0aURzHtXPk11V82FIrfzKJVqEmcgcUXC2LfV8SNK0MUr1O5o80k0IGWVxBLblrwK+TeVmUQcnFSYoOdYKVjtBKoqRiMBz4WtDtpwX4kGSWZWita4c2CjWEXdi3qbUGKTuwDj2BENKXbNzqk6Z+AqKjyGftBt1iSh/uH4/GnSjWUTk5UeQjEWnWI03TkDfga3bHzie+maj0iXrWhByd5bVzLY0t+MGSpCllqEXr62VqX1A8hJkFfm+W0jlKt73wu0dVci3LejhrieKIKKoOG5dho0zFRLT6mLaT8EkpaZqhw5FfUezrm1a1TKUQobIPSEGtINqOKsJinSPS/kCMusSf9CVFpZShHGXeiWL9UNVGjun1+l4uE19rd3JGqqsdWoA4jpAdiSAprf0SRhxqCMeRd4iCLE73GTi/Zrvqhs+BShadc77mcxLXvIQQWKUwWtcnOMVx3Jk+i2J/fneSpsRB74ngDFURFaM1WVninEXH0cbY3hjBmMZxXJdZU1ohlZxk1gnQaFLnZ4VdKJAOVf1gTZomOGv9SSqqykQOgz/yn/VJAZWCaDc3EU67SdOEUvm+0sqXmhTBMRLar4tVp3eoDmy0r2bsSZJQ1a3WU31WLQuIMJvVWgcHSbZaeU+S2mKyLAP8bE8FbkLKsH9z8nkvi+0/panKRk6zFFMalFa1LFYzPaHDmbbO7wSPIt36MQZef6RpikDUfXWCV9CdpEmQ27T1fVa1OU3TcJTjNJ+pNWfhs7F7vR5CQBIn7Vqz7SIE/qFWhwP7A7vDKUDhlBhhRegI6TNDW36gdQXf3riuJuW9bFXP2P3yoJ8BKlWddNHuwQJVSFJjkxStQgF7IeqjzarPiOqAa+HXCtveZ9XWnyoxr6pmUxtbBE5IpLShby1JyJJsu/L2mcfVGDPByVV1eNwF50IKWRss3fJTmmCymyFNM0wo0iHklCw6Pw59oX4VPh93RhbTJD2xbCaVQklfJ75alqkmK1EUt/5kLaBeghLh0BkXwsZVgpdfRZscTVrJ7jInWGtpbME/5IiwZSQUCfAx/DAgwsb7qgxZVfy97fAhcR9mtJEvH1cJUI1wzq0O5deUar/irmYTAoHRvog9cPKwgcDROlevu3ehz5SS9RqtDYqgWlefhg6Kbnqm0WZIKevjEKerlJ3eTuFCwXelFLID46yWRSEwJgonhTnOHDcnIHLV59vPC4IsJn7WWpXVFKdkcZpH52RRSIw1dSlXKb3OEFBvYap3qyxZf6ytsRVMFLULZcgEU4Ik/EqScy4kL3codV9KokhgrTzLq170dye8uS7Ar5WFEHIwPNNtr15XSr1TvKYOHw9vTs6wnuYREtm6wm06yavCWVnsXp9VDoNX1ufLYtf6a1oW9VR52lntF4SdGh3hVsuiUzgX1tKmZPB0/y2b19oa2wqXeWU3e+BeGCcZlstLtTwzmz37iQYmsxW/5fK6rE9uPkj8s/G0lsNt7sF9U25CBErL6zOfQHNxMfdGFNuk05YCP8YuaXdHDNE0uuT0XBXTsti2DRhrb2ybxLkCKs686MSeu2lM1jVm/rF+2TVe0zPIGX+i6rMu8qrWf8/+rf5f93hBLW8z+63LslgzuvhQkO7xmmASfZp0VRV9WzavZg3t2clLFRWYl9faGNvpEGODV2VWCGmZWAyv2fdYJsVl8mr6s/Ncaym85mhzs22pIgLt6LMG77pkXsvjuDRZPPP+1OsltaVJzCOHE705H681MLYnPY4mOnRVA+OidjRlnNoQPloEr2bC5jfHiQEIDYQ928Wret2E0myjLK5jnzVn5NrB63S0qiuy2P40synMXMRv/BktX5r8vl9x9r1F3GfZEIJFP1O/BrXQW8y6Kad5nX6+Dawsr4bXHLJ4U1latixO1ilPeOZnP9fIfW54kWvc81JZbKC/VsFrFo+mm7EsWeyMsZ320qbeve7FWuFVT+OCVaorXES0KvmhGhjN5Ma0hxfQGK8LZXEVvtGct720xnHL+qvCrPDm1a/RPm5NjbHznsry11tPR78W77RXWBTTzhjbCu6C3+aFX6yf7w7LxcX3vkzgxSWfW/aAqe532V3nbdd5H1vVOlATvC6Sxbaub82T7HyZWuy+LLZjjNX3vezvc7TLhdKTbcSMgEtjOM14USa9e2u2p4Smynq7ylpL25TYrLbXmW4z/nbudVrGC2a3qe6zK7T3ss9WGY/LwmxeV8tOPO86s665LFwoiw3yWgVOt2nCC65iZObps4uy4JtGU2NsvvssN5x8um/qHI/JHxu5SUiDXSi3Ts1sZwuO35jd5P67pSuKi7zkhtvSDiXomm+HW8A1r9WM5tuwVF5LnLG1p78W0I6Vc1vUeHA1tUUydDNegX+svssWxW1xeqQTxnb2eu164KJ1kg022GCDtmKRWuuCXci0NdR9GdofRj711Kc3TdcPPWzw52Q5p+vfcumZkviD8a4YzrrevZbD7WS4p/5f5zH9/NowM2sSy9j7fAZLiP9XZQcbDT22ABUvOBtuXSwW22kzy2KebkEH93q339g66hNg4JyHXL/XTAdU91iGYarX5JakBJa1BuhrTjfj/LQJK0uAWUK/rYKbP395wby8Elk6Ft1nq+J1XVwl+WyVzvrpfqvufNOebL+xDViJkju3fuFlX7taW1eVmXllRXDFxvceTQAAIABJREFUZl66PWQB8AOFEyq8jnmcm7jiOjHhOd1+dzqas4DN/svAaXk82+7ZpfK6gOmkolnG8fQY7BaviwpmnOyztuvEM/f3jTj7Htfvs84Y21Vg3cJOp7FqgV4UqpDaNDs39cdOsz7TfnfB37qF8+WxG87Q+Ti//V0fg9fts9bzvqB91217a4ztm2++yW/8xm+suhmN4p133mE8Hq8drzfffBOA3/7t3+b+/fsrbk1zGAwGAPzrf/2v167Pnj59CrB2vL7+9a8D8Hf/7t+l1+utuDXN4eHDhwD8zu/8Dt/5zndW3Jpmkec5b7/99trJ4h//8R9f+HfRBg9DCLH6RmywwQYbbLDBDeGcm7k+15qZ7V/4C3+Bf/JP/gmwmi0+M7P5brAx3TnHr/7qr/KHf/iHvPPOO/UWn2VTmx0Kv347nHP883/+z/lbf+tv8bWvfY2f+qmf8ldcSZ+d9dFu0o5Hjx7xkz/5k/zNv/k3+Tt/5+9UF1z6xqymeTnn+Pmf/3mEEPzbf/tvVyeLC+D19/7e3+Mf/aN/xLe+9S3u3r1742terx1wNqHhBmMM+KN/9+/483/+z/MP/+E/5K/9tb/mr7hsXtCo7gCw1vKZz3yGn/3Zn+V3fud3/BVboTtuxss5x1//63+d3/u93zv3M60xtkmScO/evVU3o1FEUYQQYq1CrQDb29sA3L59e636zFoLQK/XWyteAEqptZTFKnR89+7dteqzvb09AHZ2dtaKF3jjGsfx2vFKkuTCv3eiqMUGG2ywwQYbdBkbY7vBBhtssMEGC8ZaGNuZZ1WuC9aV1wYbbLDBf0DomLEVk3Myq9XsE6W9VtSsBWERByWvBmdqbq5lnWtg/YRwgw02aAStSZCaB2cOfj5TyePCb9dH8dXVhK7eAhZSNuwCo+qm/n/Ol0/wgpZtGK+5iWuUPROTY8KWWNJyfkwyGOtKQXSqgt65OHu849owYz14bNA1dGZme9lM6DKjVE+Er9+AcICxmApbN4MLr3SZgbkpL07xaZLXKSfiagFxMRW8uO4Mf/qOzc44hTi5VeD075d/t9p+c512LTIyMLl25dD6SNKc3z4hR1dt44IjHlNj+ESUbK6v3myparGRHHEDvXRTPouP5CwzCnaSV7P37cbMdo6HXSmGmbO6Gd+f9zDsizr6qgdqX/X6k3vAeY7EdcVh1n1PG9ybcLv5ADl7ssj1+2wygJqY9Tc1+CeXmW+2dfq+TZ9ANFMmpv697A6nj8Kct0Xn8bpZFOrEFc+ogOt2of/e9eRwESdGXTSO52sjVL08f3nak/08PU6bLHE76/Sfqzy3q35n1lGuTQbTOjGznXtcnPNkFupT3mCWO//3zuF1ztebMwbXnU/e/P6LeqY3TaRbhJfdxCXbuAZ+0yaJGa8W0Y75ZKZZNHHNpts1/9UuiCA2ZJzO43YdznProwWvLnRjZjsnzi2JfekJFe3FRa2tZryL4HWj8zEb0AEnjtlqkFsb+r9VfdUgmue1HE6X3aervObBtdoyw+At69CWq7T3ytwW7K+ujbGd58FeS7BOHbO39JOAFsTrzJmNKzrm76afmfWdtvPa3OvstVfRZ4t2UlYpiwu91xmd6Jbi6y36+Z09x7bZ+3Xe2C68A/xNFnPtCw6XXsbAXNQ91pXXqu+7rrwWfY9Z8tiWWfNNrz1rnHW9v1aJRfLqxJrteQ9g0+HtxSwO68rrove7hFkU1oFXhTWiUsM5d6KP1qW/Kl7TP11HZ2a26/CwZ2FdecH6cltXXnT+kPbzUfXZuvPboL3ojLG9HibK46QwTgoQVGsP0ynwXcBEeZzmBadT89uYqXoezuXlmPQVXGmPZBvgJoJ4YiXIhwKhq3026aeThto5d6bwTNP70xeJ83hV/dfdPqv4nHas/O8TWhNOneBVzYDP+Vul2iccm6+XcBnW1tg657DGYK3BWIuztg5HWGsRQiClRAiJ0golFUJKpGx3ZL1qf8XNnuIFTHgphVIKGY5Xa/ugsdZiTOn5VX2Gw1mHsxYhpe83pdBKI5UKXLvAy+BCf1nrAId1FmssUojALciiUkipOiGLZloOT8iiAUfdZ0rpWh5Fy/usGku+zyzWWS+DeDmsZBEEUvlxVsljm3kBWOsoyxLnbOAWnNsw5iaOQ9AfWgd5bLcsWucwZTnRh1Nm11pbhzSccwgp0UrX3JbVZ2trbK0xjEYjhsNjysJ3grEWrKG0BoE3tlJpkiQhSVOyLEOIqNUDxjlHPh4zHByTF3kwvDY4FQasFyapFHHsefWyHlEct55XURQMB8eMxyOssZjAq/oRwjtDOopDf/VI0wSl2ivGlUEaDgaMR0OM8c6fDca3NMbXqhESqTVpkpBmPdIsq89DbiustYxHI0ajwWSMBQNVmrI2SlIqojgmTVN6/T5xnLSal3OOIs8ZDgcURV7LorMGY0uMsbXzqnVEnKb0e33SLEMptermnwvnvKEdDo7J83EYX8FJCrJYR/2kIoljsn6fXq/Xer1Y6fvxeFQbXACcq2XRWu/gaqVJsoytre2lOuvt1VI3gHOO0hiOjo7Yf/EcYy1Sysls0NgTno/Wmq3tHZRUKKXbLVTWMhwOef78OXk+Rkp1aqbr4yTOOZRSZL0e4t59lNat9k4rBbe/v89wMKCqzeu91UmfOQdSCpI0Ze/2HbTWSNnuGYUpDUdHhxweHPg3hMAGL7wsy8DLy2MURezs3uKOVmjdclk0huFwwIsXzzGlQVRjzBjKEKEA37dSSnr9vo9K6Kj1sjjOx+zv75OPxwghvKI2pXeWjMHhsNYhBcRpCnfvEcVx6yMtpiw5PDxkMDiul2Zs5SCVJTZEJZxzRFHELXObKIrROlp108+Fcw5jLcPhgMPDg9Avk9reZVk5giVlWSKArZ0dkiQhjuOltXMtjS2AcxZTFoxHIwpribSuZxkmzCasdRhrEM6ipKLY2SVteaKB51CSj8cMRiM/+wGMCZ6p82FX47y36qwl39nF9bdW3fRLYYyhzHNGoxFCSpSUNS9rLQTP1BhDWRT0ej2s3V11sy+FdRZTlozHY6xzaKV82MuUlKXvs2pWOB6NiOO4fr/NqMZTPs7Ji6Ke1VljKMryBC9rvJIr8qITyTzWWMqiYDgaIYVECDCVc2R9WLm0Jbb0Cjzf3ukGL+t5jYYjHH7JqdIpfvbuMLakLEq0GtPr9euZYpudCOccpjSMhiOKskRJGcr32noslWVJURQ4WxInCdaYpbZxbY0tCJACpESETdhS+HUW8ELnsOGBW4QUYR2m/RDC85JKhgGjUMILl7EWh8GVPgSLoBNrSTDhJcJAQcoJL2MwwuBKvOeNDuuaXeDl/ycqWZQSFQqdOzdZU7LOzwTr3IG291mVZCJF3d5qjKkqh8A5nPPrnkJKlGr3zK9C1WdSSpwAKXyfOeXwGsOA8Y6UEAKplV97bzu1wMs31EfBBDJEyBxOWHBhjCm/Ji1l+/M9BOCEX7t1zlFanwtR5Q9UEytjSsCilAx6cXltXEtjWyU/RVFCkqSosqyToKowSVmUfj3G2v+fvTeLsSxL6/1+a9jDGWLMoTIrK7t6BkuWReMLBllCFvSLfXnpVssXCfC1aeki8IMtG3Vf+aUfuaYlWzJIF7sFDyBkhGwQFrIfSpaxuQ/dQn3vbaC76BqzKqsqK8eYTpxz9rDW8sNae58T84mIM+wTxF9VGZlxIvZa317f+qb1fd9CCk2cpESRRjacqaQQqCgiTpLRYb/WgMBaQ1GUlGVRW6JxnPrwVsPpEkKgtCJOEpKiwDnQkUIga4/ClAWZy3HOEsUJUZI0PoQM3nuI4oQkSTDW1gkn1jpMGVGUBUWeY40J7yBtfAgZqjWLwhmsrKMRDoExEUVeUJSFV7pCeLpCqLXJ8El4miiOQ7QIlPZGhDZedpRlQWYdymmiOCGOk0BXs9dMSoWOYuI4Drwo66MoVZQUZYFzOVIpoigmTtJG50QAtcGntEZHUZ0t7uW9N17LogwyscohqOi6zka+NKSUxHFEksTUvecFQekolLJYp4msRWtNkoaX33QBJyWR9kldzlgsts7Ul0KipAClsFEEzicC6CWgC0ApTRzHFEHIiXANmpQCJQVOyaCEIElaxNGSCG6piOPI0xbOjETwgqT0a2aVQkf+Z5JgRDQd3qDVwfADxyibVQoZ1k16IYgmST0vNp8TQWufIGTK8iAvCjkS7iFilKQttI4Qotm8CH7NkiQmz2KKoghOrkCEa/pk4Emto+CANP8cGnyCYRz2jxsLewtcoM2XogkpiaKoNo6uS3+mAK9bffhx2O+T5RnOunD+4FPehRQ4IYjimEhHSxGSBM9YOEeeDxkMBj55yDms85muAoFQkjhO0DpaotCdn2NR5Az6fYqiqENaxhqs84JAal2XyCwLXUIIjCkZ7vtMUH9OZn12q7UI6UNaSZqGpK/mh+7A86KzlmzYJxsO69ImY/1ZppfmkjRtEVVJeg2ny6+XxDko8ox+vx/KZfwZdVkWWOsQShJFMUortFZNd2qBkWIty4LBvq9owFHTVRoTjtQUnW4XrZdpj1W8OGA4HOKszyIvTRESEQEh0HptIeVMV1PZOod1jiLPeP70Yz547wE7W8/Js6EXAM6hooh2p0urs8q9T7yKvXOXZdgtPhGgYHdni/feeZvnTz5m0N/3zGQMQknSdodOd5Wbt+9y48ZNloEu8Nmeg/0ejz54nycffcje7jZFUWBNCQiiJKGzssrq+g1WOt2lIKuq986zIc+efMxHD9/3vDgceqHtHFpHtLpdOp01ok99ZtQ0wblGKyZnLWWRs7P1nPcfvMOLp08YDvbDmhmQkrTVptNd46W797B37i6F4K6SK/d7O3z4/gOePPqIvb1tyqLw2fHOEcUJ7ZU1bty4zcb6BkvBjFAbRk8fP+LjDx6yvfWcIst8uN8YX37W7rK6ukE7bTU9Rw8IrR0DL25vPePhu++y9fwpw0GfIs99BrIURGmLdnuVVz/9mbq+eJ64ksrWl4wU7O1s8/7bb/DG63/HzovnFEVehxik0qTtDitrm0Rac//+qzjX9MxWL7j7+z0ePXzAG9//Nzx59IhssD9qliCkryFbXScbDrn78ss491LjswmtdWTDIc8ef8Tbf/+3fPDgAb3dLUpjfEMBBzqOaXdXuXvvPjdu3MTeu9f4DFBf21iw/fwZD954nXfe/CHbL55579b4CItUEWmnw8bmTTrdDvc/8Ym6G05TV6zKYO3tbvPBg7f54d/+G549fsxwsF/Xb1a8uLK2ibOWT3zyU7iQBNZkWGsZ9Ps8/ughb/7ge3z08H16O9t1OZN1PhrWWVnj/ic/zZ2XX/bJiA2Hc5Y8z9h69oQHb77Og7feYPv5M7I8C7X6Dqk1SavD7Tt32bx5E1O8Wjcqaa788JnGvd1tPnzwDm/+4G94/vQxgxCRsMbzYpy2WNvYpNPtUGSf84095kjX1VS2QcD1ejs8+fgjHn/8EcP9PsJZEGCswyHY7w8YZjm3nz1h0N/HlAatm8tUzoExJf39Hs8ef8yjjz5g58UWzhSjzx30B336gyGd7iq9vV3KoiCOkwXP/nQ4axkO+7x49pRHH37A0yePKbIBVRs54xyiL9nvD1A6Ynf7BXmW4ayFRjcSsORZxs7Wcx59+DDw4j7Wem/d8yL0+n2KomTr+TOGwwHGWHSDd2fVhGRvd4fHH33Ao48+oLezS1nkQOjo46A/GJBlBZs3bzHo9zClwcVN3mM+VNzf3/N77MOHPHv6hHw4qJWOcSD6ksEwp9Xp0tvd8QluoZ6/qbDWkWdDtl484+MPH/Lk8ccMej2MKcMeszgEqj9AKc3u1ovQJMLQZFXhrKMocvZ2tnny8Uc8efyI3s4OeZ6FZkbOH0ENBhjr2HnxnMFggCkNRPMz1pv7Bi8BX+TsKIzDCA0ipnQDnAOFxLdHEEgUTkjy0lKU/uys6bDOUZSGwoETEaUVGIOvK8N79RaFEIrCOvKioDSjdmVNhKvC/qUhMxaDwiDIjfNnl0jAYYVECI1BkIXElaZb3c5BaUqGpaFwAoskNw7nfPKQEw4nBFJqkIqs9DWqja9tDEopK3xWrnWKwjhKS8hIxrvlUiOUJjeWvCgwoWFCk2GtJS/8mpVOUlrIy8CLIQnKKYlUkefTJeBFF0qw8rJkkJfkFkoral70JVv+PF2pGKfGedE1+kjD4ZsYDYuCrDR+vYyltCCEAmHxrTUjhNJkpSXP83rN5oUrqWyrw/I4Sbl15x7DomR3ewtbFj4DDS/cZBSRtjusbt5E6aipvHQAQgh0HLNx4zb3PvVZku4qxXCACrWZQmqk1MTtFpu3XiJOWj4Du8HEVQlEUmm6K+vcvf8qIorZ393FJ1d740FKjW6lrG9s0uqs1sk2DSYNACEVrVaH2y/fp3SCna0XWFMS6RFdKo5pr6yysr4ZMlsbniAlfBJNnLS4cesOL3+qT7KyRpENUKGvs5QaFcWknS4bt+4QxWlI0Gk2hPAtQdfWb3D3E59Exin7e7soKUIPde0z5zsdbty8RdruoEJma1OXTISSDCU17c4Kt+++QmlhZ3sLrAkNSSRKaaK0xcr6Bp3VtSAXmy0/CK13k6TFxo3b3L7XI2p1KLIMKfBerdC+rebaOqubN4iqUq3rbOTLQ0lJkqRs3riFjhOMNejQKk4pVSeuGGtptToorX2tYKOZyguCJE5YXd/g/quf5tadl5FCEEWRL6sI7deMc3WdXNMbv4MXBnEUs7K6yt1XXmV14wY2dP6KIt/X2VmHxSGV8n1N69aazaWt6p/b6XS5/dLLpJ0uZVGE0rTYe4Eu3DIjJKtrYwKu4VBK0Wq12bx5G6TmpZfvI8C391MqdPBxIIUv1YpDSVPDaZNSkqbeqHv5/qdY27yFtYZIR6H0zFcDCCl9HkFnOXjR13tHrK6ucvvuKyTtFcoyR0lJNFa6JELG/9r6ZqjSaG5oHMb2WHeFm7fvYIXk1p17PhFWyeCUS7RW6DhmbW0jlHrON9P6yirbqmNP2mrV9YtVE3QpBUVZkg2H9Pf3fQec0JKt6agadiRJytr6OptS0Wq3SJIWWiuMtWTDLJz7lUvRHKGCV0AJKyurdLsrJGlCK22HQnXn2wJmQ7I8J0ma36gDqvUS6Cii0+2StFIirUlbbZI4QUhBXhQUWcZwOCRO4qUo+6np0tor3E0Rela3SIMgK0tDnvs1cziUbv7tU9X8lFIkScr6+jqrq2skSUyaellinefFosgxpiSJo6VYM/AGUhQnrKyskIYyM3/phe8RXBQlZZFTFAXtdgup5FKsmQplWJ1Ol5shlB9FUR0lKkPPbmuNX8cgF6+V7SVRXxMFtZJtt1qkrTZxFCGkJMtzYu07RllrkFI0+VgTGNHlCJcndFdopS1arRZxkgQBVzKMB0Rakec5WqsDly40FUKEvuhS0mq3SeLY334TBIK1lmGWkQ0i9GBAFEdNdiKORZwkdHSXtJWSpi3iOAIEWZ6TDwdopXx7vAYLtoMQI15cWaGVpiRpiySOUUqRFwVZNmTY15SmDGe51d3Ei5776XD4qxzb7Q5JHJO0WqRJitIaYyxZNiQb9v0ei7wYbfpZdN2hUQiSJKXT6ZIGunSkweF5MRswHAxJ6pvCmk2XhwABOopYXV0jTRKiOPbd9YSgKHyXtiLPUaG15rxxJZUteMaKoohut0MUxbTCNXO+SDvc1Sj9AuVZ7kMlDU5wqCAArTStVgsgKFqvkJSSGBOHFmwS2e/7sxjnGp0g5VF57AlSCJJw1VwURXXCjb83VNaeh0fT6fLNVaIoptP2odS05b9WYSytNVH4e1mWnv8av14enhdTpPARFs+LKrQF9GFXJSXD4XC0x0JXnyaj5sUQUk5bbd+UI4QltVZEWjEY+DPqJWBDCF2woiii3W4Tx5VcjOrG/TqKiKNwN29YpeqYo8lyEXx7xjRNEEjSVlpfUSmkxJSGIi7I8qHPQoa5y/srq2xlSJDqdHw7xjiO0VG4CFmMNUR3+MSbKPKdU5rNT77dWBzR7nQASOKYKHgSUiq0tngbwp8fxXG0FF17wAuwVlCwcRQRJ3G4lDtc1F313g2JkV7hNp8uqRRJmmKdJdK+haiOoqBsK8PPGxRFntfWeNMhpSROYtrtbvCWEuI4QoU1c9YbgDIkLHrh1+zzP/CeXxXqj+KSOIr9mimFDEaekn7dEKKOSCwDlFIkaQsXmqkkaRI6ewXDT+nAf9L3S1bVlRnNhr/nOqLd7iKF8LKjChVLT0tURuhIUxS+V/68ibqyylYoVffJVCr0Z1V61A9TgL/K0N+1GdeCoNlsJYL1maYpBAu7EgJSSJwT4KOTPqwSlHDT4b1VTZommFIFL1bXkQgAEfoiu+AdLUOf3dEZewI4lPT31OrQulAQ+tEG5Zpr7xE23YuociJ81MhHWXSkUXpk0DrhfLfG0Hc3qs42Fzz1syCEv+yj1Uoxpb8cQivl74SueDHy/ZF9n2vf4L/pdFUGq/f+QCrfZ11r7ZMoGd3gJABjDWpJDD+E8EeG7XaQe4EPpReE3rjwOqAMBtS899iVVLZC+BT9JEmoTjn9bQ+yvm5OWFEfkGutQzbvZEqpXiJRnfPOL4bkDYMYlzqcNTVdSnql5AgNuEOZgr+se7IkqTp6uYBjmiqhIU1SSl2Gje9v6KiuPqzoEkKGrNeo8dciVvxVhceBINBUHQp3TiKEDWE8TRIndSlJUyHw3l0cx3W7PFnT5fnNhf7jQvjrIP1RR/OFtxA+OzdNU0wI64tqj1W8GK7klEL6VoBaIxreW10IUFqTJKMSLL82I4NcOImUXoY4GF320eA18/LeR05arVbNd1JUV1UGuea8gW6dDZUA19nIU4FSXhD4q8x88bIMwhvwd4qGUIpzzltBE6aC13poAedqUsr6LMIa4y9UOHCGSbgvVeMiF27wmEzZ1uQs4PypUkqkKbq6LB44kJ4fQq1Vl69lybT2ma3+jNbT5cJdvMFQcA4XPCfwCUdyQsNvkajKl6SUmNAgQApR7yOfMKRwyqGdD5svw+URUgqiSCNEK/RCrnhxnN8U4LAhybIqHWwyRAh5k3hjzt/p7Y2mw0arDrwYRbrxpT8QztjDVaJmrCHM+B6rS9EY7bFrZTsFVAqoujeUyuM74eU2Pb19HJXAclqPrpK6AnRVhkRVBw2nz39ZaKsMAynlAboQx4dUl4mu0R6zR3hxnIYjwq/hqPbYQV4clQd6z8/V2b3LsF5AbXwf4UU4loZluF4PwjxDpMuOdwIc22PjdCyCF6+ssoWxzkQzeqeHmdDNMQY7a0Y5LCir1PpZe/NHvPTZjEJVajSv6MR86KpHY17hidEeGyWuzXA05n1kc5xSmjrmnH1+mK4wiemv3WiTTfnBJw3n8z6U8uOdVmF2+bU8Py9eaWU7fZzOkKMFFDS95u4IKuv22I/GFO+cpjM9VJvi6NoJqOleuvWqcTxPVsU186RrFsL68CPHi4bmRdtslOyhdQtnpPNcs1kZDweeK+bLi37ok2XZFB4+4r9z8uIVU7azs3wXG0qZpUXf/HKni2Lc+Jngp2c5lanjLH4cD50tlSEhBOIUl2S5VukwRB0gOuHTpcVp/Li0vBhw4jHWOZ+z/Mr2iPU7nQVtwjnFwTlcHbrguDD1dJ95/t+dyhSORgGmQdsJZ7vzxMH1gmkZf8eu2ZyJbRovTg1ixlGABfJl3U1vhsp72mu4NMr2yPlo+OMir+OshZr3RqnHc9StFY+bwySWYdWo47gWjYsQAMe968PzmNjiPeFsa1F0HVY6R+ji8iqpCUL76HoBTlyqDeh4CH9xOOZoYQreVxPWjEM9uq4UXeNR6ml5y8K/sVmezy9HauAxEFx0rx7MSDv8kIUpWvALfsyczvGw+jlNCEod9sxH/1/sWYunyGOUvcmlFUbTMlknnc+Zivas5yxinx3aFxffZifz8WLW8uh85jGNRQSEZ0bXHIhZCmU7TQYef5Q7pmfwvM8UjhvvotQe4/ufOdbcIMLYxybznPW7p3+8qDU7K0w86ayadox11nQm8WjP4uGFrNkZ4e/q8vcznzX257HjzB2Os1ZtGvNqAp8ensM03/es6VuaMPIkmOSMbEENkk7ESedFlzlHalISwsE1CQKvCi0f+OyMh4jT6TotzX8WOG4uVS3ped7/WT877wbw86KrCRh/t+eab8PW7LQ5THcdXCjvmlG276RzqFtCTf/ZMLvoxFJ4tofhGFmhtRif9Gabyno9TXA7NzcrbjT/4+dwXgE3ifCeK04Q3hOv19jvnDVOEwT8LOZwTdfsMKk3e+7nTv2J58es3m/12IUt34wHnvS9nXcajVe2xyYKHfr7bN797DnpwDnkscZUE7bsxbBoy362uMq0XWMqWN6tOwEqD3DB01gyNDqMfGrt1thnPgFXnHkmM62xZ/H8KjvzAAmzsCRmvEGOyzSGKZbBLBgjeqbLb03APMopjmBu8f95Hx7N+2Bjfri4bGz+XjkPbed9DY32bE8L51Yh1tFx4NlJAucde5Y4Lnw1Ukjh/1nMYcb8Xof3qySiC4SMmwx3IIY2P5rmoQBnFVI9dcy5vcP5818TwuSzwEXpWobXMcs90GjP1sMdbIsFh1Ztduer80h0cD7jYK6cOPNer4fGmSf8mnGEZ6ogwUkkL0PyT8Uro0YFFeuE+RzX3nAJJNxhfjy6x4/Wwy4DXVDN8+Re3AdoPubzpmJ8zaoLGQ5jFCmBZfBqx3FaJO6iEaAzPVshxH0hxP8jhPiBEOL7Qoj/Knx/UwjxmhDizfB1I3xfCCH+JyHEW0KIvxFC/Pi5ZnQcxr2lBZQMXOT/cw4ym8mfOeyM6VoQvNN5kGdc8ESXmS7gIF0cmvsy08VpUZDlpqtep2PmfYDmpaMrKJwTo48jPl06nLIWF+XoLzaKAAAgAElEQVTBSTzbEvhvnXP/WgixAnxXCPEa8J8D/7dz7l8IIf458M+BrwP/MfC58P9/APzL8PVUPH78mNdee83/QxzufzI7nObtXO65jq2tLYwxI7qA46z0qY99wK+b8rOd4/XXXwfg29/+Ns+ePas/m0dSVB0JmAGNW1tbADx48ODQms0vClB5CtOkzTlHv99HCDF/ug55PdMczznHgwcPAPjL//cvWV9bP/D53CI3U5ZXzjnefPNNAL7//e8fWbNZyscjHt0Ux6p4wRjD8+fP50JXtZeOeN8zWLPHjx+f+jPivBpaCPHnwO+E//8j59wjIcRd4C+dcz8ihPifw9//1/DzP6x+7pRnLqHpc41rXOMa17jGQTjnjtXi5zqzFUJ8EvgC8B3gpTEF+jHwUvj7PeDh2K99EL53orIF+Omf/ml+4zd+4zzTaTy+8Y1v8NZbb/FHf/RHi57KVPHaa6/xu7/7u3zzm9/k05/+9KKnMzVsb2/z1a9+lS996Uv80i/90qKnM1X8+q//OlJKfvu3f/tKlWX9wR/8AX/+53/O7/3e77G+vn72LywJ3n77bb72ta/xa7/2a3zxi19c9HSmBuccv/iLv8jnP/95vvGNb1wpXvyt3/otvvOd75z4+cTKVgjRBf534L92zu0e6nDkzuudCiH+GfDPqn/fu3ePL33pS+d5ROPxO7/zOzx48ODK0VWFjn/2Z3+WL3zhCwuezfTw5MkTAD7/+c9fuTX72te+hhCCL3/5y4ueylTx7W9/G4Cf//mf59atWwuezfTw3e9+F4Af//Efv3K8qLXm9u3bV44Xz3KqJir9EUJEeEX7R865Pw3ffhzCx4SvT8L3PwTuj/36K+F7B+Cc+1+cc//IOfePJpnDNa5xjWv8Q8NV8vzGcVXpOg2TZCML4PeA151z/8PYR/8H8E/D3/8p8Odj3//PQlbyTwE7p53XXuMa17jGNa5x1TFJGPk/BH4Z+FshxL8N3/vvgH8B/IkQ4qvAe8B/Gj77P4H/BHgL6AP/xVRnfI1rXOMa17jGkuFMZeuc+1ec3Hfs5475eQf8lxed0Ik3VYSq8FH/hyuWwDznxhbXuMY1rnGN+aF5HaRO6tpR/1HVxR66vXUCRXXZK6cm+f2LjFGfX4x1kxl1CZqXAp5N79iDiXTV96p/L7lxcaCbU1i12d2MMVeIuk/32KJdAbqO8nmTLty8JK7MGh3G1VijxvVGPu6VTuUwfUyhna/ZtDg0B4FvTHH8M87LEmfdajTBA/wzxr6ed1whxhptTDFx4eilBOd7vBin6SLzEn6twj/O//unPvpgSXz1Did8++HdnI8Xj/7+LDB6dnU5hqh7DZ7jdw+8+/OPPRuENarmJiZvMHP5ec02Iajix9F7vzoJSKM1W240z7MdwzRf8FGRO7m1dPiGofHvH/HO5mxZHqbrPDbgkfdbKZBpXDo9ReFUe48TDXuYpvEuYdO5TPuyfHkokHGpcad5U89pht9kTlPVB3ikrCdas2qAsXnUkYIZ3eJ1Mdvt/O/64Lhi1AFtinLiJNomHeJS0ThmF6E68QaxOcjYysCc5kUZjfNsK5y3ldZ5BeAkPz7JMw94X+cafzIr/qKez0U+P+ypXdg6FhOu3ik/dDzZF5vPYS/+MpiFhX3RZ06TrmngOENnwl889lkHvNBpzuuSn0/6rk8yjsQJn10Epz7nHPLlMvOpZNlFZeFp81oERsd6TJWuxnq2buKQXPj5cw9w3l8463mzsbYuZsWdca486bgThw/P/3w/yHmffPl3fDmreDoCYPxMHiad0+KV6VkYOW3T80rnkQx51vv3N0lNJyoy6ZiT4EyFNMEYU6VpwjGXDdPiwcYq23PfEDDBhjn3FDhexDUpsedidB3f6H6eIfFJBNxFn3tYCM1zvSYay10kOHWUGxtH14HrLi8+tybtrwoX2mcL5MV5v8L50TWPcUZ7bZrjNVfZcjyznvRzM5qAf+3VWcEJ9zZe7NFn0zZTxgrGzCys9rOHnr2nMhuaTjK/RuPOEp4uwIk5Xro+H7pgmie0k47LHEZ0TFFsTDZiA6IByzzmrN5fY89sK5z2gud1t+XozsnpP/fU8WYINzb+tMc6af7zvIt0VuOctmbzgJty0sbZ481xrFk99wRenIcKnIHYGHv2MXSN/bnMaGJ047JotGdb4Sq++ArXtC0frulaPlxV2sbpElPOcl40Dt7xfK5fHP18HZ2EKio1/vd5YimU7TVOw4gNDzSNqCOezU+sOYqRO3DSJmtC9u354SaShctH2zF0OXeE/xYl5C6MWmifHAqeRjbvtDCpQXEgolXReOjIbDwTfNG0nYcu5yzGWKw1OGux1uFwSCGQSiGFREqBkGriipBp4coqW+cc1trqXwcSN5z1gkAwtlGE8Isgmh1ZrxjKfyVskrB5bAiNCYEIm0QEmubNWBeBtRZnLQ43og9XC4WDdMl6vZpOl3MOa0wdRnfVmjmHtW4k2IREytH/S0FXWDPrnF+7QJsLe08EOjxNKny9fDnPrGGtF9jWWqyxnk5GNEopAw9KlFIoqWpamwznHKYsMcZQmhJTlp4252n09GiUUmgdobSq163JcM5RlgV5lpENhwyGA4psSBFojXREkqbEcUKapsRJShRHyKB054Erq2ytNeRZTlkWB4WcsxhjgKBshfCMFUUkSYLWUaM3jHOOIi/I8ywIA288OGcxwZoTUgKeriiOSZKUKGo+XWVZkmdDykoAjNFljamNIqV0oCshjhOUUoue/qkwxpANhxRFFqzuoKCsoTSmrruUShOH9YqTBK11s9fMWvLcC7eyLLDGYqzFWS/IsQ6pJEIq4jghSVLSVorWUVC4zYRzjrIoGA4HZFmGKcvAkwZjSqyxKCURUhMnMWnaIm21iZMYJZrMiw5jDP1Bn0F/nywbUmQFRZljjVdKSipUFJEkKe12h1anQ6vVQk6pfnZWsNYwHAzZ2X7O7s4O+70e+/s9v+esJdYRrXaHdneF1dVVVlbX6a6sEsfzM5CupLL1gtvQ6/Xo93ve2ZOyFnLGmGClOqyzCKDV7rC5eQPVabaAs9YyGAzY292mKA3yEF3em/cek5SCOEnYvHETpVYabZ16IyJnZ2eHLBsiEDgH1pgg5Ezt8QohiOKYtfUN1tZUo73AypPo7e2xv79XR1iMMV4plSO6EII4iuiurLKxuYlS87O6LwJjLYP+gN2dbYqi8N+r1qs0WBe8WyHQWtPudNm8cZNOp9lixzlHFnhxMOjjrMOUxhsUwfBD+iiLjiJa7TYbmzfQWqOCodtEOAdlWdLb67G7s0WR5xRFSVEb7t5Q98ZsxGA4ZN05oihGKd1QqvARImMZDgfsbO+yt7vDYDCgv79PkecgBCYyOCFxiKAPFGmrRRzHc5tms7n+ErDWkmVD9nb3cAiiSIMDG2L6OG/l5UVOWeSs5gXd7gqtdnvRUz8VzjmKIme/t09WFLXHWgk4F0KTZVlQlgVpktBud2i3O4ue+pkoy5LhYMD+fh+pFEoJ7y0ZEzwmhwmhL60VURSzsrKy6GmfCRM8wP39Pg7QSmFdRYupw7HGGPp4Y6K7skqSNDvZpfYABwOyvEAp6Y0LYyjLso4mWWvB+XBsd2WFdqsFDY5G+LB/SZHnDAfD+numLClNibXVGa6DLMOUhna7g1tZqU47GgtnLaYsQ3Ss8HxnnY+4OIdyDofBDi3OQrvdxlqz6GmfCVcdY+CwDoSQKK293ACUjlDSq7s8zymLIvDl/PbYlVW2VY5QWXorlLJqqO68FTcW4y/LDOu6TTVIj8ABxnmvvAyhn6rEwDqHsV7YFXnujYwlOK+t4AJdzoIQGoeoy5Ss82HKosgRIgKakZgyCSoanABbZ69JHKZet7IsEMKfeY4SVBqOqslYoEsIiZCuziB1gR+dKXHYqbXzmwc8XcJHv4REKI10DoRPvKGOktmxZKIFT3oCCOkjfSh/fq7xyUPWmrrVqj9zt6G9ZMOJEr7doJCjozOHQ0hJlKRYa4kiTRRFPklKCpSScw+NX0llWyXQ6CgmThOcc2itkVLinMDakrLw54I+MUoTJSlRFDdewFUhuThJwtwVWivAe7dlUVKUOWVZIpQkilOiOG50CLmCVAodJ8RlCUKgQ/agCXSVZeEFn1KoKCZOkrkmOFwUUvpwY5wk3ntQKoT/HWWpKYsSRE5ZKpQUxHHa+NwB8LyotD8/d8GgU8obfiYuKPKS0vg1c+B5MYoaz4siZK5GUUQcxxjrz2hBYExMWRSUpqQs/ProKB7bYw1fMylQKhpbMxDI2kCvjm2stSgdoaMY2eAoRAWfOKlqfpRK4VLvsRvjoyxKhSQ9rVE6Rqr5HhleSWULIKUkSWLSNMUUJUIJpJA4QArtX75RIetO02q1G5+QAl4QRFFEmqR1eY+S/iyi6ppknarPWNJWi3iJjIg0TTBlgbUOpUQww0UdtlOlIdKWNG0Rx8lyCG6pfDJXkmDKss5kFaKqb3JYq9HaEIXklKYnfYHfY1EwIqr1kUHZCgH+yNZhlUUoTZq2vBGx6ImfATGWhBcVBdKYWpHKUK1Qldc5KUmSFlEU1581FRUvRnFEFMVj35dIK71hKwuMkThrfcZuFDc6J6KCd660d7DiMmST+yMNaxTWWaTwJT+Rjmq65okrq2yFED4dH8jzjKLIfcJNSHM3JoSSjQmCezm8v6qMQkiBKaus5FGmdRmSpLwH5bNbm55oU0EIgRQCZy3ZcOgtUgTW+CzQ6vyFkJiyDMYRjK1ZSAIry8KHjo3PtDZloNNZWrqFXgLvr4IUEiGgNAV5noezM39UU1TntkAcJ6HUQi5FGLnixSr3wxqf7FV7gCEqprRGR75UpvFWBBVdEqjyOkrA82IZMpIrY1BpvTR7DDwvynBUWBZl2FumPjZUSqGlRGpVRzrniSurbAlJDrvbL/jw4ftsv3hGNhhQFAXOWVSkSVsdkrTDnXuvLEUdKlTJG5bB/h6PPnzIi6eP6ff3/YG/MQgpiNMWrXaX9c2b3Lp1eynoAnDOkmdDnj75mGePP6a3t+NDdmUJOHQc0+qs0O2usbqyshT1mlWNsCkLtrdf8PTjR+xub5HnGWXgRak0SbtDK+0Q37+PCGUxzaaMOv+h3+vx8YcfsPPiOcPhIJQB+aOAOG2RtrrcuHWbJjRImARVsk02HPD86WNePH1Cv9/z9alliXWBF9tdVtc22FzfWB66Qm7A7s4Wz58+od/bC2FxX7ollCJttWm3V+m026FOv/m0gadvOBzw4vkzBr09n/xaFiF7XJGkKa1Wl0hphJq/t34llW3NUFvPeffN1/nh3/0tTx8/YjDoh5ILh1SadtcrJCkFr37yk0vQ6sw3R+j3dvnwvXf4wff+NR89fMh+b7cu+xHCp7Svbtzgk5/9PHdfftk3vnCTXeqwKNgg3J5+/CFvfv/f8v6777Dz4hmFKXHGhTO/hO7aOnfv3Wd9Yx1r7tH0PrA+e7xg+8VzHrz5A95544e8ePokeEsG63ymZLvbZePGbZI04ZVX7vvfpbkKt8o67u3t8OF77/DD73+PJ48eMej3DvJiu83qxg0+8/l/h3uvvELT1wu80ZcNBzx9/BFvvf43fPT+e+ztbIUzzXD+nCR01za4d/+T3Lh5A+fuLHraE8Bnj+9uP+f9d97g4TtvsbP1nDzPa7modESru8KNWy/R6bS4c+du4+VilfE+HPZ59vgjHrz1Q/a2XpBlgzoKIYQiabVZWdtAOMtLd15i3rx4ZZVtURTsbG/x0fvv8eijh+xtb1MWeR2ucwj2+32yLOfOy68w7A98p5EG5+47B8aU9Hq7PP7wAz54/wEvnj6jzId1uM5Y2O/36A8GdLor7O/tUpYlSdM3jLUMB32ePv6YD957l8ePPiTr74fOPT77WkrFXq+HkJLdrRchbGmB5p5vel7M2H7xjA8evMuHD99j0NvDlEXgRYdDsru3y3CY8fIr98kGA2+N6yZvT2/Q9nZ3ePTwPd5/8DZ72zuUReYz453FOIHa26W3v8/K6hr9Xs+XOkVNNvxC5Ki/z9NHH/H+u2/z9OOPKYZ9fzyDw1ifzLe7u4dSmr3tHwlRiqbvMV82uPX8OR88eIcP3n9Att+jNKWXLc6BkKitFwwHA+7eu0+eDf15Z9ONdVP6CMsHD3n44G2y/X2sKTDWVzAY58/h29svaLXbfPIzn8Gub85VAzZ5N18YVTeiYZ5ThBddOoEREhlKLnyquEIoTWmdP6+wttHeRFUbnGU5w6KktFBaR2Gpk7+cAOsEDklhLHlRUBrTaH+iskyzPGeQ5eSlpSgtuXF1ly+HwzjQSErjGOZFsFqb7bU7ZymKgv5gyCAvKEpLVlic81nKoWQTYQWldZ7+Ih/VADaWLr/HBsMh+8MhWW7IihJT2pD1L7HOgXUY4xgMM7I88x5Ug5WSc74uephl9AYDBsPcy5G89CHVYCAZY5HGMcxy+sPqeMo1es1sqNHv9ffp9fv0B0OGwxwhXMj6d1gM1kkGWUZvf58sy8fa3jYTLuTe7Pf32d7dYW+vRzbog/ONfax1lNYCJRbB1vY2g36f0pREbn7Jo1dS2YLPsIuSFhu37vJSf0jUfkaZZyE5IGTlJSndtXVWNm+gGt7OEKi6g6OimJWNG9y+9wnQCVl/P/QJFgihfShoZYW1m7eJktQnDTQdoQSr1V3lxp17ZBb2d7YRIdsa4Xu0xu0OG7duk7Q7IaN30RM/Cz6jOkpbbNy6w/4wQyUvsKYIGeMCoTRRnNJdW6PVXfWlFktBl0DqmJX1G9y4cw8RJeTDAVqpUG/rS7RanS6dtU2k0ktAl4eQirTdZf3WS+TG0u/toaT07SdRvrVmq0134yY6SkYKtuH0OQRKx3RWN1nZ3EfoHcChlabiVR0ndFfX0EmKqxoWNBxVMx+pY5LOCgaBMwatNThHZPH7LE1BxZShx/U8cSWVbVX/12m3uX3nZaK0zf08Q4baTSFlzUAyilhdXSeKEp8a3nDpLaUiTVvcuHmb0sLNO/fA2dAqLjSwwCGUotXqkqbtxjdIr5LT4jhhfX2De69+ipWNG5RljpYhI1JKhMPXDicpKytrXlk1/CICX9IUsdJd4c7L90k6K2TDAQKIogglfY20lAKhIzY2b6J1jBDNz9pVStNutbl5+w5WKG7f63kBF0Xoscb8Umta7S5x0mr8eiGqssGE9Y0bvPLJz7C6eYuyyFBSE0We52SgK05btDsrY/XezaWtKq/rdFe4/fJ9kk6XbDhEQiiF8fOXoY59dW0z7LHm0gSjkqY4TlhZv8FL9y1Flnm5GObvnG9yIbVmdXUdpaLQsGN+uJLKFqpGAjGdThelfQlMu9Mljn3YoCgKsiwjyzK0VqEGq9lMBaMGCa12m83NTW5sbtJqd0jSFK0VxliyLCfPMqyz6Eg3uul7BW8geUNifX2Dle4qcZrQarWJogjnHHkROn4VBUmaBG9+0TM/Hb5mUxLFCatra6StFkpVfVlTpBQUZUmR5WR5RpomS1GqVZUz6Tim0+1yW0gELxGn/sKBihfzcGmGEPh91nTDL2RMK61ptTvcuHGL9fUNX/+ctoiiCHDkeUlR5FhjAi82my4YlUMmScrGxgbdbhelJHHib8MRAoqirBvjdLttz4tLkJEshJeLqyurvtyxaiQTxUglKUpDmRcYU6KjaNRc5bqpxXTgG9b7bimdTod2p0McxSBgmOVkwz6Dvgo/2/ikuxqC4FV0urRSfztHkiYoqcjLkmw4YDjoUxSFb3ixJHSB7/xVNRhpdzq00hY69DjNsoxs2Gc4HBJHvl3jstBWee5pq02aprRabeI4AgRZnpMPB/T7fZRWjTcgxiHAe+4rKyRJQpq2SBJvMORFSZYNGA4GlKYMvLgcCyaCh+dlhr+eLU1SdBQFg3ZIng3Ispyo0YlsRyGEIElbdLsrnq40JY5iHJBlGXk2ZDAYkCTNb4YzDp8lHpO2WrRarXClno8S5XlONhyQDYd185V5Y7m45FwI2WftNkppOp0uaZoGYeZDe1Hoj1mWZejY03BBIPwfPpScEEcx7U7bd+aJIpSUxNYSKYWSkuFwiNJLQFeAV0gxnXY71MS166YcVctNrfwdolrr5vdsrRE8pXDLSKvVDlfo+bPNKIrItPcgrAl9dpdEKVXdsaRUtNotf52j1r7dYVkSRb5LWzYchiYCzU5og7FjjShCCkGapqRpiyiOUEphrSOKFJnWSNmvZcdSGBKhO1YrTWm1/NWASRx7OeF8d7o8HjVVqW7fWgZIIUnjhCRN6XZXSFsttFIgBHmeE8feo82yLOyxSZ46vUPrK6lsqxBXkiR0OisoJb31FvuQghQy9Kf1SR55li1Fp5QqxBXFEe12F+csSZKQJP4KLKkk2rk6JC6EXIpWjVCdJ6kQZtXEcUSa+vuFZehLK5WsrzATUoTvNx+eF1NsJ7RjTFOicDG3EBJV0wVFXnjBtwRrJqUkimPanY4XdGlCFMdopWuDSIXmAdV54TLwIuBbh7baGFOSJon31rX2l37gk/aU9IlsVa/rZYCUkiRNsc6SJglpy/Pi+B7TVX9rY3xC2BIsWc2L3S5JnATDL4T38Uc5FS8iPG9ORtj0LI0rqWzBv/w4iUmNz8bVkUZpFdrL+Vsi4jgGB0qqcFVd8zdMdTaRtlKctd6j1b5dnC//cRDjEwthIW3JLgpV9c9Vfq101S5Ojm5UEaEprQvt1xovB4QPR6ZpAjjPazoiijRSeqUqxajnbq7zpbiEAEaRiHarHfhSeyMinPNJ55Aipro5JgohvaZDCIGKItqtFsaY0HM38sk2YS/JMWOvMu6bjuqChVarhZQi8GHklW2Yv5IyXGwCpSm9Umr+LvOh8SSh2+kSxxFJnNQJiA5qQ10KGZKp5u+EXE1lK6pbO2KSxIDzQk5Wm0IIpAWkV7g60vU5U9OFnJSSSPtm9TZYnhUDyZB1hwpnTkIGpdV84e09oSisQ8j2lN5jkFLW9c9CC0TqLYkoimrh11QI/P21cZzgG9kTaFLIYCzYsSx4HXlB0fSEm0pwx3Fc1wRL6SNG1T6qwsVxMCaqHsJNpgtCrof2Bm3VK7iirapk8JUL3msSQXE1nS7wvJimqVc8UoZ7o0eeuZD+UgJSX5c7ac/4KizrFnBkNX7Zh+12audJjTka1sqwp3zP50ku+zi8nj6cfnH6rqSyFXgLLY5jL8xCUbZnLB8ucVKinEOHRJtI6zqU12TIQBf4MA8hpX1cwEkUTnnaxoVfk+FvM9JASllqnLM+/BNCreBvWFHgN5FYDgFXXTuXhs3tqsusw3VfANI5XFgnh+fFZfCU6j0m/GURDsJdoSOxIsOFGFpH6CXhRX+bkQZa9fVsIpy7V3N3LsiPoICXIUReZVmnaRpCxS6EwUf8JkNr1yqBKJ6w/8Ciz6uro8LK4ahkovdsXWi04m9Di6ytjYiJaIOp5FBcSWULwVMK6d3WOsDVZ0cVxv/edE+iQiW8kyQJNxidTpcQovHeXwUp/RmzUjq0YeSA0hkJOleH7pZhzWS4/ksqWRt+42s2Tlf188ugbKtzWCmkb4F6iBcP07Us4VagNgqs1VjnfFRFjM4vx9NmBN6QXwZeVFIiQtJh7YQcs49coFkuyXl05YR4eT+iS9bVGH61fJcvH1Y+i65pGxBXVtkCdeH5+EubzYYQdemQ/zpbK69SuAeZZRY1p+Hgd05WqxACoap6Z3Xg+zMYjXlmadchLDU2pphVPvX8aKvqo6U7LLhmxI/zpOskRTO2blNp7zrn7PNKubow+dPmvwwGRIXqfuVJ5f1ktJ3Ec+fnxSukbE8mflYMc/i58+bLWW6EA88e+/s8wkWzo+uwAhC1sJwPXTCTZJNjlHboKj238N5016zay0cVdkUXNIMXz0X1WLnJEbqqaADzo2t6SyZOVdrz3WPTa8Axes7xz6v3GG4ivXtllG0lyOYjXE5n1OrsdFpjzce7rEJ/Mx5m7jidrqUlNwjuq0bXWQJuWekCqjL5kz9bSpyttJeRtkkU9ohTxUSJYUulbA+fAYXvctAJm43CXUw4ZYy2K0XXwXFnZSCdh7bpW8NzoGuCKU97P8xqf1XPvppoflvRC+GcxyCz5J3pYXZr1Thle3ww+KhCvchTJp7DnHfGpONdnlnnu+kPKp7x7x/9uctuwkWt2WnnQ8tI1wmTOCBUpyU0G0EbR+dxJdaMQ/sPLh0dawpdFaapvOdBW+OU7ei4ZpQ0cP73cPoCTHtzXQbTXuSREoDD72GRe+WyY9d0wRGhsVDjaOwc7jLPOo4HFyHcjjUiTvi5U/eNOP3MuCmC+yLzqJOLjnMLGkLXMYfdl33gZR8wNcxKZs4ajcvprvfmrBTgYR5selTjHDigkBrWD9mN/X+5Bx3zhEWSWgvd6U9iUUbgJMOeNbezxFcjwonHCtlJ53X8zzWCLg6+/8s2YwhPueTvTwvTV4zzWrPmebZHcPTl1tltXICpxorjTnrJ82yUPk7D+PwvdfbXALrqMY/MZ5S2eN7Q1onrFbIB57Zmoxqvkz8/z7PO+Hyea3bcfKo5TJOuRsC5Ogt4GnzYJLhaSE5vrs24QMLNxEGaSgnXGWi8sj02GuJGuV9BNdV/mwSTpGrPi7FOKmO4yIae5Hfmakgca/xcbLM0jbbjiJiVEG6CkJsFbU2gaxaSuxF0TViOcu6njtE2DwU1NwTDqzr6mLSz1Hnob1wYeRzHEXxSxdMyYvEbcnaYbh3fNa5xjabh6m7v2VDWWM/2rM4fx4cg51WTejkca0TUNNV/LCWOy8iF5Qi9nY2RAXEV1uowrtZaLRhXyu37h4GxKstz/fykaKxne+aGP/Zz13hFC6eci7nZJNpMMv40nz2NkHgzMaJNzFvRzmGo49ZuHmPOA/PWe4u4/WZeuPiaNfydzNCvAuUAACAASURBVHh6jfVsYbSo82gUMOn40372ge9NfZTTx59lGHtxmbSubtVTn4dDfRbjgx/HJN01PEGmzjMQ4kBCnWBMsB/XtnEJDJ0z9/kU6FrEWxjNUYwqxE5pyHPehMFFYnzNTsqBWbZIyTgdIizYcUbTaTSfhkYr2woHhOWCcXgOxymsJsxzEowrn6tEVx3eHcugdWOfuVFC9JTKIuaIKhPa/+PgzMc+W5q1GsOJGbRLTpePiByXxDmKNS+Toh3HWdtnOdfr9MjERUVGY5RtWZb0er1FT2OqMMbgnGNvb+9KJUNlWQZAv9+/UmtW0ZLn+ZWiC/ydzlJKer09rtJhYp7ngF+7Vqu14NlMD/1+H4DhcHileLE6qvDy/mrxYlmWp34ummB5CCEWP4lrXOMa17jGNS4J58MYR9AYz/bTn/40X/ziFxc9janiL/7iL3jy5Am/8iu/suipTBWvv/46f/VXf8WXv/xlbt68uejpTA2DwYA//MM/5Md+7Mf4yZ/8yUVPZ6r44z/+YwB+4Rd+YcEzmS6+853v8L3vfY9f/uVfvlKe7dOnT/mzP/szfuZnfoYf/dEfXfR0porf//3f56WXXuIf/+N/vOipTBWvvfYa77777omfN8az/cpXvsKf/MmfLHoqU8XP/dzP8dd//dfs7u4ueipTxbe+9S1+9Vd/le9+97t84QtfWPR0pobHjx9z9+5dvv71r/Obv/mbi57OVPHZz34WKSVvvPHGoqcyVXz961/nm9/8Jo8fP+bWrVuLns7U8N3vfpef+Imf4Fvf+hZf/epXFz2dqcE5x+rqKj/1Uz/Fa6+9tujpTBVf+cpX+NM//dMTPdvGlv5c4xrzxlU6Vz+Mq0zbVcZVW7eKnqtG1yS4VrbXuMY1rnGNa8wY18r2Gte4xjWucY0Z41rZXuMa17jGNa4UmhimXipl65vbi0MvUjCvWq1ZLuBRuuAq1aAdxPzWbLY4Zs0auMkvigO0XRG6PE0XoeWq8OxVhzhwLny8zlgMGlP6cxZOanBfYWZ3zQtxsGlPteFOvPDgfNf9VWMc93dOGmJKYx5uk+dGLXzO9ayzxjkOk3RhOe8dqvXvUXU2FGMdomZH18E2g5PNUIhLvO9R77/z/d5kD+egjq14Y/LfvUzHt4uu+aTP9l/BhX082sanjSmO7P/zz3G6/Hf08eLACJO+/6PypmF0cV755Lt1XV63jjd7nQ6WRNlOxyq5aI/lEw38YwXDxRXtRSFCL2A4L11HvbJpXpp06s1NTPamzttftRao9b+pN99c6JpoHC80zqfIQk/nmrCqi+EU2xge04N47KMz51m9ltG7n0xxivFfPvB70xPkR4z1A/84vc/tZbbosYbYNJnx0Bjz8t/Ob2Bebozxf0/Kh9MYd5q0LUUYeToRgNM94ws/VYxJvwXgsHi8DF3jQvKy72fav38puiolIMSlmWk6xtEFn3HENjo5IjKFx88PJ1w56Zfr8uHbs97NySbGxZ953M/UIU2mR9dZV5GeZ36T/M5p85hmyPb0Z1zss0ut2RRoWxLPdhpw1X9Tx4XDnVNgSoc79iKBSz/3Up7FFOgau5VoqrQ1oIlLhdE7vqK45Lt2B/9oFC7Lk01oJnTxm79OOxq6PF1nGz+nh5FPnN8F53b09y4mF6+Esp30JU6LvZuwUcYxrfkcvTbqgs+dYP9OMuep0bWAaxkn/5mLz22qdJ2xZvNcr2k/67JjXVW6Jv2ZY36LcYaZOk2XtD8PXP83E6PoYs9cijDyaZg1844/381hvJPGnvWznZue23+mXTpvuuaEufLitMdaoP14VdfsoOxwV4au8efPYpzTnjnre6fHr+ScNm1L4dmeFO6YF/POapx/aHTNcsx5jXFV12zR4y0iWnTNi5cff5bPvnym9MXHngWWQtlC80K308I1XcuHq0rbNV3Lh2valgdLo2wvhlFS1GlVU4dLD5YFzrlT6YLpZV3PFwdD2seu2VLSBeO0HU8XLGfS1FWlC46jzfPfFUhwc25URgZLKQePRxXmbg7vXVll65zDWsu4wq3/tGHLCJ/5JqRACNmYTiNnwVobrL6xcyAHNnyvKisQQiCFQEi5FHSN1qw6Qx6dC9VhpbokRE4lHX8e8PO3npxAS8WPznop50tCRmu1DHTBIV60Dje+x8bpEgdpazrG18x/reTIKPlmtGYj+dF0VHup/t/amiYcgRYvP7zsGMmSpsM5i7UuyBEz4kcHUsojcn7evHhlla21ljzPMWURGAsq5WRMibOuFto6itBRRBTFKKUazVjWWsqioChyjDFBGDhwltKUOGMRSiIQNV1xHKO1Rojm5sM55zBl6dfMlF6IW4fDYozBlqYWBEpFRHFEFMdEUYQQatHTPxWeFzNMWWCMDQrKYo3FmNLXXUqJlIooTogDXXIJeLEocoo8x5QlxgbarKE0JcLhaZCSKIqJA21K60bT5ZyjLEuKPKPIc0pTYkqDc54Xcc7LCSnRUUycJGGPRUjZ3D0GgRezjDwfUuQ5RVFirJcbzlmU0iilPF1pQhKn6ChCqeXYY8P+PoNBn+FgSJ5nlGWBwxGpKKxTQtpu02p3iONkrvL+Sipbr1AN/f19Bv19nAApVPCUHKUx3qJz3vKRUpG2WqyurpGmabMFgbUMh0P29nYoS4OU0iulIAiMMVThLakkcZKyvrZOu9OhyfvFOUee5+zu7pBnmQ9tWbDWYI0X3lUIT0pFEsesrK7RXVkhjiVNCBMdh0pw7+/16Pd72GD42dJgnaEsyzo6IaUijmM63S6ra+skDY9IWGMY9Pvs7e5QFEWg1QRl6/cYQiCDsm13OqxvbNCS7UYLb+cc2XDIzvYW/UHf819RBiPdYp0NXpEkimPabU9Xd2W10crWOUdRFOxsb7O7u0WeZRRFSZHntfdeRSDiJKHT6bK+scnq2rr3DJvKi2GP7e3s8vjjj9jees6g32cwGFBkGU4ItBJEcUqr3WZjY5ObL93hxs1bpGnrWtleFtZasmzIXq+HEJI4jutzFmN8iKE0JWVRYExJO++Spi2SJFn01E+FDUppf79PUZTEceTryWzwAIN3UZYlxhiSJCZNUtJWq9ECDqA0JYP+gMGgj1QaKYWnJxgRlRFljEFrhVSKdqczdi7TTNhgIPV6+zhAKeW9Wlt5TC6sm0EKgXWWdruzFLyYZTm93j55XqCUxAY+NGUZyl08/UJAUeS02m3SJKXJll+llPb39+n1etQRsbLEBA8Q/DpJIciyIXGS0Gp3Gh8ZM2VJv7/P7s4ueV5gnaXM82AEuvrcVvYHZMMhWmvanU6IIDWTLkcw/AZ9tre22NraYjgckg2HFHmBdQ6tFDrKGAwzyrIkSVNWV9dJknRu87yyyhaCV1GUCCWRRnqrsxZsFlMa8jynLHLSNGWhxYbngKNSOiVFKVBK4azD2CpM7pVtUWQoJYNwWAI4/OY3BgloNM46rHXYsG6lMRR5hnPae07LsGZuFA6vZxuOpG04GzTWUuQFQjisMaNztCbDOSDsJecNWOGEF37hY2er4w2DaaXB213wvCdCtS4meHwSh6fNQX0UUFpDVGhvUCx6yhPAVXQZP3ccIBU4O4r2BcOvKGQdNm86qjUx1vjjDOcQUqEiAdaAlDghgiNSBOPdzHWOV1bZCiFQWqMjjcOHsrTWOAfSGorCW97GWe9tRP7MpanWWwUhBFKqQIs/O9JaAwJlS8rCeAFeegZUOkLHcaPDWxWE9GsWRREIgdIKISKkMciyqL11hEAqHehqticBni6pNDqKvGyTEiUVxlqkKiiLEucKCGfSOmr+uSYEuqSnyxL2XDD8SlNQFt4gtM6HXrWO0VHc6NyBCkJKlI6I4wTjHFr6owpjSx9Stv4IgLLaY9FSJCKKsHeiJMZJiZQCKSTG2jqnoIq4eJ6NG5874HtNgwg5D2m7S5S2ESGqUpocZ72hIYSX9SqKEXK+0ZUrqWyFEKhw7hAnKaYskTJ4tgic9MxFyCbUOiZJkpBE1FymAk9bFEckqQ9/CCnRSoEQWCvBFVhnkEi0iknSlDiKl4IurSOSJPFCzDm0ViAUSklKIUBAWRqU0iRJSpwkqIYbEZVxlCQJSZJiTFkn1yjnUFIiQ1s5LUuiyK+tVs3fmtWZZZKkvp+t9PvOAdpoCllQlMJnJitN0mo1OhxZoebFNKUsS6xzKOVlh7UxRpeUZUFOjpWKtNUmiZOlMGiVUsRpSpq20LpEKYkQEmt94mhpSoqsoJQFrVaHJE1RTedF4Q2GJE1od7qeF5U3fGxpscHg80eGhrSmf77yvuFv8eIQUvg4vZIUeclwkCOlqs/HiqLwWYbWEMcJ0bJY3EKgpEIrReacT3LIc2CUqVxag3EGoSRxFDc7uWEMMqyZBPKi8HTVoR+foGKMBSmIotgrpGWgK3h8UgqK3FAUuRdw4fy5LEsfYRECHceBF5tPl/dkJUpLyJzPbmWU2FZUlQACr7yCQmo6bSIkdWmlkFJS5hmmcKNQpSl9aYkApbU3+lTzDXUAKSSR1mitMWVJURR4I8LU/IgAFXm6KkO96aQJKdHaV16UhT+LxoETDiFBWL9WUnujL0nSuUfFrqyyrc6L+vt7PHvyhN7eDkXuQ5EuxPC9YEu5cetW8HyXoJ4snPFlwwHPnz1ld/sFeeYP/a0psRDKEVJWVtdDqK/5RgSMZ0u+YOvFcwb9fUxZBu/ClyXEaYu01WE1ZH42fb2qmlprLL29XV48e8p+b88bRWWBDbwYJylx3CKOopquptOG87W1w8GAZ08es7e7TZ5lwSjy59NRHBMnLdY2NkN5U/N5sarxLvKc7RfP2XnxnMGwP5aoZ5E6IklbdLqrbGxuBs+3+aiSiXq7O7x4/ozBfq9eL2uNL9NKUtK0Q7fdQapqjzWcFwGBIxsOePHsCcP+fm2gO+t5UeqIOE6Jowil1dx58Uoq26qWtt/b49EH7/HuGz/k6eNH9Pv74XzMoaOIdneVtY1NtFLYT7xa/XKjvSVrLdmgz9PHH/HO33+fRx8+pLe3G0IkFiEVrXab1Y0b3H/109y9e3fRU54IzlmKLGf7+VMevPX3fPDgXba3nlEUBaY0gCBOUrrrG9y+8zIbGxvehWo8HKYs6O1u8+F7b/Pg7Td58fQxw+HQZ+w6f+bXWVllffMGSRJjX3nFX+J1So/pRcM5h7GGQb/Hk48e8ubrf8eTjx8x2O9RGoM1FiElrXaH1fVNXv3M57l37x7LkNDmnCPPhrx49ph33vg+H73/Hnu7W5jShMQoiJOE7toGd+99ghs3bixHa8GQUb23s8XDB2/y/ttvsb31nKLIscY3hFBa0+p0uXHrJbqdDvbeveqXaarCraKVw0GfJ48e8sb3/5btF88pigxrQuKXkERJ4h0QHK9+8lNzF/NnKlshRAr8f0ASfv5/c859QwjxKeCPgRvAd4Ffds7lQogE+APg3weeA//EOfdgRvM/Fj4LOWdn+wUfvPM27779Q3a3tsizQd1hhHCYvr29xfrGJsPBj/rQELqhLDUyInp7uzx6/z3efvMHPH/ylHzQx4RMQosgimNevHiB1ppPf+5zWFPiXLNDk9ZaBoN9njz6gHfeeJ2PPnjIsLcXPCSHCUopfvaUPMt45f6rvrbTWmiwt+SCh7T1/CkP3vx73nnrDfq7uxRlXmcjI30TgfXtLW7eeonss5/DWktzi2M8vODe5uG7b/PmD7/P7ottynzo+dBZLL5hTPv5M+Ik4XM/8qNe+DXYiAAfAh8M9nn84UPe/uH3efLoEfmgX3eUMs75JKMnTzBlyauf+jRlqDNuMhyOosh48fQJ7775Q9575y2GvR7WljVdCJ8Ytre7y52X75F/9rMhS1k12QfBBrn48J23efON1xns7eFMCYTqDScQUtHudFlZWWPw7/57WHMT9Pycq0k82wz4WedcTwgRAf9KCPF/Af8N8D865/5YCPG7wFeBfxm+bjnnPiuE+AXgvwf+yYzmfyxc6B61v7/P3n6PwWDIMMsxpfeErANnDZaSOMvZ7w98hx9jG+3ZVg0S+v0+u7099vcHDIcZRem7ENkgvG1eIHVOfzBgOBz6RgMNFgTOOayxDIcDdvf26PX2GQyGZEVRO0LWQWkLHJrBYEB/0PcWuXOoBq+ZtZYsz9jr7bK7t0d/MGCQ5zhrEEL6ci1TYq3wtbj7PbIsw1hL1GC6nHMUZcF+f5+d3R329/sMs2FIRhSeF63BWJA6p7e/z3A4oCxL4mXgxcGA3b1der19+oMhpijqcKpfswKLptfv0+/v1009miw/rPU1+nu9PXb3dun3+2R5FrJ5Q5KU+//Ze7NYy7K0zu+3pj2c4d4bcWPMzMqhqpBRPyCQG4xliYdGfjEtNw/VakTL4gEBb1jw0GVLbakRtpDVlkxLWLYogVTmBaEWVldbQuIB+gHJCMhy2WCqKqnKyszKyIyIjOFOZ9jTWn5Ya+9z7o0733PP2ftw/5kRcca913fWWt/3rW8skRWMJ1MODmZrsc0m0Dr/fjIes7u/y+hgTDadIqAJ2KusxWIRKmPvYI/JZEJZlUQuWtrh6szf0HkufRCemvDHAf8I+Nnw+leBf4UXtv8kPAb4t8BvCSGEWyK3DxWCEdrQ39pmY/s+FYo8myLrWp9SebPk5i3i/mDpYeCXhQOQiqS/web2fbLSISdjZIh/FyhUFNEbDEkGmwilO2C4C3MmJCbpM7x9l4NpjtvdRQgXNoxEKkWSDuhvbaNMjGutDWIewqee6Zj+1jaDW/tYobFViVYKFzRuE8cMNrfQSS8w6w7MmhMgNclgk43b9yidDybSSuELI/tTUm8wJO4PQchO5A/7tajQSZ/B1h1G04LpeIRWtc9ZgpTESY90uAnKHKrl3WY4BNJEpMNbpBt7WLmPFByaM6kM/c0thImxocxt2+EAJxUmGZAON7FCIYXD1IFrwgtdk6SoOG2KeCwT51JYhC8++y7wReB/Ab4L7DjnyvCRj4HauP868H0A51wphNjFm5qfHbnmLwK/eFUCThgvSmnStMfd+68htGF7bw9bFiilQwCKQhsfcHP79p2Q/ydaq5XCLI0kSVJu37nHm2XFxvZdijxDSRWq10iUUpg4YbCxSZyk7Q/8Ej5KMjIxW1u3ef2tz5NubDEdj5DC50dLKf3jKCLpD+gPN2fVelpMm08j0QwGQx68/iY66TE+OABnMdrnZkohfU54krB1+w5ah8j4FtMFvu5xL+1x995DSgu37+9hqxKjNUoqhFQ+EjuK2Ni8RRSn7Q9qEz4POopjbt2+w+tvf4H+1m3KIvdRvEojpfJF+7Ui7Q/oD4bIhqm3l7Y6pWk43OTB629ikh5FkaGlwmhfi7su3q+jmM2t22hjWp+lcYgv3r3P5/KQ4hNFaO0zN6yFvMhxwPadB5goRi55j51L2DpfauOHhRBbwP8B/OBVb+yc+23gtwGEEAtVMerQ/SiK2djYRBvD/fsPfDH02KcfVJUvBF9WPrex9UwgoK4zOxgOuWsdt25vY4wvsq2VxjpHVc5C+E0XNkvonKKNLw135849hhubSCWJ48QXuQCqMpQCtBVpuvzQ/cvBK35JmnLr1jZJ2kMIfFH+OEFK4dN/Cp8qk6ZpKObRbrrqtJ8ojtnY3AIpeWAfNrntRisq6yhy3zRDKok2Xmlqs0Gi7lBkjKE/GHDv3n1u3b5NHHIz67WYZTnZNMNhZ6VQZYsJoxa2PoBye/sO/f6AOInpDwYYE4fSkxnj8Zg8zxgMB00uatvXowq84tat2yAk/X7Pz1ucoqQkL3J2Xu4wOtgn6aWh0cdyef6FTPHOuR0hxJ8C/ymwJYTQ4XT7BvAofOwR8DngYyGEBjbxgVJLhQjtvUwcsRm6qCRpShzFIERTpjHLMh8W3vLFNI9aQx0MhxitG0aglKIoSvI8o8i9r1a3uAbtUfjqNoq032cwHBInMWnSQxtNVXl/U5FPybKMOIpWPdxzoT7sCCH9PKWpr6Ob9oijCAfkeUGRT5lMxt7y0pm16Ivx6yhiY3OTKIrphdrHUinyoiCbTplORlSV9e4A5wVam1HzDq0N/eHQK7eDAWna8/mp1vpC92Pv0zTas9G20wWzU2Da69MfDhkONxgON4hCdbPxZMJof5f9/X2SOA7Kx6pHfR74A1acpNzeVmxv32F7+07TWGaaZSRJyovnZmXpkOeJRr4LFEHQpsB/jg96+lPgS/iI5J8D/l34ytfC8/8rvP8ny/TXzuC7VyRxgtaGJPHtlXSoYBNFEUWRo7WmDEEdbQ4imkddRcoYQ5Ik/iQR2mCVZUWeG/JMk2WZNw11hC7wJtde6htCxEnqW5cpFRowGPJchRZgGiHoBm3Ob+4kSYiiiDRND63FPM/JM3+adU0/4g7QhV+LcWRI4oReL/Wnv8j4qlllidEKJQV5niGVpC7q3+6TUl1ZTtPv9en1evT7g2A98id2rSRKMluLLN8HeDn4Aiu1UjTc3KDX6/tqbc7TIwMtxhiE7IQrurFmpklCr9fj9q1bbGxs+BNssFTYsqAsfAtPuYK86POcbB8CXw1+Wwn8gXPu/xRC/C3w+0KI/x74v4HfCZ//HeD3hBDfAV4AP3MN4z4TUkriKKbf74fyfnFjOqjrJNcCajr1QqntqBeUMYZerw/OEYcejT5JW2GM9X4KHfptah0003bvGCF89agkTVFaE4d+mjr4/xw+iMOEvrxdMG3VqM3h/X7VlGOs117tY9faP84LX+msC6iVvl6vP1Mm4sR3ZAr1u+vau76sqO7E6Q98taG010NrQ5okJGkS/JoyZIu4QIv0vveWu2pq+NNfQr/vu0qlQaFVoQd2SPBuapAr2e6UH6CpcGUib+2TQpKmvaaPtwzmpV6/T5bn5Nl0JaV5zxON/P8CP3LM6+8DP3bM61Pgny5kdJdELZTiJA5tsLzQ0SYEbgiBkq4pxyaFxJhulFvzDC4iTXvgbFAadFNvF6dCoXu/eVRg4q12lAUorUmTFKO9IDLGhM3i/XxKykYp8hWluiCURMPgHH7NGWN8Q24pfcRuqF5Wnyy0UZ0QSlL62shpr4dAEEWmWYtSSJyU3iQbaPEnpfbTVQe1JWEtmsivwzpQz5d38MqrC3WTpZLtnzHhrX1xnGArizGed+hQTUkgMBhsYumV/bk91nrKAF/CtdfrhXXpeYgKsTha+R7YvTQNZWGXr/i1OX3qSlBKEUVxOND5FlkqlIsTofC7kJI41K2NwuRcCN6OeR3DPxFS+nrHNvVVeuoGC1LWwtab6OrThD/Fq9ZvFxFO7HGSoMvS+1WEDFGt/tRQN7aujaxGm9b7N4XwazGOY5/PKP1pQUnfj1cALpzSReKDxKKmhvCqR38ymiBEE2GTFCAwN9XMmXMOHWgQ0pvyVAeC2mbC1jcx8RGtGhnqW3sziyZyQOotF12o013nncZxDEGQ1pYjH7gm0IAzFpumvlPThRpHrM794fmHD0SUECLiZUOXdA6tvYVTCF9s5VyKnxBNNberYi2Fbd3qKw6Rx7aq/Ak3pMcAs+pjSmOwaF2b9S6wYVbgzKi1NgSh3J8XrnVLtnpRKPxJUUnZiSLpMgSkJIkv3FH34JVqVsPUOYlSNPPkT0rtNt/VaWj1Wqzp8o0JZmtRKodUishFaGPCCb7lcxa6GQkhsJXvDSrVbL0555p2glr702Hbm6sDzdpK05SyKEI6kJqd2HGzlK2wPrvSMaxWIrzS4HxA3twea0zJwiu1dSOT82F1riola1eN7/KjlPaK+VyWiTERSWoxUUQSp+fLZlggj19LYQs0G8D3aqwCkzschTb/Q3cl9afeMEIIrPZNq+Hk8deKR9u1biC0+4pQSp1MV1Ce65NVF+bsKF1eCIkT/XxdoUvKubVoZ2UYj2POteDtSmxETVfdN/qV9ebAzb3XBSUCgmUsmq3FV8auQDmvHAGdUCKgTok09Ho9bFU18Sr1Wqznrg4YrWMmzoNFidu1FbYw9wO7+c1/fCj71RbUcs0n9QbxJyNXv3jiOagLmwXm6ZJzZpuTUw+6QhfM1uL603X6+LtC2/xanH/ttM93AbUCMc87Do/9MC/rCl0ws3jVVks/9tk+k9Kbj2tXW+sCpLqORf+oJ1/r8OvXnQZQR+At1NR4isCusYz0hsVvBBGue/InuklXc+UTaes2XbyyJhtquk7XkTlzM515KXNWl609Hpen+ejv5Zpyj8s5kJxm8hb1WrrQnIrZ34dCdC5Oy9oL28WhK8ndF0eXtNeL4ry0de036ELQyqVQB6RwPMsXc5/rRl7rDD6P+gS6urX8DuH0E3+oU8+i9aP6nte3Bo53y83u7+qmE+ckrN3RJRfEtWrXHd4Mp2KVhF3rvVeVhyuu3UR1kWsv1kBw/XTVV1/mzC3DpFhffxV0rXotNnO6UAvjdbKP8/1m9Xo9L12dOtl63xAc1WbmiT3qP7rKvVaOoya0NaHrcMDT4k4o7aBt/vEiaGuHoreue+woXbCIfbb6OTv6296sxfNf87rQGWHbaIbBeD7v47jkFTnJBLFsJtBscmhMEidFFp+9sNpHF5zOwM5L10n+rNXSBccHmpz7YieaoZo0jCXiOIFz2d/3tHldhaBdFGM+6x5dxWnKRlvousw4zlKilkVbh83IjqvZ61vo7zljSGeP+PSgqWX7uBZ5u1P3w9Lpmr/f1e59uhmqhWu0xhn8qS3M+SiOO8le5hqtom8RNtUWkTPDkUG16Te/BDopbE9iQY7FCJRlCqXDm9Yd8yg8P1dz6roYeluY9Kyk3YmfuEAT55M+typqT7uvu0Bh+pM+tqj1fBH4e57xGefOVgydO/U6qwhu8ltobo8dM4azxnXWKWklQVvnmbQzcFYewmqC0dypT8+D8yhFy6KtM2bkGkd9ttf1Qy2rM8mJEW3OXYsQWXbHlcswtPNe5+j7c5qH8QAAIABJREFU60hX/Zml0bYgusI3z7jVsrv/XJ221s2Xv+OxY7jQFc5JF6zSahEUuAtEAPsxnx2Rv4w568bJtuPmg1OxzrQtGV1LBbnBDW5wCVx4n59TMF98JBdCq0+2J+U5ObeE3MElpQLM329dhMWrie3rQZdHCNSCpfuKrxvLCCBaDTqWa3xurCFdcxkYx6/F+f3X/LWYWy/sSsej3SfbE6PHlmHKWLNFvES8uknW5/ReL7v1oWgObgUmwqVss+U3q1+O0rJ8usJtr/Ha5491WPRArnvKWi1s6wCR+seff37ti/m6L3+EjmWeKJZxr8N0tZ+2iwRpLWX9HXPf6/7eRYK6FoU6MuHihsHF+yQXjWUEUa2Erkvu5wutxRM/684ZLHoZHL/+z9rv56Wr1WbkeRxXzOL673lyQEA9kuP0yusITlg0jtJ29Pc9luYO0DV/32PNoiE16nRTVTvx6py9OvbFFWZYHhrFrF5za0IXzI/3sMn3lbn0Ly51bFfBqXvMv9NZd8tJQatn03w6OiNsV2nWPVWrWeI4rgOtSmFYMI6lISgVx1HXFZovo2UvP0L2EljA6aG9uNw+azvdp59AT/582+m6jrUo2kC0EML1+30ePHiw6qEsFJ988gnT6ZTPf/7zqx7KQrG3t8dnn33GG2+8QRzHqx7OwlBVFR988AFbW1tsb2+vejgLxYcffogQgjfffHPVQ1konj9/zs7ODm+//fa5+5N2AVmW8fHHH3P37l02NjZWPZyF4v333ydJEl577bVVD2WhePz4MaPRCOcjeF9Ba062QhzfdHodsG501SckKeVa0TZvJlonuuDwnK0T1nUt1rSs41qssW50nWU5as3J9ktf+hJ/8Ad/sOqhLBQ/+ZM/yV/+5V+yu7u3Vum0X/nKV/ilX/ol3n33XX7kR35k1cNZGJ48ecLDhw/58pe/zG/8xm+sejgLxRe/+EWklLz33nurHspC8eUv/wv+9b/+n3j8+DH37t1b9XAWhnfffZcf/dEf5Stf+Qo///M/v+rhLAzOOTY2NvjxH/9x/viP/7j9ro0L4Etf+hJ/+Id/eOLJdr1Ui5ZijdbTWmOdNv5RrC9tdYOS9aRv3eiaNZRpWX3pJeBG2N7gBje4wQ1ucM24EbY3uMENbnCDG1wzboTtDW5wgxvc4AbXjBthe4MbLBR/v/xQ3cPR+VlAL9gW4Lha6933iYqZb3eelo7S1ZrUn6XgAq2ZTv4+XF8pi6OFxc9XaPykKlCXv++i0dRrOuH5aV+97t/88nil+k8z1q5DNPysaVEm6FwloFfQFLmfzds6lPKfDzpaJxySr0de6OKcdUjYihMVmvOkLzUL8RJl38Srs37ha5z7+kc077NuMf9dEcZ23nEdve8h5rMI2sTRltRzm+VCcxbGd166auEwd69FdrQ5ytQOMYJz6HOX7Yp0HF319a6NtgsyuEOfP6eSdBJdixSFx3YQu+T3L807mFNerpGuC17hiGJ18XtfT/roeikO0BFhe/aCuj799LR7z042bu72FxvL9WijZ4/hNOZTC4+r1KM+M8H7DAFx/PcvSdcRzf8qzGFV89V87ISxLELgXpW2VwX1OXW2U+jyShZcZX+fuRbPuPpxCsiFlMVjX1vSfJ0xCZed8qNrbx6LEL6L2GaXtfidWAv/iuuw9T7b8yyoy0zMIpmmYN4/cpHJuC7tbVFaMxw9my4LxxfZX9TV26U1i04axdYHF6lPvKiOT0ub8Qt0s1pHXLTr2HGcYdZl6Gq/USdOtmfhPAvlWtpatcDh09V2Xcucsxu6zoEF6B9dXYvnQSvn7Kx7necza0oXXG5MC3OhHYPWn2xX3RXj6PUPPb/irc86qV0nba/Stbhrn2U1WC5d7RK0rcUZQ7+Zs8tf2zm39F7B1yk0/KXdoT9LRUf3WadPtsua5Ou6z0ltz67qo7rI/a/ruicJ3GXM2Sr7A3f9Hqu679rP2XXcqo61Oumea4gu09b6ky0c/wN3+UefR6P9Hn51FUNZKI6jax3mzGvy60cXHD6JHX2ty1hbusJpef7UvA50wZGT89zzLqMzJ9uu/9Cn4XiB232sK12E6PMFebtoU8DW4pj2OtMFbaKtHtLVaHOHp8yxyIjEq+Gqc+bcsSl5y85L7oywvQxm2pBrTiThjZCn7yuTiLk/XcC8lldHys37aOqKK54mgG7QdiJdDSM4TE9n5sw57BG6YM6vdtxahPYwu1PgnG3cHs7NKSFNEOGMli7N2Strkfn5Y44uAk2yc3TN7y9Pomvo8iR2Z85eOfnO7THnXEOTcxYcCCmQUi2VtrUVttZayrKkqkqqsqSqKqytcNZS2QqB/7GV1pgowhiDUrr1DY2dc1RV6Wlr6LI4Z6mqCpxDKYWUCm0MxkQo7elq+4ax1lIWhZ+zObpsVWGtQymJELKZM601SimEaPecWeeOp8t62oQQCClRUqGjCK0NWiukUKse+qlwzs7WofX0OGubNYqjWXdKG4wxaK2RSrV6LfrxV1RlibV+7XmlwtPlrGv4hFQKrTW6K/wj8EVrK6xzOOsViZo/zgqMCJTSfs6MQUnZauWvnhtr7ZzVyTVzSZjDsipxDrTWxEmKMeZG2F4FzjmqsmR0sM/oYJ88z6mqirIosFVFWZXgHEIqojim3x/QGwwYDIZEUdRqRmCtZTKecLC/S5Zlnp6ywlae4TlbgZRobUiSlLTfZ2O4QZwkrabLWUueZezv7zGdjIOyVAXmVlFVFgTIMGdp2mMwHNLr9dG6vQzOOUdZlhwc7DMZjyirClt5xcjZei36U4QyhiRO5tZiuxWkqrJMxxNG4wPKomwYm7MVRVXirA2KhMJoQ5L22Njwa1Gp9ioSzjmKPGc0OqAocpwj0GUbhckflQRSSaIoZjjcoDcYtFrYOucoq4rxeEyeZzjnsNYFJckr8PP+USUVvcGAjY1NZBy3yXD+Cqy1ZFlOUeSAtxR5OoJyEfh+lmVk0ylRHHPnzj3UcLi0OVtLYQtQlhUHBwe8fP7CMzSgKkvPwMvCmxakRGvNdDKhrEriOF6qpnMZWGuZTMa8fPmSLMuQUmKtpSqr5tQkBCAExozoT6dEJsJEUbsZAZDnOXu7u4xHI4QUgPAKkg2ndnxQiJSSJBkhhCCOk3C6be+cVWXJaDRib3cX5/z4q6oKilI5M2/hNe6tsiSOurEWp9mUvb09yrJCKRUEUjgVNqdBi8CR9vtEUUQUx6se+qlwzlEUBaPRqNljALbye6yqqkaJqqoSrTVCCJI0xbV8LVprmU4nTCaTxvxtbdUo7bVwyrIMZys2y4I0SVt/CCFYjyaTCTJYv8Cf3MtgAayV+b3dHaI4od8f0uv3G/P5dWNthW1tfiyKnLwsG02nshVFYNwSAVVFnmfBZGRXPOqz4bVRS1EUZHmOlAopBJW1lFWFwyEsvtyizYijiMpWqx72uVCbwvOiACFQSnoTrK2wwddSMzkphBdUHQicc7iZ+c55c7izUFrrrRHQnJrKoqDo9z29HYC3IlUUc3NhraUM5uRaoajKAq21t1R0Yc7C2IuyREqJkn4tVtZinaWqLEVZkGcZkdGUZXn1QJ5lwM0EEEKilH9e02UrS1GWZNmUqirpF4NuzBd+n1VlhZVeeHq+6K1iNV+ZTjPG47FXMtzsYLIMrK2wFVIglPfJKryZTgpJVRmkUv4EKCVSeL+Lqv1/qx74GRAC799TGq0NSIGWCqUNSpeN1u2cP01IrVp/8qshpPQ+MKNxgFIapUFWulGGbNg8UsnO+KKFqNeiQjjnAzO0QFaKMpxyKwGuAgR+LltsZq0xW4sK7ZynUUqs0ggpG2Wo/k9qjTKm1RYWCPMlvfm7jhOQIQ4CISgLgbUFzuEZdoiPaDtdgJ806WMEhBB+/8hZkFDtey+rCgEoY1C6C2tRNKVlm4C28JqQAlt4BSPLM4qi8HEfxiw13mNtha2UijiOiZMessjDopJei6siKmu9JuocxmiiOEEp3eogAAAhJFEUkaQ9vBNToLUCJ7A2+G+tN+N5M+tygwAuCyF8QEacpA2T1lqBkFjrQjBYSZ4XSCGJ4pQoijvB4Go/cxwnVJX1DFxK7wssDUVZUOQ5zlqMNsGnqTswZ9IH4cWxP0lIr1A4B9Zo8rxACG81ElosPSDlKlDKB+FVVeldMlr7k2ClkbIAAXleoIQiimLiOG6iW9uKWrjqEKiGAx2UCKUVIpzeM4R/z0TBTdOFtSiQWiGVDEFffj1KqRBSBBM5FEUJzq/FKF4u/1hLYVsvqiiKSJK4MU+6EHFnKx9IJKREao2JYiLTbp9mDSFEYMiR90FXBVUZTrPWYavSnyLCad0vqPZvFvAbP44NRRaRF7mnS9jGxGpthZQCqaJO+Ndhbi0aH43rnA/WE7aORrbYwNCVNkRJ0hklQgiBUZrIGGxZUbkKV/hTrK0sVVn4k59SGK0945Z6Zc0tzgsRXBhGK/JgefBmV9EEWlZV2QS1xUnqrUztJgvwtOlggSjLkrJ0SOnda2VRUpYF4K0UJo47tRaVlEghKGzV0AE+fqcsCyrr3WzSaJI0Ravlir+1FLbzyLMpuy9fMDrYpywK/6OHaMI4SUh7A9T2ncas0hWURcn+/g4He3sh2tpH3FlnvUaapAw3NhHiNlJ2hC4BtrKMRvvs7e4wnUxC8FeJdVWIHk9I0h7D4RAp25//1yQhWMd0MmZ3Z4fJeNQEEdX+dB1FaBMRhUC2LuQ2gjfZ5VnG7s4LxqODJvKzqioqWyGVRmpNfzBEKYVSohNCyTkoi4KDvV1GB/sURdFkORRVCXjXgIlihBTdcNXUPvWqYjw6YH9vlyL30bte8JZBIAkcgl6aNsFfbaetjqyeTsbs7+1RlgVCiCZWIitysizn4OAArQ1RFC19ztZS2PpTniWbjHn6+BEffOc9Pnvy2KdeFF7YKqXob2xye/se4gf/Affu3acLtY58WkLGzovP+ODvvsXjRx8zOtilKEpsWYEUpL0+m7e2eePNd9jevrPqIZ8LLkQT7u++5OMPvsOjjz5k9+VziqKgChG7URQz3LrF3fuvsTEcdiNww3nmNhnt8/jRR3z0vfd5+ewpWTb15nLrUEbTG2ywuXWbSBvcw9e8PFpSlOSl4BzWVmTTMc+efsr3/u7bPH/6hMlk1ER/CilJ0j6DjS1ef/NtXnvtdbogaa21fo+9fMYH3/02Tz95xMHBXoj493maURyTDjbYvvuAO9vbrZ2mefgAooKDvR0+/vC7fPr9j9jf2/HzVfjUQaUNca9Hf7DJoNfzfnnRlLlYNQnHog5mm45HPPn0Yz7+4H0O9nZD9klJWflgCKEUSM39h6/7fO8ln9jXUtjiHGVZsL+7w0ff/Tve++Zfs/PZM7Js0kTzCqmI0x47z58xGAx4653PY+2tVY/8VLjA4MajAz796EP+7pt/zdNPPyUbH2ADXQ7pc4c3NsE53njrLeyDh5zWHKANcM6SZVM+e/wp3/323/Lxhx8w2ttpAr6s88EaSdpnfHDA/QcPKT/3ZusFrk8jyXn54hnfe++bvP+db7P/8iVFkeHDBmqTXcqt7Tts3brN2++8g3VbLWZvnvVWZcn+3i4ff++7vPfN/4edZy/IM2+NsM7hkJg4YjDcQinFF774Az4PvM1KBD4yfDI+4MnH3+c79R6bjKhCgQvrQGtD3OuTTad87s23sKGgTJvhrLdCPH/6mPe//bd8/4PvMR3tNymDlXMIqTFxzO0797l3/wFVONG3e8o8v9/bec5H332P9771N0wPRriqpAyFYyoEQmmv/A2G4OzS99ZaClsb8uT293d59uwpL1+84GB0gAt+FgtQWSo3QZl9dnZeMp2MvY/QtFso+ZzNA168+Iznz58Fk0mOFL4FuXX4wglIdnZeMjo4oCgLYpeseugnw3kf33QyZuflc54/+4zdnR3y6YQ6qtBahysKitKxs7PD/t4ueZZhrW11dRtfrGPK7s5Lnj17ys7Ll0xGIx8zIASVdVhA5SVSaXZ3XjKdTj3zNmbVwz8RdS7qQdhjL549Z7S/jw17rLKOyjmmeU5lBbs7L5lMxpRlRRS59ioRzlFWJeO5Pba7u4Mtc0CEVDQHTMmKir3dHcajA4qiwDqHbKuKFNKWptMxL1885/mzp+zsvKDMs6biUllZr9ROM3SUMDrwLirX8pRIax1ZNmV35wWfPXnMy+fPKbIMgQt0VZSVwzlBUVrvxgnxBMvEWgpbCHlXQmPSIenwNtO8oiwytPTl/aTWRFFCb7iBiBIstP6UBDVdEhn3SIe3OBhlVNMxUkqk8FGuyhjvi45TXBBUbUZdedY6QEfE/U1Mb4+8tAgJSipqM1CS9DC9PlYqf3oK9V1byN6AcAJ0YIXCpEOi3pBpXuFsiZLKt92SCmNiksEGThkvgANtrVX8Qr3n0glknBL1NhhPCxy5T98CFD7oK+1vIEwc6LKtd9Y45ygtWBWhkwHC7FNVPj9aIRAOhFKkqd9jFT5lxn+ZVi7GujJUUVkKJxAmQeiEMquQEl8IQjof6R8lRGmfChXyb9s9Yz73vmKSlxQIrNDkNkcgUNIfQoQEqQwmTrFCUVZ2rub1TZ7tpVGnkaS9Hg9f/xxSR40Wp+ZyVH30Z8rtO/cwJvFBUqse/BnwKU0J23fu8/YP/CDD23fIJ+OGLqV8HrGOY7Zu3SZJ+yjV7mjCOgBDm4jNrdu88fYXiPtDRgd7CMBojZTa13uOI+9P2tjyebYtD96o12J/MOTBG28iTcz+3g62qgJdCik1ymjitM/W7TsYE7WerjrVJ0163Ln7kOkXCzZ2dqiKHB1qBEupkEoTpSm3t+8SxYlXCNtMFz6lyZiIra1tXnvzHZL+kDyb+ihepQBPm0kSNrZukfZ8mca2N4+YrcUN7j54A6cissA7mshcIdFRRH+4yWBjE6W7kckggpKwsXWH7YdT8iwLdKmZGVwqkn6fja1bPs0TwTI1ozUWtookSdm6tY2JU1574w2UUr4wv1I+dzPUy0ySJDjMRes3S53StLG5yUP7Btt37vk0hSj2uYDUoe4lUqkml6ztG8YzOMNgMOTeg4cMN7cARxRFGONLxdWVYByOfr/vGV/L6UIItNb00h537twj7Q2wtgoRkTFKetNkTVt/MPAMruXpFvUei5OErdu3QUoePCzQxhBHsS/dGKpLVdb63E4TtZ4u8M0TojhmuLnJg9de59btO2jjU5d0s8d8kRUpFUmaeutLi9dirdAaY+gPBty9/5DecCPwyQRjYoQIdFUWJ2A4HDYRu23mH77RhSJJe9y+cw9pIrRS9Pp94jhBIJpqX9Y5BoPBSpphrKWwhTnBFMdoY4giQ5r2GuGT5QX5dMJ4PEYqiTim32FbIaXXvAfDDbTymz1NUpRWlGVFNp0ynY4pqwqtvAm27fCFbXxVqF6/T78/II5j0l4PY4z3y+Q5+XTCNJsSGUMXWgf6anACqTwziJMUE9ZiHMVNcYQ8y5hMx82ptgsQAi9w44StrVsYY0jTlDhJUUqSFyV5lpFNJz4lLVhY2jxndXtDJSXGxAw3ttjakvT6PdK0h1aa0lqy6ZQ8m1KUhV+LLRdIMOOJNe/oDzfo9VL6/QHGRDjnmGYZ+XRClmUkSVcsEd7KorWhPxwSp56mzY3N0IAFJtOM0f4e48nYd0VbAU1rK2wBEII4itCBCSRJ2hTkz/OcqfFmSIvrhMZdw+EL1g/6/YauKI7RSlFWFZPIt2jL88xX9Gm9l8zD4RWJNO2RxDFJmjanCWcd02xKZrwpz0QmKEjdoM1X8/JFAtI09UU5Iq8w5HlOlk1CaUBCE4YuwI9TG00URfR6XpmIIoOUytfvzqZMtKIocmQQtq32RUM4ofrKbIN+n17qu2fFUYxUync7iiOyiWY6zTBGd2gtesWv1+sRxwmDwSAUePAxEEmWMY0M4/HY0yWhCymRXqGVpKFK2ebmFoPhAGMMOEGSTDFKBqtmFdJ+lkvX2gpbIQSRiej1+xhjSEJlHqVDeTIlG/NqluchCKcbi0prQ6/XwzlHkiReIBnt+6E6hwwarJwotNKI0Ny77VBSkiQpSkpfvCJJQp9QiWcSMpiOfWH4tlciqiGEIIpi+qHrTRJOt3UvXqUUOjwuQjnKLsyXN0tG9Ht9pJAkPa9E1D5b32/YB+5NJuPO7DEApTVpmgbekfpTbcjN9O3nfMUiP3/tjRo/CiUlSZzgrA2Wo5Q4ipvDRt10AeFThaSQYcpaGvkVIKUgjmIGgz5ae8UvSRJfzxoCPTa04pt1clom1lrYmigi7fXRShFFBmNCswGp5qoPOaTS4aTU/tNt7XdJ0h445+mKTFPj1OGaQA2BIIpN2Ejt3ShQ+11CGTWtfb3qKDQbl8qXgZbC+9Wd889bHvjl4U8ScZKEes+BLmNQoW6rD27ztOQqR5v2lzSE+T3WQwoR/OteiZBSoqxX/OqG5MaYoEi0GyL42dNej6osMSYiMrppfIHD0yV9DeFacW87Zu6MNPARX2pTm1mBByl8txxrbagAppqTfltRm5HjJKbXH3h+H0cN76gb+ziXUFVVowje+GwXgblAojT1fgelVNO5o4k61hDHPpgjiiKv0bUcPmrXkKaJrz6klE8hkarRuusgDs80QueSFY/7PNBakyQJpfY0zebMKwv1f6S1Kb0LtZH9aSIOzbdl6JJTz1utFAkhIGHWjaTla3E+WC9NUsDPn1Y+AlkKgROu4dFeMJsQH9H2OQsKbZxQ6TJ0BPN/6nx2DZ42R3fKNQaFNo7jsC5n3c5qJUhorxilLvGBbR2JRvZrMSZNCu+X1sbPWcjBF8K3WLWp7xi2irrqaylsBb5tXhyaVDtrYW5xCSFwUiKtbKJga39M2xdWXdTeJakvfABNmzMRhG3d4Uhp5dOcOtLNSGtv7i9LFaxWIjA46f3OtXk8mP9NB9qaNSf2usOIs54uWaeRhLXofNyAiUzTFKPta7FWUqmrsoWTUy14XKBJhn2ng9BqO3wQkSFJU19jlxldtbBtXDWhn7TphOInGoW2Plh4xW92YhfShjrxMljO2t+gpU5pSoL1CGfDWqv3WHDl+EeYEFm+7C5N7V/5l4SSEgIzrkv+1ekKhz7nvNlVNSeodqPeMEII31nFVZ4ZHGHOGhrB24VcOSFAa4UQMVprbGiBeJwCpMNc+s+3my7wa1HEcdNHuZ6Xo+uttkh0ZS3WQklKGXoN22DqP7zHXChRVJ/q2z5n83usqkyjSMwrESjvf9bWBbNkB+jCd9Yinu2xeboApPPxAvVa7MrJVikZ2hzKhq75+sf1flNa46zPUFm2G2pthS3QLKKZgBXhgFf/O0MXFlSNWrA65dBuZjI+jq7Ze+1HPVe1Obx+bf7fo5/vAl5h1Bwee5dpq+fLVylzJ9MViut2ha56j9X5wt7t/Ooeq5X4rtBVC6GGrvAa4nCUwPx7XaBttsdkmJNXx95EiweLy40ZecG43hOCOPzQ1RF71x9xuZxN4BnLLJh5zehacpT2Uk6rhydsKfCM7ow5W8ScziZtKTjPWlzIWl3ynK0zXaetw1UrDWskbK9fyJ05Wc37oiM5dzWOPxHP3p391S26Tp8zMSOsc3SdOmeH6IKupNvMIOr/j3nrmJNKp3DCvHV6LdY4hra1oOt4PtIcrc5J19oIW3ENwmDVmtD8GBa6UI+YjFaBa6HLX/lkRn2O8SxkBOI6lZLTFaNXx7LYw+D10taOPXcdWFe6LsJLrnvtLBpnnv7nPnceujovbI/+IIuY0HZsjMNMdVHCqQ20HefXWwe64LCP2R8qr8pcLiZcrwtH52wRTLNtcwaLU/7aQNu60gXXo6zP9uz1JG90RtgeneQFbPXjr7KCxfTqprg8gz2Nya9io7y6KS47hpPdBKui66iJ9hXFj6ut07YxtqOvXYXRtZW2tVHW15WuIxBN19orXGNekSREz1+D7a/9+QUnQHCFn0O047QAxyxgcQkb6JFrtYS0w7TVjy9NW/s2+7zP9yoXaRNd5w1QO5Nxt5Aujs7XseM7m3HX0btdwnkEbZvm6zQcOpws4NhV98SuH18XOitsr4JTTxwr9Cks6s4nkbBSf0kTdn+J74rTv7oqutzc38e+fx4GR/vm66zbnpeuq15jkWj6hM/P15GHdd/TM6/VXPCU91eNufGd97c+63Ntcbf6eXKLXUNLSHXqhBn5IsSf5+dvk5P+UAeU+XEdMi3DeSibXatF9MGRXeqrN18kim9GzukMbpl6+XFjr3//i0QAn83gltsh53S6zr+u2rTHPI4bz/mE66tfO1MbwbUgCHHxcxCasF96PbZtTRzGde+1zp5snTu8mPxzFqJ+LZNRzHyaMC8uai3bzVTyc6FN2umJ83EJrfRMc9GiNd1L4qLzdf5rrhbXMYY20HVtWGPa5mpDXPCLCx/KwtGcms/12Ytdu/XC9iRNQwhe9Zsswoe2TBzyadKpoZ8F0SK/+OKxZpN1gxssAR2QtZfCWuTZnjfPyX929uJVlcplmO2Oi1wNbxx63SFe9TVd6b4LucypNzg8LyelH7TL3H1eHKanmzSchOvLfT4Z7prSLF7Fes3VanHJjImOTMF5+f9F6W+1sD0rDWbexu7qXbtAM/J1Ct3j7rEMJnftPsA5E8zpfr4O7LpjcBn/5SLve933WD7qRIvrlrjLp23eh79uVp6j/MvNBU2cSGpHtvw8bfN7on5+2X3YamHrMQuoaV6ZO+kdYhALZhbL2CSrYHBnLZiT3r+wn3VFtMHxitJpdHch+OdYBucfEd44tph86+HCLj/xZP2qwt0V2upxnjTchubZF65/UAvEYf576J+VWEoWiaPjPuv5WeiAsOXQaWnJN+7a2j83zhMF22WcNP6u0wWn0LCyfbIYnGYFWYNpOxbrvA9PG/u60nUaWiNs3333XX7hF35h1cNNot0yAAAgAElEQVRYKL71rW8xnU7Xki6AX//1X2d7e3vFo1kcJpMJAH/0R3/Es2fPVjyaxeLp06cIIdZuLf7FX/wFAL/yK79CmqYrHs3iUK+/r371q/z5n//5ikezODjnyLKMb37zm2u3Fr/+9a+f+r44r5QWQijgr4BHzrl/LIR4B/h9YBt4F/ivnHO5ECIG/nfgPwaeA//MOffBGdfurppzgxvc4AY3uEGA85GTr+AiJ9v/GvgmsBGe/4/A/+yc+30hxP8G/Dzwv4Z/XzrnviiE+JnwuX921sV/+qd/mt/7vd/rTMmws+Cc46d+6qd49913+fTTT5fTy3RJ+N3f/V1++Zd/mT/7sz/jh3/4h1c9nIXhyZMnfOELX+BXf/VX+bVf+7W1Wos/9EM/hJSSb3zjG+tDF47/7l/+S37zN/8N77//Pvfu3Vv1kBaGr3/96/zET/wEv/Vbv8XP/dzPrc+cOcf9+/f5sR/7T/j3//5ra0MXOH72Z/85X/va1078xLmErRDiDeCngP8B+FXhf6F/BPxs+MhXgX+FF7b/JDwG+LfAbwkhhDvjCK21pt/vn2c4nYFSCiEEg8Fg1UNZKOI4BiBNU3q93opHszjU688Ys3ZrUUqJEGLt6DImAvzcrdNarE3iSZKs3ZwJIdBarR1dWp8uTs973PpN4F8ANjzfBnacc2V4/jHwenj8OvB9gPD+bvj8IQghflEI8VdCiL865xhucIMb3OAGN+gkzhS2Qoh/DDx1zr27yBs7537bOfcPnXP/cJHXvcENbnCDG9ygbTiPGfk/A/5LIcR/ASR4n+2/AbaEEDqcXt8AHoXPPwI+B3wshNDAJj5Q6gY3uMENbnCDv5c482TrnPtvnXNvOOfeBn4G+BPn3D8H/hT4UvjYzwH/Ljz+WnhOeP9PzvLXXhlr42T/e4S1nbN1pWudsa5ztq50dRNXCZH9Mj5Y6jt4n+zvhNd/B9gOr/8q8N9cbYhHEPoN1g2cxUVaWbWZwc/T5V9Yk0i9o3R1p0n12Xh1ztYDr85Zq/fOuXFFulr8E8zzxEt8uX6w0DHd4DAuVNTCOfcfgP8QHr8P/Ngxn5kC/3QBY3sFJxbvv8B3XSj9dvHD9mW/d/6xnfT8jG/PSkKf1on85Jsf6S27uGrhp9F1nrucWJbw/AO4tvJ3J9F2rts1vzmdWotn3222Fmc//UXG2G66wkP/nQuO8aJ9ji967eaxfwE431ps1u3cmrzg3a9tzkDU/wMX5XGzcV26lvmC+UdrKkidhUWdhi56mbME4VUX2Zl0nTHh9dfrf887mtPpujpjWOTptaHtAszjlS9TX+Pqm+c02s6zPxu+vcC1eN10ne/7R/7lfCto1XvsrDm7ys9y2KID80eEVdN16LNz1zzPuK5tLZ5iqRRzG+ese8zW4ryWdO4CTsfc1OMqtHWi0kKbzY4XMmNfChfXoBcBb5Fa4e9+zL0XMZqrC5TF/yYLuWYb98jChnSVC13P73KeOTv7M5cfW5t54pXouqbbn/fnus5ftTMn26viOswc9SXFNZorz5K1i6br0ibbC9/nTMIWOoI2FT6/3FhOZgNtoW3xa3ERp/Wr3+d69lh75mxxjqPlrMWzeUfoIsXF6ZrrFHjknvW7l8daCNvlNrt+9V7XJ2evj66j7eZW/Rsu8tqrout67/cqG1jovc6QSus6Z8ugaxU9kC/UMvJyNwjfW6SoPs9tr5muC97jIuiEsF1UH9LL3nsVaIWG2NF7ruucXev166imZd/3mu+xSt4xf4/rOPWvlK5jHi3kuium6zrRCZ/tSej6jw8nnZRv6Gor1rlP7nFYB7rWdy0e91r36ZpHTc060NWJky2sx499EtaVthu6uoV1pQvWlTZ3bS6sVcLNcnz885WOZnHojLC9DJoN1vgXXP1/E4Zf56XN56e1HTPGUW+2GV0wi1RsaOwIXfP0zAckzALRoPZbio7Omf/3MF0wP1f1v12kC16Zs7m/ujRnx+4x5gNlXuUdXZuz+SBE59wrkzZfAGT5tF1exNZ7zJPksNYGNwkNTULIQ/QtA2srbJ1zVGWJtRXWWqy1OOf8j+8sSirfdkxK/1ippg1Zm+Gcw1YVVVXTVc3oshaBQGmJEBIpFUorZKC17agqS1WVs/mamzPnXDM/Uim00p2ZM2sDXZWnyzq/+a2z2MoihUBIP2da687MmXMu0FWvRYdzfs4qW4EDKT1jU8rTpZRu/ZzVe6mqStxRuqoKZy11tSalFErrQ+uxzbDW+vlyFmddsx5tVVGWReO7l1KhtUEbjdYaKVXr56xWGGzgkWVVUhYFWTalzHMQIKXGRBFRFGFMhDYGpdRSxri2wrYqS8bjMZPJiKqcCVycpXIOrf0iUkpjjCGOE6I4bnrQthXWWqbZlMloRF7kOOuoKotznkYhBEprlFSYyBDFCWnawxjTarqcc5RFwWh0QJZNA1OohW6FdQ6pvGKkw3wlaY84zFlbUTPo8WjMdDr2StIcXWVVhZxmz7TjOCZNe6S9FCHaPWfWVmTTKZPJmLIog4CqAl1euRBSIKQiimKSJKU/6BPHSavpcs5R5DmTyZiiKDxdZRkYeEFZlCFTQGCiiDhO6A8G9Hr9Vgvbei1m0wllWVBVlrIoKcuCssiYTqeUZYG1Dm0iojih3x+wsblBkqStn7Oy9MpRZaswfxOm4wP29vYY7e9SFAVSaUyc0O8P2b57l9u3t5em/K2lsHXOUVYV+/t7vHzxAvCN3G1l/SZxoIxGSQkCtFIMhhtsat1qxg1e2E7GE54/f05RFGilsM55Ddw5BAKp/IaXUtDr9b32Hf60Fc458jxnd2eH8XjsmZYQ2LL09DmHkAJC0GySJGzd3sYYE+axvYygKksODvbZ39v1BwcpsWWFdRVVZZk3VRpj2NjcahTBNjM4W1nG4wm7OzuUVYkUCmu91aUqSy94nT8VSiVJe32keoAxUeuFUpZn7O3tkec5QggvcINgKoqcynqlSQgvcO/cvUccJ2jd7jmrypLReEyeZc0JPs8y8ixjMhmT5RllUQAglWJz6xbaGKIobvWcWWspiiJY/CryPGc6mbJ/MGL35Q4vXz5nMhljKwtCEqUpX7SWwWBIFEVLGeNaClvAazhFyXQywTqI4ijkulmc8yYgKy1VWSGEI44TrK1WPexzwJvu8ixjkmVEkWdctblLSIEI2p2zFikkZVmuetDngrUVRVEwnU69qVgrrHXBVA6iElg3O2X0B0N/QuR6K79cFTa4NLIsxwarSn3KqKoKgWcWZeVPinEcU1XtX4ueWftTRFaUaK0gKLpFUeCCmdyf5ksEgrIoOhGsZCsbTJCZ9+/hT055nlOWJVVZBYFbYvKcbGPDW85aDussNtBRm16LsiTLM6ZZTp7lwSpRgbPEUYyt2s0/GjdaWHdVVVLkBUXp5+9gPGY0mTAZTXDOUlUFUZ6TZdlSx7m2wlZIgVDeJytwCOn9YThwziKkbHxLSs58Zu1HGKuUSCURUgTmTfAFevpc5bCuQkgRzCSrHvfZEMF3KZUK/2qUEo3iYJ2D0vubvP9FBp9gu4kTwp9m/cnAr0UlZBOkUQdwOFvhpPdJ19aJVqMONpESpbx1QUiJQsz8m87hKofDzua25fMFh+esLt4gQmyHsBah/F6zzkLYg6oLczYXSOnwimBNmzf5C6STzcFDGYPW7XZngOd5la1mMR/OHz6sq4UxSK1wVlBVJVpr4iRGL9Hat5bCVgQHv4ljoiTBVhVxHBOZCIefGGstZVmGgBuN1qbVZpIaUkqMMURJgoNg4okQ1Kc+7y+rqgohNDqK/WZp9dmPxtccxQlRUSCEaE7t3mcW/IDWoqRFmxgTRUjZXtN4DSkVJoqJ4tj7w7Rn2tZ6K0V9YqqZgIni1puQwc+ZNoYojhHBlK+UwjmoSkNeFJRFjrUOKQRRkjZz2mYIIXwsRxQFxu0LLWgH2mhUllOUud9Tzvk1m6StDyICr0AobTCmogoBllJ4d5oPQlSUYf8JIElTv89aTld9CKljbqy1aKOJopg4TUnzHBMZqrJCSknS69PvD1BLNPuvpbAFL5TiKCJJYoqsQApxOP0nRLoKKTEmwkQGKdodJQmeERhjSJIYV1lflsQ5XBh3HZFXR4DGta+l5XQBKKVJ4ogij6isPVqcEHCeMYQAKa0N7TYg14qfJIq8UKoZmX/PJ5AI/HpV2nilsOX+sRpSSoz2dHnLSm369sJJClBKoo1BYEiSBKW6wXKUVkRRFKJ064Aowr6SOKdwJkJISZL2iEzk12bL4ZV1TVVpKApwwltTGgXD+rUnBVJI4jj1sR4t5x9CCLSUuHBS9etPoJQkjmLSXg9rE6qqREhF0usvfZ91Y+VfAvXp1lYV49G+j/rEp5fU6UBCeVNlFE5IHdgrgN/wzjqmkzFZNm18FlUIdyec1vv9QSfSEWqIoBDlWcZkPKKsKpijyznnowmjCCH9Rmo5DwDqnGBBVRSMDvbJs3yWwmVLr1hIbzpPkiSkWrTfPA742IeqYjoeMZlMmtSYypZ+/oQ3xcZJitam9dH+NQQCZy3ZdMp4dECe5zNLRFXi8Mqhjy3wmQ1CdoGuEG2dZYzHoyaSvMgL8tLTqLTCITCJIY6jVgdWzsOGKPJsOmF0cMBodMBB+DfLi7CvvDuxHzIZbvJsFwDnHLYs2H35nA/f/y57L58znU4p8hxnK4RSpL0+w41bmB/8B9gHD7pRjcU5bFUy2t/l+x9+l+dPHjMeH1AWJVVZgoAoSRlsbPH6597i3r17qx7xueGZ9gGfPvqQJ48esb+/Q5kXlKUPqtFRRH+4yfb2PTY3NjsxX7VQzbMJnz35hEcffcjuy+fkWUZRFjhrUVqT9ocMhpvEX/yPZhV03Mn1ilcPHwFf5FNevviM73/vfZ5/9sQrSYUPsBEqmOsGm9x7+BoPHjxoLzlz8GloOfu7L/n4w+/x9NNP2N/b8cE3pRe0cZrSH26yubXNxnAD2QFBW6fHjPf3+OTRhzz95BN2Xj5jOplQ5DnWWkwc0x9u0uttcPfhw06Yxuv5Gu3v8dnTxzx78pgXz56yt7sTFKUCpTX94SZpb4DUEVESE0XL9UWvpbCtoyQn4xGPP/6I73zzr3n57DPybOJzbn3+BVGSsHX7DptbW7z1zju4wZDTCmG3AdZZppMJnz1+xHe++dc8+eQR0/FBCHl3OCcwccRgYwsBvPn2Ozh7d9XDPhPOWfI848VnT/jee3/LR997n9HuTvDT+gAHpTVJf8gbb73Dg9dew77++vEFYlsEz+AKdl4854PvfIv33/sWuy+ek+dZUwhCKk2c9ri1fZeNzU3efOstH+SGaq2R3Dkoy5L93R0+fv87fOtvvsHzp0+ZTkY+j9hZEIo4TRlu3sJWJW++9TbOutYrEbaqGB3s88lHH/Dtv/kGn378MaP9nVkhGSeIk4T+cJOHr7/JnTt3sSEYrM1oeOKjj3jvb77Bo48+ZO/lC/I88xY/5zAmojfcYOv2HaLIUL3zTqv3WF1Y5eBgn0cffcB3v/X/8ej7H7Dz4jmT8QFF4SP+lTb0h0MGG5ts3bnP5956a+l8fi2FLeALCYxHPP/sCc+ePuFgd4cqhLDbEJ02mU5xSHZfviCbTny4e4v9E/6UZJlOx7x49hlPn3zKyxfPqYoc8HRZB2I6IS8tL188YzI6oCxLb3ptKV0A1jqybMLLF8958vgTnj/7jHwyBkLUpPUMejzN6fUH7O/tetOec8gWJ//4/OGM3Z0XPPnkEU+fPGY6GmFtCYgwZ4LRZErlYOfFc6bTic8HbPHudM5RFDn7uzs8/uRjHn/6iNHeHlVRNFGu1sF4MmGaFWzd2mYyGlGWpQ+oWjUBJ8AHvHvm/fTJJ3zy6Pu8ePYZZT4N1eegso7xZMxoPEWZiL3dnSaVpq2o3Uyj0T5PH3/Ko48+5MnjT8kn4ya1rrQWED4PN8+5c/8h2XRKZS1m1QScgrIs2dvZ4ZOPP+R73/07nj75hGwyoSqLMJ8VDsFkOmF0MKK0kE2nvu7CEuesxdv58miStcuSwoITmtJJKiQqlFkTQqB1hDKGrLQUhfedtZdte1hnKcqK3ALSUCEprPA+PimQCITSaBNRWkFeFD51psWMwBd1cJRlRV5ZrNBYVEOXFAKhBEiBMTFOKvKibCrGOKfaqh+FAiuWvLSUQuGE9mvSSV+MA5+mpnSEVNqvxbJsyou2VUHyaXOWrKwokThhKKygtL6ATF1hSSiD1IbCCfIy7LGWr0Vr/R4rrMAJQ+UEWen8HkN42oRCKoMVkiJUAmvqC7d0zqx1gXc4rDRYFHnpZ0oK6Q8hQmJkhJOGKgjgds8XOOvIy5JpUVEJhRWGvBpTlr4Ualk5KgQKjVMRThlKt/wmDmspbOsC08ZEbG3f5cHbn6d/axtnSx+hJhVCaoSUxL0+g61bIQR81SM/G75+rmFz6zYP33wH0x9SZlOUlEjhA74QEpOkbN29j2l5aTwP0aRbDIabPHjjLdAxk4O9EFGokFIjkKg4YuvWNkl/iGyxFaJBiEZOen3uP3yDygn2dl9iqwqjlM/dFBoZGXr9IcOt201eY5vnrY6yjpMed+6/xjjL6T37jHw6Qdd50tKgTUQ6GHDn3gOiJAkCq83wtZxNFHNr+x5vfP6LRP0B+7s7PrpaKhC+/GQ6HLJ1a5v+cNPTHBT5tsKnakVsbm3zxttfwPT6vHz2GWWeBwVJEEUJ/c1Nhpu3uH33gY/YbTFN4NOZoihm89Y2b37+Bxhs3ebZ08cc7O2Gg5cjinvcvnuXzVvb9AYD+v1hCLC88dleGVIq4ijxC+st300ljiJMyKe11vrcRluxuXULpUxTZKCtqBlwFEUMNzZ58PqbbN97SBQZjDHosGGKPKesKpK0h4niDqU0RfQHQ+49eJ3Bxi2kFEQmakrglaEEoFCaXr/fBG+0mTYBaG3o9/rcuf+QuNenqiq0UiFPWM4KwgNbm7dQ2vi81ZZDKU0vTbm9fRehNPcfvg5AZIxP8Qm5mkhJZKLAuNufhqaU8uVAb92mco5bt++S5xlKqVBj3OdzahMhlaLfHyLbnhcdisDUvOPhG2+ycWub0et7VGUVIvz9PPX6faTSJEmCCWuxrbT5OgmBrsEQ9brmzoOH3Ln3gP3dHazzdPeHQ+7evUfS65NnGUmSLL3AytoK27o7TNpL2Zbb9Ht9hhsbvgCEEGRZzujggPF45Iu+dyTVok5piuKYza0tIhMxGA5JU58PV5Ql49GYyXgEQqAD0+sChPQCdzjcYDgc0uv36fX6aG1wzjKZ+gYMRVkQh0T7ts+Zny+BMoZ+3xfhj6KItOdTD8Cb+rPphOl0QhInnYhsbfIYtSZN03CSFURx7HOglaKoKsqiCGkzVZOv2eY5q5U3qRRxkrC1dYuN4QYi5GtqY5rTkrMVeVEQR8avxVUP/hSIxnrklbyNjQ0GgwHF7duen0QxXg9SGK0pihyYddpqM6SQvvNSFDMInYp6aY/J9h0IilHS67G1uYWUgt3dHfQK6FpbYVvDt1NK2NzcpD8YNN1vsizDaIVWEmV0qJvcbt9EDYfzJtf+kOFw4Itph+43RVESGYPRMkTh+RKBnUAo0p+mKUmSMBgOSZK0abYQTyZEWjGZTDGRabdzfQ7Ot9IkimL6fUOv1/NWB+Mre03zjCzylgmlVevNdjXqMobKGAZRRBL7KllxFCOVoigK8jwjm0woyiL4qF2b3Zpz8IKp1/PzpEI+vtaaKlSfq8qCbDr15VJF+3eZFy4inNxTTFAcZOgQhgMLKAn5VFOUBULSFPRoK2qXRt0KMO2lxFHMtNcHvEIYRxH9QR+cI8+mCLl8fr/WwlZpTb/XRyvNYDggTXsoKUNBBBXqnwpfwkt0RygpqUjTBGMMg4E/AdYJ28YYpAApBNMs8z6mbpDlfehRjOv3SdKUfn9AFHmzvwu9UWU4dfjqUXRCQaqrfvX7PSITk/Z7xFGM0ir0H/ZKnzeVV54pdoQurTW9NEFJTZwkjUCSyq9FY3wnrcl44ourzDVfbyuEEOhgSnYhHabue1oXeKjLbBJO+KL90wV4E3mcJETWYoyvwOZLpaqmNrwM1jM3HoeSlKsd81kQArTSJGkPJQVJnIR+5QqEd3fEsW/x6KzFmLrLz42wXQhqBle3mIujGBOYgJwrCl/nC2rTcp9LgA9y0CRpH1tVJEnsfWRaBaHkyxqCb5FVV1tqO2oTV5KmPvAmjrwvuq5ZHRQIgWj8T0q1Nw91hjqQKKGqLMb4jW9MXU1JhipfnqnlRdH6Nm01pJREcUyvP/C1j6OZUPLNx61/LCS+96vpzFo0UUSv18c6GywOvke0N4WDrTSVsU1rxC40jhBCYLT2dFnbtBitFb7GhB786s45X2qz9WvRlwQdDAaNAujdgnM110PDeOccSdrzHdGWHBex1sLWd3ZIUNLb9Ovm40IIkGCMIU18VRXP4Nq/YQhRu3Ec46xFGzOjKwhbow0u8e32TEcaLEDQumPvi61PRCpEtgL4pk3+P4EIkaHtZgRCeKUnimKcdb5WsNZopRt/mDfuRZDi3+sCgxOiCbhJ0xQBYb50U5LRESLkQ5RuZALNqx77GagV9TRNfVnX0AHIn5b8WlRSonxXEy+UtO4EXdoYer101kfZOr9G62BD6U+1STixL7uk4WVQB432+/25OQg14gmWMKV9C0hE87naOrYsrKWwrdt+eWGaUDeIl3WLrLD5/U8dOpeEhtZtX1hSCLT2Rd1tVTV+ioYu50BrIvwG0krNFIwWo9ZA48YEFJibUk0TCeE/2NQa7oI1ojZJeqZFaDqgGgWpoUf6NVl3cWp7K7q6eUIURb6EqLWN76wRtg7cXDtIrXQTpdxmyNCcpK7LXdcen1f8BKCChUUIMKEpQZtRKxHgzeC2spShz7A/mYsQbCSbyOsoWCraDClFE/ha91C2oS2nf98r7TU/GQwGoYnGRfmH4Cqm57UUtuA1zyjytnnnbGOmbE4TgTFIpRrNtAv9KIXwXTsg9SXi8J1VlJIIpD/1BYanrTfjLbON1GVRm7hIEiqtPR1CNIwbAClROG+lqM1FLacL6hO7D2CzDQPwc0QQSApQyr9X+9/bjlrYSimbUob1nB2FczOf5//P3rvFSJac952/uJxb1r26e7pn2JyhLIqyhAUEGpaWD2tBawIL0DCgNUAbevFKXhoUYK9eJAPjJ8O7LzIswIbXgGWLXixoYNe7FEHBXMOQTBteYP1ggZo1LWtFznBunOm59L2rq7Iyzy1iHyLOycyqrKqsrJOZ5yTrD3RXVeY5J+J/4ov4vvjiiy/a3mbCe1akFJRF4Q6JN3Zs+ckNu+6MbJfsojNHIvo+U5bKbaPLRe2lACaMJaAT7QWjehpjKGXpE/k41SildEcjandwRKUHLt/HrrbGu7bKthKs0TrmKKy/+n08uKbt+zUrCEEtLNXAPVF3H6wxHkHYhYEbRmf1aq1d2wgmji2rsvdUzp/utJmoPRDWTmmzKdd3hVc1yNVtBhN97OT1XeAF1Ea5lBLtZ7Ynx48KXeI1PulQ/sQiFyQ14jHOsYu8qrYLgqiO9TipYFfBa22VLYwa4KytBo2+cCFGQZYLjiQdn5lPv4DGDosfGSVXc6HMiosMg0Z5wdKifp0sCtwBxAsvjWVGWi7PmFsur/FZetUHFjJGV2PHkriNjx8no/mbVULLbS+otgEpwlC2bjK11sq2QnPv+ILOVn0nurNnt0ZlzU79qibWTV529OvEV2Mfdo5Xjekyab1Z0l1eOC/NiY/smLm1TG7ND9Qn2k3U/3Wc18lnirE5SHO8zsuLIPy7bJrbyeddltcPhbJtAqu1ihZpIS7IWp+x7EVaviPX+iWu7QBmqevI7uuWgXQRt8W10pJkcckQCzEmT7+r8/g1LYtnLQU2jVllcdZ6rIeyFc1bu60YfCcs+6YEa5XKdawWC5gtt6HNxutgG3INto0XNNXPVi+LJ2dh0AC3KTPy5WPy3TahmKa9q2VjWhxAs7wWh25EzkCd27Pa9lF/fkKwm3hxyx7cpq0nnORVfXb1cq70iDnLPM3tvL8vXUYDz7h0mUJwcsA5zaupclaLaXXooixOq8Minrn6Fmty6ax6XhtYNY9l8uqMsuWUUTX/S2rDYnmNE1GNTfBqC7cmLeHzeK3KSdqUMm1Le8Hs9bkwX24LeVH9a+JZrVCpFU7Wp011axAtkqd50B1lOwY79v/Jz7u0PgVMRMOexctdZmfmdtZly3439py/6k9n4SXOe8LqcFF9rtpeq8JM1ZmtyVoDW226bOBlO0PjbHlePk7UZwEvvw0iugiZWmZ7dW7N9uQ6mK329VzipV30gqs9dcvEyW0o8/AapVlqQ9fAJWw4wyi6VHSiPfXL6UuW3GbT6l7V4TIduG2yeD4vmFW22sbLl3rqr7liPS64dPy5K8GUvY5XViotOKqpBVW4Ejoysx1/w1OEZgHWyTItHnuWxX3JOlguniUu1/I+c4p96XrMcsxXG7wai6hDe3g1W4+V8/Jy2Hg9Vs2L0bttkp+1qz5s76QXsKGnLolUJ5Rtl62Zi9CGQJFFoU1rds1iXXldoyms3JBYVB2u8MxGa9Oih836nluvbM9O+dbxAU9Mcls3xTSZ1m59uFXG0bq1F7QvWKtJrCuvs9G+thQNasg2UZv1Pbde2To3yPjfi3FpLR120iJautt64WXYsZ8db6sxuECbBbgeZyl3CWWsI69lltMeLK4t531uE9WZdI83164n9cwi0JEAqcW/iKmlWjuR0u8y9y3i2uZgfbDBYs3DVXCrDjBgPK0fYwFZZ+yDvFSbXbmW8+Fk/UcxdL5GU7h1QcmcPMDgZLDgtOQXXeJV1f80rxOcp3zfVoznirbYqZ1iMdmrxp/X9Lsa6ZlR3U+XMy+vjijb1WFaB1kXdM9A82QAACAASURBVGHAmgu+g9iTH4GblS69Qg3iRP2tPfu7ruFseVyNsd0czq5/t/vg+e1yHre2815E3VujbO/du8fXv/71VVejUTx48ICiKPjt3/7t1q2fXAWvvfYaAP/m3/wb3nrrrRXXpjk8e/YMgO9973trJ4tHR0cIIfj613+bdVpDf/311wH45je/yc7Ozopr0xyqfvUHf/Bttre3V1yb5mCtpSgKHjx4sHbj4r179879XrTBwhBCrL4S17jGNa5xjWtcEdbaqRZEa2a2P/uzP8vf+lt/a20sHWstf+Nv/A1ef/11vvnN/wsp14MXwL/8l/+Sv//3/z6/+Zu/yWc+85lVV6cxPHnyhL/4F/8iv/ALv8Bf/at/da1k8S//5b+MlJKvfvWra8Xrt37rt/ja177G17/+dfb29lZdpcbw+uuv89f+2l/j137t1/jCF76wVm325//8n+cnf/In+bt/9++uFa+//T/+bf79//Pvz7ymNcr2hRde4M/+2T+76mo0ir29PZRSfP7z68XrzTffBOBnfuZn+OxnP7vi2jSHBw8eAPDKK6+snSz2ej2EEGvH6/d+7/cA+DN/5s9w69atFdemOVSu45/4iZ9YuzZTSrG/v792vP7RP/pH537f+q0/17jGNa5xjWt0HdfK9hrXuMY1rnGNBeNa2V7jGte4xjWusWCsh7I9NyVgl9MFdrXe17jGNa5xjXF0T9lOHJAuxrLmnL0h3t02nlO5hUrs1MHvsx5Q0FI+NU4faF8f5D3T7d3i1u76zo4fFl7rkru75rV2bXaa12wRzE20a7PvrzXRyLPg9KEEs945OmGyuqcF24trTDtsYfZ73U/r0xPOl/GqvrNxTPb9ywlvnRZt7kZbJK/pbTZbFUV9VPFEusXZS5/zvlkePZny8Wq84FLv35d96fsu8ezRnxcc23nq9vFziueQq1G+xvnuv6Bu9e9VWczYZvU7n69ek6kLxxOkXh0neVkhZq+hqFK01r9ctvDR/UwI9NzozMx21fuxFlX+hc+dsVxx4uesZZ8+4q85nudxu6iUkx1tXl4jR0FzM5hzec1QRHXNZUVqans1zOvMxZiZiqi8SP6eWcutPAQnymqyz533pFnLueypSKP2ctzcrc0eqXllWRx7zmXqNc3zMepvVyc4jddlxrhxWbrU2DFVFqv2uxqvTijbWUieL3Tzv6RxoZroPKfcvnM9fYZrzrGmrlD0WUcXug5z9eO5rnr/tFnbfM88PdhdBYswuuZ95jivVRujVX0mP5j1xrOfdaklh1nrdQIXeQimetTmbrPRM1fdx5rESfvvqmPuDBfNVpnLPPPMe+d4zhR0yo18Hi5KHD3+kppwv508SWZhfunzHjvuuak+avDIqXbA1j9m84qd3RkW4p6cA3WzVceFNfHM1dO6glt8hgcvEJMu4mnF2wuvmQet6GNeGJusy1J4nXsCAvW42KY2WwtlO8sLmP8MxrMV9ZWPxbrASFokr0U9B5jJ6l8Ut5PtNe9z5sVMZTV0Ok+zbdZEWc2czrNMXrOWt66yeNbxeIsoa5mYuz6VxXjV50xBp5XtOh88vegyF/r8EwK71LKX8PxVlbvYNuNMxXTN6wrFX8viXM8+y0O1cF6ukIU8uxNrtlNnk+uAMxq1bVbiPJjGwVrbeW5n1b/rvM7COvA6Sxa7jh8GWRxn0nVenZnZrqvC7boAnYd15XbNq3tYV24/DLzWhWFnlO1VcFIgnZsCRvvKxiIfO4PT62PW2ol9dhW6zqv+oMu8pq7T2jGP+9hWpy7x4uyZ40lZnCabbcYkLx/MdkoUuz52nOBV/+d+aWorzzIx0W5j/W5itG8gGvyyWFtla63FGONetrUYa2o3pjHG76cCEAgpUFIhlULKdnvWrbVYY2o+xpg6yrbiK6R0/MZ4NbHVYNEwxmBM6Tm6NsOC9VxH25Nk3VZSytbzstZSlqXjMc6LShZxbSaq9pJIIREdkEXXZqbmZq2d5FW1l5SOl1TIBrbzLBq1LPr2ssbxssbJopQShECKMV6dlUWL8ePKeCYqJRVKOXkUov2y6P6NeNXjfWmAKqIcpBQopVFaL3W8X1tla4whzzKKIqcsS8qydB2oLMnLAnznwYLSmiRJ6G1sEARhqzuMtZYsz8nSIUVRYIxxnccYirJwHUYKBBKtNWEU0+v1CML28yqKgnQ4IM8zytJgSoOxJca3n5QCkOhAE4YxcRITRTFKqVVX/xy4wW04GJBl6UgOTYktS4qyrJWtVJooioiimCiO0S03kIwxZGlKmg6cLJam5lb4PiakU0RBGBJFMXGSEASBU7gthbWWPM+9LOYYU1IWBmtLytLxrI0jHRBFEUnSI4yiTshi1ceKwvctYzBlQVmWo/3NUhKGEUmvR5IkaNVuA8m1WUaRV+O98X2tpCjy2kgqjUEKSdLrsbm1TRRFS+tja6lsK4t7OBwyHA78S7eUhROoPM8oypIiz8jyHAls7+7y4oufQOug9QNcOhxyePicPM8BHL+yrPkZa7xQCZKkx63bt9lashV3WVhrybOM58+fMxwOwIKxFlMUlKakLEo/s7AIKYjimL29fbTSrZ5RWAtFUXB0dEj/6BBjnSOrLJxhVBSF4+Vn7mEYsrm9zb66gfIeibbCGMPx8THPD55RFAVCiHqAKwtnVFROPK01SW+DmzdvoZWClstilqU8f/6cdDgEcAqpksWyBBx/KSVRHLN/4ya7QYDyM942wlooi4J+v8/g+NgZRtZS5AXGFHUfq8aQQAfs+j6mEt1qZ7Iz/DKGwwFFWWJK4w3AkjzPKcuCPM+9PijY2t7hrg4IgmBpBtJaKluoFG5JluUURQE4pVSUufsszxkMBhwf9ymLDCkl+c0XWh9w4NxABekwJc0ypBS1a6jI/WCQF2R5Rlnk5FnGzu4udrPdvMC1T5amDI4HICRSuEGu6jTGOO6O/5A4Ttja3iFYdcUvQOVlOT4eYC0oJWsuRVk6N573UAyOj5FSsb21A1G728xaZ8AOh0OyPHeDViWLRVEvB5TGK+CyZGt7m6TXo9XzP2sxlSwOhgjp1EzNy5iRLBYFWZrS621gtnfO22nUChhjKPKcYZpijEV45VqUXtlaS17kZGmGFBBFEUW5f+52nFbAu5CLoiDPi9GYWOTkRU5ZFAyGQ44Oj0gHfTfe5znLDL9qr3nZCHzWF0FtYeMTS5fe1ZVlGUWeuTVA1d4ZUoWqdhbHq5opwcjNYzC1oCFo9czvJGz9z/jE4369SIh6LbAoCox160ttdkdWqGJMLGCFxQDCr8kKIdzndmRIIHADfAe4jSesdTIpEVK59WbfZmVZUhZuYJMtd42fQs1rtPZcy6I1FGWOxdR9rAvU7HibCQFCIYRrMwtYYykK5z53ywDdaDPXj/C8qNsMhPOS+TGxKAq00mitl8prLWe21SK/VBKtFaJ0L1sp6wNr3EJ5mmUIKZAyJOltELZ8XRPwvDRBEI6i7IQgIPAzwwwL5HmBtJooSgiiqNUuZHB6RSpJEISEkbNMlZKAJDDOFZTnuV93Kf11UR381WYIKdFBSBRFlMa4oBMpMSZ0nT8v3Dq1KVFSEoYxaskDwTxwa5aaIArdwCYlSinnriw1eVa4mAnjAlSCMEaHYQdkUSCVQochYVliASUlFuG8EXlBXuTOjawUOojG+ljL20xKtAoIwtAFsXlLsCwDNwvMnBzKWmZjtO6AmhCiDsJTaKQ1WC+PlWdiqIYYC1Jp4t4GQRQuNfCrA29xPgghUEqhlMba3AVEjUWiaa0ItCYMQsIwZGNjE6Xa/zqEEGitCcKgjkquZkcAWivKUqG1RilF0tsg0AFnn+fSFrh2CaOwDmqTUmCRLmRfOTdRWWiCwBInPcKwC0aE8Ao0JAwjiiJ3xqCUCGHBau9G1mhtCL1SVrLNjlYHKSVBEBCFUe2Nk352BAFWuzbTWoPSLjhKB+2XROFlMQxdHISPf3CKVPto17IeL+K4RxiEyJZH7AqvkIIwIMgCirLAzdFdi1irMEqhpELrgDhOiOIYKTtg0AqB9NHTox0aDi5SXKCERCtFGGywsblJoJe7ANV+7TIn/HkolGXO8PiYYTocBUqVBVk1UxIQxpGbJXXB3Vq54SzkeUbqebm1MeMs7rKktJYgCAjD0K2ltZwWULsYjY/eLYsCC879U7r1aIvwM8XAeS3a3l5QW91YQ56lfoaOX6d1QW3WG0yxVuggcLK46nrPgGpmUOQ5WZb6WAm/TJPn9bJAGIYEQVi7YdsOKdwI4takB367DPXyjAv+AiEVgZfFLjSYENW4WJAOjsmzHOPXN8uy8Es0FoNFhy6AqBN9DMBaF4vT75Omqe9bxo/3GVmWYzDEUUIcx0sf79dS2bp9tSV5lvL40QM+vnePZ08fMxwcu4HOGJCSIIwJ4x4bSdINRUsVvFHQPzrgw/ff5/HD+wyO+26LQllghSCMYuJkgxu3bmNf7MqGe+sjrQc8uP8Rj+5/zOHzZy6U30fs6iAk3thke3uP3e2d1u/9A7/B3lrKPOfp08c8+OhDnj97Qpam9aCttHNrJckW4csvd6S9RrJ4dPScD99/j2ePHzEcHrvo1rIAKYnihLi3yc1bt6EjslgFVw6O+3z80T0eP7hP/+i5C0D0shiEkZPFnX1u7O93RhatMWTpgEcPPubBRx9yePCULMvI88wt3WhNmPToJVts9jZcbEHb26zilaU8fnifD++9x8GTxwwGx+RZhilLrBToICKMetx9+ZWVzNbXVtnmWcaTh/d584//kLff+B6PHz5gOBz4vYAWITVRr8fejVvs7u5iTVnd3FrLuwqiOXp+wL133uJ7f/QdPv7gHv3+YT1rBzfAbe3t8aOf+Qk++fLLC0us3SSMsaTDAQ8/usf3/7/v8P677/D86WPyKvrTQhBGbGzv8NLdl9m/sY956aXWc6v2/z178oh33/hj3v7+6zx99IA0Hfp9xBalApLNTfZuvECcRNy9+0l3L+2dLI3L4gfvvMl3/+g/8fDjjxgcH7k90sYghCLu9dje3af48Z/k7ssv04Xke8YYBsd97n/wHm/85//IB+/9gMODp25LiTFgBUEUsbmzy91XfoTbt2+Pxo8Ww1pLlqY8vv8Rb/7xf+Ldt77Ps8ePyPLMyyLOfbyxya07L7G3v+eMprb3MVwfe/roAW9+9w9544//iCePHjqPROFkESEJEzfeb29v+faq/C7X+2znhrVuhvTk0X3ef/dtPrj3HsN+n7Jw7rvSGEorkP1D8qLk8OApeZa5jc9KtXaAA+cmOXz+jI/uvcd7773DweOnlLl331lLaUH2jzgeDNjdu8HguO9dYG0O3fcziUGfh/c/4t4P3uH+Rx+SDfo+EwyU1iKk4rDfRynN82dP3Wze2laH1Ls9mxlPnzzig/fe5cMP3mNweIQpcyzCuSOF5PDoiDTN+MQnXyYdDDBlCW0OTLEuYvXw+QEf3XuPe++9w+GzA8rcBegZazBWcNQ/5Ph4wM7uPoN+320vCdori9W2n+N+nwcff8j7773Dw48/Jh8OfHYsKI1FKMVR/5gwSjh6flBvdWozjDGk6YDHD+/z/g/e4aMP7jE87mNMCQhKL4vq8AiLcH0sy5xB2OJJiDGWNB3y9PFD7r37Dh/ce4/BUd95+vDjvQHZ71OWhsODA4o8wxi71LnVTOOUEOJdIcR/FkJ8RwjxB/6zfSHEt4QQ3/c/9/znQgjxPwsh3hRC/KEQ4k8tksBpuDWjLM/pD1LSvKQ0gtxAicQKhRUKpELIAKk0aV7W20najGrNaJCmDLKMorDkpSE3YJBYIbFIDC6cPytKUh9d2OaBwK1fWtI043iYkRYleVmSFs4osjhepQWsJDeGYZbV0cnt5mbI85zBMGWQFeSFJStK32YCg8RYgbGC0loGaeZmGj7jTVthsZRFyTBNGWS552XISmfwGevksLQCY2GY56Q+g1abeYFXSlnKcZqR5Ya8MGSlobC4tkLWcpmXJWmejSnbdnKrXMhplnE8TMkKQ24sWWkpvPwZP3YIqbBCkOZFnZWunawcqj52PEwZFiWllRQWXPiXxOLGe6kDVBhSGEvhs+4tE5cxnf9ra+2jsb//JvBvrbV/RwjxN/3frwJfAH7M//svgd/0P5eEKjG4JEo22H/hJdLScvj8AJNnbi+ZcYENOozY2bvBxvYOstVp1sYg3NrD1t5Nbn3iZUSYkA8Hfi+cRAgnVL3NLbb3b6KDyN224mrPAqE0vc1tbtz5BIWVHD0/QApcZK6QSKkJez32br5AlGzUkcjt5uYCuoI4Yf+FOxynGTp+gilztNJuH7FS6DBmc3uHZGsbqbTba7vqqp8Lnz83CNneu8mtlz6JDBOydICucnFLjdYhyeYWm7s3UDrsAC+nLqUK2Nja5eaLdymF4vjoOUoKv9an3PaRjU12b90hiBLHF2gtu2orvlTEvS1u3H6JtDA8P3gGtkRJ7SN6A4IkYe/GTZLNLYRUrZ3RjiCcmzjuceOFlxhkBQfPnmKKvI4QF1ITRDE7+zfY3N1DyuUHtF3FT/XzwM/5378K/N84ZfvzwD+zzsz7D0KIXSHEi9baj65S0ctACEEQhGxtbXPnE59kc3efosjdwK00wm9yxhpUGLG7u+824re1o3hUWxJ6yYYLOJGK25942W2vUNqlwfOJPKRW9HpbhFFcJ4VoK6otCVEUsbu3z91XfpTdGy9Q5BlKKbddpOo0ShKEEZteKVVJ09sKt1UrYGtzmzsvfZJ4Y5s0HSARBGHgBm/8AQtasbd7w6cMbXebgetLvd4GN1+4g1Wa23f7WGsJdOAVrmszqRVxskkYxW7wazsvqVw60P2bfPJHPs3erduURe623OnAJeywIJUijGN6G1t+8G4vLydjkjAI2d7Z5cVPfoqNnX3yPEX5iOqRYpLoMGR7e9eNly1PRDI+3t9+6S7x5rYf7902SSlkPTPXYcDOzp4zIpY83s+qbC3wr4UQFvgn1trfAm6PKdCPgdv+908A74/de89/NqFshRBfBr48b8XPQ7UpPQhDNje3SHpuT2aVBF0IQZplpMMheZYRJ1Fnjv+SUqADN8jt+33DcZwQRjFaK4qyJBumZFkGgnp7TJs7C3ilpDRRnLC7u8f29jZhGBInCdrvh8syt72kKHKiKOpEZhtnIEmCMGRre5s4SVBK+TZzSdCLwmUyy9KUKO5Iog5vIOkgYGNj029Je4EojomixO/3dklWsix1sqhUnTWrrXAzcuFzOSfs7++zu7NDGMfEcUKgNQZLllYJIArCMGg9LxjlHoiiiJ2dHTZ6GwRhQJz0/AEsro/lPjo5TqLOZMVy+4fdeB+EEVEYkvgDWKQQZHlOmrptd0GgcacALbeOsyrb/8pa+4EQ4gXgW0KI741/aa21XhHPDK+wfwvgsvdeBOFdVUIIwigiCAJ6vR5xnKADDQiGwyGDQDMcDHyGlHav/Z2E0pqNzU2SOCFJEsIoRElFVhQMB8ekA5eQ2yWGsC0PkBpBSknSSwgDp2jjOEbroD5YIhsOGKZD32FofaRkBXfIQETS2yCOY5I4IQhDALIsI00HLi+yUsj2N1MNgZPFza1t4sgZtFVSDjfADRkOjp0sKjFKm9oBSOmSwoRBSNJLiCOX2ass3Vp1NjwmTTOCNgeyTYGQkiiK2dhwp51VyUaMtaRZ5vrYYEDoFXDbu1jlwhdCuNOlYnfSWdLruckVgmGakQ6PGQ4HbrmN5fOaSUqstR/4nw+EEL8D/Axwv3IPCyFeBB74yz8APjl2+13/2RLh15OUopck/uW7dIxSubNelVIo6bL7uMxSkrYGN0zCrRtFUUQsBL3ehjuOTSt3lFlZoqVEK0maOhdRJ2hRdZaADdvzijYhCIM6BaDWilQrn3LOiW43qIn6GMfIK6QwjLzXwWVhCry7qz7mrO0jnIeUijiOnGJKekRxhNbu9JsgDF0mMylJ0yFSKH8+cfsNPyFc1i8pJUmcEHmvWHWIhNaaVEmkGvhTYzpirHvXajUuVrLoUqMKgkCT6bGzousj11sO781MkpggCNnY2CCMYpSqjkLUaC1RSlKWZiXj/YXKVgixAUhr7aH//b8B/ifgm8AvAn/H//wX/pZvAv+DEOL/wAVGHSxzvbaClJIwikmMIQojojgi0EGdvMIpXTfAl8YsPSn1PKjcwUEY0OttIIQgjmM3q1VO2VrrUssJ6aIKg7DdZ4dWqDpEkvQIdEDo28wN1i6YQfqzUS0CKUXrUzVWkH4mYTaMS2/oZVF5ZevSiiqf0zrzGb/a32bSp6HsbWwihSCKJmVRW4OS0h8WLwmC9vcx8Eaf1iS9HmVRujOG48gb6KrOleyO05MEujo4ftU1vwB+SSOKE6rjHKM4duNiPQmRtSyO8pO3nZhbTovCiF5vgyAIiOO4zlgm5GhyJYUgLwo33rdwzfY28Du+k2jgf7fW/q4Q4tvA14QQXwJ+APwlf/2/Av4c8CZwDPyVxms9A6qAG7A+nZo/5UGK0UuOnIVtTInuSFoyIV2y/qSXIHDrt1rpOuG2tcKtIfk1tTAMXUBH6+GUbRwnlNqlv6varA7cCFynqpyRqtV7oh2q+IE4dlHhWkm0DtBBZUS4AUBKN4PIM9UdWRSCIApJih5SULeXqoKjrESEYsxIDD3PdkMIgQoCkiShLEvHy+dTr4O+vPGHwLefbP0EUOCT8MduxqeVJjgxLooxLi67mWq/EYHzsIRRhKmCRQNnzFazc4FAhD42xCvbZRO7UNlaa98GfmrK54+Bz0/53AJ/vZHazYkqWX8cx3VHqGe0Unl/vfWzWddhXFL7ywWm1AcALNF9VCW1d5lsjE+yLf3xgNK5shS1WyUIgnorxsxYgRtTSjebiOOYssipjjNT1XFtVDN7CbGoIxC7YEhopYiieGIwU1K5QCjcMWdCCAhdBp8obH+e7irYJgxCbOL2BFe8lO9Hlbu4Ongh8AZGm3mBX18PAkwU1wdiuH6m6oMWKlkcBVS130CqghArZSuFmBg/qKLipYDYjQGBXu7JOPOg2qXhJlfeEFKVK1xN7ljwuwMCv0SwzCbr1sr+jBhXtm69z+Wnlcq9/OoaaxXaz5YCn/z90li6UpL1UYDGVJmh/FFSfoCTUqKtxuIGenVZZbuCtSenPF3UcVlojLU++GY0m7BSonD5W6W/vgsDXBUBKqXEWlNvGxmXN+Xz0lp/gEQXXOSTsmjA2vqYvRrWopSbrSulOhFpLaXzPsSJO4jAeKNhoh95pau09te330U+Pi5qrV2bQX3kI1R9DJQyvk/q1sui46Xq8b7iJStXv79GSYkmAGy9pLhMF/laKlsYHf9VH7mERZ5Kqu3yYlazjUt3lhUpJe07+HiWobM6hFuzaHdnqVAN3kbpOptX1SHGm6aaMbV99lehGoydsh1FhotpJ/t4hdX2AQ6my2I9kz3RLpUR2IX2AnxgTYj2sngWryrfX1dOM3K8ggmlNM5L+KwjdR+bxrmFOLOPnVF3uYI+trbKFryiERKlTirFhvaOickl9iql9aKjEisr+5SwnKjPVcuo8obWfBbsXh4f0BRq4vMFFLZUY6nq3OOysbhBbHkRpNNkcX14Cawcl8UF7TldgSyeHBcX0WbLXmab1seabLPqHdnRQM9lZHKtlS1UxmaDglRbgFO+aq6UmdBsB3FCU1m27vmny1lGt1nMYD2l0/nBYBkG0qjIxQxqJ2GrTRsd5jWtzSpesBxui+hjU3mNjSvL4VWV1twDzxwTayXVMVk8i5M49ctM3NZe2TaJWRuxWjvtCsSY0KwbLmozMeW3TuAco++8z9uO2durW7ioj3WVFyzSk7FCNOglrLAWynZi5rUQJbe6lGViwVbhyjqKWPwM5XKR5Y2VWj+rHbyaMvzaxavhkhfKbW372CUVUlOyuNDxfgFKtkKnlO00xXNSkJto0HFX6nIgxtYAxj4d49aU0l16x5+yHrWQNlv6gHZ6rWZ9eME4t6m84EprcatQQFUcwoXcOtZmSxsXV8QLfItNOXt2EQp8UWh/yKPHacUjrjQdOfflrkAfja9tuF/nqcQZEZNXeub8EGKUs7SBh7WHF77NKjm80sPaw8uVOet63gUDXMuiWOtoW3H6s3metdA2u8Qjpo6L88pki2TxlLHAVYb782VxWdw6o2zHrZfzuvlMic4ry7xDqNKnXXzV8gIRLkJVj2acmO1JF2zP+euyOK+br7Ydzy7bnv810N41yAt70Izv/MzLmmizyzxi7Nr68Pp5de15xSxZFpss7SJduixunXIjw4kXY332HbicW+uC65adKH1a3attN1V9Zn7WOdeuIgH8+fWBWbvVRbxgudb39Dazl3ZrXXRtO2RxDXhNqc94HRrrY+6CpXGz2NNdaGzwaKqPVd8vrc38NoFT8jjHeD9L21a7EhaJDilbMf3d2sUc2rVcwZpKbCEzuTacuLIoS3L1bbYYbte8Foe2eIGaxjr0samGwoLG+4nZzYLQGTdyi5Z/msVM62PXuMY1rvHDg3Uc79s9sxVMHINUBW7Ulo0dn/x3zEIdDzGveK2LlT2+5WDyv85jIouM+21ldWkai95mtirUvGB5C/9L8EtO8qr/6zbGxo6J8X4N+LVb2doq4Om8fXCLa4CF9hfvDlnFALdwV9A4tzUbuNdNEY1jFdyW4ZZcCa+JXFcLKqOKVagPnVwDrPG42G5lW2Mx65cXFzvZ8IspYjWdxFpbz6hPzkKboruqwRs4NbsWjDifHgRXJF+XwFReJ2fXUzbkd8E4mBbgNrFe13Fep9usWV7LVrRn8XK29XRul+W1ynHxsuP9rO+/I8p2taijMd0fE99NzUvbgYEAmNCuE1urnIVx2kHfFV5Q9/5T248sWFGlpJgSydl22NPtdfI792vXiFXG0BRvSMd5Ya3LfbyGvCb6mD39+7q18gAAIABJREFUnfu1W9zOi1I/bRDOjtYo2+FwyP3791ddjUaRZRnWWj7++OOVR102iefPnwPw+PHjtWqzhw8fAtDv99eKF0BRFEgp145Xv98H4MGDB/WRceuAx48fA3BwcLBWbVYdf5em6VrxAqfDzoNog9UhhFh9Ja5xjWtc4xrXuCKstVNnVq2Z2f74j/84f+Ev/IVVV6NR/PN//s/58MMP+dVf/dW1mtl+5zvf4Xd/93f5pV/6Je7cubPq6jSGfr/PP/yH/5DPfe5z/NzP/dyqq9MgLP/4H/8ThBB8+ctfXitZ/Hf/7t/x+7//+/zKr/wKGxsbq65OY/joo4/46le/yhe+8AV+6qd+atXVaQzWWv7e3/t7vPTSS/zCL/zC2siixfI73/gd3njjjTOvac3M9otf/CJf+9rXVl2VRvH5z3+eb3/727XbdV3wla98hV/+5V/mtdde47Of/eyqq9MY7j+4z4t3XuTVV1/l13/911ddnUbx6U9/GinluYNBF/Hqq6/yG7/xG9y/f59bt26tujqN4bXXXuOnf/qn+cpXvsKXvvSlVVenMVhr2d7e5nOf+xzf+ta3Vl2dRvHFL36Rb3zjG2fObDuT1KJVGGVrv8YaYdFbNVaJdZlB/LBh3dptdCDEevGaBdfKdh60wBtwjWtc4xrX6A5as2bbFZw6Y/Ea17jGNa5xjQtwPbO9BH4YXR/XuMY1rnGNq6NTyrY6tLk+vNn/u6wSnEdpznTPFQ7MnuDlPlneunBd7oLKO/Ve5ilr/gOxF4dp7b0eBtkpXmtiaJ4++H2Bcr9knOK1rm3WUV6dcSNPU2KXPYKgaSV76ozPOY9pOlnGZV3VVXarUeq0i++qM2KN3VelMrzMGZizlDPt91mKGE+07tItzl7mRBYY92Gja+1n8pqpCFFXZyLF3eyFLy4x+0Qi+CXzYkqfahBXaTNXr/FP2rSINModPw+v0bVt4gTjvOCS46KXY3vZwWOB6MTM9iIluUo756qu5aZc0/M85aRNP3naxtVxrqFymefMUebJgVVcUJ/L4HwDbJYHVLmAL1/uiMvkZ41gSq7esa9muH/y2stEd4/zmPDuNISrtNmoXldoswXNxtyzz/ruMs+Zp9zV8Jrp/vo5c46NC+DV7pntOZ3/8o86PXtsyoKeePYlntlEg06d8TfA7aqnbizCCGmK11Ws+Eba7JRUi7nqc9KguPK7udLdU+6fkdZZbV09pClZbgqzOknO81jB1XMGN89rvnc9Of4t/2CEWTBrD3NUJo2+aTnx50GrZ7ZNitIymt/a2cWsqY4yrbwm3XALW9++AIvi1YYkLjXqusw0DCyyJo2gojNKTH+1d21phfdvonWq3L5XfmYbiI3hcrymy6KlGUW7iFnlJaZAV7n5XLR6ZjvrcUczCcklFOHJ+y7yZ8zXccZXWa+AeXmd+bjlDAIXltMgr6WegTzD+zu1zj/bkzkpL4221QWiOFtZ9pTCnRfLPsf0ggsWKIuLw6VlcfYns1BZvKj0hfFa3HOg5cp2Fiy6kU+KVXONePE6ySK5nTRk2tZZrvLsdeRVPX9hh2o3ZPvNVfSattmpIL014TXx/PlWQC589lmTrKXxWgBar2xX+eIXXc5Z3NaV1yLLXFYZ69pmqy5vsW122rBdD16rlUVX2IIeO4Vb21zvl0XrlS10/yWfh3Xltq68YH25rSuvcff2umF922z9uHVC2TaDMT+Ztf7X9gedXAxb/6hEc3yvY2c5jnW0k13O0Vo/XtDlNrN1t5rKCzraZucr6u7yuliZdTVj3sW8YBV9bG2VrYuuM/4n9SBn/Xf1vksBQsiF7xtrEsYYL1AWa6rgjVE0YZUNSgiBFAIhZSd4WWsxxlBxmYhq9baS22c64tSFNrPWYo3B+oHbWuM/d7+LKhPVifZqOy+sxdhRW7l2sr6PGd/HZN3HuiSLdR/zHB2vSvHasT3PjpP07dd2bhN9zHhu3lKvXLe1LEoxMTa2GRPjvbG+/Uw93teyV7XXGLdlYW2VrTGGLMsospTSlBjjBwRTUpSlGwikRApJEEVEYYQOApRSrRYsaw1FnpNlKWVRUBrjFK41FGUBxiKVRAiJDgLCMCKMQrQOWs7LUhQFeZZSFDllaVyHMca1X1ki/UCtdEAYhoRRRBAECKFWXf1zYYwhS1PyPMOUpWszazCloSwLPwAIpNJjvMLWy6KxljzLyPOUoigwVZvVsoiTRakIgoAwigjDCK11q3lVsphlKWWee/lzvMqyxBqDVGqsj4WOV9D+PlaWBWmaUuQZZVlSFgZrS4wxGFMipUJKiVKawPMKwgAlVatn8KYsSbOUPB2S5zlFUVAUhetnpkQKhdYapTVBGBGGIYGXRSmXswN2PZWttZiy5Lh/xOHzA0rjZg+VQLlGGIXzR2HE5vY2u7u7SBm3usMYYxkOhzw/eEaWZf4z4wfx0lutDloHJL0ee/v79HoKpVqslKwlzzOeHxwwGA7AOl5lWWJMSVmW1WUorYijmO3dXba2dwilpK2u12rgPjo6pN8/8soITFFiTEFRlvUMXkpJGEZsbm2xs7tXGxdtRVmWHB8fc3h4QJEX9WemLCnLAmMN+JlEUMni3j6y12u1LBpjSIcDDg6ekaUpWCh9HzNVHxMgkKhA17K4ubnV6lmgtZYszTh4+pTB4NjPci1lUYA1lBUvIVFau3Fxa4ut7R1kJFt73rP1E6vnz57x/PkzyqLAGEOe5U7ZWosQIKVCe0M96W2ws7dHr7dxrWyvAouzurMs4+iojzEWHWiwlrIsyfPK4jEUZUlfHGGtpdfrEYbRqqt/LtzgnXN8fMxgOEQrDcJZdrURYSyFKcHPqJJejzhOWj3AWaAsSgaDIUf9fu32KQs/cHu3XjXLGITH6CBgY2MTq3WbjW4/eA/pH/WdS0spr5Dcv8q1Z4xByj4AvY1NoqjlsmgMWZbSP+qT5zlKa2xt0JbOjWfA2LI2pnq9HlEct1sWrSXPco6P+gyGQ1dX4Qykykvm3MsGrCWNIoIwpNfbWHXVL0RR5Bz3+xwdHdXuVGfQmtpQr5ZtBtrxTpIeYRi2to9ZKl5HHB4cUBqLlNJ7/kq8zefd40OUEGRZRpwkxHGMtcvxIK2lsj25Z894q00gMWPrTKW36oywGFt2KmKxGqALUaKkwlgwdsTXlCVlkVOaELxl1wVY3PpmIQQKUWcRMtVaTGnI8wwpR2ufrYf3opTeYLB+Tb0yCqu2zPMcKSzGlCxsT0WDqNbDSlNSWgNl6VP+Cc8L1/eKAlMWxGXs1wVXXfOLUY0ZpSkxWNfH8OvsuDHE9bECqeSER6m1qGaypiQvCoQUaKX9uFH9c7JYFjnGOKOwjekXx1GPhWVBlheuvVBYBAiJFa5tKs9mbg1BFNTesmVhLZUtgPRre0EYQlGiA42WiqJUSJVT5AWWHEqJEJYgiNFBu9eSwBkSslpPKUuQkkBrN5stC4q8oCidRYcU6CBEh6ELVGkxHC+3BhaEIQbQSiEQE7wMBlEKv6YUtX5dE1xsgNKaMAwojUUpty5WlgGq8OtLAkRZIKREd2BdE6o2UwRBCEK6tT4psYAuNHleeK9EibCyQ7IIUikni6XvR0ojEJSmpMgLSlNQ4AxbpQN0GDq3/6orfx6Ejw3QmjAKQQgviwpr3HpuURRupmsNUmlUEKCUbjcvACwISRi5sUNJhRRueak0BWVResMvxxqDUgFK66XK4toqWyElYRgSRTFCZEgl3cAsZb0WZrGYokRrTRTHrkN1YIALAk0YxZSli3DVWmMtddAGOc7i1pIoTnwQUbt5ASiliaKIPMsxpvTuO4n0yknmwgWqKEMUJ4Rh5DtUeyGEQEnpg4NiyrJw8iclSllUFclqLaUqCYOAKIpRqv1dU1Z9LI4RaYYQoJTC4gY7ISR5AaY0oDRxnBC0PFAP3Jql1gFRHLvgQ+EMJIFEWSeLRSFcAJiQxHFCGDhl2/Zpu/btUBbOtV8FsGGhLB23PC8QUMus64ft5iWlIopi8jh33hM5OqPbGE2pnRFR+vXZKI5rg3ZZ8tj+Hj0nhBAo5dYkTFmQ5yVUQVJlSV7kGGMxWHToFs2lbO860jhctKD062ApeZpiLd7qdtGTpXFKOAwjP/C1u7NApZgUQuAirtMUqtlEUc2SLChJEIZordsaFzUBISVaKaQUZFlBlpYgRB2NXJSFc7sK0GFIEARLC9q4CoSfGSkpsdYHpFC59dwM0FiDwRJoFxnf9qCvCkq5NkNAkeVk1smic7E6XhbXF4MgQOv2jx1uxu48YUpKsixzQZZCTET9I4STxSAg6ICHBZyyrdohyzLyrHCGknFLhMYYhA+k1FoTBOHSDfW1VbYuItlw3D/i4f2POTo8IM9yv/2iwApBEMaEUUL40kvendLeSMJxGGNJhwOePH7AsyePSYcDCh/ubrDoICSKEnb3byCl6MTADdRBNM+ePubpo0cc9w99CH+OMQalNGHSo9fbZHdntxsDdxUfUJQcHR7w+MEDjg6f++0JbgYvpHRyGCVEL3p3ZBXQ0WJUa2WDwTEP73/E4bOnZFm1DajECgjCiCjusbu3X+/dbD18fECWpTx9/JCDJ08YDPp1pLWxBh2ExEmPjc1ttre3l75ncx5Ua7JlkfP84BlPnzzi+OjQBRLVrmNFnGwQJz3CIGz9jLbe220NpizoHx3y5PEjjo+OyP22z2qrVtzrEUUJG1s7bO/sTuQZvw6QmhNV0Mbg+IiPP3yPt7/3XR7e/5Dj4z55mrmoT6VJNjbZ2b9BFAa88sqnqrtp83TJGEM6OObR/Q9567v/mQ/f/wGHhwfkWeZcQwiiXo/t3X0+9aOf4aVPfGLVVZ4J1rrB7dnjB7z7/e/y/jtv8+zJQ7IspywKLIIgDNnc2ePOS3fZv3GjEwFSLsq64Oj5U95/503effMNHj+87wykosQaiwoCeptb7N24RRJHmLt33b1LGgTmgfUBQoOjQ+5/8AO+/8d/xIOPP+K4f+SVkkFIRbKxwfbePp/6E5/hxZe6IYvGuq0/j+9/xNvf+yM+vPcehwdPPS8XLhTFMVu7+9x56S67u7tOFlseYWmtIU9Tnj1+yHtvfY/3f/AOB08ekxd5PQvUQcDG9g77N14gDAJu335h1dW+EG4f+5CDp4/54Adv8d47b/P82ROydAiANRapNBtbW2xu73L7pbvs7OzUUfLLwloqW3B7Gw8PnnHvnbd5563XOXz6lCwd+DB3ixUSFYQcHDzj1gt3GA6OMWbXh4Gvuv7T4Qa4gn7/kI/ef4+3vv9dHt+/Tzo4ri3T0gqU1jx98pQojvnM4U+6mbxt91qZ9XsbH3z0Ie98/3t88P57DI8O/X5NF7UrlCZ4/Jgiz3nlR/4ERfYjXuG2d+bu3KtuhvTum6/zzptvcPz8OUWR1bzwsvj84IAXbt8h/bHPYEpDi3fH4PqYmyG9//ZbvPn973L49BlFNqyjxw0CHQQ8ffqEKIr59I//ScqixAbtNyKO+4d8/MF7vP397/Lg4/vkw+N6P3RpQWnNkydPKIuCuy+/QpG9gunZ1kpi5YVI0wEP73/Eu2++wb33f0DaP8JYgxBup4aQiuDZE/pHR+zduMHLn/oR51oONO2chLgli3Q44MnD+7z/9pv84N23SPt98DN1l1NBcvj8KcmzJ1hjuH3nJfI7dzA9w7K62VoqW2ssRZ7TP+5zeHRI/3jA8TD1M79qi4zBFiV54Tbmp2nqAjn0DGffrRBFWTIYDHh+eEi/f8zxMK0TCtSDXGHQfs/qcDikKEqiFlvdzhNhGA6HHPaPOOofMxgMSbOCaguMa7MCRMkwzTgeDMhyt+6uZHvbzBhLlmcc9fsc9fsMBkMGfrO9FILSWAwGQ0ma5fSPj0mzjNIYAtteXtZU+737HPb7DAYpwzRzM1rhtzUZx0tnBceDAWk6pCiLWmm1E24v/mA44Kjf53iQkmYZeV44F7gQlAbKvECogsFwyMDLYpWysq2GhCkNaZrSPz6mPxyQphmpHzuUEpTWgil9H0t9m6V+L3g7RdHaUbbAwXDIIEspipLcZ/oKhHSxObbAConQGf3BgGE69IGYhmWRW0tlixBYBEIF9Lb32LlxGyM0RZbWOZGFVOgwZnNnhyDZ8NZPmweBMUhFvLnNzo3bpCWkxz4JhBAgFEoHJJubJNt7IBVdYWaFJIh7bO/f4miQIZ4/c0EdQvqtJYow2WBr7yYqaHfCh3FYBDKI2Ny9ydbNI6x6gikLtHJ7AYV025g2d3YJ4g0Wckhow3AZdQVCB/S2d9m5eRuDIs+GPnLX9TGlQ5LNTeLNbRf12noIvz9TE/a22L7xAsPczXSVlD5toazXNns7+8gw8pO+dreZz1FLEPfY2rvF0XGKPXgG1hAGgZNTqQijhM3dXYJkw7VZC5XsBIRwXq+4x8bOPpt7fYxQFHmGDgI8ccIoJtncItnaRYdRnSt5WVhPZYvbgpAkCbduvwRS88LgGEyJFLLeViGky4u8t3cTpQP34ttovo1BSpeqcP/GLT75o59h59ZtyiJHSem3LjkBUmHA5tYOUZS0PnijCr8Pg5CdnT0+8cqfoLe9Rzo8Rgrp85e6AVwFmqi3webWDrLaqtVyblppNje2uPOJTxLEPY6Pj8C6CF3pI8WVUugoZnf/Jjrwe1Fbzsv1sQ1uvnCH0gpu3H6OKQsf7aoQVZ7dMGRra4cwSpAtl0VwEcZxHLO3f5O7n/pRNvdukKdDlFQuOlcqt48/DEh6m2xubru9qKLd+2yFcOlAd3b2ePHuK8Qb2wyOj9w2nyCsc8WrQBNECbv7N+sI8rZCCFFv+9nZ2eP2i3dRYcLR4XPKPK8j+120ckiYJGxt77Cxue1yWcvljR9rqWxdA7i9cpubW0jl8gInvZ7fw6jI85zhMCXPUoIwcAN3yyMlK6WkdcDGxga3br3Ajf0bxEni99NqH6mckuUZYB23DkTtCumUatLbYP/GTba2XGeIk8QlTQDyPCfPckpTECcxSnWAlxAorYjimL29feKkh5SCKIoJoxgphXN7eW5Jz8mn7AAvNwuK2N7eRUjF7Tt3/MEXMVorjLGOV54jpUAHy0v6fhVI6baWbW5tceuFO+zt7aODoN6bCVDkpYsmt4Y4SUbJVVrabvWYGAT0Nja5efMWW9s7CIE/bCBECEFZuq1NRVmwubnp2qzlBpLbfhWSbGywv3+LKNmoM3s5I0IAsnbzK60Io+qgj+ukFo1BB5rtcIckSdja3CaK3QA3GGYMj/scH/dBCLRqbzL7cTiFi4uu29wiCkN6GxskSc/tMcsLhoMBw0GfvChc7uQOQIDfGiJJej22traJk4Re0iMIAremm6Zkw2OGw5QoDOlCezm447zCKCZKEqIoppf0XBYfBGmWkQ0HDAbHKK2Rfs2z7ai2J+kgYGt7hygMSZIeURQhlSLze6XTwTFFWdbZpdqMSqkIBEoFbGxuEmjt8uhGMUprSp9zPEsHpFnmMri1nZiHQPhtMBv0NreIoogkTtBaY4A8yynyIYPBkCAIgHY7x2sjwB80EPV6RN74CcPQZzfDxUaU1alizsO57Dbrxkg8F5xQJYnLWtPb2CDpbRCGgTvVQgVo5Qb3oiyQ7Q79nIBzB7mkDr1ejzjpEYWhF7ASrRRKSdJ0iOrAZvsKQuA4JT3iOHa8otDnb3WHSaTapZdzqTVXXePZIZVzTYZjCkkHGoFLipBqN5s12NZ7WMYhhCCMQhLp+loUxc51pxRRUZAGGq0EaeqyuLV76B6Dz9S2IXokcUKUJIT+CE5jLalvM6UGPhPRxYeWtwEWl2kuiWOiKCZOEjeW+MxfzsMS1Al+nIel/bywblyM/bGbUXVMpVY+zaahKHKyNCRNU5+ExC61zdZW2TpXXcTGxiZaKeI4cQrJD9bVDBFgmGZ+TbD9cOkanfFgjSGO4nrgVlL5fJ+jg591fSJOu/cPg8t3HCe92iqN49EZw+DSHmrpfpdSdGYgkFISRTEbGwVBEBDH8WggEBKlZR1UlOVu/b0LEMK56Tb8MWXOmIh8H3Oyp/w6oBCD7nhZhMsylCQJZWmIo8idVKQVSqo6c1TlhlRKtt7tX0EpSRzFTiZ9OlvXx6rsSsq7yt1pQFXfazOqvOpR5JabdKD9rDbwsQNV5q8ArVz8Rxg62Vxms3VD+ueA8GeDJkniffoarZXLsyskaAUEYBOvlNqf/xNGbrskSbClcQcsBNoFEQmJxCJw6y9YRnmRW65rq7y6SRyjlXKdPgh8EJFTPlK4drVYn9e1IwOBlERRhLUGrVxauSDQtdHnDAc361PZyGBqO1zmq5Ck10N6udSB9mvOEivtWJQ8dfxA2+H6mDP8rCnR2vHSSvuUf27GJ6WoT9SSqv3Gugtq08SJW3t2aQuDel0Wn8dbuiO1KIqiM4ezKOXy21fxOVq79pI+rsNaUKqs41e0doeDiCXGI6+lsq2Tv4chZZIgoD5pRflQdolCK7DhSDFfGEgkmNo0k66Ixc62hJD17Mh4y7PqIG4dwoKGUIyCc5TSFxoS03gv08VSzSaqs05dmkmFVFXn93UcC2jQMya1P3nk4rJRHbBQrXEq6bwrVYJ3YQQiEMTE/nDr9ucQHvWxyO9VxOciV342ISf2nAopR1HKLeYFoxm7id35tcpHVVcHYtRHVrr/6uUP0XJDovKKxXFCoXMfpStrfgiBrYyjyBlHYRC23tMyzkt7BVpxk1XgmrUYn45XSlHniBZLPDxiLZUtVKdARAioz5qUStVrs0LYujHCMJgpxP0sG2hcvVaBPosa1KV0gmVt4jND4U+Q0d6Cs0icgtVao5Se8Rg66/ZNLqTWF6OKso5jS6HdkV8Ip6ikH7gBlLU1n0udZrQib3Nl8MRxjJLSHTiO41UZflZKx0sqQuzEbL7NcO64sO5jk+5V3wd8H3Mz3lllcbWo+hhAWRbeKyRqg91akFiUNHXkeCdOM/I7NOLYUhTaJ3MY9TFwsiit9YaDrbfOtJlbdUpTHFkKres0rtWWnwrSWm/kOheyUnqp7v81VraiPkKpyhJSHa83jnoQr6zWc1Bd69pnrJEqNy1OeBc5e6pmgI5X4E6z8K7Kkx3CWltbeBfB+vpbX8ZqZoASIUKUch2mqr8QojZiALSfMc06CKw6cEVJifCDljP8rNuTWe3L9BRqWZyxzVaNqo9VvKqD4YU/R1Sc4FWdf9t2TPYxXdf/pLxZa9FYpA/GbLNCAryHz/UxF3TolFLVlypYa/0M0bvHWy6LbgnKjx3GnOhjp8dEd4+oj1xdFtZW2cLpznEeZu8oYury57gCXrSyqtYolPLRdOLkjHQ01553ADg5qFQp+BYZL191gPGZ7Hn1vyq3isoyIknrM5S9UbMIXv5uljmNr3i5GcQieS0X9fGBPrdu9dl513cBy+pjy8Y0XtXn592zTKy1soVmX2ibBK9S7mfX6XJ1nUUolzWMV8k7mnze9M8bK2LmepzyijT03BOf1L8tY1a/KF7jz69QGbrL4nWy/IYLqN/YiM9yelnTfawtaDOvtVK2i5xRtrUBm8D6chMzK9SuvYPLeGxW7Ua/HEa8plEU9Xdd4zXC1LYbN2i7SYuz+lvlfeswMeDqgZZro2zHrdCmOmEbBuDa5QmNCWureDXcAVfObepspdEClj4jnyj92qCdCxdx6yzzU0tY0y4ZG8M6hbP72llrwedhLZRt8510tQNaXYuxSjQV5dyGAW2CV2NKd/VtdvLdNqOYVs8LTrdZEwq3DbIIV5+xnPe8VWIRW96uwq3t3ojxWI5FNGG7w8zGUPvipwxocz/rzO8v/cgrYVp9mghsusx3i0DFq5Fyz3nOaM1wOZjkdfWCz+S1AkU7jde0+s3Spm2SxellztnvWsjrVLlz1eOicXE148dZnzdVn5PPWhTNzijbGu01jFaOkcC0w7JuEm1iNGmdL87TsNpTiM8v+6IZyoUD4YpmOBP1mra+OMM7FyefM35/i2duF6ElE/IJTHub1lKf4DMbTk/SLipjEeiMsrVUL+Xqr8ZyfqdYbncZuS4uqsNFwuUE8Ky7lz8QVOWdVe5F7XD6eeeXs2xcVOos9bpo0Fh6m11Q5qxtNpLFs5+zbJyqux2p1rodZqxYm5SqUz5TPuSySql9vKYL0SXreFHUmb2aWTvrO+vMmu1Z4f5zCcdF9yzKaT+9sDPkaV4BuEghL9eCnaXNZh28L/p+mW6uxmRxxrKWxq1RXu1qs6kdbY5+NosszhI41ByaMa5n4sVknMWl2u/SctRQf5qlXDuZ0GcWXtU2tFnXojszs3VooZ/jilj+Gk97LNem0Sar/Bo/5FhjWZy3n3XhjUw3opt5dqtntqeDF8DaKZu+6z1c9aaLq5Z8xftnKOFkdCesRwcVTGRYtpP/dR4ns0+tCy9Y3Has1UP4IWLdeLGi/asLTvaxSpwYv+BsT+Bl2XdsZssYQ3H6I6CJwW8VMrQ2Ynti/airO+wugpOR9eK1El20lDJ9IWuoHET93zIxZ6Ndtp6raK+5qK3Bmm1tifr1D3eOaf3t6esa6rmz5AxtqgyWnMt2vOzF8Ts/OGaRcOtlp2fXFzGdddazsmCssfzUJ7mc156Xq+/y281iEVZUB+v4zzih+U9vgWp6PXIRmLbGWf09dV1wjoCKVcojjK9Xnh7HruyxW2Vf4+xRed4Ru9XKtoaFad7jhRc7p1K67AC3Kox3+JNRzFcfuFcI6wfxscCFiaFbTCrirrjvx43Pib/Hvu+iK7gKUbJWnBE5OjIxusSrwlkBgVPbq2P87JTfJi/oFp8K59V6XkbdULYTM9oVlN5RgZkFZ27LWQPOl+XWFc7zbBVaeuTvXDi7n3elbS6LWSOAW4tLyuJF2wHXGaINpIUQNooidnZ2Vl2VRvH06VOKouDWrVurrkqjGAwGHB4esr+/j9YT8g99AAAgAElEQVQdsddmgDGGR48e0ev12NzcXHV1GsXjx48BuHHjxopr0iyOjo44Pj7m5s2bnTgDeFbkec7Tp0/Z2toiSZJVV6dRPHjwgCAI2NvbW3VVGsXBwQFpmmJdFO8ptGak7PV6vPzyy8DqotyqJZMmZgHWWgaDAf1+v+blsPwUfJMu4quVb63l4cOHHB4ecufOHXq9nnvqkknV7t+JaMyrccvznEePHrG9vc3du3cbeeY8mGYAX+X9Wms5ODgAmJDF5e9LPmNd7/JLlfUz7927x/HxMXfv3q0Nv7PWtpvG+HhxHuZZhjo+Pubp06fcuHGDmzdvzv2seTDZrc7mNk9drLU8evSIJElWIotnyaGvxdx93VrLW2+9RZqmZ14z08xWCLEL/FPgv/C1/O+B14H/E/gU8C7wl6y1T4V7a/8A+HPAMfBL1tr/94Ln2y9+8Yt87Wtfm4FWd/D5z3+eb3/72xwcHHTAhTc7vvKVr/DLv/zLvPbaa3z2s59ddXUaw/3793nxxRd59dVX+fVf//VVV6dRfPrTn0ZKyRtvvLHqqjSKV199ld/4jd/g448/5oUXXlh1dRrDa6+9xk//9E/zla98hS996Uurrk5jsNayvb3N5z73Ob71rW+tujqN4otf/CLf+MY3zpzZzup3+QfA71pr/yTwU8B3gb8J/Ftr7Y8B/9b/DfAF4Mf8vy8Dv3mF+q8F1knRrjPWuZ3WmRusL7914zV+FOoPGy5UtkKIHeBngf8FwFqbWWufAT8PfNVf9lXgv/W//zzwz6zDfwB2hRAvNl7za1zjGte4xjU6gllmtj8CPAT+VyHEfxRC/FMhxAZw21r7kb/mY+C2//0TwPtj99/zn01ACPFlIcQfCCH+YP7qX+Ma17jGNa7RfsyibDXwp4DftNZ+FugzchkDYN3C76XCmq21v2Wt/dPW2j99mfvOxw+fa+Ia17jGNa7RfsyibO8B96y1v+///jpO+d6v3MP+5wP//QfAJ8fuv+s/awDTDu0eP1R+Vn1//vmGZ982Xm7zmH4A9DoYECc5NHfwc/uwrryucY1rXAUXKltr7cfA+0KIH/cffR74Y+CbwC/6z34R+Bf+928C/51w+BxwMOZuvhImzn0WeMVbVfSie8XYv9mGxMl7xFge0jmV9dkFTQQOTJR3AbFTdZyxXuNGy8hYaR71+z7x/me8+9K8xu9lrvtmf/7Udz9TcZPtPW/5i8LJdz5p3HYbk7J0GU5t53/aoF0XzNf/24dZ99n+CvC/CSFC4G3gr+AU9deEEF8CfgD8JX/tv8Jt+3kTt/XnrzRR0Yte9rIOv5hI/C1mO8dwpmeuCNVrFWN/NJXo5GodZL49b9PLbPbUl/N4zeJfqd/5JQmevL7O3ojgasdfn13GSOHO1r8mUmFe4n1Pv6+5HK2CSUNoXOHOtv2xqs8l5ah6fyyGlytiuvEwazWvlsO4WS4TT54mi5bGZP2CwhtXKDMpW2vtd4Bpa6ufn3KtBf76Fes1gcvM1i6VEKCBF3pWmZe5/+Lnw1SBPuPeC+s0S5lwpXczS5udV89pt8/7rseV2zJ4zfFU5hmwxusiZlQasz6viftnba+zFLz/68LkCpd9/rTvz6vn6eWd2apzIS+44vhzgUE6w/hWG1Nc7g2f5DKOJvKNnz1mc7lE+XOM8WI0YJz67irc1ie/2Tk4c0Bf4LNnweyD2+WSfF+Y0WamIq82CMxWxOXKaENq0Wbh+IgGLPU2vJpRLn13elAT7XVVRTtbGbPnJ7a2wTztV3zOhcPHTO+/2VzFS2iuyxVwWUV7Tom2/m8+tCZdYxOYJ0H7Rc+bphCXOegvIlH5wnk1YMU0+Y7b1F6nr5m/bo3yamTCPn5E39Xq1jbDqjFltCResx8ZCU1ox7a117zwpkd9RGeTvNZmZts2IW49zjj2qwnMMm4v6j2edZzZMrDoshb6/AsevciyV9VmdsFljZ9wY7FrNUY574Vd+DucVu7iCxnxaxKdmNmucoa56M54lit54QO3K2Qxz14hr2WVsaoy15Xbsnktwz3tyql864t4OKsPOl43RbtAdELZwtiLXlbY8ZJgrXNZVOdmV591HfXpGmvMC05GmXYblZE0/nMdMM5jZAR2n5s9cf7vurbZuqAzyrbGGjZC1WnWkVkzvKonrNqMrzAidcWVST87aQuvSdfnFR90Yp9cO9AIL/Bt1oap5QhX4lbzGv+wPdzmh50YPkZiuXxu3VO2l8DI727rKMJqjUFAncxh/sQJq8EELzPadVZ1tiohxijRQje4nWyvytVX/RScbq9u8Tq5FmRrzwbjbSZdEolOcDOmXpKo1vDcn3aU/2VKu7UdZ40deI7VvttRwhY5sRe3rZjkBSODuDL8gCo8qENtNnWNdWxNuW4a33iu3eRSea2tsrXGkBcFZZFTliVlWWKtwRhDWRYIBFIplNQEUUgQBCilkbLdMWPWWsqioChyiqLAGMfJWsfLGlBKIaVEBwFBGKJ1gJTLFax5YIwhzzPKoqA0JaY0WGsxpsSUJVIphJBorWteFdc2w1pLnudOFo3BlKUbHCpZFAIhJVIqgnAki0qpVVf9XBhjKIqCsiwwZUlZOjmsZBEDUkmElGgdoIOgbrM2y6K1lrIsKPICY0pKY5xRYV3bGWNRSoIfQ4KKl9ZI2V5e4Nssz12fMgZjrGszYyhNifTGuRQSpbVvN42SqlUemJOo2swYM2Y8WM+xBDvaZy+kREmF1hql3ZiyDKytsi3Kkv7hIYdHzykLJ1hlUWJtQV44xSuEU0i9pMfG5habW5uEYdTqgcAYw2Aw4PDwgCzLsMZ6fiVl6ZSUEPL/b+9cYiTL0rv++87jPiIiH5VV3V3V1T39YMYDs4ABWdYgvDCDQINBmIVHAiHhRUvesDAS0sgICYklGwxI2BIChJEQIAwMliUjhvFIbGZsbDx29bhf1V09/ayuqax8RUbcuK/D4px7M6s6uyqzKjPiRnD/Uqoib0ZFnO+c75zv/R2U1sRxwmAwZH1jnThOOn14O+fI8xkH+/tk2bRdL3+4VZRVCXht1EYxaZqytr7OYDDstLB1zlGWJYcHB0wmY6qqUfgqXKDLx0j9miVJynA0Ym201nkFqa5rppMJh4cHlEUZDryGrgpX196KUIrIWpJ0wPrGBkmSdpwXa2bZjPH4gDyf+T1W1bi68kpgXbdWrNaGKI5ZW19nGNasq2iUvvHBPrNsSu0croa6rgJtNa15K4IxhsFwxPr6OirWnbXZ/R4ryCZT8iIHvGCta79mZVkRpG145jDWMlpbYzAYMi9WXElh662/ivHhmN2dXQCU1kH7rqjKktp5rQ5gMj6kLEui+Mha6ipcXZNlGft7+xRFgdaa2jlPW1l6Ldx5bfVQHTLLMqIoIrIxdJcsfxDkBePxmMnhYTi0xCsQrWfChYPukGyaoJQiiROcMZ0WSlVVMZlM2N8/wDmHUoqqUSLKIyvXOcehGVNVJXGcYKOo03TVdc1slnGwf0BRlmitg7Xe7DF3tGY4BsMhURwTx8mih/5QOEcQSgdkWdYK0KoKyp+rcY7WOrTWe47SdIDrsNXeeMUODw85HI9bj0odvH5VVQOerqIoEGDzUkmapJ02QholbzqdMpkcglItL7rgfQFPV57nzLIpNopQSs/VCFlJYQu0LuOyLHF4QkNUDL/1pXVRzuqKsiy8C6Lj8DEIf6AVZUkNKFE4oNFL69q1tEeR9ZrrEqRfuaA0lFWF1LXfMOAPbVxLV1V5d1dVVUtAFSGe6dejxqGdBhfWKQjaZr1UWQQrsfu82MRpy7L0vOhqBKGqa8q6bt14ZVlSVwXW2rDHur9q/vwIQid4GFqPhPPWUVmVlHnurcKqZCnowrV0OUApFay9JhzlBddsloGrGQyHnedFAb8eZcE0m+IArXTrQm4U9aosybKMbHpImg7YupyHT5hPotvKCltRPp5irPXC1hi0Nri6RpeFj3tWFWXhUCIYG6E7biGBZwlRIZ7iHEppjNHgLNr4OFOlSorSQe28pR40765DlKCMwRjj3Vja/+vpKqhURVlC7ap2bZVSnXVvtRBBtMZYQx3WTCmFrowXrmUFVQlViSjxMTLd/a3ZWEbGGGr8+mnRbe5DWVY4KcGHzEIM0MwtRvYkEKVQ2qCtaQ9v7UAp72kpqXAV1Di/H61Fqe5ateDXS4mglPGePuf8minjlaCqpCy95V5VFUqJp0ubLodrCdlOuEYhqh2VNDH2mrouKYuavJiRTSfMsowkHaJMs17zIa77O/oxICKtiyBJUmpXY4zBaL9xbGkpyoKiKFAIWmviJMHoJRC2SmFtRJwkLZ3Gervd1hWFLinKHJ/nXhMnCdbY7tMVYkRxHFOVJc6BsRoRTV1bSm0oy4LGPxHFCVEUeSWiw7SJCForojgmihLqugpJXf6wM6WhKAvymVBXFdZaojjBmG4f3BB4MYqIkgRmuT+4tbfajTUURUGRC3VZoYwQJynWdts1Dg0v+nWoyooah9EKh8LV1nsgipy6rHDKEMUpcZQshUKrtCFKYuJZHASq3z/eojWIyqnKCkGwNiKJE6/8dlylbRJCtbbUdU5TvSBKIU7jpKIsS/K8wDl/fsRRPNc1W0lhC37yo8gSxRFF7rPvCucz1HxMqQAc2mriKCaa88Q/LkQEawxRFFGXFWVdUhYhxb2uKcsCV1cordDKEidJ57M/GyiliawlN/6gLosSpElu81mvErTtOEkwS6BEQEjqMgZrDXnu3arS0FVVIdThUMZg4zjEx5aDF43WWGu85yHENJt4ZpP16i0/szS82CpI1pBrTVXmFHkVQjR18IqVOAHV8qJp++l2GUp5XtRae/d/keOQo0TEoqCqK1CCjWKieDnORSWC0QqlhLIoKMqidfU352KWzcjzPCQihvNjjmu2ssK2QZ5l7O3uMJ0cHisp8bFOa2NMFLG1daXzmZ/H4YCyyNnf22F8cEBZ5G1JU+VqtLbYOGY0Wg/W73LQBT4J5fBgn/29PbJs2pbHVLXPHrdxTJykrK+tL8mahfra2jGdHLK3s8NkcuhpClnkDjBRhDERUfQUWqulqG2EkEWeZeze2+ZwfBBKt0J2fOBFYyPW1teD+3w5aPJJUn6P7e/tMsuykKjny0tEKUwUkSQD5NKWt+i7T1obuxwf7LN7b5vpxCeH+jJCz4vK+FKmwWDQKkddZ0XnHGVRcrC3y51PPubw4IB8NmOWz3CBLocgSrMR1mvea7aSwrZNfJpOuHP7A3749k2279xmOp14QVvVKGMZjtZY39xCffFP8MzVq4se9qlQ1zXFLOPe3Tu88+brfPLRhxyO90Otoy+1SAdD1jYucf1zL3HlypVFD/lU8JulYH/3Hu/deouP3n+PvZ27FIWv4XRAFMWMNja58vQ11tfWl6Klm3NQVxWTw30+/uBd3nvnHe5t3/GHd+nLZbSxDNbW2Ni8TGQt9bVn2zaQXRW4TTLbbDrhR598xNtvvM7dO7eZHo5D7W2FUpok8OJzL7zEs89eZxkkknOOYpaxs/0jbr31Grc/fJ+Dvd1Qt+8zW6M4Ybi+ydNXn+XylSssRXJU2GMHezt8cOsm77/7Drs72+T5jDIvWl5MhiM2L11mNBwda8bS3WiNc752eLy/ywc/fIe333yNvZ17ZNMp+WxGVXnvio0ThqN1XvzCF4GzXHF6PlhJYQteezvY2+W9m2/yxms32N/eJp9lANSuBmWIkoSNjS2GoxEvvPgy9cYG3Z4Sr0RMDsd8/N67vPXaDe7evs1sehjKERw1Pj44GHlh9PznXqC6erXTBzeE2sbZlB/d/oi33/gjPnzvh0wO9nyjDp/rhbGWKB0yGR9y9dqzlM8/33mB62sbc3a373Lrzdd55+YbjHd3KYpZoMshSmPjhEtbV9i8tMVLL79M7VyXK7UAqKqSg/09Prj1Nm+9foO97W3yUL9Z1zUOhY0jhmsbGGv4/I990VtPneZFv8emk0M++eB93n7jB3zy0UfMJmPvJneO2uFDGemQMs/53IsvtY1KugznavI8Y/vObW699Ro/vPUO0/FBW4nR8KKJYsZX9rn67HXKfNZ9umpfo7+z/SPee/st3n37TabjMVVZUFa+OU7tvMU+XFtn68qVQFc91/a/XZYsjw1X+wPu4GCf7Xvb7O3ucjjxVq0SoaodDt/cQpRld3eH6XTim17Y7h4EzhHq5Mbcu7fNzr1tDsYHVEXedkepakdelJS1sLu7w+HhmLIocF2ubXSOuqrJJhN2d+5xb/su+/t75Nk02EFCWdfMioJZUbO7txuaesyo6xrd4SSpphZ1b2+H7e277O3ukk0muLpCAi/WFMyKCmUi9vd2yYLL0truJqY0SsT4YJ/t7bvs3rvH4XhMXZWAUDtH5RyzsqRGs7e7G/ZYCVF3D2+/xyomkzE7O9vs3LvHwcE+ZWiW4BxUzkFRUlawf7DP5HDsG8yEnJAu8qILe2yWTdnb22Fn5x4H+3veAAltDZtzUZWOZOJrcWezGXVVgbWLJuEz4BW7WZ5xsL/HvZ277O/tUcxyBC9kq0aRqB1mljOdTJjlM+9ZmqNHYjWFLd4SQhmS0QaD9S1mRU1Z5BitPdFNJ6K1DcQmQWPt7iHQoHZQozDJkHR9i/E0p5YpOsQvLQplDOlwhIkH1HjraZ4a3Fnhwk8FYGOS0SbRYEzh8zTQSmEQUJokGRAPRjjRodWc61g7+E+jduCUJRquE482yEtvPRnty0lQCms9L2KithlER8/tAHfEi+mQZO0SWV5TFzO/XiI4fEnMcLSGjgf+/a77Fd8OR4VCooR4tIEZTyjdIUr5NoYWQbQhHQyJhmu4kFne5T0G4VxEQEdEw3Wi4TpFJThqjAgaX6IWRSmDtU3ERm3tfneZMdzAJRqxCclwg2Rtk5oxVVWgfGMCjFJEccJofYNoMAp9Fpir938lha3PJtSkgwHPXLtOjXDp6V3KIveZeMqXlGhjiAYDtp56GmNj3xe041BKEccxl688zef+2I8xunSZPJt6JUIbRDRK+/jExqXLxMnQZ7Z2mLYmGcjaiI2NS1x/4WXi0RqT8RgVsq9Fad+vNY4YjtYZrm+iQ6lWVz0R0PCiYTgcce3659A2Znywj6srX5KlFEo02hqidMDm1hWMiVAdpwt82VmaDnjq6WvkRcXmU7tURY4JdbZNPWeUpGxuXSaKU9Scm78/DkQUcRRzaesKz730eQYbl8inE19rG8q2lNKYJGZtfZPBaP0oy7qjtLV7zESsb2zy7PMvYZMhk8kYca6tjdZKo6OIdDBi49JlrIk6TReEbH9r2djY4LkXX0YnKdPJBBdaoeJAa+MT2gZDrjz1DEmSovR8z8WVFLZAW2e7sbmFaMPVZ69jrCWOErRWVE3v3bo+St1fguxWX9IUs7a+wdVr19m6/BTaaKIoxhpfR1yVvmuK1poojpYia9cfBJbRaI2nnrnK2sYGIuLpsr7EpypDU3gcg8EgXEzQbbrA1w+ngwGXrzxFMvAdeay1bbmZ77Lk++4OhyPfXKXj5RaNQhsnCRuXtkBprl67jrGGKIp9o4vQNrWqKrQx2Kj7zVV89r4iiqKjPbZ1xe+xOMYaCw0vVhUIpEna+Ub90NSiGgZDv8dG6z6jP4pjrI1CV7ZQ2lTXrK2vL8XlCs3ZMRiu8cwz1xiubaBEiOLY7yV8t7YilDVF1mfIz/tcXElhe3QtlGekTX2JKIoYDIbEia9hnOU5+Swjm05RWnX+EICjq/OUEoy1fjNoQ5qmJEmKMZqirJhlGfkso6wq34VpCSASOhAZbwWORmvESUKaplhjqZwjn83IZxmzfEYcRf7/LHrgj0BzBZtShjhNiZLU82I6IIojQMjznHw2Y5pNfJ/djh/aDUSOlL+NzU2ssS0vKq0oipJ8NiPLplQhtu5vOOs2fZ4XffOYtbV11IYiHaSkyQBjDVVdM5vl5NmEPC98bH0JeBGa2mjDYDhkOByRJInvWW0jHPhzMZsynU6J48bb1+3Sn+bsaC5OiAdD0oauKEIQZkVBNp2STSe+yZHWzHvFluMkflwIQcgOGAwGJEnqG1CLYpbPyDKLVsr3dO249tagqRcz1jDSo/ZwiyLfbrIsS7LIkk01s1mONt1XIjz8/CvtXZNJEpOkKfExKymbZcwyg5k0XbOWoeDCQwSS0PUqTQfEcYwJFnvTHN0nernAi8tAmT+IrbXEccxgMGgvUNBKUZQlsyzzSmCRe7cd3S5p8vBj00YzHA79Hkt9M36jtRe2UcbMaLIsw4bkoWVYMX8toGIQeDAdDHyXKGtwDuI8ZxaZUIeqECVLkD/g+VApRZImGBsxHAx94wprEBRxWRBHltgairJsFaR5YmWFbeOCHA6HGG38wZ3EbWzCd1jyboQsy1CiWIbt0nSQGqQDnHMkSdq6wbX2fWp9vEyB+DhTZ/fIA9DKd3bRysfM4jTFhrs0m6bpRnmNdBlc4w28SythMCyIbNQqfVprVHDH+gJ7ociLwIvdh4+zWwbDAVo0SZp415026NC/Wyvf1Wc69fHAZYHRhjQdENmSJE7aPaa0xuLa+GbTG1pYDgVJKUWSpIDz7RjTlMhG/t7h0DmriT/XlfdGLMMu87ksCaPRms/XSRMfpgl0+fNe0Mp7Na21c6drZYVtE3dJBwN0eG2bi6uVCgkofnsoUdjILk2LPBvocrXzLSmtCbEV1WqgjRyy1nY+/geECweMd4dr39owsv7yatVc1K28kPUXQ9PpqxBbiIT2cDHO1RjtW21aa8NBoEIYQ8A5cp17bXwJFAkRIYp9eEaFBDd/8X24aME5lJI2wcZGy+EiF/FhmjQdUFclxlpsZNvEr+Y9zWulpOXRLqOJsydp4t2u2rdHtdagQsxZSVDUw2Xs2nQ/Fg1B2Caxbw8qPgRgrGmVcqVqf+YHRd1aM3eldiWF7fEkh7pKEeHIom0sIq2wWEj8od31y9UbeEaxJEnqbzDSGqV1q2X7Dv5e024yYc0SJBJJSCRKkoTS6Dbzszm4/XsaCyIFaBOnugwfY/cJbDi/fsfpalrhSdD8jDFENkKk2xZFs8esjUgTvx4NXd5iV95dbKTlRWtNa2l0Gc2lGEkSU5XBpRr4UVRYJ2kUItcKsW5ThVdotedFz5fKn4sNLzaXkIrgYkdVm7b/+GloW5Rt38y/tRFVXCGACWeI0qrlP0FwYb2aG8NO8eneKAuhjydp8LGSwhYaARoDvkG/L9NUqCBQlTvS4qyNiKxt3cpdRiNs0zSlrsrWMm8yc50DI74sQ4eG40txdaDyN60kSUJVmpa51XFh21iBgVZros4ntvmD+8g97pwLvKhb5U6cQimHkuCBWYLbjODBPVZ5y6gRSuFgahRcbXxp2jJcHdicCU0fYcI1eg0vOudQLpTJqIYXu+9BapWIOA686Hs8e6EUeFEplHOt4r4MvNhmxkeeF+uqDOulWn4Tp1Di6bXGhrDbo42QoE+diyLVfc5/TDRCqbn02YXm4Z91ODfu5a6j2TCeruYy7gddqv61v4dTLYXF3mijIjGVMe0l4/fdERrIMMbfQbwMSgQchTS01u0F3d69ej+/uRBH0kuSHd/sMSWK6lgbxpPG3gjeZbn1p+HF2h7tsZPyBEyg2SwBL3re0m1JzHG6GiWiQUPPWXhxkRFrpVRbWlbVFYS4+oN7zAQadRDEj1qz+yzZJ2xasrLCFkJCjbFhwnyfoWZuT5rkrm+WBs2md6HTEByNfZnpajTU4xt/lehqPA+N6/FhJRXLQptSCrGCdvq+LOPPGv9S0XVsj60KXfftMY4stoeNfxloO06Xcea+5w/7P/PESgtbIBRkX9Sk3n9YuiY7aQ5t2+bWOanJIgPmobvOn6756eNzsVYXQNd812zV6JJjZM2HtrnSBSuzZg9+7lnjtysvbM8Tj/bvt2Zz52/KeBAPpU2OXiwbXY02dCJ1cvSXZaULTqBtmeniUbx49Ldlo+3hVhYs65o9lC7/BmC16Hrw76ehrfuBoVPiIjSa9jPnpbmfPIoL+O5jtUEX8vmnG8PFfO/9dJ3Wr3F+Y5ELtxyO0/WobznfccyBrpXixWPfsKDzYx68eBHvXcTnXfRnr4Rle3xinjQ9+8HPg8VVz32aLnhSV9N9tC3wADj++nxcaMdd+ouLMd03pefi9uxGq7zz3mMPfuai8Cm64Fzcnp2kbUXogvtzOc7LYr5o2pbMsl3eJJmz4uSEoPP/zC7gPOjqAmnnraTJAgRt48n51LOT3vek3zNHnETXSZN7HqOa+z6bU/JgV86PxxvHwxOl5kHbUglbn8D55JOyOHfVyTjxIHiSz+oQzou2Lq7ZeYznoZ+zIHJP87WPtCYeMT+LiN/54+O+jMbH+5wO8WIbUljRPXaaZ6f5nIf9t3mx4lIJW+CRM3PaeetSrP40XlSHe+QB9ShGXMQB5xwPnezT0HXfZ534fAF08WgeWka6ToPTXP/enSPbo5nL4yP/VHYpT75m80ZL1wXz4mm+Yx54cAjnMyQ3F9qWKmZ7EsM8ThutR7137reSfAZdZ435nWYOjtfWzQefQdtZP+UUStZc6VpRXpzXHjv+ufPCg2N6XGXmNGuGnK7F4XlgfmvmcG5+oY0Tx+Mc7gLOxXmcIMtn2Z6Ai7AAuqDFXcggOkHYBcCd3kK+2GFcBC+uJl0rjQ5M18Ws2Wn8GheMC+LFi+bx5RC20rTI7tGjR4+zYpXPjvnTtsqzeZHovBv5qFGEz86cl4Y9L1dJQ9/qWQ5NBxnXCS3/vCCeEefeGWceaHkR5kfbHPz/i2gYMY/j48gFP+dmMxe4Zk/apanL6Lxle7z/71z5aU5f5hbg/pzP1wW65rxXLnou24Suua/ZxX9fy4tzpG0eTsmGpnnus3komc3+mvv5cYGE3X/eLyKh8+K+s/OWbYNFTvxZkzi6r5wzZewAAAWeSURBVI2F7Lug8QsnWDMnJHh0ny6PBxNUPjXuB2hbKrp4iDdkyelqxv8oujjpPY/6jsce3eOjFbgP4cWztvw78TsWgIvmxUXy7vHEvfOka2mE7SKxLIfWmeEl7Mnb1XUgEeJJ8LDxLzltn8mPS07XZ45/VenidBnAXcbj8mLXz9SL2GOdEbbf/e53+frXv77oYZwrXn31VbIsWzm6bt26BcA3vvENNjc3Fzya80OWZQB885vf5ObNmwsezfni9u3biMjK8eKNGzcAeOWVV9qL7FcBOzs7APzyL/8Kv/mb/2PBozlfZFnGjRs3Vo4Xv/e97z3079IFDUNEFj+IHj169OjR4wnhnDsx7tgVy3YMvLHoQawIrgB3Fz2IFUA/j+eDfh7PB/08ng8ueh5f+Kw/dEXYvuGc+/FFD2IVICK/28/lk6Ofx/NBP4/ng34ezweLnMfOl/706NGjR48ey45e2Pbo0aNHjx4XjK4I23+56AGsEPq5PB/083g+6OfxfNDP4/lgYfPYiWzkHj169OjRY5XRFcu2R48ePXr0WFksXNiKyNdE5A0RuSkiv7jo8XQZIvJvROSOiLx67NmWiHxLRN4K/14Kz0VE/nmY1z8UkT+zuJF3CyLyvIh8R0T+SER+ICK/EJ73c3kGiEgiIr8jIn8Q5vEfhecvichvh/n6TyIShedx+P1m+PuLixx/1yAiWkR+X0R+I/zez+MZISLvisgNEfm+iPxueNaJfb1QYSsiGvgXwF8GvgT8TRH50iLH1HH8W+BrDzz7ReDbzrkvAN8Ov4Of0y+En58HfmVOY1wGlMDfc859CfgK8HcC3/VzeTbMgK865/4U8GXgayLyFeAfA7/knPs8sAO8Et7/CrATnv9SeF+PI/wC8Nqx3/t5fDz8eefcl4+V+HRiXy/asv0J4KZz7h3nXA78R+BnFjymzsI597+Bew88/hngV8PrXwX++rHn/855fA/YFJFr8xlpt+Gc+9g593/D6wP8AXedfi7PhDAf4/CrDT8O+Crwa+H5g/PYzO+vAX9BznrLx4pCRJ4D/grwr8LvQj+P54VO7OtFC9vrwPvHfv8gPOtxejzjnPs4vL4NPBNe93N7CgQX3J8Gfpt+Ls+M4Pr8PnAH+BbwNrDrnCvDW47PVTuP4e97wOX5jriz+KfAN4A6/H6Zfh4fBw74nyLyeyLy8+FZJ/Z1VzpI9TgHOOdc32f69BCREfBfgL/rnNt/4Lqzfi5PAedcBXxZRDaB/wb88QUPaekgIn8VuOOc+z0R+alFj2fJ8ZPOuQ9F5GngWyLy+vE/LnJfL9qy/RB4/tjvz4VnPU6PTxrXR/j3Tnjez+1DICIWL2j/vXPuv4bH/Vw+Jpxzu8B3gD+Ld8c1ivzxuWrnMfx9A9ie81C7iD8H/DUReRcfSvsq8M/o5/HMcM59GP69g1f+foKO7OtFC9v/A3whZN1FwN8Afn3BY1o2/Drwc+H1zwH//djzvx0y7r4C7B1zpfx/jRDf+tfAa865f3LsT/1cngEi8lSwaBGRFPiL+Pj3d4CfDW97cB6b+f1Z4LdcX+iPc+7vO+eec869iD8Df8s597fo5/FMEJGhiKw1r4G/BLxKV/a1c26hP8BPA2/iYz3/YNHj6fIP8B+Aj4ECH194BR+r+TbwFvC/gK3wXsFner8N3AB+fNHj78oP8JP42M4fAt8PPz/dz+WZ5/FPAr8f5vFV4B+G5y8DvwPcBP4zEIfnSfj9Zvj7y4umoWs/wE8Bv9HP42PN3cvAH4SfHzTypCv7uu8g1aNHjx49elwwFu1G7tGjR48ePVYevbDt0aNHjx49Lhi9sO3Ro0ePHj0uGL2w7dGjR48ePS4YvbDt0aNHjx49Lhi9sO3Ro0ePHj0uGL2w7dGjR48ePS4YvbDt0aNHjx49Lhj/D6YI9fzENdoZAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"EjxVqZ9pkOPJ","colab_type":"text"},"source":["## Traversal of single dimension:"]},{"cell_type":"code","metadata":{"id":"DHYrHLzLkN28","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":98},"executionInfo":{"status":"ok","timestamp":1592991795481,"user_tz":-120,"elapsed":587,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"outputId":"9119cfec-d1fa-48c7-cafb-d3078f63162d"},"source":["traversal = viz_chairs.latent_traversal_line(cont_idx=0, size=12)\n","traversal = traversal.permute(1, 2, 0)\n","\n","fig = plt.figure(figsize=(10, 10))\n","plt.imshow(traversal.numpy())\n","plt.show()"],"execution_count":23,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"-6pImGWOkWcY","colab_type":"text"},"source":["## Reconstruction:"]},{"cell_type":"code","metadata":{"id":"1pvbzHfEknv4","colab_type":"code","colab":{}},"source":["# Get chairs test data\n","_, dataloader_chairs = torch.load('data/batch_chairs.pt')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"b4sms021kWCj","colab_type":"code","colab":{}},"source":["# Extract a batch of data\n","for batch_chairs, labels_chairs in dataloader_chairs:\n","    break\n","\n","recon_grid, recon = viz_chairs.reconstructions(batch_chairs, size=(8, 8))\n","# recon = recon.permute(1, 2, 0)\n","\n","fig = plt.figure(figsize=(10, 10))\n","recon_grid = recon_grid.permute(1, 2, 0)\n","plt.imshow(recon_grid.numpy())\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BeviVvyJkp7L","colab_type":"text"},"source":["## Encoding:"]},{"cell_type":"code","metadata":{"id":"L56PVHGkkk-6","colab_type":"code","colab":{}},"source":["encodings = model_chairs.encode(Variable(batch_chairs))\n","\n","# Continuous encodings for the first 5 examples\n","encodings['cont'][0][:5]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"EFRO74gBkuWv","colab_type":"text"},"source":[""]}]}
\ No newline at end of file
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "name": "Visualization.ipynb",
+   "provenance": [],
+   "collapsed_sections": [
+    "3bATAS0GMTqT"
+   ]
+  },
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WVOZHtI_Lgsj",
+    "colab_type": "text"
+   },
+   "source": [
+    "# Mount Drive:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vt-0rCaRK8qU",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 124
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592990829121,
+     "user_tz": -120,
+     "elapsed": 27824,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "fcea2ae8-e68e-4cf6-d2a8-def22e6911e3"
+   },
+   "source": [
+    "#Import drive\n",
+    "from google.colab import drive\n",
+    "#Mount Google Drive\n",
+    "drive.mount(\"/content/drive\")"
+   ],
+   "execution_count": 1,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+      "\n",
+      "Enter your authorization code:\n",
+      "··········\n",
+      "Mounted at /content/drive\n"
+     ],
+     "name": "stdout"
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vGK5XnB_LrNq",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 104
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592990834974,
+     "user_tz": -120,
+     "elapsed": 2845,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "09d57299-9168-4a2c-d8f5-b7283e51d8cc"
+   },
+   "source": [
+    "import os\n",
+    "os.chdir('drive/My Drive/Work/Thesis_Julien_Dejasmin/Work/code/Pytorch_CNN_mixt_representation')\n",
+    "!ls"
+   ],
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "data\t     OAR.2066986.stderr  OAR.2066988.stdout\t  reconstruction_im\n",
+      "dataloader   OAR.2066986.stdout  OAR.2066989.stderr\t  trained_models\n",
+      "Experiments  OAR.2066987.stderr  OAR.2066989.stdout\t  utils\n",
+      "img_gif      OAR.2066987.stdout  parameters_combinations  VAE_model\n",
+      "main.py      OAR.2066988.stderr  README.md\t\t  viz\n"
+     ],
+     "name": "stdout"
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EspLyKYzLv8K",
+    "colab_type": "text"
+   },
+   "source": [
+    "# Import:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "FXB9r3fxLww2",
+    "colab_type": "code",
+    "colab": {},
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592990847163,
+     "user_tz": -120,
+     "elapsed": 10085,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    }
+   },
+   "source": [
+    "from utils.load_model import load\n",
+    "from viz.visualize import Visualizer as Viz\n",
+    "import matplotlib.pyplot as plt\n",
+    "from viz.visualize import reorder_img\n",
+    "from dataloader.dataloaders import *\n",
+    "from torch.autograd import Variable"
+   ],
+   "execution_count": 3,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Z7qeqVmGL8xs",
+    "colab_type": "text"
+   },
+   "source": [
+    "# Load model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "CI7NJ2aDL77V",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "import os\n",
+    "# os.chdir('../')\n",
+    "# !ls\n",
+    "path_to_model_folder_mnist = 'trained_models/mnist/'\n",
+    "path_to_model_folder_fashion = 'trained_models/fashion_data/'\n",
+    "path_to_model_folder_dsprites = 'trained_models/dSprites/'\n",
+    "path_to_model_folder_celeba = 'trained_models/celeba_64/'\n",
+    "path_to_model_folder_chairs = 'trained_models/rendered_chairs/'\n",
+    "\n",
+    "model_mnist = load(path_to_model_folder_mnist)\n",
+    "model_fashion = load(path_to_model_folder_fashion)\n",
+    "model_dpsrites = load(path_to_model_folder_dsprites)\n",
+    "model_celeba = load(path_to_model_folder_celeba)\n",
+    "model_chairs = load(path_to_model_folder_chairs)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "yD8i2kDiaw7W",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 104
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592308168217,
+     "user_tz": -120,
+     "elapsed": 601,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "0a17e674-8a93-4e88-89a3-124b717ae680"
+   },
+   "source": [
+    "# Print the latent distribution info\n",
+    "print(model_mnist.latent_spec)\n",
+    "print(model_fashion.latent_spec)\n",
+    "print(model_dpsrites.latent_spec)\n",
+    "print(model_celeba.latent_spec)\n",
+    "print(model_chairs.latent_spec)"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "{'cont': 10}\n",
+      "{'cont': 10}\n",
+      "{'cont': 6}\n",
+      "{'cont': 32}\n",
+      "{'cont': 32}\n"
+     ],
+     "name": "stdout"
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "32FiS-PBMCGa",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592233570230,
+     "user_tz": -120,
+     "elapsed": 842,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "e28351f0-d604-4e8f-acd8-4d13e2f8e701"
+   },
+   "source": [
+    "# Print model architecture\n",
+    "print(model_mnist)\n",
+    "print(model_fashion)\n",
+    "print(model_dpsrites)\n",
+    "print(model_celeba)\n",
+    "print(model_chairs)"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "VAE(\n",
+      "  (img_to_last_conv): Sequential(\n",
+      "    (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU()\n",
+      "  )\n",
+      "  (last_conv_to_continuous_features): Sequential(\n",
+      "    (0): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "  )\n",
+      "  (features_to_hidden_continue): Sequential(\n",
+      "    (0): Linear(in_features=512, out_features=256, bias=True)\n",
+      "    (1): ReLU()\n",
+      "  )\n",
+      "  (fc_mean): Linear(in_features=256, out_features=10, bias=True)\n",
+      "  (fc_log_var): Linear(in_features=256, out_features=10, bias=True)\n",
+      "  (latent_to_features): Sequential(\n",
+      "    (0): Linear(in_features=10, out_features=256, bias=True)\n",
+      "    (1): ReLU()\n",
+      "    (2): Linear(in_features=256, out_features=512, bias=True)\n",
+      "    (3): ReLU()\n",
+      "  )\n",
+      "  (features_to_img): Sequential(\n",
+      "    (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "    (2): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU()\n",
+      "    (4): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (5): Sigmoid()\n",
+      "  )\n",
+      ")\n",
+      "VAE(\n",
+      "  (img_to_last_conv): Sequential(\n",
+      "    (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU()\n",
+      "  )\n",
+      "  (last_conv_to_continuous_features): Sequential(\n",
+      "    (0): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "  )\n",
+      "  (features_to_hidden_continue): Sequential(\n",
+      "    (0): Linear(in_features=512, out_features=256, bias=True)\n",
+      "    (1): ReLU()\n",
+      "  )\n",
+      "  (fc_mean): Linear(in_features=256, out_features=10, bias=True)\n",
+      "  (fc_log_var): Linear(in_features=256, out_features=10, bias=True)\n",
+      "  (latent_to_features): Sequential(\n",
+      "    (0): Linear(in_features=10, out_features=256, bias=True)\n",
+      "    (1): ReLU()\n",
+      "    (2): Linear(in_features=256, out_features=512, bias=True)\n",
+      "    (3): ReLU()\n",
+      "  )\n",
+      "  (features_to_img): Sequential(\n",
+      "    (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "    (2): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU()\n",
+      "    (4): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (5): Sigmoid()\n",
+      "  )\n",
+      ")\n",
+      "VAE(\n",
+      "  (img_to_last_conv): Sequential(\n",
+      "    (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU()\n",
+      "    (4): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (5): ReLU()\n",
+      "  )\n",
+      "  (last_conv_to_continuous_features): Sequential(\n",
+      "    (0): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "  )\n",
+      "  (features_to_hidden_continue): Sequential(\n",
+      "    (0): Linear(in_features=512, out_features=256, bias=True)\n",
+      "    (1): ReLU()\n",
+      "  )\n",
+      "  (fc_mean): Linear(in_features=256, out_features=6, bias=True)\n",
+      "  (fc_log_var): Linear(in_features=256, out_features=6, bias=True)\n",
+      "  (latent_to_features): Sequential(\n",
+      "    (0): Linear(in_features=6, out_features=256, bias=True)\n",
+      "    (1): ReLU()\n",
+      "    (2): Linear(in_features=256, out_features=512, bias=True)\n",
+      "    (3): ReLU()\n",
+      "  )\n",
+      "  (features_to_img): Sequential(\n",
+      "    (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "    (2): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU()\n",
+      "    (4): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (5): ReLU()\n",
+      "    (6): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (7): Sigmoid()\n",
+      "  )\n",
+      ")\n",
+      "VAE(\n",
+      "  (img_to_last_conv): Sequential(\n",
+      "    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU()\n",
+      "    (4): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (5): ReLU()\n",
+      "  )\n",
+      "  (last_conv_to_continuous_features): Sequential(\n",
+      "    (0): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "  )\n",
+      "  (features_to_hidden_continue): Sequential(\n",
+      "    (0): Linear(in_features=512, out_features=256, bias=True)\n",
+      "    (1): ReLU()\n",
+      "  )\n",
+      "  (fc_mean): Linear(in_features=256, out_features=32, bias=True)\n",
+      "  (fc_log_var): Linear(in_features=256, out_features=32, bias=True)\n",
+      "  (latent_to_features): Sequential(\n",
+      "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
+      "    (1): ReLU()\n",
+      "    (2): Linear(in_features=256, out_features=512, bias=True)\n",
+      "    (3): ReLU()\n",
+      "  )\n",
+      "  (features_to_img): Sequential(\n",
+      "    (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "    (2): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU()\n",
+      "    (4): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (5): ReLU()\n",
+      "    (6): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (7): Sigmoid()\n",
+      "  )\n",
+      ")\n",
+      "VAE(\n",
+      "  (img_to_last_conv): Sequential(\n",
+      "    (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU()\n",
+      "    (4): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (5): ReLU()\n",
+      "  )\n",
+      "  (last_conv_to_continuous_features): Sequential(\n",
+      "    (0): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "  )\n",
+      "  (features_to_hidden_continue): Sequential(\n",
+      "    (0): Linear(in_features=512, out_features=256, bias=True)\n",
+      "    (1): ReLU()\n",
+      "  )\n",
+      "  (fc_mean): Linear(in_features=256, out_features=32, bias=True)\n",
+      "  (fc_log_var): Linear(in_features=256, out_features=32, bias=True)\n",
+      "  (latent_to_features): Sequential(\n",
+      "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
+      "    (1): ReLU()\n",
+      "    (2): Linear(in_features=256, out_features=512, bias=True)\n",
+      "    (3): ReLU()\n",
+      "  )\n",
+      "  (features_to_img): Sequential(\n",
+      "    (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (1): ReLU()\n",
+      "    (2): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU()\n",
+      "    (4): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (5): ReLU()\n",
+      "    (6): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+      "    (7): Sigmoid()\n",
+      "  )\n",
+      ")\n"
+     ],
+     "name": "stdout"
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0e28tlr5MEcE",
+    "colab_type": "text"
+   },
+   "source": [
+    "# Visualize various aspects of the model:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-nvQHSM2MJ5s",
+    "colab_type": "text"
+   },
+   "source": [
+    "## Create a Visualizer for the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "VRdpRR7uMC9N",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "viz_mnist = Viz(model_mnist)\n",
+    "viz_mnist.save_images = False  # Return tensors instead of saving images\n",
+    "\n",
+    "viz_fashion = Viz(model_fashion)\n",
+    "viz_fashion.save_images = False \n",
+    "\n",
+    "viz_dsprites = Viz(model_dpsrites)\n",
+    "viz_dsprites.save_images = False \n",
+    "\n",
+    "viz_celeba = Viz(model_celeba)\n",
+    "viz_celeba.save_images = False\n",
+    "\n",
+    "viz_chairs = Viz(model_chairs)\n",
+    "viz_chairs.save_images = False"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QJrHRrt1MNbh",
+    "colab_type": "text"
+   },
+   "source": [
+    "## Samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "PlR-LCwChJjF",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "size=(8,8)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "mdHOjdtCML-T",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 612
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592308182350,
+     "user_tz": -120,
+     "elapsed": 1312,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "52286d6f-b636-4ade-e7a0-6ef54466a946"
+   },
+   "source": [
+    "samples = viz_mnist.samples(size=size)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(samples.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4eca579048>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 9
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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sXUaBn2sm28OUST6fx1dffYVKpYKvv/5aNrDZbIbhcCh6PR1QsfWFDlDYSJSMyiEBa715Mu1DvwBARLfcUGezGTKZjGzm7H3l1+vzMpNVYdXv0dERyuUy/vCHP6BcLuPrr7/e6IuWSqU2wC/ZZQYJeo/R9zyTychc0CAT+AhMWfk1Ho+FrbyvlJoGRZlMRopTTk9PZQ/hOuceq69xOp1u9DHSOjxqE8PhMNLptOwP+Xwew+EQsVgM/X4frusKy6zfZxdz5mDBkUlTa1DEhacrBbR6Pp1Ob6ByXXqoS2zJIOneEqQ4HcdBIpGQhnYApBniPgWUtG3sCB2VZkYsyxJmZDgcCmNEESCdOt9HM0V8JqCkNodjt49c8a8xL9Yom80im81uVFfQkVH82O120e/3MRqNBFBSh6Udm5+udRdmWZaUKDOy5Nxgo8x+vy8pAL8FEJ8yDQL0GiJLQK0RAwKKrDWTqIMEBmTU+FF4zTnkp0Z4tE99Fu2HTdPjxjJv+tl9pg2/xDR4IPjT1a3lcllYeYIG7iP0FUy/6+OFtL/UrJRO/3Mcqf+hjs3sm/Wl12fur1pjptk/Egha9wRA9JkER2S1AAgZsVwuZU5YloVYLAbXdZHL5bBYLJDJZDCZTDaYxl3Ol4MDR3rT1xu2rh7hxCEAqFQqop5PJBIoFAqiGYjFYtJzghEPcEv76U6enNyTyQSDwQCNRgPNZhOWZWE4HMpG6Zf0mp7crCo4OTnB8+fPUavVUCwWBQD0+310u128e/cOV1dXuLi4QL/fl3Sa2fWYX3OhUCTourdHjGjtlmYJ9jkuJhPARo+np6c4OzvDycmJlKADEPF1o9HAxcUFfvzxR/R6PTQaDUynU2FAvLoa7/v+78qYJuDG8Pr1a5ydncn5c+/evUOz2cS7d+9wfX0tEaXfzdwY2Lfp+PgYR0dH0jX96OgIk8kENzc3sG0bnU4H4/EY7XYbACSY4GZGtoFsAjdKata4gZoAcpfziZG8/r+a4eD46A3LbFQIQNjlbDaLxWIhKZNYLIbZbOZ7XaI2nTpmIFCpVFAsFvHmzRtUq9WNTvpkE9nTZzabYTAYYDKZSJ8vx3FkX6ERgFPTxbQd9zT6pNFoJFonrzTd514jn3m9nLfUFaVSKdkrWclM9pP7IwMEfRg335d7CMXqpVJJWLFkMonz83Pk83l0Oh2EQiHxGTyaaVd2cOAI8O6pQ7DEiUvww/JJCsoSiQTy+bzcbKL/bRUiZjfS5XIpYGo2m2E6nSKRSGAymfgyEtI6EDr3bDYr0Rs3dlZeDYdDjEYjmfBaYxQOh4Ul47gwWtEAKRKJiBjV1Gv5Jb3G+aLZDlZhMFojNWzb9h02jRvatnJ9c+Pgz/Z93Q9hBJrssE5wyVQr55Y5n/xuXlEzwXQ+nxeGEYCAGzKMtm1vOHNG+ACE2da6DB1EmMzRvuYM/+8v+TTNrpvsF81seGi+p9/Xht4XTMCsGWddmUX/wYO6J5OJpJSZKqIUg8cNsb0DWXj6U/NzeLF0DzF+GiTpPVA3LeVnIpPOpo5aaqArnLUUY7FYSGYnHo8L2cGgm6xRwBz9gnktKJP6owPL5XI4Pz9HoVDAy5cvpUEZ0fe2SgHgroCMlKC+Qf1+XyI/sggmVewHhoSLuFKp4OzsDKenpzg+PpbPTjHx5eWlMEY3NzeiEyEw1GcmcewIjpiXZjdULujJZALgY7pxWyS6D9NzheXmZ2dnODo6kr4klmVhPp+j1+vh+voaP/zwAxqNBi4vLzGZTDAejwVYatP3n1+bQkm/bwS/xXQ67dWrV3jx4gVevHiBcrksvVg4r5iiPYRr1+wio/h6vY5KpYLXr1+jVCqhWq0K8zMajdBoNNDtdvHzzz/DcRyMx+ON9cFNTqeYuElQbKtbQfgtrbZtY2JKhe0sotHohnaKm77ulXNfTMcuzZwTuVxOOumTcWbbD7JBbPPRbrcxHA5xdXW1wRyRPeP7cv8i05LNZqU/nQbMJhi9bzBtMpacn5RecH7yxITlcinFO+xfxLnMr/kZmdl5+fIlSqWSBBhafM5ghMEq959d2kGBo0+Zpjw5qCy1JRVIgSg3Pw2KuNFpEbF2ULq8Vpeysqki2QY/LXYNjggYmWpkHpyCcupCJpPJnUaPXg/9P3RKkwCKZ445jiMAgeO7LwbJZBnp5NLptPTi0RoBOjeODalxUzOj55KZZgA2O7zue5O7b7MsSzqtU3cRj8cRCoVkbvGh15efTc8RU1fCRzabFaaUFZ68TlZ3UnisK7v4nlqvwchab3R+F/LrwJFAQGusAGyAIq1JOoQ54GW8Bi3OZzpIM4MABPhwXpB17vV6GwCDek6t3QQ2m4ealV9MVZmtVe57TM0CARYjsYKXASQ/w3A4FOaURUtaX8X1z71hMBggHA7DcRyk0+k7c0efWbmPvfWgwZHejMgGsJqkXC7j7OwMhUIBJycnkvfmgqVTY7WISXGy5wgBBs8N4mIAPi6W0WiEarWK0Wi0odbfl2nalQ35GPU+e/YMtVoNhUJBSu57vR7a7TY+fPiA6+trQf90dFxwukpAPzjuFB9yjFmV4rofz2ezLEsij4c8B+jXjA/1UhyX09NTnJycoFgsSuXEYrGAbdu4vr7Ghw8f8PPPP6PX68G27Q3guK0dhAaEHL9dVlrswriWqMF5/fo1nj9/jnQ6DQBot9u4ubnB1dUVbm5uDkZrBEAqMqkroZaqXC7j1atXwn44joNWq4Vms4mrqyv0+310Oh0BCwTbAERnxM2U1VoEVbpPlt96AJmMOAW3TK3Yto1kMil+Q58jR/ZIb7Sm+eU6vcwrqGKwzRQrW1ewYkz7j8FggMvLS2GO2AGbxiDNsiwJ4BmwFQqFDdG1LpxhgQP9tZdO7XNNEwT8vAR0tm0jFouh0+lIxRyvlzoq+nqCJw2OtFbVcRwcHx8jFouhWCwKQ8Tml5SAkHzYpR00ONpmGjCRsibYoc3nc4xGI0HDnAB8LScFF/hqtdoQohF87KP/wq8xLmSyXNTVMN9LJ0XH5nU8iJfwUjsJ3cyONCj1WACkrJsRhqlZ2geboseFwJFsmtYa8TgH6rAoMtTvYQJzc6zYwwaAp0jfT/Plc4zXXSgUUKvVZJPgRkiHynTLIQAjkx1gAUexWESpVJK0KwA5NLPdbqPT6Ui3dBYx6PVDBldX3pA5YDrNrw1DvVhjs58Xg0odNHGT0+kQfY2HZl7+zwSN2qfpMxd1B32mljXYYgUadaHUP1L6wf2ILOVwOBRdmwbUX2q8f3rfpG6IWYDlcik+lK1OVquVpNioV+XPCY50xgDAHcbUZOJ14LkPO3hw9Kk8qwZFTJFQXU9xnHkqsLngyYawiZdlWSiVSgBuD4s0q7H2bXTG7GtUKpVwdHSEer0uAInUf6/Xw83NDdrtNrrdrmiQOJl1fyPd+0LnhJlWSSaTKJfL0gSODn84HCIcDktqRW+U99247FNjonVYjPrIevDzAx8Xbb/fR7vdxsXFBRqNhoiwgVvKW1PfjHh0dKMPjDSF/bRD3CRo1NK8evUKX3/9Nc7Pz1Eul2V9tVotXF1dodvtYjgcHsSRITpNzHX/4sULVKtVvHnzRhgDx3FEj/btt9+i0+ng3bt3soFpp870C9PwLFlm2oXNRBmB+wkg6fS3lhyQOeI61xV2wG0Jvz4eRW+2frrG32IEfGQ+gE0xPQNwirAHg4E8GHxqxp3+s1QqIZ/P4/T0FMViEaenpyLZIFvEvmqsnKXf9mIcv9R4TQAE9HAPpZ/T/e04n5mJ0acEmOl0ziGCR93vCYAnIDKD813Mm4MGR59auDoqI+LlDWE1VrvdlhJBzRzpm8hKNpYlm/9fl/r7ocJEo26vbsU6aiUwNDs8c1w1O6Y7pRIcMeIhc6S/5mvz+TxCoRCGwyEsy8JgMJD7oO/XLsdHn4vFNAcdOZ0+mTSTMSK7xHJU6rc0OOJ1aZaR18zUml/aPXyuae0djxCgsJLVaZ1OB71eT8Ch36/VTJ2Q1i8Wi3KoZjQalc2q3W4La8RWHjqo0BspgwmuDUbMTE0RMPh1nD4lxqa/1QyA1mzpDsf6b/x4ndvMvH6tU+U918UZ+vo0u8zeViyRJ3NNLRsrPelftJ8eDAYbfdYYeBGE3NeYmlIKMuCUYvDzc36TDWTWQYMikynkOOo9is/m/NBVj/toFnpQ4MgU8ZqCMd4EpsrIFPG1jOparRZs20az2ZTojYwGjQs7nU5Lp1eTFqRITbMD+04daHYkHo+jWCxKFRYrKSzLkrx1p9OR6jTmjBnZABCGRDfQZMUfgRcf+tBBAg3LsgR0MkIOh8PS+G6XIEE7bArUi8Ui8vm8AEcCIzJp7EuixyAejyObzcqREVpQyWviHCT93e12pQuyFqZrh3AoRsfGRohfffUV3rx5g0QiAdd1cXFxgaurK/zwww+4vLzcEGP71XRQwT4zumt6LpdDNpvFer3GeDzGzc0Nvv32WzQaDbx7906CMFOLR+aZc43FIY7jyByhgFUzL34ZKw0K9BjRzFMDuJnpiiPg1o8A8EUQ+bmmWQ/e80Qigfl8LkJlvg6AjAPL1TUDz67TrKTOZDKo1WriV8nG8IzL6+tr/Pjjj2g2m/jw4QNGo5E0Ib7PYgcNjvg1QY8GuVq2Qr/m1fRUrwkNihhA60aQ3N/Jwu2TTT0ocAR4l5Rq1kjnSLkZEfGSJWKJ8XA4FCEZdSbA5oGk7DWhby4AQfS8gX44SNFE5SxXJ5DhBCSA5Gf3yllr3QWZFs0QMSLU1QTa6Ah5GnuxWBShNk9kZiuAXaXWODYmC0Zg45Uq4H3nxr9er+VUbYr0vXQV3DA4XtwwdLqR//PQNgjOr2KxiFqtJs1VSZO3Wi3c3NwIQ3soR0Tooo5UKuUptp1Op+h0OvJgx28N/nT6WetI+D6cK/oICT+l5T9lXr5X+w3NOHOt6fWmq9e83s+vpucv/YT2FfP5XHyaDlAZVLPrcyKREH/KM9eYctXBKxlFx3HQ7XbRbDbRbrfR6/UkYNPatvv2I/r9zPu7DRyZn8UE+npf4trgeWwcF44tfYluKLnr/fXgwBHNK4fJQbVtG5Zlycm+zJnqdNpkMkG32xWQw783mRdSm9zczDJvlmeSfdp3uoSfX58DVavV5MRkfn6CRObDOQbAZhklUwmsGuD31E6Yjew4gTlWdA6W9bGLeLfbRTQaxWg0EjZP57cfemx0dR0fbD7GiJDlqOzTxEoKRjzZbBZHR0dyEjvni6aCuaDZ0ySdTqPT6QgzZbY24PgdgvF6X758iTdv3qBeryOTyeDHH39Ep9PBt99+i/fv36PRaAh48LPpdFosFkOhUEC1WsVXX32FWq2GcrkswtNer4cffvgB19fXePv2rWiFOH81wGK6pF6v4+joCNVqVdbVer0Wke50OpVeYPrz+M00oPFiPHWqRVeqARBWmSzBofU4omnJBrMT9Ge68IT+t1QqCSjmnqUBeKlUEnaJoIGNU4fDIZrNpjBGnU5HmNjhcLgRwD0EaNDva1mWNKekmfof8zOYqTSCZIIiHrfChqokMSg6HwwGG3q8XQdZBwmOPpVeoyAsGo3KJsSfU1tDp6Tz/mSdtOhLtzzXkQ/fj47NpP78YHRU+jwcdsUGbvtwmCWgWpCqK9FMPZEJiOjc4/E4VquVpKmYY89kMgCAXC4np0mzx8cuwJGpxTI7sPJaNDUMbJ4ZpyM+3TuL72nqL5iO1VV6FDSyTQSv+5CYA6YJTk9P8fLlS6RSKbiuKwLlTqezcfSM302DZnbW5/lwvL+sxhkMBhK904doVpld19k6hP2fOF+0Ls0UMfsRLGim3msT1Buf9gl6vZmVvWaV1yGZ1xonSOEeYIIdtnSgkYlmOxENMsjmj0Yj9Pt9XF9fi/Caom4vndFD7DvmPvup8dDfm6aDD51uJWCmFg/AhlCbB3rfp9D8t9hBgiNg8yaQmSCyZcTf7/cl9aPPLiJTokv3dQSvm7a5rrshqCSTxPdh+S57JO0rTeKVUuPio8iPlQ/a0XNMOAa8dp7ZwxOX2X+DeiI6Q276q9VKQAAnPhkmAgvbtnFycoJIJILr62vpmUFQAjx8C3w6rGQyKT2r2KbGNFQAACAASURBVLSQDo6blmVZ0kmcAnQ2BKxUKnIUDcEzzXSgo9FINsjVaoV2uy3XrsH5Q13/fZplWTKn/uqv/gp/+7d/KwzgH//4R3z//fcS5ZKx9bPpecHGsScnJ6jX6zg/PxdwNJvNcHNzg+vra7x7926DLdagm8CR3ZNPTk7w7NkzaQfA4E0zRwTjOjAzo3K/GINQ3cNLp874Go4pr42vod/QYOlQzJRucA+JxWIYjUbC+tBvEghQm+olQKbfYdDOTMTl5SXa7TZ+/vlntNttXF1dSdsVZkjMlOZDXbN+Bn49iNfzmFo+6jVZRV2pVJDNZiXAYtU4m+6yqnofxw4dLDiimXojonkKgbWinnlaolGzG60ZGXHT01UmTL9Qq0TWaJ+NDb3MROs6YuXi0pE9x4l/y5ww+/8QFHFh6E1PL3Tmy5naLBQKGzlqXenG99Sf7aGN12ke4UDTQIXMG8XYbM6mW9qbUbUZMWtGaTqdolKpYLVaod/vS7+QfYv4f63xeqg1ou6K1TO9Xg+dTmdDg+dnM9lEBkAsMGBaXWsgNNOq/45zKhqNCutUKpVE8M/gwgTOuiLHL77DyzRDQV9LBl0/vHyg/js95maKxs/Xb5rJsPPe069w7lBvaJ6goKu8mGofjUaiZbu+vpYGvebJBWYwv+tx2/b/TB2ZlqjQh1KDxyIFyjUYWBMoMmjXwYP5vx76ug8aHJmCMW7YrATRi1GXGrLPjqn5ACCvBSBoV5dX8iY6joN2u41+vy8AyQ+L28vhc+FyQWpdDcdJ9+6hcJqaCTIrwG3kqHUWwC0FO5lMhJWZz+eygfL37PmSSqXkHCamMx9yPExGjdGL1hwx1UFwHQ6HRSSZy+UEHDG1ptMI/BsNSHXFY7FYlD5Z1CgAH9Ns/Hs9ln40bgYvX77Eq1evcHp6ikKhgMvLS1xfX+P9+/d4//69rIlDSKnpqjJWMFarVZRKpQ3WiAUctm3LdXHj49ds3MeGkS9fvkS1WkW9Xpd5r4M4RsR+vd80HTTy2smEkPHQvXZMMS+vmQUYWpi9y8DoS8wEctqXkE1mup3drQmWgLsaWVZj8ZzGTqeDdruNn376Ca1WC2/fvsVwOES73Zb5d0g6RfpB7qHUrbIir1QqSY85Zic4l/r9vlQK6z1Kv7dXuve+7aDBkTaT8jQjea0n2cYY0fQmx9QQ2QJTs7OvfOgvGRegyYbp6zX1AOamrqM7Tk5WWvHoFY67rm5br9ewbVuqs7w+B21f0aMZ5ZjjovvTkFEDbs82Yvt/zQSYlRh81n1zGC2l02mMx2NhJh56oX+pWZYl6dJnz55J6f5qtdpINdGJHwJrpOcswQ0jWjI9uppSV4DqVDDvL3VF7ItUKpU2etYAmxWRXBvbhKZ+mgsa7FArpyt2WXxA/Y3pY0029VA0R17BlZZZsIWJFpwzy0AAqMGM3qNms5mc28h+WaxI43EyWhdq+m8/zQ99P3W7AjJGLHRg+w9TqM754ziOnNig+3+Z/wO4K/i+7/F4FOCIg6Lpacv6eFo2JzUnqAYNepKZA0+nySZ3hUJBytAZIVFF74f+RjS9WXMj1zQ4TYMhduzV5flc4Bp0uq4rrJxZ4UYwxZw79TWTyUSYJ51O8AKk+7BtoJEl16ZTY9muLrWls9OHUebzedGeMJLktTuOg0ajAcdxEI/HsVwuN/6XH82yLOTzeRSLRfzN3/wN/vqv/xqFQgGO4+Cf//mf8U//9E+4uLhAv98XPYSfzdQaMaKt1WrSFyyTyUgKXVfa8Aw+9q7hRklwxIZ+z58/F5aUxhSKPq7Ha8Pz2/iZLD2ZTjbYHQwGiEQior/UlVTA3fYoXsdv+I1BMoGRTr1SaF+pVKTqiqBaN9wlOAKwIfugZrXRaKDT6eCnn35Cr9fD+/fvMRwO0Wq1pAef2WXaT2ME3E2j8boJinj6wPHxsfRFKxaLojcii9rr9USIToBILS/fX/vJXwJFXzpOjwIcAd4CbWAzTaabU5l/46UZ0SW5WozNTdLsKu2niavBhnm9OlqmdoZjZvYtIsAigtfN69jIka8jC8L0nZl20/S6qbXY9bgR0GrWT2shdBqRn5PiSVLipvPSfaV4zalU6o4+RT8OQZhKYFCtVnF2doZisSiNDKfTqUS9+yi3/RwzNzwNalmIwHvDNCj9AADpvcK0G1/Pdhm6Oo2bJOcII2Rdwq9F+eZa8NtYmkwpGTDNIJnnxAGbx4l4zXe/AiPNgjBYTqVSkpZnqojzRvsNMyDXgTXBMRs52rYtYEAfqWH6SD+NEeDNGJFt5/wvFArIZrOo1+uoVCqoVCoCmJhdIEDimtC94LRumOa1RraNz+eO2aMBR8AmgwTcTZ3o15hmskaMKHm+Uj6flzPDdK6Y6SU/6Su86EYCATNaJiVsWbfVfqaWhouZjBEbbOrWBwRRZJBMFkT3/9Flr59Kbz6UmSCNwFBvlhwDAkM2FCWTQNZwMBjI9bEPVCaTESdIx0mnwRSt3oRZ1u/XCJrR8h/+8Ad88803ODs7Qzabxffff4+bmxu8ffsW796989QH+NG0UJQbnj5h3dzouD6q1aowgQQ6vK9Mv/OgWvaw4bwioGaVaL/fFyG7FnrrdJTfjGuUax2AzF3bthGPx+UwVOqpONYEn2bTVb+l1/RGr1kQiqypPz06OpLCBLJITKEDkMBR+wxWObMiq9vtotVqodPpoNVqyZzQxxbtM4D8JTPF9bq3HdNoTMOXSiX8/ve/R61WQ7Va3WiISl9q27YAxel0KvODGQyCTAZgJnPkBSK/ZMweFTgCfnkwvH5v6l54Q/SBkRTWMS1CpL9NTb9r42TRE4WLUeuDCI509MNjD/g++j35dzqdpLVbmvnQToQ9gCjGI1gw01K71mvpRUbgo8uxY7GYdLvlomRUw2unVoALmtdAkWokEpG0As2k6HVU6rcNQlso9PFcwUwmg9PTU5yfn4uwuNFoSDqAlY9+c+DbzGQGzECC81tvlqw2072++HOmo3XlkmYReMwQN4DxeLyhJ/HaBP00lqZ/0SCJa0JH/noD45rQzJGf2VI9Lwh8mUatVCrI5XICipg+JyiaTqcIhUJ3mttqYbrZ34pGX+NVweinueCVRqMUgcLrRCIhuqKjoyNhjJiS5n7qlU2gMQXLrI3WupnrRLN0mq3j7z/HHh04AjZptl+KwvXvtcMkTZ7P51Eul6VZF3OjuszSb6X8nBhsN8DqIValRSIRFItFuK6LVqu1cf1cuMCtU2c6jYDGq3kdASVBV7ValfOCGFGScXMcR1Iyux47PTZkhFitRkC8XC6l2o4OazweS/d1nndEzRGNDBGreHTHdT1OGiDp5nn8nR/mEHBLZ5fLZRwdHeGbb77BX/zFX8hRCN9++y3+8R//EdfX1wdxfhrwaZCqN3ymijgGBP5kRbk2vNJz7B7P15J5ZZl2q9VCr9fDcDgU3dE2gOQn05+Ja3Y+n8OyLClFZ9DItAjntwaNJkDyC0gy0630Z+x0nc/n8fLlSxQKBbx8+VI2ft31fDgc3qnQ0+/Jamfec/5fE2yazWj9YtqXmcEBWXJ9Tlw2m8Xvfvc7HB0d4eTkBPl8XgILfTSI2SKDjC4AkWvw2dRfcS7qZ5NF+pz19CjBkTZzUMyFqOk5/fpQKCRNAnXVCgGHbdsbanq/ODQ9YXTPCEaputMz04ZMEZqTTkfR+vq8hIrsYcFoivQzBYmMKs3u5LsYNzPaZVsHjokWjuqKGl1poiuMCA41QNS9nhjtUJipz7Sjw9RCeT/MGy8jE8IAgd2+yZayuuZQtEba9LzjfeActW0bwO1hqdzQeDAzr5fgl+uJ6SLNipitM4bDobQE0PocP+oWt5mXfzCr78xNXQcDZiWsH8zUzZC1YF8zViBWq1Vh3NmaY7VaSfCoS+7pawiyuOHrdJ1mGAHvAhG/zQcvkTrbm7BBLKvSWMRBDR77PdH0nsKxTyaTWK1WyOVyiMfjsl+Q1efY6jHW2lC9pr5E7vLowZFpXmDJnISWZQlzxNxoOBwWpoFRn98O1tSLSpeKsou34zhSOUNUXi6X4bqudBXXmgc6dh0F0Uy6nIdrHh8fy3luhUJBUlO2bcuYMaVg5tQfakx4jzXQIXs1mUyQSqUkRUjnrY+KofPXbe115Mfx4Fjw6AiW7Wstgj5uQINDP24UBNDHx8d4/vy5sKeDwQC2bePi4gI///yzaI38sAZ+q2kWyLZtRKNR9Ho9ccjcxPh7rglugNwclsulFDaQGSEYZo+km5sb3NzcoNlsyplReh1sE5z6yfR64lqyLEsOWTYLVGi6NYYuROB7+sFMlpCVyplMBufn51KBmMlkUKlUhBnUGsROpyO+l5ZKpVCtVkVvqMEXWRfqNr1San4xMyjWGiMyRvl8HsfHx8jn89IH7fj4GMViURo+alCj57zuQce5ooMRdgfnnqulDtzz9OvIan6uPTlwZJq5MRHVs2Q3l8ttiOx0Mziz/4RfTDMkPByRD537JwBcrVYYDAZYr9eyqMmIEQDo89S4uIHbY0YoVq3VarIQmKJiWorjxtTUrntEaa0RGbXRaCQpNH3EAbVTPG0+FothtVoJfZ5IJDZoYEZOFPUWi0VpbsZKC6YVebAi04rbKpX2ZXR+bG5XKpVQrVYBfCzdvry8RKfTQbfblUj5kMxkPWazGUKhkDTmbLfbSCaTmM1mApQJcsyAQXdBTqfTsu7oF5hu0meyjcdjYaHMNeCXOfBL5hV06DE1ReWakf1U1dq+zNz0uf7Z+4psOEv1AYgf4YHEbAxMDZY+UkX7Ctd17zTBpJnj5qe54CVWJ0tOP8EUfD6fR7ValQwCdUP6RABzTnBc1+u1iLp1yi0ej2OxWMgzpSIERdTWEkx96dx68uCIplkjisrOzs5QqVSk3JDAqNlsotvtSj8PPzo0Omjdkp6AhameaDSKSqWCZDIpkR+jYdLEOmdOipjPoVBIypeZi3/16hVyuRxKpRLC4TD6/T5s20a73Ua73Ua320W/379zrtQuUmsER7PZDMPhEMlkEr1eD7FYDI7jSAqV9PdsNkM2m5XFCkA2Up1y4+u11ur4+HjjsFE6TFYqcaNk3t1P4kteXy6XQ7FYxPn5OV68eAHg43mF//Iv/4L379/j8vLy4FgjvakT1HGuRyIRSamxMkmDH91hH7hlC5PJJJbLJfL5/AYLxHnWbrfRbDbx4cMHdLtddDodAckE64eSUgPuAiMzla/ZI51CYk8oNsX0i+ZIMyIEw0wRsX9RvV7f8GsEyezi/OHDB4zHY9zc3GwA53Q6jdVqhWq1Kqwi/ydZI/5MA0u/zQUz7ah1ZNlsFoVCAaenpzg5OcHp6alU8vGkBer1yJABt+0dyLjmcjkBWqvVCuPxWFg4nbocDofC4utimeFwKO1VTL3n51gAjrDJHumbbkYJFBuORiPfdwLmxq1PeO73+1Jyyv4rzJuXy2Xp48TuzSw91mWpHCdGAux5c35+jmw2K99zETCdRhE703v7ELHryJbM0XA4RCqVwmQygWVZcr+Z9y6Xy1Jt5ziORD4U6zLSZFqxUqnIIaPU6HCj5PljZA80c+QXZ8iUcjwex+npKU5PT3F0dLTBMF5fX+Pi4kLSTH743L/FtNYIgAQ41N2R0p/P57KhA9gQy3JzY+rRsqyN/kicI5PJRA6o1gdpkn3yK0vwJUY/YW6ompn1G3ME4A4bwm7nbPJI/ZHrurI5s3Eh+3yx5Qf3Ec4P/q0+Xw+4bRWie+b5MaUGePcHY3sSAiSeJ1goFDauWadQvVKufD/+j9VqtcE2MZChto0aTwbvi8Vi47SB+5hXATgyTKdIWIpIQTEP1my1Whs9Xfw0kflZOGmGwyEAoNFoSLdSMiSxWEyOsrAsS67Xtm3RXYxGo42Ox0wT8X2q1SqSySRqtZo4gXA4LJtAr9dDt9vFhw8f0Ol0hEnSwrpdCrIZ3Y7HY8RiMbTbbUSjUQwGA7iuK59fHx8xGo0QiUTgOA76/b4ALN0xl+NI4XI2mxUnMp/PMRwO0e12cX19jZubG3Q6HYxGI18dQUOnwvYV33zzDb755hu8evUK5XIZb9++RbPZxB//+Ed8//33GAwGBwmOgNu5oAX67HI+mUwQjUblZHVN9/PBvkaZTAYAxGdwbelT1pvNJlqtFtrt9kZwoLUXh25mKwTgtl+c1ibqlJrJHu1zHpkVhzw1/uTkBKVSCc+ePRNWg35tPB7j6uoKo9EIjUZDqlnJGIVCIRFvs+M6e2RpYK4PWvVjwG1WqJHx0acBlMtlHB8f4+zsTIIpnkPJ+2sKqPme9KPUDTFIJ+POMWEGh+CU0gYWiZAx0hXAX2IBOPqT8QbqnhbMe/KmUStAUa5fon0v03oKNhyLxWKoVCobLfzprCiWYxl6LpeTyFcLjwmOOPHJrmWzWcmvz+dzdLtd2LaN6+trSaWR9twXGOD9YmpkPB6j3W4jEomg2WxisVhI6lGDR0Y08/kclUpFFrfug0I2Lp1Ob+hUCDBbrZZskoPBYOPker+wB4zw2fi0XC5vHJjb7/dxc3MjPZ4OGRjptBBNH1bNCD8SiUjvGpoGAuwEzM7JPCtqsVhIdRpZI93514+pk/swr4IXU2tkVqztmz3SaTV+PrKnPFBWV1qRkTe7nHMDZ9ahUChIjx8y9tz4mS5iUYjpF/04NzQTqAPDXC4nWkse5s0ggUy7FpszEOHYcY9hNoGAiOtRA0Zd1QZsBjlmD74vHcMAHGGzmRXPBWOlEfUG0+kU/X5fHJ3fzlTTpqM3sjRXV1eYTqdylg0nL/O8rGIrl8t3KnLIkgAQAKXBlZ6o1BM1Gg10u1388Y9/RLfbxdXVlXTP3UWV2jbjIrJtG67r4vLyUkCP4zhSvUewSJFtrVa709aA0a8p6Oe8YNO/TqeDd+/e4erqChcXF+h0OpK29NNGyeupVCo4Pz/HyckJjo6OJG10c3ODf//3fxfgq0HzoZmuEtQMEin6cDiMyWQic90sv+YcYWDBnmi6dQX1Rc1mE71eTwIrsyJp3/f9c2zbZzbbU5hVrbrvkT6iZV/mpaUhwNHn5OVyOYRCt8cHscCFPo2pcTKKxWIR1WoVL1++RLFYxNHRkVwrRcbsls5+V35ljkzjGPHsyFKpJCCQGi1W6Jp9wRiYkmwg28Z9hoGFLtXXa5XzSQc33K/YnkW/15dYAI7+ZFywqVQKR0dHKJfLG0h/Pp9vaAb82vBOR8W6VHI0GsGyLFxcXGA+n0v/CIrm2IcDuE0tAtgAMDoK1LlhTniCsNFohLdv36LX66HRaEjptz5qZR+sEZ+58NjGPxaL4fr6WoSTjPJ0XxJ95AHfQzcP1P2LSJUPBgM0m000Gg1cXl7KJkmn4JcqNW4QLMmtVqvS3G6xWKDZbGK1WuHDhw9yYK4f0oD3YeZ60UzGcrncaBRp6i3IFjFSNnV2rOijRvGxpdKATTaWG6DeoMz5zbHT7TL8wB7puayF2exXxiCIc0IzSwwcdYUnD1mtVqtStcpqYMdx0O12RYepm4H6WXNEM9NszEQwSDbZHrar4DXS/zGToJsBA7eVfWaGgfsG15J+Jgt1n9W/Tx4cmaxRPp/Hs2fPcHx8LKdyMyfc7Xblhvq5QkcDAU4WshWLxQLX19dYLBZot9twHAelUkm6mjJtxnNvthknIDf5VquF8XiM7777Du12G999952AI6awvLqb7tI0o8b7x7x2LBbDcDhENpuVCj46PjpC7cB1qSiviwt1PB6j1+uh1Wrh3bt3aDQaePv2LQaDgZy2raNEfqZ9zSeuAWojWJ0Wi8UwmUxwc3ODwWCAH374AW/fvsVoNDrYlJqX6evQYm3OD6bPTPaIehIeIUFWgPf56upKzs6iA9fNQ/cNiu/DdBDGyJ2BEn0Pe50Bt41F9RErerP1w3gwrcr0mA4c2fKDzFKpVMJ8PhffSZaJPX7Oz88FPFCHNhgM0Gg05EHfzPmxb3/gZSZ45Vrg/SQLCGAjJea6rmQvBoOBFMEweOSxMwws+fcacOv/B0DaYPCZLPy2BsOfO45PHhwBtzlURgI8/4VdsXkDTXQP+GsCa2NETIaDYGA4HGK5XEq10XQ6FQqYPT0IBjgZgdsO0HxvTkJqKRqNBsbjsYCAZrMp4lN9PIIf8uma/aGz6/f7AIC3b9+i2+2Kw2NaKZ/PC1OkAU0oFBKKnIJtptJ6vR4+fPggEaKuVPJThMiUB+l/zn/XdcWZs3XFIRws+7mmWSTgtpqNjJLe4LkpkP0AIBo9MsxegMjPmpLPMQ2OWFbNFIc+Q1F3lTdTR9qv7Mv0PdEaGf3ZdX8f7g9kjVnlSKBcKpXkzDW+z3g8Ft3hzc2N6A/N6l0/mp6v5hixfH4wGEhbFH3UB4+vYuuSfr+/wSDpfoF8f/2sxx2ApOEY7PN92FrHq3/Y59iTB0eaGtS9aqgfICKls2Nq6BA2CE1H6k68pHGTySSurq6QzWblHByCpGQyKVQyIylNmxKtt1otOI6Di4sLjMdjXF9fS1qBkSQpUj9sCCZFy685LpPJBMlkEhcXFxsdcavV6kYTM93/if1sRqMRbm5uJKVCIbau+tNRkulw9mE6Sq7X63j+/DmOjo5QKpWE8Wu1WsIM8n7u+z4+lOnr4nXqzVuPVyKR2Dg2hMfwEBizg/42cPQYTKfV9HFFfKRSKbju7cHV+pgRP/hQDYj1ps/rYWCsq6Co1WQfJM2isCCDwSX3ELZSuby8xM3NDd6/f49utyuNVLVUw8/zwxwjppFZrEEWnpq9+XwuVZq9Xk8qODV7rlNqAAQEcby1iB+AvC8ZKQZtZJJMLefn2pMGR1rgxdLdbDYr4IBggAuaAOOQNgcvrQ3ztoyG9NEivV5PzpTTgmuKUrkwiNZZ8t9sNkW0zom/TXfgl7Fj1AdAqg87nY6UiPb7fTiOIyX6ZAw4lrwO9jBi6lVTxwRFXkLcfZtufMfmoNSasekanRkr7Pzy2R/azFSb14algQH7XlGEz7Ezu6Cb4/cYxlIDC51etm1b1pI+bNrroN19f37gVivDjZb9i1zXRaFQkKNPAMizqcMkmzSdTsW/2LYthw3//PPP0hCX4NlvhRnbTN9nVuIS9FCkPZlMZM8g6Ot0OhIwEzxrkGyCI4IgBqMMzAmaJpOJFLwQkHMN3mcl9JMGR8Dd40JYksjOxtTtkEEyI2e/5Ml/yXQlDh0TJzDPx2EbAwoRmTogKGAKic5OpxrNCe+lq/DLOOnPoxc773EkEkGn00EsFsPl5aVUMJIxAG573uh2Cbrygg5PC7X1JuuHMaEzZxqV4lPgtunpYDCQ3l66fN8v9/IhzCu95rVxce4wYFosFnKwLFNr9BlmheZjGz89FgwW+v0+otGopFD0eYbmod37NhPojkYjRKNR3NzcYLFYyJ5QLBalWSz1iACkSlF3USejPBgMcHFxgV6vh3fv3mE4HOL6+lrAhd8awZpmsqcEMjyPk8U90+kUmUxGSAVWdbOCmYGiTiGaWQVdqq97Y+lqQrZAIdtGmYKWKwSC7C8wLcTm5pDNZgUksDKBG7+OePR7+HEybzP9WTWtTR0NGxbqShJOTEZGBD3cKPlsps/8Coxo2/QlOsKn06KYkkBC68201kIfnWA2PPPreOiUCM+a63a7WCwWkiLUFXZ+SYc8tJkbggbRBAA8qLbdbktUy1QqtWd+PT/vvozXwyCA1Z+NRgOu+7GlQTKZlJQ1BepkWv3QANVkvgjmwuGwtCBhJ2ieT8nUkT5lnn6Q4IodtAmGhsOhpKepP/Q6KcCv80PLEAAIO9btdgUc8ZglAJLuYjpeV6VxvPl+DN6BW42R3nu0cJ9/T5Bklv7f1zp7suAIuGWNKMRmEytOfFJ2uj/PIfd2Ae6mCHRayWyGpp9pnHh6gh8CGPIyLxaA18oTnW3bvkOb67/ROfhfGge/jQk/D8EdD0gNh8PS/bff76Pdbktaza8tLB7CtjGMTJPyHDZ9uOh4PEaj0cBoNJJNQdP9Glg/hnHU4IjXDwDv37+XMUilUqJz/PDhgzSE9Qs4AjaDI54ssFwu8e7dO2SzWSwWC6TTaQwGA6liYzqaf8dUNEER9XpMqzHtTvDFueTlN/xmei0wqCQDyEN2zeNRdEWzGTDq69TaPv2gr9V7EKUdnHP6/e5bs/VkwZG+AXrj4w1ZLpeYTCZSmk3Ue5+Dv08zP78umeT3ZIo0ODJTQ17jcWhjsy1Fqh0Bx8Msad02DocyHozAXNdFo9HAZDJBs9lELBYTASW1V36vqHkoM1kFphOopdCdzh3HkdJ9M9Xs9w3wt5qe82TU2AOLadjJZIJ4PC5VrdfX19JAUZeu+2FMzJTRer0WMTEb5/Kg6lQqJUfLaOaMAm6mksiqmIeiHuKcoD/UewPnPVuiML0I3DI8GsR43WudUgOw4Wd/6x50n2P5JMGRSd+Z5/zwhlJUyAjoMTBHpm1jerRA7qnYoQCa+zJeI3UAl5eXaDQaIoBkNHyfLfkPzXitOmVMRohnsfX7ffEZZOB01cyhsAO/xczAwRwby7LQ6/XQbDYRDodFm8gqVqZc/NLXR98XbuJk/SKRCLrd7sZ5e6zmpUZPH2HBOcBnrUE0ZQf7vu7fauY4AZtNIfXrvP72c+yX3vdL33+bPUlwBNy9obzpzJHy606nI/nyfXV3DiywhzSdXtNtCszU6WPZ2D/HCH5c191gPHjoJYCNbvG6Ed5jAkXavFIjTKHQZ7IdiBYqk4Hz49iY7C+1huaRMqykYkqVwaSu5CIo0uCRrztEYGSaFwP0W17/KfsUINrVmD1ZcATcvQGM8phvtm1bDk0ltRpsFIE9RmPOPrC7ptMEGjxOJpM7R8t4CfAfw0a4zfTYALfp+NlstqHP2zYufhsTfT0EuHqf+KVjTva1ke/THuIa/TBuTxoceWkJWFFAPFkxbAAAIABJREFUcMTmXazW0bqjwAIL7GmYTiPx+20/ewqgSJvJIOmqIq1hNMfkUMblt37OQ7muwD5tTxocAdgARyzNbTabUsLPUmY2sPJTb47AAgtsd/ZUwM7nmDk2ZruHxzJmj+U6Avtle7LgSC9envvCc7D6/f5GyS7PhtEVO34QEQYWWGCB+dECvxjYoduTBEc6utGpNYrudK8G3QyQR0w8xaqdwAILLLDAAnsq9iTBEU2DJGqJ2PfILOukENOvQsLAAgsssMACC+x+7EmDI+Bu6/jAAgsssMACC+xpW+iXXxJYYIEFFlhggQX2dCwAR4EFFlhggQUWWGDKAnAUWGCBBRZYYIEFpiwAR4EFFlhggQUWWGDKAnAUWGCBBRZYYIEFpiwAR4EFFlhggQUWWGDKnnwpf2CBBRZYYPdnlmXJmWrhcBiWZSEcDiMUCm0cRgvcNuLVp9YHfeQC84MF4CiwwAILLLB7MYIiAqRIJIJQKIRYLIZQKIRIJCKvIRhar9eYzWYbh9MGxzMFtm8LwFFggQUWWGCfZRoMARCGKBaLIRKJIJPJIBaLIZPJIBqNIpFIIBKJYLlcYr1ew3EcLJdLDAYDzGYz+Z6/pwUgKbBdWwCOAgsssMAC+2wjS8T0WTgcRiKRQDQaRT6fRyqVQrFYRDKZRDqdRiQSwXw+x2q1wmg0wnQ6heu6sG0by+USlmUJiwQEwOgxG0G1Nr/c7wAcBfakzGsx/prf/RoLnHlgT8UIhkKh0IamKB6PIxqNolAoIJlM4vj4GNlsFpVKBel0GqlUCpFIBJPJBMvlEt1uF47jYD6fIxwOYz6fA4CcdblarfZ8pYF9rtGfUmfGZ86VaDQqB71zHgHAdDrFarXCfD6XY710qnVXFoCjwJ60aUDkBY4sy7oDdvTr9O8Y8Xr9zSHZl4JE2iGPQWDbTQuuCZCoLYrH44jFYshms8hkMqjVasjn86jX60ilUgKObNvGYrFAOBzGeDxGt9vFcrlENBrFcrlEKBSStQQEc+nQTM8RDaCpQwuHw0gmk4hGo0gmkzJ/AGA4HGI+nyMUCm2A5F371QAcBbZh5sa4DQj8kvnJmZmOHLgbzejFrM3rOvT7aFutVhtVN9v+3i9mbnKmfuRTIGnbdemfM9LbdcS3L/stoPK3AlC/sJLmfGEaTWuMEomEgKLT01OUSiXUajXZBC3LQiwWw3Q6heM4WK/XwjgdemDxlM0LNFuWhXg8jkgkIs+pVAqxWAzlchnJZBKFQgGxWEzufafTgeM4uLm5geM46PV6mM/nwiTtan4E4CgwMa+N0uv3n7JPTdx9OD3zmrhgzWf9MDcikx1iSbIJHDX9u8tF/DlmXrPXg68zzRwXHd17jd1T2PA+FVTcJxPnh7HUa4mPaDQqgut0Oo1CoSCgqFQqoVKpIJFIyOd3XVfYA6bUIhHv7Wjf1+s3M+eTH8bHy3eEQiEBztFoFOl0GvF4HLlcDolEAsfHx8jlciiVSkilUgA+Xks6ncZ4PMZisUAkEhEATf/K1z20PWpwtM0peVG1pjMzB//XRMqHZNuqTLjpk+bU4IGv1+NDVoD9Sbz6lWzbNPXzQ12j6ciZ39b5bg2S+Jl0WTF/pqPlaDQq/4fXSirYryDJK+rXehF9zzV4NP/eZIT4c5Zl83mxWGy81i/jcB+2jXHzYimBu/P8UwGICbr1XNLzctfjuW09UYCdSCRQKpWQzWZRr9dRq9VQr9dRKBSkao2VaJxfZFv1M+fPofY7+jWBxad+9ql9a9v82CezaAaefHBupFIpxONx1Go1pNNp0Z89e/YM+XwepVIJiUQCoVAIruuiWq1iPB4jFouh0+lgPp9jOBwKG78rVv5RgqNPRW2/xIhsYw+2ASY/RHKfa3oic2PkMyc2v9cgyrKsO6DI7FfCUlzTmZuginbfY2jeUzpz5rtJ8TKaDYfD8np+fn4uPQf42kQiIb8nELAsS/5usVjIe/ltjmhnxntMwMc0CX9nOnpqQbzEsrzni8VC5gNwOwbA4QYT2kww5PXQDQ8/xUDya/7ObJSoQRHn1j5K3L38qF5T0WgU8Xgc6XRa2IByuYxisYhcLod4PC5rbL1eyzUSDBEcmT7jkMxcK14AeBuzuu299Pdec+ZTAeguzGveaz/JdFoqlUI+n0cul8PR0RGy2SzOzs5QKBRQLBbFH7uui1QqhfF4jOFwiEgkgqurK8znc2kBsSt/+ijA0bZJ6TWhtqUNvF5vbuzbordDWcz6GnU/EqJ7Up8swaWzo8iSk50R33q9xnQ6xXK5xGg0wmw2g23bmM/n8vPJZILVaiUbJv/OZJho9zmGmh3hxk8RIKOZTCazAQgA7/uuPx/fj98vl0tMp1NEo1G4rovpdIrFYrHThfwpM+e4bsxH50VhZCwWkzHig69jIz9G+LynvF5WHfEZwAZ7dMiVR9pnmAJT81n/HrgLsDU7p8GQ/l43QeRYz2YzLBYLGfd9VPGYG18ikUAsFhN26OzsDJVKBWdnZyiXy0in08IYzWYz8ROsUhuNRnAcB47jiM/Yxjr7ybyCBj7rwEKDBX2vvK5JzwUvv8H3pS2XS1lf2rdue//7tm2Bgfl7XlcikUAqlUI6nUY6nUYikUA8HhfWkdeWTCbhui5KpRLW6zXK5TJWqxWGw6Fc7y6CrUcBjgBv52U6MRPdeoEmPQG5SerITacNzLSJHxcxzbxGsgXsR5LNZpFIJJDP55FIJIT6LBaLSCQSIqZkWooO2rZtzGYz9Ho9TCYT9Pt9cYKLxQLD4VDAAxczN8mH6ojrBY7pzJn/zmQykv8mCDDpai/Wi58vHA5vsEacSyxDDYfDvhIim46KTBF1ImzUl0gkkM1mEY/HN4BkLBYTB2aCIm54g8EA8/lcACQFlKTL/QAUP8dMcKmrbjiWZFvNOWdu7vr1mq013xvY7CA9nU43fs8+Qfxcu2SQzDXFOZLNZlEqlVCtVlEqlaScPxKJYDabYT6fw7ZtTCYTjEYjTCYTTKfTO6DPz8Gm1+Zv7jU6fa9Bkik3MN9zm+5Ks4pkVwBgPp/DsqwNlnsfPscLGOnfaX+jAVE0GpX5w8ALAGKxGFarFdLpNObzOTKZDGzb3ij555x/yLl/0OBIo3U9Mbnxk+1gPlOnDyKRyIZz0q+jk4tEIgKAFouFsCK2bWM6nWIymWA2m2E8Hm+geD8tag0YNVtAMFQul5FKpVCv15HP51Gr1ZDJZKSSgFoBM9Uym82wWq3gOA4WiwX6/T6m0yl6vR6m0ym63S4mk4nkjCmwYwkvG74RTJhO40vHUG82nAtkRZj3TiaTqFQqEgHz/2rAptOGZIn4/px3nBOMbFzXlbmziwjn14yD6bjJFMXjceTzeSSTSVSrVWQyGRSLRaRSKQFH7GrMecBNjAwRAfJwOMR0OsXV1RUcxwEAYQ4Bf6YYP2UmoCT7aFbfEFwmk8mN9aYBNL/n35sbqF5bepPhHGSaYTQaod/vYzQaSeCx6+BMrytudLlcDsViEcViEYVCQdJo8/lcmKLZbIZ+vy9B1HQ6xXA4xHg8Fl/qp7PVTHYI2Ay89T3VewvBIvcWvdcAt6lSfs335RozjePB/00/5DiOrLnJZCKBCefMLsaGn4/XosdstVohFAqJ4H4ymchnXq/XyGQycF1XmEVeG/eOfr+Pfr+/EWRFIhGRLDy0HSw48mKKzHQBnRZ7a3DyJZNJofk0o8Durfq1nMhcyI7jYDAYyPN4PJYUgl/SKKZp9K6Zk3Q6LSW3z58/R7FYxOnpqWgGksmkjJFJHxPUTCYTLBYL5HI5zGYz5HI5TCYTJBIJTCYTafjG0t1QKLTR5MuMeu5z/LyiXFK42WxWmLFYLIZ4PA7gLpul038Uk3Ic+L6z2QyhUAir1QrxeFy+187MD+bFoiUSCWQyGWSzWZTLZWSzWRFOMurneuL6YhBAoJtKpTCfzxGPx8UBhkIhjMdjrFYrRCIR6VNyKObFPHLtEDQyBc0AIpvN3glGNDjSTJ1ufvdrwNFgMJD/y3lp2/ZONsJPjYn2qTplwgBTA2nHcTAej6WEn8/0IWTm/bJeAG/Jhmb9GDCYKepcLrexdgiImTI1QSD3r3g8fmedaN/ItPZ8Ppe5QN+jwckux4afkc/0edqHLhYLAcqTyQShUAiO48j+oN+Hc4Vzg/PCTFM+tG89OHBkRmaWZW1EdDoizmazwg7o831SqZQwRToi5sTUC4AbI3U0tm2j1+thMBjg5uZGIiIyCAB8Efl45XtjsRgqlQoymQzOz89RKpUEFLGskmWWmi2ikzdpYdd1JQVJ1m2xWEgjt+l0ivV6jclkIqABACKRiBwZwOj6vjZPM6X2KSCQyWSEOeIGzsXI8lGvPD77udCZzedzARLcsMbjsTiKfYEC06GbAlqyaEyBsLKIc4BGMMtr4QbGSJ+sXDQalX4k6XQas9lM7rUWrQP+TUF7AYB4PI54PI5CoYBEIiHpIlbZFAoFEZxyfPlewEeAoxlr6tW8WD2dxgcgfoepTs5TsjC7bo5npkmYfs1kMsjn8wKqGVCOx2PMZjPpXcPv+TydTiXl7pXC3tc8MbMSvG6tXeT6p3aRsoRkMolisSi/J3iimdpLE1Cb4JP+iwEaz6DrdDqIRCLSB4hMjZYEPJR56URNIMl7yire0WgkIJCA37IsJJPJDWKB82QwGAgRoXWcu5obBwWOvFC8pjQJjqgn4bk+R0dHMnHJJHFha1SvBZQ6T8xNkukhCpSZK4/FYqI78YvT94p6OQZkiKrVKl68eIFyuYxqtSriawIWYLOUGNgs4+Y48R64ritjSRaO7BEARKNRoVR15cFDAAhzk9OAIJVKIZPJCFAiyGZ0Q4aITszUGZisJHP/8/kcqVRKrtms9trH3NBj6wWSCBjz+bw8qDGiMyLgMdM3dFRcD9FoFIvFAoVCAeFwGNlsVroe00EegijbBNYEALlcDqlUSoC1Lk3mhqgrPLX+Q1cDcq3oVgi8N6amkQEZAAETXKdawLuLMTHHhtdE38KglIzGer2W6H88HgsbsFgsMJ1OZV5pOYIf/Ke5XszUqmYPya4zFV2pVJBKpVAqlYSR5jiZ+lUK7TULwvSZBp+s9iJQJuh0XRej0Qjz+Vx0f7tcY9vuFfcFsj0EddPpVPYEMu68Ho4zANlX+Tvq1bQebRd2UOAIuMuImBOJkVw6ncbJyQlyuRzOz8+lE6ems03HogXYXtFjJpPZcABc+HR6fjAzCmEFEtNnb968Qa1Ww8uXL1GpVHB0dCRRD/O5+nRs5nvNiUmqk9fNjZKOToOkcDgsyJ+Ogc7B673vcyx0xMd7qBuPpdNpYUEWi4XoHxiVc1PX4lu9abIah1qcXC6H+XyORCKB5XK5oTvxQ4qNbB0Z1Ewmg0KhICm1VColmzIPBR0Oh5jNZnLPtSPj+AIQgMRUZafTEQ0BnR0Bp5/M3PgZrVM8WiqVkMvl8OzZM+RyOZyengoo4pwigOL8ADZTtCajZLaL0J+DYzSfzwVY2LYtWkcC+F2DCa9gg00fyRgBEL+hz05jwYautOOa0NWi+069avDHrATTZzqFyvYEXDP0oycnJ7IH6eyDWdRDX+gloOZ4cGyZttTzIRaLYT6fSxFEIpEQ0f6+xlDPYw3+6QeZVqO/18UIAGR96H5rADbmyy7n+8GBI8A7XWDmvzOZDEqlkoiMCY70AuRgc+D1gtWmN0PeuNlsJuXg+5yQXraNMSqXy6jX6zg+PsbJyclGRQlNgwQie4IZnV7i17rShqbTbfF4XEAUFw1Tcw/Z08Rrw6MzZ0kp7x8rr5hW44NAMRQKbUSA1KxxfLmIk8mkgGcCJg0e9mFe48oxYTqMIIkVJARCjPQHg4GAW2qI9KZGRpYAm/MplUphOp2K7kKnKLZ9tn2amc7QjBH9SKFQwPn5uTBIpmBdM0B6bnO8gFsdEeBdYaS1GpotmM1mGyBVb0a7At5erBrnDa+faT+K9W3bltSIufZNhnefftRMyetgiGuFJehsdZLP54VFzGazODk5EfCkBdhkPriGWLhh3jOOLf1MNpuVtUVdn95/+Hm0H973XuSlO9JNPnnNHFsd0Oux1+9nptJ2MecPEhwBd9MF3AA5gbPZrDQgI/rmYDJdQH0JNRH6FGDeIL4fq3disRhc15WUDFMQfmCONFjkBl0ul5HP5/H69WvU63W8fv1aWvpnMhkAEDA0n8/RarWkokD3LWJzRw2KyAyRheDGqCc9H9rJM/rlfdDO/j6Akl5sutxYAwHtVAiOqCdjpMvNSVedULhdKpU2qh4ZyeVyOTiOI5EmS/r9wBqZG1symRTnTj0eADiOA9u20Wg0JO9PcMTqErKpsVgMpVJJAgdWbDG1sF6vkU6nMZ1OhR3wE3NkboRsaxGPx0Wfd3p6imKxiDdv3ghzpMuPOdfJBAEQ/6JTkTolzbml5zwZI4Ly8XgsVWmDwQDdbheDwQD9fl8qZHeRajCDUc3U0w8SXOtWA5PJBLZtS6BFTab5vrR9ptbMjISXVCOZTOLo6Ej0ZkyjkTEiYDZTp5otZ8sCipDNPQyAAE7NTGu5gxYw6/nip3XlpR0jyKtUKqhUKqhWq9I6hnOKflIH417MUaA5MsxcTCaK9MoLUxtCyk47JjoYnULiZOMGryt2CIAYRXDh7Dvi0WYCRZ02qdfr0oOE9DApXm6IrVYLo9FIUip0bDpiBSDAghEUJ79ugwDcChu1dsXU9DyEAHMbu+h17wBspBPp0HV1mhZtJ5NJocV1elfrLvh6P0VywCZA4jhwnVAzR1A8HA7R7/cFHPFeaWEw1wvZwUgkstFQlGlnAoldshy/1rxAYyqVQrFYlLYW1OUxJauvQYMb/oxjyPfXUTN90Gw2k9frDYApSK5DlvDzmS0UzAaqDz1G5ljpUn6ufXO903foZqDmewL7ZRG92CszsOLaZkBM6QbBEc+OY8BpMuP6iBSOi/5/+qHTsAw2o9Go9E/TjJRmZfyk26Jpv86gPZvNolAoSMUwx8DMLvDv9T7Bn+n3fyg7KHCknbxJ2elSayJ0lgz2+33J87MxIXvxmF2c+Z5E7dlsVqIkbvq6n4Wu5NrnxDT1EmRH6vU66vU6nj9/LidkZ7NZ2QTZjO3y8hL9fh+Xl5cbLQoooNR6EwAbnW8JlMgShMNhuSe2bWM0GknUOxqNRDuhm7555d4/18z7oL/WVUGk/8kaERSxWoLzSG+cy+USqVRKQBOBN0XH+Xwew+EQyWQS4/F4r6kkvV5o2glzXpu6MY7DaDSS/jpMr/KaWVKsRexam0UGZjabiXjVT0EEcFdDw3RrpVLZ0Bh99dVXyOfzqNfrAqp1nyeuFVYqkoUej8fy/mSoOe+05oiv1ykXMi8E7GRzycboEudd+B09TmRgs9kscrmcVPGxIos+lWCO7DPXuNaTeKWC9j1HdMCj+xblcjnUajVhD8mOEhTxPjPgZtA5nU7R7/c3tFhkUvXJAzolzQCEYIHzgAxis9lEq9VCp9NBv98Xn+rHdgicN4VCAZVKBcfHxzg+PkatVhOhuQ60er0eOp0OWq2WsKRar3afe8Wn7IvAkWVZ7wCMAKwALF3X/Q+WZZUA/GcALwC8A/Dfu67b+7KPuWleOU39IErneSykt4nY+/0+HMdBq9XaSKuxuRQRLqNhTmi9iDWC32UEt83M9AApTKYXC4XCRiUSwcl4PMZ4PJaFdn19LU6NESzHE7hNp63Xa3GSwMeSYzJROopm5KiPCNDskUmVPqTpUlmd5uBmxM/GFACdGbBZbURHT20VNwyyMNxATWGh30ynhIDbg2P1GOhUKDfz5XIp18wNgalDBg96DvJnJlD0g5mMsw6KKMSmWJ3tG8LhsMwNx3GkoeFgMNhgB2zbFmDNtUT/ZPoujjU3CM0wMNDTzWe5UTw0MDJBiznfyagwSOA1kSVm8MHrZJrZBEde/2/XZko1dIpN+1M2vGT7BgIo4JYxY/dvBp8MxHV2QutYWZXGtD3nGXDLEHEO8dgV+m6CaL82Iaaf0S0fmM5nyxB9PA6DUz7olzUTBxzGwbP/jeu6bfX93wP4f1zX/V8sy/r7P33/P9/D/wHg3Y2ToIalgmwuxQoJXUnFrs22baPT6cjP6LToJFmKTg2FFlzqXKhmPvxADXOh5fN5VCoV1Ov1DcYIgHSsHY/HeP/+PbrdLt6/f49er4ebm5uNkltdWUC2gWNkWdZGqoViZ/b+ITPFvlDsWcFFYKbU7tNMIaBZMkwzwZwX4ObrAIjTp/OjJolOgI5SR4O6l8k+zWusyaLpqkE6KlMXpjU1AO6kRzVgDIfDIsjWqW2tRdv3etH6vHg8LkHEs2fPRHidzWYlZUK9FNNdHz58kFS04zhot9uSLuNGxmvVzJEWInPuccz5vQ4e1uu1gAx9zMaumGoNiigCLhQK4lNYoMCAlB292StMV/BxrZAV0QGEV4oJ2B3bqsdTXy9TrIVCQdJBrFZjgMB1Qt95c3MjjJE5Nzieuj8SK4vZa44VkNRvOY6DbreL6+trfPjwARcXF2g0Gri8vJTmxHq++MG0H8hkMqjX63j27BlOTk5Qr9dF56hJi9FohKurKzSbTVxdXUmgbso6djEnHiKt9h8B/Nd/+vp/B/D/4h7BEbAJkABI8yszpzudTgHcnoNFVMpNmlSnjui0vogLXaNfmq5I0RNy3w7fLAPVvWuoDVoul6IpabfbaLfbQmEyCtYCXGCTKdNpM7J4OtXI6JD0OhkjfYbSfQqwvcwEOZ9KQXilRb1+plsPmKBAzxENjEy2al+gwIy2to0Br49fm+Og/37bPdRl0GZ3bc2i7WssNFA1U0XU6HEzZDGGDgYYvfd6Pdi2jW63KyCJwZZmHQmOCH70muEYEkzROMf4erKb5s93MVZejDQLVAgQtGyBD4JpvQb0mAO3G6gXMNrV/NiWfjZ1edTmaXbY1FiR9dDHpIzHY3Q6HXkNgRH3G82m6QCTY0TQZdu2BJnUAnIv+1RrgH2YnjPUbDEFm8/n5fgqrTPimXu8Lh4tsw9gBHw5OHIB/F+WZbkA/lfXdf8BwJHrutd/+n0DwNEX/o/bf+Zu9lHQzjwUuj3DZTQawXVdAQQEPwRMPLuFuhKdJuGC1kdb6FJkTW8yOto3naknIkFRtVrFyckJjo6OpEldKBSSM3hubm5wc3ODy8tLdDodtNttuR6tfzBpZjoMaprYUZnPjHaoNer1euh2uxgOh6I9eAidEc0ENyZw1UBON6fUVWfs8s3NkKY7QRMEagbCpOGpRSIjCeznfDEzDa03Vj0edNJkevT16k7M/Bk3Cj702FiWJYwMf69TjftmWvV9YsXVyckJyuUyzs7OhHnlNbiuKymtTqcjLCsZaGqCCI7IBtH095qJJcA2y7s1k6fZJA1KH9q82DU2kaVInV2xCRR4Haz0zWQyMndMXwJA2LR4PC7rTgP0XbFH/F9aW6V75xEQce46jgPLsqSpLQPyXq8n7MdkMpFihn6/L/eRPlJ31z46OkKpVJJqOPZJs20b7XYbl5eXaDabeP/+vTBG1Brto0Hip4xzm2NXq9VQLpfx7NkzPH/+HNVqFYVCQcaDgUaj0UCr1cL19TXa7bakJPcBjIAvB0f/leu6HyzLqgH4vy3L+k7/0nVd90/A6Y5ZlvV3AP7uc/6pGYFrZ6L1I+ahl8zN6pSI6XSA26iNplkTvo6RoC6n3afpRc2OqqwG0JGvrtQjOidw0S3atY5KR3UcC26OpJ0ZSWqWjvdAd8PdZd7Yi/XQDlBXSWi2x4zmTKdpMkJ6/PV7mlobvm4fDJI5Dnq+6vu6Xq/lc+vx0NWeBECMngl6OI4EjdxgNPjcNh77MA32WYnElAmrkrjp63QjmzLqrs96jmtNHU0LsLkpEBwxuNIbnMng6XWzy/HS6Xp95Awf+qghXhvXDeeMXneaiQZuAxM9R3TXaH4Gvs+urlkHOnrN8xp4FJLWt5IpYpUndWhklIDbOccgyTzWiKkm6tQ43xjAtv5/9t5kubE0ydI8IAgS80SCk03uZu5RXh4eFZVVUb0ukS7pR+htL/pNetXPUA/Qi96UdK96XasUiczKjMiqyBjcw83MjSRIYp5BEkQvLD+l4uelubkbCYBmUBEKzDgA9/73H44ePap6dmYyBYDRMoqwvRSFCAaMbDqdtrpNfuzIyERrFOqo5n1/HwSOptPp4b+8nsZisf8i6X+SdBKLxfan0+lxLBbbl3R6y9/+Z0n/WZJuA1A/8tn2bw9WptOpMUJ4vl5w7bNAqDMSVR/Ch5BA+L5nWJg1Mk8NgLcoZsd79YAV6mAQw63X66rX6yZ485kkPqPLZx+RBZdMJq0PF41qt7a2lEwmbUJzeHCQsIj9ATAPYMShwiIcDAZKp9O2AXNfhUJBklQqlZRIJAwo+oKHeMLoAfiZH3d/eOBtQp+H7A3XeZ/m398De+YxzWO9NiSfz+vq6soKOMbjcVsz0+nUWCB6irHZeVCF1uDi4uLGmHHwMA/mDZDCZ8bz4nAKQybsHWQ0Iogmw41CfZIsCcRriPwe40GQd+h+DAyFgHYeY+RZxUwmo2w2q0qlot3dXSsiS50a7ok9Mp/P3wj/+Wfs10IqlVK/37d1R9gepi289/uaKyHr7L9Pn0Dqv/Fs2Vf5HnXSSGjxDDx7SC6X0/7+viqVir744gtjVQDozLd2u603b97o9evX+h//439Ywkyr1bKenl5/tmjjLPLrnzmzv7+vvb09K5aZTCbt2judjur1ukUzCEUuuq3MzwZHsVgsI2ltOp12/+Xf/4uk/0PS/yvpf5P0f/7L6/9zFxd6m/nNA8YH0ZtvaOgPyLADtA8xhAYw8Fk9vsPwvNMLo8zH6L33zoEsXWfUoScKOx5jHBqMCYcEB5uv9+G/iJOzWfjsLw+M5jHRQzrehyJ86rkXhcL5PKUbAAAgAElEQVQepFIpTSYTi4fzXv53PBAINRmeVWHMPCsTNcfmtfDDg9brWCaTyUzhOw5DNDaMgQ+7eQDox4P5h+YI3cZtWWuLAEjSzSKhvrK510Z5EOOF0vTLSiaTJpwltMQrf8/9+b3Cg6Cw5teimCIsal57hoP5QTiVecR4SrLxkW7XuPlq6hcXFwa0mGt83fdciXpfH8rxelYPcDljOA98dpU/YxhLWH2kCNvb2yqXy+aU0Z+QCAdp7aenp2q1WqZz86VQ5uVovY/5OeOZI0TmlIDxTHRYSsVHdRbJiH0Ic7Qr6b/8y6Rdl/R/TafT/y8Wi/1W0v8di8X+d0mvJP2vH36Z0cagMYkBQhz6kgytewG1T5kNwZHf1HxTRRbreDy2irXNZnOmUi3vMe/N3m+oXr/g02nJmOJ7XCsH/tXVldUtghZfW1uzyteEGGi++ezZM1vcZPTR66fdbuv09NS0GXhbHkjOCyT5cSF7LplMqt/vazq9LmiYz+fN4+/3+1bk8Pz83MbJ13fZ3d2dERYmk0kDDtlsVqVSScPh0GpKofXyQH7ezAmfzQGPKL/T6VidFs8Orq2tmTDZ98ajNhKVpGnE6kW6pO5OJhMVi0WNx2Nls1nbCJmnXNe8LQrQEj7xgIXf9SAZRla6zlhlrhCyHo/HarfbxtD5RpowTx60+3/713mPTwiKmNvlclnlclmVSsXaqPiejFyrdyqiABEgh31mMplYPz6faTwcDmcApaR7H4/QiY7H47ZPwAT5cBtaI+p9oTfj35IsNR/2dHd3V1tbW/ryyy9VLpf17NkzczbZozqdjqrVqn744Qf90z/9k46Pj/X69WurOcY8m+de+j7GvMHRoOTBzs6O9vf3rWMF2i1f7DQsOMtZvkj72eBoOp3+VdKvI75fl/Q/f8hF/Rzz3gUbDwvNC4uZ0H7z84vWsyh4kHjCLFTfTRimZFEP0l+7/x4btvd0fU0aSTMes/9dfhaPx62Stg+l0KcOoERIYTK57k1GOA3N0bxDj+G4cOD5Gj54toCf6fRtWxifqUi1dN9+BLqYe/ep+x5Qw6bx85BBCnUY9z0evDI/YPd8t/fp9FpwTQHUtbW1GXAL+OH+ffaOD6sxFr5SvS8GuSj9FRbFtkqzoS90QawZQrEwRphnl5LJpAl2Cb8BsqVrnYrXsER9SYtjA8LkAmrUhFqjUH8HI8C1h3oh7hcnFPAAY0S4hfAc+26YyHBfcyZ0qDwrNJ1ObY6zjsOeaV7H6hMd6LdWqVRUqVRMllAsFm3f4H3IgKzVajo5OVGtVlOr1ZqpNr5oViW0MHrha0Mxb2AZfTKCzyzndVlA34OqkH2beQaJOLDfgBAje0/ELzjpWpTKIQprQhd3sr1gj8hOQa+z6EnK/RBWxENHKwJahx7f3d1VMplUqVSK1F+x8QGO2BBoMlqpVGYyOPr9vsbjser1ui1s4u9RzTLnyaqx6SCQTCQSajQamkwmM12vYYfokebnghe7UzEXcOAzunK5nAaDgYEnL1rmvTg0okJs9zUGsdh1fS6Yw1arJUmWOcLv0DKHsEkqlZoBt1y7F9Pyt/4ZU0iVEAzaJEIH6L4WwYz4Q5sQRqvV0mQyUTabtYbDsEmTycQqYEsyNjV8r4uLC9OV5PN5KwZJ5hItRah/5R232wDSPMeFg4s5Sxhtd3fXmlajGwHY+OtkLWDMGa+r4vuEJqW3c5B1OZ2+zTSG5ZRuskb3OTbso+hTPdBBssF9eYbDXyNACmBQqVRUKpX04sULlUolPXnyxNrUMLfa7baOjo50eHio//7f/7uq1aq+++47q6tFNCB07BdtzF/PNlIbigy8/f19pdPpmTIvjUZDJycnFmEgpLYsAvOPAhxhIUjCQwkPo3Dg/cbA9wESHH5eUAuNCopfNMKVbt4718irP5zj8bgymYxtgr5PE4uOcJNvSBqLxYwW9eETScaoUY/Dd7b3wsx5jpX/PO8ForkKa40AgsK2MD57xXuC/J7/WZgCjxcVVcdlEeZBNOMAXV8sFmeqe/MF8AkZUuaSZz/82vIp0YxHOBaLNL8f4FQkEgkL6QyHw5lyDGQoeQ+ZV4x5Q7gIHRLZR4ReOXC9tkmaT0PNKPNOYqgZIckg1I34Ok3+mYfmw2LMEf7twzAwSR50+6zReY6LZ5olzVSI96DIh16l68xeni36PcKStKYhjA+bj16pVqvp9PRUh4eHxhjhYIa6tGWzKI0ajBFFlf1ewt5DtmeoBV60fbTgiP/zMMIwht8M+EIIyKRGU4LuRnobsgv7g4VZJYsyf/DRYb7VallbD1p9JBIJq09C53QWtRdAEm6Kx+O2GZCZw2SXrg8XxgYdiy/gtSga2GsIJKndbkuSqtWqZWMxLl6MDhsSbvY+g4nNyoNO2CUfRvKH56LCJX5uMmfR/hwfH9v4dLtdlcvlmdo1fq349+L/sCwAgJBR8GAaQB0CpHkySHw2z9Lr0S4vL+35AWA4oH39Hu4vDCXEYrGZ+7+8fNt7sNfr2e/DRoTOmr//eY1FyBjhKGQyGW1ublqdtGfPnunx48emN8LYA6NaLE2nU0uAYR8I7w8ADqtAEkA6ndbV1ZW1fmLO+ve+jznjQT5Fg2HWSVbw1++ZZR9K3tzcVLlc1osXL1Qul/X5558rl8tpb2/P2NPJZGIlVQ4PD/XmzRv9/ve/V7Va1Z/+9Cf7mWeMliHkGmV+DrFmqIPn24TglI3HY9NSVatV61oRdZ+Lso8KHEk3U/yjQFCodYg6AMnMikrrfVfG26INb8bXehoOh9ZJ3oMeT3+H7UAAmHjHMGSeAfB6Lv+5vvVElLczj7Hyn+WZI89u+YKhuVzOvFXMH6DhtXshLt/zh4uv6xOlCVuE+XvwXnGv1zOWkHtAeB2VYRb1LGOxt9mhCC3X19ftZ2HNpEUBo/AzvVPEYRiPx63tBaJ0rxUCKHHvjBcMAgekPzD5DA5Nr1vBwvk6b/Nev/f8fYNZNIawPCEThIzB35Nnr33FbP+ZMNgkhdxWF2ueYWgPkFjjlBgIr4Pr53ck2diVy2UrmEl4mTkB+KO0CowRtYzQtXrWdhmBkTcPtsMsVUkzZxOMNZnTy5D57e2jA0fSzYnjxZRRG6R0TYcDHNLptLa2tkyAjHfpC315sfGiUb0/8NbW1owhSaVSBlao4+IZjTBEwH0wScNwW+gte7aKOkKwalDBi2TWuHY8z16vp8lkosPDQ7VaLZ2fnyuTyajdbhsF7kNrHOYABjb4WCxm8+Ly8tIy9tgUQo2Nf0aLOgA9sEM7N5lMrGL8aDRSNpu17LNQb8ZzZ0x8SDkWi6nb7RqjmM/nLXOJTJx8Pm9JElHhl3mDJB9Kvbq6soNoY2ND4/HY9g2/BnwYzTOF6NCoewT7zEEPOPIAKXwu854T/iALmQ/A0MHBgQ4ODlSpVFQuly1Tz+8PvmWIZ3ZYbzhKOKKACS9Ypoq8Hx/vgM0DUPv382AE8bjfJ5m/gEnWBTrO58+fa29vT19//bXV+gEknJ+fWyHHw8NDnZyc6I9//KOq1ar+/Oc/G2gAVHpnNbzOZTTGyTPozBkymrvdrk5OTlStVq2nGnu0tBz3+FGCI+x9B9gfVkx8UnZBviH7ELJHi5y8HnhwjYSMut2u0dZoCLg/7tdbuOmEmTtY6CGG6czzLPh4m4X3IclCI91u17JjyFwjlMI4eRaAOQDbwnsi5IYt4cD1VX/DsNoivb/wAJBkjZdjsZhlVTHHff+jMNTq+zl5BgU9F7/nM1cIqy2D3ihkI/wcnk6nBoiZx4SNfPjAAx+etWfMWCf8zAPNMEFhnvMhDJWG4UFAiq+075+dB/qSIvdAmBevafS110LHzIO1cKz878+LeQ4F1jwzPwf8GALuECNvbW2pXC4baObvLy4u1Ol0rAVNtVrV0dGRJbGw9rw2axnAwvuaB4vsAbCnnJ/03PSNyJftXj9qcCTdrBAcxRx5D4EFQaYBRcpIU6ep4DKUN/fG5+Np0OdJkvX/YTLSWRsqm03Li5DDAmOARMaHhUt6aavVsqwDSuf7TXHRoSSuwzN9xLhJvaY2B8AYDzdMPZWuxemA5Hw+b8wA+iwfZgsF2d7mteFHjYnPYKNGz2g0siw1shGl2U1Puq5VI8kAZzabVaFQ0GQyMe/Rh6nDtP/wUF2E+ecBg+Tb6DCPEWSHLAtzyKfmwz5Ksiwnv8Z8bZxF9Y7y9889oQ/L5XJWriPsnSZdF8L1rBFrwzPP7JEeWDMXYN1iseu2RlEOVSiJuG/2yJ8TISiKYtvQShWLRX311Vfa3d3Vr3/9a6sLxfuMRiPrNfntt9/q7OxM3377rer1ul69emV1xyipsii2/aeaHxey1dgHSqXSzNlB/S+YMzK+/bPnPaXFMkgfPTiSbtbYuO13+JnfKDgI/Ib2rqrPiwZIXoAci8XU6/V0dXWlTqdjXiGbz8bGxoxHF4Kj28KR3nymD6G0MHV/0WPjvVuMsAk0NxkzV1dXlmXEeEkycEQqL2NB9g7j7Wsn+U10npqJ9zH/THwRU3Q1vsWIv36fnQfDwvcZv16vp0wmY2yadO1Vh9W0pdmNcB5A0T8Hf1/827OMPHNevQCX+/VJC/4Q9eyqD79gITOwKGAUxdjA9JHS7zWX0jWw9hpFz7aEWkOfsOCLboYJDuH7vOu673u8/BqJynpmHgD2crmctra2tL29bVlpzBG0mJ1OR41Gw5qsElaijpFnjPw1PATzon6SL3ybJb+u0FOFlcSX6X4/CXCERQ08Ex9DmEp5dzKZQLztdtuYIx7sMiD8kCHxafQwR61WS2dnZ5ZiSdaNpBm6nAMTUW4ikbDaJt7LZcLDqvX7fUvLXGS/uSjzAMkDQMJopG1T44dwki9qBy3M4qdmzXQ6tQwvvGsfSvJ1tt51bfM2P2d8SA3QyKHoU9b9oe+FqoQO+/2+jo6OdHV1ZdXBWWPUDUOHEIq852UeCKyvr1tRw2KxOBMu8WnasCEc3F6H5muDcSCg6/O/78NpaHXCLM5FzQUvxPbhNBge73BJmnEOmT/MEd5vOp3aGPgsOA+4PPuEYwXb5CUL4R573/uKf2/PIPH/ePxtBXgqXn/99dfa2dnRr371KxUKBe3v7xtL1uv1LGz2xz/+Uaenp/rnf/5ntVotHR8fmyh72VP1o8w7TDDDMGYI+NkTme+AQQ+OfObvMpyn0icGjjDvoXpAIF3XN/Kol43RsyPLlHKIcS2wOVzX5eWler2e/RuaH3DkDz7eg4lKocNSqXSjD5t07f3CqgEglsErDi08gKbT6Qydi/cfj8etAWYonAX4wIqQaTEYDCw91wMGrzVZNvYIiwJJ1F/xTJE/4KVrFoH7HAwGWltbs/Hw6d2MpRfa+veat4UaG0JkvjwFQCZk/sJwD0ALQOQPf9ajZwL4f/j9Ra2RUGbgNXM+WxMQKM3Wigu1Q94YT8YZ5yPMgvXZn74f5m3AaJ7mgZE023GeFhl7e3tW/Ro5Bs//4uLCmsXSXPXs7MyKOwIQlyHE/FPMzxvOTRxCtJhhuRdfcTxkjJblnMA+SXAk3QRI0nWvMbpNw65cXl5qMBjo7OxM9XrdCnP5cMSiH2oYkvAeyOXlpVqtlobDoW14XmgbFfbhZ6VSSel02kIoniXwqfuAxmUQYr/LQgYJLx4gMB6PbSPnkGCMuB+YJXRHuVxOw+HQ+qt5L8pnrDH2y7gBhh45YcQw7BIFEjjs0N10u12lUikNBgMbA2pjjcdjZTIZ69Xnu5bPK6QWAiMYH89qMDeo3UVbB+ZNPB63Kuk7OzsqFAp6+vSpstmstre3baxIW6ZmDplIdB1fVP0vPx7evIPl1wbsDnOYcfJtd8KwUwi6ABUAb9gEymvwRR0o339tGUKPAGBqGJXLZX3zzTfa2dnR119/rWKxqN3dXQPJ7LuvX7/W3/7t3xpjRKYW1dM9+/gQzIcVOTOpnVcoFFQuly3L2wvwkaRQXmZZw2nYJwuOvHkPiM3SpzADMDxr5NOYl+XBhtfhM8wAN2RrIZL0YSPvyePh+x5kkmaAgmeNlpEWfZf5a/PhEjYq0oc5uL0HDQCKau4L8PHaCp/JtKzskXQzeQELgUt4/fzfe4aekWP8wurh7xKp37eFXq8/8ClYx3pBM8Kzl67bh9BhnYOB73lwFXrMoROxLOZZY+9YeUbHM0F+DP19RIGjcO7wOV7YzbzxiRxRh+ci9hU/R5LJpMku0BiVy2XlcjljjLi3brerZrOparWq09NT1Wo1Y1YXWRz351qUXs+HY+kpifON+YgGYNuzp8t4XnzS4Mh7Oj4Nc2dnR/l83kIqeDbtdjsyS23ZLOrgZ0MOWYAwXAIgRDcBEIAu9enKPlstBBjLOC6hRbEkHOb834eFMK8x8R2lYY7YKHK5nMrlslqtlrLZrBUZ9KLdZTTPrkmzbTb4f7hJSteeIW1JWCMAIl/vqN/vWz0hHwKex72FoS3mN3qoQqEwk7xAqQOyP2GOYMC2t7dnatngVPR6PQ2HQzWbTdXrddVqNdXrddXrdcsmXbTGJBwPDq/BYGBzm7GANcWB9FlnXpflxdXMAV553mQpnZ2daTAY6OjoSL1eT81m08CD1x4tKrTmD/9kMmn1nr755httbW3pV7/6lYrFommM0FG1222dnZ3p97//vV6/fq1//Md/VK/XU71eN0HyQ2CMohwhzwKyDlKplPVS810lYAk9Y8S88tXHw/M0ZKcXYZ80OPLmadN0Oj0TMw49nGXz+G4zH2pj4vnJHQWO+DvPkLAQPPvhQ1Jh5koYoll2oBSGJKWb1dW9AaK8FxSmZHtvKszaiAJF8wgp/VSLGpfwZ/w7PFw9SPZiX8Yk7LMmzW8MQkDAZ/qijhz8gLtQD4J+JpVKWQiB8g+8fxgyouegbzu0DI6EX6dcE/sdIfPRaGSFLgmlSdfzPDTvLPkK2YBmwFG73bYSKSRzcGBGaVLmaaEGCwBdKBRUqVS0vb2tUqk0IzzmuVP1+vDwUMfHxwb66Dq/aFD8PnYbQxxq7Xxo2rdN8qUt/BnKnPJ7Zhg6XYZx+eTBEQ86kUjYRndwcKDt7W0r7uc7upOltqyT+12hNX8wR2UK+Y0AarRYLKpUKqlYLKpQKNjB4euY4BH4GkLh5y3bOIUW0vb+ukOWhBCDD5UMh0PzigFB1MqiszlsiSSrtO0FucsKkKT3K4PhGSdCBl6XRxPTXC6nQqFg1ds9I8Pf38c4hNeH6L7X6ykej6vf7xvwXVtbs2rQvpyD95g5BAghoMHr9/vqdruqVqvWab1er9v/qSC/SGDEfONeQzF+p9OR9HafQFyfy+XsFZA0Ho+VTCbt+ff7fV1cXFi2LGCA/6N97Ha7lgE8Ho+t+CG1xwg7wUrPmz0KQ8Hsgb/85S+1s7OjX/7ylzO90tCmwhj9wz/8g968eaO//du/tRpw7Bc+PLuMFhUW5dU71Dg5tEWhqjpMkm9O7Ptuwp76rO8owmHRe+EnD46kmwsBet2nLDOxQ6/4oZg/fG9jLnxYzesvEBWH1Y2j2BNfOPIhWqi1idqIQ0/bgyW/wH0Gh6+Wzebigeuyj1nUvIkCdGHIyoO/sHhiWCDT230CxfC5eS0Qh5xP4wccwXRRwJIwCiHXsGcUbYY6nY663e5M9/Flcq6iQmsAfgDj5uamptOp1e2hwfB0Op2pzcNh58GRZ4xI4SZLi98HgPH7UQzkPFmFUE+Dtmx7e9vaSnFOoMm8uLiwIsFUvq7VanZvy1K24V0W5UiH0QbfBQC5BdGWVCo1c1Z4zW7Y79MXWvX7xLLYJw2OohYATRbT6bSxBNDKYYHDh2b+gPMCQ1/mnQrGpVJJuVxOjx8/NgqZKtCIkcfjsR0E9MvxVPhDNr+BRYXXOCCYG35++H5QmUzGPCv6jvEzTzsvI2sUWnh94biEoBHQQZYT84wMNp/aD4Mxj7EAlFK3p9FoWOIBuihYLd8GJpVKGcO8vr5uejRJxiwPBgM1Gg3rm9Xv99VoNNTr9XR2djaT1blocOQ/O6wgL71tSNzr9ZROp3VycmJCZMLEvgI6ax4QBOPjBe2UQ/GOJkARJo3/8/tRACm89rs2f/ADgl68eKH9/X39u3/370xj5Hu/nZ6eql6v65/+6Z90dHSk3/72t1YNO9TULOs6vw0QsVd5PWoikTCW6PHjxyqXy3r+/LkqlYrVB8xms5aRenFxYbq7RqNhawKmcBkznD9pcCTNFoTDw/f1GYiX+q9leoA/x0IvgNCBr2xK1g0hIV+4T9KNGLLfEKPG5yEc/qGF4TVpVpwczg2vrYB58GPrCyqGG9FDGxvsNj0SY+MPNx+25StqLO77er0OgmeGiFySFeQDBAGQptOpFfSkJpYkE22TiQQ4ajabJmzu9/umpVm2DCUPapm3rGUEtZeXlxY6942rvehWkqVo+5pnsCqwUuFr1M8Zo3kfmD50isOczWZNbFwul5XNZg0cSm8Zw263a4wRWWkk7yx7ynpot4XRQu2gL1eSy+WMWKCoKswwkoOwn5ofm3A9LMM4fdLgyAOEzc1N6yVEvNR7wB4ELNPG9lPMT/RQN8GCJx314OBApVJJjx8/NtaIxqzQ5LBGnU7HdBTLVhn7Qy1Ki+QF2RysbI5nZ2caj8fGrqG5YJx5Brd9xkMzD2Z45oyLFyH7LD36dy2qGGQYQsJ7nU6nSqVS1iut2WzOFIjM5XIzffckGRsCOBqNRup0OgaSCDF5Hc0ylQHxYNFrxQBGvhhq2AyWvUSaLQZ7Wygs1KXx+f7VOx1ck/+deemMAEFffPGFtra29G/+zb/R9va2dnd3jUkk667ZbOqf//mfVa1W9d/+239To9GwfWAZiwW/r3nigLOC+c95iTidLD60hBTEHY1GajQa6na7evnyparVqg4PD2f6ky5aeH+bfbLgKETHHijclpEV6mkeoscf0qWe2cAbIIZMHDlM1/XZLL78/8cSUouykEXyc4NYOuxDIpEwoIgHLekGMFo2T+kuzLNpzBEANT8LN91FXqtP0R+NRppOp5aI4RllMtbW19c1Go3s7xF2E04j1Iz2CP2OZxT53GUxP/e8Ds6XOiCEeFttqhDU/BigCZlY//0oMDWv9eETUmgmS1IK5V0IpVEYtNPpqFarWcio2+3OZCQu04H/cy3UHfkwW1gDDvDvyzbQeovQs9feLesYfbLgSJr1FLzwGO0DKYe+cisP3r/Hsj3U0Lx3GB5MeMXZbFbpdFqlUkmZTMaqnJKJAxCiHw6MEXHjsE/Oso/JT7FQB+O9bA7Ber0uSVY917MovvM0f7/Mm8LPMdi0q6u3Xe37/b6azaZisZja7bYkzYBqX3PLMwTzGI+QpZBk85amw+vr62q1WjNFYemdh8bG9wRDXOyTE2ANw1DSMopPGXcPjKSbOhRvUazhu4BQ1Hvc9v2o97tv1sh3lD84ONDW1pZ+8YtfqFQqaX9/31hF5ne329WbN290enqq3/3ud2o0Gjo9PTVHKSz/sOwWjq8v/yJdZzgzx/v9vuLxuKrVqp0BuVzOnEa+d3Z2pl6vp8PDQ0tUYD/0iRvLtB6kTxwcSTeZlCg9gq/a6ot2PQRg9GN2G4vEIeAZEkl2EJBxwNh8bOG00KKYIz9PANG9Xk8bGxsaDAY2boxTVAXxj4U98oCDdeNLHHBYhGwafztvhiAqxAO7BXswnU7twGQjDzV3PHvC7nwfRjFKPzPP+/ypFl6Xn/c/9ru3fc/b+4Ij/17zGisAEtpLn4VFwgosaL/fV6/XM3Fxq9Uyxsgf+vO8/ruy0BmETeSe/NkIU077JULHl5eXxqA2Gg31+31ru+XlKcsIirBPFhyFNKF0veFREXcymajZbKpWq6nT6RhN+BDsNnbLH+p4ATBDYV0nGJHBYGAbPGmpR0dHVsul2+3aQnho4sOfYmH4gZDJ2tqaWq2WhV38/Lm8vDShZqPRmNk8FwEK7sJuA4qsDXQqjUZDk8lEJycnM3ob1tO75ss8xiMESD6E5HV5/hXz+hyeJ+DKh5j43fDzlt3u61nc9l6LHpewhUxY9JZ5ShThzZs3pjVifYc9w5b54L/NwuceJqAAfNjnvA7NA0jvPOAs+JpVy6gxCu2TBUfSTVrYF/bje75qa8gcPURj8/ZeAMAnDAmsr69bqMCDo/F4bICIzcL/TtSEX9YF8HPNjyMgCbqdpqTMpyjB+kNgEd5lXjPC82Y8YI2oqtztdmfWFyLtRQNpNn+vs+E6fH0WxMmwSZ5NvS3dPAr4PsTn/CnYu5gxnvF4PFYsFpupZdRsNm8wRsuWifhzzbNHnjmSZjsEeA2lD7tFMaahyH7Zx+iTBEdR8W1AUafT0eHhoXkNvsLtbcW8ltlCT8AX3II9mkwm1g/q6urKCtZBK1MKfjqdWmZPrVZTv9+3DB26jEcV9XoI4/S+FupVPCggtJZMJnV0dGTzihYSVAD+WEpCSJrZ7KDKKfAXj8fV6XR0eXmpZDJp991qtawq8m0hx3leP68eLEm6wSyHAIrfuy188tCf7adoPkw+HA5Vr9eNEbq6urLaT99++6263a6Ojo6s4jcSDNbEQ7cQ1AOSIA+k9w+HPkQH4ZMER6F5j5dUXKhCRMcUN3uIh5r38png3IcPhaCxYIFTIRdwJMkAIqI6WLWobLWHNk7va2F4DQpZknmXjCGbLWMUBYwe+hiFYIGxAAj6JsYePIXaA/9ei7DweUQJkR/6hr+y280zgpReCJ2+Xq+nbrerRqMR+fOPBRhF2ce6n99mnzQ44vBCWFav1zWZTGZaZPT7fZ2dnanb7arZbC5l+f/3Me8ZS7Mi1FgspsFgYKED/xo2pQUAMQYcblFi44/Z/HgyjxAgI+zk90Jq2f/9QzYPDnw2JNqd0WiktbU11Wq1mfIYviqyLxS4DIfKvHVPK1u8sRZZw9PpVMfHxxYyQ61p//0AACAASURBVIfkM7Cazab10QNMeRZ1ZQ/fPllw5A9xL8SOx+Oq1+sGBgaDgdVm8N7/Q10A4YHmRaOwHmE9E+lmZeiouiafoiftQZIXvnvhfjg2H+P4RFHw0+l0poecT3zw9VA8KPoYx2Zly2/MO8D6cDg02QFi/MlkYsx5v983KUaoN1vZx2GxZXiYsVhsIRcRprFTAXRjY+NGBluYmitpKTzduzQfPvixisWfIhD6KXZbKOZTsbDIqv9eGIb7FMdnZctn/jyg6K1v9+MZ8jCEtprDD9r+fjqd/ib85ifLHEnR4loybaTrTByf2fUxewerkMLd2ac+frfpd6J+Z2UrWwbz5wEMesgAh2BoNYc/XvukwZEUXQzuXb+7spWt7Kfbau2s7CHYx+z8ruyn2ScPjrytFsXKVrayla1sZStb+/FfWdnKVrayla1sZSv7dGwFjla2spWtbGUrW9nKnK3Caitb2cp+ln3qGXkrW9nKPl5bgaOVrWxl723v01V9BZRWtrKVPXRbgaOVrWxlNywKBPleY+9Kzf9Y++qtbGUr+3RsBY5WtrKVmUUVbeR7vjAejYi9+QJ5t3WoX9nKVrayh2ArcLSylX3iFoIgwA8tZKgcn8lklEwmtbm5ab3jfG2wXq9nDTtpvhs22l2BpJWtbGUPwVbgaGUrW5mkm+10AEnJZFKJREL5fF6pVErJZNKYI/rrUVVYkrXZ8X3VpJUWaWUrW9nDsRU4+hd7Vy+xT2lTD8fhU7r3T9k8MIIpSiaTWl9fV7FYVCqV0v7+vnK5nDFH4/FYk8nEGKOrqyslEgldXl5aE+O1tTXrRbiyla3s3XbbObTah+dvnzQ4itJWvMve1XvsIU7e27Ql3n7sPlf92D4eAxzReHNjY0MbGxtKp9NKpVLK5/PK5/NKJBLWqZzw2dXVlYXapNt7T9GvcGUr+xTttobMUV+hZi9Kw7daS/dnnxQ4ug0MoK3gNQQJfiKGzQf91zJ3aPb34++Re6b7NKEUfj/UiyC0lW7e/22LdhnH48fspwDln/IeYYhpGcbGz4W1tTVtbm4qmUyqUCgolUppd3dX+XxeT548UT6ft7nS6/U0Go00Ho9NX3R1daXLy0tdXl7eEGavbGWfovn9lrUTJjfgcOB0rK+vz6yl8/NzXV5e6uLiYkbH97Gur2Vwoj4ZcOQB0dra28LgHAZ+UjJhvXlgQDaOn5geQIR/twwW5aH4e/eLc319fQYkcsBxr5eXlzNAiHv3YyJFp3Evy3i8yxgr5shtTNqPgZsQjEb9fJkAknS9NpgHm5ubSqVSymazymazSiaTSiaTN8Dy+fm5Li4ubBP3oGhlK/tULQRFfp/d2NiYOXcIVQOMCE+Px2NdXl5qOBzq/PxcsVhMl5eXknRjjS1qvd2214Ukw485nKG9a6+dx31/EuAoZEkAAEzEVCpl4QO+h3kQwEQdjUYGkiaTiaF6//vT6XSGGl3kfUu6ce9eVxKPx5XNZrWxsWE6E88ccQBOp1Mbg8vLS11dXdn/Ca3w/TCVe9lZtRA8MwYhSOJe0NGEz5fnHjKQURvDMmRyhRs4G3cqlVKpVFKhUNDBwYH9e3NzU91uV+PxWO12W71eT41GQ+12W61WS8PhUKPRKHIeSMv5/Fd2d/auOR/17D/GeREmNgB+NjY2LOMzl8vZfru5ualsNqt4PK5EImH7z3g8Vr/f12AwULvd1mAwUKPRsIxQzyTNc3+9LRzoM1yjsl59uFC6dkCjzsvw7PB7Sfh6X/vLRw2OosJnHrUnEgltbm6ayDSXy1lYQZoFRoCi8XiseDxu37u4uLAH4h9iaPNe/OEGxSRNJBIGCBOJhDKZzMwYpFKpG+BoOp3aIuTgAyD2+31dXFwoHo+bENfXugFERIUpl8FuY9MSicQMDY55cHTb4gxDtHyON78Z8D7zppJDQMhGnkwmlU6nlU6nlclklE6nDURLMo92MBhYaG00Ghl75DeylX38FhWmv805CMPz7JXL7Dz9FAuBwebmphKJhNLptDY3Ny1UvbW1ZU5IMplUJpNRIpGwfScWi2k0GimVSqnX69n6HI1GkmSZoSFjP497uy364F9jsesSIF6y4fdBSIjQgWYP8Vmw/pWf87fhvd/VHPpowZFflHjEiUTC0DuvmUxGu7u7ymQyKpVKhvIxmKHxeKxut6vBYKBut6uLiwuNRiMNh0N1Oh2Nx2PLzAkPiHkv/KgJDCgiPFIsFpVOp7W1taVcLqetrS07BDc2Nm5oY2COyExqt9s6Pz9XrVazMTg/P1e/37fDk7FjQi8TgxQyap7uTiQSBgYAin48JpPJzEINQ6t+g/DeEZ/rw1GSbM4sgj3ywIiw2fb2tra2tvTkyROVSiWbG2xQrVZL7XZbx8fHarfbOjs702AwMKDMM78t3Lyyj8NCp9M7nhyKHJgYc2I0Gt1YR1HOxkOy0AHf2NhQLpdTKpXS9va2crmc9vb2lMvltLu7q1QqpUKhoGQyqWw2a2CKcen3+7bWzs7OVKvVtL6+rna7rbW1NQ2HQ9tPPZN9H/cl6QYIAvjxyvkKKMrn83aeRM0JohQ8e0LyvV5PFxcXVi9tMBjo/Pxcw+HQ/u+jFRAUd1149qMFR1K0hmJzc9NAQKlUUj6f197enoEjHrAk09gAjlKplEajkdLptM7Pz9Xr9TQYDHR1dWWH6MXFhcbj8UzYbZ5x4dvYMiZvJpOxkEk2m9Xu7q5yuZzK5bJlJSUSCXsPrhfmKJVK6fz8XJubm8YeDQYDxWIxjcdjSddAKvQAPMJfBsGd9/KI88Oc8bqxsWFjKF2HGQknjsfjGS0W9X0IX4bekqQZ4MDC9s9tXtR4CKDxZHO5nPL5vHK5nHK5nNU58ptTv9+3r/F4PCMUvY09XRYL2YzQEXiIWrl5G4CHA4+DDseT/3MwMsawi71eT+Px2OYUYAl5gvSwxj0qOsFaymaz2t7eVj6f187OjrLZrLa2tgwcIelgH+LMQV8EmOTsGY/HSiQSViojDFfd1715ALy+vq50Om0kQyKRUDabnTlf2Ts8OPJ7IzqrUKLB3MAR73Q6uri4MBKCc3ZtbW1GfxXuPStwFFgUY8QDyufzymaz2tnZUaFQ0OPHj1UqlfTZZ58ZiwKQwJsBxZ+fn9sE7fV66vf7arfbajabOjw8VL/fV6PR0HA4NGaJQ8ODpXkcfv7A29zc1Pr6uk3Ug4MDFQoFPXnyROVyWY8fP1ahUFCxWLRximJLuBdQPBO4Wq2q3+/r9PRUg8HAXlutlsXMLy4uFIvFzLthAi8KIPmNDFDEwi4UCgYieQUsecYH3Q33558x78nhgI7N0+XD4VD9ft/eLwxB3ue4RLFmiURCxWJRxWJRT58+1d7eng4ODmay09rtttrttk5PT3V6eqqTkxNbC4yBDzfeF939cyzcF6Tr+4/H4+/0PN/liUZpyUKQtQyOwF1ZGDbigNze3lYmkzFHc3t7W+l0Wvl8XhsbG3b//X5f5+fnOjo6Uq/X0+HhoXq9nmq1mu2vgGzpYQAkPyawzmj0YF8/++wz5fN5PXr0SMlk0sYlk8nY33GvyDVwVvmdtbU1c0QHg4Emk4kGg8GNBKK7vC9JM8wybFg6nVaxWFQul7NXslorlYqy2axJNtBRcQ+AIwxwBFAGFNXrdY1GIztXj4+PNRgM1Gw2NRqNbA/u9Xoz0QrYyA9ddx8VOHqXxiiVSqlYLCqbzapSqahYLOrg4EDFYlGVSsV+zsPzXjDezng81mg0Mk0GIZjJZKJOp6NY7G16M2ACuy274D7u/zb9TDKZtFo1hErK5bKxZ2iOCC+FGgHCRdSzmU6n2tjYUL/ft4KA6+vrtniJjXsWKRQrLwoY+bHCo8HbRQOQzWaVSCSUy+WMTYTpWVtbmxGoE1bzrBHifk85e3qa98JTXNS4eJCI9iGdTs94fWzWaIw6nY663a6Jr73WaNnCIiEowmHwmUOE0T37xzOVZrWH/r6iMhq9gBRnKB6Pz4Bf/56hBmdZxi3KQpYxHo/bQbmzs6Nisai9vT0VCgWVSiXLdOTgn06nGgwG5kh0Oh0NBgOtr69rOBzOOFDLFIJ/H2PteulGNps1h6NQKNhYsRfgGIWFUv29+1AUTpYvtTKP+/KMEYyQZ8O2traMFeP/RCi4Zl8ihvPFryv2Thxpkpw2NjY0mUy0sbGh4XBo5+3m5qZFbHypA3/GcP0/d/58NOAo6tDb2NjQ5uamofgXL16oXC7rxYsX2tra0pdffjkTTksmk/YeIU3nWSTSKtEf1Wo11et1HR4eqtFo6Pj4WN1uV81mU/1+X9Pp1NiB+wJI/v7DTLxkMqmtrS0VCgV99tln2t3d1eeff66trS3Tk8AwhRs9981GL8kWCUBpOBwqlUqZF9Ptdi3cBpMCuAoPnEWAJD9GHjijP0smkyqXy9rY2FCpVLJND4q33+/beKA182OWTqeNLoeBYm5Np1N1u101Gg3zABlnaX7CVA+ieZ6lUkm7u7va39+3cOvGxoZOT0/V6XT06tUrnZ6e6vXr12o0Gup2uzdS9991wIcb+n3fI8/FM8jsBwBgNFYe0DPfAb58bzgc3gid+o2Y+U3dp16vZ0AZDQn3zWf4jZ3PW0ZQEGrzaCXz7NkzbW9v61//63+tSqVitbAIqzFGzI/hcKjLy0vt7e2p1+spm82q0WhIkjqdjv2OH/dlZt5C1gi2bG9vT1tbW/riiy9UKBS0v79vISecKhJbPOhkzJB2+J95DeNt2bJ3aR4Ib2xsqFAoKJ/P6/Hjx9ra2tLTp0+1vb1toCiUZviSOWEkwssucBYl2WddXl4qkUhoPB5bKDGVSmk4HKpQKGg4HCqdTms4HCoej5u8hf1I+nAy4qMAR2GYAJSdSqWUTqdVKpVs4y+VSsYcIYZLpVL2NxjvxWZFbNSzJ3zx+yDcq6srbW5u2uGPNoc48n0t9nCRecYol8upUChYCA3ak7BPmFbJF6wArxyEfBYhyHQ6rVgspmw2K0nKZDKSpMFgIEm28TN+XO+8WRI/Tl44yVzJZrPGsPF9zwZ4kOzTaHnWIVsH8EylUga4Ly4uLOzmx35ezJFfLzApbOqFQkHZbNY0EBxWMEbtdlv9ft+yFt8XGN12HfMASKxt0qhph1Iul5VKpVSpVCwDVboGL6PRaIbZ8fownnU4p6LAEZs9G7gHXyQxECbwQtNlAQQhkKbPHhKFSqWi7e1tEx0zd/xa5378nJtMJspkMjo/P7fXMOTyECzKIfeZnr5RM/vGaDSyZ+/PLHSxkm6UlZFm9+f7ZBujpAeZTMbOkWKxqHK5rHK5bKxY2HfxtiQTGFjPNjM27LE+I401zJnK3GFd+VCdB2Efah8FOJKuB5BBTKVS2tnZUalU0ldffaXt7W39q3/1r0xTkU6nVS6XbfNiIkTR5zxgHwLhMyeTifb39y2zoNFoqFqtqlqt6uXLlzo+Ptbl5aWJySTdi96GSQGFyURl8j59+lQ7Ozv6/PPPbUOjtpEvTeBF1NPp1PQkAAEMzwDGKR6P2+FBxlqv1zN25OrqykohhGM8T0DgQR1VoNnkc7mcDg4OTKjOdQ2HQzUaDfX7fWMDG43GDTYgFrtOX4WZgJnL5XJ28K2trZmYOYomv28A7efKxsaGyuWytra29Pnnn5veqFAo2AZ+enqqo6MjvXr1SmdnZ2o2m5Yx4jc3/zz9K3abZuc+HQUYo1QqpUePHimTyejg4EC5XE5Pnz5VNpvV/v6+ifH5GzQQzFUfhqMQpt/k2aBxgjgAMZghvo9erd1u6+TkRPV6Xa9fv7a6UazFRYfa/FwhAymTyejzzz/X9va2vvnmG+3v71vfPRIYCLfCFGGElNhz0XmWy2XFYjHV6/WZUNNDYI38fkIorVKpaGtrS8Vi0VggMp77/b5l+bZaLft7BNqEqEiE8I66LyETOqx3NU7+mcOGZTIZVSoV7e7u6sWLFzo4ONCTJ09MWE5279ramrFiJGuEIml+znXDLobX7/cXzhpJSqVSkqR0Oi1J2tzctHDcbXvPz7EHD47CzR7GCFFguVzW7u6uhZDwbKB9w5BIVI0efuY3Ks8cETbiQXIItlot9ft9pVIpq490H6Ekv0jZpGkBQeYRrBHMCBu9JPOK8WbxbCeTiYEjH8/14QrugwmeyWQUi8VUKBQUj8fV6/UkyWLHniJeBHPkNzOyRGBNiKXDqCF4HI1Gpo/odDozLTP8Aca9+YPNM1RoKn7Mw7mvMQkBmN/8mBuZTMY8/tFoZKHjTqczozEK0/Tf55pvA0thKPeuzYc9vFdPNh5OAuCI9Qzbx7zHESCbkw0ehwltDc8fxphDjXE7Pz+3sDzars3NTQtLE7b3+9GiLATS2WxWhUJBW1tbqlQqNm9gOAgTU+oDEI3OhN9l7nj2NgzrPwTzrJHPCoZhY68E0PhkHpJ7fIVs5pQHQiQDMW+iNH73dU/Md/ZKquXDwrImiDJMp1NL1mGvhBHiWv0ZI+mGzpU92gOqkCnz/w7JDP7mQ+1Bg6OQ+gPBQvN+8803qlQq+uUvf2mZApubmzPqfx/DhdLzmUfSrPDSi8d8LRuqnPqwFZ/RarV0dXWldrst6VqgfRcP0LNYYX2NYrGoR48eqVKp6NmzZ9rd3VWlUlE+nzdRKhMXlE+mgE+rRITsvWeElv7++TdhI1gVQjGSLOwISzcv1ihKG5DL5YxdfPbsmXK5nPb39+0+e72ems2mms2mvv/+ew2HQzWbTdv0fZ0jPgcQlEqlTDTovR4OipAtuo/FfZvxLHEi9vf3bX7gwQ+HQ2OJfvjhBx0eHqper1s9qx+rYXQX4O9DxiDKkYFBKpVKKhaL2t/ftyxVPH+vJWJ+eiE3/yfshkNByIn79j+nYJ8H0HyfzMWXL18qm83qzZs3mk7f6tJarZYdOItgkDz7hhMBs/jVV19pZ2dHu7u7FkpnzvT7fdNdUquGbFBqyiFlgDEhceE+wiP3YeHZQzgN8EDCjgfLZOV1Oh1Vq1UDPv5M8nvJ5eWlut2u2u22arWaGo2GOp2OFV7FObtrkOQdSH+uVSoV0yNWKhVL4iENfzgcajwe6+TkRJ1OR2dnZwaUPEDi33yWL4DJnPCJEzjxjCOOKe/7rvD+h4zJgwZHGAiXbBtYIhijUqlkSJcHAOCB8uOggwrm/9LsQvD6kzDW6QW+pDry1ev1bCLdF3PkrwGU72vWkFrpN38mHKwA6ZEgfpgjJqzXKOEdhRMafUcsFjMxHaDNA4N5bn6hN8Qzgj3w4yNds2mEvwaDgbFIeHCMnyTzgvHsfBiS5+JZo6i08XkcgKE+An0VlXsJHbOZsxlTwoFN6Lb39p9x2zO+beMCjNylJs2Pq99AfRgZZ4hnQ3FOjLCxd4p8v6vhcDgDqrz2iH3Es7qAKJ8tx351fn6uRqNhoSlpNrwwLwudLp/t6jWLePgwRADo09NTO8QZd4BpyNIxvjByD8nCfYW90Tvefr75avKEDb3omTnBOcM8GwwGGg6HBkDuM+zqoxBR2by0Qgl1VL1ezyQIrVZrBhz5a8WxYuwQX8OeMQ7sqbCSgCDPoKF9jKrM/6Hj8qDBkd/kybbZ2trS119/rZ2dHf3bf/tvrTcUC9Jv2Awmmz7hkm63a56dD5n47JRkMmmiMC/o5pr4G0Iz0+lUtVpNkmzTu+2Q+alj4BenB4i7u7s6ODgwTQDptbA3HP7j8Vi1Ws1i4YABRKSXl5eWmg7AHI1GxlDhNZH6LsmKBjabTUlSNpu1RTDPzJwQOOKlbW9vq1Qq6fHjx1bWgZALVaA7nY6Ojo7UarV0enpq48UC9ewiCzmRSFiRxHw+L0kGxmKxmLEILGifEXnf6dzhXKEmycHBgZ4+fWrz4+zsTN1uV8fHxzo5OdHJyYnVGgkpcv/eUZ8TBZL4u6g6SH5j+1BmETDDITMYDLSxsWFzt9Vq2eYKg4FnyzX4e/Hr/OLiQt1uV6PRSK1Wa2YMAHj+/tj8ARjMQ7J7OBxpK/HDDz/YZ/jaLfM0AByZSltbW3r8+LGePHliGUrS21Aa1dL/+te/2nqBLZZkDBNZoJ41wonq9/sPSoztmTX2wBDkSJoJUdMjDZkF5weiZpKEJFloqtFoqNFoqFarGfOEs3LXACmUZyDEBhSXy2XTnXG+ESo8OTlRq9XSq1evVK/XVa1WLckAB0HSDfBIeQNfW44QrtdcsV+i22q1WjMtjEIt04fagwVHIa0J/UcdH/8Qw3i2Z39A84RKRqOR2u22UZ4hOIKiJ9SEoIzfkTQz6bkmdE54mXe50YVaGvRGsCMs2jAGzqKlcCXaElgSWDSoS6qyEk4DEPkDzGejTKdTy76AMn8Xo3DXFobTvFgfDRaaE8YGT41GqtT08YyRB84huOM9PN3L3PCMERvGfQgqf2xMPJAmXIInL8nWQr/ftzBr2OCS9wJA+PEGFHkv2j9z7tMza/zfA6e7GBPGmjpN6+vrarVaury8tDpOtGNgzobMkSTz7j1zxAHV7XZnrtOHRnymDWCILC0fRuIwoKURIbx5hJ2jLNxfERuzp7CWWRcko7RaLbVaLWMRfBjRz51wXnit56LCiD/HovYzP3bhmSPJmDjKPKB/46zyzKPfl2FwAQH3na3m2SPfGgT232vpCKtR3JazFOfDXyN/zz6BM8KZ6stccC38HBIDHVYYWvuxcP9PsQcJjkIPGGS7t7envb09vXjxQtvb29rb2zOQ4BchAzkejzUej62bOAJqMiZIQ59OpwbA2Cjy+bzG47HF2318lLAW4rTd3V3VajX7G7Ja7mocOIjwxNAbUa6A1GUWHmzZ+fm5ms2mMSTEtqFwfdVjACZibnqzoa8oFouSZBs+z6ZQKNhBxPiF3uF9LW5ePWgsFAoql8tWufbx48d2bZPJxEDRd999p1qtplevXlk37BAU+ev2gJdQi4+140XCTLBxhMBjHswRNDk6Airc+oKmtVpNJycnOj09NW/Xs0b+Pb1HGIaLfMiVzdA7JjBu0nUqr+84HjXW72v8LXO4Vqup1+tpMpkomUyqWq1K0syG6jVCHABhDSoAi9cphsJrdG3UuqKkCDo3ygfEYjErG1EsFtXtdlUqlXRycjITllmE3ohnSdiP6yY0JknNZlPtdlvfffedTk9PdXx8bA4WxfpIueZ9ffV4yiXArERlQC4rSLrN0eNsYs57hhlQiZNGfbVyuWz7N3O21WrpzZs3Ojs70+vXr9Vut1Wv1+3cug+tUXhfOA3oE0MA59sJ0TGCfnAUig0BC2cAr6x/H+omvO0TGQBBnNfMM6quR9Vc+xB7kOBIms1AQTtCPSOqPodsCZuX19MMBgPV63VreUF5csARG6yPhW5ubpoIk43w6urKNgwWBoAKz9xniXGQ3MXEjop7e6TvswE4mHxLFMAQB7anKL12hnHggIfW9dSuX1T+em5jEe7DwsXNcwA4whZ5toR7bjQaajabqtVqlrLuw0lRnkm4qUR5XJubmzo/P5/Ru0maOfznAYxgKWACoPHRRCHOR0wLKIq67xAUeeEyQBpnwoMk7pfQKu/pBaZRwOhDABKhUOltgoBPMffZQR4MeRDEa/je/ueeFVxfX7fwnT/4Ly4ubD4AjtEh+rXiGYdFAYSocD1smyRjWQmreE0eY+HDZ1SNBjRGsfnhengoFrX2+WLf5KySZAkRtG3y9YEo89DpdAxswBqFa/E+x8jfT9Se5bOavTwA4/7Dc459ApDlzymv4eW9fCY10QzvfEYJsu/CHiQ48pt8JpPRzs6Otre39fz5c8u8QYgcMkYwRPQB63a7Ojw8NHaA4m14//ydB2PUx2k0Gtra2tL5+bnVTGLhQ50SZydDptvtmu7mrsbCM0eAADYj/s1GhHgOxE02VqvVMgqXiefDHPwd/ybV3Zd0BxzhCfgsjhAg3ZdFMUawJLlczvrp0TIGDwXq+vvvv1e9Xtdf//pXq2fkNVLhwosCYl7ASPiuUChoOn1bA4rDxbMP8zgI2LSz2axKpZJpjajFQlbhycmJDg8PdXJyYho0Djx/z2H4jHmPaJNsFhyVXC5nhwUgjLH1r+12W+Px2NLBPyTE5se41+tpbW1N3W53xmP1obxQ98TruxisqO8x99gvGI9isWjedKVSsZpJXt8D28J7z1uMLV0/Y1hwNEeEf3AmEN7SNZ4QNO/BHri7u6vd3V3t7e1Zgkw8Hle73X6ns/UQjD3HH/TICYgoeAeBNQI44u+ReNBz7vj4WN99951puAhdeSbqPs0DI+/M+zpLODJ+H/OAGvOhZs4i7zwBoD04gkny+ldCuEhBYIxuq7n2IfagwZFPNaR8eaFQMJ0Nh7D3HInj9no9i4/X63UDTYAigFSY0o9HKMkmfLFYNGbANxD02Sm+WnJYFfku2SO/UAlv+dg3KJ/J7VMimeQeuIWLw9d28u8f/l7oSflruG/zzBVjj9gRLRpsCdoymqqSbRNWgY5iiqI+07OFHCTMExY84z4vLzBk83AqcCAIf7FG0A34GiXhe90GihKJ6ya+eMWAo3w+b2N+dfW2qjoHAokLfB6tA2Kx2ExG4M8do5CR8O+LhfqxUEf0rlB4OD/4DD9mvkYNh4jX27DRR4UIFmGeObpNaBzqPPzhF4/HDRDSyxEdqM8K9YftXWpG7tPexRaH7L0PrbH/e/G2X3ftdluNRkNnZ2eq1+vGGnmd0X0lsfD8wv0ojDbwvLh/7wQjY+FZkmnm9w0YZPYLWEQ/Vn69MUe8zojriNKpfbLMkd+YvdZof39fz58/N4AUVn6GHWi32/rhhx/U6XR0eHiodrutN2/eGGjySDgEC9PptTAbb3o0GlkKaqlUsofLNeI5kRnUaDSMar8LYBQCkTB04puegvq94Ba2CFrc96bxuhE2cqhPFoEPXXqgxFj5v/XXeF+hNc9q+JL3aIyeP39uZRbI1owAtAAAIABJREFUfKBvWKvVsnpG7XZ7xjvyno9/la4ZGViCXC6nvb0985QLhYKJ9sOsFQ7L+za/iRWLRe3s7Ojg4ECPHz+2bBHWCJuzT8X2IVMfPvPaGkDQ9va2UqmUvVLokHFgXeGEsNFRQymdTltIm3UWFdZ6X+PZwQD/lL/5uZ8HS+tDDgBIrgNhM6EC9if61oXs7bwsKgRLaRCfws1eSQgUhoTDP5FI6NmzZ6pUKvrFL35hffs4PPni3mHsPXO0zCDJj5NPhOELB0mSaVO9k4KxDzebTb1580anp6f69ttv1Wq1dHx8bOfMvFlEPs/LL3q9nu0XXocai8Xs/Lu4uLD7v7i4uMEwc6Zwfni2iPEIk4F8mx0iG2F22l2Py4MDR9JNL516PsSzvXcDZTsej+3hkoXkhWPEeUGpvnaC36DYMNDmEP9Es0MYwIMADs6oflp3NR6eMfKUflh9lAkfxofDODl/E1WmwHs+fMZPEY/eF4Pkx8CDFV/ZlaxB9GJeTOhTQsMF5wFdCJC8d82GWCwWzUuGOZJmRYfzql8TjgubuKf9JZlQHIYj6v4l2YYWgqJSqWRteehhBkvlhfqSzKPEm6YG1Gg0Ur/ft81zMpncqUZvngeL/zdrxxehhMFEZ0LKdr1eV7fbnQHOiwQJt+mffNiag9BnqdLUl67tXrPE3CdUxH66aLbsfSwM3bP+PasOkwJwwGHGOJfQbNXrdTUaDZ2enqpWq9l+tAiwGGqK2BfYKymJ4aMCXkyOYB9m2AuwfWgRkMQ4+vB5lBYvSq96n+Px4MCRn5CkylOplVoapMHygHmoUJVv3rwxBqnf78/UsJFmq2XzwHggTHzpuoNwo9FQJpNRp9Mxz9CzJV4QDJvjCzHe5dgwkQjtsBnxWR4Y8dlMPF+IiwlMHSMONRYD1LhPA/fv7T/Df4V0NP++q3Hg+vB4i8WiSqWS9vb2rE6HF5VzIB0fH6vX61kZh7CKa+jxheFL30ttb29Pz54905MnT7S7u2sHCKEVTwvPCxz5EC8AjtRx5jqMVr/fN1bHgyNAShg229nZUSaT0e7urrFyjD9jLc0mKgC8eW9YNbRd7XZbnU5H8XhctVptLuzafRnrMpVKWaf258+f6+nTp8bmttttff/99/r+++/17bffqlqt2uG4CKAQMr3h3sFzzGQy1ocPsTH7RjKZ1JdffqlyuaxHjx6Z7i8ej5uOhDo+6B4XxZb9VPN7O/smbJl32IvFooEmDMkGDnq1WtWf//xn1Wo1ff/991ZEE6d+3gJ1njWgCI1erVYzZ5gx8BEDsjGz2aytY+m6PQjniA9DStdhO/YcEp6QsAyHQ0kyUP2uxJ67HKMHB46kWUE2YQwoX0JIkgz1oqGgqzgbr6967LUlYaox4AjA5elNf4B6Dzd8aJ5F8lkadzkmmNczeQuZIo/mqd7NfQPiCFFK1+JWSTdYsCihtV88noYNvYC7Boghw0UI0PfTIxTgwx7hdXPPIX0eeo6wI4VCwVgBqgj78glkXERVc52HRYVc4/Hrfk5eU+DnNWPBM0foDxvAvVJlG/aWZ+u9QOacB0nMLzZdH54YjUb3Ol/uy/ycgWXb3t42R65UKlnDTPQm1AmCwZwXeH6X+T3Bz1sPjvhZOp225wQ48gwZDoJ0zVL2er0ZhmTR9/u+5p+t12T5sBrsOnugZ2MIJcIU1et1NZtNy0q7rwysHzPvyMJy0zGByEu/359hnT3gwfnydbqILERFMXBSGRd+H/ZRum4wS5FWQBKymfuSaDw4cOQPKTog0+vFF09D5EaqPjUjarWaDg8P1ev1dHZ2ZuG2UFvE/z04YuEzYb0WA8/Qo2NvXLNP1+V+7gMchGJ0LzL1oT4OokwmY2Di6urKMo0IkeDRkWnD4Qp9HOUReJ2Lz567uLiw1HEfqvqQcfDvE2YVwh7RdBg6329WPtNCkmmEQqGl7yHHZ1HDhvpJBwcHevbsmdWCgolC17aIlFwP3mm0C1iksBxFH73Y0ntpgB76KsHUbm9vW48yH0LkPn0/tlgsZp9L8U3mD2LtbDar6XSqfD6vq6srS3a4r3DsfZkHRjs7O/rmm2/05Zdf6le/+pXVt0Fv0mq19PLlSx0dHVnrDZ8SP29jTgJ8mL88R9Y+vRq3trZmvHpCrswRklakt04r8obT01NVq1VrbLzsguzQ+QII+YK/vok1rHosFrNxHI1GajQaOjo60p/+9CednZ3pr3/9q7rdrmq1mrHLiypp4EGL9FYDNJ1Ora0NInJ+B2E1r7SO8noiL9XwAu/JZGJdBySZs4bjxBogIrO5ual2u62rqyvTJfo6ZCvmKHZdnApNCToSNlCfBQE1R58sr3iP8uQ9exT1uf6w9Km3sDAhkuUQ9jWD7vIhegaLw94LXX22XXj9vh6Sf49QXChdd5YG9Hhdl2fS/Nj5cGRURttdm/foPN3ts23IhuLeAYeSbD4AjhDShuJDwC0HAR5yPp83kByLxWaYgV6vZ+DIM5UeuN6X+TCgZ20A+WEaNX/jQRVzwWuqqPAbxcohpuagZx5eXl6ao8Dne6bOzxF/HQ8FHIWMUblctqad29vbxqpIsnAmujfa+fjWRYsyng0NqT3b7uvSYF4wH9ZX87+DhAG9J4B8nuvhQ4z90zt9gCFC1ewXfm/2GiP0rlS+9j3T5uU03WbhGQgwpj9oq9Wy+c3PAb7T6dSeuycWcIw8i+7PCc9GwiDzNzCS+XzenNHLy0s7s/isu2aWHyQ4Ar3S7wVWgM2Zh+LTI1ut1kxBLbxkX8Mm1MhEhVpAwIRSyJjzolNPG/o03bD0+4daOIm92BcaFKFjMpm8wRhNp1NjUhC+gtQ55En1JqsGb8EXcWOBrK2tmffI9UmzgmU/hv4AvAvzzwhwB2ChSjH3y7gVCgVJUqVSMfqWuYMRPmDOAcz5XV/PiDougHVAebPZ1OnpqVWcDjtVz8M86xoyrL6rtgcoPLf19XU7APb29qyhM61YfEibOUe1dTL/ODx9IUGeCesW8O1DtKFmY9kN8ExY6cWLF/ryyy/1zTffaG9vT5VKxcYeZpvq0mSq+fFahLE/0SXg5OREsVjM5j/MIwwtIQ8Odh9u4f2urq5sbpydnalaraparerk5MQyZe9iX7wvC/cX6hQVCgXt7u6qXC7b+keX5wvoUmgY3Su9C73eahlKOHC9XDtCeeoAXl5empNHiRScAF/kM5SaAGB8qw9IDM4fgA4OE+VxcKjYS2OxmAaDgTmgMEifNDhiwDnkw8KCIFUPTHx2UBhGiWKHol49S4R34EWtbBR+U+fBE7P1BavuikHyXpZH+r77c5h1FLJGAAlJNjmJmYeeXxg643vheL3PNd8Xc8Th5Fkx7+leXV1ZiAxBcrFYNFbJMyiEddAPZbNZ2yCZWzBTXpSJxgCAjocMQF7EBhgCU2m2Vo2kG/MEls2Xb/B6Cu8McLihmeF+Scn1IV0fnvYen2cV/bUtK4sQGmOcTCa1tbWlg4MDffHFF1ZKgrZCrNHT01N999131uDYs4rSYtgT7yDCFg8GA3U6HdXrdXOUvI6P+cyzwqFi3YR7YafTMSbK999aJGPyLosKp5EFC0DIZrMG/Lnf4XBo+whrwWc2R51Ji75/1qQ/Q2OxmHWFoEEwIUNCYRsbGzfmjU9gYV/kTOLe2Q/ZF/x6528mk4ny+by1rrq6ulK325Ukq0fo95i7GL8HCY58OARRWEjdRhU69OXGvfiPLx96kDQTOvEl8NGX7O/vq1KpzBQWxHvm4ETtz2bgiwve1QLwkxgGgMwrPLJQK8O4EU5CC8J1wRgB+DyQ9GEDf5iFotn3ocfvGiCxgfmQK9Voqdzs7wXGCJYnn8/b3PHgiHR1f7hBk8MiEmLiYGBzbLfbOjk50dnZmTEDeIjz1BwBGH3tK0+b+/Ar7ChzhL50HAZ8DwaImlEXFxfWjqfRaMy0zgjZQg8OQ+dGuq7KHurmltU8Mwdj9Itf/EL/8T/+R6tIjhfc7XZ1cnKiP/zhD/qv//W/6ocfftDJycmNvlmLMs++T6dT1Wo1jcdjJRIJtdttbW1t2XxHIOsTGwgdoiuDEURjdXJyoqOjIxMh+7Daspnf37yWMZ/Pa2dnR8ViUQcHBzPteNBnotfhLCCc5h0lfyb5MVyG5y9d9zyk/tDa2tuSGzA74/HY7jmbzWo4HBpzDrDB4fbgiGeOU8VZE9ZDgoUiC67b7SqTyWg4HGp9fd0AKOvmruzBgSNvPpzEpPJiYL/J+zorCKJhSTxl7w95r8+BNaBxZKlU0vb2tvVx8/WV/MZC3ZbQU7hr+tjrmvDOhsOh1ayhB5z3gAgJYRzoeH7+AGVsfMjDs0WekSCuHAIfHwa8b1DgMw/D8CP3wwK8urqydgbeo+FvePawTT7F1n+FgDAEq2HvqUVtglwTnizPnI2fL0C+12yRcOBDp9DZfu6NRiO7P18qAu0S88vXJAuzY5i7y3hghuZ1kPl8Xru7u/rqq6/07NkzlctlC9nilFHwj/BKs9m805D7XZh3uobDoWKxmBqNhh1sOB8wrp51DFkH3o8EGPSfUTW1lsU8I865QTYmYXQyU9EZsuY7nc5MmDgejxsQ8uHSUIaxbOb3Tq9lJZHDJxcNh0Pb/728wle9X1tbu1G80e8/OLZeysCew1exWNR0OjWwFPZkuyt7sOCIwwf6jsH2yJPQGyjTFy1k0MPwEH/HRofqnoykUqmkJ0+eqFgsWvVjMuVQ63vanA7nsDiEGO7KS2LyeqYqkUhYzSXEfslk0rLEWOgwQjBe3lMHtXtGwYdEfLaWT9VnonJ/jKUHsV4A7+/jLsxvzJ6y5gstEdeGVkx6GxbKZDI3mCNYQ0/X+rlHHD1kJKk43Wq1VKvV1Gg0bB4sIm2ZuTIcDtVsNq1YG2OCRiuXy83Q3cwP2DHPJvp7xgmgThLOCgCc90GTQDYPcwpgRMkNtIGL1N68r8HMZTIZHRwc6Ouvv9Z/+k//SaVSSY8ePbL5yL7w6tUr/f3f/73+9Kc/6Q9/+MNMPZdlOCS5Bp4tPdOurq5Ur9ftAIMN4BkiP7i8vFS5XLZwiSQLubZaLSt26Zn00MFYpHnHz+v0KFtxcHCgYrGop0+fWjkLmBQYIkDlxsbbvoLMb7LAfD0571QtyxhwDZ5Bmk6nGgwGti/yM8AQTBIhtqurK3PS2T/JYg0/hzOHUCxZsABPHIxHjx4pm82qWq1qMpno7OzMAJvXSn3oGD44cOSBABR06H14psOL5xDf0i6DzZ1Yqs/i8gXriJ1Tn+Tg4MDagdBpGmDkDw1CCuhNwvol3M9djIlH9owL7MBgMLDr85k/LE5CldJsf6kwNOm1OLAsmM9EAwz5awtZm/cJuf3csfAxbw7rXq9n4NmHdRgz7pVwl88W8iHXyWRizxWmxIsM/Xt6JiXs1TbPg8ADaF/3C+0DIIZWH9vb21YFHiEkQBKdAe+L+WfPxhiGW9n8YI1gkHheVN8lBM2m6jVRy2jcM2P32WefaX9/34S5kuz+qIb8+vVrvXz50hqK+jIiy2R+PUnX+g5fpM87pKRcT6dTAxWEXgHQnU7H9uAwvLwM5vdIzgL0diR4IMT2IXQE56wzGDbYJmm2er+PMiwTKAotZN7D/Q1gwl4oyQgAwJEvncJzl67PG9ZQIpGwBCK0W2SpsfdQfNiHdTl7vfj7Q+1BgiPQKdTsYDBQOp22TRSAwqIlq+DRo0fK5XImjstms/aQWchMXGmWSSqXy3r27JlKpZL1o6ISsG+x4cMpVH+lYzXx9bvUHHl074EeaZftdtv0IT586KteExdeW1uzjYpXyh6EFZN9aMVrkNhMfJaGFznz76hQ1F1YGNJstVqSpLOzM6VSKdvUfZFCQBTpqh4cca+wKPwunqFn2gDpCO8RJZMtidbIi739M7wv47O493a7rXQ6bXorgDO1aDY3Nw0A+gwivxH592UsYQ1IUKDLNgytT55YX183gSXj2Gw21el0rO5Nu902gLSsBQLZH9LptHZ3d/XZZ5/pP/yH/6CnT59qf3/f5th4PFaz2dTx8bH+/Oc/6y9/+Yv+7u/+Tt1u1zQTy3Zv0mzhV4C9D2H4kGk8HrdaYpPJZCaTjb9vNBqqVqvWrSBk0hc9Bh7QeyBPmv7u7q6KxaKdAZlMxjQ4ZGiy9pnrgCkAAHpIogySIh3HZbAQGHFeSrJsMTJ3EWbTmN2DI8o3sMcS7eGc9glWVN6XpHw+r2w2q1gsZt0astmsnclXV1cWXgOk3lVo7UGCoyhmJpVK6fz83BYqCxcNwPr6us7Pz7WxsWHZF6RaQglysIcp51SKhjEiVZvGkWwQkgyg9Ho9q3hLLzffyPMudQXe6/DhI8RrrVbLNDWAGsYIOjzUkYQhS8/K+cXCQr5NFO/fI2TM7mMT8CE1AHQ8Hlez2bRFSmiH36fWDOL5kDmSZPobP678fSwWUyaTmdF3AZLYNMNK7PPcCKOYI5jMwWAwA3JhQNAd+WcfdtH2la79/KdcAiEXz6hK13owQA9MJz3FAJIAVp+9tSzmwy1Uv37+/LmePHmi/f19a1NDaKXRaOj777/X4eGh/vjHP+ro6Mi6rS8DKLjNmN9oCHl2rHev8/R7AeJknDLWAckpsAnLIkCWbhaS5SwA8FPMkuKuXohOFtrp6amtM8LxV1dXM3uvrw/n2fhlngfeuM4wGQfzc8Uz8jDVvsE5UQhAFvvKxcWF7bH9ft++58/oTCZj5zjzzO9ln1xYTbouJMZGenZ2png8bkgWMSmDnUi87TieSqU0HA5VKBSsvogHKZ5C9UJuigRSARjkipjOpyVzXbVaTW/evNHh4aGOj491enqqRqNxI1X3Qy1q8wL8JRIJ1Wo18+4vLy9n7tGHwLxOCBDAweU9d+4ThO5p0XDRR2UJshneB3PEtZN2GovFVK/XrQWF7zdXKpVm0sVhx+r1+gyQ82CT7DPuDU+QLA0OAMINlNvvdDozWTl+M5yXMTdjsZja7bbi8bgajYbS6fSNNGWy98JnTtaI1/QBjvDsfEib7yPYZAw7nY7NUf4/GAys5+Hx8bFGo5HVR1rGflvcF9Wvnz59qt/85jfa39/XV199pXQ6bYxjq9XS69ev9dvf/lavXr3SP/zDP8xkMi3TfUWZDy3DGoRJK54JXV9ft5o/6EfI0qIidqvVsuynZWIEPQsOuEmlUiqXyxZ9APjBrA8GA6tqXq1W7V5SqZR2dnY0mUxmRMW+yOEi6p39XPNSCOZDWBqEeeB1mJwDFDnlufv34dxmzeCcjcdjbW1taTqdqlKpzGiFC4WCLi4uLEPurkXZDw4ceS8YLUej0dDm5qY6nY4k2eYuXafhS7IWGdPp1EJNTMqQSvU0H8jUq+f5fc+yIMCt1Wo6OzvTycmJarWaFZ4M+1bd17jAqq2vr6vVamk6nVoKOnFbJliYKeG9J7/5hRPOH6jhwvDgiK8ocHTfzNH5+bkJehHik84P9esZFQ4y2C4v6iZmTmozQlPPgIQxeQp/whr5TXCeh2IYeiXc12g0LK4/Go1myv97PZoX23qHwOsyAJo+PODnAmJcwmewehcXF2q1WhoOh1YM0I/XMgp10eiRpPH555/ryZMnevLkiaW4o8Fot9t68+aNXr9+rVevXunk5GSm7tMy3NP7mr/W8LoZFxwQwlDsvV4GwXNfpmcbCrBxFLgfGCNS9dfW3laCJ2nAJ9zAwBJuJXSN88p642tRdc9+qoVj5Pd/H2nxIMqH4sJyBeG9AqrC88Prl0Kdq88+v8tMNekBgiPpmpno9Xra2NjQ8fGxJKlWq5kXD/0fDuBkMrFsHMSoHhj4sJr3fPEkODR48DALeIKnp6d69eqVfvjhB3333Xc6OTlRtVo1bcF9bIo+LoxwEq9cktrttqS39KRvn+End8hkhbF33ouf+cXghYUwSjBO0M2egboPGtkvSJge7ml9fV3dbneGJkecH4LKXq9n+hxAE+/n6WTPGvkGoh5oUR2aDfQ+Qqo/ZWwYd+7x6OjIClTmcjmrcOvbIpDM4NPu/bObTqczLSSkazEmhyBag0ajMQOC0JxQD8mDJg9Ql4k18ntEoVDQo0eP9O///b/X48eP9c0331jGH/d9cnKi3//+9/r222/1u9/9ztLYlwEQ/BwL2QMcRMaFBszlcllbW1smxkaI3Ww2jUFfZO+4KPPOIFo5WCKy0yqVijHkzN1Op2M1qrrdrjnV6+vrlvJPn0CcNTR1hI+XKbwYZVEOM/cYfnknyYfVfAVwf97wvrD+6NqQJYQAybPcfqx9mO8u7EGCIx8K6ff7qtVqWl9ft3gvgi4f48XbY0ChOqXrHi7eE/aMiZ8Q/L4/BPEcYItevnxpZfHZCHxo4L71NowNB+Hl5aWazaYkmTAwmUxqOp1ahVvPaIXeEzFzNkF+x7/y+X6Ce0p1Xp4iGggWn19U1Nhg4fnrBnDznELxuD+gGRuvv/Dg2YcZPPu0yAPRA2jSatvttl0XWUiUguA5wbqFtcC8/gTgzDijpWk2mxqNRgaCarWavVLmAn0euq/Qu5w3kHyX8ZxhEl68eKGnT5/qxYsXxhjF429b8MAYvXz50ipgk+H4UIFRlLFXwLBsbW1pd3fXar9Jb9cgySk4iX5NLIv5vZ71jLYU1ogwNPtHWMLDg6pisaidnR1r3g0bytxnPixrskGUhSxRyCIRmZFkST48b8qGsDf7/dRnq+GUwbhRQoQMNjKl/Tp6F6v5c+3BgSPPUNCV982bNzo/P9ejR480GAxULBatqzwbu3+YXhvjY+Uc9KHA0P+ep/x8Sf1qtarvvvtO1WpVL1++VLPZVLVaNc8cke59AiNJMyDHF3+U3npvm5ubGo/HFtulsjf3zt9yyPuDz2uMPJ3qwYAX24bM0X0LMP2i5F4AdITRPAjm//75eo/mNiDrxfsYjJQvLgoQWZaDnvHwIT4SBegQj4dLh3Rf8ZtxgFHlvphzMIMnJyfq9/t68+aNut2uqtWqsaqIchGG42gAxHgG96FJ+xDjQKBf2uPHj/Wb3/xGz54909/8zd/YIUqo5eTkRP/4j/+o7777zrLSer3e0tzPh5qXIgAYc7mcnjx5os8++8yyuihlcXZ2psPDQ6v5FjqLi7YwZERdIzLNAHyFQmGG+Qg1mRzu29vb2tvb0+eff24tpohWdLtdnZ6emvPgWellGIvQQoLAn6VRgNKTCqTmX11dzTQ494JpzhLqGeVyOSu9AxNZLBYNJBHJ8HvGfewVDw4cSbOePl2/NzY2dHh4aAc+YRO0Qr5gGYxRqGj32gwPhnzYxdfOIRMNluiHH37Q2dmZzs7OrI5HVKfl+xoTXj1IAdBQvbTRaEiSdTaeTCY2Kb0IF2MxEELiZ2FV0vBQo6idB0eexbnPceC5erBzG+Pl54AHyuG4hhaya/wujOLa2toMMFoW8/fIpsxm40Fkp9NRq9VSKpVSo9GYKXLKXCH7zovVLy4udHZ2ZiJVwmeeKfJ/59dY1LpbBgtLgnzxxRd6/Pixnj9/rt3dXduwYUjevHljYXWy0kaj0VLd04daGHZPpVLGlpTLZSugC7NO42+vsVm28Yg67AG9MBfoVAkVFQoFk2tIb88bQGKlUrF+awAjMpjJ3HoIrFH4nG4DSzBtOE9EaK6urkx6sLGxMVMEF7aNWoLJZFLb29tKpVLa2tqyRCiiQWStIYSnXhvn7CcPjiTZBJ1Op2q327q8vNRf/vIXnZ2daTqdWg0EugVT3TQshPiu9/eeLOGhwWCgZrOpZrOpw8ND1et1/fDDD2o0Gjo+Pra4Og8Pzzrq4L1r859BuIdwEgfT+vq6+v2+YrGYVc/O5XJWQwLzLBtj5Q+tEBx5tmYymViqOKm7iA/nBRS9KFiK7uHmvxclTL/td7GwtpOkGY8GhmZZ0tA9s8azJJ6/vv62R1EikVCz2bRskUQiYe0vANHUL+L+AEXMMeY/dZ3YDL3eIJyrUde5DAeGZxJyuZz29vb0N3/zN3r8+LF+/etfK5fLmcao2+2qXq/rj3/8o/7617/qd7/7nTlQyxZCuivDOchmsxZS29/fNwEynehPTk5sf4QxXMbx8ICPqsyk8VPDye/jOzs7Gg6H5jigL/rss89sTGBru92ujo6OdHJyMpOtt4js1fcxL6O4zRgvep2m02ljmtljKCi7trZm+ku/F5AAVSqVlEqltLe3Z6AonU5bfUKcEBI2ANzdbncmC/yuxvJBgiPP8OCpo22gdLlnSCqVilKplLVL8DV/PADgPUNlPSEi2KB6va5Op6PT01MrJYCnHWYmLcoL5vCBGSGNu9/vS5IajYYd2lwvzJrPQMK8dku6LntACNKzEYhRESET3gtTw+/7/kNm8LbfCb/nLQpA4SWxeQIiYN7Y7NAU3HYQ/Nj13ad5hoa5Gotdp2r3+30LE1JQlBA1cX+/PiaTiYlseQ0ZIt+S5V1hhGU5JNgbKOWxvb1thz/era/J8urVK71580Z/+ctfdHh4aAfgsgKBDzEPGgk10oAbXY4vt4KHv0wZardZFDj3GlSYMkna29vT+fm5SqWSAWgqzScSCWNKa7Wajo6OVK1WrTGzF2Iv61hg/nndJkPgTEUbhJG5OZ1e90PzawIwBTsESELyAXgictNut62KPhq2+xjLBwmOpNnN3XvozWZTrVZL2WxWjUbDqpoCkuiojMdD6j7siPeEWcy04aCY49nZmXVbHw6HarVa5hlEecjznPhRYULobXRD9HBCFJjL5axieFjgD6aNHmSeSvUp/oAexOdkYoQgaZ5hpqix8ObBSRQI8v/3xcUQXbIJQgcTWvEFKENNgWct3wfA3Yd5BglGkLm/tramfr8/k4DAc+bvvKbAM4pexA57FrWphtexjOYP/0wmo1KppGfPnunZs2d6/vy5SqWSVUc+Pz9XvV7XH/7wB718+VJrviU+AAAgAElEQVR/93d/Z07TMhaw/FDzYRWEs2hsqAdEqnuj0TAnEiZx2cDi+7IjrH3ODMTCnEW+RhiH+WAwUKPRsBDr999/r2azabqrsDDuspsHRl5vSHieEheUs8BhRL9I4od03ZAa59IXn6XsCnstIvjz8/MZUqJer1sE5K7rRT1YcCTdFOB6AHB5ealkMmmZMMlkUt1u17x9n56PIJksJ/Qxvuw5lbiJn3P4+VcPiv5/9t4kxpFsyxI7nGk0GufJpwiPyMjh568CqgAB2morQUDveisJAnqlvXrX294KECCgF4LUG3X3rrXQToCglTZSFapQWf9XRkbG5BPn0TiTWkSe65fPzT0iM510o7tdwEF3Op1Oe/befeeee+59d6npH2KMNMtGuhuAbOissuGE5GZPVE9GQbMn3Bh1hRZTaCxh152h/ZZfv402viu1xi9GSbo7NAGC7oytHaAfWRL9//WcNUXr+vUaFJuCdpMt9QJDD33NnzOdKrBtWzb94+NjVKtVKfaglm8wGODs7AyvX7/GxcWFMCXb6mn2kGbOfx4P4jgOstmspF51ep3Rvdc5an6YC9oP8LNRY8rmsIPBAKHQp0anZE+pq9HvoauYqTFqtVp4+/at9L0ze975mUXTpseGjLFmx3gmGgNEfdA7cH1MlZ5DDEA4lwg+eT905Sr3WTZUbrVaot8yi33uwx4NOKJTZiTH1u7JZBLdblciQEY7uskjc5ksaecXbzgZAE5qfegoqT4uCr0pPKQDMDclXUXGKiWyGjwyw7ZtYYioQWIpt84hM0ogmtcCaE7WZrMpvU2YZjEFuH5wCF7szV36I7MyQ+uNmDoyz/0z+2n5zTS4+VwU/SXvte+m02nZbBbPnz9HuVzG119/LeJQdpwfDod4//49fvzxR/zd3/0dWq0WGo3GhpbkMZne2BjdU4hdLBaRTqelySr7/rA7/OfSzA9t2i/RnzMobrfbWK1WyGazGwedEiQxUNSgqNfr4d27d2g2m/jpp59Ee8XgaZ9Sanwk6OW+R/AYjUbhuq60MUgkEjI+3G/L5fIGGNIgCLj2s9wnGFSTrGA68v379+h2u7i4uJAq0G3oWfcaHAGbztjsI8RNebFYSCNAUqP6izeSJdhExmSDtIaCz3PxaGpR06N+ioyAzRYI6/VaOj6T9qTAlo0ix+OxsGz8Wfe/0TQzI6b1eo3BYLAhytUNIM2yS36uhzaTQTJBkQmcdFpMa95oBJxMMZoRop/AoWleINF8/jEbN/5EIoF8Po9SqYSTkxNJF/EQTPYyajQa+POf/4z3798Lxf8YU2nAzT5AulkotXf0BawkGgwGkk7zM0uigyT6SfbgCofDuLi4kEIWphLN0wOWy+sz1qhL/fjxI7rdrlRu7qpqdxvG/Y3tXXhEE3Ws+hw5VmfOZjORawCQyl6CLB2Ycjx0xoatMajbYmNVtiDRB1Pf91juPTgCNs96YfRL9iccDgso0pVXjHwIigBs9K3hpqcFpbofCxcRH/k59KMfTE88jgs/L69PH8pLzRHRP49MYSqS164bgPH9dYUWHSLZNrMfiN8ApFeKTYMk/XtTvE9QqcdAl5n6rRPwl9q+fd7fa1pnRI3i4eEhXr16hUwmg2KxKELc8XiMy8tLvH//Hn/zN3+Der2Oer0uDKlfQcBvNc55XclF/0DdHYEjGbVerycaUK/0st/GRzMjZMT52XmtDBKZbdB6PDJNbGMxGAwEVHW7Xen7RuZlH+aIea/0fkIZCvVVur1HOp0G8AlAAZDqRT3G+n9QkrBcLkWPxepXNpFlvzSt+WX/vG2kKB8FOKKZg6KZClNUq3OeWnhK04yQCYZuYwD8PNFNalRPJt3dlZTndDpFLBbDYDDYYNfMtJpmUXTajpoLLhavajW/jZcXQOLzJuVOB6oFhnR6FPGbuqN90xg8NSMTyoaGtVoNtVoNpVJJUs7U37muK/3N2NjQz2XZ92Fah6b1dtwcqc1ZLpdoNBrodrsbfX28NEd+Mb3pA9c9wLhBUzg8HA6FNWNqCLg+NYFrnsx5p9OR7MM+gSIvM1PvlJHQ1wPYaOtB1shsDaL3XRIaq9VK9g6CHmYher2eMEfsl8ZMjk7T3ve4PipwRNMaCm1eAlvzUQ/ubaBnHyc2cLP/j65QIkVqiuS0A9Rm5ot1BEXwpSv2mIo06WS/jaWm1/V80I6BwIisJB0in+f1cxMlm+D3a3/KptNFtm0jl8vh5OQEBwcHqNVqSCQSSCaT4sT7/T4+fPiADx8+SETrx6af2zD6Cd0RmQUsbGdyfn6OTqcjFUUU0/p5fLjGdSUWmZF+vy+FKpwnwHWaiJs0/4aVztQbEjz49drvMq89UbcB0aCQAXUikUC325U+UYlEQgClztjQn3JdMetAcMTDw5niZPNNsvYkL7YRdH4WHIVCof8ZwH8JoL5er//il+cKAP49gFMAbwH88/V63Ql92i3/BwD/BQAXwH+9Xq//v3v7tL/Tbhu4uzQmd/3dPpo50TXrw4lKalyje51bBzajSK+fNZo3dVl+Z09MYARcg0mOjXkQLSt06ATpOMxDE/2sN3qqxvmtRcbU0JApMnUl1NLoc8L2MXX6W0yvaYpwG40GlsulHPD84cMHdDod0WFprY2f579e+zoIWi6XG0Gk9oum1EKDpH1mikwz9wvgGuwx8GXVM5sN8xgWri2tU9UNhHUGgw0dddaBAEwH3F4Znfu0L2GO/hcA/yOAf6ue+5cA/s/1ev2vQ6HQv/zl5/8ewH8O4Otfvv5TAP/TL4++tMcMgj5nJnuhc8C3MWzmc3c9r9NtwPUi2heA4DU3zHJfgh/tJPk6OtSH7HkV2JcZ56pu7WFZlqQBNOtJsT0b0VFTd989Vvxq2l+sVp8OGQaAi4sLuK4rGyDP1avX6xvau21F+fdp+rPd5RfNv3kK5iUjYUBNfZHZHw3Y7BXFn7V2Uz9q0KP/p/l/vRit+7TPgqP1ev1/h0KhU+PpfwbgP/vl+/8VwP+FT+DonwH4t+tPn/T/CYVCuVAodLBery/u6wMHtn3TE81kUG57nX69fjRfuw/AyDQdMWnHrisuNHDS4Mis0Nun636KRpAEQMqUeXaj7sTfarVET0NW5DHfXzPIYbooFApJpM+TB4BPHfhd1xW9iE6p7SuI3MfPvE0zmST+zHnhJWvx2h+8QI8XGDK/v+u5+7DfqjmqKsBzCaD6y/dHAD6o13385bkAHO2p/Rpg9JjNXKhc+F6LXbNmZlQUmD9Nl6lTC6GrDlmg4Loums2mHDBNsfFj7Glkmg4OCHgmkwkikQh6vd7GkUPsaWRW+j72MXpqtm325iHtdwuy1+v1OhQK/epRCYVC/wLAv/i9/z+wwB7CbkvJ3ibqD8y/xk2fAvqrqyvRydi2jbOzMwFM7OHF4wv0eWF8r8dsZioFgFy/rvbVOruANQ1sH+23gqMrpstCodABgPovz58BOFGvO/7luRu2Xq//DYB/AwC/BVwFFpgf7Cnr1h6LaXEnRbSRSAT1eh2xWEx6tlCEzHJlXZ7N93kK5qXJod4ksMAei/1WcPS/A/ivAPzrXx7/o3r+vwuFQv8On4TYvUBvFFhgge2DaQZJl3Fz49e/N3uFBRZYYI/LvqSU/3/DJ/F1KRQKfQTwr/AJFP2HUCj03wJ4B+Cf//Ly/wOfyvhf41Mp/3+zhc8cWGCBBXbvpoX2wKdGgIEFFtjTtJAfop4grRZYYIEFFlhggT2A/b/r9fo/MZ8Me70ysMACCyywwAIL7KlaAI4CCyywwAILLLDAlAXgKLDAAgsssMACC0xZAI4CCyywwAILLLDAlP3uJpCBPU676xwh4On0dAkssMACC+zpWQCOAts4KJZHAESjUYTD4Y3DAnWzPPZ62ZeDZAML7KEtCDgCC2x/LABHT9z0mVLhcBixWAyRSASJRAKRSERAEh03T9YOhUJYLBYbRycAgYMPLDDTzPP3vA7dNC1YR4EF9rD25MCRGb1pVoT2VByTZowIihzHQSKRQC6XQzKZRDweRyQSkeZ4PISTp5HzbCl9jhLwdMbwqZt5ltxTN72myLpGIhGEw2FEo5vuVrOuXDsmExswsoEF9jD2pMCRjty8QNJTOidLjwVPIo/FYkilUrAsC5VKBbZtI5lMIhqNysnavV5PAFEkEpHTyPWp9Y953J6afS4VZL4muPeba4rrKhqNIhaLyVhpMMS1RRaWqeuAkd1PC9Knj8MeLTjS0Rsjt3g8vvFIfQ2B0XK5FCe1WCwwmUzkDKXHtunzujk2lmUhkUigXC4jk8ng6OgImUwGiUQC4XAY0+kUi8UCoVAI8Xgcw+EQwOaBkzyEMmCQ9svMoIE/a72ZNt5TvXYAbNxzEzCbf/uYTI9bJBJBPB5HPB6HbduIx+NIp9OIxWJIJBIAID5mPp9jPp9jOp1uPDJ1Tb8TrCV/mskOan9KIEzWHcAGQzifz2W/4d7D1zxm0/7kLhDpB5/xKMGRl44mGo3CsizEYjFYliVOjK+hyJigaDqdYr1ebxwu+RgOmTQ3QoLHeDyOZDKJbDaLQqGASqWyAY5Go5E47nA4DNu2Jc3GRQ4gYJC+0LzSuQ/1GUwwpB/D4c1uH/r+8nX6hHa9mXuBpceWhtNjSI1eMpmUtZRKpVAoFMTvMNiaz+eYTCaYTCYYjUaYTCYbvigUCslGGjBz/jOvPYaBdyQSQTKZlPkAXKdQCXrJvs9msydT1HJbEGbabX5i12PzKMCROehE74lEQpySjuSy2Szi8TgymQyi0Sji8bhM2PF4jE6ng9FohFAoJIdPLpdLzOfzG3T3PpnJCnAxx2IxZLNZZDIZHBwcoFwu4+joCNlsVjbHWCyG8XiMwWCA5XIpfxeLxWTBAxCx9mNz6LelZL8kPat/p9/HS1uyKwepHZTp6OPxuKSBIpEIgGswxL8FrsGUjn51QMGKRvP3j2Ej0GPA9FkikUAymUQul0M6nUatVoNt2yiVSojFYkgmk6LTG4/H6Pf7iEajG4yrHj8NlIDbN43AdmdmUMmAmxKETCYDy7JQKBQQj8dhWdYGKJpOp5hMJuh0OhuPo9FIQPM+7zE0r73GzOCYxT70CZz/5qMZbG3bHgU4Am5OWs0U6YmbTCZRKBRgWRaKxaI4rfl8juFwiOFwKAJlRnOz2Uw2AUZy++agzA1dRzzxeBypVAqZTAb5fB6lUknSawBkowOARCKB2WyGaDQqX9wcODZmumXfzQQQ5vP6Z5p57Wa0xPHRuhP+3bbHziuCY1Ch00LUm/FzMgpmCoFjoZ0YAEkZTCYT2RQ0MDId/77OE1Ovl0wmYds2crkcstksarUa0uk0yuWypNUmkwm63a4EZGRfuZYWi8VGC40v0XwFtlvjfY9GoxuAOJVKoVwuw3EcHB8fI5FIwLZtAUez2Qyj0Qiu66LRaKDf72O9Xgsw0nvMPpvpXwiCyKpZliWkhPanTCMzrTydTiVw0JmJXQUJew2OzE2Lzp1MkeM4iMVikh4qFApIpVKoVCpIp9OoVqsCDAiO2u02lsulgKNYLCb0J9NH+wSQzNQJNzayA47jIJlM4vDwEMViEScnJ6hWqygWi7AsSzY3skRm/yOvNIzWpOzDGHnZXbS5BhIcDwDy6KW50mPCn8lGMmLUOpNtsSsmGNLrJplMIhaLIZ1OI5FIwHEcxONx+Vs6M44Fr4XXy8/uui5msxn6/T6m06no1QiW7uqRtQ/zxZwXlmUJW5DL5fD8+XNks1kcHh5KUMbXR6NRGQ+uJ37xNXpsvQD1tq9N/98vAWia3fL6nTmP/aAn+S2m7zs3d8uyBBSn02mZA4eHh0ilUsjn8zI2BEjj8RjtdhudTgelUgmtVgsfPnzAcDhEp9MR/dm+MUj0f3qP0exaNpsVciKZTCKVSonuF4D4wuFwiOl0ik6ng9lshl6vh9lsBtd1NwoXgO3On70HR8AmbccIjqidIIlOynEcFItFOI6Dg4MDSbcRHMXjcXS7XQAQZO+6rqQV9ona9kr56KiH7EA6nUY+n0e5XEapVEKxWBRAOZ1OMZvNJMWiHbj+H15pmn0YIy/TgE+zKdSTEBTxZ1ObQ2qY32vtiNaSaBGu/tttCdrNe2X2tmLrBq4XtnPweh2ZQv25GTj0ej1MJhMAwGQyEUYEgFzrbYJjv88bE1ySPUilUsjlcigWi1LMUKvVpAKU18mUtGZcvYDFQ6yl21KttwEknRq+DRx5tSbw+js/33PAW7qhU6kESo7jCAPvOA7K5bLcY86ByWSCYrGIdrst4GE0GiESiWA8HgOABBD7Yl5BVyKRQDQaRTqdRjKZRKlUknRzOp2W/Zm+k76w0+lgPB4jHo+LpnU8Hm/4Do7nNtfG3oIjr3QAK67o1AuFAhKJBLLZrDxP2judTm9EyMvlUpDscDiEZVmYz+eiEwiHw5hMJqJB8ruZjBGdHDc4phorlYpEuYeHh6hWq4Lso9GoTEgKSKmXmM1msuHpjdbLkfp9wwNuAm1qbjinMpmMbIAE1ASYZNWAaxZFVz1SyA5AhP/UngwGA9EcUI/Acb1PhtLLefGz03kVi0VZH5ZlIZ/PCyvCayUgZLqN1wRAmNXBYIDZbIZ2u43xeCxgqV6vYzKZYDAYYD6fb+gstLbAr3NFM9RM2zuOI6zrq1evUCgUcHp6KmwC/4Z6k3A4vHGNXJMaIGvbhb5Czw2CNs1q0b/qCl+TOeYGx8/LnzmfTR2aec+92hf4zbwCQQZC4/EYkUgEnU4Hy+UStm0Lc0pfwrWzWq1kfa3Xa1iWhdlshmazKUE6WRQ/rwfAu1eebdtIJBLI5/NIpVKoVquiZyVgTKVSwiwBn+YNwRH9RT6fx2AwQCKRQK/XAwCMx+MbwWcAjm4x7bDi8TgSiQTS6bQ4rmQyKY0NbduGbdtIpVJIpVJSVcLILpFIYLVaoVgsAgB6vR6WyyUsy8JisRDx5L5EOzQz9ahz5VpnVCwWkc1m4TiORDvaAXCTJzDi9dNxalC0jwySHiNqRDhfyuUybNtGuVwWVkUDhUQiscH6cLyYVmJKCfi0YbiuK+0QuHkSOPF9PpfO+K3XyOuk3ozXSMaQ+gk6N7KIBEcES6ZjInNEWjyfz2MymaDZbMJ1XYRCIYmQp9MpVqvVRsGDFvP7bc54MUZkXavVKqrVKr7++mvkcjkcHx8L0KDeKhQKYT6f37g2zjdtn3P49zk+ZvCkGVGuAS3O5yZvlq7zkXOA1zQcDjd8htajETBqYOTHe+9lJgs8m80wmUxEs9putzGZTGRMmc3QexW/X6/X6Ha7WK/XaLVaWC6Xsr685oxfzASLnDvcW+lPnj17hnw+j+PjY6mGJvus5wzniOM4MnaDwUDGlMVAOuW4TfZor8GRSf+aGhBGJLqvyHQ6xWg0AgDR1PBGMdrL5/MAPqXVAEiardfrSRWJnxeyV4SjUyNky7LZLKrVKsrlMiqVigAAikUXiwV6vR56vR4uLy/RbrfRarUwGAwwHo9lg+MENUuRTfbKr2Nl0uRkitjvKZfL4dmzZ7BtW8qytRaHxvmmwRE3Rw0myRh1u13ZcDhmZBBMduH3jJ3JinGDYxBxcHCAQqGAw8ND5PN5KUGnRoDgSDNOun+Lvv7VaiWammKxiOl0ilqtJo8Uo45GI9FZcCPRIBzw13zRoCiVSsG2bRwcHODg4AB/9Vd/hVKphBcvXsCyLKTTaXH2y+USrusKY0ZWbblcbsgBdBf627pl0+471aoZI128Ytu2VF+ZRS0EyvS32u8C1/oT13WlnxPHgizJaDSSaq1WqyVBg2aa/DQH9L3Q+qHpdArXdaUlQzKZRLvdFlaWwZNt26hUKqJ7JWhiu4fpdCrAgI13/brHAN5zx7Is1Go1ZDIZnJ6eolAoSJpZn7oQDodvVLPyi/7Qtm0AQC6XAwABkOw/qP9uG7bX4IhmAgBuBhocaZDEKjRSoawQ4SR2HAfz+Vwmqp6wmlHxs5kAyUwX2bYNx3GQy+VkQyTDRhH6YrHAaDSSjbzT6aDf78N1XdkAtT7GS7OgF7ffFvptjJplWcjlcigUCnj27JmkTJhW4+t15OtVfmoKrPk/XdeVNBwZJTpZzU7eJ4tyF/NRKBRQKpVQq9WQz+clrcZ0GzdEGgXZ5ufidfLamZrO5XKYz+dIp9NwXRfpdBrD4VAKHqhP0r3F+H5+MA0uCaBTqZSM2YsXL5DL5WTDY5pMC9HH47GsG/a2MfV7OhreNijS18Y1YOqnqM9Mp9PCJvJnMktklAiO9PuFQiHRinBOkFV8//49BoMBLi4uMBwONzruA5u6Er+Z1o9Np1O5ZqaFotEoBoPBBtPG1jGu64rcg5kMNgolANWBiF8DS72/aH/CuZPP51Gr1VAulyWdpq8LgOwxBDla56n7grFgKplMbmgYt70P7zU4ovPQExUABoOBOCj254lGo4LmmUbodruCSqkPYNm+1hQUCgUsl0u0220AkMXsx861Jltjbv46TVQqlXB4eCibouM4ErEwims2m6jX66jX6+h0Ouh2u+LsZ7OZTGo6ATo1Tng/L26dQtNUcC6Xw4sXL1AsFvHNN9+IuJavZdQ4nU43KilM5kgzUky/kS7ne2QyGSwWC2GsdDn3fS58/Vksy0IqlcLBwQFKpRJevXqFWq2GarUKx3GkuoQbHtMg3Lj5PmQDdMDAz23OCzq5xWKBSqWC8XgMy7LQ6XSQTCbR6XSwWq02KlL8sDnq6JiVraVSCdVqFd9//z0ODg5wdHQk93e5XGI0GmE4HOLq6gqz2UwAwWg0khSTTp+SWZlMJhtAYtvNAe9KtxMQ0TdQTEvtHUERWUQvLRKAG0EDmaRsNgvXdVGr1TAYDGQOvH37FoPBQIIKP8wBYLNYwOxwrcdxNpshEonAdd0b4Gg+n8vYTadTScWTjaUshGPr1wDci4nW2QjuLaVSSTSawLVeiICZLCoDBVbHMiWrq0FZFESCwqzoBO5/j9lrcARsAiQ6VNd1xVFRVKwrjPr9vvSfWCwWKBQKWK/XIswGsNE12rZtjEYjWJaFyWRyo1LHr+bFFtDBkzUqFArI5/MiTicjwBx6v99Ht9tFv9/HYDCQFAEnNY2bPtMDfmbXvMaFgIEbwcnJCUqlEp49eybPa1DDjUynTExwpDcbOs94PC7OlZo3vcHohX9f4+fFfFAQWi6XUavVUKvVhBUgKNJdfTULxo2QrJc+MkGLdjUjEQqFkE6nAQDZbFZ0WI7joNVqAQCazaaIW/n//WB6EyCTxnRBpVJBPp+XzYzgqN/vo9lsSsqFc0anZUyNmo6kOfYaIAH3twHouWUCJPo9fa2lUgmO4wgDwHvqBY44ZsB1l3Rek23bEgwQIOgU8+XlpQjX/eY/9D3QQTlwrdliYEOQxA2dvpX+Qvcz0oGLmZ3YhSD/t5i5vnWfr3Q6jWw2K0VP7I9GTVG324XruiJY5/swgNJ6JL63TuN6STa2YXsNjjhpOEE1CmVkG4lEhLbnxGMFGmk+Rq3pdHqD1tflma7rIpPJYDabIR6Py+v8lhcHvLVYRPecuMViEeVyWehzjgUj13a7jW63i6urK1xdXaHX60nky/HRjpALgM5BV1z5KXduLmgu4JOTE2SzWbx48QL5fB5fffWVRMp0arPZTABis9nEcDhEvV6X5m7c9LiZcnMBrplJveh1awTdWNGs/Pu946aBGc/PK5fL+Pbbb/H8+XMcHx9LDzBGuNzkKSjXTdnokDgfCKa4aep2B4wITf3FcrmUMe/3+8hkMhgMBmi32wIktO7mIYz3i2ChUCigWCzi66+/Rq1Ww/HxMTKZjARL7Hp9dnYmLAjTawTE3ES4uQLX60in/03NEbC9tBofyZBpoMNggIL61WolgEi3dzDnKj+3GeHz9dlsVoD1cDjE+fm56D8pwPWjmWlOzVpoxtQsqtB7D1NnHGcKr02dol+CA9PMYCuZTEqfr2KxKOw7tUOj0Qir1QqdTkc0hyxIYZDIFC3Hj2OjK/3MVjLbDqD2FhzpyWiieE15cqPWk5QO3nVdWJYF13URj8dFIc8FoKlOM8on9eenRez1WbyqUCgoTafTUrnHqJ9N+hj9UpDNNJrOD+uuyXyklsbc4PXne8jNTjNG3KjS6TQqlQoKhQJOTk6Qy+VQrVblvjOtNB6P0e12MRgMcHV1hcFggHq9jvl8LpQx2TPqtrix8v/ryh5uiuYY3fecMlNDjuOIXubo6Ei6oXMOkMlgKohOTPcaWa/X6Pf7G+CIVWy2bW9UOjGtlk6nBWwAEHF7pVJBKBSC4zgSfOijFB56vhDQMdVUrVZlvliWJT6G1UqtVgutVguNRuOGwJxBCE1fm5cY2+t127xWABsbDxvgUqcJQIJODXa8rgG4rmTlIythuSbIRDqOg8FgcEO79NA+4zbz+jxeIJZ7EABh2thCQ4MjXclngmM/mg7k9L5CIX8qlZIsAisT6Tvr9bpoDheLhWjYxuOxpNHM4h7OCS9WfVuB996CI+B6EmrqlikhPZAsh4xEIhKtcMG7risaJXatZeM7phKYciHlp3UhBGR+M5P2JAvGUkoeEZLNZmUzC4VCkgZoNptSodbtdkVICmADxZvgiGW8ZOu8tAMPwSKZoIiMEQXX33zzjXQ3tm0b2WwW6/V64ywksgK9Xg/n5+cYjUbodrsyT/RYE0zrn1mlwQicEaIW72qBIvD7NwXOAV4z+/C8fPkSz58/x+HhoTCHbNXQbrfR7/fx4cMHEeKPx+ONHkza6ZkbJTVLrIarVqvCypKN44aYTCbx8uVLFAoF9Pt91Ot1TKdTGVfq2nY9X3QKMpvNCqt4eHiIP/zhD8jlcshkMrJmBoMBzs7O0Gg08OOPP6Lb7eL8/Fw+N5k1AiSmCrimTGCk9YzbuHYzuOT81e0lmAJiuoTBAn2F3qR0Cpbvx2uNRqPIZrOwLKHOx+cAACAASURBVAtHR0fSdJbd5nUvJX42fe1+BQjA5jjqAIdBANdAoVDAwcEBvvnmGznHMhqNCuvcarXQ6XTQ6/Wk/YHfChOAm+lYrhEGmdSkUUrAop7RaITLy0t0u11hjsiskUnVvoprBbhmVoHdjsXegyNOTE1haiW7nrgaRDEiIkjiZCSiX6/XQvXxi4hfi+XuK/VxX2ZOXj3hCJDIGnHhMh8OQBiD4XAoKSRWYWgHYGoNSIlqAGVWXQEPs9DNNKOOdKgxqlQqosEisCFbokvvm80mut2uNDRkF3VGwEy5MmrSaU09b7iRkEb30pvcBzDSEV4qlUI6nUaxWBTmgw1S6ag1a9hoNNBsNqXsmgCOxkjXZMS4gebzeXGSk8kEuVwOoVBIIkNG0eVyGYlEArVaTRik+XyObre78f92ZZpBYel+JpORgKJWqyGVSkl60HVduK6LbreLVquFer0uWj2d0gSu14WZJgCuAcYuCz1MzSbL0/v9vsxPblYMHBkIMTjUoIjzl/6CRznxyCbLsgBAUq16juq14Rd/+iWm/SKwmbrX1X+FQgHValWOHAEg6Wr289ENdv2aVgM2TxGg39PdwnWwzCa3zEJwX9E6PQ2OuK/oNjHA7fNiW3Nlr8ERcJOW5gDqDZHASYuGAYjWyLZthMNh0Y3oUnZzg9NRDmk/v5i5SIFripyLNJvNSmUauzwDED0Jz/xpNptotVoYj8cboJGsAFNx+pwtjjNZBqYbvHr36M+8TTNTBRyHQqGAcrmMly9folgs4uDgYCPi4QKm3urs7Azdbhfv37+X1Ak3EwCiO6FjIPgsFArSK4abB53hYDDwFLrfl+ZAp4U0c8QNnn2MCGrJEr5+/RpXV1f46aef0Gq1RDhpivDpwDX41UCw2WzCsix0u10pdtDFDzytnik9Htb57t07RCIRtNvtjY12V6Yj4nQ6LRWd3333HUqlkgiwqUH78OEDGo0GfvjhB7RaLbx7905YaW6SZFTJNPN/mFHxNvVFXkZgpI+rYCVmv9/f6GOk+xoBm2yXBln687N56HA4RC6XQ6lUkhJtvemRoSVA93NKyTQN8Aj4eVQVdWlHR0fSLJTjxzFuNBrCOtbrdYxGI1+PgfapWojNdBoDZ97LwWAgoIgnArBgRRfDEFjRfzDFqM9n1MHjtm