diff --git a/experiments/MNIST_binary_notebook.ipynb b/experiments/MNIST_binary_notebook.ipynb
index eb5e6b61cb85749d122715d2b65f6f72998d18ad..6245dbe4845b4765c1b174cc8786a92e0900728c 100644
--- a/experiments/MNIST_binary_notebook.ipynb
+++ b/experiments/MNIST_binary_notebook.ipynb
@@ -1 +1 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"MNIST_binary_notebook.ipynb","provenance":[],"collapsed_sections":["4HOATUuzrXVC","A3JkE7cPu9lN","8DfJ-CRU93BP","5959m3vfGJXc","TSs_mcFiNcRE","8rsVSnaFQT0O","vCQ5xT6bL1jc","Nwn-bah-Kh_l","DSqdNrmQNdP5","G2QLO0jHNgrl","yiji9E6E5Njy","keD_cleEzK7u","3ofGz3He4MJ_","ks9dWFWMWofi","OYPTjl99Xxnj","NiTAZSKo5sYG","nMq_m3xy5ulb","PECJ9p4q5wbt","CZylTnk3Ofb3","oCmF35kglqnz","F5V7JpRCVAMg"],"authorship_tag":"ABX9TyPIGSBFSC0mdJpCgvCMd6UM"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zMhTyMn7oxN0","colab_type":"text"},"source":["# Mount my drive:"]},{"cell_type":"code","metadata":{"id":"_pZ0mrRAoq6w","colab_type":"code","outputId":"895e1c6c-8190-46e1-a997-a1ae9fe46822","executionInfo":{"status":"ok","timestamp":1588929700578,"user_tz":-120,"elapsed":21221,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":124}},"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":"spkJ2-HJo0j6","colab_type":"code","outputId":"b4284005-12e5-4aef-e285-6f56aa4b10c5","executionInfo":{"status":"ok","timestamp":1588929703917,"user_tz":-120,"elapsed":24544,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":69}},"source":["import os\n","os.chdir('drive/My Drive/Work/Thesis_Julien_Dejasmin/Work/code/Binary_activations_V2/MNIST_Binary_V2')\n","!ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["config.py distributions README.md\t trained_models\n","data\t experiments requirements.txt utils\n","DataLoader __pycache__ results\t visualize\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"sg5c6cpVo2rK","colab_type":"text"},"source":["# Import:"]},{"cell_type":"code","metadata":{"id":"eUgp6tuW_XWi","colab_type":"code","outputId":"afe20dcf-3712-4753-a145-39ce4e4fd14e","executionInfo":{"status":"ok","timestamp":1588858443595,"user_tz":-120,"elapsed":4125,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":82}},"source":["!pip install pytorch-ignite"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: pytorch-ignite in /usr/local/lib/python3.6/dist-packages (0.3.0)\n","Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from pytorch-ignite) (1.5.0+cu101)\n","Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->pytorch-ignite) (0.16.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch->pytorch-ignite) (1.18.3)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"4ZlfKXpS_VnV","colab_type":"code","colab":{}},"source":["try:\n"," from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\n"," from ignite.metrics import Accuracy, Loss, ConfusionMatrix\n"," from ignite.handlers import ModelCheckpoint\n"," from utils.training import run, evaluate\n","except ImportError:\n"," raise RuntimeError(\"no module Ignite, to install Ignite: 'pip install pytorch-ignite'.\")\n","\n","from tqdm import tqdm"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_0Ekxum1o3p2","colab_type":"code","colab":{}},"source":["import torch\n","from torch import nn\n","from torch.optim import SGD\n","from torch.utils.data import DataLoader\n","import torch.nn.functional as F\n","from torchvision.transforms import Compose, ToTensor, Normalize\n","from torchvision.datasets import MNIST\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from functools import partial\n","\n","from utils.models import get_my_model_MNIST, fetch_last_checkpoint_model_filename\n","from DataLoader.dataLoaders import get_mnist_dataloaders\n","from utils.functions import Hardsigmoid"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SgqKz2R6wMgp","colab_type":"text"},"source":["# Dataset:"]},{"cell_type":"code","metadata":{"id":"iAJxJUvhwL5R","colab_type":"code","outputId":"c8ce53fb-1ebb-49fe-dc11-b95c2ad43734","executionInfo":{"status":"ok","timestamp":1588929718345,"user_tz":-120,"elapsed":38958,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":69}},"source":["batch_size_train = 10000\n","batch_size_test = 1000\n","# Dataset\n","train_loader, valid_loader, test_loader, classes = get_mnist_dataloaders(batch_size_train, batch_size_test)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Number of validation examples: 6000\n","Number of training examples: 6\n","Number of testing examples: 10\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4HOATUuzrXVC","colab_type":"text"},"source":["# Training: with bias"]},{"cell_type":"markdown","metadata":{"id":"3ljVddLwIKkJ","colab_type":"text"},"source":["## Training parameters:"]},{"cell_type":"code","metadata":{"id":"l6yU1EYYIMUi","colab_type":"code","colab":{}},"source":["epochs = 50\n","lr = 1e-3\n","momentum = 0.5\n","log_interval = 10 # how many batches to wait before logging training status\n","criterion = F.nll_loss"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"A3JkE7cPu9lN","colab_type":"text"},"source":["## Run No binary network:"]},{"cell_type":"code","metadata":{"id":"3c11RL0sq_30","colab_type":"code","outputId":"b4818d56-058b-44df-8305-f4060638f53e","executionInfo":{"status":"ok","timestamp":1588673848934,"user_tz":-120,"elapsed":655,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# parameters model to load no Binary model\n","binary = False\n","\n","model, name_model = get_my_model_MNIST(binary)\n","print(name_model)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["MNIST_NonBinaryNet\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xv37jeParJ-C","colab_type":"code","outputId":"5ae42699-7f9a-423f-e4c4-0aa7bf380a41","executionInfo":{"status":"ok","timestamp":1588674928301,"user_tz":-120,"elapsed":1078546,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["path_model_checkpoint = 'trained_models/MNIST/No_binary_models/with_bias'\n","path_save_plot = 'results/MNIST_results/plot_loss_acc/'\n","\n","run(model, path_model_checkpoint, path_save_plot, name_model, train_loader, valid_loader, epochs, lr, momentum, criterion, log_interval)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["ITERATION - loss: 0.26: 100%|█████████▉| 1680/1688 [00:20<00:00, 147.59it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 1 Avg accuracy: 91.89 Avg loss: 0.32\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.19: 1%| | 20/1688 [00:21<04:12, 6.60it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 1 Avg accuracy: 92.23 Avg loss: 0.31\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.28: 1690it [00:41, 127.42it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 2 Avg accuracy: 94.13 Avg loss: 0.22\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.23: 1%| | 20/1688 [00:42<04:10, 6.66it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 2 Avg accuracy: 94.37 Avg loss: 0.22\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 1690it [01:03, 147.82it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 3 Avg accuracy: 95.11 Avg loss: 0.18\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1%| | 20/1688 [01:04<04:09, 6.69it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 3 Avg accuracy: 95.28 Avg loss: 0.18\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.18: 1690it [01:24, 129.09it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 4 Avg accuracy: 95.69 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.21: 1%| | 20/1688 [01:25<04:07, 6.74it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 4 Avg accuracy: 95.75 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.32: 1690it [01:46, 140.66it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 5 Avg accuracy: 96.10 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.18: 1%| | 20/1688 [01:47<04:07, 6.74it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 5 Avg accuracy: 96.12 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.19: 100%|█████████▉| 1680/1688 [02:07<00:00, 144.36it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 6 Avg accuracy: 96.51 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1%| | 20/1688 [02:08<04:08, 6.72it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 6 Avg accuracy: 96.42 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.18: 1690it [02:28, 143.30it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 7 Avg accuracy: 96.81 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 1%| | 20/1688 [02:29<04:18, 6.46it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 7 Avg accuracy: 96.75 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 1690it [02:50, 146.16it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 8 Avg accuracy: 97.09 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 1%| | 20/1688 [02:51<04:16, 6.51it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 8 Avg accuracy: 96.98 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1690it [03:12, 145.59it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 9 Avg accuracy: 97.25 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1%| | 20/1688 [03:13<04:08, 6.72it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 9 Avg accuracy: 97.08 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 1690it [03:33, 133.96it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 10 Avg accuracy: 97.43 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1%| | 20/1688 [03:34<04:19, 6.43it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 10 Avg accuracy: 97.23 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 100%|█████████▉| 1680/1688 [03:55<00:00, 144.27it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 11 Avg accuracy: 97.57 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.09: 1%| | 20/1688 [03:56<04:03, 6.86it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 11 Avg accuracy: 97.32 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 1690it [04:16, 144.07it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 12 Avg accuracy: 97.58 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1%| | 20/1688 [04:17<04:09, 6.68it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 12 Avg accuracy: 97.37 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 1690it [04:37, 144.24it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 13 Avg accuracy: 97.82 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 1%| | 20/1688 [04:38<04:06, 6.76it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 13 Avg accuracy: 97.63 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 1690it [04:58, 147.19it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 14 Avg accuracy: 97.81 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1%| | 20/1688 [04:59<04:07, 6.75it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 14 Avg accuracy: 97.55 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1690it [05:20, 148.37it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 15 Avg accuracy: 97.97 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.23: 1%| | 20/1688 [05:21<04:11, 6.62it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 15 Avg accuracy: 97.75 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 100%|█████████▉| 1680/1688 [05:41<00:00, 144.82it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 16 Avg accuracy: 98.04 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1%| | 20/1688 [05:42<04:03, 6.84it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 16 Avg accuracy: 97.75 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1690it [06:02, 147.27it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 17 Avg accuracy: 98.07 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1%| | 20/1688 [06:03<04:03, 6.85it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 17 Avg accuracy: 97.70 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1690it [06:23, 148.85it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 18 Avg accuracy: 98.11 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1%| | 20/1688 [06:24<04:05, 6.79it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 18 Avg accuracy: 97.80 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1690it [06:44, 136.27it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 19 Avg accuracy: 98.18 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [06:45<04:12, 6.59it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 19 Avg accuracy: 97.82 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.16: 1690it [07:06, 147.37it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 20 Avg accuracy: 98.29 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.16: 1690it [07:07, 147.37it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 20 Avg accuracy: 97.78 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 100%|█████████▉| 1680/1688 [07:27<00:00, 148.76it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 21 Avg accuracy: 98.30 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 1%| | 20/1688 [07:28<04:12, 6.61it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 21 Avg accuracy: 97.80 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1690it [07:49, 144.86it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 22 Avg accuracy: 98.36 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1%| | 20/1688 [07:50<04:04, 6.83it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 22 Avg accuracy: 97.92 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1690it [08:10, 146.10it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 23 Avg accuracy: 98.43 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1%| | 20/1688 [08:11<04:08, 6.72it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 23 Avg accuracy: 97.90 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.14: 1690it [08:31, 146.94it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 24 Avg accuracy: 98.40 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [08:32<04:02, 6.87it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 24 Avg accuracy: 97.88 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 1690it [08:51, 138.01it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 25 Avg accuracy: 98.40 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [08:52<04:02, 6.87it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 25 Avg accuracy: 97.98 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 100%|█████████▉| 1680/1688 [09:12<00:00, 150.12it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 26 Avg accuracy: 98.49 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1%| | 20/1688 [09:13<04:00, 6.93it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 26 Avg accuracy: 98.03 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1690it [09:34, 133.84it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 27 Avg accuracy: 98.59 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1%| | 20/1688 [09:35<04:18, 6.45it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 27 Avg accuracy: 98.07 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 1690it [09:56, 146.09it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 28 Avg accuracy: 98.60 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [09:57<04:07, 6.74it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 28 Avg accuracy: 97.97 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.16: 1690it [10:17, 148.30it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 29 Avg accuracy: 98.63 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [10:18<04:11, 6.63it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 29 Avg accuracy: 97.97 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1690it [10:39, 137.84it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 30 Avg accuracy: 98.65 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [10:40<04:07, 6.73it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 30 Avg accuracy: 98.05 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 100%|█████████▉| 1680/1688 [11:00<00:00, 147.55it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 31 Avg accuracy: 98.66 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1%| | 20/1688 [11:01<04:06, 6.76it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 31 Avg accuracy: 98.08 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1690it [11:21, 146.36it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 32 Avg accuracy: 98.61 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1%| | 20/1688 [11:22<04:12, 6.61it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 32 Avg accuracy: 98.00 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.15: 1690it [11:42, 127.85it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 33 Avg accuracy: 98.70 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 1%| | 20/1688 [11:43<04:05, 6.78it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 33 Avg accuracy: 98.13 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1690it [12:03, 146.65it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 34 Avg accuracy: 98.72 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 1%| | 20/1688 [12:04<04:05, 6.80it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 34 Avg accuracy: 98.15 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1690it [12:24, 145.69it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 35 Avg accuracy: 98.77 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1%| | 20/1688 [12:25<04:07, 6.74it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 35 Avg accuracy: 98.13 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 100%|█████████▉| 1680/1688 [12:46<00:00, 133.72it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 36 Avg accuracy: 98.77 Avg loss: 0.05\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1%| | 20/1688 [12:47<04:06, 6.75it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 36 Avg accuracy: 98.15 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1690it [13:08, 139.04it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 37 Avg accuracy: 98.80 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [13:09<04:12, 6.61it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 37 Avg accuracy: 98.22 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.09: 1690it [13:29, 151.09it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 38 Avg accuracy: 98.78 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1%| | 20/1688 [13:30<04:11, 6.63it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 38 Avg accuracy: 98.17 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1690it [13:50, 141.46it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 39 Avg accuracy: 98.86 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1%| | 20/1688 [13:52<04:10, 6.66it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 39 Avg accuracy: 98.20 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.00: 1690it [14:12, 147.87it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 40 Avg accuracy: 98.86 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 1%| | 20/1688 [14:13<04:14, 6.56it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 40 Avg accuracy: 98.17 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 100%|█████████▉| 1680/1688 [14:33<00:00, 148.36it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 41 Avg accuracy: 98.89 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.00: 1%| | 20/1688 [14:34<04:13, 6.59it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 41 Avg accuracy: 98.13 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1690it [14:54, 141.01it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 42 Avg accuracy: 98.89 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1%| | 20/1688 [14:55<04:05, 6.80it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 42 Avg accuracy: 98.18 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1690it [15:15, 143.54it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 43 Avg accuracy: 98.93 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.10: 1%| | 20/1688 [15:16<04:05, 6.81it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 43 Avg accuracy: 98.18 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1690it [15:37, 145.17it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 44 Avg accuracy: 98.92 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [15:38<04:17, 6.48it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 44 Avg accuracy: 98.18 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 1690it [15:58, 144.86it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 45 Avg accuracy: 98.91 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.10: 1%| | 20/1688 [15:59<04:02, 6.89it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 45 Avg accuracy: 98.27 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.20: 100%|█████████▉| 1680/1688 [16:19<00:00, 147.77it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 46 Avg accuracy: 98.96 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1%| | 20/1688 [16:20<03:57, 7.04it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 46 Avg accuracy: 98.25 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 1690it [16:40, 142.60it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 47 Avg accuracy: 98.98 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.18: 1%| | 20/1688 [16:41<04:12, 6.60it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 47 Avg accuracy: 98.23 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1690it [17:02, 145.98it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 48 Avg accuracy: 98.96 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [17:03<04:04, 6.82it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 48 Avg accuracy: 98.25 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1690it [17:23, 135.36it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 49 Avg accuracy: 98.98 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.00: 1%| | 20/1688 [17:24<04:09, 6.68it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 49 Avg accuracy: 98.20 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1690it [17:44, 142.51it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 50 Avg accuracy: 99.02 Avg loss: 0.04\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1690it [17:45, 142.51it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 50 Avg accuracy: 98.28 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":[""],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"9p-TMgHLKL8L","colab_type":"text"},"source":["### Test no binary network:"]},{"cell_type":"code","metadata":{"id":"o51hCtcA1DFo","colab_type":"code","outputId":"53dabc1a-e54c-44e7-c012-2e25f1821faf","executionInfo":{"status":"ok","timestamp":1588674929193,"user_tz":-120,"elapsed":855,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# load model pre trained no binary\n","binary = False\n","model_no_binary, name_model = get_my_model_MNIST(binary)\n","\n","path_model = 'trained_models/MNIST/No_binary_models/with_bias'\n","if torch.cuda.is_available():\n"," model_no_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_no_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model Loaded\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"9A-PvvuJPKv5","colab_type":"code","outputId":"39b06491-f48f-46c0-a2ed-b31e3f493327","executionInfo":{"status":"ok","timestamp":1588684102407,"user_tz":-120,"elapsed":1753,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["evaluate(model_no_binary, test_loader)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Test Results - Avg accuracy: 98.22 Avg loss: 0.05\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8DfJ-CRU93BP","colab_type":"text"},"source":["## Run Binary Netwwork:"]},{"cell_type":"code","metadata":{"id":"MahC-0u997vy","colab_type":"code","outputId":"a1b0ac35-6dc1-4bf2-e905-758fcd6076f2","executionInfo":{"status":"ok","timestamp":1588674930614,"user_tz":-120,"elapsed":2242,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# parameters model to load no Binary model\n","binary = True\n","\n","model, name_model = get_my_model_MNIST(binary)\n","print(name_model)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["MNIST_Stochastic_ST_first_conv_binary\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rVgZXvMM-MwZ","colab_type":"code","outputId":"10de86a4-229c-4a41-8b95-adaad2b7ab34","executionInfo":{"status":"ok","timestamp":1588676042164,"user_tz":-120,"elapsed":1113779,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["path_model_checkpoint = 'trained_models/MNIST/Binary_models/with_bias'\n","path_save_plot = 'results/MNIST_results/plot_loss_acc/'\n","\n","run(model, path_model_checkpoint, path_save_plot, name_model, train_loader, valid_loader, epochs, lr, momentum, criterion, log_interval)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["ITERATION - loss: 0.45: 100%|█████████▉| 1680/1688 [00:21<00:00, 139.72it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 1 Avg accuracy: 86.73 Avg loss: 0.50\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.38: 1%| | 20/1688 [00:22<04:14, 6.56it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 1 Avg accuracy: 86.90 Avg loss: 0.49\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.36: 1690it [00:43, 136.62it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 2 Avg accuracy: 89.43 Avg loss: 0.37\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.31: 1%| | 20/1688 [00:44<04:19, 6.42it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 2 Avg accuracy: 89.38 Avg loss: 0.36\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.24: 1690it [01:05, 134.95it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 3 Avg accuracy: 90.96 Avg loss: 0.31\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.20: 1%| | 20/1688 [01:06<04:13, 6.57it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 3 Avg accuracy: 90.95 Avg loss: 0.31\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.22: 1690it [01:27, 138.56it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 4 Avg accuracy: 91.89 Avg loss: 0.28\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.29: 1%| | 20/1688 [01:28<04:16, 6.51it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 4 Avg accuracy: 91.87 Avg loss: 0.28\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.28: 1690it [01:49, 139.77it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 5 Avg accuracy: 92.62 Avg loss: 0.25\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.30: 1%| | 20/1688 [01:50<04:19, 6.43it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 5 Avg accuracy: 92.47 Avg loss: 0.25\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.26: 100%|█████████▉| 1680/1688 [02:11<00:00, 132.54it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 6 Avg accuracy: 93.23 Avg loss: 0.23\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.17: 1%| | 20/1688 [02:12<04:08, 6.70it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 6 Avg accuracy: 93.23 Avg loss: 0.23\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.37: 1690it [02:33, 132.05it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 7 Avg accuracy: 93.71 Avg loss: 0.21\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 1%| | 20/1688 [02:34<04:19, 6.42it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 7 Avg accuracy: 93.70 Avg loss: 0.21\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.18: 1690it [02:55, 142.68it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 8 Avg accuracy: 94.15 Avg loss: 0.20\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1%| | 20/1688 [02:56<04:29, 6.18it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 8 Avg accuracy: 94.18 Avg loss: 0.20\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 1690it [03:17, 135.13it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 9 Avg accuracy: 94.48 Avg loss: 0.19\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.17: 1%| | 20/1688 [03:19<04:21, 6.38it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 9 Avg accuracy: 94.50 Avg loss: 0.19\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 1690it [03:40, 122.27it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 10 Avg accuracy: 94.76 Avg loss: 0.18\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1%| | 20/1688 [03:41<04:16, 6.50it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 10 Avg accuracy: 94.78 Avg loss: 0.18\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.19: 100%|█████████▉| 1680/1688 [04:03<00:00, 120.71it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 11 Avg accuracy: 94.86 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.14: 1%| | 20/1688 [04:04<04:27, 6.23it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 11 Avg accuracy: 94.88 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.18: 1690it [04:25, 138.12it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 12 Avg accuracy: 95.15 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 1%| | 20/1688 [04:26<04:24, 6.31it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 12 Avg accuracy: 95.17 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.18: 1690it [04:47, 137.97it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 13 Avg accuracy: 95.37 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.10: 1%| | 20/1688 [04:49<04:24, 6.31it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 13 Avg accuracy: 95.20 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.27: 1690it [05:10, 132.43it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 14 Avg accuracy: 95.58 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 1%| | 20/1688 [05:11<04:27, 6.24it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 14 Avg accuracy: 95.43 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1690it [05:32, 132.90it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 15 Avg accuracy: 95.67 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.38: 1%| | 20/1688 [05:33<04:13, 6.58it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 15 Avg accuracy: 95.53 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.17: 100%|█████████▉| 1680/1688 [05:54<00:00, 132.73it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 16 Avg accuracy: 95.82 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.23: 1%| | 20/1688 [05:55<04:19, 6.43it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 16 Avg accuracy: 95.60 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1690it [06:16, 133.45it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 17 Avg accuracy: 95.93 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1%| | 20/1688 [06:17<04:16, 6.51it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 17 Avg accuracy: 95.87 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1690it [06:38, 140.60it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 18 Avg accuracy: 96.03 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.10: 1%| | 20/1688 [06:40<04:16, 6.50it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 18 Avg accuracy: 95.88 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1690it [07:01, 134.44it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 19 Avg accuracy: 96.12 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [07:02<04:19, 6.42it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 19 Avg accuracy: 95.83 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1690it [07:23, 138.90it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 20 Avg accuracy: 96.23 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1690it [07:24, 138.90it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 20 Avg accuracy: 95.98 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.14: 100%|█████████▉| 1680/1688 [07:45<00:00, 142.32it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 21 Avg accuracy: 96.33 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 1%| | 20/1688 [07:46<04:18, 6.46it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 21 Avg accuracy: 96.08 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.17: 1690it [08:07, 135.30it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 22 Avg accuracy: 96.42 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.15: 1%| | 20/1688 [08:08<04:17, 6.48it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 22 Avg accuracy: 96.08 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 1690it [08:29, 142.85it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 23 Avg accuracy: 96.48 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1%| | 20/1688 [08:30<04:22, 6.36it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 23 Avg accuracy: 96.28 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 1690it [08:51, 137.77it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 24 Avg accuracy: 96.52 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 1%| | 20/1688 [08:52<04:14, 6.56it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 24 Avg accuracy: 96.42 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.39: 1690it [09:13, 142.70it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 25 Avg accuracy: 96.58 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1%| | 20/1688 [09:14<04:18, 6.46it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 25 Avg accuracy: 96.47 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.09: 100%|█████████▉| 1680/1688 [09:35<00:00, 136.89it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 26 Avg accuracy: 96.67 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1%| | 20/1688 [09:36<04:21, 6.37it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 26 Avg accuracy: 96.42 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1690it [09:57, 139.91it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 27 Avg accuracy: 96.73 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1%| | 20/1688 [09:58<04:19, 6.44it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 27 Avg accuracy: 96.52 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.10: 1690it [10:19, 137.66it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 28 Avg accuracy: 96.81 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1%| | 20/1688 [10:20<04:22, 6.35it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 28 Avg accuracy: 96.53 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.38: 1690it [10:41, 140.29it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 29 Avg accuracy: 96.83 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 1%| | 20/1688 [10:42<04:28, 6.22it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 29 Avg accuracy: 96.55 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1690it [11:04, 141.76it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 30 Avg accuracy: 96.87 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 1%| | 20/1688 [11:05<04:16, 6.50it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 30 Avg accuracy: 96.60 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 100%|█████████▉| 1680/1688 [11:26<00:00, 138.41it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 31 Avg accuracy: 96.91 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.21: 1%| | 20/1688 [11:27<04:33, 6.09it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 31 Avg accuracy: 96.73 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.14: 1690it [11:48, 130.16it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 32 Avg accuracy: 96.97 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1%| | 20/1688 [11:49<04:18, 6.46it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 32 Avg accuracy: 96.93 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.26: 1690it [12:10, 146.38it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 33 Avg accuracy: 96.99 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 1%| | 20/1688 [12:11<04:15, 6.54it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 33 Avg accuracy: 96.87 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 1690it [12:32, 139.06it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 34 Avg accuracy: 97.09 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 1%| | 20/1688 [12:33<04:08, 6.71it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 34 Avg accuracy: 97.00 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1690it [12:53, 140.53it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 35 Avg accuracy: 97.10 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.16: 1%| | 20/1688 [12:54<04:19, 6.43it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 35 Avg accuracy: 96.87 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 100%|█████████▉| 1680/1688 [13:16<00:00, 136.69it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 36 Avg accuracy: 97.12 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.14: 1%| | 20/1688 [13:17<04:26, 6.26it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 36 Avg accuracy: 97.03 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1690it [13:38, 141.94it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 37 Avg accuracy: 97.22 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1%| | 20/1688 [13:39<04:20, 6.40it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 37 Avg accuracy: 97.00 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 1690it [14:00, 145.19it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 38 Avg accuracy: 97.22 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.16: 1%| | 20/1688 [14:01<04:16, 6.50it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 38 Avg accuracy: 97.08 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 1690it [14:23, 134.14it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 39 Avg accuracy: 97.24 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1%| | 20/1688 [14:24<04:26, 6.25it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 39 Avg accuracy: 97.17 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1690it [14:45, 142.29it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 40 Avg accuracy: 97.30 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.22: 1%| | 20/1688 [14:47<04:28, 6.22it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 40 Avg accuracy: 97.03 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 100%|█████████▉| 1680/1688 [15:07<00:00, 135.00it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 41 Avg accuracy: 97.29 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1%| | 20/1688 [15:09<04:22, 6.35it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 41 Avg accuracy: 97.05 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1690it [15:30, 142.16it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 42 Avg accuracy: 97.37 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 1%| | 20/1688 [15:31<04:12, 6.61it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 42 Avg accuracy: 97.20 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 1690it [15:52, 137.16it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 43 Avg accuracy: 97.36 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.14: 1%| | 20/1688 [15:53<04:21, 6.38it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 43 Avg accuracy: 97.17 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1690it [16:14, 138.63it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 44 Avg accuracy: 97.41 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 1%| | 20/1688 [16:15<04:18, 6.45it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 44 Avg accuracy: 97.22 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.19: 1690it [16:36, 137.86it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 45 Avg accuracy: 97.44 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 1%| | 20/1688 [16:38<04:14, 6.55it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 45 Avg accuracy: 97.37 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.37: 100%|█████████▉| 1680/1688 [16:58<00:00, 135.37it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 46 Avg accuracy: 97.44 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1%| | 20/1688 [17:00<04:22, 6.37it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 46 Avg accuracy: 97.40 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 1690it [17:21, 142.19it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 47 Avg accuracy: 97.49 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.23: 1%| | 20/1688 [17:22<04:22, 6.35it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 47 Avg accuracy: 97.27 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1690it [17:44, 127.33it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 48 Avg accuracy: 97.54 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 1%| | 20/1688 [17:45<04:30, 6.17it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 48 Avg accuracy: 97.35 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.15: 1690it [18:06, 137.68it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 49 Avg accuracy: 97.56 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 1%| | 20/1688 [18:07<04:28, 6.21it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 49 Avg accuracy: 97.40 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.33: 1690it [18:28, 125.43it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 50 Avg accuracy: 97.56 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.33: 1690it [18:29, 125.43it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 50 Avg accuracy: 97.43 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["\rITERATION - loss: 0.33: 0%| | 0/1688 [18:30<00:13, 125.43it/s]"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":[""],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"p2H3DQ7XAQ_J","colab_type":"text"},"source":["### Test Binary network:"]},{"cell_type":"code","metadata":{"id":"UeDua8SgATa0","colab_type":"code","outputId":"68c3b7e2-2729-4305-b3b1-d9fa98341248","executionInfo":{"status":"ok","timestamp":1588676042168,"user_tz":-120,"elapsed":1113769,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# load model pre trained binary\n","binary = True \n","model_binary, name_model = get_my_model_MNIST(binary)\n","\n","path_model = 'trained_models/MNIST/Binary_models/with_bias'\n","if torch.cuda.is_available():\n"," model_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model Loaded\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"L3tFWrM6AWP9","colab_type":"code","outputId":"761ab400-195a-4168-ad4e-57fe6e1bdebe","executionInfo":{"status":"ok","timestamp":1588684107963,"user_tz":-120,"elapsed":1877,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["evaluate(model_binary, test_loader)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Test Results - Avg accuracy: 97.51 Avg loss: 0.09\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"BaWaOgvBVZhk","colab_type":"text"},"source":["# Training wihtout bias:"]},{"cell_type":"markdown","metadata":{"id":"bhaLA5o9VgxO","colab_type":"text"},"source":["## Training parameters:"]},{"cell_type":"code","metadata":{"id":"xzEaPRGEVhTN","colab_type":"code","colab":{}},"source":["epochs = 50\n","lr = 1e-3\n","momentum = 0.5\n","log_interval = 10 # how many batches to wait before logging training status\n","criterion = F.nll_loss"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OuAejo3aViZt","colab_type":"text"},"source":["## Run No binary network:"]},{"cell_type":"code","metadata":{"id":"CoJcRlrJVj8P","colab_type":"code","outputId":"4e601b10-527a-4551-93c8-4291711aa450","executionInfo":{"status":"ok","timestamp":1588858493477,"user_tz":-120,"elapsed":874,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["# parameters model to load no Binary model\n","binary = False\n","bias = False\n","\n","model, name_model = get_my_model_MNIST(binary, bias=bias)\n","print(name_model)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["MNIST_NonBinaryNet_without_bias\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wv274ImnVmwH","colab_type":"code","outputId":"c60a904c-ad13-4269-aba4-c096c4fac2ef","executionInfo":{"status":"ok","timestamp":1588859301838,"user_tz":-120,"elapsed":808956,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["path_model_checkpoint = 'trained_models/MNIST/No_binary_models/without_bias'\n","path_save_plot = 'results/MNIST_results/plot_loss_acc/'\n","\n","run(model, path_model_checkpoint, path_save_plot, name_model, train_loader, valid_loader, epochs, lr, momentum, criterion, log_interval)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["ITERATION - loss: 0.52: 100%|█████████▉| 840/844 [00:15<00:00, 94.73it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 1 Avg accuracy: 88.37 Avg loss: 0.50\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.49: 2%|▏ | 20/844 [00:16<02:19, 5.89it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 1 Avg accuracy: 88.87 Avg loss: 0.49\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.24: 100%|█████████▉| 840/844 [00:31<00:00, 93.87it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 2 Avg accuracy: 91.44 Avg loss: 0.34\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.19: 1%| | 10/844 [00:32<03:18, 4.21it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 2 Avg accuracy: 91.95 Avg loss: 0.33\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.32: 850it [00:48, 100.30it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 3 Avg accuracy: 92.89 Avg loss: 0.27\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.32: 850it [00:48, 100.30it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 3 Avg accuracy: 93.37 Avg loss: 0.27\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.26: 100%|█████████▉| 840/844 [01:04<00:00, 93.42it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 4 Avg accuracy: 93.88 Avg loss: 0.23\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.14: 2%|▏ | 20/844 [01:05<02:20, 5.85it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 4 Avg accuracy: 94.18 Avg loss: 0.23\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.21: 850it [01:20, 100.73it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 5 Avg accuracy: 94.58 Avg loss: 0.20\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.21: 850it [01:21, 100.73it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 5 Avg accuracy: 94.67 Avg loss: 0.21\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.29: 100%|█████████▉| 840/844 [01:36<00:00, 98.20it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 6 Avg accuracy: 95.02 Avg loss: 0.19\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.14: 1%| | 10/844 [01:37<03:17, 4.22it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 6 Avg accuracy: 95.03 Avg loss: 0.19\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 100%|█████████▉| 840/844 [01:52<00:00, 100.04it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 7 Avg accuracy: 95.35 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.20: 2%|▏ | 20/844 [01:53<01:42, 8.05it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 7 Avg accuracy: 95.20 Avg loss: 0.18\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.18: 850it [02:08, 99.21it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 8 Avg accuracy: 95.64 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.19: 1%| | 10/844 [02:09<03:22, 4.13it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 8 Avg accuracy: 95.47 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 100%|█████████▉| 840/844 [02:24<00:00, 99.51it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 9 Avg accuracy: 95.90 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 2%|▏ | 20/844 [02:25<02:18, 5.93it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 9 Avg accuracy: 95.75 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 850it [02:40, 96.54it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 10 Avg accuracy: 96.05 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1%| | 10/844 [02:41<03:21, 4.15it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 10 Avg accuracy: 95.88 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.09: 100%|█████████▉| 840/844 [02:57<00:00, 96.30it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 11 Avg accuracy: 96.25 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 1%| | 10/844 [02:57<03:17, 4.22it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 11 Avg accuracy: 96.05 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 100%|█████████▉| 840/844 [03:12<00:00, 94.78it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 12 Avg accuracy: 96.40 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 1%| | 10/844 [03:13<03:20, 4.15it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 12 Avg accuracy: 96.18 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 850it [03:29, 89.85it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 13 Avg accuracy: 96.56 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 1%| | 10/844 [03:30<03:19, 4.17it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 13 Avg accuracy: 96.32 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 100%|█████████▉| 840/844 [03:45<00:00, 102.47it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 14 Avg accuracy: 96.65 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.18: 2%|▏ | 20/844 [03:46<01:40, 8.19it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 14 Avg accuracy: 96.45 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 850it [04:01, 104.59it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 15 Avg accuracy: 96.79 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 850it [04:01, 104.59it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 15 Avg accuracy: 96.55 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.09: 100%|█████████▉| 840/844 [04:17<00:00, 100.80it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 16 Avg accuracy: 96.92 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 2%|▏ | 20/844 [04:18<01:42, 8.05it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 16 Avg accuracy: 96.60 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 100%|█████████▉| 840/844 [04:32<00:00, 100.75it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 17 Avg accuracy: 96.99 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 2%|▏ | 20/844 [04:33<01:41, 8.09it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 17 Avg accuracy: 96.65 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.10: 850it [04:48, 98.62it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 18 Avg accuracy: 97.08 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.17: 1%| | 10/844 [04:49<03:18, 4.20it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 18 Avg accuracy: 96.73 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.09: 100%|█████████▉| 840/844 [05:04<00:00, 100.28it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 19 Avg accuracy: 97.19 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 2%|▏ | 20/844 [05:05<01:41, 8.16it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 19 Avg accuracy: 96.72 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 850it [05:20, 101.03it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 20 Avg accuracy: 97.26 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 850it [05:21, 101.03it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 20 Avg accuracy: 96.77 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.15: 100%|█████████▉| 840/844 [05:36<00:00, 101.64it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 21 Avg accuracy: 97.34 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 2%|▏ | 20/844 [05:37<01:39, 8.25it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 21 Avg accuracy: 96.80 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.23: 100%|█████████▉| 840/844 [05:51<00:00, 102.25it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 22 Avg accuracy: 97.39 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 2%|▏ | 20/844 [05:52<01:40, 8.22it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 22 Avg accuracy: 96.85 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 850it [06:07, 102.12it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 23 Avg accuracy: 97.46 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 850it [06:08, 102.12it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 23 Avg accuracy: 96.83 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 100%|█████████▉| 840/844 [06:24<00:00, 101.30it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 24 Avg accuracy: 97.50 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.10: 2%|▏ | 20/844 [06:25<01:43, 7.98it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 24 Avg accuracy: 96.92 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.15: 850it [06:40, 95.31it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 25 Avg accuracy: 97.53 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.13: 1%| | 10/844 [06:41<03:20, 4.16it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 25 Avg accuracy: 96.98 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 100%|█████████▉| 840/844 [06:56<00:00, 99.83it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 26 Avg accuracy: 97.55 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 2%|▏ | 20/844 [06:57<01:42, 8.02it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 26 Avg accuracy: 96.95 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 100%|█████████▉| 840/844 [07:12<00:00, 97.40it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 27 Avg accuracy: 97.60 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 1%| | 10/844 [07:13<03:16, 4.25it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 27 Avg accuracy: 97.02 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.09: 850it [07:28, 99.88it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 28 Avg accuracy: 97.70 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.09: 850it [07:29, 99.88it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 28 Avg accuracy: 97.10 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 100%|█████████▉| 840/844 [07:44<00:00, 100.58it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 29 Avg accuracy: 97.72 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 2%|▏ | 20/844 [07:45<01:42, 8.04it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 29 Avg accuracy: 97.12 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 850it [08:00, 102.76it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 30 Avg accuracy: 97.77 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 850it [08:01, 102.76it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 30 Avg accuracy: 97.08 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.10: 100%|█████████▉| 840/844 [08:16<00:00, 99.99it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 31 Avg accuracy: 97.81 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 2%|▏ | 20/844 [08:17<01:42, 8.04it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 31 Avg accuracy: 97.18 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.16: 100%|█████████▉| 840/844 [08:32<00:00, 98.88it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 32 Avg accuracy: 97.88 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 1%| | 10/844 [08:33<03:19, 4.19it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 32 Avg accuracy: 97.20 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.18: 850it [08:48, 103.40it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 33 Avg accuracy: 97.87 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.10: 2%|▏ | 20/844 [08:49<01:40, 8.17it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 33 Avg accuracy: 97.25 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 100%|█████████▉| 840/844 [09:04<00:00, 101.76it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 34 Avg accuracy: 97.92 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 2%|▏ | 20/844 [09:04<01:40, 8.18it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 34 Avg accuracy: 97.23 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 850it [09:19, 104.68it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 35 Avg accuracy: 97.96 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.06: 850it [09:20, 104.68it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 35 Avg accuracy: 97.20 Avg loss: 0.10\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.11: 100%|█████████▉| 840/844 [09:35<00:00, 101.42it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 36 Avg accuracy: 97.97 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 2%|▏ | 20/844 [09:36<01:40, 8.18it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 36 Avg accuracy: 97.33 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 100%|█████████▉| 840/844 [09:51<00:00, 101.56it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 37 Avg accuracy: 98.01 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.10: 2%|▏ | 20/844 [09:52<01:40, 8.17it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 37 Avg accuracy: 97.30 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 850it [10:07, 103.13it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 38 Avg accuracy: 98.03 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 850it [10:07, 103.13it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 38 Avg accuracy: 97.30 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 100%|█████████▉| 840/844 [10:23<00:00, 99.82it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 39 Avg accuracy: 98.10 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 2%|▏ | 20/844 [10:24<01:41, 8.10it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 39 Avg accuracy: 97.37 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 850it [10:38, 103.63it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 40 Avg accuracy: 98.08 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 850it [10:39, 103.63it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 40 Avg accuracy: 97.47 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 100%|█████████▉| 840/844 [10:54<00:00, 103.03it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 41 Avg accuracy: 98.11 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 2%|▏ | 20/844 [10:55<01:40, 8.24it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 41 Avg accuracy: 97.45 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 100%|█████████▉| 840/844 [11:10<00:00, 104.64it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 42 Avg accuracy: 98.16 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 2%|▏ | 20/844 [11:11<01:41, 8.13it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 42 Avg accuracy: 97.47 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.08: 850it [11:26, 100.30it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 43 Avg accuracy: 98.16 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.04: 2%|▏ | 20/844 [11:27<01:42, 8.07it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 43 Avg accuracy: 97.50 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.16: 100%|█████████▉| 840/844 [11:42<00:00, 104.74it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 44 Avg accuracy: 98.21 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 2%|▏ | 20/844 [11:43<01:42, 8.05it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 44 Avg accuracy: 97.52 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 850it [11:57, 97.79it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 45 Avg accuracy: 98.25 Avg loss: 0.07\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 1%| | 10/844 [11:58<03:18, 4.20it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 45 Avg accuracy: 97.57 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.03: 100%|█████████▉| 840/844 [12:13<00:00, 100.05it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 46 Avg accuracy: 98.28 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.16: 2%|▏ | 20/844 [12:14<01:40, 8.17it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 46 Avg accuracy: 97.45 Avg loss: 0.09\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.12: 100%|█████████▉| 840/844 [12:29<00:00, 100.66it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 47 Avg accuracy: 98.28 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.20: 2%|▏ | 20/844 [12:30<01:41, 8.14it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 47 Avg accuracy: 97.55 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 850it [12:45, 101.99it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 48 Avg accuracy: 98.29 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.02: 850it [12:46, 101.99it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 48 Avg accuracy: 97.58 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.07: 100%|█████████▉| 840/844 [13:01<00:00, 103.98it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 49 Avg accuracy: 98.30 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.01: 2%|▏ | 20/844 [13:02<01:41, 8.12it/s] "],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 49 Avg accuracy: 97.67 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 850it [13:16, 103.38it/s]"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 50 Avg accuracy: 98.36 Avg loss: 0.06\n"],"name":"stdout"},{"output_type":"stream","text":["ITERATION - loss: 0.05: 850it [13:17, 103.38it/s]"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 50 Avg accuracy: 97.58 Avg loss: 0.08\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":[""],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"fBf2b9V8Vp6H","colab_type":"text"},"source":["### Test no binary network:"]},{"cell_type":"code","metadata":{"id":"Z7HIZwp-VqWD","colab_type":"code","outputId":"2d9046fc-7ab8-4518-acdd-d7658810b2f0","executionInfo":{"status":"ok","timestamp":1588860796320,"user_tz":-120,"elapsed":589,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["# load model pre trained no binary\n","binary = False\n","bias=False\n","model_no_binary_wt_bias, name_model = get_my_model_MNIST(binary, bias=bias)\n","\n","path_model = 'trained_models/MNIST/No_binary_models/without_bias'\n","if torch.cuda.is_available():\n"," model_no_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_no_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model Loaded MNIST_NonBinaryNet_without_bias\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"lw1KxSSWWFI7","colab_type":"code","outputId":"3212470d-3f1c-411f-c91c-78ab8bf28385","executionInfo":{"status":"ok","timestamp":1588860797931,"user_tz":-120,"elapsed":2194,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["evaluate(model_no_binary_wt_bias, test_loader)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Test Results - Avg accuracy: 98.05 Avg loss: 0.06\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"odr-PsBvVsBC","colab_type":"text"},"source":["## Run Binary Netwwork:"]},{"cell_type":"code","metadata":{"id":"IjprOyqSVvpY","colab_type":"code","outputId":"39cdacf9-6fb9-43bd-bf2e-489a931ea37b","executionInfo":{"status":"ok","timestamp":1588859890542,"user_tz":-120,"elapsed":728,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["# parameters model to load no Binary model\n","binary = True\n","bias=False\n","\n","model, name_model = get_my_model_MNIST(binary, bias=bias)\n","print(name_model)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["MNIST_Stochastic_ST_first_conv_binary_without_bias\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"42fO1UE_Vxb4","colab_type":"code","outputId":"34d273ed-be02-4ac9-fb3d-c1d5d95e2756","executionInfo":{"status":"ok","timestamp":1588860783362,"user_tz":-120,"elapsed":857415,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["path_model_checkpoint = 'trained_models/MNIST/Binary_models/without_bias'\n","path_save_plot = 'results/MNIST_results/plot_loss_acc/'\n","\n","run(model, path_model_checkpoint, path_save_plot, name_model, train_loader, valid_loader, epochs, lr, momentum, criterion, log_interval)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\n","ITERATION - loss: 0.00: 0%| | 0/844 [00:00<?, ?it/s]\u001b[A\n","ITERATION - loss: 0.47: 1%| | 10/844 [00:00<00:09, 85.79it/s]\u001b[A\n","ITERATION - loss: 0.67: 2%|▏ | 20/844 [00:00<00:09, 86.20it/s]\u001b[A\n","ITERATION - loss: 0.57: 4%|▎ | 30/844 [00:00<00:09, 87.82it/s]\u001b[A\n","ITERATION - loss: 0.49: 5%|▍ | 40/844 [00:00<00:09, 86.22it/s]\u001b[A\n","ITERATION - loss: 0.61: 6%|▌ | 50/844 [00:00<00:09, 87.78it/s]\u001b[A\n","ITERATION - loss: 0.53: 7%|▋ | 60/844 [00:00<00:08, 88.49it/s]\u001b[A\n","ITERATION - loss: 0.56: 8%|▊ | 70/844 [00:00<00:08, 87.41it/s]\u001b[A\n","ITERATION - loss: 0.61: 9%|▉ | 80/844 [00:00<00:08, 88.56it/s]\u001b[A\n","ITERATION - loss: 0.53: 11%|█ | 90/844 [00:01<00:08, 88.61it/s]\u001b[A\n","ITERATION - loss: 0.54: 12%|█▏ | 100/844 [00:01<00:08, 89.42it/s]\u001b[A\n","ITERATION - loss: 0.52: 13%|█▎ | 110/844 [00:01<00:08, 88.30it/s]\u001b[A\n","ITERATION - loss: 0.57: 14%|█▍ | 120/844 [00:01<00:08, 87.82it/s]\u001b[A\n","ITERATION - loss: 0.72: 15%|█▌ | 130/844 [00:01<00:08, 88.82it/s]\u001b[A\n","ITERATION - loss: 0.50: 17%|█▋ | 140/844 [00:01<00:07, 88.98it/s]\u001b[A\n","ITERATION - loss: 0.67: 18%|█▊ | 150/844 [00:01<00:08, 84.79it/s]\u001b[A\n","ITERATION - loss: 0.45: 19%|█▉ | 160/844 [00:01<00:08, 83.28it/s]\u001b[A\n","ITERATION - loss: 0.56: 20%|██ | 170/844 [00:01<00:07, 84.44it/s]\u001b[A\n","ITERATION - loss: 0.39: 21%|██▏ | 180/844 [00:02<00:07, 86.03it/s]\u001b[A\n","ITERATION - loss: 0.39: 23%|██▎ | 190/844 [00:02<00:07, 87.53it/s]\u001b[A\n","ITERATION - loss: 0.42: 24%|██▎ | 200/844 [00:02<00:07, 82.76it/s]\u001b[A\n","ITERATION - loss: 0.57: 25%|██▍ | 210/844 [00:02<00:07, 83.37it/s]\u001b[A\n","ITERATION - loss: 0.40: 26%|██▌ | 220/844 [00:02<00:07, 85.34it/s]\u001b[A\n","ITERATION - loss: 0.65: 27%|██▋ | 230/844 [00:02<00:07, 86.53it/s]\u001b[A\n","ITERATION - loss: 0.58: 28%|██▊ | 240/844 [00:02<00:06, 88.00it/s]\u001b[A\n","ITERATION - loss: 0.52: 30%|██▉ | 250/844 [00:02<00:07, 84.67it/s]\u001b[A\n","ITERATION - loss: 0.54: 31%|███ | 260/844 [00:02<00:06, 87.43it/s]\u001b[A\n","ITERATION - loss: 0.52: 32%|███▏ | 270/844 [00:03<00:06, 88.31it/s]\u001b[A\n","ITERATION - loss: 0.56: 33%|███▎ | 280/844 [00:03<00:06, 90.46it/s]\u001b[A\n","ITERATION - loss: 0.50: 34%|███▍ | 290/844 [00:03<00:06, 89.82it/s]\u001b[A\n","ITERATION - loss: 0.52: 36%|███▌ | 300/844 [00:03<00:06, 89.72it/s]\u001b[A\n","ITERATION - loss: 0.45: 37%|███▋ | 310/844 [00:03<00:05, 91.16it/s]\u001b[A\n","ITERATION - loss: 0.45: 38%|███▊ | 320/844 [00:03<00:05, 91.25it/s]\u001b[A\n","ITERATION - loss: 0.52: 39%|███▉ | 330/844 [00:03<00:05, 88.71it/s]\u001b[A\n","ITERATION - loss: 0.64: 40%|████ | 340/844 [00:03<00:05, 87.51it/s]\u001b[A\n","ITERATION - loss: 0.29: 41%|████▏ | 350/844 [00:03<00:05, 89.03it/s]\u001b[A\n","ITERATION - loss: 0.57: 43%|████▎ | 360/844 [00:04<00:05, 88.89it/s]\u001b[A\n","ITERATION - loss: 0.34: 44%|████▍ | 370/844 [00:04<00:05, 89.92it/s]\u001b[A\n","ITERATION - loss: 0.39: 45%|████▌ | 380/844 [00:04<00:05, 89.17it/s]\u001b[A\n","ITERATION - loss: 0.41: 46%|████▌ | 390/844 [00:04<00:05, 85.63it/s]\u001b[A\n","ITERATION - loss: 0.49: 47%|████▋ | 400/844 [00:04<00:05, 87.58it/s]\u001b[A\n","ITERATION - loss: 0.76: 49%|████▊ | 410/844 [00:04<00:04, 88.50it/s]\u001b[A\n","ITERATION - loss: 0.48: 50%|████▉ | 420/844 [00:04<00:04, 88.71it/s]\u001b[A\n","ITERATION - loss: 0.38: 51%|█████ | 430/844 [00:04<00:04, 87.97it/s]\u001b[A\n","ITERATION - loss: 0.63: 52%|█████▏ | 440/844 [00:05<00:04, 89.84it/s]\u001b[A\n","ITERATION - loss: 0.46: 53%|█████▎ | 450/844 [00:05<00:04, 89.98it/s]\u001b[A\n","ITERATION - loss: 0.51: 55%|█████▍ | 460/844 [00:05<00:04, 91.73it/s]\u001b[A\n","ITERATION - loss: 0.73: 56%|█████▌ | 470/844 [00:05<00:04, 90.17it/s]\u001b[A\n","ITERATION - loss: 0.54: 57%|█████▋ | 480/844 [00:05<00:04, 90.09it/s]\u001b[A\n","ITERATION - loss: 0.48: 58%|█████▊ | 490/844 [00:05<00:03, 91.33it/s]\u001b[A\n","ITERATION - loss: 0.57: 59%|█████▉ | 500/844 [00:05<00:03, 88.59it/s]\u001b[A\n","ITERATION - loss: 0.44: 60%|██████ | 510/844 [00:05<00:03, 89.46it/s]\u001b[A\n","ITERATION - loss: 0.39: 62%|██████▏ | 520/844 [00:05<00:03, 88.16it/s]\u001b[A\n","ITERATION - loss: 0.62: 63%|██████▎ | 530/844 [00:06<00:03, 89.47it/s]\u001b[A\n","ITERATION - loss: 0.65: 64%|██████▍ | 540/844 [00:06<00:03, 90.19it/s]\u001b[A\n","ITERATION - loss: 0.55: 65%|██████▌ | 550/844 [00:06<00:03, 90.49it/s]\u001b[A\n","ITERATION - loss: 0.52: 66%|██████▋ | 560/844 [00:06<00:03, 88.45it/s]\u001b[A\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [00:39<00:00, 93.64it/s]\n","ITERATION - loss: 0.40: 69%|██████▊ | 580/844 [00:06<00:03, 87.85it/s]\u001b[A\n","ITERATION - loss: 0.62: 70%|██████▉ | 590/844 [00:06<00:02, 86.91it/s]\u001b[A\n","ITERATION - loss: 0.53: 71%|███████ | 600/844 [00:06<00:02, 89.18it/s]\u001b[A\n","ITERATION - loss: 0.53: 72%|███████▏ | 610/844 [00:06<00:02, 89.43it/s]\u001b[A\n","ITERATION - loss: 0.55: 73%|███████▎ | 620/844 [00:07<00:02, 90.07it/s]\u001b[A\n","ITERATION - loss: 0.49: 75%|███████▍ | 630/844 [00:07<00:02, 91.38it/s]\u001b[A\n","ITERATION - loss: 0.48: 76%|███████▌ | 640/844 [00:07<00:02, 92.41it/s]\u001b[A\n","ITERATION - loss: 0.32: 77%|███████▋ | 650/844 [00:07<00:02, 89.80it/s]\u001b[A\n","ITERATION - loss: 0.37: 78%|███████▊ | 660/844 [00:07<00:02, 91.30it/s]\u001b[A\n","ITERATION - loss: 0.40: 79%|███████▉ | 670/844 [00:07<00:01, 92.38it/s]\u001b[A\n","ITERATION - loss: 0.40: 81%|████████ | 680/844 [00:07<00:01, 92.82it/s]\u001b[A\n","ITERATION - loss: 0.37: 82%|████████▏ | 690/844 [00:07<00:01, 92.41it/s]\u001b[A\n","ITERATION - loss: 0.44: 83%|████████▎ | 700/844 [00:07<00:01, 93.08it/s]\u001b[A\n","ITERATION - loss: 0.33: 84%|████████▍ | 710/844 [00:08<00:01, 90.50it/s]\u001b[A\n","ITERATION - loss: 0.44: 85%|████████▌ | 720/844 [00:08<00:01, 91.44it/s]\u001b[A\n","ITERATION - loss: 0.42: 86%|████████▋ | 730/844 [00:08<00:01, 92.65it/s]\u001b[A\n","ITERATION - loss: 0.40: 88%|████████▊ | 740/844 [00:08<00:01, 93.18it/s]\u001b[A\n","ITERATION - loss: 0.45: 89%|████████▉ | 750/844 [00:08<00:01, 86.66it/s]\u001b[A\n","ITERATION - loss: 0.38: 90%|█████████ | 760/844 [00:08<00:00, 87.19it/s]\u001b[A\n","ITERATION - loss: 0.39: 91%|█████████ | 770/844 [00:08<00:00, 85.11it/s]\u001b[A\n","ITERATION - loss: 0.37: 92%|█████████▏| 780/844 [00:08<00:00, 84.67it/s]\u001b[A\n","ITERATION - loss: 0.35: 94%|█████████▎| 790/844 [00:08<00:00, 85.47it/s]\u001b[A\n","ITERATION - loss: 0.39: 95%|█████████▍| 800/844 [00:09<00:00, 88.22it/s]\u001b[A\n","ITERATION - loss: 0.52: 96%|█████████▌| 810/844 [00:09<00:00, 90.45it/s]\u001b[A\n","ITERATION - loss: 0.52: 97%|█████████▋| 820/844 [00:09<00:00, 91.46it/s]\u001b[A\n","ITERATION - loss: 0.60: 98%|█████████▊| 830/844 [00:09<00:00, 91.36it/s]\u001b[A\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [00:09<00:00, 90.04it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [00:49<00:00, 93.64it/s]\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [00:16<00:00, 90.04it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 1 Avg accuracy: 87.64 Avg loss: 0.44\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [00:50<00:00, 93.64it/s]\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [00:17<00:00, 90.04it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 1 Avg accuracy: 87.75 Avg loss: 0.44\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.47: 1%| | 10/844 [00:17<03:31, 3.95it/s] \u001b[A\n","ITERATION - loss: 0.44: 2%|▏ | 20/844 [00:17<02:28, 5.53it/s]\u001b[A\n","ITERATION - loss: 0.61: 4%|▎ | 30/844 [00:17<01:45, 7.70it/s]\u001b[A\n","ITERATION - loss: 0.42: 5%|▍ | 40/844 [00:17<01:15, 10.62it/s]\u001b[A\n","ITERATION - loss: 0.54: 6%|▌ | 50/844 [00:18<00:55, 14.41it/s]\u001b[A\n","ITERATION - loss: 0.41: 7%|▋ | 60/844 [00:18<00:40, 19.32it/s]\u001b[A\n","ITERATION - loss: 0.45: 8%|▊ | 70/844 [00:18<00:30, 25.29it/s]\u001b[A\n","ITERATION - loss: 0.36: 9%|▉ | 80/844 [00:18<00:23, 32.38it/s]\u001b[A\n","ITERATION - loss: 0.44: 11%|█ | 90/844 [00:18<00:18, 40.29it/s]\u001b[A\n","ITERATION - loss: 0.42: 12%|█▏ | 100/844 [00:18<00:15, 48.07it/s]\u001b[A\n","ITERATION - loss: 0.41: 13%|█▎ | 110/844 [00:18<00:13, 56.05it/s]\u001b[A\n","ITERATION - loss: 0.37: 14%|█▍ | 120/844 [00:18<00:12, 60.27it/s]\u001b[A\n","ITERATION - loss: 0.52: 15%|█▌ | 130/844 [00:18<00:10, 67.94it/s]\u001b[A\n","ITERATION - loss: 0.49: 17%|█▋ | 140/844 [00:19<00:09, 72.12it/s]\u001b[A\n","ITERATION - loss: 0.32: 18%|█▊ | 150/844 [00:19<00:08, 77.58it/s]\u001b[A\n","ITERATION - loss: 0.47: 19%|█▉ | 160/844 [00:19<00:08, 81.94it/s]\u001b[A\n","ITERATION - loss: 0.46: 20%|██ | 170/844 [00:19<00:07, 84.98it/s]\u001b[A\n","ITERATION - loss: 0.38: 21%|██▏ | 180/844 [00:19<00:07, 84.80it/s]\u001b[A\n","ITERATION - loss: 0.39: 23%|██▎ | 190/844 [00:19<00:07, 84.98it/s]\u001b[A\n","ITERATION - loss: 0.39: 24%|██▎ | 200/844 [00:19<00:07, 84.89it/s]\u001b[A\n","ITERATION - loss: 0.55: 25%|██▍ | 210/844 [00:19<00:07, 85.34it/s]\u001b[A\n","ITERATION - loss: 0.44: 26%|██▌ | 220/844 [00:20<00:07, 88.09it/s]\u001b[A\n","ITERATION - loss: 0.34: 27%|██▋ | 230/844 [00:20<00:06, 88.38it/s]\u001b[A\n","ITERATION - loss: 0.35: 28%|██▊ | 240/844 [00:20<00:06, 90.39it/s]\u001b[A\n","ITERATION - loss: 0.40: 30%|██▉ | 250/844 [00:20<00:06, 90.46it/s]\u001b[A\n","ITERATION - loss: 0.39: 31%|███ | 260/844 [00:20<00:06, 91.79it/s]\u001b[A\n","ITERATION - loss: 0.42: 32%|███▏ | 270/844 [00:20<00:06, 92.57it/s]\u001b[A\n","ITERATION - loss: 0.45: 33%|███▎ | 280/844 [00:20<00:06, 90.55it/s]\u001b[A\n","ITERATION - loss: 0.44: 34%|███▍ | 290/844 [00:20<00:06, 89.78it/s]\u001b[A\n","ITERATION - loss: 0.30: 36%|███▌ | 300/844 [00:20<00:06, 88.90it/s]\u001b[A\n","ITERATION - loss: 0.25: 37%|███▋ | 310/844 [00:20<00:05, 89.67it/s]\u001b[A\n","ITERATION - loss: 0.50: 38%|███▊ | 320/844 [00:21<00:05, 89.69it/s]\u001b[A\n","ITERATION - loss: 0.32: 39%|███▉ | 330/844 [00:21<00:05, 90.97it/s]\u001b[A\n","ITERATION - loss: 0.35: 40%|████ | 340/844 [00:21<00:05, 90.71it/s]\u001b[A\n","ITERATION - loss: 0.47: 41%|████▏ | 350/844 [00:21<00:05, 91.27it/s]\u001b[A\n","ITERATION - loss: 0.31: 43%|████▎ | 360/844 [00:21<00:05, 92.08it/s]\u001b[A\n","ITERATION - loss: 0.41: 44%|████▍ | 370/844 [00:21<00:05, 89.90it/s]\u001b[A\n","ITERATION - loss: 0.39: 45%|████▌ | 380/844 [00:21<00:05, 89.08it/s]\u001b[A\n","ITERATION - loss: 0.50: 46%|████▌ | 390/844 [00:21<00:04, 91.28it/s]\u001b[A\n","ITERATION - loss: 0.24: 47%|████▋ | 400/844 [00:21<00:04, 89.02it/s]\u001b[A\n","ITERATION - loss: 0.31: 49%|████▊ | 410/844 [00:22<00:04, 91.43it/s]\u001b[A\n","ITERATION - loss: 0.53: 50%|████▉ | 420/844 [00:22<00:04, 91.37it/s]\u001b[A\n","ITERATION - loss: 0.41: 51%|█████ | 430/844 [00:22<00:04, 92.28it/s]\u001b[A\n","ITERATION - loss: 0.36: 52%|█████▏ | 440/844 [00:22<00:04, 93.75it/s]\u001b[A\n","ITERATION - loss: 0.37: 53%|█████▎ | 450/844 [00:22<00:04, 94.63it/s]\u001b[A\n","ITERATION - loss: 0.52: 55%|█████▍ | 460/844 [00:22<00:04, 93.26it/s]\u001b[A\n","ITERATION - loss: 0.51: 56%|█████▌ | 470/844 [00:22<00:04, 92.43it/s]\u001b[A\n","ITERATION - loss: 0.35: 57%|█████▋ | 480/844 [00:22<00:03, 92.74it/s]\u001b[A\n","ITERATION - loss: 0.38: 58%|█████▊ | 490/844 [00:22<00:03, 92.23it/s]\u001b[A\n","ITERATION - loss: 0.27: 59%|█████▉ | 500/844 [00:23<00:03, 93.61it/s]\u001b[A\n","ITERATION - loss: 0.29: 60%|██████ | 510/844 [00:23<00:03, 93.22it/s]\u001b[A\n","ITERATION - loss: 0.33: 62%|██████▏ | 520/844 [00:23<00:03, 94.13it/s]\u001b[A\n","ITERATION - loss: 0.47: 63%|██████▎ | 530/844 [00:23<00:03, 94.24it/s]\u001b[A\n","ITERATION - loss: 0.50: 64%|██████▍ | 540/844 [00:23<00:03, 94.68it/s]\u001b[A\n","ITERATION - loss: 0.49: 65%|██████▌ | 550/844 [00:23<00:03, 95.03it/s]\u001b[A\n","ITERATION - loss: 0.33: 66%|██████▋ | 560/844 [00:23<00:03, 92.46it/s]\u001b[A\n","ITERATION - loss: 0.33: 68%|██████▊ | 570/844 [00:23<00:03, 88.92it/s]\u001b[A\n","ITERATION - loss: 0.35: 69%|██████▊ | 580/844 [00:23<00:02, 89.05it/s]\u001b[A\n","ITERATION - loss: 0.34: 70%|██████▉ | 590/844 [00:24<00:02, 90.82it/s]\u001b[A\n","ITERATION - loss: 0.38: 71%|███████ | 600/844 [00:24<00:02, 90.73it/s]\u001b[A\n","ITERATION - loss: 0.43: 72%|███████▏ | 610/844 [00:24<00:02, 92.55it/s]\u001b[A\n","ITERATION - loss: 0.28: 73%|███████▎ | 620/844 [00:24<00:02, 93.37it/s]\u001b[A\n","ITERATION - loss: 0.41: 75%|███████▍ | 630/844 [00:24<00:02, 90.84it/s]\u001b[A\n","ITERATION - loss: 0.27: 76%|███████▌ | 640/844 [00:24<00:02, 91.13it/s]\u001b[A\n","ITERATION - loss: 0.20: 77%|███████▋ | 650/844 [00:24<00:02, 89.22it/s]\u001b[A\n","ITERATION - loss: 0.30: 78%|███████▊ | 660/844 [00:24<00:02, 89.36it/s]\u001b[A\n","ITERATION - loss: 0.33: 79%|███████▉ | 670/844 [00:24<00:01, 91.35it/s]\u001b[A\n","ITERATION - loss: 0.41: 81%|████████ | 680/844 [00:25<00:01, 92.00it/s]\u001b[A\n","ITERATION - loss: 0.36: 82%|████████▏ | 690/844 [00:25<00:01, 93.62it/s]\u001b[A\n","ITERATION - loss: 0.41: 83%|████████▎ | 700/844 [00:25<00:01, 93.16it/s]\u001b[A\n","ITERATION - loss: 0.48: 84%|████████▍ | 710/844 [00:25<00:01, 94.23it/s]\u001b[A\n","ITERATION - loss: 0.46: 85%|████████▌ | 720/844 [00:25<00:01, 94.64it/s]\u001b[A\n","ITERATION - loss: 0.36: 86%|████████▋ | 730/844 [00:25<00:01, 95.48it/s]\u001b[A\n","ITERATION - loss: 0.36: 88%|████████▊ | 740/844 [00:25<00:01, 94.83it/s]\u001b[A\n","ITERATION - loss: 0.37: 89%|████████▉ | 750/844 [00:25<00:01, 92.04it/s]\u001b[A\n","ITERATION - loss: 0.21: 90%|█████████ | 760/844 [00:25<00:00, 92.54it/s]\u001b[A\n","ITERATION - loss: 0.41: 91%|█████████ | 770/844 [00:25<00:00, 92.72it/s]\u001b[A\n","ITERATION - loss: 0.42: 92%|█████████▏| 780/844 [00:26<00:00, 92.61it/s]\u001b[A\n","ITERATION - loss: 0.52: 94%|█████████▎| 790/844 [00:26<00:00, 91.12it/s]\u001b[A\n","ITERATION - loss: 0.69: 95%|█████████▍| 800/844 [00:26<00:00, 91.12it/s]\u001b[A\n","ITERATION - loss: 0.37: 96%|█████████▌| 810/844 [00:26<00:00, 91.13it/s]\u001b[A\n","ITERATION - loss: 0.41: 97%|█████████▋| 820/844 [00:26<00:00, 91.69it/s]\u001b[A\n","ITERATION - loss: 0.37: 98%|█████████▊| 830/844 [00:26<00:00, 91.96it/s]\u001b[A\n","ITERATION - loss: 0.31: 100%|█████████▉| 840/844 [00:26<00:00, 85.76it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [01:07<00:00, 93.64it/s]\n","ITERATION - loss: 0.31: 100%|█████████▉| 840/844 [00:33<00:00, 85.76it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 2 Avg accuracy: 88.81 Avg loss: 0.39\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [01:08<00:00, 93.64it/s]\n","ITERATION - loss: 0.31: 100%|█████████▉| 840/844 [00:34<00:00, 85.76it/s]\u001b[A\n","ITERATION - loss: 0.20: 1%| | 10/844 [00:34<03:28, 4.00it/s] \u001b[A\n","ITERATION - loss: 0.37: 2%|▏ | 20/844 [00:34<02:26, 5.62it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 2 Avg accuracy: 88.85 Avg loss: 0.39\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.33: 4%|▎ | 30/844 [00:35<01:44, 7.79it/s]\u001b[A\n","ITERATION - loss: 0.22: 5%|▍ | 40/844 [00:35<01:14, 10.75it/s]\u001b[A\n","ITERATION - loss: 0.32: 6%|▌ | 50/844 [00:35<00:54, 14.62it/s]\u001b[A\n","ITERATION - loss: 0.29: 7%|▋ | 60/844 [00:35<00:40, 19.49it/s]\u001b[A\n","ITERATION - loss: 0.34: 8%|▊ | 70/844 [00:35<00:30, 25.50it/s]\u001b[A\n","ITERATION - loss: 0.43: 9%|▉ | 80/844 [00:35<00:23, 32.65it/s]\u001b[A\n","ITERATION - loss: 0.48: 11%|█ | 90/844 [00:35<00:18, 40.63it/s]\u001b[A\n","ITERATION - loss: 0.29: 12%|█▏ | 100/844 [00:35<00:15, 48.68it/s]\u001b[A\n","ITERATION - loss: 0.36: 13%|█▎ | 110/844 [00:35<00:12, 56.77it/s]\u001b[A\n","ITERATION - loss: 0.42: 14%|█▍ | 120/844 [00:36<00:11, 63.70it/s]\u001b[A\n","ITERATION - loss: 0.39: 15%|█▌ | 130/844 [00:36<00:10, 70.18it/s]\u001b[A\n","ITERATION - loss: 0.43: 17%|█▋ | 140/844 [00:36<00:09, 75.66it/s]\u001b[A\n","ITERATION - loss: 0.25: 18%|█▊ | 150/844 [00:36<00:08, 79.16it/s]\u001b[A\n","ITERATION - loss: 0.33: 19%|█▉ | 160/844 [00:36<00:08, 83.25it/s]\u001b[A\n","ITERATION - loss: 0.46: 20%|██ | 170/844 [00:36<00:07, 86.22it/s]\u001b[A\n","ITERATION - loss: 0.31: 21%|██▏ | 180/844 [00:36<00:07, 85.13it/s]\u001b[A\n","ITERATION - loss: 0.16: 23%|██▎ | 190/844 [00:36<00:07, 86.56it/s]\u001b[A\n","ITERATION - loss: 0.60: 24%|██▎ | 200/844 [00:36<00:07, 87.37it/s]\u001b[A\n","ITERATION - loss: 0.49: 25%|██▍ | 210/844 [00:37<00:07, 87.40it/s]\u001b[A\n","ITERATION - loss: 0.54: 26%|██▌ | 220/844 [00:37<00:07, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.55: 27%|██▋ | 230/844 [00:37<00:06, 90.57it/s]\u001b[A\n","ITERATION - loss: 0.46: 28%|██▊ | 240/844 [00:37<00:06, 88.77it/s]\u001b[A\n","ITERATION - loss: 0.42: 30%|██▉ | 250/844 [00:37<00:06, 90.34it/s]\u001b[A\n","ITERATION - loss: 0.35: 31%|███ | 260/844 [00:37<00:06, 91.65it/s]\u001b[A\n","ITERATION - loss: 0.47: 32%|███▏ | 270/844 [00:37<00:06, 90.41it/s]\u001b[A\n","ITERATION - loss: 0.27: 33%|███▎ | 280/844 [00:37<00:06, 92.00it/s]\u001b[A\n","ITERATION - loss: 0.32: 34%|███▍ | 290/844 [00:37<00:05, 92.78it/s]\u001b[A\n","ITERATION - loss: 0.26: 36%|███▌ | 300/844 [00:38<00:05, 92.31it/s]\u001b[A\n","ITERATION - loss: 0.25: 37%|███▋ | 310/844 [00:38<00:05, 91.32it/s]\u001b[A\n","ITERATION - loss: 0.40: 38%|███▊ | 320/844 [00:38<00:05, 92.48it/s]\u001b[A\n","ITERATION - loss: 0.56: 39%|███▉ | 330/844 [00:38<00:05, 93.17it/s]\u001b[A\n","ITERATION - loss: 0.40: 40%|████ | 340/844 [00:38<00:05, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.41: 41%|████▏ | 350/844 [00:38<00:05, 91.38it/s]\u001b[A\n","ITERATION - loss: 0.26: 43%|████▎ | 360/844 [00:38<00:05, 92.00it/s]\u001b[A\n","ITERATION - loss: 0.28: 44%|████▍ | 370/844 [00:38<00:05, 92.78it/s]\u001b[A\n","ITERATION - loss: 0.35: 45%|████▌ | 380/844 [00:38<00:05, 89.88it/s]\u001b[A\n","ITERATION - loss: 0.39: 46%|████▌ | 390/844 [00:39<00:05, 86.81it/s]\u001b[A\n","ITERATION - loss: 0.47: 47%|████▋ | 400/844 [00:39<00:05, 85.57it/s]\u001b[A\n","ITERATION - loss: 0.26: 49%|████▊ | 410/844 [00:39<00:04, 87.03it/s]\u001b[A\n","ITERATION - loss: 0.44: 50%|████▉ | 420/844 [00:39<00:04, 87.73it/s]\u001b[A\n","ITERATION - loss: 0.46: 51%|█████ | 430/844 [00:39<00:04, 87.06it/s]\u001b[A\n","ITERATION - loss: 0.65: 52%|█████▏ | 440/844 [00:39<00:04, 86.82it/s]\u001b[A\n","ITERATION - loss: 0.56: 53%|█████▎ | 450/844 [00:39<00:04, 81.08it/s]\u001b[A\n","ITERATION - loss: 0.30: 55%|█████▍ | 460/844 [00:39<00:04, 80.75it/s]\u001b[A\n","ITERATION - loss: 0.49: 56%|█████▌ | 470/844 [00:39<00:04, 84.07it/s]\u001b[A\n","ITERATION - loss: 0.30: 57%|█████▋ | 480/844 [00:40<00:04, 86.89it/s]\u001b[A\n","ITERATION - loss: 0.38: 58%|█████▊ | 490/844 [00:40<00:04, 86.08it/s]\u001b[A\n","ITERATION - loss: 0.41: 59%|█████▉ | 500/844 [00:40<00:03, 87.76it/s]\u001b[A\n","ITERATION - loss: 0.46: 60%|██████ | 510/844 [00:40<00:03, 87.30it/s]\u001b[A\n","ITERATION - loss: 0.22: 62%|██████▏ | 520/844 [00:40<00:03, 87.92it/s]\u001b[A\n","ITERATION - loss: 0.27: 63%|██████▎ | 530/844 [00:40<00:03, 89.46it/s]\u001b[A\n","ITERATION - loss: 0.22: 64%|██████▍ | 540/844 [00:40<00:03, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.40: 65%|██████▌ | 550/844 [00:40<00:03, 90.67it/s]\u001b[A\n","ITERATION - loss: 0.37: 66%|██████▋ | 560/844 [00:40<00:03, 88.22it/s]\u001b[A\n","ITERATION - loss: 0.35: 68%|██████▊ | 570/844 [00:41<00:03, 90.05it/s]\u001b[A\n","ITERATION - loss: 0.44: 69%|██████▊ | 580/844 [00:41<00:02, 88.08it/s]\u001b[A\n","ITERATION - loss: 0.40: 70%|██████▉ | 590/844 [00:41<00:02, 89.89it/s]\u001b[A\n","ITERATION - loss: 0.37: 71%|███████ | 600/844 [00:41<00:02, 89.57it/s]\u001b[A\n","ITERATION - loss: 0.42: 72%|███████▏ | 610/844 [00:41<00:02, 90.91it/s]\u001b[A\n","ITERATION - loss: 0.49: 73%|███████▎ | 620/844 [00:41<00:02, 89.44it/s]\u001b[A\n","ITERATION - loss: 0.33: 75%|███████▍ | 630/844 [00:41<00:02, 87.88it/s]\u001b[A\n","ITERATION - loss: 0.31: 76%|███████▌ | 640/844 [00:41<00:02, 87.42it/s]\u001b[A\n","ITERATION - loss: 0.32: 77%|███████▋ | 650/844 [00:41<00:02, 89.39it/s]\u001b[A\n","ITERATION - loss: 0.46: 78%|███████▊ | 660/844 [00:42<00:02, 90.69it/s]\u001b[A\n","ITERATION - loss: 0.37: 79%|███████▉ | 670/844 [00:42<00:01, 90.04it/s]\u001b[A\n","ITERATION - loss: 0.31: 81%|████████ | 680/844 [00:42<00:01, 90.52it/s]\u001b[A\n","ITERATION - loss: 0.62: 82%|████████▏ | 690/844 [00:42<00:01, 92.27it/s]\u001b[A\n","ITERATION - loss: 0.51: 83%|████████▎ | 700/844 [00:42<00:01, 91.18it/s]\u001b[A\n","ITERATION - loss: 0.28: 84%|████████▍ | 710/844 [00:42<00:01, 91.00it/s]\u001b[A\n","ITERATION - loss: 0.34: 85%|████████▌ | 720/844 [00:42<00:01, 90.55it/s]\u001b[A\n","ITERATION - loss: 0.30: 86%|████████▋ | 730/844 [00:42<00:01, 91.74it/s]\u001b[A\n","ITERATION - loss: 0.30: 88%|████████▊ | 740/844 [00:42<00:01, 91.97it/s]\u001b[A\n","ITERATION - loss: 0.48: 89%|████████▉ | 750/844 [00:43<00:01, 87.50it/s]\u001b[A\n","ITERATION - loss: 0.26: 90%|█████████ | 760/844 [00:43<00:00, 87.10it/s]\u001b[A\n","ITERATION - loss: 0.33: 91%|█████████ | 770/844 [00:43<00:00, 87.21it/s]\u001b[A\n","ITERATION - loss: 0.21: 92%|█████████▏| 780/844 [00:43<00:00, 89.28it/s]\u001b[A\n","ITERATION - loss: 0.62: 94%|█████████▎| 790/844 [00:43<00:00, 89.07it/s]\u001b[A\n","ITERATION - loss: 0.33: 95%|█████████▍| 800/844 [00:43<00:00, 90.09it/s]\u001b[A\n","ITERATION - loss: 0.23: 96%|█████████▌| 810/844 [00:43<00:00, 91.26it/s]\u001b[A\n","ITERATION - loss: 0.39: 97%|█████████▋| 820/844 [00:43<00:00, 91.33it/s]\u001b[A\n","ITERATION - loss: 0.33: 98%|█████████▊| 830/844 [00:43<00:00, 91.88it/s]\u001b[A\n","ITERATION - loss: 0.25: 100%|█████████▉| 840/844 [00:44<00:00, 91.80it/s]\u001b[A\n","ITERATION - loss: 0.41: 850it [00:44, 91.73it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [01:24<00:00, 93.64it/s]\n","ITERATION - loss: 0.41: 850it [00:51, 91.73it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 3 Avg accuracy: 89.79 Avg loss: 0.35\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [01:25<00:00, 93.64it/s]\n","ITERATION - loss: 0.41: 850it [00:52, 91.73it/s]\u001b[A\n","ITERATION - loss: 0.34: 1%| | 10/844 [00:52<03:25, 4.06it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 3 Avg accuracy: 89.57 Avg loss: 0.35\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.43: 2%|▏ | 20/844 [00:52<02:24, 5.69it/s]\u001b[A\n","ITERATION - loss: 0.48: 4%|▎ | 30/844 [00:52<01:42, 7.93it/s]\u001b[A\n","ITERATION - loss: 0.42: 5%|▍ | 40/844 [00:52<01:13, 10.89it/s]\u001b[A\n","ITERATION - loss: 0.31: 6%|▌ | 50/844 [00:52<00:53, 14.76it/s]\u001b[A\n","ITERATION - loss: 0.26: 7%|▋ | 60/844 [00:52<00:39, 19.70it/s]\u001b[A\n","ITERATION - loss: 0.22: 8%|▊ | 70/844 [00:52<00:30, 25.72it/s]\u001b[A\n","ITERATION - loss: 0.49: 9%|▉ | 80/844 [00:52<00:23, 32.84it/s]\u001b[A\n","ITERATION - loss: 0.35: 11%|█ | 90/844 [00:53<00:18, 40.77it/s]\u001b[A\n","ITERATION - loss: 0.26: 12%|█▏ | 100/844 [00:53<00:15, 49.16it/s]\u001b[A\n","ITERATION - loss: 0.33: 13%|█▎ | 110/844 [00:53<00:12, 57.38it/s]\u001b[A\n","ITERATION - loss: 0.40: 14%|█▍ | 120/844 [00:53<00:11, 64.08it/s]\u001b[A\n","ITERATION - loss: 0.45: 15%|█▌ | 130/844 [00:53<00:10, 68.69it/s]\u001b[A\n","ITERATION - loss: 0.38: 17%|█▋ | 140/844 [00:53<00:09, 74.75it/s]\u001b[A\n","ITERATION - loss: 0.27: 18%|█▊ | 150/844 [00:53<00:08, 77.71it/s]\u001b[A\n","ITERATION - loss: 0.42: 19%|█▉ | 160/844 [00:53<00:08, 81.53it/s]\u001b[A\n","ITERATION - loss: 0.37: 20%|██ | 170/844 [00:53<00:07, 85.80it/s]\u001b[A\n","ITERATION - loss: 0.24: 21%|██▏ | 180/844 [00:54<00:07, 88.21it/s]\u001b[A\n","ITERATION - loss: 0.38: 23%|██▎ | 190/844 [00:54<00:07, 86.96it/s]\u001b[A\n","ITERATION - loss: 0.35: 24%|██▎ | 200/844 [00:54<00:07, 89.92it/s]\u001b[A\n","ITERATION - loss: 0.47: 25%|██▍ | 210/844 [00:54<00:06, 91.25it/s]\u001b[A\n","ITERATION - loss: 0.25: 26%|██▌ | 220/844 [00:54<00:06, 91.13it/s]\u001b[A\n","ITERATION - loss: 0.38: 27%|██▋ | 230/844 [00:54<00:06, 92.56it/s]\u001b[A\n","ITERATION - loss: 0.23: 28%|██▊ | 240/844 [00:54<00:06, 89.12it/s]\u001b[A\n","ITERATION - loss: 0.27: 30%|██▉ | 