3vwRGwmV7zYk5MYSDTbDwNezgcwnEcTKdTKdXWN4GOjDfTdGr8X36dyGa6hxNRV6dxo6fGhKdHc5ES5XPyM/IjOAI+jT8bbHLT5cZAp6nH1IwKtjkG5jiQAi4WiwIWdSk9ox0Chnq9jl6vh0ajIYwRcK0p4LUC2GCmUqmURM/6OJHJZCIbKJk5Lv77Zo80W8b+VuxdFIvFBJAxTdhoNESI32q1ZB6Y0axO/+lx5gY6nU7luITRaIR8Po/FYoFyuSxjRRASCoWkHxL7wWgd4K6N18Fu6IVCQdhFbu7sXUS2iClXMl6sWAI2dZA6cDNZ1V36EK/AErgWhjPA0ewjf+ZnNeeq6X+52XHMdGpWXzfHS6eV/ehP7zLtXxiIUrrAatBMJiN9zXQfObIqbCSpfaYfTc8JZiXMzAqZR+3nzEo9XSzktb966bHMuaH3kvucM48CHAHeYji9+HROXKfYwuEwut0uLMuSfkiM7mKxmJQXTiYTSUFto9z6Ps1MI1EAyY63FM2RJWH/Edd10el00Gq1ZJFyg0in04jH45J20udtaXAEQBi44XAoyJ8HCOp7AewOFGkNAI94qFarorlhmTmF11dXV+h0Onj9+rUwRxwffSSEboCWyWRQqVRQKpXw/PlzlMtlPHv2TKIr5t4p7GbfKOoMNKWuHchvHSNeO9NCR0dHePXqFU5OTlCtVoU5JHt1dnaGs7MzvH79GpeXlzg/P0e/3xfHZDprLycFXBcBULg7Ho8FLBNcVKtVZLNZANdtM8rlMmKxGA4ODrBer6UHFPUJu9gstdaIqbTT01McHBxISgT41LPl7OwMV1dXwhhdXFwI6GWBiHbcuvJNC7N11as5ptu+Zh1Y8mfdZ+quogrz85q6G84B9ppLp9MClAiguXkSVLJFxi5Ti7/VeI26aSErgE9PT0XLmE6nUS6XRdJA4f5wOMTZ2RkuLi7QaDSkwa6fD501/baZuqecQAfBem7rCkd24ueYFQqFDf0rU7yUeNB3an+0Tbb10YAjABs3Q4MWk8Jl5ELhMDclDjoHmUBJHy7KRa2ZI7+yRsD15CU613lhTsLVaiVUOqNh3RaBC58AgOwRwRGdPMu/w+EwBoMBgE8NM0mbc3EA2Fj820D9pnlFdRRGckGuVisZg36/j06nI06LxxyMx2NZlF7VGkxb8gDffD4v94BRE0ujqWeaTCY32KP7YI74f6k1Y1pDt2/gBkXNTLPZRLvdluvWoFY7RAB3borANatGliWRSMhREfF4XEATU7S2bWO1WklZP+fprgMQrTVi+q9QKEiTOqYhe72esEbdbndDSOt173TqRfdreWiWwItB0gwXcLMS1gvI6deZgSPfV/sMzS5wLWgf7Fefqo33lAFQLpeTNH2lUsHJyYloGxlIsbUDm8j2+325fq5/wN/AELg5J3SmxiuQ4msob+HaN4XcOmjgvmSmXHcRLD0qcATcrMIArm8iN3vgujcSJyur2PTiJa2/Xq/huu5GKb9O0/nRTHGbrqCiqJLOiaJSigKplaAIEwCKxaJERkwXUctCB89ok6etk31iSkmDKG4gOkLcBkDSrJFlWSKqPTg4kKaHPHCYVVKdTgcfPnxAu90WDQCviZo1XjvF7ewK++LFC5TLZZyengpjwwpA13XRbrfRbDbRaDREz8Oy1tsiot9qnAO5XA6Hh4c4OTnBs2fPJEIju9FsNnF2doaffvoJb968kT49FGHfxhB97n9r5iQa/dShfjqdSjVXPp9HpVLB4eHhRo+kcrmM5XKJXC4nTVd3YV5ao+PjYxkztnXodDq4urrCjz/+iHq9jsvLS9nYtI/RwJT9xbgJ0LTeRzO+u94Yvdiqu4CR1/f6tRwDsirFYhG1Wk00W/Qr7G1F4T/TbvsADAh0E4mEMB8vXrxApVLBt99+i3w+j6OjI5lPDIwACCPS6XREh+Nnxkibl6/WoMjce3W/QM6LcDiMg4MDZLNZHB4eSvVnLBYTv9NqtSQA6Xa7N8Tq2wbQjw4cATcXtxkZERiZPSX4N9xMuWmauVA/p9P095ru5DWYImpONJZxk71gNMT3YE8kHi/Bhn58L/7ddDpFJBLZaH64Xq+FmeFGxzHXn/k+J7qZWuRn1dV6PNdI93TRlRVkTuiw9QamhYjULlHoXCgUpIdPIpEQrRHTi9R1kTVi/yhT3Hpf4IgtHPhFUb3WGjGd2mq1BCDrvlbm+971s0m5c7Mbj8cAPnXCXq1WaLfbkqolk0AxO+cZ9Ui7MK2ZYgl/LpdDLpeT+a51Iq1WC+12W/QUOlWvUy6asWVKTfshr0hbf6aH0iHd9dxdz+vfce1R60YGWvfA4aGtDED8KkTWZvoVXQnKZohkaLmX6KBQV0pr/wL4nzHyMvorLdLX64n+ksED02oERWzrEYlEMBgMhM3WfnLXerRHCY5otw0gb5y+odSZrNdrlEolYQhYUcAznwBsUOJ+BUlaO0Gmh+Jg5vw5BhokkmXSuqRYLIZSqST0MDVHZFCA69bwwCdAxINGWdFDgWe325XoSTuLbfSL0g5Mp73IFunqIWpbuBj5uViCrS2ZTMr7sCT+9PRUIkWCR6YZO50Ozs7OcH5+jp9//hn1el06bOuu4/fdFZdzgKXEPBCSrB83pHq9jo8fP6LRaMhmr+nrz5m5zszgBLhOuc5mM3z8+BG9Xg+1Wg39fl/ON6QmhRtNNpvFcDiUVPa2eoppIMO5z+NBeN4cdTP9fh/v37/HxcUFzs7O5HyoUOhTiwLNnFDvxZQcy9mLxaI4fy8hql/8ym36zS8xrj2K/09OTqQyNJVKCYtCITtZAqbz/ZxW00GXPlKG80SXseuDdJlG43FMrusCwEY3eb/c+y8x7p0UTLOiVQMYBtac49SgxWIxnJycIJPJSDaCAcNwOJQGxAxQvyTlet+BxKMGR15mCrj4Pc90YgTIL+Y9dWmlbljmV9MAyezTpMtxNdrXgjka+7kwHUBgQd2E7hTOyR+JRJBOpxEKfepZA3za+NhYjhokDVK3ZZo5M3tVscqMi1L3HSJ1TPZMvxdBYjabRbVaRbFYxNHRkZxizjEnDey6rjhFNlhk6b6uAtOR432NCWltAlqmVAk2WKHY7XY3WDIvQbjeIL0c+G3pFs4tOk720KH2iOPAMeZ9IttiVoZuyzTTY1mWBAhMpVMbw8oiMmx6rpAB44bH1CpBAoMUMob0JfoIDj9ujr9lPnLusekjO4pzbSwWiw2xLTV5fgVF2jRA4pzR958+RTPO1DNSY6h7hHHOkMXeJ9MBtleQp3WDbKoci8UkaGCxD8eE64ysmvaRuwTNTwocmU5d61Fs2xbhZTab3UDzBEikz3VJId/XL6ajYL3JsJeE1hFp1M9r0KlEpscIjrjJEmzQCejNk0xTuVyWZn48foKggOJkL+HnfY+lHg/9pVlDfg6m/ZLJpOiRWJ2kRbq2baNcLssjNUccZ5Yss5P2+fm56FPOz883+n0QMGyDLjbTqpwPdMA6nUbG6C5BuL7PX5Jq4ffcHOjg9MnlZKl0IzgTJGwbLGiGkek0buYsvV4sFhgMBjJeZEOZBmQfKX1cBtNJxWJR5gorP1nswbShnv93aXr2xTie1BrVajVUKhW5Xh7WXK/XcXFxsZGa3Kdrpg/h9VxcXGA4HMqxGDxQl/eb4uJwOCz6umg0KhWso9FI9Hp3pVsf2nRQSdDHdDOroLl/6kO9w+GwdFvP5/MblYtkiRhIatBs9n36XHB2H/akwBHN1KPoiiwCCB2tchKYCBbwj/PSG4m+Lt2fyWRMNNrnxqTF27FYTKqaWFFk9qAwnTknOsvEs9ms/Dyfz+WYCqa0dhEpe0XkvIfcuHXfDYJlHQkSWLB5ZqVSkQVPsSHHmJVnrABjU812u32j67gXMNqGKF1/8X+wjJqVMlr79CV6krv0YjoQ0SAJuD6hfT6f39rsbpc6Iz7qtgzaFwDXzDK/ptMpgOtjQSi61QBcg2lq9vha3SXbHCc/p5S+1HRKN5PJiMaPKSezr9pt+rZ9MM5lAqTVaiXsmA7ANaCgT7FtG7PZDJZlSesUXcjgZ+M81ZpKfmnQz/2HewqzD7r6U2dp9BojYH6IufHkwJGXSJdnaVE/wnOgptMpQqGQTHyv5ojcBPwwkW+LurlA6YjNVBoAAUWO42wcRqvTCgBulFJy0nLjZbqN4tXpdIp4PI5mswkAaDQaoj/hBq2jo/saSzMaJ0PGqkMecsiDU8mm8ay5VColi19X/FGHRdBIh8/yVKbQ2CfoT3/6Ey4vL/HTTz9hOBzKqfPj8XiDdvcCmvc1Bvr+kzUiMDGPheF4mYDFfM2Xskf6c+jPQpaGTCbHXzN6JkN732auEQZIPFmc+iymBKmBIHtMjZHWnRDwcDPg8wRbOhWtm+RRa2O2uNhHIyti2zZOT0/x6tUrVKtVOakduK5S63Q66HQ6Mqb7Agz5OdlQuN1uI5FIYDQaIZFIoNlsSkqR84Pd4JPJJPL5vDDTyWRS9EftdhuhUGjjzD2/Gv2/LmSh5GK1WonImv6UWQh9MDXb6XB9sY1Iu92WYJLVvKY2c9tpticFjkznrnvfkB1hPlSL6fi92YvGr4vYK+rm9dLMzVg79NVqJePAzYuvYxt3TkyyTppV0ketWJaFxWIhVTpevaK4cW5zPLmQdUUeH0OhkAhjtUCW16LniW5kx/HhtbDij4wRWwGQNTJz6Gb6apsgQLee0OBYV2nepu35NZ/rrtfqFKdO82kxqh6LbYMj/Zn0vNWHTDOyZfqA8we4PoCZc5t6Ln0sAgGg1ujxPmhG2q9p+l9rHE/d96dUKglbQHDOzZTaO5Ox3Icx0IwH/SIFx/P5HNFoFL1eb6OydbVayeHfZNam0+nGfJvP5zJOfgm8vcxkjtiTiCdNEBDqLtgMGLgPaH0WKxfZa09X85qgaBeg8UmBI2CzVF8zRmyOZ9u2nJNFVGwe+aA7JJvpmoeeyCYwMoXXnNAm00OdETsZa7BDUEHGh4sXuI5s9IbH8v7VaiVpNNLG5mZoft77Hj9eN1kSppF4dAybOhIAkkHgZ+S1aLDHxc4xICji6dqXl5f4x3/8R2GMeAgp/79XC4ltzBtu+tT5cHPXInrdtsKrYsqLMbprrL0+g05NxmIxqZx79uyZ9DixbVu0Frw/3Di37QgJjNjmgSXYdOIaHBEYAdc9fPQmoJkxrfnTFUws4SYTrTUV+1rOzXmhtUalUgnHx8c4PDwU0S196IcPH6RCstvt7pXeSIN3nRrX957NcHW1LLWsZM+0lINH5qRSKWml4kdhvjbtWzmfea26nYtXMKRburAPHLVGPIeNlcP0W/p4JVOPtY158yTBkdYZ6VJ3LVYGNnUpBEuaztPg6EvEqg9pXqk0XSHB6+ak1REucJ0CYH8O3fzRS7dhCqE1G6MZI/35tn3tWmPF0lMCPYJdfl5udkwp6giI16MjJ/YwIjVMtoidkwmqdWXatqNk3gMN8ulguFlrx+UFWjXb9GuctZeuz+wJQ5BEcAFc6zfI0DDtt+0xMkXrpPwZKHkxWfrvzPYefF6nMzUzpqtgzf4tfvQfX2KagWPg6TiOaEuoM2R7lE6nI2vjvttYbNt4jzTjxevXjDR9CnWWGmBrjSfZRa1z3BdwpCu7yaDpvk2mH9B7pl4LFKvTN2t/pQNKU58ZpNV+p3k5LiJ3CgZJg2tWZLW67nir0apOQ/A1epFo26WzMycNN0bdcIzOCMBGqbpXmoeMkUbvenPgeDHNxE1Wi275d15dsbc5uU1gS81RPB5Hr9fDer1Gp9PZ0ILwswPX/az4Pa9ZR4368FGeyH55eYk3b96g2+3KeVGmsHAXDAH/H9N8LEHnBsZo9uDgQLqD03nz3nmle24LBExARbBF8EMNz/fff49yuYyvv/5aqv509Zo+hJPnqt23eemgCIz0ocy3pSJNnRwjYN3Mkr/TgQGdPJlGVgjuM0DSG59t27AsC69evcLp6SlOT09Rq9WQTCYBAL1eD51OBxcXF3J2H8dg16Xav8f4GTkPGCTyywRHZKeHwyGy2awwqalUCrPZTATrOlDwq+lr57Vyf4lGo5JaMwNBnY3h3zMbwbYYuliFgbg+2muX7OqTAUfA7V2j9YnCXmkomrkRsOpLP2e+js/vGiBpcERUz4jGRPU6TWSyLHw/E7nrv9U6C52K0+/zJfqRbYwRx0EvTC5kXVFCYTbwiU3hAmbK0dy0OKbUTjB1xmNB2AfHqwP2Nq/XvHZu2vocNzoa9mdhw0UdIOiOz+aj/uxe7J/JGjIqZr+fYrGISqUivaI497zOUtrVURImy+mV1vDyCTS9nvQ80VWCHBdTa7QLcem2TKfTyEBTa8R2HqlUStpnMP3CtIkG4/t27ebn1YGxziys12vxKbznJihnUKlTTn42HShpRl7vM2awbcoJuAb0l8lwmzqj2wK1bdiTAEdeUaIGRTxIVWsLmAdlFQmADTAFYAMB82/p/LRxoexy8WuRWzT66eT1RCKBwWAgTdnIeOkqNq190I6c48hxIlNEESr7VfC9SI0OBoONE+gZIWy7sZdePBoY8QiLq6srjEYjAJCGlWQTtdNKp9MbjQi1EH04HKJer6PdbuPt27eo1+t4/fo1Op0OLi8vBTjpaGkXi5rG/9ntdnF1dYVGo4Fmsynl5WTGarUaYrEY+v2+lLG32230er0bTJKZVuaY6Gsye2RVKhU4joOXL18in8/j+++/Rz6fF6EuU3/D4RDdbhf1en3jMNdtAiQdLJmtDvR69gK2GiDTKDglq8rKRs4p3VCWjOK2OqTvynSQ5DiO6MlevnwpjR8JzN++fYuPHz9Kl3ECpH2qVNPmBZD4yLml9wQCSPMwc91Z2+t9/Wi8JmqkyPJokMTKXPoEzv1YLCZ+kcwRv6g11AyUF6saMEf3aLdpIHjjdLrDFH8B1/oMsiP6S1cWmAySfn4XbIFmS3RqS5/+rh0xnZuZNjSFuHrzoLBORzyMuIHro0F07wuvk+e3beZ46JL+UCiE4XCIUCgkC5h0OPVGWlxuvg91Rv1+X0pPO52ONDN76L5YvP90UDzDig5Kp0IWiwWKxaL0auG1kubmuOjr0UyIdloUIJOJKhaLyGQycu5cuVxGNpuVoISfUzeT46Y5m822Coy8TM8ZrzSXBoWcCzQv7RrnENkxPYe23c5h22YGnfq8wVwuJ3OBQWev10Oz2ZQDl83U7b6aOT+8UtAERnof0YG7+V77MCbmXmOyRdrn8pgd9kAytZCaOTJZVS+t0bbtyYAjL70R+y+wazArebSoTvfx0UcaABBhHSeBjjK9QNK2AZKZTguFrjtR93o9hEIhtFotJBIJidp1s0MAG03I9IbIE8kZCVGwSs2SrtxarT4dKjocDnFxcSEnKxM0mMzRNkGSHg+mbQAII8GmjVoYyY2Mc0OXYvMa+/0+Wq0WPn78iIuLC2GM6vW6RMkaXD+Eo+N1U3N0cXEhhz2m02npuVIul4XlefXqFY6Pj9FsNuXssFarBdd1MRgMJMpfrVYbAnutN8rlcrAsC8+fP4fjODg4OIDjODg9PUUmk8HBwYEwjgCkgvD8/Bz1eh1v3rzB1dUVut2unGm47fHTTlw3pySIJDuqj/xYr9cSKWtwwLP1mKpMp9MCUtkxfjAYCEAgCNylnuI+TAdMvO7T01McHx/j5cuXODk5gWVZWK/XaLVaaDab+Pnnn/H27Vs0m82N+bRvoPAu04EDARHPNzw6OkKtVpOeTwxeGRiwSawZVPnJzH3M3PO0T+A6YRUq108ikdhg9XVqjoG01hrtGhgBTwAcaVSumR5TJ0MkSzNvuJkb1g6Sf6dFeTQNkvjztk2jdj3hKMbmIX6MePW4EAjqNJCu2tFl2UxFafaAi12XY+uSTF36um1GRS9icyHOZjOEw2ER0bIpI6+f95ufUacemUohYBgMBlJ+SgdAhuAhnRv/v9ZG9Xo9uf+8VgIVHi48Ho8lddztdhEOhzEYDEQ0rfVYuhqLX6VSCbZt4+joSM6fY1fxdDot1UucC2S2KMTmWO5Kc+S1XryqkLj+2TyUxvXPOcJ0vQbVvA4yKDqlto89fmj0dxwXHtrLc+noN7jxExSagHCfrvlzpgMF7jXxeFw66evGurpHEL/2ASzy83npbE2AxHnP9c4KYTNlrb9MMfdDjMWjBkd6kmqdETUXPOMFgETYLO9fr9eiqXEcB8vlEoVCAev1Gv1+XyoKiITJsmiNjkk5mmm2+zQvNA9ANsHhcAgAaDabiEajUqWly2zJjnHT1DSpBg68Bj25uZH1+32Mx2O8e/cO3W4XHz9+RL/fR7PZlFPgzdTeLjcFs/qIIGk4HEoeXJ+TRQ0JS/cJMM7OznB5eYm3b9+i0WgIY0Smwy+bHTem1WqFd+/eYTaboVAoSC8S3bKATEc0GpXofzweo91uYzQayflrFGvrpoecO9FodAMMWZYl+rRMJiObKFN24/FYWLe//du/RaPRwIcPH9Dv96X9wbaZVs5vgpbBYCAgcb1eC4OYy+WEGWNZNtcdmaVisShMWaFQkJTafD6XlNLl5aV0ASZQ2FdRMkEjr/f777/Ht99+i1qtJkdjTCYTXF1dCctKvd8+HRlyW7GN/h2NwIBNY7PZLJ4/f47Dw0N89dVXyOfzsG0b4/FY9Ik///wzzs/P0W63NwK1fTGv/l5afwpc70m6gjUUCm207TB1Sw85Do8aHAG3i7G1U9dpFwDivM1jDtjqnV2kKcQkGGI6Cth0vPpz8HfbMC+mRFcFaE0HNyZORFNHQjBAkESWzLw+zRbp8uR+v3+jmZfu6/LQUQGw2ceK4JY9gDRbSODICE9HwbrqxusQ2YdmjnhNTOdQF9Xr9QToEOSQFc1mswJq5vO5OPJ0Oi0pQw2ONMAiOOK5c7pfEL/nGHEcmXJpNptotVoYjUZyavu2Uq63sYpcJ7yfTP3RoU+nU1iWheVyKSkjskU8Q43pegqzOb84dgwSdOXgQ6+FX2smG8+Ak5oyzg0C3OFwKO0ZyFzuAwAwgc9tOjUdiNN/ck5kMhkUi0U51FwzamTWdYp127297tNMxgiArHf+TDkGQZKWr5hZB5NMeMgxeBLgiKBIl+wzJbBarTAYDBAKheREYW6ORP7r9VrSDDy1fjQawbZtEeRSnEvdgskg6ZTdNk0zR0Tt/LyLxQJXV1eYz+fI5XLCmozHY5RKJViWJforTm6CIrN9Ox0cD41kPx/qVF6/fo1+v49Go4HxeIx+vy9l2rvQ4pjpTGCTQWTKg486+mcpci6XE6AQCoUwnU6lkurNmzfCArBXy5ce3LpLW6+vKxAbjQZGoxH+/u//XtoMDAYDVKtVZLNZEc86jgMAKBaL8vdMzfF7Bg+cK2QQ2PNKN0bUVPlkMkGz2cRwOMQ//MM/oNls4ocffkCn08GbN29kE90Fq6DXCsFLv9/H1dUVgE/nAOrNjMwXAIzHYxQKhY2NkK+tVqsCGtk/qtPp4OzsDO12G1dXV9IAkSBwH4CCNoIAao2eP3+OZ8+e4dWrV3jx4gUymQzC4bA0RP3xxx/x008/4fLyEt1uV1Jqfr1mUyj9OZCkA3CeUVksFvHdd9+hVCrh1atXyOVyOD4+BgBhZN+8eYPz83O8fftWWHc/pOR/jWlASPJAawo5t8k48wgm3fASuK6w9hJhPwQL/+jBEXCzSs3Md2rxF/UBWk+RSCSwWCzkHByWH2skTIEqAAETBCj8DLs0M71G0eh4PJZIJZVKodfrxO2P9QAAIABJREFUST6cf8dNjaya/iKwYR8aRjtkIlqt1oYGh+yELmH2qjzYVUrNZA/Najs2AeQ5R7qppSmmpcbIPG/OD+k0bbxvFOe3Wi2kUinU63U5T1CXEevgQVfqadCvWUa+jvNGM7Lr9VrWB3U2FOefnZ2h0Wjg/Px8o2HmLnUXem6TymczutFohHg8Lv6AoJrOnX6Ez2ezWQELOmqmPo2lypo1eshqxt9jpp4mm82iWCxupGbX67Wwg7y/Oojw67VqYHQbSNItH/RrmIrPZDLI5/M4ODiQvl5skKkZdh5mTKDst+Dq15j2r7qRpWaDvK5Ns7d3Me67HpMnAY60aXGY67qIRqMYDociOiWIoN4IgOiSVquViHRHoxGSySQGg4G8B3UIrutubA67BEZ6AmmHS4fU6XQwmUwQDodxdXWFwWCAQqGAw8NDKb/l4tYTnH9Pxojv02w24bquPF5dXcnPmk1j9dZDlC1rR870qOM4sCwL+XxeTslOpVIoFouwLAu5XE6OlFmv1xgMBmg2m1JpQ8aIG53Z9sFPzo0gnSCFOrDhcIgff/wRL1++RLlcRrVaFUEtU0MaKOkAQ5vZMJGRL1O2ZA9//vlndLtd/OlPf0Kn08Hr168lrcZ1o5uU7gIYMUgCPh2qHA6HUa/XRWM4HA6lqo86tFKpJI6eYmRuihwjHpHR7Xbx/v17tFot/Pzzz+j3+7i8vJR+LvsiwNWmU848Q+0Pf/gDvvvuO5TLZViWJWzcu3fvpELt48ePwjT7FQCYgJ9fJjvKYELLJqLRqPiQb775BoeHh/jrv/5rpFIpZLNZef1gMMDbt29xdnaGN2/eSNNYBll+HJe7TPtX3cNJSzKYlgYgHegBSEEGgwVzPQRptR2ZyX5Q/KU1BmzUBlwfrWE2jiSTRE2GTtfpcvdds0VeZjJI0+kUANDv94Xi5Lk/nKA8/JATGIBsdIzsO50OxuOxaERY7s3onxudbolgait2OfG109P3UZde8+BRPsdUKtlFHf3r6rt96FOjtVWu62K9XqPRaGCxWCAej8uGRREyr0sfnGpG0bxOM5rmWHDzJ5v48eNHtNttvH//Hu12G5eXlzKmOh29y41Trw+d+mO6PJFIYDgcYrVa3ejtpTcFXd1I3ZJmGbX+jgzaNjVV2zay6vqcPAYaWp+nz1Dzu9bI1KdqkKQBcDgcll49ukKZXeBLpRIODw9Rq9VQKpVkDTEgH41G0hRXp+T3LZ1G075Vp9rN5/mzPokCwEYpv58ChScBjrRoGLg+82kwGAgbRKFlMpnEfD6XRzpDraSnQ9MTgV/8f1404S5ZBZNB4qTTFUAUSFqWhbdv38rp0Gzk5nUIL50bS62pt2JEyI2O2iKO110i7F2yR2QEtT6ElVWaMUulUgCue/CQLfr48eNGioBC832gw03QMp/PcXV1haurK6RSKZTLZaTTaTn2gewZT6hnBYquQjE3d77/fD5Ho9GA67qiQzs/P8doNLrBKvpBoM81wjm9WCzw008/yWfNZDI4PDyEbdsoFosSIWsmjSCauqmzszNhinq9Hi4uLjAajWTtaAbFz/NGG6+Xgv3Dw0OcnJzg+PhY+let12vZ/H/++We8fv1agii/MiOmoJp+XR99Y1mWXGMmk5EgizqjZDKJSqWCTCaDo6MjeR0rFdvtNl6/fo3Ly0v88MMP6PV6OD8/lxYr+zYXgJupNK05YjU4qz0ZXDCNz0CbRS5aouAHAP3owZEGJrp6izclGo1iPB4jFotJRM18eTwel5tknv2iN0OvJlVeoIi/e6hx0HoRgqRQKCQiwGQyidFoJKknzRZoXQarLJg7Z0TEqJtAVI+3CRb1OG3TvLQDBEmM6CzLkhQaS9xZVTWfzzcqjKgV0D1J9smp6flKPdhyuUQymcRisYBt25hMJkin06JLI2CmyFKDI12hyfdluwamWM/PzyUtybO1yCx8idZgF2PCOa6Zn36/D+BT+4vZbCaMKsuQee1MH2hRN8/ZI1BiClYfF7Jrluw+zAwy2EyUeppwOCzpd7a9YFuGfanCMjVEFBqn02k5DiWfz29s9PQj5XJZKjU5L2azGQaDATqdjgQj9Xp9I7jiXNhHM1ORuju8PsiZDLTW63FPMc/93DWZ4GWPHhwBmykFAJI2YwUVN3sAUqlkWZac4E5KlJtkq9USJ8+jI3RFllke/5AOQTt+TkTSwbor+HA4lEhItzkw9SVcxNwYTSevm91pMGXmkXcBisyfdfqHizaTyUgXY125RiEt9QDv3r1Ds9lEp9MRgGSei/XQi/lzxs+l2xaEQp86oPOa4vE4Li8vpd8XHT+rPTl2el7wfnItETBTrE8miUBMA2f9uR5y3HR6jZs4U8SLxQLpdHoDNGqmmOuAkTDTZ/V6Xc4XZGUg/chDM2W/1rh2KB/I5/MoFAp48eIFvvrqK+mdRTBwdXWF8/NzXF5eih6RPsOvpn2GqVNkH6dnz56JriiRSAiDxLXCVDx7nvGMwLdv36LVauHNmzcYDAZSNey67g3/uC9mBp1Mi1F/5TgOHMeR4Ip7CgNKNgvu9XobaUa/AOlHD47MDZkTkWgV2EyzsXsnj5Zgyo2bJc994vf6kDyyS35jE0zmytQhEdAQLOmcMTdBnTowSy69GDPds+KhAKJ2dl7/3xRcai0NmwJSI3BbPyM/NXz8UjPvv960mG7Tp9PrR0aEGjhzvBj5kYlk9Y3Z/8mLafWLaR+hj5pZrVZyBp1umqoDBbIAZBjZkkAzjRpI+8U/fKnpFEoymRS9keM4GwLc5XIpa4a+keuE5tfrvq1kX899AkStxyMo0tW8BEGdTkcqMqnNJOjWOiO/jsnnTK9jnVkBcOM0CoIogmh9coMWZPuBUX3U4EhvApx8dEw6rUYhcbfblcZdyWQShUJhQ2hJLY0ZCTJS5oRnZOwHLYXXWPBnLSgkm2AKEk0RrpeD43tpcGGO9657VpiAjU6b94mi8dFoBABSicR7zfv88eNH6VHDdIk+gsWsUtsX0/fBZJLG4/HG/TQPyDQLDvS91lHwQ93732r8TNo501mT+YjFYri4uJC/0YdkcmPQfaE450zGbN82RN5zpo/y+Tyq1SpKpRIKhYIcIMzqzQ8fPuDdu3eSQvWLH7zL9GfjWuDnJsi9vLxEMpkUsb7jOPI6MrDj8Vj0dkyfMTXLvYI9f3QvvH01zu3RaIRoNIpmswkA4kvpWxhkkR0iYCS7qI828gNAetTgiGYCA72Z6fTPer2Wc5MoLNRnqOlFQuElAZNmjUxWxU+bgk6zef1sVh7p40L4ei8zq/NMxs50irseB5M11EJAnhkWjUY3WADdz4j9aXSXbz8s4PswOjANmIGbIlWv7wHvAgS+L3+vH/1s5gYJQLRRjH51tE+GUYNDgiCmBkzQuG/AiKardlnRqVPRZlWnPh/P78CIdltQRZaD18QqZQ1y5vM5BoOBFB24ristT3q93kaByj6m0bxMrwNdpZlMJkWqwRJ+sovsD8c0GttaePX9ekh7EuAIuJnyYd8XRsq8aTp9wHJNM92iD8zk+3mBIXNT8MMNB24yPvpzaXHtr21HYIIjPj4kMOL9mc/nCIVCAmjPzs6kU69lWWi1WhtCbGrKzs/PMRwOcXFxIWwhwRWB8K6vaRtm3qO77vttv3tI8HufpsEiNzI2UKXwlKZBIcGglx/wU4D0a02XY7MKqVQqoVqtIp1OyyHCw+FQ0khnZ2e4uLiQMn6m1vwKCMzPxHWtO1Z3Oh20220BiQA2QCH3Bp1SJngiUNZzZN+NulVeG3s1rddrOTeQfdMsy5Ix6/V6ct4efSv1u14NdR9qvjwZcETjYOvoV1N/vNnU3/A1NM0M3RYJmjfTj86Adtdn0+P0OTNB1pd8v23T4E8D23A4LEJIy7Kk9xOdntaX6fOgGDHqObDra9qVfW5ePHbzYhDoJ27Tspl/4/Ve+2xMqWoGCYAw6svlUhgjdhn36ozvd9P3nEU8uqO5vv+8LoIgbuxmIcpj9RUmuwYAo9EIy+VS+qexwpPgiGk1HmhNDa9Xz7iHtCcHjmimwyI44vf68XN/a36/z7ZPwO5LjIuMi44LcTabIRaLodvtIhqNwrZtcf6M+GazGXq9nlRVEFx5pZACe3xmrvMvifYf63wwW3qwOrfZbEoDxEgkIt3Ar66u0G639+K4EG06pcpNn9kF4FpmoF/vFRw/FkB8l/HatMSAAvxoNIpOp7PRD4xgmoCJ+iI++q1Y4cmCI9MeI9AJ7GYPG33mHSNaao206JwRL/s3+aUnT2APZ8H9vj4clNqifr8v1VqhUAjtdlvaXZAN2NdAQn9m3c+Lvwtscxw0AKaf1V2zKeonCCJo9uoX54fxDcBRYI/edCqV7RsIhlit5tW3R6cCHistHlhgnzOTeV0sFnj79q0AIcdxZH3U6/UNHQk75/tZb/Slts+ffZumAQ0LXryqXr0KOICbhRt+sQAcBfZkTIMkXaprvsb83m+LNrDAdm06Pc2qLQpyB4OBvIapNDJHftKQBLZd8/KXd2lW/T4fAnAU2JOzAPQEFtivM7KorusiFArBdd2Nyl7guprXbz3eAns42+f7HoCjwAILLLDAvsjMVIhX8cptVXuBBbZPFoCjwAILLLDAfpUF7Gtgj93Cn39JYIEFFlhggQUW2NOxABwFFlhggQUWWGCBKQvAUWCBBRZYYIEFFpiyABwFFlhggQUWWGCBKQvAUWCBBRZYYIEFFpiyABwFFlhggQUWWGCBKQtK+QMLLLDAAvtdZvY7uuvQ7qANQGD7YAE4CiywwAIL7Fcbz80KhUJysCi/otGoHDTKY3rW6zWm06kcXut1on1ggfnFAnAUWGCBBRbYrzINikKhkBwjEo/HEY1GkUgk5ER2fRJ7NBrFfD7HfD7fAEnL5RJAwCYF5h8LwFFggQX2oGamYPZlgzQ/912ppLt+3icz2aJoNIpoNArLshCPx1EqlZBOp1EoFJBMJhGLxeRw2ul0ilarhclkgl6vh9lsBtd1sVgsMJvNAoAUmK8sAEeB/SbjRvBUHNldp0vz93eNxVMZp7vsNl1KOLxZF2Ke3+WXsdOf20wl8Tn9On3o6nq9vvHzvmlvvNJosVhMwFEymUQul0Mmk0GxWBRwxNdPJhNMJhOEQiFMJhOs12tEIhGsVit5330Zi8B+v2mf+jn/qm1XwUYAjgK708wNwXyeE1P/vM86Aq9rpHO/6/fabtsAvTbDfRwjL7uNRdHPE0TwMRKJCPugN0ae6G6mXh5qrLwAQSQSQSKRQCwWQyqVEgaFr1mtVpjNZlgsFpJG0j/r6yJoAvw7N8wxYBotnU4jmUyiUqnAcRy8fPkShUJBmCPg03U0Gg0Mh0MAQL/fx3w+Rzgcxmw2A/Dpnu8jYPycfU6g/mttn8dHB0PaD3zp3sLndhVsPGlwdBtyve2EafO5x2jmOJjRvRklM7qn3TZx/WzmdeqFqzdyL5CkH3nd/KKW4raFvF6v9zpavo0B0g5Pjy3BkAYZGhxxjObz+Y1xeygWSYOCSCQieppYLAbbtpFMJpHJZOR5vm65XGI6nWI2m2E8HmM6ncJ1XQEFi8UCwDW7xOv3IyPrxRhFo1HEYjFYlgXLspDNZpHNZpHL5ZDNZuE4DhKJBJbLJZbLpTBMd6Ui7wtAPKR5sYvm5m++3utee7EjXv7UT/PENC//QH2a1qnRL+jXajOvm8ETH7Wv0H/ze+1JgKPbon1zAntNZK8Ib182/V9jty1qc0IzaiZYmM1mG+PC7xkJcgL7cbzMjU9rKJLJJKLRKOLxuAhNuTlyPADIxsYoeDKZYLFYCEMwnU6xXC5lnDiflsvlBmPA99oHM4EP54gGO3pM+XuKdPn7VColrwMgot3JZIL5fC4Mg+u6WC6XmM/nAHY3TiYoSiaTSCQSyGazsG0b5XIZjuPg8PAQ8XgcqVQKkUgEsVgM8/kco9EIo9EIrVYLruui2WxiPB576m0IjLzWyUPOC9MvcO3bto1EIoGDgwPkcjlhjI6PjwUshsNhuK6L2WwmvmA6nWIymWA2mwmDZv6/fVkHND1G9Iscp3g8LnPICyTpe87r1sEVgI2gywQEfmTbvMBQOBwWH2pZFqLRKGzbFuaVvsPLOE/oH0ajERaLBUajkTCyy+VSxua+AvNHDY68gJAZAWlnzfy4jt70xuaFVP246f8a83J+ekJzg+Pk5QTnRI5GoxsOjps+J/RkMhEwAPhrAevUDq+LOgnbtmXD48aonV04HL6xAGezGYbDIRaLhWzwkUgE8/kcoVBIFjCADUfIn/2+MZhgSG8EegwJoDWQ5u81ACWY4FwiYxSJRDCdTjGdThEKhaT8e5fjo32FBkepVEp0NdVqFZlMBgcHBzJXeH0aHEWjUfR6PWFQZrMZQqEQ5vM51uu1zCU/mxlIxONxJJNJOI4jjFE+n0c6nYZlWQBwAxDxaz6fSwBxn5vZQ5i5p5hVe5Zl3fAbpmmGmd8zuNQBlWYb+eg3/6HHQ4MiLdpPp9OIx+NwHAfJZBLpdFp8ggke1+u1BJxMSzP4AHAjLavH4PeOxaMDR16AyNzw6bSTyeTGzUqlUvI6TsjJZILhcIjZbCZIlRs+AcA+giQT3esNjBtXLBaTn8mkcLFz4RK1k0mh0+OEbrVastH5ASTp66bTisViSKfTSCQSKBQKsCwL5XIZqVQKxWIRiUQCmUwGsVhMdBRMjdDRM23SbrdlzkwmEwwGA4zHYwwGA8xmM0yn0w1G6TZmzU9zyQwotOZGP6ZSKcTjcZkzjuPI93rj4FzLZrMSkKxWK1lfrGgCgMlkgul0CuDT3NrFBmCCIqaOyuUy8vk8vv76axQKBbx69QqZTAa1Wm1jbjA1OB6PhTnqdrv48OED2u024vE4+v0+1utPgmRz0/PLvfdik3l/s9ksMpkMnj9/jlqthq+++grFYlHYgMFggMlkgm63i06ng/Pzc3Q6HTQaDbiuu7EevFhUv5v2I1qYzjVAkJjP55FIJOA4jqQi6XN5vdxTGIATTGqdGn0oU7P69V5M00OYyRQx0MxkMkgmkyiXy7BtG9VqFel0Wiob6SdM9ojX1e/3MZ1O0ev1MJlM0Gg0MBqNcHl5Cdd10el0MJ1OMRwOJeC4j/F4VODIix2ig6MTp7OLRqMiJszlcojFYnAcR/6OUQ/LTsfjMfr9vmx4RKwPQff/HvOiPBnl68WdzWYFPMZisY2NkBN4vb5u6mZG/67ryiKPRqMyngAe3BHqjS+RSAj44QbIBazBEZ1bIpGQa6fzItiJxWJYLBYCFMPhsABobvDcCCja5Vz189zRkaBeR5wrlmUJwNQ0OXUnOqrWKQfHcRCLxcSZxWIxAUN0rovF4taUxDav1Uyn2baNfD6PQqGAcrmMQqGAYrEozp0bH/BpbnAOABDmOZ/PY7FYwHEcLJdL9Pt9LBaLjbngNQ8ecn54Mct6THj9tm3DsiyEw2Esl0sBhr1eD91uF/1+XwDTdDqVFMk+M0ecI1wTBEXJZBKFQgG2baNSqYh/YTCmMxMaSFO4TxDENP1sNpO1x3miAyu+Fx8fYr6YjBH9QzKZlHVSq9XgOI6Ao2KxiFQqJf6CoIqm9Wr0CQSNsVgMo9EI4XAYo9EIq9VKSI37YmIfBTjyAkSMcpgioVYglUqhVCrJxNUTOJ1OSyRAQSXZgH6/j8vLS3S7XZyfn2M4HApiHY1GG4yIHxe4lz6E2gFNcbLq5OTkRAADFyVwLarle+pcuNaL0BFaloXBYIBms4npdCo6EiL8XY+BZowSiQTy+TwymQxOTk5EO5HNZnF8fCxzhqCIY8jrXSwWGI/HIrodjUZIpVISzQwGA3EWml0LhUKeglzAXwDbpMf1XGFPG8uykMvlkEqlkMlkZEyTySSy2Szi8fiNSiRuKOl0GpFIRKLjdruN8XiMaDSK4XCIfr8PAOh2uzK3tgkmTVYxkUgIYC4UCvjjH/+ISqWCv/iLv5C0WiKREMYIuE6RaI0efZBlWXAcB+FwGPV6HePxGKFQCOPxGMA1M6ZBoF/mA9dOIpGAbdsoFouoVqs4PDzEwcGBbID9fh/j8Rjv379Hq9XCP/3TP6HdbuPq6gqu695g33X66KGZjy8xc46YOrRKpYJsNovT01N5pGif4BLYlGwQHJH9mEwmEogPh0OMx2MMh0MMh0O0Wi2Mx2NJPdOXPKQW1gyeWKhQq9WQyWTw6tUrlEolvHjxQgIMy7IkmPKqWiNopMZoNpvJtdu2Ddd1EQ6HZT+hDokB6X2AxL0HR3fpiDSaJwugab1KpSLPMwLm+3DiclMkKxAOh6UkdTweY7VabaSZaH5b4Dry06Aom83CsqyNSMe2bRweHgpgYvUNr0k7b30swHK5RCKREPZkMpmgXC4LA0AgQYCh33NX169ZDAJix3FQKBSQy+VQKBSQyWQEAJAp40JjtKtTarwOnZ7k39BMwbp+zvzeL+bFFtDRp1IplMvljYZ/HDOCIqYiNdUNXANsMg2mXkNXRj7UNZtzJJvNyhzJZDKyATBK5XzWgYJubEiwlUqlYNs2UqmUVL3xmnfFjv1aM8eE7KBlWXINZIa5iXc6HUknMrXMlLKZSn7Ijf23GPcYplIZIHBPyeVyqFarMmcIKE1/QD9JdpTMIxkQ+l3gE/tImQMrHs3q0IdkF800tG3bKBQKyOfzqFQqKJfLKBaLUslI4sLcO/U1aBkGU5er1QrJZFIeydjrNXRfttfgyKR8Ne1PJ23bNg4ODuA4Dp49eyasAEERb6aXyJbC4kKhgNFohEKhgEajgUgkgkajIZN7NpvdcJJ+SZVwjAiKmAo5OjpCJpPB8fExcrkcTk9Pkc/nkcvlZLEzfUIaV4MgTmZOYFbe6OqsxWKBcrkM13Xx5s0b9Ho9/PnPf8ZgMJCxZdSzq7HgArZtG47j4OjoCOVyGX/84x+RzWZxcnIiaTQAstl56c0IkDQ9rucOX8eSbj5ybLzSCsDDA2s9Z1hVlkgkUC6XRWeSy+XkMZ/PSxNAXdnHFIJZlaRBgAaLdPa6HQKwWxBJEEC2J5fL4dmzZ6jVavj2229l46P2hpHtfD7fEOPz83PTW61WMq/K5TJWqxXa7TbW6zW63S7W67Wk6r1SJQ9tBEa2bSObzaJUKkmaJJFIyNx+//49Go0GfvjhB9TrddGHjMfjDRG21hn54fq+xDTzTFBULpeFIaIOjWuDAQP/hj4PuK6CjsViElTOZjMkk0lMJhMkk0kMh0PZ1+hv+Do+p/dAPu7SnwLXYJE+oFqtIp/P4y//8i9xdHSEFy9eSGBBHxEKhcQncm6YEg1t6/Va/AqvP51OY7lcCkgKwJFhXqJBRjekvJkbz2QyIpTT2hldQaTZEU40M0pgntSyLEynU0H6mp3wy4LXQkqK4xj1E9VnMhlks1lh2KgvIigCrrVEBD+8PoIjRoV07BTickH0ej1JpaxWKwyHw52xR15pV95Px3GQTqfl2plC0kJrlpPrsnI6es4d0ttMxbqui/F4LJoB0ucaFHlFzH6YN16MEQFzLpdDsVgUepzCXDIIDDK0Ls3cAHW7h9VqJfq04XC4MXbccHch2NVzRJdhW5YlTCJTYwwYWEHDFHKv15M5QCPLwv+hUzG2bWM4HCKZTEqV3kNpRu4yc0zo/6gVIcNBoDgcDjEYDOC6rsx/zvuHbuj5W83cX7gmWKnHxpdknqnDA64DLF3FqyvQyEjfNiam4Bq4ua4eyn/ojA3T0I7jSNCUy+VusKwEQaPRaENfRW2eroINhUKyV3t9MQuhg9L7Goe9B0fAdcWATqfpxmTMA9dqNaH8wuGwpMW4abFChmCCVCi/0uk0ZrMZCoWCbPDr9RqDwQAAMJ1OfbPoCRopjqPe6vT0FKVSSfLAZIoIZjghx+OxbFCLxQK9Xk82KwJB/h+OwXK5FGaqUCggHo9LlJxMJoViZ78XarV24Sw1I0A9TLFYxMHBAcrlMiqVilROURvlui7q9bpU3XAD185Mp4q4iVOHxvTCZDL5bKUj4B9gxPXEIKJUKsFxHHz99dcolUp4+fIlcrmciNcJivQhogTMZNT0tRJUsrCB525Rl3J+fi5VXqPRSDbYXQAkzhEyy5VKBf8/e+/yG2mWrX09EQ477vcI22lnVmZnXbpOdx/p6Aid8fcHICFmMGEA4mMAYsIIJiChb8ZlgoT0IRBiAIghQkhIjJiAEK1zjtTnfF3d1VWZ6bsd96vDjguDrN/yip3hrOyqdERkZizJikw7HH7f/e699rOe9ay1Dw8Ptbu7awJSnjUg4OTkRL1eT+fn53OMEUDCp1a2traUy+U0Ho81GAwkSe122/wR47Uuhu/Dp5bLZVWrVVUqlTeq04bDoWq1mi4uLtRsNtVoNKzSd1Fl74cCkjwwovElTAj9nWAVnz59am0OAP5eE+OrzELABWDyDDPgAe2Rb4MQ9ktbldYIXR3BE0zr06dP9ejRIwueCCZarZaGw6HOz8/V6/Ws7xfMOwwR0o9qtWqgy1e7evbZj8n7Go+PAhyFxkOD+ubLV1ThxNHC4KyYrIlEQrPZbK4KBTTL55G+86BiXTQDfgwQyRYKBR0cHGh3d1e7u7uWDoH2x4HBnFB1cnt7a+DAU5+MlXSnv4IaptcJ5ZykqYrFoiaTidLptDkPnMJDjkPIGsEAwiSy4FikXjsxHA5NfM/1+l4jPiUL8CGVEPZ1Wed0QsicwLxms1mbP/SyAQSTLvDzh7nDa9gfDCah0+nMgaN2u20bAWwSn/GQwMgDfR9kUcLv9TU+PYJovF6vq9frqdFo2HXCLozHY9so0eZ4AIb+6Pr6+kF0Ez/X/Jxg04dBQ48pyYoS+KKVx9vYIi++XVcL5wZjANteLBZVLBaNWSTAoujEBwCAHdaLl4J4lonf9WzSIk0bc22V1b/MDYJOsjS0RoE89hQnAAAgAElEQVQNBRgirO73+7q8vLQAg+BHkrGzBF0hK0SVsP96CN/6UYCjMAph0uF8oPUk2UZPwz4qA+hjxMOm9JCmbzx8IsDRaKRcLqfhcKhEImEiOc+qrHrC0ksmm83q17/+tQ4PD/XrX/9ajx49ssnLBlSr1dTv9y3SoXKIjQtwNBwO56pxfAouGo0aEzWdTq2/BbqvSqWibrerq6srY5Aow3zoBe4rkKhYrFQqVp1H7h9QdHp6aqzAYDBQq9Uy5gcQ5KMYr5fx3bEZTxg4D8zXKYIO6fF4PK5yuWznZVWrVT1//txSaoBeSQagfeqRsfJnifFKMMK6o+IEAMpru92e64D7kOPkQQAsCanDvb09m9fSayDQbrd1fHysWq2mP/zhD+r1eqrVagZ+8D+5XE7T6VSZTEaSLFihMGQ2m1mDyFqtZkHCuqTWGBMid5ijQqEw14oBcFir1QwsMi8WbVrcX6iZkdYPLHnfkcvljCGqVCr68ssvVSgUdHh4aJs5GsVer6ezszOrXqUqDQNAs7fQkR/NJswrPgV/QjsEn3JedjuE0F9Qwbi/v28CbLS8ZGQuLi7U7Xb16tUrtVotnZ2dGbNIKT7MKmlb32EcANRut9XtducE/71eb2Ea/ueMxY+Co0gk8t9J+pclXc5ms9/88L3/RNK/Lenqh7f9R7PZ7H//4Wf/oaR/S9JE0r8/m83+j598dT/BFok3fUQvyeg9yiXZ+Pr9vqFgHirI94d7m9PvrCNj5PsX0YiPniwI5aAnZ7OZRXoAFvLAUJ/8v9vtzqWFuG/f+ZXUCr2jptOp9vf3NZvNbEyz2axubm6Uz+d1e3tr1RcPJcz2rBGbFtEfYJdNHmF5v9+3viw4eRw9Dosx8L17MKJl36DNO7BlO7J3tbCEnSqtXC5nDgswLMkiXByTPxYjLNf2jBLgiDnV7Xbn0mxUNYaVTQ9tvrDDV5YxVwh+2Lja7bba7bY6nY5thnzOzs6O6RXpx0LptW+oCjPF56+iQm+RLSp2Yd2gNfJsh+/JEx4ptOhzvbZqXXxnaJ519mfJAXrRK8J0wLizp3S7XTUaDQ0GA9XrdWNPfDoKH0g6iTnm/YNni+5joFcdiPv2KOyP0nzKq9PpWN+rVqulXq9nwTHzwH8e1Xnsab5djP/CV7xvhvldmKP/XtJ/Jel/CL7/X85ms//MfyMSifxK0r8m6deSDiT9n5FI5KvZbPYwORO9eUqvF7oBgkCusAMIPunE22g0DK3jtMbjsWkEoPO8ZsVvrovanq/C/MTC4T558kSHh4f6zW9+o1/84hemm2i1WhoMBjo+Pla9Xtfvf/971et10xYBBnB0IPKw8siXIFPZRDqhUqlYZSCT/NGjR0qn06rVakqlUjo7O5sT6b3v8eAaQ+oXVoB8OIvu6upKjUZDL1++VL/ft/5MCMipKPIaBEkGGMI8OHORxRsyRtLqI+VQOwAgevz4scrlsj777DNjjDgiYzabGeNzcXFhUTIaIc8Yca+AJcAR7/N6DF5Zjz4dtwy9EeufDr6VSsXaXBC9djod1Wo16+VzcnKi4XCoVqs1l85OJpO2Gd7c3CiZTGo2m1mFF9U2RMI7OzsGoNbBvCg/nU4rl8vZuuEefBqa3l7o63yRCnMGCwNYDwrWgTVbJMBOp9M2Jx4/fqxisahyuWxgl1YG7XZb33//vTqdjk5PT61XHsDJa2MJOuj94wM56Q54UqhAIBK2EVmFLwmDCfS8pFxZ6wSYR0dHqtfrOjo6ssCTSm9/CgOVxL7sPxKJGBhqtVpqt9tqNBrWYNTrY5emOZrNZv9XJBJ59o6f969I+p9ns9lI0veRSORbSX8j6f/+yVf49muzhRQCJCJVIpmtra057Qc/o4szQlpQL1VI0jwAYPLyd73+ZJWGQ2XhpVIpo8E5A4qqGURxLN56vW6ggAkbtqcPBYD+7/ov6TUz12q1FI1G1e12lclkVCgU5nQsnjYlT/+Q4+KZI38siq+qghnyZ0EtOgfKfy4bgO/Hwfd5DdNn6wSMpMUbAXqsbDZrDAfj5XVV19fXtikCuKlo9K0NFoEjnBmpWj/nwnTBMu4fMIBmLpPJmCjUa41giWAXfVWiJGNlAc+k3ofDoeLxuKUQYGMIKPwRCuvgU6R5HRbg2Z+v6FM+MIm+ghOGOQRGi768vmRdLGSc8Vvo7nwqjWwEG3en0zEGHnYUMED6jGo/uj6HY4LP9SxqyJCs0peEvp89kaAQ/RV7rvcJkmxcAUWctUan8Z2dHUmydLxviMnZhXzu+2bSfo7m6N+LRCL/hqT/T9J/MJvNmpIOJf0/7j3HP3zvwcwDpMlkMjdRe72eotHXTRvpJcFi5ue+aopNcGdnxypOABuhWHL2g8jMn3+zag0Jiy6ZTGpvb097e3v69a9/rS+//NKqBnDq33zzjY6Pj/XNN9/o6urK9D8hPfk2bUxIiUciEetwLEn1el2//OUvJUmVSsUackajUZVKJWPnEMK/T7tPiO3bMPjyUsBQt9u1r+FwaP2b7gNHXiMiyY5K4WdvqyhZl00gFJtSycccotoT9g8d2tnZmbrdrl6+fGl0OZtkOHc8+wiVHlbu+THmd6SHHacwdcLRD9VqVQcHB9ZNnw2MCsazszOdnp5aioB0IJ9JB2Ma/M1mM2sVwUZIt/Hd3V21Wi1bE56tXrWFgNkHNTBp+FDSpBQp+NSbN+aGD2QJMJb13H/MQr+B5opilr29vTktKwDo1atXqtVq+vbbb9XtdnV5eWnMGvfmWWy+RxUkY+GlIASrodZoVcUdoU6M9cM1e0KC+cFa4DqphpX0xtlrZBcKhcKcBqter6vb7dq6o9rNV0SuAzj6ryX9p5JmP7z+55L+zT/nAyKRyD+V9E9/4t+fM4+cQa0+nYEzIqJZxPZ4B+nZBRa4PxSPh+XPCVplxYC/fsARzp1+RiDwbrerZrOpi4sLnZ+fGzVJxP8u3WsXbfBeP0BqJBaL6eLiQtvb23r+/LltrvSg8uP7kKmEECjh9BbpOxa9b/aDdsRHSJ5BxPi8SCRy7xiG47dqC1kjr6ugkorITrqrpkEcioifteArbcKqPoIIn270Tj7cHKXljZVPIZEyD3t+sVGxVrwuinXjx9QL88NT6QEQ/nw/X1XrN5xV2aIAI1w3i5gMRLqLusXzfD0j7WULfMYqAVK4pgEzCI8BRTwrmDNYDRgN1gXzI/SVPoDg+0g0GAvPxi2bUX1X88+U66Wjt/eXjCHpZeYWvfBKpdKczg8/im9B44fO775U2vsal58Ejmaz2QX/jkQi/42k/+2H/55IeuLe+viH7y36jH8u6Z//8Bk/6278gvLUZa/X09bWlobDoZVpS3pjovqy9LCRFW3hfbk2p0o3Gg2LotFHrCriw9HmcjmVSiX96le/0q9+9St98cUX2t3dNUf+8uVLff/99/q7v/s7ywGj+Xgbu/FjE87/fDqdmtj9b//2b3V6eqrnz59bU0h6WJC24Tk9hC1ydGyCIY3PAmaTmk6ncwLk0JHxmaF5x35fJck6OLYwFUsqCdbI97Lx+hLSSo1GQ81mU+1224T7XhzpG9xJsojXd1H3OqxFqYKHvn+fTiOwQEvCUSHoSdB8oHnwPZjCsmufSmR+NxoNxeNxDQYDa2wXj8ft72SzWXU6nbUo51+k1/PHnbDBez0d68UzKnRE9p322fB8ahURP13GvbZxVQDJ3zvFCfv7+yqXy8rlcqaFQUbQaDR0enqqZrNpvdzo/zaZTBYGVN5Ygz7ID4EWAcY6iLGlu0AQhggtEVpc3kNHa3zuaDSytCQtZdBvMa7IXq6urtTr9fTixQs1m029evXKgnxfAPC+AeNPAkeRSOTRbDY7++G//6qk3/3w7/9V0v8YiUT+C70WZH8p6f/92Vf5juYpfL5A3Hz5SIYNkAkpyURlPvXiT9vGuRMNLlLJr2Ky+kWMcyclsL29rU6no06no4uLC1vAbGhvW2x/7r34DQIdCZsInYN9CgcwsozNYNG9hBsk7AnOHWfkaWO/cfiImPvGua96g3sXCzcBggN/bhZAMtQWeKYAlgAgwXvC1Fr4tQ7MWvj8faEFqQ7W/fX1tRV0+CrLRevGsyoEbSFT4rU84Vivg/mx8YFA+DxDgInRR81XYuEXfI850o1oPz3jyN9b1X37dDz7QtglnRR82NsMhiSsIvYsLcwhn+n1sr5TfJh2XocAy88Dz3L5Pk0EpIirOQLEH2QNqKbnHoVTtPYADFHl5vtoPdRYvEsp//8k6Z9IqkQikWNJ/7GkfxKJRP5Kr9NqLyT9Oz8M1D9EIpH/RdI/ShpL+ndnD1ip5m3R5oTAmgqB6+vruQaO9PrxeXE641KVgCiTBX17e2uiu06nY/lO7ySXbZFIxEqPHz16pF/84hf64osv9NVXX9miu7y81NHRkf7+7/9e33zzjWq1ml07kf37uHY+g8/sdDqKxWJWkVOtVu1Q0tvbW6tueKjNYFFqK9QOeadFVUqhULDUIGlCX03DRkHajc/EkaHfCsdlnczfO3owKpLoceSPiPBVNGwcbIiAAL8pcP9h87qQWVq1s/ctDDg7jAIGnDXtLer1ui4vL614wQcX0ptVWN4fESDwXjZeAjQYOq9vXDUr4FPM/pq8RhNwQwsPqo6i0ahVg6KpwVgfHB3BhheLxdTv9zWbzeY6a0urYxMTicTcnpDP502/CrNTr9dVr9ctm0A7CumO1UdSQMEMhzZns1krfJA0V/XmK4jXjTXi73uACKsMC02KmkbAMEKz2cxS9j6VPJvNLJtBXyTO5fz+++/nOmqH2Zr3PRbvUq32ry/49n/7lvf/M0n/7Odc1E81Nq5FTgkn5KMcHppftL4SAR2A7wLMRgnlt2o0z0Im6udQSM73oTqoVqvp6OhIzWbTnLpPBTzUdYdRBc6WaJJI7CE2A39vXIcXSTM/cFgwRpPJxESCRLT0IgkjQT6fz2MTwbGHm8o6WagnQf8CUPT3yJqZzWY2Rv5w3mQyaalUgDEMUqj1uy/VuArzLKCvIENj6NMGMMZ+o1pUiOH9UAgivHbE/13PHIXatnUYn1CHJ+mN+2X+eDBAig0xOvdD0BGPx219oduhwk+6Y45gbJc1FmHgwCYf6u98xTP7AfPdj0M4twDDBIdew0RgAXt0XyPNdbFwv/WVqmRo/P2x77D/MtYEDrCzXmPUbrfnWLlltDD4KDpke/MOaTQaaWtry84+KxaLb/S88U7fNzAE0fMAmfT0vKHSLXxYyzacFgeDfvbZZ/rlL3+pg4MDFQoFnZ2dqdFo6He/+51++9vf6vvvv1ej0XhQtitkaPzG4ivq6Ko6GAwWVrX83GtgY/MbHKJYX14tyZwfvXzQ2BDZMofYMP0GAQDn1Z8Zx3EjPiJdB+fmgZE/pBlHHXY+x8lxTh+VfsPhUOVy2RqJcizAYDAwbYHv8RSKsFeZig51Nb6FgT+E2Fe3UslISjp01PgRbwBPol1YBUTZpDEBEr657Krmip+vHhj5Yx2kO8DrS/z5PxpDnzLh93zfNJ9C2d7eto2w0+nMgaJl6jlZ47BehUJB+XzezqEk4KNhLFkE0j2ejQYMoSkiKMxms9rb27NzQAFP6IvYX3xfo3UIKLz5/ZYipcFgYIF5JBJRNps1TR2FQZJsjjMXGo2GRqORMXCvXr1Ss9m0vkh0zn9oxgj76MARFjJHOzs7Gg6HikQiVprr+1eEIANKEGTLJuhPWA97/qzKPDpHSM4k5FgDqtLuSwM8lPH5IT2PLQNYev0HIA1RLABaumvyKWmuvw0Cf+5D0hyg9poJokoYFOh332l93e0+fZB0J66ELWJtbG9v20GqNFGl6SqfeV8KbdXMyCIA4DU2vmWHX/+h5iHcuMIgIVwLPlUZpi1XaWGKO7xGD1b83PCpZhh5viS9AXAIlNClULFEZ3/WK6mbZYHF+1L8IfvsheM+5c79S3dHhORyObtHHxwSiPkeYrBRvk3MujCtiyxkoMO9Naz69r8nzWt5aXZ5nxB9GQ1hsY8OHHmHRM6XHhOpVEo3Nzf2wNAUIBokYqxUKobsMaIbHhyLdtWTlYmZSqVULBa1t7enw8NDpdNpSdLZ2Zl+//vf69WrV9YXIuzZ8xDmxyQUIuIsoVA5tPahroHFx9lGnMsznU5VrVY1m81ULBZtHKfT131HfHM7Nn6fZmCOsbB5P6AhFotZh2R/Jls4Pss2fw8+hSPdCek9IPCOHgCJA8TBs0kQPUejUdNphSzDOjj6+wAAqSGf4iAiDpvPhSmyEABKmmPJPIiAUfC6LZgVL35ftTA7ZIyI9r2GzEsVSJ0RWPj3LypSQItE8BCPx60SEr2odFdhvEyA5Mef50f/HkAyzA5zgKA0k8nMne+5u7s71/DQH2ALSPTMNvMsBAarZluxMN3Ks/f3hl4XITvriuvG1/gjV9rttmq1mn2hVYVtva+q+iHsowNH0psRG/lbhNn+8FTfzRbQxAT2C4OHuKjnBH9zFcbkpAQ7l8spk8nYhjYaje6tSnvo60ILFUYNCFy9gC9MRbxP8/MBoX6/39fOzo46nY4ikYidq4fzDTfv+yJ6vo8Ox/c+Ia02m83U7XaNfWR81sW5SfPsGoCVg5glzZVxh0JqPodCBw5i3tnZsc0k7BO1btFvCOZ5rp4dBCy+jS0Kgw4/9/i5j7T9+/zvrRoUYVy/72HjN2n/6v0hbCmv3ggcfLqWOULlcC6X02g0sqOIfEHEMueOT5WGXzBepBQRITP3t7e3DRwUCoW5wML30fOModeohazUuq2ZEBj5YifYMJ95AVBzP8wX2Hzm16JeaSEjuwz7KMGRNC8Su7m5USQSsQNmo9HXZ4DhpCaTiaXUEAgzaX0+lVJNHqan+fiby57ARHV7e3v68ssv9fjxY1Wr1bmeTM1m07Qzy9JGedG7byhIJN7pdKwZ5dXV1VzvqfdpYZSC7unq6srKScmRs7Gz6L1GZtHnEsn6JqGAb/o48XwAYb5aaRUAKdSSeGOed7vdudPW0Rihh/FjIN319YnH44pEItbLqtPpaDqdGnDc2tp6UBD8Uy1ctz6FCqBm/S9q/BoyYR7keOfun7vXrbHZ+vPI1sX8usH/DQaDuZSjHyNK8/0xKLPZzAJS1haVebPZzPRI6NlSqZT29/e1tbWlq6sr+31fULOMdeNBH1oX3/mZOT+dTlUsFq2nE36DCq1UKqVqtWrj6YXankWHmfKH+IZaI39tqzDvOwC0XquHtoj7xidwbx704Vu73e4bGquQLQvX0Cat9jMtRP0+TeIflEeqi6Ii/1mh83vb316GeTEpOhmAEagcYLQsShbAEI/HVa1Wtbe3p3w+b6m+8XisdrttlXNEhQ9p/tmR1/cN+lqtlkWpRHKwKPyOnxN+k/dnTaExgCXK5XLq9/smUPTOcNXAyIutPRCANdra2rKqE4CjFwt7Fgig6LUG0Ow40nXa9EMLU40hq+PFw/zfs9OLWKRF5rU7kt5w+v4a1sn8vfq5z+ZNwEj/pxD4IVRmbVGplsvlJEnZbFaz2cyYBi9Q92ncZdmi+e2DJZ4X4mwvOOeeSasReMHA+Sa0vhWI9w3hXrTKANyPCa9eW0VxE+QCgRTgGHkLfe5YR9gi3ekiZnvZ9/1RgiM/kUCoaCCk1xokSVYdQfkoeiTeH26IobBSWkx/L/MhshFxyCxl8Swu8vcIhB96U2ZjIef8y1/+Uk+ePNHjx49VqVSMPTk+Ptbx8bFqtZpardaDMwohk8j5XrVazVo9eOEkESDVbL6PD5+Dk0un0wYacBa+R854PNbFxYVpcvg8rmsZtggM+XSXdMcctdtt01klEgm1220Du2hyuFc2Mg+COJHbH4mxSG+yLmkCL7IPe/pId3PHM8Y+tb4ozSbNH4fhBd/huIepqnXRG3kLN2wMucJgMLCDeNFjMmakxfCrdEsGNIxGIysigW0oFouaTqfKZrPWCJA59JDM0X0aPALo0WikWCxmgY7PMozHYxUKBQNSocaI9YXGDP/hmTfprgJ21W1iFpmfx15blM/n5zrLZ7NZGxOE1WGHfIAvAZg0HzysujjhowRHWMgWhPncRV/QmqTbMJ9fXVR1tcqJGwpePVu2LIYi1Bjt7++rUCjoyZMnOjg4MPqZ9vI0TcNpPuQ1suH4+UBl2XA41NbWlgnV/eaFYNzPG9JiRHpUZCHERGAKmOCE6Ww2q5ubG3OGy0ypeTDEq9c/hH19YNai0ehcnzAcPU4NJsnrJry4dB07Pr/NFj0PvxksAiz3Mcr3CVbZUMKz6nxPm3dhoJZlYeQOILwPROA/2dhZ316c75kTf8/SHUBlXvnKwVXNI88c+iwD2ikfKPB+7x+4br9GPFCGtfXVgP5vr4O9LZ2Wz+etcSrdrulj5dOhHBGDv6fNA/fsg0t8TahXW6Z99OCIRYujZ0HGYjGLAnzjsWg0aiLaVCpln8XGj4Nb1MV2VZojIlQc1yLxKO99COfiN1z0RX/xF3+hg4MD/fVf/7V2d3eVTCZ1e3ur4+NjXV1d6dtvv9X5+bnlm5eRVpPuWjxA8XY6HQNKiUTCSvvp+oyGAg0AjJN0V6ZLSoCO36Q40+m0ZrPX52vt7u5qMpno6urqwe81NL9B+zJrQBHlxThy2DFYM8aO30MvghbJM0n0cpE0p1PyDQDX1UKBuU8TLkonhlqIkFH2GyBVsT5dBBAdDof2xZpd9hwJzd8nz837lxAw4nPQjYTHgwCA8BOkk2ipEWpxmDuIez3QfujgIsw8wHagi0Fz5ANkf43sA6wxfI4HUx5UcT/+Hu+7rlWsH+YB9wuAyeVyyufz2tvbU7lc1qNHj1QqlawgiB5vnU5HNzc3ajQaGgwGdu8HBwfWAwlAhAaN9ju+MMqPwzLsowVHni1ggpMKCcGR74PEz2EEwmiAL77H66qcfqih8Yd5Eo14/QeL1F//zzGcHc3NYIyeP3+u/f1920BhIC4vL3V5ealWq6Vutzt3PMAyDCeMc0OgTak9C5fokOaOCPFZ8LwXLRcRUL/fN+dGiokI6z7NzkOa36RxQD7d5TszA358RY6n9MMNyqcUfNAAVe4jPw8g1oUVwRZdi/9emIpcRPn7zdo/V94LkPbntkl3zFHYcX/V4xMyYj7IYhx8ew4/HvjIMO3I2uMzAAvMS+aP//ve367y/mHEEKPjB3iOgL9QesEYcGRGCKB9hWyodVsX8+AInwYrTnNMjlTxwRUd81utlobDofXZIxhjr/Ks0Wx2p2/0qWe/1y7LPlpwJL15zhc5XUrKe72eptOpHTJKpJ/JZGyT4OH4zSJ8XaX5rt30g/AiYn9oItqP99GCwKfRYrGYisWiMpmM/vqv/1r7+/v6m7/5GzuPaGtrS+fn52q1Wvr22291dnam4+PjueMmHnrS+8UFRU7kij6Ga2EDRDPhO2UzZzzw7PV6Rren02lFIhFVKhXTI+VyOZXLZXU6HTsuAbbvofUT0l1FI8wFlSSAI94DCBgMBsaWebEkz3o8Hs+JLz0IJOqfzWZzn+/XzKo3/vuMjWmRdsgzbu8CbEPmiM3EN/4LmSN/9tq6jJHf8P1GxrNlPvuNDGC0qBt6CLAIHPyBrh6ULNoUHzKoCFPwXqO4tbVlpwt40bF0d0SOv3Y/BjQg9uAPcOiZ6HXQ2njz6TQCKebx3t6ednd3jTFKJpPa2toyv3F6eqpOp2PneOInS6WS/T4VvT7IymQypgfGT64ioPokwNHbtEdeaMvBh1B/9EWCNvbOMvw7q3Jm/O3r62t1u13L6/KzdDqtUqlk+WDu13fJXvSZixxQuFD8URKHh4fK5/N6+vSpqtWq5Z45SuPk5ESNRkPn5+e6vLy0BbTsjcAvNO+AYdSYH4Bj2DjfwsFHwVDiiUTCHEAmk5nTHBBpweDdp195CPMpNX/gMkfjQPtL8+fgvQ30c/2eCaFK0qebfQCxzB5b72IhMOUaEd0S1XrWDWDj06/MHelNXYbvjpzP520dwhwB1H2fl1CEu2rzwMhrMlOp1FyKBXDMvxk/30dOuqvs9EfVwDqTQvFMP88kFL4/pIX7Bczx1taW9SvrdruaTCZWAOOvy7NmpNvZUxKJhDHNAKW36dpWybT61DLPmuNUSIfReiEej0vSnAC72Wyq1WpZOo3gk2IVfIMHjfzfZ3024OgBzU90aD/yxjgkeu/MZjPTTFC5RPpk0QPy/1/FBMbBdrtdXV1dqdPpqN/vW5RbLpf17NkznZ+fGyNABHgfhe8Xp9cd+LQCm+uzZ8+Uz+f11VdfqVQq6S/+4i9MnBeNRnV2dqZOp6N/+Id/0MXFhb755hs1m03T+qxiEwjBkdcgIcaHCaJLLYJt0m84fOaT9Bo0UIUDa4fz5zgavwksCxz5jTqTyahSqahYLJp2iHnA+VY8F9YI5lMpOzs7Rqtzz56FYpNjbb0vxvIhzKdOfD+f8Xhs95tMJlUqlaw9w2Ty+iyo2Wxm4n3pbqPj2WcyGVWrVe3v7+vJkyfa3d21lht+42TzCCvXVh10MTYAOHQ3VCOxKWYyGV1fXyuTyWg2m1lFEj6X+c4ayOfzNn8KhYKBbDZFgBj6pfv6/TyUebnCdDq1lFAymTSNKtoaUoOeZfMaRX9cEeuOFLv3A2gCQ7ZkVXMg1N3B8pVKJZVKJe3v75t0IpVKGfPJUVVHR0eq1+tqtVo2jpLs3EIfgHhtkWfu/Rxctn304MizIOFk48F7Ojg8P41NMPy9VVRNLDImzvX1tR1+2O/37ZC/bDarSqWiw8NDm4iwHOE5a3we5qMGIj6ihEePHimbzerJkyd2gCKbrSQ1m02Nx2O9ePFCrVZLL1++NFBEQ7hVRcdve3b3if98NL/oi/f4CBs9gmdvlll1E2oFADWpVEq5XE65XE6pVMo2MdKynkUD5EynU9vUAUTlclnlclnZbG1SQecAACAASURBVFbpdPoN/RtH7dx3kvY6GQyQFxUzDsx90pKZTMZSK/gGH3yRRuOcw93dXVWrVZXLZeXz+bku6mgxOMgaULEOY+Q3ewIDgBwVaWxqlLAz72ezmW2olLXT9ycej1tKpVqtWgPFSCRiAJUjWvzxHMuaOyFzxHVtbW1pMBgoEnndUV96Lc1gXfE7ZCFIw/P/6XRq6ezQ/93nU/w1Lfr3Q1vIPPugiNMYSK3745k6nY4Bfh+Qe30izDMBI+PBHOA1bJWxLPvowZF0/8Tz4IjIMRp9fdI4/TdY5H5z9JtbyLIs26mxgDudjq6urlSr1dRsNi3/W6lUrFKsUqkonU7r8vJSV1dXc6dIh2MFAGTisgj29vaUzWb1m9/8RuVyWQcHB0omk3MLnTRau93W3/3d36nRaOhPf/qTnWnmS33Xybwg0jMcPh3r79NXzXhghJOASfEOxpcmh9VAD3VPpPeooisWi6pUKtrd3TUWBCaEPkzk+2mMOZu9PtIhmUzq4OBA+Xxen332mSqVivWo6Xa7mk6n5iBbrZZarZYxMeug0VtkzHkAQLfbVTqdtg2R4CCTyWg4HKpcLms2m6lWq2k0GhmQROQPAK1UKtrf39fnn3+uvb09PXnyxM5vxPF3Oh2dn5/bOVKM0yqBpA8oPTAaDofq9Xpqt9sqFosGFqPRqPb395XNZrW1tWV9sWAiAQWxWMxYIiqV/JhsbW1ZgNdqteaaxC46jeAh790DHb/OO52OxuOxEomEFe4gKJfuDinu9/uWjfAMKgyrf8b8XQ9GF93jskGRBzOeeS6Xy9rf39fu7q7K5bL5suvrazWbTV1dXanRaKjdbs+V7+NT4vG4aR+z2aylVxlrf7YcwdqGOVqS+YceNmPb3t5eqI/wvwOV+rayy2UZC2owGKjVaun4+Nh0DTicZDKp/f19i1qYwL1ez6hNJiYLlk0BtgBnWCwWlUql9Pjx4zmnxmfV63UNBgN99913arfbOjo6sgMF/URfh8gYA0D4fiVcH1EeLJDftPx88GXxXri7KOLx2pRlmL9e9GIAJfRRfPlmqGwAPK98Pq9kMqnDw0PlcjlVKpW5KhWYELqfk+JdVzDszUf83W5XmUzGKpNo8plOpy3IkKR6va7hcGgi9fF4bMAom83q8PBQ1WpVjx8/VqlUMjYXIEaE3Wg0rDhhUbfgVZpPOQJ6U6mUdX1Hb8YYjcdj8wus99lsZn4kk8koHo+rXC4bg4kOZzwem69Ar7JIm/jQYxOyNF4DxaHKtHsB7CDFICUI8+Gr2N7GCPFzz9avem8JmWd8QjqdngM1XLv3IwBKUsxkHRKJhBXqcGoCPtcXF3nGeVUp5k8GHPnB9akiABJ5cujBsDGhLzv15bg+n76qKG8ymajdbms0Gukf//Ef7TyscrlsKD2fz0uSfvnLX1pJfb/ft8XLJsZkpG8HVQjVatV6GPkSbX8uTqfT0e9+9ztdXl7qm2++UavVUr1et2MFwmhpncynnQA2vpKEjdIvVC9S9GCDKNmnY/kcX8q8TPNzPx6PWxVdqVSya4zH4xoOh9rZ2ZlzTjjtQqGgVCpl4ntYSYABqd3Ly0tdXFyoXq+r0WhY/551EhpL8ywBwujBYKCrqyttb2+r3W4rGo3aOqpUKtrZ2dFgMFAqlbIUU71eN8BDAFEqlfT8+XNVKhV9/vnndvioT4PXajVdXFzo9PRUtVrNxnwd1olngSVpOBwaWxaJRFQqlez4Dxgh2MXxeDxXGBKJRJTJZOxwVt9AlKADhhEW7fT0VPV63cZkFUGVBzWw6+g2t7e37cQFUqj4DJhjwDbBVxhse4aK/QYg4tnl0Fc89F7jGW3PeOPjfHEBPYl4PgRCYV8nSValhvYO5onginQq52769PaGOVqyhSWqkuYqMPz5N74sN9SOLCM9cp/h4KFtm82mdnZ29PLlS0uh+Fb9oPRqtapcLmcL1usIptPpXDqNiM+f29btdnVzc2Ml+S9evFC73dYf/vAHtVotXV5eWolyKMZdJ/ORWig45n7p+Dwej61hHZU4RM3ob0gzUZGBY/WHKS7byXstEKJaNBGwRDCMaEjQ1XitACJ87g+wyAZGWpcjYbzAe9Wb/X3G5uTXULfbVbfbVbvdNuEogRGHiG5vbxvoL5fL9kwRbufzeT1+/NgqekgpEBnXajWdnJzo4uLCUhCL5seqx8ynl6LRqHq9nra3t9VoNIxNQ2gNg8L/ARTRaNQCBn9AM6mY8Xisq6srdbtdHR8fq9FoqF6v21Ekq2APPHCW5is5eY5okZg7ACBAwWw2m7tfAimqRmFUML8feYE/r6vUufrCDv+FD5Tumr5ms1ljoRmvaDQ6l45Hf0fbD9Zdq9WaO4bmoU9PeJt9EuDI59C90/Hl/D7HysboJ7DvQfE2gLSq+2MDOj8/V6/XU7FY1GAw0LNnz6xahntjg2MRRyIRo8CJ8LnvUB/DmNVqNXU6Hf32t7/VxcWFfv/736vdbuvs7MwQ/6q1E/fZonQpabFYLGbl7tLrikV/9h4dstkwKdXe29tTpVLRZ599ZmJnv7GQP182QIIV4dlyqCzUNalWnLYkFQoFc9DT6XSuYovAgQqt8XhsKbSXL1/q8vJSZ2dnajabarfbxkiuMzgKU0etVkvxeFwXFxeSpMePH5vegg2OM6QQ3Uqv51IymbSUEUyT16Pc3NwYMPrmm290dHSk09NTCyTWZc2E6R78QrPZ1M3NjdLptDX0m0wm1tcLAI3hN2AHfL+ryWRiwuuXL1+qVqvpj3/8o2q1mlXX9vv9pemN7hsD6c1eeTBpsEcwrDBhBFaI9GkFgU6LlBQBFOPh/UMIkPj3stkzX6kcNuxk/6Bzfj6f183NzVw7B3zLF198oXK5bEEDkgyAUL1e18XFhWq1mskzvO/YpNUewBZVIPnBhinBAXoVfqgtYrNbN2fPQqJh4enpqSRZCq3RaFiFAUcY4LAk2UQkkvMVNT6nTBoNxugPf/iDms2mLi4u3jhgcNXlyD9mi/RAkqwPC0CYagx/thr6NISF1Wp1bnyj0ag9C8bFg6NljIuf3z5t1G631Wg0rBybQCHU0Pn+Jr4PCe0MYIyOjo7UbrctFUL387Dj87rNBc8O+PQG2pparaatrS3V6/U55pRXGINisShJVsCAroYKLErhKUg4Pj7WycmJzs/P7VDodTk2JLRFcygSiajRaGgyeX2mYLvdtv5epFp9UOXHDKBDd/zLy0v1ej396U9/UqvVMgYpPEplHeZPqD8Ke+VJmitJp50H7Eomk1GxWDQfsbOzY/MP4OxZ3UV+YtnAyL/yHLh/ry2i5x2sIdV5s9nMQODBwYEFjmi0xuOx2u22FRTRjoZgfZWBwicBjkLzk5zBj0ajdrIwYrFMJjOXbvPpiRAgefZoldoj0ljff/+9Li8vTaDNsR6PHz9WsVhUtVqduz9AD6Wn0MGdTsc2udvbW52cnBhL0Ol07PDYdYp67zP/rPyG6L9PdISAFBFuqVSaExsCIDlDrVwuK5lMWm8swFS73TaRKZU3y2RS/KY/GAwkSVdXVyYmh+GjsZ9PJ5L68OAI8HBzc2M9rL777js1m019//33ajabpjNiLvooeN0s3Pyh9yORiF69eqXRaKRqtWp6CdIG0WhUhULBxlG6C8IYNy9S73a7Oj091dnZmf7Fv/gXOj09taKFXq93byfpVVrInPj0uyQ1Gg01m02l02k9fvxY+Xxejx49mivvBiTR9BAdG5rHV69eqdfr6ejoSL1eT81mc06juA7NQ71mkFShT6uFXa5Jy/O7pB/pCo0Q3euOfPsLD47WRaPH+PNMfGNOWCTaMjCP/bOnrYxvfkkPqMvLS9VqNWOe6/X63L6zAUdLNL/YQL6JRMKaFyKqpbIESnVRt9YwqlnlAqakWJIdizGdTq3nRCqVMsYAZ8+mgNPzZwZRlUHfnvF4bHRnrVbTcDi0n6+bY3+b+WflI2K0BJTaSprTmkjzR0yQgiMNByACiLTbbdVqNdtE0GmFC/6hxos5EWpqqGxETC29ThPAeIQpVV4BOoA8jgc4Pj5Wt9u1VgD061ln1miR+TQoZ0JFo1HbuEkdoDkiIg6rivAPiNSbzaaazaaOjo50cXGhk5OTuVYaHhhxHetkHhwwBxAmz2YzO6C42Wyq3+8biPRyBAAk/oJKv6urK/X7fTWbTQuyFnUKX7Vf9f9nPHyJPmcJejDD/SPY9ucreqABu0y6O9TqrUKI7gN9r7fyomlSqPgL0qmeUCDgZI2wJqbTqR0rwmHktKHxrOEqn/0nB45CalS6O129XC6rWCxa2Xo8Hp97QCxaetmsmvYLjQVH+qvf7885eK8JoISWzZp0Gg4PepgFymZHUy7PCKzL/f855ucB1SWRSMTa/PNcfZUGehOvPfOpAxwlott6va7T01NdXFzY2XKrYFP8pi+9rgxi7tJiAeEwuiIPjHB2XDvHAQCOGo2G+v2+gT8ocebSurJGmGePJFm39KurKztCqFAoaDQaqVAoaDweW/UZmwOf4zdMSvVPTk5Uq9X03XffqVar6cWLF8aSIIpfVwAZplRIAdHTCdBIIQgiW8T9gEd+n3Qrrz748h2p19GveKCA36C0n3lCoQapJlg0Gl2is/FaRITI6PRo64DPXdUeE6abeV6tVsvIBM7Fo/Ep2k3E6dId68hcpwHxixcv1Gw29c033+jq6kpHR0dWDLGoQfGy7ZMDR948wg+Fx0xeNk7KdtkgfHSzbgtYepMdw6lJsrw2PXwAij5KAenzPX7OPa+qa+n7MH/dXqwcjUbV7/cVjUZNC0IVlyRjlNAQ+AoVHDvMSa/XU6PRsIZopC0XdcZdxr36qkwifXRRs9nMDi72zBHrgc8gJdJut60UfTAYGAXuQVHo0Nd9jvjrZONDfM8ZgbPZzAT6nFnoxfvSXWQ8Go0sZXZ5ealWq2UpaY6iCFnED2GMYCGlOzCJr2BD84Jd3+8LvRo+lXUXaljW1afyb/whbCqgIRKJWHsUScYmRSIRayjse5xxdAyNNQEGb9McLdu8j6SnV61W02z2+mBp5jpd8mHSCTC9RpfsBEHiy5cvbV1w/74v2qrXxScLjnxE65tB+r4n/nwcokDf/2XZ5/28q/nrIT3EvZErh/0IkXl4L4tShut0r3+u4aC8kwMwAHxwfKlUStPp1M6LQmMESIIyJqoiPUBKrdls6vT01DRHqyxLhvmcTqfmgCjj7/V6FtX6FhaAIypx2NDYAGCKvG4pjPo/pLni5wRgkhRhPB5Xo9FQOp3W+fm5iY/p3QNgACCTlhsMBsas0dQQjZFvELjutsgnSLJ1gF/pdDpzjCNfPkDzDPeH4ltCdhG/QXECLT5glwHVpNo4rJj3+zQVLDMl7L7n3CK5wjLGyP89gmrOl4xEIrb2SY3R5DSZTJofoXiFNBlBFSJ8inouLi6sTx5+aR1A8icFjjxbgDO/vr62g1rr9bqhfUR1ODt6b7RaLSvLDs+LWteFjXlH7BklAAPfD38ndIwfunkn4yljomLSKpQR0/CP6jSO1fCOHzbOb4DMG38EwirSBR4Q+pQoVDfRfq/XM32IZ1GZ4zADi1KxoQ7Pj/OHZGEqgbFjrbNxxeNx1Wo18xO+1QWbAdoRuscDKsOU9Idki6439B9oH0OtjnTHaC9aBx/CWPg9RJIFBugVAUWDwcAahnJYLUyapDm/QyNRAHR4APSqxsU/M+8jSSPOZjNr15HNZucO12bvJKgimEKDh96MYgUONl6Fxuo+++TAkc8XkwpotVo2qYnqENMBjlqtli4uLtRut40u9x2l1+Fhvs3CqONDiFYf0vwm7oEC5yUhzCa/7jdC39+DXib+wEyEpYAkdDjhhrhsgOTvFxDoU4leP7VoY2Ou3yeWXfc18K7mx4mUQiQSMfBYq9WsxQEsif9dP073paKlD3u87rsHQMPHaCFY8FVZVPiSekKLRqopk8nMdYuGRfO9tahW8ynqMPhets/wwTMBEidJtNtt7ezs6OTkZI559n3QfCqV6jSCBy9RWUed2ScBjhYxBZKsgVcsFrO0SiqVMpEhmySVBLT496eXf0jM0cbmzT83UmmeWaT0eHt7W91ud06kjJPzjAzRla9g8cLCVaebwr8ZMofh16KNL4z6P+Z5HwYSjBPRM92RfbUac4FxWvTcP9bx+hTMzwkqQH2qDRaJwo5er2cNVMPz0rx4H2E6jJE/03Ad5kyYCkWSgeYIUOQrE/k9wA+MMyws97iue2hkHS4mEoks5SJ8F2zADyjf92GgjwnUIGiePCv0ODQj0dI6jOXGfpqFXWjDtv3hmWjhq/SmPmtR+mAzRza2sY/HFgUV/itshxGepuCDMQ8UPoQUddhceRHb7G0d7+EH++1sNvuXwm9+EswRtiilxMSkeiQWi6ndblspJkg3ZAXWveHhxv484xn66iz+L91FiljoGMLP8a+bObKxjX2c5td5CHx8jzEfXN3XN2lRqt//jXWzt13Xul7zn2OfFDiS3hRchgxB+G//O4v+vbGPy8JnunnGG9vYxt5mP2c/WJS+/lDtY7kP7JMDR6H5B+qFZ29778c2CTa2sY1tbGPLt81esr72yYMjbAN8NraxjW1sYxvbmCRFf/wtG9vYxja2sY1tbGOfjm3A0cY2trGNbWxjG9uYsw042tjGNraxjW1sYxtztgFHG9vYxja2sY1tbGPONuBoYxvb2MY2trGNbczZBhxtbGMb29jGNraxjTnbgKONbWxjG9vYxja2MWcbcLSxjW1sYxvb2MY25mwDjja2sY1tbGMb29jGnG06ZG9sYxvb2MY2trGV2n2HeWPLPr1iA47eYuFBtPx/0eGkm+NHNraxjW1sYxubN7+P8hqJRBSNRudet7a23jjwXZLG47Fms5kmk4kdGu/33IeyDThaYIseoiR7DQ+r5Ws6nX5UpyxvbGMb29jGNvZTbRHB4MFQNBpVLBZTNBrV9va2tra27HcBQTc3N5pMJrq9vbXvYQ+5137y4Mg/NP+wtra2tLOzo1gspp2dHXuoPCTA0Hg8toc3Ho81nU6Xhmw3trGNrYeFm0D4fW9vY5mX5fg3trGHMuY8TBD7aTwet/10a2tL6XRa29vb9ppMJhWLxWwPvbm50Xg8Vrfb1c3Njb32ej2Nx+M39tv3bZ80OPJI1iPXeDyunZ0dJZNJ7ezsKJVKGdq9vb3V9fW1gaGbmxtFIhGj/pgY0+lU0sbBbWxjH7ux5vERYRrev29RCt6/LmKeNz7k07H7dDcf0hwIyQZAUQiGisWiEomECoWC4vG4MpmMtre3DfQMBgPd3NyoXq9rOBwqFotpOBxqPB5rNBrdu47el32S4MiDIhiinZ0dZbNZJRIJ5XI5JZNJ5fN5xeNxpdNpY4qur6/V6XR0c3OjwWCg4XCoaDSq0Wik6XRqeVFpA5A+FvsxoWBom+f9cVoIggiq2AR8cBWLxRSPx22TwH9Ir+fHeDzW7e2tJpOJfbEpTCYTY6VDjcWy59Z9LFioIwn/7TesT1mTuWic3vb/+8DxhzCG7KukyRKJhLa3t20/3dvbUyaTUbFYVDKZVKVSUSqVUi6XM0IComE6narf7xs4GgwGevXqlXq9niKRiPr9vjqdzlwWR3q/Y/PJgaMwjRaLxZRIJJRMJlUoFJROp+2hVSoVJRIJZTIZo/kGg4GSyaSGw6E6nY62trZ0c3MjSfY6mUwkyTRI0vpO6NA+hsjlp9iPAaBFP79vM9jozj4uW6RB9Ol3QBGbQTqd1s7OjtLptIEmHLjXUQwGAwNJt7e3lkYANEUikTlfgi1jbi0S0S4aC//+cD2EesxPbW34MUGvyvfD9/n3hyA43PzXdQw94UBwEI/Hlc/nlc1mdXh4qEKhYCBpd3dX6XRauVzOgBGBw2QyUa/X0+3trfL5vAaDgSaTiZrNptrttiTp+vra3i/d7bvvyz4pcLSIMUokEioWi8rlcnry5IkKhYIODg6UTqdVLpctR0o6rdfrKR6Pq9vtWsR4c3Oj2Wym4XBok9dPdP72Okzoty1M6W4Rh98Po9cPVRuxSCDoKyVgBcL3++e6KIJnfBa98vvrbu/KkL1NR/Mu9iGMhXQ/GIrH4waCYrGYMpmM+ZFUKqVSqWRpAjYKmCC+8CXX19caDAa6vr5Wv9+f+z9phdFoNMcsSe9/DO8TzoYVRSFrxu+wfkJ/AdDj9UNcF3+OvQ1Ah2zjovQrVVkABP8a6mvWZey8xgjGaGdnR5VKRZlMRs+ePVOpVNLz589VKpVUKpXmMjOJREKx2B0UIfuSz+c1Ho+VyWQ0HA51c3OjbDarRqOhaDSqfr9vc8xLWt7XuHxS4EiaR7fb29vGDBWLRR0cHKhYLOrp06dKp9MqlUr2/pubG/X7fcXjcU2nU21tben29nZOMLa1taXJZKJoNPogD+vn2H2U+KKoeJFuggnLq/9aF+D3YxbeV+jEeL0vVSDJnq2P5HFaPiUivUmHr5vdB4be5fuhQ/8pc31dx0V6EyT4dFkqlVI8HlehUFAikVA+n1cmk7GIeG9vT4lEQtlsVltbW+YX8Bej0UjD4VDdbleDwUDtdtvSBIPBQJ1OR/1+X9Lr+cZ8gk1632vO+4D7Uoa8ht9njPzmz7XOZjNdX19bZO9TJh+Kz/hzzI8fz92nWEM/w3swDyhHo5GB6Egk8gaYXDe/69cKpEM+n1c+n9fBwYH29/f1i1/8QuVy2ZiiVCqlWCxmY+KNcRiPx9rZ2dH19bXa7bZisZgKhYJub28Vj8d1e3trc/J9C7M/GXAUMkY4t0KhoOfPn6tSqeirr75SNptVtVrV9va24vG4JpOJRqORfVGZJske7Pb29kKHscrJex9DEl4fYCBMEVBRwM9xcixaIhrKK3F667JYvYUMEQs43PBIhTAeoTEuULk+yiPKv7m5Med2c3Nj4n0PKldlbwPF9zEH0rzQOPwcbz8W0XL/AMhl9y15V/NzxYOhUqmkRCJhEfH+/v5ceqBcLiuZTCqXyxnjjE2nU3P219fXGo1GGgwG6vf7arfbGg6HBpBqtZo6nY4x1Kw/P2bvY6wWrQu//rkHpAf824tsQ9EtzxL/0O/3NRqN1Gq1jC1jfXgmaV2e/U+xMMiKxWJKJpOWUgpFx4wn/ofnCmgeDodqtVoaDoc2N6jQQtu6TmPmAfL29ray2awymYwODw9VqVT0/Plz7e7uam9vT/l83nwvUpXr62tJd4AvZNXQ7hUKBUlSuVzWZDLR1dWVxuOxhsOhgcf3ued+MuBIupvEAJ90Oq1CoaBHjx6pWq3q4ODABGIYm5/XBACOvFPBabxrauIh7W0UeUjrhs4QipOyyp2dHaMwx+OxOTc2faoH1m3BYosYAO7X60NyuZylRjwo9Ma4eDDIWHS73blUSK/X09bWlkXNq04j3AeWFwFmP08WOSs+K0zB3icc9akWxsy/rlOaIJwrsMtoEQFF+XxeT58+VS6X0+HhoRKJhMrlsr2fsfNgED/igXO/31cymTSwRCpue3vb0vWsNYTd75OR5jrD9iVU6gKKKFbhfclk0nwH7/eVRvjJdrtt7MdgMLCilpCB/lAtXDfb29vGiqTTaVWrVXv1c4Txwp/c3t4ai9hutxWPx9XpdIxlQ89KCmmdLAwmyMaUSiXt7e1pd3dXu7u7KhQKymQy9nsQDTc3N3NM/Pb29hzpQKCayWQ0mUyUzWY1HA4NkHv/tWGO/kxj4rLZZzIZ5XI5HRwcaHd3V48fPzaqT9LcBjcajdTpdAy1397eajgczkVy68SYhBueZ0o8+OE1k8koHo8rm83OvZIP5nNgjnB2RDbn5+caDodqNpvGrvmKvXUYC+/8cfakRIrFojmvVCqlarVqEV0YEQKqpXmNEeCo1+up1Wqp3++r0Wio1Wrp9PTUUiUwbsveEBZ1og37eXkWDccdsomeGQ0BdsguSTLwg7FeYBNqtZrR5YCGVa6lRYxRKpXSo0ePjGHO5/N69uyZMpmMDg4OTDvBRof5KjTGwf+fFBlz8Pb21qp6WK/T6VSxWEw3NzeKRqO6vb21FMvPDUZCpsPrqGA4fOVuqVRSKpWa8x/Mi0gkYikSrguGudPp6Pr6WsViUb1eT0dHR+p2u/bsAU7r4jP+HPP+gWxELpdTJpPR48ePDUCnUikVi0Xbe3zwEYvFbE6MRiOl02nzwQCnaDRqelbWyPsEyD93DFgzXHOhUFCpVFKhULB9hEBxMBgYg9psNi2g9Ho6WDa0e9vb25Jka4x1SZX5Q5ESnww44gEywOiMyuWyyuXy3OLmgdVqNY1GI7Xb7Td0JT8GilYhWg5TH4tEpDg9GBNAAimBQqFgzjCZTBoahzVrNpsaDoe6vLxUr9fTbDYzwOTFcesGGEMRfiaTsZQIGx16EUCQZ9nYFABH3BvUcLfbNaffbrdtkxkMBopEIhoOh5LuKiqWNTaLGKGwKRsbHQ4HFs2nVhaBJA+8/UbJHAQoY4BDUiyRSETdblej0UjSXVS8inkTjhPjQrqsVCrp8PBQpVJJn3/+uQFqzxShLSIS9hoj7z9gE6W71Dzfj0QiBp7YSLrdrsbjsba3t+c0jT/3XkPQT2+3VCqlfD5vm3omk1G1WjX2CCDgo3YANc/v+vraAN9wONTW1pZpqSKRiHq93lxD3fcd9T+0hSlJxhDG5NGjR6pUKvriiy/mUq2JRGKhZpHMBEEJYLHT6Wg0Gml7e9vA8TpkJ6T5Ih5PPiSTSaXTaaXTaaVSKdOjERgirr66ulKv17NsBKBvNBpZKtt/PoVRiURiIWv0vsflowdHIbJFaF2pVCwnms/n54BRu91Wt9vV5eWlldxKslQLkU4YwS1KDyx7E2Sy+J4r5IETiYR2d3eVzWZVqVSUTqet5wS58UwmYwCCReq1DtlsVjc3NyoUCkaT1+t1SyYS+wAAIABJREFUXV9fW3TLZrBKC9NIYZqkUCgom82qVCopl8tZ+wbmAo0/Q3YEitfnxyeTiTKZjEVK7XbbxpvNAIC9zE3gPj0JGxxAmY2wXC5baW0ikTDQxBwiAgRc3ZeGY+zDAIJUS61WU6/X0/b2tprNpoFL0k4+ilzmOPG8k8mkksmkqtWqisWinj17pkqlomfPnimfz6tSqZhzlqThcGigCMYZv+EBgHTnD3xqN5FIGLsJWwOoAiSR0vbpyJ96r/6eeZ4+aGBdZDIZSyWWSiULqLzWKNzkfXqJuTadTpXNZjUYDBSLxdRoNDQej9VoNDSdTufYkA8BIPmx8/rVXC6nzz//XJVKRV9//bVyuZwePXo0J1KnNw97iiTzS5IsAEun0xoMBnPzbB1TkKFv9fODIqZarWb9ACORiBqNhgaDgS4uLqxCE4aMQicYuNlsZp/H37tvHN732Hz04Ei60wbx8LLZrLLZrJXwQ4eD3judjjqdjhqNhglsffphe3v7DWC0ak0JtsjpeTRfKpVULBb16NEj5fN5A0eeyvWIHMYISyaTmkwmtvFfXl5qNpvp4uLCxs7rLVZpi7Q1PEeoWcSD2WxWyWTSgAM9au7b+GezmW0CGL+fSCQkve7Dkc/n1ev1FIvFdHt7u5J75759CoVqKlKJ2WxW+/v7yuVyBpQBR7ACAGafRmMspDeF22FVI4xrOp1Wp9NRs9lUNBrV6empbm5uDIxz/ctkW8P5QYqkUChod3dX5XLZWMZcLjfHFKEh6vf7JqYdjUbGjvj1I2luDnrtTqhxAlCk02mbQz5A+6nj5H/fzw38BOxRNptVLpdTLpezueI3KmkeAE8mE9vIuR+Ex15XFY/HdXx8bALt8XhsbNiqfca7mgeBMEbFYlH7+/va3d3VkydPLPhEmwkoopEwARaBCmuLfcoL4MOAe53GyrPIHjhTfOCbNbJX9Pt9nZ+fGzsqydYB+/FgMNDOzo7ND+luvi/jjLWPGhyF9DHpNBY8okOisV6vp3a7rYuLC9OPAILYEPhc7xA8QAo3BGk1KTUf2cCU5PN57e/vq1Kp6NGjR8rlckZ7cn9Ev0T5RK84ZiY+aaPd3V1Fo1GdnJwY/cuRKusAkDA/Pp7GJqJBQzUejy2C9/fg02F+bBkHuqjjLKGFAVbLngvSvNgWkEMqlVRJPp+3KpLd3V1bGzRwA0z5kls2aJ9a9PN9ESjlfbBCzMnRaGSA1IPQZY6T9xGAFprVVSoVa/GBfoJqLKLeRqOh4XBonXxhw7zwGPaR+UGQMZvNLKXF+ALO+IJhChm7n2P4KzYeLxQnrYcPIHXszRck8BmeYWWjo8KPOVSpVBSLxVQul02ygL/gWayLz1hkHhRRuZjL5fT06VNVq1V99dVXVsJOIQsSjV6vp4uLCwOFkchrzRm9sWCuYVHeFmisg4XrXJIBwW63q0ajYYAYkEOVGbpMKtUkWYAeFnIwV6noZEzRAD+UuP+jBkfSm2WWgAVSBZQUIhajnJYH4KtDcOCS5pDrIlC06gns9TKkidLptPL5vLUwyOVyc6BPunN6IHq0RL6KLRKJmNPO5XK6ubkxsea6VOx5W3Q9/my8ra0t9ft9i2BisdjcovWiWnpc4Rwnk4ndO7/r6eVFDMsy7D5mwKdOmAeVSkXFYtEYJK8t4V59dBiuAa+ngVEDQALOotGoMUeMLUwbG7+fh8scp5BthTWDOWKtwPJIssCh1+upXq/bxjcYDOYYZ8bH63oA0vzf98byImkPiHwF5fsARt53sRZ8iw6vqwQE+WeN4BpwxOeGOrZcLmdBQywWUzab1Ww2Mz1K2P5k1X7zxywE0plMxoLOarWqvb09pdNp8w2DwcDmRLvd1snJiaVe0fnd3NxYAQinMfi/J70ZgKyjEUT6Cl72VgoLJpOJBRH0B2TdwzJ680yRB/A+gH+ocflkwBGTOaQs0Q/RY2IwGFiVGhERzomFLN2dj7TIqSzSIT20eWbCOzEfuXsgeH19baXBkkxAyXk2VOwBlhBq7+7uGtsEbQ5L8pCVAz/F/Fj4ihBJury8VCKR0PX1tRKJhNrtts2NMGILq8xgYdAUIGpnjvm/BbsQRjgPbT4o8JsrLFc+nzehcaVSMWbRiyi9roR54+e1b+HgKzdZV2EaxpeBj8djA08h67ps82l3Uo4wzKSTYAFns5kVbFxeXqrVaunFixdzrEC73bZ78qk6Ai0AJAyS730DQGfMPDvNtfrr/nPHy1+DJHum+IZ2u63JZGLSgVgsptFoZOCVZ071qu/7BhMSi8WMCalWq/b9ra0t5XI5RSIR5fN5dbtd07Gti894m/l5gsYQPdoXX3xhgUYkEjHG5PLyUo1GQ99995263a7Oz88NjFKZ6Nl75hlBmNfgrWtfKOY6DCBsIA2SkWrgR6lgZI+FqPDpZHSQyWTS5oc/aodxecg2Mh89OJLmS5m9oJRBZ9KRauGLh+5/12+aPuK6T5y9KvMbTpjaQDzKhJ1MJobiqTzrdru2oUsyrQiOXJLR6J6FW0X0v8g84xcCJEkmmp1OpwZoeMZeP+afM/9HY3B9fW0Ln7QDmwobh6d+lyUy9qyRj8phJGCQUqmUMpmMVZaETA6/FxYf+FSLr0aDXfP9bySZoweMh0zRIg3BssYoZI58EYPXXXG9+AnaWTSbTdXrdbXbbV1dXRmbhPkKx9vbW2sXQmrGV37FYjEbX88YPMTc8cwR1ybJgqbBYKDt7W2rSGWMmMsAfu8jotGosWt+k/dzL5FIGDAASK1TQHWf+Xnie/mUy2VVKhVjXVOplCaT1+eC0eKkXq+bzqZer899LkyRrwbl/8g33qXwZ5Xm/StNGZkT+D9f3ANLj08N9Vucdco6xE+ERMRDZ2o+enAUMkcMPmkDygylN4VuflETbYUbrQdUix7aMi28PjREgB2ieZijZDL5Rok+Zdbob3w65fb2VtVq1QRzpOtw+l6cvA7mwa0/zgCHziaws7Mzt6FJdw0L+RwWO2xZLpfTdDpVtVo12piFTNqBcadqaZkAwM/X+5yI1wT5ihraDjA3mAtegwLb6kEjY03vLFhGNkH+Nqxap9NRq9UylnLZXcRDEBmyR77Hymx2d04Y7HKn01G73Var1ZorSabkGnDoxc4UgtBGJJPJKJVK2d8gvd/tdnV1dWX9sjiHjXH6OdGyZ4/4HIAtInNKx2FWAY/8PeYGlUaMJ5taKpUyMMW8CEves9nsHBBfd0MPxonye3t7Ojw8ND3azs6OMUbn5+dqNBr64x//qGazaZpMmsMCkglScrmcVQMyrqTkYOmWvT7e1Xi+pFdJoUuyDuB+HUkyaUY0Gp1rwEtBCPojqsjRHHW7XQtKYOUfij36qMFRGB0CdEJxcWgezXpwwM/YLPlaxB4t20K63F8ji217e1vdbneuEg1kj2AQyhMxKWNVKBTmOuBK89GvF4uum4XMBJsLjcmgeHmvT2X48WQujEYjAwFoMXyfHzYbgOmqO0F7YPQ2J8L89REeqUGAkC8lDx2TJEu9+N4u4b1Dj3NUgo8iV+X4PTtMiiN8pl6n5tPwNIX1R+uQFvN6R0CX7wED0+IF0BQKePEp8+h9HdMT+gvmOwwopeY8Z683A/TQbsBX4zFOBF/+Wr2uy7cL8X56Xc2zi77iGT0aAJJ5DWN0dXWlVqulVqtl44XP9OJ7iiWYN4yvlzas0ocsMn8tnvFkPvjKxTCDIc1Xp1Eh6RlsX6lHMEbgTq+k0Le+T/uowZH0ZgNIJqZ3fp7W9QJSTwX7lAtMEQ5ykeNaxST2f4vNhsgOat9HqTs7O6b6p6kjeismHZMU508qDd0WWiO/aa7DwsV8+iAUN7IZwbD9GMviS1Vns5lpK3w7iMnkdQdoon4i/mWm1TyLA2sGWItGowZ+e72ePUOaDdJyYBGbGJ6v59keSabNefr0qQqFgjU1JF3F+BD9XV1dWSO4h4wA32aLNmTPpEmLRcusLz6Djc5rG2EF2EDpqcaZbBRESLJq2WazqbOzM52fn+v09FSXl5c2RoDU98VChj4KxtOviZubmzfKyv2YePZQ0lyazMsXFlVf+XTtOpsPrmH/OBKjWCxaKu329latVkv1el3ffPONms2mjo6OjGmU7tgnNH+PHz9WtVpVqVSyeUNfK/YYH5hI6wGMQgsDMAJz9kz2G/YOSXPVs7Bx2WzWBO34DMYBwAmbCkB6KFLiowdHWJhC8F9+0eLMcQI4O37mHaUXheEg1gEceCQPxY+o8vr6Wjs7O5YXxuECjBBke4cXj8dtcYYCX/7NuKz63t9mHjRI8xsjEcoiB+QBtnR3OjrVJj76940ASRetCjRyP15QzbyF1eJQS0kGnrjmq6srq7Tx0ZoHSUSEkuacHJ2A/brjejhTrNfrmdZtXYC115aEOg/PFi8Czh4EMjcQv3uWATAN2yDJ0pcAR75Iy3pm6n2Ok2eQCBhms5k9W+mOVWL9ww7yfu8r+JzQ34bAKGQy1908AKZHGOwfYnv6vDWbTV1eXlrKFZ/g/SYgi0IIjhXh74SAfNlp+Z9qYWDpgS/SA+aCP6qGPnwEWL6XGkARXwVb67WcG+boZ1j40EgJscHf3t5arjybzdrvecaISUo+GNFd2G/B/71V3Kc0nweGQYpEIqY9ur29tSiF6jTPgHmHCBjwTSVh3cINeF0tfBYeJElvHuvhN31PB29tbdnZSU+ePNHjx49VKBQUjb4+/6jRaOjy8lJHR0e6uroyQLFMJtH/Lf/8aGZKj5VYLGbtK6gsi0QiBpgbjYa1tvCbM2wkzxttAI7Na0qy2aylENBQvHr1SsfHx7q4uDDgtUoAyTgB+ACNg8FAiUTCNGP4AYAy+j169lB9RCUaR9Rwbl+hUDD/ggYL/VWj0TCm6NWrV6rVajo/P1en07HigYfaDPxn8dmeQZLuqud84OhBIesFxsz7CdJF3l/4ohcsBFDrYmE6jS7p9HAioOj3+zo9PVW9XrdydfwvaVVOJ3j27JkODg707NkzA8wAZCoeaUa8rpoj/7zC7/tUMuw0HfmZC2jOaERMhSNaJOZ6u91Wv983RpWgwafjH8I+WXDkF7bvK8Li9kak5DUXRN+L0mnrMHl91CfJNkYif74XAqL7qgDC8fKpyPDe1+H+77NFIOm+93iAhLHx0f+GklPpdfTf7/fV7XbVbrdNoBsCr2VZCNQ9AIjFYup2u/YedCWS1O125w4XxkGTRuOemANU/DGWiG5xjOh2PBCo1+vGijxkI7d3HSevtfIlw2zini3xmyVVSwRa/v7pvs4myuYAWIR58U3zGo2GarWams2mOp3OHPv4kKXc4VxhzqKf8s9b0hxA8iwR4+BZZZ9WC1m4dfcX/t4oxkA3huRAuut7xfonmODekHVQqk4bDc6whH1i/pHGpq/Ph8AaSW+Ol5ezoLkjsAYIwb7z5Sv1JM2NK9XUYQHHhjn6CeaZjVD34QWYOC/fX4RFTN6XSUp0GYowwwqnVZtPEyG05EgDtASk0ABH3tl5ASVOj00PhwfViXD0fdP+D2H3sUP3mafU0Rt8/fXXOjw8VLlctiin2+3q5OREZ2dntvn7iG+Z5v8eG52fA1wXInz6O0kyHRrgzqda/QbNHKCdQaFQULVa1cHBgR1Pk06nJb0G4kTU3333nV68eKF6vW5lvcveKEO2BJaV+45Go7q6utJ4PLZKIr8GWBej0UjZbNZ+1xcp+JPtiaQBFaQW2+22zs/PdXl5qe+++86YR7r143uWBSbCVJqvvONnvlu6pLmAKawEXtSuwQPQdWabMa9ZpS8VGzqgGcbHF7L4Kj8aYB4eHmp/f19ff/21HUkDY+tB8unpqa6urgxoLVOz+C4WgmKCBQAgZzYmEgljhIrFojFH9HfyB11TIQqIJnA/OztTs9lcOtP80YMj6c2oyKfWPBuCaNkLBQFEviLD/2xRPngdgIHXEvj8NaJqX4ETpsT8zzGvMfKN/WADFkU46zAOP2Z+nEILqx2JHEmVsOgB0GgOfHv7VeqwFqXXyN9L84woEbB01xCUlKAv5V/koH2aiVQaXbbp+4S+rd1uq16vq9lsvlHJt8r54tkMgp9+v6+dnR31+31rZeH9BUEVzAo/J1LmEGfE+j7g4rDdVqulWq2my8tLXVxcWHsDNF5e07OslOwihoe2HgjIfTrNi9HRi/gWKOHne79zn923JldhHgCEx7lI83OHe/KVWowLwUOlUrHGq9ls1vYX9Hi0cvCNeNeJlffPNMwo+P5gMEH0UqPFB8yQ74nlpRrSXesMghVSa2Ew9ZD2SYAjab6/QijAJleOQMxXXyFcxjlgixiidZm8mF9QbEI4eahb3reoZNSDSJw86B5nQHUGQrl1F2Vjfj6EugdeWfieOeK4gGq1qkKhYL1NoH5rtZpRv/6wRf/5yxwfH7H7I1AAc4uadzIXcEIhgPEC262tLTuS5unTp3r8+LH29/dVLpdtI8Wp/elPf9LJyYlOTk50eXk5J6pcJYD0GzaHg0YiEdXrdU0mExUKBXv1VaywKfl8fm5sSCdQyCFprncNa+bo6Ei1Wk3Hx8cWGcPEktIDWHKtyxwTHxSSBvSgwLfuYOMjzZzP5+2MPg+SPJPvAZdv+cB71gEgLWJH0NB4cTGBU7lctk3fp1/xG0+ePFGxWNTh4aG9j6rndrut09NTnZ2dWfd1fwzNqsfCjwd75tbWlu2bVO7BEPH80drRCZznHLbHCdlFABHMarPZvDc78RBz5ZMBR9L9qRMYJH/8BTl2hJhhyb+0nocBhkYk6GlyJmaYelkEjDCvH/BVW4hXPbj6kMyn1u6zUHNATw5fbgojA7X+tk1/FQBJ0tz1eMAUzmuu/b45EQrVYdOKxaJKpdKc8HI2m5nzR0+zTlojz5J4QABjxhEzvmTd9+rxZf9+o+T7BFkwQIhtG42Gzs/PVa/XdX5+bulNypb9+K/Ct/h54qv0/BzywYNvsLtIb7aoUk16s4s7z2OdgJH/t08p+sAJ1iSdTs/NB8YF0TWHOyNGxg8TiHc6HXW73YXtLbBVjUu47j2bhk/MZrN2oDmpaNJszIdQRO0Bs2e5fVUrRQlh+5CHtE8GHIWM0Ww2M7GhT435jQBHRyk7i95XuflFso4Wbob+yAd/GnaoZ/BjNZ1O54RzRMR8HizEQ1YOPKSFACmMFinLLpVKev78uZ4+faq9vT1Ll9zc3KjVaqnRaJiQlgrGdQDO4RzwaQDvyEMQtQi0eEEuQPHx48dWeUP1XjqdttTz1dWV6vW6Xr58qePjY+v/tAqtkbf79Gbe+XOvvnCDDY+NEIDsxxBfQu+bbrdr2onj42NdXV3p1atXarfbqtVqthHAXvkoepWsGs+HnlVhmg/2AGAEsxIGkYyPB5YePPmWIIxf+PurMn8vvhWGPwePak+OZIL1gxkjvQSLAqs6Go1sbhwdHen777/X6empOp2OZS1WXewTsujsfTBGlUrFjlKhQtNriEil+eNmmFv+udMdHHDUbretsMUH4Msag08CHIURAOZL0D069/8OhckAIha1T0esq3mamolJVOsBkLfwvqCH/WT2qYhwDD908w4BZ55Op1Uul40+3t7eto0DQSaL+Mcqi1bFHkl6IwAIr+e+5+ijemh1ouJSqfTG6fVEvrAi9O6h2mTR+KwyKl6Ucg9TiL5XDcDAp5e8fo90PA03SacBFpvN5lxvsbCh3aqBkWfVwsg+ZI/CyjQ+A/Prifcztp5d8uO/juZTgvhSAINnC9nEJ5OJgSMvVvesIscM0SPJB1er9Ks+EPLPDhBIwOy1hrBEvjKN+eDHDoM1i0QiBjb9usFXrKLVx0cLjkI61CNe8sFEtjAgVPF40bKv5AqjHq/X+FBAkk+vUZ3mf+7vw6cbOaQU5ow0A31yllFauQwLBYY0J9vb29OzZ8/0V3/1V3r06JExI2gFjo+PdXp6apqjsAfHfZH0KgBSmLZYdG3eFqUWtre3rRz5yy+/1PPnz7W/v69isWigkfLbo6MjnZ6e6vT0VBcXF2+k1Pi7q9oAvNP3Jcfh4bw4/bBbtDR/9ApAh7lxcnJiGiMqkUit+XMMfW8xxmSVFqYbffNb2ht4GYL3nfhNaZ55Q6yOH31bF21s1Sk2xoAgiMICSdazCvbQa6f8/PZBFlo1CjhgEen91Wq15sCRXyPLsBAUsc/xCjNUKpWsbxNtK+j75OcFDDX3AwjyVZ+STIMIQ4SO9b7+Xg+dgv1owVFo/oH7nhsAJJwZSJ7J7CPBMEJa5/PEQvOTCEfnWYBFk45/+wocf3wGE93rjVbt0H+OhYyBb/OQy+VULBbtyAAAIgJeUicchvg2vcgqnX3ICPzYtYTpRr/RIbwtlUoql8tGs6OjoOqr3W6r2Wza+IQN7VaZKghTqGH6zH/5Q1JDBtqvK9+BnGaxAEX0JFQi+ah41Wm0+2wRSApZdx9EhpVbi1ijULfj37eO5iuU0dBRjDGZTCyV6NdWyLKFe89kMpkDW74bOmO5SgvXuy/VTyaTJrpHQ0VLD8wDfc+2+caYkBS+8ID0m0/DhQHDIsb7fdtHDY5CxwelSQSIE4f2Pj8/t/JBdCa+f0Mmk9F0OlUmk9HNzc0cUPgQzF8nE25RytFHxPRsoXmZL8+mK6w/jTxsC7DuFjpmX2mUSqVUrVa1t7env/zLv9Tz58/12Wef2YGavV5PL1++1Pfff68//vGP1r01PA8pBAGrni/v+vfDNBpjk8vllMlk9PXXX+uzzz7T8+fPdXh4aM0Q0aCdnZ3p6upK3377rY6Pj6330zodFeKrpdDUVSoV5XI5ffbZZyoUCtrb27O5H4vF5pphSvOsFyABLQktHQBC/rysRc3s1s18qpX7QkvD/RBEcmQM4mLWSZiS9GmaRWBrncbBA8LRaKROpyNJevHihXK5nN2/F+d7UCDJwIQkex/9w66urnRxcaGTkxNjjXwfn2WyiIsYI9hzzoNLJpPa29tTLpezV/ZVjOsP13n4f59y86yhB1J+bkBYwNLx/R9jvn+qfdTgCPPakVBMDU3sm97RIBHmwGttwpTaukY699miyeO1J4uieTYQX5bLBA7R/boAgD/XwugWh8cZSNVq1QSHpI1Go5FarZaazaYajYZpBUIm7b6xWIcxChnDt5mvTkmlUtbErlAo2LhApftzpprN5hsHRYbzZJljEQJi/7zpfkzpNZU3/gT5MAL2DEnIHrEheCe/6LysdV833COpMt8rixSRJGviF6aVF1WleVtHcOj9ogdIHMY8mUxMXwdz5HsfAQrxF9LdMSyItv0Zg7DOXnu2bLuPPacijfPgisWiisWicrmcsYDe57E+vL7IVzN7xiecG/dp7rx/DgP7h7CPHhz5CZpOp5VOp000iiPn/Kh6vW6RHUg/m83O0Yqz2cwOvvvQAVKYVgvTLX6isjhA+jTm4gwgX+q8bk7ux8xHTN4ZIMDe3d3V06dPtb+/b9HwYDBQvV7XH/7wB718+VJHR0dzaaMf65i+jmN0Xz7fMyxUp1SrVT179kyff/65SqWS0um0radWq6V2u61vv/1WR0dHevHihXX79Y0xVwkIwvRgIpFQpVJRPp/X06dPlc/n9ezZMzsTjQ3ON8XkeAjfEZlx8nob3xvIR8UfAjDyzBH3j1aR+4jFYrq9vbXXRCKhQqFgLFt4j6GP8N9bN/8B+OV50rV8NpspkUio2+3OlfcTRLNW8vm86Yz8OZ7D4VDNZlOvXr3Sq1evdHR0pFarZX18lt3bSnqzFQWVZpTnP3r0SKVSyfqYkUojGCQwZH/EB4apUyQasLHZbHYu8O73+2+I3qW7g2u9ZthLRd63/uijB0fSPEDypfgMpNcHoDnyi99HlkQJi5rnfUgWTiT/b6+hwNhIeB9sm+/Lsm6O7ccs1NNIb4pHEWTDINANmaiv0Wio1WrNiQe9pus+pm7dzUfN3mkSZORyOTtfDlaF+6aRIYwaoPGhT9F+VwvT7fgFypBpapnNZucOwvQ6ETYB7nl7e9uAgnfc/B3pLl29CAis85zAF3BfkubAH88TP4lUYVFq+W3PfhFgWgfjWlj3s9nMelJ5QOQruXw/NPYQrzcajUYaDAYWXPq+RqtsirpobXAvfm1AMCQSiTlm1J9J6PcQf//sn77cn3RjOOb+9z1zxL/9/vW+9UcfNTjyEa/v8OxpQBiAWq2mWq02JzDOZDJzLc6pUOr1eoZ0oUk/RAud0aLcrV8cLARy761Wa+7U8A8NIIXpRA8CQubo0aNHymazms1eV2G9evXK9Eacnu57G61LxdHPMcaDaK1UKimXy+nrr7/W06dP9fz5cx0cHCiVSikajVrE+PLlS52fn+vbb7/VycmJ9TVahzkSMmGwHDBGVN9ls1nt7e2Zw+ZQ4cFgYN292+22Ped0Oq3d3V2l0+m5Nh8+NR8eOfGhrRVJc+AWFsQXp0iylGoikbA5AbMYAinPKtw3JqscI/wC1w3go3q52+3OVXL5po+sHbpGA5T6/b5OT0/16tWr/5+9N2mRLNu6xNa1vu8b7yLCMyIzI5P3vg8EH3wDTQQaSZOa1UyohKAm0kCggQr9ghoJaiT4QAMVCCSBBNJAEyHQQAMJXhWi9HgZLyMjMsI9vLG+7838ahC5tu97/JpHk+5m19zvBsfcPcwt7j33nH3WXnvtffD69Ws0Gg10Op2ts+9mEERwxzRapVLB3t4eyuWypNOCwaCwXaaejs+XzBnPUiNTxA7aTEnSf3KOmbpYBmBsJUNwdF+i7AcNjoCbuVPN9pj9FEgFkhXRHXH5926lp7tubhNM58gZDTG1wAjKPDV624zAXZipQWGnVy5sMo29Xk/SR9z4vXYG0l0YnSWdG08UZ6ffZDIpbAlZVzbE5PiYZ6ht20ytEUv2GRWziSVTqLx2NmqkfqrT6chGv1qtpKxbM0iaWdjg6NJgAAAgAElEQVR1X6EZJP5Mf6D9QiAQcAjO3dKIpl7ETLuZv9+m6WvQ4J7PXfeA47/H43HYti3AmGXrLGRhXyN2xPZS01jNnjO1prV48Xhc7gdwau34xc/Rlc5kYdPptBT56FMpmK40x8DUg5q6tfvSHj1YcGTSg7rRmO7ZQXqTDlzne7VIk4p83d9Ed4vddVvHolCExz4vBAdsaNfv9x3n3eyaaVqWrzxBu1gs4ptvvsHBwQHS6TQsyxLm4M9//jN+/fVXXFxcOFgRrwCAuzA6I2r1jo+Psb+/jz/+8Y94/vw5yuUy4vG4bIq1Wg2tVgt/+ctfcHp6Kn19mFLbdqm66RO0sLxUKuHZs2cSIdOhz2YzAcA8GPb09FT6oumIGIBUxTKgAD4e5Ksrc9yuCfAGEPiU6dS7riLiKwHAYDBALBbDZDKRjZ/do/VxKus6QHttLNwkB/q+mVolYGD7j6OjI1SrVSQSCVxdXaHb7aJWq+HNmzc4OTlBrVZzaPG25UPdtIVMoxUKBZTLZfnK5XKy7nlyRDwedxzMvFgspMqNICibzUogorVDWryvZSwMTmKxmKSzr66uHH2iTD2Tfla/1x4sOALWd8YGbp6krAVdGjnrCjedM/ZihPN7zQ0oMPIhY6arNggoufHpz9hloxgxkUhI2TopXWqNWq2WsCIPiTmjuaWfstksisWiNH9k9KibtrF6j4cRmw0OvTI+mhGjw2Yju3g8LsGU1iTqqiL+jul6Mgnab5j9fGheGYOvNdPvafGw2SjSLE7Q68TUIXl9XDRw00y7fvb8N6alqFWkOJ3sI/sakX136+WzDdOMKn0/yYF4PC6sUSwWkwpFnTYms7hareRstVQqJek0/r0+jFhXtNHc1pLO+phskfn9XYzhgwRHbhSb7lzLM9UAyBkxi8UC+Xxe0mjFYhEHBweis9D9K3SDqofCHNHMlAMZFIpSKVxnY7/RaCS9T3Z5HLhZMpVWLBZRLpdlDoRCIemFdXZ2hnfv3qFer8v9P6R5wDnAthWFQgGlUgkvX77EixcvcHR0hEKhgFgsJtUlg8EAr1+/xsnJCX755Zcb50N5ZfNzE5uyGodrPZlMir5ksVgIKOJxHwR8HKNcLodisSj9kMgy8jN02ws3dtEL4/KlppkUrcnhURCUKfBrPp87TmTn+PNZuH22F8fFBEYmoIlEIkilUnj69CmePXuG4+NjCa7IPl5cXEg3bAKkbQdXOnOgZSRcIyxWYLd4skKsXGRamuvCsiwBQdw7OA40CtApY+F+qtNxugjKLdC4T3uQ4GidmVQwAQCRcCKRAABBuZwM1Jro/ibrynEfinF8GDVwYpJto07roXTG1qI/rTViOhGAsCPsdKzLbh/SHHCrTkun08jn89IJm2Xrtn3dRLXdbqPZbO6ESJ/3pwXT+pBMvelxvetnrStvOE/YRFJr8wgY2PtoXRsDr43Pl5i+H/pX7Sf1YaJ8H8dPyx12xdaJgOk/CLi5h0SjUZkHPGGeINs8RscrpgGIZm00o8NAg0ESwS/fSx0W9w7uI3pO6F55WtaimUWzQm1T+82DB0ccYKrp+QCoDQgEAnjy5InkU/lQ0+m09LVJJpOYTCYintOHRn7qqIhdM05Apgv29vbw5MkTpFIpBINBzGYzEaW2Wq0b5bq7ZhoUsXKJPXx4wnwsFsN0OkWn08GbN2/w/v171Ot10Qrsan+ndaY7YWcyGXz//fd49uyZvKZSKQQCAXFoJycnODs7w08//eQYGw2OvGT6mbPfChmjWCwmzl2n3ebzOfL5vETMTL3m83m8ePECuVwOh4eH8pks9Sfj1Gq10G630ev1RIezq2tGm1uKSQtz6XPn87loFrmRMvDipklBMz/vvqqQ7soI6LSwP5fLOfqiERj1ej00Gg28e/cOHz58QKPRQK/Xu1HIsU3Te6U+ImUwGCCVSok2l3ojPkMySG6yDC1DIRBir6hOp4PJZCJH6TCg4trgz6PRSNYMx0sDKbdU7V3YgwdHgLNnghn9acGYVujzwEmWGbKEnQ9TV2I8BCcHOBkUveBJhxK164Zf2xQR3pWZ98s+PsyZUzTIzrikwtedLL/LplkjsqlMG2WzWZkLHBMKllmd1u12b1SneXVsNHukI2O9Oev0G7Uj/H08Hkc+n0epVBKtEiNu6tNY8DGZTFx9hlfH5ktMp2Q0SKJfoCbJrW+abqC4awwS4FwvoVBIGEQ2OKT+Rp+hxqDaPFNv22bukwS11JdSH0VgwrWgi5TMz+Lncf9cLBZSxKMPXuZxKvrEBfaN06DoNhH/XduDBkf6QZPxSCQSGI1GN2hxwLlJ8vXq6kq6Z//666+o1Wo4PT1Fs9mUTdIrYrrfYzoKouNPJBIoFAooFApCmVJQqIWEu3zPfM7JZBK5XA5HR0c4PDzEwcEBisWiMCSDwQDn5+d4+/Ytzs7OZAx2tUrPzejoKb48PDzE/v4+fvjhB3z77bcol8tIJpPSI4wM6k8//YS3b9/iw4cPaLfbjrOhvGxkObQDJutDi0QiUqFTKBTEn7BKjf2NmI5lynk8HqPRaKBerwtT0Gw2JRreBfB4F6aru/S9atDJ8xp1is3LrJEZQLI8vVQqyTmD+/v7SCQSkop///49Pnz4gLOzMzQaDfEfXgFGwPV+yTMDB4MBVqsVYrGYVBuOx2NUq1XR5/GkCQ1qCZ7IqpMZ6nQ6GI/HOD8/lwOYdVpRs0A6LcsCiH6/7xD63zdIepDgSEcygPNEZSLgSCQi4i+tPWAUwL/jw9XHZRAUmYJsr0zyrzW3Rc8v3SFYl+I+hHvW5avU1/CEecuyZA5QeGxqjR6K6bFgajmfz0uTRKY/ADiO3WEnbDfA6OXxoWPVh8Xy6A8dLDB1QO2hbdtSakxGie/n5sKzGvllBhRe2hTv0nRKxa0juL5vMxjdtb5xpq9kdStZI90XTc8DBg9mOs0r84HgZD6fS5PHSCSCbrcrfpJZBDYB1c+aoIj3SflBs9mUBpiDwUAKG/h3fCXTyPGhv3U7x/M+tXsPEhwBN1u+j0Yj1Ot1zGYzqTBZrVaSNiD6B64d3GQywWAwwOXlJer1Ot6+fYtOp4NarSY0uVfyxXdlpId5wCCBAheDLuE3wZGXoz03IxDmsy8UCjg6OsLe3h4KhcKNjraXl5doNpvo9Xquzm2XjfR4KBQSQPTdd9/h+fPnePr0KSqViqSNWPXJM6Goo+j1eo6eV14dFwY9FJI3Gg3Yto0PHz4gm806NDGhUAi5XA6AU6Sq0wkU2/LzLi8v0Wg08PPPP6PVauH9+/fS9E+nox/S3OGrWQpOn6CbBDKwYANFskY6tcmx8ZpPMdNoBMesbt3f30epVBLt2nQ6Rb/fx8XFBer1uvSGY4rVSwGWzrTYtu0AL+PxGLPZTKp1KTiPx+OOtJpt27I/EBwNh0M5pJud5fXeaQJikhn8PAIijpeZjryvQOyT4MiyrCcA/iWAKgAbwD/Ytv0vLMsqAPgfABwDeAfgH9u23bE+3um/APDvAxgD+Ce2bf/rO73qzzQNkPiwQqEQer0eLMtCr9cTipy0IXBd9j8YDG7oKfigzaZ/XpngX2um3sjsbwQ4+5h4PWXyKXPTVzH6Y8URBaJc4FoY6OXN/0tNjwOFxvr8NHa25UZFZoSske7943VgpFkM9uvSzDAAB0tGn6ArdGj8nuNBZpG+gl88doSAehN6iU2bCZD4vWaMTPGs/ju3Em2vASOaXi/6bD6eE6aZdj03yB6aWk0vzQPzeQHAbDYDANknqQcajUZyVinvF4ADHLEwgWkxyjJ02l2vKb0u+Hn63Da3Xln8u7u2z2GOlgD+c9u2/7VlWWkA/8qyrP8dwD8B8H/Ytv3PLcv6ZwD+GYD/AsC/B+C7377+HsB//dvrxk0zR7ZtO06PT6VS6PV6iMViKBQKiMfjyGazQrNPp1MRz1FfVK/XMZ1OMRgMHkxaCXAudjb9Yqt4Rn+M9jRjxh4nWky5a+PBKJBCylwu52j6yOrEk5MTnJ+fCztilpvusvHZ0cmXSiUcHBxgf38f+/v7ckQInRQrbX755Re8ffsWjUZDggYvi415PYyOJ5MJbNvG2dmZnJOWzWbR7/eRSqWwt7eHSCQi50ixpYMWmepqmlarhW63i5OTE3S7XZyenor/IAjTjWe9lk65C9MgG4BskuPxWET8rBZmc13d74h/61WAbTJHLNwpFovI5/NSxMHq6FarJX2NGo2G9P7S6SGvmAlGdPfvYDCI0WgkQTP3Ce4PuhCBPoAFK9wvzKag+v8E1ne45nvXMUVbE2Tbtn0B4OK37weWZf0E4BDAPwLw7/z2tv8WwP+Jj+DoHwH4l/bHK/6/LcvKWZa1/9vnbMw0JatzqAAwHA6lHwNbk8diMTnbRYMjHkyrSw5NMaUXF/HXmMkg6OZsOg9sHq66q+aWBtCdwPUmSq3RQ0un0cxUARu96TOUCAjJkFCD51ad5uWx0ekDghsAaLfbEjhNp1NhAQBIh2Pz78mgjcdjSbmy1xN/b1apPWRgpI3MCUW0kUjE0VFfb3K687FXzWQNtZ/UgnICA/oNio+5f5jsqtfmgA4iADgYT6bUg8EgJpOJQ29EI7hiQL2u7N78/0wGybym+9QXudkXaY4syzoG8G8B+H8AVBXgucTHtBvwETidqj/78NvvNgqOgOvurTrHTZATCoXQarUknUKBJXB9IC0RPnOvutvtfeY6t2E6zQTcrOThxOZC12Xs/Hu+7sJ48F4JiiigpK6q0+lIpcT5+TnOz89Rr9fFwWnnvsumnTzbNhQKBen2rM/TY0rx7du3uLi4EK0Re355TUPhZly3thKOsiJ1OBwiFouhVqshkUigUqlIQz/OEc5v+gMGUWxpwGCKDDPXj/Yb950O2JbpQFQf0kuQqYXI7XZb0rH0r5tKl9y1aQ0bj80Zj8fo9/vSCfvs7MwRYHldc2amn7mPAtfau3WAVj87tzTZ19zzNsbps8GRZVkpAP8TgP/Mtu2+gfJsy7K+6Ooty/qnAP7pl/zN1xgXLB+u7tXDQ+6IgMfjMYBrtT5BATdDDYp2afHeZusiNToq84wk3evioYADnRIkm6ArLejsOB8eUkqNppkjtuynjkAzLAQB7Pe0Lhr2uunghqAOuNZX8NBLHgdhptX0+YJseMn+Nf1+X+YQA4xNpgO2aVwT9Bfs7zMcDoVJ4u91YYcbMPKaMRuhf+Y1a20RwQ9T8t1uVxhEt476Xr1fwH2ucq18iuXz8n19jn0WOLIsK4yPwOi/s237f/7t1zWmyyzL2gdQ/+33ZwCeqD8/+u13DrNt+x8A/MNvn3+vo+iGgoFrR2iyJvwbnevk7/TrQzOOjz6HrtFoSJppuVzi4uIC7XZbFrsJFHZxbJgXHw6HOD8/RyKRECaEaZJ2uy2CQp1Cegimu0Gz/JidbCORCCaTCcLhsHS+fvXqFS4uLlCr1W40fdyFcdFpAwZClvXxPLBAIIButytFCUy5rvMPWmis0wm7yoJ8rekxZW+f1WqF09NT9Pt90XdybAgo9UkDBKle0+Jo0/sIg8VAIIDLy0vR3VGDORqNcHFxgeFwiFar5ej+7HUg+Dm2y9f+OfY51WoWgP8GwE+2bf9X6p/+VwD/IYB//tvr/6J+/59alvXf46MQu7dpvdE6M52UXoDrqMHHZObCJ1vQ6/Xkd6TCvS6+/ZTpucBcOg/U5b0tl0vR1bDSRDNHD9HIFhIsRqNRLBYLBAIB0dKwenNXWSPaOn/Aw1PNr9v+9q7SB7toZFR475xDgUAAg8EAwEf/Go1GBRgQUGv2aFeE6vo+6SfH47EImDlXmFYk277L/vIx2ucwR/82gP8AwP9nWdb/+9vv/kt8BEX/o2VZ/zGA9wD+8W//9r/hYxn/L/hYyv8f3ekV35M91smqNwbLsiSF2O/3Yds2fv75Z1xcXMgmWKvVJBJiqeYuggWCQKY+AKBer6PRaEgKhREwz1Wj/sxMre668X6m0yksy8LZ2ZmUt/McNQC4vLwUHQVBo54Duz4m5qb8JeLgXb7vuzAtXbAsy3GwKqsg9YnsBEPU4HAdepk1ApxpQwaMLFCgMJvv09KMdalV37xrn1Ot9n8BWOcl/l2X99sA/pPfeV2+bdi4WM0UQ6fTcUR3jIQeQnpJR39sXghcH21AbQQ7qz+kvlbaeD+M9vv9PoCP4KDf74tOjyX7ZI12/dDhT9lDvKf7NM2ccV5w/TDdpEu+Ccjd2ht43QgG+b1uawI4QZQPinbTLC88rPvWHPn2+cbFzRJuLczlBnqbrmIXTfdqMns26d4au36f60z3bqHWhhokXabLZ0/B6UMUpvt2N6abO5qNIQEnY/0Q5tC6+9vle3pE9q9s2/4785cP9vgQ377OdHqNR0WY/87Xh+IASIE/VuPzY0pEny22rnut/rtdf/6+3b1pJhpYn6J8KHPHXwsPz3xw5NsN01VrvlD9cZjv3H27T/PnlW+7Zj448u1W852ab7755ptvj80Cn36Lb7755ptvvvnm2+MxHxz55ptvvvnmm2++KfPBkW+++eabb7755psyHxz55ptvvvnmm2++KfPBkW+++eabb7755psyHxz55ptvvvnmm2++KfNL+X17lPapc7P8Fga++ebbXZvZQds375oPjnx70Gaeqr7upPV1xxqYZyL5Ts0333z7lLl1mNc/+13mvW8+OPLN1dwW921gYt3321r0BD88H0yfF8Yz48wz1TQgms/nWC6XmM1mcpacPmZk18+C8m175nedf7hGv+N2VqM+s5F+xjy7EfDng1fMB0e+AcANZiUYDDoWt/5Zv5+Hj3Jxmz9v4zRqXmcgEEA0GkUoFEIsFkMoFEIkEkEwGEQsFnM4Mt4XD9Sdz+dYLBYYj8dYrVZy2Op8PsfV1ZWAJP+0bd8+ZebmqNeSXif6IF/fds+0L9FBGYAbQdhqtZKgyy3geoxzwGvn7/ng6JGbCRDC4TACgQASiQQikYiAi0QigXA4LAwMJ/JkMsFyucRoNMJyuZSf+bpYLDZ6qj3vhddeKpUQi8VQKpUQiUSQTCYRiUSQSqUcJ4dHIhHYti0nz/Me2u22fD+ZTNBsNrFYLDCdTgU06QjwsTi1T2m2Pue96xiUh7JBcC4Gg0FhKyORCOLxuHzPNTKdTmUNzefzRzWXdtl0UEn/GQ6Hb/gZ+sxQKCT+ZLFYOF41aHrI/mRdqtFN7qAPu960X/DB0SM3Hc0SJITDYaTTaUSjUaRSKcfPZF5oXNy9Xg+LxQKDwUCcO4ERI2LafU5unU6jc0okEsjlcojFYshms4hGo8hkMkJxk2HSzokbVjQaFQZpOBxitVphOp0iEAhgsVgA+MieMe2m6fGHYm6aLfP724ysHI3js07nZW4KuzieOugIhUIIh8OIx+OIxWLIZDKIRCJIJBKYzWYYjUawLEtSuL5odzdMb+YEwQzKksmk+BvLsoRBCgaDWCwWCIVCmE6nuLq6QiAQEAbJDQDs8jy4TZZhSjd0ChK4vm+djaBtYkx8cPRIzdTkMO1EEHF4eIhMJoNSqYREIoFyuYx4PI54PI5Q6OO0sW0bo9EI8/kctVoN4/EYtVoNo9EI5+fnGI1G6Ha7mM/n4vg1hXwf90SAl8lkkE6n8eTJE6RSKXnd398XJokUNyM+Lkqm1WazGbrdLqbTKXq9Hvr9Pj58+IB+v4+LiwsMh0N0Oh3MZjNMJhNhksyFvGvmlmIls8g5o6NhN3E7gScAmWProkA6P83a8feMovneXTGODzfKZDKJQqGAfD6Po6Mj2Ti73S4uLi5Qq9Uwm80AQMZhW9etv1835l/7LB6C3soNFIXDYUSjUWSzWSSTSezt7SGZTKJcLss8AD76lul0im63i9FohFarhfF4jEAggPl8Ln5E65IA74+TGwhaxwjpFKMGRPQr/B3X/3w+x2q12rg/ePTgyO1BrrNPiY93ydwiHjItqVQK5XIZ2WwWlUoFyWQSxWIRiUQCsVjMwQSMx2NhikajEVarFWKxGPr9Pmzbxng8FibGtu17j4opuo7H40gmk0ilUshms8jn80ilUsjn84jH4xLRMVJnZMffLRYLzOdzBAIBzGYzRKNRxGIxLJdLxONxLJdLYZWCwSCWyyUAyALeVTMdvylm1z/zSzs9/cpxiEQiDn2N6fyp85rNZlitVvJeXg+wWwJ4DSqpd0skEigUCigWi9jb20MsFkMymQQA9Ho9WVdfkq68r+s2v3fzdbcBJ9P0ujf/dtdYMrfAIRQKiX9Ip9NIpVIoFAriN8lic+5PJhPYto1gMIjpdArgox8FIGy0uQa8OD5uAZHbHNL+RAdVWnundVr8ol+wLEvSzcDm9t5HBY7WPcx1omNtbmJjty+vm4nemSorFApIpVJ4/vw5isUiXr58iWKxiGq1KmCC+iPNAkynUywWC+zt7WE0GuHs7Ay9Xg+r1QrtdlsAhsmq3MeC58adSCSwt7eHQqGAZ8+eIZ/P47vvvhNnRe2RbdsCavQz5+9XqxUymYz8fjwe45tvvkG328Xp6Snq9Tp+/vlndDodABCaXG/+uzAnaBwDPmM+b+onYrGYpIPC4TBisZgDKOkNA4CI1xOJhMPZEQTx5+l0iuVyieFw6BDBj8djLJdL0XftghZJp9IYbJTLZezt7eHHH39EtVrF999/L5Hy6ekpxuMxBoPBVsCRuZHpzYnrHLipAzM1MW4g1nxO6wLQTQRNd2HmnkHgS8aITHWhUMDz58+RTqdRqVQEQBEYTadTtFot9Ho9hMNh9Ho92LaNyWQiwZgZRHgJIK1Lh5ngRu+rGvxoDR7fxyIZHYSxCKbdbmM2m2EwGGCxWEgW4r4ZpEcBjtwcgH5odPDU0/DhcELyQSwWCxFQ6oh31+h/kzGiDiKbzaJcLstXoVBANpsVFkaXv2t2JBQKIZVKIRgMYjQaIRAIIJfL4erqCo1GQwSIHLP7vC/qOyh4pfPi7wgA+MyYytBmAmCddiOw4/00m03Yto1+vw/LsiQSvM/04V2buT7Y7iAejyMajSKdTktUzFdGyqFQyOEUgWuQRXDEdcWfCYbo/MbjMRaLBSzLkuiQ1YL8vF3Qc7ltnhy/XC6HcrmMYrGIbDYr9xMMBh0pg22Aag2K6BO5gfHfzetapyN0Cxz1e/Rc+72M1CbtU4xRKpVCOp1GsVhEPp8X5iidTsvYkmG/urpCNBpFNBpFPB7HYrFAIpFwMO3BYPBGEOmFsXFjhcx9lP7ADQzxlX5DM9AERwy0uN9Op1OHz9Wg/T7tQYMj/SD1wufDSSQSDudPdiSbzTrQPh9Qr9fDeDxGv9/HbDbDeDx26E305Ae8tzFqYEjAUCwWkUql8PLlS1QqFfzN3/wNKpUKDg4OkE6nEYlEHGBRg6KrqysZy2KxiOVyiVgsJptdq9XCcDhEJBIREEI9yV2zALw36qKSySQSiQQSiYTopMh0WZaF0WgkQnIN2vScYXouHo9L9V4ikUA2m0U2mxXdUq1WAwB0Oh0BguZm52UzQVEqlUI0GkWlUhHmI51Oo1qtIp1O39CfcV1x7AgMqRXQLONyuXQwQovFAt1uV15nsxnq9Tomk4l8hg5IvAqQzM2CAux8Po/nz5/j+PgYf/u3f4tMJoNKpYLJZCLzpd1uYzAYYDKZCIDcxD1qv6j7f3HT15G9mSoms0oARaDL35v9e9y0Znw13+cFEKDNBL3cyGOxGHK5HDKZDA4PD1EsFvHjjz8il8vh2bNnUplI/zKbzbBcLiVwY6o1HA7j6uoK/X4fV1dXGA6HmE6nosPj/73NMXEjGNz2Uwak1GAxWLUsSwLsRCIhzKoGVaZkYzqdSvFLv98XVpm++r732gcLjtwmNDd6Vo2QFSkUCuLIyKIQ+TIdNB6PkUqlMB6PkUgkhAq/jQr1kplAkZM3mUwim82KHiKXy8m4MOJnzlenjAhydI6ZYmimo5bLJdLpNKbTKSKRCObzueP993GP2pFo7RC1UUx98ff9fl82X94DABFRptNp0RFwoTMtN51OUSgUsFwuUSwWsVqtEI1GRTfjtTngZnrMGM0R+GQyGWE8uKkzOtbzw0wHaabV1JsR7DAy5AbL3wHrNS67YBogcX1xTaXTadkYbNuWwGo0GknqcFN6Nb1m6esIirips3KVLAaNm/bV1ZWAKgJfpkx5L2Zp+jrQxM8173/ba8iUYpgaTQZhLABhdSzHjWPBjX46nQpIou6IPoVjbwYc2577t2VeOBbcT6iv4xzinAoGg5Ke58+aUWKaPhQKydzQ7JwG6psajwcHjvSD5ATjpOOmT7Hx06dPkc1mcXh4iGQyiUqlIhEfP4OM0Hg8Rrvdxmg0QqPRwGAwQL1eR7vdxvn5OQaDAVqtllQcECgB21/gNE0HZzIZpFIpPH36FOVyGS9fvsTBwYGMCRcphXAs0efCJkgiFZpIJGRDCIfDODo6QiaTQa1WQzQaRafTEfZApyrvkjkCrrVhfA6dTgfT6dSR0plOp2i325hOpxgMBuLUAchGQfapUqmgWq0Ko1IsFsURcIzYS+ndu3fodDoIBALCnnltDmjTGySfYSwWw97eHnK5HJ4/f45SqYSjoyPkcjmpXKTzN433am6MejPg2Ni2jfl8LoCVQGE6nWI8HgurZDKNXjQz8OD8yefz2Nvbw7fffouDgwOpXAI+tsCo1+u4uLjA2dkZWq2WAItN3C83Os1iUCMVjUaRy+XkWUciEXk/N/vlconlcilswHA4FDadbTA0W8Iv/fdamqAr9LymLdNpR659+s9KpYJcLoenT58il8uhUqnIJr9cLtHv96U6bTabod/vO1p/sNdVMpnEcrlEIpHAfD5HJBKR9BqAG+B0m2Og02CcO0wtkk1jcEVZA/0n55K+D/05HI/VaoVOp4PxeIx0Oo3FYiEZCA0Y75NRe1DgyHRSnMyxWOYPH8MAACAASURBVEycVSqVEufP1FGlUhHRMSMo4HqjJQuQTqclGgiHw1itVg6dyWg0EqfPjcIrptkBRjwmY0QtCSNb3ayMJe1s7sj7YzSg6VGmt66urpDNZjGdToVt02zcXRo3FDqeyWSCUCiE4XAoNOzV1ZVE6c1mE7PZTETAvB6CR27YjOJYas2IkQCaKTdGjmSUOE+8LjQ19RPxeBzpdFpSh/zKZDKiQeJ6MHs7mSwBN3rOIW6g3DD6/T7m87n8ns+GYJbgyMspSlOcyvGMRCJIp9PIZDLidwgoV6sVZrMZer0ehsOhtMPYdNpQP3tuTmRCcrmcsCC6ks4EvFonMp/PpZJTi+opomWgxeCF68PUa5oM0raeuWZVzRQkWRJWxHLc6C94r+PxWKQY/FmnD81Sdq3h8wpr5MYc0d+TCeJ4aElDOp12MMwMoPm8+YyZlmM6jnsEsw1u7PQm7EGBI8BJfdLZl8tl5HI5vHjxAvl8Hi9evBC0T+qbf0Onr1u7kz7mw06n05hMJiiVSuh0Osjlcjg7O5NIgaJSU7S4LdNjwon75MkTVKtV/OEPf8Dh4aFEPvF4XKL7+XyOZrOJ4XCIs7MzDIdDARPAR8aBJf5k3/L5PMLhsGwIL168QDqdxtnZGQAngLxrfQGZIdu2hRliqi+RSGC5XKLX62E6nUp/IqYz6Kjp3Jg773a7uLy8RLfbRalUwg8//IBsNov9/X0EAgGkUikAkCh5f38fANButwFci5K9ZtoxE/Dlcjnkcjk8efIElUoFx8fHKBQK2NvbE8E9g4HlconBYIDZbCapMv1MabZ93Qur1WpJ13Fq+Mgcce3w9bYu5F41nU7L5XI4Pj7Gixcv8P333yOZTCIej0sn7EajgdevX+Pk5AStVkvGYFNaIwKaaDSKRCKBYrGITCaDYrGIdDqNvb095PN58XlkMAhcWDnE58IKIpoZVGmNn2aW+ArAoS3zCntkAiPuKdQaUWS/t7cn+kbO+eFwiMvLS4xGI9TrdWFT6ZMIsgA4uqczsNMVoNu6dz0GOo1GjVE8HkcqlZIxSSaTok1kzzyzYINzgc+ZfpfgioEV9UUcC2Cz8+HBgCM3hK8ncj6fR6lUQi6XExEy2/hTcMhIlfQ2F7xZLcBJnUqlsFwukclk0O12EYvFZEPWSHfbQjo9sRntZDIZFAoFWeQcC05MHpfR6XQwGAxENMqJDcARMeZyOQQCAaTTaYcIL5FIyFjTyTISuGt2jdfOZxcIBGRj5gbODZn3QTBFp6VFpgAwGAwAAK1WC8FgEN1uV0Ah75MOg/ON0ZL+LC9t7G5pIKZUyHZkMhlhDfh+gh+CIgJNpsCoLTKjPAJqExQNh0NHqpafw2fFIMWLjJGbmVqjfD7v0O8BEFZlMBig2+0KyNgUMHK7Xm5MfP6aMWSxCn2aZvHI8DEI5HznvOJ7dbUi1yZ1jNwcte8xg6ZtiJHdtEaaOWLwlEwmhTkhM0iwyBQx08QEj1rMTDNL4PUeogHSpgXrJkDS+6uePzpDwy+OCz+D80ZXqur/h2PCKj19z2aLlE3YgwBHbmLjWCwm6aJvv/0W5XIZf/zjH5HJZLC/vy/vIdswHo8lymH1DDuXsqyZWhx2vGVOlQufZevmOV3bWNx6bOh8otGodOl9/vw5nj17huPjY5RKJaH9uUGRLXn37h263a7oqphWI5rv9/viIKbTqTAMrATM5XIAgGKxiNlsJrl1HplwV6YF2LZtS6qG409HzfOr6Kx0awENIufzuaTXhsMhJpOJVN6Nx2MR8RMcMcdeLpexWCxQLBYFPHhRe6QdGxt/7u3toVqt4ujoCOVyWdg/AJIGm06n0h282WzKsSq6yoppVs772WwmrIEGQ5xLZA9Y5ae7qZuiXa8axzMWiyGfz2N/fx9/+MMfcHR05Gg4Oh6PcXl5idPTU7x+/Voq8zbNMtNXklXN5/OoVCo4PDxEqVQSLQ11IDQGH4FAwJES5PVThMv+YNSLEFBr9owFEXzeZO45VsD2m39qQMCgmCL7QqGAUqkk+iOm0hk49Pt96YDd7XYBQMTH+oxKrkP6VB10bSOddNsYmABRpxgJqAuFghQf6D2Fr9Sv0teuCxwpkdDBrU7lm9d41/PkQYAjwPkACQR0d2RqapgXBiCRDzd9OvtutysMEtX0i8VCIgM2tdPdbwkG9MbgBdNOkJV4pVJJxoTXrZ03GaN2u412u41utyttDDQ40p1fCRw4ifl/634eGkzcF11sgiR2ngXgYAXJSuhFpq+Jz1CLSclq8IuiSwIfUs0EzsvlEt1u13NpNTeaXLcsoECSURwrMXnkAVOtrVYLo9HIQZMDcFSWAJDNT7N4rOAxn4nbwZu7AIw0W80qP6YZWIHDIgGuJ5Zsb1pnxFd93QwWOX91g09qzMxqM7P8Xlcx8dnTZzKInM1mwhIFg0EpVaeGc53v9Ap7ROBCX88v3eqEG/psNpOvxWLhABf6Xtx0PV7RG+lXfm9erwmYqBviM6PvnEwmItqfz+eIxWKun8vP1sJ/7a995ugzTQ8oHT2RPbUT33//PXK5HKrVqgMYtVotDAYDnJ2dSV6Y4IiOnpF1LpfDcrlEoVCQUv9kMimOoNfrIZ1OS9dTLoZtMkbAdfUVc8AvXrzA8fExvvvuOxwcHAgDwsV8cXGBTqeDV69eodls4vT0FIPBQCI8TlJ+LlmgbDaL+XyOvb090Ryxaufq6gr5fF6E2ZPJxBEV3dUYaVqerB/7GpnUrBmpr6OuA4Hrzs5kOZgWoWiQnxWJRFAsFgF8nF/hcFiq9AjStr3Rm4JKbuaFQgGVSgV7e3uSZiWrSsbo9PQUnU5HdBTtdlvSrxwjAI7mbtrREQjoykENVM1ns+2x+lzTrBGZ6hcvXuCbb76R3mlk2Or1Ov7617/i3bt3ch7htlgjzgEeLE3pAZlkplT1M2IKkOkiAlvqRrg58vkxrZzL5YRZ4sZIbQ3nEACZS+bGvOl0kjlWOv2oU5CaISFLyqIDfpEpY6GLTqfp/8ft+22ZOf5uYI0/c2woxmaFGgDZH3iOHFtXcC9lYYvWJfFZU4/IucbAe1OB086DI5qmJ/mQGL2x6oKRMDUnbOFer9clIuZmwM8kpaxZEb1g9CbgBY2RNj0mpvOjloDOi6XU3W4XzWZTWKPBYOBoUKf7czAVAnwU2dHxkTnSzpcL5r6rMHTVmvkc9O+5kd92HZpd0ToA4LpcXdO8ZMqoP9L0OefeNs10dG5aAQootSaEQvxOp4Nut4t+vy+CU2qOdES3XC4FPHO83AoddGm3ZiW8sn4+ZWZgRraa6XwypfQ5/X4f3W5X/I5eT9u4Z7e5z+dCtlA/U94HGVGepciUGH0C57peF/SR9AHRaFQKXXQn5XVaG8Ab2k2znF2zZCZrpCsu9Vhqlo33pf/N7f/2gpnjr/2JGQjp1g0ER5QnsNP1fD6XrIWbTpfFGNpHmNdxn4B5p8GRGzUcj8flTLDvvvsOxWIRR0dHwhjNZjO0Wi20Wi389NNP6Ha7+PDhgwADUoDcPNgIEIC0/tcUok4hAXAg2m0vZjqhWCyGo6MjPHv2DD/++COOj49RLpeRSqVkE2y1Wuh2u/j5559xcXEhPXuow9JVXQRHdIJMSXIjTafTEiHFYjHYto1MJoPhcCjiVL0Q7po90qXkpuh73XMxgYNZsssvXrvWJPFzLctyCBIJNubzOcLhsCPq2ZaZ+glq6XjsQS6XE4dFIemHDx/QbrdxcnIi6Va2dXATE9NR6k0PuO6izLSaWYXmhXXzpaaBUSaTkTPUDg8PxVewAu/9+/d4/fo1/vznP6PZbEoEvS3WSG+63NDIcPCsN7NnFaUI1J3xWevjhXQTSN1rjkCI4IsFE25pegahOs2qbVtgkvOZ6TQGP7xmAoFer4fBYODod6YBlW6ueVvzTze2ZpP3bq5L8zr1cU3AdcWwTqPX63VJw1NfB8BBWpinMJBpJjtpFkfpsTDH6K7GZ6fBEY2TluWQrLhhtQXZH/ZSISvS6XRkEusImNEPIwIdKZh5VTN9ANx++OImx4NOSQsIKR6kI+PmxoiWY9Pv92VzZLRo9psJBAKSPmS0ZG6WHDO3cbov02Puxh6ZpqNU/az1+WzUZnFOsHcLwRF/x8/TgN2MiNddx32bGf3qrrYEfpwXvB+mBCi+Jb3tNifM/8dk50y9yq6DIg002Wk/n89LEKWPrBkOh6LfYwS9rVYfpn/ixk7/aOo+9AbO59/pdARIEQAyAGU5Nr/nxkkgCcDRT04zKW7ak9uYZi/MGX1tes/Q32utElllXRX7qV5e2wBF/H/N69Dz3mTPdNBDloj+Q1ft8d51IMrPY2Cr24SYVePmtem061352AcDjnRF2dHREfb39/HkyRPpLzKbzQQAvHr1Cq1WC69fv8ZoNBLGSB9vwU2CWgKWpPOLwlUdEQDYSsmhORaM2kOhkDjrb775Bi9fvpRqFDojpkx+/fVXnJ2d4fXr16jVauh0OiKg44TXmxgXPzcAXcmlowPm4s2N976NY/+piMwEDOYRGjxAkv2ceD+MCBnhaudFZo2VQDw+BbgGUPoaN2FmCojpNN0MlDoT5v0ZATebTTQaDWnrQAH2bfl/sg4mUOX6MDeDXQJH5jiyT9DLly/x448/4ocffpDmqCxuOD8/x08//YRff/11axVqgHMT4bMg40GWmGyn9mPc8KihYUCpGSFWqTE1Gw6HUalU5ABW+lUybcvl0lH6r32DXpcmkAM21zHanJ/m/6W1k7rSjP6Ona4ZhDDdmk6nHZ2ydRpKM2Xr7m3TYIn/pw78dMUanxMZI9v+WMAxmUxQq9VEnsFGugTGuiCEc4nVbKPRCP1+XyQder3okn/9bPTPvxck7Tw4Mh8Y8/5sg88NmfoYCiNHo5EwHbr/BCc5Hxgr3tj7hRuIVuQD14jZ1E8A29kE6bipNSKLRvaDi3A4HKLb7Uq6hDoSzQLpiN90VFzIZgWYG8pfx65t2tyAEZ0cnbzZ/ZVaIt2jybIsEXzraEqzMnSKTBfoTsgch03fu1mhZDJHOi1J3QTntt6UNA2uKXc3FkD//17RUPwe02w1mdnDw0NUKhVH+TuDMq4vNondRl8jmgmMyBgx2GNVpg6IOG/ZxZyBEwABOZz/V1dXkk53S4tp/6RPaOcXTyDgGGufYgY7m1pHHAetNTRBPnCTJeI4WJYl2j59Vpjuom8yR14LFkxgxPtkip5Al3NLa69McMMAXusz6Tc4B5lO476i9zb+DNzsqm6Cpq+1nQZH5qbEEupqtYpyuSypEEY+nU4HzWYT9Xpd8sHsZm1ZluhJmIoqlUooFAp49uyZdAym2FKzRSxTZCpKV+5sAxhR65NIJHBwcIDj42McHh5KF9dgMCgMz4cPH1Cr1fDmzRucnZ0JyqeexC3C10YHoUsuTcevr8vN7jvy4zWYr6aGgA6bAJst8Dmv2E2YaYTFYoHRaHQjhckIej6fS/XaeDwWfQX/1gSc92X6nnX6mSXn1BoR+LMfiT7vTAcSdFCkwM00ma484XvNNJvJtu2K6XlDYFStVvH8+XP8/d//Pfb29lAoFARQ9Pt9vH37Fr/88gtev34tfW+2mVIDrs/B43EWoVBI+pBFo9EbKXJ9/AeBAf2qTo/Zti0tQ8hAaSChhdl873K5lGNH4vG4jLH2PZphcXu9j3VkMkYERUync+MnIOR9LZcfz0oDgHw+7wACmUxGvmfQYaal3FLVenw3bW6+kuSB9pFkfnRFI4s3uC8y6xAOh4V44PErTL+y3J/6Le6n9M/mOOhgTs8Ft6D+S2ynwRFNI0pT5wHcrGDSD5nACrjOg8diMTl9nF9spa8P3eTiHI1Gkl/VzfC2MZH1Rp1IJERsy/Jsom5qSTqdjlTPkCrXjNFtwMj8eR340eYF5sBc7FoXxUjIPClb08c6r64rswBI+bM+oXoymUhUzu7AJsO2ibmiQZKpn9OsmNs4aVaNjoqfx7Hg+jLBET9T6wz0tewiQAoErhsoVioVOUaCxRur1Uo2h2aziU6n4+hrtO3KRb3Zs2SaLI3u50W/qcEIgRWfHX8OBoM32EVzrXNOkKEkgOL6YPUrr5Fzg4GaBugalOvruavxcWMmTKZc9+Dh+009H++XTK2WI1CgzvWox2ydr9zWmtGVevQd+kuLqs09Q9+z1jmSNdJ9kTi2vFf6HYIvM7DgM9HVsHcBlh8MONICWreUl25LT5Ete28A1w389DEK1WrVceLy/v6+aFEYQYzHY7RaLSl/50GnZpXUJsZAb3o8J+nZs2f47rvv5F6AjzQ4WbR3797h9PQUZ2dnaDabku81mySa4MhctJzAn7Ow3VItmwIHJotCIMQz5bhgyRyxG7ruaTWbzdBut2UOEFBns1k5py+VSknFBfDxGBLqlfg7OoBNptn0WiF4Y1WdKRzXQQRTAlw/AMSZszcNNy4NjvT79P+vqe9dM2527Cr+xz/+Ed9//z2Oj49lHHl+2tnZGV69eoWTkxM0m00Zq23et97oqSlkyoypYL1B0W9q4MxnzM2IwIjvJ0Awq1IZjLCTNsHkeDyWnmFkrfg5mrnSjUPvejO8zTheBI0Mhrn+GSTxHtm/J5PJiNyDYJpGBpoMGlP09Es6MHELJjbpN01QZPZ6ymazjjWv55hmfEKhkDRH1Y1SySDy+dNHcl8GIPPSNI6hPq9P90zTIPdL7EGAI8CJ7t2qqiis5kPhJsiFzw2STk8/PDJG1GUAkIXCdBpz8aZwedNpNb3xUSfFwyO5iFl1wiZluo+RpnVvYzZ0ZKWdHsGGjiB0lOWFzVA7HX3gowYC1K3xjLFkMin3Y1L+6z7fZGbm87m0f9BVGbT7YpH0s9K/0xGwFt9qpkhHeQSQvG8+52AwKGyDBkc0/o0put1F41xnEJXNZlGpVJDP54WJpr6ROqNWqyWH6W4rnWYa5wTngD5MGrhOVZisPP9dd1Cn4DgYDIrguFQqIZFISEED02y6KEMHqtTmMG2tz28EIOkq+jD+22Qycfia+xorPSYMBDjnmSLj+HAN0a8weOJ64mdwnWlmVWuvyObqDX5bZgIks0O4Dow5V+g7mHrl+4rFopw9SLkCmURtzH6w4px7q8lY615IlmXd2WHfDwoccdIyF8wBIjAqFAqOzp3sZ8QHTnBE4EQmgA+T7AH/nmkpU6uzDQdIJ8brL5VKODg4wP7+vvRcicfjwgw1m02cnZ3h8vIS9Xr9RlXAl4gDdU6dzBoXC4WdbJVAkMS/25SZTJXui8XzsLjoqSNJpVI4ODgQoMlzgXQJrgZCbuWpbEiaTCZvdImmIzUBKXA/DJIZQOj0CRkE7djZr6pQKAgg0Kev03lTZMxoXrMKwM3+Rtt29F9rblqjJ0+e4OXLl6hWq7LxERi9efMGb968wa+//iqVO5pd2bZpqYFOmemNTp+fxe+58ZEBtG1bon92WN/f30cqlUI+n5c1oFMn7LhPZmU+n4v+ifMEgKPlCBsK8hBjdk7mtd8HQNIgkiwV2QnqYizLkmOpuMkze0FAyH4+BH6cC5ql1b3HUqkUbNuWyi/e/9eyIL/HuLdo1ogBI4kDNvUkUA4EAqKvSiQSWK1Wokva399HOp3GN998I4efkzXU1b/8PZsL6ywPi4k4lvx3irg1sP9a22lwZDp7prl6vR4ikQgGg4FEvKyA4EanGwRyM+NDpQPQ4ErTylzArHxjeasZgW/SzLQaHRIBH6+dQIXXTsGtee2fA4y4YAgyyK7pfkBE9qRKzQq4TY0NX01WhGPE3k/8HSljM9rV900jM6Q3FcDZH0tXry2XS9EiaK0CbVMpAh1MELxqZlSvC2pDdGk1nzPXA8uWOcZcP/rgSf3vu2YcDzLQlUpFzilMpVKScmUFaL1el02coHKT8/5zTa93XqO5aTMlRN0HwRE3IQZf1WpV9JpkX7mhagE/5xlTtZw/gUDgxjl9AMRHhcNh0Ujx3wm878PcGFfNHPGYIG7ODJD0nsHx09WdJhOjNUn0D9Pp1NEqgF+b9Jv8Mlkj/cVr5xzS2jNqya6urqS9BRsQaw2vvk8yRplMRph3PSf0s9BBnf6Mu7CdBkeAs804o/p3795hNBqJ0yqVSrAsS7oz88A7rWbnJOCmoaMVvakSxY/HY+n/Uq/X0el0JHLedEpNT2BuwtlsVo4JYVRj27acHddoNKSfkT437bZmZBqNc7FwIrOfEtsGEDxowMojB5gT3qRw3VzgjHyq1SoSiQRKpZKj90+1WhVhvo7WCKhjsZjMFQIqAh6+Xz8PAnQeZhwIBDAej2WeUefFdMJ9pth01Q3PiSOwZbqMTp5pZo4fy7c5BqwS1Zoj3jsAifAIDkKhkFSg7BKDxPuPRqOiP/zxxx/x4sULPHv2TBjpyWSC8/NzvH//Hj///DM+fPiAZrPp8A1eMq5pM9AE4AD2mUzGIUngHCFLSF9DxojnZpk6Ns4R27YFIFGQTcChx4gtMFqtlsxX+g/6FAACvO87vUZ2YzKZIBKJYDQaAYC0jtEVz/SPBBT8DF4vQdPV1ZWARGp06Iv7/b4U0Jh71ab8JueBZo10o2Xdo0gHPoVCwdHLioE6z+4jq6jTisFgUNhEanv1fqHZO2r7OCYEqToY/j228+AIcAKk6XSKbreLUCiEWq2GyWQikT4dvt54ONg6tUFqmZua3sT1/8N+SXrD35amYB01q6N7CjDpYJh+/FygoicbHaNuJJjL5SS6BK5byZN+Hg6Hrh1PN2EmgDRpYUY1ZJJYWqr1DJqZ0+W7unLLrOShU6GocLVa3aiS1FEvwcV9jI/bvGcUzh5guq0Bu6OvKy7g5/Bz9WZKIMo0HdsCAHAczLztqq3PMQ2sqTXK5XIol8tC/ZPxoBCbFWqDwcDByu6C6XmuNWdshKvLtsmYUmNEKYLuKG+mN+hjyTBwLWm/oPUr9CM6jcc1yOeyKaDNZ0ifafb/0nNFMxm8bq3x02JlgkACAjLw3Lf0GG7iXjVrZDLuZnWaFqLr58J74r8TVKdSKSEq6AO1f9XjwSDKHDfzWei9142h/Zq1t9PgSAvliDBt28aHDx+ERcpkMuj3+7KJc0PnBGMUMJ/PMRgMHCmDdDotZ2LxwZA16vf7qNfrqNfrUuVlLpRNIXu9IJlGLBQK0uuJzocVKf1+31G67waQTKBkpqbIiBSLRRQKBRweHuLg4ACFQgHpdFocX6/XQ6fTQaPRQKvVcgDJTbBG5gInO0Qx4N7ennSsjcfjcrZYNpsF4GQXtXMgaKaTJzOnI2OyMMlkEuVyWRrtEZBwXg6HQ3l+GhzdF0CioyEoGgwGssGPx2OJevUG4CbQZ4qUmxodoxlFM61ErUmr1ZJ1tyvsEdmAVCol3fe//fZbVCoVYQV5ePWrV6/w7t07vHv3TlhZL4MjDUbcChVYmEDxOQEQWeNIJIJCoSBBBgGVns80pl+YDmOBAnUpXKc62NB+l9eqW21QO3pfpoGJDpC5jqk1A66rdjn3tUZVV+KRIWYgQTBBQMXPZiEHx5IBxSa0R6ZUQxdmaHCjmSU+ew3q+MoUHEXYnCO6Bxb3ML5/tVqJFIG99+hL6Me0ZIM+6S4yEzsNjmgcAFYAjMdjAECj0ZDveTAkHzLw8eFrcDQcDgF8jFZYNaGjem4I7CjLlJTZ34jv3aSZzJHWweiqErIFnFj8vQmMzM/mqy7FZTUKm2USWLAXxWq1wmAwcBw7YQKxTZl2QJwLuuM5N3TNGBEYaKbF1KFxPOjUNLWvRYykj0mZLxYLcaCmeBlwMkh3bWZqjeyerqTRgFqn/HTFFeeFjpq16fnHiJHObleAkY5myZDu7e0Ja6QPb+73++h0OqI1YuDhRZ3ROtN+RPsSfc6gPmtQgyECYmr0+Fl6DmvdZiAQcAiv3UrWNQDXDD/nsGl3Nc7r5qZOP+pr02tCs1m6wk23IiBzSk1VJBIRhoSgyKwQdLumTWqQdPZEPxN9L9qf8R54fRwXk+3j57E4hPpHpqLZNJX7BwufmLnRjWrd+vN9re08ONLUGl/JkPCcoIuLC2EFyHjQ+FB0t2PqCqi45wRkxDAcDtFoNHBycoLLy0v0ej1HZ+xtOEJN+xO4aLGbFq3rlJrZ02gdY0THReeXy+WQzWZxfHyMvb09PH36FOVyWY4oYZuDi4sLnJ+fo1arSZ+XTW4YJmvE8/eq1apEwoyAySgB1wuWWgbtFKiX4MbJ8dF6Nf5ObzJaU2RZFrrdLlKpFDqdjuMcJjoUrQe5K9O6Euqfut2u3BsZVrPR6Wq1chw6y7VAxojXzHugxoBN7/j/MaXJFJ7XAZJOw5bLZRwdHeEPf/gDnj59iqOjI4lyqTU6OTnBX//6VzmLzuuskWmaHdUpZoqrtdaEQQXTxlxfujCBIAi4Bpp81aklAI5x0jIAMpzmJmi+/z61RqYmi4ECgwqTPdVMMt+nK/K4VsjS0q9yXMjm6saK2zbev07F0w/q4I6l+9QY8b40C6XXPv+Gqf1utysFQ0zHz2YzqYqlPIPrq91u32CP7iIA33lwBNw8TJEbkC6dJSXHhU/TEYBu6MWyfT5Ibn58OKxI0U0Tf2+O8/eYjrh0jljfJ6s6CIzMnkz6c2g6nxwIBER8x2NUyuWyiDHJjti2LWwERZP6ENtNjY2ZCiRryDQavwgoGfloJ0ggx8VGdpLzghsBnRrHXesN9Pt1VK0jSS5spmj4d3dlJtjivKfz4b1wHjOdDDgDDgI8vrIKlOPA1BOv3bx3vXloPYoXwYMJqll0QNaI98zAqlarodFooNfrSZnxOr2W18wcf25e3NwYcBHw6tJ+6ka0sJv3bbI7/D0BNkvz2RyT849rjhskGXqyBjySgr7sPtLPmgFeB4509S0AB5DRa0yDI+2ftdZGa6m0fscUtW/KTNkKQdF4PHZkYMiCs+KQYIjBEOcJtZW4cwAAIABJREFU/avu8wZAQC8zDLVaTarAOXYEmGSSlsulHGTLsdWg2ay8/hp7EOAIuEa1gPNhsg8LH6YJGswBpN6EeVF+MR0yHo/RaDRweXkplSgma7RNR+8mCATg6M1BB8ON2XRgOsfOz+AEp96ATNHz589RqVSkdJfpmH6/j263i/Pzc5yfn4tz27Q4VYMTnm2Uz+dFO1Eul2UB870cEwrvCZxpFLyTheTGzzHWjJKbVsv8XrcKoB6Lc+quTa8Tbirdblf6g1ADxfO1uBlopojBAL+YHpzNZjI2ACSS5s/mcQHa4XsRIPE5km18+vQpjo+P8fz5c0khEzRSa/T27Vucn5/fqMrcNWNKmOxQNpuVStRsNitaI85/znfOF5ONptE/csPrdruYz+fo9XqOjU83+Fsul+h2u3LkETdSsgtaw3gfptcMmWEG0mbQp1lUpsgmk4ncJxnTaDQqaTT6J64DtzQlgy9T3H7fxr0UgFSGca0ToDC1Sp+oe2ERWOu+WARVNB6tc3Jygm63i7dv34pWlePnVq1GwoNBm8lo/95192DAEeCMfrT2A7imbLVAV/8df8dJyo1UNy/j5tfr9eSQWbMs3QvGhWlWUdCJ6YXIDUpXFwFOjU4wGJRqrr29PaTTaRwfH6NcLkt3YNLpnJztdltE2HRk+qiCTYJIrRvRp9BTWEjnwxSSdsq8Vs0C6QNmCRopLNX9j7hR6HvlODPVlkgkRGNAx8JneF+mI0IdCetDmHV0rLVHTI9pDRbvmwHEZDIRMaV26Kxy0eDIC+kCN9Ogmh2e2deIFY6cL9TVXV5eSsm5FuZ73UydpOlHufGQZdQpIz57vflRUGzb9g3mVW+qBEXUkZAd0OlrXgM3QLIF1KVoDdx9sEf6e4IkHTBwozaN64djpjdwsnF6fuiA3WSMtmF6PLWPoKaX+wqZG/pUfU4jf85msw6mmc+LY9ftdtHr9XBxcYF2uy1SFc4RM+PAucS91zzZQV/7o0+rmWayQUT7bBymo3m+csPig00kEtjf30exWHSo5fv9Pi4uLtBoNKRvj05PbcP0feiJrB2MWxmmLr0mQKTpcQiHw9Il/Ntvv0WxWMTf/d3foVKpoFqtCsCwLEtEcu/fvxcNRq1Wk8hv00coaJYmEolI5Ev9RDablahXU+WmwI9jRWDEtAodnT6PigtWd8LVtDtBGo09unTp7n1VqvGV84RpIeD64FDS3nxW3Oh1d286I+D6nC3S6hxTjh8BIfUrdKRmmbKX2CP6hGg0inw+j2q1ihcvXuDw8FDYRuAj01er1fDhwwe8evUKzWZzp1Jq5rrXIECL8EejkWzas9lMgiU+R1bFkkXUDCjXAYMmdhBnxaaO/s31ws7SZpqNbOc69vuux4ifT6ZnPp/LWHD9ANepRbLNg8FAJBhc01dXV1Ktx/muffF4PHbodG7LdNynaXAEXDdzXS4/NuMcDofy7Mkiaq0Rj9taLBbIZDKYTCZIJpPyGaPRCLPZTPbTV69eoV6v4+zszMEIch8zAyk+dwJwPb53MUYPEhzRdL6YPwM3wZHO/YZCIREd8lA8bhSkdakpMJs+btu0U9M0I++NrIfuV8I29SZtq1F/LBbD4eEhMpkMnj9/jnw+L7oLOkdulu12G71eDx8+fMDFxQW63a5sFtsSrJu6Hx2Z8UunEQl6bNuW5mzaeZFxopCSm6hmnDgn+NmactcpT849t8qX+x4n09FqwAQ4+4foXL4ZpWmNhVu0zWdAgKrBuRfZIw2o2SCUFZmseCWoHA6HqNVqcgQPmQ9uKF7wC59jJjNChmM8HiMQCKDX6wnY0wwSg6fFYiFplEAgIGCn1+tJT7jZbIZmsym96OhPybBoVpLjNxwOHfODG7Rm5+4bGGlxubn5mpVWbEKo2SVeo/47HRBoqYcGYZpxc/MHm5pbel/RbCDT5gR7bGxJrRHF+tPp1LVfoG4Q3Ol0hEHiyQ1mpgGA41loMKQ/+67G5UGDI8CdKjbBEXAtpIvFYpIu2t/fl8odanW63S4uLy/RbDaltNAL4IjXYJZn04EQ+JH1KJVKEqWFQiGJwLhxs+qMZ4z98MMPKBaLOD4+lmqvRCIhGz91TKenp2g0Gvjzn/8slWrj8fhGRLgJ0w5I9+LQ5+fpHDmdLgDpwWH23iAQor6K4Ig0uU5PAddVbwDk/yLbRMaF4IgO3+zTcdemnYj+0k5cAzVuWGZfI+3oNRvFsednaf3KcrkUca9ZteIlCwQCiMViUpH59OlTPHnyRCper66uMJlM0Gq18PPPP+Pt27eo1+uONbdtn/C5pp8/5yvXARl3FqmMx2PpcRMMBmUDZBNINn4kKKrVahiNRsIYsb0K02n8bBN48NXslcPr5ca5iYDLDfDrceKRMZFIxFGFqduA8G8Xi4WwjjpjYTbC1MGk9pmbBEgmc6TvWxedaB9IX0k9FgsZ2AGdexQDi8FggMvLS5yfn+Pi4gLNZhO9Xs81Ne12r+sA0V2My4MHR26mHbpmj9j9tlqtolQqCXvCSU0BoKb8vCDCNqlwLb4mvc/InefKHR4eCpOkz4bjQuUp2jwkkCLUSqUiVSvsAzWfz9FoNNDv92WTYNrxcyf5fY0LX3VpPtOjwWBQ9DHsa6WPRqCT0v1IdAWPWZnC/4NAh2OjI0C+l46VujWdj9/0xuoGjnRUSz2WGeXyGglydB8cijD1MRM6TaAB0aZFpp8ysoxkWMvlshyHoY+86Ha7aLVaEhDsmtZIm94IOYct62OLAqaVCWJ4nhiDKqaUGVBYliXgiGCo0+nIutO+SbMQvA79pSs3NThyq7Td1DiZQJIgj6l0ADLv9ZFEHFfNnrr5RV1V7CZs3/Tc0vulvgbbvm7TQB/B579arYQtApzHCWkfyfJ9Eg06MNRA2bwet5/velweJTgCnHQpI1t2lv72229xdHSEdDot+W5SwZ1OR9T1m9bP3HYvXKzsL8Qu2KwQI/3NQ/9s20a1WkW9Xhcak31ugsGgHAVycHDgOGVbO0DbtkVj9O7dO9RqNfzpT3/C5eUl3r9/L2yS1qdsY2FrRm04HCIcDqPRaGC1WqFQKEgPFzoz8+/NcQZuso8mMGI/p8lkItdAB0LHzkai7FROQLWp9KPJGpmm+9OY0bt+Px19KpWSIyQymYz0jtKAU6cTvZxSi0QiEgw8ffpUTptn+pQ9vE5OTvDq1Ss0Gg1JJW2SHb0r41zj9XODW61WMpcjkQiGw6HjedKvtFotEWpfXV0JCOLBu+xRQ8ZHp8Rui/zdWH4AN8DUJuw2YMTu9/pAXO4pWtNIYMkx1PfKz6YGiz7BZKA2aab/06/0C9Rh6RJ9Vu/S75Mhp48EIMwR272wJN8NIJnXwv//U9f8e+zRgiOa1qNQMMrOt9wcKCiknoYTd9uMEXAN8rRWgB28ea2j0cjR5dayLJRKJTkaQDMXeqOjGFWf08YJSYEldUVv3rxBrVaTlCP/3YwKtzE+OlqhiLDT6cCyLBFKptNphw5GV53pqNrUAmganM5vMplI2sBsATCZTAQU8ABg6rK4Cd0nODIdDV95z2xdoe+fzADniAYAWp9G4K27R1OzxU2WTJnulO4l06nTXC6HfD6PXC4nRwlRjDsajYQhZbC07bn+e83c/GkExXquc61wsx+NRqJbXK2c3YzNBn38fJMl0tdx2zWa17sJ08G0Zlk1A8zmpgQHDIh0HzWyqFwzOrWu0/Jae+imYdsWe6S/N0Gd+Ty0fpP7DgBhA+kfNdv6NWmyz507X2o+OFKRYiaTQaFQwN7eHvL5PEKhkGx4rFKr1+vCyHiFPtcAYDabwbIsxzEGlUpFjjmgmDiRSDg2dxoXq84j61QK03Y8DuSvf/0r6vU6/vSnP6FWq+H9+/cCyLbJGPH/pHOhULLT6QiTNpvNkMvlsFgspG8NdVRMn2g9zdXVdZkynRiFgyxFZnO6brfreB4cUw0w+/2+VDd1u10R9FLjc19jph0dcN22gf1VqDdjZKhBEdMqHItAICB9Tg4ODpBOp/HkyRNks1kUCgWpylsul3KfnB+mU/SC6aaP1WoVe3t7qFarjr5G7LPz66+/4uTkRFJH2yo4uAvTAYD5zAOBj4fqMg1tNirUDT+ZJuMGbx5V9Kly6y/ZFLdhJnNE4Nfv9xEOh2Xd03/yAFWdpteFHGZfJLLHbizytteKZow0QOKc4Sv/jZpMssdmuxQGS+YB6Ob/t+5+73uuPFpwpDUVfIA8jJSliVzkmv6jU99URdHnmk4fLRYL9Ho9NBoNnJ+fO44R0eXiXJx6s9S6EJMKZfXJeDzG6ekput0u/vKXv6DRaEiPCn08iBeiaA2QyABaloV2u43VaoVsNitlyuYZa1oLw3EgGOr1esJEkUkgvU7mjg5Us5Ok0lnRMxgMJOfOsbvPvi1uZdva6XGO6IZuZvqD12iKcqvVKpLJJPL5vFR5cg3N53P0+31JtZrlt14w7Q8SiQRyuZxo73QPLzY4bTab6Ha7N9pm7LKZbABNywf43HSFq34fQcBt1VqfA4y8ZiY4YHDJarxgMIhWq4XpdCrzKJlMOj7DrJClX2HfJgZXOqW2roePl0wXv+iiE30ECseMgSrlBxoU6TSqCcJus/sYk0cNjnSUyAMlK5WKOMTVaiVao3a7fUNJ7yXTixUAms0m5vM5CoWCHEvBM9EoljW7FAPXk5MLkRsh2bL379+j0+ng3/ybfyO9KTqdDprNpmOye0GLBTjHhQuQzEy/38fV1RUymQxGo5Fozsie6CZsrNphbpwHi1JLwQhoMBg4qn302OqeUsD1uX5aQM/0wzacoE4vp9NpAT26ASbHz7ZtmUc8gqVYLMrYUXPAfibj8RitVkuqPHV/Gq84fIJDppHZ+Z3B0nL5sYNzs9lErVYTIbZZcbXrptkAzZqSHaC+hPOFukz9XhMUfYr92KVx0+zRfD5HIBBAu90WzWY6ncZqtUIikcBqtRKmiPsNwZH+HH3cEoMIN1bFC3PMDLJoek/l4cSsSuUc0elC+gXdE8wszvgUMLrPsXiU4EgLTPVp29VqFYVCQSYyI8XxeCxiMaYCvGqM5Bi91Go1WJaFXC6H5XKJcrmMTCYjqROdNtMVCayUYNVJvV4X4XWn08GbN28keiZbYjJGXjJeE5kcdnll+e1q9fG0+FarJXPCrMwi+zGdTkVLQSaRQmxulNw8dAM3OkV9Tbon1SYa2tF0BG/2LzLBrY4Eef2WZQloIDPJxoDcROnc2SFdpw5NcMRr2pZpJpnpAH0WFCN7nv3UbDYltazHa9sb1+81U1uifw84hfg62ue9myDJjS16CGYCJPpbptXY90dXcZI11rpF3WiVVX1MPVO3uS6I8Mp4mmyPZsd0LzPT1/AIq9vucZv2aMGRFqDyjK3j306Y5xlsWm+k0x5eeoA07byo71gsFsL0AECr1cLTp09RKBREPMuUAdE9kT1BwNnZGfr9Pt6/fy/n3vT7fTQaDTkIcl1JrldMj43WUugqnGazKY5Md/o1q9GoLeHm7nYwLeeHZlt0ywj+rBkkjvsmdSumfkJ/6WiOPaH0GUq6AaCOhqlXYWp3PB5LRSTFy1xPZsPMbRvbFvB+eSQGgT+rr05OTnB6eipM8i5rjdzM7T7IHPF7rg3NMPFvdUrOrCrz6gb/uabvU1fdAZD2B2SP0+m0FCxkMhmHjpHv14eqkpFm9RYPXvWijINmAiOCIy3Eps/jeHE9sf+dW8uCz7nP+x6LRweOdIRI+o8pBDp/XYmg9SBeL9PVTonptX6/j+VyidPTU6lgy2azchwIW7wTHHHD4kGxbOJ2eXkp3YCplXGjfHkdXjNTS0FnQxEpmR4eg6KrK4DraM8sRdZltnSY/H/cKGLtTDRocytdva9xMFkA3g8PzR0Oh7BtW3Q2BDxkjVjJxjmmK5sAiMPjXKHoXHeW96rDt21btGn1el3SrkwDdLtdnJycyHq4b/H8tuxzGCStCQGcDJJ+70NljgAnI621hqzkI9iez+dS6q+PQ6G2iOJkVq4y+FpXqbbtsXRLi+oxMVk1VkwzKNXFK26Nbz81ZzZx/48WHOkSy3w+L2LsWCzmAEZaiM2o2m3D2/ZkpXFy6W7LjNTj8bj0L2KJPhvbMYVAFM/2/vr0a51GMgW1XliwnzKCF0a8AKTJ42g0uiEqNO/JTTvh5rhuM7dcvduGcp9mOnYCPaYXeQQAcK3PIsMIQACkjvg0Zd7tdjGZTHB+fi4VauwmzUBjW4cQ32Z8nkyfnp2dyUnk1Kj1ej28efMG/X5f2FWvppJ/r32KQdJgX6dO+D79+pDM9AlMuXM+M23MrtA8XJr6PP4dmWcWdLAFiKk/NNseeMncronriPuPPgKG7Uz0Ae5aivA5AGlTY/CowJFe1Eyr6c69uvqAJ0bzAeoOnl6PFDW1zevl9bN9fa/XkwNTtRZGNyjkxDbTSFpj4cU02qfMzbnxd3T4WnNjvp+/+xpQo/8f83ebHEOTtSKgZhUSAVM8HhfHPp/PpQ1ENBoVypzjQOfGogUep8Eml4yIvcoaAZBoPhgM4vz8XFIb7N0zGo2kdF8LsR+qkRlyu0fNjq5Lr61bJw9hzOj/TIaNc5tsNPWqLP7Rf6uraBl8mh23TWbea6bvm0y8ZVlS8GJZHzutTyYT0Xgul0u02205ccJNX7Xu/9mUPSpwRHMDR8B19RBzoe12W740wvV6Cglwpkwsy5IJ2+12HaX6AFyd2rrqEq/e75eaeT9mamhT//82zEyTaDaUAUAoFMJoNBLBdSwWQ7fblV5I1BRocERHThBEkESgTQpdHy7qJYDEYEIHQpFIBJeXl5JSnc1mcpL8Q2aNtK2L3HVazQQIn3rddXNjyCzLkrXEVBkPY2232zeOIaIxJWf2hSJw8moQus6HsmKZjDGDK1a/MqvB1i+UaNymPdrGfT86cGQuZNLoLNPm5JxMJmg2m2g2myKSY7S4CxobbSbw0ZHeuvebi9/r9+jbl5s5FzTwZw8baidCoRD6/b5DrK7PXQMgjCKDCOr0CIZ0Ks1rkbCe72SD2LuGrQtI/bv5gMdgbiDJ1B2Z79sVH/m1Zt6rub9wLbHk3+1cQV2IocGBuU68OobmXsH1zqrp2WyGSCQi/eTIEBEU3aY70p+/aXt04EibbV93JR2NRmi1WtLwkX1Z2GmaAmUztebVCWvatieab940EzgD11U34/FYWEatxzL7HpmbgdYgaefuFae3znhtukkdcLPXyi6t+7u2dSzSY7d1LMp8Pgew/ow4c+3ozzC/96rpe+Yapy5V98Ii40yGmcGSrvj1kn94dOCIzpsRILuass8N0wVkktjXhKJkU3Pjm28PxdZFwTqNwtd1zdpM1tEtrbIL6+a2DWoXrt83b9htc8eUM9z2d143fb0aJJGR1o1DtSbLbH/iJf/wqMCRSZ3zfDTLskRnwPeR7uOJ0tRK6DOm9Gf65ttDMX9uf7THfv++3Z25zaWHNr9MYKO7Xru9V7960R4VOAKumSP9MwDpgMvfke7Twkt9rIOXEK5vvvnmm2++eclu2x93Ye98dOAIuAY/uvkdcN29+KFXWvjmm2+++ebbJmxX981HCY606Qe3rjJrVx+ub7755ptvvvn25fbowRHgM0O++eabb7755tu1BT79Ft98880333zzzbfHYz448s0333zzzTfffFPmgyPffPPNN9988803ZT448s0333zzzTfffFPmgyPffPPNN9988803ZT448s0333zzzTfffFPml/L75ptvvvnmm293avr8RX0eYzAYvHE+I0+u0IdTmydRbLrVjg+OfPPNN9988823OzECHp44EQgEHKAoHA4jEAjIF0HPfD4XgGR+0TYJkHxw9MjMDcnrL05o/T7g5qnqRPga6ev3+eabb+5mrkH9e7cji/w15ZvXTc9pgqFwOIxgMIhIJIJQKIRoNIpwOIx4PI5IJCL/znk/m83kQPj5fI7JZCLHe/HAd+43mzAfHD0iM0GQfuUk1ZQncPO8udVqhaurK6xWK9i2jeVy6QqSfPPNN6fdFphoczvY2l9XvnnVzHlNRogAKJlMIhKJIJFIIBqNIpvNIhaLIRaLIRgMyh4zHo+xXC7R6/UwnU7R7XYxnU5h27awSgA2BpAeNThye6hugIFm5kVvy496ybQTJlonio/FYgiFQojH4wiFQvKzRvWWZWGxWODq6gqj0QiLxQKj0Qjz+Rzj8RiLxUImrwZLj93MTc/td/45fg/X3MBQIBBAMBhEMBiU9EIwGJS/YeCxXC4lUmZA4q8p37xonOcEOtw70uk04vE4crkc4vE4CoUCYrEYcrkcotEootEoAoEAQqGPMGQ6nWK5XKLZbGI8HiMSiWAymci/AZB1sYn99lGCo9ucFh8WHzL/jRv+arVyfGkWZbVaOahxL5hmgHhPoVAIyWQS0WgUqVQKsVhM0DzRPUESUT0pz3a7jel0Kq+WZWE2mwGAOPOrqyvPjcNdmRvg+ZL3m0zBunTkQxy7x2Qm+8pgKxgMSvDByDkcDgOAMLGLxQKLxQKz2Ux8C/9dv/rm27ZNz3ENjsgYJZNJFAoFJJNJ7O3tIZVKyZ4TjUZlTQAfNUfL5RLhcBiDwQCr1QqDwQCj0UgCdAAOguI+95lHBY60Wp4PMxQKSU6UgIA/RyIRB3tEADSfz+WLzow5UkZ9XmGSNKoPBoNIJBKIxWIolUpIp9PI5/NIpVIolUpIJpOC6k3KczqdYrFYoNVqYTwe4+LiAsPhUF7b7TZms5kDLAK76cjXgWetydL3ZaZHCETXVWvw77XgUANtk5H0qn0pUDTNy/f2NbaOIdJgiH4lEokgmUzCsiyEQiFhXWezmbCyALBYLMTv7JK2z9Qq6t+5vW8do7or97vO3O7PLWAy7VP37YWU6zrwH4vFEIlEBAjlcjlkMhlUq1Vks1mkUilEIhFHAG5ZljBHAJBOp3F1dYV4PI7ZbIbBYIDlcolAICB+EsC9sqmPBhxppxUKhSRiC4fDwpYkk0kRjPFVb4gAxIExpTSfzzGdTjGZTG6klbbNJOmFSScdi8WE4szlcqhWq0in09jf30c6nUaxWEQ8HhdUT9aM4rhcLofJZIJgMIh+v4/5fI5QKITJZCK5Ydu2ZRIDu+XYzAVvsoqMcvQ9EQzx7znWpsCd7yP4IZDWaRS+l4vfi2O3bkO77b3ATYf+kNhFDYx0BM01R5aWwtRYLIZUKiVzYrlcik/hs6cvITByAxxesXVz4VNA4FO6K36/a7YuKNK/M98H3BTirxsDrp1tryNTiE3wT01RPB5HOp1GNptFPp9HLpdDOp2WfVdnJxiALxYLxGIxjMdjhEIhdLtdAMBwOMTV1RVms5nssfe5Jh48ODI3OC0Qy2QyiMfjKJVKAhgoHKMT0ywA8DGSm0wmmM1mGI/HmE6nGA6H6PV6qNfrGI1G6PV6QotrCnDT981XPWlTqZSAoHw+LwxSsVhEOp1GJpNBLBYTPQQtFAphtVohEAgIwqdwLhaLYTKZwLIsAUfbBoZfaqaYkHnzUCgkG1wgEEA0GnU4Op2CpRFYM5Iyo2emZFmJQYBNkD2fzyWlAmy/Yslkz7j5mxo9ky3jNRMkM+1qbvq7ugmaTKHWEtH5U3ORTCZRLBYdkXU8Hpcgiv5iOBwiEolgNBrJWjOLIbwAkswgQj97N5ZkHZOq2WkAMieoYdR6q22vg9vMjUWhX9BSDS3d0H9HP6E1ZzoLodeKqXvddsUw579mSOPxuOw1yWQSiURCxoJ7A30cszMcL4KnxWKBVCqF8XiMWCyGwWAA27ZlTdz3HvOgwdE6gBCPx5FMJgUYHBwcIJlMolqtCt2tBZNkAYCPedHRaCT092QyQa/XQzwex2q1QigUwmw2c2yC23T+2glx0yYIyuVyyGaz8nMqlRLgyIXMaw6FQsIIxeNxTKdTRKNRdDodAEAymcRisRD68/emXDZpZtRPwTpfNWBOJBI3UpWcV/x9NBpFOp12gCPTsTEtS4A9Go0wGo3kegiM+Lotc2PP3Bw+00OaRSMo4gY3n8/FoZnOHtg9cAQ4gRE3B80MpVIpVKtVZDIZ7O3tOUqao9GopOQJkCORiIwHgys9btteVybTQTBAH2lWu/K9nDt6rVmWJb6GpquS9NzfFWCkgQKftZZq8Iv+wgRHBEQMQPnstZ5Ta19peg1tKih1SyOba4Bf9Ke8VgYEV1dXDj9C6Uc4HMZyuUQsFpM9hik2cy7dlz1YcGSCgmg0KgDg8PAQuVwOz549QyaTwcHBgUR4fEDrUgeM8Jhem06nGAwGKJfLyOVyaDabiMfj6Pf76HQ6svExYt6W6WjTtm2hL6fTKYLBoJRN9no9R+qHf8tqNn4GN8l4PC5pSa3R2rYT/1zjtdJpsQcHo51cLodEIoF8Pi8bHp0by1U1IKBjjMVijnHQqTTq06bTKcbjMfr9PtrtNgaDAUKhEIbDoURIWou0yTHhvVjWdYVjIpEQxlW/UkPA58+NcrFYYLVaSYVjs9mUEl0GF9Ts7RKDpDcDXfmZTCaluCEejyOfz4t/4Vz6/9t7t9BI1y6/7//q0JJKhzqp1JK6W7uPu7/5zAyfTTCGCcG5SWzfjH0TZi5sk5iML2ZIDL6x5yYGY8hFPMaGZGCMB9tgZxiwTYYwxHFMIBh8mjGbmflms/f0Ua1zqc5VKp2q3lxIv6VVT5e6e+8tVZW03wWFTqWqt573edbzX//1X+uReiNtr2Fst9saGxuzTYPSZtLYvmHeMD4z1+7BDps+/gEwwBxg4/PgwLOOfq50u13VajUdHx+/N0dIpUijMz88OECqwTpIpVLmN9hb2IN84O33GcDQ8fGxms1mTyAOo+ylHABrX9no/fagxikESB4kAQLjOFaz2exhQgF0ZCrS6bRla8bHxy2LUSgUNDExoe3tbZ2enqper5vZCQGTAAAgAElEQVT+6DrB4K0FR9KFE7pz545F85lMRqurq1pcXNTnn3+udDqtlZUV2/h8NNuP9gfxgtwPDw81Pz9vG8TU1JSOjo40MTFhokrKEIeVZrosn89iZEMmJQZ4YkMeHx9XNpvtKb/EIfCZveO7acDIRz0zMzNWbuorLJaWlpRKpZROp8354Qz7vR5Asp/GCMDcbrfVbDY1OTlpY824EzletwP40Nh4cDQ5Oam5uTnNzMwon89bFUoqlVIulzOdmgeOR0dHOjk5UbVaVbvd1tTUlOkG2PijKBoJfd6nWsgyEnixAaZSKeXzeaVSKS0uLiqTyej+/fumafRB0vj4uDqdjvkMfj44OFC73TbNUj/WZZgstN8ESRHOz8/bumBTZHzGx8dNx8nPoYaPzf7OnTs2NwCK0hmjNErzI2SLfPod/zE3N9eTlSC1GjLNksznkl5vNBpqt9uq1+tqt9tqNBo6ODgwKQPri2vxwuRByzjCtHvIwnNNlOXDhsESsndGUdSTgsOnpNNpxXGs2dlZ0yGFbXauw24lOPIb1NTUlKWPHj58qFwup6dPnyqXy+nBgweWG0UUyebFRO12uzo5Oem50VLvopibm7NcK9H13t6eJicnVSqV7DX7tUMfhHmw59kuSVY6SSQTUvk46JWVlZ5qNi8G9CkXz6KMsvkFTGuDVCqlpaUlzc/P6969e0qn03rw4IFmZ2dVKBTe06NJvVqBfgYIPTk5Mf0Avwdo8/CValzjIM07OfRVk5OTymazmp2d1YMHD7SwsKC1tTUtLCxYZIdGwLNnaApOT0/VaDR0dHRkjGoURT3RH0HIMLR538TCOQOQZnyWlpYMSAOWSCkwHqxDv14mJibs/vvqV+YM82JYn1mSbUaAHtLvfHYAISDJa/F8mjqUKvC6+NxUKmXzZWxsTO1220D0KARd4RqBKaKnz+LiotLptNbW1sx/MBcIMFgfoWaIrAQsIqAI6Uaz2VSpVFKz2bSAo9FovNcDSBpslV+327X9E53c2NiYqtWqzek7d+7YPMYvwCLNz89renpax8fHln6GeWOudTodLSwsWOqZ+eGZt6v+rB8FR1EUPZD0jyXdlRRL+tU4jv9uFEV/Q9J/L6l4/tRfiuP4t87/569L+kuSOpL+hziO/+WVXvWHr7fHiUH7p9Np3b17V/l8XisrK8pkMsrn8xb5AYJOTk50cHBg4kg2MBa4jxiJkAEXiHClsw2i2Wzq5ORElUqlR2U/DGMRQlEfHBxYpIqeqNvt9tC1RLU4u6OjI01OTiqOY42Pj/c4bE+V+8eoWhj9A2zz+bxF+9lsVo8ePTKWxEfEPsXIuPoUmt/wPSOHY/XgiA0wrFAbhojfR3zc+3Q6rXQ6rXv37qlQKOjx48cWGfu2Dx74I6o9PT3V7OysOcRUKqVqtaooilQul9XpdCydxDWMOkAKq3LQ7KFhvHv3rv2euYJ/8WOEiB+f4YX6YS+1sIppGJ/Z+zrE5hRyUPW6sLDQU8hCrzjvK/vp1aiGPT4+tuDy9PS0hzUbFfMBYTgHVldXlc/n9fz5c5NuTE9Pa3Z21lJNPjvh5wS+mXE4PDw0tgSRs3Q2bwAk/D8+ZpDBt5+HPgD3xQXSmT+cnJzskaP4Pl6tVssYt9PTU2PnyU5MT08bcEb2MAj5xqcwR6eS/mocx/8piqJ5Sb8TRdG/Ov/b34nj+H/xT46i6IeSflbSH5G0Kun/iaLo8ziOr100EYKidDqthYUFPXr0SPl8Xo8ePdLCwoLlgUG15LlB6aByj3x9FEQqaWZmRjMzM+YQZmdnNT8/bzcM2rPRaBiihgK9bgfnUzF+8UFtjo+P26Jjs2ZDg+3w5s+1YWP3eolwkx91ASXOjfuYy+WUzWZ1//595fN5PXv2TPPz81peXjbwJKlHI+Odm9cEMS48l4pG0mlegF2tVtVoNFQul3VwcGCRINqCcHO8rvGQLjrc0iC0UChobm5OT58+VT6fN8aVdg8YrCgaKczrTqIoUjab1cTEhBYXFzU2Nqbd3V0dHx/3gOpRnjMeNKItog0GvcJIBfhKM5/C9oy21+54DaN/MI8GMQ/Czyv1ShPQhaRSKROZ379/34AhG9fY2JjNXa7b+z4CEq9RmpmZ0cnJiZrNphV9AJrDoEsafuAQ9otbW1tTJpPR48ePjTFCc8QakGR+0qeWPLDw1b5xHNsYsV+x1uipRwoSAO6F+4MaIx8gxnFs1xZFke0xBNce/HvW7OjoyEr22+22pqenjWGdmppSp9Ox/ZZA6rr3mI+CoziOtyVtn3/fiKLoS0n3PvAvPyPp1+M4PpL0OoqiF5L+uKR/ewXX+1HzkR0aERijpaUl69qJzoMKq4ODA9ug9vf31W63LSVGCbsvVaRvycLCgk1GWgMwERqNhprNpubm5nR4eGgTeJALHJDkq6QYI5wxwlBJ74EinFfYmNCXnPrHTSi57ccacS/z+bwKhYKWl5ctjegZIVIjODmfKsGIiviKVgAaHNocMXaj0VC9XtfBwYEdx+K7Iw+q8gTACItGL6wHDx5oeXnZNgBSi+iifGsLNGxRdCHiRyNA08O5uTljIfvp1EYNJPk5Q5DEhoWOkbJlQIRPTRM8oEUEaJOe9r7IN5gN02rDSDt6BohChfn5eS0uLiqXy+nevXvKZrMmppVk4lsvFvapYtgkL2aXzoKKubk5nZ6emt8ZhLbkY+ZTN77gwjPNa2tryuVyevz4sTFp+BYfTHJPSReSTfDP4b0ofGFNUr11dHSkqakp891ezuAB5KDWkb9+AmzYq4mJCdMJhUdMcY3sL7SxYN7wHNYbTHb43tdl30hzFEXRQ0l/VNK/l/TTkn4xiqK/IOm3dcYuVXQGnP6d+7cNfRhMXYmFoGh+fl4PHjzQ0tKSnjx5onQ6rcXFRVuYVGa1Wi3t7u6q1WqpWCwaY3R4eKh6vW4AwPcwgUqdnZ1VOp3W4eGhAS8mM9oD3ieKImMLcHiDZI9YdGzmTOKTk5OeVgUsMJzT3NycUqmUFhYWLDc8OTlp6bejoyPLk4e6mVHa4CT13eB8yiifz1slI5oKFirVZeghqMDyKTPPJjEuHjiEfY0QZMMkeVDkN8TrHhMcK6wHqYHHjx9rcXFRjx8/Vi6Xs+o05nCxWNTBwYFVZeLwEVRS4ZfJZHqqeUgRhBU7o2qhhpES/Ww228Okzc7OSpLde6J6X5YNwEDrNzExodPTUx0cHKjZbBpAPjw8NIAdsrHXvbY8GCQgXFhY0OzsrIEA9JvorAAxrA866ftu35JMn0ZKn9YYvh0E/mgU5oX3GewtqVRKy8vLymazev78ubLZrJ48eaL5+XktLS0Zc3h6eqpKpWJVZ0g2+Jm5EVrYTd1X+7FmwjkwTF/rg2VfzEPqj3sb7g2+sIf9yO8noe8L0/bX/fk/GRxFUTQn6Z9J+itxHNejKPoVSX9TZzqkvynpb0v6777B6/28pJ//Zpf7YfN5YFB9oVDQ3bt3LbKTLlIjtVpN9Xpdu7u7qtfr2tvbMzDDBPbdaX11yuTkpG14RBLHx8fm8EjrZTIZ5XI5NRoNy50OcuF7rYJH7bBJkkx3BF3pe/f4XhUAI0TI/hyoYVD/38ZCzQiC/Gw2a4/5+XlzcIjYYXb29/d1dHTU0+gTC3Pu5N09jc7fwqaPpKR8Wft1a488IGbdkGJcWlrS6uqqlpaWTE9CKojr3t/ft3UDCOx2uz3PZTPx4mPYlctSJqNkPgr3TCOs0ezsrGmv7ty509NB3x/OzL2FNRofH+/Rm4SMkU/XDjLY8J/XgyPWiWdWSUXDaviCFhhRdJc+UJuenjbWw+vwGJewMeQwzYMjmC5Y1Xw+b9pE2sGk02kbw9PTUwt+aE0Ac8y4+Iozxps2GQAkSe9VA38KUB5kWo2v7DFU6TJ2gCN/TdxnwDPzJ+wP6DMt/YLF6/qcnwSOoiia1Bkw+idxHP/z8wvadX//+5L+z/MfNyU9cP9+//x3PRbH8a9K+tXz//9On86zAVSPLC8v69mzZwaOSB1xaGq9XtfGxobq9bq2t7eNMWLjYuPHeflUDBO1Wq0a+3R8fGyLo1AoaGxszM6VyefzVmkQx7E9f1C9a5hUABpP5TOxAHxU3lGmTRUK1WoAI1gPHj6lNoxeGx+zMJWWSqWsEWg2m1WhULBz5qDCcfKtVkt7e3uq1WrGMtbr9R7mqJ++y8+lMPXoq5J8Q7RwIxwEQwBYIY326NEj3bt3T2tra1aNNDExYazA5uamarWa3rx5Y2vAO/nj42NLSZKCgw5n/fh0yaDA4De1fnOGfkXpdFpLS0sW/LCpwwK1Wi1VKhXrqM/roCNhswBMNJtNtVotW0+ATR94DCPFSh+zu3fvanFxUY8ePdLKyooBZs8YtVotbW1tqVaraWdnx36HLIHPT5sD9CMAMJgG6UKcHoLDQYNEPw7T09O6e/eustmsnj17pnw+rwcPHtjxGAAZginmQJiVgIn27AprkBQ/LK7vvO8rY8PAdFiyBv8+vh2HB0Vh0OPH1hMPvkVECJLRJfk1cd2B+KdUq0WS/oGkL+M4/mX3+5VzPZIk/TlJv3/+/W9K+qdRFP2yzgTZzyT9hyu96vevsSf6pfsz0c3CwoLiOLYqtGq1qkqloq2tLTUaDe3s7Oj4+Fj1er2nqiyksqXe04ehTuM41szMjEqlksbGxpTNZnt0CdDws7OzajabQ4mMuH6AHhPLA0uu1feyWVxctLQazBdMCNQ/qaBwso7KJie9HwHCiqE1ggWAHUMrQgS8v7+vWq2mzc1NHR4eqlKp2DiEFpbmsgny1dPQvsnjMAACjomqToDi3bt3e1JpY2NjlvrZ399XuVzW9va2yuWygX3f64XIUepdM/zsweQopmCxfnOGFNr8/LzNG8COJAPFPpWKboQ1J8k2OdhJHpcJsQcJDrwQe3p6WgsLC8rlclpcXFShUDBhNikUigkqlYqq1apKpZIVuHS7XZsbsIghI+/Bsm95EgZbgzR/7wEu7C2AJIJIChRY95Te7+/v2x4DOMIv8Pl981kCV6/ZCf2CH58QRA5yrHxQ6H/nMxOeFQ5ZSc8M+bEOGWVe0/vRQfiNT2GOflrSn5f0e1EUfXH+u1+S9HNRFP1IZ2m1N5L+siTFcfzjKIp+Q9If6KzS7Rfia6xU82mS+fl55XI5PX/+XKurq1pbW7PI9eDgQLu7u6pUKnr58qVqtZrevXtnQuxwIwsdt7+R3DgWebfbNS1CHMcGKOidk81mVa/XlclkdHBwYOr7QVi/CYwokmumRwtVJ3Nzc7boSZGQZsJpk1rx2ohBoPlvY54BIHWUyWSsIWg2m1U+nzfWrNPpqFwuq1ar6dWrV6rVatrY2FCz2dTe3p7pSdjcLjP0R8ypEAT5OeZz6YMYD+kCtDBXV1dXtbq6agUMOOtqtarj42Otr6+rUqno9evXqlQq2t7eVrPZNEfvK7Xo/UJXelr/E3yQTgyj3VGaO2EKFhEuPZ5yuZwVd1DtWqvVVCqVLD0mycYFfwATB6tE35pSqWRgnPEZ5KbnfRsl+zBj9+/f1/3791UoFKzLM21KWq2WsYjr6+uq1Woql8vGjkiy/m8+NTU/P2/d9X01rfcrzA9pOB2f0ZkBitbW1lQoFLS2ttbT/JfUKEzqq1evVK/XtbOzo1arpf39fUux+qoyL9D3gBSminmCz6lWq6rX68ZMUf3qtWmDDDjC/cWzweFzQpCE/2F9UQUOi8brhoH4oDS7n1Kt9m8k9RMD/NYH/udvSfpb3+G6Ptn8xgcTsLy8bMieqLfT6diRHsViUbVaTZVKxZyTT234HjXnn6fnRoR04djYmB0BkclkLMXm9U+pVMq0CV7fMyzzAkNSIJlMRoVCQfPz89buAHAp9VYleFrXd9PGRmmTk3o3Op9WQ5BNOg02kA1rd3dX1WpV29vbBqRJzzIW4fvw8JEdgLtfL6NhgYKQNUyn08rn8ya+JWolTbS/v69SqaS9vT1Vq1VVq1UdHBzYtfvqM1/Rg+PzbSIQXI4qc+TvI8wGnwP2CNCDE2djp2cPZes4fZhJqrrwOaTSWq2WibF9KfwgATPf+1QiFXm5XM6ALnObg7bL5bJKpZLp0NBthqlbXpfN3/dQ88UMXnc1jIDLMxkwO/Q740E5Pywp6bRyuaydnR3V63VtbW0ZqAk1ir7D/vT0tM0XABlsGmyUL+QIU/aDLOL4JtYvOMe8DhG/TMqd3l/4C98Ylc953XajO2QzgUkJrKys6N69e3r+/Lny+bzS6bQ6nY5FZW/fvtX+/r7evHnTwxhB/YagqF/E4ulCmnDR5n17e1upVErb29uK41iZTEZjY2MWHXHjQ2HdoMaqn8AS0TgpSMTr9G6C5gSxhwJVer4gRu1HiQ7TQq0YALpQKFh7B7rbjo2NqdVqqdFo6O3btyoWi3r37p2l1UiRhBqhfu/n00Z+k/PPH6bGxs8FdHKsn0wmY31mjo+PtbGxoXK5rBcvXqhcLpvuigocnBtFD6urq/Z6lDpPTEz0HB/gxcej2hcrBNSAIvqnETggvq5UKqa/i6LIwBBd5efn5+31aONQrVYtZVsqlWzz80zsoABkqDXi2AtYI/o5TUxMqFar6eDgQJubm9rf39e7d+9UqVSsSg0WHvDD3FheXra0LQ0kYd5Iz6G5YvMfJDi6TICNEP3evXs96WZJJrSuVqt69eqVSqWSXr58qUajoWKxaDo01r/X1uCT0um0tUbI5XJaXl62v5OiJWCDOaK6cRhz5UNjF+qMvA/G74yNjdm+QTAG6ASAk6rm86PJG9ScuPHgiMFmkKm08Q4edqhUKqlUKlmTR0ptfSMuqT8o4mcPDqSL6A+AxCOdTttC8GcK9evrMigL9RNEs7Ozs5YqIA0CXcznpsoufC3GHuA3rF4bH7IwrQZA8poR2ttTVl8ul1WpVKz3FXMlrCL6GDjq9/A2TCfm2UPGg0NTJycne5qiwhrBDEDle12Bb29B7xvaP/B+0sWaGbTQ+NtYP90JrAfUP/o0DgntdDqWtqbXGt/7z+qPiOCsPcZ1WD3DuJcEUKST8A0+vQ4gLJfLlk6C+fKMlwcZ/vVg0mEJYF9gzsIAZNDjEDLNrA/0l1w3R+TUajXt7e3ZOgEA4ze8H/LVeYwNBTxoIH3aHdbIyxm8IHvYDGyoJ/J7hZeheGDomVjWlScSQtaMueHn1rA1RyNpoXOn18SDBw909+5do36bzabW19e1sbGhN2/eWDrNn53mz6P52KD737NJ4iyoOqlUKsrlcpZC8WXjpKgGDY58JOSFhdlstsf5oUFigmJ8TlICsAFHR0fWmGxyclKtVkuSrHx3UNqqfuZBAJsVHdJhjnzzOg5JLZfL2tvbMybAN2T0FRYhCPTvy/M+RP8OExBEUWROOZvNamlpSblcziqQiOJhNIrFooEixoEW/6Rgnzx5olwuZ1WihUKhR6wM8KxUKpbS9rqaUTF/b30AQNDgxfvdbtd61cAC5HI5a+XB+GKUcVerVRWLRQOeMAP9ztkbVFrNp9PQby4uLlo3bLSSaGsIILiXaKwYryiKLEh9+PCh9cxaWlqyucE68XODgIS5MQyAyDggOaCtBWsEbWKj0VCj0dC7d+9UKpX07t071Wo1q3wm8OZ1SR0Bhih8YHw+++wzE/sTcCPsLhaLtg5h57xAGRsEw+i/9ySF72sUgiICaPYW9KwwiFTL+qKYTqdjPohDeMMy/w+l7r6L3VhwJPWm1aiwYvKycR8fH/csYo5n8KWPH2KKPmY+beKFc76JladQB80chYi+XwTs8/9h3tdTwYCMTqdj7d1TqZQkWXt7tCVssP0EeoO0cJMjSoExQ/QIkINRpGs6+od+gvxwbMP39N9f5yL+puadmXfUXkODwBRKmzlNIMF8GBsb6+maDChaXFy08SW6JSDxncB9J9xhj0toYUoWthXdEA0cea5PG5B+g3GgIg0wxRxDbzTMo0K8sbmxRgCE/vP6++jTX51Ox9gUfAj/n8/ntbi4aD56dnZWMzMz5oe97/RzbdDjEKbV8JWIr33RAe060F3BnrHHwHx53+HnEqxiNpu1juPZbNYKYPxZZL7VA+PTj1kb1ljx2XwJPuuBr+wt+Fw0WwTk/MweiVaTwAH2aFCB1I0FR17zgpB4bW3NOpR2Oh01m02Vy2VtbGxoZ2fHBKQMsGeMsE+dXN6hh8JbL47zk0a6EEIPUpAd5oJ9yseX5TMJOasniqKeaitfdYWAeXx83E5IBtkDkKTejtzDMh/9+xJsxMIwG0TFRMAsaM8ESbKqGgSkl4Ej77T8uA/TvENjPEKNGfMZFgNQTNCBMyT9dv/+fWUyGf3ET/yEFhcX9eDBA0s9oOM6ODjQzs6OdnZ2tLe3Z0f0AI6GPS6hhYAaAMmmTg8aAghJ1kmfQIO+YBMTEzYG9ACCnQQcXSZAHiRr5HvFhekvv058UOmrPwmUvJ5keXlZ6XRaP/jBD6wvUDqdtrHB7yD0LxaLKpVKBgxCvecgAoyQOeKBLtGnuZBR0O/O95HzqWSYWkAATSQ///xz5XI5ffbZZwaWCNIajYb29va0s7Oj7e1tFYtFA0dh+5RBzBOfCiQI8ICINQEICh8AbP4Oc8TpEswLSSZVabfbKpfLVv3og5F+KTzp6tbMjQRHIRuAeJB8sG/4iEMiJdDvMM/vMpheS8JrepozpOj7MQ+DFBv66/a9MnDOpBoBc0SF/A+vwSLAUbRaLY2Pj2t+fl6np6e6c+eO5Yp572Hpa6TeLtBcuz++wgumfX8ewIB3cv3KSX2zMn9vAcj+WoZtoZ6C3D8RX9hnxmu1JBmIAiBzlAKifhgTD7QQrdL/hY1k2MD5MgsBQ1hmDDACQMdxbCAC0D0zM2MBiCQ7U8+fpRcyRh/Spw3i8/pqKZg/Kn5Zw6TKfal7p9OxeTE+Pm6p+ZWVFaXTaZsbHM3jz1M7OTkxVgQQ6Uv4ub5BAsXw3rOhS70VVOHxST4g7peaJSiDLaKqmtYZd+7cMbYamYYHX5cdKcN1DWpcGBt8CCJ+gglfnci8gBXyrCKFCswXdK4w1yGb2G9fDefGVc2VGwmOJFnkm0qldO/ePd27d09LS0tKp9OSzjb17e1tbW5uant726oorrI6hv8Pm3L5a+zH2gzDPMPFGT9MXHQktBzAccVx3HPqMxMaPYWPhKIosoqChYUFO2wQNqYfSzcM8wDIVx2iSZqdndXR0ZEymYylEGGIAA+UX4eluR4Y83kvSxsNW4jtS2gBPiGjmUql1O12tbq6qnQ6bdEqjBFi65WVFc3Pz+vevXvm6CYnJy1lUqlUtL+/b9q/SqXy3loc5rh4CzUUaI0Q4yIyp/EfDOHp6alpaPzGDytSqVSsT9TGxoYBRar2RqHSKASCpA89G8Dmha5qamrKInqYpImJCdvsOHttdXXVUmyADDQ71WrVGBJSjOEm6L+/zvEJX9sXEZDiks7YEw+IELBns1mdnJwYywQDRUqOLvx05qcX38LCgvkZ+iNtbW3pxYsXxrj66rSw/9V1jIkPLJkbITPqgwHADpqhEBz5gCKKIttrOJtwYWFBk5OTFqiTdubrZcDoMqb+u47JjQVHPq2GmJhIRzrblKA80Y9cV/MozxxxbSE7NCoGQCLyocR0cnLSULrvykqEj9Njc2QDRKPCieSZTEbdblepVEpHR0c9i2HYG18YZfnrCTUGiG0BCOinKDumigJ2hNdkbKUL1ihk7IZtIUDy7Rf83wFNAGHWDwAZcAQtTnk7KQVAgy9DplIUQDAMluRj9jGtUdioTrpgUhk7X2EDEEJsTFWXZwHCcRg0m+yB82XaEQBjHMfWETqKop57SeoEcMTxQ8wNGEoYfHwO6UWvNQrvx6DGpJ/21Esn/IPn+qIbGCCfpoRN5eQB+onRi29qaqqn2SEaplKppHK5bO0N+pXtX6eFvgIQCBiiJQzaIXynZ5SYE/garpkgyzOUNBf1mscPzYkQPF92D7+N3UhwxA3jhiwtLZkAlH4q9B3a3d21aMSnu656UoUaBW40G4QvxUSsN+hNwS9yes7ACjUajZ5zfBgrUkigf7QF7XZb6XTaKgP9ie6pVEq7u7saHx9XrVa70jTmd/3sng6nQoaF2+l07P7h1CcmJmzMAE8nJyfKZDI9bSBYvLwuzoz51u+YkWGa15tRctxqtew+hrS33wxwljhJ79x86oGI7/Xr19ra2tLGxob1SPLdj0cNGPl1TLo+1N/AsAAcmFt+w9/d3VW5XNbbt2+NMeJcPlIx/XQjgx6PMD2BJMGndPANBAiAHeaGv36vRws1WrwHqZJ3795pb29PGxsbdn5hP0H2MHxl2NpDkur1ujGGcRxb4MCmvry83DMOvkoWlgWxPlo2SZZa3Nvb0/b2tl68eKGtrS29efPGgvx+LCPXetXmQZE/dxN9GX2JYFUJmAGGvrWLb3zsQS7PY64g5SDbAGDGV/hWAPjzEDgzTxLmyInm2MiI2lD4e2X/dS8yj665Fiayr8oAGQ/DmFSwHmhKEJP6tBDPAzRMTJy1sidnHkWRRT4INhcWFiRJs7OzBjxGqSlkyJz56gevF/Dl/QADtDcASsYHoAHtTlVPt9u1lMOoMYjSxVj4iPX4+LinkzFMAc/32jroc68vIDKM497ybEpxOYctFGKPAnsUMmewQVTReG2Wj4RZ59JFih3/Q1d+qphCxmiU2DMPjlgbVKMdHh72pEp83zbPmgOUvDaLjdL7AF4bRg2G3x+F0W9sBskexXFsewf61bASl8o1fAStKzzbwjFNPmVJOwBS937Poss259SFwcSHKqyvwsJ1wGegDxF9mLLZrB0RBOjjvnvGkcKdEBwxh2DZpIv1w5iQYgXw+CpZLwEIwfn3kjnyN4wKkmw2a0Lsbrfbc/QDXWc99Xsd10NUkMlk7JwyJj7VTajtd1YAACAASURBVNVq1VgsXx5+HebzxZ4yx9jIj4+Pe34fMiFhO4LDw0NbIDAoPlJcWFjQ2NjZ4btHR0c9Db2GARD8+3rH32w2LQ2A06LM2kcpvl8Riw7WEiCFI4A1aLVampubU7VaNSfabrd7gMUwzYMiomKaqMZx3FNp5u8ZoJ6vCwsLll7hHkuyzX9zc1PFYlGvXr0y3V+j0biUMRmm+VQa6UQcvz+YOKxUk/SeM6cHEAfz0puG6N9XGo3CfJAugib8QbVatQILwB7322vTwipWNJfZbNYOmfXAGWBM9dXLly9NX+P79zDHPIgeJEDCB3oGbXx83PwG/g5w5MvWpYuKLl805AMO3wGaY0eKxaLp8jjNoVKpGGMUpvKuaxxYCwQDsKacq8f+ls1mjZjwoCjU2sIQAYL4uy+IkdTTd9AfSeUDFfwW/8Oc6xdkfVeQdOPAkfT+URhoAKC3fcrEiwWvAxj5PD26BNIM3oH4wzaH0fQuzM+G4nEcdb8DDCWZcyNKbjab1vzR974h7442w0cMw/i83nxa8fDw0EqsEQ+jI/Lj4ueMB0m+AoWv9H/y/WDoVzJK+jMcCGyXb+NAlNftdt/rycXGxsaVSqUM/PnnMd9hTej/EgYp4dgOy0LdDZvYZcLkMGr1ehS/1jmKxlel9RPTSsPXonE/2JzwnwSa09PTNmd8pO/BkfdraI4YT8aWzY2jU+g/F/a+8uMzaHbNj4Wv4vUMGkx7qN1jH/LVrYiYPVD0qSFev9VqmS6Pqk7fQ2oQ4+D3CZhBf+AyvZhINYfAqJ+fg2X1GRXGol/w6QFPHMc97QBCttLvZ+xN31vmCOPmsfCgQImGoe/DRlxX+f4+0kyn01pdXbXKOc5VOzo6MlqdPkuhuOyqr0u6YIxYwGGqBCDpQZH/Go5Zp3PWIRsdTb1e18TE2RlRVOnAvlC94Z1HCM6GpTtCY1OtVo3BQzQJs+RbGzQajR4GTbrohOwPVp2bm7PPd3BwYCCDiiSfnhqm4XAAiHEca29vz1jETCZjR4j4TY2NDafkQS9RIM+hdcbr16+1sbFh5295Z+/TAsMGRh7soq3g/D360fi+WJJ6mjqycfmWBf7k9H7HPfhNbtjzwqfFYI5rtZqx2/v7+9rZ2bGeWFNTU3bNYaDne9p4UbIkq1xsNBr66quvtLW1pVevXvX0sUEb6ufaMMYIf0dgU61WJUnlctk0mKSR8GnIE0KBMPOKOeY1avRK2t7eturqnZ0d7e/v2x4WgoXrBEZSb9sTdL3sb8vLy8rn89bzi6AwzDaEqT9fEQvbhj6RMQM483q+pUQURTo8PLTxZuzw1QS0V8nG3khw5B2232w9vR1GMtcRoXmnSkUCnVTRrJBSY+H7Xg3XZWHOmMnum3ZhH6rCCCN7NlXYAy+a9AwaDuAyYDRI8+8ZsiU+KvGl915E79Nh3Dfv9GEw0aIAKE5PT416H2Za0Rt5e7+xIQQF4KA/wpGF6VjSilQ3song6EhN+qMgAAn9gMEwLWSMvL4CBhgthW914CN+5pRn4wjSfP+bkI0dlTHwcyJkjuI4tgKE4+Njq0zyZy6yLry+hCawvDYsSRzH1hncpxvRGvnUkddw+Wsd1Jj4sQD0cq7Z5OSkDg8PNTY21pOCh0X165yfue+spZA1omSd40L8XjHI1Gs/5oj1wNlv/vgcxsKnVUNJBiyb1NsWIBwbr3WN47inWhRWXpJ13ffP57V9Su27BuI3EhyFk9efR+Q3dk/3e0f4XSeaBwKAopWVFX3++edaW1uzs5QODw9VqVT07t077ezsqFQqWWm8T91clfmJDRAi7eNRO/qYOI7tXKiwD5Efo34LJnzAJPC/jHtYJj5o86CO1gVEKpOTk2o2m7YA2dhZ4L4ajXGQZB2Eu92upQ+IlAFEp6enPULEUTFPR7NmarWaHc4MG+Z7kvAZAIB0skX354+WOD4+tqq0ly9f2plTbICjojXyc9o3sZuZmdHi4qIymYzu3bvXc+CqdLZOms1mDyD0c9tXpQKOfGXaqHx+b1yH32zQ/nBmIvoTX3zhmQY2SnQpiJDxN4zD+vq6dnZ29PXXX2tjY0Pb29tWONMvoB102tGPhU+TNhoNxXFs/Ybi+KzpJ5+/3zWyZmh7gp/EHx0fH6vRaFgPsK2tLb19+9bE+2EwMYhxCINcWnbkcjkVCgUtLS1ZgYJPo3t2lLlOsAjDxuvzObjXBNl8Zf+iV9TJyYm9H/MRNp4O/n5P9SDpu9iNBEeYr5AIhVkhMLrKzTnUnMzMzGh+ft7od3pWwDyE3U0HkTP2ufDQuTGZyNP2Ay+AyH7RNYvHf/2UMR4mcwIoQMznW9FPTExYioSDDQEO/igRvsbxWaWWZ85IWU5NTVmqzqelRs1wTnEc93T8JkL2FUYwJgA/UoqAby+2JRWJ1ogjZUZRhC31bgbcv1QqZcfMkEqj2AMA4ZkCQJIkA9UhEztK4uvLzKexJPVsXvQsC9PlPGCUcrmcJNlYMofYNDlElGqsVqv1XoUa1+KvaxhjEWYjqCaTZF8PDg7eY94wglI0fN6fsv5o++B78sGyhgz+IIARX31QxJoglQg76Pddn01grXuGMXwPD44gN3wVN+uq2+1aYAJYOjw8tDQb4+wD8quyGwuOPI1NhMOE8oCFpoZhLvjbTDQPiujoubS0pMePH+v58+f64Q9/aN2iDw8PVSwWtbm5qTdv3ljE4U94v0oLU2mIR2nMRQUSzo0NEWDgF4Pv6sxrEgVQsZPL5azPBQ7ApxW8sHLQ0Z83n77w+ipfMYQzIALy4MgzamyiLM5ut2s9PnxKBiG2B0ghgBzmJumjY8bGpwhC4Mvn6HQ6tq4KhYJWVla0srJieq1yuaxqtaqvvvpKb9++Na0RzetGBRj1S6NNTU1Z1evKyooymYxyuZwxrrApgAU2DZgBST3zxgu0eYzK5+9n/VLo6ED8/A2DIy+SHRsbUz6fN93l8vKybaLFYlHlclk//vGP9fbtW717907lcrlvujW8nkFbyDbH8UUz3ImJCTt/cnZ21uYCc8QzH76Bqhcj4yPpa7S7u6utrS3t7e1ZMDEs3+mBERXYBAsECsx7zxTBlHqQGwZP7A8wPAjw+bzNZrMn+0NaH+at0+kYWzQ5Oalut2vvKSkBR958ao1BD3OV/uZ8l5RaP4dKG4G7d+/aqcpMAihTIoKrPrrkQ9foo2GEcPSo8D0ofOUR50OFkRuTzace/IJhY+F5XtAcCm8Hbf3e00fwgAEMoO3HhYUnqWcz8M9Dd8O4hp2FRymtFppnC7x2zINlnBL3k7nPgZzMeSLgcrlsKWSvRxg1YOBT0DBigD+vqZAuxPxE+549kd7X7jHPLqsyGqVx8PYxkBSyCj7wIHjKZDKam5uzUv5O5+KYkFKpZOJ8QPNljNEwLUyvnZycWNAbx7Ft5PTXAxyR7oE1mZubsz3JFw+RVqMikHPlwuaX/loGZaE8w2tVefjWHVxjv4Dat33wAWqn0zFGmb2RtHsIcLyUg6C/2+3amF+Xf73R4MhXh6RSKR0eHvYg3lwup+PjY8sNwwp8E8o2BEWwMEtLS3r69KnW1tb0kz/5k1pcXNTc3JxarZZKpZLW19f15Zdf6vXr19rZ2enp7zIIepQJRQlmoVCwlB9iWho6UnJMSgW2RLro10HUcP/+fc3Pz+v58+emywDZE2FBD3P+2HUCwk817+j95h+COq81YhFLvXQwhtaCTdV3hu1XrTdqFq4DrhGQBKPkI7hsNqvHjx/ryZMnWlxc1MzMjDG3r1690ubmpl69eqWNjQ3TMl1XK43vYmGgMzs7a1qjQqHQc8Yg7AZaE4IJ6H5KiImeveYoLA4Z9jr4VOvnI0PtpnTRg2ZxcVGLi4t6+vSpnj17ZmevNRoNtdttvXjxQuvr63r58qU2NzcvZUiGbawDD45glaMo6tEsst+w3j1rz9ongKA/FAEY6cXt7W3t7OyYOD2UXgxyXELphC/R98/xgXcUXRwdRcDINUNMsI7QJZFBoWKY9cXnxi+HDTY9o8QaBCT5oper8rU3Fhz5nCWOiI2MG8KkRN2OYh50ysT3r4mFaSpeE9U+jBEPIuhOp6Nms6laraZisdhzjtKggBHmdVEIbX3zMpgPIkAWNb+TZMifSJoNJJ/PmzAXsRyRE/fDp66Gbf3GnYXkHZGPhL1zukx75ft3MFbfRIc1CtZvbDxYks4+t290msvlzOFTrg4zQI8WHwyMyuYnvX8vuXeeFaWVAawB8xrzYmzWCuA6rJjtx8jeFLvsen06liAsnU4rm80qm832HKnRbrft8OFRnxtS79wPfQLACN/ZbrdtngCMfPBFzzd8g68GpAjCVzJ7ED1MC4GGHwMMKQVBAuPC832ftDiOTdcJMK5UKtYPzJfj83+dTscAEPt9P6B+XXYjwREpEfrVvHv3Tt1uV41Gw3qUxHGsp0+fKp1O6/DwUPv7++p0Ls7K6VeyHg68Z4v8Scr379/X2tqafuqnfkrpdFqFQkGnp6dqNBpaX1/X7/7u72p9fV1fffWVyuWyVR4MAiSEi8oDRbqa0tVYkgqFgo0jzeoovfT5XgDg8vKyic/RGkmy1OHGxob29/e1t7dnvUuGvejD9/QbIj1bfPqQRe71SF5rNjc3Z9Ub9P2AXveAi/cO59iwHd9lFgYHfG5Aw5MnT7S2tqZnz57p0aNH1tuJVMmLFy/05s0bCwoGxZR+G+uXIl9eXlY2m9Xy8rI5aF9xRtCAIBUQhc6MMvVisWhHYvi+ZqOw6X0XY514ZjqdTiudTusHP/iBHj9+rMePH2t1dVUzMzOK41jFYtEqF1+9eqX9/f2BsuhXYWE6iAoqST1nUnIoayqVMpnF6uqqHdBMwUe9XtfOzo71NioWiwaSPKAexucMgSCMKc0p5+bmeipwqdj2LVH8XI/jWPV6XScnJ9bsk/NOAcmw9AAp0tmcMsF7AaB8dRvfX0eLkBsHjvzNQ1hLCofjKuhWzeGghUJBcRzbjfXR3WU6m5ByT6fTWlpaUi6X08rKipaXl7W0tGSO0k8iji0BcPjU0qDGCPOfxTNIgIDx8XGlUinTC7BASQ+hK1pZWbHGeBw4yWLwXYE5gbyf3qTf9V3nGISRhb+/Po/t5wBagbALK1Gh74FDUzwYFICid6Y+NXcTNoLQoigyxnRhYaGHMSRyxOHXajVLpY0yM4B55ogiDsqUuf+hUNgDa1/RF8dxz4HGiFXDni+3xVgTVDD5ZpkUKHQ6HbVaLZsX9Xr9g13SR836BTb+9x4s+ZS9B0kAaMYD1ghwQEDaT5Q+LOMauF7fnqLdbuvo6Kjn0GXp/b5CrB0AYbvdtsaWgKNwb/RaWNYW+7l00YOOseqXrr7Ksbtx4Ei6SKm1221FUaTt7W2NjY1pY2ND+XzezllbXV1VNpvV3NycisWiZmdnrRspExR0zOtygzg5eWFhQffv31c+n9fDhw9NmwCNfHJyolKppM3NTX399dd69eqVvvjiC9Xrde3v7/fkUgcxLiH6x2GDsGE/OBWaCedTYHEc2+T3HaCpVIAe7na7FvFQffL111+rVCppb2+v5xy5YTlDPxZ+k/IbPvccDRvl6PwfKbTp6Wlls1llMhk9fPhQ2WxWa2tr1h+HudRqtbS/v28pJq+tuAkWCpWz2awKhYIePXqkJ0+eGDAGBL1+/doqkHZ2dno6+w7b0V9mngFhg0eITbsLUgOsATQY9O/hfCwAAEGRZwLCdTWq4/GpBgAg0FpeXtbKyoo+++wz849oc9rttl69emX9rnZ3d21DvClrwZuXWOATAEJ0lc/lcspkMlpZWenpB8YRPbu7u9rd3TWGFR/hK71CMDZI86xRu93W2NiYSqWSTk5ONDMzo8PDQzUajZ5z9gAp+AOE9hAWFGcAjqrVal/heVidxlEtIRvP+6GTRdt62VEr39ZuLDgCIJH3RedDjyEi/PHxcRUKBY2NjWllZUWzs7OG3tHdHB0d2euShkqlUsrn87YB5vN5PXjwwFJsoFnOCeKgyd3d3Z6TlIcVPYfUKF+JcOh9hF7Il7VL6gFHvnkkxkQkZ8xhm5wLREQ07I3hsrSWF1LCHMRxbOfF+UocnKKfEzhBTtsm4mFutVot05qNarXWh8yza6RkeZBGoNKkVqupXC73NPMbZWCE9dOP+QpDwJGveGUz9BoSImuOLSLKDh31bbCQbeNoiYWFBc3NzRnjRv8ezk/zTPIwN/9PNc88h+wIP7M+fKUjbJFnIKMosiCVOUI/o7AzOO89LF+JdpS9gzU+MTGher1uQQIBNSX2tLqgAo+eVkhaOOCavTo81gt/w1okhd3vdAHmkO9A76ser2r8bjQ48sp/Sfriiy909+5dTU1N2VkwExMTyuVylgLh1OzDw0PVarWeMk3fLn1mZkaZTMb6GaExQFvAmTh7e3t26viLFy9MlBr2NhnEmICucdiS7DNy7TMzM+p2u7ZwQels8ExGnL8Xm7JQjo+PbcK/fv1atVpNX331lSqVitbX123xh9HBMACip3p9uTXX5Zk09GX9SvgBilQlLi0tWSqGhdpsNrW7u6udnR3rbQVQHBVh+seMdTA+Pm5H4Tx58kQPHz7UZ599pqWlJeuNtbW11aM1IkIcpZ5GHzLvRwC1MzMzOj4+NpbUzwlSKPTuAQDs7e2pWCzqzZs3xhjzt37axptoHhBMTk7aAaRPnz7V48ePrTdUFJ11Md7Y2FCxWNTLly/1+vVrE9/eFOCM+ao8zxxRpZpOp01uMD8/b5pMemSx0RO8089of3/fQGPY0mCYY8M8j+O4h+XD79OTCUbMB5E8l4wBlYp0xwcc9wNG0kVlNK0y6ETvO/V78Ebg6c9Y89f/XcfxRoIjqZc9gsorlUqamJhQuVxWHMdWNYFuJpfLWfn60dGRHQEBSvXgaGpqytJz9PEhb4zwslQq9TTw2t/ft0k0rInO+/n+HFC6U1NTajabSqVSarfbVtbvqyzCHj2MMyidic6REDBle3t7Fg35xX4dueBvMybhg2sjKkFPRNsHrzXyjAFVWnNzc/Y3X7nSaDTscVmDu1E1vxEAAgCOBBikknxVpteT3JT04WX6Ec/2sCHSpsL3s2IzoAO+727suwTfBmCEeeCMdpHT2WHT4visHxT6S84K65dGGWXrVwnlmUbfS48O0qlUyvo70daDeYNeB+aIlGu/ZrnDMs8cSRdHStHbqV6v96TQfCUaQYYky5rAHJFuC8GLN96XYJy9NtyT/LWyxvyavUrfc6PBEQPBzdva2lKz2VQURcpmsyqXy1pYWNDq6qqlw7xWIGxOFTqyKIoMEMCalMtlra+v91Sl8L7lcrlHYzQsYCTJKFp6OyGC63a7lusmwmFR+1QkzgHNENqZvb09i5YPDg60tbVl+WRoY98KftgbQ8ioAYQPDg40NjZmR2WgN4NRo2ICp4/exJ+Xdnp6aukzSpXfvHmj/f19bW1t9bRxGGZTt081ryeZmprS4uKiCoWC7t+/r/v371tKDUe3tbVlZ2UVi8WelhU3BSARgdKfrNvtKp/PS5Jpq/wmB1vdaDS0sbGhUqmkP/zDP1StVtPOzs57WsZRCA6uwtgI8Z/Ly8vWCXtlZcWKOmAI1tfXjT0tlUrvaY1uwnj0S6sBitCn5XI56yNHixfax5BOwz9wEDPfU83oD5gdtvng2rNZFBr4o6j89fqAk//DF4QVbP0+p09Z0i4h7Efn+y0Bxti3wwq5721aDQu1RwcHB5Kkvb09HR8fa35+XoeHh4bqcXRscDBCRMIsXpAojpComMZdNHWkcRdpOr8JDhsQeM1RFF2cQ1OtVtXpdMyRnZ6enR4Pk+TBEdTq8fGxqtWq2u22dnd3DQxRgUBE5MsyR2EcMD8eniHw+WpSK9JFfygPjqDI6bvh8/GeQeHQSK83ugnsgXdOfHYfCbP54bwACN7Bj0oE/Knm1wj+g2MREJpKF8cNeYaQbuC1Wq1HZ8c4jAprehXmwQGBgp8bvqUHLD5jM+pd0r+Nea0RgYR/IE3wcwYGhZ5GXoA9bF1maB4gSeq5d/7zhPObOd+vU/zH1oJn8XkO4Miz+P75vodYmBm4Crvx4AiE2+12e4TA09PT2t/f1/z8vOVISYlks1kTI5NO8TeVBobNZlP1et0AAamkYrFodDpRJxvusCe4n6hMbhbhxMSEbQCbm5smIvSMiG9iJl0IrxHRhV8R4HIPPhQdDMPChQt4q9fr1uqfowAQ61PFRssDf4QEaVzPGAEWq9WqNjc31Ww2ValUTGw56huDT6ehrZqbm9Pdu3e1urqqTCajVCol6Wzzo8JmfX1dr169MjH2KKVSP2Rh8NBqtRTHsd69e6d6vW59warVas/REABijgRaX1+3PmtoLHyQNSpr4LuY14PQ2oPebpzQznlXBwcH2t7eVqVS0evXr/X69WsLqobZv+fbWrjhhpswKVdAEo1B2ZOoeq1Wq9rY2FC5XNbGxoaBaZ9aG7WgItxHYMFCUbp/fpiqDpnyj30+xhRj/fj3+1gPuau0Gw2OpN6b6Gnbk5MTK6GkL83R0VGP4BJgQFNEzxYhuKZhF2p7KrOgz30KaZScYT9QwPcAHlJF/oBAT2NKF+CITd5XqQGKrrPXxFWZd3DMFdgz6ezE507n7GBVnEC32zXBOgAHdgzGhDlRLpdNd+Kd3k3aEHx5u69mJBJm3BDc1+v1G80MeICETmZsbMxKjb22hA2PKpxGo2GpkTCVfNPG4VMsZBW9FtMHqF5rhD7xpujQvPmNOgRI/hECbd+2BWYZdtn3NvIMYzhfRmXe+OvxoKWfHqvf/3yX9wx/1w+Mfeh/rsJuPDiSLiavL1mH+p6cnFSxWLT+JBMTE1aKD9qn/TnMADobNrlardYDCMilhk0kR8n8ggb0RVFkvaHq9XrPERchOudrSF32+8r7jaqFQJFrx0HduXNHzWZTU1NTKpVKJsyenp5WLpezqBkHCKtIqtUfA0A3dFKsNwU0eGAUHqjqA4IoivTVV19pf3/f9EaMxU3aBPtV5Ugy8TDBE+Piq2N4Dl3AG43Ge5rFUb7X39RIafjml5LMN46Pj1sRzJdffqnd3V29e/dOe3t7Js69KesgNB9QUUk1NjZmfdDQmRJItFotA9UUrlDFS6Dtj8wItZmjOjaDACOj8J7ebgU4wkKETwqElFK73dbExIQajUbPicGAI0kGjrwmJWRJQmX8qE5oqT+S96K3y8xHTeH/9/t51I3ow0d5cRz3nMl3fHysOD5rgEmvLATc9LQBgLNB0ugMzRUC+H5t9EfdQkrbBxpscpJUqVRUKpX6drkddScvvV+Vw/0/PDy0lCspaMyvf6oxSSkzl26CP/i2FqZSYNEIQPG35XLZ5AcEmTdBc9fPuF7vN8Ku0QcHB1Z2TrBM0Nlut1Uul02LyHrxWkTvIxIbLbtV4Ai7jDVpNBqS1MOYSO+3PsfJhSzJKFKf38Zuw2f4Nua1Ajg6IkIivlKpZMJrn2JkfnhhomeGfOXETQEJoTHX6TOyt7dnoGB+ft6cOTqj7e1tK1YYlT4tn2ohmwjIgRUIfYL3A1546n9/Gy0cB7RVGxsblkJLpVKmr6OnER3ybzJrhPl1TU8dKl6lsyIXzlibnp62+URPvYODA5VKJWOcAVf9KqQTGx27leAI8w4wscQw74iYG6Ho0IvSpfeZtA8B6PA9bopx/TjvVqtlGhMaGp6cnFiVpu/pdRMdfHgvAc7eX/j7Gd7bm/iZv6l5xhXpAhW8GNWuR0dHpr3rB5hv4liFjLNPr0kysCOpJ+XoGxT79g63sf/VbbVoFG5OFEXDv4jEvvf2qaJD6eanGfuZb3pI1ZqvxCEFQAqRlORtcvSfIjb9vlnYDJWu8swNen51Oh3T0oSNcG/D2Hltpj9+KDzmwjONAESfer2JrPItt9+J4/g/C395q5mjxBL7JjZsAeCwzTNH3W7X9BSItAFH/aLf2zJet+VzXKWFqUNAj58bvprztmppQsbQSzZ8Dx7/nJtUuJJYryXgKLHEEpN04bhx+L4JYvi82wSIEvu4hfec9LPU23vmtoOAMLX6serM2zoO3wdLwFFiiSX2niXgJ7F+dpM1dddhyTjcXhv7+FMSSyyxxBJLLLHEvj+WgKPEEkssscQSSywxZwk4SiyxxBJLLLHEEnOWgKPEEkssscQSSywxZ6MiyN6X1Dr/mtjwbVHJvRglS+7H6FhyL0bHknsxOnaT78Vn/X45Ek0gJSmKot/u14gpscFbci9Gy5L7MTqW3IvRseRejI7dxnuRpNUSSyyxxBJLLLHEnCXgKLHEEkssscQSS8zZKIGjXx32BSRmltyL0bLkfoyOJfdidCy5F6Njt+5ejIzmKLHEEkssscQSS2wUbJSYo8QSSyyxxBJLLLGh20iAoyiK/lQURV9FUfQiiqK/Nuzr+b5ZFEVvoij6vSiKvoii6LfPf5eLouhfRVH0h+dfs8O+zttoURT9WhRFe1EU/b77Xd+xj87s752vk9+NouiPDe/Kb59dci/+RhRFm+dr44soiv6M+9tfP78XX0VR9F8P56pvp0VR9CCKov83iqI/iKLox1EU/Y/nv0/WxoDtA/fiVq+NoYOjKIrGJf2vkv60pB9K+rkoin443Kv6Xtp/Gcfxj1w55l+T9K/jOH4m6V+f/5zY1ds/lPSngt9dNvZ/WtKz88fPS/qVAV3j98X+od6/F5L0d87Xxo/iOP4tSTr3UT8r6Y+c/8//du7LErsaO5X0V+M4/qGkPyHpF87HPFkbg7fL7oV0i9fG0MGRpD8u6UUcx6/iOD6W9OuSfmbI15TY2T34R+ff/yNJf3aI13JrLY7j/09SOfj1ZWP/M5L+cXxm/05SJoqilcFc6e23S+7FZfYzkn49juOjOI5fS3qhM1+W2BVYHMfbcRz/p/PvG5K+avC79gAAAq9JREFUlHRPydoYuH3gXlxmt2JtjAI4uifpnft5Qx8e+MSu3mJJ/3cURb8TRdHPn//ubhzH2+ff70i6O5xL+17aZWOfrJXh2C+ep2p+zaWXk3sxIIui6KGkPyrp3ytZG0O14F5It3htjAI4Smz49p/HcfzHdEZN/0IURf+F/2N8VtKYlDUOwZKxH7r9iqQnkn4kaVvS3x7u5Xy/LIqiOUn/TNJfieO47v+WrI3BWp97cavXxiiAo01JD9zP989/l9iALI7jzfOve5L+hc4o0F1o6fOve8O7wu+dXTb2yVoZsMVxvBvHcSeO466kv6+L9EByL67Zoiia1Nlm/E/iOP7n579O1sYQrN+9uO1rYxTA0X+U9CyKokdRFN3RmZDrN4d8Td8bi6JoNoqieb6X9F9J+n2d3YO/eP60vyjp/xjOFX4v7bKx/01Jf+G8MudPSKq5FENi12CBbuXP6WxtSGf34mejKJqKouiRzoTA/2HQ13dbLYqiSNI/kPRlHMe/7P6UrI0B22X34ravjYlhX0Acx6dRFP2ipH8paVzSr8Vx/OMhX9b3ye5K+hdn818Tkv5pHMf/VxRF/1HSb0RR9JckvZX03wzxGm+tRVH0v0v6k5IWoyjakPQ/Sfqf1X/sf0vSn9GZwPFA0n878Au+xXbJvfiTURT9SGfpmzeS/rIkxXH84yiKfkPSH+ismucX4jjuDOO6b6n9tKQ/L+n3oij64vx3v6RkbQzDLrsXP3eb10bSITuxxBJLLLHEEkvM2Sik1RJLLLHEEkssscRGxhJwlFhiiSWWWGKJJeYsAUeJJZZYYoklllhizhJwlFhiiSWWWGKJJeYsAUeJJZZYYoklllhizhJwlFhiiSWWWGKJJeYsAUeJJZZYYoklllhizhJwlFhiiSWWWGKJJebs/wdE5Iesrs61wwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "v2Xz5Q66hO-K",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 612
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592308187714,
+     "user_tz": -120,
+     "elapsed": 1111,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "c08e01d8-a0ce-4e84-d3a5-c656e74564f3"
+   },
+   "source": [
+    "samples = viz_fashion.samples(size=size)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(samples.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4ec8cd66a0>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 10
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "YKxu13rwhPEZ",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 612
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592233780109,
+     "user_tz": -120,
+     "elapsed": 1588,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "1cac68a7-033e-4f3d-dbc9-8f1fdf6100ea"
+   },
+   "source": [
+    "samples = viz_dsprites.samples(size=size)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(samples.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f72db4fc470>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 32
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "QLWsKX-zrB0K",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "93ae459d-0f92-426c-fcfc-57f5ec108ea2"
+   },
+   "source": [
+    "samples = viz_celeba.samples(size=size)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(samples.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fbf980fde50>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 16
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "5uuFdjXrrCLj",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "77dac9f0-52c3-4f44-8f51-f5e3a64c177e"
+   },
+   "source": [
+    "samples = viz_chairs.samples(size=size)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(samples.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fbf98072590>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 17
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ],
+      "image/png": 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E7DsbcVkmYc8Js2wsRWlZ3pawUrAdHb8SGqB5uyUSGpIhlQUJZFmWgQTYbLzKQ/0IfwORilyImL5wnuVcoUQJ2M+z9KXj8bgxz2qfIuDk6AdHjPHrny7DJBlgYzKbyNhrRINgVGyzJqnCyoCZIjU+ygJAiPgYGdPxM0MCPGRJeD4bKNXpp+z4LdTp6R+d2Ww2Cynh+/t71HWN29vbENVxsiTJVNnkJgdgvzmbJXqMiFU3lsslAASbAR7KjCwpaN8RJ4RYdiYVR6/XX18DmveLUuLHshKA0IirtsLJENj7DkuQUiPR+rt03JodYu8lJzL1HzyO+qFL2flcs0T8PM3SpWQ7Vh7aXA2gIQeVh/bcUUc42VdVFZrS9TsANDYqtn4JSMdeCFsG1fmR9sJMM/cGu7+/R1EUIUPPoIrzLAMs6op+z3PiZMmRZbR2xcl8Psd3330XHBz3uNHjr6+vUZYlfvKTnwSHYPc3SsmQLWzUZ1+vqio03ALAhw8fwiTI9DCwV/zBYIB+v48f//jHDXnYPVtSlkkMWvqYz+dYLpf4i7/4iwZ5Xi6XIert9/v49ttvQx/ar/3arwWnptmW1BxZG2K2AiDox3K5xIcPHxo9Jqobmmn65ptvDnRDy9Cp6oZN4ccIHIOHxWIRbEV3zraZN9qK7vtk5cHPT4Ug2d8FNJev04fSNrgiCdj3IWnPDYkQs7JXV1ch86wLQHg+A45U9ER9JzNmSnJUHtwQcjabhfFpOdbK4vLyEgBCDx9L1FpasySJvyMlqM1oqZAN+zrP2h61breL6+trDAYD/OQnPwm2xL3R1I8+t42cLDlSZdIUJWue3377LW5vb8OyXI2A2FfBxsIPHz6E5ZY5NVNao9JMADMk6/Ua19fXAIDb29uQRdO0JtOdbDy8vb0N9WNNLdvvBtIzZAuVD5cn/+IXv8CHDx/CqjUSJ2C/x1Gn0wm68+2334Zl67qaMZUJrw02ewHgQD9WqxWur69xe3sbsmiqG7oSZ7vd4vb2NtiPXbiQyoQXQ1vWSLMZJEc3Nze4vb0NJUYrD5ZT1FZivkPtMxU90d+kmWU+Z9BEEnR/f4/b21sAaOyUbRtwSQxmsxk6nU6QkdoLEWsAfynEMmla9tEVfLqPHgmiXnOSQl5vkkqW7wE0iBS/NxdfCuxLjQyyv/vuO9zc3ITMu2aD2IfFeeXDhw8h2xrTC+B5ferJkiOgGQ0qMaCj//DhQ+gVIIsdDAbBgNk4Np1Ow26f1uHr/xSV12YFKAtO5NzpFtiTIx7DVWl3d3coiiLcD4m7JjMyaptYcoHqx+3tLe7v73F9fR32O+KKicFggA8fPoTVa8wc6dYQuY3dwk4C9/f3WCwWuLm5ierGcrkMkW+/3z/QDU2x8/NTthf729RvaGbx7u6ucePmNnnofchsJiB12IwWbYHZs7u7OywWi1BKVHLETDN9C7Npy+Uy7OWj97m0JSRbrkkBtu+I+s0ggrtksx8PaMqCJWldBQ3s90ZTsq1ysM3rqUJLo5xn5/M5rq+vw6a5sXl2sVhgMBgczLMxgvicSIOef2VoGYx7s9gLYFOlfMzGwxjB0M9PVWmtUmlExvRmzCDb5AGgQQLoMGzZLhdY3WDkDzR7koB9Tw4f607IfC2nyQ841A8lBaofbbph+9ZUBjxXo8DUS472t6l+WHnof6BdHpodysFWYmUMkgJO1rpAJfYHHPoOm2VjiUrtRUuP+lteErGSq+o3M2qxzJLKwj5mAMHnLNurf9XFQynI4hh0pS5XbdrmctUHO6/EFitoSVP/PwdOOnME7C8GmfdoNEK/38fZ2RmKosDd3R3m8zkAhMiYK014a4zhcIjBYBBWXKRmvG2wTo5GTWPjmLhHD8tKbJxjaph7T/T7fQwGAwyHw9BjQlmk3px+DLpT7WAwwNXVVbiR7Gw2Q13XQUcoq9FohPF43DivLMuDFV4pI6YfwH7nZ9UP7gtG3WBZhLrB1X7sS1NH+VL7lHwK7ASo8uDv59hYQuX9o2LyoCx4Dm0l9clOs0VtvZWqJ/1+P9iBTuyc8Lm7M49lvw2wDzB0exRFChlG9hypLDSDpHrOjBBXLFpZcAWX7gcGNG/NdMxeUs0cWd8BPPjEXq8X5lmtStCHcJ6l77Tz7EvPK+l7rS+ERidlWaIsS1xdXWG5XAbHNRwOw6Z/eqPM6XSKsizx7t07nJ2dhX0YciBGMdCY6cSHwyHOz89Dereu9zfKpAIrIRyNRijLEhcXFxiPx0FxKWPNCuRAENSouXX/+/fvQyNhWZa4v7/Hzc1No7TW7XZxfn4eNoH88Y9/jMvLy2zuoN0G7YmgrZydnYU+NDp0pv9pJ7yPWlmWePPmzVHdyAm6azGDgrOzs5BBIgFS+XA/MNrK5eUlJpNJ4z5qWpohUpFNm/7SB+x2u0D66EN4jt60ua7rBhmgrEajUdj2wW6kaDN2KeiMEhK9ZtR9lcdoNAp+08pCgyaSRJblB4NBIAIkYzaD9hK3z/gU2Hl2OBzi6uoK6/U6jJcLoXa7XSDJ3W4Xk8kE/X4fb9++xdnZWVj9a28j89zjP1lyZCNBYB+pjEYjvH37Fufn57i5uQk14w8fPoSMUafTCRPeeDzG1dVVMHCgmUJMHTHHQwXl2ICHrMj9/X3ok7i5uUGn08FwOES328V0Og3nXFxcBKafejT8FKhuvHv3Liy33Ww2mM1muL6+DtFOp9PB1dVV0IXpdBpW4eR401nqh6awGelSP8bjcdCNzWaD6+vrQIoAhOBhNBrhzZs3QTfoAFOZ7B6DOmKVBye4yWSC3W6H8Xgc+o+sPDqdTpDHcDhs2ArJArCf8FKTifpMtW1gv8P1eDzGZDIJvZnAnhyxl1H3u2G2mYR7MpkEAs7PVJ+aUlna/pbH5DGfzxtL8u/u7kJgQfJA2QDA+fk5xuNxmF/silfN+qemK/Za0XdQJldXVwfz7Pfff49OpxPm2Tdv3oR59vLysiGbl0xEnCw5IlTBaJzv3r0LxshN7Kqqwvn5OQCE1CgzBZPJBOPxOETEqrwpKmwb1BnTEC8vLxtp7vF4HDIDLBEwEuA54/E4RICaWUplhcmnwOrGj370o5A9fPPmTUiPf/jwAbvdDmVZhtIsDZiOkc5Rt3vIBZy0+bs5vouLCxTFww2Gh8NhiIxJikmIeH+50WgUdEOjw5zloan/q6urECVzM7uYrag8xuMxyrIMvkPvu5Ui7LWiLPjHMiEzYgwSdIsHjpN2RdkoISJ51pt/p0gY9beoHGLyYMmM5IjzA8fJjBOzTQBCOZ5kQdsV+P0pBxf2d9p5lv6D8yw3V2a2jcRxOp025lnbwvLcOHlyBKBRuyzLMuyCzeecAN++fdtIgzJlXJZlYPa8YDlFwwoanzorvS8YVwxUVYXLy8twvygaL4CQUaBzs7sp5yQPYG/cXFEynU5R1zWurq6Cbrx//z6sOuJEyUbC4XB4oB85ycL2DOi946gfdV1jMpkE4nxxcRHVDTo8JQNtupGibGzG2cpjtVphOp0CeLAV3lrl6uoq9IywrMbSEm2FsrA6khI04NMsFycz/lEGurknV1zVdY3z8/NgK0qg6FfZssDPU7KREmmMyUNXoHEMnE96vR6m02nYZHi324VsI6897YGyHY1GmEwmDfnaYAVIt+dIe8M4JmsrDKx2ux3evn0biCN9BwMMlticHD0DqMw0UE7kjIS5ud9sNkO328ViscB0Og1lJ95GRBk9kK6ixkCl5S69ZPbMDpAckRywZMDaORWctWCWDmjsmvLNRSbAfiLkWPjHSJ/LslkiuL+/D46PugHsm7RzzJAQbfpBefT7fazXa4xGo8Z9Cdt0g+cC8Zu4po5j9sKJUW0FQJAHz6HDpzxitpKyH1G/wd3fmRnjvQZV55lZZRCx2+1CGVqD06IoDjJouplsqoEFfxMrCrw9Cm2CxI+rtOgjOAcxM0Rb0B329fY0fC2WOUoRShqZFaOt2Izz/f19WL7PoIu9fdQVHftLzisnT44oXK4Y0FuC6I7XjPQ02u31ehiNRo2mO3tfnFQV1oJOmSUBbT5XJ6WRvmZJ+Do/h1kFRoapNwzGoEtzd7tdowREp8YMGx09dYU1cwBBP2L3I8sFMf2wt8/hGNm4rSUkZhCYLVFylSNpPGYvlIdeaxIDXW0D7HVDbcXusp8ibC+JNljTJnQJOsEl6iw3skTCEjSJIxv4Y6sbU5SJJbIsLfNWGGw3oDyoD1zlSf+h924kSKq0fSHHVcCUDedV2gp7kOg7NYum154ytdnEl8LJkyNeMKbveLdoNhpzq3K+rlva2yyRLi/Mrb9G2T2NkSv2aKiUR1EUYSt7vS+WNVSNpHKcANXhUSd4Xx86Nu57RN3gZEmiqKADyJEoqn7QgbMXgE5NdQNAiI454QF7W6Fji90GIAc8Zi+0lU6n09jTRVfpxT4vB3lwcif4u7VMqHv0jEYjAPu9jHg/Lfaf0L70czl5Us9Stxlr63o/xdFoFHbIZtCk94zj2KwsbDnKboVhCXTK8gH242DWnb7UzrMMrnSetfqmPsTLal8BqoAUMhus7R4bdGYkAnT6nU4nXFhOFm37cqSIWM2czqgsy7APB49lapfjB/ZRIydOEiY6N40oU08Bx6Akj7vVatSmzo3EiOcB+3tCWeKcgwysk6YcGExQP2wZQCcLEqOYreSUabXZEi0r0V6YRaIdAXt58HzqidpKrDdPvyc1cMJSGZAMMDMIoEEIlSBz8qNN6F4+9Bc286x/KclErxfHo5lV6jrnFfoKkiS1Dy0b0b/yObNHbfJIvQQLNPdIY0ZeCaFmobX8TN8RsxX7Hc+FkyZHAA6UmheF2Q69MAAaWYFerxfSozzXGkpO0MwAU9ssH+j7LCsBOMiSaOmNq9XUgHOTiV5LLRuqLDRVTqNX50YCYI05Jyh55lhIFKkzdHTMHAH7DRKBvW5wSbumxnOzGyX5jOpJjigP6oneKkK3tNBSHEvTVh4py8IGVLrfE20illHmrVNIFIE92aKtxDbWTY0UKVR3bSZNF2eo76jrh410KR8NqpXo6D5huqLRll9Tl41WWBhY0QY4zzKjyOeE+g4liC9pJydPjghVajp9jW5U2Xm8/qfSapZE308Zaox0QrqCRG8LAqARJfN8O15mBZQw5iCLNmimSLMlwH5fExqzOjtC5ZqbLGx2kY5Z9YMlJE5serzVDdqJ6kYu5Nk6+pg81HewAdXKQ8dJgsQsiUbQqYOkhteeds8l2HqTUBIDEkcdp5WHfkZOdsPfx99MPWfgoLdW4XwT86MK7W8lecxxNbQl0yTR9KXMqMXmWUJ7kV56jj15cmSVmdAVNIwGtePefgYdQy5O3kInQDp6OjwaLx0+ewf4no0MlVzZJZe5ysZGMRyH3QgwNqmpU8gVNsNK58ReGs2sHrMVOkZdup6jzVi/obsYUx60FR6ntqL6Yu0tl7KrjoGRPYBAargqjZkz6opmRXSszDIxO0CilVtGzZbV2GhNssjsCP2ozRTZ59qwT5nYzFEOUGLEx3p/OWaSrO+wc1PMZ3jm6CvBTmzWkSkR0PfsBaRzyMW5KfS3Ugk1xQvsSyQ0aDVsLclpFJBzSU2h2Q2rK2q81snp+acig1g5RMmR9qNoiUl1Q8lELpOehSWMAAI5stsUsIfRltzUVtTuUvch+rvUJ7K0zEmQfSKElqKVVPF8ykQ3PLQ6kiLs71KirNeWJUUAjWwSgAPdUB/DVZ92RWPKpUaFJThKjmwApb5UiSY/R/vyXlInXgU5ApopTU35as1c//OCxtLlOShrDBrN6VhIjqioj0FLSLlOfG1QmcQi4LZz9H+OUOemDpmRH526bVgG9r1qfE11I3US0IaYPBgwaOmI0H4zjlnLUTkQgDaor9SVqYzydfLX3kSbUVVSYDdMVftKVT6WMDKIUDJAwqiyIFHU/ht+BoAGKdKMrQ2+UoeVj/oC9adtASaf69hfctyvihxpBgTAUSJgMwinQgD099OodWKzzdd6jiWKp0iMjj1/6nm5wjokdfrag2UJkuqQOkVLjHKTk5IdRrcaERNtzl4zaSmUCT4HOpnxN3P/M5bQmFXUoIKItSjESvF8L3XECAsb1VLqaIgAACAASURBVEmKlAzGggkNRoF9KToWUOTmW/Vacvwqg8fOTUkfXg05Ag5rvW39I6cOy/Bjj4Hju4CrAp+izE5xTE+FEh4NEtS52wmT5x1r1M4ZsZKZIiYPIndbscERsx+83rYnEcBBOcXqVCw7ot+VMqzO63J03eoDOFzcATRL9fo81oSdgzws7HVuI0fHxpbCuF8VOQIOFTt24SyJOkUcc+I2Y2QnytzSvY5PQ5uNWL3Q960zz5UIxHDMVgBEydIp2oqd4G3pWfXkWNYwpiu5QYMG/f+psuBzkkV9/tSMS4o4hXn21ZEji8cyI6eONscfSwcDOHACjtNFG/GJHfca8Cnjt5mSU8ExnWgLrmKfEXucG2KysCTAEsi2z9HHOZPGNuToO149OXLEcSzdnbJCO35Y2JIK8djzU8anjPMUZWLHdCzT/NTPyBWPEaSYLGKyOUVClDucHDkcjkfxWG+IO/XXh5gOtGWVTpk8H2s74Puxcxxpw8mRw+FwOD4bx/qJYsecIuy4lSy1Hdv23JEGnBw5HA6Hw/EDwstk+SPf+x04HA6Hw+FwfAU4OXI4HA6Hw+EQODlyOBwOh8PhEDg5cjgcDofD4RA4OXI4HA6Hw+EQODlyOBwOh8PhEDg5cjgcDofD4RA4OXI4HA6Hw+EQODlyOBwOh8PhEDg5ShixrecdDsB1w8Ll0ZTBbrd7wV/y8rD64PJw+/hUJHX7EN9qvYlOx7mrwvVjD9eNJlweTXS73Zf+CUnB5bGH+9GnwT2Kw+FwOBwOhyCpzNFut3v1rLau6xAFv3Z5qCz4/LVD7/rtcHlYuDz2UN/p8nDdsHhsbk0qc/SaiQChMnjt8njt43c4HA7HyyApcuRwOBwOh8Px0nBy5HA4HA6HwyFwcuRwOBwOh8MhcHLkcDgcDofDIXBy5HA4HA6HwyFwcuRwOBwOh8MhcHLkcDgcDofDIXBy5HA4HA6HwyFIaofs58Cx3UFjmw7q8a9hU8K6rl/FOB0Oh8PhaMOrIkePbZv+lPdPjTjomDj+Y+PkMacmB4fD4XA4iFdDjuq6ftI9ZYqiiBIGfX4KxEBlEZPLU4nkKcgC+LT7DbXdo+hUZEHEdF/RZit6juN0YX3hYz7hVHyn43XgVZAjddp6M9eYMdOAd7td43nMsHM1dI7b3tj2KYRH5UA5FUWRrSyAdnnEQF1oIwp8nDs4RjteHRvlZeVxarIgjgVNrwltugE0r33buYrXKL/XANUPi1yCqpMmR5YU1XUd/hNthCdWYup0OtGLl9IFPQYdE/9IAhWfQo4+5dzUEJOHOn4gbsjW8E+RPLfJwzo7m4E8NVkQSqCBz7vDee4yAJoZ+Jjv4DGxgNP63VMj0Y8FTJ/a75ojrE9tO6YtyHxsfnlOnCw5UsHzcVVVqKoKRVGgqip0OvvFeurU67pGp9NpGL++XxQFut1u47te+kI+BiuP3W7XyI5ZWOcWk4f+kTjmIAugacT8T91oi251fCoLykcdfqfTyUYWwCHJoX60ycMSIZUj7YoyUDvLEUoS9TWLmDz4uj0uJ9hxa1B1TMdj9mJtI/esM9DeohB7/BhJ4jE5g/pxzJfGgkx9P4W55GTJEaHGvNvtUFUVgIeoZ7vdNpx322o1JUQ6CeZi3G3EqKqqg0n+qZNZp9NpyIKvEanLBDjMoKluWFiZtGWOKBNbostBHkA8q6jPNSiInWud2mNEIXVYu9HXj9mK1Y/cCRLQrhsql2O+w+pH7Njc5BLLoj2FCLVlSHIbvyKmH/r6U3QjNq++lExOjhzZbJFOfJvNJmQISBBstsiWELSU1uv10O12wwRhJ4oUFTsmj6qqAjHUDBLloRH/brcLz4Gmo6cs2khRivIADstG1A2+Z3UDQENHCGu83W63oTPHSHdKsOSZGVYrC76nuqG2ok5NZQHkR5yB5sRH4kyZ8D2+fqzErKQ5B/tQxAKr7XYLAEEX9P22yF/1A2i3FZ6bA9qCiLZMq55n9SBXGQCHc4zqhc4vhM4rfE/n2W63m4Q8To4cEXqhaNBVVWG5XIaLR8dG2GhfL2K320VZliiKAmVZotfrJT8BqnNXpaUsVqtVeA1oOnpNexLq8Hq9Hvr9PoqiwGAwCEQxZXkQsWzibrfDer1ukII20JmpYXe73YY8Yv1pqctE9YSEUQMJPdaea21lMBgEWQDx7EnKsMEVfYj2LrZB9YMZVvqLXq8XjnnpssGnIKYblEtMFnZsSqbVVvr9fpQ4poy2gFMDCXuswmaWqR/MzObgQy2srRzTj9i8or6DutHr9V60XeOkyFHMaVNpq6rCYrEIhGC73WK9XjeOZ88JsM8K8flgMMBms0FZluGz67oO2RPNrKSEmAFvt1ssl8sgC5Ki1WrVYOsxefB9kqPBYIC6rsP/lOVhnZaS5NVq1ZDHer1uRMFKHK1uKFns9/tBHkCz/JjiZKiRrk6Ax+QBoBFY2CxAv9/HZrNp6AawlwWRmiyA9v6azWaD9XrdyEJbedjyMuXR6/WCftR13SBI+j81tBHE1WoVAgnqBtAcz3a7PciI8D/1g/Lo9/vh/VyIgZJkyoIyWq/XjSqE2kqsD6/X66GqqkYlIhc5EKon6/U62Arn2cd0g8/7/T62223wGS9pKydHjmwKWB3bcrnE/f19cPqr1QrAfvKzaWFGO4yEh8Nh+EzNDKTYfBsrHaksFosF7u/vg2EDCPLg+UBTGXu9HrbbLXq9HgaDAQaDASaTSbS/JlXHb+VidYPyUN3gserYOp1OkBv1oyxLjMfjhvOjXqSmH8Dh5Ed5bDYbLBaLRjCxXq/DGGw5moSRixyGw2GwFb6n+pCaHGJQu6GOzOdzrNfrRmBlM4gxeZRlibIsMRqNQlaRx6cqC0uaOdFZ3djtdg2/ofLQUrRmFulLh8Nha+ktRXsBDoNNBtqLxSIQArWVWNlRdQNA0I/hcAgAjXlFn6cIKw+1FTvP6vFtuqF6ARwGGs+JkyJHCuv41+s1VqsV7u/vsVwusd1uQ98NsK+fay2UF4aTH2vtwEP0Q6fP81IjBDb9y6h3tVoFMkBZAGgQRJUHjZkZAspjNBqFz2dKlGPX41ORBxEjjJTHYrFo9NyoLDjOqqpCGhzYZ9FIjCgjNWjVp9TkATSDiZitaPO+ykNT4rvdDr1eD6vVKvRwUS80m6jZmRRlQVAmzJbM53MsFosgD46D9qPyoH70er1Amgn1G3peirCTH7MC1o/yWLUXYB94atmo3+9jMpk0JkDqh/rglKHBldqK+g5gn2ElGaJfrKoqZMwYZAL7zLQS7ZT1g1BbITmirdBeOH76GZ1X1HcwC6uy0O95LlmcDDlSh8vnJAM06sVigdls1sgQUAnVAQB7pkrDZjmt1+uFqFgJQIrGrFE+Ixx19Le3t5jP52Eisw22GvVQWTnmwWDQyKrRMDTblio0U0LdWC6XQTfYa6O6AexXpFA3OClQBtSDwWAQ5G2ziik6ORsJ01ZUP7RsZEmSyoP2ATwEEGorKWZYLWxAweu4Wq1wd3fX8B2W4HCMtBUrD/oR7UvJQR6qG9Z3aOZZs6UaMFp50Jda30EbykEemklbLpdBHvSxSvaAPYGmLFhWBIDhcBjKr2VZNoJSnpMydK6grcxmM8xms5Bp1SQCsC9DU2c4r0wmE3Q6HfT7/WAr1KPn1o2TIUcWKkQKl4JmlkAvDi+sOjkAgRxxpdtwOAwXvNvtotfrNYwgJcR+kyon+7AWi0V4X7MdNGhO8jyv2+1iOByiqqqQQeIxKcuDiOmGyuP+/h7APqIFmrKg49NJgSSAGUZOqhoxpwwlgLSVqqown88xn88BNHtC2JysBFBtZbfbBb2gLGwUmCI0ONDX6Dvm8zlms1mIdFUX6EP0taIoQkZxOBwGuW02m+RlQVjdoK3M5/NgKwAa19f6DvWro9EIVVWhLMtAvGkrKctEM4MK6vj9/T3m83lDNwA0MtRWFgCi+sHsmQanqUNJzG63C8kI+j9miDjPWlvpdrtBb6gjGlg9N06GHNn6rpIAsnmmQLXZVJcT8nhgv7pCFZmEwCLlMoHKRGvkTI8vl8tAjjSdzcdaXrIlI2ZI9DtSJ0ZWR2h8lAl1g4bL45gC1rFRF+jUy7IMhp+yDBRt+kFboX6og6IOMGBQh0gnSGJkdUP/p2wrABrOWXtt5vN5NIDSCLnT6TQCKFt+sj1pKUJLp226oWV5tReO2cqTtkIiEPPZKUMJkurGcrkMZFGbq5kRW61WBw38PMbqR47QBnXKw9qK+ojYggZrKy/tQ0+GHFloL4T2ejDtxxUXmvql4dtVA1xuSqJE9qsNhbapMAVoKSdW+lNCACBE/FpCo8MHmqtNgIeIZ7PZBLkxJazfDaQlE+Bwt3M+trqhPREqC/3j+51OJzSn89iYPFKTBXCoH1oKoH5okyknftUNYJ9p6/V6ODs7C3KwAUVq41fEesT4el3XWK/XwflTTt1uF/f39wdl1263i9VqFVbeUDd43Es7/6eAflT1PWYrABry0OuudqIZBCVclFEOiNkwSZLVjX6/H7JJeh77awA09IN/ipTthbD6QZLEZnWSYPpS1Q3aCoCGHmhp9iVwMuTIOhqNhklmdJ8SpnO1QW65XKLf72O1WoXsiGYNlO2r09DvS0mRNSKzxE7lwZ4jOjQ6r/V6jX6/j/V6HV4D0EiPatO1Gn+q2TRNi9toh5khTfVzaelyuQwlIxozx8gMmjYmx/QjtdVJsf4a/qdj42MtfXBi7PV6YdkynZ42XSq5UEKQoq0QqreafaY8WJZXebAXhyVU9o+wr4TyaPMdqcLaivbZUBZsMQD2mSGVBwk0CZO1lbasYsrQHhvNpnEOoW/l+Blw6wIO+hMAUf1QOaQYUClUHtQRjkm3zqFNcJ5l2Zk6w60d1DasLJ4T6RZ4HQ6Hw+FwOF4AJ5M5srDMk9GLriJgzwTTwkx/jkYjLJfLsCO2pgIZNdleipSZfSyCZ4pb07gsnfD/crnEbrdDWZZYLBaN3Ww1g8BIgZmH1KPiWFSiDZSaRWFKmCtR2Etj96vR8iKjJo2oXmK1xWOwUZlmClQ/qPfMsFEulAdLqjxHe/zUVvjZKcnAQn+bXjPVD31/uVwCQFjVNx6PQ8ld5cHo2a5USx22PA40d3RmpgDAgX5UVRV6i2gvtBXuh0P9yKHfiLAlct2KQPsSV6tVkMV6vcZkMsFms2noBoCG/1S/Yb8vVdiSGjfD1ZWbnU4nyIO2Mp1OD+ShDema2X8JnBQ5ss19VFZtKKQBq9OncXMnT1291e/3w2dxfxJ1CCk6umPlEpWFNqsD++Z1Hs8mQi2JMB2qE71tNtX/KUEnacoDaN47S0uPdOIAwiTI1Tl1vd/5mWPXBt1Yo3pqTs6mw1UG1mGzhEJ5LBYLFEXRWK3EXeS1vEg5Ula5lNXUKWuDKGXB8gDJAFf06XkkBHVdN8gESwgpQ8eqY7K6of0jSoapH2pjvM2QltTog9mfZwOKFKG+Q21E5aF9egywlBjzFhn8PJaW+MdVv3w/RVlY/wE0++p0juEdGeq6Dj5D+/S4byDn2ZgsnlsOj5Kjoij+LoB/HcBf1nX9z3587QrAfw3g1wH8PwD+rbquPxQPv/zvAPhNAHMAf6Ou6z/+Oj89+lsPmsJ44WiMugPwzc1NYxLUxlEy4F6vh7dv3zbuu6WM2E6AKSiybThWWaizogFz4r+9vT3oR+KxjBZ7vR4uLy8bqwpsZirWR/DSMgFwoBtAc+UQM2HUDY1wVW5s0mfk9/79+zBRaq+N1Q9+XwqyINRBWyfHVUnc10ZXOWpfFeXR6/Xw7t27kE1U2fIzU5aFQiczjfC5Yu3Dhw+BLHK8wF4/eP/Fb775JsiMwZWVeYqI+Q69Xhw398MC0KofRbG//+L79+8PGvz5fbnoRywA3G63YeND+g4uZuASfganZVmi3+/jm2++AYDGMTZTl6oMgPi2FxogcsXa999/H2yE1159x2AwQL/fx49+9KPGNjnWVp4bT8kc/RcA/lMAfyCv/S6A/7Gu698riuJ3Pz7/2wD+NQB/9ePfbwD4zz7+fxbEMkd8rM2l6/Uad3d3weFrdoUpQI0QZ7NZY68jVdgUMyWxjICmKpUArddrzGYzAMBsNgtj1FRmt9sNDo9NpkVRBAenx6rMU9qzxGaN9E8b01erFW5vb3F7exvGrKSSq0z0et/d3YVyAb8jph+WLLwk7O9S+WgJbb1eN+ShsgDQWNJPW6G+WN2w352i07d2rL6Bk92HDx9we3sbMgJaSuHYgYdJ7+bmppEViPmJ1CZAqw9WN9SPUjcAtOqH+la1FfqjWANySraisMEw/SuJ8vX1NW5vb8OKNV2dyDkEeNCN6+vrxuuWHKaOWOCni1poKzc3N6FFQ6suXOwDIMzJ3CcuhTLro+Soruv/pSiKXzcv/xaAf+nj498H8D/jgRz9FoA/qB8k9g+KorgoiuKbuq5/8UP94E8BHY5daTGfzwOr5XJKGu9gMMByuQwGzAuoJMoihQupsH0T+p/GSkc/m80aJSMar8pDFZjy0JSppt95bmpRsWbTdDLSUofqBnd35bgoCxJJ9qTxMTct093GrTxSgk5I1tmrc6N+UB46ccVsZbFYhC0e9IacKcviGGyQMZvNsFgscH19HSZAlQc3l+WKHPoYXcETs4+UCJK1EfUdqhu8ZQZtok0/gL29LBaL0LenBMmS9JQRy7ZWVYW7u7tP1g1gnznSloZY71Hq4O/lvHB3dxeyzlzhqfK4vb0N8hgMBqE3i4G7DSasPL62vXwuNf8RCc/H/3/l4+u/AuDP5Liff3ztAEVR/E5RFP+oKIp/9Jm/IYqYQun+LcrQVRG1hqyv8TEJkDbtfhzHwY01U4KdAOn4NEMQ+2urqdtoyN5ig5k3TRGnJJO2SUibSS3ps/1I/Bx9XzMHSgYoD00RpwyrH5p11MdA01Y0eFBCaeWj5ZocoKVRBgl2Mrfy4GMA0cUbGkylLI+2rKf6UfvXJg/1qU/RjxygmS3VDZttA9p1Q5v07VwEpOU7H4P+Vm55Yn3kY/JQ3ThmK88hlx+6ITv2i6PUt67rnwH4GQAURfGD0GPbc6R1bCoyN6kbDoc4Pz8PrJ0GX9d1WKXGHhvWiEmC+Bn2QtmI66XB30aywt+mPTF6r6PJZHKQJauqKjTTctzab8O0+LHbY6QiE2tUNE6rG+PxGBcXF1gulyEatrrB6Ad4aLzlzr/UNSXM/G5+ZwqyUGj/HJ/zd9JWJpNJI4vIyG4wGDTkwd4S1bccdIOI/RbqB/3AZDJBVVUhywjssyb2PlnD4TD0Y1Eutp8iRdiJyOqG7oQ+nU4BIJSW6rqO6oddxUTfob5UkZpuAIc+RDcvpO+4vLwMGUOSavUdvG8Y77mnNqOr2FIbextUP/jb2XM3mUzChrjMpsVsZTgcYjgchvMoi5csrX4uOfqnLJcVRfENgL/8+PrPAfyqHPdTAH/+JT/wU2EVSietwWCAsizDksrdbn8nYLJWGi+bKukMR6NRIwPAnhqbHUlBodtSj9zgUmUxHA5DaZErA5Qg6f2OyrIM91Lj8kv+7Xa7g3tnpSQTCzaYqzGXZYnpdBrKadysjbqijp2yAIDpdIrRaBQmPSVJ1rhTk4X+HnuteTd5buSmGz9SN0h+KA/KgnJSWdixp0wQOC5GtaPRCGVZhoCKkxxvDUF9oQxHoxEGgwGurq4wmUzCihtLNHKB9ofwWk8mk6AbAA7uPMDeRCXO0+kU4/H4wJfGAs2U5aO6wYn9zZs3Dd3gCi3VjaJ4uNcedQNAq36kDkuKmA0iyVF5cL5h75HaispjMpkcbCzM73pufC45+kMAvw3g9z7+//vy+t8siuLv4aER+6Z+wX4jzZBw4hoOh5hOp+ExswOskdLR08GRQLx58waTyQRlWaLb7TYMPzUjtgbG8asic3zT6TQQBBIlllPYXEtSQAXnJEF5MCLUTFVqMiFi0bASgslkgrquMRqNwl3Ht9ttaKylLDj58byLi4tAotURpu7kgcPMEeUxGo0wHo8btsLeCC5SYGaA8hgMBg3doCz0e4gUMwME5aGp/16vh9FohKurK0ynU8xms2Av19fXDd9xfn6Ofr+P4XCIy8vLQCheOhr+VNB3kBjRj47HY0wmk3DtgYftHagfd3d3QT8ABP1QW9Gs2nOXTL4EVjdIBMfjMd69e4ezszPc398Honhzc9PQjel0isFgEDJHFxcXB/qRSyk+llmkrYzHY7x9+7ZhK2xEp250u12cnZ2h3+9jNBrh4uICw+EwyIJzVZLkqCiK/woPzdfviqL4OYD/CA+k6L8piuLfA/D/Afg3Px7+R3hYxv+neFjK/+9+hd/cihjj1gmbkd75+XnIDOm9xejoSYh4zHA4DMZNItBWNkkRtrymkdzZ2VnjBohspGXqnPIgKSA5omOjLGxqPAd5qEyoG2/evEGn0wnRIGXB8TMl3u12G46f0ZItHeh3pgx1yLq89vz8PBDk0WgU5EHnRT3SMhKdG3UjN1kQlAWvdVmWePfuXbjNAcvQVVVhPB4DQMM2BoMBxuNx8B8aLedAkNSWdaLiNaetqB1o1siWWikb+hAeY31pqrDysLrx9u3bEIRSN3a7XQgwmHUnMaDO0Ga09Job1Kfyml9dXYWqAnWDtkLdoK+gbOhLSZqTLqvVdf3XW976lyPH1gD+/S/9UZ8Dy7I1a6Q1f66UAPaNlUx98maZPJ5kiRODEgLNHOl3pYRYlkR//3g8biy5Zy8F5TGdThvyYJ8BI2imQ/ln5ZEq1MlxPCRELKFRNkwDv3nzJqzIoqMnmCGgPDgx5qAfQLOxVAnveDwOe7NQF6gbFxcXIUJkzwBtJSaLWA9BqvJQaA8I9WMymYRyACfAd+/ehSZUyoOkmrKgrHLKKnY6ncY2FtQN7owONO9Bx7I99YPEmvZC2VAmSpJykIdCiR0DbVYlptNpsJW3b9827jGnugEgyIK+KIeetBjUVrSMSn9KcvTu3bvGcv7RaITdbhcII4NPlQXw/P7ipHbIVujEx8bA4XCI+XweiA03MmTJgLs/k+mzHMB0qPYvsccmBwXWbEBVVSGCo8NmWpyOkH0UHKvtL9Jske5eGsumpSofGjLr34zc6LD6/X7Y2l5vNErd0CZjTv40Zi1fpjp+C05ivP4sKzI9zq39WXpVeVAngKat6MrOlEutMWgpmoSPxHE6naIsyzD5cXds3iKC51RVhdFoFEhjrvLg9eQERt3QABFAQz+4Xw178ehLWUpiwKWlXA0iUpYNdYMZMs1+0BZIoufzOYqiwGq1wmQyAYAwJ9Hvqn7Y0jbfTxnUAfpSlgjpO7iQw9rKdDoNtrLb7cI8o7J4SVs5KXJke22A/dbsJDS8eNpMSqfHXhtr0NrAzIueS1nN9njobQtIGBkZFkURHKCSAk6Og8EgRIlk/EyRawo0deem0FV3WgIAEAyVJHq32zX6aHSZsjpJLbvmkCHgb6OtAGjYigYTXHVDW1HdoDOzZKKtUT8HaDCk41FZ0W4oK9WPqqoaZTabIUldNxRqKxwPZQAgZNupHyynsBzPTL0lFFp6zUUWnLR1XlECTD+rPtXqBn2pZt9Vp/R/DojJg3YfsxVee5apdZ6lLF8ywDwpcmSJkUZ/zJBwuSlLI7rpHffgoKO35SHNnLQtP00FtmmQiqbRjr01Bm8SyawAx0hDJwHS5ftKLnOKhoG9MZPorVarMA7KAtjvVUOjB/YThf08jXyUdKQM3eJBS0G0FU7wXI2i9xpjvwl1RSNf6kbqthKDklqWQphdZpTL2xys12t0Op3wXIkT5cHnlAe/I+WmdEJ1g9mA9XodMtHMgFAn6EMANHwHgAN/pH1Y+n2pQ+Wh2TLKg7rADLzVDfXNwD7LbMuuuUBtpSzLkFnk4hXeZoq30WHJFTjcO5B+1Abc+v85cFLkyAqOkzWdtDbRVlUV3tc0OD9HlxOyt0IbsjWdnKoSU2FJaDhhkdVr3xGwz5QACHtT8HN0Hw7tNaFMtOyWKkmymT5NBVNH2HelstDNH7XcquUEfoaVh+pJatCeI5ZFATTGon1pKg/2IgFo2MN2u230ZcVkkaJuEEpo1eEz+wGg0UujJRYGW5RTTB663YN+X2posxX1o2orwJ4AUT94rTnZUx6c9Jgl4Pmp6wZwvCmb8qBuAPtNYTWIAPa6AaDRvJ5bv5HNgKqtMHNKogPgIDCI2YqWGV/SVk6KHFloXxEnckY5nAzoBHWHU42CqdjMPNkVFpoOTlGhOQ4qKKN9Rnc22ufj9XodVWjKgxGBlhJy6htQQkBnxtUjwH7fjk6n07iliu5HQsJEMsHyiRLJXMontsdGV9McsxU6Mz7XBm3dHDM28aUuD9URZlu1v459h5SHEkw+p26wHKslxpTHr6AsGFjRj2pPCLAnlHoPSlte0sUPJANaPknZlyr4Gxlo2nmFAYU2rVM3VBYAovqRgwwUGojrggSOU+Vhx6+lNg2qXloWJ0eOYtkjXjDWNpn9IHMHEC6mfgajJQAhI8ALyPdzUGAb7egEphEM0+JFUYQVBDxPP4NOn5EfHVsuUY+WM1QeJH0sGynJ0UyCTfFSHpxALTnKQR78r+UwOjTeB4vv60q92BhzloWFZkM50fN2KRwTy/B2fJpt0R6MHDJHhAZXGmRxt2d7P0ESHc0c6V+breTiOwglA5pF5LyiuhHb5FJBedietFzlYbPMlAezY2or1g5sqfUlZXBS5EizIMDeOVHgJAM0Xi6vZNlEHQAvIJ+TGOlKrRwUWIkegJD50KZzJUcqDx2/QqMDludshiAHuXBcjFYY2WhWTXUDwIFu8DVbYrQp4dShpVe1FeqHOn87WQJ72+PkqE36NsuaC9RhawmNpRNGvNZ36Lk8R23EkutUoUEEsNdzEiPKwmYAmAXQjKPKh5kjOvT5wAAAIABJREFUZkpysxUbXGkJTeVxTDf0udWPXORAWHnoNiYMJKgbqgttumHJEb/jue3lpMgRcOhwlN3zOR0+e4yYIdFyiV5AXiSdMHKIhlVpLRmgAnNZKdC8SSYnQp309BYhlAcJ1kunQJ8K6/A1e6QOn4RRdUJloc6N/0mwYtmSVGUS0xHtobHyIDFgZoDpcWsrKgubFUhVFgorF82O2glQfYaWT3SS0wkjdZ1Q2ExYzFZ0xVrMXqgfOvYcbUVhyS+wJ0ecT6gTaivqO3ScVj/0O3KB+hAmEaytaBBmS43Avu/VZs9eQhYnR44UvFDquIH9ShpGwbokWy+Sltl0dUUs0klVkduiPxqhvTsy2T3wuDx0uWVO6WAb6QDN8hCvs94R2sqCr/F5G2lOWQ6ERrJ6jZkOVwIUWxGq8rCysEQhB3kQqidK8CiTtlWIVo48J/eJD8CBrTPKB9AIrGL2ws+hflifrN+TA3R+0edWNzQbpONT+ZyKfljf12YrsXHqOSkEUydLjmigfMyLxF4KLjnlcXrxeDF08qMjeOkO+s9BzCDZLEmSCBxO+nxsx5i7PIBDgsQIh86OPUcxUJcIjX6ts+d3pQ4rD13BaZtu7XkKzbCk4uS+BGovHE/MJuw51nfkLIfHbOXYeGyJSIniqejHl+gGX8spmHoKrDx03IQda0wOLymPkyVHABoXhRfLpvb0omlviX6GLTvYC5eDQh+TRazGrVFg7DPaWD6PywH6O3U1VqzPypbi9L9t1s5JLxQ6HpWHpr8JbTLVc/k4d1korN4fc/g8vu0zckXMVmxphFDfEfML+v8U9ENJYpvvANrHeAoyIGK2csyX6nkxH/uSOGlypKCw1ekDe8WNNcydqsNvkwXQlEdMge3jU4l41Kht5swe13auvp+zPHQMMVvhe9YR2vf0s04Bbfr/2OT32Hs5wY67LbCKHd/2OaeAx8bxGnRD8TnjTW0eeRXkqG1CA9BguPreYzXSlC7ip+CYLBQxeZxiRAzEJ7i2MX+JE8wFbRP+pzq1U5BFDJ+qE6eKtuv+FLL4lPdzxGuyg6cg57G/CnJExCLhtmzR506OuSA2vmPjfw3y0MfHegdiZPrU8CVjOkV5OPaw1/cpvSRPJUwORyp4VeQIeFq677HJ8VTw1Mju1IlRDK8x6m3Daxqr49PxFP1wHXLkhldHjhSvrQ78GFweDofD4XAAeW3F6XA4HA6Hw/GV4eTI4XA4HA6HQ+DkyOFwOBwOh0Pg5MjhcDgcDodD4OTI4XA4HA6HQ+DkyOFwOBwOh0Pg5MjhcDgcDodD4OTI4XA4HA6HQ+DkyOFwOBwOh0OQFDna7XYv/RNeHCqD1y4Pe88ml0cdfexwWLx2/Xjt43d8OZK6fUi3233pn5AUXB5NuDz24A2THQ/w29s04frRhOvHHi6Lp8EtyOFwOBwOh0Pg5MjhcDgcDodDkFRZzevED2Da0+XRTAG7PFw3LFweTbg89nDf0QTlUVXVqy+77na7R9s0XreEHA6Hw+F4RXjtxAh4Wt+VS8nhcDgcDsergZMjh8PhcDgcjk+EkyOHw+FwOBwOgZMjh8PhcDgcDoGTI4fD4XA4HA6BkyOHw+FwOBw/GE5h6wQnRw6Hw+FwOL4a6rrOjjAltQmkw+FwOByOfEESFCND+lrbhqWp3PvNM0cOh8PhcDi+GG3ZoTaiFCNSqWSZPHPkcDgcDofji3AsY9T2elEUB+dpRukls0hOjhyOFlhjbXufSCUd7HCkiJee7BxfDzbzE3tfr72SorZzeMxL6YyTo1cMKt5jKcy2Y07J0R0z6Mdq50BTRqckF8cDnprmj/VRnKI+fG7Z41QDimO+wk7ypyYDzfzwr6oqdLvdRonMjpNyaetD6nQ6L+pXXyU5+hTlPMVoxyrzMRRFgd1u1zg3puQ54zF5qHHGop22COdU5PI5yH3sCpv2b/MJ1lb0fB6fu1yOyaJNLjHiUBTFi2cGfgg8xZfq+B/LquQmCyVEwMPd7vl8s9kcjI3PY76Ux5EU7Xa7cOxL6MqrIke8aJaN8iK0Qd/PTXnbUNd1cOTHmLkqpRqAKm3bualDjXO324VxqZ7EzokZNu90TZl0Op2sHX+sWfJTJr6cHb7COn2+FisRxGzFvs/nucHayrH+kpg81NeobeQsE4K+NJY5ekw3eMyx5ykili3i33a7RVVVjeNiBMnKg7rBv6Io0O12w3c+N0F6NeQoxvDbMgJPQQ4KHIOO3ZKjp6Qv1amREBA5ycSSGysPPj42JjqAbreL3W4XDLrT6WRNqK08+BrQXiKwAUeuY1dYn6H6EXP6sfNtEEHkJJfHbOWxjJqea7MHufsQ/bMyORYgWbJsdSQHOcQIUlVV2O122G63APZBJ4BGAKnQBIWSIx6vfvU5y2yvghx9iUHrezkbMtCMgquqCjKx0fFTxtXpdNDtdhtKDOQlE9ULyoPPVRax/yonNX5GOox6nsuQfyhYW9E0eZs8eJ46+5z1AjgMIlQ/NCo+Ni6dDDQazlEudgJUO9FjCJuRt5ki+g4eqxNf6jKxpCDmOxQMluxrBH1FjrqhcwhJ0Xq9bhAl2gvR7XYbPUkAGmPvdrvodrvo9fb05CUCrpMmR8eYbaw34Bg6nU64WDbVlwNshLPb7bDZbBqysI7OZtNo5CQBg8EAu90OvV4vO+emRl1VFbbbbUgHx5x+LBXO6IjG3O/3UVUV+v1+cHb8y0Uu/G/T41VVHaTAFZwMqRv9fh91XQc55Ai1C7UX6oyOK5ZJojw6nU7DVnIizNZWONltNpsGSYqdo4jJAwAGg0HwHTwuBxzzpTF5AHHdABDspSiKbHypBtQkRXX90Ge03W6xXC5b51ptywD28wrHT/9ZlmWQgc49To5+IKiD22w2DQe3Xq8bTF0vomYAAKDX6zXYro18coASpM1mg/V6HYgBAKzX64ZR8nXNglBR+/0+drsd+v0+gAf5xMoHqcGSRJKA9XqN9XqN7XaL3W6H1WoVlQWAIAvVnV6vh16vFwyazl9Twyk7O6BJjrbbbZgAq6oKukGobth+K6sbAJ7VqX0J2kpI1A/6ke122xiPkiX1HZQHSXNd1+j1elkEWDFboQ+l76CtADiQB2H1o9frYbPZBBuhnmi2MWWZ8D8nfupGm60c0w0ADf0AEILwVAmjLSdq0mGz2WA+n2O1Wh34UoLyUN/B//1+H/1+H4PBAHVdoyxLAE2ZaKb+a8rmZMmRRi9U2s1mg8ViERR5s9k0jgeakxjTf0VRoCxLDAYDlGUZiACPB9JT4DaQ5VOJSQgAYLVaHZTYCMqD7H00GoU/W0Li8anBllPV2S+XS8zncyyXy+DwrCNUstTpdMIE0Ol0UJYlyrIMcrPp8VyIkWYGaCuaKucxGkgwW8TgYTgcYjQaAdiTp5yyJQAavoPjV3tZr9fhWO1P47WnPDqdTpCH+pXnjoI/FW22wqwAZUEywOP0eC2tqTzoR8fj8dHSW6pQQqC6sVqtUFVVIIt6rBIdtRUAGA6HDZtJOZNmfaISo9VqhdVqhcVigfv7e6xWq+BLgT2xsfJgVogZtOFwiPF4DACNLNpzB5gnS44IXsTNZoPVahUuGhWbwta6MSc+Zot6vR4GgwHG4zHqusZwOAyZI56f8uRnI0A16OVyGUgil14CCKyfTpz/67pGv98PUTQnAk0Hpw6bEl+v10E3KA8aPHVDjZpOnpMi9YPODdj3VJAkpawfCjo7kiPKg/oAINjNdrs96B/o9XqBSMWaK3OClhfVd6g86CuUFNNn0OHT3wD7yVHlkbJuxGxluVwGW9HS6zF7ISFk2YRkEUCjBM3vSt2XqG6s1+uG77C6oUGU1Q0AGI1GUf2wpDEVaBAc6zfSQFMrNTrP6ryi1ZjBYNCYh9iywLlHs3BfOxg/aXJka8LL5RJ3d3eYz+dhAtSoFkBIl/Nvs9k0yiWMfPjZmh5PHRrxrFYrzOdz3N3dhUiHGSSOTV+jA6OjOzs7CyxfyVcO4Pg0Db5YLHB7e9uIiKkbVhbUDT5X/eBjfr6WI1OH6sd6vcZsNsP9/T3W6zU2m00j8uPEAOx1Y7vdNkpqzKbxs3OQAdGWHZjNZiF7pARZs2kkPiQDm82mERXz2NSDKuDQVpgZUFvRzBnPoez4OvWDcqDvVHnw3NTloTLhvDKbzYJu8HpraVH7bDqdDrbbbSgVqVxUHjkEFNQLkmQSI86zDJR4TfU6cz6lXyE5ot8syzJ8tiYqLL6Wvpw0OSI0oqmqCrPZDIvFIghblZTpY05qJEuj0Qi73S4wWzbQ5dBLYqFR2mKxwGw2A4CglHT6jIB0kmdtnMo8mUwa0UPqER/QNCYlPlVVYT6f4/7+HkCzB4KyoG5QLsADOaB+DIfD8Fl0ejkRaKDZD7FYLHB3dxd0gK8zIqTjom70er0wbpZNtCzJz88J6thpL9R/9RNWHnzMHpTRaBR8TC6TH9AsuQIIixfu7++xWCwaGVSN8CkPfY1lRvaTrNdrlGWZTYbREloNoFQ3VBaaaVVboV9g4MEMkpasUp9but1uY9k+iQ6J0mKxaMyTGlQxC6QZtbIssd1ug27oQpkYMfqaODlypDVRYJ/242S1WCxCBKgOqqqqQHx4Hhk+nSB7dTRDkku2BGhGPdvtNqQ+SQa0XETnT8euTmC9XqPf7wfFzS2LRmdOskzdWK1WWC6XYaWFZgC63W7j2mtPFvVjOByGY3LUEasfi8Ui9A9opoS2sdlsGmSYrzPi01J1yg6+DXqt2WtD/6GZRTpuTnIAQjDGMpIufMgFeu3UVtiQzSyBlo00C2B9pRLssiwbPZ85QO2adqDZI+oG5wz1i7Sf5XIZZKRNyJxbcoFmB7WUynFQR1hS1luJqJ/Ryg0/iys7H6tIfG2fcnLkiNBmQKDp+LWxkILv9/shClJW3+v1sFqtwsoKTak+N5P9Ulhlquu60ZA9n88b6U4ydi0zcsxlWTYyULHPTxXa4KdgOYlGzXFRFrpCSSd8rj6aTqeNEkRumZJj+kHbUNJsI2hmFJfLZePzciGHFioP+hP6Ds2YqK2QILB3ZL1eB98BNEvWOSDmRwE0Vniy4Vabr9m2YD+LmenpdBoIo65cS11XVB5q3wwaSZR4zDHdIFh6Bg536ufnpwa1ewZJBF9joKmLOUiklEDyXOrAeDwO+rNerxv9aSqLrx105TW7fwJshK+slik/TnisZ67X6/CY5Gc+n4dSgZ3wUjdkC82qkekDCD1HLCkyCl4ulyENrE6PS045UeY2CWrUwzSvNlLqFge89qvVKpRImP7XyZHytI22OeGYflAulMFyuWxcewYRXK6uJdYcbQU43NqAvoTyiNkKfQQfkzCqraQ42Vno2G2ZjLbCHiSSIGsvXLih/YrMKmojLslFDrByoR7Ql1Ae9JMqC2YWVTcARPVDJ/4UM692fmUFgWOnTnAu1SzRcrlEv98PNmPbXjTTZHs21Y945ugzESMx2v+hTJTkYLFYYLPZBObKWjjTorzISrj4Wakpr8IaW1EUYS8Jjg/YGzzlcX9/j81mg7OzM2y3W4xGo7AZpqaVVQ45wOoGIzwtG3EyZBRI3WBP0WAwCJ9D/dA/qx85wGaB+v1+Qz+A/XYPbLY8Pz/Her3GdDoNeqV2YrMHOcHaC+VB0qcr81h6nE6n2G63GA6HDd9BWVA3UteJmM9QP2ptBUCj7YDB53A4xHw+b+gSj9N9xVKXB2H9PHVDt3cBEEpoABq6sdlsgh+lXVn9AJolvBTnFvUV/K/BIhvSSZa0TMZybFmWoSpDu6L/1H2j2mThmaPPhDJ7AA1yo5OYbu62WCwANG+vwcZrslhlt7rbbcrQaIdjUflwAtPl25wAgQfjZvpzMBgEB8+oOBeCpGNmlAOgYYTag6T72VA3KJO63m/0yHPp7HLsLwEOd73VrAFXa1EO1AltQNWePfYNaFScOixpsStr+Ji6wX2grK0wEqatAAi2kktZzfpPtRVdcKC6zl4TYG8v2qzLnY+1bKTBhP1LkRSojfC/+hPtT3tMN9QmmE3i52jLRoqyiPlSbcbWjJH6VAAHG+yy9KzkSrdBsNk6wjNHXwAVKo1Sm7Jvbm7C/kckSbwoXFbY7/fx4x//OOz4qaUC/Y7UobLQRlLWhQHgl7/8ZaPZ0u6YzY0wf/KTn4TUOldlpLwvB2F7BGjcugKLUc3NzU2j+ZSTQ8y5vX//PugH5atNypacpgjVY21EZ1P2L3/5y9CLRZ3RfUq+//57DIdD/PSnPz3QjdT1gtDfpgRG7WWxWGC1WuG7775r9KfFbKXf7+Obb74J8oj5jpRlwt+lkzbHSL85n89xe3sbjmPfTZu9vHv3LmSVbBk6lwxSzFZYYlwul/j++++DHLgvljZhq24AaCz/t5mRHPTDkn7VA26fowGnZpPZrtHr9XB5eRnKstZerB/lf88cfQGU2dZ1HZzZhw8fcHt7G7IkjAAAhI3bgAfFvb6+Dr0FMQPOwaAVmk2joweAu7u7EN2QtQP77e2Z9ry+vg7lpbYoODWjtpGO7RnQLOLd3R1ms1koIVndoC5xfHd3d6EJ0RIu/f6U5HEM1GdGwL/85S9xc3MTskRaSmHvQFEUmM1m+P7778OS9RwyJIpYuUszjJzsvvvuu7DPT0wetJXNZoObm5tGtjUH2Ehd/0h8GDTc3t7i7u4OwL53Ue2FfpP6f3d3h6IoDu7rGPv+VKHyoS+gbnBe0QUM6keZiV6tVri+vg6v52Qv1pfavl7OHdwcczabNcan5Vlmybg1BBcx8LiYH30u/ThZcqQCtCUk7nN0fX0dyJGWjWazGYqiwGKxCDtj251xrSLHaqIpQhW5qqqwWRcAXF9fY7FYBEfO7NnNzU2j4fbs7Cwsd9eacMpOTaN6NTqd3GjMdnNMRnwkAgDCdgYAsFwuMZlMwqTBcwBkIRsLLafd3d0FW2HvBLNFg8EAd3d36Ha7ocny/Pw86FAs2ksZxxrHaTOz2QzL5RI3NzchA6K+4/b2NgRX/X4/ZJVUHqmX1lQOaisskzDApCxIjlhGsr4UeLCRsiyxXC4xHo9D35Et4eZgL5r5UwLEeYVBN32j6kZRFFgulxgMBsGX6E7jPEdlkhqYzaGeaPaMfoObhTKbpvrPTCLHz+Z1zq/AfjPmGFF/LqS949YXwDbHEbrs1GYQgP0F5mMAB47eGrQqS+qgLBi96SoBlYOVAR0Ay456TExpU5WF/c1Kkth7RgO3GTTtrVJSqGSZpSYly7ltjMnnXKLbRnQ0Pc5StNUNPk59/ECcwGkplqUz7akAcKAT1lZigVrqmx0CT7OVth4ToOlLVS7UpTZbycFegKbNaFCk88OxeUV1yMop9h2pQXWC1w5oykD1Q/2k1RfNwgEIi34INnlrqc17jr4AKjz2h/DGsYz0uREiU6N1/XDvNADhFgjD4TA0E/Li2P6B1GFXmwAIPUTn5+cAHhRQM0mUy3A4DGx/OByGmrnKRJ196jLRqIegs+ZmjlxZwkmAhsz9nRgNAg/Nx/ZmxHr37dygN8SkfnS73VBKYq8agHCDSN48k+ewx0RtJHVZ6O+z9kI7GAwGODs7A/BQTtXo39qK+g7uhZWTrdgMgc0mqa0ACPYC7BfAcFWnXQGpPThtzfqpkmr+dmB/t3j6RO51xnut1XUdMmrUDa4U1nlGezdz8B2qGyS51AmOo9frNXwps6wszXJ/J+oG/SjHz16ktnn2a+vHSZMjYF/X3O12YWJ/8+ZNiAJJgNhfwtuJdDodjMdjDAYDXF1dYTKZhNVpuqtnTtDllnTcl5eXwcHTSLn8tqqqQIB4O4jBYIC3b9/i7OyssQdFqo5MYY2MS7GBPVGcTCYhJc69fOjcuHKR90GicU+n07B0G9g38NoIOBf51HUdnPfbt2+xXC7DJD8ajUJfmhLkyWSC4XCI9+/f4/z8PER+mqnMDdpfxvvEvXnzJjSj9/v9sIKPtsLJbTKZYDAY4PLyEtPptLHhnWabU4a1Ff52yoLtBsx+6F5g9KUaSAwGA0ynU4zH48YkSj2JZS9TghJmXams8wpL8bwVBrcC0XllNBqhLEtcXFwAACaTycGeTzlkFvU3qn5w7LxjABdnsEmb8rC6weAKQIN02WzRc/mUkyVH1vkwxd3v98P9wM7OzkLjrTZeM+rVm6teXl6GyDEXdk/EfiPLIOPxGFdXVwCAs7MzTCaTII/vvvsurK7odrs4Pz8PPVgXFxdhNZ9+R8rysOl7PqexjUajkAkZjUZhb6OqqkIvCTNHo9EoZI6GwyEuLi4CQdDoh9+XslwsWDqiI3/79i3evHkT9v+az+f48OEDgIexd7tdXF5eBnJ5dXUViBM/LzfoRKjyoL2cnZ3h9vY22Mr3338ftRX6DpJvAMkTRg38lDAzMAD2tkIdAdC4K/3d3V3wpUVRBN9JUkBb0T2CciinWVumbtCXvn37FtPpNMwrbLym7+h0Ojg7O8NgMAhyu7i4CBUNlXEOfsPOhcz2jEYjnJ2dBaLEnl32MVrdYLB5fn4egk27txjhDdmfCW2WU6ZJxzUcDvHu3buQDp1OpyHS4UZ2NGJO/qPRKPzZFGhuULZeliXevn3bIDYkR7vdDqPRKMhjPB4HhZ5MJhiPxxiNRiGrlINjU6heaMr/7OwsPB8OhyES5BgpNzo74GGioPPXTFuu+gHsS8rv378PMqKtVFWFb7/9FgBCBkF1g/qRo14odMLWTBqvKzMndV1jPB6H40ajUZDHeDxu2Ird3oDfkxJsyVn7gGgD/X4fFxcXmM1mgfTpnnEMIlkuob0wy0L/qqUT/f7UZBIDfyd9wtXVVQi6RqNRsBX6UY6fhEEDMZblcthFPZbh05IgfelkMmnoDNtWqEuqA3zMOVb9qC3NP5ft5Oe9HQ6Hw+FwOL4iTi5zFAPTfWSrvN0B0+RM97179y6sOmCzIeuj7CFgyUQbxVJm+Qpl+MC+kZgNlewnYMRzdXUV5MFIGHjIFkwmk4MsSepysKW/oijCXdO73W7Ynwd4yApwSeput8P5+XmjB4vlNQCN7CKjHu1PyimDpLYyGAywWq1Ck6kut6VusBeLtkLd0Ozqc64w+VJo5hlolg3UXthg+5g8mE1TW9HN/lKVB3+X6q7tMbPbEex2u2AvFxcXwXdy2wd+BktyzEDRVnLKxvP3qm6wp4pZd/Wj3NSQ8wr7+oAHX8NsNOWVqq1oiVXbE9i/y7/xeBz6hawvpe6wCsPyGu2GusE/ftdz77J/0uSIAqUi6h/LbHpvpE6nE/asYWNZVVWNVQV6H7IcCIGCv1VTn71eL6R3tTH9/v4eRfGw15NOjkwT23R4Lk2mwN6x8fqyZMLJj70U2+02yIRjZ/mE/TQAGo6NzkL1I4el29pbQtmofrC5FEDYrG0+nx/oBuWjfVc5Ll4A9o31uls+r/twOGzIoyiKqO9QfaE8tCE1B9BfkPyORqNwQ242EwMI91Jjk/Z2u23og12xpbfIsLtDpwxtFqadqO8YDAaBGHHlL4MMkmcuEAIQ7E37IFOVRWzLFurHdrsN5XgNOAGEwJLbg1AXNLhmiY1/9KMaYD6nXE6aHBGqdHRyNFad5JTN8o/7Tmgfkl1emAspAPYr1riRHxveAISmUzbJ0cnzOJ3sVB45RXwKGh6NkIbN68vegLIsQ3aR/SS8+zw/h/KwWxwA6Tq6GEiQGPVxAttut40l/rvdDtPpNNiJ1Q3aSW5bXih0JSabbjkm2g8bkqk/Vh4kFPQ7zB7kIg8NfmgrJAXUA4IEmfZCAsCJj3v/MEOgPXpqK7nIhnpAP8mx6KpnNmyrTei8Auy3ASA5sJnWlORhFyoQdi6lfug+TtSLwWAQAgfrS9mn16Yb9nu/Jk6eHGlKjlENDZVlFO5o2+12w86cwH4PC8tgVfE1zZg6+FsZCdvG4bIsG/LgCj9dmq7QJs0cs2h0ZEz1Uze2223Qjbpu3hyTqfBYipeTp0bFucmE/+mYSAbo2LhTOrf8t+UX2oLVjdxgM2llWWKxWAQCwNvnqC2xdHLMVijPnKDlE5ZMWE7kJAegYS/cPmUwGDSW9eveUZSd9aU5wM4r3CGeARR1gffr1OBCbYXgezndj1BJErfuICnisn3aDjOJJIKafVdfqo3dJEhtBPFry+VkyZHN6vAeP2VZHkTGtvzGDBKAoNi8SJbd63flAJ2wWCMnu6ecgOZyYyo9DZUpUiUCz7WC4Etgf6OuVKMxcnNQGi+dPTc509q3ZtwoC9tDwe9KVSYxqFzYLxAbN21FX6csmD3JacsLC73edPL0HdqbpfIgEdA9aygPuxopZZlYW+F1jNmKvgc0d34G0Fiqb32pLtVWUp0q2uYVlYdmXFU3AASfQlnwNS0r2apEilAdtivVGFTxNjGUB0mSbmmhvkNvS2SX8cdsxjeB/ELoBWTZRHtCmEnhppC8QOrc2FOiDdk5ODiFjUSU0dOQu91ucHZsxNZ0Op9rU3IsDZwDKAMaMmWhWUKOWW8oa8uwwH7zTO59RJnYrElusuFkT5LDgIIZIyVGAMJz3SssB0ffBvUdtH8tSfO/9R2EEqOYreQCDR51iwLNrCn0VhokA0oUmW3R0mtutqK6oSVG7TezuqEbR3JeAfYbRlI2uZTjrX3weuseTpwr6Cs3m80BmQb285KWXLWyEbMbzxx9IfQCMlqhslJxgWYUbBtptYbMHhslA6krsUKZPid0Ki5ZvmZN2sao0XBuk5+9djqhF0URborJY0mo+RzAwYTASIc9Fm0GnTJiUTHw4MhjuqGlQx0jSVFOTccx2OiYzlzlAewzRm26oT2MVvdSl4+VAe2EvlFvtszjGWRZ/adP5WewIVeJUeryUFjdYJZVbUV1o+1669yin8nvSA020Ab2WSCOg3MpS6yqP3ofSv7nZ/F8DSZievQcOGlyRGPUSJiKq+nZpCSZAAAgAElEQVRgvRGi/leCpClDW1bLBapcWvphCpyTmt5NWiNDlac2c+eUHbA6AeybBdkjQIdPWTBjEpMFsF9toqs0cswuEry+fKwT3TFb4fFars5ZBqonWman4z9mK/o5tJXYBpApo81WGAAwQ0LZqL0woLD2Ql/KzRCVNOYSTNhAwPpSDSoesxUAjZKrNuvnIAe9xrQTrkgkSaYvZdbVjk8/h9UdrdK8VKB50uQIaCoYDVrTgCyj8OKp87PNpdaI7efnAp3AtO5LksRxqjzUANSoc3JqhNUJGiUNmvcCAg7vNg/sb0RqZaLLlNWg7XemDJv94DgY7WnQYJ2j6kDO5FBhswM2K0Lb4GvqO/T6MytwCraiqzmZGSBUHtQPlQfHz2Ak5xWN/M22b4byUP8Zk4WON6YfqcuDv1cDZm09oK0oWbS6wUySJhxGo1HD3mx5/rlw8uQIaDp8dXYkRrEVEhop6Tm5KG4brLPT1yiTx847FXnoRK5kgOlh1Q2bQlY50TFopJN71gRoprtjumGJn7UVfS9HKAnUTBp1RJdjHysNKSnIVS7WVpQYUAa6rL/NXmKBmdpILnKJ6YaSY61MtPUR6ZhzC6Y4fqA5j1gfoMFCmy9V6CIf7Ud7CV/6KsgRcNhPoY6t7Xg16JwnO4uYLAA0IpzY8W3n5ygPNWRGM9ycTTNlbbKwy3DpJO0kmDP0Oj9libXVhxz1wkKvtxIBvvcUW8mdMFpbYXMxSQ9XXcW2t7CI2Uqu+hLTDW3Z+BQ/mhMxUti5gHJgQzVlonNu2/xDnWgjRs+NV0OOgKaRA4fGHFsa+NjzXGFZPl/7lPOeek6q0N9uy6hWN9pq5UA8u2g/PzdYYvQpY8l53DHodW4jOG2+w76eq2ysrdgWBIW2Kui51ufkTIwIneDVhyieogM5ykF/q/ZVcZNHzaDxWO3D4mfYfiySItt/5Zmjr4xjwn2Kczsl2CzIU8eZa7aoDdZ5a8Rns4f2HPs5OTq5x/AUInDqsFGuzRq1kclTIEYKO5kpMdKyiR5vz7OvnwJiwSaAJweSucuBv19Jjo6dGVc9rs1W2kppzy2jV0eOiE8hAqeO1zDGxxAzVJ0EnnJ+7PEp4tTHdwxPmeBOXT5tQaMNtvQ1+zj2PHecMvk5hrYMaVtvUYxE2s97iUyRxaslRw6HxVOi/6ec73gdeM3X22ZH2t7j80/NTp8KXst42zLptsQaO76t7OjkyOFIEK/FqTkcX4qn2Irb0+uAZg+fGlw+1qLwUnBy5HA4HA6H4wfDMWKUS8+ikyOHw+FwOBw/CI6VXO37T/mMl4KTI4fD4XA4HD842jJHbe+lBCdHDofD4XA4ngWpkyIi/218HQ6Hw+FwOH5AODlyOBwOh8PhEDg5cjgcDofD4RA4OXI4HA6Hw+EQODlyOBwOh8PhEDg5cjgcDofjleAp94p0PIEcFUXxq0VR/E9FUfxJURT/pCiKv/Xx9auiKP6Hoij+r4//Lz++XhRF8Z8URfGnRVH8b0VR/PNfexCO14HXbtSvffzH4LJpwuXhaMN2u33pn/DiqKrq0WOKx4yoKIpvAHxT1/UfF0VxBuAfA/g3APwNAN/Xdf17RVH8LoDLuq7/dlEUvwngPwDwmwB+A8Dfqev6Nx75Drdkh8PhcDgcz41/XNf1X7MvPpo5quv6F3Vd//HHx3cA/gTArwD4LQC///Gw38cDYcLH1/+gfsA/AHDxkWA5HA6Hw+FwJI9P2iG7KIpfB/DPAfhfAfyorutfAA8EqiiKv/LxsF8B8Gdy2s8/vvaLxz7fU8EP0Dsbv3Y8dp+e1wbXjSYoj6qq0Om87hbK3W6HbrcLwPUDaPqO3W6Xzc7MXwN1XQf7cN14wGP68GRyVBTFFMB/C+A/rOv69sgHx944uBpFUfwOgN956vc7HA5HG147MQLyuS3DS+C1y+a1j/9z8CSPUhRFHw/E6L+s6/q/+/jyP2W57OP/v/z4+s8B/Kqc/lMAf24/s67rn9V1/dditT6Hw+FwfBp8AnQ4fjg8ZbVaAeA/B/AndV3/x/LWHwL47Y+PfxvA35fX/52Pq9b+RQA3LL85HA6Hw+FwpI7/n713eZFte7eExoqItVa88rn34Zxzf9eGUDZsWSJIgb3Chj07FopQiFyoP8KONmxoqzpKQUE1SjtFUSCK2FSxY8sHCrZKEb3YuefsnZnxfi4b+4wvxvpirsjM/ciYK3IOSDIzMiIy5re+x/gec66XtNX+FQB/G8D/nmXZ//rHY/8egP8IwD/OsuwvAPw/AP7WH3/7b/Blp9o/BTAH8O9+10+ckJCQkJCQkPAD8exW/jf5EH9s5Y/hs8SANHR7QBrIriPpRh1JHnUkeRyQfEcdSTfqEP34uq38CQkJCQkJCQnvCYkcJSQkJCQkJCQIEjlKSEhISEhISBAkcpSQkJCQkJCQIHjVCdkJCQkJ7xUcZM2yLPgzf/fP948nJCTEj0SOEhISEp6BEh3+HNr107QTSMlUwuXh1A6wl+yaS3oRH95lW02dG79e8vyEhIT3Be8fPDE65RtCviX5ksvDS+JHky68RI8SzoN3VTkKObmm34F6ybyqqsTuE2pI1YDLxnPVopcERd+CuyR4n/jcOi9RDi9Jrj18K/bS9eQliDG+vhtypOzdH4blLwp/3+/3tcfes/K+B3gn9ZrXAQcdSfrRfoQqRPv9Hp1OB/v9/tmZo1BSFdKNNuqK+lFPDk7ZT8i+2rh+Qtex3+9PxhW99hpX9HlVVaHT6bTeh3xtFcy/7twyuHhyFGqhNZEe/T3E5i/BsSm+tZTb9vUTXkde+zqg2REmtAshXaC/2O/32O12tefrdT5FjDqdjj1Hv9qmK54QnHpOU4WEf1O0RQZ+PT6mNPmELMuMVIdiDr+UZPn3aANO+dLQWk7J49y2cdHkyPd09/v9kRKfMlafIepX2xl+qN/93FpCJLJJfm2DJ82nnhfKjlU39LG24r0R51ClSH/e7XZHxMiDVaVut1t7Lh+n3+h0OjX7aYMf8aRI/eipz+7thdURAOZD+XtbENINymO329XWE7rOXo9UJ/izf23s8PrRNEsVkofqksbVc8eWiyVHXoEZ/KiYnvgQXrGp7FRcfleG3wblVWjGw9+BQyb4UmfX1vUrmrK/lxBGlZfqRyyZz7ci5OBeu542rf9UVYDkSB+vqgrdbvfofbbbrTl9+goGPX2t6kzMuhIijmorvhJ/6n1CvqMtbbYmOez3e2y3WwBffMJmswGAk6TPt9k6nU5Nl5Q4xqwbHtRtxtmX+lKvGyHZvbUMLpYcAfWgv9vtsN1uaw6PBOmUIiqzpQLzuZ5ItQGeCHiWf8oQPaNvqwyA4woBAx/l4Uvg/jtwvN5er2cyocNrk2MDjomyJ9Gn1uLtpi1VNJ/18jt1YLvdYrPZHPkNvcZNvoO/d7td+/L60xaCpERRA6CvIp2yF5WHryIRscoACJNmrxv8Ut3wM0m+MtLtdu01vV7PHo9ZJwj1EaofaiuqC8/JIxRjzyGDiyNHoWoRmf1ut8N6vT4y7hDosHa7nTm1oiiw2+2Q5zmA9vXNfdZLmYQyIn0Nocba7XbR6/WsjXBOJf5aKDFixsefNRsOyaPT6Zj++KyvKIrWEyOVC4DaWgk6c6BeAaA8tDISK0L+AoBViug7NpvNke/QdYUID6vO3W4XeZ6j0+mgLEuTTRsqjCoXft9utzXf4f3oKXuhPIqiQJZlKIoCQHimMyb4ahHXTlmsVqug7wAOw9pKAtR2ut0uyrJElmUoyxIAaqMb/D1G+JhCu6EvVVsJxRegWTcYZ0MJxVvgoshRyCh5sXa7HZbLZU2JV6tVzdlvt1tz5j6zyfMc6/UaZVmiqipj96EZglihjm6z2ZgC07ltNpva52epWMucNO5er2dflEdbCJInAfv9Huv1Guv12pwdZcFrSlkAMCKkmV+e59jtdiiKAuv12px+W2QC1B3dZrOxSiv1Rder5MDLo9frYbvdIs9zVFW99RSjHHzwB2AOnmtfLBZYrVYWGNfrdc13hOTBv9NOiqLAfr83H+JbcjG3qX2lZLVaYbvdmh8F6p/bz1zp9zzPsd1uzUYYBM9dKWiCr4z4hIq6QV+6Wq2OZEFfonFF7YWxBYB91xijZCpGNPlS2oqPK00VRNWNqqpqupHaat8AT458WXyxWGA+n9uF40Ujk2cVREt6VMqiKNDv92utODLjthEjOrjFYmGyAID1en2UIfmZie12i16vh7Is0e/30e/3ARw7wFhl4SsFbLeuVivM53NzcqvVyq5pqMWmQ7edTgeDwQD9fh9VVZmetEEvCF8WZwAkIeAchT5Xq2OUB6sj6uBjrSD5dpoPggz8q9UKs9kMy+USy+XyaK7Et+e5Xm2T9Pt9DIfDWqD0s0gxycf7ASXMy+XyyFYUp+yl0+mY31D70YpbjPDtNJIAykLlsV6vARySCAV1gx0JABZbGE9UBvpzTDri/ajGWfWljLP++SHdYAWN/rPf71uVFXj75OGiyJGHKvJqtcJyuTQnp2VhKjEvnJ8ZYdbnqwea6ZBYxQ4q8Xq9xnw+N1kAsEwQgGVGVFr9XhSFtRiBQ89cnXxMhqzgZ/NVNBr0YrEw+RCUhZIlLXkz82Pgo17oc2KVh8I7udlsVsuItRqrtsJW8263M+I8Go1qJCrWqogSIm0R6ZwiSdF0OsVqtbIZE/UdfD31g0kWW2papVX9YJBlKyFW21GCpGRxvV7X5rSAg37QTug79vs9er0elsslxuNxrT1P36mENTYZAPXdaUwiVDcYV/xMZ8h3sDJE/QAO5En9KeUXozyA444E9YO+lHGFtuJ9qepGWZZWWVI56P9K5Ogr4PuausuELH8ymWA+n5uz0h0kwKEEyi86fJb5yG6VOHi2Hxs8GWClZDabYTqdGhEgSVIZsp1CZaUjHwwG6HQ6yPPc2gRKEGM35JBBTyYTTKdTazkyePF1PtOlvHq9nj2fWSCAmiOMVR5AeMh0tVphOp1apVVthWAbmvLYbDbo9XoYDAYW+Nhai4006zoUvN4kyAx+8/kcT09PWCwWtSoa34uOHji0nnn98zzHaDSy9ivJpxKoWOGrJUoG6Ds0SVJd8vai5Jm24n1pbPB6EqqSLBYLsxXGFeq4H1pXAqyVI5Il+tLtdmukIUa5hJJM1Y/JZGIEWqvo+nyg7kvpO9SPalx56zh7MeRIBa+gk+L33W5n7TWgntXqkB0vBo12MBigqipjtswqOazNzxCD438OVNTdbof5fI7pdAoA5rwYBP06+ZpOp2Ol9MFgYJlBW8iAQgPbfr/HfD7HbDazTEZlQf3xjq/b7Vq2w9aJ7tRoI7hWOn3aAckOiSGDPHAgS8Ph0GxFW4wxDt1q21T11idLzIBZRfIDs1p9ZZavbUYSR8qEFRO2HYnYbEcJrX6nH6WtAKhVO9ReQr40ZCv0HzGuH6hfG5UHfSQr8cChHcYEE6jPtNLXArCWGvWExIv+R8lRLPpBmYR0l9edvtTHFcqD9gHAyBGTcY0r1Ju3ji0XQ45UiYG6c/NzFHRwenHITkNZIS8MiZEnYSFFiRHqhHR2YDabATgYLx0Z20V+EJdzFKywtBWUhW+fsBxMWZAo+bVqUGBFQFuvbYPqh1ZMptOpBX0A1kZTeXAejbrhd0L+aLzWaWomq4RIM3yugXrOiitbjQz6nmDr+5EokUAD9VZLk3xi8Cm+dej9KJNMrRBpu1U3dNCn5HlusmiDrfjrw+vrdYO7GVkpUVlQr5Rc+soaxxTUBpWUEbHFGI2z9KP0HaofId3QtdDHsj3vW9X+57fAxZAj4Pj+Z16YNPL1eo3ValUbFOMF08oAM132xKnQNGzutiBiYfVN8FkxgyCd1GKxMKPs9XqYTqdH5WAGPm49VWNuG/y14vwHjVtLv6obfJ22EFUXdOagTQjJg7q+WCzMuRVFgfl8brpCApDnOVarFQaDAYD60Duz5Lf67F/7eq0KavYPwIjRfD63JImvoawYBPhatkxYFWAQZXUNqJMK76/OBfUVvp2hfpRECcCRvVAeBJNP6oKOPXhfGgt80q3tUtUZJc7c5ah/U93g63Tb/nA4rO2UZadCP0MMhJnwlURf8fO+tCnO8jVMPnu9Hm5ubmqtOv9/3woXRY5C1Rw6cBqzbsEk8ydbXS6X6PV65hh5ITqdjj1fA2KohRcrfG9YB+UWiwUAmIx6vZ5V2HiWkWa/lJGfMWpTC0mzOS8PVhVprHR8qhu6W41btHX3Ucy6EII6XwYs6ryXBwdxmTBw2z7/7isoflYpBvjPo/6Cv2tbWX2HDt1yjavVCnme12wGgOkJg6O2+GkzzJpjIUZAuGKitkLd15kj+lIGRFaedfhcKyRt8KUhPWE80UoaZaBnQJEYzufzmm4Ah12OwMHeaE+qH/yfsbWkfVUrpB9VVdV2u3rdoL/sdrs2y0qZ6q5nH9dTW+0b4TNBZrY8l4e9z6qqjBwwI2QPmIf5qYPj1vc2z5OwAkSHrEGMDq2qKtuOeXV1hc1mg36/b/JQJ9BWWaiRcQaEoBFyS/90OsVut8NoNMJms7HD/AAYaaI8dLeOvlfsUKfMVqHaD50YAMxmM2w2G9MNHcIOHabZFv3wFRJP9Lg26gV1n7s/i6IwosS5JGbLesbaqZYaEI/O+M+gvsMTyizLzJfOZjNst1uriHAIW32pBtFY9SNUOfKPc+yC15Y2wooa29NlWWK1WtnORJ3Vo35obFG5xCwfgjrrT4InceScLwfXR6ORxVvGHuqD+tFztRUvjhypMqlzJiPVORMyWs5OcNCSZUC2EFhC1+ExnT3SrxgcWhPUmWnmo5kf526qqrJZpMlkYq8lYeTPlDPnCVT+scrCG63XDWY/2nrloOVsNrO1sRVAXWBWzC8NIG2A2orqhlaQqBvaZiN5ICHgz6pjP3rHzbfqm+oxf9efKQMlwGo7DIhsF1RVVZMBZaSHrzbNlQBxVE6U/HnfofLSc9JICBaLBbIss4AIwOyDsgkFwdj8RyieqG7QV+hafCVa247UDT0aRtfKtlobEouQfqgMVD/YYtvvv2x6ybLM4guA2jwvCTRf6+MK8aP14+LIkWeymsGTDHGY8PHx0ZRRz+ugsrLS9PHjR2P1vv/uqyaxGLVHqGe929V37n3+/NkcHIkBX8tKQp7n+PXXX62C5k8DbgM069Nrx6HsxWKBz58/m17QsHXeoCgKqzT98ssvJi9/Hkmsjs1DP6cO5fPsJ+qGzutx9oRHXRRFgT/7sz+zuQtfmfyRCcS3EiOgrsM+eeDc1WazsUP/NAj6oM5K5PX1NYCDbl1fXx+1UzTInGv4NAReu5Dv0IH9x8dHe0xPR/b20u128csvv9ico7agY23L6+diKyw0ME2b0A0MSnT4PMaVbreLu7s7ALCz5ngcCOEJUgw6oQjphxYQVD+oGzrjylhaFAXyPMfPP/9suqN6QT16a/24GHLkg52v6JDFk8U+PT1hMplYZYCZAHCYE2DGOJ1OrV3gy+z+/8ekvEC4Z841MND9/vvvAGBnU/gsWnfabDYbPD4+oixLCyD+kK5Y4XWDP1MeJM4PDw94enqyoWyVBduOwKFa8PT0VJulaPq/bYHq/mq1wm+//Wa6AeBIHqy4bjYbfPr0Cf1+vzaX4vEjbOVrA4f6CL1+foaC1R7uSGJb0a+H7RXOqjF5YOVRN3xoNSZGHfHVHK2w0o9OJhM8PT0BgM2XaFDn/CLwRT6TycRkE7IV/t9Y/GjT+gHU2oPUEZ55pDej5XqoG/ydR6ho+15losl9jMlnqNrH9Xn9aNINrTo+PT3ZYLaXwzlwMeTo1OCWv2A8nIonAPMCk8Uul0s78JCDdIPBoFZC9n1hVZKYQNZNqMHtdjs72A74Ujnyu5CKosB0OkWn08FisbCZCu2P+/JvTM7tOag8ttstptOpVRW1vUrdmEwmyLKsdv80Dij7OQr/f4D4yLOH14/JZILlcomHhwcsFovaMC3l0ev1sFgs7GRsHoWgGfaPto2vlatWtnyVADi0TJn50ndwjcBB33lfPeAwnLxarexgP229qZw1KMZoO77yR92gLCaTCQAE7YW+g/ayXC5rLcpQgI0JoWtBPfFEkb6DB6cCz+sGAIstmnz75C02uSgoI9Vlrx9602avG7qZIeRHfVKbBrK/Ed7RUbBUwFCmqMyez9EhMb6XKqsOXcbm1IDwLAMfo3Fq/1wdoO7I0ufpY6EdFTHKATgminyMoPPyQd3rBttJhGbBugNJdSRWuYT0g9+1daYzMl43dO1+GP3U//hR6/ia9+d1a2oB6mwJf1ZCQ3n4x7TyyMFs/q6+Q9sIsUKPNlCi51s/ai8+cVIb0nOiiNhkECIlatdcr1aKQnpAG+JrGFcA1EiD2hqAqGOLh1a3vH6o7/BxFjjccFb9Ju2MeGvduFhyBNS3S+r5I7wR5Hg8toyQlSXuUqPj4iyFbrXlrovQhYot8wvNv7DU3+/3kec5xuOx/X06nVoGSLn0+33LfnjCL/vEPPk0xrJvCBoEvTzKskSv18NwOMTt7a3dbZukWmXB046Bgxz1fnsx3wtJ0SQPrpW3vvC6QbnwTCPebJavoTxoRz9aFq99/6YZF19F0t8BWMVsNBrVqh8ManoXcc7p6Y41JUMeDBwx6Y2fB+I1pU/kjiMAZi8AjnwHfUW/3zefQd3ge8ZGjICwnujRFLrlnLeWokz0tkw+rnBODzjcnJkEuulYB7XTWOQU0g/g4EtpJ+pLqRs8K4+6wdeobvzo89FO4aLIUci4dGt6WZYoigLD4dDOblkul7ZFmYyeis77ho3HY7vnC/8P5478/4tFaT24JjJ2BrPb21tzaFzzYrGwIMgg1+12MRwOURQF7u/vMR6Pa3fa1spRW6Dy6Pf7KMsS19fXJg+WemnUPAy00+lgMBiYc7u7u8NoNDo6p6Rt0MoA5UH9yPPcDn/kLJaS4/F4jLIs8fHjR1xfX9fOMfnWqs5L8NqAEapW+NeT2PDaM1HifBnJDnVIB9D5/DzPjSDojj1fEYitQuDnojh3qL7j6uqqtpGFa+Z8Schebm5uarbS5DtikYOv/hJ6OwzaRr/ft/YRN2cwqVLCU5Yl8jy3xIL6wetPu/HHSniifk6E9EN9aVEUuL6+rsXVPM9NX7xulGXZqBvnsouLIkfq8FS51DH1+30Mh0MAX5RUe+CPj4+m6GrQvNCj0cgyHyp7TA7tOaihc3v1cDjEhw8fAADX19eYTCa28+Lh4QGdTsfO9Lm+vjZyeX9/b5WCkBxilgkdjQ5LMuvn2obDIWazme2+eXx8RKfTsTM5rq6ujBwNBgPc3t4a+WY2zP/VBmj2l2WZBX7K4/r6Gk9PTzbEr7rR6XRwe3trunF3d2d2w/f70cT5W95bWzv0GXoMA+19MBhgPB5bBYQ7b7irj/IAYMSRVabRaGSVV622qt3EVBHw5FF1g8SY90ZjkOeMCX2p2st4PDYCcXt7a+TbV470/8cgC/UTwCGu+LbXYDCwwM7zjBhXuKGHuqEkGwCurq4wGo3Mf/gzsmKQQxP4OQnKRuPEcDi0M/O4oYcV2G63i/F4bEnEzc2NkSXvR99aDhdFjjxUoJ1Ox4jP9fW1lewGg8FR1YhKqjeKJBvmFl3fNolZgYHjzINVsY8fP9ZKlzzksKoqXF1dAfhCIgeDgbF/VtKGw6FVlfS9Y5cFUJ8V0/bp3d0dsizDaDSq6QZJoLaO6OwGgwEGg4HdUPRU6yRmKMllIKN+ZFlmbej9fo9Pnz7Z86gbZVliOBzaF3Ujdjmoj9DH9DpqJZl/4w4lgiSKwY1ESO9C7w/X1P8fqz+hTautlGWJu7u72k7W4XBoVVZtxTOhpB+hrVBeocQqlvV7ksbPpnbOdYxGI3usLEtrq2krkskTq0cATBbUj6bYEotMgPAsFnWEcfbu7s4SLPrS/X5vNkRfyrW/RDfeEhdFjrxCUbGZxfGO0HRo2hve7/e4vr62CgKNma/v9/vGaDlT4Z1qTMrbBDU+loFHoxEAmCIz4/nw4YPJitnybrez6pu23HyfPFZ4HdHshE6fM1jj8dhaBHd3d7Y1W+eN+DpWGRn8Yp6j8NDKgGbydPB0+tyiv91ug7qx3+9NN/SO4qHKwLmhawYOcyTa+qLtM6jxfle6e5WJxHA4tIoKdYSBkr6DVQE9IdnfQiMWGYU+zylbAb5UQHgKOO2F5EEramzPK7Hg/4xl/YTKwX8+Xkudy6MP3e0Op6iPRqOabrBdy3VzTsnrB79inTUKzWKp71itVhiPx6iqCuPx2EY1vC/t9/vWnlQ70bhyjrbiRZEjBZ0PdwUwg2FFaDAYWNbD81z43OFwWBuMY+bHC8YyPLOHEIuOCUriWCEjydMTr/v9vs3bzGYzdDodzOdzXF1d1XZYcP6KAQCI8/4/p0D9IBnWaiGdN28QytOg1+u1BYM8z620ztaB14+2yUP1gwkAHb/qBltIy+XSggJPg6ZuUBZAPC2S56DXi9kst6Bz/byxLlstnDHha3XzBsmRBkNt1dF2dAA1RjllWWafXW2FCSLbQ7SX+XxusmHipYO6lAnn0kgqYodWjTh3Rt2Yz+c2pgDAWku8txyraZoskBwxTmkFX/VDyUHs+qGxhXH2Jb5UfQeTTELt4i3Xf7HkiCCJUULDGRrdNcALuNvtrHesx5ZrlqBttVgV1sNvPyUp0CDGLdpZllkmTKPlmvledJAkV7E69lOgPnCrKUkB9YW3RyGJ0jKwBjo+l1/UrbbJA6ifAqzyoG5wvoKOUJ8DwDJk2krM7UVenybyRj2fzWa1XWp61llZltjv9+b0mVjxceDgO7SC5GcWY22pEbrRwOsGoQP6vP8gW4vqg7Uap62TmH2pr9xQ/wlWFkmUSY646YdJN+OPzjGxM0GZ0G5iJsweWt2ifijJ0Xg8ohMAACAASURBVFjD7gN1g76UflQT1ZB9vBUumhxpNsySnd7mgW0CPQUXwNEWS53IJ2lq2m4ZK9T5sNTPXUhaAdlut3aqLStrXJsGQP6u/ffYZeChlZJ+v4/lcmkOje3XTqdj5/2QIACoBQVfdg/NpLUB1HUGd1ZTmfWqbvB0W61+AAdZUB9ibKl5hFoEbKkxidBTv+kHmAHz+Xxcv1Q3dAcbX9MWNNkKW6kAzFfQXqgvocoQ9cPPlcRaZdRECIAlDDq3yiqRtp814WSlTXWD0PbjuWdtvga+K7FYLGy9vPlwlmUWYyk74BBHVLZacT2XHC6KHHkhUhF1G7aeUaKlTW7N9XMoZLp8vbaiYs92PNRJ807ZwOFQR+3x0pnRAbKKxkFU7Z+3jSgCdbJIZ+RviULd8GdteIKk8tCqQOzlcA+vH7QVOvjX6kaowhqrHPzMkc7SsQLC++uRNGdZhsFgYLakrTRNrHTGj1Vrv2MtVvikiteUvkNvbcE1+6qqEiQSJm3dnqtt8hroaAKJESsdq9WqFlcY2FmhZ7LtZ/A0yaT/8ZXn2Dc0qG5Q16kfXP8pX6rngnk/qmfHncN3XBQ58lCiQ1LDloC2VIDDke4AasFeW0s6dKs7cWJ3/IR3cizvct0kgt1ut3ajVV8lUQfZNhkQKgv2ufUUaOoGWwQAakQAOGSRlAdbbuc06K+FBievHwBsPoTy0MBPsJVAW2tD1UjBliJwSBR4zSkLrQAAOCKOvPYMFpRn6IBMfW6MlQJtJWnw0zN6VAcoH9qLHoqp7RZWn7TtqnoSqxwIvWYaVwAE44rO43CtKhv6DiXOSopi9a2qHz7Wcsia15y+g1VF35b0cUWTh3MlVxdLjpTpU2F1W63eBVmzAc2M+Xo6SF50OoOYGX0TVJFpnDRk7iDIsqxWFdDXEZr9xerUmhAyasqDO030NGPOHnkD9fII7TRpG7x+kCDrnbJPycPPIsUuC//ZadtMEtgKYybMVjMPRMyyzP6m76e+gZnwYDAI7mYLfZ7YoHbC9QCwW2cAh1YZiSRtga8nqB+stvG926AnjAfakaCv7HQ6Rx0IEgO+Vt+LoDyVMCpxDr0mNvDakfTz2ntfSltpIsR8LeP0OXXiYskRcFBkKil3FWmrjERJe92qkLyQfgrfD1TGDp8F6ufXthqDoQ7Y6ewE34sZgs962yALBdei26vVwXEAlbrELFDXqQGjjRVFhSdGGgy8PPh8/90H/zboBq+Tzj0AMLunHeiNMen81Va0YuSTMj+0f+6B05dCr59WAdSP8u/alvY+lXqk7ee2yEBB3dcqCX0qj3cI6UaIFAGoxRYdam9L5TUUF9SXsgjhfUdTnNWDdM+pGxdHjihEzeT8HIC2jwDUMmQNgrpDJ8uymhGESn6xQ42UhkclBA6zR96g1bD5XDrKc5U8vxX6WSkPALXrD9R36fis0Ru5Eqy2yQMIE2hNMBSeACiBAlDTDb53rAhVexRKnjmPxWoJ5aA3Im7yHbqDpy2kQAkjCY9WN5QcAYfWWpO9cK2sGrUlkfDJEKEVMuoGW+9aTdKqqreVLMtqR2CEEotY4X3GKf0I+VKdR+P1D/nRc+jGxZEjIhT82PP0BChEAnih+TODhZY7Y1fcEEKZvgZ5v2uH4HN8O6oNwa8Jeq19hqvlcI9QK0RJeOyO/hSa9IMBLmQrfp2hOZK2wBMlTQroAzQA6vyZkkXvO/QIiBAhiFlWnhjws2oFCaifmhzSDw2ibSFFHl4//BwRq0f+7vIhH6uPNbVb2yAbT5B8JYmyeM538LXnrhjZ5znbf05ISEhISEhIiBAXWzkCjlkpS6DsfZKZelYbqgyQzfqWybnZ7ddA16nZr/7NP9//3raMrwn6+bX8r7v0vHxC2Y9Wj/z7tg0h/fBVAT4v9Nq26kZIz1UHeH5Nnue1tprfycTHgfqRALrLlc9tk4z0mmpb3ldYvb2E3iM0bBy7LPzn052autuV82gh36rQypEeexGqqLVFNqGujLYV/Wv0ca67aVj7rXHR5Ag4nrPxvXCFEiZ9Pb/7ANhmqFxeooSxG+e3wF/vEEEODVT6dkHo/doKrx9N7VY+t+3whBdArZVYlqXt5uRZPSoXnuei70dyycf1TJ+m9l2s8H5Q24chf9hkL6GWSuxrD0Gvn7YYdV4RQG1sg+BrQqQgRJjbJh/qfZN+POcvY0muLp4cAc3DdAq9iKeMuM0G7fHaNVzCmpsQqgiFsr5QEL0knVCESGPoOad25LQJ/rNrUsVhWq8fei4ScLzV2ydV+v1UBS5WNFULFT7pCj23zTbjr90p3+EJc1Nnoqni2jb5PKcfL9GNpvd5a7wLcgQcB7UQm+XzTr3Hc89pMy51Xa9BiBiH2mrvEU3rvkS7CAV8vz1d/+Z3dIYcfahd0FaZfY8qc1vXDoQDPnBcFfJ/P/Vel5R8P+crXvu6c+DdkCOguQ3Cv70k843p4iX8GFxCdpvw7VCf4IkRW/CE9x3PVZ718TbjEtbwLQhVV3116JRu+Pe5NHm2eT3vihwRniQ1bS28lHZBQkLC16HJ/kOtsZf4juRHLg+n9ICPnfo9IU68S3JEPFchuFQ2n5CQ8HI0kZxTSVNbBq0Tvg9CrdavfW1CHHjX5CghISHhJfBzJHzste+R8D6QrnX70f496QkJCQlvAL+b6DW7blKwTEhoF1LlKCEhIeGVeG6OKM0ZJSS0G6lylJCQkJCQkJAgSOQoISEhISEhIUGQyFFCQkJCQkJCgiCRo4SEhISEhIQEQSJHCQkJCQkJCQmCRI4SEhJaj9ccupeQkJDwHKIiR8nBJRkkJHwNttvtuT/C2bHb7ezn5EfqUNm8R+z3e/s56cbLENU5R3qzvoR0PopHkscBSRZ1FEVx7o8QFZIvraPXiyrUnRVJN16GJKWEhISEhISEBEFUdDqV+76AVYEkj3qFJMkj6YZHkkcdSR4HJN9RR9KNOp6rvqfKUUJCQkJCQkKCIJGjhISEhISEhARBIkcJCQkJCQkJCYJEjhISEhISEhISBIkcJSQkJCQkJCQIEjlKSEhISEhISBAkcpSQkJCQkJCQIEjkKCEhISEhISFBkMhRQkJCQkJCQoIgkaOEhISEhISEBEEiRwkJCQkJCQkJgqjurfYW4H1lQveZCd1rxT8/4TLxmvsNNd2jKOlIQkIzqqpKNpLQGrwrclRVlRkofwZO35BPn08kA79MhK41wce97vBvfL3+3na8VB58rv97wuXia29eqjZySWTplK0QyVbahXdDjhjQ9KupeqS/6/NUiS9Fob8m8Onz2gyuS7/v9/vac07JwpMhL4+2ykflEXLkTUlGCG2VQUIYIZshmq51iDh0Op3a39qKU7aiyLLsyLfoezT5kDaj7cniRZMjH8j2+z32+32Q4esFrKoKnU6npsxZltlX6DVtwyljprGqrE5lRW2Ug3fu1A19LER2QrKgvlA/+HubHb/qR5OteJKkf79Eh/+11RKi7XIIJRFNyZW+RomB+ta2+9JTSZXHKT96afYSSiBfixhkcNHkiNBqkRrpKWa72+1qBCHLMguAzHqIGC7k18ArrsqD6/YEMVQtaSsJ8MRot9vZ41z3qWsdahFQR9rq/L/GVvS1oSSCaIsMQvDJxGvkwee11U4UId3wPoLfPWHWZEuTiTbKxleMTtlK0yyr/v0S7YXffaeG8IQxpDvnlMHFkyNV3u12az/r9yYH5p19t9u1YKmBsE1QQqCKq38jTs2bAKg5OH08dvj2KnXD64UabqgS5NedZRl6vZ7phupK7PDObLfb1eTjM2Nm/1ph9bah+tHmSpq3kVMOPwRPGNsmA18hYRLBn/XvTeRIfSkA9Hq9mv9sk36cksdLqkhAPeny9sLvbZEHobGFv4c6FKfWprZybhlcLDlSx05nv9vtsNlszKBViUMkoNPpYLfbodPpoNvtoigKZFmGPM/R7XaPMqPY4YkRCYEqc8ipEcz+aLxFUQAAut1uK9ZPeAKw2+2w3W4bdYOvAepESElBt9tFnufY7XbI87z2XCJ2GXkytNlsgg6/KTtmZaDb7Vrw4/e2OXpCHbwnjB76mJJH6odWndsmiybdUHKgvsP7EC8PoiiKVxHNcyOUWD1nKwoGfcoCQM1Oer2ePXZucvAa+Niy2+1eTBY1waLv4M/nXHs65yghISEhISEhQXCxlSNC2f16vcZ6vcZ2u7UqkkIzIM3wWC3abrdWFSB8eTRmaFa32WxMDmTt6/W6VgnYbrf2WlbKVB5VVRnL52ua+ucxIDQnwJbacrnEer02eaxWq9q15Qwa8EUWQP16s5pYliX2+73JR1ux/jWxIJT10VZYGaBuUAdoKwBqGTDwRRa9Xg95nmO/31uFsW1VE9UXVhcpj81m82JbYVVAdYKvix1eN1glWa1WtYqrB+UR8qW9Xg+73c70gj61TfoRshXKY7vdBm2FVVVW0ohT9tIGWQB1W6FOaFVNq8dNcRaA6Uav1zNbOVdcuThyFJoNoDNbrVaYz+dm2KvVCsChJeCHw9hWY9Dr9/vo9/tHF4rOLmaok6MRLxYLM2rgCzny8uPPLIdTHpRFWZatmb9qKoev12ssl0vTDRJpoK4bauDdbtfk1ul0UBQF+v0+9vs9yrKsbVXO87w1sqFurFYrLBYLsxXqRpOtUB6dTsdsZTAYoCzLIyIVuywUnkCz9RqylZDv6HQ6ZifAl0AYO1n24DpVN2gr2+3WbMUTqSb9oG7o33ziESN8y9DHFSbe3nfw55AsADxrL3x9rPCxZbPZYLlcmi/dbDY1fdA2q5dHURQoyxJlWZrfPJcMLo4cKTQQUolnsxmWy6UxfF4gnTWhUne7Xez3e2P14/EYwBcD5pcfOItZiYFDFrzZbLBYLMzhA6hVkfgzyRC/Ux4MmsDxcHobZKDDlKvVCsvlErPZDIvFwubTfKbDOQGVBQDTD507YBDQ/xWzo3uJrWhQ0DV53SjLslZNYPXMB5cY5aDwyZUGQPoO4CAPOnhmvpw/W6/XGI1G9r6cQWoTKAslR4vFokYGCG8/1BNWmpmQ0W/Ql/L/APHqRogIrFYrTKdTI4vqR3VzR8hWABzZCxNRtZdY5UFwjZvNxmxluVwaoeYaWFGkTrAbQ5sJkcNznYl1seQoVCGgw59Op9ZWUjYLHMrBDPbb7Ra9Xg/9ft8G5lghoKK3Bd7BTSYTzGYzM0yWP9WgKSN+UYkpFzL9UPYcK5QYsQqwWCzw9PSE2WxmDs6TX80C1dBZWaQDLIqiph8qjxjl4itq6vBns5kRAh2c5PpUN9RWAJhu0Ab9sQgxQxMeymSxWJjvWK/XNXnQVoCD79hsNsjzHMPh0NpqbR1AVjKwXq8xnU5rhCD0GvoO70vpZ1ht9clD7ODnJRlYLpdmK4wrtHu1FwBHtgLA7IX6obblW3Axg3bOODubzSyJ1kpRSDfW6zV6vR4GgwEA1NrQLFS8NS6WHKnj0aC92+2wWCwwm81qMyG8YCyhU7mpxCyj9/t9C566A6MNjg44Zt90+ABsrawUMQNSeTDgcwZlMBgcVRJihK/uecLDasBiscBisQBwyFpICikL6gszHLYZOUfB7IlZ8Tmzn+egcuHvaivz+RzT6fTIVrw8lAANh0MAMFtpIoptgH7eqqqwWCwwmUyM+HBNrBp639Htdq19720lZsJMaGsIOGy5V1loBVWDH30IA5uSI/UdqkcxzywS6json/1+j/l8jvl8XrMVACdtBQCGwyGqqkJZlkF7id1mQr51v99bIYJVMrUL2ow+lmUZhsOhzV1pZ+ccMfaiyJFeJG2J6NwA50vYPqGxk52yiqIXmYpOYydiVtgQNAskEWDwA2DOnDLL8xzL5bI2QKq9YZZM+d4xy0MrhKobesTDarWyWRtedz6/2+3WysN6vgsHblkS9oTDf45YoVuzKQ+SRTp2Pk9thaBDZ2VRdUO/twXaVttutxb8ptNp7ZBYts9IggBYy4AZsMoDiFsPCLUV1Q1NIubzeW327pQvpR2x5ajtp9jhdVjHDlg94tyiEhsSA28rSo6a7KUNaIqz1I3ZbFbbzKKtZoKVNOoGq2/AwdecAxdFjrQioL1uzX7YRqFCq9Fy2NIPW+v5E+okgPYocuhzsl1ARSQp4GwAFRs4tArYH2b5k+/dpv44UN99pga+Wq1s9wnXRGLkdUN3WHQ6HYxGI3P62+3WWgaKmOXjs3bqB22FNsTA74MibYS7j7St3Ub466QVovl8bmvL8xyz2aw2b0ZbYTKmunZOh/9a0Hfq5wdgmxaoG3wMqOuH6hQrS37HL2UYM3RcINTqou/QuALAkk3dvcchZCLPc1xfX1tc0fYTXxMrVD98RZ6+g5X4U7bCOVZu8NHWJXCeOHtR5EihVRLtgTOz5zblzWZjbbPlcmksni0RXlA6Qip16GC7mJVYwWyYslDnxt4vdxtwS6XuKuj1ejV5qAOMXQbegDX7YxlXj3tglktZUDd03kSrUDp0HSIFscpHbUUrBPv93gYraSu73c5shQGQ7SO1FT+AHuvam6COWdtEJEar1QrdbteqBhrwmAGrrag82pZU+aBH38EdSQCs1ciASF2gfqzXa5u58vKInUCH5KBVRVaV9YgD7zvUVvRYGBIirdD7dm6stqNy0UorkwgmVRpX+DPlobZCWWiL9Vzrv1hyRMEqien1epa9AAel4wWcz+fYbDYYDofYbDY2hK2VAy0Ht6kqoKAs/FbJqqpq7QHuzrm+vja58LVaRm1bFU2DtRI/EkEGAFYKSCBZDdpsNiiKwuTGqiOHVXXXGhBu98aIkDwIZnmr1QpVVR3ZSlmWtuuIwUIJuFYX2wa1F1aSKSuSA9rK1dWVyQU4nNui9tImhGyF5/Lo48CXhJFVgvl8ju12i9FohO12a/pBeejp/G3Qi6Yg7W2F/gOAVZAoC7UV3bFIW9Gdbn6OJ1Z4uVAevJsE4wsJUFVVmE6nNd0YDAY10qgtS+Ics1cXRY58e4eK6st0OmeiLZT5fI4sy2yoDoANyQGw4KmzR21w+qGsR7+4Ps5V0Kg5i8ShS85PsFrEuSPKJOaBW9UHbbGy363D+DROVpOqqqqVhvlePKyNz9GhdT7WhnkbnaPQKpjXER77UFUVZrOZvZavL4rCHD53mvg5vZjl4KHXjs6a11a3K+/3e7MV4CBH2gqAmq20qa3m/aj3Heo/WBVQe6Ge0F/wubori3MoMcMTfLUZDebUDx9X9D0A1GShvoPvE/pfsfpV4NBSDemGzmQBqPkO+gY9C44zepRFmjn6DlDlUWXWgM2WwGKxwMPDg2X6uiWVz6ez//XXX2vbmXVyvg3O3pfxdXBSd2f9/vvvWK/XNpi8XC6tfdTtdvHp0yf0+3386U9/MrnpjVZjBzMZOnnN8thq5RDy09OTGTZbsNpfVyLw4cOHmkGrHtJhansyNqh+sEIEwHZ2LhYLfP78uWYr3HFE0lwUBXq9Hn755ZcX20qs8vDgZ6S9zGYz/P7772YjJAUkljzaoSgK/Pmf/7kFSgaANrRLFF43GNToRz99+gQAZivel1I/8jyv+VKtwun/iRWaZAN1W2ErkXFF7YQ7sbyt/PzzzwBg5z5pK4n/K3aZADiKLcAh0aZ+nIoreZ6jLEv8+uuvJjO1lXO1oS+KHIUqR/yZ7J6O6vHxEU9PT5b5aRmPsyXAF4N4enqyxxhUQ0QsVkfnZaEzAsvlEr/99hsA2Dk/mgUBsF1rWZZhOp3i8+fPtjPLk4GYoWTZz9doJXEymWAymVjmpzLj3JVe7+l0Wms5qfPU/x0bTtmKksXPnz/j6enJBvabbGW9XuPp6alGFE/935j1xtsLK4mLxQJ/9Vd/hcfHR9um7OXBW8+s12t8+vQJg8HgqE3QRlRV/biT5XKJh4cHPD09AcCzvpR+l5s6dGZP/0esvjQUX1Qe6/Xa5MHqu5cFd2nRVvi4znH5/9UWqO/gWXq//fYbnp6erLKqlTGOcGTZlzPBPn/+bDNrTdXmt9SNZ8lRlmV9AP8DgPKP5/+Tqqr+/SzL/lkA/wjAPYD/GcDfrqpqnWVZCeA/A/AvAfgdwL9ZVdX//YM+/4vgy3zT6RSLxQKPj4+1HWtk9byQHCDU+275dklbZgg0O6PRbrdbI0QA8PnzZ+uPs9RbFAUmk4kNnvKkcM7ghOYoYnVuhK8kMvDpqdDT6bR2WwRWi1gaZiUA+BIUeGYL205+biB2Z6fVEW2H0VYeHh6CtjKZTGxuz9uKnzeKXQaKkP7udjs8PT1hPp/j8+fPdmCo2srj4yN6vR7m8zmKosB4PDabast8jYev7FA/WBmYTCYAYIP7Xj90WJ8bPXRzTEgmsfsQ4Hgom75DydFzcYW/N8kjZrLo4RNOtRUeB6K2wqSStjIajSyuqP8g3loOL+mFrAD8zaqq/gUAfx3Av5Zl2d8A8B8D+LtVVf1zAD4D+Is/nv8XAD5XVfXXAPzdP553VqhxM+DpMKD2PjWj0cd4kfwBgG05uIzQz8lWgO8TAziSAX/mDj5f6lSC1BZZaFm8qg7btP1XSA/UkfkhStUhIG4d8YFJb1ugtqLkpkke+lx9L60UxSiDU9DPy4oQnXzIVpR066nA+l5tJEhA/T5f2lbVL5WLzlipfvA9Qq0jbaXEDh0l8L7DV5x9XNEERGXik822yAKoy4MVISWQAI5kwJ/1sVjiyrOVo+rLJ+W0Yf7HVwXgbwL4t/94/B8C+A8A/D0A//ofPwPAPwHwn2RZllVv7BF0tgRAbTCy1+thNBrZ9n22Q5j18+ay3KFSlqWdZaPT922Bd0KURb/fR1EUuLq6sudNJhMbSKdcuPNGb77Lo+4pl7bMHPG7kjv+zB0k/X4f4/HYZmxYJt7tdrYLg9kggNqQur6P/s8YEaoIMLMD6rbCSgF1Qk+M5/BxURQ2W6LzJHpj0bZBCfRgMECe57i5uTE94bZ+VgG486YsSwwGA7MxlYk/4qANoB/Vs6xYRab+sHKivkP1g/6D83peHm2A9x26AYHVDx6C+JK4wtdqbFHf0QYdCcmj3++j1+vZ/UhZKVPdoK3wNkO8QTNP09b3OwdeFOGzLOsC+J8A/DUA/ymA/xPAQ1VVPPL1LwH86Y+f/wTg/wWAqqq2WZY9AvgA4Df3nn8HwN/51gW497Sf9WKxpM0hyaurKzuPgkbOC8YL0+kc7qZ9c3OD4XB4dA5FzBUBhWbyZPJUxvv7e3PsdOKDwcBur6JbmEejEcqyxE8//YTr6+taFQloh7PXz6i3O6CBDodDawtxRoBEQIfPSQQAYDQamaErKfdH3scuH85TATASfHNzYxUkPf9qv9/b0RidTgeDwQBFUeDu7g6j0cgG0LV64m0ldnno5+V9wD5+/IjVamVBnqcA7/d78yVqKz///DOur6/tvCPqW+xrVzCg03fwDvLX19dWTQNgNw7l7BGHbakfZVni7u4Ow+Gwph/qS2OFTyT0xHzGCcYV+oqiKIJxRUk2AIstKo+22UpTbGELMc9zOzWb8tDDc/M8x8ePHzEej+18LI0v51j/i8hRVVU7AH89y7JbAP8FgH8+9LQ/vodWcVQ1qqrq7wP4+wCQZdl3qSr54pQyWl68oigwHA5xe3trmR975Q8PD5YRAjCnNhgMcHt7a+RK7yIdu9IC4XI+dxMNBgN8+PABAHBzc4PHx0erqD08PCDLMjvv6fb21rKj+/v7GkFoG0Hy519xnaPRyDJ/7q7Y7/c2aMzKER0cgBqBLsvSzoFRQhBzUKQ8tITNYD8YDHB/f4/xeGw3Gt1sNmYrPL9mPB4bgbi7u0NZlkfnubRJRzzYJqPTv7m5wWg0wmazwWw2w+fPnwHAbjJ7d3eHPM/NVkicALQq6HnfkWWHHXmsKLLyPJlM7IT5h4cHu6UO9YPVtPv7e/Ol1D39XzFC16+VLiZCjCtMDnjT1c1mg8fHR9OdTqeD8Xhcu8nq3d2dkUfaiycEMfsO3wqjrQyHQ3z48AE3Nzd4enqynWqMK1zvzc2N3aCZcYUnZfvK0VvK4FW9oaqqHrIs++8B/A0At1mW9f6oHv05gP/vj6f9JYB/BsBfZlnWA3AD4NP3+8gvBxVMj/PP8xy3t7fIsgyj0cgO9WP1gGW+wWBg7H8wGNiXZgAxKuspaKCmAv70009WKu90OnaI3X6/x++//16TB4PhaDTCaDSyjCdkzLHBt9GA+iwQic/V1ZWd5+SrRloxorMDYERAybMeNsr/GbuMmKkx4y+KAh8/fjT9YCvat1qpGwwQ3lb0JpxtgSdytJeff/7ZqkM8HHW32+H333+357EyQnI0HA6PbKVN8CSfLaEPHz7UZtQGg0FNP9R3UJ+8fui4QtvAliB14/7+3qqvw+HwqLXI5IGyIDlilbZJHrH7DaAea7XKyvWMx+NaXAFgyRfjMnWD9qMJ5jnwkt1qPwHY/EGMBgD+VXwZsv7vAPwb+LJj7d8B8F/+8ZL/6o/f/8c//v7fvtW8kQ+AWjUiA2VFYDQaoaoqjMdja7Hd3t7a+RskAgBMkTXw+coA/2fs0MynLEtsNhvrC5PN0+F/+PDBiCMd4n6/twoLS+Y++MUsB3+9aJjM5DhUOxqNbJakqipcX19bG5akmcjz3GTCErK24DSAxJYB+s+imWtZlliv16YfJEe73Q739/c2lMt2CvVkOByareg8VhucPKGtDfUdKg/ddvzx40eTDYPDbrdDv9/HcDisyaEt7Xj1ofy9STcA1FrSbKlwPpPHGZAgUR4+uYpVJl4WwGlb8XGFZ8KxM0FbAWD2wriip4/HKo8QfGyhPCgfnhtH38Gkk7ZC3dDuzDl14yWVo18B/MPsy9xRB8A/rqrqv86y7P8A8I+yLPsPAfwvAP7BH8//BwD+8yzL/im+VIz+rR/wuV8EGqYGd2WpdG6DwQCLxcIGTUejEYDDidgsh+qJip1iqQAAIABJREFUt75P3gYlZpBmcKdBsgrQ7/dtl8F0OrUtyePx2NosPAafsvWkqA1yoJPneSskv/P53OTBXUnUEd72gH/TCgArSTpICKD2nNj1hDKhrXCmhmsrisKqAjz6gbZCndrtdjZ75W2lLbpB6PWi7fOL7SLeZmc2m9mNmkO2AiBoK22B6gYrYry+bCUDqN1aJssyrFYrG9jWG1Z7/eD/aAO0egYcKopMipg0rtdrDIdDOxtM44oOLQMw+1J7aVOFkTaisYU6wnnMxWKBLMswm83sNjPD4dBeo7ZCG9P3P4csXrJb7X8D8C8GHv+/APzLgceXAP7Wd/l03wF0zHoUuQpfDZ33ANKJeWYLDBTKatuivARlwS2krILpichcFzM99sc18LMF2TRHESv4GVUnuCYGNF57APY7D4ckOWILSWd0PKFQucZOjAgdCNXNChoYAVhVjbJS3dCNDyRWbW2bUE+4JVvbHjz1mO1V2orOYfGgVLZS2txCUt3QIKhbsrWCrD4i5Dv8TlffuosdKg8/jA/AbrDb6XTsfmqhuMLnamxpW9JNcG6Rm1l4ffk7AKvO01ZUHqobMfiO9uxH/0ooo+33+3aQIYeRN5sNeJotcCAIAI6UmBdLt162SXn5WdkGWa1WtSDP0idP+SWj1xkd4BA0KINz94ZfCj84qEGf8xK85xFLvTwsc7PZAIC10/wNjIFDmV3bakTssuH1JGFkFZE7R/r9vrWcuUOJg5e6/V8DpVYIYl9/CCRGzOwpDxIeyoN3Gfe2ovpGGbRdFiR9i8XC/CArICoPzt7QZ3hfygTTE8bY2s4haKWk2+1iMBjUbEXjCquLShC4Xj/kTV/aRgLt9SMkD8YV1ZFTvuPctnKx5MhnImSonI3gbIleCDVevReWzpqEMsA2KLI6H352Bno9hMzfK403EG2SR5uMWT+jnsGj13Y4HNp2dq2G6KGfPrujPBhE9UybNgyrEzpj422FbUP+XW+RotVDBgFPEttWGVCovZDwqO/Q1hurCHzOZrOxLFj1pi0yYNDTdbKtyKqJJl0+qFE/6HdUP/hd5RGzXEKEhv6P85g8woNESONKSBZ8Doe0/Ywe/1cb4PUDgMlD5+0AHNkKq7HUoRji7MWRI5+BkOHTebEETsbKi6e3itBeOMvpfK0/4K6NCkwSpM4eQE0eerNEb+BKNH1ZPHaos2e2z+s7GAxqbQMSAA4P8nGulX+nTrCtpk6/TcRRyRGriyFbYRWtyVaoGySMbdIPhdq2kh8vD15rtRX+znlF6kWbqomEBrWQbpAIsOqup2fzbwBMPny97vzl/2kDQnHFy4OkUTsSTCIA1GTDrkbIXtogE590cuyA5Fl3wVI3tEMDfJEP29Hacj2nHC6OHHn4bJjVEt4Px2dC+hoFe8rc7q+Brw0KDBxvYaezZpCnPMjc1fF5Ekinps5P/x4jlBgBhzapkl9mdLxZJp05ZaSOXNdK3er3+0eyiVkmQLg6wPXQkbFNwECvz/frY0WRFZM22UgIWiUADpXE52yFiGHnzfdCSDfUVkL6wdcR1I88z1snD1890iorfQcJgFaUNG7497kkewnZCuMKgKCtaPVZ263nLjxcNDlSw1SD1gBIA9cBO6/AvNg+22mrEisx4pwJcCBNzIA0O9LhVG0ztLUyoPM1zNyoJ9vttqYbrBBw7UDduWk23FQ5aoN8NCNm4PIDldo64Gv4nfJhm4B20iYZePjgTvvQ+buQ76B+cf2+Fds2eN0AcKQbIV/KSpraD6stIcLYBvmE4grXpfKgLEJJmW4GITGiPNroT71M+DPXSbvxvtTLR+fQzj26crHkyDN8EgLgcJsEZjy8YHqzSDVqXiQ9tbNtBk14Z6/kSAfjtPSpJVBdcxszYf2cdPh01sDhtgDaGqBuUKdYSdJWQ5ZlR0c+tMnJqYPSLJ/6oTeOPGUrXK+Sq7bI4BSUHFFnABw5d0+gCZ3DaZssfGVRN6ywMsDnUT/0uwZLQhPVNuqH+lGfaL40rqg+6Fxa22OLkiE/cM24EpIH3yMWGVwsOQKOW0G8ADoroNuxtZRHVsvXa/bXVoMm/BpDawZQc4J+rTEo79fCr1e33fM6aysNqO+i8cP72p7zVSP/P2NGiCA1BTqdF1B5NBHnNqy/CV5fnttRdcpW2golft4vql/1+kGSrO+jr2sjYfR2Eqqe+s09p9apFaM2VxcJlUtIHuo7uFbf1o/BXi6aHCl81l9VlW0v1CBI+IoKFddfuHNfwG+BX4NvF/jnhV7X5vUDx+0Cnw0DOCJCHjp7wp9D1YM2wFc7NPvljsWm5/N3dXKh57QVPgi+9jVth65Bg58mDadew99DpKJt0M/M6oiXB/8Weo3+3pRwt1UuSnR0ww8QloevHMUSVy6eHIWcvS/lAahlhKELE8qCY7iAX4uQo/fO7bUZcpugxptlWa1Vpn1yysa3FvW7lskvwcmpbHT3lSL0WCg4tG3tz+G59Vzaej38tfV+lAjNbob04hLkFZqvUpyyFf4cEyn4VjSRQKItceXiyRHhS3w++zvF8PWxS1FgRWg9p4jipSE0bA0cdMLP05xy9Jfm5HwWqH/zz/GPXzIufX2n0GQHTfqh3/3f245TsQI47Uffg82cIkaxr/fdkCOg2ZEDqLH4597jPeA9rdM7+ecyYX1t6P0uSXbPZbyh5yS8DzQR5qbfLxlNa32JDJKc4sS7IkdAmBDp4xosX0qYEtqNEGkOzdZ43Xgua7w0JDKU4JF0oY4kj8vBuyNHiufYflL094NQKTwEv8Ov6T0SEhISEtqLd02OEhJCODWD1fT3hISEhITLwfFwRUJCwhESIUpISEh4P0jkKCEhISEhISFBkMhRQkJCQkJCQoIgkaOEhISEhISEBEEiRwkJCQkJCQkJgkSOEhISEhISEhIEiRwlJCQkJCQkJAgSOUpISEhISEhIECRylJCQkJCQkJAgSOQoISEhISEhIUEQFTnyN/tMSFDs9/tzf4SzItlHwkuRdKWOJI+E1yKqe6t1OlFxtbMj3bKijm63e+6PEA2SbtSR5FFH8qV1JHkckGzlZUgak5CQkJCQkJAgiKpylEqfX0Bmv9/v3zXL3+/3tWrRe5dHVVWWASdb+QLqQ5LHFyR5HKC+YrfbvevqkfrSpBtf8Fwseb/a0gK8ZyIAHK8/yeN9rz8h4WvxnokRkHzH1+B9a0xC1EgGnZCQkJBwDiRylJCQkJCQcMFIiebrkchRQkJCQkJCQoIgkaOEhISEhISEBEEiRwkJCQkJCQkJgkSOEhISEhISvgFpe/zlIapzjhISEhISEtqIJoKUhqHbiUSOEhISEhISvgIvqRjxOacO6EwEKj4kcvSO8RLDfs8GXVXVyTV6p5eQkPA+4H3DS/yjPif0+uRH4kIiR+8UL+2Rh55HQ/bf24wmeTQ5Me/ogESSEt4XmghCkx1cmp1wPfr9udu3qO9oqihdinzajndHjk6VOENKeYkKW1XVVw0QesP2JOFS4deYnNv7RSgAniIDl6gLocSg6e+nXn8JslFf6uVy6vq/x0p82/CuyJEqrCr1KbYfIgFtVWK/Pn8j15DT8sEglC12Op3WOjx1bK8JfPqcENomB4/nnDsRIshtX3sI3nfwsecSBNWjSyBLXE/oJtCh9TXJp83y8D6jqiq7sa0+lmVZbX2n4k6TPNooH+B1SYR/fix4N+RIlbYpEAJhY9aL20ajDpV/+bXf708GtyzLsN/va+/Fv1MedJRtko2umetTeegam9oH3uhDN7dsgyw8QrIBwjcC1r/r61V+bYcGtOfWq7+HiFFb7MPjlA9pmr15ie/Q58aOUJVot9uZ39jtdtjv9+YHdE2aQBL+73xMv9qoL6ds5bnXKM697osmR77MqUQgpKjqwFSZNQtQoz73xXsNvGHTkPl7E1QefP5+v0e3263Jo9vtBl8bK/yavTyaHLeXhT6metLpdFrp3NSxkfQ2ZX7eZvRx/7w2IuQ/+HNoTSoPfS4Dn5LnNsnE+0wSAU9+XqIflEfb/KlPGJQcbjab2vXe7XYA6sk0/UO32z3yu/Sfp3wpnxc7VC6hv3mobmiSHYPvvGhyRPjyJx/jzyFlpPF7YtSm6kAoy6OT3+/32G63AOqVE3XmHnSGSgQ6nc6R84+dGISCnsqDj5+Shb5PlmXY7Xbodru2dqB9wdDri9cLIFwdAxC0lbZVBprQJI+X6IdPLmJx/K+FJ830nU2VIQ+uX7/TVihD1Z/Y4KuH/H273Zrv0AScRIjgujabTU0n+Df1p/raUBUqVjRVnNVWmnTE24riXGu/WHLkKyVegVWJWRoNXTw11m63W8t82hD81LHrejebTU0WwMHovUP3M0UqDxKCTqeDXq93stIQA0KEUeXh9YNr1+DG1/uMl7Lq9Q5m1ZZg6G1F2wWqO6ccnDpy6kQsju618MGQvsPLRHVAf/eBvtfrmTx8ghEzQonVer0GcCBKvkqmPuSUL6XvAA4JFd8nJrn4oM9rv9vtsN1usV6vj+wm5D97vV5NL5QU9no9kwnl2aYky5NnH3d8u1F1hI/p31Qf9O9viYslR0C9lbbb7bDb7bBer2uO3z8fqCslA2Kv10Oe58iyDHme10rDfG1sihvK9ujoadTaTgqxfVVarRx1Oh2TQ1EURoxUdrGSAk+Kt9vtSd3wlSbgy/qYPXc6HXS7XRRFYQ6A8uBrYpNBCKoHDIJqP769qL9rIOh2u8jzHPv93kgBEK9jVzSRZ5WHEgL/OoL6wYBHeVFH2qITnhCqLPgz7eAUQvrBRCLPcwAHUh2TXLw+kBAxnmw2G2w2m5o8PLnxhMhXSLrdrtkJfSlQJ0Vt8Kf8eb/fB5Nvfb5C1+V9h//7W+LiyJFeJKBODJbLJZbLpSn5er2uBf/tdnvE6vk9z3P0ej0URYGqqsygQ1Wkc8MbNGXA3vhms8FyucRqtao5N5UHgFpAZJlXM2HKY7fboSxLm0VS+OrKORHSDTo5lcd2u8VqtbKgBtRl4YM9yfNms0FZlrX/04byeEhf1uu1OfztdmvtACBsK9pKy/Mc2+0WeZ7XsmIiRhmEoFnver3GarWqJVkE5cGf1VaoG0VRoCgKAKj5Dj4vNviEgEkDbUV1g7LQIKYtag1y1A/KhIFTSXQMcgmRIiZSSg43mw0Wi4X5jv1+j9VqVYsHTLBJDIH62kgQ8zw/8qUkCaEKS0xQ30GySF+qrcSQrSjxYTxR3wHgLAnFRZGjEIPlxSIhmM/n5uTUqH0bhReO2V9RFOj3+xgMBkftlZDynhu6firpbrfDarXCZrPBbDYzoyY5YuuAWZ6Xh2bDvV4PZVliMBigLEtTfq0sxZbleN2g01utVid1g47RZzg6s1YUhTk1PkbERJyboCSQzm02m2G9XtcCIJ+ruuHbRf1+32wFqCcQMemDR6hCSHmsVqsjeVAXdHgdODh9lcdgMKjpjk86YoRvqVEvvK2sVquj1/n5IvWleZ6j3+9jOBweVVBi1A+uh/5xs9lYAkW90OSKZECry0oC1G4AGFGkL93v90Ygy7IMVo1UV5tk9hb+17ccaRvz+dwIpOpHaHRD10YZ8Dv/rm25t8JFkSOFGjWZ/nK5xHQ6xXK5tKyHF0UHDJUEMPsvisJKhcDBkHlh+bxzEwLv0ABYtsuvxWKBxWJhRq3kiNA5AionHb6SI2ZSOmBJeTNTjkEuHtQLEqHlcmnyIJFU+ZFI0cnrjrU8z825AYfgyHVrsODfY4N+Tjr++Xxu8lBy5GeyqBO0gdVqVdMlJcv8X0CcclBoG4UOf7lcWnBU/VB5UE8Y5EgitHqg8yZAvLJQO6GtkBAsFguzFeBQIdG5Gw1sTCTzPLcqtpJryoTyA95eLp6AqF0AqLXUttstFosF5vM5FouF+VKvC3yvkK0AMMK4Wq0wGo1QVRXG47HJbrVaWbWRMlGi4InSW/ta7zvUVlg54mfU2b2QrazXa4zHY4slfpPLW67tosmRGvVqtcJiscBkMjFW64eN6QyBgwJqtqNESY3AZ9HnXHPoMcphtVphvV6bQT88PJizBw5b9PW9tF/sK0fD4RDAF3JQliXW6/VRKTgmp69EVtutrCpOp1PMZjNsNpvaoW6Ugd+iy999u5XVRSXYsZbDgeMWNMnRbDbDdDrFer3GZrOpVcv4fOBQGaM9jUYjm0nr9/u1v8WmEx6h6iL1Q+XBtqIGUJ1Bo370ej0MBgObJ+n3+0dkOWaovZAc0Y9Op1OzFeDQ+lDSCNTthf6TvqYsSyMaWmn6Eet4KXHwHQh9XJMpVtAWiwUeHx+NDCjx0SSTsqAf1YoZ9YLyWS6XGAwG2G63tbakfhb/2Dl9DGVG3zGdTk0/QrbCx2grXOdwODTfQd04V3X1YsmRD4S8ANvtFvP5HPP5HABqWRwVm4ZKY2BpvKoqqxD0+31sNptaxSQWp69tQg18dPRVVZkhszzO16lj0lkkGuh2uzWnxrmcsixt0DTPc5OpEq1Yhgn9Z9DPxnmK+Xxec3AArEIWIk2UQVVVphckXdpyjRX+2mhVY7FYYDqd1oaraSP8TjlRNqwa9fv92lbnGCuILwE/736/r8mDek45qH4AhwoI2yzD4dB0IzRvERNCBEErQ95WgHqLUGd01L/Sl+52OyOL/X7fhrN/FDl6CXEI2SnXS/8JHJIIVr84g8UxBXYVPEnU6qEmGWVZWjxiokX/6SvQp/TlXFVI1RONu95WuI6QTyA54tzrcDjEdrs1gpRmjr4R3qDVkFneYwVpsViYIWqWR/KgMzR0gr6E7PG9L9zXKoMP+PysurNC5bFcLgEcV47UMegxCDokp0au8gfC8jhnMPC6odUjloOZEXoSxOvvHQBwaBtpq9ZnjLEiRKC1ZbBcLq19ovLodruW/QH1s556vZ4lIr6y2iaEqov0HRr0PQnS11MelGnboAHe+1HKQytHfE2n07GKNEE5sYpGeYSqNN8bfiYs5IeU0BD6PMogy7KaD2V1RAeRfQWedtXUHuIOLa3SeUIUSsB9VTpEoH60z/W+lEPq7FAoCWKc9ZuhQr5D5XcOXBQ58srtsxBeBCqxDlV2u11Mp9NaKZhGrudP8MKR1fqyK1/7vdbzPV7Pqpk6r6qqbKBwsVgAqJ8SzRK3lkQZ4Mjmi6KwVt1qtcJgMKjN6oQM9ZxZsjpFdTiEzlTQyQEwYsRKob4fAKuWjcfjWis3NnLYBDo3/7k4O0Znx+tJYsQ18otVMp2PoNNsG6jvCs5/UEe8fugOPgA1IqBVbAaImNFkK/yZyQTlAdSPudBE0pOATqdjlQHaFX2pT2C+53r8up5DU2Bmi1kJAdtrs9mstg7+P+oGrz27EMChojSfzzEcDrFcLjEcDmu2Q2IBHN9qJPR5n0tSvydCPt5vhNIqO+Os9x2r1cpkokRR43qqHH0lQspBIdMI/Zk2LOexV8qfSZh4EUkcSBo0oLylIr4EmnEAhxK3buVn5kOyyOfpQDqVdblcWskbOLTWdE6A8vDkIRZiBIS3rGulROWkukED57CxtlMAmJ5oS4Xy0P/Lx2NC6HMqwWPbgHrDYVrVCXVuzID9AHqIfMUOrx/MZnUbN+WxWCzsCAPdvcf5RD4eU/v9FNRWeD2ZLNE+qBMM2pQHfQd9hLaZ6Fv8DJrXjx8lo1CVSqs5oeeHfKnqADcgaJVd9Z+bUygTVhh1RywTL23B+c+s+hOSz3OVrx+JkO9gAq62wup8k+/w3QlPZt8yubwocqQIlRVZtuMFULJQVYc5HM6NcCshS8TeMfg2UizwbR91PrrbgiSRRqqnRJNQzudz5HmO5XJph2ASVPTRaGQy0d78uQy1Cd7Q1GHz3CaunethxWQ2m2G326Hf72OxWNQOfGQQYDZN5xirfniEsmruKtKWYafTsXPCZrMZttstRqOR2QyrrKpjp/QhdlAuqh8E5cSqK9sHOitBn6PzWdpqbIM8fJswyw477nT2hn+nvbD1yLkznnVFIkC/o0mJT25/hK94bTKrQdkTOU2qOIPFljxjDAD7W6/Xw2KxsANiWSXJsi9nPy0WC/OlmrwRocraS+T0FnqmtsINTErkmFhWVWUborjWwWBQsxUlmPz856gcxb9dIiEhISEhISHhDXFxlSMt77HEBxyf9cMvPbNEZ28IDg7qzI0O3v7IDPA12YG+Rr+0xMlKmVY4dLaGmT7Xz5Ne+TvnSbbbw+nHrEaFmL6Xy7nbCdpOAw6ZMNes33XYkuVhAKYjOluj+sHfdfg25gqBXidtE2s7Wge0KQ/uruF7AIfZKwA2hMwvnUOKGXqtdPOFZvOUB+dt1Hfo7i0dOmalha+lPLQiERt05kX9qOoEry+Ami9dLpdWWQu1pbytaOvuR9iLr7jo46dkr7Mv+ll17d6X+io6xzR0yJjjGwCsda+zjV4mvA5a5W1ao/7+I3d5ed8BoLZ2bUfzkExWnYGDrbCKRnloe83P56W22jfAtwiAehDUXSdPT0+13Rf6fG5L7/V6+PDhQ+0QSF9q9oTke128U4bwktcBqBE6VVbOSfC0bAC1/nCoHcLS+NXVFQBgNBphsVjg9vbWHKQOYnp5xBAEWObV68bPRAe3WCzw8PBQ0w0lfiyD02g/fPhgcwd+7ipUGj+3DBS+BauEnARgsVjg8+fPdro6v+ucWVEUyPMcP//8swULPSgVON4gESP8/IkP5PQdDw8PNqyuh15ypxKJ4q+//mry8Md++F1NseiFb2H4FjETLerG09MTABzt2OJrGfy63S7u7+9t7kY3e+j/0c/wvXBKtk3/S20BwFFyrLObbCNy2FiTLP9/SIxHoxEA2GzadDrF9fW1xRn+Dx59oHNaz63Jr/1HEE7VD6BuK7vdzna7fvr0yfSFvoPPp60URYFff/3V9EJ3RKuPSgPZXwFvVFol0B4mqwFPT094eno6usM0ABsQoxOYTqdm0KHeuH7/3mt6beVIh8c1w9GMh4pK586t/PP5vKa4fD8Ol1IedOh8XWjb5Y/KAL8Wep3U2XvCuFqtMJlMMJlMju62rUP6lAUATCYTm0vj8/310nmNmBDKNjXjXa1W+Pz5Mx4fH+34C60ucsgS+DKDNplMLJGgPZ36n7ERgtB39R2r1QoPDw926J/P7Hu9ns1lZVmGp6cnqwyEgn9MNqLQZE8H0zlzyZmhyWSCx8dHAKht7CB05irLMkynUwCo2VbIVr63nfgB8FPrVp/ruwz6nb6DftMTJR/U+bvKBwCGwyHm87kRI35evpY+t4kkvCQ+/MjKkff7tJX1eo1Pnz7h6enJqkQ6o8YuBPBFdx4fH20ntG5uORcuhhx5dqzBXYnCer22W4jwtFteYGbA0+nUgh2PddfdCT6T+lFE4LXESF/ndwpp9gvADi6bTqfWMprNZkcVEn/bFAZD3gPIkyktQ/v3Oieasictg282G9uSy9NduR7KgoRwvV5bBshsSHf+qY4oSYhBFqegn5mZMKsDOmxKW3l6ekKWZVgulyYfBonQYHqsZKAJvq3i5cFrS3lMJhNkWWY7lPR2I289fPw9oGSBdsIt/JQFK8+aPFIetBdW4nnys/pSlYkPuN9LNi/1pf7vTIhIblgBrKrKYofeeYAnqXuwbURipG0kkkcm5Pzdb/z5murJW9ibH1rnZ59MJlgul5ZYed/x+PhoyQR1g1VY9Zvn8hlxpbE/AKpMujNNA7jvraszVGXlhfW9cZbLge/L0L9HQFHnpo4eQM056cyNzhE0/U0VXfvjJGYxnhwegt76gu2AkEyAgxMDUHPo+jMrS0qq9IaaMcsCOJxvBaDWOlOd97bCn/ld5yM8OYx5/SE701ti+CqzJgTAsTyoG/q46hMRs0yAcJuNyaLqhv4OHMtDK/j0HUqE1Hd8b3vRNt/XQv2/+sH9fm8jCX4miQRIfSpjiifMTMj5XMpDZaFJXmyJhn422orftaq6ARzswR8oHBrFeGs7uZjKkYdXJL/VsN/vYzweW/kPOGQ+ZVkCgG1nLsuydusEvoduVSS+Zxb4Ne+j/9+fi6HGrcGcFRAexKbzEzpMm2WHLd5cPzOpphtFatUqliDg5450S36v1zvSDcqEswF0WtyKq0c+UKbcrusRa5VAbUX1Ic9zO9xyNpvZ/BXl0u/3bQarKAr74llQPnFQe4lJDn7WSOVBXacfGI/H1j7R9jvlAXyRIX0Hj0Sg3uhtNmKSgcIHZF/95PEG/X7fZmfYJlFSqDefpjwoTyZTpw7E/F72Qh18SauG/1NbfqrD/FmHqikT2gFwPNCu66Qf5XtXVWVVNp1DUwJEwuRjmn7mt4T//wBsjdT70WhkvoPtZtoMb9TN20/1+32zG/Uf/n+9FS6OHKkQaXgUcFEUKMsSw+HQmD4PfmQVhQpLAsULPBgMzKhJMJTV8/9978rR1xIkfh79jHTSAGpDo1RoDv4pi9fDuGjQdHJ8H+AwiOqdSEzVI/85eIw9SU9ZlhiNRqYbPPiRRICEh8SIMhgOh3bDRP6ft9CP7wF1tjr8yHte3dzcWNuVZ7GwncSzfzqdDgaDAcqyxP39PUajUe3Gw7ESwueg8zJ04NfX10YQeR8sOn09y2cwGKAoCtzc3DTKw+tGbPC2wiBOAqx+FEBtjpGvob2QNPC+lBr8drtdzXfo//6ecml6L99yVzKoVVD6QvUF6lu5u5c31uXckd+tyWoy18uDM7lLi/LQirPenT60jnPqj8YZ9R23t7d2M3K2zTi3yNjT7XYxHA5RFAXu7+8xHo+t+v4jd9q9BBdHjoimjJVVgU6nY4NwLG9OJpPajf/o4MqyxM3NDYbDYY3dqlL8iAv4re+pJElJDndaDQYDm6sCUDvptaq+bNf2d5zn7yQEvrJGw+f/jykwetIYynaGw6EFQw5a7nY7PD092Q1mGfy0wnJ7e4vRaIR+v187MDAmctiEUKCmgx4Oh7jsRKEGAAAgAElEQVS7u8NoNLL5NA4lUx6dTgfX19fI8xyDwQB3d3coy9L+1lRVjBEalHWeiA59OBzi/v4eg8HAbtq82WxMHqwcXV1dmTxubm7Q7/etauJ38QHxVRS9rRBK/kajkflUADZfxXkT+go+h77i+vrabIWkifLwCcX3wimS7ttTnijpSAIrZnwfEkUedkmSRHLU6XQwn89RFEXtxro+aacstdIIoEbImj73a9f0I+F9x/39Pa6urjCZTIw8f/78GZ1Ox0gyfQd9DWOu6t45qs0XS46AumHrlsGrqytz7CQIDIhZlpmyd7vd2p2jmT1qxsz/8yPwNQ7TOwGt4Gg5nxUjOibgEASY+dFhcc3aWiCBZADUE1H5XjFnx9riIGHM8xxXV1f2e1mWVjXR+2QxyLFyxIxYS8Ka9bUJes1ZCSKRHA6HVmkbDocAYLahpHkwGGAwGNTs5Nw7T16LUNW1KArc3t4iyzJbJ+VB31GWZU0elAUf0wqA/q+YdUWTS9o6262aePX7fRtRoA3Qjugr+EV5MuFSGfwIWeicjn//UOVIof6TX5ps6uMAzBdyZk93ujLRpEz4fAAYDAY1n6R/V38aqrB5PLem7wl/7fI8R7/fx08//WTXdjweWwWeRJD+gnIZjUaWdGsVNlWOvgOaAjENsdfrYTgc1rYJclvqfr/H7e2tsX9mgayYsLdOx+CPR//R63nt63S+SDN3JXqsoLHXTUNmK4E3P+QMCR27EkvKQmdx9P/FlBGHnC8dHTMZtlcHgwH2+8N9xa6vr21uoNfrWTAEUNMPZpJ+zoSISR4e/vpxLePx2GYEOIh7f39fO7SN82rMAKkzlNePtpXvBX4+bTUyEJIwUh7j8dj04+7uzs52on7wNaxAsy2tgTU2GwHqwU4TQNq696Pj8RgAbExhv9/j6urKWkUkisDBVsbjscmEidaPJNHaoglVUU49rkRZdXk0GtV27nY6HSwWCwyHQ2w2G9uVx/vr6Wyiyph65WeW6HN9O1Z9+nNoWtf3gr6/JoVMLEmgWYTYbre4u7urtV7pO1i5p+/0Vda3tpGLIkcKzXKooP1+3wIXFV1P8GQpdDgcmjNQlq/tEg7e+tbd98S3Ok0NdnRKy+XSyBHPz9DDyIDDjTW5JjpF4FBWp0PjLAVQn0tQYhCT4yf42XXeSAk0502KorA5it1uV9MNQvWDzkwHvYHzlIVfA14rztIwu+92uzZPxCSCW5V5fyTOYJBUUj5eFrGunVBHT7thO83rB4+0GA6HNkexWq3sgFQGL95zjmQROPgO/b8xg/7D+1G25jXRZFDk87UaAqBGArwvVRv53v7Un3MUIkqnqi3qS0l45/O5VUjZTiMx4iyarls3v+gwN5NwVpWoL9r10IqS/1yhTQT6+X+0flE29AO0Fa6h3+/XTsfOsszuyUn78roSw+GoF0uOCF4w9o11BwkJEgMkqwYsged5br1mT650xiaWylGoVK9fzP4XiwX6/b5lIDp8SDLI3RMkBHQK2jOnTBgsmFWFZkxiCgC+csQAzjXx82tbgMSAg/l0ZMDhViIh/XhJCTwWaLWRVTLKgwGMZJqtJK6Vz9HHdVdjG6HyIGlU0sc1djodu9mszpxRr/wuvnNmwy+Fr2jRPvRnbuDgc3jtmYSx/Uo7UnmQcPt5xR8lDz/c20QcPHHi63RImDZNYsw2EYfLuXWdRIi+w8cV/m+9Ee1wOKzNHakf8eRRPzOvS+h3//P3hidn9B20FcYZVpBoO6yy6qYeJqpvMdP7HC6aHDFQ05hZ2mOQHwwGtRM6vVL7LaZU0rfqk38NdAiaPwOHQ9jYNqSS+tNsdeYIOFSD9LsqO8vAfoDuR89jfSs0ONHR8zAyZjv+buoAGnWDjpRybiMp0J1DZVnancRVHt3ul7tr+x0lmukBB4evswOxwwcQzYapH2yRUB5ZltXuQUi98IO0zP6934itpUb4CoQSQrZX6TM5kK1n/DB5YItIq8rAoQWjLei3WM9r5c3nq59kZYctNR5nwfuj6QYX6oVuWwdgsQiAEWmdyQrNHp0iCc+t6UfqmPpSrRSRBHIWjTvWWJVvip3awjznrOJFkaNQFqDtNbLZ0WhkQYBOjKU9vk6dGJ0eg4b2yFV5Y4A6Xe+MWPLk3BGrQLpuZf+6XV8dOwMGMwEOr+v2XMomVufvHb4OBTIbpm74Q9lIBLSdQP1QZx97a5HwpXl+dpWHzt6ozmsywQoK18/Wi2Z/baiW8Geuk2tokoffpk95ZFlmFSdWT5Q4xywP/Uyqx37HKv0oAAuGJEh8H60o60yntl781/eWyWveT/2WtpqBQ9WM7dX1eo3r62vMZjN0u10bws6yzMiiJztMJDWRAL7scOTcog60+80/oTj3rWv+VmiiySoqALMVJXokzZQrYxV1g/L1bcS3tpOLIkcevnLEYVEtmRJ6GwwNAurcOICsJKCp1BkDVBn9TglmMmqkfG5VfTkNWJ0i16wVEpIsHXjX3TgxOn+9XvycOkegMzKUi56qrrNG/Dt1gsRZe+YxEedTUMKj7US2WllV7HQ6FgD87Ax/Z/AMEaM2QVsxbJuydejlAeDowFQ95FBbJW2TiSYKXIue9uyrhqwSeKJI36pzbBoEY00ilCiTFHP0YDAY2IYWAOZjsyyz2U2Vg+qA+l36ZD++oT4q9ha9EiQlPyFb8UPl/J1V2hh8x8WSI1/q4wVj4NNdWQz+vqesF0RnbDw5ik1RPWHjZ9XqkQ7OKjGkDBgYgeMWmRIK3V3g52xilEuoXaByYaWQhIhyU0cWAqsCati+hdIG+MoRADv7Sh/X5MKvT6sLsdpIE7Tyyt+1BQTAfAflQVvR83q0CsvEjBWENsjDfz4lBrQVZvt6SjSryXp4Ykge3peGho3PCa0eabLU6/VqhEfPzeMmF+oH5cDns0oSGtfgrlCdS9KkNCbZhMDPpj6CCQLbjYw3Wm32VSH6Yn80zDlwceRInZtmw6yY0MBp1LyALH96p68BUgOf7wfHCHVKodJ3r9ezgx8B1E50ZdYbCoLK8LVPzsdjzQAJJY0ALMtVkkxiQ1mp8ftWIV+nO7xCrZOYZQLUHZy2yujsKQ8NFk22EqqwtkEGhMqCxIh2pPKgrWiFlq9XeZAotiXYKdhaon1zU4LejBs4HAXiSQX/xt/pO7SFEqOeKEHSL/oGAFZJzPMvp8dvt1sjA5QLdYWyUKIFHKpx3MquBEl9CT9TDLJRaNLp2+96pwC9h5x+D/mOGIoPF0eOCBUoFZNsVB0ccNjmyYFTPcpdL5L2jmM0ZoU6d+AwqM3MR4kijVgrSFRmQncsce08rEvL4/p3/RyxQB0eUM+IgcMtI/xWUuoISaU/PyrLMtu27Ql0G+AdHOF3m6gcNGjyPbhePfcqRvt4CXylFECNPAMw4hySh65dyZUGypjl4hMA4HBEAX/29wxT30Gd8r7D20rMhNH7UR9XOJBOe99sNuZHWYnW+SttMamesAKvpLEtySZwrCv83EqOmmzFFzL8TjX//m+FiyVHRMgRUTF5YXSuIEQqNIAqKWqDgwPq8zU6Q8I1sUqgd8wGUKuYkBDwvXSOQomROrhY5eI/l5IYbT/6nX/e2ev76evaWDUCwsGQa/OBX2cgfMDQ18SuC03Qa67BWwOb6oAS7pB+xUwATqEp6AEH4qzDyqFdYawQ8OeQrSghj1U+/nOSGGkbvSgK86MAapVnvp5/oy2xkqbHPbA65RPwWGUDhIkkr7efT/Sv4c+hNZ8LF02O1MHxAnFXmpZL/RbepuDZRgenn1WZelVVth2ZxqrDcgp1CHrTQD2rQgljW+Sjn5M7KKgbLIWrw9fX6GPq8NtGnD18MFQbImH0z/fVuBiyvu8B/dx07j7A8W/6Gl+FC9lF22TynK3wOSF7UXhbaYOe+ABOaAI9Go1s3oi3UFFZ+AFkbat1Op3a+Vh+QD0GovBShAixtyP/XP09prVeNDkC6hdAS3ha7gPq5wOFjCF04WK4gKegayO506oRT8hm+4yVM8qCQ7iq8L6CoDsqYlPuU/Cy0cpYaBdWqDLg36OJHMYuC4/QZ1eCDJyulPCxtq37FDT71Vkaws+hnfIhbcMpWwndyuKUvajs2mQrTUkDdYKt+O12a+c+qR/1iYOvIGmLUX1oG1pqHv56h/SjaU0xxdaLJ0eKkJPSMrD+rcnZxXTxXgrv3Ojo9cRrggarmbIPkFryDZVA2ygboH6UAfBy3dD3aqscPHwFRH/m7/o99PpLQdO1JpqSqlOEqY0I2YpfU6iyGJJDG+1E1+ITTp9wa1tNB5SB40RDOxNKiJ5LQGLGqc/73FpiWeu7IUdNQe21DDaWC/daeMem80T6Nx1EPlVJ489tqRSdwnOO6FQpOPRebZaFR+jah8rl7wGvJYJtDWyn8JKg/Vp7aRO0+sXvSop8AsWWI1AnRU2Jd9vbrx5t/vzvhhwB33ah2nyRCTVa3yYA6gPYwHHrRAcL+dilGLJ3+n7tnlifqh5dOt7DGk8hrf/YVvh46O98zqUkmoRWgnTGjOMIwOHoFF+d9+2ySyRGbce7IkeK96p4p8r9oYpJqKrkX3Mpsgw575Dj988/1UNPSLhENCUGz9nLJdjKc3bP9phvx4eSU30//bntMroEvFty9J7xXObnn9v0HpeM55xUU6sxIeG94SW2cIlB/9Sohg6e+xGGpiMwQj8nnA+JHCU0zg28pCqUDDkhIeG9o6llGNrZeOo1yZ/Gg0SO3ima5gL07695bUJCQsJ7g7bP/O8vTSyTL40TiRy9Y5zKdp4jQMmYExISEsLHOujfniM/l9hyvATEe9fUhLNBDdqfW5KQkJCQEMbX7mBNxCg+JHKUUMOp3SYJCQkJCc1IyeTlIJGjhBoSGUpISEj4OnyN/0w+N06kmaOEGp67CW9Cwv/f3rvFStdlV2Fj1f1yzvluf/t3q9tJg+gHeACDLMeR82BMRIzj4DwY5IgoBrXUL0QyMgiavCQgWYKXmESJUFoxiolCsEXi2EIowbKNkofg0I1JABlEx7Jwq43b3f1/51L3y+Lh1Fg19qy1q+p836mqtavWkI5OXXZV7T3XnHONeVlrZ2RkbKIsa7Sr5+hSd5xPHTlzlFHAOW7umJGRkXFolO1uvasPKfvaNJHJUcI4Vf06G2pGRsY54dS+tIw0ZV+bLpIiR7mZrYjFYnHqUzgpeGfrsueXhmwfGfsi20rRVvQGsIdGihvnXvpc8i5IqueobCfRS0Wz2Tz1KSQFvbfbpSNHnEVkeRSRbaWIVqt16lNIBtlW9sPebMQ5V3fO/bJz7m+vnv8259wvOef+hXPuJ51zrdXr7dXzL63e/9RhTj0jIyMjIyMj4/nxlFTNDwP4FXn+lwH8mPf+0wA+AvCZ1eufAfCR9/53APix1XEZGRkZGRkZGZWA26ePwTn3SQA/AeBHAfwIgP8AwG8B+Gbv/dw5928D+C+89/+ec+7/WD3+v51zDQD/CsDH/JYfcs55oHj34kuF3qgw95hsbs1/6aA8sq08Qm95k5Hloci+o4isG0WIfnzRe/9t9v19M0d/BcCfBcAuvzcA3nrv2eX2ZQCfWD3+BIBfB4DV+7er459ysheLLIOMfZD1JCMjI+Nw2EmOnHPfB+Cr3vsv6suRQ/0e7+n3ftY59wXn3Bf2OtOMjIyMjIyMjCNgn9Vq3wngDzvnvhdAB8ANHjNJL51zjVV26JMAvrI6/ssAvgXAl1dltRcAvmG/1Hv/eQCfB9ZltYyMjIyMjIyMU2Nn5sh7/+e995/03n8KwA8C+AXv/R8D8IsAfmB12A8B+JnV459dPcfq/V/Y1m+UkZGRkZGRkZES3mdjoT8H4Eecc1/CY0/Rj69e/3EAb1av/wiAz73fKWZkZGRkZGRkHA97rVY7+EmsymopnEsKyKsK1sgrTorIulFElkcRWR5rZN9RRNaNIp5rtVpGRkZGRkZGxkUgk6OMjIyMjIyMDEEmRxkZGRkZGRkZgosnR7vqr7k+m5GRkZGRcVm4OHLkvS/88TX7vr5uj8/IuBRY23jK8RkZGRlVxT6bQJ4NYo67zJlbgqSd/udw64Z9JzF7rVYel4xzlkUZKSrTG+dcwWbOTSb2uvV6+Tz2mXOTQ0bGpeAiyJHNAMWWNPL1svdjqKrjK5vEYhOAvcGpykmPOwfErm3fzwHY0J+qQjOlsbFX6I1w9bVzJI56/bzebb4i9tq5yON9M4TnYitEbN44l2t7F+x5Q/ukbeSsyZEKns7Me4/lcllQZuvMbWlNj4sNXCqDuQuxcqFOaoQ1cqvAKqtzmAgteX7q54DNPVWqKItYGbmMBOhzG1hsI1FVhPUdCr2umLOP2UqVZRHzIfteT+zYqssDWMuiLEDYB1WXAbGvfjDw3oZTy+RiyBHwqLyLxaLwXsyJc0CtsutfvV4v/M6pB3IXrNIul8uC0wfiykh51Gq1rfJI/fp3gRPfLqdWll2KyaFKMrEBgdWPbdeltsJj9a9Wq1XCRhQx31FmL2U+hMfq9Zd9pkpQkhizA3vsNt9R9rmqoKwftawMW+Y7bBBeRejcsg85KqtCpCKLsyNHZUpK5zafz8N7NNiY81aCVKvVwnG1Wi0cp6+njLKMgCWKKg+C17pYLDYUl7LQSVA/UwWoTLZlTOxnlBDo9as8qkIILDGy+qHOzuo+oddr9UFLs1WQh0J1Qu3F2sq2IIv/Y36iSvKI2YfVnZgv3Uacq2YrilhlQl/f93osaa6yLLbpxzYybHVDcSpZnB05Iuxkt1gsMJvNCoOn5TWNcLRkoE6tVquhXq+HrFFZqSElWOLD657NZuF9q8jWUG3kqxOk/pWVmVKENebFYlGqG/qZssgPABqNRpAFSXVVHJ3qyWKxwHw+L8iD/3lN9j+wmU1sNBrBfmKEO0XEygKUh7WhmBzsBMDn9Xo9HFe1IMLaCgNM6kosS2AneRtU6V/VbAXYDLpjpIAoy5zoYwZWGnxXBTbjrL5UA4pt16U2oUmIU/qNsyJHqpA6yXHAFosFptNpeGyPtySH2QEOUrvdhnMOrVYLjcaj6OzkkCIsAaCjpyys84s5NMqA8qjX62g2m3DOodls7hUdpAZLhmazWUFn9Bg+JjRzRNJMebVarYKzT9np22wA9UNtRZ2cZoP4nCRAdaPVamGxWKDVagHYPUGkiJjvoIyYSSViBJGTvtoK5VEVskioD7G2Qt3QwMHaSkweACplK4T1CZSHyiimG4QNwlU/+D5QLb2wGWfqhxLGMihBrtVqaDQaqNfraDQaJ51bz4ocAZuRH415Pp9jNBphMpmEAZxMJoXP6iDaklmz2cRsNgtEgIOuJIkKn4pS20lPZTEej4Oz53VPp9OCo6KRc8ID1o6u0Wig0Wig2WxiuVyi1WrBe496vZ6849eoj/KZTqeYzWYFYqDQcqwSIQBBHq1WKzh9/k85EozZCvVkPB4XbGU6nRauVycA6obaynw+D7aihEDlkdpEGJv01FdoYDWZTAp6vs1W6DsoB8pF+xZTkoOizFYoi/l8HmxF9YP2otG/yoP6AVTDVmJQYjSbzYJuMCtPqK3ovMLXms0mFotFIAPMQAPp6oWFzjGqH5SH1Q3qgm1F4JxCG9FsPD9/LJwNOVL2yuca5UynU4zHYwwGgzBwSo74Wctiqdjtdhvtdhu9Xg9AMfJLsefIMnrKgiRgNBoVyCKAMAFqBKhkqV6vY7FYoFarodVqod1uo9vths+rcavjTxFKCOjgh8MhJpNJ0A07MfBzqhvAIzlot9vodDrodrtBVuoQU3Vy9hrVVlQeOgHaXj1bHlFZ8H1CdSslmdjoXktIs9lspzz0OjXLyixap9Mp6AeDKtWTVKE+pMxWeBz/2z4z1Q+VBYANW+FnUgblwcl/MBiE4Co2r6gs6EfpIymPTqcDYNNfpCqLWLmV1z8cDsNcQ7JI26etlPmObre7IQva0jFxNuQI2CyraZpvMpkEBzcajUKGQLMkPJ4TX71eD4MymUzQbrcLjlB7bVTZU1Fmm+6kLMbjMUajER4eHjAej8Mkr+lxLStZeZDddzqdQLpo9Eo6UpOHQuWiDn80GhWiQGCdfVRdoSyAR1LYbreDE2DpQEuuqWVJgHgTpdqK6gd7kGq1WsgKaDChutFqtUKZAUCQhS3J2QxcCohljqbTKSaTCQaDAcbjcZgErO8ok0ez2US/39/wG7xu+pyU5KBQAj2bzQqy0MlP5UHZqe9g4DSZTAoTZhVsxUIJwXQ6xWg0KugGddzKQhf4kCCTZGr2JGV9UNg5xuoH5UHYfj1rK5xTgM3+o2MHVWdFjmzNmxMc2exoNMLd3R2Gw2GYAGmUOsD8Lp0Im80mrq6uQpaA36sEip9LDdpTo1Hw7e1tIAMAQqrfkkxgLQ9eM3tK2Eeh6dOUGyw1u6hyGY/HuL+/x8PDQ4h4aLz8HI2cxsrnNGrKotPpFBxiqtBITskA5fHw8BAiYmsrwHoVmsqD5AhYk0aSLm1KBlB4nBrUf3Dye3h4wMPDw07fQXnwmtvtNoB1hpHySNVGFDHdmEwmQRbT6bRgF7vspdFohIwRbUWDUiBNH0rEqhMMrO7v7wvyUDthKYlZMi0rzmaz4FPb7XZBp1KWhULJkfoOVmlUN4B1qVEDplqthl6vV2hTsFnIY+KsyBEQXzlG4TJbNBqNMBwOAaCQqmM0yMHka3RqVGgawGw2Cyw/1X2PbNmErJwpYDJ8YN0TwmvRBl1ObPP5PKQ/ef3j8Rjtdjt81vYdpQabGePz5XIZJkESXk5gzITw+oF1pq1WqwWC2e12Q+TIY1OPAjWo0Ah3m61ofxJ1g49ZTqOOaCCRcrk1FhSoLas8GEhQP9R3qDxqtVooPVMe1AsbWKWGWKlRbWU4HGI4HBYygSoPtQFg7UtZkuz1esEnxzJqKUJthc+BR9nQd1AH1HfQnth6oJl1BpW9Xi8Qcs20pg5rKxx31Q/aPq9d5xUAgTzSj6rvIIk69rx6VuRIldamNZneYz8FywXKXOv1eiFdTtZKts/GOY2mrAMBTh/5xMqLSg6ZQWK5gOSIxksFbzQaG6vZeAywbrzV39HI6tRyKEOMMLJJnZOfJUH1en2jdq6TAkmBLTHo76UGG6Fy0iPxV/3QSV9LrUqmgPUE2el0CtsB6G8SKeqH6i4nNNrMZDIJGegyeajvoK0wmCBhsJmmKkDL8swOMAOtGSIeq/ai38HVSFqarBJs1pn2wrLaYDAIE7rqABe7AGvd0gUM9EFVg84t1A8tNQ4Gg8K1UjcYWALrBR7sZeU8xe8/Fc6KHMXqktYBq9OfTqfhePZSkMEquIS/0+kEYjGbzUJKWJECMYhFN7ESjzbf8jmPZ1SrSsz3SKB6vV6IhKfTaUgJEyll0GKw56YN6+PxuFAm0SiGn9NSKlPkdIg68aUqA+qJ/le7YTmaq09UHnSCdmECycD19XWhrM3SkiIFW7FQQmuzGOzHYs+MlhXp0PV47T27uroKx5TJI1VYO+ZkpmV6ABvyUNLETBtLzzyef1WD2gqAUF4jUdLFGswm6cosBp4AQkkeiG8mmTK0WVrJncpjPB6Ha+Fx6kspD+99KKVpKRc4jSzOihzFMiY2GtYVbHRUVNTxeIxms4nJZFJIi/I4KoFG2jY7YI3mFIilOXXSpiw0kwQgTGaUB8tm7L/RDIGmwykHnRxSN2zN/HF8GckyU8KtG0imdbWeRj+M/GzfhI5DyrBZP+oIMyGqK4z6x+NxyA7QVkiWaHNKumJOP8USio6Z1Q8tB6h+qO+YTqeFvhL246k8AJzU6b8LtMTO6ydZ1FI9s8mTySToiq7upDy0rARURw4K6ohWE9R3MGM0Ho+DTyURoHwAFHyHJaGpQ+1F5UGZUD/YkkIyTT1ReXBbGJXFKWVwVuSoLHOkrJUZEWYJtJ9kNBphuXzcs2c0GoWNuZhJYWpdHWUs+j11xsROzNYpa5ZAV2Vx+brKZT6fh4xZq9UK10XDV3nwu6sSCerkrbrB12u1GkajEQDg4eEB8/k89ElwqSmwjoZ04igruaYGjd5UHlxlBax3QmZPlvc+lFN6vR6Gw2HoOVPirSt3bAkPOL2dxGAnJ+qBbkpH8gggRMWUR7fbxXA4DJsbWt0gAa+KjRCUi8oC2Nw8l/JgI3+/38dsNgv6QcKkBLxKsFl5XpP2nvJ9+lPK4vr6GrPZrNB0DBTtS0lzVfyHBfXD2rnaCnWDvpQZR01aWFs5tjzOihypIG0DMo1RjVIHAUDYn0I/w0jHEoqY00+lTGCjX22eBdZ7++h/vm5Zv3MuNKB6v97DSBWVZTU24uo5pAiem71u7atg9EudIEkaDAbhe1ge0FVrlLMlzykSAY32eN7aa2P/M03OCFl1A1j3oGm2iGRJS66p2ImFJS0xu7DZV2ZdR6NRkAflyS0NSLhj8oiRxpRgbUUzA7QXyoDlV06AwKO98DvYT6JlaPVP+nupoiwrr76W+8gpGQAeAyySbcqC0KyJ9nWmqhdELBNq5cH5gbqhPlR9h65m1flZ78Cg+nFouZwVOQKKTYGquABCVoTNYnd3d4XauRIrGnC9XserV69C5ogrC6xx8PtTcPxa8rPyUOfOpkoq62Aw2Ij49btUHrVaDTc3Nxs7SWu/TSrysLCZNW28ZlP227dvg64wO6bHcxdXAPjwww8DcdAVN/ydVAmSZld1zPg6CeJoNMLt7W2Y2HXjUB7farVQr9fxwQcfhPS5ypa/YTMmKclE0/ixnjGWkYbDId6+fRsII6Ndfo760Wg08MEHH2z0KCnh4CQIpCULwuoGQVuhLIB1DyNlor2OtJdv/uZv3morRIqyIKzvYGDBjXW//vWvB7/Bnj0ezyX7rVYLH//4xwEgyIPlWN3qInXYkjnHUeXx9u3bgm7Ql1I32u02Go3Ghh/V8T9FJv4syJHNlADFZqENTd4AACAASURBVD/NkJDgDAYD3N/fByeu5Sb2TfD5w8MDnHMhTWp7jfhaKj0UVh5UVv4n+VFZAAh7uADrSBFA6K9Rlq9p0hgZSkkeRIzEel+819x0OsXbt29xe3sbmrK1MZ3lRO99kNXd3V2hXFAFpwYU9YN/mhVlxHd/f4/7+/ug/2W6QVup1Wobt6ZRmfC1U/cUWFj9pXzUd0wmk7AflvoDXhP7J6gf9/f3oVygvsPqohLJFGCz4UDRp3Lyf/v2Le7u7gCg1F6YTZzNZri7uwsZxhhR5v+U9KIMmvWibnz961/H7e1tWKWlJVgGDc49Llm/vb0FgI3NdC1SlEdMP9Sf0Jfe3t7i7u4uqhvs0QMeqzb0oww2YtmiY8riLMiRZnLs65rdIXsdDAZhybayekbAdOyMeLScoEu1Yw4uVUXWvUaWy2WIah4eHjb6aoA1uWm1WmE1ynQ6RbPZDPsaaUOmlpHKekxOjVhPmpKCxWKB+/v7kCkh+dNU+P39PZxzmE6noazG+9SRcFp9SE0OCms7PFcSo+FwGDZ101uq0FasboxGo7DXE3XDlh5iTi8V8JxsxpXyoO9Qsqj68fDwAGBTHppx2yaPlHQkls3RYJM7qHOS32YvfM5MisqB363/qwIdR24w/Pbt23AXBs0W3d7eFpqPr6+vASA0cccyqynD6odNSHBuKfOld3d3IchutVoYDofo9/uFDNMp/UU6ocqBQIfDgdEVa/YPWPcX8LFGjlR0dfg0/BS66xW2GU4naBqz3hxQe0u0NKYM3sqDBErlAyBJeVhYkgQgEOXYBAZsyoLQzInufcPvTy2DprATsj5mKcjKQ+1D5aEkk05QnwMo6EWK+mHPycpDM0WaXQLK5cH36Tusvmh5MyWURezMDOj1qSzK9EP/A+sl/vzuFPVhGzTTx/tSWv9pr5/yUV2w418VgmjPU8uBT/Wlqkt83TZlH1s3ziJzFINmjYDi3ZB5u4d+v1+4DxQnea7K4rGtVquwOaLtxtdBSyH60z4h/tkdRqmAenuDbrcb5AGsiSLLaNo7oDub6q7a9tpTkIeF7XHguXOcucKGN+Ylqfbeh1Vq2nPEmrnu8pvqrscKOyHZrAlX4XA/K90bjEGG2orqhvabbJNFivoBFKNi3aenXq+j0+ng5uam0LBP/VB5tFotNJvNQAhJGMtuNpuSLGLBFbC+SSyvjSuOAJTaC/sVebsQ9mPpPlBVg/Ud7XY7ZIOcc6GUBKwzarxtCm9irr6EC110Z+xUSTOwqR8aADjngk+kL2V/q+oG9w/kHMu+RX437U37sI6JsyJHKkQycq1r8t41vV4vZE1YO+eAkfTQ2bdaLXS7XXS73UJGhI5fBy2VyEedrJ4fHRKAcG00VGB9Y0ndKZzECEBwiLxrsl6/Rn9EKvKw0KidUUun00G73caLFy+Cg2cJkc/VgZFIAsDLly/R7/cLqy34vywLkQpUT3QlInXj6uoqpLhJllgWUVuhc+v3+8FW+P1K1GO/nQrs+eheZ9SP6+vr4E+sPEiWSaA4WfZ6vYKTL9ON1OSh4D49y+UykBwSRBKFMnvh7vGtVmvDVrSsm3JGUcFxJCGgD339+nUomTWbzXBrJgaY9L9XV1dot9t48+YNAOD6+hqNRqPQj5YyMYpB76bQbrfRarWCftA2uIeg6oZzbkM3uEhBfcdJrukkv3ogaJpPiRInQk5q/X4/sFtGw+w3IYt17nFHbD6/ublBv99Hp9MJJEFLJqkpsU15avROxez1eri6ugrKyMiPTnAwGIQJAEBw+Or0OYnqnaRTjwRtVpHpcBK/N2/eoN/vhxsnsrGQvQONRgNXV1dhMux0Onj16lWBNGomLUX9UOikDRSjYd5Nnv0iXLl3d3cX5EFboS68ePECvV4v2A9JVCxTlZpcYkEFJ3LKA3jMso5Go6Af1nf0ej00m0202+3gO7rdbrCflH2HIpaBZ+DQ7Xbx+vVr9Ho9AMBwOAyBZsxeGGi+fPkyEIptWecUQfKi/pVlMvqOFy9e4Pb2NgTeH330UbCRWq2Gly9fotVqBbm9evWqoDtVIkYx/SAZ6vV6QT9UN96+fRuut1arod/vo9lsotPp4OXLl+h2u2i324X9tE4hk7MiRwotJ/GP2RKmPhn1sVRAAqUpPj5m2pTGrlkY/T0+PjX0HOiENW3LTf56vV6hjKL3B+OkpuUBOnwSIspJJ0D+ZuxcUoLueM0xfvPmDer1Oq6vr0Nz4HK5RLfbDYS60+lslCP5p7qR6nWXQW2Fen9zcxMysCRHLC3rcXT+zLDwdY0QCbWTlGWkPoTX8+LFi7D8uNvtBt/BEgltg76D8lCiWFZWSxWWyFEWr1+/Rq1WC43FXOCyXC5DOY06QXshSbS2knpApbBEn+P8sY99LPhB+g7vfZAPdYZ+k0S71+uFbBrtqqr6AayrEq9evQqyUFthOc3OIwwutfR6Sls5K3JkHbBGwhQ2V9Jw0LiqhkqsKVA6fRo206Ec/FT7S2yqWjNonLg6nU4gicyAAOt74sTkoY6s0+mE7ACdJbCeYFPMCgCbRE17QJhJvLq6AgBcXV2FTNrr16/DbVT0zvMAAsmkoVuClKIcFHp+SnQ5qXOyWy6XYUnuzc1N2KdHbcX281lb0QwVkFb2KFYW1rHkTvHamE6y+OLFi0Lpkfdd5GPaisrDZllTkoVCe68YybfbbUyn0+BDgLW9LJfLYC/ap8UMJLNqtCWbWUwdutiCvp8+kr6DNxqez+dBFsy4qU0BCPKoIjmy86zqR5kvffnyZdjXqdFohCCT8zMDCcpUkxzHRHXoekZGRkZGRkbGEXBWmSMFWT1vDEnGrhkk51y4QzZvGMkSinbKM8phmo/QlRapMn2ybkb3s9kspC55Q0TWvlk75n5QvB0GywSUByNpRsmaQYmV1VIE9YO6oaUgpsqn0yl6vV64nxifA+vt/gGEXgJdxRdruk0ZjOKZDWJGkFkybtnA3iM2YNr7RDFlrrKwG4LGSmypQPWX10jf0W63MRqN0Ov1wmZ1nU4nRMR63yze8oDZAV3xqiV8/maKWROVBX2j2gpXqzFrHLMXlo7YcMwSm/pSbe6vArRHT0vyvN0SfSvweNeBWu3xHo1XV1ehoZ33JeT3bWvTSBXU5Zh+sLeOWbHZbBb69KgbzCpRNzgn01b0d4D4op9D4mzJEcHUNh9r3xDJADc1o5Pj6pJ2ux32XaCDpJOMEaPUlFlTngBCWns4HIZUt+7rxOOZDmajekwenEA7nU5oNLXLLlM3cE5S3EeDqVxd0UentVgswqRgSfJyuQz1c9tnk/L1A5s6wusiSaKuc1xpP1zBxl6JVqsV9jTRVZB0kjGdSEk2Vg7AWj+A9QROGwBQ2L6A24BoSYC2YuVRpZIrwUmQY6z9UxoccazVXnT8dTUTX69SEKHgObMhW32FrsSiv1SZAeud4hmU6Y3OqwZdpbvNlzIAs314LFVTFupH1ZceUzZnQ45izo3ZAQ5Wt9vFZDIJ9WA2H5MgUTkZCfE5lxNyoGKKnJrDt/JgJEwFZL+V3heK/VgkTIyEeK3q+GLyiO3RkSqUxJHocRkuiSP3cKIc6PSUcAPFHgTNMKYuAwAbOkJb6fV6QR6MhvW2InzMfgH2rWn/AaPqKjTdxnyHyqPb7YadfBeLRWgwBda7zutyfr2xKrDeRkMDM/2tFGF1AyjaCnfJ5/49MXuhz7FjT/3QoKpqoI4wa0RCzN60RqMRsqx6k1nNLuv3aAapCvKw+sFggpkzzgmcZ51zYbEP7QpYL/zRvZKoG6cMIs6GHAGbt4eg0rG8xom82+1ubFBnN7TTlV2cFGkI2jynig2k1VSpMuA1MQvGDEC/3w/kiHICENg9sE6rUwZsPAXWG0dqI2FVjJvyUZJHeWhzIQm2lkT4OoBw52iWLmNL11OUh52gdSUjJ/JerxfIMCc6zTbGbIWfp61odiBV3eDYWploCajdbofr1+Zku9Mxr79ef7xZtW4WSt+hDeqpQmXCv5gs1B4oF90hmnrDCZB2og23qduKBc9Rg4B+vx/8BHWD48vMKgkUbQVY75elZMD+Toqw+qGBofpStRWWm9VWgEc50o9qaU2J87FlcVbkCCjWJ7UWqssFmcLjewAC0wdQyArxs3QIOmh2pVqKhm3rwprm535PJJTq1Bjx6EaQKg9GBLpC65Qp0KdCz5GkkCtqVDdqtVphK3zNGGlWgBG16kbK129hyZEGEZQHnbne84gTv7UV9ixVabm2Da4YDXPiou/gdXIijEXDzDrRPtR3WJKYuq0A60m+zFYAbNiLvsf3SRy4pF2Jc+oyIGwgTHKkJUe9tRKPpU+xwZWWr1UWVZEHsM4cscKwzZeqrwDWvoMy2BZE5LLaM0AdjzYPs2GU5SMABeLA55atMhLmBMjPpBwNE3TU2jRHYyZjBxD+K6nkc6DoxDULZ+WRsiyAeMRDB0fd4HYPjGJsM6A+1p4C3citCrIAikRRs4wsL1MeSp6sbuhjkgk236qNVE0mWu5Q/bC+w96uh++pblAedhJMGTFboW4450IjOrD2m+zLsvLQbEHMVqoGlYeWUWkrJAo2+2bBgELLrVWRh+qHZpHpO1QenENj8tCyHIN3ayM5c/SesJkjoBjtcABp1GStTP8BRTLAAeOg2cxRysTIyoJsnatoyOSVHNHhcfID4vIgwdJmQkuQUpULoQ5fS6rsEVBZkDzHsh86YcQmv9TlABRloUEE0/9WHurU+HnKh5+P7Y+lskhdLrx+TnyqG5QHswXq3O130Fa0RFcVcgQUJ0AlRupTeFzMXvhZ1Q/rR6tkK0Ax+LYZEPpA6sY2WQDrxn5rI1WSRUw/OM+q79CSGm1F5cHPK9E6lV6cFTmikPmY/zlILA/xua5i4+Cpk9Mlt4yIOPnZFUmpKrKdxAAUFFibBbU8wJSwKjLlQcfIJd42BZqqLBQ2IlZD5YQOrEsBlEmMKGmGid+Vsk4orM1odkhtRUslVh7MJGn/CMl31UonsfPTCZD6TpvR/hFLkPSa1eFTztt+MyVYQqvXZxcn7LIX7cGpmq3EoAEF5aHN18DmvS6VQBGx3quqQM/V+g5mjoC474jZSqxP0f7OMXBW5AiI74DM3gh1TOrUCctqtcxGp6ikyA5aygrN81UjJSHUVQMqD923SFdYaCpZ08FVM26VhRIE249mwXHX0pL+VUkGwKbe2iZIBgTsIbCI2UqsB63s91KEtWmN8nf5Dkt8LEmMfX/qsH6D12V7jmJQe1HfWUVbUdgx1AwIbcVeVxn5rmLGyCJ2HRpEatYsdo1a0j91SQ04Q3JEWGdMZSUrJcPX93hsjGBx0CwxOhWrfQqsQvL6uSzZ9o/YSdCWkiiDbUSxKrBRj2ZSlBDGjtds06mjnPeFdUKqHzYa3qYbSqZiDq5KclG7YYaEjei6TN1+xmYllQzwGP1fFZTZig2gYp+zWfgq24oiRohUJ7atXo7ZRVVloeetY612ELMV+1x96an9xdmSI8JGPNqYrBEyV6GU9UVU3cnFHJumwIE16aESax+WKiudgVXk2G9VATEirZO+LdXaz8WWgFcROtnpaixNfwNr8hyLnPnfRpH6fpWgE78SJUsEtAHZfraq5DCGXbZClGVNzsVWFJYMx7KrPG7X95wDynqrgKLviOlSSrZy1uRIFdVO7oxw+b46vBjp0c9XiRRZ2MyHVUiNfiwJtJEAX7PfW0VY3QDKSZF9rcr6YGFJdEwe1qbKiNK5yCXmxAmbZSwj0LH3qorYuBOxns9tRPpcsE1H7OtlsjsHbAsgY+/ra6nZylmTI2Az3VfG7rcNnr5+DsZtzz3WHFomj1iUWGVZWOzruM7pmi3KJjRil63oe+ckp3fNfJyTDBRlE50tQ5/r9W/DvvPIuWIfUmSPTQ1nT46Ip052Nuv0lO+qAmwqGCjvrdHj7XvnIAuLS3Fg2xDLopVlA8o+f46IySP2/JKR5ZBBVFkXLoYcAdsJT9nx+7xWVTz1Ws6RJGaUYxdBvmRykG0hI+O8cVHkCIiXCPbJnFwCtskm47KxK5uW9SQjI+OccHHkyOLcS0TvgyyPjIyMjIxLRNp3gszIyMjIyMjIODIyOcrIyMjIyMjIEGRylJGRkZGRkZEhyOQoIyMjIyMjI0OQyVFGRkZGRkZGhiCTo4yMjIyMjIwMQSZHGRkZGRkZGRmCTI4yMjIyMjIyMgRJkaPFYnHqUzg5lstleBy7s/Ml4dKvfxtUTy4Vqh9ZVzIyMp4TSe2Q3WgkdTonR62WFHc9OfKO3WvU6/VTn0JSyLZSRLaVIrI81siy2A/Zo2RkZGRkZGRkCJJK1czn84uPiBeLRcig5VIBNu78fumgPMbjMVqtFrz3F5s1mUwm6HQ6ALJuELGbaF8q1HcsFouLtRPgsQzPuTXrBvbym0lpy6UTIyCnPDP2Q7vdBnDZ5aTsLzL2xSXbCZDnFYt95HHZGpMgshJn7IPlcnnxunLp15+RsS+yrTwdSZXVjo2y9KKmpo+tVFmJM/aBcy7o53Po6a5U+7vayiFt6dKzARkZGYfDxXqXp0wG3vtcp81IDiQbz0U6yr5nFzGyj+3xhwoyciCRkZFxKFwsOdKJhX+x1+3jjIwU8Nz6aImOEpyYbWyDZnROkX3NyMjIeF9cVFktFgHHolxF2USRkXFKPDfpUB3nBpN8Tct3+r5Cy2c87pTl6YyMjIz3wcVmjnahbElsLq9lpIBjkA0SHftb1jZInLKtZGRknAsuKnP0FGTHnnGJ2Ce7SliStC3DarNJGRmXjFyNSB8XQ45siUBfKyudxUps9jsyMs4FluTss2hhnybubCsZl4RtwURZ8PFcq04zng+5rJaRkZGRkZGRITj7zNGuMsE+78cyRpnlnz92jfG5lIrYL7Rr+4rlchlWosWyq3zfbs2fbSXj3LHP9hVl80rs8+eGfXxpaj7i7MkRsY0QbVNMbUrVtGhqA3lMpKjIz4Gn9NvEjqmiTMpswa5YI7gzty2b0U54Dys9joQplw4yzhFlNrSrhYM2pltknJN9lBHGMp+aWin+rMlRzLGXHfOUDfBiCl81vEukoop9TkYM7HZo2z7DY6qmD3rNminy3mO5XG5cj96yJLYBJN9Xp2938lZURU4ZGWUo2yB4V9+e2pINHKruW98lyExx+4+LIUdUYk39W+ZO2D1bdKNI7z3q9frG4J16IJ+Csv1ryqCTn75WNTJQBqsblhiUkaBtSF0mZYEDr30+n2OxWISbu5ZFufV6vZBl4vv1eh2LxQKtVqswEeRyW/XxlMAqVn49hzG35Ed1PDavqK/VeYWI2Yf9jiogJo9YOTEWhMZwyus/K3IUI0N8bbFYhAlwNpsVPscJ0T62qNfrhcmhaopsDVoJoiKmvPtEN6lfv0WshDSfzwvH6LUyK2JJZdUcm80YqW0AwHw+x3K5xGKxKNiKzQTVajXM5/PC9zHwqNfrqNVqmE6n4bHNOlXt3mjv0xeSqi48FVZ3gPJr0yyi/Y5YBrIKiJEi/rdZJOsj1I/aG0fTPtS29HtSRywLbcfeyqPMl/LPfvbYOCtyBGxO/Pqc0bAeR9iSESNffc7MQq1WQ6PRKGSh9HtSxDbl3VY61Oc2Q2D/yj6bIqwjWywWWCwWG2UhhfbQUDf4XSTVdHK2cTk1WPsgCeLr0+k0EEX2EQFFgsRrpm1QprSdWq0WMk8ACjIC4g3eqaKsfLLP56pmGxaxLKPNPsdQVlJNYeJ7KvT8ddKnvdAflAXWPDZGGOlPOLdoZaKKc8tyuSyQRiB+7taX8jn9p/4/RZb5bMiRzRTpQDE6XiwWIdLlANKZ8zMKKjIHsF6vF8oI6uxTdvJlcrFEUa8rBqukVNyqZdGsPKgLKg+VRZlh2iiIeqSEWYnkc1/DU51GLLOqEx3lMJ/PMZ/PMZvNCvYDoEB27KSvzk5tRSNmS5aeeh2ncJJlvgXYrudl55qybWyDyiEWZHL8CXudsaDKImXZxMaffkOzr/yz84qVlWbPSIjsnMS5JWW5AEXdoH7YrNq2TLG2NNgVr6fKop0NOQLiSsuBms/nmE6nG5Ohjfrp2PldVM7lcolms4l6vY5GowHvPZrNZiGS5u8eYjJ8V1jD5LXbTAHf4zXrpKWTvV4boxwtpShSkUEMVk9ms1mBNFpZ6Aosft6WhxqNRsgqUh6HksH7lCWsIyMZ4rVPJhNMp9OCnXAy1GzRYrEI18r31FYajUbIsG47532d/7tkbt4XsSyrJXxAvBRtS/BVzCDFAgn6TZUL399GdFUGOunHgquUYLNGOv60HSUE/FOfoas4gfX18zsbjUZhbgGwMbfwtZQQI0WcW2Ly0OwZn/M160dtIHbsDFLlyVFZREdCRKc/m80wm81CuYAKbo0b2GykIwFotVpwzqHb7QZipEZtycGpEcsWMTPA0gkdHbCZEraZJDV2GrJzLkyESpxSM2JFTB4kBWrQ9ng7ual8Go0GWq1WwcAP2Tvw1IyRJfD8b4OHxWKB6XSK2WyG8XgclQc/W1Zqq9fraDabqNVqBVshWbQRYRnpUaLB58ckSNZ+tCxv5WHPS22lVqttyKBKsJOf+lXrQ8vIoU6ItBcAwWZSJQAxH8pgwfqOmEz4OOY7qP/qSzudDprNZjiO0CxUavJRwshss/pS24KgwZJNTtRqNbTb7fCfx/GzOXO0J2yaUid6GvBkMglEYDweFwZtOp0WhK2DSGXk+41GA81mE61WC957dDqdwPhtFuGpJbZDDrpGupodoCxUeVUejHb4PCYPykQdgm2+1c+cGmVZNJUHnd10Oi18ljKKRbkkA7PZLBi04hB1c/2+2MRiJ2sNHHg9jHj1munoR6NRsBVGg/rd6tw1A0B5tFqtsFKt2+3Cex8mRB7HDJPts9Bz3nYNh4bqC8uMVh48Z23kp0+gfGgjnPQAPNlHnAI22KQcptMpJpNJ0APaSsx3aCmV7zMDT3k0m82CvPTYU0LHX6+f48+gezweh8Dbe4/JZFLIilAWDLRtNp6y4NxCv0R7IWKLGk6FMl+6XC4LvoNzsKLMlzKImM/nwY+2Wi0AKJQZiUPLIClytCstGzue/236n4rLwZlMJhgOh2ESJMsF1kze1j1p5CQCrVYLvV6v8BuaSYnV2IljK3Ms3akZtNFohOFwGCZEAMG4bfpX056UTbPZRLvdRrfbDTLgONDIT3Hdu2CNmU5tPB7j4eGhQBD0M1Y3tWGfZICEma8Rh0gHU+Y2Kt1FHmJZABKj+Xwe7OPh4QHj8TiU1zj5s/SsDl7JH4kOdYOfs0SI36XnpUTv1HqjesLJcDQaBTnRVnhsmX4we9bpdOC9L2RKTn2N26B6olkBkgH1HXZFIz+nhFflQVvpdDph8jtGKfopiM0ttidvMplgNpthOBwGQkCfwuum7PQ77cIEBpm9Xi8EsPP5HK1WK2RRbEC2z9xy6KCbiPnSwWAQCNJ4PC58TucL+g7Ko91uF/zoKQlhUuRoXyGURZSqiCRGnABo0Mpq9XgOhq5Y0l4aToCMHgGELAnTiJoGtJGQPedj1E8tQdIsyWg0wmAwwHg8LqxK4jlpFo7yYIMtI51OpxPkSPJEmekeOSk4O6A8RT6ZTIJ+UB7MqqgsqBOaUQMQiLP25bCHQPXrOcuuT3EY26I8YL1Sj7YyGo2CjoxGo8LEQHnwe7QHiTpPZz8ej3F9fQ2gaCt0lp1OBwCC7FRfrPN/auD0XKAucxJU/VD5aXA1n89DtqjRaAQSQRshAUjFLrZBbYVZIvUdGpCqvfA57YW9ZyTO2uxv+0s0OD3F9WqG1F4/x56ZxOFwGP6oGzqv2DKsEgNeM8tqnFuWyyVubm4KFQ5C7Sx2zrHnh0SZL6V+6DUBKMy71q4bjUYgnJp1pO8AiuV84LBzS1LkqMwBWoWNfc5OAFRekqHBYIDhcIi7u7tQPlHmapsMtU5O5W2323DOBUY/mUxCRKTOMHZNsXM+Fqi4lAknvrdv32I0GoXITxVPJ1J15JzEms1mcPh8PJ/PN6LilCYAjWrVoDVzNBgMQrZEU+NKvikjOr5Go4FOp1OQBb875syeA/vYyrbPqjw0I8CS2nA4xMPDA0ajUXD46qC0VGAnsmazGXps1FYajUaIiHkeMQJts0cxWzmGXnHcmSkaDAYhuzibzQoZVi1PM0iYzWah3ErC2G63CzqVkn3EoHbCbMloNMLt7W3wHRrlK7S0xtIjyRH9CkuvzCodwlZ4HfuSazuf6OPZbBYIwGw2Czby0UcfbcwrQDHoVr3WYIAyARDKa+PxeINYbvOpp/C5ShpJkkme7+/vMRgMQpKCY6vy4LlSN3jtAELwbXVDr+2iympAOQsuyxYBayev0TCwbjjlZDccDgv9FHb3X+3N4aDRwNvtNobDYUgLt1qt4PDIbnleOrHuc73brvN9od/LKBhAYPTMpgHrGrBO/vyMZoSY/qRCa6mhXq8XJkA9hxRg5azp78ViEYgBZcHz1n4tlQWAkB3w3odSEicT1Y3nRJmjLyMU+pzZDc0G0QZYEhiPx4FETyaT8L16nJIDEh9duAA8ZoVIlkgW9FxoX7GeIwsbAB0DOtF574Pj56TF86XcOOaaPaWsut3uRhYyJduIQc+NY039ZnZAdYDBofboUE+oa8w4t1otTKfT4Et05etzy2QXcSjTqdjcwse8dpIA2or6DeoBs04aZPIx5xCSRe3JUv+hJXsbQOg1HFqfYlldnl/MlwLFvssyX8og03sfsovUC91XUM/j4jJHsT4K6/Rt6tM6b5aKGOUxtU12S8Kky/Y1SlLHT6Jkm4/52CqLHbAY47XG+JwlA/1uZes6aWsExDKHRjOM6jUlCqyXnWqWZBdScf7WiLWcRHlQJjZTQkKh38Xvo9y0Nyf2u88J1dEYQYrZi+q4zQIB61K0rujka7PZLOiDBg3zs1PvKQAAIABJREFU+TyQQ9tkqrpXZi+2Wd3ad6zEdqzymsqLmbXBYIDBYLAx0ZEE6XlrllVLtakTIqBYUgIQtRWWkjRIANb9ZfZOBJQH9Yi2or91KOJrMy/7+GSOPd/j9TPg1tXPbFJn35HNwPOznN9iSQA7t1h7iem+zRjF9Ou59c1m4Hf5UvoIGzDwu3idfM+2vGw7j0MiKXLEi40t7dwlKBtVcjJTJqtp0OFwuDHAZLX8Ho1+F4sFrq+vMRqNcHV1hclkgqurqyh5sAQpRpbKrv85nENsQrETIoBg1CRH2kjIiVcnAX4XV5l0u93CFgksGcSuNYXJQB2LjpMaOPf4IXnWtC+jYEJXI9VqNVxfXxec6CHT3LpSbB/nt02vtI9ql60Aa2LGqG44HBb2/vLeo9frYTQaFWxGswc8Jz4v6087tK3sA9UZ6oHKpF6vb+gL8Og3JpNJ6K3id9msZYqIZVf5GFivBtaeRR7XaDQwGAwKpRNgvUihXq8H38k/ltcOeT16XU+Ru51btA1B+/TUVrTUyGPoY+hbmV2fz+e4urrCcDhEv9/HeDxGv9+PVkK2zS06j1n/fyjoNanO0JfSn+o8QN2x58Xm836/XwjkY/OKXu+hkBQ5stHjPq/brA9B0qJLLnVpMl9TTKdTNJtNTCaTkCWq1db3h2J0rFFzWUZrG+y12MjgfWGdmpYL6eAZ8XAiBNaZpUajETJsLJMx7WsjBZ2cbaPyc17Tc8A6OY3UdM8nlhxZBmIKnQ2D2lwMIGSNYhnPQ16LncCIbfJWW9GJjvZAh8TsmZamFePxOPRGqFy89wWb0cnAknOScJvp0ueHtpVtctLHtA3vfZCN2gfloARhPB4H/eH1xyL+FKF6pfqimWfaCEmQ9R1aaqWctCfPlkgO0Wu07bqI2DjErh9AmE/oP5kdof9QPeH3cl4Zj8dBDrQRANG5RX2pBvzbsG3uPAR4nsBmZtH6UmaP1JdSR3h+JJta0dlGZi8uc1QmiLKMxLbj+F1UWHX8bDTlcTR8DtxgMChs/qgKzu+xk8y2c9o2kNYI3hexqE8JjJ4PWTyAsMUBmT6zQp1OJyg3SZJNK9N5nILhPxWxKJK1fo4ps2ij0QjeewyHQywWC3Q6nUAMdCmuOgfKeJdevO81cEJ5apSoy/Htd2m0qpk0zcIC6xJco9HAaDQK58EJkYRJ5aGkCCj25z3l/PnZY2WOSHZ0Y0+Wy2gr7Ge8ubnBbDZDr9cLn6PD1xImzz8lu4ghRva5IpE+U3fbp91w1VGv1wv7f1F2WqpVP3rIzJFez1MCCaurwDqDQ99J+9Ayox7P3s56vY7RaFSYVwCEwMLOLWqjZfqyjw4d0k5i2XG9dRCD8eVyWepL2VvFbDRlquW1Y9m6IilypPXdp6Y9qYy6LJ2CVlJEwWtd1zaZ0oFp7VwZP0mDdXr7GneM6O3bwP2U76dMtDmSslFGr9erkQ8bcdmwrSlTAAWCRadojVmPSwHqiNXZaYOypsu1QVllAaCQFvd+vfJKCcGhoM2PsdS6hU4K9rq1YVT78vT6Y7aiPUg61vwu2owlSKpv/Myu849dy6FWNdnH1lfoStjxeAzvPQaDAbz3eHh4CGPCrT9IGiln7e1LGepXdaLXQJOPgXWgxewAgCCX5XIZGq+ZGeD32RWMh7oW/i8rRcVg/YH6BQaHDCB040Od1BlYaIChPVmaYSVh1P60bf03u8jdIRrcY+dldUArNHyN18s95NSX8jtigTt/79gkKSly9C79Ezxea7x8nYqpTJQEaTAYFDIEGtERTI33+30Aj/uyDAYDvHjxIgyarsChEdnJYl88Vx+FTppaylASR1mwsRJAaDRVJ0eZOOcCw3/16lWY+EgoCUsSUyNILMeogfPcWHrlUmVeH8k0sJYFVysCwMc+9rGNvWz4Ozaz+Fxy2GUrsUleZcDPqn5oec1mz5Q80slrqY2yYMaEqztfvnwZSDkdP6FlTcrLlp22TQqHysjx9wmVEbMDX/va1wpNuHT4JMlc0fqJT3yikGVT3ThFNPwUqCx0nKgbzMDf3d0BQCDVDLx4bK1WC77jww8/RKvV2li4oDbJ33xOn7GLRJQ9tnMLbYBjz5IatzW4v78P84kSR/1uO68A67klZi/8vljywD4uu/bn1jW1VbVj2or60ru7u6gv1SCiXq/j9evXob/XZpRjFZpDZ16TIkcUyFMuWktFmuKloWoGaTgcBsavmSQ1THVcqthsMH3x4gWm0yl6vV5hwrTKG4tQeL76mr3O5xhsmznS/zbymc1mGAwGAID7+/tCsyGhKwhIknRyjGXMytLAp4LNaKkj1izadDrF3d0d7u/vQ8lE9YCyIDEGHuXGFRhl0c1zy4LZB+tE7G/FHL9Gdlri0OyRtZXhcFiYHIF1z5BmFvi73ACStqLy18yRPc8yOcXI0iF0K+aAaTt07r/1W7+Fu7s7PDw8FGwfWE98zjnMZjN89NFHIWMSmyhTRJmtMNgkOZrNZsFWgHVGQPWBZVZ+5+3tbciQ2CDqkJPeU+cWHsfr1WyqvSkzd4PmPMPH+8wrwHpuubm5KbUXu3N4GUEqw3PLU3VC5xjNEpEsWl/K82WAzeeDwSBshmkDV/3dYyEpcqSseNuAx5wk/zhRMK3Px1R0uzEksI4IAISoRlm+lgVICNTB618ZKSgrdRCHHHQ9HzpyRru8TQSwzhzpZ9h0C6zvfWObuS3ZKCMIp4TqjJUHjZYb/en297wW9ghwczZmDIH1dhFa1j20PLTJOaZv1j6UnNt9UtRWNOKno9cVjTFb0YnQlmBsY6WW1VRvbKlsm/0cQ7diWZPFYoG7uzsMh0N89NFHIaOm2aKHhwfUarXQl3Z1dVXYOdkGErGg6dTYZSskCPQdJEecANVeHh4eAKx9BzMt2ppwDN+xa04pg+77RluhzXEuoI3QZzxlXgGwEWxpCVrbIp4ytxCHthU9H80sq35w41Q9PjavsE+NgSqPtyTsWEiKHMXShjHEBGRXwjCq1jISFdA2VFPozAqo82ZmShmvHUAakJI0S/L2cYLPGTHp98QeUxa290Yf04BVDsyQUGZ0AOrU7I7JKTl+oHg+qjdK9DQbQBJiGy1ZStK6uC2l8TeeWwbsUeD/susrI0n6XJsnbW+N2oo6KGsrPCd9jQ2WlCcbeSk3yiW2sZuNtvWctVxzaFCWzJTq+GrmmNevj23jO4+NXU+qsLbC81dbUTtRcqTlkcVivdOz+hSbSTmErfDcrF7tA/Xb6u+0/YBESdsReN3qR2PzCoCovfDzbG6OzS374F1J4btAAyCdW2LzimaMqBtqK7GMtN5GhK8dEnuRI+fcrwG4B7AAMPfef5tz7jWAnwTwKQC/BuCPeu8/co9n/F8B+F4AQwB/3Hv/D/f5nbKocY/zK3yer9nVRMC6J4Abs6mS0nD17tncnZORNTMHulmiOghG3vbcCKuo1lE+B6xBxCYZGjojXgDo9/sbde7FYlG4MzJvo6LL2S05tNf7nNf2HLDyUP1pNBrodru4uroKZRRgvQcUV92o3Nh/RCdGuR5y4ovdvJXY5gz1PSUnaitKPHidaiOaSSmzFQYRqhuxoIYOkOehOsvzjaHs+94XNmMErGXMG6VeX1/DOYfb29sQ/bIxu9vthshYb65KucSuMXWorEmQqOOdTgdXV1cAEOyF2aXFYlFYpdZutwu3luF3H+Nec0+dW0iC7VjpeWoJyHsfiD+vj1kmAKW2wscAwnJ+nVti57Mv2bEZwOeElYf2ZfE/d7zu9/uFxQkkkNxJn5kjvcmuksMy3Tg06XtK5uj3e++/Js8/B+Dnvfd/yTn3udXzPwfgDwH49Orv3wLwV1f/98K7XKyySyog699sglTnT6G32+0CaVoul+HzOmj8770Pm5Utl8sCqwew16QYS4k+t+LarAV/R0kel653Op1CylNr5977YOiUQavVCncZ5/drVKUk6VBR4LvCjgv32gAQ7gHW6/XCqhPeD4xREHWDBJHPr66uwqQIFCcQjfieUxb8/rLrtMcq1F70XoDUdZ6r2g4JEh3hLlvh+7QrGwEDKJCFsiAiZh82G3soaFmEtvLq1atQMuM2Blya3Gw2g8z6/T7a7TY++OADXF9fB5lUhRhZneV10Qe22230+/1gKwAK9kLfAazvHUZi2e12C5t+xrI5h5DPU4gFdStmK7w2zWTwunWu4LFKjKytAAjHW3uxvtT6k32u4Rhy5LxLYszFCNaXsmxG38Dr0uO5oIPXq35Uf1//HwrvU1b7fgDftXr8EwD+Hh7J0fcD+Ov+UTv+vnPupXPu497739j1hfsqr3WMWtrRY3R1CJkpIxruXcTUr/c+7HFEJaVD52TY6/VQr9fR7XYL3633BFKjiGUnLA7F7K181BmRJPV6PVxdXQWjHw6HhX4iNsiR4ZMQtNtt3Nzc4OrqKii2Gr89h1QmAj0fjhewjtwYBTvnwh4clAebrhnddLvdcLPITqeDly9fot/vo9PpFEjooUo/6jh2ZR+ph3Y8NP1PZ8WsB+9tRGdnbWU4HAaCQGcXsxVOirQR3fRNzycWNOgxFocmRtbHUNbdbhdv3rzBzc1NIAeTyQTf+MY3QnakXq/jxYsXweG/evUqBBap2MIubLMV2ke/34dzDr1eD8Cj/2BgpfbCY5hNe/nyJbrdbtAf1QklxM8pq3f9TrUV6oBmvmgrJIjMkGhZnnv5cG8oziv0mZ1OpzC3KPGKZZGeeu7PDTu3qGypH91uNxAd+lLK5fb2tmAPnU4H7XYb7XYbL168KPhR3Vvs0NlFi33JkQfwd51zHsB/573/PIAPSXi897/hnPum1bGfAPDr8tkvr17bSY4ssVBYZxUjSKpMmuLUrIbexI5sXUsG/BwbSTudTnDqAMKEqAYNbEbB+0ZBh3AEsd9UBVO23u/3C9EPm+kAFDIBnODoDNrtdqHEpuUU/l7KE4FGJyqPm5ubQBB063tu+sjrJ2kEHm+jQrJEOZA8Hwoa2VpCWkaQLFmnXltHrAGF7have7MoqWLPUqfT2XD6sWyq/s4upxfLEB3KZmK/zT8GBW/evAnn3+v1wsolkmpmVmkz/X4/RMScFO3Ko9QRsxVOZCRAwFoeWn629sKsM0mz9Rv6m8+JbXOL/V17DHWYpTPVcZ1T9DpIkoDivEKCRNsAEMgWX9Osig3yn5qNPwbRtHasvlTLa9QNkihmY2u1WihBswytN909la3sS46+03v/lRUB+jnn3D/bcmzsSjZor3PuswA+q68xQok5xNVnolGyph75GsmLcw79fh/T6RTdbhcAwm6k7LEBEJw7s0pUep0oSBC0bs4BtOe9rewRFVrJNb8LVHk18lPFbTaboVdASytcteW9x83NTaFcQDJAZb++vg5OTuvHqW5wp/pCKBnudruFFSVMBy+XS9zc3AQnZ7MAzWYTvV4PnU4n6Ibq3yFIko7pU8DzYcMwnS/f63a7hfuBOecwHo/DLuksEzBaZiZNgxLKR0mn6oYSNJYQ+J+v23PeNxP73KCNA+tSNPenYXZtsVjgzZs3hXKS3l281+sVyvlP7X85BaytUE84afV6vY3+SrUX7tfD40mgms1myCrQTtSXHkom+36v1TNb2tHS89XVVVh6T1IzGo02ggjeK4y2or4BQLAR61s4t/D87dyyzf4PbStqkwzQdI6kLwUee1mpG4vF4z1KOa9odlF1Q+epMj+aRM+R9/4rq/9fdc79NIBvB/CbblUuc859HMBXV4d/GcC3yMc/CeArke/8PIDPA8AqI7WxNHmf3gKboVEnzTTucDgMpTAqLhWWz/kbqqT8Hk4CzCBoepW/p9H3u06Gh1JiOjZGM+12G91uF6PRKDQgA+s9fNhYyYmMPQJ0XsrqlVjFMkcpTgA0NGZEGN1y7DmZzedzdDodTCaTsBSXpQS9bmbP6DhZwlKy/txyKFvGb5/HAgo9N80G0FaY0mbmiLeAYHmN197pdALhBtbN3NqozvPUUqNmjuxEUYZDyXEbKF9mTOmsSYR1C4xarRZusEuCpPpCm6FupGgXMdCn0g+qntBWOJ6LxSL4Fa7oInFgxsRmmoH1IplDjrElFPY/j4mdA+1E54Ll8vF2GP1+P9j+bDYLN9TVeYU+VzNOtBUABeIYm1u06mHPS3FsndK5heNN/VBfWqvVwnu6SSjnFV6fkkcNojTYVl9x6OvdSY6cc30ANe/9/erxHwTwFwH8LIAfAvCXVv9/ZvWRnwXwnzrn/iYeG7Fv9+k3Ajaj4TJFBYoMX52Yds2TpPR6vZAp4mBq/wT/20HTxjqSAabKOfj8Pk0t8vftuW+RcXj8nL0UMeNvtVoYDodwzoVMmTYdMjOgu4qTLFAeTC/3er1CiU1Xa1XB+VNn9Jp4LTopcKKjbpBM6Fhx8txWKnhOlEWOsd+0eqCZGs0gAWtbYR8Js0y8Xv5n5kh7A/gbWnpgFEjiqOVoDSJiOnNqHVIfQ3nx/HVjvm63GyJiLZ8A61sQqQwOSZoPCW1IZkBh+7K0/MpsPG2IflaDEZKAQ8tjn7nFgj6AvkEXFnjv0e/3w2anzBjp8nO2a+i8orIgNIPCuYUZk9jcUmbj+7x2KFBGMV8KINgAN19m0K22Qt2wpbVTzSn7ZI4+BPDTqxNrAPgb3vv/3Tn3DwD8lHPuMwD+JYA/sjr+7+BxGf+X8LiU/0/sezL71oVjn2N6T0GF401jmb67u7srpCt1/xJmUcjm+XkahzYl2z4Nft++UXCsFPecCmCJljbP9nq9wl4dAMJrXMJPudiJjd/LSVAdf1WcvWa2mD2jntTr9UAcSZrtCq1Y6ZBOTBsqDyWPd9EZ6rgGFHyd48gsGbM/9/f3hbHncmPaCh0asN7oTvvztMSmJQo9p6eWny3ZOxSUxGlJnT0Ss9kMjUYjrNLSpdsa+fI7KOeqESOOEcvKmklUn6m3vaDtqL2o77C2wt85FPaxFz0/Pqa+skzGa+H4Mwtfq9Vwf39fyITqZ7QUy2wr39e5hcQx1gPI80pNb5REsjRGP8IAisv3WV5UvxHLiFE3tN/qORMH+2AnOfLe/yqA3xN5/esA/kDkdQ/gT77LyTxl0NVBquHKeQRWyt6i6+vrcHdk3XiLEx+bwzgYVGQyXDZesl7OCdAu69x2PbEs2CGcpU0/MlvA8honLWaKgHW/CLC+OS3lq5M9v0dXI2nDZqokycqezofjx34S3bCQ76u+aJYEWDdc2gyBErDnloeObcxpKAHX4ziemmmlrbCXhOUB3kFcNzvlpKcOXAk0vxdAaEbW6HfXZPgUondIqPw4llx9xh4L6of6C93CgOUE1S/bnJ4qYpOVjh9tRffEitkL5cRr5oZ/6odUjw5tL/seF5tbSJa0ZDqbzXBzc4PhcBhe10CAQab6RvpLyoj6cn19HeyFNlMWdKaiPxwrqx+ssFAe1A3dPNnaEHWDfpRy0iDlmNed1A7Z7xMN6iAB60mMLJWd8DFoiU1rwnZg2Ghpe1QsKYgZ9ymUWSdQndSZ+WJDpWaC6PiZLVOGT2LEa9XassqBx6ZiwDEoSaBBMgJW4kBZ8HUl4HZ/LV3GH9ODQ8hjGzHa9hkew0CBtsIIVm1Fe4f0FhG0FW2a5HdSHtQ1Nqqrjuxa3WgnqrLrPSR4XnT4ukM4ZWKzCgT7U0gEUg4ctkEzA7wWll3VJmgPlIe+p8SIJJyPNfA6ZKb1fb7byoD20e12w8bAvIE3iTGJgJI/nVd0xRszUFpWii0MSkl3NODS4NtubaDtLnqjdl4/sC4/M4vPz2lP0rGRFDl66sDr4NhSAdPcNOZut4ubm5ug2HprBCq9dXxq2Bo52lVJ9rj3uaZDQAkeJy46es2ikSzxON25WL+Lr9GgtRk9NQOOQeXB8WYfAJ2aRnsssfIz+h18rAsAjuHQrO7r/23H8hp0dRgjXjq2+Xwe+mdYPmB2lZkTEiTbk2GzLLqsXXWE51VGFmKk8tjEiL+twQXLrdxdn9eq187zZWZWe7AOlRk5BGJZFO2Zoa2QDHFstaRo7YXvMxg5Vk/J+8wtNpBSf8lVe4PBINiK3jaF8wqAwnfoOVGHuJmsBhKxQCsF3bHy4bhyPmTVhfpBX6rzig2MSJgYWFndOLbtJEWO3gcxwdEZAev7PtXr9bBTp94+hEqs36ERLic/EgKNhvn+tszRqaFNuHRMjF6Y5lR50Lg1pWyzaJSBzRylKgOFXgsnPZ4/r9nKwjZ0cgLg+DNzdAw52B6KbcTIni+fc3KjHqitUFfYO0CyqLZiM6uUBbMsbNano2M0GJsw95GRvYZDQ4MuLQXaJlJrG1ZPbHkkpUluH9jgSm+hw2wQj7P2omOtsmSrAvXlFJPfLpQRJI4lwdd4g2G2IzCgsCSC10e5kUzQVnSRgw2yUpGNQuXDfiw+pn7Qd3COLdMNyoLzimaOjn3tlSdHKjCb4QDWkwiXZdO56X2zqLxkuMC6xKSpPRq0ltR0AFODVSZ1aNpbxEjI3kBVy24kSJwkaARKDKtQNohlUWjI2gtAwqgRMh0edUMnBZYZSTSsLJ5bJvY69v1+ay9cWaTlQtoKndVsNttoQLe3VVAnSFloaU2DiLKeo30J0jGhesJx1qybBhH2PTsRVpkYAWtboa7Ql9BWgLU8WE5iuYRyoQx12XaKJSPCjr+F3kSXtqL3W6Mf5XfZeYWvs4/VEqNDL+x4H8TmXvUTljzH5hVmGXUO1XnllCXFypMjCxvl8LEOAOue2mQHIGy+RRJhMwO64aFltamye6vAjHLoqHTpKVcUEJZE6PdwYo31DKQmAwt7brbUw7FV3eDkBmzeEoT6on+HlsO79FDEyAivl9DUPyc5LcPydzXLassoMVuh/LR/gM9T1xcABQKkWQQNFsqIT1XsYht43mrr9LGUgRICZiJ1AgSKBCs28aUoHz0nJTQaSHPS5yo02gqwDiR0TuJzfp5BBAM0JdMp642dX+g3gHW1goRQs9yE6gZ9iZJDlbP9vUPjbMhRTGgUqtZ+WefWCE9LaurwOAnoPhS2hBQrE6QGPS99zKXX+h5loApsyYB1cKrAqcogBnU6bDzXSJCw5SgL1YVj9Qg8x3cqOdFAgOPP/XvYg0XoLUt03GkrzAzQVujobRScYrY1BsqHfgRAwV/YY2PPU57gdkGzlMyi0geqLuwjD+s3UydGilgWEXj0D1y5xtYNvq7lb51XtCG5Vqtt3DIDwIYvSVk+1pcyW8gs0TbdUN9jdeOU80o1vFNGRkZGRkZGxpHgTrECZOMkVrcPed9z0c+TtfOPqU6WjrjCQjMl7K2wKWRt0LUb/NnSwHNF9PZ6ngM2zas7hPN9lYfuYaJRE6PosrLIc7L8sgzOc0GjO7tjukY7Kgt7btvk8dwRz3PphrUP3dXX2gqzRrQFAIXNHvmeZmSdc4UGbC3B2RLt+8joULayDbHf2lbmPGbUe2h5WL2xWWY9xtqAZuCOnV19DnloFoh/upiF84odA/Zf2XlFMybOFXfD5vk/Z+bkWLoBPM2XamYpNqfa838uyPd90Xv/bfb9symrAduFx7onULwprBIFDog2ifF7+Z5djcT3U055ErY8wP8xJ2KvX//z8TEc3KGh52zlobKwk7gau0232+9NEbFSCG1A97vSa1db0VKZXdGofXjal6d2p+dQNdjzVqd+7rB6YwNS/V82sVXNbxKxc6XeA8V5RYMO7X3V/4RdkVUWhKWO9/Gl9nEK88pZkSNFrNbNRlOtkxN2yaX9DlXcWHagSgpso7qySJjvl30P/1fl2ssQk4liV89R7PuqBup2LAtgIzu9BYl+3srPOn1LpvXYKuMcruFdYK97lxx22VmVoNfAPiNdwam2Yre92BZs2kxR1WT0XL40hXnlbMkRQSGTwWvGRJdYsmRgm7GtwlaZGBFlCrxNcWOZkthxVYWVSYxA2gxb7NqrJI9d56/ZVV3FaKNCu5KL37Mrw1olWWWs8T7jVkV/aaH+QOcTbcOwAULMl1j/uy2jUiU8hy9N4drPmhzFBoev60SvLH9XNGyj3xQG8V1gldO+tusz54gyQlg21mX6VUVoEKGlAb5nU/3Wweuqt122UnVZZaxxqWMZm+T5uvULMTKgWdoUicH7YldWyB5TJs9T4qzJkSImdGaSyph7rD/iHIiRRdl1lBn3JWDXhH4OER4RywpSz20zJWEDidiy/JiMqi6rjAziKcFk7HFVe4uein3llNr1XwQ5KisJ6XO+r1HzOTL6pyBVpc04HGLZn1hjpa6q2fZd51BGycjYhV1Z5KoShEvGRZAjoqycpu/zfxmhykqccQnYpf/bgg1975wybBkZMWwLvu1xu74nIx1cDDkqI0P71n2zk884Z+zbP1VGfMr2tbHfn5Fxjoj1cMbe2+fzGWngonbILlPAfYjRJfbdZFwetjWY6vu2DykHExkZ2xGzpWwb6eKiyNE27CqXZYKUce4oi3jLNvd7qi0camfejIyUsG2By1OOzzgtLo4cPbXJOituxiVgny0LyhpNy7JIueco41JhFyNkW6geLqbnCIj3U8R2BN7ncxkZp8RTd+1+l++1z2OZJd0csuw4HvscdpRtMSNllGWHds0zWafTw0VljratOtvWTBr7fEbGKfHc9/OK9ULY6Fd3ite/XbvGKzF639JatsGMlBHLGG37bx9npIOkyNGxehL2aTqN/eWeiYxUYG/6+VywfUb6Gh+X7SJvn8fIW54IMk6BU/jufRb6ZHtIF0mRo9lsdpTf2eb4tynrIRWZ5zSfz8NrsZLFJSG2K/MlQ+UxGo0O+lv7BhBlz4l9b03zFPDGt4eWQZWRA7kieB/NYyKl24Po9V+6HwX2s4+keo7a7fapTyEp8Ca4GY/I8ljj+vr61KeQFHIEXsSu3csvDc1m89SnkAyyH90P2YIyMjIyMjIyMgSpZI6+BmA9H2HSAAAFEklEQVSw+p+RFj5AHpdUkccmTeRxSRd5bNLEKcfl34y96FKpTTvnvuC9/7ZTn0dGEXlc0kUemzSRxyVd5LFJEymOSy6rZWRkZGRkZGQIMjnKyMjIyMjIyBCkRI4+f+oTyIgij0u6yGOTJvK4pIs8NmkiuXFJpucoIyMjIyMjIyMFpJQ5ysjIyMjIyMg4OU5Ojpxz3+Oc++fOuS855z536vO5NDjn/ppz7qvOuX8ir712zv2cc+5frP6/Wr3unHP/9Wqs/j/n3O873ZmfN5xz3+Kc+0Xn3K845/6pc+6HV6/nsTkxnHMd59z/45z7f1dj8xdWr/8259wvrcbmJ51zrdXr7dXzL63e/9Qpz//c4ZyrO+d+2Tn3t1fP87gkAOfcrznn/rFz7h85576wei1Zf3ZScuScqwP4bwH8IQC/C8B/5Jz7Xac8pwvE/wDge8xrnwPw8977TwP4+dVz4HGcPr36+yyAv3qkc7xEzAH8ae/97wTwHQD+5Mo28ticHhMA3+29/z0AvhXA9zjnvgPAXwbwY6ux+QjAZ1bHfwbAR9773wHgx1bHZRwOPwzgV+R5Hpd08Pu9998qy/aT9Wenzhx9O4Avee9/1Xs/BfA3AXz/ic/pouC9/z8BfMO8/P0AfmL1+CcA/Ify+l/3j/j7AF465z5+nDO9LHjvf8N7/w9Xj+/x6Ow/gTw2J8dKxg+rp83Vnwfw3QD+1up1OzYcs78F4A+4fL+Tg8A590kA/z6A/3713CGPS8pI1p+dmhx9AsCvy/Mvr17LOC0+9N7/BvA4SQP4ptXrebxOgFW6//cC+CXksUkCq9LNPwLwVQA/B+D/B/DWe887R6v8w9is3r8F8Oa4Z3wx+CsA/iwA3l31DfK4pAIP4O86577onPvs6rVk/dmpbx8SY+l5+Vy6yON1ZDjnrgD8LwD+lPf+bktgm8fmiPDeLwB8q3PuJYCfBvA7Y4et/uexOQKcc98H4Kve+y86576LL0cOzeNyGnyn9/4rzrlvAvBzzrl/tuXYk4/NqTNHXwbwLfL8kwC+cqJzyVjjN5nCXP3/6ur1PF5HhHOuiUdi9D957//X1ct5bBKC9/4tgL+Hx76wl845Bpwq/zA2q/dfYLOUnfH++E4Af9g592t4bNH4bjxmkvK4JADv/VdW/7+Kx4Di25GwPzs1OfoHAD69Wk3QAvCDAH72xOeU8TgGP7R6/EMAfkZe/09WKwm+A8AtU6IZz4tV78OPA/gV7/1/KW/lsTkxnHMfW2WM4JzrAvh38dgT9osAfmB1mB0bjtkPAPgFnzeYe3Z47/+89/6T3vtP4XEu+QXv/R9DHpeTwznXd85d8zGAPwjgnyBhf3byTSCdc9+LR3ZfB/DXvPc/etITujA45/5nAN+Fx7si/yaA/xzA/wbgpwD8GwD+JYA/4r3/xmrC/m/wuLptCOBPeO+/cIrzPnc45/4dAP8XgH+Mdf/Ef4bHvqM8NieEc+5347F5tI7HAPOnvPd/0Tn32/GYsXgN4JcB/Mfe+4lzrgPgf8Rj39g3APyg9/5XT3P2l4FVWe3PeO+/L4/L6bEag59ePW0A+Bve+x91zr1Bov7s5OQoIyMjIyMjIyMlnLqslpGRkZGRkZGRFDI5ysjIyMjIyMgQZHKUkZGRkZGRkSHI5CgjIyMjIyMjQ5DJUUZGRkZGRkaGIJOjjIyMjIyMjAxBJkcZGRkZGRkZGYJMjjIyMjIyMjIyBP8aYXb6jT91GdwAAAAASUVORK5CYII=\n"
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rJAV3HhbMRI8",
+    "colab_type": "text"
+   },
+   "source": [
+    "## All latent traversals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "9c0z-HzQMRwV",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 534
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592309864510,
+     "user_tz": -120,
+     "elapsed": 1216,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "7dfbb957-ca6e-48aa-ab01-47c6c8f17d6e"
+   },
+   "source": [
+    "traversals = viz_mnist.all_latent_traversals(size=12)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4ec8b87748>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "gAf8_C_Wh7It",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 612
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592234363124,
+     "user_tz": -120,
+     "elapsed": 1970,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "4c197b12-9491-4696-d5c6-27b21e853f77"
+   },
+   "source": [
+    "traversals = viz_fashion.all_latent_traversals()\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f72db2dcac8>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 44
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "KLOEa2xhh7Tc",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 488
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592234380887,
+     "user_tz": -120,
+     "elapsed": 1688,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "8fcaa188-30b9-44d7-a415-b79ebe996efc"
+   },
+   "source": [
+    "traversals = viz_dsprites.all_latent_traversals()\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f72db247278>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 45
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "9GEF-hOErHiF",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "6c0d1343-4b2d-4df5-da48-5f0ca7208989"
+   },
+   "source": [
+    "traversals = viz_celeba.all_latent_traversals()\n",
+    "\n",
+    "fig = plt.figure(figsize=(30, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fbf9b0ce4d0>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 21
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 2160x720 with 1 Axes>"
+      ],
+      "image/png": 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CylqtJvejftGlUF2UbrcrsCFRFYvFgkajIYun2WyK5a3X6wAguy8TdNVqFd1uF71eT7LMdEVqtRqKxSLy+TxyuRxKpdJr9eZUKLfKtQCAx48f48c//rFsy2NjY9jb28NPfvITwcHj8bgEUsOyzGYzNjY28Mtf/hImkwkOh0Osw/3798XFIAIxihZsNptRq9Vw//592O122O12BINBVKtVPH78WNwDh8Nh2DXUxIfZbBZ8lwuYO8Xz58+Rz+fFpVH5HVzkTEm3Wi0sLS3J53k8HtjtdqytrYnLxQWlog0qB4fZxUajIUG1xWLB7u6uuBBcIJTR7XbFivNaDg4OUKvV4HK54Ha7AUCsOZGqdrst7iStf7/fh81mg9PpRKFQQKFQgMvlEuSmXq/LHAwvkJPGqXBLuD0yEdBut/H8+XNJEROPzefzsFgsiEQiCAQChswgLQMhJG77Dx8+hN1uR7vdxsHBARqNhgSMRCI4qQzC6L/quo5Go4HFxUU4HA5J7gwGA0EHGJxRBnFdVckBCFpDeM1kMkniZThlP8yl4e6QSCRweHgo+QC73S7p6mFMWEVfLBYLbDYbPB6PzCVhUiJOtVrN4MbwXphcIyISCAQkMcRr585KZIXWnM+WKfmJiQnJSxQKBWxvbwtZLhQKQdM0eDweVCoVgVhfN06FcjcaDezv7+Pg4AClUgndblfStdz66M+ZzWYJPFwul0BfvV4P9Xod5XIZ1WoV/X7fALeZzWZ4PB6USiUJEOlPqgmgwWAgWy8jeiYcGOxy61Y5Kxx8IFxg3H0ikQh8Ph+AoweeyWRgsVgk3a8uMio2vzscDvh8PkQiEVH0Xq+HTCYDu90uFk51h1RSFAC4XC74fD5Eo1Fxo3RdR7FYhMPhQKPRgNlsljiCMsxms1hcj8eDUCgkMQozolTmYrEo8YPK9bHb7fB6vQgEAojFYjCbzYhGo+h0OsIqtNvt8jvjiuG5HR6nQrmp2AwqdF2H0+kUn1dNtnS7XeRyObx48QIXLlyA3+8HACFJMVjkAyLeCsCw3fZ6PSSTSVgsFvHbW62WbImdTgdWq1UYa1QG4Pihk9REtwCAZDj5GQ6HQyBLJmBUVAg4TpYMJ6ToCnCH4E5js9kEwrPZbPJZ3G0AiLvFgJfQncvlEhn8TP6fc0QZpCY4nU74/X5xIZiXYABOBQ0EAmi1WgL9AZAsZDAYlGQWs4/kDPG1ZE36fD6Zp9eNU6Hc7777rigFISw+MG7zU1NTqNVqKBQKKBaLePDgAX71q1/JBJw7d07wZvWLlNdutwufzwen0ykKX6lUsL6+LtZ2bGxMZKg+dK1WQ6VSEV/Y7XaLJe92u0KyAiDZT5W4NBgMsLe3Z8DA+Vp+lurrMvilDJPJhHw+j2w2Kww9BqBWq1U+c9QgAsIdLZVKyTVxUdpsNrkn1dpz4TYaDRQKBXFDVIorFd5ut8Pj8eDKlSu4fv06zGYz/uZv/gbZbBa5XA6JRALLy8uwWq0SjDJNz890uVy4efMmrl+/LkZrfn7+xHs7FcpNC6bynVUrxu1SRSX48KkQfN+w76lypunuMJunyuHPalECh1oYwASLaiVV66P+rA4VQ1f/r9JMVevP1w4Hu7SYqgsy7LOr16GiMNxt+J5hLjZ/Vl/P56Pi9+q9DM/9cDEGBzOpzAuwwIE7BRND5PoQTXndOBXKzYniVkQqJ4n5NptNvtM3Va00cJxdHJ5o+r78PxVDnXyVnK9+J96tFhqo2UR+DhWeg7JVZe33+wZ3Sa2AUVP2qmwqPCkBnBfKHk4eqdegJoYog4uaCkUZdrvdoNiq0vJ9KgVVDbzpgg0GA4PlVxcGs6SEdbmYuUMz5V+r1SRpNKpCaHicCuWm0nW7XWxubmJxcRHFYlGCOTLabDYb/H4/SqWSrG7VfaDVrdVq2NvbE+iLyMjExAS8Xq9MPhVdpauq1rlUKmF1dVUQEhK3SAfgolN9Q3Vh0KXZ3t5GNpsV5IA+7Pj4uNBH1d1CtabAEdc5lUphb29Pgli6EtPT05ienjZwW4Bja8t7ZKC8ubmJQqGAVqsl/vTk5KTMr7qDDBsV8tyz2SwymYwUcNAPv3btGs6cOWMwHIQdA4EAwuGwBI6FQgGZTAaVSgWNRgONRgMWiwX5fF5e+1njVCg3cIQgLC0t4Re/+IUgHYT16PNevnxZJoqBJyeSlrper+ODDz5ANpsVi0W/u1arYWxsTAjxVGy1AIAB7MHBARYXFw2wHPHbRqOBYDCIiYmJV3gh6m5SLpextLSERqMh1o0xBCuOmNrm56iyNO2oNGttbU0ys7TOZBKur68DAGZnZ1+ZU9WCZrNZ7O/vi2JTBjnd3W5XSFkcRJkcDoe4hplMBltbW4JIcRB56fV6uHr1qnxuPB4XpCQcDqPX6yGVSuHw8BBbW1uS4udOwGKI3/u93xN06aRxKpS72+0imUzipz/9KZrNJuLxOObn53H16lUpC8vlctja2sLY2BgikYgot5q0GAwGePToEdLpNMLhsFgkIhb5fB47OzuSpWNWU1UqXddxcHCAlZUVOBwOTExMYG5uDi6XC2azGaVSSWTUajV4vV5DATIXXLPZxO7uLkwmE+bm5qRYATgK1Pb395HL5ZDNZgV3H3YNmIHs9XqYnp7G5OSk4OqVSgWpVAr5fB7pdBqBQEAWCXBcu0iEqVKpwOFw4Pr160LiqlQqhoIHu91uUEyiKrTo5IycPXtWCGvValX45I1GA0+fPsXU1JSw/wj/kXBFnrrFYsHc3JwYr3q9jmKxiFKphMePH+P27dt46623Xqs3p0K5B4MBnj17hkqlAp/Phzt37uDb3/42rl+/Dq/XC4fDgXq9jvfffx8ff/yxcEJo/YAjK3V4eIi9vT15iJcvX8bVq1eFQM/J//DDD6XEaTgwY5UMAExNTeHNN9/EwsKC1BiS5/L48WPU63W43W5DIMssYbFYRLvdxtjYGH7rt34L8/PzBpx7a2sLH330EQ4PD9Futw1QI78zJR0Oh/HGG29gYWFBsHuWsD18+BCpVArFYtFAwVXjD9IEFhYWcPPmTbjdbqEVbGxs4NmzZ1LXODs7K9abbg1dxna7jbNnz+LOnTtwOBziXmxtbWFnZ0dcuEwmI24F6yJVHN9ut+Pdd9+Fy+VCo9FAuVxGIpHA5uYmVldXJXF2+/bt1+rNqUi/N5tNJBIJaJoGp9OJmZkZ+P1+g29ssVhw9uxZ+Hw+A6WU1rDT6WB/f1+w5Xg8jkAgIJATJ5dlVtxSaZkACH+DZCYmXiiDQQ53DwBCM6XfzsCRgVEoFBLrzveT+E8FaLfbEjMAx60RiO/7/X65DvIxLBaLoeaw0Wi8EtQSXiQfm4ucMhhDBAIBaJomFNfhnYy7iN1uRywWk/cSv2fhBfFrZjs51Ap6APD7/fB6vYYgk0Q5Ps9KpfKfVoHwFx3MwjkcDszOziKfz+P999/HgwcPcOXKFUxMTODXv/41pqenEQgEJIOlQlkcNpsNwWBQWGpLS0u4efMmHA4HHj16hGg0KgoB4JVqDyYluP2vrq7ixYsXeOutt9BsNrG4uCj8EPqkKpxIFICJKIvFgtXVVSwtLeHu3bvI5/NYW1uT7CrdEcKQlEHFpCKvrKxgaWkJly5dgtvtxqNHjwQapQy1mkcNULnw9vf3sbKygps3b0LTNDx//lwwaqbx1bQ3cXwGlFarFclkEk+ePMHdu3fRaDSwuroqxsfr9coC4bwyUGTFvq7rKJfL+OEPf4hbt25JyV+z2ZREk+ryvG6cCuUGIOSmSCSChYUFAEA0GkW5XEYkEsHly5eFeqrCRSpKYLFYEA6HRQYDyFqthng8jjNnzsh2ygpu1erSwpBQxYKHYDAohPzp6WkD4w+AwWIyaGRmc2JiAp1OBz6fTx4wCw1sNhuy2awot2oxeS12ux3RaFRK1mw2GwaDgRQ82O12kaGiR1xw3Cm4IMmpbjabOHPmjBCYcrmcQHwqt0RFchwOh/B6fD4f6vU6Lly4gFwuJ6Qy3ovKCefC4HNjCj4Wi6FSqeDChQtIJpMIhUKoVquyqL8SGUpyL9i/w+Vy4c0330Qul5M0OC3FxsaGTDYtLHCcJIhEItJb5Pbt2+KuWCwW3L17V5AQUmxVzFvN1pG/8d5776Hb7aJQKMDhcODtt98WYpfdbhda6HCyhe7UYDCQYoVarQaTyYQzZ85IsQKLhtWhymPBA4MrVtRPTEzAYrHg0aNHEiCqSR9yVmhBNU3DxYsXEQgEZFGyePqTTz6Bx+MxUBwAiIuiJo0uXLgAn8+HarWKYrGI6elpzM/PY2trS7KlZPkBwOHhobzX4/HAbDZjdnYWFy9eRLPZhM1mQywWw5kzZ7C6ugpd18VN+iw+96lS7mKxiGKxiJ2dHfFTic+y/UChUJBEj2p17Xa7BCjFYhGpVApmsxlOp1OYap1OB6VSSTgstI5qRo5+fqVSQbFYxPr6OpxOp9BCSe/MZrMC743KSJLuWiqVsLm5CZfLJUmpw8NDaTXB+1CvQ029s5IokUhIwYOanCoWi7JI1aEWPNDdSKVSMg+NRgOZTAbdblcgPPrXtLrstQIcw4r7+/sIhUISzJNWwD4xLOomxUFN9zPlX6/XEQgEBAI8ODhAr9czyGD7iteNUxFQWiwWjI2NSY+MXC4Hk8mEg4MDRKNRQweqSqUiuKzq61qtVvh8PpjNZrTbbVQqFWEQxmIxAJAHwhYC6vt5HUQjiL6Q1xGNRmWr7HQ6hkIDVbmJnTOLylKpQqGAUCgkrSUol5CmuoOoaWz6qGyx4PF44PP5hOtCVGb4OhiMUsmr1aooNouCOY/MDA6n/DnvahECmxSxfwlfQ8yc987dlYbg4OAA6XQa+XxeuhzQ1SKHhW4U+Soq6W3UOBXKbbfb4fP54PP5YDKZsLi4iJ///OcIBoNSmJrL5fDxxx+jUCjIhFLBgWPFdDqdsNvtePnyJd5//314PB643W74/X6Uy2Xcu3dPkhiqdQOOq3kYQBUKBTx48ECKA7xeLyqVCh49eoRSqfRKJhE4TntTyev1Op48eSIZTb/fD5fLhefPn0sWllZRdSlUymir1cKzZ88Er2clzdraGorFomQSVRlUbga47HOitkGz2WzY2NgQ350xjKrcauPLTqeDZDKJcrks12E2m7G5uYlsNivzr+L+rK45ODhAPp+XMrJ2uy1t6TweDxKJBGq1mlB7XS7XV6O1A2G3SCQiVmRrawvAEdbs8/mQzWaxubkpvGO1HQMHkyGkgZbLZTx58gSJREII+XRRCAGqlkolG1FGt9vF4uIi9vb2pOiB7gsDUnWowZSa/l5bW0MymRTMmBaKQ/WzVcsNQO53Z2cH6XRadjjuVqNIViSJ8f2MT0qlksCGxWIRdrsdoVDI4B6pMsjVVumu9XodOzs7aDabyGQykmIn/4aFEMCR307kiXg+75vNlRjUknrM938lAkpyjlmKxRXL31X/VUVKGAACx9UmKttNRWCIkrCpJi2R2h2JWDStHYPUWCyGYDAIq9Vq8LeHm/oAx5abPqbVaoXb7cbY2JgoYqfTQSqVkp9VKw0Y2yyrnJZYLCaLstPpIJfLoVarSSpe9b2pGPydCuj3+6XvCl0T4uTq6ylDpceyEicYDEpA7na7xVVT4xhVDt0Y7n7BYFAKHtQdW2VWNpvNz3RLToVy1+t1lEol4SvQXWg0GuLjHR4eimIOM+YAyATTrwSOHxStJUvYVKum8qv5uaz6URMv/Gq32yKfQZja+YoBGa0vsWh+t1qtht7WXAREhQBj8oScabUsjjsO/8fFSIQEMJaqMbBmT0IVt2bXAeBkzJ9GhG4Er4OuIHdDi8ViWKzAscFgwE83Uc0RMHHDXY9JtFFUXnWcCuX+8MMPYTabEQqFJINI35mWeWxsDO+++66B49BqtVCpVPDDH/4Qe3tHBzpMTExgampKOMMmkwmlUgm6riMYDEpKlwrFbCIAqUqfnJwEcExLZSDFhTA+Po6pqSlJtPCBAJCyMrWifjAYIJFIADheTJqmSZZzmJsei8UMaAlw1E01l8vJ4lF3KCZggGPrz54txK77/b6w+aj4KtV4VELs29/+tlwX44Dd3V1sbm6KEqsUVwILQQsAACAASURBVPrsKmHre9/7ntwjezCura2hUqkgl8sJCkVDEAqFJIOp7oijxqlQbpWbofqNKrmf1lotAFbrHylnFFF+mL+tFsHy//xs1X+lctGKqMR/FSLja9TrGDWG70f9u5qhVCFK9fNUPF7lWqvzw9fQijOwHQ6e1fvlNY+ylOq9q9eqLrzhuR/m6zA+4e7HHUoNpLlg6ZKdBLGq41QoN5WDD4AKTIqnSqhnvSG7GBH5UH1dVeGZPFHbiKmLhJ9DGbweNXHB9l50CWjpuI3SKqnXMbxQWB3O96pEouFiBeDVahh1XqjEo9pCDOPSqoJwt2JzIAbOo4JrygAgcYhaYKCm6eni8BkNFzyoiwgw9h9nZpVzpXbw+kq4JWr6u91uo1QqYXl5WeieDKiCwSDm5uak06vajF3dwnq9o1MVtre3sbu7K/6s1+tFJBLB+fPn4fF4XlEoVYZK7k8kEoZ2DJFIBJOTk4KfqwHgcKmWeioCcXH2TJmZmTGciqAqpuo71+t17O3tSdzBOfH7/XI6Ay3gqKoiVvvTtWFNKf3ocDiMeDwuHW05VKydz4ZF2KpyMyZg8KwuKlYfEY4kNFgulyUQplXnHKilbK/Vm8+pX/+sw+v1SofSfD6PDz74AN1u19AXpNfr4fDwELVaDRcuXJBmisOogKZpKJfL+Oijj0SZWJXCQt56vY5z584hGAy+ouAczWYTjx49kiocKj77nxSLRVy6dAnRaFSsKmAsNKjVanjx4oU0rOH/qRhEKOhjq64ZoblarYaXL19KVT8RDCaheI9s2smhBpfValWgO3I3yF6kP221WjE2NiYUCMrgtVQqFenTTRlURgaUjDdIBwAgVTbklKtHuXAXarVaMJlMEqh6vd5XjmMZNU6FcgcCAdkyyaV+44038O1vf1uolPV6HUtLS3j8+LGUmdF1AI7Lqvr9vjSMOXv2LK5fv45YLAaLxYJKpYLV1VVsbm7i4OBAmuJwcGvs9/s4ODiAruu4ePEiFhYWhO9QLpextraGTCaDdDotPbyHt3PKAI46ss7Nzclu0Wg0kEgkkE6nkUqlDOgBBxcjZUxPT2N8fFzckGq1Kqc7ZLNZeDwekQ8cLxD2AtS0o4Y3U1NTcr3s89JsNqXvXygUMiST6AIyE8oeKqTIqs18eNIEYT4ASKfT0p+QC9LhcAiDkGgY0SySy9RW1SeNU6Hc7LhKxt+ZM2fwO7/zO7h165ZQIDudDmZmZtBqtZBIJASmonKqpPpWq4ULFy7gzp07uHnzpnCheXbLT37yE6RSKcFfVf+WVrFarWJ8fBx3797F9evX5YFWq1VMT0/jyZMn2NzcRLPZNCglXQyWb8Xjcdy+fRsLCwvwer0AjhIb29vbuHfvHvb39yWpRKUaTpmHw2HcvHlTdizgaGfZ2NjA48ePcXh4iGq1KrscAIkl2EswGAzizJkzuHTpEnw+H7rdrtSIEr3I5/OGRkeVSgX1el16+VksFly6dAnnz5+Hw+FAtVrFwcEBNjc35RoODw8lgwocc3GYD/B6vbh27RpmZmYAHCVyUqmUZDmZ+WXW+nXjVCg3EzXMYPHYCLXrEP3MUCiEVColQYwa9Kg+MxtcqjAbz4UJBAI4PDw0ICHAsZ/M7dLv9wsGCxxTUZki3t7eNhzhx0H/FDhKUtAPVT+H3ZuSyaQcdqQGjwz8yJJjupyJKvbr5r3Q1aEMcj3oepAKTIjV4XCg3+8jHo8jmUyiWCxKjoDzSD4J3b9IJIJ4PC67Fd0PpunL5bKUjPE6aLU5d5OTkzh37hx8Pp+wCGk4yPsplUooFAqvQJPD49QoNwDhTJtMJvz617/Gw4cP8e6772JrawuJRAJnzpxBKBTC2NgYvF6vwR0gCsGHrus6njx5gsXFRbz77rtIJpPY3NxELBYzZNnUrU9FbLhjvHjxAisrK7h79y7S6TQ2NjbERfH7/YaECnBcRcPsncViwfLyMpaXl3H37l1kMhlsbGzI//hd9et5DdzCbTYb1tbWsLy8jPn5efj9fumBqBZWqIGtmrpmsMfPpoxHjx5Jgoc5AfYgpAyTySRHgbAI5OXLl7hw4QK8Xi+ePXsm96pyUlRqAdGWYDCIqakpJJNJ3L9/H/Pz8/B6vVhbW0O5XIbf70c+nxfXjTHJSeNUKLemHfUHabVacLlcmJiYwKVLlxCLxeB0OnH79m2pQKGCMt3OwaCTTLNz584hEomI8vBEMSoKu0upGDj9bQZGExMTmJ2dlYc9Pj6O8fFxYQyyxa6KL9PXZbqap5DxAUciEYyNjUHXdWQyGZRKJYEVVX+ZaXeT6eikibGxMQOmfufOHQwGA2QyGWlFrAbYvB52oZ2amhIOCQ3BrVu3MBgMJMjm4lAzvLwGt9uNSCSCuU/Pv3E6nej1erhx4waAIwtN3oq6czEzazabEQwGEYlEMDU1hbm5OYRCIVQqFdy8eVN6s5NiwXT+68apUG7CRPyey+Vw9uxZIQeZTCb5nQEQayyHlYoRfKvVwuTkJKxWq0z43Nyc9JEmd0S1umrqn5UjPL6uVquh2+1iamoK9XodyWQSwHEWU6Wr8nrIk5mcnISu68ID4eJMpVKiaMOJJsqmQk9NTaHX6yGfz4vrZrPZpPBChfCA45bDTOIMBgM5mDaTycBkMolrcHh4KF1sVfSISkkXUNd1hEIhWK1WWZQsTtjc3BQXTk3KBYNBWZDsMejz+RAIBKR4w+FwYHJyUo6EUfXideNUKDePzGPkXyqV8OTJE/h8PqRSKczOziKVSiGZTOLly5fodDpy2BGtndp1lUGnz+dDKBRCrVaD3+/H7u4uisWiVJwTXVDLqmhB+/0+CoWCFBo0m01YLBbs7OygVqvJCbgADL6/Wi7GgmMesUHIK5PJoNVqIZ/PC0NwVPKE11Qul0UG75M+ObvWDi8MMhgpo1KpIJlMwu12C7eFKAVpB0yhc3g8HkmkDQYDlEolJJNJsdrs4cJCAxLg1CQOzwTiPGcyGYRCIVlw3P3K5bKwFOkOfRYUeCr43GzJkEwmhWa5s7ODfD4Pr9eLVquFer0uLQSYDFArvhnVE3FJp9OyUFj13e0eHXyaSqXkdSonnA+bCkpcnXWcrJrn6QwsVFZdimGGH48G4XWMOllh+CHS2jKgKhaLACBQG3nlhCbVoJafq1bJ67oui4AuBfvyMZBTs7L8XDa7Z2zD3YuHQDmdTtTrddkpdV2XLC5lBINBCao1TZNn0mq1BCTg4V087YJw4mf1CjwVyr2/v49kMolCoQCfzwe/34/19XWUy2VBFh4+fIgPPvhAJkVNaACQjlIMiAaDgcggrJRKpfDgwQO0Wi15kKpbonZ04t+Wl5flofl8PmkaUywWX3FJAOOpCCy3Yr0ls4p2ux2Li4tyChmvRV0gKjxZrVbx9OlTgdiIi6+urp5YrEB3glY0n8/jyZMnsutRgTY3N1Eul8UFUQsNVBoxcLS4Hj58aCg0cDqdSCQSUmPKa1EzpYRse70eEokE7t27h1qtJhRcHkLFA1aJDH3WOBVuSblcRq/Xw9jYGC5cuICZmRnUajVks1k8e/ZMeggyBU40QvUzGRCZTCaEQiFEo1F4PB60Wi08fPgQJpNJDvqkj8qtUsWXAQjHIRqNIhKJ4PDwEMViUTBwWi0ONXWuZu7oT7pcLuzs7CCTyQiiYzabpWCWMoYzlPxZTU7RojUaDemlqAaiagyiultkUW5tbYkLwbIwv98vCRZ1qDBqs9kUJp/VapUjBgnf+v1+VCoV4evw+hmg1+t1ZLNZbG9vY2NjA+VyGZcuXRJeDvu4sN2bGtieNE6Fcmva0bmRly5dwrlz56TjEdO0+XxeuN10A6jg6pZOixYOhxEOh+UoaaaaLRYLyuWy4LnqgUbAMelJ7cPBRcLAjvRXujTkaKgyVGUnwZ/BHBWCgSC55WrRhOom0W/mffG4EhYrsBG9StiiDKb6eb30pz0ejxz1wSM61PZ06mJhVymeCGcymVAulzExMSHt20ikotuinjjB57ezs4OdnR0JRNlsiJlbpttZRlgul78aZWZzn/bSm56elpo6PmD6tdFoFLlcTipHOKgQbOiiKjb9PwY/rPZhqRitG62nii7wmA1unVQW9UwdKpQa0KkuCi08CwXogqisPjVxpAalavN9tac1SUpcQExecQHzXjqdDur1uuHgJLVtA90PtaCB7h5HuVxGoVBANpuVImWr1SoHoLIuVC2+4BxRzvr6usRP3KHZ2i6RSMDv90tfGBorFnh/JTpOfeMb34DJZBK/mcpCvJQKzRNvVagun88DOOrBQQYbLQ1gPMZjMBggFApJrR5fz4cfiUQMtE8mEnZ3dw20VjaUUa0k/8cTAVQCFRWE7gYVkOl4KioXtHqyAv3vXq+HnZ0dA9atadorys7BxI3aHJOWfWdnRxYGiWUM5FTXxGq1IhKJIBQKGbjzfFZbW1uCjtAg8NRkXgtb0nGx8dotlqP2yY1GA7quy+loExMTCIfDsgP83d/93Yl6cyqUW518fqkpc94o23Tx9QyY1MEAcZj+qRLpiYaof1Pfryq4KoPBo6q4tMKjZHCoVNZhC6++TnVLeA+j8HP19XwNf1avW5XBe+ZucxLfehj54b0RugOO+yNS/nBwrsKhagqdZWjq8xkueGDwPgreHB6nQrkZYJnNZrkpNtBh0oXFp+rDpdsBHLf+4uAEsjc3tz3AyLlWF8hwQKUuMrUfOB+kKmdUUKfK4C50EheG7+V1qIpJ5aTrwYWmQm7DgwqrKihlMJCk9Rzm1qiLETguFCYKwvng3DP9r/r8/M5CYu5uXCSEFvkavp8uljqnJ41Todxk1lGpm82mnNBL3jP9MQZ6XAyqcqsJnUajgWw2K92lOGHkSPB0LgAGq0VlIkKwv78v55cDR0rJk4fHxsYkNT/KEgOQUx4ODg5EhnpCQzwef8VSqds/3aadnR05Vpu0UL/fbyh4UBc/Fw4Vktg8u07RFYhGozh//ryB6spFrhY9c/dMJBJyniddkTNnzshpzirCwntVYxPSaw8ODgS2tdlsuHbtGi5evAifz/fVSr/XajXZUmu1mnT3JD5qNpslmcJEzdzcnKGAWA2EisUiksmk+Nt8wMw6soPUmTNnDAe1qg+lXq9jc3NTqlZUq0Wfvt/vY2pq6hULzEXCLqhq5M8CA7VYIR6Pv2L91blZXFyUs9J5faQKMOvJYuOTZDx9+hT1el3K8wBIw516vY433ngDfr//Fd+dVr9arQotlTshAAlYU6kUbt68Cb/fb4BpTSaToaqfFUWUQcNSLpeRzWZx48YNORfzs9qpnQrlJizXbrdRLBaFWKM+9GKxiGfPnuHhw4coFos4d+6ctBkAjmsMeWZLOBzG2NgYpqenhd5ZLpexvLyMlZUV5PN5xONxBIPBV7DmdruNw8NDWK1WXLp0CTMzM1L5w2KFdDqNXC6HcDhsOGqDrkSv1xNc+9y5c5ienpam7/V6XSzg4eGhIESq9SetNZVKQdd1TE1NyckKxI3T6bT0RWQ2kYNuRLvdxs7OjmT/5ubmpO0ceTwMmmkwVH/ZZDKh2Wxia2tLDmRlDxeLxSIIS7fbxd7enuHagWP3azAYSKKOvbhVopfNZkM6nZYcBQsZXjdOhXLTepCnEAgEMD4+jrGxMZkYu90uljCVSgGA4XBQbnlMM4fDYczNzeHs2bPiOnBii8Ui9vb2UCqVMD4+bnBtVIJ/KBTCwsKCHD0CHGXp/H4/Xrx4IRlQEpA4WJRcr9cRiURw48YNzM/PS7uHZrOJnZ0dPHjwQNqIDZ+KwEVQq9UQjUZx584dLCwsyCJgx9t79+5Jc55RBcHNZhP1eh2xWAxXr17FrVu3pCVys9nE0tISPvroI+n8pC5UxhZ0FR0OB27fvo27d+/KsSCNRgPPnj3D8+fPhR/EVDpwfDwgd1+73Y5bt25JhRSTcpubm/jVr36FfD4vi+Mr0Z+bSsVtVy0opXIz2cFTaE2mo2JdlQvOzJiu62LhmLyhlVCzi8w2qk151B4dbGWsIhW9Xk9wa8pQg0t1B9F13cCrUNPjTO4w7azyudX7MZlMCIfDwh2nJex2u2Ll2NhTLZtjEErse2JiAuPj45Iw4UKanp5GPB7H1taW9HEZDjA5bz6fDwsLC4jFYtLAv1qt4vLly+h0Onj69KmhYRFlqF/RaBTf/OY3ce7cOTndrNFoIBaLodFoYHl5Gb1eT4zd68apUG5uZVTmVColnI4bN24ICYrcbPp1DGiAY3eABbPkgLTbbXzzm98UViGrfLg1M8hUh8pKW1xcxCeffIJvfvObODg4QCKRkAXGQ4wY5QPHXZN6vZ4ssJWVFSwuLuKtt96S08B4gBRrJ1U4jni4usBWV1exsrKCS5cuwe/34969e5KKp4xhxIi/81yag4MDbG9v4xvf+AaCwSDef/99KehleZkamNO9MpvNUoWTy+Xw4x//GDdv3kQoFMIvf/lLWTDcfYZbxDFRNjk5iUgkgmaziZ/97Ge4ffs2YrEYPvroI2iahrGxMaRSKTEOX4kMJR8IIaXz588L3MWKmwsXLkhqmLyKYeiPKAIAXLhwQdwRh8OBqakpTE9PAwDW1tZQr9clmFR5HP1+X65jfHxctl+L5ajN8vj4OIAjJiNLoVRMnZabSqcWGgCQWICsw2KxKMGqukMwUeXxeDA5OYnx8XHxcU0mE95++20pQmaxAheWKoPoEH12tbqdHbz29vYkwQJgpIyxsTGpB1Xx7e985zuS1CkWi8JZJzLEwNdkMmFychIzMzN48803hQ5gNpvxrW99C5qmCV+fReKfxef+wqxATdOmNU37paZpK5qmLWma9v1P//4/aJqW1DTt6adfv62857/XNG1D07SXmqb9Z5/3s8g+o2J0Oh2cP38ewWBQEJGZmRmEQiFDUa9q7Zgl46Lo9/uYnp7GxMSEWNC5T6s/KGNUAxkGUTabDe122yBD07RXroNWfxjbpjVngQNhQ27Nk5OTspB43cM4s6Zpwm+empqSUyOYOWQRhFoozaGWqtntdvT7fVFyXls0GpVijFEMR5WgRkPh8XgwMTEhzy0Wi0ngzyBTHWx9pzYkslgsiMfj4roQkqSFp8H5j4lz9wD8t7quP9E0zQvgsaZpP/v0f/+Lruv/k/piTdMuA/jXAK4AmADwHzRNu6jr+uuXH47xVDL3dnd3RdHYD4OcblZZk2ijMvmIFZfLZWQyGXno9XpdghQGnfV6XSpDVGUijEfkhilmkpN4OFGhUBB/fhg+Uy0gixXU4gEWOhSLxZHFCsSKeX+0ZpTBdnJEO3jfw4pJmSyaSCaT0kqBcQXzAaqV5PvUwt5Wq4VSqSTV+swYDwZHp5fxcFRyYiiDle+8DpvNhv39fTlLtFwuCxuQxSTsiPVZyv2FLbeu62ld1598+nMVwAqAyde85V8B+Pe6rrd1Xd8GsAHgzuf5LHKD6fPt7+9LQMkDini8BTvzUyHol6nt0rjlk88wNjYmqEqr1ZKjnUlcUjOXapEAH3q1WkU0GhUZhBuZZBheIOquQDycD5EtEQDIyQrKnMt37krkylCG2+2G2+2W5p6sOeRQs4qq9VZdBhYVc4Hy2BBeP3cBBvlcTKxKZxLIarUim82i0WiIr6zuQACERUmyFQ+WZVW8yWQStGdvb0+aMX2edmr/vxQraJo2B+AGgAef/ul7mqY91zTt32maFvz0b5MA9pS37eP1i0EGK0OCwSACgQBKpRIePXokq9fpdCKVSmF/fx/RaFSoq6zqAI5J8Ww5kMvl8OLFC0kieL1eaWBvNptFUWmFlHsVd6PT6eDZs2dS1eL1elEqlbCxsYFqtSqNgYhsAMZKHPZK4akILFZwOBxYWVkxFBpw1wCMviZx5qdPnwpJyuPxwOFw4MWLFwJvqskS4Fi5Oer1urSoYxthi8WCJ0+eoFqtyqJWixPI66GsbreL1dVVwea9Xi80TcODBw+kKT57mjP2IepBq95oNLC0tIREIiGZYl3X8fOf/1x6lrtcLsOZlCeNf3JAqWmaB8D/BeCPdF2vaJr2vwL4HwHon37/nwH81wBGsVxG7iuapv0+gN/n7/S1Ne2orS8tHEvPTKajYtY333wT9Xodf//3f4+PP/5YEg/yYZ9aOyY0PB6PnIxLNIXYeSaTkS1dRVzU4lj2+Njd3ZU+J4PBAMHg0XomT5o+viqDn8eFsb29LddBOFFl7HEO+LO6LTMOWF9fx/7+Pmw2m1h/oi6fzqtYTTWdz+BP0zQ5UttisaBQKAgcSr9ftZYqWkEZZBWyZTTP+2SHKMYrqvXn4DxVq1Xcv38fH330kcwjT0oOBALSV0UtFh41/knKrWmaFUeK/b/ruv5/f3qTGeX/fw3g//30130A08rbpwCkRsnVdf2vAPzVpzJ09dBUXdflhF6VXM+AbH9/H6VSCaVSCblcTvpeq9tqv99HKBRCOByW47WJD9PX3NraQjabhdfrFZoqAxpaYvaLpktCl4cnquXzeVQqFeGKA8dWl8gFuTDxeFwqb3q9npz5zqY3wLFLwcWhYvNutxuxWEy2bEJ7Ko1UlUHXhl9029g7nMkTYuy0/HQNKYNzqlp2LgS1l6Cu68Jz1zRNDAaviZ/BhcokDYvDAUivmFgshsFgICcsnzS+sHJrR8v+fwOwouv6Xyh/H9d1Pf3pr/8FgMVPf/5/APwfmqb9BY4CygsAPv48n8XgiFYkEAjIyV+6rsuWrmmapLQBSEcl4Ji3zajc7XZLR1ZOKnDshtCnZ6BKGcRYAUi9ItsXsF2bWjXDahkq9zCVk7sIK2jIQweOO22RZzKcgAEgCsUvNgdlUTCLMahAKtlJ/a526FItOl9P5VUr4Jl/AIxFy6QoULmpsPwM4vf8XN4rP7vX60m1O2ti+XxUF0st5Rs1/imW+20A/xWAF5qmPf30b38K4N9omvYGjlyOHQB/8OlELWma9n8CWMYR0vLffB6kBDiaWPrbjOC3trYMiIEKr/3u7/6uWJV+v48/+ZM/kYnlSb0MKtPptLyOk2u323Hnzh2D/H/8x38Un5bugqZpEsQOy7BarTh37pzIUAsNPp0PcU1qtRpWV1dFBl0EKv6wDM6DKoP8Du5MvA52elJfCxzh6eouQkXlWZ+UQZiUQ3VFJiYmDDLUnYUGIxgMGpiU1WrVgHKcP3/+FSoxr4/3zPdzN0in05+JlAD/BOXWdf0DjPajf/Sa9/xbAP/2N/0sNT07TLBXSfB8Da3HMM49bK1UZhpw3OhSpbiq/1d/povESR6Wwc9WA8jh+xlm6KmyeZ+jZKjKO4rlp87XsAx10Y/iaXNu1S9VprrrjJKhxgLDz433qGLUKjedY1iG+oxHyThpnIoMJX1ddeJ1XRf3gdsh3YFRD31YhiqbkJMqA3hVgUcpo4rzqjLUzxqWwb+p16rKACC+60kyVFn8P10vXhu3elUGxygZVCrCj9z+1cX9WTKAYy6QKmMYZ1fnX3WFRsmge3WSjJPGqVBu1ToMBgNUKhXs7Owgl8u9Uv09MzMjNFUVERglI5VKSZGAph3VG0YiEUxPT0s7Nr5nWIau63IqwigZ5FqMkqEqpCqDSkVuy8TEhMhQH+zrZJAcdpIMdTcalpHL5STVPkoGFyyD82EZnU4H6XR6pIxQKITZ2VnpAkYZw1nPUTIYIMfjcczOzkqBg9rMc9Q4FcoNHLsI5FyzgpoWrdVqSeJkfn4ePp/P4KdShq7rqFQqWF5elrMMVcvJdr9s5g4YrRVlNJtNLC8vS5GAKoPV4ABOlKFpmmDLlEHZrCwi/4KnIqiW8iQZXNAnyeBQZdRqNaysrBjOrBwlg81BT5LBiqJRMthFi4Xao2T0ej1sbGy8IoO9GwkODMs4aZwK5ea212q1kEwmYbfbMT09LVUug8FRT7m9vT055JScE9Wf1LSjtDllxGIxTE9PS59uVQYTESpkpcpgIDo7O/uFZDAwooypqSlDYiOZTCKXy+Hw8FCSKhxqgEYZbH3BxFG1Wv3cMng6AzusBgIBWCyWV2QwmD5JBvnplGEymVCr1ZBKpVAsFoUPPkqGruvIZrOo1WrCiwkEAkKtyGQyODg4GCnjpHEqlJsTyLNjpqencfv2bZw9e1a2qFKphLW1Ndy/f9+QulV9OVoQEo0uX76M+fl56co0LKNarRoWiCqj0WggEong9u3bI2UsLi7i4OBgpAwWGlAZhmUQ+Xjw4IGw+obL3ehKUMbNmzexsLAg3PAvIuPChQu4du0awuHwSBmlUumVoglVhtvtxrVr10bKWFlZwcuXL1EsFkfKYPre4/GIDHLCe70e9vb28Pjx45EyThqnolcgixGYyAmHw3IaAZMvZvPRIayE+kiuUZEP9YThSCQifBV+mUwmkcGd4iQZun7ULJ2c7WEZoVDoRBnq8X+Uod4LM64nyQDwigwefcIU9xeRoeL1J8lQ6xqHZbDAYpSMaDQqGP5JMpgNpgy1RtbpdIqMUdcxapwKy01fdDA4OtGg3W7jk08+wYsXL3D79m05d4UcFIfDIZDVMESl67rIWFlZwdLSkpxooMpQj2fmUPFinjawurpqOBWBMtj9aZQMZid/ExkMrHgvJ8ngqQgff/zxSBnDRROqDJ49c+XKlZEy2KxSRaBUGV6v90QZwHGj+VEymPEdJeP58+cAIAH7sIyTxqlQbsJcTN1evHgRLpdLiPVMgQ8GA6ytrQnRfZicPyyD1l/X9VdkqEQnld9BjjVwdEy2iiKoMg4PDyWIGiWDBKJRMphezmQyIkMNjnkvo2SwHdlbb731mTIGg8ErMvg/VQbvRb2HUTJ8Ph8uX74sPrQqo16vG1qgDcsgIWqUjBs3bhiabQ7LOGmcCrdExVFJb52bmxOWn64f8U0CgYBwgolXq76dKqPX62F8fFyIOKNkqOluVQYzd81m80QZbID5aO8sCAAAIABJREFURWT4fD6Mj4//xjJYR0lXYJSM4TlVZRD2G5bR6XReOR+IQ5XR7x81rxwlQ9d1Q/HGKBls0KPKoKs1Nzf3ioyvlOUm5Eecmy0MdF2Xc1sIG6lKPUpGuVzG3t6e8DCAozMRG42G9O4Y9ulUGb3e0XHWJ8lgL5JRMuginSSjXC7j8PBQZGiaZqhgeZ0M9QSJzyMDgMjIZDLSdH+UDODVU5Q/rww22Vf5LcPXQRhUlcGFtb29/YqMz4IDT4XlBo5LswaDgVAyq9WqHLJKv43VK8OrmtZBlTEYDFAul4WERSyV1R7D2UxVBoAvLEPTjqvhC4XCKzLoRlAG3zd8P6NkeL1e+Hy+E2WMuh/K4MJWZWiaZrgX9VoojzJ4msVJMljEMHw/6nWR300ZXq9XztB8nYyROvPa//4nMlTyDI+Ue/r0qTR/4eFAq6urcmSFylADjvnLqoxPPvlEKsR/ExlMszcaDTx9+nSkDBYJjJIBHCtnvV4XGTabDYFAAA6HA8vLyyKDbLxhn3eUDNJWT5KhBnLDMl68eCFHdFDG0tKSQQYpAaPuBYBBBgPzpaUlJJNJA6twWAZwTGd48eIFms2myAgGg1heXhYZJMh9JdySwWBgSPnSf1xcXJRInL6lWveoZvWGZdAP3dzclCY+3W4XVqtV0AUAI2XQGlN5Nzc3JZnS6XSkKn0Yy6WM4WKFYRmkzVKGej+fJSOVSsk5MqNkqHOqyqBPrZ42XK/XhR487OaNkmE2H53sy7OCbDabyPB4POLafB4ZmUxGcgR0Pd1ut4HX/1nj1Cg3EzmDwUBKxWKxGBwOB0wmkzTHJDlfrXukDOC46psyeCArEw70++jaqFxvtQ8fiwROksE+ewBGymBzn1Eyut0uMpmMVNgPy1D97WEZPICp0+mMlMHBRUoZVGJm/1QZ9OV1XTfEEcMygCOsOxQKScsNymBPRbWZ0LAMKr/b7ZZ8A/nohUJB5A3LOGmcCuUGjJARHyC3covFIvwOJgqoxBxUCJWnfJIM1WoPN375vDLUc3WGZajkpVEyaJUog4mf4XvhUOeDP/MzhmWofqoqx+FwGDB+yuCcEs9WCwdOksEvVQZdiVEyVNeEnHm6RerCIlJC5VZljBqnQrlnZmbkRrkFs58HtzS/3y8wHtsTsIcdcESsV9Px9N1YfU1l8Pl8kuVUC18BIBqNjgyEyM2gT242mxEOh0WpaJkAjGyK2W63sb29LQuHaAIbedIl4TWyoxb/pmkaKpWK4TwZsu24eCiD1+/z+WQn5FZPAhX/plbfkB+jugSBQEDmh4zGvb09rK6uSuDHaiV+57zzOmZnZ9HtdqX9w/b2Nh4+fChHiHAOLRaLnOLAZkhqY89R41QoN7dVPjC1Ibya1lYVgL/zdWodovoetVEPdwYqourn8vfhCF3Fz4f51youPWoMy+K18b28nuFkhZpGV5VNjTNo6VRikno/nEfuQnQPyGxkuzd1vobnQM12WiwWoTzQKJClaDabBdEahvAYI9lsNlnIamyk/szdmK9hMu2kcSqUm4QaFXkgb1mForh1coLVesd2uy2JheGHTis1Kguo+pO0kvyucqTVXimqW6OmqDlUheUDJC7M61NT95SjvkdVfl3XpQtrqVTCYDCAw+Ew+M/DC2m4WZCuH1GBE4mEVPLbbDbE43FMT0+PDEzVYgg+j1KpJPx2Xh930qmpKXFR+MyY3idXhIS1drstcZbJdNRfsNFoSFzCgubXjVOj3LQC7GTE7ZcTxYknR5uJGCZG2P6Yvh+tFk8btlgshvYDnNDhE4SHrXO73UYul5MDpEKhEPx+v6ED7HD6XX1/s9lENpuVTk3AEQcjEAhgYmJCahCBV4t6qViVSgVra2vI5XKGYgWv14vx8XEh+HOb53tpcXu9nnTPKhaLhj5+6XQa/X4fly5dEiur7oK03pxHNo5XFyWP6AsEApKJ5SC5yuv1ykKgm8RdlddM9iFRseGs6/A4FcpdLBblWDlCQ/l8XpTebD5qiBkKheB2u4VYowaVVGBub9lsVo6HY6EAGWmxWMxwtiSHWtHdbreRSqXkJAG2FuP2yjMU2dZtFEbdaDSwtrYm/b7pR7N3d7FYxMWLF+XIDtX6c7BYge0PiCPTvUgkEsJ/V5MlagVMoVDAysqKQJCE/qhghUIBiUQC8/Pz0gYDMLa5q1Qq2NraQrvdFuIarTMX0Pb2NsbHx2VOAAiUSxmdTkeCycFgYOj5wtMzer2jHu0qT37UOBXKzeOZgaMbzGQyAufxIbCXnNPpFIgQMB5ISiVOp9MolUowm81CFeWiKZfLyOVycqaNysVm8NTv96Vpj8PhkAb1FosFlUpFKsi73S5mZmZeKVYAjrBsnusTj8cxPj4ulrFWq8k17u/vS5mVOthGIp1Ow263Y3JyErOzs3A6nbJwWHqWzWYRiUQMfbfp/rB4IxQKIRgMymIiJLmxsYGdnR1D1pDKxmfCYgWbzYaFhQVcuXIFwWBQjMPKygqSyaRAgmpVEBci0/Q+nw+//du/jevXr4sPbjKZ8PLlSzx79gwff/wxWq2WwISvG6dCuemPslGL1WpFLBZDLBaTQzxZAcO+duFwWCYOgOCjtVoNuVwOwWAQ586dw9WrV4WMVSqVsLKygqdPn8rDUpMxbB9WqVRQKBTg8Xhw48YNXL58GW63G91uF/l8HisrK1heXkY+n4fP5wNgDL56vZ7sROFwGLdu3cLFixfh9XoxGByddrC+vo6HDx8KI09dZHRH2CE1Ho/j1q1bmJ+fFwXmUdcPHjzA/v6+NPbkYFzAHePcuXO4ePEirl69KhUw7XYbMzMz+PDDD7G5uSmICN9LF40yzp8/j29961tYWFiQGtRer4eZmRmsrq7iZz/7mTwPyuBOSpbjnTt38N3vfhfj4+OGWGF2dhbxeByJREKa83yWcp+K9LuaKBgMBvD5fJicnMTc3BzOnTuHs2fPYmZmBlNTU3A4HEJ+J/EGOLIu6glY8XgcExMTkt4NBoNwOp3CriM8Rb8bOFZuBqm0duyJwgkfGxtDNBpFv9+XjlEqwUiNBVhooKbq2aclEokIXVTFqBloMehi83i6EfRjPR6PFEurRwkCxz43A2G/3y8Lmb60ph31TmGz0WG3iIus1+tJIbDL5TJwcCgjGo1KmzkVPVKLTZhEGg7aec/j4+OIx+NSH/uVSeKobbxsNhsODg6QTCaxsLAgtXVM87L92jCExofGgGZ7extLS0tyOsPh4SFsNhsikYg0j1EfKCccOO4VuL6+jsXFxVdOViC2y/epvHL64KzzfPnyJZaXl/Hmm2/K6Qbkq9MSDyMbtHZUpqWlJSwtLWFhYQGBQAD3798XxIWnIpDKSxkMdNlscmtrC2tra3jjjTcQDAbx4Ycfipvj9Xql+ad6+hivKRQKwePxYH19Haurq7hx4wZCoZCczkCcmoH6MEZtMpkQjUYRCASwsbGBFy9eyAkPn3zyiRiOK1euGFL1r9WZ31DH/lmGGhWbTCZBEQaDASYmJuSEAlak5HI5Q3McAAZLZLfbcfbsWalZJBtvcnISDocDL1++NGQaVVy31+uJ4o2NjSEWiwkfIh6PSynU4eGhtFMeZuPpui4LcWJiArFYTLD7aDSKsbExAEd9unmm+jDiQszY7XZjamoK4+PjMJlMkrm7e/cu+v2+cEUAGDKDhFMtFgt8Ph9mZmZEBjOCPJ2BtFe6TLwXBq3sLBUOh+Uoc+4U77zzjrgu+XxeEj4qHYEuZzgcxuTkpCgwA/hr165JgHp4eCixz1fi2BCfz2cI5rxeLyYmJpDL5ZDP59HpdDA+Pi5HfQSDQXkthxrBc0uLRCLodDpSUDw5OSnN7IczjMDxAhkMjqpPut0u4vG4uDssgODPzHRy2wWOaaZcsI1GQ4oKGPTSRUomk9JObTj5pJKmKKPdbku7YabA9/b2DPClqpgqr6RWq8Hn88Hr9eLw8FAWjtPplGb/6s5BGeyFaDKZ0Gg0pMo+m83KruFwOLC5uSl5A5UeQB44W03zuD+bzSZHpui6jnA4LLFDr9f7zPbFwClRbo/HI1aCjSlTqZQhCbO/v2/wj1kVz4dJjgUzZ+VyGTs7O8Lv6HQ6WFtbg6ZpqFarYtUIHwLHPiZ9Xp4XSfm9Xg/r6+sAjpqqcwtXT0RTlavf76NWqyGRSEiqvN/vI5FICPLCkjlVMemSEPcvlUqigLzPXC4Hi8WCUqkkJzCrsBpjB85ZPp/H/v6+0Hd1XRflYssFyuAcM+fAwLbZbCKRSMgpciSAaZqGZDKJfD4vqBKx9Gq1imw2i8PDQ2QyGZRKJVy6dOkVSPfw8BDJZBKPHz/G2NiYFIm/bpwK5eaxdy6XC7VazZCZdDqdsvqH0+kqh0ElEFHB1UpzWoRqtWpIIqjtxGht+fAZWOr6UWkYFaBarUpCRq0CB4yWm+0qmOGLRCJSgWIymcQlYbfXYdop/1Yul+V6eDQ4cX0mdob5z2pyirg/z5JkcFyr1eBwOASy9Hg8hqpz7jRUcp7grB49yIQSW0sHg0HJTQBHp1OwNwqPxX7vvffk0Foe6xIKhaR3OHcytbf3qHEq0BL6czw8yOl0olwuC9Sn60c01UKhAJPp6FzGeDwucCEAYZrxi6cA0zd3uVxoNBpIJpNS2sTXMvihb83gpt/vY2dnR5AOLpLd3V1UKhVpkq66JaqLQddmeXlZlIrp8vX1dQmS+cXBYJm7SL1ex6NHjwztkF0uF54/f45isWioJ6VC08+lP18ul/HRRx+hXC7D5XLB7XbD5XLhwYMHsgvwvbTclUoF5XJZTkaoVqv4xS9+gVwuJzIcDgfu37+P1dVV8a+5KICj4oYXL15gY2NDzp7/h3/4B6RSKXm/z+fD8vIynj9/Lq3W9vf3hf9+0jgVltvv90ulu5ro2NvbEz+OlpZHSpCLovZ/Bo6TF/S/0+k00um0BCgmk0m2RPp+qlvC3QE4hrF4OgODJcJwRGfUbKCareT1WSwW7Ozs4ODgwEBNZT+TYV6IyqEhBUEtVmChAbOl6udSVrPZlICNOLXJdHT+TL1eh9PpRL1elyM+GGADxztYNpuVrCJpxjabTc7BIXJFGYVCQZAeJrZevnwpO5Ra2ZPL5fD06VN4PB7JevLUMz5z5hBOGqdCuYlOqAGNSpYCjn1snj3udrsNikm/l9sqCU7DaIZ6wJKaaqaMYYYfz5/h4mGiiWiB6m8DxyVZdJFYNBEOhwXW6/V6cua62mKC18iFyIdM5IVZTkJ2qVTK0MQGOE4mlctlUW4uWLvdjnA4LNh9u93+/8h7s9jI0uxM7LsRwS0WMhhcgjszmcwkszKzuiprr2y15B6o1ZIAC4ZgYPzgGQwEyw9jWA34wWMLsp4GMAwvgF8GkJ4sCGNr/NCwZK3TahmF7uqurqyqzMpM7vsaJIOMfSfj+iHyOzz3j3tJZlV1qSPnBxJkBiO+uMu5/3/+c77vHGxubooYRBsw0KC3kgnIcwEgHYTb2tpQrVbx7NkzR++bYrEoDx1rbfM+0oj7+/sl8gQ0eoOy5AYfppeiPTYL5FBKVqvV0NXVJdwD+s6MHnCm0CFERh1MfoWeGTl70afX1f6Bc+Omv86oBCtXkbhFX5A4jK7wO3WWjisE3R+/349SqSSiDA5NzmciiHFmct1JKGLpCs3rBuCYfcmH4XEwumLbtnDldVcJrii1Wk2Oq1AoSAcHrdNkRzRej0QiIcfMlYJ7EnJ1OGGRBnF0dIRYLCZJrE8//VSOl/uLlyIUuLq6Kr/TAGkwNDbNha5UKo5uBwCEyG8uz/RdNcOQsxvjvdpP5Y3ka7lcTjZ0OqvGB443Ts+cfHD4sOXzeVGrm0IDvk8fB4v28HX63fPz8w62pGVZ6OnpkYyejh698sorTRiWZWF1dRWrq6sO4hObtQLO7OK3v/1tBwbfv7KyguXlZcFoa2vDzMxMUzLqT//0T/E7v/M7Dmwe3/r6OtbX1x2qp1/7tV9zTA62beNHP/qRp920hHHr2ZYGyhmWN5E/9cZJX3D+jX/XxuKGYX4/f+pwIABXDP2dbhj8ZxqMeaw8fs0kBK7eWUHH9LVBmBgcOqNr8lh0jF0nk/h3jcH/awx9bb0w9L5EY+jzIZ6ZfXYbLWHcgLN7LwDxuRgHZtLBVK6bN0xfNIYQGTbTGHyviaGxLMsSEr2JQcaieRzacL0wgEb4Uzc0uioG48dMmZsYptGYDypLlvHvGkNfE7frcREGXR0TQz/0HHRdWOPcsiyJ3ugV8TIDbxnjBs4NPJfLYXNz09FZIRgMoq+vD2NjY0K3NGduDmKws4LuaKA7KzBOa2LwBnl1RTA7K7iRfC7DMLsiuJ2LG8ZFnRUAZ60R/fOqnRV4/dwwarUa9vb2sL29LfmIrq6ups4KbrwfABK73t3dde2sMDw8jGvXrknW8qUxbi532WwW8/PzspnhLKC7ANy6daupswLfRwzdWYHYujvD2NgYhoaGPDGKxSLm5+ebuiJc1lnB9JOv2llBY3AFc8PgJvayzgpuGBd1ViCxyZwkNMbW1tYLd1bQ+xRmiQ8ODgQDgBDhOJFdU41zLxotY9yWdd4Vob29HaOjoxgbG5PZhMR+dlZw4/tqDHZW0B0NNAa7ETA8Z2JQ8W52RSCvPJfLOTDM4zg9/Wo7KxDDq7OC2eHBDYNdEUiTJcbR0ZHw2706K7BmjBsGs5PHx8eunRXoQx8eHkpRf41RLpclH3F8fCw0YXNvZI6WMG5GAHRXhDfffBPXr1+XsgXpdBrLy8uOzgpmfNnEmJ2dxezsrIgEMpmMdFZgS2at99MYZkeDSCQix6E7K5gdDehauXVFIMaX7axAEpPGMDs8uGFQrNDX1+eKcZXOCq+++moTxvr6Oubm5rCwsCAYel/EMGE6nXbFqNVq2NnZwcOHD7GwsPDydVbQpKj+/n7Xzgq9vb3CTWYnAe0nmhi6GwE3KhT4WpZ1KYbZ0YBcjxftrOCG8UU7KzDrSQmdFwb5LBqDJZDdMAA4MpluGMFg0BUjEok4uiLQZSEGXQ5mLk2MQKBR/N7srPBSzNza1w2FQtJZ4enTp3jjjTeauiIwycDNiBeGV0cDnVDxwviiXRHow3Oz9UUwNAHsi3ZWcDuOizorMCnGRJobxmWdFcifccOgmscN4/Hjx+ICuWF4jZYwbl5Ahuhu3rwpOjrbdnY0WF5elgyZ3gwSg8v7zZs3hbvhhqGLNrphAFfvrOCG8YvQWcENg26TG4Y+BzeM7u5u3LlzxxXjos4KnMkZWTEx3njjDQnbvvSdFc7OznD9+nXhQ9i2jcHBQSnvxSUNgGMJ5eaFGNRLemF8ka4IxPjH6KwQjUa/dGeF0dFRTwxzpnS7pl4YdO8uwmAtGY1Bd3FychK2/RJ3VqBgIZVKYXV1VZIX9XpdlB4sAMPNkw7BmRibm5sAznknGoOzv0nYIgZV8F4YmUzGE4NuhRcG1TMaQ/OxL8KgyoV0WWJwX6GvqYmxt7cntbjdMMif98LIZDLY29tDMplsOo5kMilxfDcMXls3jGq1inw+L2Fa27YvrVkCtMjMzSdbh/06OjpQLpeFtETDSKfTDnmZmTrXGECDTESCv8bQGxZTaEDuB5d7NwyWmHA7DsuyJFvnhgE0yEwag5/TeG4YrLRqYug0thcGGX5eGEBzvUSNwYoDAJow+IB4YfC6UvigMWq1GkKhkGBw0nopoiX0ySzLEuX748ePEYvFpBsBiUNX6YoQCASQSCSaOhqYGKY0S2P4/e5dEYjxsnRWMDH0CmJiAPDE2Nvbc0SmTAzgnMfy9OlTB0Zvby+ePXsmGLyHL4Vbwt00B12OZ8+eCU2TesXLOivwiac/59YVgQXtgf9wOitQWka1vBsGz8ULg/6wxtCdFfTsb2IA56sAMahmIj2WGOa5eI2WMG5eFHIS2CtFd1ZgPWgWYTQvpBtGOBzG0NCQ3DyNoR8EzaPmz9PT844GbhgXdVZgbFdjfN2dFdwwvDorEINxbU2Soq/Ma+OFwcJCrGJADF4P6lo1Rnd3t2xUWcHWDcNrtIRxA+exXZL42UmAsxYvfiAQEI6F+XS7YVC5QgwzFuzWFeEiDCYpqLF0w+Bxmedy1c4KJobuznDVzgqamgpAPm92VtAYfBDMh4w4xKBYRGNQmGFi8Fy0y0iMYDAonRU0Bh+my3zuljBuzmjcWft8PpycnIgSWgsNNDEfOPdx3TAKhQKWlpbkNV5gavOI44bBG2Ji0AXgDX4RDB6vFi1rDBoK66G4YZAi4HUcNO5oNNqEUa/XsbGxIcdhYvCacJgY5IgwcqMzolQJ6XujMYjN42DJDJ3N1BjmsbiNljBuGh0vIC8C/8+/c9PpJVYwMfTsx7+bBH7+3cQwL7CJYUYE3EQTbhj6Pdpg+Drw1YsV3DD0+ejryFXgqsdBLFNoYGKY94U4+t6agofLRssYt/l/Voq6qljBDeP0tNFPnFWaTLHCRRi8yCx0b2J8GbGCz+cT94DjqhjFYlFeexGxAn19Cg2AhiFeJFZ4EQydSHLD4CBRSmNYliXXg2Xn9PXwGi1j3Hzia7UaDg8Psba2JrFZ+mJ9fX2YnJxEPB6XWcAsY6YxNjc3pfA7DZMYfX19knxxwzg7a7TIWFxcdMXo7+/HxMSEKwZ/np2dIZVKOQrQE4OZPTcMPcObGFqr6YbhtkoQY2dnR4rzuGEwg8phYpRKJaysrIhyX2NEo1FMT08jGAy6YhAnn8+7YnR0dCAej7tieI2WiHNzM8Ps4cbGhgT69ZNMhQ4rtPKzXhhs3qkbSLG8Gf15L4xSqYTV1VVRh2s6aT6fd1RH0hiccWz7nNrKm0iMer0h1WLnBhODWVI3DP0AumFwuGGwt7oXhrmpNTG2trZwcnIiiniNcXh4iL29PU8MoBEC3dzcFAy6M6TEJpNJ7O7uXqp652iJmRs4Fwns7OyIWGF8fFxms1QqJSKBg4MDidtehDEwMIDx8fEmoUE2m0UikZCqSW4YWiTwIhhcwrVIYGJiwoHx8xArXAVjYGAAo6OjTWIFYnR2dl4qVvDCSKVSODk5cRUrcHLiqjEwMICxsTEpgl8ul3FwcID9/X2kUimh5142WsK4uexls1nUajWMjo7i7bffxtTUlEOssLS0hJ/85CfIZrMijTLdAGKMj49jdnYWt2/fllLGxKDgwU1o8CJihf39/V8YscJVMLRYwbKspuN4UbGCxpifnxcXzg2jWq3K30wMFjp9+PChuF9fi1jBsqwNy7KeWJb1yLKsh89fi1mW9e8ty1p+/rP3+euWZVn/u2VZK5ZlfW5Z1v2rfAfJMyx3NjAwIOXStFiBQgOSo3QoysTQYgX+o9CAEibdbs7EACCJBsbaNUYsFoPP5/vaxQpaePGiGCxbp4t3mhharGBisNGVG4YpVnDDODs7c8UgZ2dwcNCRS/i64tz/kW3bSfX/fwXg723b/h8ty/pXz///3wL4dQA3n/97B8C/ef7zwqFjqcFgENVqFZ9++imePHmCN998E4lEwiFW4PL+RcUKxAgEAheKFdra2lpWrGBitLW1XShWYKJKiwRMjHA4fKlYQSe3NMZFYoXPP/8cQGPV0xiXjZ+XW/JbAH7l+e//B4D/Dw3j/i0Af2I3zuynlmVFLcsatm37wnKdTIJwWb1165bUD6zX601Cg1KpJH/jReDvVxUrMBWsY7IaA7iaWMFMwBDjRcQKJgavx1cpVmD5Bv7NDcNtc6xLL1Bo4IZBsQIpyiaGZVmi5nnllVfEfQsEAnj99dclbEsMAJca+FcRLbEB/J1lWZ9YlvW7z1+L02Cf/xx8/voogG312Z3nrzmGZVm/a1nWQ7o53IQx1Xx6eopr1645qK7xeFyEBrqApfYxNQbFClokoDFYykzHYU0MN6EBMUyC/4tgeIkELsOg9vDLiBVGRkYEg01aNYZ2BTQGZ1MvjHq93lSr3Aujp6cHIyMjkivw+/24du2aYJjXw2t8FTP3A9u29yzLGgTw7y3LWrjgvW5H0xSwtG37jwD8EQBYlmXz6aYvrcUKLAi5uroqxeNZX5u78OeYl4oVVlZWcHZ25lDPmyKBy8QKxDBFAhpDcz2uIlZ4EQwvsQL3BOoaN2GQUeiF4fP5HA/JVTF8Pp8UUGLo1cSwbVuK8ZsYlUoF+Xwex8fHrhhe40vP3LZt7z3/eQjg+wDeBnBgWdYwADz/efj87TsAxtXHxwDsXfYdlnXe5QCAo441kxPsunB8fOzgmuhEg4nBG0YtJjEY7zXT8SaGVpeYGKzX7XYc2kiOj48dMXvbtqUv5hfBAM6L5L8oxvHxsUOeZ2JwFXPD4AbcDePsrNH2g82i3DC4GXbD4D04ODhwNJy6bOb+UsZtWVbIsqwIfwfwHQBPAfw5gH/+/G3/HMD/8/z3Pwfwz55HTd4FkLnM3wbg2IAEAgEJt/X09CASiaC/vx/ZbBYbGxtC3+QF0YkPE2NhYUHS1GzPt7W1JcswL6wXRq1Ww8LCgpQv0Bj5fN6BoQUPGqNarQpGd3c3+vv7YVkW1tbWvhBGT0+P9HLUGDqp4oWxvr6ObDaLnp4e6Tm/trYmhewvuh4ke3lhcBXwwtCkMxMjFAphY2PDgaHFG17jy7olcQDff/4EBQD8W9u2/8ayrI8B/DvLsn4HwBaA//T5+/8KwG8AWAFQBPAvrvIl3E3b9rnQAGioPqilq1arQj81fTovjMvECnRr3DAs62Kxgu6fbmK0mljBPBcvDDexgsZwExpcFQNoiBW4gphhTbfxpYzbtu01AN9wef0YwD9xed0G8C9f9HvIl6C/S6EBxQo6Jm2KFUyhgYmhRQIag+/nLGFieAkNSqUSbPvlFStoN83EAC4XK5CHbWK4iRWIcXp6ipOTE+kYYWJ4jZbIUALnmzmfz1towNARw01mVSLOEhpDiwQ0BmcHN7GrmqgWAAAgAElEQVTCZRgUK+jNlnkuwFcnVtAYVxUraAzgxcQKbivjZRharMA9AK/nZRimWMHE8BotYdy6sQ8NS4sVtL9mihV4cd0wTJGAxgDOkzZuGPV6Q9zghXGR0EBju2F8HWIFN4x6/cXECmx+y9fpS7thtLW1IRaLOe4NAAm90l3xOg4AnhheoyWMW8ea9UXQy6QZDeCN058zMfTsx58mBt/PYc4WXhj6ZpsYPBYd2tMYPB8vjK9DrEAMfb354JgY+rNeGJqu64ah75XG4DFqDPP+eY2WMG4OfQEoVmDDTvqM5obS3FFrjKuIFTSGmUSxrIvFCvo7NYZpMK0gVnBzRV4Ew0wkmRgcbhhAYzOpxQoaw2u0hHHrMBY3W+vr603G3dfXh4mJCQwODsoM4SVWODg4wNbWlkMk0N7ejlgshomJCREreGFcJDTo7++XY/nHFCtEIhGMjIx8IbECEyUa46piBTeMaDQqNR7dMIhDsQJ7WfJc2tvbEY/HhXpxFbekJcQK3ExSaLC5uSlJC00EyuVy0rLC9Lm5q9cYbiIBNm/1EhposYKb0ABodDhjoXQ3DLpMVxEruGFcVaxQLpe/sFiBceRyuSwF6K8qVjAxSqUSjo6OsLOzc6lYQWMwQsQN5NHREba3tyWqctloiZmby161WnWIFcbGxiS6kMlksLOzIzXl+LSbGBQrdHR0uIoV2Fnh4OBAmgy5YXgJDXRnhYswripWcBM8cCm/iljh6OjoymKFyzormGIF4pyeniKRSIhYQfclYmcFihV6e3sRjUabMGy7IVYwMZi1TCQSUsswFos1YbiNljBuLnuZTEbECm+99ZaIFQBI1vJnP/sZcrmcXFzTDSCG2VnBtp1ihUKh0NSNgBhXESs8e/bMtaMBZ+avWqzw5ptvYmZmRkj8WqzgJppww7ioswJVMKZI4OzsDIVCAfl83rOzAsUKc3Nz0j3BxDg9PZW/feMb38C9e/fkHrKzwscff4z5+XmkUqkmDLfRMm6Jm1jB7KwQi8UQiURwdnYmMWsd+fASK1AkoAUPluXeFeGqnRV6e3sB4OciVrDt5q4IvB5uYgWfr1k04YahOxp4CR50nJtumu6swDLK5OBwQzkwMCDFS90wKEBgZwUegylWaG9vb8LwGi0xc+t4rO6sQLHCwcEBVlZWZPn+sp0ViOElVqjX61cSGngJHrgh/Xl0Z5idnUV3d/eVOitwH2N2Vrh7924TBpNMzLC6YUQiEezu7mJlZaUJA2gkaJiE0fsIztxarKAx3MQKGsNrtIxxk6ft8/lkx0wmXl9fHwYGBmDbNpaWlqTcgw6fmRimWIEY9bpTrOCFYVnWhWKFo6OjX6jOCmYiiL9ftbMCW4DzHNwwLuuskMlkHCIQjeHz+Vw7K/j9fodYQZdCvow41TJuCaMAXp0VtNBAd1YwY9Maw+ysYGK8iEjAxCCn/Bels8JlXRHcOitosQLFG5fdFy8MGrsXD5tZzNPTUwcG3bRrz8UK/K6rjJaYuTlTMTRldlawbbups4KuZ+KFcVFnBc7+X0asoAsGaQzN9fhF76zg8/leuLOCGwa577ZtN0Wx+I+bdbOzQqVSwfLysqfgwWu0xMxtWU6RwMHBgVxQXqhgMIhcLoeTkxNHJwGdaDAx3IQGxODG0wvDsiyHSIAYoVCoSaxgYvh8PleMLypW0KIJwF1oYGLwfRrj+PjYQSPgbEwM/d1uGBQamBinp6cOoYEbxkViBa5YiUTCE8NttIRxay5Ce3u7ZOQoVujr60Mul8PGxobwqbmc6cSHiWEKDbLZLLa2tgSDxuAmEvD7/Q6RgIlRKBReGOOLihW0aMIUCZgY2vc3MbyEBlfFsCx3scL6+rrM5l4YJKLx/ZlMxiFWWF9fRyqVcu004TVawi0h3ZIXkvFadlYAmsUKernzwmhra/MUK+himm4YlmWJP3yRWMEN40XFCi+C8VWKFXRXhKtg0AW7qLPCVTGOjo4cYgXLshxiBX09vEZLGLfZFaGzsxPhcBgDAwOOzgpMKGjGm4mhxQqhUKipK0K9XkexWBThsS6S7iZWcMOwbadYQfei+arECsT6MmIFE4Nx6hfprKAxgKuJFdwwzs7OxPXhcZidFZiAMzG8RksYN3C+fDFCwk5XnLWYpOASq5dujUEiVGdnp/zTXREYfgLOEx1uGPQn3TBMsQJncw4el8/3xcUKekl+kc4KbteUGOxmcJFYwRQJmBhX6azghqHPpaurS/51dnZ6ihUuGy1h3K+99ppj9uPTe3x8jGq1ilQqhUKh4GjzRj+OYaOxsTHHBeUMyqqhVNAwDqvj5LyQrF3HJZHRmrW1Ncf7+BCSy6GXUGY/aRSWZaFYLGJ5eVkiBny4+OCYMepwOOwQCZBNt7i4KP4sV65QKIRQKOT4PuB8E+fz+STkxlQ53Tr+vaenxyGy4HFoghrQ2GxmMhmHoJgbxZ6eHsRisabj2Nrackj6Ojs7hWgVi8UQCoXEVx8dHXVkel+KmVu3mdP8X/30clnjxWZkg4ZCdQtwLiigi0J8bTAaR/NCTHI+LzLdBvNm6xvJ13gMHDQWk/6pf+rftUjAPF59jcyIhJ5lOZtr903/na+bx8trpI+d15puI1PjjGTpKI15HJxUeCylUklCn3ovoI+R9/Kl8Ln1heFPzn6MrR4fHwOAJCxYrUgbiXY3ODudnZ3h+PgYJycn0vtQuzvaYBmB0TO3ZTVEwalUCuVyGdVqVcQK3Jj6fD5Hq0H9IPF4NDmfYUm6Kvp9PBeeP8+vVCrJP7pdkUhEqAjmBls/NNwDVKtVySKGQiFh8On3ahdCryg0apZNoySMSS0aq7kRJDfFts/5Q+VyWaI3sVgMt27dwrVr12Q14HgpZm5tmLyxyWQSP/rRj5BIJCTlzA1ee3s7enp6xEiBc8PkODs7w9HRER4/fozj42OZEeh7hsNh9Pf3S+gJaJ7NarUaNjY2sLKyIkXoycPo7OzE0NAQBgYGZGbjcfB8bNtGPp/H8vKyxHA54wWDQenywEiDm+ChWq3i6OhIxBuMSLS1taGnpwfXr1/HyMiIfJ+e4QFIUmt3dxfpdBrFYhGVSkVchL6+Pty5c0cYevrB5HGcnp6iWCyiUCjIJHF2dobt7W10dnZieHgYU1NTGB4els/pDTZn/FqtJiqccrmMXC6H4+NjbG5uYnJyEt/61rfQ398vx/9SGDdwvvzRT/6rv/oracIZCoVkk0JfmjtrPXNxOa/X61haWhKD4GzPm08Jm46pmsdxenqK1dVVbG5uCptNG1ClUsHR0RE6OjoQiUQc58JlO5/P4+nTp8jlchLnprvEPYXf78fk5CQCgYAjssBrkUwmsba2hrOzM1l1aPSlUglLS0vw+/2Ix+NN15MzdiKRQCqVkn1CMBiUjXAqlcKjR4/w+uuvN3G5ea40xNPTU+H8cDavVCqC/9Zbb0kcn+fCWRs4X3W5aeRqUC6Xsb29jQ8++ADf/va3EQqFmlxAt9Eyxs1ZM5VK4aOPPkI+n8e1a9cwPj6OwcFBZDIZbGxsIJ/PSxaLMjHg3Ffz+Xw4ODgQVcjExISE4QqFgogVuDxWKhXZlOpISqFQQDKZRDAYxPDwMGKxmGQ4d3Z25GYXi0UEg0GZublhPDtr9NSxLAtDQ0MYHx931D7Z3d3F8fGxzIjciALnM2alUkEmk0EkEsHg4CBGRkYk7Z3NZrGzsyNdEaLRqCNiw4eoWCyiXC5jaGgI8XhcCFjMFPJ6JBIJx0oINFYhbu5t20Y0GsWNGzdktTo+Psbh4SG2t7dRLBaxv78v9Fkz1s28QSwWw/T0tNTi3tjYwOHhIRYWFrC/v49EIoEbN25cmsABWsi4GcP+5JNPsLCwgJs3b+LXf/3XMTk5iZ6eHpydnWF5eRkffvihzGT8pzGy2SwWFxdRLBYxNTWFd955B+Pj4wgGgygUClheXsbPfvYzicTUajXHhs+2z3vvAMDdu3fx2muvCdc4l8thaWkJi4uLclN16ItGRX5LJBLBN77xDVn6LavBm15fX8fHH3+Mvb096VqgN7NnZ2eiIR0bG8P9+/dx48YNdHZ2CgtvdXUVP/nJT3B0dIRMJtPkP1Mu197ejtnZWUxPT2NqagpdXV3S6eDJkyf46U9/ilQqhWg0ir6+Pkd4jzjhcBi3b9/GW2+9JQqpfD6PZDKJjz/+GE+ePMHx8TEymUxTSQgSs8LhMH7lV34F3/jGN2SGT6VSSKVS+Mu//Ev88Ic/xNbWFuLxuJSruGi0RPq9Wq2iWq1iaWkJjx49EiUNfWJGSSYmJjA7OyvLItBcS5rEHvqTvb29svkMBAIYGxvD5OSkg3essarVKo6Pj8VtIQWA4TS/34+hoSHxL3WCgxinp6dC8IpEIujp6RH+uBYJ9Pf3w+fzyQNmGjfQMLBoNCrGT5+dAmE2Mc3n803hM2K2tbU5MDiLdnZ2IhaLoaurSyro6nPh8fC9IyMjoiYCIMm2WCwm7oau5gU0ap8wTzAwMIDr168jEomIW8KN7djYGMLhsDy41LJeNFpi5uaFXV5eRqFQQLVaxdzcHADgu9/9Lj777DOsra3hjTfewPj4OIaGhmSmYayZkZWDgwNJ1bNz1q/+6q9id3cXW1tbGBkZQW9vL3p7e2HbNsLhsKOjQT6fRzqdFnrnxsYG9vb28Mu//Ms4ODjAxsYGenp64Pf75bt1OQL6oQx5dXR0YGlpCSsrK/jmN78pyn5mXjs7Oy+srR0Oh2FZFubm5vDo0SPcunULPT09+MlPfiLRGvqo3HgDDdemq6tLDL5er2NtbQ0PHz7ErVu30Nvbiw8++ABAI67OSFCxWHScVywWQyAQkNl0YWEB29vbmJqawtDQEH74wx+iWq3Kd7H8BHWlN2/elKzugwcP0N3dLTK90dFR3L17F3/3d3+HtbU14eKk02nJqF40WsK4KQw+PDwU6uWrr76KyclJRKNRfPOb38Tw8DCuXbuGYrGI2dlZHBwcOJ7sWq2GdDqNUqmEWq2GeDyOe/fuiS96584dDAwMoLe3F4eHhxgbG2tqlsqdPLOfsVgMd+7ckZvN/pVdXV3IZrM4OTkRyipnM7pKLPg4MjIihdbPzs4wODiIwcFGrf5kMolsNusgF3HYti2Gz1VMhwcfPHiA09NT7O7uolAoyCZY11Ckn9zR0YHJyUlEIhGUSiUpYfbNb35T6KaM5OhsK/WU/f396O3txeDgoBSO7+3tRblcxoMHD1Aul7G5uYnPP/9cojN0aaanp6W/UV9fn5ClotGolOi4d+8eJicnkcvlkEqlUKlUkMvlLo2WtIRbkkqlsLGxgVKpJBnE8fFx6WBwdnYm6pzT01NHVwQ+3dzR61DY+Pi4XNhqtYqpqSmZsRkn1pROnaBgqn1iYgLhcBidnZ2yQe3t7ZXsKElepkQMaKSaC4UCxsbGZHk+O2t0FjBFAmY4lGG5trY22RDyQW1vb0coFMLw8LAYI+PlOmavEyScTelOMfIyMjIiPSHN1DdXHm646b+Pjo4K+YtVBsi7p3GzFAV55nQlmWuYnJwU96u7uxsTExOwLEv49plMBqlU6kK7aYmZe3d3VyIYQCPt+9FHH+H1119He3s7xsbGHMUvWUBeK3Ly+byDN51IJPD48WPp9tvf34+joyNUKhWk02lp96dnbi7NvNG5XA4rKyuSXg4Gg9jd3ZWlM5/PA4ADw9QgMsoDNEhHlmVhb28PPl+jLEKxWJSHSQ/eeCZeNjc35biq1SoSiQTa29uRTqfF0PQKohNapVJJGsNSQlcoFJBKpdDZ2YlkMulI/vDh5P6Am/1wOIz9/X1xQVjP0efzSYNVPpicdRl6JSEOgPC5A4EADg8Ppentzs6OhD8ZpblotMTMzVbJWvG8tLSEYrGIUqkk/d+r1Sqy2Sx8voa8SdfIYNSCGxUWn2ckg7IwHbMlLZaznr6Ytm1LY1fOzLxB+Xweh4eHonM0Y+V8P6MBuVzOsUJ0dnZKPZFarSaJH547Z3EaK7tJ0GCYoSyXy1JMh5/XURtmYOv1OpLJpLADuerwXHg9dIQDgHA+GC/PZDLyeqVSkSzv4eEhDg8PZfZmmJXnqo+D58toC/cnS0tLqFar0j1jb28Pe3sXN+VoCePmDEb1tm3b2NrawuLiotzoZDIpkqZYLIZr165JF1oAknkDIC7F8vIyNjc3hYG2v7+Pk5MT+P1+eS0cDsuyy6gNcM6RePr0qRTf4cxFOZR+OGhUnKWA8zT8kydPJF7d09ODfD6P1dVVlMtlRwpfx+yJQUN69uwZisUiwuGwxOzn5uZQqVQc3AyTP8PEFmulHBwcIBwOIxgMIpvN4pNPPhG5HPk7WuSrqQi1Wg27u7vY399Hd3c3urq6JBTIlDrrpOg6gLwWXMn29/eRzWbR3d0tnPCPPvpI5G5US11m3C3hlnATRwNhcmZpaUku4OjoqMRxOQvopVzP2sSo1+vY29vDp59+ioGBAUl7c4Zua2tzRCpI8OFN5ax8cnKCx48fi3/f2dkpWVPTHQCaFSzt7e04Pj7Go0ePhIBEbgnT/Trtz2Pg5rGtrQ1dXV04PDxEKpUSl0ZzU+jv6s2xjpywBEQ2m8WzZ89QrVZxeHgoYoFKpdJ0HNSqcvBY0uk0Dg8Ppe6fro9CegAnDK7GzMoSe3l5GfV6HTs7O/jss8+Qy+UcqxOL0F80WsK4dZyXfl44HMa9e/cwMzOD0dFRiXlzQ6bJUcTgDMOlsLOzE9PT05IIYsyc7oReLgFn/xa/3y8FZEZHRxGPxyVtzVAhORr6ODTJqF6vo729HdFoFENDQ1IUh3XxNJcZcKrfiUUj7erqQl9fn8TbI5EIDg4OkEqlHIbAc9Hkq2q1KqHHaDQqsXUaXTableOgERJLc0S4wnD1qVar6OnpQTKZlIQYrwlnf36OPHwafm9vL/b393F0dCQRMrpGetW5aLSEcQOQUBln1+vXr+Pu3bsYHByU+iPAuVBX6/Q4tP8bDocl2aIjFZx9GI3QZCvgfPnkTWD4i5wK3ryrqLP5oJHPocsnMJGib6TeBGofmA8h2ZB8QLi6MPzIKlzEYFjUZAxytWG9E+5nGErkxp4ZTr2qMmTI49HREFNRxffz/yxgRGxyXTo7O6Wyqw5nvhTEqW9/+9sAzpdWTRo6Ojpy8JLNUr28iDMzM/I53gzbtmWzw9d0ZEJTOgEgHo8Lhs72bW1tCYGKGPo4NAY5IhqjUqlgZWUFgLOzAlcXc6Zipk77u9lsVrgqWmbX39/vOA4eM9P0+nXWR9QuDDfmJpsQgKxqfOC54hwcHMi5hEIh3Lt3z7GCEeOv//qvm8Kz5t4gEAjg3r17nhjf+973PO2mJYybS6h2NXhjNIVUk/RNxYf2FzUGcE7KMimt+vv5k5/V/q+JYb7HxHDzofk3fR7mCqRvvBlO00anj8XE0Mkkk1mn3Qxiawy+R9MRtMt1EYa+tl4Y+jvMe2/eH43hNVrCuIFmVUutVhO/jjMlM4p6+TYvlL7wLNGVz+dl+TQ7K5gYGktTS906K2guiOnr8lzcMHy+q3dWACA+7It2VtDnAjSMyquzgr5+boQlEyOTyYhB6s4KJoZ+6Dno7qTTaTkOs7OC13Ho0RLGreO7XPrW1tZcOyuMj48jHo/LbKCFBsQgwX97e9uRInfrrOCFQcrq4uLif9CdFbgqMLu4tLQkVF0Al3ZW0Cst0Ei2aQz6/B0dHY7OCsDltQJbwri1z5hMJrGysiIJB2a1yBbb3NxEIBCQrldmXFdjMJSlMYrFIjY2NlAulzE+Pu6YMbUfn8lkJI5sYjAb6vf7XTE4a2cyGczPzzdhMPO4s7PjiqFncBODKxfj0SaGuRfRGIxWkGjFuP729jb8fr9D0UMM7hvS6bQU07EsSzb5xDg6OoLP1yhAaoo/iFepVLC4uIhMJiMzPlcl224Up/f7/U0YXqMljBuAbJoSiQR6enowNTWF6elp4SRkMhksLy8LQZ80SnP5JUY0GsXw8DBu3bqFcDgM224Ujl9eXpa2IT09PQiHw03HUigUsL+/j2AwiJmZGdy6dQuRSETCgMvLyyI20MxEDp/PJ+T9YDCI2dlZ3Lx5UzAKhQLW19exvLz8QhgzMzOO9DkxksmkYOjroTFCoRBisRhmZmZE5U+MpaUlEWaQc87raVmWYNi2jampKczOzgpGPp/H+vq6tHNJp9NSu1xjsFYLANy4cQMzMzMiKatUKtjb28Pc3BxyuRzS6TRisdilNtMyxs1QFgAMDg7i9u3b0lmBmS2/349sNotisYhqtSqiVDeMgYEB3Lp1C7Ozs2LA6XQafr8f6XQahUIBpVKpiVZJf7BWq6G3t1cwdGcFZvP29/dRLBYdfi8xGPft7e3FzZs3mzordHV1IZfLIZFICIbeB5gYMzMzTZ0VXgQjGo1ienoat2/fdnRFIAa7M3R3dzcVB2KYMBgM4ubNm3jllVdEc6m/d2FhAfl8Hj09PU2bR4qLQ6GQYFC1U6vVJHY+Pz/fhOE1WiL9zkQDA//9/f2ydGqCPzsr2Lbt2RWBG9CBgQFEIhH5PGPa7KwAnGdG9c3UnRVYFYmf1xhaVcNwGTG+bGcFAK6dFXgujOe/KIbZFcENQ2cJTYxQKOTaFaG7uxsDAwPCYNSZSIYQGYNnYoyfZTLN7KxgHofbaImZW4f+wuEwqtUqHj16hKdPn7p2VmBql5lGEyMYDIp/Nzc3hwcPHnh2VtA+ITFIkGpra/tKuyJcFYPH4IZxUWcFfS4mRltbm2dXBHJt2tvbHZW8TAx2RVhbW8Mrr7zS1FmBrqJZ1oIZWyp3dnd3sbq6ildeeQU9PT14/PgxgObOCma9cHO0hHHrk7csy7OzQr3e6IrAzZy5CSOGz9fozhAKhYT34dZZwUzAmMdxlc4Kbhg01K+is0IoFLpSZwV+t4lB7vfIyMiFnRV0cscNIxgMIhKJXNhZIZvNCnFMb/S5orCzwiuvvCLhPr/fj/v374vYmlUNrhLnbgm3RCdrLuqs0NvbKxebs5yONZsYIyMjV+qsYGL4fFfvrPBFujO8SGeF9vb2K3VW8JKqMc3v1lnBDUO7Nea5UFjghkHXzA2D15hiCBPD5/NhcnJSUvKaanvRaImZmzMEfenLOivQsHTYyg3DrSuCGcrTjD5i0G++rLOCFwZnna+zswIx9DU1MSieZgTKrbOCWdCTszC59F4YukC+Gwb/mRgMAlBJ74bhNVpi5ibPgjcnkUgAgMwo9KNZoYjLnCbnmxhunRVCoZBgkDR/EYa+2F6dFdwwtJF82c4KluXszgC4d1bQ1AD9Po1B1QxT4borwmWdFSzLcnRWcMMolUqeGAwKlEolJJNJBwbfe3h46InhNlrCuDUTLBAIIJ1O49NPP0Ug0Chq3tPTg729Payvr6Nerzsq+GsmGnfp7Kzw5MkTIfj39PRgd3dXMOibM01vYpAe64VBoQExaBwmp7xareLJkycoFAoiVigWi1hcXPTE4OrB66ExeByFQsGBQZdM+/4mxurqKnZ2dhwY7A5HDF1OzcQA4ImRy+VcMfS5MKK0traG7e1thMNhqcK1sLAgGFredtFoGbeE/+hnhkIhbGxsSOCfN4DVpvg57S/rDB03QYeHhygUCrBtW3jNnB04TIISMbgpJDEfgGBoHraJoaMWFBqwNTTQWJFYb9ANg0blhsEWH4wvmxgcbhiUps3NzYkChnW29edoyCYGKQzlchnPnj0TAXRXV5cocfg5N/4PU+3EePr0KTo6OlAsFqWcMyeay/xtoEWM2+yKwJDR4OCgRC7YFaFQKEhUQ8dB3bozeHVW0IVj9NJnzuBenRXq9bqjs4LPd94VwauzAjF0tu7r6KxA8cGX6aygBQzsrMCuCLVaDYeHh5JYo1TNC4Mupj4OluXQnRU0K9JrtIRxA8560nyK3TorMJ7LjZIXhldXBD0zmTfT6zjcMCgSIIYeOkJAY9QYenW56FyI8WU7KxDjKp0VtEhbYwDuXRG4KjJS4tVZgdeY95X/NMuTGKwGe9loCeOmyJbZL7/fj0wmg3Q6Ddu2Rd+nC+Zo9wRolO0y+dIsQQw03An61zQ2RgLo2+lilNoP1hhA42ZRHMybTwweBw2CmT+KFXSpYhqInt0ACAeF/8iZIeFICx6o8gGcYoDu7m45P65ypVIJq6urwtLj3iUSiUiRTh4zAInp12o1odpmMhkkk0nJIJK6Gw6H0dvb2xQKfPDggdQ3pwZ0a2sLS0tLsqJ0dHSgv79f5HgUUASDQfzBH/yBp920hHED50uqfop5U+iL6dQsd9u6jYi+OdrgADjCZJRw0Q0yNy68+dwYEdv0Rfm92k/ng2WuBvybGa4zudt8vylI0KFO/QDq/+uhIxE8D87O+tjNh5mv8zW+zhVKv09TWXWOwLzWtm2Li0eKBKm43AswIkP3yXxI3EZLGDd3yV1dXVKjpFAoSDivXq8LmZ1GyWVLC1E5k+oNTbFYRCqVklofOsVNY9HZNB1x4CCxnmp5ChX05tF8QLThsKgNi2tyxtM4bg8If+dDTLECN2aRSEQ4OPp7ATj8d+KxJiPLaLDqVnd3t6sx0YXiLE1jpuvBB4XnoDe3xCK5jSsWdaAAJOXv9/tFG0qKBaMmF40vbNyWZc0A+DP10hSA/wFAFMB/AeDo+ev/vW3bf/X8M/8dgN8BcAbgv7Zt+2+v8l2U9TN1e3BwgKdPn0o1JaBx07q6ujA0NITR0VGHIQPnJQVonLu7u5ibm8PR0ZE8ABQJ3Lp1CwMDA7I6cGjfEGjEqD/55BMpOANA3KeJiQmMjY01YWijrNfrSKVSmJubE9EE/cpQKITx8XERCWjfX4sEKJogl5qp6Y6ODnR3d2NwcBDj4+MOgS7PlbN3rVaTOnzZbBb5fF4yuWRADg8POx58AI6H5uzszFEsk7Ov2b6F4UD9oPLvnE9sIIQAACAASURBVNlJrbAsS1iOnNyCweCVDBv4EsZt2/YigNeeH6AfwC6A7wP4FwD+N9u2/2f9fsuyXgHwTwHcATAC4AeWZd2ybfviYCUgypBAIIBEIoH5+XnxrzWRp1AoSAH6WCwmSREADibZ7u4uPvroI0fNu9PTU4kOZDIZDA0N4fr16+K7As4YdTqdxocffihqb9Y7OT09xeHhoYTlrl+/7nArOINbliX1TrRYgcs74862bWNyctIRbwfODYwl3XSfeV1A5+joCF1dXY6WHc/vH4DGg1Iul6X6LbtR0F2p1xtVaVlkSFcEMF2SYrEoGeJ6vS4GyodRbyy1K8Z7oJNULKfM5BgJYHr1+LqiJf8EwKpt25sXfOFvAfi/bNuuAFi3LGsFwNsAfnIZOGcPoFGspbOzU8rs0kerVqtYX19HOp0WF4GpbwASZz09PcXjx48xNjaG6elpMT6moBcXF/H06VPs7u5KSIozFo2rWCxiYWEBoVAI7777Lm7duiUzYy6Xw/z8PObm5rC5uYm+vr4moYFt2ygWi9jc3JT6KyRy0UVZW1vDs2fPkEgkhMprZvXK5bIIM6ampjAzMyMsvmKxiPX1dWxtbSGVSkn5Cg4+tFq7GQ6HMT09LRK7YrGI7e1t4be3t7c76m9z9meZs3K5jGAwiHv37mFgYAAdHR3I5/PY399HqVSS8sc6bk7XjdWo2EJlbGwM4+Pj6OrqkkpWfA87VXxdxv1PAfyf6v//lWVZ/wzAQwD/jW3bKQCjAH6q3rPz/LWmYVnW7wL4Xf6fMyIv6rvvvovf+I3fQDwel5hyJpPBj3/8Y/zFX/yF1BWk/wecx1EZ833rrbdw//59TE5Oor29XfrgjI6O4uDgQAo3MjbL46Bhlstl3LhxA2+99RampqYQDAblAYlEIqhWq3j8+DFyuVxTEoR8idPTUwwMDODOnTu4efOm9JcslUoIh8NS4DKfzztEy8Th8UUiEdy4cUPYkn6/X4p4Mp1dKpVcfV6eV2dnJ+LxuChg/H6/cFLIkqxUKq7KJLqMtm2L+CIejyMQCMj13t/fR6VScXSqMM+Dla3I3Y7H4xKSPDs7w9bWFtLpdFMNR6/xpdPvlmW1A/iPAfzfz1/6NwBuoOGy7AP4X/hWl4+7ppls2/4j27bftG37TQBCcAcaPtu7776LiYkJxGIxEa8ODw9L8Xm9bHLWpT9YKpWkpjVJ8YzPdnR0YHh4GMPDw7K0aw4DbwJVPoODg4hEIpJS5qYrFothbGxMvldvHrnZorCZs7IbwZ8U1FKp5NgEE4c9HNndwcTghpKhUW7ueG04MdDwY7GY+LPMH1DxQvISjZgYrFnOopWsT85r4vf70dfXh0AgICE/Hsvzey31/+jOkYpMw6erVCwWsbu7K00DzEyyOb6KmfvXAXxq2/bB84M94B8sy/pjAP/v8//uABhXnxsDcHElw+eDPldXVxdu3LiBrq4u/OhHP0KtVsN7772H7e1tLCwsIBgMYnp6GplMRupiay4FjX18fFzSu9lsFg8ePEAikcD6+roIYY+OjpqaJHEG9/sbrTr8fj/m5ubw2Wef4Z133sHh4SHW1tZkVqHxmwkLht0Y0pqfn8fTp0/x3nvv4fDw0CFWYKZPhwK5lGv248LCAubn53H79m309PTgo48+ks0b9x46eUKfnNEVv9+P7e1tLC8v44033kBPTw8+/PBDWTHoe7NAJc+FKx45LAsLC3j8+DF+6Zd+Cd3d3fjggw+kfnk+nxe+eV9fn5wPWZrc0D58+BA/+9nP8Ju/+ZsIBoP48Y9/LO1HuALQ+C8aX4Vx/2dQLollWcO2be8//+9/AuDp89//HMC/tSzrf0VjQ3kTwM+u8gXkTrD82cTEBGZmZgA0LnA0GsV3v/tdeQjq9YZgmOUF1LFJ6Ybbt2/LrMbl9sGDB6jX6/j0009FuW3ODvV6HT09PSJSZhWqs7NG06N3330X9XqjwCajPHrmpmF2dHQgGo06hAbEJkYikUAmk2kKSQLn5cY6OjowOjoq0QzGht9//32JLGWzWdmQ6lg8IzCWZaG3t1dcNEZrvvWtb6Fer2N/f19oDVpmx1WMD2sgEMCbb76JcDiMcDiMaDSK73znOwDg6DSRz+cdlXPZsY336/79+xgbG8PY2Jijv1A6ncbKygqSyeSFvBmOL2XclmUFAfwqgP9Svfw/WZb1Ghouxwb/Ztv2M8uy/h2AOQCnAP7lVSIlAORCkPyfz+cxNDSEZDIpF5xsOlJGyU3QbTIAOAj+IyMjUre7Vquhu7sb5XJZmIMAHDMEw1WanjkwMCA1wkulknR7IPmJPqeeeenqsGvY0NCQ+KPValVKCO/s7IgIgJ/TWDzOfD6PkZERCenRJens7JRSF26bL506z+Vy6OnpQX9/P46Pj2UiaG9vx/LysoRSdQ6BDwj3ROl0Gt3d3RgbG8PJyQlqtRoGBwfh9/txcHAg5DJuLgE4/O1yuSy9Ra9fvy7Rl1gsJuWhWTqCfTMvGl/KuG3bLgLoM177zy94/78G8K9f9HvK5TLa2tqQyWQQDAaRTCYdhWra2tqwuLiIQKBR6JzEKBaEBM4bk3ImJgZZfIFAAEtLSwDg6FbGTl7EAM77lWcyGezs7EhDI5/Ph9XVVQBAJpMRDN1djTM3N5XZbBbb29vyUNrPxQptbW3IZrMS+/aqYMVVamNjw6GIYYNWFvthgkknpIDzB5Zpb7a2Zvz87OxMcgEM4TGmr7OtPp9Pyh9zIwpAIiC7u7soFosiQGDmWIuzGXn5+OOPEYlEJDEHQKjEjNro4IDXaAk+N/29crmMk5MTSd4wJNTZ2YloNCq0USq2udEiBm9EtVpFMplELpdDKBRybJ4ymQyy2azU6NA9X4DzEJpt27LMcvdOHkYul5MSxBofODcmPpws78tZWNMJ+KBypTBDgTyWk5MTh9aSGOzeS8W/zmzSoPgzmUxifX3d4f6Q+MR2KqaPy5WUmd3T01Osr6/LvarX6+jq6kI6ncbu7q6sijpiQveF1Qhs20YikRAhCBVA2WwWS0tLjnLGl42WMG72Vtnb20N7ezu6urpwfHzsqGk3Pz8vJHlq73TKmksnky62bWNvbw/JZFKiA/Pz89jY2BBmnUkUIgYvLBMtmUxG2IHr6+siV+NDwEwgAKn/TaOtVCqYm5uTmh3sisAqUtzQmWl//ZDU63XJ2LI0Qjabxfz8fJPgQW+KucGmca6urkr8PhQKIZFI4MmTJ3IcjMbwOFhauLu7Gz09PYhGo9jZ2cHHH38s92BlZQWfffaZ9P7k9/GadnV1YWBgQIos9ff3o1Qq4cMPP5S2MIuLi/iHf/gHJBIJ4XvrfkdeoyW4JZlMRjZD9Xodb7/9NqLRqNS3o+/b19eHhw8fYnd3V4ycN4K+NWO/169fx+DgIHK5HD777DOUy2X09vZidHQUKysrEnHRqWKyD32+Rokvqt3z+TwePnyI09NTaVVNSRRnUhq35ltw88eCM5988okQi2KxmMTCdVkIDvrLnKU7OjqQTqfx+eefSwQlHA47fH4dCuRmlCXlenp6JAX/8OFDWY10IoubXhqVzqiymWu1WsXJyQk+/PBDKWRPjgp5I3RFgAb/m69fu3ZN/PJyuYy//du/xeDgoKwKPA66LLpyldtoCePmCQMNHR3QKIgTDoeRz+eFV1Iul5FIJPDZZ59JP3ZuDNmXkn50vV6XEmJMzQONXX0qlcL+/r5sSnkjUqmUMNhoyJ2dnVLEh8kMrhCJREJ65dC10Uvy6empxNcZHwYgNADiuBVbZzSHDw4L4pCPzU0tqQXmIEmJcX5K5bq7u6XIDw2X+xZupnk9yB0JBoMSWaJBd3d3y++kz3KF1Bxt4DwaFo/Hsbe3h4GBAaTTaVktuN8giYtuJ/sdeY2WMG5W/6fSZW1tDUNDQxLGCwQaLSeSySRSqZQ0Jw0EAhgYGAAAqYDK8BGXPE2lrFQqODk5QSaTEcFsW1ubND0lMYl8bwCSjAEaN5/JjmKxKFSA9vZ2mWUYOqOhcpbV5Xm1soW/83v5Gc6gfLBJKOLvxWIRfr9fChCZLhYJSXSTGLtmBaxQKCTpdB1V4XkC5wXs+RCyyhb3QNFoVFaqarUqRq35Opo0xU4VzNACkKZPAGRfxGjSzzUU+HWNH/zgB66UT9u2ZXbw+XwYHBx0FHShEd2+fRt/+Id/6MDkZkp3F7MsC1NTU5JdBM6THX/2Z3+G3/7t33b4vowdb21tiVEyQ/nOO+/grbfechjrH//xH2NqakqOj25CsVjE/Py8vJfGMzw8LOeseeVcaeg3W5YlDz3g1I6SymtuBlnFSUdeGLLb29uTSJTehBKXx/T9739fXBXgPGxar9exsbEhRsvzZRRGG/fv//7vw1I8dr6HRDa9d2EYl/f16+KW/FxHuVx2lPIiUadUKkmYixtNblq0ZhFwMti0wfECMrFi3lDeIP5uYmmjYzEfoFnA6sZmI65Ozuj3uQkPADh8Z77PnF1pDDpUp9+j9wL8DlNkoR80t02tjrgQmz+13E+H+rja8Ds1Z11fN252ufnm+5m80vfFa7SEcdPvq1QqODo6wvLyMg4ODhxqcRo3e8APDAxICpvv4ahUKjg8PMTe3p6EyoDGcj8wMCDtqrl061IGwDkXmyHFvb094UB3dnZieHhYNj36JnsNEr+YoCAnnMUtOcxkEv9Pw9UqFgBCouLQ1FsanMbUYgP9cNOAAeeDSBdDG6QWaehNLyM75jCLJ/n9fkcTKoYg+f28Jy/NzJ3P55HP55FIJLC0tISTkxNHLBU4F8rmcjkcHBxIWIlxbs76+XweW1tbEmVhd1zeQKpqmI6mXhE4nw3r9UZlpI2NDUlNE6OtrdGHMRwOY2xsDIODgw7Rr55xa7WacFoKhYKjQhU3u1NTUwiFQg5hrl5deE4bGxvY29uTlYrlL65fv454PC6bYBonY+o0QCZFVldXZX/C9n/Xrl2TGVYzLUmQ0g8/ab8sbMTQJDevzAcQgxOAvrbc4JKk1dHRIdUF9Mb0slh3Sxg3lSZra2vIZDKoVCoSQqLh8ObkcjmJMPT392N0tMGqpStzdHSEnZ0dcXVYRxo492s1f3x0dLSp40GxWMTW1haSyaQcA2cTvcHa3t6Gz+dzCIv1DTk6OsLGxobcUAp5aYTMMl67ds2RXdTLNDsFUyzNaAYLRy4tLQmD0W3wvXt7e9jY2HDwcUqlkrh+t2/fdjR9Bc4FBoFAQPYmCwsL2N3ddWRTo9EoRkZGMD4+7hAlAOeVX3WCbXd3FxsbGyI9Ozs7Q19fH27evCnSM37nRaMljPvo6Ajr6+uSjr5+/Tq+853vYHh4WHzcdDqNDz74AM+ePROplPZNi8Uijo+PkUgkUKvVMDo6ips3b0qfFmI8fPgQ6+vrQuNMpVIS6ajXG21FDg4OUCwWEYvFcOPGDUxPT4sgIZPJ4PHjx/IAMaSld/a2bcvs1t3djYmJCeFzky++srIilZoymYzUyAacyvvDw0PYto07d+5gdnZWwonsisAZnZUDOOhC+HyNbsMbGxuIRqN4++23pStCLpfD1tYW9vb2cHBwgKmpKQDnewVd2atarUpxeYoVSCHY3d3F7u4uQqEQbty44TBKTlK8T4eHh9jY2EAsFhPWJO/F06dP8cYbb0gm86WYuQ8PD5FOp5HP5xEMBvHuu+86qvcDEB87nU4jm80Ki41xcdbvo9j45s2buHPnDiYmJiQZQd5CsVjEzs6OhPN0K2ddQmFoaAizs7O4fv26hMwKhYIsnUtLSygUCtItjaNSqSCTyaBeryMej+POnTu4fv26GDdZiuVyWcQK5I0D55surkaDg4O4f/8+bty4IYV9CoUCYrEYqtUqEokEcrmchPsApy9Neu/9+/fx6quvSruObDaLgYEBFItFJJNJDA0NubYv6ejowNHREZLJJF599VW89tprwqs/OTnB8vIyPvnkE+zu7uLGjRuOpBaDAcy0bmxsoL+/H6+++ipGRkbQ1tYmbVo+/vhj7OzsYGRkxFECw2u0RPqdVZwsy5KKRvTTdFiJiQBGQShLAs5lZryoJMQDzo0Qs2ncoGmiEAUP/G7GponBUBgrYunPcLZiXBloGCnFsDQ0bhTb29sdiRSGGTm07xuJRByRHi20ZZ0UnV3l54lB14GCB4bmKD3r7u4WspPmuPDz1LIyDMrkFv/xnvG+mBwZHcVhIqi3txfxeByDg4MYGBhAb28vQqEQstms3MeXIlqi+SBDQ0OoVqv48MMPEQqFcP/+faTTaTx58kQI7XRV9AZKE9xZifXJkyd49uwZvvnNbyKZTGJubk7S1logwA0lLyo3OZZlYW5uDk+fPsW3vvUtHB4eYmFhQb6/s7PTESYkluZ0kxfz7NkzPHjwQMQKNHgKDrTMjkZ1enoqbbXZWWFmZkY6GvBvOuqjIxbECQQCCAaDUkz01q1b6OvrE8EDCWVMyOgohY7SRKNRnJyc4Ac/+IFoMVmwlIkviqE59Ga7Xq8jEong9PQUH330EQ4ODhCPxzE/Py+lJgqFguy5Xgrj5hIUjUbR39+PmZkZx647EAjgnXfeQSAQwOLiIoaGhpDNZh0hJN6UWCyGeDyO1157Tfxgcq/ffvttWFajMVE8Hhd/WYfxbNsW45+amsLAwIAkcyKRCN59913Yti0ZTmYKdSiShh0IBKRvJmPv9Httu9G6O5vNOmoUcjAGHQwGMTw8jKGhIUc7jffff1/IYblcTozbDC0yNR6Px2XDR0N6//33ATSqrnJyoMSO14LH4vP50NfXh9nZWdE9dnd347333pNz4WSjJx0OXsPe3l5MT0/LHoFc7mKxiMPDQ6EYm/0s3UZLuCUMa0UiEYl7MsTGJz8Wi0l9be0qcINFdYlu0BmPx0XGBQBDQ0OIxWJSUo3hJ+2+cAYiF5wdDei3E4M8ccaatZiVIa2Ojg6Uy2UMDw+jt7dXjrW7u1vEB5pyqyMQ/EkiGLtE0K3o7u5GPB4XozQNkisIZ3NWhR0eHpaoD6monEC0O6J/0m2jW8fvLZVKiMViiMVi8jDSuPmgcgJico5p+OHhYaTTaWETjo2NSfydFGh+r9doiZm7o6NDGHJbW1tSdZ8+ZFtbGxKJBE5PT7G9vY1UKiUzEg2dGLVaDScnJzg6OhJOCW88a3OTH83l1Mwo1usNYe3x8bHoLvk9a2trODs7QzabFVWMXoZ1Fq9arSKdTmNzc1NSy/V6o2BQIBCQ6k90T7RR81+tVkMmk8HW1pYYe71elyLw6XQalUqlqcgmj4MJskwmg729PeHCFAoFHB4ewufzSVdh7gVMoTLJYgyRsl1hqVTC0tKSYwUhg9EUTdBvtywLW1tbKJVKwhk6ODhAOp3Gzs6OfG8gEGiSEZqjJWbuYDCIvr4+xGIxBAIB6Wirl+uOjg6cnJwglUohHo9jdHQU8Xgc8XgcwHk9bvqf+/v78lm6BMFgEKlUColEQmZurhjAOZOO/OiDgwMxEu722eFhZ2dHVg4dCuRGiDP54eGhrEYUMPDmJxIJmVmZ3AGcJdEomtARGc5+1WpV8LVqBnCWi6tWqxIm1QV5dKcJbtB1ESTGtqvVqpRz29jYkOgH9y/Hx8c4PDxELpcTn1nvYyqVipS1Y1iSwgZukBml4j3O5/MvR5x7cHBQ/DSfz4d8Po/NzU1MTk4iHA7j9PQUm5ubSCQS6O3tFZoqWXbAeUbu7OwMoVAImUwGS0tLmJ6eliZPS0tLWFtbEwopM3Bm2huA6CPZHo/RgKWlJSwtLUmjVPrW5uaUdNFisYinT5/izp07GB0dlY3d4uKiuAqMfuj6KTpCcXp6imfPnuH27dtSfi2ZTGJ1dVWyhBxmNpEGy0ROW1sbZmdnZRLhasaNtFn16vT0VLSPrAK2sLCAqakp1Go17O3tYWtrC4VCQdT2uh4gX2fG9/j4GF1dXVheXsbMzIyEY7e2tmQPUy6XMTIyIiFLr9ESxj0yMiLLMDeWFAlks1mcnZ2ho6MDQ0ND6O7uFh/RTBbQV/P5fAiHw+jr60OpVML8/Ly8j408geamQoy28DgoRcvn83j06JHMpKy2RKGBfsgAZ11shutyuRw++eQTCQdSrMxz0CE4+qycfRntyGazePz4sawM3d3dEkbljMuHTNf10/SBs7MzPHr0SI57YGDA4d+WSiW5PhTwHh8fI5lMSt2SWq2Gubk5iWHzOjOpxQkGAH76058inU6LyBqArHQLCwuOGiz7+/uoVqvY29tDJpN5OcQK0WjUsamj/8jYLn1gpp8502jGXldXlxgUjaGzs1P4wkzf0wB0GQQtzdKqGN6EaDQqAgitpmdkwQxZ6dAeN8sUXwDn7ab5gJpcbLoDPGbSWnt6esSnpa9Kt4PXhMdCw6Rx8zpyQ0iM/f190TJyQ8jrQe58KpVytF5h9IV7iK2tLak3yLAho0esQ6JJVlwNWIsbaAiE9QNdKBReDrckHA6LUXGWppEyFU01h9b4MdtHDBokjVtTZOkz6zLIJh+EChe6J/TJGXMlN0IXbdR0TuC86CY3wuwgYGo/yQ7keegkjt7E8Xow+sIHjudAY6YCiJjc8JKHw5AnryVncc7YtVpNWqEwqpNIJJBKpaQiGNPw+XxeePalUkkMn/RVHQrka5oAx9fL5bL4/dxY633DZer3ljDuv//7v5ffeZN1Vouv8cbo5ZcXbWFhwcHB1hsrbTw69EccYpycnDgwGHt9EQwara5Am0gksLe3J+fCB4Wrk4kxNDQk38kHwbZt7OzsOEQTlmUhHo87ois879dee60Jg59ltMnn86G3txcDAwNNfOs/+ZM/wXe/+10HhhkqpMLntddeczzoxPibv/kb/N7v/Z4DW69O+t4CcMX43ve+52k3LWHcpniAF1LfRC9ivv6c/rs2FjcM8/v5U8+sAFwxeDM0jttxuG0yTTqrybkGnA8zAMfNNo/FxHCbEDh0ssoUSZhpe/5urkx60tEY+trqiIvG0IarMfR906KJizjyQIsYN3AuENBLUi6XkyQFQ1dmYXJ94bVB0bej78lEBzG04bhdRE3RdMPQ8WBz5uZxeGEADb4Il/+LMACIO0BdKAC5HiaGaTTmg1oqlaT8monB97tlBi/CoMulK1+ZD4D+SZ+b4g0AkoAjhtdx6NESxq1nQtI819fXkcvlJKvV1tYm1VWHhoYcM7uJwfgvEw5uGP39/VL4nEaqMbh8Ly0tSVUnGnd/fz/6+vowMTHhqIzFz14Fgz3T3TAuOg7uDwKBRtsQVsE13TU3jJ2dHUnY2LYtZRSIYRbk5IysefArKys4OjqSBAsxWNqYG3sOvdICDfbmysqKtPfjuTAaNjMzIzmDl2Lm1ktVMpnE2tqacCj0iTJDRiPVs50bBqukmhhMIkxMTMjrxOD/M5mMFM4xMXTv+PHxcdfjsCzLgcFiNcC5KGJnZ8cVQ8/gJgYjSIxKmBhurhAxuFnmBtnEGBkZcUjCtAuSyWSkdYllWUK95eaX2c7p6WlPjEqlgsXFRaEUs4MaN8NHR0fw+Xy4deuWg1noNVrCuDlyuRwSiYRUWL1x44aEmzKZDJaXl7GzsyOJALM4uWU1WtoRY3h4GLdu3ZJISjqdxvLysqTwWb/D7Tj29/cRDAYxMzPjirG7u4vj42NEo1FXDnSxWBSM2dlZ3Lx5U6i2FBosLy87MLR/7IURDAZhWZYDI5lMNmHQLdEYvb29uH37togViLG0tIRkMinvMTFKpRL29/dh2zampqYwOzsrGPl8Xjo85HI5kfCZGDp0aWJUKhXs7u5ifn5eMC6rWQK0kHHTPwaA8fFx3L17F5OTkxLCyuVyCAaDKBQKyGaz4otrg9AYExMTmJ2dxdTUlCg7stksQqEQCoWCtMqgtAo4d4sYy47H47h3756IBDSGz+fD1tZWU1cE7etrjKmpKcEol8uIRqOoVCquGPpc3DBocNFoFKVSCTs7O3IuHIzuaIzZ2VnMzMyIUWkMtg8xRROMObNK7t27dzE7OyvKIWJ0dXXh8ePHyGQy0s+dGEyusc7jvXv3HBisFluv1/Ho0SM5jstm75bglpDgQx+a5Hz6fFzmGHMG0KSaNjG4yWFqmzt18k/om2sMJk8YpiJfmp/XGDQyilw1ho7zapmVjrSQ2+KGwSSTiaHFCsTQYgV9LsSge8KN9EUYuhScG4bm3ZgYFEKYwgtiEMcNg365F4bXaImZWxP8WUHps88+E02d7kagKxGZISfihEIh8e/m5ubw4MEDHBwcCIa+0XrTojGowqFI4P3333dgMJHDrJ7ObDLBwve9KAaPww1jdnZWxApuGLr4PBMqPJfd3V2srKzg7t27Dgy/3y9uHstouGFEIhHs7e1hbW0Nr7zyinR44IrBXpJu94Uc8kgkgt3dXayuruLOnTvo7u7G48ePZdWioPiy7CTQQsbNLJrf78etW7fkYtt2g8vd398P27axvLwsmzl+FjifITgrsHsYZ30Tg7OluRkkhmVZwjzUnHK2jD46OkI2m23C4M1kkUndWYEYXILZFYHfrY+Dxm5iMG3/3nvvXQmD6f+RkRHpwVOv15swcrlc04aWeMTo7u7GnTt3BMPv9+P9998X14Xuoo5R6+RXV1eXuDYa4/79++KG6V6blxl4S7glwHkRGdJBr1+/LrMSAMTjcWH36QygXv5MjJGREUSjUQlxmRicoXRsVmNokQAxBgcHEY1GJRLjhsFllhjDw8MODIYBOUtyxjaTQiZGNBoVF8cLQw9u5thporu7G6Ojo54YXt3DyPc5O2s0n9IYkUgEw8PDYqha6W4eC2uRUKyh3bVr167JQ0B6wGWjJWZuPt3cbKVSKaysrDiqE62uruLs7Ew6bpmVjIhBklUqlcLm5iaA81K8GkPHnN0wSNP0wshkMp4YnLm8MLa3txEIBDwx+HkvjHq9Ibhw3gBI/wAAIABJREFUw+AwMShWoBDEDYOzq4nBvUgmk8Hu7q64DhqDxeQBJ9tSH8fp6WkTxtnZmWw2WajfxPAaLTFzc2PBm3NwcACfzyczCv1o0i+56TFT4SYGL7YbBmO+F2EcHx87bpjGODg48MTg7OOFoVl9FCVfhKENh6vVVTA0y5GaT50Kp+CBGPq73TBKpZInRiKRkOypF4bf75fGsboKFfEODg5QKpUc6fiLRksYNyMDXEIzmQwePXokpQd6enpkI0N/lrttrdYxMZ4+fSp1qXt6eqTvSr1ed+zY3TCooXzy5AmKxSIikYgDw+xooDHob2qMQqEgGMViEYuLi00Yur+PxqjVaoLBcykUCq4YmtREn5UYq6ur0p2CGEtLS9JZgVxzTWrSGJZlYXV1Fdvb200YDGeSKmFi0OUjxtbWlmD4fD4sLi4KBo/jpSjKo6MlABw7893dXTnZcDjs6LQFODkPbhjse8iLTgw37oLGsG1b+OAmBpMxOiqgMbRwgBGCXC7nwOju7hZli9u5uGFks1l8/vnnMgtqwYOZqnbDYGGhzz//3EED0KIJbjbdMEglrtfrePz4saPXEEvM8frpDb/WUwYCAeHGUzTh8/nkQSFvX0d+vEZLGLfmAdfrden4OzQ0JD4g+cnkStAQuAkyMSh16uvrk0pM1PhxluSyqLnkOsZMrWJfX5+kiikA8Pl8In64CMPna4h/NUa1WsX+/j78fr8DQxuVicHjYJEfNnvy+XxSMNTEAODAYCEg1n4xMZiSN1P4xLDthviCXBKKstnLhsdMQQNwvsGmwENHX/r6+kTRxEYAusXfZcSplnBLNC+EBkWCPl0EMsU0a8yNFkkMkvpNDM5M3Kmbsx6PgwZFDBowHy5KrNwwgHO/k8oZE+Oy4yAGADkOMu80BvkmXueiMZgjIAYJTcQAcCUMng8xeL2oY9WlHfQ94fUwj0P737q03UuhxGGGjAbr8zVayx0dHQFwcpOj0ai8j4YGQGZFjZHP56X3pMZgjxsashsGb6oXBjOdV8GgX0oM+p6keZqx8u7ubpm9TQyesz4OMvG0Ibph1Ot1ibq4YfCa6PuiMWjIW1tbTRhtbW3o7e11RI14PXTMm8e3vb2N7e1tRyYyEAjI/b3KaAnj5iynL6AbpZUXUse2NafDxOBFcsMwv9/EMG+SiaE/p//OnT4N3A3DPFbT59ZGY2Lo73DD4Pu8MHjN3DAAJ4vvRTD4Pr2pJQYzwframhjE0RiXGXlLGLc5yJXIZrMOLnYoFHJ02gWaN5Yag8ShXC7nKlbQF9rE4EUmkcrE0O0w9KyrDZu/sxMYMXw+n6vQ4DKMYrGIYrEor7kJHvS5mBjUSer+nhpDr3wvisFEkp549DXlT5/PJ3VQMpmM/I3dhN0wvEZLGLee0ShWWFtbc7D/AoEA+vr6MDk5iXg8LqEiN5EAMba3t6ULsInR19fnaFDEz3IWPDtrtI9eXFxsEhr09fWhv79fRAImBn/+vMUKbhhuq4SbWMENg/4ujdvE0GIFEyMajWJ6etrhZhFDryy5XE6ovhqjo6MD8XjcgXHZaAnj1rvzo6MjrKysiFiBswr5C2tra1JAkZ/xwmCDUU20Ika5XBaCvw5VcUOVyWQwNzfnKjTQLUDcMDjTXSRWqFQqX0qswEpOX0aswPIUxBgeHm7KcmqMjY0NpNNpWXk0RjKZhGVZmJ6eds2UWpaFcrmM5eVlwWCtRkZRNIYWWHuNljBujkKhgEQigWg0ihs3bjSJFZaWlrCxsYHDw0MhVpnuiMYYGRlxiASIwZYgbiIBYngJDShW2NnZ8cTw+a4uVtAYenhhXFWs4IbhJVbgTNrV1SWTBtAseKjX67h27Zorxvb2tvDk3TCYCbVtuwmjUqlIFS43DK/RMsZtCg3u3r2La88bEdl2QyTAOn0UDjPqoDHYjXhiYgK3b98WsUK9fi54YLuQQqHQJDSo1WrI5/NNIgGNQUPkDb2qWIEYlUqlSWhwVbEChRKXiRXcMGZmZkQkQMPXogkKDS4SK5hCAy1WePLkiScG6wVGIpEmjNPTU2FKumF4jZaJc+sqTFTO6I1MsVhEW1ujh7lt245GpBqD8WOG5Jjw4c1sa2uT0KOJUa+fd1uwLEve54bBG+OGwUSGFwZ5Ipyd3M7lIgxyNEwMvQdxw2AanhgsekNJV7ValXJ0GoPcFRZP8sJgEkh3uNDHUa/XHRjAeXWuarUqNSBNDK/REjO39ldZnX9+fh7Ly8t48803heCvW0Tz/Rza92YRy9XVVSwvLzeJBIihmXgmBmdSLwwAnhjE+XlgzMzMoKenR4QGJob2uU2Mg4MDbGxsNIkVeM24oXTDYGLNC4PvoRtpYjDz3NnZ2YTx+eefAzgvHmpieI2WMG4uXbwJ9C21WIFVkZaWliTqoC8+IxwaQ/dA1EIDihX43SYGb7Kb0IAY7IrghfGLIlYwMfi3q4oVNAbFCm4YWqzghkE6bSQSacJ4/fXXhZvihuE1WsYtofthihW00KCnp0eEBkzD0091wyDB30usYIoEiME0sBYJmBheQoOrYHydYgWNYYoEriJW0BhnZ2eeGCRLXYRB/raJ4fc3enFehOE2Wmrmrtcb1YxSqRSWl5cdRJ6VlZUvLVbQGF9UrECMfyyxAjsrvKhYIZvNYm9vz1VocJlY4SoYmnPuhsHr4oZRqVSQz+c9MbxGS8zclnXeOs6yLCQSCQCQOLVt2xLlMMUKOkpBIg7QKD5pigQ0hpfQQGOY6hKNcVWxghuGKTS4CMOyLIfggRu5FxE8EINCAwCuGDoN7oahxQpuGG5CA/7OzKwbBu/jSytWYHuPQCCAdDqNR48eSWqXIoGNjQ3Ytu0pVuDv7e3t0t5PiwTYIpobRoYSuXPXGKZIwMSgSMANg76iF4YpNDAxNDMvEAg4BA/sG+kleODn3DC0WMELQ1cEMDEAeGJks1mhJHhh8BhNjEAgcCGG12gJt0QvWz7feTeCvb097O3tifQrHA7LRtA8cY0BNGqOeIkV6OpchMEHwAuDIT83DC+hgSlWMIUGXIUuwqBI4MuIFbww+DlNojLFCuxgYWIwjOiFwd/9fn8TBnky5mbypQkF0t/lZk+LFf5/8t48Rq77Ohf8blVvtXVVV1fv3WQ3u0k2V8mSJVOWRS22vMS2osAx5CDxOJlxnBcnfwZ4L/AED3gDDN5g/nt5wBsYmMAJEPjNOMFMHgIZsmRLkUjKkijuZC/sfe/a96Vrmz+K3+lzb98iKS95Ls0PINhdXfXVXc49v7N85xzDMBcJUMtTizfD6OrqQigUsi00oIAbhvHAQoNmGJye8DAYLJpgtm57e1ti3ryR9ytWIIYuVrDD0L1PrBi60MAOgz4OlxUDQFOM9vZ2ibU3w9A7q8aoVCoIh8MmDD3Xs9l6KLPEMIy/MQwjbBjGLfVa0DCM1w3DuHvv/557rxuGYfwnwzAWDMO4YRjGY+oz37r3/ruGYXzrYb6bixqThQaMd2qbj6VHFOxqtXqAFG/FsCs00EUCOpxIDDp2vyjGvWshQml3LnwwmxUaNMPQxQofFcNaJGDFAA4WK2gMXaxgxbBeHyuGbplhxdD2tx7c9auKc/8AwH8G8HfqtX8H4Kf1ev0/Gobx7+79/m8BfAnA0Xv/PgXgvwD4lGEYQQD/HsAnAdQBfGgYxn+r1+uJB305y8CAfQ4xhwzp15hp09lLahQ7DGuRgMYADk5F0BjMUjbDuF+hgca2w6CNbS000BjUeFYMnjOP46MWK6ysrBw4l49SrFCr2Rc86M67VgxmVvVxEIPXGWjY9Sx4eJBgAw+puev1+tsA4paXfxvA3977+W8BvKxe/7t6Y/0cQMAwjCEAXwDwer1ej98T6NcBfPFhvl9HC6zxa0YHaHfT2dDeuJysBUPjWzE0Md6KweO5HwZvth2GPh8rtu6N1wxDp+qtGMRvhkGhsMPQAqPPkxg6ZW6Hof9mxbDSdTUGX7PD0OfSDKPZ+mVs7oF6vb5970u2DcPov/f6CIB19b6Ne681e/3AMgzjOwC+Y31dax7rZAUWKzC6oD9zD/MABolDLBLQGFpgrA4df6Z9TAxWe7NYQR+DxtBaSmP8IpMVNEahUBByGR2zX6ZYwYqhr+FHxbAmkqwYXA6Hw4TBv3H3sMNotn4dDqVh81r9Pq8ffLFe/z6A7wOAYRh1PrkkLrFYQQt3W1ubFAgMDAyIjWmNp2oMTlawTkU4dOjQQxUrNCs0sBYr1Ov/fSYrfJRiBY6fDofDpiKBB01W0BiFQgGLi4uCQXudzqFdoYGOeNC84mQFYuhiBVIvHsYs+WWEe9cwjKF7WnsIQPje6xsAxtT7RgFs3Xv9Ocvrbz3MF+nMoJ6swBYPwP5EA12soLWdHYZ1sgKzk9ZiBTuMZpMVmhUraAxqut+kYoU7d+48cLICixWaYbBYwQ4jHA7DMPaLFewwisWiVDZZMWq1/ckKVoxm65cR7v8G4FsA/uO9//9Jvf7nhmH8VzQcytS9B+A1AP+rcS+qAuDzAP7yYb+sXq/LZIVAIHBgsoKeihCNRm0nKwAwYQwNDdkWGnAUs12RADEepljhVzFZ4aMWPPw6JisQg8UKzTBqtfsXK2SzWSk0sGIwi1mtVjE+Po7p6WmEQiEYhiHhxJmZGVuMZuuhhNswjB+ioXVDhmFsoBH1+I8A/m/DMP4nAGsAvn7v7a8C+C0ACwDyAP4IAOr1etwwjP8FwAf33vcf6vW61UltumgfOxwOmawwbilWYJ++ZsUKLOYlhi5WsGJwssLDFglYMRwOh8w8/+9RrMBCg48yWUEXKxjGwckKdoUGtdr+1OQHFSvcuHGjKQb5I36/H2fOnDFNeKhUKhgYGEC1WpXpDGyzdr/1UMJdr9d/r8mfPmvz3jqAP2uC8zcA/uZhvlMvEnMYo+bIZO0cFgoFKTRIp9MoFosHBjFZMRh2IoYuViBZSFNc6/W6bbGCHUYgEMDa2toBDGYuH7ZYYWNjw/Y4HlSswEyexqAp1wzDWiRgxWCRgO4WxRpJmhFWDJ4LR5zfD6NerwsGfQdi8IFvb2+XMdtut/u+ctMSGUodS2Wxwp07d6RYYWdnBwsLCxL8t2qoZhgk+D/99NPY2dmRIgGS5q12nbVowjA+nsUKp06dssUg47AZhi40sMNgokkvau5a7WCxAjHsihUeZrWMcFPrGIaBY8eOSZq5Vquht7fXVKxAk0RffGoqjUGHpVarHShW0A01tVCXy+X/XxYrpNPpAw6tFaO7uxsnT56U8GCzYgXrfSE1gJMViFGr1ZoWK9hlfa2rJViBgDk9W6lUMD4+jra2NuFAW4sV7IoErBgk+GuMBxUraIzftGIFv9//Ky1W4FQEXaxgtXM1RrXamKxgh0FzkK9T++t4N0OvxNDDtMYtxQoPsreBFtHcOgTHQoPFxUUhONVqNSwuLqJSqSCTyYhN91GLFTQGtf/HpViB58Jlh7G1tSXdb+0wNPHKDoPTGewwrE32+aDy87y2dhilUknaS7CFsd3Dal0tpbl1oQGTMUxOuFwuZLNZxONxSd7otDAA22IFsuGsGHt7ew/E0IUGxLAWK1hT5NR0H6VYwXocVgwtOLxWvwhGLBaTB8AOQ3+OP+v7UiwWTRjU6OVy+YGTFUj1tcPg9SOGTsnfb7WEcFsplel0WooV9GQFFhpw22L8FNif/0gMFitkMhlbjGYTDZoVK1gxOI2AGM0mKzxMsYLdcWgM63QGu0IDhkX5OTsMFgncD8NaaMD7QgHXGHpKBAsNiMHPaQyGB60YTqcTs7OzBzAeZHO3jFminSFONGCxgi4S0I4gCT/6d2I8qFiBnwHspzPwAbhfscLDFgk8bLHCw2D8OooVOOGBn9PX9KMUK3B3scPgz3YYQIM9mMlkbDGarZYQbl1oUKvVmhYrFAoFOBz7xQrAfvmTHYa1SIAYjPEyrm2H0azQQBcr0D60w2ilYgXr9eDiA9NssoLGYKsNazMdYpTLZSnu0HwUDnaNRCJNMZqtlhBu4GDIiNtTrVYzkYLa29tNGuV+GKyQJwZvOO3NarV6oOJDY7BC24oB7HcmtcPgaobBZBMLHnhDmy2NAey3ffuoGG632xSZKZfLpsILnfTR1+NhMBjlaIZB25ojQ/gg05RipITXx4pht1pCuP/wD//QJLC8QOzPnUwmUSgUUCwWpS0DM3K0dZ999lmTqcCtsFQqIR6PI5fLiU1Ncj8vIrfC48ePA4DJNCGjLhaLiVarVCrST5qOLwWLfAl+nvb42tqanBuF1Ov1wuPxyPfxXFilQsFyOBxIpVLSHZUPn2EYcLlccj6aXsr3EiOXy6FSqSAej4ug0U4nBhcxbt26Je9hgiUej2NpaUlMEibDuLNQkHmOP/rRj2QnBfarm7izer1emdUTCoXQ3d390ImclhBubskUNoaHyGEmR4JahksXLfCp59ansaitdbKGN12HE3WWTwsoHzomL4ilP8elPX1rrFebHnzdGmUBzAR/vfSxMQpBXGsCRl8j67np46QWtvouAKTki8eov0dff+4qxNPaXt8jngMxGEXxeDzw+XzSss3tdh/gDdmtlhBubZel02msr69jbW1NvGfGTQ3DkISFdaiQNSabyWQQi8Wwu7srdFGdYNBCqys/tHOrtTYLHthnkDdN7xKAmeDPRdJQJpORSIAuvNDHw0VsCj0J/gyVMYFCn8QqePrB4oPA82QYlApBF3/oKIV+UPlg6qgKhU9fW15fKwYdaabx6SRToHk9OGtIy0Wz1RLCzWrnxcVFfPDBB1hdXUU6nUY+n5cLOjg4KEUKuiG9VRsXCgXcvXsXs7OziMViwmjT2TQ6MozRWoWzVmt0vlpaWsLCwgIKhYKQjNra2tDf3y+UAGpEa+SF9mgkEsHdu3eRyWRQKBSkzI0ZxtHRUbFV9S6ktX86nRaCP82uzs5O+Hw+jI6OYnx8XB54q1BRO1YqFaTTady9e1eqeShU09PTGBsbO/CAUCjpO/ABXFpaQjKZxN7engjk+Pg4HnnkESki4SIdQvsfPp8PDodD2kg7nU64XC5MTk7i1KlT8r3aubVbLSHcxWIR4XAYly9fxvb2NqrVqpCB9vb2ZBpCPp/H+Pi4NLgslUpi63LO4draGmZnZ5FMJgHsZ8v29vYQi8XkM8FgUBhqXBSOYrEogk1HhzPXScznTWGva20WUGvG43HhwrByhsdaKBSwurqKtrY2DA8Py2c1hsPRKHK+c+cOcrmcqbced66NjQ1JXwNmE4jnX61WsbKygkgkglqtJkJGX2FmZgZAo6e5Nl1ollBbs/tuLpcTvgl3FDYo/dSnPmW6t2Rt6hKyZDKJTCYj58LyOfpZ586de6h2ai0h3NS2kUgEXV1dePTRRzE8PIyuri7k83lsbW1hbW1NpiqEQiERWt7EcrmMeDwu40KmpqZE2wMNhyocDuPWrVuSISPvWG/hlUoFW1tb2N3dRSgUwtjYGMbGxkQYEokEZmZmsL29jXg8jp6eHtuEQzabxfb2NjweD06cOIGpqSm4XC7xH5aWljA3N4dIJILe3l5xzAAzwX9nZwft7e04e/YsTp8+DbfbLXzuhYUFLC4uSrEC4++AeepaJpNBKpXC8PAwPvnJT2JsbAxtbW3I5XLSKnplZQUOhwODg4OmiBD/VSoVLC8vw+v14gtf+IJ00c3n87h8+TKWl5cRiURw+/ZtHDt2TK4phZrmZCKRQCqVwhe+8AU88cQTCIVCqNVq+PDDD/H+++8jEong3XffxZNPPvlAp7IlhDuRSGBrawv1eh3Dw8M4ceIE+vr6JF1LMnwikUA2mzVNFqbm3tvbw87ODjKZDHp6enDy5EkMDw+LZi0Wi4hGo6hWq7h69ap8jtsusF8NFIlE4HQ6MTExgenpaQwNDcHlcqFcLiOZTKKtrU3s30qlYuq9AUD4K5VKBcPDwzhz5gwOHz4Mr9eLarWKYrEo2b2trS3RylzUnqVSCcViEf39/fjEJz6BqakpYToWCgX4/X7s7e1hdXUVyWTywKQ3/hyNRhEMBvHYY4/hk5/8pCiHvb09BAIB7O3tYWVlBTs7O6biZ9rp7e3t2NnZQbVaxblz53D+/HkMDg6iq6tLdsHLly/jwoUL2N7eRk9PD3p6GgVZ1l7oqVQKZ8+exUsvvYTR0VGxr4eHh9He3o7XXnsNu7u7mJubw8mTJ+8rNy2Rfs/n86JB+/r6ADS2Lk4i0+R2xmQDgQACgYC8nyZFW1ubkN7L5bKEwICG9nY4Gu3ayCGh3QnsU165FXu9XlNigw9JR0eHaH0mlLRw0odg9s5arFAqlWAYhkw0YDJIJ3Gq1ap8L4s36GDqIgE9WYG4PFbDaJRwkYnX2dkpzqRhGDIqfGhoSI4xnU7Lg08nkg8aTREdtaJ5MTo6KjvH7u6umIV84BgNKxaLGBwclPMlmapUKuHo0aNyPpubmwiHWbZrv1pCcwMQIe3t7UU4HMZbb70Fp9OJZ555BuFwGK+//rpktfx+v3ASeJHL5TLcbjf6+vrQ09ODZDKJf/mXf8He3h4+97nPIRaL4Z133kF3dzd6enpk7DVjysQAGqlgar1XX30VHR0deP755xGLxXDhwgXBYFJGRxwomKwGam9vx09+8hP4fD688MIL2NnZkcFGhw4dkgdR27o0j2q1msTT33zzTVy/fh1nzpxBT08PLl26hGQyiampKamysSZyiMl4+s2bN3H9+nW88MILCAaDeOedd5BIJHDixAlTCl13lGWcnr0a79y5g9nZWXz+85/HwMAAXn/9deRyOZw5cwYDAwNYX18X2xmAib1ZrTaGA2xtbeEHP/gBfvd3fxf9/f24ePEibt68iZdeeglTU1PY3d2Fw+HA+rruFHJwtYxwM6zV0dGByclJBINB5HI5HD58GIFAAIZhIB6PI5vNSriIWhzY7+QUCoXQ2dmJqakpHDlyBLlcTmzal19+GZVKBfPz8xIGY4syoGHaVKtVeL1eGIaB/v5+vPzyy6jVarK1vvLKK5KUIR1AJy0YXqTz1NfXhxMnTsj2zlYM9XodW1tbSCQSB2LN1KLUeH19fXjsscekBMvlcuGzn/0sarVGxXgkEjFxQIild7OBgQE8++yzcLvdUrP54osvAmiYLRsbGyiVSigUCqYyMgolH9avfvWrCAaDyGaz6Orqwle+8hVx/Le3tyVpxoiMDo0WCgV0dnbiy1/+Mo4cOYJqtQqfz4fPf/7zePHFF2EYBpaXl9HX1yfRsvutljBL6LRQUMvlMsbGxjAy0ujpYxiGZLJcLhf8fj8CgcCBxjYUBjpjvb29suWyZwkdHN2vjsLNCAVf4xYaCoXktb6+PrGdyfPQMV8tXB0dHSgWixJ+5Bbt8/kwODiIcrkskQtNm9XJnc7OTuTzeQwNDcmkXtZEDg8PCy3YmqTR3HByQHw+H/r7+6WYIxAIYHBwUEwOCiGFW2dO+QD6fD7p+VIqldDb24v+/n4xu/g5HcYjDk2q7u5uGdzFZkcDAwOSmWbG9UHC3RKaO5lMIpFIiK2bTCaRTCal6+fi4iK2traQzWYxOjoqW522dQuFgsnBi8fjoimq1Sqi0ShyuZzYoNSkTEgA+wQhfZFXVlaEy53P54ULHovFxIbWmTRiMKTIQgMdb2axQiKRONDIU/9ME4c9Q4BG9IEY7e3tSKfTyGQyMAzDVFDLeDNxEomEtMWgj7G1tQXDMJBOpxGJRLC3tycFBACEyESMXC6HlZUVxONx+P1+cTQNw0AkEsHOzg7C4bApeqRDl9yRbt68KQqLvtHOzo7IAU0bnVyyWy2huTnKOhwOY3d319RmuFwuizbhVmjNhgGNLCBbsJGLQoGiNnY4HIhEImhvbxcNrrWuZgUyukItyh2B/Araj3zdWiLG3SAajYompPbr7OxEqVRCIpGQODo/x0VyF80xOn8AJJFVrVbFnOD5aSy9G8ZiMaytrZmKBBi7X1paQiqVMp2rPg5Gg0qlEmZnZyWuT3ON5tHGxoYkxXT2kil17joffvihqSc40HiQdnd3JUeh6a/NVksI9+bmJiKRCJLJJMrlMra3t7G0tIRyuSxp252dHck0ktvBCww0IiHMAtbrdSSTSaytrSEej0vVNXtzUHD0TQL2Iy60McvlMpaXl5FKpeByueD1erGysoL19XUTeUmn4MltAfaLFWZmZmQ4q8/nQzablQ5Qegoyj0NnPIlx7do1Kbzo7u5GLpeTbKOmA2iBIQ4FfG5uDnfv3oXX64XP55MdgeYRr7UOk3LRN7lz5w5mZmbg8Xjg8XgQi8WwsrKCTCYjIURdeMH0OkOChtGo5H/nnXfE58nn81hcXJR7A5g5P81WS5glKysrkmonay+Xy+Hq1atIJBIy3TYQCCCfz0sPEzpNAMSJoUZyuVxwu92IxWJYXV1FOByWGDW1PHcBYujG54y+sEhgZ2dHpuBqqierwymMupqnXq9LmjybzeLKlSuiHT0ezwEnUAu3Jkm5XC54PB4pVqDABINBZDIZ5PN5EWSdCAL2bX/236tUKvjggw/g9/vh8XgwMDCATCYjzjQ/A0CusTaRHA4Hstks3nvvPQwMDKCnp0f8Gr/ff0BzDwwMyM5bLpfh9/vR1taGTCaD9957D0ePHoXf7xdzc3BwUGL/+kG1Wy0h3KlUCrVaDVtbWwiFQjKNlnFQpprJsWCrAiY0AAgltb293ZSpY1iPnVZzuZwMFiIGBSKbzYpgalOEDmwulzPZ7olEwsT/BiA3l44Y6Zs9PT2SribjkREEalb+z91JU2kZ5mRX1mq1it3dXXEkGZ/ng8KdiVThSqUCt9uNQCCA0dFRuS6pVEr4O2RR8trR8aQzyEjSwMAAJicn0d/fj/b2dhQKBXR1dSEYDMr38TgCgYA4mDT5hoeHMT09jVOnTmF0dFTML5fLhaGhoQN9IJtAIk52AAAgAElEQVStlhBuCq/X65X+ey6XS7QwY7BAw/lcWFhAqVRCMBiUaEmpVBKtT2eOnGGaIRQE8sQNw5AICgAZ0c3og2YQAvs8DQouHUZGTgBzcyAS+IlFzZrJZExakgUPVtubWNyFaHowmcRjdblconm1rcvrSv8gGAxKsQF3HyoE1nZms1l5UD0ejyS+mBwLBoPo6emRiAZn4dRqNanLpLICGg8lSWFss+H1ejE+Pi5ZTM3jHxwcxPj4+AESl91qCeF+6aWXTDeUmhZoPPnd3d04ceLEAc5zOByWC/DYY43pJdQwtLs1GWl4eBijo6OmFDWzcgAwOjpqwjAMQ4oEtIYeHh42YWibmzF5vm4YhnA7+Bqd2FAoJBia8kqziv0JmWlcXFwUG5/2Plu7EYOLITf2J6TTu729Ld0FiEEH1eVyobe313Rt+Lrenba3t7G9vS2hU02NHR4extDQkGAwCsbV1dWFSqWCmZkZzMzMCC4zloZhmDhBP/zhD5vKTUsIt442aAK+Ju3ri8gbrG+mxrAj5lPjaSqpjic3Ow5u0/qzmtaqiwfsMKyJFf1dfM1Kl9U41ri13dIY1mIEfTzW91gx9Pnq68ZdSJ8LF89fJ6Ds7o2+1rpKSV+r+2HYrZYQbrtVKpWktRYTKF6vV2xJqxNmXUwb68kKrPpg7zztuOmlb0QzDH0cduR8LdCsKCKGw+GwnYpgh8Gf6TPQoTUMw3Y6g1W4tdbWphSPTWPoa6Kvoz4O62QFYpD1Z8Wg4Otzq1Ybjeyz2ay8xuuhG/p8LIRbX4BKpSLNIincjLX29PTgyJEjGBgYkMSAtlOJUS6XEQ6Hsbq6ikQiYcIIBoMYHx9/qMkKyWRSmqWzq1N7e/uByQpWDP5frTafrKAnGpBbonetZhgfdbICd8BEIoGNjQ1Eo1EJl7J2kRi6Uy1wcBdlyI4YAKSS3e/3myYrWHuV8Lpks1ncvXtXkmx8T1dXlziq/xqTFf7VFi9GvV5HOBwWTjbT7cC+Bl1YWEBbW5uwx/RWS4xIJNIUI5fLYXFx0TRZQVfzEIPTCOymIuRyOUkS2WFQ0zWbrMAM6EedrMDqIcai7TCsO5phGEgmk5iZmTFNVnA4HIKxubkJp3N/sgKXFWNlZQWpVMqEUalUUCqVZOT15OSkqbhZ76wsatAYPJd6vS5ZXytGs9USws2tLJfLyVSEqakpTE1NSQyZWpQFC9rr1xjZbBY7OzsSfz1+/LiEBomxurqKSCRiO42AGHqiwfHjx6WRTCqVwtzcnGjBh5loYMX4VU9W0BMe7ofR09OD6elp9Pb2SpXP8vIyFhYWEIvFZLKC9d4Qo16vY/zeVAQrBhvgcyqCFYMp9nq9jiNHjuDYsWPSfL5UKglbMp/PI5FI/OomK/wmLGpmp9OJw4cP45FHHsHhw4elJ0c6nYbb7UYmk5HUrJV7oKczHDp0CCdPnsSRI0dEuImhJytYy5msEw3Onj0rGAwjUsDW19cfCsM6nYEFGM2mItwPgwLMiQalUglra2tCTdACwZ1qb28PAwMDOHbsmAimHQanIlijSSwB6+7uxunTp3Hs2DGEQiGJowcCAbjdbty8ebMpRrFYRD6fh8/nw4kTJ3D8+HFpg8FzrNVquHnzJtLp9ENNVmiJ9DvtZE2tpN2rNSGLBCgg2ta1wwDMsxSZ2eTfGCvWGIx1OxyOA4UGFAja/4Zh2GLYTUXQjp1doYHGYMzXbrICYJ6KwDAgCWFWDF00obOxuuCB4UvGrDUGTRdW7NPHMAxz0QQzj8TQ0RfdZoPsTn0uLMxohtFstYTm1k6Q2+1GqVTCq6++itHRUTzxxBPY3t7GpUuXhCrJQlvdO0R75x6PB6VSCW+88QYCgYAUCRAjEAgIq02HvvRxMDJjh0FnUBcJWG1/PmCdnZ346U9/ikAggOeffx67u7uYn59HOp3GxMSELYZOdfM4fvrTn8o8md7eXly8eFEw9LlQePmzxrhy5QouXryIZ555BsFgEJcuXUIqlcKRI0fkOACYMCjI5KNcvXoVly5dEgweB8vodB6C14N4Voynn34aoVAIt2/fxuLiIqampmTci8ZotlpCuMnRYIuyY8eO4dFHHxX23/DwML7xjW+gWq1ibm5OOMDaCWOanq8fPXoUn/jEJySNPzw8jFdeeQW1Wk0qYXTITh8HBaWvrw9nzpwRjKGhIcHY3d1FNBq1xWBcvKOjwxZjbGxMMGKx2IHrwfNihlNjMAvLYgUrhg6j6SxpX18fTp8+LbuI2+3GCy+8IE58LBY7EG/XfBU65l/4whdMGJ/97GfFX6IJZOWm6wyvy+XC5z//eblubrcbjz/+OB555BEUi0VbjGarJcwSrXXZGHF8fFzS5rVaDf39/fD7/cLX0I16iMHfuf1yGoEdho7LNsOwm4pADI4XaYZB4f5FJitojGaTFaxTEXSLimYY1okGPA7O67RONCAG7wNrMe0wmGHUJDIrBv2nQCCAkZERU6uK8fHxphjNVktobm7ltVqjGU48HpdUMwk3CwsLqFQqyGazEtKytmWgmUIMtiugUHGyAolP1KZWk4J2YiqVkokG5FIQwzrRQB8HTYyHnazwUTCaTUXQFUXNMOymIpA8ZTdZQSeommHEYjE4nU5T7JvUVus1ZeGFxiBDk6Q0O4xmqyU0t2EYppvDNgIcD1KvN8hDmUwG8XjclA7XITiNQeYfABMGJys0m0ZAzWEYhkw04E3XGL+OyQrNMH5VkxX4QGgMsgupMKg07DCoNDRGV1eXYBSLRcGwu6ZsSWHF4GeaYTRbLSHcug1YR0cHUqkUrl69Ko6M3+/H5uYmVldXxWTgjSQl1IphN1lhc3PTNFmBF1Zj6AxguVzGjRs3bDHIB7fDsBYaPGiyAjH43dR0xHiYyQq6cKIZxuLiIjY3N++LweiFHQaAphipVMpkZlh9IfoQALC0tGTCaG9vx+zsrNBvrRjNVkuZJdyWSYvc3NzE5uamUFB9Pp/U4mnyDbDv2fOCsNFiKpXCBx98YMLQ0xmAjzZZgRwX6/dZj4Nb+q9yskImk2k6FcGKQedYY3Cywo0bN+Q4fD5f08kKVgzNlbdiWCcrWLsB8HWGYuv1umAAkGvE3VZjNFstIdyMAes4NScrMGnBlLeuvgFguoj8nxGPzs5O9Pb2wu/3CwaTPEz5ftTJCsRgn3A7DGC/UJg2v3Wyws7OjvCyKVC6KQ9DaNbjYPsLu6kI2oSgQBKDZojP50MgEDgwnYGcbGvyRWMAkEaWxCgWi3IctM2tGHxYed+IQQeZXCD6UB+7yQp0fgzDkLYLvGCaFERPWmsEOwy76QwUGgqAjgtz6fAiy7lIprJiPGiyArDfbF0fh55oYIehtTAFW0cytCJgmI6C1QyDnbV0ZKbZZAU7jcnoz/0mKzDBY4dBU8MOgw8AMe53HHq1hHD/9V//tSmBooVWE5G09w6Yt64LFy4cSMbo9xJbaycr+21ra8v0er1ex/b2tjww/D5tDgH7WVD9HQzP1et17O7uIhwOm+LXfK+1IxPQaORjvQ5Aw9HmcfH7OM1Ah1MB4OTJk3Ke+v2MJrEnCBmOPG99fJ/97GcPRHF0XJ8ONgsLeJ0YX//e976H7373uwfCi8SxmlL6PPie733ve2i2WkK4rSesBYdes75w/F/zhIH9ggZ9gaxPv45IAOYqGt1fQx+bPg7rg2fF5XFw6eNjFMDqK1g/p6NA/F3b9vpBsv5dH5M+Xut5a2XC363vsYbi9PXVdAL+01lJu2uhf7feT52VtCbGmq2WEW4dCqOjol+jHawna1kvgE5c6KUvqJU4b61i0TefwsGtlktXhfO9OlXMG60jOiQOEV+bXtrh4ufthJvTGYCGycT5MVro9Oe0APJ3HT50u90IBoOmiRP62lkxeZ7EKBaLCAaDCAQCplbHD3Ov6/UG+5L1naQD6wKQB8W5W0K4tSam/UdmmRbMWq0mVTEML2nB4E22ak4KjZ3m0UKpzR1gv3MqnUceHzkj7Beie5UA+zewWq0ik8lgY2PjQMGDx+NBX18fBgYGDkxWsP4fjUZx69YtacHMWDkLbY8cOSI2vTV6w3OOxWKYmZnB/Pw8crkc6vW6TBZ74okncOzYMXHurOaC0+kUx/XChQu4dOmSONOG0agFffLJJ/HUU0+ZKuZ5Lazx82vXruFv//ZvJZ8BAGNjY/j2t7+NqakpCUc+SHu3jHDrlDXbE/NvegsjNZR0UB2X5tJZR+uN0skf3V/EejykeubzeWHg1Wo1aRPM/0na16E0fg/bjzHKQzYiNTmjDAMDAwdMCz6syWQS165dkxEdejeoVCpYWVlBW1sbxu+lr7W5QWHP5XJ48803pdJdswANw8CVK1fgcDQKDawYPJZisYg33ngDd+/eFQeXx1yrNZrHt7e34xOf+MR9Ne6bb76Jf/zHf0StVhMaMYu0v//97+PP/uzPMDY29lBy0xLCDew3sezu7sbo6CgmJycxPj4uF3F3dxdLS0uivagttRBr7eDxeNDf34/p6Wl4vV4AQCQSkXIteuNWp5MXm5qac1oYm43FYpidnUU4HEahUDA1Vud5UBhisRg6OjowNTWFEydOSIyYY1BYwkY+tN5x2trakM/nsbGxAa/Xi5GRESnBYkhyZWUFS0tL2N7elrYN+lwYebh165aE3iYnJyU0ytElq6urmJ+fF7akftjZO+bixYtIJpM4evQoDh8+LMdRLBalY9jt27dx/PhxU5mYNnXC4TDeeecdHD9+XO4tQ6ms9PnhD3+Iv/iLv3igSQK0iHBTm5J5NjExgU9/+tMYHx+Xxo/hcBgejwdXr15FNpuV7c/O+TEMA2NjYzh79qz0niaXwuPx4L333hMb3noRGdetVCro6enBuXPncPz4ceGRUxgvX76MtbU1FIvFAzV/tVqDI7O3tydTESYnJ03FCmxgs7a2hlwuJ/WLPH5GNVhocPr0aUxMTEjcP5/Py1QEmj2aF0KtmkqlUC6XMTQ0hPHxcUxNTcHv9wNopNPZE2ZnZ0eyhtZzYWFHf38/pqamMDExIe8rFAoIBoNYXFzE8vIy5ufn8cgjjxww0wzDwK1btzAyMoIjR45gYmLC1AVrd3cXtVoNGxsbQn99UIayJdLvwH7DRdqj3O7Yy4Ja0uPxmHpqW4XaMAypUGfXJj48hUIBHo9HkilWh0nbecy80Xzh+/P5PNxuN0KhkGh93YJCZ/WcTicCgYCYLcTQxQrU8tZur+VyWcJ11oIHEvr5N4ejUYihw3g8LjqguvMW482Mi1NQSUqzYnBgK3n0TFqxBwkAMc84VMsabuWUC5pEnJhBUlupVJJjXF9ff6BjCrSI5qZge71e6UL005/+FENDQ3jssccQjUZx6dIlceLcbje8Xq9pmhk1PztUORwOvPXWW/D7/Th//jzi8TguXryI7u5ueL1eU7tf7VQySsM08Ztvvolr165JocHFixcRDAbR19cnUxHsQocOR6NIoKurSwoN9GSFbDaL8fFxwdC7iM7Ssd0biybOnj1rmqzA8jVWt+hMKbtQ0U+5fPky3n77bTz77LNSaJBIJDA1NSXdoFiuBkCcS9J7HQ4H3n//fRSLRXzmM59BKBSS6QwTExNyvul02lSUzUAAr+97772HTCaDZ599Fr29vbh+/Tpu376N06dPo6urS7qHfSwGPtGR9Hq9CAaDOHr0KL761a9Kpfng4CC+/e1vI5fL4e233xaHRrMAdfbL4/FgYmICn/vc59DZ2YlcLge3240/+ZM/QT6fxwcffCAaDdgXbjq13C36+/vx4osvor29Hfl8HocOHcLZs2dllN/Ozo4MPdVRinq9Lpqtr68Pzz77rJRkHTp0CKdPn0atVpPiXjp2OmTHbKbL5UIoFMLTTz8t9NTOzk689NJLqNVqWF5elnOxmgLUwh0dHejt7cUTTzyB9vbGRDGXy4WXXnoJ9Xody8vL4mPohpgABIPhuq9//evS/9ztduOll14C0GAd3rx5U7jjuoE960G5M7/yyitCk+3q6sJTTz2F559/HpFIBLdu3UJbWxuy2az4Sk3l5peSun+lRT4zT75arUpnVDp37JVHjUbBpkDwJlKT6kbq3OLZppdcbjqDumc1NXdbW5s0eKTQFItF6TnIsB4LFrQzyJ+JQW4yjx+A7Dx8sHVMn8swDGnATweOrDnDMISwpPng1hg/U/w8Z901wDAarEs+XIyO6DCi5oNwNo7G4A6lKbOs5+Q50IchBo+D969cLkuhMjGtD5ndahnNTWcom80ikUhgbm5O7NZqtSpDPCnwAA4IJm9wsVhEMpnE6uqqCCwALCwsCAa3XKtQ0j5meIoY3CIXFhawt7cnk9a4rCl/ak5iGIYh9uX6+rqE+ThV2NrrQ095SKfTWFtbAwDTpIi2tjbpac5rqMOJmqzENszsiV2tVqURfzweF6Ej94XHoX0KdsilEioWi4hEItI4vlwum2L/+lx47BzLSKVQKBSEjLa5uSkP4cMId0tobmptZri2trbk6aet19HRIQKlt3AKLm8ML+zu7q5UmlAwOzs7kclkpPez1vbWY6nX64hGo9I2jA9RV1cXUqkUdnd3RZOxRAvYf8gYwmTIENgvVuD8xu3tbREqHj+wn4LmudkVK9CZi0ajTcOaWhtzFIu10KBSqZg6Welsq/6ZMf50Oi0PosbgCBQqBzq8zGjynFjFRIdWk6UikYjE2bPZrOQXmq0HCrdhGH9jGEbYMIxb6rX/3TCMWcMwbhiG8f8YhhG49/q4YRgFwzCu3fv3f6jPPG4Yxk3DMBYMw/hPxsMEKu8t8rd9Ph88Hg/y+Txu3ryJSqWCrq4ueDwerK2tYWtrS1hygUBAWukC+1q8vb0xzIndjWjXeTwerK+vY3V11URs0oKpM2kcqXH79m0kEgmx5YlRKpUkfa5PVYcYqUmtRROZTAZzc3OmggcAJgGj9qcpc/PmTeTzeZmswCIBYvD4Kdw8Dgrn3t4e7t69i42NDVOhwezsrNRhMspDza0FlMy9+fl5rK+vw+fzmaZEcIIFnUEKZqFQkClpLO9bXFzE4uIiPB6PzLWcnZ1FOp2WSQvcXe+3HsYs+QGA/wzg79RrrwP4y3q9XjEM438D8JcA/u29vy3W6/VHbXD+C4DvAPg5gFcBfBHAjx/i+yV5wAaTFLb19XVsbW3Jttje3o7u7m6xncvlsmhF2sL8LG1qZvgASEszcrl5M/mAUPPrbKjb7UYqlcKVK1fEtvV4PKYUs64asfJCdLHChx9+aCpWYPgOMHdqZdN5ChQjSel02lQk4Pf7Tfatzi6SOsDtnXYuANy4cUPOs6enRyimDDEy5k7aAePt7KHucDgEo17fH4ybz+fF1ON30VzJ5/OIxWLSgMjpdOLmzZti1w8NDQn/hruNtejZuh4o3PV6/W3DMMYtr/1E/fpzAL97PwzDMIYAdNfr9Xfv/f53AF7GQwo3x99x8Cm5JZxGQNIQ48104Bj6AyDEd960jo4O+P1+9Pb2ylxJag4tODQjAJhS6Qyr+Xw+IQcxzk1txAgDgAPanxlUh8Mhfa+ZGWTShFEYACZuiba3aXJwTAgHJ7HQAICpkQ4Xp/JyaCofCLfbjd7eXnGst7e35by0Awk0Mro04/QUZYZs6Txub2+L/czsLx/Uzc1NpNNpU2dYj8cDr9eLsbExwYhGo3C73VJjSTv8futX4VD+jwD+L/X7hGEYVwGkAfzP9Xr9HQAjADbUezbuvWa7DMP4DhpaHgAQCoVM9XcM81G76htHYr5VuBlNAPaLX3UUQ9NmWclOZ0fzUxgtIAZxiKsrY/i7pulqngy/iw+dtViBO4l+0ABzuRtgLprgd1gLDfgwcCvn+A4Kmp4ywe/Sx06tzfk6QMPW1w48uS08Nt4LzeqkI04fYXd3F7lcDoCZtcnjJQ9IBwTq9UYjUrY4brZ+KeE2DON7ACoA/v7eS9sADtXr9ZhhGI8D+H8NwzgFwM6+bkrpqtfr3wfw/XvfUT958iSKxSJSqZTESNPptFwIMuCYqePF16bB0NCQCCqzksTSwsmxcbyIOqJAIo/OBubzeRlxx5tI4eaDpSMCuhmlxlheXhZc7i6chExh4blwB9HUT7YPJi4FnRj3rqtcY9r3moQFABsbGweoB/Q/9MMDNHZD3dSSCoJF0jTvAIjzT44MMYLBIAYGBkxRK6b02TV2b29PHkLudD09PSYynN36hYXbMIxvAfgKgM/W751BvV4vASjd+/lDwzAWARxDQ1OPqo+PAth62O9yu93I5/OiIWgvAg0BJudYXxwKtta62gmz8hJ48yhYFCZNm9Xaxxov5g6ib5K2260YOuashVTzpnVGUv/P9+kdgdqOWFpgiclrw9f0dxFfnzffpx84fRzE4DHxNV4P646jlxVLnxd3N0ZStAnH2Dd3k/utX0i4DcP4IhoO5LP1ej2vXu8DEK/X61XDMI4AOApgqV6vxw3DyBiGcQ7AewD+BwB//bDfx8QE07SxWAz1eqPVlsvlQiwWQ39/P4LBoGhQbsvchil4gLmraDabFecyGAzC7/dLYsa6/eskBl/nAKNisShzJH0+n6kQVwvLvetkSobQKeOsRvJWmNyxCrd1keLKGD0XtTaFoNnnKSw8RoZC9/b2xIa3E2T9easAs9qdiS6aiVYF0Ox4eJ8YFNARLAYPfun0u2EYPwTwHICQYRgbAP49GtGRTgCv3zuQn9fr9X8D4DyA/2AYRgVAFcC/qdfr8XtQf4pG5MWFhiP5UM4ksD9WLp1OY2VlRbQBSUW5XA7hcBiDg4PSyotzDXXEgp8Jh8MIh8Ni91EjhMNhBINBjI6OylZuxQAgYz42NzdluCvtZaay9XarBZsYtIsjkYikyMmnZvRjZGQEIyMjEt+305K1Wk1GFG5tbclDTarByMgIxhV7Ut1Xk7Dv7e0hk8ng5s2bMgqbIc+RkREcO3bsQHRCCznxGD5cWloSW5rnMz09jf7+ftMuq+16PiT5fB4rKyu4ffu2NEgiv/vo0aPo6emR8S73Ww8TLfk9m5f/zybv/UcA/9jkb5cBnH7Q99ktZrsYc+3r68P4+DhCoZDc+Hg8jmg0CqfTid7eXtRqNbHV7n2/UDzX1tbgcDjkIjHeXCwWkclksLm5iYGBAYmx0gQiRqlUEsHmVDFqx3K5MaabrYwDgYCk+AFzcXEsFsPi4qLsQrSBGepbX1+H09mYaMBMID9LmzibzeLWrVsSaaAi4MPNyQoTExMH4u0AJNu3vLwskQuaXdTeGxsbaGtrEyqrxiBPhpnS27dvY2NjQ7St9nNmZmbQ0dFhmtCg7XtSgefn53HlyhUkEglTJpJOsGEYOHv27C8v3L8JiwM+U6kUDh8+jK985Ss4duwY+vv7JawWDofx7rvvSs882tjaLKnX61hZWUF7ezs+9alPYXx8HIODg8JpSCaTuHXrFhYXFxGJRGQ6ABdtdY7qnpycxPT0NMbGxiQiwTEea2trkr6mWQXsmzbpdBrhcFjm+HBeDE2dxcVFzM3NIRqNShhU26nkV+zs7MDpdGJ6ehrT09PisBGDM2pCoZA43ABMApxOp2VywqlTp2Q4Kh3d1dVVeZC9Xq9pB+N5FQoFmexL7crdYn19HYlEArlcDmtrawiFQiKYuVxOTA7WXs7NzaGrqwvj4+Oyk+XzeUSjURkNMzExIQzRZqslhLujowO5XA5tbW0YGRlBf3+/TLaiA+L1enHo0CEsLCwglUqJ1qXmZsuwUqmEvr4+9Pf3yzRil8slUZPx8XEpEGBCgpqDGpG0z4GBAYyOjmJwcFCm9vr9frHpWSSryVXA/jSCWq2GkZERqTwhT71UKsHtdiOXy2FjY0MYc1yaz53P59Hb24vHH38cR48eFY55oVBAKBRCtVqV2euMsgAwdeZiRdDp06fx+OOPY3BwEE6nU4T7woULmJubQzweR6lUgsfjAQAhMpXLZSQSCUSjUUxOTuLcuXMYHx9HV1cXisUiVlZWMDMzgwsXLghfhmWCNMeoHBYXF9HV1YXHH38ck5OT6OnpQUdHB2KxGN577z38/Oc/l+nMU1NT95WbluCWOJ1O9PT0oLu7G06nU2bSJBIJGRHCC88tkt1BNZ+bTg1pltlsVkZe8Hdqkfq9KhJtc1MwqLl0F1K+hw4d49P5fP5AOwg6mA5Hg4/N4+FxcpoYq3t4HNzKuc3r6Qm6XwoTMA6HQ8Z3WCdNcE49a02DwSB8Pp+pKIRDpEZGRkyxZ16HbDYrU4XJKSGPnY55uVwWijEzmzptHo/H5R+1e19fn4zHPnToEFwuF7q7u/Hoo49Kwk1HipqtltDc7e3t6O/vx9jYGILBIFZXV3H79m10dnbiySefRDabxVtvvSXNX3QYilpXV5R7PB5Eo1G8++676Orqwvnz5xGLxfD222/LiGdmATUhidwUVvvUajX8+Mc/htfrxfnz5xEOh2U6w/DwsPCNddsHAFJBxCaPb7zxBrq7u/Hcc89hZ2cH8/PzyGQyMtGAJhbPRUeB6Fj97Gc/QyAQwKOPPirFCqlUSqYz6ElpwL6jTOezu7sbs7OzuHr1Kl588UX4/X5cuHAB2WwWk5OT6Ovrk0gOMZi15D+Px4PV1VXcvXsXX/7ylxEIBPDWW29hb28PJ06cwKFDh6SUTyeCmEBiv8JKpYJ/+qd/wu/93u+hp6cH7777LnZ3d/Hss8/i7NmzmJ2dxeDg4APlpiWEGwB6enpw9OhRGIaBEydO4MknnzRp5ccffxzpdBqvvfYastmsaHBqVGrKgYEB1Ot1HDt2DOfOnYNhNEqavF4vvvvd76JUKuH9998XT18nM6hx2Syzt7cXv//7vy+FBh0dHfjjP/5j7O3tYWlpCYlEQpwuK9WU2cdgMIhvfvObcDga/fjGxsZw6tQp1Go1LC4uIpFImD7L/xmvd7lc6O/vx3P5s20AACAASURBVAsvvACn04lkMomuri68/PLLqNVqWFpakjHgOqbN4zLuUW0HBwfx4osvwuPxCMbXvvY1MWsmJiZMrZCB/bQ+/QqXy4UvfelLCAaDSKVS8Hq9+MY3vgGHw4FMJiOT2bT/wF2J5+Pz+fDKK69I03uPx4Pf/u3flocqHA4Lh8gufq5XS5glLOkCIK2+PB6PxIbJwGNlDgVIazidAKhUKqZCA3r13DYzmYxp27MmYABIpIL2J8NyjOny4dAFE8TS5o210IAPk8fjMfVCsS6dyKBdT0wKLQseSJm1w2DMmKE/+jIU5O7ublPFjl46+aSLQ0huy+VyUi3EYgXruEBtjtFs5M5mGAYymQyq1SrcbrfsgHQkHyTcLaG5dYp8bW0NXq9XEh7UzhsbG0gkEtLJX3coAiA8C0YYgsGgCDa1PEu64vG4pItpfxODx5PJZOB2u7G0tAQA4hDNzs4KFm1WcrcBc1KHESDrVASm85PJpGn8N5fOQjIEt76+LoQwXg+n0yk8DkZ6tPZnbLperyOTyWBra0topW1tbUKaSqVSyOVyQk/Q5pHO7FarVWxtbYmD6HK5EA6HJZ3OUK1uMcHPckdwOp2Yn59HZ2cnDMMQE3F7exuZTEZ8rYmJiQNZZutqCc2ty5DIMmOyQjdWj0QipjIwzcVmvJphPxY8sLspeQ8cy8z4rbVIQGuVcDgsxQQkHXk8HqRSKale4Q3TWULjHnMRgBQTUDCB/dBnOBwWDWrVvvq44vG4KTNJvk25XJYkk05rAxDyF7OPjP/r5A4d5o2NDRnPos20zs5OsZUp3BybwoeP57K+vi7ONbEAiL3NjKzD4cDq6iqSySSA/YgMzaPl5WXp0231ZayrJYSbzWdu376Ner1RJnbnzh3RJu3t7VhZWUG1WkVfX59QP7W2Y7qdZgqLFSKRiNyk1dVVxGIxeDweqSrXpo3mObA6hB3/ye3e3NyUXtJsH8HEDLD/gBCjXC7j1q1bUvDq8/mQy+Vkxg+5zQy5AebutHxYb9y4gXQ6La0pstksZmZmUCgURAtSSAAzr1zviouLixLPZlFvLpcTs0ufi35ASEeOxWK4ffu2vL62toYPP/wQy8vLEqFhWh5oUAQCgYB0k/X7/XA6nVJFX6vVsLW1hUuXLuGdd97B9vY2dnd3sbGxgXicyW/71RJmydtvv433338f29vbGB0dxalTp4Tjy7nkIyMjMl+yVCohHo8LZxlotB+ORqPCk+bU3729PVy9ehXlchl+vx+jo6OmLB1pocA+B5r8ZTaKLJVKuHbtGmq1mnBUaKZYeRfMPjJSwYaV2WwWH374oTysbKijixy4dPrdMAypJEomkzJaw+l0wuv1Ck9EmyAAJPJBk4wp8Xw+jxs3boh5Q8FnuFMnbvx+v2Roq9WqZCULhQKuX78uoUBmfpkf4HcDwKFDh4QP73Q6JbRYLBbx3nvvCWlua2sLS0tLyGazMgzqly5W+E1Yr732moTk0uk08vk8Tpw4IRqNXjazfxTURCKB3d1dAMCVK1eEJsv36mkG1MjlclluirWDP2smqcG8Xi88Ho+pWeXe3h4SiYQ4lYzJ6ggDU8r8rq6uLvT394tzykxdR0eHmEi6GQ7NGp1C5+x2hgapIalpKeDa9LE2iaeJRv58Pp9HOBw2FXBoRmUwGBRhZcSDpggr4Pf29sRe5nFoJ5tdsrhLMjfAB5ttH+bn56UgpVarIZFIfDyEm3YuIw3M2Hm9XhQKBbH3uP1S8Bg9Afa1rp4yoAlYDDWRcKSLTykQdBK55WonkTee5oZuG6G1nS4RAyBOK4+DN5nfyxusabaaUquLJjQtlSYU7XU+IJqjTa2toyT8/lqtJqNYgH1KLbkdQIMTTkHVCTKabNVqo4KewsrFLC8AeQh4DXnNGKoslUpSRG3lCf1aixX+tRbbeXG7nZ+fx+rqKvx+P/x+v2TEdBkZtQunAhw5ckS2cafTKfwQCiKdT9Za0ntnuzMAGBlpFA9p6isLk4mrK/X5T6ffNR+CGIlEAolEQl6jwLCMTUdHAIgDp18jSYp2NQVQf5+OLugWbJrrvbe3Z2qF3NPTg56engNccQDCvdF+BCNB1MIDAwPyee1oEmNyclJ2A36OWpst1qi9WUnEhFGtVsMPfvCDpnLTEsLNCwuYU+B0ikg1ZQEvL7iV9ETtqp0pYjLlrAsWKGi6SICLwqePRwu0/k4rvdQan9XFBZpgpY/T+nlqer5Ph+c0hZRa1ZoI0n+j0PJnvbvwIbL+08fBh1Q/PLSdGe0AzD3SuXQTIX3u1WoV29vbmJubQ6FQkHI2n8+H/v5+VCoV4ac0Wy0h3Lx42imyevvUuLooQRcE2AkoBY9xdFbN6+3d7jiAfeHj9gtAwoma0KXrKq1YxCFPhDFttp/QnV310oKvWX7pdNrUH4R9D+1wdNUSBZPU4fn5eUmLk47g9/tNwkwMfU0AiI3NHoG08zmuOxQKSTcB3juahbyOe3t7uHHjBv7+7/9ecg58+DweD0ZHR/Hoo4/++srM/jUXiUe8kKx6qVQqEuIbGhqy7TFiFSraorQleWOYAeWIC9qzwMEZNtSU6XQa6XRaiFd0pIaGhtDT0yN2sP68Ljhgsmd9fV20HE0av9+PQ4cOIRQKHTgO/s7tPB6P4+bNm4hGo+LYMTMYDAYxNTVlMm/0Z3mu8Xgcd+7cweLiIrLZrFzrzc1NqUQfHh6WWDSPh9eJGv/SpUu4evWqaXalw+EQ02toaAhHjhwRAbfuVqVSCT/+8Y/xz//8z6baSe5IhUIBqVQK0Wj0gU15WkK46WFru1tr6Uwmg1QqhcHBQVNSRyc9rDWVwL5pQyeVkRUmgPSFB/ZtVnr0mrNB54wzysvlMoaHhyX5ojkuFLBkMonl5WVJ4pBoRXxyz3t7e03bPjEMw5C+K+xTyLg4hSIWi8HhcGBqakratVlXNpvFhQsXpHVbT0+PiUnIRE6xWMT4PSorYK7mKZVK+OCDDzA7OyvHATToErzmuVxORoEcPnxYMIjjdDpx5coV/OQnP5Eokt7VKOTspPXzn//8vnLTEsJNoWSyYHBwEGfPnsX09DSczsa88IWFBYmYUOj1tk2eNF8LhUIYGhqSJufM5jEiYhdHdTgc4tywM+zY2BiOHDkiTDem09lXg86k1cxhLajb7caJEycwOTkp1fWFQgErKytYWFjAzs7OAZIQH0hOLXC5XDh69Cimp6clnMjSs8XFReRyOSSTSfT19ZnCiVQYS0tLaGtrw9jYGB599FH09/dLCdytW7ewsbEhEY5EIiFNdvR1np+fx/z8PEZGRnD+/Hn09fUhm81iYWEBV65cQSaTkUqaeDwuO6X2aZLJJH70ox9hbGwMf/AHf4BAIIClpSXcvXsXb731lnDBeS+j0eh95aZlhJva2+PxYHJyEsePH8fIyIiEkZxOJ8LhMADztDPNpSAnOhAIyPY4NTUlkYNDhw7hypUryGazcvMBmLQ9499OpxP9/f04duwYJicnxbnJZrNwuVy4c+cO8vm8tD7Qztbe3p7EbAcHB3Hq1ClTCVepVEIgEJCCB03Q0teEwtLX14dHH30Uk5OT8j7Ogd/b28Pa2hqy2axQbIF9rZvL5ZBKpRAMBvHYY4/hkUcegd/vl1gyH0529mIhiF6stgGAc+fO4VOf+pS0rGO0amFhQdo9UIPr4yB/pFAo4Gtf+xo+/elPo729HRMTE5icnJQx5hRuVizdb7VE+p2REPaz7u7uRiaTwcLCgolWyuoWhvT0hAVg3zNnipqallwHEn60987oCbBv2jgcjb4kdLIY4qI/oHnJjC3rChjeHHK6dfiOGIZhSP0lf9faW7eKY6cq+gMsVnA6nfLQ2XExGCuuVqvo7e01TU1zOp3SI5AYjNlbe/Sx6t7j8cDlcpnYjMw50NGmWaGpBDxv0lkDgcCBvuYDAwOym9J30ErHbrWE5marhM7OTvT19aFUKuH111/H4OAgent78fTTT+P1119HIBDA1NSUaG5NL+WFqVarkvx588034Xa7cfr0aYyNjeEf/uEf0NPTIzYukxHaCSQrjng/+9nP4PP58OlPfxrVahWvv/46enp6MDQ0ZPp+HbWhw8fiZE5WYNHEnTt3UCgUcOTIEfj9filW0MkbPgx6OgOLFXw+H9577z3EYjGMj49LRlYLlU4OsSXFlStXcOHCBXzxi19EW1sbrly5gpWVFTG7yJPnQ8KHtFAoCNPv2rVruHDhAr7+9a8jm83i2rVruHnzJsbGxqQxpk5UaT+iUqnA4/Hg8uXLePXVV/Hnf/7nuHPnDlZWVmTqRSKREF/ALqKlV0sIN8eAsDj1+PHjOH/+vPQscblc+KM/+iPMzs6awng6pkpNwj4cU1NTOHfuHLq6uhCLxTAwMIDvfve7KBaL0pCSwqkFs1ariSbr7e3FN7/5TXR2dsqQqT/90z9FpVLB4uIidnd35bPaua3X6+Ib9PX14TOf+QwcDofM5Dl58qQUGmQyGdFgmlnI8GJnZydCoRDOnz8vFNe2tjZ89atfFYxYLGbbEZUC1tXVhVAohGeeeUbOpVKp4Ld+67dQrVYxNzeHnZ0d6Wyr6aqMYrDe9Hd+53fgcrkkrPnyyy/jS1/6Eu7evSuDoXTpHrCfPKrVaggGg/jWt74l1ziVSuHxxx/HV77yFbz22mv44Q9/KMXEPP5mqyWEm+Exprzz+byQ9Le3txGJRDA8PCwcYp2c4GLLA2b/yHGmXWsYDe6wLqS1xsh1f8L29nZhJdI5pCbW3UgB+1Ai/QSmkHUjSJpg2WxWTCOrluI5trW1IZPJyMPB7Zx8E2pKHoeViMUEFqeakalIgpLH4xFWILkiVsGsVCqmAmzy7JPJJFwul/RerNfrBwSTPgy70RqGgXQ6je7ubhSLRWxtbcHv9wslVpfcPYjP3RLCrS8Aud27u7twuVwYGRkxTVTQ4T6rgJKrQM99d3cXbW1t0iIiFouJDc8tXJN8dLKD3VHZWoH01t3dXakW0mlpvXRIiyFIcjMMw0A0GpWHmNwPHbnR6W06hXSmya/hZAV2nSW+TnDppu8sA2PyyOv1IhqNIh6PCwYbUxKD5C9eJ14PRrYCgYBENFiMzcJlKweG4dBarYaFhQXR3KdOnRIHlKl4vXvcb7WEQ1mtVpHNZpHNZqX5jpWUpGPNPHlqSWA/NsyWCByCxAwaBUg7XTRDtHAyvV6v16XTv2EYEl9nlEAzCq1beVtbm2QNGRMH9glarJzX2UZ9DNpxBCBV41obd3R0YG9vD5ubm/KAUPvy3Hj+DocD2WxWxpXwnHg9VlZWpD83d1DeF14fml1sMqTpBPl8Hqurq8KoJFELgOw2ukCENaxUWkyUsdmPXbbZbrWEcHObpTZbXl7G0tKSkPlZzkRNZ8eBBvbrFx0OB3Z3dzE/P498Pi+FBixhauaFU0gpoLRHOXvS5/Nha2tLZrforCqXPj46Z7dv35Y+KeR2z8/PC4uR56IJThqjXC7j+vXrMp1BT0VgsYLm3PA4KJx8nYNQOU04n89LUQgdaD7EwP64P10ttL6+jrt370r/9EKhgFu3bmFzc1MeDM3n1pqblVUbGxu4dOkSfD4fBgYG0N7ejg8++ABLS0vyUNNUvd9qCbOEF5MMO7L6qtWqyZ6jI8MQlk4380ZSS/t8PrjdboTDYeFfM81P08fOYWHYisfCqQicJABACmK5PevjAMx9qN1ut4Q2r169Kts+i3up6fQDQmyaJbSN0+k0rl+/LllRCjlDnfoYKFD8G82vUqmEK1euyPdxPCJjylpLU7C585DAlkqlcPnyZXkPq214bzQG+32z6oYzM+/evYsPPvgAtVrNVNfZ1tYmBRi/dCPM34RFLx1oXFy2RGD0hHZwpdIYFkQtp+PcukCWwsBRJHSiWEzL+C9Df9pu1/Fo4vT29kqyI5VKSTkbU9d68RhoMzPryn4qFLDt7W1JowP75hOwz5LUFNCuri4pVmCt6c7Ojjy0VlNGFwNQ4FhexuKLQqEgx8HP23HTNf/a4/Ggp6dHGl5yN2WugsfPc+H1ZvUT0KApM0HHnZnRKKbkPzahQI/HIxEKajKOltD8YB1x4EOgn26r/c1QGmCmrLLqh/YdMWiuaG4Lf9bJCM0pZ4JHH4dmNmqqLY+Jn9HxYLZe0Oehz8FKaNKZVJa86WIFHh/NCV5PXXzAEjFtw1u1Px8u3buEVT70gVhQQm4LIzkATA8GeS0+nw99fX1SQMH7Qb4JHzSXy4WVlZWmctMSwv3JT37SJJiGYQhfmK9pslQ8Hpce3tT4x48fB2Ce6BWNRiXKoDEo8Owxzfdrsg+Pgw6uxtCOGwDhiwCQWTH6XEj84mt8OFhhQwweB+1Wr9crGMVi0TSdgefCgg5iUDjz+bwItX4wI5EIIpGICWNoaMh0LlyxWEweBG2LWzHa29sxOjqKsbGxAxisuOei0GcyGWQyGTm2trY2nDt3Dk899ZQJ437kqZZxKLXw8mcdgbD+06QpYuiaSzsiErWYZgM2w7AyBvmaPj4r3dZ6LtYYtjU+b8XQQtEMg7vSw2Lof/ycXWGDPl8dhtOf52fsMKzvs2LoRQwrRfd+GHarJTS3VcAYGmRMmZqDpPr29naT8DfDKBQKiEQiSKfTBzC4PTfDYEKEGMwkan+A9roVgzeNGPl8XjBoArB8jsknHcHRgmjFII+6s7NTyu80hhY2K0a5XJaGlHYY1qSWFYP8HI1BDg7bZTTD0NeXGBzuSn57IBAwDe6y5g+sq2WEW9+A3d1dSU3rjky9vb0YGxvD0NCQaG6tga0Ya2trSCaTMqqOGIcPH0Zvb688JHYYjEPPz8/bYoRCIRw6dMgWg//fD6O7uxtDQ0MfGYMPQTMM/VkrxsbGhjwgdhgsem6GQeqxHQZ5P+SgWDH4M2myVozOzk4MDAzYYjRbLSHcjC7Uao3G70tLS9KKgMkAVrqvrKygo6PD1C6tGQZZgCTfE2NpaQnFYhFjY2NNMdhknrFoKwbT+3YYtCM1BmPIxGCBgB2GjuNbMRhDZkbXiqFj5VYM3c/PDoMTEZphLC8vS7SKGAw3RqNROBwOTE5O2mIAjcjJ3NycCYOh2Vqt1hSj2WoJ4QYgaeadnR0EAgFMTk5icnJSIijJZBJ3797F2toawuGwzHe0Pt0aY2hoCMePH5dKao3BjqTWxo3E2N7ehtvtxvT0tC3GxsZGUwxm7TQGG8fXao2WCsvLy7h79+5HxuC2/VEwtra2pO/J9PQ0QqGQLQbL1rTWZWJta2sL9Xod4+PjthgspUsmk7YY3E01BqvrGRqdmZmxxWi2Wka4ycNwOBwYGxvDmTNnZKus1+vSSozpWpor98M4efKkFAlYMchg08WsxODc8YGBAZw5cwZHjhwRwSTxCmjMdLRiUBMxFm6HwWKFQqFgi6HPxYpB0henF2gM3TpYY1QqFQwMDOD48eOYnp4WwSFGqVTC2toaUqmUqSqIOxk5H93d3Thz5gyOHz8utGFiuFwu3LhxwxaD55zL5dDd3Y3Tp0+LcPN6DQwMoFqt2mI0Wy0RLaGdzFYDFAIdwmKjHhYhFItF4VQQg4W8xNAhPSsGsF//p3nUTH4YhmHq/UGbmBiswLE7Dk0UshYrOBwOies2w2ACpxkG2Y5WDF6/ZhhMZjH0pic3APtJGysGP+P1eoUDz+PQGKQbWDFo/pA6y6IJANIsiQ+8HUaz1RKaW3vT7Af46quvYmxsDE888QS2t7dlogHHiwBmDob2zonxxhtvIBAI4Pnnn8fOzg7effdd+Hw+wWDHJ6ut63A44PP50NnZKUUCVozh4WERGOtx6IfUikHOSzqdlqkIVgw6tEzTa4zTp0+jt7cXFy9etMWg0NhhXLlyBRcvXsQzzzxzAINRKH5OnwsxvF5vU4zDhw/LWBI7DGY37TBu376NpaUlTE5O2mI0Wy0h3NTcTGgcO3YML7zwgrw+NjaG73znOyiVSrh+/bpwmq0OlBXjueeeEy03NjaG06dPCwb5xVY2nsYIhUL4zGc+Y4vBSIw1FsubQm6KFWN0dNRUrGCHQYFgRlBjpFIpdHZ22k5WAMzZTSvGU089Jdxuu+kMVmHS5+JyueDxePDcc8/ZYkSjUWkgancuTFp5PB6cP38enZ2dgvH000/j2WefRSQSEQwd22+2WkK4gf3tyel0SpFAJpMR2qvX65Xti1vigzCY2eMF0xjNLp7GYGzbDoOFBnzIuBgTJhZj2xqDHIpmGMC+I2bFYBWN0+k8gAGYoxNWDNJaSUyyYgDmggcrBus07TBIi7Ar3NYJMJpPdKxrtUYFUigUMmFYz8VutYzNTS26t7eHdDqNra0trK+vS4vhmZkZJBIJublWqqnGYMFCLBbD3NycLQarrK3ZPU1a4vzGubk56d46MzODZDIpBRF2GPQXiBGNRk0YN27cQDQabYqhs5BWDF4jOwwdK7fDSCaTmJ2dPYChr4cdBs2d+2FkMhkp4NYYOhPJEGsikcDc3ByARieAlZUVhMPhphjNVstobh3XjEajOHToEHp6euD3+8XZ2N7eRiqVOpBWtsOIRCLSu4Q2uMYA9rWS3sqJUS6XZZzGwMAAfD6fCYPNJK0YwH7BAseGWDG8Xq8MFdUY1mQSnb50Oi0YbAk3Pj5uwrBSAuwwotEoRkdHJZFEDNKLmyW1WNRQLpebYnBKhBVDXw9qfysGbX1OsrDDsFstoblZkgU0OMPsskRHpru7G5ubm1hZWREGHm8mucYao6OjA8lkEjdv3jQR/DWG7lxlh8EiAU4eIMF/a2sLKysr0uKBXBR+zlqsYIeRy+VkR9EY+jiIQcEihi5W0BhWPo4dxsLCAjY2Nu6LobtnWTGARiUOW7Cx4GFubg7pdFoSTE7nfl9AYmjCmBWjra0Ns7Ozwuu2dvFqtlpCc+vtD9ivht/c3JQKj7a2NpmUwJuoTRM7jO7ubqTTaVy+fPkABnCweabG4APAYoXLly8L/ZVsPbsiVm0e2WFQg7FA9qNiXL9+XbrdflQMt9uNer2OGzdumI6DJgk/p80SjcGmSbVa7QAGGZa8flYM/kwMHgcfGp6fxvhYhAJ5ATXB3+12y1hqZsLIG6aW5AVvhtHZ2WmarsAiVofDYYpna5K9xnA4HPfFIF2W267G0A3lu7q6TBjslNoMQ9vsGoMdVPVgLCsGz8UOgzRajqTWRRNs8qOjHVYMoFGg7PP5EAgETBisWmds3Q6D9816HOVyGeFwuClGs9USwg3sO2KGYcgNpNetq2y45VGItK1rxSBbjhpDmwtMijAhYcWgQN0PQ09xsFvE4FZLDPb1+KgY2hb+RTBYHNAMg0JlpzH54LCySafVqWWJYZeAoX+kMXgc+t7eD8O6WkK4T506BZfLhUqlAr/fL6259BQECiRP2OFwSFbzr/7qrzA2Nmb6GwsByD+p1Wro7u4WwhWwz3bjYjqYQsSbzpYFvIl9fX3SLBIwj7PWDdP58HGUHR8SHqd1KoJuosPwIF9LJpMy3UtnblmQYF2MK1NY6vW6FF7onYnv5TEwtQ9AKvP1mJDV1VUsLCwc6ArFECiPm69zThHQiIzU63Vsb2+bwpp8MPXD+bExS1ZWVlCr1SStTftYD1Jl+peFvyzroiZlYxnSJ1msqrUthZ71fry52pFzOvcbrjNqQm1LDN18XieSuLi72O0I+nu0YOjFB0wLibZ9+Rn+jQ+N/ruO4FBItG2uHzQdi9bHw9esfBUmdvhe6w6qz40xfZbUkWXJHZEPCZUIv1v3k2m2WkK44/G42L7s1Uz2H7UiL8jIyAiOHDkiLDtmE5mYoWPDxj7r6+vCOWF7homJCYRCIXmQtH1HrcbFiWnMAno8HvT19SEQCMhUMAAmraM/X6/XpSGOnkTAnoVk8ulsqX5wKHzFYlH66LEg1+fzCYeGD5RVOIgBNISVBCZiWCcgAzBFnSjEerqadrp10keHI/ne7u5u0w7MyEwmk5GkTq1WE3NIR2c+Ful3rWFJr2SDFl3AWqlUpHbS4XAgGAwKz4S2szYDNjc3ZbY4t1vOjs/n8zAMwzTKWWtFcpTn5uZk++QDGIvFEAgEcPLkSTFPtPbVRRMsNEilUqZihVgshlQqhaNHjwojUGtingsfjsXFRSQSCXkQ29vb4fP5MDIygr6+PlN3VGBfe1Kr6oZHrE0l/2VwcBBut9sUwgP2W9TxAeGDEYvFBIN+iW5np4Wb/hMjPHyQGSDY29uTwmDulNxhPxbCTZu4UqkIHzuRSMhFZetcCm4qlcL6+jrS6TQGBgYAmDkMOzs7WFhYkBI1VqBTG0SjUeGnkKDPxQscj8dx48YNEUhWn9OJYuayra1NWIz6OICGnXznzh3BCAQCJkdWD4TVWkvzRNLpNGZnZ+VhZDlYtdqYzrC2tgans9FL3GqWAJDEydra2oGRerp93cTExIGkCYWRiaBsNoudnR2hBFerVWQyGWk70d3dbfJ7gP3x2HR62f+ROzR9AQYBGPfnv/utlhBu2rBMm6dSKfT19WFychL9/f2YmJhAIBBAOp3GzMwMVlZWZFvr7e01YRWLRayvr6NWq+Ho0aM4ceIEenp6JHS1s7ODmzdvYnd3F7u7u8ISBPZZhhzp4XA48OSTT+Lo0aMyP75arWJ5eRl37txBLBZDOBw29ewA9qvhORXh5MmTOHr0KLxeLwyjwYFeXV3F7du3EYlE0Nvba3Iu+RCx32FHR4c05KeGozZfX19HLBaTNtBc2gxgI9CRkRFMT08Lh5vDnzjPpr+/3+Sj0Cxhj5RsNivN9JmM2tjYwLVr16Qh5+DgoCgIYL9dw97eHlZXV7G7u4vh4WF88YtfhN/vRzKZxPb2Nt544w2Ew2Hpzw7g46G52d2Ty+124+TJ/4+7d42N9LzOBJ8i2WRVkXWv4v1ONtlkd6vbbl0stWRLigyM5p5DKwAAIABJREFUZQOSbCWZOMAkxsbeBDvYReBfuwgyiwUG2B87u1hgFoNkd4LAPyazDuLsTmxZQiTrYknuljqW+s77tVhkVbHurCqSdfn2R/VzeL6PX3W37Ex2qBcQ1F1d9dRX33fe9z3vOc9zziwee+wx9PT0SBXQer3RvTabzZqqGwEw+ZWVSgWjo6N45plnMDIyIr6xw+HA4OAg6vVGkRi6J+xVo1fV/f199Pf349FHH8XQ0JAUoj84OJBmTz//+c8lkmLlQJfLZVQqFfT19eH8+fMYHR2Vh3Z4eIhAICD96ZlF1SsViUUHBwfo6enBhQsXMDExIaURdGcFsunof+vBjsyhUAjnzp3D7OwsvF4v6vVGZ4X29nZcu3ZNistrkQDdmkQigXg8Dp/Ph8ceewyzs7NSdWt0dBSGYeDTTz/Fzs6OiIU56FdTaUNW4PT0tNSQGRsbw/7+Pv76r/8aS0tLmJmZMfUHbTZORPqd5WupxOYhhIfFUqmETCaDer2OwcFBUdeQkw3A1KOSxdZ5WOGBJZfLoVqtIhwOw+fzSXEZuwxlR0cHAoGAbL8M1dHXpIFboxp8H7kaVAEx/g4cFQNiJzMt1ACOfH4KEvig6XbxEMxsHxU3OjPIiZvP51GtVhEKheB2u+X8wfvGClblchmFQsFUjL9WqyGbzWJnZwf7+/sSrWKWmPeOqvVSqSRVYDlqtRqi0Shu3LghSiiWXtY8Ero02WxWevQ8aJyIlZt9Veh3AcDNmzexurqKV155BfF4HB999BG++tWv4uLFi2LcTIUDkBN5W1sb+vv74XQ6cfXqVVy/fh3PP/880uk0PvroI8zMzOCRRx7B5OQk9vf34fP5TH4ikwzhcBgejwfvvPMO/H4/nnrqKaRSKVy9ehX9/f04ffo0xsbGJLRo3UKpCu/o6MA777yDYDCIp59+GolEAouLiygWizh9+rSpb6NOU3Ni0h1644034PV6cenSJQQCAekDOTw8LCulNkyy+Bid6OrqwpUrV/DTn/4UX/va1+D3+/Gzn/0M29vb6OvrE3IWuygADZ88Foshn8/D6XRicHAQb7/9NjY3N/Hqq68iEAjgtddew/r6urhVu7u7UlUKgAiy2UH53LlzeO2113Djxg1897vfRTAYxBtvvIH33ntPlELxeFy6atxvPNC4HQ7HXwD4BoCEYRjn7r32PwL4LoDkvbf9D4ZhvHbv3/57AP8VgBqA/9YwjDfuvf7PAPzvAFoB/F+GYfzPD/puDsqLmKGqVCo4c+YMarUaJicnce7cOZw7dw4jIyNwOBzo7+8Xhh0TDnQ7SGLq7OzE448/jtbWVgQCAUxMTODChQvCNxkfH5dSCTqUBUBq+4VCITz55JNi/N3d3Th//rxEO9hFADD7h4wfc5JoscLo6CjOnj0LANjc3JTVki4AsUgtaGlpMWHk83m0trbi5ZdfRq3WqEKbSqUAmCM2uq52S0sLQqEQnnjiCXEFDg8P8c1vfhO1Wg1Xr17F9va2+Mr8LXt7e8Ktb21tlCX+vd/7PcHP5/P4rd/6LdTrdVy7dg3Ly8vi0nHCMULDnSYcDuOll16Se55IJPDiiy/im9/8Jn74wx9iYWFBdq1/DMrrXwL4twB+YHn9fzMM43/RLzgcjlkA/xzAWQD9AN50OBxT9/75/wDwVQBRAB87HI7/ZBjGnYf4fimfSxagy+USYv/m5ibC4bBJu0cXhJMBOKqrTQx2AWDF1P39fXg8HvGHNT9FZyQZ7XA6nSKuZXKBhsbQmuZc0OXQMWeKBOr1ukxEJkC4NQPmwjWAWe5mGAb29vZMoTj+9o6ODik4pIlKAKSYJH8jQ6JtbW0m96Ojo0Myn3R9dGKMSRYaM9PiumZ4W1ubqdAl7zkAOQ9wojFW39raKIuXSCQk6xuNRlGtVtHR0SE10O83HmjchmG853A4Rh/0vnvjJQD/0TCMAwCrDodjCcDj9/5tyTCMFQBwOBz/8d57H8q4aZRMcFCCtLm5iY6ODpEd8d/Y2YAGfO87Jc7qdDqxv78v/mJ/f78Uah8YGEChUJCOvlaRgA6h8bAWj8el29bdu3cRiUSQy+WEyKUPgpqYz3huOp3G9vY2IpEIDMPAzZs3MT4+LsbDicmhDYyx8lQqhVgshkAggFqtoRIfHx+X+DlT9totYSh1b28PiUQCuVxOqKnVahUrKyuygx0eHh5T4PP+GIaBbDaLzc1NZLNZ4cPv7+9jaWkJY2NjiMViqFQqos7nZOUKzAkWjUaRzWaFgJbL5ZBMJrG/v4/FxUV5lg9lNw/1LvvxLx0Ox78AcA3A9w3DyAAYAKArE0bvvQYAm5bXn2gG7HA4vgfge+rvEoLiaswDl8/nQzgcRmdnpxSAKRaLEknQ7gDj5XzIlUpF3AsePlnSgRVNrSoYAMJrrtVq2N/fR19fH4LBoKwwLIRv91keLnno40Nk1KdSqQhVlSXJrOR8zat2OBySNOnv75dVemRkBHt7e1KQkjFpjaFT5+vr6xgZGcHY2JhI9drb28Vt4Gc1LYGVXOkyra+vI5lMYnR0FPl8XgIBW1tb2NjYkJ1A3xP9efLSn3nmGYyOjiKTycDtdsPtdmNjYwOFQkEWJ2sDXLvxq0ZL/h2ACQAXAWwD+Df3XrebUsZ9XrcdhmH8uWEYjxqG8SgAqUXCh0X1Snt7u2xPhUIBiURCOtRubm5ibm5OZvvh4aFkNLkNZrNZSX4AjaqlqVRKYtmFQgH5fF62ZSY0mF2s1+tYX19HLpeTwuvxeNzUg51bNiMbnDA0/Eqlglu3bmFvb09EAsViEcvLy1JFitfHBAtXbhobBQ8MGfp8Puzt7eHGjRtS/J0Twuq3A0dihbt372Jzc1P4Oel0Gu+//z6q1So8Ho+JBUkM4vC1+fl5rK2tSRH5ra0tvPXWW3Lo5OJkbV/Ce7K3t4eVlRUsLy/L+5LJJH70ox9hf39fYuusPXi/8Sut3IZhxPlnh8PxfwL48b2/RgEMqbcOAojd+3Oz1x84EomExJDpSvBmxeNxJBIJDAwMYHh4GECDaHX9+nV5DwAp0UUctgdh62iPxyMREIYFrf4qDZTxbh6uUqkUPvroI+kg0NnZiXK5LBEAhiCBo4PcvfsoK1E2m8XVq1clusLVm9+pV1krGYsF34vFIu7cOfL0/H6/idClV25do6Reb9QLYRRpcXERBwcH0gG4tbVVWJg0UABydqF74/F4MDQ0hM7OTiwtLUl0RfeuDwaDCAQCEnEJBoNIJpMS9hsdHRVe0NbWlpRDDofD4sMPDQ1hbGwM3d3d97WbX8m4HQ5Hn2EY2/f++gqAW/f+/J8A/AeHw/G/onGgPA3gIzRW7tMOh2MMwBYah85vP+z37ezsiFFvbGxIsiYYDGJwcFBqWbCY45UrVySRwwdB33Nvb0/qSnu9XvT09Ih7Q/99Z2dHQlz1el0OpUy8sBVfrVZDMBiE3+9HMBiUFDz9be1W0Gfmis8TP1PmfOh0d+hO0CfVLo52qwAIFzsUComUi7sTi8YDZrW4zhF0dXVJLJo4tVqjOCZT4MBR2xZeByVgbrdbCoDyWrhIsAsZuSlut1sSXkCjMzDbvbjdbkxOTorbMTAwIIsF0JgIXIT6+/t//fS7w+H4KwDPAgg7HI4ogH8F4FmHw3ERDddiDcB/DQCGYdx2OBw/ROOgWAXw3xiGUbuH8y8BvIFGKPAvDMO4/aDv5rh9+zYSiQT29vbkgFWv1+F2u5FKpeQQWS6XsbKyIjUydFyXBxU+LLZxLpfLskpR9Lu1tSW+t266xJM9D6lMdWtJGcW27MDFaAKNia4N2XvkpgOQaAUPwjRo8kT4Pu1e0Lho1PTpmdDRxqiN2+VymTgauoWK5nLTUIEjX58TlZ9hwR5GsvhZuiCMZvHQryd7b28venp6xMf3er0S9jUMQ14bHh6WvAD7jVp70FvHw0RLfsfm5X9/n/f/awD/2ub11wC89qDvsxvf+MY3ROPHsCB9Px5GtHGVSiXZ0vf29vCd73wHly9fNh2i6CpodpxhNKoecbtjKK9areLv/u7vcO7cOQBH8W69ktIgaVD9/f3o6+sTX5IHWyak7t0TW3+fD9bv98Pv98u1EYP+vc5sxuNxbG9vC66OLjHGb41zc9eoVqvCH1lYWJBr5ndpbraO1PAsQhdtd3cXS0tLsjNx1+L3c2Iw+gU0BMWcwOSzxONxKbGhRR6RSEQy1JwE9xsnIkN5cHAgqyRwxEbThxJyRvjvZBHyNa6C3NKthq5Zg9RhkgeifWTNUbH+X1NIOfTKrjF01EavqHbiAKsQgddODE3F1d+joyz6PRqDu4X+vYy9M4uq75WVuqt/E5l8xWLR5Ef39vYiEAiYDJv3UWtLyZdZXV3Fzs6OqQcPIyfBYFAWOe6GzcaJMO79/X1ZsYGjRAhgJsbz4dIdoMsBHPG59WrLQaPWAmAd6dAJHavv63A0Kk7Rv+ek0w9er3Y8WAFHvO5qtSqRG+DIh9Ztuq3GzdHS0uiYsLu7K2eTWq0Gt9uNcDgs9VCsXGzWAiFWuVzGzs4O5ubmJAFF4fLs7Cz6+vqEE857RxeGv6lUKuGjjz7C9va2/C4a4dDQEB5//HGhQfD56TqI1WoVd+/elUwmJyXDhL/85S9x6tQp9PX1mfSezcaJMO5yuWwqG6DlWPw7Da9SqaBYLCKTyUhbZ+BICaPdGH6G7wHMJH6dNQTMPiu/K5/PI5PJoFAoyBbMwym7relMpR7kfTPNvr+/L9lVHvKGh4ePKWE4SekOLS0tYWFhwTQRW1pakEqlkEwmcfHiRal+y9+gs625XA4ff/wxdnZ2TOl1hk8zmQyee+45iaNzkmmV0crKCj788EOk02nTfeICMz8/j3q9jhdeeEEiWEDDp+ck29jYwN27d2Uh0vz1er1RHvrKlSsYHh6Wa7nfOBHGzWpG3Dr1Fst4Mg2chzWqahhK4wPTK6p2GbT/yj/rAyFwRDwih1mngCkHczgccmhlskav3Fp+xeLxTD3r6rQkNmUymWNdIjTO7u4u1tbWTIdHGj3Q2M43NjYwMzMDwFwIk5grKyvY2toCAFHR8xrIeVlcXBTBgb4OABKrT6fTJneI18kJwQymFknr9PyVK1dMQQA74y2Xy1hcXMQjjzzy+fC5yURjGM7tdpt6TBr3yP+MSzMZwMQPACHmcBvv7OyE3++XEmRcGdhVgd+leSY0KLo+LS0tGBwcxPDwsJzc8/k81tbWpMcND67aVwUgGUin0ymaT/bbZNu91dVVZLNZiQ5Y087lclkSJmNjY5iampL37e3tYXl5GfPz80gkEhgaGpKoB0dLS6Pn+9zcHHw+H/r7+zE+Pi5ENWIsLS0hFothe3vb1GqQEymZTCIajaKtrQ3d3d0YHByUaMb+/j5isZhwTViKmBg00K2tLdGQ8rOcaFQLkdC1vr4uh+X7jRNh3Ex80OCoYKf/xkyZrl/C1DaNm+GtarUqVNju7m50d3fD6XSiVqtJCp/FI7k66zixXoFpVIODg6K1LBaLAICVlRWpj0cXAIBpd6lWq4hEItLhgW4XY74k8ZfLZYmOcHAyVqtV9PX14Qtf+IIIHlpaWlAsFhEMBnFwcCCTRIfpuGqTzz00NISnnnoK09PT6OnpQUtLCwqFAkZHR1GtVrG0tIR4PI6pqSlTfJmcbkaann/+eZw7dw4DAwPo6OiQNipXr17FRx99hN3dXeHDA0eRpmg0KoQ0/p5IJCLx7LW1Nbz77rtYX1+XhepzYdwM77FldblcRjqdlsQDV1LN1WA8mYc0xo2t/mCxWJRDzd7ensnfZMKFw+oCMct3eHgoiQaW+2XyhJ/X5RN0Gp98cy1g5hmgq6tLfhvDaoC5o0F7e/uxbZ4CB66kGxsbIoCwHnRJqurt7ZVJyOKTJFGNjIxgbW3N9jp46Ha5XOjr65OkC2V7ZFvOzs7i+vXr4u5o0TNdOCZ5zpw5gwsXLmBmZgahUAjZbFbYirFYTHbnz4Vx0y3hzU0kErhy5QoGBwfx5JNPYmlpCXfu3EEkEsHAwIB8zhrGo3/d0tKCTCaD999/H52dnfjqV7+KnZ0dXLlyBYFAAJFIxJQy15EODoYeX3/9dXi9XrzwwgvY3t7GL37xC3R1dWFoaMjUUtoacTEMQ2L1b7zxBnw+n2AsLi4im82KbIwhO3093IE4wd9++20Eg0HMzs4iHA7j/fffR6FQwNjYmGQu9XmFu53b7cbg4CDC4TDu3LmDTz/9FF/60pfQ3d2N9957D8ViEcPDw+jt7ZWYu3UEg0EMDQ1hYmICy8vLuHv3Lh5//HEMDg7izTffRLFYxNjYmPDsdc0R3udIJILu7m709/ejUqng3XffRaVSwenTp3HlyhXMzc1hamrKVMn2c+Fz0z2o1xuFeSYnJ3H+/Hlp6jQ5OYmLFy+ipaVFkgjc9vkwuIryhkxMTOCJJ56QQykbQNVqNdy8edNU848YjHowqxcKhfD7v//7aGlp1EMZGhrC2bNnUalUsLW1JQQvbdycYLyOcDgsCSaNUa/XpfWdtTAnJwgjSJFIBF/5yleEguB2u/HKK6+gXq9jaWlJ1ONWyi4nSDgcRigUwuXLl+F0OpFMJuHxePCtb30L9Xodi4uLGBkZkeJDdCnoD7tcLvT39yMYDOK5556Dy+VCLBaD1+vF7/xOIwfIvp+8fl6HZngODg5idHQUr7zyCvx+P1ZXVxGJRPD1r38dL730EjY3N7G8vIzW1tZjzavsxokx7mw2K0UWWcqWxcgZKjQMA5lMRkSvOprBlY++e6FQQDAYxP7+PlKpFGq1miklr1d8HWHQsVcyCvf39yXdzh0ml8tJckNHAIAj0r/D4ZBKWCyNcHBwIAQldm7gb7MmUGhchUIBAITxyOukqMMuCaOjOR0dHaZQZLlclnQ+RRm6lDIH3ShWma1UKqJyZ80Riib29vbkkKhXXC4U5I1Qq8nzT6lUEv86nU7j1KlTmJqaMnUSbjZOhHHzgMfVr7u7G/V6XfonVioV3L59Gz09PVhbW8Pq6qoYOJM4DM/RR8zlckin09jY2BD23K1btxAOh6XuHv1urpxa2MoJkk6nsbW1JRNlbm4OgUBAJFjWsJnmoZB2m0qlsLW1hXA4LN0IxsfHZffQKXAO7d7kcjns7u5ie3sb4XBYuhGMjo5Kl2Wd4eV16EmbzWaRTCaxsbEhlNmVlRUMDQ0J6ayrq8u0k3HC0s2gcmZtbQ1+vx/pdBqpVAoDAwPY3d0VlqF1olOX2t/fj/X1dfnP5/MhHo9jb28PwWAQW1tbwlV/EGkKOCHGzUMaD123bt3Ck08+idOnT6NarYoBLy4uSgdaHtx0wod/Pzg4EL7w6OioELEMw8Di4qLUNbGmm/kat3iWNBgcHDTVJpyfnxfOBIvkaBydKd3d3UUsFsPAwIAkN0KhELa3t7GxsWFKV+sHyhWR2c1oNCohOJKYtra2EIvFJCOoVzpicXHI5XJYWlrC+fPnpV1eV1cXNjc3sb29ja6uLomjW42b5KlKpYKlpSVcvHhROqTRvVhZWZGOZ7oiLXeUzs5ODA8PI5fL4fbt23jqqacQDAZFZ7q+vo7NzU1MTExIKv9B40SUdigWi9LDhv+xmyy3ufn5eRGPkiHIGoAApN8MoyaFQgErKyvI5XKyXa6trWFhYUHcGSaOiMGVXNNWWQCIZP6NjQ2srq6aUvoATFEYnVCq1WqiMvF6vcLt3tjYOMYP0ZpBzQoEGnFiRjc6OzuRyWSws7Mj5Cjr53SsmtW2SqUStra2pAoUVTjMvJJWoOkD/DzrLO7v70uVq/b2diQSCWQyGenv6XK5TBwYRmaY6u/r60N7e7v8fgBC6mLXZ1ansjvc6nEiVm4tnD08PER3dzdaWlpw8+ZNMVr6hNTfab4JAAklMpUdCATgdDqxs7OD9fV1AJBQFHuvcPDPNC4SjYAGD2R3d1cEFS0tLfD7/RLCs/rLdEu4+re3t6OzsxPZbBYff/yxrNAej0douw6Hw+S3E4M4NKy9vT3cunVL3Bj2C9KkMSuBCoC4BYxc3L59WwyO95oHaWvWkBOPKz1j6bdu3UJHRwe8Xi/6+vokl2BHvmJ9cjYUIJ97fn4eLpcLPp8Pg4ODkmyrVCpCU7jfOBHGzVYRNCiKAEhNpf9L31CXuuUKRcEwVyU+cHKNte8KQJTx1uvQxXGYJWU1VgDSjYvcFE2oAo743Fy9SN8NhUJSTo0TlkZNeoAmi/Ggqnnb3MYpuojH43JApFFY3QEdy6eBUnRAKmxHR4d81urakKLL3+hyuaTOOWPZPOSSG8/Em74OHoAZ92eV2q6uLllMQqGQ3De9KzYbJ8K49Y1gZEFzIRj7zmaz8j6u3DQIXWaAkQJyvkkzpTujaan6BtJI6Htyu+fhjskTcqW50+gYtZUhqKtaMcqhK0zREA4PD23FClz5WHKOZwJOIN31V+8g/H28T4x4MH3PMw7fqycnXSEatW7f7Xa70dnZKX4+aQotLS1yprBGj5il5CJGRRBx+Dz0b6OG9n7jRBj3H/7hH8oP01t6sVgUF8TtduPSpUt47LHHABxtu7VaDf/wD/+AF1980UTmoTGR4M+VvLu7G729vfLdXCn+9m//FhcvXgRgVtFXq1Wsrq6aMFgn3IoBQFQn+jrK5TKWl5flNT5IaxcGPSkoQiAGzyW8Nq6IOtmhjVtPdn5fpVJBLBYz/RZ9+OX9ZPSIPj0xnU4nKpUKdnZ2jmHwP/1sgIbKyuqqADAp93Vs3g6j2TgRxq0J93pr5orIaIJ+ENYDhx2GNpZmGM2ugw+tGYaOqnxWDO4sVgyrr2qHoc8aD4vB92p6gh0GYN55rBgcdhjapbHD0H+3MiCBo0xmMwy7cWKMW/+5VmsUnIzH41Jjmz4n649YDcsOo1gsYnd3V9iExKDw9X4YfCBsQspVkxgMhdlh8KHbYdDN8Pl88Pv9thjaoDQGS7hRs8nCoUzz64ytHQbrjvNMQwzyRKxJEysGCWvpdFpK0TmdTvh8PnR1ddmWYtChReKT507hBTWeWiz8MONEGTcPW8vLy4hGo3Lw4tbKk/nY2JjJ/2yGwcZCZBryJvb19WFgYED8TzsMbuFsbqQrQ3m9Xng8HkxPTz8Qg1wSKwZV6KSx2hmBHQajK62trVLy4WEx1tfXRQBth8H+kFauDTFyuZwoeawYXV1dmJycPMZNt06YdDotYV5icMIHg0FbjGbjRBi3DqNRXUJOA2cykzTJZFJKLegbYIdB8pKuolqpVCSsNzIy0hSD3O9mGLlcDvF4HENDQ8cwuFUXCgWsr6/DMAw5FBKDWUONob8fgC0GQ6I8lH4WDDIJGWZjJIUY1nIKVoyNjQ1ZbKwYe3t7klG2wwAgXG2NwXONw+EQDIYtHzROhHFzFAoF7OzsSFXWiYkJOVRls1ksLCxgbW0NyWRSeq1YVwaN0dfXh+npaVmRiLGxsYF0Oi2hNTuM7e1toWcSo16vI5fLYWFhAdFoFLu7u7KVWl0aqnCIwUI0PCivrq5icXHRhKFHMwxyLpph3O86AoEAzpw5I8U8NUYqlZL3NLuOer2O0dFRnDlzBqFQSHjlq6ur2NzcxN7eHrLZrAmDuxpLE1sxuNNub29jfn5eJITW67AbJ8a4K5WKcLOHh4dx/vx5DA8PC5Uzn89LZdR8Pi+MQf0wNQZZgOPj46J11BgsLaDZZ3wIpVIJlUoFPT09OH/+PMbHx8UwC4WCqHKi0agtBnnmzTAODg7g9/tRLpcRjUZRLBaPseCaYVBUXCqVjmFYC1laMaanp3HmzBkEg0GJ4mgMVubSq6bG8Hq9OH/+PKanp8UwieFyuXDz5k3kcjlTdwbualTNe71enDt3zmTc1WoVPT09qNfrthjNxolIv/Oww6QHSfz6NE+CEYWwLIKpoyvcItnRADhSvlsxuGJoDKbcGZ7i+4hBo2K/TAC2GNVqVZI0dhjUfRLDmjxiQqsZBsssWDEYi26GoUusUSeqMUg/0IapMbq6ukylpDWG3++X62LsWz8X3ldyTzQGw4/EoDDlQcZ9IlZunXjo7OzEwcEBfvrTn2JoaAiPPvoodnZ28OGHH8Lr9YqIFTguCwMa2ygx3nrrLfh8Pjz//PPHMFhFqRkGe1w2w+jv7xeDsYbYGMLUGH6/H8899xx2dnawuLiIfD6PsbExWwwturBinDt3DqFQCB988IEthpVXrjF++ctf4oMPPsAzzzzzK2F0dXU1xRgZGYHH45HIjK6vwoXK7XabML785S8jGAzi9u3bor20w2g2ToRxc+VmRnBqagrPP/+8HCKHhobwve99DwcHB7h+/bqEB+0wmNWamprCs88+Kxm2oaEhfPe738Xh4SGuX78unAw9uMoQw9oVQWNsbGxI41XrdXAHcjqdthgUK6ysrNhi6NS9FSOXy6GjowMvv/yyLYZOglgxnnzySZO8zIpBY2qG0dnZiWeffdYWgyXudCtAjcEwqh3G5cuX8ZWvfAXJZNJUhepB40QYN3CkHGGRx9bWVhG3UsNI+iuTKQ/CcDgcQvC3w7BOEM3poEigGQZputbsG2PCvB47DIoVmmEAOIZBd4YFdSg0sGJY4+Uag1GKZhg6Jm6HQSamHUY+nze9X+9kvD5OcEZtSBw7deoUwuGw9IlvlmSzjhPhc+uti70o2UWY6vS5uTk5BDJZoQ9gxODhhRTa+fl5WwwyEZthsK6IHUY2mxWBBVPQGoNZRCsG/dEbN25gd3e3KYbmhBCD3YyZSGmGoQ3DipHNZjE3N9cUQ2cb74fR7DpIaLPqU3VWNZfLHcNYW1tDIpEwYdgtPtZxYlZuEmhaWlqwtraGQCCA/v5+IRMFAgHcuXMH6XRaKJRWFhsxeMNYJtftdsMwDAQCAczNzYmciTfQDoMlzNhqxIrBOh6aW8FBQtTh4aFgUGhgGIaoTpphcJWQcPzqAAAgAElEQVSzw2C5C4oVPgvG4uIiZmZmhLBkxdAHeDsMtgk5c+bMMQyWSLNLrnGXZcmLxcVFzM7OSmlkj8eDbDZrwrA+F7txIlZuLRigSHVpaQkHBwciEmCoiiR5/dlmGGyhrdtbpNNpyYhxZWiGAaAphsPhEAyGs4ihifrEyOVygpHJZITAZIdBLrcdBhNb2WzWFkMTyqwYqVQKOzs7TTHIAmyG0drailQqhXg8fgyDSab7YfAaU6kUtre3xQevVquSqOL3aIxm40Ss3Ny26GORnL+1tYWtrS3JZlGkys88DEY+n8fHH38sGF1dXccwuEJoDMM4oonm83lcu3ZNUuddXV1ycLTD0PRTOwxSCRgC/CwY169fl+yg1+sV96rZwVZjcPdphsHPWUUTHFTk1Ot13LhxQ34LC9EzxNkMwzAMwTAMAzdu3JCd2ePxiIiZz+Bz4ZbwIXDVY6q6t7fXlJFjKQbGSHm4aYZBaRPjxBTk8rN0YZphkGxlh8FC+XwInwXj8PAQ29vbcki0Ymg/14pByRyzeowt06WjsdDYNQbFCoFAoCmGjnYQQ9dxZAFQKwZ1qgwAWDFYY8aKwdh7MpkUDE27vd84EcYNmOVZLpdLfEsmZfjAW1tbj4mD7TBoBCTtawxtRNawEw+VwJESpxkGqQHNQlc0SisGk033w+AqbsXgvz0MhvU6dJcHYlDex8QKyzVYr4UTR5cWtsMg98aKwQgXJW8ag4dOupzNMKzjRBg3pUfMwrW0tIhQWFMuW1pa4PP5xBfTRCFmJHUmj13DgKOt0g6Dk0RjMEtZKpVsMdhizg6Dvi8PaFYMcsKbYWiOiPU6eG30X60Y/F12GPV6HZubm6bfYsXQC4bG4G/RGPoAqrO2GoPSOj1qtZrpOni/2GeUrz/I5z4RB0qeqJmO1QR9rho6GkBfzBof5ky3RlL4Gf26nT+nT/bW0/qDMDTVlNdrDWfx36zvtWLo77dGdHRY7X4YfJ2/SXOzdQza+l59T/Xr+sCqzyV8r/UZaQO3Gjc/TxdOPzP9Wz8XB0pthFx52ZqiXC5LdotVixgd0IcdPjyNUS6XkUqlJDmgBQ+63LG+Do1Rr9cFI5fLyfauhbr6DKAxdEKkXC4fEyvYCQ2sv0VjUPDAw59VrMDr1YZp/V1arEChgZ1YoRkGAMFIp9Oo1WqCwb6RVkO2Tm6gwcdJpVIimmhtbZXrsMNoNk6EcesbwJK8FCuwESlvQHd3N0bvFdqxxlOtGLFYzFZo0NfXh76+PilCaY1WABCC/9LSkpQ71oIHj8djEglYU9f0R7XQgEp51gD8LEIDO7HCwwoeqtUqYrEY1tfXJVNJDPahofDCej/4dxYHunv3ri1GV1cXpqam4Pf7bbOc/Pvu7i7u3LkjB3Ku1m1tjVaA09PTthh240QYN1fLWq0mjTdZz4KFFA8ODiSx4na7peyD9p01RiKRkMNLR0cH6vW6sN7IK2bZLjsMlh6zYjB8l8vlEI1GMTo62tS1SCaTWF9flygFMbj6spKUHQZXW2IwjMntm+XnHgYjkUhIHJmrtB0GK9dqDO4GiUQC0Wj0GAbLSe/t7WFtbQ0zMzOmXjYagyXcgEYbwEgkIhEj1iFcWVnB2bNnTd0bmo0TYdxA4yYUCgVks1n4fD6MjY2JWIFp24WFBWmOSqZbMwy/34/e3l5MTU0Jj1oLDQqFAorF4jGRADEymQxcLheGh4dNQgNisAIU215ro+YBkBVZrRhaJJDP5+U6rJOM/Wo0hp1YgdVm9W+xwwgEAlITW2MsLCwgl8vB5/OZRALEYJeIlpYWDA8PY3p6WloSspvC5uYmyuUycrmcrdCgWq1K8mt4eFj43EBjp43FYrhz5w5KpRLy+bypw0OzcSKMm9svK5YODg7i3LlzGBkZkQKPFBrwBlrFCsRgLHxoaAhnzpwRkYDGoOGR4G+HYRUJWAUPLS0t2NzctMV4kFhhf38ffr8fBwcH2NjYOCZ4AJqLFRh90EIDfR16WDGoKrIKDYhhJxKwihUoNLAKHtxuNz799FNbsUKtVpMqtz6fD+fPnxcMfkdPTw9qtRo++eQTZLPZz5dYgcVtmPHSviNvolWsoAvIWDFoBIy4WDG4ImmCv8ZwOMwiAZ7iicGaI3YY9xMaOBwOE8EfOC54YALHikEflOw6AGJkDyNW0GXiNAZXWjuhAZMvDodD+CSMp2sMv9+PU6dO3RfDMAzRR/LvZGEeHBxIUseK0WyciJWbQfyWliOhweuvvy5ihXg8jg8//BBdXV0Ih8OmWLJO7TK6QIyf/exnIjQghsfjMUVd+N1WDPrIWqygMShW0J1+icHJ0AxjYWFBRALNRBN6ov86YgWNcT+xgr4OnYrXYgWPx/NAsYK1kpf+XcT49NNPm4oVOAmZqLvfODHGzfBWS0sLpqenj4kV/uAP/kAolkyh62gJ38uw2PT0NJ577jm0tDTKs1kxuFpqV4DGQQw7oQExHiRWYPkGO4zZ2VnU6w8WK7S1tf1aYgVmMB8kVlheXkYmk7G9DiadmOF89dVX0d7efuw67ic0oKF3dHTA7Xbj2WefNWFcvnwZzz77LOLxOAqFwudPrMAtiKR4Euy5pZGwYxUaaOPkdkcMGnaxWDyGwYSRHtwmiaFFAlYMLRKwYnDSNcMg7+U/t1iB1/awYgV+rxWD94qcdmLwPlGsoJNOeifTyTcKHk6dOiUY7e3tJrECI0IPCgWeCJ9bh4sODg6Qz+exvb2NWCwm/hd52LwhvGkaQwseSPBfWFiwxSDBX6/+GqNabfRIp0jgs2DwP6vQgHF7Evw1hvYvuZXbYfD3/bpiBet1kI2nM57EIG+lmeCBCapSqWTKKhND3xNGou7evSsYq6uriMfj0i3Dek+bjROzcjO50dLSECsEg0HpRw40Dj3z8/NIpVJSmMa6cpJoRQyKFSgSoNAglUo9lFghmUxKVwRi+P1+zM/PY3NzU67XevB5WLGCxrAmpB4kViAl+NcRK1gx9Cprh7G/v4+FhQXMzs6aMKLRKJaXl8UNsmY2tVihWCyKWEFjpNNprKysiGbzYYz7RKzcZJbxMFetVrGysoJkMgmgMfsXFhawu7srvGzr9qdpmcSgVI07AycHGWw6VQ8ciRXoZwIwYdRqNSwsLCCVSkkanUalMcjY0xhsVVKr1bC+vo5YLCYsOP4WYpAVRzdJYzD60QyD7o0dRiaTwerqalMMZmB5T60YbW1tyGazWFlZEddubW1NKK/EsF6HrjNuxQAaya61tTW0tbXJ5P1cZSi5fbW0HBUpL5VKWFxclFQzoyBcWTUl0kri4QHI4XBgcXERbW1tUluaXG4r88z6d2IAwMLCgmCwrgpFAtpFaoahr8NOaGCHQRx9HTQCUgm04KHZb3E4HILR0dHRFEMfIO0wmGonBjs8eL1eadlCDDv3iJOE1cJWV1dllWY3ZC1W+FxQXnljaTBOpxMulws9PT2mnus8cOgQEbOUWthbr9eFPhkKhYTKynbaTNZwMjHlbBUatLa2CgZrpbCLGQ+rxKD7dD+xAkOYFCtYhQY6Zg+YhQa8Di1W2NnZkYMmDcqKwR2Nv7OrqwvBYNAWgwVx7AQP3BWpiAoEAujo6BAMdhO2Fp7XGLznusMDm1rRVdQCYesh2zpOhFsCHK28XB21WEFv+/Tp9HZnxWhpafCt2SGBD54TQLslVgy9g7D/PLdqYvE6m2Fw0LDpNhDDSvDnZLPbhonB6wDsRQJ8rVls2E6swOtmaxMApiZadhgUGtCl024csa0YXLH1jsoFhckpLiZ8nUm6+40TsXLbiQSSySQSiQQAs0ggEAiYtmydILBisK6gFUO3gdbhMzuMYrGIpaWlYxikeNphaPeCPJP/UsQK0WjU9Fscjobyial7PcGsGJygdhhtbW2S6WyGwWEYhrSF4bUBkGdjxWg2ToRxM3SkDz/ad2tpse9ooB+mHYZObDTD4HdZMfjQ/nNg6KiEHQbwj9tZQWNYs7Eag3/XhqUx9HOxYuhYezMM/RlrHFzfWzsMu3FijFv/uV5vkPN3dnaECKW7InBbtEtYWDGSyaSpswL952ZiBQ4+EGtnBZfLZStW0BjaPdLX8U8pVrDDaNZZQWMAaIrBuLQdhlWsoDH4d06ig4MD6T1Jd0R3VrBiNBsnyrh52FpZWcHGxobwfBmG8nq9GBgYwOjoqEk61Qxja2vrmNCAGP39/Q8tErATK3i93s8sNPgvvbOCnVhBY+RyOdy9e9cWg50VrEIDjQE0xApzc3Om1uac8Oys8LkVK0SjUSn6wlWaGTU+JBKoHoRBoQEPKZwsLKQ+NjZmWq01hlUkwMMSDYwEfzsMDiuGFis8SGhgxWCEQYsVHiR4sGIAELIXIyT6OnQbcDsM+sl2GMViEevr6+jo6LDFMIwGXXhtbQ1A42zCIvgMRTbDaDYeaNwOh+MvAHwDQMIwjHP3Xvu/AUzfe4sfQNYwjIsOh2MUwF0A8/f+7YphGH947zOXAPwlABeA1wD8d8aD9hU16vW61PeLRCK4ePGiiBUAiMTp1q1bSCQSwuq7H8bExATOnj0rBz9i3L59G+l0WhiGdhjJZBI+nw/T09O2GEtLS0in06YwoR5sRWeHwZJin376KTKZzDEMTjKNcebMGczOzkp2thmGNiaNQfHGI488glAohLa2NpTLZSwuLuL69esiaOju7m6K0draijNnzthiLC4uIh6PI51OH8MAGhGUZhisDvbJJ58gHo8jlUqhp6fnH8Ut+UsA/xbAD9TN/W3+2eFw/BsAOfX+ZcMwLtrg/DsA3wNwBQ3j/mcAfvoQ3y+rJWOtvb296O/vR09Pj3RWcLlcQqpnZSIeQIjBWnStra3o6+vDyMgIuru7RSRAjGg0ikwmg1KpJJEGjcFYaygUaoqRzWZFfaIVMMRg/NoOgxyTaDSKaDR6DAOAEPyJMTw8jO7ubumsQFeJGPwtehfhPTWMBvVgdHQU/f39CAaDJoxYLIaNjQ2puqp3AI3R1dXVFKNUKiGdTtti0N8/PDy0xSALMJlMIp1Oi3TNSmuwjgfGuQ3DeA9AuonROQD8FoC/uh+Gw+HoA+A1DOMX91brHwB4+UHfzVGv1yU2Sq6B9TTPjgasOWLtRqAxmGjQIb2WlqOuCFaxglXwwM9okYAVo5lIgImkz9pZ4UFCA41hFSsAzQUPFBpYOytYhQYAJMuof4vumEz3rBkGk0B2GMwK22FwIvt8PluMZuPX9bmfARA3DGNRvTbmcDg+AZAH8CeGYfwcwACAqHpP9N5rtsPhcHwPjVUewHGRwOHh4WfurKBP57qzgu5oQAzdz5IJBCtGs64ID9tZwU5o8Nxzzx0TK/xTdFagv/8wnRWAo8KgOhzb1dX10J0VOLH0/QBwrLMCMew6K2iMZuPXNe7fgXnV3gYwbBhG6p6P/f84HI6zAOzoW00dJsMw/hzAnwOAw+Ew+CA+a2cF7ZNxhdAYz1o6K2gMpqzvXYMthp3QgBgPI1aw66wwODj4TyZWsIomftXOCsSwdkVwuVy2nRW42xKDO59dZwWXy3Wss4IVo9n4lY3b4XC0AfgmgEvqph8AOLj3539wOBzLAKbQWKkH1ccHAcQe9rvofjABY9dZoaurS3xZBvz5Wf6fejxi0G3gTbdi6BisHcbDdlaw/hbiNsN4mM4KfLi/rlhBYzxMZwUATTGsYgWGJe/XWcEOg50ViNHe3o5QKGQSK1gx7Mavwy15AcCcYRjibjgcjojD4Wi99+dxAKcBrBiGsQ2g4HA4vnTPT/8XAP7fh/0irnYM+eXz+WOdFe7evYtMJiMGzwnBwVWGGHadFYhhFRrYYZDgb+2KwO4MWiRgxWAypRnGw3RWoKtmJ1Z42M4KVoxmnRV4Pzi5m2GwK0Kz7gx2XRE0Buu9ZDIZE8bq6ioSiYRU0LX+lmbjYUKBfwXgWQBhh8MRBfCvDMP49wD+OY4fJL8M4H9yOBxVADUAf2gYBg+jf4SjUOBP8ZCRknvXYOqssL6+jmAwiIGBAYlz+/1+3L17F9lsVsJh1lXT2p2hra0NQ0NDJrECuzOQE94Mo1Kp2AoNTnJnhWZdESia0Ad4OwxrZwXG762dFfR91SsxV2srBjsrLC4uihtk92ys44HGbRjG7zR5/fdtXvsbAH/T5P3XAJx70PfZDbLZOGMPDw+xtLSESqWCgYEB1Ot1OYSdOnVK0u9k+jXD2N7eBgBMTk6iVqtJERyy4O6HoUUChmFgcnJSroMdHpg61+nzZmIFjbG5uYlEIvFAsYIdxvDwME6dOoX19XVbDLoWdhjZbBarq6uYmprCqVOnsLGxgXg8LhnC1tbjXRE0BoUGzTA48ewwgKNFLJvNYm1tTTBSqRTW19fhcDhMoonPRfrdyolgNo5iBdbW04R4RiT0wUVjNBMaMMas3Qc7DMAsEtCCB9IxP4tYQWOQBmAVGjwMBkUCzQQPdveUGPyvGYaOjthh0Ee3w+DKfj8Muj2k8HJ3BRqhxFKpJAd9q6tmN06McQNHggO2lmBnBaBRtotREh5k7LoiaAwtNGCmjUQs3UnADsMqErDD4MP8rN0Z/qk6K1gxfpXOChrDMBpdETwej9QLtHZW0B2INYaWAVqvw9pZgUGEB40TYdzAcaEBt/x6vS4/2jAMU+EY67alV41mGADEpdBxYTsMrjB2GNT72RWP4QpMDN1Zgelm/vlBXREAc4cH4v8qGA/bWUHvILwnAIT3zUmoMege2WHwuTocDqld0qyzAhM8GqPZOBHGrVvveb1edHV1SZqZqyiTGd3d3cjn81L/ghkypp71Iai9vR3ZbBa5XIM9QCKV0+k0rRBcJZgZpTHTd+ehDYD4pcFgUCYCS7ARQ4flmF1dWVkxhTsBSGJE7zj8LfSfiUHRBI2BxqKFBnqwbBmxmeHc3Nw0ZV2Ne9QGLXjgysusI6+Dz4IVY/mbGZplOl0fBoeHh2VHKJfLqFQqKJfLmJubkzouLPPm8/nksMud5n7jRBg3/U2tagcgqyalZSyz5XQ6TZk4wNyJwKqQ58ThykCfWR+UgKN4rL4GGiJf4/dw59CTgRg0TKsvr/9N41pXSL5PGzHfYz3g8f12MWp9b4nBgqPsfUNeOld/jaH/TxyGNrPZrFBWmfUNhUIS3eLQCnqS0hYWFrC9vY29vT2J0vCZnT9/HgMDA6YDdrNxIowbOK7wAMwxUhp4e3u7iH/ppmgMPgTeNMa9ueLSaGkQ1tCXHjTAWq0mVbDY9Ehj6KETQzRkUlNLpRIACL9Fk7b00NfDMwZ7SO7t7eHg4AButxt+vx+RSAQ+n09WP32o1v8vFotIpVJYWVkxcWpaWlqEI88yylb9IwCJla+trSGdTkut85aWFqllPjw8jPHxcXHXAMhqDQDpdBqffvop0uk0Dg4OUK/XpQA9CVq3bt3C3t7eMSqx3TgRxs1Zy/h1uVxGIpGQFWJ/fx9nz57F4OCg+J/suMCbr+O0QINyms1msbOzg3g8jlqthkgkgtOnTyMUCsm2p0W1ViFwsVhELBbD2toaMpkMAAgpv7+/X4wKOL5i8oFlMhksLCwgnU7LBGHUZXh4GJOTk8dqfWjjPjg4kPoi5LQz41kqlVAoFDAxMYFAIHCMksD7sbe3h7m5OVNkRe8M8XgcDocDo6OjcDqd8nn6wg5HQ9Maj8dxcHAg9IRTp06Jzw40mrgyEMDXmEAzDAM7OzvY39+XYqQ8kPNaKQgh5djK2T9mN5/Jyv5/Gi6XS1bFer2Oa9euSVybB7YrV65gaGgIX/va1zAw0OBkabU0P0sK5UcffYSdnR0Ui0XZAba3t7G6uooLFy5gbGxMlCf6oMbwYrlcxu3bt7GzsyOqE6CxAs7NzSEej2N2dhbd3d2yK1hHNpvFnTt3cHh4KCstHyL9X5fLhcHBQVv3pF6vY2dnB+l0Gh6PB6FQSHafUqkk5wV246WxaYxarYZYLAaHwyHlJTgJDg8PUSqVUCwWJaMbDoeP7ZyHh4fSvbi7u1tYhIyicJKxLNve3p7sbsw4Mio0MTGBgYEBdHd3S/1B7p5ra2uIRqNyTuF5qtk4EcbtdrvFOH7xi1/gzp07aGlpQV9fn7gj1WoV8Xgcf/M3f4NvfOMbGBgYQEdHB8rlMoCjgx4nx/r6OkKhEB5//HEEg0E4nU6k02lcu3YNN27cQLlcxqVLl3Dq1ClZuWnA1WoVy8vLiMfjmJ6exqVLl+Dz+WQLvnLlCnZ2drCwsAC/3y8lJPTY39/H+vo62tra8Mgjj+DixYvSkrBUKuHu3bv45JNPsLm5KZpMzZcxDEM6N4RCIZw5cwanT5+WEg9MdN28eRP5fB6FQgE+n08mCF0UEpGGhoYwODiIiYkJqV1eq9WwtraG+fl5rK+vy87AQfrvwcEB2tvb4fP5MDU1heHhYSm4z8mTTCZx48YNeVY8pO/t7cmEo4Lqi1/8IsLhsOnAWC6XMTg4iA8++ADZbFZ+w/3GiTBurtyJRAIrKytob2/H2bNnMTU1JYafTqfxwQcfYG1tDdevX0dPT4+4KADERdnd3UUsFkMgEMBjjz0mN7KtrQ27u7swDANvv/02VlZWMD09LVI24Mgt4aHJ7/fj0UcfxezsLLxeL+r1OsLhMKrVKt5//31xm7q7u03+IVeqSqWCcDiMM2fOYHBwUNpqM9LD1HculzN1VuD5gy5IIBBAd3c3AoGARJH29/fR29uLWCwmekQdY+aqW6/XRbLn8/ngcrlk9T48PBTRtcvlksgPJxm/n/fX7XbLbsfD/v7+Prq6ukRwwZ2SkySfz8uuovv66IZR3AE6OjoQiUSkc9yDShmfCONmT/d0Oo1cLoeRkRFcvHgRFy5cQDgcxuHhoaxQGxsbuHXrFqampjAxMSG+XUdHBw4PD5HNZnF4eIiZmRkMDAwgEolgYGAAlUoF2WwWo6Oj8Hg8UntP7xq8oTy0jY+Py8NkhKZSqcDn86G/vx8bGxsi4dKrEJmHra2t4t/rjCqTPpFIBGtraygWi6jVaqbKV6zyBBw1oWWEhj43AEkM6Zg1AAlP0ogYo9edI3jY00VydLqeSTNOGk6Icrks94UYbGmus5RAw43jtROXZC9rZwmWdiYf/3PhlnR2dqJUKom86Etf+hJisZj4iMlkEj//+c8xMDCAkZERXL9+HclkUshIQGNloQ/qdDoxPj6O1dVVpFIpPP/880gkEvjJT36CwcFBjI6OYmtrC7lcTuKswFFYrl6vw+PxoLu7G1evXsXdu3fxG7/xG4jH43j77bcRiUTQ3d0Nl8tlKuLJwVXT4/Ggs7MT77zzjqmzwvz8PHK5HIaHhyWWrOPcOtPHUgdvvPGGiBWCwSA+/PBD5PN5TE9Po6urS3oHWfkpukLXBx98gEKhgC996UuIRCJ4//33USwW8dhjjyEQCBxLVOnConz93XffRTabxWOPPYZIJIJ33nkHtVoNjz76KHw+n4Qc6ZbwENve3o7Ozk6Uy2X87Gc/w+rqKr7whS/A6/Xik08+QaVSwfPPPy+l2pLJ5OfDuOkvF4tFBINBPP3005iYmBCKaHd3N/7kT/5E0uq7u7sSZ6VLoX3zSCSCs2fPSjipUCggEong+9//PiqVCn784x8jkUjICqnDg1wtPR4PBgcH8bWvfQ0tLS0oFosIh8P4oz/6IxweHuLOnTsYHx+Xh67dAa6gFNx+5StfQWtrK0qlEvr6+jAzMyNErt3dXZlQHFz5+JtCoRC+853viFjB5XLhlVdegWEYWF9fRy6XMylfeB28LvrL3/rWt3Dq1CkUi0U4nU68+uqraG1txfb2tvC9GaIDILkA8mHa2trw0ksvwel04vDwEC6XC9/+9rfhdDqxv7+Pubk5dHZ2SiAAgLhMdCEdDgdeeOEF1Go1keoNDg6KMiqRSMjk/lz43PTHAoGAhIjol9HNoHvCh6Xj38BRAoaEJGtnBfqXjO/ygMjP8v9MzXs8HlPBS2KGQiGJBVPwq0OQjE0Ti0IAfv7g4ECyiqVSyTbSwvAb8QqFAoDGKsi2Gl6vFx0dHSIS0NlIAGKkjAbxcNrZ2YlcLie+Mll5NCYaNL9PnyVKpRJyuZzE7Ts7O0UwHY/Hsb+/Ly6HNm5dFqNYLEqRJEZxOInj8TgymYxwzD8XSRzGm/n/WCyGoaEhrK+vo7OzE3t7e1hcXBQ+dqFQwNTUlCnCwK2Tao9sNot8Po/19XX4fD6USiXMz88jEAgITZOtAK0TBID46GymGgqFUKlUBCObzSKTyUgHLh0t0enqXC6HdDqNra0thMNhVCoV3LhxA5OTk9ILk/6rjtZw5aVIIJlMYnt7W0r9fvrppxgdHZUVl5NEE7iYZKrValKeYX19HR6PR2qgDwwMmIQGJKURg4dsHpLj8TjW1tZkEWEph3w+j3Q6LQkiPfQiwtxBoVCA3++XWL7H40Gt1miQm8lkTDHwZuNEGDd5IJFIBE6nE6+//jomJiZk2wcaD/yXv/wlrl27Jqw08oeJQQJ+e3s7rl69CrfbjdHRUeGk1Go13Lp1C3Nzc1IDXDPp+GeXyyVlE7a2ttDf3y89zuv1Ou7cuYNbt26ZeOG8DuCImFWpVJBIJASD4bNwOIxYLCb1qa3kfG0M9Xod2WwWsVjMVqzAEsR2E4wTvlaroVAoIBqN4vTp08Lf8Hq9UuZC6yd1rFz/NtYeGR0dlT6RoVAI0WhUDJuuHg2TroheRPL5PHp6eqTOOUUMu7u7yOfzQjH4XKzcPIHTNyPRiG4AY6nZbBYulwsDAwMIh8MmPjdJVNVqFS6XC7lcDtvb23A4HBgaGoJhGNjc3JQGniRnab4xSwzQD6/VatjY2EClUsHo6CgMw5BsJRmDdoWBNFPQMAwRGkxMTKBWa1TESiaTpqysNioeLrWLwypZw8PDaGtrw9rammQWaUA6Q6kNk0bP2Pvk5KQkTdLptO0Cv40AACAASURBVCmSo42KIT9r1pKVtk6dOoVYLIZcLmfaPRgRAhpRLIYiyWPh7tzT0wOHw4FcLmdqINXZ2SnMwPuNE2HcmUwG0WgUCwsLWF5eRiQSQV9fH0KhkGzRY2NjcDqdktihn8YbkM1msbu7K4dNoBFC6+7uFp91YGAADodDykfQz+SpvFQqSZaN/j2LtadSKTgcDul0Sy2g0+mUkBsASWDQQBkl6OzsxNbWljAT6SrQ76YbAeCYD8/wWHt7O2KxmJxRvF6vMPV0qhw42smYtaTL0tbWhpWVFTFEUn916p+uHjsgkKVIH75arWJxcVEmD6kPnBh6N/T7/XC5XKYYPc8Oc3NzMikZ9WK8nyLu+40TYdzXr1/H22+/jXg8jkqlgu7ubjEsHQVhnDWZTKKvr09iqwAQj8dFelUul9Hb24vW1lZTmIzhNWJQnsXtslAooFAoyIGLVFyG9Liyk5NdKpXEgGgQjNky8sBzABuM0q8tFovyZx6graQnvfqSlsqYNAtrMglCw+J1cEdgup4kM3Kp29ra5NBJ1h5gVs4w4aINmNEhVsiyrq78Hi4YkUhE1Dp8DozTk3rM30wePs9ePCw3GyfCuH/yk58IMYnUV+17cSZns1kRq37wwQe4efMmzp8/DwC4e/cudnd3JatGv7lcLptIPLpBK5MwXNmTyaQcZur1urD2tJpFb5cOh0OIXXytUqmIlK1Wq0lBHOCoWDsrY3GFIz9DGzfdFHJu2tvbj3U00KxITfgHjsKrGo9uFF0Vvk7XijwS/p3nCc25ITWW0SreH0ax+Kz43Yzja6NlxIpRL15vW1uj2pjL5ZKSD/cbJ8K4BwYGMDQ0ZOJW/OAHP5DQFDkONCz+vVAoSOVR8ok193lubg63b982cR24ejNpUalUBMPj8Zh6xgPA9vY2dnZ2TEkWoPEwenp6JKLBlYoCBOAopMfIiuZ/AxC/na8Tn4c1/X2sTqvdA76XQ7szPT09pm2dkRN+D10mHnLtMKanp48dUmns2ug5UTTbkM/ymWeeMU0yfT1cyIAj7r51/Omf/ulxg7k3ToRx0+/TKxO3Nu1KcCXjzdIPX0cruA1ryRi3b36OD1DHmLU7oJmC+v18wNpI+D7+n9+vr0P71No4eXDVK6HG0QYDQEJ1dBGsYgYdYeD7rH/neYLulP7d+vP83VahBUswU7it3TIr9VffO14fQ4+1Wg0+nw+RSEQmiB1Gs3EijJuz1+VyyWrGFC4JQexExsSKNTVL4hQfAg8+JPFr4wOODEyTg/TOQRy6RqVSyVS8Ua9Y2oCsGNymM5mMiUPCunn0+60xXe2i1Ot1ZDIZ6bJLn5c0ALISrfFl7c/u7e0hlUphYWFBZHd+vx/j4+MYHx8XH1pPJD15Wcf71q1bWFxclH8LhUK4ePEiZmZmEAqFjtF2dZWARCKBN954A3Nzc1KIn8Sup59+Gr/5m79p6nn0ICM/EcbNw4bH45EuZCwlTAYdGWnsGa7JQsBRyz5tcHwPyT1WI7QalBY/cIXLZrNIJpMyqUiiam9vlwSOHjpWXa1WkclkJEVu7azg9XoxMjJyrKMBV2New/LyMtbW1kwdvsiijMVieOSRR0wJLeuIxWK4efMmstmsSXxbKpWQz+cRjUbx9NNPm+p7A2Y+98cffyxljnW3sng8jg8++AC3b9/Giy++iP7+ftNvIcdlc3MTP/zhDyUbahiGqHD29/fx5ptv4vbt2/j+978vMrPPhXGHQiH4/X5TBwMWZmS7PJYC6OzsRDKZFGOhceuW2cBRWWBqLrlKWH1na/JEH9iYQaRim9GNSqUiqWSv12syKu2zsha4YRjS+5EPlW04EomEPEy9qxBrY2MDOzs7cg/4G/n7KP2iokcblWE0eq3Pz8+jXC4L644JH2og8/k87ty5g5mZGTkvAJCzhOZ7MwJFDO5mxWIR7733Hl588UVhKgKNaMrt27fx7rvvIpVKyS7KGD+N2DAMZLNZ/Nmf/Rn++I//WDqj3W+cCOPu7e2Fx+ORlbNer8Pv92NsbAwdHR0i6mXBd53wIOfX7XZL2I2Eq4GBAUxOTsqDKJVKWFtbw8bGBvL5vK3+kVEBuj1f/OIXMT09bQoXzs/PY3V1VSIcehfQqet0Og23242ZmRnMzs7K7nJwcIClpSV88sknUkhfF47ntry7u4tMJgOPx4PTp0/j7NmzEspjwaKbN29id3cX4XBYFPnAEad8dXUVBwcH0vvm3LlzItejCn1+fh47OztwOp2YmZkx/Qaql3gmCoVCmJmZwcTEhKTgb9y4gY2NDWQyGdy+fRuPPvqoTPhMJoN3330XW1tbstp7vV6cPn0aX/ziFzEyMgKHw4Fr167hk08+QSKRwFtvvYWvf/3rx3ZF6zgRxh0IBKQeBsNwXq9X1NT0U0kiSqVS4rrwQVA0zFU3GAxKdwZm2Pb29gBAGhbphkPAUfiMh8/Ozk6MjY0hEonIqut0OqUg+8LCgol9Bxwd2rjCRyIRjI6OIhKJyOrPAxnFGczMauM+ODgQ3zgcDmN0dBS9vb0SSWGcfHd3F6urqygUCvJvAES/ySqzXCzGx8elBiPvB2sRkrDE1btYLGJzc1MK9rS1tWF4eBgzMzPCdaeLCEB6uk9MTCAUCgEA5ufnRSRSrVbhdDoxMjKCxx57DE888QR8Pp9Uz83n87h69Sp+8Ytf4NKlS+jp6bmv3ZyIDsIs0UUD5cOj8RaLRaF10jcnI409bZhwYRyXRs4YbKVSQaFQgGEYcsKnUeqaJNrQydfWChcq2Jm1o/9oDadxlWKKn7+FB8xKpWLqkaMPtrreIDOqOiLC6BEPlpwwFOISk/F7xv11IRwdPWKC6vDw0ERXLRaLkhcAIAdgxt3JW2HCjS5XMpmUHZVnBUaPmG31+XySA+DviUQiABq74+Lioiw+zcaJWLnpBwKNg2GxWMQbb7wBj8eDl19+GR9//DHm5ubQ29uLCxcuIBQKoVQqCZEJgKlOCTHeffddOBwOvPTSS5ibm8OtW7cQCoUwNjYmanGy3gCIS8KkQ2trK/7+7/8ebrcbL7zwAtbX13Ht2jX4/X4MDQ0hEAjIAVGHBOmq0BjeeusteL1ePPfcc4jFYlhaWkI+n8f4+LhoMAFznRBOKE76t956C6FQCOfOnUN3dzfee+895HI5jI+Pw+PxoLW11SS80Iml9vZ2eDwezM3N4c6dO7h8+TIGBgbw5ptvIp1OY3p6WiZaLpcThUwikZBDbFtbo0Pw0tIS1tbW8OUvfxmTk5P48Y9/jFQqhTNnzsDtdgt3pq+vDwCwubkpka+WlkaniPX1dfzoRz/C7u4unnnmGbz22mu4fv06vvCFL4iff/PmTczMzNzXbk6EcVP0ytUkHA7jd3/3d03V/C9fviwJkWq10W2hXC7L1qrTxPTZL126JK7KI488gkuXLklmslKpiIvB1VhzJZxOJ7xeL5566ikp8TUxMYELFy4IkYurpbWkGo2bvSYvX74sLtfw8DDOnTuHer3R0aBQKJjS5gCEL8JVTQsestks3G43Xn75ZRiGgeXlZaRSKfmMDsEBkNxBf38/nn32WTidTsH45je/CaBRKJQHX+AopU7xNUcwGMS3vvUtdHV1IZ1Ow+Vy4bd/+7fR0tKC7e1tLC0tyf1g6rxQKEg2laHDb3/72wiHw0ilUnC5XHjxxRfx6quvIhaL4c0338T+/j7y+by0R282ToRbwrQwXQRu4fl8HouLi9jZ2ZGwHlcSKyOPfAkaKLdodt7d2dmR18lAYxLEWiqNoUQeGEulkhCzAJhcHEY5dHZNJ2jo83Ii5nI5iTowvGgXltQEKB3bZ0FQhhWJr9Pw+v901TiBDcOQM8fh4SFOnTol7gt/N10KGiUnu06r65Ag/XedBSamHtxdmPRhxItsQFIf2tvbUS6XkUql7ms3J2Ll5gPgCtje3o5isYitrS3RV7LdBv0w3nidnKFfSh83k8mIFpNMNpfLJQ/GylfWPm+93ijKk06nTRhzc3Po6uoy9W7R1FvgSKxAikAqlUIsFhM10Y0bNzA2NiYMRP5+fRikv03RBCdoKBTC/v4+lpeXMTExIWJmhtV0pIM7CJNI5NEEAgE5LE5OTkq3CsbKOTEYh6Y+lYKJcrmMYDCIXC6Hra0tTE5OYnd3F4eHh9J6nDuHVvZQwUPVv9/vRzweRy6XQ6FQwMrKiiwYmnzVbJwI49bF5Gmg+/v7GBkZEa1eW1ub9DnUWUidvOEge293dxdDQ0MmIv/GxoYckmjU3AF0GppZye3tbems6/P5UKvVpMGq3cTgn3UojF0RyIoLh8OIRqOIxWJCILKu/joTmkgksL29jYGBATmfdHZ2IhaLSYdinfjhPdCZ12g0is7OTlM40e12Y2trCysrK8Jx0ecHDhpcJpPB2toaLl68CJfLJbH31dVVXLlyRT7HHZLXoUcqlcLq6iouX74Mp9MpXO9oNIo333xTJiTduPuNE2HcAGQ7JGuMfGfOYDZF1XwKGgs/DxyteqymVKlUMDg4CMMwpKEpw4sk+OitnJlFGvjW1paIBOgn8zo0h1rHl7kD8fWtrS0YhoHx8XEh++/u7gpdVvNIAPNqx98WjTZaE42MjKC9vR0bGxuIRqMywa1UAH24pD8ei8XgcrkwOzsrZw9WjuW5xzpBtKvY2tqKzc1NdHV1CRtzfX0dH3/8seweVpKUXYh0bm4OTqcTTz75JOr1Rie0t99+WxpbPSzH5EQYN308+sN7e3uyVQKQsBMrKvHha4IT/870MlcuGhMPiG63W3xOK1mJxkpj0KKIaDQqhejpY/LApVdtHcYDIKGvjo4ObG5uSmaO8V26R/o6OLG46rFYu8vlQiwWk2KgwWBQRBfaleFv0a+3tjY6GnR1dclvaW1tlY4GWi/J56EPqcTgQZn96Gu1Gvx+v6mMs36mfC760EwXc3NzUw71vC/1+lF5ic+FEof14mjU6XSjh5TmMXOrYohMPwDgaJaTS8L6JV1dXfB4PHLDeJCyPjjgaHVhHJqSta6uLkmzUxKnWXLa99eELK7eNE7W7mb6npOPk1gzGzV5iK6LFitQNcQVn/dFHyy5AxqGIffw1KlTCIfDMkEzmYxkgDVPnfeSrgFdFqDh5kQiEbS1tUlfTLfbLZNEc350cSG6N1w4uFCQGuB2u+X7tFvVbJwI42bYiK4HVRrcovhQuAroldsavioUCiJR0kkhTWEFjkqn6a2Pqy4jISQ40SfW4To92ZhBBY5oo8QlZ5pKFkYMaLR0kXT9FM1KZMydIU3tdnA11Ydx7QboP3OCEh+ARJ50hEi3QGH3NRqjy+WC3+83rdC875yAegcFIKXWWG+QHR64c/K3sNBnPp8XKq01gmQdJ8K4b9y4AeBIec1VnIOroTZSwFzTW8dE+Xomk5FwktVg7DB0iTJeR6FQkMmnr0MfaDUGJwINgxhMpdMguaLrSUGMQCBg2gnIdVldXZXVnucOn89n+i00iPHx8WMYDodDdKb6fvT29pruNccTTzxh2p04WcrlMpaXl+U6fD6flGmwYnznO9+xXYEpvua1BYNBhMNhW4xm40QYt97iucIBRwcaTWjSIT/r4GpsJc7zM9aYtJV7rA92GsNq1HbXYcXQ7ooum6DpuM0wgKOYs44gWa/5Ya/Dei7R36Mx+HnrfeOf9b/rAzRXfjsMfpbXq90t/Tv0veXrn4vSDtYwGhMNyWRS/NuOjg4EAgH4fD5ZHa3UVf3Aa7WaJALYdrmjowPBYBBer1fCZ1Yhg/afydlIpVJSedTpdCIQCJh4EXYYNMx6vVHVlTFmuktc7TSG/i38fp1Q4v1g9tLv90sRTt4PbdzWe8pio6xTYofB79XXofEODg6QTqeRTqdRr9flcMxSGRx2GMTRGHR5/H4/AoFA024TduNEGLf2DQ8ODqQI+f7+vtQDbG1thc/nQ3d3N8bGxiQkqFcZjoODA6yuriIWi4lKnJEPr9eL3t5e9Pf3C/lJr6wc1CwuLS2Jz0hKLR8mSyzbYfDgaMWgr04K6tTUlIgVrIbJg+n29jYWFxdN1ZzoA4dCoaYYvD/ValVi4qxQxTOE2+1GKBTC9PS0sBatiwYxWEzfmsCiqmhqasrUCMuKATQYiMRgkojhRpZ7ZpH+z8XKrbe4ra0tJBIJYf/xkMJUbzKZFImVPmBpF2Bra0uK3jAMRwxWgarX6xgdHbUVGtRqNenzYsWgQIIKlrGxMdPqpLd8li+zYnD1zWaziEajGB0dPYbB36YxtFiBtFliWHvIaPchkUhIBzKu0hojk8lgc3MTQ0NDJkqDFYOxdo3Byrx7e3tYW1szsQ+tGIVCAcvLyzAMQxYqXgcP8isrK5idnZUQ7P3GiTBuoGHYrKsXCoXwyCOPYGJiQqIE2WwWd+/exdzcHFKplNQj0YNGl0qlEAqFMD4+jrNnz0oZYGKwh3w+n4fP5zsW687n80gmk/B6vZiensbs7OwxjOXlZcGwyrMASG0+n8+H6elpuQ6gcXBdXFzE9evXkclkpAA8B7E0xpkzZzA7OyuqonK5jMXFRWmgxCauVgy6dz6fD319fcKqbG1tFYxPPvkE6XQanZ2d6O7uborR1taG2dlZXLhwAcFg0IQxPz8vfYzsMA4PD5FIJHDq1ClMTk4KBl2yjY0NXLt2DYlEAul0+oH9cIATZNzUTLa0tGB4eBiDg4Po7e011dDgqptKpSS0ZGXTFYtFtLa2Ynh4GGNjY1KTzjCOhAbsM1Mul6XLgPU6DMMQoYEdRrlclmwlaw5qjHK5LBhjY2Po7u42cZ65O7BsnBWjVqtJMcienh65DlZtotg4mUxieXlZrkOvdjx3EGNychKDg4Pw+/0mDIom9vb2xGjtMDweDyYnJzEwMCARHWJwF2mGwXJ3Ho9H+uLwOniuYgkMFsnUcX+7cSJYgUxC1Ot1SdxwK+MJmkkP+oV0MfQhiMIBFqik38ZDJm8ijUx3B9MYDE/pjgZWDK7C1nYd/E4eMnUTVf4WxpcZy6Uvra+DMXf9m3UNP42hr+N+GPxuKwavUYuHNQZ/v/4uPhtiEN8OQ1OCOTn1dfDgzvMLn+2DxolYuXV0we124/DwEK+//jqGhobw6KOPIh6P48MPP0RXV5d0W+DndIiMGGw78bOf/X/tnW1oY1d6x/9Hfhnb8tiW5JnMOPHMZKYzZl4S0pKwGzIZJgldtqF0E9hCl9KmJS9f9sMWCm3Sfii0X7ZfuqVQSgNdaKGUtrSwy1JYwuwupJO37TqThMSWbI9sj21JI0uW38axbOn0g+7/+LlH50qayWQtGT1gbMtXP597de655zzP8z/PT3wVDd555x1TJo6bnctAjmTQG3Lt2jUnY2RkxDBkx5MdOYjBSm0PP/yw8f7Y7WC4mpWTr127ZiorxGIxXL9+3TDo/ZHRTrZDMsbHx3H9+nU8/fTTVQzZDnkudINyw6IbN244GSdPnjQZgS4GsLfp0fj4ON5++21cuXIF0WgUn376KZLJJE6fPm3OhRHkWtYSnZsXg26xsbExPPvss+bDGh0dxSuvvGLSRaX0STKY562UwtjYGJ555hl0dFT267YZnBpIjs0YHh7G5cuXnYz5+Xmj0Af8+6HIiJ6LceHCBZOEVSgUnFl4ZPT09PgYDHe/8MILToZsB6OgZDz55JNmqzrJ4PrB7kzyBmGOzNWrV9Hd3V3Vjmw2a/LkXQygMrV0MZ566ilcvXoVmUzGxzgQEUpgL5Gdi5TOzk6srKyYR2tfX5/JDZGZY7UYMsHfxZDeFheDfmkXg3kd0q8N+IMrLgb34uvp6aliSOP52YyNjQ0TMncx7OCJZDC0HsSQvnn7XLiIDWIwlsDPJiioRAZzW3Z2dnDo0CEMDw+bKK7NCLKWmXMzwWd7extra2tYWloyVXOLxSImJyeRz+fNBZEXHtgbISRjeXkZiUTCydja2vKFsW0G9wRZXl5GPB6/Kwa/ghgff/yxUZpLhrweMo9GMrjeIINlOmQUNYhRKBQwOTlprpHNkNFCyeCaKIjBABUX4jZDRiXX1tawsrKCiYkJcy43b95EOp3G2tqasx1B1hIjN0c5BmZmZ2cRjUZx/Phxs5dJJBJBPB5HNps1SVW2Aoah+2KxiNnZWXR1deHBBx80eR6RSMS4EqWg2MXY3d01Wx6wahr9xPF4HLdu3fLtliqN51IsFp2MaDRq1CgyP10GgIIYdmWFhYWFhhlTU1M4f/68qaxw+PBhLC4uYnFx0Xgm5PWwGXRh2oyFhQWTa2JXeGAnlds+T01NGfcqGfl83rc/iv25uKwlRm5m1mmtTWL8zMyMSYYql8tIJBJYXl423hR2bJnqymxBMpaWlozwtVwuIx6PI5fLmQw2XkA7PVPmlkhGqVRCIpFALpczYXQXQ2bskXHr1i3DmJ+fx9LSkk9eJqOctRj0RLAevM3gNIAMrfdyS1ZWVswmPTaD+d1yOmAzOFXkXitAZeuGVCqFzs5OH8NuB9c3nZ2dZn8Tujqz2SxmZ2d9DDu9wmUtMXJzWsKTofQIgIkSDg4OmvkatxqQd7ec2pTLZaOc7+npMSPk4OCgcZnRfScvoMzxpk+bwSLZDm7azu0K5LRCPsrJYDvm5+fNBzg4OOir4NAoI5VKmTk728I9TOS5uBjcXpliaZvBa2AnjZEhQ+2pVMonvGCMQXp6JIPX1GbwxqS+lLXi7amay1qmc7NTlstls+llJBIxHpTV1VUTkmXH4j6AksHOyQR61r9RqpJ6SlkXADP6yM3hyWCONjf+odCADHZIruzJ4E1KBrWKAwMDhsHygXfDYDuof9ze3jYlR6SPX1YhthnMpeYGnjaD2lPJkNdDKWUGDcnY2tryjfqyPqhcw9BNySoRZHAKKPOFJCPIWmJaAvg3s+H+gDLTjY9seRHt0YoMTjs4evC9ksFkrCAGj5E6RJvBUcdm8InCY9gOfriUlsnpkWTYqbWU2QF7Ka8crUOhkO9ayUCPzZD/SzJ4rmxjLQYHG3ZayeB6RT6F+X4OTLId8ppKhnytlrXEyM3IF+euSimk02mk02kAey4pav5o0vXFaKJkBAkN7DwOGWSgMUpJ6ZtsBwNFbLfNkO5BpSpbBc/MzBgu55SUjdkM5rHIdrDCGzsxO43N4FyXkUDZjnK5bKpIkMvRmLWFZIeS4Xz+T621j8GpA9NWbQavh22pVMrXDg4o/GzqdWyggZFbKTWqlPqpUmpCKfWpUuo73utRpdRbSqkp73vEe10ppf5OKTWtlPpYKfVrgvWSd/yUUuqluq3be5/5wKUHQnYmmRDvmou5GHLVLxlBfnLAv6VaLYbdDtufK7/LNsrFViMMux1B/mMXg19BaxOb4fK5S589+fY8Wr7fxXBdazmF5HW3Gfdj5N4F8Mda63Gl1GEAv1BKvQXgDwBc01p/Vyn1OoDXAfwpgN8AcNb7+gqAfwDwFaVUFMBfAHgcgPY4P9Rar9RrAE+eF7JUKpnkfK7sWeSUabD2ydudiPkK+Xze7MpEoQHDxKFQtUiAHxbgFyswSNHT04NoNGpSWMmQNwE/ME4BbMFDd3c3hoaGMDg46BQruM5FihW4IagUPMgpXBCjWCz6RAIUK0QikZpiBRlTKBaLyOVyZgs3fi6yFpBkuDrp9vY2crkclpeXffsQUgTSqNXt3FrrFICU9/O6UmoCwIMAvgHgqnfYPwP4GSqd+xsA/kVXWv+eUmpIKXXcO/YtrXXeO6m3AHwdwL/Va4Mczba3tzE3N4eFhQXfll8dHR1GaMBCo3LeJm8Ql1hBCh6OHTuG48ePNyxWcAkeDh8+7BMJNCpWoOChEbGCUsq4NKempnw11Rnej0ajGBsb+0JiBTKYyGWvH8iQYgUKDaT3Y2xsrGGxAoXc/HtXVxcikQguXLjgZLjsrubcSqlTAH4VwPsAHvA6PrTWKaUUk3QfBHBLvG3Bey3o9brGeVepVNk4RooVuMCgd2R5edmoR+QoydGFYgXmDnO7YjIoeCiXyzh58qSvQ0hGo2KFU57QQDL4vZ5YgXXlyaCxHVrvCQ2+LLECpWcLCwtVYgV+NlpX9gGfn58HACdjc3MTN2/erBIrSAaPYUBNMui5mZmZMR6hetZw51ZK9QP4LwB/pLVecy0CeKjjNV3jddf/eg3Aa+J3lMsVscLy8jJisRgeeeQRI1YAKhubx+NxJBIJk1jPtFYaOwwrDZw+fbpKaDA5OYnJyUmsrq4asYI0dtx6YgUmLFGsYJsUCdhiha2tLUxPTxuxAoUG9g1iixUuXrxodnW6W7HC0NCQbwtoW6yQy+WqxAocdDY3N5HJZHxihVgshlAoZKKWk5OTyGQygWKFnZ0dw7h06RIee+wxk/e9s7OD+fl5fPDBB0in01heXjals2tZQ51bKdWFSsf+V631f3svZ5RSx71R+zgA7p2wAGBUvP0hAEve61et13/m+n9a6zcBvOn9bw3siQQ6OzsxOjqK0dFRHDt2zHRgFt6kUoPTBDspnowTJ07glBAaADDVGNLpNFZWVoxIwCV40NotVqBoYmtrC8lk0ik0qCdW4MhNscKdO3eMd0O2wxYrHD169J7FCkeOHKkpVpiZmcH6+rpTaMB2UKxgMxiFpNAgGo2aJy6fIKxMZ4sVOI0EYLbiWF9fRywW++JiBVW5Gv8EYEJr/TfiTz8EQI/HSwB+IF7/fc9r8lUAq9705ccAvqaUiniela95r9U1Ovm5v12pVEIulzPTh83NTczOzmJ1ddW46xhul4sevsbc5kKhYPbGoMavUCiY0drFkMn5vb29WFlZMQy2o1AoGBeXrAYsGVxk9vb2Ip/P+xjJZBKZTKYmg1FUpSqq/Xw+j7m5ObPlL5ON2KHJkItCKVbghvzJZLKKQTVSqVRyCh7YtnA4jI2NDcNgpbV0Om3WNLUYQMVFyekJ25FKpUwuEHO5JSPIGhm5lpDRCwAADjlJREFUnwLwewA+UUrd8F77MwDfBfAfSqmXAcwD+G3vb/8D4HkA0wDuAPhD7yTySqm/AvBz77i/5OKyntG1xLlpZ2cn3n//fSwuLuKJJ55AOp3Gu+++i/7+fgwPD5uRWAZ3OK/r7Ow0C8Xx8XGjVCeDYgXWlediSbaDiUl9fX2GEQ6Hq8QKkuFK8A9iSLGC3OpNngv94YxKjo+Pm339YrEYPvzwwyqGTPCXYXAyEomEeWq4GLZIwD6X/v5+TE9PmxE/Go0axsmTJ02FB6m8kS5HXo+pqSkfwyVWkDdEkDXiLflfuOfLAPCc43gN4NsBrO8D+H69/2kbPwgmzVBowI534sQJXLx4sUqsIO9sHisFD88884yZF9oMFpaSHMlQak+sEApVZFEusYK9oue57JdYQTJ4o0qxgktoMDMzg5WVlbrt6Ovrw1VLaPDiiy+ahX4jYoX+/n4899xzhtHd3Y3Lly/jqidWYCUGvreWtUSEEvAn53M+zc1jdnZ20Nvba3IO6MaSPmm+V4oVlKpoL7mVsUvwYF9Al1iBuSBkcCsDV8BC+oSDBA+/TLGCbEc9sQL/rwwU8fpKsYIUGpCxurrqC+a4GEqpqqoQrDNKsYKLEdhnGuxb+2rSbUWhQSqVQiqVMll88XjciAS4f7aMvMmIFxm5XM5UxboXscL6+rpTrECV972IFaRI4F7FCi6hgUwvkAzpo64lVuCiUUY8bcb6+nqV0KARsYKMZlLwMDExYQYaKVZgVqD9ZHZZy4zcTN7Z2dlxihUoEmANSjssDcDMXUOhiuChu7sbIyMj9yxWyGazWFpa8gkemkWsQKFBo2KFfD6PRCJRJRKQYgX51HEx6PazGVKs4NpMRyaObW9vIx6P49KlS4YxODhoKi4wg7GRzt0SI7cUK4RCIbOSz2azACqjYSKRQDab9Sl25KOLCxAyGJVjVYNyuVqsIMP9ksHFHIAqwUOQWEG2wyU0kIKHRsUKWmsf44uKFSgS4NRudnbWJ1aga9U+FzKk0MAlVmD2omxHqVTyFQPgnt6M/GqtkclkjFiBmw7J6xFkLTFycxHGi9rb22sCEhzdBgYGTOEnrsblqMtVvmTIigYcIejCoidBruYlQ+tqoUEtsYI8F/kopkjg0KFD+y5W6Ovrw+HDh43QgPtt1xMr8Bq5xApyz26KFex28P284V1ihSNHjphIJ/tDUHIbrSU6t0ysZ6BEihUA+MQKu7u75gOlWCGIIcUK3NicIy3zql2CB4oV2CHok15bW/PllNM74GqHFBp82WIFnkuQ0IAu0kbECi6hgfT9Mx+FVc64WwFHW5bbsxlSrBAOhw2DcQ0pAmEeTi1riWkJjXc2H5GA/+IAftm/HCU4xSBDJvjLxz2wN793eRhkOxiYkFMXjihyOiDdXWwj56uyHVKswPlpkFhBpt42IlYgwxYasC2y1icZcvokxQqyHZJhixVkO4KEBhwIOOVxiSZ4PI+Tn3sta4mRmxFDdqJQKIRsNotsNusLJIRCIcRiMQD+mjEAfHkVXG1LoYFkRCIRXx4HGTKxXuZVTE9PVzHqiRVsxr2IFXiOSt2bWMFmlEols1OrZASJFRgMsxm2WIE3oUus0NvbW7Uw1FqbrTt4jYDKzTQwMOBMk3VZS3RuYC/tlfM92dHlSFZrBW0z5KhuM+QHT+Nx0s3YKIOcRhjSKyH/Ltvi6lQ8J/n63TDk08HFkMfJ9soOKG9CyZC+dpshF6kuhvRt24G5WtYSnZsLBzniscApd47q6ekxmW9SM0iT7ivJyGazWFtbMwxuFyyFwjaDbdJ6TyQgBQ+xWMwsVuUHSgY/dHYMyaCXpV5lBZvB6gx2ZQUKHng92LldjKDKCpIBoIohf6ZbkQwubPkksxkyQMW/kVEoFIxYYWBgAENDQz5GPWuJzk1jFO7mzZu4deuW8SbIigYjIyOmsoI9YtK4i9Hi4qJPaCAZIyMjCIfDvlFJfpciAVuskMlkMDAwgHPnzpksvVpiBRejUbGCq7ICR8O7ZczNzZkanEEM288tGaurq5iYmHAywuEwzp496yv8xM+UprVGPp+vYvCGj0QihtFIhLKlOvfu7i4WFhZw+/Zts3cJdx7lB5vJZNDf348jR4743isfh2QwwV8KDZjyWi6XTYK/nH+TYYsEbEYtkQCZdlUEKVaQjFMOsQKNDNayDBIrBDFkO7TWZmdaF4NlwOV7JYMxAxeDGZNjY2M+wQMHD0Ynk8lkFYPXVDLuq1hhv61cLpuQ+dGjR01lBQZbGPb97LPPkM1mzba8NHZKybDFCpIhRQKudtgVDWzG9PR0IENr7ayKQIYUGrgYvEGCKisw/8bFsDumXVnh0UcfNWIFycjn8+jr66tZWaGjowPnz5/3MSi8SCQSJtdeMshhpNXF2NnZwcLCAsbHxw3jyJEjB8NbAuwJDbq6ujA6OooTJ07ggQceMGIFigSk0EDOm+ku3NzcNIxTohoBAB8jl8sFVkWgWMGuaCAZtSoryAR/KXiQShxbrGALDWzBg2SEQm6xgkvwIBlnzpzBQw89hEgkglAohI2NDR+DVRGkf1ky+vv7fQx6ghhUk5UVbAanZZLB6QcT2Zj/srGxgUgkcjD83DJQwPyQ27dv+8QKyWTSiAQA+LZEkwy62kqlkknw50gqGUoFCw0YsJAigXK5XJMhbzIpNKBYQTKkSKBWO2zBg0usQKFBkFhBMu7cuYPZ2VnDSCaTvnbUEhooVakS4WJkMhnjaw9i0G9NBvcslOsByZDVGYKsJUZurvIZmg2FQnjvvfd8YgVZFaG/v9+4tWRiPRnhcBgdHR0mwZ9iBTIikYgvwd9OrGdCkBQJuMQKLpGADDMHMaRYgYIHW2hAH3IjYoVawotQaE800YhYwWbQHUuxgmS4xApcCNoMoOJ7l4xyuewTK5w5c8Yw7MCYy1qmc1MkAADnzp0zlRV2dnYwOjqKV199FcViETdu3DCFoeScjMdKhhQrkLG9vY2PPvrI5IcAfvch860BOIUGZDQiVrCFBr/MygqSQdEEKyusrq6ip6enimF7fHijkhEOh3HlyhUcOnSoimGLFWwG3aguBisr3L592zDqxTSAFunc0sPQ0dFhKpJRlVEsFhEOh83jisEUvpffOzo6TBL+5uYmlFI+kQBdf2TYgRyb4RIakFFLrMDphYvRaGUFfrhk8Fzuh1iB82SbIX3iLgY9I11dXYYRCoXQ29vrq6wA4K4YXV1dGB4eNufpYrisZebcUmiwvr5u0kTp66bQYH193VdZoRbDFglMTEwYhkskIBlBQoNGGJxmuRi1qiLQZBSSjFwud9eVFWyGFCu4GDJaKBl8spIh2/HJJ58YBsUKdFfa7SiVSlUMbqBEiZmLEWQtMXIDeyKBUCiEubk5RKNRjIyMGH/n0NCQ6eAyF1t2cJvR1dXlEwlQrJDP532797sYOzs7TqEBGUEiAaBxscLdVEX4Mior2AxeO5tBEYmrsgIZMzMzZhpkM5gQRYmbqzpDoVDAzMyMc+/1IGuJkVsKDTo79yorpNNpM/LE43EUCgVTY5J3NhcuLgYT/DkPJkNm4wUxbJGAzbBFApIRVBWBjCChgS1WcDHuVqwgGaurq0gmk4EMW2hgM6hrdTEYZXQx6MXizWIzqMIJhULm5rXXVC5riZGbK3vOsaRYgSKBSCSCYrFohAac78nFIKcU5bK7ssLQ0JCPwf/pYmjtFwnYDM7/XQx+BTGk0GB7e9skFjXCcIkV6CeWc1QXQ4oVXAwAPg+FvEa84fv7+50MKZoOYnBxajOUUojFYmZNYTOCrCU6t+0CY0L80NCQyZlYX1/35QHTdyoT/OV3W2hgM6SEysXgQsfFYJ43py82gzdJEIMiAZlI1CiDj3LJoJeH520zdnd3TUekWIEMLrx5Y3z++ee+dvCLfvggBp+kDNgEMeg1kQyWPecTht4Se99C21S9oX2/TXnbqbWtbUGmtXZOvlth5N4AEN/vRtwnGwawvN+NuI/WDOdzMugPrdC541rrx/e7EffDlFL/d1DOBWj+82kJb0nb2nYv1u7cbTuw1gqd+839bsB9tIN0LkCTn0/Te0va1rZ7tVYYudvWtnuydudu24G1pu3cSqmvK6XiqlKs9fX9bk89U/exGG0zmVKqQyn1oVLqR97vDyul3vfO59+VUt3e64e836e9v5/az3YDTdq5lVIdAP4elYKtFwB8Syl1YX9bVddYjPY8gK8C+LbX5tdRKUZ7FsA173fAX4z2NVSK0TajfQfAhPj9rwF8zzufFQAve6+/DGBFa/0rAL7nHbe/xiSgZvoC8CSAH4vf3wDwxn636y7P4QcAfh2V6Opx77XjqASlAOAfAXxLHG+Oa5YvVCrOXQPwLIAfoVI+ZhlAp/05oVK860nv507vOLWf7W/KkRtfoCBrM5iqUYwWQL1itM1kfwvgTwAwnTAGoKC15vZXss3mfLy/r3rH75s1a+duuCBrs5myitHWOtTxWtOco1LqNwHc1lr/Qr7sOFQ38Ld9sWbNLQkq1NrUpu5PMdpmsacA/JZS6nkAPQAGUBnJh5RSnd7oLNvM81lQSnUCGATQUCnGL8uadeT+OYCz3sq8G8DvoFK8tWlNVZKv70cx2qYwrfUbWuuHtNanULn+P9Fa/y6AnwL4pneYfT48z296x+/vk2i/Fy01FjPPA0gAmAHw5/vdngbaexmVx/DHAG54X8+jMu+8BmDK+x71jleoeIRmAHwC4PH9Poca53YVwI+8n08D+ACVIrr/CeCQ93qP9/u09/fT+93udvi9bQfWmnVa0ra2fWFrd+62HVhrd+62HVhrd+62HVhrd+62HVhrd+62HVhrd+62HVj7f5jzJpGuME34AAAAAElFTkSuQmCC\n"
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "JoJnXMmurHoz",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "800efecc-b99c-4e65-b06c-d973554a9aff"
+   },
+   "source": [
+    "traversals = viz_chairs.all_latent_traversals()\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fbf8a637490>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3bATAS0GMTqT",
+    "colab_type": "text"
+   },
+   "source": [
+    "## Plot a grid of two interesting traversals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "J3wQmCNnMVQR",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# Traverse 3rd continuous latent dimension across columns:\n",
+    "traversals = viz_mnist.latent_traversal_grid(cont_idx=2, cont_axis=1, disc_idx=0, disc_axis=0, size=(10, 10))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "J8axyQZansSH",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "traversals = viz_fashion.latent_traversal_grid(cont_idx=1, cont_axis=0, size=(10, 10))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "a73u9EMTnsiB",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "traversals = viz_dsprites.latent_traversal_grid(cont_idx=1, cont_axis=0, size=(6, 10))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Qj3lRa5erQU1",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "0bd62915-aba5-4468-d437-612e8df405bb"
+   },
+   "source": [
+    "traversals = viz_celeba.latent_traversal_grid(cont_idx=1, cont_axis=0, size=(6, 10))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fbf8813b750>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 23
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "YqTzdX_4rQZq",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "traversals = viz_chairs.latent_traversal_grid(cont_idx=1, cont_axis=0, size=(6, 10))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "SLbxdxkaMZAD",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# Reorder discrete latent to match order of digits\n",
+    "ordering = [0, 1, 7, 2, 4, 5, 3, 9, 6, 8]  # The 9th dimension corresponds to 0, the 3rd to 1 etc...\n",
+    "traversals = reorder_img(traversals, ordering, by_row=True)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversals.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NKa9G7gbMWjI",
+    "colab_type": "text"
+   },
+   "source": [
+    "## Plot traversal of single dimension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "V2a3xwUiMcx2",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 117
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592235503133,
+     "user_tz": -120,
+     "elapsed": 1214,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "d60225d3-ebfa-4eb5-a809-1b95c31eb7cd"
+   },
+   "source": [
+    "traversal = viz_mnist.latent_traversal_line(cont_idx=0, size=12)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversal.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f72dab0c0b8>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 66
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "WZO5C_strW4F",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 117
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592236309371,
+     "user_tz": -120,
+     "elapsed": 1255,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "f31ac497-212a-44f2-c569-51dae48513ff"
+   },
+   "source": [
+    "traversal = viz_fashion.latent_traversal_line(cont_idx=0, size=12)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversal.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f72da651b00>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 79
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "6ZK3hBTlrXBq",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 116
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592236313963,
+     "user_tz": -120,
+     "elapsed": 764,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "0244e8b9-f0ec-44f4-9398-110cb89040c2"
+   },
+   "source": [
+    "traversal = viz_dsprites.latent_traversal_line(cont_idx=0, size=12)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversal.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f72da636208>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 80
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "GRLSUXERrXId",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "c8cf4446-8f29-4737-f734-9b9d99567c09"
+   },
+   "source": [
+    "traversal = viz_celeba.latent_traversal_line(cont_idx=0, size=12)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversal.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fbf88127a90>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 24
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "qEYzXVJtrXU-",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "1633c72d-4c5e-4ef8-a9c4-2157079299f9"
+   },
+   "source": [
+    "traversal = viz_chairs.latent_traversal_line(cont_idx=0, size=12)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversal.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fbf88099890>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 25
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "V3_-nCdAMeF8",
+    "colab_type": "text"
+   },
+   "source": [
+    "## Plot reconstructions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "sil-OQR8MiZh",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# Get MNIST test data\n",
+    "_, dataloader_mnist = get_mnist_dataloaders(batch_size=32)\n",
+    "_, dataloader_fashion = get_fashion_mnist_dataloaders(batch_size=32)\n",
+    "_, dataloader_dsprites = get_dsprites_dataloader(batch_size=32)\n",
+    "dataloader_celeba = get_celeba_dataloader(batch_size=32)\n",
+    "_, dataloader_chairs = get_chairs_dataloader(batch_size=32)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "crW-qFSCMfw-",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 612
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592235831365,
+     "user_tz": -120,
+     "elapsed": 1731,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "73cd1478-7064-4c18-a28d-b29a5150a827"
+   },
+   "source": [
+    "# Extract a batch of data\n",
+    "for batch_mnist, labels_mnist in dataloader_mnist:\n",
+    "    break\n",
+    "\n",
+    "recon = viz_mnist.reconstructions(batch_mnist, size=(8, 8))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(recon.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f72da788208>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 73
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Nrwn4BsupMXC",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 612
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592235849675,
+     "user_tz": -120,
+     "elapsed": 1550,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "4ea633ef-2701-46ef-a4c0-0dd246700d8b"
+   },
+   "source": [
+    "# Extract a batch of data\n",
+    "for batch_fashion, labels_fashion in dataloader_fashion:\n",
+    "    break\n",
+    "\n",
+    "recon = viz_fashion.reconstructions(batch_fashion, size=(8, 8))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(recon.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f72da75bda0>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 74
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "nE4_xqIxpMlw",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 612
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592235864186,
+     "user_tz": -120,
+     "elapsed": 1979,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "7fffb246-a927-484c-ff89-ca98620c1cb4"
+   },
+   "source": [
+    "# Extract a batch of data\n",
+    "for batch_dsprites, labels_dsprites in dataloader_dsprites:\n",
+    "    break\n",
+    "\n",
+    "recon = viz_dsprites.reconstructions(batch_dsprites, size=(8, 8))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(recon.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f72da73f9b0>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 75
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "9GJbmVyorjlb",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "665362f9-785a-47a7-8fe2-2fee8b195c7b"
+   },
+   "source": [
+    "# Extract a batch of data\n",
+    "for batch_celeba, labels_celeba in dataloader_celeba:\n",
+    "    break\n",
+    "\n",
+    "recon = viz_celeba.reconstructions(batch_celeba, size=(8, 8))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(recon.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f2443943b50>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "zYl0tnrMrjxE",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "0bb5bb9f-30df-4913-a18c-cb7f21d699e5"
+   },
+   "source": [
+    "# Extract a batch of data\n",
+    "for batch_chairs, labels_chairs in dataloader_chairs:\n",
+    "    break\n",
+    "\n",
+    "recon = viz_chairs.reconstructions(batch_chairs, size=(8, 8))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(recon.numpy()[0, :, :], cmap='gray')"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f24439a6590>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cC8TGUi9MkPd",
+    "colab_type": "text"
+   },
+   "source": [
+    "## Encode data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "TZQzcDimMlsB",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 208
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592235871522,
+     "user_tz": -120,
+     "elapsed": 1072,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "2f662a11-4988-4b77-a1af-11abd53316ca"
+   },
+   "source": [
+    "encodings = model_mnist.encode(Variable(batch_mnist))\n",
+    "\n",
+    "# Continuous encodings for the first 5 examples\n",
+    "encodings['cont'][0][:5]"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[-1.4983e+00,  8.4277e-01, -5.2083e-03, -4.4545e-03,  1.4016e+00,\n",
+       "         -5.5452e-01, -5.7275e-03,  8.8822e-05, -1.3115e+00, -6.0071e-03],\n",
+       "        [-2.9452e-01, -2.4889e-01, -1.2956e-02, -2.1580e-02,  7.8090e-01,\n",
+       "          8.9275e-01,  2.8407e-03, -1.3607e-02,  1.0150e+00,  6.6306e-03],\n",
+       "        [-5.4490e-01,  1.5352e+00,  3.2081e-02, -2.1006e-02, -1.4876e+00,\n",
+       "          1.1628e+00, -5.9816e-03,  5.4252e-03, -6.0219e-01, -7.6609e-03],\n",
+       "        [-1.0434e+00,  7.5063e-01,  1.2917e-02, -5.3259e-03, -1.5980e-01,\n",
+       "         -3.9018e-01, -2.5563e-03,  9.1539e-03,  6.2317e-01, -1.2106e-02],\n",
+       "        [ 1.5325e+00,  1.6092e-02,  1.5755e-02, -1.1477e-03, -1.3514e+00,\n",
+       "          1.2069e+00, -5.9879e-03,  8.6866e-03,  5.2680e-02,  2.2610e-03]],\n",
+       "       grad_fn=<SliceBackward>)"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 76
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "W4zXagbHp43K",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 208
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592235912354,
+     "user_tz": -120,
+     "elapsed": 1217,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "1fc42674-d3fb-41c9-a038-1c79cff14f5c"
+   },
+   "source": [
+    "encodings = model_fashion.encode(Variable(batch_fashion))\n",
+    "\n",
+    "# Continuous encodings for the first 5 examples\n",
+    "encodings['cont'][0][:5]"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 1.7015e+00,  3.6314e-03,  5.7292e-02,  4.7376e-02,  7.4806e-01,\n",
+       "         -6.0539e-02, -1.3251e-02, -3.5400e-02,  5.7073e-02,  3.8185e-02],\n",
+       "        [-9.8607e-01,  2.6065e-02, -1.2025e-01, -1.8813e-02,  3.1769e-01,\n",
+       "          4.1996e-02,  4.5641e-01,  1.8468e-02, -7.6687e-02, -4.6500e-02],\n",
+       "        [ 8.0841e-02,  2.7969e-02, -8.1034e-02, -1.3912e-03,  8.6376e-01,\n",
+       "          7.3281e-03,  6.2990e-01, -1.4536e-02, -4.4928e-02, -2.3681e-02],\n",
+       "        [ 6.6009e-02,  3.0442e-02,  8.0643e-02,  4.6564e-02,  1.8354e+00,\n",
+       "         -4.7582e-02,  2.9833e-01, -2.8216e-02,  5.7306e-02,  2.8040e-02],\n",
+       "        [-3.5284e-01,  7.3428e-03,  2.4478e-03,  2.6899e-03, -8.7546e-01,\n",
+       "         -1.7018e-02,  1.3330e-01, -1.7264e-02, -2.2724e-02, -1.2185e-02]],\n",
+       "       grad_fn=<SliceBackward>)"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 77
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Os99CU8Ip5A9",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 190
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592235914478,
+     "user_tz": -120,
+     "elapsed": 760,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "d1353b70-8fde-4914-a524-7394b9b2a31f"
+   },
+   "source": [
+    "encodings = model_dpsrites.encode(Variable(batch_dsprites))\n",
+    "\n",
+    "# Continuous encodings for the first 5 examples\n",
+    "encodings['cont'][0][:5]"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 1.4230e-02, -9.6438e-03,  1.3452e+00, -3.8103e-03,  4.7964e-02,\n",
+       "          2.8417e-02],\n",
+       "        [ 1.0025e-02, -3.8424e-02, -9.8186e-01,  5.4055e-02, -2.1191e-02,\n",
+       "         -1.3381e+00],\n",
+       "        [-6.9912e-03,  6.3463e-03,  2.7954e-01, -9.3445e-05, -1.5486e-02,\n",
+       "          5.7476e-01],\n",
+       "        [-6.5360e-03,  1.5082e-02,  2.4653e-01, -8.7794e-03, -1.5777e-02,\n",
+       "          2.9785e-01],\n",
+       "        [ 1.7069e-02, -3.0237e-02, -9.6103e-01,  2.3664e-02, -1.6470e-02,\n",
+       "         -9.7090e-01]], grad_fn=<SliceBackward>)"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 78
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "hxv-YVxurwLL",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "c851736d-a9ad-4bb5-d574-f4305fb8c76a"
+   },
+   "source": [
+    "encodings = model_celeba.encode(Variable(batch_celeba))\n",
+    "\n",
+    "# Continuous encodings for the first 5 examples\n",
+    "encodings['cont'][0][:5]"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[-1.3779e-01, -5.1264e-01, -5.0707e-01, -6.1149e-01, -3.5573e-02,\n",
+       "          4.4275e-01, -2.9825e-03, -2.6522e-02, -6.4544e-02, -1.3954e+00,\n",
+       "          2.9860e-02,  6.5649e-01, -3.3539e-01, -9.7458e-04,  1.5257e-02,\n",
+       "          4.7139e-02,  9.4665e-01, -2.5833e-03,  1.2064e+00,  3.8720e-02,\n",
+       "          5.2554e-01,  7.7422e-02, -2.1598e-01,  1.7352e-01,  4.1477e-02,\n",
+       "          2.4813e-02,  3.8626e-02, -6.2475e-02,  2.5990e-01, -4.6569e-02,\n",
+       "          6.2493e-03, -4.6588e-02],\n",
+       "        [-7.0240e-01, -2.2950e-01,  1.9378e-01,  5.2681e-01, -1.5962e-01,\n",
+       "          2.3552e-01,  1.1604e-01, -1.9731e-02, -3.6130e-02,  4.1161e-01,\n",
+       "          2.8686e-01,  4.9956e-02,  6.7169e-02,  2.7666e-01, -4.5861e-02,\n",
+       "          8.0235e-03, -1.0799e+00, -1.2274e-01,  1.0940e+00,  7.1488e-02,\n",
+       "         -1.1883e+00, -1.1481e-01, -1.4655e-01,  5.9016e-02, -9.7642e-02,\n",
+       "          3.5401e-01, -4.0041e-03,  5.4516e-01,  1.3173e+00,  2.9270e-01,\n",
+       "          4.7857e-01,  3.8227e-02],\n",
+       "        [ 3.7233e-01,  3.3370e-01, -2.8152e-02,  5.8523e-01, -1.9884e-01,\n",
+       "         -4.1059e-01,  6.5947e-02, -2.9415e-02, -5.7276e-02, -3.9407e-01,\n",
+       "          1.6813e-01,  1.0582e-01, -4.0877e-01,  6.4707e-02, -4.2782e-03,\n",
+       "          1.5508e-02,  3.7352e-01,  5.1221e-02,  2.3878e+00,  1.0846e+00,\n",
+       "         -4.8414e-01,  5.2485e-02, -8.7318e-02, -8.0938e-01, -9.2104e-02,\n",
+       "          2.2187e-01,  1.0531e-02,  2.8334e-01, -5.6260e-01, -7.8051e-03,\n",
+       "          5.6553e-02,  9.9819e-06],\n",
+       "        [ 3.1733e-01, -4.7560e-01, -2.3807e-01,  2.9907e-01, -1.4559e-01,\n",
+       "         -7.3912e-01, -1.1918e-02, -1.0029e-02,  2.3902e-02, -1.0889e-02,\n",
+       "          1.2000e-01, -1.5203e-01, -1.7572e-01,  8.7495e-02, -2.7238e-02,\n",
+       "         -1.6006e-02, -7.6745e-01, -1.0538e-01,  2.9323e+00, -1.5575e-01,\n",
+       "         -1.5312e+00,  1.3383e-01,  4.8844e-02,  5.9153e-01, -4.8734e-02,\n",
+       "          1.8024e-01, -3.2823e-02, -3.3392e-01, -3.4411e-01, -1.1318e-02,\n",
+       "          9.4986e-02, -2.8805e-02],\n",
+       "        [ 1.7596e-01, -4.1956e-01, -3.2831e-01, -2.8357e-01, -7.9338e-02,\n",
+       "          4.2089e-01,  2.1436e-02, -2.5265e-03,  8.1027e-03, -5.1026e-01,\n",
+       "          1.7318e-02, -2.5700e-01, -5.3574e-01,  6.9628e-02, -1.9916e-02,\n",
+       "          7.2769e-03,  6.3007e-01, -3.8968e-02, -1.7731e+00,  4.4104e-02,\n",
+       "         -1.5544e-01, -6.1640e-02, -3.3437e-01, -9.6973e-02, -3.1370e-02,\n",
+       "          1.2929e-01,  5.9590e-03,  6.2087e-02,  2.2925e-01,  1.4430e-01,\n",
+       "          1.0176e-01, -5.0824e-02]], grad_fn=<SliceBackward>)"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "encodings = model_chairs.encode(Variable(batch_chairs))\n",
+    "\n",
+    "# Continuous encodings for the first 5 examples\n",
+    "encodings['cont'][0][:5]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Chairs3D:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from VAE_model.models import VAE"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "path_to_model_folder_chairs = 'trained_models/rendered_chairs/'\n",
+    "expe_name_1 = 'VAE_bs_64'\n",
+    "expe_name_2 = 'VAE_bs_256'\n",
+    "expe_name_3 = 'beta_VAE_bs_64'\n",
+    "expe_name_4 = 'beta_VAE_bs_256'\n",
+    "\n",
+    "img_size = (3, 64, 64)\n",
+    "latent_spec = {\"cont\": 10}\n",
+    "model_chairs = VAE(img_size, latent_spec=latent_spec)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "file_path = os.path.join(path_to_model_folder_chairs, expe_name_1, 'checkpoints', 'last')\n",
+    "checkpoint = torch.load(file_path, map_location=torch.device('cpu'))\n",
+    "model_chairs.load_state_dict(checkpoint['model_states']['model'])\n",
+    "\n",
+    "viz_chairs = Viz(model)\n",
+    "viz_chairs.save_images = False"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "print(model_chairs.latent_spec)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "print(model_chairs)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "viz_chairs = Viz(model_chairs)\n",
+    "viz_chairs.save_images = False"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Samples:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "size = (8, 8)\n",
+    "samples = viz_chairs.samples(size=size)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "\n",
+    "samples = samples.permute(1, 2, 0)\n",
+    "plt.imshow(samples.numpy())\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## All latent traversal:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "traversals = viz_chairs.all_latent_traversals(size=8)\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "traversals = traversals.permute(1, 2, 0)\n",
+    "\n",
+    "plt.imshow(traversals.numpy())\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Traversal of single dimension:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "traversal = viz_chairs.latent_traversal_line(cont_idx=0, size=12)\n",
+    "traversal = traversal.permute(1, 2, 0)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversal.numpy())\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Reconstruction:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# Get chairs test data\n",
+    "_, dataloader_chairs = torch.load('data/batch_chairs.pt')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# Extract a batch of data\n",
+    "for batch_chairs, labels_chairs in dataloader_chairs:\n",
+    "    break\n",
+    "\n",
+    "recon_grid, recon = viz_chairs.reconstructions(batch_chairs, size=(8, 8))\n",
+    "# recon = recon.permute(1, 2, 0)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "recon_grid = recon_grid.permute(1, 2, 0)\n",
+    "plt.imshow(recon_grid.numpy())\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Encoding:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "T7AaGReRrwQY",
+    "colab_type": "code",
+    "colab": {},
+    "outputId": "23c595a7-ad88-4462-f1f1-c4008256245a"
+   },
+   "source": [
+    "encodings = model_chairs.encode(Variable(batch_chairs))\n",
+    "\n",
+    "# Continuous encodings for the first 5 examples\n",
+    "encodings['cont'][0][:5]"
+   ],
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 0.0930,  0.1647,  0.0540,  0.1675,  0.0646,  0.0592, -0.0892,  0.1839,\n",
+       "          0.0183,  0.0508, -0.0224,  0.0921, -0.1121, -0.2782,  0.0785,  0.1275,\n",
+       "         -0.0297,  0.2134,  0.1295, -0.2447, -0.0822, -0.0591,  0.2212,  0.0884,\n",
+       "          0.1223, -0.0608,  0.0187, -0.0394, -0.1994, -0.1010, -0.1117, -0.2373],\n",
+       "        [ 0.0411, -0.0240,  0.0453, -0.0978,  0.0245,  0.0384,  0.0234, -0.0093,\n",
+       "          0.0683, -0.0912, -0.0102,  0.0345,  0.0352,  0.2328,  0.0047, -0.0060,\n",
+       "          0.0588, -0.0748,  0.0128,  0.0695, -0.0492,  0.1295, -0.0582, -0.0634,\n",
+       "         -0.0390,  0.1097,  0.0667,  0.0088,  0.0924,  0.0795, -0.0166,  0.0845],\n",
+       "        [ 0.0943,  0.1567,  0.0597,  0.1523,  0.0649,  0.0605, -0.0848,  0.1767,\n",
+       "          0.0226,  0.0392, -0.0252,  0.0935, -0.1038, -0.2513,  0.0754,  0.1249,\n",
+       "         -0.0217,  0.1986,  0.1264, -0.2326, -0.0810, -0.0435,  0.2084,  0.0799,\n",
+       "          0.1151, -0.0492,  0.0249, -0.0362, -0.1829, -0.0893, -0.1083, -0.2236],\n",
+       "        [ 0.0865,  0.1053,  0.0678,  0.0735,  0.0532,  0.0713, -0.0591,  0.1232,\n",
+       "          0.0489, -0.0129, -0.0267,  0.0796, -0.0626, -0.0902,  0.0566,  0.0956,\n",
+       "          0.0064,  0.1254,  0.1038, -0.1497, -0.0826,  0.0296,  0.1399,  0.0337,\n",
+       "          0.0650,  0.0135,  0.0425, -0.0220, -0.1069, -0.0339, -0.0899, -0.1414],\n",
+       "        [ 0.0870,  0.1220,  0.0613,  0.0875,  0.0569,  0.0649, -0.0548,  0.1284,\n",
+       "          0.0394, -0.0006, -0.0170,  0.0764, -0.0661, -0.1238,  0.0609,  0.0921,\n",
+       "          0.0013,  0.1348,  0.1052, -0.1609, -0.0756,  0.0089,  0.1501,  0.0419,\n",
+       "          0.0778, -0.0067,  0.0343, -0.0220, -0.1224, -0.0423, -0.0914, -0.1507]],\n",
+       "       grad_fn=<SliceBackward>)"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "c_uuSyI8jne1",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    ""
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kA4emg8rMoNZ",
+    "colab_type": "text"
+   },
+   "source": [
+    "# Chairs3D:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-49ZeHw7tB26",
+    "colab_type": "code",
+    "colab": {},
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592991732861,
+     "user_tz": -120,
+     "elapsed": 541,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    }
+   },
+   "source": [
+    "import torch\n",
+    "from VAE_model.models import VAE"
+   ],
+   "execution_count": 13,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dWqj-Sd8sTbv",
+    "colab_type": "code",
+    "colab": {},
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592991778452,
+     "user_tz": -120,
+     "elapsed": 585,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    }
+   },
+   "source": [
+    "path_to_model_folder_chairs = 'trained_models/rendered_chairs/'\n",
+    "expe_name_1 = 'VAE_bs_64'\n",
+    "expe_name_2 = 'VAE_bs_256'\n",
+    "expe_name_3 = 'beta_VAE_bs_64'\n",
+    "expe_name_4 = 'beta_VAE_bs_256'\n",
+    "\n",
+    "img_size = (3, 64, 64)\n",
+    "latent_spec = {\"cont\": 10}\n",
+    "model_chairs = VAE(img_size, latent_spec=latent_spec)"
+   ],
+   "execution_count": 17,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "qTRfs1OFsTfE",
+    "colab_type": "code",
+    "colab": {},
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592991779260,
+     "user_tz": -120,
+     "elapsed": 519,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    }
+   },
+   "source": [
+    "file_path = os.path.join(path_to_model_folder_chairs, expe_name_1, 'checkpoints', 'last')\n",
+    "checkpoint = torch.load(file_path, map_location=torch.device('cpu'))\n",
+    "model_chairs.load_state_dict(checkpoint['model_states']['model'])\n",
+    "\n",
+    "viz_chairs = Viz(model)\n",
+    "viz_chairs.save_images = False"
+   ],
+   "execution_count": 18,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "h49JC8-8jyoZ",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592991780840,
+     "user_tz": -120,
+     "elapsed": 642,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "305d346e-9ccd-432e-f5c0-01218076719c"
+   },
+   "source": [
+    "print(model_chairs.latent_spec)"
+   ],
+   "execution_count": 19,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "{'cont': 10}\n"
+     ],
+     "name": "stdout"
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "MnHv4uY5j2UT",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "print(model_chairs)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "yy2QEQ0oj3PY",
+    "colab_type": "code",
+    "colab": {},
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592991782739,
+     "user_tz": -120,
+     "elapsed": 571,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    }
+   },
+   "source": [
+    "viz_chairs = Viz(model_chairs)\n",
+    "viz_chairs.save_images = False"
+   ],
+   "execution_count": 20,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kBUU9Gjij7LJ",
+    "colab_type": "text"
+   },
+   "source": [
+    "## Samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "VBy86xYYj6gI",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 594
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592991785393,
+     "user_tz": -120,
+     "elapsed": 1582,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "2bd27ea9-8a2d-4564-c5c3-320884f08725"
+   },
+   "source": [
+    "size = (8, 8)\n",
+    "samples = viz_chairs.samples(size=size)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "\n",
+    "samples = samples.permute(1, 2, 0)\n",
+    "plt.imshow(samples.numpy())\n",
+    "plt.show()"
+   ],
+   "execution_count": 21,
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QyAFLLm5kHPY",
+    "colab_type": "text"
+   },
+   "source": [
+    "## All latent traversal:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "OI50-KsbkFjA",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 594
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592991791418,
+     "user_tz": -120,
+     "elapsed": 1114,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "4d09a1d0-a6a1-407f-88e8-220943b6fb57"
+   },
+   "source": [
+    "traversals = viz_chairs.all_latent_traversals(size=8)\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "traversals = traversals.permute(1, 2, 0)\n",
+    "\n",
+    "plt.imshow(traversals.numpy())\n",
+    "plt.show()"
+   ],
+   "execution_count": 22,
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EjxVqZ9pkOPJ",
+    "colab_type": "text"
+   },
+   "source": [
+    "## Traversal of single dimension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "DHYrHLzLkN28",
+    "colab_type": "code",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 98
+    },
+    "executionInfo": {
+     "status": "ok",
+     "timestamp": 1592991795481,
+     "user_tz": -120,
+     "elapsed": 587,
+     "user": {
+      "displayName": "Julien Dejasmin",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64",
+      "userId": "11938403868733315090"
+     }
+    },
+    "outputId": "9119cfec-d1fa-48c7-cafb-d3078f63162d"
+   },
+   "source": [
+    "traversal = viz_chairs.latent_traversal_line(cont_idx=0, size=12)\n",
+    "traversal = traversal.permute(1, 2, 0)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(traversal.numpy())\n",
+    "plt.show()"
+   ],
+   "execution_count": 23,
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": [],
+      "needs_background": "light"
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-6pImGWOkWcY",
+    "colab_type": "text"
+   },
+   "source": [
+    "## Reconstruction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "1pvbzHfEknv4",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# Get chairs test data\n",
+    "_, dataloader_chairs = torch.load('data/batch_chairs.pt')"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "b4sms021kWCj",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# Extract a batch of data\n",
+    "for batch_chairs, labels_chairs in dataloader_chairs:\n",
+    "    break\n",
+    "\n",
+    "recon_grid, recon = viz_chairs.reconstructions(batch_chairs, size=(8, 8))\n",
+    "# recon = recon.permute(1, 2, 0)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "recon_grid = recon_grid.permute(1, 2, 0)\n",
+    "plt.imshow(recon_grid.numpy())\n",
+    "plt.show()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BeviVvyJkp7L",
+    "colab_type": "text"
+   },
+   "source": [
+    "## Encoding:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "L56PVHGkkk-6",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "encodings = model_chairs.encode(Variable(batch_chairs))\n",
+    "\n",
+    "# Continuous encodings for the first 5 examples\n",
+    "encodings['cont'][0][:5]"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EFRO74gBkuWv",
+    "colab_type": "text"
+   },
+   "source": [
+    ""
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/Experiments/experiments.py b/Experiments/experiments.py
index e9d9f0894c35aa116e1a5aa5a787614df9859574..3a7d1efa39212c2ccd902d447368fd021bf3b44e 100644
--- a/Experiments/experiments.py
+++ b/Experiments/experiments.py
@@ -33,16 +33,31 @@ for batch_chairs, labels_chairs in dataloader_chairs:
 # torch.load('data/batch_chairs.pt')
 
 path_to_model_folder_chairs = '../trained_models/rendered_chairs/'
-expe_name_1 = 'VAE_bs_64'
-expe_name_2 = 'VAE_bs_256'
-expe_name_3 = 'beta_VAE_bs_64'
-expe_name_4 = 'beta_VAE_bs_256'
+list_expe = ['VAE_bs_64', 'VAE_bs_256', 'beta_VAE_bs_64', 'beta_VAE_bs_256', 'VAE_bs_64_ls_10_lr_1e_3',
+             'VAE_bs_64_ls_10_lr_5e_4']
 
-list_expe = [expe_name_1, expe_name_2, expe_name_3, expe_name_4]
+list_expe_ls_5 = ['VAE_bs_64_ls_5', 'beta_VAE_bs_64_ls_5']
+list_expe_ls_15 = ['VAE_bs_64_ls_15', 'beta_VAE_bs_64_ls_15']
+list_expe_ls_20 = ['VAE_bs_64_ls_20', 'beta_VAE_bs_64_ls_20']
 
 img_size = (3, 64, 64)
+
 latent_spec = {"cont": 10}
 model = VAE(img_size, latent_spec=latent_spec)
-
 for i in list_expe:
     viz_reconstruction(model, path_to_model_folder_chairs, i, batch_chairs)
+
+latent_spec = {"cont": 5}
+model = VAE(img_size, latent_spec=latent_spec)
+for i in list_expe_ls_5:
+    viz_reconstruction(model, path_to_model_folder_chairs, i, batch_chairs)
+
+latent_spec = {"cont": 15}
+model = VAE(img_size, latent_spec=latent_spec)
+for i in list_expe_ls_15:
+    viz_reconstruction(model, path_to_model_folder_chairs, i, batch_chairs)
+
+latent_spec = {"cont": 20}
+model = VAE(img_size, latent_spec=latent_spec)
+for i in list_expe_ls_20:
+    viz_reconstruction(model, path_to_model_folder_chairs, i, batch_chairs)
diff --git a/OAR.2066986.stderr b/OAR.2066986.stderr
deleted file mode 100644
index 0bb03bf57906dfc4031825f7fa552cc35aec1353..0000000000000000000000000000000000000000
--- a/OAR.2066986.stderr
+++ /dev/null
@@ -1,9 +0,0 @@
-/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py:26: UserWarning: 
-    There is an imbalance between your GPUs. You may want to exclude GPU 1 which
-    has less than 75% of the memory or cores of GPU 0. You can do so by setting
-    the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES
-    environment variable.