250/844 [00:54<00:06, 88.91it/s]\u001b[A\n","ITERATION - loss: 0.50: 31%|███ | 260/844 [00:54<00:06, 89.44it/s]\u001b[A\n","ITERATION - loss: 0.42: 32%|███▏ | 270/844 [00:55<00:06, 88.59it/s]\u001b[A\n","ITERATION - loss: 0.26: 33%|███▎ | 280/844 [00:55<00:06, 89.01it/s]\u001b[A\n","ITERATION - loss: 0.45: 34%|███▍ | 290/844 [00:55<00:06, 89.50it/s]\u001b[A\n","ITERATION - loss: 0.26: 36%|███▌ | 300/844 [00:55<00:06, 86.87it/s]\u001b[A\n","ITERATION - loss: 0.38: 37%|███▋ | 310/844 [00:55<00:06, 86.52it/s]\u001b[A\n","ITERATION - loss: 0.29: 38%|███▊ | 320/844 [00:55<00:05, 87.74it/s]\u001b[A\n","ITERATION - loss: 0.27: 39%|███▉ | 330/844 [00:55<00:06, 85.58it/s]\u001b[A\n","ITERATION - loss: 0.24: 40%|████ | 340/844 [00:55<00:05, 87.02it/s]\u001b[A\n","ITERATION - loss: 0.48: 41%|████▏ | 350/844 [00:55<00:05, 89.39it/s]\u001b[A\n","ITERATION - loss: 0.44: 43%|████▎ | 360/844 [00:56<00:05, 89.74it/s]\u001b[A\n","ITERATION - loss: 0.39: 44%|████▍ | 370/844 [00:56<00:05, 91.47it/s]\u001b[A\n","ITERATION - loss: 0.28: 45%|████▌ | 380/844 [00:56<00:05, 92.63it/s]\u001b[A\n","ITERATION - loss: 0.23: 46%|████▌ | 390/844 [00:56<00:04, 93.18it/s]\u001b[A\n","ITERATION - loss: 0.30: 47%|████▋ | 400/844 [00:56<00:04, 93.36it/s]\u001b[A\n","ITERATION - loss: 0.34: 49%|████▊ | 410/844 [00:56<00:04, 91.79it/s]\u001b[A\n","ITERATION - loss: 0.40: 50%|████▉ | 420/844 [00:56<00:04, 89.55it/s]\u001b[A\n","ITERATION - loss: 0.38: 51%|█████ | 430/844 [00:56<00:04, 90.46it/s]\u001b[A\n","ITERATION - loss: 0.32: 52%|█████▏ | 440/844 [00:56<00:04, 91.58it/s]\u001b[A\n","ITERATION - loss: 0.29: 53%|█████▎ | 450/844 [00:57<00:04, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.27: 55%|█████▍ | 460/844 [00:57<00:04, 89.37it/s]\u001b[A\n","ITERATION - loss: 0.33: 56%|█████▌ | 470/844 [00:57<00:04, 90.15it/s]\u001b[A\n","ITERATION - loss: 0.43: 57%|█████▋ | 480/844 [00:57<00:03, 91.08it/s]\u001b[A\n","ITERATION - loss: 0.28: 58%|█████▊ | 490/844 [00:57<00:03, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.36: 59%|█████▉ | 500/844 [00:57<00:03, 90.71it/s]\u001b[A\n","ITERATION - loss: 0.48: 60%|██████ | 510/844 [00:57<00:03, 86.78it/s]\u001b[A\n","ITERATION - loss: 0.45: 62%|██████▏ | 520/844 [00:57<00:03, 87.01it/s]\u001b[A\n","ITERATION - loss: 0.24: 63%|██████▎ | 530/844 [00:57<00:03, 89.70it/s]\u001b[A\n","ITERATION - loss: 0.41: 64%|██████▍ | 540/844 [00:58<00:03, 91.46it/s]\u001b[A\n","ITERATION - loss: 0.25: 65%|██████▌ | 550/844 [00:58<00:03, 93.00it/s]\u001b[A\n","ITERATION - loss: 0.21: 66%|██████▋ | 560/844 [00:58<00:03, 94.60it/s]\u001b[A\n","ITERATION - loss: 0.34: 68%|██████▊ | 570/844 [00:58<00:02, 94.50it/s]\u001b[A\n","ITERATION - loss: 0.52: 69%|██████▊ | 580/844 [00:58<00:02, 95.51it/s]\u001b[A\n","ITERATION - loss: 0.45: 70%|██████▉ | 590/844 [00:58<00:02, 94.49it/s]\u001b[A\n","ITERATION - loss: 0.52: 71%|███████ | 600/844 [00:58<00:02, 91.46it/s]\u001b[A\n","ITERATION - loss: 0.28: 72%|███████▏ | 610/844 [00:58<00:02, 90.35it/s]\u001b[A\n","ITERATION - loss: 0.34: 73%|███████▎ | 620/844 [00:58<00:02, 91.74it/s]\u001b[A\n","ITERATION - loss: 0.30: 75%|███████▍ | 630/844 [00:58<00:02, 92.78it/s]\u001b[A\n","ITERATION - loss: 0.41: 76%|███████▌ | 640/844 [00:59<00:02, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.33: 77%|███████▋ | 650/844 [00:59<00:02, 92.03it/s]\u001b[A\n","ITERATION - loss: 0.24: 78%|███████▊ | 660/844 [00:59<00:02, 91.20it/s]\u001b[A\n","ITERATION - loss: 0.28: 79%|███████▉ | 670/844 [00:59<00:01, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.23: 81%|████████ | 680/844 [00:59<00:01, 90.25it/s]\u001b[A\n","ITERATION - loss: 0.46: 82%|████████▏ | 690/844 [00:59<00:01, 85.81it/s]\u001b[A\n","ITERATION - loss: 0.28: 83%|████████▎ | 700/844 [00:59<00:01, 86.38it/s]\u001b[A\n","ITERATION - loss: 0.34: 84%|████████▍ | 710/844 [00:59<00:01, 88.30it/s]\u001b[A\n","ITERATION - loss: 0.43: 85%|████████▌ | 720/844 [00:59<00:01, 90.18it/s]\u001b[A\n","ITERATION - loss: 0.51: 86%|████████▋ | 730/844 [01:00<00:01, 91.69it/s]\u001b[A\n","ITERATION - loss: 0.33: 88%|████████▊ | 740/844 [01:00<00:01, 92.73it/s]\u001b[A\n","ITERATION - loss: 0.47: 89%|████████▉ | 750/844 [01:00<00:01, 92.65it/s]\u001b[A\n","ITERATION - loss: 0.39: 90%|█████████ | 760/844 [01:00<00:00, 90.23it/s]\u001b[A\n","ITERATION - loss: 0.21: 91%|█████████ | 770/844 [01:00<00:00, 90.76it/s]\u001b[A\n","ITERATION - loss: 0.50: 92%|█████████▏| 780/844 [01:00<00:00, 89.28it/s]\u001b[A\n","ITERATION - loss: 0.35: 94%|█████████▎| 790/844 [01:00<00:00, 88.63it/s]\u001b[A\n","ITERATION - loss: 0.30: 95%|█████████▍| 800/844 [01:00<00:00, 89.57it/s]\u001b[A\n","ITERATION - loss: 0.20: 96%|█████████▌| 810/844 [01:00<00:00, 90.22it/s]\u001b[A\n","ITERATION - loss: 0.23: 97%|█████████▋| 820/844 [01:01<00:00, 89.50it/s]\u001b[A\n","ITERATION - loss: 0.31: 98%|█████████▊| 830/844 [01:01<00:00, 90.07it/s]\u001b[A\n","ITERATION - loss: 0.38: 100%|█████████▉| 840/844 [01:01<00:00, 90.87it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [01:41<00:00, 93.64it/s]\n","ITERATION - loss: 0.38: 100%|█████████▉| 840/844 [01:08<00:00, 90.87it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 4 Avg accuracy: 90.52 Avg loss: 0.33\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [01:42<00:00, 93.64it/s]\n","ITERATION - loss: 0.38: 100%|█████████▉| 840/844 [01:09<00:00, 90.87it/s]\u001b[A\n","ITERATION - loss: 0.28: 1%| | 10/844 [01:09<03:22, 4.11it/s] \u001b[A\n","ITERATION - loss: 0.23: 2%|▏ | 20/844 [01:09<02:22, 5.77it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 4 Avg accuracy: 90.35 Avg loss: 0.33\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.42: 4%|▎ | 30/844 [01:09<01:41, 8.03it/s]\u001b[A\n","ITERATION - loss: 0.29: 5%|▍ | 40/844 [01:09<01:12, 11.08it/s]\u001b[A\n","ITERATION - loss: 0.20: 6%|▌ | 50/844 [01:09<00:52, 15.09it/s]\u001b[A\n","ITERATION - loss: 0.28: 7%|▋ | 60/844 [01:09<00:39, 20.00it/s]\u001b[A\n","ITERATION - loss: 0.32: 8%|▊ | 70/844 [01:09<00:29, 25.90it/s]\u001b[A\n","ITERATION - loss: 0.34: 9%|▉ | 80/844 [01:09<00:23, 32.64it/s]\u001b[A\n","ITERATION - loss: 0.27: 11%|█ | 90/844 [01:10<00:18, 40.19it/s]\u001b[A\n","ITERATION - loss: 0.32: 12%|█▏ | 100/844 [01:10<00:15, 48.07it/s]\u001b[A\n","ITERATION - loss: 0.36: 13%|█▎ | 110/844 [01:10<00:13, 55.95it/s]\u001b[A\n","ITERATION - loss: 0.40: 14%|█▍ | 120/844 [01:10<00:11, 63.64it/s]\u001b[A\n","ITERATION - loss: 0.31: 15%|█▌ | 130/844 [01:10<00:10, 69.41it/s]\u001b[A\n","ITERATION - loss: 0.32: 17%|█▋ | 140/844 [01:10<00:09, 75.32it/s]\u001b[A\n","ITERATION - loss: 0.36: 18%|█▊ | 150/844 [01:10<00:08, 80.13it/s]\u001b[A\n","ITERATION - loss: 0.24: 19%|█▉ | 160/844 [01:10<00:08, 77.97it/s]\u001b[A\n","ITERATION - loss: 0.34: 20%|██ | 170/844 [01:10<00:08, 80.47it/s]\u001b[A\n","ITERATION - loss: 0.28: 21%|██▏ | 180/844 [01:11<00:07, 84.89it/s]\u001b[A\n","ITERATION - loss: 0.28: 23%|██▎ | 190/844 [01:11<00:07, 87.40it/s]\u001b[A\n","ITERATION - loss: 0.32: 24%|██▎ | 200/844 [01:11<00:07, 89.65it/s]\u001b[A\n","ITERATION - loss: 0.35: 25%|██▍ | 210/844 [01:11<00:06, 92.10it/s]\u001b[A\n","ITERATION - loss: 0.19: 26%|██▌ | 220/844 [01:11<00:06, 91.41it/s]\u001b[A\n","ITERATION - loss: 0.21: 27%|██▋ | 230/844 [01:11<00:06, 93.27it/s]\u001b[A\n","ITERATION - loss: 0.28: 28%|██▊ | 240/844 [01:11<00:06, 93.75it/s]\u001b[A\n","ITERATION - loss: 0.33: 30%|██▉ | 250/844 [01:11<00:06, 93.55it/s]\u001b[A\n","ITERATION - loss: 0.36: 31%|███ | 260/844 [01:11<00:06, 89.54it/s]\u001b[A\n","ITERATION - loss: 0.22: 32%|███▏ | 270/844 [01:12<00:06, 87.15it/s]\u001b[A\n","ITERATION - loss: 0.29: 33%|███▎ | 280/844 [01:12<00:06, 88.48it/s]\u001b[A\n","ITERATION - loss: 0.30: 34%|███▍ | 290/844 [01:12<00:06, 89.55it/s]\u001b[A\n","ITERATION - loss: 0.40: 36%|███▌ | 300/844 [01:12<00:05, 91.30it/s]\u001b[A\n","ITERATION - loss: 0.41: 37%|███▋ | 310/844 [01:12<00:05, 92.85it/s]\u001b[A\n","ITERATION - loss: 0.44: 38%|███▊ | 320/844 [01:12<00:05, 93.02it/s]\u001b[A\n","ITERATION - loss: 0.34: 39%|███▉ | 330/844 [01:12<00:05, 92.07it/s]\u001b[A\n","ITERATION - loss: 0.34: 40%|████ | 340/844 [01:12<00:05, 92.40it/s]\u001b[A\n","ITERATION - loss: 0.34: 41%|████▏ | 350/844 [01:12<00:05, 89.15it/s]\u001b[A\n","ITERATION - loss: 0.46: 43%|████▎ | 360/844 [01:13<00:05, 90.70it/s]\u001b[A\n","ITERATION - loss: 0.37: 44%|████▍ | 370/844 [01:13<00:05, 91.89it/s]\u001b[A\n","ITERATION - loss: 0.46: 45%|████▌ | 380/844 [01:13<00:05, 92.18it/s]\u001b[A\n","ITERATION - loss: 0.28: 46%|████▌ | 390/844 [01:13<00:04, 92.39it/s]\u001b[A\n","ITERATION - loss: 0.24: 47%|████▋ | 400/844 [01:13<00:04, 93.09it/s]\u001b[A\n","ITERATION - loss: 0.33: 49%|████▊ | 410/844 [01:13<00:04, 93.00it/s]\u001b[A\n","ITERATION - loss: 0.25: 50%|████▉ | 420/844 [01:13<00:04, 93.56it/s]\u001b[A\n","ITERATION - loss: 0.33: 51%|█████ | 430/844 [01:13<00:04, 93.01it/s]\u001b[A\n","ITERATION - loss: 0.37: 52%|█████▏ | 440/844 [01:13<00:04, 91.74it/s]\u001b[A\n","ITERATION - loss: 0.24: 53%|█████▎ | 450/844 [01:14<00:04, 87.97it/s]\u001b[A\n","ITERATION - loss: 0.31: 55%|█████▍ | 460/844 [01:14<00:04, 88.07it/s]\u001b[A\n","ITERATION - loss: 0.26: 56%|█████▌ | 470/844 [01:14<00:04, 89.57it/s]\u001b[A\n","ITERATION - loss: 0.27: 57%|█████▋ | 480/844 [01:14<00:04, 86.87it/s]\u001b[A\n","ITERATION - loss: 0.41: 58%|█████▊ | 490/844 [01:14<00:04, 88.13it/s]\u001b[A\n","ITERATION - loss: 0.46: 59%|█████▉ | 500/844 [01:14<00:03, 87.43it/s]\u001b[A\n","ITERATION - loss: 0.29: 60%|██████ | 510/844 [01:14<00:03, 87.88it/s]\u001b[A\n","ITERATION - loss: 0.17: 62%|██████▏ | 520/844 [01:14<00:03, 89.84it/s]\u001b[A\n","ITERATION - loss: 0.33: 63%|██████▎ | 530/844 [01:14<00:03, 91.31it/s]\u001b[A\n","ITERATION - loss: 0.30: 64%|██████▍ | 540/844 [01:15<00:03, 89.91it/s]\u001b[A\n","ITERATION - loss: 0.31: 65%|██████▌ | 550/844 [01:15<00:03, 92.15it/s]\u001b[A\n","ITERATION - loss: 0.30: 66%|██████▋ | 560/844 [01:15<00:03, 93.31it/s]\u001b[A\n","ITERATION - loss: 0.32: 68%|██████▊ | 570/844 [01:15<00:02, 94.21it/s]\u001b[A\n","ITERATION - loss: 0.32: 69%|██████▊ | 580/844 [01:15<00:02, 95.35it/s]\u001b[A\n","ITERATION - loss: 0.42: 70%|██████▉ | 590/844 [01:15<00:02, 95.84it/s]\u001b[A\n","ITERATION - loss: 0.23: 71%|███████ | 600/844 [01:15<00:02, 92.03it/s]\u001b[A\n","ITERATION - loss: 0.19: 72%|███████▏ | 610/844 [01:15<00:02, 91.76it/s]\u001b[A\n","ITERATION - loss: 0.30: 73%|███████▎ | 620/844 [01:15<00:02, 92.65it/s]\u001b[A\n","ITERATION - loss: 0.26: 75%|███████▍ | 630/844 [01:15<00:02, 89.32it/s]\u001b[A\n","ITERATION - loss: 0.34: 76%|███████▌ | 640/844 [01:16<00:02, 90.74it/s]\u001b[A\n","ITERATION - loss: 0.38: 77%|███████▋ | 650/844 [01:16<00:02, 91.90it/s]\u001b[A\n","ITERATION - loss: 0.28: 78%|███████▊ | 660/844 [01:16<00:02, 90.55it/s]\u001b[A\n","ITERATION - loss: 0.34: 79%|███████▉ | 670/844 [01:16<00:01, 90.48it/s]\u001b[A\n","ITERATION - loss: 0.34: 81%|████████ | 680/844 [01:16<00:01, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.24: 82%|████████▏ | 690/844 [01:16<00:01, 92.64it/s]\u001b[A\n","ITERATION - loss: 0.39: 83%|████████▎ | 700/844 [01:16<00:01, 92.75it/s]\u001b[A\n","ITERATION - loss: 0.42: 84%|████████▍ | 710/844 [01:16<00:01, 92.81it/s]\u001b[A\n","ITERATION - loss: 0.24: 85%|████████▌ | 720/844 [01:16<00:01, 93.64it/s]\u001b[A\n","ITERATION - loss: 0.19: 86%|████████▋ | 730/844 [01:17<00:01, 89.69it/s]\u001b[A\n","ITERATION - loss: 0.32: 88%|████████▊ | 740/844 [01:17<00:01, 90.35it/s]\u001b[A\n","ITERATION - loss: 0.32: 89%|████████▉ | 750/844 [01:17<00:01, 91.33it/s]\u001b[A\n","ITERATION - loss: 0.35: 90%|█████████ | 760/844 [01:17<00:00, 91.53it/s]\u001b[A\n","ITERATION - loss: 0.31: 91%|█████████ | 770/844 [01:17<00:00, 92.57it/s]\u001b[A\n","ITERATION - loss: 0.24: 92%|█████████▏| 780/844 [01:17<00:00, 93.16it/s]\u001b[A\n","ITERATION - loss: 0.34: 94%|█████████▎| 790/844 [01:17<00:00, 92.05it/s]\u001b[A\n","ITERATION - loss: 0.18: 95%|█████████▍| 800/844 [01:17<00:00, 93.09it/s]\u001b[A\n","ITERATION - loss: 0.18: 96%|█████████▌| 810/844 [01:17<00:00, 93.72it/s]\u001b[A\n","ITERATION - loss: 0.32: 97%|█████████▋| 820/844 [01:18<00:00, 90.32it/s]\u001b[A\n","ITERATION - loss: 0.43: 98%|█████████▊| 830/844 [01:18<00:00, 90.87it/s]\u001b[A\n","ITERATION - loss: 0.18: 100%|█████████▉| 840/844 [01:18<00:00, 91.54it/s]\u001b[A\n","ITERATION - loss: 0.33: 850it [01:18, 92.70it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [01:58<00:00, 93.64it/s]\n","ITERATION - loss: 0.33: 850it [01:25, 92.70it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 5 Avg accuracy: 91.11 Avg loss: 0.31\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [01:59<00:00, 93.64it/s]\n","ITERATION - loss: 0.33: 850it [01:26, 92.70it/s]\u001b[A\n","ITERATION - loss: 0.29: 1%| | 10/844 [01:26<03:25, 4.07it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 5 Avg accuracy: 90.67 Avg loss: 0.31\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.23: 2%|▏ | 20/844 [01:26<02:24, 5.70it/s]\u001b[A\n","ITERATION - loss: 0.26: 4%|▎ | 30/844 [01:26<01:42, 7.93it/s]\u001b[A\n","ITERATION - loss: 0.22: 5%|▍ | 40/844 [01:26<01:13, 10.93it/s]\u001b[A\n","ITERATION - loss: 0.40: 6%|▌ | 50/844 [01:26<00:53, 14.86it/s]\u001b[A\n","ITERATION - loss: 0.33: 7%|▋ | 60/844 [01:26<00:39, 19.71it/s]\u001b[A\n","ITERATION - loss: 0.13: 8%|▊ | 70/844 [01:26<00:29, 25.85it/s]\u001b[A\n","ITERATION - loss: 0.21: 9%|▉ | 80/844 [01:27<00:23, 33.00it/s]\u001b[A\n","ITERATION - loss: 0.31: 11%|█ | 90/844 [01:27<00:18, 40.63it/s]\u001b[A\n","ITERATION - loss: 0.35: 12%|█▏ | 100/844 [01:27<00:15, 48.06it/s]\u001b[A\n","ITERATION - loss: 0.22: 13%|█▎ | 110/844 [01:27<00:13, 55.72it/s]\u001b[A\n","ITERATION - loss: 0.35: 14%|█▍ | 120/844 [01:27<00:11, 62.89it/s]\u001b[A\n","ITERATION - loss: 0.43: 15%|█▌ | 130/844 [01:27<00:10, 69.83it/s]\u001b[A\n","ITERATION - loss: 0.25: 17%|█▋ | 140/844 [01:27<00:09, 75.42it/s]\u001b[A\n","ITERATION - loss: 0.45: 18%|█▊ | 150/844 [01:27<00:08, 78.57it/s]\u001b[A\n","ITERATION - loss: 0.23: 19%|█▉ | 160/844 [01:27<00:08, 82.33it/s]\u001b[A\n","ITERATION - loss: 0.58: 20%|██ | 170/844 [01:28<00:08, 81.14it/s]\u001b[A\n","ITERATION - loss: 0.38: 21%|██▏ | 180/844 [01:28<00:08, 80.36it/s]\u001b[A\n","ITERATION - loss: 0.38: 23%|██▎ | 190/844 [01:28<00:07, 83.03it/s]\u001b[A\n","ITERATION - loss: 0.46: 24%|██▎ | 200/844 [01:28<00:07, 85.35it/s]\u001b[A\n","ITERATION - loss: 0.23: 25%|██▍ | 210/844 [01:28<00:07, 86.08it/s]\u001b[A\n","ITERATION - loss: 0.51: 26%|██▌ | 220/844 [01:28<00:07, 88.03it/s]\u001b[A\n","ITERATION - loss: 0.26: 27%|██▋ | 230/844 [01:28<00:06, 88.79it/s]\u001b[A\n","ITERATION - loss: 0.26: 28%|██▊ | 240/844 [01:28<00:06, 90.85it/s]\u001b[A\n","ITERATION - loss: 0.35: 30%|██▉ | 250/844 [01:29<00:06, 92.73it/s]\u001b[A\n","ITERATION - loss: 0.27: 31%|███ | 260/844 [01:29<00:06, 90.16it/s]\u001b[A\n","ITERATION - loss: 0.17: 32%|███▏ | 270/844 [01:29<00:06, 90.22it/s]\u001b[A\n","ITERATION - loss: 0.35: 33%|███▎ | 280/844 [01:29<00:06, 89.93it/s]\u001b[A\n","ITERATION - loss: 0.23: 34%|███▍ | 290/844 [01:29<00:06, 91.64it/s]\u001b[A\n","ITERATION - loss: 0.38: 36%|███▌ | 300/844 [01:29<00:05, 93.08it/s]\u001b[A\n","ITERATION - loss: 0.17: 37%|███▋ | 310/844 [01:29<00:05, 93.32it/s]\u001b[A\n","ITERATION - loss: 0.42: 38%|███▊ | 320/844 [01:29<00:05, 92.81it/s]\u001b[A\n","ITERATION - loss: 0.20: 39%|███▉ | 330/844 [01:29<00:05, 93.12it/s]\u001b[A\n","ITERATION - loss: 0.27: 40%|████ | 340/844 [01:29<00:05, 92.96it/s]\u001b[A\n","ITERATION - loss: 0.45: 41%|████▏ | 350/844 [01:30<00:05, 92.77it/s]\u001b[A\n","ITERATION - loss: 0.33: 43%|████▎ | 360/844 [01:30<00:05, 91.61it/s]\u001b[A\n","ITERATION - loss: 0.21: 44%|████▍ | 370/844 [01:30<00:05, 89.66it/s]\u001b[A\n","ITERATION - loss: 0.26: 45%|████▌ | 380/844 [01:30<00:05, 90.50it/s]\u001b[A\n","ITERATION - loss: 0.19: 46%|████▌ | 390/844 [01:30<00:04, 91.35it/s]\u001b[A\n","ITERATION - loss: 0.22: 47%|████▋ | 400/844 [01:30<00:04, 91.74it/s]\u001b[A\n","ITERATION - loss: 0.26: 49%|████▊ | 410/844 [01:30<00:04, 91.30it/s]\u001b[A\n","ITERATION - loss: 0.23: 50%|████▉ | 420/844 [01:30<00:04, 92.31it/s]\u001b[A\n","ITERATION - loss: 0.30: 51%|█████ | 430/844 [01:30<00:04, 92.78it/s]\u001b[A\n","ITERATION - loss: 0.22: 52%|█████▏ | 440/844 [01:31<00:04, 90.06it/s]\u001b[A\n","ITERATION - loss: 0.30: 53%|█████▎ | 450/844 [01:31<00:04, 88.90it/s]\u001b[A\n","ITERATION - loss: 0.39: 55%|█████▍ | 460/844 [01:31<00:04, 88.79it/s]\u001b[A\n","ITERATION - loss: 0.21: 56%|█████▌ | 470/844 [01:31<00:04, 89.09it/s]\u001b[A\n","ITERATION - loss: 0.26: 57%|█████▋ | 480/844 [01:31<00:04, 86.90it/s]\u001b[A\n","ITERATION - loss: 0.18: 58%|█████▊ | 490/844 [01:31<00:04, 88.03it/s]\u001b[A\n","ITERATION - loss: 0.30: 59%|█████▉ | 500/844 [01:31<00:03, 88.62it/s]\u001b[A\n","ITERATION - loss: 0.32: 60%|██████ | 510/844 [01:31<00:03, 89.33it/s]\u001b[A\n","ITERATION - loss: 0.15: 62%|██████▏ | 520/844 [01:31<00:03, 90.05it/s]\u001b[A\n","ITERATION - loss: 0.44: 63%|██████▎ | 530/844 [01:32<00:03, 91.01it/s]\u001b[A\n","ITERATION - loss: 0.54: 64%|██████▍ | 540/844 [01:32<00:03, 91.70it/s]\u001b[A\n","ITERATION - loss: 0.32: 65%|██████▌ | 550/844 [01:32<00:03, 91.10it/s]\u001b[A\n","ITERATION - loss: 0.22: 66%|██████▋ | 560/844 [01:32<00:03, 89.88it/s]\u001b[A\n","ITERATION - loss: 0.18: 68%|██████▊ | 570/844 [01:32<00:03, 89.55it/s]\u001b[A\n","ITERATION - loss: 0.25: 69%|██████▊ | 580/844 [01:32<00:02, 90.62it/s]\u001b[A\n","ITERATION - loss: 0.42: 70%|██████▉ | 590/844 [01:32<00:02, 91.54it/s]\u001b[A\n","ITERATION - loss: 0.57: 71%|███████ | 600/844 [01:32<00:02, 89.28it/s]\u001b[A\n","ITERATION - loss: 0.32: 72%|███████▏ | 610/844 [01:32<00:02, 89.60it/s]\u001b[A\n","ITERATION - loss: 0.31: 73%|███████▎ | 620/844 [01:33<00:02, 90.45it/s]\u001b[A\n","ITERATION - loss: 0.25: 75%|███████▍ | 630/844 [01:33<00:02, 90.24it/s]\u001b[A\n","ITERATION - loss: 0.31: 76%|███████▌ | 640/844 [01:33<00:02, 89.31it/s]\u001b[A\n","ITERATION - loss: 0.26: 77%|███████▋ | 650/844 [01:33<00:02, 88.63it/s]\u001b[A\n","ITERATION - loss: 0.37: 78%|███████▊ | 660/844 [01:33<00:02, 89.76it/s]\u001b[A\n","ITERATION - loss: 0.48: 79%|███████▉ | 670/844 [01:33<00:01, 89.23it/s]\u001b[A\n","ITERATION - loss: 0.30: 81%|████████ | 680/844 [01:33<00:01, 88.06it/s]\u001b[A\n","ITERATION - loss: 0.22: 82%|████████▏ | 690/844 [01:33<00:01, 89.27it/s]\u001b[A\n","ITERATION - loss: 0.45: 83%|████████▎ | 700/844 [01:33<00:01, 89.70it/s]\u001b[A\n","ITERATION - loss: 0.33: 84%|████████▍ | 710/844 [01:34<00:01, 91.62it/s]\u001b[A\n","ITERATION - loss: 0.14: 85%|████████▌ | 720/844 [01:34<00:01, 92.53it/s]\u001b[A\n","ITERATION - loss: 0.31: 86%|████████▋ | 730/844 [01:34<00:01, 91.31it/s]\u001b[A\n","ITERATION - loss: 0.23: 88%|████████▊ | 740/844 [01:34<00:01, 90.28it/s]\u001b[A\n","ITERATION - loss: 0.39: 89%|████████▉ | 750/844 [01:34<00:01, 91.46it/s]\u001b[A\n","ITERATION - loss: 0.24: 90%|█████████ | 760/844 [01:34<00:00, 92.27it/s]\u001b[A\n","ITERATION - loss: 0.31: 91%|█████████ | 770/844 [01:34<00:00, 91.96it/s]\u001b[A\n","ITERATION - loss: 0.19: 92%|█████████▏| 780/844 [01:34<00:00, 92.19it/s]\u001b[A\n","ITERATION - loss: 0.27: 94%|█████████▎| 790/844 [01:34<00:00, 92.95it/s]\u001b[A\n","ITERATION - loss: 0.23: 95%|█████████▍| 800/844 [01:35<00:00, 93.54it/s]\u001b[A\n","ITERATION - loss: 0.16: 96%|█████████▌| 810/844 [01:35<00:00, 90.85it/s]\u001b[A\n","ITERATION - loss: 0.25: 97%|█████████▋| 820/844 [01:35<00:00, 90.15it/s]\u001b[A\n","ITERATION - loss: 0.16: 98%|█████████▊| 830/844 [01:35<00:00, 89.39it/s]\u001b[A\n","ITERATION - loss: 0.50: 100%|█████████▉| 840/844 [01:35<00:00, 88.74it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [02:16<00:00, 93.64it/s]\n","ITERATION - loss: 0.50: 100%|█████████▉| 840/844 [01:42<00:00, 88.74it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 6 Avg accuracy: 91.56 Avg loss: 0.29\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [02:16<00:00, 93.64it/s]\n","ITERATION - loss: 0.50: 100%|█████████▉| 840/844 [01:43<00:00, 88.74it/s]\u001b[A\n","ITERATION - loss: 0.22: 1%| | 10/844 [01:43<03:26, 4.04it/s] \u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 6 Avg accuracy: 91.18 Avg loss: 0.29\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.36: 2%|▏ | 20/844 [01:43<02:26, 5.64it/s]\u001b[A\n","ITERATION - loss: 0.21: 4%|▎ | 30/844 [01:43<01:44, 7.82it/s]\u001b[A\n","ITERATION - loss: 0.21: 5%|▍ | 40/844 [01:43<01:14, 10.76it/s]\u001b[A\n","ITERATION - loss: 0.19: 6%|▌ | 50/844 [01:44<00:54, 14.61it/s]\u001b[A\n","ITERATION - loss: 0.19: 7%|▋ | 60/844 [01:44<00:40, 19.49it/s]\u001b[A\n","ITERATION - loss: 0.26: 8%|▊ | 70/844 [01:44<00:30, 25.51it/s]\u001b[A\n","ITERATION - loss: 0.25: 9%|▉ | 80/844 [01:44<00:23, 32.56it/s]\u001b[A\n","ITERATION - loss: 0.42: 11%|█ | 90/844 [01:44<00:18, 39.94it/s]\u001b[A\n","ITERATION - loss: 0.29: 12%|█▏ | 100/844 [01:44<00:15, 47.80it/s]\u001b[A\n","ITERATION - loss: 0.09: 13%|█▎ | 110/844 [01:44<00:13, 54.70it/s]\u001b[A\n","ITERATION - loss: 0.12: 14%|█▍ | 120/844 [01:44<00:11, 62.42it/s]\u001b[A\n","ITERATION - loss: 0.20: 15%|█▌ | 130/844 [01:44<00:10, 69.69it/s]\u001b[A\n","ITERATION - loss: 0.28: 17%|█▋ | 140/844 [01:45<00:09, 75.69it/s]\u001b[A\n","ITERATION - loss: 0.33: 18%|█▊ | 150/844 [01:45<00:08, 79.37it/s]\u001b[A\n","ITERATION - loss: 0.23: 19%|█▉ | 160/844 [01:45<00:08, 82.06it/s]\u001b[A\n","ITERATION - loss: 0.36: 20%|██ | 170/844 [01:45<00:07, 85.43it/s]\u001b[A\n","ITERATION - loss: 0.09: 21%|██▏ | 180/844 [01:45<00:07, 85.18it/s]\u001b[A\n","ITERATION - loss: 0.47: 23%|██▎ | 190/844 [01:45<00:07, 87.14it/s]\u001b[A\n","ITERATION - loss: 0.30: 24%|██▎ | 200/844 [01:45<00:07, 88.19it/s]\u001b[A\n","ITERATION - loss: 0.35: 25%|██▍ | 210/844 [01:45<00:07, 88.90it/s]\u001b[A\n","ITERATION - loss: 0.21: 26%|██▌ | 220/844 [01:45<00:06, 90.88it/s]\u001b[A\n","ITERATION - loss: 0.44: 27%|██▋ | 230/844 [01:45<00:06, 92.76it/s]\u001b[A\n","ITERATION - loss: 0.26: 28%|██▊ | 240/844 [01:46<00:06, 93.50it/s]\u001b[A\n","ITERATION - loss: 0.31: 30%|██▉ | 250/844 [01:46<00:06, 91.98it/s]\u001b[A\n","ITERATION - loss: 0.25: 31%|███ | 260/844 [01:46<00:06, 93.34it/s]\u001b[A\n","ITERATION - loss: 0.26: 32%|███▏ | 270/844 [01:46<00:06, 93.29it/s]\u001b[A\n","ITERATION - loss: 0.30: 33%|███▎ | 280/844 [01:46<00:06, 92.60it/s]\u001b[A\n","ITERATION - loss: 0.28: 34%|███▍ | 290/844 [01:46<00:05, 93.50it/s]\u001b[A\n","ITERATION - loss: 0.27: 36%|███▌ | 300/844 [01:46<00:06, 90.02it/s]\u001b[A\n","ITERATION - loss: 0.36: 37%|███▋ | 310/844 [01:46<00:05, 90.30it/s]\u001b[A\n","ITERATION - loss: 0.27: 38%|███▊ | 320/844 [01:46<00:05, 91.53it/s]\u001b[A\n","ITERATION - loss: 0.37: 39%|███▉ | 330/844 [01:47<00:05, 92.25it/s]\u001b[A\n","ITERATION - loss: 0.36: 40%|████ | 340/844 [01:47<00:05, 92.38it/s]\u001b[A\n","ITERATION - loss: 0.31: 41%|████▏ | 350/844 [01:47<00:05, 93.08it/s]\u001b[A\n","ITERATION - loss: 0.35: 43%|████▎ | 360/844 [01:47<00:05, 89.30it/s]\u001b[A\n","ITERATION - loss: 0.33: 44%|████▍ | 370/844 [01:47<00:05, 88.81it/s]\u001b[A\n","ITERATION - loss: 0.27: 45%|████▌ | 380/844 [01:47<00:05, 90.17it/s]\u001b[A\n","ITERATION - loss: 0.32: 46%|████▌ | 390/844 [01:47<00:05, 88.31it/s]\u001b[A\n","ITERATION - loss: 0.16: 47%|████▋ | 400/844 [01:47<00:05, 87.70it/s]\u001b[A\n","ITERATION - loss: 0.21: 49%|████▊ | 410/844 [01:47<00:04, 88.79it/s]\u001b[A\n","ITERATION - loss: 0.23: 50%|████▉ | 420/844 [01:48<00:04, 89.74it/s]\u001b[A\n","ITERATION - loss: 0.24: 51%|█████ | 430/844 [01:48<00:04, 90.74it/s]\u001b[A\n","ITERATION - loss: 0.33: 52%|█████▏ | 440/844 [01:48<00:04, 92.15it/s]\u001b[A\n","ITERATION - loss: 0.28: 53%|█████▎ | 450/844 [01:48<00:04, 91.99it/s]\u001b[A\n","ITERATION - loss: 0.50: 55%|█████▍ | 460/844 [01:48<00:04, 90.71it/s]\u001b[A\n","ITERATION - loss: 0.59: 56%|█████▌ | 470/844 [01:48<00:04, 91.54it/s]\u001b[A\n","ITERATION - loss: 0.19: 57%|█████▋ | 480/844 [01:48<00:03, 92.49it/s]\u001b[A\n","ITERATION - loss: 0.44: 58%|█████▊ | 490/844 [01:48<00:03, 88.94it/s]\u001b[A\n","ITERATION - loss: 0.23: 59%|█████▉ | 500/844 [01:48<00:03, 89.87it/s]\u001b[A\n","ITERATION - loss: 0.31: 60%|██████ | 510/844 [01:49<00:03, 91.09it/s]\u001b[A\n","ITERATION - loss: 0.21: 62%|██████▏ | 520/844 [01:49<00:03, 91.22it/s]\u001b[A\n","ITERATION - loss: 0.18: 63%|██████▎ | 530/844 [01:49<00:03, 90.42it/s]\u001b[A\n","ITERATION - loss: 0.32: 64%|██████▍ | 540/844 [01:49<00:03, 91.07it/s]\u001b[A\n","ITERATION - loss: 0.22: 65%|██████▌ | 550/844 [01:49<00:03, 88.43it/s]\u001b[A\n","ITERATION - loss: 0.27: 66%|██████▋ | 560/844 [01:49<00:03, 87.09it/s]\u001b[A\n","ITERATION - loss: 0.35: 68%|██████▊ | 570/844 [01:49<00:03, 87.76it/s]\u001b[A\n","ITERATION - loss: 0.26: 69%|██████▊ | 580/844 [01:49<00:03, 87.06it/s]\u001b[A\n","ITERATION - loss: 0.50: 70%|██████▉ | 590/844 [01:49<00:02, 87.90it/s]\u001b[A\n","ITERATION - loss: 0.25: 71%|███████ | 600/844 [01:50<00:02, 89.32it/s]\u001b[A\n","ITERATION - loss: 0.21: 72%|███████▏ | 610/844 [01:50<00:02, 89.41it/s]\u001b[A\n","ITERATION - loss: 0.36: 73%|███████▎ | 620/844 [01:50<00:02, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.30: 75%|███████▍ | 630/844 [01:50<00:02, 91.20it/s]\u001b[A\n","ITERATION - loss: 0.26: 76%|███████▌ | 640/844 [01:50<00:02, 88.83it/s]\u001b[A\n","ITERATION - loss: 0.27: 77%|███████▋ | 650/844 [01:50<00:02, 88.04it/s]\u001b[A\n","ITERATION - loss: 0.30: 78%|███████▊ | 660/844 [01:50<00:02, 89.07it/s]\u001b[A\n","ITERATION - loss: 0.44: 79%|███████▉ | 670/844 [01:50<00:02, 85.73it/s]\u001b[A\n","ITERATION - loss: 0.29: 81%|████████ | 680/844 [01:50<00:01, 88.11it/s]\u001b[A\n","ITERATION - loss: 0.50: 82%|████████▏ | 690/844 [01:51<00:01, 89.96it/s]\u001b[A\n","ITERATION - loss: 0.35: 83%|████████▎ | 700/844 [01:51<00:01, 88.48it/s]\u001b[A\n","ITERATION - loss: 0.25: 84%|████████▍ | 710/844 [01:51<00:01, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.29: 85%|████████▌ | 720/844 [01:51<00:01, 90.16it/s]\u001b[A\n","ITERATION - loss: 0.38: 86%|████████▋ | 730/844 [01:51<00:01, 90.04it/s]\u001b[A\n","ITERATION - loss: 0.37: 88%|████████▊ | 740/844 [01:51<00:01, 90.43it/s]\u001b[A\n","ITERATION - loss: 0.46: 89%|████████▉ | 750/844 [01:51<00:01, 90.71it/s]\u001b[A\n","ITERATION - loss: 0.46: 90%|█████████ | 760/844 [01:51<00:00, 88.22it/s]\u001b[A\n","ITERATION - loss: 0.40: 91%|█████████ | 770/844 [01:51<00:00, 90.20it/s]\u001b[A\n","ITERATION - loss: 0.30: 92%|█████████▏| 780/844 [01:52<00:00, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.32: 94%|█████████▎| 790/844 [01:52<00:00, 87.60it/s]\u001b[A\n","ITERATION - loss: 0.16: 95%|█████████▍| 800/844 [01:52<00:00, 87.67it/s]\u001b[A\n","ITERATION - loss: 0.21: 96%|█████████▌| 810/844 [01:52<00:00, 88.79it/s]\u001b[A\n","ITERATION - loss: 0.14: 97%|█████████▋| 820/844 [01:52<00:00, 88.54it/s]\u001b[A\n","ITERATION - loss: 0.19: 98%|█████████▊| 830/844 [01:52<00:00, 89.60it/s]\u001b[A\n","ITERATION - loss: 0.18: 100%|█████████▉| 840/844 [01:52<00:00, 90.20it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [02:33<00:00, 93.64it/s]\n","ITERATION - loss: 0.18: 100%|█████████▉| 840/844 [01:59<00:00, 90.20it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 7 Avg accuracy: 91.96 Avg loss: 0.28\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [02:34<00:00, 93.64it/s]\n","ITERATION - loss: 0.18: 100%|█████████▉| 840/844 [02:00<00:00, 90.20it/s]\u001b[A\n","ITERATION - loss: 0.26: 1%| | 10/844 [02:00<03:24, 4.08it/s] \u001b[A\n","ITERATION - loss: 0.25: 2%|▏ | 20/844 [02:00<02:23, 5.73it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 7 Avg accuracy: 91.60 Avg loss: 0.28\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.27: 4%|▎ | 30/844 [02:00<01:42, 7.97it/s]\u001b[A\n","ITERATION - loss: 0.22: 5%|▍ | 40/844 [02:01<01:13, 10.98it/s]\u001b[A\n","ITERATION - loss: 0.28: 6%|▌ | 50/844 [02:01<00:53, 14.89it/s]\u001b[A\n","ITERATION - loss: 0.22: 7%|▋ | 60/844 [02:01<00:39, 19.93it/s]\u001b[A\n","ITERATION - loss: 0.23: 8%|▊ | 70/844 [02:01<00:29, 26.01it/s]\u001b[A\n","ITERATION - loss: 0.38: 9%|▉ | 80/844 [02:01<00:22, 33.26it/s]\u001b[A\n","ITERATION - loss: 0.37: 11%|█ | 90/844 [02:01<00:18, 41.29it/s]\u001b[A\n","ITERATION - loss: 0.15: 12%|█▏ | 100/844 [02:01<00:15, 49.31it/s]\u001b[A\n","ITERATION - loss: 0.20: 13%|█▎ | 110/844 [02:01<00:12, 56.75it/s]\u001b[A\n","ITERATION - loss: 0.28: 14%|█▍ | 120/844 [02:01<00:11, 64.73it/s]\u001b[A\n","ITERATION - loss: 0.18: 15%|█▌ | 130/844 [02:01<00:09, 71.46it/s]\u001b[A\n","ITERATION - loss: 0.16: 17%|█▋ | 140/844 [02:02<00:09, 75.90it/s]\u001b[A\n","ITERATION - loss: 0.18: 18%|█▊ | 150/844 [02:02<00:08, 81.23it/s]\u001b[A\n","ITERATION - loss: 0.19: 19%|█▉ | 160/844 [02:02<00:08, 85.01it/s]\u001b[A\n","ITERATION - loss: 0.34: 20%|██ | 170/844 [02:02<00:07, 88.72it/s]\u001b[A\n","ITERATION - loss: 0.33: 21%|██▏ | 180/844 [02:02<00:07, 91.21it/s]\u001b[A\n","ITERATION - loss: 0.30: 23%|██▎ | 190/844 [02:02<00:07, 86.86it/s]\u001b[A\n","ITERATION - loss: 0.19: 24%|██▎ | 200/844 [02:02<00:07, 83.71it/s]\u001b[A\n","ITERATION - loss: 0.16: 25%|██▍ | 210/844 [02:02<00:07, 87.00it/s]\u001b[A\n","ITERATION - loss: 0.39: 26%|██▌ | 220/844 [02:02<00:06, 90.03it/s]\u001b[A\n","ITERATION - loss: 0.38: 27%|██▋ | 230/844 [02:03<00:06, 91.37it/s]\u001b[A\n","ITERATION - loss: 0.21: 28%|██▊ | 240/844 [02:03<00:06, 90.07it/s]\u001b[A\n","ITERATION - loss: 0.17: 30%|██▉ | 250/844 [02:03<00:06, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.29: 31%|███ | 260/844 [02:03<00:06, 92.49it/s]\u001b[A\n","ITERATION - loss: 0.20: 32%|███▏ | 270/844 [02:03<00:06, 93.33it/s]\u001b[A\n","ITERATION - loss: 0.35: 33%|███▎ | 280/844 [02:03<00:05, 94.67it/s]\u001b[A\n","ITERATION - loss: 0.24: 34%|███▍ | 290/844 [02:03<00:06, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.25: 36%|███▌ | 300/844 [02:03<00:05, 91.30it/s]\u001b[A\n","ITERATION - loss: 0.19: 37%|███▋ | 310/844 [02:03<00:05, 92.07it/s]\u001b[A\n","ITERATION - loss: 0.35: 38%|███▊ | 320/844 [02:04<00:05, 91.50it/s]\u001b[A\n","ITERATION - loss: 0.32: 39%|███▉ | 330/844 [02:04<00:05, 90.22it/s]\u001b[A\n","ITERATION - loss: 0.21: 40%|████ | 340/844 [02:04<00:05, 91.34it/s]\u001b[A\n","ITERATION - loss: 0.32: 41%|████▏ | 350/844 [02:04<00:05, 92.23it/s]\u001b[A\n","ITERATION - loss: 0.32: 43%|████▎ | 360/844 [02:04<00:05, 88.65it/s]\u001b[A\n","ITERATION - loss: 0.40: 44%|████▍ | 370/844 [02:04<00:05, 90.05it/s]\u001b[A\n","ITERATION - loss: 0.31: 45%|████▌ | 380/844 [02:04<00:05, 91.14it/s]\u001b[A\n","ITERATION - loss: 0.33: 46%|████▌ | 390/844 [02:04<00:05, 90.46it/s]\u001b[A\n","ITERATION - loss: 0.33: 47%|████▋ | 400/844 [02:04<00:04, 92.29it/s]\u001b[A\n","ITERATION - loss: 0.31: 49%|████▊ | 410/844 [02:05<00:04, 92.91it/s]\u001b[A\n","ITERATION - loss: 0.20: 50%|████▉ | 420/844 [02:05<00:04, 92.02it/s]\u001b[A\n","ITERATION - loss: 0.29: 51%|█████ | 430/844 [02:05<00:04, 93.08it/s]\u001b[A\n","ITERATION - loss: 0.19: 52%|█████▏ | 440/844 [02:05<00:04, 94.16it/s]\u001b[A\n","ITERATION - loss: 0.21: 53%|█████▎ | 450/844 [02:05<00:04, 94.69it/s]\u001b[A\n","ITERATION - loss: 0.35: 55%|█████▍ | 460/844 [02:05<00:04, 95.37it/s]\u001b[A\n","ITERATION - loss: 0.25: 56%|█████▌ | 470/844 [02:05<00:04, 92.11it/s]\u001b[A\n","ITERATION - loss: 0.32: 57%|█████▋ | 480/844 [02:05<00:04, 89.48it/s]\u001b[A\n","ITERATION - loss: 0.26: 58%|█████▊ | 490/844 [02:05<00:03, 90.18it/s]\u001b[A\n","ITERATION - loss: 0.38: 59%|█████▉ | 500/844 [02:06<00:03, 91.01it/s]\u001b[A\n","ITERATION - loss: 0.10: 60%|██████ | 510/844 [02:06<00:03, 90.24it/s]\u001b[A\n","ITERATION - loss: 0.25: 62%|██████▏ | 520/844 [02:06<00:03, 88.64it/s]\u001b[A\n","ITERATION - loss: 0.28: 63%|██████▎ | 530/844 [02:06<00:03, 89.43it/s]\u001b[A\n","ITERATION - loss: 0.38: 64%|██████▍ | 540/844 [02:06<00:03, 89.92it/s]\u001b[A\n","ITERATION - loss: 0.35: 65%|██████▌ | 550/844 [02:06<00:03, 90.18it/s]\u001b[A\n","ITERATION - loss: 0.28: 66%|██████▋ | 560/844 [02:06<00:03, 90.86it/s]\u001b[A\n","ITERATION - loss: 0.19: 68%|██████▊ | 570/844 [02:06<00:03, 86.72it/s]\u001b[A\n","ITERATION - loss: 0.49: 69%|██████▊ | 580/844 [02:06<00:03, 87.59it/s]\u001b[A\n","ITERATION - loss: 0.21: 70%|██████▉ | 590/844 [02:07<00:02, 89.68it/s]\u001b[A\n","ITERATION - loss: 0.36: 71%|███████ | 600/844 [02:07<00:02, 90.55it/s]\u001b[A\n","ITERATION - loss: 0.19: 72%|███████▏ | 610/844 [02:07<00:02, 89.19it/s]\u001b[A\n","ITERATION - loss: 0.24: 73%|███████▎ | 620/844 [02:07<00:02, 90.53it/s]\u001b[A\n","ITERATION - loss: 0.28: 75%|███████▍ | 630/844 [02:07<00:02, 91.98it/s]\u001b[A\n","ITERATION - loss: 0.25: 76%|███████▌ | 640/844 [02:07<00:02, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.20: 77%|███████▋ | 650/844 [02:07<00:02, 91.59it/s]\u001b[A\n","ITERATION - loss: 0.25: 78%|███████▊ | 660/844 [02:07<00:02, 88.83it/s]\u001b[A\n","ITERATION - loss: 0.53: 79%|███████▉ | 670/844 [02:07<00:01, 89.10it/s]\u001b[A\n","ITERATION - loss: 0.32: 81%|████████ | 680/844 [02:08<00:01, 90.51it/s]\u001b[A\n","ITERATION - loss: 0.08: 82%|████████▏ | 690/844 [02:08<00:01, 92.02it/s]\u001b[A\n","ITERATION - loss: 0.26: 83%|████████▎ | 700/844 [02:08<00:01, 90.16it/s]\u001b[A\n","ITERATION - loss: 0.26: 84%|████████▍ | 710/844 [02:08<00:01, 91.12it/s]\u001b[A\n","ITERATION - loss: 0.19: 85%|████████▌ | 720/844 [02:08<00:01, 91.99it/s]\u001b[A\n","ITERATION - loss: 0.25: 86%|████████▋ | 730/844 [02:08<00:01, 92.73it/s]\u001b[A\n","ITERATION - loss: 0.27: 88%|████████▊ | 740/844 [02:08<00:01, 92.83it/s]\u001b[A\n","ITERATION - loss: 0.29: 89%|████████▉ | 750/844 [02:08<00:01, 92.27it/s]\u001b[A\n","ITERATION - loss: 0.41: 90%|█████████ | 760/844 [02:08<00:00, 91.38it/s]\u001b[A\n","ITERATION - loss: 0.31: 91%|█████████ | 770/844 [02:08<00:00, 91.10it/s]\u001b[A\n","ITERATION - loss: 0.21: 92%|█████████▏| 780/844 [02:09<00:00, 90.11it/s]\u001b[A\n","ITERATION - loss: 0.18: 94%|█████████▎| 790/844 [02:09<00:00, 91.73it/s]\u001b[A\n","ITERATION - loss: 0.21: 95%|█████████▍| 800/844 [02:09<00:00, 89.22it/s]\u001b[A\n","ITERATION - loss: 0.25: 96%|█████████▌| 810/844 [02:09<00:00, 89.45it/s]\u001b[A\n","ITERATION - loss: 0.12: 97%|█████████▋| 820/844 [02:09<00:00, 89.26it/s]\u001b[A\n","ITERATION - loss: 0.36: 98%|█████████▊| 830/844 [02:09<00:00, 89.60it/s]\u001b[A\n","ITERATION - loss: 0.25: 100%|█████████▉| 840/844 [02:09<00:00, 89.94it/s]\u001b[A\n","ITERATION - loss: 0.28: 850it [02:09, 88.36it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [02:50<00:00, 93.64it/s]\n","ITERATION - loss: 0.28: 850it [02:16, 88.36it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 8 Avg accuracy: 92.35 Avg loss: 0.26\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [02:51<00:00, 93.64it/s]\n","ITERATION - loss: 0.28: 850it [02:17, 88.36it/s]\u001b[A\n","ITERATION - loss: 0.29: 1%| | 10/844 [02:17<03:25, 4.06it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 8 Avg accuracy: 92.17 Avg loss: 0.27\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.25: 2%|▏ | 20/844 [02:17<02:25, 5.68it/s]\u001b[A\n","ITERATION - loss: 0.32: 4%|▎ | 30/844 [02:18<01:43, 7.90it/s]\u001b[A\n","ITERATION - loss: 0.25: 5%|▍ | 40/844 [02:18<01:13, 10.89it/s]\u001b[A\n","ITERATION - loss: 0.26: 6%|▌ | 50/844 [02:18<00:53, 14.83it/s]\u001b[A\n","ITERATION - loss: 0.23: 7%|▋ | 60/844 [02:18<00:39, 19.86it/s]\u001b[A\n","ITERATION - loss: 0.19: 8%|▊ | 70/844 [02:18<00:30, 25.79it/s]\u001b[A\n","ITERATION - loss: 0.46: 9%|▉ | 80/844 [02:18<00:23, 32.89it/s]\u001b[A\n","ITERATION - loss: 0.31: 11%|█ | 90/844 [02:18<00:18, 40.93it/s]\u001b[A\n","ITERATION - loss: 0.29: 12%|█▏ | 100/844 [02:18<00:15, 47.98it/s]\u001b[A\n","ITERATION - loss: 0.15: 13%|█▎ | 110/844 [02:18<00:13, 55.44it/s]\u001b[A\n","ITERATION - loss: 0.37: 14%|█▍ | 120/844 [02:19<00:11, 63.03it/s]\u001b[A\n","ITERATION - loss: 0.19: 15%|█▌ | 130/844 [02:19<00:10, 67.77it/s]\u001b[A\n","ITERATION - loss: 0.16: 17%|█▋ | 140/844 [02:19<00:09, 73.91it/s]\u001b[A\n","ITERATION - loss: 0.22: 18%|█▊ | 150/844 [02:19<00:08, 78.61it/s]\u001b[A\n","ITERATION - loss: 0.39: 19%|█▉ | 160/844 [02:19<00:08, 81.12it/s]\u001b[A\n","ITERATION - loss: 0.45: 20%|██ | 170/844 [02:19<00:08, 83.04it/s]\u001b[A\n","ITERATION - loss: 0.27: 21%|██▏ | 180/844 [02:19<00:07, 85.70it/s]\u001b[A\n","ITERATION - loss: 0.21: 23%|██▎ | 190/844 [02:19<00:07, 86.58it/s]\u001b[A\n","ITERATION - loss: 0.10: 24%|██▎ | 200/844 [02:19<00:07, 87.56it/s]\u001b[A\n","ITERATION - loss: 0.24: 25%|██▍ | 210/844 [02:20<00:07, 89.50it/s]\u001b[A\n","ITERATION - loss: 0.19: 26%|██▌ | 220/844 [02:20<00:06, 90.38it/s]\u001b[A\n","ITERATION - loss: 0.17: 27%|██▋ | 230/844 [02:20<00:06, 91.10it/s]\u001b[A\n","ITERATION - loss: 0.27: 28%|██▊ | 240/844 [02:20<00:06, 91.91it/s]\u001b[A\n","ITERATION - loss: 0.15: 30%|██▉ | 250/844 [02:20<00:06, 89.64it/s]\u001b[A\n","ITERATION - loss: 0.21: 31%|███ | 260/844 [02:20<00:06, 90.34it/s]\u001b[A\n","ITERATION - loss: 0.34: 32%|███▏ | 270/844 [02:20<00:06, 91.13it/s]\u001b[A\n","ITERATION - loss: 0.25: 33%|███▎ | 280/844 [02:20<00:06, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.16: 34%|███▍ | 290/844 [02:20<00:05, 93.44it/s]\u001b[A\n","ITERATION - loss: 0.23: 36%|███▌ | 300/844 [02:21<00:05, 92.44it/s]\u001b[A\n","ITERATION - loss: 0.18: 37%|███▋ | 310/844 [02:21<00:05, 94.04it/s]\u001b[A\n","ITERATION - loss: 0.38: 38%|███▊ | 320/844 [02:21<00:05, 93.32it/s]\u001b[A\n","ITERATION - loss: 0.40: 39%|███▉ | 330/844 [02:21<00:05, 94.20it/s]\u001b[A\n","ITERATION - loss: 0.29: 40%|████ | 340/844 [02:21<00:05, 94.89it/s]\u001b[A\n","ITERATION - loss: 0.23: 41%|████▏ | 350/844 [02:21<00:05, 93.05it/s]\u001b[A\n","ITERATION - loss: 0.22: 43%|████▎ | 360/844 [02:21<00:05, 85.85it/s]\u001b[A\n","ITERATION - loss: 0.25: 44%|████▍ | 370/844 [02:21<00:05, 83.75it/s]\u001b[A\n","ITERATION - loss: 0.16: 45%|████▌ | 380/844 [02:21<00:05, 86.04it/s]\u001b[A\n","ITERATION - loss: 0.32: 46%|████▌ | 390/844 [02:22<00:05, 87.20it/s]\u001b[A\n","ITERATION - loss: 0.39: 47%|████▋ | 400/844 [02:22<00:04, 89.62it/s]\u001b[A\n","ITERATION - loss: 0.15: 49%|████▊ | 410/844 [02:22<00:04, 90.85it/s]\u001b[A\n","ITERATION - loss: 0.24: 50%|████▉ | 420/844 [02:22<00:04, 90.86it/s]\u001b[A\n","ITERATION - loss: 0.25: 51%|█████ | 430/844 [02:22<00:04, 89.74it/s]\u001b[A\n","ITERATION - loss: 0.18: 52%|█████▏ | 440/844 [02:22<00:04, 87.92it/s]\u001b[A\n","ITERATION - loss: 0.26: 53%|█████▎ | 450/844 [02:22<00:04, 89.52it/s]\u001b[A\n","ITERATION - loss: 0.20: 55%|█████▍ | 460/844 [02:22<00:04, 88.38it/s]\u001b[A\n","ITERATION - loss: 0.17: 56%|█████▌ | 470/844 [02:22<00:04, 89.70it/s]\u001b[A\n","ITERATION - loss: 0.32: 57%|█████▋ | 480/844 [02:23<00:04, 88.68it/s]\u001b[A\n","ITERATION - loss: 0.21: 58%|█████▊ | 490/844 [02:23<00:03, 90.63it/s]\u001b[A\n","ITERATION - loss: 0.19: 59%|█████▉ | 500/844 [02:23<00:03, 92.09it/s]\u001b[A\n","ITERATION - loss: 0.19: 60%|██████ | 510/844 [02:23<00:03, 92.42it/s]\u001b[A\n","ITERATION - loss: 0.24: 62%|██████▏ | 520/844 [02:23<00:03, 88.12it/s]\u001b[A\n","ITERATION - loss: 0.32: 63%|██████▎ | 530/844 [02:23<00:03, 84.29it/s]\u001b[A\n","ITERATION - loss: 0.33: 64%|██████▍ | 540/844 [02:23<00:03, 83.28it/s]\u001b[A\n","ITERATION - loss: 0.18: 65%|██████▌ | 550/844 [02:23<00:03, 87.01it/s]\u001b[A\n","ITERATION - loss: 0.33: 66%|██████▋ | 560/844 [02:23<00:03, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.14: 68%|██████▊ | 570/844 [02:24<00:03, 91.21it/s]\u001b[A\n","ITERATION - loss: 0.26: 69%|██████▊ | 580/844 [02:24<00:02, 93.24it/s]\u001b[A\n","ITERATION - loss: 0.19: 70%|██████▉ | 590/844 [02:24<00:02, 94.87it/s]\u001b[A\n","ITERATION - loss: 0.10: 71%|███████ | 600/844 [02:24<00:02, 95.22it/s]\u001b[A\n","ITERATION - loss: 0.14: 72%|███████▏ | 610/844 [02:24<00:02, 94.64it/s]\u001b[A\n","ITERATION - loss: 0.26: 73%|███████▎ | 620/844 [02:24<00:02, 92.99it/s]\u001b[A\n","ITERATION - loss: 0.30: 75%|███████▍ | 630/844 [02:24<00:02, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.26: 76%|███████▌ | 640/844 [02:24<00:02, 92.67it/s]\u001b[A\n","ITERATION - loss: 0.25: 77%|███████▋ | 650/844 [02:24<00:02, 93.21it/s]\u001b[A\n","ITERATION - loss: 0.22: 78%|███████▊ | 660/844 [02:25<00:02, 91.18it/s]\u001b[A\n","ITERATION - loss: 0.32: 79%|███████▉ | 670/844 [02:25<00:01, 92.25it/s]\u001b[A\n","ITERATION - loss: 0.20: 81%|████████ | 680/844 [02:25<00:01, 93.27it/s]\u001b[A\n","ITERATION - loss: 0.43: 82%|████████▏ | 690/844 [02:25<00:01, 92.69it/s]\u001b[A\n","ITERATION - loss: 0.27: 83%|████████▎ | 700/844 [02:25<00:01, 93.52it/s]\u001b[A\n","ITERATION - loss: 0.26: 84%|████████▍ | 710/844 [02:25<00:01, 93.97it/s]\u001b[A\n","ITERATION - loss: 0.22: 85%|████████▌ | 720/844 [02:25<00:01, 91.04it/s]\u001b[A\n","ITERATION - loss: 0.31: 86%|████████▋ | 730/844 [02:25<00:01, 90.25it/s]\u001b[A\n","ITERATION - loss: 0.31: 88%|████████▊ | 740/844 [02:25<00:01, 92.21it/s]\u001b[A\n","ITERATION - loss: 0.32: 89%|████████▉ | 750/844 [02:26<00:01, 89.95it/s]\u001b[A\n","ITERATION - loss: 0.18: 90%|█████████ | 760/844 [02:26<00:00, 86.83it/s]\u001b[A\n","ITERATION - loss: 0.30: 91%|█████████ | 770/844 [02:26<00:00, 88.41it/s]\u001b[A\n","ITERATION - loss: 0.23: 92%|█████████▏| 780/844 [02:26<00:00, 90.58it/s]\u001b[A\n","ITERATION - loss: 0.17: 94%|█████████▎| 790/844 [02:26<00:00, 91.44it/s]\u001b[A\n","ITERATION - loss: 0.19: 95%|█████████▍| 800/844 [02:26<00:00, 91.86it/s]\u001b[A\n","ITERATION - loss: 0.18: 96%|█████████▌| 810/844 [02:26<00:00, 90.08it/s]\u001b[A\n","ITERATION - loss: 0.21: 97%|█████████▋| 820/844 [02:26<00:00, 89.39it/s]\u001b[A\n","ITERATION - loss: 0.28: 98%|█████████▊| 830/844 [02:26<00:00, 90.47it/s]\u001b[A\n","ITERATION - loss: 0.25: 100%|█████████▉| 840/844 [02:27<00:00, 91.68it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [03:07<00:00, 93.64it/s]\n","ITERATION - loss: 0.25: 100%|█████████▉| 840/844 [02:34<00:00, 91.68it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 9 Avg accuracy: 92.74 Avg loss: 0.25\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [03:08<00:00, 93.64it/s]\n","ITERATION - loss: 0.25: 100%|█████████▉| 840/844 [02:34<00:00, 91.68it/s]\u001b[A\n","ITERATION - loss: 0.24: 1%| | 10/844 [02:34<03:25, 4.07it/s] \u001b[A\n","ITERATION - loss: 0.21: 2%|▏ | 20/844 [02:35<02:24, 5.70it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 9 Avg accuracy: 92.47 Avg loss: 0.26\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.21: 4%|▎ | 30/844 [02:35<01:42, 7.92it/s]\u001b[A\n","ITERATION - loss: 0.33: 5%|▍ | 40/844 [02:35<01:13, 10.91it/s]\u001b[A\n","ITERATION - loss: 0.29: 6%|▌ | 50/844 [02:35<00:53, 14.85it/s]\u001b[A\n","ITERATION - loss: 0.11: 7%|▋ | 60/844 [02:35<00:39, 19.87it/s]\u001b[A\n","ITERATION - loss: 0.23: 8%|▊ | 70/844 [02:35<00:29, 26.03it/s]\u001b[A\n","ITERATION - loss: 0.45: 9%|▉ | 80/844 [02:35<00:23, 32.80it/s]\u001b[A\n","ITERATION - loss: 0.37: 11%|█ | 90/844 [02:35<00:18, 40.64it/s]\u001b[A\n","ITERATION - loss: 0.23: 12%|█▏ | 100/844 [02:35<00:15, 48.29it/s]\u001b[A\n","ITERATION - loss: 0.25: 13%|█▎ | 110/844 [02:36<00:13, 55.16it/s]\u001b[A\n","ITERATION - loss: 0.25: 14%|█▍ | 120/844 [02:36<00:11, 62.57it/s]\u001b[A\n","ITERATION - loss: 0.55: 15%|█▌ | 130/844 [02:36<00:10, 67.85it/s]\u001b[A\n","ITERATION - loss: 0.25: 17%|█▋ | 140/844 [02:36<00:09, 73.95it/s]\u001b[A\n","ITERATION - loss: 0.14: 18%|█▊ | 150/844 [02:36<00:08, 78.74it/s]\u001b[A\n","ITERATION - loss: 0.21: 19%|█▉ | 160/844 [02:36<00:08, 82.50it/s]\u001b[A\n","ITERATION - loss: 0.23: 20%|██ | 170/844 [02:36<00:07, 85.09it/s]\u001b[A\n","ITERATION - loss: 0.23: 21%|██▏ | 180/844 [02:36<00:07, 87.46it/s]\u001b[A\n","ITERATION - loss: 0.25: 23%|██▎ | 190/844 [02:36<00:07, 86.46it/s]\u001b[A\n","ITERATION - loss: 0.31: 24%|██▎ | 200/844 [02:37<00:07, 88.56it/s]\u001b[A\n","ITERATION - loss: 0.22: 25%|██▍ | 210/844 [02:37<00:07, 90.12it/s]\u001b[A\n","ITERATION - loss: 0.34: 26%|██▌ | 220/844 [02:37<00:06, 89.97it/s]\u001b[A\n","ITERATION - loss: 0.25: 27%|██▋ | 230/844 [02:37<00:06, 90.38it/s]\u001b[A\n","ITERATION - loss: 0.30: 28%|██▊ | 240/844 [02:37<00:06, 91.82it/s]\u001b[A\n","ITERATION - loss: 0.30: 30%|██▉ | 250/844 [02:37<00:06, 93.14it/s]\u001b[A\n","ITERATION - loss: 0.31: 31%|███ | 260/844 [02:37<00:06, 88.64it/s]\u001b[A\n","ITERATION - loss: 0.21: 32%|███▏ | 270/844 [02:37<00:06, 89.99it/s]\u001b[A\n","ITERATION - loss: 0.20: 33%|███▎ | 280/844 [02:37<00:06, 89.15it/s]\u001b[A\n","ITERATION - loss: 0.23: 34%|███▍ | 290/844 [02:38<00:06, 90.43it/s]\u001b[A\n","ITERATION - loss: 0.56: 36%|███▌ | 300/844 [02:38<00:05, 90.88it/s]\u001b[A\n","ITERATION - loss: 0.18: 37%|███▋ | 310/844 [02:38<00:05, 89.94it/s]\u001b[A\n","ITERATION - loss: 0.29: 38%|███▊ | 320/844 [02:38<00:05, 87.56it/s]\u001b[A\n","ITERATION - loss: 0.31: 39%|███▉ | 330/844 [02:38<00:05, 89.29it/s]\u001b[A\n","ITERATION - loss: 0.20: 40%|████ | 340/844 [02:38<00:05, 91.04it/s]\u001b[A\n","ITERATION - loss: 0.14: 41%|████▏ | 350/844 [02:38<00:05, 91.22it/s]\u001b[A\n","ITERATION - loss: 0.23: 43%|████▎ | 360/844 [02:38<00:05, 92.25it/s]\u001b[A\n","ITERATION - loss: 0.14: 44%|████▍ | 370/844 [02:38<00:05, 92.56it/s]\u001b[A\n","ITERATION - loss: 0.17: 45%|████▌ | 380/844 [02:39<00:05, 92.14it/s]\u001b[A\n","ITERATION - loss: 0.39: 46%|████▌ | 390/844 [02:39<00:04, 93.38it/s]\u001b[A\n","ITERATION - loss: 0.25: 47%|████▋ | 400/844 [02:39<00:04, 92.16it/s]\u001b[A\n","ITERATION - loss: 0.37: 49%|████▊ | 410/844 [02:39<00:04, 93.06it/s]\u001b[A\n","ITERATION - loss: 0.29: 50%|████▉ | 420/844 [02:39<00:04, 94.03it/s]\u001b[A\n","ITERATION - loss: 0.23: 51%|█████ | 430/844 [02:39<00:04, 93.27it/s]\u001b[A\n","ITERATION - loss: 0.20: 52%|█████▏ | 440/844 [02:39<00:04, 89.30it/s]\u001b[A\n","ITERATION - loss: 0.25: 53%|█████▎ | 450/844 [02:39<00:04, 88.60it/s]\u001b[A\n","ITERATION - loss: 0.22: 55%|█████▍ | 460/844 [02:39<00:04, 88.70it/s]\u001b[A\n","ITERATION - loss: 0.28: 56%|█████▌ | 470/844 [02:40<00:04, 89.19it/s]\u001b[A\n","ITERATION - loss: 0.29: 57%|█████▋ | 480/844 [02:40<00:03, 91.44it/s]\u001b[A\n","ITERATION - loss: 0.21: 58%|█████▊ | 490/844 [02:40<00:03, 92.45it/s]\u001b[A\n","ITERATION - loss: 0.22: 59%|█████▉ | 500/844 [02:40<00:03, 92.95it/s]\u001b[A\n","ITERATION - loss: 0.32: 60%|██████ | 510/844 [02:40<00:03, 92.79it/s]\u001b[A\n","ITERATION - loss: 0.54: 62%|██████▏ | 520/844 [02:40<00:03, 94.29it/s]\u001b[A\n","ITERATION - loss: 0.27: 63%|██████▎ | 530/844 [02:40<00:03, 93.61it/s]\u001b[A\n","ITERATION - loss: 0.36: 64%|██████▍ | 540/844 [02:40<00:03, 93.02it/s]\u001b[A\n","ITERATION - loss: 0.29: 65%|██████▌ | 550/844 [02:40<00:03, 94.27it/s]\u001b[A\n","ITERATION - loss: 0.19: 66%|██████▋ | 560/844 [02:41<00:03, 92.06it/s]\u001b[A\n","ITERATION - loss: 0.26: 68%|██████▊ | 570/844 [02:41<00:02, 93.28it/s]\u001b[A\n","ITERATION - loss: 0.37: 69%|██████▊ | 580/844 [02:41<00:02, 92.75it/s]\u001b[A\n","ITERATION - loss: 0.26: 70%|██████▉ | 590/844 [02:41<00:02, 91.04it/s]\u001b[A\n","ITERATION - loss: 0.14: 71%|███████ | 600/844 [02:41<00:02, 92.70it/s]\u001b[A\n","ITERATION - loss: 0.13: 72%|███████▏ | 610/844 [02:41<00:02, 93.60it/s]\u001b[A\n","ITERATION - loss: 0.25: 73%|███████▎ | 620/844 [02:41<00:02, 93.12it/s]\u001b[A\n","ITERATION - loss: 0.12: 75%|███████▍ | 630/844 [02:41<00:02, 93.88it/s]\u001b[A\n","ITERATION - loss: 0.28: 76%|███████▌ | 640/844 [02:41<00:02, 93.54it/s]\u001b[A\n","ITERATION - loss: 0.30: 77%|███████▋ | 650/844 [02:41<00:02, 88.39it/s]\u001b[A\n","ITERATION - loss: 0.29: 78%|███████▊ | 660/844 [02:42<00:02, 87.49it/s]\u001b[A\n","ITERATION - loss: 0.28: 79%|███████▉ | 670/844 [02:42<00:01, 88.06it/s]\u001b[A\n","ITERATION - loss: 0.20: 81%|████████ | 680/844 [02:42<00:01, 88.17it/s]\u001b[A\n","ITERATION - loss: 0.23: 82%|████████▏ | 690/844 [02:42<00:01, 89.87it/s]\u001b[A\n","ITERATION - loss: 0.26: 83%|████████▎ | 700/844 [02:42<00:01, 90.25it/s]\u001b[A\n","ITERATION - loss: 0.18: 84%|████████▍ | 710/844 [02:42<00:01, 91.44it/s]\u001b[A\n","ITERATION - loss: 0.21: 85%|████████▌ | 720/844 [02:42<00:01, 92.15it/s]\u001b[A\n","ITERATION - loss: 0.63: 86%|████████▋ | 730/844 [02:42<00:01, 92.30it/s]\u001b[A\n","ITERATION - loss: 0.14: 88%|████████▊ | 740/844 [02:42<00:01, 92.36it/s]\u001b[A\n","ITERATION - loss: 0.12: 89%|████████▉ | 750/844 [02:43<00:01, 91.46it/s]\u001b[A\n","ITERATION - loss: 0.13: 90%|█████████ | 760/844 [02:43<00:00, 91.10it/s]\u001b[A\n","ITERATION - loss: 0.32: 91%|█████████ | 770/844 [02:43<00:00, 90.49it/s]\u001b[A\n","ITERATION - loss: 0.17: 92%|█████████▏| 780/844 [02:43<00:00, 91.35it/s]\u001b[A\n","ITERATION - loss: 0.18: 94%|█████████▎| 790/844 [02:43<00:00, 91.19it/s]\u001b[A\n","ITERATION - loss: 0.31: 95%|█████████▍| 800/844 [02:43<00:00, 90.92it/s]\u001b[A\n","ITERATION - loss: 0.31: 96%|█████████▌| 810/844 [02:43<00:00, 88.51it/s]\u001b[A\n","ITERATION - loss: 0.20: 97%|█████████▋| 820/844 [02:43<00:00, 89.05it/s]\u001b[A\n","ITERATION - loss: 0.33: 98%|█████████▊| 830/844 [02:44<00:00, 83.80it/s]\u001b[A\n","ITERATION - loss: 0.21: 100%|█████████▉| 840/844 [02:44<00:00, 83.65it/s]\u001b[A\n","ITERATION - loss: 0.29: 850it [02:44, 85.10it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [03:24<00:00, 93.64it/s]\n","ITERATION - loss: 0.29: 850it [02:51, 85.10it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 10 Avg accuracy: 92.94 Avg loss: 0.24\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [03:25<00:00, 93.64it/s]\n","ITERATION - loss: 0.29: 850it [02:52, 85.10it/s]\u001b[A\n","ITERATION - loss: 0.14: 1%| | 10/844 [02:52<03:26, 4.05it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 10 Avg accuracy: 92.63 Avg loss: 0.25\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.26: 2%|▏ | 20/844 [02:52<02:25, 5.67it/s]\u001b[A\n","ITERATION - loss: 0.18: 4%|▎ | 30/844 [02:52<01:43, 7.90it/s]\u001b[A\n","ITERATION - loss: 0.37: 5%|▍ | 40/844 [02:52<01:13, 10.88it/s]\u001b[A\n","ITERATION - loss: 0.17: 6%|▌ | 50/844 [02:52<00:53, 14.75it/s]\u001b[A\n","ITERATION - loss: 0.24: 7%|▋ | 60/844 [02:52<00:39, 19.70it/s]\u001b[A\n","ITERATION - loss: 0.25: 8%|▊ | 70/844 [02:52<00:30, 25.63it/s]\u001b[A\n","ITERATION - loss: 0.13: 9%|▉ | 80/844 [02:52<00:23, 32.79it/s]\u001b[A\n","ITERATION - loss: 0.29: 11%|█ | 90/844 [02:53<00:18, 40.81it/s]\u001b[A\n","ITERATION - loss: 0.24: 12%|█▏ | 100/844 [02:53<00:15, 49.20it/s]\u001b[A\n","ITERATION - loss: 0.19: 13%|█▎ | 110/844 [02:53<00:12, 56.72it/s]\u001b[A\n","ITERATION - loss: 0.12: 14%|█▍ | 120/844 [02:53<00:11, 64.28it/s]\u001b[A\n","ITERATION - loss: 0.19: 15%|█▌ | 130/844 [02:53<00:10, 70.20it/s]\u001b[A\n","ITERATION - loss: 0.14: 17%|█▋ | 140/844 [02:53<00:09, 74.00it/s]\u001b[A\n","ITERATION - loss: 0.35: 18%|█▊ | 150/844 [02:53<00:08, 78.98it/s]\u001b[A\n","ITERATION - loss: 0.49: 19%|█▉ | 160/844 [02:53<00:08, 81.64it/s]\u001b[A\n","ITERATION - loss: 0.25: 20%|██ | 170/844 [02:53<00:07, 84.65it/s]\u001b[A\n","ITERATION - loss: 0.28: 21%|██▏ | 180/844 [02:54<00:07, 87.20it/s]\u001b[A\n","ITERATION - loss: 0.36: 23%|██▎ | 190/844 [02:54<00:07, 84.92it/s]\u001b[A\n","ITERATION - loss: 0.19: 24%|██▎ | 200/844 [02:54<00:07, 85.37it/s]\u001b[A\n","ITERATION - loss: 0.26: 25%|██▍ | 210/844 [02:54<00:07, 87.48it/s]\u001b[A\n","ITERATION - loss: 0.20: 26%|██▌ | 220/844 [02:54<00:07, 87.38it/s]\u001b[A\n","ITERATION - loss: 0.14: 27%|██▋ | 230/844 [02:54<00:06, 89.41it/s]\u001b[A\n","ITERATION - loss: 0.20: 28%|██▊ | 240/844 [02:54<00:07, 85.71it/s]\u001b[A\n","ITERATION - loss: 0.13: 30%|██▉ | 250/844 [02:54<00:06, 85.20it/s]\u001b[A\n","ITERATION - loss: 0.13: 31%|███ | 260/844 [02:54<00:06, 87.00it/s]\u001b[A\n","ITERATION - loss: 0.22: 32%|███▏ | 270/844 [02:55<00:06, 89.11it/s]\u001b[A\n","ITERATION - loss: 0.28: 33%|███▎ | 280/844 [02:55<00:06, 89.49it/s]\u001b[A\n","ITERATION - loss: 0.28: 34%|███▍ | 290/844 [02:55<00:06, 89.87it/s]\u001b[A\n","ITERATION - loss: 0.31: 36%|███▌ | 300/844 [02:55<00:06, 90.04it/s]\u001b[A\n","ITERATION - loss: 0.28: 37%|███▋ | 310/844 [02:55<00:06, 88.74it/s]\u001b[A\n","ITERATION - loss: 0.14: 38%|███▊ | 320/844 [02:55<00:05, 88.58it/s]\u001b[A\n","ITERATION - loss: 0.32: 39%|███▉ | 330/844 [02:55<00:05, 87.77it/s]\u001b[A\n","ITERATION - loss: 0.28: 40%|████ | 340/844 [02:55<00:05, 89.28it/s]\u001b[A\n","ITERATION - loss: 0.16: 41%|████▏ | 350/844 [02:55<00:05, 90.67it/s]\u001b[A\n","ITERATION - loss: 0.35: 43%|████▎ | 360/844 [02:56<00:05, 90.06it/s]\u001b[A\n","ITERATION - loss: 0.10: 44%|████▍ | 370/844 [02:56<00:05, 90.04it/s]\u001b[A\n","ITERATION - loss: 0.10: 45%|████▌ | 380/844 [02:56<00:05, 91.38it/s]\u001b[A\n","ITERATION - loss: 0.18: 46%|████▌ | 390/844 [02:56<00:05, 89.92it/s]\u001b[A\n","ITERATION - loss: 0.23: 47%|████▋ | 400/844 [02:56<00:05, 88.77it/s]\u001b[A\n","ITERATION - loss: 0.13: 49%|████▊ | 410/844 [02:56<00:04, 90.35it/s]\u001b[A\n","ITERATION - loss: 0.24: 50%|████▉ | 420/844 [02:56<00:04, 87.36it/s]\u001b[A\n","ITERATION - loss: 0.40: 51%|█████ | 430/844 [02:56<00:04, 87.03it/s]\u001b[A\n","ITERATION - loss: 0.27: 52%|█████▏ | 440/844 [02:57<00:04, 88.73it/s]\u001b[A\n","ITERATION - loss: 0.29: 53%|█████▎ | 450/844 [02:57<00:04, 88.75it/s]\u001b[A\n","ITERATION - loss: 0.17: 55%|█████▍ | 460/844 [02:57<00:04, 90.38it/s]\u001b[A\n","ITERATION - loss: 0.18: 56%|█████▌ | 470/844 [02:57<00:04, 91.36it/s]\u001b[A\n","ITERATION - loss: 0.24: 57%|█████▋ | 480/844 [02:57<00:04, 89.71it/s]\u001b[A\n","ITERATION - loss: 0.28: 58%|█████▊ | 490/844 [02:57<00:03, 89.11it/s]\u001b[A\n","ITERATION - loss: 0.16: 59%|█████▉ | 500/844 [02:57<00:03, 89.12it/s]\u001b[A\n","ITERATION - loss: 0.08: 60%|██████ | 510/844 [02:57<00:03, 90.41it/s]\u001b[A\n","ITERATION - loss: 0.18: 62%|██████▏ | 520/844 [02:57<00:03, 92.18it/s]\u001b[A\n","ITERATION - loss: 0.29: 63%|██████▎ | 530/844 [02:57<00:03, 92.19it/s]\u001b[A\n","ITERATION - loss: 0.47: 64%|██████▍ | 540/844 [02:58<00:03, 93.58it/s]\u001b[A\n","ITERATION - loss: 0.18: 65%|██████▌ | 550/844 [02:58<00:03, 94.28it/s]\u001b[A\n","ITERATION - loss: 0.20: 66%|██████▋ | 560/844 [02:58<00:03, 93.06it/s]\u001b[A\n","ITERATION - loss: 0.10: 68%|██████▊ | 570/844 [02:58<00:03, 89.96it/s]\u001b[A\n","ITERATION - loss: 0.14: 69%|██████▊ | 580/844 [02:58<00:02, 90.04it/s]\u001b[A\n","ITERATION - loss: 0.12: 70%|██████▉ | 590/844 [02:58<00:02, 91.05it/s]\u001b[A\n","ITERATION - loss: 0.22: 71%|███████ | 600/844 [02:58<00:02, 90.25it/s]\u001b[A\n","ITERATION - loss: 0.20: 72%|███████▏ | 610/844 [02:58<00:02, 91.31it/s]\u001b[A\n","ITERATION - loss: 0.13: 73%|███████▎ | 620/844 [02:58<00:02, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.12: 75%|███████▍ | 630/844 [02:59<00:02, 92.68it/s]\u001b[A\n","ITERATION - loss: 0.17: 76%|███████▌ | 640/844 [02:59<00:02, 92.20it/s]\u001b[A\n","ITERATION - loss: 0.22: 77%|███████▋ | 650/844 [02:59<00:02, 92.01it/s]\u001b[A\n","ITERATION - loss: 0.22: 78%|███████▊ | 660/844 [02:59<00:02, 88.95it/s]\u001b[A\n","ITERATION - loss: 0.17: 79%|███████▉ | 670/844 [02:59<00:01, 88.74it/s]\u001b[A\n","ITERATION - loss: 0.18: 81%|████████ | 680/844 [02:59<00:01, 88.31it/s]\u001b[A\n","ITERATION - loss: 0.17: 82%|████████▏ | 690/844 [02:59<00:01, 86.96it/s]\u001b[A\n","ITERATION - loss: 0.19: 83%|████████▎ | 700/844 [02:59<00:01, 88.04it/s]\u001b[A\n","ITERATION - loss: 0.45: 84%|████████▍ | 710/844 [02:59<00:01, 89.79it/s]\u001b[A\n","ITERATION - loss: 0.12: 85%|████████▌ | 720/844 [03:00<00:01, 90.93it/s]\u001b[A\n","ITERATION - loss: 0.17: 86%|████████▋ | 730/844 [03:00<00:01, 90.74it/s]\u001b[A\n","ITERATION - loss: 0.17: 88%|████████▊ | 740/844 [03:00<00:01, 91.61it/s]\u001b[A\n","ITERATION - loss: 0.20: 89%|████████▉ | 750/844 [03:00<00:01, 92.54it/s]\u001b[A\n","ITERATION - loss: 0.17: 90%|█████████ | 760/844 [03:00<00:00, 87.14it/s]\u001b[A\n","ITERATION - loss: 0.30: 91%|█████████ | 770/844 [03:00<00:00, 86.10it/s]\u001b[A\n","ITERATION - loss: 0.21: 92%|█████████▏| 780/844 [03:00<00:00, 87.23it/s]\u001b[A\n","ITERATION - loss: 0.26: 94%|█████████▎| 790/844 [03:00<00:00, 89.27it/s]\u001b[A\n","ITERATION - loss: 0.23: 95%|█████████▍| 800/844 [03:00<00:00, 90.66it/s]\u001b[A\n","ITERATION - loss: 0.18: 96%|█████████▌| 810/844 [03:01<00:00, 90.62it/s]\u001b[A\n","ITERATION - loss: 0.22: 97%|█████████▋| 820/844 [03:01<00:00, 91.97it/s]\u001b[A\n","ITERATION - loss: 0.19: 98%|█████████▊| 830/844 [03:01<00:00, 92.71it/s]\u001b[A\n","ITERATION - loss: 0.19: 100%|█████████▉| 840/844 [03:01<00:00, 92.86it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [03:41<00:00, 93.64it/s]\n","ITERATION - loss: 0.19: 100%|█████████▉| 840/844 [03:08<00:00, 92.86it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 11 Avg accuracy: 93.29 Avg loss: 0.23\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [03:42<00:00, 93.64it/s]\n","ITERATION - loss: 0.19: 100%|█████████▉| 840/844 [03:09<00:00, 92.86it/s]\u001b[A\n","ITERATION - loss: 0.23: 1%| | 10/844 [03:09<03:24, 4.07it/s] \u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 11 Avg accuracy: 93.02 Avg loss: 0.24\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.30: 2%|▏ | 20/844 [03:09<02:24, 5.70it/s]\u001b[A\n","ITERATION - loss: 0.32: 4%|▎ | 30/844 [03:09<01:42, 7.94it/s]\u001b[A\n","ITERATION - loss: 0.24: 5%|▍ | 40/844 [03:09<01:14, 10.79it/s]\u001b[A\n","ITERATION - loss: 0.13: 6%|▌ | 50/844 [03:09<00:54, 14.69it/s]\u001b[A\n","ITERATION - loss: 0.32: 7%|▋ | 60/844 [03:09<00:40, 19.53it/s]\u001b[A\n","ITERATION - loss: 0.15: 8%|▊ | 70/844 [03:10<00:30, 25.52it/s]\u001b[A\n","ITERATION - loss: 0.23: 9%|▉ | 80/844 [03:10<00:23, 32.79it/s]\u001b[A\n","ITERATION - loss: 0.20: 11%|█ | 90/844 [03:10<00:18, 41.02it/s]\u001b[A\n","ITERATION - loss: 0.23: 12%|█▏ | 100/844 [03:10<00:15, 49.59it/s]\u001b[A\n","ITERATION - loss: 0.14: 13%|█▎ | 110/844 [03:10<00:12, 58.05it/s]\u001b[A\n","ITERATION - loss: 0.11: 14%|█▍ | 120/844 [03:10<00:10, 65.86it/s]\u001b[A\n","ITERATION - loss: 0.23: 15%|█▌ | 130/844 [03:10<00:10, 68.28it/s]\u001b[A\n","ITERATION - loss: 0.32: 17%|█▋ | 140/844 [03:10<00:09, 73.51it/s]\u001b[A\n","ITERATION - loss: 0.15: 18%|█▊ | 150/844 [03:10<00:08, 79.31it/s]\u001b[A\n","ITERATION - loss: 0.18: 19%|█▉ | 160/844 [03:11<00:08, 83.32it/s]\u001b[A\n","ITERATION - loss: 0.23: 20%|██ | 170/844 [03:11<00:07, 87.15it/s]\u001b[A\n","ITERATION - loss: 0.39: 21%|██▏ | 180/844 [03:11<00:07, 89.76it/s]\u001b[A\n","ITERATION - loss: 0.20: 23%|██▎ | 190/844 [03:11<00:07, 90.32it/s]\u001b[A\n","ITERATION - loss: 0.25: 24%|██▎ | 200/844 [03:11<00:07, 91.76it/s]\u001b[A\n","ITERATION - loss: 0.14: 25%|██▍ | 210/844 [03:11<00:06, 93.52it/s]\u001b[A\n","ITERATION - loss: 0.12: 26%|██▌ | 220/844 [03:11<00:06, 94.04it/s]\u001b[A\n","ITERATION - loss: 0.27: 27%|██▋ | 230/844 [03:11<00:06, 89.75it/s]\u001b[A\n","ITERATION - loss: 0.24: 28%|██▊ | 240/844 [03:11<00:06, 90.25it/s]\u001b[A\n","ITERATION - loss: 0.17: 30%|██▉ | 250/844 [03:11<00:06, 91.29it/s]\u001b[A\n","ITERATION - loss: 0.30: 31%|███ | 260/844 [03:12<00:06, 91.34it/s]\u001b[A\n","ITERATION - loss: 0.14: 32%|███▏ | 270/844 [03:12<00:06, 90.21it/s]\u001b[A\n","ITERATION - loss: 0.28: 33%|███▎ | 280/844 [03:12<00:06, 89.15it/s]\u001b[A\n","ITERATION - loss: 0.43: 34%|███▍ | 290/844 [03:12<00:06, 90.48it/s]\u001b[A\n","ITERATION - loss: 0.17: 36%|███▌ | 300/844 [03:12<00:06, 90.22it/s]\u001b[A\n","ITERATION - loss: 0.17: 37%|███▋ | 310/844 [03:12<00:05, 89.35it/s]\u001b[A\n","ITERATION - loss: 0.39: 38%|███▊ | 320/844 [03:12<00:06, 87.11it/s]\u001b[A\n","ITERATION - loss: 0.21: 39%|███▉ | 330/844 [03:12<00:05, 89.84it/s]\u001b[A\n","ITERATION - loss: 0.35: 40%|████ | 340/844 [03:12<00:05, 92.60it/s]\u001b[A\n","ITERATION - loss: 0.18: 41%|████▏ | 350/844 [03:13<00:05, 94.55it/s]\u001b[A\n","ITERATION - loss: 0.12: 43%|████▎ | 360/844 [03:13<00:05, 94.82it/s]\u001b[A\n","ITERATION - loss: 0.23: 44%|████▍ | 370/844 [03:13<00:04, 95.49it/s]\u001b[A\n","ITERATION - loss: 0.23: 45%|████▌ | 380/844 [03:13<00:04, 96.17it/s]\u001b[A\n","ITERATION - loss: 0.20: 46%|████▌ | 390/844 [03:13<00:04, 95.78it/s]\u001b[A\n","ITERATION - loss: 0.11: 47%|████▋ | 400/844 [03:13<00:04, 94.75it/s]\u001b[A\n","ITERATION - loss: 0.16: 49%|████▊ | 410/844 [03:13<00:04, 93.45it/s]\u001b[A\n","ITERATION - loss: 0.12: 50%|████▉ | 420/844 [03:13<00:04, 89.05it/s]\u001b[A\n","ITERATION - loss: 0.10: 51%|█████ | 430/844 [03:13<00:04, 91.36it/s]\u001b[A\n","ITERATION - loss: 0.31: 52%|█████▏ | 440/844 [03:14<00:04, 93.01it/s]\u001b[A\n","ITERATION - loss: 0.21: 53%|█████▎ | 450/844 [03:14<00:04, 93.38it/s]\u001b[A\n","ITERATION - loss: 0.20: 55%|█████▍ | 460/844 [03:14<00:04, 93.14it/s]\u001b[A\n","ITERATION - loss: 0.15: 56%|█████▌ | 470/844 [03:14<00:03, 94.29it/s]\u001b[A\n","ITERATION - loss: 0.16: 57%|█████▋ | 480/844 [03:14<00:03, 92.04it/s]\u001b[A\n","ITERATION - loss: 0.32: 58%|█████▊ | 490/844 [03:14<00:03, 92.49it/s]\u001b[A\n","ITERATION - loss: 0.27: 59%|█████▉ | 500/844 [03:14<00:03, 93.68it/s]\u001b[A\n","ITERATION - loss: 0.19: 60%|██████ | 510/844 [03:14<00:03, 88.47it/s]\u001b[A\n","ITERATION - loss: 0.30: 62%|██████▏ | 520/844 [03:14<00:03, 90.23it/s]\u001b[A\n","ITERATION - loss: 0.25: 63%|██████▎ | 530/844 [03:15<00:03, 91.33it/s]\u001b[A\n","ITERATION - loss: 0.12: 64%|██████▍ | 540/844 [03:15<00:03, 91.66it/s]\u001b[A\n","ITERATION - loss: 0.25: 65%|██████▌ | 550/844 [03:15<00:03, 92.31it/s]\u001b[A\n","ITERATION - loss: 0.11: 66%|██████▋ | 560/844 [03:15<00:03, 92.87it/s]\u001b[A\n","ITERATION - loss: 0.19: 68%|██████▊ | 570/844 [03:15<00:02, 92.62it/s]\u001b[A\n","ITERATION - loss: 0.22: 69%|██████▊ | 580/844 [03:15<00:02, 88.28it/s]\u001b[A\n","ITERATION - loss: 0.24: 70%|██████▉ | 590/844 [03:15<00:02, 90.19it/s]\u001b[A\n","ITERATION - loss: 0.27: 71%|███████ | 600/844 [03:15<00:02, 91.50it/s]\u001b[A\n","ITERATION - loss: 0.26: 72%|███████▏ | 610/844 [03:15<00:02, 89.55it/s]\u001b[A\n","ITERATION - loss: 0.11: 73%|███████▎ | 620/844 [03:16<00:02, 90.52it/s]\u001b[A\n","ITERATION - loss: 0.25: 75%|███████▍ | 630/844 [03:16<00:02, 90.75it/s]\u001b[A\n","ITERATION - loss: 0.24: 76%|███████▌ | 640/844 [03:16<00:02, 92.64it/s]\u001b[A\n","ITERATION - loss: 0.21: 77%|███████▋ | 650/844 [03:16<00:02, 93.72it/s]\u001b[A\n","ITERATION - loss: 0.16: 78%|███████▊ | 660/844 [03:16<00:01, 94.35it/s]\u001b[A\n","ITERATION - loss: 0.27: 79%|███████▉ | 670/844 [03:16<00:01, 95.21it/s]\u001b[A\n","ITERATION - loss: 0.23: 81%|████████ | 680/844 [03:16<00:01, 94.93it/s]\u001b[A\n","ITERATION - loss: 0.15: 82%|████████▏ | 690/844 [03:16<00:01, 92.72it/s]\u001b[A\n","ITERATION - loss: 0.36: 83%|████████▎ | 700/844 [03:16<00:01, 88.96it/s]\u001b[A\n","ITERATION - loss: 0.09: 84%|████████▍ | 710/844 [03:17<00:01, 89.34it/s]\u001b[A\n","ITERATION - loss: 0.51: 85%|████████▌ | 720/844 [03:17<00:01, 90.84it/s]\u001b[A\n","ITERATION - loss: 0.25: 86%|████████▋ | 730/844 [03:17<00:01, 91.78it/s]\u001b[A\n","ITERATION - loss: 0.23: 88%|████████▊ | 740/844 [03:17<00:01, 92.13it/s]\u001b[A\n","ITERATION - loss: 0.19: 89%|████████▉ | 750/844 [03:17<00:01, 92.53it/s]\u001b[A\n","ITERATION - loss: 0.38: 90%|█████████ | 760/844 [03:17<00:00, 93.29it/s]\u001b[A\n","ITERATION - loss: 0.43: 91%|█████████ | 770/844 [03:17<00:00, 91.15it/s]\u001b[A\n","ITERATION - loss: 0.16: 92%|█████████▏| 780/844 [03:17<00:00, 91.60it/s]\u001b[A\n","ITERATION - loss: 0.19: 94%|█████████▎| 790/844 [03:17<00:00, 91.43it/s]\u001b[A\n","ITERATION - loss: 0.21: 95%|█████████▍| 800/844 [03:17<00:00, 91.28it/s]\u001b[A\n","ITERATION - loss: 0.38: 96%|█████████▌| 810/844 [03:18<00:00, 90.13it/s]\u001b[A\n","ITERATION - loss: 0.30: 97%|█████████▋| 820/844 [03:18<00:00, 92.03it/s]\u001b[A\n","ITERATION - loss: 0.18: 98%|█████████▊| 830/844 [03:18<00:00, 92.43it/s]\u001b[A\n","ITERATION - loss: 0.24: 100%|█████████▉| 840/844 [03:18<00:00, 93.59it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [03:58<00:00, 93.64it/s]\n","ITERATION - loss: 0.24: 100%|█████████▉| 840/844 [03:25<00:00, 93.59it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 12 Avg accuracy: 93.59 Avg loss: 0.22\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [03:59<00:00, 93.64it/s]\n","ITERATION - loss: 0.24: 100%|█████████▉| 840/844 [03:26<00:00, 93.59it/s]\u001b[A\n","ITERATION - loss: 0.18: 1%| | 10/844 [03:26<03:25, 4.06it/s] \u001b[A\n","ITERATION - loss: 0.47: 2%|▏ | 20/844 [03:26<02:24, 5.69it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 12 Avg accuracy: 93.23 Avg loss: 0.23\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.23: 4%|▎ | 30/844 [03:26<01:43, 7.90it/s]\u001b[A\n","ITERATION - loss: 0.34: 5%|▍ | 40/844 [03:26<01:13, 10.88it/s]\u001b[A\n","ITERATION - loss: 0.22: 6%|▌ | 50/844 [03:26<00:54, 14.70it/s]\u001b[A\n","ITERATION - loss: 0.21: 7%|▋ | 60/844 [03:26<00:39, 19.62it/s]\u001b[A\n","ITERATION - loss: 0.21: 8%|▊ | 70/844 [03:27<00:30, 25.60it/s]\u001b[A\n","ITERATION - loss: 0.22: 9%|▉ | 80/844 [03:27<00:23, 32.47it/s]\u001b[A\n","ITERATION - loss: 0.24: 11%|█ | 90/844 [03:27<00:18, 40.49it/s]\u001b[A\n","ITERATION - loss: 0.24: 12%|█▏ | 100/844 [03:27<00:15, 48.69it/s]\u001b[A\n","ITERATION - loss: 0.13: 13%|█▎ | 110/844 [03:27<00:12, 56.91it/s]\u001b[A\n","ITERATION - loss: 0.21: 14%|█▍ | 120/844 [03:27<00:11, 64.30it/s]\u001b[A\n","ITERATION - loss: 0.17: 15%|█▌ | 130/844 [03:27<00:10, 70.40it/s]\u001b[A\n","ITERATION - loss: 0.24: 17%|█▋ | 140/844 [03:27<00:09, 76.07it/s]\u001b[A\n","ITERATION - loss: 0.26: 18%|█▊ | 150/844 [03:27<00:08, 80.57it/s]\u001b[A\n","ITERATION - loss: 0.17: 19%|█▉ | 160/844 [03:28<00:08, 83.47it/s]\u001b[A\n","ITERATION - loss: 0.21: 20%|██ | 170/844 [03:28<00:07, 84.32it/s]\u001b[A\n","ITERATION - loss: 0.14: 21%|██▏ | 180/844 [03:28<00:07, 87.67it/s]\u001b[A\n","ITERATION - loss: 0.16: 23%|██▎ | 190/844 [03:28<00:07, 89.31it/s]\u001b[A\n","ITERATION - loss: 0.25: 24%|██▎ | 200/844 [03:28<00:07, 87.15it/s]\u001b[A\n","ITERATION - loss: 0.21: 25%|██▍ | 210/844 [03:28<00:07, 89.23it/s]\u001b[A\n","ITERATION - loss: 0.45: 26%|██▌ | 220/844 [03:28<00:06, 90.60it/s]\u001b[A\n","ITERATION - loss: 0.20: 27%|██▋ | 230/844 [03:28<00:06, 90.90it/s]\u001b[A\n","ITERATION - loss: 0.27: 28%|██▊ | 240/844 [03:28<00:06, 91.91it/s]\u001b[A\n","ITERATION - loss: 0.44: 30%|██▉ | 250/844 [03:29<00:06, 90.32it/s]\u001b[A\n","ITERATION - loss: 0.29: 31%|███ | 260/844 [03:29<00:06, 90.65it/s]\u001b[A\n","ITERATION - loss: 0.17: 32%|███▏ | 270/844 [03:29<00:06, 90.11it/s]\u001b[A\n","ITERATION - loss: 0.25: 33%|███▎ | 280/844 [03:29<00:06, 91.80it/s]\u001b[A\n","ITERATION - loss: 0.13: 34%|███▍ | 290/844 [03:29<00:05, 92.56it/s]\u001b[A\n","ITERATION - loss: 0.16: 36%|███▌ | 300/844 [03:29<00:05, 93.31it/s]\u001b[A\n","ITERATION - loss: 0.22: 37%|███▋ | 310/844 [03:29<00:05, 91.93it/s]\u001b[A\n","ITERATION - loss: 0.26: 38%|███▊ | 320/844 [03:29<00:05, 92.57it/s]\u001b[A\n","ITERATION - loss: 0.21: 39%|███▉ | 330/844 [03:29<00:05, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.18: 40%|████ | 340/844 [03:30<00:05, 92.09it/s]\u001b[A\n","ITERATION - loss: 0.19: 41%|████▏ | 350/844 [03:30<00:05, 90.98it/s]\u001b[A\n","ITERATION - loss: 0.24: 43%|████▎ | 360/844 [03:30<00:05, 89.46it/s]\u001b[A\n","ITERATION - loss: 0.17: 44%|████▍ | 370/844 [03:30<00:05, 90.51it/s]\u001b[A\n","ITERATION - loss: 0.17: 45%|████▌ | 380/844 [03:30<00:05, 91.98it/s]\u001b[A\n","ITERATION - loss: 0.21: 46%|████▌ | 390/844 [03:30<00:04, 92.42it/s]\u001b[A\n","ITERATION - loss: 0.33: 47%|████▋ | 400/844 [03:30<00:04, 89.98it/s]\u001b[A\n","ITERATION - loss: 0.24: 49%|████▊ | 410/844 [03:30<00:04, 88.21it/s]\u001b[A\n","ITERATION - loss: 0.15: 50%|████▉ | 420/844 [03:30<00:04, 88.99it/s]\u001b[A\n","ITERATION - loss: 0.30: 51%|█████ | 430/844 [03:31<00:04, 90.56it/s]\u001b[A\n","ITERATION - loss: 0.11: 52%|█████▏ | 440/844 [03:31<00:04, 90.10it/s]\u001b[A\n","ITERATION - loss: 0.21: 53%|█████▎ | 450/844 [03:31<00:04, 89.08it/s]\u001b[A\n","ITERATION - loss: 0.32: 55%|█████▍ | 460/844 [03:31<00:04, 88.89it/s]\u001b[A\n","ITERATION - loss: 0.17: 56%|█████▌ | 470/844 [03:31<00:04, 90.07it/s]\u001b[A\n","ITERATION - loss: 0.25: 57%|█████▋ | 480/844 [03:31<00:03, 91.28it/s]\u001b[A\n","ITERATION - loss: 0.15: 58%|█████▊ | 490/844 [03:31<00:03, 89.44it/s]\u001b[A\n","ITERATION - loss: 0.23: 59%|█████▉ | 500/844 [03:31<00:03, 89.88it/s]\u001b[A\n","ITERATION - loss: 0.25: 60%|██████ | 510/844 [03:31<00:03, 88.78it/s]\u001b[A\n","ITERATION - loss: 0.10: 62%|██████▏ | 520/844 [03:32<00:03, 90.76it/s]\u001b[A\n","ITERATION - loss: 0.18: 63%|██████▎ | 530/844 [03:32<00:03, 90.26it/s]\u001b[A\n","ITERATION - loss: 0.14: 64%|██████▍ | 540/844 [03:32<00:03, 89.34it/s]\u001b[A\n","ITERATION - loss: 0.22: 65%|██████▌ | 550/844 [03:32<00:03, 91.14it/s]\u001b[A\n","ITERATION - loss: 0.38: 66%|██████▋ | 560/844 [03:32<00:03, 92.06it/s]\u001b[A\n","ITERATION - loss: 0.10: 68%|██████▊ | 570/844 [03:32<00:03, 91.16it/s]\u001b[A\n","ITERATION - loss: 0.36: 69%|██████▊ | 580/844 [03:32<00:02, 90.23it/s]\u001b[A\n","ITERATION - loss: 0.30: 70%|██████▉ | 590/844 [03:32<00:02, 91.44it/s]\u001b[A\n","ITERATION - loss: 0.19: 71%|███████ | 600/844 [03:32<00:02, 92.18it/s]\u001b[A\n","ITERATION - loss: 0.20: 72%|███████▏ | 610/844 [03:32<00:02, 92.14it/s]\u001b[A\n","ITERATION - loss: 0.16: 73%|███████▎ | 620/844 [03:33<00:02, 91.41it/s]\u001b[A\n","ITERATION - loss: 0.17: 75%|███████▍ | 630/844 [03:33<00:02, 91.47it/s]\u001b[A\n","ITERATION - loss: 0.25: 76%|███████▌ | 640/844 [03:33<00:02, 89.95it/s]\u001b[A\n","ITERATION - loss: 0.12: 77%|███████▋ | 650/844 [03:33<00:02, 89.89it/s]\u001b[A\n","ITERATION - loss: 0.28: 78%|███████▊ | 660/844 [03:33<00:02, 90.88it/s]\u001b[A\n","ITERATION - loss: 0.24: 79%|███████▉ | 670/844 [03:33<00:01, 91.10it/s]\u001b[A\n","ITERATION - loss: 0.26: 81%|████████ | 680/844 [03:33<00:01, 91.92it/s]\u001b[A\n","ITERATION - loss: 0.19: 82%|████████▏ | 690/844 [03:33<00:01, 91.70it/s]\u001b[A\n","ITERATION - loss: 0.21: 83%|████████▎ | 700/844 [03:33<00:01, 92.58it/s]\u001b[A\n","ITERATION - loss: 0.15: 84%|████████▍ | 710/844 [03:34<00:01, 90.95it/s]\u001b[A\n","ITERATION - loss: 0.29: 85%|████████▌ | 720/844 [03:34<00:01, 91.39it/s]\u001b[A\n","ITERATION - loss: 0.20: 86%|████████▋ | 730/844 [03:34<00:01, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.16: 88%|████████▊ | 740/844 [03:34<00:01, 92.76it/s]\u001b[A\n","ITERATION - loss: 0.14: 89%|████████▉ | 750/844 [03:34<00:00, 94.16it/s]\u001b[A\n","ITERATION - loss: 0.30: 90%|█████████ | 760/844 [03:34<00:00, 91.85it/s]\u001b[A\n","ITERATION - loss: 0.11: 91%|█████████ | 770/844 [03:34<00:00, 89.03it/s]\u001b[A\n","ITERATION - loss: 0.21: 92%|█████████▏| 780/844 [03:34<00:00, 88.37it/s]\u001b[A\n","ITERATION - loss: 0.13: 94%|█████████▎| 790/844 [03:34<00:00, 90.51it/s]\u001b[A\n","ITERATION - loss: 0.38: 95%|█████████▍| 800/844 [03:35<00:00, 92.60it/s]\u001b[A\n","ITERATION - loss: 0.25: 96%|█████████▌| 810/844 [03:35<00:00, 92.09it/s]\u001b[A\n","ITERATION - loss: 0.24: 97%|█████████▋| 820/844 [03:35<00:00, 92.12it/s]\u001b[A\n","ITERATION - loss: 0.23: 98%|█████████▊| 830/844 [03:35<00:00, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.14: 100%|█████████▉| 840/844 [03:35<00:00, 91.40it/s]\u001b[A\n","ITERATION - loss: 0.18: 850it [03:35, 90.61it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [04:16<00:00, 93.64it/s]\n","ITERATION - loss: 0.18: 850it [03:42, 90.61it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 13 Avg accuracy: 93.76 Avg loss: 0.22\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [04:17<00:00, 93.64it/s]\n","ITERATION - loss: 0.18: 850it [03:43, 90.61it/s]\u001b[A\n","ITERATION - loss: 0.15: 1%| | 10/844 [03:43<03:26, 4.04it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 13 Avg accuracy: 93.30 Avg loss: 0.22\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.18: 2%|▏ | 20/844 [03:43<02:25, 5.66it/s]\u001b[A\n","ITERATION - loss: 0.34: 4%|▎ | 30/844 [03:43<01:43, 7.88it/s]\u001b[A\n","ITERATION - loss: 0.27: 5%|▍ | 40/844 [03:43<01:14, 10.85it/s]\u001b[A\n","ITERATION - loss: 0.19: 6%|▌ | 50/844 [03:44<00:53, 14.78it/s]\u001b[A\n","ITERATION - loss: 0.26: 7%|▋ | 60/844 [03:44<00:39, 19.78it/s]\u001b[A\n","ITERATION - loss: 0.19: 8%|▊ | 70/844 [03:44<00:30, 25.75it/s]\u001b[A\n","ITERATION - loss: 0.31: 9%|▉ | 80/844 [03:44<00:23, 32.46it/s]\u001b[A\n","ITERATION - loss: 0.22: 11%|█ | 90/844 [03:44<00:18, 40.27it/s]\u001b[A\n","ITERATION - loss: 0.27: 12%|█▏ | 100/844 [03:44<00:15, 47.31it/s]\u001b[A\n","ITERATION - loss: 0.29: 13%|█▎ | 110/844 [03:44<00:13, 55.58it/s]\u001b[A\n","ITERATION - loss: 0.24: 14%|█▍ | 120/844 [03:44<00:11, 63.52it/s]\u001b[A\n","ITERATION - loss: 0.20: 15%|█▌ | 130/844 [03:44<00:10, 70.72it/s]\u001b[A\n","ITERATION - loss: 0.15: 17%|█▋ | 140/844 [03:45<00:09, 76.49it/s]\u001b[A\n","ITERATION - loss: 0.14: 18%|█▊ | 150/844 [03:45<00:08, 81.07it/s]\u001b[A\n","ITERATION - loss: 0.34: 19%|█▉ | 160/844 [03:45<00:08, 83.73it/s]\u001b[A\n","ITERATION - loss: 0.23: 20%|██ | 170/844 [03:45<00:07, 86.65it/s]\u001b[A\n","ITERATION - loss: 0.25: 21%|██▏ | 180/844 [03:45<00:07, 88.42it/s]\u001b[A\n","ITERATION - loss: 0.17: 23%|██▎ | 190/844 [03:45<00:07, 86.98it/s]\u001b[A\n","ITERATION - loss: 0.18: 24%|██▎ | 200/844 [03:45<00:07, 84.98it/s]\u001b[A\n","ITERATION - loss: 0.15: 25%|██▍ | 210/844 [03:45<00:07, 87.53it/s]\u001b[A\n","ITERATION - loss: 0.31: 26%|██▌ | 220/844 [03:45<00:07, 87.15it/s]\u001b[A\n","ITERATION - loss: 0.28: 27%|██▋ | 230/844 [03:46<00:06, 89.15it/s]\u001b[A\n","ITERATION - loss: 0.34: 28%|██▊ | 240/844 [03:46<00:06, 90.60it/s]\u001b[A\n","ITERATION - loss: 0.14: 30%|██▉ | 250/844 [03:46<00:06, 89.40it/s]\u001b[A\n","ITERATION - loss: 0.21: 31%|███ | 260/844 [03:46<00:06, 90.82it/s]\u001b[A\n","ITERATION - loss: 0.15: 32%|███▏ | 270/844 [03:46<00:06, 90.28it/s]\u001b[A\n","ITERATION - loss: 0.11: 33%|███▎ | 280/844 [03:46<00:06, 88.67it/s]\u001b[A\n","ITERATION - loss: 0.19: 34%|███▍ | 290/844 [03:46<00:06, 88.48it/s]\u001b[A\n","ITERATION - loss: 0.20: 36%|███▌ | 300/844 [03:46<00:06, 87.55it/s]\u001b[A\n","ITERATION - loss: 0.20: 37%|███▋ | 310/844 [03:46<00:06, 85.35it/s]\u001b[A\n","ITERATION - loss: 0.17: 38%|███▊ | 320/844 [03:47<00:05, 87.42it/s]\u001b[A\n","ITERATION - loss: 0.11: 39%|███▉ | 330/844 [03:47<00:05, 87.65it/s]\u001b[A\n","ITERATION - loss: 0.26: 40%|████ | 340/844 [03:47<00:05, 87.77it/s]\u001b[A\n","ITERATION - loss: 0.13: 41%|████▏ | 350/844 [03:47<00:05, 89.89it/s]\u001b[A\n","ITERATION - loss: 0.22: 43%|████▎ | 360/844 [03:47<00:05, 89.88it/s]\u001b[A\n","ITERATION - loss: 0.15: 44%|████▍ | 370/844 [03:47<00:05, 88.64it/s]\u001b[A\n","ITERATION - loss: 0.09: 45%|████▌ | 380/844 [03:47<00:05, 88.76it/s]\u001b[A\n","ITERATION - loss: 0.19: 46%|████▌ | 390/844 [03:47<00:05, 89.63it/s]\u001b[A\n","ITERATION - loss: 0.25: 47%|████▋ | 400/844 [03:47<00:04, 91.58it/s]\u001b[A\n","ITERATION - loss: 0.15: 49%|████▊ | 410/844 [03:48<00:04, 91.90it/s]\u001b[A\n","ITERATION - loss: 0.18: 50%|████▉ | 420/844 [03:48<00:04, 92.19it/s]\u001b[A\n","ITERATION - loss: 0.15: 51%|█████ | 430/844 [03:48<00:04, 92.42it/s]\u001b[A\n","ITERATION - loss: 0.20: 52%|█████▏ | 440/844 [03:48<00:04, 91.71it/s]\u001b[A\n","ITERATION - loss: 0.44: 53%|█████▎ | 450/844 [03:48<00:04, 93.27it/s]\u001b[A\n","ITERATION - loss: 0.15: 55%|█████▍ | 460/844 [03:48<00:04, 89.41it/s]\u001b[A\n","ITERATION - loss: 0.38: 56%|█████▌ | 470/844 [03:48<00:04, 88.11it/s]\u001b[A\n","ITERATION - loss: 0.16: 57%|█████▋ | 480/844 [03:48<00:04, 90.00it/s]\u001b[A\n","ITERATION - loss: 0.29: 58%|█████▊ | 490/844 [03:48<00:03, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.16: 59%|█████▉ | 500/844 [03:49<00:03, 90.22it/s]\u001b[A\n","ITERATION - loss: 0.17: 60%|██████ | 510/844 [03:49<00:03, 91.60it/s]\u001b[A\n","ITERATION - loss: 0.25: 62%|██████▏ | 520/844 [03:49<00:03, 92.34it/s]\u001b[A\n","ITERATION - loss: 0.15: 63%|██████▎ | 530/844 [03:49<00:03, 90.97it/s]\u001b[A\n","ITERATION - loss: 0.29: 64%|██████▍ | 540/844 [03:49<00:03, 91.40it/s]\u001b[A\n","ITERATION - loss: 0.15: 65%|██████▌ | 550/844 [03:49<00:03, 91.20it/s]\u001b[A\n","ITERATION - loss: 0.24: 66%|██████▋ | 560/844 [03:49<00:03, 88.84it/s]\u001b[A\n","ITERATION - loss: 0.17: 68%|██████▊ | 570/844 [03:49<00:03, 89.71it/s]\u001b[A\n","ITERATION - loss: 0.20: 69%|██████▊ | 580/844 [03:49<00:02, 90.25it/s]\u001b[A\n","ITERATION - loss: 0.13: 70%|██████▉ | 590/844 [03:50<00:02, 91.36it/s]\u001b[A\n","ITERATION - loss: 0.19: 71%|███████ | 600/844 [03:50<00:02, 92.19it/s]\u001b[A\n","ITERATION - loss: 0.24: 72%|███████▏ | 610/844 [03:50<00:02, 92.63it/s]\u001b[A\n","ITERATION - loss: 0.26: 73%|███████▎ | 620/844 [03:50<00:02, 91.51it/s]\u001b[A\n","ITERATION - loss: 0.19: 75%|███████▍ | 630/844 [03:50<00:02, 92.35it/s]\u001b[A\n","ITERATION - loss: 0.30: 76%|███████▌ | 640/844 [03:50<00:02, 92.86it/s]\u001b[A\n","ITERATION - loss: 0.26: 77%|███████▋ | 650/844 [03:50<00:02, 90.37it/s]\u001b[A\n","ITERATION - loss: 0.19: 78%|███████▊ | 660/844 [03:50<00:01, 92.40it/s]\u001b[A\n","ITERATION - loss: 0.24: 79%|███████▉ | 670/844 [03:50<00:01, 92.84it/s]\u001b[A\n","ITERATION - loss: 0.27: 81%|████████ | 680/844 [03:51<00:01, 93.54it/s]\u001b[A\n","ITERATION - loss: 0.29: 82%|████████▏ | 690/844 [03:51<00:01, 95.20it/s]\u001b[A\n","ITERATION - loss: 0.27: 83%|████████▎ | 700/844 [03:51<00:01, 89.67it/s]\u001b[A\n","ITERATION - loss: 0.28: 84%|████████▍ | 710/844 [03:51<00:01, 89.95it/s]\u001b[A\n","ITERATION - loss: 0.21: 85%|████████▌ | 720/844 [03:51<00:01, 91.51it/s]\u001b[A\n","ITERATION - loss: 0.21: 86%|████████▋ | 730/844 [03:51<00:01, 92.56it/s]\u001b[A\n","ITERATION - loss: 0.17: 88%|████████▊ | 740/844 [03:51<00:01, 84.59it/s]\u001b[A\n","ITERATION - loss: 0.39: 89%|████████▉ | 750/844 [03:51<00:01, 85.86it/s]\u001b[A\n","ITERATION - loss: 0.22: 90%|█████████ | 760/844 [03:51<00:00, 86.49it/s]\u001b[A\n","ITERATION - loss: 0.19: 91%|█████████ | 770/844 [03:52<00:00, 88.44it/s]\u001b[A\n","ITERATION - loss: 0.24: 92%|█████████▏| 780/844 [03:52<00:00, 90.06it/s]\u001b[A\n","ITERATION - loss: 0.18: 94%|█████████▎| 790/844 [03:52<00:00, 89.72it/s]\u001b[A\n","ITERATION - loss: 0.19: 95%|█████████▍| 800/844 [03:52<00:00, 88.47it/s]\u001b[A\n","ITERATION - loss: 0.14: 96%|█████████▌| 810/844 [03:52<00:00, 89.57it/s]\u001b[A\n","ITERATION - loss: 0.12: 97%|█████████▋| 820/844 [03:52<00:00, 89.77it/s]\u001b[A\n","ITERATION - loss: 0.22: 98%|█████████▊| 830/844 [03:52<00:00, 90.79it/s]\u001b[A\n","ITERATION - loss: 0.33: 100%|█████████▉| 840/844 [03:52<00:00, 91.73it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [04:33<00:00, 93.64it/s]\n","ITERATION - loss: 0.33: 100%|█████████▉| 840/844 [03:59<00:00, 91.73it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 14 Avg accuracy: 93.98 Avg loss: 0.21\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [04:34<00:00, 93.64it/s]\n","ITERATION - loss: 0.33: 100%|█████████▉| 840/844 [04:00<00:00, 91.73it/s]\u001b[A\n","ITERATION - loss: 0.16: 1%| | 10/844 [04:00<03:24, 4.08it/s] \u001b[A\n","ITERATION - loss: 0.25: 2%|▏ | 20/844 [04:00<02:23, 5.73it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 14 Avg accuracy: 93.63 Avg loss: 0.22\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.36: 4%|▎ | 30/844 [04:00<01:42, 7.96it/s]\u001b[A\n","ITERATION - loss: 0.21: 5%|▍ | 40/844 [04:01<01:13, 10.92it/s]\u001b[A\n","ITERATION - loss: 0.24: 6%|▌ | 50/844 [04:01<00:53, 14.86it/s]\u001b[A\n","ITERATION - loss: 0.12: 7%|▋ | 60/844 [04:01<00:39, 19.94it/s]\u001b[A\n","ITERATION - loss: 0.16: 8%|▊ | 70/844 [04:01<00:29, 26.18it/s]\u001b[A\n","ITERATION - loss: 0.20: 9%|▉ | 80/844 [04:01<00:22, 33.48it/s]\u001b[A\n","ITERATION - loss: 0.16: 11%|█ | 90/844 [04:01<00:18, 40.84it/s]\u001b[A\n","ITERATION - loss: 0.26: 12%|█▏ | 100/844 [04:01<00:15, 48.53it/s]\u001b[A\n","ITERATION - loss: 0.15: 13%|█▎ | 110/844 [04:01<00:12, 56.47it/s]\u001b[A\n","ITERATION - loss: 0.12: 14%|█▍ | 120/844 [04:01<00:11, 63.35it/s]\u001b[A\n","ITERATION - loss: 0.12: 15%|█▌ | 130/844 [04:02<00:10, 69.96it/s]\u001b[A\n","ITERATION - loss: 0.15: 17%|█▋ | 140/844 [04:02<00:09, 75.61it/s]\u001b[A\n","ITERATION - loss: 0.21: 18%|█▊ | 150/844 [04:02<00:08, 80.26it/s]\u001b[A\n","ITERATION - loss: 0.37: 19%|█▉ | 160/844 [04:02<00:08, 83.43it/s]\u001b[A\n","ITERATION - loss: 0.35: 20%|██ | 170/844 [04:02<00:07, 86.31it/s]\u001b[A\n","ITERATION - loss: 0.34: 21%|██▏ | 180/844 [04:02<00:07, 86.49it/s]\u001b[A\n","ITERATION - loss: 0.14: 23%|██▎ | 190/844 [04:02<00:07, 86.99it/s]\u001b[A\n","ITERATION - loss: 0.37: 24%|██▎ | 200/844 [04:02<00:07, 88.90it/s]\u001b[A\n","ITERATION - loss: 0.10: 25%|██▍ | 210/844 [04:02<00:07, 90.40it/s]\u001b[A\n","ITERATION - loss: 0.15: 26%|██▌ | 220/844 [04:03<00:07, 88.28it/s]\u001b[A\n","ITERATION - loss: 0.23: 27%|██▋ | 230/844 [04:03<00:06, 89.40it/s]\u001b[A\n","ITERATION - loss: 0.18: 28%|██▊ | 240/844 [04:03<00:06, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.22: 30%|██▉ | 250/844 [04:03<00:06, 89.27it/s]\u001b[A\n","ITERATION - loss: 0.41: 31%|███ | 260/844 [04:03<00:06, 91.58it/s]\u001b[A\n","ITERATION - loss: 0.23: 32%|███▏ | 270/844 [04:03<00:06, 91.24it/s]\u001b[A\n","ITERATION - loss: 0.42: 33%|███▎ | 280/844 [04:03<00:06, 90.99it/s]\u001b[A\n","ITERATION - loss: 0.28: 34%|███▍ | 290/844 [04:03<00:06, 92.00it/s]\u001b[A\n","ITERATION - loss: 0.11: 36%|███▌ | 300/844 [04:03<00:05, 91.81it/s]\u001b[A\n","ITERATION - loss: 0.26: 37%|███▋ | 310/844 [04:04<00:05, 90.86it/s]\u001b[A\n","ITERATION - loss: 0.19: 38%|███▊ | 320/844 [04:04<00:05, 91.48it/s]\u001b[A\n","ITERATION - loss: 0.20: 39%|███▉ | 330/844 [04:04<00:05, 92.68it/s]\u001b[A\n","ITERATION - loss: 0.10: 40%|████ | 340/844 [04:04<00:05, 92.48it/s]\u001b[A\n","ITERATION - loss: 0.22: 41%|████▏ | 350/844 [04:04<00:05, 92.43it/s]\u001b[A\n","ITERATION - loss: 0.16: 43%|████▎ | 360/844 [04:04<00:05, 90.64it/s]\u001b[A\n","ITERATION - loss: 0.12: 44%|████▍ | 370/844 [04:04<00:05, 90.87it/s]\u001b[A\n","ITERATION - loss: 0.25: 45%|████▌ | 380/844 [04:04<00:05, 91.04it/s]\u001b[A\n","ITERATION - loss: 0.26: 46%|████▌ | 390/844 [04:04<00:04, 92.53it/s]\u001b[A\n","ITERATION - loss: 0.27: 47%|████▋ | 400/844 [04:04<00:04, 93.51it/s]\u001b[A\n","ITERATION - loss: 0.23: 49%|████▊ | 410/844 [04:05<00:04, 92.83it/s]\u001b[A\n","ITERATION - loss: 0.38: 50%|████▉ | 420/844 [04:05<00:04, 92.94it/s]\u001b[A\n","ITERATION - loss: 0.22: 51%|█████ | 430/844 [04:05<00:04, 94.27it/s]\u001b[A\n","ITERATION - loss: 0.21: 53%|█████▎ | 450/844 [04:05<00:04, 96.60it/s]\u001b[A\n","ITERATION - loss: 0.23: 55%|█████▍ | 460/844 [04:05<00:04, 89.77it/s]\u001b[A\n","ITERATION - loss: 0.12: 56%|█████▌ | 470/844 [04:05<00:04, 88.47it/s]\u001b[A\n","ITERATION - loss: 0.21: 57%|█████▋ | 480/844 [04:05<00:04, 89.68it/s]\u001b[A\n","ITERATION - loss: 0.25: 58%|█████▊ | 490/844 [04:05<00:03, 89.76it/s]\u001b[A\n","ITERATION - loss: 0.22: 59%|█████▉ | 500/844 [04:06<00:03, 89.14it/s]\u001b[A\n","ITERATION - loss: 0.12: 60%|██████ | 510/844 [04:06<00:03, 90.68it/s]\u001b[A\n","ITERATION - loss: 0.15: 62%|██████▏ | 520/844 [04:06<00:03, 91.57it/s]\u001b[A\n","ITERATION - loss: 0.40: 63%|██████▎ | 530/844 [04:06<00:03, 92.54it/s]\u001b[A\n","ITERATION - loss: 0.15: 64%|██████▍ | 540/844 [04:06<00:03, 93.30it/s]\u001b[A\n","ITERATION - loss: 0.26: 65%|██████▌ | 550/844 [04:06<00:03, 92.47it/s]\u001b[A\n","ITERATION - loss: 0.28: 66%|██████▋ | 560/844 [04:06<00:03, 90.68it/s]\u001b[A\n","ITERATION - loss: 0.09: 68%|██████▊ | 570/844 [04:06<00:02, 91.70it/s]\u001b[A\n","ITERATION - loss: 0.19: 69%|██████▊ | 580/844 [04:06<00:02, 88.64it/s]\u001b[A\n","ITERATION - loss: 0.09: 70%|██████▉ | 590/844 [04:07<00:02, 88.47it/s]\u001b[A\n","ITERATION - loss: 0.16: 71%|███████ | 600/844 [04:07<00:02, 90.10it/s]\u001b[A\n","ITERATION - loss: 0.22: 72%|███████▏ | 610/844 [04:07<00:02, 91.79it/s]\u001b[A\n","ITERATION - loss: 0.17: 73%|███████▎ | 620/844 [04:07<00:02, 92.67it/s]\u001b[A\n","ITERATION - loss: 0.58: 75%|███████▍ | 630/844 [04:07<00:02, 93.21it/s]\u001b[A\n","ITERATION - loss: 0.16: 76%|███████▌ | 640/844 [04:07<00:02, 91.92it/s]\u001b[A\n","ITERATION - loss: 0.18: 77%|███████▋ | 650/844 [04:07<00:02, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.31: 78%|███████▊ | 660/844 [04:07<00:02, 84.94it/s]\u001b[A\n","ITERATION - loss: 0.19: 79%|███████▉ | 670/844 [04:07<00:01, 87.90it/s]\u001b[A\n","ITERATION - loss: 0.19: 81%|████████ | 680/844 [04:08<00:01, 88.09it/s]\u001b[A\n","ITERATION - loss: 0.22: 82%|████████▏ | 690/844 [04:08<00:01, 88.77it/s]\u001b[A\n","ITERATION - loss: 0.23: 83%|████████▎ | 700/844 [04:08<00:01, 90.17it/s]\u001b[A\n","ITERATION - loss: 0.23: 84%|████████▍ | 710/844 [04:08<00:01, 92.23it/s]\u001b[A\n","ITERATION - loss: 0.18: 85%|████████▌ | 720/844 [04:08<00:01, 91.20it/s]\u001b[A\n","ITERATION - loss: 0.32: 86%|████████▋ | 730/844 [04:08<00:01, 91.04it/s]\u001b[A\n","ITERATION - loss: 0.37: 88%|████████▊ | 740/844 [04:08<00:01, 91.62it/s]\u001b[A\n","ITERATION - loss: 0.18: 89%|████████▉ | 750/844 [04:08<00:01, 90.51it/s]\u001b[A\n","ITERATION - loss: 0.09: 90%|█████████ | 760/844 [04:08<00:00, 91.05it/s]\u001b[A\n","ITERATION - loss: 0.21: 91%|█████████ | 770/844 [04:09<00:00, 91.87it/s]\u001b[A\n","ITERATION - loss: 0.36: 92%|█████████▏| 780/844 [04:09<00:00, 90.42it/s]\u001b[A\n","ITERATION - loss: 0.09: 94%|█████████▎| 790/844 [04:09<00:00, 90.33it/s]\u001b[A\n","ITERATION - loss: 0.15: 95%|█████████▍| 800/844 [04:09<00:00, 91.41it/s]\u001b[A\n","ITERATION - loss: 0.24: 96%|█████████▌| 810/844 [04:09<00:00, 92.23it/s]\u001b[A\n","ITERATION - loss: 0.22: 97%|█████████▋| 820/844 [04:09<00:00, 92.53it/s]\u001b[A\n","ITERATION - loss: 0.06: 98%|█████████▊| 830/844 [04:09<00:00, 89.83it/s]\u001b[A\n","ITERATION - loss: 0.22: 100%|█████████▉| 840/844 [04:09<00:00, 91.44it/s]\u001b[A\n","ITERATION - loss: 0.06: 850it [04:09, 93.04it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [04:50<00:00, 93.64it/s]\n","ITERATION - loss: 0.06: 850it [04:16, 93.04it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 15 Avg accuracy: 94.17 Avg loss: 0.20\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [04:51<00:00, 93.64it/s]\n","ITERATION - loss: 0.06: 850it [04:17, 93.04it/s]\u001b[A\n","ITERATION - loss: 0.21: 1%| | 10/844 [04:17<03:25, 4.06it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 15 Avg accuracy: 93.80 Avg loss: 0.21\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.17: 2%|▏ | 20/844 [04:17<02:24, 5.69it/s]\u001b[A\n","ITERATION - loss: 0.27: 4%|▎ | 30/844 [04:18<01:42, 7.91it/s]\u001b[A\n","ITERATION - loss: 0.17: 5%|▍ | 40/844 [04:18<01:14, 10.77it/s]\u001b[A\n","ITERATION - loss: 0.18: 6%|▌ | 50/844 [04:18<00:54, 14.62it/s]\u001b[A\n","ITERATION - loss: 0.18: 7%|▋ | 60/844 [04:18<00:40, 19.51it/s]\u001b[A\n","ITERATION - loss: 0.14: 8%|▊ | 70/844 [04:18<00:30, 25.60it/s]\u001b[A\n","ITERATION - loss: 0.18: 9%|▉ | 80/844 [04:18<00:23, 32.74it/s]\u001b[A\n","ITERATION - loss: 0.12: 11%|█ | 90/844 [04:18<00:18, 39.95it/s]\u001b[A\n","ITERATION - loss: 0.20: 12%|█▏ | 100/844 [04:18<00:15, 48.23it/s]\u001b[A\n","ITERATION - loss: 0.20: 13%|█▎ | 110/844 [04:19<00:12, 56.58it/s]\u001b[A\n","ITERATION - loss: 0.23: 14%|█▍ | 120/844 [04:19<00:11, 63.88it/s]\u001b[A\n","ITERATION - loss: 0.26: 15%|█▌ | 130/844 [04:19<00:10, 70.56it/s]\u001b[A\n","ITERATION - loss: 0.16: 17%|█▋ | 140/844 [04:19<00:09, 75.03it/s]\u001b[A\n","ITERATION - loss: 0.11: 18%|█▊ | 150/844 [04:19<00:08, 79.23it/s]\u001b[A\n","ITERATION - loss: 0.18: 19%|█▉ | 160/844 [04:19<00:08, 82.21it/s]\u001b[A\n","ITERATION - loss: 0.16: 20%|██ | 170/844 [04:19<00:08, 82.53it/s]\u001b[A\n","ITERATION - loss: 0.07: 21%|██▏ | 180/844 [04:19<00:08, 80.16it/s]\u001b[A\n","ITERATION - loss: 0.16: 23%|██▎ | 190/844 [04:19<00:08, 78.08it/s]\u001b[A\n","ITERATION - loss: 0.19: 24%|██▎ | 200/844 [04:20<00:07, 81.46it/s]\u001b[A\n","ITERATION - loss: 0.18: 25%|██▍ | 210/844 [04:20<00:07, 84.22it/s]\u001b[A\n","ITERATION - loss: 0.09: 26%|██▌ | 220/844 [04:20<00:07, 85.81it/s]\u001b[A\n","ITERATION - loss: 0.22: 27%|██▋ | 230/844 [04:20<00:07, 85.21it/s]\u001b[A\n","ITERATION - loss: 0.22: 28%|██▊ | 240/844 [04:20<00:06, 86.73it/s]\u001b[A\n","ITERATION - loss: 0.13: 30%|██▉ | 250/844 [04:20<00:06, 87.42it/s]\u001b[A\n","ITERATION - loss: 0.28: 31%|███ | 260/844 [04:20<00:06, 89.31it/s]\u001b[A\n","ITERATION - loss: 0.43: 32%|███▏ | 270/844 [04:20<00:06, 89.39it/s]\u001b[A\n","ITERATION - loss: 0.34: 33%|███▎ | 280/844 [04:20<00:06, 90.47it/s]\u001b[A\n","ITERATION - loss: 0.21: 34%|███▍ | 290/844 [04:21<00:06, 91.93it/s]\u001b[A\n","ITERATION - loss: 0.08: 36%|███▌ | 300/844 [04:21<00:05, 92.40it/s]\u001b[A\n","ITERATION - loss: 0.20: 37%|███▋ | 310/844 [04:21<00:05, 92.91it/s]\u001b[A\n","ITERATION - loss: 0.27: 38%|███▊ | 320/844 [04:21<00:05, 89.31it/s]\u001b[A\n","ITERATION - loss: 0.15: 39%|███▉ | 330/844 [04:21<00:05, 87.08it/s]\u001b[A\n","ITERATION - loss: 0.17: 40%|████ | 340/844 [04:21<00:05, 86.97it/s]\u001b[A\n","ITERATION - loss: 0.14: 41%|████▏ | 350/844 [04:21<00:05, 88.11it/s]\u001b[A\n","ITERATION - loss: 0.13: 43%|████▎ | 360/844 [04:21<00:05, 87.00it/s]\u001b[A\n","ITERATION - loss: 0.15: 44%|████▍ | 370/844 [04:21<00:05, 88.74it/s]\u001b[A\n","ITERATION - loss: 0.16: 45%|████▌ | 380/844 [04:22<00:05, 87.73it/s]\u001b[A\n","ITERATION - loss: 0.20: 46%|████▌ | 390/844 [04:22<00:05, 87.66it/s]\u001b[A\n","ITERATION - loss: 0.32: 47%|████▋ | 400/844 [04:22<00:04, 88.99it/s]\u001b[A\n","ITERATION - loss: 0.10: 49%|████▊ | 410/844 [04:22<00:05, 86.55it/s]\u001b[A\n","ITERATION - loss: 0.13: 50%|████▉ | 420/844 [04:22<00:04, 88.14it/s]\u001b[A\n","ITERATION - loss: 0.24: 51%|█████ | 430/844 [04:22<00:04, 88.65it/s]\u001b[A\n","ITERATION - loss: 0.17: 52%|█████▏ | 440/844 [04:22<00:04, 86.59it/s]\u001b[A\n","ITERATION - loss: 0.16: 53%|█████▎ | 450/844 [04:22<00:04, 86.38it/s]\u001b[A\n","ITERATION - loss: 0.17: 55%|█████▍ | 460/844 [04:23<00:04, 88.53it/s]\u001b[A\n","ITERATION - loss: 0.16: 56%|█████▌ | 470/844 [04:23<00:04, 88.89it/s]\u001b[A\n","ITERATION - loss: 0.13: 57%|█████▋ | 480/844 [04:23<00:04, 90.95it/s]\u001b[A\n","ITERATION - loss: 0.20: 58%|█████▊ | 490/844 [04:23<00:03, 92.35it/s]\u001b[A\n","ITERATION - loss: 0.17: 59%|█████▉ | 500/844 [04:23<00:03, 91.74it/s]\u001b[A\n","ITERATION - loss: 0.17: 60%|██████ | 510/844 [04:23<00:03, 90.46it/s]\u001b[A\n","ITERATION - loss: 0.15: 62%|██████▏ | 520/844 [04:23<00:03, 87.84it/s]\u001b[A\n","ITERATION - loss: 0.17: 63%|██████▎ | 530/844 [04:23<00:03, 86.24it/s]\u001b[A\n","ITERATION - loss: 0.14: 64%|██████▍ | 540/844 [04:23<00:03, 83.30it/s]\u001b[A\n","ITERATION - loss: 0.23: 65%|██████▌ | 550/844 [04:24<00:03, 82.18it/s]\u001b[A\n","ITERATION - loss: 0.16: 66%|██████▋ | 560/844 [04:24<00:03, 83.82it/s]\u001b[A\n","ITERATION - loss: 0.24: 68%|██████▊ | 570/844 [04:24<00:03, 86.27it/s]\u001b[A\n","ITERATION - loss: 0.23: 69%|██████▊ | 580/844 [04:24<00:02, 88.34it/s]\u001b[A\n","ITERATION - loss: 0.34: 70%|██████▉ | 590/844 [04:24<00:02, 87.20it/s]\u001b[A\n","ITERATION - loss: 0.22: 71%|███████ | 600/844 [04:24<00:02, 89.81it/s]\u001b[A\n","ITERATION - loss: 0.18: 72%|███████▏ | 610/844 [04:24<00:02, 90.01it/s]\u001b[A\n","ITERATION - loss: 0.20: 73%|███████▎ | 620/844 [04:24<00:02, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.19: 75%|███████▍ | 630/844 [04:24<00:02, 86.55it/s]\u001b[A\n","ITERATION - loss: 0.41: 76%|███████▌ | 640/844 [04:25<00:02, 87.78it/s]\u001b[A\n","ITERATION - loss: 0.11: 77%|███████▋ | 650/844 [04:25<00:02, 89.16it/s]\u001b[A\n","ITERATION - loss: 0.17: 78%|███████▊ | 660/844 [04:25<00:02, 89.85it/s]\u001b[A\n","ITERATION - loss: 0.25: 79%|███████▉ | 670/844 [04:25<00:01, 90.42it/s]\u001b[A\n","ITERATION - loss: 0.21: 81%|████████ | 680/844 [04:25<00:01, 90.86it/s]\u001b[A\n","ITERATION - loss: 0.17: 82%|████████▏ | 690/844 [04:25<00:01, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.13: 83%|████████▎ | 700/844 [04:25<00:01, 89.56it/s]\u001b[A\n","ITERATION - loss: 0.23: 84%|████████▍ | 710/844 [04:25<00:01, 89.07it/s]\u001b[A\n","ITERATION - loss: 0.24: 85%|████████▌ | 720/844 [04:25<00:01, 87.22it/s]\u001b[A\n","ITERATION - loss: 0.13: 86%|████████▋ | 730/844 [04:26<00:01, 88.95it/s]\u001b[A\n","ITERATION - loss: 0.11: 88%|████████▊ | 740/844 [04:26<00:01, 90.65it/s]\u001b[A\n","ITERATION - loss: 0.36: 89%|████████▉ | 750/844 [04:26<00:01, 87.49it/s]\u001b[A\n","ITERATION - loss: 0.17: 90%|█████████ | 760/844 [04:26<00:00, 88.67it/s]\u001b[A\n","ITERATION - loss: 0.09: 91%|█████████ | 770/844 [04:26<00:00, 90.20it/s]\u001b[A\n","ITERATION - loss: 0.41: 92%|█████████▏| 780/844 [04:26<00:00, 89.37it/s]\u001b[A\n","ITERATION - loss: 0.11: 94%|█████████▎| 790/844 [04:26<00:00, 90.73it/s]\u001b[A\n","ITERATION - loss: 0.15: 95%|█████████▍| 800/844 [04:26<00:00, 91.40it/s]\u001b[A\n","ITERATION - loss: 0.29: 96%|█████████▌| 810/844 [04:26<00:00, 90.38it/s]\u001b[A\n","ITERATION - loss: 0.19: 97%|█████████▋| 820/844 [04:27<00:00, 91.84it/s]\u001b[A\n","ITERATION - loss: 0.28: 98%|█████████▊| 830/844 [04:27<00:00, 92.13it/s]\u001b[A\n","ITERATION - loss: 0.13: 100%|█████████▉| 840/844 [04:27<00:00, 92.49it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [05:07<00:00, 93.64it/s]\n","ITERATION - loss: 0.13: 100%|█████████▉| 840/844 [04:34<00:00, 92.49it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 16 Avg accuracy: 94.33 Avg loss: 0.20\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [05:08<00:00, 93.64it/s]\n","ITERATION - loss: 0.13: 100%|█████████▉| 840/844 [04:35<00:00, 92.49it/s]\u001b[A\n","ITERATION - loss: 0.22: 1%| | 10/844 [04:35<03:26, 4.03it/s] \u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 16 Avg accuracy: 93.87 Avg loss: 0.21\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.18: 2%|▏ | 20/844 [04:35<02:25, 5.65it/s]\u001b[A\n","ITERATION - loss: 0.15: 4%|▎ | 30/844 [04:35<01:43, 7.86it/s]\u001b[A\n","ITERATION - loss: 0.15: 5%|▍ | 40/844 [04:35<01:14, 10.84it/s]\u001b[A\n","ITERATION - loss: 0.37: 6%|▌ | 50/844 [04:35<00:53, 14.75it/s]\u001b[A\n","ITERATION - loss: 0.17: 7%|▋ | 60/844 [04:35<00:39, 19.64it/s]\u001b[A\n","ITERATION - loss: 0.19: 8%|▊ | 70/844 [04:35<00:30, 25.65it/s]\u001b[A\n","ITERATION - loss: 0.23: 9%|▉ | 80/844 [04:36<00:23, 32.53it/s]\u001b[A\n","ITERATION - loss: 0.21: 11%|█ | 90/844 [04:36<00:18, 39.87it/s]\u001b[A\n","ITERATION - loss: 0.16: 12%|█▏ | 100/844 [04:36<00:15, 47.59it/s]\u001b[A\n","ITERATION - loss: 0.14: 13%|█▎ | 110/844 [04:36<00:13, 55.92it/s]\u001b[A\n","ITERATION - loss: 0.19: 14%|█▍ | 120/844 [04:36<00:11, 63.15it/s]\u001b[A\n","ITERATION - loss: 0.14: 15%|█▌ | 130/844 [04:36<00:10, 69.73it/s]\u001b[A\n","ITERATION - loss: 0.15: 17%|█▋ | 140/844 [04:36<00:09, 75.24it/s]\u001b[A\n","ITERATION - loss: 0.22: 18%|█▊ | 150/844 [04:36<00:09, 77.06it/s]\u001b[A\n","ITERATION - loss: 0.19: 19%|█▉ | 160/844 [04:36<00:08, 80.46it/s]\u001b[A\n","ITERATION - loss: 0.40: 20%|██ | 170/844 [04:37<00:08, 82.31it/s]\u001b[A\n","ITERATION - loss: 0.30: 21%|██▏ | 180/844 [04:37<00:07, 85.19it/s]\u001b[A\n","ITERATION - loss: 0.16: 23%|██▎ | 190/844 [04:37<00:07, 86.36it/s]\u001b[A\n","ITERATION - loss: 0.08: 24%|██▎ | 200/844 [04:37<00:07, 88.46it/s]\u001b[A\n","ITERATION - loss: 0.35: 25%|██▍ | 210/844 [04:37<00:07, 88.68it/s]\u001b[A\n","ITERATION - loss: 0.16: 26%|██▌ | 220/844 [04:37<00:06, 90.00it/s]\u001b[A\n","ITERATION - loss: 0.17: 27%|██▋ | 230/844 [04:37<00:06, 89.85it/s]\u001b[A\n","ITERATION - loss: 0.21: 28%|██▊ | 240/844 [04:37<00:06, 89.48it/s]\u001b[A\n","ITERATION - loss: 0.12: 30%|██▉ | 250/844 [04:37<00:06, 90.40it/s]\u001b[A\n","ITERATION - loss: 0.20: 31%|███ | 260/844 [04:38<00:06, 89.35it/s]\u001b[A\n","ITERATION - loss: 0.06: 32%|███▏ | 270/844 [04:38<00:06, 90.75it/s]\u001b[A\n","ITERATION - loss: 0.11: 33%|███▎ | 280/844 [04:38<00:06, 91.43it/s]\u001b[A\n","ITERATION - loss: 0.45: 34%|███▍ | 290/844 [04:38<00:06, 88.71it/s]\u001b[A\n","ITERATION - loss: 0.27: 36%|███▌ | 300/844 [04:38<00:06, 89.78it/s]\u001b[A\n","ITERATION - loss: 0.36: 37%|███▋ | 310/844 [04:38<00:05, 89.04it/s]\u001b[A\n","ITERATION - loss: 0.30: 38%|███▊ | 320/844 [04:38<00:05, 87.59it/s]\u001b[A\n","ITERATION - loss: 0.42: 39%|███▉ | 330/844 [04:38<00:05, 87.78it/s]\u001b[A\n","ITERATION - loss: 0.17: 40%|████ | 340/844 [04:38<00:05, 88.73it/s]\u001b[A\n","ITERATION - loss: 0.09: 41%|████▏ | 350/844 [04:39<00:05, 85.53it/s]\u001b[A\n","ITERATION - loss: 0.12: 43%|████▎ | 360/844 [04:39<00:05, 88.61it/s]\u001b[A\n","ITERATION - loss: 0.19: 44%|████▍ | 370/844 [04:39<00:05, 90.11it/s]\u001b[A\n","ITERATION - loss: 0.19: 45%|████▌ | 380/844 [04:39<00:05, 91.76it/s]\u001b[A\n","ITERATION - loss: 0.13: 46%|████▌ | 390/844 [04:39<00:04, 92.75it/s]\u001b[A\n","ITERATION - loss: 0.15: 47%|████▋ | 400/844 [04:39<00:04, 89.09it/s]\u001b[A\n","ITERATION - loss: 0.28: 49%|████▊ | 410/844 [04:39<00:04, 89.97it/s]\u001b[A\n","ITERATION - loss: 0.09: 50%|████▉ | 420/844 [04:39<00:04, 88.29it/s]\u001b[A\n","ITERATION - loss: 0.24: 51%|█████ | 430/844 [04:39<00:04, 88.85it/s]\u001b[A\n","ITERATION - loss: 0.17: 52%|█████▏ | 440/844 [04:40<00:04, 88.28it/s]\u001b[A\n","ITERATION - loss: 0.16: 53%|█████▎ | 450/844 [04:40<00:04, 87.26it/s]\u001b[A\n","ITERATION - loss: 0.19: 55%|█████▍ | 460/844 [04:40<00:04, 84.66it/s]\u001b[A\n","ITERATION - loss: 0.25: 56%|█████▌ | 470/844 [04:40<00:04, 84.19it/s]\u001b[A\n","ITERATION - loss: 0.18: 57%|█████▋ | 480/844 [04:40<00:04, 85.38it/s]\u001b[A\n","ITERATION - loss: 0.17: 58%|█████▊ | 490/844 [04:40<00:04, 87.61it/s]\u001b[A\n","ITERATION - loss: 0.31: 59%|█████▉ | 500/844 [04:40<00:03, 86.04it/s]\u001b[A\n","ITERATION - loss: 0.14: 60%|██████ | 510/844 [04:40<00:03, 86.23it/s]\u001b[A\n","ITERATION - loss: 0.17: 62%|██████▏ | 520/844 [04:41<00:03, 87.27it/s]\u001b[A\n","ITERATION - loss: 0.21: 63%|██████▎ | 530/844 [04:41<00:03, 87.93it/s]\u001b[A\n","ITERATION - loss: 0.24: 64%|██████▍ | 540/844 [04:41<00:03, 89.54it/s]\u001b[A\n","ITERATION - loss: 0.14: 65%|██████▌ | 550/844 [04:41<00:03, 89.46it/s]\u001b[A\n","ITERATION - loss: 0.16: 66%|██████▋ | 560/844 [04:41<00:03, 90.52it/s]\u001b[A\n","ITERATION - loss: 0.17: 68%|██████▊ | 570/844 [04:41<00:03, 90.83it/s]\u001b[A\n","ITERATION - loss: 0.20: 69%|██████▊ | 580/844 [04:41<00:03, 86.79it/s]\u001b[A\n","ITERATION - loss: 0.13: 70%|██████▉ | 590/844 [04:41<00:02, 87.73it/s]\u001b[A\n","ITERATION - loss: 0.15: 71%|███████ | 600/844 [04:41<00:02, 87.81it/s]\u001b[A\n","ITERATION - loss: 0.16: 72%|███████▏ | 610/844 [04:42<00:02, 85.66it/s]\u001b[A\n","ITERATION - loss: 0.07: 73%|███████▎ | 620/844 [04:42<00:02, 85.76it/s]\u001b[A\n","ITERATION - loss: 0.08: 75%|███████▍ | 630/844 [04:42<00:02, 87.92it/s]\u001b[A\n","ITERATION - loss: 0.29: 76%|███████▌ | 640/844 [04:42<00:02, 88.86it/s]\u001b[A\n","ITERATION - loss: 0.17: 77%|███████▋ | 650/844 [04:42<00:02, 90.10it/s]\u001b[A\n","ITERATION - loss: 0.15: 78%|███████▊ | 660/844 [04:42<00:02, 90.94it/s]\u001b[A\n","ITERATION - loss: 0.13: 79%|███████▉ | 670/844 [04:42<00:01, 90.40it/s]\u001b[A\n","ITERATION - loss: 0.29: 81%|████████ | 680/844 [04:42<00:01, 90.49it/s]\u001b[A\n","ITERATION - loss: 0.19: 82%|████████▏ | 690/844 [04:42<00:01, 89.76it/s]\u001b[A\n","ITERATION - loss: 0.21: 83%|████████▎ | 700/844 [04:43<00:01, 91.09it/s]\u001b[A\n","ITERATION - loss: 0.31: 84%|████████▍ | 710/844 [04:43<00:01, 91.54it/s]\u001b[A\n","ITERATION - loss: 0.16: 85%|████████▌ | 720/844 [04:43<00:01, 92.41it/s]\u001b[A\n","ITERATION - loss: 0.16: 86%|████████▋ | 730/844 [04:43<00:01, 92.79it/s]\u001b[A\n","ITERATION - loss: 0.20: 88%|████████▊ | 740/844 [04:43<00:01, 93.44it/s]\u001b[A\n","ITERATION - loss: 0.25: 89%|████████▉ | 750/844 [04:43<00:01, 93.97it/s]\u001b[A\n","ITERATION - loss: 0.12: 90%|█████████ | 760/844 [04:43<00:00, 92.95it/s]\u001b[A\n","ITERATION - loss: 0.14: 91%|█████████ | 770/844 [04:43<00:00, 89.02it/s]\u001b[A\n","ITERATION - loss: 0.22: 92%|█████████▏| 780/844 [04:43<00:00, 90.68it/s]\u001b[A\n","ITERATION - loss: 0.18: 94%|█████████▎| 790/844 [04:44<00:00, 89.09it/s]\u001b[A\n","ITERATION - loss: 0.14: 95%|█████████▍| 800/844 [04:44<00:00, 91.35it/s]\u001b[A\n","ITERATION - loss: 0.13: 96%|█████████▌| 810/844 [04:44<00:00, 90.90it/s]\u001b[A\n","ITERATION - loss: 0.35: 97%|█████████▋| 820/844 [04:44<00:00, 90.83it/s]\u001b[A\n","ITERATION - loss: 0.27: 98%|█████████▊| 830/844 [04:44<00:00, 92.40it/s]\u001b[A\n","ITERATION - loss: 0.12: 100%|█████████▉| 840/844 [04:44<00:00, 93.27it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [05:25<00:00, 93.64it/s]\n","ITERATION - loss: 0.12: 100%|█████████▉| 840/844 [04:51<00:00, 93.27it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 17 Avg accuracy: 94.53 Avg loss: 0.19\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [05:26<00:00, 93.64it/s]\n","ITERATION - loss: 0.12: 100%|█████████▉| 840/844 [04:52<00:00, 93.27it/s]\u001b[A\n","ITERATION - loss: 0.17: 1%| | 10/844 [04:52<03:29, 3.99it/s] \u001b[A\n","ITERATION - loss: 0.08: 2%|▏ | 20/844 [04:52<02:27, 5.59it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 17 Avg accuracy: 94.02 Avg loss: 0.20\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.15: 4%|▎ | 30/844 [04:52<01:44, 7.78it/s]\u001b[A\n","ITERATION - loss: 0.25: 5%|▍ | 40/844 [04:53<01:15, 10.71it/s]\u001b[A\n","ITERATION - loss: 0.08: 6%|▌ | 50/844 [04:53<00:54, 14.57it/s]\u001b[A\n","ITERATION - loss: 0.15: 7%|▋ | 60/844 [04:53<00:40, 19.41it/s]\u001b[A\n","ITERATION - loss: 0.16: 8%|▊ | 70/844 [04:53<00:30, 25.27it/s]\u001b[A\n","ITERATION - loss: 0.15: 9%|▉ | 80/844 [04:53<00:23, 32.31it/s]\u001b[A\n","ITERATION - loss: 0.21: 11%|█ | 90/844 [04:53<00:18, 40.21it/s]\u001b[A\n","ITERATION - loss: 0.22: 12%|█▏ | 100/844 [04:53<00:15, 48.32it/s]\u001b[A\n","ITERATION - loss: 0.22: 13%|█▎ | 110/844 [04:53<00:13, 56.16it/s]\u001b[A\n","ITERATION - loss: 0.16: 14%|█▍ | 120/844 [04:53<00:11, 64.21it/s]\u001b[A\n","ITERATION - loss: 0.11: 15%|█▌ | 130/844 [04:53<00:09, 71.45it/s]\u001b[A\n","ITERATION - loss: 0.28: 17%|█▋ | 140/844 [04:54<00:09, 73.58it/s]\u001b[A\n","ITERATION - loss: 0.27: 18%|█▊ | 150/844 [04:54<00:08, 77.40it/s]\u001b[A\n","ITERATION - loss: 0.16: 19%|█▉ | 160/844 [04:54<00:08, 80.44it/s]\u001b[A\n","ITERATION - loss: 0.20: 20%|██ | 170/844 [04:54<00:08, 83.09it/s]\u001b[A\n","ITERATION - loss: 0.23: 21%|██▏ | 180/844 [04:54<00:07, 86.38it/s]\u001b[A\n","ITERATION - loss: 0.08: 23%|██▎ | 190/844 [04:54<00:07, 87.59it/s]\u001b[A\n","ITERATION - loss: 0.11: 24%|██▎ | 200/844 [04:54<00:07, 87.66it/s]\u001b[A\n","ITERATION - loss: 0.21: 25%|██▍ | 210/844 [04:54<00:07, 89.12it/s]\u001b[A\n","ITERATION - loss: 0.12: 26%|██▌ | 220/844 [04:54<00:06, 90.90it/s]\u001b[A\n","ITERATION - loss: 0.27: 27%|██▋ | 230/844 [04:55<00:06, 90.29it/s]\u001b[A\n","ITERATION - loss: 0.21: 28%|██▊ | 240/844 [04:55<00:06, 89.00it/s]\u001b[A\n","ITERATION - loss: 0.15: 30%|██▉ | 250/844 [04:55<00:06, 88.40it/s]\u001b[A\n","ITERATION - loss: 0.32: 31%|███ | 260/844 [04:55<00:06, 89.49it/s]\u001b[A\n","ITERATION - loss: 0.17: 32%|███▏ | 270/844 [04:55<00:06, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.12: 33%|███▎ | 280/844 [04:55<00:06, 91.08it/s]\u001b[A\n","ITERATION - loss: 0.20: 34%|███▍ | 290/844 [04:55<00:06, 90.45it/s]\u001b[A\n","ITERATION - loss: 0.09: 36%|███▌ | 300/844 [04:55<00:05, 91.71it/s]\u001b[A\n","ITERATION - loss: 0.13: 37%|███▋ | 310/844 [04:55<00:05, 92.90it/s]\u001b[A\n","ITERATION - loss: 0.10: 38%|███▊ | 320/844 [04:56<00:05, 90.48it/s]\u001b[A\n","ITERATION - loss: 0.11: 39%|███▉ | 330/844 [04:56<00:05, 92.12it/s]\u001b[A\n","ITERATION - loss: 0.22: 40%|████ | 340/844 [04:56<00:05, 91.85it/s]\u001b[A\n","ITERATION - loss: 0.24: 41%|████▏ | 350/844 [04:56<00:05, 87.59it/s]\u001b[A\n","ITERATION - loss: 0.19: 43%|████▎ | 360/844 [04:56<00:05, 89.64it/s]\u001b[A\n","ITERATION - loss: 0.12: 44%|████▍ | 370/844 [04:56<00:05, 90.93it/s]\u001b[A\n","ITERATION - loss: 0.15: 45%|████▌ | 380/844 [04:56<00:05, 89.53it/s]\u001b[A\n","ITERATION - loss: 0.12: 46%|████▌ | 390/844 [04:56<00:05, 88.96it/s]\u001b[A\n","ITERATION - loss: 0.20: 47%|████▋ | 400/844 [04:56<00:04, 90.49it/s]\u001b[A\n","ITERATION - loss: 0.21: 49%|████▊ | 410/844 [04:57<00:04, 90.39it/s]\u001b[A\n","ITERATION - loss: 0.24: 50%|████▉ | 420/844 [04:57<00:04, 91.12it/s]\u001b[A\n","ITERATION - loss: 0.43: 51%|█████ | 430/844 [04:57<00:04, 89.65it/s]\u001b[A\n","ITERATION - loss: 0.10: 52%|█████▏ | 440/844 [04:57<00:04, 88.14it/s]\u001b[A\n","ITERATION - loss: 0.18: 53%|█████▎ | 450/844 [04:57<00:04, 89.52it/s]\u001b[A\n","ITERATION - loss: 0.13: 55%|█████▍ | 460/844 [04:57<00:04, 86.12it/s]\u001b[A\n","ITERATION - loss: 0.25: 56%|█████▌ | 470/844 [04:57<00:04, 86.66it/s]\u001b[A\n","ITERATION - loss: 0.30: 57%|█████▋ | 480/844 [04:57<00:04, 88.60it/s]\u001b[A\n","ITERATION - loss: 0.28: 58%|█████▊ | 490/844 [04:58<00:03, 90.38it/s]\u001b[A\n","ITERATION - loss: 0.09: 59%|█████▉ | 500/844 [04:58<00:03, 90.43it/s]\u001b[A\n","ITERATION - loss: 0.16: 60%|██████ | 510/844 [04:58<00:03, 91.71it/s]\u001b[A\n","ITERATION - loss: 0.49: 62%|██████▏ | 520/844 [04:58<00:03, 90.25it/s]\u001b[A\n","ITERATION - loss: 0.15: 63%|██████▎ | 530/844 [04:58<00:03, 86.23it/s]\u001b[A\n","ITERATION - loss: 0.32: 64%|██████▍ | 540/844 [04:58<00:03, 88.48it/s]\u001b[A\n","ITERATION - loss: 0.13: 65%|██████▌ | 550/844 [04:58<00:03, 90.19it/s]\u001b[A\n","ITERATION - loss: 0.28: 66%|██████▋ | 560/844 [04:58<00:03, 91.16it/s]\u001b[A\n","ITERATION - loss: 0.14: 68%|██████▊ | 570/844 [04:58<00:02, 91.87it/s]\u001b[A\n","ITERATION - loss: 0.10: 69%|██████▊ | 580/844 [04:58<00:02, 93.25it/s]\u001b[A\n","ITERATION - loss: 0.10: 70%|██████▉ | 590/844 [04:59<00:02, 94.34it/s]\u001b[A\n","ITERATION - loss: 0.10: 71%|███████ | 600/844 [04:59<00:02, 93.96it/s]\u001b[A\n","ITERATION - loss: 0.23: 72%|███████▏ | 610/844 [04:59<00:02, 92.44it/s]\u001b[A\n","ITERATION - loss: 0.17: 73%|███████▎ | 620/844 [04:59<00:02, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.13: 75%|███████▍ | 630/844 [04:59<00:02, 92.43it/s]\u001b[A\n","ITERATION - loss: 0.14: 76%|███████▌ | 640/844 [04:59<00:02, 91.15it/s]\u001b[A\n","ITERATION - loss: 0.20: 77%|███████▋ | 650/844 [04:59<00:02, 91.12it/s]\u001b[A\n","ITERATION - loss: 0.15: 78%|███████▊ | 660/844 [04:59<00:02, 86.54it/s]\u001b[A\n","ITERATION - loss: 0.15: 79%|███████▉ | 670/844 [04:59<00:02, 86.13it/s]\u001b[A\n","ITERATION - loss: 0.23: 81%|████████ | 680/844 [05:00<00:01, 86.05it/s]\u001b[A\n","ITERATION - loss: 0.13: 82%|████████▏ | 690/844 [05:00<00:01, 88.18it/s]\u001b[A\n","ITERATION - loss: 0.21: 83%|████████▎ | 700/844 [05:00<00:01, 87.23it/s]\u001b[A\n","ITERATION - loss: 0.05: 84%|████████▍ | 710/844 [05:00<00:01, 87.64it/s]\u001b[A\n","ITERATION - loss: 0.22: 85%|████████▌ | 720/844 [05:00<00:01, 89.15it/s]\u001b[A\n","ITERATION - loss: 0.24: 86%|████████▋ | 730/844 [05:00<00:01, 89.65it/s]\u001b[A\n","ITERATION - loss: 0.10: 88%|████████▊ | 740/844 [05:00<00:01, 89.77it/s]\u001b[A\n","ITERATION - loss: 0.25: 89%|████████▉ | 750/844 [05:00<00:01, 91.57it/s]\u001b[A\n","ITERATION - loss: 0.16: 90%|█████████ | 760/844 [05:00<00:00, 92.17it/s]\u001b[A\n","ITERATION - loss: 0.27: 91%|█████████ | 770/844 [05:01<00:00, 93.16it/s]\u001b[A\n","ITERATION - loss: 0.21: 92%|█████████▏| 780/844 [05:01<00:00, 94.28it/s]\u001b[A\n","ITERATION - loss: 0.13: 94%|█████████▎| 790/844 [05:01<00:00, 95.24it/s]\u001b[A\n","ITERATION - loss: 0.11: 95%|█████████▍| 800/844 [05:01<00:00, 90.54it/s]\u001b[A\n","ITERATION - loss: 0.14: 96%|█████████▌| 810/844 [05:01<00:00, 92.52it/s]\u001b[A\n","ITERATION - loss: 0.22: 97%|█████████▋| 820/844 [05:01<00:00, 91.60it/s]\u001b[A\n","ITERATION - loss: 0.26: 98%|█████████▊| 830/844 [05:01<00:00, 87.84it/s]\u001b[A\n","ITERATION - loss: 0.21: 100%|█████████▉| 840/844 [05:01<00:00, 90.03it/s]\u001b[A\n","ITERATION - loss: 0.19: 850it [05:01, 92.21it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [05:42<00:00, 93.64it/s]\n","ITERATION - loss: 0.19: 850it [05:08, 92.21it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 18 Avg accuracy: 94.65 Avg loss: 0.18\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [05:43<00:00, 93.64it/s]\n","ITERATION - loss: 0.19: 850it [05:09, 92.21it/s]\u001b[A\n","ITERATION - loss: 0.19: 1%| | 10/844 [05:09<03:24, 4.07it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 18 Avg accuracy: 94.18 Avg loss: 0.20\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.13: 2%|▏ | 20/844 [05:10<02:24, 5.70it/s]\u001b[A\n","ITERATION - loss: 0.08: 4%|▎ | 30/844 [05:10<01:42, 7.93it/s]\u001b[A\n","ITERATION - loss: 0.24: 5%|▍ | 40/844 [05:10<01:13, 10.89it/s]\u001b[A\n","ITERATION - loss: 0.16: 6%|▌ | 50/844 [05:10<00:54, 14.64it/s]\u001b[A\n","ITERATION - loss: 0.29: 7%|▋ | 60/844 [05:10<00:40, 19.50it/s]\u001b[A\n","ITERATION - loss: 0.27: 8%|▊ | 70/844 [05:10<00:30, 25.09it/s]\u001b[A\n","ITERATION - loss: 0.12: 9%|▉ | 80/844 [05:10<00:23, 32.11it/s]\u001b[A\n","ITERATION - loss: 0.17: 11%|█ | 90/844 [05:10<00:18, 39.69it/s]\u001b[A\n","ITERATION - loss: 0.20: 12%|█▏ | 100/844 [05:10<00:15, 47.70it/s]\u001b[A\n","ITERATION - loss: 0.17: 13%|█▎ | 110/844 [05:11<00:13, 55.49it/s]\u001b[A\n","ITERATION - loss: 0.21: 14%|█▍ | 120/844 [05:11<00:11, 62.70it/s]\u001b[A\n","ITERATION - loss: 0.12: 15%|█▌ | 130/844 [05:11<00:10, 69.08it/s]\u001b[A\n","ITERATION - loss: 0.08: 17%|█▋ | 140/844 [05:11<00:09, 75.08it/s]\u001b[A\n","ITERATION - loss: 0.11: 18%|█▊ | 150/844 [05:11<00:08, 78.88it/s]\u001b[A\n","ITERATION - loss: 0.13: 19%|█▉ | 160/844 [05:11<00:08, 79.85it/s]\u001b[A\n","ITERATION - loss: 0.21: 20%|██ | 170/844 [05:11<00:08, 83.01it/s]\u001b[A\n","ITERATION - loss: 0.19: 21%|██▏ | 180/844 [05:11<00:07, 84.99it/s]\u001b[A\n","ITERATION - loss: 0.12: 23%|██▎ | 190/844 [05:11<00:07, 83.88it/s]\u001b[A\n","ITERATION - loss: 0.12: 24%|██▎ | 200/844 [05:12<00:07, 86.61it/s]\u001b[A\n","ITERATION - loss: 0.11: 25%|██▍ | 210/844 [05:12<00:07, 88.39it/s]\u001b[A\n","ITERATION - loss: 0.30: 26%|██▌ | 220/844 [05:12<00:06, 89.42it/s]\u001b[A\n","ITERATION - loss: 0.10: 27%|██▋ | 230/844 [05:12<00:06, 90.35it/s]\u001b[A\n","ITERATION - loss: 0.10: 28%|██▊ | 240/844 [05:12<00:06, 90.16it/s]\u001b[A\n","ITERATION - loss: 0.14: 30%|██▉ | 250/844 [05:12<00:06, 87.82it/s]\u001b[A\n","ITERATION - loss: 0.18: 31%|███ | 260/844 [05:12<00:06, 88.82it/s]\u001b[A\n","ITERATION - loss: 0.14: 32%|███▏ | 270/844 [05:12<00:06, 89.29it/s]\u001b[A\n","ITERATION - loss: 0.10: 33%|███▎ | 280/844 [05:12<00:06, 89.51it/s]\u001b[A\n","ITERATION - loss: 0.28: 34%|███▍ | 290/844 [05:13<00:06, 90.62it/s]\u001b[A\n","ITERATION - loss: 0.11: 36%|███▌ | 300/844 [05:13<00:05, 91.07it/s]\u001b[A\n","ITERATION - loss: 0.14: 37%|███▋ | 310/844 [05:13<00:05, 90.03it/s]\u001b[A\n","ITERATION - loss: 0.14: 38%|███▊ | 320/844 [05:13<00:05, 90.36it/s]\u001b[A\n","ITERATION - loss: 0.08: 39%|███▉ | 330/844 [05:13<00:05, 90.47it/s]\u001b[A\n","ITERATION - loss: 0.10: 40%|████ | 340/844 [05:13<00:05, 87.59it/s]\u001b[A\n","ITERATION - loss: 0.23: 41%|████▏ | 350/844 [05:13<00:05, 86.42it/s]\u001b[A\n","ITERATION - loss: 0.18: 43%|████▎ | 360/844 [05:13<00:05, 88.97it/s]\u001b[A\n","ITERATION - loss: 0.18: 44%|████▍ | 370/844 [05:13<00:05, 91.11it/s]\u001b[A\n","ITERATION - loss: 0.23: 45%|████▌ | 380/844 [05:14<00:05, 92.28it/s]\u001b[A\n","ITERATION - loss: 0.23: 46%|████▌ | 390/844 [05:14<00:04, 92.96it/s]\u001b[A\n","ITERATION - loss: 0.23: 47%|████▋ | 400/844 [05:14<00:04, 93.49it/s]\u001b[A\n","ITERATION - loss: 0.15: 49%|████▊ | 410/844 [05:14<00:04, 94.53it/s]\u001b[A\n","ITERATION - loss: 0.22: 50%|████▉ | 420/844 [05:14<00:04, 91.06it/s]\u001b[A\n","ITERATION - loss: 0.10: 51%|█████ | 430/844 [05:14<00:04, 89.18it/s]\u001b[A\n","ITERATION - loss: 0.06: 52%|█████▏ | 440/844 [05:14<00:04, 87.53it/s]\u001b[A\n","ITERATION - loss: 0.25: 53%|█████▎ | 450/844 [05:14<00:04, 87.62it/s]\u001b[A\n","ITERATION - loss: 0.20: 55%|█████▍ | 460/844 [05:14<00:04, 89.70it/s]\u001b[A\n","ITERATION - loss: 0.20: 56%|█████▌ | 470/844 [05:15<00:04, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.30: 57%|█████▋ | 480/844 [05:15<00:03, 91.45it/s]\u001b[A\n","ITERATION - loss: 0.21: 58%|█████▊ | 490/844 [05:15<00:03, 92.30it/s]\u001b[A\n","ITERATION - loss: 0.34: 59%|█████▉ | 500/844 [05:15<00:03, 91.88it/s]\u001b[A\n","ITERATION - loss: 0.12: 60%|██████ | 510/844 [05:15<00:03, 92.38it/s]\u001b[A\n","ITERATION - loss: 0.13: 62%|██████▏ | 520/844 [05:15<00:03, 92.19it/s]\u001b[A\n","ITERATION - loss: 0.07: 63%|██████▎ | 530/844 [05:15<00:03, 88.75it/s]\u001b[A\n","ITERATION - loss: 0.17: 64%|██████▍ | 540/844 [05:15<00:03, 89.13it/s]\u001b[A\n","ITERATION - loss: 0.07: 65%|██████▌ | 550/844 [05:15<00:03, 90.68it/s]\u001b[A\n","ITERATION - loss: 0.17: 66%|██████▋ | 560/844 [05:16<00:03, 90.89it/s]\u001b[A\n","ITERATION - loss: 0.25: 68%|██████▊ | 570/844 [05:16<00:03, 91.08it/s]\u001b[A\n","ITERATION - loss: 0.29: 69%|██████▊ | 580/844 [05:16<00:02, 91.66it/s]\u001b[A\n","ITERATION - loss: 0.11: 70%|██████▉ | 590/844 [05:16<00:02, 91.60it/s]\u001b[A\n","ITERATION - loss: 0.15: 71%|███████ | 600/844 [05:16<00:02, 89.35it/s]\u001b[A\n","ITERATION - loss: 0.19: 72%|███████▏ | 610/844 [05:16<00:02, 90.95it/s]\u001b[A\n","ITERATION - loss: 0.17: 73%|███████▎ | 620/844 [05:16<00:02, 89.14it/s]\u001b[A\n","ITERATION - loss: 0.14: 75%|███████▍ | 630/844 [05:16<00:02, 87.57it/s]\u001b[A\n","ITERATION - loss: 0.32: 76%|███████▌ | 640/844 [05:16<00:02, 89.32it/s]\u001b[A\n","ITERATION - loss: 0.23: 77%|███████▋ | 650/844 [05:17<00:02, 90.99it/s]\u001b[A\n","ITERATION - loss: 0.24: 78%|███████▊ | 660/844 [05:17<00:02, 91.06it/s]\u001b[A\n","ITERATION - loss: 0.22: 79%|███████▉ | 670/844 [05:17<00:01, 91.95it/s]\u001b[A\n","ITERATION - loss: 0.11: 81%|████████ | 680/844 [05:17<00:01, 92.60it/s]\u001b[A\n","ITERATION - loss: 0.21: 82%|████████▏ | 690/844 [05:17<00:01, 92.66it/s]\u001b[A\n","ITERATION - loss: 0.18: 83%|████████▎ | 700/844 [05:17<00:01, 91.38it/s]\u001b[A\n","ITERATION - loss: 0.32: 84%|████████▍ | 710/844 [05:17<00:01, 90.40it/s]\u001b[A\n","ITERATION - loss: 0.21: 85%|████████▌ | 720/844 [05:17<00:01, 85.23it/s]\u001b[A\n","ITERATION - loss: 0.23: 86%|████████▋ | 730/844 [05:17<00:01, 87.72it/s]\u001b[A\n","ITERATION - loss: 0.14: 88%|████████▊ | 740/844 [05:18<00:01, 89.80it/s]\u001b[A\n","ITERATION - loss: 0.24: 89%|████████▉ | 750/844 [05:18<00:01, 90.95it/s]\u001b[A\n","ITERATION - loss: 0.18: 90%|█████████ | 760/844 [05:18<00:00, 91.22it/s]\u001b[A\n","ITERATION - loss: 0.20: 91%|█████████ | 770/844 [05:18<00:00, 91.99it/s]\u001b[A\n","ITERATION - loss: 0.10: 92%|█████████▏| 780/844 [05:18<00:00, 93.04it/s]\u001b[A\n","ITERATION - loss: 0.15: 94%|█████████▎| 790/844 [05:18<00:00, 91.39it/s]\u001b[A\n","ITERATION - loss: 0.15: 95%|█████████▍| 800/844 [05:18<00:00, 91.04it/s]\u001b[A\n","ITERATION - loss: 0.34: 96%|█████████▌| 810/844 [05:18<00:00, 89.34it/s]\u001b[A\n","ITERATION - loss: 0.25: 97%|█████████▋| 820/844 [05:18<00:00, 87.66it/s]\u001b[A\n","ITERATION - loss: 0.29: 98%|█████████▊| 830/844 [05:19<00:00, 89.41it/s]\u001b[A\n","ITERATION - loss: 0.14: 100%|█████████▉| 840/844 [05:19<00:00, 91.16it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [05:59<00:00, 93.64it/s]\n","ITERATION - loss: 0.14: 100%|█████████▉| 840/844 [05:26<00:00, 91.16it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 19 Avg accuracy: 94.77 Avg loss: 0.18\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [06:00<00:00, 93.64it/s]\n","ITERATION - loss: 0.14: 100%|█████████▉| 840/844 [05:27<00:00, 91.16it/s]\u001b[A\n","ITERATION - loss: 0.11: 1%| | 10/844 [05:27<03:26, 4.04it/s] \u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 19 Avg accuracy: 94.38 Avg loss: 0.19\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.18: 2%|▏ | 20/844 [05:27<02:25, 5.66it/s]\u001b[A\n","ITERATION - loss: 0.24: 4%|▎ | 30/844 [05:27<01:43, 7.88it/s]\u001b[A\n","ITERATION - loss: 0.26: 5%|▍ | 40/844 [05:27<01:14, 10.84it/s]\u001b[A\n","ITERATION - loss: 0.32: 6%|▌ | 50/844 [05:27<00:53, 14.71it/s]\u001b[A\n","ITERATION - loss: 0.16: 7%|▋ | 60/844 [05:27<00:39, 19.63it/s]\u001b[A\n","ITERATION - loss: 0.20: 8%|▊ | 70/844 [05:27<00:30, 25.29it/s]\u001b[A\n","ITERATION - loss: 0.15: 9%|▉ | 80/844 [05:27<00:23, 32.10it/s]\u001b[A\n","ITERATION - loss: 0.10: 11%|█ | 90/844 [05:28<00:19, 39.47it/s]\u001b[A\n","ITERATION - loss: 0.14: 12%|█▏ | 100/844 [05:28<00:15, 47.66it/s]\u001b[A\n","ITERATION - loss: 0.21: 13%|█▎ | 110/844 [05:28<00:13, 55.82it/s]\u001b[A\n","ITERATION - loss: 0.09: 14%|█▍ | 120/844 [05:28<00:11, 62.76it/s]\u001b[A\n","ITERATION - loss: 0.19: 15%|█▌ | 130/844 [05:28<00:10, 69.41it/s]\u001b[A\n","ITERATION - loss: 0.36: 17%|█▋ | 140/844 [05:28<00:09, 75.24it/s]\u001b[A\n","ITERATION - loss: 0.29: 18%|█▊ | 150/844 [05:28<00:08, 79.47it/s]\u001b[A\n","ITERATION - loss: 0.16: 19%|█▉ | 160/844 [05:28<00:08, 83.74it/s]\u001b[A\n","ITERATION - loss: 0.09: 20%|██ | 170/844 [05:28<00:07, 85.44it/s]\u001b[A\n","ITERATION - loss: 0.16: 21%|██▏ | 180/844 [05:29<00:07, 85.29it/s]\u001b[A\n","ITERATION - loss: 0.20: 23%|██▎ | 190/844 [05:29<00:07, 86.64it/s]\u001b[A\n","ITERATION - loss: 0.19: 24%|██▎ | 200/844 [05:29<00:07, 87.35it/s]\u001b[A\n","ITERATION - loss: 0.11: 25%|██▍ | 210/844 [05:29<00:07, 86.52it/s]\u001b[A\n","ITERATION - loss: 0.16: 26%|██▌ | 220/844 [05:29<00:07, 88.33it/s]\u001b[A\n","ITERATION - loss: 0.22: 27%|██▋ | 230/844 [05:29<00:06, 89.32it/s]\u001b[A\n","ITERATION - loss: 0.21: 28%|██▊ | 240/844 [05:29<00:06, 91.23it/s]\u001b[A\n","ITERATION - loss: 0.15: 30%|██▉ | 250/844 [05:29<00:06, 92.84it/s]\u001b[A\n","ITERATION - loss: 0.23: 31%|███ | 260/844 [05:29<00:06, 91.96it/s]\u001b[A\n","ITERATION - loss: 0.17: 32%|███▏ | 270/844 [05:30<00:06, 92.64it/s]\u001b[A\n","ITERATION - loss: 0.17: 33%|███▎ | 280/844 [05:30<00:06, 91.81it/s]\u001b[A\n","ITERATION - loss: 0.14: 34%|███▍ | 290/844 [05:30<00:05, 92.47it/s]\u001b[A\n","ITERATION - loss: 0.20: 36%|███▌ | 300/844 [05:30<00:05, 93.10it/s]\u001b[A\n","ITERATION - loss: 0.25: 37%|███▋ | 310/844 [05:30<00:05, 93.78it/s]\u001b[A\n","ITERATION - loss: 0.31: 38%|███▊ | 320/844 [05:30<00:05, 94.11it/s]\u001b[A\n","ITERATION - loss: 0.16: 39%|███▉ | 330/844 [05:30<00:05, 94.73it/s]\u001b[A\n","ITERATION - loss: 0.09: 40%|████ | 340/844 [05:30<00:05, 93.17it/s]\u001b[A\n","ITERATION - loss: 0.14: 41%|████▏ | 350/844 [05:30<00:05, 91.17it/s]\u001b[A\n","ITERATION - loss: 0.08: 43%|████▎ | 360/844 [05:31<00:05, 86.93it/s]\u001b[A\n","ITERATION - loss: 0.09: 44%|████▍ | 370/844 [05:31<00:05, 86.71it/s]\u001b[A\n","ITERATION - loss: 0.34: 45%|████▌ | 380/844 [05:31<00:05, 89.48it/s]\u001b[A\n","ITERATION - loss: 0.15: 46%|████▌ | 390/844 [05:31<00:04, 90.85it/s]\u001b[A\n","ITERATION - loss: 0.22: 47%|████▋ | 400/844 [05:31<00:04, 92.76it/s]\u001b[A\n","ITERATION - loss: 0.20: 49%|████▊ | 410/844 [05:31<00:04, 93.16it/s]\u001b[A\n","ITERATION - loss: 0.26: 50%|████▉ | 420/844 [05:31<00:04, 90.19it/s]\u001b[A\n","ITERATION - loss: 0.12: 51%|█████ | 430/844 [05:31<00:04, 90.82it/s]\u001b[A\n","ITERATION - loss: 0.11: 52%|█████▏ | 440/844 [05:31<00:04, 88.60it/s]\u001b[A\n","ITERATION - loss: 0.30: 53%|█████▎ | 450/844 [05:32<00:04, 88.41it/s]\u001b[A\n","ITERATION - loss: 0.19: 55%|█████▍ | 460/844 [05:32<00:04, 86.62it/s]\u001b[A\n","ITERATION - loss: 0.19: 56%|█████▌ | 470/844 [05:32<00:04, 89.01it/s]\u001b[A\n","ITERATION - loss: 0.10: 57%|█████▋ | 480/844 [05:32<00:04, 90.19it/s]\u001b[A\n","ITERATION - loss: 0.17: 58%|█████▊ | 490/844 [05:32<00:03, 90.33it/s]\u001b[A\n","ITERATION - loss: 0.13: 59%|█████▉ | 500/844 [05:32<00:03, 91.21it/s]\u001b[A\n","ITERATION - loss: 0.12: 60%|██████ | 510/844 [05:32<00:03, 87.39it/s]\u001b[A\n","ITERATION - loss: 0.27: 62%|██████▏ | 520/844 [05:32<00:03, 86.21it/s]\u001b[A\n","ITERATION - loss: 0.11: 63%|██████▎ | 530/844 [05:32<00:03, 86.31it/s]\u001b[A\n","ITERATION - loss: 0.22: 64%|██████▍ | 540/844 [05:33<00:03, 87.50it/s]\u001b[A\n","ITERATION - loss: 0.13: 65%|██████▌ | 550/844 [05:33<00:03, 87.31it/s]\u001b[A\n","ITERATION - loss: 0.11: 66%|██████▋ | 560/844 [05:33<00:03, 88.33it/s]\u001b[A\n","ITERATION - loss: 0.26: 68%|██████▊ | 570/844 [05:33<00:03, 89.29it/s]\u001b[A\n","ITERATION - loss: 0.17: 69%|██████▊ | 580/844 [05:33<00:02, 90.34it/s]\u001b[A\n","ITERATION - loss: 0.10: 70%|██████▉ | 590/844 [05:33<00:02, 90.10it/s]\u001b[A\n","ITERATION - loss: 0.50: 71%|███████ | 600/844 [05:33<00:02, 86.57it/s]\u001b[A\n","ITERATION - loss: 0.17: 72%|███████▏ | 610/844 [05:33<00:02, 86.70it/s]\u001b[A\n","ITERATION - loss: 0.14: 73%|███████▎ | 620/844 [05:33<00:02, 86.55it/s]\u001b[A\n","ITERATION - loss: 0.07: 75%|███████▍ | 630/844 [05:34<00:02, 88.64it/s]\u001b[A\n","ITERATION - loss: 0.08: 76%|███████▌ | 640/844 [05:34<00:02, 87.96it/s]\u001b[A\n","ITERATION - loss: 0.10: 77%|███████▋ | 650/844 [05:34<00:02, 89.17it/s]\u001b[A\n","ITERATION - loss: 0.26: 78%|███████▊ | 660/844 [05:34<00:02, 90.23it/s]\u001b[A\n","ITERATION - loss: 0.13: 79%|███████▉ | 670/844 [05:34<00:01, 88.56it/s]\u001b[A\n","ITERATION - loss: 0.20: 81%|████████ | 680/844 [05:34<00:01, 88.77it/s]\u001b[A\n","ITERATION - loss: 0.12: 82%|████████▏ | 690/844 [05:34<00:01, 90.55it/s]\u001b[A\n","ITERATION - loss: 0.12: 83%|████████▎ | 700/844 [05:34<00:01, 90.54it/s]\u001b[A\n","ITERATION - loss: 0.45: 84%|████████▍ | 710/844 [05:34<00:01, 89.03it/s]\u001b[A\n","ITERATION - loss: 0.19: 85%|████████▌ | 720/844 [05:35<00:01, 89.58it/s]\u001b[A\n","ITERATION - loss: 0.13: 86%|████████▋ | 730/844 [05:35<00:01, 89.59it/s]\u001b[A\n","ITERATION - loss: 0.09: 88%|████████▊ | 740/844 [05:35<00:01, 88.82it/s]\u001b[A\n","ITERATION - loss: 0.27: 89%|████████▉ | 750/844 [05:35<00:01, 90.13it/s]\u001b[A\n","ITERATION - loss: 0.25: 90%|█████████ | 760/844 [05:35<00:00, 90.73it/s]\u001b[A\n","ITERATION - loss: 0.15: 91%|█████████ | 770/844 [05:35<00:00, 90.49it/s]\u001b[A\n","ITERATION - loss: 0.10: 92%|█████████▏| 780/844 [05:35<00:00, 92.32it/s]\u001b[A\n","ITERATION - loss: 0.23: 94%|█████████▎| 790/844 [05:35<00:00, 93.12it/s]\u001b[A\n","ITERATION - loss: 0.15: 95%|█████████▍| 800/844 [05:35<00:00, 92.86it/s]\u001b[A\n","ITERATION - loss: 0.14: 96%|█████████▌| 810/844 [05:36<00:00, 92.54it/s]\u001b[A\n","ITERATION - loss: 0.17: 97%|█████████▋| 820/844 [05:36<00:00, 93.96it/s]\u001b[A\n","ITERATION - loss: 0.13: 98%|█████████▊| 830/844 [05:36<00:00, 90.53it/s]\u001b[A\n","ITERATION - loss: 0.09: 100%|█████████▉| 840/844 [05:36<00:00, 92.63it/s]\u001b[A\n","ITERATION - loss: 0.18: 850it [05:36, 94.63it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [06:17<00:00, 93.64it/s]\n","ITERATION - loss: 0.18: 850it [05:43, 94.63it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 20 Avg accuracy: 94.94 Avg loss: 0.18\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [06:17<00:00, 93.64it/s]\n","ITERATION - loss: 0.18: 850it [05:44, 94.63it/s]\u001b[A\n","ITERATION - loss: 0.15: 1%| | 10/844 [05:44<03:27, 4.02it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 20 Avg accuracy: 94.57 Avg loss: 0.19\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.25: 2%|▏ | 20/844 [05:44<02:26, 5.63it/s]\u001b[A\n","ITERATION - loss: 0.13: 4%|▎ | 30/844 [05:44<01:43, 7.84it/s]\u001b[A\n","ITERATION - loss: 0.09: 5%|▍ | 40/844 [05:44<01:14, 10.73it/s]\u001b[A\n","ITERATION - loss: 0.12: 6%|▌ | 50/844 [05:44<00:54, 14.62it/s]\u001b[A\n","ITERATION - loss: 0.31: 7%|▋ | 60/844 [05:45<00:40, 19.57it/s]\u001b[A\n","ITERATION - loss: 0.23: 8%|▊ | 70/844 [05:45<00:30, 25.44it/s]\u001b[A\n","ITERATION - loss: 0.17: 9%|▉ | 80/844 [05:45<00:23, 32.58it/s]\u001b[A\n","ITERATION - loss: 0.33: 11%|█ | 90/844 [05:45<00:18, 40.66it/s]\u001b[A\n","ITERATION - loss: 0.08: 12%|█▏ | 100/844 [05:45<00:15, 48.67it/s]\u001b[A\n","ITERATION - loss: 0.11: 13%|█▎ | 110/844 [05:45<00:13, 56.15it/s]\u001b[A\n","ITERATION - loss: 0.10: 14%|█▍ | 120/844 [05:45<00:11, 61.36it/s]\u001b[A\n","ITERATION - loss: 0.11: 15%|█▌ | 130/844 [05:45<00:10, 68.57it/s]\u001b[A\n","ITERATION - loss: 0.16: 17%|█▋ | 140/844 [05:45<00:09, 74.89it/s]\u001b[A\n","ITERATION - loss: 0.07: 18%|█▊ | 150/844 [05:46<00:08, 80.94it/s]\u001b[A\n","ITERATION - loss: 0.24: 19%|█▉ | 160/844 [05:46<00:08, 81.59it/s]\u001b[A\n","ITERATION - loss: 0.17: 20%|██ | 170/844 [05:46<00:07, 85.16it/s]\u001b[A\n","ITERATION - loss: 0.27: 21%|██▏ | 180/844 [05:46<00:07, 87.57it/s]\u001b[A\n","ITERATION - loss: 0.29: 23%|██▎ | 190/844 [05:46<00:07, 84.28it/s]\u001b[A\n","ITERATION - loss: 0.14: 24%|██▎ | 200/844 [05:46<00:07, 85.38it/s]\u001b[A\n","ITERATION - loss: 0.16: 25%|██▍ | 210/844 [05:46<00:07, 84.35it/s]\u001b[A\n","ITERATION - loss: 0.15: 26%|██▌ | 220/844 [05:46<00:07, 84.58it/s]\u001b[A\n","ITERATION - loss: 0.11: 27%|██▋ | 230/844 [05:47<00:07, 87.50it/s]\u001b[A\n","ITERATION - loss: 0.17: 28%|██▊ | 240/844 [05:47<00:06, 89.32it/s]\u001b[A\n","ITERATION - loss: 0.23: 30%|██▉ | 250/844 [05:47<00:06, 87.87it/s]\u001b[A\n","ITERATION - loss: 0.14: 31%|███ | 260/844 [05:47<00:06, 89.63it/s]\u001b[A\n","ITERATION - loss: 0.39: 32%|███▏ | 270/844 [05:47<00:06, 90.87it/s]\u001b[A\n","ITERATION - loss: 0.13: 33%|███▎ | 280/844 [05:47<00:06, 89.34it/s]\u001b[A\n","ITERATION - loss: 0.12: 34%|███▍ | 290/844 [05:47<00:06, 90.18it/s]\u001b[A\n","ITERATION - loss: 0.16: 36%|███▌ | 300/844 [05:47<00:05, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.30: 37%|███▋ | 310/844 [05:47<00:05, 92.48it/s]\u001b[A\n","ITERATION - loss: 0.13: 38%|███▊ | 320/844 [05:47<00:05, 93.14it/s]\u001b[A\n","ITERATION - loss: 0.14: 39%|███▉ | 330/844 [05:48<00:05, 94.11it/s]\u001b[A\n","ITERATION - loss: 0.23: 40%|████ | 340/844 [05:48<00:05, 92.29it/s]\u001b[A\n","ITERATION - loss: 0.18: 41%|████▏ | 350/844 [05:48<00:05, 93.21it/s]\u001b[A\n","ITERATION - loss: 0.18: 43%|████▎ | 360/844 [05:48<00:05, 94.41it/s]\u001b[A\n","ITERATION - loss: 0.23: 44%|████▍ | 370/844 [05:48<00:05, 91.98it/s]\u001b[A\n","ITERATION - loss: 0.20: 45%|████▌ | 380/844 [05:48<00:04, 93.74it/s]\u001b[A\n","ITERATION - loss: 0.16: 46%|████▌ | 390/844 [05:48<00:04, 94.97it/s]\u001b[A\n","ITERATION - loss: 0.06: 47%|████▋ | 400/844 [05:48<00:04, 91.85it/s]\u001b[A\n","ITERATION - loss: 0.19: 49%|████▊ | 410/844 [05:48<00:04, 91.95it/s]\u001b[A\n","ITERATION - loss: 0.05: 50%|████▉ | 420/844 [05:49<00:04, 93.43it/s]\u001b[A\n","ITERATION - loss: 0.14: 51%|█████ | 430/844 [05:49<00:04, 94.21it/s]\u001b[A\n","ITERATION - loss: 0.07: 52%|█████▏ | 440/844 [05:49<00:04, 92.26it/s]\u001b[A\n","ITERATION - loss: 0.12: 53%|█████▎ | 450/844 [05:49<00:04, 93.42it/s]\u001b[A\n","ITERATION - loss: 0.15: 55%|█████▍ | 460/844 [05:49<00:04, 94.39it/s]\u001b[A\n","ITERATION - loss: 0.06: 56%|█████▌ | 470/844 [05:49<00:04, 91.96it/s]\u001b[A\n","ITERATION - loss: 0.15: 57%|█████▋ | 480/844 [05:49<00:03, 92.31it/s]\u001b[A\n","ITERATION - loss: 0.16: 58%|█████▊ | 490/844 [05:49<00:03, 91.61it/s]\u001b[A\n","ITERATION - loss: 0.13: 59%|█████▉ | 500/844 [05:49<00:03, 92.42it/s]\u001b[A\n","ITERATION - loss: 0.19: 60%|██████ | 510/844 [05:50<00:03, 92.80it/s]\u001b[A\n","ITERATION - loss: 0.20: 62%|██████▏ | 520/844 [05:50<00:03, 92.98it/s]\u001b[A\n","ITERATION - loss: 0.19: 63%|██████▎ | 530/844 [05:50<00:03, 91.45it/s]\u001b[A\n","ITERATION - loss: 0.18: 64%|██████▍ | 540/844 [05:50<00:03, 91.93it/s]\u001b[A\n","ITERATION - loss: 0.23: 65%|██████▌ | 550/844 [05:50<00:03, 92.07it/s]\u001b[A\n","ITERATION - loss: 0.15: 66%|██████▋ | 560/844 [05:50<00:03, 89.80it/s]\u001b[A\n","ITERATION - loss: 0.24: 68%|██████▊ | 570/844 [05:50<00:03, 87.28it/s]\u001b[A\n","ITERATION - loss: 0.29: 69%|██████▊ | 580/844 [05:50<00:02, 88.53it/s]\u001b[A\n","ITERATION - loss: 0.11: 70%|██████▉ | 590/844 [05:50<00:02, 87.61it/s]\u001b[A\n","ITERATION - loss: 0.14: 71%|███████ | 600/844 [05:51<00:02, 88.13it/s]\u001b[A\n","ITERATION - loss: 0.13: 72%|███████▏ | 610/844 [05:51<00:02, 90.01it/s]\u001b[A\n","ITERATION - loss: 0.33: 73%|███████▎ | 620/844 [05:51<00:02, 86.63it/s]\u001b[A\n","ITERATION - loss: 0.22: 75%|███████▍ | 630/844 [05:51<00:02, 87.54it/s]\u001b[A\n","ITERATION - loss: 0.11: 76%|███████▌ | 640/844 [05:51<00:02, 89.07it/s]\u001b[A\n","ITERATION - loss: 0.27: 77%|███████▋ | 650/844 [05:51<00:02, 88.26it/s]\u001b[A\n","ITERATION - loss: 0.09: 78%|███████▊ | 660/844 [05:51<00:02, 88.79it/s]\u001b[A\n","ITERATION - loss: 0.13: 79%|███████▉ | 670/844 [05:51<00:02, 85.33it/s]\u001b[A\n","ITERATION - loss: 0.26: 81%|████████ | 680/844 [05:51<00:01, 87.33it/s]\u001b[A\n","ITERATION - loss: 0.20: 82%|████████▏ | 690/844 [05:52<00:01, 88.28it/s]\u001b[A\n","ITERATION - loss: 0.20: 83%|████████▎ | 700/844 [05:52<00:01, 90.02it/s]\u001b[A\n","ITERATION - loss: 0.08: 84%|████████▍ | 710/844 [05:52<00:01, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.15: 85%|████████▌ | 720/844 [05:52<00:01, 89.69it/s]\u001b[A\n","ITERATION - loss: 0.12: 86%|████████▋ | 730/844 [05:52<00:01, 90.87it/s]\u001b[A\n","ITERATION - loss: 0.29: 88%|████████▊ | 740/844 [05:52<00:01, 88.52it/s]\u001b[A\n","ITERATION - loss: 0.10: 89%|████████▉ | 750/844 [05:52<00:01, 89.51it/s]\u001b[A\n","ITERATION - loss: 0.27: 90%|█████████ | 760/844 [05:52<00:00, 91.36it/s]\u001b[A\n","ITERATION - loss: 0.30: 91%|█████████ | 770/844 [05:52<00:00, 89.70it/s]\u001b[A\n","ITERATION - loss: 0.16: 92%|█████████▏| 780/844 [05:53<00:00, 89.17it/s]\u001b[A\n","ITERATION - loss: 0.10: 94%|█████████▎| 790/844 [05:53<00:00, 91.53it/s]\u001b[A\n","ITERATION - loss: 0.10: 95%|█████████▍| 800/844 [05:53<00:00, 89.60it/s]\u001b[A\n","ITERATION - loss: 0.13: 96%|█████████▌| 810/844 [05:53<00:00, 91.23it/s]\u001b[A\n","ITERATION - loss: 0.14: 97%|█████████▋| 820/844 [05:53<00:00, 91.88it/s]\u001b[A\n","ITERATION - loss: 0.19: 98%|█████████▊| 830/844 [05:53<00:00, 91.72it/s]\u001b[A\n","ITERATION - loss: 0.18: 100%|█████████▉| 840/844 [05:53<00:00, 90.03it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [06:34<00:00, 93.64it/s]\n","ITERATION - loss: 0.18: 100%|█████████▉| 840/844 [06:00<00:00, 90.03it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 21 Avg accuracy: 95.07 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [06:35<00:00, 93.64it/s]\n","ITERATION - loss: 0.18: 100%|█████████▉| 840/844 [06:01<00:00, 90.03it/s]\u001b[A\n","ITERATION - loss: 0.22: 1%| | 10/844 [06:01<03:25, 4.05it/s] \u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 21 Avg accuracy: 94.73 Avg loss: 0.18\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.12: 2%|▏ | 20/844 [06:01<02:25, 5.68it/s]\u001b[A\n","ITERATION - loss: 0.30: 4%|▎ | 30/844 [06:01<01:43, 7.89it/s]\u001b[A\n","ITERATION - loss: 0.16: 5%|▍ | 40/844 [06:02<01:13, 10.87it/s]\u001b[A\n","ITERATION - loss: 0.15: 6%|▌ | 50/844 [06:02<00:53, 14.80it/s]\u001b[A\n","ITERATION - loss: 0.17: 7%|▋ | 60/844 [06:02<00:39, 19.62it/s]\u001b[A\n","ITERATION - loss: 0.10: 8%|▊ | 70/844 [06:02<00:30, 25.53it/s]\u001b[A\n","ITERATION - loss: 0.15: 9%|▉ | 80/844 [06:02<00:23, 32.58it/s]\u001b[A\n","ITERATION - loss: 0.30: 11%|█ | 90/844 [06:02<00:18, 40.43it/s]\u001b[A\n","ITERATION - loss: 0.21: 12%|█▏ | 100/844 [06:02<00:15, 47.82it/s]\u001b[A\n","ITERATION - loss: 0.12: 13%|█▎ | 110/844 [06:02<00:13, 55.77it/s]\u001b[A\n","ITERATION - loss: 0.36: 14%|█▍ | 120/844 [06:02<00:11, 62.17it/s]\u001b[A\n","ITERATION - loss: 0.22: 15%|█▌ | 130/844 [06:03<00:10, 68.71it/s]\u001b[A\n","ITERATION - loss: 0.18: 17%|█▋ | 140/844 [06:03<00:09, 74.51it/s]\u001b[A\n","ITERATION - loss: 0.08: 18%|█▊ | 150/844 [06:03<00:08, 79.30it/s]\u001b[A\n","ITERATION - loss: 0.19: 19%|█▉ | 160/844 [06:03<00:08, 82.80it/s]\u001b[A\n","ITERATION - loss: 0.12: 20%|██ | 170/844 [06:03<00:07, 84.54it/s]\u001b[A\n","ITERATION - loss: 0.07: 21%|██▏ | 180/844 [06:03<00:07, 86.68it/s]\u001b[A\n","ITERATION - loss: 0.15: 23%|██▎ | 190/844 [06:03<00:07, 87.65it/s]\u001b[A\n","ITERATION - loss: 0.17: 24%|██▎ | 200/844 [06:03<00:07, 89.29it/s]\u001b[A\n","ITERATION - loss: 0.18: 25%|██▍ | 210/844 [06:03<00:07, 87.93it/s]\u001b[A\n","ITERATION - loss: 0.10: 26%|██▌ | 220/844 [06:04<00:06, 89.22it/s]\u001b[A\n","ITERATION - loss: 0.17: 27%|██▋ | 230/844 [06:04<00:06, 89.62it/s]\u001b[A\n","ITERATION - loss: 0.18: 28%|██▊ | 240/844 [06:04<00:06, 90.60it/s]\u001b[A\n","ITERATION - loss: 0.35: 30%|██▉ | 250/844 [06:04<00:06, 91.60it/s]\u001b[A\n","ITERATION - loss: 0.10: 31%|███ | 260/844 [06:04<00:06, 90.21it/s]\u001b[A\n","ITERATION - loss: 0.13: 32%|███▏ | 270/844 [06:04<00:06, 90.84it/s]\u001b[A\n","ITERATION - loss: 0.20: 33%|███▎ | 280/844 [06:04<00:06, 91.09it/s]\u001b[A\n","ITERATION - loss: 0.09: 34%|███▍ | 290/844 [06:04<00:06, 90.10it/s]\u001b[A\n","ITERATION - loss: 0.20: 36%|███▌ | 300/844 [06:04<00:06, 87.79it/s]\u001b[A\n","ITERATION - loss: 0.45: 37%|███▋ | 310/844 [06:05<00:06, 88.55it/s]\u001b[A\n","ITERATION - loss: 0.21: 38%|███▊ | 320/844 [06:05<00:05, 90.56it/s]\u001b[A\n","ITERATION - loss: 0.20: 39%|███▉ | 330/844 [06:05<00:05, 91.32it/s]\u001b[A\n","ITERATION - loss: 0.13: 40%|████ | 340/844 [06:05<00:05, 92.82it/s]\u001b[A\n","ITERATION - loss: 0.23: 41%|████▏ | 350/844 [06:05<00:05, 92.30it/s]\u001b[A\n","ITERATION - loss: 0.10: 43%|████▎ | 360/844 [06:05<00:05, 93.38it/s]\u001b[A\n","ITERATION - loss: 0.10: 44%|████▍ | 370/844 [06:05<00:05, 91.91it/s]\u001b[A\n","ITERATION - loss: 0.28: 45%|████▌ | 380/844 [06:05<00:05, 92.07it/s]\u001b[A\n","ITERATION - loss: 0.15: 46%|████▌ | 390/844 [06:05<00:04, 91.25it/s]\u001b[A\n","ITERATION - loss: 0.15: 47%|████▋ | 400/844 [06:06<00:05, 88.72it/s]\u001b[A\n","ITERATION - loss: 0.07: 49%|████▊ | 410/844 [06:06<00:04, 89.69it/s]\u001b[A\n","ITERATION - loss: 0.16: 50%|████▉ | 420/844 [06:06<00:04, 89.42it/s]\u001b[A\n","ITERATION - loss: 0.17: 51%|█████ | 430/844 [06:06<00:04, 90.28it/s]\u001b[A\n","ITERATION - loss: 0.25: 52%|█████▏ | 440/844 [06:06<00:04, 88.80it/s]\u001b[A\n","ITERATION - loss: 0.19: 53%|█████▎ | 450/844 [06:06<00:04, 89.39it/s]\u001b[A\n","ITERATION - loss: 0.12: 55%|█████▍ | 460/844 [06:06<00:04, 88.55it/s]\u001b[A\n","ITERATION - loss: 0.30: 56%|█████▌ | 470/844 [06:06<00:04, 89.73it/s]\u001b[A\n","ITERATION - loss: 0.17: 57%|█████▋ | 480/844 [06:06<00:04, 89.18it/s]\u001b[A\n","ITERATION - loss: 0.21: 58%|█████▊ | 490/844 [06:07<00:04, 85.95it/s]\u001b[A\n","ITERATION - loss: 0.12: 59%|█████▉ | 500/844 [06:07<00:03, 87.81it/s]\u001b[A\n","ITERATION - loss: 0.08: 60%|██████ | 510/844 [06:07<00:03, 88.99it/s]\u001b[A\n","ITERATION - loss: 0.15: 62%|██████▏ | 520/844 [06:07<00:03, 90.02it/s]\u001b[A\n","ITERATION - loss: 0.11: 63%|██████▎ | 530/844 [06:07<00:03, 89.51it/s]\u001b[A\n","ITERATION - loss: 0.19: 64%|██████▍ | 540/844 [06:07<00:03, 89.55it/s]\u001b[A\n","ITERATION - loss: 0.14: 65%|██████▌ | 550/844 [06:07<00:03, 90.30it/s]\u001b[A\n","ITERATION - loss: 0.17: 66%|██████▋ | 560/844 [06:07<00:03, 91.28it/s]\u001b[A\n","ITERATION - loss: 0.14: 68%|██████▊ | 570/844 [06:07<00:03, 91.08it/s]\u001b[A\n","ITERATION - loss: 0.19: 69%|██████▊ | 580/844 [06:08<00:02, 89.75it/s]\u001b[A\n","ITERATION - loss: 0.22: 70%|██████▉ | 590/844 [06:08<00:02, 91.13it/s]\u001b[A\n","ITERATION - loss: 0.12: 71%|███████ | 600/844 [06:08<00:02, 90.71it/s]\u001b[A\n","ITERATION - loss: 0.20: 72%|███████▏ | 610/844 [06:08<00:02, 91.25it/s]\u001b[A\n","ITERATION - loss: 0.10: 73%|███████▎ | 620/844 [06:08<00:02, 92.05it/s]\u001b[A\n","ITERATION - loss: 0.07: 75%|███████▍ | 630/844 [06:08<00:02, 90.35it/s]\u001b[A\n","ITERATION - loss: 0.20: 76%|███████▌ | 640/844 [06:08<00:02, 90.96it/s]\u001b[A\n","ITERATION - loss: 0.12: 77%|███████▋ | 650/844 [06:08<00:02, 91.44it/s]\u001b[A\n","ITERATION - loss: 0.28: 78%|███████▊ | 660/844 [06:08<00:02, 91.35it/s]\u001b[A\n","ITERATION - loss: 0.17: 79%|███████▉ | 670/844 [06:09<00:01, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.09: 81%|████████ | 680/844 [06:09<00:01, 92.06it/s]\u001b[A\n","ITERATION - loss: 0.16: 82%|████████▏ | 690/844 [06:09<00:01, 90.09it/s]\u001b[A\n","ITERATION - loss: 0.13: 83%|████████▎ | 700/844 [06:09<00:01, 91.39it/s]\u001b[A\n","ITERATION - loss: 0.13: 84%|████████▍ | 710/844 [06:09<00:01, 92.64it/s]\u001b[A\n","ITERATION - loss: 0.26: 85%|████████▌ | 720/844 [06:09<00:01, 90.62it/s]\u001b[A\n","ITERATION - loss: 0.21: 86%|████████▋ | 730/844 [06:09<00:01, 91.51it/s]\u001b[A\n","ITERATION - loss: 0.22: 88%|████████▊ | 740/844 [06:09<00:01, 92.56it/s]\u001b[A\n","ITERATION - loss: 0.18: 89%|████████▉ | 750/844 [06:09<00:01, 92.07it/s]\u001b[A\n","ITERATION - loss: 0.13: 90%|█████████ | 760/844 [06:09<00:00, 92.28it/s]\u001b[A\n","ITERATION - loss: 0.18: 91%|█████████ | 770/844 [06:10<00:00, 91.19it/s]\u001b[A\n","ITERATION - loss: 0.13: 92%|█████████▏| 780/844 [06:10<00:00, 90.70it/s]\u001b[A\n","ITERATION - loss: 0.20: 94%|█████████▎| 790/844 [06:10<00:00, 91.29it/s]\u001b[A\n","ITERATION - loss: 0.14: 95%|█████████▍| 800/844 [06:10<00:00, 92.29it/s]\u001b[A\n","ITERATION - loss: 0.28: 96%|█████████▌| 810/844 [06:10<00:00, 90.39it/s]\u001b[A\n","ITERATION - loss: 0.17: 97%|█████████▋| 820/844 [06:10<00:00, 91.48it/s]\u001b[A\n","ITERATION - loss: 0.16: 98%|█████████▊| 830/844 [06:10<00:00, 92.05it/s]\u001b[A\n","ITERATION - loss: 0.29: 100%|█████████▉| 840/844 [06:10<00:00, 89.90it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [06:51<00:00, 93.64it/s]\n","ITERATION - loss: 0.29: 100%|█████████▉| 840/844 [06:17<00:00, 89.90it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 22 Avg accuracy: 95.26 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [06:52<00:00, 93.64it/s]\n","ITERATION - loss: 0.29: 100%|█████████▉| 840/844 [06:18<00:00, 89.90it/s]\u001b[A\n","ITERATION - loss: 0.19: 1%| | 10/844 [06:18<03:24, 4.08it/s] \u001b[A\n","ITERATION - loss: 0.10: 2%|▏ | 20/844 [06:18<02:23, 5.73it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 22 Avg accuracy: 94.70 Avg loss: 0.18\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.18: 4%|▎ | 30/844 [06:18<01:41, 7.98it/s]\u001b[A\n","ITERATION - loss: 0.18: 5%|▍ | 40/844 [06:19<01:13, 11.01it/s]\u001b[A\n","ITERATION - loss: 0.22: 6%|▌ | 50/844 [06:19<00:53, 14.89it/s]\u001b[A\n","ITERATION - loss: 0.10: 7%|▋ | 60/844 [06:19<00:39, 19.86it/s]\u001b[A\n","ITERATION - loss: 0.12: 8%|▊ | 70/844 [06:19<00:29, 25.98it/s]\u001b[A\n","ITERATION - loss: 0.26: 9%|▉ | 80/844 [06:19<00:22, 33.30it/s]\u001b[A\n","ITERATION - loss: 0.20: 11%|█ | 90/844 [06:19<00:18, 41.39it/s]\u001b[A\n","ITERATION - loss: 0.18: 12%|█▏ | 100/844 [06:19<00:15, 49.16it/s]\u001b[A\n","ITERATION - loss: 0.24: 13%|█▎ | 110/844 [06:19<00:12, 57.73it/s]\u001b[A\n","ITERATION - loss: 0.23: 14%|█▍ | 120/844 [06:19<00:11, 65.65it/s]\u001b[A\n","ITERATION - loss: 0.38: 15%|█▌ | 130/844 [06:20<00:09, 72.60it/s]\u001b[A\n","ITERATION - loss: 0.20: 17%|█▋ | 140/844 [06:20<00:08, 78.30it/s]\u001b[A\n","ITERATION - loss: 0.10: 18%|█▊ | 150/844 [06:20<00:08, 81.87it/s]\u001b[A\n","ITERATION - loss: 0.13: 19%|█▉ | 160/844 [06:20<00:07, 86.18it/s]\u001b[A\n","ITERATION - loss: 0.26: 20%|██ | 170/844 [06:20<00:07, 89.28it/s]\u001b[A\n","ITERATION - loss: 0.22: 21%|██▏ | 180/844 [06:20<00:07, 89.99it/s]\u001b[A\n","ITERATION - loss: 0.22: 23%|██▎ | 190/844 [06:20<00:07, 88.00it/s]\u001b[A\n","ITERATION - loss: 0.12: 24%|██▎ | 200/844 [06:20<00:07, 89.36it/s]\u001b[A\n","ITERATION - loss: 0.20: 25%|██▍ | 210/844 [06:20<00:06, 90.84it/s]\u001b[A\n","ITERATION - loss: 0.19: 26%|██▌ | 220/844 [06:21<00:06, 92.87it/s]\u001b[A\n","ITERATION - loss: 0.09: 27%|██▋ | 230/844 [06:21<00:06, 93.98it/s]\u001b[A\n","ITERATION - loss: 0.21: 28%|██▊ | 240/844 [06:21<00:06, 93.28it/s]\u001b[A\n","ITERATION - loss: 0.17: 30%|██▉ | 250/844 [06:21<00:06, 90.83it/s]\u001b[A\n","ITERATION - loss: 0.10: 31%|███ | 260/844 [06:21<00:06, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.25: 32%|███▏ | 270/844 [06:21<00:06, 90.31it/s]\u001b[A\n","ITERATION - loss: 0.07: 33%|███▎ | 280/844 [06:21<00:06, 88.52it/s]\u001b[A\n","ITERATION - loss: 0.13: 34%|███▍ | 290/844 [06:21<00:06, 90.96it/s]\u001b[A\n","ITERATION - loss: 0.12: 36%|███▌ | 300/844 [06:21<00:05, 91.78it/s]\u001b[A\n","ITERATION - loss: 0.15: 37%|███▋ | 310/844 [06:21<00:05, 93.47it/s]\u001b[A\n","ITERATION - loss: 0.35: 38%|███▊ | 320/844 [06:22<00:05, 94.48it/s]\u001b[A\n","ITERATION - loss: 0.14: 39%|███▉ | 330/844 [06:22<00:05, 95.51it/s]\u001b[A\n","ITERATION - loss: 0.10: 40%|████ | 340/844 [06:22<00:05, 92.79it/s]\u001b[A\n","ITERATION - loss: 0.18: 41%|████▏ | 350/844 [06:22<00:05, 94.18it/s]\u001b[A\n","ITERATION - loss: 0.14: 43%|████▎ | 360/844 [06:22<00:05, 94.96it/s]\u001b[A\n","ITERATION - loss: 0.12: 44%|████▍ | 370/844 [06:22<00:05, 94.35it/s]\u001b[A\n","ITERATION - loss: 0.32: 45%|████▌ | 380/844 [06:22<00:05, 91.79it/s]\u001b[A\n","ITERATION - loss: 0.14: 46%|████▌ | 390/844 [06:22<00:04, 92.01it/s]\u001b[A\n","ITERATION - loss: 0.06: 47%|████▋ | 400/844 [06:22<00:05, 88.57it/s]\u001b[A\n","ITERATION - loss: 0.29: 49%|████▊ | 410/844 [06:23<00:04, 88.71it/s]\u001b[A\n","ITERATION - loss: 0.19: 50%|████▉ | 420/844 [06:23<00:04, 89.35it/s]\u001b[A\n","ITERATION - loss: 0.11: 51%|█████ | 430/844 [06:23<00:04, 90.59it/s]\u001b[A\n","ITERATION - loss: 0.11: 52%|█████▏ | 440/844 [06:23<00:04, 86.74it/s]\u001b[A\n","ITERATION - loss: 0.13: 53%|█████▎ | 450/844 [06:23<00:04, 88.69it/s]\u001b[A\n","ITERATION - loss: 0.20: 55%|█████▍ | 460/844 [06:23<00:04, 89.41it/s]\u001b[A\n","ITERATION - loss: 0.11: 56%|█████▌ | 470/844 [06:23<00:04, 86.97it/s]\u001b[A\n","ITERATION - loss: 0.23: 57%|█████▋ | 480/844 [06:23<00:04, 89.87it/s]\u001b[A\n","ITERATION - loss: 0.15: 58%|█████▊ | 490/844 [06:23<00:03, 92.16it/s]\u001b[A\n","ITERATION - loss: 0.15: 59%|█████▉ | 500/844 [06:24<00:03, 93.04it/s]\u001b[A\n","ITERATION - loss: 0.19: 60%|██████ | 510/844 [06:24<00:03, 94.11it/s]\u001b[A\n","ITERATION - loss: 0.13: 62%|██████▏ | 520/844 [06:24<00:03, 95.41it/s]\u001b[A\n","ITERATION - loss: 0.17: 63%|██████▎ | 530/844 [06:24<00:03, 92.43it/s]\u001b[A\n","ITERATION - loss: 0.14: 64%|██████▍ | 540/844 [06:24<00:03, 93.70it/s]\u001b[A\n","ITERATION - loss: 0.13: 65%|██████▌ | 550/844 [06:24<00:03, 93.97it/s]\u001b[A\n","ITERATION - loss: 0.14: 66%|██████▋ | 560/844 [06:24<00:03, 90.33it/s]\u001b[A\n","ITERATION - loss: 0.16: 68%|██████▊ | 570/844 [06:24<00:02, 91.73it/s]\u001b[A\n","ITERATION - loss: 0.08: 69%|██████▊ | 580/844 [06:24<00:02, 93.34it/s]\u001b[A\n","ITERATION - loss: 0.06: 70%|██████▉ | 590/844 [06:25<00:02, 94.27it/s]\u001b[A\n","ITERATION - loss: 0.13: 71%|███████ | 600/844 [06:25<00:02, 91.61it/s]\u001b[A\n","ITERATION - loss: 0.21: 72%|███████▏ | 610/844 [06:25<00:02, 93.25it/s]\u001b[A\n","ITERATION - loss: 0.26: 73%|███████▎ | 620/844 [06:25<00:02, 93.84it/s]\u001b[A\n","ITERATION - loss: 0.26: 75%|███████▍ | 630/844 [06:25<00:02, 92.64it/s]\u001b[A\n","ITERATION - loss: 0.23: 76%|███████▌ | 640/844 [06:25<00:02, 93.84it/s]\u001b[A\n","ITERATION - loss: 0.16: 77%|███████▋ | 650/844 [06:25<00:02, 92.91it/s]\u001b[A\n","ITERATION - loss: 0.10: 78%|███████▊ | 660/844 [06:25<00:02, 91.38it/s]\u001b[A\n","ITERATION - loss: 0.13: 79%|███████▉ | 670/844 [06:25<00:01, 93.15it/s]\u001b[A\n","ITERATION - loss: 0.12: 81%|████████ | 680/844 [06:26<00:01, 94.49it/s]\u001b[A\n","ITERATION - loss: 0.22: 82%|████████▏ | 690/844 [06:26<00:01, 94.79it/s]\u001b[A\n","ITERATION - loss: 0.18: 83%|████████▎ | 700/844 [06:26<00:01, 96.24it/s]\u001b[A\n","ITERATION - loss: 0.13: 84%|████████▍ | 710/844 [06:26<00:01, 96.63it/s]\u001b[A\n","ITERATION - loss: 0.28: 85%|████████▌ | 720/844 [06:26<00:01, 94.79it/s]\u001b[A\n","ITERATION - loss: 0.08: 86%|████████▋ | 730/844 [06:26<00:01, 94.64it/s]\u001b[A\n","ITERATION - loss: 0.21: 88%|████████▊ | 740/844 [06:26<00:01, 93.87it/s]\u001b[A\n","ITERATION - loss: 0.08: 89%|████████▉ | 750/844 [06:26<00:01, 92.48it/s]\u001b[A\n","ITERATION - loss: 0.23: 90%|█████████ | 760/844 [06:26<00:00, 91.50it/s]\u001b[A\n","ITERATION - loss: 0.17: 91%|█████████ | 770/844 [06:26<00:00, 92.08it/s]\u001b[A\n","ITERATION - loss: 0.31: 92%|█████████▏| 780/844 [06:27<00:00, 92.52it/s]\u001b[A\n","ITERATION - loss: 0.14: 94%|█████████▎| 790/844 [06:27<00:00, 92.79it/s]\u001b[A\n","ITERATION - loss: 0.14: 95%|█████████▍| 800/844 [06:27<00:00, 93.09it/s]\u001b[A\n","ITERATION - loss: 0.19: 96%|█████████▌| 810/844 [06:27<00:00, 93.71it/s]\u001b[A\n","ITERATION - loss: 0.15: 97%|█████████▋| 820/844 [06:27<00:00, 89.73it/s]\u001b[A\n","ITERATION - loss: 0.27: 98%|█████████▊| 830/844 [06:27<00:00, 87.92it/s]\u001b[A\n","ITERATION - loss: 0.09: 100%|█████████▉| 840/844 [06:27<00:00, 89.62it/s]\u001b[A\n","ITERATION - loss: 0.10: 850it [06:27, 88.43it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [07:08<00:00, 93.64it/s]\n","ITERATION - loss: 0.10: 850it [06:34, 88.43it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 23 Avg accuracy: 95.33 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [07:09<00:00, 93.64it/s]\n","ITERATION - loss: 0.10: 850it [06:35, 88.43it/s]\u001b[A\n","ITERATION - loss: 0.10: 1%| | 10/844 [06:35<03:25, 4.06it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 23 Avg accuracy: 94.88 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.22: 2%|▏ | 20/844 [06:35<02:25, 5.68it/s]\u001b[A\n","ITERATION - loss: 0.11: 4%|▎ | 30/844 [06:36<01:43, 7.90it/s]\u001b[A\n","ITERATION - loss: 0.20: 5%|▍ | 40/844 [06:36<01:13, 10.89it/s]\u001b[A\n","ITERATION - loss: 0.16: 6%|▌ | 50/844 [06:36<00:53, 14.82it/s]\u001b[A\n","ITERATION - loss: 0.06: 7%|▋ | 60/844 [06:36<00:39, 19.81it/s]\u001b[A\n","ITERATION - loss: 0.16: 8%|▊ | 70/844 [06:36<00:29, 25.89it/s]\u001b[A\n","ITERATION - loss: 0.09: 9%|▉ | 80/844 [06:36<00:23, 32.93it/s]\u001b[A\n","ITERATION - loss: 0.10: 11%|█ | 90/844 [06:36<00:19, 39.56it/s]\u001b[A\n","ITERATION - loss: 0.15: 12%|█▏ | 100/844 [06:36<00:15, 47.85it/s]\u001b[A\n","ITERATION - loss: 0.12: 13%|█▎ | 110/844 [06:36<00:13, 55.85it/s]\u001b[A\n","ITERATION - loss: 0.36: 14%|█▍ | 120/844 [06:37<00:11, 63.90it/s]\u001b[A\n","ITERATION - loss: 0.12: 15%|█▌ | 130/844 [06:37<00:10, 70.87it/s]\u001b[A\n","ITERATION - loss: 0.18: 17%|█▋ | 140/844 [06:37<00:09, 77.20it/s]\u001b[A\n","ITERATION - loss: 0.09: 18%|█▊ | 150/844 [06:37<00:08, 81.87it/s]\u001b[A\n","ITERATION - loss: 0.09: 19%|█▉ | 160/844 [06:37<00:08, 85.23it/s]\u001b[A\n","ITERATION - loss: 0.08: 20%|██ | 170/844 [06:37<00:07, 87.99it/s]\u001b[A\n","ITERATION - loss: 0.26: 21%|██▏ | 180/844 [06:37<00:07, 85.59it/s]\u001b[A\n","ITERATION - loss: 0.11: 23%|██▎ | 190/844 [06:37<00:07, 83.62it/s]\u001b[A\n","ITERATION - loss: 0.21: 24%|██▎ | 200/844 [06:37<00:07, 85.41it/s]\u001b[A\n","ITERATION - loss: 0.13: 25%|██▍ | 210/844 [06:38<00:07, 87.81it/s]\u001b[A\n","ITERATION - loss: 0.16: 26%|██▌ | 220/844 [06:38<00:06, 89.62it/s]\u001b[A\n","ITERATION - loss: 0.16: 27%|██▋ | 230/844 [06:38<00:06, 91.73it/s]\u001b[A\n","ITERATION - loss: 0.19: 28%|██▊ | 240/844 [06:38<00:06, 93.01it/s]\u001b[A\n","ITERATION - loss: 0.28: 30%|██▉ | 250/844 [06:38<00:06, 93.06it/s]\u001b[A\n","ITERATION - loss: 0.27: 31%|███ | 260/844 [06:38<00:06, 94.03it/s]\u001b[A\n","ITERATION - loss: 0.30: 32%|███▏ | 270/844 [06:38<00:06, 91.88it/s]\u001b[A\n","ITERATION - loss: 0.17: 33%|███▎ | 280/844 [06:38<00:06, 89.07it/s]\u001b[A\n","ITERATION - loss: 0.12: 34%|███▍ | 290/844 [06:38<00:06, 91.32it/s]\u001b[A\n","ITERATION - loss: 0.11: 36%|███▌ | 300/844 [06:39<00:05, 90.73it/s]\u001b[A\n","ITERATION - loss: 0.13: 37%|███▋ | 310/844 [06:39<00:05, 92.98it/s]\u001b[A\n","ITERATION - loss: 0.17: 38%|███▊ | 320/844 [06:39<00:05, 94.44it/s]\u001b[A\n","ITERATION - loss: 0.23: 39%|███▉ | 330/844 [06:39<00:05, 94.99it/s]\u001b[A\n","ITERATION - loss: 0.13: 40%|████ | 340/844 [06:39<00:05, 95.36it/s]\u001b[A\n","ITERATION - loss: 0.16: 41%|████▏ | 350/844 [06:39<00:05, 95.66it/s]\u001b[A\n","ITERATION - loss: 0.32: 43%|████▎ | 360/844 [06:39<00:05, 94.96it/s]\u001b[A\n","ITERATION - loss: 0.22: 44%|████▍ | 370/844 [06:39<00:05, 91.69it/s]\u001b[A\n","ITERATION - loss: 0.10: 45%|████▌ | 380/844 [06:39<00:05, 91.04it/s]\u001b[A\n","ITERATION - loss: 0.18: 46%|████▌ | 390/844 [06:39<00:05, 86.99it/s]\u001b[A\n","ITERATION - loss: 0.13: 47%|████▋ | 400/844 [06:40<00:04, 88.91it/s]\u001b[A\n","ITERATION - loss: 0.20: 49%|████▊ | 410/844 [06:40<00:04, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.13: 50%|████▉ | 420/844 [06:40<00:04, 90.13it/s]\u001b[A\n","ITERATION - loss: 0.11: 51%|█████ | 430/844 [06:40<00:04, 91.20it/s]\u001b[A\n","ITERATION - loss: 0.09: 52%|█████▏ | 440/844 [06:40<00:04, 91.95it/s]\u001b[A\n","ITERATION - loss: 0.13: 53%|█████▎ | 450/844 [06:40<00:04, 91.33it/s]\u001b[A\n","ITERATION - loss: 0.18: 55%|█████▍ | 460/844 [06:40<00:04, 86.10it/s]\u001b[A\n","ITERATION - loss: 0.19: 56%|█████▌ | 470/844 [06:40<00:04, 88.38it/s]\u001b[A\n","ITERATION - loss: 0.37: 57%|█████▋ | 480/844 [06:40<00:04, 88.09it/s]\u001b[A\n","ITERATION - loss: 0.29: 58%|█████▊ | 490/844 [06:41<00:03, 88.85it/s]\u001b[A\n","ITERATION - loss: 0.11: 59%|█████▉ | 500/844 [06:41<00:03, 90.31it/s]\u001b[A\n","ITERATION - loss: 0.10: 60%|██████ | 510/844 [06:41<00:03, 88.52it/s]\u001b[A\n","ITERATION - loss: 0.10: 62%|██████▏ | 520/844 [06:41<00:03, 89.39it/s]\u001b[A\n","ITERATION - loss: 0.27: 63%|██████▎ | 530/844 [06:41<00:03, 89.60it/s]\u001b[A\n","ITERATION - loss: 0.32: 64%|██████▍ | 540/844 [06:41<00:03, 90.29it/s]\u001b[A\n","ITERATION - loss: 0.07: 65%|██████▌ | 550/844 [06:41<00:03, 86.03it/s]\u001b[A\n","ITERATION - loss: 0.18: 66%|██████▋ | 560/844 [06:41<00:03, 87.76it/s]\u001b[A\n","ITERATION - loss: 0.13: 68%|██████▊ | 570/844 [06:42<00:03, 87.44it/s]\u001b[A\n","ITERATION - loss: 0.17: 69%|██████▊ | 580/844 [06:42<00:02, 89.42it/s]\u001b[A\n","ITERATION - loss: 0.13: 70%|██████▉ | 590/844 [06:42<00:02, 90.62it/s]\u001b[A\n","ITERATION - loss: 0.11: 71%|███████ | 600/844 [06:42<00:02, 90.25it/s]\u001b[A\n","ITERATION - loss: 0.15: 72%|███████▏ | 610/844 [06:42<00:02, 90.87it/s]\u001b[A\n","ITERATION - loss: 0.17: 73%|███████▎ | 620/844 [06:42<00:02, 91.86it/s]\u001b[A\n","ITERATION - loss: 0.10: 75%|███████▍ | 630/844 [06:42<00:02, 91.37it/s]\u001b[A\n","ITERATION - loss: 0.16: 76%|███████▌ | 640/844 [06:42<00:02, 91.79it/s]\u001b[A\n","ITERATION - loss: 0.24: 77%|███████▋ | 650/844 [06:42<00:02, 90.33it/s]\u001b[A\n","ITERATION - loss: 0.20: 78%|███████▊ | 660/844 [06:42<00:02, 91.25it/s]\u001b[A\n","ITERATION - loss: 0.17: 79%|███████▉ | 670/844 [06:43<00:01, 89.84it/s]\u001b[A\n","ITERATION - loss: 0.12: 81%|████████ | 680/844 [06:43<00:01, 91.40it/s]\u001b[A\n","ITERATION - loss: 0.09: 82%|████████▏ | 690/844 [06:43<00:01, 91.95it/s]\u001b[A\n","ITERATION - loss: 0.32: 83%|████████▎ | 700/844 [06:43<00:01, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.02: 84%|████████▍ | 710/844 [06:43<00:01, 92.17it/s]\u001b[A\n","ITERATION - loss: 0.08: 85%|████████▌ | 720/844 [06:43<00:01, 91.69it/s]\u001b[A\n","ITERATION - loss: 0.19: 86%|████████▋ | 730/844 [06:43<00:01, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.12: 88%|████████▊ | 740/844 [06:43<00:01, 83.51it/s]\u001b[A\n","ITERATION - loss: 0.17: 89%|████████▉ | 750/844 [06:44<00:01, 85.80it/s]\u001b[A\n","ITERATION - loss: 0.15: 90%|█████████ | 760/844 [06:44<00:00, 86.98it/s]\u001b[A\n","ITERATION - loss: 0.13: 91%|█████████ | 770/844 [06:44<00:00, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.14: 92%|█████████▏| 780/844 [06:44<00:00, 90.23it/s]\u001b[A\n","ITERATION - loss: 0.16: 94%|█████████▎| 790/844 [06:44<00:00, 91.16it/s]\u001b[A\n","ITERATION - loss: 0.20: 95%|█████████▍| 800/844 [06:44<00:00, 92.06it/s]\u001b[A\n","ITERATION - loss: 0.15: 96%|█████████▌| 810/844 [06:44<00:00, 90.91it/s]\u001b[A\n","ITERATION - loss: 0.31: 97%|█████████▋| 820/844 [06:44<00:00, 91.35it/s]\u001b[A\n","ITERATION - loss: 0.13: 98%|█████████▊| 830/844 [06:44<00:00, 90.29it/s]\u001b[A\n","ITERATION - loss: 0.22: 100%|█████████▉| 840/844 [06:44<00:00, 91.39it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [07:25<00:00, 93.64it/s]\n","ITERATION - loss: 0.22: 100%|█████████▉| 840/844 [06:52<00:00, 91.39it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 24 Avg accuracy: 95.48 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [07:26<00:00, 93.64it/s]\n","ITERATION - loss: 0.22: 100%|█████████▉| 840/844 [06:52<00:00, 91.39it/s]\u001b[A\n","ITERATION - loss: 0.07: 1%| | 10/844 [06:52<03:25, 4.06it/s] \u001b[A\n","ITERATION - loss: 0.19: 2%|▏ | 20/844 [06:53<02:24, 5.69it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 24 Avg accuracy: 94.87 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.18: 4%|▎ | 30/844 [06:53<01:42, 7.91it/s]\u001b[A\n","ITERATION - loss: 0.11: 5%|▍ | 40/844 [06:53<01:14, 10.86it/s]\u001b[A\n","ITERATION - loss: 0.28: 6%|▌ | 50/844 [06:53<00:53, 14.78it/s]\u001b[A\n","ITERATION - loss: 0.32: 7%|▋ | 60/844 [06:53<00:39, 19.78it/s]\u001b[A\n","ITERATION - loss: 0.13: 8%|▊ | 70/844 [06:53<00:29, 25.86it/s]\u001b[A\n","ITERATION - loss: 0.12: 9%|▉ | 80/844 [06:53<00:23, 33.03it/s]\u001b[A\n","ITERATION - loss: 0.15: 11%|█ | 90/844 [06:53<00:18, 40.96it/s]\u001b[A\n","ITERATION - loss: 0.20: 12%|█▏ | 100/844 [06:53<00:15, 48.96it/s]\u001b[A\n","ITERATION - loss: 0.16: 13%|█▎ | 110/844 [06:54<00:12, 57.20it/s]\u001b[A\n","ITERATION - loss: 0.15: 14%|█▍ | 120/844 [06:54<00:11, 62.18it/s]\u001b[A\n","ITERATION - loss: 0.11: 15%|█▌ | 130/844 [06:54<00:10, 67.69it/s]\u001b[A\n","ITERATION - loss: 0.09: 17%|█▋ | 140/844 [06:54<00:09, 73.64it/s]\u001b[A\n","ITERATION - loss: 0.13: 18%|█▊ | 150/844 [06:54<00:08, 78.43it/s]\u001b[A\n","ITERATION - loss: 0.13: 19%|█▉ | 160/844 [06:54<00:08, 81.99it/s]\u001b[A\n","ITERATION - loss: 0.23: 20%|██ | 170/844 [06:54<00:08, 83.25it/s]\u001b[A\n","ITERATION - loss: 0.07: 21%|██▏ | 180/844 [06:54<00:07, 85.50it/s]\u001b[A\n","ITERATION - loss: 0.07: 23%|██▎ | 190/844 [06:54<00:07, 83.78it/s]\u001b[A\n","ITERATION - loss: 0.10: 24%|██▎ | 200/844 [06:55<00:07, 84.65it/s]\u001b[A\n","ITERATION - loss: 0.13: 25%|██▍ | 210/844 [06:55<00:07, 86.15it/s]\u001b[A\n","ITERATION - loss: 0.10: 26%|██▌ | 220/844 [06:55<00:07, 85.39it/s]\u001b[A\n","ITERATION - loss: 0.11: 27%|██▋ | 230/844 [06:55<00:07, 87.25it/s]\u001b[A\n","ITERATION - loss: 0.21: 28%|██▊ | 240/844 [06:55<00:06, 88.58it/s]\u001b[A\n","ITERATION - loss: 0.18: 30%|██▉ | 250/844 [06:55<00:06, 88.28it/s]\u001b[A\n","ITERATION - loss: 0.15: 31%|███ | 260/844 [06:55<00:06, 86.85it/s]\u001b[A\n","ITERATION - loss: 0.09: 32%|███▏ | 270/844 [06:55<00:06, 88.80it/s]\u001b[A\n","ITERATION - loss: 0.09: 33%|███▎ | 280/844 [06:55<00:06, 89.75it/s]\u001b[A\n","ITERATION - loss: 0.14: 34%|███▍ | 290/844 [06:56<00:06, 91.65it/s]\u001b[A\n","ITERATION - loss: 0.25: 36%|███▌ | 300/844 [06:56<00:06, 90.57it/s]\u001b[A\n","ITERATION - loss: 0.22: 37%|███▋ | 310/844 [06:56<00:05, 89.53it/s]\u001b[A\n","ITERATION - loss: 0.19: 38%|███▊ | 320/844 [06:56<00:05, 91.32it/s]\u001b[A\n","ITERATION - loss: 0.11: 39%|███▉ | 330/844 [06:56<00:05, 91.79it/s]\u001b[A\n","ITERATION - loss: 0.11: 40%|████ | 340/844 [06:56<00:05, 91.12it/s]\u001b[A\n","ITERATION - loss: 0.16: 41%|████▏ | 350/844 [06:56<00:05, 91.00it/s]\u001b[A\n","ITERATION - loss: 0.18: 43%|████▎ | 360/844 [06:56<00:05, 92.54it/s]\u001b[A\n","ITERATION - loss: 0.05: 44%|████▍ | 370/844 [06:56<00:05, 93.24it/s]\u001b[A\n","ITERATION - loss: 0.11: 45%|████▌ | 380/844 [06:57<00:04, 93.94it/s]\u001b[A\n","ITERATION - loss: 0.23: 46%|████▌ | 390/844 [06:57<00:04, 92.97it/s]\u001b[A\n","ITERATION - loss: 0.19: 47%|████▋ | 400/844 [06:57<00:04, 90.34it/s]\u001b[A\n","ITERATION - loss: 0.22: 49%|████▊ | 410/844 [06:57<00:04, 91.46it/s]\u001b[A\n","ITERATION - loss: 0.20: 50%|████▉ | 420/844 [06:57<00:04, 93.57it/s]\u001b[A\n","ITERATION - loss: 0.12: 51%|█████ | 430/844 [06:57<00:04, 94.55it/s]\u001b[A\n","ITERATION - loss: 0.14: 52%|█████▏ | 440/844 [06:57<00:04, 94.12it/s]\u001b[A\n","ITERATION - loss: 0.08: 53%|█████▎ | 450/844 [06:57<00:04, 91.96it/s]\u001b[A\n","ITERATION - loss: 0.26: 55%|█████▍ | 460/844 [06:57<00:04, 90.71it/s]\u001b[A\n","ITERATION - loss: 0.14: 56%|█████▌ | 470/844 [06:58<00:04, 91.16it/s]\u001b[A\n","ITERATION - loss: 0.16: 57%|█████▋ | 480/844 [06:58<00:03, 92.33it/s]\u001b[A\n","ITERATION - loss: 0.15: 58%|█████▊ | 490/844 [06:58<00:03, 89.67it/s]\u001b[A\n","ITERATION - loss: 0.11: 59%|█████▉ | 500/844 [06:58<00:03, 90.40it/s]\u001b[A\n","ITERATION - loss: 0.15: 60%|██████ | 510/844 [06:58<00:03, 91.58it/s]\u001b[A\n","ITERATION - loss: 0.30: 62%|██████▏ | 520/844 [06:58<00:03, 92.18it/s]\u001b[A\n","ITERATION - loss: 0.17: 63%|██████▎ | 530/844 [06:58<00:03, 91.88it/s]\u001b[A\n","ITERATION - loss: 0.06: 64%|██████▍ | 540/844 [06:58<00:03, 91.81it/s]\u001b[A\n","ITERATION - loss: 0.13: 65%|██████▌ | 550/844 [06:58<00:03, 92.33it/s]\u001b[A\n","ITERATION - loss: 0.13: 66%|██████▋ | 560/844 [06:59<00:03, 92.10it/s]\u001b[A\n","ITERATION - loss: 0.23: 68%|██████▊ | 570/844 [06:59<00:02, 92.35it/s]\u001b[A\n","ITERATION - loss: 0.06: 69%|██████▊ | 580/844 [06:59<00:02, 91.09it/s]\u001b[A\n","ITERATION - loss: 0.12: 70%|██████▉ | 590/844 [06:59<00:02, 87.49it/s]\u001b[A\n","ITERATION - loss: 0.08: 71%|███████ | 600/844 [06:59<00:02, 89.39it/s]\u001b[A\n","ITERATION - loss: 0.07: 72%|███████▏ | 610/844 [06:59<00:02, 91.20it/s]\u001b[A\n","ITERATION - loss: 0.21: 73%|███████▎ | 620/844 [06:59<00:02, 91.18it/s]\u001b[A\n","ITERATION - loss: 0.10: 75%|███████▍ | 630/844 [06:59<00:02, 92.29it/s]\u001b[A\n","ITERATION - loss: 0.06: 76%|███████▌ | 640/844 [06:59<00:02, 93.16it/s]\u001b[A\n","ITERATION - loss: 0.17: 77%|███████▋ | 650/844 [06:59<00:02, 93.86it/s]\u001b[A\n","ITERATION - loss: 0.24: 78%|███████▊ | 660/844 [07:00<00:01, 95.30it/s]\u001b[A\n","ITERATION - loss: 0.20: 79%|███████▉ | 670/844 [07:00<00:01, 95.12it/s]\u001b[A\n","ITERATION - loss: 0.13: 81%|████████ | 680/844 [07:00<00:01, 92.44it/s]\u001b[A\n","ITERATION - loss: 0.11: 82%|████████▏ | 690/844 [07:00<00:01, 94.12it/s]\u001b[A\n","ITERATION - loss: 0.14: 83%|████████▎ | 700/844 [07:00<00:01, 95.13it/s]\u001b[A\n","ITERATION - loss: 0.14: 84%|████████▍ | 710/844 [07:00<00:01, 95.28it/s]\u001b[A\n","ITERATION - loss: 0.17: 85%|████████▌ | 720/844 [07:00<00:01, 92.99it/s]\u001b[A\n","ITERATION - loss: 0.13: 86%|████████▋ | 730/844 [07:00<00:01, 92.75it/s]\u001b[A\n","ITERATION - loss: 0.11: 88%|████████▊ | 740/844 [07:00<00:01, 92.72it/s]\u001b[A\n","ITERATION - loss: 0.16: 89%|████████▉ | 750/844 [07:01<00:01, 92.19it/s]\u001b[A\n","ITERATION - loss: 0.10: 90%|█████████ | 760/844 [07:01<00:00, 92.81it/s]\u001b[A\n","ITERATION - loss: 0.28: 91%|█████████ | 770/844 [07:01<00:00, 89.69it/s]\u001b[A\n","ITERATION - loss: 0.26: 92%|█████████▏| 780/844 [07:01<00:00, 90.30it/s]\u001b[A\n","ITERATION - loss: 0.13: 94%|█████████▎| 790/844 [07:01<00:00, 91.30it/s]\u001b[A\n","ITERATION - loss: 0.11: 95%|█████████▍| 800/844 [07:01<00:00, 92.04it/s]\u001b[A\n","ITERATION - loss: 0.18: 96%|█████████▌| 810/844 [07:01<00:00, 89.84it/s]\u001b[A\n","ITERATION - loss: 0.19: 97%|█████████▋| 820/844 [07:01<00:00, 89.66it/s]\u001b[A\n","ITERATION - loss: 0.15: 98%|█████████▊| 830/844 [07:01<00:00, 91.11it/s]\u001b[A\n","ITERATION - loss: 0.16: 100%|█████████▉| 840/844 [07:02<00:00, 92.24it/s]\u001b[A\n","ITERATION - loss: 0.14: 850it [07:02, 93.22it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [07:42<00:00, 93.64it/s]\n","ITERATION - loss: 0.14: 850it [07:09, 93.22it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 25 Avg accuracy: 95.51 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [07:43<00:00, 93.64it/s]\n","ITERATION - loss: 0.14: 850it [07:09, 93.22it/s]\u001b[A\n","ITERATION - loss: 0.22: 1%| | 10/844 [07:10<03:25, 4.07it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 25 Avg accuracy: 95.05 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.11: 2%|▏ | 20/844 [07:10<02:24, 5.70it/s]\u001b[A\n","ITERATION - loss: 0.07: 4%|▎ | 30/844 [07:10<01:42, 7.93it/s]\u001b[A\n","ITERATION - loss: 0.31: 5%|▍ | 40/844 [07:10<01:13, 10.94it/s]\u001b[A\n","ITERATION - loss: 0.14: 6%|▌ | 50/844 [07:10<00:53, 14.79it/s]\u001b[A\n","ITERATION - loss: 0.10: 7%|▋ | 60/844 [07:10<00:39, 19.63it/s]\u001b[A\n","ITERATION - loss: 0.23: 8%|▊ | 70/844 [07:10<00:30, 25.74it/s]\u001b[A\n","ITERATION - loss: 0.20: 9%|▉ | 80/844 [07:10<00:23, 33.01it/s]\u001b[A\n","ITERATION - loss: 0.10: 11%|█ | 90/844 [07:10<00:18, 41.07it/s]\u001b[A\n","ITERATION - loss: 0.12: 12%|█▏ | 100/844 [07:11<00:14, 49.66it/s]\u001b[A\n","ITERATION - loss: 0.16: 13%|█▎ | 110/844 [07:11<00:13, 55.79it/s]\u001b[A\n","ITERATION - loss: 0.22: 14%|█▍ | 120/844 [07:11<00:11, 64.16it/s]\u001b[A\n","ITERATION - loss: 0.14: 15%|█▌ | 130/844 [07:11<00:09, 71.60it/s]\u001b[A\n","ITERATION - loss: 0.13: 17%|█▋ | 140/844 [07:11<00:09, 73.61it/s]\u001b[A\n","ITERATION - loss: 0.22: 18%|█▊ | 150/844 [07:11<00:08, 77.57it/s]\u001b[A\n","ITERATION - loss: 0.11: 19%|█▉ | 160/844 [07:11<00:08, 82.35it/s]\u001b[A\n","ITERATION - loss: 0.13: 20%|██ | 170/844 [07:11<00:07, 85.07it/s]\u001b[A\n","ITERATION - loss: 0.21: 21%|██▏ | 180/844 [07:11<00:07, 86.71it/s]\u001b[A\n","ITERATION - loss: 0.13: 23%|██▎ | 190/844 [07:12<00:07, 88.94it/s]\u001b[A\n","ITERATION - loss: 0.23: 24%|██▎ | 200/844 [07:12<00:07, 89.77it/s]\u001b[A\n","ITERATION - loss: 0.07: 25%|██▍ | 210/844 [07:12<00:06, 91.39it/s]\u001b[A\n","ITERATION - loss: 0.17: 26%|██▌ | 220/844 [07:12<00:06, 92.11it/s]\u001b[A\n","ITERATION - loss: 0.32: 27%|██▋ | 230/844 [07:12<00:06, 89.58it/s]\u001b[A\n","ITERATION - loss: 0.11: 28%|██▊ | 240/844 [07:12<00:06, 88.62it/s]\u001b[A\n","ITERATION - loss: 0.25: 30%|██▉ | 250/844 [07:12<00:06, 87.28it/s]\u001b[A\n","ITERATION - loss: 0.25: 31%|███ | 260/844 [07:12<00:06, 87.83it/s]\u001b[A\n","ITERATION - loss: 0.14: 32%|███▏ | 270/844 [07:12<00:06, 89.23it/s]\u001b[A\n","ITERATION - loss: 0.19: 33%|███▎ | 280/844 [07:13<00:06, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.09: 34%|███▍ | 290/844 [07:13<00:06, 89.86it/s]\u001b[A\n","ITERATION - loss: 0.06: 36%|███▌ | 300/844 [07:13<00:06, 90.06it/s]\u001b[A\n","ITERATION - loss: 0.05: 37%|███▋ | 310/844 [07:13<00:05, 89.70it/s]\u001b[A\n","ITERATION - loss: 0.12: 38%|███▊ | 320/844 [07:13<00:05, 88.41it/s]\u001b[A\n","ITERATION - loss: 0.15: 39%|███▉ | 330/844 [07:13<00:05, 88.79it/s]\u001b[A\n","ITERATION - loss: 0.13: 40%|████ | 340/844 [07:13<00:05, 86.59it/s]\u001b[A\n","ITERATION - loss: 0.21: 41%|████▏ | 350/844 [07:13<00:05, 88.07it/s]\u001b[A\n","ITERATION - loss: 0.14: 43%|████▎ | 360/844 [07:13<00:05, 90.34it/s]\u001b[A\n","ITERATION - loss: 0.07: 44%|████▍ | 370/844 [07:14<00:05, 90.61it/s]\u001b[A\n","ITERATION - loss: 0.12: 45%|████▌ | 380/844 [07:14<00:05, 91.41it/s]\u001b[A\n","ITERATION - loss: 0.09: 46%|████▌ | 390/844 [07:14<00:04, 92.52it/s]\u001b[A\n","ITERATION - loss: 0.29: 47%|████▋ | 400/844 [07:14<00:04, 92.49it/s]\u001b[A\n","ITERATION - loss: 0.09: 49%|████▊ | 410/844 [07:14<00:04, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.25: 50%|████▉ | 420/844 [07:14<00:04, 91.06it/s]\u001b[A\n","ITERATION - loss: 0.13: 51%|█████ | 430/844 [07:14<00:04, 90.48it/s]\u001b[A\n","ITERATION - loss: 0.18: 52%|█████▏ | 440/844 [07:14<00:04, 87.78it/s]\u001b[A\n","ITERATION - loss: 0.08: 53%|█████▎ | 450/844 [07:14<00:04, 87.09it/s]\u001b[A\n","ITERATION - loss: 0.15: 55%|█████▍ | 460/844 [07:15<00:04, 88.20it/s]\u001b[A\n","ITERATION - loss: 0.09: 56%|█████▌ | 470/844 [07:15<00:04, 90.18it/s]\u001b[A\n","ITERATION - loss: 0.18: 57%|█████▋ | 480/844 [07:15<00:03, 91.40it/s]\u001b[A\n","ITERATION - loss: 0.11: 58%|█████▊ | 490/844 [07:15<00:03, 90.35it/s]\u001b[A\n","ITERATION - loss: 0.09: 59%|█████▉ | 500/844 [07:15<00:03, 87.45it/s]\u001b[A\n","ITERATION - loss: 0.11: 60%|██████ | 510/844 [07:15<00:03, 87.78it/s]\u001b[A\n","ITERATION - loss: 0.23: 62%|██████▏ | 520/844 [07:15<00:03, 90.09it/s]\u001b[A\n","ITERATION - loss: 0.17: 63%|██████▎ | 530/844 [07:15<00:03, 91.06it/s]\u001b[A\n","ITERATION - loss: 0.13: 64%|██████▍ | 540/844 [07:15<00:03, 91.11it/s]\u001b[A\n","ITERATION - loss: 0.15: 65%|██████▌ | 550/844 [07:16<00:03, 90.91it/s]\u001b[A\n","ITERATION - loss: 0.21: 66%|██████▋ | 560/844 [07:16<00:03, 91.25it/s]\u001b[A\n","ITERATION - loss: 0.11: 68%|██████▊ | 570/844 [07:16<00:03, 90.33it/s]\u001b[A\n","ITERATION - loss: 0.15: 69%|██████▊ | 580/844 [07:16<00:02, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.29: 70%|██████▉ | 590/844 [07:16<00:02, 91.72it/s]\u001b[A\n","ITERATION - loss: 0.46: 71%|███████ | 600/844 [07:16<00:02, 85.11it/s]\u001b[A\n","ITERATION - loss: 0.18: 72%|███████▏ | 610/844 [07:16<00:02, 86.66it/s]\u001b[A\n","ITERATION - loss: 0.11: 73%|███████▎ | 620/844 [07:16<00:02, 87.99it/s]\u001b[A\n","ITERATION - loss: 0.16: 75%|███████▍ | 630/844 [07:16<00:02, 89.32it/s]\u001b[A\n","ITERATION - loss: 0.06: 76%|███████▌ | 640/844 [07:17<00:02, 88.10it/s]\u001b[A\n","ITERATION - loss: 0.23: 77%|███████▋ | 650/844 [07:17<00:02, 87.13it/s]\u001b[A\n","ITERATION - loss: 0.07: 78%|███████▊ | 660/844 [07:17<00:02, 88.06it/s]\u001b[A\n","ITERATION - loss: 0.10: 79%|███████▉ | 670/844 [07:17<00:01, 89.68it/s]\u001b[A\n","ITERATION - loss: 0.08: 81%|████████ | 680/844 [07:17<00:01, 88.94it/s]\u001b[A\n","ITERATION - loss: 0.13: 82%|████████▏ | 690/844 [07:17<00:01, 88.50it/s]\u001b[A\n","ITERATION - loss: 0.15: 83%|████████▎ | 700/844 [07:17<00:01, 89.96it/s]\u001b[A\n","ITERATION - loss: 0.11: 84%|████████▍ | 710/844 [07:17<00:01, 90.44it/s]\u001b[A\n","ITERATION - loss: 0.20: 85%|████████▌ | 720/844 [07:17<00:01, 91.58it/s]\u001b[A\n","ITERATION - loss: 0.22: 86%|████████▋ | 730/844 [07:18<00:01, 92.43it/s]\u001b[A\n","ITERATION - loss: 0.11: 88%|████████▊ | 740/844 [07:18<00:01, 92.29it/s]\u001b[A\n","ITERATION - loss: 0.17: 89%|████████▉ | 750/844 [07:18<00:01, 93.14it/s]\u001b[A\n","ITERATION - loss: 0.22: 90%|█████████ | 760/844 [07:18<00:00, 93.85it/s]\u001b[A\n","ITERATION - loss: 0.12: 91%|█████████ | 770/844 [07:18<00:00, 92.98it/s]\u001b[A\n","ITERATION - loss: 0.17: 92%|█████████▏| 780/844 [07:18<00:00, 91.51it/s]\u001b[A\n","ITERATION - loss: 0.37: 94%|█████████▎| 790/844 [07:18<00:00, 89.61it/s]\u001b[A\n","ITERATION - loss: 0.12: 95%|█████████▍| 800/844 [07:18<00:00, 88.98it/s]\u001b[A\n","ITERATION - loss: 0.19: 96%|█████████▌| 810/844 [07:18<00:00, 90.55it/s]\u001b[A\n","ITERATION - loss: 0.15: 97%|█████████▋| 820/844 [07:19<00:00, 91.56it/s]\u001b[A\n","ITERATION - loss: 0.11: 98%|█████████▊| 830/844 [07:19<00:00, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.07: 100%|█████████▉| 840/844 [07:19<00:00, 90.22it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [07:59<00:00, 93.64it/s]\n","ITERATION - loss: 0.07: 100%|█████████▉| 840/844 [07:26<00:00, 90.22it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 26 Avg accuracy: 95.57 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [08:00<00:00, 93.64it/s]\n","ITERATION - loss: 0.07: 100%|█████████▉| 840/844 [07:27<00:00, 90.22it/s]\u001b[A\n","ITERATION - loss: 0.20: 1%| | 10/844 [07:27<03:22, 4.12it/s] \u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 26 Avg accuracy: 95.07 Avg loss: 0.17\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.17: 2%|▏ | 20/844 [07:27<02:22, 5.77it/s]\u001b[A\n","ITERATION - loss: 0.17: 4%|▎ | 30/844 [07:27<01:41, 8.03it/s]\u001b[A\n","ITERATION - loss: 0.07: 5%|▍ | 40/844 [07:27<01:12, 11.04it/s]\u001b[A\n","ITERATION - loss: 0.12: 6%|▌ | 50/844 [07:27<00:52, 14.99it/s]\u001b[A\n","ITERATION - loss: 0.21: 7%|▋ | 60/844 [07:27<00:39, 19.93it/s]\u001b[A\n","ITERATION - loss: 0.22: 8%|▊ | 70/844 [07:27<00:29, 25.97it/s]\u001b[A\n","ITERATION - loss: 0.22: 9%|▉ | 80/844 [07:27<00:23, 32.98it/s]\u001b[A\n","ITERATION - loss: 0.23: 11%|█ | 90/844 [07:28<00:18, 40.59it/s]\u001b[A\n","ITERATION - loss: 0.19: 12%|█▏ | 100/844 [07:28<00:15, 48.89it/s]\u001b[A\n","ITERATION - loss: 0.23: 13%|█▎ | 110/844 [07:28<00:12, 57.01it/s]\u001b[A\n","ITERATION - loss: 0.16: 14%|█▍ | 120/844 [07:28<00:11, 64.35it/s]\u001b[A\n","ITERATION - loss: 0.15: 15%|█▌ | 130/844 [07:28<00:10, 71.12it/s]\u001b[A\n","ITERATION - loss: 0.25: 17%|█▋ | 140/844 [07:28<00:09, 76.98it/s]\u001b[A\n","ITERATION - loss: 0.08: 18%|█▊ | 150/844 [07:28<00:08, 81.77it/s]\u001b[A\n","ITERATION - loss: 0.06: 19%|█▉ | 160/844 [07:28<00:08, 83.67it/s]\u001b[A\n","ITERATION - loss: 0.08: 20%|██ | 170/844 [07:28<00:07, 84.87it/s]\u001b[A\n","ITERATION - loss: 0.11: 21%|██▏ | 180/844 [07:29<00:07, 86.00it/s]\u001b[A\n","ITERATION - loss: 0.27: 23%|██▎ | 190/844 [07:29<00:07, 88.44it/s]\u001b[A\n","ITERATION - loss: 0.14: 24%|██▎ | 200/844 [07:29<00:07, 90.67it/s]\u001b[A\n","ITERATION - loss: 0.18: 25%|██▍ | 210/844 [07:29<00:06, 92.33it/s]\u001b[A\n","ITERATION - loss: 0.10: 26%|██▌ | 220/844 [07:29<00:06, 93.85it/s]\u001b[A\n","ITERATION - loss: 0.12: 27%|██▋ | 230/844 [07:29<00:06, 94.58it/s]\u001b[A\n","ITERATION - loss: 0.08: 28%|██▊ | 240/844 [07:29<00:06, 95.09it/s]\u001b[A\n","ITERATION - loss: 0.08: 30%|██▉ | 250/844 [07:29<00:06, 93.48it/s]\u001b[A\n","ITERATION - loss: 0.13: 31%|███ | 260/844 [07:29<00:06, 93.00it/s]\u001b[A\n","ITERATION - loss: 0.13: 32%|███▏ | 270/844 [07:29<00:06, 90.75it/s]\u001b[A\n","ITERATION - loss: 0.16: 33%|███▎ | 280/844 [07:30<00:06, 91.62it/s]\u001b[A\n","ITERATION - loss: 0.14: 34%|███▍ | 290/844 [07:30<00:06, 91.47it/s]\u001b[A\n","ITERATION - loss: 0.18: 36%|███▌ | 300/844 [07:30<00:05, 92.79it/s]\u001b[A\n","ITERATION - loss: 0.35: 37%|███▋ | 310/844 [07:30<00:05, 93.90it/s]\u001b[A\n","ITERATION - loss: 0.14: 38%|███▊ | 320/844 [07:30<00:05, 93.28it/s]\u001b[A\n","ITERATION - loss: 0.13: 39%|███▉ | 330/844 [07:30<00:05, 93.14it/s]\u001b[A\n","ITERATION - loss: 0.10: 40%|████ | 340/844 [07:30<00:05, 91.08it/s]\u001b[A\n","ITERATION - loss: 0.12: 41%|████▏ | 350/844 [07:30<00:05, 87.49it/s]\u001b[A\n","ITERATION - loss: 0.15: 43%|████▎ | 360/844 [07:30<00:05, 88.93it/s]\u001b[A\n","ITERATION - loss: 0.13: 44%|████▍ | 370/844 [07:31<00:05, 91.76it/s]\u001b[A\n","ITERATION - loss: 0.22: 45%|████▌ | 380/844 [07:31<00:04, 93.47it/s]\u001b[A\n","ITERATION - loss: 0.30: 46%|████▌ | 390/844 [07:31<00:04, 91.29it/s]\u001b[A\n","ITERATION - loss: 0.27: 47%|████▋ | 400/844 [07:31<00:04, 93.10it/s]\u001b[A\n","ITERATION - loss: 0.06: 49%|████▊ | 410/844 [07:31<00:04, 93.75it/s]\u001b[A\n","ITERATION - loss: 0.17: 50%|████▉ | 420/844 [07:31<00:04, 94.71it/s]\u001b[A\n","ITERATION - loss: 0.18: 51%|█████ | 430/844 [07:31<00:04, 94.21it/s]\u001b[A\n","ITERATION - loss: 0.14: 52%|█████▏ | 440/844 [07:31<00:04, 90.91it/s]\u001b[A\n","ITERATION - loss: 0.29: 53%|█████▎ | 450/844 [07:31<00:04, 89.67it/s]\u001b[A\n","ITERATION - loss: 0.14: 55%|█████▍ | 460/844 [07:32<00:04, 86.13it/s]\u001b[A\n","ITERATION - loss: 0.12: 56%|█████▌ | 470/844 [07:32<00:04, 88.14it/s]\u001b[A\n","ITERATION - loss: 0.14: 57%|█████▋ | 480/844 [07:32<00:04, 89.81it/s]\u001b[A\n","ITERATION - loss: 0.11: 58%|█████▊ | 490/844 [07:32<00:03, 90.52it/s]\u001b[A\n","ITERATION - loss: 0.16: 59%|█████▉ | 500/844 [07:32<00:03, 90.39it/s]\u001b[A\n","ITERATION - loss: 0.14: 60%|██████ | 510/844 [07:32<00:03, 90.83it/s]\u001b[A\n","ITERATION - loss: 0.12: 62%|██████▏ | 520/844 [07:32<00:03, 90.52it/s]\u001b[A\n","ITERATION - loss: 0.17: 63%|██████▎ | 530/844 [07:32<00:03, 87.22it/s]\u001b[A\n","ITERATION - loss: 0.24: 64%|██████▍ | 540/844 [07:32<00:03, 88.49it/s]\u001b[A\n","ITERATION - loss: 0.09: 65%|██████▌ | 550/844 [07:33<00:03, 88.44it/s]\u001b[A\n","ITERATION - loss: 0.34: 66%|██████▋ | 560/844 [07:33<00:03, 89.83it/s]\u001b[A\n","ITERATION - loss: 0.17: 68%|██████▊ | 570/844 [07:33<00:03, 90.58it/s]\u001b[A\n","ITERATION - loss: 0.07: 69%|██████▊ | 580/844 [07:33<00:02, 91.76it/s]\u001b[A\n","ITERATION - loss: 0.08: 70%|██████▉ | 590/844 [07:33<00:02, 91.71it/s]\u001b[A\n","ITERATION - loss: 0.11: 71%|███████ | 600/844 [07:33<00:02, 92.44it/s]\u001b[A\n","ITERATION - loss: 0.06: 72%|███████▏ | 610/844 [07:33<00:02, 92.56it/s]\u001b[A\n","ITERATION - loss: 0.27: 73%|███████▎ | 620/844 [07:33<00:02, 90.73it/s]\u001b[A\n","ITERATION - loss: 0.28: 75%|███████▍ | 630/844 [07:33<00:02, 91.85it/s]\u001b[A\n","ITERATION - loss: 0.15: 76%|███████▌ | 640/844 [07:34<00:02, 90.46it/s]\u001b[A\n","ITERATION - loss: 0.19: 77%|███████▋ | 650/844 [07:34<00:02, 90.92it/s]\u001b[A\n","ITERATION - loss: 0.11: 78%|███████▊ | 660/844 [07:34<00:01, 92.28it/s]\u001b[A\n","ITERATION - loss: 0.06: 79%|███████▉ | 670/844 [07:34<00:01, 92.64it/s]\u001b[A\n","ITERATION - loss: 0.14: 81%|████████ | 680/844 [07:34<00:01, 92.64it/s]\u001b[A\n","ITERATION - loss: 0.16: 82%|████████▏ | 690/844 [07:34<00:01, 93.07it/s]\u001b[A\n","ITERATION - loss: 0.18: 83%|████████▎ | 700/844 [07:34<00:01, 91.39it/s]\u001b[A\n","ITERATION - loss: 0.22: 84%|████████▍ | 710/844 [07:34<00:01, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.11: 85%|████████▌ | 720/844 [07:34<00:01, 90.90it/s]\u001b[A\n","ITERATION - loss: 0.17: 86%|████████▋ | 730/844 [07:35<00:01, 88.40it/s]\u001b[A\n","ITERATION - loss: 0.09: 88%|████████▊ | 740/844 [07:35<00:01, 84.91it/s]\u001b[A\n","ITERATION - loss: 0.39: 89%|████████▉ | 750/844 [07:35<00:01, 85.48it/s]\u001b[A\n","ITERATION - loss: 0.35: 90%|█████████ | 760/844 [07:35<00:00, 88.20it/s]\u001b[A\n","ITERATION - loss: 0.12: 91%|█████████ | 770/844 [07:35<00:00, 90.84it/s]\u001b[A\n","ITERATION - loss: 0.09: 92%|█████████▏| 780/844 [07:35<00:00, 86.24it/s]\u001b[A\n","ITERATION - loss: 0.07: 94%|█████████▎| 790/844 [07:35<00:00, 87.24it/s]\u001b[A\n","ITERATION - loss: 0.40: 95%|█████████▍| 800/844 [07:35<00:00, 86.81it/s]\u001b[A\n","ITERATION - loss: 0.24: 96%|█████████▌| 810/844 [07:35<00:00, 88.93it/s]\u001b[A\n","ITERATION - loss: 0.07: 97%|█████████▋| 820/844 [07:36<00:00, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.11: 98%|█████████▊| 830/844 [07:36<00:00, 89.54it/s]\u001b[A\n","ITERATION - loss: 0.15: 100%|█████████▉| 840/844 [07:36<00:00, 91.10it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [08:16<00:00, 93.64it/s]\n","ITERATION - loss: 0.15: 100%|█████████▉| 840/844 [07:43<00:00, 91.10it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 27 Avg accuracy: 95.68 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [08:17<00:00, 93.64it/s]\n","ITERATION - loss: 0.15: 100%|█████████▉| 840/844 [07:44<00:00, 91.10it/s]\u001b[A\n","ITERATION - loss: 0.17: 1%| | 10/844 [07:44<03:22, 4.11it/s] \u001b[A\n","ITERATION - loss: 0.12: 2%|▏ | 20/844 [07:44<02:22, 5.77it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 27 Avg accuracy: 95.33 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.32: 4%|▎ | 30/844 [07:44<01:41, 8.01it/s]\u001b[A\n","ITERATION - loss: 0.15: 5%|▍ | 40/844 [07:44<01:12, 11.04it/s]\u001b[A\n","ITERATION - loss: 0.09: 6%|▌ | 50/844 [07:44<00:52, 15.02it/s]\u001b[A\n","ITERATION - loss: 0.05: 7%|▋ | 60/844 [07:44<00:39, 20.05it/s]\u001b[A\n","ITERATION - loss: 0.04: 8%|▊ | 70/844 [07:44<00:29, 26.09it/s]\u001b[A\n","ITERATION - loss: 0.26: 9%|▉ | 80/844 [07:44<00:23, 32.94it/s]\u001b[A\n","ITERATION - loss: 0.14: 11%|█ | 90/844 [07:45<00:18, 40.65it/s]\u001b[A\n","ITERATION - loss: 0.11: 12%|█▏ | 100/844 [07:45<00:15, 49.17it/s]\u001b[A\n","ITERATION - loss: 0.07: 13%|█▎ | 110/844 [07:45<00:12, 57.38it/s]\u001b[A\n","ITERATION - loss: 0.08: 14%|█▍ | 120/844 [07:45<00:11, 63.99it/s]\u001b[A\n","ITERATION - loss: 0.17: 15%|█▌ | 130/844 [07:45<00:10, 70.79it/s]\u001b[A\n","ITERATION - loss: 0.13: 17%|█▋ | 140/844 [07:45<00:09, 76.02it/s]\u001b[A\n","ITERATION - loss: 0.13: 18%|█▊ | 150/844 [07:45<00:08, 80.63it/s]\u001b[A\n","ITERATION - loss: 0.13: 19%|█▉ | 160/844 [07:45<00:08, 81.52it/s]\u001b[A\n","ITERATION - loss: 0.13: 20%|██ | 170/844 [07:45<00:08, 81.53it/s]\u001b[A\n","ITERATION - loss: 0.15: 21%|██▏ | 180/844 [07:46<00:08, 77.47it/s]\u001b[A\n","ITERATION - loss: 0.16: 23%|██▎ | 190/844 [07:46<00:08, 81.45it/s]\u001b[A\n","ITERATION - loss: 0.08: 24%|██▎ | 200/844 [07:46<00:07, 83.48it/s]\u001b[A\n","ITERATION - loss: 0.10: 25%|██▍ | 210/844 [07:46<00:07, 84.42it/s]\u001b[A\n","ITERATION - loss: 0.14: 26%|██▌ | 220/844 [07:46<00:07, 