-  warnings.warn(imbalance_warn.format(device_ids[min_pos], device_ids[max_pos]))
-/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
-  warnings.warn(warning.format(ret))
-## OAR [2020-06-24 03:51:26] Job 2066986 KILLED ##
diff --git a/OAR.2066986.stdout b/OAR.2066986.stdout
deleted file mode 100644
index d44df1f0e4627fcfca61ce3f83ffa302f875433e..0000000000000000000000000000000000000000
--- a/OAR.2066986.stdout
+++ /dev/null
@@ -1,1553 +0,0 @@
-Namespace(batch_size=256, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_256', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
-creare new diretory experiment: rendered_chairs/beta_VAE_bs_256
-load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
-use 2 gpu who named:
-Tesla K40c
-Tesla K20m
-DataParallel(
-  (module): VAE(
-    (img_to_last_conv): Sequential(
-      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (1): ReLU()
-      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (3): ReLU()
-      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (5): ReLU()
-      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (7): ReLU()
-    )
-    (last_conv_to_continuous_features): Sequential(
-      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
-      (1): ReLU()
-    )
-    (features_to_hidden_continue): Sequential(
-      (0): Linear(in_features=256, out_features=20, bias=True)
-      (1): ReLU()
-    )
-    (latent_to_features): Sequential(
-      (0): Linear(in_features=10, out_features=256, bias=True)
-      (1): ReLU()
-    )
-    (features_to_img): Sequential(
-      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
-      (1): ReLU()
-      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (3): ReLU()
-      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (5): ReLU()
-      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (7): ReLU()
-      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (9): Sigmoid()
-    )
-  )
-)
-The number of parameters of model is 765335
-don't use continuous capacity
-=> loaded checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last (iter 4)'
-0/69092	Loss: 211.825
-12800/69092	Loss: 211.804
-25600/69092	Loss: 206.202
-38400/69092	Loss: 202.006
-51200/69092	Loss: 200.623
-64000/69092	Loss: 197.778
-Training time 0:03:33.530106
-Epoch: 1 Average loss: 203.12
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 5)
-0/69092	Loss: 194.363
-12800/69092	Loss: 193.513
-25600/69092	Loss: 190.886
-38400/69092	Loss: 189.957
-51200/69092	Loss: 188.767
-64000/69092	Loss: 186.445
-Training time 0:03:35.633651
-Epoch: 2 Average loss: 189.98
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 6)
-0/69092	Loss: 191.560
-12800/69092	Loss: 186.911
-25600/69092	Loss: 186.547
-38400/69092	Loss: 187.360
-51200/69092	Loss: 186.168
-64000/69092	Loss: 184.956
-Training time 0:03:34.640039
-Epoch: 3 Average loss: 186.13
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 7)
-0/69092	Loss: 188.289
-12800/69092	Loss: 182.612
-25600/69092	Loss: 181.754
-38400/69092	Loss: 177.558
-51200/69092	Loss: 178.641
-64000/69092	Loss: 178.958
-Training time 0:03:34.619088
-Epoch: 4 Average loss: 179.99
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 8)
-0/69092	Loss: 176.130
-12800/69092	Loss: 179.349
-25600/69092	Loss: 175.271
-38400/69092	Loss: 175.914
-51200/69092	Loss: 176.593
-64000/69092	Loss: 175.448
-Training time 0:03:34.581288
-Epoch: 5 Average loss: 176.39
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 9)
-0/69092	Loss: 173.057
-12800/69092	Loss: 174.321
-25600/69092	Loss: 173.044
-38400/69092	Loss: 172.272
-51200/69092	Loss: 173.259
-64000/69092	Loss: 170.322
-Training time 0:03:34.404385
-Epoch: 6 Average loss: 172.50
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 10)
-0/69092	Loss: 166.655
-12800/69092	Loss: 171.300
-25600/69092	Loss: 170.458
-38400/69092	Loss: 170.805
-51200/69092	Loss: 170.232
-64000/69092	Loss: 171.220
-Training time 0:03:34.964220
-Epoch: 7 Average loss: 170.72
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 11)
-0/69092	Loss: 178.479
-12800/69092	Loss: 170.169
-25600/69092	Loss: 169.556
-38400/69092	Loss: 171.901
-51200/69092	Loss: 168.732
-64000/69092	Loss: 169.240
-Training time 0:03:33.828026
-Epoch: 8 Average loss: 169.95
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 12)
-0/69092	Loss: 172.547
-12800/69092	Loss: 169.826
-25600/69092	Loss: 170.576
-38400/69092	Loss: 169.827
-51200/69092	Loss: 168.754
-64000/69092	Loss: 168.303
-Training time 0:03:34.457471
-Epoch: 9 Average loss: 169.32
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 13)
-0/69092	Loss: 167.148
-12800/69092	Loss: 168.280
-25600/69092	Loss: 168.913
-38400/69092	Loss: 170.169
-51200/69092	Loss: 168.747
-64000/69092	Loss: 168.010
-Training time 0:03:34.634507
-Epoch: 10 Average loss: 168.80
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 14)
-0/69092	Loss: 177.245
-12800/69092	Loss: 169.183
-25600/69092	Loss: 169.266
-38400/69092	Loss: 167.712
-51200/69092	Loss: 167.721
-64000/69092	Loss: 168.102
-Training time 0:03:34.876272
-Epoch: 11 Average loss: 168.36
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 15)
-0/69092	Loss: 176.615
-12800/69092	Loss: 167.388
-25600/69092	Loss: 168.006
-38400/69092	Loss: 167.276
-51200/69092	Loss: 168.548
-64000/69092	Loss: 166.197
-Training time 0:03:34.165047
-Epoch: 12 Average loss: 167.67
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 16)
-0/69092	Loss: 172.005
-12800/69092	Loss: 167.224
-25600/69092	Loss: 168.013
-38400/69092	Loss: 169.018
-51200/69092	Loss: 166.406
-64000/69092	Loss: 166.123
-Training time 0:03:34.981962
-Epoch: 13 Average loss: 167.29
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 17)
-0/69092	Loss: 168.780
-12800/69092	Loss: 168.028
-25600/69092	Loss: 166.477
-38400/69092	Loss: 166.556
-51200/69092	Loss: 166.215
-64000/69092	Loss: 168.803
-Training time 0:03:34.777682
-Epoch: 14 Average loss: 167.18
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 18)
-0/69092	Loss: 166.192
-12800/69092	Loss: 166.410
-25600/69092	Loss: 167.980
-38400/69092	Loss: 165.740
-51200/69092	Loss: 166.931
-64000/69092	Loss: 165.295
-Training time 0:03:33.907632
-Epoch: 15 Average loss: 166.55
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 19)
-0/69092	Loss: 168.910
-12800/69092	Loss: 166.387
-25600/69092	Loss: 168.154
-38400/69092	Loss: 167.979
-51200/69092	Loss: 165.611
-64000/69092	Loss: 164.256
-Training time 0:03:34.308635
-Epoch: 16 Average loss: 166.37
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 20)
-0/69092	Loss: 153.952
-12800/69092	Loss: 166.799
-25600/69092	Loss: 166.583
-38400/69092	Loss: 166.714
-51200/69092	Loss: 164.663
-64000/69092	Loss: 165.421
-Training time 0:03:34.306057
-Epoch: 17 Average loss: 166.10
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 21)
-0/69092	Loss: 179.877
-12800/69092	Loss: 167.473
-25600/69092	Loss: 166.346
-38400/69092	Loss: 164.610
-51200/69092	Loss: 165.784
-64000/69092	Loss: 165.548
-Training time 0:03:34.278194
-Epoch: 18 Average loss: 165.92
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 22)
-0/69092	Loss: 152.548
-12800/69092	Loss: 164.441
-25600/69092	Loss: 166.865
-38400/69092	Loss: 164.547
-51200/69092	Loss: 166.574
-64000/69092	Loss: 166.189
-Training time 0:03:34.177869
-Epoch: 19 Average loss: 165.65
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 23)
-0/69092	Loss: 169.217
-12800/69092	Loss: 166.643
-25600/69092	Loss: 166.234
-38400/69092	Loss: 164.382
-51200/69092	Loss: 164.328
-64000/69092	Loss: 165.078
-Training time 0:03:34.422146
-Epoch: 20 Average loss: 165.53
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 24)
-0/69092	Loss: 161.289
-12800/69092	Loss: 165.853
-25600/69092	Loss: 165.618
-38400/69092	Loss: 164.688
-51200/69092	Loss: 165.000
-64000/69092	Loss: 165.459
-Training time 0:03:34.461971
-Epoch: 21 Average loss: 165.37
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 25)
-0/69092	Loss: 159.510
-12800/69092	Loss: 165.153
-25600/69092	Loss: 165.337
-38400/69092	Loss: 164.817
-51200/69092	Loss: 165.308
-64000/69092	Loss: 165.527
-Training time 0:03:33.570288
-Epoch: 22 Average loss: 165.21
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 26)
-0/69092	Loss: 170.139
-12800/69092	Loss: 164.568
-25600/69092	Loss: 164.903
-38400/69092	Loss: 164.993
-51200/69092	Loss: 167.294
-64000/69092	Loss: 164.163
-Training time 0:03:34.137382
-Epoch: 23 Average loss: 165.25
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 27)
-0/69092	Loss: 160.074
-12800/69092	Loss: 164.802
-25600/69092	Loss: 165.156
-38400/69092	Loss: 165.485
-51200/69092	Loss: 165.304
-64000/69092	Loss: 164.874
-Training time 0:03:34.155758
-Epoch: 24 Average loss: 165.16
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 28)
-0/69092	Loss: 159.392
-12800/69092	Loss: 165.178
-25600/69092	Loss: 164.090
-38400/69092	Loss: 166.206
-51200/69092	Loss: 164.525
-64000/69092	Loss: 164.707
-Training time 0:03:34.617919
-Epoch: 25 Average loss: 164.87
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 29)
-0/69092	Loss: 171.862
-12800/69092	Loss: 163.451
-25600/69092	Loss: 165.349
-38400/69092	Loss: 166.284
-51200/69092	Loss: 164.517
-64000/69092	Loss: 164.092
-Training time 0:03:34.527260
-Epoch: 26 Average loss: 164.91
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 30)
-0/69092	Loss: 158.628
-12800/69092	Loss: 165.301
-25600/69092	Loss: 164.939
-38400/69092	Loss: 163.872
-51200/69092	Loss: 164.439
-64000/69092	Loss: 165.157
-Training time 0:03:35.035772
-Epoch: 27 Average loss: 164.72
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 31)
-0/69092	Loss: 159.360
-12800/69092	Loss: 165.523
-25600/69092	Loss: 164.674
-38400/69092	Loss: 164.216
-51200/69092	Loss: 163.631
-64000/69092	Loss: 165.648
-Training time 0:03:34.900889
-Epoch: 28 Average loss: 164.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 32)
-0/69092	Loss: 157.986
-12800/69092	Loss: 164.944
-25600/69092	Loss: 164.624
-38400/69092	Loss: 163.472
-51200/69092	Loss: 164.677
-64000/69092	Loss: 165.441
-Training time 0:03:34.285686
-Epoch: 29 Average loss: 164.50
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 33)
-0/69092	Loss: 161.244
-12800/69092	Loss: 163.373
-25600/69092	Loss: 165.776
-38400/69092	Loss: 164.253
-51200/69092	Loss: 164.947
-64000/69092	Loss: 164.238
-Training time 0:03:34.892902
-Epoch: 30 Average loss: 164.40
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 34)
-0/69092	Loss: 163.053
-12800/69092	Loss: 165.200
-25600/69092	Loss: 164.347
-38400/69092	Loss: 164.216
-51200/69092	Loss: 163.448
-64000/69092	Loss: 164.677
-Training time 0:03:34.057974
-Epoch: 31 Average loss: 164.38
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 35)
-0/69092	Loss: 156.522
-12800/69092	Loss: 163.038
-25600/69092	Loss: 164.385
-38400/69092	Loss: 164.930
-51200/69092	Loss: 163.186
-64000/69092	Loss: 164.172
-Training time 0:03:34.517277
-Epoch: 32 Average loss: 164.09
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 36)
-0/69092	Loss: 167.346
-12800/69092	Loss: 164.662
-25600/69092	Loss: 163.356
-38400/69092	Loss: 163.878
-51200/69092	Loss: 164.450
-64000/69092	Loss: 164.437
-Training time 0:03:34.484296
-Epoch: 33 Average loss: 164.21
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 37)
-0/69092	Loss: 150.947
-12800/69092	Loss: 164.719
-25600/69092	Loss: 162.579
-38400/69092	Loss: 164.336
-51200/69092	Loss: 165.475
-64000/69092	Loss: 163.452
-Training time 0:03:34.858968
-Epoch: 34 Average loss: 164.06
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 38)
-0/69092	Loss: 168.195
-12800/69092	Loss: 164.326
-25600/69092	Loss: 164.275
-38400/69092	Loss: 164.487
-51200/69092	Loss: 164.394
-64000/69092	Loss: 163.267
-Training time 0:03:34.799991
-Epoch: 35 Average loss: 164.21
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 39)
-0/69092	Loss: 162.548
-12800/69092	Loss: 164.372
-25600/69092	Loss: 164.171
-38400/69092	Loss: 163.765
-51200/69092	Loss: 163.489
-64000/69092	Loss: 163.863
-Training time 0:03:34.533021
-Epoch: 36 Average loss: 164.00
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 40)
-0/69092	Loss: 175.309
-12800/69092	Loss: 163.169
-25600/69092	Loss: 164.087
-38400/69092	Loss: 163.781
-51200/69092	Loss: 162.098
-64000/69092	Loss: 164.361
-Training time 0:03:34.473704
-Epoch: 37 Average loss: 163.85
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 41)
-0/69092	Loss: 165.228
-12800/69092	Loss: 163.195
-25600/69092	Loss: 164.960
-38400/69092	Loss: 164.452
-51200/69092	Loss: 164.154
-64000/69092	Loss: 162.288
-Training time 0:03:34.369595
-Epoch: 38 Average loss: 163.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 42)
-0/69092	Loss: 171.521
-12800/69092	Loss: 162.591
-25600/69092	Loss: 163.128
-38400/69092	Loss: 165.067
-51200/69092	Loss: 163.874
-64000/69092	Loss: 163.911
-Training time 0:03:34.312838
-Epoch: 39 Average loss: 163.67
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 43)
-0/69092	Loss: 155.946
-12800/69092	Loss: 163.299
-25600/69092	Loss: 162.675
-38400/69092	Loss: 165.110
-51200/69092	Loss: 164.525
-64000/69092	Loss: 164.077
-Training time 0:03:34.585969
-Epoch: 40 Average loss: 163.81
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 44)
-0/69092	Loss: 153.399
-12800/69092	Loss: 163.496
-25600/69092	Loss: 163.969
-38400/69092	Loss: 165.591
-51200/69092	Loss: 164.615
-64000/69092	Loss: 161.441
-Training time 0:03:34.709067
-Epoch: 41 Average loss: 163.67
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 45)
-0/69092	Loss: 156.591
-12800/69092	Loss: 163.524
-25600/69092	Loss: 162.621
-38400/69092	Loss: 164.014
-51200/69092	Loss: 164.112
-64000/69092	Loss: 164.345
-Training time 0:03:35.252902
-Epoch: 42 Average loss: 163.73
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 46)
-0/69092	Loss: 163.608
-12800/69092	Loss: 163.123
-25600/69092	Loss: 162.704
-38400/69092	Loss: 163.056
-51200/69092	Loss: 164.623
-64000/69092	Loss: 164.656
-Training time 0:03:34.662832
-Epoch: 43 Average loss: 163.69
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 47)
-0/69092	Loss: 153.614
-12800/69092	Loss: 163.977
-25600/69092	Loss: 163.717
-38400/69092	Loss: 163.211
-51200/69092	Loss: 163.595
-64000/69092	Loss: 162.397
-Training time 0:03:34.460078
-Epoch: 44 Average loss: 163.41
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 48)
-0/69092	Loss: 156.672
-12800/69092	Loss: 164.010
-25600/69092	Loss: 164.568
-38400/69092	Loss: 163.828
-51200/69092	Loss: 161.124
-64000/69092	Loss: 162.826
-Training time 0:03:34.715757
-Epoch: 45 Average loss: 163.37
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 49)
-0/69092	Loss: 170.756
-12800/69092	Loss: 163.939
-25600/69092	Loss: 163.215
-38400/69092	Loss: 163.414
-51200/69092	Loss: 163.922
-64000/69092	Loss: 163.683
-Training time 0:03:35.627656
-Epoch: 46 Average loss: 163.62
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 50)
-0/69092	Loss: 164.595
-12800/69092	Loss: 163.255
-25600/69092	Loss: 162.672
-38400/69092	Loss: 164.189
-51200/69092	Loss: 163.686
-64000/69092	Loss: 162.669
-Training time 0:03:34.370078
-Epoch: 47 Average loss: 163.23
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 51)
-0/69092	Loss: 165.749
-12800/69092	Loss: 162.771
-25600/69092	Loss: 163.035
-38400/69092	Loss: 164.054
-51200/69092	Loss: 161.917
-64000/69092	Loss: 162.585
-Training time 0:03:34.667025
-Epoch: 48 Average loss: 163.18
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 52)
-0/69092	Loss: 154.571
-12800/69092	Loss: 163.939
-25600/69092	Loss: 163.544
-38400/69092	Loss: 163.970
-51200/69092	Loss: 161.575
-64000/69092	Loss: 163.866
-Training time 0:03:34.864267
-Epoch: 49 Average loss: 163.40
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 53)
-0/69092	Loss: 166.004
-12800/69092	Loss: 163.521
-25600/69092	Loss: 162.988
-38400/69092	Loss: 163.130
-51200/69092	Loss: 163.261
-64000/69092	Loss: 163.026
-Training time 0:03:34.138736
-Epoch: 50 Average loss: 163.23
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 54)
-0/69092	Loss: 156.938
-12800/69092	Loss: 163.198
-25600/69092	Loss: 163.394
-38400/69092	Loss: 163.262
-51200/69092	Loss: 162.025
-64000/69092	Loss: 163.956
-Training time 0:03:34.763237
-Epoch: 51 Average loss: 163.15
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 55)
-0/69092	Loss: 164.332
-12800/69092	Loss: 163.878
-25600/69092	Loss: 163.516
-38400/69092	Loss: 163.663
-51200/69092	Loss: 162.682
-64000/69092	Loss: 161.136
-Training time 0:03:35.382783
-Epoch: 52 Average loss: 163.01
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 56)
-0/69092	Loss: 155.649
-12800/69092	Loss: 163.492
-25600/69092	Loss: 161.749
-38400/69092	Loss: 164.039
-51200/69092	Loss: 162.869
-64000/69092	Loss: 163.493
-Training time 0:03:35.305348
-Epoch: 53 Average loss: 163.03
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 57)
-0/69092	Loss: 157.357
-12800/69092	Loss: 162.833
-25600/69092	Loss: 163.294
-38400/69092	Loss: 162.755
-51200/69092	Loss: 162.863
-64000/69092	Loss: 163.724
-Training time 0:03:34.340976
-Epoch: 54 Average loss: 163.14
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 58)
-0/69092	Loss: 151.717
-12800/69092	Loss: 163.086
-25600/69092	Loss: 162.400
-38400/69092	Loss: 164.084
-51200/69092	Loss: 162.815
-64000/69092	Loss: 163.041
-Training time 0:03:35.284810
-Epoch: 55 Average loss: 163.02
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 59)
-0/69092	Loss: 158.464
-12800/69092	Loss: 163.570
-25600/69092	Loss: 161.727
-38400/69092	Loss: 164.416
-51200/69092	Loss: 162.037
-64000/69092	Loss: 163.618
-Training time 0:03:35.450159
-Epoch: 56 Average loss: 162.94
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 60)
-0/69092	Loss: 163.640
-12800/69092	Loss: 161.432
-25600/69092	Loss: 162.910
-38400/69092	Loss: 162.904
-51200/69092	Loss: 163.806
-64000/69092	Loss: 162.356
-Training time 0:03:35.267777
-Epoch: 57 Average loss: 162.80
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 61)
-0/69092	Loss: 161.019
-12800/69092	Loss: 163.341
-25600/69092	Loss: 162.614
-38400/69092	Loss: 162.124
-51200/69092	Loss: 163.792
-64000/69092	Loss: 162.896
-Training time 0:03:34.874771
-Epoch: 58 Average loss: 162.94
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 62)
-0/69092	Loss: 166.624
-12800/69092	Loss: 161.990
-25600/69092	Loss: 164.154
-38400/69092	Loss: 161.882
-51200/69092	Loss: 163.737
-64000/69092	Loss: 163.187
-Training time 0:03:34.613702
-Epoch: 59 Average loss: 162.95
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 63)
-0/69092	Loss: 154.731
-12800/69092	Loss: 162.162
-25600/69092	Loss: 164.492
-38400/69092	Loss: 161.770
-51200/69092	Loss: 162.852
-64000/69092	Loss: 163.725
-Training time 0:03:34.790103
-Epoch: 60 Average loss: 162.94
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 64)
-0/69092	Loss: 162.959
-12800/69092	Loss: 162.458
-25600/69092	Loss: 163.497
-38400/69092	Loss: 163.245
-51200/69092	Loss: 162.263
-64000/69092	Loss: 162.196
-Training time 0:03:34.640282
-Epoch: 61 Average loss: 162.72
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 65)
-0/69092	Loss: 149.759
-12800/69092	Loss: 162.575
-25600/69092	Loss: 163.300
-38400/69092	Loss: 162.595
-51200/69092	Loss: 162.451
-64000/69092	Loss: 163.141
-Training time 0:03:34.993601
-Epoch: 62 Average loss: 162.87
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 66)
-0/69092	Loss: 167.817
-12800/69092	Loss: 163.591
-25600/69092	Loss: 161.586
-38400/69092	Loss: 162.502
-51200/69092	Loss: 163.298
-64000/69092	Loss: 162.439
-Training time 0:03:34.761672
-Epoch: 63 Average loss: 162.86
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 67)
-0/69092	Loss: 162.536
-12800/69092	Loss: 163.860
-25600/69092	Loss: 161.999
-38400/69092	Loss: 162.851
-51200/69092	Loss: 161.707
-64000/69092	Loss: 161.781
-Training time 0:03:34.965002
-Epoch: 64 Average loss: 162.63
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 68)
-0/69092	Loss: 179.664
-12800/69092	Loss: 161.629
-25600/69092	Loss: 161.899
-38400/69092	Loss: 163.334
-51200/69092	Loss: 162.383
-64000/69092	Loss: 163.327
-Training time 0:03:34.568599
-Epoch: 65 Average loss: 162.67
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 69)
-0/69092	Loss: 173.059
-12800/69092	Loss: 161.902
-25600/69092	Loss: 162.708
-38400/69092	Loss: 162.622
-51200/69092	Loss: 161.650
-64000/69092	Loss: 162.824
-Training time 0:03:35.257544
-Epoch: 66 Average loss: 162.51
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 70)
-0/69092	Loss: 143.647
-12800/69092	Loss: 161.864
-25600/69092	Loss: 164.030
-38400/69092	Loss: 162.059
-51200/69092	Loss: 162.619
-64000/69092	Loss: 162.301
-Training time 0:03:35.838210
-Epoch: 67 Average loss: 162.49
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 71)
-0/69092	Loss: 166.080
-12800/69092	Loss: 162.964
-25600/69092	Loss: 163.455
-38400/69092	Loss: 162.464
-51200/69092	Loss: 162.000
-64000/69092	Loss: 161.847
-Training time 0:03:34.850876
-Epoch: 68 Average loss: 162.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 72)
-0/69092	Loss: 165.306
-12800/69092	Loss: 160.963
-25600/69092	Loss: 162.906
-38400/69092	Loss: 162.016
-51200/69092	Loss: 162.826
-64000/69092	Loss: 162.230
-Training time 0:03:35.222198
-Epoch: 69 Average loss: 162.24
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 73)
-0/69092	Loss: 159.279
-12800/69092	Loss: 161.766
-25600/69092	Loss: 162.684
-38400/69092	Loss: 162.991
-51200/69092	Loss: 163.154
-64000/69092	Loss: 161.012
-Training time 0:03:34.964302
-Epoch: 70 Average loss: 162.46
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 74)
-0/69092	Loss: 154.284
-12800/69092	Loss: 161.469
-25600/69092	Loss: 162.418
-38400/69092	Loss: 163.576
-51200/69092	Loss: 162.523
-64000/69092	Loss: 162.598
-Training time 0:03:34.914750
-Epoch: 71 Average loss: 162.55
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 75)
-0/69092	Loss: 167.185
-12800/69092	Loss: 160.845
-25600/69092	Loss: 164.084
-38400/69092	Loss: 161.897
-51200/69092	Loss: 162.055
-64000/69092	Loss: 163.471
-Training time 0:03:35.329838
-Epoch: 72 Average loss: 162.44
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 76)
-0/69092	Loss: 162.192
-12800/69092	Loss: 162.530
-25600/69092	Loss: 162.514
-38400/69092	Loss: 163.070
-51200/69092	Loss: 163.560
-64000/69092	Loss: 160.817
-Training time 0:03:35.328085
-Epoch: 73 Average loss: 162.37
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 77)
-0/69092	Loss: 166.539
-12800/69092	Loss: 162.278
-25600/69092	Loss: 163.629
-38400/69092	Loss: 162.288
-51200/69092	Loss: 160.127
-64000/69092	Loss: 162.499
-Training time 0:03:35.936274
-Epoch: 74 Average loss: 162.14
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 78)
-0/69092	Loss: 172.523
-12800/69092	Loss: 162.526
-25600/69092	Loss: 161.003
-38400/69092	Loss: 161.351
-51200/69092	Loss: 163.644
-64000/69092	Loss: 162.010
-Training time 0:03:34.917218
-Epoch: 75 Average loss: 162.09
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 79)
-0/69092	Loss: 160.719
-12800/69092	Loss: 161.441
-25600/69092	Loss: 163.190
-38400/69092	Loss: 161.822
-51200/69092	Loss: 162.764
-64000/69092	Loss: 162.811
-Training time 0:03:34.825778
-Epoch: 76 Average loss: 162.42
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 80)
-0/69092	Loss: 155.755
-12800/69092	Loss: 162.279
-25600/69092	Loss: 162.457
-38400/69092	Loss: 161.739
-51200/69092	Loss: 162.629
-64000/69092	Loss: 161.704
-Training time 0:03:34.994539
-Epoch: 77 Average loss: 162.20
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 81)
-0/69092	Loss: 167.212
-12800/69092	Loss: 161.920
-25600/69092	Loss: 162.322
-38400/69092	Loss: 163.715
-51200/69092	Loss: 162.922
-64000/69092	Loss: 161.310
-Training time 0:03:35.098604
-Epoch: 78 Average loss: 162.39
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 82)
-0/69092	Loss: 151.279
-12800/69092	Loss: 163.096
-25600/69092	Loss: 161.743
-38400/69092	Loss: 162.718
-51200/69092	Loss: 161.715
-64000/69092	Loss: 161.252
-Training time 0:03:34.378245
-Epoch: 79 Average loss: 162.15
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 83)
-0/69092	Loss: 171.928
-12800/69092	Loss: 161.661
-25600/69092	Loss: 162.137
-38400/69092	Loss: 162.428
-51200/69092	Loss: 162.382
-64000/69092	Loss: 162.189
-Training time 0:03:35.523365
-Epoch: 80 Average loss: 162.28
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 84)
-0/69092	Loss: 162.969
-12800/69092	Loss: 161.660
-25600/69092	Loss: 162.341
-38400/69092	Loss: 162.861
-51200/69092	Loss: 162.088
-64000/69092	Loss: 160.830
-Training time 0:03:35.828830
-Epoch: 81 Average loss: 162.10
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 85)
-0/69092	Loss: 163.799
-12800/69092	Loss: 160.779
-25600/69092	Loss: 161.781
-38400/69092	Loss: 162.490
-51200/69092	Loss: 162.095
-64000/69092	Loss: 162.997
-Training time 0:03:35.324407
-Epoch: 82 Average loss: 162.16
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 86)
-0/69092	Loss: 161.640
-12800/69092	Loss: 161.039
-25600/69092	Loss: 160.883
-38400/69092	Loss: 163.249
-51200/69092	Loss: 162.210
-64000/69092	Loss: 162.516
-Training time 0:03:35.899620
-Epoch: 83 Average loss: 162.13
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 87)
-0/69092	Loss: 158.307
-12800/69092	Loss: 161.081
-25600/69092	Loss: 162.671
-38400/69092	Loss: 163.349
-51200/69092	Loss: 161.665
-64000/69092	Loss: 162.282
-Training time 0:03:35.265596
-Epoch: 84 Average loss: 162.15
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 88)
-0/69092	Loss: 164.806
-12800/69092	Loss: 162.299
-25600/69092	Loss: 162.815
-38400/69092	Loss: 162.283
-51200/69092	Loss: 162.029
-64000/69092	Loss: 161.074
-Training time 0:03:35.331087
-Epoch: 85 Average loss: 162.23
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 89)
-0/69092	Loss: 155.805
-12800/69092	Loss: 160.191
-25600/69092	Loss: 162.544
-38400/69092	Loss: 162.671
-51200/69092	Loss: 162.905
-64000/69092	Loss: 161.941
-Training time 0:03:34.833442
-Epoch: 86 Average loss: 162.00
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 90)
-0/69092	Loss: 155.322
-12800/69092	Loss: 162.365
-25600/69092	Loss: 160.966
-38400/69092	Loss: 162.498
-51200/69092	Loss: 161.492
-64000/69092	Loss: 162.268
-Training time 0:03:35.194527
-Epoch: 87 Average loss: 161.91
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 91)
-0/69092	Loss: 147.863
-12800/69092	Loss: 161.758
-25600/69092	Loss: 163.217
-38400/69092	Loss: 160.943
-51200/69092	Loss: 161.846
-64000/69092	Loss: 163.150
-Training time 0:03:35.543712
-Epoch: 88 Average loss: 161.97
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 92)
-0/69092	Loss: 156.224
-12800/69092	Loss: 161.407
-25600/69092	Loss: 163.099
-38400/69092	Loss: 160.885
-51200/69092	Loss: 163.510
-64000/69092	Loss: 160.672
-Training time 0:03:34.976525
-Epoch: 89 Average loss: 162.03
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 93)
-0/69092	Loss: 163.257
-12800/69092	Loss: 160.499
-25600/69092	Loss: 161.542
-38400/69092	Loss: 161.733
-51200/69092	Loss: 162.542
-64000/69092	Loss: 162.036
-Training time 0:03:35.251829
-Epoch: 90 Average loss: 161.62
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 94)
-0/69092	Loss: 154.922
-12800/69092	Loss: 162.931
-25600/69092	Loss: 161.596
-38400/69092	Loss: 161.078
-51200/69092	Loss: 161.143
-64000/69092	Loss: 162.084
-Training time 0:03:34.964534
-Epoch: 91 Average loss: 161.71
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 95)
-0/69092	Loss: 160.102
-12800/69092	Loss: 160.236
-25600/69092	Loss: 160.102
-38400/69092	Loss: 163.086
-51200/69092	Loss: 161.250
-64000/69092	Loss: 161.530
-Training time 0:03:35.274464
-Epoch: 92 Average loss: 161.16
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 96)
-0/69092	Loss: 166.773
-12800/69092	Loss: 161.029
-25600/69092	Loss: 160.234
-38400/69092	Loss: 161.051
-51200/69092	Loss: 159.310
-64000/69092	Loss: 162.371
-Training time 0:03:34.579357
-Epoch: 93 Average loss: 160.69
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 97)
-0/69092	Loss: 156.949
-12800/69092	Loss: 160.754
-25600/69092	Loss: 161.179
-38400/69092	Loss: 160.530
-51200/69092	Loss: 160.001
-64000/69092	Loss: 160.276
-Training time 0:03:35.364384
-Epoch: 94 Average loss: 160.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 98)
-0/69092	Loss: 159.017
-12800/69092	Loss: 160.227
-25600/69092	Loss: 160.172
-38400/69092	Loss: 161.081
-51200/69092	Loss: 160.087
-64000/69092	Loss: 159.089
-Training time 0:03:35.051431
-Epoch: 95 Average loss: 160.11
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 99)
-0/69092	Loss: 158.123
-12800/69092	Loss: 161.822
-25600/69092	Loss: 161.200
-38400/69092	Loss: 159.726
-51200/69092	Loss: 159.801
-64000/69092	Loss: 159.733
-Training time 0:03:34.861642
-Epoch: 96 Average loss: 160.38
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 100)
-0/69092	Loss: 159.461
-12800/69092	Loss: 161.147
-25600/69092	Loss: 159.821
-38400/69092	Loss: 160.349
-51200/69092	Loss: 159.651
-64000/69092	Loss: 159.265
-Training time 0:03:34.392620
-Epoch: 97 Average loss: 160.05
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 101)
-0/69092	Loss: 162.921
-12800/69092	Loss: 160.283
-25600/69092	Loss: 160.020
-38400/69092	Loss: 159.135
-51200/69092	Loss: 160.403
-64000/69092	Loss: 159.789
-Training time 0:03:34.944138
-Epoch: 98 Average loss: 160.01
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 102)
-0/69092	Loss: 157.861
-12800/69092	Loss: 160.768
-25600/69092	Loss: 160.062
-38400/69092	Loss: 159.811
-51200/69092	Loss: 159.629
-64000/69092	Loss: 159.130
-Training time 0:03:35.133845
-Epoch: 99 Average loss: 159.78
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 103)
-0/69092	Loss: 158.712
-12800/69092	Loss: 161.059
-25600/69092	Loss: 159.315
-38400/69092	Loss: 158.331
-51200/69092	Loss: 160.059
-64000/69092	Loss: 159.669
-Training time 0:03:34.706540
-Epoch: 100 Average loss: 159.69
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 104)
-0/69092	Loss: 155.913
-12800/69092	Loss: 159.568
-25600/69092	Loss: 158.244
-38400/69092	Loss: 160.843
-51200/69092	Loss: 160.379
-64000/69092	Loss: 158.854
-Training time 0:03:35.405917
-Epoch: 101 Average loss: 159.61
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 105)
-0/69092	Loss: 167.682
-12800/69092	Loss: 158.469
-25600/69092	Loss: 159.905
-38400/69092	Loss: 159.218
-51200/69092	Loss: 159.482
-64000/69092	Loss: 160.674
-Training time 0:03:35.620727
-Epoch: 102 Average loss: 159.61
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 106)
-0/69092	Loss: 171.435
-12800/69092	Loss: 159.276
-25600/69092	Loss: 161.324
-38400/69092	Loss: 158.919
-51200/69092	Loss: 159.018
-64000/69092	Loss: 157.720
-Training time 0:03:35.123399
-Epoch: 103 Average loss: 159.50
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 107)
-0/69092	Loss: 157.325
-12800/69092	Loss: 160.057
-25600/69092	Loss: 159.965
-38400/69092	Loss: 158.745
-51200/69092	Loss: 158.629
-64000/69092	Loss: 160.076
-Training time 0:03:35.501608
-Epoch: 104 Average loss: 159.54
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 108)
-0/69092	Loss: 157.509
-12800/69092	Loss: 160.053
-25600/69092	Loss: 158.607
-38400/69092	Loss: 159.661
-51200/69092	Loss: 160.847
-64000/69092	Loss: 158.248
-Training time 0:03:35.400709
-Epoch: 105 Average loss: 159.56
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 109)
-0/69092	Loss: 163.613
-12800/69092	Loss: 158.970
-25600/69092	Loss: 161.421
-38400/69092	Loss: 160.211
-51200/69092	Loss: 158.902
-64000/69092	Loss: 156.983
-Training time 0:03:36.255216
-Epoch: 106 Average loss: 159.40
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 110)
-0/69092	Loss: 155.686
-12800/69092	Loss: 160.493
-25600/69092	Loss: 160.184
-38400/69092	Loss: 159.047
-51200/69092	Loss: 158.804
-64000/69092	Loss: 158.537
-Training time 0:03:35.555878
-Epoch: 107 Average loss: 159.35
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 111)
-0/69092	Loss: 156.330
-12800/69092	Loss: 159.314
-25600/69092	Loss: 159.905
-38400/69092	Loss: 159.925
-51200/69092	Loss: 159.359
-64000/69092	Loss: 157.967
-Training time 0:03:35.554763
-Epoch: 108 Average loss: 159.25
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 112)
-0/69092	Loss: 155.420
-12800/69092	Loss: 159.249
-25600/69092	Loss: 161.278
-38400/69092	Loss: 159.389
-51200/69092	Loss: 157.854
-64000/69092	Loss: 159.796
-Training time 0:03:35.583739
-Epoch: 109 Average loss: 159.31
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 113)
-0/69092	Loss: 159.001
-12800/69092	Loss: 160.341
-25600/69092	Loss: 159.930
-38400/69092	Loss: 159.423
-51200/69092	Loss: 156.880
-64000/69092	Loss: 159.597
-Training time 0:03:35.430130
-Epoch: 110 Average loss: 159.26
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 114)
-0/69092	Loss: 158.623
-12800/69092	Loss: 158.642
-25600/69092	Loss: 158.103
-38400/69092	Loss: 160.518
-51200/69092	Loss: 159.409
-64000/69092	Loss: 159.840
-Training time 0:03:34.558461
-Epoch: 111 Average loss: 159.18
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 115)
-0/69092	Loss: 152.833
-12800/69092	Loss: 159.505
-25600/69092	Loss: 159.212
-38400/69092	Loss: 158.390
-51200/69092	Loss: 159.647
-64000/69092	Loss: 158.710
-Training time 0:03:34.888443
-Epoch: 112 Average loss: 159.09
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 116)
-0/69092	Loss: 155.050
-12800/69092	Loss: 158.368
-25600/69092	Loss: 158.714
-38400/69092	Loss: 158.764
-51200/69092	Loss: 159.854
-64000/69092	Loss: 159.695
-Training time 0:03:34.671602
-Epoch: 113 Average loss: 158.90
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 117)
-0/69092	Loss: 163.357
-12800/69092	Loss: 158.739
-25600/69092	Loss: 159.252
-38400/69092	Loss: 158.969
-51200/69092	Loss: 159.156
-64000/69092	Loss: 157.985
-Training time 0:03:34.661148
-Epoch: 114 Average loss: 158.96
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 118)
-0/69092	Loss: 162.348
-12800/69092	Loss: 159.469
-25600/69092	Loss: 157.832
-38400/69092	Loss: 159.314
-51200/69092	Loss: 157.827
-64000/69092	Loss: 159.283
-Training time 0:03:35.230302
-Epoch: 115 Average loss: 158.89
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 119)
-0/69092	Loss: 162.101
-12800/69092	Loss: 159.002
-25600/69092	Loss: 158.509
-38400/69092	Loss: 159.518
-51200/69092	Loss: 159.064
-64000/69092	Loss: 158.847
-Training time 0:03:35.700575
-Epoch: 116 Average loss: 158.84
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 120)
-0/69092	Loss: 154.467
-12800/69092	Loss: 160.387
-25600/69092	Loss: 157.670
-38400/69092	Loss: 159.577
-51200/69092	Loss: 159.122
-64000/69092	Loss: 157.489
-Training time 0:03:34.765808
-Epoch: 117 Average loss: 158.96
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 121)
-0/69092	Loss: 158.054
-12800/69092	Loss: 158.487
-25600/69092	Loss: 159.933
-38400/69092	Loss: 158.417
-51200/69092	Loss: 158.789
-64000/69092	Loss: 158.287
-Training time 0:03:34.685419
-Epoch: 118 Average loss: 158.90
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 122)
-0/69092	Loss: 157.949
-12800/69092	Loss: 158.047
-25600/69092	Loss: 159.524
-38400/69092	Loss: 158.612
-51200/69092	Loss: 158.843
-64000/69092	Loss: 158.874
-Training time 0:03:35.283052
-Epoch: 119 Average loss: 158.82
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 123)
-0/69092	Loss: 158.028
-12800/69092	Loss: 158.785
-25600/69092	Loss: 159.106
-38400/69092	Loss: 158.322
-51200/69092	Loss: 159.148
-64000/69092	Loss: 158.622
-Training time 0:03:35.123444
-Epoch: 120 Average loss: 158.91
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 124)
-0/69092	Loss: 153.756
-12800/69092	Loss: 158.229
-25600/69092	Loss: 157.864
-38400/69092	Loss: 158.555
-51200/69092	Loss: 158.647
-64000/69092	Loss: 159.850
-Training time 0:03:35.094660
-Epoch: 121 Average loss: 158.70
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 125)
-0/69092	Loss: 143.921
-12800/69092	Loss: 159.522
-25600/69092	Loss: 158.944
-38400/69092	Loss: 159.566
-51200/69092	Loss: 157.765
-64000/69092	Loss: 158.544
-Training time 0:03:35.945210
-Epoch: 122 Average loss: 158.67
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 126)
-0/69092	Loss: 168.748
-12800/69092	Loss: 156.964
-25600/69092	Loss: 158.709
-38400/69092	Loss: 158.935
-51200/69092	Loss: 158.750
-64000/69092	Loss: 158.742
-Training time 0:03:35.022846
-Epoch: 123 Average loss: 158.57
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 127)
-0/69092	Loss: 174.862
-12800/69092	Loss: 159.569
-25600/69092	Loss: 157.994
-38400/69092	Loss: 159.820
-51200/69092	Loss: 159.512
-64000/69092	Loss: 156.537
-Training time 0:03:36.069587
-Epoch: 124 Average loss: 158.71
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 128)
-0/69092	Loss: 157.101
-12800/69092	Loss: 158.661
-25600/69092	Loss: 158.794
-38400/69092	Loss: 158.160
-51200/69092	Loss: 158.458
-64000/69092	Loss: 158.821
-Training time 0:03:34.841007
-Epoch: 125 Average loss: 158.81
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 129)
-0/69092	Loss: 152.880
-12800/69092	Loss: 158.483
-25600/69092	Loss: 159.524
-38400/69092	Loss: 158.460
-51200/69092	Loss: 158.845
-64000/69092	Loss: 158.793
-Training time 0:03:35.971434
-Epoch: 126 Average loss: 158.69
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 130)
-0/69092	Loss: 154.705
-12800/69092	Loss: 159.422
-25600/69092	Loss: 157.914
-38400/69092	Loss: 158.562
-51200/69092	Loss: 158.664
-64000/69092	Loss: 158.733
-Training time 0:03:35.689516
-Epoch: 127 Average loss: 158.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 131)
-0/69092	Loss: 160.352
-12800/69092	Loss: 158.191
-25600/69092	Loss: 158.584
-38400/69092	Loss: 157.742
-51200/69092	Loss: 159.398
-64000/69092	Loss: 159.173
-Training time 0:03:35.079701
-Epoch: 128 Average loss: 158.71
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 132)
-0/69092	Loss: 162.502
-12800/69092	Loss: 157.818
-25600/69092	Loss: 157.109
-38400/69092	Loss: 159.595
-51200/69092	Loss: 158.628
-64000/69092	Loss: 159.182
-Training time 0:03:35.702826
-Epoch: 129 Average loss: 158.56
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 133)
-0/69092	Loss: 157.088
-12800/69092	Loss: 158.229
-25600/69092	Loss: 157.869
-38400/69092	Loss: 159.662
-51200/69092	Loss: 158.721
-64000/69092	Loss: 157.514
-Training time 0:03:35.305305
-Epoch: 130 Average loss: 158.46
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 134)
-0/69092	Loss: 159.425
-12800/69092	Loss: 158.244
-25600/69092	Loss: 159.269
-38400/69092	Loss: 158.749
-51200/69092	Loss: 157.334
-64000/69092	Loss: 158.863
-Training time 0:03:35.265594
-Epoch: 131 Average loss: 158.43
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 135)
-0/69092	Loss: 155.062
-12800/69092	Loss: 159.819
-25600/69092	Loss: 157.603
-38400/69092	Loss: 156.517
-51200/69092	Loss: 158.284
-64000/69092	Loss: 158.365
-Training time 0:03:35.083675
-Epoch: 132 Average loss: 158.22
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 136)
-0/69092	Loss: 159.165
-12800/69092	Loss: 158.364
-25600/69092	Loss: 158.555
-38400/69092	Loss: 157.526
-51200/69092	Loss: 157.675
-64000/69092	Loss: 159.562
-Training time 0:03:34.917954
-Epoch: 133 Average loss: 158.38
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 137)
-0/69092	Loss: 164.086
-12800/69092	Loss: 158.136
-25600/69092	Loss: 158.652
-38400/69092	Loss: 158.086
-51200/69092	Loss: 158.328
-64000/69092	Loss: 158.661
-Training time 0:03:36.238460
-Epoch: 134 Average loss: 158.39
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 138)
-0/69092	Loss: 160.262
-12800/69092	Loss: 158.737
-25600/69092	Loss: 157.851
-38400/69092	Loss: 158.741
-51200/69092	Loss: 159.681
-64000/69092	Loss: 157.209
-Training time 0:03:35.637006
-Epoch: 135 Average loss: 158.49
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 139)
-0/69092	Loss: 153.405
-12800/69092	Loss: 158.161
-25600/69092	Loss: 157.899
-38400/69092	Loss: 158.128
-51200/69092	Loss: 158.549
-64000/69092	Loss: 158.466
-Training time 0:03:35.008490
-Epoch: 136 Average loss: 158.32
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 140)
-0/69092	Loss: 163.934
-12800/69092	Loss: 158.955
-25600/69092	Loss: 157.057
-38400/69092	Loss: 158.497
-51200/69092	Loss: 157.707
-64000/69092	Loss: 158.849
-Training time 0:03:35.428050
-Epoch: 137 Average loss: 158.18
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 141)
-0/69092	Loss: 155.505
-12800/69092	Loss: 157.641
-25600/69092	Loss: 158.311
-38400/69092	Loss: 158.298
-51200/69092	Loss: 159.948
-64000/69092	Loss: 159.012
-Training time 0:03:35.474795
-Epoch: 138 Average loss: 158.51
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 142)
-0/69092	Loss: 157.240
-12800/69092	Loss: 157.478
-25600/69092	Loss: 157.290
-38400/69092	Loss: 159.291
-51200/69092	Loss: 159.086
-64000/69092	Loss: 157.686
-Training time 0:03:34.563481
-Epoch: 139 Average loss: 158.23
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 143)
-0/69092	Loss: 163.161
-12800/69092	Loss: 158.980
-25600/69092	Loss: 158.207
-38400/69092	Loss: 157.897
-51200/69092	Loss: 158.807
-64000/69092	Loss: 157.534
-Training time 0:03:35.396170
-Epoch: 140 Average loss: 158.26
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 144)
-0/69092	Loss: 158.131
-12800/69092	Loss: 158.350
-25600/69092	Loss: 159.413
-38400/69092	Loss: 157.611
-51200/69092	Loss: 157.664
-64000/69092	Loss: 158.728
-Training time 0:03:35.857792
-Epoch: 141 Average loss: 158.33
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 145)
-0/69092	Loss: 157.857
-12800/69092	Loss: 156.593
-25600/69092	Loss: 158.148
-38400/69092	Loss: 158.734
-51200/69092	Loss: 157.102
-64000/69092	Loss: 160.617
-Training time 0:03:35.646313
-Epoch: 142 Average loss: 158.24
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 146)
-0/69092	Loss: 156.539
-12800/69092	Loss: 157.214
-25600/69092	Loss: 157.716
-38400/69092	Loss: 158.455
-51200/69092	Loss: 159.153
-64000/69092	Loss: 158.133
-Training time 0:03:35.056588
-Epoch: 143 Average loss: 158.25
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 147)
-0/69092	Loss: 158.767
-12800/69092	Loss: 158.426
-25600/69092	Loss: 158.740
-38400/69092	Loss: 158.653
-51200/69092	Loss: 157.037
-64000/69092	Loss: 158.165
-Training time 0:03:35.533263
-Epoch: 144 Average loss: 158.28
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 148)
-0/69092	Loss: 168.808
-12800/69092	Loss: 158.690
-25600/69092	Loss: 158.282
-38400/69092	Loss: 158.720
-51200/69092	Loss: 158.501
-64000/69092	Loss: 156.006
-Training time 0:03:35.591309
-Epoch: 145 Average loss: 158.29
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 149)
-0/69092	Loss: 154.829
-12800/69092	Loss: 157.699
-25600/69092	Loss: 158.313
-38400/69092	Loss: 158.132
-51200/69092	Loss: 159.645
-64000/69092	Loss: 157.362
-Training time 0:03:34.651382
-Epoch: 146 Average loss: 158.38
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 150)
-0/69092	Loss: 159.604
-12800/69092	Loss: 158.903
-25600/69092	Loss: 158.258
-38400/69092	Loss: 156.677
-51200/69092	Loss: 158.281
-64000/69092	Loss: 158.580
-Training time 0:03:36.077990
-Epoch: 147 Average loss: 158.26
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 151)
-0/69092	Loss: 152.812
-12800/69092	Loss: 158.197
-25600/69092	Loss: 158.080
-38400/69092	Loss: 157.186
-51200/69092	Loss: 159.838
-64000/69092	Loss: 156.717
-Training time 0:03:35.545069
-Epoch: 148 Average loss: 158.00
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 152)
-0/69092	Loss: 149.683
-12800/69092	Loss: 159.115
-25600/69092	Loss: 157.753
-38400/69092	Loss: 157.867
-51200/69092	Loss: 158.096
-64000/69092	Loss: 156.865
-Training time 0:03:35.144237
-Epoch: 149 Average loss: 158.06
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 153)
-0/69092	Loss: 156.870
-12800/69092	Loss: 158.312
-25600/69092	Loss: 158.026
-38400/69092	Loss: 157.353
-51200/69092	Loss: 158.157
-64000/69092	Loss: 158.827
-Training time 0:03:34.748605
-Epoch: 150 Average loss: 158.13
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 154)
-0/69092	Loss: 151.995
-12800/69092	Loss: 159.054
-25600/69092	Loss: 157.990
-38400/69092	Loss: 158.319
-51200/69092	Loss: 157.961
-64000/69092	Loss: 157.131
-Training time 0:03:35.971841
-Epoch: 151 Average loss: 158.06
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 155)
-0/69092	Loss: 157.716
-12800/69092	Loss: 158.155
-25600/69092	Loss: 157.841
-38400/69092	Loss: 158.173
-51200/69092	Loss: 157.463
-64000/69092	Loss: 158.897
-Training time 0:03:35.695476
-Epoch: 152 Average loss: 158.11
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 156)
-0/69092	Loss: 158.047
-12800/69092	Loss: 156.737
-25600/69092	Loss: 159.180
-38400/69092	Loss: 158.536
-51200/69092	Loss: 157.577
-64000/69092	Loss: 158.394
-Training time 0:03:35.084412
-Epoch: 153 Average loss: 158.21
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 157)
-0/69092	Loss: 153.462
-12800/69092	Loss: 159.247
-25600/69092	Loss: 155.980
-38400/69092	Loss: 157.080
-51200/69092	Loss: 158.520
-64000/69092	Loss: 158.175
-Training time 0:03:35.337585
-Epoch: 154 Average loss: 157.86
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 158)
-0/69092	Loss: 161.419
-12800/69092	Loss: 157.782
-25600/69092	Loss: 158.716
-38400/69092	Loss: 158.169
-51200/69092	Loss: 158.782
-64000/69092	Loss: 156.570
-Training time 0:03:35.909995
-Epoch: 155 Average loss: 158.07
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 159)
-0/69092	Loss: 153.223
-12800/69092	Loss: 158.127
-25600/69092	Loss: 157.457
-38400/69092	Loss: 158.450
-51200/69092	Loss: 158.295
-64000/69092	Loss: 157.931
-Training time 0:03:36.134499
-Epoch: 156 Average loss: 158.02
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 160)
-0/69092	Loss: 162.210
-12800/69092	Loss: 158.300
-25600/69092	Loss: 157.865
-38400/69092	Loss: 158.634
-51200/69092	Loss: 158.413
-64000/69092	Loss: 156.988
-Training time 0:03:35.206750
-Epoch: 157 Average loss: 157.89
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 161)
-0/69092	Loss: 161.032
-12800/69092	Loss: 158.088
-25600/69092	Loss: 157.736
-38400/69092	Loss: 157.373
-51200/69092	Loss: 158.703
-64000/69092	Loss: 158.100
-Training time 0:03:35.589710
-Epoch: 158 Average loss: 158.08
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 162)
-0/69092	Loss: 153.675
-12800/69092	Loss: 158.432
-25600/69092	Loss: 158.292
-38400/69092	Loss: 157.321
-51200/69092	Loss: 157.387
-64000/69092	Loss: 158.348
-Training time 0:03:35.281603
-Epoch: 159 Average loss: 157.84
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 163)
-0/69092	Loss: 155.890
-12800/69092	Loss: 158.047
-25600/69092	Loss: 158.035
-38400/69092	Loss: 158.387
-51200/69092	Loss: 158.192
-64000/69092	Loss: 157.601
-Training time 0:03:35.345290
-Epoch: 160 Average loss: 158.03
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 164)
-0/69092	Loss: 162.835
-12800/69092	Loss: 157.335
-25600/69092	Loss: 157.960
-38400/69092	Loss: 158.461
-51200/69092	Loss: 158.302
-64000/69092	Loss: 157.646
-Training time 0:03:36.107215
-Epoch: 161 Average loss: 157.91
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 165)
-0/69092	Loss: 156.025
-12800/69092	Loss: 156.943
-25600/69092	Loss: 157.574
-38400/69092	Loss: 158.075
-51200/69092	Loss: 158.509
-64000/69092	Loss: 159.194
-Training time 0:03:36.127311
-Epoch: 162 Average loss: 157.96
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 166)
-0/69092	Loss: 151.681
-12800/69092	Loss: 158.233
-25600/69092	Loss: 156.736
-38400/69092	Loss: 158.012
-51200/69092	Loss: 158.785
-64000/69092	Loss: 157.544
-Training time 0:03:35.675035
-Epoch: 163 Average loss: 157.85
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 167)
-0/69092	Loss: 149.342
-12800/69092	Loss: 157.504
-25600/69092	Loss: 157.447
-38400/69092	Loss: 159.814
-51200/69092	Loss: 156.905
-64000/69092	Loss: 158.121
-Training time 0:03:34.849772
-Epoch: 164 Average loss: 157.92
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 168)
-0/69092	Loss: 158.956
-12800/69092	Loss: 158.108
-25600/69092	Loss: 158.557
-38400/69092	Loss: 157.314
-51200/69092	Loss: 156.850
-64000/69092	Loss: 157.646
-Training time 0:03:35.625526
-Epoch: 165 Average loss: 157.77
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 169)
-0/69092	Loss: 157.486
-12800/69092	Loss: 157.619
-25600/69092	Loss: 158.104
-38400/69092	Loss: 159.780
-51200/69092	Loss: 157.374
-64000/69092	Loss: 157.813
-Training time 0:03:35.019341
-Epoch: 166 Average loss: 158.02
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 170)
-0/69092	Loss: 152.670
-12800/69092	Loss: 158.740
-25600/69092	Loss: 157.840
-38400/69092	Loss: 158.200
-51200/69092	Loss: 159.229
-64000/69092	Loss: 157.155
-Training time 0:03:34.361037
-Epoch: 167 Average loss: 157.98
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 171)
-0/69092	Loss: 151.640
-12800/69092	Loss: 157.037
-25600/69092	Loss: 158.233
diff --git a/OAR.2066987.stdout b/OAR.2066987.stdout
deleted file mode 100644
index e0026953c1132b772ac79dc80a441f853c1fa15f..0000000000000000000000000000000000000000
--- a/OAR.2066987.stdout
+++ /dev/null
@@ -1,7588 +0,0 @@
-Namespace(batch_size=64, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_64', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
-creare new diretory experiment: rendered_chairs/beta_VAE_bs_64
-load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
-use 2 gpu who named:
-Tesla K80
-Tesla K80
-DataParallel(
-  (module): VAE(
-    (img_to_last_conv): Sequential(
-      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (1): ReLU()
-      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (3): ReLU()
-      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (5): ReLU()
-      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (7): ReLU()
-    )
-    (last_conv_to_continuous_features): Sequential(
-      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
-      (1): ReLU()
-    )
-    (features_to_hidden_continue): Sequential(
-      (0): Linear(in_features=256, out_features=20, bias=True)
-      (1): ReLU()
-    )
-    (latent_to_features): Sequential(
-      (0): Linear(in_features=10, out_features=256, bias=True)
-      (1): ReLU()
-    )
-    (features_to_img): Sequential(
-      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
-      (1): ReLU()
-      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (3): ReLU()
-      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (5): ReLU()
-      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (7): ReLU()
-      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (9): Sigmoid()
-    )
-  )
-)
-The number of parameters of model is 765335
-don't use continuous capacity
-=> no checkpoint found at 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last'
-0/69092	Loss: 2996.283
-3200/69092	Loss: 2849.192
-6400/69092	Loss: 969.503
-9600/69092	Loss: 533.346
-12800/69092	Loss: 488.827
-16000/69092	Loss: 465.819
-19200/69092	Loss: 455.119
-22400/69092	Loss: 411.276
-25600/69092	Loss: 292.547
-28800/69092	Loss: 250.187
-32000/69092	Loss: 235.426
-35200/69092	Loss: 230.877
-38400/69092	Loss: 228.614
-41600/69092	Loss: 229.160
-44800/69092	Loss: 235.136
-48000/69092	Loss: 225.149
-51200/69092	Loss: 227.474
-54400/69092	Loss: 230.152
-57600/69092	Loss: 226.084
-60800/69092	Loss: 223.701
-64000/69092	Loss: 231.250
-67200/69092	Loss: 227.171
-Training time 0:04:11.450352
-Epoch: 1 Average loss: 447.14
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 1)
-0/69092	Loss: 251.797
-3200/69092	Loss: 225.635
-6400/69092	Loss: 228.129
-9600/69092	Loss: 223.246
-12800/69092	Loss: 214.800
-16000/69092	Loss: 216.244
-19200/69092	Loss: 213.488
-22400/69092	Loss: 204.465
-25600/69092	Loss: 210.539
-28800/69092	Loss: 208.756
-32000/69092	Loss: 211.511
-35200/69092	Loss: 201.741
-38400/69092	Loss: 208.114
-41600/69092	Loss: 203.864
-44800/69092	Loss: 207.909
-48000/69092	Loss: 200.300
-51200/69092	Loss: 202.518
-54400/69092	Loss: 202.010
-57600/69092	Loss: 199.430
-60800/69092	Loss: 199.646
-64000/69092	Loss: 191.796
-67200/69092	Loss: 192.559
-Training time 0:01:56.222555
-Epoch: 2 Average loss: 207.52
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 2)
-0/69092	Loss: 213.776
-3200/69092	Loss: 195.255
-6400/69092	Loss: 190.901
-9600/69092	Loss: 193.280
-12800/69092	Loss: 190.337
-16000/69092	Loss: 190.632
-19200/69092	Loss: 188.622
-22400/69092	Loss: 189.298
-25600/69092	Loss: 192.051
-28800/69092	Loss: 189.590
-32000/69092	Loss: 187.170
-35200/69092	Loss: 189.669
-38400/69092	Loss: 187.333
-41600/69092	Loss: 186.043
-44800/69092	Loss: 187.783
-48000/69092	Loss: 189.375
-51200/69092	Loss: 187.365
-54400/69092	Loss: 191.405
-57600/69092	Loss: 183.256
-60800/69092	Loss: 186.471
-64000/69092	Loss: 186.531
-67200/69092	Loss: 190.937
-Training time 0:01:56.276983
-Epoch: 3 Average loss: 189.10
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 3)
-0/69092	Loss: 216.677
-3200/69092	Loss: 187.545
-6400/69092	Loss: 189.138
-9600/69092	Loss: 188.047
-12800/69092	Loss: 184.307
-16000/69092	Loss: 188.439
-19200/69092	Loss: 186.454
-22400/69092	Loss: 188.196
-25600/69092	Loss: 184.643
-28800/69092	Loss: 187.148
-32000/69092	Loss: 184.818
-35200/69092	Loss: 184.574
-38400/69092	Loss: 185.366
-41600/69092	Loss: 192.044
-44800/69092	Loss: 186.806
-48000/69092	Loss: 188.283
-51200/69092	Loss: 183.873
-54400/69092	Loss: 188.628
-57600/69092	Loss: 186.013
-60800/69092	Loss: 187.894
-64000/69092	Loss: 186.695
-67200/69092	Loss: 184.645
-Training time 0:01:58.635203
-Epoch: 4 Average loss: 186.84
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 4)
-0/69092	Loss: 203.150
-3200/69092	Loss: 188.230
-6400/69092	Loss: 184.638
-9600/69092	Loss: 186.948
-12800/69092	Loss: 188.693
-16000/69092	Loss: 183.433
-19200/69092	Loss: 187.675
-22400/69092	Loss: 187.232
-25600/69092	Loss: 187.306
-28800/69092	Loss: 187.523
-32000/69092	Loss: 186.648
-35200/69092	Loss: 186.452
-38400/69092	Loss: 183.993
-41600/69092	Loss: 185.320
-44800/69092	Loss: 182.017
-48000/69092	Loss: 183.917
-51200/69092	Loss: 186.099
-54400/69092	Loss: 179.476
-57600/69092	Loss: 182.079
-60800/69092	Loss: 179.817
-64000/69092	Loss: 181.986
-67200/69092	Loss: 177.421
-Training time 0:01:57.159992
-Epoch: 5 Average loss: 184.44
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 5)
-0/69092	Loss: 201.415
-3200/69092	Loss: 180.093
-6400/69092	Loss: 179.419
-9600/69092	Loss: 178.953
-12800/69092	Loss: 178.537
-16000/69092	Loss: 181.504
-19200/69092	Loss: 179.501
-22400/69092	Loss: 179.450
-25600/69092	Loss: 176.027
-28800/69092	Loss: 178.539
-32000/69092	Loss: 176.076
-35200/69092	Loss: 175.172
-38400/69092	Loss: 177.610
-41600/69092	Loss: 177.594
-44800/69092	Loss: 176.750
-48000/69092	Loss: 177.454
-51200/69092	Loss: 176.812
-54400/69092	Loss: 181.063
-57600/69092	Loss: 176.534
-60800/69092	Loss: 177.212
-64000/69092	Loss: 173.409
-67200/69092	Loss: 175.427
-Training time 0:01:58.295935
-Epoch: 6 Average loss: 177.73
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 6)
-0/69092	Loss: 181.005
-3200/69092	Loss: 173.445
-6400/69092	Loss: 178.272
-9600/69092	Loss: 174.963
-12800/69092	Loss: 177.643
-16000/69092	Loss: 175.353
-19200/69092	Loss: 176.767
-22400/69092	Loss: 172.433
-25600/69092	Loss: 175.713
-28800/69092	Loss: 172.440
-32000/69092	Loss: 172.158
-35200/69092	Loss: 172.938
-38400/69092	Loss: 173.011
-41600/69092	Loss: 173.479
-44800/69092	Loss: 173.250
-48000/69092	Loss: 174.012
-51200/69092	Loss: 174.165
-54400/69092	Loss: 170.919
-57600/69092	Loss: 174.869
-60800/69092	Loss: 172.601
-64000/69092	Loss: 173.367
-67200/69092	Loss: 170.740
-Training time 0:01:56.841558
-Epoch: 7 Average loss: 173.80
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 7)
-0/69092	Loss: 155.570
-3200/69092	Loss: 171.951
-6400/69092	Loss: 172.733
-9600/69092	Loss: 173.404
-12800/69092	Loss: 175.665
-16000/69092	Loss: 169.749
-19200/69092	Loss: 168.763
-22400/69092	Loss: 168.842
-25600/69092	Loss: 167.193
-28800/69092	Loss: 168.480
-32000/69092	Loss: 172.957
-35200/69092	Loss: 167.509
-38400/69092	Loss: 170.859
-41600/69092	Loss: 169.670
-44800/69092	Loss: 169.974
-48000/69092	Loss: 170.610
-51200/69092	Loss: 169.231
-54400/69092	Loss: 169.917
-57600/69092	Loss: 166.675
-60800/69092	Loss: 169.919
-64000/69092	Loss: 168.816
-67200/69092	Loss: 167.087
-Training time 0:01:57.474977
-Epoch: 8 Average loss: 169.92
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 8)
-0/69092	Loss: 152.314
-3200/69092	Loss: 169.040
-6400/69092	Loss: 165.803
-9600/69092	Loss: 167.887
-12800/69092	Loss: 170.555
-16000/69092	Loss: 168.071
-19200/69092	Loss: 168.366
-22400/69092	Loss: 170.014
-25600/69092	Loss: 165.270
-28800/69092	Loss: 169.082
-32000/69092	Loss: 168.066
-35200/69092	Loss: 167.370
-38400/69092	Loss: 167.869
-41600/69092	Loss: 165.092
-44800/69092	Loss: 168.710
-48000/69092	Loss: 167.832
-51200/69092	Loss: 169.236
-54400/69092	Loss: 169.679
-57600/69092	Loss: 165.386
-60800/69092	Loss: 166.058
-64000/69092	Loss: 165.404
-67200/69092	Loss: 167.973
-Training time 0:01:58.003875
-Epoch: 9 Average loss: 167.62
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 9)
-0/69092	Loss: 186.743
-3200/69092	Loss: 166.642
-6400/69092	Loss: 165.061
-9600/69092	Loss: 165.308
-12800/69092	Loss: 168.703
-16000/69092	Loss: 168.617
-19200/69092	Loss: 170.108
-22400/69092	Loss: 167.263
-25600/69092	Loss: 165.110
-28800/69092	Loss: 167.110
-32000/69092	Loss: 168.740
-35200/69092	Loss: 167.422
-38400/69092	Loss: 166.927
-41600/69092	Loss: 165.685
-44800/69092	Loss: 166.346
-48000/69092	Loss: 167.761
-51200/69092	Loss: 165.619
-54400/69092	Loss: 162.744
-57600/69092	Loss: 167.680
-60800/69092	Loss: 166.785
-64000/69092	Loss: 166.229
-67200/69092	Loss: 164.606
-Training time 0:01:57.541170
-Epoch: 10 Average loss: 166.70
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 10)
-0/69092	Loss: 174.037
-3200/69092	Loss: 168.532
-6400/69092	Loss: 164.939
-9600/69092	Loss: 168.164
-12800/69092	Loss: 167.037
-16000/69092	Loss: 167.490
-19200/69092	Loss: 164.382
-22400/69092	Loss: 162.732
-25600/69092	Loss: 164.682
-28800/69092	Loss: 166.368
-32000/69092	Loss: 168.401
-35200/69092	Loss: 165.558
-38400/69092	Loss: 163.828
-41600/69092	Loss: 162.653
-44800/69092	Loss: 164.643
-48000/69092	Loss: 163.984
-51200/69092	Loss: 163.990
-54400/69092	Loss: 163.986
-57600/69092	Loss: 162.717
-60800/69092	Loss: 164.781
-64000/69092	Loss: 163.207
-67200/69092	Loss: 165.003
-Training time 0:01:56.971581
-Epoch: 11 Average loss: 164.96
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 11)
-0/69092	Loss: 166.630
-3200/69092	Loss: 164.078
-6400/69092	Loss: 163.281
-9600/69092	Loss: 161.550
-12800/69092	Loss: 162.644
-16000/69092	Loss: 165.764
-19200/69092	Loss: 161.325
-22400/69092	Loss: 164.922
-25600/69092	Loss: 162.244
-28800/69092	Loss: 161.489
-32000/69092	Loss: 163.428
-35200/69092	Loss: 160.567
-38400/69092	Loss: 161.385
-41600/69092	Loss: 165.746
-44800/69092	Loss: 161.974
-48000/69092	Loss: 163.419
-51200/69092	Loss: 162.336
-54400/69092	Loss: 161.887
-57600/69092	Loss: 163.420
-60800/69092	Loss: 161.745
-64000/69092	Loss: 165.138
-67200/69092	Loss: 165.768
-Training time 0:01:57.907880
-Epoch: 12 Average loss: 163.08
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 12)
-0/69092	Loss: 152.506
-3200/69092	Loss: 162.750
-6400/69092	Loss: 161.690
-9600/69092	Loss: 159.541
-12800/69092	Loss: 162.265
-16000/69092	Loss: 163.082
-19200/69092	Loss: 163.886
-22400/69092	Loss: 162.987
-25600/69092	Loss: 162.344
-28800/69092	Loss: 160.054
-32000/69092	Loss: 165.178
-35200/69092	Loss: 160.814
-38400/69092	Loss: 160.135
-41600/69092	Loss: 163.047
-44800/69092	Loss: 163.450
-48000/69092	Loss: 162.674
-51200/69092	Loss: 163.462
-54400/69092	Loss: 161.420
-57600/69092	Loss: 162.183
-60800/69092	Loss: 159.362
-64000/69092	Loss: 162.698
-67200/69092	Loss: 163.860
-Training time 0:01:57.682821
-Epoch: 13 Average loss: 162.29
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 13)
-0/69092	Loss: 151.394
-3200/69092	Loss: 160.503
-6400/69092	Loss: 162.126
-9600/69092	Loss: 161.237
-12800/69092	Loss: 161.298
-16000/69092	Loss: 161.677
-19200/69092	Loss: 161.436
-22400/69092	Loss: 158.760
-25600/69092	Loss: 163.906
-28800/69092	Loss: 158.558
-32000/69092	Loss: 162.262
-35200/69092	Loss: 161.091
-38400/69092	Loss: 162.268
-41600/69092	Loss: 166.815
-44800/69092	Loss: 161.044
-48000/69092	Loss: 161.988
-51200/69092	Loss: 165.298
-54400/69092	Loss: 165.238
-57600/69092	Loss: 163.105
-60800/69092	Loss: 163.867
-64000/69092	Loss: 158.813
-67200/69092	Loss: 163.015
-Training time 0:01:57.568042
-Epoch: 14 Average loss: 162.09
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 14)
-0/69092	Loss: 138.507
-3200/69092	Loss: 161.731
-6400/69092	Loss: 164.068
-9600/69092	Loss: 161.188
-12800/69092	Loss: 159.349
-16000/69092	Loss: 162.151
-19200/69092	Loss: 160.767
-22400/69092	Loss: 160.923
-25600/69092	Loss: 162.628
-28800/69092	Loss: 162.037
-32000/69092	Loss: 162.783
-35200/69092	Loss: 161.348
-38400/69092	Loss: 163.619
-41600/69092	Loss: 164.794
-44800/69092	Loss: 161.011
-48000/69092	Loss: 164.298
-51200/69092	Loss: 162.475
-54400/69092	Loss: 158.875
-57600/69092	Loss: 160.008
-60800/69092	Loss: 161.022
-64000/69092	Loss: 158.871
-67200/69092	Loss: 161.856
-Training time 0:01:57.727705
-Epoch: 15 Average loss: 161.58
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 15)
-0/69092	Loss: 145.301
-3200/69092	Loss: 163.730
-6400/69092	Loss: 159.274
-9600/69092	Loss: 160.533
-12800/69092	Loss: 163.232
-16000/69092	Loss: 160.052
-19200/69092	Loss: 158.232
-22400/69092	Loss: 161.700
-25600/69092	Loss: 161.574
-28800/69092	Loss: 159.018
-32000/69092	Loss: 163.837
-35200/69092	Loss: 161.704
-38400/69092	Loss: 161.651
-41600/69092	Loss: 158.541
-44800/69092	Loss: 162.820
-48000/69092	Loss: 162.896
-51200/69092	Loss: 158.152
-54400/69092	Loss: 160.193
-57600/69092	Loss: 160.873
-60800/69092	Loss: 162.326
-64000/69092	Loss: 164.243
-67200/69092	Loss: 160.145
-Training time 0:01:57.861897
-Epoch: 16 Average loss: 161.19
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 16)
-0/69092	Loss: 155.339
-3200/69092	Loss: 162.152
-6400/69092	Loss: 161.102
-9600/69092	Loss: 159.391
-12800/69092	Loss: 161.266
-16000/69092	Loss: 161.011
-19200/69092	Loss: 160.102
-22400/69092	Loss: 160.218
-25600/69092	Loss: 160.872
-28800/69092	Loss: 160.790
-32000/69092	Loss: 159.368
-35200/69092	Loss: 162.241
-38400/69092	Loss: 160.620
-41600/69092	Loss: 160.933
-44800/69092	Loss: 158.249
-48000/69092	Loss: 160.275
-51200/69092	Loss: 161.236
-54400/69092	Loss: 162.588
-57600/69092	Loss: 164.950
-60800/69092	Loss: 161.210
-64000/69092	Loss: 162.227
-67200/69092	Loss: 161.003
-Training time 0:01:57.713720
-Epoch: 17 Average loss: 161.15
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 17)
-0/69092	Loss: 148.239
-3200/69092	Loss: 159.891
-6400/69092	Loss: 164.055
-9600/69092	Loss: 159.331
-12800/69092	Loss: 161.076
-16000/69092	Loss: 158.351
-19200/69092	Loss: 158.842
-22400/69092	Loss: 162.516
-25600/69092	Loss: 164.261
-28800/69092	Loss: 160.496
-32000/69092	Loss: 162.196
-35200/69092	Loss: 161.130
-38400/69092	Loss: 163.890
-41600/69092	Loss: 158.830
-44800/69092	Loss: 158.579
-48000/69092	Loss: 157.500
-51200/69092	Loss: 159.903
-54400/69092	Loss: 158.277
-57600/69092	Loss: 157.346
-60800/69092	Loss: 161.180
-64000/69092	Loss: 159.906
-67200/69092	Loss: 163.404
-Training time 0:01:56.465208
-Epoch: 18 Average loss: 160.56
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 18)
-0/69092	Loss: 159.095
-3200/69092	Loss: 164.018
-6400/69092	Loss: 161.884
-9600/69092	Loss: 162.794
-12800/69092	Loss: 159.886
-16000/69092	Loss: 159.917
-19200/69092	Loss: 162.400
-22400/69092	Loss: 161.910
-25600/69092	Loss: 160.812
-28800/69092	Loss: 160.608
-32000/69092	Loss: 157.240
-35200/69092	Loss: 161.936
-38400/69092	Loss: 158.369
-41600/69092	Loss: 159.212
-44800/69092	Loss: 161.405
-48000/69092	Loss: 156.917
-51200/69092	Loss: 159.386
-54400/69092	Loss: 161.999
-57600/69092	Loss: 159.899
-60800/69092	Loss: 156.736
-64000/69092	Loss: 159.510
-67200/69092	Loss: 160.396
-Training time 0:01:57.294461
-Epoch: 19 Average loss: 160.37
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 19)
-0/69092	Loss: 166.383
-3200/69092	Loss: 156.651
-6400/69092	Loss: 156.774
-9600/69092	Loss: 161.875
-12800/69092	Loss: 159.251
-16000/69092	Loss: 161.151
-19200/69092	Loss: 158.966
-22400/69092	Loss: 160.892
-25600/69092	Loss: 161.681
-28800/69092	Loss: 159.795
-32000/69092	Loss: 163.193
-35200/69092	Loss: 160.392
-38400/69092	Loss: 160.293
-41600/69092	Loss: 160.649
-44800/69092	Loss: 157.463
-48000/69092	Loss: 158.044
-51200/69092	Loss: 160.192
-54400/69092	Loss: 157.318
-57600/69092	Loss: 161.197
-60800/69092	Loss: 159.937
-64000/69092	Loss: 160.161
-67200/69092	Loss: 161.669
-Training time 0:01:56.904126
-Epoch: 20 Average loss: 159.91
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 20)
-0/69092	Loss: 171.773
-3200/69092	Loss: 159.427
-6400/69092	Loss: 158.473
-9600/69092	Loss: 160.956
-12800/69092	Loss: 159.469
-16000/69092	Loss: 160.518
-19200/69092	Loss: 159.570
-22400/69092	Loss: 159.130
-25600/69092	Loss: 159.077
-28800/69092	Loss: 160.857
-32000/69092	Loss: 158.243
-35200/69092	Loss: 159.962
-38400/69092	Loss: 158.049
-41600/69092	Loss: 160.047
-44800/69092	Loss: 158.965
-48000/69092	Loss: 159.137
-51200/69092	Loss: 161.349
-54400/69092	Loss: 158.891
-57600/69092	Loss: 157.075
-60800/69092	Loss: 157.941
-64000/69092	Loss: 160.834
-67200/69092	Loss: 160.211
-Training time 0:01:56.811086
-Epoch: 21 Average loss: 159.56
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 21)
-0/69092	Loss: 172.026
-3200/69092	Loss: 158.737
-6400/69092	Loss: 159.783
-9600/69092	Loss: 158.712
-12800/69092	Loss: 161.717
-16000/69092	Loss: 157.204
-19200/69092	Loss: 158.553
-22400/69092	Loss: 159.628
-25600/69092	Loss: 159.737
-28800/69092	Loss: 158.026
-32000/69092	Loss: 157.675
-35200/69092	Loss: 158.144
-38400/69092	Loss: 159.967
-41600/69092	Loss: 157.859
-44800/69092	Loss: 159.856
-48000/69092	Loss: 158.685
-51200/69092	Loss: 160.014
-54400/69092	Loss: 160.899
-57600/69092	Loss: 159.168
-60800/69092	Loss: 160.320
-64000/69092	Loss: 160.828
-67200/69092	Loss: 160.718
-Training time 0:01:57.005308
-Epoch: 22 Average loss: 159.40
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 22)
-0/69092	Loss: 162.527
-3200/69092	Loss: 159.943
-6400/69092	Loss: 159.977
-9600/69092	Loss: 159.632
-12800/69092	Loss: 157.619
-16000/69092	Loss: 160.869
-19200/69092	Loss: 159.872
-22400/69092	Loss: 157.746
-25600/69092	Loss: 162.287
-28800/69092	Loss: 158.565
-32000/69092	Loss: 159.370
-35200/69092	Loss: 157.351
-38400/69092	Loss: 157.113
-41600/69092	Loss: 159.929
-44800/69092	Loss: 157.951
-48000/69092	Loss: 154.322
-51200/69092	Loss: 159.021
-54400/69092	Loss: 162.566
-57600/69092	Loss: 161.822
-60800/69092	Loss: 159.119
-64000/69092	Loss: 158.475
-67200/69092	Loss: 160.544
-Training time 0:01:56.138479
-Epoch: 23 Average loss: 159.25
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 23)
-0/69092	Loss: 159.601
-3200/69092	Loss: 156.261
-6400/69092	Loss: 159.154
-9600/69092	Loss: 160.216
-12800/69092	Loss: 160.138
-16000/69092	Loss: 160.859
-19200/69092	Loss: 156.390
-22400/69092	Loss: 157.362
-25600/69092	Loss: 161.009
-28800/69092	Loss: 161.188
-32000/69092	Loss: 158.415
-35200/69092	Loss: 161.258
-38400/69092	Loss: 159.534
-41600/69092	Loss: 161.277
-44800/69092	Loss: 158.023
-48000/69092	Loss: 159.033
-51200/69092	Loss: 156.571
-54400/69092	Loss: 157.854
-57600/69092	Loss: 157.330
-60800/69092	Loss: 157.524
-64000/69092	Loss: 161.840
-67200/69092	Loss: 158.968
-Training time 0:01:57.433571
-Epoch: 24 Average loss: 159.01
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 24)
-0/69092	Loss: 150.259
-3200/69092	Loss: 158.042
-6400/69092	Loss: 154.284
-9600/69092	Loss: 160.489
-12800/69092	Loss: 158.749
-16000/69092	Loss: 159.962
-19200/69092	Loss: 160.096
-22400/69092	Loss: 157.985
-25600/69092	Loss: 158.357
-28800/69092	Loss: 158.214
-32000/69092	Loss: 159.433
-35200/69092	Loss: 158.871
-38400/69092	Loss: 161.416
-41600/69092	Loss: 159.513
-44800/69092	Loss: 155.319
-48000/69092	Loss: 158.951
-51200/69092	Loss: 157.902
-54400/69092	Loss: 160.520
-57600/69092	Loss: 160.258
-60800/69092	Loss: 161.847
-64000/69092	Loss: 160.397
-67200/69092	Loss: 157.660
-Training time 0:01:56.686785
-Epoch: 25 Average loss: 158.97
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 25)
-0/69092	Loss: 175.847
-3200/69092	Loss: 157.577
-6400/69092	Loss: 157.154
-9600/69092	Loss: 157.575
-12800/69092	Loss: 156.347
-16000/69092	Loss: 158.304
-19200/69092	Loss: 155.464
-22400/69092	Loss: 159.012
-25600/69092	Loss: 159.411
-28800/69092	Loss: 160.263
-32000/69092	Loss: 160.227
-35200/69092	Loss: 159.304
-38400/69092	Loss: 158.754
-41600/69092	Loss: 159.499
-44800/69092	Loss: 159.814
-48000/69092	Loss: 158.230
-51200/69092	Loss: 157.477
-54400/69092	Loss: 159.319
-57600/69092	Loss: 159.392
-60800/69092	Loss: 156.280
-64000/69092	Loss: 161.949
-67200/69092	Loss: 158.237
-Training time 0:01:57.091565
-Epoch: 26 Average loss: 158.62
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 26)
-0/69092	Loss: 174.317
-3200/69092	Loss: 158.828
-6400/69092	Loss: 158.258
-9600/69092	Loss: 159.041
-12800/69092	Loss: 158.030
-16000/69092	Loss: 156.668
-19200/69092	Loss: 156.140
-22400/69092	Loss: 158.096
-25600/69092	Loss: 161.542
-28800/69092	Loss: 158.097
-32000/69092	Loss: 161.879
-35200/69092	Loss: 160.185
-38400/69092	Loss: 159.539
-41600/69092	Loss: 156.600
-44800/69092	Loss: 159.622
-48000/69092	Loss: 158.632
-51200/69092	Loss: 158.489
-54400/69092	Loss: 158.777
-57600/69092	Loss: 160.157
-60800/69092	Loss: 157.835
-64000/69092	Loss: 161.454
-67200/69092	Loss: 157.710
-Training time 0:01:57.573170
-Epoch: 27 Average loss: 158.77
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 27)
-0/69092	Loss: 169.950
-3200/69092	Loss: 160.448
-6400/69092	Loss: 157.658
-9600/69092	Loss: 157.383
-12800/69092	Loss: 158.649
-16000/69092	Loss: 155.918
-19200/69092	Loss: 161.217
-22400/69092	Loss: 158.561
-25600/69092	Loss: 157.231
-28800/69092	Loss: 156.257
-32000/69092	Loss: 160.891
-35200/69092	Loss: 157.534
-38400/69092	Loss: 158.610
-41600/69092	Loss: 161.252
-44800/69092	Loss: 156.420
-48000/69092	Loss: 158.420
-51200/69092	Loss: 156.531
-54400/69092	Loss: 158.198
-57600/69092	Loss: 159.361
-60800/69092	Loss: 158.509
-64000/69092	Loss: 160.518
-67200/69092	Loss: 158.676
-Training time 0:01:57.898555
-Epoch: 28 Average loss: 158.56
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 28)
-0/69092	Loss: 180.791
-3200/69092	Loss: 158.214
-6400/69092	Loss: 160.183
-9600/69092	Loss: 159.472
-12800/69092	Loss: 158.663
-16000/69092	Loss: 157.854
-19200/69092	Loss: 155.967
-22400/69092	Loss: 161.755
-25600/69092	Loss: 156.276
-28800/69092	Loss: 158.536
-32000/69092	Loss: 157.682
-35200/69092	Loss: 157.598
-38400/69092	Loss: 159.632
-41600/69092	Loss: 158.373
-44800/69092	Loss: 160.541
-48000/69092	Loss: 157.475
-51200/69092	Loss: 158.080
-54400/69092	Loss: 158.006
-57600/69092	Loss: 157.436
-60800/69092	Loss: 157.818
-64000/69092	Loss: 157.580
-67200/69092	Loss: 159.494
-Training time 0:01:57.589396
-Epoch: 29 Average loss: 158.44
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 29)
-0/69092	Loss: 154.473
-3200/69092	Loss: 154.529
-6400/69092	Loss: 157.999
-9600/69092	Loss: 157.591
-12800/69092	Loss: 158.705
-16000/69092	Loss: 158.318
-19200/69092	Loss: 156.009
-22400/69092	Loss: 155.793
-25600/69092	Loss: 158.868
-28800/69092	Loss: 156.945
-32000/69092	Loss: 157.975
-35200/69092	Loss: 158.751
-38400/69092	Loss: 158.416
-41600/69092	Loss: 162.335
-44800/69092	Loss: 157.066
-48000/69092	Loss: 160.380
-51200/69092	Loss: 161.233
-54400/69092	Loss: 161.845
-57600/69092	Loss: 158.342
-60800/69092	Loss: 154.247
-64000/69092	Loss: 156.272
-67200/69092	Loss: 159.381
-Training time 0:01:57.963856
-Epoch: 30 Average loss: 158.13
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 30)
-0/69092	Loss: 149.043
-3200/69092	Loss: 156.477
-6400/69092	Loss: 158.939
-9600/69092	Loss: 157.941
-12800/69092	Loss: 157.031
-16000/69092	Loss: 157.882
-19200/69092	Loss: 156.246
-22400/69092	Loss: 159.650
-25600/69092	Loss: 158.450
-28800/69092	Loss: 158.476
-32000/69092	Loss: 155.707
-35200/69092	Loss: 159.538
-38400/69092	Loss: 158.855
-41600/69092	Loss: 159.387
-44800/69092	Loss: 160.851
-48000/69092	Loss: 157.442
-51200/69092	Loss: 157.171
-54400/69092	Loss: 160.320
-57600/69092	Loss: 160.352
-60800/69092	Loss: 157.819
-64000/69092	Loss: 157.999
-67200/69092	Loss: 156.369
-Training time 0:01:57.846660
-Epoch: 31 Average loss: 158.17
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 31)
-0/69092	Loss: 166.044
-3200/69092	Loss: 157.123
-6400/69092	Loss: 158.867
-9600/69092	Loss: 158.108
-12800/69092	Loss: 160.383
-16000/69092	Loss: 160.470
-19200/69092	Loss: 157.369
-22400/69092	Loss: 157.906
-25600/69092	Loss: 162.166
-28800/69092	Loss: 157.768
-32000/69092	Loss: 156.997
-35200/69092	Loss: 157.754
-38400/69092	Loss: 157.299
-41600/69092	Loss: 155.928
-44800/69092	Loss: 159.950
-48000/69092	Loss: 153.524
-51200/69092	Loss: 157.030
-54400/69092	Loss: 162.447
-57600/69092	Loss: 156.819
-60800/69092	Loss: 157.239
-64000/69092	Loss: 161.310
-67200/69092	Loss: 157.672
-Training time 0:01:57.086052
-Epoch: 32 Average loss: 158.23
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 32)
-0/69092	Loss: 141.886
-3200/69092	Loss: 159.636
-6400/69092	Loss: 157.995
-9600/69092	Loss: 158.156
-12800/69092	Loss: 159.822
-16000/69092	Loss: 157.998
-19200/69092	Loss: 157.688
-22400/69092	Loss: 157.635
-25600/69092	Loss: 158.038
-28800/69092	Loss: 159.169
-32000/69092	Loss: 155.065
-35200/69092	Loss: 160.872
-38400/69092	Loss: 157.325
-41600/69092	Loss: 154.707
-44800/69092	Loss: 156.744
-48000/69092	Loss: 154.908
-51200/69092	Loss: 159.643
-54400/69092	Loss: 159.798
-57600/69092	Loss: 159.108
-60800/69092	Loss: 157.741
-64000/69092	Loss: 159.772
-67200/69092	Loss: 156.970
-Training time 0:01:56.965621
-Epoch: 33 Average loss: 158.03
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 33)
-0/69092	Loss: 149.203
-3200/69092	Loss: 159.589
-6400/69092	Loss: 158.096
-9600/69092	Loss: 160.628
-12800/69092	Loss: 158.932
-16000/69092	Loss: 157.386
-19200/69092	Loss: 155.842
-22400/69092	Loss: 158.107
-25600/69092	Loss: 158.379
-28800/69092	Loss: 158.827
-32000/69092	Loss: 160.503
-35200/69092	Loss: 157.624
-38400/69092	Loss: 158.499
-41600/69092	Loss: 158.835
-44800/69092	Loss: 158.024
-48000/69092	Loss: 157.228
-51200/69092	Loss: 159.710
-54400/69092	Loss: 163.208
-57600/69092	Loss: 159.277
-60800/69092	Loss: 156.224
-64000/69092	Loss: 155.559
-67200/69092	Loss: 154.168
-Training time 0:01:57.468334
-Epoch: 34 Average loss: 158.30
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 34)
-0/69092	Loss: 155.562
-3200/69092	Loss: 157.613
-6400/69092	Loss: 159.193
-9600/69092	Loss: 157.724
-12800/69092	Loss: 159.928
-16000/69092	Loss: 156.318
-19200/69092	Loss: 156.519
-22400/69092	Loss: 156.817
-25600/69092	Loss: 158.138
-28800/69092	Loss: 157.492
-32000/69092	Loss: 154.690
-35200/69092	Loss: 157.779
-38400/69092	Loss: 159.212
-41600/69092	Loss: 159.987
-44800/69092	Loss: 157.799
-48000/69092	Loss: 157.890
-51200/69092	Loss: 162.650
-54400/69092	Loss: 157.699
-57600/69092	Loss: 157.586
-60800/69092	Loss: 156.425
-64000/69092	Loss: 157.283
-67200/69092	Loss: 159.906
-Training time 0:01:57.655810
-Epoch: 35 Average loss: 158.11
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 35)
-0/69092	Loss: 168.523
-3200/69092	Loss: 160.645
-6400/69092	Loss: 157.500
-9600/69092	Loss: 154.921
-12800/69092	Loss: 160.682
-16000/69092	Loss: 157.425
-19200/69092	Loss: 155.782
-22400/69092	Loss: 159.781
-25600/69092	Loss: 160.874
-28800/69092	Loss: 158.889
-32000/69092	Loss: 156.878
-35200/69092	Loss: 156.351
-38400/69092	Loss: 158.159
-41600/69092	Loss: 159.099
-44800/69092	Loss: 156.303
-48000/69092	Loss: 158.400
-51200/69092	Loss: 160.983
-54400/69092	Loss: 159.926
-57600/69092	Loss: 156.810
-60800/69092	Loss: 155.804
-64000/69092	Loss: 155.990
-67200/69092	Loss: 156.750
-Training time 0:01:57.653648
-Epoch: 36 Average loss: 157.88
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 36)
-0/69092	Loss: 146.145
-3200/69092	Loss: 158.221
-6400/69092	Loss: 156.710
-9600/69092	Loss: 154.593
-12800/69092	Loss: 155.646
-16000/69092	Loss: 160.545
-19200/69092	Loss: 159.107
-22400/69092	Loss: 158.840
-25600/69092	Loss: 158.424
-28800/69092	Loss: 157.260
-32000/69092	Loss: 156.305
-35200/69092	Loss: 159.250
-38400/69092	Loss: 157.572
-41600/69092	Loss: 157.123
-44800/69092	Loss: 158.622
-48000/69092	Loss: 160.923
-51200/69092	Loss: 157.823
-54400/69092	Loss: 158.834
-57600/69092	Loss: 160.488
-60800/69092	Loss: 159.627
-64000/69092	Loss: 157.076
-67200/69092	Loss: 157.016
-Training time 0:01:56.633660
-Epoch: 37 Average loss: 158.05
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 37)
-0/69092	Loss: 159.461
-3200/69092	Loss: 157.414
-6400/69092	Loss: 156.563
-9600/69092	Loss: 157.638
-12800/69092	Loss: 159.852
-16000/69092	Loss: 158.049
-19200/69092	Loss: 159.267
-22400/69092	Loss: 157.438
-25600/69092	Loss: 161.260
-28800/69092	Loss: 157.021
-32000/69092	Loss: 158.902
-35200/69092	Loss: 157.195
-38400/69092	Loss: 157.308
-41600/69092	Loss: 158.286
-44800/69092	Loss: 157.514
-48000/69092	Loss: 159.925
-51200/69092	Loss: 157.633
-54400/69092	Loss: 151.464
-57600/69092	Loss: 159.884
-60800/69092	Loss: 158.198
-64000/69092	Loss: 159.784
-67200/69092	Loss: 156.667
-Training time 0:01:56.737842
-Epoch: 38 Average loss: 157.94
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 38)
-0/69092	Loss: 147.399
-3200/69092	Loss: 158.412
-6400/69092	Loss: 157.194
-9600/69092	Loss: 159.238
-12800/69092	Loss: 159.889
-16000/69092	Loss: 153.887
-19200/69092	Loss: 157.620
-22400/69092	Loss: 158.452
-25600/69092	Loss: 156.053
-28800/69092	Loss: 157.084
-32000/69092	Loss: 157.309
-35200/69092	Loss: 159.122
-38400/69092	Loss: 156.366
-41600/69092	Loss: 157.924
-44800/69092	Loss: 157.066
-48000/69092	Loss: 161.849
-51200/69092	Loss: 158.760
-54400/69092	Loss: 155.814
-57600/69092	Loss: 156.654
-60800/69092	Loss: 160.296
-64000/69092	Loss: 158.226
-67200/69092	Loss: 160.055
-Training time 0:01:57.367414
-Epoch: 39 Average loss: 157.93
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 39)
-0/69092	Loss: 164.177
-3200/69092	Loss: 156.984
-6400/69092	Loss: 156.742
-9600/69092	Loss: 155.836
-12800/69092	Loss: 155.307
-16000/69092	Loss: 155.141
-19200/69092	Loss: 161.373
-22400/69092	Loss: 157.936
-25600/69092	Loss: 157.795
-28800/69092	Loss: 158.593
-32000/69092	Loss: 158.277
-35200/69092	Loss: 157.714
-38400/69092	Loss: 158.840
-41600/69092	Loss: 156.470
-44800/69092	Loss: 160.162
-48000/69092	Loss: 159.038
-51200/69092	Loss: 157.774
-54400/69092	Loss: 158.154
-57600/69092	Loss: 158.726
-60800/69092	Loss: 157.381
-64000/69092	Loss: 158.056
-67200/69092	Loss: 157.962
-Training time 0:01:56.571606
-Epoch: 40 Average loss: 157.82
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 40)
-0/69092	Loss: 163.158
-3200/69092	Loss: 154.205
-6400/69092	Loss: 161.533
-9600/69092	Loss: 154.016
-12800/69092	Loss: 158.602
-16000/69092	Loss: 159.686
-19200/69092	Loss: 158.052
-22400/69092	Loss: 157.961
-25600/69092	Loss: 157.917
-28800/69092	Loss: 158.310
-32000/69092	Loss: 155.903
-35200/69092	Loss: 160.613
-38400/69092	Loss: 157.665
-41600/69092	Loss: 154.263
-44800/69092	Loss: 159.483
-48000/69092	Loss: 159.126
-51200/69092	Loss: 161.590
-54400/69092	Loss: 156.286
-57600/69092	Loss: 155.363
-60800/69092	Loss: 154.854
-64000/69092	Loss: 156.564
-67200/69092	Loss: 156.462
-Training time 0:01:57.496448
-Epoch: 41 Average loss: 157.63
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 41)
-0/69092	Loss: 142.771
-3200/69092	Loss: 157.839
-6400/69092	Loss: 156.594
-9600/69092	Loss: 157.594
-12800/69092	Loss: 157.968
-16000/69092	Loss: 158.527
-19200/69092	Loss: 160.140
-22400/69092	Loss: 158.364
-25600/69092	Loss: 161.494
-28800/69092	Loss: 154.105
-32000/69092	Loss: 158.509
-35200/69092	Loss: 159.576
-38400/69092	Loss: 155.896
-41600/69092	Loss: 157.567
-44800/69092	Loss: 156.124
-48000/69092	Loss: 158.451
-51200/69092	Loss: 157.359
-54400/69092	Loss: 156.247
-57600/69092	Loss: 158.730
-60800/69092	Loss: 156.242
-64000/69092	Loss: 155.546
-67200/69092	Loss: 157.135
-Training time 0:01:56.132207
-Epoch: 42 Average loss: 157.55
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 42)
-0/69092	Loss: 148.719
-3200/69092	Loss: 157.723
-6400/69092	Loss: 157.190
-9600/69092	Loss: 157.123
-12800/69092	Loss: 159.344
-16000/69092	Loss: 156.742
-19200/69092	Loss: 157.294
-22400/69092	Loss: 161.777
-25600/69092	Loss: 156.166
-28800/69092	Loss: 159.577
-32000/69092	Loss: 156.729
-35200/69092	Loss: 156.896
-38400/69092	Loss: 160.607
-41600/69092	Loss: 158.282
-44800/69092	Loss: 156.040
-48000/69092	Loss: 160.024
-51200/69092	Loss: 156.110
-54400/69092	Loss: 158.322
-57600/69092	Loss: 156.341
-60800/69092	Loss: 159.159
-64000/69092	Loss: 156.933
-67200/69092	Loss: 155.380
-Training time 0:01:55.631859
-Epoch: 43 Average loss: 157.74
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 43)
-0/69092	Loss: 148.240
-3200/69092	Loss: 158.520
-6400/69092	Loss: 157.521
-9600/69092	Loss: 157.891
-12800/69092	Loss: 155.299
-16000/69092	Loss: 154.953
-19200/69092	Loss: 156.328
-22400/69092	Loss: 157.241
-25600/69092	Loss: 159.548
-28800/69092	Loss: 155.270
-32000/69092	Loss: 156.408
-35200/69092	Loss: 156.868
-38400/69092	Loss: 157.401
-41600/69092	Loss: 156.812
-44800/69092	Loss: 158.932
-48000/69092	Loss: 156.356
-51200/69092	Loss: 158.910
-54400/69092	Loss: 159.543
-57600/69092	Loss: 157.286
-60800/69092	Loss: 158.413
-64000/69092	Loss: 157.405
-67200/69092	Loss: 158.833
-Training time 0:01:57.376562
-Epoch: 44 Average loss: 157.55
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 44)
-0/69092	Loss: 183.489
-3200/69092	Loss: 158.599
-6400/69092	Loss: 157.540
-9600/69092	Loss: 158.589
-12800/69092	Loss: 156.262
-16000/69092	Loss: 158.069
-19200/69092	Loss: 157.732
-22400/69092	Loss: 158.297
-25600/69092	Loss: 155.116
-28800/69092	Loss: 156.900
-32000/69092	Loss: 158.672
-35200/69092	Loss: 157.091
-38400/69092	Loss: 157.269
-41600/69092	Loss: 157.161
-44800/69092	Loss: 159.029
-48000/69092	Loss: 157.814
-51200/69092	Loss: 157.799
-54400/69092	Loss: 160.981
-57600/69092	Loss: 155.705
-60800/69092	Loss: 153.627
-64000/69092	Loss: 156.401
-67200/69092	Loss: 157.697
-Training time 0:01:57.126096
-Epoch: 45 Average loss: 157.46
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 45)
-0/69092	Loss: 134.899
-3200/69092	Loss: 155.808
-6400/69092	Loss: 155.517
-9600/69092	Loss: 160.164
-12800/69092	Loss: 158.348
-16000/69092	Loss: 155.439
-19200/69092	Loss: 156.631
-22400/69092	Loss: 158.519
-25600/69092	Loss: 157.923
-28800/69092	Loss: 155.695
-32000/69092	Loss: 158.047
-35200/69092	Loss: 156.660
-38400/69092	Loss: 155.231
-41600/69092	Loss: 156.076
-44800/69092	Loss: 158.311
-48000/69092	Loss: 158.275
-51200/69092	Loss: 155.542
-54400/69092	Loss: 158.992
-57600/69092	Loss: 157.053
-60800/69092	Loss: 156.156
-64000/69092	Loss: 155.883
-67200/69092	Loss: 160.665
-Training time 0:01:56.797464
-Epoch: 46 Average loss: 157.27
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 46)
-0/69092	Loss: 149.035
-3200/69092	Loss: 156.858
-6400/69092	Loss: 156.009
-9600/69092	Loss: 158.135
-12800/69092	Loss: 156.576
-16000/69092	Loss: 159.007
-19200/69092	Loss: 158.286
-22400/69092	Loss: 158.013
-25600/69092	Loss: 158.883
-28800/69092	Loss: 157.480
-32000/69092	Loss: 154.650
-35200/69092	Loss: 157.620
-38400/69092	Loss: 158.066
-41600/69092	Loss: 159.452
-44800/69092	Loss: 158.096
-48000/69092	Loss: 156.719
-51200/69092	Loss: 157.181
-54400/69092	Loss: 158.686
-57600/69092	Loss: 156.388
-60800/69092	Loss: 156.867
-64000/69092	Loss: 158.142
-67200/69092	Loss: 155.473
-Training time 0:01:58.057136
-Epoch: 47 Average loss: 157.39
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 47)
-0/69092	Loss: 156.587
-3200/69092	Loss: 157.950
-6400/69092	Loss: 157.832
-9600/69092	Loss: 158.014
-12800/69092	Loss: 158.341
-16000/69092	Loss: 157.709
-19200/69092	Loss: 158.444
-22400/69092	Loss: 157.614
-25600/69092	Loss: 158.293
-28800/69092	Loss: 154.710
-32000/69092	Loss: 157.538
-35200/69092	Loss: 161.075
-38400/69092	Loss: 157.889
-41600/69092	Loss: 157.793
-44800/69092	Loss: 156.859
-48000/69092	Loss: 153.925
-51200/69092	Loss: 159.580
-54400/69092	Loss: 155.482
-57600/69092	Loss: 154.306
-60800/69092	Loss: 159.191
-64000/69092	Loss: 157.118
-67200/69092	Loss: 155.726
-Training time 0:01:58.795023
-Epoch: 48 Average loss: 157.39
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 48)
-0/69092	Loss: 165.872
-3200/69092	Loss: 157.354
-6400/69092	Loss: 155.905
-9600/69092	Loss: 155.997
-12800/69092	Loss: 157.172
-16000/69092	Loss: 157.139
-19200/69092	Loss: 156.060
-22400/69092	Loss: 157.143
-25600/69092	Loss: 158.096
-28800/69092	Loss: 156.610
-32000/69092	Loss: 157.839
-35200/69092	Loss: 158.196
-38400/69092	Loss: 159.408
-41600/69092	Loss: 158.201
-44800/69092	Loss: 160.485
-48000/69092	Loss: 156.418
-51200/69092	Loss: 156.554
-54400/69092	Loss: 155.956
-57600/69092	Loss: 156.167
-60800/69092	Loss: 159.508
-64000/69092	Loss: 157.252
-67200/69092	Loss: 154.809
-Training time 0:01:56.342746
-Epoch: 49 Average loss: 157.18
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 49)
-0/69092	Loss: 160.775
-3200/69092	Loss: 155.519
-6400/69092	Loss: 158.197
-9600/69092	Loss: 156.860
-12800/69092	Loss: 159.379
-16000/69092	Loss: 158.138
-19200/69092	Loss: 154.866
-22400/69092	Loss: 157.963
-25600/69092	Loss: 155.828
-28800/69092	Loss: 158.554
-32000/69092	Loss: 156.378
-35200/69092	Loss: 154.430
-38400/69092	Loss: 159.973
-41600/69092	Loss: 156.173
-44800/69092	Loss: 156.855
-48000/69092	Loss: 158.209
-51200/69092	Loss: 154.854
-54400/69092	Loss: 153.730
-57600/69092	Loss: 157.787
-60800/69092	Loss: 155.526
-64000/69092	Loss: 157.450
-67200/69092	Loss: 161.151
-Training time 0:01:57.673239
-Epoch: 50 Average loss: 157.10
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 50)
-0/69092	Loss: 151.309
-3200/69092	Loss: 154.663
-6400/69092	Loss: 155.966
-9600/69092	Loss: 157.126
-12800/69092	Loss: 158.215
-16000/69092	Loss: 154.137
-19200/69092	Loss: 157.095
-22400/69092	Loss: 158.049
-25600/69092	Loss: 160.578
-28800/69092	Loss: 157.522
-32000/69092	Loss: 155.314
-35200/69092	Loss: 158.797
-38400/69092	Loss: 156.035
-41600/69092	Loss: 155.960
-44800/69092	Loss: 158.463
-48000/69092	Loss: 158.309
-51200/69092	Loss: 157.494
-54400/69092	Loss: 158.661
-57600/69092	Loss: 155.803
-60800/69092	Loss: 159.165
-64000/69092	Loss: 157.056
-67200/69092	Loss: 157.738
-Training time 0:01:57.105160
-Epoch: 51 Average loss: 157.25
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 51)
-0/69092	Loss: 156.573
-3200/69092	Loss: 159.189
-6400/69092	Loss: 156.278
-9600/69092	Loss: 155.030
-12800/69092	Loss: 156.196
-16000/69092	Loss: 154.702
-19200/69092	Loss: 158.979
-22400/69092	Loss: 155.708
-25600/69092	Loss: 158.506
-28800/69092	Loss: 159.577
-32000/69092	Loss: 159.355
-35200/69092	Loss: 153.567
-38400/69092	Loss: 155.650
-41600/69092	Loss: 156.753
-44800/69092	Loss: 156.222
-48000/69092	Loss: 154.584
-51200/69092	Loss: 155.190
-54400/69092	Loss: 158.051
-57600/69092	Loss: 158.151
-60800/69092	Loss: 157.054
-64000/69092	Loss: 157.889
-67200/69092	Loss: 156.688
-Training time 0:01:58.536600
-Epoch: 52 Average loss: 156.83
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 52)
-0/69092	Loss: 151.972
-3200/69092	Loss: 157.845
-6400/69092	Loss: 155.750
-9600/69092	Loss: 155.901
-12800/69092	Loss: 156.866
-16000/69092	Loss: 158.451
-19200/69092	Loss: 156.067
-22400/69092	Loss: 157.738
-25600/69092	Loss: 157.944
-28800/69092	Loss: 156.943
-32000/69092	Loss: 158.236
-35200/69092	Loss: 159.389
-38400/69092	Loss: 155.751
-41600/69092	Loss: 159.080
-44800/69092	Loss: 157.479
-48000/69092	Loss: 156.445
-51200/69092	Loss: 155.405
-54400/69092	Loss: 156.730
-57600/69092	Loss: 157.065
-60800/69092	Loss: 157.780
-64000/69092	Loss: 156.755
-67200/69092	Loss: 154.428
-Training time 0:01:57.886704
-Epoch: 53 Average loss: 157.08
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 53)
-0/69092	Loss: 174.818
-3200/69092	Loss: 154.922
-6400/69092	Loss: 156.329
-9600/69092	Loss: 156.336
-12800/69092	Loss: 156.757
-16000/69092	Loss: 152.687
-19200/69092	Loss: 159.353
-22400/69092	Loss: 160.727
-25600/69092	Loss: 156.750
-28800/69092	Loss: 155.404
-32000/69092	Loss: 152.797
-35200/69092	Loss: 152.721
-38400/69092	Loss: 156.911
-41600/69092	Loss: 157.836
-44800/69092	Loss: 158.115
-48000/69092	Loss: 160.589
-51200/69092	Loss: 160.768
-54400/69092	Loss: 154.929
-57600/69092	Loss: 157.721
-60800/69092	Loss: 156.103
-64000/69092	Loss: 157.022
-67200/69092	Loss: 156.841
-Training time 0:01:57.631492
-Epoch: 54 Average loss: 156.82
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 54)
-0/69092	Loss: 161.799
-3200/69092	Loss: 158.768
-6400/69092	Loss: 155.949
-9600/69092	Loss: 156.426
-12800/69092	Loss: 157.792
-16000/69092	Loss: 156.559
-19200/69092	Loss: 156.716
-22400/69092	Loss: 155.907
-25600/69092	Loss: 158.677
-28800/69092	Loss: 156.829
-32000/69092	Loss: 156.810
-35200/69092	Loss: 158.180
-38400/69092	Loss: 155.060
-41600/69092	Loss: 160.118
-44800/69092	Loss: 155.946
-48000/69092	Loss: 156.171
-51200/69092	Loss: 156.939
-54400/69092	Loss: 157.657
-57600/69092	Loss: 158.546
-60800/69092	Loss: 153.721
-64000/69092	Loss: 157.700
-67200/69092	Loss: 157.772
-Training time 0:01:57.196255
-Epoch: 55 Average loss: 157.06
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 55)
-0/69092	Loss: 171.154
-3200/69092	Loss: 153.401
-6400/69092	Loss: 156.834
-9600/69092	Loss: 154.542
-12800/69092	Loss: 155.035
-16000/69092	Loss: 158.024
-19200/69092	Loss: 157.822
-22400/69092	Loss: 156.419
-25600/69092	Loss: 156.718
-28800/69092	Loss: 157.653
-32000/69092	Loss: 157.035
-35200/69092	Loss: 156.710
-38400/69092	Loss: 155.312
-41600/69092	Loss: 157.849
-44800/69092	Loss: 156.143
-48000/69092	Loss: 157.220
-51200/69092	Loss: 158.816
-54400/69092	Loss: 156.738
-57600/69092	Loss: 158.093
-60800/69092	Loss: 155.736
-64000/69092	Loss: 156.589
-67200/69092	Loss: 159.281
-Training time 0:01:57.246343
-Epoch: 56 Average loss: 156.81
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 56)
-0/69092	Loss: 161.665
-3200/69092	Loss: 159.070
-6400/69092	Loss: 159.161
-9600/69092	Loss: 155.761
-12800/69092	Loss: 153.790
-16000/69092	Loss: 156.048
-19200/69092	Loss: 157.425
-22400/69092	Loss: 156.850
-25600/69092	Loss: 157.505
-28800/69092	Loss: 155.880
-32000/69092	Loss: 156.811
-35200/69092	Loss: 158.641
-38400/69092	Loss: 158.764
-41600/69092	Loss: 155.957
-44800/69092	Loss: 157.562
-48000/69092	Loss: 159.475
-51200/69092	Loss: 153.178
-54400/69092	Loss: 156.193
-57600/69092	Loss: 157.788
-60800/69092	Loss: 159.666
-64000/69092	Loss: 154.616
-67200/69092	Loss: 156.717
-Training time 0:01:57.070016
-Epoch: 57 Average loss: 156.90
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 57)
-0/69092	Loss: 154.231
-3200/69092	Loss: 158.923
-6400/69092	Loss: 158.280
-9600/69092	Loss: 157.097
-12800/69092	Loss: 157.692
-16000/69092	Loss: 156.956
-19200/69092	Loss: 157.788
-22400/69092	Loss: 157.053
-25600/69092	Loss: 157.228
-28800/69092	Loss: 155.322
-32000/69092	Loss: 156.554
-35200/69092	Loss: 158.866
-38400/69092	Loss: 156.445
-41600/69092	Loss: 154.960
-44800/69092	Loss: 157.239
-48000/69092	Loss: 158.918
-51200/69092	Loss: 157.310
-54400/69092	Loss: 155.059
-57600/69092	Loss: 155.969
-60800/69092	Loss: 154.774
-64000/69092	Loss: 156.288
-67200/69092	Loss: 154.829
-Training time 0:01:56.619470
-Epoch: 58 Average loss: 156.92
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 58)
-0/69092	Loss: 158.747
-3200/69092	Loss: 156.800
-6400/69092	Loss: 159.003
-9600/69092	Loss: 157.429
-12800/69092	Loss: 154.900
-16000/69092	Loss: 155.986
-19200/69092	Loss: 156.581
-22400/69092	Loss: 159.008
-25600/69092	Loss: 159.020
-28800/69092	Loss: 155.761
-32000/69092	Loss: 155.297
-35200/69092	Loss: 159.026
-38400/69092	Loss: 156.345
-41600/69092	Loss: 156.182
-44800/69092	Loss: 156.119
-48000/69092	Loss: 155.560
-51200/69092	Loss: 157.515
-54400/69092	Loss: 155.749
-57600/69092	Loss: 156.210
-60800/69092	Loss: 158.114
-64000/69092	Loss: 156.935
-67200/69092	Loss: 157.552
-Training time 0:01:57.085682
-Epoch: 59 Average loss: 156.87
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 59)
-0/69092	Loss: 146.586
-3200/69092	Loss: 158.287
-6400/69092	Loss: 158.391
-9600/69092	Loss: 158.880
-12800/69092	Loss: 155.636
-16000/69092	Loss: 155.746
-19200/69092	Loss: 158.401
-22400/69092	Loss: 157.398
-25600/69092	Loss: 156.920
-28800/69092	Loss: 156.823
-32000/69092	Loss: 157.231
-35200/69092	Loss: 155.793
-38400/69092	Loss: 154.992
-41600/69092	Loss: 157.846
-44800/69092	Loss: 157.710
-48000/69092	Loss: 156.173
-51200/69092	Loss: 155.872
-54400/69092	Loss: 154.709
-57600/69092	Loss: 156.277
-60800/69092	Loss: 154.646
-64000/69092	Loss: 159.004
-67200/69092	Loss: 158.058
-Training time 0:01:57.663232
-Epoch: 60 Average loss: 156.85
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 60)
-0/69092	Loss: 166.009
-3200/69092	Loss: 156.702
-6400/69092	Loss: 157.125
-9600/69092	Loss: 158.976
-12800/69092	Loss: 156.149
-16000/69092	Loss: 153.047
-19200/69092	Loss: 155.508
-22400/69092	Loss: 161.147
-25600/69092	Loss: 156.039
-28800/69092	Loss: 158.867
-32000/69092	Loss: 156.878
-35200/69092	Loss: 151.503
-38400/69092	Loss: 154.111
-41600/69092	Loss: 158.582
-44800/69092	Loss: 158.066
-48000/69092	Loss: 156.287
-51200/69092	Loss: 154.743
-54400/69092	Loss: 156.873
-57600/69092	Loss: 157.863
-60800/69092	Loss: 158.995
-64000/69092	Loss: 156.081
-67200/69092	Loss: 158.808
-Training time 0:01:56.126829
-Epoch: 61 Average loss: 156.76
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 61)
-0/69092	Loss: 149.690
-3200/69092	Loss: 157.301
-6400/69092	Loss: 154.143
-9600/69092	Loss: 157.269
-12800/69092	Loss: 153.988
-16000/69092	Loss: 159.099
-19200/69092	Loss: 160.946
-22400/69092	Loss: 155.791
-25600/69092	Loss: 155.753
-28800/69092	Loss: 158.661
-32000/69092	Loss: 155.565
-35200/69092	Loss: 158.673
-38400/69092	Loss: 156.105
-41600/69092	Loss: 158.320
-44800/69092	Loss: 155.879
-48000/69092	Loss: 158.420
-51200/69092	Loss: 155.879
-54400/69092	Loss: 159.195
-57600/69092	Loss: 156.250
-60800/69092	Loss: 152.425
-64000/69092	Loss: 157.972
-67200/69092	Loss: 157.329
-Training time 0:01:55.724764
-Epoch: 62 Average loss: 156.81
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 62)
-0/69092	Loss: 130.425
-3200/69092	Loss: 155.063
-6400/69092	Loss: 156.972
-9600/69092	Loss: 156.451
-12800/69092	Loss: 157.251
-16000/69092	Loss: 155.544
-19200/69092	Loss: 157.545
-22400/69092	Loss: 156.117
-25600/69092	Loss: 154.692
-28800/69092	Loss: 161.002
-32000/69092	Loss: 154.937
-35200/69092	Loss: 157.969
-38400/69092	Loss: 159.332
-41600/69092	Loss: 155.803
-44800/69092	Loss: 158.768
-48000/69092	Loss: 157.521
-51200/69092	Loss: 156.443
-54400/69092	Loss: 156.695
-57600/69092	Loss: 156.823
-60800/69092	Loss: 154.686
-64000/69092	Loss: 154.257
-67200/69092	Loss: 154.854
-Training time 0:01:56.869917
-Epoch: 63 Average loss: 156.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 63)
-0/69092	Loss: 168.278
-3200/69092	Loss: 156.477
-6400/69092	Loss: 155.834
-9600/69092	Loss: 156.175
-12800/69092	Loss: 156.452
-16000/69092	Loss: 160.474
-19200/69092	Loss: 156.110
-22400/69092	Loss: 155.731
-25600/69092	Loss: 157.460
-28800/69092	Loss: 158.133
-32000/69092	Loss: 157.400
-35200/69092	Loss: 156.079
-38400/69092	Loss: 155.674
-41600/69092	Loss: 157.975
-44800/69092	Loss: 156.635
-48000/69092	Loss: 160.176
-51200/69092	Loss: 155.979
-54400/69092	Loss: 155.203
-57600/69092	Loss: 155.413
-60800/69092	Loss: 156.931
-64000/69092	Loss: 156.373
-67200/69092	Loss: 155.685
-Training time 0:01:56.637123
-Epoch: 64 Average loss: 156.81
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 64)
-0/69092	Loss: 179.476
-3200/69092	Loss: 158.416
-6400/69092	Loss: 157.825
-9600/69092	Loss: 158.553
-12800/69092	Loss: 155.701
-16000/69092	Loss: 157.353
-19200/69092	Loss: 155.801
-22400/69092	Loss: 159.169
-25600/69092	Loss: 154.814
-28800/69092	Loss: 155.133
-32000/69092	Loss: 155.811
-35200/69092	Loss: 155.900
-38400/69092	Loss: 157.083
-41600/69092	Loss: 155.313
-44800/69092	Loss: 153.626
-48000/69092	Loss: 156.280
-51200/69092	Loss: 156.116
-54400/69092	Loss: 156.223
-57600/69092	Loss: 160.646
-60800/69092	Loss: 156.571
-64000/69092	Loss: 157.397
-67200/69092	Loss: 159.361
-Training time 0:01:58.626638
-Epoch: 65 Average loss: 156.71
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 65)
-0/69092	Loss: 176.773
-3200/69092	Loss: 156.387
-6400/69092	Loss: 155.365
-9600/69092	Loss: 154.489
-12800/69092	Loss: 157.076
-16000/69092	Loss: 155.513
-19200/69092	Loss: 157.775
-22400/69092	Loss: 157.222
-25600/69092	Loss: 155.243
-28800/69092	Loss: 156.085
-32000/69092	Loss: 159.660
-35200/69092	Loss: 157.171
-38400/69092	Loss: 158.023
-41600/69092	Loss: 156.154
-44800/69092	Loss: 155.216
-48000/69092	Loss: 156.061
-51200/69092	Loss: 158.243
-54400/69092	Loss: 154.301
-57600/69092	Loss: 157.221
-60800/69092	Loss: 160.235
-64000/69092	Loss: 159.062
-67200/69092	Loss: 157.388
-Training time 0:01:57.428312
-Epoch: 66 Average loss: 156.88
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 66)
-0/69092	Loss: 174.267
-3200/69092	Loss: 156.316
-6400/69092	Loss: 155.633
-9600/69092	Loss: 157.480
-12800/69092	Loss: 156.161
-16000/69092	Loss: 156.051
-19200/69092	Loss: 157.521
-22400/69092	Loss: 156.603
-25600/69092	Loss: 157.186
-28800/69092	Loss: 156.992
-32000/69092	Loss: 154.298
-35200/69092	Loss: 158.896
-38400/69092	Loss: 158.421
-41600/69092	Loss: 154.238
-44800/69092	Loss: 154.904
-48000/69092	Loss: 157.834
-51200/69092	Loss: 156.558
-54400/69092	Loss: 157.259
-57600/69092	Loss: 156.655
-60800/69092	Loss: 157.521
-64000/69092	Loss: 155.841
-67200/69092	Loss: 155.924
-Training time 0:01:58.339228
-Epoch: 67 Average loss: 156.70
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 67)
-0/69092	Loss: 140.377
-3200/69092	Loss: 154.747
-6400/69092	Loss: 155.382
-9600/69092	Loss: 156.505
-12800/69092	Loss: 156.563
-16000/69092	Loss: 154.118
-19200/69092	Loss: 155.948
-22400/69092	Loss: 154.806
-25600/69092	Loss: 159.843
-28800/69092	Loss: 156.679
-32000/69092	Loss: 154.444
-35200/69092	Loss: 157.556
-38400/69092	Loss: 158.586
-41600/69092	Loss: 156.744
-44800/69092	Loss: 159.562
-48000/69092	Loss: 155.890
-51200/69092	Loss: 157.496
-54400/69092	Loss: 159.528
-57600/69092	Loss: 155.868
-60800/69092	Loss: 156.400
-64000/69092	Loss: 157.189
-67200/69092	Loss: 154.496
-Training time 0:01:56.978773
-Epoch: 68 Average loss: 156.65
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 68)
-0/69092	Loss: 158.141
-3200/69092	Loss: 159.387
-6400/69092	Loss: 155.405
-9600/69092	Loss: 155.339
-12800/69092	Loss: 156.634
-16000/69092	Loss: 155.310
-19200/69092	Loss: 157.443
-22400/69092	Loss: 156.007
-25600/69092	Loss: 156.260
-28800/69092	Loss: 155.516
-32000/69092	Loss: 155.838
-35200/69092	Loss: 156.311
-38400/69092	Loss: 159.134
-41600/69092	Loss: 156.855
-44800/69092	Loss: 156.743
-48000/69092	Loss: 157.367
-51200/69092	Loss: 155.085
-54400/69092	Loss: 155.545
-57600/69092	Loss: 157.443
-60800/69092	Loss: 157.246
-64000/69092	Loss: 159.013
-67200/69092	Loss: 156.078
-Training time 0:01:56.638653
-Epoch: 69 Average loss: 156.74
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 69)
-0/69092	Loss: 158.210
-3200/69092	Loss: 154.090
-6400/69092	Loss: 157.629
-9600/69092	Loss: 154.256
-12800/69092	Loss: 159.237
-16000/69092	Loss: 156.519
-19200/69092	Loss: 154.338
-22400/69092	Loss: 156.687
-25600/69092	Loss: 156.197
-28800/69092	Loss: 157.974
-32000/69092	Loss: 158.141
-35200/69092	Loss: 157.055
-38400/69092	Loss: 157.476
-41600/69092	Loss: 155.278
-44800/69092	Loss: 156.339
-48000/69092	Loss: 157.029
-51200/69092	Loss: 155.831
-54400/69092	Loss: 157.420
-57600/69092	Loss: 159.868
-60800/69092	Loss: 154.644
-64000/69092	Loss: 154.695
-67200/69092	Loss: 159.722
-Training time 0:01:57.764959
-Epoch: 70 Average loss: 156.75
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 70)
-0/69092	Loss: 171.868
-3200/69092	Loss: 154.971
-6400/69092	Loss: 155.690
-9600/69092	Loss: 157.860
-12800/69092	Loss: 153.959
-16000/69092	Loss: 155.459
-19200/69092	Loss: 153.802
-22400/69092	Loss: 157.337
-25600/69092	Loss: 157.796
-28800/69092	Loss: 159.014
-32000/69092	Loss: 156.024
-35200/69092	Loss: 155.496
-38400/69092	Loss: 153.453
-41600/69092	Loss: 157.982
-44800/69092	Loss: 158.127
-48000/69092	Loss: 157.016
-51200/69092	Loss: 155.336
-54400/69092	Loss: 158.065
-57600/69092	Loss: 155.201
-60800/69092	Loss: 155.793
-64000/69092	Loss: 160.623
-67200/69092	Loss: 159.226
-Training time 0:01:57.575431
-Epoch: 71 Average loss: 156.54
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 71)
-0/69092	Loss: 165.875
-3200/69092	Loss: 155.211
-6400/69092	Loss: 155.809
-9600/69092	Loss: 154.843
-12800/69092	Loss: 156.813
-16000/69092	Loss: 154.584
-19200/69092	Loss: 156.197
-22400/69092	Loss: 156.995
-25600/69092	Loss: 156.827
-28800/69092	Loss: 156.808
-32000/69092	Loss: 155.107
-35200/69092	Loss: 155.928
-38400/69092	Loss: 155.386
-41600/69092	Loss: 157.398
-44800/69092	Loss: 159.463
-48000/69092	Loss: 157.174
-51200/69092	Loss: 155.567
-54400/69092	Loss: 156.645
-57600/69092	Loss: 156.502
-60800/69092	Loss: 158.758
-64000/69092	Loss: 156.681
-67200/69092	Loss: 156.160
-Training time 0:01:57.928112
-Epoch: 72 Average loss: 156.36
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 72)
-0/69092	Loss: 149.009
-3200/69092	Loss: 157.306
-6400/69092	Loss: 154.970
-9600/69092	Loss: 155.292
-12800/69092	Loss: 160.226
-16000/69092	Loss: 155.449
-19200/69092	Loss: 154.617
-22400/69092	Loss: 159.083
-25600/69092	Loss: 157.713
-28800/69092	Loss: 153.892
-32000/69092	Loss: 157.108
-35200/69092	Loss: 157.129
-38400/69092	Loss: 158.180
-41600/69092	Loss: 154.745
-44800/69092	Loss: 157.751
-48000/69092	Loss: 154.215
-51200/69092	Loss: 155.574
-54400/69092	Loss: 156.107
-57600/69092	Loss: 159.449
-60800/69092	Loss: 154.039
-64000/69092	Loss: 157.771
-67200/69092	Loss: 154.503
-Training time 0:01:58.927985
-Epoch: 73 Average loss: 156.45
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 73)
-0/69092	Loss: 162.335
-3200/69092	Loss: 156.992
-6400/69092	Loss: 155.441
-9600/69092	Loss: 158.319
-12800/69092	Loss: 156.799
-16000/69092	Loss: 154.601
-19200/69092	Loss: 156.184
-22400/69092	Loss: 156.119
-25600/69092	Loss: 156.730
-28800/69092	Loss: 157.873
-32000/69092	Loss: 156.744
-35200/69092	Loss: 155.650
-38400/69092	Loss: 155.064
-41600/69092	Loss: 160.364
-44800/69092	Loss: 153.693
-48000/69092	Loss: 154.281
-51200/69092	Loss: 158.761
-54400/69092	Loss: 153.293
-57600/69092	Loss: 154.834
-60800/69092	Loss: 156.792
-64000/69092	Loss: 155.921
-67200/69092	Loss: 158.465
-Training time 0:01:57.424789
-Epoch: 74 Average loss: 156.35
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 74)
-0/69092	Loss: 163.777
-3200/69092	Loss: 157.036
-6400/69092	Loss: 156.859
-9600/69092	Loss: 156.726
-12800/69092	Loss: 155.765
-16000/69092	Loss: 154.939
-19200/69092	Loss: 155.907
-22400/69092	Loss: 159.550
-25600/69092	Loss: 156.406
-28800/69092	Loss: 158.700
-32000/69092	Loss: 155.796
-35200/69092	Loss: 155.475
-38400/69092	Loss: 156.091
-41600/69092	Loss: 158.845
-44800/69092	Loss: 155.325
-48000/69092	Loss: 154.642
-51200/69092	Loss: 157.505
-54400/69092	Loss: 155.458
-57600/69092	Loss: 157.678
-60800/69092	Loss: 155.414
-64000/69092	Loss: 158.049
-67200/69092	Loss: 155.451
-Training time 0:01:58.770586
-Epoch: 75 Average loss: 156.54
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 75)
-0/69092	Loss: 153.101
-3200/69092	Loss: 155.568
-6400/69092	Loss: 157.207
-9600/69092	Loss: 156.827
-12800/69092	Loss: 156.984
-16000/69092	Loss: 157.856
-19200/69092	Loss: 154.167
-22400/69092	Loss: 158.666
-25600/69092	Loss: 157.925
-28800/69092	Loss: 157.772
-32000/69092	Loss: 157.549
-35200/69092	Loss: 155.806
-38400/69092	Loss: 156.027
-41600/69092	Loss: 157.568
-44800/69092	Loss: 158.149
-48000/69092	Loss: 157.123
-51200/69092	Loss: 154.132
-54400/69092	Loss: 156.489
-57600/69092	Loss: 154.554
-60800/69092	Loss: 155.869
-64000/69092	Loss: 154.556
-67200/69092	Loss: 156.564
-Training time 0:01:57.292697
-Epoch: 76 Average loss: 156.51
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 76)
-0/69092	Loss: 145.325
-3200/69092	Loss: 155.806
-6400/69092	Loss: 159.306
-9600/69092	Loss: 157.818
-12800/69092	Loss: 159.852
-16000/69092	Loss: 154.932
-19200/69092	Loss: 157.023
-22400/69092	Loss: 158.302
-25600/69092	Loss: 151.931
-28800/69092	Loss: 156.084
-32000/69092	Loss: 155.341
-35200/69092	Loss: 155.337
-38400/69092	Loss: 153.945
-41600/69092	Loss: 157.345
-44800/69092	Loss: 156.563
-48000/69092	Loss: 158.502
-51200/69092	Loss: 155.921
-54400/69092	Loss: 154.319
-57600/69092	Loss: 156.743
-60800/69092	Loss: 155.385
-64000/69092	Loss: 156.278
-67200/69092	Loss: 157.242
-Training time 0:01:57.657184
-Epoch: 77 Average loss: 156.38
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 77)
-0/69092	Loss: 146.821
-3200/69092	Loss: 155.702
-6400/69092	Loss: 157.032
-9600/69092	Loss: 153.347
-12800/69092	Loss: 153.843
-16000/69092	Loss: 155.661
-19200/69092	Loss: 156.902
-22400/69092	Loss: 157.238
-25600/69092	Loss: 154.921
-28800/69092	Loss: 157.825
-32000/69092	Loss: 156.451
-35200/69092	Loss: 155.924
-38400/69092	Loss: 155.369
-41600/69092	Loss: 157.591
-44800/69092	Loss: 153.504
-48000/69092	Loss: 159.252
-51200/69092	Loss: 157.205
-54400/69092	Loss: 153.367
-57600/69092	Loss: 157.783
-60800/69092	Loss: 157.550
-64000/69092	Loss: 155.960
-67200/69092	Loss: 159.255
-Training time 0:01:58.393952
-Epoch: 78 Average loss: 156.32
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 78)
-0/69092	Loss: 156.952
-3200/69092	Loss: 154.559
-6400/69092	Loss: 156.841
-9600/69092	Loss: 154.765
-12800/69092	Loss: 156.447
-16000/69092	Loss: 155.340
-19200/69092	Loss: 159.242
-22400/69092	Loss: 156.222
-25600/69092	Loss: 156.588
-28800/69092	Loss: 156.103
-32000/69092	Loss: 154.994
-35200/69092	Loss: 152.960
-38400/69092	Loss: 155.295
-41600/69092	Loss: 155.246
-44800/69092	Loss: 156.455
-48000/69092	Loss: 155.736
-51200/69092	Loss: 155.654
-54400/69092	Loss: 159.698
-57600/69092	Loss: 156.039
-60800/69092	Loss: 157.098
-64000/69092	Loss: 157.854
-67200/69092	Loss: 157.608
-Training time 0:01:57.321396
-Epoch: 79 Average loss: 156.33
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 79)
-0/69092	Loss: 158.649
-3200/69092	Loss: 156.310
-6400/69092	Loss: 158.435
-9600/69092	Loss: 159.484
-12800/69092	Loss: 156.311
-16000/69092	Loss: 157.374
-19200/69092	Loss: 154.290
-22400/69092	Loss: 155.953
-25600/69092	Loss: 157.303
-28800/69092	Loss: 156.847
-32000/69092	Loss: 157.297
-35200/69092	Loss: 154.874
-38400/69092	Loss: 153.796
-41600/69092	Loss: 155.032
-44800/69092	Loss: 155.247
-48000/69092	Loss: 155.718
-51200/69092	Loss: 158.595
-54400/69092	Loss: 156.541
-57600/69092	Loss: 155.025
-60800/69092	Loss: 158.117
-64000/69092	Loss: 155.501
-67200/69092	Loss: 154.854
-Training time 0:01:57.033627
-Epoch: 80 Average loss: 156.31
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 80)
-0/69092	Loss: 153.145
-3200/69092	Loss: 157.788
-6400/69092	Loss: 155.704
-9600/69092	Loss: 156.658
-12800/69092	Loss: 156.560
-16000/69092	Loss: 158.374
-19200/69092	Loss: 154.177
-22400/69092	Loss: 157.375
-25600/69092	Loss: 155.362
-28800/69092	Loss: 154.780
-32000/69092	Loss: 157.483
-35200/69092	Loss: 159.989
-38400/69092	Loss: 154.817
-41600/69092	Loss: 156.393
-44800/69092	Loss: 156.688
-48000/69092	Loss: 155.386
-51200/69092	Loss: 156.064
-54400/69092	Loss: 155.947
-57600/69092	Loss: 155.760
-60800/69092	Loss: 156.316
-64000/69092	Loss: 156.993
-67200/69092	Loss: 155.297
-Training time 0:01:57.857139
-Epoch: 81 Average loss: 156.47
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 81)
-0/69092	Loss: 154.239
-3200/69092	Loss: 155.129
-6400/69092	Loss: 158.601
-9600/69092	Loss: 154.721
-12800/69092	Loss: 156.024
-16000/69092	Loss: 156.336
-19200/69092	Loss: 157.672
-22400/69092	Loss: 158.059
-25600/69092	Loss: 155.845
-28800/69092	Loss: 155.871
-32000/69092	Loss: 152.471
-35200/69092	Loss: 158.093
-38400/69092	Loss: 155.734
-41600/69092	Loss: 157.672
-44800/69092	Loss: 159.303
-48000/69092	Loss: 153.724
-51200/69092	Loss: 156.120
-54400/69092	Loss: 156.064
-57600/69092	Loss: 159.324
-60800/69092	Loss: 152.366
-64000/69092	Loss: 156.259
-67200/69092	Loss: 156.098
-Training time 0:01:57.681612
-Epoch: 82 Average loss: 156.31
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 82)
-0/69092	Loss: 147.194
-3200/69092	Loss: 155.118
-6400/69092	Loss: 158.044
-9600/69092	Loss: 157.341
-12800/69092	Loss: 154.624
-16000/69092	Loss: 154.524
-19200/69092	Loss: 156.314
-22400/69092	Loss: 156.617
-25600/69092	Loss: 158.821
-28800/69092	Loss: 156.702
-32000/69092	Loss: 156.056
-35200/69092	Loss: 156.305
-38400/69092	Loss: 157.803
-41600/69092	Loss: 155.611
-44800/69092	Loss: 157.703
-48000/69092	Loss: 154.493
-51200/69092	Loss: 151.445
-54400/69092	Loss: 157.022
-57600/69092	Loss: 153.199
-60800/69092	Loss: 156.462
-64000/69092	Loss: 159.009
-67200/69092	Loss: 157.212
-Training time 0:01:56.507123
-Epoch: 83 Average loss: 156.19
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 83)
-0/69092	Loss: 175.445
-3200/69092	Loss: 156.969
-6400/69092	Loss: 157.031
-9600/69092	Loss: 156.543
-12800/69092	Loss: 158.960
-16000/69092	Loss: 153.217
-19200/69092	Loss: 156.737
-22400/69092	Loss: 158.304
-25600/69092	Loss: 155.500
-28800/69092	Loss: 159.088
-32000/69092	Loss: 156.325
-35200/69092	Loss: 158.928
-38400/69092	Loss: 157.489
-41600/69092	Loss: 154.888
-44800/69092	Loss: 154.805
-48000/69092	Loss: 156.726
-51200/69092	Loss: 153.776
-54400/69092	Loss: 153.365
-57600/69092	Loss: 159.374
-60800/69092	Loss: 155.063
-64000/69092	Loss: 155.874
-67200/69092	Loss: 154.274
-Training time 0:01:57.244601
-Epoch: 84 Average loss: 156.41
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 84)
-0/69092	Loss: 164.614
-3200/69092	Loss: 157.171
-6400/69092	Loss: 155.307
-9600/69092	Loss: 156.460
-12800/69092	Loss: 155.634
-16000/69092	Loss: 156.408
-19200/69092	Loss: 157.974
-22400/69092	Loss: 156.530
-25600/69092	Loss: 156.866
-28800/69092	Loss: 155.673
-32000/69092	Loss: 156.370
-35200/69092	Loss: 159.204
-38400/69092	Loss: 155.918
-41600/69092	Loss: 155.865
-44800/69092	Loss: 157.439
-48000/69092	Loss: 154.114
-51200/69092	Loss: 153.143
-54400/69092	Loss: 157.290
-57600/69092	Loss: 156.481
-60800/69092	Loss: 157.914
-64000/69092	Loss: 156.956
-67200/69092	Loss: 152.564
-Training time 0:01:57.380581
-Epoch: 85 Average loss: 156.19
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 85)
-0/69092	Loss: 149.693
-3200/69092	Loss: 157.577
-6400/69092	Loss: 158.061
-9600/69092	Loss: 157.667
-12800/69092	Loss: 155.177
-16000/69092	Loss: 154.303
-19200/69092	Loss: 154.208
-22400/69092	Loss: 155.968
-25600/69092	Loss: 158.216
-28800/69092	Loss: 154.347
-32000/69092	Loss: 157.054
-35200/69092	Loss: 156.202
-38400/69092	Loss: 155.253
-41600/69092	Loss: 154.441
-44800/69092	Loss: 154.887
-48000/69092	Loss: 159.465
-51200/69092	Loss: 157.362
-54400/69092	Loss: 157.643
-57600/69092	Loss: 157.378
-60800/69092	Loss: 152.115
-64000/69092	Loss: 158.885
-67200/69092	Loss: 155.049
-Training time 0:01:58.151490
-Epoch: 86 Average loss: 156.17
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 86)
-0/69092	Loss: 166.214
-3200/69092	Loss: 155.207
-6400/69092	Loss: 157.761
-9600/69092	Loss: 159.346
-12800/69092	Loss: 153.914
-16000/69092	Loss: 155.418
-19200/69092	Loss: 158.452
-22400/69092	Loss: 158.067
-25600/69092	Loss: 155.389
-28800/69092	Loss: 155.912
-32000/69092	Loss: 156.085
-35200/69092	Loss: 152.244
-38400/69092	Loss: 157.502
-41600/69092	Loss: 158.903
-44800/69092	Loss: 153.392
-48000/69092	Loss: 156.485
-51200/69092	Loss: 156.900
-54400/69092	Loss: 155.792
-57600/69092	Loss: 157.120
-60800/69092	Loss: 157.923
-64000/69092	Loss: 157.795
-67200/69092	Loss: 157.277
-Training time 0:01:57.070829
-Epoch: 87 Average loss: 156.51
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 87)
-0/69092	Loss: 159.401
-3200/69092	Loss: 155.259
-6400/69092	Loss: 152.928
-9600/69092	Loss: 155.663
-12800/69092	Loss: 153.289
-16000/69092	Loss: 157.473
-19200/69092	Loss: 153.583
-22400/69092	Loss: 155.634
-25600/69092	Loss: 158.981
-28800/69092	Loss: 158.820
-32000/69092	Loss: 157.317
-35200/69092	Loss: 156.783
-38400/69092	Loss: 156.472
-41600/69092	Loss: 157.222
-44800/69092	Loss: 157.337
-48000/69092	Loss: 160.211
-51200/69092	Loss: 155.523
-54400/69092	Loss: 155.084
-57600/69092	Loss: 153.392
-60800/69092	Loss: 157.155
-64000/69092	Loss: 156.764
-67200/69092	Loss: 154.235
-Training time 0:01:57.391573
-Epoch: 88 Average loss: 156.17
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 88)
-0/69092	Loss: 170.257
-3200/69092	Loss: 155.170
-6400/69092	Loss: 154.292
-9600/69092	Loss: 155.684
-12800/69092	Loss: 156.351
-16000/69092	Loss: 155.048
-19200/69092	Loss: 156.083
-22400/69092	Loss: 155.849
-25600/69092	Loss: 157.710
-28800/69092	Loss: 156.851
-32000/69092	Loss: 154.885
-35200/69092	Loss: 158.666
-38400/69092	Loss: 153.038
-41600/69092	Loss: 154.525
-44800/69092	Loss: 155.211
-48000/69092	Loss: 156.114
-51200/69092	Loss: 159.612
-54400/69092	Loss: 157.564
-57600/69092	Loss: 155.866
-60800/69092	Loss: 155.856
-64000/69092	Loss: 157.281
-67200/69092	Loss: 156.647
-Training time 0:01:57.305374
-Epoch: 89 Average loss: 156.09
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 89)
-0/69092	Loss: 166.612
-3200/69092	Loss: 158.085
-6400/69092	Loss: 159.125
-9600/69092	Loss: 159.112
-12800/69092	Loss: 159.096
-16000/69092	Loss: 157.196
-19200/69092	Loss: 157.357
-22400/69092	Loss: 155.583
-25600/69092	Loss: 153.914
-28800/69092	Loss: 154.987
-32000/69092	Loss: 158.178
-35200/69092	Loss: 156.370
-38400/69092	Loss: 155.198
-41600/69092	Loss: 157.549
-44800/69092	Loss: 151.950
-48000/69092	Loss: 154.985
-51200/69092	Loss: 156.409
-54400/69092	Loss: 155.406
-57600/69092	Loss: 155.511
-60800/69092	Loss: 156.110
-64000/69092	Loss: 154.917
-67200/69092	Loss: 154.276
-Training time 0:01:57.986733
-Epoch: 90 Average loss: 156.36
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 90)
-0/69092	Loss: 161.165
-3200/69092	Loss: 155.136
-6400/69092	Loss: 156.198
-9600/69092	Loss: 153.660
-12800/69092	Loss: 155.246
-16000/69092	Loss: 156.955
-19200/69092	Loss: 153.845
-22400/69092	Loss: 156.662
-25600/69092	Loss: 153.142
-28800/69092	Loss: 156.530
-32000/69092	Loss: 158.489
-35200/69092	Loss: 155.523
-38400/69092	Loss: 155.862
-41600/69092	Loss: 156.376
-44800/69092	Loss: 156.332
-48000/69092	Loss: 156.171
-51200/69092	Loss: 158.691
-54400/69092	Loss: 157.633
-57600/69092	Loss: 156.059
-60800/69092	Loss: 154.969
-64000/69092	Loss: 157.326
-67200/69092	Loss: 156.304
-Training time 0:01:57.421474
-Epoch: 91 Average loss: 156.06
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 91)
-0/69092	Loss: 151.802
-3200/69092	Loss: 156.041
-6400/69092	Loss: 154.403
-9600/69092	Loss: 154.763
-12800/69092	Loss: 156.507
-16000/69092	Loss: 156.869
-19200/69092	Loss: 156.667
-22400/69092	Loss: 155.045
-25600/69092	Loss: 154.590
-28800/69092	Loss: 157.636
-32000/69092	Loss: 157.099
-35200/69092	Loss: 156.158
-38400/69092	Loss: 156.639
-41600/69092	Loss: 156.858
-44800/69092	Loss: 156.041
-48000/69092	Loss: 154.728
-51200/69092	Loss: 157.449
-54400/69092	Loss: 156.421
-57600/69092	Loss: 154.008
-60800/69092	Loss: 157.329
-64000/69092	Loss: 156.513
-67200/69092	Loss: 156.582
-Training time 0:01:58.422619
-Epoch: 92 Average loss: 156.14
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 92)
-0/69092	Loss: 171.456
-3200/69092	Loss: 155.931
-6400/69092	Loss: 157.035
-9600/69092	Loss: 159.603
-12800/69092	Loss: 157.328
-16000/69092	Loss: 156.010
-19200/69092	Loss: 153.786
-22400/69092	Loss: 156.192
-25600/69092	Loss: 158.596
-28800/69092	Loss: 156.441
-32000/69092	Loss: 156.643
-35200/69092	Loss: 154.531
-38400/69092	Loss: 155.786
-41600/69092	Loss: 155.990
-44800/69092	Loss: 156.323
-48000/69092	Loss: 158.751
-51200/69092	Loss: 155.836
-54400/69092	Loss: 154.206
-57600/69092	Loss: 154.051
-60800/69092	Loss: 156.084
-64000/69092	Loss: 152.273
-67200/69092	Loss: 157.339
-Training time 0:01:58.731457
-Epoch: 93 Average loss: 156.16
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 93)
-0/69092	Loss: 142.786
-3200/69092	Loss: 153.297
-6400/69092	Loss: 158.310
-9600/69092	Loss: 155.520
-12800/69092	Loss: 155.443
-16000/69092	Loss: 155.411
-19200/69092	Loss: 154.829
-22400/69092	Loss: 157.129
-25600/69092	Loss: 159.370
-28800/69092	Loss: 156.973
-32000/69092	Loss: 159.210
-35200/69092	Loss: 154.596
-38400/69092	Loss: 156.608
-41600/69092	Loss: 156.005
-44800/69092	Loss: 156.838
-48000/69092	Loss: 155.210
-51200/69092	Loss: 156.871
-54400/69092	Loss: 155.138
-57600/69092	Loss: 158.078
-60800/69092	Loss: 157.500
-64000/69092	Loss: 151.739
-67200/69092	Loss: 154.873
-Training time 0:01:57.963713
-Epoch: 94 Average loss: 156.04
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 94)
-0/69092	Loss: 146.263
-3200/69092	Loss: 155.555
-6400/69092	Loss: 156.554
-9600/69092	Loss: 156.213
-12800/69092	Loss: 157.070
-16000/69092	Loss: 156.395
-19200/69092	Loss: 156.235
-22400/69092	Loss: 157.114
-25600/69092	Loss: 154.361
-28800/69092	Loss: 156.385
-32000/69092	Loss: 158.074
-35200/69092	Loss: 155.020
-38400/69092	Loss: 154.145
-41600/69092	Loss: 157.834
-44800/69092	Loss: 156.239
-48000/69092	Loss: 155.449
-51200/69092	Loss: 153.666
-54400/69092	Loss: 158.090
-57600/69092	Loss: 158.146
-60800/69092	Loss: 154.828
-64000/69092	Loss: 155.400
-67200/69092	Loss: 156.501
-Training time 0:01:57.965091
-Epoch: 95 Average loss: 156.18
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 95)
-0/69092	Loss: 155.656
-3200/69092	Loss: 157.595
-6400/69092	Loss: 153.861
-9600/69092	Loss: 155.861
-12800/69092	Loss: 155.790
-16000/69092	Loss: 157.198
-19200/69092	Loss: 156.227
-22400/69092	Loss: 156.544
-25600/69092	Loss: 158.305
-28800/69092	Loss: 157.060
-32000/69092	Loss: 154.424
-35200/69092	Loss: 156.990
-38400/69092	Loss: 157.722
-41600/69092	Loss: 157.220
-44800/69092	Loss: 155.997
-48000/69092	Loss: 158.155
-51200/69092	Loss: 154.313
-54400/69092	Loss: 156.447
-57600/69092	Loss: 155.546
-60800/69092	Loss: 156.773
-64000/69092	Loss: 156.543
-67200/69092	Loss: 155.272
-Training time 0:01:58.809980
-Epoch: 96 Average loss: 156.34
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 96)
-0/69092	Loss: 143.069
-3200/69092	Loss: 156.347
-6400/69092	Loss: 153.732
-9600/69092	Loss: 156.612
-12800/69092	Loss: 155.316
-16000/69092	Loss: 154.381
-19200/69092	Loss: 157.289
-22400/69092	Loss: 156.253
-25600/69092	Loss: 157.973
-28800/69092	Loss: 155.417
-32000/69092	Loss: 157.265
-35200/69092	Loss: 155.337
-38400/69092	Loss: 156.951
-41600/69092	Loss: 157.890
-44800/69092	Loss: 154.225
-48000/69092	Loss: 158.220
-51200/69092	Loss: 155.795
-54400/69092	Loss: 154.808
-57600/69092	Loss: 157.551
-60800/69092	Loss: 156.183
-64000/69092	Loss: 153.365
-67200/69092	Loss: 155.159
-Training time 0:01:57.791352
-Epoch: 97 Average loss: 155.96
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 97)
-0/69092	Loss: 149.637
-3200/69092	Loss: 155.549
-6400/69092	Loss: 155.544
-9600/69092	Loss: 155.894
-12800/69092	Loss: 152.778
-16000/69092	Loss: 158.410
-19200/69092	Loss: 157.024
-22400/69092	Loss: 156.053
-25600/69092	Loss: 157.290
-28800/69092	Loss: 155.204
-32000/69092	Loss: 158.822
-35200/69092	Loss: 153.736
-38400/69092	Loss: 154.738
-41600/69092	Loss: 156.868
-44800/69092	Loss: 156.905
-48000/69092	Loss: 156.006
-51200/69092	Loss: 154.121
-54400/69092	Loss: 155.837
-57600/69092	Loss: 155.911
-60800/69092	Loss: 157.958
-64000/69092	Loss: 157.863
-67200/69092	Loss: 154.774
-Training time 0:01:58.207927
-Epoch: 98 Average loss: 156.12
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 98)
-0/69092	Loss: 163.931
-3200/69092	Loss: 158.819
-6400/69092	Loss: 157.153
-9600/69092	Loss: 155.351
-12800/69092	Loss: 155.589
-16000/69092	Loss: 156.833
-19200/69092	Loss: 154.299
-22400/69092	Loss: 154.606
-25600/69092	Loss: 154.769
-28800/69092	Loss: 156.260
-32000/69092	Loss: 157.328
-35200/69092	Loss: 155.191
-38400/69092	Loss: 154.232
-41600/69092	Loss: 157.153
-44800/69092	Loss: 155.967
-48000/69092	Loss: 155.095
-51200/69092	Loss: 158.086
-54400/69092	Loss: 159.181
-57600/69092	Loss: 154.697
-60800/69092	Loss: 158.055
-64000/69092	Loss: 153.272
-67200/69092	Loss: 158.289
-Training time 0:01:57.142832
-Epoch: 99 Average loss: 156.18
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 99)
-0/69092	Loss: 174.706
-3200/69092	Loss: 157.421
-6400/69092	Loss: 158.913
-9600/69092	Loss: 156.374
-12800/69092	Loss: 155.065
-16000/69092	Loss: 151.730
-19200/69092	Loss: 157.468
-22400/69092	Loss: 154.397
-25600/69092	Loss: 153.762
-28800/69092	Loss: 157.019
-32000/69092	Loss: 156.850
-35200/69092	Loss: 155.036
-38400/69092	Loss: 155.356
-41600/69092	Loss: 157.865
-44800/69092	Loss: 157.956
-48000/69092	Loss: 155.131
-51200/69092	Loss: 154.815
-54400/69092	Loss: 156.507
-57600/69092	Loss: 155.000
-60800/69092	Loss: 155.166
-64000/69092	Loss: 159.171
-67200/69092	Loss: 154.491
-Training time 0:01:58.077721
-Epoch: 100 Average loss: 156.06
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 100)
-0/69092	Loss: 177.416
-3200/69092	Loss: 157.837
-6400/69092	Loss: 156.611
-9600/69092	Loss: 158.248
-12800/69092	Loss: 152.985
-16000/69092	Loss: 158.325
-19200/69092	Loss: 158.759
-22400/69092	Loss: 155.081
-25600/69092	Loss: 158.922
-28800/69092	Loss: 155.390
-32000/69092	Loss: 157.686
-35200/69092	Loss: 156.999
-38400/69092	Loss: 155.144
-41600/69092	Loss: 154.144
-44800/69092	Loss: 153.212
-48000/69092	Loss: 154.081
-51200/69092	Loss: 156.668
-54400/69092	Loss: 157.358
-57600/69092	Loss: 156.153
-60800/69092	Loss: 153.402
-64000/69092	Loss: 156.362
-67200/69092	Loss: 155.231
-Training time 0:01:57.508650
-Epoch: 101 Average loss: 156.11
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 101)
-0/69092	Loss: 155.446
-3200/69092	Loss: 154.474
-6400/69092	Loss: 153.953
-9600/69092	Loss: 153.650
-12800/69092	Loss: 155.206
-16000/69092	Loss: 156.317
-19200/69092	Loss: 156.065
-22400/69092	Loss: 156.366
-25600/69092	Loss: 157.411
-28800/69092	Loss: 157.308
-32000/69092	Loss: 155.609
-35200/69092	Loss: 156.758
-38400/69092	Loss: 153.468
-41600/69092	Loss: 156.464
-44800/69092	Loss: 156.841
-48000/69092	Loss: 156.027
-51200/69092	Loss: 157.217
-54400/69092	Loss: 153.982
-57600/69092	Loss: 155.583
-60800/69092	Loss: 156.712
-64000/69092	Loss: 155.515
-67200/69092	Loss: 156.003
-Training time 0:01:57.582020
-Epoch: 102 Average loss: 155.84
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 102)
-0/69092	Loss: 159.455
-3200/69092	Loss: 155.785
-6400/69092	Loss: 154.714
-9600/69092	Loss: 155.369
-12800/69092	Loss: 156.687
-16000/69092	Loss: 156.739
-19200/69092	Loss: 157.092
-22400/69092	Loss: 156.777
-25600/69092	Loss: 153.528
-28800/69092	Loss: 156.226
-32000/69092	Loss: 154.600
-35200/69092	Loss: 159.076
-38400/69092	Loss: 156.869
-41600/69092	Loss: 158.280
-44800/69092	Loss: 157.099
-48000/69092	Loss: 155.385
-51200/69092	Loss: 158.444
-54400/69092	Loss: 155.024
-57600/69092	Loss: 153.150
-60800/69092	Loss: 158.682
-64000/69092	Loss: 155.967
-67200/69092	Loss: 153.019
-Training time 0:01:58.641232
-Epoch: 103 Average loss: 156.15
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 103)
-0/69092	Loss: 158.399
-3200/69092	Loss: 155.141
-6400/69092	Loss: 158.227
-9600/69092	Loss: 154.084
-12800/69092	Loss: 156.589
-16000/69092	Loss: 157.047
-19200/69092	Loss: 155.855
-22400/69092	Loss: 155.442
-25600/69092	Loss: 156.533
-28800/69092	Loss: 156.653
-32000/69092	Loss: 157.194
-35200/69092	Loss: 159.617
-38400/69092	Loss: 154.397
-41600/69092	Loss: 154.771
-44800/69092	Loss: 154.845
-48000/69092	Loss: 154.884
-51200/69092	Loss: 157.147
-54400/69092	Loss: 154.389
-57600/69092	Loss: 155.469
-60800/69092	Loss: 155.751
-64000/69092	Loss: 157.746
-67200/69092	Loss: 154.849
-Training time 0:01:56.475910
-Epoch: 104 Average loss: 156.03
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 104)
-0/69092	Loss: 161.428
-3200/69092	Loss: 156.067
-6400/69092	Loss: 155.341
-9600/69092	Loss: 154.913
-12800/69092	Loss: 156.163
-16000/69092	Loss: 156.547
-19200/69092	Loss: 153.794
-22400/69092	Loss: 154.937
-25600/69092	Loss: 155.151
-28800/69092	Loss: 155.289
-32000/69092	Loss: 158.602
-35200/69092	Loss: 155.166
-38400/69092	Loss: 158.150
-41600/69092	Loss: 156.249
-44800/69092	Loss: 156.493
-48000/69092	Loss: 157.303
-51200/69092	Loss: 154.698
-54400/69092	Loss: 157.452
-57600/69092	Loss: 155.032
-60800/69092	Loss: 156.297
-64000/69092	Loss: 156.383
-67200/69092	Loss: 154.772
-Training time 0:01:57.094854
-Epoch: 105 Average loss: 155.98
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 105)
-0/69092	Loss: 150.408
-3200/69092	Loss: 157.610
-6400/69092	Loss: 152.280
-9600/69092	Loss: 156.733
-12800/69092	Loss: 154.552
-16000/69092	Loss: 156.122
-19200/69092	Loss: 157.433
-22400/69092	Loss: 155.877
-25600/69092	Loss: 156.331
-28800/69092	Loss: 157.846
-32000/69092	Loss: 156.285
-35200/69092	Loss: 156.583
-38400/69092	Loss: 154.465
-41600/69092	Loss: 153.643
-44800/69092	Loss: 157.368
-48000/69092	Loss: 155.520
-51200/69092	Loss: 156.080
-54400/69092	Loss: 154.398
-57600/69092	Loss: 158.109
-60800/69092	Loss: 157.425
-64000/69092	Loss: 157.451
-67200/69092	Loss: 154.696
-Training time 0:01:57.905391
-Epoch: 106 Average loss: 155.97
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 106)
-0/69092	Loss: 147.812
-3200/69092	Loss: 158.075
-6400/69092	Loss: 155.240
-9600/69092	Loss: 156.768
-12800/69092	Loss: 156.193
-16000/69092	Loss: 154.905
-19200/69092	Loss: 156.216
-22400/69092	Loss: 154.655
-25600/69092	Loss: 156.460
-28800/69092	Loss: 153.076
-32000/69092	Loss: 157.954
-35200/69092	Loss: 155.930
-38400/69092	Loss: 156.325
-41600/69092	Loss: 156.681
-44800/69092	Loss: 159.643
-48000/69092	Loss: 155.343
-51200/69092	Loss: 155.199
-54400/69092	Loss: 156.110
-57600/69092	Loss: 153.686
-60800/69092	Loss: 156.879
-64000/69092	Loss: 154.585
-67200/69092	Loss: 155.212
-Training time 0:01:56.307127
-Epoch: 107 Average loss: 155.89
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 107)
-0/69092	Loss: 154.253
-3200/69092	Loss: 153.416
-6400/69092	Loss: 152.018
-9600/69092	Loss: 156.441
-12800/69092	Loss: 156.551
-16000/69092	Loss: 155.907
-19200/69092	Loss: 154.270
-22400/69092	Loss: 158.192
-25600/69092	Loss: 157.611
-28800/69092	Loss: 157.174
-32000/69092	Loss: 156.599
-35200/69092	Loss: 156.001
-38400/69092	Loss: 155.926
-41600/69092	Loss: 156.182
-44800/69092	Loss: 156.168
-48000/69092	Loss: 155.820
-51200/69092	Loss: 156.520
-54400/69092	Loss: 157.469
-57600/69092	Loss: 153.393
-60800/69092	Loss: 156.360
-64000/69092	Loss: 158.875
-67200/69092	Loss: 156.235
-Training time 0:01:57.121476
-Epoch: 108 Average loss: 156.08
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 108)
-0/69092	Loss: 137.626
-3200/69092	Loss: 155.626
-6400/69092	Loss: 155.304
-9600/69092	Loss: 154.819
-12800/69092	Loss: 156.864
-16000/69092	Loss: 156.049
-19200/69092	Loss: 154.754
-22400/69092	Loss: 160.521
-25600/69092	Loss: 156.218
-28800/69092	Loss: 154.110
-32000/69092	Loss: 158.628
-35200/69092	Loss: 155.418
-38400/69092	Loss: 154.081
-41600/69092	Loss: 154.984
-44800/69092	Loss: 157.052
-48000/69092	Loss: 157.016
-51200/69092	Loss: 157.039
-54400/69092	Loss: 155.440
-57600/69092	Loss: 154.973
-60800/69092	Loss: 156.595
-64000/69092	Loss: 154.637
-67200/69092	Loss: 155.397
-Training time 0:01:57.346284
-Epoch: 109 Average loss: 155.98
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 109)
-0/69092	Loss: 149.738
-3200/69092	Loss: 156.390
-6400/69092	Loss: 156.148
-9600/69092	Loss: 155.724
-12800/69092	Loss: 156.560
-16000/69092	Loss: 156.340
-19200/69092	Loss: 156.251
-22400/69092	Loss: 155.574
-25600/69092	Loss: 155.729
-28800/69092	Loss: 155.537
-32000/69092	Loss: 153.385
-35200/69092	Loss: 153.714
-38400/69092	Loss: 155.649
-41600/69092	Loss: 155.944
-44800/69092	Loss: 155.621
-48000/69092	Loss: 158.474
-51200/69092	Loss: 155.418
-54400/69092	Loss: 156.086
-57600/69092	Loss: 157.631
-60800/69092	Loss: 155.373
-64000/69092	Loss: 155.928
-67200/69092	Loss: 158.605
-Training time 0:01:57.531353
-Epoch: 110 Average loss: 155.94
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 110)
-0/69092	Loss: 170.792
-3200/69092	Loss: 155.238
-6400/69092	Loss: 155.519
-9600/69092	Loss: 157.654
-12800/69092	Loss: 155.047
-16000/69092	Loss: 156.356
-19200/69092	Loss: 157.393
-22400/69092	Loss: 155.473
-25600/69092	Loss: 157.268
-28800/69092	Loss: 154.345
-32000/69092	Loss: 155.822
-35200/69092	Loss: 157.345
-38400/69092	Loss: 157.502
-41600/69092	Loss: 154.908
-44800/69092	Loss: 154.225
-48000/69092	Loss: 154.840
-51200/69092	Loss: 154.386
-54400/69092	Loss: 153.638
-57600/69092	Loss: 153.358
-60800/69092	Loss: 155.077
-64000/69092	Loss: 156.759
-67200/69092	Loss: 156.415
-Training time 0:01:56.946783
-Epoch: 111 Average loss: 155.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 111)
-0/69092	Loss: 150.964
-3200/69092	Loss: 158.559
-6400/69092	Loss: 154.329
-9600/69092	Loss: 154.166
-12800/69092	Loss: 154.935
-16000/69092	Loss: 156.629
-19200/69092	Loss: 157.566
-22400/69092	Loss: 158.841
-25600/69092	Loss: 155.628
-28800/69092	Loss: 154.152
-32000/69092	Loss: 155.909
-35200/69092	Loss: 153.850
-38400/69092	Loss: 156.465
-41600/69092	Loss: 156.930
-44800/69092	Loss: 155.522
-48000/69092	Loss: 156.088
-51200/69092	Loss: 158.124
-54400/69092	Loss: 154.659
-57600/69092	Loss: 153.904
-60800/69092	Loss: 151.762
-64000/69092	Loss: 156.022
-67200/69092	Loss: 156.427
-Training time 0:01:57.189807
-Epoch: 112 Average loss: 155.72
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 112)
-0/69092	Loss: 175.846
-3200/69092	Loss: 157.925
-6400/69092	Loss: 155.612
-9600/69092	Loss: 155.114
-12800/69092	Loss: 156.187
-16000/69092	Loss: 155.551
-19200/69092	Loss: 155.641
-22400/69092	Loss: 155.834
-25600/69092	Loss: 153.425
-28800/69092	Loss: 155.658
-32000/69092	Loss: 154.897
-35200/69092	Loss: 155.391
-38400/69092	Loss: 154.131
-41600/69092	Loss: 155.877
-44800/69092	Loss: 155.467
-48000/69092	Loss: 157.181
-51200/69092	Loss: 155.400
-54400/69092	Loss: 154.917
-57600/69092	Loss: 154.325
-60800/69092	Loss: 156.443
-64000/69092	Loss: 156.540
-67200/69092	Loss: 155.040
-Training time 0:01:57.976793
-Epoch: 113 Average loss: 155.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 113)
-0/69092	Loss: 174.227
-3200/69092	Loss: 154.453
-6400/69092	Loss: 158.337
-9600/69092	Loss: 154.710
-12800/69092	Loss: 157.299
-16000/69092	Loss: 156.532
-19200/69092	Loss: 154.051
-22400/69092	Loss: 154.384
-25600/69092	Loss: 152.617
-28800/69092	Loss: 154.337
-32000/69092	Loss: 154.008
-35200/69092	Loss: 155.621
-38400/69092	Loss: 153.317
-41600/69092	Loss: 158.356
-44800/69092	Loss: 154.623
-48000/69092	Loss: 155.293
-51200/69092	Loss: 156.157
-54400/69092	Loss: 156.266
-57600/69092	Loss: 156.518
-60800/69092	Loss: 156.546
-64000/69092	Loss: 155.152
-67200/69092	Loss: 152.980
-Training time 0:01:58.318560
-Epoch: 114 Average loss: 155.34
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 114)
-0/69092	Loss: 168.966
-3200/69092	Loss: 154.778
-6400/69092	Loss: 152.374
-9600/69092	Loss: 154.265
-12800/69092	Loss: 155.828
-16000/69092	Loss: 156.947
-19200/69092	Loss: 153.212
-22400/69092	Loss: 155.507
-25600/69092	Loss: 155.495
-28800/69092	Loss: 153.549
-32000/69092	Loss: 155.035
-35200/69092	Loss: 155.916
-38400/69092	Loss: 156.398
-41600/69092	Loss: 155.636
-44800/69092	Loss: 157.366
-48000/69092	Loss: 153.544
-51200/69092	Loss: 158.787
-54400/69092	Loss: 153.198
-57600/69092	Loss: 155.386
-60800/69092	Loss: 153.367
-64000/69092	Loss: 154.176
-67200/69092	Loss: 155.239
-Training time 0:01:57.300875
-Epoch: 115 Average loss: 155.17
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 115)
-0/69092	Loss: 153.426
-3200/69092	Loss: 152.366
-6400/69092	Loss: 153.814
-9600/69092	Loss: 156.034
-12800/69092	Loss: 154.588
-16000/69092	Loss: 158.361
-19200/69092	Loss: 155.269
-22400/69092	Loss: 153.414
-25600/69092	Loss: 157.952
-28800/69092	Loss: 155.246
-32000/69092	Loss: 154.442
-35200/69092	Loss: 157.097
-38400/69092	Loss: 155.266
-41600/69092	Loss: 155.442
-44800/69092	Loss: 153.841
-48000/69092	Loss: 154.809
-51200/69092	Loss: 150.702
-54400/69092	Loss: 156.469
-57600/69092	Loss: 155.105
-60800/69092	Loss: 157.730
-64000/69092	Loss: 155.257
-67200/69092	Loss: 151.986
-Training time 0:01:57.025523
-Epoch: 116 Average loss: 155.12
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 116)
-0/69092	Loss: 148.078
-3200/69092	Loss: 152.770
-6400/69092	Loss: 153.422
-9600/69092	Loss: 155.244
-12800/69092	Loss: 157.139
-16000/69092	Loss: 157.257
-19200/69092	Loss: 155.262
-22400/69092	Loss: 157.124
-25600/69092	Loss: 153.268
-28800/69092	Loss: 153.607
-32000/69092	Loss: 155.643
-35200/69092	Loss: 155.779
-38400/69092	Loss: 154.522
-41600/69092	Loss: 154.395
-44800/69092	Loss: 155.834
-48000/69092	Loss: 151.930
-51200/69092	Loss: 152.719
-54400/69092	Loss: 155.352
-57600/69092	Loss: 155.928
-60800/69092	Loss: 157.246
-64000/69092	Loss: 155.415
-67200/69092	Loss: 154.781
-Training time 0:01:58.664741
-Epoch: 117 Average loss: 155.05
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 117)
-0/69092	Loss: 171.513
-3200/69092	Loss: 157.710
-6400/69092	Loss: 153.195
-9600/69092	Loss: 155.382
-12800/69092	Loss: 153.891
-16000/69092	Loss: 159.238
-19200/69092	Loss: 154.755
-22400/69092	Loss: 154.061
-25600/69092	Loss: 154.306
-28800/69092	Loss: 153.344
-32000/69092	Loss: 155.959
-35200/69092	Loss: 153.530
-38400/69092	Loss: 154.895
-41600/69092	Loss: 155.108
-44800/69092	Loss: 155.699
-48000/69092	Loss: 154.761
-51200/69092	Loss: 154.865
-54400/69092	Loss: 155.258
-57600/69092	Loss: 154.297
-60800/69092	Loss: 156.943
-64000/69092	Loss: 153.107
-67200/69092	Loss: 154.885
-Training time 0:01:58.273350
-Epoch: 118 Average loss: 154.97
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 118)
-0/69092	Loss: 150.001
-3200/69092	Loss: 154.122
-6400/69092	Loss: 154.486
-9600/69092	Loss: 153.961
-12800/69092	Loss: 155.133
-16000/69092	Loss: 154.922
-19200/69092	Loss: 153.014
-22400/69092	Loss: 153.390
-25600/69092	Loss: 157.570
-28800/69092	Loss: 156.498
-32000/69092	Loss: 154.375
-35200/69092	Loss: 157.862
-38400/69092	Loss: 155.825
-41600/69092	Loss: 153.963
-44800/69092	Loss: 154.603
-48000/69092	Loss: 154.339
-51200/69092	Loss: 152.880
-54400/69092	Loss: 156.724
-57600/69092	Loss: 155.091
-60800/69092	Loss: 152.868
-64000/69092	Loss: 154.170
-67200/69092	Loss: 156.052
-Training time 0:01:58.633698
-Epoch: 119 Average loss: 154.83
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 119)
-0/69092	Loss: 160.933
-3200/69092	Loss: 155.272
-6400/69092	Loss: 156.377
-9600/69092	Loss: 152.034
-12800/69092	Loss: 155.839
-16000/69092	Loss: 154.538
-19200/69092	Loss: 155.078
-22400/69092	Loss: 153.071
-25600/69092	Loss: 155.367
-28800/69092	Loss: 154.745
-32000/69092	Loss: 153.654
-35200/69092	Loss: 152.479
-38400/69092	Loss: 153.916
-41600/69092	Loss: 154.280
-44800/69092	Loss: 152.825
-48000/69092	Loss: 156.693
-51200/69092	Loss: 154.448
-54400/69092	Loss: 156.211
-57600/69092	Loss: 157.181
-60800/69092	Loss: 156.198
-64000/69092	Loss: 155.162
-67200/69092	Loss: 154.812
-Training time 0:01:58.076896
-Epoch: 120 Average loss: 154.87
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 120)
-0/69092	Loss: 132.012
-3200/69092	Loss: 155.977
-6400/69092	Loss: 152.125
-9600/69092	Loss: 154.250
-12800/69092	Loss: 152.881
-16000/69092	Loss: 157.267
-19200/69092	Loss: 156.522
-22400/69092	Loss: 155.579
-25600/69092	Loss: 156.021
-28800/69092	Loss: 152.512
-32000/69092	Loss: 157.851
-35200/69092	Loss: 155.854
-38400/69092	Loss: 154.697
-41600/69092	Loss: 154.646
-44800/69092	Loss: 155.231
-48000/69092	Loss: 151.950
-51200/69092	Loss: 153.412
-54400/69092	Loss: 154.098
-57600/69092	Loss: 154.528
-60800/69092	Loss: 154.204
-64000/69092	Loss: 154.731
-67200/69092	Loss: 157.961
-Training time 0:01:57.341197
-Epoch: 121 Average loss: 154.90
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 121)
-0/69092	Loss: 147.670
-3200/69092	Loss: 155.720
-6400/69092	Loss: 155.014
-9600/69092	Loss: 153.611
-12800/69092	Loss: 154.024
-16000/69092	Loss: 156.432
-19200/69092	Loss: 152.719
-22400/69092	Loss: 156.579
-25600/69092	Loss: 150.242
-28800/69092	Loss: 154.217
-32000/69092	Loss: 153.524
-35200/69092	Loss: 155.488
-38400/69092	Loss: 153.052
-41600/69092	Loss: 157.173
-44800/69092	Loss: 157.166
-48000/69092	Loss: 157.039
-51200/69092	Loss: 156.565
-54400/69092	Loss: 153.906
-57600/69092	Loss: 155.050
-60800/69092	Loss: 153.772
-64000/69092	Loss: 151.165
-67200/69092	Loss: 156.539
-Training time 0:01:58.555985
-Epoch: 122 Average loss: 154.70
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 122)
-0/69092	Loss: 149.961
-3200/69092	Loss: 154.561
-6400/69092	Loss: 154.395
-9600/69092	Loss: 154.540
-12800/69092	Loss: 151.944
-16000/69092	Loss: 153.042
-19200/69092	Loss: 155.138
-22400/69092	Loss: 153.326
-25600/69092	Loss: 155.592
-28800/69092	Loss: 155.934
-32000/69092	Loss: 152.884
-35200/69092	Loss: 154.599
-38400/69092	Loss: 155.866
-41600/69092	Loss: 157.803
-44800/69092	Loss: 155.345
-48000/69092	Loss: 156.320
-51200/69092	Loss: 151.717
-54400/69092	Loss: 154.813
-57600/69092	Loss: 153.258
-60800/69092	Loss: 154.829
-64000/69092	Loss: 155.179
-67200/69092	Loss: 152.764
-Training time 0:01:58.459415
-Epoch: 123 Average loss: 154.48
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 123)
-0/69092	Loss: 143.996
-3200/69092	Loss: 154.933
-6400/69092	Loss: 155.628
-9600/69092	Loss: 154.814
-12800/69092	Loss: 153.867
-16000/69092	Loss: 155.074
-19200/69092	Loss: 153.150
-22400/69092	Loss: 153.663
-25600/69092	Loss: 154.939
-28800/69092	Loss: 150.770
-32000/69092	Loss: 154.517
-35200/69092	Loss: 155.187
-38400/69092	Loss: 156.361
-41600/69092	Loss: 155.239
-44800/69092	Loss: 154.829
-48000/69092	Loss: 154.833
-51200/69092	Loss: 154.172
-54400/69092	Loss: 155.574
-57600/69092	Loss: 155.616
-60800/69092	Loss: 155.825
-64000/69092	Loss: 155.648
-67200/69092	Loss: 154.989
-Training time 0:01:57.505004
-Epoch: 124 Average loss: 154.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 124)
-0/69092	Loss: 149.688
-3200/69092	Loss: 153.352
-6400/69092	Loss: 152.295
-9600/69092	Loss: 154.284
-12800/69092	Loss: 155.867
-16000/69092	Loss: 156.085
-19200/69092	Loss: 155.497
-22400/69092	Loss: 155.256
-25600/69092	Loss: 156.888
-28800/69092	Loss: 154.722
-32000/69092	Loss: 154.577
-35200/69092	Loss: 153.832
-38400/69092	Loss: 153.127
-41600/69092	Loss: 156.272
-44800/69092	Loss: 152.897
-48000/69092	Loss: 152.821
-51200/69092	Loss: 155.048
-54400/69092	Loss: 154.859
-57600/69092	Loss: 154.674
-60800/69092	Loss: 154.172
-64000/69092	Loss: 153.382
-67200/69092	Loss: 155.391
-Training time 0:01:57.617767
-Epoch: 125 Average loss: 154.51
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 125)
-0/69092	Loss: 174.120
-3200/69092	Loss: 154.710
-6400/69092	Loss: 155.469
-9600/69092	Loss: 154.952
-12800/69092	Loss: 154.360
-16000/69092	Loss: 153.049
-19200/69092	Loss: 155.451
-22400/69092	Loss: 155.977
-25600/69092	Loss: 153.877
-28800/69092	Loss: 153.698
-32000/69092	Loss: 155.169
-35200/69092	Loss: 155.187
-38400/69092	Loss: 154.105
-41600/69092	Loss: 151.598
-44800/69092	Loss: 154.485
-48000/69092	Loss: 155.221
-51200/69092	Loss: 153.565
-54400/69092	Loss: 156.596
-57600/69092	Loss: 156.262
-60800/69092	Loss: 151.911
-64000/69092	Loss: 155.138
-67200/69092	Loss: 153.838
-Training time 0:01:57.489586
-Epoch: 126 Average loss: 154.54
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 126)
-0/69092	Loss: 161.715
-3200/69092	Loss: 156.828
-6400/69092	Loss: 156.048
-9600/69092	Loss: 156.895
-12800/69092	Loss: 153.084
-16000/69092	Loss: 150.651
-19200/69092	Loss: 156.812
-22400/69092	Loss: 153.117
-25600/69092	Loss: 153.393
-28800/69092	Loss: 155.779
-32000/69092	Loss: 150.421
-35200/69092	Loss: 152.936
-38400/69092	Loss: 154.379
-41600/69092	Loss: 154.482
-44800/69092	Loss: 157.635
-48000/69092	Loss: 155.784
-51200/69092	Loss: 154.992
-54400/69092	Loss: 157.164
-57600/69092	Loss: 152.517
-60800/69092	Loss: 154.083
-64000/69092	Loss: 155.171
-67200/69092	Loss: 154.870
-Training time 0:01:58.320576
-Epoch: 127 Average loss: 154.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 127)
-0/69092	Loss: 149.820
-3200/69092	Loss: 155.763
-6400/69092	Loss: 154.983
-9600/69092	Loss: 153.094
-12800/69092	Loss: 154.531
-16000/69092	Loss: 153.738
-19200/69092	Loss: 154.447
-22400/69092	Loss: 154.038
-25600/69092	Loss: 152.407
-28800/69092	Loss: 152.053
-32000/69092	Loss: 156.108
-35200/69092	Loss: 157.111
-38400/69092	Loss: 154.522
-41600/69092	Loss: 154.477
-44800/69092	Loss: 155.592
-48000/69092	Loss: 155.653
-51200/69092	Loss: 155.518
-54400/69092	Loss: 155.488
-57600/69092	Loss: 153.067
-60800/69092	Loss: 153.172
-64000/69092	Loss: 155.850
-67200/69092	Loss: 152.925
-Training time 0:01:58.128835
-Epoch: 128 Average loss: 154.51
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 128)
-0/69092	Loss: 154.357
-3200/69092	Loss: 153.358
-6400/69092	Loss: 154.050
-9600/69092	Loss: 155.832
-12800/69092	Loss: 153.568
-16000/69092	Loss: 155.070
-19200/69092	Loss: 152.445
-22400/69092	Loss: 153.246
-25600/69092	Loss: 152.740
-28800/69092	Loss: 156.151
-32000/69092	Loss: 155.030
-35200/69092	Loss: 151.880
-38400/69092	Loss: 153.706
-41600/69092	Loss: 156.196
-44800/69092	Loss: 154.440
-48000/69092	Loss: 152.798
-51200/69092	Loss: 155.316
-54400/69092	Loss: 153.793
-57600/69092	Loss: 155.304
-60800/69092	Loss: 156.240
-64000/69092	Loss: 156.599
-67200/69092	Loss: 153.464
-Training time 0:01:58.853265
-Epoch: 129 Average loss: 154.32
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 129)
-0/69092	Loss: 159.408
-3200/69092	Loss: 154.166
-6400/69092	Loss: 155.684
-9600/69092	Loss: 154.080
-12800/69092	Loss: 154.837
-16000/69092	Loss: 155.003
-19200/69092	Loss: 154.744
-22400/69092	Loss: 149.973
-25600/69092	Loss: 152.992
-28800/69092	Loss: 153.851
-32000/69092	Loss: 155.261
-35200/69092	Loss: 156.042
-38400/69092	Loss: 154.715
-41600/69092	Loss: 154.610
-44800/69092	Loss: 154.538
-48000/69092	Loss: 155.196
-51200/69092	Loss: 154.074
-54400/69092	Loss: 153.846
-57600/69092	Loss: 155.952
-60800/69092	Loss: 153.240
-64000/69092	Loss: 154.886
-67200/69092	Loss: 152.854
-Training time 0:01:59.618850
-Epoch: 130 Average loss: 154.32
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 130)
-0/69092	Loss: 155.935
-3200/69092	Loss: 154.465
-6400/69092	Loss: 154.565
-9600/69092	Loss: 155.417
-12800/69092	Loss: 152.406
-16000/69092	Loss: 155.113
-19200/69092	Loss: 152.698
-22400/69092	Loss: 156.341
-25600/69092	Loss: 154.342
-28800/69092	Loss: 153.397
-32000/69092	Loss: 149.231
-35200/69092	Loss: 154.516
-38400/69092	Loss: 151.761
-41600/69092	Loss: 153.513
-44800/69092	Loss: 155.560
-48000/69092	Loss: 156.695
-51200/69092	Loss: 153.803
-54400/69092	Loss: 159.459
-57600/69092	Loss: 155.416
-60800/69092	Loss: 154.987
-64000/69092	Loss: 154.935
-67200/69092	Loss: 155.008
-Training time 0:01:57.487546
-Epoch: 131 Average loss: 154.45
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 131)
-0/69092	Loss: 145.728
-3200/69092	Loss: 152.723
-6400/69092	Loss: 155.251
-9600/69092	Loss: 155.858
-12800/69092	Loss: 155.717
-16000/69092	Loss: 157.164
-19200/69092	Loss: 156.345
-22400/69092	Loss: 152.506
-25600/69092	Loss: 155.294
-28800/69092	Loss: 156.185
-32000/69092	Loss: 154.058
-35200/69092	Loss: 152.411
-38400/69092	Loss: 152.938
-41600/69092	Loss: 154.333
-44800/69092	Loss: 152.387
-48000/69092	Loss: 151.226
-51200/69092	Loss: 152.922
-54400/69092	Loss: 153.569
-57600/69092	Loss: 153.481
-60800/69092	Loss: 153.954
-64000/69092	Loss: 155.628
-67200/69092	Loss: 156.342
-Training time 0:01:57.605448
-Epoch: 132 Average loss: 154.36
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 132)
-0/69092	Loss: 147.528
-3200/69092	Loss: 153.321
-6400/69092	Loss: 153.253
-9600/69092	Loss: 151.012
-12800/69092	Loss: 152.982
-16000/69092	Loss: 153.983
-19200/69092	Loss: 153.548
-22400/69092	Loss: 157.116
-25600/69092	Loss: 155.926
-28800/69092	Loss: 154.905
-32000/69092	Loss: 152.167
-35200/69092	Loss: 154.952
-38400/69092	Loss: 155.216
-41600/69092	Loss: 157.491
-44800/69092	Loss: 152.950
-48000/69092	Loss: 153.880
-51200/69092	Loss: 152.695
-54400/69092	Loss: 153.012
-57600/69092	Loss: 155.877
-60800/69092	Loss: 151.846
-64000/69092	Loss: 155.664
-67200/69092	Loss: 155.213
-Training time 0:01:58.702622
-Epoch: 133 Average loss: 154.23
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 133)
-0/69092	Loss: 159.723
-3200/69092	Loss: 154.466
-6400/69092	Loss: 152.954
-9600/69092	Loss: 154.046
-12800/69092	Loss: 155.107
-16000/69092	Loss: 154.238
-19200/69092	Loss: 152.850
-22400/69092	Loss: 154.230
-25600/69092	Loss: 152.999
-28800/69092	Loss: 153.305
-32000/69092	Loss: 156.465
-35200/69092	Loss: 153.700
-38400/69092	Loss: 153.750
-41600/69092	Loss: 156.218
-44800/69092	Loss: 154.095
-48000/69092	Loss: 151.172
-51200/69092	Loss: 154.330
-54400/69092	Loss: 153.509
-57600/69092	Loss: 154.224
-60800/69092	Loss: 155.738
-64000/69092	Loss: 153.959
-67200/69092	Loss: 154.808
-Training time 0:01:57.000587
-Epoch: 134 Average loss: 154.06
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 134)
-0/69092	Loss: 156.543
-3200/69092	Loss: 154.426
-6400/69092	Loss: 154.341
-9600/69092	Loss: 151.155
-12800/69092	Loss: 156.247
-16000/69092	Loss: 154.293
-19200/69092	Loss: 152.839
-22400/69092	Loss: 155.701
-25600/69092	Loss: 153.560
-28800/69092	Loss: 154.129
-32000/69092	Loss: 154.981
-35200/69092	Loss: 152.740
-38400/69092	Loss: 154.321
-41600/69092	Loss: 153.580
-44800/69092	Loss: 155.099
-48000/69092	Loss: 153.782
-51200/69092	Loss: 153.338
-54400/69092	Loss: 155.170
-57600/69092	Loss: 152.697
-60800/69092	Loss: 152.968
-64000/69092	Loss: 157.106
-67200/69092	Loss: 153.546
-Training time 0:01:57.662060
-Epoch: 135 Average loss: 154.06
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 135)
-0/69092	Loss: 159.106
-3200/69092	Loss: 153.990
-6400/69092	Loss: 155.767
-9600/69092	Loss: 154.633
-12800/69092	Loss: 156.908
-16000/69092	Loss: 152.705
-19200/69092	Loss: 153.722
-22400/69092	Loss: 154.371
-25600/69092	Loss: 155.097
-28800/69092	Loss: 153.648
-32000/69092	Loss: 153.985
-35200/69092	Loss: 153.991
-38400/69092	Loss: 152.757
-41600/69092	Loss: 156.407
-44800/69092	Loss: 150.817
-48000/69092	Loss: 155.814
-51200/69092	Loss: 153.631
-54400/69092	Loss: 156.232
-57600/69092	Loss: 153.291
-60800/69092	Loss: 152.798
-64000/69092	Loss: 150.308
-67200/69092	Loss: 155.751
-Training time 0:01:56.928577
-Epoch: 136 Average loss: 154.26
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 136)
-0/69092	Loss: 153.956
-3200/69092	Loss: 155.212
-6400/69092	Loss: 152.447
-9600/69092	Loss: 153.818
-12800/69092	Loss: 152.746
-16000/69092	Loss: 155.759
-19200/69092	Loss: 153.714
-22400/69092	Loss: 152.721
-25600/69092	Loss: 152.225
-28800/69092	Loss: 151.169
-32000/69092	Loss: 152.706
-35200/69092	Loss: 155.753
-38400/69092	Loss: 155.422
-41600/69092	Loss: 155.398
-44800/69092	Loss: 152.257
-48000/69092	Loss: 153.857
-51200/69092	Loss: 157.067
-54400/69092	Loss: 153.970
-57600/69092	Loss: 154.447
-60800/69092	Loss: 152.205
-64000/69092	Loss: 154.156
-67200/69092	Loss: 156.353
-Training time 0:01:57.550661
-Epoch: 137 Average loss: 153.92
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 137)
-0/69092	Loss: 142.783
-3200/69092	Loss: 156.137
-6400/69092	Loss: 155.682
-9600/69092	Loss: 153.996
-12800/69092	Loss: 155.705
-16000/69092	Loss: 155.974
-19200/69092	Loss: 156.330
-22400/69092	Loss: 152.064
-25600/69092	Loss: 155.111
-28800/69092	Loss: 152.343
-32000/69092	Loss: 156.257
-35200/69092	Loss: 153.007
-38400/69092	Loss: 152.673
-41600/69092	Loss: 152.809
-44800/69092	Loss: 153.557
-48000/69092	Loss: 153.619
-51200/69092	Loss: 153.444
-54400/69092	Loss: 151.786
-57600/69092	Loss: 152.920
-60800/69092	Loss: 151.331
-64000/69092	Loss: 152.122
-67200/69092	Loss: 159.084
-Training time 0:01:57.336705
-Epoch: 138 Average loss: 154.04
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 138)
-0/69092	Loss: 160.587
-3200/69092	Loss: 156.926
-6400/69092	Loss: 153.134
-9600/69092	Loss: 153.596
-12800/69092	Loss: 155.380
-16000/69092	Loss: 153.227
-19200/69092	Loss: 153.911
-22400/69092	Loss: 153.887
-25600/69092	Loss: 153.708
-28800/69092	Loss: 155.804
-32000/69092	Loss: 152.475
-35200/69092	Loss: 155.362
-38400/69092	Loss: 154.846
-41600/69092	Loss: 152.605
-44800/69092	Loss: 156.361
-48000/69092	Loss: 155.316
-51200/69092	Loss: 152.677
-54400/69092	Loss: 154.207
-57600/69092	Loss: 152.265
-60800/69092	Loss: 153.547
-64000/69092	Loss: 152.417
-67200/69092	Loss: 153.493
-Training time 0:01:58.230480
-Epoch: 139 Average loss: 154.03
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 139)
-0/69092	Loss: 139.523
-3200/69092	Loss: 154.231
-6400/69092	Loss: 155.235
-9600/69092	Loss: 154.371
-12800/69092	Loss: 154.983
-16000/69092	Loss: 154.015
-19200/69092	Loss: 154.778
-22400/69092	Loss: 156.963
-25600/69092	Loss: 150.474
-28800/69092	Loss: 150.523
-32000/69092	Loss: 154.557
-35200/69092	Loss: 155.371
-38400/69092	Loss: 154.050
-41600/69092	Loss: 153.330
-44800/69092	Loss: 156.219
-48000/69092	Loss: 152.746
-51200/69092	Loss: 155.189
-54400/69092	Loss: 151.280
-57600/69092	Loss: 153.665
-60800/69092	Loss: 154.930
-64000/69092	Loss: 154.840
-67200/69092	Loss: 155.642
-Training time 0:01:56.672850
-Epoch: 140 Average loss: 154.10
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 140)
-0/69092	Loss: 156.655
-3200/69092	Loss: 155.086
-6400/69092	Loss: 155.348
-9600/69092	Loss: 154.012
-12800/69092	Loss: 152.327
-16000/69092	Loss: 151.027
-19200/69092	Loss: 153.559
-22400/69092	Loss: 153.670
-25600/69092	Loss: 153.736
-28800/69092	Loss: 157.209
-32000/69092	Loss: 153.606
-35200/69092	Loss: 153.187
-38400/69092	Loss: 152.696
-41600/69092	Loss: 152.603
-44800/69092	Loss: 153.964
-48000/69092	Loss: 155.424
-51200/69092	Loss: 153.954
-54400/69092	Loss: 153.999
-57600/69092	Loss: 154.230
-60800/69092	Loss: 155.382
-64000/69092	Loss: 152.991
-67200/69092	Loss: 150.853
-Training time 0:01:58.026175
-Epoch: 141 Average loss: 153.86
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 141)
-0/69092	Loss: 160.142
-3200/69092	Loss: 153.358
-6400/69092	Loss: 154.774
-9600/69092	Loss: 152.377
-12800/69092	Loss: 154.675
-16000/69092	Loss: 156.249
-19200/69092	Loss: 155.458
-22400/69092	Loss: 152.909
-25600/69092	Loss: 155.081
-28800/69092	Loss: 154.258
-32000/69092	Loss: 153.205
-35200/69092	Loss: 155.777
-38400/69092	Loss: 152.785
-41600/69092	Loss: 153.439
-44800/69092	Loss: 151.158
-48000/69092	Loss: 152.683
-51200/69092	Loss: 156.369
-54400/69092	Loss: 152.324
-57600/69092	Loss: 151.797
-60800/69092	Loss: 152.179
-64000/69092	Loss: 153.761
-67200/69092	Loss: 155.343
-Training time 0:01:57.734306
-Epoch: 142 Average loss: 153.73
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 142)
-0/69092	Loss: 150.114
-3200/69092	Loss: 156.249
-6400/69092	Loss: 151.748
-9600/69092	Loss: 154.695
-12800/69092	Loss: 156.563
-16000/69092	Loss: 155.681
-19200/69092	Loss: 154.723
-22400/69092	Loss: 153.296
-25600/69092	Loss: 153.170
-28800/69092	Loss: 153.939
-32000/69092	Loss: 154.430
-35200/69092	Loss: 155.606
-38400/69092	Loss: 150.784
-41600/69092	Loss: 154.820
-44800/69092	Loss: 152.319
-48000/69092	Loss: 156.628
-51200/69092	Loss: 154.337
-54400/69092	Loss: 153.493
-57600/69092	Loss: 151.842
-60800/69092	Loss: 153.395
-64000/69092	Loss: 154.761
-67200/69092	Loss: 153.199
-Training time 0:01:57.780932
-Epoch: 143 Average loss: 153.92
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 143)
-0/69092	Loss: 169.829
-3200/69092	Loss: 152.957
-6400/69092	Loss: 152.465
-9600/69092	Loss: 153.118
-12800/69092	Loss: 152.974
-16000/69092	Loss: 153.778
-19200/69092	Loss: 152.374
-22400/69092	Loss: 155.112
-25600/69092	Loss: 155.510
-28800/69092	Loss: 153.136
-32000/69092	Loss: 153.114
-35200/69092	Loss: 155.219
-38400/69092	Loss: 154.435
-41600/69092	Loss: 155.116
-44800/69092	Loss: 153.666
-48000/69092	Loss: 154.597
-51200/69092	Loss: 153.671
-54400/69092	Loss: 154.324
-57600/69092	Loss: 155.815
-60800/69092	Loss: 154.392
-64000/69092	Loss: 152.200
-67200/69092	Loss: 153.992
-Training time 0:01:57.695235
-Epoch: 144 Average loss: 153.90
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 144)
-0/69092	Loss: 155.833
-3200/69092	Loss: 153.278
-6400/69092	Loss: 156.467
-9600/69092	Loss: 152.783
-12800/69092	Loss: 155.711
-16000/69092	Loss: 153.770
-19200/69092	Loss: 150.296
-22400/69092	Loss: 153.311
-25600/69092	Loss: 154.870
-28800/69092	Loss: 152.703
-32000/69092	Loss: 155.669
-35200/69092	Loss: 156.915
-38400/69092	Loss: 153.689
-41600/69092	Loss: 154.421
-44800/69092	Loss: 152.794
-48000/69092	Loss: 152.755
-51200/69092	Loss: 154.377
-54400/69092	Loss: 148.934
-57600/69092	Loss: 153.359
-60800/69092	Loss: 153.478
-64000/69092	Loss: 154.794
-67200/69092	Loss: 154.613
-Training time 0:01:57.431273
-Epoch: 145 Average loss: 153.83
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 145)
-0/69092	Loss: 158.462
-3200/69092	Loss: 154.293
-6400/69092	Loss: 152.808
-9600/69092	Loss: 155.977
-12800/69092	Loss: 155.423
-16000/69092	Loss: 152.008
-19200/69092	Loss: 154.136
-22400/69092	Loss: 152.260
-25600/69092	Loss: 152.057
-28800/69092	Loss: 153.241
-32000/69092	Loss: 154.236
-35200/69092	Loss: 155.114
-38400/69092	Loss: 153.480
-41600/69092	Loss: 153.581
-44800/69092	Loss: 152.582
-48000/69092	Loss: 151.147
-51200/69092	Loss: 155.744
-54400/69092	Loss: 156.545
-57600/69092	Loss: 154.778
-60800/69092	Loss: 151.076
-64000/69092	Loss: 156.301
-67200/69092	Loss: 153.371
-Training time 0:01:57.628707
-Epoch: 146 Average loss: 153.93
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 146)
-0/69092	Loss: 156.194
-3200/69092	Loss: 154.547
-6400/69092	Loss: 155.653
-9600/69092	Loss: 155.409
-12800/69092	Loss: 153.601
-16000/69092	Loss: 151.753
-19200/69092	Loss: 154.548
-22400/69092	Loss: 154.939
-25600/69092	Loss: 153.980
-28800/69092	Loss: 156.103
-32000/69092	Loss: 152.202
-35200/69092	Loss: 154.228
-38400/69092	Loss: 153.186
-41600/69092	Loss: 153.617
-44800/69092	Loss: 155.158
-48000/69092	Loss: 151.201
-51200/69092	Loss: 154.912
-54400/69092	Loss: 154.494
-57600/69092	Loss: 153.789
-60800/69092	Loss: 152.809
-64000/69092	Loss: 152.733
-67200/69092	Loss: 151.789
-Training time 0:01:58.751913
-Epoch: 147 Average loss: 153.91
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 147)
-0/69092	Loss: 162.678
-3200/69092	Loss: 154.196
-6400/69092	Loss: 153.003
-9600/69092	Loss: 155.581
-12800/69092	Loss: 155.374
-16000/69092	Loss: 154.330
-19200/69092	Loss: 153.539
-22400/69092	Loss: 151.800
-25600/69092	Loss: 152.051
-28800/69092	Loss: 154.773
-32000/69092	Loss: 151.551
-35200/69092	Loss: 156.579
-38400/69092	Loss: 153.501
-41600/69092	Loss: 155.479
-44800/69092	Loss: 153.281
-48000/69092	Loss: 154.757
-51200/69092	Loss: 152.889
-54400/69092	Loss: 152.369
-57600/69092	Loss: 151.568
-60800/69092	Loss: 154.789
-64000/69092	Loss: 156.925
-67200/69092	Loss: 151.513
-Training time 0:01:57.488062
-Epoch: 148 Average loss: 153.77
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 148)
-0/69092	Loss: 151.224
-3200/69092	Loss: 153.451
-6400/69092	Loss: 154.341
-9600/69092	Loss: 154.280
-12800/69092	Loss: 152.081
-16000/69092	Loss: 154.267
-19200/69092	Loss: 155.218
-22400/69092	Loss: 157.274
-25600/69092	Loss: 150.625
-28800/69092	Loss: 154.814
-32000/69092	Loss: 153.785
-35200/69092	Loss: 154.031
-38400/69092	Loss: 153.243
-41600/69092	Loss: 153.179
-44800/69092	Loss: 153.648
-48000/69092	Loss: 151.641
-51200/69092	Loss: 152.208
-54400/69092	Loss: 153.805
-57600/69092	Loss: 155.211
-60800/69092	Loss: 154.852
-64000/69092	Loss: 152.221
-67200/69092	Loss: 153.810
-Training time 0:01:58.789382
-Epoch: 149 Average loss: 153.73
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 149)
-0/69092	Loss: 152.636
-3200/69092	Loss: 151.073
-6400/69092	Loss: 151.507
-9600/69092	Loss: 153.848
-12800/69092	Loss: 152.529
-16000/69092	Loss: 153.900
-19200/69092	Loss: 154.187
-22400/69092	Loss: 153.347
-25600/69092	Loss: 153.988
-28800/69092	Loss: 154.171
-32000/69092	Loss: 156.736
-35200/69092	Loss: 154.913
-38400/69092	Loss: 153.681
-41600/69092	Loss: 153.000
-44800/69092	Loss: 152.467
-48000/69092	Loss: 152.460
-51200/69092	Loss: 153.296
-54400/69092	Loss: 154.302
-57600/69092	Loss: 153.158
-60800/69092	Loss: 153.864
-64000/69092	Loss: 156.101
-67200/69092	Loss: 154.313
-Training time 0:01:58.051617
-Epoch: 150 Average loss: 153.72
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 150)
-0/69092	Loss: 162.084
-3200/69092	Loss: 153.760
-6400/69092	Loss: 152.667
-9600/69092	Loss: 153.247
-12800/69092	Loss: 153.087
-16000/69092	Loss: 152.668
-19200/69092	Loss: 153.215
-22400/69092	Loss: 151.081
-25600/69092	Loss: 156.073
-28800/69092	Loss: 154.337
-32000/69092	Loss: 155.127
-35200/69092	Loss: 154.757
-38400/69092	Loss: 152.933
-41600/69092	Loss: 154.723
-44800/69092	Loss: 154.110
-48000/69092	Loss: 151.663
-51200/69092	Loss: 155.294
-54400/69092	Loss: 155.273
-57600/69092	Loss: 154.865
-60800/69092	Loss: 152.884
-64000/69092	Loss: 150.369
-67200/69092	Loss: 155.480
-Training time 0:01:58.039805
-Epoch: 151 Average loss: 153.73
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 151)
-0/69092	Loss: 154.323
-3200/69092	Loss: 154.346
-6400/69092	Loss: 156.689
-9600/69092	Loss: 154.837
-12800/69092	Loss: 152.500
-16000/69092	Loss: 154.715
-19200/69092	Loss: 153.188
-22400/69092	Loss: 154.261
-25600/69092	Loss: 154.184
-28800/69092	Loss: 154.488
-32000/69092	Loss: 155.662
-35200/69092	Loss: 152.800
-38400/69092	Loss: 151.050
-41600/69092	Loss: 152.624
-44800/69092	Loss: 152.340
-48000/69092	Loss: 154.712
-51200/69092	Loss: 155.533
-54400/69092	Loss: 155.045
-57600/69092	Loss: 154.351
-60800/69092	Loss: 154.003
-64000/69092	Loss: 153.178
-67200/69092	Loss: 156.106
-Training time 0:01:57.533614
-Epoch: 152 Average loss: 154.16
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 152)
-0/69092	Loss: 160.166
-3200/69092	Loss: 155.509
-6400/69092	Loss: 154.516
-9600/69092	Loss: 154.513
-12800/69092	Loss: 151.618
-16000/69092	Loss: 155.012
-19200/69092	Loss: 154.437
-22400/69092	Loss: 155.936
-25600/69092	Loss: 152.202
-28800/69092	Loss: 154.504
-32000/69092	Loss: 152.626
-35200/69092	Loss: 152.439
-38400/69092	Loss: 152.898
-41600/69092	Loss: 152.279
-44800/69092	Loss: 155.021
-48000/69092	Loss: 153.674
-51200/69092	Loss: 155.295
-54400/69092	Loss: 155.256
-57600/69092	Loss: 156.158
-60800/69092	Loss: 154.614
-64000/69092	Loss: 152.384
-67200/69092	Loss: 153.594
-Training time 0:01:57.634748
-Epoch: 153 Average loss: 154.01
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 153)
-0/69092	Loss: 140.068
-3200/69092	Loss: 155.298
-6400/69092	Loss: 152.207
-9600/69092	Loss: 151.929
-12800/69092	Loss: 153.416
-16000/69092	Loss: 155.561
-19200/69092	Loss: 157.925
-22400/69092	Loss: 154.117
-25600/69092	Loss: 151.465
-28800/69092	Loss: 153.599
-32000/69092	Loss: 155.049
-35200/69092	Loss: 152.796
-38400/69092	Loss: 156.962
-41600/69092	Loss: 152.889
-44800/69092	Loss: 152.001
-48000/69092	Loss: 154.006
-51200/69092	Loss: 152.195
-54400/69092	Loss: 157.328
-57600/69092	Loss: 151.284
-60800/69092	Loss: 153.628
-64000/69092	Loss: 156.930
-67200/69092	Loss: 151.567
-Training time 0:01:58.139985
-Epoch: 154 Average loss: 153.88
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 154)
-0/69092	Loss: 156.653
-3200/69092	Loss: 152.359
-6400/69092	Loss: 153.745
-9600/69092	Loss: 154.052
-12800/69092	Loss: 153.711
-16000/69092	Loss: 151.837
-19200/69092	Loss: 151.056
-22400/69092	Loss: 154.054
-25600/69092	Loss: 153.293
-28800/69092	Loss: 153.260
-32000/69092	Loss: 152.467
-35200/69092	Loss: 154.089
-38400/69092	Loss: 151.759
-41600/69092	Loss: 156.242
-44800/69092	Loss: 152.816
-48000/69092	Loss: 155.227
-51200/69092	Loss: 153.193
-54400/69092	Loss: 153.702
-57600/69092	Loss: 154.848
-60800/69092	Loss: 155.175
-64000/69092	Loss: 154.394
-67200/69092	Loss: 152.495
-Training time 0:01:57.368083
-Epoch: 155 Average loss: 153.55
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 155)
-0/69092	Loss: 167.637
-3200/69092	Loss: 151.109
-6400/69092	Loss: 153.323
-9600/69092	Loss: 155.334
-12800/69092	Loss: 153.465
-16000/69092	Loss: 153.807
-19200/69092	Loss: 154.708
-22400/69092	Loss: 154.279
-25600/69092	Loss: 155.457
-28800/69092	Loss: 155.550
-32000/69092	Loss: 152.109
-35200/69092	Loss: 152.939
-38400/69092	Loss: 152.208
-41600/69092	Loss: 155.243
-44800/69092	Loss: 154.913
-48000/69092	Loss: 152.296
-51200/69092	Loss: 152.721
-54400/69092	Loss: 153.143
-57600/69092	Loss: 155.118
-60800/69092	Loss: 154.669
-64000/69092	Loss: 153.953
-67200/69092	Loss: 151.785
-Training time 0:01:57.368878
-Epoch: 156 Average loss: 153.72
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 156)
-0/69092	Loss: 151.414
-3200/69092	Loss: 153.087
-6400/69092	Loss: 155.039
-9600/69092	Loss: 152.998
-12800/69092	Loss: 152.374
-16000/69092	Loss: 155.734
-19200/69092	Loss: 153.986
-22400/69092	Loss: 153.311
-25600/69092	Loss: 154.019
-28800/69092	Loss: 153.302
-32000/69092	Loss: 155.839
-35200/69092	Loss: 152.492
-38400/69092	Loss: 151.044
-41600/69092	Loss: 154.090
-44800/69092	Loss: 155.471
-48000/69092	Loss: 150.539
-51200/69092	Loss: 154.657
-54400/69092	Loss: 154.625
-57600/69092	Loss: 151.827
-60800/69092	Loss: 153.499
-64000/69092	Loss: 155.504
-67200/69092	Loss: 153.294
-Training time 0:01:57.937851
-Epoch: 157 Average loss: 153.69
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 157)
-0/69092	Loss: 155.203
-3200/69092	Loss: 153.649
-6400/69092	Loss: 153.099
-9600/69092	Loss: 153.397
-12800/69092	Loss: 151.938
-16000/69092	Loss: 154.316
-19200/69092	Loss: 154.341
-22400/69092	Loss: 154.392
-25600/69092	Loss: 151.844
-28800/69092	Loss: 152.773
-32000/69092	Loss: 152.386
-35200/69092	Loss: 152.962
-38400/69092	Loss: 155.712
-41600/69092	Loss: 154.281
-44800/69092	Loss: 152.907
-48000/69092	Loss: 154.915
-51200/69092	Loss: 153.984
-54400/69092	Loss: 152.315
-57600/69092	Loss: 154.558
-60800/69092	Loss: 152.245
-64000/69092	Loss: 156.315
-67200/69092	Loss: 154.819
-Training time 0:01:56.487464
-Epoch: 158 Average loss: 153.59
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 158)
-0/69092	Loss: 138.796
-3200/69092	Loss: 150.871
-6400/69092	Loss: 152.803
-9600/69092	Loss: 152.876
-12800/69092	Loss: 153.534
-16000/69092	Loss: 153.738
-19200/69092	Loss: 153.358
-22400/69092	Loss: 153.958
-25600/69092	Loss: 155.503
-28800/69092	Loss: 154.094
-32000/69092	Loss: 153.473
-35200/69092	Loss: 153.654
-38400/69092	Loss: 151.730
-41600/69092	Loss: 156.619
-44800/69092	Loss: 152.114
-48000/69092	Loss: 150.256
-51200/69092	Loss: 152.394
-54400/69092	Loss: 156.116
-57600/69092	Loss: 156.554
-60800/69092	Loss: 154.473
-64000/69092	Loss: 153.228
-67200/69092	Loss: 155.879
-Training time 0:01:56.763215
-Epoch: 159 Average loss: 153.71
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 159)
-0/69092	Loss: 135.210
-3200/69092	Loss: 153.410
-6400/69092	Loss: 154.654
-9600/69092	Loss: 151.387
-12800/69092	Loss: 153.361
-16000/69092	Loss: 154.785
-19200/69092	Loss: 153.738
-22400/69092	Loss: 154.280
-25600/69092	Loss: 151.401
-28800/69092	Loss: 153.722
-32000/69092	Loss: 156.128
-35200/69092	Loss: 152.507
-38400/69092	Loss: 155.037
-41600/69092	Loss: 155.098
-44800/69092	Loss: 150.621
-48000/69092	Loss: 156.095
-51200/69092	Loss: 156.333
-54400/69092	Loss: 154.415
-57600/69092	Loss: 152.454
-60800/69092	Loss: 156.055
-64000/69092	Loss: 150.316
-67200/69092	Loss: 151.292
-Training time 0:01:57.123982
-Epoch: 160 Average loss: 153.72
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 160)
-0/69092	Loss: 160.980
-3200/69092	Loss: 153.038
-6400/69092	Loss: 155.879
-9600/69092	Loss: 153.908
-12800/69092	Loss: 153.496
-16000/69092	Loss: 152.851
-19200/69092	Loss: 150.351
-22400/69092	Loss: 154.568
-25600/69092	Loss: 152.232
-28800/69092	Loss: 154.261
-32000/69092	Loss: 151.139
-35200/69092	Loss: 151.672
-38400/69092	Loss: 153.071
-41600/69092	Loss: 155.655
-44800/69092	Loss: 155.650
-48000/69092	Loss: 154.988
-51200/69092	Loss: 155.444
-54400/69092	Loss: 152.756
-57600/69092	Loss: 154.898
-60800/69092	Loss: 153.796
-64000/69092	Loss: 154.462
-67200/69092	Loss: 154.135
-Training time 0:01:57.824355
-Epoch: 161 Average loss: 153.73
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 161)
-0/69092	Loss: 154.934
-3200/69092	Loss: 154.769
-6400/69092	Loss: 155.408
-9600/69092	Loss: 152.720
-12800/69092	Loss: 153.010
-16000/69092	Loss: 149.867
-19200/69092	Loss: 154.809
-22400/69092	Loss: 153.012
-25600/69092	Loss: 151.028
-28800/69092	Loss: 153.618
-32000/69092	Loss: 153.146
-35200/69092	Loss: 152.735
-38400/69092	Loss: 155.770
-41600/69092	Loss: 155.319
-44800/69092	Loss: 150.476
-48000/69092	Loss: 153.693
-51200/69092	Loss: 157.593
-54400/69092	Loss: 153.628
-57600/69092	Loss: 155.057
-60800/69092	Loss: 153.639
-64000/69092	Loss: 154.853
-67200/69092	Loss: 152.783
-Training time 0:01:57.259768
-Epoch: 162 Average loss: 153.68
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 162)
-0/69092	Loss: 165.094
-3200/69092	Loss: 152.293
-6400/69092	Loss: 153.304
-9600/69092	Loss: 152.834
-12800/69092	Loss: 153.095
-16000/69092	Loss: 154.263
-19200/69092	Loss: 152.015
-22400/69092	Loss: 151.859
-25600/69092	Loss: 153.817
-28800/69092	Loss: 152.712
-32000/69092	Loss: 152.396
-35200/69092	Loss: 156.793
-38400/69092	Loss: 155.159
-41600/69092	Loss: 154.835
-44800/69092	Loss: 155.972
-48000/69092	Loss: 154.872
-51200/69092	Loss: 154.652
-54400/69092	Loss: 153.499
-57600/69092	Loss: 154.220
-60800/69092	Loss: 154.119
-64000/69092	Loss: 153.693
-67200/69092	Loss: 150.792
-Training time 0:01:57.292765
-Epoch: 163 Average loss: 153.71
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 163)
-0/69092	Loss: 148.269
-3200/69092	Loss: 153.030
-6400/69092	Loss: 153.241
-9600/69092	Loss: 154.577
-12800/69092	Loss: 153.547
-16000/69092	Loss: 151.112
-19200/69092	Loss: 152.070
-22400/69092	Loss: 153.096
-25600/69092	Loss: 152.512
-28800/69092	Loss: 150.972
-32000/69092	Loss: 151.387
-35200/69092	Loss: 153.144
-38400/69092	Loss: 155.376
-41600/69092	Loss: 156.802
-44800/69092	Loss: 152.794
-48000/69092	Loss: 154.014
-51200/69092	Loss: 155.498
-54400/69092	Loss: 151.854
-57600/69092	Loss: 152.469
-60800/69092	Loss: 153.989
-64000/69092	Loss: 155.050
-67200/69092	Loss: 154.904
-Training time 0:01:57.148237
-Epoch: 164 Average loss: 153.40
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 164)
-0/69092	Loss: 151.540
-3200/69092	Loss: 153.635
-6400/69092	Loss: 152.285
-9600/69092	Loss: 154.483
-12800/69092	Loss: 152.696
-16000/69092	Loss: 154.921
-19200/69092	Loss: 154.862
-22400/69092	Loss: 151.676
-25600/69092	Loss: 152.808
-28800/69092	Loss: 152.856
-32000/69092	Loss: 153.457
-35200/69092	Loss: 152.996
-38400/69092	Loss: 152.261
-41600/69092	Loss: 155.414
-44800/69092	Loss: 156.066
-48000/69092	Loss: 154.481
-51200/69092	Loss: 154.283
-54400/69092	Loss: 152.288
-57600/69092	Loss: 150.680
-60800/69092	Loss: 151.630
-64000/69092	Loss: 154.652
-67200/69092	Loss: 155.656
-Training time 0:01:58.221382
-Epoch: 165 Average loss: 153.58
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 165)
-0/69092	Loss: 171.343
-3200/69092	Loss: 152.834
-6400/69092	Loss: 152.541
-9600/69092	Loss: 152.125
-12800/69092	Loss: 155.896
-16000/69092	Loss: 152.651
-19200/69092	Loss: 155.593
-22400/69092	Loss: 155.573
-25600/69092	Loss: 153.585
-28800/69092	Loss: 152.051
-32000/69092	Loss: 152.165
-35200/69092	Loss: 154.546
-38400/69092	Loss: 151.660
-41600/69092	Loss: 156.363
-44800/69092	Loss: 153.298
-48000/69092	Loss: 155.658
-51200/69092	Loss: 152.386
-54400/69092	Loss: 153.483
-57600/69092	Loss: 155.383
-60800/69092	Loss: 152.277
-64000/69092	Loss: 153.315
-67200/69092	Loss: 151.966
-Training time 0:01:57.881041
-Epoch: 166 Average loss: 153.55
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 166)
-0/69092	Loss: 133.887
-3200/69092	Loss: 153.197
-6400/69092	Loss: 154.835
-9600/69092	Loss: 156.742
-12800/69092	Loss: 156.334
-16000/69092	Loss: 153.026
-19200/69092	Loss: 155.560
-22400/69092	Loss: 152.819
-25600/69092	Loss: 150.628
-28800/69092	Loss: 154.498
-32000/69092	Loss: 153.396
-35200/69092	Loss: 151.004
-38400/69092	Loss: 154.679
-41600/69092	Loss: 152.436
-44800/69092	Loss: 151.559
-48000/69092	Loss: 152.250
-51200/69092	Loss: 152.775
-54400/69092	Loss: 150.975
-57600/69092	Loss: 155.503
-60800/69092	Loss: 155.001
-64000/69092	Loss: 153.101
-67200/69092	Loss: 154.978
-Training time 0:02:00.223303
-Epoch: 167 Average loss: 153.58
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 167)
-0/69092	Loss: 149.147
-3200/69092	Loss: 153.933
-6400/69092	Loss: 153.032
-9600/69092	Loss: 154.035
-12800/69092	Loss: 152.999
-16000/69092	Loss: 155.712
-19200/69092	Loss: 152.549
-22400/69092	Loss: 153.545
-25600/69092	Loss: 151.579
-28800/69092	Loss: 154.253
-32000/69092	Loss: 152.795
-35200/69092	Loss: 154.479
-38400/69092	Loss: 153.484
-41600/69092	Loss: 153.771
-44800/69092	Loss: 152.245
-48000/69092	Loss: 154.020
-51200/69092	Loss: 151.847
-54400/69092	Loss: 154.695
-57600/69092	Loss: 153.501
-60800/69092	Loss: 154.201
-64000/69092	Loss: 153.938
-67200/69092	Loss: 152.550
-Training time 0:01:59.050741
-Epoch: 168 Average loss: 153.47
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 168)
-0/69092	Loss: 144.126
-3200/69092	Loss: 150.372
-6400/69092	Loss: 153.316
-9600/69092	Loss: 154.595
-12800/69092	Loss: 153.953
-16000/69092	Loss: 153.465
-19200/69092	Loss: 157.098
-22400/69092	Loss: 151.469
-25600/69092	Loss: 154.502
-28800/69092	Loss: 153.161
-32000/69092	Loss: 156.288
-35200/69092	Loss: 154.901
-38400/69092	Loss: 155.341
-41600/69092	Loss: 151.716
-44800/69092	Loss: 153.132
-48000/69092	Loss: 152.583
-51200/69092	Loss: 153.175
-54400/69092	Loss: 154.553
-57600/69092	Loss: 152.595
-60800/69092	Loss: 154.646
-64000/69092	Loss: 153.893
-67200/69092	Loss: 152.846
-Training time 0:01:58.963661
-Epoch: 169 Average loss: 153.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 169)
-0/69092	Loss: 154.990
-3200/69092	Loss: 152.755
-6400/69092	Loss: 155.570
-9600/69092	Loss: 151.471
-12800/69092	Loss: 153.766
-16000/69092	Loss: 156.364
-19200/69092	Loss: 152.366
-22400/69092	Loss: 154.997
-25600/69092	Loss: 153.211
-28800/69092	Loss: 151.262
-32000/69092	Loss: 152.426
-35200/69092	Loss: 153.907
-38400/69092	Loss: 153.318
-41600/69092	Loss: 153.294
-44800/69092	Loss: 153.489
-48000/69092	Loss: 154.007
-51200/69092	Loss: 152.344
-54400/69092	Loss: 153.836
-57600/69092	Loss: 152.884
-60800/69092	Loss: 155.056
-64000/69092	Loss: 154.297
-67200/69092	Loss: 153.268
-Training time 0:01:58.488707
-Epoch: 170 Average loss: 153.49
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 170)
-0/69092	Loss: 167.717
-3200/69092	Loss: 152.391
-6400/69092	Loss: 152.808
-9600/69092	Loss: 152.220
-12800/69092	Loss: 153.617
-16000/69092	Loss: 153.518
-19200/69092	Loss: 152.460
-22400/69092	Loss: 150.817
-25600/69092	Loss: 150.826
-28800/69092	Loss: 153.375
-32000/69092	Loss: 154.226
-35200/69092	Loss: 153.087
-38400/69092	Loss: 155.085
-41600/69092	Loss: 156.487
-44800/69092	Loss: 153.923
-48000/69092	Loss: 156.388
-51200/69092	Loss: 153.785
-54400/69092	Loss: 152.192
-57600/69092	Loss: 152.904
-60800/69092	Loss: 152.771
-64000/69092	Loss: 155.423
-67200/69092	Loss: 152.926
-Training time 0:01:58.307055
-Epoch: 171 Average loss: 153.49
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 171)
-0/69092	Loss: 170.096
-3200/69092	Loss: 155.635
-6400/69092	Loss: 152.207
-9600/69092	Loss: 155.079
-12800/69092	Loss: 154.785
-16000/69092	Loss: 152.576
-19200/69092	Loss: 153.121
-22400/69092	Loss: 153.258
-25600/69092	Loss: 155.704
-28800/69092	Loss: 153.085
-32000/69092	Loss: 152.489
-35200/69092	Loss: 153.985
-38400/69092	Loss: 154.194
-41600/69092	Loss: 151.834
-44800/69092	Loss: 152.536
-48000/69092	Loss: 153.344
-51200/69092	Loss: 154.594
-54400/69092	Loss: 152.373
-57600/69092	Loss: 151.567
-60800/69092	Loss: 153.130
-64000/69092	Loss: 154.112
-67200/69092	Loss: 154.415
-Training time 0:01:58.900402
-Epoch: 172 Average loss: 153.58
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 172)
-0/69092	Loss: 143.302
-3200/69092	Loss: 152.115
-6400/69092	Loss: 155.343
-9600/69092	Loss: 152.340
-12800/69092	Loss: 153.269
-16000/69092	Loss: 154.656
-19200/69092	Loss: 153.654
-22400/69092	Loss: 154.883
-25600/69092	Loss: 154.575
-28800/69092	Loss: 152.981
-32000/69092	Loss: 154.290
-35200/69092	Loss: 151.548
-38400/69092	Loss: 152.813
-41600/69092	Loss: 154.564
-44800/69092	Loss: 153.418
-48000/69092	Loss: 152.377
-51200/69092	Loss: 154.408
-54400/69092	Loss: 152.647
-57600/69092	Loss: 154.015
-60800/69092	Loss: 152.811
-64000/69092	Loss: 152.712
-67200/69092	Loss: 154.420
-Training time 0:01:57.682425
-Epoch: 173 Average loss: 153.51
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 173)
-0/69092	Loss: 161.742
-3200/69092	Loss: 152.407
-6400/69092	Loss: 154.528
-9600/69092	Loss: 152.282
-12800/69092	Loss: 152.555
-16000/69092	Loss: 158.049
-19200/69092	Loss: 152.968
-22400/69092	Loss: 155.897
-25600/69092	Loss: 154.286
-28800/69092	Loss: 151.864
-32000/69092	Loss: 154.010
-35200/69092	Loss: 155.564
-38400/69092	Loss: 153.267
-41600/69092	Loss: 153.191
-44800/69092	Loss: 151.635
-48000/69092	Loss: 152.679
-51200/69092	Loss: 150.579
-54400/69092	Loss: 155.121
-57600/69092	Loss: 155.764
-60800/69092	Loss: 153.156
-64000/69092	Loss: 152.468
-67200/69092	Loss: 151.519
-Training time 0:01:57.521824
-Epoch: 174 Average loss: 153.59
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 174)
-0/69092	Loss: 158.305
-3200/69092	Loss: 152.383
-6400/69092	Loss: 154.169
-9600/69092	Loss: 150.961
-12800/69092	Loss: 153.143
-16000/69092	Loss: 153.458
-19200/69092	Loss: 153.839
-22400/69092	Loss: 151.545
-25600/69092	Loss: 153.530
-28800/69092	Loss: 155.227
-32000/69092	Loss: 154.590
-35200/69092	Loss: 152.267
-38400/69092	Loss: 151.884
-41600/69092	Loss: 154.034
-44800/69092	Loss: 156.312
-48000/69092	Loss: 153.903
-51200/69092	Loss: 151.786
-54400/69092	Loss: 155.048
-57600/69092	Loss: 153.929
-60800/69092	Loss: 152.964
-64000/69092	Loss: 155.330
-67200/69092	Loss: 152.212
-Training time 0:01:57.989732
-Epoch: 175 Average loss: 153.45
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 175)
-0/69092	Loss: 154.773
-3200/69092	Loss: 153.349
-6400/69092	Loss: 152.404
-9600/69092	Loss: 155.498
-12800/69092	Loss: 153.516
-16000/69092	Loss: 152.032
-19200/69092	Loss: 152.516
-22400/69092	Loss: 152.473
-25600/69092	Loss: 153.110
-28800/69092	Loss: 154.600
-32000/69092	Loss: 154.944
-35200/69092	Loss: 153.640
-38400/69092	Loss: 151.123
-41600/69092	Loss: 156.020
-44800/69092	Loss: 154.271
-48000/69092	Loss: 154.586
-51200/69092	Loss: 154.334
-54400/69092	Loss: 151.005
-57600/69092	Loss: 152.191
-60800/69092	Loss: 154.740
-64000/69092	Loss: 156.242
-67200/69092	Loss: 154.027
-Training time 0:01:58.843046
-Epoch: 176 Average loss: 153.61
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 176)
-0/69092	Loss: 158.858
-3200/69092	Loss: 153.196
-6400/69092	Loss: 154.578
-9600/69092	Loss: 150.756
-12800/69092	Loss: 153.812
-16000/69092	Loss: 153.160
-19200/69092	Loss: 154.003
-22400/69092	Loss: 151.577
-25600/69092	Loss: 153.593
-28800/69092	Loss: 153.182
-32000/69092	Loss: 152.201
-35200/69092	Loss: 151.410
-38400/69092	Loss: 155.908
-41600/69092	Loss: 152.600
-44800/69092	Loss: 152.176
-48000/69092	Loss: 153.082
-51200/69092	Loss: 152.875
-54400/69092	Loss: 151.435
-57600/69092	Loss: 152.980
-60800/69092	Loss: 153.839
-64000/69092	Loss: 154.810
-67200/69092	Loss: 153.459
-Training time 0:01:58.518576
-Epoch: 177 Average loss: 153.23
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 177)
-0/69092	Loss: 157.223
-3200/69092	Loss: 153.913
-6400/69092	Loss: 153.103
-9600/69092	Loss: 154.528
-12800/69092	Loss: 153.509
-16000/69092	Loss: 151.302
-19200/69092	Loss: 152.627
-22400/69092	Loss: 152.497
-25600/69092	Loss: 155.063
-28800/69092	Loss: 155.373
-32000/69092	Loss: 153.499
-35200/69092	Loss: 154.216
-38400/69092	Loss: 153.588
-41600/69092	Loss: 153.213
-44800/69092	Loss: 153.914
-48000/69092	Loss: 154.398
-51200/69092	Loss: 154.633
-54400/69092	Loss: 153.529
-57600/69092	Loss: 153.721
-60800/69092	Loss: 154.351
-64000/69092	Loss: 152.790
-67200/69092	Loss: 152.004
-Training time 0:01:57.289607
-Epoch: 178 Average loss: 153.53
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 178)
-0/69092	Loss: 145.029
-3200/69092	Loss: 152.700
-6400/69092	Loss: 152.931
-9600/69092	Loss: 153.840
-12800/69092	Loss: 154.261
-16000/69092	Loss: 154.241
-19200/69092	Loss: 151.976
-22400/69092	Loss: 151.930
-25600/69092	Loss: 154.033
-28800/69092	Loss: 152.037
-32000/69092	Loss: 151.990
-35200/69092	Loss: 152.888
-38400/69092	Loss: 154.279
-41600/69092	Loss: 155.329
-44800/69092	Loss: 151.620
-48000/69092	Loss: 152.798
-51200/69092	Loss: 155.666
-54400/69092	Loss: 152.107
-57600/69092	Loss: 154.560
-60800/69092	Loss: 155.811
-64000/69092	Loss: 155.327
-67200/69092	Loss: 154.384
-Training time 0:01:58.109030
-Epoch: 179 Average loss: 153.47
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 179)
-0/69092	Loss: 132.821
-3200/69092	Loss: 152.968
-6400/69092	Loss: 153.225
-9600/69092	Loss: 151.330
-12800/69092	Loss: 153.036
-16000/69092	Loss: 153.537
-19200/69092	Loss: 151.809
-22400/69092	Loss: 153.752
-25600/69092	Loss: 154.048
-28800/69092	Loss: 153.331
-32000/69092	Loss: 154.121
-35200/69092	Loss: 154.223
-38400/69092	Loss: 154.314
-41600/69092	Loss: 153.762
-44800/69092	Loss: 153.451
-48000/69092	Loss: 155.277
-51200/69092	Loss: 153.193
-54400/69092	Loss: 153.819
-57600/69092	Loss: 151.237
-60800/69092	Loss: 154.401
-64000/69092	Loss: 151.327
-67200/69092	Loss: 156.048
-Training time 0:01:57.815115
-Epoch: 180 Average loss: 153.48
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 180)
-0/69092	Loss: 173.967
-3200/69092	Loss: 152.204
-6400/69092	Loss: 152.267
-9600/69092	Loss: 151.751
-12800/69092	Loss: 151.760
-16000/69092	Loss: 153.885
-19200/69092	Loss: 155.183
-22400/69092	Loss: 152.168
-25600/69092	Loss: 153.368
-28800/69092	Loss: 153.427
-32000/69092	Loss: 154.799
-35200/69092	Loss: 153.473
-38400/69092	Loss: 151.237
-41600/69092	Loss: 154.292
-44800/69092	Loss: 152.430
-48000/69092	Loss: 152.931
-51200/69092	Loss: 154.436
-54400/69092	Loss: 153.806
-57600/69092	Loss: 151.751
-60800/69092	Loss: 155.160
-64000/69092	Loss: 155.420
-67200/69092	Loss: 153.280
-Training time 0:01:57.631428
-Epoch: 181 Average loss: 153.35
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 181)
-0/69092	Loss: 160.243
-3200/69092	Loss: 152.580
-6400/69092	Loss: 154.525
-9600/69092	Loss: 152.073
-12800/69092	Loss: 155.034
-16000/69092	Loss: 153.844
-19200/69092	Loss: 153.800
-22400/69092	Loss: 156.658
-25600/69092	Loss: 153.285
-28800/69092	Loss: 154.415
-32000/69092	Loss: 153.113
-35200/69092	Loss: 154.413
-38400/69092	Loss: 152.150
-41600/69092	Loss: 151.703
-44800/69092	Loss: 152.631
-48000/69092	Loss: 152.867
-51200/69092	Loss: 152.753
-54400/69092	Loss: 150.697
-57600/69092	Loss: 154.120
-60800/69092	Loss: 153.307
-64000/69092	Loss: 153.509
-67200/69092	Loss: 154.321
-Training time 0:01:57.180327
-Epoch: 182 Average loss: 153.42
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 182)
-0/69092	Loss: 136.811
-3200/69092	Loss: 152.304
-6400/69092	Loss: 152.458
-9600/69092	Loss: 153.344
-12800/69092	Loss: 152.510
-16000/69092	Loss: 153.771
-19200/69092	Loss: 151.043
-22400/69092	Loss: 152.869
-25600/69092	Loss: 153.920
-28800/69092	Loss: 151.993
-32000/69092	Loss: 151.534
-35200/69092	Loss: 155.510
-38400/69092	Loss: 153.072
-41600/69092	Loss: 154.331
-44800/69092	Loss: 155.306
-48000/69092	Loss: 153.523
-51200/69092	Loss: 153.880
-54400/69092	Loss: 151.733
-57600/69092	Loss: 152.510
-60800/69092	Loss: 153.814
-64000/69092	Loss: 155.455
-67200/69092	Loss: 153.340
-Training time 0:01:57.497684
-Epoch: 183 Average loss: 153.21
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 183)
-0/69092	Loss: 161.665
-3200/69092	Loss: 153.367
-6400/69092	Loss: 153.018
-9600/69092	Loss: 151.798
-12800/69092	Loss: 152.768
-16000/69092	Loss: 155.262
-19200/69092	Loss: 153.670
-22400/69092	Loss: 155.870
-25600/69092	Loss: 153.954
-28800/69092	Loss: 153.844
-32000/69092	Loss: 150.597
-35200/69092	Loss: 153.193
-38400/69092	Loss: 154.559
-41600/69092	Loss: 149.616
-44800/69092	Loss: 155.597
-48000/69092	Loss: 152.084
-51200/69092	Loss: 153.694
-54400/69092	Loss: 152.873
-57600/69092	Loss: 154.909
-60800/69092	Loss: 154.249
-64000/69092	Loss: 151.734
-67200/69092	Loss: 152.525
-Training time 0:01:57.514177
-Epoch: 184 Average loss: 153.38
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 184)
-0/69092	Loss: 144.798
-3200/69092	Loss: 155.912
-6400/69092	Loss: 151.287
-9600/69092	Loss: 154.737
-12800/69092	Loss: 154.034
-16000/69092	Loss: 151.924
-19200/69092	Loss: 154.095
-22400/69092	Loss: 154.697
-25600/69092	Loss: 152.513
-28800/69092	Loss: 156.892
-32000/69092	Loss: 153.192
-35200/69092	Loss: 151.831
-38400/69092	Loss: 149.926
-41600/69092	Loss: 151.670
-44800/69092	Loss: 151.493
-48000/69092	Loss: 152.894
-51200/69092	Loss: 152.332
-54400/69092	Loss: 153.629
-57600/69092	Loss: 154.366
-60800/69092	Loss: 152.517
-64000/69092	Loss: 153.422
-67200/69092	Loss: 151.854
-Training time 0:01:56.780317
-Epoch: 185 Average loss: 153.10
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 185)
-0/69092	Loss: 153.127
-3200/69092	Loss: 153.570
-6400/69092	Loss: 152.923
-9600/69092	Loss: 153.774
-12800/69092	Loss: 150.843
-16000/69092	Loss: 153.916
-19200/69092	Loss: 153.384
-22400/69092	Loss: 153.709
-25600/69092	Loss: 153.429
-28800/69092	Loss: 153.527
-32000/69092	Loss: 152.805
-35200/69092	Loss: 153.293
-38400/69092	Loss: 153.931
-41600/69092	Loss: 155.595
-44800/69092	Loss: 152.922
-48000/69092	Loss: 154.691
-51200/69092	Loss: 151.888
-54400/69092	Loss: 151.208
-57600/69092	Loss: 155.162
-60800/69092	Loss: 151.213
-64000/69092	Loss: 151.698
-67200/69092	Loss: 153.116
-Training time 0:01:56.582316
-Epoch: 186 Average loss: 153.25
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 186)
-0/69092	Loss: 139.081
-3200/69092	Loss: 153.198
-6400/69092	Loss: 154.580
-9600/69092	Loss: 152.502
-12800/69092	Loss: 153.499
-16000/69092	Loss: 153.171
-19200/69092	Loss: 155.828
-22400/69092	Loss: 152.913
-25600/69092	Loss: 154.017
-28800/69092	Loss: 151.482
-32000/69092	Loss: 154.483
-35200/69092	Loss: 152.055
-38400/69092	Loss: 154.829
-41600/69092	Loss: 153.987
-44800/69092	Loss: 150.099
-48000/69092	Loss: 153.025
-51200/69092	Loss: 153.195
-54400/69092	Loss: 154.443
-57600/69092	Loss: 152.576
-60800/69092	Loss: 153.728
-64000/69092	Loss: 153.300
-67200/69092	Loss: 152.755
-Training time 0:01:57.961633
-Epoch: 187 Average loss: 153.30
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 187)
-0/69092	Loss: 137.805
-3200/69092	Loss: 153.069
-6400/69092	Loss: 150.559
-9600/69092	Loss: 155.338
-12800/69092	Loss: 154.055
-16000/69092	Loss: 154.296
-19200/69092	Loss: 151.792
-22400/69092	Loss: 151.791
-25600/69092	Loss: 154.987
-28800/69092	Loss: 155.171
-32000/69092	Loss: 154.879
-35200/69092	Loss: 154.590
-38400/69092	Loss: 152.051
-41600/69092	Loss: 151.056
-44800/69092	Loss: 150.419
-48000/69092	Loss: 152.993
-51200/69092	Loss: 152.944
-54400/69092	Loss: 154.770
-57600/69092	Loss: 156.868
-60800/69092	Loss: 152.806
-64000/69092	Loss: 153.789
-67200/69092	Loss: 154.109
-Training time 0:01:57.402130
-Epoch: 188 Average loss: 153.35
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 188)
-0/69092	Loss: 157.948
-3200/69092	Loss: 154.815
-6400/69092	Loss: 154.208
-9600/69092	Loss: 152.943
-12800/69092	Loss: 153.998
-16000/69092	Loss: 153.828
-19200/69092	Loss: 153.881
-22400/69092	Loss: 154.333
-25600/69092	Loss: 150.921
-28800/69092	Loss: 154.029
-32000/69092	Loss: 155.490
-35200/69092	Loss: 155.009
-38400/69092	Loss: 150.726
-41600/69092	Loss: 151.606
-44800/69092	Loss: 153.686
-48000/69092	Loss: 150.523
-51200/69092	Loss: 152.818
-54400/69092	Loss: 152.474
-57600/69092	Loss: 152.829
-60800/69092	Loss: 154.985
-64000/69092	Loss: 153.498
-67200/69092	Loss: 154.083
-Training time 0:01:56.221509
-Epoch: 189 Average loss: 153.42
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 189)
-0/69092	Loss: 153.654
-3200/69092	Loss: 156.417
-6400/69092	Loss: 153.118
-9600/69092	Loss: 149.262
-12800/69092	Loss: 153.621
-16000/69092	Loss: 154.701
-19200/69092	Loss: 154.152
-22400/69092	Loss: 152.348
-25600/69092	Loss: 153.848
-28800/69092	Loss: 156.334
-32000/69092	Loss: 153.381
-35200/69092	Loss: 155.933
-38400/69092	Loss: 151.845
-41600/69092	Loss: 152.259
-44800/69092	Loss: 154.884
-48000/69092	Loss: 151.158
-51200/69092	Loss: 150.854
-54400/69092	Loss: 152.270
-57600/69092	Loss: 154.510
-60800/69092	Loss: 152.766
-64000/69092	Loss: 150.074
-67200/69092	Loss: 151.896
-Training time 0:01:57.703280
-Epoch: 190 Average loss: 153.17
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 190)
-0/69092	Loss: 144.723
-3200/69092	Loss: 156.459
-6400/69092	Loss: 153.414
-9600/69092	Loss: 153.992
-12800/69092	Loss: 153.518
-16000/69092	Loss: 153.729
-19200/69092	Loss: 153.492
-22400/69092	Loss: 153.319
-25600/69092	Loss: 152.642
-28800/69092	Loss: 156.405
-32000/69092	Loss: 153.737
-35200/69092	Loss: 152.013
-38400/69092	Loss: 153.221
-41600/69092	Loss: 152.657
-44800/69092	Loss: 153.036
-48000/69092	Loss: 152.068
-51200/69092	Loss: 153.679
-54400/69092	Loss: 156.465
-57600/69092	Loss: 151.852
-60800/69092	Loss: 152.137
-64000/69092	Loss: 152.480
-67200/69092	Loss: 152.526
-Training time 0:01:57.576354
-Epoch: 191 Average loss: 153.49
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 191)
-0/69092	Loss: 164.213
-3200/69092	Loss: 151.961
-6400/69092	Loss: 153.876
-9600/69092	Loss: 154.495
-12800/69092	Loss: 154.759
-16000/69092	Loss: 152.287
-19200/69092	Loss: 155.725
-22400/69092	Loss: 152.406
-25600/69092	Loss: 151.840
-28800/69092	Loss: 154.640
-32000/69092	Loss: 152.285
-35200/69092	Loss: 153.189
-38400/69092	Loss: 152.761
-41600/69092	Loss: 154.928
-44800/69092	Loss: 153.206
-48000/69092	Loss: 153.663
-51200/69092	Loss: 151.281
-54400/69092	Loss: 152.788
-57600/69092	Loss: 151.861
-60800/69092	Loss: 152.905
-64000/69092	Loss: 153.191
-67200/69092	Loss: 152.214
-Training time 0:01:57.872127
-Epoch: 192 Average loss: 153.17
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 192)
-0/69092	Loss: 153.189
-3200/69092	Loss: 155.156
-6400/69092	Loss: 151.581
-9600/69092	Loss: 154.650
-12800/69092	Loss: 152.335
-16000/69092	Loss: 149.066
-19200/69092	Loss: 152.989
-22400/69092	Loss: 153.812
-25600/69092	Loss: 152.393
-28800/69092	Loss: 153.612
-32000/69092	Loss: 154.801
-35200/69092	Loss: 154.680
-38400/69092	Loss: 153.958
-41600/69092	Loss: 151.278
-44800/69092	Loss: 151.372
-48000/69092	Loss: 153.783
-51200/69092	Loss: 153.446
-54400/69092	Loss: 156.406
-57600/69092	Loss: 155.755
-60800/69092	Loss: 152.011
-64000/69092	Loss: 156.086
-67200/69092	Loss: 153.127
-Training time 0:01:57.083778
-Epoch: 193 Average loss: 153.49
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 193)
-0/69092	Loss: 154.344
-3200/69092	Loss: 153.425
-6400/69092	Loss: 152.044
-9600/69092	Loss: 156.312
-12800/69092	Loss: 152.580
-16000/69092	Loss: 152.340
-19200/69092	Loss: 153.742
-22400/69092	Loss: 152.416
-25600/69092	Loss: 151.204
-28800/69092	Loss: 154.553
-32000/69092	Loss: 154.041
-35200/69092	Loss: 154.902
-38400/69092	Loss: 155.723
-41600/69092	Loss: 149.735
-44800/69092	Loss: 153.955
-48000/69092	Loss: 154.440
-51200/69092	Loss: 154.848
-54400/69092	Loss: 151.147
-57600/69092	Loss: 152.391
-60800/69092	Loss: 152.510
-64000/69092	Loss: 154.010
-67200/69092	Loss: 153.323
-Training time 0:01:58.359636
-Epoch: 194 Average loss: 153.30
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 194)
-0/69092	Loss: 146.915
-3200/69092	Loss: 152.609
-6400/69092	Loss: 151.790
-9600/69092	Loss: 153.264
-12800/69092	Loss: 152.660
-16000/69092	Loss: 154.266
-19200/69092	Loss: 154.850
-22400/69092	Loss: 154.107
-25600/69092	Loss: 154.467
-28800/69092	Loss: 154.660
-32000/69092	Loss: 153.173
-35200/69092	Loss: 151.806
-38400/69092	Loss: 154.004
-41600/69092	Loss: 154.130
-44800/69092	Loss: 155.406
-48000/69092	Loss: 154.135
-51200/69092	Loss: 153.146
-54400/69092	Loss: 151.344
-57600/69092	Loss: 152.490
-60800/69092	Loss: 151.122
-64000/69092	Loss: 152.136
-67200/69092	Loss: 155.151
-Training time 0:01:58.474192
-Epoch: 195 Average loss: 153.33
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 195)
-0/69092	Loss: 160.613
-3200/69092	Loss: 153.715
-6400/69092	Loss: 153.313
-9600/69092	Loss: 154.246
-12800/69092	Loss: 152.969
-16000/69092	Loss: 150.915
-19200/69092	Loss: 152.274
-22400/69092	Loss: 153.307
-25600/69092	Loss: 154.993
-28800/69092	Loss: 151.508
-32000/69092	Loss: 153.728
-35200/69092	Loss: 151.085
-38400/69092	Loss: 152.811
-41600/69092	Loss: 154.267
-44800/69092	Loss: 153.282
-48000/69092	Loss: 152.042
-51200/69092	Loss: 153.633
-54400/69092	Loss: 153.821
-57600/69092	Loss: 156.052
-60800/69092	Loss: 152.132
-64000/69092	Loss: 153.222
-67200/69092	Loss: 153.562
-Training time 0:01:57.272817
-Epoch: 196 Average loss: 153.13
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 196)
-0/69092	Loss: 168.341
-3200/69092	Loss: 155.781
-6400/69092	Loss: 150.020
-9600/69092	Loss: 153.713
-12800/69092	Loss: 154.805
-16000/69092	Loss: 154.924
-19200/69092	Loss: 152.543
-22400/69092	Loss: 153.672
-25600/69092	Loss: 152.248
-28800/69092	Loss: 155.535
-32000/69092	Loss: 154.706
-35200/69092	Loss: 151.173
-38400/69092	Loss: 153.029
-41600/69092	Loss: 155.129
-44800/69092	Loss: 154.305
-48000/69092	Loss: 150.333
-51200/69092	Loss: 153.224
-54400/69092	Loss: 152.112
-57600/69092	Loss: 155.518
-60800/69092	Loss: 151.340
-64000/69092	Loss: 152.720
-67200/69092	Loss: 155.200
-Training time 0:01:57.564049
-Epoch: 197 Average loss: 153.36
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 197)
-0/69092	Loss: 152.832
-3200/69092	Loss: 155.401
-6400/69092	Loss: 152.652
-9600/69092	Loss: 153.890
-12800/69092	Loss: 150.696
-16000/69092	Loss: 151.536
-19200/69092	Loss: 153.555
-22400/69092	Loss: 152.638
-25600/69092	Loss: 152.626
-28800/69092	Loss: 151.146
-32000/69092	Loss: 152.802
-35200/69092	Loss: 155.171
-38400/69092	Loss: 153.063
-41600/69092	Loss: 154.563
-44800/69092	Loss: 155.242
-48000/69092	Loss: 151.568
-51200/69092	Loss: 155.067
-54400/69092	Loss: 153.353
-57600/69092	Loss: 154.043
-60800/69092	Loss: 152.441
-64000/69092	Loss: 152.133
-67200/69092	Loss: 152.631
-48000/69092	Loss: 153.327
-51200/69092	Loss: 151.013
-54400/69092	Loss: 152.419
-57600/69092	Loss: 153.783
-60800/69092	Loss: 153.751
-64000/69092	Loss: 152.644
-67200/69092	Loss: 154.169
-Training time 0:01:57.663572
-Epoch: 201 Average loss: 153.17
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 201)
-0/69092	Loss: 154.454
-3200/69092	Loss: 155.674
-6400/69092	Loss: 151.339
-9600/69092	Loss: 152.021
-12800/69092	Loss: 153.099
-16000/69092	Loss: 152.180
-19200/69092	Loss: 152.736
-22400/69092	Loss: 151.503
-25600/69092	Loss: 151.432
-28800/69092	Loss: 153.191
-32000/69092	Loss: 152.134
-35200/69092	Loss: 151.568
-38400/69092	Loss: 153.513
-41600/69092	Loss: 152.303
-44800/69092	Loss: 153.737
-48000/69092	Loss: 155.662
-51200/69092	Loss: 152.872
-54400/69092	Loss: 151.656
-57600/69092	Loss: 153.501
-60800/69092	Loss: 155.283
-64000/69092	Loss: 152.747
-67200/69092	Loss: 153.056
-Training time 0:01:58.155480
-Epoch: 202 Average loss: 152.98
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 202)
-0/69092	Loss: 151.555
-3200/69092	Loss: 153.526
-6400/69092	Loss: 154.845
-9600/69092	Loss: 153.318
-12800/69092	Loss: 153.101
-16000/69092	Loss: 151.746
-19200/69092	Loss: 149.802
-22400/69092	Loss: 155.046
-25600/69092	Loss: 155.019
-28800/69092	Loss: 153.662
-32000/69092	Loss: 154.378
-35200/69092	Loss: 153.605
-38400/69092	Loss: 152.214
-41600/69092	Loss: 151.754
-44800/69092	Loss: 150.080
-48000/69092	Loss: 153.390
-51200/69092	Loss: 153.520
-54400/69092	Loss: 153.503
-57600/69092	Loss: 153.312
-60800/69092	Loss: 153.914
-64000/69092	Loss: 152.388
-67200/69092	Loss: 152.194
-Training time 0:01:56.956451
-Epoch: 203 Average loss: 152.97
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 203)
-0/69092	Loss: 178.683
-3200/69092	Loss: 152.457
-6400/69092	Loss: 156.550
-9600/69092	Loss: 150.915
-12800/69092	Loss: 150.799
-16000/69092	Loss: 155.364
-19200/69092	Loss: 153.083
-22400/69092	Loss: 151.785
-25600/69092	Loss: 154.004
-28800/69092	Loss: 152.351
-32000/69092	Loss: 155.011
-35200/69092	Loss: 152.374
-38400/69092	Loss: 154.534
-41600/69092	Loss: 151.444
-44800/69092	Loss: 151.489
-48000/69092	Loss: 152.020
-51200/69092	Loss: 153.392
-54400/69092	Loss: 154.347
-57600/69092	Loss: 151.340
-60800/69092	Loss: 155.808
-64000/69092	Loss: 154.347
-67200/69092	Loss: 152.377
-Training time 0:01:56.859912
-Epoch: 204 Average loss: 153.16
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 204)
-0/69092	Loss: 138.625
-3200/69092	Loss: 152.850
-6400/69092	Loss: 152.322
-9600/69092	Loss: 154.403
-12800/69092	Loss: 153.948
-16000/69092	Loss: 151.989
-19200/69092	Loss: 154.287
-22400/69092	Loss: 151.121
-25600/69092	Loss: 151.187
-28800/69092	Loss: 153.457
-32000/69092	Loss: 153.582
-35200/69092	Loss: 153.946
-38400/69092	Loss: 150.736
-41600/69092	Loss: 155.028
-44800/69092	Loss: 153.445
-48000/69092	Loss: 153.018
-51200/69092	Loss: 149.907
-54400/69092	Loss: 153.789
-57600/69092	Loss: 151.504
-60800/69092	Loss: 153.333
-64000/69092	Loss: 154.963
-67200/69092	Loss: 155.373
-Training time 0:01:59.365824
-Epoch: 205 Average loss: 153.10
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 205)
-0/69092	Loss: 147.201
-3200/69092	Loss: 151.697
-6400/69092	Loss: 153.379
-9600/69092	Loss: 153.823
-12800/69092	Loss: 153.457
-16000/69092	Loss: 151.413
-19200/69092	Loss: 154.404
-22400/69092	Loss: 151.473
-25600/69092	Loss: 154.228
-28800/69092	Loss: 153.431
-32000/69092	Loss: 150.146
-35200/69092	Loss: 151.431
-38400/69092	Loss: 152.684
-41600/69092	Loss: 154.671
-44800/69092	Loss: 152.336
-48000/69092	Loss: 153.124
-51200/69092	Loss: 154.301
-54400/69092	Loss: 155.121
-57600/69092	Loss: 153.226
-60800/69092	Loss: 151.249
-64000/69092	Loss: 154.757
-67200/69092	Loss: 153.826
-Training time 0:01:57.188512
-Epoch: 206 Average loss: 153.05
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 206)
-0/69092	Loss: 136.333
-3200/69092	Loss: 152.612
-6400/69092	Loss: 151.811
-9600/69092	Loss: 153.699
-12800/69092	Loss: 154.377
-16000/69092	Loss: 152.827
-19200/69092	Loss: 152.339
-22400/69092	Loss: 151.878
-25600/69092	Loss: 155.262
-28800/69092	Loss: 152.071
-32000/69092	Loss: 152.793
-35200/69092	Loss: 155.137
-38400/69092	Loss: 154.037
-41600/69092	Loss: 152.279
-44800/69092	Loss: 156.727
-48000/69092	Loss: 151.432
-51200/69092	Loss: 150.464
-54400/69092	Loss: 154.882
-57600/69092	Loss: 154.085
-60800/69092	Loss: 153.740
-64000/69092	Loss: 153.330
-67200/69092	Loss: 151.401
-Training time 0:01:56.002772
-Epoch: 207 Average loss: 153.15
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 207)
-0/69092	Loss: 159.126
-3200/69092	Loss: 152.052
-6400/69092	Loss: 155.090
-9600/69092	Loss: 154.483
-12800/69092	Loss: 152.618
-16000/69092	Loss: 152.206
-19200/69092	Loss: 154.047
-22400/69092	Loss: 152.661
-25600/69092	Loss: 153.379
-28800/69092	Loss: 152.244
-32000/69092	Loss: 153.195
-35200/69092	Loss: 156.466
-38400/69092	Loss: 152.258
-41600/69092	Loss: 152.118
-44800/69092	Loss: 151.220
-48000/69092	Loss: 153.403
-51200/69092	Loss: 150.577
-54400/69092	Loss: 151.009
-57600/69092	Loss: 154.787
-60800/69092	Loss: 156.546
-64000/69092	Loss: 151.253
-67200/69092	Loss: 154.397
-Training time 0:01:56.828773
-Epoch: 208 Average loss: 153.08
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 208)
-0/69092	Loss: 151.807
-3200/69092	Loss: 154.319
-6400/69092	Loss: 153.644
-9600/69092	Loss: 153.258
-12800/69092	Loss: 152.806
-16000/69092	Loss: 153.872
-19200/69092	Loss: 154.030
-22400/69092	Loss: 155.283
-25600/69092	Loss: 151.143
-28800/69092	Loss: 153.840
-32000/69092	Loss: 154.017
-35200/69092	Loss: 150.235
-38400/69092	Loss: 154.384
-41600/69092	Loss: 155.664
-44800/69092	Loss: 151.471
-48000/69092	Loss: 154.014
-51200/69092	Loss: 150.890
-54400/69092	Loss: 155.039
-57600/69092	Loss: 151.520
-60800/69092	Loss: 153.483
-64000/69092	Loss: 153.392
-67200/69092	Loss: 156.238
-Training time 0:01:57.092329
-Epoch: 209 Average loss: 153.44
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 209)
-0/69092	Loss: 141.051
-3200/69092	Loss: 152.772
-6400/69092	Loss: 155.085
-9600/69092	Loss: 153.431
-12800/69092	Loss: 154.207
-16000/69092	Loss: 152.324
-19200/69092	Loss: 153.692
-22400/69092	Loss: 151.638
-25600/69092	Loss: 150.807
-28800/69092	Loss: 153.521
-32000/69092	Loss: 153.642
-35200/69092	Loss: 153.839
-38400/69092	Loss: 153.318
-41600/69092	Loss: 154.310
-44800/69092	Loss: 152.291
-48000/69092	Loss: 155.064
-51200/69092	Loss: 150.262
-54400/69092	Loss: 154.975
-57600/69092	Loss: 153.587
-60800/69092	Loss: 152.115
-64000/69092	Loss: 152.571
-67200/69092	Loss: 154.876
-Training time 0:01:58.064314
-Epoch: 210 Average loss: 153.18
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 210)
-0/69092	Loss: 159.396
-3200/69092	Loss: 154.733
-6400/69092	Loss: 153.565
-9600/69092	Loss: 153.966
-12800/69092	Loss: 152.109
-16000/69092	Loss: 152.466
-19200/69092	Loss: 150.720
-22400/69092	Loss: 152.711
-25600/69092	Loss: 152.942
-28800/69092	Loss: 151.933
-32000/69092	Loss: 155.083
-35200/69092	Loss: 150.069
-38400/69092	Loss: 152.721
-41600/69092	Loss: 152.702
-44800/69092	Loss: 151.334
-48000/69092	Loss: 153.130
-51200/69092	Loss: 154.547
-54400/69092	Loss: 153.815
-57600/69092	Loss: 152.741
-60800/69092	Loss: 153.496
-64000/69092	Loss: 153.536
-67200/69092	Loss: 152.980
-Training time 0:01:56.995772
-Epoch: 211 Average loss: 152.97
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 211)
-0/69092	Loss: 144.274
-3200/69092	Loss: 152.331
-6400/69092	Loss: 153.078
-9600/69092	Loss: 154.833
-12800/69092	Loss: 153.554
-16000/69092	Loss: 153.760
-19200/69092	Loss: 151.267
-22400/69092	Loss: 151.098
-25600/69092	Loss: 153.938
-28800/69092	Loss: 151.686
-32000/69092	Loss: 154.255
-35200/69092	Loss: 154.566
-38400/69092	Loss: 151.615
-41600/69092	Loss: 152.900
-44800/69092	Loss: 151.473
-48000/69092	Loss: 153.045
-51200/69092	Loss: 153.881
-54400/69092	Loss: 152.394
-57600/69092	Loss: 154.118
-60800/69092	Loss: 155.064
-64000/69092	Loss: 152.237
-67200/69092	Loss: 152.952
-Training time 0:01:57.304691
-Epoch: 212 Average loss: 152.98
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 212)
-0/69092	Loss: 152.058
-3200/69092	Loss: 152.385
-6400/69092	Loss: 154.151
-9600/69092	Loss: 154.332
-12800/69092	Loss: 154.325
-16000/69092	Loss: 153.314
-19200/69092	Loss: 151.008
-22400/69092	Loss: 154.562
-25600/69092	Loss: 154.595
-28800/69092	Loss: 150.897
-32000/69092	Loss: 153.565
-35200/69092	Loss: 151.765
-38400/69092	Loss: 152.672
-41600/69092	Loss: 153.964
-44800/69092	Loss: 154.752
-48000/69092	Loss: 153.246
-51200/69092	Loss: 152.780
-54400/69092	Loss: 153.455
-57600/69092	Loss: 154.722
-60800/69092	Loss: 149.292
-64000/69092	Loss: 153.789
-67200/69092	Loss: 151.950
-Training time 0:01:57.575948
-Epoch: 213 Average loss: 153.12
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 213)
-0/69092	Loss: 139.377
-3200/69092	Loss: 153.930
-6400/69092	Loss: 154.103
-9600/69092	Loss: 154.827
-12800/69092	Loss: 152.510
-16000/69092	Loss: 151.385
-19200/69092	Loss: 151.542
-22400/69092	Loss: 151.446
-25600/69092	Loss: 154.575
-28800/69092	Loss: 153.756
-32000/69092	Loss: 152.215
-35200/69092	Loss: 154.727
-38400/69092	Loss: 155.033
-41600/69092	Loss: 155.026
-44800/69092	Loss: 152.661
-48000/69092	Loss: 151.856
-51200/69092	Loss: 152.589
-54400/69092	Loss: 152.167
-57600/69092	Loss: 151.153
-60800/69092	Loss: 155.150
-64000/69092	Loss: 150.389
-67200/69092	Loss: 155.713
-Training time 0:01:57.480193
-Epoch: 214 Average loss: 153.12
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 214)
-0/69092	Loss: 162.559
-3200/69092	Loss: 153.853
-6400/69092	Loss: 151.702
-9600/69092	Loss: 155.117
-12800/69092	Loss: 150.545
-16000/69092	Loss: 153.676
-19200/69092	Loss: 152.117
-22400/69092	Loss: 152.922
-25600/69092	Loss: 152.732
-28800/69092	Loss: 154.555
-32000/69092	Loss: 151.590
-35200/69092	Loss: 154.411
-38400/69092	Loss: 154.215
-41600/69092	Loss: 152.622
-44800/69092	Loss: 154.972
-48000/69092	Loss: 151.488
-51200/69092	Loss: 154.537
-54400/69092	Loss: 152.975
-57600/69092	Loss: 154.043
-60800/69092	Loss: 154.166
-64000/69092	Loss: 152.604
-67200/69092	Loss: 154.063
-Training time 0:01:58.489547
-Epoch: 215 Average loss: 153.29
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 215)
-0/69092	Loss: 142.518
-3200/69092	Loss: 152.601
-6400/69092	Loss: 154.142
-9600/69092	Loss: 152.091
-12800/69092	Loss: 150.358
-16000/69092	Loss: 151.021
-19200/69092	Loss: 154.161
-22400/69092	Loss: 152.234
-25600/69092	Loss: 154.221
-28800/69092	Loss: 154.208
-32000/69092	Loss: 153.602
-35200/69092	Loss: 155.292
-38400/69092	Loss: 152.614
-41600/69092	Loss: 152.557
-44800/69092	Loss: 152.863
-48000/69092	Loss: 151.437
-51200/69092	Loss: 151.839
-54400/69092	Loss: 154.302
-57600/69092	Loss: 153.296
-60800/69092	Loss: 154.567
-64000/69092	Loss: 153.093
-67200/69092	Loss: 153.120
-Training time 0:01:58.124137
-Epoch: 216 Average loss: 153.07
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 216)
-0/69092	Loss: 145.412
-3200/69092	Loss: 153.349
-6400/69092	Loss: 151.452
-9600/69092	Loss: 151.918
-12800/69092	Loss: 154.555
-16000/69092	Loss: 151.488
-19200/69092	Loss: 155.105
-22400/69092	Loss: 155.665
-25600/69092	Loss: 153.667
-28800/69092	Loss: 152.872
-32000/69092	Loss: 155.300
-35200/69092	Loss: 150.349
-38400/69092	Loss: 151.373
-41600/69092	Loss: 152.978
-44800/69092	Loss: 152.866
-48000/69092	Loss: 154.216
-51200/69092	Loss: 154.033
-54400/69092	Loss: 152.151
-57600/69092	Loss: 152.067
-60800/69092	Loss: 152.121
-64000/69092	Loss: 154.637
-67200/69092	Loss: 151.924
-Training time 0:01:57.129648
-Epoch: 217 Average loss: 153.10
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 217)
-0/69092	Loss: 137.423
-3200/69092	Loss: 153.207
-6400/69092	Loss: 152.468
-9600/69092	Loss: 152.777
-12800/69092	Loss: 151.594
-16000/69092	Loss: 153.604
-19200/69092	Loss: 151.826
-22400/69092	Loss: 154.964
-25600/69092	Loss: 153.965
-28800/69092	Loss: 152.907
-32000/69092	Loss: 153.653
-35200/69092	Loss: 154.905
-38400/69092	Loss: 154.736
-41600/69092	Loss: 152.599
-44800/69092	Loss: 151.174
-48000/69092	Loss: 154.147
-51200/69092	Loss: 150.586
-54400/69092	Loss: 152.760
-57600/69092	Loss: 154.728
-60800/69092	Loss: 154.205
-64000/69092	Loss: 152.165
-67200/69092	Loss: 153.749
-Training time 0:01:57.834001
-Epoch: 218 Average loss: 153.21
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 218)
-0/69092	Loss: 134.265
-3200/69092	Loss: 153.752
-6400/69092	Loss: 153.571
-9600/69092	Loss: 154.039
-12800/69092	Loss: 150.626
-16000/69092	Loss: 154.971
-19200/69092	Loss: 150.849
-22400/69092	Loss: 150.640
-25600/69092	Loss: 153.325
-28800/69092	Loss: 154.799
-32000/69092	Loss: 153.318
-35200/69092	Loss: 152.523
-38400/69092	Loss: 151.084
-41600/69092	Loss: 152.294
-44800/69092	Loss: 156.620
-48000/69092	Loss: 151.964
-51200/69092	Loss: 155.712
-54400/69092	Loss: 153.685
-57600/69092	Loss: 151.700
-60800/69092	Loss: 154.930
-64000/69092	Loss: 152.310
-67200/69092	Loss: 153.247
-Training time 0:01:57.508754
-Epoch: 219 Average loss: 153.04
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 219)
-0/69092	Loss: 161.132
-3200/69092	Loss: 150.378
-6400/69092	Loss: 152.743
-9600/69092	Loss: 155.444
-12800/69092	Loss: 152.940
-16000/69092	Loss: 152.020
-19200/69092	Loss: 153.433
-22400/69092	Loss: 155.989
-25600/69092	Loss: 155.301
-28800/69092	Loss: 154.193
-32000/69092	Loss: 156.297
-35200/69092	Loss: 152.717
-38400/69092	Loss: 150.820
-41600/69092	Loss: 152.908
-44800/69092	Loss: 151.835
-48000/69092	Loss: 152.418
-51200/69092	Loss: 152.099
-54400/69092	Loss: 154.057
-57600/69092	Loss: 150.807
-60800/69092	Loss: 151.370
-64000/69092	Loss: 151.246
-67200/69092	Loss: 153.691
-Training time 0:01:59.273445
-Epoch: 220 Average loss: 153.06
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 220)
-0/69092	Loss: 134.050
-3200/69092	Loss: 151.060
-6400/69092	Loss: 153.877
-9600/69092	Loss: 151.699
-12800/69092	Loss: 152.957
-16000/69092	Loss: 152.666
-19200/69092	Loss: 153.443
-22400/69092	Loss: 151.911
-25600/69092	Loss: 151.438
-28800/69092	Loss: 154.364
-32000/69092	Loss: 152.828
-35200/69092	Loss: 152.053
-38400/69092	Loss: 153.011
-41600/69092	Loss: 153.660
-44800/69092	Loss: 156.375
-48000/69092	Loss: 156.092
-51200/69092	Loss: 151.725
-54400/69092	Loss: 153.148
-57600/69092	Loss: 150.407
-60800/69092	Loss: 154.755
-64000/69092	Loss: 153.873
-67200/69092	Loss: 151.987
-Training time 0:01:58.140186
-Epoch: 221 Average loss: 152.99
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 221)
-0/69092	Loss: 167.617
-3200/69092	Loss: 150.980
-6400/69092	Loss: 154.489
-9600/69092	Loss: 155.176
-12800/69092	Loss: 151.077
-16000/69092	Loss: 150.855
-19200/69092	Loss: 153.607
-22400/69092	Loss: 151.541
-25600/69092	Loss: 155.577
-28800/69092	Loss: 156.103
-32000/69092	Loss: 153.661
-35200/69092	Loss: 152.441
-38400/69092	Loss: 153.900
-41600/69092	Loss: 153.054
-44800/69092	Loss: 150.793
-48000/69092	Loss: 152.771
-51200/69092	Loss: 152.938
-54400/69092	Loss: 154.573
-57600/69092	Loss: 151.687
-60800/69092	Loss: 151.964
-64000/69092	Loss: 154.685
-67200/69092	Loss: 150.734
-Training time 0:01:58.151980
-Epoch: 222 Average loss: 153.10
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 222)
-0/69092	Loss: 163.035
-3200/69092	Loss: 150.475
-6400/69092	Loss: 150.694
-9600/69092	Loss: 151.595
-12800/69092	Loss: 156.462
-16000/69092	Loss: 156.534
-19200/69092	Loss: 153.539
-22400/69092	Loss: 154.385
-25600/69092	Loss: 153.351
-28800/69092	Loss: 152.876
-32000/69092	Loss: 153.442
-35200/69092	Loss: 151.764
-38400/69092	Loss: 153.304
-41600/69092	Loss: 153.636
-44800/69092	Loss: 152.515
-48000/69092	Loss: 149.859
-51200/69092	Loss: 151.391
-54400/69092	Loss: 153.922
-57600/69092	Loss: 154.066
-60800/69092	Loss: 152.113
-64000/69092	Loss: 153.166
-67200/69092	Loss: 154.064
-Training time 0:01:58.093341
-Epoch: 223 Average loss: 153.00
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 223)
-0/69092	Loss: 149.104
-3200/69092	Loss: 153.765
-6400/69092	Loss: 151.818
-9600/69092	Loss: 152.883
-12800/69092	Loss: 153.506
-16000/69092	Loss: 150.765
-19200/69092	Loss: 153.645
-22400/69092	Loss: 152.073
-25600/69092	Loss: 153.641
-28800/69092	Loss: 152.799
-32000/69092	Loss: 153.312
-35200/69092	Loss: 153.976
-38400/69092	Loss: 154.457
-41600/69092	Loss: 152.348
-44800/69092	Loss: 153.460
-48000/69092	Loss: 153.998
-51200/69092	Loss: 153.964
-54400/69092	Loss: 150.323
-57600/69092	Loss: 153.979
-60800/69092	Loss: 153.562
-64000/69092	Loss: 152.081
-67200/69092	Loss: 153.445
-Training time 0:01:57.995336
-Epoch: 224 Average loss: 152.98
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 224)
-0/69092	Loss: 149.157
-3200/69092	Loss: 154.474
-6400/69092	Loss: 155.022
-9600/69092	Loss: 151.593
-12800/69092	Loss: 154.747
-16000/69092	Loss: 152.064
-19200/69092	Loss: 152.685
-22400/69092	Loss: 153.768
-25600/69092	Loss: 152.074
-28800/69092	Loss: 152.134
-32000/69092	Loss: 150.306
-35200/69092	Loss: 153.294
-38400/69092	Loss: 156.220
-41600/69092	Loss: 152.025
-44800/69092	Loss: 154.947
-48000/69092	Loss: 153.498
-51200/69092	Loss: 152.489
-54400/69092	Loss: 153.522
-57600/69092	Loss: 152.365
-60800/69092	Loss: 153.052
-64000/69092	Loss: 151.943
-67200/69092	Loss: 149.966
-Training time 0:01:57.633472
-Epoch: 225 Average loss: 152.95
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 225)
-0/69092	Loss: 157.358
-3200/69092	Loss: 153.386
-6400/69092	Loss: 155.215
-9600/69092	Loss: 152.323
-12800/69092	Loss: 152.303
-16000/69092	Loss: 152.981
-19200/69092	Loss: 151.551
-22400/69092	Loss: 152.605
-25600/69092	Loss: 154.201
-28800/69092	Loss: 153.261
-32000/69092	Loss: 151.061
-35200/69092	Loss: 151.811
-38400/69092	Loss: 152.017
-41600/69092	Loss: 152.559
-44800/69092	Loss: 151.012
-48000/69092	Loss: 152.297
-51200/69092	Loss: 153.949
-54400/69092	Loss: 153.699
-57600/69092	Loss: 153.550
-60800/69092	Loss: 155.443
-64000/69092	Loss: 152.688
-67200/69092	Loss: 152.526
-Training time 0:01:58.981002
-Epoch: 226 Average loss: 152.88
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 226)
-0/69092	Loss: 168.753
-3200/69092	Loss: 153.817
-6400/69092	Loss: 155.469
-9600/69092	Loss: 152.652
-12800/69092	Loss: 149.638
-16000/69092	Loss: 150.607
-19200/69092	Loss: 153.912
-22400/69092	Loss: 153.587
-25600/69092	Loss: 151.851
-28800/69092	Loss: 153.092
-32000/69092	Loss: 149.984
-35200/69092	Loss: 153.251
-38400/69092	Loss: 153.295
-41600/69092	Loss: 151.924
-44800/69092	Loss: 152.871
-48000/69092	Loss: 154.267
-51200/69092	Loss: 153.253
-54400/69092	Loss: 152.931
-57600/69092	Loss: 153.489
-60800/69092	Loss: 154.847
-64000/69092	Loss: 152.248
-67200/69092	Loss: 153.873
-Training time 0:01:57.918332
-Epoch: 227 Average loss: 152.90
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 227)
-0/69092	Loss: 145.500
-3200/69092	Loss: 150.818
-6400/69092	Loss: 153.259
-9600/69092	Loss: 153.745
-12800/69092	Loss: 155.952
-16000/69092	Loss: 153.876
-19200/69092	Loss: 153.586
-22400/69092	Loss: 152.680
-25600/69092	Loss: 153.108
-28800/69092	Loss: 155.919
-32000/69092	Loss: 150.221
-35200/69092	Loss: 152.665
-38400/69092	Loss: 153.170
-41600/69092	Loss: 151.231
-44800/69092	Loss: 154.256
-48000/69092	Loss: 152.153
-51200/69092	Loss: 151.312
-54400/69092	Loss: 151.172
-57600/69092	Loss: 156.990
-60800/69092	Loss: 150.704
-64000/69092	Loss: 153.871
-67200/69092	Loss: 154.719
-Training time 0:01:57.894648
-Epoch: 228 Average loss: 153.07
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 228)
-0/69092	Loss: 180.293
-3200/69092	Loss: 153.891
-6400/69092	Loss: 151.699
-9600/69092	Loss: 151.629
-12800/69092	Loss: 155.177
-16000/69092	Loss: 153.444
-19200/69092	Loss: 152.763
-22400/69092	Loss: 155.033
-25600/69092	Loss: 154.848
-28800/69092	Loss: 152.626
-32000/69092	Loss: 152.012
-35200/69092	Loss: 155.433
-38400/69092	Loss: 151.165
-41600/69092	Loss: 150.821
-44800/69092	Loss: 153.422
-48000/69092	Loss: 150.301
-51200/69092	Loss: 153.288
-54400/69092	Loss: 153.095
-57600/69092	Loss: 153.022
-60800/69092	Loss: 155.040
-64000/69092	Loss: 152.108
-67200/69092	Loss: 151.459
-Training time 0:01:57.319584
-Epoch: 229 Average loss: 153.05
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 229)
-0/69092	Loss: 142.525
-3200/69092	Loss: 151.068
-6400/69092	Loss: 152.307
-9600/69092	Loss: 152.833
-12800/69092	Loss: 150.623
-16000/69092	Loss: 154.464
-19200/69092	Loss: 153.870
-22400/69092	Loss: 154.721
-25600/69092	Loss: 152.589
-28800/69092	Loss: 151.536
-32000/69092	Loss: 154.682
-35200/69092	Loss: 152.351
-38400/69092	Loss: 154.690
-41600/69092	Loss: 151.623
-44800/69092	Loss: 155.794
-48000/69092	Loss: 154.772
-51200/69092	Loss: 149.038
-54400/69092	Loss: 152.770
-57600/69092	Loss: 154.300
-60800/69092	Loss: 152.137
-64000/69092	Loss: 152.796
-67200/69092	Loss: 152.573
-Training time 0:01:57.791838
-Epoch: 230 Average loss: 153.00
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 230)
-0/69092	Loss: 154.642
-3200/69092	Loss: 151.589
-6400/69092	Loss: 153.849
-9600/69092	Loss: 152.458
-12800/69092	Loss: 155.827
-16000/69092	Loss: 151.786
-19200/69092	Loss: 147.413
-22400/69092	Loss: 154.911
-25600/69092	Loss: 151.117
-28800/69092	Loss: 153.292
-32000/69092	Loss: 154.569
-35200/69092	Loss: 154.036
-38400/69092	Loss: 154.870
-41600/69092	Loss: 154.462
-44800/69092	Loss: 152.763
-48000/69092	Loss: 152.092
-51200/69092	Loss: 153.746
-54400/69092	Loss: 151.517
-57600/69092	Loss: 153.789
-60800/69092	Loss: 150.274
-64000/69092	Loss: 153.312
-67200/69092	Loss: 153.244
-Training time 0:01:57.312702
-Epoch: 231 Average loss: 152.97
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 231)
-0/69092	Loss: 140.259
-3200/69092	Loss: 154.425
-6400/69092	Loss: 151.092
-9600/69092	Loss: 151.372
-12800/69092	Loss: 152.726
-16000/69092	Loss: 153.478
-19200/69092	Loss: 152.184
-22400/69092	Loss: 156.321
-25600/69092	Loss: 151.891
-28800/69092	Loss: 150.921
-32000/69092	Loss: 153.356
-35200/69092	Loss: 152.676
-38400/69092	Loss: 152.050
-41600/69092	Loss: 150.893
-44800/69092	Loss: 151.221
-48000/69092	Loss: 151.875
-51200/69092	Loss: 154.692
-54400/69092	Loss: 154.223
-57600/69092	Loss: 155.129
-60800/69092	Loss: 152.654
-64000/69092	Loss: 153.452
-67200/69092	Loss: 152.980
-Training time 0:01:56.487330
-Epoch: 232 Average loss: 152.82
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 232)
-0/69092	Loss: 171.943
-3200/69092	Loss: 154.085
-6400/69092	Loss: 153.825
-9600/69092	Loss: 154.207
-12800/69092	Loss: 150.958
-16000/69092	Loss: 152.458
-19200/69092	Loss: 152.403
-22400/69092	Loss: 152.995
-25600/69092	Loss: 152.974
-28800/69092	Loss: 151.200
-32000/69092	Loss: 152.450
-35200/69092	Loss: 153.039
-38400/69092	Loss: 151.633
-41600/69092	Loss: 154.753
-44800/69092	Loss: 153.677
-48000/69092	Loss: 152.379
-51200/69092	Loss: 156.328
-54400/69092	Loss: 151.903
-57600/69092	Loss: 155.844
-60800/69092	Loss: 150.981
-64000/69092	Loss: 151.734
-67200/69092	Loss: 154.408
-Training time 0:01:57.974114
-Epoch: 233 Average loss: 153.07
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 233)
-0/69092	Loss: 146.546
-3200/69092	Loss: 151.793
-6400/69092	Loss: 153.263
-9600/69092	Loss: 150.700
-12800/69092	Loss: 153.306
-16000/69092	Loss: 152.140
-19200/69092	Loss: 152.216
-22400/69092	Loss: 152.413
-25600/69092	Loss: 154.672
-28800/69092	Loss: 154.708
-32000/69092	Loss: 151.919
-35200/69092	Loss: 152.004
-38400/69092	Loss: 152.031
-41600/69092	Loss: 154.102
-44800/69092	Loss: 152.538
-48000/69092	Loss: 154.181
-51200/69092	Loss: 152.703
-54400/69092	Loss: 153.828
-57600/69092	Loss: 151.973
-60800/69092	Loss: 151.424
-64000/69092	Loss: 154.326
-67200/69092	Loss: 153.638
-Training time 0:01:56.738304
-Epoch: 234 Average loss: 152.94
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 234)
-0/69092	Loss: 147.142
-3200/69092	Loss: 154.610
-6400/69092	Loss: 152.188
-9600/69092	Loss: 153.607
-12800/69092	Loss: 154.154
-16000/69092	Loss: 154.403
-19200/69092	Loss: 154.347
-22400/69092	Loss: 151.795
-25600/69092	Loss: 150.659
-28800/69092	Loss: 152.914
-32000/69092	Loss: 151.999
-35200/69092	Loss: 153.316
-38400/69092	Loss: 152.318
-41600/69092	Loss: 152.568
-44800/69092	Loss: 151.948
-48000/69092	Loss: 153.530
-51200/69092	Loss: 151.766
-54400/69092	Loss: 152.731
-57600/69092	Loss: 153.901
-60800/69092	Loss: 152.770
-64000/69092	Loss: 152.304
-67200/69092	Loss: 153.580
-Training time 0:01:56.618151
-Epoch: 235 Average loss: 152.87
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 235)
-0/69092	Loss: 140.823
-3200/69092	Loss: 154.382
-6400/69092	Loss: 151.163
-9600/69092	Loss: 154.541
-12800/69092	Loss: 153.418
-16000/69092	Loss: 153.596
-19200/69092	Loss: 154.555
-22400/69092	Loss: 154.492
-25600/69092	Loss: 154.233
-28800/69092	Loss: 152.816
-32000/69092	Loss: 152.097
-35200/69092	Loss: 153.348
-38400/69092	Loss: 154.650
-41600/69092	Loss: 150.105
-44800/69092	Loss: 151.391
-48000/69092	Loss: 151.594
-51200/69092	Loss: 152.375
-54400/69092	Loss: 152.202
-57600/69092	Loss: 153.161
-60800/69092	Loss: 152.484
-64000/69092	Loss: 153.472
-67200/69092	Loss: 153.056
-Training time 0:01:57.025385
-Epoch: 236 Average loss: 152.92
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 236)
-0/69092	Loss: 150.785
-3200/69092	Loss: 153.572
-6400/69092	Loss: 151.538
-9600/69092	Loss: 152.222
-12800/69092	Loss: 151.326
-16000/69092	Loss: 151.781
-19200/69092	Loss: 154.058
-22400/69092	Loss: 151.357
-25600/69092	Loss: 151.036
-28800/69092	Loss: 155.959
-32000/69092	Loss: 152.228
-35200/69092	Loss: 151.662
-38400/69092	Loss: 154.893
-41600/69092	Loss: 150.476
-44800/69092	Loss: 153.312
-48000/69092	Loss: 152.785
-51200/69092	Loss: 154.991
-54400/69092	Loss: 151.464
-57600/69092	Loss: 153.208
-60800/69092	Loss: 153.533
-64000/69092	Loss: 151.943
-67200/69092	Loss: 153.342
-Training time 0:01:58.060189
-Epoch: 237 Average loss: 152.75
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 237)
-0/69092	Loss: 146.388
-3200/69092	Loss: 155.082
-6400/69092	Loss: 152.964
-9600/69092	Loss: 153.962
-12800/69092	Loss: 155.305
-16000/69092	Loss: 152.804
-19200/69092	Loss: 152.230
-22400/69092	Loss: 150.812
-25600/69092	Loss: 154.837
-28800/69092	Loss: 152.240
-32000/69092	Loss: 151.777
-35200/69092	Loss: 153.701
-38400/69092	Loss: 153.288
-41600/69092	Loss: 151.931
-44800/69092	Loss: 153.427
-48000/69092	Loss: 153.089
-51200/69092	Loss: 151.594
-54400/69092	Loss: 152.101
-57600/69092	Loss: 152.920
-60800/69092	Loss: 151.710
-64000/69092	Loss: 152.992
-67200/69092	Loss: 152.111
-Training time 0:01:58.970486
-Epoch: 238 Average loss: 152.89
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 238)
-0/69092	Loss: 149.429
-3200/69092	Loss: 150.371
-6400/69092	Loss: 154.537
-9600/69092	Loss: 151.209
-12800/69092	Loss: 152.965
-16000/69092	Loss: 152.941
-19200/69092	Loss: 150.059
-22400/69092	Loss: 153.176
-25600/69092	Loss: 154.018
-28800/69092	Loss: 154.078
-32000/69092	Loss: 152.689
-35200/69092	Loss: 151.744
-38400/69092	Loss: 152.659
-41600/69092	Loss: 154.504
-44800/69092	Loss: 151.904
-48000/69092	Loss: 154.087
-51200/69092	Loss: 152.256
-54400/69092	Loss: 154.235
-57600/69092	Loss: 156.127
-60800/69092	Loss: 153.656
-64000/69092	Loss: 152.386
-67200/69092	Loss: 154.235
-Training time 0:01:58.623239
-Epoch: 239 Average loss: 152.99
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 239)
-0/69092	Loss: 167.180
-3200/69092	Loss: 153.784
-6400/69092	Loss: 153.886
-9600/69092	Loss: 151.928
-12800/69092	Loss: 154.879
-16000/69092	Loss: 154.330
-19200/69092	Loss: 153.325
-22400/69092	Loss: 150.341
-25600/69092	Loss: 151.352
-28800/69092	Loss: 151.670
-32000/69092	Loss: 149.873
-35200/69092	Loss: 151.475
-38400/69092	Loss: 152.915
-41600/69092	Loss: 154.261
-44800/69092	Loss: 152.991
-48000/69092	Loss: 154.228
-51200/69092	Loss: 153.721
-54400/69092	Loss: 153.371
-57600/69092	Loss: 154.614
-60800/69092	Loss: 150.271
-64000/69092	Loss: 152.129
-67200/69092	Loss: 153.947
-Training time 0:01:58.830728
-Epoch: 240 Average loss: 152.78
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 240)
-0/69092	Loss: 153.859
-3200/69092	Loss: 149.460
-6400/69092	Loss: 153.149
-9600/69092	Loss: 154.061
-12800/69092	Loss: 152.206
-16000/69092	Loss: 152.393
-19200/69092	Loss: 149.998
-22400/69092	Loss: 153.934
-25600/69092	Loss: 153.449
-28800/69092	Loss: 153.169
-32000/69092	Loss: 152.671
-35200/69092	Loss: 151.362
-38400/69092	Loss: 151.255
-41600/69092	Loss: 152.122
-44800/69092	Loss: 152.350
-48000/69092	Loss: 152.428
-51200/69092	Loss: 154.282
-54400/69092	Loss: 154.570
-57600/69092	Loss: 153.494
-60800/69092	Loss: 149.670
-64000/69092	Loss: 152.422
-67200/69092	Loss: 154.780
-Training time 0:01:57.960246
-Epoch: 241 Average loss: 152.59
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 241)
-0/69092	Loss: 158.629
-3200/69092	Loss: 150.650
-6400/69092	Loss: 154.195
-9600/69092	Loss: 153.340
-12800/69092	Loss: 152.238
-16000/69092	Loss: 152.531
-19200/69092	Loss: 151.346
-22400/69092	Loss: 151.401
-25600/69092	Loss: 153.823
-28800/69092	Loss: 155.058
-32000/69092	Loss: 153.807
-35200/69092	Loss: 150.709
-38400/69092	Loss: 153.555
-41600/69092	Loss: 153.592
-44800/69092	Loss: 151.298
-48000/69092	Loss: 155.065
-51200/69092	Loss: 151.654
-54400/69092	Loss: 153.802
-57600/69092	Loss: 152.691
-60800/69092	Loss: 153.699
-64000/69092	Loss: 153.345
-67200/69092	Loss: 151.487
-Training time 0:01:58.264088
-Epoch: 242 Average loss: 152.88
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 242)
-0/69092	Loss: 137.126
-3200/69092	Loss: 152.149
-6400/69092	Loss: 151.933
-9600/69092	Loss: 152.520
-12800/69092	Loss: 157.330
-16000/69092	Loss: 155.815
-19200/69092	Loss: 152.166
-22400/69092	Loss: 154.238
-25600/69092	Loss: 149.851
-28800/69092	Loss: 151.604
-32000/69092	Loss: 151.911
-35200/69092	Loss: 153.001
-38400/69092	Loss: 154.620
-41600/69092	Loss: 155.193
-44800/69092	Loss: 152.859
-48000/69092	Loss: 153.107
-51200/69092	Loss: 153.861
-54400/69092	Loss: 151.699
-57600/69092	Loss: 148.180
-60800/69092	Loss: 153.639
-64000/69092	Loss: 152.561
-67200/69092	Loss: 153.073
-Training time 0:01:57.771225
-Epoch: 243 Average loss: 152.90
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 243)
-0/69092	Loss: 153.165
-3200/69092	Loss: 151.722
-6400/69092	Loss: 153.502
-9600/69092	Loss: 149.978
-12800/69092	Loss: 153.114
-16000/69092	Loss: 152.880
-19200/69092	Loss: 152.990
-22400/69092	Loss: 153.534
-25600/69092	Loss: 155.184
-28800/69092	Loss: 152.679
-32000/69092	Loss: 153.160
-35200/69092	Loss: 153.210
-38400/69092	Loss: 151.402
-41600/69092	Loss: 152.639
-44800/69092	Loss: 151.424
-48000/69092	Loss: 152.328
-51200/69092	Loss: 154.335
-54400/69092	Loss: 153.779
-57600/69092	Loss: 153.537
-60800/69092	Loss: 155.218
-64000/69092	Loss: 151.940
-67200/69092	Loss: 155.476
-Training time 0:01:57.331840
-Epoch: 244 Average loss: 153.02
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 244)
-0/69092	Loss: 163.873
-3200/69092	Loss: 151.982
-6400/69092	Loss: 153.628
-9600/69092	Loss: 152.854
-12800/69092	Loss: 152.921
-16000/69092	Loss: 152.405
-19200/69092	Loss: 152.727
-22400/69092	Loss: 153.639
-25600/69092	Loss: 150.654
-28800/69092	Loss: 151.494
-32000/69092	Loss: 154.417
-35200/69092	Loss: 152.415
-38400/69092	Loss: 154.000
-41600/69092	Loss: 154.083
-44800/69092	Loss: 153.606
-48000/69092	Loss: 152.192
-51200/69092	Loss: 151.583
-54400/69092	Loss: 153.490
-57600/69092	Loss: 151.840
-60800/69092	Loss: 152.757
-64000/69092	Loss: 156.619
-67200/69092	Loss: 151.462
-Training time 0:01:57.832230
-Epoch: 245 Average loss: 152.87
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 245)
-0/69092	Loss: 148.779
-3200/69092	Loss: 152.686
-6400/69092	Loss: 152.497
-9600/69092	Loss: 152.702
-12800/69092	Loss: 153.036
-16000/69092	Loss: 152.658
-19200/69092	Loss: 152.918
-22400/69092	Loss: 153.495
-25600/69092	Loss: 154.115
-28800/69092	Loss: 152.814
-32000/69092	Loss: 153.349
-35200/69092	Loss: 154.072
-38400/69092	Loss: 155.817
-41600/69092	Loss: 151.697
-44800/69092	Loss: 149.036
-48000/69092	Loss: 152.748
-51200/69092	Loss: 153.825
-54400/69092	Loss: 150.494
-57600/69092	Loss: 153.051
-60800/69092	Loss: 151.825
-64000/69092	Loss: 152.996
-67200/69092	Loss: 151.816
-Training time 0:01:58.694350
-Epoch: 246 Average loss: 152.79
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 246)
-0/69092	Loss: 140.414
-3200/69092	Loss: 151.577
-6400/69092	Loss: 153.748
-9600/69092	Loss: 151.823
-12800/69092	Loss: 155.714
-16000/69092	Loss: 154.473
-19200/69092	Loss: 154.174
-22400/69092	Loss: 153.460
-25600/69092	Loss: 155.288
-28800/69092	Loss: 151.393
-32000/69092	Loss: 150.425
-35200/69092	Loss: 153.623
-38400/69092	Loss: 153.749
-41600/69092	Loss: 150.976
-44800/69092	Loss: 151.228
-48000/69092	Loss: 154.616
-51200/69092	Loss: 153.523
-54400/69092	Loss: 153.865
-57600/69092	Loss: 152.582
-60800/69092	Loss: 152.402
-64000/69092	Loss: 151.277
-67200/69092	Loss: 152.981
-Training time 0:01:58.011280
-Epoch: 247 Average loss: 153.01
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 247)
-0/69092	Loss: 152.841
-3200/69092	Loss: 151.889
-6400/69092	Loss: 151.357
-9600/69092	Loss: 153.424
-12800/69092	Loss: 153.532
-16000/69092	Loss: 152.280
-19200/69092	Loss: 153.647
-22400/69092	Loss: 152.426
-25600/69092	Loss: 152.805
-28800/69092	Loss: 154.031
-32000/69092	Loss: 154.262
-35200/69092	Loss: 151.558
-38400/69092	Loss: 154.836
-41600/69092	Loss: 154.432
-44800/69092	Loss: 151.479
-48000/69092	Loss: 151.301
-51200/69092	Loss: 153.854
-54400/69092	Loss: 153.573
-57600/69092	Loss: 151.656
-60800/69092	Loss: 152.319
-64000/69092	Loss: 151.431
-67200/69092	Loss: 153.094
-Training time 0:01:57.768346
-Epoch: 248 Average loss: 152.81
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 248)
-0/69092	Loss: 166.012
-3200/69092	Loss: 153.240
-6400/69092	Loss: 152.772
-9600/69092	Loss: 151.369
-12800/69092	Loss: 152.029
-16000/69092	Loss: 152.143
-19200/69092	Loss: 154.384
-22400/69092	Loss: 150.355
-25600/69092	Loss: 153.370
-28800/69092	Loss: 152.927
-32000/69092	Loss: 153.648
-35200/69092	Loss: 151.301
-38400/69092	Loss: 155.468
-41600/69092	Loss: 156.302
-44800/69092	Loss: 151.039
-48000/69092	Loss: 153.663
-51200/69092	Loss: 151.174
-54400/69092	Loss: 154.291
-57600/69092	Loss: 152.261
-60800/69092	Loss: 151.814
-64000/69092	Loss: 153.514
-67200/69092	Loss: 151.401
-Training time 0:01:58.015001
-Epoch: 249 Average loss: 152.80
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 249)
-0/69092	Loss: 171.326
-3200/69092	Loss: 150.472
-6400/69092	Loss: 151.960
-9600/69092	Loss: 152.078
-12800/69092	Loss: 152.642
-16000/69092	Loss: 153.280
-19200/69092	Loss: 152.284
-22400/69092	Loss: 152.422
-25600/69092	Loss: 151.907
-28800/69092	Loss: 152.561
-32000/69092	Loss: 149.760
-35200/69092	Loss: 152.690
-38400/69092	Loss: 155.108
-41600/69092	Loss: 151.258
-44800/69092	Loss: 153.570
-48000/69092	Loss: 151.424
-51200/69092	Loss: 153.171
-54400/69092	Loss: 157.287
-57600/69092	Loss: 151.796
-60800/69092	Loss: 150.432
-64000/69092	Loss: 156.293
-67200/69092	Loss: 154.974
-Training time 0:01:58.922323
-Epoch: 250 Average loss: 152.78
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 250)
-0/69092	Loss: 138.233
-3200/69092	Loss: 154.458
-6400/69092	Loss: 153.750
-9600/69092	Loss: 152.624
-12800/69092	Loss: 153.770
-16000/69092	Loss: 152.733
-19200/69092	Loss: 152.485
-22400/69092	Loss: 152.774
-25600/69092	Loss: 153.434
-28800/69092	Loss: 151.495
-32000/69092	Loss: 152.250
-35200/69092	Loss: 151.584
-38400/69092	Loss: 149.970
-41600/69092	Loss: 150.455
-44800/69092	Loss: 153.378
-48000/69092	Loss: 152.234
-51200/69092	Loss: 154.340
-54400/69092	Loss: 150.703
-57600/69092	Loss: 154.407
-60800/69092	Loss: 153.559
-64000/69092	Loss: 154.581
-67200/69092	Loss: 152.552
-Training time 0:01:58.782987
-Epoch: 251 Average loss: 152.80
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 251)
-0/69092	Loss: 137.886
-3200/69092	Loss: 151.589
-6400/69092	Loss: 154.657
-9600/69092	Loss: 151.275
-12800/69092	Loss: 155.411
-16000/69092	Loss: 150.019
-19200/69092	Loss: 153.638
-22400/69092	Loss: 152.607
-25600/69092	Loss: 153.826
-28800/69092	Loss: 151.253
-32000/69092	Loss: 151.264
-35200/69092	Loss: 152.621
-38400/69092	Loss: 150.846
-41600/69092	Loss: 149.545
-44800/69092	Loss: 153.929
-48000/69092	Loss: 151.218
-51200/69092	Loss: 154.030
-54400/69092	Loss: 152.595
-57600/69092	Loss: 153.678
-60800/69092	Loss: 154.111
-64000/69092	Loss: 155.239
-67200/69092	Loss: 151.756
-Training time 0:01:57.211614
-Epoch: 252 Average loss: 152.67
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 252)
-0/69092	Loss: 152.495
-3200/69092	Loss: 150.697
-6400/69092	Loss: 152.906
-9600/69092	Loss: 154.209
-12800/69092	Loss: 151.907
-16000/69092	Loss: 153.116
-19200/69092	Loss: 153.199
-22400/69092	Loss: 157.065
-25600/69092	Loss: 153.429
-28800/69092	Loss: 150.678
-32000/69092	Loss: 154.541
-35200/69092	Loss: 151.967
-38400/69092	Loss: 150.193
-41600/69092	Loss: 152.339
-44800/69092	Loss: 152.816
-48000/69092	Loss: 153.139
-51200/69092	Loss: 152.789
-54400/69092	Loss: 154.099
-57600/69092	Loss: 151.841
-60800/69092	Loss: 149.700
-64000/69092	Loss: 151.373
-67200/69092	Loss: 154.340
-Training time 0:01:58.622072
-Epoch: 253 Average loss: 152.59
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 253)
-0/69092	Loss: 154.265
-3200/69092	Loss: 154.588
-6400/69092	Loss: 153.872
-9600/69092	Loss: 153.735
-12800/69092	Loss: 151.987
-16000/69092	Loss: 154.820
-19200/69092	Loss: 153.018
-22400/69092	Loss: 152.302
-25600/69092	Loss: 148.332
-28800/69092	Loss: 151.921
-32000/69092	Loss: 152.113
-35200/69092	Loss: 152.645
-38400/69092	Loss: 153.627
-41600/69092	Loss: 152.151
-44800/69092	Loss: 151.182
-48000/69092	Loss: 155.992
-51200/69092	Loss: 153.104
-54400/69092	Loss: 153.117
-57600/69092	Loss: 151.015
-60800/69092	Loss: 152.780
-64000/69092	Loss: 151.153
-67200/69092	Loss: 151.886
-Training time 0:01:57.786232
-Epoch: 254 Average loss: 152.65
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 254)
-0/69092	Loss: 164.848
-3200/69092	Loss: 154.808
-6400/69092	Loss: 151.647
-9600/69092	Loss: 152.834
-12800/69092	Loss: 152.288
-16000/69092	Loss: 149.960
-19200/69092	Loss: 152.225
-22400/69092	Loss: 153.333
-25600/69092	Loss: 153.560
-28800/69092	Loss: 151.350
-32000/69092	Loss: 153.601
-35200/69092	Loss: 152.374
-38400/69092	Loss: 153.109
-41600/69092	Loss: 153.594
-44800/69092	Loss: 153.065
-48000/69092	Loss: 154.874
-51200/69092	Loss: 152.092
-54400/69092	Loss: 154.703
-57600/69092	Loss: 153.148
-60800/69092	Loss: 151.717
-64000/69092	Loss: 151.697
-67200/69092	Loss: 150.450
-Training time 0:01:57.186922
-Epoch: 255 Average loss: 152.77
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 255)
-0/69092	Loss: 159.468
-3200/69092	Loss: 149.750
-6400/69092	Loss: 150.201
-9600/69092	Loss: 153.377
-12800/69092	Loss: 152.825
-16000/69092	Loss: 155.122
-19200/69092	Loss: 151.268
-22400/69092	Loss: 151.344
-25600/69092	Loss: 154.080
-28800/69092	Loss: 153.874
-32000/69092	Loss: 152.653
-35200/69092	Loss: 150.462
-38400/69092	Loss: 154.206
-41600/69092	Loss: 152.439
-44800/69092	Loss: 153.870
-48000/69092	Loss: 151.248
-51200/69092	Loss: 155.585
-54400/69092	Loss: 152.482
-57600/69092	Loss: 154.297
-60800/69092	Loss: 151.850
-64000/69092	Loss: 153.208
-67200/69092	Loss: 152.750
-Training time 0:01:57.968381
-Epoch: 256 Average loss: 152.70
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 256)
-0/69092	Loss: 156.280
-3200/69092	Loss: 152.914
-6400/69092	Loss: 152.317
-9600/69092	Loss: 152.924
-12800/69092	Loss: 155.169
-16000/69092	Loss: 150.258
-19200/69092	Loss: 151.012
-22400/69092	Loss: 150.593
-25600/69092	Loss: 152.653
-28800/69092	Loss: 155.055
-32000/69092	Loss: 155.057
-35200/69092	Loss: 151.790
-38400/69092	Loss: 153.150
-41600/69092	Loss: 154.666
-44800/69092	Loss: 150.037
-48000/69092	Loss: 152.272
-51200/69092	Loss: 151.922
-54400/69092	Loss: 155.862
-57600/69092	Loss: 151.559
-60800/69092	Loss: 154.010
-64000/69092	Loss: 153.064
-67200/69092	Loss: 151.706
-Training time 0:01:57.276543
-Epoch: 257 Average loss: 152.76
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 257)
-0/69092	Loss: 142.211
-3200/69092	Loss: 151.189
-6400/69092	Loss: 152.004
-9600/69092	Loss: 151.198
-12800/69092	Loss: 152.895
-16000/69092	Loss: 151.605
-19200/69092	Loss: 152.305
-22400/69092	Loss: 155.254
-25600/69092	Loss: 153.309
-28800/69092	Loss: 153.986
-32000/69092	Loss: 152.648
-35200/69092	Loss: 153.982
-38400/69092	Loss: 155.367
-41600/69092	Loss: 153.210
-44800/69092	Loss: 151.697
-48000/69092	Loss: 151.390
-51200/69092	Loss: 149.967
-54400/69092	Loss: 153.383
-57600/69092	Loss: 151.912
-60800/69092	Loss: 154.006
-64000/69092	Loss: 151.930
-67200/69092	Loss: 152.065
-Training time 0:01:56.878804
-Epoch: 258 Average loss: 152.65
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 258)
-0/69092	Loss: 158.911
-3200/69092	Loss: 153.411
-6400/69092	Loss: 150.013
-9600/69092	Loss: 152.422
-12800/69092	Loss: 152.190
-16000/69092	Loss: 150.353
-19200/69092	Loss: 152.823
-22400/69092	Loss: 154.977
-25600/69092	Loss: 152.330
-28800/69092	Loss: 151.853
-32000/69092	Loss: 154.677
-35200/69092	Loss: 153.071
-38400/69092	Loss: 153.771
-41600/69092	Loss: 150.395
-44800/69092	Loss: 152.291
-48000/69092	Loss: 154.876
-51200/69092	Loss: 152.035
-54400/69092	Loss: 153.179
-57600/69092	Loss: 152.952
-60800/69092	Loss: 152.539
-64000/69092	Loss: 151.809
-67200/69092	Loss: 154.798
-Training time 0:01:57.443749
-Epoch: 259 Average loss: 152.68
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 259)
-0/69092	Loss: 149.823
-3200/69092	Loss: 151.102
-6400/69092	Loss: 152.268
-9600/69092	Loss: 152.794
-12800/69092	Loss: 152.513
-16000/69092	Loss: 150.592
-19200/69092	Loss: 154.891
-22400/69092	Loss: 152.223
-25600/69092	Loss: 153.607
-28800/69092	Loss: 153.653
-32000/69092	Loss: 153.814
-35200/69092	Loss: 154.163
-38400/69092	Loss: 153.399
-41600/69092	Loss: 151.809
-44800/69092	Loss: 154.830
-48000/69092	Loss: 154.203
-51200/69092	Loss: 155.129
-54400/69092	Loss: 150.845
-57600/69092	Loss: 153.779
-60800/69092	Loss: 153.241
-64000/69092	Loss: 151.113
-67200/69092	Loss: 151.490
-Training time 0:01:57.874040
-Epoch: 260 Average loss: 152.84
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 260)
-0/69092	Loss: 160.968
-3200/69092	Loss: 153.856
-6400/69092	Loss: 151.266
-9600/69092	Loss: 153.994
-12800/69092	Loss: 154.312
-16000/69092	Loss: 153.076
-19200/69092	Loss: 153.044
-22400/69092	Loss: 150.164
-25600/69092	Loss: 153.244
-28800/69092	Loss: 151.634
-32000/69092	Loss: 151.537
-35200/69092	Loss: 151.893
-38400/69092	Loss: 153.561
-41600/69092	Loss: 152.941
-44800/69092	Loss: 150.265
-48000/69092	Loss: 154.725
-51200/69092	Loss: 151.257
-54400/69092	Loss: 153.929
-57600/69092	Loss: 157.241
-60800/69092	Loss: 151.191
-64000/69092	Loss: 150.971
-67200/69092	Loss: 150.893
-Training time 0:01:58.076517
-Epoch: 261 Average loss: 152.71
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 261)
-0/69092	Loss: 147.421
-3200/69092	Loss: 150.568
-6400/69092	Loss: 153.908
-9600/69092	Loss: 151.035
-12800/69092	Loss: 153.411
-16000/69092	Loss: 153.190
-19200/69092	Loss: 154.554
-22400/69092	Loss: 152.081
-25600/69092	Loss: 152.818
-28800/69092	Loss: 154.111
-32000/69092	Loss: 150.059
-35200/69092	Loss: 150.468
-38400/69092	Loss: 152.673
-41600/69092	Loss: 152.803
-44800/69092	Loss: 152.556
-48000/69092	Loss: 154.632
-51200/69092	Loss: 152.632
-54400/69092	Loss: 154.996
-57600/69092	Loss: 153.504
-60800/69092	Loss: 150.261
-64000/69092	Loss: 152.706
-67200/69092	Loss: 152.677
-Training time 0:01:57.302088
-Epoch: 262 Average loss: 152.69
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 262)
-0/69092	Loss: 142.283
-3200/69092	Loss: 154.785
-6400/69092	Loss: 154.332
-9600/69092	Loss: 156.019
-12800/69092	Loss: 151.168
-16000/69092	Loss: 153.149
-19200/69092	Loss: 152.787
-22400/69092	Loss: 153.482
-25600/69092	Loss: 149.799
-28800/69092	Loss: 152.477
-32000/69092	Loss: 151.953
-35200/69092	Loss: 152.393
-38400/69092	Loss: 152.471
-41600/69092	Loss: 151.076
-44800/69092	Loss: 152.994
-48000/69092	Loss: 153.052
-51200/69092	Loss: 153.551
-54400/69092	Loss: 150.230
-57600/69092	Loss: 156.880
-60800/69092	Loss: 153.239
-64000/69092	Loss: 150.834
-67200/69092	Loss: 150.893
-Training time 0:01:57.486335
-Epoch: 263 Average loss: 152.75
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 263)
-0/69092	Loss: 154.675
-3200/69092	Loss: 151.951
-6400/69092	Loss: 152.441
-9600/69092	Loss: 152.127
-12800/69092	Loss: 155.155
-16000/69092	Loss: 154.091
-19200/69092	Loss: 151.372
-22400/69092	Loss: 151.638
-25600/69092	Loss: 153.164
-28800/69092	Loss: 154.142
-32000/69092	Loss: 154.530
-35200/69092	Loss: 153.171
-38400/69092	Loss: 153.054
-41600/69092	Loss: 153.564
-44800/69092	Loss: 153.418
-48000/69092	Loss: 153.077
-51200/69092	Loss: 149.604
-54400/69092	Loss: 150.347
-57600/69092	Loss: 152.601
-60800/69092	Loss: 152.954
-64000/69092	Loss: 155.343
-67200/69092	Loss: 153.840
-Training time 0:01:59.114386
-Epoch: 264 Average loss: 152.89
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 264)
-0/69092	Loss: 139.263
-3200/69092	Loss: 154.954
-6400/69092	Loss: 153.696
-9600/69092	Loss: 152.386
-12800/69092	Loss: 152.791
-16000/69092	Loss: 151.914
-19200/69092	Loss: 149.500
-22400/69092	Loss: 154.803
-25600/69092	Loss: 153.657
-28800/69092	Loss: 152.668
-32000/69092	Loss: 153.327
-35200/69092	Loss: 153.247
-38400/69092	Loss: 152.760
-41600/69092	Loss: 152.406
-44800/69092	Loss: 154.987
-48000/69092	Loss: 152.898
-51200/69092	Loss: 153.748
-54400/69092	Loss: 151.533
-57600/69092	Loss: 153.668
-60800/69092	Loss: 149.640
-64000/69092	Loss: 151.914
-67200/69092	Loss: 152.941
-Training time 0:01:57.698918
-Epoch: 265 Average loss: 152.84
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 265)
-0/69092	Loss: 155.013
-3200/69092	Loss: 151.811
-6400/69092	Loss: 155.869
-9600/69092	Loss: 152.115
-12800/69092	Loss: 152.942
-16000/69092	Loss: 152.425
-19200/69092	Loss: 153.377
-22400/69092	Loss: 151.957
-25600/69092	Loss: 152.837
-28800/69092	Loss: 152.242
-32000/69092	Loss: 151.534
-35200/69092	Loss: 156.065
-38400/69092	Loss: 152.275
-41600/69092	Loss: 153.260
-44800/69092	Loss: 152.785
-48000/69092	Loss: 153.206
-51200/69092	Loss: 152.334
-54400/69092	Loss: 154.073
-57600/69092	Loss: 149.882
-60800/69092	Loss: 151.589
-64000/69092	Loss: 154.203
-67200/69092	Loss: 152.275
-Training time 0:01:57.469466
-Epoch: 266 Average loss: 152.77
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 266)
-0/69092	Loss: 141.726
-3200/69092	Loss: 155.509
-6400/69092	Loss: 152.069
-9600/69092	Loss: 151.864
-12800/69092	Loss: 153.164
-16000/69092	Loss: 151.388
-19200/69092	Loss: 152.751
-22400/69092	Loss: 150.445
-25600/69092	Loss: 152.793
-28800/69092	Loss: 154.118
-32000/69092	Loss: 151.966
-35200/69092	Loss: 154.259
-38400/69092	Loss: 151.459
-41600/69092	Loss: 153.852
-44800/69092	Loss: 150.929
-48000/69092	Loss: 153.467
-51200/69092	Loss: 150.057
-54400/69092	Loss: 152.735
-57600/69092	Loss: 153.921
-60800/69092	Loss: 153.803
-64000/69092	Loss: 154.605
-67200/69092	Loss: 152.399
-Training time 0:01:58.696024
-Epoch: 267 Average loss: 152.82
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 267)
-0/69092	Loss: 162.807
-3200/69092	Loss: 152.459
-6400/69092	Loss: 151.132
-9600/69092	Loss: 154.186
-12800/69092	Loss: 153.120
-16000/69092	Loss: 151.405
-19200/69092	Loss: 154.002
-22400/69092	Loss: 154.980
-25600/69092	Loss: 152.142
-28800/69092	Loss: 153.381
-32000/69092	Loss: 152.718
-35200/69092	Loss: 153.642
-38400/69092	Loss: 150.856
-41600/69092	Loss: 152.572
-44800/69092	Loss: 151.835
-48000/69092	Loss: 151.172
-51200/69092	Loss: 151.984
-54400/69092	Loss: 152.436
-57600/69092	Loss: 153.853
-60800/69092	Loss: 152.823
-64000/69092	Loss: 152.601
-67200/69092	Loss: 151.127
-Training time 0:01:58.643948
-Epoch: 268 Average loss: 152.59
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 268)
-0/69092	Loss: 146.007
-3200/69092	Loss: 152.665
-6400/69092	Loss: 148.254
-9600/69092	Loss: 152.633
-12800/69092	Loss: 153.458
-16000/69092	Loss: 152.366
-19200/69092	Loss: 152.990
-22400/69092	Loss: 150.128
-25600/69092	Loss: 155.924
-28800/69092	Loss: 152.743
-32000/69092	Loss: 152.780
-35200/69092	Loss: 153.090
-38400/69092	Loss: 153.323
-41600/69092	Loss: 154.732
-44800/69092	Loss: 152.933
-48000/69092	Loss: 150.215
-51200/69092	Loss: 154.162
-54400/69092	Loss: 153.797
-57600/69092	Loss: 152.727
-60800/69092	Loss: 151.417
-64000/69092	Loss: 153.367
-67200/69092	Loss: 152.949
-Training time 0:01:57.551861
-Epoch: 269 Average loss: 152.80
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 269)
-0/69092	Loss: 149.141
-3200/69092	Loss: 151.763
-6400/69092	Loss: 155.300
-9600/69092	Loss: 152.559
-12800/69092	Loss: 153.875
-16000/69092	Loss: 152.526
-19200/69092	Loss: 154.846
-22400/69092	Loss: 154.796
-25600/69092	Loss: 155.227
-28800/69092	Loss: 153.401
-32000/69092	Loss: 152.158
-35200/69092	Loss: 149.651
-38400/69092	Loss: 153.244
-41600/69092	Loss: 150.770
-44800/69092	Loss: 149.556
-48000/69092	Loss: 152.984
-51200/69092	Loss: 152.080
-54400/69092	Loss: 152.754
-57600/69092	Loss: 153.258
-60800/69092	Loss: 150.837
-64000/69092	Loss: 152.301
-67200/69092	Loss: 151.229
-Training time 0:01:57.921661
-Epoch: 270 Average loss: 152.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 270)
-0/69092	Loss: 142.933
-3200/69092	Loss: 152.796
-6400/69092	Loss: 150.910
-9600/69092	Loss: 153.081
-12800/69092	Loss: 153.405
-16000/69092	Loss: 152.920
-19200/69092	Loss: 152.042
-22400/69092	Loss: 154.867
-25600/69092	Loss: 153.590
-28800/69092	Loss: 152.184
-32000/69092	Loss: 153.050
-35200/69092	Loss: 153.680
-38400/69092	Loss: 151.029
-41600/69092	Loss: 154.061
-44800/69092	Loss: 152.405
-48000/69092	Loss: 150.160
-51200/69092	Loss: 151.964
-54400/69092	Loss: 154.095
-57600/69092	Loss: 151.362
-60800/69092	Loss: 153.141
-64000/69092	Loss: 154.389
-67200/69092	Loss: 154.353
-Training time 0:01:59.452528
-Epoch: 271 Average loss: 152.87
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 271)
-0/69092	Loss: 150.556
-3200/69092	Loss: 153.041
-6400/69092	Loss: 153.402
-9600/69092	Loss: 155.189
-12800/69092	Loss: 152.243
-16000/69092	Loss: 151.945
-19200/69092	Loss: 154.608
-22400/69092	Loss: 152.223
-25600/69092	Loss: 154.073
-28800/69092	Loss: 153.556
-32000/69092	Loss: 150.664
-35200/69092	Loss: 151.851
-38400/69092	Loss: 151.498
-41600/69092	Loss: 152.595
-44800/69092	Loss: 152.928
-48000/69092	Loss: 153.242
-51200/69092	Loss: 153.713
-54400/69092	Loss: 152.356
-57600/69092	Loss: 153.249
-60800/69092	Loss: 152.940
-64000/69092	Loss: 152.066
-67200/69092	Loss: 151.505
-Training time 0:01:57.362015
-Epoch: 272 Average loss: 152.81
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 272)
-0/69092	Loss: 142.003
-3200/69092	Loss: 151.796
-6400/69092	Loss: 151.929
-9600/69092	Loss: 150.793
-12800/69092	Loss: 153.583
-16000/69092	Loss: 151.567
-19200/69092	Loss: 151.955
-22400/69092	Loss: 153.185
-25600/69092	Loss: 154.144
-28800/69092	Loss: 156.695
-32000/69092	Loss: 154.361
-35200/69092	Loss: 149.322
-38400/69092	Loss: 152.257
-41600/69092	Loss: 152.399
-44800/69092	Loss: 153.998
-48000/69092	Loss: 151.968
-51200/69092	Loss: 153.755
-54400/69092	Loss: 151.921
-57600/69092	Loss: 151.026
-60800/69092	Loss: 153.841
-64000/69092	Loss: 153.881
-67200/69092	Loss: 152.031
-Training time 0:01:58.291481
-Epoch: 273 Average loss: 152.75
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 273)
-0/69092	Loss: 158.201
-3200/69092	Loss: 152.413
-6400/69092	Loss: 153.468
-9600/69092	Loss: 155.352
-12800/69092	Loss: 152.032
-16000/69092	Loss: 157.388
-19200/69092	Loss: 151.542
-22400/69092	Loss: 153.839
-25600/69092	Loss: 149.530
-28800/69092	Loss: 154.402
-32000/69092	Loss: 151.130
-35200/69092	Loss: 151.442
-38400/69092	Loss: 152.801
-41600/69092	Loss: 151.071
-44800/69092	Loss: 151.423
-48000/69092	Loss: 150.880
-51200/69092	Loss: 150.917
-54400/69092	Loss: 150.763
-57600/69092	Loss: 153.037
-60800/69092	Loss: 153.487
-64000/69092	Loss: 151.277
-67200/69092	Loss: 150.896
-Training time 0:01:58.769421
-Epoch: 274 Average loss: 152.42
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 274)
-0/69092	Loss: 160.438
-3200/69092	Loss: 152.681
-6400/69092	Loss: 152.843
-9600/69092	Loss: 152.961
-12800/69092	Loss: 152.704
-16000/69092	Loss: 153.302
-19200/69092	Loss: 153.046
-22400/69092	Loss: 153.274
-25600/69092	Loss: 153.559
-28800/69092	Loss: 152.442
-32000/69092	Loss: 153.256
-35200/69092	Loss: 154.847
-38400/69092	Loss: 152.374
-41600/69092	Loss: 153.332
-44800/69092	Loss: 151.789
-48000/69092	Loss: 153.424
-51200/69092	Loss: 150.841
-54400/69092	Loss: 151.940
-57600/69092	Loss: 151.622
-60800/69092	Loss: 149.453
-64000/69092	Loss: 152.197
-67200/69092	Loss: 153.990
-Training time 0:01:58.371440
-Epoch: 275 Average loss: 152.60
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 275)
-0/69092	Loss: 141.484
-3200/69092	Loss: 149.177
-6400/69092	Loss: 152.234
-9600/69092	Loss: 154.466
-12800/69092	Loss: 153.979
-16000/69092	Loss: 152.520
-19200/69092	Loss: 150.125
-22400/69092	Loss: 153.602
-25600/69092	Loss: 150.493
-28800/69092	Loss: 153.142
-32000/69092	Loss: 151.109
-35200/69092	Loss: 154.505
-38400/69092	Loss: 150.900
-41600/69092	Loss: 153.333
-44800/69092	Loss: 152.536
-48000/69092	Loss: 153.065
-51200/69092	Loss: 154.529
-54400/69092	Loss: 153.817
-57600/69092	Loss: 152.384
-60800/69092	Loss: 154.093
-64000/69092	Loss: 152.869
-67200/69092	Loss: 154.160
-Training time 0:01:58.752701
-Epoch: 276 Average loss: 152.78
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 276)
-0/69092	Loss: 161.542
-3200/69092	Loss: 151.479
-6400/69092	Loss: 151.229
-9600/69092	Loss: 151.512
-12800/69092	Loss: 155.455
-16000/69092	Loss: 152.145
-19200/69092	Loss: 153.118
-22400/69092	Loss: 149.699
-25600/69092	Loss: 155.748
-28800/69092	Loss: 154.457
-32000/69092	Loss: 150.352
-35200/69092	Loss: 152.252
-38400/69092	Loss: 151.361
-41600/69092	Loss: 152.664
-44800/69092	Loss: 153.569
-48000/69092	Loss: 152.681
-51200/69092	Loss: 152.369
-54400/69092	Loss: 154.724
-57600/69092	Loss: 153.242
-60800/69092	Loss: 152.829
-64000/69092	Loss: 153.175
-67200/69092	Loss: 151.537
-Training time 0:01:57.784326
-Epoch: 277 Average loss: 152.69
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 277)
-0/69092	Loss: 149.280
-3200/69092	Loss: 154.593
-6400/69092	Loss: 152.479
-9600/69092	Loss: 154.989
-12800/69092	Loss: 151.323
-16000/69092	Loss: 151.349
-19200/69092	Loss: 151.386
-22400/69092	Loss: 153.495
-25600/69092	Loss: 152.349
-28800/69092	Loss: 151.247
-32000/69092	Loss: 150.856
-35200/69092	Loss: 151.904
-38400/69092	Loss: 152.026
-41600/69092	Loss: 153.252
-44800/69092	Loss: 154.163
-48000/69092	Loss: 155.559
-51200/69092	Loss: 151.879
-54400/69092	Loss: 151.481
-57600/69092	Loss: 152.619
-60800/69092	Loss: 152.518
-64000/69092	Loss: 153.189
-67200/69092	Loss: 152.749
-Training time 0:01:57.859074
-Epoch: 278 Average loss: 152.62
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 278)
-0/69092	Loss: 156.299
-3200/69092	Loss: 151.066
-6400/69092	Loss: 151.783
-9600/69092	Loss: 155.925
-12800/69092	Loss: 152.142
-16000/69092	Loss: 152.426
-19200/69092	Loss: 151.780
-22400/69092	Loss: 152.905
-25600/69092	Loss: 149.619
-28800/69092	Loss: 151.266
-32000/69092	Loss: 156.912
-35200/69092	Loss: 151.277
-38400/69092	Loss: 151.305
-41600/69092	Loss: 153.080
-44800/69092	Loss: 153.298
-48000/69092	Loss: 152.996
-51200/69092	Loss: 152.664
-54400/69092	Loss: 153.955
-57600/69092	Loss: 154.590
-60800/69092	Loss: 149.706
-64000/69092	Loss: 152.275
-67200/69092	Loss: 152.630
-Training time 0:01:58.990335
-Epoch: 279 Average loss: 152.60
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 279)
-0/69092	Loss: 143.356
-3200/69092	Loss: 152.061
-6400/69092	Loss: 154.725
-9600/69092	Loss: 153.405
-12800/69092	Loss: 151.985
-16000/69092	Loss: 150.851
-19200/69092	Loss: 153.406
-22400/69092	Loss: 151.505
-25600/69092	Loss: 153.520
-28800/69092	Loss: 151.705
-32000/69092	Loss: 152.698
-35200/69092	Loss: 151.784
-38400/69092	Loss: 151.416
-41600/69092	Loss: 152.642
-44800/69092	Loss: 153.926
-48000/69092	Loss: 151.930
-51200/69092	Loss: 154.906
-54400/69092	Loss: 150.797
-57600/69092	Loss: 153.323
-60800/69092	Loss: 152.949
-64000/69092	Loss: 153.460
-67200/69092	Loss: 151.746
-Training time 0:01:58.308913
-Epoch: 280 Average loss: 152.63
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 280)
-0/69092	Loss: 142.265
-3200/69092	Loss: 151.712
-6400/69092	Loss: 151.126
-9600/69092	Loss: 152.569
-12800/69092	Loss: 151.902
-16000/69092	Loss: 152.489
-19200/69092	Loss: 152.019
-22400/69092	Loss: 151.205
-25600/69092	Loss: 151.586
-28800/69092	Loss: 152.102
-32000/69092	Loss: 150.318
-35200/69092	Loss: 151.045
-38400/69092	Loss: 155.814
-41600/69092	Loss: 154.385
-44800/69092	Loss: 152.161
-48000/69092	Loss: 154.467
-51200/69092	Loss: 150.515
-54400/69092	Loss: 152.695
-57600/69092	Loss: 153.329
-60800/69092	Loss: 155.119
-64000/69092	Loss: 155.157
-67200/69092	Loss: 152.908
-Training time 0:01:58.651631
-Epoch: 281 Average loss: 152.60
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 281)
-0/69092	Loss: 151.869
-3200/69092	Loss: 153.188
-6400/69092	Loss: 152.571
-9600/69092	Loss: 152.404
-12800/69092	Loss: 153.798
-16000/69092	Loss: 149.458
-19200/69092	Loss: 153.177
-22400/69092	Loss: 150.964
-25600/69092	Loss: 150.271
-28800/69092	Loss: 153.199
-32000/69092	Loss: 155.340
-35200/69092	Loss: 152.222
-38400/69092	Loss: 151.226
-41600/69092	Loss: 154.360
-44800/69092	Loss: 155.158
-48000/69092	Loss: 154.387
-51200/69092	Loss: 150.845
-54400/69092	Loss: 151.437
-57600/69092	Loss: 153.333
-60800/69092	Loss: 151.227
-64000/69092	Loss: 153.513
-67200/69092	Loss: 153.691
-Training time 0:01:57.497398
-Epoch: 282 Average loss: 152.66
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 282)
-0/69092	Loss: 165.902
-3200/69092	Loss: 151.715
-6400/69092	Loss: 153.018
-9600/69092	Loss: 151.757
-12800/69092	Loss: 151.362
-16000/69092	Loss: 152.003
-19200/69092	Loss: 150.494
-22400/69092	Loss: 154.354
-25600/69092	Loss: 154.707
-28800/69092	Loss: 154.283
-32000/69092	Loss: 151.562
-35200/69092	Loss: 155.610
-38400/69092	Loss: 149.549
-41600/69092	Loss: 152.613
-44800/69092	Loss: 149.132
-48000/69092	Loss: 150.932
-51200/69092	Loss: 152.113
-54400/69092	Loss: 153.424
-57600/69092	Loss: 153.880
-60800/69092	Loss: 153.068
-64000/69092	Loss: 154.432
-67200/69092	Loss: 154.038
-Training time 0:01:59.049903
-Epoch: 283 Average loss: 152.75
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 283)
-0/69092	Loss: 174.708
-3200/69092	Loss: 151.921
-6400/69092	Loss: 149.889
-9600/69092	Loss: 151.991
-12800/69092	Loss: 151.384
-16000/69092	Loss: 151.687
-19200/69092	Loss: 152.362
-22400/69092	Loss: 151.949
-25600/69092	Loss: 154.865
-28800/69092	Loss: 151.209
-32000/69092	Loss: 151.930
-35200/69092	Loss: 153.212
-38400/69092	Loss: 150.856
-41600/69092	Loss: 151.925
-44800/69092	Loss: 153.258
-48000/69092	Loss: 150.260
-51200/69092	Loss: 150.451
-54400/69092	Loss: 154.306
-57600/69092	Loss: 153.019
-60800/69092	Loss: 152.606
-64000/69092	Loss: 152.802
-67200/69092	Loss: 154.756
-Training time 0:01:57.222668
-Epoch: 284 Average loss: 152.33
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 284)
-0/69092	Loss: 148.232
-3200/69092	Loss: 152.646
-6400/69092	Loss: 151.825
-9600/69092	Loss: 152.101
-12800/69092	Loss: 152.151
-16000/69092	Loss: 150.848
-19200/69092	Loss: 151.938
-22400/69092	Loss: 150.148
-25600/69092	Loss: 153.628
-28800/69092	Loss: 151.971
-32000/69092	Loss: 151.637
-35200/69092	Loss: 151.882
-38400/69092	Loss: 153.685
-41600/69092	Loss: 152.497
-44800/69092	Loss: 151.789
-48000/69092	Loss: 153.182
-51200/69092	Loss: 154.075
-54400/69092	Loss: 154.834
-57600/69092	Loss: 152.172
-60800/69092	Loss: 153.122
-64000/69092	Loss: 153.420
-67200/69092	Loss: 153.720
-Training time 0:01:56.426317
-Epoch: 285 Average loss: 152.61
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 285)
-0/69092	Loss: 154.183
-3200/69092	Loss: 150.867
-6400/69092	Loss: 154.852
-9600/69092	Loss: 152.651
-12800/69092	Loss: 153.153
-16000/69092	Loss: 151.580
-19200/69092	Loss: 151.932
-22400/69092	Loss: 154.201
-25600/69092	Loss: 151.271
-28800/69092	Loss: 152.627
-32000/69092	Loss: 153.525
-35200/69092	Loss: 151.554
-38400/69092	Loss: 152.553
-41600/69092	Loss: 154.507
-44800/69092	Loss: 152.794
-48000/69092	Loss: 153.166
-51200/69092	Loss: 152.614
-54400/69092	Loss: 154.548
-57600/69092	Loss: 152.935
-60800/69092	Loss: 151.850
-64000/69092	Loss: 151.610
-67200/69092	Loss: 153.154
-Training time 0:01:57.813196
-Epoch: 286 Average loss: 152.77
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 286)
-0/69092	Loss: 148.648
-3200/69092	Loss: 154.444
-6400/69092	Loss: 153.393
-9600/69092	Loss: 151.211
-12800/69092	Loss: 153.246
-16000/69092	Loss: 152.464
-19200/69092	Loss: 151.482
-22400/69092	Loss: 152.856
-25600/69092	Loss: 151.314
-28800/69092	Loss: 150.153
-32000/69092	Loss: 151.764
-35200/69092	Loss: 152.335
-38400/69092	Loss: 150.711
-41600/69092	Loss: 154.129
-44800/69092	Loss: 152.080
-48000/69092	Loss: 153.004
-51200/69092	Loss: 153.742
-54400/69092	Loss: 153.686
-57600/69092	Loss: 152.916
-60800/69092	Loss: 154.399
-64000/69092	Loss: 151.379
-67200/69092	Loss: 152.140
-Training time 0:01:57.746516
-Epoch: 287 Average loss: 152.45
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 287)
-0/69092	Loss: 138.473
-3200/69092	Loss: 150.914
-6400/69092	Loss: 149.557
-9600/69092	Loss: 155.582
-12800/69092	Loss: 155.137
-16000/69092	Loss: 153.468
-19200/69092	Loss: 150.097
-22400/69092	Loss: 150.141
-25600/69092	Loss: 154.457
-28800/69092	Loss: 151.908
-32000/69092	Loss: 150.218
-35200/69092	Loss: 153.292
-38400/69092	Loss: 150.264
-41600/69092	Loss: 151.930
-44800/69092	Loss: 153.945
-48000/69092	Loss: 152.658
-51200/69092	Loss: 154.502
-54400/69092	Loss: 152.157
-57600/69092	Loss: 151.399
-60800/69092	Loss: 152.390
-64000/69092	Loss: 152.204
-67200/69092	Loss: 151.642
-Training time 0:01:58.206529
-Epoch: 288 Average loss: 152.32
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 288)
-0/69092	Loss: 139.143
-3200/69092	Loss: 151.870
-6400/69092	Loss: 149.610
-9600/69092	Loss: 153.246
-12800/69092	Loss: 152.547
-16000/69092	Loss: 154.961
-19200/69092	Loss: 150.978
-22400/69092	Loss: 150.914
-25600/69092	Loss: 155.167
-28800/69092	Loss: 152.139
-32000/69092	Loss: 153.613
-35200/69092	Loss: 149.871
-38400/69092	Loss: 151.635
-41600/69092	Loss: 153.673
-44800/69092	Loss: 152.072
-48000/69092	Loss: 154.959
-51200/69092	Loss: 154.649
-54400/69092	Loss: 151.008
-57600/69092	Loss: 152.606
-60800/69092	Loss: 151.091
-64000/69092	Loss: 154.021
-67200/69092	Loss: 155.655
-Training time 0:01:58.182080
-Epoch: 289 Average loss: 152.67
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 289)
-0/69092	Loss: 153.771
-3200/69092	Loss: 153.182
-6400/69092	Loss: 156.509
-9600/69092	Loss: 153.219
-12800/69092	Loss: 150.625
-16000/69092	Loss: 150.845
-19200/69092	Loss: 150.406
-22400/69092	Loss: 151.609
-25600/69092	Loss: 152.303
-28800/69092	Loss: 155.698
-32000/69092	Loss: 154.752
-35200/69092	Loss: 152.203
-38400/69092	Loss: 152.052
-41600/69092	Loss: 150.904
-44800/69092	Loss: 153.018
-48000/69092	Loss: 154.200
-51200/69092	Loss: 153.990
-54400/69092	Loss: 150.695
-57600/69092	Loss: 151.786
-60800/69092	Loss: 150.960
-64000/69092	Loss: 153.491
-67200/69092	Loss: 153.766
-Training time 0:01:56.702625
-Epoch: 290 Average loss: 152.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 290)
-0/69092	Loss: 144.858
-3200/69092	Loss: 154.124
-6400/69092	Loss: 152.808
-9600/69092	Loss: 152.267
-12800/69092	Loss: 153.314
-16000/69092	Loss: 152.880
-19200/69092	Loss: 152.399
-22400/69092	Loss: 155.578
-25600/69092	Loss: 151.986
-28800/69092	Loss: 154.957
-32000/69092	Loss: 149.377
-35200/69092	Loss: 151.953
-38400/69092	Loss: 153.675
-41600/69092	Loss: 149.530
-44800/69092	Loss: 151.295
-48000/69092	Loss: 152.107
-51200/69092	Loss: 153.384
-54400/69092	Loss: 153.480
-57600/69092	Loss: 152.505
-60800/69092	Loss: 153.533
-64000/69092	Loss: 150.302
-67200/69092	Loss: 152.367
-Training time 0:01:57.117872
-Epoch: 291 Average loss: 152.54
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 291)
-0/69092	Loss: 146.660
-3200/69092	Loss: 154.319
-6400/69092	Loss: 150.582
-9600/69092	Loss: 151.213
-12800/69092	Loss: 154.199
-16000/69092	Loss: 152.823
-19200/69092	Loss: 154.952
-22400/69092	Loss: 153.487
-25600/69092	Loss: 155.376
-28800/69092	Loss: 150.760
-32000/69092	Loss: 151.841
-35200/69092	Loss: 151.915
-38400/69092	Loss: 153.543
-41600/69092	Loss: 153.795
-44800/69092	Loss: 151.942
-48000/69092	Loss: 154.003
-51200/69092	Loss: 151.467
-54400/69092	Loss: 152.260
-57600/69092	Loss: 151.282
-60800/69092	Loss: 151.538
-64000/69092	Loss: 150.303
-67200/69092	Loss: 154.587
-Training time 0:01:58.680452
-Epoch: 292 Average loss: 152.64
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 292)
-0/69092	Loss: 145.385
-3200/69092	Loss: 151.340
-6400/69092	Loss: 152.098
-9600/69092	Loss: 152.663
-12800/69092	Loss: 155.032
-16000/69092	Loss: 152.518
-19200/69092	Loss: 151.539
-22400/69092	Loss: 154.198
-25600/69092	Loss: 150.988
-28800/69092	Loss: 152.627
-32000/69092	Loss: 149.553
-35200/69092	Loss: 154.579
-38400/69092	Loss: 153.449
-41600/69092	Loss: 152.452
-44800/69092	Loss: 153.757
-48000/69092	Loss: 153.841
-51200/69092	Loss: 154.989
-54400/69092	Loss: 151.559
-57600/69092	Loss: 151.460
-60800/69092	Loss: 150.837
-64000/69092	Loss: 153.636
-67200/69092	Loss: 153.198
-Training time 0:01:57.312848
-Epoch: 293 Average loss: 152.68
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 293)
-0/69092	Loss: 159.010
-3200/69092	Loss: 151.893
-6400/69092	Loss: 152.605
-9600/69092	Loss: 154.632
-12800/69092	Loss: 153.891
-16000/69092	Loss: 154.094
-19200/69092	Loss: 152.895
-22400/69092	Loss: 153.460
-25600/69092	Loss: 151.271
-28800/69092	Loss: 152.545
-32000/69092	Loss: 155.276
-35200/69092	Loss: 149.771
-38400/69092	Loss: 152.842
-41600/69092	Loss: 152.335
-44800/69092	Loss: 149.782
-48000/69092	Loss: 151.298
-51200/69092	Loss: 152.164
-54400/69092	Loss: 152.249
-57600/69092	Loss: 154.120
-60800/69092	Loss: 152.138
-64000/69092	Loss: 154.047
-67200/69092	Loss: 154.118
-Training time 0:01:58.901837
-Epoch: 294 Average loss: 152.70
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 294)
-0/69092	Loss: 154.872
-3200/69092	Loss: 152.256
-6400/69092	Loss: 151.741
-9600/69092	Loss: 151.242
-12800/69092	Loss: 152.359
-16000/69092	Loss: 152.685
-19200/69092	Loss: 152.911
-22400/69092	Loss: 152.940
-25600/69092	Loss: 150.561
-28800/69092	Loss: 154.254
-32000/69092	Loss: 150.908
-35200/69092	Loss: 148.987
-38400/69092	Loss: 152.038
-41600/69092	Loss: 153.540
-44800/69092	Loss: 152.610
-48000/69092	Loss: 152.271
-51200/69092	Loss: 154.108
-54400/69092	Loss: 153.427
-57600/69092	Loss: 153.687
-60800/69092	Loss: 154.644
-64000/69092	Loss: 155.805
-67200/69092	Loss: 153.871
-Training time 0:01:57.515136
-Epoch: 295 Average loss: 152.72
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 295)
-0/69092	Loss: 172.136
-3200/69092	Loss: 154.477
-6400/69092	Loss: 154.377
-9600/69092	Loss: 153.084
-12800/69092	Loss: 152.990
-16000/69092	Loss: 153.255
-19200/69092	Loss: 149.538
-22400/69092	Loss: 151.873
-25600/69092	Loss: 151.863
-28800/69092	Loss: 152.404
-32000/69092	Loss: 153.600
-35200/69092	Loss: 151.885
-38400/69092	Loss: 153.565
-41600/69092	Loss: 153.836
-44800/69092	Loss: 148.658
-48000/69092	Loss: 152.054
-51200/69092	Loss: 153.897
-54400/69092	Loss: 151.908
-57600/69092	Loss: 150.511
-60800/69092	Loss: 154.268
-64000/69092	Loss: 152.153
-67200/69092	Loss: 151.667
-Training time 0:01:58.336184
-Epoch: 296 Average loss: 152.51
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 296)
-0/69092	Loss: 154.380
-3200/69092	Loss: 152.538
-6400/69092	Loss: 155.622
-9600/69092	Loss: 152.135
-12800/69092	Loss: 149.899
-16000/69092	Loss: 152.044
-19200/69092	Loss: 152.027
-22400/69092	Loss: 154.125
-25600/69092	Loss: 153.997
-28800/69092	Loss: 151.971
-32000/69092	Loss: 154.026
-35200/69092	Loss: 153.330
-38400/69092	Loss: 153.267
-41600/69092	Loss: 152.542
-44800/69092	Loss: 153.030
-48000/69092	Loss: 150.755
-51200/69092	Loss: 150.433
-54400/69092	Loss: 151.056
-57600/69092	Loss: 151.493
-60800/69092	Loss: 150.925
-64000/69092	Loss: 153.196
-67200/69092	Loss: 153.390
-Training time 0:01:58.935444
-Epoch: 297 Average loss: 152.53
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 297)
-0/69092	Loss: 162.380
-3200/69092	Loss: 152.809
-6400/69092	Loss: 153.967
-9600/69092	Loss: 152.654
-12800/69092	Loss: 153.532
-16000/69092	Loss: 150.451
-19200/69092	Loss: 154.726
-22400/69092	Loss: 152.309
-25600/69092	Loss: 156.937
-28800/69092	Loss: 151.914
-32000/69092	Loss: 151.362
-35200/69092	Loss: 151.774
-38400/69092	Loss: 150.487
-41600/69092	Loss: 152.882
-44800/69092	Loss: 153.183
-48000/69092	Loss: 152.330
-51200/69092	Loss: 151.612
-54400/69092	Loss: 151.251
-57600/69092	Loss: 149.921
-60800/69092	Loss: 152.920
-64000/69092	Loss: 154.771
-67200/69092	Loss: 152.781
-Training time 0:01:58.921640
-Epoch: 298 Average loss: 152.66
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 298)
-0/69092	Loss: 140.366
-3200/69092	Loss: 151.046
-6400/69092	Loss: 151.239
-9600/69092	Loss: 151.586
-12800/69092	Loss: 154.627
-16000/69092	Loss: 153.641
-19200/69092	Loss: 148.075
-22400/69092	Loss: 151.608
-25600/69092	Loss: 153.601
-28800/69092	Loss: 152.342
-32000/69092	Loss: 151.909
-35200/69092	Loss: 155.055
-38400/69092	Loss: 150.865
-41600/69092	Loss: 153.405
-44800/69092	Loss: 155.919
-48000/69092	Loss: 152.391
-51200/69092	Loss: 154.713
-54400/69092	Loss: 153.119
-57600/69092	Loss: 153.990
-60800/69092	Loss: 153.354
-64000/69092	Loss: 153.947
-67200/69092	Loss: 153.847
-Training time 0:01:58.544115
-Epoch: 299 Average loss: 152.75
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 299)
-0/69092	Loss: 150.468
-3200/69092	Loss: 151.150
-6400/69092	Loss: 152.774
-9600/69092	Loss: 154.188
-12800/69092	Loss: 152.119
-16000/69092	Loss: 151.245
-19200/69092	Loss: 153.454
-22400/69092	Loss: 154.601
-25600/69092	Loss: 153.383
-28800/69092	Loss: 154.201
-32000/69092	Loss: 151.125
-35200/69092	Loss: 152.874
-38400/69092	Loss: 151.267
-41600/69092	Loss: 150.446
-44800/69092	Loss: 152.735
-48000/69092	Loss: 153.413
-51200/69092	Loss: 153.193
-54400/69092	Loss: 152.989
-57600/69092	Loss: 152.980
-60800/69092	Loss: 152.818
-64000/69092	Loss: 153.845
-67200/69092	Loss: 150.960
-Training time 0:01:58.547261
-Epoch: 300 Average loss: 152.71
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 300)
-0/69092	Loss: 143.955
-3200/69092	Loss: 151.614
-6400/69092	Loss: 153.480
-9600/69092	Loss: 150.109
-12800/69092	Loss: 154.329
-16000/69092	Loss: 153.942
-19200/69092	Loss: 153.163
-22400/69092	Loss: 150.900
-25600/69092	Loss: 153.223
-28800/69092	Loss: 153.431
-32000/69092	Loss: 151.569
-35200/69092	Loss: 156.695
-38400/69092	Loss: 150.314
-41600/69092	Loss: 152.747
-44800/69092	Loss: 150.995
-48000/69092	Loss: 151.577
-51200/69092	Loss: 153.174
-54400/69092	Loss: 150.933
-57600/69092	Loss: 153.805
-60800/69092	Loss: 150.738
-64000/69092	Loss: 151.947
-67200/69092	Loss: 151.593
-Training time 0:01:58.548118
-Epoch: 301 Average loss: 152.46
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 301)
-0/69092	Loss: 147.279
-3200/69092	Loss: 155.644
-6400/69092	Loss: 151.232
-9600/69092	Loss: 151.488
-12800/69092	Loss: 153.075
-16000/69092	Loss: 152.990
-19200/69092	Loss: 152.349
-22400/69092	Loss: 151.458
-25600/69092	Loss: 152.777
-28800/69092	Loss: 156.447
-32000/69092	Loss: 155.383
-35200/69092	Loss: 149.996
-38400/69092	Loss: 150.005
-41600/69092	Loss: 152.548
-44800/69092	Loss: 150.427
-48000/69092	Loss: 152.403
-51200/69092	Loss: 153.461
-54400/69092	Loss: 152.818
-57600/69092	Loss: 153.848
-60800/69092	Loss: 151.459
-64000/69092	Loss: 153.510
-67200/69092	Loss: 153.262
-Training time 0:01:59.635448
-Epoch: 302 Average loss: 152.62
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 302)
-0/69092	Loss: 153.877
-3200/69092	Loss: 152.488
-6400/69092	Loss: 152.250
-9600/69092	Loss: 153.468
-12800/69092	Loss: 154.536
-16000/69092	Loss: 151.385
-19200/69092	Loss: 151.035
-22400/69092	Loss: 150.293
-25600/69092	Loss: 154.147
-28800/69092	Loss: 153.163
-32000/69092	Loss: 154.560
-35200/69092	Loss: 152.221
-38400/69092	Loss: 155.581
-41600/69092	Loss: 153.473
-44800/69092	Loss: 153.722
-48000/69092	Loss: 151.588
-51200/69092	Loss: 151.839
-54400/69092	Loss: 152.329
-57600/69092	Loss: 150.435
-60800/69092	Loss: 151.320
-64000/69092	Loss: 153.641
-67200/69092	Loss: 153.587
-Training time 0:01:58.953545
-Epoch: 303 Average loss: 152.75
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 303)
-0/69092	Loss: 150.752
-3200/69092	Loss: 154.228
-6400/69092	Loss: 151.872
-9600/69092	Loss: 151.435
-12800/69092	Loss: 153.457
-16000/69092	Loss: 155.695
-19200/69092	Loss: 154.470
-22400/69092	Loss: 153.321
-25600/69092	Loss: 153.885
-28800/69092	Loss: 152.395
-32000/69092	Loss: 151.585
-35200/69092	Loss: 151.974
-38400/69092	Loss: 151.704
-41600/69092	Loss: 154.140
-44800/69092	Loss: 152.625
-48000/69092	Loss: 151.551
-51200/69092	Loss: 153.556
-54400/69092	Loss: 153.088
-57600/69092	Loss: 150.098
-60800/69092	Loss: 150.307
-64000/69092	Loss: 152.213
-67200/69092	Loss: 153.466
-Training time 0:01:59.769055
-Epoch: 304 Average loss: 152.67
-=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 304)
-0/69092	Loss: 135.868
-3200/69092	Loss: 151.608
-6400/69092	Loss: 153.318
-9600/69092	Loss: 154.543
-12800/69092	Loss: 152.205
-16000/69092	Loss: 152.045
-19200/69092	Loss: 153.663
-22400/69092	Loss: 153.126
-25600/69092	Loss: 151.074
diff --git a/OAR.2066988.stdout b/OAR.2066988.stdout
deleted file mode 100644
index 42d8ca3af1e081f42c686dc71d29f58a989a07bb..0000000000000000000000000000000000000000
--- a/OAR.2066988.stdout
+++ /dev/null
@@ -1,1336 +0,0 @@
-Namespace(batch_size=256, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_256', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
-creare new diretory experiment: rendered_chairs/VAE_bs_256
-load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
-use 2 gpu who named:
-GeForce RTX 2080 Ti
-GeForce RTX 2080 Ti
-DataParallel(
-  (module): VAE(
-    (img_to_last_conv): Sequential(
-      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (1): ReLU()
-      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (3): ReLU()
-      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (5): ReLU()
-      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (7): ReLU()
-    )
-    (last_conv_to_continuous_features): Sequential(
-      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
-      (1): ReLU()
-    )
-    (features_to_hidden_continue): Sequential(
-      (0): Linear(in_features=256, out_features=20, bias=True)
-      (1): ReLU()
-    )
-    (latent_to_features): Sequential(
-      (0): Linear(in_features=10, out_features=256, bias=True)
-      (1): ReLU()
-    )
-    (features_to_img): Sequential(
-      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
-      (1): ReLU()
-      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (3): ReLU()
-      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (5): ReLU()
-      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (7): ReLU()
-      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (9): Sigmoid()
-    )
-  )
-)
-The number of parameters of model is 765335
-don't use continuous capacity
-=> no checkpoint found at 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last'
-0/69092	Loss: 2814.439
-12800/69092	Loss: 2606.417
-25600/69092	Loss: 812.792
-38400/69092	Loss: 524.628
-51200/69092	Loss: 410.449
-64000/69092	Loss: 288.235
-Training time 0:04:00.502541
-Epoch: 1 Average loss: 888.26
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 1)
-0/69092	Loss: 254.843
-12800/69092	Loss: 241.234
-25600/69092	Loss: 230.002
-38400/69092	Loss: 228.340
-51200/69092	Loss: 221.194
-64000/69092	Loss: 215.089
-Training time 0:03:58.901498
-Epoch: 2 Average loss: 226.36
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 2)
-0/69092	Loss: 208.402
-12800/69092	Loss: 212.035
-25600/69092	Loss: 210.421
-38400/69092	Loss: 210.173
-51200/69092	Loss: 207.824
-64000/69092	Loss: 205.581
-Training time 0:03:51.885673
-Epoch: 3 Average loss: 208.84
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 3)
-0/69092	Loss: 211.543
-12800/69092	Loss: 202.477
-25600/69092	Loss: 200.710
-38400/69092	Loss: 198.968
-51200/69092	Loss: 193.583
-64000/69092	Loss: 186.156
-Training time 0:03:57.600496
-Epoch: 4 Average loss: 195.31
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 4)
-0/69092	Loss: 173.161
-12800/69092	Loss: 173.079
-25600/69092	Loss: 166.273
-38400/69092	Loss: 165.127
-51200/69092	Loss: 162.785
-64000/69092	Loss: 159.291
-Training time 0:04:05.123858
-Epoch: 5 Average loss: 164.86
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 5)
-0/69092	Loss: 156.576
-12800/69092	Loss: 154.753
-25600/69092	Loss: 151.747
-38400/69092	Loss: 151.202
-51200/69092	Loss: 148.720
-64000/69092	Loss: 147.345
-Training time 0:04:05.650963
-Epoch: 6 Average loss: 150.38
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 6)
-0/69092	Loss: 147.825
-12800/69092	Loss: 144.725
-25600/69092	Loss: 143.537
-38400/69092	Loss: 143.139
-51200/69092	Loss: 141.119
-64000/69092	Loss: 141.689
-Training time 0:04:08.122079
-Epoch: 7 Average loss: 142.63
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 7)
-0/69092	Loss: 131.992
-12800/69092	Loss: 140.162
-25600/69092	Loss: 138.903
-38400/69092	Loss: 138.267
-51200/69092	Loss: 136.916
-64000/69092	Loss: 138.901
-Training time 0:03:54.331655
-Epoch: 8 Average loss: 138.51
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 8)
-0/69092	Loss: 140.047
-12800/69092	Loss: 136.032
-25600/69092	Loss: 136.670
-38400/69092	Loss: 135.414
-51200/69092	Loss: 135.785
-64000/69092	Loss: 135.055
-Training time 0:04:09.508379
-Epoch: 9 Average loss: 135.55
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 9)
-0/69092	Loss: 131.478
-12800/69092	Loss: 134.254
-25600/69092	Loss: 133.785
-38400/69092	Loss: 133.843
-51200/69092	Loss: 134.290
-64000/69092	Loss: 132.657
-Training time 0:04:13.579643
-Epoch: 10 Average loss: 133.88
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 10)
-0/69092	Loss: 129.377
-12800/69092	Loss: 133.059
-25600/69092	Loss: 132.677
-38400/69092	Loss: 132.448
-51200/69092	Loss: 131.756
-64000/69092	Loss: 132.828
-Training time 0:04:11.038189
-Epoch: 11 Average loss: 132.56
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 11)
-0/69092	Loss: 128.447
-12800/69092	Loss: 132.007
-25600/69092	Loss: 131.486
-38400/69092	Loss: 131.955
-51200/69092	Loss: 131.331
-64000/69092	Loss: 132.508
-Training time 0:04:05.952124
-Epoch: 12 Average loss: 131.67
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 12)
-0/69092	Loss: 127.952
-12800/69092	Loss: 131.422
-25600/69092	Loss: 130.121
-38400/69092	Loss: 130.560
-51200/69092	Loss: 130.675
-64000/69092	Loss: 130.489
-Training time 0:04:03.246605
-Epoch: 13 Average loss: 130.66
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 13)
-0/69092	Loss: 136.223
-12800/69092	Loss: 129.585
-25600/69092	Loss: 129.881
-38400/69092	Loss: 130.199
-51200/69092	Loss: 129.653
-64000/69092	Loss: 129.837
-Training time 0:03:58.965557
-Epoch: 14 Average loss: 129.86
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 14)
-0/69092	Loss: 126.331
-12800/69092	Loss: 129.435
-25600/69092	Loss: 128.785
-38400/69092	Loss: 128.843
-51200/69092	Loss: 130.551
-64000/69092	Loss: 128.547
-Training time 0:04:09.519483
-Epoch: 15 Average loss: 129.28
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 15)
-0/69092	Loss: 124.206
-12800/69092	Loss: 128.104
-25600/69092	Loss: 127.977
-38400/69092	Loss: 129.959
-51200/69092	Loss: 129.623
-64000/69092	Loss: 128.646
-Training time 0:04:06.651803
-Epoch: 16 Average loss: 128.79
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 16)
-0/69092	Loss: 123.285
-12800/69092	Loss: 129.276
-25600/69092	Loss: 128.834
-38400/69092	Loss: 128.149
-51200/69092	Loss: 128.007
-64000/69092	Loss: 127.012
-Training time 0:04:09.535457
-Epoch: 17 Average loss: 128.33
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 17)
-0/69092	Loss: 122.490
-12800/69092	Loss: 128.020
-25600/69092	Loss: 128.469
-38400/69092	Loss: 127.697
-51200/69092	Loss: 127.443
-64000/69092	Loss: 127.452
-Training time 0:04:15.114925
-Epoch: 18 Average loss: 127.72
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 18)
-0/69092	Loss: 125.483
-12800/69092	Loss: 127.551
-25600/69092	Loss: 127.873
-38400/69092	Loss: 127.776
-51200/69092	Loss: 127.685
-64000/69092	Loss: 126.057
-Training time 0:04:01.902371
-Epoch: 19 Average loss: 127.38
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 19)
-0/69092	Loss: 122.885
-12800/69092	Loss: 126.344
-25600/69092	Loss: 127.344
-38400/69092	Loss: 127.723
-51200/69092	Loss: 126.600
-64000/69092	Loss: 127.361
-Training time 0:04:00.677389
-Epoch: 20 Average loss: 127.00
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 20)
-0/69092	Loss: 131.818
-12800/69092	Loss: 126.634
-25600/69092	Loss: 125.596
-38400/69092	Loss: 126.447
-51200/69092	Loss: 127.722
-64000/69092	Loss: 125.743
-Training time 0:04:08.702550
-Epoch: 21 Average loss: 126.59
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 21)
-0/69092	Loss: 120.820
-12800/69092	Loss: 125.706
-25600/69092	Loss: 127.387
-38400/69092	Loss: 126.881
-51200/69092	Loss: 125.378
-64000/69092	Loss: 126.583
-Training time 0:04:03.607133
-Epoch: 22 Average loss: 126.26
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 22)
-0/69092	Loss: 123.223
-12800/69092	Loss: 125.898
-25600/69092	Loss: 125.606
-38400/69092	Loss: 125.715
-51200/69092	Loss: 126.186
-64000/69092	Loss: 126.409
-Training time 0:04:07.609030
-Epoch: 23 Average loss: 125.96
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 23)
-0/69092	Loss: 128.143
-12800/69092	Loss: 125.965
-25600/69092	Loss: 126.009
-38400/69092	Loss: 125.024
-51200/69092	Loss: 125.283
-64000/69092	Loss: 126.305
-Training time 0:04:00.812965
-Epoch: 24 Average loss: 125.70
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 24)
-0/69092	Loss: 125.396
-12800/69092	Loss: 125.719
-25600/69092	Loss: 126.159
-38400/69092	Loss: 124.345
-51200/69092	Loss: 124.650
-64000/69092	Loss: 125.511
-Training time 0:03:58.507761
-Epoch: 25 Average loss: 125.29
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 25)
-0/69092	Loss: 136.129
-12800/69092	Loss: 126.168
-25600/69092	Loss: 125.467
-38400/69092	Loss: 124.673
-51200/69092	Loss: 124.727
-64000/69092	Loss: 124.674
-Training time 0:04:06.371973
-Epoch: 26 Average loss: 125.08
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 26)
-0/69092	Loss: 119.995
-12800/69092	Loss: 125.162
-25600/69092	Loss: 124.900
-38400/69092	Loss: 124.906
-51200/69092	Loss: 124.934
-64000/69092	Loss: 124.702
-Training time 0:04:08.636811
-Epoch: 27 Average loss: 124.80
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 27)
-0/69092	Loss: 121.814
-12800/69092	Loss: 124.749
-25600/69092	Loss: 123.778
-38400/69092	Loss: 125.225
-51200/69092	Loss: 124.118
-64000/69092	Loss: 125.262
-Training time 0:04:07.395318
-Epoch: 28 Average loss: 124.58
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 28)
-0/69092	Loss: 122.369
-12800/69092	Loss: 124.628
-25600/69092	Loss: 124.176
-38400/69092	Loss: 124.751
-51200/69092	Loss: 123.993
-64000/69092	Loss: 124.350
-Training time 0:04:13.117962
-Epoch: 29 Average loss: 124.32
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 29)
-0/69092	Loss: 121.895
-12800/69092	Loss: 124.362
-25600/69092	Loss: 125.226
-38400/69092	Loss: 124.151
-51200/69092	Loss: 123.218
-64000/69092	Loss: 124.114
-Training time 0:04:00.434000
-Epoch: 30 Average loss: 124.12
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 30)
-0/69092	Loss: 115.194
-12800/69092	Loss: 124.568
-25600/69092	Loss: 123.711
-38400/69092	Loss: 122.897
-51200/69092	Loss: 124.132
-64000/69092	Loss: 123.650
-Training time 0:04:05.715337
-Epoch: 31 Average loss: 123.82
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 31)
-0/69092	Loss: 124.334
-12800/69092	Loss: 123.616
-25600/69092	Loss: 123.198
-38400/69092	Loss: 124.313
-51200/69092	Loss: 123.299
-64000/69092	Loss: 123.692
-Training time 0:04:08.544469
-Epoch: 32 Average loss: 123.67
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 32)
-0/69092	Loss: 127.550
-12800/69092	Loss: 123.950
-25600/69092	Loss: 123.163
-38400/69092	Loss: 123.634
-51200/69092	Loss: 122.978
-64000/69092	Loss: 124.027
-Training time 0:04:13.082735
-Epoch: 33 Average loss: 123.47
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 33)
-0/69092	Loss: 123.320
-12800/69092	Loss: 123.340
-25600/69092	Loss: 122.071
-38400/69092	Loss: 123.296
-51200/69092	Loss: 123.829
-64000/69092	Loss: 123.658
-Training time 0:04:15.650392
-Epoch: 34 Average loss: 123.40
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 34)
-0/69092	Loss: 128.216
-12800/69092	Loss: 122.262
-25600/69092	Loss: 123.458
-38400/69092	Loss: 123.642
-51200/69092	Loss: 123.133
-64000/69092	Loss: 123.217
-Training time 0:04:08.276315
-Epoch: 35 Average loss: 123.08
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 35)
-0/69092	Loss: 120.675
-12800/69092	Loss: 122.984
-25600/69092	Loss: 121.834
-38400/69092	Loss: 123.413
-51200/69092	Loss: 122.305
-64000/69092	Loss: 123.635
-Training time 0:04:03.058292
-Epoch: 36 Average loss: 122.89
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 36)
-0/69092	Loss: 118.445
-12800/69092	Loss: 123.013
-25600/69092	Loss: 122.418
-38400/69092	Loss: 122.231
-51200/69092	Loss: 123.021
-64000/69092	Loss: 122.842
-Training time 0:04:06.648961
-Epoch: 37 Average loss: 122.82
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 37)
-0/69092	Loss: 121.083
-12800/69092	Loss: 123.263
-25600/69092	Loss: 122.257
-38400/69092	Loss: 122.379
-51200/69092	Loss: 122.171
-64000/69092	Loss: 123.059
-Training time 0:04:15.049360
-Epoch: 38 Average loss: 122.71
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 38)
-0/69092	Loss: 123.324
-12800/69092	Loss: 122.454
-25600/69092	Loss: 121.804
-38400/69092	Loss: 122.938
-51200/69092	Loss: 122.913
-64000/69092	Loss: 122.085
-Training time 0:04:05.371204
-Epoch: 39 Average loss: 122.43
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 39)
-0/69092	Loss: 128.633
-12800/69092	Loss: 121.116
-25600/69092	Loss: 121.840
-38400/69092	Loss: 122.284
-51200/69092	Loss: 122.594
-64000/69092	Loss: 123.471
-Training time 0:04:11.828321
-Epoch: 40 Average loss: 122.34
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 40)
-0/69092	Loss: 118.211
-12800/69092	Loss: 122.969
-25600/69092	Loss: 123.368
-38400/69092	Loss: 121.575
-51200/69092	Loss: 122.347
-64000/69092	Loss: 121.758
-Training time 0:04:09.545590
-Epoch: 41 Average loss: 122.25
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 41)
-0/69092	Loss: 122.601
-12800/69092	Loss: 122.031
-25600/69092	Loss: 123.502
-38400/69092	Loss: 123.221
-51200/69092	Loss: 121.567
-64000/69092	Loss: 121.257
-Training time 0:04:03.271690
-Epoch: 42 Average loss: 122.22
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 42)
-0/69092	Loss: 127.457
-12800/69092	Loss: 121.780
-25600/69092	Loss: 121.304
-38400/69092	Loss: 123.179
-51200/69092	Loss: 121.032
-64000/69092	Loss: 122.156
-Training time 0:04:06.866565
-Epoch: 43 Average loss: 121.91
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 43)
-0/69092	Loss: 122.912
-12800/69092	Loss: 121.686
-25600/69092	Loss: 121.672
-38400/69092	Loss: 121.170
-51200/69092	Loss: 121.757
-64000/69092	Loss: 122.824
-Training time 0:04:05.681837
-Epoch: 44 Average loss: 121.81
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 44)
-0/69092	Loss: 135.368
-12800/69092	Loss: 121.265
-25600/69092	Loss: 121.626
-38400/69092	Loss: 121.669
-51200/69092	Loss: 122.190
-64000/69092	Loss: 121.709
-Training time 0:04:11.599922
-Epoch: 45 Average loss: 121.79
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 45)
-0/69092	Loss: 121.074
-12800/69092	Loss: 121.516
-25600/69092	Loss: 120.940
-38400/69092	Loss: 122.202
-51200/69092	Loss: 121.848
-64000/69092	Loss: 122.093
-Training time 0:04:15.838166
-Epoch: 46 Average loss: 121.68
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 46)
-0/69092	Loss: 126.527
-12800/69092	Loss: 121.600
-25600/69092	Loss: 121.211
-38400/69092	Loss: 122.194
-51200/69092	Loss: 121.122
-64000/69092	Loss: 121.598
-Training time 0:04:08.633490
-Epoch: 47 Average loss: 121.51
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 47)
-0/69092	Loss: 126.953
-12800/69092	Loss: 121.570
-25600/69092	Loss: 121.045
-38400/69092	Loss: 122.628
-51200/69092	Loss: 121.052
-64000/69092	Loss: 120.897
-Training time 0:03:58.102023
-Epoch: 48 Average loss: 121.48
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 48)
-0/69092	Loss: 124.567
-12800/69092	Loss: 121.877
-25600/69092	Loss: 120.928
-38400/69092	Loss: 121.011
-51200/69092	Loss: 121.311
-64000/69092	Loss: 121.826
-Training time 0:04:06.935042
-Epoch: 49 Average loss: 121.45
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 49)
-0/69092	Loss: 121.404
-12800/69092	Loss: 121.514
-25600/69092	Loss: 121.087
-38400/69092	Loss: 121.419
-51200/69092	Loss: 121.384
-64000/69092	Loss: 121.023
-Training time 0:04:15.166332
-Epoch: 50 Average loss: 121.23
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 50)
-0/69092	Loss: 126.006
-12800/69092	Loss: 120.948
-25600/69092	Loss: 121.760
-38400/69092	Loss: 121.779
-51200/69092	Loss: 120.402
-64000/69092	Loss: 120.823
-Training time 0:04:28.909935
-Epoch: 51 Average loss: 121.15
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 51)
-0/69092	Loss: 124.286
-12800/69092	Loss: 121.074
-25600/69092	Loss: 121.402
-38400/69092	Loss: 121.318
-51200/69092	Loss: 120.578
-64000/69092	Loss: 121.595
-Training time 0:04:18.253963
-Epoch: 52 Average loss: 121.13
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 52)
-0/69092	Loss: 120.751
-12800/69092	Loss: 121.077
-25600/69092	Loss: 121.218
-38400/69092	Loss: 120.888
-51200/69092	Loss: 120.694
-64000/69092	Loss: 120.242
-Training time 0:04:06.896767
-Epoch: 53 Average loss: 120.93
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 53)
-0/69092	Loss: 127.181
-12800/69092	Loss: 120.765
-25600/69092	Loss: 120.254
-38400/69092	Loss: 120.713
-51200/69092	Loss: 120.709
-64000/69092	Loss: 121.636
-Training time 0:04:06.321434
-Epoch: 54 Average loss: 120.87
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 54)
-0/69092	Loss: 125.942
-12800/69092	Loss: 121.474
-25600/69092	Loss: 120.848
-38400/69092	Loss: 120.489
-51200/69092	Loss: 121.334
-64000/69092	Loss: 119.914
-Training time 0:04:07.908865
-Epoch: 55 Average loss: 120.75
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 55)
-0/69092	Loss: 119.879
-12800/69092	Loss: 121.761
-25600/69092	Loss: 120.564
-38400/69092	Loss: 119.969
-51200/69092	Loss: 120.840
-64000/69092	Loss: 121.243
-Training time 0:04:24.374569
-Epoch: 56 Average loss: 120.73
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 56)
-0/69092	Loss: 122.110
-12800/69092	Loss: 120.853
-25600/69092	Loss: 121.248
-38400/69092	Loss: 118.912
-51200/69092	Loss: 120.841
-64000/69092	Loss: 121.255
-Training time 0:04:19.634250
-Epoch: 57 Average loss: 120.63
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 57)
-0/69092	Loss: 119.217
-12800/69092	Loss: 119.208
-25600/69092	Loss: 119.932
-38400/69092	Loss: 121.283
-51200/69092	Loss: 120.620
-64000/69092	Loss: 120.339
-Training time 0:04:20.572270
-Epoch: 58 Average loss: 120.38
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 58)
-0/69092	Loss: 118.004
-12800/69092	Loss: 121.649
-25600/69092	Loss: 120.544
-38400/69092	Loss: 120.965
-51200/69092	Loss: 118.516
-64000/69092	Loss: 120.499
-Training time 0:04:13.101164
-Epoch: 59 Average loss: 120.46
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 59)
-0/69092	Loss: 112.881
-12800/69092	Loss: 120.567
-25600/69092	Loss: 120.420
-38400/69092	Loss: 119.364
-51200/69092	Loss: 119.955
-64000/69092	Loss: 121.037
-Training time 0:04:10.750013
-Epoch: 60 Average loss: 120.22
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 60)
-0/69092	Loss: 122.250
-12800/69092	Loss: 119.708
-25600/69092	Loss: 120.189
-38400/69092	Loss: 120.499
-51200/69092	Loss: 120.354
-64000/69092	Loss: 120.335
-Training time 0:04:13.578982
-Epoch: 61 Average loss: 120.22
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 61)
-0/69092	Loss: 117.498
-12800/69092	Loss: 120.426
-25600/69092	Loss: 120.447
-38400/69092	Loss: 119.672
-51200/69092	Loss: 120.147
-64000/69092	Loss: 119.269
-Training time 0:04:20.939895
-Epoch: 62 Average loss: 119.97
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 62)
-0/69092	Loss: 125.455
-12800/69092	Loss: 119.791
-25600/69092	Loss: 120.792
-38400/69092	Loss: 119.489
-51200/69092	Loss: 120.414
-64000/69092	Loss: 119.984
-Training time 0:04:19.158055
-Epoch: 63 Average loss: 120.14
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 63)
-0/69092	Loss: 117.290
-12800/69092	Loss: 119.601
-25600/69092	Loss: 120.635
-38400/69092	Loss: 119.446
-51200/69092	Loss: 119.494
-64000/69092	Loss: 120.180
-Training time 0:04:19.186761
-Epoch: 64 Average loss: 119.92
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 64)
-0/69092	Loss: 120.642
-12800/69092	Loss: 119.806
-25600/69092	Loss: 120.017
-38400/69092	Loss: 120.107
-51200/69092	Loss: 120.154
-64000/69092	Loss: 119.791
-Training time 0:04:17.913614
-Epoch: 65 Average loss: 119.92
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 65)
-0/69092	Loss: 119.794
-12800/69092	Loss: 118.851
-25600/69092	Loss: 120.739
-38400/69092	Loss: 120.140
-51200/69092	Loss: 120.621
-64000/69092	Loss: 118.912
-Training time 0:04:07.989220
-Epoch: 66 Average loss: 119.93
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 66)
-0/69092	Loss: 121.311
-12800/69092	Loss: 121.100
-25600/69092	Loss: 118.866
-38400/69092	Loss: 119.869
-51200/69092	Loss: 119.210
-64000/69092	Loss: 119.637
-Training time 0:04:04.809304
-Epoch: 67 Average loss: 119.71
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 67)
-0/69092	Loss: 117.776
-12800/69092	Loss: 119.768
-25600/69092	Loss: 119.897
-38400/69092	Loss: 119.051
-51200/69092	Loss: 119.888
-64000/69092	Loss: 119.118
-Training time 0:04:05.612033
-Epoch: 68 Average loss: 119.64
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 68)
-0/69092	Loss: 114.908
-12800/69092	Loss: 119.558
-25600/69092	Loss: 120.312
-38400/69092	Loss: 119.431
-51200/69092	Loss: 118.943
-64000/69092	Loss: 119.501
-Training time 0:04:18.977186
-Epoch: 69 Average loss: 119.52
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 69)
-0/69092	Loss: 122.569
-12800/69092	Loss: 118.864
-25600/69092	Loss: 119.829
-38400/69092	Loss: 119.767
-51200/69092	Loss: 119.599
-64000/69092	Loss: 119.105
-Training time 0:04:11.458453
-Epoch: 70 Average loss: 119.48
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 70)
-0/69092	Loss: 125.237
-12800/69092	Loss: 119.324
-25600/69092	Loss: 119.526
-38400/69092	Loss: 119.403
-51200/69092	Loss: 119.721
-64000/69092	Loss: 119.557
-Training time 0:04:15.062350
-Epoch: 71 Average loss: 119.49
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 71)
-0/69092	Loss: 117.608
-12800/69092	Loss: 119.207
-25600/69092	Loss: 119.193
-38400/69092	Loss: 119.453
-51200/69092	Loss: 119.239
-64000/69092	Loss: 119.616
-Training time 0:04:05.494584
-Epoch: 72 Average loss: 119.38
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 72)
-0/69092	Loss: 113.774
-12800/69092	Loss: 119.703
-25600/69092	Loss: 120.271
-38400/69092	Loss: 118.884
-51200/69092	Loss: 118.561
-64000/69092	Loss: 119.390
-Training time 0:04:04.810459
-Epoch: 73 Average loss: 119.31
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 73)
-0/69092	Loss: 121.206
-12800/69092	Loss: 119.420
-25600/69092	Loss: 119.455
-38400/69092	Loss: 119.734
-51200/69092	Loss: 118.942
-64000/69092	Loss: 118.688
-Training time 0:04:03.597461
-Epoch: 74 Average loss: 119.27
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 74)
-0/69092	Loss: 123.219
-12800/69092	Loss: 118.896
-25600/69092	Loss: 119.816
-38400/69092	Loss: 117.844
-51200/69092	Loss: 119.564
-64000/69092	Loss: 119.447
-Training time 0:04:11.406864
-Epoch: 75 Average loss: 119.14
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 75)
-0/69092	Loss: 116.660
-12800/69092	Loss: 118.787
-25600/69092	Loss: 118.336
-38400/69092	Loss: 119.625
-51200/69092	Loss: 119.877
-64000/69092	Loss: 119.186
-Training time 0:04:24.342829
-Epoch: 76 Average loss: 119.21
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 76)
-0/69092	Loss: 119.502
-12800/69092	Loss: 117.959
-25600/69092	Loss: 118.876
-38400/69092	Loss: 119.940
-51200/69092	Loss: 118.478
-64000/69092	Loss: 119.287
-Training time 0:04:07.572541
-Epoch: 77 Average loss: 119.02
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 77)
-0/69092	Loss: 112.084
-12800/69092	Loss: 117.931
-25600/69092	Loss: 118.700
-38400/69092	Loss: 119.683
-51200/69092	Loss: 118.883
-64000/69092	Loss: 118.919
-Training time 0:04:03.865588
-Epoch: 78 Average loss: 118.85
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 78)
-0/69092	Loss: 117.714
-12800/69092	Loss: 119.233
-25600/69092	Loss: 118.715
-38400/69092	Loss: 118.166
-51200/69092	Loss: 118.807
-64000/69092	Loss: 119.992
-Training time 0:04:05.190180
-Epoch: 79 Average loss: 119.03
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 79)
-0/69092	Loss: 117.169
-12800/69092	Loss: 117.980
-25600/69092	Loss: 118.887
-38400/69092	Loss: 118.441
-51200/69092	Loss: 118.812
-64000/69092	Loss: 119.412
-Training time 0:04:02.204515
-Epoch: 80 Average loss: 118.79
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 80)
-0/69092	Loss: 118.471
-12800/69092	Loss: 118.986
-25600/69092	Loss: 117.801
-38400/69092	Loss: 118.665
-51200/69092	Loss: 119.535
-64000/69092	Loss: 119.216
-Training time 0:04:14.209794
-Epoch: 81 Average loss: 118.92
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 81)
-0/69092	Loss: 113.233
-12800/69092	Loss: 118.796
-25600/69092	Loss: 120.057
-38400/69092	Loss: 118.499
-51200/69092	Loss: 118.816
-64000/69092	Loss: 117.538
-Training time 0:04:15.709971
-Epoch: 82 Average loss: 118.69
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 82)
-0/69092	Loss: 114.715
-12800/69092	Loss: 118.884
-25600/69092	Loss: 118.494
-38400/69092	Loss: 118.577
-51200/69092	Loss: 118.888
-64000/69092	Loss: 118.886
-Training time 0:04:11.655027
-Epoch: 83 Average loss: 118.71
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 83)
-0/69092	Loss: 114.184
-12800/69092	Loss: 118.511
-25600/69092	Loss: 119.265
-38400/69092	Loss: 118.358
-51200/69092	Loss: 119.018
-64000/69092	Loss: 118.696
-Training time 0:04:03.034456
-Epoch: 84 Average loss: 118.64
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 84)
-0/69092	Loss: 119.023
-12800/69092	Loss: 118.235
-25600/69092	Loss: 118.429
-38400/69092	Loss: 117.985
-51200/69092	Loss: 118.307
-64000/69092	Loss: 119.246
-Training time 0:04:07.248343
-Epoch: 85 Average loss: 118.52
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 85)
-0/69092	Loss: 121.420
-12800/69092	Loss: 118.604
-25600/69092	Loss: 118.223
-38400/69092	Loss: 118.012
-51200/69092	Loss: 118.248
-64000/69092	Loss: 119.041
-Training time 0:04:09.127944
-Epoch: 86 Average loss: 118.43
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 86)
-0/69092	Loss: 119.622
-12800/69092	Loss: 118.327
-25600/69092	Loss: 119.245
-38400/69092	Loss: 118.592
-51200/69092	Loss: 117.328
-64000/69092	Loss: 118.681
-Training time 0:04:19.338612
-Epoch: 87 Average loss: 118.44
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 87)
-0/69092	Loss: 111.174
-12800/69092	Loss: 118.593
-25600/69092	Loss: 118.148
-38400/69092	Loss: 118.586
-51200/69092	Loss: 118.532
-64000/69092	Loss: 118.767
-Training time 0:04:15.535839
-Epoch: 88 Average loss: 118.37
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 88)
-0/69092	Loss: 116.705
-12800/69092	Loss: 118.352
-25600/69092	Loss: 118.533
-38400/69092	Loss: 118.169
-51200/69092	Loss: 118.406
-64000/69092	Loss: 118.343
-Training time 0:04:20.266325
-Epoch: 89 Average loss: 118.38
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 89)
-0/69092	Loss: 115.584
-12800/69092	Loss: 117.936
-25600/69092	Loss: 118.092
-38400/69092	Loss: 118.414
-51200/69092	Loss: 118.575
-64000/69092	Loss: 117.829
-Training time 0:04:17.261602
-Epoch: 90 Average loss: 118.19
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 90)
-0/69092	Loss: 117.601
-12800/69092	Loss: 118.211
-25600/69092	Loss: 118.045
-38400/69092	Loss: 117.952
-51200/69092	Loss: 118.657
-64000/69092	Loss: 118.763
-Training time 0:04:10.787288
-Epoch: 91 Average loss: 118.30
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 91)
-0/69092	Loss: 111.534
-12800/69092	Loss: 117.956
-25600/69092	Loss: 118.221
-38400/69092	Loss: 118.217
-51200/69092	Loss: 117.930
-64000/69092	Loss: 118.685
-Training time 0:04:05.952103
-Epoch: 92 Average loss: 118.22
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 92)
-0/69092	Loss: 115.097
-12800/69092	Loss: 118.364
-25600/69092	Loss: 118.456
-38400/69092	Loss: 117.776
-51200/69092	Loss: 118.108
-64000/69092	Loss: 118.348
-Training time 0:04:14.091436
-Epoch: 93 Average loss: 118.24
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 93)
-0/69092	Loss: 117.903
-12800/69092	Loss: 118.141
-25600/69092	Loss: 117.958
-38400/69092	Loss: 118.750
-51200/69092	Loss: 116.999
-64000/69092	Loss: 119.126
-Training time 0:04:19.666973
-Epoch: 94 Average loss: 118.09
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 94)
-0/69092	Loss: 122.937
-12800/69092	Loss: 118.038
-25600/69092	Loss: 117.933
-38400/69092	Loss: 117.870
-51200/69092	Loss: 117.970
-64000/69092	Loss: 118.466
-Training time 0:04:27.989384
-Epoch: 95 Average loss: 118.03
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 95)
-0/69092	Loss: 113.251
-12800/69092	Loss: 117.549
-25600/69092	Loss: 118.034
-38400/69092	Loss: 118.375
-51200/69092	Loss: 117.229
-64000/69092	Loss: 118.759
-Training time 0:04:23.592842
-Epoch: 96 Average loss: 117.92
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 96)
-0/69092	Loss: 125.196
-12800/69092	Loss: 118.374
-25600/69092	Loss: 118.202
-38400/69092	Loss: 118.131
-51200/69092	Loss: 117.639
-64000/69092	Loss: 117.563
-Training time 0:04:07.757856
-Epoch: 97 Average loss: 117.94
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 97)
-0/69092	Loss: 116.827
-12800/69092	Loss: 118.167
-25600/69092	Loss: 117.874
-38400/69092	Loss: 118.108
-51200/69092	Loss: 117.770
-64000/69092	Loss: 117.935
-Training time 0:04:07.602487
-Epoch: 98 Average loss: 117.88
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 98)
-0/69092	Loss: 116.395
-12800/69092	Loss: 118.537
-25600/69092	Loss: 116.787
-38400/69092	Loss: 117.802
-51200/69092	Loss: 118.060
-64000/69092	Loss: 117.920
-Training time 0:04:04.077916
-Epoch: 99 Average loss: 117.86
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 99)
-0/69092	Loss: 120.129
-12800/69092	Loss: 117.395
-25600/69092	Loss: 118.096
-38400/69092	Loss: 118.253
-51200/69092	Loss: 118.095
-64000/69092	Loss: 117.124
-Training time 0:04:14.157682
-Epoch: 100 Average loss: 117.81
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 100)
-0/69092	Loss: 115.819
-12800/69092	Loss: 117.516
-25600/69092	Loss: 117.688
-38400/69092	Loss: 117.822
-51200/69092	Loss: 117.359
-64000/69092	Loss: 118.094
-Training time 0:04:19.060029
-Epoch: 101 Average loss: 117.74
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 101)
-0/69092	Loss: 117.652
-12800/69092	Loss: 117.546
-25600/69092	Loss: 117.209
-38400/69092	Loss: 118.417
-51200/69092	Loss: 117.194
-64000/69092	Loss: 117.343
-Training time 0:04:27.360355
-Epoch: 102 Average loss: 117.65
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 102)
-0/69092	Loss: 119.372
-12800/69092	Loss: 117.161
-25600/69092	Loss: 117.579
-38400/69092	Loss: 118.675
-51200/69092	Loss: 118.077
-64000/69092	Loss: 116.353
-Training time 0:04:21.829024
-Epoch: 103 Average loss: 117.65
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 103)
-0/69092	Loss: 122.145
-12800/69092	Loss: 117.681
-25600/69092	Loss: 117.564
-38400/69092	Loss: 117.854
-51200/69092	Loss: 117.792
-64000/69092	Loss: 117.095
-Training time 0:04:10.505366
-Epoch: 104 Average loss: 117.66
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 104)
-0/69092	Loss: 119.037
-12800/69092	Loss: 117.433
-25600/69092	Loss: 117.319
-38400/69092	Loss: 118.096
-51200/69092	Loss: 118.451
-64000/69092	Loss: 117.593
-Training time 0:04:13.309748
-Epoch: 105 Average loss: 117.70
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 105)
-0/69092	Loss: 115.671
-12800/69092	Loss: 118.765
-25600/69092	Loss: 117.530
-38400/69092	Loss: 116.610
-51200/69092	Loss: 117.387
-64000/69092	Loss: 118.102
-Training time 0:04:10.225632
-Epoch: 106 Average loss: 117.64
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 106)
-0/69092	Loss: 115.970
-12800/69092	Loss: 117.271
-25600/69092	Loss: 118.336
-38400/69092	Loss: 116.654
-51200/69092	Loss: 116.908
-64000/69092	Loss: 118.475
-Training time 0:04:08.311380
-Epoch: 107 Average loss: 117.52
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 107)
-0/69092	Loss: 118.297
-12800/69092	Loss: 118.045
-25600/69092	Loss: 117.715
-38400/69092	Loss: 116.549
-51200/69092	Loss: 117.337
-64000/69092	Loss: 118.109
-Training time 0:04:18.767566
-Epoch: 108 Average loss: 117.52
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 108)
-0/69092	Loss: 115.676
-12800/69092	Loss: 118.016
-25600/69092	Loss: 116.507
-38400/69092	Loss: 117.254
-51200/69092	Loss: 117.718
-64000/69092	Loss: 118.072
-Training time 0:04:27.749704
-Epoch: 109 Average loss: 117.42
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 109)
-0/69092	Loss: 118.938
-12800/69092	Loss: 116.951
-25600/69092	Loss: 117.728
-38400/69092	Loss: 118.054
-51200/69092	Loss: 117.318
-64000/69092	Loss: 117.840
-Training time 0:04:26.627106
-Epoch: 110 Average loss: 117.56
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 110)
-0/69092	Loss: 121.937
-12800/69092	Loss: 118.140
-25600/69092	Loss: 117.377
-38400/69092	Loss: 117.915
-51200/69092	Loss: 117.499
-64000/69092	Loss: 116.375
-Training time 0:04:12.037837
-Epoch: 111 Average loss: 117.39
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 111)
-0/69092	Loss: 113.947
-12800/69092	Loss: 115.591
-25600/69092	Loss: 117.857
-38400/69092	Loss: 117.139
-51200/69092	Loss: 117.992
-64000/69092	Loss: 117.336
-Training time 0:04:07.543581
-Epoch: 112 Average loss: 117.20
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 112)
-0/69092	Loss: 117.774
-12800/69092	Loss: 117.031
-25600/69092	Loss: 116.865
-38400/69092	Loss: 117.537
-51200/69092	Loss: 117.092
-64000/69092	Loss: 117.203
-Training time 0:04:06.793473
-Epoch: 113 Average loss: 117.34
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 113)
-0/69092	Loss: 122.003
-12800/69092	Loss: 117.281
-25600/69092	Loss: 116.915
-38400/69092	Loss: 117.246
-51200/69092	Loss: 117.098
-64000/69092	Loss: 117.323
-Training time 0:04:18.110371
-Epoch: 114 Average loss: 117.14
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 114)
-0/69092	Loss: 113.728
-12800/69092	Loss: 117.338
-25600/69092	Loss: 117.415
-38400/69092	Loss: 117.887
-51200/69092	Loss: 116.883
-64000/69092	Loss: 116.655
-Training time 0:04:23.082706
-Epoch: 115 Average loss: 117.17
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 115)
-0/69092	Loss: 113.460
-12800/69092	Loss: 116.962
-25600/69092	Loss: 117.133
-38400/69092	Loss: 117.736
-51200/69092	Loss: 116.920
-64000/69092	Loss: 117.195
-Training time 0:04:17.946739
-Epoch: 116 Average loss: 117.18
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 116)
-0/69092	Loss: 122.396
-12800/69092	Loss: 117.253
-25600/69092	Loss: 117.988
-38400/69092	Loss: 116.718
-51200/69092	Loss: 116.569
-64000/69092	Loss: 117.342
-Training time 0:04:09.487106
-Epoch: 117 Average loss: 117.17
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 117)
-0/69092	Loss: 120.599
-12800/69092	Loss: 118.029
-25600/69092	Loss: 116.844
-38400/69092	Loss: 116.261
-51200/69092	Loss: 117.148
-64000/69092	Loss: 116.991
-Training time 0:04:06.126837
-Epoch: 118 Average loss: 117.11
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 118)
-0/69092	Loss: 113.447
-12800/69092	Loss: 117.673
-25600/69092	Loss: 116.305
-38400/69092	Loss: 117.318
-51200/69092	Loss: 117.771
-64000/69092	Loss: 116.620
-Training time 0:04:06.952300
-Epoch: 119 Average loss: 117.09
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 119)
-0/69092	Loss: 116.694
-12800/69092	Loss: 117.533
-25600/69092	Loss: 117.339
-38400/69092	Loss: 116.549
-51200/69092	Loss: 116.852
-64000/69092	Loss: 117.144
-Training time 0:04:18.524811
-Epoch: 120 Average loss: 117.04
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 120)
-0/69092	Loss: 121.263
-12800/69092	Loss: 117.623
-25600/69092	Loss: 116.567
-38400/69092	Loss: 117.494
-51200/69092	Loss: 116.391
-64000/69092	Loss: 116.778
-Training time 0:04:26.026426
-Epoch: 121 Average loss: 116.96
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 121)
-0/69092	Loss: 119.814
-12800/69092	Loss: 117.488
-25600/69092	Loss: 116.667
-38400/69092	Loss: 116.857
-51200/69092	Loss: 116.277
-64000/69092	Loss: 116.752
-Training time 0:04:21.240310
-Epoch: 122 Average loss: 116.84
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 122)
-0/69092	Loss: 114.083
-12800/69092	Loss: 117.017
-25600/69092	Loss: 116.481
-38400/69092	Loss: 115.768
-51200/69092	Loss: 117.344
-64000/69092	Loss: 118.059
-Training time 0:04:21.731750
-Epoch: 123 Average loss: 116.92
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 123)
-0/69092	Loss: 115.396
-12800/69092	Loss: 116.999
-25600/69092	Loss: 117.044
-38400/69092	Loss: 116.922
-51200/69092	Loss: 116.681
-64000/69092	Loss: 117.204
-Training time 0:04:13.355516
-Epoch: 124 Average loss: 116.84
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 124)
-0/69092	Loss: 114.354
-12800/69092	Loss: 116.758
-25600/69092	Loss: 117.844
-38400/69092	Loss: 116.776
-51200/69092	Loss: 116.723
-64000/69092	Loss: 116.333
-Training time 0:04:11.060562
-Epoch: 125 Average loss: 116.86
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 125)
-0/69092	Loss: 112.850
-12800/69092	Loss: 116.547
-25600/69092	Loss: 117.430
-38400/69092	Loss: 116.559
-51200/69092	Loss: 116.838
-64000/69092	Loss: 117.391
-Training time 0:04:11.748621
-Epoch: 126 Average loss: 116.94
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 126)
-0/69092	Loss: 122.874
-12800/69092	Loss: 115.833
-25600/69092	Loss: 116.628
-38400/69092	Loss: 117.270
-51200/69092	Loss: 116.436
-64000/69092	Loss: 116.940
-Training time 0:04:12.916334
-Epoch: 127 Average loss: 116.83
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 127)
-0/69092	Loss: 120.091
-12800/69092	Loss: 116.054
-25600/69092	Loss: 116.758
-38400/69092	Loss: 116.714
-51200/69092	Loss: 116.977
-64000/69092	Loss: 117.389
-Training time 0:04:19.136137
-Epoch: 128 Average loss: 116.78
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 128)
-0/69092	Loss: 120.740
-12800/69092	Loss: 117.049
-25600/69092	Loss: 117.354
-38400/69092	Loss: 116.619
-51200/69092	Loss: 116.406
-64000/69092	Loss: 116.879
-Training time 0:04:14.033693
-Epoch: 129 Average loss: 116.84
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 129)
-0/69092	Loss: 115.168
-12800/69092	Loss: 117.192
-25600/69092	Loss: 115.843
-38400/69092	Loss: 116.705
-51200/69092	Loss: 116.972
-64000/69092	Loss: 117.077
-Training time 0:04:12.246856
-Epoch: 130 Average loss: 116.65
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 130)
-0/69092	Loss: 112.333
-12800/69092	Loss: 116.003
-25600/69092	Loss: 117.295
-38400/69092	Loss: 116.347
-51200/69092	Loss: 117.229
-64000/69092	Loss: 117.151
-Training time 0:04:09.115987
-Epoch: 131 Average loss: 116.72
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 131)
-0/69092	Loss: 111.468
-12800/69092	Loss: 117.271
-25600/69092	Loss: 116.352
-38400/69092	Loss: 116.958
-51200/69092	Loss: 116.093
-64000/69092	Loss: 116.560
-Training time 0:04:06.089068
-Epoch: 132 Average loss: 116.62
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 132)
-0/69092	Loss: 122.202
-12800/69092	Loss: 116.612
-25600/69092	Loss: 116.396
-38400/69092	Loss: 116.750
-51200/69092	Loss: 117.098
-64000/69092	Loss: 116.652
-Training time 0:04:12.456707
-Epoch: 133 Average loss: 116.74
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 133)
-0/69092	Loss: 115.194
-12800/69092	Loss: 115.900
-25600/69092	Loss: 117.075
-38400/69092	Loss: 116.256
-51200/69092	Loss: 116.497
-64000/69092	Loss: 116.524
-Training time 0:04:19.269725
-Epoch: 134 Average loss: 116.48
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 134)
-0/69092	Loss: 117.595
-12800/69092	Loss: 116.274
-25600/69092	Loss: 116.078
-38400/69092	Loss: 116.369
-51200/69092	Loss: 116.956
-64000/69092	Loss: 116.922
-Training time 0:04:17.374130
-Epoch: 135 Average loss: 116.57
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 135)
-0/69092	Loss: 118.568
-12800/69092	Loss: 116.903
-25600/69092	Loss: 116.886
-38400/69092	Loss: 116.347
-51200/69092	Loss: 116.076
-64000/69092	Loss: 116.188
-Training time 0:04:24.367970
-Epoch: 136 Average loss: 116.50
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 136)
-0/69092	Loss: 111.307
-12800/69092	Loss: 116.186
-25600/69092	Loss: 116.851
-38400/69092	Loss: 116.367
-51200/69092	Loss: 117.102
-64000/69092	Loss: 116.760
-Training time 0:04:08.283412
-Epoch: 137 Average loss: 116.63
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 137)
-0/69092	Loss: 120.235
-12800/69092	Loss: 116.380
-25600/69092	Loss: 116.760
-38400/69092	Loss: 116.232
-51200/69092	Loss: 116.531
-64000/69092	Loss: 116.034
-Training time 0:04:11.104504
-Epoch: 138 Average loss: 116.44
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 138)
-0/69092	Loss: 118.149
-12800/69092	Loss: 116.341
-25600/69092	Loss: 116.744
-38400/69092	Loss: 116.054
-51200/69092	Loss: 116.238
-64000/69092	Loss: 116.862
-Training time 0:04:04.615421
-Epoch: 139 Average loss: 116.47
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 139)
-0/69092	Loss: 116.938
-12800/69092	Loss: 116.694
-25600/69092	Loss: 116.568
-38400/69092	Loss: 116.814
-51200/69092	Loss: 115.828
-64000/69092	Loss: 116.409
-Training time 0:04:13.500231
-Epoch: 140 Average loss: 116.43
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 140)
-0/69092	Loss: 120.534
-12800/69092	Loss: 116.519
-25600/69092	Loss: 116.369
-38400/69092	Loss: 115.763
-51200/69092	Loss: 116.506
-64000/69092	Loss: 116.365
-Training time 0:04:16.106278
-Epoch: 141 Average loss: 116.28
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 141)
-0/69092	Loss: 119.007
-12800/69092	Loss: 116.184
-25600/69092	Loss: 117.124
-38400/69092	Loss: 115.399
-51200/69092	Loss: 115.922
-64000/69092	Loss: 116.029
-Training time 0:04:20.728542
-Epoch: 142 Average loss: 116.21
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 142)
-0/69092	Loss: 112.271
-12800/69092	Loss: 116.729
-25600/69092	Loss: 115.879
-38400/69092	Loss: 117.016
-51200/69092	Loss: 116.293
-64000/69092	Loss: 116.261
-Training time 0:04:14.548097
-Epoch: 143 Average loss: 116.39
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 143)
-0/69092	Loss: 119.247
-12800/69092	Loss: 116.008
diff --git a/OAR.2066989.stdout b/OAR.2066989.stdout
deleted file mode 100644
index d530a05bea3bf17252ed2c1e75fa5b2dfa98a56e..0000000000000000000000000000000000000000
--- a/OAR.2066989.stdout
+++ /dev/null
@@ -1,3460 +0,0 @@
-Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
-creare new diretory experiment: rendered_chairs/VAE_bs_64
-load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
-use 2 gpu who named:
-GeForce RTX 2080 Ti
-GeForce RTX 2080 Ti
-DataParallel(
-  (module): VAE(
-    (img_to_last_conv): Sequential(
-      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (1): ReLU()
-      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (3): ReLU()
-      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (5): ReLU()
-      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (7): ReLU()
-    )
-    (last_conv_to_continuous_features): Sequential(
-      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
-      (1): ReLU()
-    )
-    (features_to_hidden_continue): Sequential(
-      (0): Linear(in_features=256, out_features=20, bias=True)
-      (1): ReLU()
-    )
-    (latent_to_features): Sequential(
-      (0): Linear(in_features=10, out_features=256, bias=True)
-      (1): ReLU()
-    )
-    (features_to_img): Sequential(
-      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
-      (1): ReLU()
-      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (3): ReLU()
-      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (5): ReLU()
-      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (7): ReLU()
-      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
-      (9): Sigmoid()
-    )
-  )
-)
-The number of parameters of model is 765335
-don't use continuous capacity
-=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last (iter 3)'
-0/69092	Loss: 168.710
-3200/69092	Loss: 151.090
-6400/69092	Loss: 149.554
-9600/69092	Loss: 147.097
-12800/69092	Loss: 149.283
-16000/69092	Loss: 147.124
-19200/69092	Loss: 148.296
-22400/69092	Loss: 151.390
-25600/69092	Loss: 149.540
-28800/69092	Loss: 148.538
-32000/69092	Loss: 146.801
-35200/69092	Loss: 146.608
-38400/69092	Loss: 147.409
-41600/69092	Loss: 141.844
-44800/69092	Loss: 148.554
-48000/69092	Loss: 149.620
-51200/69092	Loss: 146.300
-54400/69092	Loss: 150.148
-57600/69092	Loss: 146.875
-60800/69092	Loss: 143.943
-64000/69092	Loss: 147.783
-67200/69092	Loss: 147.213
-Training time 0:04:24.323380
-Epoch: 1 Average loss: 147.75
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 4)
-0/69092	Loss: 158.819
-3200/69092	Loss: 145.455
-6400/69092	Loss: 146.130
-9600/69092	Loss: 144.380
-12800/69092	Loss: 144.843
-16000/69092	Loss: 146.080
-19200/69092	Loss: 145.421
-22400/69092	Loss: 149.645
-25600/69092	Loss: 148.440
-28800/69092	Loss: 144.370
-32000/69092	Loss: 147.816
-35200/69092	Loss: 146.294
-38400/69092	Loss: 145.632
-41600/69092	Loss: 141.891
-44800/69092	Loss: 143.450
-48000/69092	Loss: 143.778
-51200/69092	Loss: 145.786
-54400/69092	Loss: 143.362
-57600/69092	Loss: 147.704
-60800/69092	Loss: 147.121
-64000/69092	Loss: 145.282
-67200/69092	Loss: 145.587
-Training time 0:04:20.176991
-Epoch: 2 Average loss: 145.67
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 5)
-0/69092	Loss: 134.195
-3200/69092	Loss: 148.996
-6400/69092	Loss: 142.480
-9600/69092	Loss: 141.951
-12800/69092	Loss: 144.894
-16000/69092	Loss: 142.587
-19200/69092	Loss: 143.194
-22400/69092	Loss: 144.316
-25600/69092	Loss: 142.355
-28800/69092	Loss: 145.405
-32000/69092	Loss: 142.902
-35200/69092	Loss: 146.418
-38400/69092	Loss: 144.190
-41600/69092	Loss: 143.508
-44800/69092	Loss: 142.121
-48000/69092	Loss: 144.655
-51200/69092	Loss: 143.216
-54400/69092	Loss: 145.905
-57600/69092	Loss: 144.233
-60800/69092	Loss: 143.225
-64000/69092	Loss: 143.747
-67200/69092	Loss: 144.372
-Training time 0:04:21.847400
-Epoch: 3 Average loss: 144.14
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 6)
-0/69092	Loss: 158.546
-3200/69092	Loss: 143.724
-6400/69092	Loss: 143.463
-9600/69092	Loss: 143.337
-12800/69092	Loss: 144.160
-16000/69092	Loss: 144.717
-19200/69092	Loss: 142.720
-22400/69092	Loss: 141.418
-25600/69092	Loss: 142.358
-28800/69092	Loss: 143.985
-32000/69092	Loss: 141.719
-35200/69092	Loss: 140.359
-38400/69092	Loss: 142.268
-41600/69092	Loss: 143.411
-44800/69092	Loss: 144.112
-48000/69092	Loss: 142.263
-51200/69092	Loss: 142.091
-54400/69092	Loss: 144.641
-57600/69092	Loss: 142.175
-60800/69092	Loss: 141.229
-64000/69092	Loss: 140.349
-67200/69092	Loss: 140.640
-Training time 0:04:28.338593
-Epoch: 4 Average loss: 142.68
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 7)
-0/69092	Loss: 142.651
-3200/69092	Loss: 140.940
-6400/69092	Loss: 141.995
-9600/69092	Loss: 143.163
-12800/69092	Loss: 139.080
-16000/69092	Loss: 142.381
-19200/69092	Loss: 139.068
-22400/69092	Loss: 142.262
-25600/69092	Loss: 142.301
-28800/69092	Loss: 144.625
-32000/69092	Loss: 143.733
-35200/69092	Loss: 139.129
-38400/69092	Loss: 140.834
-41600/69092	Loss: 140.503
-44800/69092	Loss: 143.628
-48000/69092	Loss: 140.086
-51200/69092	Loss: 142.102
-54400/69092	Loss: 142.008
-57600/69092	Loss: 143.115
-60800/69092	Loss: 142.930
-64000/69092	Loss: 139.570
-67200/69092	Loss: 143.483
-Training time 0:04:32.850446
-Epoch: 5 Average loss: 141.72
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 8)
-0/69092	Loss: 148.280
-3200/69092	Loss: 139.345
-6400/69092	Loss: 140.230
-9600/69092	Loss: 143.982
-12800/69092	Loss: 139.087
-16000/69092	Loss: 140.962
-19200/69092	Loss: 141.655
-22400/69092	Loss: 140.223
-25600/69092	Loss: 141.955
-28800/69092	Loss: 140.359
-32000/69092	Loss: 141.023
-35200/69092	Loss: 138.533
-38400/69092	Loss: 140.527
-41600/69092	Loss: 138.105
-44800/69092	Loss: 140.392
-48000/69092	Loss: 143.276
-51200/69092	Loss: 141.854
-54400/69092	Loss: 137.973
-57600/69092	Loss: 143.096
-60800/69092	Loss: 141.552
-64000/69092	Loss: 140.871
-67200/69092	Loss: 141.383
-Training time 0:04:28.358295
-Epoch: 6 Average loss: 140.77
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 9)
-0/69092	Loss: 151.288
-3200/69092	Loss: 139.086
-6400/69092	Loss: 139.084
-9600/69092	Loss: 142.941
-12800/69092	Loss: 138.658
-16000/69092	Loss: 139.222
-19200/69092	Loss: 141.122
-22400/69092	Loss: 140.593
-25600/69092	Loss: 140.055
-28800/69092	Loss: 140.370
-32000/69092	Loss: 139.670
-35200/69092	Loss: 137.626
-38400/69092	Loss: 138.915
-41600/69092	Loss: 141.240
-44800/69092	Loss: 140.973
-48000/69092	Loss: 137.559
-51200/69092	Loss: 138.119
-54400/69092	Loss: 142.163
-57600/69092	Loss: 139.065
-60800/69092	Loss: 141.348
-64000/69092	Loss: 139.424
-67200/69092	Loss: 137.497
-Training time 0:04:20.120376
-Epoch: 7 Average loss: 139.74
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 10)
-0/69092	Loss: 141.026
-3200/69092	Loss: 139.577
-6400/69092	Loss: 136.711
-9600/69092	Loss: 141.149
-12800/69092	Loss: 138.626
-16000/69092	Loss: 137.874
-19200/69092	Loss: 137.986
-22400/69092	Loss: 138.027
-25600/69092	Loss: 137.398
-28800/69092	Loss: 138.577
-32000/69092	Loss: 138.277
-35200/69092	Loss: 139.278
-38400/69092	Loss: 134.269
-41600/69092	Loss: 138.488
-44800/69092	Loss: 137.161
-48000/69092	Loss: 139.269
-51200/69092	Loss: 138.194
-54400/69092	Loss: 138.302
-57600/69092	Loss: 139.736
-60800/69092	Loss: 138.145
-64000/69092	Loss: 137.397
-67200/69092	Loss: 142.087
-Training time 0:04:18.076296
-Epoch: 8 Average loss: 138.50
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 11)
-0/69092	Loss: 142.430
-3200/69092	Loss: 135.373
-6400/69092	Loss: 136.746
-9600/69092	Loss: 138.606
-12800/69092	Loss: 137.819
-16000/69092	Loss: 137.245
-19200/69092	Loss: 140.270
-22400/69092	Loss: 137.765
-25600/69092	Loss: 137.542
-28800/69092	Loss: 137.094
-32000/69092	Loss: 137.280
-35200/69092	Loss: 135.271
-38400/69092	Loss: 138.115
-41600/69092	Loss: 138.030
-44800/69092	Loss: 133.132
-48000/69092	Loss: 136.096
-51200/69092	Loss: 137.447
-54400/69092	Loss: 136.824
-57600/69092	Loss: 138.901
-60800/69092	Loss: 135.466
-64000/69092	Loss: 137.750
-67200/69092	Loss: 135.623
-Training time 0:04:24.652008
-Epoch: 9 Average loss: 137.10
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 12)
-0/69092	Loss: 133.767
-3200/69092	Loss: 132.615
-6400/69092	Loss: 135.810
-9600/69092	Loss: 136.655
-12800/69092	Loss: 137.254
-16000/69092	Loss: 138.474
-19200/69092	Loss: 135.189
-22400/69092	Loss: 135.563
-25600/69092	Loss: 135.713
-28800/69092	Loss: 136.169
-32000/69092	Loss: 136.928
-35200/69092	Loss: 134.499
-38400/69092	Loss: 135.761
-41600/69092	Loss: 133.933
-44800/69092	Loss: 137.196
-48000/69092	Loss: 135.761
-51200/69092	Loss: 133.530
-54400/69092	Loss: 135.310
-57600/69092	Loss: 134.247
-60800/69092	Loss: 133.764
-64000/69092	Loss: 132.814
-67200/69092	Loss: 132.712
-Training time 0:04:27.168263
-Epoch: 10 Average loss: 135.18
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 13)
-0/69092	Loss: 133.262
-3200/69092	Loss: 132.734
-6400/69092	Loss: 132.997
-9600/69092	Loss: 133.191
-12800/69092	Loss: 135.774
-16000/69092	Loss: 131.620
-19200/69092	Loss: 132.652
-22400/69092	Loss: 130.358
-25600/69092	Loss: 133.336
-28800/69092	Loss: 134.207
-32000/69092	Loss: 130.676
-35200/69092	Loss: 135.177
-38400/69092	Loss: 130.273
-41600/69092	Loss: 133.683
-44800/69092	Loss: 132.092
-48000/69092	Loss: 131.892
-51200/69092	Loss: 129.759
-54400/69092	Loss: 132.611
-57600/69092	Loss: 130.502
-60800/69092	Loss: 130.160
-64000/69092	Loss: 131.685
-67200/69092	Loss: 127.816
-Training time 0:04:24.848130
-Epoch: 11 Average loss: 132.05
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 14)
-0/69092	Loss: 124.336
-3200/69092	Loss: 129.374
-6400/69092	Loss: 131.302
-9600/69092	Loss: 129.214
-12800/69092	Loss: 131.172
-16000/69092	Loss: 128.778
-19200/69092	Loss: 129.312
-22400/69092	Loss: 131.400
-25600/69092	Loss: 130.371
-28800/69092	Loss: 127.324
-32000/69092	Loss: 129.498
-35200/69092	Loss: 131.702
-38400/69092	Loss: 128.119
-41600/69092	Loss: 129.399
-44800/69092	Loss: 129.674
-48000/69092	Loss: 128.298
-51200/69092	Loss: 128.708
-54400/69092	Loss: 127.717
-57600/69092	Loss: 130.327
-60800/69092	Loss: 129.181
-64000/69092	Loss: 128.923
-67200/69092	Loss: 128.455
-Training time 0:04:25.437912
-Epoch: 12 Average loss: 129.40
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 15)
-0/69092	Loss: 134.263
-3200/69092	Loss: 127.368
-6400/69092	Loss: 129.353
-9600/69092	Loss: 129.035
-12800/69092	Loss: 129.155
-16000/69092	Loss: 132.011
-19200/69092	Loss: 128.835
-22400/69092	Loss: 127.944
-25600/69092	Loss: 129.106
-28800/69092	Loss: 128.343
-32000/69092	Loss: 128.297
-35200/69092	Loss: 126.306
-38400/69092	Loss: 129.187
-41600/69092	Loss: 125.339
-44800/69092	Loss: 127.106
-48000/69092	Loss: 127.993
-51200/69092	Loss: 126.170
-54400/69092	Loss: 123.472
-57600/69092	Loss: 127.903
-60800/69092	Loss: 128.256
-64000/69092	Loss: 127.095
-67200/69092	Loss: 127.681
-Training time 0:04:32.032921
-Epoch: 13 Average loss: 127.95
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 16)
-0/69092	Loss: 131.333
-3200/69092	Loss: 126.908
-6400/69092	Loss: 127.240
-9600/69092	Loss: 125.892
-12800/69092	Loss: 128.212
-16000/69092	Loss: 127.065
-19200/69092	Loss: 128.321
-22400/69092	Loss: 128.990
-25600/69092	Loss: 128.002
-28800/69092	Loss: 128.531
-32000/69092	Loss: 124.950
-35200/69092	Loss: 127.374
-38400/69092	Loss: 124.957
-41600/69092	Loss: 123.737
-44800/69092	Loss: 127.220
-48000/69092	Loss: 125.341
-51200/69092	Loss: 127.031
-54400/69092	Loss: 126.577
-57600/69092	Loss: 127.925
-60800/69092	Loss: 125.480
-64000/69092	Loss: 125.735
-67200/69092	Loss: 129.284
-Training time 0:04:26.086614
-Epoch: 14 Average loss: 126.97
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 17)
-0/69092	Loss: 133.018
-3200/69092	Loss: 124.639
-6400/69092	Loss: 127.398
-9600/69092	Loss: 128.027
-12800/69092	Loss: 127.694
-16000/69092	Loss: 127.104
-19200/69092	Loss: 123.045
-22400/69092	Loss: 126.155
-25600/69092	Loss: 127.054
-28800/69092	Loss: 128.421
-32000/69092	Loss: 123.365
-35200/69092	Loss: 127.267
-38400/69092	Loss: 127.019
-41600/69092	Loss: 125.752
-44800/69092	Loss: 127.605
-48000/69092	Loss: 125.695
-51200/69092	Loss: 124.555
-54400/69092	Loss: 127.477
-57600/69092	Loss: 128.311
-60800/69092	Loss: 128.544
-64000/69092	Loss: 125.838
-67200/69092	Loss: 125.219
-Training time 0:04:26.177394
-Epoch: 15 Average loss: 126.43
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 18)
-0/69092	Loss: 129.000
-3200/69092	Loss: 126.913
-6400/69092	Loss: 126.775
-9600/69092	Loss: 125.272
-12800/69092	Loss: 128.050
-16000/69092	Loss: 123.353
-19200/69092	Loss: 125.428
-22400/69092	Loss: 125.317
-25600/69092	Loss: 126.913
-28800/69092	Loss: 124.705
-32000/69092	Loss: 124.564
-35200/69092	Loss: 123.664
-38400/69092	Loss: 126.124
-41600/69092	Loss: 125.927
-44800/69092	Loss: 124.006
-48000/69092	Loss: 125.397
-51200/69092	Loss: 121.915
-54400/69092	Loss: 126.219
-57600/69092	Loss: 125.470
-60800/69092	Loss: 125.073
-64000/69092	Loss: 123.041
-67200/69092	Loss: 125.522
-Training time 0:04:17.666420
-Epoch: 16 Average loss: 125.19
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 19)
-0/69092	Loss: 130.460
-3200/69092	Loss: 125.000
-6400/69092	Loss: 122.856
-9600/69092	Loss: 124.084
-12800/69092	Loss: 123.778
-16000/69092	Loss: 122.321
-19200/69092	Loss: 124.423
-22400/69092	Loss: 125.071
-25600/69092	Loss: 124.913
-28800/69092	Loss: 125.839
-32000/69092	Loss: 123.424
-35200/69092	Loss: 124.882
-38400/69092	Loss: 123.690
-41600/69092	Loss: 125.155
-44800/69092	Loss: 124.251
-48000/69092	Loss: 120.418
-51200/69092	Loss: 123.887
-54400/69092	Loss: 122.937
-57600/69092	Loss: 123.103
-60800/69092	Loss: 122.097
-64000/69092	Loss: 122.104
-67200/69092	Loss: 126.776
-Training time 0:04:17.206166
-Epoch: 17 Average loss: 123.90
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 20)
-0/69092	Loss: 125.709
-3200/69092	Loss: 122.481
-6400/69092	Loss: 124.142
-9600/69092	Loss: 125.479
-12800/69092	Loss: 122.792
-16000/69092	Loss: 122.145
-19200/69092	Loss: 122.566
-22400/69092	Loss: 124.190
-25600/69092	Loss: 123.891
-28800/69092	Loss: 122.902
-32000/69092	Loss: 124.310
-35200/69092	Loss: 122.531
-38400/69092	Loss: 121.952
-41600/69092	Loss: 121.670
-44800/69092	Loss: 121.877
-48000/69092	Loss: 121.542
-51200/69092	Loss: 121.311
-54400/69092	Loss: 123.462
-57600/69092	Loss: 122.085
-60800/69092	Loss: 122.979
-64000/69092	Loss: 122.467
-67200/69092	Loss: 125.233
-Training time 0:04:21.616950
-Epoch: 18 Average loss: 122.98
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 21)
-0/69092	Loss: 131.789
-3200/69092	Loss: 122.443
-6400/69092	Loss: 122.318
-9600/69092	Loss: 122.900
-12800/69092	Loss: 122.974
-16000/69092	Loss: 121.412
-19200/69092	Loss: 120.611
-22400/69092	Loss: 122.656
-25600/69092	Loss: 122.382
-28800/69092	Loss: 122.912
-32000/69092	Loss: 123.287
-35200/69092	Loss: 123.464
-38400/69092	Loss: 124.760
-41600/69092	Loss: 123.023
-44800/69092	Loss: 120.572
-48000/69092	Loss: 121.521
-51200/69092	Loss: 121.732
-54400/69092	Loss: 121.177
-57600/69092	Loss: 121.043
-60800/69092	Loss: 122.161
-64000/69092	Loss: 123.240
-67200/69092	Loss: 122.289
-Training time 0:04:23.710109
-Epoch: 19 Average loss: 122.31
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 22)
-0/69092	Loss: 132.011
-3200/69092	Loss: 122.395
-6400/69092	Loss: 122.687
-9600/69092	Loss: 119.839
-12800/69092	Loss: 120.750
-16000/69092	Loss: 121.405
-19200/69092	Loss: 123.091
-22400/69092	Loss: 121.585
-25600/69092	Loss: 122.142
-28800/69092	Loss: 124.710
-32000/69092	Loss: 120.898
-35200/69092	Loss: 122.051
-38400/69092	Loss: 121.603
-41600/69092	Loss: 125.219
-44800/69092	Loss: 120.014
-48000/69092	Loss: 121.117
-51200/69092	Loss: 120.134
-54400/69092	Loss: 120.445
-57600/69092	Loss: 123.741
-60800/69092	Loss: 121.813
-64000/69092	Loss: 123.181
-67200/69092	Loss: 119.608
-Training time 0:04:22.895842
-Epoch: 20 Average loss: 121.77
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 23)
-0/69092	Loss: 119.060
-3200/69092	Loss: 124.563
-6400/69092	Loss: 119.417
-9600/69092	Loss: 120.945
-12800/69092	Loss: 124.082
-16000/69092	Loss: 118.880
-19200/69092	Loss: 122.143
-22400/69092	Loss: 122.192
-25600/69092	Loss: 120.804
-28800/69092	Loss: 120.837
-32000/69092	Loss: 121.632
-35200/69092	Loss: 120.909
-38400/69092	Loss: 120.049
-41600/69092	Loss: 120.950
-44800/69092	Loss: 123.908
-48000/69092	Loss: 123.852
-51200/69092	Loss: 121.084
-54400/69092	Loss: 122.443
-57600/69092	Loss: 119.249
-60800/69092	Loss: 121.478
-64000/69092	Loss: 121.191
-67200/69092	Loss: 119.711
-Training time 0:04:25.586193
-Epoch: 21 Average loss: 121.49
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 24)
-0/69092	Loss: 147.141
-3200/69092	Loss: 122.941
-6400/69092	Loss: 121.454
-9600/69092	Loss: 121.708
-12800/69092	Loss: 120.407
-16000/69092	Loss: 120.226
-19200/69092	Loss: 120.991
-22400/69092	Loss: 121.804
-25600/69092	Loss: 120.271
-28800/69092	Loss: 119.301
-32000/69092	Loss: 120.266
-35200/69092	Loss: 119.405
-38400/69092	Loss: 122.874
-41600/69092	Loss: 121.870
-44800/69092	Loss: 120.460
-48000/69092	Loss: 122.500
-51200/69092	Loss: 122.703
-54400/69092	Loss: 120.388
-57600/69092	Loss: 121.708
-60800/69092	Loss: 121.478
-64000/69092	Loss: 119.001
-67200/69092	Loss: 120.973
-Training time 0:04:25.966213
-Epoch: 22 Average loss: 121.14
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 25)
-0/69092	Loss: 137.482
-3200/69092	Loss: 122.287
-6400/69092	Loss: 122.076
-9600/69092	Loss: 121.104
-12800/69092	Loss: 120.438
-16000/69092	Loss: 122.045
-19200/69092	Loss: 122.709
-22400/69092	Loss: 120.998
-25600/69092	Loss: 120.313
-28800/69092	Loss: 121.337
-32000/69092	Loss: 121.268
-35200/69092	Loss: 121.532
-38400/69092	Loss: 121.357
-41600/69092	Loss: 120.043
-44800/69092	Loss: 120.123
-48000/69092	Loss: 120.658
-51200/69092	Loss: 117.727
-54400/69092	Loss: 120.464
-57600/69092	Loss: 122.582
-60800/69092	Loss: 120.352
-64000/69092	Loss: 121.307
-67200/69092	Loss: 120.479
-Training time 0:04:26.382108
-Epoch: 23 Average loss: 120.90
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 26)
-0/69092	Loss: 118.231
-3200/69092	Loss: 120.758
-6400/69092	Loss: 122.165
-9600/69092	Loss: 121.244
-12800/69092	Loss: 121.333
-16000/69092	Loss: 120.261
-19200/69092	Loss: 121.975
-22400/69092	Loss: 120.148
-25600/69092	Loss: 119.612
-28800/69092	Loss: 120.985
-32000/69092	Loss: 121.173
-35200/69092	Loss: 121.716
-38400/69092	Loss: 118.881
-41600/69092	Loss: 120.434
-44800/69092	Loss: 121.482
-48000/69092	Loss: 122.284
-51200/69092	Loss: 120.594
-54400/69092	Loss: 119.384
-57600/69092	Loss: 119.849
-60800/69092	Loss: 121.753
-64000/69092	Loss: 119.563
-67200/69092	Loss: 116.662
-Training time 0:04:25.570850
-Epoch: 24 Average loss: 120.61
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 27)
-0/69092	Loss: 108.304
-3200/69092	Loss: 119.500
-6400/69092	Loss: 122.795
-9600/69092	Loss: 122.464
-12800/69092	Loss: 119.600
-16000/69092	Loss: 120.985
-19200/69092	Loss: 122.724
-22400/69092	Loss: 119.015
-25600/69092	Loss: 118.877
-28800/69092	Loss: 119.459
-32000/69092	Loss: 121.005
-35200/69092	Loss: 119.976
-38400/69092	Loss: 122.819
-41600/69092	Loss: 119.724
-44800/69092	Loss: 119.924
-48000/69092	Loss: 119.103
-51200/69092	Loss: 120.916
-54400/69092	Loss: 121.308
-57600/69092	Loss: 120.914
-60800/69092	Loss: 121.022
-64000/69092	Loss: 118.564
-67200/69092	Loss: 119.804
-Training time 0:04:18.949771
-Epoch: 25 Average loss: 120.49
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 28)
-0/69092	Loss: 118.364
-3200/69092	Loss: 120.370
-6400/69092	Loss: 120.641
-9600/69092	Loss: 119.070
-12800/69092	Loss: 119.905
-16000/69092	Loss: 119.110
-19200/69092	Loss: 121.879
-22400/69092	Loss: 121.081
-25600/69092	Loss: 122.831
-28800/69092	Loss: 121.406
-32000/69092	Loss: 121.728
-35200/69092	Loss: 120.143
-38400/69092	Loss: 122.315
-41600/69092	Loss: 117.706
-44800/69092	Loss: 119.317
-48000/69092	Loss: 119.798
-51200/69092	Loss: 119.000
-54400/69092	Loss: 119.467
-57600/69092	Loss: 120.210
-60800/69092	Loss: 119.858
-64000/69092	Loss: 119.213
-67200/69092	Loss: 119.887
-Training time 0:04:25.713850
-Epoch: 26 Average loss: 120.20
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 29)
-0/69092	Loss: 118.972
-3200/69092	Loss: 121.785
-6400/69092	Loss: 120.323
-9600/69092	Loss: 120.396
-12800/69092	Loss: 118.445
-16000/69092	Loss: 121.621
-19200/69092	Loss: 120.981
-22400/69092	Loss: 119.977
-25600/69092	Loss: 120.309
-28800/69092	Loss: 119.998
-32000/69092	Loss: 121.007
-35200/69092	Loss: 117.874
-38400/69092	Loss: 120.944
-41600/69092	Loss: 119.022
-44800/69092	Loss: 119.192
-48000/69092	Loss: 119.571
-51200/69092	Loss: 120.265
-54400/69092	Loss: 119.264
-57600/69092	Loss: 120.044
-60800/69092	Loss: 119.642
-64000/69092	Loss: 119.094
-67200/69092	Loss: 121.274
-Training time 0:04:32.947963
-Epoch: 27 Average loss: 120.08
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 30)
-0/69092	Loss: 105.711
-3200/69092	Loss: 120.707
-6400/69092	Loss: 119.270
-9600/69092	Loss: 118.434
-12800/69092	Loss: 118.723
-16000/69092	Loss: 121.686
-19200/69092	Loss: 119.470
-22400/69092	Loss: 121.206
-25600/69092	Loss: 118.162
-28800/69092	Loss: 119.135
-32000/69092	Loss: 119.909
-35200/69092	Loss: 122.402
-38400/69092	Loss: 121.348
-41600/69092	Loss: 121.857
-44800/69092	Loss: 118.933
-48000/69092	Loss: 120.389
-51200/69092	Loss: 118.926
-54400/69092	Loss: 120.242
-57600/69092	Loss: 118.897
-60800/69092	Loss: 118.025
-64000/69092	Loss: 119.202
-67200/69092	Loss: 118.477
-Training time 0:04:38.239052
-Epoch: 28 Average loss: 119.79
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 31)
-0/69092	Loss: 125.045
-3200/69092	Loss: 119.077
-6400/69092	Loss: 121.288
-9600/69092	Loss: 118.648
-12800/69092	Loss: 117.952
-16000/69092	Loss: 120.929
-19200/69092	Loss: 118.522
-22400/69092	Loss: 119.540
-25600/69092	Loss: 117.916
-28800/69092	Loss: 121.027
-32000/69092	Loss: 120.191
-35200/69092	Loss: 119.669
-38400/69092	Loss: 120.919
-41600/69092	Loss: 118.882
-44800/69092	Loss: 121.225
-48000/69092	Loss: 119.187
-51200/69092	Loss: 119.246
-54400/69092	Loss: 120.123
-57600/69092	Loss: 120.759
-60800/69092	Loss: 118.614
-64000/69092	Loss: 119.713
-67200/69092	Loss: 119.882
-Training time 0:04:30.982903
-Epoch: 29 Average loss: 119.67
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 32)
-0/69092	Loss: 116.535
-3200/69092	Loss: 120.467
-6400/69092	Loss: 120.213
-9600/69092	Loss: 116.316
-12800/69092	Loss: 122.133
-16000/69092	Loss: 118.893
-19200/69092	Loss: 119.812
-22400/69092	Loss: 121.830
-25600/69092	Loss: 118.437
-28800/69092	Loss: 119.630
-32000/69092	Loss: 119.669
-35200/69092	Loss: 117.338
-38400/69092	Loss: 118.764
-41600/69092	Loss: 118.184
-44800/69092	Loss: 118.814
-48000/69092	Loss: 121.905
-51200/69092	Loss: 119.987
-54400/69092	Loss: 117.650
-57600/69092	Loss: 120.923
-60800/69092	Loss: 119.119
-64000/69092	Loss: 120.850
-67200/69092	Loss: 119.152
-Training time 0:04:32.173868
-Epoch: 30 Average loss: 119.59
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 33)
-0/69092	Loss: 116.206
-3200/69092	Loss: 120.007
-6400/69092	Loss: 120.306
-9600/69092	Loss: 120.622
-12800/69092	Loss: 118.230
-16000/69092	Loss: 119.093
-19200/69092	Loss: 122.371
-22400/69092	Loss: 117.925
-25600/69092	Loss: 121.133
-28800/69092	Loss: 119.946
-32000/69092	Loss: 119.513
-35200/69092	Loss: 120.525
-38400/69092	Loss: 116.965
-41600/69092	Loss: 119.378
-44800/69092	Loss: 116.394
-48000/69092	Loss: 118.768
-51200/69092	Loss: 119.274
-54400/69092	Loss: 118.630
-57600/69092	Loss: 117.748
-60800/69092	Loss: 118.896
-64000/69092	Loss: 118.875
-67200/69092	Loss: 118.516
-Training time 0:04:29.045180
-Epoch: 31 Average loss: 119.29
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 34)
-0/69092	Loss: 117.266
-3200/69092	Loss: 118.274
-6400/69092	Loss: 117.234
-9600/69092	Loss: 120.030
-12800/69092	Loss: 119.236
-16000/69092	Loss: 120.456
-19200/69092	Loss: 120.226
-22400/69092	Loss: 120.690
-25600/69092	Loss: 117.134
-28800/69092	Loss: 117.551
-32000/69092	Loss: 118.243
-35200/69092	Loss: 120.714
-38400/69092	Loss: 117.411
-41600/69092	Loss: 121.130
-44800/69092	Loss: 118.548
-48000/69092	Loss: 119.227
-51200/69092	Loss: 119.998
-54400/69092	Loss: 119.477
-57600/69092	Loss: 120.617
-60800/69092	Loss: 118.561
-64000/69092	Loss: 118.560
-67200/69092	Loss: 119.088
-Training time 0:04:29.912910
-Epoch: 32 Average loss: 119.23
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 35)
-0/69092	Loss: 123.715
-3200/69092	Loss: 119.440
-6400/69092	Loss: 118.955
-9600/69092	Loss: 117.991
-12800/69092	Loss: 120.905
-16000/69092	Loss: 118.625
-19200/69092	Loss: 120.808
-22400/69092	Loss: 117.325
-25600/69092	Loss: 120.590
-28800/69092	Loss: 120.715
-32000/69092	Loss: 117.795
-35200/69092	Loss: 117.978
-38400/69092	Loss: 118.461
-41600/69092	Loss: 117.702
-44800/69092	Loss: 118.960
-48000/69092	Loss: 120.560
-51200/69092	Loss: 116.987
-54400/69092	Loss: 120.304
-57600/69092	Loss: 119.230
-60800/69092	Loss: 117.759
-64000/69092	Loss: 120.255
-67200/69092	Loss: 118.699
-Training time 0:04:33.274534
-Epoch: 33 Average loss: 119.11
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 36)
-0/69092	Loss: 118.849
-3200/69092	Loss: 120.621
-6400/69092	Loss: 119.201
-9600/69092	Loss: 118.597
-12800/69092	Loss: 119.319
-16000/69092	Loss: 117.364
-19200/69092	Loss: 118.661
-22400/69092	Loss: 117.795
-25600/69092	Loss: 119.699
-28800/69092	Loss: 120.421
-32000/69092	Loss: 118.955
-35200/69092	Loss: 118.800
-38400/69092	Loss: 118.993
-41600/69092	Loss: 120.018
-44800/69092	Loss: 118.363
-48000/69092	Loss: 119.996
-51200/69092	Loss: 120.001
-54400/69092	Loss: 118.896
-57600/69092	Loss: 116.693
-60800/69092	Loss: 121.120
-64000/69092	Loss: 118.783
-67200/69092	Loss: 120.760
-Training time 0:04:27.301002
-Epoch: 34 Average loss: 119.10
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 37)
-0/69092	Loss: 117.403
-3200/69092	Loss: 119.066
-6400/69092	Loss: 118.569
-9600/69092	Loss: 119.955
-12800/69092	Loss: 118.320
-16000/69092	Loss: 118.868
-19200/69092	Loss: 118.263
-22400/69092	Loss: 118.127
-25600/69092	Loss: 118.569
-28800/69092	Loss: 118.432
-32000/69092	Loss: 117.366
-35200/69092	Loss: 118.409
-38400/69092	Loss: 119.698
-41600/69092	Loss: 118.692
-44800/69092	Loss: 119.147
-48000/69092	Loss: 117.626
-51200/69092	Loss: 120.895
-54400/69092	Loss: 118.511
-57600/69092	Loss: 119.247
-60800/69092	Loss: 118.111
-64000/69092	Loss: 120.180
-67200/69092	Loss: 117.345
-Training time 0:04:26.693822
-Epoch: 35 Average loss: 118.71
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 38)
-0/69092	Loss: 126.114
-3200/69092	Loss: 120.061
-6400/69092	Loss: 117.689
-9600/69092	Loss: 119.253
-12800/69092	Loss: 119.081
-16000/69092	Loss: 120.007
-19200/69092	Loss: 118.019
-22400/69092	Loss: 117.523
-25600/69092	Loss: 117.614
-28800/69092	Loss: 120.150
-32000/69092	Loss: 119.956
-35200/69092	Loss: 116.728
-38400/69092	Loss: 117.896
-41600/69092	Loss: 117.262
-44800/69092	Loss: 116.199
-48000/69092	Loss: 118.588
-51200/69092	Loss: 116.918
-54400/69092	Loss: 117.918
-57600/69092	Loss: 122.362
-60800/69092	Loss: 118.200
-64000/69092	Loss: 119.610
-67200/69092	Loss: 119.881
-Training time 0:04:28.605144
-Epoch: 36 Average loss: 118.68
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 39)
-0/69092	Loss: 116.029
-3200/69092	Loss: 119.409
-6400/69092	Loss: 117.547
-9600/69092	Loss: 120.324
-12800/69092	Loss: 119.865
-16000/69092	Loss: 117.831
-19200/69092	Loss: 119.817
-22400/69092	Loss: 120.161
-25600/69092	Loss: 119.740
-28800/69092	Loss: 119.457
-32000/69092	Loss: 118.670
-35200/69092	Loss: 115.878
-38400/69092	Loss: 118.448
-41600/69092	Loss: 119.814
-44800/69092	Loss: 120.318
-48000/69092	Loss: 117.253
-51200/69092	Loss: 118.179
-54400/69092	Loss: 116.454
-57600/69092	Loss: 118.137
-60800/69092	Loss: 117.428
-64000/69092	Loss: 117.346
-67200/69092	Loss: 117.489
-Training time 0:04:26.394531
-Epoch: 37 Average loss: 118.54
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 40)
-0/69092	Loss: 131.857
-3200/69092	Loss: 119.845
-6400/69092	Loss: 118.204
-9600/69092	Loss: 119.427
-12800/69092	Loss: 118.579
-16000/69092	Loss: 118.246
-19200/69092	Loss: 117.630
-22400/69092	Loss: 118.912
-25600/69092	Loss: 118.977
-28800/69092	Loss: 120.189
-32000/69092	Loss: 119.178
-35200/69092	Loss: 120.338
-38400/69092	Loss: 119.431
-41600/69092	Loss: 116.851
-44800/69092	Loss: 118.863
-48000/69092	Loss: 117.607
-51200/69092	Loss: 120.131
-54400/69092	Loss: 116.522
-57600/69092	Loss: 117.548
-60800/69092	Loss: 120.433
-64000/69092	Loss: 119.288
-67200/69092	Loss: 117.617
-Training time 0:04:28.231272
-Epoch: 38 Average loss: 118.76
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 41)
-0/69092	Loss: 104.315
-3200/69092	Loss: 118.207
-6400/69092	Loss: 118.797
-9600/69092	Loss: 117.818
-12800/69092	Loss: 119.333
-16000/69092	Loss: 118.367
-19200/69092	Loss: 117.582
-22400/69092	Loss: 118.845
-25600/69092	Loss: 118.929
-28800/69092	Loss: 118.353
-32000/69092	Loss: 116.249
-35200/69092	Loss: 117.122
-38400/69092	Loss: 117.133
-41600/69092	Loss: 118.203
-44800/69092	Loss: 118.386
-48000/69092	Loss: 119.663
-51200/69092	Loss: 119.400
-54400/69092	Loss: 118.007
-57600/69092	Loss: 118.821
-60800/69092	Loss: 117.460
-64000/69092	Loss: 120.259
-67200/69092	Loss: 117.853
-Training time 0:04:36.544689
-Epoch: 39 Average loss: 118.36
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 42)
-0/69092	Loss: 115.324
-3200/69092	Loss: 118.398
-6400/69092	Loss: 119.789
-9600/69092	Loss: 118.277
-12800/69092	Loss: 118.077
-16000/69092	Loss: 119.158
-19200/69092	Loss: 118.147
-22400/69092	Loss: 118.761
-25600/69092	Loss: 117.942
-28800/69092	Loss: 118.611
-32000/69092	Loss: 117.219
-35200/69092	Loss: 116.300
-38400/69092	Loss: 119.515
-41600/69092	Loss: 118.987
-44800/69092	Loss: 118.719
-48000/69092	Loss: 117.598
-51200/69092	Loss: 117.573
-54400/69092	Loss: 119.454
-57600/69092	Loss: 118.737
-60800/69092	Loss: 119.045
-64000/69092	Loss: 116.808
-67200/69092	Loss: 117.400
-Training time 0:04:33.331351
-Epoch: 40 Average loss: 118.27
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 43)
-0/69092	Loss: 118.771
-3200/69092	Loss: 116.662
-6400/69092	Loss: 117.344
-9600/69092	Loss: 117.850
-12800/69092	Loss: 118.028
-16000/69092	Loss: 117.963
-19200/69092	Loss: 116.616
-22400/69092	Loss: 119.128
-25600/69092	Loss: 117.621
-28800/69092	Loss: 117.808
-32000/69092	Loss: 118.379
-35200/69092	Loss: 119.555
-38400/69092	Loss: 118.062
-41600/69092	Loss: 120.114
-44800/69092	Loss: 119.300
-48000/69092	Loss: 119.374
-51200/69092	Loss: 118.337
-54400/69092	Loss: 117.450
-57600/69092	Loss: 118.320
-60800/69092	Loss: 116.882
-64000/69092	Loss: 118.007
-67200/69092	Loss: 118.956
-Training time 0:04:29.097078
-Epoch: 41 Average loss: 118.27
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 44)
-0/69092	Loss: 121.785
-3200/69092	Loss: 118.478
-6400/69092	Loss: 119.252
-9600/69092	Loss: 116.702
-12800/69092	Loss: 117.992
-16000/69092	Loss: 117.752
-19200/69092	Loss: 120.084
-22400/69092	Loss: 118.588
-25600/69092	Loss: 117.626
-28800/69092	Loss: 118.204
-32000/69092	Loss: 118.521
-35200/69092	Loss: 118.790
-38400/69092	Loss: 115.994
-41600/69092	Loss: 117.095
-44800/69092	Loss: 119.387
-48000/69092	Loss: 119.568
-51200/69092	Loss: 118.056
-54400/69092	Loss: 119.010
-57600/69092	Loss: 118.281
-60800/69092	Loss: 116.275
-64000/69092	Loss: 118.264
-67200/69092	Loss: 118.857
-Training time 0:04:34.029356
-Epoch: 42 Average loss: 118.22
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 45)
-0/69092	Loss: 123.996
-3200/69092	Loss: 116.337
-6400/69092	Loss: 115.972
-9600/69092	Loss: 117.151
-12800/69092	Loss: 118.222
-16000/69092	Loss: 117.206
-19200/69092	Loss: 117.800
-22400/69092	Loss: 119.091
-25600/69092	Loss: 118.343
-28800/69092	Loss: 117.973
-32000/69092	Loss: 118.411
-35200/69092	Loss: 118.765
-38400/69092	Loss: 117.752
-41600/69092	Loss: 116.759
-44800/69092	Loss: 118.769
-48000/69092	Loss: 115.599
-51200/69092	Loss: 116.894
-54400/69092	Loss: 118.179
-57600/69092	Loss: 118.572
-60800/69092	Loss: 119.989
-64000/69092	Loss: 119.213
-67200/69092	Loss: 119.978
-Training time 0:04:14.785945
-Epoch: 43 Average loss: 117.97
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 46)
-0/69092	Loss: 106.408
-3200/69092	Loss: 116.920
-6400/69092	Loss: 118.497
-9600/69092	Loss: 117.738
-12800/69092	Loss: 117.219
-16000/69092	Loss: 116.886
-19200/69092	Loss: 116.969
-22400/69092	Loss: 116.356
-25600/69092	Loss: 118.912
-28800/69092	Loss: 116.484
-32000/69092	Loss: 115.970
-35200/69092	Loss: 118.229
-38400/69092	Loss: 118.897
-41600/69092	Loss: 118.444
-44800/69092	Loss: 119.632
-48000/69092	Loss: 119.609
-51200/69092	Loss: 119.555
-54400/69092	Loss: 117.546
-57600/69092	Loss: 118.634
-60800/69092	Loss: 117.183
-64000/69092	Loss: 117.965
-67200/69092	Loss: 115.849
-Training time 0:04:21.044989
-Epoch: 44 Average loss: 117.86
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 47)
-0/69092	Loss: 120.892
-3200/69092	Loss: 118.694
-6400/69092	Loss: 118.532
-9600/69092	Loss: 116.084
-12800/69092	Loss: 118.203
-16000/69092	Loss: 117.872
-19200/69092	Loss: 118.580
-22400/69092	Loss: 118.333
-25600/69092	Loss: 117.916
-28800/69092	Loss: 116.091
-32000/69092	Loss: 118.709
-35200/69092	Loss: 116.751
-38400/69092	Loss: 118.845
-41600/69092	Loss: 116.645
-44800/69092	Loss: 119.898
-48000/69092	Loss: 117.519
-51200/69092	Loss: 117.996
-54400/69092	Loss: 118.353
-57600/69092	Loss: 117.601
-60800/69092	Loss: 116.375
-64000/69092	Loss: 118.261
-67200/69092	Loss: 115.889
-Training time 0:04:12.758008
-Epoch: 45 Average loss: 117.74
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 48)
-0/69092	Loss: 118.087
-3200/69092	Loss: 117.098
-6400/69092	Loss: 118.501
-9600/69092	Loss: 116.625
-12800/69092	Loss: 119.324
-16000/69092	Loss: 119.308
-19200/69092	Loss: 117.987
-22400/69092	Loss: 117.006
-25600/69092	Loss: 117.740
-28800/69092	Loss: 117.534
-32000/69092	Loss: 117.054
-35200/69092	Loss: 118.291
-38400/69092	Loss: 115.554
-41600/69092	Loss: 115.861
-44800/69092	Loss: 118.069
-48000/69092	Loss: 119.460
-51200/69092	Loss: 118.501
-54400/69092	Loss: 117.221
-57600/69092	Loss: 116.807
-60800/69092	Loss: 116.986
-64000/69092	Loss: 118.153
-67200/69092	Loss: 117.880
-Training time 0:04:20.462099
-Epoch: 46 Average loss: 117.73
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 49)
-0/69092	Loss: 125.376
-3200/69092	Loss: 118.748
-6400/69092	Loss: 116.575
-9600/69092	Loss: 119.625
-12800/69092	Loss: 117.221
-16000/69092	Loss: 119.347
-19200/69092	Loss: 118.588
-22400/69092	Loss: 116.669
-25600/69092	Loss: 118.300
-28800/69092	Loss: 118.447
-32000/69092	Loss: 117.466
-35200/69092	Loss: 117.586
-38400/69092	Loss: 116.847
-41600/69092	Loss: 119.315
-44800/69092	Loss: 116.312
-48000/69092	Loss: 117.635
-51200/69092	Loss: 116.860
-54400/69092	Loss: 115.944
-57600/69092	Loss: 118.172
-60800/69092	Loss: 117.511
-64000/69092	Loss: 116.628
-67200/69092	Loss: 118.540
-Training time 0:04:20.977065
-Epoch: 47 Average loss: 117.77
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 50)
-0/69092	Loss: 116.650
-3200/69092	Loss: 117.711
-6400/69092	Loss: 117.896
-9600/69092	Loss: 117.998
-12800/69092	Loss: 117.463
-16000/69092	Loss: 119.810
-19200/69092	Loss: 117.047
-22400/69092	Loss: 116.851
-25600/69092	Loss: 118.149
-28800/69092	Loss: 117.825
-32000/69092	Loss: 118.743
-35200/69092	Loss: 116.654
-38400/69092	Loss: 117.213
-41600/69092	Loss: 115.864
-44800/69092	Loss: 118.279
-48000/69092	Loss: 117.977
-51200/69092	Loss: 119.500
-54400/69092	Loss: 116.919
-57600/69092	Loss: 115.241
-60800/69092	Loss: 120.465
-64000/69092	Loss: 116.324
-67200/69092	Loss: 117.485
-Training time 0:04:32.062968
-Epoch: 48 Average loss: 117.64
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 51)
-0/69092	Loss: 109.422
-3200/69092	Loss: 118.779
-6400/69092	Loss: 114.385
-9600/69092	Loss: 118.288
-12800/69092	Loss: 117.950
-16000/69092	Loss: 117.745
-19200/69092	Loss: 118.373
-22400/69092	Loss: 117.894
-25600/69092	Loss: 116.823
-28800/69092	Loss: 117.096
-32000/69092	Loss: 116.887
-35200/69092	Loss: 115.479
-38400/69092	Loss: 117.367
-41600/69092	Loss: 117.097
-44800/69092	Loss: 119.047
-48000/69092	Loss: 117.317
-51200/69092	Loss: 117.486
-54400/69092	Loss: 117.068
-57600/69092	Loss: 117.542
-60800/69092	Loss: 117.758
-64000/69092	Loss: 117.278
-67200/69092	Loss: 117.851
-Training time 0:04:23.378010
-Epoch: 49 Average loss: 117.45
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 52)
-0/69092	Loss: 134.673
-3200/69092	Loss: 116.821
-6400/69092	Loss: 118.035
-9600/69092	Loss: 117.602
-12800/69092	Loss: 116.879
-16000/69092	Loss: 118.296
-19200/69092	Loss: 116.002
-22400/69092	Loss: 118.027
-25600/69092	Loss: 118.486
-28800/69092	Loss: 119.646
-32000/69092	Loss: 118.729
-35200/69092	Loss: 117.728
-38400/69092	Loss: 117.981
-41600/69092	Loss: 116.104
-44800/69092	Loss: 117.923
-48000/69092	Loss: 116.814
-51200/69092	Loss: 119.341
-54400/69092	Loss: 117.462
-57600/69092	Loss: 115.161
-60800/69092	Loss: 117.117
-64000/69092	Loss: 117.501
-67200/69092	Loss: 117.372
-Training time 0:04:20.237584
-Epoch: 50 Average loss: 117.57
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 53)
-0/69092	Loss: 111.840
-3200/69092	Loss: 116.925
-6400/69092	Loss: 117.049
-9600/69092	Loss: 117.445
-12800/69092	Loss: 118.647
-16000/69092	Loss: 116.480
-19200/69092	Loss: 117.663
-22400/69092	Loss: 117.357
-25600/69092	Loss: 116.542
-28800/69092	Loss: 117.214
-32000/69092	Loss: 116.737
-35200/69092	Loss: 117.576
-38400/69092	Loss: 115.957
-41600/69092	Loss: 117.487
-44800/69092	Loss: 117.003
-48000/69092	Loss: 117.846
-51200/69092	Loss: 116.705
-54400/69092	Loss: 116.772
-57600/69092	Loss: 117.018
-60800/69092	Loss: 119.256
-64000/69092	Loss: 118.638
-67200/69092	Loss: 119.231
-Training time 0:04:22.076445
-Epoch: 51 Average loss: 117.41
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 54)
-0/69092	Loss: 112.129
-3200/69092	Loss: 117.937
-6400/69092	Loss: 117.977
-9600/69092	Loss: 118.404
-12800/69092	Loss: 118.683
-16000/69092	Loss: 115.423
-19200/69092	Loss: 117.976
-22400/69092	Loss: 117.606
-25600/69092	Loss: 115.392
-28800/69092	Loss: 118.396
-32000/69092	Loss: 118.410
-35200/69092	Loss: 120.169
-38400/69092	Loss: 115.286
-41600/69092	Loss: 116.985
-44800/69092	Loss: 116.913
-48000/69092	Loss: 116.859
-51200/69092	Loss: 117.496
-54400/69092	Loss: 117.475
-57600/69092	Loss: 117.096
-60800/69092	Loss: 117.043
-64000/69092	Loss: 117.797
-67200/69092	Loss: 117.296
-Training time 0:04:18.183982
-Epoch: 52 Average loss: 117.44
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 55)
-0/69092	Loss: 109.696
-3200/69092	Loss: 118.624
-6400/69092	Loss: 116.392
-9600/69092	Loss: 115.355
-12800/69092	Loss: 118.917
-16000/69092	Loss: 116.919
-19200/69092	Loss: 117.824
-22400/69092	Loss: 117.217
-25600/69092	Loss: 118.351
-28800/69092	Loss: 115.596
-32000/69092	Loss: 115.765
-35200/69092	Loss: 117.230
-38400/69092	Loss: 115.949
-41600/69092	Loss: 114.170
-44800/69092	Loss: 115.958
-48000/69092	Loss: 119.411
-51200/69092	Loss: 116.037
-54400/69092	Loss: 118.067
-57600/69092	Loss: 117.140
-60800/69092	Loss: 116.311
-64000/69092	Loss: 117.791
-67200/69092	Loss: 117.873
-Training time 0:04:28.247742
-Epoch: 53 Average loss: 117.04
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 56)
-0/69092	Loss: 119.751
-3200/69092	Loss: 118.410
-6400/69092	Loss: 116.364
-9600/69092	Loss: 117.416
-12800/69092	Loss: 116.910
-16000/69092	Loss: 117.747
-19200/69092	Loss: 119.832
-22400/69092	Loss: 117.021
-25600/69092	Loss: 117.239
-28800/69092	Loss: 115.512
-32000/69092	Loss: 116.949
-35200/69092	Loss: 117.572
-38400/69092	Loss: 117.688
-41600/69092	Loss: 115.560
-44800/69092	Loss: 118.275
-48000/69092	Loss: 117.558
-51200/69092	Loss: 115.515
-54400/69092	Loss: 117.731
-57600/69092	Loss: 115.984
-60800/69092	Loss: 117.454
-64000/69092	Loss: 115.422
-67200/69092	Loss: 118.018
-Training time 0:04:33.892244
-Epoch: 54 Average loss: 117.15
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 57)
-0/69092	Loss: 122.561
-3200/69092	Loss: 117.605
-6400/69092	Loss: 117.209
-9600/69092	Loss: 118.036
-12800/69092	Loss: 116.387
-16000/69092	Loss: 117.226
-19200/69092	Loss: 116.761
-22400/69092	Loss: 116.501
-25600/69092	Loss: 117.119
-28800/69092	Loss: 117.525
-32000/69092	Loss: 119.526
-35200/69092	Loss: 115.889
-38400/69092	Loss: 116.361
-41600/69092	Loss: 117.674
-44800/69092	Loss: 116.317
-48000/69092	Loss: 114.762
-51200/69092	Loss: 115.947
-54400/69092	Loss: 116.371
-57600/69092	Loss: 117.442
-60800/69092	Loss: 116.723
-64000/69092	Loss: 117.863
-67200/69092	Loss: 118.023
-Training time 0:04:26.042149
-Epoch: 55 Average loss: 117.06
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 58)
-0/69092	Loss: 114.835
-3200/69092	Loss: 116.415
-6400/69092	Loss: 115.747
-9600/69092	Loss: 116.319
-12800/69092	Loss: 117.885
-16000/69092	Loss: 117.722
-19200/69092	Loss: 115.351
-22400/69092	Loss: 118.106
-25600/69092	Loss: 117.478
-28800/69092	Loss: 116.495
-32000/69092	Loss: 117.339
-35200/69092	Loss: 116.400
-38400/69092	Loss: 116.613
-41600/69092	Loss: 116.669
-44800/69092	Loss: 115.792
-48000/69092	Loss: 117.015
-51200/69092	Loss: 118.780
-54400/69092	Loss: 116.552
-57600/69092	Loss: 115.665
-60800/69092	Loss: 117.378
-64000/69092	Loss: 116.443
-67200/69092	Loss: 116.465
-Training time 0:04:28.371721
-Epoch: 56 Average loss: 116.85
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 59)
-0/69092	Loss: 116.169
-3200/69092	Loss: 117.142
-6400/69092	Loss: 115.437
-9600/69092	Loss: 115.886
-12800/69092	Loss: 117.338
-16000/69092	Loss: 117.990
-19200/69092	Loss: 116.401
-22400/69092	Loss: 116.773
-25600/69092	Loss: 117.632
-28800/69092	Loss: 119.238
-32000/69092	Loss: 115.935
-35200/69092	Loss: 116.649
-38400/69092	Loss: 116.294
-41600/69092	Loss: 115.736
-44800/69092	Loss: 118.223
-48000/69092	Loss: 117.310
-51200/69092	Loss: 116.516
-54400/69092	Loss: 115.370
-57600/69092	Loss: 115.812
-60800/69092	Loss: 117.136
-64000/69092	Loss: 117.079
-67200/69092	Loss: 116.638
-Training time 0:04:26.000514
-Epoch: 57 Average loss: 116.88
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 60)
-0/69092	Loss: 110.920
-3200/69092	Loss: 114.819
-6400/69092	Loss: 118.254
-9600/69092	Loss: 116.914
-12800/69092	Loss: 115.104
-16000/69092	Loss: 117.142
-19200/69092	Loss: 116.541
-22400/69092	Loss: 116.466
-25600/69092	Loss: 117.216
-28800/69092	Loss: 116.823
-32000/69092	Loss: 118.113
-35200/69092	Loss: 118.111
-38400/69092	Loss: 115.204
-41600/69092	Loss: 119.473
-44800/69092	Loss: 116.941
-48000/69092	Loss: 117.162
-51200/69092	Loss: 117.440
-54400/69092	Loss: 118.859
-57600/69092	Loss: 117.363
-60800/69092	Loss: 116.367
-64000/69092	Loss: 115.175
-67200/69092	Loss: 115.445
-Training time 0:04:20.411956
-Epoch: 58 Average loss: 116.94
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 61)
-0/69092	Loss: 123.952
-3200/69092	Loss: 115.271
-6400/69092	Loss: 118.519
-9600/69092	Loss: 117.646
-12800/69092	Loss: 117.271
-16000/69092	Loss: 116.498
-19200/69092	Loss: 114.770
-22400/69092	Loss: 115.375
-25600/69092	Loss: 116.423
-28800/69092	Loss: 114.687
-32000/69092	Loss: 117.993
-35200/69092	Loss: 115.539
-38400/69092	Loss: 116.548
-41600/69092	Loss: 117.976
-44800/69092	Loss: 117.867
-48000/69092	Loss: 116.480
-51200/69092	Loss: 118.782
-54400/69092	Loss: 116.445
-57600/69092	Loss: 115.679
-60800/69092	Loss: 115.930
-64000/69092	Loss: 118.402
-67200/69092	Loss: 119.948
-Training time 0:04:14.535964
-Epoch: 59 Average loss: 116.89
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 62)
-0/69092	Loss: 100.414
-3200/69092	Loss: 115.779
-6400/69092	Loss: 116.562
-9600/69092	Loss: 115.648
-12800/69092	Loss: 117.149
-16000/69092	Loss: 116.193
-19200/69092	Loss: 117.466
-22400/69092	Loss: 119.299
-25600/69092	Loss: 115.709
-28800/69092	Loss: 119.611
-32000/69092	Loss: 117.682
-35200/69092	Loss: 114.740
-38400/69092	Loss: 118.007
-41600/69092	Loss: 116.426
-44800/69092	Loss: 118.943
-48000/69092	Loss: 117.323
-51200/69092	Loss: 116.706
-54400/69092	Loss: 115.251
-57600/69092	Loss: 115.733
-60800/69092	Loss: 115.655
-64000/69092	Loss: 117.807
-67200/69092	Loss: 117.300
-Training time 0:04:16.028042
-Epoch: 60 Average loss: 116.90
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 63)
-0/69092	Loss: 115.661
-3200/69092	Loss: 116.403
-6400/69092	Loss: 117.127
-9600/69092	Loss: 116.554
-12800/69092	Loss: 117.257
-16000/69092	Loss: 117.434
-19200/69092	Loss: 115.576
-22400/69092	Loss: 116.923
-25600/69092	Loss: 117.401
-28800/69092	Loss: 116.043
-32000/69092	Loss: 116.124
-35200/69092	Loss: 114.900
-38400/69092	Loss: 116.566
-41600/69092	Loss: 116.817
-44800/69092	Loss: 115.295
-48000/69092	Loss: 117.047
-51200/69092	Loss: 116.223
-54400/69092	Loss: 117.439
-57600/69092	Loss: 114.738
-60800/69092	Loss: 116.195
-64000/69092	Loss: 117.599
-67200/69092	Loss: 116.314
-Training time 0:04:18.673967
-Epoch: 61 Average loss: 116.50
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 64)
-0/69092	Loss: 116.701
-3200/69092	Loss: 115.635
-6400/69092	Loss: 115.918
-9600/69092	Loss: 116.463
-12800/69092	Loss: 114.782
-16000/69092	Loss: 119.606
-19200/69092	Loss: 116.417
-22400/69092	Loss: 116.438
-25600/69092	Loss: 116.582
-28800/69092	Loss: 116.684
-32000/69092	Loss: 117.507
-35200/69092	Loss: 115.514
-38400/69092	Loss: 118.919
-41600/69092	Loss: 115.495
-44800/69092	Loss: 117.188
-48000/69092	Loss: 118.238
-51200/69092	Loss: 115.925
-54400/69092	Loss: 114.867
-57600/69092	Loss: 116.680
-60800/69092	Loss: 117.989
-64000/69092	Loss: 115.270
-67200/69092	Loss: 116.514
-Training time 0:04:21.632812
-Epoch: 62 Average loss: 116.60
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 65)
-0/69092	Loss: 107.765
-3200/69092	Loss: 115.992
-6400/69092	Loss: 114.613
-9600/69092	Loss: 116.039
-12800/69092	Loss: 116.738
-16000/69092	Loss: 116.278
-19200/69092	Loss: 117.953
-22400/69092	Loss: 117.638
-25600/69092	Loss: 116.648
-28800/69092	Loss: 116.693
-32000/69092	Loss: 117.494
-35200/69092	Loss: 115.466
-38400/69092	Loss: 116.476
-41600/69092	Loss: 116.449
-44800/69092	Loss: 115.923
-48000/69092	Loss: 118.222
-51200/69092	Loss: 115.599
-54400/69092	Loss: 117.616
-57600/69092	Loss: 114.726
-60800/69092	Loss: 116.805
-64000/69092	Loss: 116.337
-67200/69092	Loss: 116.728
-Training time 0:04:21.771630
-Epoch: 63 Average loss: 116.49
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 66)
-0/69092	Loss: 112.972
-3200/69092	Loss: 116.920
-6400/69092	Loss: 116.534
-9600/69092	Loss: 115.370
-12800/69092	Loss: 117.104
-16000/69092	Loss: 117.011
-19200/69092	Loss: 117.300
-22400/69092	Loss: 114.896
-25600/69092	Loss: 112.888
-28800/69092	Loss: 116.259
-32000/69092	Loss: 116.728
-35200/69092	Loss: 117.515
-38400/69092	Loss: 117.990
-41600/69092	Loss: 116.353
-44800/69092	Loss: 115.560
-48000/69092	Loss: 117.958
-51200/69092	Loss: 115.790
-54400/69092	Loss: 117.354
-57600/69092	Loss: 117.081
-60800/69092	Loss: 116.480
-64000/69092	Loss: 118.028
-67200/69092	Loss: 116.265
-Training time 0:04:21.300752
-Epoch: 64 Average loss: 116.53
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 67)
-0/69092	Loss: 127.194
-3200/69092	Loss: 115.406
-6400/69092	Loss: 116.160
-9600/69092	Loss: 117.016
-12800/69092	Loss: 116.635
-16000/69092	Loss: 117.400
-19200/69092	Loss: 117.031
-22400/69092	Loss: 114.807
-25600/69092	Loss: 116.996
-28800/69092	Loss: 117.163
-32000/69092	Loss: 114.979
-35200/69092	Loss: 117.914
-38400/69092	Loss: 115.196
-41600/69092	Loss: 115.262
-44800/69092	Loss: 116.426
-48000/69092	Loss: 115.990
-51200/69092	Loss: 117.046
-54400/69092	Loss: 116.395
-57600/69092	Loss: 116.468
-60800/69092	Loss: 117.116
-64000/69092	Loss: 117.099
-67200/69092	Loss: 117.352
-Training time 0:04:19.523237
-Epoch: 65 Average loss: 116.47
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 68)
-0/69092	Loss: 130.036
-3200/69092	Loss: 116.596
-6400/69092	Loss: 115.924
-9600/69092	Loss: 116.912
-12800/69092	Loss: 117.363
-16000/69092	Loss: 115.103
-19200/69092	Loss: 116.001
-22400/69092	Loss: 117.190
-25600/69092	Loss: 117.382
-28800/69092	Loss: 117.636
-32000/69092	Loss: 116.078
-35200/69092	Loss: 114.790
-38400/69092	Loss: 115.516
-41600/69092	Loss: 117.044
-44800/69092	Loss: 115.866
-48000/69092	Loss: 115.281
-51200/69092	Loss: 116.932
-54400/69092	Loss: 116.562
-57600/69092	Loss: 114.892
-60800/69092	Loss: 115.262
-64000/69092	Loss: 118.491
-67200/69092	Loss: 117.641
-Training time 0:04:13.537568
-Epoch: 66 Average loss: 116.39
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 69)
-0/69092	Loss: 131.276
-3200/69092	Loss: 116.788
-6400/69092	Loss: 115.669
-9600/69092	Loss: 116.492
-12800/69092	Loss: 114.834
-16000/69092	Loss: 117.516
-19200/69092	Loss: 116.285
-22400/69092	Loss: 116.431
-25600/69092	Loss: 115.076
-28800/69092	Loss: 116.645
-32000/69092	Loss: 115.335
-35200/69092	Loss: 115.405
-38400/69092	Loss: 115.994
-41600/69092	Loss: 114.355
-44800/69092	Loss: 116.930
-48000/69092	Loss: 115.433
-51200/69092	Loss: 116.551
-54400/69092	Loss: 117.376
-57600/69092	Loss: 116.016
-60800/69092	Loss: 117.925
-64000/69092	Loss: 117.088
-67200/69092	Loss: 118.403
-Training time 0:04:15.694500
-Epoch: 67 Average loss: 116.33
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 70)
-0/69092	Loss: 116.447
-3200/69092	Loss: 117.105
-6400/69092	Loss: 114.875
-9600/69092	Loss: 117.971
-12800/69092	Loss: 117.665
-16000/69092	Loss: 116.713
-19200/69092	Loss: 115.762
-22400/69092	Loss: 116.025
-25600/69092	Loss: 115.859
-28800/69092	Loss: 117.123
-32000/69092	Loss: 115.941
-35200/69092	Loss: 114.252
-38400/69092	Loss: 116.231
-41600/69092	Loss: 115.251
-44800/69092	Loss: 117.061
-48000/69092	Loss: 117.370
-51200/69092	Loss: 116.626
-54400/69092	Loss: 115.745
-57600/69092	Loss: 115.975
-60800/69092	Loss: 115.250
-64000/69092	Loss: 117.407
-67200/69092	Loss: 117.801
-Training time 0:04:23.256883
-Epoch: 68 Average loss: 116.39
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 71)
-0/69092	Loss: 109.427
-3200/69092	Loss: 115.791
-6400/69092	Loss: 117.890
-9600/69092	Loss: 117.328
-12800/69092	Loss: 116.337
-16000/69092	Loss: 115.928
-19200/69092	Loss: 115.436
-22400/69092	Loss: 117.851
-25600/69092	Loss: 116.486
-28800/69092	Loss: 116.733
-32000/69092	Loss: 117.120
-35200/69092	Loss: 116.794
-38400/69092	Loss: 115.890
-41600/69092	Loss: 117.253
-44800/69092	Loss: 116.706
-48000/69092	Loss: 114.991
-51200/69092	Loss: 115.969
-54400/69092	Loss: 115.819
-57600/69092	Loss: 115.382
-60800/69092	Loss: 116.838
-64000/69092	Loss: 116.069
-67200/69092	Loss: 115.233
-Training time 0:04:14.798986
-Epoch: 69 Average loss: 116.27
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 72)
-0/69092	Loss: 115.002
-3200/69092	Loss: 117.518
-6400/69092	Loss: 115.190
-9600/69092	Loss: 115.954
-12800/69092	Loss: 115.045
-16000/69092	Loss: 116.130
-19200/69092	Loss: 116.050
-22400/69092	Loss: 115.581
-25600/69092	Loss: 117.877
-28800/69092	Loss: 115.889
-32000/69092	Loss: 114.806
-35200/69092	Loss: 116.736
-38400/69092	Loss: 115.884
-41600/69092	Loss: 115.327
-44800/69092	Loss: 116.250
-48000/69092	Loss: 115.486
-51200/69092	Loss: 115.868
-54400/69092	Loss: 118.572
-57600/69092	Loss: 114.840
-60800/69092	Loss: 116.344
-64000/69092	Loss: 115.752
-67200/69092	Loss: 116.489
-Training time 0:04:19.510293
-Epoch: 70 Average loss: 116.12
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 73)
-0/69092	Loss: 134.101
-3200/69092	Loss: 117.654
-6400/69092	Loss: 115.580
-9600/69092	Loss: 117.954
-12800/69092	Loss: 115.616
-16000/69092	Loss: 114.816
-19200/69092	Loss: 116.003
-22400/69092	Loss: 118.480
-25600/69092	Loss: 114.473
-28800/69092	Loss: 116.164
-32000/69092	Loss: 116.078
-35200/69092	Loss: 115.936
-38400/69092	Loss: 115.966
-41600/69092	Loss: 115.125
-44800/69092	Loss: 116.449
-48000/69092	Loss: 116.312
-51200/69092	Loss: 115.841
-54400/69092	Loss: 115.415
-57600/69092	Loss: 117.123
-60800/69092	Loss: 116.921
-64000/69092	Loss: 117.044
-67200/69092	Loss: 115.881
-Training time 0:04:21.271139
-Epoch: 71 Average loss: 116.21
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 74)
-0/69092	Loss: 110.241
-3200/69092	Loss: 115.037
-6400/69092	Loss: 113.940
-9600/69092	Loss: 116.558
-12800/69092	Loss: 117.040
-16000/69092	Loss: 114.286
-19200/69092	Loss: 118.157
-22400/69092	Loss: 116.347
-25600/69092	Loss: 115.526
-28800/69092	Loss: 115.577
-32000/69092	Loss: 116.441
-35200/69092	Loss: 115.329
-38400/69092	Loss: 117.232
-41600/69092	Loss: 117.204
-44800/69092	Loss: 116.180
-48000/69092	Loss: 113.763
-51200/69092	Loss: 113.985
-54400/69092	Loss: 116.504
-57600/69092	Loss: 117.151
-60800/69092	Loss: 117.439
-64000/69092	Loss: 115.862
-67200/69092	Loss: 116.257
-Training time 0:04:20.527632
-Epoch: 72 Average loss: 116.07
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 75)
-0/69092	Loss: 117.426
-3200/69092	Loss: 114.095
-6400/69092	Loss: 116.426
-9600/69092	Loss: 117.106
-12800/69092	Loss: 116.094
-16000/69092	Loss: 116.348
-19200/69092	Loss: 116.034
-22400/69092	Loss: 116.527
-25600/69092	Loss: 115.802
-28800/69092	Loss: 115.645
-32000/69092	Loss: 115.092
-35200/69092	Loss: 117.360
-38400/69092	Loss: 116.234
-41600/69092	Loss: 115.253
-44800/69092	Loss: 116.481
-48000/69092	Loss: 115.347
-51200/69092	Loss: 114.886
-54400/69092	Loss: 116.217
-57600/69092	Loss: 117.852
-60800/69092	Loss: 114.498
-64000/69092	Loss: 116.421
-67200/69092	Loss: 117.095
-Training time 0:04:14.108358
-Epoch: 73 Average loss: 116.01
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 76)
-0/69092	Loss: 120.607
-3200/69092	Loss: 115.399
-6400/69092	Loss: 114.639
-9600/69092	Loss: 115.672
-12800/69092	Loss: 114.804
-16000/69092	Loss: 115.667
-19200/69092	Loss: 116.190
-22400/69092	Loss: 117.506
-25600/69092	Loss: 114.107
-28800/69092	Loss: 116.319
-32000/69092	Loss: 114.787
-35200/69092	Loss: 116.422
-38400/69092	Loss: 117.309
-41600/69092	Loss: 118.066
-44800/69092	Loss: 114.058
-48000/69092	Loss: 116.455
-51200/69092	Loss: 115.945
-54400/69092	Loss: 115.025
-57600/69092	Loss: 119.706
-60800/69092	Loss: 114.159
-64000/69092	Loss: 117.580
-67200/69092	Loss: 115.744
-Training time 0:04:18.325905
-Epoch: 74 Average loss: 115.99
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 77)
-0/69092	Loss: 118.108
-3200/69092	Loss: 115.449
-6400/69092	Loss: 117.178
-9600/69092	Loss: 115.026
-12800/69092	Loss: 114.951
-16000/69092	Loss: 118.810
-19200/69092	Loss: 118.613
-22400/69092	Loss: 114.244
-25600/69092	Loss: 114.633
-28800/69092	Loss: 115.905
-32000/69092	Loss: 115.582
-35200/69092	Loss: 116.536
-38400/69092	Loss: 116.985
-41600/69092	Loss: 116.062
-44800/69092	Loss: 116.445
-48000/69092	Loss: 116.440
-51200/69092	Loss: 116.525
-54400/69092	Loss: 113.789
-57600/69092	Loss: 115.686
-60800/69092	Loss: 116.835
-64000/69092	Loss: 116.446
-67200/69092	Loss: 114.254
-Training time 0:04:11.717487
-Epoch: 75 Average loss: 115.98
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 78)
-0/69092	Loss: 110.195
-3200/69092	Loss: 116.650
-6400/69092	Loss: 116.428
-9600/69092	Loss: 116.469
-12800/69092	Loss: 115.338
-16000/69092	Loss: 115.323
-19200/69092	Loss: 115.777
-22400/69092	Loss: 115.717
-25600/69092	Loss: 114.637
-28800/69092	Loss: 115.820
-32000/69092	Loss: 115.664
-35200/69092	Loss: 115.927
-38400/69092	Loss: 117.323
-41600/69092	Loss: 116.202
-44800/69092	Loss: 113.799
-48000/69092	Loss: 117.076
-51200/69092	Loss: 115.777
-54400/69092	Loss: 117.773
-57600/69092	Loss: 114.421
-60800/69092	Loss: 114.618
-64000/69092	Loss: 115.741
-67200/69092	Loss: 115.039
-Training time 0:04:20.126153
-Epoch: 76 Average loss: 115.82
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 79)
-0/69092	Loss: 115.282
-3200/69092	Loss: 115.687
-6400/69092	Loss: 116.064
-9600/69092	Loss: 115.918
-12800/69092	Loss: 113.280
-16000/69092	Loss: 114.392
-19200/69092	Loss: 115.886
-22400/69092	Loss: 117.267
-25600/69092	Loss: 116.556
-28800/69092	Loss: 117.617
-32000/69092	Loss: 116.096
-35200/69092	Loss: 116.449
-38400/69092	Loss: 116.663
-41600/69092	Loss: 116.042
-44800/69092	Loss: 115.932
-48000/69092	Loss: 114.773
-51200/69092	Loss: 116.468
-54400/69092	Loss: 114.870
-57600/69092	Loss: 115.219
-60800/69092	Loss: 116.082
-64000/69092	Loss: 115.782
-67200/69092	Loss: 114.948
-Training time 0:04:26.667052
-Epoch: 77 Average loss: 115.86
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 80)
-0/69092	Loss: 113.016
-3200/69092	Loss: 116.797
-6400/69092	Loss: 116.327
-9600/69092	Loss: 114.864
-12800/69092	Loss: 113.603
-16000/69092	Loss: 118.688
-19200/69092	Loss: 117.221
-22400/69092	Loss: 114.753
-25600/69092	Loss: 115.706
-28800/69092	Loss: 116.332
-32000/69092	Loss: 115.016
-35200/69092	Loss: 114.848
-38400/69092	Loss: 115.691
-41600/69092	Loss: 114.871
-44800/69092	Loss: 114.361
-48000/69092	Loss: 115.572
-51200/69092	Loss: 115.656
-54400/69092	Loss: 115.938
-57600/69092	Loss: 115.503
-60800/69092	Loss: 117.543
-64000/69092	Loss: 116.509
-67200/69092	Loss: 116.613
-Training time 0:04:27.455867
-Epoch: 78 Average loss: 115.73
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 81)
-0/69092	Loss: 116.266
-3200/69092	Loss: 116.131
-6400/69092	Loss: 116.205
-9600/69092	Loss: 113.487
-12800/69092	Loss: 116.034
-16000/69092	Loss: 115.570
-19200/69092	Loss: 116.025
-22400/69092	Loss: 115.053
-25600/69092	Loss: 113.316
-28800/69092	Loss: 116.254
-32000/69092	Loss: 117.574
-35200/69092	Loss: 117.817
-38400/69092	Loss: 116.883
-41600/69092	Loss: 116.216
-44800/69092	Loss: 117.161
-48000/69092	Loss: 117.616
-51200/69092	Loss: 114.720
-54400/69092	Loss: 115.970
-57600/69092	Loss: 114.982
-60800/69092	Loss: 114.933
-64000/69092	Loss: 118.061
-67200/69092	Loss: 113.611
-Training time 0:04:25.561758
-Epoch: 79 Average loss: 115.93
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 82)
-0/69092	Loss: 105.227
-3200/69092	Loss: 116.596
-6400/69092	Loss: 115.248
-9600/69092	Loss: 117.734
-12800/69092	Loss: 116.540
-16000/69092	Loss: 116.902
-19200/69092	Loss: 114.568
-22400/69092	Loss: 114.365
-25600/69092	Loss: 115.724
-28800/69092	Loss: 115.997
-32000/69092	Loss: 114.831
-35200/69092	Loss: 115.820
-38400/69092	Loss: 115.015
-41600/69092	Loss: 114.732
-44800/69092	Loss: 115.605
-48000/69092	Loss: 117.712
-51200/69092	Loss: 117.044
-54400/69092	Loss: 115.373
-57600/69092	Loss: 114.918
-60800/69092	Loss: 114.857
-64000/69092	Loss: 115.588
-67200/69092	Loss: 115.213
-Training time 0:04:21.035061
-Epoch: 80 Average loss: 115.73
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 83)
-0/69092	Loss: 116.023
-3200/69092	Loss: 114.750
-6400/69092	Loss: 115.606
-9600/69092	Loss: 114.526
-12800/69092	Loss: 116.967
-16000/69092	Loss: 115.071
-19200/69092	Loss: 116.907
-22400/69092	Loss: 116.157
-25600/69092	Loss: 115.547
-28800/69092	Loss: 115.020
-32000/69092	Loss: 115.856
-35200/69092	Loss: 116.306
-38400/69092	Loss: 115.244
-41600/69092	Loss: 115.245
-44800/69092	Loss: 113.337
-48000/69092	Loss: 115.717
-51200/69092	Loss: 114.111
-54400/69092	Loss: 116.606
-57600/69092	Loss: 115.800
-60800/69092	Loss: 117.631
-64000/69092	Loss: 115.104
-67200/69092	Loss: 115.512
-Training time 0:04:27.606268
-Epoch: 81 Average loss: 115.63
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 84)
-0/69092	Loss: 109.763
-3200/69092	Loss: 114.796
-6400/69092	Loss: 114.820
-9600/69092	Loss: 116.260
-12800/69092	Loss: 115.517
-16000/69092	Loss: 114.184
-19200/69092	Loss: 112.686
-22400/69092	Loss: 114.702
-25600/69092	Loss: 113.757
-28800/69092	Loss: 116.156
-32000/69092	Loss: 116.493
-35200/69092	Loss: 117.036
-38400/69092	Loss: 114.748
-41600/69092	Loss: 116.401
-44800/69092	Loss: 117.682
-48000/69092	Loss: 115.540
-51200/69092	Loss: 116.903
-54400/69092	Loss: 114.747
-57600/69092	Loss: 118.514
-60800/69092	Loss: 116.045
-64000/69092	Loss: 115.743
-67200/69092	Loss: 114.532
-Training time 0:04:21.981919
-Epoch: 82 Average loss: 115.55
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 85)
-0/69092	Loss: 102.658
-3200/69092	Loss: 115.777
-6400/69092	Loss: 115.979
-9600/69092	Loss: 114.755
-12800/69092	Loss: 116.289
-16000/69092	Loss: 113.141
-19200/69092	Loss: 113.872
-22400/69092	Loss: 116.325
-25600/69092	Loss: 115.921
-28800/69092	Loss: 116.949
-32000/69092	Loss: 115.509
-35200/69092	Loss: 115.380
-38400/69092	Loss: 114.348
-41600/69092	Loss: 113.686
-44800/69092	Loss: 115.715
-48000/69092	Loss: 115.677
-51200/69092	Loss: 117.759
-54400/69092	Loss: 117.281
-57600/69092	Loss: 117.353
-60800/69092	Loss: 116.770
-64000/69092	Loss: 115.188
-67200/69092	Loss: 115.187
-Training time 0:04:15.339523
-Epoch: 83 Average loss: 115.65
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 86)
-0/69092	Loss: 106.183
-3200/69092	Loss: 115.604
-6400/69092	Loss: 114.901
-9600/69092	Loss: 115.998
-12800/69092	Loss: 115.279
-16000/69092	Loss: 115.837
-19200/69092	Loss: 114.004
-22400/69092	Loss: 114.471
-25600/69092	Loss: 116.697
-28800/69092	Loss: 116.087
-32000/69092	Loss: 115.580
-35200/69092	Loss: 114.502
-38400/69092	Loss: 114.836
-41600/69092	Loss: 116.212
-44800/69092	Loss: 115.260
-48000/69092	Loss: 116.323
-51200/69092	Loss: 115.060
-54400/69092	Loss: 115.965
-57600/69092	Loss: 115.483
-60800/69092	Loss: 116.460
-64000/69092	Loss: 115.958
-67200/69092	Loss: 114.216
-Training time 0:04:24.070150
-Epoch: 84 Average loss: 115.45
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 87)
-0/69092	Loss: 109.772
-3200/69092	Loss: 114.663
-6400/69092	Loss: 116.979
-9600/69092	Loss: 115.478
-12800/69092	Loss: 115.829
-16000/69092	Loss: 113.845
-19200/69092	Loss: 116.495
-22400/69092	Loss: 114.270
-25600/69092	Loss: 117.338
-28800/69092	Loss: 117.073
-32000/69092	Loss: 115.038
-35200/69092	Loss: 114.788
-38400/69092	Loss: 115.930
-41600/69092	Loss: 115.175
-44800/69092	Loss: 114.302
-48000/69092	Loss: 114.691
-51200/69092	Loss: 117.297
-54400/69092	Loss: 115.718
-57600/69092	Loss: 116.222
-60800/69092	Loss: 115.254
-64000/69092	Loss: 113.235
-67200/69092	Loss: 116.336
-Training time 0:04:27.389755
-Epoch: 85 Average loss: 115.56
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 88)
-0/69092	Loss: 114.321
-3200/69092	Loss: 114.351
-6400/69092	Loss: 115.291
-9600/69092	Loss: 116.430
-12800/69092	Loss: 117.276
-16000/69092	Loss: 116.460
-19200/69092	Loss: 114.705
-22400/69092	Loss: 114.293
-25600/69092	Loss: 115.415
-28800/69092	Loss: 113.700
-32000/69092	Loss: 115.014
-35200/69092	Loss: 115.317
-38400/69092	Loss: 116.014
-41600/69092	Loss: 116.513
-44800/69092	Loss: 117.764
-48000/69092	Loss: 114.385
-51200/69092	Loss: 115.029
-54400/69092	Loss: 114.609
-57600/69092	Loss: 116.770
-60800/69092	Loss: 115.598
-64000/69092	Loss: 116.776
-67200/69092	Loss: 115.444
-Training time 0:04:35.227404
-Epoch: 86 Average loss: 115.54
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 89)
-0/69092	Loss: 110.096
-3200/69092	Loss: 115.085
-6400/69092	Loss: 115.375
-9600/69092	Loss: 113.848
-12800/69092	Loss: 115.607
-16000/69092	Loss: 115.240
-19200/69092	Loss: 115.920
-22400/69092	Loss: 113.823
-25600/69092	Loss: 114.585
-28800/69092	Loss: 115.852
-32000/69092	Loss: 115.286
-35200/69092	Loss: 115.811
-38400/69092	Loss: 116.294
-41600/69092	Loss: 115.652
-44800/69092	Loss: 115.174
-48000/69092	Loss: 115.841
-51200/69092	Loss: 116.753
-54400/69092	Loss: 115.245
-57600/69092	Loss: 114.727
-60800/69092	Loss: 115.222
-64000/69092	Loss: 114.703
-67200/69092	Loss: 116.976
-Training time 0:04:32.743549
-Epoch: 87 Average loss: 115.41
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 90)
-0/69092	Loss: 119.462
-3200/69092	Loss: 115.533
-6400/69092	Loss: 117.001
-9600/69092	Loss: 116.389
-12800/69092	Loss: 115.264
-16000/69092	Loss: 114.015
-19200/69092	Loss: 115.133
-22400/69092	Loss: 115.934
-25600/69092	Loss: 113.928
-28800/69092	Loss: 116.988
-32000/69092	Loss: 113.673
-35200/69092	Loss: 114.373
-38400/69092	Loss: 116.277
-41600/69092	Loss: 117.572
-44800/69092	Loss: 111.675
-48000/69092	Loss: 117.552
-51200/69092	Loss: 115.310
-54400/69092	Loss: 115.944
-57600/69092	Loss: 116.559
-60800/69092	Loss: 114.095
-64000/69092	Loss: 114.392
-67200/69092	Loss: 114.071
-Training time 0:04:25.855823
-Epoch: 88 Average loss: 115.29
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 91)
-0/69092	Loss: 112.185
-3200/69092	Loss: 115.037
-6400/69092	Loss: 114.506
-9600/69092	Loss: 115.579
-12800/69092	Loss: 116.375
-16000/69092	Loss: 114.867
-19200/69092	Loss: 114.462
-22400/69092	Loss: 116.254
-25600/69092	Loss: 114.250
-28800/69092	Loss: 118.013
-32000/69092	Loss: 116.511
-35200/69092	Loss: 112.674
-38400/69092	Loss: 114.184
-41600/69092	Loss: 116.883
-44800/69092	Loss: 115.688
-48000/69092	Loss: 114.781
-51200/69092	Loss: 116.460
-54400/69092	Loss: 114.772
-57600/69092	Loss: 114.616
-60800/69092	Loss: 114.424
-64000/69092	Loss: 115.626
-67200/69092	Loss: 114.778
-Training time 0:04:18.054649
-Epoch: 89 Average loss: 115.32
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 92)
-0/69092	Loss: 117.381
-3200/69092	Loss: 116.538
-6400/69092	Loss: 114.395
-9600/69092	Loss: 114.199
-12800/69092	Loss: 115.076
-16000/69092	Loss: 113.660
-19200/69092	Loss: 115.942
-22400/69092	Loss: 115.406
-25600/69092	Loss: 115.577
-28800/69092	Loss: 116.055
-32000/69092	Loss: 115.750
-35200/69092	Loss: 115.183
-38400/69092	Loss: 114.756
-41600/69092	Loss: 116.439
-44800/69092	Loss: 116.094
-48000/69092	Loss: 115.108
-51200/69092	Loss: 116.191
-54400/69092	Loss: 117.091
-57600/69092	Loss: 114.440
-60800/69092	Loss: 114.310
-64000/69092	Loss: 116.254
-67200/69092	Loss: 115.486
-Training time 0:04:11.502557
-Epoch: 90 Average loss: 115.44
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 93)
-0/69092	Loss: 109.876
-3200/69092	Loss: 114.018
-6400/69092	Loss: 112.307
-9600/69092	Loss: 113.206
-12800/69092	Loss: 116.645
-16000/69092	Loss: 114.336
-19200/69092	Loss: 115.819
-22400/69092	Loss: 114.077
-25600/69092	Loss: 113.695
-28800/69092	Loss: 116.631
-32000/69092	Loss: 116.626
-35200/69092	Loss: 115.507
-38400/69092	Loss: 116.458
-41600/69092	Loss: 117.837
-44800/69092	Loss: 115.667
-48000/69092	Loss: 114.105
-51200/69092	Loss: 115.868
-54400/69092	Loss: 113.209
-57600/69092	Loss: 116.659
-60800/69092	Loss: 113.839
-64000/69092	Loss: 115.627
-67200/69092	Loss: 115.893
-Training time 0:04:23.661572
-Epoch: 91 Average loss: 115.23
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 94)
-0/69092	Loss: 109.875
-3200/69092	Loss: 116.904
-6400/69092	Loss: 114.350
-9600/69092	Loss: 113.917
-12800/69092	Loss: 115.405
-16000/69092	Loss: 114.182
-19200/69092	Loss: 115.203
-22400/69092	Loss: 116.412
-25600/69092	Loss: 115.923
-28800/69092	Loss: 114.897
-32000/69092	Loss: 115.843
-35200/69092	Loss: 115.061
-38400/69092	Loss: 113.299
-41600/69092	Loss: 113.805
-44800/69092	Loss: 114.774
-48000/69092	Loss: 115.476
-51200/69092	Loss: 115.628
-54400/69092	Loss: 115.072
-57600/69092	Loss: 114.085
-60800/69092	Loss: 116.230
-64000/69092	Loss: 115.288
-67200/69092	Loss: 116.407
-Training time 0:04:18.970973
-Epoch: 92 Average loss: 115.28
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 95)
-0/69092	Loss: 107.112
-3200/69092	Loss: 116.615
-6400/69092	Loss: 115.904
-9600/69092	Loss: 115.694
-12800/69092	Loss: 115.271
-16000/69092	Loss: 114.946
-19200/69092	Loss: 114.214
-22400/69092	Loss: 115.723
-25600/69092	Loss: 115.411
-28800/69092	Loss: 114.224
-32000/69092	Loss: 115.103
-35200/69092	Loss: 117.514
-38400/69092	Loss: 114.115
-41600/69092	Loss: 116.881
-44800/69092	Loss: 117.588
-48000/69092	Loss: 113.807
-51200/69092	Loss: 115.843
-54400/69092	Loss: 116.029
-57600/69092	Loss: 115.897
-60800/69092	Loss: 112.874
-64000/69092	Loss: 115.341
-67200/69092	Loss: 113.812
-Training time 0:04:34.910466
-Epoch: 93 Average loss: 115.33
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 96)
-0/69092	Loss: 111.090
-3200/69092	Loss: 113.802
-6400/69092	Loss: 113.778
-9600/69092	Loss: 116.835
-12800/69092	Loss: 116.298
-16000/69092	Loss: 115.383
-19200/69092	Loss: 115.244
-22400/69092	Loss: 117.004
-25600/69092	Loss: 113.987
-28800/69092	Loss: 114.258
-32000/69092	Loss: 116.204
-35200/69092	Loss: 114.571
-38400/69092	Loss: 115.871
-41600/69092	Loss: 114.701
-44800/69092	Loss: 114.940
-48000/69092	Loss: 115.518
-51200/69092	Loss: 115.979
-54400/69092	Loss: 114.040
-57600/69092	Loss: 115.293
-60800/69092	Loss: 113.554
-64000/69092	Loss: 115.140
-67200/69092	Loss: 114.921
-Training time 0:04:29.937189
-Epoch: 94 Average loss: 115.17
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 97)
-0/69092	Loss: 104.309
-3200/69092	Loss: 114.970
-6400/69092	Loss: 114.448
-9600/69092	Loss: 116.391
-12800/69092	Loss: 116.613
-16000/69092	Loss: 116.624
-19200/69092	Loss: 114.606
-22400/69092	Loss: 114.459
-25600/69092	Loss: 114.573
-28800/69092	Loss: 114.351
-32000/69092	Loss: 114.737
-35200/69092	Loss: 114.211
-38400/69092	Loss: 116.090
-41600/69092	Loss: 115.409
-44800/69092	Loss: 113.350
-48000/69092	Loss: 116.947
-51200/69092	Loss: 116.175
-54400/69092	Loss: 114.535
-57600/69092	Loss: 113.567
-60800/69092	Loss: 113.530
-64000/69092	Loss: 115.104
-67200/69092	Loss: 115.522
-Training time 0:04:32.566710
-Epoch: 95 Average loss: 115.10
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 98)
-0/69092	Loss: 103.548
-3200/69092	Loss: 114.404
-6400/69092	Loss: 115.306
-9600/69092	Loss: 115.076
-12800/69092	Loss: 116.447
-16000/69092	Loss: 113.969
-19200/69092	Loss: 116.127
-22400/69092	Loss: 115.642
-25600/69092	Loss: 114.994
-28800/69092	Loss: 116.458
-32000/69092	Loss: 115.971
-35200/69092	Loss: 117.573
-38400/69092	Loss: 117.198
-41600/69092	Loss: 114.652
-44800/69092	Loss: 113.356
-48000/69092	Loss: 115.118
-51200/69092	Loss: 114.814
-54400/69092	Loss: 114.915
-57600/69092	Loss: 113.385
-60800/69092	Loss: 114.671
-64000/69092	Loss: 114.218
-67200/69092	Loss: 115.762
-Training time 0:04:30.297416
-Epoch: 96 Average loss: 115.27
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 99)
-0/69092	Loss: 103.183
-3200/69092	Loss: 114.371
-6400/69092	Loss: 114.578
-9600/69092	Loss: 114.177
-12800/69092	Loss: 113.374
-16000/69092	Loss: 114.539
-19200/69092	Loss: 115.190
-22400/69092	Loss: 115.282
-25600/69092	Loss: 115.299
-28800/69092	Loss: 115.313
-32000/69092	Loss: 115.930
-35200/69092	Loss: 115.794
-38400/69092	Loss: 115.233
-41600/69092	Loss: 115.526
-44800/69092	Loss: 115.670
-48000/69092	Loss: 116.022
-51200/69092	Loss: 115.881
-54400/69092	Loss: 114.486
-57600/69092	Loss: 116.493
-60800/69092	Loss: 113.559
-64000/69092	Loss: 114.572
-67200/69092	Loss: 113.786
-Training time 0:04:14.350046
-Epoch: 97 Average loss: 114.96
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 100)
-0/69092	Loss: 109.205
-3200/69092	Loss: 114.470
-6400/69092	Loss: 115.518
-9600/69092	Loss: 115.455
-12800/69092	Loss: 114.463
-16000/69092	Loss: 114.735
-19200/69092	Loss: 112.726
-22400/69092	Loss: 115.038
-25600/69092	Loss: 115.942
-28800/69092	Loss: 116.597
-32000/69092	Loss: 114.920
-35200/69092	Loss: 115.183
-38400/69092	Loss: 115.178
-41600/69092	Loss: 115.187
-44800/69092	Loss: 115.023
-48000/69092	Loss: 114.013
-51200/69092	Loss: 115.620
-54400/69092	Loss: 113.888
-57600/69092	Loss: 115.136
-60800/69092	Loss: 117.265
-64000/69092	Loss: 114.032
-67200/69092	Loss: 115.695
-Training time 0:04:16.813142
-Epoch: 98 Average loss: 115.06
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 101)
-0/69092	Loss: 102.324
-3200/69092	Loss: 117.226
-6400/69092	Loss: 111.502
-9600/69092	Loss: 115.173
-12800/69092	Loss: 116.427
-16000/69092	Loss: 117.208
-19200/69092	Loss: 113.589
-22400/69092	Loss: 116.157
-25600/69092	Loss: 113.960
-28800/69092	Loss: 113.542
-32000/69092	Loss: 115.706
-35200/69092	Loss: 114.351
-38400/69092	Loss: 113.532
-41600/69092	Loss: 115.807
-44800/69092	Loss: 114.047
-48000/69092	Loss: 116.583
-51200/69092	Loss: 114.274
-54400/69092	Loss: 115.270
-57600/69092	Loss: 114.983
-60800/69092	Loss: 113.886
-64000/69092	Loss: 115.446
-67200/69092	Loss: 116.695
-Training time 0:04:16.901688
-Epoch: 99 Average loss: 115.03
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 102)
-0/69092	Loss: 101.450
-3200/69092	Loss: 114.254
-6400/69092	Loss: 114.502
-9600/69092	Loss: 115.191
-12800/69092	Loss: 115.722
-16000/69092	Loss: 115.505
-19200/69092	Loss: 115.682
-22400/69092	Loss: 115.095
-25600/69092	Loss: 113.668
-28800/69092	Loss: 116.141
-32000/69092	Loss: 116.021
-35200/69092	Loss: 114.208
-38400/69092	Loss: 114.481
-41600/69092	Loss: 116.893
-44800/69092	Loss: 115.935
-48000/69092	Loss: 114.079
-51200/69092	Loss: 115.014
-54400/69092	Loss: 116.096
-57600/69092	Loss: 114.209
-60800/69092	Loss: 114.125
-64000/69092	Loss: 116.074
-67200/69092	Loss: 113.280
-Training time 0:04:30.717629
-Epoch: 100 Average loss: 115.05
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 103)
-0/69092	Loss: 110.561
-3200/69092	Loss: 114.694
-6400/69092	Loss: 115.096
-9600/69092	Loss: 112.462
-12800/69092	Loss: 114.749
-16000/69092	Loss: 113.297
-19200/69092	Loss: 117.057
-22400/69092	Loss: 115.330
-25600/69092	Loss: 116.169
-28800/69092	Loss: 115.596
-32000/69092	Loss: 115.761
-35200/69092	Loss: 114.065
-38400/69092	Loss: 114.156
-41600/69092	Loss: 114.239
-44800/69092	Loss: 113.600
-48000/69092	Loss: 116.481
-51200/69092	Loss: 115.040
-54400/69092	Loss: 116.844
-57600/69092	Loss: 115.482
-60800/69092	Loss: 114.866
-64000/69092	Loss: 114.837
-67200/69092	Loss: 113.714
-Training time 0:04:33.068145
-Epoch: 101 Average loss: 114.95
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 104)
-0/69092	Loss: 111.877
-3200/69092	Loss: 113.528
-6400/69092	Loss: 116.126
-9600/69092	Loss: 115.060
-12800/69092	Loss: 115.645
-16000/69092	Loss: 112.514
-19200/69092	Loss: 114.612
-22400/69092	Loss: 114.462
-25600/69092	Loss: 115.992
-28800/69092	Loss: 114.581
-32000/69092	Loss: 117.691
-35200/69092	Loss: 113.213
-38400/69092	Loss: 114.303
-41600/69092	Loss: 114.591
-44800/69092	Loss: 114.572
-48000/69092	Loss: 113.757
-51200/69092	Loss: 115.127
-54400/69092	Loss: 114.971
-57600/69092	Loss: 115.206
-60800/69092	Loss: 114.473
-64000/69092	Loss: 113.442
-67200/69092	Loss: 114.790
-Training time 0:04:26.031708
-Epoch: 102 Average loss: 114.77
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 105)
-0/69092	Loss: 123.542
-3200/69092	Loss: 115.210
-6400/69092	Loss: 114.367
-9600/69092	Loss: 116.109
-12800/69092	Loss: 113.503
-16000/69092	Loss: 115.242
-19200/69092	Loss: 115.365
-22400/69092	Loss: 115.884
-25600/69092	Loss: 113.157
-28800/69092	Loss: 115.895
-32000/69092	Loss: 114.043
-35200/69092	Loss: 115.025
-38400/69092	Loss: 115.140
-41600/69092	Loss: 114.808
-44800/69092	Loss: 114.618
-48000/69092	Loss: 115.046
-51200/69092	Loss: 115.224
-54400/69092	Loss: 116.526
-57600/69092	Loss: 114.561
-60800/69092	Loss: 114.281
-64000/69092	Loss: 114.436
-67200/69092	Loss: 112.579
-Training time 0:04:25.144287
-Epoch: 103 Average loss: 114.78
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 106)
-0/69092	Loss: 105.940
-3200/69092	Loss: 115.154
-6400/69092	Loss: 115.266
-9600/69092	Loss: 114.273
-12800/69092	Loss: 116.441
-16000/69092	Loss: 114.447
-19200/69092	Loss: 114.707
-22400/69092	Loss: 114.581
-25600/69092	Loss: 115.075
-28800/69092	Loss: 112.858
-32000/69092	Loss: 115.417
-35200/69092	Loss: 113.227
-38400/69092	Loss: 114.756
-41600/69092	Loss: 115.456
-44800/69092	Loss: 115.085
-48000/69092	Loss: 114.573
-51200/69092	Loss: 113.830
-54400/69092	Loss: 114.057
-57600/69092	Loss: 115.467
-60800/69092	Loss: 116.554
-64000/69092	Loss: 115.321
-67200/69092	Loss: 114.775
-Training time 0:04:26.405936
-Epoch: 104 Average loss: 114.83
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 107)
-0/69092	Loss: 105.310
-3200/69092	Loss: 114.426
-6400/69092	Loss: 113.549
-9600/69092	Loss: 113.799
-12800/69092	Loss: 115.263
-16000/69092	Loss: 113.263
-19200/69092	Loss: 114.692
-22400/69092	Loss: 113.127
-25600/69092	Loss: 112.946
-28800/69092	Loss: 117.490
-32000/69092	Loss: 114.970
-35200/69092	Loss: 115.424
-38400/69092	Loss: 114.136
-41600/69092	Loss: 114.439
-44800/69092	Loss: 116.172
-48000/69092	Loss: 114.843
-51200/69092	Loss: 114.015
-54400/69092	Loss: 117.099
-57600/69092	Loss: 115.218
-60800/69092	Loss: 115.403
-64000/69092	Loss: 115.615
-67200/69092	Loss: 115.623
-Training time 0:04:30.908597
-Epoch: 105 Average loss: 114.84
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 108)
-0/69092	Loss: 116.573
-3200/69092	Loss: 113.115
-6400/69092	Loss: 116.044
-9600/69092	Loss: 114.502
-12800/69092	Loss: 115.132
-16000/69092	Loss: 114.569
-19200/69092	Loss: 115.861
-22400/69092	Loss: 115.374
-25600/69092	Loss: 115.477
-28800/69092	Loss: 114.479
-32000/69092	Loss: 113.733
-35200/69092	Loss: 114.816
-38400/69092	Loss: 113.667
-41600/69092	Loss: 113.461
-44800/69092	Loss: 114.692
-48000/69092	Loss: 115.788
-51200/69092	Loss: 114.131
-54400/69092	Loss: 115.042
-57600/69092	Loss: 114.190
-60800/69092	Loss: 115.706
-64000/69092	Loss: 113.984
-67200/69092	Loss: 114.659
-Training time 0:04:22.615178
-Epoch: 106 Average loss: 114.72
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 109)
-0/69092	Loss: 116.218
-3200/69092	Loss: 116.439
-6400/69092	Loss: 114.330
-9600/69092	Loss: 114.518
-12800/69092	Loss: 114.244
-16000/69092	Loss: 115.165
-19200/69092	Loss: 116.537
-22400/69092	Loss: 114.890
-25600/69092	Loss: 115.084
-28800/69092	Loss: 113.722
-32000/69092	Loss: 115.406
-35200/69092	Loss: 117.413
-38400/69092	Loss: 116.108
-41600/69092	Loss: 114.091
-44800/69092	Loss: 114.189
-48000/69092	Loss: 113.336
-51200/69092	Loss: 114.854
-54400/69092	Loss: 114.296
-57600/69092	Loss: 114.604
-60800/69092	Loss: 113.472
-64000/69092	Loss: 114.589
-67200/69092	Loss: 113.700
-Training time 0:04:15.871955
-Epoch: 107 Average loss: 114.78
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 110)
-0/69092	Loss: 110.384
-3200/69092	Loss: 115.819
-6400/69092	Loss: 115.273
-9600/69092	Loss: 113.674
-12800/69092	Loss: 113.988
-16000/69092	Loss: 115.226
-19200/69092	Loss: 114.639
-22400/69092	Loss: 115.910
-25600/69092	Loss: 116.044
-28800/69092	Loss: 115.133
-32000/69092	Loss: 113.307
-35200/69092	Loss: 113.846
-38400/69092	Loss: 113.773
-41600/69092	Loss: 114.995
-44800/69092	Loss: 116.288
-48000/69092	Loss: 117.788
-51200/69092	Loss: 115.265
-54400/69092	Loss: 113.632
-57600/69092	Loss: 113.777
-60800/69092	Loss: 112.919
-64000/69092	Loss: 114.026
-67200/69092	Loss: 114.251
-Training time 0:04:15.986438
-Epoch: 108 Average loss: 114.82
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 111)
-0/69092	Loss: 110.926
-3200/69092	Loss: 115.009
-6400/69092	Loss: 113.864
-9600/69092	Loss: 112.513
-12800/69092	Loss: 115.393
-16000/69092	Loss: 115.601
-19200/69092	Loss: 114.252
-22400/69092	Loss: 114.202
-25600/69092	Loss: 114.249
-28800/69092	Loss: 115.185
-32000/69092	Loss: 114.265
-35200/69092	Loss: 113.490
-38400/69092	Loss: 113.867
-41600/69092	Loss: 115.186
-44800/69092	Loss: 115.967
-48000/69092	Loss: 111.777
-51200/69092	Loss: 113.831
-54400/69092	Loss: 116.026
-57600/69092	Loss: 113.335
-60800/69092	Loss: 115.290
-64000/69092	Loss: 116.204
-67200/69092	Loss: 114.208
-Training time 0:04:20.591390
-Epoch: 109 Average loss: 114.50
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 112)
-0/69092	Loss: 115.884
-3200/69092	Loss: 117.003
-6400/69092	Loss: 116.712
-9600/69092	Loss: 114.350
-12800/69092	Loss: 114.647
-16000/69092	Loss: 116.077
-19200/69092	Loss: 113.585
-22400/69092	Loss: 115.143
-25600/69092	Loss: 113.691
-28800/69092	Loss: 115.322
-32000/69092	Loss: 114.797
-35200/69092	Loss: 115.077
-38400/69092	Loss: 118.525
-41600/69092	Loss: 113.060
-44800/69092	Loss: 113.783
-48000/69092	Loss: 116.291
-51200/69092	Loss: 113.717
-54400/69092	Loss: 115.111
-57600/69092	Loss: 113.641
-60800/69092	Loss: 114.002
-64000/69092	Loss: 113.758
-67200/69092	Loss: 113.087
-Training time 0:04:26.836611
-Epoch: 110 Average loss: 114.81
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 113)
-0/69092	Loss: 125.851
-3200/69092	Loss: 116.071
-6400/69092	Loss: 112.736
-9600/69092	Loss: 115.678
-12800/69092	Loss: 114.922
-16000/69092	Loss: 113.817
-19200/69092	Loss: 115.561
-22400/69092	Loss: 113.305
-25600/69092	Loss: 112.979
-28800/69092	Loss: 116.145
-32000/69092	Loss: 114.480
-35200/69092	Loss: 116.515
-38400/69092	Loss: 113.043
-41600/69092	Loss: 112.938
-44800/69092	Loss: 114.279
-48000/69092	Loss: 114.374
-51200/69092	Loss: 114.732
-54400/69092	Loss: 113.942
-57600/69092	Loss: 115.297
-60800/69092	Loss: 114.086
-64000/69092	Loss: 114.664
-67200/69092	Loss: 115.492
-Training time 0:04:35.629081
-Epoch: 111 Average loss: 114.54
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 114)
-0/69092	Loss: 104.279
-3200/69092	Loss: 115.154
-6400/69092	Loss: 113.715
-9600/69092	Loss: 113.930
-12800/69092	Loss: 114.900
-16000/69092	Loss: 113.268
-19200/69092	Loss: 113.740
-22400/69092	Loss: 114.593
-25600/69092	Loss: 114.678
-28800/69092	Loss: 115.832
-32000/69092	Loss: 115.583
-35200/69092	Loss: 114.260
-38400/69092	Loss: 115.078
-41600/69092	Loss: 113.106
-44800/69092	Loss: 115.660
-48000/69092	Loss: 115.305
-51200/69092	Loss: 115.483
-54400/69092	Loss: 113.941
-57600/69092	Loss: 114.990
-60800/69092	Loss: 115.176
-64000/69092	Loss: 113.339
-67200/69092	Loss: 114.725
-Training time 0:04:29.687402
-Epoch: 112 Average loss: 114.54
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 115)
-0/69092	Loss: 102.917
-3200/69092	Loss: 114.706
-6400/69092	Loss: 115.422
-9600/69092	Loss: 116.626
-12800/69092	Loss: 117.345
-16000/69092	Loss: 115.406
-19200/69092	Loss: 113.189
-22400/69092	Loss: 114.186
-25600/69092	Loss: 113.471
-28800/69092	Loss: 113.179
-32000/69092	Loss: 112.903
-35200/69092	Loss: 112.962
-38400/69092	Loss: 112.716
-41600/69092	Loss: 114.636
-44800/69092	Loss: 117.224
-48000/69092	Loss: 113.902
-51200/69092	Loss: 114.505
-54400/69092	Loss: 114.530
-57600/69092	Loss: 114.777
-60800/69092	Loss: 114.589
-64000/69092	Loss: 114.345
-67200/69092	Loss: 114.994
-Training time 0:04:30.520088
-Epoch: 113 Average loss: 114.54
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 116)
-0/69092	Loss: 113.905
-3200/69092	Loss: 117.563
-6400/69092	Loss: 113.548
-9600/69092	Loss: 115.576
-12800/69092	Loss: 114.451
-16000/69092	Loss: 115.835
-19200/69092	Loss: 114.568
-22400/69092	Loss: 114.740
-25600/69092	Loss: 114.518
-28800/69092	Loss: 115.925
-32000/69092	Loss: 111.706
-35200/69092	Loss: 113.438
-38400/69092	Loss: 113.781
-41600/69092	Loss: 115.378
-44800/69092	Loss: 114.766
-48000/69092	Loss: 112.897
-51200/69092	Loss: 111.760
-54400/69092	Loss: 113.607
-57600/69092	Loss: 114.396
-60800/69092	Loss: 115.229
-64000/69092	Loss: 115.306
-67200/69092	Loss: 115.853
-Training time 0:04:29.241112
-Epoch: 114 Average loss: 114.59
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 117)
-0/69092	Loss: 104.253
-3200/69092	Loss: 114.970
-6400/69092	Loss: 113.504
-9600/69092	Loss: 114.310
-12800/69092	Loss: 114.699
-16000/69092	Loss: 113.594
-19200/69092	Loss: 113.744
-22400/69092	Loss: 114.847
-25600/69092	Loss: 114.582
-28800/69092	Loss: 114.003
-32000/69092	Loss: 114.017
-35200/69092	Loss: 113.475
-38400/69092	Loss: 115.256
-41600/69092	Loss: 116.657
-44800/69092	Loss: 114.122
-48000/69092	Loss: 113.792
-51200/69092	Loss: 113.483
-54400/69092	Loss: 114.718
-57600/69092	Loss: 115.763
-60800/69092	Loss: 115.972
-64000/69092	Loss: 114.327
-67200/69092	Loss: 114.012
-Training time 0:04:27.381340
-Epoch: 115 Average loss: 114.45
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 118)
-0/69092	Loss: 110.148
-3200/69092	Loss: 114.791
-6400/69092	Loss: 115.172
-9600/69092	Loss: 114.780
-12800/69092	Loss: 115.101
-16000/69092	Loss: 115.442
-19200/69092	Loss: 114.977
-22400/69092	Loss: 113.353
-25600/69092	Loss: 114.346
-28800/69092	Loss: 115.027
-32000/69092	Loss: 113.964
-35200/69092	Loss: 115.722
-38400/69092	Loss: 113.181
-41600/69092	Loss: 114.651
-44800/69092	Loss: 111.235
-48000/69092	Loss: 114.792
-51200/69092	Loss: 113.957
-54400/69092	Loss: 113.560
-57600/69092	Loss: 116.211
-60800/69092	Loss: 115.072
-64000/69092	Loss: 113.563
-67200/69092	Loss: 113.980
-Training time 0:04:18.154084
-Epoch: 116 Average loss: 114.42
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 119)
-0/69092	Loss: 120.825
-3200/69092	Loss: 115.325
-6400/69092	Loss: 113.553
-9600/69092	Loss: 113.736
-12800/69092	Loss: 116.343
-16000/69092	Loss: 115.969
-19200/69092	Loss: 115.205
-22400/69092	Loss: 115.081
-25600/69092	Loss: 113.776
-28800/69092	Loss: 114.976
-32000/69092	Loss: 114.686
-35200/69092	Loss: 113.304
-38400/69092	Loss: 115.544
-41600/69092	Loss: 112.245
-44800/69092	Loss: 113.748
-48000/69092	Loss: 113.974
-51200/69092	Loss: 114.691
-54400/69092	Loss: 115.770
-57600/69092	Loss: 113.741
-60800/69092	Loss: 113.169
-64000/69092	Loss: 114.951
-67200/69092	Loss: 116.248
-Training time 0:04:18.164247
-Epoch: 117 Average loss: 114.55
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 120)
-0/69092	Loss: 109.928
-3200/69092	Loss: 115.008
-6400/69092	Loss: 113.864
-9600/69092	Loss: 114.838
-12800/69092	Loss: 114.410
-16000/69092	Loss: 114.373
-19200/69092	Loss: 114.011
-22400/69092	Loss: 117.361
-25600/69092	Loss: 116.671
-28800/69092	Loss: 112.738
-32000/69092	Loss: 115.302
-35200/69092	Loss: 115.476
-38400/69092	Loss: 112.458
-41600/69092	Loss: 115.842
-44800/69092	Loss: 114.128
-48000/69092	Loss: 116.531
-51200/69092	Loss: 113.945
-54400/69092	Loss: 113.735
-57600/69092	Loss: 113.663
-60800/69092	Loss: 113.893
-64000/69092	Loss: 114.535
-67200/69092	Loss: 111.640
-Training time 0:04:21.234648
-Epoch: 118 Average loss: 114.48
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 121)
-0/69092	Loss: 112.347
-3200/69092	Loss: 112.206
-6400/69092	Loss: 113.684
-9600/69092	Loss: 115.071
-12800/69092	Loss: 114.938
-16000/69092	Loss: 113.291
-19200/69092	Loss: 114.991
-22400/69092	Loss: 114.087
-25600/69092	Loss: 113.994
-28800/69092	Loss: 114.996
-32000/69092	Loss: 114.884
-35200/69092	Loss: 113.972
-38400/69092	Loss: 115.015
-41600/69092	Loss: 114.189
-44800/69092	Loss: 115.831
-48000/69092	Loss: 115.978
-51200/69092	Loss: 115.122
-54400/69092	Loss: 113.542
-57600/69092	Loss: 113.660
-60800/69092	Loss: 115.409
-64000/69092	Loss: 113.369
-67200/69092	Loss: 113.812
-Training time 0:04:21.509968
-Epoch: 119 Average loss: 114.42
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 122)
-0/69092	Loss: 110.610
-3200/69092	Loss: 114.091
-6400/69092	Loss: 114.390
-9600/69092	Loss: 113.732
-12800/69092	Loss: 113.679
-16000/69092	Loss: 114.955
-19200/69092	Loss: 112.877
-22400/69092	Loss: 115.253
-25600/69092	Loss: 113.215
-28800/69092	Loss: 116.279
-32000/69092	Loss: 114.028
-35200/69092	Loss: 113.391
-38400/69092	Loss: 114.243
-41600/69092	Loss: 114.268
-44800/69092	Loss: 113.596
-48000/69092	Loss: 116.315
-51200/69092	Loss: 114.467
-54400/69092	Loss: 113.734
-57600/69092	Loss: 115.470
-60800/69092	Loss: 114.426
-64000/69092	Loss: 112.766
-67200/69092	Loss: 116.240
-Training time 0:04:26.174176
-Epoch: 120 Average loss: 114.31
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 123)
-0/69092	Loss: 108.468
-3200/69092	Loss: 113.823
-6400/69092	Loss: 113.926
-9600/69092	Loss: 112.867
-12800/69092	Loss: 115.001
-16000/69092	Loss: 115.812
-19200/69092	Loss: 115.371
-22400/69092	Loss: 114.633
-25600/69092	Loss: 113.848
-28800/69092	Loss: 115.192
-32000/69092	Loss: 115.266
-35200/69092	Loss: 113.069
-38400/69092	Loss: 113.941
-41600/69092	Loss: 113.058
-44800/69092	Loss: 115.085
-48000/69092	Loss: 114.492
-51200/69092	Loss: 115.389
-54400/69092	Loss: 114.802
-57600/69092	Loss: 114.942
-60800/69092	Loss: 116.039
-64000/69092	Loss: 115.730
-67200/69092	Loss: 113.697
-Training time 0:04:26.866437
-Epoch: 121 Average loss: 114.57
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 124)
-0/69092	Loss: 120.564
-3200/69092	Loss: 113.837
-6400/69092	Loss: 113.033
-9600/69092	Loss: 114.391
-12800/69092	Loss: 112.768
-16000/69092	Loss: 115.145
-19200/69092	Loss: 113.677
-22400/69092	Loss: 114.920
-25600/69092	Loss: 113.847
-28800/69092	Loss: 113.706
-32000/69092	Loss: 114.759
-35200/69092	Loss: 113.610
-38400/69092	Loss: 111.903
-41600/69092	Loss: 114.020
-44800/69092	Loss: 115.250
-48000/69092	Loss: 113.698
-51200/69092	Loss: 115.873
-54400/69092	Loss: 114.153
-57600/69092	Loss: 116.196
-60800/69092	Loss: 114.086
-64000/69092	Loss: 115.693
-67200/69092	Loss: 112.606
-Training time 0:04:26.091665
-Epoch: 122 Average loss: 114.24
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 125)
-0/69092	Loss: 116.260
-3200/69092	Loss: 115.572
-6400/69092	Loss: 113.364
-9600/69092	Loss: 114.549
-12800/69092	Loss: 112.625
-16000/69092	Loss: 114.697
-19200/69092	Loss: 114.014
-22400/69092	Loss: 114.915
-25600/69092	Loss: 113.741
-28800/69092	Loss: 113.853
-32000/69092	Loss: 113.749
-35200/69092	Loss: 114.529
-38400/69092	Loss: 115.581
-41600/69092	Loss: 113.452
-44800/69092	Loss: 114.885
-48000/69092	Loss: 114.452
-51200/69092	Loss: 113.929
-54400/69092	Loss: 113.919
-57600/69092	Loss: 112.803
-60800/69092	Loss: 116.090
-64000/69092	Loss: 113.673
-67200/69092	Loss: 114.878
-Training time 0:04:20.195962
-Epoch: 123 Average loss: 114.29
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 126)
-0/69092	Loss: 102.904
-3200/69092	Loss: 114.950
-6400/69092	Loss: 114.285
-9600/69092	Loss: 113.059
-12800/69092	Loss: 114.756
-16000/69092	Loss: 114.081
-19200/69092	Loss: 116.571
-22400/69092	Loss: 115.653
-25600/69092	Loss: 114.392
-28800/69092	Loss: 112.085
-32000/69092	Loss: 115.196
-35200/69092	Loss: 115.576
-38400/69092	Loss: 112.741
-41600/69092	Loss: 114.079
-44800/69092	Loss: 112.967
-48000/69092	Loss: 114.430
-51200/69092	Loss: 113.740
-54400/69092	Loss: 112.241
-57600/69092	Loss: 115.875
-60800/69092	Loss: 115.158
-64000/69092	Loss: 113.297
-67200/69092	Loss: 113.612
-Training time 0:04:21.333011
-Epoch: 124 Average loss: 114.22
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 127)
-0/69092	Loss: 121.503
-3200/69092	Loss: 113.770
-6400/69092	Loss: 113.968
-9600/69092	Loss: 115.570
-12800/69092	Loss: 113.710
-16000/69092	Loss: 115.004
-19200/69092	Loss: 115.766
-22400/69092	Loss: 110.885
-25600/69092	Loss: 116.284
-28800/69092	Loss: 115.173
-32000/69092	Loss: 115.608
-35200/69092	Loss: 113.924
-38400/69092	Loss: 113.265
-41600/69092	Loss: 111.167
-44800/69092	Loss: 114.134
-48000/69092	Loss: 113.143
-51200/69092	Loss: 111.826
-54400/69092	Loss: 114.397
-57600/69092	Loss: 113.879
-60800/69092	Loss: 115.124
-64000/69092	Loss: 115.703
-67200/69092	Loss: 113.662
-Training time 0:04:15.476898
-Epoch: 125 Average loss: 114.16
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 128)
-0/69092	Loss: 126.295
-3200/69092	Loss: 116.617
-6400/69092	Loss: 114.015
-9600/69092	Loss: 113.958
-12800/69092	Loss: 113.577
-16000/69092	Loss: 115.547
-19200/69092	Loss: 113.954
-22400/69092	Loss: 113.498
-25600/69092	Loss: 115.607
-28800/69092	Loss: 111.954
-32000/69092	Loss: 113.496
-35200/69092	Loss: 113.677
-38400/69092	Loss: 114.765
-41600/69092	Loss: 115.236
-44800/69092	Loss: 114.410
-48000/69092	Loss: 114.308
-51200/69092	Loss: 111.934
-54400/69092	Loss: 113.136
-57600/69092	Loss: 115.013
-60800/69092	Loss: 114.129
-64000/69092	Loss: 113.522
-67200/69092	Loss: 113.718
-Training time 0:04:28.347039
-Epoch: 126 Average loss: 114.13
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 129)
-0/69092	Loss: 119.682
-3200/69092	Loss: 114.543
-6400/69092	Loss: 112.717
-9600/69092	Loss: 114.208
-12800/69092	Loss: 112.800
-16000/69092	Loss: 112.286
-19200/69092	Loss: 114.785
-22400/69092	Loss: 112.812
-25600/69092	Loss: 114.896
-28800/69092	Loss: 113.281
-32000/69092	Loss: 114.762
-35200/69092	Loss: 114.394
-38400/69092	Loss: 114.450
-41600/69092	Loss: 115.436
-44800/69092	Loss: 115.494
-48000/69092	Loss: 115.467
-51200/69092	Loss: 116.115
-54400/69092	Loss: 113.398
-57600/69092	Loss: 115.479
-60800/69092	Loss: 113.288
-64000/69092	Loss: 112.189
-67200/69092	Loss: 113.367
-Training time 0:04:24.226082
-Epoch: 127 Average loss: 114.09
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 130)
-0/69092	Loss: 108.846
-3200/69092	Loss: 113.563
-6400/69092	Loss: 113.164
-9600/69092	Loss: 113.377
-12800/69092	Loss: 113.232
-16000/69092	Loss: 115.017
-19200/69092	Loss: 114.418
-22400/69092	Loss: 113.556
-25600/69092	Loss: 113.549
-28800/69092	Loss: 114.477
-32000/69092	Loss: 113.487
-35200/69092	Loss: 115.755
-38400/69092	Loss: 113.114
-41600/69092	Loss: 112.750
-44800/69092	Loss: 114.507
-48000/69092	Loss: 113.253
-51200/69092	Loss: 114.938
-54400/69092	Loss: 114.313
-57600/69092	Loss: 113.574
-60800/69092	Loss: 115.895
-64000/69092	Loss: 112.618
-67200/69092	Loss: 115.091
-Training time 0:04:26.704741
-Epoch: 128 Average loss: 114.00
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 131)
-0/69092	Loss: 116.501
-3200/69092	Loss: 115.050
-6400/69092	Loss: 113.112
-9600/69092	Loss: 113.652
-12800/69092	Loss: 114.119
-16000/69092	Loss: 112.435
-19200/69092	Loss: 113.488
-22400/69092	Loss: 113.293
-25600/69092	Loss: 114.508
-28800/69092	Loss: 113.832
-32000/69092	Loss: 115.152
-35200/69092	Loss: 114.316
-38400/69092	Loss: 113.723
-41600/69092	Loss: 112.864
-44800/69092	Loss: 114.576
-48000/69092	Loss: 114.918
-51200/69092	Loss: 114.874
-54400/69092	Loss: 115.212
-57600/69092	Loss: 114.723
-60800/69092	Loss: 114.521
-64000/69092	Loss: 114.774
-67200/69092	Loss: 114.068
-Training time 0:04:24.372506
-Epoch: 129 Average loss: 114.13
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 132)
-0/69092	Loss: 115.373
-3200/69092	Loss: 112.991
-6400/69092	Loss: 115.433
-9600/69092	Loss: 114.669
-12800/69092	Loss: 114.506
-16000/69092	Loss: 113.683
-19200/69092	Loss: 113.663
-22400/69092	Loss: 113.430
-25600/69092	Loss: 114.815
-28800/69092	Loss: 112.627
-32000/69092	Loss: 115.622
-35200/69092	Loss: 112.702
-38400/69092	Loss: 112.591
-41600/69092	Loss: 115.371
-44800/69092	Loss: 115.227
-48000/69092	Loss: 114.349
-51200/69092	Loss: 115.599
-54400/69092	Loss: 114.853
-57600/69092	Loss: 113.642
-60800/69092	Loss: 112.977
-64000/69092	Loss: 112.816
-67200/69092	Loss: 115.588
-Training time 0:04:25.369487
-Epoch: 130 Average loss: 114.15
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 133)
-0/69092	Loss: 107.789
-3200/69092	Loss: 112.483
-6400/69092	Loss: 115.576
-9600/69092	Loss: 115.011
-12800/69092	Loss: 113.660
-16000/69092	Loss: 115.363
-19200/69092	Loss: 115.619
-22400/69092	Loss: 113.759
-25600/69092	Loss: 111.395
-28800/69092	Loss: 112.099
-32000/69092	Loss: 114.761
-35200/69092	Loss: 113.568
-38400/69092	Loss: 114.094
-41600/69092	Loss: 114.263
-44800/69092	Loss: 115.484
-48000/69092	Loss: 113.398
-51200/69092	Loss: 114.657
-54400/69092	Loss: 114.476
-57600/69092	Loss: 113.724
-60800/69092	Loss: 113.923
-64000/69092	Loss: 113.543
-67200/69092	Loss: 113.137
-Training time 0:04:23.321508
-Epoch: 131 Average loss: 114.05
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 134)
-0/69092	Loss: 123.004
-3200/69092	Loss: 114.564
-6400/69092	Loss: 113.602
-9600/69092	Loss: 113.447
-12800/69092	Loss: 113.275
-16000/69092	Loss: 114.056
-19200/69092	Loss: 114.602
-22400/69092	Loss: 114.670
-25600/69092	Loss: 113.045
-28800/69092	Loss: 113.388
-32000/69092	Loss: 113.867
-35200/69092	Loss: 115.408
-38400/69092	Loss: 114.099
-41600/69092	Loss: 113.591
-44800/69092	Loss: 113.610
-48000/69092	Loss: 113.645
-51200/69092	Loss: 113.597
-54400/69092	Loss: 113.680
-57600/69092	Loss: 114.309
-60800/69092	Loss: 113.205
-64000/69092	Loss: 113.916
-67200/69092	Loss: 117.059
-Training time 0:04:20.153807
-Epoch: 132 Average loss: 114.00
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 135)
-0/69092	Loss: 108.133
-3200/69092	Loss: 112.305
-6400/69092	Loss: 113.728
-9600/69092	Loss: 111.871
-12800/69092	Loss: 112.839
-16000/69092	Loss: 113.411
-19200/69092	Loss: 114.898
-22400/69092	Loss: 112.742
-25600/69092	Loss: 113.579
-28800/69092	Loss: 113.920
-32000/69092	Loss: 115.454
-35200/69092	Loss: 112.449
-38400/69092	Loss: 113.912
-41600/69092	Loss: 113.215
-44800/69092	Loss: 114.048
-48000/69092	Loss: 115.217
-51200/69092	Loss: 113.958
-54400/69092	Loss: 115.548
-57600/69092	Loss: 115.074
-60800/69092	Loss: 114.615
-64000/69092	Loss: 114.327
-67200/69092	Loss: 114.783
-Training time 0:04:13.651886
-Epoch: 133 Average loss: 113.98
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 136)
-0/69092	Loss: 112.990
-3200/69092	Loss: 112.850
-6400/69092	Loss: 111.781
-9600/69092	Loss: 114.440
-12800/69092	Loss: 113.950
-16000/69092	Loss: 113.776
-19200/69092	Loss: 116.008
-22400/69092	Loss: 114.935
-25600/69092	Loss: 113.381
-28800/69092	Loss: 115.506
-32000/69092	Loss: 113.739
-35200/69092	Loss: 115.350
-38400/69092	Loss: 113.398
-41600/69092	Loss: 112.783
-44800/69092	Loss: 114.051
-48000/69092	Loss: 113.729
-51200/69092	Loss: 111.615
-54400/69092	Loss: 112.875
-57600/69092	Loss: 113.566
-60800/69092	Loss: 112.446
-64000/69092	Loss: 115.136
-67200/69092	Loss: 113.092
-Training time 0:04:03.871304
-Epoch: 134 Average loss: 113.86
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 137)
-0/69092	Loss: 113.820
-3200/69092	Loss: 113.023
-6400/69092	Loss: 113.605
-9600/69092	Loss: 115.006
-12800/69092	Loss: 113.062
-16000/69092	Loss: 113.313
-19200/69092	Loss: 113.548
-22400/69092	Loss: 113.821
-25600/69092	Loss: 113.696
-28800/69092	Loss: 113.847
-32000/69092	Loss: 112.898
-35200/69092	Loss: 115.648
-38400/69092	Loss: 115.076
-41600/69092	Loss: 112.041
-44800/69092	Loss: 112.616
-48000/69092	Loss: 114.295
-51200/69092	Loss: 114.063
-54400/69092	Loss: 114.141
-57600/69092	Loss: 113.066
-60800/69092	Loss: 114.087
-64000/69092	Loss: 116.195
-67200/69092	Loss: 113.690
-Training time 0:04:07.117686
-Epoch: 135 Average loss: 113.87
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 138)
-0/69092	Loss: 117.427
-3200/69092	Loss: 114.269
-6400/69092	Loss: 115.480
-9600/69092	Loss: 112.917
-12800/69092	Loss: 114.796
-16000/69092	Loss: 113.584
-19200/69092	Loss: 113.838
-22400/69092	Loss: 114.711
-25600/69092	Loss: 112.946
-28800/69092	Loss: 112.712
-32000/69092	Loss: 113.911
-35200/69092	Loss: 113.644
-38400/69092	Loss: 115.387
-41600/69092	Loss: 114.737
-44800/69092	Loss: 113.171
-48000/69092	Loss: 113.791
-51200/69092	Loss: 113.560
-54400/69092	Loss: 113.901
-57600/69092	Loss: 115.148
-60800/69092	Loss: 112.369
-64000/69092	Loss: 112.151
-67200/69092	Loss: 115.114
-Training time 0:04:02.716749
-Epoch: 136 Average loss: 113.90
-=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 139)
-0/69092	Loss: 109.092
-3200/69092	Loss: 115.821
-6400/69092	Loss: 112.875
-9600/69092	Loss: 114.357
-12800/69092	Loss: 114.035
-16000/69092	Loss: 112.059
-19200/69092	Loss: 114.598
-22400/69092	Loss: 113.213
-25600/69092	Loss: 113.436
-28800/69092	Loss: 115.048
-32000/69092	Loss: 113.937
-35200/69092	Loss: 112.612
-38400/69092	Loss: 114.134
diff --git a/OAR.2066988.stderr b/OAR.2068271.stderr
similarity index 82%
rename from OAR.2066988.stderr
rename to OAR.2068271.stderr
index e842733f51f7c87aabeda14f050c0fa2ff041395..64fb0257ff2d4940108fdddee63b89ebd4dda025 100644
--- a/OAR.2066988.stderr
+++ b/OAR.2068271.stderr
@@ -1,3 +1,3 @@
 /data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
   warnings.warn(warning.format(ret))
-## OAR [2020-06-24 06:43:10] Job 2066988 KILLED ##
+## OAR [2020-06-24 16:29:57] Job 2068271 KILLED ##
diff --git a/OAR.2068271.stdout b/OAR.2068271.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..e1e66006f73bafdd83f9f52654868534917a9953
--- /dev/null
+++ b/OAR.2068271.stdout
@@ -0,0 +1,68 @@
+Namespace(batch_size=256, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_256', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=True, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last (iter 171)'
+0/69092	Loss: 147.665
+12800/69092	Loss: 158.181
+25600/69092	Loss: 159.590
+38400/69092	Loss: 158.551
+51200/69092	Loss: 158.112
+64000/69092	Loss: 157.238
+Training time 0:03:48.393375
+Epoch: 1 Average loss: 158.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 172)
+0/69092	Loss: 159.925
+12800/69092	Loss: 158.833
+25600/69092	Loss: 159.460
+38400/69092	Loss: 157.054
+51200/69092	Loss: 157.525
+64000/69092	Loss: 158.482
+Training time 0:03:41.025229
+Epoch: 2 Average loss: 158.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 173)
+0/69092	Loss: 152.283
+12800/69092	Loss: 157.230
+25600/69092	Loss: 157.103
+38400/69092	Loss: 158.817
diff --git a/OAR.2066989.stderr b/OAR.2068272.stderr
similarity index 82%
rename from OAR.2066989.stderr
rename to OAR.2068272.stderr
index 234323fdd0c0cd9b7dadab71ac41d247f46869b1..234dbc35579aaa2431331d87e98153eda0f1c969 100644
--- a/OAR.2066989.stderr
+++ b/OAR.2068272.stderr
@@ -1,3 +1,3 @@
 /data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
   warnings.warn(warning.format(ret))
-## OAR [2020-06-24 07:00:02] Job 2066989 KILLED ##
+## OAR [2020-06-24 16:29:57] Job 2068272 KILLED ##
diff --git a/OAR.2068272.stdout b/OAR.2068272.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..1f62614dd1e4133b46f4e3e6465ff77705ee0df7
--- /dev/null
+++ b/OAR.2068272.stdout
@@ -0,0 +1,174 @@
+Namespace(batch_size=64, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_64', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=True, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+Tesla K80
+Tesla K80
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last (iter 304)'
+0/69092	Loss: 153.033
+3200/69092	Loss: 151.558
+6400/69092	Loss: 150.062
+9600/69092	Loss: 154.221
+12800/69092	Loss: 151.615
+16000/69092	Loss: 153.931
+19200/69092	Loss: 153.015
+22400/69092	Loss: 153.433
+25600/69092	Loss: 151.584
+28800/69092	Loss: 154.002
+32000/69092	Loss: 153.302
+35200/69092	Loss: 151.154
+38400/69092	Loss: 152.741
+41600/69092	Loss: 153.137
+44800/69092	Loss: 152.327
+48000/69092	Loss: 152.095
+51200/69092	Loss: 150.511
+54400/69092	Loss: 151.692
+57600/69092	Loss: 151.058
+60800/69092	Loss: 155.055
+64000/69092	Loss: 150.898
+67200/69092	Loss: 155.036
+Training time 0:01:58.162781
+Epoch: 1 Average loss: 152.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 305)
+0/69092	Loss: 143.092
+3200/69092	Loss: 153.010
+6400/69092	Loss: 155.530
+9600/69092	Loss: 155.364
+12800/69092	Loss: 151.123
+16000/69092	Loss: 154.058
+19200/69092	Loss: 151.657
+22400/69092	Loss: 153.832
+25600/69092	Loss: 151.606
+28800/69092	Loss: 153.287
+32000/69092	Loss: 150.634
+35200/69092	Loss: 152.244
+38400/69092	Loss: 152.817
+41600/69092	Loss: 152.458
+44800/69092	Loss: 153.017
+48000/69092	Loss: 151.141
+51200/69092	Loss: 154.387
+54400/69092	Loss: 149.705
+57600/69092	Loss: 151.770
+60800/69092	Loss: 151.618
+64000/69092	Loss: 152.999
+67200/69092	Loss: 154.086
+Training time 0:01:57.931589
+Epoch: 2 Average loss: 152.66
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 306)
+0/69092	Loss: 149.850
+3200/69092	Loss: 153.249
+6400/69092	Loss: 153.873
+9600/69092	Loss: 153.335
+12800/69092	Loss: 153.749
+16000/69092	Loss: 148.933
+19200/69092	Loss: 154.976
+22400/69092	Loss: 153.382
+25600/69092	Loss: 151.603
+28800/69092	Loss: 152.808
+32000/69092	Loss: 151.032
+35200/69092	Loss: 151.926
+38400/69092	Loss: 155.662
+41600/69092	Loss: 150.252
+44800/69092	Loss: 152.976
+48000/69092	Loss: 153.162
+51200/69092	Loss: 153.542
+54400/69092	Loss: 152.422
+57600/69092	Loss: 150.808
+60800/69092	Loss: 152.001
+64000/69092	Loss: 153.256
+67200/69092	Loss: 152.930
+Training time 0:01:57.656130
+Epoch: 3 Average loss: 152.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 307)
+0/69092	Loss: 161.186
+3200/69092	Loss: 151.119
+6400/69092	Loss: 154.165
+9600/69092	Loss: 150.897
+12800/69092	Loss: 152.695
+16000/69092	Loss: 151.460
+19200/69092	Loss: 154.138
+22400/69092	Loss: 153.239
+25600/69092	Loss: 152.014
+28800/69092	Loss: 151.173
+32000/69092	Loss: 155.464
+35200/69092	Loss: 154.407
+38400/69092	Loss: 150.135
+41600/69092	Loss: 154.000
+44800/69092	Loss: 152.920
+48000/69092	Loss: 151.982
+51200/69092	Loss: 155.144
+54400/69092	Loss: 151.637
+57600/69092	Loss: 150.082
+60800/69092	Loss: 153.631
+64000/69092	Loss: 152.659
+67200/69092	Loss: 153.400
+Training time 0:01:58.079109
+Epoch: 4 Average loss: 152.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 308)
+0/69092	Loss: 173.856
+3200/69092	Loss: 152.023
+6400/69092	Loss: 153.829
+9600/69092	Loss: 150.343
+12800/69092	Loss: 151.734
+16000/69092	Loss: 152.576
+19200/69092	Loss: 153.110
+22400/69092	Loss: 155.344
+25600/69092	Loss: 151.083
+28800/69092	Loss: 151.468
+32000/69092	Loss: 150.122
+35200/69092	Loss: 150.961
+38400/69092	Loss: 152.861
+41600/69092	Loss: 150.400
+44800/69092	Loss: 154.243
+48000/69092	Loss: 156.053
+51200/69092	Loss: 151.848
+54400/69092	Loss: 154.020
+57600/69092	Loss: 152.957
+60800/69092	Loss: 154.637
+64000/69092	Loss: 151.895
+67200/69092	Loss: 152.106
+Training time 0:01:58.719240
+Epoch: 5 Average loss: 152.53
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 309)
+0/69092	Loss: 161.423
+3200/69092	Loss: 151.636
+6400/69092	Loss: 150.785
diff --git a/OAR.2066987.stderr b/OAR.2068273.stderr
similarity index 82%
rename from OAR.2066987.stderr
rename to OAR.2068273.stderr
index 01f042ddbf3b684dce92eb842907f4494d809311..178871f770f24b43d66f4c5d4b6679a4329d7068 100644
--- a/OAR.2066987.stderr
+++ b/OAR.2068273.stderr
@@ -1,3 +1,3 @@
 /data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
   warnings.warn(warning.format(ret))
-## OAR [2020-06-24 03:51:26] Job 2066987 KILLED ##
+## OAR [2020-06-24 16:29:57] Job 2068273 KILLED ##
diff --git a/OAR.2068273.stdout b/OAR.2068273.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..bfda5c064b356aaa2f4601727900e8151c8b804a
--- /dev/null
+++ b/OAR.2068273.stdout
@@ -0,0 +1,60 @@
+Namespace(batch_size=256, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_256', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=True, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce GTX 1080 Ti
+GeForce GTX 1080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last (iter 143)'
+0/69092	Loss: 116.257
+12800/69092	Loss: 116.865
+25600/69092	Loss: 115.704
+38400/69092	Loss: 116.840
+51200/69092	Loss: 116.758
+64000/69092	Loss: 116.974
+Training time 0:05:34.767176
+Epoch: 1 Average loss: 116.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 144)
+0/69092	Loss: 115.425
+12800/69092	Loss: 116.366
+25600/69092	Loss: 115.519
+38400/69092	Loss: 116.832
+51200/69092	Loss: 116.514
diff --git a/OAR.2068274.stderr b/OAR.2068274.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..50eb79e2c931abe5c8da266838dc52573b76f71d
--- /dev/null
+++ b/OAR.2068274.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-24 16:29:57] Job 2068274 KILLED ##
diff --git a/OAR.2068274.stdout b/OAR.2068274.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..6b9b463ae6a29afa45236062556d897e73a1f4f3
--- /dev/null
+++ b/OAR.2068274.stdout
@@ -0,0 +1,102 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=True, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last (iter 139)'
+0/69092	Loss: 110.847
+3200/69092	Loss: 115.390
+6400/69092	Loss: 112.621
+9600/69092	Loss: 112.659
+12800/69092	Loss: 114.223
+16000/69092	Loss: 112.621
+19200/69092	Loss: 115.178
+22400/69092	Loss: 113.874
+25600/69092	Loss: 113.745
+28800/69092	Loss: 114.605
+32000/69092	Loss: 113.612
+35200/69092	Loss: 114.793
+38400/69092	Loss: 116.839
+41600/69092	Loss: 114.658
+44800/69092	Loss: 114.111
+48000/69092	Loss: 112.756
+51200/69092	Loss: 114.016
+54400/69092	Loss: 114.316
+57600/69092	Loss: 112.581
+60800/69092	Loss: 112.926
+64000/69092	Loss: 112.703
+67200/69092	Loss: 112.203
+Training time 0:04:30.608806
+Epoch: 1 Average loss: 113.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 140)
+0/69092	Loss: 102.792
+3200/69092	Loss: 112.805
+6400/69092	Loss: 114.190
+9600/69092	Loss: 114.078
+12800/69092	Loss: 113.312
+16000/69092	Loss: 112.534
+19200/69092	Loss: 113.381
+22400/69092	Loss: 114.327
+25600/69092	Loss: 114.343
+28800/69092	Loss: 114.635
+32000/69092	Loss: 114.228
+35200/69092	Loss: 113.063
+38400/69092	Loss: 113.853
+41600/69092	Loss: 114.526
+44800/69092	Loss: 113.829
+48000/69092	Loss: 114.176
+51200/69092	Loss: 113.607
+54400/69092	Loss: 113.869
+57600/69092	Loss: 113.135
+60800/69092	Loss: 113.620
+64000/69092	Loss: 114.794
+67200/69092	Loss: 113.848
+Training time 0:04:18.213843
+Epoch: 2 Average loss: 113.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 141)
+0/69092	Loss: 123.686
+3200/69092	Loss: 115.639
+6400/69092	Loss: 113.484
+9600/69092	Loss: 112.216
+12800/69092	Loss: 112.320
+16000/69092	Loss: 115.109
diff --git a/OAR.2068275.stderr b/OAR.2068275.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..b7fe79eccf10a8ea5c7ba3395907fbb9cc7d3077
--- /dev/null
+++ b/OAR.2068275.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-24 16:29:57] Job 2068275 KILLED ##
diff --git a/OAR.2068275.stdout b/OAR.2068275.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..437dd91e0f83cbc4b1e7b49daf49f31a9a540ff3
--- /dev/null
+++ b/OAR.2068275.stdout
@@ -0,0 +1,92 @@
+Namespace(batch_size=64, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_64_ls_15', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=15, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/beta_VAE_bs_64_ls_15
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=30, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=15, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 769185
+don't use continuous capacity
+=> no checkpoint found at 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last'
+0/69092	Loss: 2891.769
+3200/69092	Loss: 2825.642
+6400/69092	Loss: 1205.228
+9600/69092	Loss: 556.734
+12800/69092	Loss: 484.027
+16000/69092	Loss: 465.437
+19200/69092	Loss: 448.356
+22400/69092	Loss: 437.048
+25600/69092	Loss: 401.074
+28800/69092	Loss: 289.548
+32000/69092	Loss: 238.668
+35200/69092	Loss: 234.046
+38400/69092	Loss: 226.796
+41600/69092	Loss: 222.254
+44800/69092	Loss: 225.392
+48000/69092	Loss: 221.208
+51200/69092	Loss: 220.618
+54400/69092	Loss: 213.913
+57600/69092	Loss: 210.644
+60800/69092	Loss: 213.087
+64000/69092	Loss: 216.019
+67200/69092	Loss: 208.903
+Training time 0:04:53.937253
+Epoch: 1 Average loss: 460.47
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 1)
+0/69092	Loss: 188.720
+3200/69092	Loss: 204.200
+6400/69092	Loss: 204.811
+9600/69092	Loss: 200.863
+12800/69092	Loss: 202.928
+16000/69092	Loss: 205.920
+19200/69092	Loss: 195.467
+22400/69092	Loss: 199.383
+25600/69092	Loss: 200.195
+28800/69092	Loss: 196.327
+32000/69092	Loss: 196.155
+35200/69092	Loss: 193.812
+38400/69092	Loss: 193.730
+41600/69092	Loss: 194.544
+44800/69092	Loss: 196.956
+48000/69092	Loss: 190.843
+51200/69092	Loss: 192.657
+54400/69092	Loss: 189.354
+57600/69092	Loss: 192.162
+60800/69092	Loss: 189.223
diff --git a/OAR.2068276.stderr b/OAR.2068276.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..7819e1f177926fa86947793548686a04effb6ff1
--- /dev/null
+++ b/OAR.2068276.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-24 16:39:19] Job 2068276 KILLED ##
diff --git a/OAR.2068276.stdout b/OAR.2068276.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..c4b04675921651e4a6be48a71f59c3078a86c930
--- /dev/null
+++ b/OAR.2068276.stdout
@@ -0,0 +1,102 @@
+Namespace(batch_size=64, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_64_ls_20', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=20, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/beta_VAE_bs_64_ls_20
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=40, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=20, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 773035
+don't use continuous capacity
+=> no checkpoint found at 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last'
+0/69092	Loss: 3095.371
+3200/69092	Loss: 3013.162
+6400/69092	Loss: 1142.397
+9600/69092	Loss: 583.239
+12800/69092	Loss: 500.184
+16000/69092	Loss: 479.822
+19200/69092	Loss: 462.136
+22400/69092	Loss: 446.272
+25600/69092	Loss: 445.532
+28800/69092	Loss: 451.249
+32000/69092	Loss: 444.728
+35200/69092	Loss: 436.249
+38400/69092	Loss: 434.357
+41600/69092	Loss: 450.662
+44800/69092	Loss: 436.829
+48000/69092	Loss: 444.385
+51200/69092	Loss: 442.255
+54400/69092	Loss: 431.708
+57600/69092	Loss: 444.837
+60800/69092	Loss: 445.616
+64000/69092	Loss: 438.358
+67200/69092	Loss: 448.892
+Training time 0:04:22.264375
+Epoch: 1 Average loss: 608.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 1)
+0/69092	Loss: 419.617
+3200/69092	Loss: 441.728
+6400/69092	Loss: 436.680
+9600/69092	Loss: 440.386
+12800/69092	Loss: 437.215
+16000/69092	Loss: 439.624
+19200/69092	Loss: 436.731
+22400/69092	Loss: 436.506
+25600/69092	Loss: 439.104
+28800/69092	Loss: 433.798
+32000/69092	Loss: 443.554
+35200/69092	Loss: 442.369
+38400/69092	Loss: 441.461
+41600/69092	Loss: 428.932
+44800/69092	Loss: 458.124
+48000/69092	Loss: 440.610
+51200/69092	Loss: 433.183
+54400/69092	Loss: 445.103
+57600/69092	Loss: 448.074
+60800/69092	Loss: 437.783
+64000/69092	Loss: 442.622
+67200/69092	Loss: 435.505
+Training time 0:04:38.790417
+Epoch: 2 Average loss: 439.99
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 2)
+0/69092	Loss: 419.540
+3200/69092	Loss: 431.518
+6400/69092	Loss: 444.740
+9600/69092	Loss: 437.046
+12800/69092	Loss: 446.255
diff --git a/OAR.2068277.stderr b/OAR.2068277.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..be3f6a32321736ff39490147ea8294581f0c24d4
--- /dev/null
+++ b/OAR.2068277.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-24 16:40:03] Job 2068277 KILLED ##
diff --git a/OAR.2068277.stdout b/OAR.2068277.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..ce73d369bd9ab42767584651b3d97ac9e427a6d5
--- /dev/null
+++ b/OAR.2068277.stdout
@@ -0,0 +1,100 @@
+Namespace(batch_size=64, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_64_ls_5', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=5, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/beta_VAE_bs_64_ls_5
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=10, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=5, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 761485
+don't use continuous capacity
+=> no checkpoint found at 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last'
+0/69092	Loss: 2840.949
+3200/69092	Loss: 2696.334
+6400/69092	Loss: 878.800
+9600/69092	Loss: 529.528
+12800/69092	Loss: 473.136
+16000/69092	Loss: 459.979
+19200/69092	Loss: 427.518
+22400/69092	Loss: 312.154
+25600/69092	Loss: 251.443
+28800/69092	Loss: 243.150
+32000/69092	Loss: 238.743
+35200/69092	Loss: 232.739
+38400/69092	Loss: 233.067
+41600/69092	Loss: 230.312
+44800/69092	Loss: 223.895
+48000/69092	Loss: 230.252
+51200/69092	Loss: 228.705
+54400/69092	Loss: 232.979
+57600/69092	Loss: 229.967
+60800/69092	Loss: 224.165
+64000/69092	Loss: 224.971
+67200/69092	Loss: 228.978
+Training time 0:04:33.155919
+Epoch: 1 Average loss: 426.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 1)
+0/69092	Loss: 220.633
+3200/69092	Loss: 223.511
+6400/69092	Loss: 224.541
+9600/69092	Loss: 225.067
+12800/69092	Loss: 222.152
+16000/69092	Loss: 222.201
+19200/69092	Loss: 225.091
+22400/69092	Loss: 220.790
+25600/69092	Loss: 219.536
+28800/69092	Loss: 214.187
+32000/69092	Loss: 221.030
+35200/69092	Loss: 217.724
+38400/69092	Loss: 214.937
+41600/69092	Loss: 221.160
+44800/69092	Loss: 214.006
+48000/69092	Loss: 216.696
+51200/69092	Loss: 208.186
+54400/69092	Loss: 198.221
+57600/69092	Loss: 202.476
+60800/69092	Loss: 191.258
+64000/69092	Loss: 196.500
+67200/69092	Loss: 196.036
+Training time 0:04:43.151928
+Epoch: 2 Average loss: 213.44
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 2)
+0/69092	Loss: 181.796
+3200/69092	Loss: 190.527
+6400/69092	Loss: 192.437
diff --git a/OAR.2068278.stderr b/OAR.2068278.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..c77cae3370838accee6eb94abf3456bba5e2289c
--- /dev/null
+++ b/OAR.2068278.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-24 16:40:04] Job 2068278 KILLED ##
diff --git a/OAR.2068278.stdout b/OAR.2068278.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..b700e16e3787041ad67b307f1e9a11e16e3a49e2
--- /dev/null
+++ b/OAR.2068278.stdout
@@ -0,0 +1,90 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64_ls_5', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=5, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64_ls_5
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=10, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=5, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 761485
+don't use continuous capacity
+=> no checkpoint found at 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last'
+0/69092	Loss: 2903.199
+3200/69092	Loss: 2704.227
+6400/69092	Loss: 982.409
+9600/69092	Loss: 523.329
+12800/69092	Loss: 363.221
+16000/69092	Loss: 276.723
+19200/69092	Loss: 250.323
+22400/69092	Loss: 229.455
+25600/69092	Loss: 220.906
+28800/69092	Loss: 220.425
+32000/69092	Loss: 216.671
+35200/69092	Loss: 213.795
+38400/69092	Loss: 211.775
+41600/69092	Loss: 205.819
+44800/69092	Loss: 206.077
+48000/69092	Loss: 208.456
+51200/69092	Loss: 210.820
+54400/69092	Loss: 204.282
+57600/69092	Loss: 204.736
+60800/69092	Loss: 204.576
+64000/69092	Loss: 200.975
+67200/69092	Loss: 198.954
+Training time 0:05:45.368750
+Epoch: 1 Average loss: 390.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 1)
+0/69092	Loss: 193.590
+3200/69092	Loss: 194.946
+6400/69092	Loss: 199.709
+9600/69092	Loss: 200.276
+12800/69092	Loss: 193.180
+16000/69092	Loss: 192.735
+19200/69092	Loss: 190.256
+22400/69092	Loss: 190.013
+25600/69092	Loss: 188.325
+28800/69092	Loss: 189.229
+32000/69092	Loss: 189.882
+35200/69092	Loss: 190.248
+38400/69092	Loss: 188.566
+41600/69092	Loss: 185.072
+44800/69092	Loss: 189.564
+48000/69092	Loss: 181.238
+51200/69092	Loss: 181.543
+54400/69092	Loss: 182.526
diff --git a/OAR.2068279.stderr b/OAR.2068279.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..f9f388689b9788de750abba5c69e331b20d79694
--- /dev/null
+++ b/OAR.2068279.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-24 16:40:20] Job 2068279 KILLED ##
diff --git a/OAR.2068279.stdout b/OAR.2068279.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..5bf0bda7e7f175c90c5538aeaa5f3738fd10535c
--- /dev/null
+++ b/OAR.2068279.stdout
@@ -0,0 +1,111 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64_ls_15', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=15, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64_ls_15
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=30, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=15, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 769185
+don't use continuous capacity
+=> no checkpoint found at 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last'
+0/69092	Loss: 3015.823
+3200/69092	Loss: 2867.494
+6400/69092	Loss: 899.528
+9600/69092	Loss: 536.005
+12800/69092	Loss: 478.343
+16000/69092	Loss: 455.459
+19200/69092	Loss: 457.437
+22400/69092	Loss: 370.021
+25600/69092	Loss: 263.633
+28800/69092	Loss: 232.440
+32000/69092	Loss: 210.459
+35200/69092	Loss: 217.661
+38400/69092	Loss: 216.086
+41600/69092	Loss: 215.461
+44800/69092	Loss: 208.221
+48000/69092	Loss: 205.981
+51200/69092	Loss: 208.176
+54400/69092	Loss: 204.615
+57600/69092	Loss: 205.887
+60800/69092	Loss: 203.826
+64000/69092	Loss: 202.141
+67200/69092	Loss: 198.343
+Training time 0:03:51.208936
+Epoch: 1 Average loss: 427.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 1)
+0/69092	Loss: 187.281
+3200/69092	Loss: 200.427
+6400/69092	Loss: 196.083
+9600/69092	Loss: 202.029
+12800/69092	Loss: 196.254
+16000/69092	Loss: 196.466
+19200/69092	Loss: 195.587
+22400/69092	Loss: 192.062
+25600/69092	Loss: 197.137
+28800/69092	Loss: 196.870
+32000/69092	Loss: 193.763
+35200/69092	Loss: 196.194
+38400/69092	Loss: 193.444
+41600/69092	Loss: 186.353
+44800/69092	Loss: 184.125
+48000/69092	Loss: 179.607
+51200/69092	Loss: 181.214
+54400/69092	Loss: 179.105
+57600/69092	Loss: 173.470
+60800/69092	Loss: 163.793
+64000/69092	Loss: 163.068
+67200/69092	Loss: 164.580
+Training time 0:03:46.574199
+Epoch: 2 Average loss: 186.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 2)
+0/69092	Loss: 164.363
+3200/69092	Loss: 157.112
+6400/69092	Loss: 152.674
+9600/69092	Loss: 154.297
+12800/69092	Loss: 155.036
+16000/69092	Loss: 151.871
+19200/69092	Loss: 151.537
+22400/69092	Loss: 152.374
+25600/69092	Loss: 150.578
+28800/69092	Loss: 152.244
+32000/69092	Loss: 150.801
+35200/69092	Loss: 147.880
+38400/69092	Loss: 148.003
+41600/69092	Loss: 147.567
diff --git a/OAR.2068280.stderr b/OAR.2068280.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..048a3f185656040515f6e14835e645a9d37a919f
--- /dev/null
+++ b/OAR.2068280.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-24 16:40:20] Job 2068280 KILLED ##
diff --git a/OAR.2068280.stdout b/OAR.2068280.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..b2affcdd22ea7b2410a0b210923bdffa26cba17b
--- /dev/null
+++ b/OAR.2068280.stdout
@@ -0,0 +1,174 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64_ls_20', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=20, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64_ls_20
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+Tesla K80
+Tesla K80
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=40, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=20, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 773035
+don't use continuous capacity
+=> no checkpoint found at 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last'
+0/69092	Loss: 2881.933
+3200/69092	Loss: 2719.794
+6400/69092	Loss: 873.853
+9600/69092	Loss: 554.953
+12800/69092	Loss: 492.325
+16000/69092	Loss: 482.132
+19200/69092	Loss: 452.762
+22400/69092	Loss: 330.168
+25600/69092	Loss: 255.698
+28800/69092	Loss: 239.942
+32000/69092	Loss: 228.030
+35200/69092	Loss: 218.689
+38400/69092	Loss: 215.508
+41600/69092	Loss: 216.039
+44800/69092	Loss: 210.728
+48000/69092	Loss: 214.618
+51200/69092	Loss: 213.710
+54400/69092	Loss: 209.650
+57600/69092	Loss: 205.299
+60800/69092	Loss: 203.167
+64000/69092	Loss: 204.888
+67200/69092	Loss: 199.354
+Training time 0:01:59.952372
+Epoch: 1 Average loss: 422.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 1)
+0/69092	Loss: 213.822
+3200/69092	Loss: 195.598
+6400/69092	Loss: 188.690
+9600/69092	Loss: 189.246
+12800/69092	Loss: 185.152
+16000/69092	Loss: 182.199
+19200/69092	Loss: 178.716
+22400/69092	Loss: 175.190
+25600/69092	Loss: 173.751
+28800/69092	Loss: 173.114
+32000/69092	Loss: 169.251
+35200/69092	Loss: 165.858
+38400/69092	Loss: 162.986
+41600/69092	Loss: 160.892
+44800/69092	Loss: 157.779
+48000/69092	Loss: 159.470
+51200/69092	Loss: 155.455
+54400/69092	Loss: 153.642
+57600/69092	Loss: 152.026
+60800/69092	Loss: 154.318
+64000/69092	Loss: 149.577
+67200/69092	Loss: 149.924
+Training time 0:01:58.317271
+Epoch: 2 Average loss: 167.76
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 2)
+0/69092	Loss: 165.835
+3200/69092	Loss: 147.308
+6400/69092	Loss: 149.297
+9600/69092	Loss: 142.005
+12800/69092	Loss: 146.004
+16000/69092	Loss: 143.574
+19200/69092	Loss: 144.306
+22400/69092	Loss: 142.412
+25600/69092	Loss: 141.625
+28800/69092	Loss: 141.184
+32000/69092	Loss: 140.178
+35200/69092	Loss: 142.290
+38400/69092	Loss: 140.434
+41600/69092	Loss: 138.432
+44800/69092	Loss: 141.047
+48000/69092	Loss: 138.527
+51200/69092	Loss: 140.962
+54400/69092	Loss: 137.973
+57600/69092	Loss: 137.226
+60800/69092	Loss: 136.121
+64000/69092	Loss: 138.238
+67200/69092	Loss: 138.245
+Training time 0:01:59.670387
+Epoch: 3 Average loss: 141.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 3)
+0/69092	Loss: 142.078
+3200/69092	Loss: 136.431
+6400/69092	Loss: 135.250
+9600/69092	Loss: 136.257
+12800/69092	Loss: 135.379
+16000/69092	Loss: 135.078
+19200/69092	Loss: 134.206
+22400/69092	Loss: 135.185
+25600/69092	Loss: 132.867
+28800/69092	Loss: 137.278
+32000/69092	Loss: 134.216
+35200/69092	Loss: 134.020
+38400/69092	Loss: 130.385
+41600/69092	Loss: 135.043
+44800/69092	Loss: 133.981
+48000/69092	Loss: 132.545
+51200/69092	Loss: 135.545
+54400/69092	Loss: 134.631
+57600/69092	Loss: 131.294
+60800/69092	Loss: 132.418
+64000/69092	Loss: 131.634
+67200/69092	Loss: 133.258
+Training time 0:01:58.240577
+Epoch: 4 Average loss: 134.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 4)
+0/69092	Loss: 142.708
+3200/69092	Loss: 132.561
+6400/69092	Loss: 130.874
+9600/69092	Loss: 131.468
+12800/69092	Loss: 131.411
+16000/69092	Loss: 131.824
+19200/69092	Loss: 131.005
+22400/69092	Loss: 132.192
+25600/69092	Loss: 132.671
+28800/69092	Loss: 131.997
+32000/69092	Loss: 128.867
+35200/69092	Loss: 130.574
+38400/69092	Loss: 132.202
+41600/69092	Loss: 129.819
+44800/69092	Loss: 131.265
+48000/69092	Loss: 129.098
+51200/69092	Loss: 130.616
+54400/69092	Loss: 130.498
+57600/69092	Loss: 126.580
+60800/69092	Loss: 130.212
+64000/69092	Loss: 132.173
+67200/69092	Loss: 129.980
+Training time 0:01:58.078942
+Epoch: 5 Average loss: 130.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 5)
+0/69092	Loss: 124.281
+3200/69092	Loss: 128.822
diff --git a/OAR.2068281.stderr b/OAR.2068281.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..ed1a13f56d41ee95a0e31c7cb44f71d80ba6a091
--- /dev/null
+++ b/OAR.2068281.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-24 16:40:20] Job 2068281 KILLED ##
diff --git a/OAR.2068281.stdout b/OAR.2068281.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..88b79673c8ff4bd3b31434331aeba59f81b14c85
--- /dev/null
+++ b/OAR.2068281.stdout
@@ -0,0 +1,90 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64_ls_10_lr_5e_4', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0005, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64_ls_10_lr_5e_4
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce GTX 1080 Ti
+GeForce GTX 1080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> no checkpoint found at 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last'
+0/69092	Loss: 2961.939
+3200/69092	Loss: 1584.693
+6400/69092	Loss: 429.344
+9600/69092	Loss: 254.546
+12800/69092	Loss: 231.380
+16000/69092	Loss: 223.665
+19200/69092	Loss: 223.704
+22400/69092	Loss: 222.083
+25600/69092	Loss: 204.693
+28800/69092	Loss: 191.011
+32000/69092	Loss: 182.565
+35200/69092	Loss: 181.646
+38400/69092	Loss: 177.863
+41600/69092	Loss: 177.561
+44800/69092	Loss: 177.871
+48000/69092	Loss: 177.182
+51200/69092	Loss: 175.946
+54400/69092	Loss: 173.444
+57600/69092	Loss: 169.927
+60800/69092	Loss: 172.724
+64000/69092	Loss: 167.277
+67200/69092	Loss: 167.102
+Training time 0:05:38.196312
+Epoch: 1 Average loss: 269.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 1)
+0/69092	Loss: 163.101
+3200/69092	Loss: 165.132
+6400/69092	Loss: 164.033
+9600/69092	Loss: 164.653
+12800/69092	Loss: 165.870
+16000/69092	Loss: 162.765
+19200/69092	Loss: 159.483
+22400/69092	Loss: 160.846
+25600/69092	Loss: 150.316
+28800/69092	Loss: 153.128
+32000/69092	Loss: 151.751
+35200/69092	Loss: 149.352
+38400/69092	Loss: 153.455
+41600/69092	Loss: 151.018
+44800/69092	Loss: 146.968
+48000/69092	Loss: 149.090
+51200/69092	Loss: 146.556
+54400/69092	Loss: 146.958
diff --git a/OAR.2068282.stderr b/OAR.2068282.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..36785ebedbe71031f3320131a958815069374403
--- /dev/null
+++ b/OAR.2068282.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-24 16:43:44] Job 2068282 KILLED ##
diff --git a/OAR.2068282.stdout b/OAR.2068282.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..7edde933fe3841fcbfe63fc20e8fb3abc9736ec7
--- /dev/null
+++ b/OAR.2068282.stdout
@@ -0,0 +1,105 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64_ls_10_lr_1e_3', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64_ls_10_lr_1e_3
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce GTX 1080 Ti
+GeForce GTX 1080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> no checkpoint found at 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last'
+0/69092	Loss: 2701.321
+3200/69092	Loss: 815.006
+6400/69092	Loss: 448.151
+9600/69092	Loss: 444.065
+12800/69092	Loss: 443.395
+16000/69092	Loss: 429.927
+19200/69092	Loss: 437.021
+22400/69092	Loss: 446.268
+25600/69092	Loss: 442.520
+28800/69092	Loss: 452.733
+32000/69092	Loss: 441.294
+35200/69092	Loss: 440.683
+38400/69092	Loss: 451.406
+41600/69092	Loss: 443.954
+44800/69092	Loss: 444.240
+48000/69092	Loss: 425.311
+51200/69092	Loss: 436.951
+54400/69092	Loss: 441.969
+57600/69092	Loss: 439.883
+60800/69092	Loss: 432.937
+64000/69092	Loss: 430.354
+67200/69092	Loss: 438.986
+Training time 0:04:22.675834
+Epoch: 1 Average loss: 460.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 1)
+0/69092	Loss: 477.468
+3200/69092	Loss: 444.829
+6400/69092	Loss: 439.752
+9600/69092	Loss: 448.288
+12800/69092	Loss: 433.830
+16000/69092	Loss: 441.858
+19200/69092	Loss: 441.838
+22400/69092	Loss: 440.030
+25600/69092	Loss: 441.060
+28800/69092	Loss: 443.189
+32000/69092	Loss: 444.328
+35200/69092	Loss: 444.173
+38400/69092	Loss: 425.570
+41600/69092	Loss: 442.802
+44800/69092	Loss: 443.597
+48000/69092	Loss: 435.120
+51200/69092	Loss: 447.508
+54400/69092	Loss: 446.497
+57600/69092	Loss: 438.670
+60800/69092	Loss: 433.847
+64000/69092	Loss: 438.785
+67200/69092	Loss: 439.632
+Training time 0:04:12.735653
+Epoch: 2 Average loss: 440.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 2)
+0/69092	Loss: 393.189
+3200/69092	Loss: 435.335
+6400/69092	Loss: 442.619
+9600/69092	Loss: 437.330
+12800/69092	Loss: 445.204
+16000/69092	Loss: 446.610
+19200/69092	Loss: 443.931
+22400/69092	Loss: 444.131
diff --git a/OAR.2068284.stderr b/OAR.2068284.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..8761745c12850dafcd110c49d5952d90d703a49a
--- /dev/null
+++ b/OAR.2068284.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-25 02:59:26] Job 2068284 KILLED ##
diff --git a/OAR.2068284.stdout b/OAR.2068284.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..4d1747234620e406dae49729a37f84b70d855075
--- /dev/null
+++ b/OAR.2068284.stdout
@@ -0,0 +1,1347 @@
+Namespace(batch_size=256, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_256', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=True, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce GTX 1080 Ti
+GeForce GTX 1080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last (iter 173)'
+0/69092	Loss: 155.952
+12800/69092	Loss: 159.121
+25600/69092	Loss: 160.204
+38400/69092	Loss: 157.853
+51200/69092	Loss: 158.496
+64000/69092	Loss: 155.627
+Training time 0:04:07.256582
+Epoch: 1 Average loss: 158.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 174)
+0/69092	Loss: 156.559
+12800/69092	Loss: 157.571
+25600/69092	Loss: 157.691
+38400/69092	Loss: 158.235
+51200/69092	Loss: 158.045
+64000/69092	Loss: 157.110
+Training time 0:04:09.060156
+Epoch: 2 Average loss: 157.78
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 175)
+0/69092	Loss: 169.037
+12800/69092	Loss: 159.161
+25600/69092	Loss: 158.117
+38400/69092	Loss: 157.120
+51200/69092	Loss: 158.061
+64000/69092	Loss: 158.438
+Training time 0:04:11.693603
+Epoch: 3 Average loss: 158.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 176)
+0/69092	Loss: 173.992
+12800/69092	Loss: 157.694
+25600/69092	Loss: 158.061
+38400/69092	Loss: 158.073
+51200/69092	Loss: 157.170
+64000/69092	Loss: 158.600
+Training time 0:04:08.950306
+Epoch: 4 Average loss: 158.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 177)
+0/69092	Loss: 152.509
+12800/69092	Loss: 157.509
+25600/69092	Loss: 157.259
+38400/69092	Loss: 157.133
+51200/69092	Loss: 158.958
+64000/69092	Loss: 157.564
+Training time 0:04:04.422023
+Epoch: 5 Average loss: 157.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 178)
+0/69092	Loss: 159.325
+12800/69092	Loss: 157.663
+25600/69092	Loss: 158.119
+38400/69092	Loss: 157.028
+51200/69092	Loss: 157.416
+64000/69092	Loss: 158.792
+Training time 0:04:05.803442
+Epoch: 6 Average loss: 157.93
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 179)
+0/69092	Loss: 170.144
+12800/69092	Loss: 158.356
+25600/69092	Loss: 158.823
+38400/69092	Loss: 158.388
+51200/69092	Loss: 157.626
+64000/69092	Loss: 157.173
+Training time 0:04:07.837678
+Epoch: 7 Average loss: 158.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 180)
+0/69092	Loss: 155.297
+12800/69092	Loss: 158.118
+25600/69092	Loss: 158.781
+38400/69092	Loss: 156.992
+51200/69092	Loss: 157.078
+64000/69092	Loss: 157.590
+Training time 0:04:10.990231
+Epoch: 8 Average loss: 157.78
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 181)
+0/69092	Loss: 151.523
+12800/69092	Loss: 158.502
+25600/69092	Loss: 156.810
+38400/69092	Loss: 158.807
+51200/69092	Loss: 157.819
+64000/69092	Loss: 157.962
+Training time 0:04:08.934011
+Epoch: 9 Average loss: 158.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 182)
+0/69092	Loss: 158.328
+12800/69092	Loss: 156.602
+25600/69092	Loss: 157.674
+38400/69092	Loss: 158.318
+51200/69092	Loss: 158.263
+64000/69092	Loss: 156.871
+Training time 0:04:07.837835
+Epoch: 10 Average loss: 157.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 183)
+0/69092	Loss: 168.085
+12800/69092	Loss: 157.082
+25600/69092	Loss: 157.931
+38400/69092	Loss: 157.634
+51200/69092	Loss: 158.448
+64000/69092	Loss: 156.862
+Training time 0:04:04.238534
+Epoch: 11 Average loss: 157.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 184)
+0/69092	Loss: 152.943
+12800/69092	Loss: 157.047
+25600/69092	Loss: 159.241
+38400/69092	Loss: 156.479
+51200/69092	Loss: 157.264
+64000/69092	Loss: 157.732
+Training time 0:04:06.931607
+Epoch: 12 Average loss: 157.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 185)
+0/69092	Loss: 149.970
+12800/69092	Loss: 158.554
+25600/69092	Loss: 157.538
+38400/69092	Loss: 156.891
+51200/69092	Loss: 158.801
+64000/69092	Loss: 157.180
+Training time 0:04:06.729115
+Epoch: 13 Average loss: 157.86
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 186)
+0/69092	Loss: 168.819
+12800/69092	Loss: 157.445
+25600/69092	Loss: 158.959
+38400/69092	Loss: 157.423
+51200/69092	Loss: 158.772
+64000/69092	Loss: 155.783
+Training time 0:04:07.010972
+Epoch: 14 Average loss: 157.76
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 187)
+0/69092	Loss: 160.036
+12800/69092	Loss: 159.004
+25600/69092	Loss: 157.329
+38400/69092	Loss: 157.758
+51200/69092	Loss: 156.844
+64000/69092	Loss: 157.920
+Training time 0:04:08.909317
+Epoch: 15 Average loss: 157.76
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 188)
+0/69092	Loss: 149.585
+12800/69092	Loss: 157.099
+25600/69092	Loss: 157.843
+38400/69092	Loss: 156.822
+51200/69092	Loss: 156.968
+64000/69092	Loss: 158.730
+Training time 0:04:07.691350
+Epoch: 16 Average loss: 157.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 189)
+0/69092	Loss: 167.834
+12800/69092	Loss: 158.222
+25600/69092	Loss: 158.600
+38400/69092	Loss: 157.595
+51200/69092	Loss: 157.795
+64000/69092	Loss: 157.077
+Training time 0:04:07.152289
+Epoch: 17 Average loss: 157.83
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 190)
+0/69092	Loss: 157.181
+12800/69092	Loss: 157.831
+25600/69092	Loss: 158.101
+38400/69092	Loss: 158.576
+51200/69092	Loss: 157.180
+64000/69092	Loss: 158.019
+Training time 0:04:08.809209
+Epoch: 18 Average loss: 157.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 191)
+0/69092	Loss: 153.267
+12800/69092	Loss: 158.127
+25600/69092	Loss: 157.503
+38400/69092	Loss: 157.463
+51200/69092	Loss: 158.788
+64000/69092	Loss: 157.668
+Training time 0:04:11.227325
+Epoch: 19 Average loss: 157.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 192)
+0/69092	Loss: 161.668
+12800/69092	Loss: 157.593
+25600/69092	Loss: 157.789
+38400/69092	Loss: 157.822
+51200/69092	Loss: 157.680
+64000/69092	Loss: 158.281
+Training time 0:04:09.122571
+Epoch: 20 Average loss: 157.93
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 193)
+0/69092	Loss: 150.352
+12800/69092	Loss: 157.402
+25600/69092	Loss: 157.315
+38400/69092	Loss: 158.616
+51200/69092	Loss: 156.236
+64000/69092	Loss: 158.431
+Training time 0:04:11.388727
+Epoch: 21 Average loss: 157.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 194)
+0/69092	Loss: 156.163
+12800/69092	Loss: 157.171
+25600/69092	Loss: 156.466
+38400/69092	Loss: 158.786
+51200/69092	Loss: 158.157
+64000/69092	Loss: 157.648
+Training time 0:04:05.055387
+Epoch: 22 Average loss: 157.76
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 195)
+0/69092	Loss: 147.419
+12800/69092	Loss: 157.830
+25600/69092	Loss: 157.611
+38400/69092	Loss: 157.757
+51200/69092	Loss: 158.056
+64000/69092	Loss: 157.455
+Training time 0:04:05.411815
+Epoch: 23 Average loss: 157.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 196)
+0/69092	Loss: 171.791
+12800/69092	Loss: 156.112
+25600/69092	Loss: 157.859
+38400/69092	Loss: 158.415
+51200/69092	Loss: 158.944
+64000/69092	Loss: 156.379
+Training time 0:04:08.043169
+Epoch: 24 Average loss: 157.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 197)
+0/69092	Loss: 160.278
+12800/69092	Loss: 157.984
+25600/69092	Loss: 158.727
+38400/69092	Loss: 158.111
+51200/69092	Loss: 157.447
+64000/69092	Loss: 156.952
+Training time 0:04:12.129530
+Epoch: 25 Average loss: 157.79
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 198)
+0/69092	Loss: 152.806
+12800/69092	Loss: 156.814
+25600/69092	Loss: 158.617
+38400/69092	Loss: 158.246
+51200/69092	Loss: 156.832
+64000/69092	Loss: 156.933
+Training time 0:04:08.390553
+Epoch: 26 Average loss: 157.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 199)
+0/69092	Loss: 156.767
+12800/69092	Loss: 157.931
+25600/69092	Loss: 157.088
+38400/69092	Loss: 157.270
+51200/69092	Loss: 158.061
+64000/69092	Loss: 158.765
+Training time 0:04:08.695474
+Epoch: 27 Average loss: 157.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 200)
+0/69092	Loss: 156.520
+12800/69092	Loss: 157.872
+25600/69092	Loss: 159.027
+38400/69092	Loss: 158.514
+51200/69092	Loss: 157.492
+64000/69092	Loss: 156.484
+Training time 0:04:05.689479
+Epoch: 28 Average loss: 157.95
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 201)
+0/69092	Loss: 150.646
+12800/69092	Loss: 158.405
+25600/69092	Loss: 158.932
+38400/69092	Loss: 156.635
+51200/69092	Loss: 157.135
+64000/69092	Loss: 157.422
+Training time 0:04:06.258930
+Epoch: 29 Average loss: 157.66
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 202)
+0/69092	Loss: 165.311
+12800/69092	Loss: 157.452
+25600/69092	Loss: 157.317
+38400/69092	Loss: 156.785
+51200/69092	Loss: 158.505
+64000/69092	Loss: 158.541
+Training time 0:04:09.311575
+Epoch: 30 Average loss: 157.68
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 203)
+0/69092	Loss: 152.277
+12800/69092	Loss: 156.063
+25600/69092	Loss: 157.964
+38400/69092	Loss: 158.164
+51200/69092	Loss: 157.314
+64000/69092	Loss: 158.910
+Training time 0:04:08.771677
+Epoch: 31 Average loss: 157.58
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 204)
+0/69092	Loss: 164.293
+12800/69092	Loss: 157.703
+25600/69092	Loss: 158.666
+38400/69092	Loss: 157.075
+51200/69092	Loss: 157.553
+64000/69092	Loss: 158.131
+Training time 0:04:11.246236
+Epoch: 32 Average loss: 157.63
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 205)
+0/69092	Loss: 154.875
+12800/69092	Loss: 158.356
+25600/69092	Loss: 158.025
+38400/69092	Loss: 156.657
+51200/69092	Loss: 158.189
+64000/69092	Loss: 157.547
+Training time 0:04:09.750177
+Epoch: 33 Average loss: 157.53
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 206)
+0/69092	Loss: 153.610
+12800/69092	Loss: 158.994
+25600/69092	Loss: 158.683
+38400/69092	Loss: 158.389
+51200/69092	Loss: 157.156
+64000/69092	Loss: 155.605
+Training time 0:04:04.536860
+Epoch: 34 Average loss: 157.76
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 207)
+0/69092	Loss: 160.609
+12800/69092	Loss: 158.284
+25600/69092	Loss: 157.730
+38400/69092	Loss: 157.647
+51200/69092	Loss: 157.609
+64000/69092	Loss: 156.316
+Training time 0:04:07.207110
+Epoch: 35 Average loss: 157.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 208)
+0/69092	Loss: 157.581
+12800/69092	Loss: 157.032
+25600/69092	Loss: 157.428
+38400/69092	Loss: 158.491
+51200/69092	Loss: 157.662
+64000/69092	Loss: 157.269
+Training time 0:04:07.801669
+Epoch: 36 Average loss: 157.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 209)
+0/69092	Loss: 159.224
+12800/69092	Loss: 158.065
+25600/69092	Loss: 157.590
+38400/69092	Loss: 156.078
+51200/69092	Loss: 158.977
+64000/69092	Loss: 158.306
+Training time 0:04:09.360865
+Epoch: 37 Average loss: 157.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 210)
+0/69092	Loss: 165.091
+12800/69092	Loss: 158.108
+25600/69092	Loss: 157.228
+38400/69092	Loss: 157.449
+51200/69092	Loss: 156.462
+64000/69092	Loss: 157.308
+Training time 0:04:08.158248
+Epoch: 38 Average loss: 157.41
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 211)
+0/69092	Loss: 157.792
+12800/69092	Loss: 157.611
+25600/69092	Loss: 158.170
+38400/69092	Loss: 158.744
+51200/69092	Loss: 157.334
+64000/69092	Loss: 156.975
+Training time 0:04:07.028698
+Epoch: 39 Average loss: 157.68
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 212)
+0/69092	Loss: 170.346
+12800/69092	Loss: 156.963
+25600/69092	Loss: 157.363
+38400/69092	Loss: 156.976
+51200/69092	Loss: 156.863
+64000/69092	Loss: 158.115
+Training time 0:04:06.447876
+Epoch: 40 Average loss: 157.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 213)
+0/69092	Loss: 153.107
+12800/69092	Loss: 157.775
+25600/69092	Loss: 156.232
+38400/69092	Loss: 158.089
+51200/69092	Loss: 156.741
+64000/69092	Loss: 158.053
+Training time 0:04:07.363092
+Epoch: 41 Average loss: 157.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 214)
+0/69092	Loss: 158.205
+12800/69092	Loss: 157.932
+25600/69092	Loss: 157.873
+38400/69092	Loss: 157.378
+51200/69092	Loss: 157.124
+64000/69092	Loss: 156.899
+Training time 0:04:08.284864
+Epoch: 42 Average loss: 157.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 215)
+0/69092	Loss: 159.169
+12800/69092	Loss: 156.975
+25600/69092	Loss: 156.550
+38400/69092	Loss: 157.878
+51200/69092	Loss: 157.209
+64000/69092	Loss: 157.833
+Training time 0:04:08.949711
+Epoch: 43 Average loss: 157.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 216)
+0/69092	Loss: 152.923
+12800/69092	Loss: 156.564
+25600/69092	Loss: 157.336
+38400/69092	Loss: 157.758
+51200/69092	Loss: 158.955
+64000/69092	Loss: 157.532
+Training time 0:04:09.563251
+Epoch: 44 Average loss: 157.46
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 217)
+0/69092	Loss: 159.523
+12800/69092	Loss: 157.357
+25600/69092	Loss: 157.871
+38400/69092	Loss: 158.002
+51200/69092	Loss: 156.907
+64000/69092	Loss: 156.788
+Training time 0:04:07.358115
+Epoch: 45 Average loss: 157.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 218)
+0/69092	Loss: 166.447
+12800/69092	Loss: 156.163
+25600/69092	Loss: 158.640
+38400/69092	Loss: 157.564
+51200/69092	Loss: 155.737
+64000/69092	Loss: 158.560
+Training time 0:04:04.926735
+Epoch: 46 Average loss: 157.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 219)
+0/69092	Loss: 157.227
+12800/69092	Loss: 157.724
+25600/69092	Loss: 157.674
+38400/69092	Loss: 158.769
+51200/69092	Loss: 156.429
+64000/69092	Loss: 157.198
+Training time 0:04:09.950528
+Epoch: 47 Average loss: 157.59
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 220)
+0/69092	Loss: 165.830
+12800/69092	Loss: 156.446
+25600/69092	Loss: 157.076
+38400/69092	Loss: 158.443
+51200/69092	Loss: 157.780
+64000/69092	Loss: 158.231
+Training time 0:04:10.794276
+Epoch: 48 Average loss: 157.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 221)
+0/69092	Loss: 161.042
+12800/69092	Loss: 156.000
+25600/69092	Loss: 157.124
+38400/69092	Loss: 157.936
+51200/69092	Loss: 158.877
+64000/69092	Loss: 157.673
+Training time 0:04:11.640587
+Epoch: 49 Average loss: 157.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 222)
+0/69092	Loss: 157.060
+12800/69092	Loss: 157.399
+25600/69092	Loss: 157.788
+38400/69092	Loss: 156.679
+51200/69092	Loss: 158.832
+64000/69092	Loss: 156.170
+Training time 0:04:10.365157
+Epoch: 50 Average loss: 157.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 223)
+0/69092	Loss: 161.609
+12800/69092	Loss: 158.738
+25600/69092	Loss: 158.430
+38400/69092	Loss: 156.129
+51200/69092	Loss: 156.666
+64000/69092	Loss: 157.170
+Training time 0:04:09.316831
+Epoch: 51 Average loss: 157.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 224)
+0/69092	Loss: 153.683
+12800/69092	Loss: 157.725
+25600/69092	Loss: 156.806
+38400/69092	Loss: 157.850
+51200/69092	Loss: 158.526
+64000/69092	Loss: 156.939
+Training time 0:04:05.028103
+Epoch: 52 Average loss: 157.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 225)
+0/69092	Loss: 158.885
+12800/69092	Loss: 157.245
+25600/69092	Loss: 158.704
+38400/69092	Loss: 157.207
+51200/69092	Loss: 156.306
+64000/69092	Loss: 157.821
+Training time 0:04:10.674155
+Epoch: 53 Average loss: 157.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 226)
+0/69092	Loss: 155.339
+12800/69092	Loss: 158.253
+25600/69092	Loss: 158.159
+38400/69092	Loss: 155.812
+51200/69092	Loss: 156.602
+64000/69092	Loss: 157.555
+Training time 0:04:12.632021
+Epoch: 54 Average loss: 157.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 227)
+0/69092	Loss: 155.890
+12800/69092	Loss: 156.798
+25600/69092	Loss: 158.212
+38400/69092	Loss: 157.738
+51200/69092	Loss: 157.282
+64000/69092	Loss: 157.701
+Training time 0:04:11.221483
+Epoch: 55 Average loss: 157.68
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 228)
+0/69092	Loss: 161.760
+12800/69092	Loss: 158.265
+25600/69092	Loss: 157.248
+38400/69092	Loss: 156.036
+51200/69092	Loss: 158.352
+64000/69092	Loss: 156.961
+Training time 0:04:12.344839
+Epoch: 56 Average loss: 157.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 229)
+0/69092	Loss: 156.050
+12800/69092	Loss: 158.169
+25600/69092	Loss: 157.276
+38400/69092	Loss: 158.034
+51200/69092	Loss: 157.169
+64000/69092	Loss: 157.876
+Training time 0:04:06.652882
+Epoch: 57 Average loss: 157.70
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 230)
+0/69092	Loss: 155.364
+12800/69092	Loss: 157.337
+25600/69092	Loss: 156.698
+38400/69092	Loss: 158.144
+51200/69092	Loss: 157.125
+64000/69092	Loss: 158.089
+Training time 0:04:05.451386
+Epoch: 58 Average loss: 157.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 231)
+0/69092	Loss: 155.917
+12800/69092	Loss: 157.703
+25600/69092	Loss: 155.820
+38400/69092	Loss: 158.629
+51200/69092	Loss: 157.808
+64000/69092	Loss: 157.381
+Training time 0:04:10.231175
+Epoch: 59 Average loss: 157.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 232)
+0/69092	Loss: 154.034
+12800/69092	Loss: 156.802
+25600/69092	Loss: 157.941
+38400/69092	Loss: 158.557
+51200/69092	Loss: 156.309
+64000/69092	Loss: 157.781
+Training time 0:04:09.659678
+Epoch: 60 Average loss: 157.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 233)
+0/69092	Loss: 152.551
+12800/69092	Loss: 156.890
+25600/69092	Loss: 158.074
+38400/69092	Loss: 157.366
+51200/69092	Loss: 158.784
+64000/69092	Loss: 156.646
+Training time 0:04:12.622080
+Epoch: 61 Average loss: 157.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 234)
+0/69092	Loss: 159.347
+12800/69092	Loss: 157.794
+25600/69092	Loss: 157.253
+38400/69092	Loss: 157.027
+51200/69092	Loss: 156.760
+64000/69092	Loss: 157.832
+Training time 0:04:09.804689
+Epoch: 62 Average loss: 157.52
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 235)
+0/69092	Loss: 161.108
+12800/69092	Loss: 156.700
+25600/69092	Loss: 158.598
+38400/69092	Loss: 156.907
+51200/69092	Loss: 156.723
+64000/69092	Loss: 156.703
+Training time 0:04:06.690097
+Epoch: 63 Average loss: 157.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 236)
+0/69092	Loss: 151.689
+12800/69092	Loss: 159.341
+25600/69092	Loss: 156.846
+38400/69092	Loss: 157.478
+51200/69092	Loss: 158.575
+64000/69092	Loss: 156.002
+Training time 0:04:07.036244
+Epoch: 64 Average loss: 157.57
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 237)
+0/69092	Loss: 160.479
+12800/69092	Loss: 156.621
+25600/69092	Loss: 157.561
+38400/69092	Loss: 156.412
+51200/69092	Loss: 159.183
+64000/69092	Loss: 158.383
+Training time 0:04:12.176953
+Epoch: 65 Average loss: 157.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 238)
+0/69092	Loss: 168.077
+12800/69092	Loss: 156.993
+25600/69092	Loss: 158.328
+38400/69092	Loss: 156.861
+51200/69092	Loss: 157.901
+64000/69092	Loss: 157.954
+Training time 0:04:10.374660
+Epoch: 66 Average loss: 157.58
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 239)
+0/69092	Loss: 147.819
+12800/69092	Loss: 158.114
+25600/69092	Loss: 156.945
+38400/69092	Loss: 157.846
+51200/69092	Loss: 156.692
+64000/69092	Loss: 157.591
+Training time 0:04:09.441636
+Epoch: 67 Average loss: 157.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 240)
+0/69092	Loss: 158.824
+12800/69092	Loss: 156.585
+25600/69092	Loss: 157.306
+38400/69092	Loss: 158.213
+51200/69092	Loss: 157.915
+64000/69092	Loss: 157.248
+Training time 0:04:08.608228
+Epoch: 68 Average loss: 157.43
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 241)
+0/69092	Loss: 159.264
+12800/69092	Loss: 157.138
+25600/69092	Loss: 158.863
+38400/69092	Loss: 158.558
+51200/69092	Loss: 156.178
+64000/69092	Loss: 157.639
+Training time 0:04:03.071162
+Epoch: 69 Average loss: 157.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 242)
+0/69092	Loss: 152.968
+12800/69092	Loss: 156.544
+25600/69092	Loss: 156.787
+38400/69092	Loss: 158.234
+51200/69092	Loss: 156.915
+64000/69092	Loss: 157.555
+Training time 0:04:07.455852
+Epoch: 70 Average loss: 157.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 243)
+0/69092	Loss: 160.421
+12800/69092	Loss: 158.576
+25600/69092	Loss: 157.594
+38400/69092	Loss: 157.761
+51200/69092	Loss: 156.322
+64000/69092	Loss: 156.756
+Training time 0:04:08.703059
+Epoch: 71 Average loss: 157.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 244)
+0/69092	Loss: 153.485
+12800/69092	Loss: 157.888
+25600/69092	Loss: 157.596
+38400/69092	Loss: 156.846
+51200/69092	Loss: 157.866
+64000/69092	Loss: 156.987
+Training time 0:04:12.067903
+Epoch: 72 Average loss: 157.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 245)
+0/69092	Loss: 163.371
+12800/69092	Loss: 157.914
+25600/69092	Loss: 156.977
+38400/69092	Loss: 156.872
+51200/69092	Loss: 157.466
+64000/69092	Loss: 158.811
+Training time 0:04:10.709059
+Epoch: 73 Average loss: 157.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 246)
+0/69092	Loss: 161.437
+12800/69092	Loss: 158.193
+25600/69092	Loss: 156.608
+38400/69092	Loss: 158.900
+51200/69092	Loss: 156.847
+64000/69092	Loss: 156.355
+Training time 0:04:09.862156
+Epoch: 74 Average loss: 157.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 247)
+0/69092	Loss: 161.102
+12800/69092	Loss: 157.671
+25600/69092	Loss: 157.514
+38400/69092	Loss: 156.699
+51200/69092	Loss: 158.679
+64000/69092	Loss: 157.702
+Training time 0:04:07.326575
+Epoch: 75 Average loss: 157.59
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 248)
+0/69092	Loss: 151.403
+12800/69092	Loss: 157.397
+25600/69092	Loss: 157.554
+38400/69092	Loss: 156.725
+51200/69092	Loss: 158.962
+64000/69092	Loss: 157.685
+Training time 0:04:11.968574
+Epoch: 76 Average loss: 157.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 249)
+0/69092	Loss: 157.915
+12800/69092	Loss: 155.784
+25600/69092	Loss: 158.362
+38400/69092	Loss: 156.531
+51200/69092	Loss: 157.851
+64000/69092	Loss: 157.451
+Training time 0:04:09.654373
+Epoch: 77 Average loss: 157.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 250)
+0/69092	Loss: 148.656
+12800/69092	Loss: 157.064
+25600/69092	Loss: 156.904
+38400/69092	Loss: 157.858
+51200/69092	Loss: 157.146
+64000/69092	Loss: 157.414
+Training time 0:04:08.727900
+Epoch: 78 Average loss: 157.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 251)
+0/69092	Loss: 155.678
+12800/69092	Loss: 156.509
+25600/69092	Loss: 157.133
+38400/69092	Loss: 157.766
+51200/69092	Loss: 157.756
+64000/69092	Loss: 157.893
+Training time 0:04:07.309731
+Epoch: 79 Average loss: 157.33
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 252)
+0/69092	Loss: 144.090
+12800/69092	Loss: 156.885
+25600/69092	Loss: 157.186
+38400/69092	Loss: 156.099
+51200/69092	Loss: 157.945
+64000/69092	Loss: 157.640
+Training time 0:04:08.898807
+Epoch: 80 Average loss: 157.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 253)
+0/69092	Loss: 158.216
+12800/69092	Loss: 158.188
+25600/69092	Loss: 156.013
+38400/69092	Loss: 156.550
+51200/69092	Loss: 157.594
+64000/69092	Loss: 157.669
+Training time 0:04:04.894205
+Epoch: 81 Average loss: 157.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 254)
+0/69092	Loss: 163.928
+12800/69092	Loss: 158.098
+25600/69092	Loss: 156.798
+38400/69092	Loss: 157.754
+51200/69092	Loss: 156.268
+64000/69092	Loss: 157.443
+Training time 0:04:08.553407
+Epoch: 82 Average loss: 157.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 255)
+0/69092	Loss: 157.476
+12800/69092	Loss: 156.176
+25600/69092	Loss: 158.057
+38400/69092	Loss: 157.450
+51200/69092	Loss: 158.515
+64000/69092	Loss: 157.737
+Training time 0:04:11.952887
+Epoch: 83 Average loss: 157.47
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 256)
+0/69092	Loss: 152.488
+12800/69092	Loss: 158.060
+25600/69092	Loss: 157.019
+38400/69092	Loss: 155.443
+51200/69092	Loss: 157.548
+64000/69092	Loss: 157.624
+Training time 0:04:10.425186
+Epoch: 84 Average loss: 157.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 257)
+0/69092	Loss: 162.522
+12800/69092	Loss: 159.263
+25600/69092	Loss: 157.610
+38400/69092	Loss: 156.635
+51200/69092	Loss: 156.299
+64000/69092	Loss: 157.541
+Training time 0:04:11.270785
+Epoch: 85 Average loss: 157.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 258)
+0/69092	Loss: 153.684
+12800/69092	Loss: 158.437
+25600/69092	Loss: 155.698
+38400/69092	Loss: 157.179
+51200/69092	Loss: 157.263
+64000/69092	Loss: 158.020
+Training time 0:04:10.949805
+Epoch: 86 Average loss: 157.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 259)
+0/69092	Loss: 156.394
+12800/69092	Loss: 157.345
+25600/69092	Loss: 157.588
+38400/69092	Loss: 157.866
+51200/69092	Loss: 155.943
+64000/69092	Loss: 157.875
+Training time 0:04:04.290943
+Epoch: 87 Average loss: 157.27
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 260)
+0/69092	Loss: 151.419
+12800/69092	Loss: 156.644
+25600/69092	Loss: 156.534
+38400/69092	Loss: 158.318
+51200/69092	Loss: 156.783
+64000/69092	Loss: 156.739
+Training time 0:04:15.388469
+Epoch: 88 Average loss: 157.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 261)
+0/69092	Loss: 161.832
+12800/69092	Loss: 157.948
+25600/69092	Loss: 156.336
+38400/69092	Loss: 158.019
+51200/69092	Loss: 156.740
+64000/69092	Loss: 157.256
+Training time 0:04:12.798415
+Epoch: 89 Average loss: 157.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 262)
+0/69092	Loss: 167.103
+12800/69092	Loss: 157.328
+25600/69092	Loss: 157.233
+38400/69092	Loss: 157.854
+51200/69092	Loss: 155.752
+64000/69092	Loss: 156.840
+Training time 0:04:12.797798
+Epoch: 90 Average loss: 157.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 263)
+0/69092	Loss: 155.901
+12800/69092	Loss: 157.552
+25600/69092	Loss: 157.195
+38400/69092	Loss: 157.300
+51200/69092	Loss: 156.765
+64000/69092	Loss: 157.187
+Training time 0:04:11.831708
+Epoch: 91 Average loss: 157.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 264)
+0/69092	Loss: 154.262
+12800/69092	Loss: 158.352
+25600/69092	Loss: 156.119
+38400/69092	Loss: 158.913
+51200/69092	Loss: 157.989
+64000/69092	Loss: 156.361
+Training time 0:04:08.926917
+Epoch: 92 Average loss: 157.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 265)
+0/69092	Loss: 155.008
+12800/69092	Loss: 157.280
+25600/69092	Loss: 158.019
+38400/69092	Loss: 155.993
+51200/69092	Loss: 157.609
+64000/69092	Loss: 156.842
+Training time 0:04:03.073599
+Epoch: 93 Average loss: 157.24
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 266)
+0/69092	Loss: 162.025
+12800/69092	Loss: 157.524
+25600/69092	Loss: 157.398
+38400/69092	Loss: 157.294
+51200/69092	Loss: 157.970
+64000/69092	Loss: 156.351
+Training time 0:04:07.584761
+Epoch: 94 Average loss: 157.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 267)
+0/69092	Loss: 168.699
+12800/69092	Loss: 156.917
+25600/69092	Loss: 156.806
+38400/69092	Loss: 157.386
+51200/69092	Loss: 156.886
+64000/69092	Loss: 157.273
+Training time 0:04:09.591231
+Epoch: 95 Average loss: 157.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 268)
+0/69092	Loss: 158.137
+12800/69092	Loss: 157.558
+25600/69092	Loss: 157.538
+38400/69092	Loss: 157.434
+51200/69092	Loss: 157.487
+64000/69092	Loss: 156.290
+Training time 0:04:12.204420
+Epoch: 96 Average loss: 157.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 269)
+0/69092	Loss: 162.844
+12800/69092	Loss: 156.509
+25600/69092	Loss: 157.572
+38400/69092	Loss: 158.294
+51200/69092	Loss: 156.501
+64000/69092	Loss: 157.894
+Training time 0:04:10.931413
+Epoch: 97 Average loss: 157.46
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 270)
+0/69092	Loss: 160.477
+12800/69092	Loss: 157.578
+25600/69092	Loss: 157.279
+38400/69092	Loss: 157.476
+51200/69092	Loss: 157.491
+64000/69092	Loss: 156.596
+Training time 0:04:08.272934
+Epoch: 98 Average loss: 157.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 271)
+0/69092	Loss: 161.273
+12800/69092	Loss: 157.254
+25600/69092	Loss: 157.126
+38400/69092	Loss: 157.503
+51200/69092	Loss: 158.235
+64000/69092	Loss: 156.365
+Training time 0:04:03.909884
+Epoch: 99 Average loss: 157.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 272)
+0/69092	Loss: 157.615
+12800/69092	Loss: 157.470
+25600/69092	Loss: 157.726
+38400/69092	Loss: 158.092
+51200/69092	Loss: 156.651
+64000/69092	Loss: 157.146
+Training time 0:04:11.181057
+Epoch: 100 Average loss: 157.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 273)
+0/69092	Loss: 155.768
+12800/69092	Loss: 156.706
+25600/69092	Loss: 157.099
+38400/69092	Loss: 156.770
+51200/69092	Loss: 156.383
+64000/69092	Loss: 157.121
+Training time 0:04:09.140018
+Epoch: 101 Average loss: 156.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 274)
+0/69092	Loss: 146.619
+12800/69092	Loss: 158.031
+25600/69092	Loss: 157.539
+38400/69092	Loss: 157.488
+51200/69092	Loss: 156.214
+64000/69092	Loss: 157.160
+Training time 0:04:10.464033
+Epoch: 102 Average loss: 157.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 275)
+0/69092	Loss: 149.681
+12800/69092	Loss: 157.090
+25600/69092	Loss: 157.236
+38400/69092	Loss: 157.710
+51200/69092	Loss: 157.181
+64000/69092	Loss: 157.986
+Training time 0:04:09.876653
+Epoch: 103 Average loss: 157.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 276)
+0/69092	Loss: 163.643
+12800/69092	Loss: 157.128
+25600/69092	Loss: 157.302
+38400/69092	Loss: 156.973
+51200/69092	Loss: 156.821
+64000/69092	Loss: 157.241
+Training time 0:04:07.838607
+Epoch: 104 Average loss: 157.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 277)
+0/69092	Loss: 158.093
+12800/69092	Loss: 157.244
+25600/69092	Loss: 157.186
+38400/69092	Loss: 157.452
+51200/69092	Loss: 155.985
+64000/69092	Loss: 158.488
+Training time 0:04:03.601052
+Epoch: 105 Average loss: 157.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 278)
+0/69092	Loss: 154.813
+12800/69092	Loss: 157.052
+25600/69092	Loss: 157.161
+38400/69092	Loss: 157.203
+51200/69092	Loss: 157.232
+64000/69092	Loss: 157.310
+Training time 0:04:07.292869
+Epoch: 106 Average loss: 157.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 279)
+0/69092	Loss: 158.664
+12800/69092	Loss: 156.515
+25600/69092	Loss: 157.229
+38400/69092	Loss: 157.361
+51200/69092	Loss: 157.873
+64000/69092	Loss: 157.616
+Training time 0:04:06.809608
+Epoch: 107 Average loss: 157.24
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 280)
+0/69092	Loss: 156.387
+12800/69092	Loss: 158.136
+25600/69092	Loss: 157.099
+38400/69092	Loss: 156.837
+51200/69092	Loss: 156.747
+64000/69092	Loss: 156.586
+Training time 0:04:09.066369
+Epoch: 108 Average loss: 157.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 281)
+0/69092	Loss: 156.363
+12800/69092	Loss: 156.955
+25600/69092	Loss: 157.579
+38400/69092	Loss: 156.693
+51200/69092	Loss: 157.130
+64000/69092	Loss: 157.739
+Training time 0:04:07.344154
+Epoch: 109 Average loss: 157.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 282)
+0/69092	Loss: 154.839
+12800/69092	Loss: 156.584
+25600/69092	Loss: 156.852
+38400/69092	Loss: 156.705
+51200/69092	Loss: 157.566
+64000/69092	Loss: 157.846
+Training time 0:04:02.596162
+Epoch: 110 Average loss: 157.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 283)
+0/69092	Loss: 163.107
+12800/69092	Loss: 156.967
+25600/69092	Loss: 156.324
+38400/69092	Loss: 158.065
+51200/69092	Loss: 156.880
+64000/69092	Loss: 157.599
+Training time 0:04:03.459253
+Epoch: 111 Average loss: 157.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 284)
+0/69092	Loss: 152.234
+12800/69092	Loss: 155.948
+25600/69092	Loss: 156.527
+38400/69092	Loss: 157.628
+51200/69092	Loss: 157.727
+64000/69092	Loss: 156.804
+Training time 0:04:06.196250
+Epoch: 112 Average loss: 156.93
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 285)
+0/69092	Loss: 166.500
+12800/69092	Loss: 157.189
+25600/69092	Loss: 156.956
+38400/69092	Loss: 155.700
+51200/69092	Loss: 157.823
+64000/69092	Loss: 157.373
+Training time 0:04:08.707100
+Epoch: 113 Average loss: 157.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 286)
+0/69092	Loss: 161.993
+12800/69092	Loss: 157.234
+25600/69092	Loss: 158.382
+38400/69092	Loss: 156.551
+51200/69092	Loss: 157.351
+64000/69092	Loss: 156.157
+Training time 0:04:07.990283
+Epoch: 114 Average loss: 157.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 287)
+0/69092	Loss: 161.841
+12800/69092	Loss: 157.712
+25600/69092	Loss: 155.495
+38400/69092	Loss: 157.577
+51200/69092	Loss: 157.636
+64000/69092	Loss: 156.955
+Training time 0:04:09.016317
+Epoch: 115 Average loss: 157.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 288)
+0/69092	Loss: 155.560
+12800/69092	Loss: 157.573
+25600/69092	Loss: 158.010
+38400/69092	Loss: 156.599
+51200/69092	Loss: 156.843
+64000/69092	Loss: 156.326
+Training time 0:03:59.434990
+Epoch: 116 Average loss: 157.03
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 289)
+0/69092	Loss: 153.375
+12800/69092	Loss: 156.316
+25600/69092	Loss: 158.547
+38400/69092	Loss: 157.258
+51200/69092	Loss: 157.724
+64000/69092	Loss: 157.224
+Training time 0:04:06.722800
+Epoch: 117 Average loss: 157.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 290)
+0/69092	Loss: 155.603
+12800/69092	Loss: 157.760
+25600/69092	Loss: 156.922
+38400/69092	Loss: 157.641
+51200/69092	Loss: 157.616
+64000/69092	Loss: 156.164
+Training time 0:04:07.593237
+Epoch: 118 Average loss: 157.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 291)
+0/69092	Loss: 154.914
+12800/69092	Loss: 156.693
+25600/69092	Loss: 157.227
+38400/69092	Loss: 156.418
+51200/69092	Loss: 157.796
+64000/69092	Loss: 156.511
+Training time 0:04:06.697634
+Epoch: 119 Average loss: 157.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 292)
+0/69092	Loss: 162.489
+12800/69092	Loss: 156.861
+25600/69092	Loss: 157.719
+38400/69092	Loss: 158.311
+51200/69092	Loss: 155.898
+64000/69092	Loss: 156.442
+Training time 0:04:08.848071
+Epoch: 120 Average loss: 157.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 293)
+0/69092	Loss: 159.276
+12800/69092	Loss: 156.682
+25600/69092	Loss: 156.932
+38400/69092	Loss: 157.718
+51200/69092	Loss: 156.803
+64000/69092	Loss: 156.573
+Training time 0:04:05.920002
+Epoch: 121 Average loss: 157.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 294)
+0/69092	Loss: 155.874
+12800/69092	Loss: 157.463
+25600/69092	Loss: 157.989
+38400/69092	Loss: 155.701
+51200/69092	Loss: 156.886
+64000/69092	Loss: 156.351
+Training time 0:04:03.654210
+Epoch: 122 Average loss: 156.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 295)
+0/69092	Loss: 149.058
+12800/69092	Loss: 156.329
+25600/69092	Loss: 155.563
+38400/69092	Loss: 157.396
+51200/69092	Loss: 158.430
+64000/69092	Loss: 156.105
+Training time 0:04:06.935410
+Epoch: 123 Average loss: 156.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 296)
+0/69092	Loss: 158.406
+12800/69092	Loss: 156.088
+25600/69092	Loss: 156.076
+38400/69092	Loss: 157.194
+51200/69092	Loss: 158.158
+64000/69092	Loss: 157.273
+Training time 0:04:11.321915
+Epoch: 124 Average loss: 157.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 297)
+0/69092	Loss: 156.820
+12800/69092	Loss: 156.939
+25600/69092	Loss: 155.882
+38400/69092	Loss: 157.324
+51200/69092	Loss: 156.343
+64000/69092	Loss: 157.685
+Training time 0:04:13.570882
+Epoch: 125 Average loss: 156.86
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 298)
+0/69092	Loss: 153.471
+12800/69092	Loss: 156.685
+25600/69092	Loss: 157.746
+38400/69092	Loss: 156.922
+51200/69092	Loss: 158.207
+64000/69092	Loss: 157.401
+Training time 0:04:10.702523
+Epoch: 126 Average loss: 157.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 299)
+0/69092	Loss: 160.194
+12800/69092	Loss: 155.556
+25600/69092	Loss: 156.484
+38400/69092	Loss: 157.497
+51200/69092	Loss: 156.744
+64000/69092	Loss: 157.462
+Training time 0:04:04.298316
+Epoch: 127 Average loss: 156.81
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 300)
+0/69092	Loss: 145.845
+12800/69092	Loss: 157.552
+25600/69092	Loss: 157.499
+38400/69092	Loss: 156.503
+51200/69092	Loss: 158.020
+64000/69092	Loss: 157.439
+Training time 0:04:04.537136
+Epoch: 128 Average loss: 157.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 301)
+0/69092	Loss: 172.349
+12800/69092	Loss: 157.380
+25600/69092	Loss: 157.161
+38400/69092	Loss: 157.727
+51200/69092	Loss: 155.646
+64000/69092	Loss: 157.910
+Training time 0:04:08.531440
+Epoch: 129 Average loss: 157.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 302)
+0/69092	Loss: 155.418
+12800/69092	Loss: 156.609
+25600/69092	Loss: 157.015
+38400/69092	Loss: 158.265
+51200/69092	Loss: 157.511
+64000/69092	Loss: 157.121
+Training time 0:04:06.752566
+Epoch: 130 Average loss: 157.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 303)
+0/69092	Loss: 150.533
+12800/69092	Loss: 156.525
+25600/69092	Loss: 157.079
+38400/69092	Loss: 157.841
+51200/69092	Loss: 157.257
+64000/69092	Loss: 157.765
+Training time 0:04:08.646985
+Epoch: 131 Average loss: 157.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 304)
+0/69092	Loss: 155.461
+12800/69092	Loss: 156.606
+25600/69092	Loss: 156.778
+38400/69092	Loss: 156.201
+51200/69092	Loss: 156.135
+64000/69092	Loss: 158.307
+Training time 0:04:09.419583
+Epoch: 132 Average loss: 157.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 305)
+0/69092	Loss: 156.535
+12800/69092	Loss: 156.648
+25600/69092	Loss: 157.401
+38400/69092	Loss: 158.188
+51200/69092	Loss: 156.639
+64000/69092	Loss: 157.362
+Training time 0:04:02.464969
+Epoch: 133 Average loss: 157.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 306)
+0/69092	Loss: 166.057
+12800/69092	Loss: 156.458
+25600/69092	Loss: 156.344
+38400/69092	Loss: 157.892
+51200/69092	Loss: 157.120
+64000/69092	Loss: 158.209
+Training time 0:04:07.555364
+Epoch: 134 Average loss: 157.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 307)
+0/69092	Loss: 155.097
+12800/69092	Loss: 157.837
+25600/69092	Loss: 156.164
+38400/69092	Loss: 156.819
+51200/69092	Loss: 157.538
+64000/69092	Loss: 156.176
+Training time 0:04:09.609917
+Epoch: 135 Average loss: 157.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 308)
+0/69092	Loss: 152.057
+12800/69092	Loss: 156.457
+25600/69092	Loss: 156.684
+38400/69092	Loss: 158.750
+51200/69092	Loss: 156.853
+64000/69092	Loss: 157.246
+Training time 0:04:07.395100
+Epoch: 136 Average loss: 157.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 309)
+0/69092	Loss: 144.699
+12800/69092	Loss: 158.049
+25600/69092	Loss: 155.358
+38400/69092	Loss: 158.175
+51200/69092	Loss: 156.777
+64000/69092	Loss: 157.143
+Training time 0:04:08.038622
+Epoch: 137 Average loss: 157.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 310)
+0/69092	Loss: 154.776
+12800/69092	Loss: 158.213
+25600/69092	Loss: 157.469
+38400/69092	Loss: 156.029
+51200/69092	Loss: 155.871
+64000/69092	Loss: 158.126
+Training time 0:04:07.007746
+Epoch: 138 Average loss: 157.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 311)
+0/69092	Loss: 166.088
+12800/69092	Loss: 155.682
+25600/69092	Loss: 156.094
+38400/69092	Loss: 157.821
+51200/69092	Loss: 157.103
+64000/69092	Loss: 156.859
+Training time 0:04:04.057358
+Epoch: 139 Average loss: 156.84
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 312)
+0/69092	Loss: 148.434
+12800/69092	Loss: 156.650
+25600/69092	Loss: 156.832
+38400/69092	Loss: 156.339
+51200/69092	Loss: 157.166
+64000/69092	Loss: 157.624
+Training time 0:04:10.618231
+Epoch: 140 Average loss: 156.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 313)
+0/69092	Loss: 161.419
+12800/69092	Loss: 155.633
+25600/69092	Loss: 156.680
+38400/69092	Loss: 156.857
+51200/69092	Loss: 157.910
+64000/69092	Loss: 157.843
+Training time 0:04:11.862477
+Epoch: 141 Average loss: 157.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 314)
+0/69092	Loss: 159.316
+12800/69092	Loss: 156.805
+25600/69092	Loss: 157.847
+38400/69092	Loss: 155.797
+51200/69092	Loss: 156.922
+64000/69092	Loss: 156.482
+Training time 0:04:10.162818
+Epoch: 142 Average loss: 156.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 315)
+0/69092	Loss: 146.955
+12800/69092	Loss: 157.627
+25600/69092	Loss: 156.975
+38400/69092	Loss: 157.253
+51200/69092	Loss: 156.910
+64000/69092	Loss: 157.968
+Training time 0:04:08.477279
+Epoch: 143 Average loss: 157.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 316)
+0/69092	Loss: 154.526
+12800/69092	Loss: 157.462
+25600/69092	Loss: 158.502
+38400/69092	Loss: 155.874
+51200/69092	Loss: 157.289
+64000/69092	Loss: 156.534
+Training time 0:04:07.828540
+Epoch: 144 Average loss: 157.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 317)
+0/69092	Loss: 163.826
+12800/69092	Loss: 156.522
+25600/69092	Loss: 156.078
+38400/69092	Loss: 156.854
+51200/69092	Loss: 157.322
diff --git a/OAR.2068285.stderr b/OAR.2068285.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..5a9b289cbb2890a886fefa5ff4fddee20ba54283
--- /dev/null
+++ b/OAR.2068285.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-25 02:59:26] Job 2068285 KILLED ##
diff --git a/OAR.2068285.stdout b/OAR.2068285.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..7a2085697db9960746377b4eedca6a055bdc30b7
--- /dev/null
+++ b/OAR.2068285.stdout
@@ -0,0 +1,3089 @@
+Namespace(batch_size=64, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_64', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=True, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce GTX 1080 Ti
+GeForce GTX 1080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last (iter 309)'
+0/69092	Loss: 152.183
+3200/69092	Loss: 151.650
+6400/69092	Loss: 152.285
+9600/69092	Loss: 150.815
+12800/69092	Loss: 153.354
+16000/69092	Loss: 152.782
+19200/69092	Loss: 155.529
+22400/69092	Loss: 154.036
+25600/69092	Loss: 148.649
+28800/69092	Loss: 150.179
+32000/69092	Loss: 152.932
+35200/69092	Loss: 151.995
+38400/69092	Loss: 152.076
+41600/69092	Loss: 153.395
+44800/69092	Loss: 151.396
+48000/69092	Loss: 154.387
+51200/69092	Loss: 153.050
+54400/69092	Loss: 154.637
+57600/69092	Loss: 154.287
+60800/69092	Loss: 148.589
+64000/69092	Loss: 153.666
+67200/69092	Loss: 151.120
+Training time 0:04:52.357383
+Epoch: 1 Average loss: 152.43
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 310)
+0/69092	Loss: 159.442
+3200/69092	Loss: 154.781
+6400/69092	Loss: 152.621
+9600/69092	Loss: 150.426
+12800/69092	Loss: 150.802
+16000/69092	Loss: 152.935
+19200/69092	Loss: 150.630
+22400/69092	Loss: 150.863
+25600/69092	Loss: 154.977
+28800/69092	Loss: 150.534
+32000/69092	Loss: 155.075
+35200/69092	Loss: 150.436
+38400/69092	Loss: 152.814
+41600/69092	Loss: 152.529
+44800/69092	Loss: 151.246
+48000/69092	Loss: 151.439
+51200/69092	Loss: 152.896
+54400/69092	Loss: 153.654
+57600/69092	Loss: 151.745
+60800/69092	Loss: 154.735
+64000/69092	Loss: 152.990
+67200/69092	Loss: 154.547
+Training time 0:04:51.856329
+Epoch: 2 Average loss: 152.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 311)
+0/69092	Loss: 134.734
+3200/69092	Loss: 152.843
+6400/69092	Loss: 152.510
+9600/69092	Loss: 150.592
+12800/69092	Loss: 153.914
+16000/69092	Loss: 152.556
+19200/69092	Loss: 149.478
+22400/69092	Loss: 152.516
+25600/69092	Loss: 152.359
+28800/69092	Loss: 151.001
+32000/69092	Loss: 153.504
+35200/69092	Loss: 153.543
+38400/69092	Loss: 152.340
+41600/69092	Loss: 152.565
+44800/69092	Loss: 153.622
+48000/69092	Loss: 154.323
+51200/69092	Loss: 150.575
+54400/69092	Loss: 151.611
+57600/69092	Loss: 151.667
+60800/69092	Loss: 153.052
+64000/69092	Loss: 153.388
+67200/69092	Loss: 153.600
+Training time 0:04:50.139861
+Epoch: 3 Average loss: 152.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 312)
+0/69092	Loss: 142.408
+3200/69092	Loss: 149.926
+6400/69092	Loss: 154.006
+9600/69092	Loss: 154.148
+12800/69092	Loss: 151.793
+16000/69092	Loss: 151.222
+19200/69092	Loss: 152.250
+22400/69092	Loss: 151.391
+25600/69092	Loss: 155.184
+28800/69092	Loss: 151.960
+32000/69092	Loss: 153.657
+35200/69092	Loss: 155.231
+38400/69092	Loss: 152.180
+41600/69092	Loss: 152.465
+44800/69092	Loss: 152.076
+48000/69092	Loss: 149.416
+51200/69092	Loss: 152.701
+54400/69092	Loss: 151.919
+57600/69092	Loss: 152.060
+60800/69092	Loss: 150.976
+64000/69092	Loss: 151.854
+67200/69092	Loss: 154.316
+Training time 0:04:55.009685
+Epoch: 4 Average loss: 152.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 313)
+0/69092	Loss: 153.333
+3200/69092	Loss: 152.545
+6400/69092	Loss: 150.896
+9600/69092	Loss: 150.357
+12800/69092	Loss: 152.456
+16000/69092	Loss: 152.458
+19200/69092	Loss: 152.289
+22400/69092	Loss: 152.380
+25600/69092	Loss: 152.213
+28800/69092	Loss: 152.459
+32000/69092	Loss: 152.800
+35200/69092	Loss: 154.103
+38400/69092	Loss: 152.956
+41600/69092	Loss: 152.906
+44800/69092	Loss: 151.564
+48000/69092	Loss: 153.002
+51200/69092	Loss: 151.203
+54400/69092	Loss: 151.133
+57600/69092	Loss: 154.644
+60800/69092	Loss: 152.807
+64000/69092	Loss: 154.770
+67200/69092	Loss: 150.738
+Training time 0:04:51.641555
+Epoch: 5 Average loss: 152.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 314)
+0/69092	Loss: 148.495
+3200/69092	Loss: 154.320
+6400/69092	Loss: 153.600
+9600/69092	Loss: 153.046
+12800/69092	Loss: 150.433
+16000/69092	Loss: 151.382
+19200/69092	Loss: 152.088
+22400/69092	Loss: 152.638
+25600/69092	Loss: 152.848
+28800/69092	Loss: 149.897
+32000/69092	Loss: 155.370
+35200/69092	Loss: 153.207
+38400/69092	Loss: 152.994
+41600/69092	Loss: 152.902
+44800/69092	Loss: 152.503
+48000/69092	Loss: 151.920
+51200/69092	Loss: 152.251
+54400/69092	Loss: 150.518
+57600/69092	Loss: 153.685
+60800/69092	Loss: 151.117
+64000/69092	Loss: 153.273
+67200/69092	Loss: 152.338
+Training time 0:04:49.531773
+Epoch: 6 Average loss: 152.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 315)
+0/69092	Loss: 159.999
+3200/69092	Loss: 152.523
+6400/69092	Loss: 151.299
+9600/69092	Loss: 152.631
+12800/69092	Loss: 154.044
+16000/69092	Loss: 152.559
+19200/69092	Loss: 153.762
+22400/69092	Loss: 152.462
+25600/69092	Loss: 149.804
+28800/69092	Loss: 154.240
+32000/69092	Loss: 153.769
+35200/69092	Loss: 150.327
+38400/69092	Loss: 155.003
+41600/69092	Loss: 151.960
+44800/69092	Loss: 150.458
+48000/69092	Loss: 152.349
+51200/69092	Loss: 152.027
+54400/69092	Loss: 153.317
+57600/69092	Loss: 152.629
+60800/69092	Loss: 152.926
+64000/69092	Loss: 151.968
+67200/69092	Loss: 151.694
+Training time 0:04:58.512221
+Epoch: 7 Average loss: 152.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 316)
+0/69092	Loss: 163.111
+3200/69092	Loss: 153.264
+6400/69092	Loss: 154.351
+9600/69092	Loss: 154.740
+12800/69092	Loss: 153.918
+16000/69092	Loss: 151.023
+19200/69092	Loss: 152.130
+22400/69092	Loss: 154.274
+25600/69092	Loss: 152.311
+28800/69092	Loss: 148.902
+32000/69092	Loss: 152.154
+35200/69092	Loss: 152.465
+38400/69092	Loss: 152.427
+41600/69092	Loss: 151.234
+44800/69092	Loss: 151.904
+48000/69092	Loss: 154.259
+51200/69092	Loss: 148.981
+54400/69092	Loss: 151.418
+57600/69092	Loss: 153.284
+60800/69092	Loss: 153.432
+64000/69092	Loss: 154.522
+67200/69092	Loss: 151.813
+Training time 0:04:52.326015
+Epoch: 8 Average loss: 152.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 317)
+0/69092	Loss: 145.411
+3200/69092	Loss: 154.616
+6400/69092	Loss: 154.145
+9600/69092	Loss: 151.741
+12800/69092	Loss: 153.260
+16000/69092	Loss: 153.434
+19200/69092	Loss: 149.795
+22400/69092	Loss: 151.875
+25600/69092	Loss: 154.525
+28800/69092	Loss: 152.066
+32000/69092	Loss: 151.676
+35200/69092	Loss: 153.090
+38400/69092	Loss: 153.029
+41600/69092	Loss: 152.771
+44800/69092	Loss: 150.900
+48000/69092	Loss: 152.190
+51200/69092	Loss: 151.611
+54400/69092	Loss: 153.291
+57600/69092	Loss: 150.412
+60800/69092	Loss: 150.282
+64000/69092	Loss: 153.127
+67200/69092	Loss: 153.294
+Training time 0:04:53.794871
+Epoch: 9 Average loss: 152.44
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 318)
+0/69092	Loss: 128.179
+3200/69092	Loss: 149.781
+6400/69092	Loss: 152.331
+9600/69092	Loss: 154.396
+12800/69092	Loss: 151.316
+16000/69092	Loss: 152.371
+19200/69092	Loss: 152.552
+22400/69092	Loss: 153.303
+25600/69092	Loss: 153.643
+28800/69092	Loss: 150.225
+32000/69092	Loss: 150.622
+35200/69092	Loss: 152.424
+38400/69092	Loss: 151.834
+41600/69092	Loss: 152.264
+44800/69092	Loss: 154.677
+48000/69092	Loss: 153.459
+51200/69092	Loss: 153.347
+54400/69092	Loss: 152.326
+57600/69092	Loss: 153.728
+60800/69092	Loss: 152.451
+64000/69092	Loss: 148.630
+67200/69092	Loss: 152.763
+Training time 0:04:49.848114
+Epoch: 10 Average loss: 152.24
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 319)
+0/69092	Loss: 142.905
+3200/69092	Loss: 153.824
+6400/69092	Loss: 155.878
+9600/69092	Loss: 150.336
+12800/69092	Loss: 153.642
+16000/69092	Loss: 151.411
+19200/69092	Loss: 154.567
+22400/69092	Loss: 153.036
+25600/69092	Loss: 150.087
+28800/69092	Loss: 151.030
+32000/69092	Loss: 151.418
+35200/69092	Loss: 151.023
+38400/69092	Loss: 148.444
+41600/69092	Loss: 151.903
+44800/69092	Loss: 152.763
+48000/69092	Loss: 154.169
+51200/69092	Loss: 151.868
+54400/69092	Loss: 152.579
+57600/69092	Loss: 153.067
+60800/69092	Loss: 152.149
+64000/69092	Loss: 153.555
+67200/69092	Loss: 151.000
+Training time 0:04:57.013579
+Epoch: 11 Average loss: 152.29
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 320)
+0/69092	Loss: 151.394
+3200/69092	Loss: 152.447
+6400/69092	Loss: 152.888
+9600/69092	Loss: 152.646
+12800/69092	Loss: 151.644
+16000/69092	Loss: 152.469
+19200/69092	Loss: 152.163
+22400/69092	Loss: 150.452
+25600/69092	Loss: 151.902
+28800/69092	Loss: 152.144
+32000/69092	Loss: 150.738
+35200/69092	Loss: 153.309
+38400/69092	Loss: 153.811
+41600/69092	Loss: 152.597
+44800/69092	Loss: 151.872
+48000/69092	Loss: 153.172
+51200/69092	Loss: 153.323
+54400/69092	Loss: 150.141
+57600/69092	Loss: 152.336
+60800/69092	Loss: 154.109
+64000/69092	Loss: 152.203
+67200/69092	Loss: 153.926
+Training time 0:04:48.153389
+Epoch: 12 Average loss: 152.41
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 321)
+0/69092	Loss: 157.636
+3200/69092	Loss: 152.608
+6400/69092	Loss: 152.308
+9600/69092	Loss: 153.485
+12800/69092	Loss: 153.940
+16000/69092	Loss: 151.878
+19200/69092	Loss: 149.654
+22400/69092	Loss: 151.707
+25600/69092	Loss: 153.842
+28800/69092	Loss: 150.141
+32000/69092	Loss: 152.478
+35200/69092	Loss: 152.337
+38400/69092	Loss: 149.935
+41600/69092	Loss: 152.342
+44800/69092	Loss: 154.538
+48000/69092	Loss: 153.898
+51200/69092	Loss: 152.160
+54400/69092	Loss: 153.422
+57600/69092	Loss: 152.781
+60800/69092	Loss: 153.877
+64000/69092	Loss: 154.009
+67200/69092	Loss: 151.143
+Training time 0:04:45.355561
+Epoch: 13 Average loss: 152.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 322)
+0/69092	Loss: 162.517
+3200/69092	Loss: 152.929
+6400/69092	Loss: 152.477
+9600/69092	Loss: 150.981
+12800/69092	Loss: 152.707
+16000/69092	Loss: 155.280
+19200/69092	Loss: 151.793
+22400/69092	Loss: 152.082
+25600/69092	Loss: 155.199
+28800/69092	Loss: 151.704
+32000/69092	Loss: 150.883
+35200/69092	Loss: 153.300
+38400/69092	Loss: 150.486
+41600/69092	Loss: 155.252
+44800/69092	Loss: 155.059
+48000/69092	Loss: 152.528
+51200/69092	Loss: 153.416
+54400/69092	Loss: 154.628
+57600/69092	Loss: 150.056
+60800/69092	Loss: 152.059
+64000/69092	Loss: 151.454
+67200/69092	Loss: 149.021
+Training time 0:04:48.906956
+Epoch: 14 Average loss: 152.60
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 323)
+0/69092	Loss: 157.050
+3200/69092	Loss: 153.959
+6400/69092	Loss: 154.344
+9600/69092	Loss: 152.152
+12800/69092	Loss: 151.359
+16000/69092	Loss: 152.589
+19200/69092	Loss: 150.047
+22400/69092	Loss: 152.870
+25600/69092	Loss: 149.748
+28800/69092	Loss: 153.648
+32000/69092	Loss: 156.431
+35200/69092	Loss: 152.113
+38400/69092	Loss: 152.905
+41600/69092	Loss: 152.468
+44800/69092	Loss: 153.602
+48000/69092	Loss: 152.497
+51200/69092	Loss: 151.044
+54400/69092	Loss: 155.048
+57600/69092	Loss: 151.216
+60800/69092	Loss: 150.927
+64000/69092	Loss: 152.557
+67200/69092	Loss: 153.362
+Training time 0:04:54.813450
+Epoch: 15 Average loss: 152.59
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 324)
+0/69092	Loss: 148.821
+3200/69092	Loss: 152.991
+6400/69092	Loss: 152.562
+9600/69092	Loss: 150.855
+12800/69092	Loss: 153.888
+16000/69092	Loss: 153.659
+19200/69092	Loss: 152.960
+22400/69092	Loss: 152.873
+25600/69092	Loss: 152.063
+28800/69092	Loss: 151.277
+32000/69092	Loss: 151.158
+35200/69092	Loss: 152.072
+38400/69092	Loss: 153.704
+41600/69092	Loss: 153.907
+44800/69092	Loss: 149.131
+48000/69092	Loss: 151.833
+51200/69092	Loss: 151.896
+54400/69092	Loss: 152.958
+57600/69092	Loss: 151.339
+60800/69092	Loss: 151.675
+64000/69092	Loss: 152.641
+67200/69092	Loss: 151.787
+Training time 0:04:57.074914
+Epoch: 16 Average loss: 152.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 325)
+0/69092	Loss: 139.830
+3200/69092	Loss: 153.831
+6400/69092	Loss: 154.674
+9600/69092	Loss: 151.321
+12800/69092	Loss: 153.510
+16000/69092	Loss: 148.394
+19200/69092	Loss: 151.813
+22400/69092	Loss: 153.058
+25600/69092	Loss: 154.548
+28800/69092	Loss: 150.580
+32000/69092	Loss: 155.687
+35200/69092	Loss: 152.704
+38400/69092	Loss: 152.194
+41600/69092	Loss: 153.185
+44800/69092	Loss: 148.863
+48000/69092	Loss: 153.429
+51200/69092	Loss: 154.257
+54400/69092	Loss: 152.202
+57600/69092	Loss: 152.896
+60800/69092	Loss: 151.201
+64000/69092	Loss: 153.511
+67200/69092	Loss: 152.102
+Training time 0:04:56.971456
+Epoch: 17 Average loss: 152.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 326)
+0/69092	Loss: 144.373
+3200/69092	Loss: 152.078
+6400/69092	Loss: 153.584
+9600/69092	Loss: 149.502
+12800/69092	Loss: 152.185
+16000/69092	Loss: 152.809
+19200/69092	Loss: 153.496
+22400/69092	Loss: 154.004
+25600/69092	Loss: 154.795
+28800/69092	Loss: 151.465
+32000/69092	Loss: 154.518
+35200/69092	Loss: 149.687
+38400/69092	Loss: 153.964
+41600/69092	Loss: 152.572
+44800/69092	Loss: 149.701
+48000/69092	Loss: 150.567
+51200/69092	Loss: 151.033
+54400/69092	Loss: 153.177
+57600/69092	Loss: 151.122
+60800/69092	Loss: 151.542
+64000/69092	Loss: 152.469
+67200/69092	Loss: 153.430
+Training time 0:05:01.046983
+Epoch: 18 Average loss: 152.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 327)
+0/69092	Loss: 152.464
+3200/69092	Loss: 153.488
+6400/69092	Loss: 152.579
+9600/69092	Loss: 152.127
+12800/69092	Loss: 150.396
+16000/69092	Loss: 154.485
+19200/69092	Loss: 150.241
+22400/69092	Loss: 152.079
+25600/69092	Loss: 151.118
+28800/69092	Loss: 153.015
+32000/69092	Loss: 151.977
+35200/69092	Loss: 149.929
+38400/69092	Loss: 149.426
+41600/69092	Loss: 151.497
+44800/69092	Loss: 152.594
+48000/69092	Loss: 155.580
+51200/69092	Loss: 151.766
+54400/69092	Loss: 152.328
+57600/69092	Loss: 154.382
+60800/69092	Loss: 151.399
+64000/69092	Loss: 153.803
+67200/69092	Loss: 153.180
+Training time 0:04:55.558946
+Epoch: 19 Average loss: 152.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 328)
+0/69092	Loss: 146.869
+3200/69092	Loss: 153.782
+6400/69092	Loss: 153.934
+9600/69092	Loss: 153.725
+12800/69092	Loss: 153.885
+16000/69092	Loss: 154.710
+19200/69092	Loss: 152.246
+22400/69092	Loss: 152.874
+25600/69092	Loss: 152.616
+28800/69092	Loss: 153.899
+32000/69092	Loss: 150.965
+35200/69092	Loss: 152.105
+38400/69092	Loss: 150.924
+41600/69092	Loss: 151.229
+44800/69092	Loss: 150.414
+48000/69092	Loss: 151.099
+51200/69092	Loss: 151.417
+54400/69092	Loss: 151.783
+57600/69092	Loss: 152.924
+60800/69092	Loss: 151.453
+64000/69092	Loss: 151.253
+67200/69092	Loss: 151.988
+Training time 0:05:00.999168
+Epoch: 20 Average loss: 152.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 329)
+0/69092	Loss: 147.028
+3200/69092	Loss: 151.092
+6400/69092	Loss: 153.710
+9600/69092	Loss: 152.265
+12800/69092	Loss: 152.751
+16000/69092	Loss: 153.640
+19200/69092	Loss: 152.519
+22400/69092	Loss: 152.768
+25600/69092	Loss: 153.118
+28800/69092	Loss: 151.120
+32000/69092	Loss: 152.780
+35200/69092	Loss: 152.773
+38400/69092	Loss: 151.942
+41600/69092	Loss: 151.585
+44800/69092	Loss: 153.718
+48000/69092	Loss: 152.276
+51200/69092	Loss: 154.530
+54400/69092	Loss: 152.744
+57600/69092	Loss: 150.482
+60800/69092	Loss: 155.222
+64000/69092	Loss: 150.784
+67200/69092	Loss: 151.592
+Training time 0:04:54.733607
+Epoch: 21 Average loss: 152.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 330)
+0/69092	Loss: 151.133
+3200/69092	Loss: 151.407
+6400/69092	Loss: 152.002
+9600/69092	Loss: 152.132
+12800/69092	Loss: 150.633
+16000/69092	Loss: 150.642
+19200/69092	Loss: 153.192
+22400/69092	Loss: 153.700
+25600/69092	Loss: 150.160
+28800/69092	Loss: 152.733
+32000/69092	Loss: 152.790
+35200/69092	Loss: 150.569
+38400/69092	Loss: 152.402
+41600/69092	Loss: 152.672
+44800/69092	Loss: 153.412
+48000/69092	Loss: 155.049
+51200/69092	Loss: 152.697
+54400/69092	Loss: 154.992
+57600/69092	Loss: 151.886
+60800/69092	Loss: 153.533
+64000/69092	Loss: 150.066
+67200/69092	Loss: 151.130
+Training time 0:04:51.479730
+Epoch: 22 Average loss: 152.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 331)
+0/69092	Loss: 134.860
+3200/69092	Loss: 153.536
+6400/69092	Loss: 152.443
+9600/69092	Loss: 149.892
+12800/69092	Loss: 150.728
+16000/69092	Loss: 152.499
+19200/69092	Loss: 153.774
+22400/69092	Loss: 152.431
+25600/69092	Loss: 149.763
+28800/69092	Loss: 154.345
+32000/69092	Loss: 151.940
+35200/69092	Loss: 152.357
+38400/69092	Loss: 152.972
+41600/69092	Loss: 150.494
+44800/69092	Loss: 151.210
+48000/69092	Loss: 154.224
+51200/69092	Loss: 151.207
+54400/69092	Loss: 150.331
+57600/69092	Loss: 152.456
+60800/69092	Loss: 153.180
+64000/69092	Loss: 153.737
+67200/69092	Loss: 151.679
+Training time 0:04:55.682500
+Epoch: 23 Average loss: 152.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 332)
+0/69092	Loss: 146.132
+3200/69092	Loss: 151.199
+6400/69092	Loss: 151.551
+9600/69092	Loss: 155.865
+12800/69092	Loss: 150.800
+16000/69092	Loss: 151.015
+19200/69092	Loss: 152.506
+22400/69092	Loss: 151.661
+25600/69092	Loss: 151.519
+28800/69092	Loss: 151.153
+32000/69092	Loss: 152.509
+35200/69092	Loss: 150.394
+38400/69092	Loss: 152.259
+41600/69092	Loss: 153.364
+44800/69092	Loss: 152.367
+48000/69092	Loss: 153.211
+51200/69092	Loss: 153.377
+54400/69092	Loss: 152.013
+57600/69092	Loss: 149.970
+60800/69092	Loss: 152.552
+64000/69092	Loss: 152.334
+67200/69092	Loss: 152.434
+Training time 0:04:46.518119
+Epoch: 24 Average loss: 152.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 333)
+0/69092	Loss: 149.840
+3200/69092	Loss: 154.191
+6400/69092	Loss: 152.417
+9600/69092	Loss: 150.376
+12800/69092	Loss: 152.603
+16000/69092	Loss: 152.176
+19200/69092	Loss: 151.488
+22400/69092	Loss: 151.300
+25600/69092	Loss: 153.556
+28800/69092	Loss: 151.875
+32000/69092	Loss: 151.179
+35200/69092	Loss: 151.816
+38400/69092	Loss: 153.076
+41600/69092	Loss: 151.797
+44800/69092	Loss: 150.499
+48000/69092	Loss: 151.741
+51200/69092	Loss: 152.232
+54400/69092	Loss: 154.147
+57600/69092	Loss: 152.086
+60800/69092	Loss: 152.965
+64000/69092	Loss: 154.207
+67200/69092	Loss: 153.088
+Training time 0:04:50.798790
+Epoch: 25 Average loss: 152.34
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 334)
+0/69092	Loss: 148.167
+3200/69092	Loss: 154.325
+6400/69092	Loss: 152.557
+9600/69092	Loss: 152.038
+12800/69092	Loss: 150.379
+16000/69092	Loss: 152.418
+19200/69092	Loss: 151.297
+22400/69092	Loss: 151.685
+25600/69092	Loss: 153.702
+28800/69092	Loss: 153.929
+32000/69092	Loss: 151.989
+35200/69092	Loss: 152.220
+38400/69092	Loss: 153.127
+41600/69092	Loss: 153.456
+44800/69092	Loss: 153.248
+48000/69092	Loss: 151.492
+51200/69092	Loss: 152.005
+54400/69092	Loss: 150.737
+57600/69092	Loss: 155.506
+60800/69092	Loss: 148.446
+64000/69092	Loss: 152.352
+67200/69092	Loss: 151.310
+Training time 0:04:56.322250
+Epoch: 26 Average loss: 152.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 335)
+0/69092	Loss: 141.427
+3200/69092	Loss: 152.090
+6400/69092	Loss: 152.904
+9600/69092	Loss: 152.459
+12800/69092	Loss: 151.812
+16000/69092	Loss: 150.855
+19200/69092	Loss: 153.354
+22400/69092	Loss: 150.437
+25600/69092	Loss: 153.535
+28800/69092	Loss: 151.582
+32000/69092	Loss: 152.588
+35200/69092	Loss: 149.283
+38400/69092	Loss: 151.001
+41600/69092	Loss: 150.085
+44800/69092	Loss: 154.701
+48000/69092	Loss: 156.761
+51200/69092	Loss: 152.037
+54400/69092	Loss: 153.141
+57600/69092	Loss: 152.711
+60800/69092	Loss: 151.207
+64000/69092	Loss: 153.963
+67200/69092	Loss: 151.814
+Training time 0:04:57.506762
+Epoch: 27 Average loss: 152.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 336)
+0/69092	Loss: 133.221
+3200/69092	Loss: 153.101
+6400/69092	Loss: 153.749
+9600/69092	Loss: 152.099
+12800/69092	Loss: 150.121
+16000/69092	Loss: 153.229
+19200/69092	Loss: 152.229
+22400/69092	Loss: 153.361
+25600/69092	Loss: 152.052
+28800/69092	Loss: 152.372
+32000/69092	Loss: 151.172
+35200/69092	Loss: 149.881
+38400/69092	Loss: 152.041
+41600/69092	Loss: 152.504
+44800/69092	Loss: 150.522
+48000/69092	Loss: 152.769
+51200/69092	Loss: 151.048
+54400/69092	Loss: 152.903
+57600/69092	Loss: 151.750
+60800/69092	Loss: 152.186
+64000/69092	Loss: 149.461
+67200/69092	Loss: 154.764
+Training time 0:04:56.992493
+Epoch: 28 Average loss: 152.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 337)
+0/69092	Loss: 130.269
+3200/69092	Loss: 153.670
+6400/69092	Loss: 152.220
+9600/69092	Loss: 151.508
+12800/69092	Loss: 153.114
+16000/69092	Loss: 151.541
+19200/69092	Loss: 152.177
+22400/69092	Loss: 153.604
+25600/69092	Loss: 152.756
+28800/69092	Loss: 153.729
+32000/69092	Loss: 152.179
+35200/69092	Loss: 151.586
+38400/69092	Loss: 150.351
+41600/69092	Loss: 152.783
+44800/69092	Loss: 154.172
+48000/69092	Loss: 150.299
+51200/69092	Loss: 151.737
+54400/69092	Loss: 152.123
+57600/69092	Loss: 151.734
+60800/69092	Loss: 147.117
+64000/69092	Loss: 151.109
+67200/69092	Loss: 153.096
+Training time 0:04:51.204427
+Epoch: 29 Average loss: 152.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 338)
+0/69092	Loss: 138.425
+3200/69092	Loss: 152.477
+6400/69092	Loss: 150.676
+9600/69092	Loss: 153.611
+12800/69092	Loss: 150.906
+16000/69092	Loss: 152.130
+19200/69092	Loss: 150.774
+22400/69092	Loss: 153.285
+25600/69092	Loss: 153.297
+28800/69092	Loss: 153.633
+32000/69092	Loss: 151.448
+35200/69092	Loss: 153.320
+38400/69092	Loss: 152.201
+41600/69092	Loss: 152.348
+44800/69092	Loss: 150.531
+48000/69092	Loss: 151.512
+51200/69092	Loss: 150.381
+54400/69092	Loss: 152.497
+57600/69092	Loss: 151.720
+60800/69092	Loss: 152.262
+64000/69092	Loss: 155.182
+67200/69092	Loss: 150.858
+Training time 0:04:51.996438
+Epoch: 30 Average loss: 152.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 339)
+0/69092	Loss: 149.449
+3200/69092	Loss: 153.819
+6400/69092	Loss: 150.767
+9600/69092	Loss: 154.335
+12800/69092	Loss: 154.126
+16000/69092	Loss: 149.557
+19200/69092	Loss: 151.935
+22400/69092	Loss: 152.038
+25600/69092	Loss: 151.220
+28800/69092	Loss: 151.983
+32000/69092	Loss: 152.670
+35200/69092	Loss: 151.051
+38400/69092	Loss: 153.609
+41600/69092	Loss: 152.595
+44800/69092	Loss: 151.606
+48000/69092	Loss: 150.659
+51200/69092	Loss: 153.891
+54400/69092	Loss: 152.512
+57600/69092	Loss: 152.411
+60800/69092	Loss: 155.702
+64000/69092	Loss: 151.627
+67200/69092	Loss: 152.198
+Training time 0:04:51.963552
+Epoch: 31 Average loss: 152.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 340)
+0/69092	Loss: 134.457
+3200/69092	Loss: 153.652
+6400/69092	Loss: 154.143
+9600/69092	Loss: 150.744
+12800/69092	Loss: 154.232
+16000/69092	Loss: 149.880
+19200/69092	Loss: 153.318
+22400/69092	Loss: 152.315
+25600/69092	Loss: 151.022
+28800/69092	Loss: 152.031
+32000/69092	Loss: 150.523
+35200/69092	Loss: 154.217
+38400/69092	Loss: 150.024
+41600/69092	Loss: 151.019
+44800/69092	Loss: 152.273
+48000/69092	Loss: 152.065
+51200/69092	Loss: 156.054
+54400/69092	Loss: 152.644
+57600/69092	Loss: 150.708
+60800/69092	Loss: 150.233
+64000/69092	Loss: 151.841
+67200/69092	Loss: 153.538
+Training time 0:04:59.586796
+Epoch: 32 Average loss: 152.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 341)
+0/69092	Loss: 184.830
+3200/69092	Loss: 149.674
+6400/69092	Loss: 151.520
+9600/69092	Loss: 153.057
+12800/69092	Loss: 152.992
+16000/69092	Loss: 150.488
+19200/69092	Loss: 151.317
+22400/69092	Loss: 153.007
+25600/69092	Loss: 152.180
+28800/69092	Loss: 153.014
+32000/69092	Loss: 153.689
+35200/69092	Loss: 149.681
+38400/69092	Loss: 151.869
+41600/69092	Loss: 153.280
+44800/69092	Loss: 153.019
+48000/69092	Loss: 152.745
+51200/69092	Loss: 152.673
+54400/69092	Loss: 154.760
+57600/69092	Loss: 150.124
+60800/69092	Loss: 150.195
+64000/69092	Loss: 151.911
+67200/69092	Loss: 152.179
+Training time 0:04:44.373168
+Epoch: 33 Average loss: 152.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 342)
+0/69092	Loss: 149.493
+3200/69092	Loss: 153.542
+6400/69092	Loss: 152.415
+9600/69092	Loss: 154.209
+12800/69092	Loss: 150.385
+16000/69092	Loss: 153.775
+19200/69092	Loss: 150.375
+22400/69092	Loss: 151.428
+25600/69092	Loss: 151.631
+28800/69092	Loss: 153.269
+32000/69092	Loss: 151.129
+35200/69092	Loss: 151.653
+38400/69092	Loss: 155.009
+41600/69092	Loss: 155.339
+44800/69092	Loss: 151.274
+48000/69092	Loss: 153.453
+51200/69092	Loss: 150.441
+54400/69092	Loss: 152.416
+57600/69092	Loss: 153.149
+60800/69092	Loss: 151.520
+64000/69092	Loss: 152.832
+67200/69092	Loss: 152.487
+Training time 0:04:56.752912
+Epoch: 34 Average loss: 152.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 343)
+0/69092	Loss: 156.455
+3200/69092	Loss: 153.220
+6400/69092	Loss: 151.141
+9600/69092	Loss: 151.671
+12800/69092	Loss: 151.010
+16000/69092	Loss: 155.255
+19200/69092	Loss: 153.328
+22400/69092	Loss: 151.027
+25600/69092	Loss: 155.107
+28800/69092	Loss: 151.477
+32000/69092	Loss: 151.732
+35200/69092	Loss: 156.207
+38400/69092	Loss: 150.103
+41600/69092	Loss: 153.276
+44800/69092	Loss: 150.903
+48000/69092	Loss: 149.701
+51200/69092	Loss: 151.371
+54400/69092	Loss: 152.385
+57600/69092	Loss: 153.020
+60800/69092	Loss: 149.253
+64000/69092	Loss: 152.212
+67200/69092	Loss: 151.646
+Training time 0:04:41.271757
+Epoch: 35 Average loss: 152.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 344)
+0/69092	Loss: 152.728
+3200/69092	Loss: 154.176
+6400/69092	Loss: 152.111
+9600/69092	Loss: 153.827
+12800/69092	Loss: 148.727
+16000/69092	Loss: 154.085
+19200/69092	Loss: 150.397
+22400/69092	Loss: 152.221
+25600/69092	Loss: 152.440
+28800/69092	Loss: 151.169
+32000/69092	Loss: 150.328
+35200/69092	Loss: 153.477
+38400/69092	Loss: 154.612
+41600/69092	Loss: 151.677
+44800/69092	Loss: 153.689
+48000/69092	Loss: 150.797
+51200/69092	Loss: 151.514
+54400/69092	Loss: 149.755
+57600/69092	Loss: 152.959
+60800/69092	Loss: 151.039
+64000/69092	Loss: 150.996
+67200/69092	Loss: 157.028
+Training time 0:04:50.749251
+Epoch: 36 Average loss: 152.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 345)
+0/69092	Loss: 144.968
+3200/69092	Loss: 150.944
+6400/69092	Loss: 151.882
+9600/69092	Loss: 151.432
+12800/69092	Loss: 153.335
+16000/69092	Loss: 151.410
+19200/69092	Loss: 151.090
+22400/69092	Loss: 151.332
+25600/69092	Loss: 150.389
+28800/69092	Loss: 151.724
+32000/69092	Loss: 153.236
+35200/69092	Loss: 151.028
+38400/69092	Loss: 153.111
+41600/69092	Loss: 152.455
+44800/69092	Loss: 150.457
+48000/69092	Loss: 151.080
+51200/69092	Loss: 152.359
+54400/69092	Loss: 154.452
+57600/69092	Loss: 153.823
+60800/69092	Loss: 152.210
+64000/69092	Loss: 156.284
+67200/69092	Loss: 153.959
+Training time 0:04:56.092557
+Epoch: 37 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 346)
+0/69092	Loss: 154.519
+3200/69092	Loss: 152.953
+6400/69092	Loss: 151.713
+9600/69092	Loss: 151.209
+12800/69092	Loss: 152.897
+16000/69092	Loss: 152.985
+19200/69092	Loss: 150.601
+22400/69092	Loss: 152.633
+25600/69092	Loss: 152.365
+28800/69092	Loss: 152.170
+32000/69092	Loss: 153.599
+35200/69092	Loss: 154.194
+38400/69092	Loss: 151.321
+41600/69092	Loss: 151.657
+44800/69092	Loss: 152.809
+48000/69092	Loss: 149.193
+51200/69092	Loss: 151.585
+54400/69092	Loss: 147.995
+57600/69092	Loss: 154.751
+60800/69092	Loss: 152.405
+64000/69092	Loss: 151.126
+67200/69092	Loss: 153.054
+Training time 0:04:46.340005
+Epoch: 38 Average loss: 152.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 347)
+0/69092	Loss: 155.517
+3200/69092	Loss: 151.532
+6400/69092	Loss: 151.045
+9600/69092	Loss: 150.674
+12800/69092	Loss: 149.913
+16000/69092	Loss: 153.018
+19200/69092	Loss: 151.897
+22400/69092	Loss: 151.769
+25600/69092	Loss: 151.634
+28800/69092	Loss: 152.942
+32000/69092	Loss: 153.543
+35200/69092	Loss: 148.720
+38400/69092	Loss: 153.905
+41600/69092	Loss: 151.111
+44800/69092	Loss: 153.790
+48000/69092	Loss: 151.695
+51200/69092	Loss: 152.201
+54400/69092	Loss: 152.448
+57600/69092	Loss: 154.993
+60800/69092	Loss: 152.544
+64000/69092	Loss: 154.550
+67200/69092	Loss: 153.987
+Training time 0:04:51.157197
+Epoch: 39 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 348)
+0/69092	Loss: 164.980
+3200/69092	Loss: 152.534
+6400/69092	Loss: 151.433
+9600/69092	Loss: 148.842
+12800/69092	Loss: 153.554
+16000/69092	Loss: 153.431
+19200/69092	Loss: 152.971
+22400/69092	Loss: 152.744
+25600/69092	Loss: 151.805
+28800/69092	Loss: 154.479
+32000/69092	Loss: 151.800
+35200/69092	Loss: 152.465
+38400/69092	Loss: 152.071
+41600/69092	Loss: 150.298
+44800/69092	Loss: 153.446
+48000/69092	Loss: 151.722
+51200/69092	Loss: 154.719
+54400/69092	Loss: 149.358
+57600/69092	Loss: 151.644
+60800/69092	Loss: 152.203
+64000/69092	Loss: 153.698
+67200/69092	Loss: 153.300
+Training time 0:04:58.104908
+Epoch: 40 Average loss: 152.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 349)
+0/69092	Loss: 155.595
+3200/69092	Loss: 150.947
+6400/69092	Loss: 154.446
+9600/69092	Loss: 153.506
+12800/69092	Loss: 151.536
+16000/69092	Loss: 153.836
+19200/69092	Loss: 153.533
+22400/69092	Loss: 150.537
+25600/69092	Loss: 152.646
+28800/69092	Loss: 151.218
+32000/69092	Loss: 153.552
+35200/69092	Loss: 152.001
+38400/69092	Loss: 152.176
+41600/69092	Loss: 152.006
+44800/69092	Loss: 151.491
+48000/69092	Loss: 150.715
+51200/69092	Loss: 151.317
+54400/69092	Loss: 152.502
+57600/69092	Loss: 154.138
+60800/69092	Loss: 150.647
+64000/69092	Loss: 152.744
+67200/69092	Loss: 150.142
+Training time 0:04:54.278531
+Epoch: 41 Average loss: 152.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 350)
+0/69092	Loss: 165.297
+3200/69092	Loss: 150.804
+6400/69092	Loss: 151.645
+9600/69092	Loss: 153.291
+12800/69092	Loss: 152.469
+16000/69092	Loss: 150.577
+19200/69092	Loss: 154.290
+22400/69092	Loss: 150.442
+25600/69092	Loss: 154.266
+28800/69092	Loss: 154.679
+32000/69092	Loss: 152.875
+35200/69092	Loss: 151.537
+38400/69092	Loss: 151.901
+41600/69092	Loss: 151.255
+44800/69092	Loss: 151.752
+48000/69092	Loss: 152.236
+51200/69092	Loss: 152.769
+54400/69092	Loss: 151.213
+57600/69092	Loss: 153.572
+60800/69092	Loss: 152.105
+64000/69092	Loss: 149.684
+67200/69092	Loss: 152.700
+Training time 0:05:07.562855
+Epoch: 42 Average loss: 152.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 351)
+0/69092	Loss: 148.370
+3200/69092	Loss: 152.623
+6400/69092	Loss: 152.860
+9600/69092	Loss: 153.164
+12800/69092	Loss: 152.447
+16000/69092	Loss: 152.457
+19200/69092	Loss: 153.142
+22400/69092	Loss: 150.295
+25600/69092	Loss: 152.441
+28800/69092	Loss: 150.415
+32000/69092	Loss: 154.880
+35200/69092	Loss: 154.536
+38400/69092	Loss: 153.023
+41600/69092	Loss: 152.579
+44800/69092	Loss: 153.105
+48000/69092	Loss: 150.858
+51200/69092	Loss: 148.467
+54400/69092	Loss: 150.451
+57600/69092	Loss: 152.593
+60800/69092	Loss: 150.685
+64000/69092	Loss: 151.622
+67200/69092	Loss: 153.743
+Training time 0:04:51.185614
+Epoch: 43 Average loss: 152.22
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 352)
+0/69092	Loss: 167.932
+3200/69092	Loss: 151.753
+6400/69092	Loss: 153.492
+9600/69092	Loss: 153.002
+12800/69092	Loss: 151.284
+16000/69092	Loss: 152.758
+19200/69092	Loss: 152.294
+22400/69092	Loss: 150.516
+25600/69092	Loss: 152.514
+28800/69092	Loss: 152.163
+32000/69092	Loss: 147.794
+35200/69092	Loss: 152.661
+38400/69092	Loss: 152.315
+41600/69092	Loss: 152.247
+44800/69092	Loss: 155.118
+48000/69092	Loss: 152.939
+51200/69092	Loss: 153.689
+54400/69092	Loss: 152.181
+57600/69092	Loss: 149.681
+60800/69092	Loss: 150.995
+64000/69092	Loss: 151.173
+67200/69092	Loss: 153.536
+Training time 0:04:55.612014
+Epoch: 44 Average loss: 152.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 353)
+0/69092	Loss: 145.841
+3200/69092	Loss: 152.229
+6400/69092	Loss: 151.560
+9600/69092	Loss: 151.520
+12800/69092	Loss: 152.022
+16000/69092	Loss: 149.459
+19200/69092	Loss: 151.850
+22400/69092	Loss: 151.131
+25600/69092	Loss: 154.602
+28800/69092	Loss: 153.778
+32000/69092	Loss: 151.886
+35200/69092	Loss: 149.483
+38400/69092	Loss: 150.160
+41600/69092	Loss: 150.472
+44800/69092	Loss: 153.515
+48000/69092	Loss: 153.779
+51200/69092	Loss: 151.000
+54400/69092	Loss: 153.249
+57600/69092	Loss: 152.344
+60800/69092	Loss: 154.424
+64000/69092	Loss: 152.395
+67200/69092	Loss: 155.873
+Training time 0:04:51.201725
+Epoch: 45 Average loss: 152.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 354)
+0/69092	Loss: 137.913
+3200/69092	Loss: 152.157
+6400/69092	Loss: 152.798
+9600/69092	Loss: 152.298
+12800/69092	Loss: 152.906
+16000/69092	Loss: 154.373
+19200/69092	Loss: 151.240
+22400/69092	Loss: 153.380
+25600/69092	Loss: 153.667
+28800/69092	Loss: 152.635
+32000/69092	Loss: 151.434
+35200/69092	Loss: 150.640
+38400/69092	Loss: 151.175
+41600/69092	Loss: 150.284
+44800/69092	Loss: 151.346
+48000/69092	Loss: 149.708
+51200/69092	Loss: 153.606
+54400/69092	Loss: 151.059
+57600/69092	Loss: 150.702
+60800/69092	Loss: 153.747
+64000/69092	Loss: 152.882
+67200/69092	Loss: 152.776
+Training time 0:04:48.809736
+Epoch: 46 Average loss: 152.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 355)
+0/69092	Loss: 155.595
+3200/69092	Loss: 151.454
+6400/69092	Loss: 150.406
+9600/69092	Loss: 153.454
+12800/69092	Loss: 151.006
+16000/69092	Loss: 151.426
+19200/69092	Loss: 156.457
+22400/69092	Loss: 150.815
+25600/69092	Loss: 151.828
+28800/69092	Loss: 151.957
+32000/69092	Loss: 151.945
+35200/69092	Loss: 152.698
+38400/69092	Loss: 152.272
+41600/69092	Loss: 150.738
+44800/69092	Loss: 152.915
+48000/69092	Loss: 153.258
+51200/69092	Loss: 151.136
+54400/69092	Loss: 153.077
+57600/69092	Loss: 152.956
+60800/69092	Loss: 149.251
+64000/69092	Loss: 151.603
+67200/69092	Loss: 153.848
+Training time 0:04:50.084359
+Epoch: 47 Average loss: 152.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 356)
+0/69092	Loss: 156.728
+3200/69092	Loss: 153.443
+6400/69092	Loss: 150.638
+9600/69092	Loss: 151.727
+12800/69092	Loss: 152.303
+16000/69092	Loss: 151.170
+19200/69092	Loss: 152.111
+22400/69092	Loss: 152.699
+25600/69092	Loss: 150.311
+28800/69092	Loss: 150.221
+32000/69092	Loss: 155.142
+35200/69092	Loss: 152.097
+38400/69092	Loss: 152.748
+41600/69092	Loss: 155.204
+44800/69092	Loss: 152.113
+48000/69092	Loss: 152.013
+51200/69092	Loss: 152.560
+54400/69092	Loss: 154.394
+57600/69092	Loss: 151.125
+60800/69092	Loss: 153.749
+64000/69092	Loss: 152.106
+67200/69092	Loss: 151.150
+Training time 0:04:56.589392
+Epoch: 48 Average loss: 152.34
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 357)
+0/69092	Loss: 162.978
+3200/69092	Loss: 153.373
+6400/69092	Loss: 151.620
+9600/69092	Loss: 151.033
+12800/69092	Loss: 152.945
+16000/69092	Loss: 151.605
+19200/69092	Loss: 150.252
+22400/69092	Loss: 151.784
+25600/69092	Loss: 152.188
+28800/69092	Loss: 151.456
+32000/69092	Loss: 152.454
+35200/69092	Loss: 153.653
+38400/69092	Loss: 152.103
+41600/69092	Loss: 153.231
+44800/69092	Loss: 150.651
+48000/69092	Loss: 152.846
+51200/69092	Loss: 151.844
+54400/69092	Loss: 152.032
+57600/69092	Loss: 153.584
+60800/69092	Loss: 151.616
+64000/69092	Loss: 153.219
+67200/69092	Loss: 152.786
+Training time 0:04:47.694802
+Epoch: 49 Average loss: 152.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 358)
+0/69092	Loss: 146.221
+3200/69092	Loss: 152.938
+6400/69092	Loss: 153.933
+9600/69092	Loss: 152.109
+12800/69092	Loss: 153.504
+16000/69092	Loss: 153.531
+19200/69092	Loss: 153.283
+22400/69092	Loss: 150.464
+25600/69092	Loss: 152.537
+28800/69092	Loss: 153.610
+32000/69092	Loss: 152.708
+35200/69092	Loss: 152.029
+38400/69092	Loss: 148.441
+41600/69092	Loss: 149.923
+44800/69092	Loss: 151.397
+48000/69092	Loss: 151.045
+51200/69092	Loss: 152.944
+54400/69092	Loss: 151.273
+57600/69092	Loss: 153.945
+60800/69092	Loss: 153.427
+64000/69092	Loss: 153.790
+67200/69092	Loss: 151.499
+Training time 0:04:50.751972
+Epoch: 50 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 359)
+0/69092	Loss: 165.142
+3200/69092	Loss: 151.661
+6400/69092	Loss: 151.705
+9600/69092	Loss: 151.387
+12800/69092	Loss: 153.068
+16000/69092	Loss: 153.747
+19200/69092	Loss: 150.545
+22400/69092	Loss: 150.692
+25600/69092	Loss: 152.346
+28800/69092	Loss: 151.423
+32000/69092	Loss: 153.013
+35200/69092	Loss: 149.609
+38400/69092	Loss: 153.360
+41600/69092	Loss: 150.800
+44800/69092	Loss: 153.717
+48000/69092	Loss: 151.040
+51200/69092	Loss: 152.198
+54400/69092	Loss: 152.882
+57600/69092	Loss: 153.217
+60800/69092	Loss: 151.976
+64000/69092	Loss: 152.568
+67200/69092	Loss: 154.505
+Training time 0:04:53.556238
+Epoch: 51 Average loss: 152.22
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 360)
+0/69092	Loss: 155.354
+3200/69092	Loss: 151.415
+6400/69092	Loss: 148.914
+9600/69092	Loss: 151.348
+12800/69092	Loss: 152.186
+16000/69092	Loss: 154.559
+19200/69092	Loss: 153.989
+22400/69092	Loss: 154.117
+25600/69092	Loss: 152.078
+28800/69092	Loss: 152.098
+32000/69092	Loss: 151.568
+35200/69092	Loss: 151.049
+38400/69092	Loss: 151.978
+41600/69092	Loss: 153.583
+44800/69092	Loss: 152.448
+48000/69092	Loss: 148.927
+51200/69092	Loss: 153.927
+54400/69092	Loss: 152.828
+57600/69092	Loss: 152.888
+60800/69092	Loss: 150.125
+64000/69092	Loss: 150.730
+67200/69092	Loss: 152.808
+Training time 0:05:00.744422
+Epoch: 52 Average loss: 152.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 361)
+0/69092	Loss: 146.191
+3200/69092	Loss: 151.267
+6400/69092	Loss: 155.417
+9600/69092	Loss: 149.679
+12800/69092	Loss: 152.965
+16000/69092	Loss: 151.732
+19200/69092	Loss: 152.283
+22400/69092	Loss: 151.821
+25600/69092	Loss: 151.954
+28800/69092	Loss: 156.055
+32000/69092	Loss: 152.704
+35200/69092	Loss: 152.981
+38400/69092	Loss: 149.679
+41600/69092	Loss: 151.232
+44800/69092	Loss: 151.826
+48000/69092	Loss: 151.640
+51200/69092	Loss: 150.887
+54400/69092	Loss: 153.202
+57600/69092	Loss: 152.679
+60800/69092	Loss: 151.314
+64000/69092	Loss: 150.668
+67200/69092	Loss: 153.902
+Training time 0:04:54.495637
+Epoch: 53 Average loss: 152.24
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 362)
+0/69092	Loss: 166.947
+3200/69092	Loss: 154.093
+6400/69092	Loss: 154.242
+9600/69092	Loss: 152.789
+12800/69092	Loss: 154.342
+16000/69092	Loss: 149.782
+19200/69092	Loss: 153.207
+22400/69092	Loss: 151.134
+25600/69092	Loss: 152.531
+28800/69092	Loss: 151.491
+32000/69092	Loss: 149.411
+35200/69092	Loss: 153.941
+38400/69092	Loss: 152.423
+41600/69092	Loss: 151.690
+44800/69092	Loss: 150.280
+48000/69092	Loss: 152.689
+51200/69092	Loss: 149.195
+54400/69092	Loss: 153.367
+57600/69092	Loss: 154.523
+60800/69092	Loss: 152.483
+64000/69092	Loss: 152.741
+67200/69092	Loss: 149.173
+Training time 0:04:45.627212
+Epoch: 54 Average loss: 152.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 363)
+0/69092	Loss: 157.367
+3200/69092	Loss: 150.394
+6400/69092	Loss: 150.663
+9600/69092	Loss: 153.807
+12800/69092	Loss: 151.130
+16000/69092	Loss: 154.581
+19200/69092	Loss: 152.572
+22400/69092	Loss: 152.268
+25600/69092	Loss: 155.387
+28800/69092	Loss: 150.463
+32000/69092	Loss: 151.389
+35200/69092	Loss: 151.641
+38400/69092	Loss: 153.353
+41600/69092	Loss: 152.714
+44800/69092	Loss: 152.886
+48000/69092	Loss: 153.651
+51200/69092	Loss: 152.755
+54400/69092	Loss: 150.921
+57600/69092	Loss: 151.642
+60800/69092	Loss: 150.403
+64000/69092	Loss: 151.311
+67200/69092	Loss: 151.678
+Training time 0:05:02.730608
+Epoch: 55 Average loss: 152.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 364)
+0/69092	Loss: 145.930
+3200/69092	Loss: 150.429
+6400/69092	Loss: 151.513
+9600/69092	Loss: 152.365
+12800/69092	Loss: 151.374
+16000/69092	Loss: 152.184
+19200/69092	Loss: 152.533
+22400/69092	Loss: 154.206
+25600/69092	Loss: 153.850
+28800/69092	Loss: 151.375
+32000/69092	Loss: 154.196
+35200/69092	Loss: 152.400
+38400/69092	Loss: 151.774
+41600/69092	Loss: 153.905
+44800/69092	Loss: 151.187
+48000/69092	Loss: 150.063
+51200/69092	Loss: 152.525
+54400/69092	Loss: 150.089
+57600/69092	Loss: 153.965
+60800/69092	Loss: 152.555
+64000/69092	Loss: 150.469
+67200/69092	Loss: 153.792
+Training time 0:05:08.792934
+Epoch: 56 Average loss: 152.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 365)
+0/69092	Loss: 152.498
+3200/69092	Loss: 150.841
+6400/69092	Loss: 149.979
+9600/69092	Loss: 154.737
+12800/69092	Loss: 152.760
+16000/69092	Loss: 153.164
+19200/69092	Loss: 151.122
+22400/69092	Loss: 150.475
+25600/69092	Loss: 153.124
+28800/69092	Loss: 150.368
+32000/69092	Loss: 152.099
+35200/69092	Loss: 154.059
+38400/69092	Loss: 152.283
+41600/69092	Loss: 153.770
+44800/69092	Loss: 151.144
+48000/69092	Loss: 153.797
+51200/69092	Loss: 153.163
+54400/69092	Loss: 151.551
+57600/69092	Loss: 149.193
+60800/69092	Loss: 152.865
+64000/69092	Loss: 151.365
+67200/69092	Loss: 151.513
+Training time 0:04:57.827078
+Epoch: 57 Average loss: 152.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 366)
+0/69092	Loss: 147.381
+3200/69092	Loss: 153.777
+6400/69092	Loss: 153.114
+9600/69092	Loss: 149.780
+12800/69092	Loss: 153.229
+16000/69092	Loss: 150.720
+19200/69092	Loss: 151.541
+22400/69092	Loss: 152.261
+25600/69092	Loss: 151.935
+28800/69092	Loss: 150.915
+32000/69092	Loss: 153.301
+35200/69092	Loss: 151.364
+38400/69092	Loss: 151.890
+41600/69092	Loss: 153.117
+44800/69092	Loss: 151.360
+48000/69092	Loss: 152.360
+51200/69092	Loss: 152.068
+54400/69092	Loss: 149.764
+57600/69092	Loss: 152.226
+60800/69092	Loss: 151.177
+64000/69092	Loss: 153.096
+67200/69092	Loss: 153.029
+Training time 0:05:02.011952
+Epoch: 58 Average loss: 151.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 367)
+0/69092	Loss: 150.569
+3200/69092	Loss: 152.745
+6400/69092	Loss: 153.195
+9600/69092	Loss: 150.142
+12800/69092	Loss: 153.325
+16000/69092	Loss: 149.827
+19200/69092	Loss: 153.633
+22400/69092	Loss: 152.257
+25600/69092	Loss: 151.171
+28800/69092	Loss: 148.631
+32000/69092	Loss: 153.539
+35200/69092	Loss: 154.201
+38400/69092	Loss: 151.118
+41600/69092	Loss: 153.224
+44800/69092	Loss: 151.894
+48000/69092	Loss: 153.593
+51200/69092	Loss: 150.882
+54400/69092	Loss: 153.404
+57600/69092	Loss: 152.346
+60800/69092	Loss: 154.515
+64000/69092	Loss: 151.724
+67200/69092	Loss: 151.763
+Training time 0:04:51.397189
+Epoch: 59 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 368)
+0/69092	Loss: 152.698
+3200/69092	Loss: 150.510
+6400/69092	Loss: 154.781
+9600/69092	Loss: 152.349
+12800/69092	Loss: 152.191
+16000/69092	Loss: 150.223
+19200/69092	Loss: 150.018
+22400/69092	Loss: 153.098
+25600/69092	Loss: 152.559
+28800/69092	Loss: 150.995
+32000/69092	Loss: 150.916
+35200/69092	Loss: 155.911
+38400/69092	Loss: 151.247
+41600/69092	Loss: 151.564
+44800/69092	Loss: 154.736
+48000/69092	Loss: 150.521
+51200/69092	Loss: 151.808
+54400/69092	Loss: 153.132
+57600/69092	Loss: 152.417
+60800/69092	Loss: 155.007
+64000/69092	Loss: 151.645
+67200/69092	Loss: 151.255
+Training time 0:04:56.358551
+Epoch: 60 Average loss: 152.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 369)
+0/69092	Loss: 144.129
+3200/69092	Loss: 151.475
+6400/69092	Loss: 151.476
+9600/69092	Loss: 152.586
+12800/69092	Loss: 151.600
+16000/69092	Loss: 150.702
+19200/69092	Loss: 150.883
+22400/69092	Loss: 151.202
+25600/69092	Loss: 153.367
+28800/69092	Loss: 152.773
+32000/69092	Loss: 151.591
+35200/69092	Loss: 152.391
+38400/69092	Loss: 152.063
+41600/69092	Loss: 153.060
+44800/69092	Loss: 151.395
+48000/69092	Loss: 154.362
+51200/69092	Loss: 150.850
+54400/69092	Loss: 154.596
+57600/69092	Loss: 151.363
+60800/69092	Loss: 153.536
+64000/69092	Loss: 155.335
+67200/69092	Loss: 151.891
+Training time 0:05:23.467534
+Epoch: 61 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 370)
+0/69092	Loss: 163.940
+3200/69092	Loss: 150.827
+6400/69092	Loss: 149.793
+9600/69092	Loss: 151.618
+12800/69092	Loss: 151.544
+16000/69092	Loss: 152.441
+19200/69092	Loss: 152.698
+22400/69092	Loss: 152.034
+25600/69092	Loss: 150.992
+28800/69092	Loss: 149.667
+32000/69092	Loss: 153.019
+35200/69092	Loss: 154.537
+38400/69092	Loss: 155.126
+41600/69092	Loss: 150.875
+44800/69092	Loss: 151.684
+48000/69092	Loss: 150.959
+51200/69092	Loss: 152.445
+54400/69092	Loss: 152.994
+57600/69092	Loss: 151.815
+60800/69092	Loss: 153.775
+64000/69092	Loss: 151.573
+67200/69092	Loss: 155.133
+Training time 0:05:53.166138
+Epoch: 62 Average loss: 152.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 371)
+0/69092	Loss: 160.420
+3200/69092	Loss: 153.931
+6400/69092	Loss: 152.696
+9600/69092	Loss: 152.333
+12800/69092	Loss: 151.992
+16000/69092	Loss: 150.342
+19200/69092	Loss: 149.804
+22400/69092	Loss: 151.871
+25600/69092	Loss: 150.169
+28800/69092	Loss: 157.273
+32000/69092	Loss: 152.391
+35200/69092	Loss: 153.342
+38400/69092	Loss: 153.075
+41600/69092	Loss: 155.212
+44800/69092	Loss: 152.789
+48000/69092	Loss: 153.142
+51200/69092	Loss: 149.485
+54400/69092	Loss: 152.921
+57600/69092	Loss: 150.541
+60800/69092	Loss: 152.106
+64000/69092	Loss: 151.806
+67200/69092	Loss: 150.912
+Training time 0:05:24.650218
+Epoch: 63 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 372)
+0/69092	Loss: 157.216
+3200/69092	Loss: 152.605
+6400/69092	Loss: 154.170
+9600/69092	Loss: 150.242
+12800/69092	Loss: 150.847
+16000/69092	Loss: 153.558
+19200/69092	Loss: 149.197
+22400/69092	Loss: 152.052
+25600/69092	Loss: 153.600
+28800/69092	Loss: 150.805
+32000/69092	Loss: 151.154
+35200/69092	Loss: 151.345
+38400/69092	Loss: 153.986
+41600/69092	Loss: 153.242
+44800/69092	Loss: 151.297
+48000/69092	Loss: 152.481
+51200/69092	Loss: 154.252
+54400/69092	Loss: 150.514
+57600/69092	Loss: 151.360
+60800/69092	Loss: 151.142
+64000/69092	Loss: 152.341
+67200/69092	Loss: 152.235
+Training time 0:05:23.349995
+Epoch: 64 Average loss: 152.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 373)
+0/69092	Loss: 156.083
+3200/69092	Loss: 148.343
+6400/69092	Loss: 151.406
+9600/69092	Loss: 152.402
+12800/69092	Loss: 152.789
+16000/69092	Loss: 152.097
+19200/69092	Loss: 151.255
+22400/69092	Loss: 150.589
+25600/69092	Loss: 151.193
+28800/69092	Loss: 151.768
+32000/69092	Loss: 152.436
+35200/69092	Loss: 151.905
+38400/69092	Loss: 151.210
+41600/69092	Loss: 153.031
+44800/69092	Loss: 155.077
+48000/69092	Loss: 152.141
+51200/69092	Loss: 152.639
+54400/69092	Loss: 152.202
+57600/69092	Loss: 153.911
+60800/69092	Loss: 150.886
+64000/69092	Loss: 151.991
+67200/69092	Loss: 153.081
+Training time 0:05:27.113903
+Epoch: 65 Average loss: 152.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 374)
+0/69092	Loss: 146.668
+3200/69092	Loss: 154.008
+6400/69092	Loss: 155.628
+9600/69092	Loss: 152.317
+12800/69092	Loss: 152.248
+16000/69092	Loss: 153.502
+19200/69092	Loss: 153.896
+22400/69092	Loss: 151.885
+25600/69092	Loss: 153.051
+28800/69092	Loss: 150.148
+32000/69092	Loss: 151.484
+35200/69092	Loss: 151.041
+38400/69092	Loss: 152.862
+41600/69092	Loss: 150.271
+44800/69092	Loss: 153.355
+48000/69092	Loss: 150.979
+51200/69092	Loss: 150.087
+54400/69092	Loss: 150.249
+57600/69092	Loss: 153.382
+60800/69092	Loss: 150.308
+64000/69092	Loss: 152.263
+67200/69092	Loss: 150.980
+Training time 0:05:23.714057
+Epoch: 66 Average loss: 152.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 375)
+0/69092	Loss: 142.300
+3200/69092	Loss: 152.648
+6400/69092	Loss: 153.002
+9600/69092	Loss: 151.178
+12800/69092	Loss: 151.427
+16000/69092	Loss: 150.636
+19200/69092	Loss: 153.439
+22400/69092	Loss: 153.312
+25600/69092	Loss: 151.814
+28800/69092	Loss: 153.635
+32000/69092	Loss: 152.744
+35200/69092	Loss: 152.227
+38400/69092	Loss: 152.585
+41600/69092	Loss: 151.846
+44800/69092	Loss: 151.106
+48000/69092	Loss: 152.238
+51200/69092	Loss: 150.460
+54400/69092	Loss: 153.362
+57600/69092	Loss: 154.661
+60800/69092	Loss: 153.270
+64000/69092	Loss: 150.172
+67200/69092	Loss: 152.478
+Training time 0:05:33.072789
+Epoch: 67 Average loss: 152.20
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 376)
+0/69092	Loss: 167.086
+3200/69092	Loss: 152.539
+6400/69092	Loss: 153.615
+9600/69092	Loss: 153.984
+12800/69092	Loss: 153.109
+16000/69092	Loss: 151.658
+19200/69092	Loss: 153.113
+22400/69092	Loss: 151.606
+25600/69092	Loss: 149.857
+28800/69092	Loss: 152.291
+32000/69092	Loss: 152.001
+35200/69092	Loss: 150.874
+38400/69092	Loss: 152.269
+41600/69092	Loss: 152.812
+44800/69092	Loss: 152.633
+48000/69092	Loss: 153.407
+51200/69092	Loss: 149.529
+54400/69092	Loss: 150.778
+57600/69092	Loss: 154.074
+60800/69092	Loss: 155.265
+64000/69092	Loss: 150.943
+67200/69092	Loss: 152.413
+Training time 0:04:59.252105
+Epoch: 68 Average loss: 152.32
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 377)
+0/69092	Loss: 142.886
+3200/69092	Loss: 150.915
+6400/69092	Loss: 151.632
+9600/69092	Loss: 152.223
+12800/69092	Loss: 150.318
+16000/69092	Loss: 151.817
+19200/69092	Loss: 152.761
+22400/69092	Loss: 152.089
+25600/69092	Loss: 154.266
+28800/69092	Loss: 150.921
+32000/69092	Loss: 151.730
+35200/69092	Loss: 153.607
+38400/69092	Loss: 153.081
+41600/69092	Loss: 150.853
+44800/69092	Loss: 152.767
+48000/69092	Loss: 152.460
+51200/69092	Loss: 150.523
+54400/69092	Loss: 151.378
+57600/69092	Loss: 153.249
+60800/69092	Loss: 152.830
+64000/69092	Loss: 151.944
+67200/69092	Loss: 152.016
+Training time 0:05:16.139701
+Epoch: 69 Average loss: 152.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 378)
+0/69092	Loss: 171.625
+3200/69092	Loss: 154.513
+6400/69092	Loss: 151.640
+9600/69092	Loss: 150.764
+12800/69092	Loss: 150.490
+16000/69092	Loss: 151.810
+19200/69092	Loss: 153.845
+22400/69092	Loss: 150.604
+25600/69092	Loss: 152.091
+28800/69092	Loss: 150.351
+32000/69092	Loss: 154.173
+35200/69092	Loss: 153.935
+38400/69092	Loss: 151.496
+41600/69092	Loss: 150.508
+44800/69092	Loss: 151.278
+48000/69092	Loss: 148.709
+51200/69092	Loss: 152.223
+54400/69092	Loss: 154.862
+57600/69092	Loss: 152.224
+60800/69092	Loss: 153.854
+64000/69092	Loss: 150.635
+67200/69092	Loss: 150.417
+Training time 0:05:20.814982
+Epoch: 70 Average loss: 152.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 379)
+0/69092	Loss: 159.456
+3200/69092	Loss: 151.843
+6400/69092	Loss: 152.962
+9600/69092	Loss: 153.162
+12800/69092	Loss: 152.416
+16000/69092	Loss: 152.438
+19200/69092	Loss: 154.884
+22400/69092	Loss: 152.882
+25600/69092	Loss: 152.923
+28800/69092	Loss: 151.315
+32000/69092	Loss: 153.602
+35200/69092	Loss: 151.506
+38400/69092	Loss: 153.189
+41600/69092	Loss: 151.808
+44800/69092	Loss: 149.524
+48000/69092	Loss: 152.428
+51200/69092	Loss: 151.998
+54400/69092	Loss: 151.662
+57600/69092	Loss: 149.739
+60800/69092	Loss: 152.809
+64000/69092	Loss: 154.365
+67200/69092	Loss: 150.230
+Training time 0:04:41.908707
+Epoch: 71 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 380)
+0/69092	Loss: 147.989
+3200/69092	Loss: 152.401
+6400/69092	Loss: 151.690
+9600/69092	Loss: 151.527
+12800/69092	Loss: 151.547
+16000/69092	Loss: 148.992
+19200/69092	Loss: 153.467
+22400/69092	Loss: 150.681
+25600/69092	Loss: 153.436
+28800/69092	Loss: 152.665
+32000/69092	Loss: 153.847
+35200/69092	Loss: 149.465
+38400/69092	Loss: 154.151
+41600/69092	Loss: 152.815
+44800/69092	Loss: 152.269
+48000/69092	Loss: 150.504
+51200/69092	Loss: 153.604
+54400/69092	Loss: 153.192
+57600/69092	Loss: 151.053
+60800/69092	Loss: 153.720
+64000/69092	Loss: 153.013
+67200/69092	Loss: 152.351
+Training time 0:04:49.550108
+Epoch: 72 Average loss: 152.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 381)
+0/69092	Loss: 155.271
+3200/69092	Loss: 153.494
+6400/69092	Loss: 152.078
+9600/69092	Loss: 149.940
+12800/69092	Loss: 150.048
+16000/69092	Loss: 150.825
+19200/69092	Loss: 150.739
+22400/69092	Loss: 152.702
+25600/69092	Loss: 155.752
+28800/69092	Loss: 154.054
+32000/69092	Loss: 151.410
+35200/69092	Loss: 154.361
+38400/69092	Loss: 150.217
+41600/69092	Loss: 153.970
+44800/69092	Loss: 151.603
+48000/69092	Loss: 150.173
+51200/69092	Loss: 151.283
+54400/69092	Loss: 150.320
+57600/69092	Loss: 154.031
+60800/69092	Loss: 151.250
+64000/69092	Loss: 152.557
+67200/69092	Loss: 152.158
+Training time 0:04:51.031669
+Epoch: 73 Average loss: 152.07
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 382)
+0/69092	Loss: 167.281
+3200/69092	Loss: 151.140
+6400/69092	Loss: 151.214
+9600/69092	Loss: 154.212
+12800/69092	Loss: 152.252
+16000/69092	Loss: 148.862
+19200/69092	Loss: 149.399
+22400/69092	Loss: 152.893
+25600/69092	Loss: 153.142
+28800/69092	Loss: 152.190
+32000/69092	Loss: 151.733
+35200/69092	Loss: 152.655
+38400/69092	Loss: 154.997
+41600/69092	Loss: 151.070
+44800/69092	Loss: 153.855
+48000/69092	Loss: 152.491
+51200/69092	Loss: 150.044
+54400/69092	Loss: 150.720
+57600/69092	Loss: 150.193
+60800/69092	Loss: 152.856
+64000/69092	Loss: 152.537
+67200/69092	Loss: 152.294
+Training time 0:04:55.081420
+Epoch: 74 Average loss: 152.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 383)
+0/69092	Loss: 154.157
+3200/69092	Loss: 154.255
+6400/69092	Loss: 153.611
+9600/69092	Loss: 151.472
+12800/69092	Loss: 153.355
+16000/69092	Loss: 150.815
+19200/69092	Loss: 153.617
+22400/69092	Loss: 153.628
+25600/69092	Loss: 150.988
+28800/69092	Loss: 151.479
+32000/69092	Loss: 152.324
+35200/69092	Loss: 153.211
+38400/69092	Loss: 150.005
+41600/69092	Loss: 148.870
+44800/69092	Loss: 154.888
+48000/69092	Loss: 152.478
+51200/69092	Loss: 152.040
+54400/69092	Loss: 151.494
+57600/69092	Loss: 151.065
+60800/69092	Loss: 153.344
+64000/69092	Loss: 153.279
+67200/69092	Loss: 154.722
+Training time 0:04:50.357753
+Epoch: 75 Average loss: 152.45
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 384)
+0/69092	Loss: 148.367
+3200/69092	Loss: 149.811
+6400/69092	Loss: 152.498
+9600/69092	Loss: 153.230
+12800/69092	Loss: 151.580
+16000/69092	Loss: 149.368
+19200/69092	Loss: 153.172
+22400/69092	Loss: 152.466
+25600/69092	Loss: 149.360
+28800/69092	Loss: 150.754
+32000/69092	Loss: 150.235
+35200/69092	Loss: 152.744
+38400/69092	Loss: 152.375
+41600/69092	Loss: 152.117
+44800/69092	Loss: 149.865
+48000/69092	Loss: 152.296
+51200/69092	Loss: 150.958
+54400/69092	Loss: 154.242
+57600/69092	Loss: 154.513
+60800/69092	Loss: 153.132
+64000/69092	Loss: 154.810
+67200/69092	Loss: 152.240
+Training time 0:04:56.023137
+Epoch: 76 Average loss: 152.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 385)
+0/69092	Loss: 159.299
+3200/69092	Loss: 151.701
+6400/69092	Loss: 154.116
+9600/69092	Loss: 153.099
+12800/69092	Loss: 153.676
+16000/69092	Loss: 152.018
+19200/69092	Loss: 152.899
+22400/69092	Loss: 152.236
+25600/69092	Loss: 154.659
+28800/69092	Loss: 150.001
+32000/69092	Loss: 151.848
+35200/69092	Loss: 153.828
+38400/69092	Loss: 149.875
+41600/69092	Loss: 154.095
+44800/69092	Loss: 152.501
+48000/69092	Loss: 150.692
+51200/69092	Loss: 151.368
+54400/69092	Loss: 152.377
+57600/69092	Loss: 154.053
+60800/69092	Loss: 150.230
+64000/69092	Loss: 151.682
+67200/69092	Loss: 150.104
+Training time 0:05:38.090144
+Epoch: 77 Average loss: 152.20
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 386)
+0/69092	Loss: 133.769
+3200/69092	Loss: 150.945
+6400/69092	Loss: 152.139
+9600/69092	Loss: 150.133
+12800/69092	Loss: 153.125
+16000/69092	Loss: 152.958
+19200/69092	Loss: 153.307
+22400/69092	Loss: 154.924
+25600/69092	Loss: 150.651
+28800/69092	Loss: 152.592
+32000/69092	Loss: 151.322
+35200/69092	Loss: 151.483
+38400/69092	Loss: 151.906
+41600/69092	Loss: 151.097
+44800/69092	Loss: 152.955
+48000/69092	Loss: 152.512
+51200/69092	Loss: 153.579
+54400/69092	Loss: 150.903
+57600/69092	Loss: 151.697
+60800/69092	Loss: 152.023
+64000/69092	Loss: 152.772
+67200/69092	Loss: 152.888
+Training time 0:04:55.780531
+Epoch: 78 Average loss: 152.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 387)
+0/69092	Loss: 177.367
+3200/69092	Loss: 153.016
+6400/69092	Loss: 154.626
+9600/69092	Loss: 152.591
+12800/69092	Loss: 152.371
+16000/69092	Loss: 150.365
+19200/69092	Loss: 151.905
+22400/69092	Loss: 152.857
+25600/69092	Loss: 151.943
+28800/69092	Loss: 153.557
+32000/69092	Loss: 153.086
+35200/69092	Loss: 150.043
+38400/69092	Loss: 152.453
+41600/69092	Loss: 149.896
+44800/69092	Loss: 149.321
+48000/69092	Loss: 153.453
+51200/69092	Loss: 153.388
+54400/69092	Loss: 151.923
+57600/69092	Loss: 150.125
+60800/69092	Loss: 153.940
+64000/69092	Loss: 151.747
+67200/69092	Loss: 151.802
+Training time 0:04:55.208007
+Epoch: 79 Average loss: 152.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 388)
+0/69092	Loss: 150.818
+3200/69092	Loss: 151.441
+6400/69092	Loss: 154.883
+9600/69092	Loss: 152.123
+12800/69092	Loss: 152.493
+16000/69092	Loss: 152.560
+19200/69092	Loss: 152.359
+22400/69092	Loss: 152.105
+25600/69092	Loss: 152.571
+28800/69092	Loss: 150.197
+32000/69092	Loss: 151.594
+35200/69092	Loss: 148.968
+38400/69092	Loss: 153.790
+41600/69092	Loss: 154.585
+44800/69092	Loss: 152.791
+48000/69092	Loss: 151.996
+51200/69092	Loss: 151.946
+54400/69092	Loss: 151.222
+57600/69092	Loss: 151.251
+60800/69092	Loss: 153.577
+64000/69092	Loss: 148.282
+67200/69092	Loss: 153.347
+Training time 0:04:59.494419
+Epoch: 80 Average loss: 152.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 389)
+0/69092	Loss: 160.390
+3200/69092	Loss: 152.723
+6400/69092	Loss: 154.673
+9600/69092	Loss: 152.125
+12800/69092	Loss: 149.842
+16000/69092	Loss: 150.631
+19200/69092	Loss: 154.081
+22400/69092	Loss: 153.013
+25600/69092	Loss: 154.062
+28800/69092	Loss: 153.230
+32000/69092	Loss: 151.247
+35200/69092	Loss: 151.991
+38400/69092	Loss: 148.997
+41600/69092	Loss: 153.138
+44800/69092	Loss: 152.746
+48000/69092	Loss: 152.123
+51200/69092	Loss: 153.045
+54400/69092	Loss: 153.534
+57600/69092	Loss: 149.361
+60800/69092	Loss: 149.920
+64000/69092	Loss: 150.953
+67200/69092	Loss: 151.708
+Training time 0:04:59.060240
+Epoch: 81 Average loss: 152.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 390)
+0/69092	Loss: 153.963
+3200/69092	Loss: 152.424
+6400/69092	Loss: 155.083
+9600/69092	Loss: 151.304
+12800/69092	Loss: 152.461
+16000/69092	Loss: 152.443
+19200/69092	Loss: 152.779
+22400/69092	Loss: 152.083
+25600/69092	Loss: 151.778
+28800/69092	Loss: 152.084
+32000/69092	Loss: 151.633
+35200/69092	Loss: 153.404
+38400/69092	Loss: 148.104
+41600/69092	Loss: 155.486
+44800/69092	Loss: 148.950
+48000/69092	Loss: 152.170
+51200/69092	Loss: 154.032
+54400/69092	Loss: 152.849
+57600/69092	Loss: 150.474
+60800/69092	Loss: 153.086
+64000/69092	Loss: 151.313
+67200/69092	Loss: 149.878
+Training time 0:04:57.080448
+Epoch: 82 Average loss: 151.97
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 391)
+0/69092	Loss: 154.336
+3200/69092	Loss: 151.494
+6400/69092	Loss: 152.967
+9600/69092	Loss: 149.309
+12800/69092	Loss: 151.182
+16000/69092	Loss: 150.112
+19200/69092	Loss: 153.050
+22400/69092	Loss: 150.839
+25600/69092	Loss: 153.711
+28800/69092	Loss: 151.765
+32000/69092	Loss: 151.727
+35200/69092	Loss: 152.602
+38400/69092	Loss: 154.348
+41600/69092	Loss: 152.824
+44800/69092	Loss: 150.893
+48000/69092	Loss: 150.305
+51200/69092	Loss: 152.051
+54400/69092	Loss: 153.804
+57600/69092	Loss: 154.280
+60800/69092	Loss: 152.271
+64000/69092	Loss: 152.635
+67200/69092	Loss: 149.864
+Training time 0:04:57.010780
+Epoch: 83 Average loss: 151.99
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 392)
+0/69092	Loss: 182.028
+3200/69092	Loss: 152.429
+6400/69092	Loss: 150.760
+9600/69092	Loss: 151.331
+12800/69092	Loss: 151.735
+16000/69092	Loss: 151.469
+19200/69092	Loss: 151.580
+22400/69092	Loss: 151.156
+25600/69092	Loss: 153.199
+28800/69092	Loss: 151.023
+32000/69092	Loss: 152.648
+35200/69092	Loss: 151.730
+38400/69092	Loss: 155.580
+41600/69092	Loss: 152.280
+44800/69092	Loss: 153.323
+48000/69092	Loss: 151.427
+51200/69092	Loss: 154.983
+54400/69092	Loss: 150.072
+57600/69092	Loss: 153.436
+60800/69092	Loss: 152.252
+64000/69092	Loss: 150.017
+67200/69092	Loss: 152.116
+Training time 0:04:45.735179
+Epoch: 84 Average loss: 152.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 393)
+0/69092	Loss: 154.702
+3200/69092	Loss: 152.774
+6400/69092	Loss: 152.440
+9600/69092	Loss: 154.457
+12800/69092	Loss: 154.161
+16000/69092	Loss: 150.860
+19200/69092	Loss: 151.098
+22400/69092	Loss: 150.214
+25600/69092	Loss: 148.884
+28800/69092	Loss: 149.376
+32000/69092	Loss: 151.280
+35200/69092	Loss: 153.219
+38400/69092	Loss: 150.948
+41600/69092	Loss: 155.880
+44800/69092	Loss: 151.803
+48000/69092	Loss: 152.655
+51200/69092	Loss: 153.246
+54400/69092	Loss: 151.673
+57600/69092	Loss: 153.024
+60800/69092	Loss: 153.541
+64000/69092	Loss: 153.738
+67200/69092	Loss: 151.754
+Training time 0:04:57.023906
+Epoch: 85 Average loss: 152.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 394)
+0/69092	Loss: 142.829
+3200/69092	Loss: 152.490
+6400/69092	Loss: 151.030
+9600/69092	Loss: 150.666
+12800/69092	Loss: 151.519
+16000/69092	Loss: 150.399
+19200/69092	Loss: 153.108
+22400/69092	Loss: 151.066
+25600/69092	Loss: 152.339
+28800/69092	Loss: 152.410
+32000/69092	Loss: 155.271
+35200/69092	Loss: 153.059
+38400/69092	Loss: 151.213
+41600/69092	Loss: 152.088
+44800/69092	Loss: 154.145
+48000/69092	Loss: 150.306
+51200/69092	Loss: 150.901
+54400/69092	Loss: 151.373
+57600/69092	Loss: 150.391
+60800/69092	Loss: 150.261
+64000/69092	Loss: 153.969
+67200/69092	Loss: 151.814
+Training time 0:04:55.390241
+Epoch: 86 Average loss: 151.91
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 395)
+0/69092	Loss: 149.056
+3200/69092	Loss: 149.947
+6400/69092	Loss: 154.324
+9600/69092	Loss: 153.580
+12800/69092	Loss: 152.930
+16000/69092	Loss: 152.680
+19200/69092	Loss: 153.038
+22400/69092	Loss: 151.125
+25600/69092	Loss: 151.951
+28800/69092	Loss: 152.586
+32000/69092	Loss: 152.570
+35200/69092	Loss: 149.354
+38400/69092	Loss: 148.427
+41600/69092	Loss: 151.235
+44800/69092	Loss: 152.589
+48000/69092	Loss: 152.086
+51200/69092	Loss: 150.932
+54400/69092	Loss: 153.294
+57600/69092	Loss: 152.074
+60800/69092	Loss: 151.371
+64000/69092	Loss: 152.852
+67200/69092	Loss: 152.169
+Training time 0:04:48.814648
+Epoch: 87 Average loss: 151.95
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 396)
+0/69092	Loss: 157.786
+3200/69092	Loss: 152.570
+6400/69092	Loss: 153.078
+9600/69092	Loss: 152.408
+12800/69092	Loss: 152.629
+16000/69092	Loss: 151.153
+19200/69092	Loss: 151.928
+22400/69092	Loss: 151.346
+25600/69092	Loss: 150.992
+28800/69092	Loss: 150.583
+32000/69092	Loss: 149.188
+35200/69092	Loss: 152.389
+38400/69092	Loss: 152.509
+41600/69092	Loss: 154.821
+44800/69092	Loss: 150.531
+48000/69092	Loss: 151.399
+51200/69092	Loss: 150.111
+54400/69092	Loss: 152.454
+57600/69092	Loss: 152.133
+60800/69092	Loss: 150.629
+64000/69092	Loss: 153.384
+67200/69092	Loss: 153.505
+Training time 0:04:54.803282
+Epoch: 88 Average loss: 151.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 397)
+0/69092	Loss: 171.129
+3200/69092	Loss: 151.790
+6400/69092	Loss: 153.028
+9600/69092	Loss: 152.004
+12800/69092	Loss: 151.282
+16000/69092	Loss: 150.597
+19200/69092	Loss: 153.276
+22400/69092	Loss: 150.037
+25600/69092	Loss: 151.375
+28800/69092	Loss: 152.042
+32000/69092	Loss: 151.800
+35200/69092	Loss: 152.210
+38400/69092	Loss: 152.840
+41600/69092	Loss: 151.627
+44800/69092	Loss: 151.665
+48000/69092	Loss: 151.353
+51200/69092	Loss: 152.797
+54400/69092	Loss: 151.047
+57600/69092	Loss: 151.778
+60800/69092	Loss: 153.219
+64000/69092	Loss: 153.312
+67200/69092	Loss: 151.992
+Training time 0:04:50.539237
+Epoch: 89 Average loss: 152.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 398)
+0/69092	Loss: 163.351
+3200/69092	Loss: 151.922
+6400/69092	Loss: 152.530
+9600/69092	Loss: 154.250
+12800/69092	Loss: 150.779
+16000/69092	Loss: 153.006
+19200/69092	Loss: 152.350
+22400/69092	Loss: 150.987
+25600/69092	Loss: 153.924
+28800/69092	Loss: 152.805
+32000/69092	Loss: 152.224
+35200/69092	Loss: 151.677
+38400/69092	Loss: 155.644
+41600/69092	Loss: 150.963
+44800/69092	Loss: 152.313
+48000/69092	Loss: 152.735
+51200/69092	Loss: 149.950
+54400/69092	Loss: 150.979
+57600/69092	Loss: 153.317
+60800/69092	Loss: 153.071
+64000/69092	Loss: 152.851
+67200/69092	Loss: 151.237
+Training time 0:04:49.878713
+Epoch: 90 Average loss: 152.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 399)
+0/69092	Loss: 163.554
+3200/69092	Loss: 151.718
+6400/69092	Loss: 153.950
+9600/69092	Loss: 151.830
+12800/69092	Loss: 152.770
+16000/69092	Loss: 153.396
+19200/69092	Loss: 149.883
+22400/69092	Loss: 150.290
+25600/69092	Loss: 149.773
+28800/69092	Loss: 152.480
+32000/69092	Loss: 151.067
+35200/69092	Loss: 156.999
+38400/69092	Loss: 152.950
+41600/69092	Loss: 152.975
+44800/69092	Loss: 153.519
+48000/69092	Loss: 152.611
+51200/69092	Loss: 152.230
+54400/69092	Loss: 151.480
+57600/69092	Loss: 150.186
+60800/69092	Loss: 150.945
+64000/69092	Loss: 149.248
+67200/69092	Loss: 151.699
+Training time 0:04:47.200851
+Epoch: 91 Average loss: 152.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 400)
+0/69092	Loss: 150.296
+3200/69092	Loss: 149.735
+6400/69092	Loss: 153.625
+9600/69092	Loss: 150.936
+12800/69092	Loss: 152.938
+16000/69092	Loss: 150.642
+19200/69092	Loss: 153.460
+22400/69092	Loss: 152.283
+25600/69092	Loss: 153.096
+28800/69092	Loss: 152.937
+32000/69092	Loss: 151.133
+35200/69092	Loss: 154.050
+38400/69092	Loss: 152.118
+41600/69092	Loss: 152.096
+44800/69092	Loss: 151.038
+48000/69092	Loss: 149.925
+51200/69092	Loss: 151.219
+54400/69092	Loss: 152.395
+57600/69092	Loss: 153.124
+60800/69092	Loss: 151.859
+64000/69092	Loss: 151.257
+67200/69092	Loss: 149.621
+Training time 0:04:52.972326
+Epoch: 92 Average loss: 151.90
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 401)
+0/69092	Loss: 160.210
+3200/69092	Loss: 150.249
+6400/69092	Loss: 151.565
+9600/69092	Loss: 149.957
+12800/69092	Loss: 151.506
+16000/69092	Loss: 152.402
+19200/69092	Loss: 152.908
+22400/69092	Loss: 149.944
+25600/69092	Loss: 153.508
+28800/69092	Loss: 150.672
+32000/69092	Loss: 155.991
+35200/69092	Loss: 151.940
+38400/69092	Loss: 149.855
+41600/69092	Loss: 152.922
+44800/69092	Loss: 151.392
+48000/69092	Loss: 154.734
+51200/69092	Loss: 150.691
+54400/69092	Loss: 151.314
+57600/69092	Loss: 152.757
+60800/69092	Loss: 151.927
+64000/69092	Loss: 153.325
+67200/69092	Loss: 149.930
+Training time 0:04:55.110473
+Epoch: 93 Average loss: 151.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 402)
+0/69092	Loss: 141.758
+3200/69092	Loss: 148.836
+6400/69092	Loss: 153.720
+9600/69092	Loss: 151.733
+12800/69092	Loss: 150.266
+16000/69092	Loss: 153.532
+19200/69092	Loss: 151.682
+22400/69092	Loss: 152.700
+25600/69092	Loss: 151.377
+28800/69092	Loss: 151.040
+32000/69092	Loss: 148.090
+35200/69092	Loss: 153.011
+38400/69092	Loss: 152.989
+41600/69092	Loss: 153.283
+44800/69092	Loss: 155.993
+48000/69092	Loss: 152.637
+51200/69092	Loss: 152.229
+54400/69092	Loss: 151.351
+57600/69092	Loss: 154.748
+60800/69092	Loss: 151.491
+64000/69092	Loss: 151.453
+67200/69092	Loss: 152.492
+Training time 0:04:49.920553
+Epoch: 94 Average loss: 152.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 403)
+0/69092	Loss: 160.858
+3200/69092	Loss: 151.767
+6400/69092	Loss: 149.789
+9600/69092	Loss: 152.571
+12800/69092	Loss: 150.421
+16000/69092	Loss: 151.720
+19200/69092	Loss: 150.676
+22400/69092	Loss: 150.497
+25600/69092	Loss: 151.545
+28800/69092	Loss: 152.169
+32000/69092	Loss: 152.427
+35200/69092	Loss: 153.442
+38400/69092	Loss: 149.878
+41600/69092	Loss: 152.922
+44800/69092	Loss: 151.869
+48000/69092	Loss: 151.706
+51200/69092	Loss: 154.346
+54400/69092	Loss: 153.269
+57600/69092	Loss: 151.879
+60800/69092	Loss: 152.733
+64000/69092	Loss: 154.100
+67200/69092	Loss: 153.658
+Training time 0:04:46.535271
+Epoch: 95 Average loss: 152.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 404)
+0/69092	Loss: 151.968
+3200/69092	Loss: 153.705
+6400/69092	Loss: 152.080
+9600/69092	Loss: 152.701
+12800/69092	Loss: 151.054
+16000/69092	Loss: 152.064
+19200/69092	Loss: 152.099
+22400/69092	Loss: 152.200
+25600/69092	Loss: 152.526
+28800/69092	Loss: 151.792
+32000/69092	Loss: 150.670
+35200/69092	Loss: 155.599
+38400/69092	Loss: 148.762
+41600/69092	Loss: 151.034
+44800/69092	Loss: 153.194
+48000/69092	Loss: 154.365
+51200/69092	Loss: 151.639
+54400/69092	Loss: 152.496
+57600/69092	Loss: 151.936
+60800/69092	Loss: 152.478
+64000/69092	Loss: 150.782
+67200/69092	Loss: 153.092
+Training time 0:04:45.636976
+Epoch: 96 Average loss: 152.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 405)
+0/69092	Loss: 146.729
+3200/69092	Loss: 149.252
+6400/69092	Loss: 148.245
+9600/69092	Loss: 149.307
+12800/69092	Loss: 153.481
+16000/69092	Loss: 151.573
+19200/69092	Loss: 152.993
+22400/69092	Loss: 151.453
+25600/69092	Loss: 154.485
+28800/69092	Loss: 149.507
+32000/69092	Loss: 153.101
+35200/69092	Loss: 153.367
+38400/69092	Loss: 153.710
+41600/69092	Loss: 154.064
+44800/69092	Loss: 152.640
+48000/69092	Loss: 152.065
+51200/69092	Loss: 153.477
+54400/69092	Loss: 151.558
+57600/69092	Loss: 152.573
+60800/69092	Loss: 151.899
+64000/69092	Loss: 153.708
+67200/69092	Loss: 154.891
+Training time 0:04:59.854441
+Epoch: 97 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 406)
+0/69092	Loss: 137.998
+3200/69092	Loss: 151.489
+6400/69092	Loss: 151.412
+9600/69092	Loss: 150.391
+12800/69092	Loss: 151.562
+16000/69092	Loss: 152.893
+19200/69092	Loss: 152.713
+22400/69092	Loss: 150.260
+25600/69092	Loss: 151.633
+28800/69092	Loss: 151.326
+32000/69092	Loss: 154.725
+35200/69092	Loss: 150.661
+38400/69092	Loss: 152.762
+41600/69092	Loss: 151.188
+44800/69092	Loss: 152.194
+48000/69092	Loss: 152.163
+51200/69092	Loss: 150.429
+54400/69092	Loss: 153.507
+57600/69092	Loss: 154.009
+60800/69092	Loss: 153.067
+64000/69092	Loss: 154.646
+67200/69092	Loss: 151.797
+Training time 0:04:46.845983
+Epoch: 98 Average loss: 152.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 407)
+0/69092	Loss: 169.630
+3200/69092	Loss: 151.264
+6400/69092	Loss: 153.226
+9600/69092	Loss: 151.168
+12800/69092	Loss: 154.012
+16000/69092	Loss: 152.439
+19200/69092	Loss: 151.221
+22400/69092	Loss: 150.469
+25600/69092	Loss: 151.142
+28800/69092	Loss: 153.160
+32000/69092	Loss: 153.687
+35200/69092	Loss: 151.872
+38400/69092	Loss: 151.593
+41600/69092	Loss: 152.981
+44800/69092	Loss: 151.639
+48000/69092	Loss: 152.625
+51200/69092	Loss: 152.690
+54400/69092	Loss: 149.891
+57600/69092	Loss: 149.604
+60800/69092	Loss: 152.135
+64000/69092	Loss: 152.302
+67200/69092	Loss: 150.975
+Training time 0:04:57.776910
+Epoch: 99 Average loss: 151.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 408)
+0/69092	Loss: 157.478
+3200/69092	Loss: 148.961
+6400/69092	Loss: 154.174
+9600/69092	Loss: 149.814
+12800/69092	Loss: 149.233
+16000/69092	Loss: 151.266
+19200/69092	Loss: 149.778
+22400/69092	Loss: 155.607
+25600/69092	Loss: 150.476
+28800/69092	Loss: 152.955
+32000/69092	Loss: 151.325
+35200/69092	Loss: 152.565
+38400/69092	Loss: 155.373
+41600/69092	Loss: 150.391
+44800/69092	Loss: 151.788
+48000/69092	Loss: 152.091
+51200/69092	Loss: 151.354
+54400/69092	Loss: 152.939
+57600/69092	Loss: 154.037
+60800/69092	Loss: 152.035
+64000/69092	Loss: 152.074
+67200/69092	Loss: 153.509
+Training time 0:04:53.823615
+Epoch: 100 Average loss: 152.03
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 409)
+0/69092	Loss: 169.415
+3200/69092	Loss: 153.103
+6400/69092	Loss: 151.163
+9600/69092	Loss: 153.687
+12800/69092	Loss: 152.283
+16000/69092	Loss: 150.388
+19200/69092	Loss: 152.896
+22400/69092	Loss: 153.909
+25600/69092	Loss: 151.392
+28800/69092	Loss: 151.566
+32000/69092	Loss: 152.565
+35200/69092	Loss: 150.287
+38400/69092	Loss: 151.676
+41600/69092	Loss: 152.150
+44800/69092	Loss: 150.827
+48000/69092	Loss: 150.386
+51200/69092	Loss: 150.106
+54400/69092	Loss: 152.285
+57600/69092	Loss: 150.452
+60800/69092	Loss: 151.801
+64000/69092	Loss: 153.773
+67200/69092	Loss: 155.499
+Training time 0:04:48.562618
+Epoch: 101 Average loss: 152.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 410)
+0/69092	Loss: 158.078
+3200/69092	Loss: 147.879
+6400/69092	Loss: 151.176
+9600/69092	Loss: 150.107
+12800/69092	Loss: 154.523
+16000/69092	Loss: 149.720
+19200/69092	Loss: 153.969
+22400/69092	Loss: 152.776
+25600/69092	Loss: 152.456
+28800/69092	Loss: 150.317
+32000/69092	Loss: 154.425
+35200/69092	Loss: 152.331
+38400/69092	Loss: 151.896
+41600/69092	Loss: 153.088
+44800/69092	Loss: 152.307
+48000/69092	Loss: 152.278
+51200/69092	Loss: 154.122
+54400/69092	Loss: 151.100
+57600/69092	Loss: 150.320
+60800/69092	Loss: 150.146
+64000/69092	Loss: 154.554
+67200/69092	Loss: 150.885
+Training time 0:04:47.864876
+Epoch: 102 Average loss: 151.93
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 411)
+0/69092	Loss: 161.600
+3200/69092	Loss: 150.294
+6400/69092	Loss: 152.689
+9600/69092	Loss: 152.596
+12800/69092	Loss: 150.389
+16000/69092	Loss: 153.032
+19200/69092	Loss: 153.226
+22400/69092	Loss: 154.488
+25600/69092	Loss: 149.508
+28800/69092	Loss: 152.821
+32000/69092	Loss: 153.566
+35200/69092	Loss: 152.721
+38400/69092	Loss: 153.682
+41600/69092	Loss: 152.280
+44800/69092	Loss: 151.708
+48000/69092	Loss: 152.183
+51200/69092	Loss: 152.264
+54400/69092	Loss: 152.192
+57600/69092	Loss: 150.605
+60800/69092	Loss: 152.570
+64000/69092	Loss: 153.640
+67200/69092	Loss: 150.774
+Training time 0:04:46.565216
+Epoch: 103 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 412)
+0/69092	Loss: 142.357
+3200/69092	Loss: 153.350
+6400/69092	Loss: 152.448
+9600/69092	Loss: 150.219
+12800/69092	Loss: 153.586
+16000/69092	Loss: 153.335
+19200/69092	Loss: 151.796
+22400/69092	Loss: 151.756
+25600/69092	Loss: 151.087
+28800/69092	Loss: 153.957
+32000/69092	Loss: 151.021
+35200/69092	Loss: 151.751
+38400/69092	Loss: 150.219
+41600/69092	Loss: 153.643
+44800/69092	Loss: 151.641
+48000/69092	Loss: 151.229
+51200/69092	Loss: 152.683
+54400/69092	Loss: 150.299
+57600/69092	Loss: 151.652
+60800/69092	Loss: 152.452
+64000/69092	Loss: 152.588
+67200/69092	Loss: 155.900
+Training time 0:04:53.206867
+Epoch: 104 Average loss: 152.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 413)
+0/69092	Loss: 147.985
+3200/69092	Loss: 150.459
+6400/69092	Loss: 151.480
+9600/69092	Loss: 151.722
+12800/69092	Loss: 152.108
+16000/69092	Loss: 150.949
+19200/69092	Loss: 155.608
+22400/69092	Loss: 151.986
+25600/69092	Loss: 152.775
+28800/69092	Loss: 150.901
+32000/69092	Loss: 150.172
+35200/69092	Loss: 154.703
+38400/69092	Loss: 151.604
+41600/69092	Loss: 151.956
+44800/69092	Loss: 150.413
+48000/69092	Loss: 153.019
+51200/69092	Loss: 153.686
+54400/69092	Loss: 151.706
+57600/69092	Loss: 152.662
+60800/69092	Loss: 152.065
+64000/69092	Loss: 151.596
+67200/69092	Loss: 152.116
+Training time 0:04:58.640244
+Epoch: 105 Average loss: 152.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 414)
+0/69092	Loss: 149.987
+3200/69092	Loss: 149.893
+6400/69092	Loss: 152.323
+9600/69092	Loss: 152.562
+12800/69092	Loss: 151.810
+16000/69092	Loss: 152.911
+19200/69092	Loss: 153.191
+22400/69092	Loss: 150.899
+25600/69092	Loss: 153.012
+28800/69092	Loss: 153.163
+32000/69092	Loss: 151.207
+35200/69092	Loss: 152.882
+38400/69092	Loss: 150.813
+41600/69092	Loss: 152.901
+44800/69092	Loss: 152.755
+48000/69092	Loss: 150.881
+51200/69092	Loss: 151.376
+54400/69092	Loss: 152.687
+57600/69092	Loss: 153.730
+60800/69092	Loss: 150.189
+64000/69092	Loss: 151.934
+67200/69092	Loss: 151.211
+Training time 0:04:48.120273
+Epoch: 106 Average loss: 152.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 415)
+0/69092	Loss: 151.823
+3200/69092	Loss: 148.643
+6400/69092	Loss: 152.817
+9600/69092	Loss: 151.382
+12800/69092	Loss: 153.022
+16000/69092	Loss: 150.716
+19200/69092	Loss: 153.632
+22400/69092	Loss: 151.172
+25600/69092	Loss: 148.932
+28800/69092	Loss: 151.189
+32000/69092	Loss: 151.965
+35200/69092	Loss: 151.846
+38400/69092	Loss: 149.539
+41600/69092	Loss: 152.411
+44800/69092	Loss: 153.926
+48000/69092	Loss: 153.859
+51200/69092	Loss: 150.885
+54400/69092	Loss: 153.050
+57600/69092	Loss: 153.645
+60800/69092	Loss: 150.253
+64000/69092	Loss: 152.771
+67200/69092	Loss: 150.985
+Training time 0:04:48.540351
+Epoch: 107 Average loss: 151.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 416)
+0/69092	Loss: 131.866
+3200/69092	Loss: 149.503
+6400/69092	Loss: 152.488
+9600/69092	Loss: 152.815
+12800/69092	Loss: 151.525
+16000/69092	Loss: 151.414
+19200/69092	Loss: 153.750
+22400/69092	Loss: 152.786
+25600/69092	Loss: 152.018
+28800/69092	Loss: 153.919
+32000/69092	Loss: 152.371
+35200/69092	Loss: 153.434
+38400/69092	Loss: 151.454
+41600/69092	Loss: 152.944
+44800/69092	Loss: 151.085
+48000/69092	Loss: 153.937
+51200/69092	Loss: 151.860
+54400/69092	Loss: 150.493
+57600/69092	Loss: 148.695
+60800/69092	Loss: 154.028
+64000/69092	Loss: 152.080
+67200/69092	Loss: 154.185
+Training time 0:04:55.298100
+Epoch: 108 Average loss: 152.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 417)
+0/69092	Loss: 147.412
+3200/69092	Loss: 151.994
+6400/69092	Loss: 153.347
+9600/69092	Loss: 150.016
+12800/69092	Loss: 152.968
+16000/69092	Loss: 150.501
+19200/69092	Loss: 152.418
+22400/69092	Loss: 151.831
+25600/69092	Loss: 152.847
+28800/69092	Loss: 150.512
+32000/69092	Loss: 149.003
+35200/69092	Loss: 149.976
+38400/69092	Loss: 154.265
+41600/69092	Loss: 148.565
+44800/69092	Loss: 150.216
+48000/69092	Loss: 152.008
+51200/69092	Loss: 153.583
+54400/69092	Loss: 153.803
+57600/69092	Loss: 152.243
+60800/69092	Loss: 152.050
+64000/69092	Loss: 154.021
+67200/69092	Loss: 152.084
+Training time 0:04:41.255307
+Epoch: 109 Average loss: 151.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 418)
+0/69092	Loss: 162.086
+3200/69092	Loss: 152.745
+6400/69092	Loss: 152.261
+9600/69092	Loss: 150.936
+12800/69092	Loss: 153.044
+16000/69092	Loss: 151.620
+19200/69092	Loss: 150.673
+22400/69092	Loss: 151.599
+25600/69092	Loss: 153.168
+28800/69092	Loss: 152.770
+32000/69092	Loss: 153.143
+35200/69092	Loss: 152.202
+38400/69092	Loss: 153.055
+41600/69092	Loss: 151.643
+44800/69092	Loss: 150.894
+48000/69092	Loss: 150.257
+51200/69092	Loss: 152.914
+54400/69092	Loss: 152.266
+57600/69092	Loss: 150.819
+60800/69092	Loss: 151.397
+64000/69092	Loss: 152.063
+67200/69092	Loss: 152.324
+Training time 0:04:47.438624
+Epoch: 110 Average loss: 152.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 419)
+0/69092	Loss: 160.629
+3200/69092	Loss: 152.910
+6400/69092	Loss: 152.561
+9600/69092	Loss: 152.619
+12800/69092	Loss: 151.571
+16000/69092	Loss: 153.697
+19200/69092	Loss: 152.958
+22400/69092	Loss: 152.440
+25600/69092	Loss: 151.144
+28800/69092	Loss: 150.991
+32000/69092	Loss: 151.666
+35200/69092	Loss: 152.099
+38400/69092	Loss: 150.653
+41600/69092	Loss: 152.933
+44800/69092	Loss: 152.994
+48000/69092	Loss: 151.900
+51200/69092	Loss: 150.365
+54400/69092	Loss: 152.757
+57600/69092	Loss: 152.901
+60800/69092	Loss: 150.266
+64000/69092	Loss: 151.742
+67200/69092	Loss: 153.295
+Training time 0:04:51.982336
+Epoch: 111 Average loss: 152.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 420)
+0/69092	Loss: 157.400
+3200/69092	Loss: 149.721
+6400/69092	Loss: 154.515
+9600/69092	Loss: 151.512
+12800/69092	Loss: 154.721
+16000/69092	Loss: 150.363
+19200/69092	Loss: 151.800
+22400/69092	Loss: 152.965
+25600/69092	Loss: 151.072
+28800/69092	Loss: 152.065
+32000/69092	Loss: 151.845
+35200/69092	Loss: 150.387
+38400/69092	Loss: 150.705
+41600/69092	Loss: 151.653
+44800/69092	Loss: 154.105
+48000/69092	Loss: 149.193
+51200/69092	Loss: 150.544
+54400/69092	Loss: 150.828
+57600/69092	Loss: 154.232
+60800/69092	Loss: 152.992
+64000/69092	Loss: 151.455
+67200/69092	Loss: 151.612
+Training time 0:04:54.074097
+Epoch: 112 Average loss: 151.84
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 421)
+0/69092	Loss: 157.065
+3200/69092	Loss: 150.346
+6400/69092	Loss: 151.884
+9600/69092	Loss: 152.009
+12800/69092	Loss: 149.326
+16000/69092	Loss: 148.875
+19200/69092	Loss: 152.735
+22400/69092	Loss: 152.689
+25600/69092	Loss: 153.586
+28800/69092	Loss: 152.923
+32000/69092	Loss: 150.834
+35200/69092	Loss: 152.571
+38400/69092	Loss: 153.652
+41600/69092	Loss: 152.052
+44800/69092	Loss: 150.601
+48000/69092	Loss: 152.516
+51200/69092	Loss: 150.682
+54400/69092	Loss: 153.087
+57600/69092	Loss: 152.169
+60800/69092	Loss: 153.943
+64000/69092	Loss: 153.933
+67200/69092	Loss: 153.847
+Training time 0:04:45.471707
+Epoch: 113 Average loss: 152.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 422)
+0/69092	Loss: 166.283
+3200/69092	Loss: 152.282
+6400/69092	Loss: 152.711
+9600/69092	Loss: 154.190
+12800/69092	Loss: 148.430
+16000/69092	Loss: 154.423
+19200/69092	Loss: 154.683
+22400/69092	Loss: 150.249
+25600/69092	Loss: 151.769
+28800/69092	Loss: 151.643
+32000/69092	Loss: 150.683
+35200/69092	Loss: 152.734
+38400/69092	Loss: 152.737
+41600/69092	Loss: 150.408
+44800/69092	Loss: 153.608
+48000/69092	Loss: 149.757
+51200/69092	Loss: 150.082
+54400/69092	Loss: 151.735
+57600/69092	Loss: 152.117
+60800/69092	Loss: 152.901
+64000/69092	Loss: 150.976
+67200/69092	Loss: 152.441
+Training time 0:04:50.508285
+Epoch: 114 Average loss: 151.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 423)
+0/69092	Loss: 156.983
+3200/69092	Loss: 153.281
+6400/69092	Loss: 148.878
+9600/69092	Loss: 152.051
+12800/69092	Loss: 152.789
+16000/69092	Loss: 150.928
+19200/69092	Loss: 150.311
+22400/69092	Loss: 149.966
+25600/69092	Loss: 153.276
+28800/69092	Loss: 151.430
+32000/69092	Loss: 155.032
+35200/69092	Loss: 152.147
+38400/69092	Loss: 152.673
+41600/69092	Loss: 154.257
+44800/69092	Loss: 152.049
+48000/69092	Loss: 150.658
+51200/69092	Loss: 149.756
+54400/69092	Loss: 153.817
+57600/69092	Loss: 151.338
+60800/69092	Loss: 153.225
+64000/69092	Loss: 153.599
+67200/69092	Loss: 152.351
+Training time 0:04:54.961885
+Epoch: 115 Average loss: 152.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 424)
+0/69092	Loss: 132.478
+3200/69092	Loss: 149.838
+6400/69092	Loss: 152.855
+9600/69092	Loss: 152.989
+12800/69092	Loss: 153.865
+16000/69092	Loss: 150.047
+19200/69092	Loss: 151.295
+22400/69092	Loss: 151.470
+25600/69092	Loss: 150.768
+28800/69092	Loss: 151.075
+32000/69092	Loss: 150.120
+35200/69092	Loss: 156.213
+38400/69092	Loss: 152.625
+41600/69092	Loss: 152.245
+44800/69092	Loss: 153.139
+48000/69092	Loss: 151.104
+51200/69092	Loss: 151.805
+54400/69092	Loss: 153.466
+57600/69092	Loss: 149.804
+60800/69092	Loss: 154.504
+64000/69092	Loss: 148.703
+67200/69092	Loss: 153.108
+Training time 0:04:47.408569
+Epoch: 116 Average loss: 151.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 425)
+0/69092	Loss: 161.084
+3200/69092	Loss: 149.878
+6400/69092	Loss: 154.645
+9600/69092	Loss: 152.513
+12800/69092	Loss: 154.098
+16000/69092	Loss: 151.261
+19200/69092	Loss: 153.206
+22400/69092	Loss: 151.152
+25600/69092	Loss: 150.373
+28800/69092	Loss: 151.378
+32000/69092	Loss: 150.651
+35200/69092	Loss: 153.225
+38400/69092	Loss: 154.637
+41600/69092	Loss: 152.478
+44800/69092	Loss: 151.924
+48000/69092	Loss: 151.923
+51200/69092	Loss: 151.490
+54400/69092	Loss: 151.390
+57600/69092	Loss: 151.142
+60800/69092	Loss: 150.049
+64000/69092	Loss: 152.262
+67200/69092	Loss: 154.271
+Training time 0:04:42.203575
+Epoch: 117 Average loss: 152.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 426)
+0/69092	Loss: 167.484
+3200/69092	Loss: 151.594
+6400/69092	Loss: 151.849
+9600/69092	Loss: 151.773
+12800/69092	Loss: 154.473
+16000/69092	Loss: 153.150
+19200/69092	Loss: 151.681
+22400/69092	Loss: 151.531
+25600/69092	Loss: 151.840
+28800/69092	Loss: 152.144
+32000/69092	Loss: 155.200
+35200/69092	Loss: 152.617
+38400/69092	Loss: 152.047
+41600/69092	Loss: 150.749
+44800/69092	Loss: 152.802
+48000/69092	Loss: 153.691
+51200/69092	Loss: 151.513
+54400/69092	Loss: 149.877
+57600/69092	Loss: 149.372
+60800/69092	Loss: 151.032
+64000/69092	Loss: 147.679
+67200/69092	Loss: 153.573
+Training time 0:04:54.279119
+Epoch: 118 Average loss: 151.86
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 427)
+0/69092	Loss: 133.374
+3200/69092	Loss: 153.390
+6400/69092	Loss: 153.883
+9600/69092	Loss: 150.555
+12800/69092	Loss: 151.884
+16000/69092	Loss: 152.527
+19200/69092	Loss: 154.051
+22400/69092	Loss: 150.880
+25600/69092	Loss: 152.859
+28800/69092	Loss: 151.407
+32000/69092	Loss: 150.339
+35200/69092	Loss: 151.008
+38400/69092	Loss: 152.450
+41600/69092	Loss: 151.903
+44800/69092	Loss: 152.252
+48000/69092	Loss: 153.607
+51200/69092	Loss: 149.811
+54400/69092	Loss: 149.198
+57600/69092	Loss: 154.233
+60800/69092	Loss: 151.883
+64000/69092	Loss: 154.476
+67200/69092	Loss: 148.915
+Training time 0:04:42.952877
+Epoch: 119 Average loss: 152.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 428)
+0/69092	Loss: 130.876
+3200/69092	Loss: 153.569
+6400/69092	Loss: 149.717
+9600/69092	Loss: 152.734
+12800/69092	Loss: 150.330
+16000/69092	Loss: 152.841
+19200/69092	Loss: 153.176
+22400/69092	Loss: 152.644
+25600/69092	Loss: 153.954
+28800/69092	Loss: 149.216
+32000/69092	Loss: 154.519
+35200/69092	Loss: 151.584
+38400/69092	Loss: 150.392
+41600/69092	Loss: 149.239
+44800/69092	Loss: 152.308
+48000/69092	Loss: 152.019
+51200/69092	Loss: 150.558
+54400/69092	Loss: 153.810
+57600/69092	Loss: 151.494
+60800/69092	Loss: 154.102
+64000/69092	Loss: 151.360
+67200/69092	Loss: 150.479
+Training time 0:04:46.836761
+Epoch: 120 Average loss: 151.86
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 429)
+0/69092	Loss: 142.914
+3200/69092	Loss: 151.008
+6400/69092	Loss: 150.469
+9600/69092	Loss: 152.556
+12800/69092	Loss: 151.596
+16000/69092	Loss: 151.404
+19200/69092	Loss: 152.845
+22400/69092	Loss: 153.279
+25600/69092	Loss: 149.563
+28800/69092	Loss: 149.553
+32000/69092	Loss: 151.925
+35200/69092	Loss: 152.067
+38400/69092	Loss: 151.782
+41600/69092	Loss: 152.750
+44800/69092	Loss: 152.136
+48000/69092	Loss: 152.894
+51200/69092	Loss: 152.172
+54400/69092	Loss: 151.977
+57600/69092	Loss: 149.534
+60800/69092	Loss: 152.757
+64000/69092	Loss: 151.173
+67200/69092	Loss: 154.301
+Training time 0:04:35.789460
+Epoch: 121 Average loss: 151.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 430)
+0/69092	Loss: 150.952
+3200/69092	Loss: 152.128
+6400/69092	Loss: 152.453
+9600/69092	Loss: 151.920
+12800/69092	Loss: 153.240
+16000/69092	Loss: 153.071
+19200/69092	Loss: 151.953
+22400/69092	Loss: 147.788
+25600/69092	Loss: 150.992
+28800/69092	Loss: 153.508
+32000/69092	Loss: 151.623
+35200/69092	Loss: 154.380
+38400/69092	Loss: 150.654
+41600/69092	Loss: 153.483
+44800/69092	Loss: 154.157
+48000/69092	Loss: 150.010
+51200/69092	Loss: 155.253
+54400/69092	Loss: 152.480
diff --git a/OAR.2068286.stderr b/OAR.2068286.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..8656ad55ec4813c05222efb991750f556ca3d607
--- /dev/null
+++ b/OAR.2068286.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-25 02:59:26] Job 2068286 KILLED ##
diff --git a/OAR.2068286.stdout b/OAR.2068286.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..1a2cfa4114642bd76f45907940892279f91c9dff
--- /dev/null
+++ b/OAR.2068286.stdout
@@ -0,0 +1,1509 @@
+Namespace(batch_size=256, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_256', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=True, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last (iter 144)'
+0/69092	Loss: 120.459
+12800/69092	Loss: 116.572
+25600/69092	Loss: 116.237
+38400/69092	Loss: 115.864
+51200/69092	Loss: 116.059
+64000/69092	Loss: 117.337
+Training time 0:03:42.281008
+Epoch: 1 Average loss: 116.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 145)
+0/69092	Loss: 120.358
+12800/69092	Loss: 116.899
+25600/69092	Loss: 115.721
+38400/69092	Loss: 116.798
+51200/69092	Loss: 116.771
+64000/69092	Loss: 116.356
+Training time 0:03:40.681431
+Epoch: 2 Average loss: 116.47
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 146)
+0/69092	Loss: 119.181
+12800/69092	Loss: 115.598
+25600/69092	Loss: 117.185
+38400/69092	Loss: 116.847
+51200/69092	Loss: 116.143
+64000/69092	Loss: 117.005
+Training time 0:03:40.721636
+Epoch: 3 Average loss: 116.55
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 147)
+0/69092	Loss: 115.099
+12800/69092	Loss: 116.701
+25600/69092	Loss: 116.450
+38400/69092	Loss: 116.328
+51200/69092	Loss: 115.482
+64000/69092	Loss: 116.387
+Training time 0:03:40.701471
+Epoch: 4 Average loss: 116.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 148)
+0/69092	Loss: 119.770
+12800/69092	Loss: 115.991
+25600/69092	Loss: 116.217
+38400/69092	Loss: 116.309
+51200/69092	Loss: 116.911
+64000/69092	Loss: 116.120
+Training time 0:03:39.979969
+Epoch: 5 Average loss: 116.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 149)
+0/69092	Loss: 118.151
+12800/69092	Loss: 115.808
+25600/69092	Loss: 116.703
+38400/69092	Loss: 116.090
+51200/69092	Loss: 116.427
+64000/69092	Loss: 117.572
+Training time 0:03:40.579498
+Epoch: 6 Average loss: 116.51
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 150)
+0/69092	Loss: 113.875
+12800/69092	Loss: 116.600
+25600/69092	Loss: 116.295
+38400/69092	Loss: 115.668
+51200/69092	Loss: 116.840
+64000/69092	Loss: 115.933
+Training time 0:03:41.221395
+Epoch: 7 Average loss: 116.36
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 151)
+0/69092	Loss: 114.324
+12800/69092	Loss: 116.549
+25600/69092	Loss: 115.837
+38400/69092	Loss: 116.300
+51200/69092	Loss: 116.452
+64000/69092	Loss: 116.383
+Training time 0:03:42.000145
+Epoch: 8 Average loss: 116.26
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 152)
+0/69092	Loss: 112.656
+12800/69092	Loss: 116.205
+25600/69092	Loss: 116.463
+38400/69092	Loss: 116.948
+51200/69092	Loss: 116.328
+64000/69092	Loss: 115.736
+Training time 0:03:39.929666
+Epoch: 9 Average loss: 116.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 153)
+0/69092	Loss: 121.883
+12800/69092	Loss: 116.664
+25600/69092	Loss: 115.788
+38400/69092	Loss: 116.076
+51200/69092	Loss: 116.190
+64000/69092	Loss: 116.272
+Training time 0:03:40.616174
+Epoch: 10 Average loss: 116.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 154)
+0/69092	Loss: 115.453
+12800/69092	Loss: 116.273
+25600/69092	Loss: 115.309
+38400/69092	Loss: 117.375
+51200/69092	Loss: 116.806
+64000/69092	Loss: 116.037
+Training time 0:03:40.447946
+Epoch: 11 Average loss: 116.18
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 155)
+0/69092	Loss: 119.812
+12800/69092	Loss: 116.018
+25600/69092	Loss: 116.276
+38400/69092	Loss: 115.628
+51200/69092	Loss: 115.753
+64000/69092	Loss: 116.374
+Training time 0:03:40.825373
+Epoch: 12 Average loss: 116.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 156)
+0/69092	Loss: 112.388
+12800/69092	Loss: 115.852
+25600/69092	Loss: 116.388
+38400/69092	Loss: 116.094
+51200/69092	Loss: 116.621
+64000/69092	Loss: 115.358
+Training time 0:03:40.482848
+Epoch: 13 Average loss: 116.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 157)
+0/69092	Loss: 117.393
+12800/69092	Loss: 116.509
+25600/69092	Loss: 115.161
+38400/69092	Loss: 116.172
+51200/69092	Loss: 116.397
+64000/69092	Loss: 116.203
+Training time 0:03:40.998442
+Epoch: 14 Average loss: 116.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 158)
+0/69092	Loss: 119.346
+12800/69092	Loss: 116.319
+25600/69092	Loss: 115.947
+38400/69092	Loss: 116.468
+51200/69092	Loss: 116.241
+64000/69092	Loss: 116.013