87.44it/s]\u001b[A\n","ITERATION - loss: 0.25: 27%|██▋ | 230/844 [07:46<00:06, 88.63it/s]\u001b[A\n","ITERATION - loss: 0.15: 28%|██▊ | 240/844 [07:46<00:06, 87.30it/s]\u001b[A\n","ITERATION - loss: 0.19: 30%|██▉ | 250/844 [07:46<00:06, 87.82it/s]\u001b[A\n","ITERATION - loss: 0.18: 31%|███ | 260/844 [07:46<00:06, 88.99it/s]\u001b[A\n","ITERATION - loss: 0.14: 32%|███▏ | 270/844 [07:47<00:06, 88.50it/s]\u001b[A\n","ITERATION - loss: 0.23: 33%|███▎ | 280/844 [07:47<00:06, 90.19it/s]\u001b[A\n","ITERATION - loss: 0.23: 34%|███▍ | 290/844 [07:47<00:06, 90.14it/s]\u001b[A\n","ITERATION - loss: 0.08: 36%|███▌ | 300/844 [07:47<00:06, 88.61it/s]\u001b[A\n","ITERATION - loss: 0.29: 37%|███▋ | 310/844 [07:47<00:05, 90.22it/s]\u001b[A\n","ITERATION - loss: 0.14: 38%|███▊ | 320/844 [07:47<00:05, 91.76it/s]\u001b[A\n","ITERATION - loss: 0.11: 39%|███▉ | 330/844 [07:47<00:05, 91.51it/s]\u001b[A\n","ITERATION - loss: 0.10: 40%|████ | 340/844 [07:47<00:05, 92.44it/s]\u001b[A\n","ITERATION - loss: 0.12: 41%|████▏ | 350/844 [07:47<00:05, 93.17it/s]\u001b[A\n","ITERATION - loss: 0.09: 43%|████▎ | 360/844 [07:48<00:05, 91.76it/s]\u001b[A\n","ITERATION - loss: 0.14: 44%|████▍ | 370/844 [07:48<00:05, 90.20it/s]\u001b[A\n","ITERATION - loss: 0.11: 45%|████▌ | 380/844 [07:48<00:05, 91.61it/s]\u001b[A\n","ITERATION - loss: 0.12: 46%|████▌ | 390/844 [07:48<00:05, 90.77it/s]\u001b[A\n","ITERATION - loss: 0.08: 47%|████▋ | 400/844 [07:48<00:04, 91.33it/s]\u001b[A\n","ITERATION - loss: 0.17: 49%|████▊ | 410/844 [07:48<00:04, 91.65it/s]\u001b[A\n","ITERATION - loss: 0.09: 50%|████▉ | 420/844 [07:48<00:04, 90.01it/s]\u001b[A\n","ITERATION - loss: 0.07: 51%|█████ | 430/844 [07:48<00:04, 91.07it/s]\u001b[A\n","ITERATION - loss: 0.17: 52%|█████▏ | 440/844 [07:48<00:04, 92.93it/s]\u001b[A\n","ITERATION - loss: 0.14: 53%|█████▎ | 450/844 [07:49<00:04, 94.63it/s]\u001b[A\n","ITERATION - loss: 0.13: 55%|█████▍ | 460/844 [07:49<00:04, 92.99it/s]\u001b[A\n","ITERATION - loss: 0.11: 56%|█████▌ | 470/844 [07:49<00:03, 94.93it/s]\u001b[A\n","ITERATION - loss: 0.08: 57%|█████▋ | 480/844 [07:49<00:03, 95.54it/s]\u001b[A\n","ITERATION - loss: 0.15: 58%|█████▊ | 490/844 [07:49<00:03, 93.47it/s]\u001b[A\n","ITERATION - loss: 0.20: 59%|█████▉ | 500/844 [07:49<00:03, 95.05it/s]\u001b[A\n","ITERATION - loss: 0.15: 60%|██████ | 510/844 [07:49<00:03, 96.32it/s]\u001b[A\n","ITERATION - loss: 0.08: 62%|██████▏ | 520/844 [07:49<00:03, 96.75it/s]\u001b[A\n","ITERATION - loss: 0.05: 63%|██████▎ | 530/844 [07:49<00:03, 96.03it/s]\u001b[A\n","ITERATION - loss: 0.14: 64%|██████▍ | 540/844 [07:49<00:03, 96.09it/s]\u001b[A\n","ITERATION - loss: 0.19: 65%|██████▌ | 550/844 [07:50<00:03, 93.94it/s]\u001b[A\n","ITERATION - loss: 0.11: 66%|██████▋ | 560/844 [07:50<00:03, 90.61it/s]\u001b[A\n","ITERATION - loss: 0.06: 68%|██████▊ | 570/844 [07:50<00:02, 91.59it/s]\u001b[A\n","ITERATION - loss: 0.07: 69%|██████▊ | 580/844 [07:50<00:02, 92.42it/s]\u001b[A\n","ITERATION - loss: 0.08: 70%|██████▉ | 590/844 [07:50<00:02, 89.75it/s]\u001b[A\n","ITERATION - loss: 0.15: 71%|███████ | 600/844 [07:50<00:02, 91.28it/s]\u001b[A\n","ITERATION - loss: 0.14: 72%|███████▏ | 610/844 [07:50<00:02, 92.27it/s]\u001b[A\n","ITERATION - loss: 0.12: 73%|███████▎ | 620/844 [07:50<00:02, 91.90it/s]\u001b[A\n","ITERATION - loss: 0.17: 75%|███████▍ | 630/844 [07:50<00:02, 92.77it/s]\u001b[A\n","ITERATION - loss: 0.22: 76%|███████▌ | 640/844 [07:51<00:02, 93.76it/s]\u001b[A\n","ITERATION - loss: 0.13: 77%|███████▋ | 650/844 [07:51<00:02, 91.67it/s]\u001b[A\n","ITERATION - loss: 0.25: 78%|███████▊ | 660/844 [07:51<00:02, 89.86it/s]\u001b[A\n","ITERATION - loss: 0.23: 79%|███████▉ | 670/844 [07:51<00:01, 91.32it/s]\u001b[A\n","ITERATION - loss: 0.16: 81%|████████ | 680/844 [07:51<00:01, 90.06it/s]\u001b[A\n","ITERATION - loss: 0.14: 82%|████████▏ | 690/844 [07:51<00:01, 90.77it/s]\u001b[A\n","ITERATION - loss: 0.17: 83%|████████▎ | 700/844 [07:51<00:01, 92.53it/s]\u001b[A\n","ITERATION - loss: 0.07: 84%|████████▍ | 710/844 [07:51<00:01, 92.18it/s]\u001b[A\n","ITERATION - loss: 0.14: 85%|████████▌ | 720/844 [07:51<00:01, 92.73it/s]\u001b[A\n","ITERATION - loss: 0.11: 86%|████████▋ | 730/844 [07:52<00:01, 92.98it/s]\u001b[A\n","ITERATION - loss: 0.15: 88%|████████▊ | 740/844 [07:52<00:01, 89.58it/s]\u001b[A\n","ITERATION - loss: 0.13: 89%|████████▉ | 750/844 [07:52<00:01, 90.67it/s]\u001b[A\n","ITERATION - loss: 0.10: 90%|█████████ | 760/844 [07:52<00:00, 91.87it/s]\u001b[A\n","ITERATION - loss: 0.14: 91%|█████████ | 770/844 [07:52<00:00, 90.43it/s]\u001b[A\n","ITERATION - loss: 0.12: 92%|█████████▏| 780/844 [07:52<00:00, 91.60it/s]\u001b[A\n","ITERATION - loss: 0.15: 94%|█████████▎| 790/844 [07:52<00:00, 91.90it/s]\u001b[A\n","ITERATION - loss: 0.14: 95%|█████████▍| 800/844 [07:52<00:00, 92.49it/s]\u001b[A\n","ITERATION - loss: 0.16: 96%|█████████▌| 810/844 [07:52<00:00, 93.96it/s]\u001b[A\n","ITERATION - loss: 0.26: 97%|█████████▋| 820/844 [07:53<00:00, 94.75it/s]\u001b[A\n","ITERATION - loss: 0.05: 98%|█████████▊| 830/844 [07:53<00:00, 94.30it/s]\u001b[A\n","ITERATION - loss: 0.04: 100%|█████████▉| 840/844 [07:53<00:00, 93.68it/s]\u001b[A\n","ITERATION - loss: 0.23: 850it [07:53, 95.03it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [08:33<00:00, 93.64it/s]\n","ITERATION - loss: 0.23: 850it [08:00, 95.03it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 28 Avg accuracy: 95.75 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [08:34<00:00, 93.64it/s]\n","ITERATION - loss: 0.23: 850it [08:01, 95.03it/s]\u001b[A\n","ITERATION - loss: 0.15: 1%| | 10/844 [08:01<03:22, 4.11it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 28 Avg accuracy: 95.33 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.29: 2%|▏ | 20/844 [08:01<02:23, 5.76it/s]\u001b[A\n","ITERATION - loss: 0.12: 4%|▎ | 30/844 [08:01<01:41, 8.01it/s]\u001b[A\n","ITERATION - loss: 0.09: 5%|▍ | 40/844 [08:01<01:12, 11.05it/s]\u001b[A\n","ITERATION - loss: 0.13: 6%|▌ | 50/844 [08:01<00:52, 15.03it/s]\u001b[A\n","ITERATION - loss: 0.14: 7%|▋ | 60/844 [08:01<00:39, 20.00it/s]\u001b[A\n","ITERATION - loss: 0.10: 8%|▊ | 70/844 [08:01<00:29, 26.15it/s]\u001b[A\n","ITERATION - loss: 0.18: 9%|▉ | 80/844 [08:01<00:22, 33.52it/s]\u001b[A\n","ITERATION - loss: 0.19: 11%|█ | 90/844 [08:02<00:18, 41.69it/s]\u001b[A\n","ITERATION - loss: 0.10: 12%|█▏ | 100/844 [08:02<00:14, 50.28it/s]\u001b[A\n","ITERATION - loss: 0.08: 13%|█▎ | 110/844 [08:02<00:12, 57.08it/s]\u001b[A\n","ITERATION - loss: 0.15: 14%|█▍ | 120/844 [08:02<00:11, 64.89it/s]\u001b[A\n","ITERATION - loss: 0.13: 15%|█▌ | 130/844 [08:02<00:09, 71.74it/s]\u001b[A\n","ITERATION - loss: 0.21: 17%|█▋ | 140/844 [08:02<00:09, 77.19it/s]\u001b[A\n","ITERATION - loss: 0.09: 18%|█▊ | 150/844 [08:02<00:08, 81.08it/s]\u001b[A\n","ITERATION - loss: 0.05: 19%|█▉ | 160/844 [08:02<00:08, 82.85it/s]\u001b[A\n","ITERATION - loss: 0.09: 20%|██ | 170/844 [08:02<00:07, 84.49it/s]\u001b[A\n","ITERATION - loss: 0.21: 21%|██▏ | 180/844 [08:03<00:07, 85.49it/s]\u001b[A\n","ITERATION - loss: 0.09: 23%|██▎ | 190/844 [08:03<00:07, 87.00it/s]\u001b[A\n","ITERATION - loss: 0.14: 24%|██▎ | 200/844 [08:03<00:07, 88.50it/s]\u001b[A\n","ITERATION - loss: 0.18: 25%|██▍ | 210/844 [08:03<00:07, 88.79it/s]\u001b[A\n","ITERATION - loss: 0.16: 26%|██▌ | 220/844 [08:03<00:06, 89.86it/s]\u001b[A\n","ITERATION - loss: 0.22: 27%|██▋ | 230/844 [08:03<00:06, 90.13it/s]\u001b[A\n","ITERATION - loss: 0.05: 28%|██▊ | 240/844 [08:03<00:06, 91.19it/s]\u001b[A\n","ITERATION - loss: 0.14: 30%|██▉ | 250/844 [08:03<00:06, 89.56it/s]\u001b[A\n","ITERATION - loss: 0.10: 31%|███ | 260/844 [08:03<00:06, 90.30it/s]\u001b[A\n","ITERATION - loss: 0.17: 32%|███▏ | 270/844 [08:04<00:06, 91.90it/s]\u001b[A\n","ITERATION - loss: 0.17: 33%|███▎ | 280/844 [08:04<00:06, 92.78it/s]\u001b[A\n","ITERATION - loss: 0.14: 34%|███▍ | 290/844 [08:04<00:05, 93.20it/s]\u001b[A\n","ITERATION - loss: 0.09: 36%|███▌ | 300/844 [08:04<00:05, 92.36it/s]\u001b[A\n","ITERATION - loss: 0.26: 37%|███▋ | 310/844 [08:04<00:05, 92.70it/s]\u001b[A\n","ITERATION - loss: 0.09: 38%|███▊ | 320/844 [08:04<00:05, 92.69it/s]\u001b[A\n","ITERATION - loss: 0.30: 39%|███▉ | 330/844 [08:04<00:05, 93.60it/s]\u001b[A\n","ITERATION - loss: 0.05: 40%|████ | 340/844 [08:04<00:05, 92.29it/s]\u001b[A\n","ITERATION - loss: 0.11: 41%|████▏ | 350/844 [08:04<00:05, 92.90it/s]\u001b[A\n","ITERATION - loss: 0.19: 43%|████▎ | 360/844 [08:04<00:05, 94.51it/s]\u001b[A\n","ITERATION - loss: 0.14: 44%|████▍ | 370/844 [08:05<00:05, 93.00it/s]\u001b[A\n","ITERATION - loss: 0.14: 45%|████▌ | 380/844 [08:05<00:04, 93.01it/s]\u001b[A\n","ITERATION - loss: 0.17: 46%|████▌ | 390/844 [08:05<00:04, 93.11it/s]\u001b[A\n","ITERATION - loss: 0.12: 47%|████▋ | 400/844 [08:05<00:04, 93.33it/s]\u001b[A\n","ITERATION - loss: 0.10: 49%|████▊ | 410/844 [08:05<00:04, 94.23it/s]\u001b[A\n","ITERATION - loss: 0.23: 50%|████▉ | 420/844 [08:05<00:04, 93.63it/s]\u001b[A\n","ITERATION - loss: 0.21: 51%|█████ | 430/844 [08:05<00:04, 92.94it/s]\u001b[A\n","ITERATION - loss: 0.18: 52%|█████▏ | 440/844 [08:05<00:04, 91.18it/s]\u001b[A\n","ITERATION - loss: 0.09: 53%|█████▎ | 450/844 [08:05<00:04, 92.54it/s]\u001b[A\n","ITERATION - loss: 0.14: 55%|█████▍ | 460/844 [08:06<00:04, 92.14it/s]\u001b[A\n","ITERATION - loss: 0.26: 56%|█████▌ | 470/844 [08:06<00:04, 90.99it/s]\u001b[A\n","ITERATION - loss: 0.15: 57%|█████▋ | 480/844 [08:06<00:03, 92.08it/s]\u001b[A\n","ITERATION - loss: 0.18: 58%|█████▊ | 490/844 [08:06<00:03, 90.08it/s]\u001b[A\n","ITERATION - loss: 0.12: 59%|█████▉ | 500/844 [08:06<00:03, 90.18it/s]\u001b[A\n","ITERATION - loss: 0.16: 60%|██████ | 510/844 [08:06<00:03, 90.66it/s]\u001b[A\n","ITERATION - loss: 0.13: 62%|██████▏ | 520/844 [08:06<00:03, 86.27it/s]\u001b[A\n","ITERATION - loss: 0.13: 63%|██████▎ | 530/844 [08:06<00:03, 80.30it/s]\u001b[A\n","ITERATION - loss: 0.08: 64%|██████▍ | 540/844 [08:06<00:03, 83.37it/s]\u001b[A\n","ITERATION - loss: 0.10: 65%|██████▌ | 550/844 [08:07<00:03, 86.09it/s]\u001b[A\n","ITERATION - loss: 0.11: 66%|██████▋ | 560/844 [08:07<00:03, 88.82it/s]\u001b[A\n","ITERATION - loss: 0.16: 68%|██████▊ | 570/844 [08:07<00:03, 90.34it/s]\u001b[A\n","ITERATION - loss: 0.08: 69%|██████▊ | 580/844 [08:07<00:02, 90.16it/s]\u001b[A\n","ITERATION - loss: 0.28: 70%|██████▉ | 590/844 [08:07<00:02, 91.69it/s]\u001b[A\n","ITERATION - loss: 0.17: 71%|███████ | 600/844 [08:07<00:02, 91.56it/s]\u001b[A\n","ITERATION - loss: 0.15: 72%|███████▏ | 610/844 [08:07<00:02, 92.24it/s]\u001b[A\n","ITERATION - loss: 0.16: 73%|███████▎ | 620/844 [08:07<00:02, 90.51it/s]\u001b[A\n","ITERATION - loss: 0.12: 75%|███████▍ | 630/844 [08:07<00:02, 91.13it/s]\u001b[A\n","ITERATION - loss: 0.05: 76%|███████▌ | 640/844 [08:08<00:02, 92.67it/s]\u001b[A\n","ITERATION - loss: 0.08: 77%|███████▋ | 650/844 [08:08<00:02, 93.84it/s]\u001b[A\n","ITERATION - loss: 0.05: 78%|███████▊ | 660/844 [08:08<00:01, 93.17it/s]\u001b[A\n","ITERATION - loss: 0.14: 79%|███████▉ | 670/844 [08:08<00:01, 92.32it/s]\u001b[A\n","ITERATION - loss: 0.14: 81%|████████ | 680/844 [08:08<00:01, 93.10it/s]\u001b[A\n","ITERATION - loss: 0.16: 82%|████████▏ | 690/844 [08:08<00:01, 92.02it/s]\u001b[A\n","ITERATION - loss: 0.19: 83%|████████▎ | 700/844 [08:08<00:01, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.19: 84%|████████▍ | 710/844 [08:08<00:01, 91.64it/s]\u001b[A\n","ITERATION - loss: 0.05: 85%|████████▌ | 720/844 [08:08<00:01, 89.53it/s]\u001b[A\n","ITERATION - loss: 0.25: 86%|████████▋ | 730/844 [08:09<00:01, 91.31it/s]\u001b[A\n","ITERATION - loss: 0.08: 88%|████████▊ | 740/844 [08:09<00:01, 92.73it/s]\u001b[A\n","ITERATION - loss: 0.14: 89%|████████▉ | 750/844 [08:09<00:01, 92.34it/s]\u001b[A\n","ITERATION - loss: 0.04: 90%|█████████ | 760/844 [08:09<00:00, 91.26it/s]\u001b[A\n","ITERATION - loss: 0.17: 91%|█████████ | 770/844 [08:09<00:00, 92.39it/s]\u001b[A\n","ITERATION - loss: 0.11: 92%|█████████▏| 780/844 [08:09<00:00, 92.35it/s]\u001b[A\n","ITERATION - loss: 0.06: 94%|█████████▎| 790/844 [08:09<00:00, 93.07it/s]\u001b[A\n","ITERATION - loss: 0.14: 95%|█████████▍| 800/844 [08:09<00:00, 93.63it/s]\u001b[A\n","ITERATION - loss: 0.17: 96%|█████████▌| 810/844 [08:09<00:00, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.05: 97%|█████████▋| 820/844 [08:10<00:00, 92.20it/s]\u001b[A\n","ITERATION - loss: 0.06: 98%|█████████▊| 830/844 [08:10<00:00, 93.56it/s]\u001b[A\n","ITERATION - loss: 0.13: 100%|█████████▉| 840/844 [08:10<00:00, 94.09it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [08:50<00:00, 93.64it/s]\n","ITERATION - loss: 0.13: 100%|█████████▉| 840/844 [08:17<00:00, 94.09it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 29 Avg accuracy: 95.83 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [08:51<00:00, 93.64it/s]\n","ITERATION - loss: 0.13: 100%|█████████▉| 840/844 [08:18<00:00, 94.09it/s]\u001b[A\n","ITERATION - loss: 0.08: 1%| | 10/844 [08:18<03:25, 4.06it/s] \u001b[A\n","ITERATION - loss: 0.13: 2%|▏ | 20/844 [08:18<02:24, 5.70it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 29 Avg accuracy: 95.48 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.09: 4%|▎ | 30/844 [08:18<01:42, 7.94it/s]\u001b[A\n","ITERATION - loss: 0.17: 5%|▍ | 40/844 [08:18<01:13, 10.91it/s]\u001b[A\n","ITERATION - loss: 0.12: 6%|▌ | 50/844 [08:18<00:53, 14.84it/s]\u001b[A\n","ITERATION - loss: 0.12: 7%|▋ | 60/844 [08:18<00:39, 19.88it/s]\u001b[A\n","ITERATION - loss: 0.14: 8%|▊ | 70/844 [08:18<00:30, 25.75it/s]\u001b[A\n","ITERATION - loss: 0.16: 9%|▉ | 80/844 [08:18<00:23, 33.06it/s]\u001b[A\n","ITERATION - loss: 0.16: 11%|█ | 90/844 [08:19<00:18, 41.27it/s]\u001b[A\n","ITERATION - loss: 0.17: 12%|█▏ | 100/844 [08:19<00:15, 48.90it/s]\u001b[A\n","ITERATION - loss: 0.12: 13%|█▎ | 110/844 [08:19<00:12, 57.46it/s]\u001b[A\n","ITERATION - loss: 0.06: 14%|█▍ | 120/844 [08:19<00:11, 65.49it/s]\u001b[A\n","ITERATION - loss: 0.08: 15%|█▌ | 130/844 [08:19<00:09, 72.41it/s]\u001b[A\n","ITERATION - loss: 0.20: 17%|█▋ | 140/844 [08:19<00:09, 76.57it/s]\u001b[A\n","ITERATION - loss: 0.12: 18%|█▊ | 150/844 [08:19<00:08, 80.51it/s]\u001b[A\n","ITERATION - loss: 0.24: 19%|█▉ | 160/844 [08:19<00:08, 82.29it/s]\u001b[A\n","ITERATION - loss: 0.06: 20%|██ | 170/844 [08:19<00:07, 85.51it/s]\u001b[A\n","ITERATION - loss: 0.07: 21%|██▏ | 180/844 [08:20<00:07, 87.79it/s]\u001b[A\n","ITERATION - loss: 0.25: 23%|██▎ | 190/844 [08:20<00:07, 86.72it/s]\u001b[A\n","ITERATION - loss: 0.10: 24%|██▎ | 200/844 [08:20<00:07, 88.89it/s]\u001b[A\n","ITERATION - loss: 0.17: 25%|██▍ | 210/844 [08:20<00:07, 90.34it/s]\u001b[A\n","ITERATION - loss: 0.12: 26%|██▌ | 220/844 [08:20<00:06, 90.59it/s]\u001b[A\n","ITERATION - loss: 0.19: 27%|██▋ | 230/844 [08:20<00:06, 90.16it/s]\u001b[A\n","ITERATION - loss: 0.21: 28%|██▊ | 240/844 [08:20<00:07, 85.69it/s]\u001b[A\n","ITERATION - loss: 0.12: 30%|██▉ | 250/844 [08:20<00:07, 84.25it/s]\u001b[A\n","ITERATION - loss: 0.17: 31%|███ | 260/844 [08:20<00:06, 87.08it/s]\u001b[A\n","ITERATION - loss: 0.09: 32%|███▏ | 270/844 [08:21<00:06, 88.58it/s]\u001b[A\n","ITERATION - loss: 0.12: 33%|███▎ | 280/844 [08:21<00:06, 88.57it/s]\u001b[A\n","ITERATION - loss: 0.13: 34%|███▍ | 290/844 [08:21<00:06, 89.86it/s]\u001b[A\n","ITERATION - loss: 0.16: 36%|███▌ | 300/844 [08:21<00:06, 90.22it/s]\u001b[A\n","ITERATION - loss: 0.21: 37%|███▋ | 310/844 [08:21<00:05, 91.89it/s]\u001b[A\n","ITERATION - loss: 0.24: 38%|███▊ | 320/844 [08:21<00:05, 90.41it/s]\u001b[A\n","ITERATION - loss: 0.31: 39%|███▉ | 330/844 [08:21<00:05, 91.53it/s]\u001b[A\n","ITERATION - loss: 0.20: 40%|████ | 340/844 [08:21<00:05, 92.91it/s]\u001b[A\n","ITERATION - loss: 0.10: 41%|████▏ | 350/844 [08:21<00:05, 93.43it/s]\u001b[A\n","ITERATION - loss: 0.16: 43%|████▎ | 360/844 [08:22<00:05, 93.88it/s]\u001b[A\n","ITERATION - loss: 0.18: 44%|████▍ | 370/844 [08:22<00:05, 94.46it/s]\u001b[A\n","ITERATION - loss: 0.07: 45%|████▌ | 380/844 [08:22<00:05, 92.78it/s]\u001b[A\n","ITERATION - loss: 0.14: 46%|████▌ | 390/844 [08:22<00:04, 93.59it/s]\u001b[A\n","ITERATION - loss: 0.17: 47%|████▋ | 400/844 [08:22<00:04, 94.83it/s]\u001b[A\n","ITERATION - loss: 0.13: 49%|████▊ | 410/844 [08:22<00:04, 94.89it/s]\u001b[A\n","ITERATION - loss: 0.13: 50%|████▉ | 420/844 [08:22<00:04, 92.97it/s]\u001b[A\n","ITERATION - loss: 0.14: 51%|█████ | 430/844 [08:22<00:04, 92.59it/s]\u001b[A\n","ITERATION - loss: 0.16: 52%|█████▏ | 440/844 [08:22<00:04, 94.07it/s]\u001b[A\n","ITERATION - loss: 0.20: 53%|█████▎ | 450/844 [08:22<00:04, 92.61it/s]\u001b[A\n","ITERATION - loss: 0.06: 55%|█████▍ | 460/844 [08:23<00:04, 92.76it/s]\u001b[A\n","ITERATION - loss: 0.12: 56%|█████▌ | 470/844 [08:23<00:04, 91.42it/s]\u001b[A\n","ITERATION - loss: 0.05: 57%|█████▋ | 480/844 [08:23<00:03, 92.19it/s]\u001b[A\n","ITERATION - loss: 0.30: 58%|█████▊ | 490/844 [08:23<00:03, 93.86it/s]\u001b[A\n","ITERATION - loss: 0.14: 59%|█████▉ | 500/844 [08:23<00:03, 94.87it/s]\u001b[A\n","ITERATION - loss: 0.13: 60%|██████ | 510/844 [08:23<00:03, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.12: 62%|██████▏ | 520/844 [08:23<00:03, 91.95it/s]\u001b[A\n","ITERATION - loss: 0.09: 63%|██████▎ | 530/844 [08:23<00:03, 92.71it/s]\u001b[A\n","ITERATION - loss: 0.10: 64%|██████▍ | 540/844 [08:23<00:03, 91.94it/s]\u001b[A\n","ITERATION - loss: 0.15: 65%|██████▌ | 550/844 [08:24<00:03, 92.85it/s]\u001b[A\n","ITERATION - loss: 0.09: 66%|██████▋ | 560/844 [08:24<00:03, 93.46it/s]\u001b[A\n","ITERATION - loss: 0.13: 68%|██████▊ | 570/844 [08:24<00:03, 91.06it/s]\u001b[A\n","ITERATION - loss: 0.12: 69%|██████▊ | 580/844 [08:24<00:02, 92.53it/s]\u001b[A\n","ITERATION - loss: 0.21: 70%|██████▉ | 590/844 [08:24<00:02, 93.28it/s]\u001b[A\n","ITERATION - loss: 0.18: 71%|███████ | 600/844 [08:24<00:02, 90.88it/s]\u001b[A\n","ITERATION - loss: 0.14: 72%|███████▏ | 610/844 [08:24<00:02, 90.65it/s]\u001b[A\n","ITERATION - loss: 0.25: 73%|███████▎ | 620/844 [08:24<00:02, 91.76it/s]\u001b[A\n","ITERATION - loss: 0.20: 75%|███████▍ | 630/844 [08:24<00:02, 92.32it/s]\u001b[A\n","ITERATION - loss: 0.24: 76%|███████▌ | 640/844 [08:25<00:02, 90.18it/s]\u001b[A\n","ITERATION - loss: 0.07: 77%|███████▋ | 650/844 [08:25<00:02, 91.06it/s]\u001b[A\n","ITERATION - loss: 0.16: 78%|███████▊ | 660/844 [08:25<00:02, 89.72it/s]\u001b[A\n","ITERATION - loss: 0.14: 79%|███████▉ | 670/844 [08:25<00:01, 91.08it/s]\u001b[A\n","ITERATION - loss: 0.14: 81%|████████ | 680/844 [08:25<00:01, 92.39it/s]\u001b[A\n","ITERATION - loss: 0.05: 82%|████████▏ | 690/844 [08:25<00:01, 92.05it/s]\u001b[A\n","ITERATION - loss: 0.14: 83%|████████▎ | 700/844 [08:25<00:01, 89.89it/s]\u001b[A\n","ITERATION - loss: 0.09: 84%|████████▍ | 710/844 [08:25<00:01, 90.46it/s]\u001b[A\n","ITERATION - loss: 0.18: 85%|████████▌ | 720/844 [08:25<00:01, 90.02it/s]\u001b[A\n","ITERATION - loss: 0.11: 86%|████████▋ | 730/844 [08:26<00:01, 90.54it/s]\u001b[A\n","ITERATION - loss: 0.14: 88%|████████▊ | 740/844 [08:26<00:01, 90.68it/s]\u001b[A\n","ITERATION - loss: 0.12: 89%|████████▉ | 750/844 [08:26<00:01, 88.99it/s]\u001b[A\n","ITERATION - loss: 0.30: 90%|█████████ | 760/844 [08:26<00:00, 89.83it/s]\u001b[A\n","ITERATION - loss: 0.06: 91%|█████████ | 770/844 [08:26<00:00, 90.28it/s]\u001b[A\n","ITERATION - loss: 0.10: 92%|█████████▏| 780/844 [08:26<00:00, 89.56it/s]\u001b[A\n","ITERATION - loss: 0.16: 94%|█████████▎| 790/844 [08:26<00:00, 89.27it/s]\u001b[A\n","ITERATION - loss: 0.11: 95%|█████████▍| 800/844 [08:26<00:00, 86.61it/s]\u001b[A\n","ITERATION - loss: 0.07: 96%|█████████▌| 810/844 [08:26<00:00, 89.72it/s]\u001b[A\n","ITERATION - loss: 0.14: 97%|█████████▋| 820/844 [08:27<00:00, 90.89it/s]\u001b[A\n","ITERATION - loss: 0.09: 98%|█████████▊| 830/844 [08:27<00:00, 90.40it/s]\u001b[A\n","ITERATION - loss: 0.20: 100%|█████████▉| 840/844 [08:27<00:00, 90.40it/s]\u001b[A\n","ITERATION - loss: 0.05: 850it [08:27, 91.30it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [09:07<00:00, 93.64it/s]\n","ITERATION - loss: 0.05: 850it [08:34, 91.30it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 30 Avg accuracy: 95.87 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [09:08<00:00, 93.64it/s]\n","ITERATION - loss: 0.05: 850it [08:35, 91.30it/s]\u001b[A\n","ITERATION - loss: 0.11: 1%| | 10/844 [08:35<03:24, 4.08it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 30 Avg accuracy: 95.52 Avg loss: 0.16\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.12: 2%|▏ | 20/844 [08:35<02:24, 5.71it/s]\u001b[A\n","ITERATION - loss: 0.27: 4%|▎ | 30/844 [08:35<01:42, 7.93it/s]\u001b[A\n","ITERATION - loss: 0.07: 5%|▍ | 40/844 [08:35<01:13, 10.94it/s]\u001b[A\n","ITERATION - loss: 0.12: 6%|▌ | 50/844 [08:35<00:53, 14.88it/s]\u001b[A\n","ITERATION - loss: 0.16: 7%|▋ | 60/844 [08:35<00:40, 19.58it/s]\u001b[A\n","ITERATION - loss: 0.10: 8%|▊ | 70/844 [08:35<00:30, 25.61it/s]\u001b[A\n","ITERATION - loss: 0.09: 9%|▉ | 80/844 [08:36<00:23, 32.55it/s]\u001b[A\n","ITERATION - loss: 0.12: 11%|█ | 90/844 [08:36<00:18, 39.97it/s]\u001b[A\n","ITERATION - loss: 0.14: 12%|█▏ | 100/844 [08:36<00:15, 48.17it/s]\u001b[A\n","ITERATION - loss: 0.08: 13%|█▎ | 110/844 [08:36<00:13, 55.78it/s]\u001b[A\n","ITERATION - loss: 0.18: 14%|█▍ | 120/844 [08:36<00:11, 62.09it/s]\u001b[A\n","ITERATION - loss: 0.14: 15%|█▌ | 130/844 [08:36<00:10, 66.76it/s]\u001b[A\n","ITERATION - loss: 0.14: 17%|█▋ | 140/844 [08:36<00:09, 71.73it/s]\u001b[A\n","ITERATION - loss: 0.13: 18%|█▊ | 150/844 [08:36<00:09, 76.73it/s]\u001b[A\n","ITERATION - loss: 0.16: 19%|█▉ | 160/844 [08:37<00:08, 80.47it/s]\u001b[A\n","ITERATION - loss: 0.22: 20%|██ | 170/844 [08:37<00:07, 84.38it/s]\u001b[A\n","ITERATION - loss: 0.14: 21%|██▏ | 180/844 [08:37<00:07, 86.32it/s]\u001b[A\n","ITERATION - loss: 0.06: 23%|██▎ | 190/844 [08:37<00:07, 87.53it/s]\u001b[A\n","ITERATION - loss: 0.09: 24%|██▎ | 200/844 [08:37<00:07, 89.85it/s]\u001b[A\n","ITERATION - loss: 0.18: 25%|██▍ | 210/844 [08:37<00:07, 88.85it/s]\u001b[A\n","ITERATION - loss: 0.13: 26%|██▌ | 220/844 [08:37<00:07, 88.94it/s]\u001b[A\n","ITERATION - loss: 0.18: 27%|██▋ | 230/844 [08:37<00:06, 90.46it/s]\u001b[A\n","ITERATION - loss: 0.19: 28%|██▊ | 240/844 [08:37<00:06, 88.00it/s]\u001b[A\n","ITERATION - loss: 0.18: 30%|██▉ | 250/844 [08:38<00:06, 85.39it/s]\u001b[A\n","ITERATION - loss: 0.09: 31%|███ | 260/844 [08:38<00:06, 86.41it/s]\u001b[A\n","ITERATION - loss: 0.18: 32%|███▏ | 270/844 [08:38<00:06, 88.75it/s]\u001b[A\n","ITERATION - loss: 0.09: 33%|███▎ | 280/844 [08:38<00:06, 89.98it/s]\u001b[A\n","ITERATION - loss: 0.09: 34%|███▍ | 290/844 [08:38<00:06, 91.25it/s]\u001b[A\n","ITERATION - loss: 0.13: 36%|███▌ | 300/844 [08:38<00:06, 90.16it/s]\u001b[A\n","ITERATION - loss: 0.14: 37%|███▋ | 310/844 [08:38<00:05, 89.59it/s]\u001b[A\n","ITERATION - loss: 0.17: 38%|███▊ | 320/844 [08:38<00:05, 88.27it/s]\u001b[A\n","ITERATION - loss: 0.11: 39%|███▉ | 330/844 [08:38<00:05, 88.12it/s]\u001b[A\n","ITERATION - loss: 0.19: 40%|████ | 340/844 [08:39<00:05, 89.59it/s]\u001b[A\n","ITERATION - loss: 0.14: 41%|████▏ | 350/844 [08:39<00:05, 90.69it/s]\u001b[A\n","ITERATION - loss: 0.12: 43%|████▎ | 360/844 [08:39<00:05, 91.97it/s]\u001b[A\n","ITERATION - loss: 0.18: 44%|████▍ | 370/844 [08:39<00:05, 92.57it/s]\u001b[A\n","ITERATION - loss: 0.15: 45%|████▌ | 380/844 [08:39<00:05, 92.40it/s]\u001b[A\n","ITERATION - loss: 0.12: 46%|████▌ | 390/844 [08:39<00:04, 91.75it/s]\u001b[A\n","ITERATION - loss: 0.33: 47%|████▋ | 400/844 [08:39<00:04, 89.51it/s]\u001b[A\n","ITERATION - loss: 0.09: 49%|████▊ | 410/844 [08:39<00:04, 89.35it/s]\u001b[A\n","ITERATION - loss: 0.08: 50%|████▉ | 420/844 [08:39<00:04, 88.92it/s]\u001b[A\n","ITERATION - loss: 0.19: 51%|█████ | 430/844 [08:40<00:04, 88.52it/s]\u001b[A\n","ITERATION - loss: 0.06: 52%|█████▏ | 440/844 [08:40<00:04, 88.10it/s]\u001b[A\n","ITERATION - loss: 0.11: 53%|█████▎ | 450/844 [08:40<00:04, 88.20it/s]\u001b[A\n","ITERATION - loss: 0.11: 55%|█████▍ | 460/844 [08:40<00:04, 89.49it/s]\u001b[A\n","ITERATION - loss: 0.21: 56%|█████▌ | 470/844 [08:40<00:04, 91.00it/s]\u001b[A\n","ITERATION - loss: 0.21: 57%|█████▋ | 480/844 [08:40<00:03, 91.20it/s]\u001b[A\n","ITERATION - loss: 0.09: 58%|█████▊ | 490/844 [08:40<00:04, 88.47it/s]\u001b[A\n","ITERATION - loss: 0.19: 59%|█████▉ | 500/844 [08:40<00:03, 88.80it/s]\u001b[A\n","ITERATION - loss: 0.13: 60%|██████ | 510/844 [08:40<00:03, 86.15it/s]\u001b[A\n","ITERATION - loss: 0.15: 62%|██████▏ | 520/844 [08:41<00:03, 89.47it/s]\u001b[A\n","ITERATION - loss: 0.05: 63%|██████▎ | 530/844 [08:41<00:03, 91.50it/s]\u001b[A\n","ITERATION - loss: 0.05: 64%|██████▍ | 540/844 [08:41<00:03, 92.08it/s]\u001b[A\n","ITERATION - loss: 0.24: 65%|██████▌ | 550/844 [08:41<00:03, 93.24it/s]\u001b[A\n","ITERATION - loss: 0.10: 66%|██████▋ | 560/844 [08:41<00:03, 93.83it/s]\u001b[A\n","ITERATION - loss: 0.13: 68%|██████▊ | 570/844 [08:41<00:02, 93.72it/s]\u001b[A\n","ITERATION - loss: 0.15: 69%|██████▊ | 580/844 [08:41<00:02, 90.45it/s]\u001b[A\n","ITERATION - loss: 0.15: 70%|██████▉ | 590/844 [08:41<00:02, 91.31it/s]\u001b[A\n","ITERATION - loss: 0.15: 71%|███████ | 600/844 [08:41<00:02, 90.11it/s]\u001b[A\n","ITERATION - loss: 0.11: 72%|███████▏ | 610/844 [08:41<00:02, 90.88it/s]\u001b[A\n","ITERATION - loss: 0.10: 73%|███████▎ | 620/844 [08:42<00:02, 87.76it/s]\u001b[A\n","ITERATION - loss: 0.11: 75%|███████▍ | 630/844 [08:42<00:02, 88.48it/s]\u001b[A\n","ITERATION - loss: 0.18: 76%|███████▌ | 640/844 [08:42<00:02, 90.77it/s]\u001b[A\n","ITERATION - loss: 0.14: 77%|███████▋ | 650/844 [08:42<00:02, 91.45it/s]\u001b[A\n","ITERATION - loss: 0.16: 78%|███████▊ | 660/844 [08:42<00:01, 92.35it/s]\u001b[A\n","ITERATION - loss: 0.06: 79%|███████▉ | 670/844 [08:42<00:01, 88.69it/s]\u001b[A\n","ITERATION - loss: 0.13: 81%|████████ | 680/844 [08:42<00:01, 89.12it/s]\u001b[A\n","ITERATION - loss: 0.22: 82%|████████▏ | 690/844 [08:42<00:01, 85.30it/s]\u001b[A\n","ITERATION - loss: 0.08: 83%|████████▎ | 700/844 [08:43<00:01, 88.24it/s]\u001b[A\n","ITERATION - loss: 0.08: 84%|████████▍ | 710/844 [08:43<00:01, 89.02it/s]\u001b[A\n","ITERATION - loss: 0.21: 85%|████████▌ | 720/844 [08:43<00:01, 90.85it/s]\u001b[A\n","ITERATION - loss: 0.05: 86%|████████▋ | 730/844 [08:43<00:01, 92.18it/s]\u001b[A\n","ITERATION - loss: 0.11: 88%|████████▊ | 740/844 [08:43<00:01, 91.95it/s]\u001b[A\n","ITERATION - loss: 0.07: 89%|████████▉ | 750/844 [08:43<00:01, 92.46it/s]\u001b[A\n","ITERATION - loss: 0.21: 90%|█████████ | 760/844 [08:43<00:00, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.04: 91%|█████████ | 770/844 [08:43<00:00, 88.37it/s]\u001b[A\n","ITERATION - loss: 0.06: 92%|█████████▏| 780/844 [08:43<00:00, 88.52it/s]\u001b[A\n","ITERATION - loss: 0.10: 94%|█████████▎| 790/844 [08:44<00:00, 90.87it/s]\u001b[A\n","ITERATION - loss: 0.15: 95%|█████████▍| 800/844 [08:44<00:00, 92.14it/s]\u001b[A\n","ITERATION - loss: 0.16: 96%|█████████▌| 810/844 [08:44<00:00, 93.74it/s]\u001b[A\n","ITERATION - loss: 0.14: 97%|█████████▋| 820/844 [08:44<00:00, 94.63it/s]\u001b[A\n","ITERATION - loss: 0.15: 98%|█████████▊| 830/844 [08:44<00:00, 94.92it/s]\u001b[A\n","ITERATION - loss: 0.21: 100%|█████████▉| 840/844 [08:44<00:00, 95.41it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [09:25<00:00, 93.64it/s]\n","ITERATION - loss: 0.21: 100%|█████████▉| 840/844 [08:51<00:00, 95.41it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 31 Avg accuracy: 95.98 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [09:25<00:00, 93.64it/s]\n","ITERATION - loss: 0.21: 100%|█████████▉| 840/844 [08:52<00:00, 95.41it/s]\u001b[A\n","ITERATION - loss: 0.12: 1%| | 10/844 [08:52<03:25, 4.07it/s] \u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 31 Avg accuracy: 95.63 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.13: 2%|▏ | 20/844 [08:52<02:24, 5.70it/s]\u001b[A\n","ITERATION - loss: 0.12: 4%|▎ | 30/844 [08:52<01:42, 7.90it/s]\u001b[A\n","ITERATION - loss: 0.23: 5%|▍ | 40/844 [08:52<01:13, 10.88it/s]\u001b[A\n","ITERATION - loss: 0.09: 6%|▌ | 50/844 [08:52<00:53, 14.77it/s]\u001b[A\n","ITERATION - loss: 0.18: 7%|▋ | 60/844 [08:53<00:39, 19.72it/s]\u001b[A\n","ITERATION - loss: 0.10: 8%|▊ | 70/844 [08:53<00:29, 25.85it/s]\u001b[A\n","ITERATION - loss: 0.07: 9%|▉ | 80/844 [08:53<00:23, 33.02it/s]\u001b[A\n","ITERATION - loss: 0.10: 11%|█ | 90/844 [08:53<00:18, 41.11it/s]\u001b[A\n","ITERATION - loss: 0.10: 12%|█▏ | 100/844 [08:53<00:15, 49.49it/s]\u001b[A\n","ITERATION - loss: 0.07: 13%|█▎ | 110/844 [08:53<00:12, 56.61it/s]\u001b[A\n","ITERATION - loss: 0.10: 14%|█▍ | 120/844 [08:53<00:11, 64.79it/s]\u001b[A\n","ITERATION - loss: 0.08: 15%|█▌ | 130/844 [08:53<00:10, 69.25it/s]\u001b[A\n","ITERATION - loss: 0.11: 17%|█▋ | 140/844 [08:53<00:09, 73.92it/s]\u001b[A\n","ITERATION - loss: 0.12: 18%|█▊ | 150/844 [08:54<00:08, 78.95it/s]\u001b[A\n","ITERATION - loss: 0.31: 19%|█▉ | 160/844 [08:54<00:08, 81.60it/s]\u001b[A\n","ITERATION - loss: 0.19: 20%|██ | 170/844 [08:54<00:07, 85.04it/s]\u001b[A\n","ITERATION - loss: 0.14: 21%|██▏ | 180/844 [08:54<00:07, 87.34it/s]\u001b[A\n","ITERATION - loss: 0.17: 23%|██▎ | 190/844 [08:54<00:07, 87.77it/s]\u001b[A\n","ITERATION - loss: 0.18: 24%|██▎ | 200/844 [08:54<00:07, 89.34it/s]\u001b[A\n","ITERATION - loss: 0.14: 25%|██▍ | 210/844 [08:54<00:07, 86.47it/s]\u001b[A\n","ITERATION - loss: 0.09: 26%|██▌ | 220/844 [08:54<00:07, 86.82it/s]\u001b[A\n","ITERATION - loss: 0.14: 27%|██▋ | 230/844 [08:54<00:07, 83.91it/s]\u001b[A\n","ITERATION - loss: 0.09: 28%|██▊ | 240/844 [08:55<00:07, 85.88it/s]\u001b[A\n","ITERATION - loss: 0.08: 30%|██▉ | 250/844 [08:55<00:06, 87.08it/s]\u001b[A\n","ITERATION - loss: 0.11: 31%|███ | 260/844 [08:55<00:06, 89.16it/s]\u001b[A\n","ITERATION - loss: 0.15: 32%|███▏ | 270/844 [08:55<00:06, 90.31it/s]\u001b[A\n","ITERATION - loss: 0.16: 33%|███▎ | 280/844 [08:55<00:06, 91.10it/s]\u001b[A\n","ITERATION - loss: 0.13: 34%|███▍ | 290/844 [08:55<00:06, 90.98it/s]\u001b[A\n","ITERATION - loss: 0.08: 36%|███▌ | 300/844 [08:55<00:06, 88.79it/s]\u001b[A\n","ITERATION - loss: 0.06: 37%|███▋ | 310/844 [08:55<00:05, 90.19it/s]\u001b[A\n","ITERATION - loss: 0.25: 38%|███▊ | 320/844 [08:55<00:05, 90.28it/s]\u001b[A\n","ITERATION - loss: 0.06: 39%|███▉ | 330/844 [08:56<00:05, 89.79it/s]\u001b[A\n","ITERATION - loss: 0.07: 40%|████ | 340/844 [08:56<00:05, 89.80it/s]\u001b[A\n","ITERATION - loss: 0.07: 41%|████▏ | 350/844 [08:56<00:05, 91.35it/s]\u001b[A\n","ITERATION - loss: 0.11: 43%|████▎ | 360/844 [08:56<00:05, 92.79it/s]\u001b[A\n","ITERATION - loss: 0.13: 44%|████▍ | 370/844 [08:56<00:05, 92.88it/s]\u001b[A\n","ITERATION - loss: 0.20: 45%|████▌ | 380/844 [08:56<00:04, 93.00it/s]\u001b[A\n","ITERATION - loss: 0.15: 46%|████▌ | 390/844 [08:56<00:04, 93.15it/s]\u001b[A\n","ITERATION - loss: 0.08: 47%|████▋ | 400/844 [08:56<00:04, 92.90it/s]\u001b[A\n","ITERATION - loss: 0.06: 49%|████▊ | 410/844 [08:56<00:04, 92.76it/s]\u001b[A\n","ITERATION - loss: 0.06: 50%|████▉ | 420/844 [08:57<00:04, 91.98it/s]\u001b[A\n","ITERATION - loss: 0.09: 51%|█████ | 430/844 [08:57<00:04, 91.44it/s]\u001b[A\n","ITERATION - loss: 0.11: 52%|█████▏ | 440/844 [08:57<00:04, 92.09it/s]\u001b[A\n","ITERATION - loss: 0.26: 53%|█████▎ | 450/844 [08:57<00:04, 93.79it/s]\u001b[A\n","ITERATION - loss: 0.24: 55%|█████▍ | 460/844 [08:57<00:04, 90.30it/s]\u001b[A\n","ITERATION - loss: 0.19: 56%|█████▌ | 470/844 [08:57<00:04, 91.71it/s]\u001b[A\n","ITERATION - loss: 0.10: 57%|█████▋ | 480/844 [08:57<00:03, 92.22it/s]\u001b[A\n","ITERATION - loss: 0.21: 58%|█████▊ | 490/844 [08:57<00:03, 89.33it/s]\u001b[A\n","ITERATION - loss: 0.12: 59%|█████▉ | 500/844 [08:57<00:03, 90.45it/s]\u001b[A\n","ITERATION - loss: 0.13: 60%|██████ | 510/844 [08:58<00:03, 89.38it/s]\u001b[A\n","ITERATION - loss: 0.12: 62%|██████▏ | 520/844 [08:58<00:03, 87.70it/s]\u001b[A\n","ITERATION - loss: 0.20: 63%|██████▎ | 530/844 [08:58<00:03, 89.10it/s]\u001b[A\n","ITERATION - loss: 0.08: 64%|██████▍ | 540/844 [08:58<00:03, 90.56it/s]\u001b[A\n","ITERATION - loss: 0.11: 65%|██████▌ | 550/844 [08:58<00:03, 91.20it/s]\u001b[A\n","ITERATION - loss: 0.08: 66%|██████▋ | 560/844 [08:58<00:03, 92.24it/s]\u001b[A\n","ITERATION - loss: 0.10: 68%|██████▊ | 570/844 [08:58<00:03, 89.78it/s]\u001b[A\n","ITERATION - loss: 0.06: 69%|██████▊ | 580/844 [08:58<00:02, 88.65it/s]\u001b[A\n","ITERATION - loss: 0.09: 70%|██████▉ | 590/844 [08:58<00:02, 86.51it/s]\u001b[A\n","ITERATION - loss: 0.10: 71%|███████ | 600/844 [08:59<00:02, 86.91it/s]\u001b[A\n","ITERATION - loss: 0.21: 72%|███████▏ | 610/844 [08:59<00:02, 84.82it/s]\u001b[A\n","ITERATION - loss: 0.19: 73%|███████▎ | 620/844 [08:59<00:02, 87.59it/s]\u001b[A\n","ITERATION - loss: 0.15: 75%|███████▍ | 630/844 [08:59<00:02, 86.54it/s]\u001b[A\n","ITERATION - loss: 0.17: 76%|███████▌ | 640/844 [08:59<00:02, 88.67it/s]\u001b[A\n","ITERATION - loss: 0.20: 77%|███████▋ | 650/844 [08:59<00:02, 89.92it/s]\u001b[A\n","ITERATION - loss: 0.07: 78%|███████▊ | 660/844 [08:59<00:02, 89.90it/s]\u001b[A\n","ITERATION - loss: 0.10: 79%|███████▉ | 670/844 [08:59<00:01, 88.94it/s]\u001b[A\n","ITERATION - loss: 0.10: 81%|████████ | 680/844 [08:59<00:01, 90.41it/s]\u001b[A\n","ITERATION - loss: 0.11: 82%|████████▏ | 690/844 [09:00<00:01, 91.43it/s]\u001b[A\n","ITERATION - loss: 0.06: 83%|████████▎ | 700/844 [09:00<00:01, 89.49it/s]\u001b[A\n","ITERATION - loss: 0.15: 84%|████████▍ | 710/844 [09:00<00:01, 90.36it/s]\u001b[A\n","ITERATION - loss: 0.14: 85%|████████▌ | 720/844 [09:00<00:01, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.16: 86%|████████▋ | 730/844 [09:00<00:01, 91.91it/s]\u001b[A\n","ITERATION - loss: 0.12: 88%|████████▊ | 740/844 [09:00<00:01, 92.98it/s]\u001b[A\n","ITERATION - loss: 0.08: 89%|████████▉ | 750/844 [09:00<00:01, 93.07it/s]\u001b[A\n","ITERATION - loss: 0.08: 90%|█████████ | 760/844 [09:00<00:00, 92.28it/s]\u001b[A\n","ITERATION - loss: 0.08: 91%|█████████ | 770/844 [09:00<00:00, 93.98it/s]\u001b[A\n","ITERATION - loss: 0.08: 92%|█████████▏| 780/844 [09:00<00:00, 93.51it/s]\u001b[A\n","ITERATION - loss: 0.13: 94%|█████████▎| 790/844 [09:01<00:00, 92.34it/s]\u001b[A\n","ITERATION - loss: 0.28: 95%|█████████▍| 800/844 [09:01<00:00, 92.45it/s]\u001b[A\n","ITERATION - loss: 0.06: 96%|█████████▌| 810/844 [09:01<00:00, 91.91it/s]\u001b[A\n","ITERATION - loss: 0.13: 97%|█████████▋| 820/844 [09:01<00:00, 92.46it/s]\u001b[A\n","ITERATION - loss: 0.16: 98%|█████████▊| 830/844 [09:01<00:00, 93.85it/s]\u001b[A\n","ITERATION - loss: 0.28: 100%|█████████▉| 840/844 [09:01<00:00, 92.99it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [09:42<00:00, 93.64it/s]\n","ITERATION - loss: 0.28: 100%|█████████▉| 840/844 [09:08<00:00, 92.99it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 32 Avg accuracy: 96.04 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [09:43<00:00, 93.64it/s]\n","ITERATION - loss: 0.28: 100%|█████████▉| 840/844 [09:09<00:00, 92.99it/s]\u001b[A\n","ITERATION - loss: 0.13: 1%| | 10/844 [09:09<03:24, 4.07it/s] \u001b[A\n","ITERATION - loss: 0.09: 2%|▏ | 20/844 [09:09<02:24, 5.72it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 32 Avg accuracy: 95.58 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.11: 4%|▎ | 30/844 [09:09<01:42, 7.95it/s]\u001b[A\n","ITERATION - loss: 0.12: 5%|▍ | 40/844 [09:09<01:13, 10.95it/s]\u001b[A\n","ITERATION - loss: 0.18: 6%|▌ | 50/844 [09:10<00:53, 14.86it/s]\u001b[A\n","ITERATION - loss: 0.14: 7%|▋ | 60/844 [09:10<00:39, 19.95it/s]\u001b[A\n","ITERATION - loss: 0.19: 8%|▊ | 70/844 [09:10<00:29, 26.23it/s]\u001b[A\n","ITERATION - loss: 0.09: 9%|▉ | 80/844 [09:10<00:23, 33.06it/s]\u001b[A\n","ITERATION - loss: 0.07: 11%|█ | 90/844 [09:10<00:18, 41.28it/s]\u001b[A\n","ITERATION - loss: 0.05: 12%|█▏ | 100/844 [09:10<00:14, 49.99it/s]\u001b[A\n","ITERATION - loss: 0.11: 13%|█▎ | 110/844 [09:10<00:12, 58.52it/s]\u001b[A\n","ITERATION - loss: 0.19: 14%|█▍ | 120/844 [09:10<00:10, 66.23it/s]\u001b[A\n","ITERATION - loss: 0.17: 15%|█▌ | 130/844 [09:10<00:09, 72.71it/s]\u001b[A\n","ITERATION - loss: 0.10: 17%|█▋ | 140/844 [09:10<00:09, 77.87it/s]\u001b[A\n","ITERATION - loss: 0.26: 18%|█▊ | 150/844 [09:11<00:08, 82.75it/s]\u001b[A\n","ITERATION - loss: 0.06: 19%|█▉ | 160/844 [09:11<00:07, 86.45it/s]\u001b[A\n","ITERATION - loss: 0.14: 20%|██ | 170/844 [09:11<00:07, 88.86it/s]\u001b[A\n","ITERATION - loss: 0.25: 21%|██▏ | 180/844 [09:11<00:07, 86.56it/s]\u001b[A\n","ITERATION - loss: 0.13: 23%|██▎ | 190/844 [09:11<00:07, 89.15it/s]\u001b[A\n","ITERATION - loss: 0.23: 24%|██▎ | 200/844 [09:11<00:07, 88.43it/s]\u001b[A\n","ITERATION - loss: 0.11: 25%|██▍ | 210/844 [09:11<00:07, 90.04it/s]\u001b[A\n","ITERATION - loss: 0.05: 26%|██▌ | 220/844 [09:11<00:06, 90.41it/s]\u001b[A\n","ITERATION - loss: 0.09: 27%|██▋ | 230/844 [09:11<00:06, 90.42it/s]\u001b[A\n","ITERATION - loss: 0.07: 28%|██▊ | 240/844 [09:12<00:06, 92.04it/s]\u001b[A\n","ITERATION - loss: 0.17: 30%|██▉ | 250/844 [09:12<00:06, 92.92it/s]\u001b[A\n","ITERATION - loss: 0.09: 31%|███ | 260/844 [09:12<00:06, 92.45it/s]\u001b[A\n","ITERATION - loss: 0.15: 32%|███▏ | 270/844 [09:12<00:06, 86.34it/s]\u001b[A\n","ITERATION - loss: 0.29: 33%|███▎ | 280/844 [09:12<00:06, 88.46it/s]\u001b[A\n","ITERATION - loss: 0.05: 34%|███▍ | 290/844 [09:12<00:06, 90.04it/s]\u001b[A\n","ITERATION - loss: 0.09: 36%|███▌ | 300/844 [09:12<00:05, 91.35it/s]\u001b[A\n","ITERATION - loss: 0.27: 37%|███▋ | 310/844 [09:12<00:05, 90.30it/s]\u001b[A\n","ITERATION - loss: 0.10: 38%|███▊ | 320/844 [09:12<00:05, 90.95it/s]\u001b[A\n","ITERATION - loss: 0.19: 39%|███▉ | 330/844 [09:13<00:05, 91.74it/s]\u001b[A\n","ITERATION - loss: 0.12: 40%|████ | 340/844 [09:13<00:05, 92.73it/s]\u001b[A\n","ITERATION - loss: 0.04: 41%|████▏ | 350/844 [09:13<00:05, 93.46it/s]\u001b[A\n","ITERATION - loss: 0.18: 43%|████▎ | 360/844 [09:13<00:05, 92.43it/s]\u001b[A\n","ITERATION - loss: 0.19: 44%|████▍ | 370/844 [09:13<00:05, 91.29it/s]\u001b[A\n","ITERATION - loss: 0.08: 45%|████▌ | 380/844 [09:13<00:05, 92.07it/s]\u001b[A\n","ITERATION - loss: 0.15: 46%|████▌ | 390/844 [09:13<00:04, 92.72it/s]\u001b[A\n","ITERATION - loss: 0.10: 47%|████▋ | 400/844 [09:13<00:04, 92.82it/s]\u001b[A\n","ITERATION - loss: 0.07: 49%|████▊ | 410/844 [09:13<00:04, 94.11it/s]\u001b[A\n","ITERATION - loss: 0.15: 50%|████▉ | 420/844 [09:13<00:04, 93.83it/s]\u001b[A\n","ITERATION - loss: 0.13: 51%|█████ | 430/844 [09:14<00:04, 92.72it/s]\u001b[A\n","ITERATION - loss: 0.14: 52%|█████▏ | 440/844 [09:14<00:04, 94.39it/s]\u001b[A\n","ITERATION - loss: 0.23: 53%|█████▎ | 450/844 [09:14<00:04, 95.50it/s]\u001b[A\n","ITERATION - loss: 0.06: 55%|█████▍ | 460/844 [09:14<00:04, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.17: 56%|█████▌ | 470/844 [09:14<00:04, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.06: 57%|█████▋ | 480/844 [09:14<00:04, 90.49it/s]\u001b[A\n","ITERATION - loss: 0.17: 58%|█████▊ | 490/844 [09:14<00:03, 89.72it/s]\u001b[A\n","ITERATION - loss: 0.17: 59%|█████▉ | 500/844 [09:14<00:03, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.12: 62%|██████▏ | 520/844 [09:15<00:03, 93.84it/s]\u001b[A\n","ITERATION - loss: 0.13: 63%|██████▎ | 530/844 [09:15<00:03, 94.86it/s]\u001b[A\n","ITERATION - loss: 0.14: 64%|██████▍ | 540/844 [09:15<00:03, 96.02it/s]\u001b[A\n","ITERATION - loss: 0.29: 65%|██████▌ | 550/844 [09:15<00:03, 96.36it/s]\u001b[A\n","ITERATION - loss: 0.10: 66%|██████▋ | 560/844 [09:15<00:03, 92.77it/s]\u001b[A\n","ITERATION - loss: 0.12: 68%|██████▊ | 570/844 [09:15<00:02, 94.16it/s]\u001b[A\n","ITERATION - loss: 0.13: 69%|██████▊ | 580/844 [09:15<00:02, 93.13it/s]\u001b[A\n","ITERATION - loss: 0.16: 70%|██████▉ | 590/844 [09:15<00:02, 91.99it/s]\u001b[A\n","ITERATION - loss: 0.07: 71%|███████ | 600/844 [09:15<00:02, 93.18it/s]\u001b[A\n","ITERATION - loss: 0.14: 72%|███████▏ | 610/844 [09:16<00:02, 93.68it/s]\u001b[A\n","ITERATION - loss: 0.04: 73%|███████▎ | 620/844 [09:16<00:02, 93.75it/s]\u001b[A\n","ITERATION - loss: 0.11: 75%|███████▍ | 630/844 [09:16<00:02, 94.10it/s]\u001b[A\n","ITERATION - loss: 0.39: 76%|███████▌ | 640/844 [09:16<00:02, 93.42it/s]\u001b[A\n","ITERATION - loss: 0.06: 77%|███████▋ | 650/844 [09:16<00:02, 87.73it/s]\u001b[A\n","ITERATION - loss: 0.07: 78%|███████▊ | 660/844 [09:16<00:02, 89.97it/s]\u001b[A\n","ITERATION - loss: 0.16: 79%|███████▉ | 670/844 [09:16<00:01, 90.39it/s]\u001b[A\n","ITERATION - loss: 0.05: 81%|████████ | 680/844 [09:16<00:01, 91.34it/s]\u001b[A\n","ITERATION - loss: 0.10: 82%|████████▏ | 690/844 [09:16<00:01, 92.07it/s]\u001b[A\n","ITERATION - loss: 0.30: 83%|████████▎ | 700/844 [09:17<00:01, 92.83it/s]\u001b[A\n","ITERATION - loss: 0.06: 84%|████████▍ | 710/844 [09:17<00:01, 92.94it/s]\u001b[A\n","ITERATION - loss: 0.04: 85%|████████▌ | 720/844 [09:17<00:01, 92.93it/s]\u001b[A\n","ITERATION - loss: 0.08: 86%|████████▋ | 730/844 [09:17<00:01, 93.38it/s]\u001b[A\n","ITERATION - loss: 0.09: 88%|████████▊ | 740/844 [09:17<00:01, 93.40it/s]\u001b[A\n","ITERATION - loss: 0.07: 89%|████████▉ | 750/844 [09:17<00:01, 90.62it/s]\u001b[A\n","ITERATION - loss: 0.21: 90%|█████████ | 760/844 [09:17<00:00, 91.68it/s]\u001b[A\n","ITERATION - loss: 0.27: 91%|█████████ | 770/844 [09:17<00:00, 91.90it/s]\u001b[A\n","ITERATION - loss: 0.16: 92%|█████████▏| 780/844 [09:17<00:00, 92.08it/s]\u001b[A\n","ITERATION - loss: 0.12: 94%|█████████▎| 790/844 [09:17<00:00, 92.57it/s]\u001b[A\n","ITERATION - loss: 0.09: 95%|█████████▍| 800/844 [09:18<00:00, 91.76it/s]\u001b[A\n","ITERATION - loss: 0.09: 96%|█████████▌| 810/844 [09:18<00:00, 92.64it/s]\u001b[A\n","ITERATION - loss: 0.18: 97%|█████████▋| 820/844 [09:18<00:00, 92.62it/s]\u001b[A\n","ITERATION - loss: 0.15: 98%|█████████▊| 830/844 [09:18<00:00, 91.51it/s]\u001b[A\n","ITERATION - loss: 0.09: 100%|█████████▉| 840/844 [09:18<00:00, 87.93it/s]\u001b[A\n","ITERATION - loss: 0.32: 850it [09:18, 89.24it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [09:59<00:00, 93.64it/s]\n","ITERATION - loss: 0.32: 850it [09:25, 89.24it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 33 Avg accuracy: 96.09 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [09:59<00:00, 93.64it/s]\n","ITERATION - loss: 0.32: 850it [09:26, 89.24it/s]\u001b[A\n","ITERATION - loss: 0.07: 1%| | 10/844 [09:26<03:24, 4.07it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 33 Avg accuracy: 95.65 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.12: 2%|▏ | 20/844 [09:26<02:24, 5.68it/s]\u001b[A\n","ITERATION - loss: 0.05: 4%|▎ | 30/844 [09:26<01:42, 7.91it/s]\u001b[A\n","ITERATION - loss: 0.10: 5%|▍ | 40/844 [09:26<01:13, 10.90it/s]\u001b[A\n","ITERATION - loss: 0.11: 6%|▌ | 50/844 [09:27<00:53, 14.83it/s]\u001b[A\n","ITERATION - loss: 0.13: 7%|▋ | 60/844 [09:27<00:39, 19.80it/s]\u001b[A\n","ITERATION - loss: 0.09: 8%|▊ | 70/844 [09:27<00:29, 25.83it/s]\u001b[A\n","ITERATION - loss: 0.16: 9%|▉ | 80/844 [09:27<00:23, 32.95it/s]\u001b[A\n","ITERATION - loss: 0.07: 11%|█ | 90/844 [09:27<00:18, 40.61it/s]\u001b[A\n","ITERATION - loss: 0.13: 12%|█▏ | 100/844 [09:27<00:15, 48.67it/s]\u001b[A\n","ITERATION - loss: 0.08: 13%|█▎ | 110/844 [09:27<00:13, 55.84it/s]\u001b[A\n","ITERATION - loss: 0.06: 14%|█▍ | 120/844 [09:27<00:11, 62.06it/s]\u001b[A\n","ITERATION - loss: 0.15: 15%|█▌ | 130/844 [09:27<00:10, 68.74it/s]\u001b[A\n","ITERATION - loss: 0.17: 17%|█▋ | 140/844 [09:28<00:09, 74.24it/s]\u001b[A\n","ITERATION - loss: 0.12: 18%|█▊ | 150/844 [09:28<00:08, 78.37it/s]\u001b[A\n","ITERATION - loss: 0.20: 19%|█▉ | 160/844 [09:28<00:08, 81.64it/s]\u001b[A\n","ITERATION - loss: 0.12: 20%|██ | 170/844 [09:28<00:07, 84.36it/s]\u001b[A\n","ITERATION - loss: 0.09: 21%|██▏ | 180/844 [09:28<00:07, 85.62it/s]\u001b[A\n","ITERATION - loss: 0.17: 23%|██▎ | 190/844 [09:28<00:07, 82.76it/s]\u001b[A\n","ITERATION - loss: 0.09: 24%|██▎ | 200/844 [09:28<00:07, 80.80it/s]\u001b[A\n","ITERATION - loss: 0.20: 25%|██▍ | 210/844 [09:28<00:07, 84.24it/s]\u001b[A\n","ITERATION - loss: 0.10: 26%|██▌ | 220/844 [09:28<00:07, 87.23it/s]\u001b[A\n","ITERATION - loss: 0.12: 27%|██▋ | 230/844 [09:29<00:06, 89.81it/s]\u001b[A\n","ITERATION - loss: 0.09: 28%|██▊ | 240/844 [09:29<00:06, 91.13it/s]\u001b[A\n","ITERATION - loss: 0.12: 30%|██▉ | 250/844 [09:29<00:06, 92.27it/s]\u001b[A\n","ITERATION - loss: 0.24: 31%|███ | 260/844 [09:29<00:06, 93.20it/s]\u001b[A\n","ITERATION - loss: 0.08: 32%|███▏ | 270/844 [09:29<00:06, 92.92it/s]\u001b[A\n","ITERATION - loss: 0.06: 33%|███▎ | 280/844 [09:29<00:06, 93.50it/s]\u001b[A\n","ITERATION - loss: 0.06: 34%|███▍ | 290/844 [09:29<00:06, 92.20it/s]\u001b[A\n","ITERATION - loss: 0.09: 36%|███▌ | 300/844 [09:29<00:06, 88.13it/s]\u001b[A\n","ITERATION - loss: 0.22: 37%|███▋ | 310/844 [09:29<00:05, 89.28it/s]\u001b[A\n","ITERATION - loss: 0.14: 38%|███▊ | 320/844 [09:30<00:06, 86.82it/s]\u001b[A\n","ITERATION - loss: 0.06: 39%|███▉ | 330/844 [09:30<00:06, 84.83it/s]\u001b[A\n","ITERATION - loss: 0.16: 40%|████ | 340/844 [09:30<00:05, 86.37it/s]\u001b[A\n","ITERATION - loss: 0.08: 41%|████▏ | 350/844 [09:30<00:05, 88.20it/s]\u001b[A\n","ITERATION - loss: 0.09: 43%|████▎ | 360/844 [09:30<00:05, 88.63it/s]\u001b[A\n","ITERATION - loss: 0.20: 44%|████▍ | 370/844 [09:30<00:05, 88.96it/s]\u001b[A\n","ITERATION - loss: 0.13: 45%|████▌ | 380/844 [09:30<00:05, 88.35it/s]\u001b[A\n","ITERATION - loss: 0.19: 46%|████▌ | 390/844 [09:30<00:05, 87.92it/s]\u001b[A\n","ITERATION - loss: 0.18: 47%|████▋ | 400/844 [09:30<00:04, 89.57it/s]\u001b[A\n","ITERATION - loss: 0.17: 49%|████▊ | 410/844 [09:31<00:04, 90.91it/s]\u001b[A\n","ITERATION - loss: 0.07: 50%|████▉ | 420/844 [09:31<00:04, 91.86it/s]\u001b[A\n","ITERATION - loss: 0.03: 51%|█████ | 430/844 [09:31<00:04, 92.42it/s]\u001b[A\n","ITERATION - loss: 0.20: 52%|█████▏ | 440/844 [09:31<00:04, 93.67it/s]\u001b[A\n","ITERATION - loss: 0.17: 53%|█████▎ | 450/844 [09:31<00:04, 91.38it/s]\u001b[A\n","ITERATION - loss: 0.18: 55%|█████▍ | 460/844 [09:31<00:04, 91.73it/s]\u001b[A\n","ITERATION - loss: 0.11: 56%|█████▌ | 470/844 [09:31<00:04, 90.33it/s]\u001b[A\n","ITERATION - loss: 0.10: 57%|█████▋ | 480/844 [09:31<00:04, 87.21it/s]\u001b[A\n","ITERATION - loss: 0.12: 58%|█████▊ | 490/844 [09:31<00:04, 84.43it/s]\u001b[A\n","ITERATION - loss: 0.14: 59%|█████▉ | 500/844 [09:32<00:03, 86.33it/s]\u001b[A\n","ITERATION - loss: 0.04: 60%|██████ | 510/844 [09:32<00:03, 88.83it/s]\u001b[A\n","ITERATION - loss: 0.16: 62%|██████▏ | 520/844 [09:32<00:03, 89.93it/s]\u001b[A\n","ITERATION - loss: 0.10: 63%|██████▎ | 530/844 [09:32<00:03, 91.93it/s]\u001b[A\n","ITERATION - loss: 0.18: 64%|██████▍ | 540/844 [09:32<00:03, 93.46it/s]\u001b[A\n","ITERATION - loss: 0.13: 65%|██████▌ | 550/844 [09:32<00:03, 91.21it/s]\u001b[A\n","ITERATION - loss: 0.08: 66%|██████▋ | 560/844 [09:32<00:03, 89.95it/s]\u001b[A\n","ITERATION - loss: 0.12: 68%|██████▊ | 570/844 [09:32<00:03, 88.95it/s]\u001b[A\n","ITERATION - loss: 0.13: 69%|██████▊ | 580/844 [09:32<00:02, 89.89it/s]\u001b[A\n","ITERATION - loss: 0.11: 70%|██████▉ | 590/844 [09:33<00:02, 90.19it/s]\u001b[A\n","ITERATION - loss: 0.12: 71%|███████ | 600/844 [09:33<00:02, 91.19it/s]\u001b[A\n","ITERATION - loss: 0.16: 72%|███████▏ | 610/844 [09:33<00:02, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.15: 73%|███████▎ | 620/844 [09:33<00:02, 92.10it/s]\u001b[A\n","ITERATION - loss: 0.11: 75%|███████▍ | 630/844 [09:33<00:02, 91.88it/s]\u001b[A\n","ITERATION - loss: 0.09: 76%|███████▌ | 640/844 [09:33<00:02, 92.21it/s]\u001b[A\n","ITERATION - loss: 0.19: 77%|███████▋ | 650/844 [09:33<00:02, 89.33it/s]\u001b[A\n","ITERATION - loss: 0.17: 78%|███████▊ | 660/844 [09:33<00:02, 90.48it/s]\u001b[A\n","ITERATION - loss: 0.09: 79%|███████▉ | 670/844 [09:33<00:01, 89.60it/s]\u001b[A\n","ITERATION - loss: 0.13: 81%|████████ | 680/844 [09:34<00:01, 86.43it/s]\u001b[A\n","ITERATION - loss: 0.19: 82%|████████▏ | 690/844 [09:34<00:01, 88.35it/s]\u001b[A\n","ITERATION - loss: 0.13: 83%|████████▎ | 700/844 [09:34<00:01, 89.03it/s]\u001b[A\n","ITERATION - loss: 0.09: 84%|████████▍ | 710/844 [09:34<00:01, 90.97it/s]\u001b[A\n","ITERATION - loss: 0.11: 85%|████████▌ | 720/844 [09:34<00:01, 92.14it/s]\u001b[A\n","ITERATION - loss: 0.27: 86%|████████▋ | 730/844 [09:34<00:01, 91.41it/s]\u001b[A\n","ITERATION - loss: 0.24: 88%|████████▊ | 740/844 [09:34<00:01, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.09: 89%|████████▉ | 750/844 [09:34<00:01, 92.79it/s]\u001b[A\n","ITERATION - loss: 0.17: 90%|█████████ | 760/844 [09:34<00:00, 91.69it/s]\u001b[A\n","ITERATION - loss: 0.07: 91%|█████████ | 770/844 [09:35<00:00, 92.51it/s]\u001b[A\n","ITERATION - loss: 0.17: 92%|█████████▏| 780/844 [09:35<00:00, 93.70it/s]\u001b[A\n","ITERATION - loss: 0.10: 94%|█████████▎| 790/844 [09:35<00:00, 94.21it/s]\u001b[A\n","ITERATION - loss: 0.12: 95%|█████████▍| 800/844 [09:35<00:00, 94.72it/s]\u001b[A\n","ITERATION - loss: 0.18: 96%|█████████▌| 810/844 [09:35<00:00, 95.66it/s]\u001b[A\n","ITERATION - loss: 0.19: 97%|█████████▋| 820/844 [09:35<00:00, 96.66it/s]\u001b[A\n","ITERATION - loss: 0.13: 98%|█████████▊| 830/844 [09:35<00:00, 95.18it/s]\u001b[A\n","ITERATION - loss: 0.16: 100%|█████████▉| 840/844 [09:35<00:00, 93.70it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [10:16<00:00, 93.64it/s]\n","ITERATION - loss: 0.16: 100%|█████████▉| 840/844 [09:42<00:00, 93.70it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 34 Avg accuracy: 96.17 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [10:17<00:00, 93.64it/s]\n","ITERATION - loss: 0.16: 100%|█████████▉| 840/844 [09:43<00:00, 93.70it/s]\u001b[A\n","ITERATION - loss: 0.17: 1%| | 10/844 [09:43<03:22, 4.11it/s] \u001b[A\n","ITERATION - loss: 0.09: 2%|▏ | 20/844 [09:43<02:22, 5.77it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 34 Avg accuracy: 95.87 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.03: 4%|▎ | 30/844 [09:43<01:41, 8.03it/s]\u001b[A\n","ITERATION - loss: 0.30: 5%|▍ | 40/844 [09:43<01:12, 11.05it/s]\u001b[A\n","ITERATION - loss: 0.22: 6%|▌ | 50/844 [09:44<00:52, 15.03it/s]\u001b[A\n","ITERATION - loss: 0.05: 7%|▋ | 60/844 [09:44<00:39, 20.05it/s]\u001b[A\n","ITERATION - loss: 0.21: 8%|▊ | 70/844 [09:44<00:29, 26.28it/s]\u001b[A\n","ITERATION - loss: 0.08: 9%|▉ | 80/844 [09:44<00:22, 33.30it/s]\u001b[A\n","ITERATION - loss: 0.07: 11%|█ | 90/844 [09:44<00:18, 41.50it/s]\u001b[A\n","ITERATION - loss: 0.19: 12%|█▏ | 100/844 [09:44<00:15, 49.08it/s]\u001b[A\n","ITERATION - loss: 0.24: 13%|█▎ | 110/844 [09:44<00:12, 56.60it/s]\u001b[A\n","ITERATION - loss: 0.04: 14%|█▍ | 120/844 [09:44<00:11, 64.54it/s]\u001b[A\n","ITERATION - loss: 0.08: 15%|█▌ | 130/844 [09:44<00:10, 69.94it/s]\u001b[A\n","ITERATION - loss: 0.05: 17%|█▋ | 140/844 [09:45<00:09, 76.31it/s]\u001b[A\n","ITERATION - loss: 0.08: 18%|█▊ | 150/844 [09:45<00:08, 79.91it/s]\u001b[A\n","ITERATION - loss: 0.10: 19%|█▉ | 160/844 [09:45<00:08, 83.63it/s]\u001b[A\n","ITERATION - loss: 0.09: 20%|██ | 170/844 [09:45<00:07, 86.99it/s]\u001b[A\n","ITERATION - loss: 0.09: 21%|██▏ | 180/844 [09:45<00:07, 89.48it/s]\u001b[A\n","ITERATION - loss: 0.09: 23%|██▎ | 190/844 [09:45<00:07, 89.69it/s]\u001b[A\n","ITERATION - loss: 0.16: 24%|██▎ | 200/844 [09:45<00:07, 89.88it/s]\u001b[A\n","ITERATION - loss: 0.14: 25%|██▍ | 210/844 [09:45<00:06, 90.99it/s]\u001b[A\n","ITERATION - loss: 0.19: 26%|██▌ | 220/844 [09:45<00:06, 89.27it/s]\u001b[A\n","ITERATION - loss: 0.10: 27%|██▋ | 230/844 [09:46<00:06, 90.59it/s]\u001b[A\n","ITERATION - loss: 0.09: 28%|██▊ | 240/844 [09:46<00:06, 89.80it/s]\u001b[A\n","ITERATION - loss: 0.12: 30%|██▉ | 250/844 [09:46<00:06, 89.08it/s]\u001b[A\n","ITERATION - loss: 0.07: 31%|███ | 260/844 [09:46<00:06, 89.87it/s]\u001b[A\n","ITERATION - loss: 0.08: 32%|███▏ | 270/844 [09:46<00:06, 90.26it/s]\u001b[A\n","ITERATION - loss: 0.21: 33%|███▎ | 280/844 [09:46<00:06, 88.11it/s]\u001b[A\n","ITERATION - loss: 0.28: 34%|███▍ | 290/844 [09:46<00:06, 85.95it/s]\u001b[A\n","ITERATION - loss: 0.09: 36%|███▌ | 300/844 [09:46<00:06, 84.97it/s]\u001b[A\n","ITERATION - loss: 0.13: 37%|███▋ | 310/844 [09:46<00:06, 84.45it/s]\u001b[A\n","ITERATION - loss: 0.04: 38%|███▊ | 320/844 [09:47<00:06, 86.61it/s]\u001b[A\n","ITERATION - loss: 0.19: 39%|███▉ | 330/844 [09:47<00:05, 86.50it/s]\u001b[A\n","ITERATION - loss: 0.08: 40%|████ | 340/844 [09:47<00:05, 88.77it/s]\u001b[A\n","ITERATION - loss: 0.07: 41%|████▏ | 350/844 [09:47<00:05, 91.41it/s]\u001b[A\n","ITERATION - loss: 0.18: 43%|████▎ | 360/844 [09:47<00:05, 93.03it/s]\u001b[A\n","ITERATION - loss: 0.17: 44%|████▍ | 370/844 [09:47<00:05, 93.09it/s]\u001b[A\n","ITERATION - loss: 0.05: 45%|████▌ | 380/844 [09:47<00:04, 93.44it/s]\u001b[A\n","ITERATION - loss: 0.16: 46%|████▌ | 390/844 [09:47<00:04, 94.24it/s]\u001b[A\n","ITERATION - loss: 0.44: 47%|████▋ | 400/844 [09:47<00:04, 93.07it/s]\u001b[A\n","ITERATION - loss: 0.12: 49%|████▊ | 410/844 [09:48<00:04, 92.70it/s]\u001b[A\n","ITERATION - loss: 0.13: 50%|████▉ | 420/844 [09:48<00:04, 89.82it/s]\u001b[A\n","ITERATION - loss: 0.19: 51%|█████ | 430/844 [09:48<00:04, 87.29it/s]\u001b[A\n","ITERATION - loss: 0.09: 52%|█████▏ | 440/844 [09:48<00:04, 89.58it/s]\u001b[A\n","ITERATION - loss: 0.17: 53%|█████▎ | 450/844 [09:48<00:04, 90.65it/s]\u001b[A\n","ITERATION - loss: 0.10: 55%|█████▍ | 460/844 [09:48<00:04, 91.59it/s]\u001b[A\n","ITERATION - loss: 0.13: 56%|█████▌ | 470/844 [09:48<00:04, 92.68it/s]\u001b[A\n","ITERATION - loss: 0.06: 57%|█████▋ | 480/844 [09:48<00:03, 92.11it/s]\u001b[A\n","ITERATION - loss: 0.16: 58%|█████▊ | 490/844 [09:48<00:03, 92.64it/s]\u001b[A\n","ITERATION - loss: 0.13: 59%|█████▉ | 500/844 [09:49<00:03, 90.48it/s]\u001b[A\n","ITERATION - loss: 0.10: 60%|██████ | 510/844 [09:49<00:03, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.18: 62%|██████▏ | 520/844 [09:49<00:03, 90.76it/s]\u001b[A\n","ITERATION - loss: 0.12: 63%|██████▎ | 530/844 [09:49<00:03, 90.40it/s]\u001b[A\n","ITERATION - loss: 0.21: 64%|██████▍ | 540/844 [09:49<00:03, 91.32it/s]\u001b[A\n","ITERATION - loss: 0.11: 65%|██████▌ | 550/844 [09:49<00:03, 89.12it/s]\u001b[A\n","ITERATION - loss: 0.15: 66%|██████▋ | 560/844 [09:49<00:03, 89.53it/s]\u001b[A\n","ITERATION - loss: 0.14: 68%|██████▊ | 570/844 [09:49<00:03, 91.17it/s]\u001b[A\n","ITERATION - loss: 0.08: 69%|██████▊ | 580/844 [09:49<00:02, 92.10it/s]\u001b[A\n","ITERATION - loss: 0.19: 70%|██████▉ | 590/844 [09:50<00:02, 90.01it/s]\u001b[A\n","ITERATION - loss: 0.12: 71%|███████ | 600/844 [09:50<00:02, 89.32it/s]\u001b[A\n","ITERATION - loss: 0.05: 72%|███████▏ | 610/844 [09:50<00:02, 87.41it/s]\u001b[A\n","ITERATION - loss: 0.14: 73%|███████▎ | 620/844 [09:50<00:02, 88.91it/s]\u001b[A\n","ITERATION - loss: 0.07: 75%|███████▍ | 630/844 [09:50<00:02, 90.18it/s]\u001b[A\n","ITERATION - loss: 0.16: 76%|███████▌ | 640/844 [09:50<00:02, 91.36it/s]\u001b[A\n","ITERATION - loss: 0.12: 77%|███████▋ | 650/844 [09:50<00:02, 92.31it/s]\u001b[A\n","ITERATION - loss: 0.15: 78%|███████▊ | 660/844 [09:50<00:02, 89.51it/s]\u001b[A\n","ITERATION - loss: 0.09: 79%|███████▉ | 670/844 [09:50<00:01, 90.23it/s]\u001b[A\n","ITERATION - loss: 0.07: 81%|████████ | 680/844 [09:51<00:01, 90.81it/s]\u001b[A\n","ITERATION - loss: 0.16: 82%|████████▏ | 690/844 [09:51<00:01, 93.21it/s]\u001b[A\n","ITERATION - loss: 0.16: 83%|████████▎ | 700/844 [09:51<00:01, 92.10it/s]\u001b[A\n","ITERATION - loss: 0.12: 84%|████████▍ | 710/844 [09:51<00:01, 91.38it/s]\u001b[A\n","ITERATION - loss: 0.04: 85%|████████▌ | 720/844 [09:51<00:01, 93.29it/s]\u001b[A\n","ITERATION - loss: 0.18: 86%|████████▋ | 730/844 [09:51<00:01, 94.14it/s]\u001b[A\n","ITERATION - loss: 0.06: 88%|████████▊ | 740/844 [09:51<00:01, 92.10it/s]\u001b[A\n","ITERATION - loss: 0.08: 89%|████████▉ | 750/844 [09:51<00:01, 92.62it/s]\u001b[A\n","ITERATION - loss: 0.08: 90%|█████████ | 760/844 [09:51<00:00, 90.03it/s]\u001b[A\n","ITERATION - loss: 0.12: 91%|█████████ | 770/844 [09:52<00:00, 87.09it/s]\u001b[A\n","ITERATION - loss: 0.15: 92%|█████████▏| 780/844 [09:52<00:00, 89.32it/s]\u001b[A\n","ITERATION - loss: 0.28: 94%|█████████▎| 790/844 [09:52<00:00, 90.78it/s]\u001b[A\n","ITERATION - loss: 0.10: 95%|█████████▍| 800/844 [09:52<00:00, 89.61it/s]\u001b[A\n","ITERATION - loss: 0.14: 96%|█████████▌| 810/844 [09:52<00:00, 90.71it/s]\u001b[A\n","ITERATION - loss: 0.14: 97%|█████████▋| 820/844 [09:52<00:00, 91.66it/s]\u001b[A\n","ITERATION - loss: 0.18: 98%|█████████▊| 830/844 [09:52<00:00, 88.68it/s]\u001b[A\n","ITERATION - loss: 0.13: 100%|█████████▉| 840/844 [09:52<00:00, 89.56it/s]\u001b[A\n","ITERATION - loss: 0.07: 850it [09:52, 91.63it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [10:33<00:00, 93.64it/s]\n","ITERATION - loss: 0.07: 850it [09:59, 91.63it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 35 Avg accuracy: 96.22 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [10:34<00:00, 93.64it/s]\n","ITERATION - loss: 0.07: 850it [10:00, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.13: 1%| | 10/844 [10:00<03:21, 4.13it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 35 Avg accuracy: 95.78 Avg loss: 0.15\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.27: 2%|▏ | 20/844 [10:00<02:22, 5.78it/s]\u001b[A\n","ITERATION - loss: 0.08: 4%|▎ | 30/844 [10:00<01:41, 8.04it/s]\u001b[A\n","ITERATION - loss: 0.11: 5%|▍ | 40/844 [10:01<01:12, 11.09it/s]\u001b[A\n","ITERATION - loss: 0.20: 6%|▌ | 50/844 [10:01<00:52, 15.02it/s]\u001b[A\n","ITERATION - loss: 0.05: 7%|▋ | 60/844 [10:01<00:39, 19.85it/s]\u001b[A\n","ITERATION - loss: 0.03: 8%|▊ | 70/844 [10:01<00:29, 26.00it/s]\u001b[A\n","ITERATION - loss: 0.14: 9%|▉ | 80/844 [10:01<00:23, 33.21it/s]\u001b[A\n","ITERATION - loss: 0.09: 11%|█ | 90/844 [10:01<00:18, 40.91it/s]\u001b[A\n","ITERATION - loss: 0.26: 12%|█▏ | 100/844 [10:01<00:15, 49.16it/s]\u001b[A\n","ITERATION - loss: 0.06: 13%|█▎ | 110/844 [10:01<00:12, 56.97it/s]\u001b[A\n","ITERATION - loss: 0.16: 14%|█▍ | 120/844 [10:01<00:11, 64.61it/s]\u001b[A\n","ITERATION - loss: 0.11: 15%|█▌ | 130/844 [10:02<00:09, 71.44it/s]\u001b[A\n","ITERATION - loss: 0.11: 17%|█▋ | 140/844 [10:02<00:09, 76.31it/s]\u001b[A\n","ITERATION - loss: 0.18: 18%|█▊ | 150/844 [10:02<00:08, 80.32it/s]\u001b[A\n","ITERATION - loss: 0.10: 19%|█▉ | 160/844 [10:02<00:08, 83.95it/s]\u001b[A\n","ITERATION - loss: 0.13: 20%|██ | 170/844 [10:02<00:07, 86.25it/s]\u001b[A\n","ITERATION - loss: 0.13: 21%|██▏ | 180/844 [10:02<00:07, 85.53it/s]\u001b[A\n","ITERATION - loss: 0.24: 23%|██▎ | 190/844 [10:02<00:07, 85.46it/s]\u001b[A\n","ITERATION - loss: 0.13: 24%|██▎ | 200/844 [10:02<00:07, 87.97it/s]\u001b[A\n","ITERATION - loss: 0.17: 25%|██▍ | 210/844 [10:02<00:06, 90.70it/s]\u001b[A\n","ITERATION - loss: 0.16: 26%|██▌ | 220/844 [10:03<00:06, 91.79it/s]\u001b[A\n","ITERATION - loss: 0.13: 27%|██▋ | 230/844 [10:03<00:06, 93.85it/s]\u001b[A\n","ITERATION - loss: 0.14: 28%|██▊ | 240/844 [10:03<00:06, 93.17it/s]\u001b[A\n","ITERATION - loss: 0.05: 30%|██▉ | 250/844 [10:03<00:06, 92.59it/s]\u001b[A\n","ITERATION - loss: 0.09: 31%|███ | 260/844 [10:03<00:06, 94.53it/s]\u001b[A\n","ITERATION - loss: 0.06: 32%|███▏ | 270/844 [10:03<00:06, 92.33it/s]\u001b[A\n","ITERATION - loss: 0.14: 33%|███▎ | 280/844 [10:03<00:06, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.27: 34%|███▍ | 290/844 [10:03<00:06, 90.69it/s]\u001b[A\n","ITERATION - loss: 0.15: 36%|███▌ | 300/844 [10:03<00:05, 91.20it/s]\u001b[A\n","ITERATION - loss: 0.07: 37%|███▋ | 310/844 [10:03<00:05, 91.96it/s]\u001b[A\n","ITERATION - loss: 0.08: 38%|███▊ | 320/844 [10:04<00:05, 93.11it/s]\u001b[A\n","ITERATION - loss: 0.15: 39%|███▉ | 330/844 [10:04<00:05, 91.13it/s]\u001b[A\n","ITERATION - loss: 0.25: 40%|████ | 340/844 [10:04<00:05, 92.32it/s]\u001b[A\n","ITERATION - loss: 0.18: 41%|████▏ | 350/844 [10:04<00:05, 93.60it/s]\u001b[A\n","ITERATION - loss: 0.11: 43%|████▎ | 360/844 [10:04<00:05, 90.69it/s]\u001b[A\n","ITERATION - loss: 0.17: 44%|████▍ | 370/844 [10:04<00:05, 88.45it/s]\u001b[A\n","ITERATION - loss: 0.38: 45%|████▌ | 380/844 [10:04<00:05, 89.82it/s]\u001b[A\n","ITERATION - loss: 0.15: 46%|████▌ | 390/844 [10:04<00:05, 90.03it/s]\u001b[A\n","ITERATION - loss: 0.19: 47%|████▋ | 400/844 [10:04<00:04, 92.08it/s]\u001b[A\n","ITERATION - loss: 0.11: 49%|████▊ | 410/844 [10:05<00:04, 94.03it/s]\u001b[A\n","ITERATION - loss: 0.15: 50%|████▉ | 420/844 [10:05<00:04, 94.70it/s]\u001b[A\n","ITERATION - loss: 0.13: 51%|█████ | 430/844 [10:05<00:04, 93.49it/s]\u001b[A\n","ITERATION - loss: 0.10: 52%|█████▏ | 440/844 [10:05<00:04, 95.14it/s]\u001b[A\n","ITERATION - loss: 0.06: 53%|█████▎ | 450/844 [10:05<00:04, 95.89it/s]\u001b[A\n","ITERATION - loss: 0.17: 55%|█████▍ | 460/844 [10:05<00:04, 91.87it/s]\u001b[A\n","ITERATION - loss: 0.09: 56%|█████▌ | 470/844 [10:05<00:04, 92.15it/s]\u001b[A\n","ITERATION - loss: 0.21: 57%|█████▋ | 480/844 [10:05<00:03, 93.20it/s]\u001b[A\n","ITERATION - loss: 0.10: 58%|█████▊ | 490/844 [10:05<00:03, 93.27it/s]\u001b[A\n","ITERATION - loss: 0.11: 59%|█████▉ | 500/844 [10:06<00:03, 92.97it/s]\u001b[A\n","ITERATION - loss: 0.04: 60%|██████ | 510/844 [10:06<00:03, 93.69it/s]\u001b[A\n","ITERATION - loss: 0.13: 62%|██████▏ | 520/844 [10:06<00:03, 91.94it/s]\u001b[A\n","ITERATION - loss: 0.13: 63%|██████▎ | 530/844 [10:06<00:03, 92.32it/s]\u001b[A\n","ITERATION - loss: 0.13: 64%|██████▍ | 540/844 [10:06<00:03, 92.84it/s]\u001b[A\n","ITERATION - loss: 0.22: 65%|██████▌ | 550/844 [10:06<00:03, 92.81it/s]\u001b[A\n","ITERATION - loss: 0.12: 66%|██████▋ | 560/844 [10:06<00:03, 90.65it/s]\u001b[A\n","ITERATION - loss: 0.19: 68%|██████▊ | 570/844 [10:06<00:02, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.19: 69%|██████▊ | 580/844 [10:06<00:02, 89.06it/s]\u001b[A\n","ITERATION - loss: 0.19: 70%|██████▉ | 590/844 [10:07<00:02, 90.15it/s]\u001b[A\n","ITERATION - loss: 0.05: 71%|███████ | 600/844 [10:07<00:02, 91.79it/s]\u001b[A\n","ITERATION - loss: 0.11: 72%|███████▏ | 610/844 [10:07<00:02, 90.98it/s]\u001b[A\n","ITERATION - loss: 0.16: 73%|███████▎ | 620/844 [10:07<00:02, 91.52it/s]\u001b[A\n","ITERATION - loss: 0.09: 75%|███████▍ | 630/844 [10:07<00:02, 92.43it/s]\u001b[A\n","ITERATION - loss: 0.21: 76%|███████▌ | 640/844 [10:07<00:02, 92.67it/s]\u001b[A\n","ITERATION - loss: 0.10: 77%|███████▋ | 650/844 [10:07<00:02, 90.45it/s]\u001b[A\n","ITERATION - loss: 0.16: 78%|███████▊ | 660/844 [10:07<00:02, 91.69it/s]\u001b[A\n","ITERATION - loss: 0.24: 79%|███████▉ | 670/844 [10:07<00:01, 91.67it/s]\u001b[A\n","ITERATION - loss: 0.18: 81%|████████ | 680/844 [10:08<00:01, 92.86it/s]\u001b[A\n","ITERATION - loss: 0.05: 82%|████████▏ | 690/844 [10:08<00:01, 93.26it/s]\u001b[A\n","ITERATION - loss: 0.07: 83%|████████▎ | 700/844 [10:08<00:01, 93.06it/s]\u001b[A\n","ITERATION - loss: 0.07: 84%|████████▍ | 710/844 [10:08<00:01, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.06: 85%|████████▌ | 720/844 [10:08<00:01, 92.32it/s]\u001b[A\n","ITERATION - loss: 0.24: 86%|████████▋ | 730/844 [10:08<00:01, 92.27it/s]\u001b[A\n","ITERATION - loss: 0.03: 88%|████████▊ | 740/844 [10:08<00:01, 92.31it/s]\u001b[A\n","ITERATION - loss: 0.24: 89%|████████▉ | 750/844 [10:08<00:01, 88.62it/s]\u001b[A\n","ITERATION - loss: 0.16: 90%|█████████ | 760/844 [10:08<00:00, 87.41it/s]\u001b[A\n","ITERATION - loss: 0.15: 91%|█████████ | 770/844 [10:09<00:00, 89.76it/s]\u001b[A\n","ITERATION - loss: 0.09: 92%|█████████▏| 780/844 [10:09<00:00, 90.32it/s]\u001b[A\n","ITERATION - loss: 0.12: 94%|█████████▎| 790/844 [10:09<00:00, 92.50it/s]\u001b[A\n","ITERATION - loss: 0.10: 95%|█████████▍| 800/844 [10:09<00:00, 91.43it/s]\u001b[A\n","ITERATION - loss: 0.11: 96%|█████████▌| 810/844 [10:09<00:00, 91.94it/s]\u001b[A\n","ITERATION - loss: 0.12: 97%|█████████▋| 820/844 [10:09<00:00, 92.82it/s]\u001b[A\n","ITERATION - loss: 0.09: 98%|█████████▊| 830/844 [10:09<00:00, 93.23it/s]\u001b[A\n","ITERATION - loss: 0.18: 100%|█████████▉| 840/844 [10:09<00:00, 90.21it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [10:50<00:00, 93.64it/s]\n","ITERATION - loss: 0.18: 100%|█████████▉| 840/844 [10:16<00:00, 90.21it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 36 Avg accuracy: 96.24 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [10:51<00:00, 93.64it/s]\n","ITERATION - loss: 0.18: 100%|█████████▉| 840/844 [10:17<00:00, 90.21it/s]\u001b[A\n","ITERATION - loss: 0.09: 1%| | 10/844 [10:17<03:26, 4.04it/s] \u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 36 Avg accuracy: 95.68 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.14: 2%|▏ | 20/844 [10:17<02:25, 5.67it/s]\u001b[A\n","ITERATION - loss: 0.13: 4%|▎ | 30/844 [10:17<01:43, 7.88it/s]\u001b[A\n","ITERATION - loss: 0.08: 5%|▍ | 40/844 [10:18<01:14, 10.80it/s]\u001b[A\n","ITERATION - loss: 0.14: 6%|▌ | 50/844 [10:18<00:53, 14.71it/s]\u001b[A\n","ITERATION - loss: 0.11: 7%|▋ | 60/844 [10:18<00:39, 19.66it/s]\u001b[A\n","ITERATION - loss: 0.14: 8%|▊ | 70/844 [10:18<00:30, 25.70it/s]\u001b[A\n","ITERATION - loss: 0.05: 9%|▉ | 80/844 [10:18<00:23, 32.86it/s]\u001b[A\n","ITERATION - loss: 0.05: 11%|█ | 90/844 [10:18<00:18, 40.59it/s]\u001b[A\n","ITERATION - loss: 0.15: 12%|█▏ | 100/844 [10:18<00:15, 48.39it/s]\u001b[A\n","ITERATION - loss: 0.07: 13%|█▎ | 110/844 [10:18<00:12, 56.74it/s]\u001b[A\n","ITERATION - loss: 0.13: 14%|█▍ | 120/844 [10:18<00:11, 63.26it/s]\u001b[A\n","ITERATION - loss: 0.15: 15%|█▌ | 130/844 [10:19<00:10, 70.28it/s]\u001b[A\n","ITERATION - loss: 0.10: 17%|█▋ | 140/844 [10:19<00:09, 75.71it/s]\u001b[A\n","ITERATION - loss: 0.14: 18%|█▊ | 150/844 [10:19<00:08, 80.25it/s]\u001b[A\n","ITERATION - loss: 0.09: 19%|█▉ | 160/844 [10:19<00:08, 84.12it/s]\u001b[A\n","ITERATION - loss: 0.15: 20%|██ | 170/844 [10:19<00:07, 84.88it/s]\u001b[A\n","ITERATION - loss: 0.11: 21%|██▏ | 180/844 [10:19<00:07, 87.27it/s]\u001b[A\n","ITERATION - loss: 0.15: 23%|██▎ | 190/844 [10:19<00:07, 87.32it/s]\u001b[A\n","ITERATION - loss: 0.20: 24%|██▎ | 200/844 [10:19<00:07, 88.33it/s]\u001b[A\n","ITERATION - loss: 0.11: 25%|██▍ | 210/844 [10:19<00:07, 88.59it/s]\u001b[A\n","ITERATION - loss: 0.19: 26%|██▌ | 220/844 [10:20<00:06, 90.59it/s]\u001b[A\n","ITERATION - loss: 0.12: 27%|██▋ | 230/844 [10:20<00:06, 89.48it/s]\u001b[A\n","ITERATION - loss: 0.04: 28%|██▊ | 240/844 [10:20<00:06, 90.51it/s]\u001b[A\n","ITERATION - loss: 0.18: 30%|██▉ | 250/844 [10:20<00:06, 91.18it/s]\u001b[A\n","ITERATION - loss: 0.09: 31%|███ | 260/844 [10:20<00:06, 90.29it/s]\u001b[A\n","ITERATION - loss: 0.05: 32%|███▏ | 270/844 [10:20<00:06, 90.46it/s]\u001b[A\n","ITERATION - loss: 0.13: 33%|███▎ | 280/844 [10:20<00:06, 90.70it/s]\u001b[A\n","ITERATION - loss: 0.14: 34%|███▍ | 290/844 [10:20<00:06, 88.69it/s]\u001b[A\n","ITERATION - loss: 0.13: 36%|███▌ | 300/844 [10:20<00:06, 88.75it/s]\u001b[A\n","ITERATION - loss: 0.18: 37%|███▋ | 310/844 [10:21<00:06, 88.73it/s]\u001b[A\n","ITERATION - loss: 0.11: 38%|███▊ | 320/844 [10:21<00:05, 89.25it/s]\u001b[A\n","ITERATION - loss: 0.17: 39%|███▉ | 330/844 [10:21<00:05, 89.12it/s]\u001b[A\n","ITERATION - loss: 0.06: 40%|████ | 340/844 [10:21<00:05, 89.45it/s]\u001b[A\n","ITERATION - loss: 0.14: 41%|████▏ | 350/844 [10:21<00:05, 88.64it/s]\u001b[A\n","ITERATION - loss: 0.11: 43%|████▎ | 360/844 [10:21<00:05, 86.89it/s]\u001b[A\n","ITERATION - loss: 0.21: 44%|████▍ | 370/844 [10:21<00:05, 88.56it/s]\u001b[A\n","ITERATION - loss: 0.06: 45%|████▌ | 380/844 [10:21<00:05, 86.34it/s]\u001b[A\n","ITERATION - loss: 0.13: 46%|████▌ | 390/844 [10:22<00:05, 80.93it/s]\u001b[A\n","ITERATION - loss: 0.14: 47%|████▋ | 400/844 [10:22<00:05, 84.71it/s]\u001b[A\n","ITERATION - loss: 0.14: 49%|████▊ | 410/844 [10:22<00:04, 87.70it/s]\u001b[A\n","ITERATION - loss: 0.10: 50%|████▉ | 420/844 [10:22<00:04, 89.56it/s]\u001b[A\n","ITERATION - loss: 0.08: 51%|█████ | 430/844 [10:22<00:04, 90.77it/s]\u001b[A\n","ITERATION - loss: 0.21: 52%|█████▏ | 440/844 [10:22<00:04, 90.09it/s]\u001b[A\n","ITERATION - loss: 0.05: 53%|█████▎ | 450/844 [10:22<00:04, 90.12it/s]\u001b[A\n","ITERATION - loss: 0.25: 55%|█████▍ | 460/844 [10:22<00:04, 87.84it/s]\u001b[A\n","ITERATION - loss: 0.23: 56%|█████▌ | 470/844 [10:22<00:04, 90.24it/s]\u001b[A\n","ITERATION - loss: 0.15: 57%|█████▋ | 480/844 [10:22<00:03, 91.73it/s]\u001b[A\n","ITERATION - loss: 0.09: 58%|█████▊ | 490/844 [10:23<00:03, 91.53it/s]\u001b[A\n","ITERATION - loss: 0.34: 59%|█████▉ | 500/844 [10:23<00:03, 93.60it/s]\u001b[A\n","ITERATION - loss: 0.10: 60%|██████ | 510/844 [10:23<00:03, 95.09it/s]\u001b[A\n","ITERATION - loss: 0.22: 62%|██████▏ | 520/844 [10:23<00:03, 96.40it/s]\u001b[A\n","ITERATION - loss: 0.15: 63%|██████▎ | 530/844 [10:23<00:03, 94.16it/s]\u001b[A\n","ITERATION - loss: 0.14: 64%|██████▍ | 540/844 [10:23<00:03, 92.73it/s]\u001b[A\n","ITERATION - loss: 0.11: 65%|██████▌ | 550/844 [10:23<00:03, 92.78it/s]\u001b[A\n","ITERATION - loss: 0.14: 66%|██████▋ | 560/844 [10:23<00:03, 90.08it/s]\u001b[A\n","ITERATION - loss: 0.07: 68%|██████▊ | 570/844 [10:23<00:03, 91.28it/s]\u001b[A\n","ITERATION - loss: 0.18: 69%|██████▊ | 580/844 [10:24<00:02, 90.22it/s]\u001b[A\n","ITERATION - loss: 0.16: 70%|██████▉ | 590/844 [10:24<00:02, 91.45it/s]\u001b[A\n","ITERATION - loss: 0.11: 71%|███████ | 600/844 [10:24<00:02, 92.34it/s]\u001b[A\n","ITERATION - loss: 0.05: 72%|███████▏ | 610/844 [10:24<00:02, 90.56it/s]\u001b[A\n","ITERATION - loss: 0.14: 73%|███████▎ | 620/844 [10:24<00:02, 89.83it/s]\u001b[A\n","ITERATION - loss: 0.19: 75%|███████▍ | 630/844 [10:24<00:02, 90.43it/s]\u001b[A\n","ITERATION - loss: 0.22: 76%|███████▌ | 640/844 [10:24<00:02, 91.26it/s]\u001b[A\n","ITERATION - loss: 0.10: 77%|███████▋ | 650/844 [10:24<00:02, 92.22it/s]\u001b[A\n","ITERATION - loss: 0.08: 78%|███████▊ | 660/844 [10:24<00:01, 94.29it/s]\u001b[A\n","ITERATION - loss: 0.07: 81%|████████ | 680/844 [10:25<00:01, 95.18it/s]\u001b[A\n","ITERATION - loss: 0.08: 83%|████████▎ | 700/844 [10:25<00:01, 97.75it/s]\u001b[A\n","ITERATION - loss: 0.19: 84%|████████▍ | 710/844 [10:25<00:01, 95.51it/s]\u001b[A\n","ITERATION - loss: 0.09: 85%|████████▌ | 720/844 [10:25<00:01, 92.72it/s]\u001b[A\n","ITERATION - loss: 0.14: 86%|████████▋ | 730/844 [10:25<00:01, 94.77it/s]\u001b[A\n","ITERATION - loss: 0.09: 88%|████████▊ | 740/844 [10:25<00:01, 93.68it/s]\u001b[A\n","ITERATION - loss: 0.05: 89%|████████▉ | 750/844 [10:25<00:00, 94.86it/s]\u001b[A\n","ITERATION - loss: 0.10: 90%|█████████ | 760/844 [10:25<00:00, 94.63it/s]\u001b[A\n","ITERATION - loss: 0.21: 91%|█████████ | 770/844 [10:26<00:00, 90.70it/s]\u001b[A\n","ITERATION - loss: 0.15: 92%|█████████▏| 780/844 [10:26<00:00, 92.64it/s]\u001b[A\n","ITERATION - loss: 0.07: 94%|█████████▎| 790/844 [10:26<00:00, 92.59it/s]\u001b[A\n","ITERATION - loss: 0.18: 95%|█████████▍| 800/844 [10:26<00:00, 94.12it/s]\u001b[A\n","ITERATION - loss: 0.14: 96%|█████████▌| 810/844 [10:26<00:00, 95.26it/s]\u001b[A\n","ITERATION - loss: 0.12: 97%|█████████▋| 820/844 [10:26<00:00, 93.73it/s]\u001b[A\n","ITERATION - loss: 0.16: 98%|█████████▊| 830/844 [10:26<00:00, 93.92it/s]\u001b[A\n","ITERATION - loss: 0.11: 100%|█████████▉| 840/844 [10:26<00:00, 94.52it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [11:07<00:00, 93.64it/s]\n","ITERATION - loss: 0.11: 100%|█████████▉| 840/844 [10:33<00:00, 94.52it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 37 Avg accuracy: 96.32 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [11:08<00:00, 93.64it/s]\n","ITERATION - loss: 0.11: 100%|█████████▉| 840/844 [10:34<00:00, 94.52it/s]\u001b[A\n","ITERATION - loss: 0.13: 1%| | 10/844 [10:34<03:22, 4.12it/s] \u001b[A\n","ITERATION - loss: 0.10: 2%|▏ | 20/844 [10:34<02:22, 5.78it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 37 Avg accuracy: 95.83 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.08: 4%|▎ | 30/844 [10:34<01:41, 8.04it/s]\u001b[A\n","ITERATION - loss: 0.13: 5%|▍ | 40/844 [10:35<01:12, 11.06it/s]\u001b[A\n","ITERATION - loss: 0.08: 6%|▌ | 50/844 [10:35<00:52, 15.05it/s]\u001b[A\n","ITERATION - loss: 0.16: 7%|▋ | 60/844 [10:35<00:38, 20.13it/s]\u001b[A\n","ITERATION - loss: 0.13: 8%|▊ | 70/844 [10:35<00:29, 26.21it/s]\u001b[A\n","ITERATION - loss: 0.07: 9%|▉ | 80/844 [10:35<00:22, 33.45it/s]\u001b[A\n","ITERATION - loss: 0.07: 11%|█ | 90/844 [10:35<00:18, 41.37it/s]\u001b[A\n","ITERATION - loss: 0.14: 12%|█▏ | 100/844 [10:35<00:15, 49.58it/s]\u001b[A\n","ITERATION - loss: 0.19: 13%|█▎ | 110/844 [10:35<00:12, 57.12it/s]\u001b[A\n","ITERATION - loss: 0.10: 14%|█▍ | 120/844 [10:35<00:11, 64.30it/s]\u001b[A\n","ITERATION - loss: 0.08: 15%|█▌ | 130/844 [10:35<00:10, 71.37it/s]\u001b[A\n","ITERATION - loss: 0.05: 17%|█▋ | 140/844 [10:36<00:09, 76.94it/s]\u001b[A\n","ITERATION - loss: 0.17: 18%|█▊ | 150/844 [10:36<00:08, 79.16it/s]\u001b[A\n","ITERATION - loss: 0.05: 19%|█▉ | 160/844 [10:36<00:08, 82.81it/s]\u001b[A\n","ITERATION - loss: 0.19: 20%|██ | 170/844 [10:36<00:08, 84.15it/s]\u001b[A\n","ITERATION - loss: 0.22: 21%|██▏ | 180/844 [10:36<00:07, 87.08it/s]\u001b[A\n","ITERATION - loss: 0.05: 23%|██▎ | 190/844 [10:36<00:07, 86.71it/s]\u001b[A\n","ITERATION - loss: 0.06: 24%|██▎ | 200/844 [10:36<00:07, 87.54it/s]\u001b[A\n","ITERATION - loss: 0.32: 25%|██▍ | 210/844 [10:36<00:07, 90.44it/s]\u001b[A\n","ITERATION - loss: 0.22: 26%|██▌ | 220/844 [10:36<00:06, 92.57it/s]\u001b[A\n","ITERATION - loss: 0.15: 27%|██▋ | 230/844 [10:37<00:06, 93.77it/s]\u001b[A\n","ITERATION - loss: 0.16: 28%|██▊ | 240/844 [10:37<00:06, 95.23it/s]\u001b[A\n","ITERATION - loss: 0.07: 30%|██▉ | 250/844 [10:37<00:06, 95.84it/s]\u001b[A\n","ITERATION - loss: 0.05: 31%|███ | 260/844 [10:37<00:06, 93.47it/s]\u001b[A\n","ITERATION - loss: 0.11: 32%|███▏ | 270/844 [10:37<00:06, 94.68it/s]\u001b[A\n","ITERATION - loss: 0.07: 33%|███▎ | 280/844 [10:37<00:05, 95.93it/s]\u001b[A\n","ITERATION - loss: 0.03: 34%|███▍ | 290/844 [10:37<00:05, 95.90it/s]\u001b[A\n","ITERATION - loss: 0.11: 36%|███▌ | 300/844 [10:37<00:06, 89.52it/s]\u001b[A\n","ITERATION - loss: 0.05: 37%|███▋ | 310/844 [10:37<00:05, 89.70it/s]\u001b[A\n","ITERATION - loss: 0.06: 38%|███▊ | 320/844 [10:38<00:05, 90.44it/s]\u001b[A\n","ITERATION - loss: 0.09: 39%|███▉ | 330/844 [10:38<00:05, 92.03it/s]\u001b[A\n","ITERATION - loss: 0.07: 40%|████ | 340/844 [10:38<00:05, 93.67it/s]\u001b[A\n","ITERATION - loss: 0.11: 41%|████▏ | 350/844 [10:38<00:05, 89.55it/s]\u001b[A\n","ITERATION - loss: 0.04: 43%|████▎ | 360/844 [10:38<00:05, 90.23it/s]\u001b[A\n","ITERATION - loss: 0.08: 44%|████▍ | 370/844 [10:38<00:05, 91.51it/s]\u001b[A\n","ITERATION - loss: 0.12: 45%|████▌ | 380/844 [10:38<00:05, 91.95it/s]\u001b[A\n","ITERATION - loss: 0.09: 46%|████▌ | 390/844 [10:38<00:04, 91.02it/s]\u001b[A\n","ITERATION - loss: 0.18: 47%|████▋ | 400/844 [10:38<00:04, 91.42it/s]\u001b[A\n","ITERATION - loss: 0.15: 49%|████▊ | 410/844 [10:39<00:04, 91.90it/s]\u001b[A\n","ITERATION - loss: 0.09: 50%|████▉ | 420/844 [10:39<00:04, 93.47it/s]\u001b[A\n","ITERATION - loss: 0.16: 51%|█████ | 430/844 [10:39<00:04, 95.11it/s]\u001b[A\n","ITERATION - loss: 0.16: 52%|█████▏ | 440/844 [10:39<00:04, 95.17it/s]\u001b[A\n","ITERATION - loss: 0.15: 53%|█████▎ | 450/844 [10:39<00:04, 93.67it/s]\u001b[A\n","ITERATION - loss: 0.10: 55%|█████▍ | 460/844 [10:39<00:04, 94.86it/s]\u001b[A\n","ITERATION - loss: 0.21: 56%|█████▌ | 470/844 [10:39<00:04, 89.38it/s]\u001b[A\n","ITERATION - loss: 0.36: 57%|█████▋ | 480/844 [10:39<00:04, 89.40it/s]\u001b[A\n","ITERATION - loss: 0.18: 58%|█████▊ | 490/844 [10:39<00:03, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.08: 59%|█████▉ | 500/844 [10:39<00:03, 93.31it/s]\u001b[A\n","ITERATION - loss: 0.09: 60%|██████ | 510/844 [10:40<00:03, 91.12it/s]\u001b[A\n","ITERATION - loss: 0.09: 62%|██████▏ | 520/844 [10:40<00:03, 93.39it/s]\u001b[A\n","ITERATION - loss: 0.11: 63%|██████▎ | 530/844 [10:40<00:03, 93.81it/s]\u001b[A\n","ITERATION - loss: 0.08: 64%|██████▍ | 540/844 [10:40<00:03, 94.80it/s]\u001b[A\n","ITERATION - loss: 0.16: 65%|██████▌ | 550/844 [10:40<00:03, 94.18it/s]\u001b[A\n","ITERATION - loss: 0.10: 66%|██████▋ | 560/844 [10:40<00:03, 94.13it/s]\u001b[A\n","ITERATION - loss: 0.05: 68%|██████▊ | 570/844 [10:40<00:02, 92.19it/s]\u001b[A\n","ITERATION - loss: 0.11: 69%|██████▊ | 580/844 [10:40<00:02, 90.72it/s]\u001b[A\n","ITERATION - loss: 0.10: 70%|██████▉ | 590/844 [10:40<00:02, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.11: 71%|███████ | 600/844 [10:41<00:02, 91.34it/s]\u001b[A\n","ITERATION - loss: 0.06: 72%|███████▏ | 610/844 [10:41<00:02, 92.38it/s]\u001b[A\n","ITERATION - loss: 0.05: 73%|███████▎ | 620/844 [10:41<00:02, 91.81it/s]\u001b[A\n","ITERATION - loss: 0.13: 75%|███████▍ | 630/844 [10:41<00:02, 92.55it/s]\u001b[A\n","ITERATION - loss: 0.15: 76%|███████▌ | 640/844 [10:41<00:02, 91.01it/s]\u001b[A\n","ITERATION - loss: 0.24: 77%|███████▋ | 650/844 [10:41<00:02, 91.00it/s]\u001b[A\n","ITERATION - loss: 0.10: 78%|███████▊ | 660/844 [10:41<00:02, 91.93it/s]\u001b[A\n","ITERATION - loss: 0.12: 79%|███████▉ | 670/844 [10:41<00:01, 90.86it/s]\u001b[A\n","ITERATION - loss: 0.05: 81%|████████ | 680/844 [10:41<00:01, 91.35it/s]\u001b[A\n","ITERATION - loss: 0.12: 82%|████████▏ | 690/844 [10:42<00:01, 91.54it/s]\u001b[A\n","ITERATION - loss: 0.11: 83%|████████▎ | 700/844 [10:42<00:01, 92.34it/s]\u001b[A\n","ITERATION - loss: 0.06: 84%|████████▍ | 710/844 [10:42<00:01, 92.94it/s]\u001b[A\n","ITERATION - loss: 0.08: 85%|████████▌ | 720/844 [10:42<00:01, 92.57it/s]\u001b[A\n","ITERATION - loss: 0.12: 86%|████████▋ | 730/844 [10:42<00:01, 87.52it/s]\u001b[A\n","ITERATION - loss: 0.13: 88%|████████▊ | 740/844 [10:42<00:01, 84.80it/s]\u001b[A\n","ITERATION - loss: 0.10: 89%|████████▉ | 750/844 [10:42<00:01, 83.35it/s]\u001b[A\n","ITERATION - loss: 0.06: 90%|█████████ | 760/844 [10:42<00:01, 82.85it/s]\u001b[A\n","ITERATION - loss: 0.13: 91%|█████████ | 770/844 [10:42<00:00, 85.46it/s]\u001b[A\n","ITERATION - loss: 0.14: 92%|█████████▏| 780/844 [10:43<00:00, 87.52it/s]\u001b[A\n","ITERATION - loss: 0.09: 94%|█████████▎| 790/844 [10:43<00:00, 89.71it/s]\u001b[A\n","ITERATION - loss: 0.03: 95%|█████████▍| 800/844 [10:43<00:00, 90.91it/s]\u001b[A\n","ITERATION - loss: 0.22: 96%|█████████▌| 810/844 [10:43<00:00, 91.56it/s]\u001b[A\n","ITERATION - loss: 0.15: 97%|█████████▋| 820/844 [10:43<00:00, 89.45it/s]\u001b[A\n","ITERATION - loss: 0.13: 98%|█████████▊| 830/844 [10:43<00:00, 88.50it/s]\u001b[A\n","ITERATION - loss: 0.09: 100%|█████████▉| 840/844 [10:43<00:00, 84.09it/s]\u001b[A\n","ITERATION - loss: 0.17: 850it [10:43, 81.55it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [11:24<00:00, 93.64it/s]\n","ITERATION - loss: 0.17: 850it [10:50, 81.55it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 38 Avg accuracy: 96.41 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [11:25<00:00, 93.64it/s]\n","ITERATION - loss: 0.17: 850it [10:51, 81.55it/s]\u001b[A\n","ITERATION - loss: 0.08: 1%| | 10/844 [10:51<03:24, 4.07it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 38 Avg accuracy: 95.95 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.26: 2%|▏ | 20/844 [10:51<02:24, 5.71it/s]\u001b[A\n","ITERATION - loss: 0.18: 4%|▎ | 30/844 [10:52<01:42, 7.92it/s]\u001b[A\n","ITERATION - loss: 0.13: 5%|▍ | 40/844 [10:52<01:13, 10.87it/s]\u001b[A\n","ITERATION - loss: 0.13: 6%|▌ | 50/844 [10:52<00:53, 14.77it/s]\u001b[A\n","ITERATION - loss: 0.10: 7%|▋ | 60/844 [10:52<00:39, 19.70it/s]\u001b[A\n","ITERATION - loss: 0.33: 8%|▊ | 70/844 [10:52<00:30, 25.74it/s]\u001b[A\n","ITERATION - loss: 0.10: 9%|▉ | 80/844 [10:52<00:23, 32.86it/s]\u001b[A\n","ITERATION - loss: 0.06: 11%|█ | 90/844 [10:52<00:18, 40.16it/s]\u001b[A\n","ITERATION - loss: 0.19: 12%|█▏ | 100/844 [10:52<00:15, 48.70it/s]\u001b[A\n","ITERATION - loss: 0.11: 13%|█▎ | 110/844 [10:52<00:12, 57.35it/s]\u001b[A\n","ITERATION - loss: 0.15: 14%|█▍ | 120/844 [10:53<00:11, 62.78it/s]\u001b[A\n","ITERATION - loss: 0.14: 15%|█▌ | 130/844 [10:53<00:10, 69.25it/s]\u001b[A\n","ITERATION - loss: 0.11: 17%|█▋ | 140/844 [10:53<00:09, 75.54it/s]\u001b[A\n","ITERATION - loss: 0.06: 18%|█▊ | 150/844 [10:53<00:08, 81.06it/s]\u001b[A\n","ITERATION - loss: 0.10: 19%|█▉ | 160/844 [10:53<00:08, 85.17it/s]\u001b[A\n","ITERATION - loss: 0.06: 20%|██ | 170/844 [10:53<00:07, 88.19it/s]\u001b[A\n","ITERATION - loss: 0.13: 21%|██▏ | 180/844 [10:53<00:07, 90.68it/s]\u001b[A\n","ITERATION - loss: 0.22: 23%|██▎ | 190/844 [10:53<00:07, 86.44it/s]\u001b[A\n","ITERATION - loss: 0.11: 24%|██▎ | 200/844 [10:53<00:07, 86.99it/s]\u001b[A\n","ITERATION - loss: 0.19: 25%|██▍ | 210/844 [10:54<00:07, 84.94it/s]\u001b[A\n","ITERATION - loss: 0.18: 26%|██▌ | 220/844 [10:54<00:07, 86.56it/s]\u001b[A\n","ITERATION - loss: 0.28: 27%|██▋ | 230/844 [10:54<00:06, 88.43it/s]\u001b[A\n","ITERATION - loss: 0.10: 28%|██▊ | 240/844 [10:54<00:06, 88.88it/s]\u001b[A\n","ITERATION - loss: 0.15: 30%|██▉ | 250/844 [10:54<00:06, 89.02it/s]\u001b[A\n","ITERATION - loss: 0.10: 31%|███ | 260/844 [10:54<00:06, 88.75it/s]\u001b[A\n","ITERATION - loss: 0.09: 32%|███▏ | 270/844 [10:54<00:06, 89.35it/s]\u001b[A\n","ITERATION - loss: 0.14: 33%|███▎ | 280/844 [10:54<00:06, 88.24it/s]\u001b[A\n","ITERATION - loss: 0.12: 34%|███▍ | 290/844 [10:54<00:06, 89.47it/s]\u001b[A\n","ITERATION - loss: 0.26: 36%|███▌ | 300/844 [10:55<00:06, 89.54it/s]\u001b[A\n","ITERATION - loss: 0.14: 37%|███▋ | 310/844 [10:55<00:05, 90.36it/s]\u001b[A\n","ITERATION - loss: 0.11: 38%|███▊ | 320/844 [10:55<00:05, 91.32it/s]\u001b[A\n","ITERATION - loss: 0.07: 39%|███▉ | 330/844 [10:55<00:05, 92.47it/s]\u001b[A\n","ITERATION - loss: 0.15: 40%|████ | 340/844 [10:55<00:05, 92.91it/s]\u001b[A\n","ITERATION - loss: 0.07: 41%|████▏ | 350/844 [10:55<00:05, 92.54it/s]\u001b[A\n","ITERATION - loss: 0.17: 43%|████▎ | 360/844 [10:55<00:05, 93.62it/s]\u001b[A\n","ITERATION - loss: 0.13: 44%|████▍ | 370/844 [10:55<00:05, 90.82it/s]\u001b[A\n","ITERATION - loss: 0.17: 45%|████▌ | 380/844 [10:55<00:05, 89.53it/s]\u001b[A\n","ITERATION - loss: 0.18: 46%|████▌ | 390/844 [10:56<00:05, 88.89it/s]\u001b[A\n","ITERATION - loss: 0.05: 47%|████▋ | 400/844 [10:56<00:05, 88.35it/s]\u001b[A\n","ITERATION - loss: 0.08: 49%|████▊ | 410/844 [10:56<00:04, 87.30it/s]\u001b[A\n","ITERATION - loss: 0.07: 50%|████▉ | 420/844 [10:56<00:04, 88.49it/s]\u001b[A\n","ITERATION - loss: 0.10: 51%|█████ | 430/844 [10:56<00:04, 89.50it/s]\u001b[A\n","ITERATION - loss: 0.15: 52%|█████▏ | 440/844 [10:56<00:04, 88.47it/s]\u001b[A\n","ITERATION - loss: 0.12: 53%|█████▎ | 450/844 [10:56<00:04, 87.43it/s]\u001b[A\n","ITERATION - loss: 0.12: 55%|█████▍ | 460/844 [10:56<00:04, 85.32it/s]\u001b[A\n","ITERATION - loss: 0.17: 56%|█████▌ | 470/844 [10:56<00:04, 87.86it/s]\u001b[A\n","ITERATION - loss: 0.06: 57%|█████▋ | 480/844 [10:57<00:04, 88.24it/s]\u001b[A\n","ITERATION - loss: 0.08: 58%|█████▊ | 490/844 [10:57<00:03, 89.22it/s]\u001b[A\n","ITERATION - loss: 0.18: 59%|█████▉ | 500/844 [10:57<00:03, 90.91it/s]\u001b[A\n","ITERATION - loss: 0.10: 60%|██████ | 510/844 [10:57<00:03, 92.02it/s]\u001b[A\n","ITERATION - loss: 0.21: 62%|██████▏ | 520/844 [10:57<00:03, 92.77it/s]\u001b[A\n","ITERATION - loss: 0.08: 63%|██████▎ | 530/844 [10:57<00:03, 92.31it/s]\u001b[A\n","ITERATION - loss: 0.10: 64%|██████▍ | 540/844 [10:57<00:03, 92.95it/s]\u001b[A\n","ITERATION - loss: 0.12: 65%|██████▌ | 550/844 [10:57<00:03, 92.24it/s]\u001b[A\n","ITERATION - loss: 0.11: 66%|██████▋ | 560/844 [10:57<00:03, 90.73it/s]\u001b[A\n","ITERATION - loss: 0.27: 68%|██████▊ | 570/844 [10:58<00:03, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.16: 69%|██████▊ | 580/844 [10:58<00:02, 89.42it/s]\u001b[A\n","ITERATION - loss: 0.14: 70%|██████▉ | 590/844 [10:58<00:02, 90.69it/s]\u001b[A\n","ITERATION - loss: 0.12: 71%|███████ | 600/844 [10:58<00:02, 91.58it/s]\u001b[A\n","ITERATION - loss: 0.13: 72%|███████▏ | 610/844 [10:58<00:02, 92.13it/s]\u001b[A\n","ITERATION - loss: 0.34: 73%|███████▎ | 620/844 [10:58<00:02, 93.12it/s]\u001b[A\n","ITERATION - loss: 0.10: 75%|███████▍ | 630/844 [10:58<00:02, 93.76it/s]\u001b[A\n","ITERATION - loss: 0.05: 76%|███████▌ | 640/844 [10:58<00:02, 93.90it/s]\u001b[A\n","ITERATION - loss: 0.17: 77%|███████▋ | 650/844 [10:58<00:02, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.16: 78%|███████▊ | 660/844 [10:59<00:02, 91.55it/s]\u001b[A\n","ITERATION - loss: 0.19: 79%|███████▉ | 670/844 [10:59<00:01, 90.51it/s]\u001b[A\n","ITERATION - loss: 0.17: 81%|████████ | 680/844 [10:59<00:01, 91.65it/s]\u001b[A\n","ITERATION - loss: 0.12: 82%|████████▏ | 690/844 [10:59<00:01, 91.80it/s]\u001b[A\n","ITERATION - loss: 0.12: 83%|████████▎ | 700/844 [10:59<00:01, 92.03it/s]\u001b[A\n","ITERATION - loss: 0.14: 84%|████████▍ | 710/844 [10:59<00:01, 92.30it/s]\u001b[A\n","ITERATION - loss: 0.07: 85%|████████▌ | 720/844 [10:59<00:01, 92.71it/s]\u001b[A\n","ITERATION - loss: 0.21: 86%|████████▋ | 730/844 [10:59<00:01, 92.48it/s]\u001b[A\n","ITERATION - loss: 0.10: 88%|████████▊ | 740/844 [10:59<00:01, 90.87it/s]\u001b[A\n","ITERATION - loss: 0.16: 89%|████████▉ | 750/844 [10:59<00:01, 91.60it/s]\u001b[A\n","ITERATION - loss: 0.10: 90%|█████████ | 760/844 [11:00<00:00, 90.03it/s]\u001b[A\n","ITERATION - loss: 0.17: 91%|█████████ | 770/844 [11:00<00:00, 91.43it/s]\u001b[A\n","ITERATION - loss: 0.08: 92%|█████████▏| 780/844 [11:00<00:00, 92.49it/s]\u001b[A\n","ITERATION - loss: 0.15: 94%|█████████▎| 790/844 [11:00<00:00, 89.84it/s]\u001b[A\n","ITERATION - loss: 0.13: 95%|█████████▍| 800/844 [11:00<00:00, 91.44it/s]\u001b[A\n","ITERATION - loss: 0.10: 96%|█████████▌| 810/844 [11:00<00:00, 92.26it/s]\u001b[A\n","ITERATION - loss: 0.15: 97%|█████████▋| 820/844 [11:00<00:00, 91.07it/s]\u001b[A\n","ITERATION - loss: 0.06: 98%|█████████▊| 830/844 [11:00<00:00, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.17: 100%|█████████▉| 840/844 [11:00<00:00, 90.65it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [11:41<00:00, 93.64it/s]\n","ITERATION - loss: 0.17: 100%|█████████▉| 840/844 [11:07<00:00, 90.65it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 39 Avg accuracy: 96.43 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [11:42<00:00, 93.64it/s]\n","ITERATION - loss: 0.17: 100%|█████████▉| 840/844 [11:08<00:00, 90.65it/s]\u001b[A\n","ITERATION - loss: 0.12: 1%| | 10/844 [11:08<03:23, 4.10it/s] \u001b[A\n","ITERATION - loss: 0.04: 2%|▏ | 20/844 [11:08<02:23, 5.75it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 39 Avg accuracy: 95.92 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.10: 4%|▎ | 30/844 [11:09<01:41, 8.00it/s]\u001b[A\n","ITERATION - loss: 0.09: 5%|▍ | 40/844 [11:09<01:13, 10.99it/s]\u001b[A\n","ITERATION - loss: 0.12: 6%|▌ | 50/844 [11:09<00:53, 14.91it/s]\u001b[A\n","ITERATION - loss: 0.09: 7%|▋ | 60/844 [11:09<00:39, 19.95it/s]\u001b[A\n","ITERATION - loss: 0.09: 8%|▊ | 70/844 [11:09<00:29, 26.11it/s]\u001b[A\n","ITERATION - loss: 0.13: 9%|▉ | 80/844 [11:09<00:22, 33.26it/s]\u001b[A\n","ITERATION - loss: 0.10: 11%|█ | 90/844 [11:09<00:18, 41.31it/s]\u001b[A\n","ITERATION - loss: 0.12: 12%|█▏ | 100/844 [11:09<00:15, 49.56it/s]\u001b[A\n","ITERATION - loss: 0.28: 13%|█▎ | 110/844 [11:09<00:12, 56.58it/s]\u001b[A\n","ITERATION - loss: 0.05: 14%|█▍ | 120/844 [11:10<00:11, 64.27it/s]\u001b[A\n","ITERATION - loss: 0.21: 15%|█▌ | 130/844 [11:10<00:10, 69.68it/s]\u001b[A\n","ITERATION - loss: 0.14: 17%|█▋ | 140/844 [11:10<00:09, 74.33it/s]\u001b[A\n","ITERATION - loss: 0.21: 18%|█▊ | 150/844 [11:10<00:08, 79.10it/s]\u001b[A\n","ITERATION - loss: 0.20: 19%|█▉ | 160/844 [11:10<00:08, 81.91it/s]\u001b[A\n","ITERATION - loss: 0.07: 20%|██ | 170/844 [11:10<00:07, 85.26it/s]\u001b[A\n","ITERATION - loss: 0.19: 21%|██▏ | 180/844 [11:10<00:07, 87.89it/s]\u001b[A\n","ITERATION - loss: 0.13: 23%|██▎ | 190/844 [11:10<00:07, 88.12it/s]\u001b[A\n","ITERATION - loss: 0.11: 24%|██▎ | 200/844 [11:10<00:07, 89.77it/s]\u001b[A\n","ITERATION - loss: 0.09: 25%|██▍ | 210/844 [11:11<00:06, 91.45it/s]\u001b[A\n","ITERATION - loss: 0.10: 26%|██▌ | 220/844 [11:11<00:06, 89.45it/s]\u001b[A\n","ITERATION - loss: 0.12: 27%|██▋ | 230/844 [11:11<00:06, 89.35it/s]\u001b[A\n","ITERATION - loss: 0.14: 28%|██▊ | 240/844 [11:11<00:06, 90.79it/s]\u001b[A\n","ITERATION - loss: 0.10: 30%|██▉ | 250/844 [11:11<00:06, 91.78it/s]\u001b[A\n","ITERATION - loss: 0.07: 31%|███ | 260/844 [11:11<00:06, 93.07it/s]\u001b[A\n","ITERATION - loss: 0.28: 32%|███▏ | 270/844 [11:11<00:06, 88.72it/s]\u001b[A\n","ITERATION - loss: 0.09: 33%|███▎ | 280/844 [11:11<00:06, 88.90it/s]\u001b[A\n","ITERATION - loss: 0.12: 34%|███▍ | 290/844 [11:11<00:06, 90.02it/s]\u001b[A\n","ITERATION - loss: 0.19: 36%|███▌ | 300/844 [11:12<00:05, 90.82it/s]\u001b[A\n","ITERATION - loss: 0.09: 37%|███▋ | 310/844 [11:12<00:06, 87.26it/s]\u001b[A\n","ITERATION - loss: 0.06: 38%|███▊ | 320/844 [11:12<00:06, 86.54it/s]\u001b[A\n","ITERATION - loss: 0.05: 39%|███▉ | 330/844 [11:12<00:05, 88.69it/s]\u001b[A\n","ITERATION - loss: 0.20: 40%|████ | 340/844 [11:12<00:05, 89.23it/s]\u001b[A\n","ITERATION - loss: 0.13: 41%|████▏ | 350/844 [11:12<00:05, 88.43it/s]\u001b[A\n","ITERATION - loss: 0.08: 43%|████▎ | 360/844 [11:12<00:05, 90.31it/s]\u001b[A\n","ITERATION - loss: 0.11: 44%|████▍ | 370/844 [11:12<00:05, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.09: 45%|████▌ | 380/844 [11:12<00:05, 91.08it/s]\u001b[A\n","ITERATION - loss: 0.05: 46%|████▌ | 390/844 [11:13<00:05, 90.73it/s]\u001b[A\n","ITERATION - loss: 0.19: 47%|████▋ | 400/844 [11:13<00:04, 90.41it/s]\u001b[A\n","ITERATION - loss: 0.18: 49%|████▊ | 410/844 [11:13<00:04, 88.42it/s]\u001b[A\n","ITERATION - loss: 0.21: 50%|████▉ | 420/844 [11:13<00:04, 90.44it/s]\u001b[A\n","ITERATION - loss: 0.13: 51%|█████ | 430/844 [11:13<00:04, 90.57it/s]\u001b[A\n","ITERATION - loss: 0.23: 52%|█████▏ | 440/844 [11:13<00:04, 91.15it/s]\u001b[A\n","ITERATION - loss: 0.19: 53%|█████▎ | 450/844 [11:13<00:04, 92.18it/s]\u001b[A\n","ITERATION - loss: 0.17: 55%|█████▍ | 460/844 [11:13<00:04, 84.67it/s]\u001b[A\n","ITERATION - loss: 0.09: 56%|█████▌ | 470/844 [11:13<00:04, 87.45it/s]\u001b[A\n","ITERATION - loss: 0.04: 58%|█████▊ | 490/844 [11:14<00:03, 91.80it/s]\u001b[A\n","ITERATION - loss: 0.05: 59%|█████▉ | 500/844 [11:14<00:03, 91.67it/s]\u001b[A\n","ITERATION - loss: 0.26: 60%|██████ | 510/844 [11:14<00:03, 91.81it/s]\u001b[A\n","ITERATION - loss: 0.11: 62%|██████▏ | 520/844 [11:14<00:03, 93.72it/s]\u001b[A\n","ITERATION - loss: 0.06: 63%|██████▎ | 530/844 [11:14<00:03, 95.02it/s]\u001b[A\n","ITERATION - loss: 0.11: 64%|██████▍ | 540/844 [11:14<00:03, 93.66it/s]\u001b[A\n","ITERATION - loss: 0.13: 65%|██████▌ | 550/844 [11:14<00:03, 93.14it/s]\u001b[A\n","ITERATION - loss: 0.14: 66%|██████▋ | 560/844 [11:14<00:03, 93.10it/s]\u001b[A\n","ITERATION - loss: 0.07: 68%|██████▊ | 570/844 [11:15<00:02, 92.36it/s]\u001b[A\n","ITERATION - loss: 0.14: 69%|██████▊ | 580/844 [11:15<00:02, 92.94it/s]\u001b[A\n","ITERATION - loss: 0.05: 70%|██████▉ | 590/844 [11:15<00:02, 93.51it/s]\u001b[A\n","ITERATION - loss: 0.07: 71%|███████ | 600/844 [11:15<00:02, 90.07it/s]\u001b[A\n","ITERATION - loss: 0.20: 72%|███████▏ | 610/844 [11:15<00:02, 90.75it/s]\u001b[A\n","ITERATION - loss: 0.07: 73%|███████▎ | 620/844 [11:15<00:02, 91.67it/s]\u001b[A\n","ITERATION - loss: 0.07: 75%|███████▍ | 630/844 [11:15<00:02, 89.50it/s]\u001b[A\n","ITERATION - loss: 0.20: 76%|███████▌ | 640/844 [11:15<00:02, 91.46it/s]\u001b[A\n","ITERATION - loss: 0.08: 77%|███████▋ | 650/844 [11:15<00:02, 88.24it/s]\u001b[A\n","ITERATION - loss: 0.09: 78%|███████▊ | 660/844 [11:16<00:02, 90.28it/s]\u001b[A\n","ITERATION - loss: 0.05: 79%|███████▉ | 670/844 [11:16<00:01, 91.97it/s]\u001b[A\n","ITERATION - loss: 0.18: 81%|████████ | 680/844 [11:16<00:01, 91.99it/s]\u001b[A\n","ITERATION - loss: 0.09: 82%|████████▏ | 690/844 [11:16<00:01, 89.07it/s]\u001b[A\n","ITERATION - loss: 0.18: 83%|████████▎ | 700/844 [11:16<00:01, 90.19it/s]\u001b[A\n","ITERATION - loss: 0.16: 84%|████████▍ | 710/844 [11:16<00:01, 91.00it/s]\u001b[A\n","ITERATION - loss: 0.11: 85%|████████▌ | 720/844 [11:16<00:01, 92.09it/s]\u001b[A\n","ITERATION - loss: 0.10: 86%|████████▋ | 730/844 [11:16<00:01, 92.05it/s]\u001b[A\n","ITERATION - loss: 0.16: 88%|████████▊ | 740/844 [11:16<00:01, 92.30it/s]\u001b[A\n","ITERATION - loss: 0.29: 89%|████████▉ | 750/844 [11:16<00:01, 92.95it/s]\u001b[A\n","ITERATION - loss: 0.22: 90%|█████████ | 760/844 [11:17<00:00, 92.84it/s]\u001b[A\n","ITERATION - loss: 0.28: 91%|█████████ | 770/844 [11:17<00:00, 92.49it/s]\u001b[A\n","ITERATION - loss: 0.18: 92%|█████████▏| 780/844 [11:17<00:00, 91.00it/s]\u001b[A\n","ITERATION - loss: 0.20: 94%|█████████▎| 790/844 [11:17<00:00, 90.20it/s]\u001b[A\n","ITERATION - loss: 0.09: 95%|█████████▍| 800/844 [11:17<00:00, 90.30it/s]\u001b[A\n","ITERATION - loss: 0.14: 96%|█████████▌| 810/844 [11:17<00:00, 89.85it/s]\u001b[A\n","ITERATION - loss: 0.04: 97%|█████████▋| 820/844 [11:17<00:00, 89.46it/s]\u001b[A\n","ITERATION - loss: 0.09: 98%|█████████▊| 830/844 [11:17<00:00, 90.38it/s]\u001b[A\n","ITERATION - loss: 0.09: 100%|█████████▉| 840/844 [11:17<00:00, 88.15it/s]\u001b[A\n","ITERATION - loss: 0.10: 850it [11:18, 90.59it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [11:58<00:00, 93.64it/s]\n","ITERATION - loss: 0.10: 850it [11:25, 90.59it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 40 Avg accuracy: 96.50 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [11:59<00:00, 93.64it/s]\n","ITERATION - loss: 0.10: 850it [11:25, 90.59it/s]\u001b[A\n","ITERATION - loss: 0.14: 1%| | 10/844 [11:26<03:24, 4.08it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 40 Avg accuracy: 96.13 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.08: 2%|▏ | 20/844 [11:26<02:24, 5.71it/s]\u001b[A\n","ITERATION - loss: 0.16: 4%|▎ | 30/844 [11:26<01:42, 7.95it/s]\u001b[A\n","ITERATION - loss: 0.28: 5%|▍ | 40/844 [11:26<01:13, 10.93it/s]\u001b[A\n","ITERATION - loss: 0.08: 6%|▌ | 50/844 [11:26<00:53, 14.88it/s]\u001b[A\n","ITERATION - loss: 0.18: 7%|▋ | 60/844 [11:26<00:39, 19.70it/s]\u001b[A\n","ITERATION - loss: 0.09: 8%|▊ | 70/844 [11:26<00:30, 25.51it/s]\u001b[A\n","ITERATION - loss: 0.29: 9%|▉ | 80/844 [11:26<00:23, 32.62it/s]\u001b[A\n","ITERATION - loss: 0.09: 11%|█ | 90/844 [11:26<00:18, 40.36it/s]\u001b[A\n","ITERATION - loss: 0.06: 12%|█▏ | 100/844 [11:27<00:15, 48.70it/s]\u001b[A\n","ITERATION - loss: 0.09: 13%|█▎ | 110/844 [11:27<00:12, 57.30it/s]\u001b[A\n","ITERATION - loss: 0.22: 14%|█▍ | 120/844 [11:27<00:11, 64.67it/s]\u001b[A\n","ITERATION - loss: 0.14: 15%|█▌ | 130/844 [11:27<00:09, 71.62it/s]\u001b[A\n","ITERATION - loss: 0.08: 17%|█▋ | 140/844 [11:27<00:09, 77.48it/s]\u001b[A\n","ITERATION - loss: 0.18: 18%|█▊ | 150/844 [11:27<00:08, 80.28it/s]\u001b[A\n","ITERATION - loss: 0.10: 19%|█▉ | 160/844 [11:27<00:08, 82.96it/s]\u001b[A\n","ITERATION - loss: 0.09: 20%|██ | 170/844 [11:27<00:07, 85.27it/s]\u001b[A\n","ITERATION - loss: 0.14: 21%|██▏ | 180/844 [11:27<00:07, 87.98it/s]\u001b[A\n","ITERATION - loss: 0.20: 23%|██▎ | 190/844 [11:27<00:07, 89.47it/s]\u001b[A\n","ITERATION - loss: 0.05: 24%|██▎ | 200/844 [11:28<00:07, 89.17it/s]\u001b[A\n","ITERATION - loss: 0.08: 25%|██▍ | 210/844 [11:28<00:07, 90.37it/s]\u001b[A\n","ITERATION - loss: 0.08: 26%|██▌ | 220/844 [11:28<00:06, 90.42it/s]\u001b[A\n","ITERATION - loss: 0.16: 27%|██▋ | 230/844 [11:28<00:06, 90.36it/s]\u001b[A\n","ITERATION - loss: 0.09: 28%|██▊ | 240/844 [11:28<00:06, 90.85it/s]\u001b[A\n","ITERATION - loss: 0.11: 30%|██▉ | 250/844 [11:28<00:06, 88.92it/s]\u001b[A\n","ITERATION - loss: 0.08: 31%|███ | 260/844 [11:28<00:06, 89.64it/s]\u001b[A\n","ITERATION - loss: 0.14: 32%|███▏ | 270/844 [11:28<00:06, 89.77it/s]\u001b[A\n","ITERATION - loss: 0.15: 33%|███▎ | 280/844 [11:29<00:06, 86.59it/s]\u001b[A\n","ITERATION - loss: 0.06: 34%|███▍ | 290/844 [11:29<00:06, 86.00it/s]\u001b[A\n","ITERATION - loss: 0.20: 36%|███▌ | 300/844 [11:29<00:06, 86.12it/s]\u001b[A\n","ITERATION - loss: 0.08: 37%|███▋ | 310/844 [11:29<00:06, 87.74it/s]\u001b[A\n","ITERATION - loss: 0.06: 38%|███▊ | 320/844 [11:29<00:05, 88.58it/s]\u001b[A\n","ITERATION - loss: 0.13: 39%|███▉ | 330/844 [11:29<00:05, 87.81it/s]\u001b[A\n","ITERATION - loss: 0.02: 40%|████ | 340/844 [11:29<00:05, 86.90it/s]\u001b[A\n","ITERATION - loss: 0.13: 41%|████▏ | 350/844 [11:29<00:05, 86.36it/s]\u001b[A\n","ITERATION - loss: 0.14: 43%|████▎ | 360/844 [11:29<00:05, 88.36it/s]\u001b[A\n","ITERATION - loss: 0.23: 44%|████▍ | 370/844 [11:30<00:05, 89.76it/s]\u001b[A\n","ITERATION - loss: 0.22: 45%|████▌ | 380/844 [11:30<00:05, 90.69it/s]\u001b[A\n","ITERATION - loss: 0.15: 46%|████▌ | 390/844 [11:30<00:05, 90.28it/s]\u001b[A\n","ITERATION - loss: 0.07: 47%|████▋ | 400/844 [11:30<00:04, 90.69it/s]\u001b[A\n","ITERATION - loss: 0.14: 49%|████▊ | 410/844 [11:30<00:04, 91.37it/s]\u001b[A\n","ITERATION - loss: 0.11: 50%|████▉ | 420/844 [11:30<00:04, 88.77it/s]\u001b[A\n","ITERATION - loss: 0.15: 51%|█████ | 430/844 [11:30<00:04, 87.30it/s]\u001b[A\n","ITERATION - loss: 0.19: 52%|█████▏ | 440/844 [11:30<00:04, 88.18it/s]\u001b[A\n","ITERATION - loss: 0.11: 53%|█████▎ | 450/844 [11:30<00:04, 88.99it/s]\u001b[A\n","ITERATION - loss: 0.16: 55%|█████▍ | 460/844 [11:31<00:04, 90.74it/s]\u001b[A\n","ITERATION - loss: 0.12: 56%|█████▌ | 470/844 [11:31<00:04, 92.04it/s]\u001b[A\n","ITERATION - loss: 0.10: 57%|█████▋ | 480/844 [11:31<00:04, 89.17it/s]\u001b[A\n","ITERATION - loss: 0.12: 58%|█████▊ | 490/844 [11:31<00:03, 90.84it/s]\u001b[A\n","ITERATION - loss: 0.06: 59%|█████▉ | 500/844 [11:31<00:03, 92.26it/s]\u001b[A\n","ITERATION - loss: 0.13: 60%|██████ | 510/844 [11:31<00:03, 92.24it/s]\u001b[A\n","ITERATION - loss: 0.09: 62%|██████▏ | 520/844 [11:31<00:03, 86.47it/s]\u001b[A\n","ITERATION - loss: 0.11: 63%|██████▎ | 530/844 [11:31<00:03, 88.37it/s]\u001b[A\n","ITERATION - loss: 0.32: 64%|██████▍ | 540/844 [11:31<00:03, 89.17it/s]\u001b[A\n","ITERATION - loss: 0.11: 65%|██████▌ | 550/844 [11:32<00:03, 91.32it/s]\u001b[A\n","ITERATION - loss: 0.12: 66%|██████▋ | 560/844 [11:32<00:03, 91.42it/s]\u001b[A\n","ITERATION - loss: 0.28: 68%|██████▊ | 570/844 [11:32<00:02, 92.99it/s]\u001b[A\n","ITERATION - loss: 0.15: 69%|██████▊ | 580/844 [11:32<00:02, 93.51it/s]\u001b[A\n","ITERATION - loss: 0.09: 70%|██████▉ | 590/844 [11:32<00:02, 93.19it/s]\u001b[A\n","ITERATION - loss: 0.16: 71%|███████ | 600/844 [11:32<00:02, 94.20it/s]\u001b[A\n","ITERATION - loss: 0.25: 72%|███████▏ | 610/844 [11:32<00:02, 91.75it/s]\u001b[A\n","ITERATION - loss: 0.38: 73%|███████▎ | 620/844 [11:32<00:02, 87.65it/s]\u001b[A\n","ITERATION - loss: 0.15: 75%|███████▍ | 630/844 [11:32<00:02, 89.81it/s]\u001b[A\n","ITERATION - loss: 0.06: 76%|███████▌ | 640/844 [11:33<00:02, 90.05it/s]\u001b[A\n","ITERATION - loss: 0.05: 77%|███████▋ | 650/844 [11:33<00:02, 91.99it/s]\u001b[A\n","ITERATION - loss: 0.04: 78%|███████▊ | 660/844 [11:33<00:01, 93.22it/s]\u001b[A\n","ITERATION - loss: 0.06: 79%|███████▉ | 670/844 [11:33<00:01, 92.95it/s]\u001b[A\n","ITERATION - loss: 0.06: 81%|████████ | 680/844 [11:33<00:01, 93.97it/s]\u001b[A\n","ITERATION - loss: 0.10: 82%|████████▏ | 690/844 [11:33<00:01, 94.34it/s]\u001b[A\n","ITERATION - loss: 0.08: 83%|████████▎ | 700/844 [11:33<00:01, 91.48it/s]\u001b[A\n","ITERATION - loss: 0.07: 84%|████████▍ | 710/844 [11:33<00:01, 87.75it/s]\u001b[A\n","ITERATION - loss: 0.07: 85%|████████▌ | 720/844 [11:33<00:01, 89.70it/s]\u001b[A\n","ITERATION - loss: 0.09: 86%|████████▋ | 730/844 [11:33<00:01, 90.87it/s]\u001b[A\n","ITERATION - loss: 0.09: 88%|████████▊ | 740/844 [11:34<00:01, 91.67it/s]\u001b[A\n","ITERATION - loss: 0.15: 89%|████████▉ | 750/844 [11:34<00:01, 92.63it/s]\u001b[A\n","ITERATION - loss: 0.23: 90%|█████████ | 760/844 [11:34<00:00, 92.50it/s]\u001b[A\n","ITERATION - loss: 0.07: 91%|█████████ | 770/844 [11:34<00:00, 93.10it/s]\u001b[A\n","ITERATION - loss: 0.16: 92%|█████████▏| 780/844 [11:34<00:00, 89.15it/s]\u001b[A\n","ITERATION - loss: 0.18: 94%|█████████▎| 790/844 [11:34<00:00, 85.65it/s]\u001b[A\n","ITERATION - loss: 0.10: 95%|█████████▍| 800/844 [11:34<00:00, 86.79it/s]\u001b[A\n","ITERATION - loss: 0.11: 96%|█████████▌| 810/844 [11:34<00:00, 89.85it/s]\u001b[A\n","ITERATION - loss: 0.10: 97%|█████████▋| 820/844 [11:34<00:00, 92.36it/s]\u001b[A\n","ITERATION - loss: 0.17: 98%|█████████▊| 830/844 [11:35<00:00, 94.17it/s]\u001b[A\n","ITERATION - loss: 0.05: 100%|█████████▉| 840/844 [11:35<00:00, 95.27it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [12:15<00:00, 93.64it/s]\n","ITERATION - loss: 0.05: 100%|█████████▉| 840/844 [11:42<00:00, 95.27it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 41 Avg accuracy: 96.54 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [12:16<00:00, 93.64it/s]\n","ITERATION - loss: 0.05: 100%|█████████▉| 840/844 [11:42<00:00, 95.27it/s]\u001b[A\n","ITERATION - loss: 0.13: 1%| | 10/844 [11:43<03:21, 4.13it/s] \u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 41 Avg accuracy: 96.12 Avg loss: 0.14\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.10: 2%|▏ | 20/844 [11:43<02:22, 5.79it/s]\u001b[A\n","ITERATION - loss: 0.13: 4%|▎ | 30/844 [11:43<01:41, 8.03it/s]\u001b[A\n","ITERATION - loss: 0.06: 5%|▍ | 40/844 [11:43<01:12, 11.04it/s]\u001b[A\n","ITERATION - loss: 0.12: 6%|▌ | 50/844 [11:43<00:52, 15.02it/s]\u001b[A\n","ITERATION - loss: 0.13: 7%|▋ | 60/844 [11:43<00:39, 20.05it/s]\u001b[A\n","ITERATION - loss: 0.23: 8%|▊ | 70/844 [11:43<00:29, 26.23it/s]\u001b[A\n","ITERATION - loss: 0.10: 9%|▉ | 80/844 [11:43<00:22, 33.48it/s]\u001b[A\n","ITERATION - loss: 0.20: 11%|█ | 90/844 [11:43<00:18, 41.35it/s]\u001b[A\n","ITERATION - loss: 0.20: 12%|█▏ | 100/844 [11:43<00:15, 49.34it/s]\u001b[A\n","ITERATION - loss: 0.14: 13%|█▎ | 110/844 [11:44<00:12, 58.00it/s]\u001b[A\n","ITERATION - loss: 0.12: 14%|█▍ | 120/844 [11:44<00:11, 65.61it/s]\u001b[A\n","ITERATION - loss: 0.14: 15%|█▌ | 130/844 [11:44<00:09, 72.75it/s]\u001b[A\n","ITERATION - loss: 0.16: 17%|█▋ | 140/844 [11:44<00:08, 78.42it/s]\u001b[A\n","ITERATION - loss: 0.10: 18%|█▊ | 150/844 [11:44<00:08, 82.71it/s]\u001b[A\n","ITERATION - loss: 0.10: 19%|█▉ | 160/844 [11:44<00:07, 86.47it/s]\u001b[A\n","ITERATION - loss: 0.13: 20%|██ | 170/844 [11:44<00:07, 88.50it/s]\u001b[A\n","ITERATION - loss: 0.15: 21%|██▏ | 180/844 [11:44<00:08, 82.61it/s]\u001b[A\n","ITERATION - loss: 0.04: 23%|██▎ | 190/844 [11:44<00:08, 80.80it/s]\u001b[A\n","ITERATION - loss: 0.14: 24%|██▎ | 200/844 [11:45<00:07, 84.28it/s]\u001b[A\n","ITERATION - loss: 0.12: 25%|██▍ | 210/844 [11:45<00:07, 85.50it/s]\u001b[A\n","ITERATION - loss: 0.07: 26%|██▌ | 220/844 [11:45<00:07, 88.11it/s]\u001b[A\n","ITERATION - loss: 0.10: 27%|██▋ | 230/844 [11:45<00:06, 90.37it/s]\u001b[A\n","ITERATION - loss: 0.12: 28%|██▊ | 240/844 [11:45<00:06, 91.67it/s]\u001b[A\n","ITERATION - loss: 0.14: 30%|██▉ | 250/844 [11:45<00:06, 92.12it/s]\u001b[A\n","ITERATION - loss: 0.10: 31%|███ | 260/844 [11:45<00:06, 91.51it/s]\u001b[A\n","ITERATION - loss: 0.03: 32%|███▏ | 270/844 [11:45<00:06, 88.64it/s]\u001b[A\n","ITERATION - loss: 0.13: 33%|███▎ | 280/844 [11:45<00:06, 90.89it/s]\u001b[A\n","ITERATION - loss: 0.15: 34%|███▍ | 290/844 [11:46<00:06, 90.39it/s]\u001b[A\n","ITERATION - loss: 0.09: 36%|███▌ | 300/844 [11:46<00:05, 90.69it/s]\u001b[A\n","ITERATION - loss: 0.14: 37%|███▋ | 310/844 [11:46<00:05, 92.39it/s]\u001b[A\n","ITERATION - loss: 0.05: 38%|███▊ | 320/844 [11:46<00:05, 93.11it/s]\u001b[A\n","ITERATION - loss: 0.10: 39%|███▉ | 330/844 [11:46<00:05, 92.56it/s]\u001b[A\n","ITERATION - loss: 0.24: 40%|████ | 340/844 [11:46<00:05, 92.78it/s]\u001b[A\n","ITERATION - loss: 0.11: 41%|████▏ | 350/844 [11:46<00:05, 93.59it/s]\u001b[A\n","ITERATION - loss: 0.15: 43%|████▎ | 360/844 [11:46<00:05, 89.25it/s]\u001b[A\n","ITERATION - loss: 0.14: 44%|████▍ | 370/844 [11:46<00:05, 90.24it/s]\u001b[A\n","ITERATION - loss: 0.07: 45%|████▌ | 380/844 [11:47<00:05, 89.83it/s]\u001b[A\n","ITERATION - loss: 0.06: 46%|████▌ | 390/844 [11:47<00:04, 91.40it/s]\u001b[A\n","ITERATION - loss: 0.10: 47%|████▋ | 400/844 [11:47<00:04, 92.27it/s]\u001b[A\n","ITERATION - loss: 0.13: 49%|████▊ | 410/844 [11:47<00:04, 93.07it/s]\u001b[A\n","ITERATION - loss: 0.20: 50%|████▉ | 420/844 [11:47<00:04, 93.20it/s]\u001b[A\n","ITERATION - loss: 0.04: 51%|█████ | 430/844 [11:47<00:04, 90.16it/s]\u001b[A\n","ITERATION - loss: 0.20: 52%|█████▏ | 440/844 [11:47<00:04, 90.92it/s]\u001b[A\n","ITERATION - loss: 0.15: 53%|█████▎ | 450/844 [11:47<00:04, 91.41it/s]\u001b[A\n","ITERATION - loss: 0.10: 55%|█████▍ | 460/844 [11:47<00:04, 90.75it/s]\u001b[A\n","ITERATION - loss: 0.05: 56%|█████▌ | 470/844 [11:48<00:04, 90.92it/s]\u001b[A\n","ITERATION - loss: 0.05: 57%|█████▋ | 480/844 [11:48<00:03, 91.15it/s]\u001b[A\n","ITERATION - loss: 0.11: 58%|█████▊ | 490/844 [11:48<00:03, 93.01it/s]\u001b[A\n","ITERATION - loss: 0.11: 59%|█████▉ | 500/844 [11:48<00:03, 93.73it/s]\u001b[A\n","ITERATION - loss: 0.15: 60%|██████ | 510/844 [11:48<00:03, 93.29it/s]\u001b[A\n","ITERATION - loss: 0.06: 62%|██████▏ | 520/844 [11:48<00:03, 93.83it/s]\u001b[A\n","ITERATION - loss: 0.08: 63%|██████▎ | 530/844 [11:48<00:03, 93.84it/s]\u001b[A\n","ITERATION - loss: 0.09: 64%|██████▍ | 540/844 [11:48<00:03, 90.62it/s]\u001b[A\n","ITERATION - loss: 0.12: 65%|██████▌ | 550/844 [11:48<00:03, 89.21it/s]\u001b[A\n","ITERATION - loss: 0.13: 66%|██████▋ | 560/844 [11:49<00:03, 90.63it/s]\u001b[A\n","ITERATION - loss: 0.03: 68%|██████▊ | 570/844 [11:49<00:03, 88.83it/s]\u001b[A\n","ITERATION - loss: 0.19: 69%|██████▊ | 580/844 [11:49<00:02, 90.22it/s]\u001b[A\n","ITERATION - loss: 0.19: 70%|██████▉ | 590/844 [11:49<00:02, 90.83it/s]\u001b[A\n","ITERATION - loss: 0.29: 71%|███████ | 600/844 [11:49<00:02, 90.82it/s]\u001b[A\n","ITERATION - loss: 0.08: 72%|███████▏ | 610/844 [11:49<00:02, 91.26it/s]\u001b[A\n","ITERATION - loss: 0.17: 73%|███████▎ | 620/844 [11:49<00:02, 91.55it/s]\u001b[A\n","ITERATION - loss: 0.08: 75%|███████▍ | 630/844 [11:49<00:02, 91.31it/s]\u001b[A\n","ITERATION - loss: 0.13: 76%|███████▌ | 640/844 [11:49<00:02, 90.12it/s]\u001b[A\n","ITERATION - loss: 0.15: 77%|███████▋ | 650/844 [11:50<00:02, 89.97it/s]\u001b[A\n","ITERATION - loss: 0.18: 78%|███████▊ | 660/844 [11:50<00:02, 89.05it/s]\u001b[A\n","ITERATION - loss: 0.06: 79%|███████▉ | 670/844 [11:50<00:01, 90.39it/s]\u001b[A\n","ITERATION - loss: 0.25: 81%|████████ | 680/844 [11:50<00:01, 90.02it/s]\u001b[A\n","ITERATION - loss: 0.08: 82%|████████▏ | 690/844 [11:50<00:01, 90.55it/s]\u001b[A\n","ITERATION - loss: 0.10: 83%|████████▎ | 700/844 [11:50<00:01, 90.88it/s]\u001b[A\n","ITERATION - loss: 0.08: 84%|████████▍ | 710/844 [11:50<00:01, 89.23it/s]\u001b[A\n","ITERATION - loss: 0.29: 85%|████████▌ | 720/844 [11:50<00:01, 88.25it/s]\u001b[A\n","ITERATION - loss: 0.09: 86%|████████▋ | 730/844 [11:50<00:01, 87.85it/s]\u001b[A\n","ITERATION - loss: 0.20: 88%|████████▊ | 740/844 [11:51<00:01, 89.70it/s]\u001b[A\n","ITERATION - loss: 0.43: 89%|████████▉ | 750/844 [11:51<00:01, 89.02it/s]\u001b[A\n","ITERATION - loss: 0.24: 90%|█████████ | 760/844 [11:51<00:00, 88.24it/s]\u001b[A\n","ITERATION - loss: 0.09: 91%|█████████ | 770/844 [11:51<00:00, 89.11it/s]\u001b[A\n","ITERATION - loss: 0.14: 92%|█████████▏| 780/844 [11:51<00:00, 90.67it/s]\u001b[A\n","ITERATION - loss: 0.23: 94%|█████████▎| 790/844 [11:51<00:00, 91.21it/s]\u001b[A\n","ITERATION - loss: 0.14: 95%|█████████▍| 800/844 [11:51<00:00, 92.59it/s]\u001b[A\n","ITERATION - loss: 0.13: 96%|█████████▌| 810/844 [11:51<00:00, 92.47it/s]\u001b[A\n","ITERATION - loss: 0.05: 97%|█████████▋| 820/844 [11:51<00:00, 92.86it/s]\u001b[A\n","ITERATION - loss: 0.20: 98%|█████████▊| 830/844 [11:52<00:00, 91.42it/s]\u001b[A\n","ITERATION - loss: 0.16: 100%|█████████▉| 840/844 [11:52<00:00, 90.17it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [12:32<00:00, 93.64it/s]\n","ITERATION - loss: 0.16: 100%|█████████▉| 840/844 [11:59<00:00, 90.17it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 42 Avg accuracy: 96.61 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [12:33<00:00, 93.64it/s]\n","ITERATION - loss: 0.16: 100%|█████████▉| 840/844 [11:59<00:00, 90.17it/s]\u001b[A\n","ITERATION - loss: 0.11: 1%| | 10/844 [12:00<03:23, 4.10it/s] \u001b[A\n","ITERATION - loss: 0.09: 2%|▏ | 20/844 [12:00<02:23, 5.74it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 42 Avg accuracy: 96.03 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.08: 4%|▎ | 30/844 [12:00<01:41, 7.99it/s]\u001b[A\n","ITERATION - loss: 0.14: 5%|▍ | 40/844 [12:00<01:13, 10.94it/s]\u001b[A\n","ITERATION - loss: 0.07: 6%|▌ | 50/844 [12:00<00:53, 14.85it/s]\u001b[A\n","ITERATION - loss: 0.13: 7%|▋ | 60/844 [12:00<00:39, 19.86it/s]\u001b[A\n","ITERATION - loss: 0.08: 8%|▊ | 70/844 [12:00<00:29, 25.86it/s]\u001b[A\n","ITERATION - loss: 0.10: 9%|▉ | 80/844 [12:00<00:23, 32.78it/s]\u001b[A\n","ITERATION - loss: 0.05: 11%|█ | 90/844 [12:00<00:18, 40.92it/s]\u001b[A\n","ITERATION - loss: 0.06: 12%|█▏ | 100/844 [12:01<00:15, 49.20it/s]\u001b[A\n","ITERATION - loss: 0.10: 13%|█▎ | 110/844 [12:01<00:12, 57.16it/s]\u001b[A\n","ITERATION - loss: 0.19: 14%|█▍ | 120/844 [12:01<00:11, 65.07it/s]\u001b[A\n","ITERATION - loss: 0.06: 15%|█▌ | 130/844 [12:01<00:10, 70.83it/s]\u001b[A\n","ITERATION - loss: 0.09: 17%|█▋ | 140/844 [12:01<00:09, 76.44it/s]\u001b[A\n","ITERATION - loss: 0.05: 18%|█▊ | 150/844 [12:01<00:08, 81.61it/s]\u001b[A\n","ITERATION - loss: 0.05: 19%|█▉ | 160/844 [12:01<00:08, 82.43it/s]\u001b[A\n","ITERATION - loss: 0.16: 20%|██ | 170/844 [12:01<00:07, 85.83it/s]\u001b[A\n","ITERATION - loss: 0.07: 21%|██▏ | 180/844 [12:01<00:07, 88.66it/s]\u001b[A\n","ITERATION - loss: 0.07: 23%|██▎ | 190/844 [12:01<00:07, 90.16it/s]\u001b[A\n","ITERATION - loss: 0.13: 24%|██▎ | 200/844 [12:02<00:07, 89.40it/s]\u001b[A\n","ITERATION - loss: 0.21: 25%|██▍ | 210/844 [12:02<00:06, 90.68it/s]\u001b[A\n","ITERATION - loss: 0.17: 26%|██▌ | 220/844 [12:02<00:06, 91.30it/s]\u001b[A\n","ITERATION - loss: 0.07: 27%|██▋ | 230/844 [12:02<00:06, 88.63it/s]\u001b[A\n","ITERATION - loss: 0.04: 28%|██▊ | 240/844 [12:02<00:06, 89.53it/s]\u001b[A\n","ITERATION - loss: 0.22: 30%|██▉ | 250/844 [12:02<00:06, 91.32it/s]\u001b[A\n","ITERATION - loss: 0.04: 31%|███ | 260/844 [12:02<00:06, 91.49it/s]\u001b[A\n","ITERATION - loss: 0.13: 32%|███▏ | 270/844 [12:02<00:06, 93.58it/s]\u001b[A\n","ITERATION - loss: 0.13: 33%|███▎ | 280/844 [12:02<00:05, 94.47it/s]\u001b[A\n","ITERATION - loss: 0.06: 34%|███▍ | 290/844 [12:03<00:05, 94.87it/s]\u001b[A\n","ITERATION - loss: 0.11: 36%|███▌ | 300/844 [12:03<00:05, 93.53it/s]\u001b[A\n","ITERATION - loss: 0.11: 37%|███▋ | 310/844 [12:03<00:05, 94.94it/s]\u001b[A\n","ITERATION - loss: 0.12: 38%|███▊ | 320/844 [12:03<00:05, 95.79it/s]\u001b[A\n","ITERATION - loss: 0.23: 39%|███▉ | 330/844 [12:03<00:05, 94.41it/s]\u001b[A\n","ITERATION - loss: 0.09: 40%|████ | 340/844 [12:03<00:05, 95.69it/s]\u001b[A\n","ITERATION - loss: 0.08: 41%|████▏ | 350/844 [12:03<00:05, 95.34it/s]\u001b[A\n","ITERATION - loss: 0.13: 43%|████▎ | 360/844 [12:03<00:05, 92.41it/s]\u001b[A\n","ITERATION - loss: 0.11: 44%|████▍ | 370/844 [12:03<00:05, 93.27it/s]\u001b[A\n","ITERATION - loss: 0.09: 45%|████▌ | 380/844 [12:04<00:04, 93.03it/s]\u001b[A\n","ITERATION - loss: 0.12: 46%|████▌ | 390/844 [12:04<00:04, 91.88it/s]\u001b[A\n","ITERATION - loss: 0.14: 47%|████▋ | 400/844 [12:04<00:04, 93.36it/s]\u001b[A\n","ITERATION - loss: 0.11: 49%|████▊ | 410/844 [12:04<00:04, 93.19it/s]\u001b[A\n","ITERATION - loss: 0.09: 50%|████▉ | 420/844 [12:04<00:04, 92.03it/s]\u001b[A\n","ITERATION - loss: 0.17: 51%|█████ | 430/844 [12:04<00:04, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.09: 52%|█████▏ | 440/844 [12:04<00:04, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.09: 53%|█████▎ | 450/844 [12:04<00:04, 92.12it/s]\u001b[A\n","ITERATION - loss: 0.09: 55%|█████▍ | 460/844 [12:04<00:04, 93.83it/s]\u001b[A\n","ITERATION - loss: 0.19: 56%|█████▌ | 470/844 [12:04<00:04, 92.84it/s]\u001b[A\n","ITERATION - loss: 0.10: 57%|█████▋ | 480/844 [12:05<00:03, 94.06it/s]\u001b[A\n","ITERATION - loss: 0.11: 58%|█████▊ | 490/844 [12:05<00:03, 93.86it/s]\u001b[A\n","ITERATION - loss: 0.14: 59%|█████▉ | 500/844 [12:05<00:03, 94.95it/s]\u001b[A\n","ITERATION - loss: 0.05: 60%|██████ | 510/844 [12:05<00:03, 96.37it/s]\u001b[A\n","ITERATION - loss: 0.07: 62%|██████▏ | 520/844 [12:05<00:03, 94.37it/s]\u001b[A\n","ITERATION - loss: 0.07: 63%|██████▎ | 530/844 [12:05<00:03, 92.05it/s]\u001b[A\n","ITERATION - loss: 0.11: 64%|██████▍ | 540/844 [12:05<00:03, 84.87it/s]\u001b[A\n","ITERATION - loss: 0.08: 65%|██████▌ | 550/844 [12:05<00:03, 85.79it/s]\u001b[A\n","ITERATION - loss: 0.23: 66%|██████▋ | 560/844 [12:05<00:03, 87.46it/s]\u001b[A\n","ITERATION - loss: 0.04: 68%|██████▊ | 570/844 [12:06<00:03, 89.05it/s]\u001b[A\n","ITERATION - loss: 0.13: 69%|██████▊ | 580/844 [12:06<00:03, 85.71it/s]\u001b[A\n","ITERATION - loss: 0.14: 70%|██████▉ | 590/844 [12:06<00:02, 86.26it/s]\u001b[A\n","ITERATION - loss: 0.09: 71%|███████ | 600/844 [12:06<00:02, 87.37it/s]\u001b[A\n","ITERATION - loss: 0.03: 72%|███████▏ | 610/844 [12:06<00:02, 87.22it/s]\u001b[A\n","ITERATION - loss: 0.28: 73%|███████▎ | 620/844 [12:06<00:02, 88.70it/s]\u001b[A\n","ITERATION - loss: 0.16: 75%|███████▍ | 630/844 [12:06<00:02, 89.28it/s]\u001b[A\n","ITERATION - loss: 0.07: 76%|███████▌ | 640/844 [12:06<00:02, 90.78it/s]\u001b[A\n","ITERATION - loss: 0.08: 77%|███████▋ | 650/844 [12:06<00:02, 92.26it/s]\u001b[A\n","ITERATION - loss: 0.13: 78%|███████▊ | 660/844 [12:07<00:01, 92.65it/s]\u001b[A\n","ITERATION - loss: 0.17: 79%|███████▉ | 670/844 [12:07<00:01, 92.55it/s]\u001b[A\n","ITERATION - loss: 0.18: 81%|████████ | 680/844 [12:07<00:01, 93.70it/s]\u001b[A\n","ITERATION - loss: 0.07: 82%|████████▏ | 690/844 [12:07<00:01, 93.50it/s]\u001b[A\n","ITERATION - loss: 0.13: 83%|████████▎ | 700/844 [12:07<00:01, 92.93it/s]\u001b[A\n","ITERATION - loss: 0.11: 84%|████████▍ | 710/844 [12:07<00:01, 94.76it/s]\u001b[A\n","ITERATION - loss: 0.19: 85%|████████▌ | 720/844 [12:07<00:01, 93.65it/s]\u001b[A\n","ITERATION - loss: 0.12: 86%|████████▋ | 730/844 [12:07<00:01, 93.38it/s]\u001b[A\n","ITERATION - loss: 0.13: 88%|████████▊ | 740/844 [12:07<00:01, 94.85it/s]\u001b[A\n","ITERATION - loss: 0.07: 89%|████████▉ | 750/844 [12:08<00:01, 93.32it/s]\u001b[A\n","ITERATION - loss: 0.12: 90%|█████████ | 760/844 [12:08<00:00, 91.99it/s]\u001b[A\n","ITERATION - loss: 0.12: 91%|█████████ | 770/844 [12:08<00:00, 93.62it/s]\u001b[A\n","ITERATION - loss: 0.11: 92%|█████████▏| 780/844 [12:08<00:00, 92.27it/s]\u001b[A\n","ITERATION - loss: 0.11: 94%|█████████▎| 790/844 [12:08<00:00, 92.02it/s]\u001b[A\n","ITERATION - loss: 0.08: 95%|█████████▍| 800/844 [12:08<00:00, 90.98it/s]\u001b[A\n","ITERATION - loss: 0.11: 96%|█████████▌| 810/844 [12:08<00:00, 90.71it/s]\u001b[A\n","ITERATION - loss: 0.10: 97%|█████████▋| 820/844 [12:08<00:00, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.17: 98%|█████████▊| 830/844 [12:08<00:00, 93.35it/s]\u001b[A\n","ITERATION - loss: 0.04: 100%|█████████▉| 840/844 [12:09<00:00, 93.96it/s]\u001b[A\n","ITERATION - loss: 0.12: 850it [12:09, 94.78it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [12:49<00:00, 93.64it/s]\n","ITERATION - loss: 0.12: 850it [12:16, 94.78it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 43 Avg accuracy: 96.64 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [12:50<00:00, 93.64it/s]\n","ITERATION - loss: 0.12: 850it [12:16, 94.78it/s]\u001b[A\n","ITERATION - loss: 0.03: 1%| | 10/844 [12:16<03:22, 4.11it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 43 Avg accuracy: 96.07 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.07: 2%|▏ | 20/844 [12:17<02:22, 5.77it/s]\u001b[A\n","ITERATION - loss: 0.08: 4%|▎ | 30/844 [12:17<01:41, 8.03it/s]\u001b[A\n","ITERATION - loss: 0.29: 5%|▍ | 40/844 [12:17<01:12, 11.03it/s]\u001b[A\n","ITERATION - loss: 0.07: 6%|▌ | 50/844 [12:17<00:52, 15.03it/s]\u001b[A\n","ITERATION - loss: 0.06: 7%|▋ | 60/844 [12:17<00:38, 20.11it/s]\u001b[A\n","ITERATION - loss: 0.08: 8%|▊ | 70/844 [12:17<00:29, 26.12it/s]\u001b[A\n","ITERATION - loss: 0.09: 9%|▉ | 80/844 [12:17<00:23, 32.96it/s]\u001b[A\n","ITERATION - loss: 0.09: 11%|█ | 90/844 [12:17<00:18, 40.75it/s]\u001b[A\n","ITERATION - loss: 0.06: 12%|█▏ | 100/844 [12:17<00:15, 48.95it/s]\u001b[A\n","ITERATION - loss: 0.04: 13%|█▎ | 110/844 [12:18<00:12, 57.03it/s]\u001b[A\n","ITERATION - loss: 0.25: 14%|█▍ | 120/844 [12:18<00:11, 64.16it/s]\u001b[A\n","ITERATION - loss: 0.05: 15%|█▌ | 130/844 [12:18<00:10, 70.61it/s]\u001b[A\n","ITERATION - loss: 0.16: 17%|█▋ | 140/844 [12:18<00:09, 75.48it/s]\u001b[A\n","ITERATION - loss: 0.07: 18%|█▊ | 150/844 [12:18<00:08, 77.41it/s]\u001b[A\n","ITERATION - loss: 0.08: 19%|█▉ | 160/844 [12:18<00:08, 81.92it/s]\u001b[A\n","ITERATION - loss: 0.12: 20%|██ | 170/844 [12:18<00:08, 82.51it/s]\u001b[A\n","ITERATION - loss: 0.22: 21%|██▏ | 180/844 [12:18<00:07, 84.97it/s]\u001b[A\n","ITERATION - loss: 0.10: 23%|██▎ | 190/844 [12:18<00:07, 85.92it/s]\u001b[A\n","ITERATION - loss: 0.06: 24%|██▎ | 200/844 [12:19<00:07, 88.05it/s]\u001b[A\n","ITERATION - loss: 0.04: 25%|██▍ | 210/844 [12:19<00:07, 88.09it/s]\u001b[A\n","ITERATION - loss: 0.06: 26%|██▌ | 220/844 [12:19<00:06, 89.55it/s]\u001b[A\n","ITERATION - loss: 0.07: 27%|██▋ | 230/844 [12:19<00:06, 88.49it/s]\u001b[A\n","ITERATION - loss: 0.21: 28%|██▊ | 240/844 [12:19<00:06, 89.49it/s]\u001b[A\n","ITERATION - loss: 0.05: 30%|██▉ | 250/844 [12:19<00:06, 90.70it/s]\u001b[A\n","ITERATION - loss: 0.05: 31%|███ | 260/844 [12:19<00:06, 87.79it/s]\u001b[A\n","ITERATION - loss: 0.03: 32%|███▏ | 270/844 [12:19<00:06, 89.37it/s]\u001b[A\n","ITERATION - loss: 0.06: 33%|███▎ | 280/844 [12:19<00:06, 92.24it/s]\u001b[A\n","ITERATION - loss: 0.15: 34%|███▍ | 290/844 [12:20<00:05, 94.03it/s]\u001b[A\n","ITERATION - loss: 0.25: 36%|███▌ | 300/844 [12:20<00:05, 95.19it/s]\u001b[A\n","ITERATION - loss: 0.09: 38%|███▊ | 320/844 [12:20<00:05, 96.93it/s]\u001b[A\n","ITERATION - loss: 0.13: 39%|███▉ | 330/844 [12:20<00:05, 94.08it/s]\u001b[A\n","ITERATION - loss: 0.20: 40%|████ | 340/844 [12:20<00:05, 92.70it/s]\u001b[A\n","ITERATION - loss: 0.06: 41%|████▏ | 350/844 [12:20<00:05, 92.48it/s]\u001b[A\n","ITERATION - loss: 0.18: 43%|████▎ | 360/844 [12:20<00:05, 90.57it/s]\u001b[A\n","ITERATION - loss: 0.09: 44%|████▍ | 370/844 [12:20<00:05, 92.29it/s]\u001b[A\n","ITERATION - loss: 0.21: 45%|████▌ | 380/844 [12:21<00:04, 92.96it/s]\u001b[A\n","ITERATION - loss: 0.06: 46%|████▌ | 390/844 [12:21<00:04, 93.66it/s]\u001b[A\n","ITERATION - loss: 0.08: 47%|████▋ | 400/844 [12:21<00:04, 93.99it/s]\u001b[A\n","ITERATION - loss: 0.11: 49%|████▊ | 410/844 [12:21<00:04, 93.74it/s]\u001b[A\n","ITERATION - loss: 0.10: 50%|████▉ | 420/844 [12:21<00:04, 92.70it/s]\u001b[A\n","ITERATION - loss: 0.26: 51%|█████ | 430/844 [12:21<00:04, 93.59it/s]\u001b[A\n","ITERATION - loss: 0.15: 52%|█████▏ | 440/844 [12:21<00:04, 94.09it/s]\u001b[A\n","ITERATION - loss: 0.11: 53%|█████▎ | 450/844 [12:21<00:04, 92.44it/s]\u001b[A\n","ITERATION - loss: 0.11: 55%|█████▍ | 460/844 [12:21<00:04, 88.64it/s]\u001b[A\n","ITERATION - loss: 0.09: 56%|█████▌ | 470/844 [12:22<00:04, 89.67it/s]\u001b[A\n","ITERATION - loss: 0.19: 57%|█████▋ | 480/844 [12:22<00:03, 91.57it/s]\u001b[A\n","ITERATION - loss: 0.11: 58%|█████▊ | 490/844 [12:22<00:03, 93.14it/s]\u001b[A\n","ITERATION - loss: 0.07: 59%|█████▉ | 500/844 [12:22<00:03, 92.18it/s]\u001b[A\n","ITERATION - loss: 0.17: 60%|██████ | 510/844 [12:22<00:03, 89.69it/s]\u001b[A\n","ITERATION - loss: 0.07: 62%|██████▏ | 520/844 [12:22<00:03, 90.81it/s]\u001b[A\n","ITERATION - loss: 0.16: 63%|██████▎ | 530/844 [12:22<00:03, 91.95it/s]\u001b[A\n","ITERATION - loss: 0.08: 64%|██████▍ | 540/844 [12:22<00:03, 90.02it/s]\u001b[A\n","ITERATION - loss: 0.20: 65%|██████▌ | 550/844 [12:22<00:03, 88.41it/s]\u001b[A\n","ITERATION - loss: 0.12: 66%|██████▋ | 560/844 [12:23<00:03, 89.86it/s]\u001b[A\n","ITERATION - loss: 0.09: 68%|██████▊ | 570/844 [12:23<00:03, 90.27it/s]\u001b[A\n","ITERATION - loss: 0.09: 69%|██████▊ | 580/844 [12:23<00:02, 91.43it/s]\u001b[A\n","ITERATION - loss: 0.11: 70%|██████▉ | 590/844 [12:23<00:02, 91.53it/s]\u001b[A\n","ITERATION - loss: 0.09: 71%|███████ | 600/844 [12:23<00:02, 89.84it/s]\u001b[A\n","ITERATION - loss: 0.10: 72%|███████▏ | 610/844 [12:23<00:02, 90.53it/s]\u001b[A\n","ITERATION - loss: 0.11: 73%|███████▎ | 620/844 [12:23<00:02, 87.99it/s]\u001b[A\n","ITERATION - loss: 0.10: 75%|███████▍ | 630/844 [12:23<00:02, 89.93it/s]\u001b[A\n","ITERATION - loss: 0.13: 76%|███████▌ | 640/844 [12:23<00:02, 89.65it/s]\u001b[A\n","ITERATION - loss: 0.08: 77%|███████▋ | 650/844 [12:24<00:02, 89.92it/s]\u001b[A\n","ITERATION - loss: 0.05: 78%|███████▊ | 660/844 [12:24<00:02, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.03: 79%|███████▉ | 670/844 [12:24<00:01, 93.21it/s]\u001b[A\n","ITERATION - loss: 0.11: 81%|████████ | 680/844 [12:24<00:01, 91.17it/s]\u001b[A\n","ITERATION - loss: 0.16: 82%|████████▏ | 690/844 [12:24<00:01, 90.48it/s]\u001b[A\n","ITERATION - loss: 0.18: 83%|████████▎ | 700/844 [12:24<00:01, 92.22it/s]\u001b[A\n","ITERATION - loss: 0.07: 84%|████████▍ | 710/844 [12:24<00:01, 92.69it/s]\u001b[A\n","ITERATION - loss: 0.16: 85%|████████▌ | 720/844 [12:24<00:01, 92.25it/s]\u001b[A\n","ITERATION - loss: 0.03: 86%|████████▋ | 730/844 [12:24<00:01, 93.11it/s]\u001b[A\n","ITERATION - loss: 0.14: 88%|████████▊ | 740/844 [12:24<00:01, 92.24it/s]\u001b[A\n","ITERATION - loss: 0.06: 89%|████████▉ | 750/844 [12:25<00:01, 93.18it/s]\u001b[A\n","ITERATION - loss: 0.09: 90%|█████████ | 760/844 [12:25<00:00, 92.55it/s]\u001b[A\n","ITERATION - loss: 0.07: 91%|█████████ | 770/844 [12:25<00:00, 93.38it/s]\u001b[A\n","ITERATION - loss: 0.15: 92%|█████████▏| 780/844 [12:25<00:00, 93.82it/s]\u001b[A\n","ITERATION - loss: 0.12: 94%|█████████▎| 790/844 [12:25<00:00, 91.55it/s]\u001b[A\n","ITERATION - loss: 0.21: 95%|█████████▍| 800/844 [12:25<00:00, 92.82it/s]\u001b[A\n","ITERATION - loss: 0.13: 96%|█████████▌| 810/844 [12:25<00:00, 93.62it/s]\u001b[A\n","ITERATION - loss: 0.17: 97%|█████████▋| 820/844 [12:25<00:00, 92.77it/s]\u001b[A\n","ITERATION - loss: 0.21: 98%|█████████▊| 830/844 [12:25<00:00, 91.69it/s]\u001b[A\n","ITERATION - loss: 0.20: 100%|█████████▉| 840/844 [12:26<00:00, 93.17it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [13:06<00:00, 93.64it/s]\n","ITERATION - loss: 0.20: 100%|█████████▉| 840/844 [12:33<00:00, 93.17it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 44 Avg accuracy: 96.71 Avg loss: 0.12\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [13:07<00:00, 93.64it/s]\n","ITERATION - loss: 0.20: 100%|█████████▉| 840/844 [12:33<00:00, 93.17it/s]\u001b[A\n","ITERATION - loss: 0.09: 1%| | 10/844 [12:34<03:25, 4.07it/s] \u001b[A\n","ITERATION - loss: 0.10: 2%|▏ | 20/844 [12:34<02:24, 5.70it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 44 Avg accuracy: 96.18 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.06: 4%|▎ | 30/844 [12:34<01:42, 7.94it/s]\u001b[A\n","ITERATION - loss: 0.18: 5%|▍ | 40/844 [12:34<01:13, 10.94it/s]\u001b[A\n","ITERATION - loss: 0.07: 6%|▌ | 50/844 [12:34<00:53, 14.89it/s]\u001b[A\n","ITERATION - loss: 0.08: 7%|▋ | 60/844 [12:34<00:39, 19.96it/s]\u001b[A\n","ITERATION - loss: 0.09: 8%|▊ | 70/844 [12:34<00:30, 25.79it/s]\u001b[A\n","ITERATION - loss: 0.23: 9%|▉ | 80/844 [12:34<00:23, 32.99it/s]\u001b[A\n","ITERATION - loss: 0.03: 11%|█ | 90/844 [12:34<00:18, 40.85it/s]\u001b[A\n","ITERATION - loss: 0.21: 12%|█▏ | 100/844 [12:34<00:15, 49.11it/s]\u001b[A\n","ITERATION - loss: 0.10: 13%|█▎ | 110/844 [12:35<00:12, 57.67it/s]\u001b[A\n","ITERATION - loss: 0.16: 14%|█▍ | 120/844 [12:35<00:11, 64.84it/s]\u001b[A\n","ITERATION - loss: 0.07: 15%|█▌ | 130/844 [12:35<00:09, 71.75it/s]\u001b[A\n","ITERATION - loss: 0.22: 17%|█▋ | 140/844 [12:35<00:09, 77.02it/s]\u001b[A\n","ITERATION - loss: 0.27: 18%|█▊ | 150/844 [12:35<00:08, 82.02it/s]\u001b[A\n","ITERATION - loss: 0.07: 19%|█▉ | 160/844 [12:35<00:08, 84.02it/s]\u001b[A\n","ITERATION - loss: 0.11: 20%|██ | 170/844 [12:35<00:07, 86.71it/s]\u001b[A\n","ITERATION - loss: 0.07: 21%|██▏ | 180/844 [12:35<00:07, 88.67it/s]\u001b[A\n","ITERATION - loss: 0.16: 23%|██▎ | 190/844 [12:35<00:07, 90.29it/s]\u001b[A\n","ITERATION - loss: 0.15: 24%|██▎ | 200/844 [12:36<00:07, 91.81it/s]\u001b[A\n","ITERATION - loss: 0.05: 25%|██▍ | 210/844 [12:36<00:06, 93.86it/s]\u001b[A\n","ITERATION - loss: 0.10: 26%|██▌ | 220/844 [12:36<00:06, 93.06it/s]\u001b[A\n","ITERATION - loss: 0.08: 27%|██▋ | 230/844 [12:36<00:06, 94.54it/s]\u001b[A\n","ITERATION - loss: 0.10: 28%|██▊ | 240/844 [12:36<00:06, 94.90it/s]\u001b[A\n","ITERATION - loss: 0.17: 30%|██▉ | 250/844 [12:36<00:06, 95.26it/s]\u001b[A\n","ITERATION - loss: 0.06: 31%|███ | 260/844 [12:36<00:06, 93.51it/s]\u001b[A\n","ITERATION - loss: 0.20: 32%|███▏ | 270/844 [12:36<00:06, 91.29it/s]\u001b[A\n","ITERATION - loss: 0.08: 33%|███▎ | 280/844 [12:36<00:06, 87.64it/s]\u001b[A\n","ITERATION - loss: 0.06: 34%|███▍ | 290/844 [12:37<00:06, 88.22it/s]\u001b[A\n","ITERATION - loss: 0.08: 36%|███▌ | 300/844 [12:37<00:06, 90.12it/s]\u001b[A\n","ITERATION - loss: 0.19: 37%|███▋ | 310/844 [12:37<00:05, 89.77it/s]\u001b[A\n","ITERATION - loss: 0.08: 38%|███▊ | 320/844 [12:37<00:05, 91.48it/s]\u001b[A\n","ITERATION - loss: 0.10: 39%|███▉ | 330/844 [12:37<00:05, 92.22it/s]\u001b[A\n","ITERATION - loss: 0.30: 40%|████ | 340/844 [12:37<00:05, 93.27it/s]\u001b[A\n","ITERATION - loss: 0.08: 41%|████▏ | 350/844 [12:37<00:05, 91.99it/s]\u001b[A\n","ITERATION - loss: 0.22: 43%|████▎ | 360/844 [12:37<00:05, 92.00it/s]\u001b[A\n","ITERATION - loss: 0.12: 44%|████▍ | 370/844 [12:37<00:05, 91.85it/s]\u001b[A\n","ITERATION - loss: 0.10: 45%|████▌ | 380/844 [12:37<00:05, 92.69it/s]\u001b[A\n","ITERATION - loss: 0.14: 46%|████▌ | 390/844 [12:38<00:04, 92.84it/s]\u001b[A\n","ITERATION - loss: 0.11: 47%|████▋ | 400/844 [12:38<00:04, 91.95it/s]\u001b[A\n","ITERATION - loss: 0.08: 49%|████▊ | 410/844 [12:38<00:04, 92.65it/s]\u001b[A\n","ITERATION - loss: 0.12: 50%|████▉ | 420/844 [12:38<00:04, 92.37it/s]\u001b[A\n","ITERATION - loss: 0.13: 51%|█████ | 430/844 [12:38<00:04, 93.18it/s]\u001b[A\n","ITERATION - loss: 0.07: 52%|█████▏ | 440/844 [12:38<00:04, 91.45it/s]\u001b[A\n","ITERATION - loss: 0.09: 53%|█████▎ | 450/844 [12:38<00:04, 91.86it/s]\u001b[A\n","ITERATION - loss: 0.09: 55%|█████▍ | 460/844 [12:38<00:04, 92.44it/s]\u001b[A\n","ITERATION - loss: 0.05: 56%|█████▌ | 470/844 [12:38<00:04, 91.81it/s]\u001b[A\n","ITERATION - loss: 0.09: 57%|█████▋ | 480/844 [12:39<00:03, 92.43it/s]\u001b[A\n","ITERATION - loss: 0.10: 58%|█████▊ | 490/844 [12:39<00:03, 91.26it/s]\u001b[A\n","ITERATION - loss: 0.27: 59%|█████▉ | 500/844 [12:39<00:03, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.11: 60%|██████ | 510/844 [12:39<00:03, 91.94it/s]\u001b[A\n","ITERATION - loss: 0.05: 62%|██████▏ | 520/844 [12:39<00:03, 92.05it/s]\u001b[A\n","ITERATION - loss: 0.14: 63%|██████▎ | 530/844 [12:39<00:03, 91.45it/s]\u001b[A\n","ITERATION - loss: 0.08: 64%|██████▍ | 540/844 [12:39<00:03, 87.76it/s]\u001b[A\n","ITERATION - loss: 0.17: 65%|██████▌ | 550/844 [12:39<00:03, 89.19it/s]\u001b[A\n","ITERATION - loss: 0.09: 66%|██████▋ | 560/844 [12:39<00:03, 90.53it/s]\u001b[A\n","ITERATION - loss: 0.05: 68%|██████▊ | 570/844 [12:40<00:02, 91.85it/s]\u001b[A\n","ITERATION - loss: 0.09: 69%|██████▊ | 580/844 [12:40<00:02, 92.92it/s]\u001b[A\n","ITERATION - loss: 0.08: 70%|██████▉ | 590/844 [12:40<00:02, 91.07it/s]\u001b[A\n","ITERATION - loss: 0.18: 71%|███████ | 600/844 [12:40<00:02, 92.31it/s]\u001b[A\n","ITERATION - loss: 0.04: 72%|███████▏ | 610/844 [12:40<00:02, 92.39it/s]\u001b[A\n","ITERATION - loss: 0.11: 73%|███████▎ | 620/844 [12:40<00:02, 93.50it/s]\u001b[A\n","ITERATION - loss: 0.08: 75%|███████▍ | 630/844 [12:40<00:02, 92.48it/s]\u001b[A\n","ITERATION - loss: 0.10: 76%|███████▌ | 640/844 [12:40<00:02, 92.44it/s]\u001b[A\n","ITERATION - loss: 0.19: 77%|███████▋ | 650/844 [12:40<00:02, 92.02it/s]\u001b[A\n","ITERATION - loss: 0.09: 78%|███████▊ | 660/844 [12:41<00:01, 92.56it/s]\u001b[A\n","ITERATION - loss: 0.17: 79%|███████▉ | 670/844 [12:41<00:01, 92.96it/s]\u001b[A\n","ITERATION - loss: 0.07: 81%|████████ | 680/844 [12:41<00:01, 92.76it/s]\u001b[A\n","ITERATION - loss: 0.09: 82%|████████▏ | 690/844 [12:41<00:01, 88.55it/s]\u001b[A\n","ITERATION - loss: 0.16: 83%|████████▎ | 700/844 [12:41<00:01, 89.77it/s]\u001b[A\n","ITERATION - loss: 0.10: 84%|████████▍ | 710/844 [12:41<00:01, 90.30it/s]\u001b[A\n","ITERATION - loss: 0.06: 85%|████████▌ | 720/844 [12:41<00:01, 89.62it/s]\u001b[A\n","ITERATION - loss: 0.07: 86%|████████▋ | 730/844 [12:41<00:01, 92.14it/s]\u001b[A\n","ITERATION - loss: 0.15: 88%|████████▊ | 740/844 [12:41<00:01, 94.03it/s]\u001b[A\n","ITERATION - loss: 0.27: 89%|████████▉ | 750/844 [12:42<00:00, 95.20it/s]\u001b[A\n","ITERATION - loss: 0.21: 90%|█████████ | 760/844 [12:42<00:00, 95.78it/s]\u001b[A\n","ITERATION - loss: 0.25: 92%|█████████▏| 780/844 [12:42<00:00, 97.16it/s]\u001b[A\n","ITERATION - loss: 0.05: 94%|█████████▎| 790/844 [12:42<00:00, 96.28it/s]\u001b[A\n","ITERATION - loss: 0.22: 95%|█████████▍| 800/844 [12:42<00:00, 96.55it/s]\u001b[A\n","ITERATION - loss: 0.07: 96%|█████████▌| 810/844 [12:42<00:00, 95.90it/s]\u001b[A\n","ITERATION - loss: 0.18: 97%|█████████▋| 820/844 [12:42<00:00, 92.88it/s]\u001b[A\n","ITERATION - loss: 0.20: 98%|█████████▊| 830/844 [12:42<00:00, 93.46it/s]\u001b[A\n","ITERATION - loss: 0.07: 100%|█████████▉| 840/844 [12:42<00:00, 94.36it/s]\u001b[A\n","ITERATION - loss: 0.08: 850it [12:43, 94.25it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [13:23<00:00, 93.64it/s]\n","ITERATION - loss: 0.08: 850it [12:50, 94.25it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 45 Avg accuracy: 96.71 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [13:24<00:00, 93.64it/s]\n","ITERATION - loss: 0.08: 850it [12:50, 94.25it/s]\u001b[A\n","ITERATION - loss: 0.05: 1%| | 10/844 [12:51<03:24, 4.07it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 45 Avg accuracy: 96.05 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.04: 2%|▏ | 20/844 [12:51<02:24, 5.71it/s]\u001b[A\n","ITERATION - loss: 0.13: 4%|▎ | 30/844 [12:51<01:42, 7.94it/s]\u001b[A\n","ITERATION - loss: 0.10: 5%|▍ | 40/844 [12:51<01:13, 10.94it/s]\u001b[A\n","ITERATION - loss: 0.06: 6%|▌ | 50/844 [12:51<00:53, 14.86it/s]\u001b[A\n","ITERATION - loss: 0.16: 7%|▋ | 60/844 [12:51<00:39, 19.74it/s]\u001b[A\n","ITERATION - loss: 0.08: 8%|▊ | 70/844 [12:51<00:30, 25.68it/s]\u001b[A\n","ITERATION - loss: 0.14: 9%|▉ | 80/844 [12:51<00:23, 32.49it/s]\u001b[A\n","ITERATION - loss: 0.10: 11%|█ | 90/844 [12:51<00:18, 39.75it/s]\u001b[A\n","ITERATION - loss: 0.04: 12%|█▏ | 100/844 [12:52<00:15, 48.10it/s]\u001b[A\n","ITERATION - loss: 0.08: 13%|█▎ | 110/844 [12:52<00:12, 56.55it/s]\u001b[A\n","ITERATION - loss: 0.09: 14%|█▍ | 120/844 [12:52<00:11, 64.04it/s]\u001b[A\n","ITERATION - loss: 0.17: 15%|█▌ | 130/844 [12:52<00:10, 71.02it/s]\u001b[A\n","ITERATION - loss: 0.08: 17%|█▋ | 140/844 [12:52<00:09, 77.12it/s]\u001b[A\n","ITERATION - loss: 0.06: 18%|█▊ | 150/844 [12:52<00:08, 77.53it/s]\u001b[A\n","ITERATION - loss: 0.06: 19%|█▉ | 160/844 [12:52<00:08, 81.48it/s]\u001b[A\n","ITERATION - loss: 0.10: 20%|██ | 170/844 [12:52<00:08, 83.14it/s]\u001b[A\n","ITERATION - loss: 0.10: 21%|██▏ | 180/844 [12:52<00:08, 79.88it/s]\u001b[A\n","ITERATION - loss: 0.06: 23%|██▎ | 190/844 [12:53<00:07, 84.21it/s]\u001b[A\n","ITERATION - loss: 0.09: 24%|██▎ | 200/844 [12:53<00:07, 87.04it/s]\u001b[A\n","ITERATION - loss: 0.08: 25%|██▍ | 210/844 [12:53<00:07, 88.61it/s]\u001b[A\n","ITERATION - loss: 0.10: 26%|██▌ | 220/844 [12:53<00:06, 90.87it/s]\u001b[A\n","ITERATION - loss: 0.06: 27%|██▋ | 230/844 [12:53<00:06, 92.04it/s]\u001b[A\n","ITERATION - loss: 0.15: 28%|██▊ | 240/844 [12:53<00:06, 91.47it/s]\u001b[A\n","ITERATION - loss: 0.09: 30%|██▉ | 250/844 [12:53<00:06, 90.76it/s]\u001b[A\n","ITERATION - loss: 0.25: 31%|███ | 260/844 [12:53<00:06, 90.53it/s]\u001b[A\n","ITERATION - loss: 0.08: 32%|███▏ | 270/844 [12:53<00:06, 89.00it/s]\u001b[A\n","ITERATION - loss: 0.14: 33%|███▎ | 280/844 [12:54<00:06, 89.84it/s]\u001b[A\n","ITERATION - loss: 0.08: 34%|███▍ | 290/844 [12:54<00:06, 87.81it/s]\u001b[A\n","ITERATION - loss: 0.10: 36%|███▌ | 300/844 [12:54<00:06, 90.32it/s]\u001b[A\n","ITERATION - loss: 0.05: 37%|███▋ | 310/844 [12:54<00:05, 91.88it/s]\u001b[A\n","ITERATION - loss: 0.04: 38%|███▊ | 320/844 [12:54<00:05, 89.78it/s]\u001b[A\n","ITERATION - loss: 0.13: 39%|███▉ | 330/844 [12:54<00:05, 91.22it/s]\u001b[A\n","ITERATION - loss: 0.10: 40%|████ | 340/844 [12:54<00:05, 90.42it/s]\u001b[A\n","ITERATION - loss: 0.11: 41%|████▏ | 350/844 [12:54<00:05, 91.03it/s]\u001b[A\n","ITERATION - loss: 0.27: 43%|████▎ | 360/844 [12:54<00:05, 89.71it/s]\u001b[A\n","ITERATION - loss: 0.14: 44%|████▍ | 370/844 [12:55<00:05, 89.72it/s]\u001b[A\n","ITERATION - loss: 0.09: 45%|████▌ | 380/844 [12:55<00:05, 90.88it/s]\u001b[A\n","ITERATION - loss: 0.10: 46%|████▌ | 390/844 [12:55<00:04, 91.40it/s]\u001b[A\n","ITERATION - loss: 0.10: 47%|████▋ | 400/844 [12:55<00:04, 90.65it/s]\u001b[A\n","ITERATION - loss: 0.09: 49%|████▊ | 410/844 [12:55<00:04, 91.44it/s]\u001b[A\n","ITERATION - loss: 0.08: 50%|████▉ | 420/844 [12:55<00:04, 92.06it/s]\u001b[A\n","ITERATION - loss: 0.13: 51%|█████ | 430/844 [12:55<00:04, 89.28it/s]\u001b[A\n","ITERATION - loss: 0.12: 52%|█████▏ | 440/844 [12:55<00:04, 87.46it/s]\u001b[A\n","ITERATION - loss: 0.22: 53%|█████▎ | 450/844 [12:55<00:04, 85.68it/s]\u001b[A\n","ITERATION - loss: 0.15: 55%|█████▍ | 460/844 [12:56<00:04, 88.10it/s]\u001b[A\n","ITERATION - loss: 0.15: 56%|█████▌ | 470/844 [12:56<00:04, 90.14it/s]\u001b[A\n","ITERATION - loss: 0.05: 57%|█████▋ | 480/844 [12:56<00:04, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.15: 58%|█████▊ | 490/844 [12:56<00:03, 92.41it/s]\u001b[A\n","ITERATION - loss: 0.14: 59%|█████▉ | 500/844 [12:56<00:03, 92.90it/s]\u001b[A\n","ITERATION - loss: 0.07: 60%|██████ | 510/844 [12:56<00:03, 89.66it/s]\u001b[A\n","ITERATION - loss: 0.12: 62%|██████▏ | 520/844 [12:56<00:03, 89.35it/s]\u001b[A\n","ITERATION - loss: 0.05: 63%|██████▎ | 530/844 [12:56<00:03, 91.12it/s]\u001b[A\n","ITERATION - loss: 0.05: 64%|██████▍ | 540/844 [12:56<00:03, 88.72it/s]\u001b[A\n","ITERATION - loss: 0.20: 65%|██████▌ | 550/844 [12:57<00:03, 88.23it/s]\u001b[A\n","ITERATION - loss: 0.07: 66%|██████▋ | 560/844 [12:57<00:03, 89.53it/s]\u001b[A\n","ITERATION - loss: 0.14: 68%|██████▊ | 570/844 [12:57<00:03, 91.30it/s]\u001b[A\n","ITERATION - loss: 0.10: 69%|██████▊ | 580/844 [12:57<00:02, 92.72it/s]\u001b[A\n","ITERATION - loss: 0.09: 70%|██████▉ | 590/844 [12:57<00:02, 92.49it/s]\u001b[A\n","ITERATION - loss: 0.05: 71%|███████ | 600/844 [12:57<00:02, 90.96it/s]\u001b[A\n","ITERATION - loss: 0.21: 72%|███████▏ | 610/844 [12:57<00:02, 88.00it/s]\u001b[A\n","ITERATION - loss: 0.16: 73%|███████▎ | 620/844 [12:57<00:02, 87.00it/s]\u001b[A\n","ITERATION - loss: 0.04: 75%|███████▍ | 630/844 [12:57<00:02, 89.03it/s]\u001b[A\n","ITERATION - loss: 0.13: 76%|███████▌ | 640/844 [12:58<00:02, 88.76it/s]\u001b[A\n","ITERATION - loss: 0.09: 77%|███████▋ | 650/844 [12:58<00:02, 90.62it/s]\u001b[A\n","ITERATION - loss: 0.15: 78%|███████▊ | 660/844 [12:58<00:01, 92.15it/s]\u001b[A\n","ITERATION - loss: 0.12: 79%|███████▉ | 670/844 [12:58<00:01, 93.02it/s]\u001b[A\n","ITERATION - loss: 0.04: 81%|████████ | 680/844 [12:58<00:01, 93.23it/s]\u001b[A\n","ITERATION - loss: 0.06: 82%|████████▏ | 690/844 [12:58<00:01, 94.20it/s]\u001b[A\n","ITERATION - loss: 0.23: 83%|████████▎ | 700/844 [12:58<00:01, 94.01it/s]\u001b[A\n","ITERATION - loss: 0.12: 84%|████████▍ | 710/844 [12:58<00:01, 91.94it/s]\u001b[A\n","ITERATION - loss: 0.05: 85%|████████▌ | 720/844 [12:58<00:01, 93.25it/s]\u001b[A\n","ITERATION - loss: 0.08: 86%|████████▋ | 730/844 [12:58<00:01, 92.67it/s]\u001b[A\n","ITERATION - loss: 0.23: 88%|████████▊ | 740/844 [12:59<00:01, 93.71it/s]\u001b[A\n","ITERATION - loss: 0.08: 89%|████████▉ | 750/844 [12:59<00:00, 94.68it/s]\u001b[A\n","ITERATION - loss: 0.11: 90%|█████████ | 760/844 [12:59<00:00, 95.31it/s]\u001b[A\n","ITERATION - loss: 0.14: 91%|█████████ | 770/844 [12:59<00:00, 95.64it/s]\u001b[A\n","ITERATION - loss: 0.10: 92%|█████████▏| 780/844 [12:59<00:00, 96.30it/s]\u001b[A\n","ITERATION - loss: 0.16: 94%|█████████▎| 790/844 [12:59<00:00, 95.83it/s]\u001b[A\n","ITERATION - loss: 0.07: 95%|█████████▍| 800/844 [12:59<00:00, 88.67it/s]\u001b[A\n","ITERATION - loss: 0.17: 96%|█████████▌| 810/844 [12:59<00:00, 89.94it/s]\u001b[A\n","ITERATION - loss: 0.16: 97%|█████████▋| 820/844 [12:59<00:00, 91.81it/s]\u001b[A\n","ITERATION - loss: 0.13: 98%|█████████▊| 830/844 [13:00<00:00, 88.22it/s]\u001b[A\n","ITERATION - loss: 0.05: 100%|█████████▉| 840/844 [13:00<00:00, 89.17it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [13:40<00:00, 93.64it/s]\n","ITERATION - loss: 0.05: 100%|█████████▉| 840/844 [13:07<00:00, 89.17it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 46 Avg accuracy: 96.76 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [13:41<00:00, 93.64it/s]\n","ITERATION - loss: 0.05: 100%|█████████▉| 840/844 [13:07<00:00, 89.17it/s]\u001b[A\n","ITERATION - loss: 0.06: 1%| | 10/844 [13:07<03:21, 4.13it/s] \u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 46 Avg accuracy: 96.12 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.18: 2%|▏ | 20/844 [13:08<02:22, 5.78it/s]\u001b[A\n","ITERATION - loss: 0.05: 4%|▎ | 30/844 [13:08<01:41, 8.03it/s]\u001b[A\n","ITERATION - loss: 0.12: 5%|▍ | 40/844 [13:08<01:12, 11.04it/s]\u001b[A\n","ITERATION - loss: 0.06: 6%|▌ | 50/844 [13:08<00:52, 15.02it/s]\u001b[A\n","ITERATION - loss: 0.18: 7%|▋ | 60/844 [13:08<00:39, 20.05it/s]\u001b[A\n","ITERATION - loss: 0.07: 8%|▊ | 70/844 [13:08<00:29, 26.23it/s]\u001b[A\n","ITERATION - loss: 0.15: 9%|▉ | 80/844 [13:08<00:22, 33.57it/s]\u001b[A\n","ITERATION - loss: 0.15: 11%|█ | 90/844 [13:08<00:18, 41.29it/s]\u001b[A\n","ITERATION - loss: 0.13: 12%|█▏ | 100/844 [13:08<00:15, 49.26it/s]\u001b[A\n","ITERATION - loss: 0.07: 13%|█▎ | 110/844 [13:09<00:12, 57.64it/s]\u001b[A\n","ITERATION - loss: 0.12: 14%|█▍ | 120/844 [13:09<00:11, 64.53it/s]\u001b[A\n","ITERATION - loss: 0.09: 15%|█▌ | 130/844 [13:09<00:09, 71.67it/s]\u001b[A\n","ITERATION - loss: 0.05: 17%|█▋ | 140/844 [13:09<00:09, 77.83it/s]\u001b[A\n","ITERATION - loss: 0.13: 18%|█▊ | 150/844 [13:09<00:08, 82.51it/s]\u001b[A\n","ITERATION - loss: 0.04: 19%|█▉ | 160/844 [13:09<00:08, 84.82it/s]\u001b[A\n","ITERATION - loss: 0.21: 20%|██ | 170/844 [13:09<00:07, 86.80it/s]\u001b[A\n","ITERATION - loss: 0.07: 21%|██▏ | 180/844 [13:09<00:07, 89.53it/s]\u001b[A\n","ITERATION - loss: 0.20: 23%|██▎ | 190/844 [13:09<00:07, 88.98it/s]\u001b[A\n","ITERATION - loss: 0.19: 24%|██▎ | 200/844 [13:10<00:07, 90.53it/s]\u001b[A\n","ITERATION - loss: 0.08: 25%|██▍ | 210/844 [13:10<00:06, 91.11it/s]\u001b[A\n","ITERATION - loss: 0.07: 26%|██▌ | 220/844 [13:10<00:06, 89.23it/s]\u001b[A\n","ITERATION - loss: 0.09: 27%|██▋ | 230/844 [13:10<00:06, 91.59it/s]\u001b[A\n","ITERATION - loss: 0.03: 28%|██▊ | 240/844 [13:10<00:06, 92.44it/s]\u001b[A\n","ITERATION - loss: 0.05: 30%|██▉ | 250/844 [13:10<00:06, 93.07it/s]\u001b[A\n","ITERATION - loss: 0.11: 31%|███ | 260/844 [13:10<00:06, 93.83it/s]\u001b[A\n","ITERATION - loss: 0.03: 32%|███▏ | 270/844 [13:10<00:06, 90.24it/s]\u001b[A\n","ITERATION - loss: 0.16: 33%|███▎ | 280/844 [13:10<00:06, 90.63it/s]\u001b[A\n","ITERATION - loss: 0.11: 34%|███▍ | 290/844 [13:11<00:06, 89.96it/s]\u001b[A\n","ITERATION - loss: 0.08: 36%|███▌ | 300/844 [13:11<00:06, 89.02it/s]\u001b[A\n","ITERATION - loss: 0.23: 37%|███▋ | 310/844 [13:11<00:05, 90.52it/s]\u001b[A\n","ITERATION - loss: 0.05: 38%|███▊ | 320/844 [13:11<00:05, 91.25it/s]\u001b[A\n","ITERATION - loss: 0.18: 39%|███▉ | 330/844 [13:11<00:05, 91.01it/s]\u001b[A\n","ITERATION - loss: 0.06: 40%|████ | 340/844 [13:11<00:05, 92.33it/s]\u001b[A\n","ITERATION - loss: 0.15: 41%|████▏ | 350/844 [13:11<00:05, 92.91it/s]\u001b[A\n","ITERATION - loss: 0.08: 43%|████▎ | 360/844 [13:11<00:05, 92.58it/s]\u001b[A\n","ITERATION - loss: 0.04: 44%|████▍ | 370/844 [13:11<00:05, 93.32it/s]\u001b[A\n","ITERATION - loss: 0.16: 45%|████▌ | 380/844 [13:12<00:05, 92.17it/s]\u001b[A\n","ITERATION - loss: 0.05: 46%|████▌ | 390/844 [13:12<00:04, 90.88it/s]\u001b[A\n","ITERATION - loss: 0.17: 47%|████▋ | 400/844 [13:12<00:04, 89.52it/s]\u001b[A\n","ITERATION - loss: 0.04: 49%|████▊ | 410/844 [13:12<00:04, 90.79it/s]\u001b[A\n","ITERATION - loss: 0.07: 50%|████▉ | 420/844 [13:12<00:04, 92.56it/s]\u001b[A\n","ITERATION - loss: 0.12: 51%|█████ | 430/844 [13:12<00:04, 92.88it/s]\u001b[A\n","ITERATION - loss: 0.18: 52%|█████▏ | 440/844 [13:12<00:04, 92.42it/s]\u001b[A\n","ITERATION - loss: 0.12: 53%|█████▎ | 450/844 [13:12<00:04, 92.34it/s]\u001b[A\n","ITERATION - loss: 0.25: 55%|█████▍ | 460/844 [13:12<00:04, 92.85it/s]\u001b[A\n","ITERATION - loss: 0.18: 56%|█████▌ | 470/844 [13:12<00:03, 94.13it/s]\u001b[A\n","ITERATION - loss: 0.07: 57%|█████▋ | 480/844 [13:13<00:03, 93.52it/s]\u001b[A\n","ITERATION - loss: 0.09: 58%|█████▊ | 490/844 [13:13<00:03, 92.53it/s]\u001b[A\n","ITERATION - loss: 0.05: 60%|██████ | 510/844 [13:13<00:03, 95.89it/s]\u001b[A\n","ITERATION - loss: 0.15: 62%|██████▏ | 520/844 [13:13<00:03, 95.85it/s]\u001b[A\n","ITERATION - loss: 0.11: 63%|██████▎ | 530/844 [13:13<00:03, 96.13it/s]\u001b[A\n","ITERATION - loss: 0.11: 64%|██████▍ | 540/844 [13:13<00:03, 95.98it/s]\u001b[A\n","ITERATION - loss: 0.15: 65%|██████▌ | 550/844 [13:13<00:03, 94.40it/s]\u001b[A\n","ITERATION - loss: 0.05: 66%|██████▋ | 560/844 [13:13<00:03, 89.47it/s]\u001b[A\n","ITERATION - loss: 0.09: 68%|██████▊ | 570/844 [13:14<00:03, 88.84it/s]\u001b[A\n","ITERATION - loss: 0.16: 69%|██████▊ | 580/844 [13:14<00:02, 90.36it/s]\u001b[A\n","ITERATION - loss: 0.19: 70%|██████▉ | 590/844 [13:14<00:02, 89.99it/s]\u001b[A\n","ITERATION - loss: 0.07: 71%|███████ | 600/844 [13:14<00:02, 91.31it/s]\u001b[A\n","ITERATION - loss: 0.21: 72%|███████▏ | 610/844 [13:14<00:02, 92.80it/s]\u001b[A\n","ITERATION - loss: 0.08: 73%|███████▎ | 620/844 [13:14<00:02, 93.02it/s]\u001b[A\n","ITERATION - loss: 0.08: 75%|███████▍ | 630/844 [13:14<00:02, 92.68it/s]\u001b[A\n","ITERATION - loss: 0.10: 76%|███████▌ | 640/844 [13:14<00:02, 93.12it/s]\u001b[A\n","ITERATION - loss: 0.07: 77%|███████▋ | 650/844 [13:14<00:02, 93.51it/s]\u001b[A\n","ITERATION - loss: 0.14: 78%|███████▊ | 660/844 [13:15<00:01, 92.71it/s]\u001b[A\n","ITERATION - loss: 0.19: 79%|███████▉ | 670/844 [13:15<00:01, 91.03it/s]\u001b[A\n","ITERATION - loss: 0.10: 81%|████████ | 680/844 [13:15<00:01, 89.87it/s]\u001b[A\n","ITERATION - loss: 0.20: 82%|████████▏ | 690/844 [13:15<00:01, 90.21it/s]\u001b[A\n","ITERATION - loss: 0.05: 83%|████████▎ | 700/844 [13:15<00:01, 91.34it/s]\u001b[A\n","ITERATION - loss: 0.12: 84%|████████▍ | 710/844 [13:15<00:01, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.17: 85%|████████▌ | 720/844 [13:15<00:01, 89.03it/s]\u001b[A\n","ITERATION - loss: 0.06: 86%|████████▋ | 730/844 [13:15<00:01, 89.90it/s]\u001b[A\n","ITERATION - loss: 0.17: 88%|████████▊ | 740/844 [13:15<00:01, 90.26it/s]\u001b[A\n","ITERATION - loss: 0.15: 89%|████████▉ | 750/844 [13:16<00:01, 91.57it/s]\u001b[A\n","ITERATION - loss: 0.15: 90%|█████████ | 760/844 [13:16<00:00, 90.69it/s]\u001b[A\n","ITERATION - loss: 0.17: 91%|█████████ | 770/844 [13:16<00:00, 89.75it/s]\u001b[A\n","ITERATION - loss: 0.10: 92%|█████████▏| 780/844 [13:16<00:00, 91.54it/s]\u001b[A\n","ITERATION - loss: 0.10: 94%|█████████▎| 790/844 [13:16<00:00, 92.40it/s]\u001b[A\n","ITERATION - loss: 0.13: 95%|█████████▍| 800/844 [13:16<00:00, 91.69it/s]\u001b[A\n","ITERATION - loss: 0.08: 96%|█████████▌| 810/844 [13:16<00:00, 92.65it/s]\u001b[A\n","ITERATION - loss: 0.25: 97%|█████████▋| 820/844 [13:16<00:00, 92.98it/s]\u001b[A\n","ITERATION - loss: 0.28: 98%|█████████▊| 830/844 [13:16<00:00, 93.04it/s]\u001b[A\n","ITERATION - loss: 0.25: 100%|█████████▉| 840/844 [13:16<00:00, 93.74it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [13:57<00:00, 93.64it/s]\n","ITERATION - loss: 0.25: 100%|█████████▉| 840/844 [13:23<00:00, 93.74it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 47 Avg accuracy: 96.79 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [13:58<00:00, 93.64it/s]\n","ITERATION - loss: 0.25: 100%|█████████▉| 840/844 [13:24<00:00, 93.74it/s]\u001b[A\n","ITERATION - loss: 0.16: 1%| | 10/844 [13:24<03:19, 4.17it/s] \u001b[A\n","ITERATION - loss: 0.26: 2%|▏ | 20/844 [13:24<02:20, 5.85it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 47 Avg accuracy: 96.30 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.09: 4%|▎ | 30/844 [13:24<01:40, 8.13it/s]\u001b[A\n","ITERATION - loss: 0.09: 5%|▍ | 40/844 [13:25<01:11, 11.20it/s]\u001b[A\n","ITERATION - loss: 0.17: 6%|▌ | 50/844 [13:25<00:52, 15.23it/s]\u001b[A\n","ITERATION - loss: 0.09: 7%|▋ | 60/844 [13:25<00:38, 20.36it/s]\u001b[A\n","ITERATION - loss: 0.09: 8%|▊ | 70/844 [13:25<00:29, 26.32it/s]\u001b[A\n","ITERATION - loss: 0.09: 9%|▉ | 80/844 [13:25<00:22, 33.49it/s]\u001b[A\n","ITERATION - loss: 0.06: 11%|█ | 90/844 [13:25<00:18, 41.63it/s]\u001b[A\n","ITERATION - loss: 0.18: 12%|█▏ | 100/844 [13:25<00:14, 49.69it/s]\u001b[A\n","ITERATION - loss: 0.09: 13%|█▎ | 110/844 [13:25<00:12, 57.10it/s]\u001b[A\n","ITERATION - loss: 0.19: 14%|█▍ | 120/844 [13:25<00:11, 64.97it/s]\u001b[A\n","ITERATION - loss: 0.10: 15%|█▌ | 130/844 [13:26<00:09, 71.45it/s]\u001b[A\n","ITERATION - loss: 0.17: 17%|█▋ | 140/844 [13:26<00:09, 77.58it/s]\u001b[A\n","ITERATION - loss: 0.23: 18%|█▊ | 150/844 [13:26<00:08, 82.18it/s]\u001b[A\n","ITERATION - loss: 0.09: 19%|█▉ | 160/844 [13:26<00:08, 82.48it/s]\u001b[A\n","ITERATION - loss: 0.14: 20%|██ | 170/844 [13:26<00:07, 85.71it/s]\u001b[A\n","ITERATION - loss: 0.07: 21%|██▏ | 180/844 [13:26<00:07, 88.22it/s]\u001b[A\n","ITERATION - loss: 0.09: 23%|██▎ | 190/844 [13:26<00:07, 88.04it/s]\u001b[A\n","ITERATION - loss: 0.07: 24%|██▎ | 200/844 [13:26<00:07, 89.89it/s]\u001b[A\n","ITERATION - loss: 0.18: 25%|██▍ | 210/844 [13:26<00:06, 91.70it/s]\u001b[A\n","ITERATION - loss: 0.08: 26%|██▌ | 220/844 [13:27<00:06, 92.07it/s]\u001b[A\n","ITERATION - loss: 0.09: 27%|██▋ | 230/844 [13:27<00:06, 93.35it/s]\u001b[A\n","ITERATION - loss: 0.15: 28%|██▊ | 240/844 [13:27<00:06, 93.98it/s]\u001b[A\n","ITERATION - loss: 0.04: 30%|██▉ | 250/844 [13:27<00:06, 93.78it/s]\u001b[A\n","ITERATION - loss: 0.12: 31%|███ | 260/844 [13:27<00:06, 89.85it/s]\u001b[A\n","ITERATION - loss: 0.09: 32%|███▏ | 270/844 [13:27<00:06, 91.42it/s]\u001b[A\n","ITERATION - loss: 0.19: 33%|███▎ | 280/844 [13:27<00:06, 91.33it/s]\u001b[A\n","ITERATION - loss: 0.04: 34%|███▍ | 290/844 [13:27<00:05, 92.93it/s]\u001b[A\n","ITERATION - loss: 0.10: 36%|███▌ | 300/844 [13:27<00:05, 93.05it/s]\u001b[A\n","ITERATION - loss: 0.11: 37%|███▋ | 310/844 [13:27<00:05, 90.74it/s]\u001b[A\n","ITERATION - loss: 0.06: 38%|███▊ | 320/844 [13:28<00:05, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.08: 39%|███▉ | 330/844 [13:28<00:05, 92.39it/s]\u001b[A\n","ITERATION - loss: 0.11: 40%|████ | 340/844 [13:28<00:05, 93.72it/s]\u001b[A\n","ITERATION - loss: 0.04: 41%|████▏ | 350/844 [13:28<00:05, 90.77it/s]\u001b[A\n","ITERATION - loss: 0.06: 43%|████▎ | 360/844 [13:28<00:05, 91.09it/s]\u001b[A\n","ITERATION - loss: 0.09: 44%|████▍ | 370/844 [13:28<00:05, 92.27it/s]\u001b[A\n","ITERATION - loss: 0.13: 45%|████▌ | 380/844 [13:28<00:05, 86.17it/s]\u001b[A\n","ITERATION - loss: 0.05: 46%|████▌ | 390/844 [13:28<00:05, 87.22it/s]\u001b[A\n","ITERATION - loss: 0.05: 47%|████▋ | 400/844 [13:28<00:05, 88.66it/s]\u001b[A\n","ITERATION - loss: 0.10: 49%|████▊ | 410/844 [13:29<00:05, 86.09it/s]\u001b[A\n","ITERATION - loss: 0.13: 50%|████▉ | 420/844 [13:29<00:04, 88.66it/s]\u001b[A\n","ITERATION - loss: 0.09: 51%|█████ | 430/844 [13:29<00:04, 90.77it/s]\u001b[A\n","ITERATION - loss: 0.16: 52%|█████▏ | 440/844 [13:29<00:04, 88.37it/s]\u001b[A\n","ITERATION - loss: 0.14: 53%|█████▎ | 450/844 [13:29<00:04, 87.62it/s]\u001b[A\n","ITERATION - loss: 0.09: 55%|█████▍ | 460/844 [13:29<00:04, 89.78it/s]\u001b[A\n","ITERATION - loss: 0.19: 56%|█████▌ | 470/844 [13:29<00:04, 88.43it/s]\u001b[A\n","ITERATION - loss: 0.15: 57%|█████▋ | 480/844 [13:29<00:04, 86.10it/s]\u001b[A\n","ITERATION - loss: 0.08: 58%|█████▊ | 490/844 [13:30<00:04, 88.15it/s]\u001b[A\n","ITERATION - loss: 0.08: 59%|█████▉ | 500/844 [13:30<00:03, 89.24it/s]\u001b[A\n","ITERATION - loss: 0.10: 60%|██████ | 510/844 [13:30<00:03, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.13: 62%|██████▏ | 520/844 [13:30<00:03, 91.83it/s]\u001b[A\n","ITERATION - loss: 0.13: 63%|██████▎ | 530/844 [13:30<00:03, 89.30it/s]\u001b[A\n","ITERATION - loss: 0.28: 64%|██████▍ | 540/844 [13:30<00:03, 90.08it/s]\u001b[A\n","ITERATION - loss: 0.12: 65%|██████▌ | 550/844 [13:30<00:03, 90.26it/s]\u001b[A\n","ITERATION - loss: 0.18: 66%|██████▋ | 560/844 [13:30<00:03, 88.52it/s]\u001b[A\n","ITERATION - loss: 0.07: 68%|██████▊ | 570/844 [13:30<00:03, 88.59it/s]\u001b[A\n","ITERATION - loss: 0.07: 69%|██████▊ | 580/844 [13:31<00:02, 89.73it/s]\u001b[A\n","ITERATION - loss: 0.07: 70%|██████▉ | 590/844 [13:31<00:02, 90.70it/s]\u001b[A\n","ITERATION - loss: 0.08: 71%|███████ | 600/844 [13:31<00:02, 91.61it/s]\u001b[A\n","ITERATION - loss: 0.06: 72%|███████▏ | 610/844 [13:31<00:02, 90.86it/s]\u001b[A\n","ITERATION - loss: 0.19: 73%|███████▎ | 620/844 [13:31<00:02, 91.39it/s]\u001b[A\n","ITERATION - loss: 0.07: 75%|███████▍ | 630/844 [13:31<00:02, 88.25it/s]\u001b[A\n","ITERATION - loss: 0.12: 76%|███████▌ | 640/844 [13:31<00:02, 85.09it/s]\u001b[A\n","ITERATION - loss: 0.04: 77%|███████▋ | 650/844 [13:31<00:02, 87.42it/s]\u001b[A\n","ITERATION - loss: 0.15: 78%|███████▊ | 660/844 [13:31<00:02, 86.44it/s]\u001b[A\n","ITERATION - loss: 0.12: 79%|███████▉ | 670/844 [13:32<00:01, 87.52it/s]\u001b[A\n","ITERATION - loss: 0.06: 81%|████████ | 680/844 [13:32<00:01, 89.83it/s]\u001b[A\n","ITERATION - loss: 0.02: 82%|████████▏ | 690/844 [13:32<00:01, 91.78it/s]\u001b[A\n","ITERATION - loss: 0.20: 83%|████████▎ | 700/844 [13:32<00:01, 92.52it/s]\u001b[A\n","ITERATION - loss: 0.13: 84%|████████▍ | 710/844 [13:32<00:01, 93.80it/s]\u001b[A\n","ITERATION - loss: 0.19: 85%|████████▌ | 720/844 [13:32<00:01, 90.93it/s]\u001b[A\n","ITERATION - loss: 0.13: 86%|████████▋ | 730/844 [13:32<00:01, 91.34it/s]\u001b[A\n","ITERATION - loss: 0.21: 88%|████████▊ | 740/844 [13:32<00:01, 89.33it/s]\u001b[A\n","ITERATION - loss: 0.07: 89%|████████▉ | 750/844 [13:32<00:01, 90.80it/s]\u001b[A\n","ITERATION - loss: 0.07: 90%|█████████ | 760/844 [13:33<00:00, 90.73it/s]\u001b[A\n","ITERATION - loss: 0.15: 91%|█████████ | 770/844 [13:33<00:00, 92.40it/s]\u001b[A\n","ITERATION - loss: 0.14: 92%|█████████▏| 780/844 [13:33<00:00, 93.21it/s]\u001b[A\n","ITERATION - loss: 0.06: 94%|█████████▎| 790/844 [13:33<00:00, 93.82it/s]\u001b[A\n","ITERATION - loss: 0.08: 95%|█████████▍| 800/844 [13:33<00:00, 95.30it/s]\u001b[A\n","ITERATION - loss: 0.08: 96%|█████████▌| 810/844 [13:33<00:00, 91.95it/s]\u001b[A\n","ITERATION - loss: 0.09: 97%|█████████▋| 820/844 [13:33<00:00, 88.97it/s]\u001b[A\n","ITERATION - loss: 0.12: 98%|█████████▊| 830/844 [13:33<00:00, 90.23it/s]\u001b[A\n","ITERATION - loss: 0.10: 100%|█████████▉| 840/844 [13:33<00:00, 91.53it/s]\u001b[A\n","ITERATION - loss: 0.08: 850it [13:33, 92.61it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [14:14<00:00, 93.64it/s]\n","ITERATION - loss: 0.08: 850it [13:40, 92.61it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 48 Avg accuracy: 96.81 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [14:15<00:00, 93.64it/s]\n","ITERATION - loss: 0.08: 850it [13:41, 92.61it/s]\u001b[A\n","ITERATION - loss: 0.08: 1%| | 10/844 [13:41<03:23, 4.09it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 48 Avg accuracy: 96.30 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.10: 2%|▏ | 20/844 [13:41<02:23, 5.74it/s]\u001b[A\n","ITERATION - loss: 0.18: 4%|▎ | 30/844 [13:42<01:41, 8.00it/s]\u001b[A\n","ITERATION - loss: 0.05: 5%|▍ | 40/844 [13:42<01:12, 11.02it/s]\u001b[A\n","ITERATION - loss: 0.19: 6%|▌ | 50/844 [13:42<00:53, 14.98it/s]\u001b[A\n","ITERATION - loss: 0.14: 7%|▋ | 60/844 [13:42<00:39, 20.06it/s]\u001b[A\n","ITERATION - loss: 0.17: 8%|▊ | 70/844 [13:42<00:29, 26.27it/s]\u001b[A\n","ITERATION - loss: 0.24: 9%|▉ | 80/844 [13:42<00:22, 33.56it/s]\u001b[A\n","ITERATION - loss: 0.03: 11%|█ | 90/844 [13:42<00:18, 40.82it/s]\u001b[A\n","ITERATION - loss: 0.07: 12%|█▏ | 100/844 [13:42<00:15, 48.93it/s]\u001b[A\n","ITERATION - loss: 0.11: 13%|█▎ | 110/844 [13:42<00:12, 57.35it/s]\u001b[A\n","ITERATION - loss: 0.08: 14%|█▍ | 120/844 [13:43<00:11, 64.61it/s]\u001b[A\n","ITERATION - loss: 0.11: 15%|█▌ | 130/844 [13:43<00:09, 71.88it/s]\u001b[A\n","ITERATION - loss: 0.07: 17%|█▋ | 140/844 [13:43<00:09, 77.58it/s]\u001b[A\n","ITERATION - loss: 0.11: 18%|█▊ | 150/844 [13:43<00:08, 82.04it/s]\u001b[A\n","ITERATION - loss: 0.07: 19%|█▉ | 160/844 [13:43<00:08, 85.26it/s]\u001b[A\n","ITERATION - loss: 0.08: 20%|██ | 170/844 [13:43<00:07, 88.29it/s]\u001b[A\n","ITERATION - loss: 0.16: 21%|██▏ | 180/844 [13:43<00:07, 88.82it/s]\u001b[A\n","ITERATION - loss: 0.21: 23%|██▎ | 190/844 [13:43<00:07, 85.90it/s]\u001b[A\n","ITERATION - loss: 0.06: 24%|██▎ | 200/844 [13:43<00:07, 87.08it/s]\u001b[A\n","ITERATION - loss: 0.08: 25%|██▍ | 210/844 [13:44<00:07, 89.32it/s]\u001b[A\n","ITERATION - loss: 0.06: 26%|██▌ | 220/844 [13:44<00:06, 90.58it/s]\u001b[A\n","ITERATION - loss: 0.10: 27%|██▋ | 230/844 [13:44<00:06, 90.44it/s]\u001b[A\n","ITERATION - loss: 0.09: 28%|██▊ | 240/844 [13:44<00:06, 91.00it/s]\u001b[A\n","ITERATION - loss: 0.07: 30%|██▉ | 250/844 [13:44<00:06, 92.42it/s]\u001b[A\n","ITERATION - loss: 0.07: 31%|███ | 260/844 [13:44<00:06, 92.79it/s]\u001b[A\n","ITERATION - loss: 0.10: 32%|███▏ | 270/844 [13:44<00:06, 92.78it/s]\u001b[A\n","ITERATION - loss: 0.17: 33%|███▎ | 280/844 [13:44<00:06, 87.58it/s]\u001b[A\n","ITERATION - loss: 0.04: 34%|███▍ | 290/844 [13:44<00:06, 87.57it/s]\u001b[A\n","ITERATION - loss: 0.09: 36%|███▌ | 300/844 [13:45<00:06, 88.09it/s]\u001b[A\n","ITERATION - loss: 0.12: 37%|███▋ | 310/844 [13:45<00:06, 88.32it/s]\u001b[A\n","ITERATION - loss: 0.15: 38%|███▊ | 320/844 [13:45<00:06, 86.25it/s]\u001b[A\n","ITERATION - loss: 0.08: 39%|███▉ | 330/844 [13:45<00:05, 88.34it/s]\u001b[A\n","ITERATION - loss: 0.10: 40%|████ | 340/844 [13:45<00:05, 90.36it/s]\u001b[A\n","ITERATION - loss: 0.07: 41%|████▏ | 350/844 [13:45<00:05, 90.86it/s]\u001b[A\n","ITERATION - loss: 0.05: 43%|████▎ | 360/844 [13:45<00:05, 88.13it/s]\u001b[A\n","ITERATION - loss: 0.10: 44%|████▍ | 370/844 [13:45<00:05, 87.47it/s]\u001b[A\n","ITERATION - loss: 0.17: 45%|████▌ | 380/844 [13:45<00:05, 88.51it/s]\u001b[A\n","ITERATION - loss: 0.02: 46%|████▌ | 390/844 [13:46<00:05, 90.42it/s]\u001b[A\n","ITERATION - loss: 0.15: 47%|████▋ | 400/844 [13:46<00:04, 91.61it/s]\u001b[A\n","ITERATION - loss: 0.14: 49%|████▊ | 410/844 [13:46<00:04, 91.08it/s]\u001b[A\n","ITERATION - loss: 0.23: 50%|████▉ | 420/844 [13:46<00:04, 92.44it/s]\u001b[A\n","ITERATION - loss: 0.07: 51%|█████ | 430/844 [13:46<00:04, 92.42it/s]\u001b[A\n","ITERATION - loss: 0.07: 52%|█████▏ | 440/844 [13:46<00:04, 91.79it/s]\u001b[A\n","ITERATION - loss: 0.16: 53%|█████▎ | 450/844 [13:46<00:04, 88.97it/s]\u001b[A\n","ITERATION - loss: 0.21: 55%|█████▍ | 460/844 [13:46<00:04, 86.95it/s]\u001b[A\n","ITERATION - loss: 0.07: 56%|█████▌ | 470/844 [13:46<00:04, 87.30it/s]\u001b[A\n","ITERATION - loss: 0.05: 57%|█████▋ | 480/844 [13:47<00:04, 88.78it/s]\u001b[A\n","ITERATION - loss: 0.14: 58%|█████▊ | 490/844 [13:47<00:04, 86.60it/s]\u001b[A\n","ITERATION - loss: 0.01: 59%|█████▉ | 500/844 [13:47<00:03, 87.60it/s]\u001b[A\n","ITERATION - loss: 0.10: 60%|██████ | 510/844 [13:47<00:03, 88.80it/s]\u001b[A\n","ITERATION - loss: 0.05: 62%|██████▏ | 520/844 [13:47<00:03, 88.79it/s]\u001b[A\n","ITERATION - loss: 0.12: 63%|██████▎ | 530/844 [13:47<00:03, 89.85it/s]\u001b[A\n","ITERATION - loss: 0.13: 64%|██████▍ | 540/844 [13:47<00:03, 90.25it/s]\u001b[A\n","ITERATION - loss: 0.07: 65%|██████▌ | 550/844 [13:47<00:03, 88.64it/s]\u001b[A\n","ITERATION - loss: 0.22: 66%|██████▋ | 560/844 [13:47<00:03, 88.16it/s]\u001b[A\n","ITERATION - loss: 0.08: 68%|██████▊ | 570/844 [13:48<00:03, 89.16it/s]\u001b[A\n","ITERATION - loss: 0.08: 69%|██████▊ | 580/844 [13:48<00:03, 87.58it/s]\u001b[A\n","ITERATION - loss: 0.07: 70%|██████▉ | 590/844 [13:48<00:02, 87.64it/s]\u001b[A\n","ITERATION - loss: 0.17: 71%|███████ | 600/844 [13:48<00:02, 89.25it/s]\u001b[A\n","ITERATION - loss: 0.08: 72%|███████▏ | 610/844 [13:48<00:02, 91.10it/s]\u001b[A\n","ITERATION - loss: 0.09: 73%|███████▎ | 620/844 [13:48<00:02, 90.18it/s]\u001b[A\n","ITERATION - loss: 0.13: 75%|███████▍ | 630/844 [13:48<00:02, 91.52it/s]\u001b[A\n","ITERATION - loss: 0.18: 76%|███████▌ | 640/844 [13:48<00:02, 90.45it/s]\u001b[A\n","ITERATION - loss: 0.09: 77%|███████▋ | 650/844 [13:48<00:02, 88.64it/s]\u001b[A\n","ITERATION - loss: 0.05: 78%|███████▊ | 660/844 [13:49<00:02, 88.92it/s]\u001b[A\n","ITERATION - loss: 0.31: 79%|███████▉ | 670/844 [13:49<00:01, 90.10it/s]\u001b[A\n","ITERATION - loss: 0.08: 81%|████████ | 680/844 [13:49<00:01, 91.10it/s]\u001b[A\n","ITERATION - loss: 0.07: 82%|████████▏ | 690/844 [13:49<00:01, 91.55it/s]\u001b[A\n","ITERATION - loss: 0.07: 83%|████████▎ | 700/844 [13:49<00:01, 87.81it/s]\u001b[A\n","ITERATION - loss: 0.09: 84%|████████▍ | 710/844 [13:49<00:01, 86.42it/s]\u001b[A\n","ITERATION - loss: 0.16: 85%|████████▌ | 720/844 [13:49<00:01, 85.32it/s]\u001b[A\n","ITERATION - loss: 0.15: 86%|████████▋ | 730/844 [13:49<00:01, 86.24it/s]\u001b[A\n","ITERATION - loss: 0.09: 88%|████████▊ | 740/844 [13:49<00:01, 86.55it/s]\u001b[A\n","ITERATION - loss: 0.16: 89%|████████▉ | 750/844 [13:50<00:01, 89.16it/s]\u001b[A\n","ITERATION - loss: 0.07: 90%|█████████ | 760/844 [13:50<00:00, 91.17it/s]\u001b[A\n","ITERATION - loss: 0.11: 91%|█████████ | 770/844 [13:50<00:00, 92.53it/s]\u001b[A\n","ITERATION - loss: 0.17: 92%|█████████▏| 780/844 [13:50<00:00, 92.53it/s]\u001b[A\n","ITERATION - loss: 0.14: 94%|█████████▎| 790/844 [13:50<00:00, 93.26it/s]\u001b[A\n","ITERATION - loss: 0.23: 95%|█████████▍| 800/844 [13:50<00:00, 93.95it/s]\u001b[A\n","ITERATION - loss: 0.10: 96%|█████████▌| 810/844 [13:50<00:00, 91.35it/s]\u001b[A\n","ITERATION - loss: 0.15: 97%|█████████▋| 820/844 [13:50<00:00, 89.12it/s]\u001b[A\n","ITERATION - loss: 0.07: 98%|█████████▊| 830/844 [13:50<00:00, 88.51it/s]\u001b[A\n","ITERATION - loss: 0.12: 100%|█████████▉| 840/844 [13:51<00:00, 89.77it/s]\u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [14:31<00:00, 93.64it/s]\n","ITERATION - loss: 0.12: 100%|█████████▉| 840/844 [13:58<00:00, 89.77it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 49 Avg accuracy: 96.89 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [14:32<00:00, 93.64it/s]\n","ITERATION - loss: 0.12: 100%|█████████▉| 840/844 [13:58<00:00, 89.77it/s]\u001b[A\n","ITERATION - loss: 0.08: 1%| | 10/844 [13:59<03:25, 4.07it/s] \u001b[A\n","ITERATION - loss: 0.06: 2%|▏ | 20/844 [13:59<02:24, 5.70it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 49 Avg accuracy: 96.37 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.05: 4%|▎ | 30/844 [13:59<01:42, 7.93it/s]\u001b[A\n","ITERATION - loss: 0.24: 5%|▍ | 40/844 [13:59<01:13, 10.93it/s]\u001b[A\n","ITERATION - loss: 0.08: 6%|▌ | 50/844 [13:59<00:53, 14.86it/s]\u001b[A\n","ITERATION - loss: 0.18: 7%|▋ | 60/844 [13:59<00:39, 19.90it/s]\u001b[A\n","ITERATION - loss: 0.13: 8%|▊ | 70/844 [13:59<00:29, 25.85it/s]\u001b[A\n","ITERATION - loss: 0.08: 9%|▉ | 80/844 [13:59<00:23, 32.19it/s]\u001b[A\n","ITERATION - loss: 0.26: 11%|█ | 90/844 [13:59<00:18, 39.89it/s]\u001b[A\n","ITERATION - loss: 0.07: 12%|█▏ | 100/844 [14:00<00:15, 47.31it/s]\u001b[A\n","ITERATION - loss: 0.16: 13%|█▎ | 110/844 [14:00<00:13, 55.30it/s]\u001b[A\n","ITERATION - loss: 0.19: 14%|█▍ | 120/844 [14:00<00:11, 61.21it/s]\u001b[A\n","ITERATION - loss: 0.08: 15%|█▌ | 130/844 [14:00<00:10, 65.87it/s]\u001b[A\n","ITERATION - loss: 0.19: 17%|█▋ | 140/844 [14:00<00:09, 72.42it/s]\u001b[A\n","ITERATION - loss: 0.14: 18%|█▊ | 150/844 [14:00<00:08, 77.49it/s]\u001b[A\n","ITERATION - loss: 0.18: 19%|█▉ | 160/844 [14:00<00:08, 81.59it/s]\u001b[A\n","ITERATION - loss: 0.06: 20%|██ | 170/844 [14:00<00:07, 85.09it/s]\u001b[A\n","ITERATION - loss: 0.04: 21%|██▏ | 180/844 [14:00<00:07, 87.87it/s]\u001b[A\n","ITERATION - loss: 0.10: 23%|██▎ | 190/844 [14:01<00:07, 87.55it/s]\u001b[A\n","ITERATION - loss: 0.15: 24%|██▎ | 200/844 [14:01<00:07, 88.78it/s]\u001b[A\n","ITERATION - loss: 0.04: 25%|██▍ | 210/844 [14:01<00:07, 88.48it/s]\u001b[A\n","ITERATION - loss: 0.04: 26%|██▌ | 220/844 [14:01<00:06, 90.06it/s]\u001b[A\n","ITERATION - loss: 0.14: 27%|██▋ | 230/844 [14:01<00:06, 90.99it/s]\u001b[A\n","ITERATION - loss: 0.09: 28%|██▊ | 240/844 [14:01<00:06, 91.47it/s]\u001b[A\n","ITERATION - loss: 0.16: 30%|██▉ | 250/844 [14:01<00:06, 92.70it/s]\u001b[A\n","ITERATION - loss: 0.08: 31%|███ | 260/844 [14:01<00:06, 92.95it/s]\u001b[A\n","ITERATION - loss: 0.06: 32%|███▏ | 270/844 [14:01<00:06, 92.58it/s]\u001b[A\n","ITERATION - loss: 0.03: 33%|███▎ | 280/844 [14:02<00:06, 91.64it/s]\u001b[A\n","ITERATION - loss: 0.03: 34%|███▍ | 290/844 [14:02<00:06, 91.41it/s]\u001b[A\n","ITERATION - loss: 0.07: 36%|███▌ | 300/844 [14:02<00:06, 86.92it/s]\u001b[A\n","ITERATION - loss: 0.07: 37%|███▋ | 310/844 [14:02<00:06, 88.62it/s]\u001b[A\n","ITERATION - loss: 0.16: 38%|███▊ | 320/844 [14:02<00:05, 90.64it/s]\u001b[A\n","ITERATION - loss: 0.07: 39%|███▉ | 330/844 [14:02<00:05, 92.06it/s]\u001b[A\n","ITERATION - loss: 0.04: 40%|████ | 340/844 [14:02<00:05, 92.59it/s]\u001b[A\n","ITERATION - loss: 0.19: 41%|████▏ | 350/844 [14:02<00:05, 91.52it/s]\u001b[A\n","ITERATION - loss: 0.09: 43%|████▎ | 360/844 [14:02<00:05, 92.00it/s]\u001b[A\n","ITERATION - loss: 0.15: 44%|████▍ | 370/844 [14:02<00:05, 90.49it/s]\u001b[A\n","ITERATION - loss: 0.06: 45%|████▌ | 380/844 [14:03<00:05, 91.05it/s]\u001b[A\n","ITERATION - loss: 0.21: 46%|████▌ | 390/844 [14:03<00:05, 90.64it/s]\u001b[A\n","ITERATION - loss: 0.07: 47%|████▋ | 400/844 [14:03<00:04, 89.82it/s]\u001b[A\n","ITERATION - loss: 0.10: 49%|████▊ | 410/844 [14:03<00:04, 91.16it/s]\u001b[A\n","ITERATION - loss: 0.16: 50%|████▉ | 420/844 [14:03<00:04, 92.28it/s]\u001b[A\n","ITERATION - loss: 0.07: 51%|█████ | 430/844 [14:03<00:04, 92.49it/s]\u001b[A\n","ITERATION - loss: 0.20: 52%|█████▏ | 440/844 [14:03<00:04, 92.68it/s]\u001b[A\n","ITERATION - loss: 0.10: 53%|█████▎ | 450/844 [14:03<00:04, 93.56it/s]\u001b[A\n","ITERATION - loss: 0.18: 55%|█████▍ | 460/844 [14:03<00:04, 92.90it/s]\u001b[A\n","ITERATION - loss: 0.05: 56%|█████▌ | 470/844 [14:04<00:04, 88.35it/s]\u001b[A\n","ITERATION - loss: 0.05: 57%|█████▋ | 480/844 [14:04<00:04, 90.86it/s]\u001b[A\n","ITERATION - loss: 0.08: 58%|█████▊ | 490/844 [14:04<00:03, 89.48it/s]\u001b[A\n","ITERATION - loss: 0.10: 59%|█████▉ | 500/844 [14:04<00:03, 91.31it/s]\u001b[A\n","ITERATION - loss: 0.08: 60%|██████ | 510/844 [14:04<00:03, 91.57it/s]\u001b[A\n","ITERATION - loss: 0.10: 62%|██████▏ | 520/844 [14:04<00:03, 91.29it/s]\u001b[A\n","ITERATION - loss: 0.03: 63%|██████▎ | 530/844 [14:04<00:03, 92.31it/s]\u001b[A\n","ITERATION - loss: 0.13: 64%|██████▍ | 540/844 [14:04<00:03, 92.46it/s]\u001b[A\n","ITERATION - loss: 0.20: 65%|██████▌ | 550/844 [14:04<00:03, 92.85it/s]\u001b[A\n","ITERATION - loss: 0.07: 66%|██████▋ | 560/844 [14:05<00:03, 91.93it/s]\u001b[A\n","ITERATION - loss: 0.16: 68%|██████▊ | 570/844 [14:05<00:02, 92.82it/s]\u001b[A\n","ITERATION - loss: 0.06: 69%|██████▊ | 580/844 [14:05<00:02, 90.93it/s]\u001b[A\n","ITERATION - loss: 0.08: 70%|██████▉ | 590/844 [14:05<00:02, 92.13it/s]\u001b[A\n","ITERATION - loss: 0.08: 71%|███████ | 600/844 [14:05<00:02, 92.25it/s]\u001b[A\n","ITERATION - loss: 0.20: 72%|███████▏ | 610/844 [14:05<00:02, 93.15it/s]\u001b[A\n","ITERATION - loss: 0.10: 73%|███████▎ | 620/844 [14:05<00:02, 93.61it/s]\u001b[A\n","ITERATION - loss: 0.17: 75%|███████▍ | 630/844 [14:05<00:02, 94.22it/s]\u001b[A\n","ITERATION - loss: 0.07: 76%|███████▌ | 640/844 [14:05<00:02, 93.21it/s]\u001b[A\n","ITERATION - loss: 0.06: 77%|███████▋ | 650/844 [14:06<00:02, 91.73it/s]\u001b[A\n","ITERATION - loss: 0.19: 78%|███████▊ | 660/844 [14:06<00:01, 92.74it/s]\u001b[A\n","ITERATION - loss: 0.08: 79%|███████▉ | 670/844 [14:06<00:01, 89.92it/s]\u001b[A\n","ITERATION - loss: 0.15: 81%|████████ | 680/844 [14:06<00:01, 88.66it/s]\u001b[A\n","ITERATION - loss: 0.08: 82%|████████▏ | 690/844 [14:06<00:01, 90.58it/s]\u001b[A\n","ITERATION - loss: 0.13: 83%|████████▎ | 700/844 [14:06<00:01, 90.67it/s]\u001b[A\n","ITERATION - loss: 0.08: 84%|████████▍ | 710/844 [14:06<00:01, 91.09it/s]\u001b[A\n","ITERATION - loss: 0.15: 85%|████████▌ | 720/844 [14:06<00:01, 91.88it/s]\u001b[A\n","ITERATION - loss: 0.10: 86%|████████▋ | 730/844 [14:06<00:01, 91.63it/s]\u001b[A\n","ITERATION - loss: 0.06: 88%|████████▊ | 740/844 [14:07<00:01, 93.07it/s]\u001b[A\n","ITERATION - loss: 0.15: 89%|████████▉ | 750/844 [14:07<00:01, 92.81it/s]\u001b[A\n","ITERATION - loss: 0.12: 90%|█████████ | 760/844 [14:07<00:00, 93.68it/s]\u001b[A\n","ITERATION - loss: 0.12: 91%|█████████ | 770/844 [14:07<00:00, 91.77it/s]\u001b[A\n","ITERATION - loss: 0.01: 92%|█████████▏| 780/844 [14:07<00:00, 93.09it/s]\u001b[A\n","ITERATION - loss: 0.15: 94%|█████████▎| 790/844 [14:07<00:00, 94.12it/s]\u001b[A\n","ITERATION - loss: 0.12: 95%|█████████▍| 800/844 [14:07<00:00, 92.53it/s]\u001b[A\n","ITERATION - loss: 0.11: 96%|█████████▌| 810/844 [14:07<00:00, 92.78it/s]\u001b[A\n","ITERATION - loss: 0.04: 97%|█████████▋| 820/844 [14:07<00:00, 91.34it/s]\u001b[A\n","ITERATION - loss: 0.12: 98%|█████████▊| 830/844 [14:07<00:00, 92.17it/s]\u001b[A\n","ITERATION - loss: 0.10: 100%|█████████▉| 840/844 [14:08<00:00, 90.94it/s]\u001b[A\n","ITERATION - loss: 0.14: 850it [14:08, 90.17it/s] \u001b[A\n","\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [14:48<00:00, 93.64it/s]\n","ITERATION - loss: 0.14: 850it [14:15, 90.17it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Training Results - Epoch: 50 Avg accuracy: 96.89 Avg loss: 0.11\n"],"name":"stdout"},{"output_type":"stream","text":["\n","ITERATION - loss: 0.44: 100%|█████████▉| 840/844 [14:49<00:00, 93.64it/s]\n","ITERATION - loss: 0.14: 850it [14:15, 90.17it/s]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["Validation Results - Epoch: 50 Avg accuracy: 96.27 Avg loss: 0.13\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["\n"," \u001b[A"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"K0M_D7KcVvAD","colab_type":"text"},"source":["### Test Binary network:"]},{"cell_type":"code","metadata":{"id":"vGe1V4R4VzC3","colab_type":"code","outputId":"cf55f699-11d8-4127-dca9-cba8725be74f","executionInfo":{"status":"ok","timestamp":1588860797934,"user_tz":-120,"elapsed":2187,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["# load model pre trained binary\n","binary = True \n","bias=False\n","model_binary_wt_bias, name_model = get_my_model_MNIST(binary, bias=bias)\n","\n","path_model = 'trained_models/MNIST/Binary_models/without_bias'\n","if torch.cuda.is_available():\n"," model_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model Loaded MNIST_Stochastic_ST_first_conv_binary_without_bias\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-jCPLDGhVz_Q","colab_type":"code","outputId":"9a99f4a2-fba0-44ef-f0ae-7464637e5418","executionInfo":{"status":"ok","timestamp":1588860799208,"user_tz":-120,"elapsed":3453,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["evaluate(model_binary_wt_bias, test_loader)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Test Results - Avg accuracy: 96.92 Avg loss: 0.11\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"-_4XKTA569_T","colab_type":"text"},"source":["# Visualization:"]},{"cell_type":"code","metadata":{"id":"n0o9-iFNDI1m","colab_type":"code","colab":{}},"source":["from visualize.viz import visTensor, get_activation, viz_activations, viz_filters\n","from visualize.viz import viz_heatmap, test_predict_few_examples, standardize_and_clip, format_for_plotting\n","from visualize.viz import apply_transforms, GradientAscent, get_filter_layer2\n","from visualize.viz import get_region_layer1, get_region_layer2, get_regions_interest, get_all_regions_max\n","\n","# for regions extraction\n","import collections\n","from functools import partial\n","import cv2"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zWjDVnSAFyUr","colab_type":"text"},"source":["## Load model:"]},{"cell_type":"code","metadata":{"id":"LlTceHneFzkp","colab_type":"code","outputId":"032f4fac-ceb1-418d-905f-f6c3a51a0f29","executionInfo":{"status":"ok","timestamp":1588929722174,"user_tz":-120,"elapsed":3805,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# load model pre trained no binary\n","binary = False\n","model_no_binary, name_model = get_my_model_MNIST(binary)\n","\n","path_model = 'trained_models/MNIST/No_binary_models/with_bias'\n","if torch.cuda.is_available():\n"," model_no_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_no_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Model Loaded MNIST_NonBinaryNet\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"5qw9xVKbGFDs","colab_type":"code","outputId":"328e7769-8dac-436b-e20f-8d6ce9a09376","executionInfo":{"status":"ok","timestamp":1588929722907,"user_tz":-120,"elapsed":4522,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# load model pre trained binary\n","binary = True \n","model_binary, name_model = get_my_model_MNIST(binary)\n","\n","path_model = 'trained_models/MNIST/Binary_models/with_bias'\n","if torch.cuda.is_available():\n"," model_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Model Loaded MNIST_Stochastic_ST_first_conv_binary\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"UwKq4tgwWNv9","colab_type":"code","outputId":"892e3465-7b9c-4c63-b693-ee98970aa2b7","executionInfo":{"status":"ok","timestamp":1588929723171,"user_tz":-120,"elapsed":4772,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# load model pre trained no binary\n","binary = False\n","bias=False\n","model_no_binary_wt_bias, name_model = get_my_model_MNIST(binary, bias=bias)\n","\n","path_model = 'trained_models/MNIST/No_binary_models/without_bias'\n","if torch.cuda.is_available():\n"," model_no_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_no_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":9,"outputs":[{"output_type":"stream","text":["Model Loaded MNIST_NonBinaryNet_without_bias\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xonXN-e4WOCf","colab_type":"code","outputId":"5e149904-1722-4bcc-f0ed-73be13576a98","executionInfo":{"status":"ok","timestamp":1588929724015,"user_tz":-120,"elapsed":5603,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# load model pre trained binary\n","binary = True \n","bias=False\n","model_binary_wt_bias, name_model = get_my_model_MNIST(binary, bias=bias)\n","\n","path_model = 'trained_models/MNIST/Binary_models/without_bias'\n","if torch.cuda.is_available():\n"," model_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Model Loaded MNIST_Stochastic_ST_first_conv_binary_without_bias\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rHWEBMGBNGhP","colab_type":"code","outputId":"2b27aa9f-f38a-472b-cf82-72b4a0ca2c77","executionInfo":{"status":"ok","timestamp":1588860837938,"user_tz":-120,"elapsed":475,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":163}},"source":["print(model_no_binary)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["NoBinaryNetMnist(\n"," (layer1): Conv2d(1, 10, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n"," (batchnorm1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (act_layer1): ReLU()\n"," (layer2): Conv2d(10, 20, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n"," (batchnorm2): BatchNorm2d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (act_layer2): ReLU()\n"," (fc): Linear(in_features=980, out_features=10, bias=True)\n",")\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"j1CP_zaINIat","colab_type":"code","outputId":"e27e3e73-25eb-4501-c22e-963ebf58fc38","executionInfo":{"status":"ok","timestamp":1588860839024,"user_tz":-120,"elapsed":1344,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":228}},"source":["print(model_binary)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["BinaryNetMNIST(\n"," (layer1): Conv2d(1, 10, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n"," (batchnorm1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (act_layer1): StochasticBinaryActivation(\n"," (act): Hardsigmoid(\n"," (act): Hardtanh(min_val=-1.0, max_val=1.0)\n"," )\n"," )\n"," (layer2): Conv2d(10, 20, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n"," (batchnorm2): BatchNorm2d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (act_layer2): ReLU()\n"," (fc): Linear(in_features=980, out_features=10, bias=True)\n",")\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"k4ALBHjqWbgZ","colab_type":"code","outputId":"ce43606d-c5e1-4920-d813-749d3c3864a9","executionInfo":{"status":"ok","timestamp":1588860839026,"user_tz":-120,"elapsed":1119,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":163}},"source":["print(model_no_binary_wt_bias)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["NoBinaryNetMnist(\n"," (layer1): Conv2d(1, 10, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (batchnorm1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (act_layer1): ReLU()\n"," (layer2): Conv2d(10, 20, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (batchnorm2): BatchNorm2d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (act_layer2): ReLU()\n"," (fc): Linear(in_features=980, out_features=10, bias=True)\n",")\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-kitDGciWb-Y","colab_type":"code","outputId":"64406349-962e-4d7d-eae4-ae114e9b91ae","executionInfo":{"status":"ok","timestamp":1588860839654,"user_tz":-120,"elapsed":1444,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":228}},"source":["print(model_binary_wt_bias)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["BinaryNetMNIST(\n"," (layer1): Conv2d(1, 10, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (batchnorm1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (act_layer1): StochasticBinaryActivation(\n"," (act): Hardsigmoid(\n"," (act): Hardtanh(min_val=-1.0, max_val=1.0)\n"," )\n"," )\n"," (layer2): Conv2d(10, 20, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (batchnorm2): BatchNorm2d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (act_layer2): ReLU()\n"," (fc): Linear(in_features=980, out_features=10, bias=True)\n",")\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5959m3vfGJXc","colab_type":"text"},"source":["## Visualization few predictions:"]},{"cell_type":"code","metadata":{"id":"-DNeZnOcGN1d","colab_type":"code","outputId":"e1443145-19eb-4d68-88ed-ce1bfdbb9e7b","executionInfo":{"status":"ok","timestamp":1588681174607,"user_tz":-120,"elapsed":1307,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":230}},"source":["print('No binary model')\n","test_predict_few_examples(model_no_binary, test_loader)\n","plt.show()\n","print('Binary model')\n","test_predict_few_examples(model_binary, test_loader)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["No binary model\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1800x288 with 10 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Binary