+Training time 0:03:42.395751
+Epoch: 15 Average loss: 116.17
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 159)
+0/69092	Loss: 113.202
+12800/69092	Loss: 116.460
+25600/69092	Loss: 115.730
+38400/69092	Loss: 115.797
+51200/69092	Loss: 116.281
+64000/69092	Loss: 115.448
+Training time 0:03:41.078200
+Epoch: 16 Average loss: 116.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 160)
+0/69092	Loss: 112.005
+12800/69092	Loss: 115.957
+25600/69092	Loss: 116.704
+38400/69092	Loss: 116.893
+51200/69092	Loss: 115.449
+64000/69092	Loss: 115.642
+Training time 0:03:40.431808
+Epoch: 17 Average loss: 116.13
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 161)
+0/69092	Loss: 113.287
+12800/69092	Loss: 116.447
+25600/69092	Loss: 115.693
+38400/69092	Loss: 116.821
+51200/69092	Loss: 116.644
+64000/69092	Loss: 115.405
+Training time 0:03:40.430938
+Epoch: 18 Average loss: 116.02
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 162)
+0/69092	Loss: 112.196
+12800/69092	Loss: 117.175
+25600/69092	Loss: 115.399
+38400/69092	Loss: 115.678
+51200/69092	Loss: 115.329
+64000/69092	Loss: 116.300
+Training time 0:03:40.572838
+Epoch: 19 Average loss: 116.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 163)
+0/69092	Loss: 117.480
+12800/69092	Loss: 117.243
+25600/69092	Loss: 115.588
+38400/69092	Loss: 115.861
+51200/69092	Loss: 115.026
+64000/69092	Loss: 115.228
+Training time 0:03:40.556184
+Epoch: 20 Average loss: 115.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 164)
+0/69092	Loss: 118.064
+12800/69092	Loss: 115.452
+25600/69092	Loss: 116.303
+38400/69092	Loss: 115.384
+51200/69092	Loss: 115.665
+64000/69092	Loss: 116.274
+Training time 0:03:40.790851
+Epoch: 21 Average loss: 115.88
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 165)
+0/69092	Loss: 112.775
+12800/69092	Loss: 116.776
+25600/69092	Loss: 116.287
+38400/69092	Loss: 115.689
+51200/69092	Loss: 115.688
+64000/69092	Loss: 115.419
+Training time 0:03:41.559039
+Epoch: 22 Average loss: 115.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 166)
+0/69092	Loss: 118.464
+12800/69092	Loss: 114.966
+25600/69092	Loss: 115.504
+38400/69092	Loss: 116.800
+51200/69092	Loss: 114.923
+64000/69092	Loss: 116.779
+Training time 0:03:40.445243
+Epoch: 23 Average loss: 115.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 167)
+0/69092	Loss: 117.857
+12800/69092	Loss: 116.386
+25600/69092	Loss: 115.667
+38400/69092	Loss: 114.653
+51200/69092	Loss: 116.496
+64000/69092	Loss: 115.861
+Training time 0:03:40.728070
+Epoch: 24 Average loss: 115.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 168)
+0/69092	Loss: 118.362
+12800/69092	Loss: 116.060
+25600/69092	Loss: 115.154
+38400/69092	Loss: 116.675
+51200/69092	Loss: 115.458
+64000/69092	Loss: 115.521
+Training time 0:03:41.048771
+Epoch: 25 Average loss: 115.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 169)
+0/69092	Loss: 119.678
+12800/69092	Loss: 115.659
+25600/69092	Loss: 115.170
+38400/69092	Loss: 116.955
+51200/69092	Loss: 116.197
+64000/69092	Loss: 115.340
+Training time 0:03:40.247926
+Epoch: 26 Average loss: 115.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 170)
+0/69092	Loss: 112.441
+12800/69092	Loss: 115.019
+25600/69092	Loss: 115.888
+38400/69092	Loss: 116.372
+51200/69092	Loss: 116.046
+64000/69092	Loss: 115.798
+Training time 0:03:40.481684
+Epoch: 27 Average loss: 115.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 171)
+0/69092	Loss: 118.454
+12800/69092	Loss: 115.568
+25600/69092	Loss: 116.489
+38400/69092	Loss: 115.309
+51200/69092	Loss: 115.959
+64000/69092	Loss: 115.868
+Training time 0:03:40.730148
+Epoch: 28 Average loss: 115.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 172)
+0/69092	Loss: 118.467
+12800/69092	Loss: 115.553
+25600/69092	Loss: 115.273
+38400/69092	Loss: 115.216
+51200/69092	Loss: 116.678
+64000/69092	Loss: 116.455
+Training time 0:03:41.245003
+Epoch: 29 Average loss: 115.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 173)
+0/69092	Loss: 114.006
+12800/69092	Loss: 115.621
+25600/69092	Loss: 116.126
+38400/69092	Loss: 115.985
+51200/69092	Loss: 113.654
+64000/69092	Loss: 116.662
+Training time 0:03:40.563781
+Epoch: 30 Average loss: 115.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 174)
+0/69092	Loss: 114.707
+12800/69092	Loss: 115.550
+25600/69092	Loss: 115.860
+38400/69092	Loss: 115.116
+51200/69092	Loss: 116.279
+64000/69092	Loss: 115.989
+Training time 0:03:40.619515
+Epoch: 31 Average loss: 115.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 175)
+0/69092	Loss: 116.031
+12800/69092	Loss: 115.936
+25600/69092	Loss: 116.239
+38400/69092	Loss: 114.555
+51200/69092	Loss: 115.844
+64000/69092	Loss: 116.013
+Training time 0:03:40.575515
+Epoch: 32 Average loss: 115.80
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 176)
+0/69092	Loss: 111.837
+12800/69092	Loss: 116.479
+25600/69092	Loss: 115.491
+38400/69092	Loss: 115.912
+51200/69092	Loss: 114.634
+64000/69092	Loss: 115.763
+Training time 0:03:40.491551
+Epoch: 33 Average loss: 115.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 177)
+0/69092	Loss: 117.520
+12800/69092	Loss: 116.071
+25600/69092	Loss: 116.613
+38400/69092	Loss: 114.784
+51200/69092	Loss: 115.905
+64000/69092	Loss: 115.579
+Training time 0:03:41.179735
+Epoch: 34 Average loss: 115.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 178)
+0/69092	Loss: 123.567
+12800/69092	Loss: 116.828
+25600/69092	Loss: 115.420
+38400/69092	Loss: 115.197
+51200/69092	Loss: 115.964
+64000/69092	Loss: 115.175
+Training time 0:03:40.153394
+Epoch: 35 Average loss: 115.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 179)
+0/69092	Loss: 116.028
+12800/69092	Loss: 115.196
+25600/69092	Loss: 115.596
+38400/69092	Loss: 115.706
+51200/69092	Loss: 116.102
+64000/69092	Loss: 115.148
+Training time 0:03:41.520109
+Epoch: 36 Average loss: 115.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 180)
+0/69092	Loss: 114.294
+12800/69092	Loss: 115.208
+25600/69092	Loss: 116.025
+38400/69092	Loss: 116.365
+51200/69092	Loss: 115.723
+64000/69092	Loss: 115.075
+Training time 0:03:41.725098
+Epoch: 37 Average loss: 115.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 181)
+0/69092	Loss: 108.952
+12800/69092	Loss: 115.211
+25600/69092	Loss: 115.684
+38400/69092	Loss: 114.832
+51200/69092	Loss: 116.676
+64000/69092	Loss: 115.385
+Training time 0:03:40.430924
+Epoch: 38 Average loss: 115.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 182)
+0/69092	Loss: 115.457
+12800/69092	Loss: 115.327
+25600/69092	Loss: 115.543
+38400/69092	Loss: 115.617
+51200/69092	Loss: 115.977
+64000/69092	Loss: 115.014
+Training time 0:03:41.206588
+Epoch: 39 Average loss: 115.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 183)
+0/69092	Loss: 116.693
+12800/69092	Loss: 114.557
+25600/69092	Loss: 115.086
+38400/69092	Loss: 115.546
+51200/69092	Loss: 116.112
+64000/69092	Loss: 114.996
+Training time 0:03:40.908944
+Epoch: 40 Average loss: 115.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 184)
+0/69092	Loss: 112.386
+12800/69092	Loss: 115.443
+25600/69092	Loss: 115.660
+38400/69092	Loss: 115.033
+51200/69092	Loss: 115.429
+64000/69092	Loss: 115.723
+Training time 0:03:40.941674
+Epoch: 41 Average loss: 115.53
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 185)
+0/69092	Loss: 116.749
+12800/69092	Loss: 115.176
+25600/69092	Loss: 116.024
+38400/69092	Loss: 115.643
+51200/69092	Loss: 115.313
+64000/69092	Loss: 115.243
+Training time 0:03:41.117220
+Epoch: 42 Average loss: 115.55
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 186)
+0/69092	Loss: 121.403
+12800/69092	Loss: 115.205
+25600/69092	Loss: 115.911
+38400/69092	Loss: 115.502
+51200/69092	Loss: 115.076
+64000/69092	Loss: 115.854
+Training time 0:03:41.143228
+Epoch: 43 Average loss: 115.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 187)
+0/69092	Loss: 112.070
+12800/69092	Loss: 115.371
+25600/69092	Loss: 115.118
+38400/69092	Loss: 115.911
+51200/69092	Loss: 114.989
+64000/69092	Loss: 115.609
+Training time 0:03:42.112486
+Epoch: 44 Average loss: 115.46
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 188)
+0/69092	Loss: 106.050
+12800/69092	Loss: 115.710
+25600/69092	Loss: 116.172
+38400/69092	Loss: 115.340
+51200/69092	Loss: 114.929
+64000/69092	Loss: 115.512
+Training time 0:03:40.727732
+Epoch: 45 Average loss: 115.51
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 189)
+0/69092	Loss: 117.641
+12800/69092	Loss: 114.826
+25600/69092	Loss: 115.081
+38400/69092	Loss: 115.612
+51200/69092	Loss: 115.316
+64000/69092	Loss: 115.945
+Training time 0:03:40.648978
+Epoch: 46 Average loss: 115.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 190)
+0/69092	Loss: 112.434
+12800/69092	Loss: 115.572
+25600/69092	Loss: 115.967
+38400/69092	Loss: 116.601
+51200/69092	Loss: 114.836
+64000/69092	Loss: 114.873
+Training time 0:03:40.988167
+Epoch: 47 Average loss: 115.47
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 191)
+0/69092	Loss: 115.484
+12800/69092	Loss: 114.976
+25600/69092	Loss: 114.461
+38400/69092	Loss: 116.200
+51200/69092	Loss: 115.965
+64000/69092	Loss: 115.243
+Training time 0:03:41.030348
+Epoch: 48 Average loss: 115.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 192)
+0/69092	Loss: 114.590
+12800/69092	Loss: 115.792
+25600/69092	Loss: 115.040
+38400/69092	Loss: 114.508
+51200/69092	Loss: 115.620
+64000/69092	Loss: 116.456
+Training time 0:03:40.662849
+Epoch: 49 Average loss: 115.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 193)
+0/69092	Loss: 122.536
+12800/69092	Loss: 115.258
+25600/69092	Loss: 115.104
+38400/69092	Loss: 115.037
+51200/69092	Loss: 116.723
+64000/69092	Loss: 114.876
+Training time 0:03:40.648263
+Epoch: 50 Average loss: 115.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 194)
+0/69092	Loss: 110.866
+12800/69092	Loss: 115.340
+25600/69092	Loss: 115.662
+38400/69092	Loss: 114.955
+51200/69092	Loss: 115.300
+64000/69092	Loss: 115.256
+Training time 0:03:41.139315
+Epoch: 51 Average loss: 115.37
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 195)
+0/69092	Loss: 117.302
+12800/69092	Loss: 115.389
+25600/69092	Loss: 114.703
+38400/69092	Loss: 115.806
+51200/69092	Loss: 115.654
+64000/69092	Loss: 115.386
+Training time 0:03:40.901612
+Epoch: 52 Average loss: 115.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 196)
+0/69092	Loss: 113.879
+12800/69092	Loss: 115.001
+25600/69092	Loss: 115.614
+38400/69092	Loss: 115.384
+51200/69092	Loss: 114.834
+64000/69092	Loss: 115.560
+Training time 0:03:40.568950
+Epoch: 53 Average loss: 115.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 197)
+0/69092	Loss: 122.464
+12800/69092	Loss: 114.625
+25600/69092	Loss: 116.063
+38400/69092	Loss: 114.064
+51200/69092	Loss: 114.791
+64000/69092	Loss: 116.462
+Training time 0:03:40.821567
+Epoch: 54 Average loss: 115.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 198)
+0/69092	Loss: 127.387
+12800/69092	Loss: 115.084
+25600/69092	Loss: 115.119
+38400/69092	Loss: 114.816
+51200/69092	Loss: 115.599
+64000/69092	Loss: 116.237
+Training time 0:03:41.123891
+Epoch: 55 Average loss: 115.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 199)
+0/69092	Loss: 113.509
+12800/69092	Loss: 115.800
+25600/69092	Loss: 114.487
+38400/69092	Loss: 114.952
+51200/69092	Loss: 115.551
+64000/69092	Loss: 116.056
+Training time 0:03:40.488129
+Epoch: 56 Average loss: 115.26
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 200)
+0/69092	Loss: 113.927
+12800/69092	Loss: 113.790
+25600/69092	Loss: 115.493
+38400/69092	Loss: 116.131
+51200/69092	Loss: 115.332
+64000/69092	Loss: 115.806
+Training time 0:03:40.772788
+Epoch: 57 Average loss: 115.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 201)
+0/69092	Loss: 111.797
+12800/69092	Loss: 114.642
+25600/69092	Loss: 116.307
+38400/69092	Loss: 114.750
+51200/69092	Loss: 114.781
+64000/69092	Loss: 116.396
+Training time 0:03:41.710917
+Epoch: 58 Average loss: 115.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 202)
+0/69092	Loss: 116.348
+12800/69092	Loss: 114.825
+25600/69092	Loss: 114.921
+38400/69092	Loss: 114.994
+51200/69092	Loss: 115.013
+64000/69092	Loss: 115.435
+Training time 0:03:40.356029
+Epoch: 59 Average loss: 115.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 203)
+0/69092	Loss: 111.710
+12800/69092	Loss: 115.723
+25600/69092	Loss: 115.646
+38400/69092	Loss: 115.310
+51200/69092	Loss: 114.750
+64000/69092	Loss: 114.651
+Training time 0:03:41.163611
+Epoch: 60 Average loss: 115.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 204)
+0/69092	Loss: 114.197
+12800/69092	Loss: 115.492
+25600/69092	Loss: 115.614
+38400/69092	Loss: 115.454
+51200/69092	Loss: 114.582
+64000/69092	Loss: 115.071
+Training time 0:03:40.874169
+Epoch: 61 Average loss: 115.20
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 205)
+0/69092	Loss: 118.631
+12800/69092	Loss: 115.554
+25600/69092	Loss: 115.449
+38400/69092	Loss: 115.311
+51200/69092	Loss: 115.563
+64000/69092	Loss: 115.179
+Training time 0:03:41.192234
+Epoch: 62 Average loss: 115.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 206)
+0/69092	Loss: 115.212
+12800/69092	Loss: 115.093
+25600/69092	Loss: 115.891
+38400/69092	Loss: 114.202
+51200/69092	Loss: 114.780
+64000/69092	Loss: 115.348
+Training time 0:03:37.915514
+Epoch: 63 Average loss: 115.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 207)
+0/69092	Loss: 119.185
+12800/69092	Loss: 115.877
+25600/69092	Loss: 115.202
+38400/69092	Loss: 114.906
+51200/69092	Loss: 115.212
+64000/69092	Loss: 114.694
+Training time 0:03:39.470830
+Epoch: 64 Average loss: 115.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 208)
+0/69092	Loss: 112.690
+12800/69092	Loss: 115.238
+25600/69092	Loss: 115.583
+38400/69092	Loss: 114.951
+51200/69092	Loss: 115.899
+64000/69092	Loss: 114.965
+Training time 0:03:40.621222
+Epoch: 65 Average loss: 115.26
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 209)
+0/69092	Loss: 111.283
+12800/69092	Loss: 115.453
+25600/69092	Loss: 115.282
+38400/69092	Loss: 115.247
+51200/69092	Loss: 115.254
+64000/69092	Loss: 114.364
+Training time 0:03:39.657240
+Epoch: 66 Average loss: 115.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 210)
+0/69092	Loss: 114.298
+12800/69092	Loss: 115.087
+25600/69092	Loss: 115.529
+38400/69092	Loss: 114.790
+51200/69092	Loss: 114.523
+64000/69092	Loss: 115.252
+Training time 0:03:39.587864
+Epoch: 67 Average loss: 115.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 211)
+0/69092	Loss: 119.244
+12800/69092	Loss: 114.677
+25600/69092	Loss: 115.632
+38400/69092	Loss: 115.939
+51200/69092	Loss: 113.807
+64000/69092	Loss: 114.473
+Training time 0:03:39.266111
+Epoch: 68 Average loss: 115.01
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 212)
+0/69092	Loss: 116.596
+12800/69092	Loss: 115.531
+25600/69092	Loss: 115.348
+38400/69092	Loss: 114.819
+51200/69092	Loss: 115.305
+64000/69092	Loss: 114.165
+Training time 0:03:39.576071
+Epoch: 69 Average loss: 115.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 213)
+0/69092	Loss: 111.364
+12800/69092	Loss: 115.789
+25600/69092	Loss: 115.457
+38400/69092	Loss: 114.520
+51200/69092	Loss: 114.835
+64000/69092	Loss: 114.175
+Training time 0:03:40.669528
+Epoch: 70 Average loss: 114.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 214)
+0/69092	Loss: 109.991
+12800/69092	Loss: 114.868
+25600/69092	Loss: 114.949
+38400/69092	Loss: 114.404
+51200/69092	Loss: 115.914
+64000/69092	Loss: 114.747
+Training time 0:03:39.913414
+Epoch: 71 Average loss: 115.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 215)
+0/69092	Loss: 115.842
+12800/69092	Loss: 114.590
+25600/69092	Loss: 115.731
+38400/69092	Loss: 114.622
+51200/69092	Loss: 115.623
+64000/69092	Loss: 114.875
+Training time 0:03:40.583036
+Epoch: 72 Average loss: 114.96
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 216)
+0/69092	Loss: 110.377
+12800/69092	Loss: 115.047
+25600/69092	Loss: 115.273
+38400/69092	Loss: 115.216
+51200/69092	Loss: 114.854
+64000/69092	Loss: 114.817
+Training time 0:03:40.331125
+Epoch: 73 Average loss: 115.01
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 217)
+0/69092	Loss: 106.582
+12800/69092	Loss: 115.858
+25600/69092	Loss: 114.577
+38400/69092	Loss: 115.438
+51200/69092	Loss: 114.821
+64000/69092	Loss: 114.224
+Training time 0:03:39.740569
+Epoch: 74 Average loss: 114.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 218)
+0/69092	Loss: 124.641
+12800/69092	Loss: 113.975
+25600/69092	Loss: 115.571
+38400/69092	Loss: 114.576
+51200/69092	Loss: 114.249
+64000/69092	Loss: 115.583
+Training time 0:03:39.557484
+Epoch: 75 Average loss: 114.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 219)
+0/69092	Loss: 115.628
+12800/69092	Loss: 114.197
+25600/69092	Loss: 114.989
+38400/69092	Loss: 115.503
+51200/69092	Loss: 114.342
+64000/69092	Loss: 114.788
+Training time 0:03:39.911937
+Epoch: 76 Average loss: 114.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 220)
+0/69092	Loss: 118.019
+12800/69092	Loss: 115.567
+25600/69092	Loss: 115.781
+38400/69092	Loss: 114.912
+51200/69092	Loss: 115.020
+64000/69092	Loss: 113.646
+Training time 0:03:39.957911
+Epoch: 77 Average loss: 114.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 221)
+0/69092	Loss: 109.805
+12800/69092	Loss: 114.832
+25600/69092	Loss: 114.996
+38400/69092	Loss: 114.294
+51200/69092	Loss: 114.873
+64000/69092	Loss: 114.860
+Training time 0:03:39.574652
+Epoch: 78 Average loss: 114.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 222)
+0/69092	Loss: 114.975
+12800/69092	Loss: 114.704
+25600/69092	Loss: 114.546
+38400/69092	Loss: 114.155
+51200/69092	Loss: 115.018
+64000/69092	Loss: 115.606
+Training time 0:03:40.653183
+Epoch: 79 Average loss: 114.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 223)
+0/69092	Loss: 118.612
+12800/69092	Loss: 115.064
+25600/69092	Loss: 113.899
+38400/69092	Loss: 114.684
+51200/69092	Loss: 115.660
+64000/69092	Loss: 114.569
+Training time 0:03:39.065048
+Epoch: 80 Average loss: 114.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 224)
+0/69092	Loss: 109.031
+12800/69092	Loss: 114.216
+25600/69092	Loss: 114.796
+38400/69092	Loss: 114.840
+51200/69092	Loss: 114.877
+64000/69092	Loss: 114.433
+Training time 0:03:39.553370
+Epoch: 81 Average loss: 114.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 225)
+0/69092	Loss: 111.758
+12800/69092	Loss: 115.345
+25600/69092	Loss: 114.349
+38400/69092	Loss: 114.239
+51200/69092	Loss: 115.207
+64000/69092	Loss: 115.440
+Training time 0:03:39.635807
+Epoch: 82 Average loss: 114.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 226)
+0/69092	Loss: 113.457
+12800/69092	Loss: 115.237
+25600/69092	Loss: 114.856
+38400/69092	Loss: 115.088
+51200/69092	Loss: 114.461
+64000/69092	Loss: 114.783
+Training time 0:03:39.479383
+Epoch: 83 Average loss: 114.88
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 227)
+0/69092	Loss: 118.418
+12800/69092	Loss: 115.666
+25600/69092	Loss: 114.994
+38400/69092	Loss: 114.782
+51200/69092	Loss: 114.864
+64000/69092	Loss: 113.881
+Training time 0:03:39.779647
+Epoch: 84 Average loss: 114.88
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 228)
+0/69092	Loss: 118.901
+12800/69092	Loss: 115.459
+25600/69092	Loss: 113.995
+38400/69092	Loss: 114.473
+51200/69092	Loss: 115.358
+64000/69092	Loss: 115.174
+Training time 0:03:39.825373
+Epoch: 85 Average loss: 114.89
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 229)
+0/69092	Loss: 116.887
+12800/69092	Loss: 115.089
+25600/69092	Loss: 114.870
+38400/69092	Loss: 114.173
+51200/69092	Loss: 114.921
+64000/69092	Loss: 114.568
+Training time 0:03:40.558272
+Epoch: 86 Average loss: 114.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 230)
+0/69092	Loss: 119.022
+12800/69092	Loss: 114.155
+25600/69092	Loss: 115.116
+38400/69092	Loss: 114.351
+51200/69092	Loss: 115.333
+64000/69092	Loss: 114.804
+Training time 0:03:39.110950
+Epoch: 87 Average loss: 114.76
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 231)
+0/69092	Loss: 114.052
+12800/69092	Loss: 114.419
+25600/69092	Loss: 115.095
+38400/69092	Loss: 114.998
+51200/69092	Loss: 113.998
+64000/69092	Loss: 114.826
+Training time 0:03:38.998349
+Epoch: 88 Average loss: 114.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 232)
+0/69092	Loss: 107.197
+12800/69092	Loss: 114.560
+25600/69092	Loss: 115.108
+38400/69092	Loss: 114.551
+51200/69092	Loss: 114.927
+64000/69092	Loss: 114.435
+Training time 0:03:42.260425
+Epoch: 89 Average loss: 114.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 233)
+0/69092	Loss: 107.413
+12800/69092	Loss: 114.444
+25600/69092	Loss: 114.685
+38400/69092	Loss: 114.867
+51200/69092	Loss: 115.129
+64000/69092	Loss: 113.986
+Training time 0:03:50.393656
+Epoch: 90 Average loss: 114.63
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 234)
+0/69092	Loss: 112.908
+12800/69092	Loss: 115.659
+25600/69092	Loss: 114.256
+38400/69092	Loss: 114.956
+51200/69092	Loss: 113.938
+64000/69092	Loss: 115.278
+Training time 0:03:41.978122
+Epoch: 91 Average loss: 114.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 235)
+0/69092	Loss: 113.401
+12800/69092	Loss: 114.613
+25600/69092	Loss: 115.830
+38400/69092	Loss: 114.113
+51200/69092	Loss: 114.794
+64000/69092	Loss: 114.232
+Training time 0:03:44.706303
+Epoch: 92 Average loss: 114.66
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 236)
+0/69092	Loss: 118.505
+12800/69092	Loss: 114.510
+25600/69092	Loss: 114.844
+38400/69092	Loss: 114.507
+51200/69092	Loss: 114.165
+64000/69092	Loss: 114.310
+Training time 0:03:42.290089
+Epoch: 93 Average loss: 114.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 237)
+0/69092	Loss: 111.298
+12800/69092	Loss: 115.110
+25600/69092	Loss: 114.177
+38400/69092	Loss: 114.547
+51200/69092	Loss: 114.346
+64000/69092	Loss: 114.125
+Training time 0:03:40.739105
+Epoch: 94 Average loss: 114.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 238)
+0/69092	Loss: 115.497
+12800/69092	Loss: 114.533
+25600/69092	Loss: 113.951
+38400/69092	Loss: 114.258
+51200/69092	Loss: 115.078
+64000/69092	Loss: 114.435
+Training time 0:03:40.520623
+Epoch: 95 Average loss: 114.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 239)
+0/69092	Loss: 120.449
+12800/69092	Loss: 114.306
+25600/69092	Loss: 114.458
+38400/69092	Loss: 113.983
+51200/69092	Loss: 114.979
+64000/69092	Loss: 115.746
+Training time 0:03:40.739195
+Epoch: 96 Average loss: 114.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 240)
+0/69092	Loss: 116.587
+12800/69092	Loss: 114.370
+25600/69092	Loss: 115.026
+38400/69092	Loss: 114.646
+51200/69092	Loss: 114.849
+64000/69092	Loss: 114.088
+Training time 0:03:40.295166
+Epoch: 97 Average loss: 114.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 241)
+0/69092	Loss: 113.646
+12800/69092	Loss: 114.517
+25600/69092	Loss: 114.350
+38400/69092	Loss: 114.589
+51200/69092	Loss: 114.638
+64000/69092	Loss: 114.851
+Training time 0:03:39.879273
+Epoch: 98 Average loss: 114.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 242)
+0/69092	Loss: 108.064
+12800/69092	Loss: 114.233
+25600/69092	Loss: 114.721
+38400/69092	Loss: 114.504
+51200/69092	Loss: 114.622
+64000/69092	Loss: 114.088
+Training time 0:03:46.401228
+Epoch: 99 Average loss: 114.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 243)
+0/69092	Loss: 112.234
+12800/69092	Loss: 115.063
+25600/69092	Loss: 114.201
+38400/69092	Loss: 115.500
+51200/69092	Loss: 114.187
+64000/69092	Loss: 113.749
+Training time 0:03:43.651188
+Epoch: 100 Average loss: 114.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 244)
+0/69092	Loss: 108.880
+12800/69092	Loss: 114.441
+25600/69092	Loss: 114.546
+38400/69092	Loss: 114.229
+51200/69092	Loss: 114.399
+64000/69092	Loss: 114.490
+Training time 0:03:41.775324
+Epoch: 101 Average loss: 114.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 245)
+0/69092	Loss: 116.923
+12800/69092	Loss: 114.301
+25600/69092	Loss: 114.859
+38400/69092	Loss: 114.571
+51200/69092	Loss: 114.437
+64000/69092	Loss: 114.160
+Training time 0:03:41.341339
+Epoch: 102 Average loss: 114.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 246)
+0/69092	Loss: 118.843
+12800/69092	Loss: 113.869
+25600/69092	Loss: 114.652
+38400/69092	Loss: 114.754
+51200/69092	Loss: 114.434
+64000/69092	Loss: 114.532
+Training time 0:03:40.704962
+Epoch: 103 Average loss: 114.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 247)
+0/69092	Loss: 108.865
+12800/69092	Loss: 114.752
+25600/69092	Loss: 114.883
+38400/69092	Loss: 113.607
+51200/69092	Loss: 114.288
+64000/69092	Loss: 114.525
+Training time 0:03:42.934816
+Epoch: 104 Average loss: 114.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 248)
+0/69092	Loss: 119.276
+12800/69092	Loss: 113.929
+25600/69092	Loss: 114.603
+38400/69092	Loss: 114.846
+51200/69092	Loss: 114.323
+64000/69092	Loss: 115.003
+Training time 0:03:41.347376
+Epoch: 105 Average loss: 114.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 249)
+0/69092	Loss: 117.378
+12800/69092	Loss: 114.512
+25600/69092	Loss: 114.029
+38400/69092	Loss: 113.996
+51200/69092	Loss: 114.640
+64000/69092	Loss: 114.273
+Training time 0:03:41.384372
+Epoch: 106 Average loss: 114.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 250)
+0/69092	Loss: 116.733
+12800/69092	Loss: 114.053
+25600/69092	Loss: 115.784
+38400/69092	Loss: 114.504
+51200/69092	Loss: 113.391
+64000/69092	Loss: 115.021
+Training time 0:03:43.748070
+Epoch: 107 Average loss: 114.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 251)
+0/69092	Loss: 109.071
+12800/69092	Loss: 115.350
+25600/69092	Loss: 114.421
+38400/69092	Loss: 113.817
+51200/69092	Loss: 114.571
+64000/69092	Loss: 113.952
+Training time 0:03:42.178234
+Epoch: 108 Average loss: 114.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 252)
+0/69092	Loss: 110.194
+12800/69092	Loss: 113.623
+25600/69092	Loss: 114.909
+38400/69092	Loss: 114.726
+51200/69092	Loss: 114.983
+64000/69092	Loss: 114.253
+Training time 0:03:42.143474
+Epoch: 109 Average loss: 114.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 253)
+0/69092	Loss: 112.906
+12800/69092	Loss: 113.083
+25600/69092	Loss: 114.752
+38400/69092	Loss: 114.171
+51200/69092	Loss: 114.487
+64000/69092	Loss: 114.980
+Training time 0:03:41.290923
+Epoch: 110 Average loss: 114.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 254)
+0/69092	Loss: 124.385
+12800/69092	Loss: 113.826
+25600/69092	Loss: 114.997
+38400/69092	Loss: 114.641
+51200/69092	Loss: 114.734
+64000/69092	Loss: 113.799
+Training time 0:03:41.705886
+Epoch: 111 Average loss: 114.40
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 255)
+0/69092	Loss: 111.269
+12800/69092	Loss: 113.420
+25600/69092	Loss: 115.922
+38400/69092	Loss: 113.670
+51200/69092	Loss: 113.914
+64000/69092	Loss: 114.474
+Training time 0:03:40.924247
+Epoch: 112 Average loss: 114.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 256)
+0/69092	Loss: 115.978
+12800/69092	Loss: 115.311
+25600/69092	Loss: 114.722
+38400/69092	Loss: 113.684
+51200/69092	Loss: 113.768
+64000/69092	Loss: 113.957
+Training time 0:03:40.834863
+Epoch: 113 Average loss: 114.26
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 257)
+0/69092	Loss: 109.364
+12800/69092	Loss: 114.242
+25600/69092	Loss: 114.552
+38400/69092	Loss: 114.000
+51200/69092	Loss: 114.802
+64000/69092	Loss: 113.713
+Training time 0:03:43.156358
+Epoch: 114 Average loss: 114.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 258)
+0/69092	Loss: 111.265
+12800/69092	Loss: 114.377
+25600/69092	Loss: 114.272
+38400/69092	Loss: 113.863
+51200/69092	Loss: 114.741
+64000/69092	Loss: 114.591
+Training time 0:03:41.998148
+Epoch: 115 Average loss: 114.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 259)
+0/69092	Loss: 117.682
+12800/69092	Loss: 115.080
+25600/69092	Loss: 113.807
+38400/69092	Loss: 113.662
+51200/69092	Loss: 115.175
+64000/69092	Loss: 114.527
+Training time 0:03:42.627598
+Epoch: 116 Average loss: 114.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 260)
+0/69092	Loss: 113.881
+12800/69092	Loss: 114.402
+25600/69092	Loss: 114.172
+38400/69092	Loss: 114.510
+51200/69092	Loss: 114.053
+64000/69092	Loss: 114.174
+Training time 0:03:42.035927
+Epoch: 117 Average loss: 114.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 261)
+0/69092	Loss: 113.673
+12800/69092	Loss: 114.281
+25600/69092	Loss: 114.134
+38400/69092	Loss: 114.204
+51200/69092	Loss: 114.304
+64000/69092	Loss: 114.797
+Training time 0:03:41.551131
+Epoch: 118 Average loss: 114.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 262)
+0/69092	Loss: 111.412
+12800/69092	Loss: 113.519
+25600/69092	Loss: 114.681
+38400/69092	Loss: 114.317
+51200/69092	Loss: 113.760
+64000/69092	Loss: 113.961
+Training time 0:03:44.184585
+Epoch: 119 Average loss: 114.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 263)
+0/69092	Loss: 111.933
+12800/69092	Loss: 114.447
+25600/69092	Loss: 113.720
+38400/69092	Loss: 113.964
+51200/69092	Loss: 113.929
+64000/69092	Loss: 114.812
+Training time 0:03:42.196323
+Epoch: 120 Average loss: 114.26
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 264)
+0/69092	Loss: 113.074
+12800/69092	Loss: 114.431
+25600/69092	Loss: 113.384
+38400/69092	Loss: 115.006
+51200/69092	Loss: 114.743
+64000/69092	Loss: 113.431
+Training time 0:03:42.197650
+Epoch: 121 Average loss: 114.17
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 265)
+0/69092	Loss: 115.437
+12800/69092	Loss: 114.323
+25600/69092	Loss: 113.790
+38400/69092	Loss: 114.352
+51200/69092	Loss: 114.947
+64000/69092	Loss: 114.044
+Training time 0:03:41.562076
+Epoch: 122 Average loss: 114.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 266)
+0/69092	Loss: 113.162
+12800/69092	Loss: 113.852
+25600/69092	Loss: 113.980
+38400/69092	Loss: 114.593
+51200/69092	Loss: 113.447
+64000/69092	Loss: 115.204
+Training time 0:03:40.874992
+Epoch: 123 Average loss: 114.20
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 267)
+0/69092	Loss: 116.120
+12800/69092	Loss: 114.274
+25600/69092	Loss: 114.634
+38400/69092	Loss: 114.158
+51200/69092	Loss: 114.676
+64000/69092	Loss: 112.900
+Training time 0:03:41.653124
+Epoch: 124 Average loss: 114.17
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 268)
+0/69092	Loss: 117.180
+12800/69092	Loss: 113.294
+25600/69092	Loss: 114.609
+38400/69092	Loss: 114.933
+51200/69092	Loss: 114.103
+64000/69092	Loss: 113.871
+Training time 0:03:41.242283
+Epoch: 125 Average loss: 114.11
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 269)
+0/69092	Loss: 109.756
+12800/69092	Loss: 114.033
+25600/69092	Loss: 114.634
+38400/69092	Loss: 114.371
+51200/69092	Loss: 114.184
+64000/69092	Loss: 113.549
+Training time 0:03:42.196836
+Epoch: 126 Average loss: 114.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 270)
+0/69092	Loss: 111.929
+12800/69092	Loss: 114.828
+25600/69092	Loss: 113.790
+38400/69092	Loss: 114.042
+51200/69092	Loss: 114.953
+64000/69092	Loss: 113.364
+Training time 0:03:41.900956
+Epoch: 127 Average loss: 114.20
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 271)
+0/69092	Loss: 110.864
+12800/69092	Loss: 114.467
+25600/69092	Loss: 114.406
+38400/69092	Loss: 113.131
+51200/69092	Loss: 114.844
+64000/69092	Loss: 114.251
+Training time 0:03:43.068656
+Epoch: 128 Average loss: 114.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 272)
+0/69092	Loss: 112.706
+12800/69092	Loss: 113.940
+25600/69092	Loss: 113.914
+38400/69092	Loss: 115.086
+51200/69092	Loss: 113.297
+64000/69092	Loss: 114.017
+Training time 0:03:42.137844
+Epoch: 129 Average loss: 114.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 273)
+0/69092	Loss: 114.385
+12800/69092	Loss: 113.581
+25600/69092	Loss: 113.566
+38400/69092	Loss: 114.429
+51200/69092	Loss: 114.653
+64000/69092	Loss: 113.894
+Training time 0:03:41.510301
+Epoch: 130 Average loss: 114.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 274)
+0/69092	Loss: 110.028
+12800/69092	Loss: 113.881
+25600/69092	Loss: 114.392
+38400/69092	Loss: 113.452
+51200/69092	Loss: 114.865
+64000/69092	Loss: 114.058
+Training time 0:03:41.511612
+Epoch: 131 Average loss: 114.13
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 275)
+0/69092	Loss: 110.665
+12800/69092	Loss: 114.053
+25600/69092	Loss: 114.680
+38400/69092	Loss: 113.985
+51200/69092	Loss: 113.684
+64000/69092	Loss: 114.159
+Training time 0:03:41.726226
+Epoch: 132 Average loss: 114.13
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 276)
+0/69092	Loss: 118.540
+12800/69092	Loss: 113.363
+25600/69092	Loss: 113.460
+38400/69092	Loss: 114.502
+51200/69092	Loss: 113.836
+64000/69092	Loss: 114.899
+Training time 0:03:41.569514
+Epoch: 133 Average loss: 114.02
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 277)
+0/69092	Loss: 117.445
+12800/69092	Loss: 114.008
+25600/69092	Loss: 114.618
+38400/69092	Loss: 114.031
+51200/69092	Loss: 113.772
+64000/69092	Loss: 113.839
+Training time 0:03:41.186443
+Epoch: 134 Average loss: 114.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 278)
+0/69092	Loss: 113.595
+12800/69092	Loss: 113.980
+25600/69092	Loss: 113.805
+38400/69092	Loss: 114.087
+51200/69092	Loss: 113.955
+64000/69092	Loss: 113.742
+Training time 0:03:42.152164
+Epoch: 135 Average loss: 113.96
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 279)
+0/69092	Loss: 112.821
+12800/69092	Loss: 114.496
+25600/69092	Loss: 113.962
+38400/69092	Loss: 114.649
+51200/69092	Loss: 113.409
+64000/69092	Loss: 113.551
+Training time 0:03:41.361916
+Epoch: 136 Average loss: 114.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 280)
+0/69092	Loss: 115.241
+12800/69092	Loss: 113.896
+25600/69092	Loss: 114.136
+38400/69092	Loss: 112.995
+51200/69092	Loss: 113.532
+64000/69092	Loss: 114.980
+Training time 0:03:40.680527
+Epoch: 137 Average loss: 113.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 281)
+0/69092	Loss: 117.064
+12800/69092	Loss: 113.764
+25600/69092	Loss: 114.953
+38400/69092	Loss: 113.620
+51200/69092	Loss: 113.511
+64000/69092	Loss: 114.963
+Training time 0:03:41.352191
+Epoch: 138 Average loss: 114.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 282)
+0/69092	Loss: 108.923
+12800/69092	Loss: 114.116
+25600/69092	Loss: 114.152
+38400/69092	Loss: 113.833
+51200/69092	Loss: 114.022
+64000/69092	Loss: 113.697
+Training time 0:03:40.793996
+Epoch: 139 Average loss: 113.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 283)
+0/69092	Loss: 115.468
+12800/69092	Loss: 114.883
+25600/69092	Loss: 113.313
+38400/69092	Loss: 114.564
+51200/69092	Loss: 114.127
+64000/69092	Loss: 113.590
+Training time 0:03:41.558375
+Epoch: 140 Average loss: 114.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 284)
+0/69092	Loss: 111.618
+12800/69092	Loss: 114.210
+25600/69092	Loss: 113.877
+38400/69092	Loss: 113.493
+51200/69092	Loss: 113.761
+64000/69092	Loss: 113.488
+Training time 0:03:40.687252
+Epoch: 141 Average loss: 113.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 285)
+0/69092	Loss: 115.856
+12800/69092	Loss: 113.995
+25600/69092	Loss: 113.398
+38400/69092	Loss: 114.115
+51200/69092	Loss: 115.273
+64000/69092	Loss: 113.940
+Training time 0:03:41.465886
+Epoch: 142 Average loss: 114.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 286)
+0/69092	Loss: 109.752
+12800/69092	Loss: 114.166
+25600/69092	Loss: 113.758
+38400/69092	Loss: 114.394
+51200/69092	Loss: 113.661
+64000/69092	Loss: 114.022
+Training time 0:03:40.046346
+Epoch: 143 Average loss: 113.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 287)
+0/69092	Loss: 113.853
+12800/69092	Loss: 114.157
+25600/69092	Loss: 113.092
+38400/69092	Loss: 113.822
+51200/69092	Loss: 113.737
+64000/69092	Loss: 113.995
+Training time 0:03:40.139976
+Epoch: 144 Average loss: 113.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 288)
+0/69092	Loss: 113.345
+12800/69092	Loss: 113.668
+25600/69092	Loss: 113.303
+38400/69092	Loss: 113.832
+51200/69092	Loss: 113.686
+64000/69092	Loss: 114.217
+Training time 0:03:40.291585
+Epoch: 145 Average loss: 113.76
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 289)
+0/69092	Loss: 110.422
+12800/69092	Loss: 113.919
+25600/69092	Loss: 114.204
+38400/69092	Loss: 114.164
+51200/69092	Loss: 113.983
+64000/69092	Loss: 113.053
+Training time 0:03:40.165304
+Epoch: 146 Average loss: 113.88
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 290)
+0/69092	Loss: 111.442
+12800/69092	Loss: 113.895
+25600/69092	Loss: 113.671
+38400/69092	Loss: 113.587
+51200/69092	Loss: 113.714
+64000/69092	Loss: 113.411
+Training time 0:03:40.558246
+Epoch: 147 Average loss: 113.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 291)
+0/69092	Loss: 114.554
+12800/69092	Loss: 113.458
+25600/69092	Loss: 113.886
+38400/69092	Loss: 114.594
+51200/69092	Loss: 113.528
+64000/69092	Loss: 113.846
+Training time 0:03:40.836498
+Epoch: 148 Average loss: 113.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 292)
+0/69092	Loss: 115.352
+12800/69092	Loss: 114.107
+25600/69092	Loss: 114.243
+38400/69092	Loss: 113.824
+51200/69092	Loss: 113.277
+64000/69092	Loss: 113.832
+Training time 0:03:41.716480
+Epoch: 149 Average loss: 113.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 293)
+0/69092	Loss: 110.963
+12800/69092	Loss: 113.638
+25600/69092	Loss: 113.369
+38400/69092	Loss: 114.120
+51200/69092	Loss: 113.222
+64000/69092	Loss: 114.146
+Training time 0:03:40.436857
+Epoch: 150 Average loss: 113.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 294)
+0/69092	Loss: 107.337
+12800/69092	Loss: 114.409
+25600/69092	Loss: 113.354
+38400/69092	Loss: 113.109
+51200/69092	Loss: 115.007
+64000/69092	Loss: 113.664
+Training time 0:03:40.938804
+Epoch: 151 Average loss: 113.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 295)
+0/69092	Loss: 109.025
+12800/69092	Loss: 114.129
+25600/69092	Loss: 113.999
+38400/69092	Loss: 112.536
+51200/69092	Loss: 114.157
+64000/69092	Loss: 113.699
+Training time 0:03:40.882615
+Epoch: 152 Average loss: 113.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 296)
+0/69092	Loss: 113.340
+12800/69092	Loss: 114.084
+25600/69092	Loss: 113.610
+38400/69092	Loss: 113.778
+51200/69092	Loss: 113.365
+64000/69092	Loss: 114.084
+Training time 0:03:41.200797
+Epoch: 153 Average loss: 113.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 297)
+0/69092	Loss: 107.227
+12800/69092	Loss: 113.711
+25600/69092	Loss: 113.918
+38400/69092	Loss: 113.109
+51200/69092	Loss: 114.343
+64000/69092	Loss: 114.783
+Training time 0:03:40.808502
+Epoch: 154 Average loss: 113.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 298)
+0/69092	Loss: 118.423
+12800/69092	Loss: 113.903
+25600/69092	Loss: 114.294
+38400/69092	Loss: 113.249
+51200/69092	Loss: 113.512
+64000/69092	Loss: 113.888
+Training time 0:03:40.464494
+Epoch: 155 Average loss: 113.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 299)
+0/69092	Loss: 110.552
+12800/69092	Loss: 114.813
+25600/69092	Loss: 113.985
+38400/69092	Loss: 113.234
+51200/69092	Loss: 113.633
+64000/69092	Loss: 112.940
+Training time 0:03:41.316922
+Epoch: 156 Average loss: 113.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 300)
+0/69092	Loss: 107.383
+12800/69092	Loss: 114.037
+25600/69092	Loss: 113.720
+38400/69092	Loss: 114.365
+51200/69092	Loss: 114.352
+64000/69092	Loss: 113.392
+Training time 0:03:40.482092
+Epoch: 157 Average loss: 113.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 301)
+0/69092	Loss: 117.259
+12800/69092	Loss: 113.602
+25600/69092	Loss: 114.473
+38400/69092	Loss: 114.025
+51200/69092	Loss: 113.492
+64000/69092	Loss: 113.360
+Training time 0:03:41.407732
+Epoch: 158 Average loss: 113.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 302)
+0/69092	Loss: 112.966
+12800/69092	Loss: 115.213
+25600/69092	Loss: 113.350
+38400/69092	Loss: 113.020
+51200/69092	Loss: 113.828
+64000/69092	Loss: 112.916
+Training time 0:03:40.707541
+Epoch: 159 Average loss: 113.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 303)
+0/69092	Loss: 112.165
+12800/69092	Loss: 113.725
+25600/69092	Loss: 113.370
+38400/69092	Loss: 113.152
+51200/69092	Loss: 114.303
+64000/69092	Loss: 113.987
+Training time 0:03:40.781130
+Epoch: 160 Average loss: 113.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 304)
+0/69092	Loss: 118.550
+12800/69092	Loss: 113.662
+25600/69092	Loss: 113.729
+38400/69092	Loss: 112.721
+51200/69092	Loss: 114.058
+64000/69092	Loss: 113.786
+Training time 0:03:40.647435
+Epoch: 161 Average loss: 113.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 305)
+0/69092	Loss: 110.329
+12800/69092	Loss: 113.751
+25600/69092	Loss: 113.522
+38400/69092	Loss: 113.958
+51200/69092	Loss: 113.470
+64000/69092	Loss: 114.256
+Training time 0:03:40.584303
+Epoch: 162 Average loss: 113.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 306)
+0/69092	Loss: 115.073
+12800/69092	Loss: 113.487
+25600/69092	Loss: 112.933
+38400/69092	Loss: 114.015
+51200/69092	Loss: 113.915
diff --git a/OAR.2068287.stderr b/OAR.2068287.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..8fc598446edda5fd81a9f933f553b623353c5554
--- /dev/null
+++ b/OAR.2068287.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-25 02:59:26] Job 2068287 KILLED ##
diff --git a/OAR.2068287.stdout b/OAR.2068287.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..6505e10a528e0204a913473c8a8fa59ed465bd91
--- /dev/null
+++ b/OAR.2068287.stdout
@@ -0,0 +1,7690 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=True, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+Tesla K80
+Tesla K80
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last (iter 141)'
+0/69092	Loss: 125.079
+3200/69092	Loss: 115.007
+6400/69092	Loss: 112.859
+9600/69092	Loss: 111.069
+12800/69092	Loss: 112.965
+16000/69092	Loss: 115.110
+19200/69092	Loss: 114.558
+22400/69092	Loss: 112.883
+25600/69092	Loss: 115.052
+28800/69092	Loss: 113.553
+32000/69092	Loss: 113.282
+35200/69092	Loss: 113.634
+38400/69092	Loss: 115.232
+41600/69092	Loss: 114.954
+44800/69092	Loss: 114.793
+48000/69092	Loss: 113.861
+51200/69092	Loss: 115.127
+54400/69092	Loss: 114.662
+57600/69092	Loss: 113.465
+60800/69092	Loss: 115.242
+64000/69092	Loss: 113.780
+67200/69092	Loss: 115.152
+Training time 0:01:56.869585
+Epoch: 1 Average loss: 114.13
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 142)
+0/69092	Loss: 112.760
+3200/69092	Loss: 113.538
+6400/69092	Loss: 113.977
+9600/69092	Loss: 114.566
+12800/69092	Loss: 112.319
+16000/69092	Loss: 111.585
+19200/69092	Loss: 114.728
+22400/69092	Loss: 114.281
+25600/69092	Loss: 114.036
+28800/69092	Loss: 112.604
+32000/69092	Loss: 114.703
+35200/69092	Loss: 112.797
+38400/69092	Loss: 113.396
+41600/69092	Loss: 113.250
+44800/69092	Loss: 114.304
+48000/69092	Loss: 114.082
+51200/69092	Loss: 113.120
+54400/69092	Loss: 114.240
+57600/69092	Loss: 114.220
+60800/69092	Loss: 114.049
+64000/69092	Loss: 114.315
+67200/69092	Loss: 115.325
+Training time 0:01:55.978440
+Epoch: 2 Average loss: 113.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 143)
+0/69092	Loss: 104.286
+3200/69092	Loss: 113.911
+6400/69092	Loss: 113.413
+9600/69092	Loss: 114.114
+12800/69092	Loss: 114.172
+16000/69092	Loss: 112.455
+19200/69092	Loss: 113.278
+22400/69092	Loss: 112.483
+25600/69092	Loss: 114.161
+28800/69092	Loss: 113.708
+32000/69092	Loss: 113.457
+35200/69092	Loss: 114.794
+38400/69092	Loss: 115.137
+41600/69092	Loss: 113.252
+44800/69092	Loss: 113.677
+48000/69092	Loss: 114.658
+51200/69092	Loss: 114.810
+54400/69092	Loss: 114.506
+57600/69092	Loss: 114.287
+60800/69092	Loss: 112.400
+64000/69092	Loss: 113.262
+67200/69092	Loss: 113.660
+Training time 0:01:56.425483
+Epoch: 3 Average loss: 113.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 144)
+0/69092	Loss: 110.025
+3200/69092	Loss: 113.539
+6400/69092	Loss: 113.187
+9600/69092	Loss: 114.277
+12800/69092	Loss: 114.722
+16000/69092	Loss: 115.005
+19200/69092	Loss: 115.202
+22400/69092	Loss: 114.601
+25600/69092	Loss: 114.374
+28800/69092	Loss: 113.862
+32000/69092	Loss: 113.593
+35200/69092	Loss: 112.949
+38400/69092	Loss: 114.994
+41600/69092	Loss: 110.398
+44800/69092	Loss: 114.510
+48000/69092	Loss: 112.255
+51200/69092	Loss: 113.327
+54400/69092	Loss: 113.922
+57600/69092	Loss: 114.309
+60800/69092	Loss: 113.514
+64000/69092	Loss: 113.504
+67200/69092	Loss: 112.617
+Training time 0:01:57.257287
+Epoch: 4 Average loss: 113.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 145)
+0/69092	Loss: 115.686
+3200/69092	Loss: 113.514
+6400/69092	Loss: 113.764
+9600/69092	Loss: 114.790
+12800/69092	Loss: 114.248
+16000/69092	Loss: 115.185
+19200/69092	Loss: 114.162
+22400/69092	Loss: 113.379
+25600/69092	Loss: 112.616
+28800/69092	Loss: 111.764
+32000/69092	Loss: 111.180
+35200/69092	Loss: 113.116
+38400/69092	Loss: 112.823
+41600/69092	Loss: 114.039
+44800/69092	Loss: 115.470
+48000/69092	Loss: 112.761
+51200/69092	Loss: 114.183
+54400/69092	Loss: 114.356
+57600/69092	Loss: 113.520
+60800/69092	Loss: 113.811
+64000/69092	Loss: 114.090
+67200/69092	Loss: 114.403
+Training time 0:01:56.836488
+Epoch: 5 Average loss: 113.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 146)
+0/69092	Loss: 109.627
+3200/69092	Loss: 116.425
+6400/69092	Loss: 115.631
+9600/69092	Loss: 113.910
+12800/69092	Loss: 113.174
+16000/69092	Loss: 115.867
+19200/69092	Loss: 114.272
+22400/69092	Loss: 113.119
+25600/69092	Loss: 113.460
+28800/69092	Loss: 113.324
+32000/69092	Loss: 113.904
+35200/69092	Loss: 112.535
+38400/69092	Loss: 113.853
+41600/69092	Loss: 114.495
+44800/69092	Loss: 113.832
+48000/69092	Loss: 113.648
+51200/69092	Loss: 113.347
+54400/69092	Loss: 112.514
+57600/69092	Loss: 114.683
+60800/69092	Loss: 113.183
+64000/69092	Loss: 113.315
+67200/69092	Loss: 113.213
+Training time 0:01:57.404128
+Epoch: 6 Average loss: 113.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 147)
+0/69092	Loss: 109.914
+3200/69092	Loss: 114.036
+6400/69092	Loss: 114.713
+9600/69092	Loss: 113.437
+12800/69092	Loss: 114.746
+16000/69092	Loss: 111.768
+19200/69092	Loss: 114.115
+22400/69092	Loss: 114.814
+25600/69092	Loss: 115.645
+28800/69092	Loss: 112.973
+32000/69092	Loss: 113.810
+35200/69092	Loss: 115.210
+38400/69092	Loss: 113.221
+41600/69092	Loss: 114.990
+44800/69092	Loss: 114.650
+48000/69092	Loss: 113.660
+51200/69092	Loss: 112.482
+54400/69092	Loss: 113.060
+57600/69092	Loss: 113.071
+60800/69092	Loss: 113.850
+64000/69092	Loss: 112.558
+67200/69092	Loss: 112.789
+Training time 0:01:57.441482
+Epoch: 7 Average loss: 113.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 148)
+0/69092	Loss: 121.824
+3200/69092	Loss: 112.704
+6400/69092	Loss: 113.783
+9600/69092	Loss: 113.949
+12800/69092	Loss: 112.764
+16000/69092	Loss: 113.602
+19200/69092	Loss: 112.738
+22400/69092	Loss: 115.579
+25600/69092	Loss: 113.357
+28800/69092	Loss: 114.605
+32000/69092	Loss: 114.020
+35200/69092	Loss: 113.004
+38400/69092	Loss: 112.137
+41600/69092	Loss: 115.544
+44800/69092	Loss: 113.168
+48000/69092	Loss: 114.932
+51200/69092	Loss: 114.134
+54400/69092	Loss: 111.579
+57600/69092	Loss: 113.492
+60800/69092	Loss: 112.961
+64000/69092	Loss: 114.972
+67200/69092	Loss: 114.606
+Training time 0:01:58.520655
+Epoch: 8 Average loss: 113.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 149)
+0/69092	Loss: 110.463
+3200/69092	Loss: 113.137
+6400/69092	Loss: 114.297
+9600/69092	Loss: 112.593
+12800/69092	Loss: 116.417
+16000/69092	Loss: 115.145
+19200/69092	Loss: 111.854
+22400/69092	Loss: 112.939
+25600/69092	Loss: 111.441
+28800/69092	Loss: 112.811
+32000/69092	Loss: 114.812
+35200/69092	Loss: 115.321
+38400/69092	Loss: 114.408
+41600/69092	Loss: 113.814
+44800/69092	Loss: 112.967
+48000/69092	Loss: 111.880
+51200/69092	Loss: 113.830
+54400/69092	Loss: 113.349
+57600/69092	Loss: 114.363
+60800/69092	Loss: 113.460
+64000/69092	Loss: 113.752
+67200/69092	Loss: 113.491
+Training time 0:01:58.384435
+Epoch: 9 Average loss: 113.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 150)
+0/69092	Loss: 121.917
+3200/69092	Loss: 115.084
+6400/69092	Loss: 114.664
+9600/69092	Loss: 113.384
+12800/69092	Loss: 112.379
+16000/69092	Loss: 112.492
+19200/69092	Loss: 113.140
+22400/69092	Loss: 115.757
+25600/69092	Loss: 114.930
+28800/69092	Loss: 112.177
+32000/69092	Loss: 111.538
+35200/69092	Loss: 115.418
+38400/69092	Loss: 114.796
+41600/69092	Loss: 112.498
+44800/69092	Loss: 112.340
+48000/69092	Loss: 113.766
+51200/69092	Loss: 113.419
+54400/69092	Loss: 114.525
+57600/69092	Loss: 114.386
+60800/69092	Loss: 115.346
+64000/69092	Loss: 115.220
+67200/69092	Loss: 114.505
+Training time 0:01:57.951281
+Epoch: 10 Average loss: 113.89
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 151)
+0/69092	Loss: 108.863
+3200/69092	Loss: 112.518
+6400/69092	Loss: 113.433
+9600/69092	Loss: 112.106
+12800/69092	Loss: 113.909
+16000/69092	Loss: 113.413
+19200/69092	Loss: 112.693
+22400/69092	Loss: 113.943
+25600/69092	Loss: 113.133
+28800/69092	Loss: 111.835
+32000/69092	Loss: 115.210
+35200/69092	Loss: 114.977
+38400/69092	Loss: 113.770
+41600/69092	Loss: 114.100
+44800/69092	Loss: 113.332
+48000/69092	Loss: 113.625
+51200/69092	Loss: 111.323
+54400/69092	Loss: 113.913
+57600/69092	Loss: 113.490
+60800/69092	Loss: 116.104
+64000/69092	Loss: 114.764
+67200/69092	Loss: 114.384
+Training time 0:01:57.412617
+Epoch: 11 Average loss: 113.66
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 152)
+0/69092	Loss: 104.733
+3200/69092	Loss: 115.578
+6400/69092	Loss: 112.918
+9600/69092	Loss: 113.655
+12800/69092	Loss: 114.260
+16000/69092	Loss: 114.817
+19200/69092	Loss: 114.822
+22400/69092	Loss: 114.628
+25600/69092	Loss: 113.353
+28800/69092	Loss: 111.861
+32000/69092	Loss: 113.800
+35200/69092	Loss: 112.308
+38400/69092	Loss: 111.741
+41600/69092	Loss: 114.310
+44800/69092	Loss: 114.432
+48000/69092	Loss: 113.419
+51200/69092	Loss: 113.757
+54400/69092	Loss: 114.367
+57600/69092	Loss: 115.230
+60800/69092	Loss: 112.206
+64000/69092	Loss: 112.024
+67200/69092	Loss: 113.345
+Training time 0:01:57.934890
+Epoch: 12 Average loss: 113.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 153)
+0/69092	Loss: 107.076
+3200/69092	Loss: 114.470
+6400/69092	Loss: 112.783
+9600/69092	Loss: 111.472
+12800/69092	Loss: 113.910
+16000/69092	Loss: 112.457
+19200/69092	Loss: 114.655
+22400/69092	Loss: 111.367
+25600/69092	Loss: 114.837
+28800/69092	Loss: 114.223
+32000/69092	Loss: 114.428
+35200/69092	Loss: 114.466
+38400/69092	Loss: 114.307
+41600/69092	Loss: 113.122
+44800/69092	Loss: 112.442
+48000/69092	Loss: 112.000
+51200/69092	Loss: 114.755
+54400/69092	Loss: 115.889
+57600/69092	Loss: 115.738
+60800/69092	Loss: 113.443
+64000/69092	Loss: 113.503
+67200/69092	Loss: 112.557
+Training time 0:01:57.624968
+Epoch: 13 Average loss: 113.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 154)
+0/69092	Loss: 106.276
+3200/69092	Loss: 113.254
+6400/69092	Loss: 113.480
+9600/69092	Loss: 113.790
+12800/69092	Loss: 113.209
+16000/69092	Loss: 115.360
+19200/69092	Loss: 115.386
+22400/69092	Loss: 113.469
+25600/69092	Loss: 114.159
+28800/69092	Loss: 112.560
+32000/69092	Loss: 112.678
+35200/69092	Loss: 112.689
+38400/69092	Loss: 113.637
+41600/69092	Loss: 113.831
+44800/69092	Loss: 113.137
+48000/69092	Loss: 114.083
+51200/69092	Loss: 114.013
+54400/69092	Loss: 113.474
+57600/69092	Loss: 114.993
+60800/69092	Loss: 112.344
+64000/69092	Loss: 112.172
+67200/69092	Loss: 114.178
+Training time 0:01:58.010337
+Epoch: 14 Average loss: 113.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 155)
+0/69092	Loss: 108.611
+3200/69092	Loss: 111.197
+6400/69092	Loss: 112.415
+9600/69092	Loss: 114.600
+12800/69092	Loss: 114.010
+16000/69092	Loss: 112.331
+19200/69092	Loss: 115.730
+22400/69092	Loss: 114.922
+25600/69092	Loss: 113.552
+28800/69092	Loss: 114.249
+32000/69092	Loss: 113.767
+35200/69092	Loss: 112.832
+38400/69092	Loss: 113.465
+41600/69092	Loss: 113.522
+44800/69092	Loss: 111.865
+48000/69092	Loss: 113.941
+51200/69092	Loss: 114.104
+54400/69092	Loss: 113.277
+57600/69092	Loss: 114.511
+60800/69092	Loss: 114.295
+64000/69092	Loss: 114.379
+67200/69092	Loss: 115.290
+Training time 0:01:57.048396
+Epoch: 15 Average loss: 113.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 156)
+0/69092	Loss: 107.534
+3200/69092	Loss: 115.344
+6400/69092	Loss: 111.464
+9600/69092	Loss: 116.030
+12800/69092	Loss: 113.621
+16000/69092	Loss: 114.181
+19200/69092	Loss: 111.447
+22400/69092	Loss: 112.626
+25600/69092	Loss: 114.451
+28800/69092	Loss: 114.031
+32000/69092	Loss: 114.145
+35200/69092	Loss: 114.513
+38400/69092	Loss: 112.339
+41600/69092	Loss: 113.435
+44800/69092	Loss: 112.827
+48000/69092	Loss: 113.485
+51200/69092	Loss: 111.628
+54400/69092	Loss: 115.204
+57600/69092	Loss: 114.545
+60800/69092	Loss: 112.454
+64000/69092	Loss: 114.114
+67200/69092	Loss: 113.067
+Training time 0:01:57.457423
+Epoch: 16 Average loss: 113.60
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 157)
+0/69092	Loss: 116.660
+3200/69092	Loss: 114.853
+6400/69092	Loss: 112.667
+9600/69092	Loss: 112.774
+12800/69092	Loss: 114.292
+16000/69092	Loss: 114.432
+19200/69092	Loss: 112.169
+22400/69092	Loss: 113.521
+25600/69092	Loss: 113.267
+28800/69092	Loss: 113.237
+32000/69092	Loss: 114.095
+35200/69092	Loss: 113.309
+38400/69092	Loss: 113.006
+41600/69092	Loss: 114.433
+44800/69092	Loss: 112.666
+48000/69092	Loss: 113.657
+51200/69092	Loss: 113.695
+54400/69092	Loss: 112.925
+57600/69092	Loss: 114.781
+60800/69092	Loss: 112.507
+64000/69092	Loss: 113.606
+67200/69092	Loss: 113.461
+Training time 0:01:57.301919
+Epoch: 17 Average loss: 113.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 158)
+0/69092	Loss: 122.441
+3200/69092	Loss: 112.092
+6400/69092	Loss: 113.156
+9600/69092	Loss: 113.583
+12800/69092	Loss: 111.844
+16000/69092	Loss: 114.031
+19200/69092	Loss: 114.414
+22400/69092	Loss: 113.251
+25600/69092	Loss: 113.530
+28800/69092	Loss: 113.606
+32000/69092	Loss: 113.308
+35200/69092	Loss: 114.011
+38400/69092	Loss: 114.065
+41600/69092	Loss: 112.315
+44800/69092	Loss: 113.055
+48000/69092	Loss: 115.181
+51200/69092	Loss: 112.946
+54400/69092	Loss: 112.808
+57600/69092	Loss: 112.249
+60800/69092	Loss: 114.315
+64000/69092	Loss: 113.936
+67200/69092	Loss: 114.932
+Training time 0:01:57.483391
+Epoch: 18 Average loss: 113.47
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 159)
+0/69092	Loss: 122.759
+3200/69092	Loss: 113.321
+6400/69092	Loss: 113.648
+9600/69092	Loss: 112.375
+12800/69092	Loss: 112.852
+16000/69092	Loss: 112.071
+19200/69092	Loss: 113.528
+22400/69092	Loss: 114.628
+25600/69092	Loss: 112.113
+28800/69092	Loss: 113.940
+32000/69092	Loss: 113.889
+35200/69092	Loss: 113.388
+38400/69092	Loss: 112.044
+41600/69092	Loss: 115.349
+44800/69092	Loss: 115.419
+48000/69092	Loss: 115.057
+51200/69092	Loss: 111.960
+54400/69092	Loss: 113.128
+57600/69092	Loss: 114.014
+60800/69092	Loss: 114.916
+64000/69092	Loss: 112.238
+67200/69092	Loss: 114.478
+Training time 0:01:57.380616
+Epoch: 19 Average loss: 113.53
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 160)
+0/69092	Loss: 122.416
+3200/69092	Loss: 113.065
+6400/69092	Loss: 112.945
+9600/69092	Loss: 114.812
+12800/69092	Loss: 113.286
+16000/69092	Loss: 113.571
+19200/69092	Loss: 112.787
+22400/69092	Loss: 114.123
+25600/69092	Loss: 111.963
+28800/69092	Loss: 114.746
+32000/69092	Loss: 114.841
+35200/69092	Loss: 113.247
+38400/69092	Loss: 114.276
+41600/69092	Loss: 113.018
+44800/69092	Loss: 113.369
+48000/69092	Loss: 115.743
+51200/69092	Loss: 112.478
+54400/69092	Loss: 114.102
+57600/69092	Loss: 111.813
+60800/69092	Loss: 114.739
+64000/69092	Loss: 114.274
+67200/69092	Loss: 111.381
+Training time 0:01:56.380362
+Epoch: 20 Average loss: 113.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 161)
+0/69092	Loss: 112.697
+3200/69092	Loss: 111.970
+6400/69092	Loss: 113.422
+9600/69092	Loss: 114.740
+12800/69092	Loss: 112.861
+16000/69092	Loss: 113.943
+19200/69092	Loss: 114.139
+22400/69092	Loss: 114.140
+25600/69092	Loss: 112.929
+28800/69092	Loss: 113.136
+32000/69092	Loss: 113.414
+35200/69092	Loss: 112.107
+38400/69092	Loss: 113.728
+41600/69092	Loss: 115.102
+44800/69092	Loss: 113.559
+48000/69092	Loss: 112.958
+51200/69092	Loss: 114.589
+54400/69092	Loss: 113.106
+57600/69092	Loss: 113.833
+60800/69092	Loss: 113.401
+64000/69092	Loss: 113.879
+67200/69092	Loss: 113.091
+Training time 0:01:56.913742
+Epoch: 21 Average loss: 113.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 162)
+0/69092	Loss: 112.838
+3200/69092	Loss: 112.310
+6400/69092	Loss: 113.759
+9600/69092	Loss: 112.544
+12800/69092	Loss: 112.362
+16000/69092	Loss: 112.802
+19200/69092	Loss: 112.718
+22400/69092	Loss: 113.442
+25600/69092	Loss: 112.484
+28800/69092	Loss: 115.515
+32000/69092	Loss: 114.749
+35200/69092	Loss: 113.935
+38400/69092	Loss: 113.959
+41600/69092	Loss: 114.361
+44800/69092	Loss: 113.340
+48000/69092	Loss: 114.839
+51200/69092	Loss: 112.398
+54400/69092	Loss: 111.141
+57600/69092	Loss: 113.406
+60800/69092	Loss: 114.487
+64000/69092	Loss: 112.059
+67200/69092	Loss: 113.190
+Training time 0:01:56.616397
+Epoch: 22 Average loss: 113.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 163)
+0/69092	Loss: 112.265
+3200/69092	Loss: 113.519
+6400/69092	Loss: 114.359
+9600/69092	Loss: 114.245
+12800/69092	Loss: 113.577
+16000/69092	Loss: 110.586
+19200/69092	Loss: 114.820
+22400/69092	Loss: 113.391
+25600/69092	Loss: 113.191
+28800/69092	Loss: 113.830
+32000/69092	Loss: 115.596
+35200/69092	Loss: 111.519
+38400/69092	Loss: 113.771
+41600/69092	Loss: 112.730
+44800/69092	Loss: 113.291
+48000/69092	Loss: 113.354
+51200/69092	Loss: 113.362
+54400/69092	Loss: 114.491
+57600/69092	Loss: 112.943
+60800/69092	Loss: 115.163
+64000/69092	Loss: 114.269
+67200/69092	Loss: 111.660
+Training time 0:01:57.499579
+Epoch: 23 Average loss: 113.46
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 164)
+0/69092	Loss: 110.541
+3200/69092	Loss: 113.010
+6400/69092	Loss: 112.319
+9600/69092	Loss: 110.574
+12800/69092	Loss: 112.917
+16000/69092	Loss: 115.502
+19200/69092	Loss: 113.512
+22400/69092	Loss: 114.476
+25600/69092	Loss: 115.393
+28800/69092	Loss: 112.487
+32000/69092	Loss: 113.305
+35200/69092	Loss: 111.957
+38400/69092	Loss: 113.179
+41600/69092	Loss: 113.447
+44800/69092	Loss: 112.649
+48000/69092	Loss: 113.721
+51200/69092	Loss: 113.584
+54400/69092	Loss: 113.247
+57600/69092	Loss: 113.686
+60800/69092	Loss: 114.066
+64000/69092	Loss: 113.360
+67200/69092	Loss: 112.081
+Training time 0:01:57.703437
+Epoch: 24 Average loss: 113.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 165)
+0/69092	Loss: 105.358
+3200/69092	Loss: 114.464
+6400/69092	Loss: 112.813
+9600/69092	Loss: 113.094
+12800/69092	Loss: 113.297
+16000/69092	Loss: 114.835
+19200/69092	Loss: 115.511
+22400/69092	Loss: 112.595
+25600/69092	Loss: 113.581
+28800/69092	Loss: 113.866
+32000/69092	Loss: 114.044
+35200/69092	Loss: 114.533
+38400/69092	Loss: 115.093
+41600/69092	Loss: 114.654
+44800/69092	Loss: 114.217
+48000/69092	Loss: 113.844
+51200/69092	Loss: 111.626
+54400/69092	Loss: 112.837
+57600/69092	Loss: 112.028
+60800/69092	Loss: 111.412
+64000/69092	Loss: 111.944
+67200/69092	Loss: 114.354
+Training time 0:01:57.128045
+Epoch: 25 Average loss: 113.53
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 166)
+0/69092	Loss: 119.216
+3200/69092	Loss: 113.760
+6400/69092	Loss: 114.056
+9600/69092	Loss: 113.799
+12800/69092	Loss: 113.218
+16000/69092	Loss: 113.157
+19200/69092	Loss: 113.767
+22400/69092	Loss: 112.843
+25600/69092	Loss: 114.296
+28800/69092	Loss: 111.594
+32000/69092	Loss: 113.287
+35200/69092	Loss: 112.529
+38400/69092	Loss: 115.394
+41600/69092	Loss: 113.424
+44800/69092	Loss: 113.577
+48000/69092	Loss: 113.916
+51200/69092	Loss: 113.749
+54400/69092	Loss: 112.013
+57600/69092	Loss: 112.941
+60800/69092	Loss: 115.017
+64000/69092	Loss: 112.619
+67200/69092	Loss: 112.699
+Training time 0:01:56.817717
+Epoch: 26 Average loss: 113.44
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 167)
+0/69092	Loss: 112.080
+3200/69092	Loss: 113.181
+6400/69092	Loss: 113.334
+9600/69092	Loss: 113.410
+12800/69092	Loss: 114.304
+16000/69092	Loss: 115.035
+19200/69092	Loss: 110.356
+22400/69092	Loss: 113.695
+25600/69092	Loss: 111.821
+28800/69092	Loss: 112.289
+32000/69092	Loss: 112.570
+35200/69092	Loss: 113.323
+38400/69092	Loss: 112.747
+41600/69092	Loss: 114.286
+44800/69092	Loss: 114.734
+48000/69092	Loss: 115.015
+51200/69092	Loss: 113.396
+54400/69092	Loss: 114.657
+57600/69092	Loss: 112.889
+60800/69092	Loss: 113.635
+64000/69092	Loss: 114.906
+67200/69092	Loss: 113.901
+Training time 0:01:56.991105
+Epoch: 27 Average loss: 113.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 168)
+0/69092	Loss: 118.096
+3200/69092	Loss: 112.825
+6400/69092	Loss: 111.038
+9600/69092	Loss: 113.668
+12800/69092	Loss: 112.929
+16000/69092	Loss: 116.022
+19200/69092	Loss: 112.779
+22400/69092	Loss: 113.887
+25600/69092	Loss: 112.522
+28800/69092	Loss: 113.807
+32000/69092	Loss: 113.408
+35200/69092	Loss: 111.301
+38400/69092	Loss: 113.108
+41600/69092	Loss: 115.450
+44800/69092	Loss: 114.732
+48000/69092	Loss: 112.078
+51200/69092	Loss: 111.123
+54400/69092	Loss: 113.296
+57600/69092	Loss: 113.732
+60800/69092	Loss: 115.135
+64000/69092	Loss: 112.423
+67200/69092	Loss: 113.397
+Training time 0:01:57.699817
+Epoch: 28 Average loss: 113.29
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 169)
+0/69092	Loss: 129.896
+3200/69092	Loss: 111.535
+6400/69092	Loss: 113.759
+9600/69092	Loss: 112.517
+12800/69092	Loss: 113.311
+16000/69092	Loss: 113.559
+19200/69092	Loss: 113.697
+22400/69092	Loss: 114.203
+25600/69092	Loss: 112.384
+28800/69092	Loss: 111.674
+32000/69092	Loss: 114.226
+35200/69092	Loss: 111.903
+38400/69092	Loss: 111.676
+41600/69092	Loss: 114.267
+44800/69092	Loss: 114.515
+48000/69092	Loss: 114.730
+51200/69092	Loss: 113.357
+54400/69092	Loss: 113.161
+57600/69092	Loss: 113.563
+60800/69092	Loss: 113.460
+64000/69092	Loss: 112.999
+67200/69092	Loss: 113.924
+Training time 0:01:57.600318
+Epoch: 29 Average loss: 113.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 170)
+0/69092	Loss: 116.106
+3200/69092	Loss: 113.221
+6400/69092	Loss: 114.010
+9600/69092	Loss: 112.702
+12800/69092	Loss: 113.932
+16000/69092	Loss: 112.952
+19200/69092	Loss: 113.270
+22400/69092	Loss: 111.887
+25600/69092	Loss: 113.317
+28800/69092	Loss: 115.396
+32000/69092	Loss: 113.389
+35200/69092	Loss: 114.309
+38400/69092	Loss: 115.338
+41600/69092	Loss: 115.297
+44800/69092	Loss: 111.782
+48000/69092	Loss: 111.820
+51200/69092	Loss: 113.843
+54400/69092	Loss: 113.087
+57600/69092	Loss: 112.162
+60800/69092	Loss: 113.825
+64000/69092	Loss: 111.508
+67200/69092	Loss: 111.641
+Training time 0:01:57.824421
+Epoch: 30 Average loss: 113.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 171)
+0/69092	Loss: 111.976
+3200/69092	Loss: 113.670
+6400/69092	Loss: 112.234
+9600/69092	Loss: 112.258
+12800/69092	Loss: 111.372
+16000/69092	Loss: 113.058
+19200/69092	Loss: 114.690
+22400/69092	Loss: 113.186
+25600/69092	Loss: 112.399
+28800/69092	Loss: 113.581
+32000/69092	Loss: 112.355
+35200/69092	Loss: 114.725
+38400/69092	Loss: 114.342
+41600/69092	Loss: 113.915
+44800/69092	Loss: 114.097
+48000/69092	Loss: 111.121
+51200/69092	Loss: 112.346
+54400/69092	Loss: 112.699
+57600/69092	Loss: 114.326
+60800/69092	Loss: 115.077
+64000/69092	Loss: 113.883
+67200/69092	Loss: 112.444
+Training time 0:01:56.239890
+Epoch: 31 Average loss: 113.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 172)
+0/69092	Loss: 107.572
+3200/69092	Loss: 112.417
+6400/69092	Loss: 113.643
+9600/69092	Loss: 112.115
+12800/69092	Loss: 113.451
+16000/69092	Loss: 114.296
+19200/69092	Loss: 112.702
+22400/69092	Loss: 112.452
+25600/69092	Loss: 111.976
+28800/69092	Loss: 113.433
+32000/69092	Loss: 114.964
+35200/69092	Loss: 114.163
+38400/69092	Loss: 114.011
+41600/69092	Loss: 113.044
+44800/69092	Loss: 112.864
+48000/69092	Loss: 113.486
+51200/69092	Loss: 114.419
+54400/69092	Loss: 111.944
+57600/69092	Loss: 113.216
+60800/69092	Loss: 112.574
+64000/69092	Loss: 113.551
+67200/69092	Loss: 112.291
+Training time 0:01:58.274545
+Epoch: 32 Average loss: 113.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 173)
+0/69092	Loss: 128.844
+3200/69092	Loss: 113.094
+6400/69092	Loss: 113.790
+9600/69092	Loss: 114.675
+12800/69092	Loss: 113.787
+16000/69092	Loss: 113.817
+19200/69092	Loss: 111.743
+22400/69092	Loss: 112.467
+25600/69092	Loss: 113.830
+28800/69092	Loss: 113.495
+32000/69092	Loss: 113.034
+35200/69092	Loss: 112.246
+38400/69092	Loss: 112.938
+41600/69092	Loss: 113.047
+44800/69092	Loss: 112.097
+48000/69092	Loss: 115.020
+51200/69092	Loss: 112.659
+54400/69092	Loss: 114.513
+57600/69092	Loss: 112.853
+60800/69092	Loss: 112.763
+64000/69092	Loss: 112.027
+67200/69092	Loss: 114.172
+Training time 0:01:57.798408
+Epoch: 33 Average loss: 113.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 174)
+0/69092	Loss: 104.629
+3200/69092	Loss: 112.173
+6400/69092	Loss: 114.126
+9600/69092	Loss: 113.989
+12800/69092	Loss: 112.428
+16000/69092	Loss: 113.403
+19200/69092	Loss: 112.374
+22400/69092	Loss: 114.004
+25600/69092	Loss: 111.297
+28800/69092	Loss: 113.265
+32000/69092	Loss: 114.151
+35200/69092	Loss: 113.076
+38400/69092	Loss: 112.095
+41600/69092	Loss: 112.621
+44800/69092	Loss: 113.332
+48000/69092	Loss: 113.742
+51200/69092	Loss: 112.438
+54400/69092	Loss: 114.087
+57600/69092	Loss: 112.684
+60800/69092	Loss: 113.479
+64000/69092	Loss: 111.662
+67200/69092	Loss: 114.712
+Training time 0:01:57.837601
+Epoch: 34 Average loss: 113.12
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 175)
+0/69092	Loss: 103.858
+3200/69092	Loss: 112.354
+6400/69092	Loss: 112.711
+9600/69092	Loss: 112.340
+12800/69092	Loss: 114.180
+16000/69092	Loss: 114.228
+19200/69092	Loss: 112.943
+22400/69092	Loss: 113.147
+25600/69092	Loss: 113.512
+28800/69092	Loss: 113.014
+32000/69092	Loss: 112.682
+35200/69092	Loss: 113.589
+38400/69092	Loss: 113.333
+41600/69092	Loss: 110.257
+44800/69092	Loss: 113.071
+48000/69092	Loss: 113.393
+51200/69092	Loss: 112.867
+54400/69092	Loss: 112.875
+57600/69092	Loss: 114.227
+60800/69092	Loss: 114.129
+64000/69092	Loss: 113.988
+67200/69092	Loss: 113.577
+Training time 0:01:57.205795
+Epoch: 35 Average loss: 113.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 176)
+0/69092	Loss: 103.413
+3200/69092	Loss: 112.778
+6400/69092	Loss: 111.808
+9600/69092	Loss: 113.004
+12800/69092	Loss: 112.850
+16000/69092	Loss: 112.508
+19200/69092	Loss: 112.487
+22400/69092	Loss: 112.307
+25600/69092	Loss: 112.598
+28800/69092	Loss: 113.347
+32000/69092	Loss: 113.953
+35200/69092	Loss: 112.537
+38400/69092	Loss: 113.105
+41600/69092	Loss: 113.944
+44800/69092	Loss: 112.627
+48000/69092	Loss: 113.469
+51200/69092	Loss: 113.354
+54400/69092	Loss: 111.951
+57600/69092	Loss: 114.612
+60800/69092	Loss: 115.151
+64000/69092	Loss: 114.095
+67200/69092	Loss: 112.999
+Training time 0:01:58.093735
+Epoch: 36 Average loss: 113.13
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 177)
+0/69092	Loss: 118.704
+3200/69092	Loss: 115.288
+6400/69092	Loss: 112.416
+9600/69092	Loss: 113.091
+12800/69092	Loss: 113.384
+16000/69092	Loss: 111.939
+19200/69092	Loss: 115.027
+22400/69092	Loss: 113.573
+25600/69092	Loss: 111.731
+28800/69092	Loss: 112.633
+32000/69092	Loss: 113.658
+35200/69092	Loss: 111.934
+38400/69092	Loss: 113.779
+41600/69092	Loss: 112.805
+44800/69092	Loss: 113.871
+48000/69092	Loss: 112.855
+51200/69092	Loss: 113.678
+54400/69092	Loss: 111.995
+57600/69092	Loss: 113.649
+60800/69092	Loss: 113.059
+64000/69092	Loss: 112.577
+67200/69092	Loss: 113.533
+Training time 0:01:57.311183
+Epoch: 37 Average loss: 113.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 178)
+0/69092	Loss: 98.776
+3200/69092	Loss: 115.477
+6400/69092	Loss: 112.687
+9600/69092	Loss: 113.104
+12800/69092	Loss: 113.899
+16000/69092	Loss: 112.074
+19200/69092	Loss: 113.378
+22400/69092	Loss: 113.883
+25600/69092	Loss: 112.427
+28800/69092	Loss: 112.084
+32000/69092	Loss: 113.294
+35200/69092	Loss: 114.481
+38400/69092	Loss: 111.956
+41600/69092	Loss: 111.803
+44800/69092	Loss: 115.324
+48000/69092	Loss: 113.409
+51200/69092	Loss: 113.415
+54400/69092	Loss: 112.898
+57600/69092	Loss: 112.548
+60800/69092	Loss: 111.415
+64000/69092	Loss: 113.569
+67200/69092	Loss: 113.263
+Training time 0:01:58.672983
+Epoch: 38 Average loss: 113.18
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 179)
+0/69092	Loss: 117.852
+3200/69092	Loss: 114.132
+6400/69092	Loss: 113.622
+9600/69092	Loss: 114.413
+12800/69092	Loss: 113.895
+16000/69092	Loss: 113.178
+19200/69092	Loss: 112.793
+22400/69092	Loss: 113.263
+25600/69092	Loss: 112.936
+28800/69092	Loss: 112.303
+32000/69092	Loss: 112.990
+35200/69092	Loss: 111.565
+38400/69092	Loss: 112.377
+41600/69092	Loss: 113.479
+44800/69092	Loss: 115.034
+48000/69092	Loss: 112.626
+51200/69092	Loss: 111.186
+54400/69092	Loss: 112.106
+57600/69092	Loss: 114.955
+60800/69092	Loss: 113.966
+64000/69092	Loss: 112.044
+67200/69092	Loss: 113.459
+Training time 0:01:57.760976
+Epoch: 39 Average loss: 113.18
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 180)
+0/69092	Loss: 112.574
+3200/69092	Loss: 113.277
+6400/69092	Loss: 113.513
+9600/69092	Loss: 112.636
+12800/69092	Loss: 114.511
+16000/69092	Loss: 111.913
+19200/69092	Loss: 113.311
+22400/69092	Loss: 112.848
+25600/69092	Loss: 113.098
+28800/69092	Loss: 111.790
+32000/69092	Loss: 115.993
+35200/69092	Loss: 112.678
+38400/69092	Loss: 112.988
+41600/69092	Loss: 113.563
+44800/69092	Loss: 112.193
+48000/69092	Loss: 111.674
+51200/69092	Loss: 112.468
+54400/69092	Loss: 114.340
+57600/69092	Loss: 112.935
+60800/69092	Loss: 114.753
+64000/69092	Loss: 114.156
+67200/69092	Loss: 114.243
+Training time 0:01:56.222605
+Epoch: 40 Average loss: 113.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 181)
+0/69092	Loss: 105.331
+3200/69092	Loss: 114.259
+6400/69092	Loss: 111.239
+9600/69092	Loss: 115.819
+12800/69092	Loss: 111.548
+16000/69092	Loss: 112.908
+19200/69092	Loss: 113.682
+22400/69092	Loss: 113.371
+25600/69092	Loss: 112.336
+28800/69092	Loss: 113.930
+32000/69092	Loss: 113.883
+35200/69092	Loss: 113.407
+38400/69092	Loss: 112.290
+41600/69092	Loss: 112.645
+44800/69092	Loss: 113.480
+48000/69092	Loss: 114.097
+51200/69092	Loss: 114.638
+54400/69092	Loss: 112.103
+57600/69092	Loss: 112.390
+60800/69092	Loss: 113.538
+64000/69092	Loss: 112.240
+67200/69092	Loss: 113.869
+Training time 0:01:57.349000
+Epoch: 41 Average loss: 113.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 182)
+0/69092	Loss: 106.944
+3200/69092	Loss: 112.803
+6400/69092	Loss: 110.267
+9600/69092	Loss: 112.340
+12800/69092	Loss: 113.424
+16000/69092	Loss: 113.995
+19200/69092	Loss: 113.619
+22400/69092	Loss: 111.961
+25600/69092	Loss: 114.552
+28800/69092	Loss: 113.481
+32000/69092	Loss: 113.119
+35200/69092	Loss: 111.295
+38400/69092	Loss: 114.473
+41600/69092	Loss: 111.617
+44800/69092	Loss: 113.792
+48000/69092	Loss: 114.302
+51200/69092	Loss: 114.581
+54400/69092	Loss: 113.555
+57600/69092	Loss: 112.925
+60800/69092	Loss: 110.887
+64000/69092	Loss: 113.113
+67200/69092	Loss: 111.808
+Training time 0:01:56.222317
+Epoch: 42 Average loss: 112.96
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 183)
+0/69092	Loss: 118.230
+3200/69092	Loss: 114.454
+6400/69092	Loss: 114.169
+9600/69092	Loss: 113.332
+12800/69092	Loss: 113.019
+16000/69092	Loss: 112.611
+19200/69092	Loss: 113.410
+22400/69092	Loss: 112.536
+25600/69092	Loss: 113.521
+28800/69092	Loss: 112.724
+32000/69092	Loss: 113.471
+35200/69092	Loss: 113.396
+38400/69092	Loss: 112.500
+41600/69092	Loss: 112.662
+44800/69092	Loss: 112.447
+48000/69092	Loss: 113.205
+51200/69092	Loss: 113.619
+54400/69092	Loss: 112.108
+57600/69092	Loss: 112.777
+60800/69092	Loss: 111.704
+64000/69092	Loss: 113.784
+67200/69092	Loss: 114.549
+Training time 0:01:56.173590
+Epoch: 43 Average loss: 113.11
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 184)
+0/69092	Loss: 110.065
+3200/69092	Loss: 111.767
+6400/69092	Loss: 113.636
+9600/69092	Loss: 112.554
+12800/69092	Loss: 113.104
+16000/69092	Loss: 112.124
+19200/69092	Loss: 112.675
+22400/69092	Loss: 113.358
+25600/69092	Loss: 113.541
+28800/69092	Loss: 114.161
+32000/69092	Loss: 113.763
+35200/69092	Loss: 113.449
+38400/69092	Loss: 111.399
+41600/69092	Loss: 112.777
+44800/69092	Loss: 111.883
+48000/69092	Loss: 114.067
+51200/69092	Loss: 113.131
+54400/69092	Loss: 113.204
+57600/69092	Loss: 114.136
+60800/69092	Loss: 114.549
+64000/69092	Loss: 113.641
+67200/69092	Loss: 111.212
+Training time 0:01:56.315797
+Epoch: 44 Average loss: 113.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 185)
+0/69092	Loss: 107.930
+3200/69092	Loss: 112.748
+6400/69092	Loss: 113.215
+9600/69092	Loss: 112.162
+12800/69092	Loss: 112.736
+16000/69092	Loss: 113.186
+19200/69092	Loss: 112.042
+22400/69092	Loss: 111.728
+25600/69092	Loss: 113.111
+28800/69092	Loss: 112.076
+32000/69092	Loss: 112.213
+35200/69092	Loss: 113.642
+38400/69092	Loss: 113.701
+41600/69092	Loss: 113.927
+44800/69092	Loss: 112.954
+48000/69092	Loss: 113.721
+51200/69092	Loss: 112.651
+54400/69092	Loss: 111.437
+57600/69092	Loss: 113.122
+60800/69092	Loss: 113.259
+64000/69092	Loss: 114.487
+67200/69092	Loss: 112.311
+Training time 0:01:57.252377
+Epoch: 45 Average loss: 112.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 186)
+0/69092	Loss: 107.370
+3200/69092	Loss: 112.774
+6400/69092	Loss: 111.116
+9600/69092	Loss: 115.065
+12800/69092	Loss: 110.289
+16000/69092	Loss: 112.626
+19200/69092	Loss: 114.124
+22400/69092	Loss: 114.952
+25600/69092	Loss: 112.964
+28800/69092	Loss: 111.603
+32000/69092	Loss: 114.307
+35200/69092	Loss: 113.034
+38400/69092	Loss: 111.020
+41600/69092	Loss: 112.118
+44800/69092	Loss: 113.457
+48000/69092	Loss: 113.852
+51200/69092	Loss: 113.247
+54400/69092	Loss: 114.377
+57600/69092	Loss: 112.041
+60800/69092	Loss: 112.349
+64000/69092	Loss: 113.902
+67200/69092	Loss: 113.314
+Training time 0:01:57.677153
+Epoch: 46 Average loss: 113.02
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 187)
+0/69092	Loss: 108.191
+3200/69092	Loss: 111.180
+6400/69092	Loss: 114.511
+9600/69092	Loss: 114.274
+12800/69092	Loss: 112.695
+16000/69092	Loss: 112.708
+19200/69092	Loss: 113.813
+22400/69092	Loss: 113.495
+25600/69092	Loss: 113.813
+28800/69092	Loss: 113.657
+32000/69092	Loss: 113.259
+35200/69092	Loss: 114.526
+38400/69092	Loss: 113.245
+41600/69092	Loss: 112.102
+44800/69092	Loss: 113.019
+48000/69092	Loss: 113.146
+51200/69092	Loss: 111.802
+54400/69092	Loss: 111.874
+57600/69092	Loss: 112.954
+60800/69092	Loss: 112.509
+64000/69092	Loss: 112.301
+67200/69092	Loss: 113.756
+Training time 0:01:57.270936
+Epoch: 47 Average loss: 113.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 188)
+0/69092	Loss: 114.384
+3200/69092	Loss: 112.098
+6400/69092	Loss: 113.737
+9600/69092	Loss: 112.405
+12800/69092	Loss: 112.935
+16000/69092	Loss: 112.704
+19200/69092	Loss: 112.897
+22400/69092	Loss: 111.671
+25600/69092	Loss: 111.944
+28800/69092	Loss: 112.637
+32000/69092	Loss: 112.662
+35200/69092	Loss: 113.128
+38400/69092	Loss: 113.058
+41600/69092	Loss: 114.302
+44800/69092	Loss: 112.805
+48000/69092	Loss: 113.744
+51200/69092	Loss: 112.352
+54400/69092	Loss: 113.272
+57600/69092	Loss: 113.209
+60800/69092	Loss: 112.415
+64000/69092	Loss: 113.384
+67200/69092	Loss: 114.061
+Training time 0:01:56.621897
+Epoch: 48 Average loss: 112.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 189)
+0/69092	Loss: 122.424
+3200/69092	Loss: 113.558
+6400/69092	Loss: 114.006
+9600/69092	Loss: 113.438
+12800/69092	Loss: 112.103
+16000/69092	Loss: 111.731
+19200/69092	Loss: 113.225
+22400/69092	Loss: 113.965
+25600/69092	Loss: 112.907
+28800/69092	Loss: 113.415
+32000/69092	Loss: 113.648
+35200/69092	Loss: 112.892
+38400/69092	Loss: 113.315
+41600/69092	Loss: 113.611
+44800/69092	Loss: 112.368
+48000/69092	Loss: 111.456
+51200/69092	Loss: 114.291
+54400/69092	Loss: 112.338
+57600/69092	Loss: 112.937
+60800/69092	Loss: 113.357
+64000/69092	Loss: 112.928
+67200/69092	Loss: 110.368
+Training time 0:01:57.034065
+Epoch: 49 Average loss: 112.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 190)
+0/69092	Loss: 103.725
+3200/69092	Loss: 112.181
+6400/69092	Loss: 110.256
+9600/69092	Loss: 112.561
+12800/69092	Loss: 113.508
+16000/69092	Loss: 112.477
+19200/69092	Loss: 113.481
+22400/69092	Loss: 113.713
+25600/69092	Loss: 111.214
+28800/69092	Loss: 112.821
+32000/69092	Loss: 114.706
+35200/69092	Loss: 113.510
+38400/69092	Loss: 113.152
+41600/69092	Loss: 111.306
+44800/69092	Loss: 113.479
+48000/69092	Loss: 112.801
+51200/69092	Loss: 113.999
+54400/69092	Loss: 114.534
+57600/69092	Loss: 114.387
+60800/69092	Loss: 111.725
+64000/69092	Loss: 114.551
+67200/69092	Loss: 111.415
+Training time 0:01:57.144276
+Epoch: 50 Average loss: 112.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 191)
+0/69092	Loss: 123.784
+3200/69092	Loss: 113.886
+6400/69092	Loss: 112.050
+9600/69092	Loss: 112.913
+12800/69092	Loss: 112.992
+16000/69092	Loss: 113.581
+19200/69092	Loss: 112.858
+22400/69092	Loss: 109.655
+25600/69092	Loss: 113.699
+28800/69092	Loss: 113.077
+32000/69092	Loss: 113.015
+35200/69092	Loss: 113.601
+38400/69092	Loss: 113.509
+41600/69092	Loss: 112.657
+44800/69092	Loss: 112.097
+48000/69092	Loss: 112.557
+51200/69092	Loss: 113.076
+54400/69092	Loss: 111.945
+57600/69092	Loss: 112.558
+60800/69092	Loss: 113.805
+64000/69092	Loss: 113.686
+67200/69092	Loss: 112.579
+Training time 0:01:58.141268
+Epoch: 51 Average loss: 112.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 192)
+0/69092	Loss: 102.234
+3200/69092	Loss: 113.874
+6400/69092	Loss: 112.134
+9600/69092	Loss: 111.494
+12800/69092	Loss: 112.315
+16000/69092	Loss: 114.314
+19200/69092	Loss: 112.447
+22400/69092	Loss: 113.243
+25600/69092	Loss: 112.554
+28800/69092	Loss: 112.491
+32000/69092	Loss: 114.047
+35200/69092	Loss: 114.010
+38400/69092	Loss: 113.350
+41600/69092	Loss: 111.099
+44800/69092	Loss: 112.864
+48000/69092	Loss: 113.585
+51200/69092	Loss: 113.284
+54400/69092	Loss: 113.734
+57600/69092	Loss: 111.351
+60800/69092	Loss: 113.639
+64000/69092	Loss: 111.110
+67200/69092	Loss: 112.782
+Training time 0:01:57.109686
+Epoch: 52 Average loss: 112.89
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 193)
+0/69092	Loss: 125.149
+3200/69092	Loss: 113.593
+6400/69092	Loss: 113.225
+9600/69092	Loss: 113.240
+12800/69092	Loss: 113.332
+16000/69092	Loss: 112.498
+19200/69092	Loss: 111.564
+22400/69092	Loss: 112.087
+25600/69092	Loss: 114.866
+28800/69092	Loss: 113.822
+32000/69092	Loss: 112.653
+35200/69092	Loss: 112.648
+38400/69092	Loss: 113.448
+41600/69092	Loss: 112.285
+44800/69092	Loss: 111.339
+48000/69092	Loss: 114.992
+51200/69092	Loss: 112.286
+54400/69092	Loss: 113.181
+57600/69092	Loss: 114.641
+60800/69092	Loss: 112.185
+64000/69092	Loss: 110.240
+67200/69092	Loss: 111.789
+Training time 0:01:57.890895
+Epoch: 53 Average loss: 112.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 194)
+0/69092	Loss: 120.738
+3200/69092	Loss: 113.921
+6400/69092	Loss: 112.629
+9600/69092	Loss: 112.500
+12800/69092	Loss: 112.924
+16000/69092	Loss: 113.851
+19200/69092	Loss: 112.452
+22400/69092	Loss: 112.171
+25600/69092	Loss: 113.169
+28800/69092	Loss: 113.749
+32000/69092	Loss: 111.723
+35200/69092	Loss: 114.696
+38400/69092	Loss: 111.455
+41600/69092	Loss: 111.961
+44800/69092	Loss: 111.725
+48000/69092	Loss: 112.909
+51200/69092	Loss: 114.163
+54400/69092	Loss: 112.770
+57600/69092	Loss: 113.329
+60800/69092	Loss: 112.858
+64000/69092	Loss: 115.067
+67200/69092	Loss: 112.942
+Training time 0:01:58.375331
+Epoch: 54 Average loss: 113.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 195)
+0/69092	Loss: 113.161
+3200/69092	Loss: 112.216
+6400/69092	Loss: 114.245
+9600/69092	Loss: 112.978
+12800/69092	Loss: 111.724
+16000/69092	Loss: 112.694
+19200/69092	Loss: 114.218
+22400/69092	Loss: 113.346
+25600/69092	Loss: 113.955
+28800/69092	Loss: 112.076
+32000/69092	Loss: 112.785
+35200/69092	Loss: 113.738
+38400/69092	Loss: 112.492
+41600/69092	Loss: 111.618
+44800/69092	Loss: 112.485
+48000/69092	Loss: 110.495
+51200/69092	Loss: 112.329
+54400/69092	Loss: 113.543
+57600/69092	Loss: 112.926
+60800/69092	Loss: 111.947
+64000/69092	Loss: 113.947
+67200/69092	Loss: 113.614
+Training time 0:01:57.921067
+Epoch: 55 Average loss: 112.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 196)
+0/69092	Loss: 98.536
+3200/69092	Loss: 113.967
+6400/69092	Loss: 113.678
+9600/69092	Loss: 112.032
+12800/69092	Loss: 113.125
+16000/69092	Loss: 112.978
+19200/69092	Loss: 112.244
+22400/69092	Loss: 111.852
+25600/69092	Loss: 112.795
+28800/69092	Loss: 112.706
+32000/69092	Loss: 112.926
+35200/69092	Loss: 113.740
+38400/69092	Loss: 113.753
+41600/69092	Loss: 112.075
+44800/69092	Loss: 110.197
+48000/69092	Loss: 113.637
+51200/69092	Loss: 114.269
+54400/69092	Loss: 113.479
+57600/69092	Loss: 112.259
+60800/69092	Loss: 113.970
+64000/69092	Loss: 112.493
+67200/69092	Loss: 112.385
+Training time 0:01:57.750009
+Epoch: 56 Average loss: 112.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 197)
+0/69092	Loss: 111.093
+3200/69092	Loss: 113.880
+6400/69092	Loss: 112.762
+9600/69092	Loss: 111.905
+12800/69092	Loss: 114.283
+16000/69092	Loss: 112.578
+19200/69092	Loss: 113.268
+22400/69092	Loss: 113.933
+25600/69092	Loss: 113.673
+28800/69092	Loss: 113.308
+32000/69092	Loss: 113.911
+35200/69092	Loss: 112.552
+38400/69092	Loss: 112.965
+41600/69092	Loss: 112.686
+44800/69092	Loss: 112.111
+48000/69092	Loss: 112.260
+51200/69092	Loss: 113.688
+54400/69092	Loss: 110.520
+57600/69092	Loss: 112.602
+60800/69092	Loss: 111.985
+64000/69092	Loss: 113.892
+67200/69092	Loss: 111.503
+Training time 0:01:57.478060
+Epoch: 57 Average loss: 112.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 198)
+0/69092	Loss: 125.914
+3200/69092	Loss: 113.873
+6400/69092	Loss: 113.726
+9600/69092	Loss: 115.227
+12800/69092	Loss: 110.942
+16000/69092	Loss: 112.000
+19200/69092	Loss: 111.971
+22400/69092	Loss: 113.533
+25600/69092	Loss: 112.320
+28800/69092	Loss: 112.569
+32000/69092	Loss: 111.833
+35200/69092	Loss: 113.412
+38400/69092	Loss: 112.716
+41600/69092	Loss: 112.908
+44800/69092	Loss: 112.154
+48000/69092	Loss: 113.787
+51200/69092	Loss: 112.760
+54400/69092	Loss: 113.770
+57600/69092	Loss: 113.666
+60800/69092	Loss: 112.642
+64000/69092	Loss: 112.393
+67200/69092	Loss: 113.339
+Training time 0:01:57.454998
+Epoch: 58 Average loss: 112.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 199)
+0/69092	Loss: 113.249
+3200/69092	Loss: 112.831
+6400/69092	Loss: 113.055
+9600/69092	Loss: 112.264
+12800/69092	Loss: 113.239
+16000/69092	Loss: 114.807
+19200/69092	Loss: 111.828
+22400/69092	Loss: 112.956
+25600/69092	Loss: 112.355
+28800/69092	Loss: 114.544
+32000/69092	Loss: 110.842
+35200/69092	Loss: 113.752
+38400/69092	Loss: 114.699
+41600/69092	Loss: 113.010
+44800/69092	Loss: 113.437
+48000/69092	Loss: 114.445
+51200/69092	Loss: 112.138
+54400/69092	Loss: 112.515
+57600/69092	Loss: 111.633
+60800/69092	Loss: 110.929
+64000/69092	Loss: 112.556
+67200/69092	Loss: 111.946
+Training time 0:01:57.979320
+Epoch: 59 Average loss: 112.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 200)
+0/69092	Loss: 118.560
+3200/69092	Loss: 113.390
+6400/69092	Loss: 115.115
+9600/69092	Loss: 113.015
+12800/69092	Loss: 110.703
+16000/69092	Loss: 112.370
+19200/69092	Loss: 111.884
+22400/69092	Loss: 112.114
+25600/69092	Loss: 113.264
+28800/69092	Loss: 111.140
+32000/69092	Loss: 112.199
+35200/69092	Loss: 111.933
+38400/69092	Loss: 115.160
+41600/69092	Loss: 112.984
+44800/69092	Loss: 113.587
+48000/69092	Loss: 111.713
+51200/69092	Loss: 112.597
+54400/69092	Loss: 114.407
+57600/69092	Loss: 111.939
+60800/69092	Loss: 112.508
+64000/69092	Loss: 112.346
+67200/69092	Loss: 113.437
+Training time 0:01:57.129662
+Epoch: 60 Average loss: 112.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 201)
+0/69092	Loss: 106.730
+3200/69092	Loss: 111.336
+6400/69092	Loss: 112.730
+9600/69092	Loss: 112.802
+12800/69092	Loss: 112.511
+16000/69092	Loss: 112.847
+19200/69092	Loss: 112.665
+22400/69092	Loss: 112.093
+25600/69092	Loss: 111.901
+28800/69092	Loss: 113.367
+32000/69092	Loss: 111.384
+35200/69092	Loss: 114.479
+38400/69092	Loss: 113.044
+41600/69092	Loss: 112.871
+44800/69092	Loss: 111.875
+48000/69092	Loss: 113.852
+51200/69092	Loss: 114.161
+54400/69092	Loss: 113.598
+57600/69092	Loss: 112.559
+60800/69092	Loss: 111.755
+64000/69092	Loss: 113.378
+67200/69092	Loss: 114.233
+Training time 0:01:57.502646
+Epoch: 61 Average loss: 112.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 202)
+0/69092	Loss: 110.732
+3200/69092	Loss: 112.066
+6400/69092	Loss: 110.979
+9600/69092	Loss: 111.672
+12800/69092	Loss: 112.490
+16000/69092	Loss: 110.993
+19200/69092	Loss: 113.076
+22400/69092	Loss: 112.079
+25600/69092	Loss: 113.152
+28800/69092	Loss: 112.425
+32000/69092	Loss: 112.395
+35200/69092	Loss: 112.097
+38400/69092	Loss: 112.282
+41600/69092	Loss: 112.552
+44800/69092	Loss: 112.215
+48000/69092	Loss: 113.234
+51200/69092	Loss: 112.832
+54400/69092	Loss: 113.246
+57600/69092	Loss: 112.555
+60800/69092	Loss: 113.089
+64000/69092	Loss: 114.522
+67200/69092	Loss: 114.644
+Training time 0:01:56.714733
+Epoch: 62 Average loss: 112.66
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 203)
+0/69092	Loss: 125.102
+3200/69092	Loss: 114.078
+6400/69092	Loss: 114.435
+9600/69092	Loss: 113.205
+12800/69092	Loss: 112.800
+16000/69092	Loss: 110.416
+19200/69092	Loss: 113.880
+22400/69092	Loss: 111.915
+25600/69092	Loss: 112.619
+28800/69092	Loss: 112.915
+32000/69092	Loss: 111.166
+35200/69092	Loss: 113.828
+38400/69092	Loss: 113.011
+41600/69092	Loss: 113.585
+44800/69092	Loss: 112.541
+48000/69092	Loss: 113.303
+51200/69092	Loss: 113.605
+54400/69092	Loss: 112.948
+57600/69092	Loss: 110.535
+60800/69092	Loss: 113.138
+64000/69092	Loss: 112.730
+67200/69092	Loss: 112.324
+Training time 0:01:56.675149
+Epoch: 63 Average loss: 112.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 204)
+0/69092	Loss: 107.751
+3200/69092	Loss: 113.080
+6400/69092	Loss: 111.117
+9600/69092	Loss: 113.675
+12800/69092	Loss: 111.980
+16000/69092	Loss: 112.702
+19200/69092	Loss: 112.221
+22400/69092	Loss: 114.652
+25600/69092	Loss: 111.969
+28800/69092	Loss: 112.533
+32000/69092	Loss: 113.737
+35200/69092	Loss: 113.486
+38400/69092	Loss: 114.099
+41600/69092	Loss: 111.737
+44800/69092	Loss: 112.225
+48000/69092	Loss: 111.938
+51200/69092	Loss: 114.247
+54400/69092	Loss: 111.343
+57600/69092	Loss: 110.885
+60800/69092	Loss: 113.434
+64000/69092	Loss: 111.832
+67200/69092	Loss: 112.673
+Training time 0:01:57.304249
+Epoch: 64 Average loss: 112.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 205)
+0/69092	Loss: 119.555
+3200/69092	Loss: 111.338
+6400/69092	Loss: 111.867
+9600/69092	Loss: 111.428
+12800/69092	Loss: 111.916
+16000/69092	Loss: 114.298
+19200/69092	Loss: 112.599
+22400/69092	Loss: 113.052
+25600/69092	Loss: 112.724
+28800/69092	Loss: 112.868
+32000/69092	Loss: 115.266
+35200/69092	Loss: 113.234
+38400/69092	Loss: 113.548
+41600/69092	Loss: 111.175
+44800/69092	Loss: 113.618
+48000/69092	Loss: 112.329
+51200/69092	Loss: 111.662
+54400/69092	Loss: 111.407
+57600/69092	Loss: 114.493
+60800/69092	Loss: 113.059
+64000/69092	Loss: 112.701
+67200/69092	Loss: 112.004
+Training time 0:01:56.345180
+Epoch: 65 Average loss: 112.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 206)
+0/69092	Loss: 115.853
+3200/69092	Loss: 113.659
+6400/69092	Loss: 112.134
+9600/69092	Loss: 112.609
+12800/69092	Loss: 110.304
+16000/69092	Loss: 114.111
+19200/69092	Loss: 111.842
+22400/69092	Loss: 112.393
+25600/69092	Loss: 111.813
+28800/69092	Loss: 112.723
+32000/69092	Loss: 111.490
+35200/69092	Loss: 112.234
+38400/69092	Loss: 113.776
+41600/69092	Loss: 112.868
+44800/69092	Loss: 111.690
+48000/69092	Loss: 113.217
+51200/69092	Loss: 114.886
+54400/69092	Loss: 113.434
+57600/69092	Loss: 113.007
+60800/69092	Loss: 114.535
+64000/69092	Loss: 113.078
+67200/69092	Loss: 111.854
+Training time 0:01:57.293184
+Epoch: 66 Average loss: 112.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 207)
+0/69092	Loss: 121.578
+3200/69092	Loss: 112.014
+6400/69092	Loss: 111.408
+9600/69092	Loss: 114.871
+12800/69092	Loss: 112.817
+16000/69092	Loss: 111.106
+19200/69092	Loss: 111.748
+22400/69092	Loss: 111.850
+25600/69092	Loss: 113.682
+28800/69092	Loss: 112.155
+32000/69092	Loss: 111.136
+35200/69092	Loss: 114.829
+38400/69092	Loss: 109.859
+41600/69092	Loss: 114.682
+44800/69092	Loss: 112.475
+48000/69092	Loss: 113.490
+51200/69092	Loss: 113.920
+54400/69092	Loss: 113.714
+57600/69092	Loss: 112.002
+60800/69092	Loss: 113.716
+64000/69092	Loss: 113.480
+67200/69092	Loss: 112.792
+Training time 0:01:57.191413
+Epoch: 67 Average loss: 112.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 208)
+0/69092	Loss: 112.806
+3200/69092	Loss: 113.068
+6400/69092	Loss: 114.299
+9600/69092	Loss: 113.946
+12800/69092	Loss: 113.426
+16000/69092	Loss: 114.277
+19200/69092	Loss: 111.715
+22400/69092	Loss: 112.805
+25600/69092	Loss: 113.436
+28800/69092	Loss: 111.918
+32000/69092	Loss: 110.794
+35200/69092	Loss: 113.398
+38400/69092	Loss: 112.557
+41600/69092	Loss: 113.606
+44800/69092	Loss: 112.019
+48000/69092	Loss: 112.819
+51200/69092	Loss: 112.972
+54400/69092	Loss: 112.156
+57600/69092	Loss: 114.157
+60800/69092	Loss: 112.676
+64000/69092	Loss: 110.638
+67200/69092	Loss: 112.451
+Training time 0:01:57.261387
+Epoch: 68 Average loss: 112.76
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 209)
+0/69092	Loss: 113.386
+3200/69092	Loss: 114.170
+6400/69092	Loss: 110.506
+9600/69092	Loss: 113.760
+12800/69092	Loss: 111.703
+16000/69092	Loss: 112.979
+19200/69092	Loss: 113.631
+22400/69092	Loss: 112.239
+25600/69092	Loss: 113.287
+28800/69092	Loss: 112.579
+32000/69092	Loss: 112.529
+35200/69092	Loss: 112.516
+38400/69092	Loss: 112.248
+41600/69092	Loss: 110.628
+44800/69092	Loss: 111.668
+48000/69092	Loss: 114.073
+51200/69092	Loss: 114.264
+54400/69092	Loss: 111.930
+57600/69092	Loss: 113.082
+60800/69092	Loss: 113.034
+64000/69092	Loss: 111.445
+67200/69092	Loss: 113.708
+Training time 0:01:57.350971
+Epoch: 69 Average loss: 112.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 210)
+0/69092	Loss: 125.102
+3200/69092	Loss: 113.944
+6400/69092	Loss: 113.237
+9600/69092	Loss: 112.432
+12800/69092	Loss: 113.079
+16000/69092	Loss: 113.065
+19200/69092	Loss: 113.352
+22400/69092	Loss: 111.868
+25600/69092	Loss: 111.362
+28800/69092	Loss: 111.899
+32000/69092	Loss: 112.378
+35200/69092	Loss: 113.345
+38400/69092	Loss: 113.601
+41600/69092	Loss: 113.423
+44800/69092	Loss: 113.835
+48000/69092	Loss: 113.524
+51200/69092	Loss: 111.625
+54400/69092	Loss: 110.301
+57600/69092	Loss: 113.257
+60800/69092	Loss: 112.360
+64000/69092	Loss: 113.661
+67200/69092	Loss: 111.989
+Training time 0:01:57.706960
+Epoch: 70 Average loss: 112.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 211)
+0/69092	Loss: 109.951
+3200/69092	Loss: 111.585
+6400/69092	Loss: 112.386
+9600/69092	Loss: 114.688
+12800/69092	Loss: 112.558
+16000/69092	Loss: 114.412
+19200/69092	Loss: 110.693
+22400/69092	Loss: 112.368
+25600/69092	Loss: 111.187
+28800/69092	Loss: 110.824
+32000/69092	Loss: 111.415
+35200/69092	Loss: 113.049
+38400/69092	Loss: 112.739
+41600/69092	Loss: 114.434
+44800/69092	Loss: 112.295
+48000/69092	Loss: 111.453
+51200/69092	Loss: 113.783
+54400/69092	Loss: 114.709
+57600/69092	Loss: 114.544
+60800/69092	Loss: 111.354
+64000/69092	Loss: 113.425
+67200/69092	Loss: 114.318
+Training time 0:01:56.501884
+Epoch: 71 Average loss: 112.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 212)
+0/69092	Loss: 115.720
+3200/69092	Loss: 112.429
+6400/69092	Loss: 110.584
+9600/69092	Loss: 113.790
+12800/69092	Loss: 112.467
+16000/69092	Loss: 112.473
+19200/69092	Loss: 113.625
+22400/69092	Loss: 113.141
+25600/69092	Loss: 111.571
+28800/69092	Loss: 113.934
+32000/69092	Loss: 113.007
+35200/69092	Loss: 113.262
+38400/69092	Loss: 112.649
+41600/69092	Loss: 111.434
+44800/69092	Loss: 113.323
+48000/69092	Loss: 111.536
+51200/69092	Loss: 114.455
+54400/69092	Loss: 112.240
+57600/69092	Loss: 113.238
+60800/69092	Loss: 111.717
+64000/69092	Loss: 112.014
+67200/69092	Loss: 111.735
+Training time 0:01:58.567226
+Epoch: 72 Average loss: 112.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 213)
+0/69092	Loss: 111.391
+3200/69092	Loss: 110.615
+6400/69092	Loss: 111.063
+9600/69092	Loss: 113.281
+12800/69092	Loss: 113.252
+16000/69092	Loss: 113.083
+19200/69092	Loss: 112.328
+22400/69092	Loss: 112.919
+25600/69092	Loss: 113.872
+28800/69092	Loss: 112.001
+32000/69092	Loss: 112.789
+35200/69092	Loss: 112.941
+38400/69092	Loss: 113.896
+41600/69092	Loss: 112.776
+44800/69092	Loss: 112.682
+48000/69092	Loss: 111.300
+51200/69092	Loss: 112.580
+54400/69092	Loss: 113.257
+57600/69092	Loss: 113.428
+60800/69092	Loss: 111.667
+64000/69092	Loss: 112.985
+67200/69092	Loss: 113.264
+Training time 0:01:59.109756
+Epoch: 73 Average loss: 112.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 214)
+0/69092	Loss: 105.142
+3200/69092	Loss: 112.594
+6400/69092	Loss: 110.573
+9600/69092	Loss: 111.890
+12800/69092	Loss: 110.356
+16000/69092	Loss: 112.265
+19200/69092	Loss: 112.690
+22400/69092	Loss: 112.969
+25600/69092	Loss: 113.605
+28800/69092	Loss: 111.696
+32000/69092	Loss: 113.205
+35200/69092	Loss: 111.634
+38400/69092	Loss: 111.312
+41600/69092	Loss: 112.915
+44800/69092	Loss: 114.137
+48000/69092	Loss: 113.250
+51200/69092	Loss: 113.176
+54400/69092	Loss: 113.504
+57600/69092	Loss: 113.093
+60800/69092	Loss: 112.143
+64000/69092	Loss: 112.619
+67200/69092	Loss: 113.034
+Training time 0:01:57.727148
+Epoch: 74 Average loss: 112.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 215)
+0/69092	Loss: 106.662
+3200/69092	Loss: 111.343
+6400/69092	Loss: 113.590
+9600/69092	Loss: 113.685
+12800/69092	Loss: 111.313
+16000/69092	Loss: 113.363
+19200/69092	Loss: 111.301
+22400/69092	Loss: 112.669
+25600/69092	Loss: 111.878
+28800/69092	Loss: 114.197
+32000/69092	Loss: 112.956
+35200/69092	Loss: 112.539
+38400/69092	Loss: 112.137
+41600/69092	Loss: 113.329
+44800/69092	Loss: 112.944
+48000/69092	Loss: 113.005
+51200/69092	Loss: 112.354
+54400/69092	Loss: 111.350
+57600/69092	Loss: 113.151
+60800/69092	Loss: 113.402
+64000/69092	Loss: 112.928
+67200/69092	Loss: 111.612
+Training time 0:01:58.503154
+Epoch: 75 Average loss: 112.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 216)
+0/69092	Loss: 109.634
+3200/69092	Loss: 112.922
+6400/69092	Loss: 111.558
+9600/69092	Loss: 111.909
+12800/69092	Loss: 112.965
+16000/69092	Loss: 114.224
+19200/69092	Loss: 109.836
+22400/69092	Loss: 112.878
+25600/69092	Loss: 113.737
+28800/69092	Loss: 111.219
+32000/69092	Loss: 112.265
+35200/69092	Loss: 111.559
+38400/69092	Loss: 112.683
+41600/69092	Loss: 112.252
+44800/69092	Loss: 111.319
+48000/69092	Loss: 112.530
+51200/69092	Loss: 112.719
+54400/69092	Loss: 114.480
+57600/69092	Loss: 113.720
+60800/69092	Loss: 112.721
+64000/69092	Loss: 111.756
+67200/69092	Loss: 114.433
+Training time 0:01:57.915950
+Epoch: 76 Average loss: 112.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 217)
+0/69092	Loss: 110.370
+3200/69092	Loss: 113.485
+6400/69092	Loss: 111.071
+9600/69092	Loss: 112.981
+12800/69092	Loss: 113.115
+16000/69092	Loss: 113.565
+19200/69092	Loss: 112.656
+22400/69092	Loss: 112.244
+25600/69092	Loss: 113.620
+28800/69092	Loss: 112.454
+32000/69092	Loss: 113.654
+35200/69092	Loss: 110.934
+38400/69092	Loss: 110.700
+41600/69092	Loss: 111.663
+44800/69092	Loss: 111.770
+48000/69092	Loss: 113.938
+51200/69092	Loss: 112.635
+54400/69092	Loss: 111.136
+57600/69092	Loss: 114.183
+60800/69092	Loss: 110.725
+64000/69092	Loss: 111.686
+67200/69092	Loss: 113.193
+Training time 0:01:58.140659
+Epoch: 77 Average loss: 112.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 218)
+0/69092	Loss: 119.513
+3200/69092	Loss: 112.164
+6400/69092	Loss: 115.295
+9600/69092	Loss: 112.260
+12800/69092	Loss: 115.003
+16000/69092	Loss: 113.505
+19200/69092	Loss: 111.126
+22400/69092	Loss: 112.373
+25600/69092	Loss: 114.709
+28800/69092	Loss: 113.183
+32000/69092	Loss: 112.834
+35200/69092	Loss: 110.947
+38400/69092	Loss: 114.188
+41600/69092	Loss: 113.869
+44800/69092	Loss: 111.811
+48000/69092	Loss: 112.425
+51200/69092	Loss: 111.222
+54400/69092	Loss: 109.423
+57600/69092	Loss: 111.643
+60800/69092	Loss: 111.004
+64000/69092	Loss: 112.427
+67200/69092	Loss: 110.218
+Training time 0:01:58.233680
+Epoch: 78 Average loss: 112.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 219)
+0/69092	Loss: 112.880
+3200/69092	Loss: 111.231
+6400/69092	Loss: 111.246
+9600/69092	Loss: 112.264
+12800/69092	Loss: 111.231
+16000/69092	Loss: 113.573
+19200/69092	Loss: 112.453
+22400/69092	Loss: 111.672
+25600/69092	Loss: 111.678
+28800/69092	Loss: 112.624
+32000/69092	Loss: 113.164
+35200/69092	Loss: 112.180
+38400/69092	Loss: 111.770
+41600/69092	Loss: 114.413
+44800/69092	Loss: 113.653
+48000/69092	Loss: 112.630
+51200/69092	Loss: 114.465
+54400/69092	Loss: 111.538
+57600/69092	Loss: 111.864
+60800/69092	Loss: 113.904
+64000/69092	Loss: 113.190
+67200/69092	Loss: 112.678
+Training time 0:01:58.886331
+Epoch: 79 Average loss: 112.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 220)
+0/69092	Loss: 128.631
+3200/69092	Loss: 112.969
+6400/69092	Loss: 113.126
+9600/69092	Loss: 112.544
+12800/69092	Loss: 114.528
+16000/69092	Loss: 113.954
+19200/69092	Loss: 113.491
+22400/69092	Loss: 112.045
+25600/69092	Loss: 112.418
+28800/69092	Loss: 112.261
+32000/69092	Loss: 112.555
+35200/69092	Loss: 112.481
+38400/69092	Loss: 113.040
+41600/69092	Loss: 112.316
+44800/69092	Loss: 113.381
+48000/69092	Loss: 110.421
+51200/69092	Loss: 112.938
+54400/69092	Loss: 112.496
+57600/69092	Loss: 111.130
+60800/69092	Loss: 112.314
+64000/69092	Loss: 111.236
+67200/69092	Loss: 112.486
+Training time 0:01:59.192499
+Epoch: 80 Average loss: 112.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 221)
+0/69092	Loss: 110.983
+3200/69092	Loss: 111.795
+6400/69092	Loss: 114.281
+9600/69092	Loss: 114.297
+12800/69092	Loss: 111.781
+16000/69092	Loss: 113.590
+19200/69092	Loss: 111.945
+22400/69092	Loss: 111.954
+25600/69092	Loss: 114.341
+28800/69092	Loss: 111.819
+32000/69092	Loss: 111.738
+35200/69092	Loss: 112.450
+38400/69092	Loss: 112.232
+41600/69092	Loss: 113.709
+44800/69092	Loss: 109.591
+48000/69092	Loss: 111.978
+51200/69092	Loss: 113.746
+54400/69092	Loss: 112.940
+57600/69092	Loss: 111.191
+60800/69092	Loss: 113.764
+64000/69092	Loss: 110.196
+67200/69092	Loss: 113.870
+Training time 0:01:58.037191
+Epoch: 81 Average loss: 112.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 222)
+0/69092	Loss: 106.766
+3200/69092	Loss: 110.676
+6400/69092	Loss: 112.739
+9600/69092	Loss: 114.432
+12800/69092	Loss: 111.230
+16000/69092	Loss: 113.746
+19200/69092	Loss: 112.254
+22400/69092	Loss: 110.426
+25600/69092	Loss: 112.839
+28800/69092	Loss: 111.624
+32000/69092	Loss: 111.983
+35200/69092	Loss: 112.184
+38400/69092	Loss: 113.878
+41600/69092	Loss: 112.466
+44800/69092	Loss: 113.243
+48000/69092	Loss: 113.959
+51200/69092	Loss: 111.869
+54400/69092	Loss: 114.875
+57600/69092	Loss: 113.464
+60800/69092	Loss: 111.022
+64000/69092	Loss: 112.748
+67200/69092	Loss: 113.343
+Training time 0:01:57.151935
+Epoch: 82 Average loss: 112.60
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 223)
+0/69092	Loss: 112.459
+3200/69092	Loss: 113.521
+6400/69092	Loss: 112.971
+9600/69092	Loss: 114.863
+12800/69092	Loss: 113.266
+16000/69092	Loss: 111.280
+19200/69092	Loss: 113.492
+22400/69092	Loss: 111.211
+25600/69092	Loss: 112.837
+28800/69092	Loss: 111.302
+32000/69092	Loss: 112.393
+35200/69092	Loss: 113.066
+38400/69092	Loss: 111.966
+41600/69092	Loss: 113.211
+44800/69092	Loss: 112.303
+48000/69092	Loss: 112.610
+51200/69092	Loss: 111.012
+54400/69092	Loss: 113.453
+57600/69092	Loss: 113.841
+60800/69092	Loss: 111.124
+64000/69092	Loss: 111.964
+67200/69092	Loss: 112.159
+Training time 0:01:57.861526
+Epoch: 83 Average loss: 112.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 224)
+0/69092	Loss: 112.569
+3200/69092	Loss: 112.110
+6400/69092	Loss: 111.643
+9600/69092	Loss: 111.477
+12800/69092	Loss: 111.725
+16000/69092	Loss: 112.504
+19200/69092	Loss: 114.204
+22400/69092	Loss: 111.431
+25600/69092	Loss: 113.620
+28800/69092	Loss: 112.256
+32000/69092	Loss: 110.583
+35200/69092	Loss: 111.800
+38400/69092	Loss: 111.793
+41600/69092	Loss: 114.786
+44800/69092	Loss: 112.921
+48000/69092	Loss: 111.340
+51200/69092	Loss: 112.890
+54400/69092	Loss: 111.795
+57600/69092	Loss: 115.177
+60800/69092	Loss: 112.495
+64000/69092	Loss: 113.175
+67200/69092	Loss: 113.448
+Training time 0:01:56.508485
+Epoch: 84 Average loss: 112.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 225)
+0/69092	Loss: 108.268
+3200/69092	Loss: 113.082
+6400/69092	Loss: 113.174
+9600/69092	Loss: 112.854
+12800/69092	Loss: 111.730
+16000/69092	Loss: 112.991
+19200/69092	Loss: 112.370
+22400/69092	Loss: 112.805
+25600/69092	Loss: 112.939
+28800/69092	Loss: 110.554
+32000/69092	Loss: 112.918
+35200/69092	Loss: 112.305
+38400/69092	Loss: 112.502
+41600/69092	Loss: 112.318
+44800/69092	Loss: 114.760
+48000/69092	Loss: 110.678
+51200/69092	Loss: 111.979
+54400/69092	Loss: 113.421
+57600/69092	Loss: 111.349
+60800/69092	Loss: 111.670
+64000/69092	Loss: 110.914
+67200/69092	Loss: 115.127
+Training time 0:01:57.954607
+Epoch: 85 Average loss: 112.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 226)
+0/69092	Loss: 121.964
+3200/69092	Loss: 111.323
+6400/69092	Loss: 112.121
+9600/69092	Loss: 113.463
+12800/69092	Loss: 113.063
+16000/69092	Loss: 112.890
+19200/69092	Loss: 113.526
+22400/69092	Loss: 112.961
+25600/69092	Loss: 111.565
+28800/69092	Loss: 111.969
+32000/69092	Loss: 113.186
+35200/69092	Loss: 112.689
+38400/69092	Loss: 112.469
+41600/69092	Loss: 113.112
+44800/69092	Loss: 113.887
+48000/69092	Loss: 112.435
+51200/69092	Loss: 113.260
+54400/69092	Loss: 112.285
+57600/69092	Loss: 111.916
+60800/69092	Loss: 112.957
+64000/69092	Loss: 111.678
+67200/69092	Loss: 110.248
+Training time 0:01:56.265853
+Epoch: 86 Average loss: 112.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 227)
+0/69092	Loss: 109.157
+3200/69092	Loss: 113.487
+6400/69092	Loss: 111.748
+9600/69092	Loss: 113.971
+12800/69092	Loss: 113.269
+16000/69092	Loss: 111.564
+19200/69092	Loss: 112.596
+22400/69092	Loss: 113.633
+25600/69092	Loss: 112.949
+28800/69092	Loss: 112.985
+32000/69092	Loss: 113.670
+35200/69092	Loss: 111.751
+38400/69092	Loss: 109.655
+41600/69092	Loss: 113.779
+44800/69092	Loss: 112.315
+48000/69092	Loss: 113.399
+51200/69092	Loss: 111.939
+54400/69092	Loss: 112.200
+57600/69092	Loss: 112.255
+60800/69092	Loss: 113.125
+64000/69092	Loss: 110.611
+67200/69092	Loss: 111.268
+Training time 0:01:55.365660
+Epoch: 87 Average loss: 112.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 228)
+0/69092	Loss: 116.874
+3200/69092	Loss: 113.586
+6400/69092	Loss: 112.407
+9600/69092	Loss: 112.673
+12800/69092	Loss: 112.387
+16000/69092	Loss: 111.351
+19200/69092	Loss: 112.815
+22400/69092	Loss: 113.230
+25600/69092	Loss: 112.361
+28800/69092	Loss: 113.541
+32000/69092	Loss: 111.372
+35200/69092	Loss: 114.097
+38400/69092	Loss: 113.668
+41600/69092	Loss: 112.039
+44800/69092	Loss: 111.892
+48000/69092	Loss: 111.060
+51200/69092	Loss: 111.540
+54400/69092	Loss: 112.278
+57600/69092	Loss: 113.199
+60800/69092	Loss: 112.627
+64000/69092	Loss: 113.202
+67200/69092	Loss: 113.085
+Training time 0:01:57.042352
+Epoch: 88 Average loss: 112.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 229)
+0/69092	Loss: 114.875
+3200/69092	Loss: 112.138
+6400/69092	Loss: 111.415
+9600/69092	Loss: 111.738
+12800/69092	Loss: 110.809
+16000/69092	Loss: 113.625
+19200/69092	Loss: 111.668
+22400/69092	Loss: 114.136
+25600/69092	Loss: 111.754
+28800/69092	Loss: 112.277
+32000/69092	Loss: 113.830
+35200/69092	Loss: 113.253
+38400/69092	Loss: 110.957
+41600/69092	Loss: 109.613
+44800/69092	Loss: 114.308
+48000/69092	Loss: 110.400
+51200/69092	Loss: 113.213
+54400/69092	Loss: 114.264
+57600/69092	Loss: 113.775
+60800/69092	Loss: 113.685
+64000/69092	Loss: 114.123
+67200/69092	Loss: 112.675
+Training time 0:01:56.362619
+Epoch: 89 Average loss: 112.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 230)
+0/69092	Loss: 113.780
+3200/69092	Loss: 110.638
+6400/69092	Loss: 110.601
+9600/69092	Loss: 113.225
+12800/69092	Loss: 111.759
+16000/69092	Loss: 111.304
+19200/69092	Loss: 111.822
+22400/69092	Loss: 111.951
+25600/69092	Loss: 114.009
+28800/69092	Loss: 113.582
+32000/69092	Loss: 111.096
+35200/69092	Loss: 112.199
+38400/69092	Loss: 112.879
+41600/69092	Loss: 112.579
+44800/69092	Loss: 112.746
+48000/69092	Loss: 115.082
+51200/69092	Loss: 110.698
+54400/69092	Loss: 114.595
+57600/69092	Loss: 111.723
+60800/69092	Loss: 113.068
+64000/69092	Loss: 113.374
+67200/69092	Loss: 111.968
+Training time 0:01:57.460918
+Epoch: 90 Average loss: 112.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 231)
+0/69092	Loss: 128.725
+3200/69092	Loss: 113.530
+6400/69092	Loss: 110.484
+9600/69092	Loss: 111.388
+12800/69092	Loss: 111.532
+16000/69092	Loss: 114.390
+19200/69092	Loss: 112.773
+22400/69092	Loss: 112.701
+25600/69092	Loss: 111.224
+28800/69092	Loss: 112.133
+32000/69092	Loss: 112.773
+35200/69092	Loss: 111.604
+38400/69092	Loss: 112.374
+41600/69092	Loss: 112.812
+44800/69092	Loss: 112.262
+48000/69092	Loss: 113.658
+51200/69092	Loss: 112.086
+54400/69092	Loss: 112.476
+57600/69092	Loss: 111.860
+60800/69092	Loss: 114.189
+64000/69092	Loss: 111.349
+67200/69092	Loss: 113.362
+Training time 0:01:57.583728
+Epoch: 91 Average loss: 112.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 232)
+0/69092	Loss: 112.169
+3200/69092	Loss: 112.838
+6400/69092	Loss: 112.009
+9600/69092	Loss: 112.562
+12800/69092	Loss: 110.737
+16000/69092	Loss: 112.397
+19200/69092	Loss: 111.796
+22400/69092	Loss: 113.050
+25600/69092	Loss: 112.202
+28800/69092	Loss: 111.759
+32000/69092	Loss: 112.713
+35200/69092	Loss: 113.893
+38400/69092	Loss: 112.594
+41600/69092	Loss: 114.052
+44800/69092	Loss: 112.531
+48000/69092	Loss: 112.984
+51200/69092	Loss: 111.920
+54400/69092	Loss: 113.424
+57600/69092	Loss: 111.371
+60800/69092	Loss: 110.712
+64000/69092	Loss: 112.798
+67200/69092	Loss: 110.935
+Training time 0:01:56.638898
+Epoch: 92 Average loss: 112.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 233)
+0/69092	Loss: 104.901
+3200/69092	Loss: 113.506
+6400/69092	Loss: 110.374
+9600/69092	Loss: 113.009
+12800/69092	Loss: 113.245
+16000/69092	Loss: 111.917
+19200/69092	Loss: 112.796
+22400/69092	Loss: 110.250
+25600/69092	Loss: 110.654
+28800/69092	Loss: 112.481
+32000/69092	Loss: 113.008
+35200/69092	Loss: 111.767
+38400/69092	Loss: 110.780
+41600/69092	Loss: 113.789
+44800/69092	Loss: 112.085
+48000/69092	Loss: 113.199
+51200/69092	Loss: 112.478
+54400/69092	Loss: 114.154
+57600/69092	Loss: 113.327
+60800/69092	Loss: 112.483
+64000/69092	Loss: 113.620
+67200/69092	Loss: 112.331
+Training time 0:01:58.264885
+Epoch: 93 Average loss: 112.40
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 234)
+0/69092	Loss: 102.670
+3200/69092	Loss: 111.317
+6400/69092	Loss: 113.022
+9600/69092	Loss: 112.766
+12800/69092	Loss: 112.702
+16000/69092	Loss: 111.284
+19200/69092	Loss: 110.661
+22400/69092	Loss: 111.850
+25600/69092	Loss: 113.525
+28800/69092	Loss: 113.103
+32000/69092	Loss: 111.017
+35200/69092	Loss: 111.895
+38400/69092	Loss: 113.335
+41600/69092	Loss: 113.631
+44800/69092	Loss: 111.002
+48000/69092	Loss: 113.389
+51200/69092	Loss: 112.564
+54400/69092	Loss: 115.150
+57600/69092	Loss: 112.661
+60800/69092	Loss: 109.555
+64000/69092	Loss: 111.753
+67200/69092	Loss: 113.512
+Training time 0:01:57.561475
+Epoch: 94 Average loss: 112.35
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 235)
+0/69092	Loss: 111.616
+3200/69092	Loss: 111.908
+6400/69092	Loss: 114.024
+9600/69092	Loss: 112.815
+12800/69092	Loss: 112.405
+16000/69092	Loss: 112.368
+19200/69092	Loss: 112.817
+22400/69092	Loss: 112.361
+25600/69092	Loss: 112.703
+28800/69092	Loss: 113.290
+32000/69092	Loss: 112.128
+35200/69092	Loss: 111.069
+38400/69092	Loss: 112.229
+41600/69092	Loss: 111.405
+44800/69092	Loss: 112.453
+48000/69092	Loss: 111.708
+51200/69092	Loss: 110.954
+54400/69092	Loss: 112.996
+57600/69092	Loss: 110.041
+60800/69092	Loss: 113.277
+64000/69092	Loss: 111.377
+67200/69092	Loss: 112.470
+Training time 0:01:57.399955
+Epoch: 95 Average loss: 112.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 236)
+0/69092	Loss: 108.858
+3200/69092	Loss: 112.788
+6400/69092	Loss: 113.286
+9600/69092	Loss: 112.635
+12800/69092	Loss: 111.946
+16000/69092	Loss: 112.513
+19200/69092	Loss: 113.211
+22400/69092	Loss: 112.744
+25600/69092	Loss: 113.010
+28800/69092	Loss: 110.110
+32000/69092	Loss: 114.224
+35200/69092	Loss: 113.327
+38400/69092	Loss: 111.222
+41600/69092	Loss: 112.321
+44800/69092	Loss: 112.465
+48000/69092	Loss: 112.242
+51200/69092	Loss: 114.034
+54400/69092	Loss: 111.427
+57600/69092	Loss: 112.722
+60800/69092	Loss: 110.585
+64000/69092	Loss: 110.285
+67200/69092	Loss: 112.110
+Training time 0:01:57.737669
+Epoch: 96 Average loss: 112.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 237)
+0/69092	Loss: 113.734
+3200/69092	Loss: 112.927
+6400/69092	Loss: 110.756
+9600/69092	Loss: 112.624
+12800/69092	Loss: 113.738
+16000/69092	Loss: 113.159
+19200/69092	Loss: 110.637
+22400/69092	Loss: 111.946
+25600/69092	Loss: 111.776
+28800/69092	Loss: 112.537
+32000/69092	Loss: 111.712
+35200/69092	Loss: 111.581
+38400/69092	Loss: 113.931
+41600/69092	Loss: 113.069
+44800/69092	Loss: 113.205
+48000/69092	Loss: 111.657
+51200/69092	Loss: 112.759
+54400/69092	Loss: 113.272
+57600/69092	Loss: 112.965
+60800/69092	Loss: 113.063
+64000/69092	Loss: 112.486
+67200/69092	Loss: 113.419
+Training time 0:01:58.457412
+Epoch: 97 Average loss: 112.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 238)
+0/69092	Loss: 108.410
+3200/69092	Loss: 113.605
+6400/69092	Loss: 112.954
+9600/69092	Loss: 111.622
+12800/69092	Loss: 112.280
+16000/69092	Loss: 112.006
+19200/69092	Loss: 111.360
+22400/69092	Loss: 112.516
+25600/69092	Loss: 112.452
+28800/69092	Loss: 111.874
+32000/69092	Loss: 113.323
+35200/69092	Loss: 111.920
+38400/69092	Loss: 112.724
+41600/69092	Loss: 111.703
+44800/69092	Loss: 112.722
+48000/69092	Loss: 111.623
+51200/69092	Loss: 111.738
+54400/69092	Loss: 111.144
+57600/69092	Loss: 112.676
+60800/69092	Loss: 113.212
+64000/69092	Loss: 111.363
+67200/69092	Loss: 112.736
+Training time 0:01:59.224965
+Epoch: 98 Average loss: 112.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 239)
+0/69092	Loss: 111.845
+3200/69092	Loss: 113.693
+6400/69092	Loss: 111.175
+9600/69092	Loss: 113.933
+12800/69092	Loss: 112.771
+16000/69092	Loss: 110.274
+19200/69092	Loss: 111.342
+22400/69092	Loss: 110.797
+25600/69092	Loss: 111.887
+28800/69092	Loss: 111.159
+32000/69092	Loss: 112.912
+35200/69092	Loss: 112.362
+38400/69092	Loss: 111.300
+41600/69092	Loss: 112.362
+44800/69092	Loss: 113.710
+48000/69092	Loss: 113.416
+51200/69092	Loss: 114.248
+54400/69092	Loss: 113.388
+57600/69092	Loss: 113.971
+60800/69092	Loss: 112.745
+64000/69092	Loss: 111.748
+67200/69092	Loss: 111.991
+Training time 0:01:58.678730
+Epoch: 99 Average loss: 112.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 240)
+0/69092	Loss: 110.661
+3200/69092	Loss: 112.339
+6400/69092	Loss: 111.748
+9600/69092	Loss: 111.444
+12800/69092	Loss: 111.422
+16000/69092	Loss: 111.832
+19200/69092	Loss: 110.530
+22400/69092	Loss: 112.211
+25600/69092	Loss: 112.419
+28800/69092	Loss: 113.769
+32000/69092	Loss: 111.190
+35200/69092	Loss: 112.705
+38400/69092	Loss: 110.066
+41600/69092	Loss: 111.861
+44800/69092	Loss: 113.482
+48000/69092	Loss: 113.957
+51200/69092	Loss: 112.604
+54400/69092	Loss: 113.089
+57600/69092	Loss: 112.564
+60800/69092	Loss: 113.803
+64000/69092	Loss: 112.737
+67200/69092	Loss: 113.359
+Training time 0:01:56.680057
+Epoch: 100 Average loss: 112.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 241)
+0/69092	Loss: 110.930
+3200/69092	Loss: 112.815
+6400/69092	Loss: 112.960
+9600/69092	Loss: 113.546
+12800/69092	Loss: 112.789
+16000/69092	Loss: 112.152
+19200/69092	Loss: 112.332
+22400/69092	Loss: 112.596
+25600/69092	Loss: 110.187
+28800/69092	Loss: 111.092
+32000/69092	Loss: 112.499
+35200/69092	Loss: 110.721
+38400/69092	Loss: 111.915
+41600/69092	Loss: 114.503
+44800/69092	Loss: 112.824
+48000/69092	Loss: 112.665
+51200/69092	Loss: 111.630
+54400/69092	Loss: 113.112
+57600/69092	Loss: 112.477
+60800/69092	Loss: 111.280
+64000/69092	Loss: 111.140
+67200/69092	Loss: 112.843
+Training time 0:01:57.646950
+Epoch: 101 Average loss: 112.32
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 242)
+0/69092	Loss: 115.128
+3200/69092	Loss: 112.378
+6400/69092	Loss: 110.980
+9600/69092	Loss: 113.601
+12800/69092	Loss: 111.631
+16000/69092	Loss: 111.741
+19200/69092	Loss: 114.039
+22400/69092	Loss: 113.053
+25600/69092	Loss: 112.787
+28800/69092	Loss: 112.469
+32000/69092	Loss: 111.676
+35200/69092	Loss: 111.143
+38400/69092	Loss: 112.707
+41600/69092	Loss: 112.902
+44800/69092	Loss: 115.000
+48000/69092	Loss: 114.135
+51200/69092	Loss: 111.745
+54400/69092	Loss: 111.527
+57600/69092	Loss: 111.419
+60800/69092	Loss: 112.648
+64000/69092	Loss: 112.654
+67200/69092	Loss: 110.848
+Training time 0:01:57.675385
+Epoch: 102 Average loss: 112.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 243)
+0/69092	Loss: 122.110
+3200/69092	Loss: 111.696
+6400/69092	Loss: 113.218
+9600/69092	Loss: 111.855
+12800/69092	Loss: 111.998
+16000/69092	Loss: 111.435
+19200/69092	Loss: 114.606
+22400/69092	Loss: 111.574
+25600/69092	Loss: 112.168
+28800/69092	Loss: 113.102
+32000/69092	Loss: 110.812
+35200/69092	Loss: 111.244
+38400/69092	Loss: 110.454
+41600/69092	Loss: 111.764
+44800/69092	Loss: 111.940
+48000/69092	Loss: 112.859
+51200/69092	Loss: 111.696
+54400/69092	Loss: 112.131
+57600/69092	Loss: 114.864
+60800/69092	Loss: 112.273
+64000/69092	Loss: 113.124
+67200/69092	Loss: 112.506
+Training time 0:01:57.176206
+Epoch: 103 Average loss: 112.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 244)
+0/69092	Loss: 107.943
+3200/69092	Loss: 112.101
+6400/69092	Loss: 114.077
+9600/69092	Loss: 112.517
+12800/69092	Loss: 112.820
+16000/69092	Loss: 114.419
+19200/69092	Loss: 111.560
+22400/69092	Loss: 112.843
+25600/69092	Loss: 112.677
+28800/69092	Loss: 111.454
+32000/69092	Loss: 113.242
+35200/69092	Loss: 111.730
+38400/69092	Loss: 112.198
+41600/69092	Loss: 111.994
+44800/69092	Loss: 111.727
+48000/69092	Loss: 111.415
+51200/69092	Loss: 114.657
+54400/69092	Loss: 111.494
+57600/69092	Loss: 112.025
+60800/69092	Loss: 111.606
+64000/69092	Loss: 111.702
+67200/69092	Loss: 110.875
+Training time 0:01:57.934002
+Epoch: 104 Average loss: 112.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 245)
+0/69092	Loss: 118.837
+3200/69092	Loss: 112.914
+6400/69092	Loss: 112.777
+9600/69092	Loss: 113.202
+12800/69092	Loss: 112.745
+16000/69092	Loss: 112.602
+19200/69092	Loss: 111.085
+22400/69092	Loss: 111.904
+25600/69092	Loss: 113.905
+28800/69092	Loss: 112.016
+32000/69092	Loss: 114.198
+35200/69092	Loss: 112.954
+38400/69092	Loss: 112.641
+41600/69092	Loss: 110.650
+44800/69092	Loss: 110.040
+48000/69092	Loss: 110.839
+51200/69092	Loss: 111.753
+54400/69092	Loss: 111.339
+57600/69092	Loss: 112.851
+60800/69092	Loss: 110.588
+64000/69092	Loss: 112.322
+67200/69092	Loss: 111.523
+Training time 0:01:56.932822
+Epoch: 105 Average loss: 112.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 246)
+0/69092	Loss: 119.116
+3200/69092	Loss: 112.507
+6400/69092	Loss: 114.622
+9600/69092	Loss: 111.569
+12800/69092	Loss: 112.558
+16000/69092	Loss: 111.289
+19200/69092	Loss: 110.495
+22400/69092	Loss: 111.210
+25600/69092	Loss: 113.024
+28800/69092	Loss: 113.384
+32000/69092	Loss: 112.152
+35200/69092	Loss: 112.843
+38400/69092	Loss: 113.555
+41600/69092	Loss: 112.324
+44800/69092	Loss: 112.075
+48000/69092	Loss: 111.592
+51200/69092	Loss: 113.754
+54400/69092	Loss: 112.188
+57600/69092	Loss: 112.554
+60800/69092	Loss: 112.263
+64000/69092	Loss: 111.831
+67200/69092	Loss: 110.805
+Training time 0:01:56.950941
+Epoch: 106 Average loss: 112.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 247)
+0/69092	Loss: 104.412
+3200/69092	Loss: 111.679
+6400/69092	Loss: 112.090
+9600/69092	Loss: 111.886
+12800/69092	Loss: 112.062
+16000/69092	Loss: 112.733
+19200/69092	Loss: 112.301
+22400/69092	Loss: 112.985
+25600/69092	Loss: 113.072
+28800/69092	Loss: 112.380
+32000/69092	Loss: 112.159
+35200/69092	Loss: 110.853
+38400/69092	Loss: 113.615
+41600/69092	Loss: 111.124
+44800/69092	Loss: 112.985
+48000/69092	Loss: 111.388
+51200/69092	Loss: 114.311
+54400/69092	Loss: 112.027
+57600/69092	Loss: 111.420
+60800/69092	Loss: 111.063
+64000/69092	Loss: 112.437
+67200/69092	Loss: 113.936
+Training time 0:01:57.014313
+Epoch: 107 Average loss: 112.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 248)
+0/69092	Loss: 115.203
+3200/69092	Loss: 112.016
+6400/69092	Loss: 111.749
+9600/69092	Loss: 111.528
+12800/69092	Loss: 112.093
+16000/69092	Loss: 113.914
+19200/69092	Loss: 112.226
+22400/69092	Loss: 112.427
+25600/69092	Loss: 111.981
+28800/69092	Loss: 112.209
+32000/69092	Loss: 111.937
+35200/69092	Loss: 111.763
+38400/69092	Loss: 112.318
+41600/69092	Loss: 112.454
+44800/69092	Loss: 113.071
+48000/69092	Loss: 112.119
+51200/69092	Loss: 112.597
+54400/69092	Loss: 112.414
+57600/69092	Loss: 113.179
+60800/69092	Loss: 112.642
+64000/69092	Loss: 111.958
+67200/69092	Loss: 112.401
+Training time 0:01:57.301862
+Epoch: 108 Average loss: 112.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 249)
+0/69092	Loss: 122.546
+3200/69092	Loss: 113.128
+6400/69092	Loss: 112.474
+9600/69092	Loss: 110.420
+12800/69092	Loss: 112.803
+16000/69092	Loss: 110.452
+19200/69092	Loss: 112.079
+22400/69092	Loss: 110.320
+25600/69092	Loss: 112.415
+28800/69092	Loss: 112.520
+32000/69092	Loss: 110.496
+35200/69092	Loss: 112.416
+38400/69092	Loss: 111.564
+41600/69092	Loss: 112.560
+44800/69092	Loss: 112.120
+48000/69092	Loss: 113.670
+51200/69092	Loss: 111.537
+54400/69092	Loss: 113.272
+57600/69092	Loss: 113.247
+60800/69092	Loss: 112.408
+64000/69092	Loss: 112.795
+67200/69092	Loss: 112.773
+Training time 0:01:56.308761
+Epoch: 109 Average loss: 112.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 250)
+0/69092	Loss: 98.938
+3200/69092	Loss: 111.445
+6400/69092	Loss: 113.297
+9600/69092	Loss: 112.665
+12800/69092	Loss: 112.216
+16000/69092	Loss: 112.548
+19200/69092	Loss: 113.707
+22400/69092	Loss: 112.265
+25600/69092	Loss: 112.643
+28800/69092	Loss: 111.430
+32000/69092	Loss: 111.949
+35200/69092	Loss: 112.830
+38400/69092	Loss: 112.370
+41600/69092	Loss: 110.079
+44800/69092	Loss: 111.474
+48000/69092	Loss: 109.858
+51200/69092	Loss: 112.089
+54400/69092	Loss: 114.173
+57600/69092	Loss: 114.180
+60800/69092	Loss: 111.812
+64000/69092	Loss: 112.274
+67200/69092	Loss: 113.646
+Training time 0:01:56.748136
+Epoch: 110 Average loss: 112.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 251)
+0/69092	Loss: 109.140
+3200/69092	Loss: 112.765
+6400/69092	Loss: 113.035
+9600/69092	Loss: 111.078
+12800/69092	Loss: 113.376
+16000/69092	Loss: 113.253
+19200/69092	Loss: 111.920
+22400/69092	Loss: 111.738
+25600/69092	Loss: 109.345
+28800/69092	Loss: 111.622
+32000/69092	Loss: 112.487
+35200/69092	Loss: 111.012
+38400/69092	Loss: 110.377
+41600/69092	Loss: 113.165
+44800/69092	Loss: 113.685
+48000/69092	Loss: 111.998
+51200/69092	Loss: 112.723
+54400/69092	Loss: 112.647
+57600/69092	Loss: 112.257
+60800/69092	Loss: 111.227
+64000/69092	Loss: 112.463
+67200/69092	Loss: 111.712
+Training time 0:01:56.515083
+Epoch: 111 Average loss: 112.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 252)
+0/69092	Loss: 110.661
+3200/69092	Loss: 111.328
+6400/69092	Loss: 111.800
+9600/69092	Loss: 112.689
+12800/69092	Loss: 111.939
+16000/69092	Loss: 111.356
+19200/69092	Loss: 113.590
+22400/69092	Loss: 110.786
+25600/69092	Loss: 110.686
+28800/69092	Loss: 111.201
+32000/69092	Loss: 112.002
+35200/69092	Loss: 112.562
+38400/69092	Loss: 112.656
+41600/69092	Loss: 110.836
+44800/69092	Loss: 112.719
+48000/69092	Loss: 112.621
+51200/69092	Loss: 113.685
+54400/69092	Loss: 112.028
+57600/69092	Loss: 115.000
+60800/69092	Loss: 113.152
+64000/69092	Loss: 110.856
+67200/69092	Loss: 111.474
+Training time 0:01:56.597435
+Epoch: 112 Average loss: 112.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 253)
+0/69092	Loss: 109.960
+3200/69092	Loss: 110.964
+6400/69092	Loss: 112.876
+9600/69092	Loss: 110.982
+12800/69092	Loss: 113.332
+16000/69092	Loss: 110.655
+19200/69092	Loss: 112.564
+22400/69092	Loss: 112.240
+25600/69092	Loss: 112.795
+28800/69092	Loss: 112.056
+32000/69092	Loss: 112.768
+35200/69092	Loss: 111.929
+38400/69092	Loss: 110.701
+41600/69092	Loss: 113.828
+44800/69092	Loss: 111.824
+48000/69092	Loss: 111.996
+51200/69092	Loss: 112.019
+54400/69092	Loss: 111.082
+57600/69092	Loss: 112.899
+60800/69092	Loss: 111.659
+64000/69092	Loss: 111.178
+67200/69092	Loss: 113.213
+Training time 0:01:57.918296
+Epoch: 113 Average loss: 112.12
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 254)
+0/69092	Loss: 118.086
+3200/69092	Loss: 111.598
+6400/69092	Loss: 114.653
+9600/69092	Loss: 112.980
+12800/69092	Loss: 112.017
+16000/69092	Loss: 112.215
+19200/69092	Loss: 112.215
+22400/69092	Loss: 111.565
+25600/69092	Loss: 111.433
+28800/69092	Loss: 111.878
+32000/69092	Loss: 112.721
+35200/69092	Loss: 110.890
+38400/69092	Loss: 110.228
+41600/69092	Loss: 112.251
+44800/69092	Loss: 110.539
+48000/69092	Loss: 113.110
+51200/69092	Loss: 111.918
+54400/69092	Loss: 112.831
+57600/69092	Loss: 111.676
+60800/69092	Loss: 111.244
+64000/69092	Loss: 112.928
+67200/69092	Loss: 112.688
+Training time 0:01:56.785403
+Epoch: 114 Average loss: 112.12
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 255)
+0/69092	Loss: 105.504
+3200/69092	Loss: 111.509
+6400/69092	Loss: 110.423
+9600/69092	Loss: 111.608
+12800/69092	Loss: 112.015
+16000/69092	Loss: 111.526
+19200/69092	Loss: 112.261
+22400/69092	Loss: 110.048
+25600/69092	Loss: 113.101
+28800/69092	Loss: 113.714
+32000/69092	Loss: 111.425
+35200/69092	Loss: 113.042
+38400/69092	Loss: 113.132
+41600/69092	Loss: 112.322
+44800/69092	Loss: 114.220
+48000/69092	Loss: 113.080
+51200/69092	Loss: 110.365
+54400/69092	Loss: 109.480
+57600/69092	Loss: 112.605
+60800/69092	Loss: 111.615
+64000/69092	Loss: 113.207
+67200/69092	Loss: 114.524
+Training time 0:01:57.574441
+Epoch: 115 Average loss: 112.20
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 256)
+0/69092	Loss: 112.621
+3200/69092	Loss: 111.410
+6400/69092	Loss: 113.177
+9600/69092	Loss: 111.835
+12800/69092	Loss: 113.086
+16000/69092	Loss: 110.949
+19200/69092	Loss: 111.810
+22400/69092	Loss: 111.389
+25600/69092	Loss: 114.276
+28800/69092	Loss: 111.165
+32000/69092	Loss: 112.905
+35200/69092	Loss: 111.984
+38400/69092	Loss: 112.424
+41600/69092	Loss: 111.574
+44800/69092	Loss: 111.835
+48000/69092	Loss: 111.637
+51200/69092	Loss: 112.245
+54400/69092	Loss: 113.529
+57600/69092	Loss: 112.601
+60800/69092	Loss: 112.377
+64000/69092	Loss: 111.574
+67200/69092	Loss: 111.307
+Training time 0:01:57.667118
+Epoch: 116 Average loss: 112.12
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 257)
+0/69092	Loss: 107.236
+3200/69092	Loss: 111.610
+6400/69092	Loss: 112.600
+9600/69092	Loss: 111.004
+12800/69092	Loss: 111.948
+16000/69092	Loss: 110.685
+19200/69092	Loss: 110.344
+22400/69092	Loss: 113.975
+25600/69092	Loss: 112.237
+28800/69092	Loss: 112.586
+32000/69092	Loss: 113.264
+35200/69092	Loss: 111.315
+38400/69092	Loss: 112.652
+41600/69092	Loss: 113.324
+44800/69092	Loss: 111.615
+48000/69092	Loss: 113.560
+51200/69092	Loss: 112.039
+54400/69092	Loss: 113.206
+57600/69092	Loss: 112.907
+60800/69092	Loss: 111.778
+64000/69092	Loss: 113.576
+67200/69092	Loss: 109.730
+Training time 0:01:58.268045
+Epoch: 117 Average loss: 112.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 258)
+0/69092	Loss: 119.309
+3200/69092	Loss: 109.940
+6400/69092	Loss: 110.648
+9600/69092	Loss: 112.478
+12800/69092	Loss: 112.512
+16000/69092	Loss: 111.950
+19200/69092	Loss: 112.965
+22400/69092	Loss: 113.168
+25600/69092	Loss: 112.653
+28800/69092	Loss: 111.329
+32000/69092	Loss: 112.938
+35200/69092	Loss: 112.107
+38400/69092	Loss: 112.749
+41600/69092	Loss: 111.167
+44800/69092	Loss: 113.240
+48000/69092	Loss: 112.599
+51200/69092	Loss: 111.117
+54400/69092	Loss: 111.381
+57600/69092	Loss: 112.368
+60800/69092	Loss: 111.555
+64000/69092	Loss: 113.408
+67200/69092	Loss: 112.892
+Training time 0:01:58.493072
+Epoch: 118 Average loss: 112.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 259)
+0/69092	Loss: 110.086
+3200/69092	Loss: 113.020
+6400/69092	Loss: 111.594
+9600/69092	Loss: 109.827
+12800/69092	Loss: 112.473
+16000/69092	Loss: 113.471
+19200/69092	Loss: 112.421
+22400/69092	Loss: 113.545
+25600/69092	Loss: 112.571
+28800/69092	Loss: 111.506
+32000/69092	Loss: 111.066
+35200/69092	Loss: 111.101
+38400/69092	Loss: 112.553
+41600/69092	Loss: 112.108
+44800/69092	Loss: 112.328
+48000/69092	Loss: 113.641
+51200/69092	Loss: 111.265
+54400/69092	Loss: 111.934
+57600/69092	Loss: 113.249
+60800/69092	Loss: 111.569
+64000/69092	Loss: 111.621
+67200/69092	Loss: 110.803
+Training time 0:01:57.863728
+Epoch: 119 Average loss: 112.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 260)
+0/69092	Loss: 107.729
+3200/69092	Loss: 111.098
+6400/69092	Loss: 112.683
+9600/69092	Loss: 114.338
+12800/69092	Loss: 111.751
+16000/69092	Loss: 111.751
+19200/69092	Loss: 111.278
+22400/69092	Loss: 110.961
+25600/69092	Loss: 112.530
+28800/69092	Loss: 114.040
+32000/69092	Loss: 112.281
+35200/69092	Loss: 112.416
+38400/69092	Loss: 111.593
+41600/69092	Loss: 110.715
+44800/69092	Loss: 112.425
+48000/69092	Loss: 111.945
+51200/69092	Loss: 113.088
+54400/69092	Loss: 113.703
+57600/69092	Loss: 113.058
+60800/69092	Loss: 111.201
+64000/69092	Loss: 109.807
+67200/69092	Loss: 112.162
+Training time 0:01:58.318562
+Epoch: 120 Average loss: 112.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 261)
+0/69092	Loss: 115.668
+3200/69092	Loss: 113.417
+6400/69092	Loss: 111.360
+9600/69092	Loss: 112.868
+12800/69092	Loss: 111.201
+16000/69092	Loss: 112.403
+19200/69092	Loss: 111.328
+22400/69092	Loss: 111.108
+25600/69092	Loss: 111.842
+28800/69092	Loss: 111.713
+32000/69092	Loss: 113.858
+35200/69092	Loss: 111.048
+38400/69092	Loss: 110.803
+41600/69092	Loss: 112.040
+44800/69092	Loss: 112.643
+48000/69092	Loss: 111.528
+51200/69092	Loss: 113.667
+54400/69092	Loss: 111.525
+57600/69092	Loss: 112.965
+60800/69092	Loss: 112.586
+64000/69092	Loss: 109.281
+67200/69092	Loss: 113.376
+Training time 0:01:59.138927
+Epoch: 121 Average loss: 112.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 262)
+0/69092	Loss: 102.175
+3200/69092	Loss: 109.960
+6400/69092	Loss: 112.384
+9600/69092	Loss: 110.948
+12800/69092	Loss: 111.415
+16000/69092	Loss: 113.569
+19200/69092	Loss: 111.811
+22400/69092	Loss: 112.280
+25600/69092	Loss: 113.318
+28800/69092	Loss: 111.813
+32000/69092	Loss: 112.473
+35200/69092	Loss: 111.266
+38400/69092	Loss: 113.206
+41600/69092	Loss: 112.823
+44800/69092	Loss: 110.928
+48000/69092	Loss: 112.257
+51200/69092	Loss: 113.370
+54400/69092	Loss: 112.357
+57600/69092	Loss: 111.404
+60800/69092	Loss: 111.087
+64000/69092	Loss: 110.713
+67200/69092	Loss: 111.838
+Training time 0:01:58.268645
+Epoch: 122 Average loss: 111.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 263)
+0/69092	Loss: 114.347
+3200/69092	Loss: 112.021
+6400/69092	Loss: 110.714
+9600/69092	Loss: 112.481
+12800/69092	Loss: 112.010
+16000/69092	Loss: 112.858
+19200/69092	Loss: 114.146
+22400/69092	Loss: 113.468
+25600/69092	Loss: 113.793
+28800/69092	Loss: 110.912
+32000/69092	Loss: 112.297
+35200/69092	Loss: 110.685
+38400/69092	Loss: 111.899
+41600/69092	Loss: 111.598
+44800/69092	Loss: 110.782
+48000/69092	Loss: 110.557
+51200/69092	Loss: 112.468
+54400/69092	Loss: 114.146
+57600/69092	Loss: 113.223
+60800/69092	Loss: 110.101
+64000/69092	Loss: 112.696
+67200/69092	Loss: 112.284
+Training time 0:01:58.717772
+Epoch: 123 Average loss: 112.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 264)
+0/69092	Loss: 114.796
+3200/69092	Loss: 111.597
+6400/69092	Loss: 112.164
+9600/69092	Loss: 112.020
+12800/69092	Loss: 113.329
+16000/69092	Loss: 112.340
+19200/69092	Loss: 113.006
+22400/69092	Loss: 112.646
+25600/69092	Loss: 111.458
+28800/69092	Loss: 111.624
+32000/69092	Loss: 110.047
+35200/69092	Loss: 113.653
+38400/69092	Loss: 110.712
+41600/69092	Loss: 112.988
+44800/69092	Loss: 111.665
+48000/69092	Loss: 109.792
+51200/69092	Loss: 113.108
+54400/69092	Loss: 112.833
+57600/69092	Loss: 113.314
+60800/69092	Loss: 111.704
+64000/69092	Loss: 110.928
+67200/69092	Loss: 112.795
+Training time 0:01:58.193320
+Epoch: 124 Average loss: 112.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 265)
+0/69092	Loss: 108.997
+3200/69092	Loss: 111.090
+6400/69092	Loss: 111.651
+9600/69092	Loss: 112.592
+12800/69092	Loss: 113.259
+16000/69092	Loss: 111.718
+19200/69092	Loss: 111.622
+22400/69092	Loss: 110.863
+25600/69092	Loss: 111.885
+28800/69092	Loss: 110.308
+32000/69092	Loss: 113.694
+35200/69092	Loss: 110.378
+38400/69092	Loss: 112.791
+41600/69092	Loss: 113.794
+44800/69092	Loss: 111.802
+48000/69092	Loss: 110.771
+51200/69092	Loss: 111.244
+54400/69092	Loss: 113.381
+57600/69092	Loss: 111.620
+60800/69092	Loss: 110.666
+64000/69092	Loss: 111.954
+67200/69092	Loss: 111.739
+Training time 0:01:57.941828
+Epoch: 125 Average loss: 111.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 266)
+0/69092	Loss: 123.242
+3200/69092	Loss: 110.574
+6400/69092	Loss: 114.011
+9600/69092	Loss: 111.405
+12800/69092	Loss: 111.924
+16000/69092	Loss: 112.379
+19200/69092	Loss: 112.513
+22400/69092	Loss: 112.633
+25600/69092	Loss: 111.569
+28800/69092	Loss: 109.637
+32000/69092	Loss: 111.933
+35200/69092	Loss: 112.171
+38400/69092	Loss: 112.258
+41600/69092	Loss: 113.604
+44800/69092	Loss: 112.474
+48000/69092	Loss: 111.926
+51200/69092	Loss: 110.240
+54400/69092	Loss: 113.540
+57600/69092	Loss: 113.718
+60800/69092	Loss: 112.365
+64000/69092	Loss: 112.068
+67200/69092	Loss: 113.282
+Training time 0:01:58.633467
+Epoch: 126 Average loss: 112.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 267)
+0/69092	Loss: 118.893
+3200/69092	Loss: 112.783
+6400/69092	Loss: 111.837
+9600/69092	Loss: 112.242
+12800/69092	Loss: 112.555
+16000/69092	Loss: 113.339
+19200/69092	Loss: 112.081
+22400/69092	Loss: 111.319
+25600/69092	Loss: 111.447
+28800/69092	Loss: 113.518
+32000/69092	Loss: 112.991
+35200/69092	Loss: 112.440
+38400/69092	Loss: 111.251
+41600/69092	Loss: 111.765
+44800/69092	Loss: 109.843
+48000/69092	Loss: 110.788
+51200/69092	Loss: 110.088
+54400/69092	Loss: 112.768
+57600/69092	Loss: 114.083
+60800/69092	Loss: 112.801
+64000/69092	Loss: 112.033
+67200/69092	Loss: 112.089
+Training time 0:01:57.055968
+Epoch: 127 Average loss: 112.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 268)
+0/69092	Loss: 110.885
+3200/69092	Loss: 112.572
+6400/69092	Loss: 111.447
+9600/69092	Loss: 112.685
+12800/69092	Loss: 111.153
+16000/69092	Loss: 110.326
+19200/69092	Loss: 113.495
+22400/69092	Loss: 111.638
+25600/69092	Loss: 112.952
+28800/69092	Loss: 109.724
+32000/69092	Loss: 112.432
+35200/69092	Loss: 114.034
+38400/69092	Loss: 112.584
+41600/69092	Loss: 112.523
+44800/69092	Loss: 110.940
+48000/69092	Loss: 111.957
+51200/69092	Loss: 110.980
+54400/69092	Loss: 111.456
+57600/69092	Loss: 109.690
+60800/69092	Loss: 112.595
+64000/69092	Loss: 114.273
+67200/69092	Loss: 112.951
+Training time 0:01:58.018575
+Epoch: 128 Average loss: 112.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 269)
+0/69092	Loss: 109.695
+3200/69092	Loss: 111.374
+6400/69092	Loss: 111.361
+9600/69092	Loss: 112.371
+12800/69092	Loss: 113.973
+16000/69092	Loss: 112.413
+19200/69092	Loss: 112.429
+22400/69092	Loss: 112.522
+25600/69092	Loss: 111.950
+28800/69092	Loss: 112.936
+32000/69092	Loss: 110.791
+35200/69092	Loss: 111.810
+38400/69092	Loss: 111.177
+41600/69092	Loss: 110.538
+44800/69092	Loss: 111.373
+48000/69092	Loss: 113.135
+51200/69092	Loss: 111.911
+54400/69092	Loss: 110.473
+57600/69092	Loss: 112.694
+60800/69092	Loss: 111.446
+64000/69092	Loss: 112.469
+67200/69092	Loss: 112.830
+Training time 0:01:57.592824
+Epoch: 129 Average loss: 112.01
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 270)
+0/69092	Loss: 110.452
+3200/69092	Loss: 110.466
+6400/69092	Loss: 111.432
+9600/69092	Loss: 112.794
+12800/69092	Loss: 112.774
+16000/69092	Loss: 109.937
+19200/69092	Loss: 112.586
+22400/69092	Loss: 112.148
+25600/69092	Loss: 113.248
+28800/69092	Loss: 109.771
+32000/69092	Loss: 111.443
+35200/69092	Loss: 109.777
+38400/69092	Loss: 111.336
+41600/69092	Loss: 112.573
+44800/69092	Loss: 113.080
+48000/69092	Loss: 114.813
+51200/69092	Loss: 111.562
+54400/69092	Loss: 113.657
+57600/69092	Loss: 112.000
+60800/69092	Loss: 112.768
+64000/69092	Loss: 110.541
+67200/69092	Loss: 113.268
+Training time 0:01:57.598200
+Epoch: 130 Average loss: 112.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 271)
+0/69092	Loss: 105.434
+3200/69092	Loss: 111.917
+6400/69092	Loss: 111.433
+9600/69092	Loss: 112.869
+12800/69092	Loss: 111.079
+16000/69092	Loss: 110.561
+19200/69092	Loss: 113.303
+22400/69092	Loss: 113.539
+25600/69092	Loss: 110.971
+28800/69092	Loss: 111.493
+32000/69092	Loss: 113.151
+35200/69092	Loss: 111.416
+38400/69092	Loss: 113.769
+41600/69092	Loss: 112.592
+44800/69092	Loss: 112.700
+48000/69092	Loss: 110.438
+51200/69092	Loss: 111.709
+54400/69092	Loss: 112.481
+57600/69092	Loss: 111.177
+60800/69092	Loss: 111.158
+64000/69092	Loss: 111.630
+67200/69092	Loss: 112.780
+Training time 0:01:57.694994
+Epoch: 131 Average loss: 111.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 272)
+0/69092	Loss: 112.573
+3200/69092	Loss: 111.776
+6400/69092	Loss: 112.478
+9600/69092	Loss: 111.609
+12800/69092	Loss: 110.617
+16000/69092	Loss: 111.480
+19200/69092	Loss: 112.747
+22400/69092	Loss: 113.442
+25600/69092	Loss: 112.510
+28800/69092	Loss: 112.243
+32000/69092	Loss: 113.979
+35200/69092	Loss: 111.217
+38400/69092	Loss: 112.713
+41600/69092	Loss: 110.079
+44800/69092	Loss: 111.208
+48000/69092	Loss: 114.122
+51200/69092	Loss: 111.058
+54400/69092	Loss: 111.174
+57600/69092	Loss: 111.158
+60800/69092	Loss: 110.395
+64000/69092	Loss: 113.077
+67200/69092	Loss: 111.988
+Training time 0:01:58.058514
+Epoch: 132 Average loss: 111.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 273)
+0/69092	Loss: 121.018
+3200/69092	Loss: 112.661
+6400/69092	Loss: 112.562
+9600/69092	Loss: 110.916
+12800/69092	Loss: 112.616
+16000/69092	Loss: 110.914
+19200/69092	Loss: 112.020
+22400/69092	Loss: 111.536
+25600/69092	Loss: 110.976
+28800/69092	Loss: 111.438
+32000/69092	Loss: 112.881
+35200/69092	Loss: 111.717
+38400/69092	Loss: 110.565
+41600/69092	Loss: 111.493
+44800/69092	Loss: 110.398
+48000/69092	Loss: 110.814
+51200/69092	Loss: 113.235
+54400/69092	Loss: 111.401
+57600/69092	Loss: 113.059
+60800/69092	Loss: 112.742
+64000/69092	Loss: 112.204
+67200/69092	Loss: 112.430
+Training time 0:01:56.792400
+Epoch: 133 Average loss: 111.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 274)
+0/69092	Loss: 110.742
+3200/69092	Loss: 113.340
+6400/69092	Loss: 113.245
+9600/69092	Loss: 110.026
+12800/69092	Loss: 113.185
+16000/69092	Loss: 111.091
+19200/69092	Loss: 111.806
+22400/69092	Loss: 111.200
+25600/69092	Loss: 111.964
+28800/69092	Loss: 109.868
+32000/69092	Loss: 111.438
+35200/69092	Loss: 112.407
+38400/69092	Loss: 111.131
+41600/69092	Loss: 111.926
+44800/69092	Loss: 112.104
+48000/69092	Loss: 113.281
+51200/69092	Loss: 112.860
+54400/69092	Loss: 112.034
+57600/69092	Loss: 112.700
+60800/69092	Loss: 113.316
+64000/69092	Loss: 110.939
+67200/69092	Loss: 111.988
+Training time 0:01:57.272370
+Epoch: 134 Average loss: 111.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 275)
+0/69092	Loss: 127.176
+3200/69092	Loss: 111.731
+6400/69092	Loss: 110.770
+9600/69092	Loss: 112.029
+12800/69092	Loss: 112.023
+16000/69092	Loss: 112.091
+19200/69092	Loss: 112.280
+22400/69092	Loss: 111.250
+25600/69092	Loss: 112.418
+28800/69092	Loss: 111.083
+32000/69092	Loss: 111.736
+35200/69092	Loss: 111.939
+38400/69092	Loss: 113.224
+41600/69092	Loss: 111.375
+44800/69092	Loss: 113.684
+48000/69092	Loss: 113.034
+51200/69092	Loss: 111.617
+54400/69092	Loss: 113.043
+57600/69092	Loss: 111.940
+60800/69092	Loss: 111.944
+64000/69092	Loss: 111.805
+67200/69092	Loss: 111.653
+Training time 0:01:57.058910
+Epoch: 135 Average loss: 112.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 276)
+0/69092	Loss: 118.687
+3200/69092	Loss: 112.916
+6400/69092	Loss: 112.381
+9600/69092	Loss: 114.137
+12800/69092	Loss: 111.492
+16000/69092	Loss: 109.961
+19200/69092	Loss: 113.096
+22400/69092	Loss: 111.977
+25600/69092	Loss: 111.565
+28800/69092	Loss: 111.526
+32000/69092	Loss: 111.217
+35200/69092	Loss: 111.367
+38400/69092	Loss: 110.970
+41600/69092	Loss: 112.607
+44800/69092	Loss: 114.355
+48000/69092	Loss: 111.468
+51200/69092	Loss: 112.745
+54400/69092	Loss: 110.527
+57600/69092	Loss: 111.736
+60800/69092	Loss: 110.270
+64000/69092	Loss: 113.704
+67200/69092	Loss: 111.488
+Training time 0:01:56.959955
+Epoch: 136 Average loss: 112.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 277)
+0/69092	Loss: 109.122
+3200/69092	Loss: 111.330
+6400/69092	Loss: 111.645
+9600/69092	Loss: 113.376
+12800/69092	Loss: 110.622
+16000/69092	Loss: 111.457
+19200/69092	Loss: 113.859
+22400/69092	Loss: 111.271
+25600/69092	Loss: 109.988
+28800/69092	Loss: 110.879
+32000/69092	Loss: 112.654
+35200/69092	Loss: 113.028
+38400/69092	Loss: 110.386
+41600/69092	Loss: 111.067
+44800/69092	Loss: 109.077
+48000/69092	Loss: 113.784
+51200/69092	Loss: 113.707
+54400/69092	Loss: 112.856
+57600/69092	Loss: 112.763
+60800/69092	Loss: 113.247
+64000/69092	Loss: 112.301
+67200/69092	Loss: 111.590
+Training time 0:01:57.133902
+Epoch: 137 Average loss: 111.93
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 278)
+0/69092	Loss: 107.406
+3200/69092	Loss: 112.618
+6400/69092	Loss: 113.154
+9600/69092	Loss: 112.063
+12800/69092	Loss: 112.073
+16000/69092	Loss: 111.946
+19200/69092	Loss: 112.897
+22400/69092	Loss: 112.109
+25600/69092	Loss: 111.530
+28800/69092	Loss: 112.458
+32000/69092	Loss: 112.763
+35200/69092	Loss: 110.965
+38400/69092	Loss: 113.469
+41600/69092	Loss: 110.809
+44800/69092	Loss: 111.943
+48000/69092	Loss: 114.101
+51200/69092	Loss: 113.021
+54400/69092	Loss: 110.293
+57600/69092	Loss: 111.147
+60800/69092	Loss: 111.424
+64000/69092	Loss: 111.865
+67200/69092	Loss: 111.375
+Training time 0:01:57.266665
+Epoch: 138 Average loss: 112.02
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 279)
+0/69092	Loss: 103.212
+3200/69092	Loss: 112.472
+6400/69092	Loss: 113.479
+9600/69092	Loss: 112.589
+12800/69092	Loss: 112.073
+16000/69092	Loss: 111.552
+19200/69092	Loss: 111.969
+22400/69092	Loss: 111.225
+25600/69092	Loss: 113.232
+28800/69092	Loss: 112.642
+32000/69092	Loss: 111.045
+35200/69092	Loss: 110.319
+38400/69092	Loss: 109.832
+41600/69092	Loss: 110.909
+44800/69092	Loss: 111.229
+48000/69092	Loss: 113.238
+51200/69092	Loss: 112.517
+54400/69092	Loss: 113.034
+57600/69092	Loss: 112.834
+60800/69092	Loss: 112.623
+64000/69092	Loss: 111.474
+67200/69092	Loss: 112.129
+Training time 0:01:58.435114
+Epoch: 139 Average loss: 112.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 280)
+0/69092	Loss: 108.457
+3200/69092	Loss: 110.087
+6400/69092	Loss: 110.082
+9600/69092	Loss: 111.008
+12800/69092	Loss: 112.120
+16000/69092	Loss: 112.566
+19200/69092	Loss: 111.473
+22400/69092	Loss: 111.214
+25600/69092	Loss: 113.012
+28800/69092	Loss: 113.351
+32000/69092	Loss: 113.902
+35200/69092	Loss: 111.173
+38400/69092	Loss: 112.956
+41600/69092	Loss: 110.527
+44800/69092	Loss: 114.892
+48000/69092	Loss: 110.396
+51200/69092	Loss: 113.245
+54400/69092	Loss: 110.763
+57600/69092	Loss: 110.205
+60800/69092	Loss: 111.772
+64000/69092	Loss: 112.627
+67200/69092	Loss: 111.441
+Training time 0:01:56.262381
+Epoch: 140 Average loss: 111.89
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 281)
+0/69092	Loss: 98.658
+3200/69092	Loss: 113.159
+6400/69092	Loss: 110.102
+9600/69092	Loss: 112.839
+12800/69092	Loss: 112.983
+16000/69092	Loss: 110.094
+19200/69092	Loss: 111.538
+22400/69092	Loss: 112.158
+25600/69092	Loss: 112.301
+28800/69092	Loss: 111.748
+32000/69092	Loss: 110.355
+35200/69092	Loss: 110.992
+38400/69092	Loss: 112.652
+41600/69092	Loss: 113.077
+44800/69092	Loss: 111.689
+48000/69092	Loss: 112.482
+51200/69092	Loss: 111.524
+54400/69092	Loss: 111.967
+57600/69092	Loss: 111.668
+60800/69092	Loss: 110.932
+64000/69092	Loss: 112.046
+67200/69092	Loss: 113.020
+Training time 0:01:58.535137
+Epoch: 141 Average loss: 111.89
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 282)
+0/69092	Loss: 124.952
+3200/69092	Loss: 112.607
+6400/69092	Loss: 113.206
+9600/69092	Loss: 110.438
+12800/69092	Loss: 112.742
+16000/69092	Loss: 112.216
+19200/69092	Loss: 112.843
+22400/69092	Loss: 111.532
+25600/69092	Loss: 111.301
+28800/69092	Loss: 110.823
+32000/69092	Loss: 111.241
+35200/69092	Loss: 112.160
+38400/69092	Loss: 111.847
+41600/69092	Loss: 112.071
+44800/69092	Loss: 112.332
+48000/69092	Loss: 112.300
+51200/69092	Loss: 111.658
+54400/69092	Loss: 112.571
+57600/69092	Loss: 110.150
+60800/69092	Loss: 112.637
+64000/69092	Loss: 111.443
+67200/69092	Loss: 112.552
+Training time 0:01:57.326723
+Epoch: 142 Average loss: 111.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 283)
+0/69092	Loss: 108.045
+3200/69092	Loss: 112.897
+6400/69092	Loss: 112.089
+9600/69092	Loss: 110.165
+12800/69092	Loss: 111.870
+16000/69092	Loss: 112.836
+19200/69092	Loss: 113.111
+22400/69092	Loss: 111.287
+25600/69092	Loss: 111.836
+28800/69092	Loss: 112.930
+32000/69092	Loss: 111.349
+35200/69092	Loss: 112.133
+38400/69092	Loss: 111.580
+41600/69092	Loss: 113.092
+44800/69092	Loss: 112.667
+48000/69092	Loss: 111.006
+51200/69092	Loss: 111.750
+54400/69092	Loss: 110.065
+57600/69092	Loss: 112.065
+60800/69092	Loss: 111.136
+64000/69092	Loss: 111.657
+67200/69092	Loss: 113.054
+Training time 0:01:57.236775
+Epoch: 143 Average loss: 111.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 284)
+0/69092	Loss: 106.834
+3200/69092	Loss: 111.079
+6400/69092	Loss: 111.140
+9600/69092	Loss: 113.726
+12800/69092	Loss: 111.682
+16000/69092	Loss: 111.500
+19200/69092	Loss: 112.017
+22400/69092	Loss: 112.366
+25600/69092	Loss: 113.172
+28800/69092	Loss: 110.644
+32000/69092	Loss: 111.564
+35200/69092	Loss: 111.866
+38400/69092	Loss: 111.532
+41600/69092	Loss: 111.272
+44800/69092	Loss: 112.606
+48000/69092	Loss: 112.309
+51200/69092	Loss: 112.685
+54400/69092	Loss: 111.293
+57600/69092	Loss: 111.289
+60800/69092	Loss: 110.347
+64000/69092	Loss: 112.299
+67200/69092	Loss: 110.649
+Training time 0:01:58.010957
+Epoch: 144 Average loss: 111.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 285)
+0/69092	Loss: 118.306
+3200/69092	Loss: 111.237
+6400/69092	Loss: 111.661
+9600/69092	Loss: 114.291
+12800/69092	Loss: 113.517
+16000/69092	Loss: 110.445
+19200/69092	Loss: 111.158
+22400/69092	Loss: 109.768
+25600/69092	Loss: 110.498
+28800/69092	Loss: 113.142
+32000/69092	Loss: 113.399
+35200/69092	Loss: 111.526
+38400/69092	Loss: 113.117
+41600/69092	Loss: 110.777
+44800/69092	Loss: 111.227
+48000/69092	Loss: 112.151
+51200/69092	Loss: 111.684
+54400/69092	Loss: 110.881
+57600/69092	Loss: 111.018
+60800/69092	Loss: 113.214
+64000/69092	Loss: 111.659
+67200/69092	Loss: 111.615
+Training time 0:01:58.616269
+Epoch: 145 Average loss: 111.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 286)
+0/69092	Loss: 115.363
+3200/69092	Loss: 111.312
+6400/69092	Loss: 112.252
+9600/69092	Loss: 112.526
+12800/69092	Loss: 112.737
+16000/69092	Loss: 111.285
+19200/69092	Loss: 112.019
+22400/69092	Loss: 112.912
+25600/69092	Loss: 110.939
+28800/69092	Loss: 113.678
+32000/69092	Loss: 109.269
+35200/69092	Loss: 110.789
+38400/69092	Loss: 113.603
+41600/69092	Loss: 112.517
+44800/69092	Loss: 111.067
+48000/69092	Loss: 112.921
+51200/69092	Loss: 112.439
+54400/69092	Loss: 110.862
+57600/69092	Loss: 112.110
+60800/69092	Loss: 113.408
+64000/69092	Loss: 109.576
+67200/69092	Loss: 112.647
+Training time 0:01:57.659672
+Epoch: 146 Average loss: 111.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 287)
+0/69092	Loss: 117.260
+3200/69092	Loss: 111.658
+6400/69092	Loss: 109.211
+9600/69092	Loss: 113.671
+12800/69092	Loss: 111.070
+16000/69092	Loss: 112.197
+19200/69092	Loss: 113.150
+22400/69092	Loss: 112.106
+25600/69092	Loss: 110.923
+28800/69092	Loss: 110.465
+32000/69092	Loss: 112.369
+35200/69092	Loss: 111.273
+38400/69092	Loss: 111.069
+41600/69092	Loss: 112.165
+44800/69092	Loss: 110.222
+48000/69092	Loss: 113.154
+51200/69092	Loss: 114.592
+54400/69092	Loss: 112.268
+57600/69092	Loss: 112.081
+60800/69092	Loss: 112.133
+64000/69092	Loss: 112.107
+67200/69092	Loss: 111.921
+Training time 0:01:58.866951
+Epoch: 147 Average loss: 111.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 288)
+0/69092	Loss: 125.864
+3200/69092	Loss: 112.431
+6400/69092	Loss: 111.947
+9600/69092	Loss: 112.225
+12800/69092	Loss: 112.134
+16000/69092	Loss: 112.516
+19200/69092	Loss: 112.602
+22400/69092	Loss: 109.668
+25600/69092	Loss: 113.651
+28800/69092	Loss: 111.084
+32000/69092	Loss: 113.826
+35200/69092	Loss: 111.270
+38400/69092	Loss: 112.044
+41600/69092	Loss: 110.729
+44800/69092	Loss: 111.775
+48000/69092	Loss: 111.153
+51200/69092	Loss: 112.430
+54400/69092	Loss: 111.239
+57600/69092	Loss: 111.362
+60800/69092	Loss: 111.584
+64000/69092	Loss: 110.666
+67200/69092	Loss: 112.932
+Training time 0:01:56.854656
+Epoch: 148 Average loss: 111.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 289)
+0/69092	Loss: 120.989
+3200/69092	Loss: 110.863
+6400/69092	Loss: 111.532
+9600/69092	Loss: 110.060
+12800/69092	Loss: 111.442
+16000/69092	Loss: 112.615
+19200/69092	Loss: 110.756
+22400/69092	Loss: 113.074
+25600/69092	Loss: 111.051
+28800/69092	Loss: 114.605
+32000/69092	Loss: 112.238
+35200/69092	Loss: 112.525
+38400/69092	Loss: 112.964
+41600/69092	Loss: 110.887
+44800/69092	Loss: 109.753
+48000/69092	Loss: 110.362
+51200/69092	Loss: 113.599
+54400/69092	Loss: 111.841
+57600/69092	Loss: 109.317
+60800/69092	Loss: 111.099
+64000/69092	Loss: 113.230
+67200/69092	Loss: 111.380
+Training time 0:01:57.507897
+Epoch: 149 Average loss: 111.70
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 290)
+0/69092	Loss: 117.476
+3200/69092	Loss: 112.599
+6400/69092	Loss: 110.336
+9600/69092	Loss: 112.918
+12800/69092	Loss: 110.651
+16000/69092	Loss: 111.573
+19200/69092	Loss: 112.520
+22400/69092	Loss: 111.845
+25600/69092	Loss: 110.945
+28800/69092	Loss: 113.548
+32000/69092	Loss: 111.766
+35200/69092	Loss: 112.943
+38400/69092	Loss: 111.785
+41600/69092	Loss: 111.330
+44800/69092	Loss: 111.686
+48000/69092	Loss: 112.200
+51200/69092	Loss: 109.573
+54400/69092	Loss: 112.740
+57600/69092	Loss: 112.079
+60800/69092	Loss: 112.911
+64000/69092	Loss: 112.236
+67200/69092	Loss: 112.986
+Training time 0:01:58.333631
+Epoch: 150 Average loss: 111.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 291)
+0/69092	Loss: 115.953
+3200/69092	Loss: 113.134
+6400/69092	Loss: 113.103
+9600/69092	Loss: 110.199
+12800/69092	Loss: 113.194
+16000/69092	Loss: 111.184
+19200/69092	Loss: 111.698
+22400/69092	Loss: 112.935
+25600/69092	Loss: 111.881
+28800/69092	Loss: 111.115
+32000/69092	Loss: 112.603
+35200/69092	Loss: 112.020
+38400/69092	Loss: 112.642
+41600/69092	Loss: 111.432
+44800/69092	Loss: 112.172
+48000/69092	Loss: 110.613
+51200/69092	Loss: 111.053
+54400/69092	Loss: 111.428
+57600/69092	Loss: 112.518
+60800/69092	Loss: 111.422
+64000/69092	Loss: 111.675
+67200/69092	Loss: 112.371
+Training time 0:01:58.128314
+Epoch: 151 Average loss: 111.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 292)
+0/69092	Loss: 104.421
+3200/69092	Loss: 111.188
+6400/69092	Loss: 112.041
+9600/69092	Loss: 111.957
+12800/69092	Loss: 110.362
+16000/69092	Loss: 112.861
+19200/69092	Loss: 112.682
+22400/69092	Loss: 111.382
+25600/69092	Loss: 111.200
+28800/69092	Loss: 109.572
+32000/69092	Loss: 111.507
+35200/69092	Loss: 114.643
+38400/69092	Loss: 113.705
+41600/69092	Loss: 111.291
+44800/69092	Loss: 113.218
+48000/69092	Loss: 111.520
+51200/69092	Loss: 111.699
+54400/69092	Loss: 111.856
+57600/69092	Loss: 112.596
+60800/69092	Loss: 111.065
+64000/69092	Loss: 112.072
+67200/69092	Loss: 111.599
+Training time 0:01:56.836098
+Epoch: 152 Average loss: 111.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 293)
+0/69092	Loss: 107.049
+3200/69092	Loss: 111.545
+6400/69092	Loss: 112.860
+9600/69092	Loss: 109.550
+12800/69092	Loss: 109.521
+16000/69092	Loss: 111.000
+19200/69092	Loss: 112.895
+22400/69092	Loss: 109.538
+25600/69092	Loss: 111.365
+28800/69092	Loss: 113.803
+32000/69092	Loss: 111.668
+35200/69092	Loss: 112.163
+38400/69092	Loss: 112.722
+41600/69092	Loss: 112.485
+44800/69092	Loss: 112.295
+48000/69092	Loss: 112.679
+51200/69092	Loss: 113.590
+54400/69092	Loss: 110.211
+57600/69092	Loss: 109.907
+60800/69092	Loss: 113.576
+64000/69092	Loss: 112.936
+67200/69092	Loss: 110.808
+Training time 0:01:57.354715
+Epoch: 153 Average loss: 111.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 294)
+0/69092	Loss: 111.501
+3200/69092	Loss: 111.638
+6400/69092	Loss: 110.784
+9600/69092	Loss: 113.868
+12800/69092	Loss: 110.259
+16000/69092	Loss: 112.660
+19200/69092	Loss: 111.296
+22400/69092	Loss: 112.705
+25600/69092	Loss: 110.795
+28800/69092	Loss: 111.370
+32000/69092	Loss: 112.506
+35200/69092	Loss: 111.228
+38400/69092	Loss: 111.229
+41600/69092	Loss: 110.885
+44800/69092	Loss: 111.343
+48000/69092	Loss: 112.372
+51200/69092	Loss: 111.562
+54400/69092	Loss: 112.554
+57600/69092	Loss: 112.194
+60800/69092	Loss: 110.748
+64000/69092	Loss: 113.162
+67200/69092	Loss: 111.802
+Training time 0:01:57.258471
+Epoch: 154 Average loss: 111.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 295)
+0/69092	Loss: 108.742
+3200/69092	Loss: 110.511
+6400/69092	Loss: 111.703
+9600/69092	Loss: 110.358
+12800/69092	Loss: 112.914
+16000/69092	Loss: 111.439
+19200/69092	Loss: 112.426
+22400/69092	Loss: 112.589
+25600/69092	Loss: 111.170
+28800/69092	Loss: 111.646
+32000/69092	Loss: 111.722
+35200/69092	Loss: 110.836
+38400/69092	Loss: 112.782
+41600/69092	Loss: 111.605
+44800/69092	Loss: 113.442
+48000/69092	Loss: 110.468
+51200/69092	Loss: 110.794
+54400/69092	Loss: 110.828
+57600/69092	Loss: 113.813
+60800/69092	Loss: 110.367
+64000/69092	Loss: 112.361
+67200/69092	Loss: 111.840
+Training time 0:01:57.111745
+Epoch: 155 Average loss: 111.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 296)
+0/69092	Loss: 118.872
+3200/69092	Loss: 110.466
+6400/69092	Loss: 114.033
+9600/69092	Loss: 111.255
+12800/69092	Loss: 113.188
+16000/69092	Loss: 111.942
+19200/69092	Loss: 110.293
+22400/69092	Loss: 112.721
+25600/69092	Loss: 110.724
+28800/69092	Loss: 111.917
+32000/69092	Loss: 111.823
+35200/69092	Loss: 111.921
+38400/69092	Loss: 112.379
+41600/69092	Loss: 111.464
+44800/69092	Loss: 109.217
+48000/69092	Loss: 111.438
+51200/69092	Loss: 112.555
+54400/69092	Loss: 113.719
+57600/69092	Loss: 110.811
+60800/69092	Loss: 112.150
+64000/69092	Loss: 112.173
+67200/69092	Loss: 110.787
+Training time 0:01:56.623030
+Epoch: 156 Average loss: 111.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 297)
+0/69092	Loss: 114.869
+3200/69092	Loss: 111.270
+6400/69092	Loss: 110.590
+9600/69092	Loss: 111.682
+12800/69092	Loss: 111.369
+16000/69092	Loss: 111.500
+19200/69092	Loss: 113.862
+22400/69092	Loss: 112.474
+25600/69092	Loss: 113.168
+28800/69092	Loss: 110.683
+32000/69092	Loss: 111.306
+35200/69092	Loss: 109.768
+38400/69092	Loss: 111.974
+41600/69092	Loss: 112.443
+44800/69092	Loss: 113.173
+48000/69092	Loss: 111.066
+51200/69092	Loss: 110.422
+54400/69092	Loss: 110.778
+57600/69092	Loss: 110.822
+60800/69092	Loss: 112.678
+64000/69092	Loss: 111.867
+67200/69092	Loss: 111.467
+Training time 0:01:57.529709
+Epoch: 157 Average loss: 111.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 298)
+0/69092	Loss: 124.694
+3200/69092	Loss: 111.638
+6400/69092	Loss: 110.301
+9600/69092	Loss: 112.122
+12800/69092	Loss: 110.529
+16000/69092	Loss: 109.903
+19200/69092	Loss: 112.519
+22400/69092	Loss: 112.301
+25600/69092	Loss: 110.694
+28800/69092	Loss: 113.052
+32000/69092	Loss: 112.189
+35200/69092	Loss: 113.203
+38400/69092	Loss: 110.898
+41600/69092	Loss: 110.987
+44800/69092	Loss: 110.335
+48000/69092	Loss: 110.953
+51200/69092	Loss: 112.360
+54400/69092	Loss: 112.887
+57600/69092	Loss: 112.167
+60800/69092	Loss: 113.126
+64000/69092	Loss: 113.502
+67200/69092	Loss: 110.894
+Training time 0:01:57.557864
+Epoch: 158 Average loss: 111.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 299)
+0/69092	Loss: 114.132
+3200/69092	Loss: 112.711
+6400/69092	Loss: 112.266
+9600/69092	Loss: 111.940
+12800/69092	Loss: 110.040
+16000/69092	Loss: 110.274
+19200/69092	Loss: 111.464
+22400/69092	Loss: 114.249
+25600/69092	Loss: 110.451
+28800/69092	Loss: 111.813
+32000/69092	Loss: 110.874
+35200/69092	Loss: 111.192
+38400/69092	Loss: 110.178
+41600/69092	Loss: 111.566
+44800/69092	Loss: 112.302
+48000/69092	Loss: 112.227
+51200/69092	Loss: 112.962
+54400/69092	Loss: 112.561
+57600/69092	Loss: 111.218
+60800/69092	Loss: 113.534
+64000/69092	Loss: 111.048
+67200/69092	Loss: 110.810
+Training time 0:01:58.071249
+Epoch: 159 Average loss: 111.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 300)
+0/69092	Loss: 123.650
+3200/69092	Loss: 111.714
+6400/69092	Loss: 113.229
+9600/69092	Loss: 113.051
+12800/69092	Loss: 111.433
+16000/69092	Loss: 110.213
+19200/69092	Loss: 111.912
+22400/69092	Loss: 112.486
+25600/69092	Loss: 111.917
+28800/69092	Loss: 112.611
+32000/69092	Loss: 110.129
+35200/69092	Loss: 112.950
+38400/69092	Loss: 112.496
+41600/69092	Loss: 111.032
+44800/69092	Loss: 112.336
+48000/69092	Loss: 109.904
+51200/69092	Loss: 111.151
+54400/69092	Loss: 111.449
+57600/69092	Loss: 111.840
+60800/69092	Loss: 110.888
+64000/69092	Loss: 112.554
+67200/69092	Loss: 110.121
+Training time 0:01:58.034978
+Epoch: 160 Average loss: 111.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 301)
+0/69092	Loss: 116.410
+3200/69092	Loss: 110.600
+6400/69092	Loss: 112.400
+9600/69092	Loss: 111.510
+12800/69092	Loss: 110.163
+16000/69092	Loss: 110.768
+19200/69092	Loss: 114.241
+22400/69092	Loss: 112.413
+25600/69092	Loss: 113.094
+28800/69092	Loss: 110.829
+32000/69092	Loss: 113.481
+35200/69092	Loss: 112.277
+38400/69092	Loss: 112.755
+41600/69092	Loss: 112.485
+44800/69092	Loss: 111.657
+48000/69092	Loss: 111.142
+51200/69092	Loss: 110.248
+54400/69092	Loss: 109.791
+57600/69092	Loss: 112.472
+60800/69092	Loss: 112.471
+64000/69092	Loss: 112.048
+67200/69092	Loss: 111.838
+Training time 0:01:56.366043
+Epoch: 161 Average loss: 111.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 302)
+0/69092	Loss: 113.542
+3200/69092	Loss: 110.087
+6400/69092	Loss: 113.418
+9600/69092	Loss: 112.001
+12800/69092	Loss: 113.136
+16000/69092	Loss: 111.554
+19200/69092	Loss: 112.779
+22400/69092	Loss: 111.375
+25600/69092	Loss: 110.408
+28800/69092	Loss: 112.812
+32000/69092	Loss: 112.009
+35200/69092	Loss: 111.353
+38400/69092	Loss: 110.976
+41600/69092	Loss: 113.415
+44800/69092	Loss: 112.298
+48000/69092	Loss: 110.774
+51200/69092	Loss: 108.745
+54400/69092	Loss: 112.864
+57600/69092	Loss: 109.988
+60800/69092	Loss: 112.738
+64000/69092	Loss: 110.572
+67200/69092	Loss: 112.847
+Training time 0:01:58.019849
+Epoch: 162 Average loss: 111.70
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 303)
+0/69092	Loss: 119.715
+3200/69092	Loss: 110.518
+6400/69092	Loss: 111.559
+9600/69092	Loss: 112.434
+12800/69092	Loss: 112.339
+16000/69092	Loss: 112.397
+19200/69092	Loss: 110.979
+22400/69092	Loss: 110.737
+25600/69092	Loss: 111.644
+28800/69092	Loss: 112.902
+32000/69092	Loss: 111.472
+35200/69092	Loss: 113.155
+38400/69092	Loss: 109.615
+41600/69092	Loss: 112.107
+44800/69092	Loss: 111.096
+48000/69092	Loss: 112.698
+51200/69092	Loss: 112.322
+54400/69092	Loss: 111.918
+57600/69092	Loss: 112.837
+60800/69092	Loss: 110.629
+64000/69092	Loss: 111.170
+67200/69092	Loss: 112.125
+Training time 0:01:57.907451
+Epoch: 163 Average loss: 111.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 304)
+0/69092	Loss: 134.733
+3200/69092	Loss: 111.368
+6400/69092	Loss: 112.738
+9600/69092	Loss: 112.785
+12800/69092	Loss: 111.969
+16000/69092	Loss: 111.528
+19200/69092	Loss: 109.242
+22400/69092	Loss: 113.343
+25600/69092	Loss: 113.095
+28800/69092	Loss: 110.104
+32000/69092	Loss: 111.320
+35200/69092	Loss: 110.740
+38400/69092	Loss: 109.703
+41600/69092	Loss: 111.375
+44800/69092	Loss: 111.857
+48000/69092	Loss: 112.100
+51200/69092	Loss: 112.985
+54400/69092	Loss: 111.972
+57600/69092	Loss: 109.736
+60800/69092	Loss: 112.205
+64000/69092	Loss: 111.661
+67200/69092	Loss: 112.903
+Training time 0:01:58.142396
+Epoch: 164 Average loss: 111.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 305)
+0/69092	Loss: 118.115
+3200/69092	Loss: 110.009
+6400/69092	Loss: 111.570
+9600/69092	Loss: 112.055
+12800/69092	Loss: 113.302
+16000/69092	Loss: 110.916
+19200/69092	Loss: 111.357
+22400/69092	Loss: 111.904
+25600/69092	Loss: 110.185
+28800/69092	Loss: 111.550
+32000/69092	Loss: 113.098
+35200/69092	Loss: 110.133
+38400/69092	Loss: 111.114
+41600/69092	Loss: 112.392
+44800/69092	Loss: 114.921
+48000/69092	Loss: 111.498
+51200/69092	Loss: 111.717
+54400/69092	Loss: 111.010
+57600/69092	Loss: 111.393
+60800/69092	Loss: 111.221
+64000/69092	Loss: 111.821
+67200/69092	Loss: 111.310
+Training time 0:01:57.883301
+Epoch: 165 Average loss: 111.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 306)
+0/69092	Loss: 109.505
+3200/69092	Loss: 109.239
+6400/69092	Loss: 112.679
+9600/69092	Loss: 111.230
+12800/69092	Loss: 112.153
+16000/69092	Loss: 108.978
+19200/69092	Loss: 112.635
+22400/69092	Loss: 111.655
+25600/69092	Loss: 110.654
+28800/69092	Loss: 111.396
+32000/69092	Loss: 110.772
+35200/69092	Loss: 110.620
+38400/69092	Loss: 111.977
+41600/69092	Loss: 112.527
+44800/69092	Loss: 112.819
+48000/69092	Loss: 113.921
+51200/69092	Loss: 112.138
+54400/69092	Loss: 111.674
+57600/69092	Loss: 111.156
+60800/69092	Loss: 112.003
+64000/69092	Loss: 112.026
+67200/69092	Loss: 113.795
+Training time 0:01:57.628814
+Epoch: 166 Average loss: 111.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 307)
+0/69092	Loss: 111.059
+3200/69092	Loss: 111.078
+6400/69092	Loss: 111.255
+9600/69092	Loss: 111.719
+12800/69092	Loss: 110.882
+16000/69092	Loss: 114.077
+19200/69092	Loss: 111.082
+22400/69092	Loss: 112.027
+25600/69092	Loss: 110.732
+28800/69092	Loss: 111.248
+32000/69092	Loss: 112.801
+35200/69092	Loss: 110.367
+38400/69092	Loss: 111.870
+41600/69092	Loss: 111.445
+44800/69092	Loss: 112.290
+48000/69092	Loss: 112.549
+51200/69092	Loss: 111.689
+54400/69092	Loss: 111.940
+57600/69092	Loss: 112.155
+60800/69092	Loss: 112.263
+64000/69092	Loss: 112.639
+67200/69092	Loss: 111.075
+Training time 0:01:58.999863
+Epoch: 167 Average loss: 111.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 308)
+0/69092	Loss: 105.557
+3200/69092	Loss: 112.981
+6400/69092	Loss: 112.326
+9600/69092	Loss: 110.460
+12800/69092	Loss: 111.842
+16000/69092	Loss: 112.710
+19200/69092	Loss: 113.543
+22400/69092	Loss: 112.468
+25600/69092	Loss: 110.899
+28800/69092	Loss: 113.226
+32000/69092	Loss: 111.981
+35200/69092	Loss: 110.594
+38400/69092	Loss: 110.432
+41600/69092	Loss: 110.462
+44800/69092	Loss: 112.145
+48000/69092	Loss: 112.282
+51200/69092	Loss: 110.518
+54400/69092	Loss: 112.326
+57600/69092	Loss: 112.281
+60800/69092	Loss: 112.349
+64000/69092	Loss: 112.677
+67200/69092	Loss: 111.289
+Training time 0:01:57.916369
+Epoch: 168 Average loss: 111.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 309)
+0/69092	Loss: 106.652
+3200/69092	Loss: 112.091
+6400/69092	Loss: 112.037
+9600/69092	Loss: 111.819
+12800/69092	Loss: 111.874
+16000/69092	Loss: 110.806
+19200/69092	Loss: 112.183
+22400/69092	Loss: 112.422
+25600/69092	Loss: 109.044
+28800/69092	Loss: 112.361
+32000/69092	Loss: 111.146
+35200/69092	Loss: 112.348
+38400/69092	Loss: 110.026
+41600/69092	Loss: 112.366
+44800/69092	Loss: 112.497
+48000/69092	Loss: 111.515
+51200/69092	Loss: 111.187
+54400/69092	Loss: 111.332
+57600/69092	Loss: 109.368
+60800/69092	Loss: 111.638
+64000/69092	Loss: 114.175
+67200/69092	Loss: 110.968
+Training time 0:01:58.337494
+Epoch: 169 Average loss: 111.60
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 310)
+0/69092	Loss: 116.036
+3200/69092	Loss: 109.581
+6400/69092	Loss: 112.270
+9600/69092	Loss: 111.920
+12800/69092	Loss: 111.055
+16000/69092	Loss: 110.885
+19200/69092	Loss: 112.197
+22400/69092	Loss: 111.740
+25600/69092	Loss: 111.951
+28800/69092	Loss: 111.953
+32000/69092	Loss: 109.690
+35200/69092	Loss: 111.384
+38400/69092	Loss: 109.769
+41600/69092	Loss: 111.808
+44800/69092	Loss: 111.351
+48000/69092	Loss: 111.708
+51200/69092	Loss: 112.546
+54400/69092	Loss: 111.851
+57600/69092	Loss: 111.209
+60800/69092	Loss: 113.422
+64000/69092	Loss: 113.189
+67200/69092	Loss: 111.935
+Training time 0:01:57.384316
+Epoch: 170 Average loss: 111.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 311)
+0/69092	Loss: 127.703
+3200/69092	Loss: 111.678
+6400/69092	Loss: 110.477
+9600/69092	Loss: 111.366
+12800/69092	Loss: 111.567
+16000/69092	Loss: 113.184
+19200/69092	Loss: 112.240
+22400/69092	Loss: 111.619
+25600/69092	Loss: 108.892
+28800/69092	Loss: 112.422
+32000/69092	Loss: 112.121
+35200/69092	Loss: 111.129
+38400/69092	Loss: 112.726
+41600/69092	Loss: 110.563
+44800/69092	Loss: 111.391
+48000/69092	Loss: 111.781
+51200/69092	Loss: 112.103
+54400/69092	Loss: 110.624
+57600/69092	Loss: 111.868
+60800/69092	Loss: 111.465
+64000/69092	Loss: 112.831
+67200/69092	Loss: 113.136
+Training time 0:01:57.705388
+Epoch: 171 Average loss: 111.70
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 312)
+0/69092	Loss: 108.844
+3200/69092	Loss: 111.695
+6400/69092	Loss: 111.223
+9600/69092	Loss: 112.083
+12800/69092	Loss: 112.880
+16000/69092	Loss: 110.638
+19200/69092	Loss: 112.117
+22400/69092	Loss: 111.752
+25600/69092	Loss: 110.880
+28800/69092	Loss: 111.832
+32000/69092	Loss: 110.838
+35200/69092	Loss: 112.321
+38400/69092	Loss: 111.527
+41600/69092	Loss: 112.675
+44800/69092	Loss: 110.984
+48000/69092	Loss: 113.043
+51200/69092	Loss: 109.742
+54400/69092	Loss: 110.143
+57600/69092	Loss: 110.711
+60800/69092	Loss: 112.018
+64000/69092	Loss: 112.209
+67200/69092	Loss: 112.814
+Training time 0:01:57.964064
+Epoch: 172 Average loss: 111.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 313)
+0/69092	Loss: 117.573
+3200/69092	Loss: 111.334
+6400/69092	Loss: 112.929
+9600/69092	Loss: 111.225
+12800/69092	Loss: 111.788
+16000/69092	Loss: 110.620
+19200/69092	Loss: 110.578
+22400/69092	Loss: 111.633
+25600/69092	Loss: 112.730
+28800/69092	Loss: 112.205
+32000/69092	Loss: 111.285
+35200/69092	Loss: 111.097
+38400/69092	Loss: 110.843
+41600/69092	Loss: 110.864
+44800/69092	Loss: 110.815
+48000/69092	Loss: 112.478
+51200/69092	Loss: 113.079
+54400/69092	Loss: 111.633
+57600/69092	Loss: 110.176
+60800/69092	Loss: 112.667
+64000/69092	Loss: 110.104
+67200/69092	Loss: 112.285
+Training time 0:01:56.752130
+Epoch: 173 Average loss: 111.55
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 314)
+0/69092	Loss: 110.782
+3200/69092	Loss: 111.362
+6400/69092	Loss: 113.374
+9600/69092	Loss: 112.839
+12800/69092	Loss: 112.448
+16000/69092	Loss: 112.099
+19200/69092	Loss: 111.209
+22400/69092	Loss: 110.317
+25600/69092	Loss: 111.654
+28800/69092	Loss: 110.356
+32000/69092	Loss: 110.643
+35200/69092	Loss: 111.474
+38400/69092	Loss: 111.918
+41600/69092	Loss: 111.560
+44800/69092	Loss: 111.214
+48000/69092	Loss: 111.769
+51200/69092	Loss: 110.218
+54400/69092	Loss: 112.135
+57600/69092	Loss: 112.353
+60800/69092	Loss: 112.673
+64000/69092	Loss: 110.937
+67200/69092	Loss: 112.363
+Training time 0:01:58.067145
+Epoch: 174 Average loss: 111.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 315)
+0/69092	Loss: 114.900
+3200/69092	Loss: 111.194
+6400/69092	Loss: 110.765
+9600/69092	Loss: 113.184
+12800/69092	Loss: 111.068
+16000/69092	Loss: 111.820
+19200/69092	Loss: 110.786
+22400/69092	Loss: 110.856
+25600/69092	Loss: 112.766
+28800/69092	Loss: 112.088
+32000/69092	Loss: 110.329
+35200/69092	Loss: 112.775
+38400/69092	Loss: 111.490
+41600/69092	Loss: 112.312
+44800/69092	Loss: 112.025
+48000/69092	Loss: 110.544
+51200/69092	Loss: 112.408
+54400/69092	Loss: 110.298
+57600/69092	Loss: 111.694
+60800/69092	Loss: 111.988
+64000/69092	Loss: 111.013
+67200/69092	Loss: 110.715
+Training time 0:01:58.014468
+Epoch: 175 Average loss: 111.51
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 316)
+0/69092	Loss: 119.735
+3200/69092	Loss: 110.581
+6400/69092	Loss: 110.471
+9600/69092	Loss: 110.051
+12800/69092	Loss: 111.062
+16000/69092	Loss: 111.943
+19200/69092	Loss: 112.570
+22400/69092	Loss: 113.682
+25600/69092	Loss: 110.904
+28800/69092	Loss: 110.676
+32000/69092	Loss: 111.977
+35200/69092	Loss: 111.398
+38400/69092	Loss: 110.633
+41600/69092	Loss: 111.087
+44800/69092	Loss: 113.643
+48000/69092	Loss: 111.851
+51200/69092	Loss: 112.379
+54400/69092	Loss: 111.891
+57600/69092	Loss: 109.544
+60800/69092	Loss: 111.967
+64000/69092	Loss: 113.614
+67200/69092	Loss: 112.276
+Training time 0:01:58.495056
+Epoch: 176 Average loss: 111.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 317)
+0/69092	Loss: 124.552
+3200/69092	Loss: 112.319
+6400/69092	Loss: 111.483
+9600/69092	Loss: 110.606
+12800/69092	Loss: 112.892
+16000/69092	Loss: 112.674
+19200/69092	Loss: 111.931
+22400/69092	Loss: 109.751
+25600/69092	Loss: 111.548
+28800/69092	Loss: 109.975
+32000/69092	Loss: 111.960
+35200/69092	Loss: 110.546
+38400/69092	Loss: 111.756
+41600/69092	Loss: 111.660
+44800/69092	Loss: 112.089
+48000/69092	Loss: 112.292
+51200/69092	Loss: 111.803
+54400/69092	Loss: 112.917
+57600/69092	Loss: 109.461
+60800/69092	Loss: 109.814
+64000/69092	Loss: 112.297
+67200/69092	Loss: 112.958
+Training time 0:01:57.255709
+Epoch: 177 Average loss: 111.60
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 318)
+0/69092	Loss: 120.511
+3200/69092	Loss: 113.576
+6400/69092	Loss: 111.405
+9600/69092	Loss: 113.479
+12800/69092	Loss: 112.399
+16000/69092	Loss: 109.682
+19200/69092	Loss: 113.299
+22400/69092	Loss: 111.035
+25600/69092	Loss: 111.170
+28800/69092	Loss: 111.269
+32000/69092	Loss: 110.564
+35200/69092	Loss: 111.389
+38400/69092	Loss: 111.394
+41600/69092	Loss: 111.550
+44800/69092	Loss: 113.036
+48000/69092	Loss: 110.169
+51200/69092	Loss: 110.245
+54400/69092	Loss: 110.791
+57600/69092	Loss: 111.674
+60800/69092	Loss: 111.160
+64000/69092	Loss: 113.037
+67200/69092	Loss: 112.097
+Training time 0:01:57.057106
+Epoch: 178 Average loss: 111.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 319)
+0/69092	Loss: 103.304
+3200/69092	Loss: 111.873
+6400/69092	Loss: 110.717
+9600/69092	Loss: 112.062
+12800/69092	Loss: 111.397
+16000/69092	Loss: 111.597
+19200/69092	Loss: 112.798
+22400/69092	Loss: 110.245
+25600/69092	Loss: 110.977
+28800/69092	Loss: 111.191
+32000/69092	Loss: 110.798
+35200/69092	Loss: 110.867
+38400/69092	Loss: 112.236
+41600/69092	Loss: 112.333
+44800/69092	Loss: 110.852
+48000/69092	Loss: 111.670
+51200/69092	Loss: 110.712
+54400/69092	Loss: 111.992
+57600/69092	Loss: 112.176
+60800/69092	Loss: 112.691
+64000/69092	Loss: 111.358
+67200/69092	Loss: 112.990
+Training time 0:01:57.803994
+Epoch: 179 Average loss: 111.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 320)
+0/69092	Loss: 107.170
+3200/69092	Loss: 112.843
+6400/69092	Loss: 111.024
+9600/69092	Loss: 111.109
+12800/69092	Loss: 111.462
+16000/69092	Loss: 110.910
+19200/69092	Loss: 113.197
+22400/69092	Loss: 109.887
+25600/69092	Loss: 111.829
+28800/69092	Loss: 109.722
+32000/69092	Loss: 111.730
+35200/69092	Loss: 112.916
+38400/69092	Loss: 112.945
+41600/69092	Loss: 112.207
+44800/69092	Loss: 109.817
+48000/69092	Loss: 112.512
+51200/69092	Loss: 112.084
+54400/69092	Loss: 111.714
+57600/69092	Loss: 111.316
+60800/69092	Loss: 111.814
+64000/69092	Loss: 110.556
+67200/69092	Loss: 111.118
+Training time 0:01:56.358556
+Epoch: 180 Average loss: 111.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 321)
+0/69092	Loss: 100.743
+3200/69092	Loss: 111.787
+6400/69092	Loss: 111.899
+9600/69092	Loss: 111.281
+12800/69092	Loss: 111.201
+16000/69092	Loss: 110.951
+19200/69092	Loss: 111.522
+22400/69092	Loss: 111.217
+25600/69092	Loss: 110.644
+28800/69092	Loss: 112.240
+32000/69092	Loss: 113.481
+35200/69092	Loss: 110.947
+38400/69092	Loss: 111.085
+41600/69092	Loss: 110.284
+44800/69092	Loss: 111.467
+48000/69092	Loss: 112.270
+51200/69092	Loss: 112.145
+54400/69092	Loss: 111.475
+57600/69092	Loss: 113.179
+60800/69092	Loss: 111.228
+64000/69092	Loss: 111.896
+67200/69092	Loss: 110.584
+Training time 0:01:57.502158
+Epoch: 181 Average loss: 111.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 322)
+0/69092	Loss: 108.357
+3200/69092	Loss: 111.731
+6400/69092	Loss: 111.733
+9600/69092	Loss: 113.481
+12800/69092	Loss: 111.365
+16000/69092	Loss: 111.726
+19200/69092	Loss: 110.502
+22400/69092	Loss: 110.389
+25600/69092	Loss: 112.975
+28800/69092	Loss: 110.518
+32000/69092	Loss: 112.618
+35200/69092	Loss: 112.790
+38400/69092	Loss: 109.776
+41600/69092	Loss: 110.367
+44800/69092	Loss: 111.609
+48000/69092	Loss: 111.711
+51200/69092	Loss: 110.099
+54400/69092	Loss: 112.283
+57600/69092	Loss: 110.904
+60800/69092	Loss: 113.059
+64000/69092	Loss: 112.030
+67200/69092	Loss: 113.089
+Training time 0:01:56.875677
+Epoch: 182 Average loss: 111.63
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 323)
+0/69092	Loss: 114.072
+3200/69092	Loss: 110.401
+6400/69092	Loss: 110.559
+9600/69092	Loss: 111.795
+12800/69092	Loss: 111.781
+16000/69092	Loss: 111.667
+19200/69092	Loss: 111.338
+22400/69092	Loss: 111.120
+25600/69092	Loss: 110.425
+28800/69092	Loss: 111.761
+32000/69092	Loss: 111.229
+35200/69092	Loss: 111.569
+38400/69092	Loss: 112.074
+41600/69092	Loss: 110.837
+44800/69092	Loss: 113.152
+48000/69092	Loss: 112.021
+51200/69092	Loss: 111.156
+54400/69092	Loss: 113.387
+57600/69092	Loss: 111.168
+60800/69092	Loss: 112.002
+64000/69092	Loss: 111.889
+67200/69092	Loss: 111.340
+Training time 0:01:57.008256
+Epoch: 183 Average loss: 111.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 324)
+0/69092	Loss: 120.016
+3200/69092	Loss: 111.444
+6400/69092	Loss: 109.558
+9600/69092	Loss: 111.469
+12800/69092	Loss: 111.536
+16000/69092	Loss: 111.952
+19200/69092	Loss: 111.813
+22400/69092	Loss: 113.079
+25600/69092	Loss: 109.439
+28800/69092	Loss: 110.270
+32000/69092	Loss: 110.863
+35200/69092	Loss: 111.457
+38400/69092	Loss: 112.581
+41600/69092	Loss: 110.833
+44800/69092	Loss: 112.133
+48000/69092	Loss: 113.678
+51200/69092	Loss: 110.353
+54400/69092	Loss: 111.541
+57600/69092	Loss: 111.633
+60800/69092	Loss: 112.184
+64000/69092	Loss: 112.424
+67200/69092	Loss: 111.424
+Training time 0:01:58.523298
+Epoch: 184 Average loss: 111.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 325)
+0/69092	Loss: 107.876
+3200/69092	Loss: 109.747
+6400/69092	Loss: 109.868
+9600/69092	Loss: 111.135
+12800/69092	Loss: 111.407
+16000/69092	Loss: 112.379
+19200/69092	Loss: 111.545
+22400/69092	Loss: 113.547
+25600/69092	Loss: 110.568
+28800/69092	Loss: 113.485
+32000/69092	Loss: 112.639
+35200/69092	Loss: 111.609
+38400/69092	Loss: 109.332
+41600/69092	Loss: 113.248
+44800/69092	Loss: 112.174
+48000/69092	Loss: 110.729
+51200/69092	Loss: 112.713
+54400/69092	Loss: 112.936
+57600/69092	Loss: 110.470
+60800/69092	Loss: 111.682
+64000/69092	Loss: 111.546
+67200/69092	Loss: 110.812
+Training time 0:01:58.360094
+Epoch: 185 Average loss: 111.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 326)
+0/69092	Loss: 109.357
+3200/69092	Loss: 111.876
+6400/69092	Loss: 110.766
+9600/69092	Loss: 110.595
+12800/69092	Loss: 109.909
+16000/69092	Loss: 111.825
+19200/69092	Loss: 113.635
+22400/69092	Loss: 111.603
+25600/69092	Loss: 110.578
+28800/69092	Loss: 109.896
+32000/69092	Loss: 112.974
+35200/69092	Loss: 112.931
+38400/69092	Loss: 110.565
+41600/69092	Loss: 111.909
+44800/69092	Loss: 112.094
+48000/69092	Loss: 111.724
+51200/69092	Loss: 109.451
+54400/69092	Loss: 112.351
+57600/69092	Loss: 111.552
+60800/69092	Loss: 111.427
+64000/69092	Loss: 112.138
+67200/69092	Loss: 112.850
+Training time 0:01:58.267326
+Epoch: 186 Average loss: 111.66
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 327)
+0/69092	Loss: 106.456
+3200/69092	Loss: 111.164
+6400/69092	Loss: 111.993
+9600/69092	Loss: 112.414
+12800/69092	Loss: 109.983
+16000/69092	Loss: 112.061
+19200/69092	Loss: 111.702
+22400/69092	Loss: 111.505
+25600/69092	Loss: 110.846
+28800/69092	Loss: 112.006
+32000/69092	Loss: 111.262
+35200/69092	Loss: 111.303
+38400/69092	Loss: 112.328
+41600/69092	Loss: 111.673
+44800/69092	Loss: 112.157
+48000/69092	Loss: 110.761
+51200/69092	Loss: 110.008
+54400/69092	Loss: 112.026
+57600/69092	Loss: 112.987
+60800/69092	Loss: 111.907
+64000/69092	Loss: 112.970
+67200/69092	Loss: 111.228
+Training time 0:01:58.723759
+Epoch: 187 Average loss: 111.66
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 328)
+0/69092	Loss: 113.900
+3200/69092	Loss: 111.702
+6400/69092	Loss: 112.092
+9600/69092	Loss: 110.643
+12800/69092	Loss: 111.959
+16000/69092	Loss: 111.351
+19200/69092	Loss: 111.236
+22400/69092	Loss: 112.940
+25600/69092	Loss: 111.317
+28800/69092	Loss: 110.408
+32000/69092	Loss: 111.312
+35200/69092	Loss: 111.465
+38400/69092	Loss: 111.462
+41600/69092	Loss: 112.111
+44800/69092	Loss: 111.153
+48000/69092	Loss: 113.464
+51200/69092	Loss: 112.607
+54400/69092	Loss: 112.634
+57600/69092	Loss: 110.603
+60800/69092	Loss: 110.935
+64000/69092	Loss: 111.933
+67200/69092	Loss: 110.884
+Training time 0:01:56.799651
+Epoch: 188 Average loss: 111.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 329)
+0/69092	Loss: 109.241
+3200/69092	Loss: 112.366
+6400/69092	Loss: 110.846
+9600/69092	Loss: 111.406
+12800/69092	Loss: 110.057
+16000/69092	Loss: 110.629
+19200/69092	Loss: 111.649
+22400/69092	Loss: 112.120
+25600/69092	Loss: 113.910
+28800/69092	Loss: 109.390
+32000/69092	Loss: 112.291
+35200/69092	Loss: 112.187
+38400/69092	Loss: 109.937
+41600/69092	Loss: 112.167
+44800/69092	Loss: 111.798
+48000/69092	Loss: 112.998
+51200/69092	Loss: 110.693
+54400/69092	Loss: 110.759
+57600/69092	Loss: 112.801
+60800/69092	Loss: 113.271
+64000/69092	Loss: 109.608
+67200/69092	Loss: 110.332
+Training time 0:01:57.246459
+Epoch: 189 Average loss: 111.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 330)
+0/69092	Loss: 97.416
+3200/69092	Loss: 111.024
+6400/69092	Loss: 110.710
+9600/69092	Loss: 111.905
+12800/69092	Loss: 112.514
+16000/69092	Loss: 112.099
+19200/69092	Loss: 110.833
+22400/69092	Loss: 110.053
+25600/69092	Loss: 111.712
+28800/69092	Loss: 112.495
+32000/69092	Loss: 110.644
+35200/69092	Loss: 112.581
+38400/69092	Loss: 110.519
+41600/69092	Loss: 110.523
+44800/69092	Loss: 112.045
+48000/69092	Loss: 114.001
+51200/69092	Loss: 113.558
+54400/69092	Loss: 113.206
+57600/69092	Loss: 111.365
+60800/69092	Loss: 110.648
+64000/69092	Loss: 109.599
+67200/69092	Loss: 111.489
+Training time 0:01:58.534265
+Epoch: 190 Average loss: 111.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 331)
+0/69092	Loss: 109.889
+3200/69092	Loss: 111.807
+6400/69092	Loss: 111.917
+9600/69092	Loss: 112.418
+12800/69092	Loss: 111.399
+16000/69092	Loss: 111.778
+19200/69092	Loss: 110.268
+22400/69092	Loss: 112.134
+25600/69092	Loss: 111.793
+28800/69092	Loss: 113.554
+32000/69092	Loss: 111.343
+35200/69092	Loss: 111.764
+38400/69092	Loss: 111.731
+41600/69092	Loss: 111.383
+44800/69092	Loss: 110.959
+48000/69092	Loss: 111.216
+51200/69092	Loss: 112.441
+54400/69092	Loss: 110.586
+57600/69092	Loss: 111.640
+60800/69092	Loss: 109.830
+64000/69092	Loss: 111.446
+67200/69092	Loss: 108.799
+Training time 0:01:57.102272
+Epoch: 191 Average loss: 111.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 332)
+0/69092	Loss: 108.920
+3200/69092	Loss: 111.195
+6400/69092	Loss: 111.135
+9600/69092	Loss: 110.951
+12800/69092	Loss: 112.638
+16000/69092	Loss: 112.347
+19200/69092	Loss: 112.486
+22400/69092	Loss: 112.524
+25600/69092	Loss: 112.107
+28800/69092	Loss: 110.149
+32000/69092	Loss: 111.548
+35200/69092	Loss: 111.022
+38400/69092	Loss: 111.766
+41600/69092	Loss: 109.564
+44800/69092	Loss: 111.678
+48000/69092	Loss: 110.788
+51200/69092	Loss: 111.531
+54400/69092	Loss: 110.344
+57600/69092	Loss: 112.534
+60800/69092	Loss: 111.576
+64000/69092	Loss: 112.879
+67200/69092	Loss: 111.351
+Training time 0:01:59.373875
+Epoch: 192 Average loss: 111.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 333)
+0/69092	Loss: 94.938
+3200/69092	Loss: 109.984
+6400/69092	Loss: 113.110
+9600/69092	Loss: 112.400
+12800/69092	Loss: 110.083
+16000/69092	Loss: 112.644
+19200/69092	Loss: 110.717
+22400/69092	Loss: 110.447
+25600/69092	Loss: 111.353
+28800/69092	Loss: 111.059
+32000/69092	Loss: 112.197
+35200/69092	Loss: 110.996
+38400/69092	Loss: 111.752
+41600/69092	Loss: 111.362
+44800/69092	Loss: 111.330
+48000/69092	Loss: 110.749
+51200/69092	Loss: 112.118
+54400/69092	Loss: 111.676
+57600/69092	Loss: 111.923
+60800/69092	Loss: 111.846
+64000/69092	Loss: 112.101
+67200/69092	Loss: 111.094
+Training time 0:01:57.080547
+Epoch: 193 Average loss: 111.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 334)
+0/69092	Loss: 103.360
+3200/69092	Loss: 109.909
+6400/69092	Loss: 110.607
+9600/69092	Loss: 111.329
+12800/69092	Loss: 111.241
+16000/69092	Loss: 111.323
+19200/69092	Loss: 111.070
+22400/69092	Loss: 112.863
+25600/69092	Loss: 110.287
+28800/69092	Loss: 111.865
+32000/69092	Loss: 111.984
+35200/69092	Loss: 110.044
+38400/69092	Loss: 111.127
+41600/69092	Loss: 112.657
+44800/69092	Loss: 111.520
+48000/69092	Loss: 111.684
+51200/69092	Loss: 112.632
+54400/69092	Loss: 110.193
+57600/69092	Loss: 112.449
+60800/69092	Loss: 111.390
+64000/69092	Loss: 112.835
+67200/69092	Loss: 110.220
+Training time 0:01:56.919221
+Epoch: 194 Average loss: 111.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 335)
+0/69092	Loss: 114.571
+3200/69092	Loss: 109.905
+6400/69092	Loss: 112.086
+9600/69092	Loss: 113.124
+12800/69092	Loss: 111.455
+16000/69092	Loss: 111.385
+19200/69092	Loss: 110.461
+22400/69092	Loss: 113.651
+25600/69092	Loss: 111.032
+28800/69092	Loss: 111.218
+32000/69092	Loss: 110.235
+35200/69092	Loss: 111.196
+38400/69092	Loss: 110.942
+41600/69092	Loss: 111.118
+44800/69092	Loss: 112.162
+48000/69092	Loss: 112.270
+51200/69092	Loss: 112.336
+54400/69092	Loss: 112.304
+57600/69092	Loss: 112.400
+60800/69092	Loss: 110.947
+64000/69092	Loss: 112.175
+67200/69092	Loss: 110.084
+Training time 0:01:58.159029
+Epoch: 195 Average loss: 111.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 336)
+0/69092	Loss: 108.121
+3200/69092	Loss: 110.551
+6400/69092	Loss: 110.122
+9600/69092	Loss: 111.087
+12800/69092	Loss: 109.713
+16000/69092	Loss: 112.398
+19200/69092	Loss: 111.586
+22400/69092	Loss: 111.953
+25600/69092	Loss: 110.096
+28800/69092	Loss: 113.473
+32000/69092	Loss: 111.779
+35200/69092	Loss: 109.830
+38400/69092	Loss: 113.223
+41600/69092	Loss: 112.166
+44800/69092	Loss: 111.385
+48000/69092	Loss: 110.130
+51200/69092	Loss: 112.360
+54400/69092	Loss: 112.056
+57600/69092	Loss: 111.081
+60800/69092	Loss: 111.881
+64000/69092	Loss: 112.729
+67200/69092	Loss: 110.063
+Training time 0:01:58.104471
+Epoch: 196 Average loss: 111.44
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 337)
+0/69092	Loss: 106.781
+3200/69092	Loss: 110.821
+6400/69092	Loss: 111.953
+9600/69092	Loss: 109.727
+12800/69092	Loss: 111.328
+16000/69092	Loss: 111.844
+19200/69092	Loss: 112.256
+22400/69092	Loss: 111.124
+25600/69092	Loss: 110.867
+28800/69092	Loss: 113.414
+32000/69092	Loss: 112.425
+35200/69092	Loss: 110.892
+38400/69092	Loss: 112.804
+41600/69092	Loss: 112.032
+44800/69092	Loss: 111.316
+48000/69092	Loss: 110.373
+51200/69092	Loss: 110.897
+54400/69092	Loss: 111.082
+57600/69092	Loss: 111.780
+60800/69092	Loss: 111.888
+64000/69092	Loss: 111.758
+67200/69092	Loss: 110.124
+Training time 0:01:58.603707
+Epoch: 197 Average loss: 111.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 338)
+0/69092	Loss: 113.594
+3200/69092	Loss: 113.698
+6400/69092	Loss: 112.309
+9600/69092	Loss: 112.653
+12800/69092	Loss: 111.578
+16000/69092	Loss: 111.554
+19200/69092	Loss: 112.596
+22400/69092	Loss: 113.017
+25600/69092	Loss: 111.630
+28800/69092	Loss: 110.219
+32000/69092	Loss: 112.802
+35200/69092	Loss: 111.337
+38400/69092	Loss: 110.153
+41600/69092	Loss: 110.940
+44800/69092	Loss: 111.857
+48000/69092	Loss: 110.170
+51200/69092	Loss: 110.449
+54400/69092	Loss: 110.685
+57600/69092	Loss: 112.279
+60800/69092	Loss: 111.142
+64000/69092	Loss: 111.796
+67200/69092	Loss: 112.044
+Training time 0:01:57.167400
+Epoch: 198 Average loss: 111.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 339)
+0/69092	Loss: 112.419
+3200/69092	Loss: 109.955
+6400/69092	Loss: 112.138
+9600/69092	Loss: 111.481
+12800/69092	Loss: 112.328
+16000/69092	Loss: 111.303
+19200/69092	Loss: 111.574
+22400/69092	Loss: 110.819
+25600/69092	Loss: 111.268
+28800/69092	Loss: 111.733
+32000/69092	Loss: 112.241
+35200/69092	Loss: 112.829
+38400/69092	Loss: 112.505
+41600/69092	Loss: 112.321
+44800/69092	Loss: 113.180
+48000/69092	Loss: 110.595
+51200/69092	Loss: 109.521
+54400/69092	Loss: 112.243
+57600/69092	Loss: 112.375
+60800/69092	Loss: 110.801
+64000/69092	Loss: 111.653
+67200/69092	Loss: 109.770
+Training time 0:01:57.924023
+Epoch: 199 Average loss: 111.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 340)
+0/69092	Loss: 111.472
+3200/69092	Loss: 110.743
+6400/69092	Loss: 113.299
+9600/69092	Loss: 113.510
+12800/69092	Loss: 111.098
+16000/69092	Loss: 110.945
+19200/69092	Loss: 112.532
+22400/69092	Loss: 110.176
+25600/69092	Loss: 111.653
+28800/69092	Loss: 112.327
+32000/69092	Loss: 111.306
+35200/69092	Loss: 109.258
+38400/69092	Loss: 113.309
+41600/69092	Loss: 111.966
+44800/69092	Loss: 109.731
+48000/69092	Loss: 110.473
+51200/69092	Loss: 111.429
+54400/69092	Loss: 110.927
+57600/69092	Loss: 112.217
+60800/69092	Loss: 110.853
+64000/69092	Loss: 111.186
+67200/69092	Loss: 111.871
+Training time 0:01:58.334315
+Epoch: 200 Average loss: 111.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 341)
+0/69092	Loss: 115.150
+3200/69092	Loss: 110.023
+6400/69092	Loss: 112.227
+9600/69092	Loss: 111.714
+12800/69092	Loss: 112.349
+16000/69092	Loss: 110.676
+19200/69092	Loss: 110.853
+22400/69092	Loss: 113.538
+25600/69092	Loss: 110.516
+28800/69092	Loss: 111.096
+32000/69092	Loss: 111.750
+35200/69092	Loss: 111.765
+38400/69092	Loss: 111.635
+41600/69092	Loss: 110.206
+44800/69092	Loss: 112.139
+48000/69092	Loss: 111.607
+51200/69092	Loss: 111.363
+54400/69092	Loss: 112.149
+57600/69092	Loss: 110.888
+60800/69092	Loss: 110.482
+64000/69092	Loss: 112.155
+67200/69092	Loss: 111.006
+Training time 0:01:57.005626
+Epoch: 201 Average loss: 111.51
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 342)
+0/69092	Loss: 113.854
+3200/69092	Loss: 111.091
+6400/69092	Loss: 112.947
+9600/69092	Loss: 111.028
+12800/69092	Loss: 112.363
+16000/69092	Loss: 110.452
+19200/69092	Loss: 112.219
+22400/69092	Loss: 110.810
+25600/69092	Loss: 111.414
+28800/69092	Loss: 109.000
+32000/69092	Loss: 112.222
+35200/69092	Loss: 111.294
+38400/69092	Loss: 112.423
+41600/69092	Loss: 109.885
+44800/69092	Loss: 110.768
+48000/69092	Loss: 111.757
+51200/69092	Loss: 111.989
+54400/69092	Loss: 111.814
+57600/69092	Loss: 111.490
+60800/69092	Loss: 110.375
+64000/69092	Loss: 113.477
+67200/69092	Loss: 111.864
+Training time 0:01:58.205028
+Epoch: 202 Average loss: 111.47
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 343)
+0/69092	Loss: 122.803
+3200/69092	Loss: 109.099
+6400/69092	Loss: 111.211
+9600/69092	Loss: 112.652
+12800/69092	Loss: 110.796
+16000/69092	Loss: 112.224
+19200/69092	Loss: 111.626
+22400/69092	Loss: 110.358
+25600/69092	Loss: 110.847
+28800/69092	Loss: 109.929
+32000/69092	Loss: 112.566
+35200/69092	Loss: 111.338
+38400/69092	Loss: 112.282
+41600/69092	Loss: 112.631
+44800/69092	Loss: 111.255
+48000/69092	Loss: 111.783
+51200/69092	Loss: 110.747
+54400/69092	Loss: 112.228
+57600/69092	Loss: 111.133
+60800/69092	Loss: 112.582
+64000/69092	Loss: 112.125
+67200/69092	Loss: 110.639
+Training time 0:01:57.600120
+Epoch: 203 Average loss: 111.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 344)
+0/69092	Loss: 93.580
+3200/69092	Loss: 110.922
+6400/69092	Loss: 110.754
+9600/69092	Loss: 112.235
+12800/69092	Loss: 111.213
+16000/69092	Loss: 110.708
+19200/69092	Loss: 112.412
+22400/69092	Loss: 112.055
+25600/69092	Loss: 111.254
+28800/69092	Loss: 110.863
+32000/69092	Loss: 111.556
+35200/69092	Loss: 110.194
+38400/69092	Loss: 110.994
+41600/69092	Loss: 112.317
+44800/69092	Loss: 110.494
+48000/69092	Loss: 111.593
+51200/69092	Loss: 112.344
+54400/69092	Loss: 111.820
+57600/69092	Loss: 111.345
+60800/69092	Loss: 110.048
+64000/69092	Loss: 111.832
+67200/69092	Loss: 112.054
+Training time 0:01:56.237356
+Epoch: 204 Average loss: 111.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 345)
+0/69092	Loss: 112.522
+3200/69092	Loss: 111.112
+6400/69092	Loss: 110.098
+9600/69092	Loss: 108.869
+12800/69092	Loss: 112.934
+16000/69092	Loss: 111.414
+19200/69092	Loss: 111.944
+22400/69092	Loss: 113.090
+25600/69092	Loss: 112.490
+28800/69092	Loss: 112.373
+32000/69092	Loss: 112.170
+35200/69092	Loss: 111.583
+38400/69092	Loss: 112.493
+41600/69092	Loss: 111.659
+44800/69092	Loss: 109.277
+48000/69092	Loss: 111.841
+51200/69092	Loss: 111.398
+54400/69092	Loss: 110.888
+57600/69092	Loss: 110.742
+60800/69092	Loss: 111.845
+64000/69092	Loss: 113.225
+67200/69092	Loss: 111.037
+Training time 0:01:57.617241
+Epoch: 205 Average loss: 111.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 346)
+0/69092	Loss: 98.533
+3200/69092	Loss: 111.355
+6400/69092	Loss: 111.611
+9600/69092	Loss: 110.095
+12800/69092	Loss: 111.926
+16000/69092	Loss: 112.762
+19200/69092	Loss: 112.127
+22400/69092	Loss: 109.491
+25600/69092	Loss: 111.587
+28800/69092	Loss: 111.030
+32000/69092	Loss: 111.695
+35200/69092	Loss: 109.720
+38400/69092	Loss: 112.026
+41600/69092	Loss: 110.734
+44800/69092	Loss: 110.859
+48000/69092	Loss: 112.773
+51200/69092	Loss: 110.691
+54400/69092	Loss: 111.574
+57600/69092	Loss: 111.777
+60800/69092	Loss: 111.960
+64000/69092	Loss: 111.923
+67200/69092	Loss: 111.407
+Training time 0:01:56.652164
+Epoch: 206 Average loss: 111.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 347)
+0/69092	Loss: 108.875
+3200/69092	Loss: 110.916
+6400/69092	Loss: 113.648
+9600/69092	Loss: 110.534
+12800/69092	Loss: 111.209
+16000/69092	Loss: 111.274
+19200/69092	Loss: 112.621
+22400/69092	Loss: 110.061
+25600/69092	Loss: 112.428
+28800/69092	Loss: 111.467
+32000/69092	Loss: 111.706
+35200/69092	Loss: 111.864
+38400/69092	Loss: 111.089
+41600/69092	Loss: 111.207
+44800/69092	Loss: 111.968
+48000/69092	Loss: 110.630
+51200/69092	Loss: 109.912
+54400/69092	Loss: 111.795
+57600/69092	Loss: 110.983
+60800/69092	Loss: 110.989
+64000/69092	Loss: 111.718
+67200/69092	Loss: 112.320
+Training time 0:01:56.916312
+Epoch: 207 Average loss: 111.40
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 348)
+0/69092	Loss: 102.599
+3200/69092	Loss: 110.473
+6400/69092	Loss: 111.614
+9600/69092	Loss: 111.271
+12800/69092	Loss: 111.222
+16000/69092	Loss: 111.518
+19200/69092	Loss: 110.553
+22400/69092	Loss: 111.711
+25600/69092	Loss: 112.280
+28800/69092	Loss: 113.308
+32000/69092	Loss: 111.122
+35200/69092	Loss: 110.948
+38400/69092	Loss: 110.553
+41600/69092	Loss: 111.291
+44800/69092	Loss: 111.090
+48000/69092	Loss: 112.110
+51200/69092	Loss: 109.851
+54400/69092	Loss: 111.271
+57600/69092	Loss: 110.337
+60800/69092	Loss: 112.488
+64000/69092	Loss: 112.346
+67200/69092	Loss: 111.697
+Training time 0:01:56.969007
+Epoch: 208 Average loss: 111.35
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 349)
+0/69092	Loss: 113.835
+3200/69092	Loss: 111.938
+6400/69092	Loss: 111.895
+9600/69092	Loss: 109.699
+12800/69092	Loss: 111.054
+16000/69092	Loss: 112.884
+19200/69092	Loss: 111.537
+22400/69092	Loss: 110.177
+25600/69092	Loss: 111.210
+28800/69092	Loss: 110.937
+32000/69092	Loss: 110.941
+35200/69092	Loss: 111.862
+38400/69092	Loss: 112.476
+41600/69092	Loss: 111.416
+44800/69092	Loss: 109.835
+48000/69092	Loss: 113.591
+51200/69092	Loss: 110.922
+54400/69092	Loss: 112.708
+57600/69092	Loss: 112.562
+60800/69092	Loss: 111.865
+64000/69092	Loss: 109.793
+67200/69092	Loss: 111.836
+Training time 0:01:56.360459
+Epoch: 209 Average loss: 111.51
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 350)
+0/69092	Loss: 120.409
+3200/69092	Loss: 110.850
+6400/69092	Loss: 112.257
+9600/69092	Loss: 113.017
+12800/69092	Loss: 111.456
+16000/69092	Loss: 110.195
+19200/69092	Loss: 111.982
+22400/69092	Loss: 112.190
+25600/69092	Loss: 112.127
+28800/69092	Loss: 111.933
+32000/69092	Loss: 110.376
+35200/69092	Loss: 111.161
+38400/69092	Loss: 112.101
+41600/69092	Loss: 110.839
+44800/69092	Loss: 110.107
+48000/69092	Loss: 111.470
+51200/69092	Loss: 112.179
+54400/69092	Loss: 111.840
+57600/69092	Loss: 110.614
+60800/69092	Loss: 110.808
+64000/69092	Loss: 111.620
+67200/69092	Loss: 111.510
+Training time 0:01:57.590427
+Epoch: 210 Average loss: 111.47
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 351)
+0/69092	Loss: 119.344
+3200/69092	Loss: 112.807
+6400/69092	Loss: 112.348
+9600/69092	Loss: 110.871
+12800/69092	Loss: 111.955
+16000/69092	Loss: 110.985
+19200/69092	Loss: 110.206
+22400/69092	Loss: 111.836
+25600/69092	Loss: 110.782
+28800/69092	Loss: 111.066
+32000/69092	Loss: 113.356
+35200/69092	Loss: 108.950
+38400/69092	Loss: 110.221
+41600/69092	Loss: 109.766
+44800/69092	Loss: 110.668
+48000/69092	Loss: 109.244
+51200/69092	Loss: 112.367
+54400/69092	Loss: 111.290
+57600/69092	Loss: 113.432
+60800/69092	Loss: 112.470
+64000/69092	Loss: 111.769
+67200/69092	Loss: 110.955
+Training time 0:01:57.536802
+Epoch: 211 Average loss: 111.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 352)
+0/69092	Loss: 120.097
+3200/69092	Loss: 110.348
+6400/69092	Loss: 109.759
+9600/69092	Loss: 111.939
+12800/69092	Loss: 111.628
+16000/69092	Loss: 109.937
+19200/69092	Loss: 111.015
+22400/69092	Loss: 109.874
+25600/69092	Loss: 111.871
+28800/69092	Loss: 112.431
+32000/69092	Loss: 111.558
+35200/69092	Loss: 111.477
+38400/69092	Loss: 111.061
+41600/69092	Loss: 111.284
+44800/69092	Loss: 111.224
+48000/69092	Loss: 111.538
+51200/69092	Loss: 110.273
+54400/69092	Loss: 113.683
+57600/69092	Loss: 114.031
+60800/69092	Loss: 111.889
+64000/69092	Loss: 110.906
+67200/69092	Loss: 112.257
+Training time 0:01:59.027080
+Epoch: 212 Average loss: 111.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 353)
+0/69092	Loss: 112.744
+3200/69092	Loss: 111.743
+6400/69092	Loss: 111.099
+9600/69092	Loss: 112.636
+12800/69092	Loss: 110.038
+16000/69092	Loss: 110.172
+19200/69092	Loss: 111.920
+22400/69092	Loss: 111.602
+25600/69092	Loss: 111.908
+28800/69092	Loss: 112.016
+32000/69092	Loss: 112.443
+35200/69092	Loss: 109.461
+38400/69092	Loss: 110.080
+41600/69092	Loss: 111.385
+44800/69092	Loss: 112.291
+48000/69092	Loss: 112.444
+51200/69092	Loss: 110.867
+54400/69092	Loss: 111.104
+57600/69092	Loss: 112.389
+60800/69092	Loss: 111.302
+64000/69092	Loss: 111.587
+67200/69092	Loss: 110.853
+Training time 0:01:57.615160
+Epoch: 213 Average loss: 111.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 354)
+0/69092	Loss: 114.279
+3200/69092	Loss: 112.340
+6400/69092	Loss: 110.850
+9600/69092	Loss: 112.539
+12800/69092	Loss: 111.477
+16000/69092	Loss: 110.505
+19200/69092	Loss: 111.526
+22400/69092	Loss: 111.796
+25600/69092	Loss: 112.174
+28800/69092	Loss: 110.404
+32000/69092	Loss: 111.231
+35200/69092	Loss: 111.565
+38400/69092	Loss: 111.532
+41600/69092	Loss: 111.646
+44800/69092	Loss: 109.560
+48000/69092	Loss: 110.412
+51200/69092	Loss: 111.165
+54400/69092	Loss: 111.110
+57600/69092	Loss: 111.434
+60800/69092	Loss: 111.814
+64000/69092	Loss: 112.276
+67200/69092	Loss: 111.237
+Training time 0:01:58.346116
+Epoch: 214 Average loss: 111.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 355)
+0/69092	Loss: 127.044
+3200/69092	Loss: 110.452
+6400/69092	Loss: 111.756
+9600/69092	Loss: 112.551
+12800/69092	Loss: 111.003
+16000/69092	Loss: 112.250
+19200/69092	Loss: 110.165
+22400/69092	Loss: 112.154
+25600/69092	Loss: 110.438
+28800/69092	Loss: 112.979
+32000/69092	Loss: 110.254
+35200/69092	Loss: 109.706
+38400/69092	Loss: 111.473
+41600/69092	Loss: 112.342
+44800/69092	Loss: 111.705
+48000/69092	Loss: 111.665
+51200/69092	Loss: 112.091
+54400/69092	Loss: 111.668
+57600/69092	Loss: 112.842
+60800/69092	Loss: 111.255
+64000/69092	Loss: 110.401
+67200/69092	Loss: 110.209
+Training time 0:01:58.014856
+Epoch: 215 Average loss: 111.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 356)
+0/69092	Loss: 112.351
+3200/69092	Loss: 111.519
+6400/69092	Loss: 111.246
+9600/69092	Loss: 109.853
+12800/69092	Loss: 109.595
+16000/69092	Loss: 111.626
+19200/69092	Loss: 111.506
+22400/69092	Loss: 111.250
+25600/69092	Loss: 111.395
+28800/69092	Loss: 111.913
+32000/69092	Loss: 111.892
+35200/69092	Loss: 111.181
+38400/69092	Loss: 112.370
+41600/69092	Loss: 110.939
+44800/69092	Loss: 110.783
+48000/69092	Loss: 111.743
+51200/69092	Loss: 110.026
+54400/69092	Loss: 110.932
+57600/69092	Loss: 112.053
+60800/69092	Loss: 111.906
+64000/69092	Loss: 111.053
+67200/69092	Loss: 113.081
+Training time 0:01:58.788414
+Epoch: 216 Average loss: 111.35
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 357)
+0/69092	Loss: 121.562
+3200/69092	Loss: 111.542
+6400/69092	Loss: 109.944
+9600/69092	Loss: 112.213
+12800/69092	Loss: 111.913
+16000/69092	Loss: 110.020
+19200/69092	Loss: 111.637
+22400/69092	Loss: 110.192
+25600/69092	Loss: 111.485
+28800/69092	Loss: 111.960
+32000/69092	Loss: 111.698
+35200/69092	Loss: 110.886
+38400/69092	Loss: 111.271
+41600/69092	Loss: 110.022
+44800/69092	Loss: 111.836
+48000/69092	Loss: 110.569
+51200/69092	Loss: 113.301
+54400/69092	Loss: 110.978
+57600/69092	Loss: 111.605
+60800/69092	Loss: 111.127
+64000/69092	Loss: 111.314
+67200/69092	Loss: 112.614
+Training time 0:01:58.957951
+Epoch: 217 Average loss: 111.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 358)
+0/69092	Loss: 102.586
+3200/69092	Loss: 110.779
+6400/69092	Loss: 112.011
+9600/69092	Loss: 110.499
+12800/69092	Loss: 112.280
+16000/69092	Loss: 113.763
+19200/69092	Loss: 112.573
+22400/69092	Loss: 108.902
+25600/69092	Loss: 111.236
+28800/69092	Loss: 109.855
+32000/69092	Loss: 110.905
+35200/69092	Loss: 112.341
+38400/69092	Loss: 111.872
+41600/69092	Loss: 111.001
+44800/69092	Loss: 112.656
+48000/69092	Loss: 108.933
+51200/69092	Loss: 112.074
+54400/69092	Loss: 111.367
+57600/69092	Loss: 111.971
+60800/69092	Loss: 111.967
+64000/69092	Loss: 109.766
+67200/69092	Loss: 111.624
+Training time 0:01:57.840309
+Epoch: 218 Average loss: 111.32
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 359)
+0/69092	Loss: 124.881
+3200/69092	Loss: 109.882
+6400/69092	Loss: 111.695
+9600/69092	Loss: 111.945
+12800/69092	Loss: 111.044
+16000/69092	Loss: 110.048
+19200/69092	Loss: 110.531
+22400/69092	Loss: 111.482
+25600/69092	Loss: 112.433
+28800/69092	Loss: 110.267
+32000/69092	Loss: 112.630
+35200/69092	Loss: 112.747
+38400/69092	Loss: 111.090
+41600/69092	Loss: 111.716
+44800/69092	Loss: 109.714
+48000/69092	Loss: 110.786
+51200/69092	Loss: 110.437
+54400/69092	Loss: 110.069
+57600/69092	Loss: 112.107
+60800/69092	Loss: 111.284
+64000/69092	Loss: 112.616
+67200/69092	Loss: 110.709
+Training time 0:01:57.654049
+Epoch: 219 Average loss: 111.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 360)
+0/69092	Loss: 107.601
+3200/69092	Loss: 112.450
+6400/69092	Loss: 111.540
+9600/69092	Loss: 111.025
+12800/69092	Loss: 109.177
+16000/69092	Loss: 111.226
+19200/69092	Loss: 109.213
+22400/69092	Loss: 110.742
+25600/69092	Loss: 111.340
+28800/69092	Loss: 110.020
+32000/69092	Loss: 111.061
+35200/69092	Loss: 111.604
+38400/69092	Loss: 113.606
+41600/69092	Loss: 111.543
+44800/69092	Loss: 111.912
+48000/69092	Loss: 110.423
+51200/69092	Loss: 112.450
+54400/69092	Loss: 111.991
+57600/69092	Loss: 112.207
+60800/69092	Loss: 110.035
+64000/69092	Loss: 112.878
+67200/69092	Loss: 111.034
+Training time 0:01:58.261768
+Epoch: 220 Average loss: 111.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 361)
+0/69092	Loss: 110.842
+3200/69092	Loss: 110.316
+6400/69092	Loss: 111.318
+9600/69092	Loss: 111.450
+12800/69092	Loss: 112.726
+16000/69092	Loss: 109.595
+19200/69092	Loss: 110.400
+22400/69092	Loss: 110.424
+25600/69092	Loss: 112.292
+28800/69092	Loss: 112.729
+32000/69092	Loss: 110.775
+35200/69092	Loss: 111.294
+38400/69092	Loss: 110.349
+41600/69092	Loss: 109.950
+44800/69092	Loss: 111.693
+48000/69092	Loss: 113.166
+51200/69092	Loss: 111.447
+54400/69092	Loss: 110.477
+57600/69092	Loss: 112.263
+60800/69092	Loss: 113.131
+64000/69092	Loss: 112.114
+67200/69092	Loss: 110.500
+Training time 0:01:57.627358
+Epoch: 221 Average loss: 111.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 362)
+0/69092	Loss: 109.257
+3200/69092	Loss: 111.263
+6400/69092	Loss: 110.685
+9600/69092	Loss: 112.333
+12800/69092	Loss: 110.178
+16000/69092	Loss: 109.855
+19200/69092	Loss: 110.781
+22400/69092	Loss: 110.541
+25600/69092	Loss: 111.465
+28800/69092	Loss: 113.951
+32000/69092	Loss: 109.545
+35200/69092	Loss: 109.706
+38400/69092	Loss: 109.152
+41600/69092	Loss: 112.902
+44800/69092	Loss: 112.605
+48000/69092	Loss: 111.139
+51200/69092	Loss: 111.587
+54400/69092	Loss: 111.706
+57600/69092	Loss: 111.629
+60800/69092	Loss: 111.455
+64000/69092	Loss: 112.777
+67200/69092	Loss: 112.410
+Training time 0:01:58.640351
+Epoch: 222 Average loss: 111.29
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 363)
+0/69092	Loss: 127.826
+3200/69092	Loss: 111.929
+6400/69092	Loss: 110.048
+9600/69092	Loss: 110.230
+12800/69092	Loss: 110.655
+16000/69092	Loss: 112.476
+19200/69092	Loss: 111.954
+22400/69092	Loss: 112.187
+25600/69092	Loss: 111.901
+28800/69092	Loss: 111.431
+32000/69092	Loss: 110.900
+35200/69092	Loss: 110.733
+38400/69092	Loss: 110.865
+41600/69092	Loss: 111.837
+44800/69092	Loss: 111.534
+48000/69092	Loss: 110.931
+51200/69092	Loss: 111.468
+54400/69092	Loss: 110.263
+57600/69092	Loss: 111.144
+60800/69092	Loss: 111.499
+64000/69092	Loss: 113.455
+67200/69092	Loss: 111.727
+Training time 0:01:57.344704
+Epoch: 223 Average loss: 111.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 364)
+0/69092	Loss: 115.922
+3200/69092	Loss: 110.742
+6400/69092	Loss: 112.816
+9600/69092	Loss: 113.036
+12800/69092	Loss: 108.655
+16000/69092	Loss: 110.480
+19200/69092	Loss: 111.792
+22400/69092	Loss: 112.388
+25600/69092	Loss: 111.947
+28800/69092	Loss: 112.240
+32000/69092	Loss: 111.292
+35200/69092	Loss: 109.624
+38400/69092	Loss: 109.701
+41600/69092	Loss: 112.976
+44800/69092	Loss: 111.058
+48000/69092	Loss: 110.137
+51200/69092	Loss: 111.092
+54400/69092	Loss: 109.685
+57600/69092	Loss: 112.904
+60800/69092	Loss: 111.244
+64000/69092	Loss: 110.958
+67200/69092	Loss: 110.772
+Training time 0:01:57.242663
+Epoch: 224 Average loss: 111.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 365)
+0/69092	Loss: 105.513
+3200/69092	Loss: 111.115
+6400/69092	Loss: 110.288
+9600/69092	Loss: 111.020
+12800/69092	Loss: 113.001
+16000/69092	Loss: 110.015
+19200/69092	Loss: 111.040
+22400/69092	Loss: 112.315
+25600/69092	Loss: 111.176
+28800/69092	Loss: 111.426
+32000/69092	Loss: 110.235
+35200/69092	Loss: 112.121
+38400/69092	Loss: 110.873
+41600/69092	Loss: 112.275
+44800/69092	Loss: 111.597
+48000/69092	Loss: 110.714
+51200/69092	Loss: 112.752
+54400/69092	Loss: 111.250
+57600/69092	Loss: 110.235
+60800/69092	Loss: 111.397
+64000/69092	Loss: 109.267
+67200/69092	Loss: 110.353
+Training time 0:01:58.334069
+Epoch: 225 Average loss: 111.20
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 366)
+0/69092	Loss: 118.099
+3200/69092	Loss: 110.454
+6400/69092	Loss: 110.810
+9600/69092	Loss: 110.643
+12800/69092	Loss: 112.621
+16000/69092	Loss: 113.077
+19200/69092	Loss: 112.072
+22400/69092	Loss: 111.309
+25600/69092	Loss: 111.833
+28800/69092	Loss: 110.233
+32000/69092	Loss: 112.047
+35200/69092	Loss: 110.325
+38400/69092	Loss: 110.898
+41600/69092	Loss: 110.207
+44800/69092	Loss: 111.931
+48000/69092	Loss: 111.206
+51200/69092	Loss: 112.358
+54400/69092	Loss: 112.203
+57600/69092	Loss: 111.354
+60800/69092	Loss: 110.269
+64000/69092	Loss: 111.339
+67200/69092	Loss: 112.139
+Training time 0:01:56.571158
+Epoch: 226 Average loss: 111.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 367)
+0/69092	Loss: 105.174
+3200/69092	Loss: 111.525
+6400/69092	Loss: 110.272
+9600/69092	Loss: 111.046
+12800/69092	Loss: 109.603
+16000/69092	Loss: 110.997
+19200/69092	Loss: 110.737
+22400/69092	Loss: 111.180
+25600/69092	Loss: 112.482
+28800/69092	Loss: 112.495
+32000/69092	Loss: 112.216
+35200/69092	Loss: 109.527
+38400/69092	Loss: 110.979
+41600/69092	Loss: 110.004
+44800/69092	Loss: 111.313
+48000/69092	Loss: 111.064
+51200/69092	Loss: 111.682
+54400/69092	Loss: 110.034
+57600/69092	Loss: 110.902
+60800/69092	Loss: 111.099
+64000/69092	Loss: 111.124
+67200/69092	Loss: 112.292
+Training time 0:01:57.296885
+Epoch: 227 Average loss: 111.12
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 368)
+0/69092	Loss: 106.661
+3200/69092	Loss: 110.437
+6400/69092	Loss: 110.442
+9600/69092	Loss: 111.701
+12800/69092	Loss: 110.894
+16000/69092	Loss: 110.809
+19200/69092	Loss: 110.898
+22400/69092	Loss: 111.743
+25600/69092	Loss: 113.289
+28800/69092	Loss: 111.461
+32000/69092	Loss: 109.851
+35200/69092	Loss: 111.512
+38400/69092	Loss: 110.425
+41600/69092	Loss: 109.910
+44800/69092	Loss: 110.566
+48000/69092	Loss: 110.228
+51200/69092	Loss: 112.979
+54400/69092	Loss: 111.993
+57600/69092	Loss: 113.125
+60800/69092	Loss: 111.460
+64000/69092	Loss: 112.063
+67200/69092	Loss: 110.471
+Training time 0:01:57.932701
+Epoch: 228 Average loss: 111.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 369)
+0/69092	Loss: 109.460
+3200/69092	Loss: 109.768
+6400/69092	Loss: 111.855
+9600/69092	Loss: 111.658
+12800/69092	Loss: 110.377
+16000/69092	Loss: 111.898
+19200/69092	Loss: 110.089
+22400/69092	Loss: 111.923
+25600/69092	Loss: 112.154
+28800/69092	Loss: 111.484
+32000/69092	Loss: 110.921
+35200/69092	Loss: 110.930
+38400/69092	Loss: 110.767
+41600/69092	Loss: 111.373
+44800/69092	Loss: 111.323
+48000/69092	Loss: 111.928
+51200/69092	Loss: 111.844
+54400/69092	Loss: 110.907
+57600/69092	Loss: 111.087
+60800/69092	Loss: 111.255
+64000/69092	Loss: 111.467
+67200/69092	Loss: 110.572
+Training time 0:01:56.896235
+Epoch: 229 Average loss: 111.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 370)
+0/69092	Loss: 100.697
+3200/69092	Loss: 111.961
+6400/69092	Loss: 110.156
+9600/69092	Loss: 110.939
+12800/69092	Loss: 111.157
+16000/69092	Loss: 112.250
+19200/69092	Loss: 111.242
+22400/69092	Loss: 111.208
+25600/69092	Loss: 111.491
+28800/69092	Loss: 110.459
+32000/69092	Loss: 109.915
+35200/69092	Loss: 111.903
+38400/69092	Loss: 109.792
+41600/69092	Loss: 111.766
+44800/69092	Loss: 111.266
+48000/69092	Loss: 113.149
+51200/69092	Loss: 111.914
+54400/69092	Loss: 110.529
+57600/69092	Loss: 111.267
+60800/69092	Loss: 110.886
+64000/69092	Loss: 111.560
+67200/69092	Loss: 111.441
+Training time 0:01:57.565134
+Epoch: 230 Average loss: 111.25
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 371)
+0/69092	Loss: 107.621
+3200/69092	Loss: 111.732
+6400/69092	Loss: 110.538
+9600/69092	Loss: 112.264
+12800/69092	Loss: 109.527
+16000/69092	Loss: 111.364
+19200/69092	Loss: 113.838
+22400/69092	Loss: 112.676
+25600/69092	Loss: 110.422
+28800/69092	Loss: 112.439
+32000/69092	Loss: 110.913
+35200/69092	Loss: 110.436
+38400/69092	Loss: 111.448
+41600/69092	Loss: 109.956
+44800/69092	Loss: 110.899
+48000/69092	Loss: 111.218
+51200/69092	Loss: 109.792
+54400/69092	Loss: 112.106
+57600/69092	Loss: 109.574
+60800/69092	Loss: 110.445
+64000/69092	Loss: 111.070
+67200/69092	Loss: 111.572
+Training time 0:01:57.721162
+Epoch: 231 Average loss: 111.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 372)
+0/69092	Loss: 118.985
+3200/69092	Loss: 111.589
+6400/69092	Loss: 110.570
+9600/69092	Loss: 112.050
+12800/69092	Loss: 110.363
+16000/69092	Loss: 110.479
+19200/69092	Loss: 110.818
+22400/69092	Loss: 110.467
+25600/69092	Loss: 111.968
+28800/69092	Loss: 112.022
+32000/69092	Loss: 112.321
+35200/69092	Loss: 110.381
+38400/69092	Loss: 112.018
+41600/69092	Loss: 110.860
+44800/69092	Loss: 111.434
+48000/69092	Loss: 111.930
+51200/69092	Loss: 109.133
+54400/69092	Loss: 110.783
+57600/69092	Loss: 109.955
+60800/69092	Loss: 112.031
+64000/69092	Loss: 113.308
+67200/69092	Loss: 110.796
+Training time 0:01:56.902076
+Epoch: 232 Average loss: 111.25
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 373)
+0/69092	Loss: 107.782
+3200/69092	Loss: 112.229
+6400/69092	Loss: 111.837
+9600/69092	Loss: 111.382
+12800/69092	Loss: 110.122
+16000/69092	Loss: 112.720
+19200/69092	Loss: 110.917
+22400/69092	Loss: 110.515
+25600/69092	Loss: 111.300
+28800/69092	Loss: 111.422
+32000/69092	Loss: 110.690
+35200/69092	Loss: 111.913
+38400/69092	Loss: 111.768
+41600/69092	Loss: 111.117
+44800/69092	Loss: 110.936
+48000/69092	Loss: 109.291
+51200/69092	Loss: 111.271
+54400/69092	Loss: 110.415
+57600/69092	Loss: 111.206
+60800/69092	Loss: 111.269
+64000/69092	Loss: 112.456
+67200/69092	Loss: 112.000
+Training time 0:01:58.284692
+Epoch: 233 Average loss: 111.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 374)
+0/69092	Loss: 122.656
+3200/69092	Loss: 110.646
+6400/69092	Loss: 110.087
+9600/69092	Loss: 111.240
+12800/69092	Loss: 111.472
+16000/69092	Loss: 111.305
+19200/69092	Loss: 110.153
+22400/69092	Loss: 112.367
+25600/69092	Loss: 111.038
+28800/69092	Loss: 110.239
+32000/69092	Loss: 110.406
+35200/69092	Loss: 111.731
+38400/69092	Loss: 110.514
+41600/69092	Loss: 108.850
+44800/69092	Loss: 111.103
+48000/69092	Loss: 112.646
+51200/69092	Loss: 113.490
+54400/69092	Loss: 111.335
+57600/69092	Loss: 111.706
+60800/69092	Loss: 112.250
+64000/69092	Loss: 111.484
+67200/69092	Loss: 112.740
+Training time 0:01:56.956137
+Epoch: 234 Average loss: 111.28
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 375)
+0/69092	Loss: 110.892
+3200/69092	Loss: 110.887
+6400/69092	Loss: 110.948
+9600/69092	Loss: 111.023
+12800/69092	Loss: 111.547
+16000/69092	Loss: 112.227
+19200/69092	Loss: 111.496
+22400/69092	Loss: 112.579
+25600/69092	Loss: 110.428
+28800/69092	Loss: 110.210
+32000/69092	Loss: 110.786
+35200/69092	Loss: 111.905
+38400/69092	Loss: 110.357
+41600/69092	Loss: 111.679
+44800/69092	Loss: 110.700
+48000/69092	Loss: 109.927
+51200/69092	Loss: 112.556
+54400/69092	Loss: 111.367
+57600/69092	Loss: 110.971
+60800/69092	Loss: 111.714
+64000/69092	Loss: 111.642
+67200/69092	Loss: 110.445
+Training time 0:01:56.701332
+Epoch: 235 Average loss: 111.24
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 376)
+0/69092	Loss: 102.721
+3200/69092	Loss: 110.008
+6400/69092	Loss: 111.771
+9600/69092	Loss: 110.820
+12800/69092	Loss: 110.292
+16000/69092	Loss: 111.429
+19200/69092	Loss: 110.661
+22400/69092	Loss: 111.941
+25600/69092	Loss: 110.882
+28800/69092	Loss: 111.950
+32000/69092	Loss: 112.068
+35200/69092	Loss: 110.569
+38400/69092	Loss: 112.274
+41600/69092	Loss: 109.851
+44800/69092	Loss: 110.529
+48000/69092	Loss: 112.117
+51200/69092	Loss: 110.190
+54400/69092	Loss: 111.345
+57600/69092	Loss: 110.456
+60800/69092	Loss: 112.395
+64000/69092	Loss: 111.087
+67200/69092	Loss: 112.002
+Training time 0:01:57.784069
+Epoch: 236 Average loss: 111.18
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 377)
+0/69092	Loss: 108.712
+3200/69092	Loss: 112.343
+6400/69092	Loss: 112.327
+9600/69092	Loss: 109.230
+12800/69092	Loss: 112.186
+16000/69092	Loss: 109.064
+19200/69092	Loss: 113.283
+22400/69092	Loss: 111.513
+25600/69092	Loss: 110.349
+28800/69092	Loss: 112.609
+32000/69092	Loss: 109.786
+35200/69092	Loss: 109.445
+38400/69092	Loss: 112.362
+41600/69092	Loss: 112.131
+44800/69092	Loss: 110.724
+48000/69092	Loss: 112.368
+51200/69092	Loss: 110.325
+54400/69092	Loss: 111.311
+57600/69092	Loss: 110.564
+60800/69092	Loss: 112.719
+64000/69092	Loss: 112.450
+67200/69092	Loss: 112.526
+Training time 0:01:57.479259
+Epoch: 237 Average loss: 111.36
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 378)
+0/69092	Loss: 109.185
+3200/69092	Loss: 110.635
+6400/69092	Loss: 111.992
+9600/69092	Loss: 111.282
+12800/69092	Loss: 108.316
+16000/69092	Loss: 111.221
+19200/69092	Loss: 111.703
+22400/69092	Loss: 110.000
+25600/69092	Loss: 112.576
+28800/69092	Loss: 111.549
+32000/69092	Loss: 110.746
+35200/69092	Loss: 111.515
+38400/69092	Loss: 109.555
+41600/69092	Loss: 110.974
+44800/69092	Loss: 110.739
+48000/69092	Loss: 111.645
+51200/69092	Loss: 112.599
+54400/69092	Loss: 112.298
+57600/69092	Loss: 112.291
+60800/69092	Loss: 109.475
+64000/69092	Loss: 112.657
+67200/69092	Loss: 111.482
+Training time 0:01:58.070004
+Epoch: 238 Average loss: 111.12
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 379)
+0/69092	Loss: 103.191
+3200/69092	Loss: 111.912
+6400/69092	Loss: 110.286
+9600/69092	Loss: 111.739
+12800/69092	Loss: 110.873
+16000/69092	Loss: 111.077
+19200/69092	Loss: 112.737
+22400/69092	Loss: 111.359
+25600/69092	Loss: 110.602
+28800/69092	Loss: 111.192
+32000/69092	Loss: 109.452
+35200/69092	Loss: 110.769
+38400/69092	Loss: 109.890
+41600/69092	Loss: 114.046
+44800/69092	Loss: 110.270
+48000/69092	Loss: 110.798
+51200/69092	Loss: 111.544
+54400/69092	Loss: 111.508
+57600/69092	Loss: 110.659
+60800/69092	Loss: 110.040
+64000/69092	Loss: 112.591
+67200/69092	Loss: 111.184
+Training time 0:01:57.766793
+Epoch: 239 Average loss: 111.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 380)
+0/69092	Loss: 113.920
+3200/69092	Loss: 109.719
+6400/69092	Loss: 110.938
+9600/69092	Loss: 111.372
+12800/69092	Loss: 110.764
+16000/69092	Loss: 110.970
+19200/69092	Loss: 111.281
+22400/69092	Loss: 112.916
+25600/69092	Loss: 110.118
+28800/69092	Loss: 111.521
+32000/69092	Loss: 110.514
+35200/69092	Loss: 110.994
+38400/69092	Loss: 111.896
+41600/69092	Loss: 112.232
+44800/69092	Loss: 112.411
+48000/69092	Loss: 111.327
+51200/69092	Loss: 110.128
+54400/69092	Loss: 111.865
+57600/69092	Loss: 112.046
+60800/69092	Loss: 110.568
+64000/69092	Loss: 108.884
+67200/69092	Loss: 112.514
+Training time 0:01:58.447590
+Epoch: 240 Average loss: 111.18
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 381)
+0/69092	Loss: 106.667
+3200/69092	Loss: 110.994
+6400/69092	Loss: 111.828
+9600/69092	Loss: 110.403
+12800/69092	Loss: 110.869
+16000/69092	Loss: 111.339
+19200/69092	Loss: 110.626
+22400/69092	Loss: 112.181
+25600/69092	Loss: 109.524
+28800/69092	Loss: 110.179
+32000/69092	Loss: 112.797
+35200/69092	Loss: 112.763
+38400/69092	Loss: 112.085
+41600/69092	Loss: 111.966
+44800/69092	Loss: 108.199
+48000/69092	Loss: 111.263
+51200/69092	Loss: 111.702
+54400/69092	Loss: 111.092
+57600/69092	Loss: 111.441
+60800/69092	Loss: 110.747
+64000/69092	Loss: 109.926
+67200/69092	Loss: 111.399
+Training time 0:01:57.672062
+Epoch: 241 Average loss: 111.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 382)
+0/69092	Loss: 118.522
+3200/69092	Loss: 113.003
+6400/69092	Loss: 111.845
+9600/69092	Loss: 110.895
+12800/69092	Loss: 110.848
+16000/69092	Loss: 111.250
+19200/69092	Loss: 110.447
+22400/69092	Loss: 112.390
+25600/69092	Loss: 111.955
+28800/69092	Loss: 109.747
+32000/69092	Loss: 110.723
+35200/69092	Loss: 112.436
+38400/69092	Loss: 109.578
+41600/69092	Loss: 109.520
+44800/69092	Loss: 112.748
+48000/69092	Loss: 113.196
+51200/69092	Loss: 110.835
+54400/69092	Loss: 112.373
+57600/69092	Loss: 112.555
+60800/69092	Loss: 110.195
+64000/69092	Loss: 112.207
+67200/69092	Loss: 110.139
+Training time 0:01:58.554527
+Epoch: 242 Average loss: 111.32
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 383)
+0/69092	Loss: 110.692
+3200/69092	Loss: 109.538
+6400/69092	Loss: 111.412
+9600/69092	Loss: 111.209
+12800/69092	Loss: 110.019
+16000/69092	Loss: 113.119
+19200/69092	Loss: 111.463
+22400/69092	Loss: 110.923
+25600/69092	Loss: 111.187
+28800/69092	Loss: 110.448
+32000/69092	Loss: 111.356
+35200/69092	Loss: 111.989
+38400/69092	Loss: 112.339
+41600/69092	Loss: 111.679
+44800/69092	Loss: 111.292
+48000/69092	Loss: 110.369
+51200/69092	Loss: 110.691
+54400/69092	Loss: 110.423
+57600/69092	Loss: 110.764
+60800/69092	Loss: 111.110
+64000/69092	Loss: 111.982
+67200/69092	Loss: 109.894
+Training time 0:01:57.696635
+Epoch: 243 Average loss: 111.11
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 384)
+0/69092	Loss: 113.028
+3200/69092	Loss: 111.658
+6400/69092	Loss: 111.408
+9600/69092	Loss: 110.169
+12800/69092	Loss: 110.419
+16000/69092	Loss: 111.114
+19200/69092	Loss: 108.767
+22400/69092	Loss: 110.102
+25600/69092	Loss: 110.452
+28800/69092	Loss: 109.772
+32000/69092	Loss: 110.774
+35200/69092	Loss: 110.432
+38400/69092	Loss: 111.535
+41600/69092	Loss: 112.125
+44800/69092	Loss: 112.251
+48000/69092	Loss: 110.962
+51200/69092	Loss: 112.463
+54400/69092	Loss: 110.419
+57600/69092	Loss: 111.509
+60800/69092	Loss: 111.829
+64000/69092	Loss: 111.827
+67200/69092	Loss: 111.953
+Training time 0:01:57.499486
+Epoch: 244 Average loss: 111.11
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 385)
+0/69092	Loss: 111.727
+3200/69092	Loss: 111.671
+6400/69092	Loss: 111.128
+9600/69092	Loss: 110.674
+12800/69092	Loss: 110.573
+16000/69092	Loss: 111.281
+19200/69092	Loss: 111.655
+22400/69092	Loss: 111.609
+25600/69092	Loss: 109.946
+28800/69092	Loss: 110.985
+32000/69092	Loss: 112.240
+35200/69092	Loss: 111.988
+38400/69092	Loss: 110.245
+41600/69092	Loss: 110.574
+44800/69092	Loss: 111.236
+48000/69092	Loss: 111.256
+51200/69092	Loss: 112.173
+54400/69092	Loss: 110.117
+57600/69092	Loss: 110.748
+60800/69092	Loss: 109.949
+64000/69092	Loss: 111.060
+67200/69092	Loss: 112.144
+Training time 0:01:57.728370
+Epoch: 245 Average loss: 111.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 386)
+0/69092	Loss: 110.880
+3200/69092	Loss: 111.216
+6400/69092	Loss: 111.107
+9600/69092	Loss: 110.549
+12800/69092	Loss: 112.441
+16000/69092	Loss: 111.091
+19200/69092	Loss: 111.000
+22400/69092	Loss: 110.637
+25600/69092	Loss: 111.768
+28800/69092	Loss: 109.734
+32000/69092	Loss: 111.293
+35200/69092	Loss: 111.020
+38400/69092	Loss: 111.124
+41600/69092	Loss: 111.152
+44800/69092	Loss: 112.918
+48000/69092	Loss: 110.081
+51200/69092	Loss: 109.203
+54400/69092	Loss: 111.268
+57600/69092	Loss: 112.532
+60800/69092	Loss: 111.184
+64000/69092	Loss: 110.975
+67200/69092	Loss: 110.599
+Training time 0:01:59.189433
+Epoch: 246 Average loss: 111.12
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 387)
+0/69092	Loss: 119.276
+3200/69092	Loss: 109.900
+6400/69092	Loss: 111.798
+9600/69092	Loss: 110.056
+12800/69092	Loss: 110.956
+16000/69092	Loss: 111.421
+19200/69092	Loss: 110.138
+22400/69092	Loss: 110.850
+25600/69092	Loss: 112.589
+28800/69092	Loss: 110.275
+32000/69092	Loss: 112.987
+35200/69092	Loss: 110.354
+38400/69092	Loss: 110.669
+41600/69092	Loss: 111.464
+44800/69092	Loss: 112.296
+48000/69092	Loss: 107.889
+51200/69092	Loss: 110.713
+54400/69092	Loss: 111.676
+57600/69092	Loss: 110.659
+60800/69092	Loss: 112.807
+64000/69092	Loss: 111.888
+67200/69092	Loss: 111.914
+Training time 0:01:57.338323
+Epoch: 247 Average loss: 111.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 388)
+0/69092	Loss: 114.700
+3200/69092	Loss: 111.241
+6400/69092	Loss: 111.656
+9600/69092	Loss: 112.026
+12800/69092	Loss: 110.048
+16000/69092	Loss: 110.723
+19200/69092	Loss: 110.779
+22400/69092	Loss: 109.860
+25600/69092	Loss: 111.066
+28800/69092	Loss: 110.062
+32000/69092	Loss: 112.445
+35200/69092	Loss: 109.649
+38400/69092	Loss: 111.172
+41600/69092	Loss: 110.447
+44800/69092	Loss: 112.813
+48000/69092	Loss: 110.219
+51200/69092	Loss: 112.625
+54400/69092	Loss: 113.060
+57600/69092	Loss: 109.788
+60800/69092	Loss: 113.112
+64000/69092	Loss: 111.282
+67200/69092	Loss: 110.880
+Training time 0:01:58.430439
+Epoch: 248 Average loss: 111.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 389)
+0/69092	Loss: 103.736
+3200/69092	Loss: 109.705
+6400/69092	Loss: 111.352
+9600/69092	Loss: 110.496
+12800/69092	Loss: 110.735
+16000/69092	Loss: 109.864
+19200/69092	Loss: 110.467
+22400/69092	Loss: 111.431
+25600/69092	Loss: 110.229
+28800/69092	Loss: 112.154
+32000/69092	Loss: 110.671
+35200/69092	Loss: 111.736
+38400/69092	Loss: 112.159
+41600/69092	Loss: 110.002
+44800/69092	Loss: 113.132
+48000/69092	Loss: 111.088
+51200/69092	Loss: 111.827
+54400/69092	Loss: 111.049
+57600/69092	Loss: 111.497
+60800/69092	Loss: 111.336
+64000/69092	Loss: 111.809
+67200/69092	Loss: 111.159
+Training time 0:01:57.369860
+Epoch: 249 Average loss: 111.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 390)
+0/69092	Loss: 123.498
+3200/69092	Loss: 111.731
+6400/69092	Loss: 109.836
+9600/69092	Loss: 111.213
+12800/69092	Loss: 111.127
+16000/69092	Loss: 112.301
+19200/69092	Loss: 111.047
+22400/69092	Loss: 112.960
+25600/69092	Loss: 111.261
+28800/69092	Loss: 111.291
+32000/69092	Loss: 112.719
+35200/69092	Loss: 110.935
+38400/69092	Loss: 111.477
+41600/69092	Loss: 111.206
+44800/69092	Loss: 110.188
+48000/69092	Loss: 109.406
+51200/69092	Loss: 110.845
+54400/69092	Loss: 111.418
+57600/69092	Loss: 112.339
+60800/69092	Loss: 109.618
+64000/69092	Loss: 110.230
+67200/69092	Loss: 110.452
+Training time 0:01:57.411439
+Epoch: 250 Average loss: 111.17
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 391)
+0/69092	Loss: 106.357
+3200/69092	Loss: 110.279
+6400/69092	Loss: 110.361
+9600/69092	Loss: 113.076
+12800/69092	Loss: 111.444
+16000/69092	Loss: 110.781
+19200/69092	Loss: 109.138
+22400/69092	Loss: 111.925
+25600/69092	Loss: 109.538
+28800/69092	Loss: 111.920
+32000/69092	Loss: 111.026
+35200/69092	Loss: 110.586
+38400/69092	Loss: 113.298
+41600/69092	Loss: 111.047
+44800/69092	Loss: 110.734
+48000/69092	Loss: 110.385
+51200/69092	Loss: 111.447
+54400/69092	Loss: 111.839
+57600/69092	Loss: 111.870
+60800/69092	Loss: 111.131
+64000/69092	Loss: 110.217
+67200/69092	Loss: 111.671
+Training time 0:01:57.431767
+Epoch: 251 Average loss: 111.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 392)
+0/69092	Loss: 102.804
+3200/69092	Loss: 110.917
+6400/69092	Loss: 111.240
+9600/69092	Loss: 111.921
+12800/69092	Loss: 111.686
+16000/69092	Loss: 110.836
+19200/69092	Loss: 110.360
+22400/69092	Loss: 112.078
+25600/69092	Loss: 109.073
+28800/69092	Loss: 113.623
+32000/69092	Loss: 112.233
+35200/69092	Loss: 111.018
+38400/69092	Loss: 110.364
+41600/69092	Loss: 111.294
+44800/69092	Loss: 111.601
+48000/69092	Loss: 110.258
+51200/69092	Loss: 111.271
+54400/69092	Loss: 111.440
+57600/69092	Loss: 111.215
+60800/69092	Loss: 111.404
+64000/69092	Loss: 110.972
+67200/69092	Loss: 110.816
+Training time 0:01:57.637421
+Epoch: 252 Average loss: 111.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 393)
+0/69092	Loss: 108.239
+3200/69092	Loss: 110.886
+6400/69092	Loss: 113.579
+9600/69092	Loss: 110.798
+12800/69092	Loss: 108.050
+16000/69092	Loss: 110.297
+19200/69092	Loss: 110.946
+22400/69092	Loss: 112.451
+25600/69092	Loss: 113.043
+28800/69092	Loss: 110.665
+32000/69092	Loss: 110.870
+35200/69092	Loss: 111.911
+38400/69092	Loss: 112.313
+41600/69092	Loss: 109.459
+44800/69092	Loss: 110.712
+48000/69092	Loss: 110.312
+51200/69092	Loss: 111.116
+54400/69092	Loss: 110.275
+57600/69092	Loss: 112.778
+60800/69092	Loss: 112.580
+64000/69092	Loss: 109.848
+67200/69092	Loss: 112.238
+Training time 0:01:56.937335
+Epoch: 253 Average loss: 111.20
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 394)
+0/69092	Loss: 113.377
+3200/69092	Loss: 112.926
+6400/69092	Loss: 111.311
+9600/69092	Loss: 109.904
+12800/69092	Loss: 110.220
+16000/69092	Loss: 111.905
+19200/69092	Loss: 110.879
+22400/69092	Loss: 110.318
+25600/69092	Loss: 111.719
+28800/69092	Loss: 111.584
+32000/69092	Loss: 110.900
+35200/69092	Loss: 112.835
+38400/69092	Loss: 111.440
+41600/69092	Loss: 112.075
+44800/69092	Loss: 111.887
+48000/69092	Loss: 112.226
+51200/69092	Loss: 109.887
+54400/69092	Loss: 110.785
+57600/69092	Loss: 110.074
+60800/69092	Loss: 110.514
+64000/69092	Loss: 110.812
+67200/69092	Loss: 111.522
+Training time 0:01:56.496175
+Epoch: 254 Average loss: 111.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 395)
+0/69092	Loss: 125.020
+3200/69092	Loss: 111.732
+6400/69092	Loss: 111.201
+9600/69092	Loss: 109.565
+12800/69092	Loss: 112.074
+16000/69092	Loss: 110.631
+19200/69092	Loss: 109.777
+22400/69092	Loss: 111.223
+25600/69092	Loss: 112.449
+28800/69092	Loss: 112.911
+32000/69092	Loss: 109.928
+35200/69092	Loss: 110.396
+38400/69092	Loss: 112.070
+41600/69092	Loss: 111.047
+44800/69092	Loss: 111.813
+48000/69092	Loss: 109.342
+51200/69092	Loss: 112.822
+54400/69092	Loss: 108.774
+57600/69092	Loss: 109.953
+60800/69092	Loss: 110.226
+64000/69092	Loss: 110.831
+67200/69092	Loss: 111.720
+Training time 0:01:57.038958
+Epoch: 255 Average loss: 111.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 396)
+0/69092	Loss: 109.533
+3200/69092	Loss: 111.231
+6400/69092	Loss: 109.497
+9600/69092	Loss: 109.054
+12800/69092	Loss: 110.814
+16000/69092	Loss: 109.865
+19200/69092	Loss: 111.885
+22400/69092	Loss: 113.264
+25600/69092	Loss: 111.223
+28800/69092	Loss: 111.529
+32000/69092	Loss: 110.043
+35200/69092	Loss: 110.608
+38400/69092	Loss: 110.200
+41600/69092	Loss: 110.879
+44800/69092	Loss: 112.661
+48000/69092	Loss: 111.049
+51200/69092	Loss: 110.760
+54400/69092	Loss: 110.193
+57600/69092	Loss: 111.437
+60800/69092	Loss: 110.753
+64000/69092	Loss: 110.284
+67200/69092	Loss: 110.475
+Training time 0:01:57.300419
+Epoch: 256 Average loss: 110.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 397)
+0/69092	Loss: 119.043
+3200/69092	Loss: 110.694
+6400/69092	Loss: 112.449
+9600/69092	Loss: 110.888
+12800/69092	Loss: 112.096
+16000/69092	Loss: 111.366
+19200/69092	Loss: 111.170
+22400/69092	Loss: 111.517
+25600/69092	Loss: 112.610
+28800/69092	Loss: 110.832
+32000/69092	Loss: 110.499
+35200/69092	Loss: 111.410
+38400/69092	Loss: 111.158
+41600/69092	Loss: 110.478
+44800/69092	Loss: 111.160
+48000/69092	Loss: 110.847
+51200/69092	Loss: 110.402
+54400/69092	Loss: 112.098
+57600/69092	Loss: 109.922
+60800/69092	Loss: 110.733
+64000/69092	Loss: 113.063
+67200/69092	Loss: 110.606
+Training time 0:01:57.780238
+Epoch: 257 Average loss: 111.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 398)
+0/69092	Loss: 117.179
+3200/69092	Loss: 111.384
+6400/69092	Loss: 109.683
+9600/69092	Loss: 109.482
+12800/69092	Loss: 110.988
+16000/69092	Loss: 111.890
+19200/69092	Loss: 111.074
+22400/69092	Loss: 111.664
+25600/69092	Loss: 110.090
+28800/69092	Loss: 110.135
+32000/69092	Loss: 110.975
+35200/69092	Loss: 112.127
+38400/69092	Loss: 112.769
+41600/69092	Loss: 110.558
+44800/69092	Loss: 112.651
+48000/69092	Loss: 109.083
+51200/69092	Loss: 110.386
+54400/69092	Loss: 110.048
+57600/69092	Loss: 112.158
+60800/69092	Loss: 111.103
+64000/69092	Loss: 112.057
+67200/69092	Loss: 111.740
+Training time 0:01:58.561590
+Epoch: 258 Average loss: 111.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 399)
+0/69092	Loss: 118.334
+3200/69092	Loss: 109.569
+6400/69092	Loss: 111.863
+9600/69092	Loss: 110.048
+12800/69092	Loss: 111.313
+16000/69092	Loss: 111.699
+19200/69092	Loss: 110.905
+22400/69092	Loss: 112.432
+25600/69092	Loss: 111.223
+28800/69092	Loss: 109.268
+32000/69092	Loss: 111.676
+35200/69092	Loss: 113.159
+38400/69092	Loss: 111.500
+41600/69092	Loss: 109.357
+44800/69092	Loss: 110.880
+48000/69092	Loss: 109.824
+51200/69092	Loss: 110.635
+54400/69092	Loss: 111.235
+57600/69092	Loss: 110.958
+60800/69092	Loss: 111.464
+64000/69092	Loss: 111.591
+67200/69092	Loss: 111.618
+Training time 0:01:56.658825
+Epoch: 259 Average loss: 111.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 400)
+0/69092	Loss: 99.918
+3200/69092	Loss: 110.536
+6400/69092	Loss: 111.087
+9600/69092	Loss: 112.079
+12800/69092	Loss: 111.291
+16000/69092	Loss: 111.590
+19200/69092	Loss: 111.152
+22400/69092	Loss: 109.990
+25600/69092	Loss: 111.403
+28800/69092	Loss: 111.198
+32000/69092	Loss: 109.055
+35200/69092	Loss: 111.210
+38400/69092	Loss: 111.335
+41600/69092	Loss: 108.108
+44800/69092	Loss: 112.186
+48000/69092	Loss: 111.355
+51200/69092	Loss: 111.126
+54400/69092	Loss: 110.002
+57600/69092	Loss: 111.158
+60800/69092	Loss: 112.760
+64000/69092	Loss: 111.019
+67200/69092	Loss: 110.575
+Training time 0:01:57.564294
+Epoch: 260 Average loss: 110.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 401)
+0/69092	Loss: 102.919
+3200/69092	Loss: 111.334
+6400/69092	Loss: 110.592
+9600/69092	Loss: 111.426
+12800/69092	Loss: 110.922
+16000/69092	Loss: 111.627
+19200/69092	Loss: 112.994
+22400/69092	Loss: 109.935
+25600/69092	Loss: 109.856
+28800/69092	Loss: 109.845
+32000/69092	Loss: 110.726
+35200/69092	Loss: 111.638
+38400/69092	Loss: 111.053
+41600/69092	Loss: 110.047
+44800/69092	Loss: 110.895
+48000/69092	Loss: 110.560
+51200/69092	Loss: 112.562
+54400/69092	Loss: 110.502
+57600/69092	Loss: 111.007
+60800/69092	Loss: 110.608
+64000/69092	Loss: 111.131
+67200/69092	Loss: 111.897
+Training time 0:01:57.132493
+Epoch: 261 Average loss: 111.01
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 402)
+0/69092	Loss: 114.218
+3200/69092	Loss: 112.573
+6400/69092	Loss: 112.008
+9600/69092	Loss: 111.699
+12800/69092	Loss: 109.915
+16000/69092	Loss: 111.076
+19200/69092	Loss: 111.227
+22400/69092	Loss: 111.564
+25600/69092	Loss: 110.558
+28800/69092	Loss: 110.465
+32000/69092	Loss: 110.430
+35200/69092	Loss: 110.942
+38400/69092	Loss: 111.891
+41600/69092	Loss: 112.028
+44800/69092	Loss: 110.570
+48000/69092	Loss: 111.672
+51200/69092	Loss: 112.246
+54400/69092	Loss: 111.485
+57600/69092	Loss: 112.011
+60800/69092	Loss: 109.935
+64000/69092	Loss: 110.762
+67200/69092	Loss: 111.097
+Training time 0:01:58.383614
+Epoch: 262 Average loss: 111.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 403)
+0/69092	Loss: 112.604
+3200/69092	Loss: 110.540
+6400/69092	Loss: 111.252
+9600/69092	Loss: 109.829
+12800/69092	Loss: 113.017
+16000/69092	Loss: 113.760
+19200/69092	Loss: 110.520
+22400/69092	Loss: 111.341
+25600/69092	Loss: 110.474
+28800/69092	Loss: 110.952
+32000/69092	Loss: 110.309
+35200/69092	Loss: 109.334
+38400/69092	Loss: 110.914
+41600/69092	Loss: 110.116
+44800/69092	Loss: 111.209
+48000/69092	Loss: 112.935
+51200/69092	Loss: 110.746
+54400/69092	Loss: 110.242
+57600/69092	Loss: 110.549
+60800/69092	Loss: 112.742
+64000/69092	Loss: 111.958
+67200/69092	Loss: 111.216
+Training time 0:01:57.953651
+Epoch: 263 Average loss: 111.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 404)
+0/69092	Loss: 101.946
+3200/69092	Loss: 110.426
+6400/69092	Loss: 110.260
+9600/69092	Loss: 109.664
+12800/69092	Loss: 110.935
+16000/69092	Loss: 111.888
+19200/69092	Loss: 112.961
+22400/69092	Loss: 110.784
+25600/69092	Loss: 110.923
+28800/69092	Loss: 109.775
+32000/69092	Loss: 111.445
+35200/69092	Loss: 110.206
+38400/69092	Loss: 112.993
+41600/69092	Loss: 111.066
+44800/69092	Loss: 110.433
+48000/69092	Loss: 110.588
+51200/69092	Loss: 111.304
+54400/69092	Loss: 111.200
+57600/69092	Loss: 112.353
+60800/69092	Loss: 111.725
+64000/69092	Loss: 110.548
+67200/69092	Loss: 110.856
+Training time 0:01:57.917637
+Epoch: 264 Average loss: 111.02
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 405)
+0/69092	Loss: 102.223
+3200/69092	Loss: 110.544
+6400/69092	Loss: 110.596
+9600/69092	Loss: 109.487
+12800/69092	Loss: 110.647
+16000/69092	Loss: 110.012
+19200/69092	Loss: 111.867
+22400/69092	Loss: 109.929
+25600/69092	Loss: 112.987
+28800/69092	Loss: 110.354
+32000/69092	Loss: 109.552
+35200/69092	Loss: 111.469
+38400/69092	Loss: 111.168
+41600/69092	Loss: 110.773
+44800/69092	Loss: 110.498
+48000/69092	Loss: 112.108
+51200/69092	Loss: 110.388
+54400/69092	Loss: 113.500
+57600/69092	Loss: 109.864
+60800/69092	Loss: 112.753
+64000/69092	Loss: 111.999
+67200/69092	Loss: 112.182
+Training time 0:01:57.302165
+Epoch: 265 Average loss: 111.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 406)
+0/69092	Loss: 103.748
+3200/69092	Loss: 111.518
+6400/69092	Loss: 111.662
+9600/69092	Loss: 110.320
+12800/69092	Loss: 112.069
+16000/69092	Loss: 111.370
+19200/69092	Loss: 110.221
+22400/69092	Loss: 111.720
+25600/69092	Loss: 110.837
+28800/69092	Loss: 111.321
+32000/69092	Loss: 110.262
+35200/69092	Loss: 111.120
+38400/69092	Loss: 108.821
+41600/69092	Loss: 109.257
+44800/69092	Loss: 111.489
+48000/69092	Loss: 111.358
+51200/69092	Loss: 109.158
+54400/69092	Loss: 111.183
+57600/69092	Loss: 112.635
+60800/69092	Loss: 110.459
+64000/69092	Loss: 110.898
+67200/69092	Loss: 111.642
+Training time 0:01:58.368381
+Epoch: 266 Average loss: 110.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 407)
+0/69092	Loss: 112.892
+3200/69092	Loss: 111.153
+6400/69092	Loss: 111.807
+9600/69092	Loss: 111.807
+12800/69092	Loss: 112.844
+16000/69092	Loss: 111.230
+19200/69092	Loss: 111.024
+22400/69092	Loss: 111.635
+25600/69092	Loss: 112.329
+28800/69092	Loss: 110.557
+32000/69092	Loss: 111.422
+35200/69092	Loss: 110.001
+38400/69092	Loss: 110.677
+41600/69092	Loss: 111.130
+44800/69092	Loss: 110.490
+48000/69092	Loss: 111.001
+51200/69092	Loss: 110.854
+54400/69092	Loss: 111.521
+57600/69092	Loss: 110.090
+60800/69092	Loss: 111.625
+64000/69092	Loss: 109.477
+67200/69092	Loss: 109.777
+Training time 0:01:58.427540
+Epoch: 267 Average loss: 111.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 408)
+0/69092	Loss: 105.202
+3200/69092	Loss: 110.029
+6400/69092	Loss: 109.795
+9600/69092	Loss: 110.878
+12800/69092	Loss: 111.447
+16000/69092	Loss: 114.239
+19200/69092	Loss: 111.138
+22400/69092	Loss: 110.883
+25600/69092	Loss: 110.844
+28800/69092	Loss: 111.197
+32000/69092	Loss: 110.699
+35200/69092	Loss: 110.040
+38400/69092	Loss: 111.646
+41600/69092	Loss: 110.489
+44800/69092	Loss: 110.871
+48000/69092	Loss: 111.598
+51200/69092	Loss: 110.340
+54400/69092	Loss: 112.259
+57600/69092	Loss: 109.082
+60800/69092	Loss: 111.659
+64000/69092	Loss: 112.772
+67200/69092	Loss: 112.814
+Training time 0:01:57.560979
+Epoch: 268 Average loss: 111.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 409)
+0/69092	Loss: 114.172
+3200/69092	Loss: 109.790
+6400/69092	Loss: 111.874
+9600/69092	Loss: 112.924
+12800/69092	Loss: 112.108
+16000/69092	Loss: 110.863
+19200/69092	Loss: 110.759
+22400/69092	Loss: 111.317
+25600/69092	Loss: 111.183
+28800/69092	Loss: 112.582
+32000/69092	Loss: 110.774
+35200/69092	Loss: 109.770
+38400/69092	Loss: 111.539
+41600/69092	Loss: 111.438
+44800/69092	Loss: 112.325
+48000/69092	Loss: 109.711
+51200/69092	Loss: 110.771
+54400/69092	Loss: 111.276
+57600/69092	Loss: 111.158
+60800/69092	Loss: 109.779
+64000/69092	Loss: 109.416
+67200/69092	Loss: 111.248
+Training time 0:01:58.904154
+Epoch: 269 Average loss: 111.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 410)
+0/69092	Loss: 105.736
+3200/69092	Loss: 109.660
+6400/69092	Loss: 110.559
+9600/69092	Loss: 111.273
+12800/69092	Loss: 111.583
+16000/69092	Loss: 111.526
+19200/69092	Loss: 109.822
+22400/69092	Loss: 111.871
+25600/69092	Loss: 109.665
+28800/69092	Loss: 110.681
+32000/69092	Loss: 111.805
+35200/69092	Loss: 111.811
+38400/69092	Loss: 112.030
+41600/69092	Loss: 110.044
+44800/69092	Loss: 111.697
+48000/69092	Loss: 109.874
+51200/69092	Loss: 111.497
+54400/69092	Loss: 112.470
+57600/69092	Loss: 110.808
+60800/69092	Loss: 111.034
+64000/69092	Loss: 110.285
+67200/69092	Loss: 109.798
+Training time 0:01:57.119963
+Epoch: 270 Average loss: 110.89
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 411)
+0/69092	Loss: 107.418
+3200/69092	Loss: 113.195
+6400/69092	Loss: 111.603
+9600/69092	Loss: 110.949
+12800/69092	Loss: 110.224
+16000/69092	Loss: 111.228
+19200/69092	Loss: 111.496
+22400/69092	Loss: 111.003
+25600/69092	Loss: 110.157
+28800/69092	Loss: 111.891
+32000/69092	Loss: 111.249
+35200/69092	Loss: 111.203
+38400/69092	Loss: 111.227
+41600/69092	Loss: 110.084
+44800/69092	Loss: 112.262
+48000/69092	Loss: 111.512
+51200/69092	Loss: 112.289
+54400/69092	Loss: 110.747
+57600/69092	Loss: 110.910
+60800/69092	Loss: 109.941
+64000/69092	Loss: 112.352
+67200/69092	Loss: 110.064
+Training time 0:01:58.405551
+Epoch: 271 Average loss: 111.17
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 412)
+0/69092	Loss: 112.247
+3200/69092	Loss: 111.532
+6400/69092	Loss: 109.866
+9600/69092	Loss: 109.927
+12800/69092	Loss: 110.300
+16000/69092	Loss: 110.468
+19200/69092	Loss: 109.192
+22400/69092	Loss: 111.327
+25600/69092	Loss: 110.400
+28800/69092	Loss: 112.408
+32000/69092	Loss: 109.738
+35200/69092	Loss: 111.481
+38400/69092	Loss: 112.698
+41600/69092	Loss: 108.841
+44800/69092	Loss: 109.635
+48000/69092	Loss: 111.447
+51200/69092	Loss: 111.715
+54400/69092	Loss: 111.377
+57600/69092	Loss: 112.041
+60800/69092	Loss: 112.644
+64000/69092	Loss: 111.499
+67200/69092	Loss: 112.792
+Training time 0:01:57.107391
+Epoch: 272 Average loss: 111.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 413)
+0/69092	Loss: 113.593
+3200/69092	Loss: 111.851
+6400/69092	Loss: 112.942
+9600/69092	Loss: 109.614
+12800/69092	Loss: 113.096
+16000/69092	Loss: 110.529
+19200/69092	Loss: 110.020
+22400/69092	Loss: 111.430
+25600/69092	Loss: 111.389
+28800/69092	Loss: 112.372
+32000/69092	Loss: 110.273
+35200/69092	Loss: 109.844
+38400/69092	Loss: 110.619
+41600/69092	Loss: 110.420
+44800/69092	Loss: 111.002
+48000/69092	Loss: 111.528
+51200/69092	Loss: 111.237
+54400/69092	Loss: 113.031
+57600/69092	Loss: 110.168
+60800/69092	Loss: 109.557
+64000/69092	Loss: 110.396
+67200/69092	Loss: 113.071
+Training time 0:01:58.378197
+Epoch: 273 Average loss: 111.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 414)
+0/69092	Loss: 107.776
+3200/69092	Loss: 110.438
+6400/69092	Loss: 109.587
+9600/69092	Loss: 111.353
+12800/69092	Loss: 109.622
+16000/69092	Loss: 111.800
+19200/69092	Loss: 111.342
+22400/69092	Loss: 110.491
+25600/69092	Loss: 112.317
+28800/69092	Loss: 111.947
+32000/69092	Loss: 111.256
+35200/69092	Loss: 111.003
+38400/69092	Loss: 112.212
+41600/69092	Loss: 110.820
+44800/69092	Loss: 111.593
+48000/69092	Loss: 110.804
+51200/69092	Loss: 110.570
+54400/69092	Loss: 111.628
+57600/69092	Loss: 111.308
+60800/69092	Loss: 109.944
+64000/69092	Loss: 110.376
+67200/69092	Loss: 109.365
+Training time 0:01:58.140014
+Epoch: 274 Average loss: 110.89
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 415)
+0/69092	Loss: 107.265
+3200/69092	Loss: 110.911
+6400/69092	Loss: 111.722
+9600/69092	Loss: 111.788
+12800/69092	Loss: 111.163
+16000/69092	Loss: 111.562
+19200/69092	Loss: 112.048
+22400/69092	Loss: 112.247
+25600/69092	Loss: 110.505
+28800/69092	Loss: 111.820
+32000/69092	Loss: 110.298
+35200/69092	Loss: 110.960
+38400/69092	Loss: 112.540
+41600/69092	Loss: 110.044
+44800/69092	Loss: 109.915
+48000/69092	Loss: 110.118
+51200/69092	Loss: 110.464
+54400/69092	Loss: 110.355
+57600/69092	Loss: 111.397
+60800/69092	Loss: 111.731
+64000/69092	Loss: 110.044
+67200/69092	Loss: 110.554
+Training time 0:01:56.970570
+Epoch: 275 Average loss: 111.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 416)
+0/69092	Loss: 109.308
+3200/69092	Loss: 109.620
+6400/69092	Loss: 112.496
+9600/69092	Loss: 111.210
+12800/69092	Loss: 109.303
+16000/69092	Loss: 109.302
+19200/69092	Loss: 111.062
+22400/69092	Loss: 111.424
+25600/69092	Loss: 112.844
+28800/69092	Loss: 110.963
+32000/69092	Loss: 111.105
+35200/69092	Loss: 110.907
+38400/69092	Loss: 110.317
+41600/69092	Loss: 111.495
+44800/69092	Loss: 111.257
+48000/69092	Loss: 111.045
+51200/69092	Loss: 111.204
+54400/69092	Loss: 110.498
+57600/69092	Loss: 110.776
+60800/69092	Loss: 112.857
+64000/69092	Loss: 109.549
+67200/69092	Loss: 110.512
+Training time 0:01:57.871726
+Epoch: 276 Average loss: 110.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 417)
+0/69092	Loss: 114.632
+3200/69092	Loss: 111.907
+6400/69092	Loss: 109.742
+9600/69092	Loss: 109.988
+12800/69092	Loss: 109.891
+16000/69092	Loss: 110.597
+19200/69092	Loss: 109.888
+22400/69092	Loss: 110.783
+25600/69092	Loss: 110.870
+28800/69092	Loss: 112.833
+32000/69092	Loss: 111.495
+35200/69092	Loss: 112.458
+38400/69092	Loss: 112.049
+41600/69092	Loss: 111.632
+44800/69092	Loss: 112.290
+48000/69092	Loss: 111.698
+51200/69092	Loss: 108.472
+54400/69092	Loss: 111.141
+57600/69092	Loss: 110.113
+60800/69092	Loss: 110.784
+64000/69092	Loss: 109.813
+67200/69092	Loss: 109.572
+Training time 0:01:57.464278
+Epoch: 277 Average loss: 110.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 418)
+0/69092	Loss: 127.052
+3200/69092	Loss: 110.904
+6400/69092	Loss: 110.255
+9600/69092	Loss: 109.091
+12800/69092	Loss: 110.646
+16000/69092	Loss: 111.554
+19200/69092	Loss: 110.131
+22400/69092	Loss: 110.198
+25600/69092	Loss: 108.659
+28800/69092	Loss: 110.733
+32000/69092	Loss: 110.423
+35200/69092	Loss: 112.402
+38400/69092	Loss: 111.252
+41600/69092	Loss: 110.627
+44800/69092	Loss: 111.488
+48000/69092	Loss: 112.702
+51200/69092	Loss: 111.199
+54400/69092	Loss: 111.644
+57600/69092	Loss: 111.951
+60800/69092	Loss: 111.907
+64000/69092	Loss: 109.767
+67200/69092	Loss: 112.209
+Training time 0:01:56.721931
+Epoch: 278 Average loss: 110.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 419)
+0/69092	Loss: 111.806
+3200/69092	Loss: 113.157
+6400/69092	Loss: 110.903
+9600/69092	Loss: 110.516
+12800/69092	Loss: 111.144
+16000/69092	Loss: 109.884
+19200/69092	Loss: 111.071
+22400/69092	Loss: 112.457
+25600/69092	Loss: 109.489
+28800/69092	Loss: 110.938
+32000/69092	Loss: 109.966
+35200/69092	Loss: 112.436
+38400/69092	Loss: 111.449
+41600/69092	Loss: 111.393
+44800/69092	Loss: 111.038
+48000/69092	Loss: 111.123
+51200/69092	Loss: 111.270
+54400/69092	Loss: 110.161
+57600/69092	Loss: 111.428
+60800/69092	Loss: 111.725
+64000/69092	Loss: 110.831
+67200/69092	Loss: 110.678
+Training time 0:01:57.508080
+Epoch: 279 Average loss: 111.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 420)
+0/69092	Loss: 105.821
+3200/69092	Loss: 112.053
+6400/69092	Loss: 110.937
+9600/69092	Loss: 110.877
+12800/69092	Loss: 110.348
+16000/69092	Loss: 109.876
+19200/69092	Loss: 110.762
+22400/69092	Loss: 110.478
+25600/69092	Loss: 109.307
+28800/69092	Loss: 110.364
+32000/69092	Loss: 110.561
+35200/69092	Loss: 110.215
+38400/69092	Loss: 112.803
+41600/69092	Loss: 113.309
+44800/69092	Loss: 110.400
+48000/69092	Loss: 110.345
+51200/69092	Loss: 110.096
+54400/69092	Loss: 111.199
+57600/69092	Loss: 110.091
+60800/69092	Loss: 112.631
+64000/69092	Loss: 113.145
+67200/69092	Loss: 109.860
+Training time 0:01:57.990605
+Epoch: 280 Average loss: 110.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 421)
+0/69092	Loss: 99.305
+3200/69092	Loss: 111.363
+6400/69092	Loss: 111.338
+9600/69092	Loss: 111.436
+12800/69092	Loss: 110.589
+16000/69092	Loss: 108.550
+19200/69092	Loss: 111.130
+22400/69092	Loss: 111.056
+25600/69092	Loss: 110.729
+28800/69092	Loss: 111.964
+32000/69092	Loss: 110.826
+35200/69092	Loss: 110.334
+38400/69092	Loss: 112.357
+41600/69092	Loss: 111.353
+44800/69092	Loss: 110.260
+48000/69092	Loss: 111.335
+51200/69092	Loss: 110.534
+54400/69092	Loss: 111.128
+57600/69092	Loss: 110.575
+60800/69092	Loss: 110.258
+64000/69092	Loss: 111.142
+67200/69092	Loss: 110.867
+Training time 0:01:57.845171
+Epoch: 281 Average loss: 110.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 422)
+0/69092	Loss: 109.939
+3200/69092	Loss: 110.927
+6400/69092	Loss: 111.372
+9600/69092	Loss: 113.477
+12800/69092	Loss: 110.871
+16000/69092	Loss: 110.388
+19200/69092	Loss: 110.984
+22400/69092	Loss: 109.603
+25600/69092	Loss: 110.984
+28800/69092	Loss: 110.942
+32000/69092	Loss: 110.955
+35200/69092	Loss: 113.097
+38400/69092	Loss: 110.582
+41600/69092	Loss: 110.519
+44800/69092	Loss: 110.225
+48000/69092	Loss: 111.144
+51200/69092	Loss: 110.632
+54400/69092	Loss: 110.743
+57600/69092	Loss: 110.619
+60800/69092	Loss: 109.524
+64000/69092	Loss: 110.168
+67200/69092	Loss: 111.639
+Training time 0:01:57.405325
+Epoch: 282 Average loss: 110.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 423)
+0/69092	Loss: 119.652
+3200/69092	Loss: 111.000
+6400/69092	Loss: 109.335
+9600/69092	Loss: 110.609
+12800/69092	Loss: 110.800
+16000/69092	Loss: 112.095
+19200/69092	Loss: 109.557
+22400/69092	Loss: 110.808
+25600/69092	Loss: 110.399
+28800/69092	Loss: 111.826
+32000/69092	Loss: 110.780
+35200/69092	Loss: 111.985
+38400/69092	Loss: 112.738
+41600/69092	Loss: 111.138
+44800/69092	Loss: 110.412
+48000/69092	Loss: 111.163
+51200/69092	Loss: 112.704
+54400/69092	Loss: 111.012
+57600/69092	Loss: 110.059
+60800/69092	Loss: 111.216
+64000/69092	Loss: 111.620
+67200/69092	Loss: 110.888
+Training time 0:01:57.440733
+Epoch: 283 Average loss: 111.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 424)
+0/69092	Loss: 106.990
+3200/69092	Loss: 107.762
+6400/69092	Loss: 111.007
+9600/69092	Loss: 109.851
+12800/69092	Loss: 111.823
+16000/69092	Loss: 111.459
+19200/69092	Loss: 111.446
+22400/69092	Loss: 112.483
+25600/69092	Loss: 110.370
+28800/69092	Loss: 110.804
+32000/69092	Loss: 110.054
+35200/69092	Loss: 110.605
+38400/69092	Loss: 110.478
+41600/69092	Loss: 110.785
+44800/69092	Loss: 113.107
+48000/69092	Loss: 111.193
+51200/69092	Loss: 112.808
+54400/69092	Loss: 111.634
+57600/69092	Loss: 110.049
+60800/69092	Loss: 110.342
+64000/69092	Loss: 109.923
+67200/69092	Loss: 111.048
+Training time 0:01:58.276838
+Epoch: 284 Average loss: 110.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 425)
+0/69092	Loss: 126.258
+3200/69092	Loss: 110.308
+6400/69092	Loss: 108.240
+9600/69092	Loss: 111.404
+12800/69092	Loss: 110.676
+16000/69092	Loss: 110.967
+19200/69092	Loss: 113.284
+22400/69092	Loss: 109.787
+25600/69092	Loss: 109.907
+28800/69092	Loss: 112.063
+32000/69092	Loss: 111.203
+35200/69092	Loss: 112.119
+38400/69092	Loss: 110.590
+41600/69092	Loss: 110.458
+44800/69092	Loss: 113.380
+48000/69092	Loss: 111.126
+51200/69092	Loss: 111.631
+54400/69092	Loss: 110.385
+57600/69092	Loss: 109.505
+60800/69092	Loss: 109.918
+64000/69092	Loss: 110.778
+67200/69092	Loss: 111.695
+Training time 0:01:58.765267
+Epoch: 285 Average loss: 110.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 426)
+0/69092	Loss: 110.888
+3200/69092	Loss: 109.617
+6400/69092	Loss: 110.353
+9600/69092	Loss: 110.585
+12800/69092	Loss: 110.895
+16000/69092	Loss: 110.390
+19200/69092	Loss: 110.729
+22400/69092	Loss: 112.426
+25600/69092	Loss: 110.992
+28800/69092	Loss: 111.520
+32000/69092	Loss: 110.369
+35200/69092	Loss: 111.826
+38400/69092	Loss: 110.541
+41600/69092	Loss: 111.892
+44800/69092	Loss: 109.360
+48000/69092	Loss: 112.748
+51200/69092	Loss: 109.524
+54400/69092	Loss: 111.490
+57600/69092	Loss: 112.498
+60800/69092	Loss: 111.301
+64000/69092	Loss: 111.848
+67200/69092	Loss: 110.673
+Training time 0:01:57.282262
+Epoch: 286 Average loss: 110.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 427)
+0/69092	Loss: 114.157
+3200/69092	Loss: 110.901
+6400/69092	Loss: 108.332
+9600/69092	Loss: 111.945
+12800/69092	Loss: 110.487
+16000/69092	Loss: 110.610
+19200/69092	Loss: 110.866
+22400/69092	Loss: 111.028
+25600/69092	Loss: 110.484
+28800/69092	Loss: 112.734
+32000/69092	Loss: 110.830
+35200/69092	Loss: 111.092
+38400/69092	Loss: 109.632
+41600/69092	Loss: 109.797
+44800/69092	Loss: 111.363
+48000/69092	Loss: 110.378
+51200/69092	Loss: 111.159
+54400/69092	Loss: 109.174
+57600/69092	Loss: 111.778
+60800/69092	Loss: 111.520
+64000/69092	Loss: 111.976
+67200/69092	Loss: 111.374
+Training time 0:01:58.552811
+Epoch: 287 Average loss: 110.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 428)
+0/69092	Loss: 98.511
+3200/69092	Loss: 108.557
+6400/69092	Loss: 110.954
+9600/69092	Loss: 111.782
+12800/69092	Loss: 111.856
+16000/69092	Loss: 111.708
+19200/69092	Loss: 110.535
+22400/69092	Loss: 111.989
+25600/69092	Loss: 109.486
+28800/69092	Loss: 111.154
+32000/69092	Loss: 110.285
+35200/69092	Loss: 112.020
+38400/69092	Loss: 112.430
+41600/69092	Loss: 109.311
+44800/69092	Loss: 111.011
+48000/69092	Loss: 109.668
+51200/69092	Loss: 111.056
+54400/69092	Loss: 110.999
+57600/69092	Loss: 111.689
+60800/69092	Loss: 110.654
+64000/69092	Loss: 111.223
+67200/69092	Loss: 111.608
+Training time 0:01:57.676074
+Epoch: 288 Average loss: 110.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 429)
+0/69092	Loss: 104.075
+3200/69092	Loss: 111.203
+6400/69092	Loss: 110.838
+9600/69092	Loss: 110.986
+12800/69092	Loss: 110.098
+16000/69092	Loss: 109.012
+19200/69092	Loss: 111.120
+22400/69092	Loss: 109.926
+25600/69092	Loss: 112.095
+28800/69092	Loss: 111.210
+32000/69092	Loss: 110.441
+35200/69092	Loss: 112.471
+38400/69092	Loss: 110.142
+41600/69092	Loss: 111.790
+44800/69092	Loss: 112.376
+48000/69092	Loss: 110.861
+51200/69092	Loss: 111.021
+54400/69092	Loss: 109.097
+57600/69092	Loss: 111.030
+60800/69092	Loss: 111.385
+64000/69092	Loss: 110.457
+67200/69092	Loss: 112.128
+Training time 0:01:58.374407
+Epoch: 289 Average loss: 110.98
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 430)
+0/69092	Loss: 102.637
+3200/69092	Loss: 110.249
+6400/69092	Loss: 110.317
+9600/69092	Loss: 111.740
+12800/69092	Loss: 110.958
+16000/69092	Loss: 109.061
+19200/69092	Loss: 109.024
+22400/69092	Loss: 110.359
+25600/69092	Loss: 111.071
+28800/69092	Loss: 109.974
+32000/69092	Loss: 111.436
+35200/69092	Loss: 111.265
+38400/69092	Loss: 111.014
+41600/69092	Loss: 110.954
+44800/69092	Loss: 110.811
+48000/69092	Loss: 111.528
+51200/69092	Loss: 110.617
+54400/69092	Loss: 111.308
+57600/69092	Loss: 112.040
+60800/69092	Loss: 109.778
+64000/69092	Loss: 111.718
+67200/69092	Loss: 111.476
+Training time 0:01:57.290321
+Epoch: 290 Average loss: 110.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 431)
+0/69092	Loss: 112.755
+3200/69092	Loss: 111.344
+6400/69092	Loss: 110.573
+9600/69092	Loss: 110.679
+12800/69092	Loss: 111.196
+16000/69092	Loss: 111.228
+19200/69092	Loss: 109.418
+22400/69092	Loss: 110.008
+25600/69092	Loss: 112.658
+28800/69092	Loss: 111.429
+32000/69092	Loss: 110.372
+35200/69092	Loss: 111.216
+38400/69092	Loss: 111.777
+41600/69092	Loss: 110.837
+44800/69092	Loss: 111.508
+48000/69092	Loss: 109.906
+51200/69092	Loss: 109.060
+54400/69092	Loss: 111.110
+57600/69092	Loss: 109.381
+60800/69092	Loss: 111.219
+64000/69092	Loss: 112.256
+67200/69092	Loss: 111.393
+Training time 0:01:58.204894
+Epoch: 291 Average loss: 110.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 432)
+0/69092	Loss: 112.736
+3200/69092	Loss: 110.317
+6400/69092	Loss: 110.182
+9600/69092	Loss: 111.489
+12800/69092	Loss: 109.635
+16000/69092	Loss: 109.835
+19200/69092	Loss: 112.105
+22400/69092	Loss: 111.390
+25600/69092	Loss: 111.282
+28800/69092	Loss: 109.946
+32000/69092	Loss: 110.244
+35200/69092	Loss: 113.222
+38400/69092	Loss: 113.057
+41600/69092	Loss: 112.111
+44800/69092	Loss: 110.511
+48000/69092	Loss: 110.272
+51200/69092	Loss: 110.620
+54400/69092	Loss: 110.659
+57600/69092	Loss: 110.869
+60800/69092	Loss: 110.452
+64000/69092	Loss: 112.389
+67200/69092	Loss: 110.010
+Training time 0:01:57.528478
+Epoch: 292 Average loss: 110.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 433)
+0/69092	Loss: 114.500
+3200/69092	Loss: 110.128
+6400/69092	Loss: 109.510
+9600/69092	Loss: 111.423
+12800/69092	Loss: 110.941
+16000/69092	Loss: 108.992
+19200/69092	Loss: 111.361
+22400/69092	Loss: 110.340
+25600/69092	Loss: 110.495
+28800/69092	Loss: 110.377
+32000/69092	Loss: 110.545
+35200/69092	Loss: 109.933
+38400/69092	Loss: 109.964
+41600/69092	Loss: 112.998
+44800/69092	Loss: 111.314
+48000/69092	Loss: 111.898
+51200/69092	Loss: 111.896
+54400/69092	Loss: 111.930
+57600/69092	Loss: 109.081
+60800/69092	Loss: 111.646
+64000/69092	Loss: 110.968
+67200/69092	Loss: 110.556
+Training time 0:01:57.517016
+Epoch: 293 Average loss: 110.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 434)
+0/69092	Loss: 128.226
+3200/69092	Loss: 113.884
+6400/69092	Loss: 110.194
+9600/69092	Loss: 112.417
+12800/69092	Loss: 110.099
+16000/69092	Loss: 111.266
+19200/69092	Loss: 110.346
+22400/69092	Loss: 110.674
+25600/69092	Loss: 111.557
+28800/69092	Loss: 109.910
+32000/69092	Loss: 110.767
+35200/69092	Loss: 108.370
+38400/69092	Loss: 109.809
+41600/69092	Loss: 112.768
+44800/69092	Loss: 110.918
+48000/69092	Loss: 110.599
+51200/69092	Loss: 109.976
+54400/69092	Loss: 110.991
+57600/69092	Loss: 111.904
+60800/69092	Loss: 110.660
+64000/69092	Loss: 108.824
+67200/69092	Loss: 109.690
+Training time 0:01:58.568456
+Epoch: 294 Average loss: 110.76
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 435)
+0/69092	Loss: 101.908
+3200/69092	Loss: 111.186
+6400/69092	Loss: 110.414
+9600/69092	Loss: 110.318
+12800/69092	Loss: 112.272
+16000/69092	Loss: 109.021
+19200/69092	Loss: 113.014
+22400/69092	Loss: 110.897
+25600/69092	Loss: 110.764
+28800/69092	Loss: 110.582
+32000/69092	Loss: 112.341
+35200/69092	Loss: 112.834
+38400/69092	Loss: 111.393
+41600/69092	Loss: 110.558
+44800/69092	Loss: 111.250
+48000/69092	Loss: 110.628
+51200/69092	Loss: 109.401
+54400/69092	Loss: 110.663
+57600/69092	Loss: 110.167
+60800/69092	Loss: 111.613
+64000/69092	Loss: 109.191
+67200/69092	Loss: 113.185
+Training time 0:01:58.401494
+Epoch: 295 Average loss: 111.02
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 436)
+0/69092	Loss: 111.187
+3200/69092	Loss: 110.383
+6400/69092	Loss: 113.301
+9600/69092	Loss: 111.514
+12800/69092	Loss: 110.436
+16000/69092	Loss: 109.524
+19200/69092	Loss: 110.445
+22400/69092	Loss: 109.889
+25600/69092	Loss: 111.973
+28800/69092	Loss: 112.284
+32000/69092	Loss: 111.488
+35200/69092	Loss: 110.241
+38400/69092	Loss: 109.937
+41600/69092	Loss: 110.844
+44800/69092	Loss: 109.690
+48000/69092	Loss: 111.326
+51200/69092	Loss: 111.018
+54400/69092	Loss: 109.443
+57600/69092	Loss: 110.248
+60800/69092	Loss: 109.418
+64000/69092	Loss: 110.942
+67200/69092	Loss: 112.532
+Training time 0:01:58.054422
+Epoch: 296 Average loss: 110.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 437)
+0/69092	Loss: 109.589
+3200/69092	Loss: 108.906
+6400/69092	Loss: 111.589
+9600/69092	Loss: 110.672
+12800/69092	Loss: 111.534
+16000/69092	Loss: 111.271
+19200/69092	Loss: 111.011
+22400/69092	Loss: 110.344
+25600/69092	Loss: 110.352
+28800/69092	Loss: 111.733
+32000/69092	Loss: 111.576
+35200/69092	Loss: 109.561
+38400/69092	Loss: 113.457
+41600/69092	Loss: 112.402
+44800/69092	Loss: 110.143
+48000/69092	Loss: 109.951
+51200/69092	Loss: 112.914
+54400/69092	Loss: 112.174
+57600/69092	Loss: 109.020
+60800/69092	Loss: 109.533
+64000/69092	Loss: 111.442
+67200/69092	Loss: 111.365
+Training time 0:01:57.368761
+Epoch: 297 Average loss: 110.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 438)
+0/69092	Loss: 108.749
+3200/69092	Loss: 111.171
+6400/69092	Loss: 110.433
+9600/69092	Loss: 111.856
+12800/69092	Loss: 108.539
+16000/69092	Loss: 110.187
+19200/69092	Loss: 111.049
+22400/69092	Loss: 111.446
+25600/69092	Loss: 109.760
+28800/69092	Loss: 110.100
+32000/69092	Loss: 111.754
+35200/69092	Loss: 113.237
+38400/69092	Loss: 111.183
+41600/69092	Loss: 110.450
+44800/69092	Loss: 111.432
+48000/69092	Loss: 113.090
+51200/69092	Loss: 109.832
+54400/69092	Loss: 110.826
+57600/69092	Loss: 112.865
+60800/69092	Loss: 111.927
+64000/69092	Loss: 110.442
+67200/69092	Loss: 109.851
+Training time 0:01:56.984332
+Epoch: 298 Average loss: 110.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 439)
+0/69092	Loss: 105.931
+3200/69092	Loss: 110.126
+6400/69092	Loss: 110.617
+9600/69092	Loss: 110.992
+12800/69092	Loss: 110.139
+16000/69092	Loss: 112.035
+19200/69092	Loss: 110.818
+22400/69092	Loss: 110.201
+25600/69092	Loss: 111.025
+28800/69092	Loss: 110.035
+32000/69092	Loss: 111.795
+35200/69092	Loss: 112.614
+38400/69092	Loss: 111.208
+41600/69092	Loss: 110.589
+44800/69092	Loss: 110.215
+48000/69092	Loss: 111.576
+51200/69092	Loss: 111.711
+54400/69092	Loss: 109.680
+57600/69092	Loss: 112.130
+60800/69092	Loss: 110.922
+64000/69092	Loss: 110.125
+67200/69092	Loss: 110.402
+Training time 0:01:57.751606
+Epoch: 299 Average loss: 110.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 440)
+0/69092	Loss: 108.554
+3200/69092	Loss: 109.777
+6400/69092	Loss: 110.337
+9600/69092	Loss: 112.371
+12800/69092	Loss: 111.388
+16000/69092	Loss: 109.166
+19200/69092	Loss: 110.062
+22400/69092	Loss: 112.056
+25600/69092	Loss: 110.879
+28800/69092	Loss: 112.380
+32000/69092	Loss: 111.925
+35200/69092	Loss: 110.586
+38400/69092	Loss: 109.672
+41600/69092	Loss: 110.674
+44800/69092	Loss: 109.983
+48000/69092	Loss: 110.466
+51200/69092	Loss: 110.676
+54400/69092	Loss: 111.481
+57600/69092	Loss: 110.750
+60800/69092	Loss: 111.140
+64000/69092	Loss: 109.790
+67200/69092	Loss: 110.965
+Training time 0:01:57.502604
+Epoch: 300 Average loss: 110.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 441)
+0/69092	Loss: 126.165
+3200/69092	Loss: 110.285
+6400/69092	Loss: 110.129
+9600/69092	Loss: 111.424
+12800/69092	Loss: 109.740
+16000/69092	Loss: 111.854
+19200/69092	Loss: 110.794
+22400/69092	Loss: 110.792
+25600/69092	Loss: 111.706
+28800/69092	Loss: 112.301
+32000/69092	Loss: 111.723
+35200/69092	Loss: 112.221
+38400/69092	Loss: 111.395
+41600/69092	Loss: 111.282
+44800/69092	Loss: 110.389
+48000/69092	Loss: 109.643
+51200/69092	Loss: 110.450
+54400/69092	Loss: 111.629
+57600/69092	Loss: 111.873
+60800/69092	Loss: 110.584
+64000/69092	Loss: 111.131
+67200/69092	Loss: 109.687
+Training time 0:01:57.204475
+Epoch: 301 Average loss: 111.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 442)
+0/69092	Loss: 106.093
+3200/69092	Loss: 109.878
+6400/69092	Loss: 110.295
+9600/69092	Loss: 111.979
+12800/69092	Loss: 113.320
+16000/69092	Loss: 111.759
+19200/69092	Loss: 111.122
+22400/69092	Loss: 109.408
+25600/69092	Loss: 110.433
+28800/69092	Loss: 109.775
+32000/69092	Loss: 108.749
+35200/69092	Loss: 110.879
+38400/69092	Loss: 111.396
+41600/69092	Loss: 111.396
+44800/69092	Loss: 111.190
+48000/69092	Loss: 109.918
+51200/69092	Loss: 110.460
+54400/69092	Loss: 111.901
+57600/69092	Loss: 110.748
+60800/69092	Loss: 110.686
+64000/69092	Loss: 110.852
+67200/69092	Loss: 112.430
+Training time 0:01:58.077791
+Epoch: 302 Average loss: 110.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 443)
+0/69092	Loss: 108.815
+3200/69092	Loss: 112.556
+6400/69092	Loss: 110.766
+9600/69092	Loss: 108.460
+12800/69092	Loss: 111.993
+16000/69092	Loss: 110.540
+19200/69092	Loss: 109.040
+22400/69092	Loss: 110.837
+25600/69092	Loss: 111.466
+28800/69092	Loss: 110.389
+32000/69092	Loss: 108.280
+35200/69092	Loss: 110.833
+38400/69092	Loss: 110.869
+41600/69092	Loss: 108.830
+44800/69092	Loss: 112.886
+48000/69092	Loss: 110.929
+51200/69092	Loss: 110.975
+54400/69092	Loss: 111.118
+57600/69092	Loss: 110.810
+60800/69092	Loss: 110.648
+64000/69092	Loss: 112.793
+67200/69092	Loss: 110.700
+Training time 0:01:57.392230
+Epoch: 303 Average loss: 110.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 444)
+0/69092	Loss: 102.485
+3200/69092	Loss: 110.565
+6400/69092	Loss: 112.579
+9600/69092	Loss: 110.242
+12800/69092	Loss: 109.614
+16000/69092	Loss: 110.495
+19200/69092	Loss: 110.945
+22400/69092	Loss: 112.366
+25600/69092	Loss: 110.999
+28800/69092	Loss: 109.653
+32000/69092	Loss: 110.920
+35200/69092	Loss: 111.993
+38400/69092	Loss: 111.538
+41600/69092	Loss: 111.368
+44800/69092	Loss: 110.520
+48000/69092	Loss: 111.310
+51200/69092	Loss: 109.049
+54400/69092	Loss: 112.195
+57600/69092	Loss: 111.088
+60800/69092	Loss: 109.198
+64000/69092	Loss: 111.151
+67200/69092	Loss: 112.724
+Training time 0:01:57.946444
+Epoch: 304 Average loss: 110.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 445)
+0/69092	Loss: 102.396
+3200/69092	Loss: 110.045
+6400/69092	Loss: 111.459
+9600/69092	Loss: 110.740
+12800/69092	Loss: 109.375
+16000/69092	Loss: 109.528
+19200/69092	Loss: 110.296
+22400/69092	Loss: 111.529
+25600/69092	Loss: 112.015
+28800/69092	Loss: 112.480
+32000/69092	Loss: 111.684
+35200/69092	Loss: 109.674
+38400/69092	Loss: 109.920
+41600/69092	Loss: 110.739
+44800/69092	Loss: 112.200
+48000/69092	Loss: 110.242
+51200/69092	Loss: 109.654
+54400/69092	Loss: 110.177
+57600/69092	Loss: 111.492
+60800/69092	Loss: 110.973
+64000/69092	Loss: 114.455
+67200/69092	Loss: 110.318
+Training time 0:01:56.605594
+Epoch: 305 Average loss: 110.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 446)
+0/69092	Loss: 109.967
+3200/69092	Loss: 111.850
+6400/69092	Loss: 112.518
+9600/69092	Loss: 110.590
+12800/69092	Loss: 110.210
+16000/69092	Loss: 109.241
+19200/69092	Loss: 110.525
+22400/69092	Loss: 110.654
+25600/69092	Loss: 111.915
+28800/69092	Loss: 111.632
+32000/69092	Loss: 110.208
+35200/69092	Loss: 109.360
+38400/69092	Loss: 110.583
+41600/69092	Loss: 110.789
+44800/69092	Loss: 111.279
+48000/69092	Loss: 110.052
+51200/69092	Loss: 110.062
+54400/69092	Loss: 110.449
+57600/69092	Loss: 110.957
diff --git a/OAR.2068288.stderr b/OAR.2068288.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..179126132225796ec105b2d6a4d42d0c489edc55
--- /dev/null
+++ b/OAR.2068288.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-25 02:59:26] Job 2068288 KILLED ##
diff --git a/OAR.2068288.stdout b/OAR.2068288.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..ede880f175881dea951eb877778177b8a0a40847
--- /dev/null
+++ b/OAR.2068288.stdout
@@ -0,0 +1,3001 @@
+Namespace(batch_size=64, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_64_ls_15', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=15, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/beta_VAE_bs_64_ls_15
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=30, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=15, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 769185
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last (iter 1)'
+0/69092	Loss: 206.887
+3200/69092	Loss: 207.842
+6400/69092	Loss: 208.285
+9600/69092	Loss: 202.771
+12800/69092	Loss: 200.673
+16000/69092	Loss: 204.444
+19200/69092	Loss: 200.745
+22400/69092	Loss: 196.177
+25600/69092	Loss: 196.550
+28800/69092	Loss: 196.250
+32000/69092	Loss: 193.874
+35200/69092	Loss: 190.245
+38400/69092	Loss: 194.581
+41600/69092	Loss: 192.099
+44800/69092	Loss: 192.691
+48000/69092	Loss: 187.078
+51200/69092	Loss: 190.474
+54400/69092	Loss: 187.622
+57600/69092	Loss: 183.145
+60800/69092	Loss: 187.360
+64000/69092	Loss: 180.957
+67200/69092	Loss: 182.479
+Training time 0:05:05.562599
+Epoch: 1 Average loss: 193.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 2)
+0/69092	Loss: 170.944
+3200/69092	Loss: 181.493
+6400/69092	Loss: 178.386
+9600/69092	Loss: 180.497
+12800/69092	Loss: 178.568
+16000/69092	Loss: 176.458
+19200/69092	Loss: 182.626
+22400/69092	Loss: 177.960
+25600/69092	Loss: 174.158
+28800/69092	Loss: 180.979
+32000/69092	Loss: 175.474
+35200/69092	Loss: 173.373
+38400/69092	Loss: 175.485
+41600/69092	Loss: 172.948
+44800/69092	Loss: 174.871
+48000/69092	Loss: 173.946
+51200/69092	Loss: 174.915
+54400/69092	Loss: 172.485
+57600/69092	Loss: 174.387
+60800/69092	Loss: 173.445
+64000/69092	Loss: 171.097
+67200/69092	Loss: 171.948
+Training time 0:05:04.217780
+Epoch: 2 Average loss: 175.77
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 3)
+0/69092	Loss: 166.616
+3200/69092	Loss: 174.563
+6400/69092	Loss: 169.681
+9600/69092	Loss: 169.392
+12800/69092	Loss: 169.089
+16000/69092	Loss: 169.595
+19200/69092	Loss: 169.412
+22400/69092	Loss: 171.284
+25600/69092	Loss: 168.897
+28800/69092	Loss: 165.861
+32000/69092	Loss: 164.709
+35200/69092	Loss: 166.149
+38400/69092	Loss: 167.966
+41600/69092	Loss: 167.646
+44800/69092	Loss: 168.247
+48000/69092	Loss: 165.073
+51200/69092	Loss: 165.861
+54400/69092	Loss: 166.124
+57600/69092	Loss: 171.008
+60800/69092	Loss: 166.149
+64000/69092	Loss: 172.057
+67200/69092	Loss: 165.177
+Training time 0:05:12.167257
+Epoch: 3 Average loss: 168.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 4)
+0/69092	Loss: 165.766
+3200/69092	Loss: 163.782
+6400/69092	Loss: 165.200
+9600/69092	Loss: 166.316
+12800/69092	Loss: 165.267
+16000/69092	Loss: 166.998
+19200/69092	Loss: 169.749
+22400/69092	Loss: 164.441
+25600/69092	Loss: 162.002
+28800/69092	Loss: 161.511
+32000/69092	Loss: 165.594
+35200/69092	Loss: 165.813
+38400/69092	Loss: 163.929
+41600/69092	Loss: 165.160
+44800/69092	Loss: 166.075
+48000/69092	Loss: 167.417
+51200/69092	Loss: 162.397
+54400/69092	Loss: 163.676
+57600/69092	Loss: 162.870
+60800/69092	Loss: 166.312
+64000/69092	Loss: 163.025
+67200/69092	Loss: 162.398
+Training time 0:05:07.147008
+Epoch: 4 Average loss: 164.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 5)
+0/69092	Loss: 153.476
+3200/69092	Loss: 159.726
+6400/69092	Loss: 165.582
+9600/69092	Loss: 161.601
+12800/69092	Loss: 162.702
+16000/69092	Loss: 164.992
+19200/69092	Loss: 166.401
+22400/69092	Loss: 163.438
+25600/69092	Loss: 159.738
+28800/69092	Loss: 161.781
+32000/69092	Loss: 164.914
+35200/69092	Loss: 165.694
+38400/69092	Loss: 163.410
+41600/69092	Loss: 162.084
+44800/69092	Loss: 160.367
+48000/69092	Loss: 161.416
+51200/69092	Loss: 160.890
+54400/69092	Loss: 166.457
+57600/69092	Loss: 164.688
+60800/69092	Loss: 164.736
+64000/69092	Loss: 164.117
+67200/69092	Loss: 164.351
+Training time 0:05:06.414312
+Epoch: 5 Average loss: 163.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 6)
+0/69092	Loss: 189.969
+3200/69092	Loss: 164.013
+6400/69092	Loss: 161.849
+9600/69092	Loss: 162.317
+12800/69092	Loss: 162.261
+16000/69092	Loss: 160.441
+19200/69092	Loss: 162.060
+22400/69092	Loss: 162.608
+25600/69092	Loss: 162.462
+28800/69092	Loss: 162.498
+32000/69092	Loss: 160.510
+35200/69092	Loss: 165.184
+38400/69092	Loss: 162.527
+41600/69092	Loss: 162.811
+44800/69092	Loss: 159.004
+48000/69092	Loss: 162.282
+51200/69092	Loss: 164.850
+54400/69092	Loss: 159.039
+57600/69092	Loss: 160.952
+60800/69092	Loss: 158.907
+64000/69092	Loss: 160.586
+67200/69092	Loss: 159.604
+Training time 0:04:58.356152
+Epoch: 6 Average loss: 161.86
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 7)
+0/69092	Loss: 163.107
+3200/69092	Loss: 159.555
+6400/69092	Loss: 160.722
+9600/69092	Loss: 160.986
+12800/69092	Loss: 161.709
+16000/69092	Loss: 161.245
+19200/69092	Loss: 162.102
+22400/69092	Loss: 160.872
+25600/69092	Loss: 159.378
+28800/69092	Loss: 163.367
+32000/69092	Loss: 160.811
+35200/69092	Loss: 162.435
+38400/69092	Loss: 159.574
+41600/69092	Loss: 160.831
+44800/69092	Loss: 161.828
+48000/69092	Loss: 158.710
+51200/69092	Loss: 158.553
+54400/69092	Loss: 159.628
+57600/69092	Loss: 160.123
+60800/69092	Loss: 158.915
+64000/69092	Loss: 161.189
+67200/69092	Loss: 163.030
+Training time 0:05:01.055401
+Epoch: 7 Average loss: 160.66
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 8)
+0/69092	Loss: 174.514
+3200/69092	Loss: 161.884
+6400/69092	Loss: 161.307
+9600/69092	Loss: 163.065
+12800/69092	Loss: 161.624
+16000/69092	Loss: 161.000
+19200/69092	Loss: 161.357
+22400/69092	Loss: 158.717
+25600/69092	Loss: 160.997
+28800/69092	Loss: 158.610
+32000/69092	Loss: 160.508
+35200/69092	Loss: 160.528
+38400/69092	Loss: 159.435
+41600/69092	Loss: 160.706
+44800/69092	Loss: 159.112
+48000/69092	Loss: 158.584
+51200/69092	Loss: 161.281
+54400/69092	Loss: 158.506
+57600/69092	Loss: 155.892
+60800/69092	Loss: 159.104
+64000/69092	Loss: 159.219
+67200/69092	Loss: 159.272
+Training time 0:05:04.411575
+Epoch: 8 Average loss: 160.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 9)
+0/69092	Loss: 151.763
+3200/69092	Loss: 156.918
+6400/69092	Loss: 158.726
+9600/69092	Loss: 158.688
+12800/69092	Loss: 159.796
+16000/69092	Loss: 161.199
+19200/69092	Loss: 160.557
+22400/69092	Loss: 155.504
+25600/69092	Loss: 158.230
+28800/69092	Loss: 159.681
+32000/69092	Loss: 163.094
+35200/69092	Loss: 159.575
+38400/69092	Loss: 158.562
+41600/69092	Loss: 159.907
+44800/69092	Loss: 158.514
+48000/69092	Loss: 157.097
+51200/69092	Loss: 157.089
+54400/69092	Loss: 160.598
+57600/69092	Loss: 161.355
+60800/69092	Loss: 159.777
+64000/69092	Loss: 160.242
+67200/69092	Loss: 158.511
+Training time 0:04:58.204644
+Epoch: 9 Average loss: 159.24
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 10)
+0/69092	Loss: 134.625
+3200/69092	Loss: 157.098
+6400/69092	Loss: 159.415
+9600/69092	Loss: 156.766
+12800/69092	Loss: 159.311
+16000/69092	Loss: 156.983
+19200/69092	Loss: 158.991
+22400/69092	Loss: 162.854
+25600/69092	Loss: 158.652
+28800/69092	Loss: 156.597
+32000/69092	Loss: 162.327
+35200/69092	Loss: 159.612
+38400/69092	Loss: 160.586
+41600/69092	Loss: 157.079
+44800/69092	Loss: 156.920
+48000/69092	Loss: 161.475
+51200/69092	Loss: 157.764
+54400/69092	Loss: 157.025
+57600/69092	Loss: 156.038
+60800/69092	Loss: 157.571
+64000/69092	Loss: 158.655
+67200/69092	Loss: 156.811
+Training time 0:04:59.827318
+Epoch: 10 Average loss: 158.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 11)
+0/69092	Loss: 159.620
+3200/69092	Loss: 157.281
+6400/69092	Loss: 159.662
+9600/69092	Loss: 157.641
+12800/69092	Loss: 159.027
+16000/69092	Loss: 154.571
+19200/69092	Loss: 157.737
+22400/69092	Loss: 155.758
+25600/69092	Loss: 158.316
+28800/69092	Loss: 157.215
+32000/69092	Loss: 159.709
+35200/69092	Loss: 157.757
+38400/69092	Loss: 161.319
+41600/69092	Loss: 161.856
+44800/69092	Loss: 159.194
+48000/69092	Loss: 156.638
+51200/69092	Loss: 159.944
+54400/69092	Loss: 156.483
+57600/69092	Loss: 158.072
+60800/69092	Loss: 160.427
+64000/69092	Loss: 158.403
+67200/69092	Loss: 157.545
+Training time 0:05:07.402879
+Epoch: 11 Average loss: 158.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 12)
+0/69092	Loss: 154.575
+3200/69092	Loss: 155.621
+6400/69092	Loss: 159.010
+9600/69092	Loss: 157.663
+12800/69092	Loss: 156.741
+16000/69092	Loss: 160.086
+19200/69092	Loss: 159.133
+22400/69092	Loss: 158.643
+25600/69092	Loss: 157.256
+28800/69092	Loss: 158.074
+32000/69092	Loss: 157.626
+35200/69092	Loss: 160.211
+38400/69092	Loss: 158.422
+41600/69092	Loss: 156.553
+44800/69092	Loss: 156.754
+48000/69092	Loss: 155.479
+51200/69092	Loss: 156.734
+54400/69092	Loss: 161.558
+57600/69092	Loss: 158.854
+60800/69092	Loss: 157.962
+64000/69092	Loss: 157.696
+67200/69092	Loss: 155.864
+Training time 0:04:57.898314
+Epoch: 12 Average loss: 157.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 13)
+0/69092	Loss: 198.587
+3200/69092	Loss: 156.337
+6400/69092	Loss: 157.083
+9600/69092	Loss: 158.551
+12800/69092	Loss: 155.835
+16000/69092	Loss: 159.414
+19200/69092	Loss: 156.997
+22400/69092	Loss: 156.781
+25600/69092	Loss: 157.049
+28800/69092	Loss: 159.597
+32000/69092	Loss: 158.982
+35200/69092	Loss: 156.480
+38400/69092	Loss: 157.809
+41600/69092	Loss: 157.435
+44800/69092	Loss: 155.803
+48000/69092	Loss: 157.486
+51200/69092	Loss: 159.577
+54400/69092	Loss: 156.059
+57600/69092	Loss: 155.216
+60800/69092	Loss: 157.501
+64000/69092	Loss: 159.789
+67200/69092	Loss: 157.693
+Training time 0:05:00.335381
+Epoch: 13 Average loss: 157.53
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 14)
+0/69092	Loss: 171.126
+3200/69092	Loss: 158.834
+6400/69092	Loss: 158.648
+9600/69092	Loss: 156.490
+12800/69092	Loss: 156.551
+16000/69092	Loss: 155.481
+19200/69092	Loss: 158.480
+22400/69092	Loss: 157.740
+25600/69092	Loss: 156.230
+28800/69092	Loss: 155.460
+32000/69092	Loss: 158.201
+35200/69092	Loss: 157.160
+38400/69092	Loss: 155.383
+41600/69092	Loss: 157.695
+44800/69092	Loss: 158.694
+48000/69092	Loss: 158.045
+51200/69092	Loss: 159.016
+54400/69092	Loss: 158.704
+57600/69092	Loss: 158.473
+60800/69092	Loss: 155.494
+64000/69092	Loss: 157.108
+67200/69092	Loss: 154.708
+Training time 0:05:01.352674
+Epoch: 14 Average loss: 157.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 15)
+0/69092	Loss: 158.885
+3200/69092	Loss: 159.121
+6400/69092	Loss: 156.716
+9600/69092	Loss: 155.387
+12800/69092	Loss: 154.486
+16000/69092	Loss: 155.538
+19200/69092	Loss: 159.955
+22400/69092	Loss: 157.290
+25600/69092	Loss: 155.298
+28800/69092	Loss: 157.796
+32000/69092	Loss: 155.302
+35200/69092	Loss: 158.811
+38400/69092	Loss: 157.280
+41600/69092	Loss: 155.210
+44800/69092	Loss: 160.083
+48000/69092	Loss: 154.914
+51200/69092	Loss: 154.685
+54400/69092	Loss: 160.132
+57600/69092	Loss: 157.141
+60800/69092	Loss: 156.621
+64000/69092	Loss: 156.775
+67200/69092	Loss: 159.171
+Training time 0:04:56.771282
+Epoch: 15 Average loss: 156.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 16)
+0/69092	Loss: 180.610
+3200/69092	Loss: 158.926
+6400/69092	Loss: 157.400
+9600/69092	Loss: 154.938
+12800/69092	Loss: 158.403
+16000/69092	Loss: 156.749
+19200/69092	Loss: 154.579
+22400/69092	Loss: 156.441
+25600/69092	Loss: 155.450
+28800/69092	Loss: 155.572
+32000/69092	Loss: 155.950
+35200/69092	Loss: 155.745
+38400/69092	Loss: 156.225
+41600/69092	Loss: 156.569
+44800/69092	Loss: 157.336
+48000/69092	Loss: 156.346
+51200/69092	Loss: 156.521
+54400/69092	Loss: 156.633
+57600/69092	Loss: 156.478
+60800/69092	Loss: 157.656
+64000/69092	Loss: 157.980
+67200/69092	Loss: 156.007
+Training time 0:04:57.181037
+Epoch: 16 Average loss: 156.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 17)
+0/69092	Loss: 166.976
+3200/69092	Loss: 155.996
+6400/69092	Loss: 156.284
+9600/69092	Loss: 157.531
+12800/69092	Loss: 157.001
+16000/69092	Loss: 156.033
+19200/69092	Loss: 156.092
+22400/69092	Loss: 155.495
+25600/69092	Loss: 154.071
+28800/69092	Loss: 158.731
+32000/69092	Loss: 155.976
+35200/69092	Loss: 158.330
+38400/69092	Loss: 157.550
+41600/69092	Loss: 153.981
+44800/69092	Loss: 156.093
+48000/69092	Loss: 155.275
+51200/69092	Loss: 156.546
+54400/69092	Loss: 157.030
+57600/69092	Loss: 158.271
+60800/69092	Loss: 157.585
+64000/69092	Loss: 157.200
+67200/69092	Loss: 157.208
+Training time 0:05:09.402899
+Epoch: 17 Average loss: 156.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 18)
+0/69092	Loss: 138.008
+3200/69092	Loss: 155.972
+6400/69092	Loss: 155.708
+9600/69092	Loss: 157.606
+12800/69092	Loss: 158.033
+16000/69092	Loss: 155.677
+19200/69092	Loss: 155.332
+22400/69092	Loss: 159.563
+25600/69092	Loss: 154.693
+28800/69092	Loss: 153.843
+32000/69092	Loss: 154.891
+35200/69092	Loss: 152.642
+38400/69092	Loss: 158.392
+41600/69092	Loss: 156.441
+44800/69092	Loss: 156.736
+48000/69092	Loss: 153.157
+51200/69092	Loss: 158.228
+54400/69092	Loss: 157.247
+57600/69092	Loss: 156.135
+60800/69092	Loss: 156.527
+64000/69092	Loss: 157.145
+67200/69092	Loss: 157.334
+Training time 0:05:11.412854
+Epoch: 18 Average loss: 156.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 19)
+0/69092	Loss: 175.512
+3200/69092	Loss: 159.676
+6400/69092	Loss: 155.888
+9600/69092	Loss: 155.238
+12800/69092	Loss: 154.457
+16000/69092	Loss: 157.052
+19200/69092	Loss: 154.044
+22400/69092	Loss: 158.530
+25600/69092	Loss: 155.509
+28800/69092	Loss: 154.811
+32000/69092	Loss: 156.581
+35200/69092	Loss: 157.237
+38400/69092	Loss: 155.297
+41600/69092	Loss: 153.913
+44800/69092	Loss: 154.750
+48000/69092	Loss: 153.670
+51200/69092	Loss: 157.841
+54400/69092	Loss: 156.308
+57600/69092	Loss: 153.487
+60800/69092	Loss: 157.768
+64000/69092	Loss: 154.807
+67200/69092	Loss: 154.463
+Training time 0:05:10.181806
+Epoch: 19 Average loss: 155.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 20)
+0/69092	Loss: 149.774
+3200/69092	Loss: 156.927
+6400/69092	Loss: 155.198
+9600/69092	Loss: 155.500
+12800/69092	Loss: 158.390
+16000/69092	Loss: 153.901
+19200/69092	Loss: 157.590
+22400/69092	Loss: 156.058
+25600/69092	Loss: 155.266
+28800/69092	Loss: 157.283
+32000/69092	Loss: 158.147
+35200/69092	Loss: 156.172
+38400/69092	Loss: 154.989
+41600/69092	Loss: 154.753
+44800/69092	Loss: 155.295
+48000/69092	Loss: 156.432
+51200/69092	Loss: 154.009
+54400/69092	Loss: 155.243
+57600/69092	Loss: 157.107
+60800/69092	Loss: 157.627
+64000/69092	Loss: 155.015
+67200/69092	Loss: 153.090
+Training time 0:05:18.014695
+Epoch: 20 Average loss: 155.87
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 21)
+0/69092	Loss: 156.897
+3200/69092	Loss: 152.205
+6400/69092	Loss: 157.276
+9600/69092	Loss: 157.334
+12800/69092	Loss: 155.175
+16000/69092	Loss: 156.639
+19200/69092	Loss: 156.498
+22400/69092	Loss: 156.934
+25600/69092	Loss: 155.024
+28800/69092	Loss: 155.736
+32000/69092	Loss: 156.889
+35200/69092	Loss: 156.288
+38400/69092	Loss: 156.362
+41600/69092	Loss: 157.268
+44800/69092	Loss: 153.435
+48000/69092	Loss: 156.287
+51200/69092	Loss: 154.534
+54400/69092	Loss: 155.425
+57600/69092	Loss: 155.554
+60800/69092	Loss: 155.930
+64000/69092	Loss: 156.817
+67200/69092	Loss: 156.358
+Training time 0:05:00.028455
+Epoch: 21 Average loss: 155.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 22)
+0/69092	Loss: 166.798
+3200/69092	Loss: 155.961
+6400/69092	Loss: 157.307
+9600/69092	Loss: 155.340
+12800/69092	Loss: 154.392
+16000/69092	Loss: 152.580
+19200/69092	Loss: 157.830
+22400/69092	Loss: 155.235
+25600/69092	Loss: 157.458
+28800/69092	Loss: 155.417
+32000/69092	Loss: 155.424
+35200/69092	Loss: 154.403
+38400/69092	Loss: 156.144
+41600/69092	Loss: 158.808
+44800/69092	Loss: 157.301
+48000/69092	Loss: 155.940
+51200/69092	Loss: 152.464
+54400/69092	Loss: 155.455
+57600/69092	Loss: 154.166
+60800/69092	Loss: 154.344
+64000/69092	Loss: 153.303
+67200/69092	Loss: 156.709
+Training time 0:05:06.625068
+Epoch: 22 Average loss: 155.57
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 23)
+0/69092	Loss: 153.292
+3200/69092	Loss: 155.550
+6400/69092	Loss: 154.465
+9600/69092	Loss: 154.747
+12800/69092	Loss: 156.483
+16000/69092	Loss: 154.716
+19200/69092	Loss: 154.990
+22400/69092	Loss: 158.698
+25600/69092	Loss: 153.843
+28800/69092	Loss: 157.427
+32000/69092	Loss: 155.258
+35200/69092	Loss: 156.867
+38400/69092	Loss: 155.848
+41600/69092	Loss: 154.291
+44800/69092	Loss: 155.653
+48000/69092	Loss: 153.495
+51200/69092	Loss: 156.850
+54400/69092	Loss: 153.608
+57600/69092	Loss: 156.122
+60800/69092	Loss: 155.608
+64000/69092	Loss: 153.933
+67200/69092	Loss: 156.125
+Training time 0:05:02.092503
+Epoch: 23 Average loss: 155.32
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 24)
+0/69092	Loss: 148.688
+3200/69092	Loss: 157.104
+6400/69092	Loss: 155.251
+9600/69092	Loss: 157.003
+12800/69092	Loss: 153.265
+16000/69092	Loss: 157.457
+19200/69092	Loss: 157.067
+22400/69092	Loss: 155.820
+25600/69092	Loss: 157.289
+28800/69092	Loss: 154.638
+32000/69092	Loss: 154.275
+35200/69092	Loss: 153.192
+38400/69092	Loss: 154.359
+41600/69092	Loss: 153.652
+44800/69092	Loss: 154.943
+48000/69092	Loss: 154.444
+51200/69092	Loss: 153.575
+54400/69092	Loss: 155.056
+57600/69092	Loss: 156.695
+60800/69092	Loss: 156.227
+64000/69092	Loss: 150.975
+67200/69092	Loss: 158.196
+Training time 0:05:02.910400
+Epoch: 24 Average loss: 155.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 25)
+0/69092	Loss: 151.762
+3200/69092	Loss: 155.785
+6400/69092	Loss: 154.801
+9600/69092	Loss: 153.452
+12800/69092	Loss: 154.596
+16000/69092	Loss: 155.320
+19200/69092	Loss: 154.723
+22400/69092	Loss: 151.396
+25600/69092	Loss: 155.391
+28800/69092	Loss: 153.849
+32000/69092	Loss: 154.842
+35200/69092	Loss: 156.054
+38400/69092	Loss: 153.651
+41600/69092	Loss: 157.530
+44800/69092	Loss: 153.593
+48000/69092	Loss: 156.526
+51200/69092	Loss: 158.754
+54400/69092	Loss: 154.276
+57600/69092	Loss: 152.490
+60800/69092	Loss: 156.267
+64000/69092	Loss: 156.384
+67200/69092	Loss: 153.558
+Training time 0:05:08.903150
+Epoch: 25 Average loss: 154.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 26)
+0/69092	Loss: 145.520
+3200/69092	Loss: 154.470
+6400/69092	Loss: 157.175
+9600/69092	Loss: 153.897
+12800/69092	Loss: 155.593
+16000/69092	Loss: 155.791
+19200/69092	Loss: 154.346
+22400/69092	Loss: 153.382
+25600/69092	Loss: 157.153
+28800/69092	Loss: 155.913
+32000/69092	Loss: 151.357
+35200/69092	Loss: 153.224
+38400/69092	Loss: 155.464
+41600/69092	Loss: 155.946
+44800/69092	Loss: 155.148
+48000/69092	Loss: 155.896
+51200/69092	Loss: 153.044
+54400/69092	Loss: 151.947
+57600/69092	Loss: 154.547
+60800/69092	Loss: 153.721
+64000/69092	Loss: 155.764
+67200/69092	Loss: 155.832
+Training time 0:05:02.145435
+Epoch: 26 Average loss: 154.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 27)
+0/69092	Loss: 145.025
+3200/69092	Loss: 155.914
+6400/69092	Loss: 154.117
+9600/69092	Loss: 154.782
+12800/69092	Loss: 156.164
+16000/69092	Loss: 158.107
+19200/69092	Loss: 150.796
+22400/69092	Loss: 155.807
+25600/69092	Loss: 156.047
+28800/69092	Loss: 154.962
+32000/69092	Loss: 155.689
+35200/69092	Loss: 155.341
+38400/69092	Loss: 154.023
+41600/69092	Loss: 154.776
+44800/69092	Loss: 158.038
+48000/69092	Loss: 156.683
+51200/69092	Loss: 154.205
+54400/69092	Loss: 155.036
+57600/69092	Loss: 154.203
+60800/69092	Loss: 153.761
+64000/69092	Loss: 152.981
+67200/69092	Loss: 153.593
+Training time 0:05:04.721903
+Epoch: 27 Average loss: 154.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 28)
+0/69092	Loss: 142.085
+3200/69092	Loss: 156.566
+6400/69092	Loss: 154.072
+9600/69092	Loss: 155.197
+12800/69092	Loss: 155.686
+16000/69092	Loss: 155.226
+19200/69092	Loss: 155.577
+22400/69092	Loss: 156.916
+25600/69092	Loss: 153.739
+28800/69092	Loss: 156.855
+32000/69092	Loss: 156.404
+35200/69092	Loss: 154.426
+38400/69092	Loss: 153.957
+41600/69092	Loss: 153.818
+44800/69092	Loss: 151.411
+48000/69092	Loss: 155.809
+51200/69092	Loss: 155.386
+54400/69092	Loss: 155.290
+57600/69092	Loss: 156.896
+60800/69092	Loss: 155.407
+64000/69092	Loss: 154.888
+67200/69092	Loss: 155.350
+Training time 0:05:03.047716
+Epoch: 28 Average loss: 155.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 29)
+0/69092	Loss: 151.373
+3200/69092	Loss: 153.773
+6400/69092	Loss: 156.066
+9600/69092	Loss: 156.678
+12800/69092	Loss: 153.829
+16000/69092	Loss: 155.105
+19200/69092	Loss: 155.393
+22400/69092	Loss: 152.258
+25600/69092	Loss: 154.484
+28800/69092	Loss: 157.004
+32000/69092	Loss: 154.659
+35200/69092	Loss: 153.899
+38400/69092	Loss: 153.183
+41600/69092	Loss: 154.263
+44800/69092	Loss: 155.750
+48000/69092	Loss: 156.017
+51200/69092	Loss: 156.395
+54400/69092	Loss: 152.766
+57600/69092	Loss: 154.067
+60800/69092	Loss: 154.765
+64000/69092	Loss: 158.121
+67200/69092	Loss: 153.901
+Training time 0:05:03.827168
+Epoch: 29 Average loss: 154.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 30)
+0/69092	Loss: 161.455
+3200/69092	Loss: 156.467
+6400/69092	Loss: 153.161
+9600/69092	Loss: 153.883
+12800/69092	Loss: 156.245
+16000/69092	Loss: 152.822
+19200/69092	Loss: 154.706
+22400/69092	Loss: 155.953
+25600/69092	Loss: 153.495
+28800/69092	Loss: 154.108
+32000/69092	Loss: 155.972
+35200/69092	Loss: 152.512
+38400/69092	Loss: 153.367
+41600/69092	Loss: 154.328
+44800/69092	Loss: 154.372
+48000/69092	Loss: 153.476
+51200/69092	Loss: 155.928
+54400/69092	Loss: 154.817
+57600/69092	Loss: 156.931
+60800/69092	Loss: 154.815
+64000/69092	Loss: 152.635
+67200/69092	Loss: 156.441
+Training time 0:05:03.619466
+Epoch: 30 Average loss: 154.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 31)
+0/69092	Loss: 163.201
+3200/69092	Loss: 154.643
+6400/69092	Loss: 154.043
+9600/69092	Loss: 154.239
+12800/69092	Loss: 158.436
+16000/69092	Loss: 156.100
+19200/69092	Loss: 153.272
+22400/69092	Loss: 154.454
+25600/69092	Loss: 155.075
+28800/69092	Loss: 155.023
+32000/69092	Loss: 156.125
+35200/69092	Loss: 155.432
+38400/69092	Loss: 153.544
+41600/69092	Loss: 154.850
+44800/69092	Loss: 154.166
+48000/69092	Loss: 154.606
+51200/69092	Loss: 153.678
+54400/69092	Loss: 154.670
+57600/69092	Loss: 153.692
+60800/69092	Loss: 158.491
+64000/69092	Loss: 153.935
+67200/69092	Loss: 151.687
+Training time 0:05:04.037156
+Epoch: 31 Average loss: 154.77
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 32)
+0/69092	Loss: 141.922
+3200/69092	Loss: 154.850
+6400/69092	Loss: 153.576
+9600/69092	Loss: 154.580
+12800/69092	Loss: 157.847
+16000/69092	Loss: 156.746
+19200/69092	Loss: 154.366
+22400/69092	Loss: 155.767
+25600/69092	Loss: 153.426
+28800/69092	Loss: 151.863
+32000/69092	Loss: 154.862
+35200/69092	Loss: 155.273
+38400/69092	Loss: 156.388
+41600/69092	Loss: 153.722
+44800/69092	Loss: 153.381
+48000/69092	Loss: 154.076
+51200/69092	Loss: 156.021
+54400/69092	Loss: 152.544
+57600/69092	Loss: 152.952
+60800/69092	Loss: 153.821
+64000/69092	Loss: 154.748
+67200/69092	Loss: 152.921
+Training time 0:05:06.533599
+Epoch: 32 Average loss: 154.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 33)
+0/69092	Loss: 147.477
+3200/69092	Loss: 154.419
+6400/69092	Loss: 151.117
+9600/69092	Loss: 153.667
+12800/69092	Loss: 152.558
+16000/69092	Loss: 152.046
+19200/69092	Loss: 157.615
+22400/69092	Loss: 157.424
+25600/69092	Loss: 153.632
+28800/69092	Loss: 153.371
+32000/69092	Loss: 155.240
+35200/69092	Loss: 153.214
+38400/69092	Loss: 154.187
+41600/69092	Loss: 152.769
+44800/69092	Loss: 154.383
+48000/69092	Loss: 156.063
+51200/69092	Loss: 153.722
+54400/69092	Loss: 155.292
+57600/69092	Loss: 155.070
+60800/69092	Loss: 155.242
+64000/69092	Loss: 154.664
+67200/69092	Loss: 153.232
+Training time 0:05:06.860989
+Epoch: 33 Average loss: 154.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 34)
+0/69092	Loss: 150.105
+3200/69092	Loss: 154.872
+6400/69092	Loss: 153.334
+9600/69092	Loss: 153.557
+12800/69092	Loss: 157.195
+16000/69092	Loss: 154.167
+19200/69092	Loss: 153.944
+22400/69092	Loss: 153.382
+25600/69092	Loss: 153.936
+28800/69092	Loss: 154.048
+32000/69092	Loss: 151.582
+35200/69092	Loss: 150.827
+38400/69092	Loss: 152.395
+41600/69092	Loss: 155.515
+44800/69092	Loss: 154.176
+48000/69092	Loss: 155.271
+51200/69092	Loss: 154.451
+54400/69092	Loss: 155.946
+57600/69092	Loss: 158.292
+60800/69092	Loss: 153.092
+64000/69092	Loss: 154.001
+67200/69092	Loss: 153.687
+Training time 0:05:02.044776
+Epoch: 34 Average loss: 154.22
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 35)
+0/69092	Loss: 154.792
+3200/69092	Loss: 153.265
+6400/69092	Loss: 152.430
+9600/69092	Loss: 155.663
+12800/69092	Loss: 153.035
+16000/69092	Loss: 152.774
+19200/69092	Loss: 153.099
+22400/69092	Loss: 153.285
+25600/69092	Loss: 154.487
+28800/69092	Loss: 155.288
+32000/69092	Loss: 155.312
+35200/69092	Loss: 151.985
+38400/69092	Loss: 153.029
+41600/69092	Loss: 153.710
+44800/69092	Loss: 154.096
+48000/69092	Loss: 152.608
+51200/69092	Loss: 153.097
+54400/69092	Loss: 157.047
+57600/69092	Loss: 155.492
+60800/69092	Loss: 155.266
+64000/69092	Loss: 155.379
+67200/69092	Loss: 156.074
+Training time 0:05:01.698349
+Epoch: 35 Average loss: 154.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 36)
+0/69092	Loss: 147.266
+3200/69092	Loss: 154.854
+6400/69092	Loss: 156.191
+9600/69092	Loss: 153.243
+12800/69092	Loss: 155.336
+16000/69092	Loss: 156.092
+19200/69092	Loss: 153.358
+22400/69092	Loss: 154.779
+25600/69092	Loss: 151.964
+28800/69092	Loss: 152.475
+32000/69092	Loss: 155.118
+35200/69092	Loss: 153.731
+38400/69092	Loss: 153.870
+41600/69092	Loss: 153.622
+44800/69092	Loss: 153.203
+48000/69092	Loss: 153.439
+51200/69092	Loss: 153.529
+54400/69092	Loss: 153.085
+57600/69092	Loss: 151.578
+60800/69092	Loss: 157.822
+64000/69092	Loss: 153.030
+67200/69092	Loss: 153.405
+Training time 0:04:58.065118
+Epoch: 36 Average loss: 153.96
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 37)
+0/69092	Loss: 144.321
+3200/69092	Loss: 152.110
+6400/69092	Loss: 154.992
+9600/69092	Loss: 154.468
+12800/69092	Loss: 154.868
+16000/69092	Loss: 153.470
+19200/69092	Loss: 151.740
+22400/69092	Loss: 151.488
+25600/69092	Loss: 155.486
+28800/69092	Loss: 154.514
+32000/69092	Loss: 155.513
+35200/69092	Loss: 156.860
+38400/69092	Loss: 152.545
+41600/69092	Loss: 150.724
+44800/69092	Loss: 156.680
+48000/69092	Loss: 154.619
+51200/69092	Loss: 153.913
+54400/69092	Loss: 154.099
+57600/69092	Loss: 156.039
+60800/69092	Loss: 154.873
+64000/69092	Loss: 153.594
+67200/69092	Loss: 153.795
+Training time 0:05:08.408368
+Epoch: 37 Average loss: 154.07
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 38)
+0/69092	Loss: 151.149
+3200/69092	Loss: 154.316
+6400/69092	Loss: 153.311
+9600/69092	Loss: 152.132
+12800/69092	Loss: 156.712
+16000/69092	Loss: 155.304
+19200/69092	Loss: 153.195
+22400/69092	Loss: 154.007
+25600/69092	Loss: 152.999
+28800/69092	Loss: 154.118
+32000/69092	Loss: 155.476
+35200/69092	Loss: 154.149
+38400/69092	Loss: 151.813
+41600/69092	Loss: 153.062
+44800/69092	Loss: 151.518
+48000/69092	Loss: 155.054
+51200/69092	Loss: 156.870
+54400/69092	Loss: 155.662
+57600/69092	Loss: 153.237
+60800/69092	Loss: 153.279
+64000/69092	Loss: 153.719
+67200/69092	Loss: 154.816
+Training time 0:04:59.677722
+Epoch: 38 Average loss: 154.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 39)
+0/69092	Loss: 166.044
+3200/69092	Loss: 152.716
+6400/69092	Loss: 156.488
+9600/69092	Loss: 152.505
+12800/69092	Loss: 154.847
+16000/69092	Loss: 153.333
+19200/69092	Loss: 153.921
+22400/69092	Loss: 155.176
+25600/69092	Loss: 156.477
+28800/69092	Loss: 153.947
+32000/69092	Loss: 153.339
+35200/69092	Loss: 153.645
+38400/69092	Loss: 153.167
+41600/69092	Loss: 153.730
+44800/69092	Loss: 153.016
+48000/69092	Loss: 153.397
+51200/69092	Loss: 153.121
+54400/69092	Loss: 156.223
+57600/69092	Loss: 151.676
+60800/69092	Loss: 156.338
+64000/69092	Loss: 152.792
+67200/69092	Loss: 155.497
+Training time 0:05:01.546897
+Epoch: 39 Average loss: 154.07
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 40)
+0/69092	Loss: 145.958
+3200/69092	Loss: 153.693
+6400/69092	Loss: 155.230
+9600/69092	Loss: 152.551
+12800/69092	Loss: 152.835
+16000/69092	Loss: 153.111
+19200/69092	Loss: 155.037
+22400/69092	Loss: 150.292
+25600/69092	Loss: 151.004
+28800/69092	Loss: 156.137
+32000/69092	Loss: 154.216
+35200/69092	Loss: 155.649
+38400/69092	Loss: 153.518
+41600/69092	Loss: 158.186
+44800/69092	Loss: 155.724
+48000/69092	Loss: 154.573
+51200/69092	Loss: 151.834
+54400/69092	Loss: 151.491
+57600/69092	Loss: 153.863
+60800/69092	Loss: 153.605
+64000/69092	Loss: 152.668
+67200/69092	Loss: 155.306
+Training time 0:05:06.053928
+Epoch: 40 Average loss: 153.99
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 41)
+0/69092	Loss: 152.745
+3200/69092	Loss: 153.341
+6400/69092	Loss: 152.335
+9600/69092	Loss: 154.571
+12800/69092	Loss: 152.477
+16000/69092	Loss: 153.298
+19200/69092	Loss: 154.614
+22400/69092	Loss: 152.732
+25600/69092	Loss: 152.089
+28800/69092	Loss: 155.235
+32000/69092	Loss: 155.665
+35200/69092	Loss: 155.164
+38400/69092	Loss: 155.002
+41600/69092	Loss: 154.969
+44800/69092	Loss: 155.071
+48000/69092	Loss: 151.430
+51200/69092	Loss: 154.898
+54400/69092	Loss: 153.429
+57600/69092	Loss: 151.567
+60800/69092	Loss: 152.725
+64000/69092	Loss: 152.782
+67200/69092	Loss: 155.043
+Training time 0:05:07.448444
+Epoch: 41 Average loss: 153.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 42)
+0/69092	Loss: 147.857
+3200/69092	Loss: 153.241
+6400/69092	Loss: 155.718
+9600/69092	Loss: 153.425
+12800/69092	Loss: 154.952
+16000/69092	Loss: 153.958
+19200/69092	Loss: 153.363
+22400/69092	Loss: 153.638
+25600/69092	Loss: 151.926
+28800/69092	Loss: 153.577
+32000/69092	Loss: 152.298
+35200/69092	Loss: 153.688
+38400/69092	Loss: 154.614
+41600/69092	Loss: 154.486
+44800/69092	Loss: 157.781
+48000/69092	Loss: 154.365
+51200/69092	Loss: 155.802
+54400/69092	Loss: 152.274
+57600/69092	Loss: 152.661
+60800/69092	Loss: 153.740
+64000/69092	Loss: 153.539
+67200/69092	Loss: 155.345
+Training time 0:05:08.560162
+Epoch: 42 Average loss: 153.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 43)
+0/69092	Loss: 171.613
+3200/69092	Loss: 149.764
+6400/69092	Loss: 151.743
+9600/69092	Loss: 154.044
+12800/69092	Loss: 156.217
+16000/69092	Loss: 153.436
+19200/69092	Loss: 152.853
+22400/69092	Loss: 153.352
+25600/69092	Loss: 154.759
+28800/69092	Loss: 155.015
+32000/69092	Loss: 152.561
+35200/69092	Loss: 154.654
+38400/69092	Loss: 151.119
+41600/69092	Loss: 152.056
+44800/69092	Loss: 152.853
+48000/69092	Loss: 154.311
+51200/69092	Loss: 153.144
+54400/69092	Loss: 155.307
+57600/69092	Loss: 155.020
+60800/69092	Loss: 156.720
+64000/69092	Loss: 152.289
+67200/69092	Loss: 153.449
+Training time 0:05:04.432898
+Epoch: 43 Average loss: 153.66
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 44)
+0/69092	Loss: 170.133
+3200/69092	Loss: 152.235
+6400/69092	Loss: 152.641
+9600/69092	Loss: 155.498
+12800/69092	Loss: 154.923
+16000/69092	Loss: 152.892
+19200/69092	Loss: 152.627
+22400/69092	Loss: 151.357
+25600/69092	Loss: 152.230
+28800/69092	Loss: 154.684
+32000/69092	Loss: 154.487
+35200/69092	Loss: 152.331
+38400/69092	Loss: 152.898
+41600/69092	Loss: 151.549
+44800/69092	Loss: 156.044
+48000/69092	Loss: 153.885
+51200/69092	Loss: 151.917
+54400/69092	Loss: 153.655
+57600/69092	Loss: 154.280
+60800/69092	Loss: 152.941
+64000/69092	Loss: 155.576
+67200/69092	Loss: 155.964
+Training time 0:05:04.038218
+Epoch: 44 Average loss: 153.53
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 45)
+0/69092	Loss: 156.539
+3200/69092	Loss: 151.731
+6400/69092	Loss: 152.422
+9600/69092	Loss: 153.158
+12800/69092	Loss: 153.571
+16000/69092	Loss: 151.715
+19200/69092	Loss: 155.852
+22400/69092	Loss: 152.424
+25600/69092	Loss: 154.561
+28800/69092	Loss: 153.384
+32000/69092	Loss: 153.394
+35200/69092	Loss: 154.395
+38400/69092	Loss: 155.487
+41600/69092	Loss: 153.853
+44800/69092	Loss: 152.225
+48000/69092	Loss: 152.628
+51200/69092	Loss: 155.227
+54400/69092	Loss: 152.513
+57600/69092	Loss: 154.089
+60800/69092	Loss: 152.235
+64000/69092	Loss: 155.785
+67200/69092	Loss: 152.158
+Training time 0:05:04.734548
+Epoch: 45 Average loss: 153.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 46)
+0/69092	Loss: 160.141
+3200/69092	Loss: 153.474
+6400/69092	Loss: 152.989
+9600/69092	Loss: 154.237
+12800/69092	Loss: 152.351
+16000/69092	Loss: 153.528
+19200/69092	Loss: 154.957
+22400/69092	Loss: 151.499
+25600/69092	Loss: 152.737
+28800/69092	Loss: 154.073
+32000/69092	Loss: 152.931
+35200/69092	Loss: 156.743
+38400/69092	Loss: 155.835
+41600/69092	Loss: 154.301
+44800/69092	Loss: 152.964
+48000/69092	Loss: 152.606
+51200/69092	Loss: 153.143
+54400/69092	Loss: 153.386
+57600/69092	Loss: 150.341
+60800/69092	Loss: 154.751
+64000/69092	Loss: 153.371
+67200/69092	Loss: 155.081
+Training time 0:05:05.560478
+Epoch: 46 Average loss: 153.65
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 47)
+0/69092	Loss: 159.554
+3200/69092	Loss: 154.737
+6400/69092	Loss: 152.041
+9600/69092	Loss: 154.259
+12800/69092	Loss: 150.843
+16000/69092	Loss: 154.812
+19200/69092	Loss: 153.575
+22400/69092	Loss: 153.562
+25600/69092	Loss: 154.008
+28800/69092	Loss: 153.427
+32000/69092	Loss: 154.091
+35200/69092	Loss: 152.106
+38400/69092	Loss: 158.513
+41600/69092	Loss: 154.801
+44800/69092	Loss: 154.234
+48000/69092	Loss: 154.945
+51200/69092	Loss: 154.535
+54400/69092	Loss: 154.542
+57600/69092	Loss: 153.441
+60800/69092	Loss: 150.218
+64000/69092	Loss: 152.690
+67200/69092	Loss: 152.164
+Training time 0:05:05.756269
+Epoch: 47 Average loss: 153.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 48)
+0/69092	Loss: 134.804
+3200/69092	Loss: 153.924
+6400/69092	Loss: 151.148
+9600/69092	Loss: 150.673
+12800/69092	Loss: 155.309
+16000/69092	Loss: 152.124
+19200/69092	Loss: 155.303
+22400/69092	Loss: 151.643
+25600/69092	Loss: 151.390
+28800/69092	Loss: 150.736
+32000/69092	Loss: 156.025
+35200/69092	Loss: 154.392
+38400/69092	Loss: 153.438
+41600/69092	Loss: 152.407
+44800/69092	Loss: 155.137
+48000/69092	Loss: 154.130
+51200/69092	Loss: 153.248
+54400/69092	Loss: 153.686
+57600/69092	Loss: 155.877
+60800/69092	Loss: 152.973
+64000/69092	Loss: 153.756
+67200/69092	Loss: 153.518
+Training time 0:05:10.223096
+Epoch: 48 Average loss: 153.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 49)
+0/69092	Loss: 137.311
+3200/69092	Loss: 155.135
+6400/69092	Loss: 153.290
+9600/69092	Loss: 155.428
+12800/69092	Loss: 152.749
+16000/69092	Loss: 153.186
+19200/69092	Loss: 153.337
+22400/69092	Loss: 157.355
+25600/69092	Loss: 154.726
+28800/69092	Loss: 152.976
+32000/69092	Loss: 152.562
+35200/69092	Loss: 153.569
+38400/69092	Loss: 153.438
+41600/69092	Loss: 155.214
+44800/69092	Loss: 152.062
+48000/69092	Loss: 153.686
+51200/69092	Loss: 152.035
+54400/69092	Loss: 155.127
+57600/69092	Loss: 155.570
+60800/69092	Loss: 152.989
+64000/69092	Loss: 151.805
+67200/69092	Loss: 151.512
+Training time 0:05:06.435324
+Epoch: 49 Average loss: 153.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 50)
+0/69092	Loss: 176.282
+3200/69092	Loss: 152.492
+6400/69092	Loss: 151.182
+9600/69092	Loss: 153.315
+12800/69092	Loss: 150.967
+16000/69092	Loss: 155.656
+19200/69092	Loss: 154.845
+22400/69092	Loss: 155.301
+25600/69092	Loss: 154.970
+28800/69092	Loss: 152.298
+32000/69092	Loss: 155.756
+35200/69092	Loss: 156.072
+38400/69092	Loss: 154.876
+41600/69092	Loss: 157.088
+44800/69092	Loss: 155.463
+48000/69092	Loss: 153.898
+51200/69092	Loss: 152.215
+54400/69092	Loss: 152.863
+57600/69092	Loss: 153.437
+60800/69092	Loss: 152.373
+64000/69092	Loss: 151.388
+67200/69092	Loss: 150.655
+Training time 0:05:03.710498
+Epoch: 50 Average loss: 153.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 51)
+0/69092	Loss: 158.625
+3200/69092	Loss: 154.152
+6400/69092	Loss: 153.690
+9600/69092	Loss: 152.061
+12800/69092	Loss: 154.185
+16000/69092	Loss: 149.322
+19200/69092	Loss: 153.205
+22400/69092	Loss: 154.816
+25600/69092	Loss: 151.939
+28800/69092	Loss: 156.743
+32000/69092	Loss: 153.360
+35200/69092	Loss: 151.926
+38400/69092	Loss: 156.276
+41600/69092	Loss: 155.058
+44800/69092	Loss: 153.286
+48000/69092	Loss: 154.900
+51200/69092	Loss: 151.956
+54400/69092	Loss: 152.775
+57600/69092	Loss: 151.664
+60800/69092	Loss: 152.328
+64000/69092	Loss: 155.246
+67200/69092	Loss: 151.667
+Training time 0:05:04.672720
+Epoch: 51 Average loss: 153.40
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 52)
+0/69092	Loss: 156.898
+3200/69092	Loss: 150.986
+6400/69092	Loss: 152.840
+9600/69092	Loss: 151.740
+12800/69092	Loss: 153.018
+16000/69092	Loss: 156.755
+19200/69092	Loss: 154.863
+22400/69092	Loss: 153.028
+25600/69092	Loss: 149.774
+28800/69092	Loss: 153.092
+32000/69092	Loss: 152.882
+35200/69092	Loss: 155.499
+38400/69092	Loss: 152.468
+41600/69092	Loss: 152.468
+44800/69092	Loss: 151.979
+48000/69092	Loss: 153.114
+51200/69092	Loss: 154.031
+54400/69092	Loss: 154.356
+57600/69092	Loss: 154.272
+60800/69092	Loss: 152.246
+64000/69092	Loss: 153.062
+67200/69092	Loss: 153.387
+Training time 0:05:08.002440
+Epoch: 52 Average loss: 153.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 53)
+0/69092	Loss: 161.253
+3200/69092	Loss: 155.004
+6400/69092	Loss: 153.371
+9600/69092	Loss: 152.802
+12800/69092	Loss: 152.663
+16000/69092	Loss: 155.737
+19200/69092	Loss: 151.118
+22400/69092	Loss: 154.458
+25600/69092	Loss: 156.289
+28800/69092	Loss: 154.404
+32000/69092	Loss: 150.858
+35200/69092	Loss: 154.569
+38400/69092	Loss: 151.450
+41600/69092	Loss: 153.888
+44800/69092	Loss: 152.719
+48000/69092	Loss: 154.929
+51200/69092	Loss: 152.535
+54400/69092	Loss: 151.346
+57600/69092	Loss: 152.216
+60800/69092	Loss: 153.693
+64000/69092	Loss: 156.593
+67200/69092	Loss: 153.910
+Training time 0:05:06.410454
+Epoch: 53 Average loss: 153.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 54)
+0/69092	Loss: 155.533
+3200/69092	Loss: 153.937
+6400/69092	Loss: 155.412
+9600/69092	Loss: 154.931
+12800/69092	Loss: 153.251
+16000/69092	Loss: 152.131
+19200/69092	Loss: 153.399
+22400/69092	Loss: 153.270
+25600/69092	Loss: 154.298
+28800/69092	Loss: 154.523
+32000/69092	Loss: 151.808
+35200/69092	Loss: 153.407
+38400/69092	Loss: 151.423
+41600/69092	Loss: 153.940
+44800/69092	Loss: 155.125
+48000/69092	Loss: 151.519
+51200/69092	Loss: 154.659
+54400/69092	Loss: 153.851
+57600/69092	Loss: 152.930
+60800/69092	Loss: 151.557
+64000/69092	Loss: 152.873
+67200/69092	Loss: 153.245
+Training time 0:05:04.874587
+Epoch: 54 Average loss: 153.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 55)
+0/69092	Loss: 164.339
+3200/69092	Loss: 153.000
+6400/69092	Loss: 153.157
+9600/69092	Loss: 151.128
+12800/69092	Loss: 154.678
+16000/69092	Loss: 153.077
+19200/69092	Loss: 151.972
+22400/69092	Loss: 154.141
+25600/69092	Loss: 154.754
+28800/69092	Loss: 153.184
+32000/69092	Loss: 154.772
+35200/69092	Loss: 152.511
+38400/69092	Loss: 153.568
+41600/69092	Loss: 152.005
+44800/69092	Loss: 149.800
+48000/69092	Loss: 152.716
+51200/69092	Loss: 154.639
+54400/69092	Loss: 153.018
+57600/69092	Loss: 154.460
+60800/69092	Loss: 154.754
+64000/69092	Loss: 153.897
+67200/69092	Loss: 152.005
+Training time 0:05:04.064776
+Epoch: 55 Average loss: 153.22
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 56)
+0/69092	Loss: 145.024
+3200/69092	Loss: 154.929
+6400/69092	Loss: 152.530
+9600/69092	Loss: 152.945
+12800/69092	Loss: 155.670
+16000/69092	Loss: 154.351
+19200/69092	Loss: 152.745
+22400/69092	Loss: 155.064
+25600/69092	Loss: 152.553
+28800/69092	Loss: 153.270
+32000/69092	Loss: 152.030
+35200/69092	Loss: 152.364
+38400/69092	Loss: 153.618
+41600/69092	Loss: 153.368
+44800/69092	Loss: 153.049
+48000/69092	Loss: 152.522
+51200/69092	Loss: 154.650
+54400/69092	Loss: 152.192
+57600/69092	Loss: 152.361
+60800/69092	Loss: 155.067
+64000/69092	Loss: 150.842
+67200/69092	Loss: 153.224
+Training time 0:05:05.357819
+Epoch: 56 Average loss: 153.33
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 57)
+0/69092	Loss: 153.625
+3200/69092	Loss: 151.436
+6400/69092	Loss: 152.714
+9600/69092	Loss: 153.801
+12800/69092	Loss: 154.746
+16000/69092	Loss: 154.916
+19200/69092	Loss: 155.349
+22400/69092	Loss: 152.170
+25600/69092	Loss: 153.511
+28800/69092	Loss: 151.470
+32000/69092	Loss: 152.545
+35200/69092	Loss: 155.377
+38400/69092	Loss: 151.887
+41600/69092	Loss: 154.537
+44800/69092	Loss: 154.658
+48000/69092	Loss: 153.163
+51200/69092	Loss: 152.460
+54400/69092	Loss: 152.746
+57600/69092	Loss: 154.616
+60800/69092	Loss: 151.210
+64000/69092	Loss: 153.386
+67200/69092	Loss: 152.204
+Training time 0:05:00.132235
+Epoch: 57 Average loss: 153.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 58)
+0/69092	Loss: 140.014
+3200/69092	Loss: 156.273
+6400/69092	Loss: 153.502
+9600/69092	Loss: 152.811
+12800/69092	Loss: 152.082
+16000/69092	Loss: 150.071
+19200/69092	Loss: 152.512
+22400/69092	Loss: 151.939
+25600/69092	Loss: 151.793
+28800/69092	Loss: 151.631
+32000/69092	Loss: 155.074
+35200/69092	Loss: 151.940
+38400/69092	Loss: 153.641
+41600/69092	Loss: 154.657
+44800/69092	Loss: 155.468
+48000/69092	Loss: 153.665
+51200/69092	Loss: 154.009
+54400/69092	Loss: 154.453
+57600/69092	Loss: 155.003
+60800/69092	Loss: 149.373
+64000/69092	Loss: 154.071
+67200/69092	Loss: 153.819
+Training time 0:05:08.698422
+Epoch: 58 Average loss: 153.29
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 59)
+0/69092	Loss: 143.812
+3200/69092	Loss: 153.009
+6400/69092	Loss: 153.208
+9600/69092	Loss: 154.279
+12800/69092	Loss: 152.098
+16000/69092	Loss: 151.400
+19200/69092	Loss: 153.523
+22400/69092	Loss: 153.810
+25600/69092	Loss: 155.014
+28800/69092	Loss: 154.882
+32000/69092	Loss: 152.385
+35200/69092	Loss: 152.551
+38400/69092	Loss: 151.502
+41600/69092	Loss: 153.572
+44800/69092	Loss: 153.259
+48000/69092	Loss: 150.445
+51200/69092	Loss: 150.321
+54400/69092	Loss: 152.052
+57600/69092	Loss: 152.770
+60800/69092	Loss: 151.706
+64000/69092	Loss: 154.925
+67200/69092	Loss: 152.911
+Training time 0:05:04.608892
+Epoch: 59 Average loss: 152.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 60)
+0/69092	Loss: 162.215
+3200/69092	Loss: 152.426
+6400/69092	Loss: 152.394
+9600/69092	Loss: 149.961
+12800/69092	Loss: 152.450
+16000/69092	Loss: 153.276
+19200/69092	Loss: 152.965
+22400/69092	Loss: 153.926
+25600/69092	Loss: 153.751
+28800/69092	Loss: 152.589
+32000/69092	Loss: 154.798
+35200/69092	Loss: 151.504
+38400/69092	Loss: 156.993
+41600/69092	Loss: 151.467
+44800/69092	Loss: 152.940
+48000/69092	Loss: 152.895
+51200/69092	Loss: 153.160
+54400/69092	Loss: 156.046
+57600/69092	Loss: 154.143
+60800/69092	Loss: 153.214
+64000/69092	Loss: 152.232
+67200/69092	Loss: 154.575
+Training time 0:05:11.751397
+Epoch: 60 Average loss: 153.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 61)
+0/69092	Loss: 148.478
+3200/69092	Loss: 154.240
+6400/69092	Loss: 154.791
+9600/69092	Loss: 151.924
+12800/69092	Loss: 152.483
+16000/69092	Loss: 152.506
+19200/69092	Loss: 152.950
+22400/69092	Loss: 151.456
+25600/69092	Loss: 154.445
+28800/69092	Loss: 150.913
+32000/69092	Loss: 152.421
+35200/69092	Loss: 155.222
+38400/69092	Loss: 156.020
+41600/69092	Loss: 151.382
+44800/69092	Loss: 153.835
+48000/69092	Loss: 153.079
+51200/69092	Loss: 154.136
+54400/69092	Loss: 153.399
+57600/69092	Loss: 154.339
+60800/69092	Loss: 155.080
+64000/69092	Loss: 152.277
+67200/69092	Loss: 148.640
+Training time 0:05:05.818863
+Epoch: 61 Average loss: 153.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 62)
+0/69092	Loss: 162.908
+3200/69092	Loss: 152.068
+6400/69092	Loss: 153.853
+9600/69092	Loss: 151.146
+12800/69092	Loss: 153.924
+16000/69092	Loss: 151.657
+19200/69092	Loss: 150.882
+22400/69092	Loss: 151.974
+25600/69092	Loss: 154.947
+28800/69092	Loss: 153.533
+32000/69092	Loss: 153.462
+35200/69092	Loss: 152.480
+38400/69092	Loss: 152.152
+41600/69092	Loss: 153.849
+44800/69092	Loss: 154.626
+48000/69092	Loss: 151.176
+51200/69092	Loss: 155.849
+54400/69092	Loss: 150.300
+57600/69092	Loss: 151.026
+60800/69092	Loss: 151.228
+64000/69092	Loss: 155.838
+67200/69092	Loss: 156.099
+Training time 0:05:08.367117
+Epoch: 62 Average loss: 152.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 63)
+0/69092	Loss: 158.083
+3200/69092	Loss: 152.139
+6400/69092	Loss: 151.515
+9600/69092	Loss: 154.007
+12800/69092	Loss: 154.114
+16000/69092	Loss: 152.373
+19200/69092	Loss: 156.591
+22400/69092	Loss: 153.921
+25600/69092	Loss: 153.131
+28800/69092	Loss: 153.269
+32000/69092	Loss: 153.428
+35200/69092	Loss: 153.374
+38400/69092	Loss: 154.027
+41600/69092	Loss: 153.133
+44800/69092	Loss: 151.447
+48000/69092	Loss: 153.021
+51200/69092	Loss: 153.346
+54400/69092	Loss: 153.267
+57600/69092	Loss: 153.133
+60800/69092	Loss: 150.994
+64000/69092	Loss: 151.683
+67200/69092	Loss: 152.689
+Training time 0:05:00.343405
+Epoch: 63 Average loss: 153.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 64)
+0/69092	Loss: 155.382
+3200/69092	Loss: 150.348
+6400/69092	Loss: 153.559
+9600/69092	Loss: 151.789
+12800/69092	Loss: 152.339
+16000/69092	Loss: 152.841
+19200/69092	Loss: 153.235
+22400/69092	Loss: 155.057
+25600/69092	Loss: 152.698
+28800/69092	Loss: 154.833
+32000/69092	Loss: 151.222
+35200/69092	Loss: 154.029
+38400/69092	Loss: 150.603
+41600/69092	Loss: 153.714
+44800/69092	Loss: 152.634
+48000/69092	Loss: 155.500
+51200/69092	Loss: 152.528
+54400/69092	Loss: 150.831
+57600/69092	Loss: 156.847
+60800/69092	Loss: 151.564
+64000/69092	Loss: 153.409
+67200/69092	Loss: 154.167
+Training time 0:05:03.212436
+Epoch: 64 Average loss: 153.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 65)
+0/69092	Loss: 139.758
+3200/69092	Loss: 151.157
+6400/69092	Loss: 152.249
+9600/69092	Loss: 154.095
+12800/69092	Loss: 153.313
+16000/69092	Loss: 150.597
+19200/69092	Loss: 155.302
+22400/69092	Loss: 152.118
+25600/69092	Loss: 153.546
+28800/69092	Loss: 154.313
+32000/69092	Loss: 153.620
+35200/69092	Loss: 154.113
+38400/69092	Loss: 155.124
+41600/69092	Loss: 152.480
+44800/69092	Loss: 152.689
+48000/69092	Loss: 152.772
+51200/69092	Loss: 152.237
+54400/69092	Loss: 152.833
+57600/69092	Loss: 151.780
+60800/69092	Loss: 152.019
+64000/69092	Loss: 151.113
+67200/69092	Loss: 154.489
+Training time 0:05:05.074899
+Epoch: 65 Average loss: 152.91
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 66)
+0/69092	Loss: 159.979
+3200/69092	Loss: 151.053
+6400/69092	Loss: 154.034
+9600/69092	Loss: 152.561
+12800/69092	Loss: 156.672
+16000/69092	Loss: 152.556
+19200/69092	Loss: 152.532
+22400/69092	Loss: 153.240
+25600/69092	Loss: 155.443
+28800/69092	Loss: 154.030
+32000/69092	Loss: 153.251
+35200/69092	Loss: 152.415
+38400/69092	Loss: 155.896
+41600/69092	Loss: 151.039
+44800/69092	Loss: 151.409
+48000/69092	Loss: 151.106
+51200/69092	Loss: 151.377
+54400/69092	Loss: 150.559
+57600/69092	Loss: 154.342
+60800/69092	Loss: 150.862
+64000/69092	Loss: 152.216
+67200/69092	Loss: 151.325
+Training time 0:05:00.575013
+Epoch: 66 Average loss: 152.76
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 67)
+0/69092	Loss: 142.966
+3200/69092	Loss: 152.295
+6400/69092	Loss: 155.944
+9600/69092	Loss: 152.407
+12800/69092	Loss: 153.805
+16000/69092	Loss: 152.183
+19200/69092	Loss: 153.307
+22400/69092	Loss: 150.859
+25600/69092	Loss: 154.866
+28800/69092	Loss: 154.007
+32000/69092	Loss: 153.482
+35200/69092	Loss: 153.582
+38400/69092	Loss: 152.513
+41600/69092	Loss: 152.289
+44800/69092	Loss: 152.738
+48000/69092	Loss: 149.751
+51200/69092	Loss: 154.121
+54400/69092	Loss: 151.372
+57600/69092	Loss: 152.082
+60800/69092	Loss: 151.721
+64000/69092	Loss: 154.688
+67200/69092	Loss: 152.273
+Training time 0:05:03.398712
+Epoch: 67 Average loss: 152.86
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 68)
+0/69092	Loss: 156.820
+3200/69092	Loss: 153.983
+6400/69092	Loss: 152.582
+9600/69092	Loss: 152.875
+12800/69092	Loss: 154.775
+16000/69092	Loss: 151.311
+19200/69092	Loss: 154.312
+22400/69092	Loss: 153.475
+25600/69092	Loss: 153.559
+28800/69092	Loss: 152.839
+32000/69092	Loss: 151.645
+35200/69092	Loss: 152.993
+38400/69092	Loss: 152.570
+41600/69092	Loss: 152.728
+44800/69092	Loss: 155.204
+48000/69092	Loss: 151.278
+51200/69092	Loss: 150.405
+54400/69092	Loss: 151.839
+57600/69092	Loss: 152.835
+60800/69092	Loss: 152.176
+64000/69092	Loss: 153.671
+67200/69092	Loss: 152.518
+Training time 0:05:07.168211
+Epoch: 68 Average loss: 152.74
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 69)
+0/69092	Loss: 154.987
+3200/69092	Loss: 153.001
+6400/69092	Loss: 152.196
+9600/69092	Loss: 152.795
+12800/69092	Loss: 151.874
+16000/69092	Loss: 150.412
+19200/69092	Loss: 152.470
+22400/69092	Loss: 156.112
+25600/69092	Loss: 153.249
+28800/69092	Loss: 153.358
+32000/69092	Loss: 153.126
+35200/69092	Loss: 152.111
+38400/69092	Loss: 151.914
+41600/69092	Loss: 152.241
+44800/69092	Loss: 156.576
+48000/69092	Loss: 153.261
+51200/69092	Loss: 151.098
+54400/69092	Loss: 152.671
+57600/69092	Loss: 150.513
+60800/69092	Loss: 153.903
+64000/69092	Loss: 152.987
+67200/69092	Loss: 153.839
+Training time 0:05:01.142378
+Epoch: 69 Average loss: 152.87
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 70)
+0/69092	Loss: 138.401
+3200/69092	Loss: 155.595
+6400/69092	Loss: 151.267
+9600/69092	Loss: 150.606
+12800/69092	Loss: 151.684
+16000/69092	Loss: 151.296
+19200/69092	Loss: 155.351
+22400/69092	Loss: 153.372
+25600/69092	Loss: 150.808
+28800/69092	Loss: 154.447
+32000/69092	Loss: 154.128
+35200/69092	Loss: 152.911
+38400/69092	Loss: 154.448
+41600/69092	Loss: 153.440
+44800/69092	Loss: 151.671
+48000/69092	Loss: 151.455
+51200/69092	Loss: 151.722
+54400/69092	Loss: 154.920
+57600/69092	Loss: 152.498
+60800/69092	Loss: 152.582
+64000/69092	Loss: 153.175
+67200/69092	Loss: 152.935
+Training time 0:05:01.443628
+Epoch: 70 Average loss: 152.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 71)
+0/69092	Loss: 148.997
+3200/69092	Loss: 153.403
+6400/69092	Loss: 152.238
+9600/69092	Loss: 152.631
+12800/69092	Loss: 154.329
+16000/69092	Loss: 152.442
+19200/69092	Loss: 155.095
+22400/69092	Loss: 148.772
+25600/69092	Loss: 150.420
+28800/69092	Loss: 150.991
+32000/69092	Loss: 150.446
+35200/69092	Loss: 153.889
+38400/69092	Loss: 151.610
+41600/69092	Loss: 157.595
+44800/69092	Loss: 156.073
+48000/69092	Loss: 151.608
+51200/69092	Loss: 152.426
+54400/69092	Loss: 153.357
+57600/69092	Loss: 155.688
+60800/69092	Loss: 152.993
+64000/69092	Loss: 152.860
+67200/69092	Loss: 151.448
+Training time 0:05:06.476809
+Epoch: 71 Average loss: 152.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 72)
+0/69092	Loss: 141.329
+3200/69092	Loss: 150.459
+6400/69092	Loss: 150.223
+9600/69092	Loss: 149.934
+12800/69092	Loss: 155.668
+16000/69092	Loss: 153.889
+19200/69092	Loss: 151.707
+22400/69092	Loss: 155.100
+25600/69092	Loss: 153.854
+28800/69092	Loss: 154.796
+32000/69092	Loss: 150.782
+35200/69092	Loss: 154.409
+38400/69092	Loss: 149.620
+41600/69092	Loss: 152.567
+44800/69092	Loss: 154.245
+48000/69092	Loss: 154.795
+51200/69092	Loss: 152.696
+54400/69092	Loss: 151.767
+57600/69092	Loss: 152.658
+60800/69092	Loss: 151.507
+64000/69092	Loss: 154.653
+67200/69092	Loss: 155.114
+Training time 0:04:59.466738
+Epoch: 72 Average loss: 152.78
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 73)
+0/69092	Loss: 151.954
+3200/69092	Loss: 150.543
+6400/69092	Loss: 150.064
+9600/69092	Loss: 151.341
+12800/69092	Loss: 155.051
+16000/69092	Loss: 151.767
+19200/69092	Loss: 151.707
+22400/69092	Loss: 150.051
+25600/69092	Loss: 153.169
+28800/69092	Loss: 152.199
+32000/69092	Loss: 153.218
+35200/69092	Loss: 154.742
+38400/69092	Loss: 153.789
+41600/69092	Loss: 152.391
+44800/69092	Loss: 153.139
+48000/69092	Loss: 151.313
+51200/69092	Loss: 153.902
+54400/69092	Loss: 153.355
+57600/69092	Loss: 155.380
+60800/69092	Loss: 154.568
+64000/69092	Loss: 154.107
+67200/69092	Loss: 154.334
+Training time 0:05:03.158249
+Epoch: 73 Average loss: 152.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 74)
+0/69092	Loss: 149.423
+3200/69092	Loss: 151.679
+6400/69092	Loss: 153.798
+9600/69092	Loss: 152.668
+12800/69092	Loss: 153.307
+16000/69092	Loss: 153.359
+19200/69092	Loss: 153.498
+22400/69092	Loss: 153.059
+25600/69092	Loss: 149.113
+28800/69092	Loss: 150.271
+32000/69092	Loss: 151.695
+35200/69092	Loss: 153.754
+38400/69092	Loss: 154.245
+41600/69092	Loss: 153.353
+44800/69092	Loss: 155.702
+48000/69092	Loss: 154.674
+51200/69092	Loss: 154.443
+54400/69092	Loss: 151.039
+57600/69092	Loss: 152.744
+60800/69092	Loss: 151.841
+64000/69092	Loss: 150.926
+67200/69092	Loss: 152.025
+Training time 0:05:01.050127
+Epoch: 74 Average loss: 152.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 75)
+0/69092	Loss: 166.404
+3200/69092	Loss: 152.402
+6400/69092	Loss: 153.553
+9600/69092	Loss: 152.141
+12800/69092	Loss: 154.060
+16000/69092	Loss: 153.407
+19200/69092	Loss: 151.435
+22400/69092	Loss: 153.794
+25600/69092	Loss: 153.962
+28800/69092	Loss: 151.479
+32000/69092	Loss: 152.428
+35200/69092	Loss: 150.827
+38400/69092	Loss: 154.074
+41600/69092	Loss: 150.292
+44800/69092	Loss: 153.294
+48000/69092	Loss: 155.552
+51200/69092	Loss: 151.826
+54400/69092	Loss: 148.807
+57600/69092	Loss: 153.112
+60800/69092	Loss: 152.712
+64000/69092	Loss: 152.365
+67200/69092	Loss: 156.062
+Training time 0:05:00.279982
+Epoch: 75 Average loss: 152.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 76)
+0/69092	Loss: 160.236
+3200/69092	Loss: 156.036
+6400/69092	Loss: 151.301
+9600/69092	Loss: 154.022
+12800/69092	Loss: 152.363
+16000/69092	Loss: 152.940
+19200/69092	Loss: 152.926
+22400/69092	Loss: 155.772
+25600/69092	Loss: 149.718
+28800/69092	Loss: 153.058
+32000/69092	Loss: 154.940
+35200/69092	Loss: 149.198
+38400/69092	Loss: 150.990
+41600/69092	Loss: 153.861
+44800/69092	Loss: 152.412
+48000/69092	Loss: 151.850
+51200/69092	Loss: 151.471
+54400/69092	Loss: 152.056
+57600/69092	Loss: 153.746
+60800/69092	Loss: 152.218
+64000/69092	Loss: 154.474
+67200/69092	Loss: 153.115
+Training time 0:05:03.586743
+Epoch: 76 Average loss: 152.70
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 77)
+0/69092	Loss: 151.925
+3200/69092	Loss: 152.772
+6400/69092	Loss: 153.458
+9600/69092	Loss: 153.755
+12800/69092	Loss: 150.433
+16000/69092	Loss: 155.720
+19200/69092	Loss: 154.717
+22400/69092	Loss: 151.831
+25600/69092	Loss: 152.225
+28800/69092	Loss: 155.586
+32000/69092	Loss: 150.702
+35200/69092	Loss: 152.576
+38400/69092	Loss: 153.101
+41600/69092	Loss: 152.526
+44800/69092	Loss: 151.885
+48000/69092	Loss: 153.356
+51200/69092	Loss: 153.625
+54400/69092	Loss: 152.734
+57600/69092	Loss: 153.902
+60800/69092	Loss: 150.747
+64000/69092	Loss: 151.922
+67200/69092	Loss: 151.135
+Training time 0:05:01.913817
+Epoch: 77 Average loss: 152.74
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 78)
+0/69092	Loss: 150.499
+3200/69092	Loss: 150.364
+6400/69092	Loss: 152.616
+9600/69092	Loss: 150.805
+12800/69092	Loss: 152.270
+16000/69092	Loss: 154.041
+19200/69092	Loss: 151.174
+22400/69092	Loss: 152.580
+25600/69092	Loss: 150.905
+28800/69092	Loss: 151.016
+32000/69092	Loss: 155.241
+35200/69092	Loss: 153.416
+38400/69092	Loss: 155.948
+41600/69092	Loss: 151.825
+44800/69092	Loss: 152.065
+48000/69092	Loss: 153.484
+51200/69092	Loss: 154.511
+54400/69092	Loss: 153.743
+57600/69092	Loss: 153.570
+60800/69092	Loss: 152.180
+64000/69092	Loss: 149.114
+67200/69092	Loss: 151.693
+Training time 0:05:04.301251
+Epoch: 78 Average loss: 152.53
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 79)
+0/69092	Loss: 139.963
+3200/69092	Loss: 152.064
+6400/69092	Loss: 154.636
+9600/69092	Loss: 151.479
+12800/69092	Loss: 154.645
+16000/69092	Loss: 155.208
+19200/69092	Loss: 154.139
+22400/69092	Loss: 154.556
+25600/69092	Loss: 154.659
+28800/69092	Loss: 153.859
+32000/69092	Loss: 152.297
+35200/69092	Loss: 152.349
+38400/69092	Loss: 151.648
+41600/69092	Loss: 154.945
+44800/69092	Loss: 152.920
+48000/69092	Loss: 153.521
+51200/69092	Loss: 150.717
+54400/69092	Loss: 150.692
+57600/69092	Loss: 151.252
+60800/69092	Loss: 150.369
+64000/69092	Loss: 151.706
+67200/69092	Loss: 152.179
+Training time 0:05:00.712610
+Epoch: 79 Average loss: 152.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 80)
+0/69092	Loss: 145.566
+3200/69092	Loss: 150.740
+6400/69092	Loss: 150.905
+9600/69092	Loss: 153.793
+12800/69092	Loss: 152.054
+16000/69092	Loss: 151.727
+19200/69092	Loss: 153.509
+22400/69092	Loss: 152.940
+25600/69092	Loss: 154.239
+28800/69092	Loss: 153.820
+32000/69092	Loss: 152.859
+35200/69092	Loss: 151.372
+38400/69092	Loss: 154.048
+41600/69092	Loss: 153.518
+44800/69092	Loss: 151.242
+48000/69092	Loss: 150.291
+51200/69092	Loss: 151.278
+54400/69092	Loss: 150.139
+57600/69092	Loss: 153.476
+60800/69092	Loss: 152.763
+64000/69092	Loss: 153.223
+67200/69092	Loss: 154.283
+Training time 0:05:01.484190
+Epoch: 80 Average loss: 152.58
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 81)
+0/69092	Loss: 152.637
+3200/69092	Loss: 150.512
+6400/69092	Loss: 151.159
+9600/69092	Loss: 152.371
+12800/69092	Loss: 151.326
+16000/69092	Loss: 153.093
+19200/69092	Loss: 151.106
+22400/69092	Loss: 153.780
+25600/69092	Loss: 156.551
+28800/69092	Loss: 152.412
+32000/69092	Loss: 152.912
+35200/69092	Loss: 151.983
+38400/69092	Loss: 153.083
+41600/69092	Loss: 153.586
+44800/69092	Loss: 152.433
+48000/69092	Loss: 153.503
+51200/69092	Loss: 152.919
+54400/69092	Loss: 151.444
+57600/69092	Loss: 152.309
+60800/69092	Loss: 153.188
+64000/69092	Loss: 152.829
+67200/69092	Loss: 152.593
+Training time 0:05:14.836802
+Epoch: 81 Average loss: 152.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 82)
+0/69092	Loss: 127.290
+3200/69092	Loss: 152.351
+6400/69092	Loss: 152.768
+9600/69092	Loss: 152.386
+12800/69092	Loss: 154.187
+16000/69092	Loss: 153.321
+19200/69092	Loss: 153.269
+22400/69092	Loss: 151.541
+25600/69092	Loss: 152.779
+28800/69092	Loss: 152.687
+32000/69092	Loss: 150.497
+35200/69092	Loss: 154.914
+38400/69092	Loss: 151.950
+41600/69092	Loss: 152.179
+44800/69092	Loss: 153.137
+48000/69092	Loss: 152.031
+51200/69092	Loss: 150.718
+54400/69092	Loss: 152.290
+57600/69092	Loss: 152.399
+60800/69092	Loss: 153.191
+64000/69092	Loss: 152.428
+67200/69092	Loss: 153.222
+Training time 0:05:03.126978
+Epoch: 82 Average loss: 152.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 83)
+0/69092	Loss: 170.106
+3200/69092	Loss: 151.821
+6400/69092	Loss: 152.948
+9600/69092	Loss: 155.070
+12800/69092	Loss: 151.771
+16000/69092	Loss: 151.659
+19200/69092	Loss: 150.362
+22400/69092	Loss: 153.987
+25600/69092	Loss: 150.692
+28800/69092	Loss: 154.158
+32000/69092	Loss: 150.786
+35200/69092	Loss: 151.568
+38400/69092	Loss: 155.342
+41600/69092	Loss: 151.864
+44800/69092	Loss: 153.326
+48000/69092	Loss: 150.261
+51200/69092	Loss: 152.559
+54400/69092	Loss: 154.103
+57600/69092	Loss: 151.534
+60800/69092	Loss: 152.258
+64000/69092	Loss: 150.644
+67200/69092	Loss: 154.056
+Training time 0:05:04.365485
+Epoch: 83 Average loss: 152.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 84)
+0/69092	Loss: 156.607
+3200/69092	Loss: 153.684
+6400/69092	Loss: 152.161
+9600/69092	Loss: 151.058
+12800/69092	Loss: 153.163
+16000/69092	Loss: 151.401
+19200/69092	Loss: 150.793
+22400/69092	Loss: 151.713
+25600/69092	Loss: 153.818
+28800/69092	Loss: 155.153
+32000/69092	Loss: 153.687
+35200/69092	Loss: 153.718
+38400/69092	Loss: 153.344
+41600/69092	Loss: 154.692
+44800/69092	Loss: 153.889
+48000/69092	Loss: 151.061
+51200/69092	Loss: 152.466
+54400/69092	Loss: 153.934
+57600/69092	Loss: 152.357
+60800/69092	Loss: 150.489
+64000/69092	Loss: 151.883
+67200/69092	Loss: 153.254
+Training time 0:05:05.027352
+Epoch: 84 Average loss: 152.66
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 85)
+0/69092	Loss: 144.233
+3200/69092	Loss: 151.305
+6400/69092	Loss: 152.445
+9600/69092	Loss: 152.617
+12800/69092	Loss: 154.848
+16000/69092	Loss: 151.736
+19200/69092	Loss: 154.041
+22400/69092	Loss: 154.408
+25600/69092	Loss: 152.803
+28800/69092	Loss: 152.694
+32000/69092	Loss: 149.173
+35200/69092	Loss: 149.996
+38400/69092	Loss: 150.598
+41600/69092	Loss: 152.484
+44800/69092	Loss: 149.742
+48000/69092	Loss: 154.882
+51200/69092	Loss: 149.077
+54400/69092	Loss: 153.066
+57600/69092	Loss: 153.498
+60800/69092	Loss: 153.081
+64000/69092	Loss: 152.484
+67200/69092	Loss: 155.103
+Training time 0:05:00.247594
+Epoch: 85 Average loss: 152.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 86)
+0/69092	Loss: 141.634
+3200/69092	Loss: 154.162
+6400/69092	Loss: 155.657
+9600/69092	Loss: 152.385
+12800/69092	Loss: 150.410
+16000/69092	Loss: 152.131
+19200/69092	Loss: 150.595
+22400/69092	Loss: 150.497
+25600/69092	Loss: 153.052
+28800/69092	Loss: 153.005
+32000/69092	Loss: 152.996
+35200/69092	Loss: 150.578
+38400/69092	Loss: 154.163
+41600/69092	Loss: 150.851
+44800/69092	Loss: 150.714
+48000/69092	Loss: 151.768
+51200/69092	Loss: 153.833
+54400/69092	Loss: 152.679
+57600/69092	Loss: 152.159
+60800/69092	Loss: 152.186
+64000/69092	Loss: 151.733
+67200/69092	Loss: 152.934
+Training time 0:04:59.334738
+Epoch: 86 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 87)
+0/69092	Loss: 132.613
+3200/69092	Loss: 154.505
+6400/69092	Loss: 150.723
+9600/69092	Loss: 154.777
+12800/69092	Loss: 154.341
+16000/69092	Loss: 152.327
+19200/69092	Loss: 152.538
+22400/69092	Loss: 152.846
+25600/69092	Loss: 152.652
+28800/69092	Loss: 152.130
+32000/69092	Loss: 151.218
+35200/69092	Loss: 151.767
+38400/69092	Loss: 150.011
+41600/69092	Loss: 151.972
+44800/69092	Loss: 152.368
+48000/69092	Loss: 150.778
+51200/69092	Loss: 151.855
+54400/69092	Loss: 151.421
+57600/69092	Loss: 153.227
+60800/69092	Loss: 153.404
+64000/69092	Loss: 154.412
+67200/69092	Loss: 155.687
+Training time 0:05:04.861271
+Epoch: 87 Average loss: 152.58
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 88)
+0/69092	Loss: 149.474
+3200/69092	Loss: 150.297
+6400/69092	Loss: 152.666
+9600/69092	Loss: 152.328
+12800/69092	Loss: 154.612
+16000/69092	Loss: 153.454
+19200/69092	Loss: 150.684
+22400/69092	Loss: 151.669
+25600/69092	Loss: 154.238
+28800/69092	Loss: 152.287
+32000/69092	Loss: 154.842
+35200/69092	Loss: 154.491
+38400/69092	Loss: 154.811
+41600/69092	Loss: 150.886
+44800/69092	Loss: 152.775
+48000/69092	Loss: 151.452
+51200/69092	Loss: 153.143
+54400/69092	Loss: 149.978
+57600/69092	Loss: 154.368
+60800/69092	Loss: 150.601
+64000/69092	Loss: 154.155
+67200/69092	Loss: 150.809
+Training time 0:04:59.932954
+Epoch: 88 Average loss: 152.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 89)
+0/69092	Loss: 143.299
+3200/69092	Loss: 152.911
+6400/69092	Loss: 151.744
+9600/69092	Loss: 154.087
+12800/69092	Loss: 153.153
+16000/69092	Loss: 153.537
+19200/69092	Loss: 152.830
+22400/69092	Loss: 150.626
+25600/69092	Loss: 149.894
+28800/69092	Loss: 151.881
+32000/69092	Loss: 152.581
+35200/69092	Loss: 154.882
+38400/69092	Loss: 152.432
+41600/69092	Loss: 152.900
+44800/69092	Loss: 151.728
+48000/69092	Loss: 151.693
+51200/69092	Loss: 151.184
+54400/69092	Loss: 151.568
+57600/69092	Loss: 153.244
+60800/69092	Loss: 152.252
+64000/69092	Loss: 151.284
+67200/69092	Loss: 151.461
+Training time 0:04:58.809173
+Epoch: 89 Average loss: 152.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 90)
+0/69092	Loss: 144.438
+3200/69092	Loss: 151.207
+6400/69092	Loss: 152.556
+9600/69092	Loss: 153.540
+12800/69092	Loss: 152.265
+16000/69092	Loss: 151.794
+19200/69092	Loss: 154.257
+22400/69092	Loss: 153.766
+25600/69092	Loss: 152.606
+28800/69092	Loss: 150.654
+32000/69092	Loss: 151.568
+35200/69092	Loss: 152.547
+38400/69092	Loss: 153.567
+41600/69092	Loss: 152.986
+44800/69092	Loss: 154.000
+48000/69092	Loss: 152.426
+51200/69092	Loss: 152.760
+54400/69092	Loss: 152.946
+57600/69092	Loss: 152.907
+60800/69092	Loss: 152.971
+64000/69092	Loss: 150.503
+67200/69092	Loss: 151.103
+Training time 0:05:05.040677
+Epoch: 90 Average loss: 152.57
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 91)
+0/69092	Loss: 151.201
+3200/69092	Loss: 148.363
+6400/69092	Loss: 152.795
+9600/69092	Loss: 154.857
+12800/69092	Loss: 154.865
+16000/69092	Loss: 151.746
+19200/69092	Loss: 151.830
+22400/69092	Loss: 152.672
+25600/69092	Loss: 153.510
+28800/69092	Loss: 152.644
+32000/69092	Loss: 152.946
+35200/69092	Loss: 152.825
+38400/69092	Loss: 152.690
+41600/69092	Loss: 151.726
+44800/69092	Loss: 151.685
+48000/69092	Loss: 149.414
+51200/69092	Loss: 152.569
+54400/69092	Loss: 150.847
+57600/69092	Loss: 154.336
+60800/69092	Loss: 153.217
+64000/69092	Loss: 156.152
+67200/69092	Loss: 149.649
+Training time 0:05:07.174822
+Epoch: 91 Average loss: 152.41
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 92)
+0/69092	Loss: 146.879
+3200/69092	Loss: 151.679
+6400/69092	Loss: 154.180
+9600/69092	Loss: 152.580
+12800/69092	Loss: 154.908
+16000/69092	Loss: 151.998
+19200/69092	Loss: 154.043
+22400/69092	Loss: 152.445
+25600/69092	Loss: 152.331
+28800/69092	Loss: 152.467
+32000/69092	Loss: 154.391
+35200/69092	Loss: 149.140
+38400/69092	Loss: 154.011
+41600/69092	Loss: 152.635
+44800/69092	Loss: 153.557
+48000/69092	Loss: 151.022
+51200/69092	Loss: 153.452
+54400/69092	Loss: 151.311
+57600/69092	Loss: 150.852
+60800/69092	Loss: 152.605
+64000/69092	Loss: 152.167
+67200/69092	Loss: 149.958
+Training time 0:05:02.091466
+Epoch: 92 Average loss: 152.46
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 93)
+0/69092	Loss: 155.889
+3200/69092	Loss: 151.318
+6400/69092	Loss: 153.910
+9600/69092	Loss: 149.093
+12800/69092	Loss: 152.369
+16000/69092	Loss: 149.840
+19200/69092	Loss: 154.737
+22400/69092	Loss: 150.456
+25600/69092	Loss: 153.290
+28800/69092	Loss: 150.556
+32000/69092	Loss: 153.934
+35200/69092	Loss: 152.128
+38400/69092	Loss: 155.152
+41600/69092	Loss: 151.227
+44800/69092	Loss: 152.402
+48000/69092	Loss: 152.533
+51200/69092	Loss: 153.016
+54400/69092	Loss: 152.008
+57600/69092	Loss: 154.355
+60800/69092	Loss: 151.960
+64000/69092	Loss: 151.035
+67200/69092	Loss: 151.408
+Training time 0:05:09.818408
+Epoch: 93 Average loss: 152.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 94)
+0/69092	Loss: 160.898
+3200/69092	Loss: 152.832
+6400/69092	Loss: 152.692
+9600/69092	Loss: 149.815
+12800/69092	Loss: 150.418
+16000/69092	Loss: 153.161
+19200/69092	Loss: 153.317
+22400/69092	Loss: 154.669
+25600/69092	Loss: 151.977
+28800/69092	Loss: 151.650
+32000/69092	Loss: 151.620
+35200/69092	Loss: 150.944
+38400/69092	Loss: 152.662
+41600/69092	Loss: 152.309
+44800/69092	Loss: 153.141
+48000/69092	Loss: 152.710
+51200/69092	Loss: 149.739
+54400/69092	Loss: 152.727
+57600/69092	Loss: 151.518
+60800/69092	Loss: 154.450
+64000/69092	Loss: 153.961
+67200/69092	Loss: 150.943
+Training time 0:05:11.518790
+Epoch: 94 Average loss: 152.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 95)
+0/69092	Loss: 158.054
+3200/69092	Loss: 154.231
+6400/69092	Loss: 154.395
+9600/69092	Loss: 153.585
+12800/69092	Loss: 153.048
+16000/69092	Loss: 154.041
+19200/69092	Loss: 150.861
+22400/69092	Loss: 153.845
+25600/69092	Loss: 149.978
+28800/69092	Loss: 151.273
+32000/69092	Loss: 155.098
+35200/69092	Loss: 152.181
+38400/69092	Loss: 154.399
+41600/69092	Loss: 150.771
+44800/69092	Loss: 149.101
+48000/69092	Loss: 150.421
+51200/69092	Loss: 154.368
+54400/69092	Loss: 147.994
+57600/69092	Loss: 150.609
+60800/69092	Loss: 152.076
+64000/69092	Loss: 153.247
+67200/69092	Loss: 151.974
+Training time 0:05:19.096335
+Epoch: 95 Average loss: 152.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 96)
+0/69092	Loss: 167.858
+3200/69092	Loss: 151.973
+6400/69092	Loss: 150.961
+9600/69092	Loss: 153.146
+12800/69092	Loss: 153.905
+16000/69092	Loss: 150.775
+19200/69092	Loss: 151.109
+22400/69092	Loss: 153.245
+25600/69092	Loss: 151.288
+28800/69092	Loss: 149.723
+32000/69092	Loss: 153.029
+35200/69092	Loss: 154.187
+38400/69092	Loss: 152.289
+41600/69092	Loss: 152.623
+44800/69092	Loss: 150.950
+48000/69092	Loss: 151.842
+51200/69092	Loss: 151.985
+54400/69092	Loss: 151.905
+57600/69092	Loss: 154.471
+60800/69092	Loss: 148.848
+64000/69092	Loss: 151.029
+67200/69092	Loss: 154.073
+Training time 0:05:14.758375
+Epoch: 96 Average loss: 152.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 97)
+0/69092	Loss: 147.837
+3200/69092	Loss: 154.152
+6400/69092	Loss: 149.796
+9600/69092	Loss: 151.266
+12800/69092	Loss: 154.179
+16000/69092	Loss: 153.537
+19200/69092	Loss: 152.437
+22400/69092	Loss: 153.179
+25600/69092	Loss: 154.426
+28800/69092	Loss: 151.582
+32000/69092	Loss: 150.323
+35200/69092	Loss: 151.787
+38400/69092	Loss: 151.679
+41600/69092	Loss: 151.968
+44800/69092	Loss: 150.813
+48000/69092	Loss: 150.975
+51200/69092	Loss: 151.489
+54400/69092	Loss: 152.246
+57600/69092	Loss: 153.647
+60800/69092	Loss: 151.336
+64000/69092	Loss: 151.397
+67200/69092	Loss: 155.549
+Training time 0:05:10.379359
+Epoch: 97 Average loss: 152.33
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 98)
+0/69092	Loss: 154.740
+3200/69092	Loss: 151.913
+6400/69092	Loss: 150.169
+9600/69092	Loss: 150.706
+12800/69092	Loss: 152.045
+16000/69092	Loss: 152.562
+19200/69092	Loss: 150.442
+22400/69092	Loss: 153.113
+25600/69092	Loss: 153.983
+28800/69092	Loss: 152.689
+32000/69092	Loss: 151.500
+35200/69092	Loss: 154.215
+38400/69092	Loss: 151.321
+41600/69092	Loss: 151.483
+44800/69092	Loss: 153.338
+48000/69092	Loss: 151.855
+51200/69092	Loss: 155.090
+54400/69092	Loss: 152.879
+57600/69092	Loss: 152.352
+60800/69092	Loss: 153.363
+64000/69092	Loss: 152.709
+67200/69092	Loss: 152.203
+Training time 0:05:14.910973
+Epoch: 98 Average loss: 152.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 99)
+0/69092	Loss: 142.389
+3200/69092	Loss: 151.556
+6400/69092	Loss: 150.432
+9600/69092	Loss: 152.263
+12800/69092	Loss: 152.895
+16000/69092	Loss: 153.922
+19200/69092	Loss: 152.318
+22400/69092	Loss: 151.243
+25600/69092	Loss: 155.285
+28800/69092	Loss: 153.173
+32000/69092	Loss: 153.491
+35200/69092	Loss: 152.379
+38400/69092	Loss: 153.217
+41600/69092	Loss: 153.880
+44800/69092	Loss: 152.068
+48000/69092	Loss: 150.619
+51200/69092	Loss: 152.586
+54400/69092	Loss: 151.715
+57600/69092	Loss: 151.369
+60800/69092	Loss: 152.483
+64000/69092	Loss: 151.930
+67200/69092	Loss: 153.290
+Training time 0:05:17.595488
+Epoch: 99 Average loss: 152.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 100)
+0/69092	Loss: 158.684
+3200/69092	Loss: 153.906
+6400/69092	Loss: 153.327
+9600/69092	Loss: 151.818
+12800/69092	Loss: 155.821
+16000/69092	Loss: 152.444
+19200/69092	Loss: 152.891
+22400/69092	Loss: 149.164
+25600/69092	Loss: 152.786
+28800/69092	Loss: 149.422
+32000/69092	Loss: 150.534
+35200/69092	Loss: 151.991
+38400/69092	Loss: 152.149
+41600/69092	Loss: 152.764
+44800/69092	Loss: 153.696
+48000/69092	Loss: 153.265
+51200/69092	Loss: 153.180
+54400/69092	Loss: 150.813
+57600/69092	Loss: 155.719
+60800/69092	Loss: 153.404
+64000/69092	Loss: 151.654
+67200/69092	Loss: 151.188
+Training time 0:05:03.418084
+Epoch: 100 Average loss: 152.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 101)
+0/69092	Loss: 167.760
+3200/69092	Loss: 152.876
+6400/69092	Loss: 153.562
+9600/69092	Loss: 152.892
+12800/69092	Loss: 151.356
+16000/69092	Loss: 152.326
+19200/69092	Loss: 152.955
+22400/69092	Loss: 150.492
+25600/69092	Loss: 153.585
+28800/69092	Loss: 152.329
+32000/69092	Loss: 151.782
+35200/69092	Loss: 152.113
+38400/69092	Loss: 149.783
+41600/69092	Loss: 152.142
+44800/69092	Loss: 151.179
+48000/69092	Loss: 151.627
+51200/69092	Loss: 155.051
+54400/69092	Loss: 152.449
+57600/69092	Loss: 154.746
+60800/69092	Loss: 153.757
+64000/69092	Loss: 149.538
+67200/69092	Loss: 152.841
+Training time 0:04:51.914784
+Epoch: 101 Average loss: 152.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 102)
+0/69092	Loss: 137.340
+3200/69092	Loss: 153.577
+6400/69092	Loss: 150.329
+9600/69092	Loss: 154.083
+12800/69092	Loss: 151.225
+16000/69092	Loss: 150.840
+19200/69092	Loss: 154.126
+22400/69092	Loss: 153.644
+25600/69092	Loss: 151.214
+28800/69092	Loss: 148.798
+32000/69092	Loss: 152.823
+35200/69092	Loss: 149.150
+38400/69092	Loss: 152.960
+41600/69092	Loss: 151.685
+44800/69092	Loss: 150.527
+48000/69092	Loss: 152.596
+51200/69092	Loss: 154.297
+54400/69092	Loss: 151.212
+57600/69092	Loss: 155.284
+60800/69092	Loss: 155.288
+64000/69092	Loss: 151.360
+67200/69092	Loss: 153.971
+Training time 0:05:01.022271
+Epoch: 102 Average loss: 152.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 103)
+0/69092	Loss: 153.573
+3200/69092	Loss: 155.110
+6400/69092	Loss: 151.305
+9600/69092	Loss: 153.206
+12800/69092	Loss: 152.802
+16000/69092	Loss: 149.800
+19200/69092	Loss: 150.971
+22400/69092	Loss: 151.738
+25600/69092	Loss: 151.675
+28800/69092	Loss: 152.290
+32000/69092	Loss: 154.392
+35200/69092	Loss: 154.004
+38400/69092	Loss: 152.356
+41600/69092	Loss: 152.916
+44800/69092	Loss: 152.282
+48000/69092	Loss: 149.664
+51200/69092	Loss: 154.644
+54400/69092	Loss: 152.245
+57600/69092	Loss: 151.714
+60800/69092	Loss: 151.316
+64000/69092	Loss: 152.527
+67200/69092	Loss: 152.642
+Training time 0:05:07.864949
+Epoch: 103 Average loss: 152.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 104)
+0/69092	Loss: 151.662
+3200/69092	Loss: 151.814
+6400/69092	Loss: 149.252
+9600/69092	Loss: 152.869
+12800/69092	Loss: 151.850
+16000/69092	Loss: 151.858
+19200/69092	Loss: 153.972
+22400/69092	Loss: 152.033
+25600/69092	Loss: 150.453
+28800/69092	Loss: 152.936
+32000/69092	Loss: 151.613
+35200/69092	Loss: 152.562
+38400/69092	Loss: 152.823
+41600/69092	Loss: 153.282
+44800/69092	Loss: 150.677
+48000/69092	Loss: 151.082
+51200/69092	Loss: 153.963
+54400/69092	Loss: 150.317
+57600/69092	Loss: 151.814
+60800/69092	Loss: 152.419
+64000/69092	Loss: 151.160
+67200/69092	Loss: 151.677
+Training time 0:05:03.611881
+Epoch: 104 Average loss: 152.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 105)
+0/69092	Loss: 161.888
+3200/69092	Loss: 151.001
+6400/69092	Loss: 153.504
+9600/69092	Loss: 153.895
+12800/69092	Loss: 150.216
+16000/69092	Loss: 151.263
+19200/69092	Loss: 152.214
+22400/69092	Loss: 153.599
+25600/69092	Loss: 150.967
+28800/69092	Loss: 151.131
+32000/69092	Loss: 151.013
+35200/69092	Loss: 151.392
+38400/69092	Loss: 153.402
+41600/69092	Loss: 154.157
+44800/69092	Loss: 150.464
+48000/69092	Loss: 152.472
+51200/69092	Loss: 153.008
+54400/69092	Loss: 152.619
+57600/69092	Loss: 151.042
+60800/69092	Loss: 152.681
+64000/69092	Loss: 152.854
+67200/69092	Loss: 150.712
+Training time 0:05:04.874558
+Epoch: 105 Average loss: 152.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 106)
+0/69092	Loss: 163.787
+3200/69092	Loss: 153.259
+6400/69092	Loss: 153.108
+9600/69092	Loss: 153.607
+12800/69092	Loss: 152.003
+16000/69092	Loss: 155.934
+19200/69092	Loss: 150.254
+22400/69092	Loss: 152.237
+25600/69092	Loss: 154.861
+28800/69092	Loss: 149.868
+32000/69092	Loss: 151.238
+35200/69092	Loss: 150.747
+38400/69092	Loss: 153.263
+41600/69092	Loss: 153.709
+44800/69092	Loss: 151.564
+48000/69092	Loss: 152.925
+51200/69092	Loss: 150.700
+54400/69092	Loss: 151.824
+57600/69092	Loss: 151.186
+60800/69092	Loss: 151.589
+64000/69092	Loss: 151.741
+67200/69092	Loss: 153.103
+Training time 0:05:04.314858
+Epoch: 106 Average loss: 152.27
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 107)
+0/69092	Loss: 150.281
+3200/69092	Loss: 153.516
+6400/69092	Loss: 150.627
+9600/69092	Loss: 150.680
+12800/69092	Loss: 150.820
+16000/69092	Loss: 154.541
+19200/69092	Loss: 151.983
+22400/69092	Loss: 150.967
+25600/69092	Loss: 153.063
+28800/69092	Loss: 152.155
+32000/69092	Loss: 152.521
+35200/69092	Loss: 150.335
+38400/69092	Loss: 154.174
+41600/69092	Loss: 150.998
+44800/69092	Loss: 153.771
+48000/69092	Loss: 151.923
+51200/69092	Loss: 152.697
+54400/69092	Loss: 152.294
+57600/69092	Loss: 152.125
+60800/69092	Loss: 150.568
+64000/69092	Loss: 150.760
+67200/69092	Loss: 152.534
+Training time 0:05:02.166449
+Epoch: 107 Average loss: 152.03
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 108)
+0/69092	Loss: 162.576
+3200/69092	Loss: 152.626
+6400/69092	Loss: 151.675
+9600/69092	Loss: 149.449
+12800/69092	Loss: 153.519
+16000/69092	Loss: 153.674
+19200/69092	Loss: 151.671
+22400/69092	Loss: 151.299
+25600/69092	Loss: 151.974
+28800/69092	Loss: 151.358
+32000/69092	Loss: 150.880
+35200/69092	Loss: 152.578
+38400/69092	Loss: 151.399
+41600/69092	Loss: 153.304
+44800/69092	Loss: 151.126
+48000/69092	Loss: 153.111
+51200/69092	Loss: 151.278
+54400/69092	Loss: 153.105
+57600/69092	Loss: 152.167
+60800/69092	Loss: 151.647
+64000/69092	Loss: 151.658
+67200/69092	Loss: 153.319
+Training time 0:05:03.899225
+Epoch: 108 Average loss: 152.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 109)
+0/69092	Loss: 162.644
+3200/69092	Loss: 151.071
+6400/69092	Loss: 151.951
+9600/69092	Loss: 150.950
+12800/69092	Loss: 153.295
+16000/69092	Loss: 151.607
+19200/69092	Loss: 155.580
+22400/69092	Loss: 149.835
+25600/69092	Loss: 152.906
+28800/69092	Loss: 151.171
+32000/69092	Loss: 150.889
+35200/69092	Loss: 150.368
+38400/69092	Loss: 151.140
+41600/69092	Loss: 155.550
+44800/69092	Loss: 154.019
+48000/69092	Loss: 153.075
+51200/69092	Loss: 152.970
+54400/69092	Loss: 152.861
+57600/69092	Loss: 152.184
+60800/69092	Loss: 152.178
+64000/69092	Loss: 151.419
+67200/69092	Loss: 150.563
+Training time 0:05:02.399302
+Epoch: 109 Average loss: 152.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 110)
+0/69092	Loss: 154.988
+3200/69092	Loss: 153.248
+6400/69092	Loss: 151.155
+9600/69092	Loss: 152.526
+12800/69092	Loss: 151.358
+16000/69092	Loss: 150.776
+19200/69092	Loss: 151.902
+22400/69092	Loss: 152.815
+25600/69092	Loss: 150.157
+28800/69092	Loss: 152.073
+32000/69092	Loss: 152.516
+35200/69092	Loss: 151.435
+38400/69092	Loss: 150.491
+41600/69092	Loss: 153.070
+44800/69092	Loss: 153.470
+48000/69092	Loss: 152.637
+51200/69092	Loss: 154.199
+54400/69092	Loss: 151.909
+57600/69092	Loss: 152.136
+60800/69092	Loss: 152.336
+64000/69092	Loss: 152.451
+67200/69092	Loss: 153.406
+Training time 0:05:05.714831
+Epoch: 110 Average loss: 152.22
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 111)
+0/69092	Loss: 162.069
+3200/69092	Loss: 151.802
+6400/69092	Loss: 153.021
+9600/69092	Loss: 152.369
+12800/69092	Loss: 152.280
+16000/69092	Loss: 151.387
+19200/69092	Loss: 152.338
+22400/69092	Loss: 155.283
+25600/69092	Loss: 151.629
+28800/69092	Loss: 152.203
+32000/69092	Loss: 152.868
+35200/69092	Loss: 150.479
+38400/69092	Loss: 152.421
+41600/69092	Loss: 151.306
+44800/69092	Loss: 152.975
+48000/69092	Loss: 152.016
+51200/69092	Loss: 151.654
+54400/69092	Loss: 152.664
+57600/69092	Loss: 151.651
+60800/69092	Loss: 152.255
+64000/69092	Loss: 149.480
+67200/69092	Loss: 152.793
+Training time 0:05:02.300465
+Epoch: 111 Average loss: 152.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 112)
+0/69092	Loss: 156.487
+3200/69092	Loss: 151.962
+6400/69092	Loss: 151.294
+9600/69092	Loss: 152.823
+12800/69092	Loss: 153.223
+16000/69092	Loss: 154.456
+19200/69092	Loss: 153.151
+22400/69092	Loss: 153.360
+25600/69092	Loss: 151.618
+28800/69092	Loss: 153.194
+32000/69092	Loss: 150.388
+35200/69092	Loss: 150.987
+38400/69092	Loss: 154.060
+41600/69092	Loss: 153.648
+44800/69092	Loss: 151.719
+48000/69092	Loss: 150.867
+51200/69092	Loss: 151.067
+54400/69092	Loss: 151.764
+57600/69092	Loss: 150.370
+60800/69092	Loss: 150.257
+64000/69092	Loss: 152.464
+67200/69092	Loss: 151.794
+Training time 0:05:02.461204
+Epoch: 112 Average loss: 152.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 113)
+0/69092	Loss: 134.647
+3200/69092	Loss: 152.720
+6400/69092	Loss: 151.996
+9600/69092	Loss: 151.881
+12800/69092	Loss: 150.558
+16000/69092	Loss: 151.079
+19200/69092	Loss: 152.303
+22400/69092	Loss: 152.675
+25600/69092	Loss: 151.991
+28800/69092	Loss: 151.762
+32000/69092	Loss: 153.496
+35200/69092	Loss: 153.372
+38400/69092	Loss: 151.300
+41600/69092	Loss: 154.439
+44800/69092	Loss: 151.378
+48000/69092	Loss: 149.762
+51200/69092	Loss: 153.833
+54400/69092	Loss: 154.696
+57600/69092	Loss: 150.390
+60800/69092	Loss: 153.278
+64000/69092	Loss: 153.532
+67200/69092	Loss: 150.833
+Training time 0:05:00.977990
+Epoch: 113 Average loss: 152.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 114)
+0/69092	Loss: 147.473
+3200/69092	Loss: 151.169
+6400/69092	Loss: 152.872
+9600/69092	Loss: 151.215
+12800/69092	Loss: 152.837
+16000/69092	Loss: 152.389
+19200/69092	Loss: 153.817
+22400/69092	Loss: 150.561
+25600/69092	Loss: 150.609
+28800/69092	Loss: 151.353
+32000/69092	Loss: 155.208
+35200/69092	Loss: 149.628
+38400/69092	Loss: 152.854
+41600/69092	Loss: 149.109
+44800/69092	Loss: 151.593
+48000/69092	Loss: 148.380
+51200/69092	Loss: 155.033
+54400/69092	Loss: 154.760
+57600/69092	Loss: 153.577
+60800/69092	Loss: 150.653
+64000/69092	Loss: 151.115
+67200/69092	Loss: 152.697
+Training time 0:05:02.626567
+Epoch: 114 Average loss: 152.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 115)
+0/69092	Loss: 166.235
+3200/69092	Loss: 151.045
+6400/69092	Loss: 151.566
+9600/69092	Loss: 153.103
+12800/69092	Loss: 150.612
+16000/69092	Loss: 152.364
+19200/69092	Loss: 153.273
+22400/69092	Loss: 149.959
+25600/69092	Loss: 150.041
+28800/69092	Loss: 152.056
+32000/69092	Loss: 151.269
+35200/69092	Loss: 151.116
+38400/69092	Loss: 153.598
+41600/69092	Loss: 154.428
+44800/69092	Loss: 151.714
+48000/69092	Loss: 154.260
+51200/69092	Loss: 152.895
+54400/69092	Loss: 149.122
+57600/69092	Loss: 149.939
+60800/69092	Loss: 152.893
+64000/69092	Loss: 151.964
+67200/69092	Loss: 153.125
+Training time 0:05:02.931710
+Epoch: 115 Average loss: 151.91
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 116)
+0/69092	Loss: 153.137
+3200/69092	Loss: 154.553
+6400/69092	Loss: 150.445
+9600/69092	Loss: 150.450
+12800/69092	Loss: 152.588
+16000/69092	Loss: 151.747
+19200/69092	Loss: 151.415
+22400/69092	Loss: 151.264
+25600/69092	Loss: 151.927
+28800/69092	Loss: 151.327
+32000/69092	Loss: 152.850
+35200/69092	Loss: 152.286
+38400/69092	Loss: 151.161
+41600/69092	Loss: 152.494
+44800/69092	Loss: 152.301
+48000/69092	Loss: 155.223
+51200/69092	Loss: 154.050
+54400/69092	Loss: 152.871
+57600/69092	Loss: 151.519
+60800/69092	Loss: 152.448
+64000/69092	Loss: 147.826
+67200/69092	Loss: 150.735
+Training time 0:05:03.193442
+Epoch: 116 Average loss: 152.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 117)
+0/69092	Loss: 157.562
+3200/69092	Loss: 152.486
+6400/69092	Loss: 150.864
+9600/69092	Loss: 152.991
+12800/69092	Loss: 150.016
+16000/69092	Loss: 149.548
+19200/69092	Loss: 150.238
+22400/69092	Loss: 153.190
+25600/69092	Loss: 151.756
+28800/69092	Loss: 153.476
+32000/69092	Loss: 151.726
+35200/69092	Loss: 152.100
+38400/69092	Loss: 153.736
+41600/69092	Loss: 151.745
+44800/69092	Loss: 151.568
+48000/69092	Loss: 153.706
+51200/69092	Loss: 151.723
+54400/69092	Loss: 152.113
+57600/69092	Loss: 155.143
+60800/69092	Loss: 153.267
+64000/69092	Loss: 152.065
+67200/69092	Loss: 150.600
+Training time 0:05:04.841105
+Epoch: 117 Average loss: 152.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 118)
+0/69092	Loss: 155.602
+3200/69092	Loss: 153.534
+6400/69092	Loss: 152.894
+9600/69092	Loss: 153.559
+12800/69092	Loss: 150.108
+16000/69092	Loss: 153.907
+19200/69092	Loss: 151.515
+22400/69092	Loss: 150.308
+25600/69092	Loss: 153.708
+28800/69092	Loss: 153.366
+32000/69092	Loss: 149.893
+35200/69092	Loss: 149.675
+38400/69092	Loss: 150.179
+41600/69092	Loss: 150.500
+44800/69092	Loss: 152.019
+48000/69092	Loss: 151.848
+51200/69092	Loss: 150.390
+54400/69092	Loss: 152.176
+57600/69092	Loss: 151.036
+60800/69092	Loss: 151.798
+64000/69092	Loss: 154.119
+67200/69092	Loss: 152.002
+Training time 0:05:10.346446
+Epoch: 118 Average loss: 151.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_15/checkpoints/last' (iter 119)
+0/69092	Loss: 164.591
+3200/69092	Loss: 152.554
+6400/69092	Loss: 152.542
+9600/69092	Loss: 152.283
diff --git a/OAR.2068289.stderr b/OAR.2068289.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..62059a6fde84cf373602f4f41a34c77fc9d2061e
--- /dev/null
+++ b/OAR.2068289.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-25 02:59:26] Job 2068289 KILLED ##
diff --git a/OAR.2068289.stdout b/OAR.2068289.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..0105d9f49542fd9fa913fad3856aa4f28e36598b
--- /dev/null
+++ b/OAR.2068289.stdout
@@ -0,0 +1,3003 @@
+Namespace(batch_size=64, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_64_ls_20', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=20, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/beta_VAE_bs_64_ls_20
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=40, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=20, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 773035
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last (iter 2)'
+0/69092	Loss: 430.567
+3200/69092	Loss: 451.552
+6400/69092	Loss: 438.499
+9600/69092	Loss: 445.077
+12800/69092	Loss: 447.616
+16000/69092	Loss: 435.337
+19200/69092	Loss: 450.837
+22400/69092	Loss: 439.799
+25600/69092	Loss: 439.213
+28800/69092	Loss: 437.965
+32000/69092	Loss: 435.058
+35200/69092	Loss: 438.174
+38400/69092	Loss: 448.690
+41600/69092	Loss: 431.653
+44800/69092	Loss: 438.952
+48000/69092	Loss: 436.764
+51200/69092	Loss: 431.678
+54400/69092	Loss: 432.029
+57600/69092	Loss: 452.793
+60800/69092	Loss: 435.165
+64000/69092	Loss: 444.115
+67200/69092	Loss: 441.251
+Training time 0:05:05.893259
+Epoch: 1 Average loss: 440.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 3)
+0/69092	Loss: 444.272
+3200/69092	Loss: 445.115
+6400/69092	Loss: 441.781
+9600/69092	Loss: 446.416
+12800/69092	Loss: 442.182
+16000/69092	Loss: 433.913
+19200/69092	Loss: 437.256
+22400/69092	Loss: 441.802
+25600/69092	Loss: 431.982
+28800/69092	Loss: 439.460
+32000/69092	Loss: 438.135
+35200/69092	Loss: 439.063
+38400/69092	Loss: 448.086
+41600/69092	Loss: 440.595
+44800/69092	Loss: 437.003
+48000/69092	Loss: 433.917
+51200/69092	Loss: 447.033
+54400/69092	Loss: 440.830
+57600/69092	Loss: 435.229
+60800/69092	Loss: 439.483
+64000/69092	Loss: 440.454
+67200/69092	Loss: 448.095
+Training time 0:05:03.050238
+Epoch: 2 Average loss: 440.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 4)
+0/69092	Loss: 462.525
+3200/69092	Loss: 433.152
+6400/69092	Loss: 434.208
+9600/69092	Loss: 441.110
+12800/69092	Loss: 444.663
+16000/69092	Loss: 446.222
+19200/69092	Loss: 432.492
+22400/69092	Loss: 441.121
+25600/69092	Loss: 441.951
+28800/69092	Loss: 437.930
+32000/69092	Loss: 433.192
+35200/69092	Loss: 446.659
+38400/69092	Loss: 444.017
+41600/69092	Loss: 449.571
+44800/69092	Loss: 439.392
+48000/69092	Loss: 438.912
+51200/69092	Loss: 449.640
+54400/69092	Loss: 437.776
+57600/69092	Loss: 426.500
+60800/69092	Loss: 443.331
+64000/69092	Loss: 443.030
+67200/69092	Loss: 441.288
+Training time 0:05:11.274975
+Epoch: 3 Average loss: 440.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 5)
+0/69092	Loss: 447.306
+3200/69092	Loss: 443.481
+6400/69092	Loss: 446.084
+9600/69092	Loss: 434.249
+12800/69092	Loss: 433.784
+16000/69092	Loss: 445.353
+19200/69092	Loss: 441.356
+22400/69092	Loss: 443.973
+25600/69092	Loss: 439.298
+28800/69092	Loss: 439.510
+32000/69092	Loss: 440.013
+35200/69092	Loss: 443.306
+38400/69092	Loss: 435.255
+41600/69092	Loss: 442.483
+44800/69092	Loss: 445.100
+48000/69092	Loss: 439.007
+51200/69092	Loss: 427.231
+54400/69092	Loss: 436.510
+57600/69092	Loss: 436.720
+60800/69092	Loss: 450.904
+64000/69092	Loss: 439.393
+67200/69092	Loss: 442.527
+Training time 0:05:06.700535
+Epoch: 4 Average loss: 440.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 6)
+0/69092	Loss: 418.644
+3200/69092	Loss: 438.218
+6400/69092	Loss: 450.252
+9600/69092	Loss: 439.158
+12800/69092	Loss: 441.716
+16000/69092	Loss: 447.580
+19200/69092	Loss: 445.912
+22400/69092	Loss: 438.749
+25600/69092	Loss: 436.598
+28800/69092	Loss: 437.339
+32000/69092	Loss: 433.433
+35200/69092	Loss: 431.786
+38400/69092	Loss: 448.120
+41600/69092	Loss: 435.548
+44800/69092	Loss: 445.933
+48000/69092	Loss: 441.889
+51200/69092	Loss: 442.513
+54400/69092	Loss: 435.773
+57600/69092	Loss: 430.723
+60800/69092	Loss: 443.641
+64000/69092	Loss: 438.866
+67200/69092	Loss: 442.804
+Training time 0:05:05.878531
+Epoch: 5 Average loss: 440.27
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 7)
+0/69092	Loss: 501.531
+3200/69092	Loss: 436.080
+6400/69092	Loss: 430.934
+9600/69092	Loss: 440.531
+12800/69092	Loss: 443.256
+16000/69092	Loss: 443.942
+19200/69092	Loss: 439.735
+22400/69092	Loss: 441.888
+25600/69092	Loss: 443.789
+28800/69092	Loss: 443.138
+32000/69092	Loss: 282.311
+35200/69092	Loss: 222.722
+38400/69092	Loss: 224.461
+41600/69092	Loss: 216.463
+44800/69092	Loss: 216.539
+48000/69092	Loss: 205.753
+51200/69092	Loss: 198.170
+54400/69092	Loss: 199.128
+57600/69092	Loss: 195.972
+60800/69092	Loss: 191.918
+64000/69092	Loss: 192.646
+67200/69092	Loss: 189.203
+Training time 0:04:56.607394
+Epoch: 6 Average loss: 306.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 8)
+0/69092	Loss: 193.317
+3200/69092	Loss: 190.651
+6400/69092	Loss: 188.764
+9600/69092	Loss: 188.883
+12800/69092	Loss: 189.042
+16000/69092	Loss: 184.939
+19200/69092	Loss: 187.289
+22400/69092	Loss: 186.018
+25600/69092	Loss: 188.386
+28800/69092	Loss: 185.676
+32000/69092	Loss: 188.149
+35200/69092	Loss: 183.489
+38400/69092	Loss: 182.190
+41600/69092	Loss: 180.228
+44800/69092	Loss: 179.100
+48000/69092	Loss: 178.929
+51200/69092	Loss: 180.279
+54400/69092	Loss: 180.885
+57600/69092	Loss: 180.871
+60800/69092	Loss: 175.996
+64000/69092	Loss: 175.814
+67200/69092	Loss: 172.733
+Training time 0:05:01.248720
+Epoch: 7 Average loss: 183.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 9)
+0/69092	Loss: 175.976
+3200/69092	Loss: 175.519
+6400/69092	Loss: 172.875
+9600/69092	Loss: 168.530
+12800/69092	Loss: 174.181
+16000/69092	Loss: 174.451
+19200/69092	Loss: 173.476
+22400/69092	Loss: 177.952
+25600/69092	Loss: 173.038
+28800/69092	Loss: 173.493
+32000/69092	Loss: 169.433
+35200/69092	Loss: 175.888
+38400/69092	Loss: 172.573
+41600/69092	Loss: 171.296
+44800/69092	Loss: 173.126
+48000/69092	Loss: 170.911
+51200/69092	Loss: 172.181
+54400/69092	Loss: 176.282
+57600/69092	Loss: 171.192
+60800/69092	Loss: 171.903
+64000/69092	Loss: 172.167
+67200/69092	Loss: 170.300
+Training time 0:05:02.991306
+Epoch: 8 Average loss: 172.77
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 10)
+0/69092	Loss: 177.808
+3200/69092	Loss: 167.293
+6400/69092	Loss: 171.747
+9600/69092	Loss: 172.320
+12800/69092	Loss: 169.716
+16000/69092	Loss: 168.703
+19200/69092	Loss: 171.163
+22400/69092	Loss: 165.480
+25600/69092	Loss: 171.134
+28800/69092	Loss: 169.034
+32000/69092	Loss: 170.347
+35200/69092	Loss: 171.707
+38400/69092	Loss: 168.689
+41600/69092	Loss: 168.946
+44800/69092	Loss: 166.259
+48000/69092	Loss: 169.122
+51200/69092	Loss: 167.689
+54400/69092	Loss: 172.184
+57600/69092	Loss: 170.305
+60800/69092	Loss: 168.594
+64000/69092	Loss: 167.623
+67200/69092	Loss: 169.057
+Training time 0:04:59.365652
+Epoch: 9 Average loss: 169.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 11)
+0/69092	Loss: 170.726
+3200/69092	Loss: 169.164
+6400/69092	Loss: 168.319
+9600/69092	Loss: 165.079
+12800/69092	Loss: 167.098
+16000/69092	Loss: 164.978
+19200/69092	Loss: 170.563
+22400/69092	Loss: 167.007
+25600/69092	Loss: 167.459
+28800/69092	Loss: 170.687
+32000/69092	Loss: 165.900
+35200/69092	Loss: 166.946
+38400/69092	Loss: 164.479
+41600/69092	Loss: 169.314
+44800/69092	Loss: 165.872
+48000/69092	Loss: 165.364
+51200/69092	Loss: 163.238
+54400/69092	Loss: 165.130
+57600/69092	Loss: 166.742
+60800/69092	Loss: 167.505
+64000/69092	Loss: 168.180
+67200/69092	Loss: 167.454
+Training time 0:05:00.056367
+Epoch: 10 Average loss: 166.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 12)
+0/69092	Loss: 165.274
+3200/69092	Loss: 164.180
+6400/69092	Loss: 165.239
+9600/69092	Loss: 168.142
+12800/69092	Loss: 168.498
+16000/69092	Loss: 167.716
+19200/69092	Loss: 163.388
+22400/69092	Loss: 165.186
+25600/69092	Loss: 170.132
+28800/69092	Loss: 165.804
+32000/69092	Loss: 164.222
+35200/69092	Loss: 167.129
+38400/69092	Loss: 165.750
+41600/69092	Loss: 163.277
+44800/69092	Loss: 162.054
+48000/69092	Loss: 163.924
+51200/69092	Loss: 163.566
+54400/69092	Loss: 164.816
+57600/69092	Loss: 161.576
+60800/69092	Loss: 164.528
+64000/69092	Loss: 165.856
+67200/69092	Loss: 165.561
+Training time 0:05:06.804340
+Epoch: 11 Average loss: 165.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 13)
+0/69092	Loss: 183.655
+3200/69092	Loss: 162.179
+6400/69092	Loss: 166.925
+9600/69092	Loss: 163.074
+12800/69092	Loss: 161.481
+16000/69092	Loss: 165.606
+19200/69092	Loss: 163.444
+22400/69092	Loss: 162.572
+25600/69092	Loss: 166.145
+28800/69092	Loss: 164.023
+32000/69092	Loss: 163.924
+35200/69092	Loss: 163.503
+38400/69092	Loss: 162.495
+41600/69092	Loss: 165.627
+44800/69092	Loss: 162.917
+48000/69092	Loss: 162.868
+51200/69092	Loss: 160.486
+54400/69092	Loss: 162.442
+57600/69092	Loss: 159.867
+60800/69092	Loss: 160.743
+64000/69092	Loss: 167.472
+67200/69092	Loss: 162.736
+Training time 0:04:58.903187
+Epoch: 12 Average loss: 163.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 14)
+0/69092	Loss: 146.602
+3200/69092	Loss: 164.487
+6400/69092	Loss: 160.674
+9600/69092	Loss: 166.910
+12800/69092	Loss: 162.449
+16000/69092	Loss: 159.800
+19200/69092	Loss: 162.522
+22400/69092	Loss: 161.967
+25600/69092	Loss: 160.885
+28800/69092	Loss: 160.508
+32000/69092	Loss: 162.803
+35200/69092	Loss: 162.870
+38400/69092	Loss: 162.641
+41600/69092	Loss: 164.704
+44800/69092	Loss: 161.753
+48000/69092	Loss: 161.745
+51200/69092	Loss: 163.318
+54400/69092	Loss: 159.323
+57600/69092	Loss: 163.804
+60800/69092	Loss: 164.845
+64000/69092	Loss: 163.711
+67200/69092	Loss: 162.224
+Training time 0:04:59.047005
+Epoch: 13 Average loss: 162.60
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 15)
+0/69092	Loss: 180.344
+3200/69092	Loss: 162.784
+6400/69092	Loss: 164.695
+9600/69092	Loss: 163.425
+12800/69092	Loss: 162.526
+16000/69092	Loss: 162.231
+19200/69092	Loss: 158.073
+22400/69092	Loss: 164.558
+25600/69092	Loss: 162.173
+28800/69092	Loss: 162.845
+32000/69092	Loss: 160.427
+35200/69092	Loss: 160.872
+38400/69092	Loss: 161.094
+41600/69092	Loss: 159.888
+44800/69092	Loss: 159.363
+48000/69092	Loss: 162.448
+51200/69092	Loss: 159.365
+54400/69092	Loss: 162.186
+57600/69092	Loss: 162.648
+60800/69092	Loss: 161.662
+64000/69092	Loss: 158.286
+67200/69092	Loss: 161.052
+Training time 0:05:00.164209
+Epoch: 14 Average loss: 161.58
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 16)
+0/69092	Loss: 199.840
+3200/69092	Loss: 163.063
+6400/69092	Loss: 160.443
+9600/69092	Loss: 160.690
+12800/69092	Loss: 160.661
+16000/69092	Loss: 161.505
+19200/69092	Loss: 162.851
+22400/69092	Loss: 163.128
+25600/69092	Loss: 164.712
+28800/69092	Loss: 160.490
+32000/69092	Loss: 161.206
+35200/69092	Loss: 164.118
+38400/69092	Loss: 162.320
+41600/69092	Loss: 161.073
+44800/69092	Loss: 162.087
+48000/69092	Loss: 156.887
+51200/69092	Loss: 161.298
+54400/69092	Loss: 162.237
+57600/69092	Loss: 159.090
+60800/69092	Loss: 157.578
+64000/69092	Loss: 160.340
+67200/69092	Loss: 160.287
+Training time 0:04:58.618335
+Epoch: 15 Average loss: 161.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 17)
+0/69092	Loss: 179.801
+3200/69092	Loss: 159.282
+6400/69092	Loss: 162.081
+9600/69092	Loss: 162.562
+12800/69092	Loss: 161.926
+16000/69092	Loss: 158.826
+19200/69092	Loss: 160.103
+22400/69092	Loss: 158.152
+25600/69092	Loss: 163.123
+28800/69092	Loss: 161.471
+32000/69092	Loss: 159.271
+35200/69092	Loss: 160.391
+38400/69092	Loss: 163.551
+41600/69092	Loss: 161.165
+44800/69092	Loss: 160.229
+48000/69092	Loss: 160.965
+51200/69092	Loss: 158.097
+54400/69092	Loss: 160.917
+57600/69092	Loss: 159.306
+60800/69092	Loss: 160.436
+64000/69092	Loss: 161.442
+67200/69092	Loss: 161.968
+Training time 0:04:57.306732
+Epoch: 16 Average loss: 160.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 18)
+0/69092	Loss: 149.385
+3200/69092	Loss: 159.807
+6400/69092	Loss: 161.194
+9600/69092	Loss: 161.355
+12800/69092	Loss: 160.187
+16000/69092	Loss: 162.113
+19200/69092	Loss: 157.555
+22400/69092	Loss: 162.597
+25600/69092	Loss: 159.544
+28800/69092	Loss: 160.797
+32000/69092	Loss: 159.622
+35200/69092	Loss: 158.874
+38400/69092	Loss: 159.822
+41600/69092	Loss: 158.641
+44800/69092	Loss: 158.873
+48000/69092	Loss: 158.707
+51200/69092	Loss: 162.068
+54400/69092	Loss: 163.116
+57600/69092	Loss: 160.427
+60800/69092	Loss: 160.234
+64000/69092	Loss: 159.714
+67200/69092	Loss: 162.192
+Training time 0:05:10.648769
+Epoch: 17 Average loss: 160.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 19)
+0/69092	Loss: 172.314
+3200/69092	Loss: 159.519
+6400/69092	Loss: 159.834
+9600/69092	Loss: 160.876
+12800/69092	Loss: 157.965
+16000/69092	Loss: 158.430
+19200/69092	Loss: 162.003
+22400/69092	Loss: 158.368
+25600/69092	Loss: 161.281
+28800/69092	Loss: 161.379
+32000/69092	Loss: 157.398
+35200/69092	Loss: 161.975
+38400/69092	Loss: 160.614
+41600/69092	Loss: 159.682
+44800/69092	Loss: 160.802
+48000/69092	Loss: 160.805
+51200/69092	Loss: 158.886
+54400/69092	Loss: 158.829
+57600/69092	Loss: 157.948
+60800/69092	Loss: 161.549
+64000/69092	Loss: 162.151
+67200/69092	Loss: 158.295
+Training time 0:05:12.274791
+Epoch: 18 Average loss: 159.96
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 20)
+0/69092	Loss: 131.326
+3200/69092	Loss: 159.722
+6400/69092	Loss: 161.068
+9600/69092	Loss: 162.357
+12800/69092	Loss: 158.572
+16000/69092	Loss: 156.072
+19200/69092	Loss: 159.042
+22400/69092	Loss: 160.605
+25600/69092	Loss: 160.488
+28800/69092	Loss: 157.250
+32000/69092	Loss: 157.668
+35200/69092	Loss: 159.510
+38400/69092	Loss: 160.301
+41600/69092	Loss: 157.766
+44800/69092	Loss: 163.092
+48000/69092	Loss: 160.065
+51200/69092	Loss: 159.585
+54400/69092	Loss: 160.448
+57600/69092	Loss: 160.693
+60800/69092	Loss: 155.794
+64000/69092	Loss: 159.446
+67200/69092	Loss: 160.260
+Training time 0:05:10.358980
+Epoch: 19 Average loss: 159.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 21)
+0/69092	Loss: 139.467
+3200/69092	Loss: 156.805
+6400/69092	Loss: 163.923
+9600/69092	Loss: 156.384
+12800/69092	Loss: 160.907
+16000/69092	Loss: 157.463
+19200/69092	Loss: 159.306
+22400/69092	Loss: 160.186
+25600/69092	Loss: 160.608
+28800/69092	Loss: 157.611
+32000/69092	Loss: 161.119
+35200/69092	Loss: 161.114
+38400/69092	Loss: 155.444
+41600/69092	Loss: 157.161
+44800/69092	Loss: 159.646
+48000/69092	Loss: 157.669
+51200/69092	Loss: 157.876
+54400/69092	Loss: 161.787
+57600/69092	Loss: 161.446
+60800/69092	Loss: 160.716
+64000/69092	Loss: 158.617
+67200/69092	Loss: 157.310
+Training time 0:05:18.605031
+Epoch: 20 Average loss: 159.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 22)
+0/69092	Loss: 162.804
+3200/69092	Loss: 157.607
+6400/69092	Loss: 158.703
+9600/69092	Loss: 159.519
+12800/69092	Loss: 156.560
+16000/69092	Loss: 157.732
+19200/69092	Loss: 157.927
+22400/69092	Loss: 157.652
+25600/69092	Loss: 160.784
+28800/69092	Loss: 158.147
+32000/69092	Loss: 160.244
+35200/69092	Loss: 158.372
+38400/69092	Loss: 160.944
+41600/69092	Loss: 158.488
+44800/69092	Loss: 159.586
+48000/69092	Loss: 158.742
+51200/69092	Loss: 162.743
+54400/69092	Loss: 161.818
+57600/69092	Loss: 160.912
+60800/69092	Loss: 157.601
+64000/69092	Loss: 156.800
+67200/69092	Loss: 158.901
+Training time 0:04:59.334097
+Epoch: 21 Average loss: 159.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 23)
+0/69092	Loss: 178.919
+3200/69092	Loss: 156.956
+6400/69092	Loss: 155.858
+9600/69092	Loss: 158.408
+12800/69092	Loss: 159.608
+16000/69092	Loss: 158.151
+19200/69092	Loss: 156.806
+22400/69092	Loss: 159.397
+25600/69092	Loss: 158.664
+28800/69092	Loss: 159.260
+32000/69092	Loss: 158.235
+35200/69092	Loss: 155.953
+38400/69092	Loss: 158.616
+41600/69092	Loss: 161.979
+44800/69092	Loss: 158.240
+48000/69092	Loss: 159.231
+51200/69092	Loss: 159.557
+54400/69092	Loss: 158.783
+57600/69092	Loss: 158.981
+60800/69092	Loss: 160.020
+64000/69092	Loss: 157.398
+67200/69092	Loss: 156.409
+Training time 0:05:06.096368
+Epoch: 22 Average loss: 158.52
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 24)
+0/69092	Loss: 198.471
+3200/69092	Loss: 159.577
+6400/69092	Loss: 157.152
+9600/69092	Loss: 159.531
+12800/69092	Loss: 156.278
+16000/69092	Loss: 157.974
+19200/69092	Loss: 159.375
+22400/69092	Loss: 159.431
+25600/69092	Loss: 155.183
+28800/69092	Loss: 159.021
+32000/69092	Loss: 156.130
+35200/69092	Loss: 158.486
+38400/69092	Loss: 159.190
+41600/69092	Loss: 161.333
+44800/69092	Loss: 156.929
+48000/69092	Loss: 157.362
+51200/69092	Loss: 159.342
+54400/69092	Loss: 159.642
+57600/69092	Loss: 158.222
+60800/69092	Loss: 157.330
+64000/69092	Loss: 159.937
+67200/69092	Loss: 157.708
+Training time 0:05:01.714042
+Epoch: 23 Average loss: 158.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 25)
+0/69092	Loss: 154.421
+3200/69092	Loss: 161.038
+6400/69092	Loss: 159.909
+9600/69092	Loss: 160.374
+12800/69092	Loss: 155.509
+16000/69092	Loss: 158.463
+19200/69092	Loss: 157.168
+22400/69092	Loss: 156.711
+25600/69092	Loss: 159.442
+28800/69092	Loss: 159.541
+32000/69092	Loss: 158.115
+35200/69092	Loss: 154.931
+38400/69092	Loss: 161.294
+41600/69092	Loss: 156.948
+44800/69092	Loss: 157.822
+48000/69092	Loss: 158.018
+51200/69092	Loss: 156.660
+54400/69092	Loss: 158.641
+57600/69092	Loss: 155.128
+60800/69092	Loss: 158.553
+64000/69092	Loss: 158.320
+67200/69092	Loss: 157.628
+Training time 0:05:02.542967
+Epoch: 24 Average loss: 158.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 26)
+0/69092	Loss: 145.903
+3200/69092	Loss: 155.523
+6400/69092	Loss: 158.970
+9600/69092	Loss: 158.780
+12800/69092	Loss: 157.718
+16000/69092	Loss: 155.180
+19200/69092	Loss: 161.513
+22400/69092	Loss: 160.515
+25600/69092	Loss: 157.197
+28800/69092	Loss: 159.840
+32000/69092	Loss: 160.121
+35200/69092	Loss: 156.714
+38400/69092	Loss: 158.600
+41600/69092	Loss: 155.920
+44800/69092	Loss: 155.065
+48000/69092	Loss: 159.233
+51200/69092	Loss: 156.716
+54400/69092	Loss: 158.423
+57600/69092	Loss: 158.802
+60800/69092	Loss: 157.774
+64000/69092	Loss: 156.853
+67200/69092	Loss: 156.542
+Training time 0:05:08.574867
+Epoch: 25 Average loss: 158.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 27)
+0/69092	Loss: 183.912
+3200/69092	Loss: 159.267
+6400/69092	Loss: 156.931
+9600/69092	Loss: 155.880
+12800/69092	Loss: 156.799
+16000/69092	Loss: 157.466
+19200/69092	Loss: 157.293
+22400/69092	Loss: 161.534
+25600/69092	Loss: 158.723
+28800/69092	Loss: 158.959
+32000/69092	Loss: 155.528
+35200/69092	Loss: 155.695
+38400/69092	Loss: 158.558
+41600/69092	Loss: 158.691
+44800/69092	Loss: 156.499
+48000/69092	Loss: 159.375
+51200/69092	Loss: 156.073
+54400/69092	Loss: 156.345
+57600/69092	Loss: 158.272
+60800/69092	Loss: 159.055
+64000/69092	Loss: 159.254
+67200/69092	Loss: 158.598
+Training time 0:05:01.677203
+Epoch: 26 Average loss: 157.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 28)
+0/69092	Loss: 158.319
+3200/69092	Loss: 155.226
+6400/69092	Loss: 159.075
+9600/69092	Loss: 158.067
+12800/69092	Loss: 158.444
+16000/69092	Loss: 157.934
+19200/69092	Loss: 158.273
+22400/69092	Loss: 158.384
+25600/69092	Loss: 156.721
+28800/69092	Loss: 157.274
+32000/69092	Loss: 160.808
+35200/69092	Loss: 158.591
+38400/69092	Loss: 159.001
+41600/69092	Loss: 158.926
+44800/69092	Loss: 155.346
+48000/69092	Loss: 157.825
+51200/69092	Loss: 154.177
+54400/69092	Loss: 157.157
+57600/69092	Loss: 155.185
+60800/69092	Loss: 157.648
+64000/69092	Loss: 156.520
+67200/69092	Loss: 157.222
+Training time 0:05:04.085619
+Epoch: 27 Average loss: 157.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 29)
+0/69092	Loss: 168.794
+3200/69092	Loss: 157.004
+6400/69092	Loss: 157.799
+9600/69092	Loss: 157.924
+12800/69092	Loss: 157.496
+16000/69092	Loss: 154.202
+19200/69092	Loss: 156.467
+22400/69092	Loss: 154.233
+25600/69092	Loss: 157.350
+28800/69092	Loss: 156.450
+32000/69092	Loss: 155.983
+35200/69092	Loss: 158.488
+38400/69092	Loss: 158.456
+41600/69092	Loss: 158.286
+44800/69092	Loss: 155.845
+48000/69092	Loss: 155.172
+51200/69092	Loss: 161.770
+54400/69092	Loss: 157.202
+57600/69092	Loss: 159.669
+60800/69092	Loss: 156.999
+64000/69092	Loss: 154.768
+67200/69092	Loss: 157.480
+Training time 0:05:02.893539
+Epoch: 28 Average loss: 157.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 30)
+0/69092	Loss: 173.289
+3200/69092	Loss: 157.316
+6400/69092	Loss: 158.827
+9600/69092	Loss: 155.158
+12800/69092	Loss: 157.116
+16000/69092	Loss: 158.760
+19200/69092	Loss: 157.836
+22400/69092	Loss: 156.273
+25600/69092	Loss: 158.301
+28800/69092	Loss: 156.450
+32000/69092	Loss: 158.055
+35200/69092	Loss: 157.311
+38400/69092	Loss: 156.183
+41600/69092	Loss: 157.349
+44800/69092	Loss: 152.971
+48000/69092	Loss: 159.318
+51200/69092	Loss: 157.521
+54400/69092	Loss: 154.562
+57600/69092	Loss: 156.533
+60800/69092	Loss: 158.231
+64000/69092	Loss: 156.484
+67200/69092	Loss: 157.459
+Training time 0:05:03.158730
+Epoch: 29 Average loss: 157.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 31)
+0/69092	Loss: 152.272
+3200/69092	Loss: 154.144
+6400/69092	Loss: 158.409
+9600/69092	Loss: 156.469
+12800/69092	Loss: 156.355
+16000/69092	Loss: 156.064
+19200/69092	Loss: 158.342
+22400/69092	Loss: 156.801
+25600/69092	Loss: 156.990
+28800/69092	Loss: 157.833
+32000/69092	Loss: 157.819
+35200/69092	Loss: 153.839
+38400/69092	Loss: 154.517
+41600/69092	Loss: 158.042
+44800/69092	Loss: 156.666
+48000/69092	Loss: 158.158
+51200/69092	Loss: 159.296
+54400/69092	Loss: 158.656
+57600/69092	Loss: 160.197
+60800/69092	Loss: 155.470
+64000/69092	Loss: 155.759
+67200/69092	Loss: 156.717
+Training time 0:05:02.535085
+Epoch: 30 Average loss: 157.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 32)
+0/69092	Loss: 159.900
+3200/69092	Loss: 155.428
+6400/69092	Loss: 156.153
+9600/69092	Loss: 158.252
+12800/69092	Loss: 154.153
+16000/69092	Loss: 155.715
+19200/69092	Loss: 156.701
+22400/69092	Loss: 157.210
+25600/69092	Loss: 155.482
+28800/69092	Loss: 155.486
+32000/69092	Loss: 159.990
+35200/69092	Loss: 159.849
+38400/69092	Loss: 159.775
+41600/69092	Loss: 157.037
+44800/69092	Loss: 153.946
+48000/69092	Loss: 156.716
+51200/69092	Loss: 158.174
+54400/69092	Loss: 159.367
+57600/69092	Loss: 156.563
+60800/69092	Loss: 158.856
+64000/69092	Loss: 158.591
+67200/69092	Loss: 155.222
+Training time 0:05:03.584235
+Epoch: 31 Average loss: 157.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 33)
+0/69092	Loss: 138.630
+3200/69092	Loss: 157.491
+6400/69092	Loss: 158.921
+9600/69092	Loss: 158.418
+12800/69092	Loss: 155.203
+16000/69092	Loss: 154.800
+19200/69092	Loss: 156.910
+22400/69092	Loss: 157.309
+25600/69092	Loss: 156.536
+28800/69092	Loss: 156.003
+32000/69092	Loss: 156.527
+35200/69092	Loss: 156.284
+38400/69092	Loss: 158.071
+41600/69092	Loss: 154.295
+44800/69092	Loss: 156.268
+48000/69092	Loss: 155.979
+51200/69092	Loss: 158.383
+54400/69092	Loss: 157.090
+57600/69092	Loss: 158.942
+60800/69092	Loss: 155.958
+64000/69092	Loss: 155.065
+67200/69092	Loss: 158.969
+Training time 0:05:00.048382
+Epoch: 32 Average loss: 156.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 34)
+0/69092	Loss: 154.280
+3200/69092	Loss: 156.005
+6400/69092	Loss: 157.047
+9600/69092	Loss: 155.668
+12800/69092	Loss: 156.591
+16000/69092	Loss: 157.436
+19200/69092	Loss: 157.341
+22400/69092	Loss: 157.175
+25600/69092	Loss: 158.463
+28800/69092	Loss: 157.588
+32000/69092	Loss: 158.312
+35200/69092	Loss: 157.513
+38400/69092	Loss: 159.358
+41600/69092	Loss: 155.965
+44800/69092	Loss: 156.304
+48000/69092	Loss: 154.301
+51200/69092	Loss: 155.460
+54400/69092	Loss: 157.155
+57600/69092	Loss: 155.685
+60800/69092	Loss: 153.439
+64000/69092	Loss: 156.254
+67200/69092	Loss: 156.532
+Training time 0:05:06.067095
+Epoch: 33 Average loss: 156.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 35)
+0/69092	Loss: 141.958
+3200/69092	Loss: 154.117
+6400/69092	Loss: 157.673
+9600/69092	Loss: 154.604
+12800/69092	Loss: 158.539
+16000/69092	Loss: 159.025
+19200/69092	Loss: 159.947
+22400/69092	Loss: 157.176
+25600/69092	Loss: 154.619
+28800/69092	Loss: 159.329
+32000/69092	Loss: 157.699
+35200/69092	Loss: 156.895
+38400/69092	Loss: 157.851
+41600/69092	Loss: 154.119
+44800/69092	Loss: 157.337
+48000/69092	Loss: 156.373
+51200/69092	Loss: 155.055
+54400/69092	Loss: 154.255
+57600/69092	Loss: 158.908
+60800/69092	Loss: 155.901
+64000/69092	Loss: 156.265
+67200/69092	Loss: 156.322
+Training time 0:05:02.584854
+Epoch: 34 Average loss: 156.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 36)
+0/69092	Loss: 141.024
+3200/69092	Loss: 157.381
+6400/69092	Loss: 154.569
+9600/69092	Loss: 156.749
+12800/69092	Loss: 156.400
+16000/69092	Loss: 156.175
+19200/69092	Loss: 154.078
+22400/69092	Loss: 158.045
+25600/69092	Loss: 158.648
+28800/69092	Loss: 156.584
+32000/69092	Loss: 157.413
+35200/69092	Loss: 155.964
+38400/69092	Loss: 158.223
+41600/69092	Loss: 156.824
+44800/69092	Loss: 154.100
+48000/69092	Loss: 157.217
+51200/69092	Loss: 157.154
+54400/69092	Loss: 155.985
+57600/69092	Loss: 154.258
+60800/69092	Loss: 158.663
+64000/69092	Loss: 153.862
+67200/69092	Loss: 156.158
+Training time 0:04:58.572646
+Epoch: 35 Average loss: 156.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 37)
+0/69092	Loss: 149.548
+3200/69092	Loss: 158.175
+6400/69092	Loss: 157.057
+9600/69092	Loss: 154.916
+12800/69092	Loss: 152.153
+16000/69092	Loss: 155.552
+19200/69092	Loss: 159.669
+22400/69092	Loss: 156.470
+25600/69092	Loss: 155.376
+28800/69092	Loss: 153.088
+32000/69092	Loss: 154.291
+35200/69092	Loss: 154.271
+38400/69092	Loss: 158.879
+41600/69092	Loss: 157.144
+44800/69092	Loss: 156.685
+48000/69092	Loss: 156.017
+51200/69092	Loss: 155.343
+54400/69092	Loss: 155.829
+57600/69092	Loss: 156.472
+60800/69092	Loss: 160.351
+64000/69092	Loss: 156.204
+67200/69092	Loss: 156.556
+Training time 0:04:58.032654
+Epoch: 36 Average loss: 156.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 38)
+0/69092	Loss: 166.897
+3200/69092	Loss: 157.064
+6400/69092	Loss: 156.608
+9600/69092	Loss: 155.527
+12800/69092	Loss: 155.202
+16000/69092	Loss: 157.688
+19200/69092	Loss: 155.319
+22400/69092	Loss: 155.936
+25600/69092	Loss: 153.880
+28800/69092	Loss: 155.470
+32000/69092	Loss: 156.070
+35200/69092	Loss: 155.959
+38400/69092	Loss: 157.210
+41600/69092	Loss: 152.925
+44800/69092	Loss: 156.254
+48000/69092	Loss: 156.256
+51200/69092	Loss: 156.374
+54400/69092	Loss: 156.948
+57600/69092	Loss: 155.010
+60800/69092	Loss: 157.953
+64000/69092	Loss: 155.792
+67200/69092	Loss: 157.263
+Training time 0:05:12.924819
+Epoch: 37 Average loss: 156.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 39)
+0/69092	Loss: 148.838
+3200/69092	Loss: 157.990
+6400/69092	Loss: 155.146
+9600/69092	Loss: 156.503
+12800/69092	Loss: 156.882
+16000/69092	Loss: 156.628
+19200/69092	Loss: 157.286
+22400/69092	Loss: 155.862
+25600/69092	Loss: 154.377
+28800/69092	Loss: 156.522
+32000/69092	Loss: 153.395
+35200/69092	Loss: 155.686
+38400/69092	Loss: 156.584
+41600/69092	Loss: 155.208
+44800/69092	Loss: 155.178
+48000/69092	Loss: 154.740
+51200/69092	Loss: 159.219
+54400/69092	Loss: 156.950
+57600/69092	Loss: 157.241
+60800/69092	Loss: 156.995
+64000/69092	Loss: 157.894
+67200/69092	Loss: 155.692
+Training time 0:04:54.323092
+Epoch: 38 Average loss: 156.24
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 40)
+0/69092	Loss: 147.868
+3200/69092	Loss: 157.768
+6400/69092	Loss: 155.189
+9600/69092	Loss: 158.650
+12800/69092	Loss: 157.251
+16000/69092	Loss: 156.936
+19200/69092	Loss: 155.778
+22400/69092	Loss: 154.944
+25600/69092	Loss: 153.205
+28800/69092	Loss: 157.779
+32000/69092	Loss: 154.850
+35200/69092	Loss: 158.655
+38400/69092	Loss: 154.072
+41600/69092	Loss: 154.439
+44800/69092	Loss: 157.815
+48000/69092	Loss: 154.913
+51200/69092	Loss: 154.039
+54400/69092	Loss: 156.694
+57600/69092	Loss: 156.639
+60800/69092	Loss: 156.112
+64000/69092	Loss: 152.171
+67200/69092	Loss: 156.980
+Training time 0:04:53.604248
+Epoch: 39 Average loss: 156.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 41)
+0/69092	Loss: 147.705
+3200/69092	Loss: 155.556
+6400/69092	Loss: 155.200
+9600/69092	Loss: 157.521
+12800/69092	Loss: 153.459
+16000/69092	Loss: 156.978
+19200/69092	Loss: 153.923
+22400/69092	Loss: 157.482
+25600/69092	Loss: 154.741
+28800/69092	Loss: 155.055
+32000/69092	Loss: 158.463
+35200/69092	Loss: 154.147
+38400/69092	Loss: 155.114
+41600/69092	Loss: 155.337
+44800/69092	Loss: 156.893
+48000/69092	Loss: 158.580
+51200/69092	Loss: 156.684
+54400/69092	Loss: 154.755
+57600/69092	Loss: 156.090
+60800/69092	Loss: 155.736
+64000/69092	Loss: 154.606
+67200/69092	Loss: 158.278
+Training time 0:05:13.506864
+Epoch: 40 Average loss: 155.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 42)
+0/69092	Loss: 160.277
+3200/69092	Loss: 156.898
+6400/69092	Loss: 154.616
+9600/69092	Loss: 156.852
+12800/69092	Loss: 151.225
+16000/69092	Loss: 157.091
+19200/69092	Loss: 159.233
+22400/69092	Loss: 155.176
+25600/69092	Loss: 157.237
+28800/69092	Loss: 156.804
+32000/69092	Loss: 156.285
+35200/69092	Loss: 154.726
+38400/69092	Loss: 155.144
+41600/69092	Loss: 156.868
+44800/69092	Loss: 156.826
+48000/69092	Loss: 156.344
+51200/69092	Loss: 156.396
+54400/69092	Loss: 154.604
+57600/69092	Loss: 155.950
+60800/69092	Loss: 154.924
+64000/69092	Loss: 154.222
+67200/69092	Loss: 157.096
+Training time 0:05:07.493872
+Epoch: 41 Average loss: 156.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 43)
+0/69092	Loss: 176.641
+3200/69092	Loss: 156.157
+6400/69092	Loss: 157.115
+9600/69092	Loss: 150.653
+12800/69092	Loss: 154.181
+16000/69092	Loss: 158.271
+19200/69092	Loss: 155.148
+22400/69092	Loss: 156.107
+25600/69092	Loss: 157.770
+28800/69092	Loss: 155.937
+32000/69092	Loss: 154.601
+35200/69092	Loss: 155.916
+38400/69092	Loss: 154.866
+41600/69092	Loss: 153.659
+44800/69092	Loss: 156.341
+48000/69092	Loss: 157.204
+51200/69092	Loss: 156.436
+54400/69092	Loss: 155.775
+57600/69092	Loss: 154.015
+60800/69092	Loss: 154.759
+64000/69092	Loss: 157.842
+67200/69092	Loss: 157.727
+Training time 0:05:09.750887
+Epoch: 42 Average loss: 155.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 44)
+0/69092	Loss: 146.066
+3200/69092	Loss: 156.428
+6400/69092	Loss: 154.703
+9600/69092	Loss: 153.493
+12800/69092	Loss: 154.055
+16000/69092	Loss: 154.985
+19200/69092	Loss: 158.085
+22400/69092	Loss: 155.330
+25600/69092	Loss: 154.810
+28800/69092	Loss: 155.093
+32000/69092	Loss: 158.393
+35200/69092	Loss: 157.086
+38400/69092	Loss: 155.725
+41600/69092	Loss: 154.598
+44800/69092	Loss: 154.570
+48000/69092	Loss: 157.881
+51200/69092	Loss: 157.361
+54400/69092	Loss: 154.023
+57600/69092	Loss: 152.908
+60800/69092	Loss: 156.139
+64000/69092	Loss: 155.932
+67200/69092	Loss: 154.440
+Training time 0:04:59.423167
+Epoch: 43 Average loss: 155.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 45)
+0/69092	Loss: 200.474
+3200/69092	Loss: 153.884
+6400/69092	Loss: 156.203
+9600/69092	Loss: 155.631
+12800/69092	Loss: 157.309
+16000/69092	Loss: 153.582
+19200/69092	Loss: 158.401
+22400/69092	Loss: 154.351
+25600/69092	Loss: 157.492
+28800/69092	Loss: 154.397
+32000/69092	Loss: 157.346
+35200/69092	Loss: 154.203
+38400/69092	Loss: 153.720
+41600/69092	Loss: 156.197
+44800/69092	Loss: 153.916
+48000/69092	Loss: 155.614
+51200/69092	Loss: 157.565
+54400/69092	Loss: 153.797
+57600/69092	Loss: 155.992
+60800/69092	Loss: 157.435
+64000/69092	Loss: 152.022
+67200/69092	Loss: 158.247
+Training time 0:05:03.481951
+Epoch: 44 Average loss: 155.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 46)
+0/69092	Loss: 150.111
+3200/69092	Loss: 157.995
+6400/69092	Loss: 156.446
+9600/69092	Loss: 154.183
+12800/69092	Loss: 154.716
+16000/69092	Loss: 153.516
+19200/69092	Loss: 152.982
+22400/69092	Loss: 153.300
+25600/69092	Loss: 156.466
+28800/69092	Loss: 157.172
+32000/69092	Loss: 157.166
+35200/69092	Loss: 156.265
+38400/69092	Loss: 154.529
+41600/69092	Loss: 156.155
+44800/69092	Loss: 156.316
+48000/69092	Loss: 154.215
+51200/69092	Loss: 155.968
+54400/69092	Loss: 156.190
+57600/69092	Loss: 156.931
+60800/69092	Loss: 154.928
+64000/69092	Loss: 154.245
+67200/69092	Loss: 155.043
+Training time 0:05:06.014997
+Epoch: 45 Average loss: 155.40
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 47)
+0/69092	Loss: 164.460
+3200/69092	Loss: 155.164
+6400/69092	Loss: 153.712
+9600/69092	Loss: 158.241
+12800/69092	Loss: 154.462
+16000/69092	Loss: 153.172
+19200/69092	Loss: 155.025
+22400/69092	Loss: 156.956
+25600/69092	Loss: 157.186
+28800/69092	Loss: 154.228
+32000/69092	Loss: 155.207
+35200/69092	Loss: 155.690
+38400/69092	Loss: 153.174
+41600/69092	Loss: 154.424
+44800/69092	Loss: 156.082
+48000/69092	Loss: 155.082
+51200/69092	Loss: 155.209
+54400/69092	Loss: 156.691
+57600/69092	Loss: 154.057
+60800/69092	Loss: 157.915
+64000/69092	Loss: 155.094
+67200/69092	Loss: 156.608
+Training time 0:05:06.110059
+Epoch: 46 Average loss: 155.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 48)
+0/69092	Loss: 157.285
+3200/69092	Loss: 155.880
+6400/69092	Loss: 157.244
+9600/69092	Loss: 154.071
+12800/69092	Loss: 156.788
+16000/69092	Loss: 154.833
+19200/69092	Loss: 155.581
+22400/69092	Loss: 155.328
+25600/69092	Loss: 155.566
+28800/69092	Loss: 155.714
+32000/69092	Loss: 154.491
+35200/69092	Loss: 154.973
+38400/69092	Loss: 154.087
+41600/69092	Loss: 154.297
+44800/69092	Loss: 156.104
+48000/69092	Loss: 154.182
+51200/69092	Loss: 156.398
+54400/69092	Loss: 155.229
+57600/69092	Loss: 152.827
+60800/69092	Loss: 157.835
+64000/69092	Loss: 156.431
+67200/69092	Loss: 157.561
+Training time 0:05:04.778110
+Epoch: 47 Average loss: 155.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 49)
+0/69092	Loss: 155.640
+3200/69092	Loss: 157.163
+6400/69092	Loss: 155.718
+9600/69092	Loss: 153.309
+12800/69092	Loss: 157.666
+16000/69092	Loss: 154.540
+19200/69092	Loss: 155.487
+22400/69092	Loss: 154.137
+25600/69092	Loss: 155.672
+28800/69092	Loss: 153.656
+32000/69092	Loss: 155.147
+35200/69092	Loss: 154.278
+38400/69092	Loss: 154.301
+41600/69092	Loss: 153.384
+44800/69092	Loss: 158.994
+48000/69092	Loss: 155.656
+51200/69092	Loss: 155.256
+54400/69092	Loss: 156.492
+57600/69092	Loss: 151.754
+60800/69092	Loss: 154.231
+64000/69092	Loss: 156.473
+67200/69092	Loss: 156.620
+Training time 0:05:05.030386
+Epoch: 48 Average loss: 155.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 50)
+0/69092	Loss: 152.081
+3200/69092	Loss: 154.616
+6400/69092	Loss: 153.586
+9600/69092	Loss: 151.007
+12800/69092	Loss: 153.013
+16000/69092	Loss: 153.165
+19200/69092	Loss: 157.482
+22400/69092	Loss: 155.414
+25600/69092	Loss: 153.239
+28800/69092	Loss: 151.686
+32000/69092	Loss: 156.170
+35200/69092	Loss: 158.453
+38400/69092	Loss: 153.003
+41600/69092	Loss: 156.861
+44800/69092	Loss: 153.598
+48000/69092	Loss: 154.978
+51200/69092	Loss: 157.901
+54400/69092	Loss: 157.818
+57600/69092	Loss: 153.674
+60800/69092	Loss: 154.840
+64000/69092	Loss: 156.388
+67200/69092	Loss: 157.618
+Training time 0:05:05.663659
+Epoch: 49 Average loss: 154.95
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 51)
+0/69092	Loss: 138.055
+3200/69092	Loss: 157.310
+6400/69092	Loss: 155.433
+9600/69092	Loss: 156.493
+12800/69092	Loss: 154.718
+16000/69092	Loss: 155.934
+19200/69092	Loss: 153.202
+22400/69092	Loss: 153.749
+25600/69092	Loss: 155.077
+28800/69092	Loss: 155.993
+32000/69092	Loss: 152.357
+35200/69092	Loss: 152.738
+38400/69092	Loss: 157.730
+41600/69092	Loss: 155.163
+44800/69092	Loss: 155.048
+48000/69092	Loss: 154.018
+51200/69092	Loss: 153.513
+54400/69092	Loss: 154.700
+57600/69092	Loss: 154.419
+60800/69092	Loss: 156.469
+64000/69092	Loss: 156.642
+67200/69092	Loss: 157.263
+Training time 0:05:02.768568
+Epoch: 50 Average loss: 155.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 52)
+0/69092	Loss: 161.086
+3200/69092	Loss: 154.211
+6400/69092	Loss: 154.670
+9600/69092	Loss: 155.008
+12800/69092	Loss: 155.333
+16000/69092	Loss: 155.166
+19200/69092	Loss: 155.091
+22400/69092	Loss: 154.827
+25600/69092	Loss: 153.501
+28800/69092	Loss: 155.039
+32000/69092	Loss: 154.895
+35200/69092	Loss: 155.657
+38400/69092	Loss: 157.103
+41600/69092	Loss: 155.500
+44800/69092	Loss: 151.399
+48000/69092	Loss: 157.694
+51200/69092	Loss: 156.642
+54400/69092	Loss: 154.087
+57600/69092	Loss: 153.673
+60800/69092	Loss: 154.877
+64000/69092	Loss: 154.903
+67200/69092	Loss: 156.111
+Training time 0:05:05.459720
+Epoch: 51 Average loss: 155.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 53)
+0/69092	Loss: 141.273
+3200/69092	Loss: 154.704
+6400/69092	Loss: 160.726
+9600/69092	Loss: 158.246
+12800/69092	Loss: 154.867
+16000/69092	Loss: 154.588
+19200/69092	Loss: 155.334
+22400/69092	Loss: 154.317
+25600/69092	Loss: 156.342
+28800/69092	Loss: 151.124
+32000/69092	Loss: 153.697
+35200/69092	Loss: 155.627
+38400/69092	Loss: 156.727
+41600/69092	Loss: 155.011
+44800/69092	Loss: 152.628
+48000/69092	Loss: 156.980
+51200/69092	Loss: 152.179
+54400/69092	Loss: 156.922
+57600/69092	Loss: 154.467
+60800/69092	Loss: 154.816
+64000/69092	Loss: 155.005
+67200/69092	Loss: 153.558
+Training time 0:05:11.431072
+Epoch: 52 Average loss: 155.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 54)
+0/69092	Loss: 164.586
+3200/69092	Loss: 156.119
+6400/69092	Loss: 153.139
+9600/69092	Loss: 152.056
+12800/69092	Loss: 153.613
+16000/69092	Loss: 153.200
+19200/69092	Loss: 155.015
+22400/69092	Loss: 154.981
+25600/69092	Loss: 154.164
+28800/69092	Loss: 154.798
+32000/69092	Loss: 153.374
+35200/69092	Loss: 154.654
+38400/69092	Loss: 153.589
+41600/69092	Loss: 152.973
+44800/69092	Loss: 155.552
+48000/69092	Loss: 154.761
+51200/69092	Loss: 156.590
+54400/69092	Loss: 153.793
+57600/69092	Loss: 156.273
+60800/69092	Loss: 155.909
+64000/69092	Loss: 154.876
+67200/69092	Loss: 157.661
+Training time 0:05:05.996288
+Epoch: 53 Average loss: 154.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 55)
+0/69092	Loss: 146.012
+3200/69092	Loss: 152.299
+6400/69092	Loss: 152.976
+9600/69092	Loss: 152.541
+12800/69092	Loss: 155.811
+16000/69092	Loss: 154.429
+19200/69092	Loss: 156.677
+22400/69092	Loss: 151.371
+25600/69092	Loss: 157.504
+28800/69092	Loss: 159.227
+32000/69092	Loss: 155.371
+35200/69092	Loss: 153.324
+38400/69092	Loss: 155.056
+41600/69092	Loss: 154.234
+44800/69092	Loss: 154.490
+48000/69092	Loss: 153.918
+51200/69092	Loss: 152.108
+54400/69092	Loss: 154.187
+57600/69092	Loss: 155.906
+60800/69092	Loss: 153.891
+64000/69092	Loss: 155.826
+67200/69092	Loss: 155.579
+Training time 0:05:04.230799
+Epoch: 54 Average loss: 154.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 56)
+0/69092	Loss: 153.508
+3200/69092	Loss: 155.734
+6400/69092	Loss: 154.875
+9600/69092	Loss: 155.674
+12800/69092	Loss: 154.985
+16000/69092	Loss: 154.900
+19200/69092	Loss: 155.885
+22400/69092	Loss: 152.871
+25600/69092	Loss: 153.240
+28800/69092	Loss: 155.101
+32000/69092	Loss: 155.479
+35200/69092	Loss: 154.778
+38400/69092	Loss: 154.601
+41600/69092	Loss: 152.157
+44800/69092	Loss: 152.879
+48000/69092	Loss: 155.989
+51200/69092	Loss: 155.628
+54400/69092	Loss: 157.303
+57600/69092	Loss: 152.704
+60800/69092	Loss: 152.557
+64000/69092	Loss: 153.799
+67200/69092	Loss: 153.185
+Training time 0:05:03.901663
+Epoch: 55 Average loss: 154.57
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 57)
+0/69092	Loss: 154.122
+3200/69092	Loss: 152.552
+6400/69092	Loss: 152.658
+9600/69092	Loss: 153.987
+12800/69092	Loss: 156.042
+16000/69092	Loss: 155.590
+19200/69092	Loss: 153.177
+22400/69092	Loss: 154.028
+25600/69092	Loss: 155.959
+28800/69092	Loss: 154.585
+32000/69092	Loss: 154.518
+35200/69092	Loss: 152.070
+38400/69092	Loss: 158.937
+41600/69092	Loss: 154.355
+44800/69092	Loss: 154.037
+48000/69092	Loss: 152.917
+51200/69092	Loss: 156.300
+54400/69092	Loss: 154.668
+57600/69092	Loss: 155.386
+60800/69092	Loss: 156.333
+64000/69092	Loss: 153.739
+67200/69092	Loss: 152.806
+Training time 0:05:04.243139
+Epoch: 56 Average loss: 154.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 58)
+0/69092	Loss: 144.614
+3200/69092	Loss: 153.546
+6400/69092	Loss: 153.930
+9600/69092	Loss: 153.363
+12800/69092	Loss: 155.139
+16000/69092	Loss: 155.062
+19200/69092	Loss: 154.600
+22400/69092	Loss: 153.238
+25600/69092	Loss: 156.407
+28800/69092	Loss: 155.126
+32000/69092	Loss: 152.581
+35200/69092	Loss: 154.254
+38400/69092	Loss: 153.591
+41600/69092	Loss: 154.453
+44800/69092	Loss: 153.838
+48000/69092	Loss: 155.885
+51200/69092	Loss: 157.253
+54400/69092	Loss: 155.813
+57600/69092	Loss: 154.260
+60800/69092	Loss: 153.679
+64000/69092	Loss: 153.516
+67200/69092	Loss: 151.820
+Training time 0:04:59.879109
+Epoch: 57 Average loss: 154.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 59)
+0/69092	Loss: 156.545
+3200/69092	Loss: 152.711
+6400/69092	Loss: 155.152
+9600/69092	Loss: 153.643
+12800/69092	Loss: 153.460
+16000/69092	Loss: 155.417
+19200/69092	Loss: 155.859
+22400/69092	Loss: 156.568
+25600/69092	Loss: 156.311
+28800/69092	Loss: 154.010
+32000/69092	Loss: 154.787
+35200/69092	Loss: 155.393
+38400/69092	Loss: 154.792
+41600/69092	Loss: 154.848
+44800/69092	Loss: 151.788
+48000/69092	Loss: 153.340
+51200/69092	Loss: 154.351
+54400/69092	Loss: 151.239
+57600/69092	Loss: 154.593
+60800/69092	Loss: 153.693
+64000/69092	Loss: 154.874
+67200/69092	Loss: 153.618
+Training time 0:05:07.218632
+Epoch: 58 Average loss: 154.34
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 60)
+0/69092	Loss: 156.600
+3200/69092	Loss: 156.684
+6400/69092	Loss: 151.265
+9600/69092	Loss: 151.986
+12800/69092	Loss: 153.862
+16000/69092	Loss: 154.563
+19200/69092	Loss: 156.867
+22400/69092	Loss: 153.331
+25600/69092	Loss: 156.700
+28800/69092	Loss: 157.730
+32000/69092	Loss: 153.682
+35200/69092	Loss: 152.577
+38400/69092	Loss: 154.478
+41600/69092	Loss: 153.996
+44800/69092	Loss: 151.927
+48000/69092	Loss: 152.702
+51200/69092	Loss: 154.391
+54400/69092	Loss: 153.501
+57600/69092	Loss: 154.020
+60800/69092	Loss: 153.457
+64000/69092	Loss: 154.137
+67200/69092	Loss: 156.023
+Training time 0:05:04.719617
+Epoch: 59 Average loss: 154.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 61)
+0/69092	Loss: 170.459
+3200/69092	Loss: 152.318
+6400/69092	Loss: 154.422
+9600/69092	Loss: 152.764
+12800/69092	Loss: 154.167
+16000/69092	Loss: 152.276
+19200/69092	Loss: 157.502
+22400/69092	Loss: 152.620
+25600/69092	Loss: 152.902
+28800/69092	Loss: 155.460
+32000/69092	Loss: 155.917
+35200/69092	Loss: 152.836
+38400/69092	Loss: 154.717
+41600/69092	Loss: 153.879
+44800/69092	Loss: 152.771
+48000/69092	Loss: 154.452
+51200/69092	Loss: 154.147
+54400/69092	Loss: 156.133
+57600/69092	Loss: 156.922
+60800/69092	Loss: 153.447
+64000/69092	Loss: 153.968
+67200/69092	Loss: 153.248
+Training time 0:05:06.522886
+Epoch: 60 Average loss: 154.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 62)
+0/69092	Loss: 147.740
+3200/69092	Loss: 152.669
+6400/69092	Loss: 155.595
+9600/69092	Loss: 151.748
+12800/69092	Loss: 156.285
+16000/69092	Loss: 154.100
+19200/69092	Loss: 158.269
+22400/69092	Loss: 154.795
+25600/69092	Loss: 154.597
+28800/69092	Loss: 154.787
+32000/69092	Loss: 154.627
+35200/69092	Loss: 155.593
+38400/69092	Loss: 155.354
+41600/69092	Loss: 152.669
+44800/69092	Loss: 153.115
+48000/69092	Loss: 155.852
+51200/69092	Loss: 156.078
+54400/69092	Loss: 151.349
+57600/69092	Loss: 151.340
+60800/69092	Loss: 151.443
+64000/69092	Loss: 150.492
+67200/69092	Loss: 152.684
+Training time 0:05:05.323525
+Epoch: 61 Average loss: 154.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 63)
+0/69092	Loss: 139.416
+3200/69092	Loss: 153.701
+6400/69092	Loss: 155.274
+9600/69092	Loss: 154.927
+12800/69092	Loss: 152.724
+16000/69092	Loss: 155.177
+19200/69092	Loss: 155.993
+22400/69092	Loss: 153.200
+25600/69092	Loss: 153.818
+28800/69092	Loss: 153.500
+32000/69092	Loss: 153.379
+35200/69092	Loss: 154.827
+38400/69092	Loss: 152.315
+41600/69092	Loss: 154.844
+44800/69092	Loss: 153.337
+48000/69092	Loss: 154.495
+51200/69092	Loss: 156.173
+54400/69092	Loss: 152.639
+57600/69092	Loss: 151.143
+60800/69092	Loss: 156.477
+64000/69092	Loss: 151.459
+67200/69092	Loss: 156.094
+Training time 0:05:08.764411
+Epoch: 62 Average loss: 154.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 64)
+0/69092	Loss: 150.931
+3200/69092	Loss: 153.194
+6400/69092	Loss: 153.936
+9600/69092	Loss: 155.239
+12800/69092	Loss: 153.442
+16000/69092	Loss: 155.337
+19200/69092	Loss: 155.263
+22400/69092	Loss: 154.418
+25600/69092	Loss: 153.767
+28800/69092	Loss: 152.669
+32000/69092	Loss: 153.968
+35200/69092	Loss: 155.063
+38400/69092	Loss: 154.774
+41600/69092	Loss: 155.956
+44800/69092	Loss: 149.058
+48000/69092	Loss: 153.635
+51200/69092	Loss: 153.272
+54400/69092	Loss: 154.152
+57600/69092	Loss: 155.237
+60800/69092	Loss: 153.538
+64000/69092	Loss: 152.544
+67200/69092	Loss: 155.601
+Training time 0:04:58.135277
+Epoch: 63 Average loss: 153.99
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 65)
+0/69092	Loss: 149.957
+3200/69092	Loss: 154.703
+6400/69092	Loss: 154.330
+9600/69092	Loss: 153.689
+12800/69092	Loss: 152.845
+16000/69092	Loss: 153.162
+19200/69092	Loss: 153.399
+22400/69092	Loss: 157.306
+25600/69092	Loss: 152.055
+28800/69092	Loss: 153.684
+32000/69092	Loss: 151.968
+35200/69092	Loss: 152.251
+38400/69092	Loss: 151.774
+41600/69092	Loss: 154.926
+44800/69092	Loss: 155.528
+48000/69092	Loss: 152.358
+51200/69092	Loss: 154.989
+54400/69092	Loss: 153.860
+57600/69092	Loss: 151.102
+60800/69092	Loss: 156.806
+64000/69092	Loss: 156.172
+67200/69092	Loss: 152.239
+Training time 0:05:04.264074
+Epoch: 64 Average loss: 153.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 66)
+0/69092	Loss: 135.616
+3200/69092	Loss: 152.446
+6400/69092	Loss: 154.534
+9600/69092	Loss: 156.478
+12800/69092	Loss: 152.469
+16000/69092	Loss: 153.438
+19200/69092	Loss: 152.151
+22400/69092	Loss: 151.988
+25600/69092	Loss: 152.486
+28800/69092	Loss: 154.608
+32000/69092	Loss: 152.458
+35200/69092	Loss: 155.160
+38400/69092	Loss: 152.668
+41600/69092	Loss: 153.771
+44800/69092	Loss: 155.444
+48000/69092	Loss: 153.273
+51200/69092	Loss: 154.682
+54400/69092	Loss: 156.136
+57600/69092	Loss: 154.308
+60800/69092	Loss: 154.201
+64000/69092	Loss: 154.921
+67200/69092	Loss: 151.464
+Training time 0:05:11.196563
+Epoch: 65 Average loss: 153.87
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 67)
+0/69092	Loss: 153.509
+3200/69092	Loss: 153.018
+6400/69092	Loss: 151.510
+9600/69092	Loss: 153.944
+12800/69092	Loss: 153.229
+16000/69092	Loss: 154.579
+19200/69092	Loss: 152.519
+22400/69092	Loss: 155.319
+25600/69092	Loss: 154.698
+28800/69092	Loss: 154.007
+32000/69092	Loss: 153.458
+35200/69092	Loss: 153.842
+38400/69092	Loss: 152.014
+41600/69092	Loss: 153.029
+44800/69092	Loss: 153.387
+48000/69092	Loss: 156.787
+51200/69092	Loss: 155.629
+54400/69092	Loss: 153.123
+57600/69092	Loss: 156.001
+60800/69092	Loss: 153.081
+64000/69092	Loss: 154.751
+67200/69092	Loss: 152.536
+Training time 0:04:59.973290
+Epoch: 66 Average loss: 153.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 68)
+0/69092	Loss: 158.298
+3200/69092	Loss: 152.507
+6400/69092	Loss: 152.727
+9600/69092	Loss: 153.121
+12800/69092	Loss: 150.498
+16000/69092	Loss: 151.056
+19200/69092	Loss: 157.246
+22400/69092	Loss: 155.845
+25600/69092	Loss: 155.414
+28800/69092	Loss: 155.750
+32000/69092	Loss: 151.761
+35200/69092	Loss: 155.314
+38400/69092	Loss: 157.279
+41600/69092	Loss: 155.597
+44800/69092	Loss: 153.331
+48000/69092	Loss: 153.527
+51200/69092	Loss: 152.233
+54400/69092	Loss: 152.478
+57600/69092	Loss: 154.482
+60800/69092	Loss: 153.493
+64000/69092	Loss: 152.558
+67200/69092	Loss: 156.084
+Training time 0:05:03.196744
+Epoch: 67 Average loss: 153.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 69)
+0/69092	Loss: 137.431
+3200/69092	Loss: 153.184
+6400/69092	Loss: 153.018
+9600/69092	Loss: 153.017
+12800/69092	Loss: 154.140
+16000/69092	Loss: 153.353
+19200/69092	Loss: 153.756
+22400/69092	Loss: 155.243
+25600/69092	Loss: 152.656
+28800/69092	Loss: 150.268
+32000/69092	Loss: 154.123
+35200/69092	Loss: 152.991
+38400/69092	Loss: 155.381
+41600/69092	Loss: 152.844
+44800/69092	Loss: 157.580
+48000/69092	Loss: 155.649
+51200/69092	Loss: 152.598
+54400/69092	Loss: 155.049
+57600/69092	Loss: 156.392
+60800/69092	Loss: 151.240
+64000/69092	Loss: 155.197
+67200/69092	Loss: 153.459
+Training time 0:05:07.525183
+Epoch: 68 Average loss: 153.81
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 70)
+0/69092	Loss: 151.003
+3200/69092	Loss: 154.731
+6400/69092	Loss: 151.691
+9600/69092	Loss: 156.213
+12800/69092	Loss: 154.119
+16000/69092	Loss: 156.713
+19200/69092	Loss: 158.253
+22400/69092	Loss: 152.825
+25600/69092	Loss: 153.418
+28800/69092	Loss: 151.424
+32000/69092	Loss: 151.944
+35200/69092	Loss: 150.554
+38400/69092	Loss: 156.523
+41600/69092	Loss: 154.309
+44800/69092	Loss: 151.041
+48000/69092	Loss: 154.112
+51200/69092	Loss: 152.367
+54400/69092	Loss: 154.360
+57600/69092	Loss: 152.664
+60800/69092	Loss: 152.367
+64000/69092	Loss: 152.254
+67200/69092	Loss: 155.735
+Training time 0:04:59.974978
+Epoch: 69 Average loss: 153.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 71)
+0/69092	Loss: 161.774
+3200/69092	Loss: 155.306
+6400/69092	Loss: 152.665
+9600/69092	Loss: 151.905
+12800/69092	Loss: 155.717
+16000/69092	Loss: 152.308
+19200/69092	Loss: 153.469
+22400/69092	Loss: 153.628
+25600/69092	Loss: 152.235
+28800/69092	Loss: 151.682
+32000/69092	Loss: 154.698
+35200/69092	Loss: 154.138
+38400/69092	Loss: 155.426
+41600/69092	Loss: 149.700
+44800/69092	Loss: 153.198
+48000/69092	Loss: 155.658
+51200/69092	Loss: 154.721
+54400/69092	Loss: 154.164
+57600/69092	Loss: 149.495
+60800/69092	Loss: 155.010
+64000/69092	Loss: 155.860
+67200/69092	Loss: 152.009
+Training time 0:05:03.293064
+Epoch: 70 Average loss: 153.46
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 72)
+0/69092	Loss: 144.245
+3200/69092	Loss: 154.381
+6400/69092	Loss: 153.375
+9600/69092	Loss: 150.919
+12800/69092	Loss: 153.996
+16000/69092	Loss: 153.696
+19200/69092	Loss: 153.120
+22400/69092	Loss: 153.994
+25600/69092	Loss: 154.056
+28800/69092	Loss: 155.968
+32000/69092	Loss: 151.166
+35200/69092	Loss: 152.345
+38400/69092	Loss: 154.135
+41600/69092	Loss: 152.586
+44800/69092	Loss: 154.086
+48000/69092	Loss: 156.236
+51200/69092	Loss: 154.236
+54400/69092	Loss: 153.280
+57600/69092	Loss: 153.596
+60800/69092	Loss: 154.071
+64000/69092	Loss: 152.977
+67200/69092	Loss: 154.226
+Training time 0:05:06.396825
+Epoch: 71 Average loss: 153.66
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 73)
+0/69092	Loss: 156.194
+3200/69092	Loss: 151.805
+6400/69092	Loss: 154.363
+9600/69092	Loss: 154.537
+12800/69092	Loss: 152.933
+16000/69092	Loss: 156.908
+19200/69092	Loss: 156.385
+22400/69092	Loss: 153.351
+25600/69092	Loss: 153.926
+28800/69092	Loss: 154.872
+32000/69092	Loss: 154.015
+35200/69092	Loss: 150.921
+38400/69092	Loss: 153.676
+41600/69092	Loss: 152.123
+44800/69092	Loss: 151.742
+48000/69092	Loss: 151.958
+51200/69092	Loss: 154.512
+54400/69092	Loss: 151.513
+57600/69092	Loss: 154.801
+60800/69092	Loss: 153.840
+64000/69092	Loss: 152.705
+67200/69092	Loss: 151.378
+Training time 0:05:00.657293
+Epoch: 72 Average loss: 153.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 74)
+0/69092	Loss: 156.484
+3200/69092	Loss: 155.144
+6400/69092	Loss: 151.167
+9600/69092	Loss: 154.190
+12800/69092	Loss: 151.972
+16000/69092	Loss: 155.841
+19200/69092	Loss: 153.245
+22400/69092	Loss: 153.017
+25600/69092	Loss: 154.034
+28800/69092	Loss: 154.449
+32000/69092	Loss: 151.774
+35200/69092	Loss: 154.554
+38400/69092	Loss: 152.260
+41600/69092	Loss: 153.270
+44800/69092	Loss: 156.584
+48000/69092	Loss: 156.922
+51200/69092	Loss: 149.480
+54400/69092	Loss: 152.737
+57600/69092	Loss: 152.815
+60800/69092	Loss: 152.341
+64000/69092	Loss: 155.473
+67200/69092	Loss: 153.995
+Training time 0:05:02.853466
+Epoch: 73 Average loss: 153.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 75)
+0/69092	Loss: 148.939
+3200/69092	Loss: 153.304
+6400/69092	Loss: 154.032
+9600/69092	Loss: 151.501
+12800/69092	Loss: 155.538
+16000/69092	Loss: 155.840
+19200/69092	Loss: 154.375
+22400/69092	Loss: 152.371
+25600/69092	Loss: 153.850
+28800/69092	Loss: 155.079
+32000/69092	Loss: 155.369
+35200/69092	Loss: 153.096
+38400/69092	Loss: 153.323
+41600/69092	Loss: 153.568
+44800/69092	Loss: 152.036
+48000/69092	Loss: 150.695
+51200/69092	Loss: 152.184
+54400/69092	Loss: 153.827
+57600/69092	Loss: 151.439
+60800/69092	Loss: 153.644
+64000/69092	Loss: 155.684
+67200/69092	Loss: 153.418
+Training time 0:05:00.557330
+Epoch: 74 Average loss: 153.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 76)
+0/69092	Loss: 150.033
+3200/69092	Loss: 153.176
+6400/69092	Loss: 153.239
+9600/69092	Loss: 151.274
+12800/69092	Loss: 153.676
+16000/69092	Loss: 155.003
+19200/69092	Loss: 152.913
+22400/69092	Loss: 153.146
+25600/69092	Loss: 152.478
+28800/69092	Loss: 153.191
+32000/69092	Loss: 154.477
+35200/69092	Loss: 151.997
+38400/69092	Loss: 150.608
+41600/69092	Loss: 155.911
+44800/69092	Loss: 154.714
+48000/69092	Loss: 151.517
+51200/69092	Loss: 153.886
+54400/69092	Loss: 152.867
+57600/69092	Loss: 153.793
+60800/69092	Loss: 152.574
+64000/69092	Loss: 151.439
+67200/69092	Loss: 154.768
+Training time 0:05:01.203807
+Epoch: 75 Average loss: 153.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 77)
+0/69092	Loss: 142.014
+3200/69092	Loss: 154.271
+6400/69092	Loss: 154.913
+9600/69092	Loss: 154.524
+12800/69092	Loss: 153.158
+16000/69092	Loss: 154.462
+19200/69092	Loss: 153.590
+22400/69092	Loss: 152.747
+25600/69092	Loss: 152.127
+28800/69092	Loss: 150.609
+32000/69092	Loss: 151.544
+35200/69092	Loss: 153.632
+38400/69092	Loss: 152.679
+41600/69092	Loss: 154.565
+44800/69092	Loss: 153.910
+48000/69092	Loss: 151.477
+51200/69092	Loss: 154.229
+54400/69092	Loss: 151.742
+57600/69092	Loss: 153.524
+60800/69092	Loss: 150.082
+64000/69092	Loss: 154.862
+67200/69092	Loss: 156.026
+Training time 0:05:04.062959
+Epoch: 76 Average loss: 153.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 78)
+0/69092	Loss: 148.453
+3200/69092	Loss: 152.221
+6400/69092	Loss: 153.214
+9600/69092	Loss: 155.322
+12800/69092	Loss: 153.553
+16000/69092	Loss: 152.134
+19200/69092	Loss: 153.447
+22400/69092	Loss: 154.105
+25600/69092	Loss: 153.733
+28800/69092	Loss: 151.577
+32000/69092	Loss: 155.673
+35200/69092	Loss: 153.258
+38400/69092	Loss: 152.589
+41600/69092	Loss: 152.946
+44800/69092	Loss: 152.928
+48000/69092	Loss: 152.054
+51200/69092	Loss: 153.723
+54400/69092	Loss: 151.409
+57600/69092	Loss: 152.121
+60800/69092	Loss: 153.611
+64000/69092	Loss: 152.776
+67200/69092	Loss: 153.087
+Training time 0:05:01.937133
+Epoch: 77 Average loss: 153.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 79)
+0/69092	Loss: 158.441
+3200/69092	Loss: 155.406
+6400/69092	Loss: 152.158
+9600/69092	Loss: 155.231
+12800/69092	Loss: 150.286
+16000/69092	Loss: 154.259
+19200/69092	Loss: 151.001
+22400/69092	Loss: 154.950
+25600/69092	Loss: 152.324
+28800/69092	Loss: 154.063
+32000/69092	Loss: 153.091
+35200/69092	Loss: 153.493
+38400/69092	Loss: 150.938
+41600/69092	Loss: 154.376
+44800/69092	Loss: 153.107
+48000/69092	Loss: 152.342
+51200/69092	Loss: 155.247
+54400/69092	Loss: 152.103
+57600/69092	Loss: 154.678
+60800/69092	Loss: 152.135
+64000/69092	Loss: 152.381
+67200/69092	Loss: 153.750
+Training time 0:05:03.291943
+Epoch: 78 Average loss: 153.20
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 80)
+0/69092	Loss: 151.019
+3200/69092	Loss: 151.870
+6400/69092	Loss: 152.273
+9600/69092	Loss: 153.427
+12800/69092	Loss: 155.270
+16000/69092	Loss: 154.600
+19200/69092	Loss: 150.932
+22400/69092	Loss: 154.699
+25600/69092	Loss: 153.568
+28800/69092	Loss: 149.703
+32000/69092	Loss: 151.962
+35200/69092	Loss: 153.908
+38400/69092	Loss: 152.027
+41600/69092	Loss: 154.136
+44800/69092	Loss: 154.786
+48000/69092	Loss: 155.455
+51200/69092	Loss: 153.557
+54400/69092	Loss: 154.232
+57600/69092	Loss: 152.527
+60800/69092	Loss: 151.458
+64000/69092	Loss: 155.241
+67200/69092	Loss: 155.344
+Training time 0:05:00.654061
+Epoch: 79 Average loss: 153.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 81)
+0/69092	Loss: 164.827
+3200/69092	Loss: 152.071
+6400/69092	Loss: 154.103
+9600/69092	Loss: 151.613
+12800/69092	Loss: 153.057
+16000/69092	Loss: 153.029
+19200/69092	Loss: 153.164
+22400/69092	Loss: 155.791
+25600/69092	Loss: 150.832
+28800/69092	Loss: 153.147
+32000/69092	Loss: 154.802
+35200/69092	Loss: 155.089
+38400/69092	Loss: 149.886
+41600/69092	Loss: 154.534
+44800/69092	Loss: 152.561
+48000/69092	Loss: 154.211
+51200/69092	Loss: 152.915
+54400/69092	Loss: 154.393
+57600/69092	Loss: 151.915
+60800/69092	Loss: 153.240
+64000/69092	Loss: 152.048
+67200/69092	Loss: 153.093
+Training time 0:04:59.990244
+Epoch: 80 Average loss: 153.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 82)
+0/69092	Loss: 151.023
+3200/69092	Loss: 150.561
+6400/69092	Loss: 152.307
+9600/69092	Loss: 152.909
+12800/69092	Loss: 154.198
+16000/69092	Loss: 152.977
+19200/69092	Loss: 151.994
+22400/69092	Loss: 154.000
+25600/69092	Loss: 152.126
+28800/69092	Loss: 155.862
+32000/69092	Loss: 153.807
+35200/69092	Loss: 156.478
+38400/69092	Loss: 153.013
+41600/69092	Loss: 153.210
+44800/69092	Loss: 153.690
+48000/69092	Loss: 151.425
+51200/69092	Loss: 154.031
+54400/69092	Loss: 155.327
+57600/69092	Loss: 151.086
+60800/69092	Loss: 152.455
+64000/69092	Loss: 151.816
+67200/69092	Loss: 155.467
+Training time 0:05:06.066471
+Epoch: 81 Average loss: 153.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 83)
+0/69092	Loss: 170.955
+3200/69092	Loss: 151.483
+6400/69092	Loss: 153.425
+9600/69092	Loss: 151.804
+12800/69092	Loss: 154.874
+16000/69092	Loss: 153.667
+19200/69092	Loss: 150.724
+22400/69092	Loss: 152.623
+25600/69092	Loss: 152.325
+28800/69092	Loss: 154.891
+32000/69092	Loss: 152.610
+35200/69092	Loss: 154.654
+38400/69092	Loss: 150.852
+41600/69092	Loss: 150.880
+44800/69092	Loss: 154.887
+48000/69092	Loss: 154.362
+51200/69092	Loss: 153.672
+54400/69092	Loss: 151.659
+57600/69092	Loss: 152.698
+60800/69092	Loss: 152.635
+64000/69092	Loss: 153.886
+67200/69092	Loss: 152.832
+Training time 0:05:04.369737
+Epoch: 82 Average loss: 152.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 84)
+0/69092	Loss: 147.327
+3200/69092	Loss: 151.784
+6400/69092	Loss: 154.445
+9600/69092	Loss: 154.886
+12800/69092	Loss: 152.869
+16000/69092	Loss: 153.140
+19200/69092	Loss: 151.202
+22400/69092	Loss: 154.594
+25600/69092	Loss: 154.053
+28800/69092	Loss: 152.765
+32000/69092	Loss: 154.873
+35200/69092	Loss: 150.885
+38400/69092	Loss: 154.746
+41600/69092	Loss: 156.010
+44800/69092	Loss: 152.345
+48000/69092	Loss: 154.142
+51200/69092	Loss: 151.205
+54400/69092	Loss: 151.321
+57600/69092	Loss: 152.261
+60800/69092	Loss: 155.695
+64000/69092	Loss: 151.443
+67200/69092	Loss: 151.694
+Training time 0:05:02.456637
+Epoch: 83 Average loss: 153.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 85)
+0/69092	Loss: 157.304
+3200/69092	Loss: 153.795
+6400/69092	Loss: 151.412
+9600/69092	Loss: 153.938
+12800/69092	Loss: 157.122
+16000/69092	Loss: 154.438
+19200/69092	Loss: 155.162
+22400/69092	Loss: 155.787
+25600/69092	Loss: 153.335
+28800/69092	Loss: 154.145
+32000/69092	Loss: 154.408
+35200/69092	Loss: 152.654
+38400/69092	Loss: 153.887
+41600/69092	Loss: 151.372
+44800/69092	Loss: 153.267
+48000/69092	Loss: 151.122
+51200/69092	Loss: 151.413
+54400/69092	Loss: 152.751
+57600/69092	Loss: 152.389
+60800/69092	Loss: 151.229
+64000/69092	Loss: 152.515
+67200/69092	Loss: 150.938
+Training time 0:05:04.469644
+Epoch: 84 Average loss: 153.20
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 86)
+0/69092	Loss: 155.411
+3200/69092	Loss: 152.728
+6400/69092	Loss: 153.224
+9600/69092	Loss: 153.888
+12800/69092	Loss: 150.202
+16000/69092	Loss: 151.087
+19200/69092	Loss: 151.997
+22400/69092	Loss: 153.448
+25600/69092	Loss: 153.868
+28800/69092	Loss: 154.632
+32000/69092	Loss: 152.747
+35200/69092	Loss: 151.499
+38400/69092	Loss: 152.371
+41600/69092	Loss: 155.379
+44800/69092	Loss: 150.440
+48000/69092	Loss: 152.814
+51200/69092	Loss: 153.243
+54400/69092	Loss: 154.576
+57600/69092	Loss: 153.910
+60800/69092	Loss: 152.324
+64000/69092	Loss: 156.518
+67200/69092	Loss: 151.910
+Training time 0:04:59.088313
+Epoch: 85 Average loss: 153.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 87)
+0/69092	Loss: 160.774
+3200/69092	Loss: 155.434
+6400/69092	Loss: 157.195
+9600/69092	Loss: 152.280
+12800/69092	Loss: 156.440
+16000/69092	Loss: 151.277
+19200/69092	Loss: 152.130
+22400/69092	Loss: 153.580
+25600/69092	Loss: 154.009
+28800/69092	Loss: 152.155
+32000/69092	Loss: 153.223
+35200/69092	Loss: 153.253
+38400/69092	Loss: 151.555
+41600/69092	Loss: 152.075
+44800/69092	Loss: 152.142
+48000/69092	Loss: 156.967
+51200/69092	Loss: 151.809
+54400/69092	Loss: 149.804
+57600/69092	Loss: 150.972
+60800/69092	Loss: 151.268
+64000/69092	Loss: 154.066
+67200/69092	Loss: 151.888
+Training time 0:04:58.442294
+Epoch: 86 Average loss: 153.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 88)
+0/69092	Loss: 152.322
+3200/69092	Loss: 151.590
+6400/69092	Loss: 153.602
+9600/69092	Loss: 151.057
+12800/69092	Loss: 151.128
+16000/69092	Loss: 154.163
+19200/69092	Loss: 153.779
+22400/69092	Loss: 152.040
+25600/69092	Loss: 154.772
+28800/69092	Loss: 153.669
+32000/69092	Loss: 151.816
+35200/69092	Loss: 151.665
+38400/69092	Loss: 153.741
+41600/69092	Loss: 155.616
+44800/69092	Loss: 154.520
+48000/69092	Loss: 151.972
+51200/69092	Loss: 152.793
+54400/69092	Loss: 154.131
+57600/69092	Loss: 152.470
+60800/69092	Loss: 153.640
+64000/69092	Loss: 153.099
+67200/69092	Loss: 150.981
+Training time 0:05:07.376272
+Epoch: 87 Average loss: 153.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 89)
+0/69092	Loss: 159.553
+3200/69092	Loss: 153.347
+6400/69092	Loss: 152.530
+9600/69092	Loss: 154.021
+12800/69092	Loss: 153.439
+16000/69092	Loss: 149.131
+19200/69092	Loss: 150.333
+22400/69092	Loss: 151.817
+25600/69092	Loss: 152.307
+28800/69092	Loss: 154.859
+32000/69092	Loss: 154.099
+35200/69092	Loss: 151.406
+38400/69092	Loss: 152.829
+41600/69092	Loss: 153.314
+44800/69092	Loss: 154.028
+48000/69092	Loss: 156.256
+51200/69092	Loss: 152.179
+54400/69092	Loss: 153.508
+57600/69092	Loss: 154.960
+60800/69092	Loss: 148.960
+64000/69092	Loss: 155.890
+67200/69092	Loss: 152.293
+Training time 0:04:55.985734
+Epoch: 88 Average loss: 152.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 90)
+0/69092	Loss: 136.290
+3200/69092	Loss: 152.589
+6400/69092	Loss: 151.043
+9600/69092	Loss: 155.248
+12800/69092	Loss: 151.427
+16000/69092	Loss: 151.395
+19200/69092	Loss: 151.643
+22400/69092	Loss: 154.406
+25600/69092	Loss: 155.140
+28800/69092	Loss: 156.893
+32000/69092	Loss: 151.823
+35200/69092	Loss: 153.980
+38400/69092	Loss: 151.603
+41600/69092	Loss: 154.155
+44800/69092	Loss: 151.745
+48000/69092	Loss: 152.713
+51200/69092	Loss: 156.490
+54400/69092	Loss: 151.307
+57600/69092	Loss: 154.355
+60800/69092	Loss: 152.788
+64000/69092	Loss: 150.361
+67200/69092	Loss: 151.625
+Training time 0:05:00.287694
+Epoch: 89 Average loss: 152.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 91)
+0/69092	Loss: 173.193
+3200/69092	Loss: 153.656
+6400/69092	Loss: 152.859
+9600/69092	Loss: 153.125
+12800/69092	Loss: 154.441
+16000/69092	Loss: 153.359
+19200/69092	Loss: 152.302
+22400/69092	Loss: 153.237
+25600/69092	Loss: 153.937
+28800/69092	Loss: 151.052
+32000/69092	Loss: 155.033
+35200/69092	Loss: 153.576
+38400/69092	Loss: 154.512
+41600/69092	Loss: 152.978
+44800/69092	Loss: 151.260
+48000/69092	Loss: 151.452
+51200/69092	Loss: 155.488
+54400/69092	Loss: 154.502
+57600/69092	Loss: 153.495
+60800/69092	Loss: 151.899
+64000/69092	Loss: 151.552
+67200/69092	Loss: 152.370
+Training time 0:05:07.109067
+Epoch: 90 Average loss: 153.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 92)
+0/69092	Loss: 138.774
+3200/69092	Loss: 151.979
+6400/69092	Loss: 152.541
+9600/69092	Loss: 153.044
+12800/69092	Loss: 154.177
+16000/69092	Loss: 151.911
+19200/69092	Loss: 153.195
+22400/69092	Loss: 152.966
+25600/69092	Loss: 151.744
+28800/69092	Loss: 151.799
+32000/69092	Loss: 151.514
+35200/69092	Loss: 154.238
+38400/69092	Loss: 153.472
+41600/69092	Loss: 152.963
+44800/69092	Loss: 153.216
+48000/69092	Loss: 150.231
+51200/69092	Loss: 154.025
+54400/69092	Loss: 153.853
+57600/69092	Loss: 151.647
+60800/69092	Loss: 153.048
+64000/69092	Loss: 151.840
+67200/69092	Loss: 153.029
+Training time 0:05:03.215848
+Epoch: 91 Average loss: 152.69
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 93)
+0/69092	Loss: 150.631
+3200/69092	Loss: 155.801
+6400/69092	Loss: 152.577
+9600/69092	Loss: 149.986
+12800/69092	Loss: 150.401
+16000/69092	Loss: 151.807
+19200/69092	Loss: 150.984
+22400/69092	Loss: 153.995
+25600/69092	Loss: 151.320
+28800/69092	Loss: 151.361
+32000/69092	Loss: 150.865
+35200/69092	Loss: 154.256
+38400/69092	Loss: 155.717
+41600/69092	Loss: 152.501
+44800/69092	Loss: 152.904
+48000/69092	Loss: 155.577
+51200/69092	Loss: 153.366
+54400/69092	Loss: 154.010
+57600/69092	Loss: 153.506
+60800/69092	Loss: 151.959
+64000/69092	Loss: 152.378
+67200/69092	Loss: 153.409
+Training time 0:05:02.467104
+Epoch: 92 Average loss: 152.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 94)
+0/69092	Loss: 137.588
+3200/69092	Loss: 154.422
+6400/69092	Loss: 153.005
+9600/69092	Loss: 152.546
+12800/69092	Loss: 151.804
+16000/69092	Loss: 152.492
+19200/69092	Loss: 149.259
+22400/69092	Loss: 152.988
+25600/69092	Loss: 154.934
+28800/69092	Loss: 154.740
+32000/69092	Loss: 153.126
+35200/69092	Loss: 151.362
+38400/69092	Loss: 153.404
+41600/69092	Loss: 152.740
+44800/69092	Loss: 152.464
+48000/69092	Loss: 153.898
+51200/69092	Loss: 152.364
+54400/69092	Loss: 152.889
+57600/69092	Loss: 150.061
+60800/69092	Loss: 153.786
+64000/69092	Loss: 154.129
+67200/69092	Loss: 151.211
+Training time 0:05:21.121234
+Epoch: 93 Average loss: 152.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 95)
+0/69092	Loss: 159.301
+3200/69092	Loss: 152.549
+6400/69092	Loss: 152.793
+9600/69092	Loss: 154.778
+12800/69092	Loss: 152.887
+16000/69092	Loss: 154.832
+19200/69092	Loss: 151.559
+22400/69092	Loss: 152.364
+25600/69092	Loss: 152.966
+28800/69092	Loss: 154.015
+32000/69092	Loss: 151.286
+35200/69092	Loss: 154.054
+38400/69092	Loss: 152.238
+41600/69092	Loss: 152.510
+44800/69092	Loss: 151.685
+48000/69092	Loss: 151.848
+51200/69092	Loss: 150.794
+54400/69092	Loss: 151.815
+57600/69092	Loss: 154.456
+60800/69092	Loss: 153.243
+64000/69092	Loss: 151.379
+67200/69092	Loss: 152.289
+Training time 0:05:05.718220
+Epoch: 94 Average loss: 152.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 96)
+0/69092	Loss: 148.685
+3200/69092	Loss: 153.959
+6400/69092	Loss: 154.756
+9600/69092	Loss: 153.469
+12800/69092	Loss: 151.996
+16000/69092	Loss: 151.456
+19200/69092	Loss: 153.570
+22400/69092	Loss: 151.253
+25600/69092	Loss: 153.916
+28800/69092	Loss: 151.222
+32000/69092	Loss: 153.491
+35200/69092	Loss: 151.802
+38400/69092	Loss: 151.863
+41600/69092	Loss: 152.482
+44800/69092	Loss: 157.120
+48000/69092	Loss: 153.743
+51200/69092	Loss: 153.713
+54400/69092	Loss: 151.019
+57600/69092	Loss: 152.423
+60800/69092	Loss: 153.373
+64000/69092	Loss: 152.027
+67200/69092	Loss: 153.569
+Training time 0:05:13.770123
+Epoch: 95 Average loss: 153.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 97)
+0/69092	Loss: 144.888
+3200/69092	Loss: 154.294
+6400/69092	Loss: 155.633
+9600/69092	Loss: 150.376
+12800/69092	Loss: 151.350
+16000/69092	Loss: 150.670
+19200/69092	Loss: 151.202
+22400/69092	Loss: 154.200
+25600/69092	Loss: 155.299
+28800/69092	Loss: 153.456
+32000/69092	Loss: 151.662
+35200/69092	Loss: 153.122
+38400/69092	Loss: 152.119
+41600/69092	Loss: 155.510
+44800/69092	Loss: 153.847
+48000/69092	Loss: 151.220
+51200/69092	Loss: 152.173
+54400/69092	Loss: 152.944
+57600/69092	Loss: 150.199
+60800/69092	Loss: 153.262
+64000/69092	Loss: 152.992
+67200/69092	Loss: 151.225
+Training time 0:05:14.512258
+Epoch: 96 Average loss: 152.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 98)
+0/69092	Loss: 145.908
+3200/69092	Loss: 156.202
+6400/69092	Loss: 153.557
+9600/69092	Loss: 153.879
+12800/69092	Loss: 151.261
+16000/69092	Loss: 151.924
+19200/69092	Loss: 155.570
+22400/69092	Loss: 149.156
+25600/69092	Loss: 153.326
+28800/69092	Loss: 153.375
+32000/69092	Loss: 150.357
+35200/69092	Loss: 149.965
+38400/69092	Loss: 152.330
+41600/69092	Loss: 155.561
+44800/69092	Loss: 153.511
+48000/69092	Loss: 155.264
+51200/69092	Loss: 153.119
+54400/69092	Loss: 153.118
+57600/69092	Loss: 150.697
+60800/69092	Loss: 151.908
+64000/69092	Loss: 152.887
+67200/69092	Loss: 152.033
+Training time 0:05:09.046628
+Epoch: 97 Average loss: 152.84
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 99)
+0/69092	Loss: 163.816
+3200/69092	Loss: 151.839
+6400/69092	Loss: 150.942
+9600/69092	Loss: 151.094
+12800/69092	Loss: 152.994
+16000/69092	Loss: 151.583
+19200/69092	Loss: 151.724
+22400/69092	Loss: 151.680
+25600/69092	Loss: 154.411
+28800/69092	Loss: 153.341
+32000/69092	Loss: 152.108
+35200/69092	Loss: 151.711
+38400/69092	Loss: 154.571
+41600/69092	Loss: 152.383
+44800/69092	Loss: 153.287
+48000/69092	Loss: 153.545
+51200/69092	Loss: 151.304
+54400/69092	Loss: 154.108
+57600/69092	Loss: 156.060
+60800/69092	Loss: 150.395
+64000/69092	Loss: 151.598
+67200/69092	Loss: 151.042
+Training time 0:05:17.460411
+Epoch: 98 Average loss: 152.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 100)
+0/69092	Loss: 142.495
+3200/69092	Loss: 152.631
+6400/69092	Loss: 153.448
+9600/69092	Loss: 154.208
+12800/69092	Loss: 152.384
+16000/69092	Loss: 152.287
+19200/69092	Loss: 153.442
+22400/69092	Loss: 152.089
+25600/69092	Loss: 151.928
+28800/69092	Loss: 152.185
+32000/69092	Loss: 154.098
+35200/69092	Loss: 152.993
+38400/69092	Loss: 151.704
+41600/69092	Loss: 151.850
+44800/69092	Loss: 153.045
+48000/69092	Loss: 151.880
+51200/69092	Loss: 153.108
+54400/69092	Loss: 149.770
+57600/69092	Loss: 153.855
+60800/69092	Loss: 152.210
+64000/69092	Loss: 152.729
+67200/69092	Loss: 151.185
+Training time 0:05:17.488419
+Epoch: 99 Average loss: 152.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 101)
+0/69092	Loss: 152.357
+3200/69092	Loss: 152.105
+6400/69092	Loss: 151.490
+9600/69092	Loss: 153.552
+12800/69092	Loss: 152.941
+16000/69092	Loss: 148.692
+19200/69092	Loss: 152.597
+22400/69092	Loss: 151.824
+25600/69092	Loss: 154.107
+28800/69092	Loss: 155.843
+32000/69092	Loss: 151.404
+35200/69092	Loss: 154.285
+38400/69092	Loss: 149.636
+41600/69092	Loss: 153.175
+44800/69092	Loss: 153.762
+48000/69092	Loss: 150.530
+51200/69092	Loss: 152.657
+54400/69092	Loss: 150.277
+57600/69092	Loss: 151.790
+60800/69092	Loss: 151.414
+64000/69092	Loss: 152.574
+67200/69092	Loss: 154.462
+Training time 0:05:04.079356
+Epoch: 100 Average loss: 152.46
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 102)
+0/69092	Loss: 150.764
+3200/69092	Loss: 156.045
+6400/69092	Loss: 153.180
+9600/69092	Loss: 150.329
+12800/69092	Loss: 151.842
+16000/69092	Loss: 151.438
+19200/69092	Loss: 149.832
+22400/69092	Loss: 152.585
+25600/69092	Loss: 155.156
+28800/69092	Loss: 152.419
+32000/69092	Loss: 154.063
+35200/69092	Loss: 151.402
+38400/69092	Loss: 154.315
+41600/69092	Loss: 150.096
+44800/69092	Loss: 152.664
+48000/69092	Loss: 154.527
+51200/69092	Loss: 153.800
+54400/69092	Loss: 152.519
+57600/69092	Loss: 153.290
+60800/69092	Loss: 155.294
+64000/69092	Loss: 152.205
+67200/69092	Loss: 152.099
+Training time 0:04:59.508990
+Epoch: 101 Average loss: 152.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 103)
+0/69092	Loss: 164.819
+3200/69092	Loss: 154.112
+6400/69092	Loss: 153.043
+9600/69092	Loss: 153.420
+12800/69092	Loss: 153.918
+16000/69092	Loss: 151.298
+19200/69092	Loss: 153.300
+22400/69092	Loss: 152.069
+25600/69092	Loss: 152.772
+28800/69092	Loss: 153.973
+32000/69092	Loss: 153.526
+35200/69092	Loss: 154.607
+38400/69092	Loss: 153.641
+41600/69092	Loss: 152.778
+44800/69092	Loss: 150.596
+48000/69092	Loss: 150.833
+51200/69092	Loss: 151.411
+54400/69092	Loss: 151.794
+57600/69092	Loss: 152.379
+60800/69092	Loss: 150.641
+64000/69092	Loss: 153.430
+67200/69092	Loss: 153.745
+Training time 0:04:58.759844
+Epoch: 102 Average loss: 152.79
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 104)
+0/69092	Loss: 155.212
+3200/69092	Loss: 150.710
+6400/69092	Loss: 150.372
+9600/69092	Loss: 152.780
+12800/69092	Loss: 154.372
+16000/69092	Loss: 152.948
+19200/69092	Loss: 154.913
+22400/69092	Loss: 154.924
+25600/69092	Loss: 151.664
+28800/69092	Loss: 153.776
+32000/69092	Loss: 151.676
+35200/69092	Loss: 152.477
+38400/69092	Loss: 149.843
+41600/69092	Loss: 151.344
+44800/69092	Loss: 153.824
+48000/69092	Loss: 151.946
+51200/69092	Loss: 152.546
+54400/69092	Loss: 151.850
+57600/69092	Loss: 153.006
+60800/69092	Loss: 150.892
+64000/69092	Loss: 153.496
+67200/69092	Loss: 151.937
+Training time 0:05:01.081316
+Epoch: 103 Average loss: 152.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 105)
+0/69092	Loss: 158.482
+3200/69092	Loss: 155.715
+6400/69092	Loss: 150.633
+9600/69092	Loss: 150.715
+12800/69092	Loss: 151.132
+16000/69092	Loss: 152.029
+19200/69092	Loss: 152.349
+22400/69092	Loss: 149.221
+25600/69092	Loss: 152.625
+28800/69092	Loss: 150.371
+32000/69092	Loss: 152.691
+35200/69092	Loss: 152.896
+38400/69092	Loss: 156.667
+41600/69092	Loss: 152.666
+44800/69092	Loss: 151.124
+48000/69092	Loss: 153.126
+51200/69092	Loss: 152.280
+54400/69092	Loss: 153.000
+57600/69092	Loss: 151.603
+60800/69092	Loss: 153.182
+64000/69092	Loss: 153.217
+67200/69092	Loss: 153.639
+Training time 0:05:05.078701
+Epoch: 104 Average loss: 152.43
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 106)
+0/69092	Loss: 162.693
+3200/69092	Loss: 152.524
+6400/69092	Loss: 152.094
+9600/69092	Loss: 152.530
+12800/69092	Loss: 151.146
+16000/69092	Loss: 151.000
+19200/69092	Loss: 153.157
+22400/69092	Loss: 152.684
+25600/69092	Loss: 152.236
+28800/69092	Loss: 151.991
+32000/69092	Loss: 149.786
+35200/69092	Loss: 154.276
+38400/69092	Loss: 153.001
+41600/69092	Loss: 152.500
+44800/69092	Loss: 150.808
+48000/69092	Loss: 152.246
+51200/69092	Loss: 152.803
+54400/69092	Loss: 150.922
+57600/69092	Loss: 151.247
+60800/69092	Loss: 154.350
+64000/69092	Loss: 154.206
+67200/69092	Loss: 152.359
+Training time 0:05:08.495428
+Epoch: 105 Average loss: 152.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 107)
+0/69092	Loss: 157.806
+3200/69092	Loss: 154.320
+6400/69092	Loss: 154.119
+9600/69092	Loss: 152.087
+12800/69092	Loss: 151.915
+16000/69092	Loss: 151.474
+19200/69092	Loss: 150.175
+22400/69092	Loss: 152.247
+25600/69092	Loss: 155.800
+28800/69092	Loss: 152.738
+32000/69092	Loss: 155.295
+35200/69092	Loss: 153.964
+38400/69092	Loss: 155.813
+41600/69092	Loss: 154.893
+44800/69092	Loss: 151.783
+48000/69092	Loss: 150.853
+51200/69092	Loss: 150.650
+54400/69092	Loss: 153.105
+57600/69092	Loss: 150.999
+60800/69092	Loss: 151.310
+64000/69092	Loss: 152.381
+67200/69092	Loss: 153.032
+Training time 0:05:01.722781
+Epoch: 106 Average loss: 152.78
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 108)
+0/69092	Loss: 164.493
+3200/69092	Loss: 151.558
+6400/69092	Loss: 153.669
+9600/69092	Loss: 153.055
+12800/69092	Loss: 152.687
+16000/69092	Loss: 150.611
+19200/69092	Loss: 147.977
+22400/69092	Loss: 152.367
+25600/69092	Loss: 152.304
+28800/69092	Loss: 153.159
+32000/69092	Loss: 152.434
+35200/69092	Loss: 151.772
+38400/69092	Loss: 154.893
+41600/69092	Loss: 154.256
+44800/69092	Loss: 148.339
+48000/69092	Loss: 154.205
+51200/69092	Loss: 152.878
+54400/69092	Loss: 152.201
+57600/69092	Loss: 153.168
+60800/69092	Loss: 152.438
+64000/69092	Loss: 155.415
+67200/69092	Loss: 153.441
+Training time 0:05:02.547415
+Epoch: 107 Average loss: 152.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 109)
+0/69092	Loss: 148.809
+3200/69092	Loss: 150.910
+6400/69092	Loss: 151.943
+9600/69092	Loss: 152.889
+12800/69092	Loss: 152.411
+16000/69092	Loss: 153.348
+19200/69092	Loss: 152.371
+22400/69092	Loss: 152.439
+25600/69092	Loss: 152.269
+28800/69092	Loss: 152.576
+32000/69092	Loss: 151.970
+35200/69092	Loss: 150.969
+38400/69092	Loss: 151.487
+41600/69092	Loss: 152.641
+44800/69092	Loss: 151.826
+48000/69092	Loss: 151.155
+51200/69092	Loss: 152.168
+54400/69092	Loss: 149.807
+57600/69092	Loss: 155.031
+60800/69092	Loss: 153.892
+64000/69092	Loss: 151.999
+67200/69092	Loss: 151.738
+Training time 0:05:04.695620
+Epoch: 108 Average loss: 152.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 110)
+0/69092	Loss: 158.586
+3200/69092	Loss: 151.509
+6400/69092	Loss: 151.652
+9600/69092	Loss: 152.892
+12800/69092	Loss: 153.234
+16000/69092	Loss: 153.368
+19200/69092	Loss: 153.232
+22400/69092	Loss: 152.319
+25600/69092	Loss: 149.443
+28800/69092	Loss: 150.087
+32000/69092	Loss: 154.087
+35200/69092	Loss: 155.178
+38400/69092	Loss: 150.814
+41600/69092	Loss: 153.138
+44800/69092	Loss: 151.443
+48000/69092	Loss: 150.927
+51200/69092	Loss: 151.618
+54400/69092	Loss: 153.429
+57600/69092	Loss: 153.643
+60800/69092	Loss: 152.830
+64000/69092	Loss: 152.977
+67200/69092	Loss: 151.568
+Training time 0:05:02.439395
+Epoch: 109 Average loss: 152.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 111)
+0/69092	Loss: 160.520
+3200/69092	Loss: 153.134
+6400/69092	Loss: 151.785
+9600/69092	Loss: 151.450
+12800/69092	Loss: 153.406
+16000/69092	Loss: 150.892
+19200/69092	Loss: 152.349
+22400/69092	Loss: 151.413
+25600/69092	Loss: 155.517
+28800/69092	Loss: 154.485
+32000/69092	Loss: 154.868
+35200/69092	Loss: 153.424
+38400/69092	Loss: 153.438
+41600/69092	Loss: 152.351
+44800/69092	Loss: 154.254
+48000/69092	Loss: 151.103
+51200/69092	Loss: 152.773
+54400/69092	Loss: 149.550
+57600/69092	Loss: 152.296
+60800/69092	Loss: 151.777
+64000/69092	Loss: 151.505
+67200/69092	Loss: 151.820
+Training time 0:05:06.358995
+Epoch: 110 Average loss: 152.60
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 112)
+0/69092	Loss: 144.156
+3200/69092	Loss: 152.230
+6400/69092	Loss: 151.840
+9600/69092	Loss: 153.787
+12800/69092	Loss: 151.389
+16000/69092	Loss: 154.197
+19200/69092	Loss: 154.010
+22400/69092	Loss: 153.567
+25600/69092	Loss: 151.977
+28800/69092	Loss: 151.630
+32000/69092	Loss: 148.550
+35200/69092	Loss: 154.312
+38400/69092	Loss: 154.578
+41600/69092	Loss: 153.901
+44800/69092	Loss: 150.806
+48000/69092	Loss: 150.984
+51200/69092	Loss: 154.965
+54400/69092	Loss: 152.275
+57600/69092	Loss: 149.143
+60800/69092	Loss: 149.454
+64000/69092	Loss: 151.215
+67200/69092	Loss: 151.462
+Training time 0:05:02.575174
+Epoch: 111 Average loss: 152.20
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 113)
+0/69092	Loss: 137.242
+3200/69092	Loss: 154.129
+6400/69092	Loss: 152.299
+9600/69092	Loss: 151.966
+12800/69092	Loss: 154.456
+16000/69092	Loss: 154.436
+19200/69092	Loss: 152.158
+22400/69092	Loss: 150.708
+25600/69092	Loss: 152.748
+28800/69092	Loss: 151.616
+32000/69092	Loss: 151.754
+35200/69092	Loss: 149.865
+38400/69092	Loss: 152.191
+41600/69092	Loss: 152.820
+44800/69092	Loss: 150.216
+48000/69092	Loss: 149.721
+51200/69092	Loss: 152.029
+54400/69092	Loss: 155.722
+57600/69092	Loss: 151.542
+60800/69092	Loss: 152.518
+64000/69092	Loss: 154.387
+67200/69092	Loss: 151.074
+Training time 0:05:02.396935
+Epoch: 112 Average loss: 152.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 114)
+0/69092	Loss: 165.902
+3200/69092	Loss: 150.377
+6400/69092	Loss: 151.814
+9600/69092	Loss: 153.642
+12800/69092	Loss: 151.890
+16000/69092	Loss: 153.804
+19200/69092	Loss: 150.675
+22400/69092	Loss: 153.077
+25600/69092	Loss: 152.290
+28800/69092	Loss: 155.946
+32000/69092	Loss: 151.610
+35200/69092	Loss: 152.848
+38400/69092	Loss: 151.772
+41600/69092	Loss: 150.248
+44800/69092	Loss: 153.183
+48000/69092	Loss: 151.950
+51200/69092	Loss: 153.444
+54400/69092	Loss: 154.098
+57600/69092	Loss: 153.529
+60800/69092	Loss: 150.748
+64000/69092	Loss: 152.207
+67200/69092	Loss: 151.174
+Training time 0:05:00.621314
+Epoch: 113 Average loss: 152.43
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 115)
+0/69092	Loss: 147.808
+3200/69092	Loss: 153.776
+6400/69092	Loss: 151.598
+9600/69092	Loss: 148.984
+12800/69092	Loss: 154.844
+16000/69092	Loss: 155.733
+19200/69092	Loss: 154.050
+22400/69092	Loss: 150.272
+25600/69092	Loss: 150.759
+28800/69092	Loss: 153.905
+32000/69092	Loss: 155.563
+35200/69092	Loss: 152.909
+38400/69092	Loss: 151.906
+41600/69092	Loss: 149.198
+44800/69092	Loss: 151.171
+48000/69092	Loss: 154.471
+51200/69092	Loss: 151.954
+54400/69092	Loss: 149.824
+57600/69092	Loss: 153.078
+60800/69092	Loss: 149.280
+64000/69092	Loss: 152.360
+67200/69092	Loss: 153.188
+Training time 0:05:07.754681
+Epoch: 114 Average loss: 152.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 116)
+0/69092	Loss: 162.564
+3200/69092	Loss: 152.357
+6400/69092	Loss: 150.666
+9600/69092	Loss: 152.617
+12800/69092	Loss: 152.975
+16000/69092	Loss: 154.865
+19200/69092	Loss: 153.846
+22400/69092	Loss: 150.764
+25600/69092	Loss: 150.878
+28800/69092	Loss: 150.933
+32000/69092	Loss: 152.093
+35200/69092	Loss: 152.873
+38400/69092	Loss: 152.629
+41600/69092	Loss: 151.633
+44800/69092	Loss: 151.503
+48000/69092	Loss: 152.399
+51200/69092	Loss: 154.762
+54400/69092	Loss: 153.077
+57600/69092	Loss: 149.121
+60800/69092	Loss: 151.332
+64000/69092	Loss: 154.033
+67200/69092	Loss: 151.749
+Training time 0:05:02.491970
+Epoch: 115 Average loss: 152.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 117)
+0/69092	Loss: 143.778
+3200/69092	Loss: 151.167
+6400/69092	Loss: 149.055
+9600/69092	Loss: 154.806
+12800/69092	Loss: 151.053
+16000/69092	Loss: 151.978
+19200/69092	Loss: 153.226
+22400/69092	Loss: 152.385
+25600/69092	Loss: 154.677
+28800/69092	Loss: 150.408
+32000/69092	Loss: 152.199
+35200/69092	Loss: 152.255
+38400/69092	Loss: 154.065
+41600/69092	Loss: 150.001
+44800/69092	Loss: 152.624
+48000/69092	Loss: 151.635
+51200/69092	Loss: 153.096
+54400/69092	Loss: 155.753
+57600/69092	Loss: 152.518
+60800/69092	Loss: 153.385
+64000/69092	Loss: 150.671
+67200/69092	Loss: 149.546
+Training time 0:05:04.539182
+Epoch: 116 Average loss: 152.24
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 118)
+0/69092	Loss: 145.364
+3200/69092	Loss: 155.135
+6400/69092	Loss: 149.908
+9600/69092	Loss: 151.907
+12800/69092	Loss: 148.680
+16000/69092	Loss: 151.505
+19200/69092	Loss: 152.239
+22400/69092	Loss: 151.608
+25600/69092	Loss: 149.992
+28800/69092	Loss: 152.539
+32000/69092	Loss: 155.070
+35200/69092	Loss: 151.357
+38400/69092	Loss: 154.963
+41600/69092	Loss: 151.068
+44800/69092	Loss: 154.297
+48000/69092	Loss: 152.450
+51200/69092	Loss: 152.996
+54400/69092	Loss: 149.902
+57600/69092	Loss: 153.720
+60800/69092	Loss: 152.241
+64000/69092	Loss: 152.892
+67200/69092	Loss: 152.491
+Training time 0:05:04.691764
+Epoch: 117 Average loss: 152.29
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 119)
+0/69092	Loss: 158.828
+3200/69092	Loss: 153.695
+6400/69092	Loss: 154.094
+9600/69092	Loss: 150.939
+12800/69092	Loss: 153.622
+16000/69092	Loss: 152.381
+19200/69092	Loss: 149.955
+22400/69092	Loss: 149.864
+25600/69092	Loss: 153.791
+28800/69092	Loss: 150.729
+32000/69092	Loss: 152.179
+35200/69092	Loss: 150.721
+38400/69092	Loss: 151.006
+41600/69092	Loss: 150.271
+44800/69092	Loss: 149.664
+48000/69092	Loss: 152.665
+51200/69092	Loss: 151.087
+54400/69092	Loss: 153.225
+57600/69092	Loss: 157.378
+60800/69092	Loss: 152.752
+64000/69092	Loss: 155.546
+67200/69092	Loss: 153.239
+Training time 0:05:11.812518
+Epoch: 118 Average loss: 152.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_20/checkpoints/last' (iter 120)
+0/69092	Loss: 156.434
+3200/69092	Loss: 151.044
+6400/69092	Loss: 152.033
+9600/69092	Loss: 152.175
+12800/69092	Loss: 153.270
+16000/69092	Loss: 154.147
diff --git a/OAR.2068290.stderr b/OAR.2068290.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..2c95ab0472390ff0f76d8b29d7ea3e05a640e0e0
--- /dev/null
+++ b/OAR.2068290.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-25 02:59:26] Job 2068290 KILLED ##
diff --git a/OAR.2068290.stdout b/OAR.2068290.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..37eedf72076eee31f9ac96ad1b88fda3f5d4ba58
--- /dev/null
+++ b/OAR.2068290.stdout
@@ -0,0 +1,2690 @@
+Namespace(batch_size=64, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_64_ls_5', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=5, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/beta_VAE_bs_64_ls_5
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=10, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=5, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 761485
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last (iter 2)'
+0/69092	Loss: 206.284
+3200/69092	Loss: 191.772
+6400/69092	Loss: 186.580
+9600/69092	Loss: 187.553
+12800/69092	Loss: 179.883
+16000/69092	Loss: 182.176
+19200/69092	Loss: 187.932
+22400/69092	Loss: 184.194
+25600/69092	Loss: 180.651
+28800/69092	Loss: 178.819
+32000/69092	Loss: 175.748
+35200/69092	Loss: 185.757
+38400/69092	Loss: 184.852
+41600/69092	Loss: 178.318
+44800/69092	Loss: 180.974
+48000/69092	Loss: 176.737
+51200/69092	Loss: 184.122
+54400/69092	Loss: 177.643
+57600/69092	Loss: 179.564
+60800/69092	Loss: 179.818
+64000/69092	Loss: 178.485
+67200/69092	Loss: 180.491
+Training time 0:05:42.886828
+Epoch: 1 Average loss: 181.99
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 3)
+0/69092	Loss: 185.484
+3200/69092	Loss: 177.918
+6400/69092	Loss: 178.950
+9600/69092	Loss: 175.949
+12800/69092	Loss: 180.625
+16000/69092	Loss: 179.795
+19200/69092	Loss: 176.926
+22400/69092	Loss: 177.637
+25600/69092	Loss: 182.322
+28800/69092	Loss: 175.535
+32000/69092	Loss: 179.663
+35200/69092	Loss: 178.396
+38400/69092	Loss: 177.509
+41600/69092	Loss: 178.524
+44800/69092	Loss: 180.868
+48000/69092	Loss: 179.206
+51200/69092	Loss: 175.411
+54400/69092	Loss: 175.164
+57600/69092	Loss: 175.484
+60800/69092	Loss: 177.437
+64000/69092	Loss: 183.367
+67200/69092	Loss: 175.420
+Training time 0:05:50.560333
+Epoch: 2 Average loss: 178.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 4)
+0/69092	Loss: 165.081
+3200/69092	Loss: 177.282
+6400/69092	Loss: 177.009
+9600/69092	Loss: 175.280
+12800/69092	Loss: 173.575
+16000/69092	Loss: 177.322
+19200/69092	Loss: 173.360
+22400/69092	Loss: 179.046
+25600/69092	Loss: 179.528
+28800/69092	Loss: 179.841
+32000/69092	Loss: 175.505
+35200/69092	Loss: 173.406
+38400/69092	Loss: 178.157
+41600/69092	Loss: 181.415
+44800/69092	Loss: 177.471
+48000/69092	Loss: 178.392
+51200/69092	Loss: 179.621
+54400/69092	Loss: 179.774
+57600/69092	Loss: 179.728
+60800/69092	Loss: 176.719
+64000/69092	Loss: 177.497
+67200/69092	Loss: 174.821
+Training time 0:06:03.416294
+Epoch: 3 Average loss: 177.34
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 5)
+0/69092	Loss: 177.347
+3200/69092	Loss: 176.306
+6400/69092	Loss: 174.916
+9600/69092	Loss: 178.468
+12800/69092	Loss: 175.838
+16000/69092	Loss: 179.554
+19200/69092	Loss: 175.765
+22400/69092	Loss: 178.247
+25600/69092	Loss: 173.473
+28800/69092	Loss: 177.255
+32000/69092	Loss: 175.557
+35200/69092	Loss: 177.057
+38400/69092	Loss: 176.364
+41600/69092	Loss: 177.415
+44800/69092	Loss: 176.233
+48000/69092	Loss: 176.041
+51200/69092	Loss: 177.231
+54400/69092	Loss: 177.484
+57600/69092	Loss: 177.772
+60800/69092	Loss: 179.202
+64000/69092	Loss: 177.597
+67200/69092	Loss: 176.004
+Training time 0:05:43.414806
+Epoch: 4 Average loss: 176.76
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 6)
+0/69092	Loss: 192.912
+3200/69092	Loss: 177.919
+6400/69092	Loss: 176.670
+9600/69092	Loss: 176.543
+12800/69092	Loss: 176.900
+16000/69092	Loss: 173.263
+19200/69092	Loss: 177.434
+22400/69092	Loss: 174.874
+25600/69092	Loss: 178.713
+28800/69092	Loss: 174.574
+32000/69092	Loss: 174.557
+35200/69092	Loss: 178.241
+38400/69092	Loss: 174.689
+41600/69092	Loss: 174.315
+44800/69092	Loss: 178.442
+48000/69092	Loss: 178.501
+51200/69092	Loss: 176.855
+54400/69092	Loss: 175.434
+57600/69092	Loss: 176.113
+60800/69092	Loss: 177.818
+64000/69092	Loss: 172.621
+67200/69092	Loss: 176.531
+Training time 0:05:29.381799
+Epoch: 5 Average loss: 176.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 7)
+0/69092	Loss: 185.043
+3200/69092	Loss: 172.702
+6400/69092	Loss: 179.136
+9600/69092	Loss: 176.406
+12800/69092	Loss: 175.405
+16000/69092	Loss: 175.605
+19200/69092	Loss: 174.544
+22400/69092	Loss: 172.518
+25600/69092	Loss: 175.201
+28800/69092	Loss: 173.957
+32000/69092	Loss: 178.255
+35200/69092	Loss: 175.895
+38400/69092	Loss: 174.356
+41600/69092	Loss: 174.803
+44800/69092	Loss: 175.339
+48000/69092	Loss: 177.531
+51200/69092	Loss: 173.707
+54400/69092	Loss: 174.617
+57600/69092	Loss: 179.951
+60800/69092	Loss: 176.160
+64000/69092	Loss: 177.427
+67200/69092	Loss: 174.210
+Training time 0:05:32.869675
+Epoch: 6 Average loss: 175.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 8)
+0/69092	Loss: 175.536
+3200/69092	Loss: 176.146
+6400/69092	Loss: 182.585
+9600/69092	Loss: 179.227
+12800/69092	Loss: 173.100
+16000/69092	Loss: 177.521
+19200/69092	Loss: 176.860
+22400/69092	Loss: 174.105
+25600/69092	Loss: 175.694
+28800/69092	Loss: 176.292
+32000/69092	Loss: 172.337
+35200/69092	Loss: 173.839
+38400/69092	Loss: 175.045
+41600/69092	Loss: 178.532
+44800/69092	Loss: 178.498
+48000/69092	Loss: 176.122
+51200/69092	Loss: 172.301
+54400/69092	Loss: 173.076
+57600/69092	Loss: 175.092
+60800/69092	Loss: 174.465
+64000/69092	Loss: 170.611
+67200/69092	Loss: 177.806
+Training time 0:05:26.216188
+Epoch: 7 Average loss: 175.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 9)
+0/69092	Loss: 185.987
+3200/69092	Loss: 178.445
+6400/69092	Loss: 170.073
+9600/69092	Loss: 174.401
+12800/69092	Loss: 179.827
+16000/69092	Loss: 175.722
+19200/69092	Loss: 174.884
+22400/69092	Loss: 174.974
+25600/69092	Loss: 172.651
+28800/69092	Loss: 172.641
+32000/69092	Loss: 178.871
+35200/69092	Loss: 173.840
+38400/69092	Loss: 175.191
+41600/69092	Loss: 175.336
+44800/69092	Loss: 177.951
+48000/69092	Loss: 172.772
+51200/69092	Loss: 177.344
+54400/69092	Loss: 174.130
+57600/69092	Loss: 173.331
+60800/69092	Loss: 176.248
+64000/69092	Loss: 174.978
+67200/69092	Loss: 175.957
+Training time 0:05:32.889948
+Epoch: 8 Average loss: 175.34
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 10)
+0/69092	Loss: 195.713
+3200/69092	Loss: 172.833
+6400/69092	Loss: 173.741
+9600/69092	Loss: 177.423
+12800/69092	Loss: 179.955
+16000/69092	Loss: 174.259
+19200/69092	Loss: 177.071
+22400/69092	Loss: 173.643
+25600/69092	Loss: 173.985
+28800/69092	Loss: 177.484
+32000/69092	Loss: 175.859
+35200/69092	Loss: 172.818
+38400/69092	Loss: 178.825
+41600/69092	Loss: 174.300
+44800/69092	Loss: 175.437
+48000/69092	Loss: 175.735
+51200/69092	Loss: 174.531
+54400/69092	Loss: 173.547
+57600/69092	Loss: 174.477
+60800/69092	Loss: 175.797
+64000/69092	Loss: 173.868
+67200/69092	Loss: 174.690
+Training time 0:05:29.560786
+Epoch: 9 Average loss: 175.34
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 11)
+0/69092	Loss: 173.612
+3200/69092	Loss: 174.081
+6400/69092	Loss: 174.155
+9600/69092	Loss: 173.137
+12800/69092	Loss: 176.574
+16000/69092	Loss: 177.726
+19200/69092	Loss: 174.834
+22400/69092	Loss: 175.774
+25600/69092	Loss: 176.048
+28800/69092	Loss: 176.829
+32000/69092	Loss: 175.205
+35200/69092	Loss: 175.138
+38400/69092	Loss: 174.141
+41600/69092	Loss: 173.852
+44800/69092	Loss: 175.199
+48000/69092	Loss: 179.661
+51200/69092	Loss: 174.070
+54400/69092	Loss: 173.991
+57600/69092	Loss: 172.956
+60800/69092	Loss: 173.462
+64000/69092	Loss: 176.472
+67200/69092	Loss: 173.347
+Training time 0:05:42.417051
+Epoch: 10 Average loss: 175.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 12)
+0/69092	Loss: 163.116
+3200/69092	Loss: 175.304
+6400/69092	Loss: 177.005
+9600/69092	Loss: 173.286
+12800/69092	Loss: 174.734
+16000/69092	Loss: 174.939
+19200/69092	Loss: 173.188
+22400/69092	Loss: 173.347
+25600/69092	Loss: 175.969
+28800/69092	Loss: 173.679
+32000/69092	Loss: 175.613
+35200/69092	Loss: 177.046
+38400/69092	Loss: 172.615
+41600/69092	Loss: 176.622
+44800/69092	Loss: 174.378
+48000/69092	Loss: 172.333
+51200/69092	Loss: 174.156
+54400/69092	Loss: 178.344
+57600/69092	Loss: 174.554
+60800/69092	Loss: 171.129
+64000/69092	Loss: 173.789
+67200/69092	Loss: 174.153
+Training time 0:05:38.281182
+Epoch: 11 Average loss: 174.53
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 13)
+0/69092	Loss: 167.593
+3200/69092	Loss: 172.577
+6400/69092	Loss: 175.898
+9600/69092	Loss: 173.126
+12800/69092	Loss: 174.301
+16000/69092	Loss: 173.796
+19200/69092	Loss: 176.480
+22400/69092	Loss: 176.278
+25600/69092	Loss: 174.386
+28800/69092	Loss: 175.487
+32000/69092	Loss: 172.144
+35200/69092	Loss: 168.589
+38400/69092	Loss: 178.079
+41600/69092	Loss: 178.203
+44800/69092	Loss: 172.678
+48000/69092	Loss: 175.696
+51200/69092	Loss: 173.820
+54400/69092	Loss: 172.014
+57600/69092	Loss: 174.640
+60800/69092	Loss: 177.710
+64000/69092	Loss: 175.304
+67200/69092	Loss: 171.286
+Training time 0:05:22.413960
+Epoch: 12 Average loss: 174.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 14)
+0/69092	Loss: 180.297
+3200/69092	Loss: 171.896
+6400/69092	Loss: 173.761
+9600/69092	Loss: 176.759
+12800/69092	Loss: 174.684
+16000/69092	Loss: 173.839
+19200/69092	Loss: 174.083
+22400/69092	Loss: 175.820
+25600/69092	Loss: 176.575
+28800/69092	Loss: 173.056
+32000/69092	Loss: 173.990
+35200/69092	Loss: 171.532
+38400/69092	Loss: 177.350
+41600/69092	Loss: 174.512
+44800/69092	Loss: 175.190
+48000/69092	Loss: 171.103
+51200/69092	Loss: 177.047
+54400/69092	Loss: 177.925
+57600/69092	Loss: 173.641
+60800/69092	Loss: 176.523
+64000/69092	Loss: 173.404
+67200/69092	Loss: 172.554
+Training time 0:05:33.331409
+Epoch: 13 Average loss: 174.60
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 15)
+0/69092	Loss: 190.760
+3200/69092	Loss: 172.674
+6400/69092	Loss: 172.813
+9600/69092	Loss: 173.694
+12800/69092	Loss: 175.784
+16000/69092	Loss: 175.043
+19200/69092	Loss: 176.451
+22400/69092	Loss: 172.945
+25600/69092	Loss: 172.757
+28800/69092	Loss: 177.959
+32000/69092	Loss: 172.057
+35200/69092	Loss: 173.982
+38400/69092	Loss: 174.324
+41600/69092	Loss: 173.081
+44800/69092	Loss: 177.249
+48000/69092	Loss: 173.428
+51200/69092	Loss: 175.714
+54400/69092	Loss: 173.761
+57600/69092	Loss: 172.590
+60800/69092	Loss: 175.677
+64000/69092	Loss: 172.918
+67200/69092	Loss: 173.185
+Training time 0:05:22.947126
+Epoch: 14 Average loss: 174.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 16)
+0/69092	Loss: 167.152
+3200/69092	Loss: 175.923
+6400/69092	Loss: 173.443
+9600/69092	Loss: 175.274
+12800/69092	Loss: 173.762
+16000/69092	Loss: 176.352
+19200/69092	Loss: 172.607
+22400/69092	Loss: 172.997
+25600/69092	Loss: 178.061
+28800/69092	Loss: 172.353
+32000/69092	Loss: 175.860
+35200/69092	Loss: 171.380
+38400/69092	Loss: 173.802
+41600/69092	Loss: 177.668
+44800/69092	Loss: 175.795
+48000/69092	Loss: 173.208
+51200/69092	Loss: 175.804
+54400/69092	Loss: 170.799
+57600/69092	Loss: 173.943
+60800/69092	Loss: 176.869
+64000/69092	Loss: 173.862
+67200/69092	Loss: 171.966
+Training time 0:05:44.882489
+Epoch: 15 Average loss: 174.33
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 17)
+0/69092	Loss: 175.757
+3200/69092	Loss: 175.039
+6400/69092	Loss: 173.789
+9600/69092	Loss: 176.758
+12800/69092	Loss: 176.867
+16000/69092	Loss: 176.634
+19200/69092	Loss: 172.219
+22400/69092	Loss: 171.303
+25600/69092	Loss: 171.701
+28800/69092	Loss: 173.745
+32000/69092	Loss: 173.220
+35200/69092	Loss: 172.173
+38400/69092	Loss: 174.064
+41600/69092	Loss: 173.507
+44800/69092	Loss: 174.430
+48000/69092	Loss: 175.702
+51200/69092	Loss: 168.808
+54400/69092	Loss: 175.641
+57600/69092	Loss: 170.829
+60800/69092	Loss: 177.189
+64000/69092	Loss: 171.159
+67200/69092	Loss: 175.589
+Training time 0:05:51.200178
+Epoch: 16 Average loss: 173.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 18)
+0/69092	Loss: 158.718
+3200/69092	Loss: 173.612
+6400/69092	Loss: 177.566
+9600/69092	Loss: 174.291
+12800/69092	Loss: 169.936
+16000/69092	Loss: 170.902
+19200/69092	Loss: 177.548
+22400/69092	Loss: 174.334
+25600/69092	Loss: 175.364
+28800/69092	Loss: 173.103
+32000/69092	Loss: 176.042
+35200/69092	Loss: 177.162
+38400/69092	Loss: 176.477
+41600/69092	Loss: 173.526
+44800/69092	Loss: 171.050
+48000/69092	Loss: 175.433
+51200/69092	Loss: 173.217
+54400/69092	Loss: 173.919
+57600/69092	Loss: 173.317
+60800/69092	Loss: 171.425
+64000/69092	Loss: 172.770
+67200/69092	Loss: 171.839
+Training time 0:05:37.227078
+Epoch: 17 Average loss: 173.91
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 19)
+0/69092	Loss: 175.521
+3200/69092	Loss: 169.495
+6400/69092	Loss: 175.040
+9600/69092	Loss: 174.591
+12800/69092	Loss: 173.541
+16000/69092	Loss: 174.730
+19200/69092	Loss: 176.340
+22400/69092	Loss: 170.779
+25600/69092	Loss: 174.685
+28800/69092	Loss: 173.111
+32000/69092	Loss: 175.287
+35200/69092	Loss: 177.107
+38400/69092	Loss: 173.228
+41600/69092	Loss: 173.567
+44800/69092	Loss: 174.867
+48000/69092	Loss: 176.990
+51200/69092	Loss: 176.325
+54400/69092	Loss: 172.071
+57600/69092	Loss: 169.826
+60800/69092	Loss: 176.096
+64000/69092	Loss: 171.550
+67200/69092	Loss: 174.078
+Training time 0:06:00.896832
+Epoch: 18 Average loss: 173.96
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 20)
+0/69092	Loss: 181.706
+3200/69092	Loss: 174.942
+6400/69092	Loss: 174.335
+9600/69092	Loss: 175.405
+12800/69092	Loss: 174.920
+16000/69092	Loss: 174.579
+19200/69092	Loss: 174.273
+22400/69092	Loss: 175.236
+25600/69092	Loss: 172.553
+28800/69092	Loss: 173.624
+32000/69092	Loss: 173.970
+35200/69092	Loss: 174.625
+38400/69092	Loss: 176.944
+41600/69092	Loss: 177.543
+44800/69092	Loss: 171.156
+48000/69092	Loss: 175.112
+51200/69092	Loss: 168.709
+54400/69092	Loss: 173.187
+57600/69092	Loss: 173.014
+60800/69092	Loss: 173.705
+64000/69092	Loss: 172.995
+67200/69092	Loss: 172.727
+Training time 0:05:33.461102
+Epoch: 19 Average loss: 173.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 21)
+0/69092	Loss: 181.324
+3200/69092	Loss: 175.460
+6400/69092	Loss: 173.600
+9600/69092	Loss: 172.653
+12800/69092	Loss: 176.877
+16000/69092	Loss: 173.990
+19200/69092	Loss: 175.041
+22400/69092	Loss: 173.715
+25600/69092	Loss: 174.660
+28800/69092	Loss: 174.793
+32000/69092	Loss: 174.699
+35200/69092	Loss: 170.826
+38400/69092	Loss: 173.354
+41600/69092	Loss: 172.968
+44800/69092	Loss: 171.330
+48000/69092	Loss: 173.645
+51200/69092	Loss: 173.527
+54400/69092	Loss: 174.024
+57600/69092	Loss: 172.847
+60800/69092	Loss: 170.919
+64000/69092	Loss: 173.528
+67200/69092	Loss: 176.333
+Training time 0:05:41.627131
+Epoch: 20 Average loss: 173.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 22)
+0/69092	Loss: 175.522
+3200/69092	Loss: 171.681
+6400/69092	Loss: 174.306
+9600/69092	Loss: 173.824
+12800/69092	Loss: 173.190
+16000/69092	Loss: 177.138
+19200/69092	Loss: 173.399
+22400/69092	Loss: 175.519
+25600/69092	Loss: 174.662
+28800/69092	Loss: 173.911
+32000/69092	Loss: 171.048
+35200/69092	Loss: 172.223
+38400/69092	Loss: 173.460
+41600/69092	Loss: 175.998
+44800/69092	Loss: 173.967
+48000/69092	Loss: 172.762
+51200/69092	Loss: 174.501
+54400/69092	Loss: 174.181
+57600/69092	Loss: 174.943
+60800/69092	Loss: 170.237
+64000/69092	Loss: 177.731
+67200/69092	Loss: 175.039
+Training time 0:05:32.326405
+Epoch: 21 Average loss: 174.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 23)
+0/69092	Loss: 169.470
+3200/69092	Loss: 173.431
+6400/69092	Loss: 173.364
+9600/69092	Loss: 173.243
+12800/69092	Loss: 175.181
+16000/69092	Loss: 172.482
+19200/69092	Loss: 172.325
+22400/69092	Loss: 170.139
+25600/69092	Loss: 174.497
+28800/69092	Loss: 178.208
+32000/69092	Loss: 171.218
+35200/69092	Loss: 173.980
+38400/69092	Loss: 177.087
+41600/69092	Loss: 175.343
+44800/69092	Loss: 171.970
+48000/69092	Loss: 177.428
+51200/69092	Loss: 172.604
+54400/69092	Loss: 173.524
+57600/69092	Loss: 175.285
+60800/69092	Loss: 169.347
+64000/69092	Loss: 175.777
+67200/69092	Loss: 173.082
+Training time 0:05:41.385094
+Epoch: 22 Average loss: 173.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 24)
+0/69092	Loss: 170.264
+3200/69092	Loss: 175.610
+6400/69092	Loss: 173.777
+9600/69092	Loss: 174.105
+12800/69092	Loss: 173.542
+16000/69092	Loss: 173.334
+19200/69092	Loss: 170.956
+22400/69092	Loss: 177.598
+25600/69092	Loss: 176.880
+28800/69092	Loss: 172.293
+32000/69092	Loss: 174.877
+35200/69092	Loss: 171.622
+38400/69092	Loss: 172.211
+41600/69092	Loss: 172.787
+44800/69092	Loss: 176.438
+48000/69092	Loss: 173.580
+51200/69092	Loss: 173.973
+54400/69092	Loss: 174.231
+57600/69092	Loss: 176.727
+60800/69092	Loss: 172.584
+64000/69092	Loss: 171.992
+67200/69092	Loss: 172.850
+Training time 0:05:31.432612
+Epoch: 23 Average loss: 173.84
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 25)
+0/69092	Loss: 192.459
+3200/69092	Loss: 174.192
+6400/69092	Loss: 172.172
+9600/69092	Loss: 173.914
+12800/69092	Loss: 173.817
+16000/69092	Loss: 175.222
+19200/69092	Loss: 174.602
+22400/69092	Loss: 170.613
+25600/69092	Loss: 173.318
+28800/69092	Loss: 174.815
+32000/69092	Loss: 173.949
+35200/69092	Loss: 173.028
+38400/69092	Loss: 173.344
+41600/69092	Loss: 172.672
+44800/69092	Loss: 173.504
+48000/69092	Loss: 176.420
+51200/69092	Loss: 173.166
+54400/69092	Loss: 172.663
+57600/69092	Loss: 173.213
+60800/69092	Loss: 173.389
+64000/69092	Loss: 174.746
+67200/69092	Loss: 173.229
+Training time 0:05:41.747589
+Epoch: 24 Average loss: 173.78
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 26)
+0/69092	Loss: 187.258
+3200/69092	Loss: 174.271
+6400/69092	Loss: 169.356
+9600/69092	Loss: 173.209
+12800/69092	Loss: 171.349
+16000/69092	Loss: 176.658
+19200/69092	Loss: 175.129
+22400/69092	Loss: 169.299
+25600/69092	Loss: 176.399
+28800/69092	Loss: 177.179
+32000/69092	Loss: 175.130
+35200/69092	Loss: 175.582
+38400/69092	Loss: 171.445
+41600/69092	Loss: 173.623
+44800/69092	Loss: 173.105
+48000/69092	Loss: 174.830
+51200/69092	Loss: 173.433
+54400/69092	Loss: 174.068
+57600/69092	Loss: 172.694
+60800/69092	Loss: 172.232
+64000/69092	Loss: 170.319
+67200/69092	Loss: 175.323
+Training time 0:05:48.043514
+Epoch: 25 Average loss: 173.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 27)
+0/69092	Loss: 159.018
+3200/69092	Loss: 174.298
+6400/69092	Loss: 172.624
+9600/69092	Loss: 173.771
+12800/69092	Loss: 171.477
+16000/69092	Loss: 176.488
+19200/69092	Loss: 177.637
+22400/69092	Loss: 174.962
+25600/69092	Loss: 173.725
+28800/69092	Loss: 175.619
+32000/69092	Loss: 172.961
+35200/69092	Loss: 169.719
+38400/69092	Loss: 173.452
+41600/69092	Loss: 174.124
+44800/69092	Loss: 174.044
+48000/69092	Loss: 171.758
+51200/69092	Loss: 173.624
+54400/69092	Loss: 172.592
+57600/69092	Loss: 171.208
+60800/69092	Loss: 176.300
+64000/69092	Loss: 174.036
+67200/69092	Loss: 173.975
+Training time 0:05:29.049309
+Epoch: 26 Average loss: 173.78
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 28)
+0/69092	Loss: 160.580
+3200/69092	Loss: 175.324
+6400/69092	Loss: 174.976
+9600/69092	Loss: 174.375
+12800/69092	Loss: 173.432
+16000/69092	Loss: 173.015
+19200/69092	Loss: 174.784
+22400/69092	Loss: 170.553
+25600/69092	Loss: 174.186
+28800/69092	Loss: 178.069
+32000/69092	Loss: 175.331
+35200/69092	Loss: 177.122
+38400/69092	Loss: 173.582
+41600/69092	Loss: 170.883
+44800/69092	Loss: 168.975
+48000/69092	Loss: 171.320
+51200/69092	Loss: 171.969
+54400/69092	Loss: 171.927
+57600/69092	Loss: 175.958
+60800/69092	Loss: 177.487
+64000/69092	Loss: 171.726
+67200/69092	Loss: 173.001
+Training time 0:05:40.474273
+Epoch: 27 Average loss: 173.70
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 29)
+0/69092	Loss: 186.247
+3200/69092	Loss: 172.054
+6400/69092	Loss: 174.711
+9600/69092	Loss: 173.377
+12800/69092	Loss: 174.325
+16000/69092	Loss: 171.554
+19200/69092	Loss: 170.114
+22400/69092	Loss: 172.368
+25600/69092	Loss: 178.603
+28800/69092	Loss: 172.321
+32000/69092	Loss: 175.067
+35200/69092	Loss: 172.821
+38400/69092	Loss: 173.497
+41600/69092	Loss: 170.781
+44800/69092	Loss: 172.645
+48000/69092	Loss: 174.624
+51200/69092	Loss: 174.692
+54400/69092	Loss: 174.437
+57600/69092	Loss: 173.175
+60800/69092	Loss: 175.592
+64000/69092	Loss: 176.727
+67200/69092	Loss: 171.446
+Training time 0:05:54.306120
+Epoch: 28 Average loss: 173.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 30)
+0/69092	Loss: 177.635
+3200/69092	Loss: 176.320
+6400/69092	Loss: 173.105
+9600/69092	Loss: 173.753
+12800/69092	Loss: 173.966
+16000/69092	Loss: 173.023
+19200/69092	Loss: 177.296
+22400/69092	Loss: 171.875
+25600/69092	Loss: 172.370
+28800/69092	Loss: 171.588
+32000/69092	Loss: 174.452
+35200/69092	Loss: 172.140
+38400/69092	Loss: 172.938
+41600/69092	Loss: 171.031
+44800/69092	Loss: 174.442
+48000/69092	Loss: 169.778
+51200/69092	Loss: 175.924
+54400/69092	Loss: 172.634
+57600/69092	Loss: 173.484
+60800/69092	Loss: 175.750
+64000/69092	Loss: 174.191
+67200/69092	Loss: 172.983
+Training time 0:05:23.217377
+Epoch: 29 Average loss: 173.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 31)
+0/69092	Loss: 177.443
+3200/69092	Loss: 172.360
+6400/69092	Loss: 171.361
+9600/69092	Loss: 171.755
+12800/69092	Loss: 174.928
+16000/69092	Loss: 174.394
+19200/69092	Loss: 174.640
+22400/69092	Loss: 171.706
+25600/69092	Loss: 174.741
+28800/69092	Loss: 172.728
+32000/69092	Loss: 175.243
+35200/69092	Loss: 170.282
+38400/69092	Loss: 173.597
+41600/69092	Loss: 175.927
+44800/69092	Loss: 171.773
+48000/69092	Loss: 172.405
+51200/69092	Loss: 176.730
+54400/69092	Loss: 176.713
+57600/69092	Loss: 172.287
+60800/69092	Loss: 173.667
+64000/69092	Loss: 172.699
+67200/69092	Loss: 174.272
+Training time 0:05:40.304543
+Epoch: 30 Average loss: 173.62
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 32)
+0/69092	Loss: 191.708
+3200/69092	Loss: 173.430
+6400/69092	Loss: 173.703
+9600/69092	Loss: 171.621
+12800/69092	Loss: 175.206
+16000/69092	Loss: 173.444
+19200/69092	Loss: 173.582
+22400/69092	Loss: 172.806
+25600/69092	Loss: 175.794
+28800/69092	Loss: 171.370
+32000/69092	Loss: 172.763
+35200/69092	Loss: 173.796
+38400/69092	Loss: 172.945
+41600/69092	Loss: 171.384
+44800/69092	Loss: 174.757
+48000/69092	Loss: 173.012
+51200/69092	Loss: 173.523
+54400/69092	Loss: 177.514
+57600/69092	Loss: 174.484
+60800/69092	Loss: 173.689
+64000/69092	Loss: 175.510
+67200/69092	Loss: 171.662
+Training time 0:05:25.356098
+Epoch: 31 Average loss: 173.58
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 33)
+0/69092	Loss: 196.864
+3200/69092	Loss: 174.341
+6400/69092	Loss: 173.206
+9600/69092	Loss: 175.148
+12800/69092	Loss: 174.531
+16000/69092	Loss: 173.104
+19200/69092	Loss: 174.909
+22400/69092	Loss: 171.156
+25600/69092	Loss: 172.036
+28800/69092	Loss: 173.442
+32000/69092	Loss: 172.939
+35200/69092	Loss: 173.429
+38400/69092	Loss: 174.354
+41600/69092	Loss: 174.152
+44800/69092	Loss: 173.010
+48000/69092	Loss: 173.626
+51200/69092	Loss: 169.262
+54400/69092	Loss: 170.032
+57600/69092	Loss: 178.830
+60800/69092	Loss: 176.600
+64000/69092	Loss: 172.144
+67200/69092	Loss: 172.936
+Training time 0:05:23.495012
+Epoch: 32 Average loss: 173.53
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 34)
+0/69092	Loss: 166.554
+3200/69092	Loss: 174.301
+6400/69092	Loss: 173.805
+9600/69092	Loss: 171.245
+12800/69092	Loss: 174.749
+16000/69092	Loss: 175.356
+19200/69092	Loss: 173.674
+22400/69092	Loss: 171.741
+25600/69092	Loss: 174.273
+28800/69092	Loss: 174.210
+32000/69092	Loss: 171.078
+35200/69092	Loss: 175.898
+38400/69092	Loss: 178.527
+41600/69092	Loss: 174.504
+44800/69092	Loss: 174.795
+48000/69092	Loss: 171.500
+51200/69092	Loss: 173.709
+54400/69092	Loss: 171.968
+57600/69092	Loss: 174.119
+60800/69092	Loss: 175.209
+64000/69092	Loss: 171.790
+67200/69092	Loss: 173.900
+Training time 0:05:39.295017
+Epoch: 33 Average loss: 173.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 35)
+0/69092	Loss: 186.714
+3200/69092	Loss: 176.883
+6400/69092	Loss: 172.812
+9600/69092	Loss: 171.623
+12800/69092	Loss: 174.599
+16000/69092	Loss: 173.348
+19200/69092	Loss: 173.608
+22400/69092	Loss: 172.481
+25600/69092	Loss: 175.913
+28800/69092	Loss: 174.323
+32000/69092	Loss: 172.932
+35200/69092	Loss: 172.995
+38400/69092	Loss: 173.248
+41600/69092	Loss: 172.981
+44800/69092	Loss: 172.659
+48000/69092	Loss: 173.360
+51200/69092	Loss: 171.643
+54400/69092	Loss: 173.445
+57600/69092	Loss: 174.796
+60800/69092	Loss: 174.537
+64000/69092	Loss: 173.874
+67200/69092	Loss: 172.268
+Training time 0:05:44.055745
+Epoch: 34 Average loss: 173.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 36)
+0/69092	Loss: 164.509
+3200/69092	Loss: 169.188
+6400/69092	Loss: 173.661
+9600/69092	Loss: 172.126
+12800/69092	Loss: 175.549
+16000/69092	Loss: 173.874
+19200/69092	Loss: 174.934
+22400/69092	Loss: 172.382
+25600/69092	Loss: 171.855
+28800/69092	Loss: 172.607
+32000/69092	Loss: 174.699
+35200/69092	Loss: 170.735
+38400/69092	Loss: 172.636
+41600/69092	Loss: 174.432
+44800/69092	Loss: 171.385
+48000/69092	Loss: 176.124
+51200/69092	Loss: 172.042
+54400/69092	Loss: 175.066
+57600/69092	Loss: 174.499
+60800/69092	Loss: 174.652
+64000/69092	Loss: 173.362
+67200/69092	Loss: 176.318
+Training time 0:05:29.571042
+Epoch: 35 Average loss: 173.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 37)
+0/69092	Loss: 184.585
+3200/69092	Loss: 175.565
+6400/69092	Loss: 172.125
+9600/69092	Loss: 170.228
+12800/69092	Loss: 171.469
+16000/69092	Loss: 172.525
+19200/69092	Loss: 173.128
+22400/69092	Loss: 172.829
+25600/69092	Loss: 173.167
+28800/69092	Loss: 170.336
+32000/69092	Loss: 175.812
+35200/69092	Loss: 173.563
+38400/69092	Loss: 177.054
+41600/69092	Loss: 173.328
+44800/69092	Loss: 173.700
+48000/69092	Loss: 173.092
+51200/69092	Loss: 172.745
+54400/69092	Loss: 170.423
+57600/69092	Loss: 172.372
+60800/69092	Loss: 171.590
+64000/69092	Loss: 175.804
+67200/69092	Loss: 171.773
+Training time 0:05:51.309468
+Epoch: 36 Average loss: 173.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 38)
+0/69092	Loss: 173.427
+3200/69092	Loss: 173.175
+6400/69092	Loss: 172.144
+9600/69092	Loss: 173.344
+12800/69092	Loss: 174.643
+16000/69092	Loss: 178.121
+19200/69092	Loss: 173.095
+22400/69092	Loss: 174.251
+25600/69092	Loss: 172.400
+28800/69092	Loss: 174.409
+32000/69092	Loss: 177.024
+35200/69092	Loss: 171.541
+38400/69092	Loss: 175.171
+41600/69092	Loss: 171.738
+44800/69092	Loss: 174.493
+48000/69092	Loss: 173.316
+51200/69092	Loss: 170.099
+54400/69092	Loss: 173.562
+57600/69092	Loss: 170.568
+60800/69092	Loss: 172.148
+64000/69092	Loss: 173.017
+67200/69092	Loss: 173.565
+Training time 0:05:40.488864
+Epoch: 37 Average loss: 173.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 39)
+0/69092	Loss: 186.108
+3200/69092	Loss: 170.907
+6400/69092	Loss: 173.202
+9600/69092	Loss: 178.612
+12800/69092	Loss: 171.577
+16000/69092	Loss: 172.614
+19200/69092	Loss: 173.740
+22400/69092	Loss: 176.534
+25600/69092	Loss: 174.375
+28800/69092	Loss: 173.829
+32000/69092	Loss: 171.273
+35200/69092	Loss: 172.430
+38400/69092	Loss: 172.139
+41600/69092	Loss: 171.459
+44800/69092	Loss: 174.351
+48000/69092	Loss: 173.663
+51200/69092	Loss: 174.352
+54400/69092	Loss: 170.678
+57600/69092	Loss: 170.146
+60800/69092	Loss: 173.981
+64000/69092	Loss: 174.069
+67200/69092	Loss: 171.602
+Training time 0:05:40.008656
+Epoch: 38 Average loss: 173.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 40)
+0/69092	Loss: 187.263
+3200/69092	Loss: 174.044
+6400/69092	Loss: 171.880
+9600/69092	Loss: 170.916
+12800/69092	Loss: 172.835
+16000/69092	Loss: 169.904
+19200/69092	Loss: 173.127
+22400/69092	Loss: 172.923
+25600/69092	Loss: 175.621
+28800/69092	Loss: 177.994
+32000/69092	Loss: 172.789
+35200/69092	Loss: 174.507
+38400/69092	Loss: 174.986
+41600/69092	Loss: 171.516
+44800/69092	Loss: 170.562
+48000/69092	Loss: 174.539
+51200/69092	Loss: 173.680
+54400/69092	Loss: 172.400
+57600/69092	Loss: 172.262
+60800/69092	Loss: 174.315
+64000/69092	Loss: 171.795
+67200/69092	Loss: 173.152
+Training time 0:05:33.916960
+Epoch: 39 Average loss: 173.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 41)
+0/69092	Loss: 180.096
+3200/69092	Loss: 174.381
+6400/69092	Loss: 170.518
+9600/69092	Loss: 172.870
+12800/69092	Loss: 172.021
+16000/69092	Loss: 172.750
+19200/69092	Loss: 170.749
+22400/69092	Loss: 172.568
+25600/69092	Loss: 175.968
+28800/69092	Loss: 172.411
+32000/69092	Loss: 176.875
+35200/69092	Loss: 171.088
+38400/69092	Loss: 172.786
+41600/69092	Loss: 177.546
+44800/69092	Loss: 174.277
+48000/69092	Loss: 175.076
+51200/69092	Loss: 170.425
+54400/69092	Loss: 172.672
+57600/69092	Loss: 177.248
+60800/69092	Loss: 173.309
+64000/69092	Loss: 170.583
+67200/69092	Loss: 173.451
+Training time 0:05:47.549711
+Epoch: 40 Average loss: 173.44
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 42)
+0/69092	Loss: 166.892
+3200/69092	Loss: 169.907
+6400/69092	Loss: 174.729
+9600/69092	Loss: 174.300
+12800/69092	Loss: 171.541
+16000/69092	Loss: 169.615
+19200/69092	Loss: 171.974
+22400/69092	Loss: 170.669
+25600/69092	Loss: 175.969
+28800/69092	Loss: 173.754
+32000/69092	Loss: 172.225
+35200/69092	Loss: 172.512
+38400/69092	Loss: 173.071
+41600/69092	Loss: 170.008
+44800/69092	Loss: 171.252
+48000/69092	Loss: 170.339
+51200/69092	Loss: 172.338
+54400/69092	Loss: 174.364
+57600/69092	Loss: 173.408
+60800/69092	Loss: 170.907
+64000/69092	Loss: 172.668
+67200/69092	Loss: 173.325
+Training time 0:05:35.665365
+Epoch: 41 Average loss: 172.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 43)
+0/69092	Loss: 183.274
+3200/69092	Loss: 172.073
+6400/69092	Loss: 175.359
+9600/69092	Loss: 170.925
+12800/69092	Loss: 174.301
+16000/69092	Loss: 172.649
+19200/69092	Loss: 170.175
+22400/69092	Loss: 171.567
+25600/69092	Loss: 170.073
+28800/69092	Loss: 170.343
+32000/69092	Loss: 166.742
+35200/69092	Loss: 172.750
+38400/69092	Loss: 171.848
+41600/69092	Loss: 171.209
+44800/69092	Loss: 169.259
+48000/69092	Loss: 174.016
+51200/69092	Loss: 171.689
+54400/69092	Loss: 168.837
+57600/69092	Loss: 168.469
+60800/69092	Loss: 169.180
+64000/69092	Loss: 174.771
+67200/69092	Loss: 173.935
+Training time 0:05:54.282279
+Epoch: 42 Average loss: 171.47
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 44)
+0/69092	Loss: 167.563
+3200/69092	Loss: 167.655
+6400/69092	Loss: 170.149
+9600/69092	Loss: 171.828
+12800/69092	Loss: 172.133
+16000/69092	Loss: 171.540
+19200/69092	Loss: 166.793
+22400/69092	Loss: 171.767
+25600/69092	Loss: 171.824
+28800/69092	Loss: 169.961
+32000/69092	Loss: 172.404
+35200/69092	Loss: 171.086
+38400/69092	Loss: 171.440
+41600/69092	Loss: 171.000
+44800/69092	Loss: 167.727
+48000/69092	Loss: 168.990
+51200/69092	Loss: 169.421
+54400/69092	Loss: 171.666
+57600/69092	Loss: 172.731
+60800/69092	Loss: 168.360
+64000/69092	Loss: 169.549
+67200/69092	Loss: 170.794
+Training time 0:05:54.221089
+Epoch: 43 Average loss: 170.41
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 45)
+0/69092	Loss: 173.246
+3200/69092	Loss: 171.212
+6400/69092	Loss: 171.112
+9600/69092	Loss: 171.287
+12800/69092	Loss: 173.460
+16000/69092	Loss: 170.604
+19200/69092	Loss: 170.470
+22400/69092	Loss: 169.744
+25600/69092	Loss: 169.659
+28800/69092	Loss: 170.463
+32000/69092	Loss: 167.962
+35200/69092	Loss: 168.192
+38400/69092	Loss: 169.308
+41600/69092	Loss: 166.174
+44800/69092	Loss: 167.972
+48000/69092	Loss: 167.101
+51200/69092	Loss: 168.082
+54400/69092	Loss: 168.402
+57600/69092	Loss: 167.455
+60800/69092	Loss: 168.489
+64000/69092	Loss: 168.424
+67200/69092	Loss: 166.649
+Training time 0:05:42.435056
+Epoch: 44 Average loss: 169.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 46)
+0/69092	Loss: 153.235
+3200/69092	Loss: 167.863
+6400/69092	Loss: 168.738
+9600/69092	Loss: 170.254
+12800/69092	Loss: 165.962
+16000/69092	Loss: 169.147
+19200/69092	Loss: 165.075
+22400/69092	Loss: 166.501
+25600/69092	Loss: 167.458
+28800/69092	Loss: 170.880
+32000/69092	Loss: 166.437
+35200/69092	Loss: 166.887
+38400/69092	Loss: 169.201
+41600/69092	Loss: 167.544
+44800/69092	Loss: 167.635
+48000/69092	Loss: 164.696
+51200/69092	Loss: 167.352
+54400/69092	Loss: 168.781
+57600/69092	Loss: 169.274
+60800/69092	Loss: 170.615
+64000/69092	Loss: 167.813
+67200/69092	Loss: 167.527
+Training time 0:05:32.692638
+Epoch: 45 Average loss: 167.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 47)
+0/69092	Loss: 157.577
+3200/69092	Loss: 168.256
+6400/69092	Loss: 170.025
+9600/69092	Loss: 170.829
+12800/69092	Loss: 164.074
+16000/69092	Loss: 167.900
+19200/69092	Loss: 168.318
+22400/69092	Loss: 167.668
+25600/69092	Loss: 165.187
+28800/69092	Loss: 164.678
+32000/69092	Loss: 167.627
+35200/69092	Loss: 165.355
+38400/69092	Loss: 165.897
+41600/69092	Loss: 167.423
+44800/69092	Loss: 166.143
+48000/69092	Loss: 165.399
+51200/69092	Loss: 166.400
+54400/69092	Loss: 165.740
+57600/69092	Loss: 167.359
+60800/69092	Loss: 167.019
+64000/69092	Loss: 164.991
+67200/69092	Loss: 165.534
+Training time 0:05:40.355541
+Epoch: 46 Average loss: 166.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 48)
+0/69092	Loss: 167.794
+3200/69092	Loss: 166.358
+6400/69092	Loss: 167.976
+9600/69092	Loss: 161.915
+12800/69092	Loss: 170.618
+16000/69092	Loss: 169.478
+19200/69092	Loss: 168.012
+22400/69092	Loss: 168.640
+25600/69092	Loss: 166.508
+28800/69092	Loss: 164.664
+32000/69092	Loss: 165.563
+35200/69092	Loss: 165.242
+38400/69092	Loss: 165.173
+41600/69092	Loss: 166.427
+44800/69092	Loss: 164.840
+48000/69092	Loss: 164.091
+51200/69092	Loss: 165.624
+54400/69092	Loss: 167.620
+57600/69092	Loss: 167.491
+60800/69092	Loss: 167.892
+64000/69092	Loss: 166.079
+67200/69092	Loss: 165.601
+Training time 0:05:39.830287
+Epoch: 47 Average loss: 166.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 49)
+0/69092	Loss: 160.000
+3200/69092	Loss: 168.413
+6400/69092	Loss: 166.428
+9600/69092	Loss: 167.653
+12800/69092	Loss: 166.982
+16000/69092	Loss: 167.007
+19200/69092	Loss: 166.679
+22400/69092	Loss: 166.577
+25600/69092	Loss: 165.914
+28800/69092	Loss: 165.350
+32000/69092	Loss: 164.527
+35200/69092	Loss: 164.677
+38400/69092	Loss: 169.371
+41600/69092	Loss: 165.501
+44800/69092	Loss: 165.035
+48000/69092	Loss: 167.083
+51200/69092	Loss: 165.342
+54400/69092	Loss: 168.917
+57600/69092	Loss: 164.038
+60800/69092	Loss: 162.979
+64000/69092	Loss: 163.487
+67200/69092	Loss: 165.938
+Training time 0:05:38.988145
+Epoch: 48 Average loss: 166.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 50)
+0/69092	Loss: 154.051
+3200/69092	Loss: 168.800
+6400/69092	Loss: 164.603
+9600/69092	Loss: 168.144
+12800/69092	Loss: 166.208
+16000/69092	Loss: 167.659
+19200/69092	Loss: 162.595
+22400/69092	Loss: 166.029
+25600/69092	Loss: 169.441
+28800/69092	Loss: 164.896
+32000/69092	Loss: 163.644
+35200/69092	Loss: 169.101
+38400/69092	Loss: 164.853
+41600/69092	Loss: 166.421
+44800/69092	Loss: 166.245
+48000/69092	Loss: 166.465
+51200/69092	Loss: 165.259
+54400/69092	Loss: 164.067
+57600/69092	Loss: 165.839
+60800/69092	Loss: 165.805
+64000/69092	Loss: 164.429
+67200/69092	Loss: 165.264
+Training time 0:05:57.260261
+Epoch: 49 Average loss: 165.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 51)
+0/69092	Loss: 181.015
+3200/69092	Loss: 164.637
+6400/69092	Loss: 164.384
+9600/69092	Loss: 166.718
+12800/69092	Loss: 167.925
+16000/69092	Loss: 164.983
+19200/69092	Loss: 166.862
+22400/69092	Loss: 164.006
+25600/69092	Loss: 168.377
+28800/69092	Loss: 164.324
+32000/69092	Loss: 163.546
+35200/69092	Loss: 164.518
+38400/69092	Loss: 163.273
+41600/69092	Loss: 162.711
+44800/69092	Loss: 167.412
+48000/69092	Loss: 166.993
+51200/69092	Loss: 166.282
+54400/69092	Loss: 167.241
+57600/69092	Loss: 162.450
+60800/69092	Loss: 165.818
+64000/69092	Loss: 164.673
+67200/69092	Loss: 167.188
+Training time 0:05:50.119307
+Epoch: 50 Average loss: 165.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 52)
+0/69092	Loss: 198.353
+3200/69092	Loss: 168.760
+6400/69092	Loss: 163.387
+9600/69092	Loss: 165.692
+12800/69092	Loss: 164.378
+16000/69092	Loss: 166.856
+19200/69092	Loss: 165.326
+22400/69092	Loss: 164.655
+25600/69092	Loss: 164.794
+28800/69092	Loss: 165.226
+32000/69092	Loss: 166.955
+35200/69092	Loss: 161.901
+38400/69092	Loss: 164.587
+41600/69092	Loss: 164.124
+44800/69092	Loss: 166.599
+48000/69092	Loss: 163.628
+51200/69092	Loss: 166.996
+54400/69092	Loss: 166.418
+57600/69092	Loss: 165.492
+60800/69092	Loss: 165.077
+64000/69092	Loss: 162.775
+67200/69092	Loss: 166.733
+Training time 0:05:36.376033
+Epoch: 51 Average loss: 165.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 53)
+0/69092	Loss: 143.459
+3200/69092	Loss: 163.731
+6400/69092	Loss: 163.375
+9600/69092	Loss: 168.696
+12800/69092	Loss: 165.597
+16000/69092	Loss: 165.569
+19200/69092	Loss: 166.215
+22400/69092	Loss: 167.393
+25600/69092	Loss: 167.570
+28800/69092	Loss: 163.246
+32000/69092	Loss: 166.556
+35200/69092	Loss: 162.497
+38400/69092	Loss: 166.182
+41600/69092	Loss: 167.587
+44800/69092	Loss: 161.856
+48000/69092	Loss: 163.982
+51200/69092	Loss: 165.737
+54400/69092	Loss: 163.250
+57600/69092	Loss: 165.125
+60800/69092	Loss: 163.595
+64000/69092	Loss: 162.587
+67200/69092	Loss: 165.027
+Training time 0:05:40.020361
+Epoch: 52 Average loss: 165.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 54)
+0/69092	Loss: 150.567
+3200/69092	Loss: 166.590
+6400/69092	Loss: 166.091
+9600/69092	Loss: 162.325
+12800/69092	Loss: 165.441
+16000/69092	Loss: 162.639
+19200/69092	Loss: 166.093
+22400/69092	Loss: 163.996
+25600/69092	Loss: 164.310
+28800/69092	Loss: 165.598
+32000/69092	Loss: 165.352
+35200/69092	Loss: 163.505
+38400/69092	Loss: 163.557
+41600/69092	Loss: 165.363
+44800/69092	Loss: 167.443
+48000/69092	Loss: 163.115
+51200/69092	Loss: 164.136
+54400/69092	Loss: 169.178
+57600/69092	Loss: 161.254
+60800/69092	Loss: 165.040
+64000/69092	Loss: 163.431
+67200/69092	Loss: 163.072
+Training time 0:05:34.292027
+Epoch: 53 Average loss: 164.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 55)
+0/69092	Loss: 179.397
+3200/69092	Loss: 169.331
+6400/69092	Loss: 165.416
+9600/69092	Loss: 163.820
+12800/69092	Loss: 164.717
+16000/69092	Loss: 167.858
+19200/69092	Loss: 165.986
+22400/69092	Loss: 162.102
+25600/69092	Loss: 162.791
+28800/69092	Loss: 162.377
+32000/69092	Loss: 161.870
+35200/69092	Loss: 164.394
+38400/69092	Loss: 167.809
+41600/69092	Loss: 162.293
+44800/69092	Loss: 164.169
+48000/69092	Loss: 163.266
+51200/69092	Loss: 164.188
+54400/69092	Loss: 164.553
+57600/69092	Loss: 163.790
+60800/69092	Loss: 163.379
+64000/69092	Loss: 163.940
+67200/69092	Loss: 163.795
+Training time 0:05:52.653168
+Epoch: 54 Average loss: 164.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 56)
+0/69092	Loss: 158.589
+3200/69092	Loss: 164.807
+6400/69092	Loss: 161.015
+9600/69092	Loss: 162.975
+12800/69092	Loss: 164.167
+16000/69092	Loss: 162.519
+19200/69092	Loss: 166.933
+22400/69092	Loss: 162.555
+25600/69092	Loss: 163.479
+28800/69092	Loss: 163.457
+32000/69092	Loss: 167.122
+35200/69092	Loss: 165.168
+38400/69092	Loss: 162.780
+41600/69092	Loss: 162.968
+44800/69092	Loss: 162.150
+48000/69092	Loss: 164.946
+51200/69092	Loss: 163.820
+54400/69092	Loss: 164.089
+57600/69092	Loss: 163.518
+60800/69092	Loss: 162.881
+64000/69092	Loss: 159.904
+67200/69092	Loss: 162.548
+Training time 0:05:52.862775
+Epoch: 55 Average loss: 163.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 57)
+0/69092	Loss: 150.236
+3200/69092	Loss: 162.965
+6400/69092	Loss: 161.524
+9600/69092	Loss: 165.274
+12800/69092	Loss: 162.213
+16000/69092	Loss: 162.425
+19200/69092	Loss: 163.687
+22400/69092	Loss: 161.616
+25600/69092	Loss: 160.336
+28800/69092	Loss: 166.733
+32000/69092	Loss: 165.842
+35200/69092	Loss: 164.410
+38400/69092	Loss: 164.495
+41600/69092	Loss: 164.165
+44800/69092	Loss: 160.374
+48000/69092	Loss: 160.507
+51200/69092	Loss: 161.214
+54400/69092	Loss: 162.758
+57600/69092	Loss: 163.086
+60800/69092	Loss: 163.382
+64000/69092	Loss: 161.542
+67200/69092	Loss: 160.924
+Training time 0:05:42.539817
+Epoch: 56 Average loss: 162.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 58)
+0/69092	Loss: 139.150
+3200/69092	Loss: 159.976
+6400/69092	Loss: 164.714
+9600/69092	Loss: 158.648
+12800/69092	Loss: 163.976
+16000/69092	Loss: 163.032
+19200/69092	Loss: 165.323
+22400/69092	Loss: 162.589
+25600/69092	Loss: 161.895
+28800/69092	Loss: 161.899
+32000/69092	Loss: 161.471
+35200/69092	Loss: 162.316
+38400/69092	Loss: 159.002
+41600/69092	Loss: 161.996
+44800/69092	Loss: 162.421
+48000/69092	Loss: 161.444
+51200/69092	Loss: 162.999
+54400/69092	Loss: 160.922
+57600/69092	Loss: 164.080
+60800/69092	Loss: 163.423
+64000/69092	Loss: 163.086
+67200/69092	Loss: 163.945
+Training time 0:05:46.353508
+Epoch: 57 Average loss: 162.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 59)
+0/69092	Loss: 178.841
+3200/69092	Loss: 162.692
+6400/69092	Loss: 159.783
+9600/69092	Loss: 160.010
+12800/69092	Loss: 163.379
+16000/69092	Loss: 160.886
+19200/69092	Loss: 162.199
+22400/69092	Loss: 162.560
+25600/69092	Loss: 168.001
+28800/69092	Loss: 161.574
+32000/69092	Loss: 160.602
+35200/69092	Loss: 160.272
+38400/69092	Loss: 158.592
+41600/69092	Loss: 162.144
+44800/69092	Loss: 161.838
+48000/69092	Loss: 164.109
+51200/69092	Loss: 162.542
+54400/69092	Loss: 158.274
+57600/69092	Loss: 159.163
+60800/69092	Loss: 163.266
+64000/69092	Loss: 162.224
+67200/69092	Loss: 165.458
+Training time 0:05:39.626714
+Epoch: 58 Average loss: 161.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 60)
+0/69092	Loss: 166.984
+3200/69092	Loss: 162.239
+6400/69092	Loss: 162.115
+9600/69092	Loss: 162.349
+12800/69092	Loss: 159.904
+16000/69092	Loss: 158.859
+19200/69092	Loss: 162.854
+22400/69092	Loss: 160.167
+25600/69092	Loss: 160.621
+28800/69092	Loss: 158.050
+32000/69092	Loss: 162.151
+35200/69092	Loss: 161.174
+38400/69092	Loss: 160.051
+41600/69092	Loss: 162.315
+44800/69092	Loss: 161.528
+48000/69092	Loss: 162.895
+51200/69092	Loss: 162.563
+54400/69092	Loss: 163.136
+57600/69092	Loss: 160.897
+60800/69092	Loss: 162.557
+64000/69092	Loss: 160.214
+67200/69092	Loss: 163.037
+Training time 0:05:33.048844
+Epoch: 59 Average loss: 161.45
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 61)
+0/69092	Loss: 149.507
+3200/69092	Loss: 160.715
+6400/69092	Loss: 161.707
+9600/69092	Loss: 161.779
+12800/69092	Loss: 160.168
+16000/69092	Loss: 160.552
+19200/69092	Loss: 159.908
+22400/69092	Loss: 161.218
+25600/69092	Loss: 163.683
+28800/69092	Loss: 159.321
+32000/69092	Loss: 159.532
+35200/69092	Loss: 159.671
+38400/69092	Loss: 161.547
+41600/69092	Loss: 158.484
+44800/69092	Loss: 159.405
+48000/69092	Loss: 161.779
+51200/69092	Loss: 159.974
+54400/69092	Loss: 162.963
+57600/69092	Loss: 160.340
+60800/69092	Loss: 161.868
+64000/69092	Loss: 158.539
+67200/69092	Loss: 160.137
+Training time 0:05:40.333830
+Epoch: 60 Average loss: 160.77
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 62)
+0/69092	Loss: 168.603
+3200/69092	Loss: 159.598
+6400/69092	Loss: 160.441
+9600/69092	Loss: 159.780
+12800/69092	Loss: 164.112
+16000/69092	Loss: 163.368
+19200/69092	Loss: 160.479
+22400/69092	Loss: 162.530
+25600/69092	Loss: 161.251
+28800/69092	Loss: 162.324
+32000/69092	Loss: 162.983
+35200/69092	Loss: 163.757
+38400/69092	Loss: 159.764
+41600/69092	Loss: 160.018
+44800/69092	Loss: 160.194
+48000/69092	Loss: 158.096
+51200/69092	Loss: 161.157
+54400/69092	Loss: 159.922
+57600/69092	Loss: 160.651
+60800/69092	Loss: 157.688
+64000/69092	Loss: 159.952
+67200/69092	Loss: 160.075
+Training time 0:05:45.995703
+Epoch: 61 Average loss: 160.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 63)
+0/69092	Loss: 164.432
+3200/69092	Loss: 159.754
+6400/69092	Loss: 159.381
+9600/69092	Loss: 161.880
+12800/69092	Loss: 161.369
+16000/69092	Loss: 157.463
+19200/69092	Loss: 161.791
+22400/69092	Loss: 159.944
+25600/69092	Loss: 160.871
+28800/69092	Loss: 162.126
+32000/69092	Loss: 162.310
+35200/69092	Loss: 161.692
+38400/69092	Loss: 162.270
+41600/69092	Loss: 158.490
+44800/69092	Loss: 162.484
+48000/69092	Loss: 158.736
+51200/69092	Loss: 158.702
+54400/69092	Loss: 160.241
+57600/69092	Loss: 160.938
+60800/69092	Loss: 158.529
+64000/69092	Loss: 161.997
+67200/69092	Loss: 161.102
+Training time 0:05:40.618788
+Epoch: 62 Average loss: 160.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 64)
+0/69092	Loss: 154.568
+3200/69092	Loss: 160.887
+6400/69092	Loss: 161.419
+9600/69092	Loss: 159.361
+12800/69092	Loss: 159.252
+16000/69092	Loss: 160.597
+19200/69092	Loss: 158.492
+22400/69092	Loss: 160.724
+25600/69092	Loss: 163.020
+28800/69092	Loss: 159.280
+32000/69092	Loss: 160.119
+35200/69092	Loss: 157.753
+38400/69092	Loss: 159.272
+41600/69092	Loss: 161.015
+44800/69092	Loss: 159.210
+48000/69092	Loss: 160.275
+51200/69092	Loss: 160.902
+54400/69092	Loss: 157.824
+57600/69092	Loss: 161.885
+60800/69092	Loss: 161.744
+64000/69092	Loss: 162.985
+67200/69092	Loss: 160.771
+Training time 0:05:53.095656
+Epoch: 63 Average loss: 160.34
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 65)
+0/69092	Loss: 172.859
+3200/69092	Loss: 162.614
+6400/69092	Loss: 159.534
+9600/69092	Loss: 161.002
+12800/69092	Loss: 162.184
+16000/69092	Loss: 160.792
+19200/69092	Loss: 160.219
+22400/69092	Loss: 161.261
+25600/69092	Loss: 161.704
+28800/69092	Loss: 157.483
+32000/69092	Loss: 159.020
+35200/69092	Loss: 161.533
+38400/69092	Loss: 158.825
+41600/69092	Loss: 158.501
+44800/69092	Loss: 159.131
+48000/69092	Loss: 159.244
+51200/69092	Loss: 160.231
+54400/69092	Loss: 160.575
+57600/69092	Loss: 159.540
+60800/69092	Loss: 160.942
+64000/69092	Loss: 160.067
+67200/69092	Loss: 162.011
+Training time 0:05:34.212499
+Epoch: 64 Average loss: 160.41
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 66)
+0/69092	Loss: 139.824
+3200/69092	Loss: 158.205
+6400/69092	Loss: 160.618
+9600/69092	Loss: 156.324
+12800/69092	Loss: 162.737
+16000/69092	Loss: 160.543
+19200/69092	Loss: 161.895
+22400/69092	Loss: 162.182
+25600/69092	Loss: 158.472
+28800/69092	Loss: 158.899
+32000/69092	Loss: 156.941
+35200/69092	Loss: 159.754
+38400/69092	Loss: 161.669
+41600/69092	Loss: 159.106
+44800/69092	Loss: 160.160
+48000/69092	Loss: 161.184
+51200/69092	Loss: 159.042
+54400/69092	Loss: 156.636
+57600/69092	Loss: 163.306
+60800/69092	Loss: 159.992
+64000/69092	Loss: 160.094
+67200/69092	Loss: 163.599
+Training time 0:05:32.575799
+Epoch: 65 Average loss: 160.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 67)
+0/69092	Loss: 155.559
+3200/69092	Loss: 162.071
+6400/69092	Loss: 158.085
+9600/69092	Loss: 163.591
+12800/69092	Loss: 160.074
+16000/69092	Loss: 162.083
+19200/69092	Loss: 160.218
+22400/69092	Loss: 160.989
+25600/69092	Loss: 160.541
+28800/69092	Loss: 156.759
+32000/69092	Loss: 157.632
+35200/69092	Loss: 160.790
+38400/69092	Loss: 158.259
+41600/69092	Loss: 161.889
+44800/69092	Loss: 162.124
+48000/69092	Loss: 159.639
+51200/69092	Loss: 159.816
+54400/69092	Loss: 157.599
+57600/69092	Loss: 160.068
+60800/69092	Loss: 160.466
+64000/69092	Loss: 160.161
+67200/69092	Loss: 161.598
+Training time 0:05:33.207884
+Epoch: 66 Average loss: 160.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 68)
+0/69092	Loss: 154.558
+3200/69092	Loss: 161.224
+6400/69092	Loss: 160.700
+9600/69092	Loss: 160.048
+12800/69092	Loss: 160.292
+16000/69092	Loss: 161.326
+19200/69092	Loss: 158.982
+22400/69092	Loss: 161.091
+25600/69092	Loss: 162.158
+28800/69092	Loss: 158.306
+32000/69092	Loss: 161.938
+35200/69092	Loss: 158.099
+38400/69092	Loss: 160.225
+41600/69092	Loss: 161.799
+44800/69092	Loss: 159.889
+48000/69092	Loss: 161.780
+51200/69092	Loss: 157.643
+54400/69092	Loss: 161.322
+57600/69092	Loss: 159.164
+60800/69092	Loss: 158.339
+64000/69092	Loss: 159.711
+67200/69092	Loss: 159.860
+Training time 0:05:33.566629
+Epoch: 67 Average loss: 160.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 69)
+0/69092	Loss: 169.675
+3200/69092	Loss: 160.542
+6400/69092	Loss: 158.814
+9600/69092	Loss: 160.833
+12800/69092	Loss: 163.534
+16000/69092	Loss: 158.616
+19200/69092	Loss: 157.820
+22400/69092	Loss: 162.494
+25600/69092	Loss: 159.611
+28800/69092	Loss: 159.206
+32000/69092	Loss: 163.601
+35200/69092	Loss: 160.216
+38400/69092	Loss: 158.062
+41600/69092	Loss: 160.071
+44800/69092	Loss: 161.188
+48000/69092	Loss: 161.149
+51200/69092	Loss: 159.584
+54400/69092	Loss: 158.012
+57600/69092	Loss: 159.519
+60800/69092	Loss: 160.839
+64000/69092	Loss: 160.357
+67200/69092	Loss: 160.408
+Training time 0:05:35.774903
+Epoch: 68 Average loss: 160.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 70)
+0/69092	Loss: 161.484
+3200/69092	Loss: 161.311
+6400/69092	Loss: 159.259
+9600/69092	Loss: 157.865
+12800/69092	Loss: 160.388
+16000/69092	Loss: 161.656
+19200/69092	Loss: 159.835
+22400/69092	Loss: 157.867
+25600/69092	Loss: 159.708
+28800/69092	Loss: 163.847
+32000/69092	Loss: 161.511
+35200/69092	Loss: 158.605
+38400/69092	Loss: 160.856
+41600/69092	Loss: 161.425
+44800/69092	Loss: 158.509
+48000/69092	Loss: 158.459
+51200/69092	Loss: 157.175
+54400/69092	Loss: 158.936
+57600/69092	Loss: 158.645
+60800/69092	Loss: 162.137
+64000/69092	Loss: 161.631
+67200/69092	Loss: 161.586
+Training time 0:05:45.968104
+Epoch: 69 Average loss: 160.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 71)
+0/69092	Loss: 164.481
+3200/69092	Loss: 159.260
+6400/69092	Loss: 161.784
+9600/69092	Loss: 160.517
+12800/69092	Loss: 159.791
+16000/69092	Loss: 157.795
+19200/69092	Loss: 159.406
+22400/69092	Loss: 158.184
+25600/69092	Loss: 157.690
+28800/69092	Loss: 159.254
+32000/69092	Loss: 160.635
+35200/69092	Loss: 162.649
+38400/69092	Loss: 160.130
+41600/69092	Loss: 159.409
+44800/69092	Loss: 160.040
+48000/69092	Loss: 159.570
+51200/69092	Loss: 160.380
+54400/69092	Loss: 158.930
+57600/69092	Loss: 161.539
+60800/69092	Loss: 158.730
+64000/69092	Loss: 160.679
+67200/69092	Loss: 159.969
+Training time 0:05:32.651529
+Epoch: 70 Average loss: 159.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 72)
+0/69092	Loss: 142.716
+3200/69092	Loss: 157.992
+6400/69092	Loss: 157.551
+9600/69092	Loss: 160.012
+12800/69092	Loss: 159.935
+16000/69092	Loss: 159.061
+19200/69092	Loss: 161.256
+22400/69092	Loss: 159.462
+25600/69092	Loss: 162.119
+28800/69092	Loss: 161.915
+32000/69092	Loss: 156.964
+35200/69092	Loss: 160.437
+38400/69092	Loss: 161.939
+41600/69092	Loss: 162.232
+44800/69092	Loss: 158.483
+48000/69092	Loss: 159.101
+51200/69092	Loss: 157.089
+54400/69092	Loss: 158.380
+57600/69092	Loss: 160.195
+60800/69092	Loss: 161.569
+64000/69092	Loss: 160.382
+67200/69092	Loss: 158.295
+Training time 0:05:31.268910
+Epoch: 71 Average loss: 159.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 73)
+0/69092	Loss: 144.982
+3200/69092	Loss: 158.636
+6400/69092	Loss: 159.587
+9600/69092	Loss: 158.902
+12800/69092	Loss: 158.461
+16000/69092	Loss: 160.561
+19200/69092	Loss: 158.414
+22400/69092	Loss: 159.442
+25600/69092	Loss: 155.782
+28800/69092	Loss: 159.598
+32000/69092	Loss: 160.719
+35200/69092	Loss: 160.212
+38400/69092	Loss: 158.779
+41600/69092	Loss: 160.444
+44800/69092	Loss: 160.412
+48000/69092	Loss: 160.666
+51200/69092	Loss: 159.817
+54400/69092	Loss: 159.676
+57600/69092	Loss: 157.572
+60800/69092	Loss: 159.114
+64000/69092	Loss: 164.526
+67200/69092	Loss: 160.948
+Training time 0:05:50.763759
+Epoch: 72 Average loss: 159.63
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 74)
+0/69092	Loss: 145.163
+3200/69092	Loss: 159.608
+6400/69092	Loss: 159.834
+9600/69092	Loss: 160.672
+12800/69092	Loss: 161.739
+16000/69092	Loss: 158.802
+19200/69092	Loss: 161.271
+22400/69092	Loss: 160.589
+25600/69092	Loss: 158.441
+28800/69092	Loss: 160.220
+32000/69092	Loss: 159.768
+35200/69092	Loss: 158.483
+38400/69092	Loss: 160.513
+41600/69092	Loss: 159.295
+44800/69092	Loss: 157.276
+48000/69092	Loss: 157.452
+51200/69092	Loss: 158.179
+54400/69092	Loss: 160.086
+57600/69092	Loss: 159.868
+60800/69092	Loss: 160.561
+64000/69092	Loss: 160.033
+67200/69092	Loss: 159.217
+Training time 0:05:27.953398
+Epoch: 73 Average loss: 159.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 75)
+0/69092	Loss: 182.593
+3200/69092	Loss: 157.735
+6400/69092	Loss: 162.623
+9600/69092	Loss: 157.961
+12800/69092	Loss: 156.636
+16000/69092	Loss: 159.952
+19200/69092	Loss: 159.249
+22400/69092	Loss: 161.514
+25600/69092	Loss: 155.918
+28800/69092	Loss: 158.620
+32000/69092	Loss: 158.486
+35200/69092	Loss: 160.225
+38400/69092	Loss: 161.200
+41600/69092	Loss: 158.838
+44800/69092	Loss: 161.555
+48000/69092	Loss: 160.739
+51200/69092	Loss: 157.590
+54400/69092	Loss: 157.939
+57600/69092	Loss: 159.183
+60800/69092	Loss: 161.986
+64000/69092	Loss: 157.023
+67200/69092	Loss: 161.347
+Training time 0:05:26.295751
+Epoch: 74 Average loss: 159.40
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 76)
+0/69092	Loss: 158.681
+3200/69092	Loss: 161.179
+6400/69092	Loss: 158.464
+9600/69092	Loss: 157.540
+12800/69092	Loss: 158.278
+16000/69092	Loss: 160.927
+19200/69092	Loss: 158.889
+22400/69092	Loss: 158.557
+25600/69092	Loss: 159.444
+28800/69092	Loss: 158.354
+32000/69092	Loss: 158.480
+35200/69092	Loss: 159.606
+38400/69092	Loss: 158.638
+41600/69092	Loss: 158.121
+44800/69092	Loss: 161.871
+48000/69092	Loss: 159.119
+51200/69092	Loss: 157.051
+54400/69092	Loss: 158.179
+57600/69092	Loss: 162.269
+60800/69092	Loss: 161.076
+64000/69092	Loss: 159.565
+67200/69092	Loss: 159.024
+Training time 0:05:37.460671
+Epoch: 75 Average loss: 159.33
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 77)
+0/69092	Loss: 180.183
+3200/69092	Loss: 161.326
+6400/69092	Loss: 156.454
+9600/69092	Loss: 158.102
+12800/69092	Loss: 160.033
+16000/69092	Loss: 157.371
+19200/69092	Loss: 158.610
+22400/69092	Loss: 161.016
+25600/69092	Loss: 158.187
+28800/69092	Loss: 158.732
+32000/69092	Loss: 158.551
+35200/69092	Loss: 164.114
+38400/69092	Loss: 158.244
+41600/69092	Loss: 160.820
+44800/69092	Loss: 161.380
+48000/69092	Loss: 160.957
+51200/69092	Loss: 160.380
+54400/69092	Loss: 159.550
+57600/69092	Loss: 159.288
+60800/69092	Loss: 159.340
+64000/69092	Loss: 159.191
+67200/69092	Loss: 160.314
+Training time 0:05:39.624340
+Epoch: 76 Average loss: 159.65
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 78)
+0/69092	Loss: 154.192
+3200/69092	Loss: 160.147
+6400/69092	Loss: 160.512
+9600/69092	Loss: 163.486
+12800/69092	Loss: 160.083
+16000/69092	Loss: 161.902
+19200/69092	Loss: 159.175
+22400/69092	Loss: 159.717
+25600/69092	Loss: 157.231
+28800/69092	Loss: 156.782
+32000/69092	Loss: 161.465
+35200/69092	Loss: 156.727
+38400/69092	Loss: 160.297
+41600/69092	Loss: 160.974
+44800/69092	Loss: 158.703
+48000/69092	Loss: 160.782
+51200/69092	Loss: 158.618
+54400/69092	Loss: 156.340
+57600/69092	Loss: 157.774
+60800/69092	Loss: 160.375
+64000/69092	Loss: 159.188
+67200/69092	Loss: 161.189
+Training time 0:05:34.005221
+Epoch: 77 Average loss: 159.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 79)
+0/69092	Loss: 163.315
+3200/69092	Loss: 159.521
+6400/69092	Loss: 157.050
+9600/69092	Loss: 158.928
+12800/69092	Loss: 159.364
+16000/69092	Loss: 160.024
+19200/69092	Loss: 159.391
+22400/69092	Loss: 160.335
+25600/69092	Loss: 158.621
+28800/69092	Loss: 159.980
+32000/69092	Loss: 157.115
+35200/69092	Loss: 158.873
+38400/69092	Loss: 162.329
+41600/69092	Loss: 161.074
+44800/69092	Loss: 157.698
+48000/69092	Loss: 158.620
+51200/69092	Loss: 160.713
+54400/69092	Loss: 158.261
+57600/69092	Loss: 160.761
+60800/69092	Loss: 161.576
+64000/69092	Loss: 159.373
+67200/69092	Loss: 160.216
+Training time 0:05:52.913169
+Epoch: 78 Average loss: 159.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 80)
+0/69092	Loss: 172.063
+3200/69092	Loss: 160.270
+6400/69092	Loss: 157.432
+9600/69092	Loss: 158.220
+12800/69092	Loss: 157.986
+16000/69092	Loss: 160.562
+19200/69092	Loss: 157.943
+22400/69092	Loss: 160.751
+25600/69092	Loss: 158.696
+28800/69092	Loss: 158.822
+32000/69092	Loss: 160.434
+35200/69092	Loss: 158.596
+38400/69092	Loss: 158.962
+41600/69092	Loss: 165.257
+44800/69092	Loss: 157.434
+48000/69092	Loss: 159.686
+51200/69092	Loss: 162.117
+54400/69092	Loss: 159.919
+57600/69092	Loss: 162.438
+60800/69092	Loss: 158.443
+64000/69092	Loss: 159.690
+67200/69092	Loss: 156.254
+Training time 0:05:32.739756
+Epoch: 79 Average loss: 159.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 81)
+0/69092	Loss: 156.964
+3200/69092	Loss: 161.015
+6400/69092	Loss: 156.071
+9600/69092	Loss: 159.688
+12800/69092	Loss: 157.272
+16000/69092	Loss: 160.853
+19200/69092	Loss: 162.442
+22400/69092	Loss: 159.761
+25600/69092	Loss: 159.435
+28800/69092	Loss: 162.287
+32000/69092	Loss: 160.241
+35200/69092	Loss: 158.339
+38400/69092	Loss: 159.539
+41600/69092	Loss: 158.518
+44800/69092	Loss: 162.535
+48000/69092	Loss: 157.626
+51200/69092	Loss: 158.148
+54400/69092	Loss: 156.977
+57600/69092	Loss: 160.558
+60800/69092	Loss: 159.007
+64000/69092	Loss: 158.975
+67200/69092	Loss: 160.422
+Training time 0:05:38.436039
+Epoch: 80 Average loss: 159.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 82)
+0/69092	Loss: 166.420
+3200/69092	Loss: 158.251
+6400/69092	Loss: 159.005
+9600/69092	Loss: 159.200
+12800/69092	Loss: 160.833
+16000/69092	Loss: 159.219
+19200/69092	Loss: 161.466
+22400/69092	Loss: 157.718
+25600/69092	Loss: 159.957
+28800/69092	Loss: 158.104
+32000/69092	Loss: 157.485
+35200/69092	Loss: 160.498
+38400/69092	Loss: 159.825
+41600/69092	Loss: 158.072
+44800/69092	Loss: 158.347
+48000/69092	Loss: 156.493
+51200/69092	Loss: 159.301
+54400/69092	Loss: 161.004
+57600/69092	Loss: 161.581
+60800/69092	Loss: 158.647
+64000/69092	Loss: 160.269
+67200/69092	Loss: 161.770
+Training time 0:05:37.385188
+Epoch: 81 Average loss: 159.47
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 83)
+0/69092	Loss: 147.358
+3200/69092	Loss: 158.292
+6400/69092	Loss: 157.895
+9600/69092	Loss: 159.892
+12800/69092	Loss: 158.760
+16000/69092	Loss: 159.902
+19200/69092	Loss: 157.565
+22400/69092	Loss: 159.770
+25600/69092	Loss: 161.968
+28800/69092	Loss: 160.955
+32000/69092	Loss: 159.616
+35200/69092	Loss: 160.013
+38400/69092	Loss: 159.763
+41600/69092	Loss: 157.884
+44800/69092	Loss: 160.106
+48000/69092	Loss: 157.624
+51200/69092	Loss: 160.414
+54400/69092	Loss: 156.007
+57600/69092	Loss: 162.964
+60800/69092	Loss: 158.520
+64000/69092	Loss: 159.945
+67200/69092	Loss: 160.523
+Training time 0:05:26.791158
+Epoch: 82 Average loss: 159.45
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 84)
+0/69092	Loss: 151.199
+3200/69092	Loss: 159.845
+6400/69092	Loss: 159.746
+9600/69092	Loss: 158.563
+12800/69092	Loss: 158.639
+16000/69092	Loss: 158.074
+19200/69092	Loss: 158.288
+22400/69092	Loss: 157.029
+25600/69092	Loss: 160.350
+28800/69092	Loss: 161.038
+32000/69092	Loss: 161.095
+35200/69092	Loss: 158.589
+38400/69092	Loss: 162.509
+41600/69092	Loss: 160.726
+44800/69092	Loss: 157.305
+48000/69092	Loss: 159.656
+51200/69092	Loss: 159.312
+54400/69092	Loss: 157.474
+57600/69092	Loss: 157.230
+60800/69092	Loss: 158.984
+64000/69092	Loss: 159.303
+67200/69092	Loss: 161.517
+Training time 0:05:48.321003
+Epoch: 83 Average loss: 159.27
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 85)
+0/69092	Loss: 167.506
+3200/69092	Loss: 158.734
+6400/69092	Loss: 154.474
+9600/69092	Loss: 158.548
+12800/69092	Loss: 159.065
+16000/69092	Loss: 161.150
+19200/69092	Loss: 156.751
+22400/69092	Loss: 161.333
+25600/69092	Loss: 158.619
+28800/69092	Loss: 159.785
+32000/69092	Loss: 158.721
+35200/69092	Loss: 160.114
+38400/69092	Loss: 159.841
+41600/69092	Loss: 158.137
+44800/69092	Loss: 161.397
+48000/69092	Loss: 157.815
+51200/69092	Loss: 159.810
+54400/69092	Loss: 159.795
+57600/69092	Loss: 162.666
+60800/69092	Loss: 160.183
+64000/69092	Loss: 161.473
+67200/69092	Loss: 161.339
+Training time 0:05:36.990551
+Epoch: 84 Average loss: 159.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 86)
+0/69092	Loss: 155.993
+3200/69092	Loss: 160.144
+6400/69092	Loss: 159.036
+9600/69092	Loss: 158.457
+12800/69092	Loss: 156.089
+16000/69092	Loss: 160.999
+19200/69092	Loss: 159.388
+22400/69092	Loss: 160.120
+25600/69092	Loss: 161.818
+28800/69092	Loss: 158.264
+32000/69092	Loss: 159.129
+35200/69092	Loss: 155.037
+38400/69092	Loss: 156.492
+41600/69092	Loss: 161.887
+44800/69092	Loss: 157.376
+48000/69092	Loss: 160.773
+51200/69092	Loss: 159.116
+54400/69092	Loss: 158.015
+57600/69092	Loss: 160.229
+60800/69092	Loss: 160.340
+64000/69092	Loss: 161.229
+67200/69092	Loss: 158.952
+Training time 0:06:00.143695
+Epoch: 85 Average loss: 159.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 87)
+0/69092	Loss: 155.338
+3200/69092	Loss: 156.856
+6400/69092	Loss: 159.420
+9600/69092	Loss: 158.050
+12800/69092	Loss: 156.955
+16000/69092	Loss: 161.199
+19200/69092	Loss: 161.122
+22400/69092	Loss: 160.793
+25600/69092	Loss: 160.091
+28800/69092	Loss: 157.460
+32000/69092	Loss: 158.015
+35200/69092	Loss: 160.317
+38400/69092	Loss: 162.705
+41600/69092	Loss: 156.314
+44800/69092	Loss: 157.986
+48000/69092	Loss: 157.577
+51200/69092	Loss: 157.721
+54400/69092	Loss: 160.059
+57600/69092	Loss: 159.799
+60800/69092	Loss: 159.100
+64000/69092	Loss: 160.676
+67200/69092	Loss: 161.405
+Training time 0:06:06.903945
+Epoch: 86 Average loss: 159.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 88)
+0/69092	Loss: 149.605
+3200/69092	Loss: 158.922
+6400/69092	Loss: 161.236
+9600/69092	Loss: 159.632
+12800/69092	Loss: 159.879
+16000/69092	Loss: 159.513
+19200/69092	Loss: 159.728
+22400/69092	Loss: 156.810
+25600/69092	Loss: 159.908
+28800/69092	Loss: 158.473
+32000/69092	Loss: 162.172
+35200/69092	Loss: 158.501
+38400/69092	Loss: 156.194
+41600/69092	Loss: 159.270
+44800/69092	Loss: 156.212
+48000/69092	Loss: 159.069
+51200/69092	Loss: 162.970
+54400/69092	Loss: 159.473
+57600/69092	Loss: 160.206
+60800/69092	Loss: 158.938
+64000/69092	Loss: 161.160
+67200/69092	Loss: 157.509
+Training time 0:05:50.139203
+Epoch: 87 Average loss: 159.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 89)
+0/69092	Loss: 177.011
+3200/69092	Loss: 160.157
+6400/69092	Loss: 157.318
+9600/69092	Loss: 157.788
+12800/69092	Loss: 159.433
+16000/69092	Loss: 161.363
+19200/69092	Loss: 160.101
+22400/69092	Loss: 158.566
+25600/69092	Loss: 158.240
+28800/69092	Loss: 158.962
+32000/69092	Loss: 160.175
+35200/69092	Loss: 160.883
+38400/69092	Loss: 163.155
+41600/69092	Loss: 161.841
+44800/69092	Loss: 158.269
+48000/69092	Loss: 156.448
+51200/69092	Loss: 157.790
+54400/69092	Loss: 157.862
+57600/69092	Loss: 156.747
+60800/69092	Loss: 158.219
+64000/69092	Loss: 158.478
+67200/69092	Loss: 161.865
+Training time 0:05:43.728689
+Epoch: 88 Average loss: 159.24
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 90)
+0/69092	Loss: 166.930
+3200/69092	Loss: 159.466
+6400/69092	Loss: 159.608
+9600/69092	Loss: 156.597
+12800/69092	Loss: 161.365
+16000/69092	Loss: 158.517
+19200/69092	Loss: 160.347
+22400/69092	Loss: 158.586
+25600/69092	Loss: 156.568
+28800/69092	Loss: 157.357
+32000/69092	Loss: 157.222
+35200/69092	Loss: 159.279
+38400/69092	Loss: 161.612
+41600/69092	Loss: 160.618
+44800/69092	Loss: 159.613
+48000/69092	Loss: 156.892
+51200/69092	Loss: 161.148
+54400/69092	Loss: 158.872
+57600/69092	Loss: 157.522
+60800/69092	Loss: 159.689
+64000/69092	Loss: 162.225
+67200/69092	Loss: 160.018
+Training time 0:05:50.054965
+Epoch: 89 Average loss: 159.20
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 91)
+0/69092	Loss: 163.660
+3200/69092	Loss: 160.243
+6400/69092	Loss: 160.221
+9600/69092	Loss: 159.913
+12800/69092	Loss: 159.190
+16000/69092	Loss: 159.877
+19200/69092	Loss: 157.558
+22400/69092	Loss: 156.779
+25600/69092	Loss: 160.367
+28800/69092	Loss: 159.827
+32000/69092	Loss: 157.915
+35200/69092	Loss: 159.848
+38400/69092	Loss: 162.339
+41600/69092	Loss: 159.374
+44800/69092	Loss: 158.487
+48000/69092	Loss: 157.560
+51200/69092	Loss: 157.324
+54400/69092	Loss: 162.203
+57600/69092	Loss: 159.657
+60800/69092	Loss: 156.430
+64000/69092	Loss: 158.719
+67200/69092	Loss: 161.346
+Training time 0:05:38.412112
+Epoch: 90 Average loss: 159.33
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 92)
+0/69092	Loss: 150.490
+3200/69092	Loss: 158.934
+6400/69092	Loss: 159.617
+9600/69092	Loss: 159.273
+12800/69092	Loss: 158.963
+16000/69092	Loss: 157.031
+19200/69092	Loss: 159.648
+22400/69092	Loss: 161.084
+25600/69092	Loss: 161.378
+28800/69092	Loss: 157.690
+32000/69092	Loss: 157.451
+35200/69092	Loss: 157.694
+38400/69092	Loss: 156.956
+41600/69092	Loss: 157.919
+44800/69092	Loss: 160.401
+48000/69092	Loss: 158.635
+51200/69092	Loss: 159.358
+54400/69092	Loss: 160.336
+57600/69092	Loss: 160.171
+60800/69092	Loss: 160.302
+64000/69092	Loss: 160.705
+67200/69092	Loss: 157.521
+Training time 0:05:28.990469
+Epoch: 91 Average loss: 159.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 93)
+0/69092	Loss: 145.606
+3200/69092	Loss: 157.411
+6400/69092	Loss: 158.385
+9600/69092	Loss: 161.953
+12800/69092	Loss: 162.363
+16000/69092	Loss: 160.688
+19200/69092	Loss: 159.999
+22400/69092	Loss: 160.088
+25600/69092	Loss: 157.191
+28800/69092	Loss: 157.325
+32000/69092	Loss: 157.672
+35200/69092	Loss: 157.772
+38400/69092	Loss: 159.461
+41600/69092	Loss: 158.805
+44800/69092	Loss: 160.401
+48000/69092	Loss: 159.929
+51200/69092	Loss: 155.478
+54400/69092	Loss: 159.072
+57600/69092	Loss: 160.275
+60800/69092	Loss: 158.119
+64000/69092	Loss: 155.665
+67200/69092	Loss: 162.841
+Training time 0:05:38.189466
+Epoch: 92 Average loss: 159.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 94)
+0/69092	Loss: 149.699
+3200/69092	Loss: 160.871
+6400/69092	Loss: 160.415
+9600/69092	Loss: 158.102
+12800/69092	Loss: 159.485
+16000/69092	Loss: 160.179
+19200/69092	Loss: 157.111
+22400/69092	Loss: 159.198
+25600/69092	Loss: 158.266
+28800/69092	Loss: 159.081
+32000/69092	Loss: 159.866
+35200/69092	Loss: 160.600
+38400/69092	Loss: 161.372
+41600/69092	Loss: 161.123
+44800/69092	Loss: 157.819
+48000/69092	Loss: 160.199
+51200/69092	Loss: 157.882
+54400/69092	Loss: 158.645
+57600/69092	Loss: 156.973
+60800/69092	Loss: 157.073
+64000/69092	Loss: 158.881
+67200/69092	Loss: 160.097
+Training time 0:05:51.833121
+Epoch: 93 Average loss: 159.22
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 95)
+0/69092	Loss: 184.254
+3200/69092	Loss: 159.065
+6400/69092	Loss: 159.760
+9600/69092	Loss: 159.856
+12800/69092	Loss: 161.118
+16000/69092	Loss: 158.212
+19200/69092	Loss: 160.134
+22400/69092	Loss: 158.819
+25600/69092	Loss: 159.705
+28800/69092	Loss: 157.535
+32000/69092	Loss: 158.722
+35200/69092	Loss: 158.514
+38400/69092	Loss: 160.212
+41600/69092	Loss: 156.201
+44800/69092	Loss: 160.091
+48000/69092	Loss: 161.874
+51200/69092	Loss: 157.687
+54400/69092	Loss: 160.520
+57600/69092	Loss: 158.559
+60800/69092	Loss: 159.642
+64000/69092	Loss: 157.527
+67200/69092	Loss: 157.909
+Training time 0:05:30.468429
+Epoch: 94 Average loss: 159.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 96)
+0/69092	Loss: 158.403
+3200/69092	Loss: 161.799
+6400/69092	Loss: 160.832
+9600/69092	Loss: 159.543
+12800/69092	Loss: 157.111
+16000/69092	Loss: 159.294
+19200/69092	Loss: 158.326
+22400/69092	Loss: 157.586
+25600/69092	Loss: 159.439
+28800/69092	Loss: 158.697
+32000/69092	Loss: 160.257
+35200/69092	Loss: 161.020
+38400/69092	Loss: 158.668
+41600/69092	Loss: 157.037
+44800/69092	Loss: 159.130
+48000/69092	Loss: 159.078
+51200/69092	Loss: 159.145
+54400/69092	Loss: 158.187
+57600/69092	Loss: 158.109
+60800/69092	Loss: 156.643
+64000/69092	Loss: 159.603
+67200/69092	Loss: 161.188
+Training time 0:05:38.293450
+Epoch: 95 Average loss: 159.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 97)
+0/69092	Loss: 168.477
+3200/69092	Loss: 161.501
+6400/69092	Loss: 159.521
+9600/69092	Loss: 158.598
+12800/69092	Loss: 156.909
+16000/69092	Loss: 161.964
+19200/69092	Loss: 157.111
+22400/69092	Loss: 162.194
+25600/69092	Loss: 159.024
+28800/69092	Loss: 158.036
+32000/69092	Loss: 160.642
+35200/69092	Loss: 156.836
+38400/69092	Loss: 158.410
+41600/69092	Loss: 159.573
+44800/69092	Loss: 160.553
+48000/69092	Loss: 159.129
+51200/69092	Loss: 160.196
+54400/69092	Loss: 158.417
+57600/69092	Loss: 161.815
+60800/69092	Loss: 156.995
+64000/69092	Loss: 154.083
+67200/69092	Loss: 158.133
+Training time 0:05:28.475772
+Epoch: 96 Average loss: 159.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 98)
+0/69092	Loss: 140.255
+3200/69092	Loss: 156.551
+6400/69092	Loss: 159.354
+9600/69092	Loss: 160.916
+12800/69092	Loss: 158.278
+16000/69092	Loss: 159.341
+19200/69092	Loss: 161.566
+22400/69092	Loss: 158.895
+25600/69092	Loss: 158.972
+28800/69092	Loss: 157.996
+32000/69092	Loss: 158.795
+35200/69092	Loss: 159.576
+38400/69092	Loss: 160.755
+41600/69092	Loss: 160.168
+44800/69092	Loss: 159.245
+48000/69092	Loss: 157.132
+51200/69092	Loss: 159.715
+54400/69092	Loss: 157.385
+57600/69092	Loss: 159.976
+60800/69092	Loss: 161.275
+64000/69092	Loss: 157.620
+67200/69092	Loss: 159.525
+Training time 0:05:40.654846
+Epoch: 97 Average loss: 159.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 99)
+0/69092	Loss: 153.552
+3200/69092	Loss: 157.359
+6400/69092	Loss: 156.474
+9600/69092	Loss: 157.425
+12800/69092	Loss: 157.220
+16000/69092	Loss: 160.235
+19200/69092	Loss: 158.997
+22400/69092	Loss: 159.980
+25600/69092	Loss: 159.940
+28800/69092	Loss: 156.694
+32000/69092	Loss: 160.209
+35200/69092	Loss: 156.144
+38400/69092	Loss: 161.132
+41600/69092	Loss: 160.131
+44800/69092	Loss: 160.084
+48000/69092	Loss: 161.853
+51200/69092	Loss: 157.055
+54400/69092	Loss: 161.999
+57600/69092	Loss: 160.006
+60800/69092	Loss: 159.206
+64000/69092	Loss: 157.588
+67200/69092	Loss: 157.070
+Training time 0:05:38.871933
+Epoch: 98 Average loss: 158.93
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 100)
+0/69092	Loss: 173.034
+3200/69092	Loss: 158.607
+6400/69092	Loss: 157.463
+9600/69092	Loss: 160.543
+12800/69092	Loss: 155.781
+16000/69092	Loss: 157.382
+19200/69092	Loss: 160.133
+22400/69092	Loss: 159.512
+25600/69092	Loss: 158.401
+28800/69092	Loss: 162.084
+32000/69092	Loss: 157.081
+35200/69092	Loss: 160.956
+38400/69092	Loss: 156.895
+41600/69092	Loss: 159.678
+44800/69092	Loss: 160.463
+48000/69092	Loss: 160.401
+51200/69092	Loss: 158.272
+54400/69092	Loss: 157.737
+57600/69092	Loss: 159.711
+60800/69092	Loss: 159.560
+64000/69092	Loss: 160.014
+67200/69092	Loss: 158.850
+Training time 0:05:35.589953
+Epoch: 99 Average loss: 158.99
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 101)
+0/69092	Loss: 167.127
+3200/69092	Loss: 158.691
+6400/69092	Loss: 160.346
+9600/69092	Loss: 158.123
+12800/69092	Loss: 161.401
+16000/69092	Loss: 155.992
+19200/69092	Loss: 155.775
+22400/69092	Loss: 157.855
+25600/69092	Loss: 157.884
+28800/69092	Loss: 158.127
+32000/69092	Loss: 157.958
+35200/69092	Loss: 157.580
+38400/69092	Loss: 159.400
+41600/69092	Loss: 159.374
+44800/69092	Loss: 159.242
+48000/69092	Loss: 159.683
+51200/69092	Loss: 161.591
+54400/69092	Loss: 160.516
+57600/69092	Loss: 163.235
+60800/69092	Loss: 157.712
+64000/69092	Loss: 158.136
+67200/69092	Loss: 160.390
+Training time 0:05:58.043103
+Epoch: 100 Average loss: 158.96
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 102)
+0/69092	Loss: 152.027
+3200/69092	Loss: 158.885
+6400/69092	Loss: 159.788
+9600/69092	Loss: 161.860
+12800/69092	Loss: 158.560
+16000/69092	Loss: 159.234
+19200/69092	Loss: 161.745
+22400/69092	Loss: 158.516
+25600/69092	Loss: 157.561
+28800/69092	Loss: 159.708
+32000/69092	Loss: 156.264
+35200/69092	Loss: 158.669
+38400/69092	Loss: 159.436
+41600/69092	Loss: 160.343
+44800/69092	Loss: 156.492
+48000/69092	Loss: 158.668
+51200/69092	Loss: 156.608
+54400/69092	Loss: 159.002
+57600/69092	Loss: 158.732
+60800/69092	Loss: 158.290
+64000/69092	Loss: 160.763
+67200/69092	Loss: 161.906
+Training time 0:05:35.318233
+Epoch: 101 Average loss: 159.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 103)
+0/69092	Loss: 157.941
+3200/69092	Loss: 159.008
+6400/69092	Loss: 157.562
+9600/69092	Loss: 159.699
+12800/69092	Loss: 161.177
+16000/69092	Loss: 159.552
+19200/69092	Loss: 158.248
+22400/69092	Loss: 159.358
+25600/69092	Loss: 158.911
+28800/69092	Loss: 159.869
+32000/69092	Loss: 159.056
+35200/69092	Loss: 161.017
+38400/69092	Loss: 157.137
+41600/69092	Loss: 157.186
+44800/69092	Loss: 163.476
+48000/69092	Loss: 157.922
+51200/69092	Loss: 159.202
+54400/69092	Loss: 156.061
+57600/69092	Loss: 159.155
+60800/69092	Loss: 161.382
+64000/69092	Loss: 156.198
+67200/69092	Loss: 159.163
+Training time 0:05:42.512486
+Epoch: 102 Average loss: 159.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 104)
+0/69092	Loss: 165.301
+3200/69092	Loss: 157.779
+6400/69092	Loss: 158.741
+9600/69092	Loss: 158.514
+12800/69092	Loss: 159.785
+16000/69092	Loss: 158.431
+19200/69092	Loss: 160.162
+22400/69092	Loss: 155.917
+25600/69092	Loss: 160.767
+28800/69092	Loss: 157.529
+32000/69092	Loss: 157.990
+35200/69092	Loss: 157.774
+38400/69092	Loss: 159.274
+41600/69092	Loss: 160.234
+44800/69092	Loss: 160.363
+48000/69092	Loss: 159.043
+51200/69092	Loss: 159.252
+54400/69092	Loss: 158.529
+57600/69092	Loss: 161.563
+60800/69092	Loss: 155.825
+64000/69092	Loss: 158.326
+67200/69092	Loss: 159.363
+Training time 0:05:38.708248
+Epoch: 103 Average loss: 158.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 105)
+0/69092	Loss: 162.340
+3200/69092	Loss: 159.264
+6400/69092	Loss: 158.435
+9600/69092	Loss: 159.761
+12800/69092	Loss: 158.671
+16000/69092	Loss: 159.417
+19200/69092	Loss: 159.279
+22400/69092	Loss: 158.103
+25600/69092	Loss: 158.076
+28800/69092	Loss: 161.202
+32000/69092	Loss: 158.187
+35200/69092	Loss: 161.701
+38400/69092	Loss: 157.858
+41600/69092	Loss: 159.545
+44800/69092	Loss: 161.386
+48000/69092	Loss: 160.321
+51200/69092	Loss: 157.574
+54400/69092	Loss: 155.936
+57600/69092	Loss: 157.492
+60800/69092	Loss: 159.926
+64000/69092	Loss: 159.998
+67200/69092	Loss: 159.038
+Training time 0:05:38.603309
+Epoch: 104 Average loss: 159.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 106)
+0/69092	Loss: 152.111
+3200/69092	Loss: 159.423
+6400/69092	Loss: 162.587
+9600/69092	Loss: 160.640
+12800/69092	Loss: 159.132
+16000/69092	Loss: 159.707
+19200/69092	Loss: 158.382
+22400/69092	Loss: 156.301
+25600/69092	Loss: 159.051
+28800/69092	Loss: 158.924
+32000/69092	Loss: 157.140
+35200/69092	Loss: 162.650
+38400/69092	Loss: 157.653
+41600/69092	Loss: 159.198
+44800/69092	Loss: 159.082
+48000/69092	Loss: 158.785
+51200/69092	Loss: 156.259
+54400/69092	Loss: 158.097
+57600/69092	Loss: 157.105
+60800/69092	Loss: 163.704
+64000/69092	Loss: 158.391
+67200/69092	Loss: 156.931
+Training time 0:05:34.796696
+Epoch: 105 Average loss: 159.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64_ls_5/checkpoints/last' (iter 107)
+0/69092	Loss: 170.103
+3200/69092	Loss: 156.306
+6400/69092	Loss: 160.654
+9600/69092	Loss: 159.796
+12800/69092	Loss: 157.796
+16000/69092	Loss: 157.300
+19200/69092	Loss: 157.026
+22400/69092	Loss: 158.475
+25600/69092	Loss: 158.968
+28800/69092	Loss: 159.848
+32000/69092	Loss: 158.780
+35200/69092	Loss: 159.463
+38400/69092	Loss: 160.543
+41600/69092	Loss: 159.262
+44800/69092	Loss: 158.482
+48000/69092	Loss: 157.139
+51200/69092	Loss: 160.624
+54400/69092	Loss: 160.324
diff --git a/OAR.2068291.stderr b/OAR.2068291.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..bef21b425da6ed5bf2b6378f1e117febd8fd876c
--- /dev/null
+++ b/OAR.2068291.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-25 02:59:26] Job 2068291 KILLED ##
diff --git a/OAR.2068291.stdout b/OAR.2068291.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..8cab011240c9de830441b9c0202e96c8a4edf1f9
--- /dev/null
+++ b/OAR.2068291.stdout
@@ -0,0 +1,3168 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64_ls_5', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=5, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64_ls_5
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=10, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=5, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 761485
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last (iter 1)'
+0/69092	Loss: 160.718
+3200/69092	Loss: 195.348
+6400/69092	Loss: 196.738
+9600/69092	Loss: 194.883
+12800/69092	Loss: 190.485
+16000/69092	Loss: 187.904
+19200/69092	Loss: 191.408
+22400/69092	Loss: 192.884
+25600/69092	Loss: 190.201
+28800/69092	Loss: 187.534
+32000/69092	Loss: 188.378
+35200/69092	Loss: 187.631
+38400/69092	Loss: 187.406
+41600/69092	Loss: 188.709
+44800/69092	Loss: 186.751
+48000/69092	Loss: 191.667
+51200/69092	Loss: 183.246
+54400/69092	Loss: 187.353
+57600/69092	Loss: 188.015
+60800/69092	Loss: 183.729
+64000/69092	Loss: 184.278
+67200/69092	Loss: 186.790
+Training time 0:04:47.348882
+Epoch: 1 Average loss: 189.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 2)
+0/69092	Loss: 168.150
+3200/69092	Loss: 183.479
+6400/69092	Loss: 179.790
+9600/69092	Loss: 183.119
+12800/69092	Loss: 181.762
+16000/69092	Loss: 179.022
+19200/69092	Loss: 185.978
+22400/69092	Loss: 183.064
+25600/69092	Loss: 181.177
+28800/69092	Loss: 181.881
+32000/69092	Loss: 184.199
+35200/69092	Loss: 181.692
+38400/69092	Loss: 181.997
+41600/69092	Loss: 180.545
+44800/69092	Loss: 184.930
+48000/69092	Loss: 181.591
+51200/69092	Loss: 183.077
+54400/69092	Loss: 182.514
+57600/69092	Loss: 184.202
+60800/69092	Loss: 175.696
+64000/69092	Loss: 178.647
+67200/69092	Loss: 178.508
+Training time 0:04:50.598290
+Epoch: 2 Average loss: 181.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 3)
+0/69092	Loss: 160.616
+3200/69092	Loss: 176.036
+6400/69092	Loss: 181.527
+9600/69092	Loss: 180.076
+12800/69092	Loss: 180.270
+16000/69092	Loss: 180.807
+19200/69092	Loss: 177.929
+22400/69092	Loss: 178.709
+25600/69092	Loss: 181.522
+28800/69092	Loss: 178.841
+32000/69092	Loss: 180.035
+35200/69092	Loss: 178.275
+38400/69092	Loss: 182.683
+41600/69092	Loss: 177.399
+44800/69092	Loss: 180.405
+48000/69092	Loss: 181.841
+51200/69092	Loss: 176.475
+54400/69092	Loss: 177.734
+57600/69092	Loss: 182.002
+60800/69092	Loss: 180.416
+64000/69092	Loss: 176.499
+67200/69092	Loss: 179.069
+Training time 0:04:58.449314
+Epoch: 3 Average loss: 179.47
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 4)
+0/69092	Loss: 166.288
+3200/69092	Loss: 180.779
+6400/69092	Loss: 177.722
+9600/69092	Loss: 178.878
+12800/69092	Loss: 176.309
+16000/69092	Loss: 176.302
+19200/69092	Loss: 181.046
+22400/69092	Loss: 181.669
+25600/69092	Loss: 180.611
+28800/69092	Loss: 177.631
+32000/69092	Loss: 175.672
+35200/69092	Loss: 176.264
+38400/69092	Loss: 180.020
+41600/69092	Loss: 178.528
+44800/69092	Loss: 181.005
+48000/69092	Loss: 176.038
+51200/69092	Loss: 178.528
+54400/69092	Loss: 177.017
+57600/69092	Loss: 176.450
+60800/69092	Loss: 178.622
+64000/69092	Loss: 173.096
+67200/69092	Loss: 178.671
+Training time 0:04:53.296224
+Epoch: 4 Average loss: 178.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 5)
+0/69092	Loss: 162.201
+3200/69092	Loss: 175.820
+6400/69092	Loss: 179.074
+9600/69092	Loss: 177.050
+12800/69092	Loss: 175.576
+16000/69092	Loss: 177.369
+19200/69092	Loss: 177.303
+22400/69092	Loss: 175.484
+25600/69092	Loss: 176.561
+28800/69092	Loss: 180.101
+32000/69092	Loss: 175.739
+35200/69092	Loss: 179.139
+38400/69092	Loss: 173.434
+41600/69092	Loss: 181.191
+44800/69092	Loss: 178.676
+48000/69092	Loss: 177.746
+51200/69092	Loss: 177.446
+54400/69092	Loss: 175.045
+57600/69092	Loss: 177.810
+60800/69092	Loss: 176.667
+64000/69092	Loss: 176.333
+67200/69092	Loss: 178.492
+Training time 0:04:51.420617
+Epoch: 5 Average loss: 177.25
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 6)
+0/69092	Loss: 160.424
+3200/69092	Loss: 178.602
+6400/69092	Loss: 175.228
+9600/69092	Loss: 173.148
+12800/69092	Loss: 173.935
+16000/69092	Loss: 176.762
+19200/69092	Loss: 177.120
+22400/69092	Loss: 176.178
+25600/69092	Loss: 178.346
+28800/69092	Loss: 173.847
+32000/69092	Loss: 175.982
+35200/69092	Loss: 176.556
+38400/69092	Loss: 177.775
+41600/69092	Loss: 176.511
+44800/69092	Loss: 177.889
+48000/69092	Loss: 172.558
+51200/69092	Loss: 174.388
+54400/69092	Loss: 175.100
+57600/69092	Loss: 177.807
+60800/69092	Loss: 173.273
+64000/69092	Loss: 173.717
+67200/69092	Loss: 176.009
+Training time 0:04:44.187596
+Epoch: 6 Average loss: 175.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 7)
+0/69092	Loss: 154.626
+3200/69092	Loss: 175.090
+6400/69092	Loss: 173.648
+9600/69092	Loss: 177.601
+12800/69092	Loss: 175.078
+16000/69092	Loss: 171.486
+19200/69092	Loss: 174.718
+22400/69092	Loss: 175.500
+25600/69092	Loss: 175.546
+28800/69092	Loss: 174.822
+32000/69092	Loss: 172.752
+35200/69092	Loss: 176.297
+38400/69092	Loss: 173.670
+41600/69092	Loss: 177.349
+44800/69092	Loss: 177.002
+48000/69092	Loss: 171.563
+51200/69092	Loss: 173.142
+54400/69092	Loss: 175.472
+57600/69092	Loss: 177.937
+60800/69092	Loss: 177.506
+64000/69092	Loss: 173.922
+67200/69092	Loss: 176.779
+Training time 0:04:42.086637
+Epoch: 7 Average loss: 175.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 8)
+0/69092	Loss: 166.842
+3200/69092	Loss: 176.393
+6400/69092	Loss: 177.910
+9600/69092	Loss: 174.545
+12800/69092	Loss: 175.173
+16000/69092	Loss: 172.209
+19200/69092	Loss: 177.664
+22400/69092	Loss: 174.725
+25600/69092	Loss: 173.448
+28800/69092	Loss: 174.825
+32000/69092	Loss: 176.125
+35200/69092	Loss: 172.737
+38400/69092	Loss: 175.656
+41600/69092	Loss: 173.128
+44800/69092	Loss: 172.895
+48000/69092	Loss: 172.324
+51200/69092	Loss: 172.439
+54400/69092	Loss: 175.153
+57600/69092	Loss: 176.990
+60800/69092	Loss: 173.385
+64000/69092	Loss: 171.504
+67200/69092	Loss: 175.988
+Training time 0:04:50.085384
+Epoch: 8 Average loss: 174.55
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 9)
+0/69092	Loss: 192.984
+3200/69092	Loss: 168.908
+6400/69092	Loss: 174.075
+9600/69092	Loss: 177.689
+12800/69092	Loss: 173.688
+16000/69092	Loss: 177.886
+19200/69092	Loss: 174.232
+22400/69092	Loss: 173.340
+25600/69092	Loss: 172.025
+28800/69092	Loss: 172.520
+32000/69092	Loss: 171.036
+35200/69092	Loss: 173.630
+38400/69092	Loss: 172.711
+41600/69092	Loss: 173.286
+44800/69092	Loss: 171.935
+48000/69092	Loss: 175.494
+51200/69092	Loss: 175.592
+54400/69092	Loss: 171.378
+57600/69092	Loss: 176.081
+60800/69092	Loss: 171.243
+64000/69092	Loss: 175.991
+67200/69092	Loss: 171.297
+Training time 0:04:46.070348
+Epoch: 9 Average loss: 173.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 10)
+0/69092	Loss: 146.981
+3200/69092	Loss: 171.780
+6400/69092	Loss: 173.784
+9600/69092	Loss: 177.090
+12800/69092	Loss: 170.615
+16000/69092	Loss: 168.159
+19200/69092	Loss: 172.559
+22400/69092	Loss: 170.943
+25600/69092	Loss: 169.955
+28800/69092	Loss: 167.313
+32000/69092	Loss: 169.852
+35200/69092	Loss: 168.104
+38400/69092	Loss: 167.028
+41600/69092	Loss: 162.734
+44800/69092	Loss: 162.262
+48000/69092	Loss: 167.438
+51200/69092	Loss: 164.043
+54400/69092	Loss: 163.100
+57600/69092	Loss: 164.585
+60800/69092	Loss: 162.346
+64000/69092	Loss: 161.962
+67200/69092	Loss: 166.577
+Training time 0:04:47.102107
+Epoch: 10 Average loss: 167.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 11)
+0/69092	Loss: 151.183
+3200/69092	Loss: 165.384
+6400/69092	Loss: 167.018
+9600/69092	Loss: 163.825
+12800/69092	Loss: 162.573
+16000/69092	Loss: 161.033
+19200/69092	Loss: 162.465
+22400/69092	Loss: 163.940
+25600/69092	Loss: 158.944
+28800/69092	Loss: 162.664
+32000/69092	Loss: 161.330
+35200/69092	Loss: 163.406
+38400/69092	Loss: 158.683
+41600/69092	Loss: 160.531
+44800/69092	Loss: 160.417
+48000/69092	Loss: 164.075
+51200/69092	Loss: 163.731
+54400/69092	Loss: 163.045
+57600/69092	Loss: 164.244
+60800/69092	Loss: 157.949
+64000/69092	Loss: 160.197
+67200/69092	Loss: 162.859
+Training time 0:04:51.746640
+Epoch: 11 Average loss: 162.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 12)
+0/69092	Loss: 154.924
+3200/69092	Loss: 163.273
+6400/69092	Loss: 159.163
+9600/69092	Loss: 155.884
+12800/69092	Loss: 162.201
+16000/69092	Loss: 159.360
+19200/69092	Loss: 156.143
+22400/69092	Loss: 156.272
+25600/69092	Loss: 156.757
+28800/69092	Loss: 157.364
+32000/69092	Loss: 158.780
+35200/69092	Loss: 156.232
+38400/69092	Loss: 154.412
+41600/69092	Loss: 155.536
+44800/69092	Loss: 158.557
+48000/69092	Loss: 153.966
+51200/69092	Loss: 155.738
+54400/69092	Loss: 153.539
+57600/69092	Loss: 157.388
+60800/69092	Loss: 155.193
+64000/69092	Loss: 156.084
+67200/69092	Loss: 154.706
+Training time 0:04:39.651515
+Epoch: 12 Average loss: 156.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 13)
+0/69092	Loss: 142.486
+3200/69092	Loss: 156.168
+6400/69092	Loss: 155.495
+9600/69092	Loss: 154.547
+12800/69092	Loss: 150.812
+16000/69092	Loss: 152.557
+19200/69092	Loss: 155.233
+22400/69092	Loss: 153.208
+25600/69092	Loss: 152.687
+28800/69092	Loss: 154.943
+32000/69092	Loss: 153.995
+35200/69092	Loss: 155.391
+38400/69092	Loss: 150.432
+41600/69092	Loss: 152.702
+44800/69092	Loss: 154.048
+48000/69092	Loss: 153.700
+51200/69092	Loss: 153.710
+54400/69092	Loss: 153.347
+57600/69092	Loss: 156.340
+60800/69092	Loss: 155.228
+64000/69092	Loss: 153.031
+67200/69092	Loss: 156.122
+Training time 0:04:47.079566
+Epoch: 13 Average loss: 154.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 14)
+0/69092	Loss: 167.874
+3200/69092	Loss: 153.950
+6400/69092	Loss: 153.100
+9600/69092	Loss: 154.868
+12800/69092	Loss: 153.843
+16000/69092	Loss: 152.569
+19200/69092	Loss: 152.143
+22400/69092	Loss: 156.714
+25600/69092	Loss: 149.704
+28800/69092	Loss: 150.733
+32000/69092	Loss: 148.052
+35200/69092	Loss: 155.286
+38400/69092	Loss: 151.164
+41600/69092	Loss: 150.744
+44800/69092	Loss: 149.292
+48000/69092	Loss: 151.365
+51200/69092	Loss: 153.081
+54400/69092	Loss: 148.012
+57600/69092	Loss: 153.617
+60800/69092	Loss: 154.546
+64000/69092	Loss: 153.127
+67200/69092	Loss: 153.229
+Training time 0:04:38.197224
+Epoch: 14 Average loss: 152.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 15)
+0/69092	Loss: 142.733
+3200/69092	Loss: 153.018
+6400/69092	Loss: 151.895
+9600/69092	Loss: 151.264
+12800/69092	Loss: 148.139
+16000/69092	Loss: 150.293
+19200/69092	Loss: 152.738
+22400/69092	Loss: 152.494
+25600/69092	Loss: 149.747
+28800/69092	Loss: 151.198
+32000/69092	Loss: 150.283
+35200/69092	Loss: 154.543
+38400/69092	Loss: 149.681
+41600/69092	Loss: 150.284
+44800/69092	Loss: 151.567
+48000/69092	Loss: 153.972
+51200/69092	Loss: 153.460
+54400/69092	Loss: 150.492
+57600/69092	Loss: 151.530
+60800/69092	Loss: 153.527
+64000/69092	Loss: 152.394
+67200/69092	Loss: 151.078
+Training time 0:04:39.605667
+Epoch: 15 Average loss: 151.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 16)
+0/69092	Loss: 135.762
+3200/69092	Loss: 151.223
+6400/69092	Loss: 149.636
+9600/69092	Loss: 151.390
+12800/69092	Loss: 152.078
+16000/69092	Loss: 152.049
+19200/69092	Loss: 148.491
+22400/69092	Loss: 149.151
+25600/69092	Loss: 153.019
+28800/69092	Loss: 147.553
+32000/69092	Loss: 153.056
+35200/69092	Loss: 151.313
+38400/69092	Loss: 149.743
+41600/69092	Loss: 149.528
+44800/69092	Loss: 149.730
+48000/69092	Loss: 152.423
+51200/69092	Loss: 153.822
+54400/69092	Loss: 150.835
+57600/69092	Loss: 151.424
+60800/69092	Loss: 151.928
+64000/69092	Loss: 146.308
+67200/69092	Loss: 150.844
+Training time 0:04:41.437413
+Epoch: 16 Average loss: 150.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 17)
+0/69092	Loss: 140.489
+3200/69092	Loss: 152.261
+6400/69092	Loss: 149.076
+9600/69092	Loss: 147.913
+12800/69092	Loss: 151.479
+16000/69092	Loss: 152.871
+19200/69092	Loss: 149.458
+22400/69092	Loss: 153.279
+25600/69092	Loss: 150.869
+28800/69092	Loss: 150.986
+32000/69092	Loss: 152.924
+35200/69092	Loss: 148.119
+38400/69092	Loss: 150.243
+41600/69092	Loss: 148.496
+44800/69092	Loss: 150.882
+48000/69092	Loss: 149.852
+51200/69092	Loss: 149.254
+54400/69092	Loss: 149.813
+57600/69092	Loss: 148.314
+60800/69092	Loss: 151.928
+64000/69092	Loss: 150.182
+67200/69092	Loss: 151.590
+Training time 0:04:41.424319
+Epoch: 17 Average loss: 150.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 18)
+0/69092	Loss: 165.653
+3200/69092	Loss: 150.708
+6400/69092	Loss: 150.738
+9600/69092	Loss: 149.234
+12800/69092	Loss: 150.091
+16000/69092	Loss: 153.208
+19200/69092	Loss: 150.966
+22400/69092	Loss: 148.370
+25600/69092	Loss: 150.041
+28800/69092	Loss: 152.196
+32000/69092	Loss: 147.806
+35200/69092	Loss: 146.719
+38400/69092	Loss: 149.678
+41600/69092	Loss: 151.082
+44800/69092	Loss: 151.379
+48000/69092	Loss: 149.959
+51200/69092	Loss: 150.877
+54400/69092	Loss: 149.647
+57600/69092	Loss: 148.742
+60800/69092	Loss: 151.242
+64000/69092	Loss: 148.770
+67200/69092	Loss: 151.824
+Training time 0:04:48.453964
+Epoch: 18 Average loss: 150.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 19)
+0/69092	Loss: 137.928
+3200/69092	Loss: 145.544
+6400/69092	Loss: 151.132
+9600/69092	Loss: 153.427
+12800/69092	Loss: 152.087
+16000/69092	Loss: 148.224
+19200/69092	Loss: 151.954
+22400/69092	Loss: 151.094
+25600/69092	Loss: 149.743
+28800/69092	Loss: 150.293
+32000/69092	Loss: 149.179
+35200/69092	Loss: 148.198
+38400/69092	Loss: 149.964
+41600/69092	Loss: 149.894
+44800/69092	Loss: 148.807
+48000/69092	Loss: 152.734
+51200/69092	Loss: 148.849
+54400/69092	Loss: 147.120
+57600/69092	Loss: 149.641
+60800/69092	Loss: 147.192
+64000/69092	Loss: 149.263
+67200/69092	Loss: 149.469
+Training time 0:04:56.394880
+Epoch: 19 Average loss: 149.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 20)
+0/69092	Loss: 129.201
+3200/69092	Loss: 149.012
+6400/69092	Loss: 149.960
+9600/69092	Loss: 149.647
+12800/69092	Loss: 151.793
+16000/69092	Loss: 149.258
+19200/69092	Loss: 148.539
+22400/69092	Loss: 147.455
+25600/69092	Loss: 149.341
+28800/69092	Loss: 149.944
+32000/69092	Loss: 152.197
+35200/69092	Loss: 146.482
+38400/69092	Loss: 148.127
+41600/69092	Loss: 146.162
+44800/69092	Loss: 151.764
+48000/69092	Loss: 150.575
+51200/69092	Loss: 150.623
+54400/69092	Loss: 149.253
+57600/69092	Loss: 147.238
+60800/69092	Loss: 149.014
+64000/69092	Loss: 150.219
+67200/69092	Loss: 148.554
+Training time 0:04:45.573469
+Epoch: 20 Average loss: 149.37
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 21)
+0/69092	Loss: 133.640
+3200/69092	Loss: 148.624
+6400/69092	Loss: 149.967
+9600/69092	Loss: 148.680
+12800/69092	Loss: 147.118
+16000/69092	Loss: 150.804
+19200/69092	Loss: 148.523
+22400/69092	Loss: 152.628
+25600/69092	Loss: 150.004
+28800/69092	Loss: 149.583
+32000/69092	Loss: 148.045
+35200/69092	Loss: 147.617
+38400/69092	Loss: 152.470
+41600/69092	Loss: 150.862
+44800/69092	Loss: 149.814
+48000/69092	Loss: 149.141
+51200/69092	Loss: 148.093
+54400/69092	Loss: 152.332
+57600/69092	Loss: 146.968
+60800/69092	Loss: 149.775
+64000/69092	Loss: 147.789
+67200/69092	Loss: 147.252
+Training time 0:04:59.276110
+Epoch: 21 Average loss: 149.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 22)
+0/69092	Loss: 131.701
+3200/69092	Loss: 148.449
+6400/69092	Loss: 151.597
+9600/69092	Loss: 148.279
+12800/69092	Loss: 149.448
+16000/69092	Loss: 147.015
+19200/69092	Loss: 147.292
+22400/69092	Loss: 150.862
+25600/69092	Loss: 151.634
+28800/69092	Loss: 146.623
+32000/69092	Loss: 150.266
+35200/69092	Loss: 150.815
+38400/69092	Loss: 148.912
+41600/69092	Loss: 148.657
+44800/69092	Loss: 151.418
+48000/69092	Loss: 146.808
+51200/69092	Loss: 148.590
+54400/69092	Loss: 144.899
+57600/69092	Loss: 149.390
+60800/69092	Loss: 148.584
+64000/69092	Loss: 150.349
+67200/69092	Loss: 152.418
+Training time 0:04:46.599533
+Epoch: 22 Average loss: 149.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 23)
+0/69092	Loss: 147.441
+3200/69092	Loss: 146.450
+6400/69092	Loss: 147.865
+9600/69092	Loss: 149.093
+12800/69092	Loss: 149.175
+16000/69092	Loss: 148.235
+19200/69092	Loss: 145.462
+22400/69092	Loss: 148.329
+25600/69092	Loss: 149.039
+28800/69092	Loss: 150.100
+32000/69092	Loss: 149.619
+35200/69092	Loss: 148.932
+38400/69092	Loss: 151.061
+41600/69092	Loss: 150.732
+44800/69092	Loss: 147.543
+48000/69092	Loss: 148.581
+51200/69092	Loss: 149.412
+54400/69092	Loss: 150.560
+57600/69092	Loss: 147.948
+60800/69092	Loss: 148.273
+64000/69092	Loss: 148.835
+67200/69092	Loss: 150.739
+Training time 0:04:49.055498
+Epoch: 23 Average loss: 148.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 24)
+0/69092	Loss: 148.316
+3200/69092	Loss: 150.705
+6400/69092	Loss: 145.427
+9600/69092	Loss: 145.590
+12800/69092	Loss: 146.992
+16000/69092	Loss: 151.595
+19200/69092	Loss: 150.990
+22400/69092	Loss: 149.186
+25600/69092	Loss: 148.401
+28800/69092	Loss: 149.080
+32000/69092	Loss: 149.414
+35200/69092	Loss: 150.053
+38400/69092	Loss: 147.450
+41600/69092	Loss: 151.939
+44800/69092	Loss: 146.092
+48000/69092	Loss: 149.252
+51200/69092	Loss: 149.241
+54400/69092	Loss: 147.512
+57600/69092	Loss: 148.757
+60800/69092	Loss: 150.917
+64000/69092	Loss: 144.102
+67200/69092	Loss: 148.888
+Training time 0:04:42.031507
+Epoch: 24 Average loss: 148.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 25)
+0/69092	Loss: 152.213
+3200/69092	Loss: 149.305
+6400/69092	Loss: 147.650
+9600/69092	Loss: 147.823
+12800/69092	Loss: 150.604
+16000/69092	Loss: 146.576
+19200/69092	Loss: 150.881
+22400/69092	Loss: 149.893
+25600/69092	Loss: 148.029
+28800/69092	Loss: 150.403
+32000/69092	Loss: 149.834
+35200/69092	Loss: 150.335
+38400/69092	Loss: 149.817
+41600/69092	Loss: 148.180
+44800/69092	Loss: 148.837
+48000/69092	Loss: 148.287
+51200/69092	Loss: 145.299
+54400/69092	Loss: 150.729
+57600/69092	Loss: 146.837
+60800/69092	Loss: 146.870
+64000/69092	Loss: 147.973
+67200/69092	Loss: 147.860
+Training time 0:04:47.412749
+Epoch: 25 Average loss: 148.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 26)
+0/69092	Loss: 147.533
+3200/69092	Loss: 149.722
+6400/69092	Loss: 145.848
+9600/69092	Loss: 149.544
+12800/69092	Loss: 147.232
+16000/69092	Loss: 146.528
+19200/69092	Loss: 144.404
+22400/69092	Loss: 148.635
+25600/69092	Loss: 150.073
+28800/69092	Loss: 148.495
+32000/69092	Loss: 148.930
+35200/69092	Loss: 151.213
+38400/69092	Loss: 149.984
+41600/69092	Loss: 149.236
+44800/69092	Loss: 148.683
+48000/69092	Loss: 149.080
+51200/69092	Loss: 147.438
+54400/69092	Loss: 148.451
+57600/69092	Loss: 146.744
+60800/69092	Loss: 150.171
+64000/69092	Loss: 148.242
+67200/69092	Loss: 149.408
+Training time 0:04:50.951991
+Epoch: 26 Average loss: 148.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 27)
+0/69092	Loss: 152.592
+3200/69092	Loss: 149.182
+6400/69092	Loss: 148.681
+9600/69092	Loss: 147.861
+12800/69092	Loss: 148.158
+16000/69092	Loss: 147.307
+19200/69092	Loss: 148.734
+22400/69092	Loss: 148.499
+25600/69092	Loss: 147.275
+28800/69092	Loss: 151.277
+32000/69092	Loss: 149.926
+35200/69092	Loss: 150.180
+38400/69092	Loss: 147.927
+41600/69092	Loss: 147.626
+44800/69092	Loss: 147.453
+48000/69092	Loss: 148.842
+51200/69092	Loss: 151.201
+54400/69092	Loss: 149.597
+57600/69092	Loss: 147.173
+60800/69092	Loss: 150.191
+64000/69092	Loss: 148.148
+67200/69092	Loss: 148.408
+Training time 0:04:53.876524
+Epoch: 27 Average loss: 148.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 28)
+0/69092	Loss: 136.799
+3200/69092	Loss: 149.160
+6400/69092	Loss: 148.577
+9600/69092	Loss: 148.877
+12800/69092	Loss: 148.757
+16000/69092	Loss: 148.481
+19200/69092	Loss: 148.485
+22400/69092	Loss: 145.469
+25600/69092	Loss: 147.464
+28800/69092	Loss: 150.880
+32000/69092	Loss: 148.038
+35200/69092	Loss: 150.974
+38400/69092	Loss: 148.349
+41600/69092	Loss: 146.056
+44800/69092	Loss: 152.184
+48000/69092	Loss: 146.304
+51200/69092	Loss: 148.212
+54400/69092	Loss: 147.954
+57600/69092	Loss: 146.696
+60800/69092	Loss: 148.265
+64000/69092	Loss: 148.102
+67200/69092	Loss: 148.043
+Training time 0:04:48.916889
+Epoch: 28 Average loss: 148.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 29)
+0/69092	Loss: 139.670
+3200/69092	Loss: 149.241
+6400/69092	Loss: 146.362
+9600/69092	Loss: 149.768
+12800/69092	Loss: 150.836
+16000/69092	Loss: 151.187
+19200/69092	Loss: 148.298
+22400/69092	Loss: 146.347
+25600/69092	Loss: 147.607
+28800/69092	Loss: 148.707
+32000/69092	Loss: 149.895
+35200/69092	Loss: 149.527
+38400/69092	Loss: 148.237
+41600/69092	Loss: 144.982
+44800/69092	Loss: 151.784
+48000/69092	Loss: 147.750
+51200/69092	Loss: 146.564
+54400/69092	Loss: 146.418
+57600/69092	Loss: 149.342
+60800/69092	Loss: 148.502
+64000/69092	Loss: 149.298
+67200/69092	Loss: 147.498
+Training time 0:04:45.409229
+Epoch: 29 Average loss: 148.46
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 30)
+0/69092	Loss: 151.405
+3200/69092	Loss: 146.274
+6400/69092	Loss: 149.452
+9600/69092	Loss: 146.109
+12800/69092	Loss: 149.210
+16000/69092	Loss: 146.885
+19200/69092	Loss: 148.982
+22400/69092	Loss: 149.403
+25600/69092	Loss: 150.823
+28800/69092	Loss: 149.720
+32000/69092	Loss: 146.378
+35200/69092	Loss: 151.609
+38400/69092	Loss: 151.925
+41600/69092	Loss: 150.469
+44800/69092	Loss: 148.398
+48000/69092	Loss: 148.156
+51200/69092	Loss: 146.210
+54400/69092	Loss: 147.480
+57600/69092	Loss: 147.065
+60800/69092	Loss: 145.786
+64000/69092	Loss: 150.431
+67200/69092	Loss: 146.721
+Training time 0:04:50.737945
+Epoch: 30 Average loss: 148.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 31)
+0/69092	Loss: 150.116
+3200/69092	Loss: 150.399
+6400/69092	Loss: 148.203
+9600/69092	Loss: 151.006
+12800/69092	Loss: 147.144
+16000/69092	Loss: 150.393
+19200/69092	Loss: 146.829
+22400/69092	Loss: 147.297
+25600/69092	Loss: 145.868
+28800/69092	Loss: 148.685
+32000/69092	Loss: 147.328
+35200/69092	Loss: 150.047
+38400/69092	Loss: 144.112
+41600/69092	Loss: 145.091
+44800/69092	Loss: 147.921
+48000/69092	Loss: 147.783
+51200/69092	Loss: 147.961
+54400/69092	Loss: 149.061
+57600/69092	Loss: 147.789
+60800/69092	Loss: 146.963
+64000/69092	Loss: 149.435
+67200/69092	Loss: 146.020
+Training time 0:04:48.621983
+Epoch: 31 Average loss: 147.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 32)
+0/69092	Loss: 139.254
+3200/69092	Loss: 147.274
+6400/69092	Loss: 148.368
+9600/69092	Loss: 147.824
+12800/69092	Loss: 144.687
+16000/69092	Loss: 147.647
+19200/69092	Loss: 145.607
+22400/69092	Loss: 145.909
+25600/69092	Loss: 147.128
+28800/69092	Loss: 147.337
+32000/69092	Loss: 147.407
+35200/69092	Loss: 147.988
+38400/69092	Loss: 150.538
+41600/69092	Loss: 151.803
+44800/69092	Loss: 146.955
+48000/69092	Loss: 147.923
+51200/69092	Loss: 147.718
+54400/69092	Loss: 149.130
+57600/69092	Loss: 149.352
+60800/69092	Loss: 147.535
+64000/69092	Loss: 149.475
+67200/69092	Loss: 147.609
+Training time 0:04:48.911700
+Epoch: 32 Average loss: 147.93
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 33)
+0/69092	Loss: 135.456
+3200/69092	Loss: 147.926
+6400/69092	Loss: 145.118
+9600/69092	Loss: 149.077
+12800/69092	Loss: 150.177
+16000/69092	Loss: 148.397
+19200/69092	Loss: 147.117
+22400/69092	Loss: 145.922
+25600/69092	Loss: 146.261
+28800/69092	Loss: 148.776
+32000/69092	Loss: 149.062
+35200/69092	Loss: 149.170
+38400/69092	Loss: 147.219
+41600/69092	Loss: 148.891
+44800/69092	Loss: 147.959
+48000/69092	Loss: 150.057
+51200/69092	Loss: 147.330
+54400/69092	Loss: 147.750
+57600/69092	Loss: 148.495
+60800/69092	Loss: 147.875
+64000/69092	Loss: 148.009
+67200/69092	Loss: 149.262
+Training time 0:04:50.174805
+Epoch: 33 Average loss: 148.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 34)
+0/69092	Loss: 182.166
+3200/69092	Loss: 148.901
+6400/69092	Loss: 145.378
+9600/69092	Loss: 148.315
+12800/69092	Loss: 146.911
+16000/69092	Loss: 147.523
+19200/69092	Loss: 147.612
+22400/69092	Loss: 146.449
+25600/69092	Loss: 147.696
+28800/69092	Loss: 150.118
+32000/69092	Loss: 148.327
+35200/69092	Loss: 147.242
+38400/69092	Loss: 148.225
+41600/69092	Loss: 146.721
+44800/69092	Loss: 149.915
+48000/69092	Loss: 147.828
+51200/69092	Loss: 146.816
+54400/69092	Loss: 148.654
+57600/69092	Loss: 147.533
+60800/69092	Loss: 148.067
+64000/69092	Loss: 148.144
+67200/69092	Loss: 150.079
+Training time 0:04:43.649297
+Epoch: 34 Average loss: 147.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 35)
+0/69092	Loss: 144.509
+3200/69092	Loss: 148.918
+6400/69092	Loss: 149.180
+9600/69092	Loss: 143.877
+12800/69092	Loss: 147.871
+16000/69092	Loss: 148.619
+19200/69092	Loss: 148.622
+22400/69092	Loss: 149.938
+25600/69092	Loss: 151.149
+28800/69092	Loss: 145.947
+32000/69092	Loss: 146.845
+35200/69092	Loss: 143.968
+38400/69092	Loss: 148.796
+41600/69092	Loss: 145.774
+44800/69092	Loss: 147.522
+48000/69092	Loss: 150.373
+51200/69092	Loss: 150.224
+54400/69092	Loss: 144.202
+57600/69092	Loss: 147.134
+60800/69092	Loss: 147.678
+64000/69092	Loss: 147.897
+67200/69092	Loss: 147.136
+Training time 0:04:52.302760
+Epoch: 35 Average loss: 147.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 36)
+0/69092	Loss: 171.568
+3200/69092	Loss: 147.211
+6400/69092	Loss: 151.186
+9600/69092	Loss: 147.847
+12800/69092	Loss: 147.045
+16000/69092	Loss: 147.694
+19200/69092	Loss: 147.207
+22400/69092	Loss: 146.437
+25600/69092	Loss: 148.529
+28800/69092	Loss: 147.193
+32000/69092	Loss: 147.475
+35200/69092	Loss: 149.954
+38400/69092	Loss: 146.927
+41600/69092	Loss: 147.328
+44800/69092	Loss: 147.303
+48000/69092	Loss: 147.689
+51200/69092	Loss: 145.557
+54400/69092	Loss: 149.372
+57600/69092	Loss: 149.927
+60800/69092	Loss: 148.130
+64000/69092	Loss: 145.447
+67200/69092	Loss: 147.423
+Training time 0:04:41.499130
+Epoch: 36 Average loss: 147.66
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 37)
+0/69092	Loss: 126.942
+3200/69092	Loss: 147.392
+6400/69092	Loss: 149.148
+9600/69092	Loss: 148.443
+12800/69092	Loss: 146.970
+16000/69092	Loss: 147.117
+19200/69092	Loss: 145.697
+22400/69092	Loss: 150.965
+25600/69092	Loss: 150.003
+28800/69092	Loss: 144.241
+32000/69092	Loss: 148.380
+35200/69092	Loss: 143.263
+38400/69092	Loss: 146.500
+41600/69092	Loss: 147.698
+44800/69092	Loss: 148.868
+48000/69092	Loss: 149.101
+51200/69092	Loss: 146.882
+54400/69092	Loss: 149.080
+57600/69092	Loss: 145.908
+60800/69092	Loss: 150.441
+64000/69092	Loss: 149.058
+67200/69092	Loss: 148.011
+Training time 0:04:43.312257
+Epoch: 37 Average loss: 147.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 38)
+0/69092	Loss: 138.473
+3200/69092	Loss: 148.439
+6400/69092	Loss: 146.342
+9600/69092	Loss: 149.556
+12800/69092	Loss: 147.739
+16000/69092	Loss: 147.844
+19200/69092	Loss: 150.256
+22400/69092	Loss: 147.245
+25600/69092	Loss: 148.738
+28800/69092	Loss: 145.625
+32000/69092	Loss: 149.568
+35200/69092	Loss: 144.465
+38400/69092	Loss: 145.087
+41600/69092	Loss: 148.033
+44800/69092	Loss: 147.266
+48000/69092	Loss: 147.949
+51200/69092	Loss: 147.969
+54400/69092	Loss: 151.305
+57600/69092	Loss: 144.169
+60800/69092	Loss: 148.036
+64000/69092	Loss: 146.607
+67200/69092	Loss: 148.207
+Training time 0:04:37.742191
+Epoch: 38 Average loss: 147.66
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 39)
+0/69092	Loss: 140.590
+3200/69092	Loss: 147.467
+6400/69092	Loss: 145.737
+9600/69092	Loss: 148.149
+12800/69092	Loss: 148.441
+16000/69092	Loss: 147.550
+19200/69092	Loss: 148.744
+22400/69092	Loss: 145.392
+25600/69092	Loss: 147.454
+28800/69092	Loss: 148.207
+32000/69092	Loss: 147.382
+35200/69092	Loss: 148.161
+38400/69092	Loss: 148.098
+41600/69092	Loss: 146.294
+44800/69092	Loss: 147.684
+48000/69092	Loss: 146.768
+51200/69092	Loss: 148.378
+54400/69092	Loss: 148.982
+57600/69092	Loss: 146.299
+60800/69092	Loss: 148.486
+64000/69092	Loss: 146.811
+67200/69092	Loss: 148.951
+Training time 0:04:50.802320
+Epoch: 39 Average loss: 147.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 40)
+0/69092	Loss: 136.610
+3200/69092	Loss: 150.866
+6400/69092	Loss: 147.610
+9600/69092	Loss: 148.863
+12800/69092	Loss: 147.901
+16000/69092	Loss: 146.124
+19200/69092	Loss: 147.856
+22400/69092	Loss: 147.756
+25600/69092	Loss: 146.895
+28800/69092	Loss: 148.882
+32000/69092	Loss: 149.392
+35200/69092	Loss: 145.366
+38400/69092	Loss: 146.326
+41600/69092	Loss: 148.099
+44800/69092	Loss: 147.129
+48000/69092	Loss: 147.240
+51200/69092	Loss: 146.275
+54400/69092	Loss: 150.795
+57600/69092	Loss: 146.048
+60800/69092	Loss: 145.345
+64000/69092	Loss: 146.640
+67200/69092	Loss: 147.691
+Training time 0:04:46.034226
+Epoch: 40 Average loss: 147.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 41)
+0/69092	Loss: 130.478
+3200/69092	Loss: 145.856
+6400/69092	Loss: 148.837
+9600/69092	Loss: 148.716
+12800/69092	Loss: 147.260
+16000/69092	Loss: 146.766
+19200/69092	Loss: 148.677
+22400/69092	Loss: 144.870
+25600/69092	Loss: 146.645
+28800/69092	Loss: 149.022
+32000/69092	Loss: 149.616
+35200/69092	Loss: 147.131
+38400/69092	Loss: 147.440
+41600/69092	Loss: 146.554
+44800/69092	Loss: 149.245
+48000/69092	Loss: 150.220
+51200/69092	Loss: 147.618
+54400/69092	Loss: 149.107
+57600/69092	Loss: 147.340
+60800/69092	Loss: 147.957
+64000/69092	Loss: 146.702
+67200/69092	Loss: 147.134
+Training time 0:04:32.941870
+Epoch: 41 Average loss: 147.76
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 42)
+0/69092	Loss: 142.196
+3200/69092	Loss: 149.834
+6400/69092	Loss: 147.423
+9600/69092	Loss: 147.878
+12800/69092	Loss: 148.993
+16000/69092	Loss: 146.516
+19200/69092	Loss: 147.415
+22400/69092	Loss: 148.012
+25600/69092	Loss: 147.496
+28800/69092	Loss: 148.991
+32000/69092	Loss: 147.641
+35200/69092	Loss: 144.425
+38400/69092	Loss: 148.495
+41600/69092	Loss: 146.185
+44800/69092	Loss: 145.948
+48000/69092	Loss: 148.388
+51200/69092	Loss: 144.057
+54400/69092	Loss: 149.661
+57600/69092	Loss: 149.148
+60800/69092	Loss: 148.142
+64000/69092	Loss: 146.454
+67200/69092	Loss: 147.064
+Training time 0:04:51.137364
+Epoch: 42 Average loss: 147.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 43)
+0/69092	Loss: 146.976
+3200/69092	Loss: 145.914
+6400/69092	Loss: 149.067
+9600/69092	Loss: 147.212
+12800/69092	Loss: 149.855
+16000/69092	Loss: 147.618
+19200/69092	Loss: 147.907
+22400/69092	Loss: 143.236
+25600/69092	Loss: 146.884
+28800/69092	Loss: 146.931
+32000/69092	Loss: 149.960
+35200/69092	Loss: 147.290
+38400/69092	Loss: 146.588
+41600/69092	Loss: 148.242
+44800/69092	Loss: 147.682
+48000/69092	Loss: 149.333
+51200/69092	Loss: 145.939
+54400/69092	Loss: 148.984
+57600/69092	Loss: 147.244
+60800/69092	Loss: 146.377
+64000/69092	Loss: 147.587
+67200/69092	Loss: 148.987
+Training time 0:04:52.995865
+Epoch: 43 Average loss: 147.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 44)
+0/69092	Loss: 147.195
+3200/69092	Loss: 145.492
+6400/69092	Loss: 148.713
+9600/69092	Loss: 147.604
+12800/69092	Loss: 148.692
+16000/69092	Loss: 149.684
+19200/69092	Loss: 150.388
+22400/69092	Loss: 146.937
+25600/69092	Loss: 149.534
+28800/69092	Loss: 148.582
+32000/69092	Loss: 147.982
+35200/69092	Loss: 150.096
+38400/69092	Loss: 146.612
+41600/69092	Loss: 143.746
+44800/69092	Loss: 145.780
+48000/69092	Loss: 146.939
+51200/69092	Loss: 144.991
+54400/69092	Loss: 149.095
+57600/69092	Loss: 146.711
+60800/69092	Loss: 145.298
+64000/69092	Loss: 146.612
+67200/69092	Loss: 147.295
+Training time 0:04:48.267435
+Epoch: 44 Average loss: 147.44
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 45)
+0/69092	Loss: 144.535
+3200/69092	Loss: 149.252
+6400/69092	Loss: 147.248
+9600/69092	Loss: 145.847
+12800/69092	Loss: 147.830
+16000/69092	Loss: 145.908
+19200/69092	Loss: 148.936
+22400/69092	Loss: 147.652
+25600/69092	Loss: 146.594
+28800/69092	Loss: 148.105
+32000/69092	Loss: 147.654
+35200/69092	Loss: 147.288
+38400/69092	Loss: 146.380
+41600/69092	Loss: 150.282
+44800/69092	Loss: 146.159
+48000/69092	Loss: 149.498
+51200/69092	Loss: 146.705
+54400/69092	Loss: 148.901
+57600/69092	Loss: 148.219
+60800/69092	Loss: 144.109
+64000/69092	Loss: 146.984
+67200/69092	Loss: 146.319
+Training time 0:04:46.182034
+Epoch: 45 Average loss: 147.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 46)
+0/69092	Loss: 136.896
+3200/69092	Loss: 149.550
+6400/69092	Loss: 148.753
+9600/69092	Loss: 148.302
+12800/69092	Loss: 148.910
+16000/69092	Loss: 146.299
+19200/69092	Loss: 148.390
+22400/69092	Loss: 145.611
+25600/69092	Loss: 147.391
+28800/69092	Loss: 148.267
+32000/69092	Loss: 146.850
+35200/69092	Loss: 147.362
+38400/69092	Loss: 147.291
+41600/69092	Loss: 149.410
+44800/69092	Loss: 147.307
+48000/69092	Loss: 144.954
+51200/69092	Loss: 148.240
+54400/69092	Loss: 147.475
+57600/69092	Loss: 145.484
+60800/69092	Loss: 143.873
+64000/69092	Loss: 148.499
+67200/69092	Loss: 147.200
+Training time 0:04:42.458226
+Epoch: 46 Average loss: 147.40
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 47)
+0/69092	Loss: 150.699
+3200/69092	Loss: 147.425
+6400/69092	Loss: 147.047
+9600/69092	Loss: 145.380
+12800/69092	Loss: 149.754
+16000/69092	Loss: 149.451
+19200/69092	Loss: 147.034
+22400/69092	Loss: 147.372
+25600/69092	Loss: 147.991
+28800/69092	Loss: 148.776
+32000/69092	Loss: 149.615
+35200/69092	Loss: 147.608
+38400/69092	Loss: 148.215
+41600/69092	Loss: 149.303
+44800/69092	Loss: 146.046
+48000/69092	Loss: 145.386
+51200/69092	Loss: 145.610
+54400/69092	Loss: 146.915
+57600/69092	Loss: 148.651
+60800/69092	Loss: 146.439
+64000/69092	Loss: 145.994
+67200/69092	Loss: 145.309
+Training time 0:04:50.171637
+Epoch: 47 Average loss: 147.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 48)
+0/69092	Loss: 152.689
+3200/69092	Loss: 148.242
+6400/69092	Loss: 149.818
+9600/69092	Loss: 150.796
+12800/69092	Loss: 146.022
+16000/69092	Loss: 146.036
+19200/69092	Loss: 148.187
+22400/69092	Loss: 146.057
+25600/69092	Loss: 148.854
+28800/69092	Loss: 146.305
+32000/69092	Loss: 147.919
+35200/69092	Loss: 147.342
+38400/69092	Loss: 145.379
+41600/69092	Loss: 147.849
+44800/69092	Loss: 145.081
+48000/69092	Loss: 148.374
+51200/69092	Loss: 148.579
+54400/69092	Loss: 148.269
+57600/69092	Loss: 148.248
+60800/69092	Loss: 145.525
+64000/69092	Loss: 143.879
+67200/69092	Loss: 146.685
+Training time 0:04:51.065306
+Epoch: 48 Average loss: 147.28
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 49)
+0/69092	Loss: 138.702
+3200/69092	Loss: 149.051
+6400/69092	Loss: 146.844
+9600/69092	Loss: 146.851
+12800/69092	Loss: 148.108
+16000/69092	Loss: 149.468
+19200/69092	Loss: 146.898
+22400/69092	Loss: 147.229
+25600/69092	Loss: 148.109
+28800/69092	Loss: 144.980
+32000/69092	Loss: 146.640
+35200/69092	Loss: 147.083
+38400/69092	Loss: 144.511
+41600/69092	Loss: 149.689
+44800/69092	Loss: 146.317
+48000/69092	Loss: 148.143
+51200/69092	Loss: 149.500
+54400/69092	Loss: 146.935
+57600/69092	Loss: 145.848
+60800/69092	Loss: 149.408
+64000/69092	Loss: 145.568
+67200/69092	Loss: 146.664
+Training time 0:04:52.656015
+Epoch: 49 Average loss: 147.28
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 50)
+0/69092	Loss: 141.442
+3200/69092	Loss: 145.800
+6400/69092	Loss: 148.988
+9600/69092	Loss: 146.610
+12800/69092	Loss: 149.453
+16000/69092	Loss: 146.489
+19200/69092	Loss: 147.201
+22400/69092	Loss: 146.820
+25600/69092	Loss: 146.664
+28800/69092	Loss: 144.953
+32000/69092	Loss: 150.625
+35200/69092	Loss: 145.450
+38400/69092	Loss: 145.755
+41600/69092	Loss: 147.921
+44800/69092	Loss: 146.116
+48000/69092	Loss: 147.734
+51200/69092	Loss: 144.139
+54400/69092	Loss: 143.210
+57600/69092	Loss: 146.801
+60800/69092	Loss: 149.121
+64000/69092	Loss: 150.397
+67200/69092	Loss: 147.114
+Training time 0:04:52.556216
+Epoch: 50 Average loss: 147.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 51)
+0/69092	Loss: 148.842
+3200/69092	Loss: 149.280
+6400/69092	Loss: 144.520
+9600/69092	Loss: 145.122
+12800/69092	Loss: 148.532
+16000/69092	Loss: 148.949
+19200/69092	Loss: 152.320
+22400/69092	Loss: 147.693
+25600/69092	Loss: 148.352
+28800/69092	Loss: 147.083
+32000/69092	Loss: 143.256
+35200/69092	Loss: 147.945
+38400/69092	Loss: 148.295
+41600/69092	Loss: 146.062
+44800/69092	Loss: 148.280
+48000/69092	Loss: 148.141
+51200/69092	Loss: 144.961
+54400/69092	Loss: 146.155
+57600/69092	Loss: 149.486
+60800/69092	Loss: 147.682
+64000/69092	Loss: 144.341
+67200/69092	Loss: 146.745
+Training time 0:04:52.407796
+Epoch: 51 Average loss: 147.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 52)
+0/69092	Loss: 144.874
+3200/69092	Loss: 148.552
+6400/69092	Loss: 146.392
+9600/69092	Loss: 149.637
+12800/69092	Loss: 142.479
+16000/69092	Loss: 145.334
+19200/69092	Loss: 147.575
+22400/69092	Loss: 147.806
+25600/69092	Loss: 144.501
+28800/69092	Loss: 146.850
+32000/69092	Loss: 146.706
+35200/69092	Loss: 148.118
+38400/69092	Loss: 146.641
+41600/69092	Loss: 146.493
+44800/69092	Loss: 148.939
+48000/69092	Loss: 146.910
+51200/69092	Loss: 148.734
+54400/69092	Loss: 147.240
+57600/69092	Loss: 146.749
+60800/69092	Loss: 148.595
+64000/69092	Loss: 145.173
+67200/69092	Loss: 146.752
+Training time 0:04:46.414580
+Epoch: 52 Average loss: 146.96
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 53)
+0/69092	Loss: 159.264
+3200/69092	Loss: 146.521
+6400/69092	Loss: 145.872
+9600/69092	Loss: 147.680
+12800/69092	Loss: 146.508
+16000/69092	Loss: 143.673
+19200/69092	Loss: 149.822
+22400/69092	Loss: 147.306
+25600/69092	Loss: 144.225
+28800/69092	Loss: 148.730
+32000/69092	Loss: 147.515
+35200/69092	Loss: 147.099
+38400/69092	Loss: 148.525
+41600/69092	Loss: 148.024
+44800/69092	Loss: 146.560
+48000/69092	Loss: 148.455
+51200/69092	Loss: 150.450
+54400/69092	Loss: 147.113
+57600/69092	Loss: 147.433
+60800/69092	Loss: 147.747
+64000/69092	Loss: 147.956
+67200/69092	Loss: 145.113
+Training time 0:04:51.452545
+Epoch: 53 Average loss: 147.28
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 54)
+0/69092	Loss: 141.546
+3200/69092	Loss: 145.539
+6400/69092	Loss: 149.190
+9600/69092	Loss: 146.784
+12800/69092	Loss: 146.082
+16000/69092	Loss: 146.864
+19200/69092	Loss: 145.810
+22400/69092	Loss: 147.325
+25600/69092	Loss: 143.982
+28800/69092	Loss: 150.492
+32000/69092	Loss: 147.060
+35200/69092	Loss: 146.891
+38400/69092	Loss: 146.113
+41600/69092	Loss: 147.908
+44800/69092	Loss: 146.118
+48000/69092	Loss: 146.011
+51200/69092	Loss: 146.489
+54400/69092	Loss: 147.377
+57600/69092	Loss: 146.536
+60800/69092	Loss: 148.937
+64000/69092	Loss: 147.756
+67200/69092	Loss: 147.634
+Training time 0:04:50.742064
+Epoch: 54 Average loss: 147.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 55)
+0/69092	Loss: 138.250
+3200/69092	Loss: 145.860
+6400/69092	Loss: 145.939
+9600/69092	Loss: 148.053
+12800/69092	Loss: 145.854
+16000/69092	Loss: 146.061
+19200/69092	Loss: 146.897
+22400/69092	Loss: 147.777
+25600/69092	Loss: 148.758
+28800/69092	Loss: 144.013
+32000/69092	Loss: 146.965
+35200/69092	Loss: 148.122
+38400/69092	Loss: 145.971
+41600/69092	Loss: 148.565
+44800/69092	Loss: 146.701
+48000/69092	Loss: 145.711
+51200/69092	Loss: 144.412
+54400/69092	Loss: 147.006
+57600/69092	Loss: 148.684
+60800/69092	Loss: 150.994
+64000/69092	Loss: 148.301
+67200/69092	Loss: 146.625
+Training time 0:04:53.544549
+Epoch: 55 Average loss: 146.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 56)
+0/69092	Loss: 153.768
+3200/69092	Loss: 148.208
+6400/69092	Loss: 146.056
+9600/69092	Loss: 144.783
+12800/69092	Loss: 146.773
+16000/69092	Loss: 142.310
+19200/69092	Loss: 148.937
+22400/69092	Loss: 146.681
+25600/69092	Loss: 145.761
+28800/69092	Loss: 149.047
+32000/69092	Loss: 150.048
+35200/69092	Loss: 146.347
+38400/69092	Loss: 147.055
+41600/69092	Loss: 144.490
+44800/69092	Loss: 152.141
+48000/69092	Loss: 146.734
+51200/69092	Loss: 148.234
+54400/69092	Loss: 146.772
+57600/69092	Loss: 148.011
+60800/69092	Loss: 145.731
+64000/69092	Loss: 147.526
+67200/69092	Loss: 144.554
+Training time 0:04:53.486139
+Epoch: 56 Average loss: 146.96
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 57)
+0/69092	Loss: 146.591
+3200/69092	Loss: 145.821
+6400/69092	Loss: 150.520
+9600/69092	Loss: 147.664
+12800/69092	Loss: 146.785
+16000/69092	Loss: 148.951
+19200/69092	Loss: 147.684
+22400/69092	Loss: 146.618
+25600/69092	Loss: 147.101
+28800/69092	Loss: 145.667
+32000/69092	Loss: 147.255
+35200/69092	Loss: 146.929
+38400/69092	Loss: 145.768
+41600/69092	Loss: 148.278
+44800/69092	Loss: 145.624
+48000/69092	Loss: 146.251
+51200/69092	Loss: 146.753
+54400/69092	Loss: 144.288
+57600/69092	Loss: 149.160
+60800/69092	Loss: 145.712
+64000/69092	Loss: 147.056
+67200/69092	Loss: 148.634
+Training time 0:04:51.065809
+Epoch: 57 Average loss: 147.01
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 58)
+0/69092	Loss: 143.716
+3200/69092	Loss: 148.458
+6400/69092	Loss: 147.682
+9600/69092	Loss: 146.822
+12800/69092	Loss: 144.305
+16000/69092	Loss: 147.238
+19200/69092	Loss: 148.305
+22400/69092	Loss: 146.991
+25600/69092	Loss: 146.660
+28800/69092	Loss: 147.319
+32000/69092	Loss: 147.859
+35200/69092	Loss: 147.335
+38400/69092	Loss: 147.587
+41600/69092	Loss: 146.720
+44800/69092	Loss: 145.439
+48000/69092	Loss: 147.053
+51200/69092	Loss: 146.829
+54400/69092	Loss: 145.434
+57600/69092	Loss: 144.668
+60800/69092	Loss: 145.682
+64000/69092	Loss: 146.102
+67200/69092	Loss: 147.807
+Training time 0:04:46.504364
+Epoch: 58 Average loss: 146.76
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 59)
+0/69092	Loss: 137.024
+3200/69092	Loss: 146.156
+6400/69092	Loss: 146.917
+9600/69092	Loss: 145.676
+12800/69092	Loss: 144.955
+16000/69092	Loss: 145.394
+19200/69092	Loss: 148.735
+22400/69092	Loss: 151.117
+25600/69092	Loss: 143.244
+28800/69092	Loss: 148.260
+32000/69092	Loss: 145.791
+35200/69092	Loss: 148.731
+38400/69092	Loss: 147.829
+41600/69092	Loss: 144.755
+44800/69092	Loss: 146.574
+48000/69092	Loss: 146.108
+51200/69092	Loss: 148.643
+54400/69092	Loss: 148.252
+57600/69092	Loss: 146.390
+60800/69092	Loss: 148.153
+64000/69092	Loss: 145.236
+67200/69092	Loss: 146.518
+Training time 0:04:51.503963
+Epoch: 59 Average loss: 146.93
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 60)
+0/69092	Loss: 138.822
+3200/69092	Loss: 147.527
+6400/69092	Loss: 146.079
+9600/69092	Loss: 145.572
+12800/69092	Loss: 147.603
+16000/69092	Loss: 144.474
+19200/69092	Loss: 145.038
+22400/69092	Loss: 144.862
+25600/69092	Loss: 146.587
+28800/69092	Loss: 149.152
+32000/69092	Loss: 145.570
+35200/69092	Loss: 147.617
+38400/69092	Loss: 147.989
+41600/69092	Loss: 146.229
+44800/69092	Loss: 147.906
+48000/69092	Loss: 151.285
+51200/69092	Loss: 143.997
+54400/69092	Loss: 145.932
+57600/69092	Loss: 150.376
+60800/69092	Loss: 146.700
+64000/69092	Loss: 148.486
+67200/69092	Loss: 146.345
+Training time 0:04:46.929217
+Epoch: 60 Average loss: 146.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 61)
+0/69092	Loss: 166.519
+3200/69092	Loss: 145.672
+6400/69092	Loss: 147.772
+9600/69092	Loss: 148.168
+12800/69092	Loss: 146.215
+16000/69092	Loss: 146.079
+19200/69092	Loss: 147.056
+22400/69092	Loss: 147.892
+25600/69092	Loss: 146.288
+28800/69092	Loss: 148.293
+32000/69092	Loss: 148.328
+35200/69092	Loss: 143.436
+38400/69092	Loss: 145.152
+41600/69092	Loss: 146.213
+44800/69092	Loss: 148.472
+48000/69092	Loss: 145.735
+51200/69092	Loss: 147.423
+54400/69092	Loss: 148.034
+57600/69092	Loss: 148.200
+60800/69092	Loss: 146.902
+64000/69092	Loss: 147.962
+67200/69092	Loss: 145.349
+Training time 0:04:50.557487
+Epoch: 61 Average loss: 146.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 62)
+0/69092	Loss: 152.315
+3200/69092	Loss: 145.624
+6400/69092	Loss: 144.994
+9600/69092	Loss: 146.370
+12800/69092	Loss: 145.070
+16000/69092	Loss: 147.258
+19200/69092	Loss: 146.463
+22400/69092	Loss: 146.461
+25600/69092	Loss: 146.188
+28800/69092	Loss: 149.226
+32000/69092	Loss: 146.179
+35200/69092	Loss: 143.983
+38400/69092	Loss: 145.782
+41600/69092	Loss: 149.198
+44800/69092	Loss: 145.018
+48000/69092	Loss: 149.167
+51200/69092	Loss: 147.337
+54400/69092	Loss: 146.529
+57600/69092	Loss: 148.921
+60800/69092	Loss: 148.086
+64000/69092	Loss: 148.350
+67200/69092	Loss: 148.678
+Training time 0:04:52.219880
+Epoch: 62 Average loss: 146.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 63)
+0/69092	Loss: 150.422
+3200/69092	Loss: 145.658
+6400/69092	Loss: 148.464
+9600/69092	Loss: 144.912
+12800/69092	Loss: 146.988
+16000/69092	Loss: 145.140
+19200/69092	Loss: 147.034
+22400/69092	Loss: 149.184
+25600/69092	Loss: 143.026
+28800/69092	Loss: 145.987
+32000/69092	Loss: 144.233
+35200/69092	Loss: 148.456
+38400/69092	Loss: 144.521
+41600/69092	Loss: 146.424
+44800/69092	Loss: 146.320
+48000/69092	Loss: 146.144
+51200/69092	Loss: 149.693
+54400/69092	Loss: 147.905
+57600/69092	Loss: 145.137
+60800/69092	Loss: 147.358
+64000/69092	Loss: 146.544
+67200/69092	Loss: 146.999
+Training time 0:04:41.221411
+Epoch: 63 Average loss: 146.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 64)
+0/69092	Loss: 171.182
+3200/69092	Loss: 144.032
+6400/69092	Loss: 149.165
+9600/69092	Loss: 146.192
+12800/69092	Loss: 145.012
+16000/69092	Loss: 147.469
+19200/69092	Loss: 146.261
+22400/69092	Loss: 147.494
+25600/69092	Loss: 147.959
+28800/69092	Loss: 144.652
+32000/69092	Loss: 147.345
+35200/69092	Loss: 145.761
+38400/69092	Loss: 147.931
+41600/69092	Loss: 148.879
+44800/69092	Loss: 146.097
+48000/69092	Loss: 148.339
+51200/69092	Loss: 146.139
+54400/69092	Loss: 145.088
+57600/69092	Loss: 147.679
+60800/69092	Loss: 144.936
+64000/69092	Loss: 146.873
+67200/69092	Loss: 146.842
+Training time 0:04:50.096951
+Epoch: 64 Average loss: 146.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 65)
+0/69092	Loss: 133.547
+3200/69092	Loss: 146.622
+6400/69092	Loss: 145.129
+9600/69092	Loss: 145.236
+12800/69092	Loss: 149.343
+16000/69092	Loss: 145.954
+19200/69092	Loss: 148.716
+22400/69092	Loss: 149.692
+25600/69092	Loss: 144.304
+28800/69092	Loss: 148.359
+32000/69092	Loss: 147.637
+35200/69092	Loss: 144.991
+38400/69092	Loss: 144.874
+41600/69092	Loss: 146.725
+44800/69092	Loss: 149.184
+48000/69092	Loss: 147.321
+51200/69092	Loss: 148.040
+54400/69092	Loss: 146.115
+57600/69092	Loss: 145.868
+60800/69092	Loss: 146.311
+64000/69092	Loss: 147.308
+67200/69092	Loss: 143.867
+Training time 0:04:57.613096
+Epoch: 65 Average loss: 146.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 66)
+0/69092	Loss: 137.107
+3200/69092	Loss: 144.685
+6400/69092	Loss: 146.227
+9600/69092	Loss: 146.986
+12800/69092	Loss: 145.772
+16000/69092	Loss: 144.567
+19200/69092	Loss: 149.107
+22400/69092	Loss: 146.743
+25600/69092	Loss: 146.766
+28800/69092	Loss: 146.158
+32000/69092	Loss: 147.916
+35200/69092	Loss: 143.342
+38400/69092	Loss: 146.238
+41600/69092	Loss: 148.321
+44800/69092	Loss: 147.586
+48000/69092	Loss: 146.530
+51200/69092	Loss: 146.869
+54400/69092	Loss: 148.187
+57600/69092	Loss: 149.371
+60800/69092	Loss: 149.565
+64000/69092	Loss: 145.194
+67200/69092	Loss: 147.560
+Training time 0:04:44.057703
+Epoch: 66 Average loss: 146.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 67)
+0/69092	Loss: 132.834
+3200/69092	Loss: 150.361
+6400/69092	Loss: 146.893
+9600/69092	Loss: 147.579
+12800/69092	Loss: 146.295
+16000/69092	Loss: 145.771
+19200/69092	Loss: 146.264
+22400/69092	Loss: 146.407
+25600/69092	Loss: 147.373
+28800/69092	Loss: 144.665
+32000/69092	Loss: 145.544
+35200/69092	Loss: 146.769
+38400/69092	Loss: 146.017
+41600/69092	Loss: 148.503
+44800/69092	Loss: 145.678
+48000/69092	Loss: 144.804
+51200/69092	Loss: 149.534
+54400/69092	Loss: 145.693
+57600/69092	Loss: 147.367
+60800/69092	Loss: 147.390
+64000/69092	Loss: 147.381
+67200/69092	Loss: 146.191
+Training time 0:04:46.229026
+Epoch: 67 Average loss: 146.80
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 68)
+0/69092	Loss: 156.265
+3200/69092	Loss: 147.226
+6400/69092	Loss: 144.118
+9600/69092	Loss: 150.272
+12800/69092	Loss: 148.052
+16000/69092	Loss: 147.857
+19200/69092	Loss: 147.257
+22400/69092	Loss: 147.120
+25600/69092	Loss: 147.883
+28800/69092	Loss: 146.170
+32000/69092	Loss: 146.023
+35200/69092	Loss: 148.848
+38400/69092	Loss: 145.555
+41600/69092	Loss: 146.862
+44800/69092	Loss: 147.628
+48000/69092	Loss: 148.706
+51200/69092	Loss: 145.668
+54400/69092	Loss: 146.020
+57600/69092	Loss: 143.698
+60800/69092	Loss: 145.418
+64000/69092	Loss: 146.778
+67200/69092	Loss: 147.013
+Training time 0:04:44.444230
+Epoch: 68 Average loss: 146.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 69)
+0/69092	Loss: 141.757
+3200/69092	Loss: 146.667
+6400/69092	Loss: 148.135
+9600/69092	Loss: 144.239
+12800/69092	Loss: 146.948
+16000/69092	Loss: 145.072
+19200/69092	Loss: 147.376
+22400/69092	Loss: 147.459
+25600/69092	Loss: 147.408
+28800/69092	Loss: 146.334
+32000/69092	Loss: 145.263
+35200/69092	Loss: 146.575
+38400/69092	Loss: 144.863
+41600/69092	Loss: 149.402
+44800/69092	Loss: 146.329
+48000/69092	Loss: 146.814
+51200/69092	Loss: 148.077
+54400/69092	Loss: 147.105
+57600/69092	Loss: 148.299
+60800/69092	Loss: 146.524
+64000/69092	Loss: 143.302
+67200/69092	Loss: 147.207
+Training time 0:04:46.047948
+Epoch: 69 Average loss: 146.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 70)
+0/69092	Loss: 141.410
+3200/69092	Loss: 146.306
+6400/69092	Loss: 150.284
+9600/69092	Loss: 147.467
+12800/69092	Loss: 145.598
+16000/69092	Loss: 145.802
+19200/69092	Loss: 145.539
+22400/69092	Loss: 144.361
+25600/69092	Loss: 147.736
+28800/69092	Loss: 145.281
+32000/69092	Loss: 145.185
+35200/69092	Loss: 146.842
+38400/69092	Loss: 147.779
+41600/69092	Loss: 147.431
+44800/69092	Loss: 145.748
+48000/69092	Loss: 147.031
+51200/69092	Loss: 144.653
+54400/69092	Loss: 147.337
+57600/69092	Loss: 147.096
+60800/69092	Loss: 149.250
+64000/69092	Loss: 146.275
+67200/69092	Loss: 145.122
+Training time 0:04:45.749935
+Epoch: 70 Average loss: 146.53
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 71)
+0/69092	Loss: 146.901
+3200/69092	Loss: 148.857
+6400/69092	Loss: 145.309
+9600/69092	Loss: 148.464
+12800/69092	Loss: 145.992
+16000/69092	Loss: 145.157
+19200/69092	Loss: 144.117
+22400/69092	Loss: 150.274
+25600/69092	Loss: 146.916
+28800/69092	Loss: 146.169
+32000/69092	Loss: 143.758
+35200/69092	Loss: 150.341
+38400/69092	Loss: 149.049
+41600/69092	Loss: 146.319
+44800/69092	Loss: 146.374
+48000/69092	Loss: 147.975
+51200/69092	Loss: 148.639
+54400/69092	Loss: 145.146
+57600/69092	Loss: 146.491
+60800/69092	Loss: 145.525
+64000/69092	Loss: 148.042
+67200/69092	Loss: 144.941
+Training time 0:04:44.314991
+Epoch: 71 Average loss: 146.89
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 72)
+0/69092	Loss: 136.750
+3200/69092	Loss: 143.544
+6400/69092	Loss: 143.704
+9600/69092	Loss: 148.213
+12800/69092	Loss: 148.150
+16000/69092	Loss: 147.150
+19200/69092	Loss: 145.401
+22400/69092	Loss: 149.390
+25600/69092	Loss: 146.899
+28800/69092	Loss: 147.619
+32000/69092	Loss: 146.977
+35200/69092	Loss: 146.808
+38400/69092	Loss: 148.002
+41600/69092	Loss: 147.236
+44800/69092	Loss: 145.674
+48000/69092	Loss: 146.777
+51200/69092	Loss: 146.922
+54400/69092	Loss: 145.737
+57600/69092	Loss: 149.854
+60800/69092	Loss: 145.690
+64000/69092	Loss: 147.044
+67200/69092	Loss: 144.801
+Training time 0:04:44.887256
+Epoch: 72 Average loss: 146.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 73)
+0/69092	Loss: 143.328
+3200/69092	Loss: 146.134
+6400/69092	Loss: 147.602
+9600/69092	Loss: 145.674
+12800/69092	Loss: 146.106
+16000/69092	Loss: 144.240
+19200/69092	Loss: 146.586
+22400/69092	Loss: 146.861
+25600/69092	Loss: 146.332
+28800/69092	Loss: 146.855
+32000/69092	Loss: 146.523
+35200/69092	Loss: 145.526
+38400/69092	Loss: 147.427
+41600/69092	Loss: 147.800
+44800/69092	Loss: 149.240
+48000/69092	Loss: 147.022
+51200/69092	Loss: 148.705
+54400/69092	Loss: 147.315
+57600/69092	Loss: 147.294
+60800/69092	Loss: 145.819
+64000/69092	Loss: 144.981
+67200/69092	Loss: 147.104
+Training time 0:04:45.578236
+Epoch: 73 Average loss: 146.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 74)
+0/69092	Loss: 148.127
+3200/69092	Loss: 147.190
+6400/69092	Loss: 145.206
+9600/69092	Loss: 146.966
+12800/69092	Loss: 144.832
+16000/69092	Loss: 146.914
+19200/69092	Loss: 146.511
+22400/69092	Loss: 146.988
+25600/69092	Loss: 149.029
+28800/69092	Loss: 145.859
+32000/69092	Loss: 147.268
+35200/69092	Loss: 148.214
+38400/69092	Loss: 146.391
+41600/69092	Loss: 146.535
+44800/69092	Loss: 148.129
+48000/69092	Loss: 148.398
+51200/69092	Loss: 146.996
+54400/69092	Loss: 145.574
+57600/69092	Loss: 145.142
+60800/69092	Loss: 148.766
+64000/69092	Loss: 147.201
+67200/69092	Loss: 144.287
+Training time 0:04:47.385231
+Epoch: 74 Average loss: 146.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 75)
+0/69092	Loss: 145.387
+3200/69092	Loss: 147.010
+6400/69092	Loss: 146.720
+9600/69092	Loss: 145.023
+12800/69092	Loss: 144.783
+16000/69092	Loss: 145.967
+19200/69092	Loss: 147.753
+22400/69092	Loss: 149.264
+25600/69092	Loss: 147.752
+28800/69092	Loss: 145.571
+32000/69092	Loss: 145.894
+35200/69092	Loss: 146.341
+38400/69092	Loss: 150.516
+41600/69092	Loss: 144.471
+44800/69092	Loss: 147.365
+48000/69092	Loss: 146.712
+51200/69092	Loss: 147.938
+54400/69092	Loss: 147.917
+57600/69092	Loss: 145.684
+60800/69092	Loss: 146.698
+64000/69092	Loss: 145.271
+67200/69092	Loss: 146.297
+Training time 0:04:52.850931
+Epoch: 75 Average loss: 146.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 76)
+0/69092	Loss: 134.811
+3200/69092	Loss: 147.197
+6400/69092	Loss: 144.804
+9600/69092	Loss: 144.591
+12800/69092	Loss: 143.718
+16000/69092	Loss: 147.373
+19200/69092	Loss: 148.129
+22400/69092	Loss: 146.322
+25600/69092	Loss: 147.842
+28800/69092	Loss: 146.666
+32000/69092	Loss: 142.722
+35200/69092	Loss: 148.314
+38400/69092	Loss: 145.398
+41600/69092	Loss: 149.611
+44800/69092	Loss: 145.691
+48000/69092	Loss: 146.587
+51200/69092	Loss: 147.103
+54400/69092	Loss: 147.075
+57600/69092	Loss: 146.253
+60800/69092	Loss: 147.973
+64000/69092	Loss: 147.565
+67200/69092	Loss: 144.309
+Training time 0:04:38.922155
+Epoch: 76 Average loss: 146.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 77)
+0/69092	Loss: 161.470
+3200/69092	Loss: 148.837
+6400/69092	Loss: 145.034
+9600/69092	Loss: 146.081
+12800/69092	Loss: 146.551
+16000/69092	Loss: 150.407
+19200/69092	Loss: 145.112
+22400/69092	Loss: 147.386
+25600/69092	Loss: 145.021
+28800/69092	Loss: 144.185
+32000/69092	Loss: 150.343
+35200/69092	Loss: 145.518
+38400/69092	Loss: 143.175
+41600/69092	Loss: 145.601
+44800/69092	Loss: 146.190
+48000/69092	Loss: 147.611
+51200/69092	Loss: 144.955
+54400/69092	Loss: 145.267
+57600/69092	Loss: 143.007
+60800/69092	Loss: 145.417
+64000/69092	Loss: 148.023
+67200/69092	Loss: 148.796
+Training time 0:04:49.948629
+Epoch: 77 Average loss: 146.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 78)
+0/69092	Loss: 132.900
+3200/69092	Loss: 144.037
+6400/69092	Loss: 147.456
+9600/69092	Loss: 147.700
+12800/69092	Loss: 142.977
+16000/69092	Loss: 144.563
+19200/69092	Loss: 148.978
+22400/69092	Loss: 146.363
+25600/69092	Loss: 146.626
+28800/69092	Loss: 143.578
+32000/69092	Loss: 146.683
+35200/69092	Loss: 145.173
+38400/69092	Loss: 145.595
+41600/69092	Loss: 147.735
+44800/69092	Loss: 145.994
+48000/69092	Loss: 147.550
+51200/69092	Loss: 149.236
+54400/69092	Loss: 149.362
+57600/69092	Loss: 145.351
+60800/69092	Loss: 145.817
+64000/69092	Loss: 147.280
+67200/69092	Loss: 148.197
+Training time 0:04:48.329674
+Epoch: 78 Average loss: 146.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 79)
+0/69092	Loss: 146.488
+3200/69092	Loss: 148.755
+6400/69092	Loss: 147.160
+9600/69092	Loss: 147.761
+12800/69092	Loss: 146.850
+16000/69092	Loss: 146.065
+19200/69092	Loss: 148.065
+22400/69092	Loss: 146.303
+25600/69092	Loss: 144.103
+28800/69092	Loss: 145.871
+32000/69092	Loss: 145.392
+35200/69092	Loss: 149.758
+38400/69092	Loss: 143.850
+41600/69092	Loss: 144.490
+44800/69092	Loss: 147.981
+48000/69092	Loss: 147.345
+51200/69092	Loss: 144.965
+54400/69092	Loss: 145.190
+57600/69092	Loss: 145.986
+60800/69092	Loss: 147.443
+64000/69092	Loss: 146.229
+67200/69092	Loss: 147.378
+Training time 0:04:48.934611
+Epoch: 79 Average loss: 146.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 80)
+0/69092	Loss: 157.176
+3200/69092	Loss: 145.926
+6400/69092	Loss: 143.998
+9600/69092	Loss: 145.831
+12800/69092	Loss: 146.750
+16000/69092	Loss: 144.284
+19200/69092	Loss: 146.421
+22400/69092	Loss: 146.518
+25600/69092	Loss: 146.410
+28800/69092	Loss: 146.470
+32000/69092	Loss: 148.159
+35200/69092	Loss: 148.378
+38400/69092	Loss: 147.588
+41600/69092	Loss: 146.371
+44800/69092	Loss: 146.708
+48000/69092	Loss: 146.941
+51200/69092	Loss: 148.051
+54400/69092	Loss: 148.573
+57600/69092	Loss: 145.871
+60800/69092	Loss: 145.712
+64000/69092	Loss: 144.300
+67200/69092	Loss: 148.144
+Training time 0:04:52.228795
+Epoch: 80 Average loss: 146.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 81)
+0/69092	Loss: 153.261
+3200/69092	Loss: 148.666
+6400/69092	Loss: 148.354
+9600/69092	Loss: 147.904
+12800/69092	Loss: 146.296
+16000/69092	Loss: 143.740
+19200/69092	Loss: 147.392
+22400/69092	Loss: 147.917
+25600/69092	Loss: 145.985
+28800/69092	Loss: 148.436
+32000/69092	Loss: 148.712
+35200/69092	Loss: 145.638
+38400/69092	Loss: 145.446
+41600/69092	Loss: 144.139
+44800/69092	Loss: 143.804
+48000/69092	Loss: 147.053
+51200/69092	Loss: 145.991
+54400/69092	Loss: 147.518
+57600/69092	Loss: 148.761
+60800/69092	Loss: 146.601
+64000/69092	Loss: 144.337
+67200/69092	Loss: 145.645
+Training time 0:04:45.350943
+Epoch: 81 Average loss: 146.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 82)
+0/69092	Loss: 153.126
+3200/69092	Loss: 148.162
+6400/69092	Loss: 143.942
+9600/69092	Loss: 144.100
+12800/69092	Loss: 145.566
+16000/69092	Loss: 147.425
+19200/69092	Loss: 149.394
+22400/69092	Loss: 148.007
+25600/69092	Loss: 144.938
+28800/69092	Loss: 146.931
+32000/69092	Loss: 145.399
+35200/69092	Loss: 144.522
+38400/69092	Loss: 146.210
+41600/69092	Loss: 148.657
+44800/69092	Loss: 147.164
+48000/69092	Loss: 147.403
+51200/69092	Loss: 145.348
+54400/69092	Loss: 143.764
+57600/69092	Loss: 147.150
+60800/69092	Loss: 146.995
+64000/69092	Loss: 148.026
+67200/69092	Loss: 145.771
+Training time 0:04:51.941539
+Epoch: 82 Average loss: 146.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 83)
+0/69092	Loss: 176.068
+3200/69092	Loss: 145.124
+6400/69092	Loss: 143.828
+9600/69092	Loss: 149.005
+12800/69092	Loss: 148.229
+16000/69092	Loss: 147.771
+19200/69092	Loss: 144.125
+22400/69092	Loss: 147.251
+25600/69092	Loss: 146.498
+28800/69092	Loss: 146.091
+32000/69092	Loss: 147.282
+35200/69092	Loss: 145.029
+38400/69092	Loss: 146.293
+41600/69092	Loss: 147.910
+44800/69092	Loss: 149.290
+48000/69092	Loss: 146.987
+51200/69092	Loss: 145.287
+54400/69092	Loss: 150.488
+57600/69092	Loss: 145.710
+60800/69092	Loss: 144.541
+64000/69092	Loss: 145.557
+67200/69092	Loss: 145.058
+Training time 0:04:48.348697
+Epoch: 83 Average loss: 146.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 84)
+0/69092	Loss: 144.708
+3200/69092	Loss: 144.832
+6400/69092	Loss: 145.334
+9600/69092	Loss: 146.718
+12800/69092	Loss: 147.947
+16000/69092	Loss: 146.680
+19200/69092	Loss: 147.667
+22400/69092	Loss: 145.028
+25600/69092	Loss: 147.608
+28800/69092	Loss: 145.457
+32000/69092	Loss: 145.508
+35200/69092	Loss: 148.174
+38400/69092	Loss: 149.388
+41600/69092	Loss: 146.454
+44800/69092	Loss: 147.681
+48000/69092	Loss: 146.904
+51200/69092	Loss: 145.659
+54400/69092	Loss: 144.355
+57600/69092	Loss: 148.931
+60800/69092	Loss: 144.741
+64000/69092	Loss: 147.586
+67200/69092	Loss: 144.604
+Training time 0:04:47.092381
+Epoch: 84 Average loss: 146.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 85)
+0/69092	Loss: 150.844
+3200/69092	Loss: 149.226
+6400/69092	Loss: 145.229
+9600/69092	Loss: 146.837
+12800/69092	Loss: 145.042
+16000/69092	Loss: 147.181
+19200/69092	Loss: 146.465
+22400/69092	Loss: 147.421
+25600/69092	Loss: 147.043
+28800/69092	Loss: 146.730
+32000/69092	Loss: 144.857
+35200/69092	Loss: 148.278
+38400/69092	Loss: 144.500
+41600/69092	Loss: 145.502
+44800/69092	Loss: 148.351
+48000/69092	Loss: 143.943
+51200/69092	Loss: 147.422
+54400/69092	Loss: 145.589
+57600/69092	Loss: 147.053
+60800/69092	Loss: 145.999
+64000/69092	Loss: 147.959
+67200/69092	Loss: 148.060
+Training time 0:04:54.056603
+Epoch: 85 Average loss: 146.51
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 86)
+0/69092	Loss: 150.501
+3200/69092	Loss: 146.975
+6400/69092	Loss: 145.075
+9600/69092	Loss: 145.210
+12800/69092	Loss: 147.565
+16000/69092	Loss: 146.472
+19200/69092	Loss: 146.963
+22400/69092	Loss: 147.741
+25600/69092	Loss: 143.369
+28800/69092	Loss: 147.038
+32000/69092	Loss: 146.497
+35200/69092	Loss: 148.039
+38400/69092	Loss: 146.521
+41600/69092	Loss: 147.316
+44800/69092	Loss: 144.388
+48000/69092	Loss: 144.874
+51200/69092	Loss: 147.348
+54400/69092	Loss: 144.612
+57600/69092	Loss: 144.866
+60800/69092	Loss: 147.650
+64000/69092	Loss: 148.534
+67200/69092	Loss: 147.284
+Training time 0:04:42.164306
+Epoch: 86 Average loss: 146.40
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 87)
+0/69092	Loss: 130.774
+3200/69092	Loss: 143.932
+6400/69092	Loss: 146.004
+9600/69092	Loss: 145.332
+12800/69092	Loss: 147.397
+16000/69092	Loss: 149.202
+19200/69092	Loss: 145.596
+22400/69092	Loss: 143.158
+25600/69092	Loss: 149.671
+28800/69092	Loss: 148.668
+32000/69092	Loss: 146.308
+35200/69092	Loss: 147.665
+38400/69092	Loss: 144.408
+41600/69092	Loss: 145.956
+44800/69092	Loss: 145.521
+48000/69092	Loss: 146.520
+51200/69092	Loss: 147.369
+54400/69092	Loss: 145.813
+57600/69092	Loss: 146.935
+60800/69092	Loss: 143.657
+64000/69092	Loss: 148.843
+67200/69092	Loss: 147.984
+Training time 0:04:49.511242
+Epoch: 87 Average loss: 146.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 88)
+0/69092	Loss: 172.170
+3200/69092	Loss: 147.932
+6400/69092	Loss: 148.160
+9600/69092	Loss: 146.456
+12800/69092	Loss: 147.134
+16000/69092	Loss: 145.960
+19200/69092	Loss: 145.012
+22400/69092	Loss: 144.202
+25600/69092	Loss: 148.634
+28800/69092	Loss: 142.529
+32000/69092	Loss: 145.319
+35200/69092	Loss: 145.394
+38400/69092	Loss: 145.681
+41600/69092	Loss: 146.299
+44800/69092	Loss: 146.569
+48000/69092	Loss: 147.196
+51200/69092	Loss: 146.142
+54400/69092	Loss: 145.017
+57600/69092	Loss: 146.816
+60800/69092	Loss: 148.059
+64000/69092	Loss: 147.033
+67200/69092	Loss: 146.888
+Training time 0:04:46.596728
+Epoch: 88 Average loss: 146.32
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 89)
+0/69092	Loss: 141.474
+3200/69092	Loss: 144.988
+6400/69092	Loss: 144.828
+9600/69092	Loss: 148.709
+12800/69092	Loss: 145.847
+16000/69092	Loss: 147.807
+19200/69092	Loss: 143.096
+22400/69092	Loss: 147.022
+25600/69092	Loss: 145.785
+28800/69092	Loss: 147.062
+32000/69092	Loss: 145.481
+35200/69092	Loss: 146.118
+38400/69092	Loss: 148.299
+41600/69092	Loss: 149.001
+44800/69092	Loss: 147.231
+48000/69092	Loss: 149.250
+51200/69092	Loss: 145.316
+54400/69092	Loss: 144.862
+57600/69092	Loss: 146.098
+60800/69092	Loss: 147.844
+64000/69092	Loss: 145.150
+67200/69092	Loss: 145.787
+Training time 0:04:45.924385
+Epoch: 89 Average loss: 146.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 90)
+0/69092	Loss: 150.736
+3200/69092	Loss: 143.700
+6400/69092	Loss: 147.926
+9600/69092	Loss: 146.266
+12800/69092	Loss: 147.853
+16000/69092	Loss: 146.538
+19200/69092	Loss: 146.596
+22400/69092	Loss: 144.553
+25600/69092	Loss: 144.404
+28800/69092	Loss: 145.696
+32000/69092	Loss: 144.545
+35200/69092	Loss: 144.873
+38400/69092	Loss: 148.271
+41600/69092	Loss: 149.252
+44800/69092	Loss: 145.313
+48000/69092	Loss: 146.939
+51200/69092	Loss: 145.722
+54400/69092	Loss: 147.297
+57600/69092	Loss: 146.017
+60800/69092	Loss: 143.556
+64000/69092	Loss: 147.418
+67200/69092	Loss: 149.259
+Training time 0:04:36.654075
+Epoch: 90 Average loss: 146.26
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 91)
+0/69092	Loss: 130.866
+3200/69092	Loss: 147.000
+6400/69092	Loss: 146.036
+9600/69092	Loss: 145.496
+12800/69092	Loss: 143.556
+16000/69092	Loss: 143.745
+19200/69092	Loss: 146.638
+22400/69092	Loss: 147.189
+25600/69092	Loss: 148.213
+28800/69092	Loss: 148.934
+32000/69092	Loss: 147.896
+35200/69092	Loss: 146.729
+38400/69092	Loss: 143.678
+41600/69092	Loss: 143.533
+44800/69092	Loss: 147.984
+48000/69092	Loss: 144.601
+51200/69092	Loss: 145.526
+54400/69092	Loss: 146.910
+57600/69092	Loss: 145.254
+60800/69092	Loss: 143.548
+64000/69092	Loss: 149.972
+67200/69092	Loss: 146.744
+Training time 0:04:45.640072
+Epoch: 91 Average loss: 146.25
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 92)
+0/69092	Loss: 143.790
+3200/69092	Loss: 148.452
+6400/69092	Loss: 148.212
+9600/69092	Loss: 146.093
+12800/69092	Loss: 146.753
+16000/69092	Loss: 146.704
+19200/69092	Loss: 145.288
+22400/69092	Loss: 145.378
+25600/69092	Loss: 145.909
+28800/69092	Loss: 144.579
+32000/69092	Loss: 147.110
+35200/69092	Loss: 149.035
+38400/69092	Loss: 146.199
+41600/69092	Loss: 147.460
+44800/69092	Loss: 147.125
+48000/69092	Loss: 145.227
+51200/69092	Loss: 143.031
+54400/69092	Loss: 143.526
+57600/69092	Loss: 146.274