model\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABXEAAACECAYAAADWdmg4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3de9hU4x7/8c/qgIhUKjl0UHJKSondVoq2Q0SRRNqIYiunCJuYpsTvV+SstK/tUMghRZI2IYS2Xagtxa/sSnQ+UUqn9ftj6nbfq1nTPNMzM+uZ5/26Ltf+3r5rzXz31e2eNXdrfcfzfV8AAAAAAAAAgGgqk+8CAAAAAAAAAADh2MQFAAAAAAAAgAhjExcAAAAAAAAAIoxNXAAAAAAAAACIMDZxAQAAAAAAACDC2MQFAAAAAAAAgAgrl+8CcsmLe9UkfSKpiR/zN+7m2BqSpkhq7Mf833NQHiKKeYNMMG+QCS/uHStppKST/Jjv7+bY9pIu92P+JTkpDpHFvEFR8RmFTLDWIBOsN8iI98e8kZ963sjzGkkaLt9vkYvSEF2lYb0p0Zu4XtxbH/hXFSQ95cf8G0JOuVPSczv/ML24N1tSbSu/j6R3/Jjf3o/5y7y496GknpIeL+bSkSde3Ntb0lOS2kqqImm+pL/7Mf+dFKcF582Dki6QdLCknyTd78f8kZLEvClcXtzrLelKScdLGu3H/Ct3c0pw3nSWdLOkxpK+8GN+650HMm8Klxf3qkj6p6QzJa1UYr15KcUpAyU9uPPLcarz/Zj/lhf3HvDiXiM/5s/K5v8P5IcX946U9F9JY/yYf3mKQ515k+p85k1h4jMKe4K1BkXhxb1jJD0pqamkFZL6+jF/XIpTguvNoUp8H2sp6TdJ9/kxf7jEelPwPK+LpJikWpKWSrpSvv9JyNF3SnrObOB63nOSLpO02Tqmknx/m3x/ljxvrTyvvXz/razVj5xjvdlViW6n4Mf8ijv/UWJDbaOk15Idu2Pz7gpJL1jnH2edv7+kHwPnvyjp2mzVj7wop8Sf82mSKknqJ+lVL+7VSXZwsnkjaYOk9jvOv0LSo17cs//Wj3lTmH6WdJ+kZ3Z3YMi8WS3pEUn/J+Q05k1helKJi80akrpKGubFveOSHejFvZqS2kh6owjnj1biwgOF6UlJ/0l1QMi82d35zJvCw2cU9gRrDdLixb1ykt6UNEGJG2J6SnrBi3sNQo5Ptt68IOl/SlzbnCvpfi/utbHyrDeFyPP+Iun/SrpKib2XVpJ+CDk22byRpMHy/YrWP9usHPOmwLDeJFeiN3EDLpK0XIlbp5M5WdJaP+YvDsm3knSQpNetf/dvSUd4ca928lNQ0vgxf4Mf8/v7MX+BH/O3+zF/ghL/UTcNOWWXeePH/Jgf8+fuOP/fSsy5P1nnMG8KkB/zx/ox/w1Jq9I4PNm8mezH/FeV+KKdDPOmwHhxbz8lPpvu8WP+ej/mT5U0XlK3kFP+IulLP+ZvKsL5U5S4IEGB8eJeF0lrJb2/m0OdeZPm+VPEvCkofEYhU6w1KKKjJR0i6WE/5m/zY/4Hkj5V+LWNs954ca+ipNaSBvkxf4sf82dKGiOpu3UO601hiksaIN+fJt/fLt//Sb7/U8ixJ0taKz907yaZKZLO2LEBjMLAepNEIW3iXiFpZIr+TMdL+m4357/ux/wNO/+FH/O3Spon6YRiqxKRsqMPSgNJs0MOSTlvvLhXQdJJ9vnMG2j3680umDcFqYGkrX7M/976dzMlJb0TV7vOm3TOnyOpjhf3DiiGehERO/48B0jqk8bhu6w3aZzPvCnd+IyCJNYaFBtPUsOQXHDeeIH/3eV81psC5HllJTWTVE2eN0+et1ie94Q8r0LIGWGfU9fL81bL82bI8y5yMokN4S2SjirO0hE5pX69KYhN3B275qdJej7FYQdK+jXk/H0ldZL0XJL0rzvORYHx4l55JW6ff96P+XNDDgudNzsMV2JT5V+Bf8+8Kd12N2/CMG8KS0VJvwT+3TolHiFLJjhv0jl/5/HMm8IyUNI/Uzw9ZEu23uzufOZN6cZnFHZirUFRfafE0699vbhX3ot7ZyrxPXzfkOOdeePH/F+VuJPuHi/u7ePFvROVeOooeD7rTWGpIam8EnsuLZXov95EidaGySRbbx6TdKSk6pLukfScPO/PgWOYN4WF9SaJgtjEVeJ26ql+zP9fimPWKPyL84VK9AL7KElufyUeEUIB8eJeGUmjlOg12TvFoaHzxot7Q5T4W5zOSe4AZ96UbqnWm1SYN4VlvaTg3UcHKHzzJDhv0jl/5/HMmwLhxb3GSvz45sNpnuLMmzTPZ96UbnxGgbUGGfFj/hZJHZRok7FU0q2SXpUUtpGfbL3pKqmuEr9TMkyJnpXB81lvCsvGHf/7uHx/iXx/paShktqFHL/rvPH9L+X7q+T7W+X7E5W4GevCwHnMmwLCepNcuXwXUEz+qvAfYthplqRbQnJJWzHsaKRcX4k7LVEgvLjnKfFr7zUktduxOIRJOm+8uBeXdI6k0/yY/0sgx7xBqvUmKeZNQfpeUjkv7h3px/z/t+PfnaDw9i2zlPg8Ksr5x0haEFyHUKK1llRH0iIv7kmJO7LLenHvWD/mn5jk+OC8Sed85k3pxmcUJNYaZMiP+bOUuBtOkuTFvc8U/kTsLuuNH/MXSjrPOv8lSV9YY9abQuP7a+R5iyXZ+y1hbTCl9D6nfNmPyXveoZL2UhHbBSHaWG92VeI3cb2410LSoZJe282hX0g60It7h/qxPxpoe3HvMCV+afW6JOc0V+LCY2Fx1YtIGKbERWVbP+Zv3M2xu8wbL+79XdJlklr6MT/ZD4gwbwrQjgW+nKSySnxJ2UeJfqVbkxyebN6UVeIxonKSyuw4f5v1lwjMmwLjx/wNXtwbK2mAF/euUeLRsQsktQg55T1Jj3pxbx8/5m9K8/zTJL2Tvf8XyIMRkl62xrcpsVHyt5DjnXmT5vnMmwLDZxQywFqDjHhxr5ESf9FcRtL1kmoqeVtCKfl6c4wSd8L9LqmzpDOV+G62E+tNYXpW0g3yvElK9K69RdKEkGO/kHSgPO9Q8+NnntdJ0iRJvynxFMDlktpb55wm6QP5/u/ZKR/5wHqzq0Jop3CFpLE7+l2E8mP+ZiX+sC8PpLpJ+tyP+fOTnNZViZ6nKBA7+idfq8RmyFIv7q3f8U/XZMeHzJv7JdWSNM86/y4rz7wpTP2UeBToTiXmw0aF9HEKmTfddpwzTIleUBsl/cPKM28K0/WSKijRz2m0pL/5MT/pnbh+zF8m6QMlNmrTPf9SSU9noW7kiR/zf/Nj/tKd/yjRVmOTH/NXhBzvzJs0z2feFB4+o1AkrDXYA90kLVHi2uQMSX/xY8k3zkLWm7Mk/aDEo8/XSTo7MG9YbwrTQEn/UWJDbo6kryQNSnqkn3Te3CTpJyUeex8iqYd8f4qVZ94UJtabAM/fpZVn4fLiXjVJn0hqsrs7ML24V12JHrlNdvxtM0op5g0ywbxBJry4d6wSjwg1T9JrO3hse0nd/JjfOSfFIbKYNygqPqOQCdYaZIL1Bhnx/pg38nfz9KznNZL0tHz/T7koDdFVGtabUrWJCwAAAAAAAAAlTSG0UwAAAAAAAACAgsUmLgAAAAAAAABEGJu4AAAAAAAAABBh5YpysOd5NNCNjpW+71fLdxHpYN5Eh+/7Xr5rSAdzJlJYa5AJ5g0ywbxBJpg3yATzBplg3qDI+A6ODISuNdyJW3ItzHcBAEoF1hpkgnmDTDBvkAnmDTLBvEEmmDcAciF0rWETFwAAAAAAAAAijE1cAAAAAAAAAIgwNnEBAAAAAAAAIMLYxAUAAAAAAACACGMTFwAAAAAAAAAijE1cAAAAAAAAAIgwNnEBAAAAAAAAIMLYxAUAAAAAAACACGMTFwAAAAAAAAAirFy+CwBy6bbbbnPGFSpUMHGjRo2cXKdOnUJfZ9iwYc74888/N/GoUaP2pEQAAAAAAADAwZ24AAAAAAAAABBhbOICAAAAAAAAQITRTgEF75VXXjFxqhYJQdu3bw/NXXvttc64bdu2Jv7oo4+c3KJFi9J+T5QeDRo0MPHcuXOd3E033WTixx9/PGc1ITf2228/ZzxkyBATB9eWGTNmOOOLL77YxAsXLsxCdQAAAEDJUrlyZWdcq1attM4LXk/fcsstJv7mm2+c3Pfff2/imTNnFrVEoFhwJy4AAAAAAAAARBibuAAAAAAAAAAQYWziAgAAAAAAAECE0RMXBcfugSul3wc32Jf0X//6l4mPOOIIJ9e+fXtnXK9ePRN37drVyT3wwANpvT9KlyZNmpg42H958eLFuS4HOVSzZk1n3KNHDxMH50LTpk2d8XnnnWfiJ598MgvVIZ9OPPFEZzx27FgT16lTJ+vvf+aZZzrjOXPmmPjHH3/M+vsjWuxrnfHjxzu53r17m3j48OFObtu2bdktDBmrXr26iV999VUn99lnn5l4xIgRTm7BggVZrSuoUqVKzrhVq1YmnjRpkpPbsmVLTmoCkH/nnnuuMz7//PNN3Lp1aydXv379tF7T7nMrSbVr1zbx3nvvHXpe2bJl03p9oLhxJy4AAAAAAAAARBibuAAAAAAAAAAQYbRTQEFo1qyZiTt27Bh63OzZs52x/QjGypUrndz69etNvNdeezm5adOmOeMTTjjBxFWrVk2jYpR2jRs3NvGGDRuc3Lhx43JdDrKsWrVqJn7++efzWAmi7KyzznLGqR7jy4Zgq6Du3bubuEuXLjmtBbkXvH556qmnQo994oknTPzMM884uY0bNxZvYchY5cqVnbF9HRxsWbBs2TIT57p9guTWM2PGDCdnf4YG2wzNmzcvu4UhpQMOOMDEwRZyDRs2NHHbtm2dHG0wsJPdllCSevXqZWK75ZgkVahQwRl7nrfH79+gQYM9fg0gl7gTFwAAAAAAAAAijE1cAAAAAAAAAIgwNnEBAAAAAAAAIMLy3hO3U6dOztjue/Lzzz87uU2bNpn4xRdfdHJLly41Mb2RSp+aNWuaONgbx+7/Few3uGTJkrRe/9Zbb3XGxx57bOixb7/9dlqvidLF7gsmSb179zbxqFGjcl0OsuzGG290xh06dDBx8+bNM37dVq1ambhMGffvYWfOnGnijz/+OOP3QG6VK/fHpVi7du3yWMmufSj79Olj4v3228/JBXt5o+Sz1xdJOuyww0KPHT16tInt63Pk30EHHWTiV155xclVqVLFxMGexzfccEN2C9uNfv36mbhu3bpO7tprrzUx3/Pyq2vXrs540KBBJj788MNDz7N750rSqlWrircwlFjBz5qbbrop6+85d+5cEwd/MwclT/369U1sfwZKu/5eUuvWrU28fft2Jzd8+HATf/rpp04uSp893IkLAAAAAAAAABHGJi4AAAAAAAAARFje2ykMHjzYGdepUyet8+zHaiTp119/NXE+bolfvHixiYP/n6ZPn57rckqdt956y8T27fSSOzdWr16d0et36dLFGZcvXz6j10HpdfTRRztj+9Hk4OOOKPkefvhhZxx8XCdTF154YdJYkhYuXGjiSy65xMkFH5NHdLRp08bEf/rTn5xc8Hoi2ypXruyM7dZB++67r5OjnULJt/feezvju+++O+1z7TZAvu8XW03YcyeeeKKJ7cdGgwYMGJCDasIdd9xxzthuXTZu3Dgnx3VSftmPuz/yyCNOrmrVqiZOtRY8/vjjzthuKyZl/h0N0RF8jN1uixB8NH3SpEkm/v33353cunXrTBy81gi2dnr33XdN/M033zi5f//73yb+6quvnNzGjRtD3wPRZLcmDK4f9nei4DwsipNPPtnEW7dudXLfffediadOnerk7Lm+efPmjN8/XdyJCwAAAAAAAAARxiYuAAAAAAAAAEQYm7gAAAAAAAAAEGF574nbo0cPZ9yoUSMTz5kzx8kdc8wxJrb7PUluz6dTTjnFyf34448mPvzww9OuLdgHY8WKFSauWbNm6HmLFi1yxvTEzS27L+Se6Nu3r4kbNGiQ8li7544dAzvdfvvtztiep6wRhWHixIkmLlOmeP6OdNWqVc54/fr1Jq5du7aTq1u3rom/+OILJ1e2bNliqQd7zu7pJUmjR4828fz5853c/fffn5Oadrrgggty+n7Ir+OPP94ZN23aNPTY4DXxO++8k5WaUHTVq1d3xhdddFHosVdffbWJ7e81uWL3wZ08eXLoccGeuPbvWyD3brvtNhNXqVIlo9cI9uo/++yznfGgQYNMHOyfm4sek8iM3aPW7k8rSSeccIKJO3bsGPoa06ZNc8b2Ps+CBQucXK1atZyx/btExfX7E8gfey+wV69eTs5eQw444IDQ1/jpp5+c8SeffOKM//e//5k4+P3c/g2R5s2bOzl77WvXrp2TmzlzpomHDx8eWltx4U5cAAAAAAAAAIgwNnEBAAAAAAAAIMLy3k7h/fffTzm2TZo0KTRXuXJlEzdu3NjJ2bdFn3TSSWnXtmnTJmf8/fffmzjY6sG+vTr4OCRKhvPOO88ZDxgwwMR77bWXk1u+fLkz/vvf/27i3377LQvVoaSpU6eOM27WrJkztteTDRs25KIkFLPTTjvNGR911FEmDj7Sle4jXsFHcIKPpq1bt87Ep59+upO7++67Q1/3b3/7m4mHDRuWVi3Ijn79+jlj+1HE4OOldvuMbLGvX4JzmkcTC1uqx+6DgmsRouOhhx5yxpdffrmJ7e9AkvTaa6/lpKYwLVu2NHGNGjWc3HPPPWfiF154IVclIYlgu6arrroq9NhZs2aZeNmyZU6ubdu2oedVqlTJGdstG1588UUnt3Tp0vBikVPB78QvvfSSie32CZLbEipV+5SgYAsFW7BtJUq2p59+2hnbbTcOOuig0POCe4b//e9/TXzXXXc5ueCenq1FixbO2P6+9Mwzzzg5e48xuNY9+eSTJn799dedXDZaF3EnLgAAAAAAAABEGJu4AAAAAAAAABBhbOICAAAAAAAAQITlvSducVmzZo2JP/zww9DjUvXc3R27d5jdg1dy+3C88sorGb8H8ifYszTY88cW/DP+6KOPslITSq5gb8mgbPTHQfbZvY5ffvllJ5eqd5Nt4cKFztjunRSPx51cqh7bwdfp2bOniatVq+bkBg8ebOJ99tnHyT3xxBMm3rJlS+j7IXOdOnUycbt27ZzcvHnzTDx9+vSc1bST3Us52AN3ypQpJl67dm2uSkKOtGrVKmV+8+bNJk7Vcxv55fu+M7b/O/7555+dnP1nmi0VKlQwcbA34fXXX2/iYN3du3fPbmFIW/D3Zfbff38Tf/LJJ07Ovt4NXl9ceumlJg7OhXr16jnjgw8+2MRvvvmmkzvnnHNMvHr16pS1o/hVrFjRxPbvwEjub8qsXLnSyT344IMm5jdjSq/gunD77beb+JprrnFynueZOPhd2f5NjyFDhji5TH9fpmrVqs64bNmyJu7fv7+Ts3+fK9g3PNe4ExcAAAAAAAAAIoxNXAAAAAAAAACIsIJpp5AN1atXd8ZPPfWUicuUcfe/BwwYYGIe8yg53njjDROfeeaZoceNHDnSGffr1y9rNaEwHH/88Snz9uPtKDnKlfvjYzPd9gmS23KlS5cuTi74+Fm6gu0UHnjgARMPHTrUye27774mDs698ePHm3j+/PkZ1YLULr74YhPbfxaSe22RC3ZLEEnq2rWribdt2+bk7rvvPhPTaqMwtGjRImmcjP144tdff521mpA95557rjN+9913TRxskWI/qloUwfZRrVu3NvEpp5wSet6YMWMyej9k39577+2M7dYXDz/8cOh5mzZtcsbPPvusie3PQUk64ogjQl8n+Oh9LtqAIFyHDh1MfOeddzq5RYsWmbhly5ZObt26ddktDCWC/ZkgSX379jWx3T5Bkn766ScT261MJemLL77I6P3tFgmSdPjhh5s4uMczceJEEwfbp9qCdY8aNcrEuWg/xp24AAAAAAAAABBhbOICAAAAAAAAQISxiQsAAAAAAAAAEUZP3BR69erljKtVq2biNWvWOLnvvvsuJzVhz9SsWdMZ2/3ggv2f7D6Vdl9ASVq/fn0WqkNJZ/d+u+qqq5zcV1995Yzfe++9nNSE/Jg+fboz7t69u4kz7YG7O3ZvW7vPqSSddNJJWXlPJFepUiVnnKovZKZ9KDPVs2dPZ2z3dp4zZ46T+/DDD3NSE3KnKGtBrucmMvPoo4864zZt2pj4kEMOcXKtWrUycbCn3/nnn5/R+wdfx+6fGvTDDz+Y+K677sro/ZB9l156aWgu2GfZ/n2RVJo1a5b2+0+bNs0Z870rv1L1T7e/3yxevDgX5aCECfakDf7+gm3r1q0mPvnkk51cp06dTHz00UeHvsbGjRud8THHHBM6Dn4nq1GjRujr2pYtW+aMc/0bEtyJCwAAAAAAAAARxiYuAAAAAAAAAEQY7RQC/vznP5v4zjvvDD2uQ4cOzvibb77JWk0oPq+//rozrlq1auixL7zwgonnz5+ftZpQONq2bWviKlWqOLlJkyY5402bNuWkJmRPmTLhfw8afAQoF+xHWoO1paq1f//+Ju7WrVux11UaBdvzHHrooSYePXp0rstx1KtXLzTHtUzhS/VI89q1a50x7RRKhhkzZjjjRo0ambhx48ZO7uyzzzZx3759ndyKFStM/Pzzz6f9/qNGjXLGM2fODD32s88+MzHX1tEV/JyyW20EW7LYjzUff/zxTq5jx44mrly5spMLrjd2vkePHk7OnmPffvttytpR/OzH2IPsNSUWizm5N99808Rff/118ReGEuGDDz5wxnarLvu7syTVqlXLxI899piTS9Wqx27REGzfkEqq9gnbt293xuPGjTPxjTfe6OSWLFmS9nsWB+7EBQAAAAAAAIAIYxMXAAAAAAAAACKMTVwAAAAAAAAAiDB64ga0a9fOxOXLl3dy77//vok///zznNWEPWP3cTrxxBNDj5syZYozDvb1AXbnhBNOMHGwb8+YMWNyXQ6y4LrrrjNxsFdSvrVv397ETZo0cXJ2rcG67Z64KB6//vqrM7Z7wdn9KiW3f/bq1auzUk/16tVNnKq33dSpU7Py/sifU0891RlfdtlloceuW7fOGS9evDgrNSG71qxZY2K792BwfMcddxTL+x1xxBHO2O7PHuyDedtttxXLeyK7Jk+e7IzttSHY99buUZuqZ2XwNXv16uWMJ0yYYOIjjzzSydn9J+3rMORGtWrVTBy8hrR/A+Dee+91cv369TPx8OHDndy0adNMbPdBlaR58+aZePbs2SlrO+6440wc3J/hMywaNm7c6IztXtkHHnigk7N/k8r+rSpJWrVqlYkXLVrk5Ox5aH8fl6TmzZsXseKEESNGOOO77rrLxMGe3rnGnbgAAAAAAAAAEGFs4gIAAAAAAABAhLGJCwAAAAAAAAARVup74laoUMEZn3322SbevHmzk7N7pG7ZsiW7hSFjVatWdcZ2/5Jgn2NbsG/X+vXri7cwFJyDDz7YGbds2dLE3333nZMbN25cTmpCdtl9Z/PB7kt27LHHOjl7rUtlxYoVzpjPs+IX7P81f/58E1900UVO7u233zbx0KFDM3q/hg0bOuNgj8o6deqYOFXPwqj1ecaeC14TlSkTfv/Ge++9l+1yUICCfTDtNSbYdzf4+YNoCvZn79y5s4mDv/FQqVKl0Nd5/PHHTRycC5s2bXLGY8eONbHdF1OSzjrrLBPXq1fPydmfr8iOBx980MR9+vRJ+zz78+b66693csFxcQiuL/bv3XTp0qXY3w97LthbNvjffiZGjhzpjFP1xA3+hoU9v5977jknt23btj2urbhwJy4AAAAAAAAARBibuAAAAAAAAAAQYaW+nULfvn2dcZMmTUw8adIkJ/fZZ5/lpCbsmVtvvdUZn3TSSaHHvvHGGya222UA6bjyyiudcfXq1U38zjvv5LgalAZ33323iXv16pX2eQsWLDDxFVdc4eQWLVq0x3UhNfvzxfM8J3fuueeaePTo0Rm9/sqVK51xsGXCQQcdlNbrBB8dQ8nXqVOn0FzwMcann3462+WgAFx88cXO+K9//aszth9PXbVqVU5qQnZNnjzZxME15bLLLjNxcE2xW20E2ycEDRw40MTHHHOMkzv//POTvqa06zUNip/9iPsrr7zi5F566SUTlyvnbi0dfvjhJk7Vyqe42C3HJHeu9uvXz8ndd999Wa8HuXP77bebuCitM6677jpnnOl1eK5xJy4AAAAAAAAARBibuAAAAAAAAAAQYWziAgAAAAAAAECElbqeuHbvOUm65557nPEvv/xi4gEDBuSkJhSvPn36pH1s7969Tbx+/fpslIMCVrt27dDcmjVrclgJCtXEiROd8VFHHZXR63z77bcmnjp16h7VhKKbO3euiTt37uzkGjdubOL69etn9PpjxoxJmX/++edN3LVr19DjNm7cmNH7I1oOO+wwE9v9KoMWL17sjKdPn561mlA4zjnnnJT5CRMmmPjLL7/MdjnIMbs/brJxpuzPn2DfVbsnbps2bZxclSpVTLx69epiqQWubdu2mTj4OdGgQYPQ88444wwTly9f3sn179/fxKl+v2ZP2L9B0LRp06y8B/LjmmuuccZ2z+Ngb+ag2bNnm3js2LHFW1iOcCcuAAAAAAAAAEQYm7gAAAAAAAAAEGGlop1C1apVTfzYY485ubJlyzpj+9HVadOmZbcw5J39CM6WLVsyfp1169aFvo79+EilSpVCX+PAAw90xum2hbAfcZGkO+64w8S//fZbWq+BzJx33nmhubfeeiuHlSBX7EezypQJ/3vQVI+bjhgxwhkfcsghoccG32P79u27KzGp9u3bZ3Qesu/rr79OGhenH374Ia3jGjZs6Iy/+eabbJSDLGvRooWJU61Tb7zxRi7KQYEJfr5t2LDBGT/00EO5LAcF6NVXX3XGdjuFSy65xMnZrfFohRgt77//fmjObiUVbKewdetWEz/77LNO7h//+Iczvvnmm02cqn0QSr7mzZubOPg5U7FixdDzgi0zr7vuOhP//vvvxVRdbnEnLgAAAAAAAABEGJu4AAAAAAAAABBhbOICAJxFOAcAAAcKSURBVAAAAAAAQIQVZE/cYJ/bSZMmmbhu3bpObv78+c74nnvuyV5hiJxZs2YVy+u89tprJl6yZImTq1GjhomDfZyyYenSpSYeNGhQ1t+vtDn11FNNfPDBB+exEuTDsGHDTDx48ODQ4yZMmOCMU/WyLUqf23SPHT58eNqvicJn93K24yB64BYG+7cgglauXGniRx99NBfloADYPQTt61pJWr58uTP+8ssvc1ITClfwWse+3rrgggucXCwWM/HLL7/s5L7//vssVIfi8O6775o4+H21XLk/tqh69Ojh5OrXr++MW7dundb7LV68uIgVImrs3/fYf//9Q48L9mm3e2pL0qefflq8heUBd+ICAAAAAAAAQISxiQsAAAAAAAAAEVaQ7RTq1avnjJs2bRp6bJ8+fZxxsL0CSp6JEyc64+BjN9lw8cUXZ3Te1q1bTZzqMenx48c74+nTp4ce+8knn2RUC9LTsWNHEwdbt3z11Vcm/vjjj3NWE3Jn7NixJu7bt6+Tq1atWtbff8WKFSaeM2eOk+vZs6eJg21dULr5vp80RmE666yzQnOLFi0y8bp163JRDgqA3U4huIa8/fbboecFH3mtXLmyie25CKTy9ddfm/jee+91ckOGDDHx/fff7+S6detm4o0bN2apOmTCvoZ99dVXnVznzp1Dz2vTpk1obtu2bc7YXpvuvPPOopaIPAt+ftx+++1pnffiiy864ylTphRXSZHBnbgAAAAAAAAAEGFs4gIAAAAAAABAhLGJCwAAAAAAAAARVjA9cWvXrm3id999N/S4YA/DCRMmZK0m5MeFF17ojO3+KeXLl0/7dY477jgTX3LJJWmf98wzzzjjBQsWhB77+uuvm3ju3LlpvwdyZ99993XG7dq1Cz12zJgxJg72ZUJhWLhwoYm7dOni5Dp06GDim266KSvvP2jQIBM/+eSTWXkPFJ599tknNEefwJIveG0T/G0I26ZNm0y8ZcuWrNWE0iN4vdO1a1cT33LLLU5u9uzZJr7iiiuyWxgK0siRI53xtddea+Lgd8ABAwaYeNasWdktDEViX3vcfPPNTq5ixYombtasmZOrXr26M7a/Z48aNcrJ9e/ffw+rRK7Zf/bffvutk0u1j2P/9x2cT4WIO3EBAAAAAAAAIMLYxAUAAAAAAACACCuYdgo9e/Y0ca1atUKP++ijj5yx7/tZqwnRMHjw4D1+jcsuu6wYKkFJFHzcdM2aNSYeP368k3v00UdzUhOi4eOPPw4dB9v62J9R7du3d3L2PBoxYoST8zzPGQcfLQLScdVVV5l47dq1Tm7gwIG5LgfFbPv27c54+vTpJm7YsKGTmzdvXk5qQulxzTXXOOOrr77axP/85z+dHOsN9tSKFSuccdu2bU0cbGF3xx13mNhu84FoWbZsmTO2r5O7devm5E455RRnHI/HTbx8+fIsVIdcOv3000182GGHOblU+3Z26x67bVSh4k5cAAAAAAAAAIgwNnEBAAAAAAAAIMLYxAUAAAAAAACACCuxPXFPPfVUZ3zDDTfkqRIAhSzYE7dFixZ5qgQlyaRJk1KOgVz6z3/+Y+KhQ4c6uQ8//DDX5aCYbdu2zRnffffdJg72kJsxY0ZOakJh6d27t4kHDBjg5IL94YcNG2Zi+3cEJGnz5s1ZqA6l2aJFi0w8efJkJ3f++eeb+Nhjj3Vy/MZAyTBq1KiUYxQWu296qh64Q4YMccal7VqWO3EBAAAAAAAAIMLYxAUAAAAAAACACCux7RRatmzpjCtWrBh67Pz58028fv36rNUEAAAQNe3bt893Ccihn3/+2cTdu3fPYyUoFFOnTjXx6aefnsdKgHCdOnVyxjNnzjRx/fr1nRztFIDoqVKliok9z3Nyy5cvN/EjjzySs5qiiDtxAQAAAAAAACDC2MQFAAAAAAAAgAhjExcAAAAAAAAAIqzE9sRNxe5/I0lnnHGGiVevXp3rcgAAAAAAQJb88ssvzrhu3bp5qgRAJoYOHZo0lqSBAweaeMmSJTmrKYq4ExcAAAAAAAAAIoxNXAAAAAAAAACIMM/3/fQP9rz0D0a2zfB9v1m+i0gH8yY6fN/38l1DOpgzkcJag0wwb5AJ5g0ywbxBJpg3yATzBkXGd3BkIHSt4U5cAAAAAAAAAIgwNnEBAAAAAAAAIMLYxAUAAAAAAACACCtXxONXSlqYjUJQZLXzXUARMG+igTmDTDBvkAnmDTLBvEEmmDfIBPMGmWDeoKiYM8hE6Lwp0g+bAQAAAAAAAAByi3YKAAAAAAAAABBhbOICAAAAAAAAQISxiQsAAAAAAAAAEcYmLgAAAAAAAABEGJu4AAAAAAAAABBhbOICAAAAAAAAQISxiQsAAAAAAAAAEcYmLgAAAAAAAABEGJu4AAAAAAAAABBh/x91SLDSbWG30QAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 1800x288 with 10 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"ksVNO6-vNFnN","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"TSs_mcFiNcRE","colab_type":"text"},"source":["## Visualization Activations values for a specific data:"]},{"cell_type":"code","metadata":{"id":"jRztIz4ONl-z","colab_type":"code","outputId":"2ced852e-251f-4eb4-fc91-17c59477822b","executionInfo":{"status":"ok","timestamp":1588681185402,"user_tz":-120,"elapsed":921,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":390}},"source":["index_data = 10\n","viz_activations(model_no_binary, test_loader, index_data)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["act_layer1 for label 0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["act_layer2 for label 0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"ssercF6si1Wb","colab_type":"code","outputId":"fd9235bc-f89d-468d-ad1b-2db57aaeeb36","executionInfo":{"status":"ok","timestamp":1588681188107,"user_tz":-120,"elapsed":775,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":390}},"source":["index_data = 10\n","viz_activations(model_binary, test_loader, index_data)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["act_layer1.act for label 0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["act_layer2 for label 0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"8rsVSnaFQT0O","colab_type":"text"},"source":["## Visualization heatmap for a specific data:\n"]},{"cell_type":"code","metadata":{"id":"iXdLsVXjQY92","colab_type":"code","outputId":"1e4ea230-0de7-4b40-c9b0-37c3ff7113fe","executionInfo":{"status":"ok","timestamp":1588681196761,"user_tz":-120,"elapsed":1153,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":560}},"source":["index_data = 10\n","viz_heatmap(model_no_binary, name_model, test_loader, index_data)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["layer:act_layer1 :heatrmap for an image of label 0 with model MNIST_Stochastic_ST_first_conv_binary\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["layer:act_layer2 :heatrmap for an image of label 0 with model MNIST_Stochastic_ST_first_conv_binary\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"MCSsDrVSk7Jc","colab_type":"code","outputId":"34f7e822-f9aa-43e1-8e6a-e59cec3eebd5","executionInfo":{"status":"ok","timestamp":1588681199963,"user_tz":-120,"elapsed":1064,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":560}},"source":["index_data = 10\n","viz_heatmap(model_binary, name_model, test_loader, index_data)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["layer:act_layer1.act :heatrmap for an image of label 0 with model MNIST_Stochastic_ST_first_conv_binary\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAS4AAAD+CAYAAACECejYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAVJ0lEQVR4nO3df5BdZX3H8feHBIogDaYpDPlhk2kDNWIrzg5imam2YAnKEGfqdMAfhUqb6Ywo/pg6MHa0Q6cdW1tbnVLtFiNUKWij1kwbTfAHtT8ACUIpSUQyoLABDRikTClNdvfTP+6J3r2b3Xuy5+4952w+r5kze8+55zzfJ5vkO8/znOc8R7aJiGiTY+quQETEkUriiojWSeKKiNZJ4oqI1kniiojWSeKKiNZpROKStF7SA5L2SLp6iHFXSfqapF2Sdkq6alixu+qwSNI9kv5pyHFPlrRZ0rck7Zb0iiHGfmfx+75f0s2Sjp/neJsk7ZN0f9expZJulfRg8fMFQ4z9weL3fp+kz0s6eT5iL2S1Jy5Ji4DrgAuBdcClktYNKfw48G7b64BzgLcOMfYhVwG7hxwT4MPAl2z/PPCLw6qDpBXA24ER22cCi4BL5jnsDcD6nmNXA1+xvRb4SrE/rNi3Amfa/gXg28A18xR7wao9cQFnA3tsP2T7AHALsGEYgW0/bvubxedn6PznXTGM2ACSVgKvBa4fVswi7hLgl4GPA9g+YPuHQ6zCYuB5khYDJwCPzWcw218H9vcc3gDcWHy+EXjdsGLb3m57vNi9A1g5H7EXsiYkrhXAo137YwwxeRwiaTVwFnDnEMP+JfAeYHKIMQHWAE8Anyi6qddLOnEYgW3vBf4MeAR4HHja9vZhxO5xqu3Hi8/fA06toQ4AbwG+WFPs1mpC4qqdpOcDnwXeYfu/hxTzImCf7buHEa/HYuBlwEdtnwX8D/PXVZqiGEvaQCd5LgdOlPSmYcSeiTvPvQ392TdJ76UzXHHTsGO3XRMS115gVdf+yuLYUEg6lk7Susn254YVFzgXuFjSd+h0j39V0qeGFHsMGLN9qHW5mU4iG4bzgYdtP2H7IPA54JeGFLvb9yWdBlD83DfM4JIuBy4C3ug8MHzEmpC47gLWSloj6Tg6A7VbhhFYkuiM8+y2/aFhxDzE9jW2V9peTefP/FXbQ2l52P4e8KikM4pD5wG7hhGbThfxHEknFL//86jn5sQW4LLi82XAF4YVWNJ6OkMEF9t+dlhxF5LaE1cxSHklsI3OP+DP2N45pPDnAm+m09q5t9heM6TYdXsbcJOk+4CXAn88jKBFK28z8E3gv+j8Gxydz5iSbgZuB86QNCbpCuADwKslPUinFfiBIcb+K+Ak4Nbi39zH5iP2Qqa0UiOibWpvcUVEHKkkrohonSSuiGidJK6IaJ1GJS5JGxM7sRN74TjcQ+Y930vSR4oFFu6TVGo+YaMSF1DnX2hiJ/bREHvYbmD6Q+bdLgTWFttG4KNlCm1a4oqIBWSGB9y7bQD+zh13ACcfeqJhNosHVcEylixd7FNXHDvj96csX8zpL3leLRPLEjuxF0Ls7+89yNP7x1WljAt+5UT/YP9EqXPvvu//dgLPdR0atX0kE4pnWmTh8cOf3jHUxHXqimP5yBfWDDNkxFHl7RserlzGD/ZP8I1tLyx17qLTHnzO9kjloEdoqIkrIprPwOTwVlqa0yILGeOKiCmMOeiJUtsAbAF+s7i7eA6d9dlm7SZCWlwRcRiDanEVD5m/ClgmaQx4P3AsgO2PAVuB1wB7gGeB3ypTbqXEVSzP8WE664Zfb3tenrCPiOExZmJAiy/YvrTP9wbeeqTlzrmrWPNLLiJiHk3iUltdqoxx1faSi4iYPwYmcKmtLlUSV6mXXEjaKGmHpB1P7x/v/ToiGqjpLa55H5wvJqONArVN+IuI8gwcbPgCo1USV60vuYiI+eGau4FlVElcP3rJBZ2EdQnwhoHUKiLqY5hodt6ae+KyPS7p0EsuFgGbhviSi4iYJ52Z881WaYzL9lY6E8giYsEQE1R6TnveZeZ8REzRGZxP4oqIFunM40riioiWmUyLKyLaJC2uiGgdIyYavuJVEldETJOuYkS0ihEHvKjuaswqiSsipuhMQE1XMSJaJoPzEdEqtphwWlwR0TKTaXFFRJt0BuebnRqaXbuIGLoMzkdEK01kHldEtElmzkdEK03mrmJEtEnnIeskrqPe/onnV7r+2r+b9WXAs9r11r+uFPuC5S+tdH0VF+78YaXr3/GC78z52u3PHlspdpsZcTCP/EREm9hkAmpEtI0yATUi2sWkxRURLdT0wfk5107SKklfk7RL0k5JVw2yYhFRDyMmXW6rS5UW1zjwbtvflHQScLekW23vGlDdIqIGndeTNbszVuVN1o8Djxefn5G0G1gBJHFFtNpR8kJYSauBs4A7D/PdRmAjwCnLm53FI6J4yLrhg/OVayfp+cBngXfY/u/e722P2h6xPbJkaRJXRBtMFK2ufltdKiUuScfSSVo32f7cYKoUEXWyxaSPKbWVIWm9pAck7ZF09WG+f2Fxo+8eSfdJek2/MufcBJIk4OPAbtsfmms5EdEsncH5wTzyI2kRcB3wamAMuEvSlp6beL8PfMb2RyWtA7YCq2crt0qL61zgzcCvSrq32Ppmyohous6a82W2Es4G9th+yPYB4BZgQ885Bn6y+LwEeKxfoVXuKv4bNPzWQ0Qcsc7gfOn/2ssk7ejaH7U92rW/Ani0a38MeHlPGX8AbJf0NuBE4Px+QTNaHhHTHMHM+Sdtj1QMdylwg+0/l/QK4JOSzrQ9OdMFSVwRMcWhmfMDshdY1bW/sjjW7QpgPYDt2yUdDywD9s1UaBLXEHzijJ+pdP0q/mPO117wR/Wtp1XVF198crXrmfuffdtj91aK3fb1vAb4soy7gLWS1tBJWJcAb+g55xHgPOAGSS8CjgeemK3QJK6ImMKGg5ODSVy2xyVdCWwDFgGbbO+UdC2ww/YW4N3A30p6J50htstte7Zyk7giYopOV3FwM+dtb6UzxaH72Pu6Pu+iM0uhtCSuiJjmqHhWMSIWjiOcDlGLJK6I6DHYruJ8SOKKiGmy5nxEtErnrmJeTxYRLTLgCajzIokrIqZJVzEiWiV3FSOilXJXMSJaxRbjSVwR0TbpKkZEq2SMK1rvop1PVbr+Jcc/2v+kGbzqeTOuI1fKBcvnvqzN+hdWWxvvXd/6z0rX1y2JKyJaJfO4IqKVMo8rIlrFhvEBLSQ4X5K4ImKaBd9VLF74uAPYa/ui6lWKiDodLWNcVwG7+fELHSOi5dzwxFWpIytpJfBa4PrBVCcimmASldrqUrXF9ZfAe4CTZjpB0kZgI8ApyzOkFtF0dvPHuObc4pJ0EbDP9t2znWd71PaI7ZElS5O4IppPTEweU2qrS5VMci5wsaTX0HmB409K+pTtNw2mahFRlwU7xmX7Gtsrba+m83baryZpRbTfoWcVy2x1Sd8tIqZyZ5yryQaSuGzfBtw2iLIion555CciWsXF4HyTJXFFxDRHRVfxaHD/c6vqrsKcvHvPzlrjH/Dc38+3/dn63u3n8fHaYjdB0+8qJnFFxBR2EldEtFDTZ84ncUXENBnjiohWMWIydxUjom0a3uCqtqxNRCxAxeB8ma0MSeslPSBpj6SrZzjnNyTtkrRT0t/3KzMtroiYbkBNrmKF5OuAVwNjwF2Sttje1XXOWuAa4FzbT0k6pV+5aXFFxDQDbHGdDeyx/ZDtA8AtwIaec34HuM72U53Y3tev0CSuiJjCwOSkSm3AMkk7uraNPcWtALrfCjxWHOt2OnC6pH+XdIek9f3qmK5iRExloPw8ridtV3vtdycPrQVeBawEvi7pJbZ/ONMFaXFFxDR2ua2EvUD383Iri2PdxoAttg/afhj4Np1ENqMkroiYziW3/u4C1kpaI+k4OouObuk55x/ptLaQtIxO1/Gh2QpNVzEiepSf6tCP7XFJVwLbgEXAJts7JV0L7LC9pfju1yTtAiaA37P9g9nKTeKKiOkGOAPV9lZga8+x93V9NvCuYisliaukL79y9ZyvfeaS0yvFPumWOypdH3FEDJ7MQ9YR0TpJXBHRNg1/WDGJKyKmS+KKiFY5sgmotag0j0vSyZI2S/qWpN2SXjGoikVEfQY4AXVeVG1xfRj4ku3XF5PLThhAnSKibgv1rqKkJcAvA5cDFE9+HxhMtSKiTmr4GFeVruIa4AngE5LukXS9pBN7T5K08dCT40/vP7pf+RTRCmUf96kxuVVJXIuBlwEftX0W8D/AtNUNbY/aHrE9smRp7gVENJ86g/NltppUSVxjwJjtO4v9zXQSWUS03UJtcdn+HvCopDOKQ+cBu2a5JCLaYrLkVpOqfbe3ATcVdxQfAn6repUiolYtmMdVKXHZvheouvphRDRM0+8qZrQ8IqZreOLKCqgR0TppcZX0xIYz+p80g6Wbbq8U+9jbTqtw9c5KsePolK5iRLSLWbiP/ETEApYWV0S0TbqKEdE+SVwR0TpJXBHRJnK6ihHRRrmrGBFtkxZXRLRPEldEtErGuCKilZK4IqJtVOMigWVkdYiIaJ20uCJiunQVI6JVMji/cFRdU6uKt6/8cm2x63TbMy+quwpHrySuiGidJK6IaBORu4oR0Tb+8YPW/bYyJK2X9ICkPZKmve2+67xfl2RJfd8cVilxSXqnpJ2S7pd0s6Tjq5QXEQ0xoDdZS1oEXAdcCKwDLpW07jDnnQRcBdxZpnpzTlySVgBvB0ZsnwksAi6Za3kR0SADSlzA2cAe2w/ZPgDcAmw4zHl/CPwJ8FyZQqt2FRcDz5O0GDgBeKxieRHRAEfQVVwmaUfXtrGnqBXAo137Y8WxH8eSXgassv3PZes358F523sl/RnwCPC/wHbb23vPK/4gGwFOWZ57ARGtUP6u4pO25/w2e0nHAB8CLj+S66p0FV9Ap8m3BlgOnCjpTb3n2R61PWJ7ZMnSJK6IxnPnrmKZrYS9wKqu/ZXFsUNOAs4EbpP0HeAcYEu/AfoqXcXzgYdtP2H7IPA54JcqlBcRTTG4Ma67gLWS1kg6js44+JYfhbGftr3M9mrbq4E7gItt75it0CqJ6xHgHEknSBJwHrC7QnkR0RCDmg5hexy4EthGJz98xvZOSddKuniu9asyxnWnpM3AN4Fx4B5gdK7lRUSDDHDmvO2twNaeY++b4dxXlSmz0qCT7fcD769SRkQ0TPluYG0yWh4RU4isDhERLZTEFUe1gyya87V3n1VtfvTkK8+a87XH/Ms9lWK3XhJXRLROEldEtEpWQI2IVkriioi2afpCgklcETFNuooR0S6ZgBoRrZTEFRFtkpnzEdFKmmx25kriioipMsYVEW2UrmJEtE8SV0S0TVpcEdE+SVwR0SrOIz8Lxrc/dvacrz39d79RKfavnXCw0vV1umD5i2uLXWVNrZP+ddkAa9IumccVEe3kZmeuJK6ImCYtroholxZMQO27qLekTZL2Sbq/69hSSbdKerD4+YL5rWZEDJMmy211KfM2ghuA9T3Hrga+Ynst8JViPyIWiNYnLttfB/b3HN4A3Fh8vhF43YDrFRF1MZ3B+TJbTeY6xnWq7ceLz98DTp3pREkbgY0ApyzPkFpEGzR9cL7ai+sA27MO5dketT1ie2TJ0iSuiFZwya0mc01c35d0GkDxc9/gqhQRdTo0AbXMVpe5Jq4twGXF58uALwymOhFROxtNltvqUmY6xM3A7cAZksYkXQF8AHi1pAeB84v9iFgoGt5V7DvoZPvSGb46b8B1iYiGaPrgfEbLI2IqA1lzPiJap9l5K4mrrIcvHp3ztRf87ksrxb5gebXrj1bfvfYVc772I6ddP8CatM8gu4qS1gMfBhYB19v+QM/37wJ+GxgHngDeYvu7s5VZeR5XRCw8g7qrKGkRcB1wIbAOuFTSup7T7gFGbP8CsBn4037lJnFFxFRl7yiWa5WdDeyx/ZDtA8AtdB4Z/HE4+2u2ny127wBW9is0XcWImKIzAbV0X3GZpB1d+6O2u8dVVgCPdu2PAS+fpbwrgC/2C5rEFRHTlV/54UnbI4MIKelNwAjwyn7nJnFFxDRH0OLqZy+wqmt/ZXFsajzpfOC9wCtt/1+/QjPGFRFTDXaM6y5graQ1ko4DLqHzyOCPSDoL+BvgYtulnntOiysiegzuOUTb45KuBLbRmQ6xyfZOSdcCO2xvAT4IPB/4B0kAj9i+eLZyk7giYroBLhJoeyuwtefY+7o+n3+kZSZxRcRUeSFsRLRS3qsYEa3T7LyVxBUR02my2X3FJK6ImMocyQTUWiRxRcQUwoOcgDovkrgiYrokroVh+7PHzvnabY/dO8CatMsvfmOmlb/7++CZmytG31nx+qNYEldEtErGuCKijXJXMSJaxo3vKpZ5r+ImSfsk3d917IOSviXpPkmfl3Ty/FYzIobGdBJXma0mZZa1uQFY33PsVuDMYo3obwPXDLheEVGnyZJbTfomLttfB/b3HNtue7zYLbVGdES0h+xSW10GMcb1FuDTAygnIpqi4WNclRKXpPfSeRfaTbOcsxHYCHDK8twLiGg8GyaafVdxzks3S7ocuAh4oz1zerY9anvE9siSpUlcEa3Q8MH5OWWS4s2076GzsP2z/c6PiJZpeFexzHSIm4HbgTMkjUm6Avgr4CTgVkn3SvrYPNczIobFwKTLbTXp2+KyfbiHzT4+D3WJiEYwuNljXBl0ioipTOMH55O4ImK6ho9xJXENQZUlcdqu+tI0UYskrohol+Y/ZJ3EFRFTGciyNhHROmlxRUS7NP+RnySuiJjK4MzjiojWqXFWfBlJXBExXca4IqJV7NxVjIgWSosrItrFeGKi7krMKokrIqY6tKxNgyVxRcR0DZ8OMeelmyNiYTLgSZfaypC0XtIDkvZIuvow3/+EpE8X398paXW/MpO4ImIqFwsJltn6kLQIuA64EFgHXCppXc9pVwBP2f454C+AP+lXbhJXREzjiYlSWwlnA3tsP2T7AHALsKHnnA3AjcXnzcB5kjRboUMd43rw/ueevPBnd393llOWAU8Oqz6JndgLMPbPVC3gGZ7a9mVvXlby9OMl7ejaH7U92rW/Ani0a38MeHlPGT86x/a4pKeBn2KW39FQE5ftn57te0k7bI8Mqz6JndhHW+wybK+vuw79pKsYEfNpL7Cqa39lceyw50haDCwBfjBboUlcETGf7gLWSloj6TjgEmBLzzlbgMuKz68HvjrbS6ahefO4RvufktiJndhtUYxZXQlsAxYBm2zvlHQtsMP2FjqvO/ykpD3AfjrJbVbqk9giIhonXcWIaJ0krohonSSuiGidJK6IaJ0krohonSSuiGidJK6IaJ3/B2sicAI6P51bAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["layer:act_layer2 :heatrmap for an image of label 0 with model MNIST_Stochastic_ST_first_conv_binary\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAScAAAD+CAYAAAB4HMMSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAUQklEQVR4nO3dfbBd1V3G8e/DDUkgvARMVUqiyWisMvUF5pq2g9NSaWuoDPGlo6BU66D5w1KptHaodqjS8Y/qiC8jg0aILdqKlVrnjk1LOy0V6wAmFKQkFEwjLRepKX2zUEnIvY9/nH319JLcs2/u2fusc/fzmdnD2fvus34rL/yy1tprryXbRESU5oRRVyAi4miSnCKiSElOEVGkJKeIKFKSU0QUKckpIopUVHKStFXSw5L2S7qmxbg7JR2U9GBbMftib5B0h6R9kvZKuqrF2Ksl/aukf6ti/05bsfvqMCHpPkn/2HLcRyV9WtL9kva0HHutpNskfUbSQ5Je0mb8caFS5jlJmgAeAV4JTAO7gcts72sh9kuBp4BbbL+w6XjzYp8FnGX7U5JOBe4FfqKlX7eANbafknQi8EngKtt3Nx27rw5XA5PAabYvbjHuo8Ck7SfbitkX+93AP9u+SdJK4GTbX227HqUrqeW0Bdhv+4Dtw8CtwLY2Atu+E/hyG7GOEvsJ25+qPn8deAg4u6XYtv1UdXpidbT2r5Wk9cCPAze1FXPUJJ0OvBS4GcD24SSmoyspOZ0NPNZ3Pk1L/5OWQtJG4FzgnhZjTki6HzgIfNR2a7GBPwLeAsy2GHOOgY9IulfS9hbjbgK+CPxl1Z29SdKaFuOPjZKSU6dJOgV4P/BG2//dVlzbM7Z/CFgPbJHUSrdW0sXAQdv3thHvKH7E9nnARcDrq659G1YA5wE32j4XeBpobXx1nJSUnB4HNvSdr6+uLXvVeM/7gffY/vtR1KHqWtwBbG0p5PnAJdXYz63Aj0r665ZiY/vx6r8HgQ/QG1ZowzQw3ddCvY1esop5SkpOu4HNkjZVg4SXAlMjrlPjqkHpm4GHbF/fcuznSVpbfT6J3sOIz7QR2/Zbba+3vZHen/XHbV/eRmxJa6qHD1RdqlcBrTyptf0F4DFJL6guXQg0/vBjHK0YdQXm2D4i6UrgdmAC2Gl7bxuxJf0NcAGwTtI08HbbN7cRm14L4rXAp6uxH4DftL2rhdhnAe+unpSeALzPdquP9Efk24AP9P5dYAXwXtsfbjH+G4D3VP8IHwB+qcXYY6OYqQQREf1K6tZFRPyfJKeIKFKSU0QUKckpIopUXHJqebZuYid252I3YdDL8+r5k+ql/gckDZzbVVxyAkb5h5bYid2F2E14FwtP4L0I2Fwd24EbBxVYYnKKiDFT4+X5bfRW/XC16sXaakWOY2pkEuZKrfJqju9dxtWczGk687gnXx1+/vG/Q7ni9DNYffaG44594lPHP2ds1eq1nHr6+uMuwEv4Z2bVSWs55Yzjjw1Ab0Ljoq08eS2nnHn8v+cAnji+761ccwZrnre02BNPPn1c31vq3/NneJrDPnR8v+mVH3v5Gn/pyzO17r33gUN7gWf6Lu2wvWMR4Y71Yv8Tx/pCI8lpNWt4kS5souiBHv3V0a3b9fx/OTKy2DOrlvT3dMlmV4wu/qHTR9cBOHPnXSOJe48/tuQyvvTlGf719u+ode/EWf/+jO3JJQddhGJeX4mIdhmYbW+1mkW/2J8xp4iOMuZZz9Q6hmAK+IXqqd2Lga/ZPmaXDtJyiui0YbWcjvbyPL2VVbH9Z8Au4NXAfuAb1HjZOckpoqOMmRnSi/+2LxvwcwOvX0yZSU4RHTbb3pLxi5bkFNFRBmaSnCKiRGk5RURxDDxb8GKTSU4RHWWcbl1EFMgwU25uSnKK6KreDPFy1ZohLmmrpIertViyAWDEsiBmah6jMLDlVG0bdAO9Pc2mgd2Spmxnr62IMdYbEB/tC+MLqdNy2gLst33A9mF6u7Nua7ZaEdG03jynMW45cfR1WF40/6Zq2dHt0FurJiLKN1twy2loA+LVwlM7gCUtohUR7ZhrOZWqTnJa9DosEVE+I2YKXjWpTnLaDWyWtIleUroU+LlGaxURrRjrbp3tI5KuBG4HJoCdtvc2XrOIaJQRh493AfYW1Bpzsr2L3mJREbFM9CZhjne3LiKWqXEfEI+IZcgWM0vZU6xhSU4RHTabllNElKY3IF5uCii3ZhHRqAyIR0SxZsZ5nlNELE/LYYZ4RCxTs3laFxGl6b3427HkNHvGGp5+xXNWVWnFzp+/YSRxAd724p8cWeyTXvm5kcUetRUX/fDIYl//6F0jiXvpxU8tuQwjnh3311ciYvmxySTMiCiRMgkzIspj0nKKiEJ1bkA8IspnNN6LzUXE8tTbGqrcFFBuzSKiYaPb9qmOJKeIjjKZIR4RhSq55VRu2oyIRtli1ifUOgaRtFXSw5L2S7rmKD//Dkl3SLpP0gOSXj2ozLScIjqqNyC+9NdXJE0ANwCvpLcj+G5JU7b39d32NuB9tm+UdA69DVM2LlRuklNEZw1tDfEtwH7bBwAk3QpsA/qTk4HTqs+nA/85qNCBNZO0U9JBSQ8uusoRUazegLhqHcA6SXv6ju19RZ0NPNZ3Pl1d6/fbwOWSpum1mt4wqH51Wk7vAv4UuKXGvRExRhYxQ/xJ25NLCHUZ8C7bfyDpJcBfSXqh7dljfaHOjr93Stq4hEpFRIGGOEP8cWBD3/n66lq/K4CtALbvkrQaWAccPFahQ3taJ2n7XJPv2UNLX2smIpo3ywm1jgF2A5slbZK0ErgUmJp3z+eBCwEkfR+wGvjiQoUObUDc9g5gB8ApZ27wsMqNiGbY8Ozs0tsnto9IuhK4HZgAdtreK+k6YI/tKeBNwF9I+nV6w12vs71gnsjTuoiO6nXrhtN5sr2L3kB3/7Vr+z7vA85fTJlJThEdNtYzxCX9DXAX8AJJ05KuaL5aEdG0RU4laF2dp3WXtVGRiGjb8Lp1TUi3LqLDsoZ4RBSn97QuW0NFRGGyTG9EFCvduogoztzTulIlOUV0WJ7WRURxbHEkySkiSpRuXUQUp5NjTid85WnW3HZPE0UP9Isv/tWRxAX4rjffPbLYXbbqQ7tHFvvqjS8ZSdzH/LGhlNO55BQR5cs8p4goVuY5RURxbDgyhMXmmpLkFNFh6dZFRHEy5hQRxXKSU0SUKAPiEVEcO2NOEVEkMZOndRFRoow5RURxOvluXUSMAffGnUpVZ9+6DZLukLRP0l5JV7VRsYho3iyqdYxCnZbTEeBNtj8l6VTgXkkfrbYXjogx5XEfELf9BPBE9fnrkh4CzgaSnCLGXMndukWNOUnaCJwLPGexJknbge0Aqzl5CFWLiKYti6d1kk4B3g+80fZ/z/+57R3ADoDTdGbB+TgioNdqGvvkJOlEeonpPbb/vtkqRURbxnoqgSQBNwMP2b6++SpFRFvGfczpfOC1wKcl3V9d+03bu5qrVkQ0zYjZMX9a90ko+NXliDhuBTecBk/CjIhlqhoQr3MMImmrpIcl7Zd0zTHu+Zm+ydzvHVRmXl+J6LIhNJ0kTQA3AK8EpoHdkqb6J2pL2gy8FTjf9lckfeugctNyiuiwIbWctgD7bR+wfRi4Fdg2755fAW6w/ZVeXB8cVGiSU0RHGZidVa0DWCdpT9+xva+os4HH+s6nq2v9vgf4Hkn/IuluSVsH1S/duoiuMlB/ntOTtieXEG0FsBm4AFgP3Cnp+21/9VhfSMsposPsescAjwMb+s7XV9f6TQNTtp+1/R/AI/SS1TElOUV0mWseC9sNbJa0SdJK4FJgat49/0Cv1YSkdfS6eQcWKjTduojOqjdNYBDbRyRdCdwOTAA7be+VdB2wx/ZU9bNXSdoHzAC/YftLC5Wb5BTRZUOahVm9MbJr3rVr+z4buLo6amkkOWnVSlas39hE0QPd/bN/MJK4AD//5vNHFnvVP337yGLDaF8gffaCJ0YWe6wZPFvuyx9pOUV0WpJTRJSo4JfrkpwiuizJKSKKs7hJmK1LcorosHFfbC4ilqs8rYuIEiktp4goTr1XU0YmySmis5QB8YgoVFpOEVGk2VFX4NiSnCK6atznOUlaDdwJrKruv83225uuWEQ0b9yf1h0CftT2U9W25J+U9CHbdzdct4ho2jgnp2odlqeq0xOro+BfUkQsB7WW6ZU0UW1FfhD4qO17jnLP9rmdGQ7PfGPY9YyIBsj1jlGolZxsz9j+IXoLl2+R9MKj3LPD9qTtyZUTJw+7nhExbKb3+kqdYwQWtcFBtY3LHcDAPaciYgwMZ4ODRgxMTpKeJ2lt9fkkelsOf6bpikVE80ru1tV5WncW8O5qP/QTgPfZ/sdmqxURrSj40Vadp3UPAOe2UJeIaNs4J6eIWJ5G2WWrI8kposuy2FxElCgtp4goU5JTRBQnY04RUawkp4gokQpebG5Rr69ERLQlLaeILku3LiKK08UB8cNrT+TzP/X8Jooe6Cd/7eqRxAU4mecsc9Wab1n19MhiA7zijH0ji30LG0YWe+x1LTlFxJhIcoqI0og8rYuIEtVcy6nOuJSkrZIelrRf0jUL3PfTkixpclCZSU4RXTaElTCrtd5uAC4CzgEuk3TOUe47FbgK6g3OJjlFdNlwlundAuy3fcD2YeBWYNtR7nsH8E7gmTpVS3KK6LBFdOvWze2uVB3b+4o5G3is73y6uvb/caTzgA22P1i3bhkQj+iy+k/rnrQ9cJzoaCSdAFwPvG4x30tyiugqD+1p3ePwTZPN1lfX5pwKvBD4hCSAbwemJF1ie8+xCk1yiuiy4cxz2g1slrSJXlK6FPi5/wthfw1YN3cu6RPAmxdKTJAxp4hOG8ZUAttHgCuB24GH6O3QtFfSdZIuOd66peUU0WVDmiFuexewa961a49x7wV1yqzdcpI0Iek+SdmzLmI5qDuNoOBNNedcRa/JdlpDdYmIFomyVyWo1XKStB74ceCmZqsTEW0qeTvyut26PwLeAhzzwaOk7XMTtGb+Z7TLd0RETQV36wYmJ0kXAwdt37vQfbZ32J60PTlx0pqhVTAiGlRwcqoz5nQ+cImkVwOrgdMk/bXty5utWkQ0qvCVMAe2nGy/1fZ62xvpTa76eBJTxDIx5i2niFimSl5sblHJyfYngE80UpOIaF3J3bq0nCK6aoRdtjqSnCK6LMkpIkpT+gzxJKeIDtNsudkpySmiqzLmFBGlSrcuIsqU5BQRJUrLKSLKlOQUEcUZ3u4rjWgkOa386hE2TP1XE0UPNPPIZ0cSd9T+4x3fO9L4t3zw6yONPyqP/PkPjyTuod+9a8llZJ5TRJTL5WanJKeIDkvLKSLKk0mYEVGqzg2IR8R4SHKKiPKYDIhHRJkyIB4RZUpyiojSZBJmRJTJzmJzEVGocnNTveQk6VHg68AMcMT2ZJOVioh2LJdu3cttP9lYTSKiXQbSrYuIIpWbmzih5n0GPiLpXknbj3aDpO2S9kjac3jmG8OrYUQ0Rq53DCxH2irpYUn7JV1zlJ9fLWmfpAckfUzSdw4qs25y+hHb5wEXAa+X9NL5N9jeYXvS9uTKiZNrFhsRo6RZ1zoWLEOaAG6glx/OAS6TdM682+4DJm3/AHAb8HuD6lYrOdl+vPrvQeADwJY634uIgnkRx8K2APttH7B9GLgV2PZNoew7bM91qe4G1g8qdGBykrRG0qlzn4FXAQ8OrG5EFK03CdO1DmDd3LBNdfQP75wNPNZ3Pl1dO5YrgA8Nql+dAfFvAz4gae7+99r+cI3vRUTp6q9K8OQwphBJuhyYBF426N6Bycn2AeAHl1qpiCiPhrMqwePAhr7z9dW1b44lvQL4LeBltg8NKrTugHhELDfDG3PaDWyWtEnSSuBSYKr/BknnAn8OXFKNXQ+UeU4RnTWcd+tsH5F0JXA7MAHstL1X0nXAHttTwO8DpwB/Vw0Rfd72JQuVm+QU0WVDWmzO9i5g17xr1/Z9fsViy0xyiuiqLm6qGRFjIsv0RkSRys1NSU4RXabZcvt1SU4RXWUWMwmzdUlOER0lPKxJmI1Icorosq4lJx86xMwjn22i6IEOfWTjSOICrHrVo6OL/cHdI4s9atce+NTIYp+/+v6RxN3yp0NalLZrySkixkDGnCKiVHlaFxEFcrp1EVEgk+QUEYUqt1eX5BTRZZnnFBFlSnKKiOLYMFNuvy7JKaLL0nKKiCIlOUVEcQwMYQ3xptTafUXSWkm3SfqMpIckvaTpikVE0wyerXeMQN2W0x8DH7b9mmrrl5MbrFNEtMGM94C4pNOBlwKvA6j2Qj/cbLUiohUFjznV6dZtAr4I/KWk+yTdJGnN/JskbZ/bR/1ZBm7mGRElsOsdI1AnOa0AzgNutH0u8DRwzfybbO+wPWl78kRWDbmaETF8NRNTwclpGpi2fU91fhu9ZBUR48zA7Gy9YwQGJifbXwAek/SC6tKFwL5GaxUR7Si45VT3ad0bgPdUT+oOAL/UXJUioh3L4PUV2/cDkw3XJSLaZPCI5jDVkRniEV1W8AzxJKeILit4nlOSU0RX2SN7EldHklNEl6XlFBHlMZ6ZGXUljinJKaKrCl8yJckpossKnkpQaz2niFh+DHjWtY5BJG2V9LCk/ZKe8+6tpFWS/rb6+T2SNg4qM8kpoqs8nMXmJE0ANwAXAecAl0k6Z95tVwBfsf3dwB8C7xxUvSSniA7zzEytY4AtwH7bB6r13m4Fts27Zxvw7urzbcCFkrRQoXIDjxIlfRH43HF+fR3w5BCrk9iJvRxjf6ft5y2lApI+XNWjjtXAM33nO2zvqMp5DbDV9i9X568FXmT7yr5YD1b3TFfnn63uOebvQSMD4kv5TZO0x/ZI3uNL7MTuQuw5treOMv4g6dZFxFI9DmzoO19fXTvqPZJWAKcDX1qo0CSniFiq3cBmSZuqZZUuBabm3TMF/GL1+TXAxz1gTKnEeU47EjuxE3t82D4i6UrgdmAC2Gl7r6TrgD22p4Cbgb+StB/4Mr0EtqBGBsQjIpYq3bqIKFKSU0QUKckpIoqU5BQRRUpyiogiJTlFRJGSnCKiSP8Lg0vYDMplNcUAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"vCQ5xT6bL1jc","colab_type":"text"},"source":["## Visualization filters trained:"]},{"cell_type":"code","metadata":{"id":"le3aS1MNl2fS","colab_type":"code","colab":{}},"source":["for name, m in model_no_binary.named_modules():\n"," if type(m) == nn.Conv2d:\n"," filters = m.weight.data.clone()\n"," break"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wl64zUbLmBMs","colab_type":"code","colab":{}},"source":["filter_0 = filters[0][0]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"AfKvYa8jmFz5","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":69},"outputId":"c98c6592-48f1-44f2-ac16-7a59a74a0b61","executionInfo":{"status":"ok","timestamp":1588929897139,"user_tz":-120,"elapsed":461,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}}},"source":["filter_0"],"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[-0.0100, 0.2919, 0.1254],\n"," [ 0.0844, 0.0426, 0.0630],\n"," [-0.2353, -0.3480, -0.0394]])"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"zYdypB1FmQOJ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":286},"outputId":"aca66007-e60c-4da8-8b3b-83139b4c98ec","executionInfo":{"status":"ok","timestamp":1588929898616,"user_tz":-120,"elapsed":798,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}}},"source":["plt.imshow(filter_0, cmap='gray')"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f929756dc50>"]},"metadata":{"tags":[]},"execution_count":26},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"hvX1IYa-nF90","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":356},"outputId":"92b54385-d4ab-426b-c5be-788ac058c5ea","executionInfo":{"status":"ok","timestamp":1588930172264,"user_tz":-120,"elapsed":625,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}}},"source":["# normalized between 0 et 1:\n","val_min = filter_0.min()\n","val_max = filter_0.max()\n","\n","filter_0_normalized = (filter_0 - val_min)/(val_max - val_min)\n","\n","print(val_min, val_max)\n","print(filter_0_normalized)\n","\n","plt.imshow(filter_0_normalized, cmap='gray')"],"execution_count":29,"outputs":[{"output_type":"stream","text":["tensor(-0.3480) tensor(0.2919)\n","tensor([[0.5283, 1.0000, 0.7398],\n"," [0.6758, 0.6104, 0.6423],\n"," [0.1761, 0.0000, 0.4822]])\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f92974e2390>"]},"metadata":{"tags":[]},"execution_count":29},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"V56DbYo7GW1c","colab_type":"code","outputId":"0fd63142-22f7-4c3b-c91e-f05a4602b942","executionInfo":{"status":"ok","timestamp":1588929724760,"user_tz":-120,"elapsed":4997,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":385}},"source":["viz_filters(model_no_binary)"],"execution_count":11,"outputs":[{"output_type":"stream","text":["Visualization filters learned for layer: layer1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Visualization filters learned for layer: layer2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"7JqXez-RU44M","colab_type":"code","outputId":"33188954-4f32-487f-8b53-a2bd946f3f6c","executionInfo":{"status":"ok","timestamp":1588860843354,"user_tz":-120,"elapsed":1055,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["viz_filters(model_binary)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Visualization filters learned for layer: layer1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Visualization filters learned for layer: layer2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Lk46uch7aZDz","colab_type":"code","outputId":"8c3e7a60-6781-4128-c190-056221e0ff4f","executionInfo":{"status":"ok","timestamp":1588860844163,"user_tz":-120,"elapsed":970,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["viz_filters(model_no_binary_wt_bias)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Visualization filters learned for layer: layer1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Visualization filters learned for layer: layer2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"OVH9IlEnaZI_","colab_type":"code","outputId":"47c4af2c-78ad-43b7-9d13-407ea591291c","executionInfo":{"status":"ok","timestamp":1588860844551,"user_tz":-120,"elapsed":952,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["viz_filters(model_binary_wt_bias)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Visualization filters learned for layer: layer1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Visualization filters learned for layer: layer2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Nwn-bah-Kh_l","colab_type":"text"},"source":["## Visualization image that maximizes a specific activation in a specific layer for a specifc filter:"]},{"cell_type":"markdown","metadata":{"id":"DSqdNrmQNdP5","colab_type":"text"},"source":["### No binary model:"]},{"cell_type":"code","metadata":{"id":"0YRM3DpSNihu","colab_type":"code","colab":{}},"source":["g_ascent_no_binary = GradientAscent(model_no_binary, nb_channels=1, img_size=28)\n","g_ascent_no_binary.use_gpu = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"aE17LvBuNwVD","colab_type":"code","colab":{}},"source":["conv1_no_binary = model_no_binary.layer1\n","conv1_filters_no_binary = [0,1,2,3,4,5,6,7,8,9]\n","mean_gradient_layer1 = False\n","ind_x_layer1 = 7\n","ind_y_layer1 = 7\n","\n","conv2_no_binary = model_no_binary.layer2\n","conv2_filters_no_binary = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","mean_gradient_layer2 = False\n","ind_x_layer2 = 3\n","ind_y_layer2 = 3\n","\n","lr=0.0001\n","num_iter=1000\n","MNIST = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Sh16ddpuUfUp","colab_type":"code","outputId":"dee289b0-88c1-4395-dded-485db893dbd1","executionInfo":{"status":"ok","timestamp":1588681434184,"user_tz":-120,"elapsed":762,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":386}},"source":["viz_filters(model_no_binary)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Visualization filters learned for layer: layer1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Visualization filters learned for layer: layer2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"JP1bsOUzN79c","colab_type":"code","outputId":"e6112da7-f267-4aeb-96a7-8b288e80dc52","executionInfo":{"status":"ok","timestamp":1588681513079,"user_tz":-120,"elapsed":76456,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["g_ascent_no_binary.visualize(conv1_no_binary, MNIST, conv1_filters_no_binary, mean_gradient_layer1,\n"," ind_x_layer1, ind_y_layer1, lr=lr, num_iter=num_iter, title='No binary model: conv layer 1')\n","g_ascent_no_binary.visualize(conv2_no_binary, MNIST, conv2_filters_no_binary, mean_gradient_layer2,\n"," ind_x_layer2, ind_y_layer2, lr=lr, num_iter=num_iter, title='No binary model: conv layer 2')"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x1080 with 11 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x1800 with 21 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"RmOpOt7P6t95","colab_type":"code","colab":{}},"source":["g_ascent_no_binary = GradientAscent(model_no_binary, nb_channels=1, img_size=28)\n","g_ascent_no_binary.use_gpu = True\n","\n","conv1_no_binary = model_no_binary.layer1\n","conv1_filters_no_binary = [0,1,2,3,4,5,6,7,8,9]\n","mean_gradient_layer1 = True\n","ind_x_layer1 = 7\n","ind_y_layer1 = 7\n","\n","conv2_no_binary = model_no_binary.layer2\n","conv2_filters_no_binary = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","mean_gradient_layer2 = True\n","ind_x_layer2 = 3\n","ind_y_layer2 = 3\n","\n","lr=0.0001\n","num_iter=1000\n","MNIST = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"xDCPM5x16xDv","colab_type":"code","outputId":"3603e5bb-bf4e-4c2c-ec06-b6afac1aee51","executionInfo":{"status":"ok","timestamp":1588684552588,"user_tz":-120,"elapsed":75271,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["g_ascent_no_binary.visualize(conv1_no_binary, MNIST, conv1_filters_no_binary, mean_gradient_layer1,\n"," ind_x_layer1, ind_y_layer1, lr=lr, num_iter=num_iter, title='No binary model: conv layer 1')\n","g_ascent_no_binary.visualize(conv2_no_binary, MNIST, conv2_filters_no_binary, mean_gradient_layer2,\n"," ind_x_layer2, ind_y_layer2, lr=lr, num_iter=num_iter, title='No binary model: conv layer 2')"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x1080 with 11 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x1800 with 21 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"G2QLO0jHNgrl","colab_type":"text"},"source":["### Binary model:"]},{"cell_type":"code","metadata":{"id":"REf3YEamNi8X","colab_type":"code","colab":{}},"source":["g_ascent_binary = GradientAscent(model_binary, nb_channels=1, img_size=28)\n","g_ascent_binary.use_gpu = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LeMtt18nO4MG","colab_type":"code","colab":{}},"source":["conv1_binary = model_binary.layer1\n","conv1_filters_binary = [0,1,2,3,4,5,6,7,8,9]\n","mean_gradient_layer1 = False\n","ind_x_layer1 = 7\n","ind_y_layer1 = 7\n","\n","conv2_binary = model_binary.layer2\n","conv2_filters_binary = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","mean_gradient_layer2 = False\n","ind_x_layer2 = 3\n","ind_y_layer2 = 3\n","\n","lr=0.0001\n","num_iter=1000\n","MNIST = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"vTvyDQcica_V","colab_type":"code","outputId":"1544b368-f63a-4cfc-de4b-2dd93d9a2ddb","executionInfo":{"status":"ok","timestamp":1588681517163,"user_tz":-120,"elapsed":801,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":386}},"source":["viz_filters(model_binary)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Visualization filters learned for layer: layer1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Visualization filters learned for layer: layer2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"bKPVj-UZO8gX","colab_type":"code","outputId":"15eda554-3b48-4d35-f9d7-782fcdca0d34","executionInfo":{"status":"ok","timestamp":1588681598007,"user_tz":-120,"elapsed":81623,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["g_ascent_binary.visualize(conv1_binary, MNIST, conv1_filters_binary, mean_gradient_layer1,\n"," ind_x_layer1, ind_y_layer1, lr=lr, num_iter=num_iter, title='Binary model: conv layer 1')\n","g_ascent_binary.visualize(conv2_binary, MNIST, conv2_filters_binary, mean_gradient_layer2,\n"," ind_x_layer2, ind_y_layer2, lr=lr, num_iter=num_iter, title='Binary model: conv layer 2')"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x1080 with 11 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x1800 with 21 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"dPw0S2MPKfJd","colab_type":"text"},"source":["## Visuazation regions that maximizes a specific layer and filter:"]},{"cell_type":"markdown","metadata":{"id":"yiji9E6E5Njy","colab_type":"text"},"source":["### Run:"]},{"cell_type":"markdown","metadata":{"id":"_9e1_CIzy3tw","colab_type":"text"},"source":["#### Extract and save regions and activations:"]},{"cell_type":"markdown","metadata":{"id":"keD_cleEzK7u","colab_type":"text"},"source":["##### No binary model:"]},{"cell_type":"code","metadata":{"id":"WxtX6_3F0tjw","colab_type":"code","colab":{}},"source":["activations_no_binary = collections.defaultdict(list)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3N74Ty7m0soN","colab_type":"code","colab":{}},"source":["def save_activation_no_binary(name, mod, inp, out):\n"," activations_no_binary[name].append(out.cpu())\n"," "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wvBPUzNx0tqH","colab_type":"code","outputId":"de6a6339-6935-4a07-92eb-fa147af9c75a","executionInfo":{"status":"ok","timestamp":1588861075211,"user_tz":-120,"elapsed":2385,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["for name, m in model_no_binary.named_modules():\n"," if type(m)==nn.Conv2d:\n"," # partial to assign the layer name to each hook\n"," m.register_forward_hook(partial(save_activation_no_binary, name))\n","\n","for batch in train_loader:\n"," out = model_no_binary(batch[0])\n"," break # for only one batch\n","\n","activations_no_binary = {name: torch.cat(outputs, 0) for name, outputs in activations_no_binary.items()}\n","\n","for k,v in activations_no_binary.items():\n"," print (k, v.size())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["layer1 torch.Size([10000, 10, 14, 14])\n","layer2 torch.Size([10000, 20, 7, 7])\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"aMhtlGzBhdpu","colab_type":"code","colab":{}},"source":["stride = 2\n","padding = 1\n","filter_size = 3\n","len_img_w = 28\n","len_img_h = 28\n","loader = train_loader\n","\n","region_final, activation_final, activation_final_normalized = get_all_regions_max(loader, activations_no_binary, stride, padding, filter_size, len_img_h, len_img_w)\n","\n","region_layer1_no_binary = region_final['layer1']\n","region_layer2_no_binary = region_final['layer2']\n","activation_layer1_no_binary = activation_final['layer1']\n","activation_layer2_no_binary = activation_final['layer2']\n","activation_layer1_no_binary_normalized = activation_final_normalized['layer1']\n","activation_layer2_no_binary_normalized = activation_final_normalized['layer2']\n","\n","print(region_layer1_no_binary.shape)\n","print(region_layer2_no_binary.shape)\n","print(activation_layer1_no_binary.shape)\n","print(activation_layer2_no_binary.shape)\n","print(activation_layer1_no_binary_normalized.shape)\n","print(activation_layer2_no_binary_normalized.shape)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7maQ5JoavpfO","colab_type":"code","colab":{}},"source":["np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_regions_max_layer1.npy', region_layer1_no_binary)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_regions_max_layer2.npy', region_layer2_no_binary)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer1.npy', activation_layer1_no_binary)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer2.npy', activation_layer2_no_binary)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer1_normalized.npy', activation_layer1_no_binary_normalized)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer2_normalized.npy', activation_layer2_no_binary_normalized)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3ofGz3He4MJ_","colab_type":"text"},"source":["##### Binary model:"]},{"cell_type":"code","metadata":{"id":"o3rnEV5s4WlQ","colab_type":"code","colab":{}},"source":["activations_binary = collections.defaultdict(list)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Fu8-ujxQ4lNl","colab_type":"code","colab":{}},"source":["def save_activation_binary(name, mod, inp, out):\n"," activations_binary[name].append(out.cpu())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"iU5S2TdA4nyZ","colab_type":"code","outputId":"16e6814d-28e9-47fe-db49-b90eee388d02","executionInfo":{"status":"ok","timestamp":1588846740075,"user_tz":-120,"elapsed":4249,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["for name, m in model_binary.named_modules():\n"," if type(m)==nn.Conv2d:\n"," # partial to assign the layer name to each hook\n"," m.register_forward_hook(partial(save_activation_binary, name))\n","\n","for batch in train_loader:\n"," out = model_binary(batch[0])\n"," break # for only one batch\n","\n","activations_binary = {name: torch.cat(outputs, 0) for name, outputs in activations_binary.items()}\n","\n","for k,v in activations_binary.items():\n"," print (k, v.size())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["layer1 torch.Size([10000, 10, 14, 14])\n","layer2 torch.Size([10000, 20, 7, 7])\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"m6yu7Vqx4QUy","colab_type":"code","colab":{}},"source":["stride = 2\n","padding = 1\n","filter_size = 3\n","len_img_w = 28\n","len_img_h = 28\n","loader = train_loader\n","\n","region_final, activation_final, activation_final_normalized = get_all_regions_max(loader, activations_binary, stride, padding, filter_size, len_img_h, len_img_w)\n","\n","region_layer1_binary = region_final['layer1']\n","region_layer2_binary = region_final['layer2']\n","activation_layer1_binary = activation_final['layer1']\n","activation_layer2_binary = activation_final['layer2']\n","activation_layer1_binary_normalized = activation_final_normalized['layer1']\n","activation_layer2_binary_normalized = activation_final_normalized['layer2']\n","\n","print(region_layer1_binary.shape)\n","print(region_layer2_binary.shape)\n","print(activation_layer1_binary.shape)\n","print(activation_layer2_binary.shape)\n","print(activation_layer1_binary_normalized.shape)\n","print(activation_layer2_binary_normalized.shape)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Wk4vOhOM4NbY","colab_type":"code","colab":{}},"source":["np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_regions_max_layer1.npy', region_layer1_binary)\n","np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_regions_max_layer2.npy', region_layer2_binary)\n","np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer1.npy', activation_layer1_binary)\n","np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer2.npy', activation_layer2_binary)\n","np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer1_normalized.npy', activation_layer1_binary_normalized)\n","np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer2_normalized.npy', activation_layer2_binary_normalized)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ks9dWFWMWofi","colab_type":"text"},"source":["##### No binary model without bias:"]},{"cell_type":"code","metadata":{"id":"3KUqNNGBWuFw","colab_type":"code","colab":{}},"source":["activations_no_binary_without_bias = collections.defaultdict(list)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"onfbqtOtW4QE","colab_type":"code","colab":{}},"source":["def save_activation_no_binary(name, mod, inp, out):\n"," activations_no_binary_without_bias[name].append(out.cpu())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UQaH3O97XFKQ","colab_type":"code","outputId":"e5283089-e697-4a47-af82-232390b8f9ed","executionInfo":{"status":"error","timestamp":1588861360341,"user_tz":-120,"elapsed":510,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":223}},"source":["for name, m in model_no_binary_wt_bias.named_modules():\n"," if type(m)==nn.Conv2d:\n"," # partial to assign the layer name to each hook\n"," m.register_forward_hook(partial(activations_no_binary_without_bias, name))\n","\n","for batch in train_loader:\n"," out = model_no_binary_wt_bias(batch[0])\n"," break # for only one batch\n","\n","activations_no_binary_without_bias = {name: torch.cat(outputs, 0) for name, outputs in activations_no_binary_without_bias.items()}\n","\n","for k,v in activations_no_binary_without_bias.items():\n"," print (k, v.size())"],"execution_count":0,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-9-345090f05e43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# partial to assign the layer name to each hook\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_forward_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactivations_no_binary_without_bias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: the first argument must be callable"]}]},{"cell_type":"code","metadata":{"id":"CSuIypahXXJd","colab_type":"code","colab":{}},"source":["stride = 2\n","padding = 1\n","filter_size = 3\n","len_img_w = 28\n","len_img_h = 28\n","loader = train_loader\n","\n","region_final, activation_final, activation_final_normalized = get_all_regions_max(loader, activations_no_binary_without_bias, stride, padding, filter_size, len_img_h, len_img_w)\n","\n","region_layer1_no_binary_without_bias = region_final['layer1']\n","region_layer2_no_binary_without_bias = region_final['layer2']\n","activation_layer1_no_binary_without_bias = activation_final['layer1']\n","activation_layer2_no_binary_without_bias = activation_final['layer2']\n","activation_layer1_no_binary_normalized_without_bias = activation_final_normalized['layer1']\n","activation_layer2_no_binary_normalized_without_bias = activation_final_normalized['layer2']\n","\n","print(region_layer1_no_binary_without_bias.shape)\n","print(region_layer2_no_binary_without_bias.shape)\n","print(activation_layer1_no_binary_without_bias.shape)\n","print(activation_layer2_no_binary_without_bias.shape)\n","print(activation_layer1_no_binary_normalized_without_bias.shape)\n","print(activation_layer2_no_binary_normalized_without_bias.shape)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1-b-BJs0Xm1n","colab_type":"code","colab":{}},"source":["np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer1.npy', region_layer1_no_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer2.npy', region_layer2_no_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1.npy', activation_layer1_no_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2.npy', activation_layer2_no_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1_normalized.npy', activation_layer1_no_binary_normalized_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2_normalized.npy', activation_layer2_no_binary_normalized_without_bias)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OYPTjl99Xxnj","colab_type":"text"},"source":["##### Binary model without bias:"]},{"cell_type":"code","metadata":{"id":"hTpppx4dX1Fa","colab_type":"code","colab":{}},"source":["activations_binary_without_bias = collections.defaultdict(list)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zAMvT3iIX23w","colab_type":"code","colab":{}},"source":["def save_activation_binary(name, mod, inp, out):\n"," activations_binary_without_bias[name].append(out.cpu())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8Fw__7XFX28h","colab_type":"code","colab":{}},"source":["for name, m in model_binary_wt_bias.named_modules():\n"," if type(m)==nn.Conv2d:\n"," # partial to assign the layer name to each hook\n"," m.register_forward_hook(partial(activations_binary_without_bias, name))\n","\n","for batch in train_loader:\n"," out = model_binary_wt_bias(batch[0])\n"," break # for only one batch\n","\n","activations_binary_without_bias = {name: torch.cat(outputs, 0) for name, outputs in activations_binary_without_bias.items()}\n","\n","for k,v in activations_binary_without_bias.items():\n"," print (k, v.size())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6vkB_0jTX2_X","colab_type":"code","colab":{}},"source":["stride = 2\n","padding = 1\n","filter_size = 3\n","len_img_w = 28\n","len_img_h = 28\n","loader = train_loader\n","\n","region_final, activation_final, activation_final_normalized = get_all_regions_max(loader, activations_binary_without_bias, stride, padding, filter_size, len_img_h, len_img_w)\n","\n","region_layer1_binary_without_bias = region_final['layer1']\n","region_layer2_binary_without_bias = region_final['layer2']\n","activation_layer1_binary_without_bias = activation_final['layer1']\n","activation_layer2_binary_without_bias = activation_final['layer2']\n","activation_layer1_binary_normalized_without_bias = activation_final_normalized['layer1']\n","activation_layer2_binary_normalized_without_bias = activation_final_normalized['layer2']\n","\n","print(region_layer1_binary_without_bias.shape)\n","print(region_layer2_binary_without_bias.shape)\n","print(activation_layer1_binary_without_bias.shape)\n","print(activation_layer2_binary_without_bias.shape)\n","print(activation_layer1_binary_normalized_without_bias.shape)\n","print(activation_layer2_binary_normalized_without_bias.shape)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cDAVPpvLX26x","colab_type":"code","colab":{}},"source":["np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer1.npy', region_layer1_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer2.npy', region_layer2_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1.npy', activation_layer1_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2.npy', activation_layer2_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1_normalized.npy', activation_layer1_binary_normalized_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2_normalized.npy', activation_layer2_binary_normalized_without_bias)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ipyNhwCxyMDr","colab_type":"text"},"source":["### Visualize max regions:"]},{"cell_type":"markdown","metadata":{"id":"zJsdFgDOySdN","colab_type":"text"},"source":["#### Load regions and activations:"]},{"cell_type":"code","metadata":{"id":"qYSQEIpcyRen","colab_type":"code","colab":{}},"source":["region_layer1_no_binary = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_regions_max_layer1.npy', allow_pickle=True)\n","region_layer2_no_binary = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_regions_max_layer2.npy', allow_pickle=True)\n","activation_layer1_no_binary = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer1.npy', allow_pickle=True)\n","activation_layer2_no_binary = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer2.npy', allow_pickle=True)\n","activation_layer1_no_binary_normalized = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer1_normalized.npy', allow_pickle=True)\n","activation_layer2_no_binary_normalized = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer2_normalized.npy', allow_pickle=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Inn2qU5E39-s","colab_type":"code","colab":{}},"source":["region_layer1_binary = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_regions_max_layer1.npy', allow_pickle=True)\n","region_layer2_binary = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_regions_max_layer2.npy', allow_pickle=True)\n","activation_layer1_binary = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer1.npy', allow_pickle=True)\n","activation_layer2_binary = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer2.npy', allow_pickle=True)\n","activation_layer1_binary_normalized = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer1_normalized.npy', allow_pickle=True)\n","activation_layer2_binary_normalized = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer2_normalized.npy', allow_pickle=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"52jIwyOVYSAh","colab_type":"code","colab":{}},"source":["region_layer1_no_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer1.npy', allow_pickle=True)\n","region_layer2_no_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer2.npy', allow_pickle=True)\n","activation_layer1_no_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1.npy', allow_pickle=True)\n","activation_layer2_no_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2.npy', allow_pickle=True)\n","activation_layer1_no_binary_normalized_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1_normalized.npy', allow_pickle=True)\n","activation_layer2_no_binary_normalized_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2_normalized.npy', allow_pickle=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"R6ZkRCNBYSVa","colab_type":"code","colab":{}},"source":["region_layer1_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer1.npy', allow_pickle=True)\n","region_layer2_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer2.npy', allow_pickle=True)\n","activation_layer1_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1.npy', allow_pickle=True)\n","activation_layer2_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2.npy', allow_pickle=True)\n","activation_layer1_binary_normalized_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1_normalized.npy', allow_pickle=True)\n","activation_layer2_binary_normalized_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2_normalized.npy', allow_pickle=True)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f03V578Tyv1E","colab_type":"text"},"source":["#### Viz:"]},{"cell_type":"code","metadata":{"id":"zY2oBF5gArgV","colab_type":"code","outputId":"ae67eac4-9cef-46d2-f4a0-a13ec7754a18","executionInfo":{"status":"ok","timestamp":1588845254434,"user_tz":-120,"elapsed":759,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":383}},"source":["viz_filters(model_no_binary)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Visualization filters learned for layer: layer1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Visualization filters learned for layer: layer2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"wV3Y7ZUT5n9Y","colab_type":"text"},"source":["##### No binary model layer1:"]},{"cell_type":"code","metadata":{"id":"HFAQzzf-1LHK","colab_type":"code","outputId":"b8772267-93eb-4362-bf7b-430ac71e2d61","executionInfo":{"status":"ok","timestamp":1588847623157,"user_tz":-120,"elapsed":7010,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# parameters\n","list_filter_interest_layer1 = [0,1,2,3,4,5,6,7,8,9]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 1\n","nrow=14\n","\n","# regions and activation of interest\n","regions = region_layer1_no_binary\n","activations = activation_layer1_no_binary\n","activations_normalized = activation_layer1_no_binary_normalized\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer1, nrow=nrow)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Interest of filters: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n","Consider 1% image regions = 100 images\n","mean image:\n","mean regions of 100 regions more=True or worst=False active for filter number: 0 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 100 regions more=True or worst=False active for filter number: 1 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAFoAAABVCAYAAADJ/vPXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAECElEQVR4nO2cz2sdVRTHP1/bPl2YvEUqT9GiFooQV0oo+GNRzEazsBsXdSHupEhBwY0guPAPcNcgBQsGBIs/kCKRIGgWLqzG0AppUVJBVCTRJLzYlS/JcfHG+mxLZt68ueeV8Xxg4L55d84cPlxm5s5hrsyMID23DDuB/wsh2okQ7USIdiJEO7E3RdBms2mtVmvgOGtraxVk02Vzc3PgGNvb2+zs7KjMsUlEt1otpqenB44zMzNTQTZd5ubmBo6xvr5e+ti4dDgRop0I0U6EaCcKiZb0pKTvJS1LejV1UnUkV7SkPcBJ4ClgHHhW0njqxOpGkRF9GFg2sx/N7C/gPeBo2rTqRxHRdwM/9/z+JdsX9EFlN0NJL0hakLTQbrerClsbioj+FTjQ8/uebN9/MLNTZjZhZhPNZrOq/GpDEdHfAIck3S+pARwDzqZNq37kvuswsy1JJ4A5YA9w2syWkmdWMwq9VDKzWWA2cS61JmaGToRoJ0K0E0le/I+OjjI5OTlwnPn5+cGTyVhdXa0sVhliRDsRop0I0U6EaCdCtBMh2okQ7USIdiJEOxGinQjRToRoJ0K0EyHaiRDtRIh2IkQ7EaKdSFLK6nQ6rKysDBxnY2Ojgmy6NBqNgWN0Op3Sx8aIdiJEOxGinQjRThT5tOKApC8kXZS0JOklj8TqRpGnji3gFTNblDQCfCvpMzO7mDi3WpE7os3sNzNbzNp/ApeITyv6pq9rtKT7gIeAcymSqTOFRUu6HfgQeNnMrlsqoPcblipXJagLRT/o3EdX8rtm9tGN+vR+wzI2NlZljrWgyFOHgLeBS2b2ZvqU6kmREf0Y8BzwhKTz2TaVOK/aUeRjoS+BUquuBP8SM0MnQrQTIdqJEO2EUiwCK+l34KecbvuBPyo/eVoeMLORMgcmKWWZ2R15fSQtmNlEivOnQtJC2WPj0uFEiHZimKJPDfHcZSmdc5KbYXA9celwIkQ7kVR03sKEkm6VdCb7/1xWwRkqRYrRko5Iave8zXw9N7CZJdnoLgt0GTgINIALwPg1fV4E3srax4AzqfLpI++7gIez9gjwww3yPgJ80k/clCO6yMKER4F3svYHwGRWaBgaqYrRKUUXWZjwah8z2wLawE1TB8spRj8i6YKkTyU9mBcryRS8DuQUoxeBe83sSlZt+hg4tFu8lCO6yMKEV/tI2gs0gaGX0POK0Wa2aWZXsvYssE/S/t1iphRdZGHCs8DzWfsZ4HMb8gyqSDFa0p3/3EskHabrcfcBkvgOPkX3rn0ZeC3b9wbwdNa+DXgfWAa+Bg7eBE8djwMGfAecz7Yp4DhwPOtzAlii+yT1FfBoXtyYgjsRM0MnQrQTIdqJEO1EiHYiRDsRop34G7lvxM0KOcxuAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 100 regions more=True or worst=False active for filter number: 2 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 100 regions more=True or worst=False active for filter number: 3 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 100 regions more=True or worst=False active for filter number: 4 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAFoAAABVCAYAAADJ/vPXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAD/ElEQVR4nO2cwWscVRzHP1+3u0oweHADmqS4FooQT0ootHooetEc7MVDPYi3UKSg4EUQPPgHeCtIoQUPQktVpEhEBL14sCYurZAWJRXEBA/RQ2tPsuHnYceSmpB5O7vvt3X6+8DA2903v/nth8fMvHnznsyMID/3jTuBe4UQ7USIdiJEOxGindiXI2i73bZOpzN0nG63O3wyBc1mc+gYvV6Pra0tVdk3i+hOp8Py8vLQcSYmJkaQTZ/p6emhY6yvr1feN04dToRoJ0K0EyHaiSTRkl6Q9JOkNUlv506qjpSKltQATgEvAnPAK5LmcidWN1Ja9CFgzcx+MbO/gXPAsbxp1Y8U0TPAb9s+rxffBQMwsouhpEVJK5JWNjc3RxW2NqSI3gD2b/s8W3x3B2Z22szmzWx+ampqVPnVhhTRy8BBSY9LagHHgYt506ofpc86zKwn6STwJdAAzprZavbMakbSQyUzWwKWMudSa6Jn6ESIdiJEO6Ec73VIMqnSQMQd3I3vnJhZpT8WLdqJEO1EiHYiRDsRop0I0U6EaCdCtBMh2okQ7USIdiJEOxGinQjRToRoJ0K0EyHaiRDtRJY5LK1Wi5mZ4V/P29jY8UJUZWZnZ4eOEXNY/geEaCdCtBMh2omUqRX7JX0j6aqkVUlveCRWN1LuOnrAW2bWlTQJ/CDpKzO7mjm3WlHaos3sdzPrFuW/gGvE1IqBGegcLakDPAVcypFMnUnusEh6EPgEeNPMbu7y+yKwCNBoNEaWYF1IndDZpC/5IzP7dLc62+ewhOidpNx1CDgDXDOz9/OnVE9SWvQzwKvAc5IuF9tC5rxqR8pkoW+B4V92vseJnqETIdqJEO1EiHYi12ShTeDXkmpt4I+RHzwvT5jZZJUdswxlmVnprHtJK2Y2n+P4uZC0UnXfOHU4EaKdGKfo02M8dlUq55zlYhjsJE4dToRoJ7KKLluYUNL9ks4Xv18qRnDGSspgtKSjkm5se5r5bmlgM8uy0V8W6DpwAGgBV4C5/9R5HfigKB8HzufKZ4C8HwWeLsqTwM+75H0U+HyQuDlbdMrChMeAD4vyx8DzGsX6E0OQazA6p+iUhQlv1zGzHnADeDhjTgNRMhh9WNIVSV9IerIsVpYueB0oGYzuAo+Z2a1itOkz4OBe8XK26JSFCW/XkbQPeAj4M2NOSZQNRpvZTTO7VZSXgKak9l4xc4pOWZjwIvBaUX4Z+NrG3INKGYyW9Mi/1xJJh+h73LuBZL6CL9C/al8H3im+ew94qSg/AFwA1oDvgQN3wV3Hs4ABPwKXi20BOAGcKOqcBFbp30l9BxwpixtdcCeiZ+hEiHYiRDsRop0I0U6EaCdCtBP/AJvluT9kDBH9AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 100 regions more=True or worst=False active for filter number: 5 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 100 regions more=True or worst=False active for filter number: 6 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 100 regions more=True or worst=False active for filter number: 7 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 100 regions more=True or worst=False active for filter number: 8 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 100 regions more=True or worst=False active for filter number: 9 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid image\n","grid regions of 100 regions more=True or worst=False active for filter number: 0 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 100 regions more=True or worst=False active for filter number: 1 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 100 regions more=True or worst=False active for filter number: 2 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 100 regions more=True or worst=False active for filter number: 3 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 100 regions more=True or worst=False active for filter number: 4 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 100 regions more=True or worst=False active for filter number: 5 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 100 regions more=True or worst=False active for filter number: 6 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAxEAAAHSCAYAAACThEZVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAf/ElEQVR4nO3df5CddX0v8M8nWagUi4IsDFUhiiCNVeF6htG2VqCKoXauOAOI9VrqWNFSW2i1gD+AeAvTMkP91SqVCi0t0EqrEWoRS71Yb51byyGFBpKbBh2cGgMJrVqCpZLN9/6xh7mpXdh8nuz5kd3XayaTPc8573w/z55n9+S9z55zsrUWAAAAu2vZuAcAAAD2LkoEAABQokQAAAAlSgQAAFCiRAAAACVKBAAAUDI1ysUOPvjgtmLFilEuCQAAdHD//ffHQw89lHNdN9ISsWLFiuj3+6NcEgAA6KDX6z3hdX6dCQAAKNmjEpGZqzJzY2bel5kXLtRQAADA5OpcIjJzeUR8NCJOiYiVEfGGzFy5UIMBAACTaU/ORBwfEfe11r7WWvteRPxpRLx2YcYCAAAm1Z6UiGdGxD/vcvkbg20AAMAiNvQnVmfm2ZnZz8z+tm3bhr0cAAAwZHtSIjZHxLN3ufyswbb/pLV2VWut11rrTU9P78FyAADAJNiTEnFHRByVmc/JzH0j4syIuHlhxgIAACZV5zeba63tyMx3RMTnI2J5RFzTWrt3wSYDAAAm0h69Y3Vr7ZaIuGWBZgEAAPYC3rEaAAAo2aMzEaOwc+fOcubWW2/ttFa/3y9nLr744nJm/fr15cx3vvOdcqarl73sZeXMueeeW8585CMfKWe6aK2VM5k5hEkWjn2atRj3qcv3vD/6oz8qZ9785jeXM132JyLine98ZznzgQ98oNNaVaM87vbbb79y5vzzzy9nVq9eXc788R//cTnzcz/3c+VMV4vt+0PXr6VHH320nHnJS15SznT5f8piu48iuu3TZZddVs68733vK2e66nrszcWZCAAAoESJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoESJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqmxj3AMJx44omdcqtWrVrgSeb2/Oc/fyTrjNLBBx887hFgybr77rvHPcKTOu+888qZz33uc+XMhg0byplR+oVf+IVy5n3ve98QJmFv9cADD5QzN998cznzvOc9r5xh1jnnnFPOfOhDH+q01kMPPdQpt1CciQAAAEqUCAAAoESJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoESJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqmxj3AMPzAD/zAuEcAGJnrr79+3CM8qcMOO6ycueKKK8qZ17zmNeVMF3/wB3/QKXfqqacu8CQLZ926deMegd3wK7/yK+XMjTfeWM4cffTR5QyzDjjggHJm+fLlQ5hk+JyJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoESJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoCRbayNbrNfrtX6/P7L1AACAbnq9XvT7/ZzrOmciAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKJka9wDz2bhxYzlz5JFHdlrrk5/8ZDnzxje+sZzJzHJmlFpr5cwk71OX/ZmZmem0VpfPQ5f5li9fXs5M8n0UsfiOu4hu+7Rjx45y5l/+5V/KmVNOOaWcWbt2bTkTMdn3U5f7qEtmT3JVy5bVfz44yfdRxOi+T+7cubOc6aLrsdD1sWkUuny+r7vuuk5rnXnmmeVMl68LX0tPzpkIAACgRIkAAABK9ujXmTLz/oh4OCJmImJHa623EEMBAACTayGeE3Fia+2hBfh3AACAvYBfZwIAAEr2tES0iPirzLwzM8+e6waZeXZm9jOzv23btj1cDgAAGLc9LRE/0Vr7bxFxSkT8Umb+5PffoLV2VWut11rrTU9P7+FyAADAuO1RiWitbR78vTUi1kTE8QsxFAAAMLk6l4jM3D8zf+jxjyPi5Ii4Z6EGAwAAJtOevDrToRGxZvDOfFMRcUNr7dYFmQoAAJhYnUtEa+1rEfHiBZwFAADYC3iJVwAAoCRbayNbrNfrtX6/X8ps3769vM6DDz5YzkRErFq1qpzZtGlTOTP4FbCJ1eWYmOR96rI/MzMzndbq8nnoMt/y5cvLmUm+jyIW33EXMbpj7/TTTy9n1qxZU850fbyY5Pupyz59/vOf77TW8cfXX3vkgAMOKGd8f5g1yfvU9WvppptuWuBJ5vaTP/lfXmxzXgceeGA5M8n3UcTiO+4i6vvU6/Wi3+/PuVPORAAAACVKBAAAUKJEAAAAJUoEAABQokQAAAAlSgQAAFCiRAAAACVKBAAAUKJEAAAAJUoEAABQokQAAAAlSgQAAFAyNe4B5vPAAw+UM5deemmnte67775OORafqanJ/tJorY17BIbksssuK2fWrFkzhEl4IqtWreqUe+UrX1nO/Pqv/3o5c/LJJ5cz7B1OPfXUkaxz3HHHlTNr164dwiRMMmciAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKMnW2sgW6/V6rd/vj2w9AACgm16vF/1+P+e6zpkIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoESJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoESJAAAASpQIAACgZGrcA8wnM8c9wpNqrZUz9mm0uuxPl8wodfl8T/J9FLH4jruIxbdPXb8uFts+7dy5cwiTzO3SSy8tZy6++OJy5uijjy5nNm3aVM501eV+mpmZKWde+MIXljMbNmwoZ3wtzZrk/YmwT/NxJgIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAICSqXEPAABLwSOPPFLO/N7v/V45c/HFF5czxxxzTDmzadOmcmbSvf71ry9nVq9evfCDsODOOeeccY+w6DgTAQAAlCgRAABAybwlIjOvycytmXnPLtsOyszbMnPT4O8DhzsmAAAwKXbnTMQfRsSq79t2YUR8obV2VER8YXAZAABYAuYtEa21L0XEv37f5tdGxLWDj6+NiFMXeC4AAGBCdX1OxKGttS2Djx+IiEOf6IaZeXZm9jOzv23bto7LAQAAk2KPn1jdWmsR0Z7k+qtaa73WWm96enpPlwMAAMasa4l4MDMPi4gY/L114UYCAAAmWdcScXNEnDX4+KyIuGlhxgEAACbd7rzE659ExP+JiOdn5jcy8y0R8VsR8arM3BQRrxxcBgAAloCp+W7QWnvDE1z1Uws8CwAAsBfwjtUAAEDJvGciYCk68cQTO+X+5m/+ZoEnmdvsi6LVfOxjHytnPvzhD5czEREbN27slJtUBx100LhHYDccfPDB4x7hSX35y18uZ7Zs2TL/jRbAm970pnLmL/7iL4YwCUvJypUrO+XuvvvuBZ6ELpyJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoESJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoCRbayNbrNfrtX6/P7L1AACAbnq9XvT7/ZzrOmciAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKJka9wDzycxxj/CkWmvlTJd9+t3f/d1y5hd/8RfLmYiIZcvq3XJ6erqc+ad/+qdy5mlPe1o502V/HHejZ59m7bfffuXMgQceWM6cf/755cx5551XzkRE7Ny5s5zp8rl75JFHypkDDjignFmMx92OHTuGMMl/9cM//MOdclu3bi1nuhx3F1xwQTlzxRVXlDNd7qOIiKmp+n/bZmZmOq1V1WWfutxHERFr1qwpZ0477bRyxuPSk3MmAgAAKFEiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKJka9wBL0Zvf/OZy5m1ve9sQJlk473jHO8qZpz3taeXMQw89VM4ccsgh5QyMy6OPPlrObNmypZx597vfXc6cd9555cwoXXnlleXMBRdcMIRJ9j6ZuajWiYhYt25dOXPdddcNYZKFMzMzM5J1li2b7J8x33HHHeMegXAmAgAAKFIiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgJKpcQ+wFH30ox8tZ5Ytq/e93/md3ylnIiLOPffccua9731vOfOd73ynnDn88MPLmUcffbScWYz22Wefcuaxxx4bwiRMgkn/usjMcub6668vZy644IJyZjFqrZUzX/7yl8uZLt/3u3rrW99azjzwwANDmGThHHXUUeXMy1/+8nLm1a9+dTnD0uNMBAAAUDJvicjMazJza2bes8u21Zm5OTPvGvz56eGOCQAATIrdORPxhxGxao7tH2ytHTv4c8vCjgUAAEyqeUtEa+1LEfGvI5gFAADYC+zJcyLekZn/OPh1pwMXbCIAAGCidS0RV0bEkRFxbERsiYjffqIbZubZmdnPzP62bds6LgcAAEyKTiWitfZga22mtbYzIn4/Io5/ktte1VrrtdZ609PTXecEAAAmRKcSkZmH7XLxdRFxzxPdFgAAWFzmfbO5zPyTiDghIg7OzG9ExCURcUJmHhsRLSLuj4i3DXFGAABggsxbIlprb5hj89VDmAUAANgLeMdqAACgRIkAAABKsrU2ssV6vV7r9/sjWw8AAOim1+tFv9/Pua5zJgIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAICSqXEPMJ/MHNla+++/fzmzffv2cmaU+9RFa62cGdU+XXnlleXM29/+9nLGfTR6XfZpx44d5cyKFSvKmc2bN5czEd326cd+7MfKma9//evlzDe/+c1ypsv+7EluFLp8XXzkIx/ptNa5557bKVfl+8Osfffdt5xZuXJlOdPFXXfd1Sl3yimnlDOveMUrypnXv/715cxznvOccubyyy8vZyIiLrzwwk65Kl9LT86ZCAAAoESJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoESJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqytTayxXq9Xuv3+6VMZpbX6ZKJiLjooovKmfe///3lTNf5RqXLMdFln5761KeWM3/3d39XzrzgBS8oZ9xHo9dln9auXVvOvOIVryhntm/fXs5ELL77qevjxSgfZ6q6fL6npqY6rTUzM9MpV7XYjruIbvu0evXqcqbL/wO6WL58eafcqO6nH/zBHyxnHnnkkXJm06ZN5UxExNFHH90pV+VrKaLX60W/359zp5yJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoESJAAAASpQIAACgRIkAAABKlAgAAKBEiQAAAEqUCAAAoGRq3AMMwxFHHNEpd8kllyzwJHP767/+63Lm4x//eDlzyimnlDOjtP/++5czxxxzzBAmYW/12c9+tpzZvn37ECZhEnzxi18sZ0488cRyZmZmppxh9C666KJxj7DX+u53vzvuEdgLOBMBAACUKBEAAECJEgEAAJQoEQAAQIkSAQAAlCgRAABAiRIBAACUKBEAAECJEgEAAJQoEQAAQIkSAQAAlCgRAABAiRIBAACUZGttZIv1er3W7/dHth4AANBNr9eLfr+fc13nTAQAAFAyb4nIzGdn5u2ZuT4z783McwfbD8rM2zJz0+DvA4c/LgAAMG67cyZiR0S8s7W2MiJeGhG/lJkrI+LCiPhCa+2oiPjC4DIAALDIzVsiWmtbWmtrBx8/HBEbIuKZEfHaiLh2cLNrI+LUYQ0JAABMjtJzIjJzRUQcFxFfiYhDW2tbBlc9EBGHLuhkAADARNrtEpGZT42IT0XEea21f9v1ujb7Ek9zvsxTZp6dmf3M7G/btm2PhgUAAMZvt0pEZu4TswXi+tbapwebH8zMwwbXHxYRW+fKttauaq31Wmu96enphZgZAAAYo915daaMiKsjYkNr7QO7XHVzRJw1+PisiLhp4ccDAAAmzdRu3ObHI+JNEbEuM+8abHtPRPxWRNyYmW+JiK9HxBnDGREAAJgk85aI1trfRsSc71QXET+1sOMAAACTzjtWAwAAJUoEAABQsjvPiRir2ed1T67ZV7eteeyxx8qZb37zm+VMV0cccUQ5861vfauc+cu//Mty5u1vf3s5s3379nJmMR539mn0uuzT+vXry5mf//mfL2fuuOOOcqbL/kREXH755eXMu971rnKmy/GwbFn9Z2mL8bjrsk9XX311OdPlWI1YfPdT16+lxbZPXb43RERceOGFnXJVXfbpN3/zN8uZCy64oJyJiPjqV79azhx11FGd1pqLMxEAAECJEgEAAJQoEQAAQIkSAQAAlCgRAABAiRIBAACUKBEAAECJEgEAAJQoEQAAQIkSAQAAlCgRAABAiRIBAACUTI17gKXo1FNPLWduueWWIUwyt9ZaOfOqV72qnLnzzjvLGVjsfvVXf7WcueOOO4YwycL5tV/7tZGsc//995czz33ucxd+kL3Q1VdfXc787M/+7BAmYSl573vfO+4R9mo33HBDOXPJJZcs2PrORAAAACVKBAAAUKJEAAAAJUoEAABQokQAAAAlSgQAAFCiRAAAACVKBAAAUKJEAAAAJUoEAABQokQAAAAlSgQAAFCSrbWRLdbr9Vq/3y9lMnNI0yyMLp8/+zRai21/IuzT4+zTaHV9vJiZmVngSeZ2xhlnlDOf+tSnyplJvo8iut1P3/3ud8uZpzzlKeVMV8uW1X/mOcn3U9evpcW2T5O8PxHd9mndunXlzAte8IJyJiLihBNOKGe+9KUvlW7f6/Wi3+/PeUc5EwEAAJQoEQAAQIkSAQAAlCgRAABAiRIBAACUKBEAAECJEgEAAJQoEQAAQIkSAQAAlCgRAABAiRIBAACUKBEAAEBJttZGtliv12v9fn9k6wEAAN30er3o9/s513XORAAAACVKBAAAUKJEAAAAJUoEAABQokQAAAAlSgQAAFCiRAAAACVKBAAAUKJEAAAAJUoEAABQokQAAAAlSgQAAFCiRAAAACVT4x5gPpk57hGeVGutnFmM+7Rz584hTLIwli2rd+Wu99Fjjz1WznT53O27777lzMzMTDmzcePGciYi4pxzzilnvvjFL5YzXfbp7rvvLmde/vKXlzMREY888kg50+Xrr4s/+7M/K2fOOOOMTmtt3ry5nDnkkEPKmbPOOqucueGGG8qZxfg93D51t3bt2nLmuOOO67TW7bffXs6cdNJJndaqmuT7qKtR7dNFF11UzkRErF69upzp8n+iJ/y3FuxfAgAAloR5S0RmPjszb8/M9Zl5b2aeO9i+OjM3Z+Zdgz8/PfxxAQCAcdudX2faERHvbK2tzcwfiog7M/O2wXUfbK1dMbzxAACASTNviWitbYmILYOPH87MDRHxzGEPBgAATKbScyIyc0VEHBcRXxlsekdm/mNmXpOZBy7wbAAAwATa7RKRmU+NiE9FxHmttX+LiCsj4siIODZmz1T89hPkzs7Mfmb2t23btgAjAwAA47RbJSIz94nZAnF9a+3TERGttQdbazOttZ0R8fsRcfxc2dbaVa21XmutNz09vVBzAwAAY7I7r86UEXF1RGxorX1gl+2H7XKz10XEPQs/HgAAMGl259WZfjwi3hQR6zLzrsG290TEGzLz2IhoEXF/RLxtKBMCAAATZXdenelvI2Kut9+7ZeHHAQAAJp13rAYAAEqUCAAAoGR3nhMBE2Pjxo3lzI/8yI+UM0ceeWQ5ExGxfPnyTrlR+Pd///dy5rTTTuu01oYNGzrlRuHFL35xOXPNNdcMYZLxOv3000e21sknn1zOfOYznylnPvGJT5QzsKf6/X45c9xxx3Va66STTuqUY3T233//cuaXf/mXhzDJ8DkTAQAAlCgRAABAiRIBAACUKBEAAECJEgEAAJQoEQAAQIkSAQAAlCgRAABAiRIBAACUKBEAAECJEgEAAJQoEQAAQMnUuAdg6frc5z5Xzpx55pnlzMMPP1zOfPrTny5nIiJmZmbKmZ07d3Zaq+rjH/94ObNhw4YhTLJwXve615Uzv/Ebv1HOnHbaaeVMV8uW1X+2c/rpp5czV1xxRTlz+OGHlzMREevXry9n3vOe95Qz1113XTkDe+rss88uZ9761rcOYRImwa233lrOPOMZzxjCJMPnTAQAAFCiRAAAACVKBAAAUKJEAAAAJUoEAABQokQAAAAlSgQAAFCiRAAAACVKBAAAUKJEAAAAJUoEAABQokQAAAAl2Vob2WK9Xq/1+/2RrQcAAHTT6/Wi3+/nXNc5EwEAAJQoEQAAQIkSAQAAlCgRAABAiRIBAACUKBEAAECJEgEAAJQoEQAAQIkSAQAAlCgRAABAiRIBAACUKBEAAEDJ1LgHmM/OnTvLme9973ud1nrJS15Sztx7773lzMzMTDmzYcOGcuaFL3xhORMR0VrrlAMAYGlwJgIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAICSqXEPMJ/MLGeuuuqqTmutX7++Uw4AAJYSZyIAAIASJQIAACiZt0Rk5lMy8+8z8+7MvDcz3z/Y/pzM/Epm3peZn8zMfYc/LgAAMG67cybiPyLipNbaiyPi2IhYlZkvjYjLI+KDrbXnRcS3IuItwxsTAACYFPOWiDZr++DiPoM/LSJOiog/H2y/NiJOHcqEAADARNmt50Rk5vLMvCsitkbEbRHx1Yj4dmttx+Am34iIZw5nRAAAYJLsVolorc201o6NiGdFxPERcczuLpCZZ2dmPzP727Zt6zgmAAAwKUqvztRa+3ZE3B4RL4uIp2fm4+8z8ayI2PwEmataa73WWm96enqPhgUAAMZvd16daToznz74eL+IeFVEbIjZMnHa4GZnRcRNwxoSAACYHLvzjtWHRcS1mbk8ZkvHja21z2bm+oj408y8NCL+ISKuHuKcAADAhJi3RLTW/jEijptj+9di9vkRAADAEuIdqwEAgBIlAgAAKNmd50SM1bp168qZSy+9dAiTAAAAEc5EAAAARUoEAABQokQAAAAlSgQAAFCiRAAAACVKBAAAUKJEAAAAJUoEAABQokQAAAAlSgQAAFCiRAAAACVKBAAAUDI17gHm86IXvaic2bp16xAmWTjLly8vZ370R3+0nGmtlTMAADAfZyIAAIASJQIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoUSIAAIASJQIAAChRIgAAgBIlAgAAKFEiAACAEiUCAAAoydba6BbL3BYRX3+Cqw+OiIdGNgyTzLHArhwP7MrxwK4cDzzOsTAcR7TWpue6YqQl4slkZr+11hv3HIyfY4FdOR7YleOBXTkeeJxjYfT8OhMAAFCiRAAAACWTVCKuGvcATAzHArtyPLArxwO7cjzwOMfCiE3McyIAAIC9wySdiQAAAPYCYy8RmbkqMzdm5n2ZeeG452G0MvOazNyamffssu2gzLwtMzcN/j5wnDMyOpn57My8PTPXZ+a9mXnuYLtjYonJzKdk5t9n5t2DY+H9g+3PycyvDB4zPpmZ+457VkYnM5dn5j9k5mcHlx0PS1Rm3p+Z6zLzrszsD7Z5rBihsZaIzFweER+NiFMiYmVEvCEzV45zJkbuDyNi1fdtuzAivtBaOyoivjC4zNKwIyLe2VpbGREvjYhfGnxPcEwsPf8RESe11l4cEcdGxKrMfGlEXB4RH2ytPS8ivhURbxnjjIzeuRGxYZfLjoel7cTW2rG7vLSrx4oRGveZiOMj4r7W2tdaa9+LiD+NiNeOeSZGqLX2pYj41+/b/NqIuHbw8bURcepIh2JsWmtbWmtrBx8/HLP/WXhmOCaWnDZr++DiPoM/LSJOiog/H2x3LCwhmfmsiHhNRHxicDnD8cB/5rFihMZdIp4ZEf+8y+VvDLaxtB3aWtsy+PiBiDh0nMMwHpm5IiKOi4ivhGNiSRr86spdEbE1Im6LiK9GxLdbazsGN/GYsbR8KCLOj4idg8vPCMfDUtYi4q8y887MPHuwzWPFCE2NewB4Mq21lpleQmyJycynRsSnIuK81tq/zf7AcZZjYulorc1ExLGZ+fSIWBMRx4x5JMYkM38mIra21u7MzBPGPQ8T4Sdaa5sz85CIuC0z/++uV3qsGL5xn4nYHBHP3uXyswbbWNoezMzDIiIGf28d8zyMUGbuE7MF4vrW2qcHmx0TS1hr7dsRcXtEvCwinp6Zj/8AzGPG0vHjEfHfM/P+mP3V55Mi4sPheFiyWmubB39vjdkfMhwfHitGatwl4o6IOGrw6gr7RsSZEXHzmGdi/G6OiLMGH58VETeNcRZGaPA7zldHxIbW2gd2ucoxscRk5vTgDERk5n4R8aqYfY7M7RFx2uBmjoUlorX27tbas1prK2L2/wr/q7X2xnA8LEmZuX9m/tDjH0fEyRFxT3isGKmxv9lcZv50zP6e4/KIuKa1dtlYB2KkMvNPIuKEiDg4Ih6MiEsi4jMRcWNEHB4RX4+IM1pr3//kaxahzPyJiPjfEbEu/v/vPb8nZp8X4ZhYQjLzRTH7xMjlMfsDrxtba/8zM58bsz+JPigi/iEi/kdr7T/GNymjNvh1pne11n7G8bA0De73NYOLUxFxQ2vtssx8RnisGJmxlwgAAGDvMu5fZwIAAPYySgQAAFCiRAAAACVKBAAAUKJEAAAAJUoEAABQokQAAAAlSgQAAFDy/wC1V1DtSIzzPQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 100 regions more=True or worst=False active for filter number: 7 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAxEAAAHSCAYAAACThEZVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAaj0lEQVR4nO3dX6zc51kn8O9DTisQILV1zkZR292wUCXNxZKuRlFxrFUpCwosio2EENUuipSgcAFSkdjGXW6A1SJhdaG7FwgpuBa54F8F1LZQtUvUrcQ6XhWmEGjaxGqpgmgUGjtQUW5Aad+9OGNxqOyc84znzMyZ8/lI1pn5zbx6n8e/d87463f+1BgjAAAA+/V1qy4AAAA4XIQIAACgRYgAAABahAgAAKBFiAAAAFqECAAAoGVrmZPdfvvt46677lrmlAAAwBxeeOGFXLt2rW5021JDxF133ZXpdLrMKQEAgDlMJpOb3ublTAAAQMsthYiqerCqrlTV56rq/YsqCgAAWF9zh4iqui3JLyf53iT3JnlPVd27qMIAAID1dCs7Efcn+dwY4/NjjH9M8ltJTi6mLAAAYF3dSoh4c5K/2nX9C7NjAADABjvwN1ZX1WNVNa2q6dWrVw96OgAA4IDdSoh4Mclbd11/y+zYPzPGeGKMMRljTLa3t29hOgAAYB3cSoj44yRvq6pvqarXJ/nhJBcXUxYAALCu5v6yuTHGq1X1E0n+d5LbkpwbY3x6YZUBAABr6Za+sXqM8dEkH11QLQAAwCHgG6sBAICWW9qJWIaqWtpcx44da4+5du1ae8wye5rHGKM95vbbb2+PeeWVV9pj5jFPP87RjmWdo8R5uu6ee+5pj7ly5Up7zDzm6SdZ7/Nk3e3Q03J5LO1Y534SPe3FTgQAANAiRAAAAC1CBAAA0CJEAAAALUIEAADQIkQAAAAtQgQAANAiRAAAAC1CBAAA0CJEAAAALUIEAADQIkQAAAAtW6suYJ288sorqy7h0PJ3t/6co8PhypUrqy4BAPZkJwIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGjZWnUBHJxjx46tugT2wXma3913390ec+XKlQOohHUwz3pY5jzrvPaOHz/eHnP58uUDqGRxNrGnZVnWY2mZ5lkP627VPdmJAAAAWoQIAACgRYgAAABahAgAAKBFiAAAAFqECAAAoEWIAAAAWoQIAACgRYgAAABahAgAAKBFiAAAAFqECAAAoGVr1QXsZYyx6hIWTk/rb9P6STazp+eff37VJSzcJp6nTetpE9fd008/veoSFm4Te9q0x9Km9ZNsZk83YycCAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAlq1VF7CXqlp1Ca9pjNEes6yejh07Nte4a9eutces83la53M0Lz3t0NNyzdNPkpw8ebI95uLFi3PN1bVp5yjR03Wbtu6SzetpE9fdJvZ0M3YiAACAFiECAABouaWXM1XVC0m+nOQrSV4dY0wWURQAALC+FvGeiO8cY/RfRA8AABxKXs4EAAC03GqIGEn+oKo+WVWP3egOVfVYVU2ranr16tVbnA4AAFi1Ww0RJ8YY/zbJ9yb58ar6d197hzHGE2OMyRhjsr29fYvTAQAAq3ZLIWKM8eLs58tJPpLk/kUUBQAArK+5Q0RVfWNVffP1y0m+J8mziyoMAABYT7fy6Ux3JPnI7Jv5tpL8xhjjfy2kKgAAYG3NHSLGGJ9P8u0LrAUAADgEfMQrAADQUmOMpU02mUzGdDptjZm9XGptzfP3p6fl2rR+Ej1dp6flmvf5YtN6Wud+Ej1dt849eSztWOd+Ej0lyWQyyXQ6vWFTdiIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABo2Vp1Aevk+PHjqy5hoe6+++6lzfXQQw+1x1y8ePEAKjl8Nm3dJZvX0zzre17z/N1dvnz5ACphHSxz7S3Dpv1uYDWOHTvWHnPy5MkDqGS1Vv37wU4EAADQIkQAAAAtQgQAANAiRAAAAC1CBAAA0CJEAAAALUIEAADQIkQAAAAtQgQAANAiRAAAAC1CBAAA0CJEAAAALVurLmAvY4xVl7Bwm9jThQsXVl3CQm3iOdLT4fD000+vuoSF27TztGn9JHo6LDatp03rJ9nMnm7GTgQAANAiRAAAAC1CBAAA0CJEAAAALUIEAADQIkQAAAAtQgQAANAiRAAAAC1CBAAA0CJEAAAALUIEAADQIkQAAAAtQgQAANCyteoC9lJVqy7hNY0x2mP0tFyb1k+ip+v0NL+77767Peb555+fa651Pk/rfI6S5KGHHmqPuXDhQnvMOp+jZP3PU9c8/SSb19M695PoaS92IgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGjZWnUBACzflStXVl0C+3Dx4sVVlwAH5pFHHplr3Llz5xZcCfOwEwEAALQIEQAAQMueIaKqzlXVy1X17K5jb6qqp6rqs7OfbzzYMgEAgHWxn52IX0vy4Ncce3+Sj40x3pbkY7PrAADAEbBniBhj/GGSv/mawyeTPDm7/GSSUwuuCwAAWFPzvifijjHGS7PLf53kjpvdsaoeq6ppVU2vXr0653QAAMC6uOU3Vo8xRpLxGrc/McaYjDEm29vbtzodAACwYvOGiC9W1Z1JMvv58uJKAgAA1tm8IeJikodnlx9OcmEx5QAAAOtuPx/x+ptJ/l+Su6vqC1X1aJJfSPLdVfXZJP9+dh0AADgCtva6wxjjPTe56bsWXAsAAHAI+MZqAACgZc+diKPkoYceWnUJC/XII48sba7jx4+3x1y+fPkAKjl8lnWe5pnn3LlzB1DJ4bPMxxLze9/73rfqEg6teX6H33PPPe0x165da4+Z1zzP6RcvXjyAShZn036Pr3Nth8GZM2dWOr+dCAAAoEWIAAAAWoQIAACgRYgAAABahAgAAKBFiAAAAFqECAAAoEWIAAAAWoQIAACgRYgAAABahAgAAKBFiAAAAFpqjLG0ySaTyZhOp0ubDwAAmM9kMsl0Oq0b3WYnAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgJatVRewl6padQmvaYzRHqOn5dq0fhI9XbeJPT3++OPtMR/4wAfaY+YxTz/Jep8n627HJvZ04cKF9pjTp0+3x1y5cqU9xmNpxzr3k+hpL3YiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAlq1VFwDAP/nABz6w6hJgI5w6dWrVJcBGsxMBAAC0CBEAAECLEAEAALQIEQAAQIsQAQAAtAgRAABAixABAAC0CBEAAECLEAEAALQIEQAAQIsQAQAAtAgRAABAy9aqC9jLmTNn2mNOnz59AJUszvHjx9tjLl++fACVHD7zrId1d/bs2VWXsHCPPPLIUubxWDocNvH3+LIs67G0LJv4O3wTnT9/finzzLse1vn3w1F6XrITAQAAtOwZIqrqXFW9XFXP7jr2s1X1YlU9M/vzfQdbJgAAsC72sxPxa0kevMHxD44x7pv9+ehiywIAANbVniFijPGHSf5mCbUAAACHwK28J+InqurPZy93euPCKgIAANbavCHiV5J8a5L7kryU5BdvdseqeqyqplU1vXr16pzTAQAA62KuEDHG+OIY4ytjjK8m+dUk97/GfZ8YY0zGGJPt7e156wQAANbEXCGiqu7cdfUHkjx7s/sCAACbZc8vm6uq30zyriS3V9UXkvxMkndV1X1JRpIXkvzYAdYIAACskT1DxBjjPTc4/KEDqAUAADgEfGM1AADQIkQAAAAtNcZY2mSTyWRMp9OlzQcAAMxnMplkOp3WjW6zEwEAALQIEQAAQIsQAQAAtAgRAABAixABAAC0CBEAAECLEAEAALQIEQAAQIsQAQAAtAgRAABAixABAAC0CBEAAEDL1qoL2EtVrbqE1zTGaI/R03JtWj+Jnq7T03LN00+yeT2tcz/Jevd09uzZucY9+uij7THrfJ48lnascz/JfD098MAD7TGXL19uj0mSM2fOtMc8/vjjc811I3YiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaKkxxtImm0wmYzqdtsZU1QFVsxjz/P3pabk2rZ9ET9dtYk+33357e8wrr7zSHjOPeZ8v1vk8WXc79LRcHks71rmfRE9JMplMMp1Ob9iUnQgAAKBFiAAAAFqECAAAoEWIAAAAWoQIAACgRYgAAABahAgAAKBFiAAAAFqECAAAoEWIAAAAWoQIAACgRYgAAABatlZdwDo5e/bsqktYqGX2c/78+faYU6dOHUAli3HmzJm5xp0+fXrBlRxOm/ZYOn78+NLmOnnyZHvMuXPnDqCSw2ee30PrbtMeS5toE8/RpUuX1nqeEydOLLgS5mEnAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaKkxxtImm0wmYzqdLm0+AABgPpPJJNPptG50m50IAACgZc8QUVVvraqPV9VnqurTVfXe2fE3VdVTVfXZ2c83Hny5AADAqu1nJ+LVJD81xrg3yTuT/HhV3Zvk/Uk+NsZ4W5KPza4DAAAbbs8QMcZ4aYzxJ7PLX07yXJI3JzmZ5MnZ3Z5McuqgigQAANZH6z0RVXVXknck+USSO8YYL81u+uskdyy0MgAAYC3tO0RU1Tcl+d0kPznG+Lvdt42dj3i64cc8VdVjVTWtqunVq1dvqVgAAGD19hUiqup12QkQvz7G+L3Z4S9W1Z2z2+9M8vKNxo4xnhhjTMYYk+3t7UXUDAAArNB+Pp2pknwoyXNjjF/addPFJA/PLj+c5MLiywMAANbN1j7u80CSH0nyqap6Znbsp5P8QpIPV9WjSf4yyQ8dTIkAAMA62TNEjDEuJbnhN9Ul+a7FlgMAAKw731gNAAC0CBEAAEDLft4TsVInT55sj7l48eIBVHJjO59u27PzXvX1tWk9bVo/iZ6u09NyzdNPsnk9rXM/iZ6uW+eePJZ2rHM/iZ72YicCAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAlq1VF7CXixcvrroEAABgFzsRAABAixABAAC0CBEAAECLEAEAALQIEQAAQIsQAQAAtAgRAABAixABAAC0CBEAAECLEAEAALQIEQAAQIsQAQAAtGytuoCj6NKlS+0xJ06cOIBKFufs2bPtMT/6oz96AJUcPvOsh3W3aT1tWj9J8txzz626hIXbxPO0rJ428XlpWd73vvetuoSFO3/+/FLmOXPmzFzjTp8+veBKDqdlnaebsRMBAAC0CBEAAECLEAEAALQIEQAAQIsQAQAAtAgRAABAixABAAC0CBEAAECLEAEAALQIEQAAQIsQAQAAtAgRAABAS40xljbZZDIZ0+l0afMBAADzmUwmmU6ndaPb7EQAAAAtQgQAANAiRAAAAC1CBAAA0CJEAAAALUIEAADQIkQAAAAtQgQAANAiRAAAAC1CBAAA0CJEAAAALUIEAADQIkQAAAAtW6suYC9VteoSXtMYoz1GT/M7f/58e8zJkyfbYzbxHD399NPtMSdOnGiPmdc8PT3//PPtMadPn26PuXjxYntMst6PpXnM00+SXLt2rT1me3t7rrm6Nu0cJXq6bp17mvextGk9rXM/iZ72YicCAABo2TNEVNVbq+rjVfWZqvp0Vb13dvxnq+rFqnpm9uf7Dr5cAABg1fbzcqZXk/zUGONPquqbk3yyqp6a3fbBMcZ/P7jyAACAdbNniBhjvJTkpdnlL1fVc0nefNCFAQAA66n1noiquivJO5J8YnboJ6rqz6vqXFW9ccG1AQAAa2jfIaKqvinJ7yb5yTHG3yX5lSTfmuS+7OxU/OJNxj1WVdOqml69enUBJQMAAKu0rxBRVa/LToD49THG7yXJGOOLY4yvjDG+muRXk9x/o7FjjCfGGJMxxmRZH9kHAAAcnP18OlMl+VCS58YYv7Tr+J277vYDSZ5dfHkAAMC62c+nMz2Q5EeSfKqqnpkd++kk76mq+5KMJC8k+bEDqRAAAFgr+/l0pktJbvT1ex9dfDkAAMC6843VAABAixABAAC07Oc9EbA2Tp061R4zxjiASg6fEydOrLqEhXv729++6hLYB5/MB7B57EQAAAAtQgQAANAiRAAAAC1CBAAA0CJEAAAALUIEAADQIkQAAAAtQgQAANAiRAAAAC1CBAAA0CJEAAAALUIEAADQsrXqAvby1a9+tT3m7W9/+1xzXblyZa5xyzDP38PXfZ2MeBhcunRp1SWwRjzW2e3q1atLmefMmTNLmWcTnT17dmlznT9/vj3m1KlTB1DJYlh3t2ZZvx9uxjMPAADQIkQAAAAtQgQAANAiRAAAAC1CBAAA0CJEAAAALUIEAADQIkQAAAAtQgQAANAiRAAAAC1CBAAA0CJEAAAALTXGWNpkk8lkTKfTpc0HAADMZzKZZDqd1o1usxMBAAC0CBEAAECLEAEAALQIEQAAQIsQAQAAtAgRAABAixABAAC0CBEAAECLEAEAALQIEQAAQIsQAQAAtAgRAABAy9aqC9jLtWvX2mO2t7cPoJIbG2O0x1TVAVSyOPP0BADA0WEnAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgBYhAgAAaBEiAACAFiECAABoESIAAIAWIQIAAGgRIgAAgJatVRewl+3t7VWXAAAA7GInAgAAaBEiAACAlj1DRFV9fVX9UVX9WVV9uqp+bnb8W6rqE1X1uar67ap6/cGXCwAArNp+diL+Icm7xxjfnuS+JA9W1TuTnEnywTHGtyX52ySPHlyZAADAutgzRIwdfz+7+rrZn5Hk3Ul+Z3b8ySSnDqRCAABgrezrPRFVdVtVPZPk5SRPJfmLJF8aY7w6u8sXkrz5YEoEAADWyb5CxBjjK2OM+5K8Jcn9Se7Z7wRV9VhVTatqevXq1TnLBAAA1kXr05nGGF9K8vEk35HkDVV1/Xsm3pLkxZuMeWKMMRljTHznAwAAHH77+XSm7ap6w+zyNyT57iTPZSdM/ODsbg8nuXBQRQIAAOtjP99YfWeSJ6vqtuyEjg+PMX6/qj6T5Leq6r8l+dMkHzrAOgEAgDWxZ4gYY/x5knfc4Pjns/P+CAAA4AjxjdUAAECLEAEAALTs5z0RLNilS5faY06cOHEAlQAAQJ+dCAAAoEWIAAAAWoQIAACgRYgAAABahAgAAKBFiAAAAFqECAAAoEWIAAAAWoQIAACgRYgAAABahAgAAKBFiAAAAFq2Vl3AXsYYqy5h4R544IH2mE38ewAA4HCyEwEAALQIEQAAQIsQAQAAtAgRAABAixABAAC0CBEAAECLEAEAALQIEQAAQIsQAQAAtAgRAABAixABAAC0CBEAAEBLjTGWN1nV1SR/eZObb09ybWnFsM6sBXazHtjNemA364HrrIWD8a/GGNs3umGpIeK1VNV0jDFZdR2snrXAbtYDu1kP7GY9cJ21sHxezgQAALQIEQAAQMs6hYgnVl0Aa8NaYDfrgd2sB3azHrjOWliytXlPBAAAcDis004EAABwCKw8RFTVg1V1pao+V1XvX3U9LFdVnauql6vq2V3H3lRVT1XVZ2c/37jKGlmeqnprVX28qj5TVZ+uqvfOjlsTR0xVfX1V/VFV/dlsLfzc7Pi3VNUnZs8Zv11Vr191rSxPVd1WVX9aVb8/u249HFFV9UJVfaqqnqmq6eyY54olWmmIqKrbkvxyku9Ncm+S91TVvausiaX7tSQPfs2x9yf52BjjbUk+NrvO0fBqkp8aY9yb5J1Jfnz2O8GaOHr+Icm7xxjfnuS+JA9W1TuTnEnywTHGtyX52ySPrrBGlu+9SZ7bdd16ONq+c4xx366PdvVcsUSr3om4P8nnxhifH2P8Y5LfSnJyxTWxRGOMP0zyN19z+GSSJ2eXn0xyaqlFsTJjjJfGGH8yu/zl7Pxj4c2xJo6csePvZ1dfN/szkrw7ye/MjlsLR0hVvSXJf0hydna9Yj3wz3muWKJVh4g3J/mrXde/MDvG0XbHGOOl2eW/TnLHKothNarqriTvSPKJWBNH0uylK88keTnJU0n+IsmXxhivzu7iOeNo+R9JHk/y1dn1Y7EejrKR5A+q6pNV9djsmOeKJdpadQHwWsYYo6p8hNgRU1XflOR3k/zkGOPvdv7DcYc1cXSMMb6S5L6qekOSjyS5Z8UlsSJV9f1JXh5jfLKq3rXqelgLJ8YYL1bVv0jyVFU9v/tGzxUHb9U7ES8meeuu62+ZHeNo+2JV3Zkks58vr7gelqiqXpedAPHrY4zfmx22Jo6wMcaXknw8yXckeUNVXf8PMM8ZR8cDSR6qqhey89Lndyf5n7Eejqwxxouzny9n5z8Z7o/niqVadYj44yRvm326wuuT/HCSiyuuidW7mOTh2eWHk1xYYS0s0ew1zh9K8twY45d23WRNHDFVtT3bgUhVfUOS787Oe2Q+nuQHZ3ezFo6IMcZ/GWO8ZYxxV3b+rfB/xhj/MdbDkVRV31hV33z9cpLvSfJsPFcs1cq/bK6qvi87r3O8Lcm5McbPr7QglqqqfjPJu5LcnuSLSX4myfkkH07yL5P8ZZIfGmN87Zuv2UBVdSLJ/03yqfzT655/Ojvvi7AmjpCq+jfZeWPkbdn5D68PjzH+a1X96+z8T/Sbkvxpkv80xviH1VXKss1ezvSfxxjfbz0cTbPz/pHZ1a0kvzHG+PmqOhbPFUuz8hABAAAcLqt+ORMAAHDICBEAAECLEAEAALQIEQAAQIsQAQAAtAgRAABAixABAAC0CBEAAEDL/wdyinufZqz1igAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 100 regions more=True or worst=False active for filter number: 8 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 100 regions more=True or worst=False active for filter number: 9 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"NcCGRhD2txj2","colab_type":"code","outputId":"cbe52687-5d94-496e-f20f-148ebe324e2e","executionInfo":{"status":"ok","timestamp":1588699200062,"user_tz":-120,"elapsed":42620,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["activation_values"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[array([0.79804754, 0.79804754, 0.79558432, 0.79558432, 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.793082 ,\n"," 0.793082 , 0.793082 , 0.793082 , 0.793082 , 0.7923367 ,\n"," 0.79191238, 0.79183894, 0.79148471, 0.7914781 , 0.79117286,\n"," 0.79107058, 0.79107058, 0.79098642, 0.79073942, 0.79073942,\n"," 0.79073942, 0.79073942, 0.79073942, 0.79073942, 0.79073942,\n"," 0.79073942, 0.79073942, 0.79073942, 0.79073942, 0.79073942,\n"," 0.79073942, 0.79073942, 0.79073942, 0.79073942, 0.79073942,\n"," 0.79073942, 0.79073942, 0.79073942, 0.79073942, 0.79073942,\n"," 0.79073942, 0.79073942, 0.79073942, 0.79073942, 0.79073942,\n"," 0.79073942, 0.79073942, 0.79073942, 0.79073942, 0.79073942,\n"," 0.79073942, 0.79073942, 0.79073942, 0.79073942, 0.79073942,\n"," 0.79073942, 0.79073942, 0.79073942, 0.79073942, 0.79073942,\n"," 0.79050815, 0.79049242, 0.79040825, 0.79024631, 0.79024541,\n"," 0.79010814, 0.79010814, 0.79010814, 0.79010814, 0.7899999 ,\n"," 0.78999412, 0.7899217 , 0.7898826 , 0.78987581, 0.7898742 ,\n"," 0.7897653 , 0.78972965, 0.78970867, 0.78970867, 0.78970867,\n"," 0.78970867, 0.78970867, 0.78970867, 0.78970867, 0.78970867,\n"," 0.78970867, 0.78954154, 0.78953105, 0.78952992, 0.78950435,\n"," 0.78949159, 0.78926438, 0.78924197, 0.78913552, 0.78913552,\n"," 0.78913552, 0.78913552, 0.78913552, 0.78913552, 0.78913552,\n"," 0.78913552, 0.78913552, 0.78913552, 0.78913552, 0.78913492,\n"," 0.78913492, 0.78909808, 0.78906149, 0.78898096, 0.78896344,\n"," 0.78896344, 0.78896344, 0.78896344, 0.78896344, 0.78896344,\n"," 0.78896344, 0.78896344, 0.78896344, 0.78877372, 0.78876281,\n"," 0.7886889 , 0.7886889 , 0.7886889 , 0.7886889 , 0.78868234,\n"," 0.78868234, 0.78868234, 0.78868234, 0.78868234, 0.78868234,\n"," 0.78868234, 0.78868234, 0.78864324, 0.78855741, 0.78851628,\n"," 0.78851628, 0.78851521, 0.78851521, 0.78851521, 0.78851521,\n"," 0.78851521, 0.78851521, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78839684,\n"," 0.78839684, 0.78839684, 0.78839684, 0.78839684, 0.78838521,\n"," 0.78838521, 0.78838521, 0.78838521, 0.78838521, 0.78838521,\n"," 0.78838521, 0.78838521, 0.78838521, 0.78826928, 0.78812623,\n"," 0.78810626, 0.78802228, 0.78775835, 0.78770083, 0.78770083,\n"," 0.78769714, 0.78760666, 0.78736615, 0.78736615, 0.78736615,\n"," 0.78736615, 0.78736615, 0.78736615, 0.78736615, 0.78736615,\n"," 0.78736615, 0.78736615, 0.78736615, 0.78736615, 0.78736615,\n"," 0.78736615, 0.78736615, 0.78736615, 0.78736615, 0.78736615,\n"," 0.78736615, 0.78736615, 0.78736615, 0.78736615, 0.78736615,\n"," 0.78736615, 0.78736615, 0.78736615, 0.78736615, 0.78736615,\n"," 0.78729743, 0.78728473, 0.78726953, 0.78712833, 0.78708041,\n"," 0.78701311, 0.78701311, 0.78691477, 0.78683633, 0.78679693,\n"," 0.78679305, 0.78679305, 0.78679305, 0.78679305, 0.78679305,\n"," 0.78679305, 0.78679305, 0.78679305, 0.78679305, 0.78679305,\n"," 0.78679305, 0.78679305, 0.78679305, 0.78679305, 0.78679305,\n"," 0.78679305, 0.78679305, 0.78679305, 