+60800/69092	Loss: 144.178
+64000/69092	Loss: 147.213
+67200/69092	Loss: 146.719
+Training time 0:04:46.616076
+Epoch: 92 Average loss: 146.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 93)
+0/69092	Loss: 131.844
+3200/69092	Loss: 146.233
+6400/69092	Loss: 145.560
+9600/69092	Loss: 145.973
+12800/69092	Loss: 145.216
+16000/69092	Loss: 146.969
+19200/69092	Loss: 148.277
+22400/69092	Loss: 144.590
+25600/69092	Loss: 146.804
+28800/69092	Loss: 144.767
+32000/69092	Loss: 147.660
+35200/69092	Loss: 148.570
+38400/69092	Loss: 144.681
+41600/69092	Loss: 146.550
+44800/69092	Loss: 146.291
+48000/69092	Loss: 146.049
+51200/69092	Loss: 148.134
+54400/69092	Loss: 145.314
+57600/69092	Loss: 147.903
+60800/69092	Loss: 147.337
+64000/69092	Loss: 147.311
+67200/69092	Loss: 146.239
+Training time 0:04:33.958303
+Epoch: 93 Average loss: 146.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 94)
+0/69092	Loss: 159.987
+3200/69092	Loss: 144.610
+6400/69092	Loss: 145.484
+9600/69092	Loss: 147.181
+12800/69092	Loss: 144.933
+16000/69092	Loss: 146.598
+19200/69092	Loss: 145.728
+22400/69092	Loss: 147.576
+25600/69092	Loss: 149.163
+28800/69092	Loss: 146.879
+32000/69092	Loss: 145.968
+35200/69092	Loss: 143.184
+38400/69092	Loss: 143.644
+41600/69092	Loss: 147.400
+44800/69092	Loss: 150.723
+48000/69092	Loss: 145.739
+51200/69092	Loss: 148.812
+54400/69092	Loss: 146.706
+57600/69092	Loss: 143.072
+60800/69092	Loss: 146.473
+64000/69092	Loss: 148.354
+67200/69092	Loss: 146.095
+Training time 0:04:45.355013
+Epoch: 94 Average loss: 146.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 95)
+0/69092	Loss: 147.164
+3200/69092	Loss: 145.659
+6400/69092	Loss: 148.269
+9600/69092	Loss: 145.315
+12800/69092	Loss: 144.934
+16000/69092	Loss: 147.971
+19200/69092	Loss: 146.946
+22400/69092	Loss: 146.300
+25600/69092	Loss: 145.977
+28800/69092	Loss: 146.625
+32000/69092	Loss: 145.866
+35200/69092	Loss: 147.237
+38400/69092	Loss: 144.933
+41600/69092	Loss: 145.057
+44800/69092	Loss: 147.038
+48000/69092	Loss: 147.101
+51200/69092	Loss: 144.668
+54400/69092	Loss: 146.394
+57600/69092	Loss: 145.030
+60800/69092	Loss: 144.753
+64000/69092	Loss: 142.859
+67200/69092	Loss: 150.341
+Training time 0:04:49.958558
+Epoch: 95 Average loss: 146.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 96)
+0/69092	Loss: 158.442
+3200/69092	Loss: 142.351
+6400/69092	Loss: 142.983
+9600/69092	Loss: 145.565
+12800/69092	Loss: 144.412
+16000/69092	Loss: 145.774
+19200/69092	Loss: 146.258
+22400/69092	Loss: 146.872
+25600/69092	Loss: 147.029
+28800/69092	Loss: 147.865
+32000/69092	Loss: 148.307
+35200/69092	Loss: 145.273
+38400/69092	Loss: 144.069
+41600/69092	Loss: 145.431
+44800/69092	Loss: 146.413
+48000/69092	Loss: 146.679
+51200/69092	Loss: 144.460
+54400/69092	Loss: 145.622
+57600/69092	Loss: 147.939
+60800/69092	Loss: 147.975
+64000/69092	Loss: 146.828
+67200/69092	Loss: 149.784
+Training time 0:04:49.432118
+Epoch: 96 Average loss: 146.24
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 97)
+0/69092	Loss: 150.812
+3200/69092	Loss: 147.672
+6400/69092	Loss: 148.727
+9600/69092	Loss: 144.173
+12800/69092	Loss: 147.766
+16000/69092	Loss: 144.169
+19200/69092	Loss: 145.384
+22400/69092	Loss: 145.715
+25600/69092	Loss: 145.146
+28800/69092	Loss: 148.175
+32000/69092	Loss: 145.735
+35200/69092	Loss: 146.501
+38400/69092	Loss: 149.295
+41600/69092	Loss: 146.113
+44800/69092	Loss: 142.665
+48000/69092	Loss: 146.194
+51200/69092	Loss: 147.126
+54400/69092	Loss: 146.652
+57600/69092	Loss: 145.176
+60800/69092	Loss: 146.656
+64000/69092	Loss: 146.244
+67200/69092	Loss: 146.630
+Training time 0:04:48.152766
+Epoch: 97 Average loss: 146.25
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 98)
+0/69092	Loss: 137.869
+3200/69092	Loss: 144.554
+6400/69092	Loss: 147.447
+9600/69092	Loss: 147.078
+12800/69092	Loss: 147.414
+16000/69092	Loss: 145.274
+19200/69092	Loss: 143.583
+22400/69092	Loss: 146.877
+25600/69092	Loss: 145.375
+28800/69092	Loss: 144.806
+32000/69092	Loss: 147.440
+35200/69092	Loss: 144.985
+38400/69092	Loss: 145.597
+41600/69092	Loss: 145.825
+44800/69092	Loss: 146.902
+48000/69092	Loss: 146.497
+51200/69092	Loss: 147.173
+54400/69092	Loss: 147.590
+57600/69092	Loss: 143.967
+60800/69092	Loss: 146.571
+64000/69092	Loss: 147.073
+67200/69092	Loss: 148.854
+Training time 0:04:50.807013
+Epoch: 98 Average loss: 146.28
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 99)
+0/69092	Loss: 154.239
+3200/69092	Loss: 144.396
+6400/69092	Loss: 147.155
+9600/69092	Loss: 144.404
+12800/69092	Loss: 144.719
+16000/69092	Loss: 144.030
+19200/69092	Loss: 145.726
+22400/69092	Loss: 145.985
+25600/69092	Loss: 146.530
+28800/69092	Loss: 146.298
+32000/69092	Loss: 145.471
+35200/69092	Loss: 146.877
+38400/69092	Loss: 149.247
+41600/69092	Loss: 147.390
+44800/69092	Loss: 146.625
+48000/69092	Loss: 148.406
+51200/69092	Loss: 146.172
+54400/69092	Loss: 147.904
+57600/69092	Loss: 143.994
+60800/69092	Loss: 145.696
+64000/69092	Loss: 144.117
+67200/69092	Loss: 147.643
+Training time 0:04:49.392991
+Epoch: 99 Average loss: 146.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 100)
+0/69092	Loss: 138.864
+3200/69092	Loss: 144.660
+6400/69092	Loss: 146.022
+9600/69092	Loss: 148.185
+12800/69092	Loss: 146.598
+16000/69092	Loss: 146.159
+19200/69092	Loss: 146.482
+22400/69092	Loss: 147.925
+25600/69092	Loss: 144.232
+28800/69092	Loss: 142.620
+32000/69092	Loss: 146.098
+35200/69092	Loss: 145.490
+38400/69092	Loss: 148.119
+41600/69092	Loss: 148.468
+44800/69092	Loss: 146.462
+48000/69092	Loss: 143.704
+51200/69092	Loss: 144.804
+54400/69092	Loss: 145.402
+57600/69092	Loss: 147.141
+60800/69092	Loss: 148.049
+64000/69092	Loss: 147.209
+67200/69092	Loss: 145.067
+Training time 0:05:02.259688
+Epoch: 100 Average loss: 146.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 101)
+0/69092	Loss: 139.731
+3200/69092	Loss: 146.653
+6400/69092	Loss: 146.633
+9600/69092	Loss: 146.183
+12800/69092	Loss: 146.290
+16000/69092	Loss: 145.528
+19200/69092	Loss: 146.658
+22400/69092	Loss: 144.775
+25600/69092	Loss: 147.836
+28800/69092	Loss: 145.933
+32000/69092	Loss: 145.502
+35200/69092	Loss: 149.519
+38400/69092	Loss: 146.157
+41600/69092	Loss: 143.665
+44800/69092	Loss: 146.125
+48000/69092	Loss: 145.846
+51200/69092	Loss: 145.099
+54400/69092	Loss: 145.854
+57600/69092	Loss: 149.682
+60800/69092	Loss: 146.248
+64000/69092	Loss: 145.447
+67200/69092	Loss: 145.757
+Training time 0:04:50.492272
+Epoch: 101 Average loss: 146.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 102)
+0/69092	Loss: 137.845
+3200/69092	Loss: 146.728
+6400/69092	Loss: 148.161
+9600/69092	Loss: 145.300
+12800/69092	Loss: 145.649
+16000/69092	Loss: 146.977
+19200/69092	Loss: 146.316
+22400/69092	Loss: 144.493
+25600/69092	Loss: 148.020
+28800/69092	Loss: 145.930
+32000/69092	Loss: 147.541
+35200/69092	Loss: 143.148
+38400/69092	Loss: 144.819
+41600/69092	Loss: 145.984
+44800/69092	Loss: 146.177
+48000/69092	Loss: 147.886
+51200/69092	Loss: 147.379
+54400/69092	Loss: 146.163
+57600/69092	Loss: 145.780
+60800/69092	Loss: 143.685
+64000/69092	Loss: 147.160
+67200/69092	Loss: 146.238
+Training time 0:04:56.379840
+Epoch: 102 Average loss: 146.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 103)
+0/69092	Loss: 146.581
+3200/69092	Loss: 145.429
+6400/69092	Loss: 145.708
+9600/69092	Loss: 147.823
+12800/69092	Loss: 145.072
+16000/69092	Loss: 145.139
+19200/69092	Loss: 148.050
+22400/69092	Loss: 145.983
+25600/69092	Loss: 146.083
+28800/69092	Loss: 147.766
+32000/69092	Loss: 145.270
+35200/69092	Loss: 146.375
+38400/69092	Loss: 146.013
+41600/69092	Loss: 146.810
+44800/69092	Loss: 144.908
+48000/69092	Loss: 150.633
+51200/69092	Loss: 144.139
+54400/69092	Loss: 147.514
+57600/69092	Loss: 146.007
+60800/69092	Loss: 146.419
+64000/69092	Loss: 144.330
+67200/69092	Loss: 146.160
+Training time 0:04:54.375537
+Epoch: 103 Average loss: 146.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 104)
+0/69092	Loss: 130.534
+3200/69092	Loss: 145.072
+6400/69092	Loss: 144.192
+9600/69092	Loss: 146.945
+12800/69092	Loss: 144.152
+16000/69092	Loss: 146.369
+19200/69092	Loss: 147.721
+22400/69092	Loss: 145.291
+25600/69092	Loss: 148.231
+28800/69092	Loss: 147.630
+32000/69092	Loss: 146.201
+35200/69092	Loss: 145.965
+38400/69092	Loss: 147.652
+41600/69092	Loss: 143.750
+44800/69092	Loss: 144.310
+48000/69092	Loss: 146.166
+51200/69092	Loss: 146.873
+54400/69092	Loss: 146.049
+57600/69092	Loss: 146.260
+60800/69092	Loss: 145.330
+64000/69092	Loss: 146.802
+67200/69092	Loss: 146.804
+Training time 0:04:59.427512
+Epoch: 104 Average loss: 146.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 105)
+0/69092	Loss: 132.117
+3200/69092	Loss: 146.253
+6400/69092	Loss: 145.019
+9600/69092	Loss: 148.625
+12800/69092	Loss: 145.470
+16000/69092	Loss: 147.295
+19200/69092	Loss: 146.672
+22400/69092	Loss: 145.614
+25600/69092	Loss: 146.372
+28800/69092	Loss: 144.464
+32000/69092	Loss: 144.707
+35200/69092	Loss: 143.969
+38400/69092	Loss: 148.209
+41600/69092	Loss: 147.600
+44800/69092	Loss: 148.195
+48000/69092	Loss: 147.528
+51200/69092	Loss: 145.292
+54400/69092	Loss: 145.971
+57600/69092	Loss: 143.792
+60800/69092	Loss: 148.743
+64000/69092	Loss: 147.708
+67200/69092	Loss: 145.900
+Training time 0:04:59.014296
+Epoch: 105 Average loss: 146.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 106)
+0/69092	Loss: 145.947
+3200/69092	Loss: 148.659
+6400/69092	Loss: 144.583
+9600/69092	Loss: 147.626
+12800/69092	Loss: 146.635
+16000/69092	Loss: 144.842
+19200/69092	Loss: 145.490
+22400/69092	Loss: 144.518
+25600/69092	Loss: 143.298
+28800/69092	Loss: 149.115
+32000/69092	Loss: 145.910
+35200/69092	Loss: 145.557
+38400/69092	Loss: 145.652
+41600/69092	Loss: 145.566
+44800/69092	Loss: 145.848
+48000/69092	Loss: 147.078
+51200/69092	Loss: 147.832
+54400/69092	Loss: 146.577
+57600/69092	Loss: 144.249
+60800/69092	Loss: 146.791
+64000/69092	Loss: 144.408
+67200/69092	Loss: 146.397
+Training time 0:04:50.408624
+Epoch: 106 Average loss: 146.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 107)
+0/69092	Loss: 151.039
+3200/69092	Loss: 148.167
+6400/69092	Loss: 144.562
+9600/69092	Loss: 145.521
+12800/69092	Loss: 148.811
+16000/69092	Loss: 145.789
+19200/69092	Loss: 146.616
+22400/69092	Loss: 146.180
+25600/69092	Loss: 144.805
+28800/69092	Loss: 145.103
+32000/69092	Loss: 148.776
+35200/69092	Loss: 144.775
+38400/69092	Loss: 144.048
+41600/69092	Loss: 144.765
+44800/69092	Loss: 149.169
+48000/69092	Loss: 147.047
+51200/69092	Loss: 147.648
+54400/69092	Loss: 146.018
+57600/69092	Loss: 143.473
+60800/69092	Loss: 147.100
+64000/69092	Loss: 144.868
+67200/69092	Loss: 145.224
+Training time 0:04:37.447307
+Epoch: 107 Average loss: 146.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 108)
+0/69092	Loss: 128.266
+3200/69092	Loss: 145.199
+6400/69092	Loss: 147.147
+9600/69092	Loss: 149.362
+12800/69092	Loss: 144.580
+16000/69092	Loss: 146.901
+19200/69092	Loss: 148.808
+22400/69092	Loss: 144.811
+25600/69092	Loss: 144.553
+28800/69092	Loss: 146.868
+32000/69092	Loss: 147.421
+35200/69092	Loss: 145.371
+38400/69092	Loss: 146.525
+41600/69092	Loss: 145.474
+44800/69092	Loss: 146.642
+48000/69092	Loss: 144.745
+51200/69092	Loss: 145.890
+54400/69092	Loss: 143.847
+57600/69092	Loss: 147.177
+60800/69092	Loss: 145.012
+64000/69092	Loss: 147.952
+67200/69092	Loss: 146.501
+Training time 0:04:49.045111
+Epoch: 108 Average loss: 146.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 109)
+0/69092	Loss: 148.765
+3200/69092	Loss: 146.100
+6400/69092	Loss: 144.642
+9600/69092	Loss: 147.783
+12800/69092	Loss: 150.626
+16000/69092	Loss: 148.139
+19200/69092	Loss: 143.067
+22400/69092	Loss: 143.564
+25600/69092	Loss: 145.103
+28800/69092	Loss: 144.103
+32000/69092	Loss: 146.391
+35200/69092	Loss: 145.805
+38400/69092	Loss: 147.241
+41600/69092	Loss: 146.699
+44800/69092	Loss: 144.942
+48000/69092	Loss: 143.257
+51200/69092	Loss: 147.046
+54400/69092	Loss: 145.775
+57600/69092	Loss: 146.831
+60800/69092	Loss: 146.036
+64000/69092	Loss: 144.623
+67200/69092	Loss: 146.934
+Training time 0:04:50.016618
+Epoch: 109 Average loss: 146.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 110)
+0/69092	Loss: 130.376
+3200/69092	Loss: 149.106
+6400/69092	Loss: 145.386
+9600/69092	Loss: 146.282
+12800/69092	Loss: 148.713
+16000/69092	Loss: 147.315
+19200/69092	Loss: 145.703
+22400/69092	Loss: 145.796
+25600/69092	Loss: 147.294
+28800/69092	Loss: 147.374
+32000/69092	Loss: 145.776
+35200/69092	Loss: 146.005
+38400/69092	Loss: 147.978
+41600/69092	Loss: 143.488
+44800/69092	Loss: 143.420
+48000/69092	Loss: 145.115
+51200/69092	Loss: 143.473
+54400/69092	Loss: 144.648
+57600/69092	Loss: 145.152
+60800/69092	Loss: 144.988
+64000/69092	Loss: 144.863
+67200/69092	Loss: 148.045
+Training time 0:04:50.847158
+Epoch: 110 Average loss: 146.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 111)
+0/69092	Loss: 165.203
+3200/69092	Loss: 145.252
+6400/69092	Loss: 145.382
+9600/69092	Loss: 147.041
+12800/69092	Loss: 147.485
+16000/69092	Loss: 145.525
+19200/69092	Loss: 146.576
+22400/69092	Loss: 145.825
+25600/69092	Loss: 146.910
+28800/69092	Loss: 145.976
+32000/69092	Loss: 146.487
+35200/69092	Loss: 147.970
+38400/69092	Loss: 142.838
+41600/69092	Loss: 147.194
+44800/69092	Loss: 146.962
+48000/69092	Loss: 148.049
+51200/69092	Loss: 143.582
+54400/69092	Loss: 144.774
+57600/69092	Loss: 145.327
+60800/69092	Loss: 144.627
+64000/69092	Loss: 146.615
+67200/69092	Loss: 147.865
+Training time 0:04:51.248216
+Epoch: 111 Average loss: 146.11
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 112)
+0/69092	Loss: 136.520
+3200/69092	Loss: 144.773
+6400/69092	Loss: 144.679
+9600/69092	Loss: 146.260
+12800/69092	Loss: 147.670
+16000/69092	Loss: 144.617
+19200/69092	Loss: 144.163
+22400/69092	Loss: 145.293
+25600/69092	Loss: 143.857
+28800/69092	Loss: 147.111
+32000/69092	Loss: 146.718
+35200/69092	Loss: 146.649
+38400/69092	Loss: 145.887
+41600/69092	Loss: 147.043
+44800/69092	Loss: 144.844
+48000/69092	Loss: 146.134
+51200/69092	Loss: 146.961
+54400/69092	Loss: 145.410
+57600/69092	Loss: 146.247
+60800/69092	Loss: 146.507
+64000/69092	Loss: 151.692
+67200/69092	Loss: 146.187
+Training time 0:04:45.679753
+Epoch: 112 Average loss: 146.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 113)
+0/69092	Loss: 146.427
+3200/69092	Loss: 145.019
+6400/69092	Loss: 144.179
+9600/69092	Loss: 143.454
+12800/69092	Loss: 145.496
+16000/69092	Loss: 147.107
+19200/69092	Loss: 148.987
+22400/69092	Loss: 147.540
+25600/69092	Loss: 144.192
+28800/69092	Loss: 145.857
+32000/69092	Loss: 146.907
+35200/69092	Loss: 147.025
+38400/69092	Loss: 147.464
+41600/69092	Loss: 145.127
+44800/69092	Loss: 145.134
+48000/69092	Loss: 146.232
+51200/69092	Loss: 148.749
+54400/69092	Loss: 147.042
+57600/69092	Loss: 146.374
+60800/69092	Loss: 145.059
+64000/69092	Loss: 145.683
+67200/69092	Loss: 143.474
+Training time 0:04:46.129673
+Epoch: 113 Average loss: 145.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 114)
+0/69092	Loss: 144.778
+3200/69092	Loss: 143.392
+6400/69092	Loss: 147.666
+9600/69092	Loss: 146.264
+12800/69092	Loss: 143.568
+16000/69092	Loss: 145.713
+19200/69092	Loss: 144.667
+22400/69092	Loss: 144.799
+25600/69092	Loss: 147.714
+28800/69092	Loss: 147.228
+32000/69092	Loss: 144.913
+35200/69092	Loss: 143.135
+38400/69092	Loss: 145.958
+41600/69092	Loss: 147.487
+44800/69092	Loss: 147.618
+48000/69092	Loss: 145.319
+51200/69092	Loss: 147.119
+54400/69092	Loss: 146.600
+57600/69092	Loss: 146.191
+60800/69092	Loss: 147.204
+64000/69092	Loss: 146.261
+67200/69092	Loss: 146.122
+Training time 0:04:51.105844
+Epoch: 114 Average loss: 146.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 115)
+0/69092	Loss: 133.509
+3200/69092	Loss: 147.741
+6400/69092	Loss: 142.576
+9600/69092	Loss: 145.737
+12800/69092	Loss: 146.453
+16000/69092	Loss: 149.904
+19200/69092	Loss: 145.035
+22400/69092	Loss: 145.878
+25600/69092	Loss: 145.561
+28800/69092	Loss: 147.577
+32000/69092	Loss: 148.763
+35200/69092	Loss: 144.254
+38400/69092	Loss: 146.629
+41600/69092	Loss: 145.588
+44800/69092	Loss: 145.786
+48000/69092	Loss: 144.087
+51200/69092	Loss: 147.574
+54400/69092	Loss: 146.626
+57600/69092	Loss: 147.096
+60800/69092	Loss: 145.142
+64000/69092	Loss: 142.579
+67200/69092	Loss: 144.149
+Training time 0:04:43.645364
+Epoch: 115 Average loss: 146.01
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 116)
+0/69092	Loss: 161.732
+3200/69092	Loss: 144.177
+6400/69092	Loss: 146.406
+9600/69092	Loss: 147.298
+12800/69092	Loss: 143.329
+16000/69092	Loss: 144.917
+19200/69092	Loss: 146.169
+22400/69092	Loss: 145.983
+25600/69092	Loss: 144.672
+28800/69092	Loss: 146.303
+32000/69092	Loss: 144.998
+35200/69092	Loss: 146.761
+38400/69092	Loss: 147.210
+41600/69092	Loss: 146.413
+44800/69092	Loss: 145.364
+48000/69092	Loss: 148.832
+51200/69092	Loss: 144.940
+54400/69092	Loss: 144.519
+57600/69092	Loss: 145.309
+60800/69092	Loss: 147.307
+64000/69092	Loss: 148.331
+67200/69092	Loss: 146.517
+Training time 0:04:44.782513
+Epoch: 116 Average loss: 145.93
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 117)
+0/69092	Loss: 148.347
+3200/69092	Loss: 144.033
+6400/69092	Loss: 145.816
+9600/69092	Loss: 148.012
+12800/69092	Loss: 147.761
+16000/69092	Loss: 143.092
+19200/69092	Loss: 146.285
+22400/69092	Loss: 147.739
+25600/69092	Loss: 149.156
+28800/69092	Loss: 145.611
+32000/69092	Loss: 145.875
+35200/69092	Loss: 147.979
+38400/69092	Loss: 145.146
+41600/69092	Loss: 145.820
+44800/69092	Loss: 144.963
+48000/69092	Loss: 146.284
+51200/69092	Loss: 145.179
+54400/69092	Loss: 146.234
+57600/69092	Loss: 144.831
+60800/69092	Loss: 147.571
+64000/69092	Loss: 147.710
+67200/69092	Loss: 144.079
+Training time 0:04:46.924920
+Epoch: 117 Average loss: 146.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 118)
+0/69092	Loss: 157.480
+3200/69092	Loss: 149.274
+6400/69092	Loss: 147.107
+9600/69092	Loss: 143.046
+12800/69092	Loss: 144.197
+16000/69092	Loss: 147.376
+19200/69092	Loss: 145.885
+22400/69092	Loss: 144.901
+25600/69092	Loss: 144.866
+28800/69092	Loss: 146.456
+32000/69092	Loss: 148.189
+35200/69092	Loss: 146.113
+38400/69092	Loss: 144.089
+41600/69092	Loss: 148.923
+44800/69092	Loss: 147.606
+48000/69092	Loss: 146.035
+51200/69092	Loss: 145.660
+54400/69092	Loss: 144.232
+57600/69092	Loss: 147.091
+60800/69092	Loss: 144.818
+64000/69092	Loss: 143.162
+67200/69092	Loss: 146.966
+Training time 0:04:45.661094
+Epoch: 118 Average loss: 145.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 119)
+0/69092	Loss: 153.837
+3200/69092	Loss: 148.322
+6400/69092	Loss: 146.598
+9600/69092	Loss: 145.814
+12800/69092	Loss: 146.202
+16000/69092	Loss: 144.343
+19200/69092	Loss: 145.251
+22400/69092	Loss: 145.708
+25600/69092	Loss: 145.235
+28800/69092	Loss: 145.780
+32000/69092	Loss: 144.486
+35200/69092	Loss: 146.611
+38400/69092	Loss: 146.383
+41600/69092	Loss: 148.458
+44800/69092	Loss: 145.451
+48000/69092	Loss: 147.772
+51200/69092	Loss: 145.517
+54400/69092	Loss: 144.958
+57600/69092	Loss: 142.384
+60800/69092	Loss: 143.981
+64000/69092	Loss: 146.866
+67200/69092	Loss: 145.221
+Training time 0:04:48.385159
+Epoch: 119 Average loss: 145.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 120)
+0/69092	Loss: 136.371
+3200/69092	Loss: 143.163
+6400/69092	Loss: 143.475
+9600/69092	Loss: 148.267
+12800/69092	Loss: 147.337
+16000/69092	Loss: 146.614
+19200/69092	Loss: 144.409
+22400/69092	Loss: 147.758
+25600/69092	Loss: 147.001
+28800/69092	Loss: 145.738
+32000/69092	Loss: 144.724
+35200/69092	Loss: 144.732
+38400/69092	Loss: 146.099
+41600/69092	Loss: 145.078
+44800/69092	Loss: 147.099
+48000/69092	Loss: 146.569
+51200/69092	Loss: 146.935
+54400/69092	Loss: 146.290
+57600/69092	Loss: 145.528
+60800/69092	Loss: 147.599
+64000/69092	Loss: 144.916
+67200/69092	Loss: 148.928
+Training time 0:04:36.875365
+Epoch: 120 Average loss: 146.11
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 121)
+0/69092	Loss: 131.305
+3200/69092	Loss: 143.638
+6400/69092	Loss: 146.782
+9600/69092	Loss: 143.325
+12800/69092	Loss: 150.015
+16000/69092	Loss: 144.729
+19200/69092	Loss: 144.394
+22400/69092	Loss: 145.765
+25600/69092	Loss: 145.712
+28800/69092	Loss: 145.295
+32000/69092	Loss: 145.650
+35200/69092	Loss: 146.135
+38400/69092	Loss: 146.447
+41600/69092	Loss: 146.660
+44800/69092	Loss: 148.607
+48000/69092	Loss: 147.215
+51200/69092	Loss: 147.149
+54400/69092	Loss: 144.786
+57600/69092	Loss: 143.834
+60800/69092	Loss: 146.769
+64000/69092	Loss: 143.324
+67200/69092	Loss: 145.913
+Training time 0:04:52.196409
+Epoch: 121 Average loss: 145.80
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 122)
+0/69092	Loss: 155.197
+3200/69092	Loss: 147.635
+6400/69092	Loss: 142.454
+9600/69092	Loss: 146.415
+12800/69092	Loss: 147.256
+16000/69092	Loss: 144.683
+19200/69092	Loss: 146.449
+22400/69092	Loss: 145.164
+25600/69092	Loss: 148.890
+28800/69092	Loss: 144.380
+32000/69092	Loss: 144.353
+35200/69092	Loss: 147.569
+38400/69092	Loss: 144.548
+41600/69092	Loss: 144.247
+44800/69092	Loss: 148.184
+48000/69092	Loss: 146.054
+51200/69092	Loss: 144.699
+54400/69092	Loss: 144.041
+57600/69092	Loss: 148.090
+60800/69092	Loss: 145.390
+64000/69092	Loss: 143.471
+67200/69092	Loss: 145.281
+Training time 0:04:43.886909
+Epoch: 122 Average loss: 145.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 123)
+0/69092	Loss: 148.461
+3200/69092	Loss: 144.364
+6400/69092	Loss: 145.360
+9600/69092	Loss: 146.712
+12800/69092	Loss: 149.114
+16000/69092	Loss: 145.473
+19200/69092	Loss: 147.030
+22400/69092	Loss: 148.086
+25600/69092	Loss: 145.227
+28800/69092	Loss: 144.656
+32000/69092	Loss: 144.907
+35200/69092	Loss: 146.060
+38400/69092	Loss: 146.030
+41600/69092	Loss: 149.171
+44800/69092	Loss: 145.563
+48000/69092	Loss: 146.399
+51200/69092	Loss: 144.539
+54400/69092	Loss: 143.456
+57600/69092	Loss: 142.815
+60800/69092	Loss: 146.142
+64000/69092	Loss: 147.865
+67200/69092	Loss: 144.077
+Training time 0:04:45.941821
+Epoch: 123 Average loss: 145.96
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 124)
+0/69092	Loss: 133.072
+3200/69092	Loss: 145.802
+6400/69092	Loss: 146.011
+9600/69092	Loss: 146.894
+12800/69092	Loss: 143.769
+16000/69092	Loss: 148.883
+19200/69092	Loss: 145.309
+22400/69092	Loss: 146.619
+25600/69092	Loss: 145.833
+28800/69092	Loss: 146.381
+32000/69092	Loss: 145.016
+35200/69092	Loss: 147.641
+38400/69092	Loss: 147.790
+41600/69092	Loss: 145.403
+44800/69092	Loss: 145.743
+48000/69092	Loss: 147.073
+51200/69092	Loss: 146.143
+54400/69092	Loss: 143.506
+57600/69092	Loss: 145.625
+60800/69092	Loss: 147.074
+64000/69092	Loss: 144.900
+67200/69092	Loss: 147.176
+Training time 0:04:42.112605
+Epoch: 124 Average loss: 146.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last' (iter 125)
+0/69092	Loss: 148.691
+3200/69092	Loss: 144.481
+6400/69092	Loss: 145.141
+9600/69092	Loss: 146.340
+12800/69092	Loss: 144.990
+16000/69092	Loss: 148.193
+19200/69092	Loss: 143.628
+22400/69092	Loss: 145.318
+25600/69092	Loss: 146.684
+28800/69092	Loss: 146.355
+32000/69092	Loss: 144.701
+35200/69092	Loss: 144.713
+38400/69092	Loss: 146.332
+41600/69092	Loss: 146.015
+44800/69092	Loss: 147.595
+48000/69092	Loss: 144.976
+51200/69092	Loss: 145.766
+54400/69092	Loss: 146.489
+57600/69092	Loss: 147.861
+60800/69092	Loss: 146.934
+64000/69092	Loss: 143.582
diff --git a/OAR.2068292.stderr b/OAR.2068292.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..a65b71902ff90f1db731fadbf6618b2c49090c5a
--- /dev/null
+++ b/OAR.2068292.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
+## OAR [2020-06-25 10:08:28] Job 2068292 KILLED ##
diff --git a/OAR.2068292.stdout b/OAR.2068292.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..5e37e87eac416a7c8ce456b645f66511a49f17ed
--- /dev/null
+++ b/OAR.2068292.stdout
@@ -0,0 +1,2903 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64_ls_15', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=15, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64_ls_15
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+Tesla K40c
+Tesla K80
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=30, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=15, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 769185
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last (iter 2)'
+0/69092	Loss: 164.068
+3200/69092	Loss: 159.062
+6400/69092	Loss: 157.224
+9600/69092	Loss: 154.582
+12800/69092	Loss: 154.183
+16000/69092	Loss: 153.739
+19200/69092	Loss: 153.701
+22400/69092	Loss: 150.246
+25600/69092	Loss: 153.596
+28800/69092	Loss: 150.894
+32000/69092	Loss: 151.965
+35200/69092	Loss: 149.944
+38400/69092	Loss: 148.097
+41600/69092	Loss: 147.112
+44800/69092	Loss: 147.547
+48000/69092	Loss: 143.439
+51200/69092	Loss: 143.876
+54400/69092	Loss: 143.881
+57600/69092	Loss: 141.629
+60800/69092	Loss: 141.141
+64000/69092	Loss: 140.164
+67200/69092	Loss: 142.035
+Training time 0:04:58.146411
+Epoch: 1 Average loss: 148.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 3)
+0/69092	Loss: 152.479
+3200/69092	Loss: 142.726
+6400/69092	Loss: 144.389
+9600/69092	Loss: 138.407
+12800/69092	Loss: 138.264
+16000/69092	Loss: 140.209
+19200/69092	Loss: 139.579
+22400/69092	Loss: 136.847
+25600/69092	Loss: 134.427
+28800/69092	Loss: 134.863
+32000/69092	Loss: 135.759
+35200/69092	Loss: 137.380
+38400/69092	Loss: 136.066
+41600/69092	Loss: 135.911
+44800/69092	Loss: 135.465
+48000/69092	Loss: 134.008
+51200/69092	Loss: 133.120
+54400/69092	Loss: 136.413
+57600/69092	Loss: 132.427
+60800/69092	Loss: 135.123
+64000/69092	Loss: 136.686
+67200/69092	Loss: 133.240
+Training time 0:04:50.508829
+Epoch: 2 Average loss: 136.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 4)
+0/69092	Loss: 137.475
+3200/69092	Loss: 134.191
+6400/69092	Loss: 132.651
+9600/69092	Loss: 132.042
+12800/69092	Loss: 134.187
+16000/69092	Loss: 133.448
+19200/69092	Loss: 136.283
+22400/69092	Loss: 130.460
+25600/69092	Loss: 133.009
+28800/69092	Loss: 134.494
+32000/69092	Loss: 133.096
+35200/69092	Loss: 130.866
+38400/69092	Loss: 132.377
+41600/69092	Loss: 133.557
+44800/69092	Loss: 133.696
+48000/69092	Loss: 128.877
+51200/69092	Loss: 130.718
+54400/69092	Loss: 130.170
+57600/69092	Loss: 131.031
+60800/69092	Loss: 128.449
+64000/69092	Loss: 131.945
+67200/69092	Loss: 132.108
+Training time 0:04:49.348248
+Epoch: 3 Average loss: 132.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 5)
+0/69092	Loss: 112.655
+3200/69092	Loss: 131.264
+6400/69092	Loss: 130.131
+9600/69092	Loss: 131.474
+12800/69092	Loss: 130.229
+16000/69092	Loss: 131.610
+19200/69092	Loss: 128.390
+22400/69092	Loss: 130.507
+25600/69092	Loss: 131.762
+28800/69092	Loss: 130.924
+32000/69092	Loss: 129.825
+35200/69092	Loss: 130.224
+38400/69092	Loss: 130.196
+41600/69092	Loss: 129.426
+44800/69092	Loss: 130.307
+48000/69092	Loss: 131.456
+51200/69092	Loss: 128.890
+54400/69092	Loss: 130.250
+57600/69092	Loss: 128.794
+60800/69092	Loss: 131.355
+64000/69092	Loss: 130.152
+67200/69092	Loss: 131.066
+Training time 0:04:50.908864
+Epoch: 4 Average loss: 130.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 6)
+0/69092	Loss: 127.888
+3200/69092	Loss: 130.891
+6400/69092	Loss: 129.799
+9600/69092	Loss: 130.955
+12800/69092	Loss: 126.172
+16000/69092	Loss: 130.069
+19200/69092	Loss: 130.856
+22400/69092	Loss: 127.463
+25600/69092	Loss: 128.853
+28800/69092	Loss: 128.933
+32000/69092	Loss: 129.473
+35200/69092	Loss: 130.088
+38400/69092	Loss: 129.832
+41600/69092	Loss: 130.128
+44800/69092	Loss: 128.135
+48000/69092	Loss: 127.895
+51200/69092	Loss: 125.976
+54400/69092	Loss: 127.525
+57600/69092	Loss: 130.168
+60800/69092	Loss: 127.672
+64000/69092	Loss: 126.680
+67200/69092	Loss: 128.760
+Training time 0:04:49.413921
+Epoch: 5 Average loss: 128.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 7)
+0/69092	Loss: 136.036
+3200/69092	Loss: 127.881
+6400/69092	Loss: 128.480
+9600/69092	Loss: 126.520
+12800/69092	Loss: 127.070
+16000/69092	Loss: 127.472
+19200/69092	Loss: 127.496
+22400/69092	Loss: 129.317
+25600/69092	Loss: 126.693
+28800/69092	Loss: 126.789
+32000/69092	Loss: 128.037
+35200/69092	Loss: 126.847
+38400/69092	Loss: 127.274
+41600/69092	Loss: 127.458
+44800/69092	Loss: 127.357
+48000/69092	Loss: 129.112
+51200/69092	Loss: 129.610
+54400/69092	Loss: 127.765
+57600/69092	Loss: 127.420
+60800/69092	Loss: 126.488
+64000/69092	Loss: 126.994
+67200/69092	Loss: 128.856
+Training time 0:04:52.057289
+Epoch: 6 Average loss: 127.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 8)
+0/69092	Loss: 114.712
+3200/69092	Loss: 127.520
+6400/69092	Loss: 124.320
+9600/69092	Loss: 126.230
+12800/69092	Loss: 129.335
+16000/69092	Loss: 125.961
+19200/69092	Loss: 127.808
+22400/69092	Loss: 127.900
+25600/69092	Loss: 125.901
+28800/69092	Loss: 127.676
+32000/69092	Loss: 123.257
+35200/69092	Loss: 125.715
+38400/69092	Loss: 126.342
+41600/69092	Loss: 127.061
+44800/69092	Loss: 124.434
+48000/69092	Loss: 127.263
+51200/69092	Loss: 126.219
+54400/69092	Loss: 127.302
+57600/69092	Loss: 126.033
+60800/69092	Loss: 126.737
+64000/69092	Loss: 127.853
+67200/69092	Loss: 125.695
+Training time 0:04:49.157151
+Epoch: 7 Average loss: 126.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 9)
+0/69092	Loss: 144.076
+3200/69092	Loss: 125.569
+6400/69092	Loss: 125.122
+9600/69092	Loss: 126.678
+12800/69092	Loss: 123.121
+16000/69092	Loss: 126.292
+19200/69092	Loss: 124.290
+22400/69092	Loss: 125.286
+25600/69092	Loss: 127.713
+28800/69092	Loss: 128.740
+32000/69092	Loss: 126.587
+35200/69092	Loss: 125.263
+38400/69092	Loss: 127.627
+41600/69092	Loss: 122.766
+44800/69092	Loss: 123.776
+48000/69092	Loss: 124.783
+51200/69092	Loss: 127.640
+54400/69092	Loss: 125.066
+57600/69092	Loss: 127.092
+60800/69092	Loss: 126.302
+64000/69092	Loss: 124.086
+67200/69092	Loss: 126.229
+Training time 0:04:51.763548
+Epoch: 8 Average loss: 125.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 10)
+0/69092	Loss: 143.115
+3200/69092	Loss: 124.100
+6400/69092	Loss: 127.471
+9600/69092	Loss: 124.016
+12800/69092	Loss: 124.874
+16000/69092	Loss: 125.503
+19200/69092	Loss: 123.509
+22400/69092	Loss: 123.893
+25600/69092	Loss: 124.253
+28800/69092	Loss: 125.839
+32000/69092	Loss: 124.033
+35200/69092	Loss: 122.995
+38400/69092	Loss: 126.921
+41600/69092	Loss: 126.131
+44800/69092	Loss: 125.028
+48000/69092	Loss: 124.421
+51200/69092	Loss: 124.513
+54400/69092	Loss: 125.414
+57600/69092	Loss: 123.846
+60800/69092	Loss: 123.672
+64000/69092	Loss: 124.252
+67200/69092	Loss: 125.823
+Training time 0:04:50.687557
+Epoch: 9 Average loss: 124.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 11)
+0/69092	Loss: 132.530
+3200/69092	Loss: 124.715
+6400/69092	Loss: 126.497
+9600/69092	Loss: 123.316
+12800/69092	Loss: 124.901
+16000/69092	Loss: 125.205
+19200/69092	Loss: 122.765
+22400/69092	Loss: 126.498
+25600/69092	Loss: 124.976
+28800/69092	Loss: 124.783
+32000/69092	Loss: 123.165
+35200/69092	Loss: 123.086
+38400/69092	Loss: 124.304
+41600/69092	Loss: 122.915
+44800/69092	Loss: 125.451
+48000/69092	Loss: 124.571
+51200/69092	Loss: 124.476
+54400/69092	Loss: 122.600
+57600/69092	Loss: 124.240
+60800/69092	Loss: 123.787
+64000/69092	Loss: 122.615
+67200/69092	Loss: 124.837
+Training time 0:04:52.461847
+Epoch: 10 Average loss: 124.29
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 12)
+0/69092	Loss: 123.180
+3200/69092	Loss: 123.575
+6400/69092	Loss: 122.534
+9600/69092	Loss: 123.632
+12800/69092	Loss: 124.860
+16000/69092	Loss: 123.442
+19200/69092	Loss: 124.136
+22400/69092	Loss: 123.966
+25600/69092	Loss: 123.843
+28800/69092	Loss: 124.526
+32000/69092	Loss: 122.504
+35200/69092	Loss: 124.491
+38400/69092	Loss: 123.499
+41600/69092	Loss: 124.616
+44800/69092	Loss: 122.246
+48000/69092	Loss: 123.037
+51200/69092	Loss: 124.100
+54400/69092	Loss: 126.530
+57600/69092	Loss: 121.947
+60800/69092	Loss: 122.122
+64000/69092	Loss: 124.514
+67200/69092	Loss: 123.882
+Training time 0:04:51.755454
+Epoch: 11 Average loss: 123.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 13)
+0/69092	Loss: 124.780
+3200/69092	Loss: 123.718
+6400/69092	Loss: 125.246
+9600/69092	Loss: 125.144
+12800/69092	Loss: 121.691
+16000/69092	Loss: 121.736
+19200/69092	Loss: 123.536
+22400/69092	Loss: 124.053
+25600/69092	Loss: 124.311
+28800/69092	Loss: 124.009
+32000/69092	Loss: 123.313
+35200/69092	Loss: 123.694
+38400/69092	Loss: 123.137
+41600/69092	Loss: 122.437
+44800/69092	Loss: 122.643
+48000/69092	Loss: 121.283
+51200/69092	Loss: 122.192
+54400/69092	Loss: 121.984
+57600/69092	Loss: 121.845
+60800/69092	Loss: 123.966
+64000/69092	Loss: 123.677
+67200/69092	Loss: 121.343
+Training time 0:04:49.593080
+Epoch: 12 Average loss: 123.01
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 14)
+0/69092	Loss: 113.463
+3200/69092	Loss: 121.899
+6400/69092	Loss: 122.716
+9600/69092	Loss: 121.791
+12800/69092	Loss: 122.534
+16000/69092	Loss: 122.608
+19200/69092	Loss: 123.967
+22400/69092	Loss: 123.682
+25600/69092	Loss: 121.969
+28800/69092	Loss: 123.313
+32000/69092	Loss: 122.102
+35200/69092	Loss: 124.053
+38400/69092	Loss: 121.807
+41600/69092	Loss: 121.690
+44800/69092	Loss: 123.093
+48000/69092	Loss: 122.188
+51200/69092	Loss: 123.149
+54400/69092	Loss: 120.774
+57600/69092	Loss: 121.591
+60800/69092	Loss: 125.514
+64000/69092	Loss: 122.014
+67200/69092	Loss: 121.635
+Training time 0:04:51.107930
+Epoch: 13 Average loss: 122.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 15)
+0/69092	Loss: 141.649
+3200/69092	Loss: 121.660
+6400/69092	Loss: 124.211
+9600/69092	Loss: 123.860
+12800/69092	Loss: 120.946
+16000/69092	Loss: 120.423
+19200/69092	Loss: 122.185
+22400/69092	Loss: 122.621
+25600/69092	Loss: 123.143
+28800/69092	Loss: 122.857
+32000/69092	Loss: 122.491
+35200/69092	Loss: 122.411
+38400/69092	Loss: 119.921
+41600/69092	Loss: 123.385
+44800/69092	Loss: 122.616
+48000/69092	Loss: 121.728
+51200/69092	Loss: 121.045
+54400/69092	Loss: 123.097
+57600/69092	Loss: 121.515
+60800/69092	Loss: 123.073
+64000/69092	Loss: 121.583
+67200/69092	Loss: 121.666
+Training time 0:04:49.451576
+Epoch: 14 Average loss: 122.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 16)
+0/69092	Loss: 124.186
+3200/69092	Loss: 120.439
+6400/69092	Loss: 121.248
+9600/69092	Loss: 120.874
+12800/69092	Loss: 121.578
+16000/69092	Loss: 122.464
+19200/69092	Loss: 121.549
+22400/69092	Loss: 120.158
+25600/69092	Loss: 120.951
+28800/69092	Loss: 124.429
+32000/69092	Loss: 121.813
+35200/69092	Loss: 121.571
+38400/69092	Loss: 123.013
+41600/69092	Loss: 120.008
+44800/69092	Loss: 122.563
+48000/69092	Loss: 122.886
+51200/69092	Loss: 120.782
+54400/69092	Loss: 123.421
+57600/69092	Loss: 121.868
+60800/69092	Loss: 121.298
+64000/69092	Loss: 119.484
+67200/69092	Loss: 123.969
+Training time 0:04:50.416095
+Epoch: 15 Average loss: 121.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 17)
+0/69092	Loss: 147.280
+3200/69092	Loss: 124.071
+6400/69092	Loss: 122.413
+9600/69092	Loss: 121.878
+12800/69092	Loss: 118.349
+16000/69092	Loss: 122.346
+19200/69092	Loss: 119.379
+22400/69092	Loss: 119.008
+25600/69092	Loss: 123.249
+28800/69092	Loss: 120.557
+32000/69092	Loss: 120.716
+35200/69092	Loss: 120.112
+38400/69092	Loss: 120.230
+41600/69092	Loss: 123.128
+44800/69092	Loss: 122.548
+48000/69092	Loss: 121.832
+51200/69092	Loss: 122.497
+54400/69092	Loss: 120.060
+57600/69092	Loss: 121.200
+60800/69092	Loss: 122.492
+64000/69092	Loss: 121.126
+67200/69092	Loss: 119.935
+Training time 0:04:49.960933
+Epoch: 16 Average loss: 121.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 18)
+0/69092	Loss: 124.367
+3200/69092	Loss: 122.409
+6400/69092	Loss: 122.262
+9600/69092	Loss: 119.387
+12800/69092	Loss: 121.039
+16000/69092	Loss: 119.182
+19200/69092	Loss: 121.419
+22400/69092	Loss: 121.514
+25600/69092	Loss: 122.545
+28800/69092	Loss: 122.114
+32000/69092	Loss: 119.501
+35200/69092	Loss: 122.873
+38400/69092	Loss: 121.808
+41600/69092	Loss: 120.921
+44800/69092	Loss: 121.306
+48000/69092	Loss: 120.496
+51200/69092	Loss: 120.772
+54400/69092	Loss: 118.943
+57600/69092	Loss: 120.649
+60800/69092	Loss: 120.235
+64000/69092	Loss: 122.692
+67200/69092	Loss: 119.990
+Training time 0:04:49.027354
+Epoch: 17 Average loss: 121.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 19)
+0/69092	Loss: 121.317
+3200/69092	Loss: 120.732
+6400/69092	Loss: 122.482
+9600/69092	Loss: 119.718
+12800/69092	Loss: 119.866
+16000/69092	Loss: 120.726
+19200/69092	Loss: 121.990
+22400/69092	Loss: 119.675
+25600/69092	Loss: 121.545
+28800/69092	Loss: 120.568
+32000/69092	Loss: 122.772
+35200/69092	Loss: 121.967
+38400/69092	Loss: 121.649
+41600/69092	Loss: 117.995
+44800/69092	Loss: 121.247
+48000/69092	Loss: 120.534
+51200/69092	Loss: 119.402
+54400/69092	Loss: 119.726
+57600/69092	Loss: 122.539
+60800/69092	Loss: 119.981
+64000/69092	Loss: 118.654
+67200/69092	Loss: 119.318
+Training time 0:04:49.797799
+Epoch: 18 Average loss: 120.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 20)
+0/69092	Loss: 118.134
+3200/69092	Loss: 120.396
+6400/69092	Loss: 120.969
+9600/69092	Loss: 120.209
+12800/69092	Loss: 121.274
+16000/69092	Loss: 121.006
+19200/69092	Loss: 122.580
+22400/69092	Loss: 119.853
+25600/69092	Loss: 119.243
+28800/69092	Loss: 118.694
+32000/69092	Loss: 121.411
+35200/69092	Loss: 121.019
+38400/69092	Loss: 120.749
+41600/69092	Loss: 121.177
+44800/69092	Loss: 120.289
+48000/69092	Loss: 118.386
+51200/69092	Loss: 119.777
+54400/69092	Loss: 120.387
+57600/69092	Loss: 119.084
+60800/69092	Loss: 122.402
+64000/69092	Loss: 119.486
+67200/69092	Loss: 120.203
+Training time 0:04:48.928545
+Epoch: 19 Average loss: 120.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 21)
+0/69092	Loss: 132.927
+3200/69092	Loss: 121.500
+6400/69092	Loss: 121.023
+9600/69092	Loss: 119.467
+12800/69092	Loss: 119.294
+16000/69092	Loss: 120.119
+19200/69092	Loss: 121.221
+22400/69092	Loss: 121.108
+25600/69092	Loss: 120.540
+28800/69092	Loss: 119.922
+32000/69092	Loss: 119.803
+35200/69092	Loss: 118.895
+38400/69092	Loss: 120.113
+41600/69092	Loss: 119.533
+44800/69092	Loss: 120.517
+48000/69092	Loss: 118.937
+51200/69092	Loss: 119.558
+54400/69092	Loss: 118.833
+57600/69092	Loss: 119.291
+60800/69092	Loss: 118.990
+64000/69092	Loss: 120.532
+67200/69092	Loss: 121.909
+Training time 0:04:51.148482
+Epoch: 20 Average loss: 120.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 22)
+0/69092	Loss: 118.307
+3200/69092	Loss: 119.423
+6400/69092	Loss: 118.216
+9600/69092	Loss: 119.984
+12800/69092	Loss: 120.158
+16000/69092	Loss: 117.909
+19200/69092	Loss: 119.399
+22400/69092	Loss: 120.739
+25600/69092	Loss: 121.521
+28800/69092	Loss: 120.108
+32000/69092	Loss: 118.571
+35200/69092	Loss: 119.629
+38400/69092	Loss: 118.709
+41600/69092	Loss: 119.575
+44800/69092	Loss: 118.780
+48000/69092	Loss: 119.657
+51200/69092	Loss: 119.952
+54400/69092	Loss: 119.715
+57600/69092	Loss: 118.939
+60800/69092	Loss: 117.817
+64000/69092	Loss: 120.944
+67200/69092	Loss: 121.950
+Training time 0:04:49.804652
+Epoch: 21 Average loss: 119.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 23)
+0/69092	Loss: 121.398
+3200/69092	Loss: 118.355
+6400/69092	Loss: 118.077
+9600/69092	Loss: 119.630
+12800/69092	Loss: 120.356
+16000/69092	Loss: 119.896
+19200/69092	Loss: 121.023
+22400/69092	Loss: 120.690
+25600/69092	Loss: 119.712
+28800/69092	Loss: 121.213
+32000/69092	Loss: 122.260
+35200/69092	Loss: 118.701
+38400/69092	Loss: 120.645
+41600/69092	Loss: 118.089
+44800/69092	Loss: 119.693
+48000/69092	Loss: 118.592
+51200/69092	Loss: 119.260
+54400/69092	Loss: 119.355
+57600/69092	Loss: 117.261
+60800/69092	Loss: 118.825
+64000/69092	Loss: 117.297
+67200/69092	Loss: 119.719
+Training time 0:04:50.067787
+Epoch: 22 Average loss: 119.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 24)
+0/69092	Loss: 111.638
+3200/69092	Loss: 119.813
+6400/69092	Loss: 118.761
+9600/69092	Loss: 118.287
+12800/69092	Loss: 118.752
+16000/69092	Loss: 118.375
+19200/69092	Loss: 118.758
+22400/69092	Loss: 120.132
+25600/69092	Loss: 117.772
+28800/69092	Loss: 120.166
+32000/69092	Loss: 120.307
+35200/69092	Loss: 119.838
+38400/69092	Loss: 118.112
+41600/69092	Loss: 118.822
+44800/69092	Loss: 118.199
+48000/69092	Loss: 119.910
+51200/69092	Loss: 120.456
+54400/69092	Loss: 121.213
+57600/69092	Loss: 119.889
+60800/69092	Loss: 119.915
+64000/69092	Loss: 116.545
+67200/69092	Loss: 118.453
+Training time 0:04:52.012971
+Epoch: 23 Average loss: 119.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 25)
+0/69092	Loss: 140.434
+3200/69092	Loss: 118.041
+6400/69092	Loss: 120.979
+9600/69092	Loss: 116.782
+12800/69092	Loss: 119.718
+16000/69092	Loss: 119.394
+19200/69092	Loss: 117.417
+22400/69092	Loss: 118.923
+25600/69092	Loss: 118.774
+28800/69092	Loss: 118.152
+32000/69092	Loss: 118.696
+35200/69092	Loss: 119.377
+38400/69092	Loss: 120.148
+41600/69092	Loss: 119.314
+44800/69092	Loss: 120.440
+48000/69092	Loss: 118.426
+51200/69092	Loss: 119.611
+54400/69092	Loss: 119.685
+57600/69092	Loss: 119.266
+60800/69092	Loss: 118.629
+64000/69092	Loss: 118.080
+67200/69092	Loss: 119.973
+Training time 0:04:49.487557
+Epoch: 24 Average loss: 119.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 26)
+0/69092	Loss: 124.300
+3200/69092	Loss: 117.881
+6400/69092	Loss: 118.144
+9600/69092	Loss: 120.593
+12800/69092	Loss: 117.324
+16000/69092	Loss: 118.725
+19200/69092	Loss: 118.808
+22400/69092	Loss: 120.655
+25600/69092	Loss: 118.664
+28800/69092	Loss: 120.388
+32000/69092	Loss: 118.245
+35200/69092	Loss: 120.707
+38400/69092	Loss: 118.805
+41600/69092	Loss: 119.746
+44800/69092	Loss: 119.556
+48000/69092	Loss: 118.753
+51200/69092	Loss: 117.709
+54400/69092	Loss: 118.975
+57600/69092	Loss: 119.539
+60800/69092	Loss: 118.077
+64000/69092	Loss: 118.615
+67200/69092	Loss: 118.794
+Training time 0:04:52.128706
+Epoch: 25 Average loss: 118.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 27)
+0/69092	Loss: 113.090
+3200/69092	Loss: 119.358
+6400/69092	Loss: 118.229
+9600/69092	Loss: 119.189
+12800/69092	Loss: 117.514
+16000/69092	Loss: 118.845
+19200/69092	Loss: 118.498
+22400/69092	Loss: 117.974
+25600/69092	Loss: 116.578
+28800/69092	Loss: 118.265
+32000/69092	Loss: 118.847
+35200/69092	Loss: 121.428
+38400/69092	Loss: 120.392
+41600/69092	Loss: 118.957
+44800/69092	Loss: 119.120
+48000/69092	Loss: 119.334
+51200/69092	Loss: 120.669
+54400/69092	Loss: 120.009
+57600/69092	Loss: 117.227
+60800/69092	Loss: 118.158
+64000/69092	Loss: 119.327
+67200/69092	Loss: 118.300
+Training time 0:04:49.223185
+Epoch: 26 Average loss: 118.80
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 28)
+0/69092	Loss: 116.295
+3200/69092	Loss: 117.970
+6400/69092	Loss: 119.817
+9600/69092	Loss: 117.504
+12800/69092	Loss: 120.582
+16000/69092	Loss: 120.024
+19200/69092	Loss: 119.205
+22400/69092	Loss: 116.581
+25600/69092	Loss: 117.436
+28800/69092	Loss: 118.478
+32000/69092	Loss: 120.577
+35200/69092	Loss: 116.665
+38400/69092	Loss: 119.224
+41600/69092	Loss: 118.680
+44800/69092	Loss: 118.032
+48000/69092	Loss: 118.059
+51200/69092	Loss: 118.905
+54400/69092	Loss: 118.619
+57600/69092	Loss: 117.789
+60800/69092	Loss: 118.975
+64000/69092	Loss: 117.807
+67200/69092	Loss: 118.468
+Training time 0:04:51.033925
+Epoch: 27 Average loss: 118.55
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 29)
+0/69092	Loss: 107.462
+3200/69092	Loss: 117.137
+6400/69092	Loss: 119.733
+9600/69092	Loss: 119.552
+12800/69092	Loss: 118.056
+16000/69092	Loss: 118.923
+19200/69092	Loss: 117.516
+22400/69092	Loss: 120.202
+25600/69092	Loss: 118.220
+28800/69092	Loss: 120.580
+32000/69092	Loss: 117.011
+35200/69092	Loss: 117.457
+38400/69092	Loss: 120.626
+41600/69092	Loss: 118.098
+44800/69092	Loss: 118.663
+48000/69092	Loss: 118.516
+51200/69092	Loss: 116.312
+54400/69092	Loss: 117.661
+57600/69092	Loss: 117.157
+60800/69092	Loss: 120.903
+64000/69092	Loss: 118.162
+67200/69092	Loss: 116.963
+Training time 0:04:51.592031
+Epoch: 28 Average loss: 118.35
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 30)
+0/69092	Loss: 102.629
+3200/69092	Loss: 119.291
+6400/69092	Loss: 117.435
+9600/69092	Loss: 119.788
+12800/69092	Loss: 116.903
+16000/69092	Loss: 118.794
+19200/69092	Loss: 118.863
+22400/69092	Loss: 117.916
+25600/69092	Loss: 120.067
+28800/69092	Loss: 120.635
+32000/69092	Loss: 117.094
+35200/69092	Loss: 117.373
+38400/69092	Loss: 118.355
+41600/69092	Loss: 116.381
+44800/69092	Loss: 118.091
+48000/69092	Loss: 117.962
+51200/69092	Loss: 119.181
+54400/69092	Loss: 117.127
+57600/69092	Loss: 118.752
+60800/69092	Loss: 118.904
+64000/69092	Loss: 116.458
+67200/69092	Loss: 118.868
+Training time 0:04:51.587348
+Epoch: 29 Average loss: 118.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 31)
+0/69092	Loss: 117.959
+3200/69092	Loss: 118.615
+6400/69092	Loss: 116.865
+9600/69092	Loss: 117.357
+12800/69092	Loss: 117.407
+16000/69092	Loss: 118.332
+19200/69092	Loss: 117.742
+22400/69092	Loss: 118.900
+25600/69092	Loss: 118.546
+28800/69092	Loss: 118.227
+32000/69092	Loss: 116.875
+35200/69092	Loss: 117.978
+38400/69092	Loss: 118.425
+41600/69092	Loss: 117.657
+44800/69092	Loss: 118.104
+48000/69092	Loss: 119.604
+51200/69092	Loss: 119.600
+54400/69092	Loss: 121.515
+57600/69092	Loss: 118.742
+60800/69092	Loss: 116.864
+64000/69092	Loss: 115.990
+67200/69092	Loss: 118.275
+Training time 0:04:50.048876
+Epoch: 30 Average loss: 118.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 32)
+0/69092	Loss: 106.593
+3200/69092	Loss: 119.711
+6400/69092	Loss: 116.807
+9600/69092	Loss: 117.608
+12800/69092	Loss: 118.867
+16000/69092	Loss: 117.906
+19200/69092	Loss: 117.232
+22400/69092	Loss: 115.240
+25600/69092	Loss: 117.749
+28800/69092	Loss: 118.540
+32000/69092	Loss: 119.195
+35200/69092	Loss: 118.320
+38400/69092	Loss: 116.502
+41600/69092	Loss: 117.607
+44800/69092	Loss: 117.263
+48000/69092	Loss: 117.957
+51200/69092	Loss: 117.742
+54400/69092	Loss: 118.916
+57600/69092	Loss: 117.189
+60800/69092	Loss: 120.184
+64000/69092	Loss: 116.788
+67200/69092	Loss: 118.234
+Training time 0:04:50.015429
+Epoch: 31 Average loss: 117.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 33)
+0/69092	Loss: 128.475
+3200/69092	Loss: 117.685
+6400/69092	Loss: 116.569
+9600/69092	Loss: 119.111
+12800/69092	Loss: 117.263
+16000/69092	Loss: 116.626
+19200/69092	Loss: 117.213
+22400/69092	Loss: 117.793
+25600/69092	Loss: 117.037
+28800/69092	Loss: 119.234
+32000/69092	Loss: 117.317
+35200/69092	Loss: 118.113
+38400/69092	Loss: 117.912
+41600/69092	Loss: 117.433
+44800/69092	Loss: 117.601
+48000/69092	Loss: 118.597
+51200/69092	Loss: 119.003
+54400/69092	Loss: 117.660
+57600/69092	Loss: 117.688
+60800/69092	Loss: 117.206
+64000/69092	Loss: 119.188
+67200/69092	Loss: 118.233
+Training time 0:04:52.524545
+Epoch: 32 Average loss: 117.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 34)
+0/69092	Loss: 108.132
+3200/69092	Loss: 118.912
+6400/69092	Loss: 115.902
+9600/69092	Loss: 117.341
+12800/69092	Loss: 117.619
+16000/69092	Loss: 116.339
+19200/69092	Loss: 118.875
+22400/69092	Loss: 118.306
+25600/69092	Loss: 118.466
+28800/69092	Loss: 116.170
+32000/69092	Loss: 115.511
+35200/69092	Loss: 120.453
+38400/69092	Loss: 118.390
+41600/69092	Loss: 115.965
+44800/69092	Loss: 116.297
+48000/69092	Loss: 119.445
+51200/69092	Loss: 119.516
+54400/69092	Loss: 115.973
+57600/69092	Loss: 118.658
+60800/69092	Loss: 116.730
+64000/69092	Loss: 119.248
+67200/69092	Loss: 116.062
+Training time 0:04:50.447285
+Epoch: 33 Average loss: 117.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 35)
+0/69092	Loss: 121.678
+3200/69092	Loss: 117.583
+6400/69092	Loss: 116.046
+9600/69092	Loss: 118.373
+12800/69092	Loss: 115.075
+16000/69092	Loss: 116.979
+19200/69092	Loss: 117.002
+22400/69092	Loss: 116.698
+25600/69092	Loss: 118.600
+28800/69092	Loss: 118.987
+32000/69092	Loss: 116.339
+35200/69092	Loss: 116.992
+38400/69092	Loss: 117.991
+41600/69092	Loss: 116.828
+44800/69092	Loss: 118.113
+48000/69092	Loss: 116.288
+51200/69092	Loss: 116.670
+54400/69092	Loss: 116.627
+57600/69092	Loss: 117.844
+60800/69092	Loss: 119.019
+64000/69092	Loss: 117.406
+67200/69092	Loss: 118.033
+Training time 0:04:51.258306
+Epoch: 34 Average loss: 117.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 36)
+0/69092	Loss: 122.526
+3200/69092	Loss: 117.809
+6400/69092	Loss: 116.431
+9600/69092	Loss: 115.905
+12800/69092	Loss: 118.919
+16000/69092	Loss: 118.223
+19200/69092	Loss: 117.447
+22400/69092	Loss: 119.590
+25600/69092	Loss: 117.410
+28800/69092	Loss: 116.450
+32000/69092	Loss: 119.641
+35200/69092	Loss: 118.163
+38400/69092	Loss: 116.956
+41600/69092	Loss: 116.597
+44800/69092	Loss: 117.110
+48000/69092	Loss: 118.238
+51200/69092	Loss: 116.821
+54400/69092	Loss: 115.176
+57600/69092	Loss: 116.725
+60800/69092	Loss: 116.664
+64000/69092	Loss: 117.485
+67200/69092	Loss: 117.773
+Training time 0:04:50.190005
+Epoch: 35 Average loss: 117.37
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 37)
+0/69092	Loss: 123.229
+3200/69092	Loss: 117.976
+6400/69092	Loss: 116.481
+9600/69092	Loss: 117.942
+12800/69092	Loss: 117.082
+16000/69092	Loss: 116.883
+19200/69092	Loss: 116.089
+22400/69092	Loss: 116.840
+25600/69092	Loss: 118.900
+28800/69092	Loss: 116.752
+32000/69092	Loss: 115.466
+35200/69092	Loss: 116.356
+38400/69092	Loss: 114.889
+41600/69092	Loss: 119.188
+44800/69092	Loss: 120.328
+48000/69092	Loss: 116.525
+51200/69092	Loss: 117.148
+54400/69092	Loss: 117.533
+57600/69092	Loss: 117.055
+60800/69092	Loss: 116.720
+64000/69092	Loss: 117.952
+67200/69092	Loss: 117.484
+Training time 0:04:50.280765
+Epoch: 36 Average loss: 117.29
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 38)
+0/69092	Loss: 111.605
+3200/69092	Loss: 117.366
+6400/69092	Loss: 116.807
+9600/69092	Loss: 114.905
+12800/69092	Loss: 117.049
+16000/69092	Loss: 115.857
+19200/69092	Loss: 118.030
+22400/69092	Loss: 117.065
+25600/69092	Loss: 121.040
+28800/69092	Loss: 116.276
+32000/69092	Loss: 116.552
+35200/69092	Loss: 117.029
+38400/69092	Loss: 116.262
+41600/69092	Loss: 116.357
+44800/69092	Loss: 116.982
+48000/69092	Loss: 117.109
+51200/69092	Loss: 117.132
+54400/69092	Loss: 117.274
+57600/69092	Loss: 116.951
+60800/69092	Loss: 119.223
+64000/69092	Loss: 117.742
+67200/69092	Loss: 118.431
+Training time 0:04:52.490880
+Epoch: 37 Average loss: 117.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 39)
+0/69092	Loss: 115.327
+3200/69092	Loss: 117.210
+6400/69092	Loss: 117.962
+9600/69092	Loss: 115.766
+12800/69092	Loss: 116.867
+16000/69092	Loss: 116.569
+19200/69092	Loss: 118.385
+22400/69092	Loss: 118.070
+25600/69092	Loss: 115.436
+28800/69092	Loss: 116.705
+32000/69092	Loss: 115.048
+35200/69092	Loss: 118.282
+38400/69092	Loss: 117.069
+41600/69092	Loss: 115.870
+44800/69092	Loss: 117.483
+48000/69092	Loss: 116.781
+51200/69092	Loss: 118.254
+54400/69092	Loss: 115.811
+57600/69092	Loss: 116.128
+60800/69092	Loss: 116.566
+64000/69092	Loss: 115.452
+67200/69092	Loss: 118.371
+Training time 0:04:52.498969
+Epoch: 38 Average loss: 116.88
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 40)
+0/69092	Loss: 125.033
+3200/69092	Loss: 120.041
+6400/69092	Loss: 116.207
+9600/69092	Loss: 118.136
+12800/69092	Loss: 116.748
+16000/69092	Loss: 116.243
+19200/69092	Loss: 116.841
+22400/69092	Loss: 116.977
+25600/69092	Loss: 116.668
+28800/69092	Loss: 116.521
+32000/69092	Loss: 117.136
+35200/69092	Loss: 116.983
+38400/69092	Loss: 117.618
+41600/69092	Loss: 114.372
+44800/69092	Loss: 115.211
+48000/69092	Loss: 118.201
+51200/69092	Loss: 116.028
+54400/69092	Loss: 115.890
+57600/69092	Loss: 114.996
+60800/69092	Loss: 116.487
+64000/69092	Loss: 116.983
+67200/69092	Loss: 117.058
+Training time 0:04:50.387457
+Epoch: 39 Average loss: 116.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 41)
+0/69092	Loss: 110.258
+3200/69092	Loss: 117.252
+6400/69092	Loss: 117.309
+9600/69092	Loss: 116.297
+12800/69092	Loss: 117.236
+16000/69092	Loss: 117.996
+19200/69092	Loss: 118.234
+22400/69092	Loss: 118.257
+25600/69092	Loss: 116.130
+28800/69092	Loss: 117.185
+32000/69092	Loss: 115.816
+35200/69092	Loss: 115.462
+38400/69092	Loss: 117.363
+41600/69092	Loss: 116.068
+44800/69092	Loss: 116.302
+48000/69092	Loss: 117.931
+51200/69092	Loss: 114.693
+54400/69092	Loss: 116.760
+57600/69092	Loss: 114.889
+60800/69092	Loss: 116.937
+64000/69092	Loss: 116.777
+67200/69092	Loss: 117.384
+Training time 0:04:50.472581
+Epoch: 40 Average loss: 116.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 42)
+0/69092	Loss: 109.066
+3200/69092	Loss: 116.320
+6400/69092	Loss: 116.240
+9600/69092	Loss: 116.514
+12800/69092	Loss: 117.777
+16000/69092	Loss: 115.869
+19200/69092	Loss: 116.254
+22400/69092	Loss: 116.256
+25600/69092	Loss: 117.526
+28800/69092	Loss: 118.255
+32000/69092	Loss: 116.091
+35200/69092	Loss: 115.097
+38400/69092	Loss: 114.364
+41600/69092	Loss: 116.590
+44800/69092	Loss: 118.679
+48000/69092	Loss: 116.334
+51200/69092	Loss: 118.734
+54400/69092	Loss: 119.049
+57600/69092	Loss: 117.248
+60800/69092	Loss: 117.773
+64000/69092	Loss: 115.110
+67200/69092	Loss: 114.249
+Training time 0:04:50.321341
+Epoch: 41 Average loss: 116.70
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 43)
+0/69092	Loss: 116.922
+3200/69092	Loss: 115.485
+6400/69092	Loss: 118.106
+9600/69092	Loss: 116.983
+12800/69092	Loss: 115.947
+16000/69092	Loss: 116.718
+19200/69092	Loss: 118.177
+22400/69092	Loss: 117.735
+25600/69092	Loss: 116.123
+28800/69092	Loss: 117.093
+32000/69092	Loss: 117.513
+35200/69092	Loss: 116.709
+38400/69092	Loss: 116.535
+41600/69092	Loss: 117.350
+44800/69092	Loss: 118.415
+48000/69092	Loss: 116.725
+51200/69092	Loss: 117.575
+54400/69092	Loss: 116.148
+57600/69092	Loss: 115.328
+60800/69092	Loss: 116.508
+64000/69092	Loss: 117.187
+67200/69092	Loss: 115.227
+Training time 0:04:50.882292
+Epoch: 42 Average loss: 116.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 44)
+0/69092	Loss: 126.416
+3200/69092	Loss: 117.159
+6400/69092	Loss: 115.931
+9600/69092	Loss: 117.318
+12800/69092	Loss: 115.581
+16000/69092	Loss: 115.776
+19200/69092	Loss: 115.857
+22400/69092	Loss: 115.716
+25600/69092	Loss: 115.505
+28800/69092	Loss: 117.308
+32000/69092	Loss: 115.968
+35200/69092	Loss: 116.318
+38400/69092	Loss: 117.705
+41600/69092	Loss: 116.935
+44800/69092	Loss: 116.748
+48000/69092	Loss: 116.868
+51200/69092	Loss: 115.906
+54400/69092	Loss: 115.920
+57600/69092	Loss: 116.019
+60800/69092	Loss: 117.345
+64000/69092	Loss: 116.749
+67200/69092	Loss: 117.800
+Training time 0:04:50.692964
+Epoch: 43 Average loss: 116.44
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 45)
+0/69092	Loss: 113.226
+3200/69092	Loss: 116.414
+6400/69092	Loss: 115.403
+9600/69092	Loss: 114.424
+12800/69092	Loss: 116.078
+16000/69092	Loss: 118.299
+19200/69092	Loss: 115.333
+22400/69092	Loss: 118.434
+25600/69092	Loss: 117.068
+28800/69092	Loss: 117.351
+32000/69092	Loss: 116.913
+35200/69092	Loss: 116.053
+38400/69092	Loss: 115.261
+41600/69092	Loss: 113.541
+44800/69092	Loss: 117.611
+48000/69092	Loss: 115.486
+51200/69092	Loss: 116.020
+54400/69092	Loss: 114.984
+57600/69092	Loss: 116.429
+60800/69092	Loss: 117.387
+64000/69092	Loss: 118.603
+67200/69092	Loss: 117.236
+Training time 0:04:50.906052
+Epoch: 44 Average loss: 116.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 46)
+0/69092	Loss: 114.154
+3200/69092	Loss: 115.486
+6400/69092	Loss: 115.256
+9600/69092	Loss: 115.306
+12800/69092	Loss: 117.701
+16000/69092	Loss: 115.663
+19200/69092	Loss: 115.530
+22400/69092	Loss: 115.508
+25600/69092	Loss: 116.523
+28800/69092	Loss: 117.027
+32000/69092	Loss: 116.623
+35200/69092	Loss: 116.793
+38400/69092	Loss: 114.976
+41600/69092	Loss: 116.407
+44800/69092	Loss: 117.925
+48000/69092	Loss: 115.193
+51200/69092	Loss: 117.126
+54400/69092	Loss: 117.248
+57600/69092	Loss: 113.484
+60800/69092	Loss: 117.698
+64000/69092	Loss: 116.944
+67200/69092	Loss: 115.727
+Training time 0:04:49.102354
+Epoch: 45 Average loss: 116.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 47)
+0/69092	Loss: 110.757
+3200/69092	Loss: 115.191
+6400/69092	Loss: 115.617
+9600/69092	Loss: 116.033
+12800/69092	Loss: 115.286
+16000/69092	Loss: 116.425
+19200/69092	Loss: 116.590
+22400/69092	Loss: 115.571
+25600/69092	Loss: 116.207
+28800/69092	Loss: 116.555
+32000/69092	Loss: 117.130
+35200/69092	Loss: 114.536
+38400/69092	Loss: 115.110
+41600/69092	Loss: 116.440
+44800/69092	Loss: 114.962
+48000/69092	Loss: 117.549
+51200/69092	Loss: 116.202
+54400/69092	Loss: 116.490
+57600/69092	Loss: 115.999
+60800/69092	Loss: 115.435
+64000/69092	Loss: 117.503
+67200/69092	Loss: 117.120
+Training time 0:04:50.815465
+Epoch: 46 Average loss: 116.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 48)
+0/69092	Loss: 115.975
+3200/69092	Loss: 115.669
+6400/69092	Loss: 116.485
+9600/69092	Loss: 115.950
+12800/69092	Loss: 114.916
+16000/69092	Loss: 118.731
+19200/69092	Loss: 114.510
+22400/69092	Loss: 116.273
+25600/69092	Loss: 116.265
+28800/69092	Loss: 117.772
+32000/69092	Loss: 114.696
+35200/69092	Loss: 114.409
+38400/69092	Loss: 115.990
+41600/69092	Loss: 115.251
+44800/69092	Loss: 115.582
+48000/69092	Loss: 117.679
+51200/69092	Loss: 116.516
+54400/69092	Loss: 117.912
+57600/69092	Loss: 116.147
+60800/69092	Loss: 116.550
+64000/69092	Loss: 115.431
+67200/69092	Loss: 116.913
+Training time 0:04:50.529880
+Epoch: 47 Average loss: 116.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 49)
+0/69092	Loss: 118.941
+3200/69092	Loss: 115.989
+6400/69092	Loss: 116.560
+9600/69092	Loss: 118.031
+12800/69092	Loss: 116.526
+16000/69092	Loss: 118.645
+19200/69092	Loss: 116.125
+22400/69092	Loss: 116.495
+25600/69092	Loss: 116.235
+28800/69092	Loss: 116.240
+32000/69092	Loss: 116.055
+35200/69092	Loss: 114.656
+38400/69092	Loss: 116.154
+41600/69092	Loss: 117.614
+44800/69092	Loss: 114.164
+48000/69092	Loss: 116.587
+51200/69092	Loss: 114.979
+54400/69092	Loss: 114.746
+57600/69092	Loss: 114.571
+60800/69092	Loss: 115.249
+64000/69092	Loss: 115.959
+67200/69092	Loss: 115.955
+Training time 0:04:50.316161
+Epoch: 48 Average loss: 116.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 50)
+0/69092	Loss: 118.445
+3200/69092	Loss: 116.011
+6400/69092	Loss: 115.451
+9600/69092	Loss: 116.933
+12800/69092	Loss: 116.725
+16000/69092	Loss: 114.365
+19200/69092	Loss: 116.176
+22400/69092	Loss: 115.867
+25600/69092	Loss: 113.811
+28800/69092	Loss: 117.353
+32000/69092	Loss: 115.446
+35200/69092	Loss: 115.863
+38400/69092	Loss: 117.233
+41600/69092	Loss: 114.420
+44800/69092	Loss: 116.427
+48000/69092	Loss: 115.379
+51200/69092	Loss: 116.464
+54400/69092	Loss: 115.967
+57600/69092	Loss: 114.872
+60800/69092	Loss: 116.183
+64000/69092	Loss: 116.950
+67200/69092	Loss: 116.495
+Training time 0:04:51.000314
+Epoch: 49 Average loss: 115.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 51)
+0/69092	Loss: 110.965
+3200/69092	Loss: 114.970
+6400/69092	Loss: 116.543
+9600/69092	Loss: 115.009
+12800/69092	Loss: 116.755
+16000/69092	Loss: 114.706
+19200/69092	Loss: 114.190
+22400/69092	Loss: 114.569
+25600/69092	Loss: 115.313
+28800/69092	Loss: 115.033
+32000/69092	Loss: 116.078
+35200/69092	Loss: 115.569
+38400/69092	Loss: 116.802
+41600/69092	Loss: 116.311
+44800/69092	Loss: 117.974
+48000/69092	Loss: 115.641
+51200/69092	Loss: 116.793
+54400/69092	Loss: 116.699
+57600/69092	Loss: 116.751
+60800/69092	Loss: 116.524
+64000/69092	Loss: 113.234
+67200/69092	Loss: 116.150
+Training time 0:04:51.109183
+Epoch: 50 Average loss: 115.80
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 52)
+0/69092	Loss: 108.017
+3200/69092	Loss: 114.934
+6400/69092	Loss: 114.856
+9600/69092	Loss: 116.531
+12800/69092	Loss: 113.770
+16000/69092	Loss: 115.197
+19200/69092	Loss: 116.244
+22400/69092	Loss: 115.346
+25600/69092	Loss: 116.879
+28800/69092	Loss: 116.504
+32000/69092	Loss: 114.772
+35200/69092	Loss: 117.467
+38400/69092	Loss: 114.945
+41600/69092	Loss: 115.933
+44800/69092	Loss: 116.462
+48000/69092	Loss: 115.551
+51200/69092	Loss: 115.669
+54400/69092	Loss: 115.515
+57600/69092	Loss: 115.213
+60800/69092	Loss: 117.749
+64000/69092	Loss: 115.684
+67200/69092	Loss: 116.791
+Training time 0:04:50.668655
+Epoch: 51 Average loss: 115.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 53)
+0/69092	Loss: 117.423
+3200/69092	Loss: 115.116
+6400/69092	Loss: 116.887
+9600/69092	Loss: 114.589
+12800/69092	Loss: 116.855
+16000/69092	Loss: 115.924
+19200/69092	Loss: 116.141
+22400/69092	Loss: 116.903
+25600/69092	Loss: 114.613
+28800/69092	Loss: 113.224
+32000/69092	Loss: 117.233
+35200/69092	Loss: 116.103
+38400/69092	Loss: 114.733
+41600/69092	Loss: 116.245
+44800/69092	Loss: 115.208
+48000/69092	Loss: 116.370
+51200/69092	Loss: 115.745
+54400/69092	Loss: 113.581
+57600/69092	Loss: 116.676
+60800/69092	Loss: 116.732
+64000/69092	Loss: 117.132
+67200/69092	Loss: 115.330
+Training time 0:04:51.758798
+Epoch: 52 Average loss: 115.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 54)
+0/69092	Loss: 107.363
+3200/69092	Loss: 115.455
+6400/69092	Loss: 114.363
+9600/69092	Loss: 113.316
+12800/69092	Loss: 114.088
+16000/69092	Loss: 115.876
+19200/69092	Loss: 114.352
+22400/69092	Loss: 115.823
+25600/69092	Loss: 114.822
+28800/69092	Loss: 116.164
+32000/69092	Loss: 117.430
+35200/69092	Loss: 115.201
+38400/69092	Loss: 117.835
+41600/69092	Loss: 117.573
+44800/69092	Loss: 116.864
+48000/69092	Loss: 113.686
+51200/69092	Loss: 114.353
+54400/69092	Loss: 116.330
+57600/69092	Loss: 116.880
+60800/69092	Loss: 115.311
+64000/69092	Loss: 117.652
+67200/69092	Loss: 115.619
+Training time 0:04:49.333246
+Epoch: 53 Average loss: 115.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 55)
+0/69092	Loss: 111.250
+3200/69092	Loss: 115.047
+6400/69092	Loss: 116.211
+9600/69092	Loss: 114.196
+12800/69092	Loss: 114.638
+16000/69092	Loss: 114.877
+19200/69092	Loss: 117.125
+22400/69092	Loss: 116.822
+25600/69092	Loss: 116.239
+28800/69092	Loss: 115.250
+32000/69092	Loss: 116.520
+35200/69092	Loss: 116.383
+38400/69092	Loss: 116.305
+41600/69092	Loss: 115.319
+44800/69092	Loss: 115.305
+48000/69092	Loss: 114.337
+51200/69092	Loss: 114.042
+54400/69092	Loss: 115.618
+57600/69092	Loss: 115.546
+60800/69092	Loss: 115.192
+64000/69092	Loss: 115.025
+67200/69092	Loss: 113.886
+Training time 0:04:51.174373
+Epoch: 54 Average loss: 115.46
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 56)
+0/69092	Loss: 121.345
+3200/69092	Loss: 116.380
+6400/69092	Loss: 114.931
+9600/69092	Loss: 115.219
+12800/69092	Loss: 113.502
+16000/69092	Loss: 115.090
+19200/69092	Loss: 117.525
+22400/69092	Loss: 115.704
+25600/69092	Loss: 114.509
+28800/69092	Loss: 116.143
+32000/69092	Loss: 117.079
+35200/69092	Loss: 115.237
+38400/69092	Loss: 114.852
+41600/69092	Loss: 116.465
+44800/69092	Loss: 114.581
+48000/69092	Loss: 115.045
+51200/69092	Loss: 114.057
+54400/69092	Loss: 116.492
+57600/69092	Loss: 114.078
+60800/69092	Loss: 112.629
+64000/69092	Loss: 114.679
+67200/69092	Loss: 115.498
+Training time 0:04:52.204344
+Epoch: 55 Average loss: 115.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 57)
+0/69092	Loss: 105.918
+3200/69092	Loss: 114.693
+6400/69092	Loss: 115.644
+9600/69092	Loss: 114.654
+12800/69092	Loss: 115.317
+16000/69092	Loss: 115.527
+19200/69092	Loss: 113.368
+22400/69092	Loss: 116.289
+25600/69092	Loss: 114.841
+28800/69092	Loss: 115.621
+32000/69092	Loss: 116.214
+35200/69092	Loss: 116.268
+38400/69092	Loss: 116.120
+41600/69092	Loss: 115.684
+44800/69092	Loss: 114.851
+48000/69092	Loss: 117.124
+51200/69092	Loss: 116.559
+54400/69092	Loss: 113.316
+57600/69092	Loss: 113.417
+60800/69092	Loss: 115.656
+64000/69092	Loss: 114.335
+67200/69092	Loss: 117.134
+Training time 0:04:49.365066
+Epoch: 56 Average loss: 115.40
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 58)
+0/69092	Loss: 130.230
+3200/69092	Loss: 113.360
+6400/69092	Loss: 115.075
+9600/69092	Loss: 113.792
+12800/69092	Loss: 115.990
+16000/69092	Loss: 116.490
+19200/69092	Loss: 117.062
+22400/69092	Loss: 115.459
+25600/69092	Loss: 112.523
+28800/69092	Loss: 115.314
+32000/69092	Loss: 115.161
+35200/69092	Loss: 115.395
+38400/69092	Loss: 115.698
+41600/69092	Loss: 115.437
+44800/69092	Loss: 116.958
+48000/69092	Loss: 115.300
+51200/69092	Loss: 115.323
+54400/69092	Loss: 113.655
+57600/69092	Loss: 115.774
+60800/69092	Loss: 116.198
+64000/69092	Loss: 116.066
+67200/69092	Loss: 117.171
+Training time 0:04:50.333766
+Epoch: 57 Average loss: 115.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 59)
+0/69092	Loss: 110.380
+3200/69092	Loss: 117.242
+6400/69092	Loss: 115.368
+9600/69092	Loss: 116.128
+12800/69092	Loss: 115.609
+16000/69092	Loss: 114.974
+19200/69092	Loss: 114.545
+22400/69092	Loss: 113.922
+25600/69092	Loss: 116.304
+28800/69092	Loss: 112.777
+32000/69092	Loss: 116.925
+35200/69092	Loss: 115.065
+38400/69092	Loss: 115.531
+41600/69092	Loss: 113.043
+44800/69092	Loss: 115.650
+48000/69092	Loss: 114.513
+51200/69092	Loss: 114.802
+54400/69092	Loss: 114.561
+57600/69092	Loss: 114.275
+60800/69092	Loss: 116.770
+64000/69092	Loss: 115.084
+67200/69092	Loss: 114.219
+Training time 0:04:50.861188
+Epoch: 58 Average loss: 115.18
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 60)
+0/69092	Loss: 120.938
+3200/69092	Loss: 116.526
+6400/69092	Loss: 113.273
+9600/69092	Loss: 114.060
+12800/69092	Loss: 115.242
+16000/69092	Loss: 116.480
+19200/69092	Loss: 114.736
+22400/69092	Loss: 115.080
+25600/69092	Loss: 114.591
+28800/69092	Loss: 114.889
+32000/69092	Loss: 114.226
+35200/69092	Loss: 114.526
+38400/69092	Loss: 114.164
+41600/69092	Loss: 114.240
+44800/69092	Loss: 116.406
+48000/69092	Loss: 113.433
+51200/69092	Loss: 117.690
+54400/69092	Loss: 115.850
+57600/69092	Loss: 113.394
+60800/69092	Loss: 115.963
+64000/69092	Loss: 114.316
+67200/69092	Loss: 116.932
+Training time 0:04:51.653567
+Epoch: 59 Average loss: 115.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 61)
+0/69092	Loss: 129.070
+3200/69092	Loss: 113.538
+6400/69092	Loss: 116.309
+9600/69092	Loss: 115.728
+12800/69092	Loss: 114.899
+16000/69092	Loss: 114.760
+19200/69092	Loss: 116.447
+22400/69092	Loss: 115.839
+25600/69092	Loss: 114.601
+28800/69092	Loss: 112.923
+32000/69092	Loss: 115.536
+35200/69092	Loss: 116.740
+38400/69092	Loss: 114.354
+41600/69092	Loss: 116.054
+44800/69092	Loss: 114.711
+48000/69092	Loss: 114.170
+51200/69092	Loss: 114.636
+54400/69092	Loss: 115.156
+57600/69092	Loss: 113.717
+60800/69092	Loss: 116.622
+64000/69092	Loss: 115.291
+67200/69092	Loss: 115.842
+Training time 0:04:49.531087
+Epoch: 60 Average loss: 115.18
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 62)
+0/69092	Loss: 113.572
+3200/69092	Loss: 115.474
+6400/69092	Loss: 116.251
+9600/69092	Loss: 116.395
+12800/69092	Loss: 114.333
+16000/69092	Loss: 114.263
+19200/69092	Loss: 112.245
+22400/69092	Loss: 114.744
+25600/69092	Loss: 115.087
+28800/69092	Loss: 117.191
+32000/69092	Loss: 114.861
+35200/69092	Loss: 115.426
+38400/69092	Loss: 115.288
+41600/69092	Loss: 114.900
+44800/69092	Loss: 114.223
+48000/69092	Loss: 114.517
+51200/69092	Loss: 114.747
+54400/69092	Loss: 116.455
+57600/69092	Loss: 115.121
+60800/69092	Loss: 113.595
+64000/69092	Loss: 115.817
+67200/69092	Loss: 113.993
+Training time 0:04:49.861094
+Epoch: 61 Average loss: 115.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 63)
+0/69092	Loss: 120.034
+3200/69092	Loss: 114.612
+6400/69092	Loss: 114.035
+9600/69092	Loss: 117.415
+12800/69092	Loss: 115.067
+16000/69092	Loss: 113.959
+19200/69092	Loss: 113.977
+22400/69092	Loss: 112.873
+25600/69092	Loss: 114.675
+28800/69092	Loss: 113.659
+32000/69092	Loss: 116.643
+35200/69092	Loss: 116.369
+38400/69092	Loss: 116.391
+41600/69092	Loss: 114.723
+44800/69092	Loss: 115.430
+48000/69092	Loss: 115.085
+51200/69092	Loss: 113.667
+54400/69092	Loss: 114.991
+57600/69092	Loss: 115.769
+60800/69092	Loss: 114.969
+64000/69092	Loss: 112.662
+67200/69092	Loss: 115.065
+Training time 0:04:50.082754
+Epoch: 62 Average loss: 114.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 64)
+0/69092	Loss: 107.914
+3200/69092	Loss: 115.811
+6400/69092	Loss: 114.845
+9600/69092	Loss: 115.219
+12800/69092	Loss: 113.002
+16000/69092	Loss: 114.682
+19200/69092	Loss: 114.980
+22400/69092	Loss: 114.454
+25600/69092	Loss: 113.652
+28800/69092	Loss: 115.837
+32000/69092	Loss: 115.660
+35200/69092	Loss: 114.064
+38400/69092	Loss: 116.172
+41600/69092	Loss: 114.529
+44800/69092	Loss: 115.625
+48000/69092	Loss: 115.497
+51200/69092	Loss: 116.070
+54400/69092	Loss: 114.471
+57600/69092	Loss: 115.314
+60800/69092	Loss: 114.247
+64000/69092	Loss: 116.192
+67200/69092	Loss: 113.941
+Training time 0:04:49.957412
+Epoch: 63 Average loss: 114.98
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 65)
+0/69092	Loss: 116.641
+3200/69092	Loss: 114.474
+6400/69092	Loss: 112.499
+9600/69092	Loss: 116.596
+12800/69092	Loss: 114.035
+16000/69092	Loss: 114.961
+19200/69092	Loss: 115.245
+22400/69092	Loss: 115.777
+25600/69092	Loss: 114.787
+28800/69092	Loss: 113.626
+32000/69092	Loss: 114.013
+35200/69092	Loss: 115.915
+38400/69092	Loss: 114.022
+41600/69092	Loss: 114.241
+44800/69092	Loss: 115.958
+48000/69092	Loss: 113.939
+51200/69092	Loss: 116.508
+54400/69092	Loss: 114.077
+57600/69092	Loss: 115.458
+60800/69092	Loss: 113.704
+64000/69092	Loss: 115.936
+67200/69092	Loss: 114.465
+Training time 0:04:49.658180
+Epoch: 64 Average loss: 114.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 66)
+0/69092	Loss: 112.635
+3200/69092	Loss: 114.548
+6400/69092	Loss: 114.231
+9600/69092	Loss: 115.587
+12800/69092	Loss: 114.641
+16000/69092	Loss: 115.595
+19200/69092	Loss: 113.876
+22400/69092	Loss: 113.440
+25600/69092	Loss: 116.155
+28800/69092	Loss: 114.903
+32000/69092	Loss: 112.486
+35200/69092	Loss: 113.886
+38400/69092	Loss: 116.748
+41600/69092	Loss: 116.410
+44800/69092	Loss: 114.976
+48000/69092	Loss: 115.530
+51200/69092	Loss: 116.601
+54400/69092	Loss: 114.131
+57600/69092	Loss: 115.115
+60800/69092	Loss: 113.630
+64000/69092	Loss: 114.185
+67200/69092	Loss: 114.594
+Training time 0:04:50.297879
+Epoch: 65 Average loss: 114.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 67)
+0/69092	Loss: 125.901
+3200/69092	Loss: 115.050
+6400/69092	Loss: 112.895
+9600/69092	Loss: 114.888
+12800/69092	Loss: 114.904
+16000/69092	Loss: 114.360
+19200/69092	Loss: 114.818
+22400/69092	Loss: 114.196
+25600/69092	Loss: 114.682
+28800/69092	Loss: 114.934
+32000/69092	Loss: 114.733
+35200/69092	Loss: 114.040
+38400/69092	Loss: 115.150
+41600/69092	Loss: 115.889
+44800/69092	Loss: 115.538
+48000/69092	Loss: 115.784
+51200/69092	Loss: 113.973
+54400/69092	Loss: 117.135
+57600/69092	Loss: 114.236
+60800/69092	Loss: 114.949
+64000/69092	Loss: 113.168
+67200/69092	Loss: 116.649
+Training time 0:04:51.649768
+Epoch: 66 Average loss: 114.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 68)
+0/69092	Loss: 109.445
+3200/69092	Loss: 114.568
+6400/69092	Loss: 115.636
+9600/69092	Loss: 114.090
+12800/69092	Loss: 117.041
+16000/69092	Loss: 115.497
+19200/69092	Loss: 114.000
+22400/69092	Loss: 114.204
+25600/69092	Loss: 115.507
+28800/69092	Loss: 114.216
+32000/69092	Loss: 115.093
+35200/69092	Loss: 113.535
+38400/69092	Loss: 115.401
+41600/69092	Loss: 113.445
+44800/69092	Loss: 115.084
+48000/69092	Loss: 113.895
+51200/69092	Loss: 116.076
+54400/69092	Loss: 112.761
+57600/69092	Loss: 114.978
+60800/69092	Loss: 114.853
+64000/69092	Loss: 113.223
+67200/69092	Loss: 115.037
+Training time 0:04:50.448239
+Epoch: 67 Average loss: 114.70
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 69)
+0/69092	Loss: 121.913
+3200/69092	Loss: 113.246
+6400/69092	Loss: 114.522
+9600/69092	Loss: 116.150
+12800/69092	Loss: 114.766
+16000/69092	Loss: 114.983
+19200/69092	Loss: 114.599
+22400/69092	Loss: 115.377
+25600/69092	Loss: 114.733
+28800/69092	Loss: 113.788
+32000/69092	Loss: 115.150
+35200/69092	Loss: 116.280
+38400/69092	Loss: 114.409
+41600/69092	Loss: 114.290
+44800/69092	Loss: 114.097
+48000/69092	Loss: 113.125
+51200/69092	Loss: 114.332
+54400/69092	Loss: 115.385
+57600/69092	Loss: 114.851
+60800/69092	Loss: 113.286
+64000/69092	Loss: 115.712
+67200/69092	Loss: 115.736
+Training time 0:04:51.138483
+Epoch: 68 Average loss: 114.63
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 70)
+0/69092	Loss: 125.051
+3200/69092	Loss: 115.102
+6400/69092	Loss: 114.087
+9600/69092	Loss: 116.201
+12800/69092	Loss: 113.721
+16000/69092	Loss: 114.431
+19200/69092	Loss: 115.753
+22400/69092	Loss: 115.861
+25600/69092	Loss: 114.995
+28800/69092	Loss: 113.949
+32000/69092	Loss: 115.319
+35200/69092	Loss: 114.642
+38400/69092	Loss: 114.189
+41600/69092	Loss: 113.771
+44800/69092	Loss: 113.618
+48000/69092	Loss: 115.785
+51200/69092	Loss: 113.695
+54400/69092	Loss: 114.677
+57600/69092	Loss: 114.020
+60800/69092	Loss: 113.694
+64000/69092	Loss: 113.486
+67200/69092	Loss: 112.500
+Training time 0:04:49.815245
+Epoch: 69 Average loss: 114.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 71)
+0/69092	Loss: 118.067
+3200/69092	Loss: 114.432
+6400/69092	Loss: 116.424
+9600/69092	Loss: 113.103
+12800/69092	Loss: 113.420
+16000/69092	Loss: 114.150
+19200/69092	Loss: 112.896
+22400/69092	Loss: 114.493
+25600/69092	Loss: 116.477
+28800/69092	Loss: 114.754
+32000/69092	Loss: 113.572
+35200/69092	Loss: 112.137
+38400/69092	Loss: 114.255
+41600/69092	Loss: 114.041
+44800/69092	Loss: 114.902
+48000/69092	Loss: 114.853
+51200/69092	Loss: 114.115
+54400/69092	Loss: 111.929
+57600/69092	Loss: 113.867
+60800/69092	Loss: 115.578
+64000/69092	Loss: 115.253
+67200/69092	Loss: 114.495
+Training time 0:04:51.925960
+Epoch: 70 Average loss: 114.37
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 72)
+0/69092	Loss: 110.626
+3200/69092	Loss: 115.597
+6400/69092	Loss: 113.847
+9600/69092	Loss: 112.283
+12800/69092	Loss: 114.362
+16000/69092	Loss: 114.564
+19200/69092	Loss: 115.204
+22400/69092	Loss: 114.233
+25600/69092	Loss: 114.334
+28800/69092	Loss: 114.784
+32000/69092	Loss: 112.240
+35200/69092	Loss: 114.379
+38400/69092	Loss: 115.718
+41600/69092	Loss: 114.241
+44800/69092	Loss: 115.565
+48000/69092	Loss: 114.088
+51200/69092	Loss: 115.310
+54400/69092	Loss: 113.681
+57600/69092	Loss: 112.798
+60800/69092	Loss: 114.490
+64000/69092	Loss: 115.532
+67200/69092	Loss: 115.908
+Training time 0:04:51.263301
+Epoch: 71 Average loss: 114.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 73)
+0/69092	Loss: 126.524
+3200/69092	Loss: 112.766
+6400/69092	Loss: 114.966
+9600/69092	Loss: 114.645
+12800/69092	Loss: 115.025
+16000/69092	Loss: 115.586
+19200/69092	Loss: 114.933
+22400/69092	Loss: 113.922
+25600/69092	Loss: 115.301
+28800/69092	Loss: 113.919
+32000/69092	Loss: 116.193
+35200/69092	Loss: 114.761
+38400/69092	Loss: 111.971
+41600/69092	Loss: 116.336
+44800/69092	Loss: 113.653
+48000/69092	Loss: 115.769
+51200/69092	Loss: 112.701
+54400/69092	Loss: 114.297
+57600/69092	Loss: 112.923
+60800/69092	Loss: 111.982
+64000/69092	Loss: 114.084
+67200/69092	Loss: 115.607
+Training time 0:04:52.005985
+Epoch: 72 Average loss: 114.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 74)
+0/69092	Loss: 112.065
+3200/69092	Loss: 114.408
+6400/69092	Loss: 114.604
+9600/69092	Loss: 114.376
+12800/69092	Loss: 113.403
+16000/69092	Loss: 114.143
+19200/69092	Loss: 115.974
+22400/69092	Loss: 113.870
+25600/69092	Loss: 114.257
+28800/69092	Loss: 114.431
+32000/69092	Loss: 115.319
+35200/69092	Loss: 113.325
+38400/69092	Loss: 115.692
+41600/69092	Loss: 114.905
+44800/69092	Loss: 113.285
+48000/69092	Loss: 112.058
+51200/69092	Loss: 115.133
+54400/69092	Loss: 114.319
+57600/69092	Loss: 115.527
+60800/69092	Loss: 112.734
+64000/69092	Loss: 113.817
+67200/69092	Loss: 115.289
+Training time 0:04:50.372596
+Epoch: 73 Average loss: 114.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 75)
+0/69092	Loss: 108.900
+3200/69092	Loss: 113.069
+6400/69092	Loss: 116.169
+9600/69092	Loss: 113.440
+12800/69092	Loss: 114.160
+16000/69092	Loss: 115.020
+19200/69092	Loss: 116.341
+22400/69092	Loss: 113.578
+25600/69092	Loss: 113.593
+28800/69092	Loss: 114.390
+32000/69092	Loss: 113.834
+35200/69092	Loss: 113.907
+38400/69092	Loss: 116.457
+41600/69092	Loss: 113.770
+44800/69092	Loss: 114.490
+48000/69092	Loss: 113.662
+51200/69092	Loss: 114.060
+54400/69092	Loss: 111.935
+57600/69092	Loss: 114.180
+60800/69092	Loss: 113.947
+64000/69092	Loss: 116.748
+67200/69092	Loss: 114.355
+Training time 0:04:50.022412
+Epoch: 74 Average loss: 114.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 76)
+0/69092	Loss: 119.328
+3200/69092	Loss: 113.290
+6400/69092	Loss: 115.541
+9600/69092	Loss: 114.173
+12800/69092	Loss: 115.024
+16000/69092	Loss: 113.129
+19200/69092	Loss: 114.460
+22400/69092	Loss: 114.829
+25600/69092	Loss: 114.816
+28800/69092	Loss: 112.927
+32000/69092	Loss: 113.915
+35200/69092	Loss: 113.821
+38400/69092	Loss: 113.344
+41600/69092	Loss: 113.747
+44800/69092	Loss: 113.595
+48000/69092	Loss: 114.600
+51200/69092	Loss: 113.716
+54400/69092	Loss: 116.395
+57600/69092	Loss: 114.278
+60800/69092	Loss: 113.421
+64000/69092	Loss: 115.272
+67200/69092	Loss: 114.376
+Training time 0:04:50.965335
+Epoch: 75 Average loss: 114.24
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 77)
+0/69092	Loss: 106.325
+3200/69092	Loss: 114.237
+6400/69092	Loss: 112.202
+9600/69092	Loss: 114.797
+12800/69092	Loss: 113.953
+16000/69092	Loss: 114.647
+19200/69092	Loss: 113.164
+22400/69092	Loss: 114.101
+25600/69092	Loss: 115.652
+28800/69092	Loss: 116.110
+32000/69092	Loss: 112.867
+35200/69092	Loss: 113.098
+38400/69092	Loss: 115.475
+41600/69092	Loss: 115.695
+44800/69092	Loss: 115.099
+48000/69092	Loss: 114.189
+51200/69092	Loss: 113.810
+54400/69092	Loss: 113.467
+57600/69092	Loss: 113.638
+60800/69092	Loss: 113.796
+64000/69092	Loss: 112.408
+67200/69092	Loss: 115.216
+Training time 0:04:48.314100
+Epoch: 76 Average loss: 114.17
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 78)
+0/69092	Loss: 111.949
+3200/69092	Loss: 114.600
+6400/69092	Loss: 115.056
+9600/69092	Loss: 114.120
+12800/69092	Loss: 114.041
+16000/69092	Loss: 113.095
+19200/69092	Loss: 115.825
+22400/69092	Loss: 112.967
+25600/69092	Loss: 113.657
+28800/69092	Loss: 113.907
+32000/69092	Loss: 114.831
+35200/69092	Loss: 116.393
+38400/69092	Loss: 114.758
+41600/69092	Loss: 111.627
+44800/69092	Loss: 113.137
+48000/69092	Loss: 114.459
+51200/69092	Loss: 114.188
+54400/69092	Loss: 114.140
+57600/69092	Loss: 113.776
+60800/69092	Loss: 114.968
+64000/69092	Loss: 114.500
+67200/69092	Loss: 111.762
+Training time 0:04:50.516608
+Epoch: 77 Average loss: 114.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 79)
+0/69092	Loss: 120.149
+3200/69092	Loss: 113.093
+6400/69092	Loss: 111.723
+9600/69092	Loss: 113.978
+12800/69092	Loss: 114.171
+16000/69092	Loss: 113.628
+19200/69092	Loss: 114.345
+22400/69092	Loss: 114.132
+25600/69092	Loss: 114.419
+28800/69092	Loss: 113.464
+32000/69092	Loss: 112.627
+35200/69092	Loss: 114.483
+38400/69092	Loss: 113.634
+41600/69092	Loss: 116.167
+44800/69092	Loss: 113.841
+48000/69092	Loss: 114.498
+51200/69092	Loss: 114.056
+54400/69092	Loss: 114.758
+57600/69092	Loss: 113.521
+60800/69092	Loss: 113.663
+64000/69092	Loss: 115.677
+67200/69092	Loss: 115.055
+Training time 0:04:51.375221
+Epoch: 78 Average loss: 114.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 80)
+0/69092	Loss: 112.982
+3200/69092	Loss: 114.917
+6400/69092	Loss: 114.844
+9600/69092	Loss: 112.990
+12800/69092	Loss: 112.300
+16000/69092	Loss: 114.665
+19200/69092	Loss: 113.423
+22400/69092	Loss: 114.391
+25600/69092	Loss: 112.353
+28800/69092	Loss: 114.761
+32000/69092	Loss: 113.442
+35200/69092	Loss: 114.386
+38400/69092	Loss: 114.219
+41600/69092	Loss: 113.040
+44800/69092	Loss: 113.246
+48000/69092	Loss: 113.242
+51200/69092	Loss: 114.584
+54400/69092	Loss: 113.588
+57600/69092	Loss: 115.343
+60800/69092	Loss: 113.627
+64000/69092	Loss: 115.992
+67200/69092	Loss: 113.429
+Training time 0:04:52.295794
+Epoch: 79 Average loss: 113.96
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 81)
+0/69092	Loss: 114.161
+3200/69092	Loss: 113.462
+6400/69092	Loss: 113.622
+9600/69092	Loss: 114.331
+12800/69092	Loss: 114.920
+16000/69092	Loss: 115.949
+19200/69092	Loss: 115.160
+22400/69092	Loss: 113.351
+25600/69092	Loss: 115.150
+28800/69092	Loss: 112.688
+32000/69092	Loss: 111.680
+35200/69092	Loss: 113.813
+38400/69092	Loss: 113.434
+41600/69092	Loss: 113.293
+44800/69092	Loss: 115.529
+48000/69092	Loss: 114.092
+51200/69092	Loss: 113.028
+54400/69092	Loss: 112.844
+57600/69092	Loss: 113.923
+60800/69092	Loss: 113.820
+64000/69092	Loss: 114.425
+67200/69092	Loss: 113.621
+Training time 0:04:51.764568
+Epoch: 80 Average loss: 113.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 82)
+0/69092	Loss: 107.403
+3200/69092	Loss: 114.295
+6400/69092	Loss: 112.806
+9600/69092	Loss: 115.963
+12800/69092	Loss: 115.544
+16000/69092	Loss: 114.910
+19200/69092	Loss: 112.273
+22400/69092	Loss: 115.473
+25600/69092	Loss: 112.662
+28800/69092	Loss: 115.452
+32000/69092	Loss: 113.634
+35200/69092	Loss: 113.874
+38400/69092	Loss: 111.303
+41600/69092	Loss: 114.504
+44800/69092	Loss: 112.888
+48000/69092	Loss: 113.844
+51200/69092	Loss: 114.398
+54400/69092	Loss: 113.549
+57600/69092	Loss: 116.015
+60800/69092	Loss: 112.868
+64000/69092	Loss: 112.874
+67200/69092	Loss: 112.393
+Training time 0:04:52.298415
+Epoch: 81 Average loss: 113.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 83)
+0/69092	Loss: 107.947
+3200/69092	Loss: 114.589
+6400/69092	Loss: 114.348
+9600/69092	Loss: 114.530
+12800/69092	Loss: 113.244
+16000/69092	Loss: 114.148
+19200/69092	Loss: 114.319
+22400/69092	Loss: 113.858
+25600/69092	Loss: 116.558
+28800/69092	Loss: 112.606
+32000/69092	Loss: 114.654
+35200/69092	Loss: 113.615
+38400/69092	Loss: 114.364
+41600/69092	Loss: 114.591
+44800/69092	Loss: 113.994
+48000/69092	Loss: 114.206
+51200/69092	Loss: 112.193
+54400/69092	Loss: 111.868
+57600/69092	Loss: 112.703
+60800/69092	Loss: 113.730
+64000/69092	Loss: 113.579
+67200/69092	Loss: 113.643
+Training time 0:04:51.209246
+Epoch: 82 Average loss: 113.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 84)
+0/69092	Loss: 107.467
+3200/69092	Loss: 114.237
+6400/69092	Loss: 114.535
+9600/69092	Loss: 112.398
+12800/69092	Loss: 113.000
+16000/69092	Loss: 113.300
+19200/69092	Loss: 114.881
+22400/69092	Loss: 113.460
+25600/69092	Loss: 112.801
+28800/69092	Loss: 115.524
+32000/69092	Loss: 114.338
+35200/69092	Loss: 113.895
+38400/69092	Loss: 113.508
+41600/69092	Loss: 112.388
+44800/69092	Loss: 115.688
+48000/69092	Loss: 113.361
+51200/69092	Loss: 114.276
+54400/69092	Loss: 112.706
+57600/69092	Loss: 114.713
+60800/69092	Loss: 113.868
+64000/69092	Loss: 114.698
+67200/69092	Loss: 113.222
+Training time 0:04:52.280438
+Epoch: 83 Average loss: 113.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 85)
+0/69092	Loss: 106.944
+3200/69092	Loss: 115.518
+6400/69092	Loss: 115.470
+9600/69092	Loss: 114.716
+12800/69092	Loss: 113.934
+16000/69092	Loss: 113.233
+19200/69092	Loss: 113.723
+22400/69092	Loss: 114.237
+25600/69092	Loss: 110.741
+28800/69092	Loss: 114.205
+32000/69092	Loss: 113.444
+35200/69092	Loss: 113.357
+38400/69092	Loss: 111.711
+41600/69092	Loss: 114.080
+44800/69092	Loss: 114.114
+48000/69092	Loss: 115.278
+51200/69092	Loss: 112.557
+54400/69092	Loss: 114.194
+57600/69092	Loss: 113.822
+60800/69092	Loss: 114.116
+64000/69092	Loss: 113.442
+67200/69092	Loss: 114.152
+Training time 0:04:54.264285
+Epoch: 84 Average loss: 113.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 86)
+0/69092	Loss: 112.850
+3200/69092	Loss: 113.493
+6400/69092	Loss: 113.656
+9600/69092	Loss: 114.496
+12800/69092	Loss: 112.854
+16000/69092	Loss: 114.891
+19200/69092	Loss: 113.072
+22400/69092	Loss: 113.396
+25600/69092	Loss: 112.900
+28800/69092	Loss: 113.637
+32000/69092	Loss: 113.876
+35200/69092	Loss: 114.509
+38400/69092	Loss: 114.604
+41600/69092	Loss: 112.058
+44800/69092	Loss: 113.134
+48000/69092	Loss: 113.515
+51200/69092	Loss: 114.492
+54400/69092	Loss: 113.979
+57600/69092	Loss: 114.504
+60800/69092	Loss: 112.922
+64000/69092	Loss: 113.027
+67200/69092	Loss: 115.655
+Training time 0:04:51.105966
+Epoch: 85 Average loss: 113.80
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 87)
+0/69092	Loss: 94.388
+3200/69092	Loss: 111.242
+6400/69092	Loss: 112.348
+9600/69092	Loss: 115.769
+12800/69092	Loss: 115.243
+16000/69092	Loss: 113.438
+19200/69092	Loss: 113.098
+22400/69092	Loss: 112.425
+25600/69092	Loss: 112.287
+28800/69092	Loss: 112.773
+32000/69092	Loss: 112.341
+35200/69092	Loss: 114.409
+38400/69092	Loss: 113.970
+41600/69092	Loss: 114.154
+44800/69092	Loss: 114.033
+48000/69092	Loss: 115.759
+51200/69092	Loss: 114.301
+54400/69092	Loss: 113.919
+57600/69092	Loss: 114.798
+60800/69092	Loss: 113.820
+64000/69092	Loss: 115.980
+67200/69092	Loss: 112.752
+Training time 0:04:52.219598
+Epoch: 86 Average loss: 113.70
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 88)
+0/69092	Loss: 121.401
+3200/69092	Loss: 112.879
+6400/69092	Loss: 114.243
+9600/69092	Loss: 112.702
+12800/69092	Loss: 112.158
+16000/69092	Loss: 113.485
+19200/69092	Loss: 114.173
+22400/69092	Loss: 112.978
+25600/69092	Loss: 114.152
+28800/69092	Loss: 114.511
+32000/69092	Loss: 112.840
+35200/69092	Loss: 113.681
+38400/69092	Loss: 114.121
+41600/69092	Loss: 113.551
+44800/69092	Loss: 113.046
+48000/69092	Loss: 113.967
+51200/69092	Loss: 112.369
+54400/69092	Loss: 112.924
+57600/69092	Loss: 113.448
+60800/69092	Loss: 116.035
+64000/69092	Loss: 115.074
+67200/69092	Loss: 112.418
+Training time 0:04:51.213202
+Epoch: 87 Average loss: 113.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 89)
+0/69092	Loss: 111.812
+3200/69092	Loss: 113.849
+6400/69092	Loss: 113.464
+9600/69092	Loss: 115.864
+12800/69092	Loss: 112.305
+16000/69092	Loss: 113.882
+19200/69092	Loss: 112.665
+22400/69092	Loss: 115.261
+25600/69092	Loss: 113.349
+28800/69092	Loss: 112.552
+32000/69092	Loss: 113.600
+35200/69092	Loss: 114.422
+38400/69092	Loss: 114.059
+41600/69092	Loss: 113.211
+44800/69092	Loss: 113.649
+48000/69092	Loss: 114.113
+51200/69092	Loss: 113.820
+54400/69092	Loss: 112.448
+57600/69092	Loss: 113.484
+60800/69092	Loss: 112.836
+64000/69092	Loss: 115.360
+67200/69092	Loss: 114.395
+Training time 0:04:52.346897
+Epoch: 88 Average loss: 113.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 90)
+0/69092	Loss: 123.009
+3200/69092	Loss: 114.672
+6400/69092	Loss: 114.283
+9600/69092	Loss: 113.250
+12800/69092	Loss: 114.013
+16000/69092	Loss: 115.927
+19200/69092	Loss: 112.359
+22400/69092	Loss: 114.315
+25600/69092	Loss: 113.557
+28800/69092	Loss: 113.454
+32000/69092	Loss: 114.627
+35200/69092	Loss: 112.778
+38400/69092	Loss: 114.176
+41600/69092	Loss: 112.950
+44800/69092	Loss: 113.211
+48000/69092	Loss: 113.908
+51200/69092	Loss: 112.994
+54400/69092	Loss: 113.669
+57600/69092	Loss: 113.994
+60800/69092	Loss: 113.695
+64000/69092	Loss: 113.448
+67200/69092	Loss: 112.353
+Training time 0:04:51.132729
+Epoch: 89 Average loss: 113.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 91)
+0/69092	Loss: 107.751
+3200/69092	Loss: 115.992
+6400/69092	Loss: 111.825
+9600/69092	Loss: 115.858
+12800/69092	Loss: 114.290
+16000/69092	Loss: 112.762
+19200/69092	Loss: 112.139
+22400/69092	Loss: 113.851
+25600/69092	Loss: 112.673
+28800/69092	Loss: 113.076
+32000/69092	Loss: 114.495
+35200/69092	Loss: 113.640
+38400/69092	Loss: 116.184
+41600/69092	Loss: 114.355
+44800/69092	Loss: 111.952
+48000/69092	Loss: 113.720
+51200/69092	Loss: 113.271
+54400/69092	Loss: 112.506
+57600/69092	Loss: 110.996
+60800/69092	Loss: 112.843
+64000/69092	Loss: 113.444
+67200/69092	Loss: 114.445
+Training time 0:04:50.625156
+Epoch: 90 Average loss: 113.53
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 92)
+0/69092	Loss: 112.598
+3200/69092	Loss: 113.812
+6400/69092	Loss: 114.151
+9600/69092	Loss: 112.680
+12800/69092	Loss: 112.075
+16000/69092	Loss: 114.050
+19200/69092	Loss: 112.628
+22400/69092	Loss: 114.042
+25600/69092	Loss: 113.074
+28800/69092	Loss: 113.462
+32000/69092	Loss: 114.582
+35200/69092	Loss: 113.098
+38400/69092	Loss: 112.913
+41600/69092	Loss: 115.821
+44800/69092	Loss: 112.552
+48000/69092	Loss: 114.369
+51200/69092	Loss: 112.121
+54400/69092	Loss: 112.954
+57600/69092	Loss: 112.156
+60800/69092	Loss: 113.913
+64000/69092	Loss: 113.602
+67200/69092	Loss: 113.076
+Training time 0:04:50.194425
+Epoch: 91 Average loss: 113.46
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 93)
+0/69092	Loss: 115.386
+3200/69092	Loss: 112.377
+6400/69092	Loss: 113.867
+9600/69092	Loss: 113.624
+12800/69092	Loss: 114.453
+16000/69092	Loss: 113.860
+19200/69092	Loss: 112.540
+22400/69092	Loss: 113.809
+25600/69092	Loss: 113.196
+28800/69092	Loss: 112.945
+32000/69092	Loss: 113.234
+35200/69092	Loss: 114.203
+38400/69092	Loss: 112.276
+41600/69092	Loss: 111.515
+44800/69092	Loss: 115.496
+48000/69092	Loss: 112.570
+51200/69092	Loss: 111.446
+54400/69092	Loss: 114.333
+57600/69092	Loss: 113.735
+60800/69092	Loss: 115.819
+64000/69092	Loss: 113.408
+67200/69092	Loss: 114.424
+Training time 0:04:51.056869
+Epoch: 92 Average loss: 113.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 94)
+0/69092	Loss: 115.292
+3200/69092	Loss: 112.917
+6400/69092	Loss: 113.135
+9600/69092	Loss: 114.095
+12800/69092	Loss: 112.343
+16000/69092	Loss: 111.457
+19200/69092	Loss: 113.123
+22400/69092	Loss: 113.382
+25600/69092	Loss: 114.794
+28800/69092	Loss: 113.786
+32000/69092	Loss: 111.974
+35200/69092	Loss: 113.622
+38400/69092	Loss: 112.727
+41600/69092	Loss: 113.396
+44800/69092	Loss: 114.468
+48000/69092	Loss: 113.996
+51200/69092	Loss: 112.353
+54400/69092	Loss: 113.359
+57600/69092	Loss: 112.007
+60800/69092	Loss: 113.062
+64000/69092	Loss: 115.791
+67200/69092	Loss: 114.567
+Training time 0:04:50.570403
+Epoch: 93 Average loss: 113.36
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 95)
+0/69092	Loss: 109.136
+3200/69092	Loss: 114.790
+6400/69092	Loss: 113.016
+9600/69092	Loss: 113.913
+12800/69092	Loss: 113.639
+16000/69092	Loss: 114.177
+19200/69092	Loss: 113.522
+22400/69092	Loss: 111.878
+25600/69092	Loss: 113.327
+28800/69092	Loss: 113.775
+32000/69092	Loss: 112.777
+35200/69092	Loss: 113.935
+38400/69092	Loss: 113.537
+41600/69092	Loss: 112.504
+44800/69092	Loss: 112.843
+48000/69092	Loss: 111.545
+51200/69092	Loss: 113.418
+54400/69092	Loss: 114.000
+57600/69092	Loss: 112.889
+60800/69092	Loss: 113.918
+64000/69092	Loss: 112.917
+67200/69092	Loss: 112.881
+Training time 0:04:50.509614
+Epoch: 94 Average loss: 113.26
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 96)
+0/69092	Loss: 120.211
+3200/69092	Loss: 112.835
+6400/69092	Loss: 114.498
+9600/69092	Loss: 110.539
+12800/69092	Loss: 112.930
+16000/69092	Loss: 114.047
+19200/69092	Loss: 114.101
+22400/69092	Loss: 113.367
+25600/69092	Loss: 112.753
+28800/69092	Loss: 114.776
+32000/69092	Loss: 112.135
+35200/69092	Loss: 115.300
+38400/69092	Loss: 112.598
+41600/69092	Loss: 113.166
+44800/69092	Loss: 114.484
+48000/69092	Loss: 112.204
+51200/69092	Loss: 113.411
+54400/69092	Loss: 114.884
+57600/69092	Loss: 113.061
+60800/69092	Loss: 112.235
+64000/69092	Loss: 113.967
+67200/69092	Loss: 113.788
+Training time 0:04:50.899388
+Epoch: 95 Average loss: 113.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 97)
+0/69092	Loss: 111.181
+3200/69092	Loss: 113.883
+6400/69092	Loss: 113.542
+9600/69092	Loss: 114.806
+12800/69092	Loss: 113.439
+16000/69092	Loss: 113.058
+19200/69092	Loss: 113.155
+22400/69092	Loss: 112.833
+25600/69092	Loss: 112.554
+28800/69092	Loss: 114.190
+32000/69092	Loss: 113.610
+35200/69092	Loss: 111.993
+38400/69092	Loss: 113.532
+41600/69092	Loss: 113.269
+44800/69092	Loss: 114.041
+48000/69092	Loss: 111.756
+51200/69092	Loss: 115.580
+54400/69092	Loss: 112.936
+57600/69092	Loss: 114.655
+60800/69092	Loss: 112.729
+64000/69092	Loss: 113.405
+67200/69092	Loss: 113.330
+Training time 0:04:51.675494
+Epoch: 96 Average loss: 113.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 98)
+0/69092	Loss: 129.512
+3200/69092	Loss: 112.647
+6400/69092	Loss: 112.876
+9600/69092	Loss: 112.423
+12800/69092	Loss: 114.425
+16000/69092	Loss: 112.080
+19200/69092	Loss: 113.824
+22400/69092	Loss: 114.857
+25600/69092	Loss: 112.271
+28800/69092	Loss: 112.538
+32000/69092	Loss: 115.135
+35200/69092	Loss: 111.871
+38400/69092	Loss: 113.502
+41600/69092	Loss: 113.204
+44800/69092	Loss: 112.465
+48000/69092	Loss: 111.558
+51200/69092	Loss: 112.963
+54400/69092	Loss: 113.559
+57600/69092	Loss: 114.097
+60800/69092	Loss: 111.680
+64000/69092	Loss: 116.714
+67200/69092	Loss: 114.457
+Training time 0:04:50.365147
+Epoch: 97 Average loss: 113.32
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 99)
+0/69092	Loss: 118.832
+3200/69092	Loss: 114.116
+6400/69092	Loss: 113.636
+9600/69092	Loss: 112.787
+12800/69092	Loss: 111.858
+16000/69092	Loss: 112.529
+19200/69092	Loss: 112.109
+22400/69092	Loss: 112.187
+25600/69092	Loss: 112.936
+28800/69092	Loss: 115.427
+32000/69092	Loss: 112.267
+35200/69092	Loss: 111.076
+38400/69092	Loss: 111.865
+41600/69092	Loss: 113.033
+44800/69092	Loss: 112.791
+48000/69092	Loss: 114.817
+51200/69092	Loss: 113.882
+54400/69092	Loss: 114.492
+57600/69092	Loss: 113.355
+60800/69092	Loss: 114.096
+64000/69092	Loss: 112.991
+67200/69092	Loss: 113.376
+Training time 0:04:49.462065
+Epoch: 98 Average loss: 113.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 100)
+0/69092	Loss: 108.530
+3200/69092	Loss: 113.168
+6400/69092	Loss: 113.990
+9600/69092	Loss: 113.376
+12800/69092	Loss: 115.051
+16000/69092	Loss: 111.984
+19200/69092	Loss: 112.483
+22400/69092	Loss: 110.864
+25600/69092	Loss: 113.915
+28800/69092	Loss: 113.825
+32000/69092	Loss: 114.106
+35200/69092	Loss: 113.509
+38400/69092	Loss: 113.246
+41600/69092	Loss: 114.283
+44800/69092	Loss: 111.216
+48000/69092	Loss: 113.894
+51200/69092	Loss: 112.492
+54400/69092	Loss: 114.035
+57600/69092	Loss: 112.132
+60800/69092	Loss: 111.788
+64000/69092	Loss: 114.670
+67200/69092	Loss: 113.927
+Training time 0:04:50.711497
+Epoch: 99 Average loss: 113.24
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 101)
+0/69092	Loss: 104.715
+3200/69092	Loss: 114.996
+6400/69092	Loss: 111.712
+9600/69092	Loss: 114.057
+12800/69092	Loss: 113.130
+16000/69092	Loss: 114.854
+19200/69092	Loss: 112.510
+22400/69092	Loss: 112.570
+25600/69092	Loss: 113.299
+28800/69092	Loss: 112.590
+32000/69092	Loss: 112.673
+35200/69092	Loss: 111.300
+38400/69092	Loss: 111.996
+41600/69092	Loss: 114.006
+44800/69092	Loss: 114.334
+48000/69092	Loss: 111.544
+51200/69092	Loss: 114.231
+54400/69092	Loss: 111.873
+57600/69092	Loss: 114.207
+60800/69092	Loss: 112.306
+64000/69092	Loss: 111.566
+67200/69092	Loss: 113.902
+Training time 0:04:50.773052
+Epoch: 100 Average loss: 113.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 102)
+0/69092	Loss: 103.241
+3200/69092	Loss: 114.012
+6400/69092	Loss: 113.032
+9600/69092	Loss: 111.289
+12800/69092	Loss: 113.076
+16000/69092	Loss: 113.746
+19200/69092	Loss: 113.834
+22400/69092	Loss: 112.645
+25600/69092	Loss: 114.432
+28800/69092	Loss: 112.254
+32000/69092	Loss: 114.221
+35200/69092	Loss: 111.278
+38400/69092	Loss: 113.230
+41600/69092	Loss: 113.947
+44800/69092	Loss: 112.927
+48000/69092	Loss: 113.256
+51200/69092	Loss: 111.359
+54400/69092	Loss: 113.623
+57600/69092	Loss: 112.886
+60800/69092	Loss: 113.248
+64000/69092	Loss: 113.053
+67200/69092	Loss: 112.776
+Training time 0:04:51.880454
+Epoch: 101 Average loss: 113.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 103)
+0/69092	Loss: 104.510
+3200/69092	Loss: 115.825
+6400/69092	Loss: 113.438
+9600/69092	Loss: 113.008
+12800/69092	Loss: 112.949
+16000/69092	Loss: 114.365
+19200/69092	Loss: 112.928
+22400/69092	Loss: 111.763
+25600/69092	Loss: 114.226
+28800/69092	Loss: 112.180
+32000/69092	Loss: 111.872
+35200/69092	Loss: 112.966
+38400/69092	Loss: 113.134
+41600/69092	Loss: 111.910
+44800/69092	Loss: 112.458
+48000/69092	Loss: 113.513
+51200/69092	Loss: 112.349
+54400/69092	Loss: 113.011
+57600/69092	Loss: 113.556
+60800/69092	Loss: 112.649
+64000/69092	Loss: 113.863
+67200/69092	Loss: 113.112
+Training time 0:04:53.868210
+Epoch: 102 Average loss: 113.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 104)
+0/69092	Loss: 112.749
+3200/69092	Loss: 112.414
+6400/69092	Loss: 112.463
+9600/69092	Loss: 112.118
+12800/69092	Loss: 111.886
+16000/69092	Loss: 111.227
+19200/69092	Loss: 112.808
+22400/69092	Loss: 112.538
+25600/69092	Loss: 112.087
+28800/69092	Loss: 113.498
+32000/69092	Loss: 112.696
+35200/69092	Loss: 113.405
+38400/69092	Loss: 113.021
+41600/69092	Loss: 114.543
+44800/69092	Loss: 114.788
+48000/69092	Loss: 114.305
+51200/69092	Loss: 113.947
+54400/69092	Loss: 113.979
+57600/69092	Loss: 113.925
+60800/69092	Loss: 113.345
+64000/69092	Loss: 113.074
+67200/69092	Loss: 111.675
+Training time 0:04:51.015212
+Epoch: 103 Average loss: 113.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 105)
+0/69092	Loss: 113.049
+3200/69092	Loss: 112.966
+6400/69092	Loss: 112.076
+9600/69092	Loss: 113.396
+12800/69092	Loss: 113.117
+16000/69092	Loss: 113.435
+19200/69092	Loss: 111.830
+22400/69092	Loss: 110.879
+25600/69092	Loss: 112.320
+28800/69092	Loss: 113.011
+32000/69092	Loss: 112.395
+35200/69092	Loss: 113.857
+38400/69092	Loss: 111.612
+41600/69092	Loss: 113.616
+44800/69092	Loss: 113.028
+48000/69092	Loss: 113.891
+51200/69092	Loss: 115.016
+54400/69092	Loss: 112.686
+57600/69092	Loss: 114.493
+60800/69092	Loss: 112.483
+64000/69092	Loss: 113.863
+67200/69092	Loss: 112.239
+Training time 0:04:50.883705
+Epoch: 104 Average loss: 113.02
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 106)
+0/69092	Loss: 117.649
+3200/69092	Loss: 111.403
+6400/69092	Loss: 114.627
+9600/69092	Loss: 113.497
+12800/69092	Loss: 114.286
+16000/69092	Loss: 111.266
+19200/69092	Loss: 113.161
+22400/69092	Loss: 112.569
+25600/69092	Loss: 111.745
+28800/69092	Loss: 112.846
+32000/69092	Loss: 111.716
+35200/69092	Loss: 112.420
+38400/69092	Loss: 112.767
+41600/69092	Loss: 111.755
+44800/69092	Loss: 114.990
+48000/69092	Loss: 114.644
+51200/69092	Loss: 113.324
+54400/69092	Loss: 112.092
+57600/69092	Loss: 115.715
+60800/69092	Loss: 112.178
+64000/69092	Loss: 112.530
+67200/69092	Loss: 111.036
+Training time 0:04:50.722033
+Epoch: 105 Average loss: 112.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 107)
+0/69092	Loss: 110.335
+3200/69092	Loss: 112.968
+6400/69092	Loss: 112.965
+9600/69092	Loss: 113.754
+12800/69092	Loss: 112.037
+16000/69092	Loss: 113.292
+19200/69092	Loss: 112.486
+22400/69092	Loss: 113.055
+25600/69092	Loss: 112.109
+28800/69092	Loss: 111.938
+32000/69092	Loss: 113.729
+35200/69092	Loss: 114.021
+38400/69092	Loss: 112.244
+41600/69092	Loss: 111.851
+44800/69092	Loss: 113.689
+48000/69092	Loss: 112.606
+51200/69092	Loss: 114.633
+54400/69092	Loss: 115.295
+57600/69092	Loss: 110.716
+60800/69092	Loss: 112.370
+64000/69092	Loss: 113.886
+67200/69092	Loss: 114.530
+Training time 0:04:49.583058
+Epoch: 106 Average loss: 112.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 108)
+0/69092	Loss: 109.728
+3200/69092	Loss: 111.651
+6400/69092	Loss: 113.660
+9600/69092	Loss: 110.784
+12800/69092	Loss: 112.393
+16000/69092	Loss: 112.028
+19200/69092	Loss: 113.139
+22400/69092	Loss: 114.301
+25600/69092	Loss: 112.913
+28800/69092	Loss: 114.420
+32000/69092	Loss: 113.733
+35200/69092	Loss: 111.859
+38400/69092	Loss: 112.927
+41600/69092	Loss: 115.408
+44800/69092	Loss: 111.712
+48000/69092	Loss: 115.124
+51200/69092	Loss: 112.678
+54400/69092	Loss: 111.074
+57600/69092	Loss: 113.910
+60800/69092	Loss: 112.468
+64000/69092	Loss: 112.889
+67200/69092	Loss: 112.525
+Training time 0:04:50.873563
+Epoch: 107 Average loss: 112.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 109)
+0/69092	Loss: 100.595
+3200/69092	Loss: 111.699
+6400/69092	Loss: 112.685
+9600/69092	Loss: 111.818
+12800/69092	Loss: 112.431
+16000/69092	Loss: 114.224
+19200/69092	Loss: 111.799
+22400/69092	Loss: 113.780
+25600/69092	Loss: 112.470
+28800/69092	Loss: 113.491
+32000/69092	Loss: 113.645
+35200/69092	Loss: 112.084
+38400/69092	Loss: 112.113
+41600/69092	Loss: 112.947
+44800/69092	Loss: 113.417
+48000/69092	Loss: 113.985
+51200/69092	Loss: 112.549
+54400/69092	Loss: 112.278
+57600/69092	Loss: 114.006
+60800/69092	Loss: 114.476
+64000/69092	Loss: 112.118
+67200/69092	Loss: 113.907
+Training time 0:04:50.246159
+Epoch: 108 Average loss: 112.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 110)
+0/69092	Loss: 109.749
+3200/69092	Loss: 111.728
+6400/69092	Loss: 114.343
+9600/69092	Loss: 115.068
+12800/69092	Loss: 111.811
+16000/69092	Loss: 112.032
+19200/69092	Loss: 112.554
+22400/69092	Loss: 112.011
+25600/69092	Loss: 113.259
+28800/69092	Loss: 112.044
+32000/69092	Loss: 110.983
+35200/69092	Loss: 113.468
+38400/69092	Loss: 114.091
+41600/69092	Loss: 112.747
+44800/69092	Loss: 112.890
+48000/69092	Loss: 113.464
+51200/69092	Loss: 111.830
+54400/69092	Loss: 113.043
+57600/69092	Loss: 113.324
+60800/69092	Loss: 112.852
+64000/69092	Loss: 114.433
+67200/69092	Loss: 112.236
+Training time 0:04:49.864885
+Epoch: 109 Average loss: 112.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 111)
+0/69092	Loss: 108.662
+3200/69092	Loss: 112.250
+6400/69092	Loss: 112.018
+9600/69092	Loss: 113.160
+12800/69092	Loss: 112.030
+16000/69092	Loss: 110.437
+19200/69092	Loss: 112.932
+22400/69092	Loss: 112.913
+25600/69092	Loss: 114.639
+28800/69092	Loss: 111.360
+32000/69092	Loss: 114.006
+35200/69092	Loss: 112.941
+38400/69092	Loss: 112.100
+41600/69092	Loss: 114.785
+44800/69092	Loss: 111.514
+48000/69092	Loss: 113.532
+51200/69092	Loss: 113.963
+54400/69092	Loss: 112.818
+57600/69092	Loss: 114.311
+60800/69092	Loss: 115.810
+64000/69092	Loss: 110.706
+67200/69092	Loss: 113.092
+Training time 0:04:52.403239
+Epoch: 110 Average loss: 112.93
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 112)
+0/69092	Loss: 117.085
+3200/69092	Loss: 112.570
+6400/69092	Loss: 112.889
+9600/69092	Loss: 113.498
+12800/69092	Loss: 113.033
+16000/69092	Loss: 113.505
+19200/69092	Loss: 110.694
+22400/69092	Loss: 112.606
+25600/69092	Loss: 114.135
+28800/69092	Loss: 112.351
+32000/69092	Loss: 113.568
+35200/69092	Loss: 111.569
+38400/69092	Loss: 113.105
+41600/69092	Loss: 113.619
+44800/69092	Loss: 113.033
+48000/69092	Loss: 110.961
+51200/69092	Loss: 113.473
+54400/69092	Loss: 113.197
+57600/69092	Loss: 112.174
+60800/69092	Loss: 114.522
+64000/69092	Loss: 114.344
+67200/69092	Loss: 113.659
+Training time 0:04:51.452453
+Epoch: 111 Average loss: 112.93
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 113)
+0/69092	Loss: 114.975
+3200/69092	Loss: 112.918
+6400/69092	Loss: 112.470
+9600/69092	Loss: 113.404
+12800/69092	Loss: 112.227
+16000/69092	Loss: 113.907
+19200/69092	Loss: 112.203
+22400/69092	Loss: 112.680
+25600/69092	Loss: 110.716
+28800/69092	Loss: 113.804
+32000/69092	Loss: 113.425
+35200/69092	Loss: 113.265
+38400/69092	Loss: 111.771
+41600/69092	Loss: 113.441
+44800/69092	Loss: 113.071
+48000/69092	Loss: 112.056
+51200/69092	Loss: 113.299
+54400/69092	Loss: 112.062
+57600/69092	Loss: 113.936
+60800/69092	Loss: 111.318
+64000/69092	Loss: 112.291
+67200/69092	Loss: 115.086
+Training time 0:04:51.975763
+Epoch: 112 Average loss: 112.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 114)
+0/69092	Loss: 102.922
+3200/69092	Loss: 115.186
+6400/69092	Loss: 111.882
+9600/69092	Loss: 113.402
+12800/69092	Loss: 112.680
+16000/69092	Loss: 113.579
+19200/69092	Loss: 111.947
+22400/69092	Loss: 111.836
+25600/69092	Loss: 113.005
+28800/69092	Loss: 112.644
+32000/69092	Loss: 112.913
+35200/69092	Loss: 110.705
+38400/69092	Loss: 112.131
+41600/69092	Loss: 113.813
+44800/69092	Loss: 115.153
+48000/69092	Loss: 112.444
+51200/69092	Loss: 112.657
+54400/69092	Loss: 111.252
+57600/69092	Loss: 114.383
+60800/69092	Loss: 114.278
+64000/69092	Loss: 111.896
+67200/69092	Loss: 113.764
+Training time 0:04:50.679658
+Epoch: 113 Average loss: 112.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 115)
+0/69092	Loss: 112.672
+3200/69092	Loss: 112.993
+6400/69092	Loss: 112.206
+9600/69092	Loss: 113.897
+12800/69092	Loss: 113.445
+16000/69092	Loss: 112.395
+19200/69092	Loss: 113.587
+22400/69092	Loss: 114.301
+25600/69092	Loss: 112.975
+28800/69092	Loss: 113.426
+32000/69092	Loss: 112.050
+35200/69092	Loss: 113.016
+38400/69092	Loss: 114.072
+41600/69092	Loss: 110.919
+44800/69092	Loss: 113.175
+48000/69092	Loss: 111.370
+51200/69092	Loss: 113.587
+54400/69092	Loss: 112.240
+57600/69092	Loss: 111.858
+60800/69092	Loss: 112.010
+64000/69092	Loss: 112.690
+67200/69092	Loss: 112.855
+Training time 0:04:50.348314
+Epoch: 114 Average loss: 112.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_15/checkpoints/last' (iter 116)
+0/69092	Loss: 104.999
+3200/69092	Loss: 113.136
+6400/69092	Loss: 112.056
+9600/69092	Loss: 112.611
+12800/69092	Loss: 111.995
+16000/69092	Loss: 112.782
diff --git a/OAR.2068293.stderr b/OAR.2068293.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..a9c628a29c58ebc65df754dd5dfbbd99e2282b5f
--- /dev/null
+++ b/OAR.2068293.stderr
@@ -0,0 +1,2 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
diff --git a/OAR.2068293.stdout b/OAR.2068293.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..b7f0ce43886cbbe53160b7c68fbbac4033a01a70
--- /dev/null
+++ b/OAR.2068293.stdout
@@ -0,0 +1,2050 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64_ls_20', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=20, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64_ls_20
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=40, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=20, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 773035
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last (iter 5)'
+0/69092	Loss: 123.071
+3200/69092	Loss: 129.671
+6400/69092	Loss: 127.398
+9600/69092	Loss: 127.951
+12800/69092	Loss: 128.455
+16000/69092	Loss: 129.429
+19200/69092	Loss: 126.305
+22400/69092	Loss: 128.984
+25600/69092	Loss: 130.783
+28800/69092	Loss: 129.975
+32000/69092	Loss: 128.062
+35200/69092	Loss: 127.186
+38400/69092	Loss: 128.455
+41600/69092	Loss: 128.958
+44800/69092	Loss: 129.323
+48000/69092	Loss: 126.212
+51200/69092	Loss: 128.191
+54400/69092	Loss: 127.190
+57600/69092	Loss: 126.934
+60800/69092	Loss: 127.484
+64000/69092	Loss: 128.661
+67200/69092	Loss: 124.744
+Training time 0:04:50.464730
+Epoch: 1 Average loss: 128.18
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 6)
+0/69092	Loss: 129.943
+3200/69092	Loss: 129.051
+6400/69092	Loss: 125.219
+9600/69092	Loss: 123.455
+12800/69092	Loss: 127.291
+16000/69092	Loss: 127.190
+19200/69092	Loss: 126.336
+22400/69092	Loss: 127.079
+25600/69092	Loss: 126.929
+28800/69092	Loss: 126.119
+32000/69092	Loss: 126.972
+35200/69092	Loss: 124.950
+38400/69092	Loss: 125.826
+41600/69092	Loss: 126.043
+44800/69092	Loss: 123.989
+48000/69092	Loss: 124.989
+51200/69092	Loss: 125.214
+54400/69092	Loss: 124.362
+57600/69092	Loss: 125.383
+60800/69092	Loss: 125.831
+64000/69092	Loss: 123.904
+67200/69092	Loss: 127.904
+Training time 0:04:51.734344
+Epoch: 2 Average loss: 125.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 7)
+0/69092	Loss: 151.090
+3200/69092	Loss: 123.873
+6400/69092	Loss: 124.226
+9600/69092	Loss: 126.185
+12800/69092	Loss: 125.767
+16000/69092	Loss: 124.022
+19200/69092	Loss: 126.055
+22400/69092	Loss: 124.320
+25600/69092	Loss: 124.870
+28800/69092	Loss: 127.822
+32000/69092	Loss: 124.268
+35200/69092	Loss: 125.595
+38400/69092	Loss: 123.975
+41600/69092	Loss: 122.331
+44800/69092	Loss: 121.933
+48000/69092	Loss: 123.773
+51200/69092	Loss: 124.910
+54400/69092	Loss: 123.662
+57600/69092	Loss: 122.927
+60800/69092	Loss: 123.387
+64000/69092	Loss: 123.426
+67200/69092	Loss: 125.656
+Training time 0:04:45.516245
+Epoch: 3 Average loss: 124.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 8)
+0/69092	Loss: 116.913
+3200/69092	Loss: 121.551
+6400/69092	Loss: 123.757
+9600/69092	Loss: 120.772
+12800/69092	Loss: 124.840
+16000/69092	Loss: 122.135
+19200/69092	Loss: 124.674
+22400/69092	Loss: 124.362
+25600/69092	Loss: 124.429
+28800/69092	Loss: 121.847
+32000/69092	Loss: 122.697
+35200/69092	Loss: 121.337
+38400/69092	Loss: 123.855
+41600/69092	Loss: 123.472
+44800/69092	Loss: 123.395
+48000/69092	Loss: 123.456
+51200/69092	Loss: 122.075
+54400/69092	Loss: 123.076
+57600/69092	Loss: 124.115
+60800/69092	Loss: 123.259
+64000/69092	Loss: 123.464
+67200/69092	Loss: 121.002
+Training time 0:04:52.057476
+Epoch: 4 Average loss: 123.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 9)
+0/69092	Loss: 99.405
+3200/69092	Loss: 122.589
+6400/69092	Loss: 123.487
+9600/69092	Loss: 119.019
+12800/69092	Loss: 121.564
+16000/69092	Loss: 123.554
+19200/69092	Loss: 121.925
+22400/69092	Loss: 120.459
+25600/69092	Loss: 121.809
+28800/69092	Loss: 121.263
+32000/69092	Loss: 122.560
+35200/69092	Loss: 120.117
+38400/69092	Loss: 121.506
+41600/69092	Loss: 123.250
+44800/69092	Loss: 121.267
+48000/69092	Loss: 122.438
+51200/69092	Loss: 121.474
+54400/69092	Loss: 123.530
+57600/69092	Loss: 122.750
+60800/69092	Loss: 122.617
+64000/69092	Loss: 123.213
+67200/69092	Loss: 121.717
+Training time 0:04:47.144073
+Epoch: 5 Average loss: 121.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 10)
+0/69092	Loss: 116.159
+3200/69092	Loss: 122.455
+6400/69092	Loss: 120.286
+9600/69092	Loss: 119.735
+12800/69092	Loss: 122.867
+16000/69092	Loss: 120.996
+19200/69092	Loss: 121.540
+22400/69092	Loss: 122.140
+25600/69092	Loss: 120.710
+28800/69092	Loss: 121.041
+32000/69092	Loss: 122.114
+35200/69092	Loss: 119.510
+38400/69092	Loss: 120.339
+41600/69092	Loss: 121.481
+44800/69092	Loss: 120.913
+48000/69092	Loss: 120.790
+51200/69092	Loss: 121.752
+54400/69092	Loss: 120.170
+57600/69092	Loss: 120.647
+60800/69092	Loss: 120.284
+64000/69092	Loss: 121.987
+67200/69092	Loss: 121.628
+Training time 0:04:53.401766
+Epoch: 6 Average loss: 121.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 11)
+0/69092	Loss: 129.555
+3200/69092	Loss: 119.839
+6400/69092	Loss: 118.692
+9600/69092	Loss: 121.241
+12800/69092	Loss: 118.470
+16000/69092	Loss: 121.128
+19200/69092	Loss: 120.905
+22400/69092	Loss: 120.691
+25600/69092	Loss: 119.656
+28800/69092	Loss: 119.566
+32000/69092	Loss: 120.417
+35200/69092	Loss: 119.614
+38400/69092	Loss: 119.080
+41600/69092	Loss: 119.992
+44800/69092	Loss: 122.269
+48000/69092	Loss: 120.826
+51200/69092	Loss: 120.305
+54400/69092	Loss: 119.281
+57600/69092	Loss: 120.017
+60800/69092	Loss: 122.424
+64000/69092	Loss: 120.654
+67200/69092	Loss: 121.758
+Training time 0:04:53.550179
+Epoch: 7 Average loss: 120.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 12)
+0/69092	Loss: 110.400
+3200/69092	Loss: 120.276
+6400/69092	Loss: 121.587
+9600/69092	Loss: 119.223
+12800/69092	Loss: 120.454
+16000/69092	Loss: 120.013
+19200/69092	Loss: 117.803
+22400/69092	Loss: 121.672
+25600/69092	Loss: 118.489
+28800/69092	Loss: 120.078
+32000/69092	Loss: 119.809
+35200/69092	Loss: 119.278
+38400/69092	Loss: 117.270
+41600/69092	Loss: 120.507
+44800/69092	Loss: 118.501
+48000/69092	Loss: 119.447
+51200/69092	Loss: 120.600
+54400/69092	Loss: 120.683
+57600/69092	Loss: 118.504
+60800/69092	Loss: 118.999
+64000/69092	Loss: 118.737
+67200/69092	Loss: 121.232
+Training time 0:04:49.394686
+Epoch: 8 Average loss: 119.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 13)
+0/69092	Loss: 104.134
+3200/69092	Loss: 120.123
+6400/69092	Loss: 117.816
+9600/69092	Loss: 117.857
+12800/69092	Loss: 118.330
+16000/69092	Loss: 117.953
+19200/69092	Loss: 118.829
+22400/69092	Loss: 120.242
+25600/69092	Loss: 119.360
+28800/69092	Loss: 118.183
+32000/69092	Loss: 118.660
+35200/69092	Loss: 116.001
+38400/69092	Loss: 118.471
+41600/69092	Loss: 119.110
+44800/69092	Loss: 120.752
+48000/69092	Loss: 118.350
+51200/69092	Loss: 118.216
+54400/69092	Loss: 119.695
+57600/69092	Loss: 119.219
+60800/69092	Loss: 118.008
+64000/69092	Loss: 117.196
+67200/69092	Loss: 117.916
+Training time 0:04:53.527974
+Epoch: 9 Average loss: 118.63
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 14)
+0/69092	Loss: 134.504
+3200/69092	Loss: 118.385
+6400/69092	Loss: 117.437
+9600/69092	Loss: 120.115
+12800/69092	Loss: 116.593
+16000/69092	Loss: 116.214
+19200/69092	Loss: 116.874
+22400/69092	Loss: 118.624
+25600/69092	Loss: 118.921
+28800/69092	Loss: 117.498
+32000/69092	Loss: 117.532
+35200/69092	Loss: 118.473
+38400/69092	Loss: 117.333
+41600/69092	Loss: 117.636
+44800/69092	Loss: 118.679
+48000/69092	Loss: 118.222
+51200/69092	Loss: 116.884
+54400/69092	Loss: 116.768
+57600/69092	Loss: 118.938
+60800/69092	Loss: 118.071
+64000/69092	Loss: 119.501
+67200/69092	Loss: 117.280
+Training time 0:04:54.806029
+Epoch: 10 Average loss: 117.98
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 15)
+0/69092	Loss: 109.254
+3200/69092	Loss: 119.447
+6400/69092	Loss: 117.376
+9600/69092	Loss: 115.992
+12800/69092	Loss: 117.909
+16000/69092	Loss: 116.937
+19200/69092	Loss: 116.312
+22400/69092	Loss: 117.740
+25600/69092	Loss: 119.293
+28800/69092	Loss: 116.433
+32000/69092	Loss: 118.364
+35200/69092	Loss: 117.022
+38400/69092	Loss: 117.941
+41600/69092	Loss: 116.341
+44800/69092	Loss: 118.821
+48000/69092	Loss: 117.934
+51200/69092	Loss: 118.055
+54400/69092	Loss: 116.848
+57600/69092	Loss: 114.801
+60800/69092	Loss: 117.061
+64000/69092	Loss: 116.393
+67200/69092	Loss: 117.030
+Training time 0:04:53.313837
+Epoch: 11 Average loss: 117.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 16)
+0/69092	Loss: 124.562
+3200/69092	Loss: 117.708
+6400/69092	Loss: 116.378
+9600/69092	Loss: 115.341
+12800/69092	Loss: 118.288
+16000/69092	Loss: 116.312
+19200/69092	Loss: 115.873
+22400/69092	Loss: 116.824
+25600/69092	Loss: 114.939
+28800/69092	Loss: 117.201
+32000/69092	Loss: 117.600
+35200/69092	Loss: 114.187
+38400/69092	Loss: 117.203
+41600/69092	Loss: 116.734
+44800/69092	Loss: 117.038
+48000/69092	Loss: 116.328
+51200/69092	Loss: 117.843
+54400/69092	Loss: 117.899
+57600/69092	Loss: 117.246
+60800/69092	Loss: 117.224
+64000/69092	Loss: 117.388
+67200/69092	Loss: 117.255
+Training time 0:04:48.196398
+Epoch: 12 Average loss: 116.80
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 17)
+0/69092	Loss: 103.586
+3200/69092	Loss: 116.581
+6400/69092	Loss: 116.557
+9600/69092	Loss: 117.937
+12800/69092	Loss: 117.568
+16000/69092	Loss: 117.730
+19200/69092	Loss: 116.240
+22400/69092	Loss: 116.533
+25600/69092	Loss: 114.281
+28800/69092	Loss: 115.136
+32000/69092	Loss: 115.581
+35200/69092	Loss: 117.259
+38400/69092	Loss: 115.315
+41600/69092	Loss: 115.175
+44800/69092	Loss: 115.283
+48000/69092	Loss: 116.809
+51200/69092	Loss: 115.756
+54400/69092	Loss: 117.455
+57600/69092	Loss: 116.416
+60800/69092	Loss: 118.661
+64000/69092	Loss: 115.042
+67200/69092	Loss: 117.673
+Training time 0:04:49.374363
+Epoch: 13 Average loss: 116.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 18)
+0/69092	Loss: 114.152
+3200/69092	Loss: 115.526
+6400/69092	Loss: 116.044
+9600/69092	Loss: 114.991
+12800/69092	Loss: 116.347
+16000/69092	Loss: 115.274
+19200/69092	Loss: 115.281
+22400/69092	Loss: 116.965
+25600/69092	Loss: 116.550
+28800/69092	Loss: 115.085
+32000/69092	Loss: 115.061
+35200/69092	Loss: 115.124
+38400/69092	Loss: 115.807
+41600/69092	Loss: 115.448
+44800/69092	Loss: 116.142
+48000/69092	Loss: 116.700
+51200/69092	Loss: 116.097
+54400/69092	Loss: 116.449
+57600/69092	Loss: 117.906
+60800/69092	Loss: 114.443
+64000/69092	Loss: 117.303
+67200/69092	Loss: 116.183
+Training time 0:04:47.466840
+Epoch: 14 Average loss: 115.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 19)
+0/69092	Loss: 115.756
+3200/69092	Loss: 113.816
+6400/69092	Loss: 116.148
+9600/69092	Loss: 116.477
+12800/69092	Loss: 116.312
+16000/69092	Loss: 116.970
+19200/69092	Loss: 115.761
+22400/69092	Loss: 116.004
+25600/69092	Loss: 114.904
+28800/69092	Loss: 114.584
+32000/69092	Loss: 116.777
+35200/69092	Loss: 116.189
+38400/69092	Loss: 113.972
+41600/69092	Loss: 117.633
+44800/69092	Loss: 116.672
+48000/69092	Loss: 114.872
+51200/69092	Loss: 115.771
+54400/69092	Loss: 115.499
+57600/69092	Loss: 114.661
+60800/69092	Loss: 115.201
+64000/69092	Loss: 114.100
+67200/69092	Loss: 113.874
+Training time 0:04:51.327746
+Epoch: 15 Average loss: 115.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 20)
+0/69092	Loss: 115.941
+3200/69092	Loss: 115.412
+6400/69092	Loss: 116.467
+9600/69092	Loss: 115.764
+12800/69092	Loss: 116.308
+16000/69092	Loss: 114.297
+19200/69092	Loss: 114.467
+22400/69092	Loss: 114.919
+25600/69092	Loss: 114.402
+28800/69092	Loss: 114.454
+32000/69092	Loss: 115.140
+35200/69092	Loss: 114.877
+38400/69092	Loss: 115.791
+41600/69092	Loss: 116.370
+44800/69092	Loss: 114.254
+48000/69092	Loss: 115.377
+51200/69092	Loss: 115.315
+54400/69092	Loss: 114.060
+57600/69092	Loss: 114.105
+60800/69092	Loss: 115.245
+64000/69092	Loss: 114.178
+67200/69092	Loss: 114.546
+Training time 0:04:41.171591
+Epoch: 16 Average loss: 115.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 21)
+0/69092	Loss: 141.782
+3200/69092	Loss: 114.003
+6400/69092	Loss: 115.411
+9600/69092	Loss: 115.911
+12800/69092	Loss: 114.007
+16000/69092	Loss: 114.354
+19200/69092	Loss: 116.028
+22400/69092	Loss: 113.100
+25600/69092	Loss: 115.666
+28800/69092	Loss: 113.996
+32000/69092	Loss: 116.153
+35200/69092	Loss: 115.098
+38400/69092	Loss: 112.231
+41600/69092	Loss: 115.095
+44800/69092	Loss: 113.056
+48000/69092	Loss: 113.455
+51200/69092	Loss: 113.613
+54400/69092	Loss: 115.574
+57600/69092	Loss: 114.387
+60800/69092	Loss: 113.743
+64000/69092	Loss: 115.058
+67200/69092	Loss: 114.973
+Training time 0:04:36.060267
+Epoch: 17 Average loss: 114.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 22)
+0/69092	Loss: 107.420
+3200/69092	Loss: 113.793
+6400/69092	Loss: 115.466
+9600/69092	Loss: 113.951
+12800/69092	Loss: 115.332
+16000/69092	Loss: 115.308
+19200/69092	Loss: 114.394
+22400/69092	Loss: 113.850
+25600/69092	Loss: 116.228
+28800/69092	Loss: 114.637
+32000/69092	Loss: 113.228
+35200/69092	Loss: 113.624
+38400/69092	Loss: 114.285
+41600/69092	Loss: 112.589
+44800/69092	Loss: 114.494
+48000/69092	Loss: 112.654
+51200/69092	Loss: 115.949
+54400/69092	Loss: 114.853
+57600/69092	Loss: 112.992
+60800/69092	Loss: 114.541
+64000/69092	Loss: 114.248
+67200/69092	Loss: 113.553
+Training time 0:04:37.523009
+Epoch: 18 Average loss: 114.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 23)
+0/69092	Loss: 115.435
+3200/69092	Loss: 112.490
+6400/69092	Loss: 114.217
+9600/69092	Loss: 112.386
+12800/69092	Loss: 113.442
+16000/69092	Loss: 114.289
+19200/69092	Loss: 115.442
+22400/69092	Loss: 112.385
+25600/69092	Loss: 114.455
+28800/69092	Loss: 115.032
+32000/69092	Loss: 115.131
+35200/69092	Loss: 113.942
+38400/69092	Loss: 114.186
+41600/69092	Loss: 114.002
+44800/69092	Loss: 113.893
+48000/69092	Loss: 113.659
+51200/69092	Loss: 114.449
+54400/69092	Loss: 114.045
+57600/69092	Loss: 114.498
+60800/69092	Loss: 113.359
+64000/69092	Loss: 114.983
+67200/69092	Loss: 112.828
+Training time 0:04:33.872554
+Epoch: 19 Average loss: 113.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 24)
+0/69092	Loss: 111.737
+3200/69092	Loss: 114.558
+6400/69092	Loss: 115.141
+9600/69092	Loss: 115.882
+12800/69092	Loss: 112.989
+16000/69092	Loss: 113.441
+19200/69092	Loss: 114.143
+22400/69092	Loss: 113.532
+25600/69092	Loss: 114.813
+28800/69092	Loss: 112.059
+32000/69092	Loss: 113.061
+35200/69092	Loss: 112.920
+38400/69092	Loss: 113.187
+41600/69092	Loss: 112.812
+44800/69092	Loss: 114.557
+48000/69092	Loss: 112.577
+51200/69092	Loss: 113.078
+54400/69092	Loss: 114.110
+57600/69092	Loss: 113.877
+60800/69092	Loss: 112.964
+64000/69092	Loss: 113.965
+67200/69092	Loss: 113.553
+Training time 0:04:39.906828
+Epoch: 20 Average loss: 113.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 25)
+0/69092	Loss: 123.589
+3200/69092	Loss: 113.380
+6400/69092	Loss: 114.676
+9600/69092	Loss: 114.405
+12800/69092	Loss: 113.494
+16000/69092	Loss: 114.177
+19200/69092	Loss: 113.390
+22400/69092	Loss: 111.949
+25600/69092	Loss: 114.273
+28800/69092	Loss: 113.334
+32000/69092	Loss: 112.502
+35200/69092	Loss: 114.189
+38400/69092	Loss: 114.932
+41600/69092	Loss: 115.119
+44800/69092	Loss: 111.780
+48000/69092	Loss: 111.857
+51200/69092	Loss: 111.502
+54400/69092	Loss: 113.287
+57600/69092	Loss: 112.345
+60800/69092	Loss: 113.466
+64000/69092	Loss: 113.066
+67200/69092	Loss: 113.099
+Training time 0:04:39.348429
+Epoch: 21 Average loss: 113.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 26)
+0/69092	Loss: 110.674
+3200/69092	Loss: 112.143
+6400/69092	Loss: 112.617
+9600/69092	Loss: 114.939
+12800/69092	Loss: 113.844
+16000/69092	Loss: 113.021
+19200/69092	Loss: 114.569
+22400/69092	Loss: 112.865
+25600/69092	Loss: 112.809
+28800/69092	Loss: 114.086
+32000/69092	Loss: 112.677
+35200/69092	Loss: 114.229
+38400/69092	Loss: 112.291
+41600/69092	Loss: 111.985
+44800/69092	Loss: 112.205
+48000/69092	Loss: 112.236
+51200/69092	Loss: 113.672
+54400/69092	Loss: 113.601
+57600/69092	Loss: 112.585
+60800/69092	Loss: 111.314
+64000/69092	Loss: 113.152
+67200/69092	Loss: 112.903
+Training time 0:04:34.061168
+Epoch: 22 Average loss: 113.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 27)
+0/69092	Loss: 109.967
+3200/69092	Loss: 112.520
+6400/69092	Loss: 114.405
+9600/69092	Loss: 113.686
+12800/69092	Loss: 112.579
+16000/69092	Loss: 113.520
+19200/69092	Loss: 113.800
+22400/69092	Loss: 111.771
+25600/69092	Loss: 111.360
+28800/69092	Loss: 113.635
+32000/69092	Loss: 113.484
+35200/69092	Loss: 111.351
+38400/69092	Loss: 114.812
+41600/69092	Loss: 112.548
+44800/69092	Loss: 113.236
+48000/69092	Loss: 112.303
+51200/69092	Loss: 114.013
+54400/69092	Loss: 112.735
+57600/69092	Loss: 111.526
+60800/69092	Loss: 112.256
+64000/69092	Loss: 111.369
+67200/69092	Loss: 113.282
+Training time 0:04:39.859457
+Epoch: 23 Average loss: 112.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 28)
+0/69092	Loss: 119.190
+3200/69092	Loss: 111.744
+6400/69092	Loss: 111.066
+9600/69092	Loss: 111.289
+12800/69092	Loss: 111.358
+16000/69092	Loss: 111.865
+19200/69092	Loss: 114.916
+22400/69092	Loss: 113.265
+25600/69092	Loss: 111.379
+28800/69092	Loss: 112.894
+32000/69092	Loss: 113.767
+35200/69092	Loss: 112.440
+38400/69092	Loss: 112.766
+41600/69092	Loss: 112.426
+44800/69092	Loss: 113.225
+48000/69092	Loss: 112.531
+51200/69092	Loss: 114.364
+54400/69092	Loss: 111.020
+57600/69092	Loss: 111.747
+60800/69092	Loss: 114.497
+64000/69092	Loss: 113.869
+67200/69092	Loss: 111.078
+Training time 0:04:39.936371
+Epoch: 24 Average loss: 112.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 29)
+0/69092	Loss: 110.290
+3200/69092	Loss: 111.822
+6400/69092	Loss: 111.413
+9600/69092	Loss: 112.422
+12800/69092	Loss: 111.954
+16000/69092	Loss: 113.762
+19200/69092	Loss: 114.692
+22400/69092	Loss: 110.497
+25600/69092	Loss: 114.033
+28800/69092	Loss: 111.619
+32000/69092	Loss: 115.025
+35200/69092	Loss: 112.577
+38400/69092	Loss: 113.097
+41600/69092	Loss: 113.049
+44800/69092	Loss: 113.574
+48000/69092	Loss: 112.155
+51200/69092	Loss: 110.795
+54400/69092	Loss: 113.053
+57600/69092	Loss: 112.099
+60800/69092	Loss: 110.614
+64000/69092	Loss: 111.320
+67200/69092	Loss: 112.674
+Training time 0:04:45.041794
+Epoch: 25 Average loss: 112.47
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 30)
+0/69092	Loss: 110.546
+3200/69092	Loss: 112.299
+6400/69092	Loss: 112.430
+9600/69092	Loss: 110.809
+12800/69092	Loss: 113.117
+16000/69092	Loss: 111.603
+19200/69092	Loss: 111.411
+22400/69092	Loss: 111.766
+25600/69092	Loss: 111.472
+28800/69092	Loss: 112.941
+32000/69092	Loss: 113.351
+35200/69092	Loss: 113.431
+38400/69092	Loss: 111.543
+41600/69092	Loss: 114.145
+44800/69092	Loss: 111.926
+48000/69092	Loss: 112.800
+51200/69092	Loss: 112.201
+54400/69092	Loss: 112.540
+57600/69092	Loss: 111.984
+60800/69092	Loss: 111.264
+64000/69092	Loss: 110.867
+67200/69092	Loss: 111.477
+Training time 0:04:43.517833
+Epoch: 26 Average loss: 112.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 31)
+0/69092	Loss: 116.476
+3200/69092	Loss: 111.689
+6400/69092	Loss: 111.937
+9600/69092	Loss: 112.337
+12800/69092	Loss: 112.217
+16000/69092	Loss: 114.211
+19200/69092	Loss: 111.085
+22400/69092	Loss: 110.283
+25600/69092	Loss: 111.542
+28800/69092	Loss: 112.261
+32000/69092	Loss: 111.289
+35200/69092	Loss: 111.992
+38400/69092	Loss: 110.785
+41600/69092	Loss: 112.940
+44800/69092	Loss: 112.285
+48000/69092	Loss: 114.579
+51200/69092	Loss: 110.708
+54400/69092	Loss: 112.470
+57600/69092	Loss: 113.620
+60800/69092	Loss: 110.872
+64000/69092	Loss: 111.970
+67200/69092	Loss: 112.708
+Training time 0:04:43.768186
+Epoch: 27 Average loss: 112.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 32)
+0/69092	Loss: 109.204
+3200/69092	Loss: 112.435
+6400/69092	Loss: 114.026
+9600/69092	Loss: 111.097
+12800/69092	Loss: 110.929
+16000/69092	Loss: 111.349
+19200/69092	Loss: 110.908
+22400/69092	Loss: 110.745
+25600/69092	Loss: 114.152
+28800/69092	Loss: 110.816
+32000/69092	Loss: 111.625
+35200/69092	Loss: 111.684
+38400/69092	Loss: 110.873
+41600/69092	Loss: 112.146
+44800/69092	Loss: 111.570
+48000/69092	Loss: 111.101
+51200/69092	Loss: 110.831
+54400/69092	Loss: 112.227
+57600/69092	Loss: 111.256
+60800/69092	Loss: 109.046
+64000/69092	Loss: 111.628
+67200/69092	Loss: 112.160
+Training time 0:04:41.229393
+Epoch: 28 Average loss: 111.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 33)
+0/69092	Loss: 104.209
+3200/69092	Loss: 112.197
+6400/69092	Loss: 110.449
+9600/69092	Loss: 110.083
+12800/69092	Loss: 109.298
+16000/69092	Loss: 112.898
+19200/69092	Loss: 112.408
+22400/69092	Loss: 112.143
+25600/69092	Loss: 111.547
+28800/69092	Loss: 110.064
+32000/69092	Loss: 110.612
+35200/69092	Loss: 111.170
+38400/69092	Loss: 112.284
+41600/69092	Loss: 111.592
+44800/69092	Loss: 110.504
+48000/69092	Loss: 112.900
+51200/69092	Loss: 111.123
+54400/69092	Loss: 113.507
+57600/69092	Loss: 111.303
+60800/69092	Loss: 113.287
+64000/69092	Loss: 110.679
+67200/69092	Loss: 115.029
+Training time 0:04:43.681844
+Epoch: 29 Average loss: 111.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 34)
+0/69092	Loss: 121.056
+3200/69092	Loss: 111.290
+6400/69092	Loss: 110.832
+9600/69092	Loss: 112.305
+12800/69092	Loss: 112.942
+16000/69092	Loss: 112.884
+19200/69092	Loss: 112.636
+22400/69092	Loss: 112.394
+25600/69092	Loss: 112.482
+28800/69092	Loss: 110.956
+32000/69092	Loss: 109.826
+35200/69092	Loss: 112.321
+38400/69092	Loss: 111.822
+41600/69092	Loss: 110.904
+44800/69092	Loss: 110.287
+48000/69092	Loss: 111.784
+51200/69092	Loss: 109.850
+54400/69092	Loss: 110.845
+57600/69092	Loss: 110.811
+60800/69092	Loss: 111.698
+64000/69092	Loss: 109.814
+67200/69092	Loss: 111.012
+Training time 0:04:43.069539
+Epoch: 30 Average loss: 111.35
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 35)
+0/69092	Loss: 108.077
+3200/69092	Loss: 111.317
+6400/69092	Loss: 110.567
+9600/69092	Loss: 110.230
+12800/69092	Loss: 111.407
+16000/69092	Loss: 111.366
+19200/69092	Loss: 111.396
+22400/69092	Loss: 111.792
+25600/69092	Loss: 112.813
+28800/69092	Loss: 113.976
+32000/69092	Loss: 109.683
+35200/69092	Loss: 109.564
+38400/69092	Loss: 109.532
+41600/69092	Loss: 111.487
+44800/69092	Loss: 110.475
+48000/69092	Loss: 110.605
+51200/69092	Loss: 110.499
+54400/69092	Loss: 111.540
+57600/69092	Loss: 112.921
+60800/69092	Loss: 110.256
+64000/69092	Loss: 111.300
+67200/69092	Loss: 111.619
+Training time 0:04:49.022954
+Epoch: 31 Average loss: 111.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 36)
+0/69092	Loss: 127.145
+3200/69092	Loss: 110.557
+6400/69092	Loss: 110.201
+9600/69092	Loss: 111.831
+12800/69092	Loss: 110.124
+16000/69092	Loss: 110.563
+19200/69092	Loss: 111.827
+22400/69092	Loss: 112.174
+25600/69092	Loss: 111.700
+28800/69092	Loss: 111.491
+32000/69092	Loss: 110.017
+35200/69092	Loss: 111.848
+38400/69092	Loss: 111.125
+41600/69092	Loss: 110.740
+44800/69092	Loss: 111.447
+48000/69092	Loss: 110.298
+51200/69092	Loss: 111.226
+54400/69092	Loss: 110.337
+57600/69092	Loss: 113.737
+60800/69092	Loss: 110.593
+64000/69092	Loss: 111.048
+67200/69092	Loss: 109.999
+Training time 0:04:34.240853
+Epoch: 32 Average loss: 111.13
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 37)
+0/69092	Loss: 107.446
+3200/69092	Loss: 111.796
+6400/69092	Loss: 110.517
+9600/69092	Loss: 109.203
+12800/69092	Loss: 112.325
+16000/69092	Loss: 111.188
+19200/69092	Loss: 109.881
+22400/69092	Loss: 111.257
+25600/69092	Loss: 111.082
+28800/69092	Loss: 109.237
+32000/69092	Loss: 111.067
+35200/69092	Loss: 111.960
+38400/69092	Loss: 110.869
+41600/69092	Loss: 110.585
+44800/69092	Loss: 111.741
+48000/69092	Loss: 110.282
+51200/69092	Loss: 111.195
+54400/69092	Loss: 111.672
+57600/69092	Loss: 112.134
+60800/69092	Loss: 111.049
+64000/69092	Loss: 110.333
+67200/69092	Loss: 111.257
+Training time 0:04:43.764296
+Epoch: 33 Average loss: 110.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 38)
+0/69092	Loss: 115.077
+3200/69092	Loss: 111.501
+6400/69092	Loss: 110.084
+9600/69092	Loss: 108.942
+12800/69092	Loss: 109.901
+16000/69092	Loss: 111.592
+19200/69092	Loss: 110.374
+22400/69092	Loss: 111.268
+25600/69092	Loss: 111.209
+28800/69092	Loss: 108.915
+32000/69092	Loss: 110.874
+35200/69092	Loss: 110.405
+38400/69092	Loss: 110.662
+41600/69092	Loss: 111.986
+44800/69092	Loss: 111.237
+48000/69092	Loss: 111.136
+51200/69092	Loss: 110.622
+54400/69092	Loss: 111.210
+57600/69092	Loss: 111.007
+60800/69092	Loss: 110.189
+64000/69092	Loss: 110.597
+67200/69092	Loss: 110.604
+Training time 0:04:34.058617
+Epoch: 34 Average loss: 110.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 39)
+0/69092	Loss: 114.591
+3200/69092	Loss: 111.557
+6400/69092	Loss: 112.004
+9600/69092	Loss: 110.475
+12800/69092	Loss: 110.930
+16000/69092	Loss: 110.553
+19200/69092	Loss: 111.408
+22400/69092	Loss: 110.119
+25600/69092	Loss: 109.701
+28800/69092	Loss: 110.129
+32000/69092	Loss: 110.820
+35200/69092	Loss: 111.306
+38400/69092	Loss: 112.009
+41600/69092	Loss: 110.124
+44800/69092	Loss: 110.473
+48000/69092	Loss: 110.975
+51200/69092	Loss: 109.217
+54400/69092	Loss: 109.901
+57600/69092	Loss: 109.916
+60800/69092	Loss: 110.830
+64000/69092	Loss: 109.267
+67200/69092	Loss: 109.708
+Training time 0:04:41.568257
+Epoch: 35 Average loss: 110.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 40)
+0/69092	Loss: 107.984
+3200/69092	Loss: 111.464
+6400/69092	Loss: 110.381
+9600/69092	Loss: 108.964
+12800/69092	Loss: 109.341
+16000/69092	Loss: 110.254
+19200/69092	Loss: 111.665
+22400/69092	Loss: 110.508
+25600/69092	Loss: 109.982
+28800/69092	Loss: 110.859
+32000/69092	Loss: 110.971
+35200/69092	Loss: 109.513
+38400/69092	Loss: 110.977
+41600/69092	Loss: 110.812
+44800/69092	Loss: 109.692
+48000/69092	Loss: 109.992
+51200/69092	Loss: 110.592
+54400/69092	Loss: 111.265
+57600/69092	Loss: 110.588
+60800/69092	Loss: 110.761
+64000/69092	Loss: 108.940
+67200/69092	Loss: 110.958
+Training time 0:04:40.277525
+Epoch: 36 Average loss: 110.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 41)
+0/69092	Loss: 114.775
+3200/69092	Loss: 108.106
+6400/69092	Loss: 111.209
+9600/69092	Loss: 109.911
+12800/69092	Loss: 109.625
+16000/69092	Loss: 110.445
+19200/69092	Loss: 110.535
+22400/69092	Loss: 111.299
+25600/69092	Loss: 110.666
+28800/69092	Loss: 110.410
+32000/69092	Loss: 109.227
+35200/69092	Loss: 111.651
+38400/69092	Loss: 109.915
+41600/69092	Loss: 110.830
+44800/69092	Loss: 109.736
+48000/69092	Loss: 110.278
+51200/69092	Loss: 110.908
+54400/69092	Loss: 110.394
+57600/69092	Loss: 110.202
+60800/69092	Loss: 110.999
+64000/69092	Loss: 110.175
+67200/69092	Loss: 108.871
+Training time 0:04:37.605560
+Epoch: 37 Average loss: 110.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 42)
+0/69092	Loss: 104.960
+3200/69092	Loss: 108.889
+6400/69092	Loss: 112.234
+9600/69092	Loss: 109.699
+12800/69092	Loss: 109.142
+16000/69092	Loss: 108.497
+19200/69092	Loss: 109.407
+22400/69092	Loss: 110.011
+25600/69092	Loss: 108.630
+28800/69092	Loss: 110.500
+32000/69092	Loss: 108.327
+35200/69092	Loss: 109.930
+38400/69092	Loss: 110.265
+41600/69092	Loss: 110.566
+44800/69092	Loss: 111.602
+48000/69092	Loss: 109.075
+51200/69092	Loss: 112.328
+54400/69092	Loss: 111.355
+57600/69092	Loss: 110.336
+60800/69092	Loss: 108.419
+64000/69092	Loss: 110.706
+67200/69092	Loss: 109.177
+Training time 0:04:42.874293
+Epoch: 38 Average loss: 110.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 43)
+0/69092	Loss: 101.099
+3200/69092	Loss: 108.840
+6400/69092	Loss: 111.816
+9600/69092	Loss: 108.919
+12800/69092	Loss: 111.016
+16000/69092	Loss: 108.929
+19200/69092	Loss: 109.597
+22400/69092	Loss: 110.688
+25600/69092	Loss: 108.692
+28800/69092	Loss: 112.614
+32000/69092	Loss: 109.458
+35200/69092	Loss: 111.066
+38400/69092	Loss: 109.306
+41600/69092	Loss: 110.359
+44800/69092	Loss: 111.017
+48000/69092	Loss: 108.748
+51200/69092	Loss: 109.679
+54400/69092	Loss: 110.205
+57600/69092	Loss: 110.115
+60800/69092	Loss: 109.525
+64000/69092	Loss: 109.737
+67200/69092	Loss: 110.562
+Training time 0:04:44.660730
+Epoch: 39 Average loss: 110.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 44)
+0/69092	Loss: 113.374
+3200/69092	Loss: 110.626
+6400/69092	Loss: 110.426
+9600/69092	Loss: 110.897
+12800/69092	Loss: 109.861
+16000/69092	Loss: 108.550
+19200/69092	Loss: 109.533
+22400/69092	Loss: 109.485
+25600/69092	Loss: 111.144
+28800/69092	Loss: 109.704
+32000/69092	Loss: 109.563
+35200/69092	Loss: 110.768
+38400/69092	Loss: 108.485
+41600/69092	Loss: 109.076
+44800/69092	Loss: 108.626
+48000/69092	Loss: 111.067
+51200/69092	Loss: 110.033
+54400/69092	Loss: 110.220
+57600/69092	Loss: 110.535
+60800/69092	Loss: 110.658
+64000/69092	Loss: 110.981
+67200/69092	Loss: 109.433
+Training time 0:04:42.924883
+Epoch: 40 Average loss: 110.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 45)
+0/69092	Loss: 108.721
+3200/69092	Loss: 111.064
+6400/69092	Loss: 109.979
+9600/69092	Loss: 109.450
+12800/69092	Loss: 110.368
+16000/69092	Loss: 109.995
+19200/69092	Loss: 110.070
+22400/69092	Loss: 109.947
+25600/69092	Loss: 110.112
+28800/69092	Loss: 109.921
+32000/69092	Loss: 107.978
+35200/69092	Loss: 109.070
+38400/69092	Loss: 109.520
+41600/69092	Loss: 108.034
+44800/69092	Loss: 109.744
+48000/69092	Loss: 109.552
+51200/69092	Loss: 109.638
+54400/69092	Loss: 109.456
+57600/69092	Loss: 109.720
+60800/69092	Loss: 111.156
+64000/69092	Loss: 108.989
+67200/69092	Loss: 111.889
+Training time 0:04:51.701796
+Epoch: 41 Average loss: 109.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 46)
+0/69092	Loss: 102.802
+3200/69092	Loss: 111.136
+6400/69092	Loss: 109.180
+9600/69092	Loss: 109.834
+12800/69092	Loss: 108.788
+16000/69092	Loss: 109.064
+19200/69092	Loss: 110.622
+22400/69092	Loss: 108.998
+25600/69092	Loss: 109.969
+28800/69092	Loss: 109.226
+32000/69092	Loss: 109.222
+35200/69092	Loss: 109.802
+38400/69092	Loss: 110.785
+41600/69092	Loss: 109.166
+44800/69092	Loss: 109.259
+48000/69092	Loss: 110.716
+51200/69092	Loss: 108.746
+54400/69092	Loss: 110.182
+57600/69092	Loss: 110.703
+60800/69092	Loss: 109.418
+64000/69092	Loss: 109.296
+67200/69092	Loss: 108.989
+Training time 0:04:43.843168
+Epoch: 42 Average loss: 109.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 47)
+0/69092	Loss: 126.465
+3200/69092	Loss: 109.168
+6400/69092	Loss: 108.719
+9600/69092	Loss: 109.867
+12800/69092	Loss: 108.087
+16000/69092	Loss: 109.965
+19200/69092	Loss: 110.314
+22400/69092	Loss: 108.599
+25600/69092	Loss: 109.118
+28800/69092	Loss: 112.310
+32000/69092	Loss: 110.493
+35200/69092	Loss: 110.416
+38400/69092	Loss: 109.973
+41600/69092	Loss: 111.803
+44800/69092	Loss: 108.155
+48000/69092	Loss: 109.102
+51200/69092	Loss: 108.966
+54400/69092	Loss: 108.444
+57600/69092	Loss: 109.125
+60800/69092	Loss: 108.695
+64000/69092	Loss: 110.524
+67200/69092	Loss: 110.741
+Training time 0:04:50.546091
+Epoch: 43 Average loss: 109.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 48)
+0/69092	Loss: 103.647
+3200/69092	Loss: 109.899
+6400/69092	Loss: 110.280
+9600/69092	Loss: 109.248
+12800/69092	Loss: 110.468
+16000/69092	Loss: 111.977
+19200/69092	Loss: 109.311
+22400/69092	Loss: 111.485
+25600/69092	Loss: 109.807
+28800/69092	Loss: 109.574
+32000/69092	Loss: 107.953
+35200/69092	Loss: 109.336
+38400/69092	Loss: 108.080
+41600/69092	Loss: 108.483
+44800/69092	Loss: 109.126
+48000/69092	Loss: 108.139
+51200/69092	Loss: 108.291
+54400/69092	Loss: 108.933
+57600/69092	Loss: 108.613
+60800/69092	Loss: 109.202
+64000/69092	Loss: 109.614
+67200/69092	Loss: 110.292
+Training time 0:04:49.733684
+Epoch: 44 Average loss: 109.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 49)
+0/69092	Loss: 109.167
+3200/69092	Loss: 109.525
+6400/69092	Loss: 108.399
+9600/69092	Loss: 108.729
+12800/69092	Loss: 108.766
+16000/69092	Loss: 110.111
+19200/69092	Loss: 109.067
+22400/69092	Loss: 109.113
+25600/69092	Loss: 110.619
+28800/69092	Loss: 110.376
+32000/69092	Loss: 109.246
+35200/69092	Loss: 108.491
+38400/69092	Loss: 110.233
+41600/69092	Loss: 108.926
+44800/69092	Loss: 108.867
+48000/69092	Loss: 109.026
+51200/69092	Loss: 110.182
+54400/69092	Loss: 109.406
+57600/69092	Loss: 110.103
+60800/69092	Loss: 109.449
+64000/69092	Loss: 107.732
+67200/69092	Loss: 110.404
+Training time 0:04:53.380546
+Epoch: 45 Average loss: 109.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 50)
+0/69092	Loss: 102.441
+3200/69092	Loss: 109.258
+6400/69092	Loss: 109.261
+9600/69092	Loss: 109.454
+12800/69092	Loss: 109.680
+16000/69092	Loss: 110.244
+19200/69092	Loss: 109.354
+22400/69092	Loss: 108.090
+25600/69092	Loss: 108.010
+28800/69092	Loss: 109.430
+32000/69092	Loss: 111.084
+35200/69092	Loss: 108.300
+38400/69092	Loss: 107.124
+41600/69092	Loss: 106.001
+44800/69092	Loss: 109.506
+48000/69092	Loss: 111.813
+51200/69092	Loss: 109.929
+54400/69092	Loss: 108.852
+57600/69092	Loss: 107.228
+60800/69092	Loss: 110.214
+64000/69092	Loss: 108.539
+67200/69092	Loss: 108.468
+Training time 0:04:42.198571
+Epoch: 46 Average loss: 109.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 51)
+0/69092	Loss: 110.935
+3200/69092	Loss: 109.027
+6400/69092	Loss: 108.949
+9600/69092	Loss: 108.656
+12800/69092	Loss: 108.127
+16000/69092	Loss: 109.599
+19200/69092	Loss: 107.615
+22400/69092	Loss: 111.287
+25600/69092	Loss: 110.431
+28800/69092	Loss: 109.253
+32000/69092	Loss: 110.841
+35200/69092	Loss: 108.992
+38400/69092	Loss: 108.798
+41600/69092	Loss: 110.392
+44800/69092	Loss: 107.892
+48000/69092	Loss: 108.384
+51200/69092	Loss: 109.177
+54400/69092	Loss: 109.331
+57600/69092	Loss: 107.822
+60800/69092	Loss: 106.257
+64000/69092	Loss: 108.215
+67200/69092	Loss: 110.373
+Training time 0:04:49.803242
+Epoch: 47 Average loss: 108.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 52)
+0/69092	Loss: 107.273
+3200/69092	Loss: 108.980
+6400/69092	Loss: 109.240
+9600/69092	Loss: 107.524
+12800/69092	Loss: 108.102
+16000/69092	Loss: 109.265
+19200/69092	Loss: 110.584
+22400/69092	Loss: 109.721
+25600/69092	Loss: 107.613
+28800/69092	Loss: 108.611
+32000/69092	Loss: 109.184
+35200/69092	Loss: 108.064
+38400/69092	Loss: 108.106
+41600/69092	Loss: 108.937
+44800/69092	Loss: 107.993
+48000/69092	Loss: 108.745
+51200/69092	Loss: 110.025
+54400/69092	Loss: 108.768
+57600/69092	Loss: 108.040
+60800/69092	Loss: 108.474
+64000/69092	Loss: 108.278
+67200/69092	Loss: 108.626
+Training time 0:04:50.519463
+Epoch: 48 Average loss: 108.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 53)
+0/69092	Loss: 102.906
+3200/69092	Loss: 108.175
+6400/69092	Loss: 110.078
+9600/69092	Loss: 108.896
+12800/69092	Loss: 108.041
+16000/69092	Loss: 108.613
+19200/69092	Loss: 109.001
+22400/69092	Loss: 108.239
+25600/69092	Loss: 108.157
+28800/69092	Loss: 109.316
+32000/69092	Loss: 108.273
+35200/69092	Loss: 108.219
+38400/69092	Loss: 107.372
+41600/69092	Loss: 107.124
+44800/69092	Loss: 108.016
+48000/69092	Loss: 110.386
+51200/69092	Loss: 108.787
+54400/69092	Loss: 108.155
+57600/69092	Loss: 108.254
+60800/69092	Loss: 108.866
+64000/69092	Loss: 109.982
+67200/69092	Loss: 109.006
+Training time 0:04:49.696891
+Epoch: 49 Average loss: 108.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 54)
+0/69092	Loss: 104.153
+3200/69092	Loss: 108.018
+6400/69092	Loss: 108.156
+9600/69092	Loss: 108.956
+12800/69092	Loss: 110.080
+16000/69092	Loss: 109.224
+19200/69092	Loss: 109.280
+22400/69092	Loss: 107.590
+25600/69092	Loss: 108.249
+28800/69092	Loss: 106.179
+32000/69092	Loss: 108.142
+35200/69092	Loss: 108.132
+38400/69092	Loss: 108.733
+41600/69092	Loss: 109.026
+44800/69092	Loss: 107.957
+48000/69092	Loss: 106.913
+51200/69092	Loss: 108.879
+54400/69092	Loss: 108.485
+57600/69092	Loss: 108.608
+60800/69092	Loss: 109.192
+64000/69092	Loss: 107.740
+67200/69092	Loss: 109.164
+Training time 0:04:42.383065
+Epoch: 50 Average loss: 108.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 55)
+0/69092	Loss: 103.210
+3200/69092	Loss: 107.998
+6400/69092	Loss: 110.100
+9600/69092	Loss: 108.056
+12800/69092	Loss: 106.873
+16000/69092	Loss: 106.977
+19200/69092	Loss: 108.452
+22400/69092	Loss: 108.645
+25600/69092	Loss: 108.029
+28800/69092	Loss: 108.975
+32000/69092	Loss: 109.396
+35200/69092	Loss: 107.864
+38400/69092	Loss: 108.605
+41600/69092	Loss: 108.646
+44800/69092	Loss: 108.115
+48000/69092	Loss: 106.863
+51200/69092	Loss: 108.656
+54400/69092	Loss: 107.829
+57600/69092	Loss: 108.720
+60800/69092	Loss: 107.200
+64000/69092	Loss: 107.129
+67200/69092	Loss: 108.928
+Training time 0:04:53.753077
+Epoch: 51 Average loss: 108.24
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 56)
+0/69092	Loss: 99.226
+3200/69092	Loss: 109.379
+6400/69092	Loss: 107.912
+9600/69092	Loss: 108.517
+12800/69092	Loss: 108.567
+16000/69092	Loss: 107.621
+19200/69092	Loss: 108.156
+22400/69092	Loss: 108.049
+25600/69092	Loss: 106.849
+28800/69092	Loss: 107.496
+32000/69092	Loss: 107.815
+35200/69092	Loss: 110.221
+38400/69092	Loss: 107.563
+41600/69092	Loss: 107.106
+44800/69092	Loss: 107.033
+48000/69092	Loss: 109.990
+51200/69092	Loss: 109.967
+54400/69092	Loss: 108.879
+57600/69092	Loss: 107.200
+60800/69092	Loss: 108.082
+64000/69092	Loss: 108.172
+67200/69092	Loss: 108.027
+Training time 0:04:49.744157
+Epoch: 52 Average loss: 108.25
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 57)
+0/69092	Loss: 104.733
+3200/69092	Loss: 107.449
+6400/69092	Loss: 107.652
+9600/69092	Loss: 108.413
+12800/69092	Loss: 107.699
+16000/69092	Loss: 108.465
+19200/69092	Loss: 106.801
+22400/69092	Loss: 106.146
+25600/69092	Loss: 109.091
+28800/69092	Loss: 107.616
+32000/69092	Loss: 109.353
+35200/69092	Loss: 107.828
+38400/69092	Loss: 107.516
+41600/69092	Loss: 108.168
+44800/69092	Loss: 106.649
+48000/69092	Loss: 109.267
+51200/69092	Loss: 109.144
+54400/69092	Loss: 107.902
+57600/69092	Loss: 108.065
+60800/69092	Loss: 109.866
+64000/69092	Loss: 109.355
+67200/69092	Loss: 106.590
+Training time 0:04:56.545482
+Epoch: 53 Average loss: 108.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 58)
+0/69092	Loss: 103.578
+3200/69092	Loss: 107.679
+6400/69092	Loss: 107.674
+9600/69092	Loss: 108.424
+12800/69092	Loss: 108.361
+16000/69092	Loss: 106.618
+19200/69092	Loss: 109.062
+22400/69092	Loss: 107.414
+25600/69092	Loss: 110.398
+28800/69092	Loss: 106.737
+32000/69092	Loss: 106.625
+35200/69092	Loss: 106.643
+38400/69092	Loss: 108.122
+41600/69092	Loss: 107.716
+44800/69092	Loss: 108.184
+48000/69092	Loss: 109.113
+51200/69092	Loss: 107.916
+54400/69092	Loss: 107.116
+57600/69092	Loss: 105.350
+60800/69092	Loss: 109.847
+64000/69092	Loss: 107.041
+67200/69092	Loss: 107.282
+Training time 0:04:49.920582
+Epoch: 54 Average loss: 107.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 59)
+0/69092	Loss: 109.314
+3200/69092	Loss: 106.938
+6400/69092	Loss: 106.937
+9600/69092	Loss: 108.338
+12800/69092	Loss: 106.591
+16000/69092	Loss: 106.293
+19200/69092	Loss: 107.046
+22400/69092	Loss: 108.085
+25600/69092	Loss: 109.090
+28800/69092	Loss: 106.335
+32000/69092	Loss: 109.177
+35200/69092	Loss: 108.154
+38400/69092	Loss: 108.860
+41600/69092	Loss: 107.963
+44800/69092	Loss: 106.935
+48000/69092	Loss: 108.159
+51200/69092	Loss: 107.759
+54400/69092	Loss: 107.512
+57600/69092	Loss: 107.193
+60800/69092	Loss: 107.152
+64000/69092	Loss: 107.224
+67200/69092	Loss: 108.543
+Training time 0:04:51.018882
+Epoch: 55 Average loss: 107.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 60)
+0/69092	Loss: 91.358
+3200/69092	Loss: 106.304
+6400/69092	Loss: 108.153
+9600/69092	Loss: 109.371
+12800/69092	Loss: 107.301
+16000/69092	Loss: 108.816
+19200/69092	Loss: 107.875
+22400/69092	Loss: 107.227
+25600/69092	Loss: 108.243
+28800/69092	Loss: 108.738
+32000/69092	Loss: 107.627
+35200/69092	Loss: 107.309
+38400/69092	Loss: 108.692
+41600/69092	Loss: 108.226
+44800/69092	Loss: 106.392
+48000/69092	Loss: 107.722
+51200/69092	Loss: 106.610
+54400/69092	Loss: 108.167
+57600/69092	Loss: 108.127
+60800/69092	Loss: 107.082
+64000/69092	Loss: 107.069
+67200/69092	Loss: 106.531
+Training time 0:04:43.520874
+Epoch: 56 Average loss: 107.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 61)
+0/69092	Loss: 110.477
+3200/69092	Loss: 108.627
+6400/69092	Loss: 107.287
+9600/69092	Loss: 105.823
+12800/69092	Loss: 109.205
+16000/69092	Loss: 106.500
+19200/69092	Loss: 106.957
+22400/69092	Loss: 108.999
+25600/69092	Loss: 106.936
+28800/69092	Loss: 106.688
+32000/69092	Loss: 108.377
+35200/69092	Loss: 106.603
+38400/69092	Loss: 106.404
+41600/69092	Loss: 106.618
+44800/69092	Loss: 108.963
+48000/69092	Loss: 105.955
+51200/69092	Loss: 108.686
+54400/69092	Loss: 107.060
+57600/69092	Loss: 108.829
+60800/69092	Loss: 109.190
+64000/69092	Loss: 109.119
+67200/69092	Loss: 108.589
+Training time 0:04:41.173054
+Epoch: 57 Average loss: 107.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 62)
+0/69092	Loss: 112.057
+3200/69092	Loss: 108.878
+6400/69092	Loss: 107.226
+9600/69092	Loss: 108.297
+12800/69092	Loss: 108.657
+16000/69092	Loss: 106.209
+19200/69092	Loss: 105.882
+22400/69092	Loss: 106.071
+25600/69092	Loss: 108.803
+28800/69092	Loss: 107.810
+32000/69092	Loss: 105.625
+35200/69092	Loss: 106.806
+38400/69092	Loss: 108.175
+41600/69092	Loss: 107.626
+44800/69092	Loss: 107.604
+48000/69092	Loss: 107.837
+51200/69092	Loss: 106.685
+54400/69092	Loss: 110.453
+57600/69092	Loss: 108.153
+60800/69092	Loss: 107.209
+64000/69092	Loss: 107.714
+67200/69092	Loss: 106.547
+Training time 0:04:43.993388
+Epoch: 58 Average loss: 107.55
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 63)
+0/69092	Loss: 123.563
+3200/69092	Loss: 106.403
+6400/69092	Loss: 107.181
+9600/69092	Loss: 108.489
+12800/69092	Loss: 107.953
+16000/69092	Loss: 110.204
+19200/69092	Loss: 109.196
+22400/69092	Loss: 106.157
+25600/69092	Loss: 107.468
+28800/69092	Loss: 105.458
+32000/69092	Loss: 107.226
+35200/69092	Loss: 106.051
+38400/69092	Loss: 107.864
+41600/69092	Loss: 109.366
+44800/69092	Loss: 108.387
+48000/69092	Loss: 107.860
+51200/69092	Loss: 106.375
+54400/69092	Loss: 108.123
+57600/69092	Loss: 106.196
+60800/69092	Loss: 106.480
+64000/69092	Loss: 106.830
+67200/69092	Loss: 106.591
+Training time 0:04:37.546204
+Epoch: 59 Average loss: 107.51
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 64)
+0/69092	Loss: 95.213
+3200/69092	Loss: 106.027
+6400/69092	Loss: 107.618
+9600/69092	Loss: 105.784
+12800/69092	Loss: 107.630
+16000/69092	Loss: 106.266
+19200/69092	Loss: 106.681
+22400/69092	Loss: 108.355
+25600/69092	Loss: 108.582
+28800/69092	Loss: 107.247
+32000/69092	Loss: 106.418
+35200/69092	Loss: 107.866
+38400/69092	Loss: 107.643
+41600/69092	Loss: 107.719
+44800/69092	Loss: 106.770
+48000/69092	Loss: 108.965
+51200/69092	Loss: 107.024
+54400/69092	Loss: 107.825
+57600/69092	Loss: 107.277
+60800/69092	Loss: 106.770
+64000/69092	Loss: 106.849
+67200/69092	Loss: 107.868
+Training time 0:04:43.409421
+Epoch: 60 Average loss: 107.24
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 65)
+0/69092	Loss: 99.140
+3200/69092	Loss: 108.373
+6400/69092	Loss: 108.361
+9600/69092	Loss: 106.322
+12800/69092	Loss: 106.600
+16000/69092	Loss: 105.737
+19200/69092	Loss: 107.075
+22400/69092	Loss: 107.897
+25600/69092	Loss: 106.637
+28800/69092	Loss: 109.254
+32000/69092	Loss: 108.755
+35200/69092	Loss: 107.586
+38400/69092	Loss: 106.973
+41600/69092	Loss: 106.574
+44800/69092	Loss: 107.630
+48000/69092	Loss: 107.069
+51200/69092	Loss: 106.450
+54400/69092	Loss: 106.582
+57600/69092	Loss: 107.387
+60800/69092	Loss: 107.566
+64000/69092	Loss: 107.241
+67200/69092	Loss: 107.083
+Training time 0:04:45.168993
+Epoch: 61 Average loss: 107.29
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 66)
+0/69092	Loss: 108.632
+3200/69092	Loss: 108.459
+6400/69092	Loss: 106.302
+9600/69092	Loss: 107.831
+12800/69092	Loss: 105.810
+16000/69092	Loss: 105.939
+19200/69092	Loss: 109.040
+22400/69092	Loss: 106.610
+25600/69092	Loss: 108.444
+28800/69092	Loss: 107.125
+32000/69092	Loss: 105.910
+35200/69092	Loss: 106.708
+38400/69092	Loss: 105.734
+41600/69092	Loss: 107.290
+44800/69092	Loss: 107.097
+48000/69092	Loss: 107.371
+51200/69092	Loss: 107.602
+54400/69092	Loss: 108.115
+57600/69092	Loss: 107.538
+60800/69092	Loss: 106.859
+64000/69092	Loss: 105.613
+67200/69092	Loss: 107.768
+Training time 0:04:46.909384
+Epoch: 62 Average loss: 107.12
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 67)
+0/69092	Loss: 96.787
+3200/69092	Loss: 106.211
+6400/69092	Loss: 106.251
+9600/69092	Loss: 106.681
+12800/69092	Loss: 106.072
+16000/69092	Loss: 106.618
+19200/69092	Loss: 107.220
+22400/69092	Loss: 108.668
+25600/69092	Loss: 106.418
+28800/69092	Loss: 108.128
+32000/69092	Loss: 106.958
+35200/69092	Loss: 107.550
+38400/69092	Loss: 105.766
+41600/69092	Loss: 108.187
+44800/69092	Loss: 109.119
+48000/69092	Loss: 106.322
+51200/69092	Loss: 108.572
+54400/69092	Loss: 107.911
+57600/69092	Loss: 106.371
+60800/69092	Loss: 106.815
+64000/69092	Loss: 105.432
+67200/69092	Loss: 107.124
+Training time 0:04:45.988190
+Epoch: 63 Average loss: 107.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 68)
+0/69092	Loss: 101.009
+3200/69092	Loss: 107.848
+6400/69092	Loss: 107.981
+9600/69092	Loss: 105.931
+12800/69092	Loss: 105.062
+16000/69092	Loss: 107.260
+19200/69092	Loss: 106.238
+22400/69092	Loss: 106.388
+25600/69092	Loss: 107.564
+28800/69092	Loss: 106.940
+32000/69092	Loss: 107.675
+35200/69092	Loss: 107.333
+38400/69092	Loss: 106.242
+41600/69092	Loss: 106.396
+44800/69092	Loss: 107.305
+48000/69092	Loss: 107.249
+51200/69092	Loss: 107.218
+54400/69092	Loss: 108.159
+57600/69092	Loss: 107.802
+60800/69092	Loss: 105.884
+64000/69092	Loss: 107.416
+67200/69092	Loss: 107.177
+Training time 0:04:50.115311
+Epoch: 64 Average loss: 107.01
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 69)
+0/69092	Loss: 99.700
+3200/69092	Loss: 105.408
+6400/69092	Loss: 107.577
+9600/69092	Loss: 107.187
+12800/69092	Loss: 106.684
+16000/69092	Loss: 107.859
+19200/69092	Loss: 106.757
+22400/69092	Loss: 107.083
+25600/69092	Loss: 107.706
+28800/69092	Loss: 107.304
+32000/69092	Loss: 106.535
+35200/69092	Loss: 107.133
+38400/69092	Loss: 108.241
+41600/69092	Loss: 108.464
+44800/69092	Loss: 107.018
+48000/69092	Loss: 106.956
+51200/69092	Loss: 107.139
+54400/69092	Loss: 105.645
+57600/69092	Loss: 106.116
+60800/69092	Loss: 107.512
+64000/69092	Loss: 105.906
+67200/69092	Loss: 106.503
+Training time 0:04:50.923955
+Epoch: 65 Average loss: 106.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 70)
+0/69092	Loss: 113.871
+3200/69092	Loss: 106.510
+6400/69092	Loss: 104.945
+9600/69092	Loss: 106.325
+12800/69092	Loss: 104.192
+16000/69092	Loss: 108.807
+19200/69092	Loss: 107.005
+22400/69092	Loss: 106.568
+25600/69092	Loss: 109.132
+28800/69092	Loss: 106.207
+32000/69092	Loss: 106.092
+35200/69092	Loss: 107.904
+38400/69092	Loss: 107.723
+41600/69092	Loss: 107.919
+44800/69092	Loss: 106.389
+48000/69092	Loss: 106.156
+51200/69092	Loss: 106.327
+54400/69092	Loss: 108.188
+57600/69092	Loss: 106.585
+60800/69092	Loss: 106.378
+64000/69092	Loss: 106.329
+67200/69092	Loss: 108.926
+Training time 0:04:50.597860
+Epoch: 66 Average loss: 106.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 71)
+0/69092	Loss: 106.674
+3200/69092	Loss: 105.739
+6400/69092	Loss: 105.293
+9600/69092	Loss: 106.417
+12800/69092	Loss: 106.795
+16000/69092	Loss: 106.054
+19200/69092	Loss: 104.626
+22400/69092	Loss: 108.555
+25600/69092	Loss: 107.928
+28800/69092	Loss: 106.134
+32000/69092	Loss: 106.989
+35200/69092	Loss: 106.796
+38400/69092	Loss: 106.665
+41600/69092	Loss: 105.272
+44800/69092	Loss: 108.432
+48000/69092	Loss: 106.274
+51200/69092	Loss: 106.274
+54400/69092	Loss: 105.491
+57600/69092	Loss: 107.484
+60800/69092	Loss: 106.835
+64000/69092	Loss: 106.077
+67200/69092	Loss: 108.613
+Training time 0:04:50.856114
+Epoch: 67 Average loss: 106.60
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 72)
+0/69092	Loss: 107.316
+3200/69092	Loss: 107.972
+6400/69092	Loss: 108.050
+9600/69092	Loss: 106.102
+12800/69092	Loss: 106.441
+16000/69092	Loss: 108.152
+19200/69092	Loss: 107.119
+22400/69092	Loss: 106.215
+25600/69092	Loss: 107.264
+28800/69092	Loss: 105.527
+32000/69092	Loss: 107.794
+35200/69092	Loss: 105.042
+38400/69092	Loss: 107.403
+41600/69092	Loss: 105.311
+44800/69092	Loss: 106.848
+48000/69092	Loss: 106.986
+51200/69092	Loss: 107.933
+54400/69092	Loss: 105.659
+57600/69092	Loss: 107.058
+60800/69092	Loss: 107.427
+64000/69092	Loss: 106.436
+67200/69092	Loss: 105.850
+Training time 0:04:42.894581
+Epoch: 68 Average loss: 106.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 73)
+0/69092	Loss: 103.258
+3200/69092	Loss: 105.467
+6400/69092	Loss: 105.508
+9600/69092	Loss: 107.575
+12800/69092	Loss: 107.472
+16000/69092	Loss: 105.982
+19200/69092	Loss: 105.868
+22400/69092	Loss: 106.597
+25600/69092	Loss: 106.147
+28800/69092	Loss: 106.502
+32000/69092	Loss: 106.497
+35200/69092	Loss: 106.906
+38400/69092	Loss: 107.811
+41600/69092	Loss: 107.281
+44800/69092	Loss: 106.072
+48000/69092	Loss: 106.723
+51200/69092	Loss: 106.482
+54400/69092	Loss: 106.787
+57600/69092	Loss: 107.622
+60800/69092	Loss: 105.791
+64000/69092	Loss: 107.319
+67200/69092	Loss: 106.772
+Training time 0:04:54.501528
+Epoch: 69 Average loss: 106.62
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 74)
+0/69092	Loss: 107.553
+3200/69092	Loss: 106.098
+6400/69092	Loss: 107.191
+9600/69092	Loss: 106.159
+12800/69092	Loss: 104.771
+16000/69092	Loss: 109.163
+19200/69092	Loss: 107.074
+22400/69092	Loss: 105.869
+25600/69092	Loss: 106.491
+28800/69092	Loss: 106.822
+32000/69092	Loss: 107.059
+35200/69092	Loss: 105.834
+38400/69092	Loss: 104.738
+41600/69092	Loss: 106.728
+44800/69092	Loss: 107.754
+48000/69092	Loss: 106.263
+51200/69092	Loss: 105.967
+54400/69092	Loss: 105.614
+57600/69092	Loss: 108.203
+60800/69092	Loss: 106.293
+64000/69092	Loss: 105.167
+67200/69092	Loss: 106.777
+Training time 0:04:52.868330
+Epoch: 70 Average loss: 106.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 75)
+0/69092	Loss: 98.173
+3200/69092	Loss: 107.283
+6400/69092	Loss: 106.688
+9600/69092	Loss: 106.175
+12800/69092	Loss: 106.923
+16000/69092	Loss: 105.406
+19200/69092	Loss: 105.920
+22400/69092	Loss: 107.011
+25600/69092	Loss: 107.188
+28800/69092	Loss: 106.831
+32000/69092	Loss: 105.050
+35200/69092	Loss: 107.188
+38400/69092	Loss: 105.814
+41600/69092	Loss: 107.216
+44800/69092	Loss: 106.493
+48000/69092	Loss: 105.505
+51200/69092	Loss: 105.077
+54400/69092	Loss: 105.549
+57600/69092	Loss: 107.144
+60800/69092	Loss: 106.992
+64000/69092	Loss: 105.153
+67200/69092	Loss: 105.536
+Training time 0:04:55.646175
+Epoch: 71 Average loss: 106.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 76)
+0/69092	Loss: 104.828
+3200/69092	Loss: 106.681
+6400/69092	Loss: 104.585
+9600/69092	Loss: 104.697
+12800/69092	Loss: 107.388
+16000/69092	Loss: 106.748
+19200/69092	Loss: 106.904
+22400/69092	Loss: 105.430
+25600/69092	Loss: 107.294
+28800/69092	Loss: 106.562
+32000/69092	Loss: 107.229
+35200/69092	Loss: 104.755
+38400/69092	Loss: 106.461
+41600/69092	Loss: 105.027
+44800/69092	Loss: 107.103
+48000/69092	Loss: 107.121
+51200/69092	Loss: 107.508
+54400/69092	Loss: 106.154
+57600/69092	Loss: 107.660
+60800/69092	Loss: 105.422
+64000/69092	Loss: 105.391
+67200/69092	Loss: 107.187
+Training time 0:04:52.971490
+Epoch: 72 Average loss: 106.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 77)
+0/69092	Loss: 102.193
+3200/69092	Loss: 105.875
+6400/69092	Loss: 107.181
+9600/69092	Loss: 105.227
+12800/69092	Loss: 107.922
+16000/69092	Loss: 105.979
+19200/69092	Loss: 106.287
+22400/69092	Loss: 104.530
+25600/69092	Loss: 104.866
+28800/69092	Loss: 106.620
+32000/69092	Loss: 105.745
+35200/69092	Loss: 106.743
+38400/69092	Loss: 106.674
+41600/69092	Loss: 106.348
+44800/69092	Loss: 106.103
+48000/69092	Loss: 106.401
+51200/69092	Loss: 106.476
+54400/69092	Loss: 107.128
+57600/69092	Loss: 104.708
+60800/69092	Loss: 106.286
+64000/69092	Loss: 107.405
+67200/69092	Loss: 106.975
+Training time 0:04:52.079726
+Epoch: 73 Average loss: 106.29
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 78)
+0/69092	Loss: 115.106
+3200/69092	Loss: 106.613
+6400/69092	Loss: 105.516
+9600/69092	Loss: 106.279
+12800/69092	Loss: 107.035
+16000/69092	Loss: 106.901
+19200/69092	Loss: 106.259
+22400/69092	Loss: 106.482
+25600/69092	Loss: 106.685
+28800/69092	Loss: 105.713
+32000/69092	Loss: 106.677
+35200/69092	Loss: 105.898
+38400/69092	Loss: 105.959
+41600/69092	Loss: 106.056
+44800/69092	Loss: 104.058
+48000/69092	Loss: 107.636
+51200/69092	Loss: 106.408
+54400/69092	Loss: 105.510
+57600/69092	Loss: 107.126
+60800/69092	Loss: 102.794
+64000/69092	Loss: 105.883
+67200/69092	Loss: 106.210
+Training time 0:04:48.463217
+Epoch: 74 Average loss: 106.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 79)
+0/69092	Loss: 108.485
+3200/69092	Loss: 105.781
+6400/69092	Loss: 106.366
+9600/69092	Loss: 106.327
+12800/69092	Loss: 106.386
+16000/69092	Loss: 105.807
+19200/69092	Loss: 107.269
+22400/69092	Loss: 107.495
+25600/69092	Loss: 106.904
+28800/69092	Loss: 106.073
+32000/69092	Loss: 107.340
+35200/69092	Loss: 104.801
+38400/69092	Loss: 105.712
+41600/69092	Loss: 104.574
+44800/69092	Loss: 106.045
+48000/69092	Loss: 106.166
+51200/69092	Loss: 105.577
+54400/69092	Loss: 106.264
+57600/69092	Loss: 104.153
+60800/69092	Loss: 107.094
+64000/69092	Loss: 105.029
+67200/69092	Loss: 106.907
+Training time 0:04:46.581238
+Epoch: 75 Average loss: 106.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 80)
+0/69092	Loss: 133.000
+3200/69092	Loss: 106.878
+6400/69092	Loss: 107.797
+9600/69092	Loss: 106.549
+12800/69092	Loss: 106.853
+16000/69092	Loss: 105.499
+19200/69092	Loss: 105.092
+22400/69092	Loss: 106.505
+25600/69092	Loss: 106.679
+28800/69092	Loss: 102.969
+32000/69092	Loss: 105.854
+35200/69092	Loss: 106.818
+38400/69092	Loss: 106.764
+41600/69092	Loss: 105.524
+44800/69092	Loss: 107.438
+48000/69092	Loss: 105.218
+51200/69092	Loss: 104.353
+54400/69092	Loss: 106.234
+57600/69092	Loss: 105.975
+60800/69092	Loss: 106.486
+64000/69092	Loss: 105.349
+67200/69092	Loss: 106.522
+Training time 0:04:52.247822
+Epoch: 76 Average loss: 106.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 81)
+0/69092	Loss: 113.320
+3200/69092	Loss: 106.065
+6400/69092	Loss: 105.636
+9600/69092	Loss: 105.594
+12800/69092	Loss: 104.921
+16000/69092	Loss: 105.310
+19200/69092	Loss: 106.557
+22400/69092	Loss: 106.530
+25600/69092	Loss: 106.007
+28800/69092	Loss: 104.959
+32000/69092	Loss: 105.296
+35200/69092	Loss: 105.649
+38400/69092	Loss: 105.838
+41600/69092	Loss: 104.787
+44800/69092	Loss: 105.916
+48000/69092	Loss: 104.483
+51200/69092	Loss: 108.540
+54400/69092	Loss: 106.935
+57600/69092	Loss: 107.094
+60800/69092	Loss: 105.542
+64000/69092	Loss: 107.094
+67200/69092	Loss: 106.696
+Training time 0:04:50.465072
+Epoch: 77 Average loss: 106.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 82)
+0/69092	Loss: 105.775
+3200/69092	Loss: 104.143
+6400/69092	Loss: 106.376
+9600/69092	Loss: 105.853
+12800/69092	Loss: 107.442
+16000/69092	Loss: 105.974
+19200/69092	Loss: 106.224
+22400/69092	Loss: 106.614
+25600/69092	Loss: 104.898
+28800/69092	Loss: 105.351
+32000/69092	Loss: 106.364
+35200/69092	Loss: 106.209
+38400/69092	Loss: 107.988
+41600/69092	Loss: 106.163
+44800/69092	Loss: 105.920
+48000/69092	Loss: 104.891
+51200/69092	Loss: 105.154
+54400/69092	Loss: 105.039
+57600/69092	Loss: 107.874
+60800/69092	Loss: 106.659
+64000/69092	Loss: 104.838
+67200/69092	Loss: 106.004
+Training time 0:04:47.906519
+Epoch: 78 Average loss: 106.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 83)
+0/69092	Loss: 104.938
+3200/69092	Loss: 105.438
+6400/69092	Loss: 105.059
+9600/69092	Loss: 106.616
+12800/69092	Loss: 105.004
+16000/69092	Loss: 107.055
+19200/69092	Loss: 106.784
+22400/69092	Loss: 105.539
+25600/69092	Loss: 105.332
+28800/69092	Loss: 105.271
+32000/69092	Loss: 105.947
+35200/69092	Loss: 106.141
+38400/69092	Loss: 106.491
+41600/69092	Loss: 104.884
+44800/69092	Loss: 106.111
+48000/69092	Loss: 105.816
+51200/69092	Loss: 104.976
+54400/69092	Loss: 106.576
+57600/69092	Loss: 105.387
+60800/69092	Loss: 106.023
+64000/69092	Loss: 106.223
+67200/69092	Loss: 105.289
+Training time 0:04:49.954373
+Epoch: 79 Average loss: 105.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 84)
+0/69092	Loss: 111.414
+3200/69092	Loss: 104.501
+6400/69092	Loss: 105.996
+9600/69092	Loss: 105.728
+12800/69092	Loss: 105.972
+16000/69092	Loss: 103.711
+19200/69092	Loss: 105.339
+22400/69092	Loss: 105.824
+25600/69092	Loss: 106.456
+28800/69092	Loss: 105.027
+32000/69092	Loss: 105.114
+35200/69092	Loss: 105.825
+38400/69092	Loss: 106.253
+41600/69092	Loss: 105.802
+44800/69092	Loss: 105.359
+48000/69092	Loss: 105.120
+51200/69092	Loss: 105.310
+54400/69092	Loss: 106.832
+57600/69092	Loss: 107.461
+60800/69092	Loss: 106.991
+64000/69092	Loss: 108.579
+67200/69092	Loss: 105.452
+Training time 0:04:52.101022
+Epoch: 80 Average loss: 105.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_20/checkpoints/last' (iter 85)
+0/69092	Loss: 118.695
+3200/69092	Loss: 104.423
+6400/69092	Loss: 104.555
diff --git a/OAR.2068294.stderr b/OAR.2068294.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..a9c628a29c58ebc65df754dd5dfbbd99e2282b5f
--- /dev/null
+++ b/OAR.2068294.stderr
@@ -0,0 +1,2 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
diff --git a/OAR.2068294.stdout b/OAR.2068294.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..b48bdf14b49a86f47a140ff1124f9c5976cdeb98
--- /dev/null
+++ b/OAR.2068294.stdout
@@ -0,0 +1,2052 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64_ls_10_lr_5e_4', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0005, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64_ls_10_lr_5e_4
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last (iter 1)'
+0/69092	Loss: 159.842
+3200/69092	Loss: 169.274
+6400/69092	Loss: 166.739
+9600/69092	Loss: 164.403
+12800/69092	Loss: 163.047
+16000/69092	Loss: 158.285
+19200/69092	Loss: 160.616
+22400/69092	Loss: 155.277
+25600/69092	Loss: 154.275
+28800/69092	Loss: 151.988
+32000/69092	Loss: 149.324
+35200/69092	Loss: 150.873
+38400/69092	Loss: 151.049
+41600/69092	Loss: 150.226
+44800/69092	Loss: 150.034
+48000/69092	Loss: 148.424
+51200/69092	Loss: 145.290
+54400/69092	Loss: 151.523
+57600/69092	Loss: 147.486
+60800/69092	Loss: 146.221
+64000/69092	Loss: 149.535
+67200/69092	Loss: 146.699
+Training time 0:04:50.557788
+Epoch: 1 Average loss: 153.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 2)
+0/69092	Loss: 143.173
+3200/69092	Loss: 148.837
+6400/69092	Loss: 149.340
+9600/69092	Loss: 147.622
+12800/69092	Loss: 146.403
+16000/69092	Loss: 146.955
+19200/69092	Loss: 145.442
+22400/69092	Loss: 147.009
+25600/69092	Loss: 145.950
+28800/69092	Loss: 143.687
+32000/69092	Loss: 145.683
+35200/69092	Loss: 145.375
+38400/69092	Loss: 145.855
+41600/69092	Loss: 145.754
+44800/69092	Loss: 145.672
+48000/69092	Loss: 143.268
+51200/69092	Loss: 143.225
+54400/69092	Loss: 144.677
+57600/69092	Loss: 145.922
+60800/69092	Loss: 146.299
+64000/69092	Loss: 143.574
+67200/69092	Loss: 143.527
+Training time 0:04:51.692162
+Epoch: 2 Average loss: 145.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 3)
+0/69092	Loss: 153.519
+3200/69092	Loss: 145.651
+6400/69092	Loss: 141.828
+9600/69092	Loss: 145.531
+12800/69092	Loss: 145.265
+16000/69092	Loss: 142.549
+19200/69092	Loss: 140.299
+22400/69092	Loss: 147.157
+25600/69092	Loss: 144.192
+28800/69092	Loss: 143.237
+32000/69092	Loss: 141.736
+35200/69092	Loss: 142.677
+38400/69092	Loss: 142.500
+41600/69092	Loss: 144.968
+44800/69092	Loss: 140.901
+48000/69092	Loss: 142.025
+51200/69092	Loss: 144.095
+54400/69092	Loss: 141.622
+57600/69092	Loss: 140.296
+60800/69092	Loss: 142.888
+64000/69092	Loss: 139.783
+67200/69092	Loss: 139.524
+Training time 0:04:46.026650
+Epoch: 3 Average loss: 142.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 4)
+0/69092	Loss: 153.974
+3200/69092	Loss: 143.761
+6400/69092	Loss: 142.291
+9600/69092	Loss: 139.206
+12800/69092	Loss: 141.900
+16000/69092	Loss: 138.278
+19200/69092	Loss: 140.812
+22400/69092	Loss: 139.452
+25600/69092	Loss: 142.395
+28800/69092	Loss: 141.577
+32000/69092	Loss: 137.828
+35200/69092	Loss: 141.852
+38400/69092	Loss: 142.464
+41600/69092	Loss: 140.110
+44800/69092	Loss: 140.493
+48000/69092	Loss: 139.024
+51200/69092	Loss: 137.492
+54400/69092	Loss: 139.615
+57600/69092	Loss: 139.125
+60800/69092	Loss: 143.667
+64000/69092	Loss: 139.027
+67200/69092	Loss: 137.685
+Training time 0:04:49.303581
+Epoch: 4 Average loss: 140.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 5)
+0/69092	Loss: 112.383
+3200/69092	Loss: 137.454
+6400/69092	Loss: 139.073
+9600/69092	Loss: 140.830
+12800/69092	Loss: 139.548
+16000/69092	Loss: 138.777
+19200/69092	Loss: 140.528
+22400/69092	Loss: 137.749
+25600/69092	Loss: 138.787
+28800/69092	Loss: 141.040
+32000/69092	Loss: 138.785
+35200/69092	Loss: 139.628
+38400/69092	Loss: 140.765
+41600/69092	Loss: 139.658
+44800/69092	Loss: 138.988
+48000/69092	Loss: 139.052
+51200/69092	Loss: 138.653
+54400/69092	Loss: 139.797
+57600/69092	Loss: 139.011
+60800/69092	Loss: 138.346
+64000/69092	Loss: 139.014
+67200/69092	Loss: 140.627
+Training time 0:04:47.368582
+Epoch: 5 Average loss: 139.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 6)
+0/69092	Loss: 130.235
+3200/69092	Loss: 138.492
+6400/69092	Loss: 138.328
+9600/69092	Loss: 139.536
+12800/69092	Loss: 138.059
+16000/69092	Loss: 136.499
+19200/69092	Loss: 139.865
+22400/69092	Loss: 137.590
+25600/69092	Loss: 138.390
+28800/69092	Loss: 138.681
+32000/69092	Loss: 138.109
+35200/69092	Loss: 141.501
+38400/69092	Loss: 137.944
+41600/69092	Loss: 137.168
+44800/69092	Loss: 137.908
+48000/69092	Loss: 138.282
+51200/69092	Loss: 136.059
+54400/69092	Loss: 137.297
+57600/69092	Loss: 140.392
+60800/69092	Loss: 134.949
+64000/69092	Loss: 137.993
+67200/69092	Loss: 137.138
+Training time 0:04:53.281922
+Epoch: 6 Average loss: 138.11
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 7)
+0/69092	Loss: 140.970
+3200/69092	Loss: 137.488
+6400/69092	Loss: 140.878
+9600/69092	Loss: 137.161
+12800/69092	Loss: 139.734
+16000/69092	Loss: 137.984
+19200/69092	Loss: 139.772
+22400/69092	Loss: 136.809
+25600/69092	Loss: 139.014
+28800/69092	Loss: 134.192
+32000/69092	Loss: 140.247
+35200/69092	Loss: 137.572
+38400/69092	Loss: 139.433
+41600/69092	Loss: 137.778
+44800/69092	Loss: 137.263
+48000/69092	Loss: 136.331
+51200/69092	Loss: 136.103
+54400/69092	Loss: 136.096
+57600/69092	Loss: 137.410
+60800/69092	Loss: 136.121
+64000/69092	Loss: 136.672
+67200/69092	Loss: 137.533
+Training time 0:04:53.955557
+Epoch: 7 Average loss: 137.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 8)
+0/69092	Loss: 125.774
+3200/69092	Loss: 139.187
+6400/69092	Loss: 136.501
+9600/69092	Loss: 137.414
+12800/69092	Loss: 135.825
+16000/69092	Loss: 137.348
+19200/69092	Loss: 137.275
+22400/69092	Loss: 136.744
+25600/69092	Loss: 137.035
+28800/69092	Loss: 137.127
+32000/69092	Loss: 134.687
+35200/69092	Loss: 138.643
+38400/69092	Loss: 138.699
+41600/69092	Loss: 134.890
+44800/69092	Loss: 137.422
+48000/69092	Loss: 136.792
+51200/69092	Loss: 137.172
+54400/69092	Loss: 132.974
+57600/69092	Loss: 137.482
+60800/69092	Loss: 138.539
+64000/69092	Loss: 137.681
+67200/69092	Loss: 137.094
+Training time 0:04:50.852102
+Epoch: 8 Average loss: 136.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 9)
+0/69092	Loss: 128.512
+3200/69092	Loss: 136.849
+6400/69092	Loss: 136.887
+9600/69092	Loss: 135.035
+12800/69092	Loss: 135.584
+16000/69092	Loss: 137.715
+19200/69092	Loss: 136.939
+22400/69092	Loss: 138.728
+25600/69092	Loss: 137.430
+28800/69092	Loss: 136.728
+32000/69092	Loss: 136.111
+35200/69092	Loss: 136.315
+38400/69092	Loss: 136.075
+41600/69092	Loss: 135.117
+44800/69092	Loss: 137.334
+48000/69092	Loss: 137.614
+51200/69092	Loss: 135.375
+54400/69092	Loss: 136.036
+57600/69092	Loss: 136.917
+60800/69092	Loss: 136.521
+64000/69092	Loss: 137.985
+67200/69092	Loss: 136.001
+Training time 0:04:53.617479
+Epoch: 9 Average loss: 136.62
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 10)
+0/69092	Loss: 116.711
+3200/69092	Loss: 139.440
+6400/69092	Loss: 135.457
+9600/69092	Loss: 138.314
+12800/69092	Loss: 134.946
+16000/69092	Loss: 133.214
+19200/69092	Loss: 136.892
+22400/69092	Loss: 136.203
+25600/69092	Loss: 137.560
+28800/69092	Loss: 138.216
+32000/69092	Loss: 136.824
+35200/69092	Loss: 138.758
+38400/69092	Loss: 135.570
+41600/69092	Loss: 137.090
+44800/69092	Loss: 134.380
+48000/69092	Loss: 135.627
+51200/69092	Loss: 135.116
+54400/69092	Loss: 135.653
+57600/69092	Loss: 135.568
+60800/69092	Loss: 135.086
+64000/69092	Loss: 137.637
+67200/69092	Loss: 135.876
+Training time 0:04:54.556594
+Epoch: 10 Average loss: 136.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 11)
+0/69092	Loss: 130.440
+3200/69092	Loss: 136.629
+6400/69092	Loss: 136.639
+9600/69092	Loss: 136.358
+12800/69092	Loss: 137.923
+16000/69092	Loss: 136.105
+19200/69092	Loss: 135.169
+22400/69092	Loss: 136.566
+25600/69092	Loss: 135.791
+28800/69092	Loss: 137.898
+32000/69092	Loss: 136.301
+35200/69092	Loss: 135.944
+38400/69092	Loss: 139.066
+41600/69092	Loss: 135.071
+44800/69092	Loss: 137.581
+48000/69092	Loss: 135.287
+51200/69092	Loss: 134.512
+54400/69092	Loss: 134.847
+57600/69092	Loss: 133.324
+60800/69092	Loss: 133.098
+64000/69092	Loss: 138.197
+67200/69092	Loss: 137.772
+Training time 0:04:52.905411
+Epoch: 11 Average loss: 136.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 12)
+0/69092	Loss: 142.560
+3200/69092	Loss: 138.019
+6400/69092	Loss: 137.816
+9600/69092	Loss: 136.478
+12800/69092	Loss: 134.968
+16000/69092	Loss: 134.737
+19200/69092	Loss: 134.154
+22400/69092	Loss: 134.689
+25600/69092	Loss: 136.335
+28800/69092	Loss: 136.210
+32000/69092	Loss: 134.062
+35200/69092	Loss: 137.574
+38400/69092	Loss: 134.143
+41600/69092	Loss: 135.749
+44800/69092	Loss: 137.421
+48000/69092	Loss: 136.107
+51200/69092	Loss: 136.956
+54400/69092	Loss: 135.313
+57600/69092	Loss: 134.547
+60800/69092	Loss: 137.678
+64000/69092	Loss: 137.972
+67200/69092	Loss: 136.143
+Training time 0:04:47.934248
+Epoch: 12 Average loss: 136.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 13)
+0/69092	Loss: 138.820
+3200/69092	Loss: 134.840
+6400/69092	Loss: 135.779
+9600/69092	Loss: 136.047
+12800/69092	Loss: 136.017
+16000/69092	Loss: 133.767
+19200/69092	Loss: 137.659
+22400/69092	Loss: 136.069
+25600/69092	Loss: 136.581
+28800/69092	Loss: 137.223
+32000/69092	Loss: 134.748
+35200/69092	Loss: 137.429
+38400/69092	Loss: 133.493
+41600/69092	Loss: 136.301
+44800/69092	Loss: 133.949
+48000/69092	Loss: 137.032
+51200/69092	Loss: 136.085
+54400/69092	Loss: 135.442
+57600/69092	Loss: 134.689
+60800/69092	Loss: 134.591
+64000/69092	Loss: 137.789
+67200/69092	Loss: 134.512
+Training time 0:04:49.043560
+Epoch: 13 Average loss: 135.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 14)
+0/69092	Loss: 138.798
+3200/69092	Loss: 136.422
+6400/69092	Loss: 135.985
+9600/69092	Loss: 133.135
+12800/69092	Loss: 134.340
+16000/69092	Loss: 136.762
+19200/69092	Loss: 136.748
+22400/69092	Loss: 134.862
+25600/69092	Loss: 135.796
+28800/69092	Loss: 136.190
+32000/69092	Loss: 134.759
+35200/69092	Loss: 131.705
+38400/69092	Loss: 136.131
+41600/69092	Loss: 136.145
+44800/69092	Loss: 134.797
+48000/69092	Loss: 137.117
+51200/69092	Loss: 134.780
+54400/69092	Loss: 134.688
+57600/69092	Loss: 135.539
+60800/69092	Loss: 135.482
+64000/69092	Loss: 135.210
+67200/69092	Loss: 138.155
+Training time 0:04:47.415866
+Epoch: 14 Average loss: 135.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 15)
+0/69092	Loss: 134.309
+3200/69092	Loss: 137.721
+6400/69092	Loss: 135.463
+9600/69092	Loss: 134.279
+12800/69092	Loss: 135.122
+16000/69092	Loss: 133.930
+19200/69092	Loss: 133.664
+22400/69092	Loss: 136.379
+25600/69092	Loss: 135.420
+28800/69092	Loss: 134.670
+32000/69092	Loss: 135.000
+35200/69092	Loss: 135.351
+38400/69092	Loss: 136.120
+41600/69092	Loss: 135.211
+44800/69092	Loss: 135.834
+48000/69092	Loss: 134.783
+51200/69092	Loss: 134.572
+54400/69092	Loss: 136.132
+57600/69092	Loss: 134.998
+60800/69092	Loss: 135.242
+64000/69092	Loss: 136.464
+67200/69092	Loss: 136.273
+Training time 0:04:50.106540
+Epoch: 15 Average loss: 135.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 16)
+0/69092	Loss: 135.949
+3200/69092	Loss: 134.864
+6400/69092	Loss: 134.832
+9600/69092	Loss: 133.051
+12800/69092	Loss: 133.823
+16000/69092	Loss: 133.778
+19200/69092	Loss: 135.979
+22400/69092	Loss: 136.273
+25600/69092	Loss: 135.099
+28800/69092	Loss: 134.681
+32000/69092	Loss: 135.605
+35200/69092	Loss: 138.182
+38400/69092	Loss: 136.193
+41600/69092	Loss: 136.388
+44800/69092	Loss: 135.354
+48000/69092	Loss: 137.457
+51200/69092	Loss: 133.089
+54400/69092	Loss: 135.397
+57600/69092	Loss: 133.427
+60800/69092	Loss: 135.073
+64000/69092	Loss: 136.909
+67200/69092	Loss: 134.642
+Training time 0:04:38.435256
+Epoch: 16 Average loss: 135.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 17)
+0/69092	Loss: 138.000
+3200/69092	Loss: 136.111
+6400/69092	Loss: 133.658
+9600/69092	Loss: 133.701
+12800/69092	Loss: 135.034
+16000/69092	Loss: 135.448
+19200/69092	Loss: 134.607
+22400/69092	Loss: 137.125
+25600/69092	Loss: 134.265
+28800/69092	Loss: 136.148
+32000/69092	Loss: 134.616
+35200/69092	Loss: 136.364
+38400/69092	Loss: 135.393
+41600/69092	Loss: 134.187
+44800/69092	Loss: 136.165
+48000/69092	Loss: 135.929
+51200/69092	Loss: 134.787
+54400/69092	Loss: 133.402
+57600/69092	Loss: 135.915
+60800/69092	Loss: 135.325
+64000/69092	Loss: 135.651
+67200/69092	Loss: 135.385
+Training time 0:04:41.886585
+Epoch: 17 Average loss: 135.25
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 18)
+0/69092	Loss: 143.136
+3200/69092	Loss: 135.909
+6400/69092	Loss: 134.992
+9600/69092	Loss: 133.596
+12800/69092	Loss: 135.802
+16000/69092	Loss: 134.036
+19200/69092	Loss: 133.975
+22400/69092	Loss: 136.420
+25600/69092	Loss: 135.745
+28800/69092	Loss: 133.899
+32000/69092	Loss: 136.081
+35200/69092	Loss: 135.021
+38400/69092	Loss: 135.222
+41600/69092	Loss: 134.817
+44800/69092	Loss: 135.857
+48000/69092	Loss: 136.124
+51200/69092	Loss: 133.457
+54400/69092	Loss: 133.075
+57600/69092	Loss: 134.068
+60800/69092	Loss: 136.092
+64000/69092	Loss: 133.002
+67200/69092	Loss: 134.536
+Training time 0:04:37.640146
+Epoch: 18 Average loss: 134.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 19)
+0/69092	Loss: 136.190
+3200/69092	Loss: 135.955
+6400/69092	Loss: 133.368
+9600/69092	Loss: 136.835
+12800/69092	Loss: 135.033
+16000/69092	Loss: 134.636
+19200/69092	Loss: 134.387
+22400/69092	Loss: 134.776
+25600/69092	Loss: 135.802
+28800/69092	Loss: 135.218
+32000/69092	Loss: 134.476
+35200/69092	Loss: 134.542
+38400/69092	Loss: 135.400
+41600/69092	Loss: 134.663
+44800/69092	Loss: 133.927
+48000/69092	Loss: 136.387
+51200/69092	Loss: 133.757
+54400/69092	Loss: 134.504
+57600/69092	Loss: 135.201
+60800/69092	Loss: 134.295
+64000/69092	Loss: 132.394
+67200/69092	Loss: 133.272
+Training time 0:04:33.461645
+Epoch: 19 Average loss: 134.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 20)
+0/69092	Loss: 141.672
+3200/69092	Loss: 136.147
+6400/69092	Loss: 134.504
+9600/69092	Loss: 135.077
+12800/69092	Loss: 132.699
+16000/69092	Loss: 135.500
+19200/69092	Loss: 132.953
+22400/69092	Loss: 134.542
+25600/69092	Loss: 135.023
+28800/69092	Loss: 135.674
+32000/69092	Loss: 133.153
+35200/69092	Loss: 133.527
+38400/69092	Loss: 135.516
+41600/69092	Loss: 135.942
+44800/69092	Loss: 134.288
+48000/69092	Loss: 134.031
+51200/69092	Loss: 137.359
+54400/69092	Loss: 133.247
+57600/69092	Loss: 135.630
+60800/69092	Loss: 134.472
+64000/69092	Loss: 134.720
+67200/69092	Loss: 135.098
+Training time 0:04:40.036596
+Epoch: 20 Average loss: 134.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 21)
+0/69092	Loss: 150.207
+3200/69092	Loss: 135.195
+6400/69092	Loss: 134.344
+9600/69092	Loss: 135.940
+12800/69092	Loss: 134.407
+16000/69092	Loss: 134.870
+19200/69092	Loss: 133.250
+22400/69092	Loss: 133.484
+25600/69092	Loss: 136.036
+28800/69092	Loss: 134.513
+32000/69092	Loss: 134.567
+35200/69092	Loss: 132.468
+38400/69092	Loss: 134.329
+41600/69092	Loss: 135.064
+44800/69092	Loss: 133.131
+48000/69092	Loss: 134.148
+51200/69092	Loss: 136.914
+54400/69092	Loss: 134.930
+57600/69092	Loss: 136.108
+60800/69092	Loss: 133.987
+64000/69092	Loss: 136.280
+67200/69092	Loss: 134.166
+Training time 0:04:39.091547
+Epoch: 21 Average loss: 134.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 22)
+0/69092	Loss: 119.053
+3200/69092	Loss: 135.355
+6400/69092	Loss: 135.607
+9600/69092	Loss: 136.246
+12800/69092	Loss: 134.407
+16000/69092	Loss: 131.474
+19200/69092	Loss: 134.511
+22400/69092	Loss: 134.013
+25600/69092	Loss: 133.152
+28800/69092	Loss: 132.573
+32000/69092	Loss: 135.316
+35200/69092	Loss: 134.359
+38400/69092	Loss: 135.460
+41600/69092	Loss: 135.286
+44800/69092	Loss: 134.391
+48000/69092	Loss: 135.731
+51200/69092	Loss: 137.241
+54400/69092	Loss: 136.328
+57600/69092	Loss: 132.285
+60800/69092	Loss: 135.411
+64000/69092	Loss: 134.402
+67200/69092	Loss: 134.102
+Training time 0:04:34.055154
+Epoch: 22 Average loss: 134.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 23)
+0/69092	Loss: 132.497
+3200/69092	Loss: 134.809
+6400/69092	Loss: 136.661
+9600/69092	Loss: 132.698
+12800/69092	Loss: 134.736
+16000/69092	Loss: 133.905
+19200/69092	Loss: 136.538
+22400/69092	Loss: 135.342
+25600/69092	Loss: 133.752
+28800/69092	Loss: 134.078
+32000/69092	Loss: 135.883
+35200/69092	Loss: 135.296
+38400/69092	Loss: 135.743
+41600/69092	Loss: 134.805
+44800/69092	Loss: 135.000
+48000/69092	Loss: 133.954
+51200/69092	Loss: 134.722
+54400/69092	Loss: 134.012
+57600/69092	Loss: 134.085
+60800/69092	Loss: 134.561
+64000/69092	Loss: 133.512
+67200/69092	Loss: 132.733
+Training time 0:04:39.774768
+Epoch: 23 Average loss: 134.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 24)
+0/69092	Loss: 139.944
+3200/69092	Loss: 131.760
+6400/69092	Loss: 136.860
+9600/69092	Loss: 136.123
+12800/69092	Loss: 135.383
+16000/69092	Loss: 135.544
+19200/69092	Loss: 135.580
+22400/69092	Loss: 135.211
+25600/69092	Loss: 131.185
+28800/69092	Loss: 132.956
+32000/69092	Loss: 134.480
+35200/69092	Loss: 135.644
+38400/69092	Loss: 134.626
+41600/69092	Loss: 132.784
+44800/69092	Loss: 133.034
+48000/69092	Loss: 135.432
+51200/69092	Loss: 136.171
+54400/69092	Loss: 134.883
+57600/69092	Loss: 136.160
+60800/69092	Loss: 133.469
+64000/69092	Loss: 133.819
+67200/69092	Loss: 134.401
+Training time 0:04:40.808800
+Epoch: 24 Average loss: 134.60
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 25)
+0/69092	Loss: 145.018
+3200/69092	Loss: 132.701
+6400/69092	Loss: 132.960
+9600/69092	Loss: 134.206
+12800/69092	Loss: 133.399
+16000/69092	Loss: 135.152
+19200/69092	Loss: 134.454
+22400/69092	Loss: 134.089
+25600/69092	Loss: 133.050
+28800/69092	Loss: 133.241
+32000/69092	Loss: 135.133
+35200/69092	Loss: 134.018
+38400/69092	Loss: 133.902
+41600/69092	Loss: 135.801
+44800/69092	Loss: 136.048
+48000/69092	Loss: 135.188
+51200/69092	Loss: 136.588
+54400/69092	Loss: 133.573
+57600/69092	Loss: 132.799
+60800/69092	Loss: 134.888
+64000/69092	Loss: 135.027
+67200/69092	Loss: 135.773
+Training time 0:04:44.718572
+Epoch: 25 Average loss: 134.40
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 26)
+0/69092	Loss: 127.012
+3200/69092	Loss: 134.702
+6400/69092	Loss: 136.845
+9600/69092	Loss: 134.568
+12800/69092	Loss: 133.714
+16000/69092	Loss: 135.752
+19200/69092	Loss: 133.098
+22400/69092	Loss: 136.472
+25600/69092	Loss: 134.800
+28800/69092	Loss: 133.131
+32000/69092	Loss: 132.874
+35200/69092	Loss: 133.481
+38400/69092	Loss: 134.428
+41600/69092	Loss: 135.679
+44800/69092	Loss: 134.405
+48000/69092	Loss: 134.641
+51200/69092	Loss: 134.593
+54400/69092	Loss: 134.779
+57600/69092	Loss: 134.973
+60800/69092	Loss: 132.792
+64000/69092	Loss: 132.360
+67200/69092	Loss: 132.379
+Training time 0:04:44.102291
+Epoch: 26 Average loss: 134.25
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 27)
+0/69092	Loss: 149.808
+3200/69092	Loss: 132.259
+6400/69092	Loss: 134.341
+9600/69092	Loss: 135.164
+12800/69092	Loss: 133.393
+16000/69092	Loss: 134.844
+19200/69092	Loss: 133.896
+22400/69092	Loss: 132.075
+25600/69092	Loss: 133.649
+28800/69092	Loss: 134.832
+32000/69092	Loss: 133.606
+35200/69092	Loss: 134.642
+38400/69092	Loss: 133.741
+41600/69092	Loss: 132.305
+44800/69092	Loss: 133.431
+48000/69092	Loss: 136.227
+51200/69092	Loss: 133.519
+54400/69092	Loss: 135.129
+57600/69092	Loss: 132.867
+60800/69092	Loss: 132.726
+64000/69092	Loss: 134.911
+67200/69092	Loss: 135.453
+Training time 0:04:44.718513
+Epoch: 27 Average loss: 133.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 28)
+0/69092	Loss: 140.091
+3200/69092	Loss: 133.067
+6400/69092	Loss: 135.230
+9600/69092	Loss: 133.767
+12800/69092	Loss: 133.414
+16000/69092	Loss: 132.176
+19200/69092	Loss: 134.166
+22400/69092	Loss: 135.205
+25600/69092	Loss: 136.393
+28800/69092	Loss: 132.047
+32000/69092	Loss: 131.969
+35200/69092	Loss: 133.022
+38400/69092	Loss: 134.558
+41600/69092	Loss: 133.753
+44800/69092	Loss: 134.982
+48000/69092	Loss: 136.384
+51200/69092	Loss: 133.967
+54400/69092	Loss: 132.168
+57600/69092	Loss: 134.359
+60800/69092	Loss: 136.309
+64000/69092	Loss: 134.633
+67200/69092	Loss: 135.702
+Training time 0:04:41.116094
+Epoch: 28 Average loss: 134.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 29)
+0/69092	Loss: 124.378
+3200/69092	Loss: 131.196
+6400/69092	Loss: 133.288
+9600/69092	Loss: 133.099
+12800/69092	Loss: 132.631
+16000/69092	Loss: 133.875
+19200/69092	Loss: 135.789
+22400/69092	Loss: 133.333
+25600/69092	Loss: 131.898
+28800/69092	Loss: 135.044
+32000/69092	Loss: 136.767
+35200/69092	Loss: 135.353
+38400/69092	Loss: 135.699
+41600/69092	Loss: 133.328
+44800/69092	Loss: 137.368
+48000/69092	Loss: 135.211
+51200/69092	Loss: 134.633
+54400/69092	Loss: 131.426
+57600/69092	Loss: 133.014
+60800/69092	Loss: 134.384
+64000/69092	Loss: 134.334
+67200/69092	Loss: 134.622
+Training time 0:04:43.813486
+Epoch: 29 Average loss: 134.17
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 30)
+0/69092	Loss: 135.568
+3200/69092	Loss: 134.305
+6400/69092	Loss: 132.871
+9600/69092	Loss: 132.481
+12800/69092	Loss: 134.599
+16000/69092	Loss: 131.658
+19200/69092	Loss: 135.084
+22400/69092	Loss: 132.367
+25600/69092	Loss: 135.661
+28800/69092	Loss: 132.998
+32000/69092	Loss: 135.432
+35200/69092	Loss: 136.519
+38400/69092	Loss: 133.184
+41600/69092	Loss: 136.257
+44800/69092	Loss: 136.150
+48000/69092	Loss: 131.348
+51200/69092	Loss: 132.628
+54400/69092	Loss: 134.070
+57600/69092	Loss: 133.399
+60800/69092	Loss: 133.182
+64000/69092	Loss: 135.141
+67200/69092	Loss: 133.544
+Training time 0:04:43.219094
+Epoch: 30 Average loss: 133.88
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 31)
+0/69092	Loss: 124.188
+3200/69092	Loss: 134.833
+6400/69092	Loss: 133.584
+9600/69092	Loss: 133.853
+12800/69092	Loss: 132.266
+16000/69092	Loss: 135.120
+19200/69092	Loss: 135.788
+22400/69092	Loss: 131.430
+25600/69092	Loss: 136.732
+28800/69092	Loss: 133.887
+32000/69092	Loss: 135.501
+35200/69092	Loss: 134.410
+38400/69092	Loss: 133.129
+41600/69092	Loss: 131.547
+44800/69092	Loss: 133.732
+48000/69092	Loss: 134.267
+51200/69092	Loss: 136.209
+54400/69092	Loss: 131.541
+57600/69092	Loss: 133.454
+60800/69092	Loss: 132.622
+64000/69092	Loss: 132.528
+67200/69092	Loss: 132.466
+Training time 0:04:48.333091
+Epoch: 31 Average loss: 133.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 32)
+0/69092	Loss: 129.738
+3200/69092	Loss: 134.777
+6400/69092	Loss: 135.615
+9600/69092	Loss: 132.873
+12800/69092	Loss: 135.517
+16000/69092	Loss: 132.053
+19200/69092	Loss: 133.392
+22400/69092	Loss: 136.432
+25600/69092	Loss: 134.465
+28800/69092	Loss: 136.804
+32000/69092	Loss: 131.439
+35200/69092	Loss: 135.418
+38400/69092	Loss: 133.925
+41600/69092	Loss: 135.465
+44800/69092	Loss: 131.826
+48000/69092	Loss: 134.965
+51200/69092	Loss: 132.440
+54400/69092	Loss: 134.992
+57600/69092	Loss: 135.859
+60800/69092	Loss: 130.496
+64000/69092	Loss: 132.862
+67200/69092	Loss: 134.359
+Training time 0:04:34.723577
+Epoch: 32 Average loss: 134.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 33)
+0/69092	Loss: 135.288
+3200/69092	Loss: 133.811
+6400/69092	Loss: 134.418
+9600/69092	Loss: 133.275
+12800/69092	Loss: 132.395
+16000/69092	Loss: 131.969
+19200/69092	Loss: 134.218
+22400/69092	Loss: 133.016
+25600/69092	Loss: 133.718
+28800/69092	Loss: 135.349
+32000/69092	Loss: 134.404
+35200/69092	Loss: 135.265
+38400/69092	Loss: 133.873
+41600/69092	Loss: 134.478
+44800/69092	Loss: 135.259
+48000/69092	Loss: 135.084
+51200/69092	Loss: 133.549
+54400/69092	Loss: 133.415
+57600/69092	Loss: 132.021
+60800/69092	Loss: 133.636
+64000/69092	Loss: 135.127
+67200/69092	Loss: 132.057
+Training time 0:04:44.494181
+Epoch: 33 Average loss: 133.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 34)
+0/69092	Loss: 142.448
+3200/69092	Loss: 133.348
+6400/69092	Loss: 136.045
+9600/69092	Loss: 135.147
+12800/69092	Loss: 133.005
+16000/69092	Loss: 133.364
+19200/69092	Loss: 133.808
+22400/69092	Loss: 130.967
+25600/69092	Loss: 134.049
+28800/69092	Loss: 132.894
+32000/69092	Loss: 131.265
+35200/69092	Loss: 132.280
+38400/69092	Loss: 134.631
+41600/69092	Loss: 134.471
+44800/69092	Loss: 133.223
+48000/69092	Loss: 133.106
+51200/69092	Loss: 135.413
+54400/69092	Loss: 133.348
+57600/69092	Loss: 135.116
+60800/69092	Loss: 131.990
+64000/69092	Loss: 132.751
+67200/69092	Loss: 135.112
+Training time 0:04:34.204443
+Epoch: 34 Average loss: 133.63
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 35)
+0/69092	Loss: 123.990
+3200/69092	Loss: 132.792
+6400/69092	Loss: 135.419
+9600/69092	Loss: 133.988
+12800/69092	Loss: 133.357
+16000/69092	Loss: 134.003
+19200/69092	Loss: 132.330
+22400/69092	Loss: 133.200
+25600/69092	Loss: 133.000
+28800/69092	Loss: 134.942
+32000/69092	Loss: 132.583
+35200/69092	Loss: 132.112
+38400/69092	Loss: 134.837
+41600/69092	Loss: 133.017
+44800/69092	Loss: 132.499
+48000/69092	Loss: 134.986
+51200/69092	Loss: 134.932
+54400/69092	Loss: 133.095
+57600/69092	Loss: 133.156
+60800/69092	Loss: 132.966
+64000/69092	Loss: 135.842
+67200/69092	Loss: 133.402
+Training time 0:04:42.218022
+Epoch: 35 Average loss: 133.62
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 36)
+0/69092	Loss: 133.238
+3200/69092	Loss: 134.758
+6400/69092	Loss: 133.690
+9600/69092	Loss: 134.546
+12800/69092	Loss: 134.473
+16000/69092	Loss: 133.391
+19200/69092	Loss: 132.560
+22400/69092	Loss: 134.921
+25600/69092	Loss: 132.042
+28800/69092	Loss: 131.305
+32000/69092	Loss: 133.672
+35200/69092	Loss: 134.956
+38400/69092	Loss: 134.512
+41600/69092	Loss: 135.354
+44800/69092	Loss: 131.522
+48000/69092	Loss: 136.540
+51200/69092	Loss: 133.360
+54400/69092	Loss: 133.874
+57600/69092	Loss: 133.071
+60800/69092	Loss: 133.754
+64000/69092	Loss: 135.372
+67200/69092	Loss: 131.328
+Training time 0:04:40.897872
+Epoch: 36 Average loss: 133.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 37)
+0/69092	Loss: 128.988
+3200/69092	Loss: 132.509
+6400/69092	Loss: 132.893
+9600/69092	Loss: 132.500
+12800/69092	Loss: 134.516
+16000/69092	Loss: 131.733
+19200/69092	Loss: 134.720
+22400/69092	Loss: 136.080
+25600/69092	Loss: 130.821
+28800/69092	Loss: 134.869
+32000/69092	Loss: 133.506
+35200/69092	Loss: 133.271
+38400/69092	Loss: 134.171
+41600/69092	Loss: 134.311
+44800/69092	Loss: 133.292
+48000/69092	Loss: 132.155
+51200/69092	Loss: 134.171
+54400/69092	Loss: 133.322
+57600/69092	Loss: 134.706
+60800/69092	Loss: 133.661
+64000/69092	Loss: 133.638
+67200/69092	Loss: 133.638
+Training time 0:04:38.412299
+Epoch: 37 Average loss: 133.46
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 38)
+0/69092	Loss: 124.622
+3200/69092	Loss: 132.056
+6400/69092	Loss: 135.011
+9600/69092	Loss: 133.184
+12800/69092	Loss: 133.030
+16000/69092	Loss: 133.174
+19200/69092	Loss: 133.255
+22400/69092	Loss: 133.345
+25600/69092	Loss: 135.452
+28800/69092	Loss: 133.048
+32000/69092	Loss: 133.322
+35200/69092	Loss: 135.453
+38400/69092	Loss: 133.312
+41600/69092	Loss: 134.087
+44800/69092	Loss: 132.115
+48000/69092	Loss: 135.058
+51200/69092	Loss: 134.513
+54400/69092	Loss: 131.484
+57600/69092	Loss: 133.567
+60800/69092	Loss: 135.834
+64000/69092	Loss: 134.272
+67200/69092	Loss: 129.925
+Training time 0:04:42.429171
+Epoch: 38 Average loss: 133.53
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 39)
+0/69092	Loss: 156.193
+3200/69092	Loss: 133.821
+6400/69092	Loss: 132.816
+9600/69092	Loss: 130.703
+12800/69092	Loss: 134.637
+16000/69092	Loss: 136.554
+19200/69092	Loss: 135.034
+22400/69092	Loss: 132.533
+25600/69092	Loss: 133.841
+28800/69092	Loss: 133.199
+32000/69092	Loss: 133.368
+35200/69092	Loss: 133.641
+38400/69092	Loss: 133.051
+41600/69092	Loss: 129.854
+44800/69092	Loss: 133.528
+48000/69092	Loss: 134.050
+51200/69092	Loss: 133.928
+54400/69092	Loss: 135.090
+57600/69092	Loss: 132.527
+60800/69092	Loss: 131.953
+64000/69092	Loss: 136.588
+67200/69092	Loss: 133.985
+Training time 0:04:45.375654
+Epoch: 39 Average loss: 133.62
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 40)
+0/69092	Loss: 121.759
+3200/69092	Loss: 135.346
+6400/69092	Loss: 132.908
+9600/69092	Loss: 132.013
+12800/69092	Loss: 134.725
+16000/69092	Loss: 132.235
+19200/69092	Loss: 130.104
+22400/69092	Loss: 132.965
+25600/69092	Loss: 133.479
+28800/69092	Loss: 135.865
+32000/69092	Loss: 131.262
+35200/69092	Loss: 134.122
+38400/69092	Loss: 134.475
+41600/69092	Loss: 134.452
+44800/69092	Loss: 134.810
+48000/69092	Loss: 136.456
+51200/69092	Loss: 133.344
+54400/69092	Loss: 134.291
+57600/69092	Loss: 131.519
+60800/69092	Loss: 134.138
+64000/69092	Loss: 134.223
+67200/69092	Loss: 133.954
+Training time 0:04:48.930774
+Epoch: 40 Average loss: 133.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 41)
+0/69092	Loss: 144.336
+3200/69092	Loss: 133.839
+6400/69092	Loss: 133.075
+9600/69092	Loss: 132.904
+12800/69092	Loss: 134.981
+16000/69092	Loss: 133.528
+19200/69092	Loss: 133.937
+22400/69092	Loss: 131.403
+25600/69092	Loss: 134.301
+28800/69092	Loss: 131.515
+32000/69092	Loss: 134.074
+35200/69092	Loss: 133.805
+38400/69092	Loss: 133.653
+41600/69092	Loss: 134.400
+44800/69092	Loss: 132.125
+48000/69092	Loss: 133.792
+51200/69092	Loss: 132.654
+54400/69092	Loss: 132.679
+57600/69092	Loss: 134.043
+60800/69092	Loss: 134.716
+64000/69092	Loss: 136.102
+67200/69092	Loss: 132.645
+Training time 0:04:47.339056
+Epoch: 41 Average loss: 133.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 42)
+0/69092	Loss: 132.451
+3200/69092	Loss: 135.299
+6400/69092	Loss: 131.848
+9600/69092	Loss: 133.216
+12800/69092	Loss: 132.636
+16000/69092	Loss: 132.065
+19200/69092	Loss: 133.161
+22400/69092	Loss: 133.787
+25600/69092	Loss: 133.244
+28800/69092	Loss: 132.750
+32000/69092	Loss: 132.239
+35200/69092	Loss: 133.696
+38400/69092	Loss: 129.928
+41600/69092	Loss: 134.737
+44800/69092	Loss: 133.275
+48000/69092	Loss: 135.322
+51200/69092	Loss: 132.460
+54400/69092	Loss: 134.073
+57600/69092	Loss: 133.371
+60800/69092	Loss: 135.080
+64000/69092	Loss: 133.444
+67200/69092	Loss: 131.956
+Training time 0:04:43.830576
+Epoch: 42 Average loss: 133.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 43)
+0/69092	Loss: 135.158
+3200/69092	Loss: 132.319
+6400/69092	Loss: 134.570
+9600/69092	Loss: 132.404
+12800/69092	Loss: 134.118
+16000/69092	Loss: 134.321
+19200/69092	Loss: 134.189
+22400/69092	Loss: 134.232
+25600/69092	Loss: 131.641
+28800/69092	Loss: 132.989
+32000/69092	Loss: 135.760
+35200/69092	Loss: 133.028
+38400/69092	Loss: 132.821
+41600/69092	Loss: 132.379
+44800/69092	Loss: 132.276
+48000/69092	Loss: 132.132
+51200/69092	Loss: 132.759
+54400/69092	Loss: 133.374
+57600/69092	Loss: 132.124
+60800/69092	Loss: 133.663
+64000/69092	Loss: 135.621
+67200/69092	Loss: 133.664
+Training time 0:04:50.468097
+Epoch: 43 Average loss: 133.35
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 44)
+0/69092	Loss: 139.048
+3200/69092	Loss: 133.552
+6400/69092	Loss: 133.540
+9600/69092	Loss: 133.927
+12800/69092	Loss: 132.372
+16000/69092	Loss: 134.623
+19200/69092	Loss: 132.679
+22400/69092	Loss: 132.230
+25600/69092	Loss: 136.383
+28800/69092	Loss: 135.167
+32000/69092	Loss: 134.343
+35200/69092	Loss: 132.917
+38400/69092	Loss: 134.341
+41600/69092	Loss: 132.956
+44800/69092	Loss: 131.317
+48000/69092	Loss: 133.880
+51200/69092	Loss: 131.834
+54400/69092	Loss: 131.631
+57600/69092	Loss: 133.360
+60800/69092	Loss: 134.007
+64000/69092	Loss: 134.175
+67200/69092	Loss: 132.231
+Training time 0:04:50.108150
+Epoch: 44 Average loss: 133.36
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 45)
+0/69092	Loss: 135.992
+3200/69092	Loss: 131.969
+6400/69092	Loss: 132.230
+9600/69092	Loss: 133.682
+12800/69092	Loss: 132.256
+16000/69092	Loss: 133.592
+19200/69092	Loss: 132.326
+22400/69092	Loss: 134.299
+25600/69092	Loss: 135.507
+28800/69092	Loss: 132.121
+32000/69092	Loss: 132.063
+35200/69092	Loss: 133.704
+38400/69092	Loss: 132.375
+41600/69092	Loss: 132.320
+44800/69092	Loss: 133.848
+48000/69092	Loss: 134.989
+51200/69092	Loss: 133.256
+54400/69092	Loss: 133.317
+57600/69092	Loss: 134.129
+60800/69092	Loss: 134.256
+64000/69092	Loss: 133.321
+67200/69092	Loss: 132.003
+Training time 0:04:53.508367
+Epoch: 45 Average loss: 133.26
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 46)
+0/69092	Loss: 130.517
+3200/69092	Loss: 132.984
+6400/69092	Loss: 131.636
+9600/69092	Loss: 131.881
+12800/69092	Loss: 132.526
+16000/69092	Loss: 134.043
+19200/69092	Loss: 133.470
+22400/69092	Loss: 132.409
+25600/69092	Loss: 131.709
+28800/69092	Loss: 132.902
+32000/69092	Loss: 134.319
+35200/69092	Loss: 136.253
+38400/69092	Loss: 134.041
+41600/69092	Loss: 130.727
+44800/69092	Loss: 133.832
+48000/69092	Loss: 132.790
+51200/69092	Loss: 132.107
+54400/69092	Loss: 132.977
+57600/69092	Loss: 134.921
+60800/69092	Loss: 133.574
+64000/69092	Loss: 134.376
+67200/69092	Loss: 133.520
+Training time 0:04:42.061703
+Epoch: 46 Average loss: 133.20
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 47)
+0/69092	Loss: 133.775
+3200/69092	Loss: 133.730
+6400/69092	Loss: 131.377
+9600/69092	Loss: 131.189
+12800/69092	Loss: 130.373
+16000/69092	Loss: 134.994
+19200/69092	Loss: 132.828
+22400/69092	Loss: 132.458
+25600/69092	Loss: 132.872
+28800/69092	Loss: 133.617
+32000/69092	Loss: 133.669
+35200/69092	Loss: 133.018
+38400/69092	Loss: 131.874
+41600/69092	Loss: 133.839
+44800/69092	Loss: 132.748
+48000/69092	Loss: 136.165
+51200/69092	Loss: 132.377
+54400/69092	Loss: 133.373
+57600/69092	Loss: 132.232
+60800/69092	Loss: 132.530
+64000/69092	Loss: 135.841
+67200/69092	Loss: 132.767
+Training time 0:04:47.580389
+Epoch: 47 Average loss: 133.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 48)
+0/69092	Loss: 128.723
+3200/69092	Loss: 130.819
+6400/69092	Loss: 133.273
+9600/69092	Loss: 134.754
+12800/69092	Loss: 132.760
+16000/69092	Loss: 134.709
+19200/69092	Loss: 134.013
+22400/69092	Loss: 134.015
+25600/69092	Loss: 132.572
+28800/69092	Loss: 134.936
+32000/69092	Loss: 132.879
+35200/69092	Loss: 131.608
+38400/69092	Loss: 132.359
+41600/69092	Loss: 132.794
+44800/69092	Loss: 131.539
+48000/69092	Loss: 133.287
+51200/69092	Loss: 132.672
+54400/69092	Loss: 131.857
+57600/69092	Loss: 132.390
+60800/69092	Loss: 132.856
+64000/69092	Loss: 135.911
+67200/69092	Loss: 133.497
+Training time 0:04:49.268835
+Epoch: 48 Average loss: 133.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 49)
+0/69092	Loss: 129.483
+3200/69092	Loss: 133.579
+6400/69092	Loss: 134.122
+9600/69092	Loss: 132.421
+12800/69092	Loss: 132.922
+16000/69092	Loss: 133.830
+19200/69092	Loss: 135.214
+22400/69092	Loss: 134.288
+25600/69092	Loss: 132.616
+28800/69092	Loss: 131.659
+32000/69092	Loss: 134.710
+35200/69092	Loss: 131.626
+38400/69092	Loss: 131.865
+41600/69092	Loss: 131.776
+44800/69092	Loss: 135.127
+48000/69092	Loss: 132.649
+51200/69092	Loss: 132.262
+54400/69092	Loss: 134.266
+57600/69092	Loss: 131.577
+60800/69092	Loss: 133.486
+64000/69092	Loss: 132.942
+67200/69092	Loss: 134.161
+Training time 0:04:49.351053
+Epoch: 49 Average loss: 133.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 50)
+0/69092	Loss: 147.384
+3200/69092	Loss: 133.079
+6400/69092	Loss: 134.019
+9600/69092	Loss: 133.171
+12800/69092	Loss: 132.717
+16000/69092	Loss: 134.023
+19200/69092	Loss: 134.378
+22400/69092	Loss: 133.032
+25600/69092	Loss: 132.334
+28800/69092	Loss: 132.942
+32000/69092	Loss: 134.698
+35200/69092	Loss: 133.306
+38400/69092	Loss: 130.680
+41600/69092	Loss: 131.174
+44800/69092	Loss: 133.474
+48000/69092	Loss: 130.451
+51200/69092	Loss: 135.306
+54400/69092	Loss: 132.507
+57600/69092	Loss: 132.798
+60800/69092	Loss: 131.816
+64000/69092	Loss: 133.174
+67200/69092	Loss: 133.875
+Training time 0:04:41.682554
+Epoch: 50 Average loss: 133.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 51)
+0/69092	Loss: 136.488
+3200/69092	Loss: 135.509
+6400/69092	Loss: 131.770
+9600/69092	Loss: 135.026
+12800/69092	Loss: 131.669
+16000/69092	Loss: 131.227
+19200/69092	Loss: 132.927
+22400/69092	Loss: 131.692
+25600/69092	Loss: 132.917
+28800/69092	Loss: 133.707
+32000/69092	Loss: 131.803
+35200/69092	Loss: 134.959
+38400/69092	Loss: 133.376
+41600/69092	Loss: 130.779
+44800/69092	Loss: 133.152
+48000/69092	Loss: 132.008
+51200/69092	Loss: 133.650
+54400/69092	Loss: 133.612
+57600/69092	Loss: 135.377
+60800/69092	Loss: 131.977
+64000/69092	Loss: 132.687
+67200/69092	Loss: 132.218
+Training time 0:04:52.903941
+Epoch: 51 Average loss: 132.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 52)
+0/69092	Loss: 128.212
+3200/69092	Loss: 132.653
+6400/69092	Loss: 132.919
+9600/69092	Loss: 133.486
+12800/69092	Loss: 131.899
+16000/69092	Loss: 134.405
+19200/69092	Loss: 132.317
+22400/69092	Loss: 133.405
+25600/69092	Loss: 132.550
+28800/69092	Loss: 133.849
+32000/69092	Loss: 132.646
+35200/69092	Loss: 136.405
+38400/69092	Loss: 131.433
+41600/69092	Loss: 133.545
+44800/69092	Loss: 132.170
+48000/69092	Loss: 134.529
+51200/69092	Loss: 133.212
+54400/69092	Loss: 131.058
+57600/69092	Loss: 134.522
+60800/69092	Loss: 132.641
+64000/69092	Loss: 131.853
+67200/69092	Loss: 132.450
+Training time 0:04:49.937765
+Epoch: 52 Average loss: 133.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 53)
+0/69092	Loss: 154.664
+3200/69092	Loss: 131.569
+6400/69092	Loss: 133.267
+9600/69092	Loss: 131.108
+12800/69092	Loss: 134.072
+16000/69092	Loss: 130.141
+19200/69092	Loss: 135.100
+22400/69092	Loss: 133.228
+25600/69092	Loss: 133.742
+28800/69092	Loss: 133.085
+32000/69092	Loss: 134.728
+35200/69092	Loss: 134.777
+38400/69092	Loss: 134.701
+41600/69092	Loss: 132.244
+44800/69092	Loss: 135.134
+48000/69092	Loss: 131.380
+51200/69092	Loss: 131.166
+54400/69092	Loss: 133.751
+57600/69092	Loss: 130.564
+60800/69092	Loss: 132.434
+64000/69092	Loss: 133.248
+67200/69092	Loss: 134.456
+Training time 0:04:55.322000
+Epoch: 53 Average loss: 133.13
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 54)
+0/69092	Loss: 121.583
+3200/69092	Loss: 133.230
+6400/69092	Loss: 136.075
+9600/69092	Loss: 132.229
+12800/69092	Loss: 133.163
+16000/69092	Loss: 132.531
+19200/69092	Loss: 133.423
+22400/69092	Loss: 133.894
+25600/69092	Loss: 132.111
+28800/69092	Loss: 132.092
+32000/69092	Loss: 134.509
+35200/69092	Loss: 132.290
+38400/69092	Loss: 131.369
+41600/69092	Loss: 132.263
+44800/69092	Loss: 132.489
+48000/69092	Loss: 132.776
+51200/69092	Loss: 133.100
+54400/69092	Loss: 133.091
+57600/69092	Loss: 134.334
+60800/69092	Loss: 133.931
+64000/69092	Loss: 134.194
+67200/69092	Loss: 133.505
+Training time 0:04:50.053210
+Epoch: 54 Average loss: 133.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 55)
+0/69092	Loss: 139.116
+3200/69092	Loss: 132.280
+6400/69092	Loss: 131.085
+9600/69092	Loss: 133.199
+12800/69092	Loss: 131.715
+16000/69092	Loss: 134.194
+19200/69092	Loss: 133.526
+22400/69092	Loss: 134.003
+25600/69092	Loss: 132.579
+28800/69092	Loss: 130.548
+32000/69092	Loss: 130.813
+35200/69092	Loss: 133.355
+38400/69092	Loss: 133.445
+41600/69092	Loss: 131.297
+44800/69092	Loss: 133.953
+48000/69092	Loss: 132.609
+51200/69092	Loss: 134.504
+54400/69092	Loss: 132.477
+57600/69092	Loss: 134.692
+60800/69092	Loss: 134.461
+64000/69092	Loss: 132.301
+67200/69092	Loss: 131.675
+Training time 0:04:50.185496
+Epoch: 55 Average loss: 132.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 56)
+0/69092	Loss: 139.767
+3200/69092	Loss: 130.033
+6400/69092	Loss: 136.771
+9600/69092	Loss: 133.974
+12800/69092	Loss: 134.293
+16000/69092	Loss: 135.151
+19200/69092	Loss: 133.709
+22400/69092	Loss: 134.872
+25600/69092	Loss: 132.963
+28800/69092	Loss: 132.037
+32000/69092	Loss: 134.976
+35200/69092	Loss: 134.066
+38400/69092	Loss: 133.453
+41600/69092	Loss: 131.751
+44800/69092	Loss: 132.321
+48000/69092	Loss: 131.545
+51200/69092	Loss: 132.053
+54400/69092	Loss: 133.989
+57600/69092	Loss: 129.672
+60800/69092	Loss: 131.938
+64000/69092	Loss: 132.031
+67200/69092	Loss: 131.946
+Training time 0:04:42.928901
+Epoch: 56 Average loss: 133.01
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 57)
+0/69092	Loss: 144.037
+3200/69092	Loss: 132.071
+6400/69092	Loss: 132.160
+9600/69092	Loss: 134.628
+12800/69092	Loss: 129.619
+16000/69092	Loss: 131.743
+19200/69092	Loss: 134.225
+22400/69092	Loss: 132.648
+25600/69092	Loss: 130.468
+28800/69092	Loss: 130.525
+32000/69092	Loss: 136.156
+35200/69092	Loss: 134.091
+38400/69092	Loss: 131.885
+41600/69092	Loss: 133.657
+44800/69092	Loss: 134.782
+48000/69092	Loss: 131.491
+51200/69092	Loss: 133.445
+54400/69092	Loss: 133.638
+57600/69092	Loss: 133.323
+60800/69092	Loss: 134.983
+64000/69092	Loss: 133.721
+67200/69092	Loss: 131.053
+Training time 0:04:40.165173
+Epoch: 57 Average loss: 132.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 58)
+0/69092	Loss: 129.738
+3200/69092	Loss: 132.425
+6400/69092	Loss: 131.857
+9600/69092	Loss: 135.928
+12800/69092	Loss: 135.517
+16000/69092	Loss: 132.770
+19200/69092	Loss: 131.028
+22400/69092	Loss: 133.902
+25600/69092	Loss: 133.489
+28800/69092	Loss: 134.372
+32000/69092	Loss: 132.467
+35200/69092	Loss: 131.959
+38400/69092	Loss: 134.314
+41600/69092	Loss: 131.457
+44800/69092	Loss: 132.516
+48000/69092	Loss: 131.558
+51200/69092	Loss: 133.222
+54400/69092	Loss: 132.404
+57600/69092	Loss: 130.771
+60800/69092	Loss: 133.937
+64000/69092	Loss: 132.910
+67200/69092	Loss: 133.500
+Training time 0:04:42.612048
+Epoch: 58 Average loss: 132.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 59)
+0/69092	Loss: 122.984
+3200/69092	Loss: 132.611
+6400/69092	Loss: 135.339
+9600/69092	Loss: 131.863
+12800/69092	Loss: 133.527
+16000/69092	Loss: 130.291
+19200/69092	Loss: 133.049
+22400/69092	Loss: 135.233
+25600/69092	Loss: 132.212
+28800/69092	Loss: 134.731
+32000/69092	Loss: 130.560
+35200/69092	Loss: 134.372
+38400/69092	Loss: 131.422
+41600/69092	Loss: 130.815
+44800/69092	Loss: 131.701
+48000/69092	Loss: 131.929
+51200/69092	Loss: 135.147
+54400/69092	Loss: 132.449
+57600/69092	Loss: 133.829
+60800/69092	Loss: 133.501
+64000/69092	Loss: 133.877
+67200/69092	Loss: 129.602
+Training time 0:04:37.324057
+Epoch: 59 Average loss: 132.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 60)
+0/69092	Loss: 122.513
+3200/69092	Loss: 134.482
+6400/69092	Loss: 132.177
+9600/69092	Loss: 133.577
+12800/69092	Loss: 133.506
+16000/69092	Loss: 133.168
+19200/69092	Loss: 133.895
+22400/69092	Loss: 133.565
+25600/69092	Loss: 131.319
+28800/69092	Loss: 130.678
+32000/69092	Loss: 131.773
+35200/69092	Loss: 135.546
+38400/69092	Loss: 133.470
+41600/69092	Loss: 133.065
+44800/69092	Loss: 133.927
+48000/69092	Loss: 132.890
+51200/69092	Loss: 130.915
+54400/69092	Loss: 131.842
+57600/69092	Loss: 129.366
+60800/69092	Loss: 131.157
+64000/69092	Loss: 133.777
+67200/69092	Loss: 132.089
+Training time 0:04:40.821442
+Epoch: 60 Average loss: 132.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 61)
+0/69092	Loss: 149.925
+3200/69092	Loss: 134.578
+6400/69092	Loss: 133.580
+9600/69092	Loss: 132.310
+12800/69092	Loss: 134.810
+16000/69092	Loss: 134.253
+19200/69092	Loss: 133.320
+22400/69092	Loss: 131.336
+25600/69092	Loss: 134.131
+28800/69092	Loss: 131.634
+32000/69092	Loss: 134.895
+35200/69092	Loss: 130.358
+38400/69092	Loss: 134.110
+41600/69092	Loss: 132.002
+44800/69092	Loss: 131.523
+48000/69092	Loss: 129.541
+51200/69092	Loss: 131.935
+54400/69092	Loss: 133.334
+57600/69092	Loss: 131.391
+60800/69092	Loss: 132.812
+64000/69092	Loss: 133.295
+67200/69092	Loss: 131.466
+Training time 0:04:44.859402
+Epoch: 61 Average loss: 132.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 62)
+0/69092	Loss: 131.649
+3200/69092	Loss: 130.858
+6400/69092	Loss: 131.698
+9600/69092	Loss: 130.840
+12800/69092	Loss: 134.111
+16000/69092	Loss: 135.759
+19200/69092	Loss: 134.759
+22400/69092	Loss: 133.252
+25600/69092	Loss: 131.654
+28800/69092	Loss: 132.767
+32000/69092	Loss: 133.286
+35200/69092	Loss: 133.181
+38400/69092	Loss: 131.110
+41600/69092	Loss: 130.842
+44800/69092	Loss: 131.715
+48000/69092	Loss: 130.865
+51200/69092	Loss: 132.387
+54400/69092	Loss: 131.835
+57600/69092	Loss: 134.237
+60800/69092	Loss: 133.573
+64000/69092	Loss: 132.774
+67200/69092	Loss: 132.712
+Training time 0:04:41.388189
+Epoch: 62 Average loss: 132.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 63)
+0/69092	Loss: 128.223
+3200/69092	Loss: 131.041
+6400/69092	Loss: 133.689
+9600/69092	Loss: 133.526
+12800/69092	Loss: 134.404
+16000/69092	Loss: 133.655
+19200/69092	Loss: 130.344
+22400/69092	Loss: 133.791
+25600/69092	Loss: 132.568
+28800/69092	Loss: 131.957
+32000/69092	Loss: 131.541
+35200/69092	Loss: 132.421
+38400/69092	Loss: 133.257
+41600/69092	Loss: 132.507
+44800/69092	Loss: 135.825
+48000/69092	Loss: 132.617
+51200/69092	Loss: 132.709
+54400/69092	Loss: 131.571
+57600/69092	Loss: 132.242
+60800/69092	Loss: 132.903
+64000/69092	Loss: 131.835
+67200/69092	Loss: 133.631
+Training time 0:04:49.618462
+Epoch: 63 Average loss: 132.76
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 64)
+0/69092	Loss: 141.970
+3200/69092	Loss: 132.906
+6400/69092	Loss: 131.589
+9600/69092	Loss: 132.945
+12800/69092	Loss: 131.993
+16000/69092	Loss: 133.720
+19200/69092	Loss: 131.937
+22400/69092	Loss: 133.572
+25600/69092	Loss: 133.384
+28800/69092	Loss: 132.476
+32000/69092	Loss: 134.767
+35200/69092	Loss: 130.999
+38400/69092	Loss: 133.096
+41600/69092	Loss: 132.213
+44800/69092	Loss: 134.635
+48000/69092	Loss: 133.338
+51200/69092	Loss: 131.336
+54400/69092	Loss: 132.595
+57600/69092	Loss: 130.424
+60800/69092	Loss: 133.307
+64000/69092	Loss: 130.981
+67200/69092	Loss: 134.853
+Training time 0:04:49.486074
+Epoch: 64 Average loss: 132.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 65)
+0/69092	Loss: 142.410
+3200/69092	Loss: 132.879
+6400/69092	Loss: 131.708
+9600/69092	Loss: 134.260
+12800/69092	Loss: 134.587
+16000/69092	Loss: 132.342
+19200/69092	Loss: 132.129
+22400/69092	Loss: 133.000
+25600/69092	Loss: 132.074
+28800/69092	Loss: 131.301
+32000/69092	Loss: 134.566
+35200/69092	Loss: 131.667
+38400/69092	Loss: 133.092
+41600/69092	Loss: 131.948
+44800/69092	Loss: 133.808
+48000/69092	Loss: 131.241
+51200/69092	Loss: 135.670
+54400/69092	Loss: 132.278
+57600/69092	Loss: 132.134
+60800/69092	Loss: 130.886
+64000/69092	Loss: 135.610
+67200/69092	Loss: 133.141
+Training time 0:04:50.348031
+Epoch: 65 Average loss: 132.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 66)
+0/69092	Loss: 139.822
+3200/69092	Loss: 130.727
+6400/69092	Loss: 132.551
+9600/69092	Loss: 134.175
+12800/69092	Loss: 133.198
+16000/69092	Loss: 132.715
+19200/69092	Loss: 130.373
+22400/69092	Loss: 133.920
+25600/69092	Loss: 132.062
+28800/69092	Loss: 133.211
+32000/69092	Loss: 133.075
+35200/69092	Loss: 130.958
+38400/69092	Loss: 131.023
+41600/69092	Loss: 135.211
+44800/69092	Loss: 132.886
+48000/69092	Loss: 133.733
+51200/69092	Loss: 133.364
+54400/69092	Loss: 132.806
+57600/69092	Loss: 133.334
+60800/69092	Loss: 132.378
+64000/69092	Loss: 131.962
+67200/69092	Loss: 133.470
+Training time 0:04:49.848154
+Epoch: 66 Average loss: 132.76
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 67)
+0/69092	Loss: 122.181
+3200/69092	Loss: 132.670
+6400/69092	Loss: 131.164
+9600/69092	Loss: 133.716
+12800/69092	Loss: 133.805
+16000/69092	Loss: 132.016
+19200/69092	Loss: 133.238
+22400/69092	Loss: 131.701
+25600/69092	Loss: 133.658
+28800/69092	Loss: 134.396
+32000/69092	Loss: 134.192
+35200/69092	Loss: 133.033
+38400/69092	Loss: 132.378
+41600/69092	Loss: 134.240
+44800/69092	Loss: 132.282
+48000/69092	Loss: 134.094
+51200/69092	Loss: 132.881
+54400/69092	Loss: 131.479
+57600/69092	Loss: 129.058
+60800/69092	Loss: 132.371
+64000/69092	Loss: 132.129
+67200/69092	Loss: 132.735
+Training time 0:04:51.138665
+Epoch: 67 Average loss: 132.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 68)
+0/69092	Loss: 124.340
+3200/69092	Loss: 130.412
+6400/69092	Loss: 132.574
+9600/69092	Loss: 130.767
+12800/69092	Loss: 131.837
+16000/69092	Loss: 134.062
+19200/69092	Loss: 132.242
+22400/69092	Loss: 133.401
+25600/69092	Loss: 131.134
+28800/69092	Loss: 132.820
+32000/69092	Loss: 132.169
+35200/69092	Loss: 131.609
+38400/69092	Loss: 134.158
+41600/69092	Loss: 132.800
+44800/69092	Loss: 134.012
+48000/69092	Loss: 133.530
+51200/69092	Loss: 132.636
+54400/69092	Loss: 132.712
+57600/69092	Loss: 131.364
+60800/69092	Loss: 132.467
+64000/69092	Loss: 135.751
+67200/69092	Loss: 133.907
+Training time 0:04:43.856306
+Epoch: 68 Average loss: 132.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 69)
+0/69092	Loss: 131.655
+3200/69092	Loss: 133.725
+6400/69092	Loss: 130.032
+9600/69092	Loss: 132.432
+12800/69092	Loss: 133.568
+16000/69092	Loss: 135.816
+19200/69092	Loss: 131.504
+22400/69092	Loss: 133.019
+25600/69092	Loss: 130.538
+28800/69092	Loss: 134.134
+32000/69092	Loss: 131.309
+35200/69092	Loss: 131.254
+38400/69092	Loss: 133.626
+41600/69092	Loss: 134.476
+44800/69092	Loss: 134.004
+48000/69092	Loss: 132.131
+51200/69092	Loss: 133.296
+54400/69092	Loss: 132.150
+57600/69092	Loss: 134.057
+60800/69092	Loss: 132.065
+64000/69092	Loss: 133.015
+67200/69092	Loss: 130.422
+Training time 0:04:53.279530
+Epoch: 69 Average loss: 132.66
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 70)
+0/69092	Loss: 125.589
+3200/69092	Loss: 131.118
+6400/69092	Loss: 132.382
+9600/69092	Loss: 133.215
+12800/69092	Loss: 130.796
+16000/69092	Loss: 135.467
+19200/69092	Loss: 132.306
+22400/69092	Loss: 134.098
+25600/69092	Loss: 130.094
+28800/69092	Loss: 132.910
+32000/69092	Loss: 131.109
+35200/69092	Loss: 132.451
+38400/69092	Loss: 131.878
+41600/69092	Loss: 133.722
+44800/69092	Loss: 132.581
+48000/69092	Loss: 133.228
+51200/69092	Loss: 132.824
+54400/69092	Loss: 133.179
+57600/69092	Loss: 132.546
+60800/69092	Loss: 133.130
+64000/69092	Loss: 133.600
+67200/69092	Loss: 133.109
+Training time 0:04:51.803614
+Epoch: 70 Average loss: 132.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 71)
+0/69092	Loss: 117.361
+3200/69092	Loss: 134.069
+6400/69092	Loss: 132.470
+9600/69092	Loss: 133.053
+12800/69092	Loss: 132.501
+16000/69092	Loss: 132.771
+19200/69092	Loss: 133.054
+22400/69092	Loss: 134.746
+25600/69092	Loss: 129.443
+28800/69092	Loss: 132.636
+32000/69092	Loss: 130.268
+35200/69092	Loss: 133.105
+38400/69092	Loss: 132.794
+41600/69092	Loss: 133.814
+44800/69092	Loss: 130.128
+48000/69092	Loss: 131.660
+51200/69092	Loss: 134.778
+54400/69092	Loss: 132.476
+57600/69092	Loss: 133.365
+60800/69092	Loss: 133.058
+64000/69092	Loss: 134.067
+67200/69092	Loss: 132.029
+Training time 0:04:55.168694
+Epoch: 71 Average loss: 132.62
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 72)
+0/69092	Loss: 136.164
+3200/69092	Loss: 131.908
+6400/69092	Loss: 133.864
+9600/69092	Loss: 132.912
+12800/69092	Loss: 132.428
+16000/69092	Loss: 131.046
+19200/69092	Loss: 133.465
+22400/69092	Loss: 132.551
+25600/69092	Loss: 134.417
+28800/69092	Loss: 132.658
+32000/69092	Loss: 130.542
+35200/69092	Loss: 131.479
+38400/69092	Loss: 133.827
+41600/69092	Loss: 132.156
+44800/69092	Loss: 131.939
+48000/69092	Loss: 132.566
+51200/69092	Loss: 132.774
+54400/69092	Loss: 132.511
+57600/69092	Loss: 135.735
+60800/69092	Loss: 133.289
+64000/69092	Loss: 131.164
+67200/69092	Loss: 133.477
+Training time 0:04:51.320410
+Epoch: 72 Average loss: 132.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 73)
+0/69092	Loss: 137.239
+3200/69092	Loss: 131.684
+6400/69092	Loss: 130.119
+9600/69092	Loss: 135.505
+12800/69092	Loss: 131.383
+16000/69092	Loss: 131.100
+19200/69092	Loss: 133.959
+22400/69092	Loss: 131.316
+25600/69092	Loss: 132.879
+28800/69092	Loss: 135.066
+32000/69092	Loss: 130.489
+35200/69092	Loss: 133.174
+38400/69092	Loss: 133.194
+41600/69092	Loss: 133.004
+44800/69092	Loss: 132.902
+48000/69092	Loss: 133.927
+51200/69092	Loss: 133.512
+54400/69092	Loss: 133.162
+57600/69092	Loss: 132.015
+60800/69092	Loss: 132.341
+64000/69092	Loss: 130.212
+67200/69092	Loss: 131.414
+Training time 0:04:50.639351
+Epoch: 73 Average loss: 132.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 74)
+0/69092	Loss: 128.461
+3200/69092	Loss: 133.643
+6400/69092	Loss: 133.056
+9600/69092	Loss: 134.341
+12800/69092	Loss: 130.799
+16000/69092	Loss: 131.329
+19200/69092	Loss: 132.097
+22400/69092	Loss: 133.080
+25600/69092	Loss: 132.457
+28800/69092	Loss: 134.632
+32000/69092	Loss: 131.857
+35200/69092	Loss: 133.437
+38400/69092	Loss: 134.191
+41600/69092	Loss: 132.604
+44800/69092	Loss: 130.977
+48000/69092	Loss: 133.419
+51200/69092	Loss: 132.324
+54400/69092	Loss: 134.801
+57600/69092	Loss: 131.169
+60800/69092	Loss: 133.855
+64000/69092	Loss: 130.546
+67200/69092	Loss: 131.256
+Training time 0:04:43.400141
+Epoch: 74 Average loss: 132.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 75)
+0/69092	Loss: 129.280
+3200/69092	Loss: 128.060
+6400/69092	Loss: 132.713
+9600/69092	Loss: 131.357
+12800/69092	Loss: 132.191
+16000/69092	Loss: 132.008
+19200/69092	Loss: 133.235
+22400/69092	Loss: 133.112
+25600/69092	Loss: 131.919
+28800/69092	Loss: 134.392
+32000/69092	Loss: 134.172
+35200/69092	Loss: 131.547
+38400/69092	Loss: 132.146
+41600/69092	Loss: 132.063
+44800/69092	Loss: 129.239
+48000/69092	Loss: 132.211
+51200/69092	Loss: 133.981
+54400/69092	Loss: 134.989
+57600/69092	Loss: 131.962
+60800/69092	Loss: 133.478
+64000/69092	Loss: 135.140
+67200/69092	Loss: 132.177
+Training time 0:04:49.077664
+Epoch: 75 Average loss: 132.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 76)
+0/69092	Loss: 132.039
+3200/69092	Loss: 132.490
+6400/69092	Loss: 133.999
+9600/69092	Loss: 131.021
+12800/69092	Loss: 133.773
+16000/69092	Loss: 134.095
+19200/69092	Loss: 132.457
+22400/69092	Loss: 133.266
+25600/69092	Loss: 132.737
+28800/69092	Loss: 131.414
+32000/69092	Loss: 132.399
+35200/69092	Loss: 134.236
+38400/69092	Loss: 131.446
+41600/69092	Loss: 132.047
+44800/69092	Loss: 130.692
+48000/69092	Loss: 132.875
+51200/69092	Loss: 134.827
+54400/69092	Loss: 130.094
+57600/69092	Loss: 132.695
+60800/69092	Loss: 132.648
+64000/69092	Loss: 131.729
+67200/69092	Loss: 132.372
+Training time 0:04:50.271656
+Epoch: 76 Average loss: 132.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 77)
+0/69092	Loss: 138.312
+3200/69092	Loss: 133.760
+6400/69092	Loss: 134.814
+9600/69092	Loss: 132.228
+12800/69092	Loss: 130.811
+16000/69092	Loss: 130.341
+19200/69092	Loss: 134.031
+22400/69092	Loss: 133.910
+25600/69092	Loss: 128.844
+28800/69092	Loss: 132.982
+32000/69092	Loss: 132.897
+35200/69092	Loss: 130.602
+38400/69092	Loss: 133.486
+41600/69092	Loss: 131.232
+44800/69092	Loss: 131.308
+48000/69092	Loss: 130.775
+51200/69092	Loss: 134.724
+54400/69092	Loss: 133.027
+57600/69092	Loss: 134.760
+60800/69092	Loss: 129.936
+64000/69092	Loss: 130.772
+67200/69092	Loss: 134.359
+Training time 0:04:49.884341
+Epoch: 77 Average loss: 132.40
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 78)
+0/69092	Loss: 136.987
+3200/69092	Loss: 133.288
+6400/69092	Loss: 134.047
+9600/69092	Loss: 129.458
+12800/69092	Loss: 133.686
+16000/69092	Loss: 130.544
+19200/69092	Loss: 133.436
+22400/69092	Loss: 133.693
+25600/69092	Loss: 131.974
+28800/69092	Loss: 134.353
+32000/69092	Loss: 129.646
+35200/69092	Loss: 131.835
+38400/69092	Loss: 132.434
+41600/69092	Loss: 132.363
+44800/69092	Loss: 131.716
+48000/69092	Loss: 133.753
+51200/69092	Loss: 131.988
+54400/69092	Loss: 131.147
+57600/69092	Loss: 131.306
+60800/69092	Loss: 130.084
+64000/69092	Loss: 134.025
+67200/69092	Loss: 133.127
+Training time 0:04:47.500596
+Epoch: 78 Average loss: 132.32
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 79)
+0/69092	Loss: 142.447
+3200/69092	Loss: 132.908
+6400/69092	Loss: 133.935
+9600/69092	Loss: 131.297
+12800/69092	Loss: 130.220
+16000/69092	Loss: 132.283
+19200/69092	Loss: 132.330
+22400/69092	Loss: 132.251
+25600/69092	Loss: 133.363
+28800/69092	Loss: 130.896
+32000/69092	Loss: 132.771
+35200/69092	Loss: 133.911
+38400/69092	Loss: 128.730
+41600/69092	Loss: 132.264
+44800/69092	Loss: 132.691
+48000/69092	Loss: 132.675
+51200/69092	Loss: 132.761
+54400/69092	Loss: 134.194
+57600/69092	Loss: 133.052
+60800/69092	Loss: 133.198
+64000/69092	Loss: 131.895
+67200/69092	Loss: 131.477
+Training time 0:04:42.610289
+Epoch: 79 Average loss: 132.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 80)
+0/69092	Loss: 133.878
+3200/69092	Loss: 131.946
+6400/69092	Loss: 131.534
+9600/69092	Loss: 134.315
+12800/69092	Loss: 131.719
+16000/69092	Loss: 133.018
+19200/69092	Loss: 131.079
+22400/69092	Loss: 133.072
+25600/69092	Loss: 131.219
+28800/69092	Loss: 132.463
+32000/69092	Loss: 129.932
+35200/69092	Loss: 132.161
+38400/69092	Loss: 131.843
+41600/69092	Loss: 134.582
+44800/69092	Loss: 131.029
+48000/69092	Loss: 136.120
+51200/69092	Loss: 131.440
+54400/69092	Loss: 133.946
+57600/69092	Loss: 131.097
+60800/69092	Loss: 131.184
+64000/69092	Loss: 134.055
+67200/69092	Loss: 133.600
+Training time 0:04:50.732455
+Epoch: 80 Average loss: 132.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last' (iter 81)
+0/69092	Loss: 147.651
+3200/69092	Loss: 133.330
+6400/69092	Loss: 132.364
+9600/69092	Loss: 131.480
+12800/69092	Loss: 134.336
diff --git a/OAR.2068295.stderr b/OAR.2068295.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..a9c628a29c58ebc65df754dd5dfbbd99e2282b5f
--- /dev/null
+++ b/OAR.2068295.stderr
@@ -0,0 +1,2 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+  warnings.warn(warning.format(ret))
diff --git a/OAR.2068295.stdout b/OAR.2068295.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..dfa55f8083d1d3fcbcdfc745044cc4de402325bf
--- /dev/null
+++ b/OAR.2068295.stdout
@@ -0,0 +1,2193 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64_ls_10_lr_1e_3', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64_ls_10_lr_1e_3
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+  (module): VAE(
+    (img_to_last_conv): Sequential(
+      (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (1): ReLU()
+      (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+    )
+    (last_conv_to_continuous_features): Sequential(
+      (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+    )
+    (features_to_hidden_continue): Sequential(
+      (0): Linear(in_features=256, out_features=20, bias=True)
+      (1): ReLU()
+    )
+    (latent_to_features): Sequential(
+      (0): Linear(in_features=10, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (features_to_img): Sequential(
+      (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+      (1): ReLU()
+      (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (3): ReLU()
+      (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (5): ReLU()
+      (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (7): ReLU()
+      (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+      (9): Sigmoid()
+    )
+  )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last (iter 2)'
+0/69092	Loss: 375.625
+3200/69092	Loss: 446.785
+6400/69092	Loss: 440.296
+9600/69092	Loss: 442.574
+12800/69092	Loss: 432.406
+16000/69092	Loss: 444.233
+19200/69092	Loss: 435.788
+22400/69092	Loss: 440.217
+25600/69092	Loss: 443.591
+28800/69092	Loss: 444.238
+32000/69092	Loss: 434.090
+35200/69092	Loss: 427.514
+38400/69092	Loss: 439.384
+41600/69092	Loss: 434.977
+44800/69092	Loss: 449.689
+48000/69092	Loss: 446.574
+51200/69092	Loss: 439.118
+54400/69092	Loss: 444.995
+57600/69092	Loss: 434.926
+60800/69092	Loss: 441.292
+64000/69092	Loss: 427.845
+67200/69092	Loss: 432.656
+Training time 0:04:34.051671
+Epoch: 1 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 3)
+0/69092	Loss: 393.832
+3200/69092	Loss: 438.616
+6400/69092	Loss: 431.874
+9600/69092	Loss: 455.541
+12800/69092	Loss: 440.252
+16000/69092	Loss: 440.665
+19200/69092	Loss: 435.808
+22400/69092	Loss: 437.461
+25600/69092	Loss: 444.880
+28800/69092	Loss: 435.920
+32000/69092	Loss: 447.494
+35200/69092	Loss: 431.046
+38400/69092	Loss: 437.620
+41600/69092	Loss: 442.615
+44800/69092	Loss: 436.568
+48000/69092	Loss: 443.203
+51200/69092	Loss: 440.405
+54400/69092	Loss: 436.639
+57600/69092	Loss: 432.852
+60800/69092	Loss: 432.166
+64000/69092	Loss: 434.953
+67200/69092	Loss: 441.264
+Training time 0:04:31.395650
+Epoch: 2 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 4)
+0/69092	Loss: 422.917
+3200/69092	Loss: 431.748
+6400/69092	Loss: 447.314
+9600/69092	Loss: 436.361
+12800/69092	Loss: 444.990
+16000/69092	Loss: 442.628
+19200/69092	Loss: 444.761
+22400/69092	Loss: 442.232
+25600/69092	Loss: 438.262
+28800/69092	Loss: 431.871
+32000/69092	Loss: 439.934
+35200/69092	Loss: 435.968
+38400/69092	Loss: 426.521
+41600/69092	Loss: 441.102
+44800/69092	Loss: 444.118
+48000/69092	Loss: 434.464
+51200/69092	Loss: 430.311
+54400/69092	Loss: 445.226
+57600/69092	Loss: 444.504
+60800/69092	Loss: 438.852
+64000/69092	Loss: 440.893
+67200/69092	Loss: 436.566
+Training time 0:04:29.906735
+Epoch: 3 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 5)
+0/69092	Loss: 415.262
+3200/69092	Loss: 448.800
+6400/69092	Loss: 437.634
+9600/69092	Loss: 438.002
+12800/69092	Loss: 431.911
+16000/69092	Loss: 442.412
+19200/69092	Loss: 435.215
+22400/69092	Loss: 441.085
+25600/69092	Loss: 445.418
+28800/69092	Loss: 448.736
+32000/69092	Loss: 435.353
+35200/69092	Loss: 439.455
+38400/69092	Loss: 435.531
+41600/69092	Loss: 437.956
+44800/69092	Loss: 446.065
+48000/69092	Loss: 444.811
+51200/69092	Loss: 431.132
+54400/69092	Loss: 432.654
+57600/69092	Loss: 433.304
+60800/69092	Loss: 436.254
+64000/69092	Loss: 445.370
+67200/69092	Loss: 438.517
+Training time 0:04:26.361455
+Epoch: 4 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 6)
+0/69092	Loss: 449.209
+3200/69092	Loss: 440.689
+6400/69092	Loss: 436.107
+9600/69092	Loss: 434.786
+12800/69092	Loss: 437.954
+16000/69092	Loss: 439.338
+19200/69092	Loss: 439.716
+22400/69092	Loss: 441.164
+25600/69092	Loss: 430.412
+28800/69092	Loss: 440.419
+32000/69092	Loss: 447.638
+35200/69092	Loss: 435.944
+38400/69092	Loss: 441.802
+41600/69092	Loss: 435.020
+44800/69092	Loss: 435.082
+48000/69092	Loss: 443.907
+51200/69092	Loss: 427.800
+54400/69092	Loss: 447.827
+57600/69092	Loss: 435.002
+60800/69092	Loss: 445.449
+64000/69092	Loss: 431.835
+67200/69092	Loss: 452.837
+Training time 0:04:35.682005
+Epoch: 5 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 7)
+0/69092	Loss: 413.826
+3200/69092	Loss: 443.325
+6400/69092	Loss: 437.337
+9600/69092	Loss: 434.842
+12800/69092	Loss: 435.281
+16000/69092	Loss: 444.859
+19200/69092	Loss: 438.488
+22400/69092	Loss: 438.252
+25600/69092	Loss: 441.747
+28800/69092	Loss: 447.335
+32000/69092	Loss: 447.572
+35200/69092	Loss: 435.608
+38400/69092	Loss: 434.339
+41600/69092	Loss: 435.066
+44800/69092	Loss: 432.960
+48000/69092	Loss: 435.379
+51200/69092	Loss: 431.384
+54400/69092	Loss: 440.550
+57600/69092	Loss: 450.134
+60800/69092	Loss: 431.891
+64000/69092	Loss: 435.316
+67200/69092	Loss: 446.095
+Training time 0:04:32.055193
+Epoch: 6 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 8)
+0/69092	Loss: 410.914
+3200/69092	Loss: 443.680
+6400/69092	Loss: 421.163
+9600/69092	Loss: 446.949
+12800/69092	Loss: 433.857
+16000/69092	Loss: 436.575
+19200/69092	Loss: 434.585
+22400/69092	Loss: 435.087
+25600/69092	Loss: 449.742
+28800/69092	Loss: 440.855
+32000/69092	Loss: 443.160
+35200/69092	Loss: 437.213
+38400/69092	Loss: 443.910
+41600/69092	Loss: 438.458
+44800/69092	Loss: 438.994
+48000/69092	Loss: 441.874
+51200/69092	Loss: 438.175
+54400/69092	Loss: 430.585
+57600/69092	Loss: 445.506
+60800/69092	Loss: 440.340
+64000/69092	Loss: 440.323
+67200/69092	Loss: 439.424
+Training time 0:04:24.081298
+Epoch: 7 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 9)
+0/69092	Loss: 422.780
+3200/69092	Loss: 448.578
+6400/69092	Loss: 430.252
+9600/69092	Loss: 441.269
+12800/69092	Loss: 436.205
+16000/69092	Loss: 443.399
+19200/69092	Loss: 441.904
+22400/69092	Loss: 444.786
+25600/69092	Loss: 437.944
+28800/69092	Loss: 450.518
+32000/69092	Loss: 432.025
+35200/69092	Loss: 432.558
+38400/69092	Loss: 439.379
+41600/69092	Loss: 446.042
+44800/69092	Loss: 436.374
+48000/69092	Loss: 451.215
+51200/69092	Loss: 432.161
+54400/69092	Loss: 424.488
+57600/69092	Loss: 434.085
+60800/69092	Loss: 438.740
+64000/69092	Loss: 440.643
+67200/69092	Loss: 441.024
+Training time 0:04:22.898986
+Epoch: 8 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 10)
+0/69092	Loss: 442.409
+3200/69092	Loss: 445.771
+6400/69092	Loss: 437.414
+9600/69092	Loss: 450.510
+12800/69092	Loss: 441.865
+16000/69092	Loss: 429.933
+19200/69092	Loss: 434.439
+22400/69092	Loss: 436.837
+25600/69092	Loss: 443.226
+28800/69092	Loss: 445.913
+32000/69092	Loss: 435.719
+35200/69092	Loss: 436.262
+38400/69092	Loss: 426.778
+41600/69092	Loss: 444.944
+44800/69092	Loss: 438.147
+48000/69092	Loss: 437.067
+51200/69092	Loss: 431.721
+54400/69092	Loss: 443.098
+57600/69092	Loss: 450.341
+60800/69092	Loss: 437.374
+64000/69092	Loss: 441.980
+67200/69092	Loss: 429.471
+Training time 0:04:25.848194
+Epoch: 9 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 11)
+0/69092	Loss: 464.626
+3200/69092	Loss: 432.685
+6400/69092	Loss: 438.780
+9600/69092	Loss: 446.017
+12800/69092	Loss: 434.559
+16000/69092	Loss: 439.762
+19200/69092	Loss: 439.569
+22400/69092	Loss: 438.673
+25600/69092	Loss: 440.022
+28800/69092	Loss: 444.908
+32000/69092	Loss: 436.992
+35200/69092	Loss: 442.352
+38400/69092	Loss: 441.576
+41600/69092	Loss: 436.198
+44800/69092	Loss: 433.849
+48000/69092	Loss: 440.041
+51200/69092	Loss: 437.511
+54400/69092	Loss: 440.434
+57600/69092	Loss: 430.454
+60800/69092	Loss: 441.230
+64000/69092	Loss: 436.647
+67200/69092	Loss: 447.057
+Training time 0:04:28.407033
+Epoch: 10 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 12)
+0/69092	Loss: 451.777
+3200/69092	Loss: 431.351
+6400/69092	Loss: 436.055
+9600/69092	Loss: 441.894
+12800/69092	Loss: 443.788
+16000/69092	Loss: 441.224
+19200/69092	Loss: 442.755
+22400/69092	Loss: 429.945
+25600/69092	Loss: 435.642
+28800/69092	Loss: 439.928
+32000/69092	Loss: 436.072
+35200/69092	Loss: 435.795
+38400/69092	Loss: 433.844
+41600/69092	Loss: 438.198
+44800/69092	Loss: 442.656
+48000/69092	Loss: 439.857
+51200/69092	Loss: 437.720
+54400/69092	Loss: 440.369
+57600/69092	Loss: 439.407
+60800/69092	Loss: 445.087
+64000/69092	Loss: 446.632
+67200/69092	Loss: 446.490
+Training time 0:04:36.721568
+Epoch: 11 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 13)
+0/69092	Loss: 445.309
+3200/69092	Loss: 445.610
+6400/69092	Loss: 440.409
+9600/69092	Loss: 435.306
+12800/69092	Loss: 446.171
+16000/69092	Loss: 428.434
+19200/69092	Loss: 435.219
+22400/69092	Loss: 440.951
+25600/69092	Loss: 437.595
+28800/69092	Loss: 445.892
+32000/69092	Loss: 444.479
+35200/69092	Loss: 429.088
+38400/69092	Loss: 439.665
+41600/69092	Loss: 439.643
+44800/69092	Loss: 437.927
+48000/69092	Loss: 436.874
+51200/69092	Loss: 432.499
+54400/69092	Loss: 440.864
+57600/69092	Loss: 434.907
+60800/69092	Loss: 435.699
+64000/69092	Loss: 451.284
+67200/69092	Loss: 441.631
+Training time 0:04:31.425226
+Epoch: 12 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 14)
+0/69092	Loss: 391.987
+3200/69092	Loss: 439.455
+6400/69092	Loss: 438.929
+9600/69092	Loss: 438.123
+12800/69092	Loss: 438.160
+16000/69092	Loss: 436.873
+19200/69092	Loss: 432.016
+22400/69092	Loss: 443.506
+25600/69092	Loss: 442.866
+28800/69092	Loss: 432.541
+32000/69092	Loss: 439.689
+35200/69092	Loss: 432.838
+38400/69092	Loss: 438.098
+41600/69092	Loss: 433.655
+44800/69092	Loss: 454.539
+48000/69092	Loss: 433.450
+51200/69092	Loss: 429.925
+54400/69092	Loss: 435.827
+57600/69092	Loss: 443.370
+60800/69092	Loss: 449.191
+64000/69092	Loss: 437.884
+67200/69092	Loss: 442.179
+Training time 0:04:31.222503
+Epoch: 13 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 15)
+0/69092	Loss: 395.253
+3200/69092	Loss: 443.331
+6400/69092	Loss: 451.115
+9600/69092	Loss: 444.352
+12800/69092	Loss: 434.585
+16000/69092	Loss: 433.416
+19200/69092	Loss: 439.680
+22400/69092	Loss: 434.093
+25600/69092	Loss: 434.002
+28800/69092	Loss: 432.102
+32000/69092	Loss: 444.747
+35200/69092	Loss: 441.676
+38400/69092	Loss: 454.103
+41600/69092	Loss: 454.494
+44800/69092	Loss: 438.996
+48000/69092	Loss: 434.560
+51200/69092	Loss: 439.567
+54400/69092	Loss: 432.892
+57600/69092	Loss: 437.043
+60800/69092	Loss: 430.473
+64000/69092	Loss: 428.671
+67200/69092	Loss: 439.237
+Training time 0:04:29.380603
+Epoch: 14 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 16)
+0/69092	Loss: 400.240
+3200/69092	Loss: 450.573
+6400/69092	Loss: 442.114
+9600/69092	Loss: 442.227
+12800/69092	Loss: 448.687
+16000/69092	Loss: 428.751
+19200/69092	Loss: 439.393
+22400/69092	Loss: 439.926
+25600/69092	Loss: 446.699
+28800/69092	Loss: 437.067
+32000/69092	Loss: 444.480
+35200/69092	Loss: 436.639
+38400/69092	Loss: 441.261
+41600/69092	Loss: 442.127
+44800/69092	Loss: 427.793
+48000/69092	Loss: 432.717
+51200/69092	Loss: 434.227
+54400/69092	Loss: 431.206
+57600/69092	Loss: 434.003
+60800/69092	Loss: 436.200
+64000/69092	Loss: 437.519
+67200/69092	Loss: 441.965
+Training time 0:04:28.812572
+Epoch: 15 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 17)
+0/69092	Loss: 498.240
+3200/69092	Loss: 440.474
+6400/69092	Loss: 443.826
+9600/69092	Loss: 427.633
+12800/69092	Loss: 432.060
+16000/69092	Loss: 437.623
+19200/69092	Loss: 446.722
+22400/69092	Loss: 447.293
+25600/69092	Loss: 443.333
+28800/69092	Loss: 440.026
+32000/69092	Loss: 431.689
+35200/69092	Loss: 445.803
+38400/69092	Loss: 425.248
+41600/69092	Loss: 435.430
+44800/69092	Loss: 430.713
+48000/69092	Loss: 449.797
+51200/69092	Loss: 445.763
+54400/69092	Loss: 444.885
+57600/69092	Loss: 443.544
+60800/69092	Loss: 442.726
+64000/69092	Loss: 433.580
+67200/69092	Loss: 434.089
+Training time 0:04:27.019803
+Epoch: 16 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 18)
+0/69092	Loss: 466.280
+3200/69092	Loss: 444.208
+6400/69092	Loss: 430.691
+9600/69092	Loss: 440.609
+12800/69092	Loss: 444.150
+16000/69092	Loss: 433.251
+19200/69092	Loss: 440.078
+22400/69092	Loss: 434.438
+25600/69092	Loss: 437.736
+28800/69092	Loss: 442.102
+32000/69092	Loss: 448.164
+35200/69092	Loss: 429.432
+38400/69092	Loss: 452.784
+41600/69092	Loss: 442.916
+44800/69092	Loss: 437.249
+48000/69092	Loss: 440.515
+51200/69092	Loss: 426.367
+54400/69092	Loss: 437.090
+57600/69092	Loss: 442.061
+60800/69092	Loss: 436.515
+64000/69092	Loss: 439.174
+67200/69092	Loss: 436.353
+Training time 0:04:19.161998
+Epoch: 17 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 19)
+0/69092	Loss: 387.399
+3200/69092	Loss: 443.479
+6400/69092	Loss: 437.514
+9600/69092	Loss: 438.330
+12800/69092	Loss: 436.930
+16000/69092	Loss: 438.073
+19200/69092	Loss: 429.599
+22400/69092	Loss: 452.687
+25600/69092	Loss: 442.682
+28800/69092	Loss: 433.250
+32000/69092	Loss: 432.935
+35200/69092	Loss: 443.827
+38400/69092	Loss: 443.910
+41600/69092	Loss: 428.557
+44800/69092	Loss: 435.339
+48000/69092	Loss: 439.639
+51200/69092	Loss: 443.590
+54400/69092	Loss: 443.773
+57600/69092	Loss: 443.634
+60800/69092	Loss: 433.950
+64000/69092	Loss: 440.524
+67200/69092	Loss: 433.771
+Training time 0:04:27.899715
+Epoch: 18 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 20)
+0/69092	Loss: 517.758
+3200/69092	Loss: 437.515
+6400/69092	Loss: 438.680
+9600/69092	Loss: 430.177
+12800/69092	Loss: 442.590
+16000/69092	Loss: 444.649
+19200/69092	Loss: 439.902
+22400/69092	Loss: 434.947
+25600/69092	Loss: 446.782
+28800/69092	Loss: 448.020
+32000/69092	Loss: 449.653
+35200/69092	Loss: 436.316
+38400/69092	Loss: 440.670
+41600/69092	Loss: 440.831
+44800/69092	Loss: 433.954
+48000/69092	Loss: 436.466
+51200/69092	Loss: 433.538
+54400/69092	Loss: 446.983
+57600/69092	Loss: 433.267
+60800/69092	Loss: 435.412
+64000/69092	Loss: 439.729
+67200/69092	Loss: 433.043
+Training time 0:04:18.605166
+Epoch: 19 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 21)
+0/69092	Loss: 468.855
+3200/69092	Loss: 435.587
+6400/69092	Loss: 433.519
+9600/69092	Loss: 441.110
+12800/69092	Loss: 448.535
+16000/69092	Loss: 435.916
+19200/69092	Loss: 442.000
+22400/69092	Loss: 432.518
+25600/69092	Loss: 437.813
+28800/69092	Loss: 440.261
+32000/69092	Loss: 451.433
+35200/69092	Loss: 432.196
+38400/69092	Loss: 429.665
+41600/69092	Loss: 434.087
+44800/69092	Loss: 452.219
+48000/69092	Loss: 441.694
+51200/69092	Loss: 443.547
+54400/69092	Loss: 428.570
+57600/69092	Loss: 434.825
+60800/69092	Loss: 442.841
+64000/69092	Loss: 446.499
+67200/69092	Loss: 435.330
+Training time 0:04:26.089858
+Epoch: 20 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 22)
+0/69092	Loss: 399.025
+3200/69092	Loss: 439.286
+6400/69092	Loss: 437.546
+9600/69092	Loss: 437.893
+12800/69092	Loss: 439.831
+16000/69092	Loss: 441.731
+19200/69092	Loss: 435.240
+22400/69092	Loss: 446.213
+25600/69092	Loss: 440.672
+28800/69092	Loss: 436.112
+32000/69092	Loss: 437.153
+35200/69092	Loss: 432.120
+38400/69092	Loss: 434.109
+41600/69092	Loss: 448.263
+44800/69092	Loss: 444.124
+48000/69092	Loss: 443.655
+51200/69092	Loss: 439.393
+54400/69092	Loss: 440.949
+57600/69092	Loss: 428.376
+60800/69092	Loss: 444.703
+64000/69092	Loss: 439.446
+67200/69092	Loss: 438.540
+Training time 0:04:24.569566
+Epoch: 21 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 23)
+0/69092	Loss: 447.055
+3200/69092	Loss: 444.572
+6400/69092	Loss: 442.419
+9600/69092	Loss: 446.406
+12800/69092	Loss: 429.402
+16000/69092	Loss: 442.351
+19200/69092	Loss: 435.744
+22400/69092	Loss: 435.037
+25600/69092	Loss: 432.900
+28800/69092	Loss: 430.895
+32000/69092	Loss: 445.457
+35200/69092	Loss: 443.495
+38400/69092	Loss: 440.882
+41600/69092	Loss: 439.615
+44800/69092	Loss: 435.154
+48000/69092	Loss: 433.825
+51200/69092	Loss: 442.148
+54400/69092	Loss: 441.451
+57600/69092	Loss: 434.547
+60800/69092	Loss: 441.309
+64000/69092	Loss: 435.940
+67200/69092	Loss: 439.713
+Training time 0:04:22.992704
+Epoch: 22 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 24)
+0/69092	Loss: 389.986
+3200/69092	Loss: 437.453
+6400/69092	Loss: 428.187
+9600/69092	Loss: 437.500
+12800/69092	Loss: 440.849
+16000/69092	Loss: 446.903
+19200/69092	Loss: 435.664
+22400/69092	Loss: 435.958
+25600/69092	Loss: 437.770
+28800/69092	Loss: 439.976
+32000/69092	Loss: 434.114
+35200/69092	Loss: 450.093
+38400/69092	Loss: 446.469
+41600/69092	Loss: 423.855
+44800/69092	Loss: 442.047
+48000/69092	Loss: 445.983
+51200/69092	Loss: 439.791
+54400/69092	Loss: 430.888
+57600/69092	Loss: 446.467
+60800/69092	Loss: 430.531
+64000/69092	Loss: 451.369
+67200/69092	Loss: 440.697
+Training time 0:04:27.871353
+Epoch: 23 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 25)
+0/69092	Loss: 534.593
+3200/69092	Loss: 435.368
+6400/69092	Loss: 443.011
+9600/69092	Loss: 443.323
+12800/69092	Loss: 443.329
+16000/69092	Loss: 439.366
+19200/69092	Loss: 435.409
+22400/69092	Loss: 434.417
+25600/69092	Loss: 447.081
+28800/69092	Loss: 427.311
+32000/69092	Loss: 437.893
+35200/69092	Loss: 448.450
+38400/69092	Loss: 430.901
+41600/69092	Loss: 441.879
+44800/69092	Loss: 433.479
+48000/69092	Loss: 431.285
+51200/69092	Loss: 449.887
+54400/69092	Loss: 438.692
+57600/69092	Loss: 439.121
+60800/69092	Loss: 438.396
+64000/69092	Loss: 448.568
+67200/69092	Loss: 434.511
+Training time 0:04:20.548591
+Epoch: 24 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 26)
+0/69092	Loss: 476.164
+3200/69092	Loss: 436.696
+6400/69092	Loss: 446.213
+9600/69092	Loss: 450.422
+12800/69092	Loss: 432.899
+16000/69092	Loss: 436.943
+19200/69092	Loss: 436.398
+22400/69092	Loss: 436.425
+25600/69092	Loss: 439.782
+28800/69092	Loss: 433.574
+32000/69092	Loss: 436.817
+35200/69092	Loss: 446.180
+38400/69092	Loss: 443.506
+41600/69092	Loss: 439.607
+44800/69092	Loss: 440.577
+48000/69092	Loss: 438.207
+51200/69092	Loss: 433.010
+54400/69092	Loss: 444.056
+57600/69092	Loss: 439.147
+60800/69092	Loss: 440.633
+64000/69092	Loss: 440.489
+67200/69092	Loss: 436.375
+Training time 0:04:19.057597
+Epoch: 25 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 27)
+0/69092	Loss: 374.643
+3200/69092	Loss: 445.066
+6400/69092	Loss: 437.406
+9600/69092	Loss: 433.901
+12800/69092	Loss: 445.341
+16000/69092	Loss: 445.741
+19200/69092	Loss: 427.750
+22400/69092	Loss: 430.690
+25600/69092	Loss: 430.201
+28800/69092	Loss: 432.028
+32000/69092	Loss: 450.946
+35200/69092	Loss: 435.227
+38400/69092	Loss: 446.144
+41600/69092	Loss: 438.084
+44800/69092	Loss: 432.434
+48000/69092	Loss: 442.399
+51200/69092	Loss: 442.481
+54400/69092	Loss: 438.879
+57600/69092	Loss: 442.300
+60800/69092	Loss: 440.708
+64000/69092	Loss: 433.623
+67200/69092	Loss: 451.991
+Training time 0:04:26.867117
+Epoch: 26 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 28)
+0/69092	Loss: 411.831
+3200/69092	Loss: 438.035
+6400/69092	Loss: 444.905
+9600/69092	Loss: 433.037
+12800/69092	Loss: 436.386
+16000/69092	Loss: 443.999
+19200/69092	Loss: 430.444
+22400/69092	Loss: 440.358
+25600/69092	Loss: 430.060
+28800/69092	Loss: 429.699
+32000/69092	Loss: 440.840
+35200/69092	Loss: 432.152
+38400/69092	Loss: 436.628
+41600/69092	Loss: 440.554
+44800/69092	Loss: 455.091
+48000/69092	Loss: 442.408
+51200/69092	Loss: 450.611
+54400/69092	Loss: 430.677
+57600/69092	Loss: 447.235
+60800/69092	Loss: 442.394
+64000/69092	Loss: 437.629
+67200/69092	Loss: 443.945
+Training time 0:04:19.868080
+Epoch: 27 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 29)
+0/69092	Loss: 463.077
+3200/69092	Loss: 455.815
+6400/69092	Loss: 431.184
+9600/69092	Loss: 431.235
+12800/69092	Loss: 440.467
+16000/69092	Loss: 442.644
+19200/69092	Loss: 450.845
+22400/69092	Loss: 444.505
+25600/69092	Loss: 441.152
+28800/69092	Loss: 433.715
+32000/69092	Loss: 437.446
+35200/69092	Loss: 437.977
+38400/69092	Loss: 442.337
+41600/69092	Loss: 428.539
+44800/69092	Loss: 445.475
+48000/69092	Loss: 427.866
+51200/69092	Loss: 440.335
+54400/69092	Loss: 423.947
+57600/69092	Loss: 441.449
+60800/69092	Loss: 436.196
+64000/69092	Loss: 443.439
+67200/69092	Loss: 439.903
+Training time 0:04:30.814036
+Epoch: 28 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 30)
+0/69092	Loss: 405.135
+3200/69092	Loss: 452.535
+6400/69092	Loss: 435.374
+9600/69092	Loss: 436.959
+12800/69092	Loss: 444.434
+16000/69092	Loss: 440.027
+19200/69092	Loss: 440.817
+22400/69092	Loss: 444.350
+25600/69092	Loss: 441.136
+28800/69092	Loss: 438.674
+32000/69092	Loss: 434.921
+35200/69092	Loss: 434.902
+38400/69092	Loss: 444.388
+41600/69092	Loss: 427.179
+44800/69092	Loss: 435.924
+48000/69092	Loss: 436.628
+51200/69092	Loss: 443.645
+54400/69092	Loss: 428.798
+57600/69092	Loss: 440.182
+60800/69092	Loss: 436.767
+64000/69092	Loss: 442.607
+67200/69092	Loss: 438.407
+Training time 0:04:24.017043
+Epoch: 29 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 31)
+0/69092	Loss: 476.353
+3200/69092	Loss: 433.756
+6400/69092	Loss: 440.207
+9600/69092	Loss: 448.995
+12800/69092	Loss: 444.782
+16000/69092	Loss: 429.639
+19200/69092	Loss: 444.561
+22400/69092	Loss: 447.386
+25600/69092	Loss: 438.468
+28800/69092	Loss: 433.601
+32000/69092	Loss: 431.844
+35200/69092	Loss: 450.702
+38400/69092	Loss: 442.945
+41600/69092	Loss: 443.027
+44800/69092	Loss: 434.272
+48000/69092	Loss: 442.576
+51200/69092	Loss: 433.732
+54400/69092	Loss: 437.670
+57600/69092	Loss: 434.728
+60800/69092	Loss: 440.396
+64000/69092	Loss: 431.227
+67200/69092	Loss: 434.757
+Training time 0:04:30.715730
+Epoch: 30 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 32)
+0/69092	Loss: 481.012
+3200/69092	Loss: 444.196
+6400/69092	Loss: 443.710
+9600/69092	Loss: 437.834
+12800/69092	Loss: 434.136
+16000/69092	Loss: 438.710
+19200/69092	Loss: 449.580
+22400/69092	Loss: 434.762
+25600/69092	Loss: 437.236
+28800/69092	Loss: 433.502
+32000/69092	Loss: 445.858
+35200/69092	Loss: 436.958
+38400/69092	Loss: 432.003
+41600/69092	Loss: 435.567
+44800/69092	Loss: 430.610
+48000/69092	Loss: 438.893
+51200/69092	Loss: 438.334
+54400/69092	Loss: 448.751
+57600/69092	Loss: 437.368
+60800/69092	Loss: 437.962
+64000/69092	Loss: 440.413
+67200/69092	Loss: 447.322
+Training time 0:04:26.265103
+Epoch: 31 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 33)
+0/69092	Loss: 400.108
+3200/69092	Loss: 437.015
+6400/69092	Loss: 437.442
+9600/69092	Loss: 435.463
+12800/69092	Loss: 442.450
+16000/69092	Loss: 439.221
+19200/69092	Loss: 438.020
+22400/69092	Loss: 436.137
+25600/69092	Loss: 436.487
+28800/69092	Loss: 427.598
+32000/69092	Loss: 430.596
+35200/69092	Loss: 437.139
+38400/69092	Loss: 445.967
+41600/69092	Loss: 445.456
+44800/69092	Loss: 435.217
+48000/69092	Loss: 446.861
+51200/69092	Loss: 449.337
+54400/69092	Loss: 441.523
+57600/69092	Loss: 430.402
+60800/69092	Loss: 443.866
+64000/69092	Loss: 445.363
+67200/69092	Loss: 439.684
+Training time 0:04:25.301353
+Epoch: 32 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 34)
+0/69092	Loss: 448.684
+3200/69092	Loss: 435.749
+6400/69092	Loss: 432.473
+9600/69092	Loss: 435.465
+12800/69092	Loss: 435.356
+16000/69092	Loss: 440.362
+19200/69092	Loss: 444.648
+22400/69092	Loss: 435.983
+25600/69092	Loss: 441.671
+28800/69092	Loss: 435.129
+32000/69092	Loss: 431.571
+35200/69092	Loss: 440.624
+38400/69092	Loss: 437.530
+41600/69092	Loss: 428.413
+44800/69092	Loss: 432.629
+48000/69092	Loss: 443.885
+51200/69092	Loss: 444.145
+54400/69092	Loss: 452.289
+57600/69092	Loss: 444.870
+60800/69092	Loss: 436.989
+64000/69092	Loss: 441.231
+67200/69092	Loss: 444.663
+Training time 0:04:33.404934
+Epoch: 33 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 35)
+0/69092	Loss: 436.446
+3200/69092	Loss: 447.517
+6400/69092	Loss: 440.830
+9600/69092	Loss: 435.077
+12800/69092	Loss: 444.663
+16000/69092	Loss: 438.343
+19200/69092	Loss: 436.011
+22400/69092	Loss: 444.873
+25600/69092	Loss: 441.715
+28800/69092	Loss: 436.985
+32000/69092	Loss: 440.298
+35200/69092	Loss: 433.009
+38400/69092	Loss: 437.177
+41600/69092	Loss: 450.954
+44800/69092	Loss: 439.261
+48000/69092	Loss: 437.136
+51200/69092	Loss: 433.217
+54400/69092	Loss: 440.091
+57600/69092	Loss: 435.368
+60800/69092	Loss: 436.326
+64000/69092	Loss: 432.034
+67200/69092	Loss: 439.265
+Training time 0:04:16.514513
+Epoch: 34 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 36)
+0/69092	Loss: 403.806
+3200/69092	Loss: 438.807
+6400/69092	Loss: 432.434
+9600/69092	Loss: 443.323
+12800/69092	Loss: 446.873
+16000/69092	Loss: 437.214
+19200/69092	Loss: 434.220
+22400/69092	Loss: 442.977
+25600/69092	Loss: 437.363
+28800/69092	Loss: 431.058
+32000/69092	Loss: 439.841
+35200/69092	Loss: 437.261
+38400/69092	Loss: 437.091
+41600/69092	Loss: 445.562
+44800/69092	Loss: 447.495
+48000/69092	Loss: 449.808
+51200/69092	Loss: 437.370
+54400/69092	Loss: 443.357
+57600/69092	Loss: 436.630
+60800/69092	Loss: 435.718
+64000/69092	Loss: 427.944
+67200/69092	Loss: 438.632
+Training time 0:04:22.187666
+Epoch: 35 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 37)
+0/69092	Loss: 454.148
+3200/69092	Loss: 430.643
+6400/69092	Loss: 449.694
+9600/69092	Loss: 444.262
+12800/69092	Loss: 435.892
+16000/69092	Loss: 437.207
+19200/69092	Loss: 448.424
+22400/69092	Loss: 433.562
+25600/69092	Loss: 438.894
+28800/69092	Loss: 428.642
+32000/69092	Loss: 433.974
+35200/69092	Loss: 432.721
+38400/69092	Loss: 431.000
+41600/69092	Loss: 443.778
+44800/69092	Loss: 437.898
+48000/69092	Loss: 437.710
+51200/69092	Loss: 446.022
+54400/69092	Loss: 442.456
+57600/69092	Loss: 443.119
+60800/69092	Loss: 441.017
+64000/69092	Loss: 438.171
+67200/69092	Loss: 441.328
+Training time 0:04:23.534664
+Epoch: 36 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 38)
+0/69092	Loss: 503.709
+3200/69092	Loss: 452.426
+6400/69092	Loss: 433.778
+9600/69092	Loss: 437.960
+12800/69092	Loss: 437.121
+16000/69092	Loss: 438.996
+19200/69092	Loss: 440.897
+22400/69092	Loss: 434.344
+25600/69092	Loss: 434.731
+28800/69092	Loss: 442.679
+32000/69092	Loss: 442.099
+35200/69092	Loss: 436.466
+38400/69092	Loss: 430.395
+41600/69092	Loss: 439.344
+44800/69092	Loss: 442.088
+48000/69092	Loss: 432.574
+51200/69092	Loss: 454.989
+54400/69092	Loss: 446.804
+57600/69092	Loss: 438.708
+60800/69092	Loss: 432.784
+64000/69092	Loss: 432.696
+67200/69092	Loss: 437.383
+Training time 0:04:23.355301
+Epoch: 37 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 39)
+0/69092	Loss: 447.649
+3200/69092	Loss: 438.415
+6400/69092	Loss: 437.275
+9600/69092	Loss: 433.935
+12800/69092	Loss: 444.876
+16000/69092	Loss: 436.280
+19200/69092	Loss: 442.748
+22400/69092	Loss: 433.677
+25600/69092	Loss: 434.464
+28800/69092	Loss: 436.915
+32000/69092	Loss: 434.070
+35200/69092	Loss: 433.907
+38400/69092	Loss: 444.981
+41600/69092	Loss: 438.346
+44800/69092	Loss: 435.798
+48000/69092	Loss: 439.504
+51200/69092	Loss: 449.858
+54400/69092	Loss: 438.362
+57600/69092	Loss: 435.984
+60800/69092	Loss: 453.773
+64000/69092	Loss: 438.298
+67200/69092	Loss: 443.329
+Training time 0:04:29.106721
+Epoch: 38 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 40)
+0/69092	Loss: 391.537
+3200/69092	Loss: 440.161
+6400/69092	Loss: 446.423
+9600/69092	Loss: 437.502
+12800/69092	Loss: 442.225
+16000/69092	Loss: 446.088
+19200/69092	Loss: 437.018
+22400/69092	Loss: 437.129
+25600/69092	Loss: 442.543
+28800/69092	Loss: 425.001
+32000/69092	Loss: 455.314
+35200/69092	Loss: 442.058
+38400/69092	Loss: 437.978
+41600/69092	Loss: 435.451
+44800/69092	Loss: 442.741
+48000/69092	Loss: 435.259
+51200/69092	Loss: 441.030
+54400/69092	Loss: 437.358
+57600/69092	Loss: 441.818
+60800/69092	Loss: 430.050
+64000/69092	Loss: 433.974
+67200/69092	Loss: 440.024
+Training time 0:04:27.133206
+Epoch: 39 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 41)
+0/69092	Loss: 437.809
+3200/69092	Loss: 445.816
+6400/69092	Loss: 426.942
+9600/69092	Loss: 436.363
+12800/69092	Loss: 442.467
+16000/69092	Loss: 440.726
+19200/69092	Loss: 433.769
+22400/69092	Loss: 438.185
+25600/69092	Loss: 441.295
+28800/69092	Loss: 437.113
+32000/69092	Loss: 424.040
+35200/69092	Loss: 437.843
+38400/69092	Loss: 446.272
+41600/69092	Loss: 449.939
+44800/69092	Loss: 436.133
+48000/69092	Loss: 432.491
+51200/69092	Loss: 435.159
+54400/69092	Loss: 445.164
+57600/69092	Loss: 442.093
+60800/69092	Loss: 445.885
+64000/69092	Loss: 438.135
+67200/69092	Loss: 441.220
+Training time 0:04:30.637297
+Epoch: 40 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 42)
+0/69092	Loss: 409.611
+3200/69092	Loss: 444.389
+6400/69092	Loss: 440.365
+9600/69092	Loss: 442.838
+12800/69092	Loss: 449.627
+16000/69092	Loss: 436.579
+19200/69092	Loss: 431.977
+22400/69092	Loss: 434.451
+25600/69092	Loss: 436.311
+28800/69092	Loss: 440.259
+32000/69092	Loss: 441.167
+35200/69092	Loss: 439.849
+38400/69092	Loss: 441.058
+41600/69092	Loss: 438.793
+44800/69092	Loss: 440.302
+48000/69092	Loss: 435.157
+51200/69092	Loss: 439.652
+54400/69092	Loss: 444.732
+57600/69092	Loss: 435.939
+60800/69092	Loss: 434.778
+64000/69092	Loss: 439.681
+67200/69092	Loss: 432.230
+Training time 0:04:26.600357
+Epoch: 41 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 43)
+0/69092	Loss: 507.415
+3200/69092	Loss: 438.228
+6400/69092	Loss: 438.391
+9600/69092	Loss: 437.827
+12800/69092	Loss: 437.751
+16000/69092	Loss: 445.539
+19200/69092	Loss: 436.618
+22400/69092	Loss: 443.044
+25600/69092	Loss: 449.848
+28800/69092	Loss: 434.835
+32000/69092	Loss: 436.210
+35200/69092	Loss: 440.821
+38400/69092	Loss: 430.715
+41600/69092	Loss: 443.028
+44800/69092	Loss: 442.057
+48000/69092	Loss: 442.289
+51200/69092	Loss: 435.588
+54400/69092	Loss: 436.053
+57600/69092	Loss: 432.229
+60800/69092	Loss: 447.308
+64000/69092	Loss: 441.927
+67200/69092	Loss: 435.013
+Training time 0:04:30.007899
+Epoch: 42 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 44)
+0/69092	Loss: 407.972
+3200/69092	Loss: 432.577
+6400/69092	Loss: 440.557
+9600/69092	Loss: 433.031
+12800/69092	Loss: 440.819
+16000/69092	Loss: 431.605
+19200/69092	Loss: 436.227
+22400/69092	Loss: 440.783
+25600/69092	Loss: 442.717
+28800/69092	Loss: 444.362
+32000/69092	Loss: 436.995
+35200/69092	Loss: 447.956
+38400/69092	Loss: 449.645
+41600/69092	Loss: 432.915
+44800/69092	Loss: 443.741
+48000/69092	Loss: 442.266
+51200/69092	Loss: 436.813
+54400/69092	Loss: 444.252
+57600/69092	Loss: 427.523
+60800/69092	Loss: 437.849
+64000/69092	Loss: 439.495
+67200/69092	Loss: 440.288
+Training time 0:04:29.266357
+Epoch: 43 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 45)
+0/69092	Loss: 398.038
+3200/69092	Loss: 431.172
+6400/69092	Loss: 441.445
+9600/69092	Loss: 444.381
+12800/69092	Loss: 440.471
+16000/69092	Loss: 433.104
+19200/69092	Loss: 453.816
+22400/69092	Loss: 439.002
+25600/69092	Loss: 441.156
+28800/69092	Loss: 437.415
+32000/69092	Loss: 438.586
+35200/69092	Loss: 430.243
+38400/69092	Loss: 441.148
+41600/69092	Loss: 446.183
+44800/69092	Loss: 442.891
+48000/69092	Loss: 442.371
+51200/69092	Loss: 440.565
+54400/69092	Loss: 438.831
+57600/69092	Loss: 429.123
+60800/69092	Loss: 435.269
+64000/69092	Loss: 435.936
+67200/69092	Loss: 439.151
+Training time 0:04:17.281301
+Epoch: 44 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 46)
+0/69092	Loss: 385.241
+3200/69092	Loss: 439.788
+6400/69092	Loss: 439.399
+9600/69092	Loss: 437.780
+12800/69092	Loss: 444.044
+16000/69092	Loss: 442.072
+19200/69092	Loss: 434.022
+22400/69092	Loss: 443.063
+25600/69092	Loss: 443.804
+28800/69092	Loss: 445.599
+32000/69092	Loss: 437.145
+35200/69092	Loss: 436.727
+38400/69092	Loss: 437.106
+41600/69092	Loss: 447.407
+44800/69092	Loss: 437.015
+48000/69092	Loss: 434.691
+51200/69092	Loss: 433.837
+54400/69092	Loss: 434.865
+57600/69092	Loss: 434.589
+60800/69092	Loss: 436.302
+64000/69092	Loss: 442.040
+67200/69092	Loss: 440.525
+Training time 0:04:24.839865
+Epoch: 45 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 47)
+0/69092	Loss: 494.956
+3200/69092	Loss: 429.617
+6400/69092	Loss: 433.782
+9600/69092	Loss: 443.090
+12800/69092	Loss: 433.299
+16000/69092	Loss: 434.624
+19200/69092	Loss: 440.676
+22400/69092	Loss: 444.434
+25600/69092	Loss: 439.572
+28800/69092	Loss: 430.997
+32000/69092	Loss: 442.559
+35200/69092	Loss: 430.993
+38400/69092	Loss: 443.031
+41600/69092	Loss: 437.318
+44800/69092	Loss: 441.607
+48000/69092	Loss: 439.600
+51200/69092	Loss: 436.554
+54400/69092	Loss: 445.179
+57600/69092	Loss: 443.965
+60800/69092	Loss: 440.311
+64000/69092	Loss: 445.184
+67200/69092	Loss: 435.747
+Training time 0:04:26.880554
+Epoch: 46 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 48)
+0/69092	Loss: 412.232
+3200/69092	Loss: 452.045
+6400/69092	Loss: 446.762
+9600/69092	Loss: 444.849
+12800/69092	Loss: 437.523
+16000/69092	Loss: 428.655
+19200/69092	Loss: 440.258
+22400/69092	Loss: 445.879
+25600/69092	Loss: 435.195
+28800/69092	Loss: 436.907
+32000/69092	Loss: 429.976
+35200/69092	Loss: 431.490
+38400/69092	Loss: 442.729
+41600/69092	Loss: 443.433
+44800/69092	Loss: 439.090
+48000/69092	Loss: 444.125
+51200/69092	Loss: 438.630
+54400/69092	Loss: 429.891
+57600/69092	Loss: 439.128
+60800/69092	Loss: 443.925
+64000/69092	Loss: 440.835
+67200/69092	Loss: 438.906
+Training time 0:04:32.843146
+Epoch: 47 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 49)
+0/69092	Loss: 426.367
+3200/69092	Loss: 442.251
+6400/69092	Loss: 436.144
+9600/69092	Loss: 444.448
+12800/69092	Loss: 439.454
+16000/69092	Loss: 442.244
+19200/69092	Loss: 437.906
+22400/69092	Loss: 435.093
+25600/69092	Loss: 430.616
+28800/69092	Loss: 441.967
+32000/69092	Loss: 436.438
+35200/69092	Loss: 447.880
+38400/69092	Loss: 434.073
+41600/69092	Loss: 440.352
+44800/69092	Loss: 443.125
+48000/69092	Loss: 438.794
+51200/69092	Loss: 434.574
+54400/69092	Loss: 434.909
+57600/69092	Loss: 440.548
+60800/69092	Loss: 447.929
+64000/69092	Loss: 433.344
+67200/69092	Loss: 432.947
+Training time 0:04:32.968524
+Epoch: 48 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 50)
+0/69092	Loss: 426.598
+3200/69092	Loss: 437.581
+6400/69092	Loss: 447.624
+9600/69092	Loss: 441.424
+12800/69092	Loss: 435.978
+16000/69092	Loss: 439.993
+19200/69092	Loss: 440.253
+22400/69092	Loss: 441.978
+25600/69092	Loss: 440.757
+28800/69092	Loss: 438.010
+32000/69092	Loss: 436.918
+35200/69092	Loss: 437.191
+38400/69092	Loss: 431.716
+41600/69092	Loss: 443.954
+44800/69092	Loss: 446.419
+48000/69092	Loss: 436.451
+51200/69092	Loss: 444.939
+54400/69092	Loss: 435.050
+57600/69092	Loss: 441.813
+60800/69092	Loss: 430.161
+64000/69092	Loss: 432.653
+67200/69092	Loss: 439.996
+Training time 0:04:28.575223
+Epoch: 49 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 51)
+0/69092	Loss: 422.456
+3200/69092	Loss: 435.311
+6400/69092	Loss: 436.360
+9600/69092	Loss: 437.095
+12800/69092	Loss: 436.947
+16000/69092	Loss: 445.358
+19200/69092	Loss: 440.554
+22400/69092	Loss: 429.427
+25600/69092	Loss: 436.670
+28800/69092	Loss: 429.370
+32000/69092	Loss: 443.846
+35200/69092	Loss: 438.409
+38400/69092	Loss: 437.534
+41600/69092	Loss: 432.698
+44800/69092	Loss: 452.084
+48000/69092	Loss: 448.768
+51200/69092	Loss: 440.683
+54400/69092	Loss: 437.061
+57600/69092	Loss: 448.541
+60800/69092	Loss: 429.059
+64000/69092	Loss: 443.735
+67200/69092	Loss: 437.904
+Training time 0:04:27.925841
+Epoch: 50 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 52)
+0/69092	Loss: 550.071
+3200/69092	Loss: 442.956
+6400/69092	Loss: 438.831
+9600/69092	Loss: 438.188
+12800/69092	Loss: 442.906
+16000/69092	Loss: 444.248
+19200/69092	Loss: 442.256
+22400/69092	Loss: 441.740
+25600/69092	Loss: 446.092
+28800/69092	Loss: 437.966
+32000/69092	Loss: 432.234
+35200/69092	Loss: 442.237
+38400/69092	Loss: 431.455
+41600/69092	Loss: 438.076
+44800/69092	Loss: 439.315
+48000/69092	Loss: 441.768
+51200/69092	Loss: 442.594
+54400/69092	Loss: 436.191
+57600/69092	Loss: 433.962
+60800/69092	Loss: 438.392
+64000/69092	Loss: 437.155
+67200/69092	Loss: 431.720
+Training time 0:04:29.821254
+Epoch: 51 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 53)
+0/69092	Loss: 437.337
+3200/69092	Loss: 446.035
+6400/69092	Loss: 442.904
+9600/69092	Loss: 435.249
+12800/69092	Loss: 439.279
+16000/69092	Loss: 443.838
+19200/69092	Loss: 436.515
+22400/69092	Loss: 439.223
+25600/69092	Loss: 441.838
+28800/69092	Loss: 442.334
+32000/69092	Loss: 440.988
+35200/69092	Loss: 427.747
+38400/69092	Loss: 441.443
+41600/69092	Loss: 444.276
+44800/69092	Loss: 445.065
+48000/69092	Loss: 439.623
+51200/69092	Loss: 431.319
+54400/69092	Loss: 441.970
+57600/69092	Loss: 433.791
+60800/69092	Loss: 442.944
+64000/69092	Loss: 437.015
+67200/69092	Loss: 428.847
+Training time 0:04:25.797223
+Epoch: 52 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 54)
+0/69092	Loss: 417.672
+3200/69092	Loss: 443.598
+6400/69092	Loss: 437.574
+9600/69092	Loss: 438.813
+12800/69092	Loss: 444.055
+16000/69092	Loss: 437.293
+19200/69092	Loss: 449.733
+22400/69092	Loss: 441.537
+25600/69092	Loss: 425.452
+28800/69092	Loss: 444.374
+32000/69092	Loss: 435.388
+35200/69092	Loss: 440.291
+38400/69092	Loss: 431.105
+41600/69092	Loss: 442.119
+44800/69092	Loss: 439.724
+48000/69092	Loss: 432.880
+51200/69092	Loss: 453.698
+54400/69092	Loss: 433.217
+57600/69092	Loss: 437.590
+60800/69092	Loss: 442.609
+64000/69092	Loss: 438.135
+67200/69092	Loss: 439.851
+Training time 0:04:22.740984
+Epoch: 53 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 55)
+0/69092	Loss: 381.935
+3200/69092	Loss: 428.765
+6400/69092	Loss: 443.327
+9600/69092	Loss: 436.975
+12800/69092	Loss: 439.071
+16000/69092	Loss: 437.715
+19200/69092	Loss: 433.178
+22400/69092	Loss: 443.541
+25600/69092	Loss: 436.405
+28800/69092	Loss: 447.054
+32000/69092	Loss: 442.356
+35200/69092	Loss: 436.860
+38400/69092	Loss: 442.425
+41600/69092	Loss: 442.004
+44800/69092	Loss: 430.222
+48000/69092	Loss: 438.636
+51200/69092	Loss: 447.515
+54400/69092	Loss: 443.500
+57600/69092	Loss: 437.393
+60800/69092	Loss: 442.845
+64000/69092	Loss: 440.101
+67200/69092	Loss: 436.235
+Training time 0:04:21.278154
+Epoch: 54 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 56)
+0/69092	Loss: 413.646
+3200/69092	Loss: 431.481
+6400/69092	Loss: 447.569
+9600/69092	Loss: 437.780
+12800/69092	Loss: 433.669
+16000/69092	Loss: 438.410
+19200/69092	Loss: 445.867
+22400/69092	Loss: 444.858
+25600/69092	Loss: 445.017
+28800/69092	Loss: 445.177
+32000/69092	Loss: 439.514
+35200/69092	Loss: 431.732
+38400/69092	Loss: 439.518
+41600/69092	Loss: 431.592
+44800/69092	Loss: 444.482
+48000/69092	Loss: 438.297
+51200/69092	Loss: 432.212
+54400/69092	Loss: 442.138
+57600/69092	Loss: 438.929
+60800/69092	Loss: 437.359
+64000/69092	Loss: 436.536
+67200/69092	Loss: 441.419
+Training time 0:04:27.817203
+Epoch: 55 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 57)
+0/69092	Loss: 427.898
+3200/69092	Loss: 447.176
+6400/69092	Loss: 440.106
+9600/69092	Loss: 439.614
+12800/69092	Loss: 429.972
+16000/69092	Loss: 446.450
+19200/69092	Loss: 435.340
+22400/69092	Loss: 439.818
+25600/69092	Loss: 436.800
+28800/69092	Loss: 441.428
+32000/69092	Loss: 440.438
+35200/69092	Loss: 431.536
+38400/69092	Loss: 442.916
+41600/69092	Loss: 431.127
+44800/69092	Loss: 439.969
+48000/69092	Loss: 438.947
+51200/69092	Loss: 442.249
+54400/69092	Loss: 441.265
+57600/69092	Loss: 443.976
+60800/69092	Loss: 427.512
+64000/69092	Loss: 436.935
+67200/69092	Loss: 445.539
+Training time 0:04:33.632200
+Epoch: 56 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 58)
+0/69092	Loss: 411.760
+3200/69092	Loss: 433.892
+6400/69092	Loss: 439.177
+9600/69092	Loss: 432.515
+12800/69092	Loss: 437.680
+16000/69092	Loss: 444.365
+19200/69092	Loss: 438.557
+22400/69092	Loss: 445.234
+25600/69092	Loss: 440.781
+28800/69092	Loss: 435.052
+32000/69092	Loss: 442.516
+35200/69092	Loss: 437.830
+38400/69092	Loss: 435.828
+41600/69092	Loss: 442.848
+44800/69092	Loss: 446.832
+48000/69092	Loss: 445.540
+51200/69092	Loss: 436.871
+54400/69092	Loss: 440.483
+57600/69092	Loss: 431.503
+60800/69092	Loss: 435.062
+64000/69092	Loss: 436.744
+67200/69092	Loss: 439.370
+Training time 0:04:33.756435
+Epoch: 57 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 59)
+0/69092	Loss: 438.751
+3200/69092	Loss: 448.455
+6400/69092	Loss: 434.567
+9600/69092	Loss: 438.998
+12800/69092	Loss: 442.327
+16000/69092	Loss: 439.686
+19200/69092	Loss: 439.175
+22400/69092	Loss: 443.437
+25600/69092	Loss: 434.127
+28800/69092	Loss: 434.975
+32000/69092	Loss: 435.085
+35200/69092	Loss: 451.537
+38400/69092	Loss: 438.002
+41600/69092	Loss: 439.895
+44800/69092	Loss: 429.652
+48000/69092	Loss: 450.670
+51200/69092	Loss: 426.567
+54400/69092	Loss: 432.979
+57600/69092	Loss: 434.381
+60800/69092	Loss: 438.850
+64000/69092	Loss: 436.625
+67200/69092	Loss: 443.626
+Training time 0:04:34.159196
+Epoch: 58 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 60)
+0/69092	Loss: 481.370
+3200/69092	Loss: 443.856
+6400/69092	Loss: 438.814
+9600/69092	Loss: 441.666
+12800/69092	Loss: 437.547
+16000/69092	Loss: 434.459
+19200/69092	Loss: 431.864
+22400/69092	Loss: 433.989
+25600/69092	Loss: 430.929
+28800/69092	Loss: 439.915
+32000/69092	Loss: 447.245
+35200/69092	Loss: 447.208
+38400/69092	Loss: 433.403
+41600/69092	Loss: 438.187
+44800/69092	Loss: 435.722
+48000/69092	Loss: 438.726
+51200/69092	Loss: 432.387
+54400/69092	Loss: 439.987
+57600/69092	Loss: 440.465
+60800/69092	Loss: 441.431
+64000/69092	Loss: 449.919
+67200/69092	Loss: 450.584
+Training time 0:04:34.858693
+Epoch: 59 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 61)
+0/69092	Loss: 429.961
+3200/69092	Loss: 429.342
+6400/69092	Loss: 442.421
+9600/69092	Loss: 435.096
+12800/69092	Loss: 441.566
+16000/69092	Loss: 442.757
+19200/69092	Loss: 436.914
+22400/69092	Loss: 443.071
+25600/69092	Loss: 442.141
+28800/69092	Loss: 448.962
+32000/69092	Loss: 443.628
+35200/69092	Loss: 430.613
+38400/69092	Loss: 427.441
+41600/69092	Loss: 434.592
+44800/69092	Loss: 436.874
+48000/69092	Loss: 447.286
+51200/69092	Loss: 429.857
+54400/69092	Loss: 436.978
+57600/69092	Loss: 447.167
+60800/69092	Loss: 438.259
+64000/69092	Loss: 438.108
+67200/69092	Loss: 447.906
+Training time 0:04:28.926358
+Epoch: 60 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 62)
+0/69092	Loss: 453.265
+3200/69092	Loss: 438.843
+6400/69092	Loss: 432.844
+9600/69092	Loss: 441.518
+12800/69092	Loss: 434.637
+16000/69092	Loss: 441.445
+19200/69092	Loss: 445.523
+22400/69092	Loss: 442.913
+25600/69092	Loss: 438.194
+28800/69092	Loss: 449.389
+32000/69092	Loss: 441.307
+35200/69092	Loss: 437.638
+38400/69092	Loss: 438.045
+41600/69092	Loss: 439.874
+44800/69092	Loss: 419.166
+48000/69092	Loss: 439.177
+51200/69092	Loss: 438.055
+54400/69092	Loss: 454.875
+57600/69092	Loss: 442.716
+60800/69092	Loss: 433.500
+64000/69092	Loss: 442.145
+67200/69092	Loss: 425.070
+Training time 0:04:17.812471
+Epoch: 61 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 63)
+0/69092	Loss: 435.152
+3200/69092	Loss: 425.168
+6400/69092	Loss: 436.629
+9600/69092	Loss: 440.108
+12800/69092	Loss: 440.200
+16000/69092	Loss: 441.054
+19200/69092	Loss: 442.874
+22400/69092	Loss: 440.906
+25600/69092	Loss: 437.788
+28800/69092	Loss: 439.653
+32000/69092	Loss: 450.036
+35200/69092	Loss: 447.640
+38400/69092	Loss: 440.043
+41600/69092	Loss: 431.116
+44800/69092	Loss: 440.889
+48000/69092	Loss: 439.093
+51200/69092	Loss: 444.772
+54400/69092	Loss: 432.654
+57600/69092	Loss: 439.986
+60800/69092	Loss: 434.186
+64000/69092	Loss: 450.730
+67200/69092	Loss: 429.098
+Training time 0:04:24.809857
+Epoch: 62 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 64)
+0/69092	Loss: 440.093
+3200/69092	Loss: 433.018
+6400/69092	Loss: 429.474
+9600/69092	Loss: 441.433
+12800/69092	Loss: 442.660
+16000/69092	Loss: 446.788
+19200/69092	Loss: 441.570
+22400/69092	Loss: 441.901
+25600/69092	Loss: 439.729
+28800/69092	Loss: 441.023
+32000/69092	Loss: 433.298
+35200/69092	Loss: 440.624
+38400/69092	Loss: 433.511
+41600/69092	Loss: 441.454
+44800/69092	Loss: 439.079
+48000/69092	Loss: 438.031
+51200/69092	Loss: 442.159
+54400/69092	Loss: 445.902
+57600/69092	Loss: 430.249
+60800/69092	Loss: 440.677
+64000/69092	Loss: 432.880
+67200/69092	Loss: 437.562
+Training time 0:04:20.804945
+Epoch: 63 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 65)
+0/69092	Loss: 436.817
+3200/69092	Loss: 440.182
+6400/69092	Loss: 435.733
+9600/69092	Loss: 439.587
+12800/69092	Loss: 431.296
+16000/69092	Loss: 441.980
+19200/69092	Loss: 442.104
+22400/69092	Loss: 438.614
+25600/69092	Loss: 440.386
+28800/69092	Loss: 434.225
+32000/69092	Loss: 442.410
+35200/69092	Loss: 437.588
+38400/69092	Loss: 442.037
+41600/69092	Loss: 442.591
+44800/69092	Loss: 436.520
+48000/69092	Loss: 438.737
+51200/69092	Loss: 434.122
+54400/69092	Loss: 429.386
+57600/69092	Loss: 433.496
+60800/69092	Loss: 442.233
+64000/69092	Loss: 445.463
+67200/69092	Loss: 448.857
+Training time 0:04:19.942842
+Epoch: 64 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 66)
+0/69092	Loss: 398.998
+3200/69092	Loss: 443.840
+6400/69092	Loss: 439.399
+9600/69092	Loss: 435.081
+12800/69092	Loss: 445.893
+16000/69092	Loss: 431.500
+19200/69092	Loss: 435.736
+22400/69092	Loss: 439.064
+25600/69092	Loss: 436.566
+28800/69092	Loss: 431.796
+32000/69092	Loss: 431.160
+35200/69092	Loss: 435.912
+38400/69092	Loss: 436.780
+41600/69092	Loss: 445.401
+44800/69092	Loss: 434.821
+48000/69092	Loss: 438.186
+51200/69092	Loss: 434.963
+54400/69092	Loss: 441.965
+57600/69092	Loss: 445.562
+60800/69092	Loss: 441.051
+64000/69092	Loss: 440.830
+67200/69092	Loss: 454.219
+Training time 0:04:28.375923
+Epoch: 65 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 67)
+0/69092	Loss: 481.965
+3200/69092	Loss: 444.284
+6400/69092	Loss: 430.930
+9600/69092	Loss: 439.995
+12800/69092	Loss: 445.742
+16000/69092	Loss: 442.147
+19200/69092	Loss: 433.298
+22400/69092	Loss: 438.165
+25600/69092	Loss: 443.967
+28800/69092	Loss: 437.553
+32000/69092	Loss: 435.797
+35200/69092	Loss: 439.222
+38400/69092	Loss: 441.535
+41600/69092	Loss: 435.819
+44800/69092	Loss: 437.121
+48000/69092	Loss: 432.600
+51200/69092	Loss: 442.415
+54400/69092	Loss: 436.416
+57600/69092	Loss: 441.440
+60800/69092	Loss: 441.323
+64000/69092	Loss: 431.728
+67200/69092	Loss: 442.462
+Training time 0:04:25.304930
+Epoch: 66 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 68)
+0/69092	Loss: 485.893
+3200/69092	Loss: 439.189
+6400/69092	Loss: 429.988
+9600/69092	Loss: 435.731
+12800/69092	Loss: 437.687
+16000/69092	Loss: 436.549
+19200/69092	Loss: 440.563
+22400/69092	Loss: 443.955
+25600/69092	Loss: 443.521
+28800/69092	Loss: 436.155
+32000/69092	Loss: 437.393
+35200/69092	Loss: 443.298
+38400/69092	Loss: 441.077
+41600/69092	Loss: 438.236
+44800/69092	Loss: 438.087
+48000/69092	Loss: 438.055
+51200/69092	Loss: 444.033
+54400/69092	Loss: 443.158
+57600/69092	Loss: 440.457
+60800/69092	Loss: 446.044
+64000/69092	Loss: 426.677
+67200/69092	Loss: 438.979
+Training time 0:04:33.185552
+Epoch: 67 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 69)
+0/69092	Loss: 420.831
+3200/69092	Loss: 442.669
+6400/69092	Loss: 452.660
+9600/69092	Loss: 431.214
+12800/69092	Loss: 443.841
+16000/69092	Loss: 433.611
+19200/69092	Loss: 436.799
+22400/69092	Loss: 435.424
+25600/69092	Loss: 440.612
+28800/69092	Loss: 444.618
+32000/69092	Loss: 432.802
+35200/69092	Loss: 436.876
+38400/69092	Loss: 440.315
+41600/69092	Loss: 438.914
+44800/69092	Loss: 447.854
+48000/69092	Loss: 439.244
+51200/69092	Loss: 444.912
+54400/69092	Loss: 439.080
+57600/69092	Loss: 433.689
+60800/69092	Loss: 435.428
+64000/69092	Loss: 435.146
+67200/69092	Loss: 442.037
+Training time 0:04:28.518043
+Epoch: 68 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 70)
+0/69092	Loss: 393.491
+3200/69092	Loss: 432.490
+6400/69092	Loss: 436.490
+9600/69092	Loss: 432.180
+12800/69092	Loss: 437.639
+16000/69092	Loss: 426.939
+19200/69092	Loss: 442.415
+22400/69092	Loss: 429.092
+25600/69092	Loss: 444.033
+28800/69092	Loss: 450.195
+32000/69092	Loss: 436.750
+35200/69092	Loss: 439.721
+38400/69092	Loss: 435.390
+41600/69092	Loss: 438.870
+44800/69092	Loss: 445.929
+48000/69092	Loss: 439.922
+51200/69092	Loss: 444.181
+54400/69092	Loss: 446.175
+57600/69092	Loss: 439.737
+60800/69092	Loss: 437.200
+64000/69092	Loss: 451.240
+67200/69092	Loss: 433.368
+Training time 0:04:34.362609
+Epoch: 69 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 71)
+0/69092	Loss: 486.449
+3200/69092	Loss: 440.920
+6400/69092	Loss: 436.375
+9600/69092	Loss: 435.286
+12800/69092	Loss: 440.093
+16000/69092	Loss: 444.735
+19200/69092	Loss: 437.497
+22400/69092	Loss: 433.487
+25600/69092	Loss: 435.702
+28800/69092	Loss: 447.285
+32000/69092	Loss: 433.168
+35200/69092	Loss: 444.765
+38400/69092	Loss: 433.999
+41600/69092	Loss: 435.072
+44800/69092	Loss: 448.299
+48000/69092	Loss: 438.131
+51200/69092	Loss: 443.913
+54400/69092	Loss: 440.995
+57600/69092	Loss: 444.716
+60800/69092	Loss: 436.394
+64000/69092	Loss: 434.067
+67200/69092	Loss: 444.127
+Training time 0:04:23.119485
+Epoch: 70 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 72)
+0/69092	Loss: 456.388
+3200/69092	Loss: 440.627
+6400/69092	Loss: 434.655
+9600/69092	Loss: 425.941
+12800/69092	Loss: 442.158
+16000/69092	Loss: 438.968
+19200/69092	Loss: 436.301
+22400/69092	Loss: 443.149
+25600/69092	Loss: 443.818
+28800/69092	Loss: 441.077
+32000/69092	Loss: 437.158
+35200/69092	Loss: 441.001
+38400/69092	Loss: 436.902
+41600/69092	Loss: 450.161
+44800/69092	Loss: 440.306
+48000/69092	Loss: 428.515
+51200/69092	Loss: 446.683
+54400/69092	Loss: 435.140
+57600/69092	Loss: 448.877
+60800/69092	Loss: 433.218
+64000/69092	Loss: 439.427
+67200/69092	Loss: 441.373
+Training time 0:04:28.330593
+Epoch: 71 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 73)
+0/69092	Loss: 458.070
+3200/69092	Loss: 444.052
+6400/69092	Loss: 442.543
+9600/69092	Loss: 427.966
+12800/69092	Loss: 428.694
+16000/69092	Loss: 435.232
+19200/69092	Loss: 442.810
+22400/69092	Loss: 440.732
+25600/69092	Loss: 439.676
+28800/69092	Loss: 438.011
+32000/69092	Loss: 436.440
+35200/69092	Loss: 436.617
+38400/69092	Loss: 440.179
+41600/69092	Loss: 435.382
+44800/69092	Loss: 442.662
+48000/69092	Loss: 439.867
+51200/69092	Loss: 440.253
+54400/69092	Loss: 435.741
+57600/69092	Loss: 441.780
+60800/69092	Loss: 444.592
+64000/69092	Loss: 442.122
+67200/69092	Loss: 441.525
+Training time 0:04:22.613738
+Epoch: 72 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 74)
+0/69092	Loss: 469.953
+3200/69092	Loss: 438.776
+6400/69092	Loss: 435.868
+9600/69092	Loss: 441.209
+12800/69092	Loss: 441.959
+16000/69092	Loss: 429.666
+19200/69092	Loss: 439.287
+22400/69092	Loss: 436.681
+25600/69092	Loss: 444.585
+28800/69092	Loss: 448.134
+32000/69092	Loss: 427.122
+35200/69092	Loss: 433.999
+38400/69092	Loss: 440.035
+41600/69092	Loss: 432.372
+44800/69092	Loss: 447.241
+48000/69092	Loss: 442.884
+51200/69092	Loss: 444.846
+54400/69092	Loss: 438.735
+57600/69092	Loss: 439.255
+60800/69092	Loss: 449.132
+64000/69092	Loss: 432.687
+67200/69092	Loss: 435.702
+Training time 0:04:20.623353
+Epoch: 73 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 75)
+0/69092	Loss: 427.240
+3200/69092	Loss: 429.469
+6400/69092	Loss: 444.833
+9600/69092	Loss: 434.928
+12800/69092	Loss: 444.967
+16000/69092	Loss: 423.495
+19200/69092	Loss: 434.935
+22400/69092	Loss: 449.202
+25600/69092	Loss: 423.320
+28800/69092	Loss: 442.072
+32000/69092	Loss: 447.337
+35200/69092	Loss: 437.285
+38400/69092	Loss: 442.135
+41600/69092	Loss: 446.631
+44800/69092	Loss: 433.983
+48000/69092	Loss: 431.751
+51200/69092	Loss: 441.727
+54400/69092	Loss: 442.096
+57600/69092	Loss: 437.589
+60800/69092	Loss: 448.595
+64000/69092	Loss: 441.812
+67200/69092	Loss: 436.986
+Training time 0:04:35.874284
+Epoch: 74 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 76)
+0/69092	Loss: 431.358
+3200/69092	Loss: 443.052
+6400/69092	Loss: 443.236
+9600/69092	Loss: 433.132
+12800/69092	Loss: 434.050
+16000/69092	Loss: 437.981
+19200/69092	Loss: 441.862
+22400/69092	Loss: 440.291
+25600/69092	Loss: 429.166
+28800/69092	Loss: 441.906
+32000/69092	Loss: 431.980
+35200/69092	Loss: 447.021
+38400/69092	Loss: 436.277
+41600/69092	Loss: 452.453
+44800/69092	Loss: 442.244
+48000/69092	Loss: 442.442
+51200/69092	Loss: 436.932
+54400/69092	Loss: 437.544
+57600/69092	Loss: 430.479
+60800/69092	Loss: 441.225
+64000/69092	Loss: 439.020
+67200/69092	Loss: 437.369
+Training time 0:04:28.609695
+Epoch: 75 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 77)
+0/69092	Loss: 430.895
+3200/69092	Loss: 429.181
+6400/69092	Loss: 436.251
+9600/69092	Loss: 434.145
+12800/69092	Loss: 447.277
+16000/69092	Loss: 438.238
+19200/69092	Loss: 434.316
+22400/69092	Loss: 433.676
+25600/69092	Loss: 449.608
+28800/69092	Loss: 436.183
+32000/69092	Loss: 438.880
+35200/69092	Loss: 435.693
+38400/69092	Loss: 441.719
+41600/69092	Loss: 441.783
+44800/69092	Loss: 440.828
+48000/69092	Loss: 440.520
+51200/69092	Loss: 449.355
+54400/69092	Loss: 441.894
+57600/69092	Loss: 443.175
+60800/69092	Loss: 431.229
+64000/69092	Loss: 443.335
+67200/69092	Loss: 437.127
+Training time 0:04:37.820950
+Epoch: 76 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 78)
+0/69092	Loss: 430.771
+3200/69092	Loss: 438.633
+6400/69092	Loss: 444.544
+9600/69092	Loss: 444.716
+12800/69092	Loss: 434.922
+16000/69092	Loss: 437.631
+19200/69092	Loss: 435.571
+22400/69092	Loss: 441.155
+25600/69092	Loss: 437.357
+28800/69092	Loss: 445.235
+32000/69092	Loss: 438.067
+35200/69092	Loss: 433.339
+38400/69092	Loss: 437.645
+41600/69092	Loss: 439.892
+44800/69092	Loss: 453.014
+48000/69092	Loss: 436.742
+51200/69092	Loss: 439.820
+54400/69092	Loss: 432.332
+57600/69092	Loss: 442.311
+60800/69092	Loss: 443.235
+64000/69092	Loss: 434.287
+67200/69092	Loss: 434.622
+Training time 0:04:35.927628
+Epoch: 77 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 79)
+0/69092	Loss: 446.499
+3200/69092	Loss: 436.133
+6400/69092	Loss: 441.444
+9600/69092	Loss: 427.256
+12800/69092	Loss: 436.374
+16000/69092	Loss: 436.849
+19200/69092	Loss: 441.694
+22400/69092	Loss: 440.956
+25600/69092	Loss: 436.733
+28800/69092	Loss: 440.706
+32000/69092	Loss: 439.587
+35200/69092	Loss: 432.551
+38400/69092	Loss: 443.022
+41600/69092	Loss: 437.109
+44800/69092	Loss: 436.916
+48000/69092	Loss: 442.528
+51200/69092	Loss: 443.058
+54400/69092	Loss: 440.018
+57600/69092	Loss: 430.461
+60800/69092	Loss: 452.985
+64000/69092	Loss: 448.857
+67200/69092	Loss: 439.490
+Training time 0:04:32.511036
+Epoch: 78 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 80)
+0/69092	Loss: 487.504
+3200/69092	Loss: 442.350
+6400/69092	Loss: 446.787
+9600/69092	Loss: 441.357
+12800/69092	Loss: 432.835
+16000/69092	Loss: 443.573
+19200/69092	Loss: 444.299
+22400/69092	Loss: 434.448
+25600/69092	Loss: 446.956
+28800/69092	Loss: 449.180
+32000/69092	Loss: 430.925
+35200/69092	Loss: 433.882
+38400/69092	Loss: 446.180
+41600/69092	Loss: 441.777
+44800/69092	Loss: 434.901
+48000/69092	Loss: 431.769
+51200/69092	Loss: 439.595
+54400/69092	Loss: 437.004
+57600/69092	Loss: 430.203
+60800/69092	Loss: 443.179
+64000/69092	Loss: 433.426
+67200/69092	Loss: 434.029
+Training time 0:04:30.890610
+Epoch: 79 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 81)
+0/69092	Loss: 481.668
+3200/69092	Loss: 437.355
+6400/69092	Loss: 432.293
+9600/69092	Loss: 434.117
+12800/69092	Loss: 447.442
+16000/69092	Loss: 439.925
+19200/69092	Loss: 434.140
+22400/69092	Loss: 427.423
+25600/69092	Loss: 442.359
+28800/69092	Loss: 439.085
+32000/69092	Loss: 449.415
+35200/69092	Loss: 435.332
+38400/69092	Loss: 433.305
+41600/69092	Loss: 445.405
+44800/69092	Loss: 430.264
+48000/69092	Loss: 438.950
+51200/69092	Loss: 446.735
+54400/69092	Loss: 436.979
+57600/69092	Loss: 441.830
+60800/69092	Loss: 438.962
+64000/69092	Loss: 437.526
+67200/69092	Loss: 447.985
+Training time 0:04:23.198038
+Epoch: 80 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 82)
+0/69092	Loss: 378.888
+3200/69092	Loss: 431.912
+6400/69092	Loss: 443.355
+9600/69092	Loss: 437.282
+12800/69092	Loss: 435.850
+16000/69092	Loss: 441.770
+19200/69092	Loss: 425.460
+22400/69092	Loss: 429.830
+25600/69092	Loss: 434.771
+28800/69092	Loss: 428.194
+32000/69092	Loss: 448.500
+35200/69092	Loss: 446.295
+38400/69092	Loss: 455.919
+41600/69092	Loss: 437.209
+44800/69092	Loss: 444.666
+48000/69092	Loss: 443.734
+51200/69092	Loss: 442.007
+54400/69092	Loss: 437.688
+57600/69092	Loss: 448.615
+60800/69092	Loss: 442.213
+64000/69092	Loss: 437.041
+67200/69092	Loss: 429.636
+Training time 0:04:30.661318
+Epoch: 81 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 83)
+0/69092	Loss: 449.163
+3200/69092	Loss: 439.526
+6400/69092	Loss: 444.871
+9600/69092	Loss: 441.900
+12800/69092	Loss: 439.481
+16000/69092	Loss: 442.925
+19200/69092	Loss: 448.002
+22400/69092	Loss: 434.180
+25600/69092	Loss: 432.900
+28800/69092	Loss: 436.394
+32000/69092	Loss: 437.555
+35200/69092	Loss: 427.933
+38400/69092	Loss: 442.859
+41600/69092	Loss: 442.253
+44800/69092	Loss: 430.309
+48000/69092	Loss: 438.446
+51200/69092	Loss: 429.865
+54400/69092	Loss: 455.650
+57600/69092	Loss: 430.378
+60800/69092	Loss: 443.486
+64000/69092	Loss: 435.465
+67200/69092	Loss: 443.477
+Training time 0:04:19.914743
+Epoch: 82 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 84)
+0/69092	Loss: 406.290
+3200/69092	Loss: 444.287
+6400/69092	Loss: 444.629
+9600/69092	Loss: 437.308
+12800/69092	Loss: 438.079
+16000/69092	Loss: 441.510
+19200/69092	Loss: 437.281
+22400/69092	Loss: 441.529
+25600/69092	Loss: 431.017
+28800/69092	Loss: 439.013
+32000/69092	Loss: 437.763
+35200/69092	Loss: 440.027
+38400/69092	Loss: 442.800
+41600/69092	Loss: 434.031
+44800/69092	Loss: 442.341
+48000/69092	Loss: 445.696
+51200/69092	Loss: 441.060
+54400/69092	Loss: 442.858
+57600/69092	Loss: 441.939
+60800/69092	Loss: 431.163
+64000/69092	Loss: 440.905
+67200/69092	Loss: 434.195
+Training time 0:04:23.488505
+Epoch: 83 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 85)
+0/69092	Loss: 441.828
+3200/69092	Loss: 434.512
+6400/69092	Loss: 434.845
+9600/69092	Loss: 448.229
+12800/69092	Loss: 436.518
+16000/69092	Loss: 446.633
+19200/69092	Loss: 429.827
+22400/69092	Loss: 431.421
+25600/69092	Loss: 433.879
+28800/69092	Loss: 451.642
+32000/69092	Loss: 438.861
+35200/69092	Loss: 436.175
+38400/69092	Loss: 441.431
+41600/69092	Loss: 442.288
+44800/69092	Loss: 437.448
+48000/69092	Loss: 441.520
+51200/69092	Loss: 443.343
+54400/69092	Loss: 448.508
+57600/69092	Loss: 437.061
+60800/69092	Loss: 440.716
+64000/69092	Loss: 429.753
+67200/69092	Loss: 432.530
+Training time 0:04:30.260231
+Epoch: 84 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 86)
+0/69092	Loss: 425.748
+3200/69092	Loss: 438.217
+6400/69092	Loss: 438.140
+9600/69092	Loss: 442.356
+12800/69092	Loss: 437.696
+16000/69092	Loss: 444.825
+19200/69092	Loss: 443.557
+22400/69092	Loss: 436.350
+25600/69092	Loss: 439.677
+28800/69092	Loss: 439.924
+32000/69092	Loss: 443.346
+35200/69092	Loss: 430.511
+38400/69092	Loss: 440.147
+41600/69092	Loss: 442.451
+44800/69092	Loss: 434.681
+48000/69092	Loss: 438.830
+51200/69092	Loss: 441.980
+54400/69092	Loss: 441.627
+57600/69092	Loss: 436.089
+60800/69092	Loss: 448.313
+64000/69092	Loss: 434.959
+67200/69092	Loss: 429.999
+Training time 0:04:26.453752
+Epoch: 85 Average loss: 439.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_1e_3/checkpoints/last' (iter 87)
+0/69092	Loss: 437.104
+3200/69092	Loss: 445.228
+6400/69092	Loss: 447.248
+9600/69092	Loss: 436.153
+12800/69092	Loss: 427.372
+16000/69092	Loss: 450.446
+19200/69092	Loss: 443.578
+22400/69092	Loss: 443.433
+25600/69092	Loss: 433.324
+28800/69092	Loss: 430.439
+32000/69092	Loss: 434.079
+35200/69092	Loss: 434.142
+38400/69092	Loss: 441.583
+41600/69092	Loss: 439.060
+44800/69092	Loss: 434.916
+48000/69092	Loss: 434.462
+51200/69092	Loss: 441.623
+54400/69092	Loss: 442.552
+57600/69092	Loss: 440.673
+60800/69092	Loss: 438.891
+64000/69092	Loss: 447.394
diff --git a/data/batch_chairs.pt b/data/batch_chairs.pt
index f76235874fcf41cddbfd0ab0a2e12b341a4478e7..6ed6a19ef67d74b27ac113a5f9c63582e36187ae 100644
Binary files a/data/batch_chairs.pt and b/data/batch_chairs.pt differ
diff --git a/parameters_combinations/param_combinations_chairs.txt b/parameters_combinations/param_combinations_chairs.txt
index e1228ff44808c90a06ca62bdef045378612d1926..0abd899eb8a5dd146824f4621ed1e4870f9fdfe7 100644
--- a/parameters_combinations/param_combinations_chairs.txt
+++ b/parameters_combinations/param_combinations_chairs.txt
@@ -1,4 +1,12 @@
---batch-size=256 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --is-beta-VAE=True --beta=4 --lr=1e-4 --experiment-name=beta_VAE_bs_256 --gpu-devices 0 1 --experiment-name=beta_VAE_bs_256
---batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --is-beta-VAE=True --beta=4 --lr=1e-4 --experiment-name=beta_VAE_bs_64 --gpu-devices 0 1 --experiment-name=beta_VAE_bs_64
---batch-size=256 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --lr=1e-4 --experiment-name=VAE_bs_256 --gpu-devices 0 1 --experiment-name=VAE_bs_256
---batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --lr=1e-4 --experiment-name=VAE_bs_64 --gpu-devices 0 1 --experiment-name=VAE_bs_64
\ No newline at end of file
+--batch-size=256 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --is-beta-VAE=True --beta=4 --lr=1e-4 --experiment-name=beta_VAE_bs_256 --gpu-devices 0 1 --load-model-checkpoint=True
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --is-beta-VAE=True --beta=4 --lr=1e-4 --experiment-name=beta_VAE_bs_64 --gpu-devices 0 1 --load-model-checkpoint=True
+--batch-size=256 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --lr=1e-4 --experiment-name=VAE_bs_256 --gpu-devices 0 1 --load-model-checkpoint=True
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --lr=1e-4 --experiment-name=VAE_bs_64 --gpu-devices 0 1 --load-model-checkpoint=True
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=15 --is-beta-VAE=True --beta=4 --lr=1e-4 --gpu-devices 0 1 --experiment-name=beta_VAE_bs_64_ls_15
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=20 --is-beta-VAE=True --beta=4 --lr=1e-4 --gpu-devices 0 1 --experiment-name=beta_VAE_bs_64_ls_20
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=5 --is-beta-VAE=True --beta=4 --lr=1e-4 --gpu-devices 0 1 --experiment-name=beta_VAE_bs_64_ls_5
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=5 --lr=1e-4 --gpu-devices 0 1 --experiment-name=VAE_bs_64_ls_5
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=15 --lr=1e-4 --gpu-devices 0 1 --experiment-name=VAE_bs_64_ls_15
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=20 --lr=1e-4 --gpu-devices 0 1 --experiment-name=VAE_bs_64_ls_20
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --lr=5e-4 --gpu-devices 0 1 --experiment-name=VAE_bs_64_ls_10_lr_5e_4
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --lr=1e-3 --gpu-devices 0 1 --experiment-name=VAE_bs_64_ls_10_lr_1e_3
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diff --git a/trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last b/trained_models/rendered_chairs/VAE_bs_64_ls_10_lr_5e_4/checkpoints/last
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diff --git a/trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last b/trained_models/rendered_chairs/VAE_bs_64_ls_5/checkpoints/last
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