0.78679305, 0.78679305,\n"," 0.78679305, 0.78679305, 0.78679305, 0.78679305, 0.78679305,\n"," 0.78679305, 0.78679305, 0.78679305, 0.78679305, 0.78679305,\n"," 0.78679305, 0.78678721, 0.78676569, 0.78674114, 0.78654021,\n"," 0.78640997, 0.78634638, 0.78634638, 0.78634638, 0.78634638,\n"," 0.78634638, 0.78634638, 0.78634638, 0.78634638, 0.78634638,\n"," 0.78634638, 0.78634638, 0.78634638, 0.78634638, 0.78634638,\n"," 0.78634638, 0.78634638, 0.78633535, 0.78633535, 0.78633535,\n"," 0.78633535, 0.78633535, 0.78633535, 0.78633535, 0.78633535,\n"," 0.78633535, 0.78626788, 0.78626788, 0.7861737 , 0.78610343,\n"," 0.78610343, 0.78607887, 0.78605437, 0.78605437, 0.78605437,\n"," 0.78605437, 0.78605437, 0.78605437, 0.78605437, 0.78605437,\n"," 0.78605437, 0.78605437, 0.78605437, 0.78605437, 0.78605437,\n"," 0.78605437, 0.78605437, 0.78605437, 0.78605437, 0.78605437,\n"," 0.78605437, 0.78605437, 0.78605437, 0.78605437, 0.78605437,\n"," 0.78605437, 0.78605437, 0.78605437, 0.78605437, 0.78605437,\n"," 0.78605437, 0.78605437, 0.78605437, 0.78605437, 0.78605437,\n"," 0.78605437, 0.78605437, 0.78605437, 0.78605437, 0.78605437,\n"," 0.78605437, 0.78605437, 0.78605437, 0.78605437, 0.78605437,\n"," 0.78605437, 0.78605437, 0.78605437, 0.78604615, 0.7859211 ,\n"," 0.78591865, 0.78591865, 0.78581071, 0.78579915, 0.78574771,\n"," 0.78574771, 0.78574771, 0.78574771, 0.78567731, 0.78555799,\n"," 0.78555214, 0.7854777 , 0.78532618, 0.78530937, 0.78530514,\n"," 0.78530276, 0.78530276, 0.78518915, 0.78518915, 0.78518915,\n"," 0.78518915, 0.78518915, 0.78518915, 0.78518915, 0.78518564,\n"," 0.78495502, 0.78477883, 0.78474104, 0.78469163, 0.78469163,\n"," 0.78467059, 0.78467059, 0.78467059, 0.78449953, 0.78447455,\n"," 0.78444463, 0.78439045, 0.78437024, 0.78436214, 0.78432292,\n"," 0.7842958 , 0.7842958 , 0.78426337, 0.78425789, 0.78409201,\n"," 0.7840547 , 0.7840547 , 0.78399283, 0.78399283, 0.78399283,\n"," 0.78399283, 0.78399283, 0.78395253, 0.78395057, 0.78395057,\n"," 0.78392529, 0.78392529, 0.78388959, 0.78383368, 0.78383124,\n"," 0.78383124, 0.7837854 , 0.78374207, 0.78371173, 0.78371173,\n"," 0.78371173, 0.78371173, 0.78371173, 0.78371173, 0.78371173,\n"," 0.78371173, 0.78371173, 0.78371173, 0.78371173, 0.78371173,\n"," 0.78371173, 0.78348529, 0.78348529, 0.78345656, 0.78345424,\n"," 0.78345424, 0.78345424, 0.78342974, 0.78340518, 0.78340518,\n"," 0.78339243, 0.78339243, 0.78330231, 0.78328681, 0.78318727,\n"," 0.78318727, 0.78318727, 0.78318727, 0.78318006, 0.7830863 ,\n"," 0.78296256, 0.78284657, 0.7828204 , 0.78279203, 0.78276742,\n"," 0.78275412, 0.78271317, 0.78262466, 0.78259385, 0.78259385,\n"," 0.78254157, 0.78252244, 0.7824685 , 0.7824685 , 0.78244746,\n"," 0.78243476, 0.78238732, 0.78238732, 0.78234059, 0.78227007,\n"," 0.78223872, 0.7822215 , 0.78217888, 0.78217888, 0.78217888,\n"," 0.7821297 , 0.78210515, 0.78210217, 0.78210217, 0.78197449,\n"," 0.78195333, 0.78193468, 0.78191543, 0.78184694, 0.7817986 ,\n"," 0.78173673, 0.78172749, 0.78172749, 0.78172314, 0.78171134,\n"," 0.78168952, 0.78168952, 0.78151327, 0.78146744, 0.78146178,\n"," 0.78139514, 0.78136915, 0.78135049, 0.78123343, 0.78123343,\n"," 0.78123343, 0.78123343, 0.78123343, 0.78118813, 0.78115821,\n"," 0.78114021, 0.78113043, 0.78108722, 0.78105104, 0.78105104,\n"," 0.78105104, 0.78098643, 0.78097051, 0.78097051, 0.78097051,\n"," 0.78097051, 0.78097051, 0.78094429, 0.7808671 , 0.78086221,\n"," 0.78085887, 0.78078061, 0.78078061, 0.78074151, 0.78073943,\n"," 0.78061771, 0.78048593, 0.78040349, 0.78038061, 0.78036487,\n"," 0.78029871, 0.7802276 , 0.7802276 , 0.7802276 , 0.78019905,\n"," 0.7801702 , 0.7801702 , 0.7801041 , 0.78010309, 0.77999836,\n"," 0.77999836, 0.7799477 , 0.77994514, 0.77981716, 0.77981174,\n"," 0.77981174, 0.77980536, 0.7797755 , 0.77975136, 0.77972001,\n"," 0.77968109, 0.7796728 , 0.77958453, 0.77956098, 0.77950436,\n"," 0.77950436, 0.77950436, 0.77945602, 0.77945602, 0.77945602,\n"," 0.77943152, 0.77937269, 0.77929229, 0.77925736, 0.77922159,\n"," 0.77914947, 0.77909714, 0.77908289, 0.77908289, 0.77908289,\n"," 0.77890885, 0.7788828 , 0.77887809, 0.77884656, 0.77884561,\n"," 0.77884561, 0.77882123, 0.77881831, 0.77879864, 0.77876329,\n"," 0.77876329, 0.77875566, 0.77873802, 0.77872068, 0.77870846,\n"," 0.77868897, 0.77868897, 0.7786358 , 0.7785216 , 0.77851629,\n"," 0.77846259, 0.77846259, 0.77846259, 0.77844989, 0.77844197,\n"," 0.77844197, 0.77844197, 0.77844197, 0.77833277, 0.77832168,\n"," 0.77818745, 0.77817774, 0.77817535, 0.77817053, 0.77813143,\n"," 0.77813143, 0.77807152, 0.77802229, 0.77799773, 0.77796322,\n"," 0.77789474, 0.77789474, 0.77789241, 0.77788329, 0.77780509,\n"," 0.77778149, 0.77776122, 0.7777229 , 0.77770114, 0.77770007,\n"," 0.77763051, 0.77752823, 0.77752751, 0.77752107, 0.77749372,\n"," 0.77746379, 0.77743006, 0.77735776, 0.77734184, 0.77732474,\n"," 0.77728122, 0.77727932, 0.77727318, 0.77727318, 0.77727318,\n"," 0.77727318, 0.77727318, 0.77727318, 0.77727318, 0.77727318,\n"," 0.77727318, 0.77727318, 0.77727318, 0.77727318, 0.77727318,\n"," 0.77721798, 0.77721798, 0.77721709, 0.77715373, 0.7771427 ,\n"," 0.77714014, 0.77713805, 0.77713805, 0.77713501, 0.77703422,\n"," 0.77695984, 0.77695984, 0.77690876, 0.77686399, 0.77683145,\n"," 0.77678239, 0.77678239, 0.7767477 , 0.77666783, 0.77666783,\n"," 0.77663392, 0.77659208, 0.7765795 , 0.7765795 , 0.77657539,\n"," 0.77656728, 0.77655214, 0.77650028, 0.77647573, 0.77647573,\n"," 0.77641267, 0.77629322, 0.77629322, 0.77625006, 0.77622074,\n"," 0.77621984, 0.77618986, 0.77618837, 0.77618724, 0.77618569,\n"," 0.77618569, 0.77617383, 0.77608603, 0.77604616, 0.77592838,\n"," 0.77588928, 0.77588928, 0.7758342 , 0.77579916, 0.77579892,\n"," 0.77578896, 0.77578896, 0.77578896, 0.77578896, 0.77575058,\n"," 0.77574384, 0.77568054, 0.77567983, 0.77567983, 0.77564406,\n"," 0.77561414, 0.77555215, 0.77555215, 0.77555215, 0.77554113,\n"," 0.77549386, 0.7754823 , 0.7754823 , 0.77545524, 0.77540922,\n"," 0.77538103, 0.77538103, 0.77538103, 0.77530515, 0.77530515,\n"," 0.77524614, 0.77520305, 0.77516752, 0.7751078 , 0.77508897,\n"," 0.77506047, 0.77505815, 0.77505815, 0.77498221, 0.77498221,\n"," 0.77498221, 0.77498221, 0.77498221, 0.77488422, 0.77487546,\n"," 0.77485311, 0.77482003, 0.77481109, 0.77475882, 0.77469546,\n"," 0.77466208, 0.77462977, 0.77460366, 0.77457362, 0.77456176,\n"," 0.77456176, 0.77456176, 0.77451348, 0.77450216, 0.77448887,\n"," 0.77448887, 0.77448887, 0.77448887, 0.77445817, 0.77434373,\n"," 0.77433974, 0.77423418, 0.77420688, 0.77418232, 0.77418149,\n"," 0.77416509, 0.77415776, 0.77415776, 0.77410859, 0.77407008,\n"," 0.7740671 , 0.7740671 , 0.77406329, 0.77402943, 0.77402401,\n"," 0.77394253, 0.77383125, 0.7738207 , 0.7738207 , 0.7738021 ,\n"," 0.7738021 , 0.77377081, 0.77376533, 0.77374345, 0.7736519 ,\n"," 0.77361828, 0.77361453, 0.77361453, 0.77360433, 0.77350241,\n"," 0.77350056, 0.77344853, 0.77337408, 0.77332902, 0.77332669,\n"," 0.77331489, 0.77331299, 0.7732926 , 0.7732926 , 0.7732926 ,\n"," 0.7732926 , 0.7732926 , 0.7732926 , 0.77325469, 0.77325469,\n"," 0.77324373, 0.77324075, 0.77320969, 0.77320969, 0.77320969,\n"," 0.77320498, 0.77316433, 0.7730909 , 0.7730484 , 0.77298445,\n"," 0.77296269, 0.77296251, 0.77291179, 0.77290958, 0.77288151,\n"," 0.77285928, 0.77284843, 0.77284658, 0.77280867, 0.77275091,\n"," 0.77273989, 0.7726962 , 0.77266306, 0.77265912, 0.77261794,\n"," 0.77256864, 0.772551 , 0.77250206, 0.77250206, 0.77250206,\n"," 0.77247751, 0.77247751, 0.77247751, 0.7724756 , 0.77246004,\n"," 0.77244353, 0.77241361, 0.77240062, 0.77240062, 0.77239484,\n"," 0.77236193, 0.7723304 , 0.77221346, 0.77221346, 0.77216452,\n"," 0.77215087, 0.7721079 , 0.7721079 , 0.77209395, 0.77208591]),\n"," array([1.02109706, 1.02109706, 1.01435256, 1.01435256, 1.01435256,\n"," 1.01423132, 1.01423132, 1.00543964, 1.00543964, 1.00109005,\n"," 0.99869514, 0.99840319, 0.99807096, 0.99691278, 0.99523753,\n"," 0.99205601, 0.99163759, 0.98797542, 0.98622477, 0.98568857,\n"," 0.9854328 , 0.9854328 , 0.98263645, 0.98263645, 0.98263645,\n"," 0.98263645, 0.98263645, 0.98263645, 0.98153871, 0.98153871,\n"," 0.98139566, 0.98139566, 0.98139566, 0.98027492, 0.97957587,\n"," 0.978567 , 0.97852427, 0.97797543, 0.97729224, 0.9767592 ,\n"," 0.97670037, 0.97655743, 0.97622108, 0.97607785, 0.97601312,\n"," 0.97583544, 0.97549891, 0.97533011, 0.97507393, 0.97502714,\n"," 0.97496122, 0.97482824, 0.97383881, 0.97383153, 0.97370309,\n"," 0.97347009, 0.97321957, 0.97297138, 0.97275639, 0.97252035,\n"," 0.9712882 , 0.9709543 , 0.97062439, 0.97058463, 0.97044677,\n"," 0.97041243, 0.97033268, 0.97033268, 0.97010106, 0.96967065,\n"," 0.96938241, 0.96926856, 0.96915472, 0.96914744, 0.96914744,\n"," 0.96914744, 0.96914744, 0.96914744, 0.96914744, 0.96896631,\n"," 0.96895653, 0.96888947, 0.96886533, 0.96877891, 0.96875083,\n"," 0.96870661, 0.96836191, 0.96828389, 0.96803892, 0.96798456,\n"," 0.96774745, 0.96748859, 0.96700162, 0.96693957, 0.96688896,\n"," 0.96687055, 0.96665657, 0.9666546 , 0.96655697, 0.96639609,\n"," 0.96618146, 0.96604139, 0.96591723, 0.96591169, 0.96588129,\n"," 0.96585089, 0.96583128, 0.96581131, 0.96571255, 0.96566755,\n"," 0.96552849, 0.96551788, 0.96551472, 0.96543819, 0.96542579,\n"," 0.96538049, 0.96526599, 0.96517313, 0.96517092, 0.96515244,\n"," 0.96503556, 0.96497506, 0.96480769, 0.96436691, 0.96424311,\n"," 0.96414006, 0.96385825, 0.96358812, 0.96302086, 0.96286768,\n"," 0.96262944, 0.96262944, 0.96262944, 0.96262944, 0.96262944,\n"," 0.96249866, 0.96217221, 0.96217209, 0.96215647, 0.96212053,\n"," 0.96197534, 0.96191216, 0.96189761, 0.96178114, 0.96159446,\n"," 0.9615317 , 0.9615317 , 0.96140653, 0.96138877, 0.96138877,\n"," 0.96138877, 0.96137518, 0.96086526, 0.96086502, 0.96082264,\n"," 0.96082264, 0.96074897, 0.96069986, 0.96048468, 0.96047133,\n"," 0.96037793, 0.96035582, 0.96028507, 0.96015126, 0.96001965,\n"," 0.95991021, 0.95984972, 0.95982617, 0.95963579, 0.95956826,\n"," 0.95954937, 0.95946133, 0.95937228, 0.95935059, 0.95926082,\n"," 0.9591791 , 0.95913672, 0.95909536, 0.95900279, 0.95900083,\n"," 0.95894527, 0.95886666, 0.95884049, 0.95883024, 0.95876372,\n"," 0.95871854, 0.95851851, 0.95846653, 0.95836496, 0.95833665,\n"," 0.95814222, 0.95812774, 0.95796347, 0.95769328, 0.95751661,\n"," 0.95746893, 0.95735461, 0.9572798 , 0.95712709, 0.95704812,\n"," 0.956949 , 0.95684093, 0.95682573, 0.95681262, 0.95675027,\n"," 0.95667505, 0.95664853, 0.95664662, 0.95664525, 0.95654958,\n"," 0.95643711, 0.95640254, 0.95636922, 0.95630956, 0.95622921,\n"," 0.95621043, 0.95617497, 0.95615947, 0.95602769, 0.95600611,\n"," 0.95600611, 0.95600611, 0.9559862 , 0.9559564 , 0.95590013,\n"," 0.95586073, 0.95582342, 0.9557423 , 0.95569187, 0.95553654,\n"," 0.95542228, 0.955351 , 0.955329 , 0.95530611, 0.95524746,\n"," 0.95502269, 0.95502096, 0.95501953, 0.95483297, 0.9547556 ,\n"," 0.95467627, 0.95466602, 0.95455158, 0.95443439, 0.954427 ,\n"," 0.95433289, 0.95426404, 0.95423377, 0.95422846, 0.95418537,\n"," 0.95416856, 0.95407075, 0.95397222, 0.95396376, 0.95392972,\n"," 0.95382148, 0.95377827, 0.95367223, 0.95361125, 0.95361125,\n"," 0.95355457, 0.9534964 , 0.95343596, 0.95321262, 0.95317316,\n"," 0.95310897, 0.95308542, 0.95305359, 0.9530158 , 0.95299602,\n"," 0.95291507, 0.95285755, 0.95285082, 0.9528352 , 0.95267183,\n"," 0.95266277, 0.95265794, 0.95256329, 0.95252985, 0.9524976 ,\n"," 0.95248145, 0.952465 , 0.9523865 , 0.95220864, 0.95218039,\n"," 0.95208907, 0.95193553, 0.95188528, 0.9518438 , 0.95176631,\n"," 0.95165658, 0.95163095, 0.9515236 , 0.95150417, 0.95137262,\n"," 0.95135784, 0.95130193, 0.95129526, 0.95124048, 0.95123428,\n"," 0.95119184, 0.95117813, 0.95111829, 0.95108926, 0.95093614,\n"," 0.95093614, 0.9509033 , 0.95089734, 0.95088696, 0.95082647,\n"," 0.95081192, 0.95077837, 0.95073509, 0.95070106, 0.95067799,\n"," 0.95064092, 0.95056754, 0.95053631, 0.95050097, 0.95044768,\n"," 0.95043576, 0.95043576, 0.95041955, 0.95040393, 0.95032579,\n"," 0.95032579, 0.95032144, 0.95032144, 0.95032144, 0.95020449,\n"," 0.95020449, 0.95020086, 0.95015103, 0.95013613, 0.95008695,\n"," 0.95002574, 0.94993961, 0.94990635, 0.9499054 , 0.94988191,\n"," 0.94986987, 0.94985944, 0.94985867, 0.94985282, 0.94982654,\n"," 0.94981277, 0.94980127, 0.94971389, 0.94971389, 0.94967049,\n"," 0.94965953, 0.94960284, 0.94959807, 0.94948334, 0.94944483,\n"," 0.94944447, 0.94935751, 0.94925511, 0.94922996, 0.94922531,\n"," 0.94922161, 0.94922161, 0.94912136, 0.94909245, 0.94895124,\n"," 0.9489395 , 0.94888622, 0.94886762, 0.94886345, 0.94881475,\n"," 0.94881296, 0.94881296, 0.94880879, 0.94879168, 0.94878131,\n"," 0.94872278, 0.94872278, 0.94860846, 0.94857496, 0.94850743,\n"," 0.94847268, 0.94846326, 0.94839525, 0.94839412, 0.948394 ,\n"," 0.94838065, 0.94836491, 0.94835216, 0.94832218, 0.94829154,\n"," 0.948237 , 0.94821835, 0.94813561, 0.94803208, 0.94801599,\n"," 0.94800597, 0.94800597, 0.94793612, 0.94787443, 0.94780207,\n"," 0.94769853, 0.94767052, 0.94757491, 0.94754416, 0.94753021,\n"," 0.94748265, 0.94732058, 0.94729215, 0.94716799, 0.94711399,\n"," 0.94708741, 0.94708538, 0.94690287, 0.94688612, 0.94684082,\n"," 0.94676161, 0.94669527, 0.94666249, 0.9466536 , 0.94659829,\n"," 0.94659466, 0.94655657, 0.94654727, 0.94636452, 0.94636136,\n"," 0.94635409, 0.9463079 , 0.94630003, 0.94628537, 0.94626027,\n"," 0.94620121, 0.94619763, 0.94609106, 0.94602329, 0.94601583,\n"," 0.94594812, 0.94585568, 0.94579268, 0.94577712, 0.9457531 ,\n"," 0.9457531 , 0.94569427, 0.94564712, 0.94564521, 0.94551241,\n"," 0.94550258, 0.94548267, 0.94536912, 0.94519728, 0.94517142,\n"," 0.94508636, 0.94502461, 0.94497055, 0.94487768, 0.94487071,\n"," 0.94485962, 0.94485646, 0.94481957, 0.94478005, 0.94477862,\n"," 0.94473207, 0.94467908, 0.94466275, 0.94465077, 0.94455653,\n"," 0.94455034, 0.94454521, 0.9445371 , 0.94448698, 0.94448495,\n"," 0.9444595 , 0.94437367, 0.94434291, 0.94431162, 0.94429326,\n"," 0.94428933, 0.94425964, 0.94424349, 0.94419158, 0.94419158,\n"," 0.94418007, 0.94406641, 0.94404304, 0.94403547, 0.94400603,\n"," 0.94400126, 0.9439885 , 0.94397807, 0.94389331, 0.94388026,\n"," 0.94380552, 0.94380093, 0.9437567 , 0.94374734, 0.94368249,\n"," 0.94359195, 0.94357896, 0.94357276, 0.9435696 , 0.94352931,\n"," 0.94351816, 0.94348621, 0.94346011, 0.94338447, 0.94335604,\n"," 0.94334292, 0.94330108, 0.94326395, 0.94315404, 0.94314814,\n"," 0.94312847, 0.94312847, 0.94311506, 0.94311506, 0.94310468,\n"," 0.94305187, 0.94300199, 0.942949 , 0.94291502, 0.94285971,\n"," 0.94285619, 0.94285262, 0.94280756, 0.94280422, 0.94278216,\n"," 0.94277501, 0.94273603, 0.94271892, 0.94269222, 0.94263643,\n"," 0.9426043 , 0.94258219, 0.94254214, 0.94251776, 0.94247711,\n"," 0.94245201, 0.94244868, 0.94243133, 0.94235837, 0.94231266,\n"," 0.94226938, 0.94225341, 0.94223517, 0.9422099 , 0.94219828,\n"," 0.94208163, 0.94198483, 0.94197661, 0.94193685, 0.94187909,\n"," 0.94183725, 0.94182575, 0.9418174 , 0.94181347, 0.94180018,\n"," 0.94177896, 0.94173193, 0.9417209 , 0.94172019, 0.94166416,\n"," 0.94163334, 0.94162166, 0.94158274, 0.94155276, 0.94153416,\n"," 0.94152385, 0.94146508, 0.94145703, 0.94144797, 0.94144183,\n"," 0.94144118, 0.94131958, 0.94129103, 0.94128197, 0.94125062,\n"," 0.9412393 , 0.94123912, 0.94122487, 0.94116873, 0.94114524,\n"," 0.94111693, 0.94108558, 0.94097716, 0.94096011, 0.94091946,\n"," 0.94089895, 0.94088036, 0.94085509, 0.94085222, 0.94082695,\n"," 0.94081873, 0.94080561, 0.94077587, 0.94076252, 0.94075632,\n"," 0.94069642, 0.94069535, 0.94064426, 0.94061619, 0.94053519,\n"," 0.94051129, 0.94038796, 0.94035614, 0.9403488 , 0.94031596,\n"," 0.94031292, 0.9403035 , 0.9402619 , 0.94022763, 0.94022763,\n"," 0.94022763, 0.94022739, 0.94016743, 0.94015652, 0.94014633,\n"," 0.94008106, 0.94007057, 0.94006765, 0.94006515, 0.9400599 ,\n"," 0.93998957, 0.93996561, 0.93994844, 0.93993419, 0.93993247,\n"," 0.9398995 , 0.93987697, 0.93985623, 0.93978405, 0.93977976,\n"," 0.93969882, 0.93969363, 0.9396472 , 0.93959183, 0.93959057,\n"," 0.93957472, 0.93956822, 0.93955475, 0.93953997, 0.93940294,\n"," 0.93938214, 0.93936217, 0.93931496, 0.93931258, 0.93930447,\n"," 0.93928838, 0.93926638, 0.93920887, 0.93905807, 0.93904251,\n"," 0.93901813, 0.93901151, 0.93900859, 0.93893069, 0.93882662,\n"," 0.93874109, 0.93872154, 0.93865806, 0.93865281, 0.93864346,\n"," 0.93862659, 0.93859112, 0.93854487, 0.93849099, 0.93848926,\n"," 0.9384886 , 0.93847859, 0.9384433 , 0.93839419, 0.93837243,\n"," 0.93832099, 0.93831939, 0.93831939, 0.93828171, 0.93826926,\n"," 0.93821847, 0.93810999, 0.93808049, 0.93807417, 0.93806529,\n"," 0.93804789, 0.93804252, 0.93802917, 0.93801183, 0.93800074,\n"," 0.93798506, 0.93797302, 0.93794358, 0.93783432, 0.93779254,\n"," 0.9377836 , 0.93777251, 0.93764001, 0.93759936, 0.93758529,\n"," 0.93755245, 0.93755245, 0.93755245, 0.93755245, 0.93755245,\n"," 0.93753195, 0.93746322, 0.93744904, 0.93742687, 0.93740028,\n"," 0.93734729, 0.93733257, 0.93732947, 0.93730181, 0.93730181,\n"," 0.93730181, 0.93727744, 0.93726993, 0.9372586 , 0.93725067,\n"," 0.93723589, 0.93722576, 0.93720573, 0.93715173, 0.93710393,\n"," 0.93710041, 0.93709773, 0.93709272, 0.93703789, 0.93702978,\n"," 0.9370259 , 0.93697602, 0.9369573 , 0.93692786, 0.93691963,\n"," 0.93686539, 0.93685824, 0.93685335, 0.93683231, 0.9368192 ,\n"," 0.93681335, 0.93680012, 0.93678969, 0.936786 , 0.93678558,\n"," 0.9367578 , 0.93674922, 0.93668133, 0.93665063, 0.93664306,\n"," 0.93663037, 0.93658751, 0.9365381 , 0.93653023, 0.93649101,\n"," 0.93645471, 0.93640947, 0.93640423, 0.93639612, 0.93636048,\n"," 0.93632531, 0.93631178, 0.93631178, 0.93631178, 0.93631148,\n"," 0.93628931, 0.93627083, 0.93626499, 0.93620414, 0.93619865,\n"," 0.93615758, 0.93615466, 0.93612939, 0.93608111, 0.93607849,\n"," 0.93607342, 0.93607324, 0.93606389, 0.93604726, 0.93604714,\n"," 0.93598437, 0.93597418, 0.93595326, 0.93590826, 0.93590796,\n"," 0.93584168, 0.93581587, 0.93580365, 0.93579078, 0.93578237,\n"," 0.93575245, 0.93574375, 0.93573385, 0.9357326 , 0.93569773,\n"," 0.93569529, 0.93564767, 0.9356004 , 0.9355948 , 0.93553501,\n"," 0.93552154, 0.93548191, 0.93545079, 0.93540776, 0.93539709,\n"," 0.9353404 , 0.93525416, 0.93517804, 0.93515873, 0.93511879,\n"," 0.93510783, 0.93510211, 0.93508399, 0.93504751, 0.93503064,\n"," 0.93502188, 0.93500388, 0.93500149, 0.93497586, 0.93494385,\n"," 0.93493158, 0.9349156 , 0.93490219, 0.93489206, 0.93489093,\n"," 0.93485773, 0.93481117, 0.93474865, 0.93472695, 0.93472695,\n"," 0.93470991, 0.9346754 , 0.93466848, 0.9346683 , 0.93464047,\n"," 0.93460441, 0.93458271, 0.93457931, 0.9345755 , 0.93455476,\n"," 0.93453819, 0.93448299, 0.93447733, 0.9344061 , 0.93439096,\n"," 0.93435502, 0.93434119, 0.93424785, 0.93423569, 0.93423259,\n"," 0.93423045, 0.93417519, 0.93414754, 0.9341253 , 0.9340806 ,\n"," 0.93406397, 0.93404078, 0.9339835 , 0.93398273, 0.93395412,\n"," 0.93395382, 0.93392283, 0.93391263, 0.93385315, 0.93384713,\n"," 0.93381333, 0.93376559, 0.93372542, 0.93368632, 0.93359524,\n"," 0.93359435, 0.93358356, 0.93349874, 0.93348312, 0.93348312,\n"," 0.93348312, 0.93348312, 0.93348312, 0.93348312, 0.93345219,\n"," 0.93342048, 0.93340522, 0.93340504, 0.93338031, 0.93334466,\n"," 0.9333365 , 0.9333173 , 0.93327272, 0.93324983, 0.93324059,\n"," 0.9331876 , 0.93317878, 0.93314278, 0.93312931, 0.93304169,\n"," 0.93304026, 0.93300545, 0.93297976, 0.93297845, 0.93296582,\n"," 0.93295026, 0.9329378 , 0.93293697, 0.93293595, 0.93288046,\n"," 0.93283814, 0.93271893, 0.93265194, 0.93263537, 0.93258119,\n"," 0.93257493, 0.93253648, 0.93253338, 0.93241876, 0.93241823,\n"," 0.93240052, 0.93239903, 0.93235868, 0.93228841, 0.93227458,\n"," 0.93226576, 0.93224686, 0.93224275, 0.93217504, 0.93216646,\n"," 0.93204474, 0.93202621, 0.9320125 , 0.93195647, 0.93195456,\n"," 0.93194157, 0.93193573, 0.93192923, 0.93191636, 0.9318462 ,\n"," 0.9318406 , 0.93179256, 0.93178725, 0.93177974, 0.93174303,\n"," 0.93171334, 0.9317084 , 0.93169349, 0.9316619 , 0.93163109,\n"," 0.93161112, 0.93158376, 0.93157506, 0.9315632 , 0.93156177,\n"," 0.93154931, 0.93154573, 0.93154478, 0.93149781, 0.93143928,\n"," 0.93143821, 0.93142968, 0.93138468, 0.9313575 , 0.93132246,\n"," 0.93124658, 0.93122149, 0.93121344, 0.93116134, 0.93116069,\n"," 0.93114722, 0.93112439, 0.93111396, 0.93111062, 0.93107057,\n"," 0.93102986, 0.93102735, 0.93094003, 0.93093073, 0.93092924,\n"," 0.93092924, 0.93091142, 0.93088841, 0.9308508 , 0.93080795,\n"," 0.93080795, 0.93080795, 0.93080795, 0.9307996 , 0.93077427,\n"," 0.93073255, 0.93070751, 0.93067598, 0.93057573, 0.93056887,\n"," 0.93055779, 0.93050838, 0.93050653, 0.93049866, 0.93046474,\n"," 0.93045193, 0.93044108, 0.93040007, 0.93039715, 0.93038219,\n"," 0.93036747, 0.93036509, 0.93035573, 0.93033993, 0.93033367]),\n"," array([0.82445461, 0.82445461, 0.82445461, 0.82445461, 0.82445461,\n"," 0.82445461, 0.82445461, 0.82445461, 0.82445461, 0.81446624,\n"," 0.81234968, 0.81192631, 0.811589 , 0.81105775, 0.80920559,\n"," 0.8088873 , 0.80768472, 0.80768472, 0.80768472, 0.80768472,\n"," 0.80768472, 0.80768472, 0.80768472, 0.80768472, 0.80768472,\n"," 0.80768472, 0.80641806, 0.80641806, 0.80641806, 0.80641806,\n"," 0.80641806, 0.80641806, 0.80593282, 0.80548984, 0.80546492,\n"," 0.80544436, 0.80457217, 0.80399102, 0.80382949, 0.80253464,\n"," 0.80221808, 0.80189878, 0.80167931, 0.80138999, 0.80086863,\n"," 0.80054176, 0.80032021, 0.80013084, 0.80010325, 0.80008054,\n"," 0.79942781, 0.79884374, 0.79878461, 0.79878461, 0.79867768,\n"," 0.79807991, 0.79707205, 0.79696476, 0.79695034, 0.79688793,\n"," 0.79660332, 0.79641128, 0.79615355, 0.79526174, 0.79518342,\n"," 0.79517609, 0.79475862, 0.79449856, 0.79436189, 0.79431343,\n"," 0.79424894, 0.79341358, 0.79329634, 0.79316479, 0.79251152,\n"," 0.7921226 , 0.79169077, 0.79167795, 0.79156303, 0.79130369,\n"," 0.79089028, 0.79084349, 0.79077852, 0.79064864, 0.79064864,\n"," 0.79064864, 0.79053909, 0.79052228, 0.79047078, 0.79032809,\n"," 0.79032809, 0.7903052 , 0.79016966, 0.79016966, 0.78989148,\n"," 0.78982013, 0.78974169, 0.78967404, 0.78945941, 0.78938198,\n"," 0.78938198, 0.78938198, 0.78938198, 0.78938198, 0.78938198,\n"," 0.78938198, 0.78938198, 0.78938198, 0.78938198, 0.78938198,\n"," 0.78937089, 0.78927773, 0.78915352, 0.7891407 , 0.78912783,\n"," 0.78909576, 0.78889853, 0.78882897, 0.7887798 , 0.7887798 ,\n"," 0.78841788, 0.78817427, 0.78808403, 0.78806573, 0.78798544,\n"," 0.78771156, 0.7876721 , 0.78762811, 0.78751636, 0.78719777,\n"," 0.78710741, 0.78689009, 0.78674299, 0.78667825, 0.78662199,\n"," 0.7866106 , 0.78657305, 0.78636563, 0.78628564, 0.78622216,\n"," 0.785855 , 0.78581125, 0.78566051, 0.78561562, 0.78548372,\n"," 0.78507423, 0.78468019, 0.78466624, 0.78464943, 0.78457111,\n"," 0.78442383, 0.78441453, 0.78415555, 0.78384483, 0.78384119,\n"," 0.78370267, 0.78367954, 0.78342521, 0.78339767, 0.78337204,\n"," 0.78322059, 0.78312337, 0.7830255 , 0.78296107, 0.78287691,\n"," 0.78283185, 0.78248793, 0.78248739, 0.78245401, 0.78239328,\n"," 0.78238255, 0.7823379 , 0.78223008, 0.78201616, 0.78173989,\n"," 0.78144056, 0.78130984, 0.78117067, 0.78112417, 0.78112417,\n"," 0.78106618, 0.78092414, 0.78092009, 0.78090656, 0.7808457 ,\n"," 0.78082681, 0.78076977, 0.78076977, 0.78076977, 0.78075653,\n"," 0.78071302, 0.78057134, 0.7805326 , 0.7805137 , 0.78037208,\n"," 0.78031152, 0.78002292, 0.78001219, 0.7797336 , 0.77967149,\n"," 0.7796551 , 0.77952439, 0.77947974, 0.77925348, 0.77924937,\n"," 0.77918905, 0.77878332, 0.77876091, 0.77868891, 0.77861488,\n"," 0.77843601, 0.77843601, 0.77842176, 0.77839094, 0.77833521,\n"," 0.77823371, 0.77816862, 0.77807301, 0.77793616, 0.7778759 ,\n"," 0.7778641 , 0.7778641 , 0.77779847, 0.77768332, 0.77760404,\n"," 0.77759022, 0.77757406, 0.77742964, 0.77723378, 0.77720845,\n"," 0.77720642, 0.77716398, 0.77709955, 0.77708507, 0.77703309,\n"," 0.77680784, 0.77675074, 0.77672404, 0.7765767 , 0.77657145,\n"," 0.77657145, 0.7765246 , 0.77646369, 0.776411 , 0.77630281,\n"," 0.77626222, 0.77623028, 0.77621418, 0.77619302, 0.77619302,\n"," 0.77618915, 0.77618134, 0.77614057, 0.77608377, 0.77600831,\n"," 0.7757358 , 0.7757262 , 0.77557379, 0.77552563, 0.77545983,\n"," 0.77533424, 0.77524054, 0.77508652, 0.77508652, 0.77508652,\n"," 0.77508438, 0.77502054, 0.77501541, 0.77495795, 0.77492577,\n"," 0.77479148, 0.77469194, 0.77459544, 0.77457392, 0.77455747,\n"," 0.77455235, 0.77450037, 0.77435124, 0.77417076, 0.7741704 ,\n"," 0.77407479, 0.77399594, 0.77396554, 0.77393568, 0.77388781,\n"," 0.77388489, 0.77385741, 0.77385741, 0.7737571 , 0.77366585,\n"," 0.7736311 , 0.7736125 , 0.77355367, 0.77353835, 0.77353352,\n"," 0.77345598, 0.77333581, 0.77302074, 0.77292013, 0.77292013,\n"," 0.77291375, 0.77290505, 0.77288926, 0.7728281 , 0.77279359,\n"," 0.77270043, 0.77268159, 0.77265191, 0.77261204, 0.77261204,\n"," 0.77261204, 0.77261204, 0.77261204, 0.77261204, 0.77261204,\n"," 0.77261204, 0.77261204, 0.77257675, 0.77255684, 0.77255279,\n"," 0.77252626, 0.77247733, 0.7724306 , 0.77240753, 0.77229023,\n"," 0.77221495, 0.77215171, 0.77209365, 0.77207798, 0.7720508 ,\n"," 0.77191556, 0.77185655, 0.77181613, 0.77173579, 0.7717225 ,\n"," 0.77169579, 0.77159727, 0.77153814, 0.77150249, 0.77145445,\n"," 0.77141964, 0.77137738, 0.77134544, 0.77134544, 0.77134544,\n"," 0.77134544, 0.77134544, 0.77134544, 0.77134544, 0.77134544,\n"," 0.77134544, 0.77134544, 0.77134544, 0.77134544, 0.77134544,\n"," 0.77134544, 0.77134544, 0.77134544, 0.77134544, 0.77134544,\n"," 0.77134544, 0.77134544, 0.77130812, 0.77130294, 0.77128112,\n"," 0.77124447, 0.77114552, 0.77105272, 0.77082795, 0.77082795,\n"," 0.77079654, 0.77072984, 0.77071279, 0.77070051, 0.7706688 ,\n"," 0.77048963, 0.7704891 , 0.77046913, 0.77040273, 0.77020812,\n"," 0.77007949, 0.77004141, 0.76996356, 0.76994103, 0.76990873,\n"," 0.76989943, 0.76981354, 0.76973748, 0.76971519, 0.76961607,\n"," 0.76956218, 0.76949239, 0.76938444, 0.76938438, 0.76937753,\n"," 0.7692889 , 0.76923501, 0.76922405, 0.76910824, 0.76910824,\n"," 0.7691049 , 0.7691049 , 0.76904821, 0.76903313, 0.76903313,\n"," 0.76899362, 0.76878387, 0.76865357, 0.76865184, 0.76864225,\n"," 0.76848394, 0.76848394, 0.76848394, 0.76848394, 0.76848346,\n"," 0.76831639, 0.76830041, 0.7682851 , 0.7682851 , 0.7682851 ,\n"," 0.76827621, 0.76823586, 0.76817608, 0.76792526, 0.76783431,\n"," 0.76783431, 0.76774949, 0.76773697, 0.76760721, 0.76760721,\n"," 0.76758116, 0.76754999, 0.76752198, 0.76752198, 0.76751733,\n"," 0.76741105, 0.76736522, 0.76735783, 0.76735783, 0.76734805,\n"," 0.76734805, 0.76731348, 0.76731169, 0.76730829, 0.76721251,\n"," 0.76714873, 0.76713943, 0.76704425, 0.76696289, 0.76679713,\n"," 0.76674604, 0.76672006, 0.76671541, 0.76657104, 0.76653033,\n"," 0.76647228, 0.76647228, 0.76647228, 0.76626801, 0.7662642 ,\n"," 0.76619202, 0.76617271, 0.76617271, 0.76601809, 0.76599926,\n"," 0.76594478, 0.76592708, 0.76582319, 0.76581013, 0.76579952,\n"," 0.76576889, 0.76565009, 0.76565009, 0.76556075, 0.76549268,\n"," 0.76548374, 0.76536942, 0.76534259, 0.7653299 , 0.7653299 ,\n"," 0.76526231, 0.7651372 , 0.76512921, 0.76512879, 0.76512295,\n"," 0.76512051, 0.76510209, 0.76510209, 0.76505357, 0.76500809,\n"," 0.76482612, 0.76480001, 0.76472861, 0.76463801, 0.7645846 ,\n"," 0.76455855, 0.76455408, 0.76455408, 0.76451242, 0.76444507,\n"," 0.76439899, 0.76435107, 0.76433194, 0.76423889, 0.76419377,\n"," 0.76412833, 0.76410139, 0.76399076, 0.7638936 , 0.76384479,\n"," 0.76379418, 0.76378822, 0.76375508, 0.76375508, 0.76373851,\n"," 0.76373851, 0.76373279, 0.76368058, 0.76359397, 0.76359397,\n"," 0.763583 , 0.7635448 , 0.76348186, 0.7634756 , 0.76344299,\n"," 0.76344299, 0.7634275 , 0.7634275 , 0.76340407, 0.76338494,\n"," 0.76321834, 0.76316166, 0.76314199, 0.76313359, 0.7631042 ,\n"," 0.76307386, 0.76302958, 0.76298189, 0.76297742, 0.76297742,\n"," 0.76293117, 0.76281905, 0.76276177, 0.76264501, 0.76264501,\n"," 0.76264423, 0.76244503, 0.76243508, 0.76242661, 0.76242155,\n"," 0.7624166 , 0.76236069, 0.76231182, 0.76225322, 0.76223773,\n"," 0.76221299, 0.76218593, 0.7621085 , 0.76208425, 0.76206869,\n"," 0.76206869, 0.76204306, 0.76204187, 0.76200891, 0.76189691,\n"," 0.76189291, 0.76185739, 0.76183116, 0.76177156, 0.76157928,\n"," 0.76150352, 0.76148534, 0.76139432, 0.7613765 , 0.76133335,\n"," 0.76131737, 0.7611295 , 0.76105529, 0.76105154, 0.76101738,\n"," 0.76101738, 0.76101738, 0.76094168, 0.76091677, 0.76088989,\n"," 0.76085967, 0.76080257, 0.76079041, 0.76078171, 0.76077545,\n"," 0.76072246, 0.76071048, 0.76070482, 0.76060426, 0.76060104,\n"," 0.76060104, 0.76057196, 0.76053149, 0.76047677, 0.76043481,\n"," 0.76033658, 0.76033068, 0.76031554, 0.76026964, 0.76024079,\n"," 0.7602213 , 0.7601431 , 0.76007318, 0.76005912, 0.76005518,\n"," 0.76005262, 0.75994968, 0.75993925, 0.75993925, 0.75990772,\n"," 0.7599051 , 0.75989217, 0.75984478, 0.75981516, 0.75981116,\n"," 0.75980246, 0.75980246, 0.75980246, 0.75978518, 0.75970691,\n"," 0.7596572 , 0.75964528, 0.75963944, 0.75955856, 0.75955856,\n"," 0.75954592, 0.75953543, 0.75953269, 0.75953269, 0.75945812,\n"," 0.75945812, 0.75941402, 0.75916958, 0.75904489, 0.75901997,\n"," 0.75898641, 0.75898641, 0.75898135, 0.75898135, 0.75888848,\n"," 0.75888592, 0.75887889, 0.75883198, 0.75880027, 0.75873774,\n"," 0.75871068, 0.75868255, 0.75868112, 0.75862414, 0.75862348,\n"," 0.75862348, 0.75853401, 0.7584995 , 0.75836951, 0.75831658,\n"," 0.75831658, 0.75831658, 0.75831658, 0.75824487, 0.75822324,\n"," 0.75820142, 0.75817341, 0.75800514, 0.75799787, 0.75799745,\n"," 0.75775611, 0.75774741, 0.75773519, 0.75757384, 0.75752771,\n"," 0.75743443, 0.75743443, 0.7573998 , 0.7573998 , 0.75730455,\n"," 0.7572155 , 0.75718671, 0.75718671, 0.75718671, 0.75718671,\n"," 0.75718671, 0.75718671, 0.75713992, 0.75708467, 0.75704998,\n"," 0.75704998, 0.75694931, 0.75694931, 0.75683528, 0.75681782,\n"," 0.75680774, 0.75676727, 0.75670248, 0.75665087, 0.75662845,\n"," 0.75660062, 0.75660062, 0.75649679, 0.75643253, 0.75643253,\n"," 0.75642979, 0.75638735, 0.75635147, 0.75635147, 0.75627559,\n"," 0.75625944, 0.75625134, 0.75622928, 0.75620586, 0.75618416,\n"," 0.75613511, 0.75613075, 0.75608623, 0.75600916, 0.75596124,\n"," 0.75590861, 0.75590569, 0.75590014, 0.755808 , 0.75579029,\n"," 0.75579029, 0.75574076, 0.75572246, 0.7556836 , 0.7556529 ,\n"," 0.7556529 , 0.7556529 , 0.75563878, 0.75562906, 0.75560117,\n"," 0.75552392, 0.7555095 , 0.7555095 , 0.7554732 , 0.75536782,\n"," 0.75534362, 0.75534201, 0.7553317 , 0.75527728, 0.75527728,\n"," 0.75519466, 0.75519466, 0.75517446, 0.75514525, 0.75492555,\n"," 0.75492555, 0.75491774, 0.75471407, 0.75468987, 0.75465411,\n"," 0.75464851, 0.75462645, 0.75457644, 0.75447828, 0.7543093 ,\n"," 0.7543093 , 0.7543093 , 0.7543093 , 0.7543093 , 0.7543093 ,\n"," 0.7543093 , 0.7543093 , 0.7543093 , 0.7543093 , 0.7543093 ,\n"," 0.7543093 , 0.7543093 , 0.7543093 , 0.7543093 , 0.7543093 ,\n"," 0.7543093 , 0.7543093 , 0.7543093 , 0.75429976, 0.75422919,\n"," 0.75413394, 0.75413394, 0.7541163 , 0.7541123 , 0.75409496,\n"," 0.75409496, 0.75398022, 0.75394559, 0.75394541, 0.75392741,\n"," 0.75392741, 0.75392741, 0.75384468, 0.75381744, 0.75379783,\n"," 0.75378954, 0.75378692, 0.75367552, 0.75366122, 0.7536608 ,\n"," 0.7536608 , 0.75364053, 0.75362033, 0.75350124, 0.75350124,\n"," 0.75347334, 0.75342745, 0.75342584, 0.75341475, 0.75338346,\n"," 0.7533589 , 0.75327373, 0.75327373, 0.75323194, 0.75322133,\n"," 0.75320798, 0.75317329, 0.7531721 , 0.75314248, 0.75311321,\n"," 0.75311321, 0.75311321, 0.75302714, 0.75300795, 0.75292581,\n"," 0.75284666, 0.75276816, 0.75270069, 0.75269604, 0.75266194,\n"," 0.7526145 , 0.75256479, 0.75251627, 0.75251544, 0.75249034,\n"," 0.75248706, 0.75241089, 0.75239396, 0.75233966, 0.75232804,\n"," 0.75228339, 0.75220048, 0.75217724, 0.75217724, 0.75216508,\n"," 0.75209832, 0.75208604, 0.75202084, 0.75192535, 0.75191742,\n"," 0.75191575, 0.75187564, 0.75185591, 0.75175059, 0.7517162 ,\n"," 0.75170141, 0.75166094, 0.75160432, 0.75159895, 0.75159794,\n"," 0.75156289, 0.75153255, 0.75153124, 0.75152791, 0.7514655 ,\n"," 0.7514655 , 0.7514655 , 0.7514655 , 0.75138676, 0.75131601,\n"," 0.75130922, 0.75130922, 0.75130922, 0.75130922, 0.75128025,\n"," 0.75128025, 0.75124991, 0.75122619, 0.7512157 , 0.75118297,\n"," 0.75116765, 0.75116765, 0.75114375, 0.75112844, 0.75106859,\n"," 0.75104624, 0.75102806, 0.75098741, 0.75093162, 0.75093114,\n"," 0.75092119, 0.75090945, 0.75090116, 0.75089705, 0.7508738 ,\n"," 0.75086147, 0.75086105, 0.75080591, 0.75075704, 0.75068998,\n"," 0.75068998, 0.75066686, 0.75066686, 0.75066686, 0.75063884,\n"," 0.75063497, 0.75062746, 0.75059193, 0.75053173, 0.75051719,\n"," 0.75038886, 0.75036985, 0.75036985, 0.75024199, 0.75021845,\n"," 0.75021321, 0.75021321, 0.75021321, 0.75021321, 0.75021219,\n"," 0.75021219, 0.75016749, 0.75015759, 0.75014132, 0.75013268,\n"," 0.75012374, 0.75010854, 0.75009727, 0.75008041, 0.75005722,\n"," 0.75005722, 0.75005722, 0.75005722, 0.75005722, 0.75003588,\n"," 0.75002658, 0.74998564, 0.74998522, 0.74998146, 0.74995261,\n"," 0.74985254, 0.74981022, 0.7497856 , 0.74974763, 0.74956268,\n"," 0.74955797, 0.74955797, 0.74954307, 0.74947083, 0.74943352,\n"," 0.74943352, 0.74942982, 0.7494027 , 0.74936759, 0.74931949,\n"," 0.74930549, 0.74928904, 0.74927455, 0.74925786, 0.74924576,\n"," 0.74921471, 0.74921191, 0.7490806 , 0.74890554, 0.74890518,\n"," 0.74879253, 0.74879253, 0.74879253, 0.74878383, 0.74878073,\n"," 0.74873364, 0.74866629, 0.74862355, 0.7486136 , 0.74853742,\n"," 0.74853742, 0.74844688, 0.74843514, 0.74841928, 0.74841225,\n"," 0.74841225, 0.74840415, 0.74836844, 0.74834877, 0.74832898,\n"," 0.74832767, 0.74830556, 0.74827468, 0.74816388, 0.7481333 ,\n"," 0.74805969, 0.74803913, 0.74795681, 0.74794394, 0.74785459,\n"," 0.74784577, 0.74779469, 0.74776888, 0.74775726, 0.74768573]),\n"," array([ 0.11844413, 0.09660473, 0.06089146, 0.05539076, 0.04944766,\n"," 0.03160222, 0.01994678, 0.01865607, 0.01010864, 0.00608411,\n"," 0.00608411, 0.00608411, 0.00608411, 0.00608411, 0.0036988 ,\n"," 0.00250615, 0.00250615, 0.00250615, 0.00236699, 0.00124047,\n"," 0.000356 , -0.00023391, -0.00034034, -0.0008908 , -0.00217705,\n"," -0.00260212, -0.00306144, -0.00394677, -0.00488438, -0.00512409,\n"," -0.0052064 , -0.00577518, -0.00579939, -0.00589417, -0.00594369,\n"," -0.00650051, -0.0067357 , -0.00691772, -0.00722598, -0.00739508,\n"," -0.00892353, -0.00941984, -0.00976786, -0.01173659, -0.01261895,\n"," -0.01320209, -0.01415533, -0.01416909, -0.01498224, -0.01522556,\n"," -0.01542283, -0.01566752, -0.01578484, -0.01580916, -0.01599546,\n"," -0.01622158, -0.01678032, -0.0169607 , -0.0172176 , -0.01726815,\n"," -0.01768958, -0.01768958, -0.01920426, -0.01920426, -0.01926534,\n"," -0.01945416, -0.01963231, -0.01974611, -0.01994399, -0.02057978,\n"," -0.02066141, -0.02070962, -0.02070962, -0.02089505, -0.02120168,\n"," -0.02155962, -0.02167313, -0.02192399, -0.02205075, -0.02205075,\n"," -0.02236531, -0.0224959 , -0.02271246, -0.02280045, -0.02280045,\n"," -0.0228265 , -0.02306053, -0.02332251, -0.02368039, -0.0237483 ,\n"," -0.02437858, -0.02457521, -0.02461893, -0.02461893, -0.02475133,\n"," -0.02527176, -0.02557903, -0.02608989, -0.02621142, -0.02622905,\n"," -0.02622905, -0.02622905, -0.02622905, -0.02622905, -0.02622905,\n"," -0.02622905, -0.02622905, -0.02622905, -0.02622905, -0.02622905,\n"," -0.02622905, -0.02638438, -0.02638634, -0.02641192, -0.02641192,\n"," -0.02641916, -0.02654673, -0.02660719, -0.02675068, -0.02682137,\n"," -0.02709968, -0.02710507, -0.02718146, -0.02737443, -0.02740794,\n"," -0.02742348, -0.02746542, -0.02746542, -0.02748092, -0.02750913,\n"," -0.02757704, -0.02760457, -0.02767879, -0.0279266 , -0.02798556,\n"," -0.02805869, -0.02818369, -0.02822499, -0.0283061 , -0.02843149,\n"," -0.02843909, -0.02846292, -0.02846681, -0.02858683, -0.02860108,\n"," -0.02879723, -0.02879723, -0.02879723, -0.02879723, -0.02894842,\n"," -0.02902314, -0.02905042, -0.02905468, -0.02911926, -0.02911926,\n"," -0.02916984, -0.02916984, -0.02930212, -0.02940896, -0.02962248,\n"," -0.02969551, -0.02974954, -0.02992992, -0.02994617, -0.03003267,\n"," -0.03021454, -0.03031191, -0.03063393, -0.03104784, -0.03141208,\n"," -0.03147316, -0.03148643, -0.03153221, -0.03205224, -0.03208082,\n"," -0.03209354, -0.03211121, -0.03211121, -0.03221125, -0.0325296 ,\n"," -0.03274 , -0.0328203 , -0.03294228, -0.0330345 , -0.03310785,\n"," -0.0332348 , -0.03323651, -0.03324482, -0.03329121, -0.03342038,\n"," -0.03370693, -0.03375666, -0.03397018, -0.03401028, -0.03402213,\n"," -0.03428131, -0.03434484, -0.03434657, -0.03448679, -0.03456833,\n"," -0.03486133, -0.03506316, -0.03527972, -0.03529349, -0.03529349,\n"," -0.03530336, -0.03531427, -0.03536084, -0.03536084, -0.03536084,\n"," -0.03548359, -0.0354977 , -0.03551766, -0.03561681, -0.03566802,\n"," -0.03573767, -0.03578363, -0.03618776, -0.03637649, -0.03650978,\n"," -0.03650978, -0.03658103, -0.03668278, -0.03669265, -0.03675162,\n"," -0.03675162, -0.03675162, -0.03687968, -0.03713799, -0.037258 ,\n"," -0.03731696, -0.0373367 , -0.03738041, -0.0373814 , -0.03754883,\n"," -0.03760139, -0.03765665, -0.03768893, -0.03778329, -0.03784939,\n"," -0.03788623, -0.03793737, -0.03807378, -0.03819356, -0.03820733,\n"," -0.03824106, -0.03828374, -0.0383224 , -0.03843688, -0.03845128,\n"," -0.03845398, -0.03853325, -0.03871223, -0.03871223, -0.03871223,\n"," -0.03871223, -0.03871223, -0.03875753, -0.03881368, -0.03881368,\n"," -0.03887469, -0.03899685, -0.03910791, -0.03913003, -0.03914379,\n"," -0.03921712, -0.03926015, -0.03931887, -0.03934251, -0.0393464 ,\n"," -0.0394329 , -0.0394441 , -0.03966663, -0.03966855, -0.03978927,\n"," -0.03987805, -0.03988273, -0.040103 , -0.04019425, -0.04030112,\n"," -0.04037831, -0.04069439, -0.04070426, -0.04070426, -0.04070426,\n"," -0.04070426, -0.04070426, -0.0407318 , -0.04092005, -0.04097512,\n"," -0.04105382, -0.04118701, -0.04123669, -0.04126849, -0.04126849,\n"," -0.04136626, -0.04138754, -0.04143481, -0.04143871, -0.04155493,\n"," -0.04162206, -0.04162306, -0.04163535, -0.04163535, -0.04166288,\n"," -0.04172781, -0.04174158, -0.04191762, -0.04199028, -0.04204101,\n"," -0.0421127 , -0.04229706, -0.04231784, -0.04244849, -0.04252813,\n"," -0.04261396, -0.04275137, -0.04275137, -0.04275137, -0.04291598,\n"," -0.04291747, -0.04291747, -0.04295836, -0.04303911, -0.04303911,\n"," -0.04306353, -0.0432425 , -0.0432425 , -0.04337568, -0.04337568,\n"," -0.04343664, -0.04366163, -0.04376116, -0.04385304, -0.04385304,\n"," -0.04386681, -0.04390811, -0.04393635, -0.0440372 , -0.04408318,\n"," -0.04421034, -0.04430976, -0.04433139, -0.04439403, -0.04459249,\n"," -0.04468088, -0.04477094, -0.04478055, -0.04478504, -0.04479369,\n"," -0.0448954 , -0.0449458 , -0.04518048, -0.04518243, -0.0452896 ,\n"," -0.04538538, -0.04538538, -0.04538538, -0.04562283, -0.04562283,\n"," -0.04563405, -0.04574749, -0.04605896, -0.04609677, -0.04614647,\n"," -0.04618087, -0.04621679, -0.04626197, -0.0462988 , -0.04631327,\n"," -0.04646849, -0.04660765, -0.04663467, -0.0466353 , -0.04671043,\n"," -0.04676298, -0.04676298, -0.0469276 , -0.04695825, -0.04709877,\n"," -0.04721811, -0.04723585, -0.0472937 , -0.04729482, -0.04729541,\n"," -0.04729914, -0.04730921, -0.04732094, -0.04732294, -0.04744626,\n"," -0.04761691, -0.04765815, -0.04771321, -0.0478003 , -0.0478003 ,\n"," -0.04781955, -0.04793946, -0.04804752, -0.04811421, -0.04830519,\n"," -0.04831058, -0.04836279, -0.04836286, -0.04836408, -0.04858412,\n"," -0.0486817 , -0.04876897, -0.0488056 , -0.04881009, -0.04885465,\n"," -0.04886067, -0.04889359, -0.04903586, -0.04906147, -0.04906673,\n"," -0.04907871, -0.04913211, -0.04915189, -0.04917919, -0.04920938,\n"," -0.04930353, -0.04931498, -0.04932247, -0.04940241, -0.0494796 ,\n"," -0.04954353, -0.04958895, -0.04963612, -0.04985279, -0.04988961,\n"," -0.04996526, -0.05006723, -0.05008624, -0.05012754, -0.05012991,\n"," -0.0502068 , -0.05028047, -0.05032922, -0.05043431, -0.05049157,\n"," -0.05050689, -0.05055513, -0.05055513, -0.05064378, -0.05068899,\n"," -0.05068899, -0.05072461, -0.05082966, -0.0508607 , -0.05087411,\n"," -0.05095779, -0.05098815, -0.05101253, -0.05109487, -0.0511472 ,\n"," -0.0512017 , -0.0512017 , -0.0512017 , -0.0512337 , -0.05133455,\n"," -0.05147371, -0.05147553, -0.0514969 , -0.05157936, -0.05163758,\n"," -0.05171011, -0.05175195, -0.05183944, -0.05183944, -0.05183944,\n"," -0.05195567, -0.05196661, -0.0519825 , -0.05199627, -0.05199879,\n"," -0.05202232, -0.05202232, -0.05202232, -0.05202232, -0.05202232,\n"," -0.05202232, -0.05203036, -0.05203798, -0.05207828, -0.05208517,\n"," -0.05224233, -0.0522818 , -0.05233596, -0.05254636, -0.05257722,\n"," -0.05262834, -0.05267954, -0.05269339, -0.05275286, -0.05282709,\n"," -0.05291896, -0.05293176, -0.0529878 , -0.0530743 , -0.0530934 ,\n"," -0.05319623, -0.05322437, -0.0532949 , -0.05333913, -0.05335413,\n"," -0.05335413, -0.05337234, -0.05341006, -0.05343053, -0.053537 ,\n"," -0.05358906, -0.05362535, -0.05377015, -0.05385843, -0.05397583,\n"," -0.0540188 , -0.0540387 , -0.05404189, -0.05404189, -0.05404189,\n"," -0.05404189, -0.05404189, -0.05404189, -0.05404189, -0.05404189,\n"," -0.05405265, -0.05405607, -0.054121 , -0.054121 , -0.05413952,\n"," -0.05417656, -0.05426306, -0.05439149, -0.05454678, -0.05464015,\n"," -0.05466215, -0.05474326, -0.05476237, -0.05478965, -0.05479414,\n"," -0.05483842, -0.05496429, -0.05499694, -0.05500331, -0.05518807,\n"," -0.05524752, -0.0552498 , -0.0553737 , -0.05538331, -0.055388 ,\n"," -0.05539022, -0.05545174, -0.05548325, -0.05558855, -0.0556722 ,\n"," -0.05570266, -0.05573829, -0.05573944, -0.05587818, -0.05593392,\n"," -0.05607215, -0.05616092, -0.05619854, -0.0563377 , -0.05638348,\n"," -0.05638348, -0.05638348, -0.05639374, -0.05642731, -0.05644279,\n"," -0.05652958, -0.05657222, -0.05658958, -0.05664198, -0.05669116,\n"," -0.05672799, -0.05687339, -0.05687461, -0.05688838, -0.05701714,\n"," -0.05705838, -0.05709318, -0.05709612, -0.05709968, -0.05710417,\n"," -0.05718929, -0.0572104 , -0.05729894, -0.05731661, -0.05739327,\n"," -0.05746152, -0.05766113, -0.05766113, -0.05766113, -0.05766113,\n"," -0.05766113, -0.05766113, -0.05766113, -0.05766113, -0.05766113,\n"," -0.05766113, -0.05766113, -0.05766113, -0.05766113, -0.05766113,\n"," -0.05766113, -0.05766113, -0.05766113, -0.05768795, -0.0577153 ,\n"," -0.05772848, -0.05783861, -0.05793056, -0.05795316, -0.05798825,\n"," -0.0580092 , -0.05803391, -0.0580544 , -0.05808103, -0.05808103,\n"," -0.0581585 , -0.05819144, -0.05821868, -0.05823976, -0.0582609 ,\n"," -0.05830965, -0.05840306, -0.05842862, -0.05852871, -0.05854047,\n"," -0.05854222, -0.05854222, -0.05854222, -0.05854222, -0.05854222,\n"," -0.05854222, -0.05854222, -0.05854222, -0.05854222, -0.05854222,\n"," -0.05854222, -0.05854222, -0.05854222, -0.05854222, -0.05854222,\n"," -0.05854222, -0.05854222, -0.05854222, -0.05854222, -0.05854222,\n"," -0.05857766, -0.05858593, -0.05859093, -0.05860152, -0.05861368,\n"," -0.05862118, -0.05871077, -0.0587245 , -0.05879028, -0.05884659,\n"," -0.05884659, -0.05887444, -0.05890795, -0.05891581, -0.05911046,\n"," -0.0591519 , -0.0591565 , -0.05923175, -0.05924705, -0.05929332,\n"," -0.05929332, -0.05934101, -0.05958284, -0.05958639, -0.05959571,\n"," -0.05960191, -0.05962748, -0.05974854, -0.05985198, -0.05987829,\n"," -0.06004482, -0.06005631, -0.06006366, -0.06011437, -0.06011437,\n"," -0.06012904, -0.06012904, -0.06024307, -0.06024307, -0.06024937,\n"," -0.06026648, -0.06038493, -0.06043043, -0.06051384, -0.06051726,\n"," -0.06055368, -0.06061926, -0.06061926, -0.06061926, -0.06063419,\n"," -0.06064339, -0.06074466, -0.06074818, -0.06079914, -0.06080232,\n"," -0.06081341, -0.06084582, -0.06108376, -0.06112416, -0.06119782,\n"," -0.06143183, -0.06145502, -0.06159852, -0.06160709, -0.0616883 ,\n"," -0.06182659, -0.06184094, -0.06187008, -0.06193731, -0.06193731,\n"," -0.06193731, -0.06196911, -0.06212018, -0.06212018, -0.06212018,\n"," -0.06213395, -0.06219959, -0.06226265, -0.06236213, -0.06238265,\n"," -0.06246766, -0.06248591, -0.06250061, -0.06252725, -0.06255383,\n"," -0.06272411, -0.06275726, -0.06276423, -0.06284709, -0.06287826,\n"," -0.06293333, -0.06293333, -0.0629471 , -0.0629471 , -0.06307249,\n"," -0.06312065, -0.06320209, -0.06326912, -0.06328373, -0.06337409,\n"," -0.06340026, -0.0634082 , -0.06343823, -0.06345199, -0.06345785,\n"," -0.06355882, -0.06368826, -0.06378633, -0.06386051, -0.06386051,\n"," -0.06394312, -0.06394312, -0.06395689, -0.06399135, -0.06400245,\n"," -0.06400356, -0.06412689, -0.06413174, -0.06413975, -0.06413975,\n"," -0.06415442, -0.06428819, -0.06432262, -0.06432544, -0.06434802,\n"," -0.06438651, -0.06446178, -0.06446809, -0.06462804, -0.06468925,\n"," -0.06469021, -0.06472692, -0.06480628, -0.06486024, -0.0648712 ,\n"," -0.0649179 , -0.06494522, -0.06496667, -0.06496667, -0.06496667,\n"," -0.06496667, -0.06496667, -0.06498044, -0.06502654, -0.06509268,\n"," -0.06510583, -0.06511154, -0.06511446, -0.06512304, -0.06512364,\n"," -0.06513306, -0.06516508, -0.06516508, -0.06519934, -0.06521332,\n"," -0.06522056, -0.06526183, -0.06528869, -0.06530577, -0.06537234,\n"," -0.06545379, -0.06546491, -0.06547157, -0.06548017, -0.06567358,\n"," -0.06571099, -0.06581162, -0.06582502, -0.06583926, -0.06588101,\n"," -0.06589534, -0.06597646, -0.06597646, -0.06597646, -0.06598046,\n"," -0.06598339, -0.06603487, -0.06606386, -0.06606663, -0.06614646,\n"," -0.06614646, -0.06618129, -0.06623069, -0.06627452, -0.0662979 ,\n"," -0.06629848, -0.06629848, -0.06631556, -0.06631614, -0.06634583,\n"," -0.06634798, -0.06636973, -0.06638663, -0.06648135, -0.06648319,\n"," -0.06651278, -0.06654991, -0.06655408, -0.06655946, -0.06660353,\n"," -0.0666514 , -0.06666422, -0.0666851 , -0.06680337, -0.06680337,\n"," -0.06680727, -0.06682193, -0.06683941, -0.06687067, -0.06695007,\n"," -0.06702765, -0.0670739 , -0.06712376, -0.06725161, -0.0672945 ,\n"," -0.06730827, -0.06731158, -0.06731643, -0.06736723, -0.0674262 ,\n"," -0.06744435, -0.06749114, -0.06750149, -0.06757912, -0.06759503,\n"," -0.06760458, -0.06760458, -0.0676183 , -0.06763813, -0.067674 ,\n"," -0.06767493, -0.06768925, -0.06771438, -0.06771772, -0.06774764,\n"," -0.06774885, -0.0677526 , -0.06776737, -0.06782693, -0.06799062,\n"," -0.06801459, -0.06812532, -0.06815653, -0.06816606, -0.0681789 ,\n"," -0.06821123, -0.0682146 , -0.06827372, -0.06830889, -0.06831805,\n"," -0.06832499, -0.06833661, -0.06836785, -0.06848073, -0.06848716,\n"," -0.06848716, -0.06850092, -0.06855485, -0.06858411, -0.06864892,\n"," -0.06867291, -0.06872339, -0.06873082, -0.06873187, -0.06882295,\n"," -0.06892599, -0.06892599, -0.06901008, -0.06901061, -0.06905045,\n"," -0.06908667, -0.06918868, -0.06918868, -0.06919837, -0.06920497,\n"," -0.06925132, -0.06925432, -0.06927396, -0.06932784, -0.06935032,\n"," -0.06940388, -0.0694062 , -0.06949694, -0.06951071, -0.06951071,\n"," -0.0696568 , -0.06965766, -0.06965766, -0.06968988, -0.06969316,\n"," -0.06969358, -0.06969689, -0.06969689, -0.06972716, -0.0699782 ,\n"," -0.07000273, -0.0700156 , -0.07007771, -0.07008114, -0.07008923,\n"," -0.07008923, -0.07010329, -0.07013386, -0.07013643, -0.07015187,\n"," -0.07019847, -0.07020327, -0.07021224, -0.07023139, -0.07031399,\n"," -0.07033581, -0.0703601 , -0.07039749, -0.07042268, -0.07046543,\n"," -0.0705205 , -0.0705205 , -0.0705205 , -0.07056313, -0.07058039,\n"," -0.0705969 , -0.0705969 , -0.0705969 , -0.07065511, -0.07069397,\n"," -0.07070336, -0.07076381, -0.07076381, -0.07076381, -0.07080331,\n"," -0.07080331, -0.07084252, -0.07084252, -0.07084252, -0.07088277,\n"," -0.07091377, -0.07091377, -0.07101946, -0.07102539, -0.07102539,\n"," -0.07106754, -0.07109902, -0.07110243, -0.07112684, -0.07114955,\n"," -0.07120826, -0.07120826, -0.07120826, -0.07123461, -0.07125827]),\n"," array([0.35529953, 0.33367708, 0.33367708, 0.33367708, 0.33222875,\n"," 0.32887942, 0.32743704, 0.32722399, 0.32660159, 0.32658222,\n"," 0.32546347, 0.32546347, 0.32542107, 0.32529646, 0.32529646,\n"," 0.32529646, 0.32529646, 0.32475951, 0.3239986 , 0.32265151,\n"," 0.3225477 , 0.32203707, 0.32131475, 0.32019743, 0.3200748 ,\n"," 0.31928921, 0.31928661, 0.31928661, 0.31905583, 0.31855744,\n"," 0.31855744, 0.31855744, 0.31855744, 0.31855744, 0.31855744,\n"," 0.31855744, 0.31855744, 0.31855744, 0.31855744, 0.31855744,\n"," 0.31855744, 0.31855744, 0.31855744, 0.31855744, 0.31855744,\n"," 0.31855744, 0.31855744, 0.31855744, 0.31855744, 0.31851646,\n"," 0.31822622, 0.31805563, 0.31776437, 0.31752002, 0.31722468,\n"," 0.31721744, 0.31717554, 0.31717268, 0.31681871, 0.31654018,\n"," 0.31632322, 0.31543908, 0.31534189, 0.31511232, 0.31475222,\n"," 0.31464964, 0.31418946, 0.31410661, 0.31407109, 0.31404051,\n"," 0.31380913, 0.31352371, 0.31347525, 0.3132728 , 0.31319454,\n"," 0.3131783 , 0.31313932, 0.31313905, 0.31309581, 0.31292939,\n"," 0.31289479, 0.31287864, 0.31281471, 0.31260228, 0.31246457,\n"," 0.3123925 , 0.3123925 , 0.3123925 , 0.3123925 , 0.3123925 ,\n"," 0.31219137, 0.31211367, 0.31210107, 0.31189877, 0.3116971 ,\n"," 0.31162095, 0.31148785, 0.31141943, 0.31141603, 0.31133917,\n"," 0.31090862, 0.3107498 , 0.31064683, 0.31059736, 0.31034702,\n"," 0.31034383, 0.31034383, 0.31034383, 0.31034383, 0.31034383,\n"," 0.31029278, 0.31021649, 0.31020802, 0.31020802, 0.31017679,\n"," 0.31017679, 0.31017679, 0.31017679, 0.31011274, 0.31010759,\n"," 0.3100431 , 0.31002524, 0.31 , 0.30998591, 0.30997166,\n"," 0.30993348, 0.30991697, 0.30991325, 0.30978161, 0.30971771,\n"," 0.30969822, 0.30966035, 0.30957544, 0.30940592, 0.30939803,\n"," 0.30932179, 0.30918407, 0.30912891, 0.30911016, 0.30897096,\n"," 0.30892721, 0.30890077, 0.30866763, 0.30866548, 0.30864647,\n"," 0.30848888, 0.30848223, 0.30848223, 0.30834138, 0.30829358,\n"," 0.30825523, 0.30821031, 0.30783048, 0.30771297, 0.307702 ,\n"," 0.30769914, 0.30769914, 0.30769914, 0.30769914, 0.30769414,\n"," 0.30767041, 0.30759168, 0.30757311, 0.307542 , 0.30754137,\n"," 0.30749807, 0.30748504, 0.30744699, 0.3074162 , 0.30739942,\n"," 0.30732554, 0.30726287, 0.30721363, 0.30710441, 0.30706123,\n"," 0.30694896, 0.30690885, 0.30683252, 0.30679083, 0.30667973,\n"," 0.30667874, 0.30664787, 0.30664191, 0.30663636, 0.30654916,\n"," 0.30650145, 0.30650145, 0.30649695, 0.30648711, 0.30647808,\n"," 0.30642745, 0.30618894, 0.30617085, 0.30615422, 0.30613914,\n"," 0.30609348, 0.30607474, 0.30604368, 0.30596456, 0.30595747,\n"," 0.30591732, 0.30591494, 0.30588287, 0.30580893, 0.30547377,\n"," 0.30546319, 0.30544654, 0.30541608, 0.30536664, 0.30532786,\n"," 0.30503899, 0.30503583, 0.30502531, 0.30502021, 0.30482522,\n"," 0.30481747, 0.30476969, 0.30476758, 0.30465186, 0.30465075,\n"," 0.30464172, 0.30456755, 0.304533 , 0.30452675, 0.30450946,\n"," 0.30447531, 0.30436707, 0.30434796, 0.30425194, 0.30423105,\n"," 0.30421159, 0.30406493, 0.30405259, 0.30404606, 0.30404568,\n"," 0.30402291, 0.3040061 , 0.3040061 , 0.3039223 , 0.30388838,\n"," 0.30384102, 0.30384102, 0.30384102, 0.30384102, 0.30384102,\n"," 0.30384102, 0.30384102, 0.30370563, 0.30367401, 0.30367401,\n"," 0.30367401, 0.30367401, 0.30367401, 0.30367401, 0.30367401,\n"," 0.30367401, 0.30367401, 0.30367401, 0.30367401, 0.30367401,\n"," 0.30367401, 0.30367401, 0.30367401, 0.30367401, 0.30367401,\n"," 0.30367401, 0.30367401, 0.30367401, 0.30367401, 0.30367401,\n"," 0.30367401, 0.30367401, 0.30367401, 0.30367401, 0.30367032,\n"," 0.30357644, 0.30357045, 0.30340153, 0.30333161, 0.3032161 ,\n"," 0.30319247, 0.30307433, 0.30302173, 0.30301651, 0.30300987,\n"," 0.30295238, 0.30288702, 0.3027727 , 0.30275619, 0.30273154,\n"," 0.30270943, 0.30267128, 0.30265123, 0.30263373, 0.30262214,\n"," 0.30260497, 0.30249014, 0.30241755, 0.30241755, 0.30241755,\n"," 0.30231225, 0.30231225, 0.30231225, 0.30231225, 0.30231225,\n"," 0.30230999, 0.30223313, 0.30222461, 0.30222061, 0.30221909,\n"," 0.30218902, 0.30212021, 0.30204329, 0.3020038 , 0.3019923 ,\n"," 0.301967 , 0.30191559, 0.30191329, 0.30188823, 0.30185005,\n"," 0.30182645, 0.30182454, 0.30180922, 0.30170411, 0.30170411,\n"," 0.30170304, 0.30152389, 0.30149302, 0.30146843, 0.30144837,\n"," 0.30142963, 0.30141291, 0.3014034 , 0.30131626, 0.30130899,\n"," 0.30128384, 0.3012659 , 0.30125004, 0.30125004, 0.30116671,\n"," 0.30114898, 0.30110627, 0.30110565, 0.30106193, 0.30105332,\n"," 0.30103919, 0.30102792, 0.30082962, 0.30080768, 0.30078927,\n"," 0.30077565, 0.30072081, 0.30071807, 0.30070734, 0.30063918,\n"," 0.30062005, 0.30060655, 0.3005836 , 0.30057383, 0.30056581,\n"," 0.30056581, 0.30054769, 0.30050042, 0.30045494, 0.30043533,\n"," 0.30043533, 0.30040449, 0.30039605, 0.30037901, 0.30035979,\n"," 0.30032369, 0.30032238, 0.30030176, 0.30028379, 0.30026254,\n"," 0.30023304, 0.30022159, 0.30022159, 0.30018935, 0.30011466,\n"," 0.30011073, 0.30004212, 0.29995862, 0.29993615, 0.29993615,\n"," 0.29988474, 0.29987144, 0.299835 , 0.29978934, 0.29971278,\n"," 0.29965577, 0.29965034, 0.29965034, 0.29964876, 0.29964492,\n"," 0.29963186, 0.29957932, 0.29944959, 0.29938418, 0.29935595,\n"," 0.29931247, 0.29930353, 0.29925761, 0.29925334, 0.29922771,\n"," 0.29921106, 0.29919764, 0.29919371, 0.29919371, 0.29918027,\n"," 0.29916337, 0.29915041, 0.29913056, 0.29912052, 0.29907724,\n"," 0.29903537, 0.29899624, 0.29892546, 0.29888791, 0.29883489,\n"," 0.29877469, 0.29875514, 0.29873684, 0.29873675, 0.29871386,\n"," 0.29862151, 0.29858628, 0.29854628, 0.29852128, 0.29851949,\n"," 0.29851645, 0.29841882, 0.29840112, 0.29837534, 0.29826325,\n"," 0.29825163, 0.29818821, 0.29818475, 0.29817683, 0.29817235,\n"," 0.2981599 , 0.29812506, 0.29804632, 0.29804492, 0.29792625,\n"," 0.29791656, 0.29788324, 0.29788217, 0.29787767, 0.29784188,\n"," 0.29782993, 0.29776198, 0.29773778, 0.29772189, 0.29764712,\n"," 0.2976298 , 0.29759359, 0.29756066, 0.29753906, 0.29753619,\n"," 0.29752311, 0.29751918, 0.29742122, 0.2974143 , 0.29740745,\n"," 0.29739553, 0.29735541, 0.297326 , 0.2973105 , 0.29727286,\n"," 0.29727286, 0.29727286, 0.29727286, 0.29727286, 0.29727286,\n"," 0.29727286, 0.29720646, 0.29719922, 0.29719809, 0.29715779,\n"," 0.29713985, 0.29711261, 0.297111 , 0.29709697, 0.2970764 ,\n"," 0.29703513, 0.29702914, 0.29697198, 0.2968891 , 0.29683954,\n"," 0.29683205, 0.29679239, 0.29674593, 0.29672769, 0.29670379,\n"," 0.29665262, 0.29664084, 0.29661664, 0.2965543 , 0.29653901,\n"," 0.29651839, 0.29650578, 0.29649401, 0.29647073, 0.29643968,\n"," 0.29641765, 0.29641101, 0.29637355, 0.29635167, 0.29632881,\n"," 0.29632512, 0.29631802, 0.29629686, 0.29628912, 0.29627201,\n"," 0.29625517, 0.29615477, 0.29614013, 0.29611671, 0.29609632,\n"," 0.29603076, 0.29602429, 0.29597038, 0.29595608, 0.29591033,\n"," 0.29589114, 0.29587877, 0.29586193, 0.2958346 , 0.2958301 ,\n"," 0.29578215, 0.2957536 , 0.29572147, 0.29571518, 0.2956607 ,\n"," 0.29565895, 0.2956506 , 0.29563349, 0.29562587, 0.29561767,\n"," 0.29557627, 0.29557568, 0.29556012, 0.29555777, 0.29552841,\n"," 0.29552472, 0.29552472, 0.29552335, 0.29551974, 0.29551342,\n"," 0.29551342, 0.29551274, 0.29549918, 0.29547498, 0.2954742 ,\n"," 0.2954604 , 0.2954604 , 0.2954604 , 0.2954604 , 0.2954368 ,\n"," 0.29537192, 0.29535851, 0.29531649, 0.29528806, 0.29528174,\n"," 0.29525614, 0.29521081, 0.29518318, 0.29517522, 0.29513389,\n"," 0.29512694, 0.29512653, 0.29509673, 0.29509372, 0.29509372,\n"," 0.29505712, 0.29505712, 0.29505712, 0.29505712, 0.29505712,\n"," 0.29505712, 0.29504013, 0.29503301, 0.2949588 , 0.29493639,\n"," 0.294833 , 0.29475766, 0.29474685, 0.2947287 , 0.2947261 ,\n"," 0.29469427, 0.29462206, 0.29458955, 0.29455319, 0.29455319,\n"," 0.2945323 , 0.29450464, 0.29448986, 0.29447731, 0.29441392,\n"," 0.29440254, 0.29437205, 0.29435995, 0.29435837, 0.29432699,\n"," 0.29429272, 0.29427546, 0.29425862, 0.29416701, 0.29415759,\n"," 0.29409686, 0.29409686, 0.29406589, 0.29402691, 0.29399857,\n"," 0.29396644, 0.29394782, 0.29392251, 0.29390395, 0.29388061,\n"," 0.29387558, 0.29387018, 0.29386652, 0.29385015, 0.29384616,\n"," 0.29384476, 0.29380754, 0.29380497, 0.29380336, 0.29374805,\n"," 0.29374462, 0.29372454, 0.29370907, 0.29368311, 0.29367605,\n"," 0.2936745 , 0.29367301, 0.29363912, 0.29355159, 0.29351529,\n"," 0.29348338, 0.29346633, 0.29346597, 0.29345313, 0.29345262,\n"," 0.29344514, 0.29340965, 0.29339889, 0.29335994, 0.29333568,\n"," 0.29330403, 0.2933017 , 0.29328614, 0.29327172, 0.29318982,\n"," 0.29314068, 0.29310715, 0.29308149, 0.29303041, 0.29300833,\n"," 0.29298198, 0.29296118, 0.29295951, 0.2929576 , 0.29294854,\n"," 0.29294771, 0.29294097, 0.29293409, 0.2929306 , 0.29289603,\n"," 0.29286847, 0.2928673 , 0.29286686, 0.29283726, 0.29283494,\n"," 0.29283211, 0.29280052, 0.292788 , 0.29276642, 0.29275382,\n"," 0.29274902, 0.2927345 , 0.2927312 , 0.29272789, 0.29272768,\n"," 0.29272699, 0.29272446, 0.29270563, 0.2926262 , 0.29262054,\n"," 0.29261893, 0.29261178, 0.29259655, 0.29258958, 0.29255438,\n"," 0.29252639, 0.29251739, 0.2924937 , 0.29248247, 0.29248247,\n"," 0.29247952, 0.29247826, 0.29246214, 0.29243073, 0.2923547 ,\n"," 0.2923547 , 0.29226756, 0.29226357, 0.2922619 , 0.29225475,\n"," 0.29224423, 0.29222488, 0.29221708, 0.29218984, 0.29218543,\n"," 0.29215047, 0.2921066 , 0.2921066 , 0.29207876, 0.29207754,\n"," 0.29207337, 0.29202199, 0.29201707, 0.29201707, 0.29201669,\n"," 0.29199421, 0.291987 , 0.2919786 , 0.29196966, 0.29195994,\n"," 0.2919316 , 0.29189199, 0.29189199, 0.29183173, 0.29178125,\n"," 0.29178116, 0.29177552, 0.29177189, 0.2917597 , 0.29174453,\n"," 0.29174402, 0.29172811, 0.29172549, 0.29168811, 0.29166642,\n"," 0.29165354, 0.29162318, 0.29160523, 0.29160452, 0.29158878,\n"," 0.29158682, 0.29158118, 0.29157677, 0.29156843, 0.29156578,\n"," 0.29155582, 0.29149947, 0.29144758, 0.29142621, 0.29142311,\n"," 0.29141045, 0.29140741, 0.29139045, 0.29136476, 0.29134077,\n"," 0.29133675, 0.29132444, 0.29132131, 0.29129368, 0.29124853,\n"," 0.29124492, 0.29124492, 0.29124066, 0.29123756, 0.29123065,\n"," 0.29123065, 0.29123065, 0.29122996, 0.29121903, 0.29121867,\n"," 0.29121789, 0.29120779, 0.29118162, 0.29109725, 0.29109725,\n"," 0.2910476 , 0.29102582, 0.2910246 , 0.29101428, 0.2910037 ,\n"," 0.29100204, 0.29099253, 0.29099253, 0.29098296, 0.29098198,\n"," 0.290961 , 0.29092869, 0.29091689, 0.29090464, 0.29086691,\n"," 0.29085931, 0.29084945, 0.29082742, 0.29079804, 0.29079336,\n"," 0.29078901, 0.29077008, 0.29077008, 0.29076892, 0.29073796,\n"," 0.29072672, 0.29069179, 0.29068214, 0.29065219, 0.29057178,\n"," 0.29055968, 0.29054809, 0.29052374, 0.29051253, 0.29050645,\n"," 0.29048997, 0.29043666, 0.29043466, 0.2904337 , 0.29042798,\n"," 0.29041457, 0.29038122, 0.29037634, 0.2903662 , 0.29031822,\n"," 0.29029858, 0.2902905 , 0.29027149, 0.29026422, 0.29024875,\n"," 0.29022795, 0.29021922, 0.29018852, 0.29018259, 0.29017234,\n"," 0.29016367, 0.29015294, 0.29014054, 0.29013473, 0.29012552,\n"," 0.29010096, 0.29008898, 0.29008576, 0.2900852 , 0.29006636,\n"," 0.29005161, 0.29002964, 0.29002219, 0.29001915, 0.29001758,\n"," 0.2900165 , 0.29000849, 0.29000849, 0.29000264, 0.28998595,\n"," 0.28998271, 0.28997797, 0.28996521, 0.28996146, 0.28995797,\n"," 0.28995532, 0.28993466, 0.2899228 , 0.28991958, 0.2899048 ,\n"," 0.28985268, 0.28985268, 0.28983492, 0.28982294, 0.28981817,\n"," 0.2898075 , 0.28977737, 0.28976464, 0.28976145, 0.28973341,\n"," 0.28972939, 0.28972724, 0.2897245 , 0.28969958, 0.28969911,\n"," 0.28968206, 0.28964761, 0.28963453, 0.28960568, 0.28960228,\n"," 0.28957725, 0.28956699, 0.2895245 , 0.28952399, 0.28950769,\n"," 0.28947932, 0.28943855, 0.28940487, 0.28938636, 0.28937796,\n"," 0.28937501, 0.28934285, 0.28932124, 0.28931984, 0.28929642,\n"," 0.28928226, 0.28926796, 0.2892656 , 0.28926158, 0.28924817,\n"," 0.28920805, 0.28920189, 0.28920007, 0.28919995, 0.28919417,\n"," 0.28918687, 0.2891764 , 0.28914765, 0.28914651, 0.28913376,\n"," 0.28910181, 0.28909877, 0.28905585, 0.28905585, 0.28904873,\n"," 0.28891814, 0.28890195, 0.288899 , 0.28889143, 0.28884473,\n"," 0.28883693, 0.28883314, 0.2888287 , 0.28882143, 0.28880146,\n"," 0.28874418, 0.28872263, 0.28872138, 0.28872138, 0.28872138,\n"," 0.28872138, 0.28871629, 0.28870544, 0.28869814, 0.28869238,\n"," 0.28867397, 0.2886672 , 0.2886633 , 0.28865665, 0.28865221,\n"," 0.28865203, 0.28863004, 0.28860882, 0.28855437, 0.28855437,\n"," 0.28855437, 0.28855437, 0.28855437, 0.28855437, 0.28855437,\n"," 0.28855437, 0.28855437, 0.28855437, 0.28855437, 0.28855437,\n"," 0.28855437, 0.28855437, 0.28855437, 0.28855437, 0.28855437,\n"," 0.28855437, 0.28855437, 0.28854331, 0.28853914, 0.2885353 ,\n"," 0.2885294 , 0.28852919, 0.28847492, 0.28842816, 0.28842402,\n"," 0.28842393, 0.28840935, 0.28837782, 0.28835228, 0.28832966,\n"," 0.28830099, 0.28829521, 0.28828731, 0.28828385, 0.28828385,\n"," 0.28827438, 0.2882635 , 0.28824785, 0.28823665, 0.28822282,\n"," 0.28820816, 0.28820765, 0.28818232, 0.28816715, 0.28815535]),\n"," array([1.30334723, 1.30334723, 1.27480578, 1.27454305, 1.2575053 ,\n"," 1.25169444, 1.25169444, 1.24955356, 1.24388289, 1.24015284,\n"," 1.23892653, 1.23749638, 1.23677289, 1.23652029, 1.23625422,\n"," 1.23608601, 1.23508036, 1.23416042, 1.23237646, 1.2316283 ,\n"," 1.23027277, 1.22914624, 1.22893071, 1.22893071, 1.22893071,\n"," 1.22848248, 1.22822845, 1.22766292, 1.22648764, 1.22582054,\n"," 1.22336257, 1.22283924, 1.22195101, 1.2177192 , 1.21757376,\n"," 1.21697056, 1.21668816, 1.21581388, 1.21496773, 1.21380615,\n"," 1.21186376, 1.21159756, 1.21093738, 1.21093738, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21067464, 1.21067464, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21067464, 1.21067464, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21067464, 1.21067464, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21067464, 1.21067464, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21067464, 1.21067464, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21067464, 1.21067464, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21067464, 1.21067464, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21067464, 1.21067464, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21067464, 1.21067464, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21067464, 1.21067464, 1.21067464,\n"," 1.21067464, 1.21067464, 1.21064603, 1.20989537, 1.20943964,\n"," 1.20938957, 1.20874369, 1.2086091 , 1.20823395, 1.20823181,\n"," 1.20802546, 1.20799291, 1.20792651, 1.20779562, 1.20750964,\n"," 1.20715535, 1.20631051, 1.20625877, 1.20604026, 1.20586061,\n"," 1.20586061, 1.20535815, 1.20526731, 1.20496011, 1.20428038,\n"," 1.20420861, 1.20412052, 1.20389235, 1.20383179, 1.20380497,\n"," 1.20341349, 1.20341349, 1.2030654 , 1.20304573, 1.20300305,\n"," 1.20296323, 1.20296323, 1.20290232, 1.20242572, 1.20242572,\n"," 1.20219827, 1.20217752, 1.20217752, 1.20217752, 1.20217752,\n"," 1.20217752, 1.20217752, 1.20217752, 1.20104635, 1.20104635,\n"," 1.20104635, 1.20104635, 1.20104635, 1.20104635, 1.20104635,\n"," 1.20104635, 1.20104635, 1.20104635, 1.20104635, 1.20104635,\n"," 1.20104635, 1.20104635, 1.20104635, 1.20104635, 1.20104635,\n"," 1.20104635, 1.20104635, 1.20104635, 1.20104635, 1.20104635,\n"," 1.20104635, 1.20104635, 1.20104635, 1.20104635, 1.20104635,\n"," 1.20104635, 1.20068681, 1.20068681, 1.20068681, 1.20068681,\n"," 1.20068681, 1.20037413, 1.20006609, 1.20004177, 1.20004177,\n"," 1.20004177, 1.20004177, 1.20004177, 1.20004177, 1.20004177,\n"," 1.20004177, 1.20004177, 1.20004177, 1.19983792, 1.19970274,\n"," 1.19963133, 1.19963133, 1.19963133, 1.19963133, 1.19958043,\n"," 1.19910395, 1.19903779, 1.19903779, 1.19903779, 1.19903779,\n"," 1.19903779, 1.19903779, 1.19876182, 1.1983856 , 1.19816673,\n"," 1.1981492 , 1.1981492 , 1.1981492 , 1.1981492 , 1.1981492 ,\n"," 1.1981492 , 1.1981492 , 1.1981492 , 1.1981492 , 1.1981492 ,\n"," 1.1981492 , 1.1981492 , 1.19803894, 1.19776988, 1.19776988,\n"," 1.19776988, 1.19776988, 1.19776988, 1.19776988, 1.19776988,\n"," 1.19776988, 1.19776988, 1.19776988, 1.19776988, 1.19776988,\n"," 1.19776988, 1.19776988, 1.19775903, 1.19767356, 1.19760501,\n"," 1.1975919 , 1.1975919 , 1.1975919 , 1.1975919 , 1.1975919 ,\n"," 1.1975919 , 1.1975919 , 1.1975919 , 1.1975466 , 1.19725454,\n"," 1.19702566, 1.19652915, 1.19640756, 1.19639957, 1.19637454,\n"," 1.19623232, 1.19623232, 1.19623232, 1.19623232, 1.19623232,\n"," 1.19623232, 1.19623232, 1.19623232, 1.19623232, 1.19623232,\n"," 1.19623232, 1.19623232, 1.19623232, 1.19623232, 1.19623232,\n"," 1.19623232, 1.19623232, 1.19623232, 1.19623232, 1.19618547,\n"," 1.19600379, 1.19594455, 1.19579279, 1.1955837 , 1.1954726 ,\n"," 1.19547153, 1.19525194, 1.19525194, 1.19525194, 1.19525194,\n"," 1.19521165, 1.19515336, 1.19498491, 1.19494045, 1.19488657,\n"," 1.19481719, 1.19481719, 1.19481719, 1.19481719, 1.19481719,\n"," 1.19481719, 1.19481719, 1.19481289, 1.19480836, 1.19479203,\n"," 1.19479203, 1.19477308, 1.19464815, 1.19456458, 1.19449329,\n"," 1.19449329, 1.19449329, 1.19449329, 1.19449329, 1.19449329,\n"," 1.19447732, 1.19424021, 1.19413733, 1.19413733, 1.19395268,\n"," 1.19395268, 1.19393158, 1.19368958, 1.19355869, 1.19314408,\n"," 1.19285691, 1.19282806, 1.19274402, 1.19271159, 1.19269764,\n"," 1.19258416, 1.1925801 , 1.1923275 , 1.19232559, 1.19231474,\n"," 1.19156039, 1.19156039, 1.19141805, 1.19141805, 1.19141805,\n"," 1.19141805, 1.19141805, 1.19141805, 1.19129658, 1.19126022,\n"," 1.19119823, 1.19115365, 1.19107974, 1.19107974, 1.19107974,\n"," 1.19091058, 1.190552 , 1.19043779, 1.19043779, 1.19043779,\n"," 1.19043779, 1.19043779, 1.19043779, 1.19039297, 1.19037437,\n"," 1.19027507, 1.19024789, 1.19015968, 1.18995106, 1.18979752,\n"," 1.18966484, 1.18966317, 1.18966317, 1.18955576, 1.18949187,\n"," 1.18949187, 1.18949187, 1.18949187, 1.18949187, 1.18949187,\n"," 1.18949187, 1.18949187, 1.18949187, 1.18943489, 1.18939817,\n"," 1.1889677 , 1.18895316, 1.18895316, 1.18889201, 1.18878579,\n"," 1.18874228, 1.18843675, 1.18802774, 1.18784177, 1.18781781,\n"," 1.18776596, 1.18776476, 1.18751001, 1.18746805, 1.18734229,\n"," 1.18734229, 1.18726945, 1.18717313, 1.18669248, 1.18663466,\n"," 1.18662035, 1.1866039 , 1.18635106, 1.18618023, 1.18613303,\n"," 1.18609643, 1.18609643, 1.18609643, 1.18596208, 1.18586874,\n"," 1.18586874, 1.18586874, 1.18541336, 1.18525112, 1.1851424 ,\n"," 1.1851424 , 1.18502319, 1.18501043, 1.18485606, 1.18485057,\n"," 1.18485057, 1.18471396, 1.18470013, 1.18468142, 1.18453026,\n"," 1.18452227, 1.18447471, 1.18430388, 1.18429351, 1.18420005,\n"," 1.18411016, 1.18397164, 1.18377399, 1.18377399, 1.18368733,\n"," 1.18360472, 1.18360472, 1.18360472, 1.18343556, 1.18294966,\n"," 1.18292975, 1.18289638, 1.1828289 , 1.18278039, 1.18276989,\n"," 1.18271637, 1.18263245, 1.18260074, 1.18259442, 1.18251216,\n"," 1.18250692, 1.18244219, 1.18243551, 1.18240511, 1.18229532,\n"," 1.18225682, 1.18215919, 1.18207455, 1.18207455, 1.18207455,\n"," 1.18207455, 1.18206322, 1.18198729, 1.18189514, 1.18187284,\n"," 1.18180764, 1.18180275, 1.18176734, 1.18172669, 1.18171775,\n"," 1.18166959, 1.18153858, 1.18135417, 1.18119705, 1.18119705,\n"," 1.18114257, 1.1809715 , 1.18094409, 1.18090522, 1.18087935,\n"," 1.18064642, 1.18060923, 1.18043709, 1.18043208, 1.18016255,\n"," 1.17986727, 1.17986727, 1.17977548, 1.17939663, 1.17929256,\n"," 1.17928755, 1.1791575 , 1.17911124, 1.17902303, 1.17902303,\n"," 1.17899299, 1.17880297, 1.17879069, 1.17879069, 1.17864752,\n"," 1.17862141, 1.17862141, 1.17862141, 1.17845237, 1.17841959,\n"," 1.17840934, 1.17831445, 1.17824566, 1.17823017, 1.17808473,\n"," 1.17800963, 1.17792952, 1.17784798, 1.17782545, 1.17778575,\n"," 1.17777586, 1.17773831, 1.17773366, 1.17773366, 1.17772579,\n"," 1.17755556, 1.17740262, 1.17740262, 1.17737567, 1.17737567,\n"," 1.17730129, 1.17728388, 1.17728007, 1.17727792, 1.17727077,\n"," 1.17725372, 1.17720652, 1.17707372, 1.17706728, 1.17690957,\n"," 1.17657769, 1.1765753 , 1.17655563, 1.1763829 , 1.1763829 ,\n"," 1.1763829 , 1.1763829 , 1.1763829 , 1.17624724, 1.17609715,\n"," 1.17607439, 1.17594564, 1.17593122, 1.17584991, 1.17584658,\n"," 1.17575657, 1.17575324, 1.17575324, 1.1756438 , 1.17555666,\n"," 1.17555249, 1.1755054 , 1.17548764, 1.17540872, 1.17536318,\n"," 1.17536318, 1.17527735, 1.17513001, 1.17511249, 1.17499161,\n"," 1.1749804 , 1.17475426, 1.17471492, 1.17467666, 1.17461634,\n"," 1.17454708, 1.17449999, 1.17446649, 1.17433798, 1.17428958,\n"," 1.17420042, 1.17409801, 1.17405272, 1.17375195, 1.17363822,\n"," 1.17360818, 1.17358434, 1.17355061, 1.17340529, 1.17333663,\n"," 1.17330909, 1.17330205, 1.1732111 , 1.17312074, 1.17309666,\n"," 1.17307913, 1.17298687, 1.17298687, 1.17298687, 1.17294192,\n"," 1.17292297, 1.17288578, 1.17282712, 1.17268419, 1.17258847,\n"," 1.17258847, 1.17248631, 1.17246211, 1.17245793, 1.17235768,\n"," 1.1722585 , 1.17222321, 1.17219245, 1.17218852, 1.17206836,\n"," 1.17196512, 1.17189085, 1.17155695, 1.17146957, 1.17141557,\n"," 1.17135251, 1.17133689, 1.17117333, 1.17114651, 1.17114651,\n"," 1.17114651, 1.17104399, 1.17097199, 1.17093945, 1.17092216,\n"," 1.1709075 , 1.17081058, 1.17079306, 1.17078495, 1.17075872,\n"," 1.17070508, 1.17061722, 1.17054152, 1.17053008, 1.17051411,\n"," 1.17051411, 1.17047381, 1.17045808, 1.17044199, 1.17040133,\n"," 1.17037356, 1.17037344, 1.17022753, 1.17017758, 1.17006993,\n"," 1.16996348, 1.16993392, 1.16993392, 1.16991699, 1.16990066,\n"," 1.16990066, 1.16985047, 1.16981375, 1.16977513, 1.16974998,\n"," 1.1697315 , 1.16967154, 1.16967154, 1.1696372 , 1.16959798,\n"," 1.16958475, 1.16955853, 1.16955853, 1.16946638, 1.16946638,\n"," 1.16946411, 1.16945601, 1.16938102, 1.16937053, 1.16934288,\n"," 1.16933572, 1.16933525, 1.16928875, 1.16922975, 1.1692152 ,\n"," 1.16920316, 1.16916668, 1.16913676, 1.16906619, 1.16895711,\n"," 1.16889322, 1.16889191, 1.168872 , 1.16884601, 1.16882408,\n"," 1.16873217, 1.16860282, 1.16860282, 1.16853678, 1.16853678,\n"," 1.16851377, 1.16848576, 1.16848576, 1.16848576, 1.16847992,\n"," 1.16820729, 1.16818988, 1.16806042, 1.16803181, 1.16797543,\n"," 1.16793633, 1.16791654, 1.16787755, 1.16785598, 1.16783154,\n"," 1.16777813, 1.1677742 , 1.16773987, 1.16762578, 1.16758728,\n"," 1.16757822, 1.16753817, 1.16753817, 1.16752279, 1.16742921,\n"," 1.16740894, 1.16740894, 1.16740894, 1.167395 , 1.16737902,\n"," 1.16737723, 1.16735208, 1.16733813, 1.16733813, 1.16733181,\n"," 1.16732216, 1.16729605, 1.16727769, 1.16723979, 1.16722906,\n"," 1.16702414, 1.1669842 , 1.1669786 , 1.16696823, 1.16689682,\n"," 1.16689682, 1.16689682, 1.1667577 , 1.16672814, 1.16657162,\n"," 1.16652536, 1.16649091, 1.16646767, 1.16642606, 1.16642082,\n"," 1.16641009, 1.16633832, 1.16633677, 1.16627789, 1.16627789,\n"," 1.16627789, 1.16627789, 1.16618538, 1.16616321, 1.16613269,\n"," 1.166116 , 1.16601944, 1.16601944, 1.16601944, 1.16600466,\n"," 1.16599655, 1.16599655, 1.16583741, 1.16581643, 1.16575992,\n"," 1.16569626, 1.16550553, 1.16549349, 1.16545796, 1.16539824,\n"," 1.16527152, 1.16526055, 1.16516531, 1.16515672, 1.16499972,\n"," 1.16499972, 1.16499972, 1.16493499, 1.16491735, 1.16490543,\n"," 1.16476274, 1.1647203 , 1.16469324, 1.16460109, 1.16460109,\n"," 1.16460109, 1.16460109, 1.16460109, 1.16460109, 1.16460109,\n"," 1.16460109, 1.16460109, 1.16460109, 1.16460109, 1.16460109,\n"," 1.1645987 , 1.16455674, 1.16453302, 1.16450059, 1.16449213,\n"," 1.16449213, 1.16439152, 1.16434765, 1.16425192, 1.1642381 ,\n"," 1.16420889, 1.16419506, 1.16413736, 1.1641345 , 1.16412675,\n"," 1.16408277, 1.16396856, 1.16381717, 1.16376662, 1.16376567,\n"," 1.16368556, 1.16367149, 1.16367149, 1.16367149, 1.16363037,\n"," 1.16361213, 1.16355932, 1.16350365, 1.16342807, 1.16335428,\n"," 1.16334701, 1.16333961, 1.16326594, 1.16321468, 1.16321468,\n"," 1.16311693, 1.16310513, 1.16299319, 1.16298783, 1.16293752,\n"," 1.16293013, 1.16290152, 1.16289175, 1.16289175, 1.16288662,\n"," 1.16288662, 1.16288662, 1.16288662, 1.16285431, 1.16282475,\n"," 1.1627568 , 1.16270864, 1.16262245, 1.16262209, 1.16250598,\n"," 1.16250277, 1.16241157, 1.16233516, 1.16227925, 1.16223931,\n"," 1.16202021, 1.16202021, 1.16188729, 1.1618768 , 1.16185451,\n"," 1.16185451, 1.16175365, 1.16166961, 1.16165745, 1.16165042,\n"," 1.16164744, 1.16163397, 1.16161704, 1.16160095, 1.16158581,\n"," 1.16155076, 1.16136634, 1.16134906, 1.16134703, 1.16130793,\n"," 1.16128027, 1.16120529, 1.16120529, 1.16120529, 1.16120279,\n"," 1.1611799 , 1.1611799 , 1.16115773, 1.16114664, 1.1611433 ,\n"," 1.1610949 , 1.16103983, 1.16101074, 1.16100669, 1.16096938,\n"," 1.16088617, 1.16079891, 1.16073358, 1.16070569, 1.16069353,\n"," 1.16066647, 1.16059697, 1.16059458, 1.1605823 , 1.16054976,\n"," 1.16049063, 1.16045034, 1.16044152, 1.16043675, 1.16040862,\n"," 1.16037405, 1.16034806, 1.16028285, 1.1602515 , 1.16024804,\n"," 1.16023743, 1.16023743, 1.16023743, 1.16023743, 1.16018033,\n"," 1.16014266, 1.1601032 , 1.1601032 , 1.16005731, 1.1600343 ,\n"," 1.1599946 , 1.15993416, 1.15993202, 1.15988219, 1.15985775,\n"," 1.15976501, 1.15976501, 1.15967762, 1.1596545 , 1.1595943 ,\n"," 1.15953243, 1.15950298, 1.15946007, 1.15941417, 1.1594038 ,\n"," 1.15937054, 1.15934587, 1.15929222, 1.15929222, 1.15928459,\n"," 1.15927732, 1.15918171, 1.15918171, 1.1591804 , 1.15911877,\n"," 1.1590836 , 1.15904558, 1.15902185, 1.15902185, 1.15902185,\n"," 1.15902185, 1.15902185, 1.15902185, 1.15902185, 1.15902185,\n"," 1.15902185, 1.15901792, 1.15888262, 1.15880728, 1.15879154,\n"," 1.15872359, 1.15870035, 1.15868831, 1.15865195, 1.15862179,\n"," 1.15851915, 1.15851915, 1.1584723 , 1.15843058, 1.15843058,\n"," 1.15843058, 1.15840805, 1.15839875, 1.15839422, 1.15833616,\n"," 1.15831363, 1.15830803, 1.15830433, 1.15830278, 1.15829301,\n"," 1.15816677, 1.15815806, 1.15813792, 1.15805066, 1.15805066,\n"," 1.15797269, 1.15793777, 1.15786529, 1.15781724, 1.15779233,\n"," 1.15775073, 1.15768564, 1.15766621, 1.1576643 , 1.15760589,\n"," 1.15758109, 1.15751493, 1.1574403 , 1.15743196, 1.15742576,\n"," 1.15741181, 1.15741098, 1.15741098, 1.15735281, 1.15733266,\n"," 1.15732038, 1.1573168 , 1.15730381, 1.15729892, 1.15727329]),\n"," array([0.63222402, 0.63222402, 0.61497915, 0.61497915, 0.61497915,\n"," 0.61497915, 0.61427593, 0.61403656, 0.61403656, 0.60591125,\n"," 0.60320616, 0.60268444, 0.60268444, 0.60246551, 0.60246551,\n"," 0.60246551, 0.60246551, 0.60246551, 0.60246551, 0.60246551,\n"," 0.60246551, 0.60177392, 0.60161936, 0.60150659, 0.59919351,\n"," 0.59911472, 0.59904915, 0.59861249, 0.59857452, 0.59828466,\n"," 0.59800375, 0.59800375, 0.59800375, 0.59800375, 0.59800375,\n"," 0.59800375, 0.59800375, 0.59800375, 0.59800375, 0.5974822 ,\n"," 0.59747046, 0.59721226, 0.59679168, 0.59679168, 0.59679168,\n"," 0.59679168, 0.59679168, 0.59679168, 0.59679168, 0.59679168,\n"," 0.59679168, 0.59673136, 0.59622771, 0.59620339, 0.59584904,\n"," 0.59584904, 0.59584904, 0.59584904, 0.59582072, 0.5957312 ,\n"," 0.595438 , 0.59540433, 0.59540433, 0.59540433, 0.59510553,\n"," 0.59470189, 0.59453309, 0.59420484, 0.59400994, 0.59338754,\n"," 0.59338725, 0.59321672, 0.59312242, 0.59282297, 0.5924378 ,\n"," 0.59239399, 0.5923053 , 0.59220362, 0.59165013, 0.59164697,\n"," 0.59154743, 0.59122175, 0.59110802, 0.59106362, 0.59105837,\n"," 0.59100223, 0.59098679, 0.59098458, 0.59098458, 0.59075481,\n"," 0.59067106, 0.59056264, 0.59046561, 0.59039527, 0.59036165,\n"," 0.59030443, 0.59030443, 0.59030443, 0.58983904, 0.58959621,\n"," 0.58947617, 0.58943969, 0.5894143 , 0.58930719, 0.58925408,\n"," 0.58925408, 0.58913267, 0.5891102 , 0.5891102 , 0.58901501,\n"," 0.58882517, 0.58864373, 0.58861929, 0.58835006, 0.58825535,\n"," 0.58824468, 0.58820379, 0.58812916, 0.58812916, 0.58812916,\n"," 0.58802229, 0.58799505, 0.58783454, 0.58776838, 0.58766466,\n"," 0.58762151, 0.58762151, 0.58762151, 0.58762151, 0.58741617,\n"," 0.58725804, 0.58719319, 0.58712572, 0.58695674, 0.58683741,\n"," 0.58673579, 0.58667892, 0.5866369 , 0.58661926, 0.58654648,\n"," 0.58635974, 0.58631533, 0.58626455, 0.58615988, 0.58606923,\n"," 0.58606923, 0.58602881, 0.58601224, 0.58601093, 0.58596015,\n"," 0.58585876, 0.58585876, 0.58569038, 0.58567709, 0.58564001,\n"," 0.58562899, 0.58560812, 0.58551627, 0.58522063, 0.58522063,\n"," 0.58522063, 0.58522063, 0.58522063, 0.58522063, 0.58522063,\n"," 0.58522063, 0.58522063, 0.58522063, 0.58522063, 0.58522063,\n"," 0.58522063, 0.58522063, 0.58522063, 0.58522063, 0.58522063,\n"," 0.58522063, 0.58508497, 0.5850122 , 0.58489299, 0.58488125,\n"," 0.58481586, 0.5847553 , 0.5846312 , 0.58454871, 0.58453661,\n"," 0.58453661, 0.58453661, 0.58453661, 0.58453661, 0.58453661,\n"," 0.58453661, 0.58453661, 0.58453661, 0.58452266, 0.58450949,\n"," 0.58445358, 0.58442199, 0.58434105, 0.58431029, 0.58427805,\n"," 0.58427805, 0.58427805, 0.58427805, 0.58427805, 0.58425379,\n"," 0.58402872, 0.58401996, 0.58401138, 0.58400995, 0.58400041,\n"," 0.58397782, 0.58388233, 0.58378017, 0.58377016, 0.58367848,\n"," 0.58367628, 0.58359581, 0.58359069, 0.58355874, 0.58355874,\n"," 0.58350718, 0.58343786, 0.58339286, 0.58335799, 0.58334351,\n"," 0.58327848, 0.58326185, 0.58312643, 0.58301383, 0.58299607,\n"," 0.58290893, 0.58284825, 0.58267057, 0.58256286, 0.58249491,\n"," 0.5824641 , 0.58240271, 0.58233678, 0.58231914, 0.58231694,\n"," 0.58230925, 0.5822373 , 0.5822019 , 0.58216965, 0.58213037,\n"," 0.58211672, 0.58211082, 0.58209157, 0.58207256, 0.581963 ,\n"," 0.58194947, 0.58193886, 0.58189613, 0.58185673, 0.5817433 ,\n"," 0.58165008, 0.58165008, 0.58160174, 0.5815869 , 0.5815751 ,\n"," 0.58153683, 0.58150733, 0.58149201, 0.58143902, 0.58140892,\n"," 0.58139199, 0.58137602, 0.58133858, 0.58131117, 0.58129334,\n"," 0.58129025, 0.58125508, 0.58116764, 0.58114135, 0.58111131,\n"," 0.58109087, 0.58103073, 0.580984 , 0.58094174, 0.58086503,\n"," 0.58084857, 0.58078265, 0.58076745, 0.58075887, 0.58075887,\n"," 0.58075887, 0.58075887, 0.58075887, 0.58075887, 0.58075887,\n"," 0.58075321, 0.58073837, 0.58060801, 0.58056521, 0.58046103,\n"," 0.58045286, 0.58044177, 0.58043677, 0.58039057, 0.58038878,\n"," 0.58036393, 0.58029807, 0.58024251, 0.58022851, 0.58022439,\n"," 0.58015102, 0.58014756, 0.58012998, 0.58010966, 0.58010584,\n"," 0.58009326, 0.57993543, 0.57991749, 0.57987434, 0.57982725,\n"," 0.57981622, 0.57981622, 0.57981622, 0.57981622, 0.57981622,\n"," 0.57979983, 0.57975036, 0.57966715, 0.57965446, 0.57960552,\n"," 0.57960552, 0.57959396, 0.57958245, 0.57953382, 0.57952273,\n"," 0.57951516, 0.57948494, 0.57942098, 0.57931876, 0.57930315,\n"," 0.5792923 , 0.57924271, 0.57921672, 0.57920545, 0.57917255,\n"," 0.57912594, 0.57912159, 0.57907188, 0.57905728, 0.5790512 ,\n"," 0.57900566, 0.57898551, 0.5789634 , 0.57893986, 0.57893687,\n"," 0.57885462, 0.57878405, 0.57878405, 0.57874352, 0.57871109,\n"," 0.57870948, 0.57870555, 0.57870555, 0.57868099, 0.57867128,\n"," 0.57866597, 0.57861847, 0.57860416, 0.57860416, 0.57860416,\n"," 0.57860416, 0.57860416, 0.57853764, 0.57852006, 0.57851899,\n"," 0.57851785, 0.57847339, 0.57843351, 0.57839227, 0.57838982,\n"," 0.57838035, 0.57836992, 0.57834721, 0.57833141, 0.57832849,\n"," 0.57832247, 0.57828122, 0.57822692, 0.57821655, 0.57819051,\n"," 0.57817984, 0.57816708, 0.57814795, 0.57809669, 0.57807934,\n"," 0.57805806, 0.57801992, 0.57798231, 0.57797259, 0.5779652 ,\n"," 0.57794988, 0.57793623, 0.57793599, 0.57790536, 0.57789433,\n"," 0.57786995, 0.57778877, 0.57776642, 0.57776082, 0.57775521,\n"," 0.57773417, 0.57773221, 0.57766515, 0.57756376, 0.57755542,\n"," 0.57754391, 0.57752657, 0.57752132, 0.57749683, 0.57749438,\n"," 0.5774852 , 0.57747304, 0.57747251, 0.57742518, 0.57740098,\n"," 0.57736009, 0.57735097, 0.57734346, 0.57731777, 0.57729232,\n"," 0.57728738, 0.57727146, 0.57727015, 0.57726812, 0.5772599 ,\n"," 0.57723176, 0.5772264 , 0.57720852, 0.57717854, 0.57715631,\n"," 0.57715046, 0.57710195, 0.57707995, 0.57705778, 0.57703966,\n"," 0.57702297, 0.57701963, 0.57700264, 0.57699138, 0.57698452,\n"," 0.57693696, 0.57689267, 0.576877 , 0.57683629, 0.5768007 ,\n"," 0.57679325, 0.57671803, 0.57671046, 0.57665426, 0.57659405,\n"," 0.5765897 , 0.57656139, 0.57654339, 0.57650048, 0.57641363,\n"," 0.57638085, 0.57633579, 0.57632941, 0.57630575, 0.57630384,\n"," 0.57620913, 0.57618439, 0.57616818, 0.57613081, 0.57612222,\n"," 0.57608259, 0.57605565, 0.57604909, 0.57602817, 0.57602817,\n"," 0.57601351, 0.57600242, 0.57600188, 0.57599622, 0.57595128,\n"," 0.57595122, 0.57595122, 0.57594955, 0.57594711, 0.57593423,\n"," 0.57591283, 0.57589483, 0.57588273, 0.57585979, 0.57581782,\n"," 0.57576925, 0.57575828, 0.5757488 , 0.57571292, 0.57571292,\n"," 0.57567853, 0.5756703 , 0.57565886, 0.57564586, 0.57558835,\n"," 0.57554621, 0.57552278, 0.57551652, 0.57549798, 0.57549596,\n"," 0.57549065, 0.57549 , 0.57548958, 0.57548022, 0.57547486,\n"," 0.57541943, 0.57541627, 0.57537758, 0.57536489, 0.57536232,\n"," 0.57536113, 0.57530576, 0.57530427, 0.57529491, 0.5752781 ,\n"," 0.57523954, 0.57523954, 0.57520378, 0.57514906, 0.57514542,\n"," 0.57513869, 0.57511234, 0.57510787, 0.57510787, 0.57510787,\n"," 0.57509816, 0.57500291, 0.57498425, 0.57498413, 0.57496947,\n"," 0.57496452, 0.57496357, 0.57496125, 0.57494575, 0.57488316,\n"," 0.57487386, 0.57487035, 0.57483923, 0.57480532, 0.57479811,\n"," 0.57479608, 0.57479042, 0.57477486, 0.57476383, 0.57476038,\n"," 0.57473123, 0.57473099, 0.5747053 , 0.57470316, 0.57470077,\n"," 0.57465535, 0.57465225, 0.57464486, 0.57463229, 0.57462311,\n"," 0.57459754, 0.57458436, 0.57455707, 0.57453787, 0.57449013,\n"," 0.57445395, 0.57443935, 0.57441425, 0.57433784, 0.57433784,\n"," 0.57433271, 0.57431704, 0.57429034, 0.57429016, 0.57428825,\n"," 0.57426977, 0.57423717, 0.5742324 , 0.57420641, 0.57416749,\n"," 0.57412452, 0.57410675, 0.57408202, 0.57407254, 0.57399368,\n"," 0.57398683, 0.57398593, 0.57396477, 0.57395148, 0.57392043,\n"," 0.5739097 , 0.5739097 , 0.57388288, 0.57383156, 0.57383084,\n"," 0.57379019, 0.57378936, 0.57371801, 0.57368171, 0.57367706,\n"," 0.57367069, 0.57364571, 0.57362205, 0.57360911, 0.57358456,\n"," 0.57358074, 0.57355446, 0.57352507, 0.57352239, 0.57349169,\n"," 0.5734812 , 0.57346028, 0.57344311, 0.57342893, 0.57340121,\n"," 0.57338953, 0.57338375, 0.57337373, 0.57331008, 0.57328051,\n"," 0.57327038, 0.5732609 , 0.57325238, 0.57322776, 0.57322556,\n"," 0.57320559, 0.57320249, 0.57318705, 0.57318592, 0.57317454,\n"," 0.5731613 , 0.57315749, 0.57314032, 0.57313859, 0.57312155,\n"," 0.57311606, 0.57311559, 0.57309526, 0.57309008, 0.57308912,\n"," 0.57306617, 0.57304603, 0.57302368, 0.57301211, 0.5730027 ,\n"," 0.57299 , 0.57298124, 0.5729689 , 0.57295424, 0.57291305,\n"," 0.57288712, 0.57286835, 0.57286835, 0.57285666, 0.57284749,\n"," 0.57284278, 0.57284123, 0.57279891, 0.57278162, 0.57275581,\n"," 0.57275367, 0.57273489, 0.57273054, 0.57272565, 0.57271266,\n"," 0.57270706, 0.57270706, 0.57270706, 0.57270706, 0.57270706,\n"," 0.57270706, 0.57270706, 0.57270706, 0.57270706, 0.57270706,\n"," 0.57270706, 0.5726971 , 0.57267064, 0.57263297, 0.57262266,\n"," 0.57261777, 0.57261747, 0.57258075, 0.57256663, 0.5725497 ,\n"," 0.57253391, 0.57252789, 0.57251132, 0.57251042, 0.57250589,\n"," 0.57249886, 0.57248503, 0.57246315, 0.57245237, 0.57244897,\n"," 0.57244807, 0.57244426, 0.57242268, 0.57240492, 0.57240474,\n"," 0.57239604, 0.572384 , 0.57237822, 0.57235342, 0.57235181,\n"," 0.57235181, 0.57234532, 0.57232702, 0.57232153, 0.57231581,\n"," 0.57231194, 0.57230902, 0.57230401, 0.57229233, 0.57228822,\n"," 0.57223833, 0.57222623, 0.57222337, 0.57222182, 0.57221192,\n"," 0.57219446, 0.57219112, 0.57218748, 0.57215625, 0.57214457,\n"," 0.57214355, 0.572142 , 0.57212949, 0.57212651, 0.57212615,\n"," 0.57211077, 0.57211077, 0.57210684, 0.57209927, 0.57209218,\n"," 0.57207751, 0.57207453, 0.57206988, 0.57206237, 0.5720607 ,\n"," 0.57205123, 0.57203901, 0.57203662, 0.57203662, 0.57203656,\n"," 0.57203442, 0.57201314, 0.5720098 , 0.57198858, 0.57198006,\n"," 0.57197195, 0.57195139, 0.57195055, 0.57188553, 0.5718832 ,\n"," 0.57188016, 0.57187688, 0.57185799, 0.57185459, 0.57184118,\n"," 0.5718407 , 0.57182539, 0.57178825, 0.57177758, 0.57175356,\n"," 0.57175356, 0.57173473, 0.57169414, 0.5716911 , 0.57166731,\n"," 0.57166159, 0.57164639, 0.57162899, 0.57158774, 0.57157344,\n"," 0.57156205, 0.57150972, 0.5715062 , 0.57148623, 0.57144666,\n"," 0.57143825, 0.57140779, 0.57139695, 0.57138354, 0.57138002,\n"," 0.57137775, 0.57137775, 0.57136613, 0.57132649, 0.57131368,\n"," 0.57128882, 0.57127994, 0.57127994, 0.57127994, 0.57127994,\n"," 0.57127994, 0.57127994, 0.57127994, 0.57127994, 0.57127994,\n"," 0.57127994, 0.57127994, 0.57127994, 0.57127994, 0.57127994,\n"," 0.57126284, 0.57125199, 0.57124388, 0.5712139 , 0.5712024 ,\n"," 0.57119542, 0.57119483, 0.57118255, 0.57117844, 0.57117409,\n"," 0.5711695 , 0.57116485, 0.57115459, 0.57113945, 0.57113057,\n"," 0.57112008, 0.57110721, 0.57105893, 0.57104969, 0.57104784,\n"," 0.57102001, 0.571015 , 0.57100415, 0.57100153, 0.57099724,\n"," 0.57099688, 0.57099551, 0.57098979, 0.57098603, 0.57096791,\n"," 0.5709635 , 0.57096201, 0.57095361, 0.57094222, 0.5709281 ,\n"," 0.57092583, 0.57092178, 0.57091349, 0.5708999 , 0.57087338,\n"," 0.57087296, 0.57086772, 0.57085574, 0.57084084, 0.57080805,\n"," 0.57080126, 0.57076997, 0.57074589, 0.5707404 , 0.57072324,\n"," 0.5707162 , 0.57071334, 0.57070994, 0.57070845, 0.5706901 ,\n"," 0.57067966, 0.57067198, 0.57065499, 0.57064611, 0.57064611,\n"," 0.57064611, 0.57064611, 0.57064611, 0.57064611, 0.57064378,\n"," 0.57063812, 0.57062936, 0.57061571, 0.57061034, 0.57060617,\n"," 0.57057577, 0.5705716 , 0.57054597, 0.57053649, 0.57053256,\n"," 0.57051182, 0.57050955, 0.57050049, 0.57049829, 0.57045752,\n"," 0.57045752, 0.57044685, 0.57044208, 0.57042873, 0.5704276 ,\n"," 0.57041454, 0.57039541, 0.57039469, 0.57036591, 0.57035786,\n"," 0.57035029, 0.57031858, 0.57029831, 0.57028717, 0.57028371,\n"," 0.57027209, 0.57026869, 0.57026446, 0.57026011, 0.57024682,\n"," 0.57024354, 0.57021713, 0.57021624, 0.57021254, 0.57019389,\n"," 0.57019043, 0.57016844, 0.57015634, 0.57011956, 0.57010895,\n"," 0.57009417, 0.5700888 , 0.57008845, 0.570086 , 0.570086 ,\n"," 0.57008451, 0.57007486, 0.57007092, 0.57005304, 0.57004654,\n"," 0.57004023, 0.57003522, 0.57002777, 0.57001126, 0.56999671,\n"," 0.56995702, 0.56994361, 0.56993663, 0.56992853, 0.5699116 ,\n"," 0.56990796, 0.56990403, 0.56989121, 0.56988639, 0.56987989,\n"," 0.56987327, 0.56987029, 0.56986368, 0.56986284, 0.56982708,\n"," 0.56980288, 0.56977737, 0.56976897, 0.5697214 , 0.56970143,\n"," 0.56965965, 0.56965965, 0.56965131, 0.56964236, 0.56963986,\n"," 0.56963509, 0.56962252, 0.56962031, 0.56955528, 0.56954414,\n"," 0.56953239, 0.56952363, 0.56950623, 0.56949204, 0.56948656,\n"," 0.56946504, 0.56945789, 0.56945026, 0.56944948, 0.56943405,\n"," 0.56943405, 0.56943405, 0.56943405, 0.56943405, 0.56943405,\n"," 0.56943405, 0.56943405, 0.56943405, 0.56943405, 0.56943291,\n"," 0.56942409, 0.56942075, 0.56942058, 0.56941992, 0.56940567,\n"," 0.56939471, 0.56936592, 0.56936246, 0.56935167, 0.56933177,\n"," 0.569318 , 0.56930226, 0.56928736, 0.56927407, 0.56925362,\n"," 0.56924903, 0.56923795, 0.5692333 , 0.56922531, 0.56918997]),\n"," array([0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60420626, 0.60420626,\n"," 0.60420626, 0.60420626, 0.60420626, 0.60382879, 0.60382879,\n"," 0.60382879, 0.60382879, 0.60382879, 0.60382879, 0.60382879,\n"," 0.60382879, 0.60382879, 0.60360277, 0.60360277, 0.60360277,\n"," 0.60360277, 0.60360277, 0.60360277, 0.60360277, 0.60360277,\n"," 0.60360277, 0.60360277, 0.60360277, 0.60360277, 0.60360277,\n"," 0.60360277, 0.60360277, 0.60360277, 0.60336739, 0.60336739,\n"," 0.60336739, 0.60336739, 0.60336739, 0.60336739, 0.60336739,\n"," 0.60336739, 0.60336739, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60322529, 0.60322529,\n"," 0.60322529, 0.60322529, 0.60322529, 0.60299921, 0.60299921,\n"," 0.60299921, 0.60299921, 0.60299921, 0.60299921, 0.60299921,\n"," 0.60299921, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60298991, 0.60298991, 0.60298991,\n"," 0.60298991, 0.60298991, 0.60276383, 0.60276383, 0.60276383,\n"," 0.60276383, 0.60276383, 0.60276383, 0.60276383, 0.60276383,\n"," 0.60276383, 0.60276383, 0.60276383, 0.60276383, 0.60276383,\n"," 0.60276383, 0.60276383, 0.60276383, 0.60276383, 0.60276383,\n"," 0.60276383, 0.60276383, 0.60276383, 0.60276383, 0.60276383,\n"," 0.60276383, 0.60276383, 0.60276383, 0.60276383, 0.60276383,\n"," 0.60276383, 0.60276383, 0.60276383, 0.60276383, 0.60276383,\n"," 0.60276383, 0.60276383, 0.60276383, 0.60276383, 0.60276383,\n"," 0.60276383, 0.60276383, 0.60276383, 0.60276383, 0.60276383,\n"," 0.60276383, 0.60276383, 0.60276383, 0.60276383, 0.60276383,\n"," 0.60262173, 0.60262173, 0.60262173, 0.60261244, 0.60247034,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60238636, 0.60238636, 0.60238636, 0.60238636,\n"," 0.60238636, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60224426, 0.60224426, 0.60224426,\n"," 0.60224426, 0.60224426, 0.60185748, 0.60185748, 0.60185748,\n"," 0.60179216, 0.60178286, 0.60178286, 0.60178286, 0.60178286,\n"," 0.60178286, 0.60178286, 0.60178286, 0.60178286, 0.60178286,\n"," 0.60178286, 0.60178286, 0.60178286, 0.60178286, 0.60178286,\n"," 0.60178286, 0.60178286, 0.60178286, 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 , 0.6017735 ,\n"," 0.6017735 , 0.60171539, 0.60171539, 0.60148001, 0.60140538,\n"," 0.60140538, 0.60140538, 0.60140538, 0.60140538, 0.60140538,\n"," 0.60140538, 0.60140538, 0.60133791, 0.6013214 , 0.6013214 ,\n"," 0.6013214 , 0.6013214 , 0.6013214 , 0.6013214 , 0.6013214 ,\n"," 0.6013214 , 0.6013214 , 0.6013214 , 0.6013214 , 0.6013214 ,\n"," 0.6013214 , 0.6013214 , 0.6013214 , 0.6013214 , 0.6013214 ,\n"," 0.6013214 , 0.6013214 , 0.6013214 , 0.6013214 , 0.6013214 ,\n"," 0.6013214 , 0.6013214 , 0.6013214 , 0.60117 , 0.60117 ,\n"," 0.60117 , 0.60117 , 0.60117 , 0.60117 , 0.60117 ,\n"," 0.60117 , 0.60117 , 0.60117 , 0.60117 , 0.60117 ,\n"," 0.60117 , 0.60117 , 0.60117 , 0.60117 , 0.60117 ,\n"," 0.60117 , 0.60117 , 0.60117 , 0.60110253, 0.60108602,\n"," 0.60108602, 0.60094392, 0.60087645, 0.60087645, 0.60085064,\n"," 0.60072505, 0.60070854, 0.60058296, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60056645,\n"," 0.60056645, 0.60056645, 0.60056645, 0.60056645, 0.60055715,\n"," 0.60049897, 0.6004315 , 0.6004315 , 0.6004315 , 0.60042435,\n"," 0.60042435, 0.60042435, 0.60034758, 0.5999701 , 0.59995359,\n"," 0.59995359, 0.599828 , 0.59967655, 0.59963429, 0.59959263,\n"," 0.59903079, 0.59903079, 0.59903079, 0.59898907, 0.59884697,\n"," 0.59856933, 0.59833395, 0.59831804, 0.59831804, 0.59823412,\n"," 0.59808266, 0.59795648, 0.59794056, 0.59770519, 0.59762126,\n"," 0.59732771, 0.59716916, 0.59695023, 0.59689152, 0.59665614,\n"," 0.59657276, 0.59652859, 0.59651405, 0.59648877, 0.59641421,\n"," 0.59634668, 0.59627867, 0.59605265, 0.5959692 , 0.5959692 ,\n"," 0.5959692 , 0.5958271 , 0.5957756 , 0.5956592 , 0.59559172,\n"," 0.59544033, 0.59544033, 0.59521425, 0.59506285, 0.59502065,\n"," 0.59497839, 0.59492075, 0.59492075, 0.59492075, 0.59483677,\n"," 0.59483629, 0.59454328, 0.59452677, 0.59452677, 0.59452677,\n"," 0.59437484, 0.59437484, 0.5943079 , 0.59423327, 0.59423327,\n"," 0.59423327, 0.59422392, 0.59422392, 0.59405857, 0.59385526,\n"," 0.59385526, 0.59385526, 0.59384644, 0.59378833, 0.59378833,\n"," 0.59377182, 0.59370434, 0.59363687, 0.59347832, 0.5933845 ,\n"," 0.5933845 , 0.59332687, 0.59328687, 0.59328687, 0.59328687,\n"," 0.59328687, 0.59325939, 0.59325939, 0.59325939, 0.59303337,\n"," 0.59294939, 0.59294939, 0.59294939, 0.59269702, 0.59269702,\n"," 0.5926559 , 0.59263939, 0.59257191, 0.59257191, 0.59242982,\n"," 0.59242052, 0.59233654, 0.59233654, 0.59233606, 0.59227842,\n"," 0.59204304, 0.59204304, 0.59204304, 0.59190094, 0.59188443,\n"," 0.59188443, 0.59181696, 0.59170669, 0.59170669, 0.59170669,\n"," 0.59152347, 0.59148067, 0.59148067, 0.59148067, 0.59143949,\n"," 0.59124529, 0.59106201, 0.59106201, 0.59101927, 0.59101927,\n"," 0.59075201, 0.59075201, 0.59054244, 0.59054244, 0.59054244,\n"," 0.59054244, 0.59054244, 0.59053314, 0.59044427, 0.59037453,\n"," 0.59037453, 0.59018034, 0.59015507, 0.58994496, 0.58994496,\n"," 0.58992958, 0.58988953, 0.58980286, 0.58969367, 0.58964705,\n"," 0.58963609, 0.58963609, 0.5895521 , 0.5894531 , 0.58940071,\n"," 0.58940071, 0.58910936, 0.58898097, 0.58896399, 0.58886093,\n"," 0.58879715, 0.58856177, 0.58856177, 0.58856177, 0.58850253,\n"," 0.58850253, 0.58841968, 0.58827758, 0.58827758, 0.58827758,\n"," 0.58826828, 0.58812618, 0.58812505, 0.58798295, 0.58798295,\n"," 0.5878908 , 0.5878908 , 0.58782715, 0.5877322 , 0.58766365,\n"," 0.58751333, 0.58751225, 0.58737123, 0.58732545, 0.58732545,\n"," 0.58728725, 0.58728617, 0.58713585, 0.58690977, 0.58661628,\n"," 0.58661628, 0.58661628, 0.5864473 , 0.5864473 , 0.5864473 ,\n"," 0.5863809 , 0.58629692, 0.5862388 , 0.58622229, 0.58621192,\n"," 0.5860709 , 0.5860709 , 0.58600724, 0.58600724, 0.58600724,\n"," 0.58600724, 0.58600724, 0.58600724, 0.58600342, 0.58600235,\n"," 0.58600235, 0.58600235, 0.58599281, 0.58591944, 0.58586133,\n"," 0.58586133, 0.58577734, 0.58573794, 0.58570987, 0.58562595,\n"," 0.58562595, 0.58560836, 0.58560836, 0.58550555, 0.5854997 ,\n"," 0.58546734, 0.58546734, 0.58539987, 0.58539987, 0.58538234,\n"," 0.58538234, 0.58533239, 0.58528328, 0.58524847, 0.58517385,\n"," 0.58482182, 0.58479089, 0.58471239, 0.58471239, 0.58471239,\n"," 0.58454341, 0.58454341, 0.58454341, 0.58439201, 0.58439201,\n"," 0.58439201, 0.58430803, 0.58426744, 0.58418733, 0.58418733,\n"," 0.5841769 , 0.58407271, 0.58407271, 0.58403206, 0.58397394,\n"," 0.58395743, 0.58395743, 0.58395743, 0.58393055, 0.58391804,\n"," 0.58388996, 0.58388996, 0.58388996, 0.58386344, 0.58378845,\n"," 0.58369523, 0.58369523, 0.58369523, 0.58359647, 0.58351249,\n"," 0.58321899, 0.58321899, 0.58313501, 0.58309168, 0.58309168,\n"," 0.58299536, 0.58299536, 0.58269006, 0.58269006, 0.58269006]),\n"," array([1.13297248, 1.13297248, 1.13297248, 1.13297248, 1.12802839,\n"," 1.12381315, 1.12381315, 1.11046445, 1.11046445, 1.11046445,\n"," 1.10980058, 1.10980058, 1.10980058, 1.10980058, 1.10980058,\n"," 1.10980058, 1.10520232, 1.10353208, 1.10346889, 1.10272157,\n"," 1.10136521, 1.09988236, 1.09798276, 1.09775043, 1.09757233,\n"," 1.09718621, 1.09653258, 1.09521806, 1.09425938, 1.09293973,\n"," 1.09288001, 1.0920639 , 1.08949316, 1.08939481, 1.08906209,\n"," 1.08830237, 1.08795643, 1.08795643, 1.08795643, 1.08795643,\n"," 1.08795643, 1.08795643, 1.08795643, 1.08795643, 1.08795643,\n"," 1.08756173, 1.08729243, 1.08729243, 1.08729243, 1.08729243,\n"," 1.08729243, 1.08729243, 1.08710265, 1.08699059, 1.08699059,\n"," 1.08699059, 1.08699059, 1.08699059, 1.08699059, 1.08699059,\n"," 1.08699059, 1.08647656, 1.08620322, 1.08580208, 1.08538532,\n"," 1.08537006, 1.08485305, 1.08483732, 1.08478677, 1.08478129,\n"," 1.08471036, 1.08422244, 1.08364534, 1.08364534, 1.08364534,\n"," 1.08364534, 1.08296406, 1.08282077, 1.0826441 , 1.08160472,\n"," 1.08142543, 1.08100188, 1.08081949, 1.08081949, 1.08072209,\n"," 1.0802325 , 1.07985878, 1.07963669, 1.07940114, 1.07915914,\n"," 1.07908487, 1.07861793, 1.07837629, 1.07804477, 1.07791841,\n"," 1.07783926, 1.07740808, 1.0773648 , 1.07602799, 1.07577407,\n"," 1.0757376 , 1.07554877, 1.07554877, 1.07530892, 1.07524097,\n"," 1.07520795, 1.07506537, 1.0750649 , 1.07464826, 1.07447064,\n"," 1.07444584, 1.07438862, 1.07431757, 1.07403827, 1.07366395,\n"," 1.07330501, 1.07318544, 1.07277524, 1.07271695, 1.07249248,\n"," 1.07201767, 1.07191849, 1.07180607, 1.07178402, 1.07178402,\n"," 1.07172275, 1.07170594, 1.07162583, 1.07158065, 1.07140851,\n"," 1.07124722, 1.07106721, 1.07100785, 1.07089233, 1.07089043,\n"," 1.07056379, 1.07055509, 1.07052493, 1.07046044, 1.07046032,\n"," 1.07008111, 1.06997085, 1.06992197, 1.06989729, 1.0698936 ,\n"," 1.06979203, 1.06962252, 1.06944609, 1.06932056, 1.0692836 ,\n"," 1.06924331, 1.0692153 , 1.06893373, 1.0686574 , 1.06852841,\n"," 1.06833446, 1.06831241, 1.0681684 , 1.06810534, 1.06791079,\n"," 1.06768775, 1.06753588, 1.06728911, 1.06708145, 1.06688702,\n"," 1.06666088, 1.06647158, 1.06614804, 1.0660708 , 1.06603622,\n"," 1.06599987, 1.06595254, 1.06592178, 1.06579995, 1.06579995,\n"," 1.06579995, 1.06579995, 1.06579995, 1.06579995, 1.06579995,\n"," 1.06579995, 1.06579995, 1.06579995, 1.06579995, 1.06579995,\n"," 1.06579995, 1.06579995, 1.06579995, 1.06579995, 1.06571686,\n"," 1.06566477, 1.06542659, 1.06526506, 1.0650816 , 1.06501579,\n"," 1.06478441, 1.06478441, 1.06478441, 1.0646168 , 1.06448245,\n"," 1.06448245, 1.06448245, 1.06448245, 1.06448245, 1.06448245,\n"," 1.06448245, 1.06448245, 1.06448245, 1.06448245, 1.0643152 ,\n"," 1.06422734, 1.06420958, 1.06389904, 1.06381857, 1.06381857,\n"," 1.06381857, 1.06381857, 1.06381857, 1.06381857, 1.06379998,\n"," 1.06337583, 1.06336975, 1.06331277, 1.06315207, 1.06314194,\n"," 1.06306911, 1.06306088, 1.06303012, 1.06296134, 1.062958 ,\n"," 1.06289077, 1.06282365, 1.06272078, 1.06255662, 1.06242824,\n"," 1.06236696, 1.06233168, 1.06230485, 1.06225181, 1.06215024,\n"," 1.06215024, 1.06213665, 1.06201303, 1.06201303, 1.0618763 ,\n"," 1.06187546, 1.06186903, 1.06179392, 1.06179392, 1.06179392,\n"," 1.06171739, 1.06158984, 1.06153059, 1.06152856, 1.06150103,\n"," 1.06121528, 1.06113851, 1.06107688, 1.06103921, 1.06103158,\n"," 1.06095099, 1.06094968, 1.06082654, 1.06082654, 1.06075168,\n"," 1.06064022, 1.06042504, 1.06015205, 1.06012118, 1.06012118,\n"," 1.0600996 , 1.06003165, 1.0599879 , 1.05982208, 1.05978036,\n"," 1.05973041, 1.05956376, 1.05946505, 1.05943727, 1.05937886,\n"," 1.05924857, 1.05906105, 1.05901098, 1.05889952, 1.05885923,\n"," 1.05870056, 1.05869007, 1.0586437 , 1.05863941, 1.05861557,\n"," 1.05836213, 1.05829227, 1.05811942, 1.05809617, 1.05800664,\n"," 1.05798411, 1.05797148, 1.05794775, 1.05787814, 1.05781877,\n"," 1.05779195, 1.057778 , 1.05761266, 1.05744565, 1.05744565,\n"," 1.05727315, 1.05711806, 1.05701005, 1.0567919 , 1.05675352,\n"," 1.05668688, 1.05651486, 1.05651486, 1.05651486, 1.05642247,\n"," 1.05638218, 1.05615795, 1.05613053, 1.05601275, 1.0559628 ,\n"," 1.05594122, 1.0559355 , 1.05583549, 1.05570126, 1.05560064,\n"," 1.05558276, 1.05557895, 1.05553913, 1.05545795, 1.0553416 ,\n"," 1.05524564, 1.05524421, 1.05519485, 1.05502117, 1.05499959,\n"," 1.05497205, 1.054932 , 1.05492246, 1.05491936, 1.0549078 ,\n"," 1.05486488, 1.05484033, 1.05468106, 1.0546304 , 1.05459177,\n"," 1.0545156 , 1.05444443, 1.05429983, 1.05424595, 1.05418801,\n"," 1.05410135, 1.05407858, 1.05404937, 1.05395675, 1.05395675,\n"," 1.05370224, 1.05365384, 1.05362654, 1.05350685, 1.05346024,\n"," 1.05333948, 1.05324674, 1.05323923, 1.0529772 , 1.05290902,\n"," 1.052899 , 1.05288172, 1.05283999, 1.05282366, 1.05278957,\n"," 1.05277145, 1.05272043, 1.05271459, 1.05270934, 1.05267131,\n"," 1.05265832, 1.05261827, 1.05259359, 1.0525316 , 1.05252934,\n"," 1.05243158, 1.0523665 , 1.05231631, 1.05222917, 1.0520736 ,\n"," 1.05207157, 1.05202639, 1.05201733, 1.05192482, 1.05176008,\n"," 1.05175161, 1.05173397, 1.0517236 , 1.05147886, 1.05144799,\n"," 1.05140746, 1.05135655, 1.05134261, 1.05130935, 1.05128908,\n"," 1.05127251, 1.05125558, 1.05114627, 1.05105793, 1.05100954,\n"," 1.05093157, 1.05090952, 1.0508976 , 1.05080712, 1.0507828 ,\n"," 1.05075169, 1.05074906, 1.05069923, 1.05063498, 1.05052543,\n"," 1.05047178, 1.05047166, 1.05047119, 1.05045438, 1.05043364,\n"," 1.05039883, 1.0503906 , 1.05037439, 1.05027425, 1.05020857,\n"," 1.05013442, 1.05006683, 1.0498873 , 1.04987526, 1.0498265 ,\n"," 1.04966879, 1.04966414, 1.04952145, 1.04948604, 1.04932475,\n"," 1.04918051, 1.04916394, 1.04912269, 1.04910111, 1.04903018,\n"," 1.04896784, 1.0489285 , 1.0489217 , 1.04890478, 1.0488894 ,\n"," 1.04883742, 1.04879832, 1.04879737, 1.04873395, 1.04870498,\n"," 1.04865229, 1.04865015, 1.0486474 , 1.0486362 , 1.04863095,\n"," 1.04858029, 1.04858017, 1.04838312, 1.04829192, 1.04825544,\n"," 1.04817736, 1.04816699, 1.0480988 , 1.04795921, 1.04781604,\n"," 1.04765368, 1.04761922, 1.04759121, 1.04756081, 1.04750502,\n"," 1.04743659, 1.04741716, 1.04741657, 1.04737329, 1.04732549,\n"," 1.04729557, 1.04726434, 1.04721248, 1.04720831, 1.04714227,\n"," 1.04711413, 1.0470928 , 1.04706955, 1.04704165, 1.04701173,\n"," 1.04700398, 1.04698932, 1.04693699, 1.0468775 , 1.04684103,\n"," 1.04681432, 1.0468142 , 1.04681075, 1.04679847, 1.04677427,\n"," 1.04676616, 1.04676211, 1.04673827, 1.04669547, 1.04667723,\n"," 1.04664981, 1.04662776, 1.04662001, 1.04660237, 1.04659212,\n"," 1.04650867, 1.04650187, 1.04649985, 1.04649353, 1.04642582,\n"," 1.04639518, 1.04638362, 1.0463655 , 1.0463388 , 1.04627204,\n"," 1.04625571, 1.0462054 , 1.04613686, 1.04612362, 1.04610968,\n"," 1.0460012 , 1.04599023, 1.04596722, 1.04596317, 1.04589415,\n"," 1.04586768, 1.04583538, 1.04583502, 1.04578817, 1.04578435,\n"," 1.04572093, 1.04565334, 1.04551005, 1.04541898, 1.04536736,\n"," 1.04509592, 1.04506266, 1.04502928, 1.04502594, 1.04502547,\n"," 1.04490805, 1.04488194, 1.0448519 , 1.04485106, 1.04481912,\n"," 1.04474115, 1.04472113, 1.04466927, 1.04442704, 1.04435849,\n"," 1.04434824, 1.04433417, 1.04428911, 1.04427552, 1.04421544,\n"," 1.04415059, 1.04411435, 1.04410493, 1.04406142, 1.0440383 ,\n"," 1.04402053, 1.04394269, 1.04388964, 1.04375243, 1.0437324 ,\n"," 1.0436945 , 1.04350019, 1.04339838, 1.04338408, 1.04336762,\n"," 1.04335511, 1.04329193, 1.04329193, 1.04329193, 1.04329193,\n"," 1.04329193, 1.04329193, 1.04329193, 1.04329193, 1.04329193,\n"," 1.04328513, 1.04327226, 1.04322839, 1.04320967, 1.04309487,\n"," 1.04300487, 1.04300094, 1.04297018, 1.04289019, 1.04286659,\n"," 1.0427897 , 1.04276752, 1.04274523, 1.04273307, 1.04271734,\n"," 1.04269147, 1.04269147, 1.04269147, 1.04267406, 1.04266691,\n"," 1.0426122 , 1.04256999, 1.04256427, 1.04256117, 1.04255235,\n"," 1.04253447, 1.04253399, 1.04253101, 1.0424614 , 1.04245746,\n"," 1.04234922, 1.04231489, 1.04231048, 1.04230368, 1.04228175,\n"," 1.04216349, 1.04215157, 1.04213738, 1.04205585, 1.04199803,\n"," 1.04199374, 1.04197443, 1.04197443, 1.04197443, 1.04197443,\n"," 1.04197443, 1.04197443, 1.04197443, 1.04197443, 1.04197443,\n"," 1.04192162, 1.04191899, 1.04184246, 1.04178202, 1.04178023,\n"," 1.04176593, 1.04175842, 1.04172134, 1.04170632, 1.04161847,\n"," 1.04161656, 1.04160678, 1.04155242, 1.04152846, 1.04150748,\n"," 1.0414443 , 1.0414443 , 1.04143071, 1.04136765, 1.04131186,\n"," 1.04131055, 1.04128635, 1.04120517, 1.04108274, 1.04107428,\n"," 1.04104018, 1.04103291, 1.04097843, 1.04096234, 1.04094815,\n"," 1.04091668, 1.04079688, 1.04072583, 1.04066873, 1.04064655,\n"," 1.04064655, 1.04064655, 1.04064655, 1.04064655, 1.04064655,\n"," 1.04064655, 1.04064655, 1.04064655, 1.04064655, 1.04064655,\n"," 1.04064655, 1.04064655, 1.04064655, 1.04064655, 1.04064655,\n"," 1.04064655, 1.04064655, 1.04064655, 1.04064655, 1.04064655,\n"," 1.04064655, 1.04064655, 1.04064655, 1.04064655, 1.04064655,\n"," 1.04064655, 1.04064655, 1.04064655, 1.04064655, 1.04064655,\n"," 1.04064655, 1.04064655, 1.04064655, 1.04064655, 1.04064655,\n"," 1.04064655, 1.04064655, 1.04064655, 1.0406177 , 1.04059553,\n"," 1.04059553, 1.04055572, 1.04050994, 1.04049611, 1.04048836,\n"," 1.04045737, 1.04045522, 1.0404278 , 1.0404278 , 1.0404278 ,\n"," 1.0404278 , 1.04038692, 1.04038358, 1.04038358, 1.04037058,\n"," 1.04034424, 1.04026842, 1.04024553, 1.04023218, 1.04023206,\n"," 1.04019773, 1.04015744, 1.04014504, 1.04014146, 1.04014146,\n"," 1.04009473, 1.03999782, 1.0399859 , 1.03998303, 1.03996015,\n"," 1.03995037, 1.03990698, 1.03988039, 1.03980899, 1.03977895,\n"," 1.03974044, 1.03974044, 1.03972852, 1.03970587, 1.03968549,\n"," 1.03960705, 1.03954244, 1.03946996, 1.03946996, 1.03936541,\n"," 1.03935957, 1.03931916, 1.0392859 , 1.0392859 , 1.0392859 ,\n"," 1.0392859 , 1.0392859 , 1.0392859 , 1.0392859 , 1.0392859 ,\n"," 1.0392611 , 1.03924441, 1.0392226 , 1.03915 , 1.03913105,\n"," 1.03911769, 1.03911483, 1.03910911, 1.03908265, 1.03908002,\n"," 1.03906167, 1.03906131, 1.03903985, 1.03902924, 1.03902209,\n"," 1.03899074, 1.03897822, 1.03892899, 1.03892183, 1.03889966,\n"," 1.03889251, 1.03884733, 1.03880656, 1.03877628, 1.03863394,\n"," 1.0386219 , 1.03860068, 1.03859174, 1.03855205, 1.03852129,\n"," 1.03844154, 1.03842902, 1.03841531, 1.0383395 , 1.03821146,\n"," 1.03818834, 1.03817749, 1.03817558, 1.03816569, 1.03811276,\n"," 1.0380758 , 1.0380044 , 1.03800273, 1.03795767, 1.03794789,\n"," 1.03789246, 1.03788924, 1.03785956, 1.03784692, 1.0378108 ,\n"," 1.03778386, 1.03777051, 1.03773582, 1.03772128, 1.03771961,\n"," 1.03770053, 1.03763819, 1.03763115, 1.03760326, 1.03754306,\n"," 1.0375396 , 1.03744888, 1.03741622, 1.03740788, 1.03731322,\n"," 1.0372777 , 1.03723359, 1.03713417, 1.0371151 , 1.03709853,\n"," 1.03707099, 1.03707099, 1.03707099, 1.03707099, 1.03706264,\n"," 1.03704321, 1.03699434, 1.03696871, 1.03690577, 1.03690064,\n"," 1.03689075, 1.03688192, 1.03688014, 1.03687978, 1.03687322,\n"," 1.03685546, 1.03685367, 1.03682411, 1.03679335, 1.03679335,\n"," 1.03674889, 1.03674746, 1.03673923, 1.03673387, 1.03668845,\n"," 1.03663146, 1.03660548, 1.03659534, 1.03659141, 1.03656697,\n"," 1.03652525, 1.03651392, 1.03651118, 1.03650129, 1.03649795,\n"," 1.03649104, 1.03648317, 1.03644311, 1.03644311, 1.03643012,\n"," 1.03642511, 1.03638673, 1.03636158, 1.03635764, 1.0363524 ,\n"," 1.03634512, 1.03634512, 1.03633499, 1.03633046, 1.03631449,\n"," 1.03631449, 1.03631449, 1.03626585, 1.03624237, 1.03621411,\n"," 1.0361979 , 1.03616762, 1.03611767, 1.03611767, 1.03611767,\n"," 1.03611767, 1.03611767, 1.03606451, 1.03606451, 1.03606451,\n"," 1.03602433, 1.03601408, 1.03600585, 1.03600585, 1.03598654,\n"," 1.03597522, 1.03597522, 1.03597522, 1.03597522, 1.03597224,\n"," 1.03595877, 1.03595877, 1.03595877, 1.03595877, 1.03589761,\n"," 1.03589308, 1.03586316, 1.03585982, 1.03584194, 1.03580797,\n"," 1.03579497, 1.03569448, 1.03569067, 1.03569067, 1.03567445,\n"," 1.03565955, 1.03561163, 1.03557444, 1.03557444, 1.03557444,\n"," 1.03554928, 1.03554094, 1.03550863, 1.03548992, 1.03548992,\n"," 1.03548992, 1.03548992, 1.03548992, 1.03548992, 1.03548992,\n"," 1.03548992, 1.03548992, 1.03548992, 1.03548992, 1.03548992,\n"," 1.03548992, 1.03548992, 1.03548992, 1.03548992, 1.03548992,\n"," 1.03548992, 1.03546178, 1.03543842, 1.03543639, 1.03541565,\n"," 1.03540611, 1.03540385, 1.03537548, 1.03533101, 1.03533101,\n"," 1.03533101, 1.03533101, 1.03533101, 1.03533101, 1.03533101,\n"," 1.03533101, 1.03533101, 1.03533101, 1.03533101, 1.03533101,\n"," 1.03533101, 1.03533101, 1.03533101, 1.03533101, 1.03533101,\n"," 1.03533101, 1.03533101, 1.03533101, 1.03533101, 1.03533101,\n"," 1.03533101, 1.03533101, 1.03533101, 1.03533101, 1.03533101,\n"," 1.03533101, 1.03533101, 1.03533101, 1.03533101, 1.03529966,\n"," 1.03528357, 1.03528321, 1.03527641, 1.0352608 , 1.03523576,\n"," 1.0352354 , 1.03523374, 1.03522646, 1.03520596, 1.03510225,\n"," 1.03510225, 1.03504097, 1.03502572, 1.03502202, 1.03500557]),\n"," array([0.25088719, 0.2490904 , 0.2490904 , 0.2490904 , 0.24811119,\n"," 0.24734654, 0.24604921, 0.24553286, 0.24124771, 0.24050711,\n"," 0.24029763, 0.23972301, 0.23807791, 0.23682503, 0.23677978,\n"," 0.23514891, 0.23473553, 0.23438707, 0.23254904, 0.23192208,\n"," 0.23188303, 0.2316931 , 0.23086245, 0.22865191, 0.22785768,\n"," 0.22726044, 0.22715057, 0.22709703, 0.22708079, 0.22701202,\n"," 0.22673033, 0.2263297 , 0.22600068, 0.22600068, 0.22487615,\n"," 0.22479193, 0.22426097, 0.22420017, 0.22386359, 0.2232803 ,\n"," 0.2232803 , 0.2232803 , 0.22295126, 0.22264561, 0.22259839,\n"," 0.22259839, 0.22259839, 0.22255589, 0.22187813, 0.22155418,\n"," 0.22136758, 0.22128832, 0.22089952, 0.22060257, 0.22033884,\n"," 0.22026633, 0.22021377, 0.21985504, 0.21978863, 0.21940009,\n"," 0.21916327, 0.21892893, 0.21890472, 0.21890472, 0.21890472,\n"," 0.21873894, 0.21864228, 0.21819441, 0.21812366, 0.21742544,\n"," 0.21732923, 0.21714427, 0.2170475 , 0.21690038, 0.21687633,\n"," 0.21682683, 0.21654427, 0.21633402, 0.21620035, 0.21584125,\n"," 0.21563081, 0.21559153, 0.21559153, 0.21544993, 0.21496728,\n"," 0.21489944, 0.21488741, 0.21475394, 0.2147297 , 0.2144931 ,\n"," 0.21443729, 0.21424633, 0.21415277, 0.21383508, 0.21380308,\n"," 0.21366338, 0.21359245, 0.21326986, 0.21326032, 0.21321313,\n"," 0.21287607, 0.21267852, 0.21260697, 0.2126039 , 0.21235909,\n"," 0.21218924, 0.21218924, 0.21210982, 0.21206053, 0.21206053,\n"," 0.21206053, 0.21206053, 0.21206053, 0.21206053, 0.21202746,\n"," 0.21198858, 0.21191704, 0.21173133, 0.21170098, 0.21162784,\n"," 0.21153072, 0.21145573, 0.21139562, 0.21136479, 0.21135291,\n"," 0.21125093, 0.21118505, 0.2111759 , 0.21112515, 0.21110858,\n"," 0.21094845, 0.21084003, 0.2108106 , 0.21063383, 0.21049345,\n"," 0.21042411, 0.21034995, 0.21033251, 0.2102955 , 0.21002418,\n"," 0.21001057, 0.2099618 , 0.20994498, 0.20972534, 0.2096945 ,\n"," 0.20933586, 0.2092887 , 0.20915715, 0.20902148, 0.20887488,\n"," 0.20876208, 0.20870863, 0.20869839, 0.20865823, 0.20865823,\n"," 0.20865823, 0.20865823, 0.20858726, 0.20853981, 0.20834053,\n"," 0.20816191, 0.20807973, 0.20776118, 0.20763922, 0.20762056,\n"," 0.20757218, 0.20753936, 0.20749733, 0.20735623, 0.20702618,\n"," 0.20700236, 0.2067692 , 0.20676154, 0.20672458, 0.20670371,\n"," 0.20667654, 0.20667654, 0.20667124, 0.20665273, 0.20662044,\n"," 0.20656495, 0.20646922, 0.20625284, 0.20601444, 0.20600218,\n"," 0.20595376, 0.20589672, 0.20556013, 0.20555513, 0.20541514,\n"," 0.20538525, 0.20537226, 0.2052696 , 0.20515683, 0.20514598,\n"," 0.20505768, 0.20497392, 0.20487979, 0.20487815, 0.2048678 ,\n"," 0.20473902, 0.20470275, 0.20468585, 0.20440896, 0.20440045,\n"," 0.20425604, 0.20418413, 0.20418005, 0.20409086, 0.20401876,\n"," 0.20383228, 0.2038016 , 0.20364076, 0.20358142, 0.20355678,\n"," 0.20352671, 0.20350108, 0.20344475, 0.2034421 , 0.20336315,\n"," 0.20326759, 0.20322566, 0.20321086, 0.20316488, 0.20311923,\n"," 0.20306282, 0.20305239, 0.20303981, 0.20292994, 0.20290321,\n"," 0.20287053, 0.20286094, 0.20284966, 0.20274352, 0.20269623,\n"," 0.20265137, 0.20262013, 0.20256276, 0.20254932, 0.20254368,\n"," 0.20243739, 0.20236599, 0.20235544, 0.20232491, 0.20230456,\n"," 0.20229411, 0.20227991, 0.20225671, 0.20222586, 0.20222364,\n"," 0.20201384, 0.20193523, 0.20192753, 0.20181002, 0.2017584 ,\n"," 0.20165138, 0.20165138, 0.20165138, 0.20165138, 0.20165138,\n"," 0.20165138, 0.20165138, 0.20165138, 0.20164572, 0.20164514,\n"," 0.20164274, 0.20164092, 0.20163149, 0.20158914, 0.20158914,\n"," 0.20158418, 0.20147201, 0.20146658, 0.20137861, 0.20135579,\n"," 0.20135084, 0.20125592, 0.20122735, 0.2011313 , 0.20111112,\n"," 0.20111112, 0.20109773, 0.20108531, 0.20104231, 0.20102072,\n"," 0.20090465, 0.20079045, 0.20077549, 0.20077077, 0.20077077,\n"," 0.20077077, 0.20077077, 0.20077077, 0.20066345, 0.20062134,\n"," 0.20057224, 0.20054653, 0.20047478, 0.20043281, 0.20042618,\n"," 0.20038977, 0.20030747, 0.20030527, 0.20027688, 0.20021191,\n"," 0.20016886, 0.20016132, 0.20013799, 0.20006526, 0.2000026 ,\n"," 0.19997115, 0.1999298 , 0.19992971, 0.19989684, 0.19983771,\n"," 0.19982345, 0.19981201, 0.19974016, 0.19972652, 0.19968016,\n"," 0.19961229, 0.19956939, 0.19945657, 0.19944076, 0.19943552,\n"," 0.19935133, 0.19932857, 0.19931667, 0.19927043, 0.19926101,\n"," 0.19922398, 0.19920012, 0.19908705, 0.19904919, 0.19889456,\n"," 0.19880019, 0.19872917, 0.19869819, 0.19868244, 0.19867565,\n"," 0.19865075, 0.19859873, 0.19859117, 0.19858988, 0.19856089,\n"," 0.19852024, 0.19848871, 0.19847362, 0.19846044, 0.19839251,\n"," 0.19829054, 0.19824909, 0.19824909, 0.19824909, 0.19824909,\n"," 0.19824909, 0.19824909, 0.19814008, 0.1981106 , 0.19810812,\n"," 0.19806109, 0.1979623 , 0.19794694, 0.19790763, 0.19789515,\n"," 0.19787395, 0.19784527, 0.19783448, 0.19766459, 0.1975099 ,\n"," 0.19748035, 0.19747387, 0.19744943, 0.19743678, 0.19736847,\n"," 0.19736752, 0.1972902 , 0.19728951, 0.1972501 , 0.19722424,\n"," 0.19720726, 0.19710544, 0.19710064, 0.19704255, 0.19703574,\n"," 0.1970094 , 0.1970094 , 0.19700764, 0.19698991, 0.19691756,\n"," 0.19686823, 0.19672897, 0.19672757, 0.1966754 , 0.19665074,\n"," 0.1966358 , 0.19660747, 0.19660512, 0.19655542, 0.19653055,\n"," 0.19652328, 0.19651784, 0.19651304, 0.19649528, 0.1964304 ,\n"," 0.1963878 , 0.19633488, 0.1962875 , 0.19618067, 0.19608463,\n"," 0.19604243, 0.19601957, 0.19599777, 0.19598591, 0.19595629,\n"," 0.19595629, 0.19591257, 0.19591244, 0.1958072 , 0.19577409,\n"," 0.19565631, 0.19560826, 0.19547996, 0.19543435, 0.19540446,\n"," 0.19540069, 0.19533496, 0.19532397, 0.195217 , 0.19519015,\n"," 0.19517548, 0.19514062, 0.19513281, 0.19507574, 0.19506438,\n"," 0.19505703, 0.1949359 , 0.1949359 , 0.1949359 , 0.1949359 ,\n"," 0.1949359 , 0.1949359 , 0.1949359 , 0.1949359 , 0.1949359 ,\n"," 0.1949359 , 0.1949359 , 0.19493271, 0.19491708, 0.19490016,\n"," 0.19474715, 0.19474418, 0.19468257, 0.19467273, 0.19465759,\n"," 0.1946376 , 0.19460836, 0.19459487, 0.19458707, 0.1945421 ,\n"," 0.19447535, 0.194454 , 0.1944339 , 0.19439544, 0.19436431,\n"," 0.19428694, 0.19427943, 0.19427331, 0.19422513, 0.19420861,\n"," 0.19419444, 0.19418979, 0.1941441 , 0.19413763, 0.19413701,\n"," 0.19409356, 0.1940624 , 0.19404702, 0.19398358, 0.19396318,\n"," 0.19395293, 0.19392958, 0.19391985, 0.19389713, 0.19379558,\n"," 0.19377567, 0.19376348, 0.19375598, 0.19369379, 0.19367836,\n"," 0.1936748 , 0.1936748 , 0.19358625, 0.19353707, 0.19351925,\n"," 0.19348809, 0.19345322, 0.19344108, 0.1934126 , 0.19340919,\n"," 0.19337003, 0.19336805, 0.19336805, 0.19334093, 0.19333513,\n"," 0.1933286 , 0.19330667, 0.19327198, 0.19325489, 0.19325303,\n"," 0.19324587, 0.19319172, 0.19318014, 0.19313964, 0.19311777,\n"," 0.19309933, 0.19300044, 0.1929777 , 0.19296624, 0.19295421,\n"," 0.19295421, 0.19295421, 0.19294329, 0.19289161, 0.19278443,\n"," 0.19276597, 0.19268675, 0.19257978, 0.1925749 , 0.19256727,\n"," 0.19253995, 0.19253893, 0.19253555, 0.19253555, 0.19252183,\n"," 0.19248751, 0.19247194, 0.19245508, 0.19239306, 0.19238321,\n"," 0.19234225, 0.19231489, 0.19228822, 0.19215694, 0.19213831,\n"," 0.19212906, 0.19198512, 0.19197558, 0.19197531, 0.19196405,\n"," 0.19193064, 0.191917 , 0.1918909 , 0.19183044, 0.19168285,\n"," 0.19165854, 0.19164482, 0.19162852, 0.19160807, 0.19158082,\n"," 0.1915652 , 0.19155754, 0.19155701, 0.19153142, 0.19151042,\n"," 0.1915053 , 0.1914973 , 0.19139923, 0.19139923, 0.19139634,\n"," 0.19139421, 0.19138485, 0.1913784 , 0.19137256, 0.19136243,\n"," 0.19130334, 0.19127141, 0.19125071, 0.19124223, 0.19124223,\n"," 0.19124223, 0.19124223, 0.19124223, 0.19124223, 0.19124223,\n"," 0.19123803, 0.19120309, 0.19119833, 0.19117257, 0.1911006 ,\n"," 0.19108857, 0.19108471, 0.19108187, 0.19096623, 0.19094837,\n"," 0.19093005, 0.19086607, 0.19085358, 0.19082481, 0.19080801,\n"," 0.19074334, 0.19074334, 0.19074117, 0.19073139, 0.19072019,\n"," 0.19065274, 0.19064006, 0.19062433, 0.19062077, 0.19060756,\n"," 0.19060595, 0.19059932, 0.19059561, 0.19053935, 0.19052602,\n"," 0.19051696, 0.19051452, 0.19051331, 0.19048372, 0.19047002,\n"," 0.19044857, 0.19043829, 0.19036432, 0.19036162, 0.19036162,\n"," 0.19036162, 0.19036162, 0.19036162, 0.19036162, 0.19036162,\n"," 0.19033851, 0.19031627, 0.19030249, 0.1902964 , 0.19028018,\n"," 0.19023976, 0.19023474, 0.1902231 , 0.19020812, 0.19019666,\n"," 0.19019441, 0.190166 , 0.19014299, 0.19014251, 0.19013466,\n"," 0.19012712, 0.19006141, 0.19000842, 0.18999544, 0.189987 ,\n"," 0.18996987, 0.18993746, 0.18988706, 0.18988638, 0.18988322,\n"," 0.18986978, 0.18985005, 0.18984561, 0.18983683, 0.18983556,\n"," 0.18977794, 0.18975067, 0.18974794, 0.18971296, 0.18969132,\n"," 0.1896826 , 0.1896683 , 0.18964767, 0.18962294, 0.18960448,\n"," 0.18960056, 0.18957382, 0.18955907, 0.1895559 , 0.18945351,\n"," 0.1893961 , 0.18938842, 0.18938842, 0.18930416, 0.18929335,\n"," 0.18928269, 0.18927529, 0.18926318, 0.18925725, 0.18920693,\n"," 0.18920316, 0.18920265, 0.18918844, 0.18915181, 0.1891329 ,\n"," 0.18912506, 0.18912007, 0.18911093, 0.18910372, 0.18905477,\n"," 0.18899509, 0.18895628, 0.18894671, 0.18892942, 0.18892831,\n"," 0.1889246 , 0.18890364, 0.18889995, 0.18889205, 0.18885733,\n"," 0.18885547, 0.1888501 , 0.18875018, 0.18872543, 0.1887137 ,\n"," 0.18870221, 0.18866818, 0.18866052, 0.18865231, 0.18862864,\n"," 0.18859847, 0.1885957 , 0.18857235, 0.18854029, 0.18852888,\n"," 0.18852487, 0.18851727, 0.18848138, 0.18839477, 0.18839274,\n"," 0.18833908, 0.18833485, 0.18832374, 0.18829861, 0.18828094,\n"," 0.18825245, 0.18823473, 0.18823102, 0.18814591, 0.18808895,\n"," 0.18808772, 0.1880811 , 0.18805972, 0.18803392, 0.18801196,\n"," 0.18800409, 0.18799071, 0.18796363, 0.18794693, 0.18792474,\n"," 0.18791905, 0.18789457, 0.18789209, 0.18788017, 0.18787946,\n"," 0.18787119, 0.18783228, 0.18775453, 0.18775043, 0.18774968,\n"," 0.18774968, 0.18772252, 0.18771122, 0.18771122, 0.18768829,\n"," 0.18767978, 0.18765093, 0.18760434, 0.18757284, 0.18754214,\n"," 0.18753786, 0.18750569, 0.18750355, 0.18747607, 0.18747532,\n"," 0.18747117, 0.18747103, 0.18746735, 0.18740045, 0.18740045,\n"," 0.18738315, 0.18735833, 0.18731986, 0.1873102 , 0.18726544,\n"," 0.18719171, 0.18717964, 0.18717964, 0.18717931, 0.18717271,\n"," 0.18715008, 0.18713313, 0.18712218, 0.18710516, 0.18708974,\n"," 0.18701877, 0.18696539, 0.18692927, 0.18691565, 0.18688317,\n"," 0.18685688, 0.18683061, 0.18683061, 0.18683061, 0.18683061,\n"," 0.18683061, 0.18683061, 0.18683061, 0.18683061, 0.1868159 ,\n"," 0.18681577, 0.1868137 , 0.18681231, 0.18674284, 0.18674044,\n"," 0.18672936, 0.18670094, 0.186627 , 0.18658234, 0.18649383,\n"," 0.1864706 , 0.18645331, 0.1864209 , 0.18640019, 0.1863976 ,\n"," 0.18639737, 0.1863741 , 0.18633968, 0.18632837, 0.18632731,\n"," 0.18632104, 0.1863032 , 0.18628059, 0.18627383, 0.18627343,\n"," 0.18626419, 0.18625036, 0.18624845, 0.1862271 , 0.18622328,\n"," 0.18622313, 0.1862126 , 0.18619715, 0.18619505, 0.18617535,\n"," 0.18610723, 0.18607357, 0.18605971, 0.18604258, 0.1860307 ,\n"," 0.18601915, 0.18601859, 0.18601292, 0.18596458, 0.18596458,\n"," 0.18595071, 0.18595071, 0.18594886, 0.18589522, 0.18586807,\n"," 0.18584564, 0.18584563, 0.18584345, 0.18580958, 0.18580578,\n"," 0.18577458, 0.18574461, 0.1857148 , 0.1857086 , 0.18570179,\n"," 0.18569781, 0.18568826, 0.18567668, 0.18565795, 0.18558763,\n"," 0.18556996, 0.18553618, 0.18553366, 0.18552555, 0.18552363,\n"," 0.18552172, 0.18551615, 0.18550968, 0.18550624, 0.18550424,\n"," 0.18547222, 0.18544039, 0.18543352, 0.18541366, 0.1853856 ,\n"," 0.18538553, 0.18536748, 0.18534188, 0.18533951, 0.18533623,\n"," 0.18532294, 0.1853155 , 0.18526448, 0.18526313, 0.18523064,\n"," 0.18521583, 0.18518838, 0.18518203, 0.1851813 , 0.18518062,\n"," 0.18513273, 0.18511213, 0.18509488, 0.18507147, 0.18503882,\n"," 0.18502182, 0.18502024, 0.18501189, 0.18501051, 0.18501051,\n"," 0.18499333, 0.18498303, 0.18496655, 0.18488391, 0.18487754,\n"," 0.18487571, 0.18486053, 0.18478793, 0.18476169, 0.18474619,\n"," 0.18472576, 0.18472497, 0.18472283, 0.18462913, 0.18459448,\n"," 0.18458749, 0.18456052, 0.18455806, 0.18454118, 0.18453562,\n"," 0.18452676, 0.18452676, 0.18452676, 0.18452676, 0.18452676,\n"," 0.18452676, 0.18452676, 0.18452676, 0.18452676, 0.18452676,\n"," 0.18451375, 0.18451005, 0.18450375, 0.18445566, 0.18442181,\n"," 0.18441787, 0.18439469, 0.18437965, 0.1843743 , 0.18436612,\n"," 0.184359 , 0.18430807, 0.18429917, 0.18428993, 0.18428952,\n"," 0.18427785, 0.18427405, 0.1842491 , 0.1842408 , 0.18417199,\n"," 0.18417196, 0.18416914, 0.18414012, 0.1841256 , 0.1841078 ,\n"," 0.18410388, 0.18407899, 0.18407536, 0.18406817, 0.18401189,\n"," 0.18396994, 0.18392102, 0.18390948, 0.18389438, 0.18388358,\n"," 0.18388037, 0.18385923, 0.18385699, 0.18383521, 0.18376791,\n"," 0.18372865, 0.18372799, 0.18365732, 0.18364736, 0.18363246,\n"," 0.18363027, 0.18361296, 0.18350972, 0.18348831, 0.1834732 ,\n"," 0.18345617, 0.18343833, 0.18342832, 0.18342832, 0.18342832,\n"," 0.18342309, 0.1834098 , 0.18339853, 0.18335912, 0.18335092])]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"Hnb8cGd3EXD0","colab_type":"code","outputId":"f6f26e0e-4d9d-41a6-e9b6-ca3cd01589ba","executionInfo":{"status":"ok","timestamp":1588699200292,"user_tz":-120,"elapsed":42601,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["activation_values_normalized"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]),\n"," array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]),\n"," array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]),\n"," array([ inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf,\n"," 0.11944094, 0.03948138, 0.02241481, 0.01856063, 0.01705125,\n"," 0.01661285, 0.00673699, 0.00245872, 0.00126298, 0.00041566,\n"," 0.00013214, -0.00109713, -0.00193772, -0.00197338, -0.00296957,\n"," -0.00315971, -0.00403285, -0.00407015, -0.00508718, -0.00531292,\n"," -0.00563766, -0.00653598, -0.0065441 , -0.00695248, -0.0072135 ,\n"," -0.00800902, -0.00835001, -0.00862192, -0.00887357, -0.00897465,\n"," -0.00917914, -0.00972576, -0.00979875, -0.01000494, -0.01033477,\n"," -0.01133585, -0.01201361, -0.01217324, -0.01239017, -0.01252452,\n"," -0.01269126, -0.01279784, -0.01284128, -0.01290407, -0.01301621,\n"," -0.01311935, -0.01315534, -0.0131613 , -0.01319017, -0.01326173,\n"," -0.01326283, -0.01336794, -0.01351243, -0.01365967, -0.01371147,\n"," -0.01416805, -0.01451112, -0.0147299 , -0.0147425 , -0.01474865,\n"," -0.01491574, -0.01499236, -0.01514026, -0.01530856, -0.01530952,\n"," -0.01548706, -0.01554475, -0.0156587 , -0.01606586, -0.01615557,\n"," -0.01621887, -0.01645863, -0.01652265, -0.01702073, -0.01713273,\n"," -0.01730904, -0.017356 , -0.01747785, -0.01775003, -0.0177834 ,\n"," -0.01808523, -0.0180978 , -0.01815218, -0.01820669, -0.01854145,\n"," -0.01871954, -0.01884168, -0.01915053, -0.01928313, -0.01934397,\n"," -0.01937232, -0.01947043, -0.01955049, -0.01957366, -0.01961732,\n"," -0.01964436, -0.01966834, -0.01966834, -0.01969276, -0.01975168,\n"," -0.01999564, -0.02002086, -0.02019398, -0.02026638, -0.02043799,\n"," -0.02046853, -0.02056391, -0.02085736, -0.02103273, -0.02108283,\n"," -0.02111499, -0.02136776, -0.02144934, -0.02146571, -0.02154151,\n"," -0.02157498, -0.02194053, -0.02196631, -0.02202872, -0.02208664,\n"," -0.02210529, -0.02213538, -0.02222506, -0.02224469, -0.02225018,\n"," -0.02230381, -0.02238109, -0.02254212, -0.02273142, -0.02291795,\n"," -0.02302442, -0.02302883, -0.02303193, -0.02304553, -0.02311114,\n"," -0.02315325, -0.02315919, -0.02317194, -0.02322301, -0.0233151 ,\n"," -0.02332142, -0.02333037, -0.02342918, -0.02344143, -0.02354307,\n"," -0.02359552, -0.02378099, -0.02388207, -0.02388328, -0.02388773,\n"," -0.02393258, -0.0240234 , -0.02402697, -0.02404312, -0.02407258,\n"," -0.02409335, -0.02410165, -0.02411601, -0.02422684, -0.02426364,\n"," -0.02433386, -0.0244652 , -0.02468505, -0.02471043, -0.02473041,\n"," -0.02475219, -0.02486811, -0.02489128, -0.02498827, -0.0251279 ,\n"," -0.02520407, -0.02522044, -0.02533355, -0.02543577, -0.02549428,\n"," -0.02557146, -0.02558098, -0.02559461, -0.02561806, -0.02568172,\n"," -0.02569814, -0.02569917, -0.02578924, -0.02584881, -0.02586558,\n"," -0.02596128, -0.02598082, -0.02598766, -0.02600601, -0.02601646,\n"," -0.02607966, -0.02611154, -0.02611356, -0.02612131, -0.02612832,\n"," -0.02619769, -0.02625631, -0.02630104, -0.0265159 , -0.02653533,\n"," -0.02654699, -0.02678209, -0.02693294, -0.02700417, -0.0270097 ,\n"," -0.02704885, -0.02705111, -0.0270742 , -0.02713225, -0.02715967,\n"," -0.02740094, -0.02747105, -0.02750453, -0.02753164, -0.0275929 ,\n"," -0.02763564, -0.02764439, -0.02766908, -0.02767327, -0.02776529,\n"," -0.02776553, -0.02777434, -0.02779585, -0.02782006, -0.02788474,\n"," -0.02796677, -0.02798991, -0.02800641, -0.02802144, -0.02809074,\n"," -0.02816342, -0.02821922, -0.02822337, -0.02822449, -0.02825454,\n"," -0.02833245, -0.02833796, -0.02851535, -0.02853806, -0.02862353,\n"," -0.02869825, -0.02872121, -0.02874705, -0.02887336, -0.028893 ,\n"," -0.02894082, -0.02899365, -0.02900214, -0.02901741, -0.02905858,\n"," -0.02914005, -0.02915666, -0.02916794, -0.02917773, -0.02926437,\n"," -0.02926838, -0.02927111, -0.02928049, -0.02934391, -0.02939858,\n"," -0.02940813, -0.02948104, -0.02948122, -0.02948163, -0.02949901,\n"," -0.0295025 , -0.02957217, -0.02961588, -0.02973783, -0.02975561,\n"," -0.02978895, -0.02981784, -0.02986653, -0.02989817, -0.02991687,\n"," -0.03015664, -0.03017545, -0.03018105, -0.03018874, -0.0302354 ,\n"," -0.03027645, -0.0302876 , -0.03029479, -0.03029479, -0.03030613,\n"," -0.03033158, -0.03035392, -0.03035491, -0.03035491, -0.03035492,\n"," -0.03037916, -0.03038867, -0.03040259, -0.03044027, -0.03053822,\n"," -0.03057069, -0.03057965, -0.03061217, -0.03066059, -0.03072409,\n"," -0.03072927, -0.03074283, -0.03075297, -0.03078832, -0.03080243,\n"," -0.03091139, -0.03092169, -0.0309934 , -0.03099904, -0.03100601,\n"," -0.03102776, -0.03103291, -0.03104991, -0.03105506, -0.0310988 ,\n"," -0.0311153 , -0.03115634, -0.03116094, -0.03119808, -0.03120112,\n"," -0.03120716, -0.03125265, -0.0312568 , -0.03126623, -0.03127602,\n"," -0.03129491, -0.03129713, -0.03131483, -0.03132338, -0.03133518,\n"," -0.03133538, -0.03134523, -0.03137387, -0.0314546 , -0.03145811,\n"," -0.03146349, -0.03147455, -0.03148465, -0.03149512, -0.03152416,\n"," -0.03160837, -0.03162019, -0.03162185, -0.03163294, -0.03163485,\n"," -0.0316628 , -0.03166791, -0.03167715, -0.03167951, -0.03168103,\n"," -0.03177845, -0.03182371, -0.03189515, -0.031898 , -0.03189972,\n"," -0.03193026, -0.03194616, -0.03197198, -0.03201196, -0.032017 ,\n"," -0.03206489, -0.03213483, -0.03215928, -0.03216974, -0.03217323,\n"," -0.03219389, -0.03219613, -0.03227161, -0.03232692, -0.03233747,\n"," -0.03234272, -0.03235511, -0.03239058, -0.03240081, -0.03242938,\n"," -0.03245086, -0.032455 , -0.03250008, -0.03251998, -0.03252654,\n"," -0.032532 , -0.0325492 , -0.03256868, -0.03257082, -0.03261896,\n"," -0.03262462, -0.03263815, -0.03264167, -0.03265025, -0.03265917,\n"," -0.03268718, -0.03270469, -0.03275499, -0.03277258, -0.03277826,\n"," -0.03278 , -0.03280038, -0.03286437, -0.03288432, -0.03289773,\n"," -0.03293579, -0.03297313, -0.0330174 , -0.0330325 , -0.03305194,\n"," -0.03305883, -0.03308592, -0.03308866, -0.03310365, -0.03314874,\n"," -0.03320371, -0.03327433, -0.03332355, -0.03333468, -0.03335091,\n"," -0.03335823, -0.03336233, -0.03336845, -0.03343767, -0.03344204,\n"," -0.03344731, -0.03348822, -0.03353375, -0.03354286, -0.03356284,\n"," -0.03364006, -0.03364584, -0.03364599, -0.03366769, -0.03368941,\n"," -0.03369329, -0.03371158, -0.03376465, -0.03378133, -0.03378219,\n"," -0.03380175, -0.03387171, -0.03391489, -0.03393734, -0.03394318,\n"," -0.03403488, -0.03403874, -0.03404019, -0.03405462, -0.03406426,\n"," -0.03409698, -0.03411337, -0.03411778, -0.03419431, -0.03421583,\n"," -0.03423775, -0.0342515 , -0.03425558, -0.03429952, -0.03432012,\n"," -0.03432734, -0.03434484, -0.0343491 , -0.03439277, -0.03442258,\n"," -0.03443259, -0.03446338, -0.03451315, -0.03452202, -0.03453397,\n"," -0.03454499, -0.03456089, -0.03456358, -0.03463345, -0.03464259,\n"," -0.03464616, -0.03465589, -0.03465736, -0.03466863, -0.0347066 ,\n"," -0.0347756 , -0.03483546, -0.03483576, -0.03484813, -0.03484973,\n"," -0.03486611, -0.03487119, -0.03487385, -0.0348915 , -0.03491266,\n"," -0.0349891 , -0.03502207, -0.03502749, -0.03504288, -0.03504508,\n"," -0.03510733, -0.03512967, -0.03513691, -0.0351488 , -0.03515156,\n"," -0.03516131, -0.03518124, -0.03523655, -0.03523663, -0.03523944,\n"," -0.03525652, -0.03525732, -0.03528517, -0.03530498, -0.03532873,\n"," -0.03535024, -0.03539237, -0.035397 , -0.03541971, -0.03543359,\n"," -0.03545231, -0.03548665, -0.03549283, -0.0355129 , -0.03556542,\n"," -0.03557126, -0.03557504, -0.03561416, -0.03564643, -0.03565728,\n"," -0.0356616 , -0.03568557, -0.0357087 , -0.03572865, -0.03574241,\n"," -0.03575737, -0.03577362, -0.03577718, -0.03578652, -0.03578874,\n"," -0.03579574, -0.03580362, -0.03584294, -0.03587354, -0.03588559,\n"," -0.03592198, -0.03596931, -0.03603472, -0.03608244, -0.03611262,\n"," -0.03613231, -0.03613891, -0.03614028, -0.03614544, -0.03619428,\n"," -0.03622843, -0.03625798, -0.03626814, -0.03628235, -0.0362856 ,\n"," -0.0363907 , -0.03643993, -0.03644194, -0.03648018, -0.03649009,\n"," -0.03650614, -0.03651222, -0.03651709, -0.03656609, -0.03659569,\n"," -0.03665851, -0.03666385, -0.03670661, -0.03671625, -0.03675402,\n"," -0.03679116, -0.03680295, -0.03683305, -0.03686221, -0.03688207,\n"," -0.03691355, -0.03699445, -0.03699856, -0.03702394, -0.0370605 ,\n"," -0.03706948, -0.03710757, -0.03711773, -0.03713102, -0.03720845,\n"," -0.03723989, -0.03725625, -0.03726307, -0.0372733 , -0.03729454,\n"," -0.03729702, -0.03729904, -0.03730211, -0.0373132 , -0.03732538,\n"," -0.03732555, -0.03734018, -0.03736541, -0.0373803 , -0.03742769,\n"," -0.03743259, -0.03743484, -0.03747019, -0.03747969, -0.03753689,\n"," -0.0375467 , -0.03758353, -0.03760106, -0.03760643, -0.03760932,\n"," -0.03761182, -0.03765303, -0.03768184, -0.03768466, -0.03768543,\n"," -0.03769526, -0.03771064, -0.03772927, -0.03773479, -0.03776099,\n"," -0.03777249, -0.037812 , -0.03789684, -0.03792915, -0.03797236,\n"," -0.03797341, -0.03798515, -0.03800131, -0.03801813, -0.03803755,\n"," -0.03804899, -0.03810167, -0.03810679, -0.03811562, -0.03812863,\n"," -0.03814175, -0.0381878 , -0.03822938, -0.0382318 , -0.03823712,\n"," -0.03825606, -0.03827792, -0.03829636, -0.03829983, -0.03838415,\n"," -0.03841677, -0.03844274, -0.03850943, -0.0385196 , -0.03854125,\n"," -0.03855849, -0.03860299, -0.03862937, -0.0386574 , -0.03865965,\n"," -0.03867961, -0.03868398, -0.03870812, -0.03871154, -0.03872544,\n"," -0.03872912, -0.03873289, -0.03874775, -0.03875788, -0.03876787,\n"," -0.03877456, -0.03889144, -0.03890973, -0.03891779, -0.03891876,\n"," -0.0389471 , -0.03895769, -0.03896825, -0.03899004, -0.03901798,\n"," -0.03902431, -0.03903076, -0.03903975, -0.03904536, -0.0390478 ,\n"," -0.03908238, -0.03908965, -0.03909426, -0.03910032, -0.03912177,\n"," -0.03913464, -0.03917384, -0.03922042, -0.03923995, -0.03928791,\n"," -0.03932714, -0.03935562, -0.03941163, -0.03943571, -0.03943857,\n"," -0.0394441 , -0.03944777, -0.03946999, -0.03949592, -0.03950286,\n"," -0.03951343, -0.03951656, -0.03951865, -0.03958407, -0.03958469,\n"," -0.0395957 , -0.0395963 , -0.03959935, -0.03960032, -0.03960175,\n"," -0.03960824, -0.03962366, -0.03964503, -0.03968344, -0.03969101,\n"," -0.039743 , -0.03974882, -0.03976725, -0.03979471, -0.03980479,\n"," -0.03981666, -0.03981982, -0.03982592, -0.0398623 , -0.03988767,\n"," -0.03990136, -0.03993335, -0.0399554 , -0.0399677 , -0.039978 ,\n"," -0.03999593, -0.0400384 , -0.04004985, -0.04006623, -0.04007224,\n"," -0.0400977 , -0.04009907, -0.04011669, -0.04011813, -0.04014758,\n"," -0.04015033, -0.04016238, -0.04017201, -0.04017263, -0.04018424,\n"," -0.04028537, -0.04031558, -0.04033784, -0.04039469, -0.04040268,\n"," -0.04040469, -0.04042171, -0.04042307, -0.04043249, -0.04045592,\n"," -0.04045762, -0.04046213, -0.04046765, -0.04048218, -0.04050671,\n"," -0.0405095 , -0.04055955, -0.04059581, -0.04060039, -0.04061576,\n"," -0.04064329, -0.04065071, -0.04066472, -0.04068771, -0.04070188,\n"," -0.0407026 , -0.04077955, -0.04079501, -0.04079501, -0.04079958,\n"," -0.04080969, -0.04081294, -0.04083157, -0.04083479, -0.04085524,\n"," -0.04085777, -0.04086804, -0.04087139, -0.04088501, -0.04089047,\n"," -0.04089277, -0.04089735, -0.04090245, -0.04090738, -0.04091624,\n"," -0.04091631, -0.04091809, -0.04091887, -0.04093831, -0.04096439,\n"," -0.04098027, -0.04098759, -0.04099194, -0.04099918, -0.04102629,\n"," -0.04103066, -0.04103414, -0.04107379, -0.04113632, -0.04113944,\n"," -0.04115534, -0.0412056 , -0.04121111, -0.04121252, -0.04121428,\n"," -0.04126124, -0.04126852, -0.04127176, -0.04130829, -0.04133144,\n"," -0.04133558, -0.04136678, -0.04137015, -0.04139344, -0.04139927,\n"," -0.04144217, -0.04144218, -0.04145474, -0.04145477, -0.04148045,\n"," -0.04150917, -0.04151569, -0.04153143, -0.0415337 , -0.04154354,\n"," -0.04155302, -0.04156474, -0.04156899, -0.04159028, -0.0416113 ,\n"," -0.04162829, -0.04163366, -0.04164624, -0.04166173, -0.0416667 ,\n"," -0.04166834, -0.04168149, -0.04169017, -0.04169686, -0.04170704,\n"," -0.04172755, -0.04175705, -0.04178334, -0.04178412, -0.04179126,\n"," -0.04180041, -0.04183861, -0.04187781, -0.04188192, -0.04188523,\n"," -0.04190431, -0.04190782, -0.04191964, -0.04192097, -0.04193037,\n"," -0.04193871, -0.04194421, -0.04194844, -0.04196357, -0.04196457,\n"," -0.04196949, -0.04197048, -0.04201069, -0.04201966, -0.04202634,\n"," -0.04202877, -0.04203324, -0.04204579, -0.04208494, -0.04208938,\n"," -0.04209236, -0.0421158 , -0.04211716, -0.04213666, -0.04214971,\n"," -0.04215972, -0.04216499, -0.04217249, -0.04218065, -0.04218433,\n"," -0.04219847, -0.04224315, -0.04227389, -0.04232656, -0.04234279,\n"," -0.04234504, -0.04235712, -0.04240301, -0.04242102, -0.04242295,\n"," -0.04242563, -0.04242711, -0.04244277, -0.0424752 , -0.04248764,\n"," -0.0425116 , -0.04256286, -0.04260233, -0.04261097, -0.04263899,\n"," -0.04266166, -0.04266267, -0.04266974, -0.04267759, -0.0426947 ,\n"," -0.04270292, -0.04271089, -0.04271407, -0.04274323, -0.04276208,\n"," -0.04278993, -0.04280124, -0.04280705, -0.04284703, -0.04284721,\n"," -0.04287563, -0.04288034, -0.04288905, -0.04290393, -0.04290429,\n"," -0.04290912, -0.04291983, -0.04292466, -0.0429352 , -0.04295221,\n"," -0.04296598, -0.04296827, -0.04297008, -0.04297053, -0.04298119,\n"," -0.04298278, -0.0430119 , -0.04302214, -0.04302411, -0.043034 ,\n"," -0.04305554, -0.04307271, -0.04310754, -0.04310856, -0.04314167,\n"," -0.04316657, -0.04317058, -0.04319298, -0.04319379, -0.0431949 ,\n"," -0.04325007, -0.04326322, -0.04327093, -0.0432907 , -0.04330377,\n"," -0.04330667, -0.04331565, -0.04332327, -0.04337735, -0.04338399,\n"," -0.04339117, -0.04340173, -0.0434345 , -0.04343911, -0.04346976,\n"," -0.04347922, -0.04348388, -0.04351846, -0.04352798, -0.04355102,\n"," -0.04358177, -0.04358716, -0.04361402, -0.04362575, -0.04363953,\n"," -0.04365458, -0.04366362, -0.04366528, -0.04366801, -0.04370557,\n"," -0.04370639, -0.04372588, -0.0437271 , -0.04373262, -0.04375954,\n"," -0.04377214, -0.04377922, -0.04380081, -0.04385643, -0.0438651 ,\n"," -0.04387301, -0.04388072, -0.04388396, -0.04388776, -0.04390343]),\n"," array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]),\n"," array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]),\n"," array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]),\n"," array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]),\n"," array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]),\n"," array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,\n"," inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf])]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"NiTAZSKo5sYG","colab_type":"text"},"source":["##### No binary model layer2:"]},{"cell_type":"code","metadata":{"id":"eUkV73iknQ1U","colab_type":"code","outputId":"b0029029-1671-41ec-b4ef-36e01aa77c9c","executionInfo":{"status":"ok","timestamp":1588699219957,"user_tz":-120,"elapsed":60922,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1Da-S0ssNo0mBy5LDYiem2t6wHMArBNxV"}},"source":["# parameters\n","list_filter_interest_layer2 = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 10\n","\n","# regions and activation of interest\n","regions = region_layer2_no_binary\n","activations = activation_layer2_no_binary\n","activations_normalized = activation_layer2_no_binary_normalized\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer2)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"nMq_m3xy5ulb","colab_type":"text"},"source":["##### Binary model layer1:"]},{"cell_type":"code","metadata":{"id":"nAGXpIHV1W7G","colab_type":"code","outputId":"17b22da8-17b8-4e2c-e85f-20a3963aa05b","executionInfo":{"status":"ok","timestamp":1588699220286,"user_tz":-120,"elapsed":59805,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":386}},"source":["viz_filters(model_binary)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Visualization filters learned for layer: layer1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Visualization filters learned for layer: layer2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"XJygUtZ5vPhB","colab_type":"code","outputId":"76be8f9f-16ae-4d85-fdea-edfbe79c75c6","executionInfo":{"status":"ok","timestamp":1588699229494,"user_tz":-120,"elapsed":68804,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# parameters\n","list_filter_interest_layer1 = [0,1,2,3,4,5,6,7,8,9]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 10\n","\n","# regions and activation of interest\n","regions = region_layer1_binary\n","activations = activation_layer1_binary\n","activations_normalized = activation_layer1_binary_normalized\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer1)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Interest of filters: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n","Consider 10% image regions = 1000 images\n","mean image:\n","mean regions of 1000 regions more=True or worst=False active for filter number: 0 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 1000 regions more=True or worst=False active for filter number: 1 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAFoAAABVCAYAAADJ/vPXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAADjklEQVR4nO2cz6tWRRjHP99Sa6G4yKBLSSlIkCsjhMKF0CbuIl20cOdORARdRkGL/gDXcqGgRZCUESKGm1q40dKLP7hKchXEwo0FmquQvi3eUa5Xfc+8L3ee93p6PjAw7zkz8z58GOacOXPOyDZJe56bdAD/F1J0ECk6iBQdRIoOYkWLRiX19lbGtsaplz06iBQdRIoOIkUHkaKDqBIt6QNJv0mal/Rx66B6ie2hCXgeuAZsBFYBF4C3Ouq4r6nL19NSTY/eCszbvm77H+AbYEdFvWQBNaJfBW4u+P17OZaMwJLNDCXtAfYsVXt9o0b0H8D6Bb9fK8cewfYMMAP9noKPS83Q8SuwSdIGSauAXcCxtmH1j84ebfu+pP3ASQZ3IF/anmseWc9QizXDPg8d+fRumZOig0jRQaToIFJ0ECk6iBQdRIoOIkUHkaKDSNFBpOggUnQQKTqIFB1Eig4iRQeRooNI0UGk6CBSdBApOogUHUSKDiJFB5Gig0jRQXSKlrRe0s+SLkuak3QgIrC+0fmSo6QpYMr2rKQ1wDlgp+3LQ+rkS46L6OzRtm/Zni35v4Er5KcVIzPSGC3pDWALcKZFMH2m+hsWSauBo8BB23efcD6/YRlC1YvoklYCx4GTtg9VlM8xehE1F0MBXwF/2T5Y1WiKfowa0duAU8Al4N9y+BPbJ4bUSdGLyG9YRiS/YVnmpOggUnQQKTqIJtuxAbeBGx1l1pVyzxJvjluxiWjbL3eVkXTW9jst/r8Vks6OWzeHjiBSdBCTFD0zwf8el7FjbjIzTB4nh44gUnQQTUV3bUwo6QVJR8r5M2UFZ6LULEZL2i7pjqTzJX3W2fC4G+YtxcaEwD7gcMnvAo60imeEuKeAt0t+DXD1CXFvB44v9QaD41KzMeEOBosKAN8B75eFhonRajG6peiajQkflrF9H7gDvNQwppHoWIx+V9IFST9K2tzVVqtnHc88HYvRs8Drtu9JmgZ+ADYNa69lj67ZmPBhGUkrgLXAnw1jqqIsRh8Fvrb9/eLztu/avlfyJ4CVktYNa7Ol6JqNCY8Bu0v+I+AnT3gGVa4RXwBXnrbiL+mVB9cSSVsZeBzeQRpfwacZXLWvAZ+WY58DH5b8i8C3wDzwC7BxGdx1bGOwhfFF4HxJ08BeYG8psx+YY3AndRp4r6vdnIIHkTPDIFJ0ECk6iBQdRIoOIkUHkaKD+A/J1jZxEuBimQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 1000 regions more=True or worst=False active for filter number: 2 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 1000 regions more=True or worst=False active for filter number: 3 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 1000 regions more=True or worst=False active for filter number: 4 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 1000 regions more=True or worst=False active for filter number: 5 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 1000 regions more=True or worst=False active for filter number: 6 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 1000 regions more=True or worst=False active for filter number: 7 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 1000 regions more=True or worst=False active for filter number: 8 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["mean regions of 1000 regions more=True or worst=False active for filter number: 9 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized region:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x72 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid image\n","grid regions of 1000 regions more=True or worst=False active for filter number: 0 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 1000 regions more=True or worst=False active for filter number: 1 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 1000 regions more=True or worst=False active for filter number: 2 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQ0AAA22CAYAAACiv7nKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdX+iwaXrQ9+vpxsZCqdFkCGF3YQMuFE+qdogWTyRBiGnp5kCtbZEgC3uSgEWljT3xpAfxpKlCERZXuiml2zQtZJFACVGRQhXHKrEmB25DZHeJ7kSTtEWspH16ML/AuN3Hme/82Xm8ns8Hlnl/f2au333y5X7f+937Ps7zHIC361/6oH8A4F8sogEkogEkogEkogEkogEk3/B+/EeP4/jumfnTM/Ohmflz53n+0D/v+7/lW77l/NjHPvZ+/CjAO/DzP//z84u/+IvH1/raex6N4zg+NDP/5cz8npn50sz89eM4Pn+e589c/Tsf+9jH5rXXXnuvfxTgHXr11Vcvv/Z+/PbkO2bmC+d5/tx5nv90Zj43M594H+YAH4D3IxofnpkvvunjL7187p9xHMenjuN47TiO115//fX34ccA3g8f2B+Enuf56fM8Xz3P89VXXnnlg/oxgOj9iMaXZ+ajb/r4Iy+fAxZ4P05P/vrMfPw4jm+fN2LxB2fmP3in/7Hj+Jp/gPuu/PP+T3rmvXfzNq/tCfOuvOfROM/zV4/j+IGZ+Z/mjSPXP3+e5995r+cAH4z35e9pnOf5EzPzE+/Hfxv4YPkboUAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkDyvlz39156JxefmnePeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye3vCN3+9N3meZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67bn77bPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye3vCN3+9N3meZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67bn77bPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/o7Q7U/fbZ63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv3pu83zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIbn96sv3pu83zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+9N3meZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye3vCN3+9N3meZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67bn77bPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V95yp3Ecx58/juMrx3H8b2/63G86juMnj+P4uy///I0vnz+O4/gzx3F84TiOnz6O47e/nz888PX3dn578l/NzHd/1ed+cGZ+6jzPj8/MT718PDPze2fm4y//+9TM/Nn35scE7uIto3Ge51+ZmX/0VZ/+xMx89uXXn52Z733T53/kfMNfnZlvOo7j296rHxb44L3TPwj91vM8f+Hl139/Zr715dcfnpkvvun7vvTyuf+f4zg+dRzHa8dxvPb666+/wx8D+Hp716cn5xt/OpP/hOY8z0+f5/nqeZ6vvvLKK+/2xwC+Tt5pNP7Br/224+WfX3n5/Jdn5qNv+r6PvHwOWOKdHrl+fma+b2Z+6OWfP/6mz//AcRyfm5nfMTO/8qbfxrwj29/L3Dxv89qeMO/KW0bjOI7/dmZ+98x8y3EcX5qZPzlvxOJHj+P45Mz8vZn5Ay/f/hMz8z0z84WZ+ccz84fzTwTc2ltG4zzPf//iS9/1Nb73nJnvf7c/FHBf/ho5kIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkHiW0bwVs8z7+rHTABLRABLRABLRABLRABLRAJLbH7luf/pu87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRAJLbn55sf/pu87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+ztCtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/an7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/I3T703eb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7luf/pu87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRAJLbn55sf/pu87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+ztCtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/an7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXXnLncZxHB89juMvHcfxM8dx/J3jOP7Iy+d/03EcP3kcx999+edvfPn8cRzHnzmO4wvHcfz0cRy//f1eBPD183Z+e/KrM/PHzvP8LTPzO2fm+4/j+C0z84Mz81PneX58Zn7q5eOZmd87Mx9/+d+nZubPvuc/NfCBectonOf5C+d5/q8vv/4/Z+ZnZ+bDM/OJmfnsy7d9dma+9+XXn5iZHznf8Fdn5puO4/i29/wnBz4Q6Q9Cj+P42Mz8tpn5azPzred5/sLLl/7+zHzry68/PDNffNO/9qWXz331f+tTx3G8dhzHa6+//nr8sYEPytuOxnEc/+rM/A8z8x+d5/l/vPlr5xt/QpP+lOY8z0+f5/nqeZ6vvvLKK+VfBT5Abysax3H8unkjGP/NeZ7/48un/8Gv/bbj5Z9fefn8l2fmo2/61z/y8jlggbc8cj3eeEDyMzPzs+d5/udv+tLnZ+b7ZuaHXv7542/6/A8cx/G5mfkdM/Mrb/ptTLb9vczN8zav7Qnzrrydv6fxu2bmD83M3z6O42+9fO4/nTdi8aPHcXxyZv7ezPyBl6/9xMx8z8x8YWb+8cz84fxTAbf1ltE4z/N/npmrxH3X1/j+c2a+/13+XMBN+WvkQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQOItV/NWzDLv68dOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7e8I3f703eZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtufvts8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7e8I3f703eZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtufvts8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+jtDtT99tnrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/em7zfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIwb1d4AACAASURBVIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0huf3qy/em7zfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v703eZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7e8I3f703eZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtufvts8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkLzlxcLHcfz6mfkrM/ONL9//Y+d5/snjOL59Zj43M988M39jZv7QeZ7/9DiOb5yZH5mZf3Nm/uHM/Hvnef78O/0Bt7+XuXne5rU9Yd6Vt7PT+L9n5jvP8/w3Zua3zsx3H8fxO2fmT83MD5/n+Ztn5pdm5pMv3//Jmfmll8//8Mv3AUu8ZTTON/xfLx/+upf/nTPznTPzYy+f/+zMfO/Lrz/x8vG8fP27jvfj7nXgA/G2/kzjOI4PHcfxt2bmKzPzkzPzv8/ML5/n+asv3/Klmfnwy68/PDNfnJl5+fqvzBu/hfnq/+anjuN47TiO115//fV3twrg6+ZtReM8z//nPM/fOjMfmZnvmJl//d0OPs/z0+d5vnqe56uvvPLKu/3PAV8n6fTkPM9fnpm/NDP/1sx803Ecv/YHqR+ZmS+//PrLM/PRmZmXr/+GeeMPRIEF3jIax3G8chzHN738+l+Zmd8zMz87b8Tj97182/fNzI+//PrzLx/Py9f/4nmXP/YF3rW385brt83MZ4/j+NC8EZkfPc/zLxzH8TMz87njOP6zmfmbM/OZl+//zMz818dxfGFm/tHM/MF38wNufy9z87zNa3vCvCtvGY3zPH96Zn7b1/j8z80bf77x1Z//JzPz+/NPAvwLwd8IBRLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRAJK3c7HwB2r7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+ztCtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/an7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0guf3pyfan7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj703eb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/I3T703eb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7luf/pu87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+ztCtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/an7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0guf3pyfan7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj703eb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/I3T703eb521e2xPmXXnbO43jOD50HMffPI7jL7x8/O3Hcfy14zi+cBzHf3ccx7/88vlvfPn4Cy9f/9j786MDH4Ty25M/MjM/+6aP/9TM/PB5nr95Zn5pZj758vlPzswvvXz+h1++D1jibUXjOI6PzMy/PTN/7uXjY2a+c2Z+7OVbPjsz3/vy60+8fDwvX/+u4/24ex34QLzdncZ/MTP/8cz8vy8ff/PM/PJ5nr/68vGXZubDL7/+8Mx8cWbm5eu/8vL9/4zjOD51HMdrx3G89vrrr7/DHx/4envLaBzH8e/MzFfO8/wb7+Xg8zw/fZ7nq+d5vvrKK6+8l/9p4H30dk5PftfM/LvHcXzPzPz6mfnXZuZPz8w3HcfxDS+7iY/MzJdfvv/LM/PRmfnScRzfMDO/YWb+4Xv+kwMfiLeMxnmef2Jm/sTMzHEcv3tm/vh5nv/hcRz//cz8vpn53Mx838z8+Mu/8vmXj/+Xl6//xfNdnBVtf/pu87zNa3vCvCvv5i93/Scz80eP4/jCvPFnFp95+fxnZuabXz7/R2fmB9/FDOBm0l/uOs/zL8/MX3759c/NzHd8je/5JzPz+9+Dnw24IX+NHEhEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0i85Wreilnmff3YaQCJaACJaACJaACJaACJaADJ7Y9ct7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0guf3Fwtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkNz+yHX7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRAJLbXyy8/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaADJ7Y9ct7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0guf3Fwtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkNz+yHX7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRgP+vvfsJuey+6zj++VESFVuoaWsJWq2RgHQhMYRQIRQUlJqNCiJdWUUIiAVduAgIUhcuFBQEoRJRqOKf+he7NJaAK1ujJmlq1aYS0RCbSK1WXGjrz8W9gTHM6cwnnTz39HdeLxjmmTsz/Z5zyPPu7z5nnvOjIhpARTSAimgAFdEAKqIBVEQDqOz+GaGrb3238ryVz+0I87ZYaQAV0QAqogFURAOoiAZQEQ2gsvtbrqtvfbfyvJXP7QjztlhpABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVEQDqOz+wcKr75e58ryVz+0I87ZYaQAV0QAqogFURAOoiAZQEQ2gsvtbrqvvl7nyvJXP7QjztlhpABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVEQDqOz+wcKr75e58ryVz+0I87ZYaQAV0QAqogFURAOoiAZQEQ2gsvtbrqvvl7nyvJXP7QjztlhpABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVEQDqOz+wcKr75e58ryVz+0I87ZYaQAV0QAqogFURAOoiAZQEQ2gsvtbrqvvl7nyvJXP7QjztlhpABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVHb/jNDVt75bed7K53aEeVusNICKaAAV0QAqogFURAOoiAZQ2f0t19W3vlt53srndoR5W6w0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVHb/YOHV98tced7K53aEeVusNICKaAAV0QAqogFURAOoiAZQ2f0t19X3y1x53srndoR5W6w0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVHb/YOHV98tced7K53aEeVusNICKaAAV0QAqogFURAOoiAZQ2f0t19X3y1x53srndoR5W6w0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVHb/YOHV98tced7K53aEeVusNICKaAAV0QAqogFURAOoiAZQ2f0t19X3y1x53srndoR5W6w0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVHb/YOHV98tced7K53aEeVusNICKaAAV0QAqogFURAOoiAZQ2f0t19X3y1x53srndoR5W6w0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKrt/RujqW9+tPG/lczvCvC1WGkBFNICKaAAV0QAqogFURAOo7P6W6+pb3608b+VzO8K8LVYaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKrt/sPDq+2WuPG/lczvCvC1WGkBFNICKaAAV0QAqogFURAOo7P6W6+r7Za48b+VzO8K8LVYaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKrt/sPDq+2WuPG/lczvCvC1WGkBFNICKaAAV0QAqogFURAOo7P6W6+r7Za48b+VzO8K8LVYaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKrt/sPDq+2WuPG/lczvCvC1WGkBFNICKaAAV0QAqogFURAOo7P6W6+r7Za48b+VzO8K8LVYaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKrt/sPDq+2WuPG/lczvCvC1WGkBFNICKaAAV0QAqogFUdn/3ZPWt71aet/K5HWHeFisNoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAld0/WHj1re9WnrfyuR1h3hYrDaAiGkBFNICKaAAV0QAqogFUdn/LdfX9Mleet/K5HWHeFisNoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAld0/WHj1/TJXnrfyuR1h3hYrDaAiGkBFNICKaAAV0QAqogFUdn/LdfX9Mleet/K5HWHeFisNoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAld0/WHj1/TJXnrfyuR1h3hYrDaAiGkBFNICKaAAV0QAqogFUdn/LdfX9Mleet/K5HWHeFisNoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAld0/WHj1/TJXnrfyuR1h3hYrDaAiGkBFNICKaAAV0QAqogFUdn/LdfX9Mleet/K5HWHeFisNoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAyu6fEbr61ncrz1v53I4wb4uVBlARDaAiGkBFNICKaAAV0QAqu7/luvrWdyvPW/ncjjBvi5UGUBENoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAyu4fLLz6fpkrz1v53I4wb4uVBlARDaAiGkBFNICKaAAV0QAqu7/luvp+mSvPW/ncjjBvi5UGUBENoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAyk09WHiM8WySzyb5fJLPzTnvG2PckeQDSd6a5Nkk3z/n/LdxevrpLyZ5MMl/JfnBOedfvdIDXH2/zJXnrXxuR5i3pVlpfNuc8545533nXz+c5ENzzruTfOj86yT5riR3n388lOR9t+pggcv7Yt6efHeS958/fn+S77nm9V+fJ3+e5PVjjDu/iDnAjtxsNGaSPxlj/OUY46Hza2+ecz5//vhfkrz5/PHXJPmna/7uP59f+3/GGA+NMR4fYzz+4osvvoJDBy7hZjdLemDO+dwY46uTPDrG+Ntrf3POOccY1RuuOecjSR5Jkvvuu28fb9aAG7qplcac87nzzy8k+aMk9yf51EtvO84/v3D+488lecs1f/1rz68BC7hhNMYYXznGeN1LHyf5ziRPJ/lgknef/9i7k/zx+eMPJvmBcfL2JP9+zdsY4EvcuNFtnDHGXTmtLpLT25nfmnP+zBjjDUl+N8nXJfnHnG65fvp8y/WXkrwzp1uuPzTnfPwGM148/2+85I1J/vUVnM+ttpfjSBzL9ezlOJL1juXr55xvut5v3DAalzDGePyaW7uHP47Esez5OJJjHYt/EQpURAOo7DUaj1z6AM72chyJY7mevRxHcqBj2eXXNID92utKA9gp0QAqu4rGGOOdY4y/G2M8M8Z4+MZ/41U9lmfHGB8dYzwxxviC/87kVZj9a2OMF8YYT1/z2h1jjEfHGJ84//xVFzqO944xnjtflyfGGA++2sdxnvuWMcZjY4y/GWN8bIzxY+fXL3Fdto7lSq/NOQU51gAAArBJREFUGOPLxxgfGWM8eT6Onz6//g1jjA+fP48+MMa4/ZYOnnPu4keS1yT5ZJK7ktye5Mkkb7vg8Tyb5I0Xmv2OJPcmefqa134uycPnjx9O8rMXOo73JvmJC1yTO5Pce/74dUn+PsnbLnRdto7lSq9NkpHkteePb0vy4SRvz+kfXb7r/PovJ/mRWzl3TyuN+5M8M+f8hznnfyf5nZy+zf5w5px/luTTL3t561EEV30cFzHnfH6eH+Y05/xsko/n9N3Tl7guW8dypebJf55/edv5x0zy7Ul+//z6Lb8me4rGTX1L/RW63uMALmnrUQSX8J4xxlPnty+v+tuBlxtjvDXJt+T0/6wXvS4vO5bkiq/NGOM1Y4wncvqG0UdzWq1/Zs75ufMfueWfR3uKxt48MOe8N6cnkf3oGOMdlz6gl8zTuvNS98rfl+Qbk9yT5PkkP3+Vw8cYr03yB0l+fM75H9f+3lVfl+scy5Vfmznn5+ec9+T03eT3J/mmV3vmnqKxq2+pn9d/HMAlbT2K4ErNOT91/g/1f5P8Sq7wuowxbsvpk/Q355x/eH75ItflesdyyWsz5/xMkseSfGtOT8t76Vk5t/zzaE/R+Iskd5+/8nt7knfl9G32V+4LPA7gkrYeRXClXvboxu/NFV2X83dP/2qSj885f+Ga37ry67J1LFd9bcYYbxpjvP788Vck+Y6cvr7yWJLvO/+xW39NruorvTf51eAHc/pK9CeT/OQFj+OunO7ePJnkY1d9LEl+O6fl7f/k9J70h5O8IacHOH8iyZ8mueNCx/EbST6a5KmcPmHvvKJr8kBObz2eSvLE+ceDF7ouW8dypdcmyTcn+evzvKeT/NQ1//1+JMkzSX4vyZfdyrn+GTlQ2dPbE+BLgGgAFdEAKqIBVEQDqIgGUBENoPJ/NiFYefZ2oJwAAAAASUVORK5CYII=\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 1000 regions more=True or worst=False active for filter number: 3 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 1000 regions more=True or worst=False active for filter number: 4 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 1000 regions more=True or worst=False active for filter number: 5 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 1000 regions more=True or worst=False active for filter number: 6 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 1000 regions more=True or worst=False active for filter number: 7 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQ0AAA22CAYAAACiv7nKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdX+iwaXrQ9+vpxsZCqdFkCGF3YQMuFE+qdogWTyRBiGnp5kCtbZEgC3uSgEWljT3xpAfxpKlCERZXuiml2zQtZJFACVGRQhXHKrEmB25DZHeJ7kSTtEWspH16ML/AuN3Hme/82Xm8ns8Hlnl/f2au333y5X7f+937Ps7zHIC361/6oH8A4F8sogEkogEkogEkogEkogEk3/B+/EeP4/jumfnTM/Ohmflz53n+0D/v+7/lW77l/NjHPvZ+/CjAO/DzP//z84u/+IvH1/raex6N4zg+NDP/5cz8npn50sz89eM4Pn+e589c/Tsf+9jH5rXXXnuvfxTgHXr11Vcvv/Z+/PbkO2bmC+d5/tx5nv90Zj43M594H+YAH4D3IxofnpkvvunjL7187p9xHMenjuN47TiO115//fX34ccA3g8f2B+Enuf56fM8Xz3P89VXXnnlg/oxgOj9iMaXZ+ajb/r4Iy+fAxZ4P05P/vrMfPw4jm+fN2LxB2fmP3in/7Hj+Jp/gPuu/PP+T3rmvXfzNq/tCfOuvOfROM/zV4/j+IGZ+Z/mjSPXP3+e5995r+cAH4z35e9pnOf5EzPzE+/Hfxv4YPkboUAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkDyvlz39156JxefmnePeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye3vCN3+9N3meZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67bn77bPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye3vCN3+9N3meZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67bn77bPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/o7Q7U/fbZ63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv3pu83zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIbn96sv3pu83zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+9N3meZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye3vCN3+9N3meZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67bn77bPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V+w0gEQ0gEQ0gEQ0gEQ0gEQ0gOT2R67b38vcPG/z2p4w74qdBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpCIBpDc/mLh7e9lbp63eW1PmHfFTgNIRANIRANIRANIRANIRANIbn/kuv29zM3zNq/tCfOu2GkAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAiWgAye0vFt7+XubmeZvX9oR5V95yp3Ecx58/juMrx3H8b2/63G86juMnj+P4uy///I0vnz+O4/gzx3F84TiOnz6O47e/nz888PX3dn578l/NzHd/1ed+cGZ+6jzPj8/MT718PDPze2fm4y//+9TM/Nn35scE7uIto3Ge51+ZmX/0VZ/+xMx89uXXn52Z733T53/kfMNfnZlvOo7j296rHxb44L3TPwj91vM8f+Hl139/Zr715dcfnpkvvun7vvTyuf+f4zg+dRzHa8dxvPb666+/wx8D+Hp716cn5xt/OpP/hOY8z0+f5/nqeZ6vvvLKK+/2xwC+Tt5pNP7Br/224+WfX3n5/Jdn5qNv+r6PvHwOWOKdHrl+fma+b2Z+6OWfP/6mz//AcRyfm5nfMTO/8qbfxrwj29/L3Dxv89qeMO/KW0bjOI7/dmZ+98x8y3EcX5qZPzlvxOJHj+P45Mz8vZn5Ay/f/hMz8z0z84WZ+ccz84fzTwTc2ltG4zzPf//iS9/1Nb73nJnvf7c/FHBf/ho5kIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkHiW0bwVs8z7+rHTABLRABLRABLRABLRABLRAJLbH7luf/pu87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRAJLbn55sf/pu87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+ztCtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/an7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/I3T703eb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7luf/pu87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRAJLbn55sf/pu87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+ztCtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/an7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXXnLncZxHB89juMvHcfxM8dx/J3jOP7Iy+d/03EcP3kcx999+edvfPn8cRzHnzmO4wvHcfz0cRy//f1eBPD183Z+e/KrM/PHzvP8LTPzO2fm+4/j+C0z84Mz81PneX58Zn7q5eOZmd87Mx9/+d+nZubPvuc/NfCBectonOf5C+d5/q8vv/4/Z+ZnZ+bDM/OJmfnsy7d9dma+9+XXn5iZHznf8Fdn5puO4/i29/wnBz4Q6Q9Cj+P42Mz8tpn5azPzred5/sLLl/7+zHzry68/PDNffNO/9qWXz331f+tTx3G8dhzHa6+//nr8sYEPytuOxnEc/+rM/A8z8x+d5/l/vPlr5xt/QpP+lOY8z0+f5/nqeZ6vvvLKK+VfBT5Abysax3H8unkjGP/NeZ7/48un/8Gv/bbj5Z9fefn8l2fmo2/61z/y8jlggbc8cj3eeEDyMzPzs+d5/udv+tLnZ+b7ZuaHXv7542/6/A8cx/G5mfkdM/Mrb/ptTLb9vczN8zav7Qnzrrydv6fxu2bmD83M3z6O42+9fO4/nTdi8aPHcXxyZv7ezPyBl6/9xMx8z8x8YWb+8cz84fxTAbf1ltE4z/N/npmrxH3X1/j+c2a+/13+XMBN+WvkQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQCIaQOItV/NWzDLv68dOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7e8I3f703eZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtufvts8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7e8I3f703eZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtufvts8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+jtDtT99tnrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/em7zfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIwb1d4AACAASURBVIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0huf3qy/em7zfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v703eZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7e8I3f703eZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtufvts8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkNz+YuHt72Vunrd5bU+Yd8VOA0hEA0hEA0hEA0hEA0hEA0huf+S6/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaACJaADJ7S8W3v5e5uZ5m9f2hHlX7DSARDSARDSARDSARDSARDSA5PZHrtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkIgGkLzlxcLHcfz6mfkrM/ONL9//Y+d5/snjOL59Zj43M988M39jZv7QeZ7/9DiOb5yZH5mZf3Nm/uHM/Hvnef78O/0Bt7+XuXne5rU9Yd6Vt7PT+L9n5jvP8/w3Zua3zsx3H8fxO2fmT83MD5/n+Ztn5pdm5pMv3//Jmfmll8//8Mv3AUu8ZTTON/xfLx/+upf/nTPznTPzYy+f/+zMfO/Lrz/x8vG8fP27jvfj7nXgA/G2/kzjOI4PHcfxt2bmKzPzkzPzv8/ML5/n+asv3/Klmfnwy68/PDNfnJl5+fqvzBu/hfnq/+anjuN47TiO115//fV3twrg6+ZtReM8z//nPM/fOjMfmZnvmJl//d0OPs/z0+d5vnqe56uvvPLKu/3PAV8n6fTkPM9fnpm/NDP/1sx803Ecv/YHqR+ZmS+//PrLM/PRmZmXr/+GeeMPRIEF3jIax3G8chzHN738+l+Zmd8zMz87b8Tj97182/fNzI+//PrzLx/Py9f/4nmXP/YF3rW385brt83MZ4/j+NC8EZkfPc/zLxzH8TMz87njOP6zmfmbM/OZl+//zMz818dxfGFm/tHM/MF38wNufy9z87zNa3vCvCtvGY3zPH96Zn7b1/j8z80bf77x1Z//JzPz+/NPAvwLwd8IBRLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRAJK3c7HwB2r7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+ztCtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/an7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0guf3pyfan7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj703eb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/I3T703eb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7luf/pu87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+ztCtz99t3ne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/an7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0guf3pyfan7zbP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj703eb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/WHj7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRAJLbH7lufy9z87zNa3vCvCt2GkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkAiGkBy+4uFt7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0guf2R6/b3MjfP27y2J8y7YqcBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJKIBJLe/I3T703eb521e2xPmXXnbO43jOD50HMffPI7jL7x8/O3Hcfy14zi+cBzHf3ccx7/88vlvfPn4Cy9f/9j786MDH4Ty25M/MjM/+6aP/9TM/PB5nr95Zn5pZj758vlPzswvvXz+h1++D1jibUXjOI6PzMy/PTN/7uXjY2a+c2Z+7OVbPjsz3/vy60+8fDwvX/+u4/24ex34QLzdncZ/MTP/8cz8vy8ff/PM/PJ5nr/68vGXZubDL7/+8Mx8cWbm5eu/8vL9/4zjOD51HMdrx3G89vrrr7/DHx/4envLaBzH8e/MzFfO8/wb7+Xg8zw/fZ7nq+d5vvrKK6+8l/9p4H30dk5PftfM/LvHcXzPzPz6mfnXZuZPz8w3HcfxDS+7iY/MzJdfvv/LM/PRmfnScRzfMDO/YWb+4Xv+kwMfiLeMxnmef2Jm/sTMzHEcv3tm/vh5nv/hcRz//cz8vpn53Mx838z8+Mu/8vmXj/+Xl6//xfNdnBVtf/pu87zNa3vCvCvv5i93/Scz80eP4/jCvPFnFp95+fxnZuabXz7/R2fmB9/FDOBm0l/uOs/zL8/MX3759c/NzHd8je/5JzPz+9+Dnw24IX+NHEhEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0hEA0i85Wreilnmff3YaQCJaACJaACJaACJaACJaADJ7Y9ct7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0guf3Fwtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkNz+yHX7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRAJLbXyy8/b3MzfM2r+0J867YaQCJaACJaACJaACJaACJaADJ7Y9ct7+XuXne5rU9Yd4VOw0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0gEQ0guf3Fwtvfy9w8b/PanjDvip0GkIgGkIgGkIgGkIgGkIgGkNz+yHX7e5mb521e2xPmXbHTABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRABLRgP+vvfsJuey+6zj++VESFVuoaWsJWq2RgHQhMYRQIRQUlJqNCiJdWUUIiAVduAgIUhcuFBQEoRJRqOKf+he7NJaAK1ujJmlq1aYS0RCbSK1WXGjrz8W9gTHM6cwnnTz39HdeLxjmmTsz/Z5zyPPu7z5nnvOjIhpARTSAimgAFdEAKqIBVEQDqOz+GaGrb3238ryVz+0I87ZYaQAV0QAqogFURAOoiAZQEQ2gsvtbrqtvfbfyvJXP7QjztlhpABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVEQDqOz+wcKr75e58ryVz+0I87ZYaQAV0QAqogFURAOoiAZQEQ2gsvtbrqvvl7nyvJXP7QjztlhpABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVEQDqOz+wcKr75e58ryVz+0I87ZYaQAV0QAqogFURAOoiAZQEQ2gsvtbrqvvl7nyvJXP7QjztlhpABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVEQDqOz+wcKr75e58ryVz+0I87ZYaQAV0QAqogFURAOoiAZQEQ2gsvtbrqvvl7nyvJXP7QjztlhpABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVHb/jNDVt75bed7K53aEeVusNICKaAAV0QAqogFURAOoiAZQ2f0t19W3vlt53srndoR5W6w0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVHb/YOHV98tced7K53aEeVusNICKaAAV0QAqogFURAOoiAZQ2f0t19X3y1x53srndoR5W6w0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVHb/YOHV98tced7K53aEeVusNICKaAAV0QAqogFURAOoiAZQ2f0t19X3y1x53srndoR5W6w0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVHb/YOHV98tced7K53aEeVusNICKaAAV0QAqogFURAOoiAZQ2f0t19X3y1x53srndoR5W6w0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKqIBVHb/YOHV98tced7K53aEeVusNICKaAAV0QAqogFURAOoiAZQ2f0t19X3y1x53srndoR5W6w0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKrt/RujqW9+tPG/lczvCvC1WGkBFNICKaAAV0QAqogFURAOo7P6W6+pb3608b+VzO8K8LVYaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKrt/sPDq+2WuPG/lczvCvC1WGkBFNICKaAAV0QAqogFURAOo7P6W6+r7Za48b+VzO8K8LVYaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKrt/sPDq+2WuPG/lczvCvC1WGkBFNICKaAAV0QAqogFURAOo7P6W6+r7Za48b+VzO8K8LVYaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKrt/sPDq+2WuPG/lczvCvC1WGkBFNICKaAAV0QAqogFURAOo7P6W6+r7Za48b+VzO8K8LVYaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAFdEAKrt/sPDq+2WuPG/lczvCvC1WGkBFNICKaAAV0QAqogFUdn/3ZPWt71aet/K5HWHeFisNoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAld0/WHj1re9WnrfyuR1h3hYrDaAiGkBFNICKaAAV0QAqogFUdn/LdfX9Mleet/K5HWHeFisNoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAld0/WHj1/TJXnrfyuR1h3hYrDaAiGkBFNICKaAAV0QAqogFUdn/LdfX9Mleet/K5HWHeFisNoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAld0/WHj1/TJXnrfyuR1h3hYrDaAiGkBFNICKaAAV0QAqogFUdn/LdfX9Mleet/K5HWHeFisNoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAimgAld0/WHj1/TJXnrfyuR1h3hYrDaAiGkBFNICKaAAV0QAqogFUdn/LdfX9Mleet/K5HWHeFisNoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAyu6fEbr61ncrz1v53I4wb4uVBlARDaAiGkBFNICKaAAV0QAqu7/luvrWdyvPW/ncjjBvi5UGUBENoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAyu4fLLz6fpkrz1v53I4wb4uVBlARDaAiGkBFNICKaAAV0QAqu7/luvp+mSvPW/ncjjBvi5UGUBENoCIaQEU0gIpoABXRACqiAVREA6iIBlARDaAiGkBFNICKaAAV0QAqogFURAOoiAZQEQ2gIhpARTSAyk09WHiM8WySzyb5fJLPzTnvG2PckeQDSd6a5Nkk3z/n/LdxevrpLyZ5MMl/JfnBOedfvdIDXH2/zJXnrXxuR5i3pVlpfNuc8545533nXz+c5ENzzruTfOj86yT5riR3n388lOR9t+pggcv7Yt6efHeS958/fn+S77nm9V+fJ3+e5PVjjDu/iDnAjtxsNGaSPxlj/OUY46Hza2+ecz5//vhfkrz5/PHXJPmna/7uP59f+3/GGA+NMR4fYzz+4osvvoJDBy7hZjdLemDO+dwY46uTPDrG+Ntrf3POOccY1RuuOecjSR5Jkvvuu28fb9aAG7qplcac87nzzy8k+aMk9yf51EtvO84/v3D+488lecs1f/1rz68BC7hhNMYYXznGeN1LHyf5ziRPJ/lgknef/9i7k/zx+eMPJvmBcfL2JP9+zdsY4EvcuNFtnDHGXTmtLpLT25nfmnP+zBjjDUl+N8nXJfnHnG65fvp8y/WXkrwzp1uuPzTnfPwGM148/2+85I1J/vUVnM+ttpfjSBzL9ezlOJL1juXr55xvut5v3DAalzDGePyaW7uHP47Esez5OJJjHYt/EQpURAOo7DUaj1z6AM72chyJY7mevRxHcqBj2eXXNID92utKA9gp0QAqu4rGGOOdY4y/G2M8M8Z4+MZ/41U9lmfHGB8dYzwxxviC/87kVZj9a2OMF8YYT1/z2h1jjEfHGJ84//xVFzqO944xnjtflyfGGA++2sdxnvuWMcZjY4y/GWN8bIzxY+fXL3Fdto7lSq/NOQU51gAAArBJREFUGOPLxxgfGWM8eT6Onz6//g1jjA+fP48+MMa4/ZYOnnPu4keS1yT5ZJK7ktye5Mkkb7vg8Tyb5I0Xmv2OJPcmefqa134uycPnjx9O8rMXOo73JvmJC1yTO5Pce/74dUn+PsnbLnRdto7lSq9NkpHkteePb0vy4SRvz+kfXb7r/PovJ/mRWzl3TyuN+5M8M+f8hznnfyf5nZy+zf5w5px/luTTL3t561EEV30cFzHnfH6eH+Y05/xsko/n9N3Tl7guW8dypebJf55/edv5x0zy7Ul+//z6Lb8me4rGTX1L/RW63uMALmnrUQSX8J4xxlPnty+v+tuBlxtjvDXJt+T0/6wXvS4vO5bkiq/NGOM1Y4wncvqG0UdzWq1/Zs75ufMfueWfR3uKxt48MOe8N6cnkf3oGOMdlz6gl8zTuvNS98rfl+Qbk9yT5PkkP3+Vw8cYr03yB0l+fM75H9f+3lVfl+scy5Vfmznn5+ec9+T03eT3J/mmV3vmnqKxq2+pn9d/HMAlbT2K4ErNOT91/g/1f5P8Sq7wuowxbsvpk/Q355x/eH75ItflesdyyWsz5/xMkseSfGtOT8t76Vk5t/zzaE/R+Iskd5+/8nt7knfl9G32V+4LPA7gkrYeRXClXvboxu/NFV2X83dP/2qSj885f+Ga37ry67J1LFd9bcYYbxpjvP788Vck+Y6cvr7yWJLvO/+xW39NruorvTf51eAHc/pK9CeT/OQFj+OunO7ePJnkY1d9LEl+O6fl7f/k9J70h5O8IacHOH8iyZ8mueNCx/EbST6a5KmcPmHvvKJr8kBObz2eSvLE+ceDF7ouW8dypdcmyTcn+evzvKeT/NQ1//1+JMkzSX4vyZfdyrn+GTlQ2dPbE+BLgGgAFdEAKqIBVEQDqIgGUBENoPJ/NiFYefZ2oJwAAAAASUVORK5CYII=\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 1000 regions more=True or worst=False active for filter number: 8 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["grid regions of 1000 regions more=True or worst=False active for filter number: 9 :\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["normalized regions:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x4608 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"PECJ9p4q5wbt","colab_type":"text"},"source":["##### Binary model layer2:"]},{"cell_type":"code","metadata":{"id":"dhAgS9Qv5yNP","colab_type":"code","outputId":"f076d306-86a5-4f49-c6aa-683717aa3b90","executionInfo":{"status":"ok","timestamp":1588699249976,"user_tz":-120,"elapsed":87956,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"18mdbRj9P4zF7NsheiAYs7obwrEigJbqF"}},"source":["# parameters\n","list_filter_interest_layer2 = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 10\n","\n","# regions and activation of interest\n","regions = region_layer2_binary\n","activations = activation_layer2_binary\n","activations_normalized = activation_layer2_binary_normalized\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer2)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"KQPBG4iXZP4s","colab_type":"text"},"source":["##### No binary model without bias layer1:"]},{"cell_type":"code","metadata":{"id":"iYck4UZcZSID","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer1 = [0,1,2,3,4,5,6,7,8,9]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 1\n","nrow=14\n","\n","# regions and activation of interest\n","regions = region_layer1_no_binary_without_bias\n","activations = activation_layer1_no_binary_without_bias\n","activations_normalized = activation_layer1_no_binary_normalized_without_bias\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer1, nrow=nrow)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OGvbyZm2ZSOk","colab_type":"text"},"source":["##### No binary model without bias layer2:"]},{"cell_type":"code","metadata":{"id":"Z41hkhkBZS8n","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer2 = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 1\n","nrow=14\n","\n","# regions and activation of interest\n","regions = region_layer2_no_binary_without_bias\n","activations = activation_layer2_no_binary_without_bias\n","activations_normalized = activation_layer2_no_binary_normalized_without_bias\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer2, nrow=nrow)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7XW5dzurZbeS","colab_type":"text"},"source":["##### Binary model without bias layer1:"]},{"cell_type":"code","metadata":{"id":"nsUeDv2gZbj9","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer1 = [0,1,2,3,4,5,6,7,8,9]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 1\n","nrow=14\n","\n","# regions and activation of interest\n","regions = region_layer1_binary_without_bias\n","activations = activation_layer1_binary_without_bias\n","activations_normalized = activation_layer1_binary_normalized_without_bias\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer1, nrow=nrow)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vfWIgzyWZb9H","colab_type":"text"},"source":["##### Binary model without bias layer2:"]},{"cell_type":"code","metadata":{"id":"a0UMeeKwZcJ1","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer2 = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 1\n","nrow=14\n","\n","# regions and activation of interest\n","regions = region_layer2_binary_without_bias\n","activations = activation_layer2_binary_without_bias\n","activations_normalized = activation_layer2_binary_normalized_without_bias\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer2, nrow=nrow)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CZylTnk3Ofb3","colab_type":"text"},"source":["# Draft"]},{"cell_type":"markdown","metadata":{"id":"oCmF35kglqnz","colab_type":"text"},"source":["## Test region's score:"]},{"cell_type":"code","metadata":{"id":"-mcd_tTm6oYe","colab_type":"code","colab":{}},"source":["from numpy import linalg as LA"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9GuHiS03FJE3","colab_type":"code","colab":{}},"source":["def get_activation(name, activation):\n"," def hook(model, input, output):\n"," activation[name] = output.detach()\n"," return hook\n","\n","def test_score_region(model, filter_choice):\n","\n"," dataiter = iter(train_loader)\n"," images, _ = dataiter.next()\n","\n"," index = np.random.randint(0,1000)\n"," image = images[index]\n"," print('image test number: {} with shape: {}'.format(index, image.shape))\n"," plt.imshow(image[0], cmap='gray')\n"," plt.show()\n","\n"," activation = {}\n","\n"," for name, m in model.named_modules():\n"," if type(m) == nn.Conv2d:\n"," m.register_forward_hook(get_activation(name, activation))\n","\n"," out = model(image.unsqueeze(0)) \n","\n"," activation_layer1 = activation['layer1'][0]\n"," activation_layer2 = activation['layer2'][0]\n","\n"," print('prediction:{}'.format(out.data.numpy().argmax())) \n","\n"," filter = filter_choice\n"," act_max_layer1 = activation_layer1[filter].max()\n"," print('value activation max for filte {} :{}'.format(filter, act_max_layer1))\n","\n"," ind_x = int((np.where(activation_layer1[filter] == act_max_layer1)[0])[0]) \n"," ind_y = int((np.where(activation_layer1[filter] == act_max_layer1)[1])[0])\n","\n"," print('index of max value: x: {}, y: {}'.format(ind_x, ind_y))\n","\n"," name = 'layer1'\n"," stride = 2\n"," padding=1\n"," filter_size=3\n"," len_img_h=28\n"," len_img_w=28\n"," im = image[0]\n","\n"," region, begin_col, end_col, begin_raw, end_raw = get_region_layer1(im, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w, return_all=True)\n","\n"," print('region extracted: {}'.format(region))\n"," plt.imshow(region, cmap='gray')\n"," plt.show()\n","\n"," random_im = np.uint8(np.random.uniform(0, 255, (28, 28)))/255\n"," print('random image generated:')\n"," plt.imshow(random_im, cmap='gray')\n"," plt.show()\n","\n"," random_im[begin_col:end_col, begin_raw:end_raw] = region\n"," plt.imshow(random_im, cmap='gray')\n"," print('random image generated with region that maximize filter activation:')\n"," plt.show()\n","\n"," activation_random_im = {}\n","\n"," for name, m in model_no_binary.named_modules():\n"," if type(m) == nn.Conv2d:\n"," m.register_forward_hook(get_activation(name, activation_random_im))\n","\n"," random_image = (torch.tensor(random_im.reshape((1,1,28,28))))\n"," out = model_no_binary(random_image.float())\n"," activation_layer1_random = activation_random_im['layer1'][0]\n","\n"," act_max_random = activation_layer1_random[filter].max()\n","\n"," ind_x_random = int((np.where(activation_layer1_random[filter] == act_max_random)[0])[0]) \n"," ind_y_random = int((np.where(activation_layer1_random[filter] == act_max_random)[1])[0])\n","\n"," activation_value_index_random = activation_layer1_random[filter][ind_x][ind_y]\n"," activation_value_index = activation_layer1[filter][ind_x][ind_y]\n","\n"," print('activation max for image: {} with index: x:{}, y:{}'.format(act_max_layer1, ind_x, ind_y))\n"," print('activation max for random image with region: {} with index: x:{}, y:{}'.format(act_max_random, ind_x_random, ind_y_random)) \n","\n"," print('activation value for ind_x: {} and ind_y: {} = {}'.format(ind_x, ind_y, activation_value_index))\n"," print('random activation value for ind_x: {} and ind_y: {} = {}'.format(ind_x_random, ind_y_random, activation_value_index_random))\n"," \n"," region_new_random, begin_col, end_col, begin_raw, end_raw = get_region_layer1(random_im, ind_x_random, ind_y_random, name, stride, padding, filter_size, len_img_h, len_img_w, return_all=True)\n","\n"," plt.imshow(region_new_random, cmap='gray')\n"," print('region_new_random')\n"," plt.show()\n","\n"," return region"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"IiVsMemoGBHD","colab_type":"code","outputId":"c4148172-134c-4e0d-c656-f4d7fbb858f4","executionInfo":{"status":"ok","timestamp":1588862518715,"user_tz":-120,"elapsed":1924,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["model = model_no_binary\n","filter_choice = 9\n","\n","region = test_score_region(model, filter_choice)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["image test number: 295 with shape: torch.Size([1, 28, 28])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAN90lEQVR4nO3df4wUdZrH8c9z3gLGRQNrJETw2MNBszGRNQQv0Vz8tRv1H0STZTG5cO7GWcNqWD3jmT0R4uXMcjm4v8zqEAyc4SQbGYVsDAuHm1OjMeCP09FxwSMgMCNoNFnAP5DhuT+m5jLC1Ldnuqq6mnner6TT3fVMdT2Wfqzqqq76mrsLwPj3F3U3AKA1CDsQBGEHgiDsQBCEHQjiL1u5MDPj0D9QMXe3kaYX2rKb2a1m9icz+8TMHi3yWQCqZc2eZzez8yTtkfQjSYck7ZK02N0/SszDlh2oWBVb9vmSPnH3fe5+UtImSQsKfB6AChUJ+6WSDg57fyib9i1m1mlmu81sd4FlASio8gN07t4lqUtiNx6oU5Et+2FJM4e9n5FNA9CGioR9l6QOM/u+mU2Q9FNJW8tpC0DZmt6Nd/dTZna/pD9IOk/Ss+7+YWmdAShV06femloY39mBylXyoxoA5w7CDgRB2IEgCDsQBGEHgiDsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EARhB4Jo6ZDNiGfGjBm5tdtuu63QZ3d0dCTrDz/8cNOf/cQTTyTrmzZtStY//vjjppddFbbsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAE59mRdPXVVyfrzzzzTLJ+0UUX5dbmzJnTVE+jVWSE4uXLlyfrBw8eTNbb8Tx7obCb2X5JxyQNSDrl7vPKaApA+crYst/o7l+U8DkAKsR3diCIomF3SdvN7G0z6xzpD8ys08x2m9nugssCUEDR3fjr3f2wmV0iaYeZfezurw7/A3fvktQlSWbW/BETAIUU2rK7++Hs+aikFyXNL6MpAOVrOuxmdoGZTR56LenHknrKagxAuYrsxk+T9KKZDX3Of7r7tlK6QstcddVVyXp3d3eyPmvWrBK7QZWaDru775OU/sUFgLbBqTcgCMIOBEHYgSAIOxAEYQeCsCKXAY55YfyCruXmzp2brO/YsSNZnzp1apntnDN27dqVrF933XXJ+sDAQJntjIm720jT2bIDQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBDcSnocWL16dW7trrvuSs5b9Xn0EydO5NYef/zx5LyzZ89O1pcuXdpUT6OxatWqZL3O8+jNYssOBEHYgSAIOxAEYQeCIOxAEIQdCIKwA0Fwnv0c0GjY5IULF+bWZs6cWXY7Y/LQQw/l1jZu3Jic9+WXXy67nVHbu3dvbcuuClt2IAjCDgRB2IEgCDsQBGEHgiDsQBCEHQiC8+zngPPPPz9ZnzRpUmXLfv3115P1devWJet9fX25tf7+/uS8kydPTtYbSV1z/sADDyTn7e3tLbTsdtRwy25mz5rZUTPrGTZtqpntMLO92fOUatsEUNRoduPXS7r1jGmPStrp7h2SdmbvAbSxhmF391clfXnG5AWSNmSvN0i6o+S+AJSs2e/s09x96AvXZ5Km5f2hmXVK6mxyOQBKUvgAnbt7asBGd++S1CUxsCNQp2ZPvR0xs+mSlD0fLa8lAFVoNuxbJS3JXi+RtKWcdgBUpeH47Gb2vKQbJF0s6YikFZJekvQ7SZdJOiDpJ+5+5kG8kT6L3fgKXHPNNbm1e+65JznvK6+8kqy/+eabyfrEiROT9bVr1+bWbr755uS8RT355JO5teXLl1e67Drljc/e8Du7uy/OKVX7bwpAqfi5LBAEYQeCIOxAEIQdCIKwA0E0PPVW6sI49XbOue+++5L1p556qkWdnG3//v3J+oIFC3JrPT09ubVzXd6pN7bsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAEt5IO7pFHHknWV65cWdmyv/nmm2T9wQcfTNY3bdqUrH/11Vdj7mk8Y8sOBEHYgSAIOxAEYQeCIOxAEIQdCIKwA0FwPfs4MGVK/iC6ixYtSs67Zs2aZL3RraIbeffdd3Nrjz32WHLebdu2FVp2VFzPDgRH2IEgCDsQBGEHgiDsQBCEHQiCsANBcD37OJC6rvuWW25pYSdn27BhQ26N8+it1XDLbmbPmtlRM+sZNm2lmR02s/eyx+3VtgmgqNHsxq+XdOsI0//d3edmj5fLbQtA2RqG3d1flfRlC3oBUKEiB+juN7P3s9383B9nm1mnme02s90FlgWgoGbD/ltJsyXNldQvaXXeH7p7l7vPc/d5TS4LQAmaCru7H3H3AXc/LWmtpPnltgWgbE2F3cymD3u7UNL4Hf8WGCcanmc3s+cl3SDpYjM7JGmFpBvMbK4kl7Rf0i8q7HHcu+KKK5L1l156KVm/7LLLymznWz7//PNkff789E5dX19fme2ggIZhd/fFI0xeV0EvACrEz2WBIAg7EARhB4Ig7EAQhB0IgktcSzBhwoRkfcWKFcn6nXfemazPmTNnzD2VZfLkycn6jTfemKxv3rw5t3b8+PGmekJz2LIDQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBAM2VyC5557Llm/++67W9RJ+9mzZ09ubd269r14MnULbKnxpb91YshmIDjCDgRB2IEgCDsQBGEHgiDsQBCEHQiC8+yZK6+8MllP3c758ssvT85rNuJpT7SxAwcOJOsnT55M1hv991QlzrMDwRF2IAjCDgRB2IEgCDsQBGEHgiDsQBBh7hs/adKkZH3p0qXJekdHR5nttMypU6cK1Rv9RmDixIlj7qkdDAwMJOsnTpxI1letWlVmOy3RcMtuZjPN7I9m9pGZfWhmy7LpU81sh5ntzZ6nVN8ugGaNZjf+lKR/cPcfSPobSb80sx9IelTSTnfvkLQzew+gTTUMu7v3u/s72etjknolXSppgaShe/dskHRHVU0CKG5M39nNbJakH0p6S9I0d+/PSp9JmpYzT6ekzuZbBFCGUR+NN7PvStos6Vfu/ufhNR+8mmbEi1zcvcvd57n7vEKdAihkVGE3s+9oMOgb3b07m3zEzKZn9emSjlbTIoAyNNyNt8FzL+sk9br7mmGlrZKWSPpN9rylkg5L8vXXXyfrrbzUt2xbtuSv+qeffjo57/bt25P1Cy+8MFnftm1bsn7ttdcm60X09fUl693d3bm11157LTnvCy+80FRP7Ww039mvk/R3kj4ws/eyab/WYMh/Z2Y/l3RA0k+qaRFAGRqG3d1fl5T3y4qby20HQFX4uSwQBGEHgiDsQBCEHQiCsANBhLmV9OnTp5P1Os+zf/rpp8n6okWLkvXe3t7c2rFjx5rqabQuueSSZP2mm27Krd17773JeZctW5asN/rtxL59+5L18YpbSQPBEXYgCMIOBEHYgSAIOxAEYQeCIOxAEGHOszf652x0Hj7ljTfeSNbXr1+frL/11lvJek9Pz1hbQmCcZweCI+xAEIQdCIKwA0EQdiAIwg4EQdiBIMIM2dxo6GFgvGPLDgRB2IEgCDsQBGEHgiDsQBCEHQiCsANBNAy7mc00sz+a2Udm9qGZLcumrzSzw2b2Xva4vfp2ATSr4c0rzGy6pOnu/o6ZTZb0tqQ7NDge+3F3/7dRL6zGm1cAUeTdvGI047P3S+rPXh8zs15Jl5bbHoCqjek7u5nNkvRDSUP3UbrfzN43s2fNbErOPJ1mttvMdhfqFEAho74HnZl9V9J/S/oXd+82s2mSvpDkkv5Zg7v6P2vwGezGAxXL240fVdjN7DuSfi/pD+6+ZoT6LEm/d/erGnwOYQcq1vQNJ23wcrF1knqHBz07cDdkoSRugQq0sdEcjb9e0muSPpA0dL/lX0taLGmuBnfj90v6RXYwL/VZbNmBihXajS8LYQeqx33jgeAIOxAEYQeCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EARhB4Ig7EAQhB0IgrADQbR6yOYvJB0Y9v7ibFo7atfe2rUvid6aVWZvf5VXaOn17Gct3Gy3u8+rrYGEdu2tXfuS6K1ZreqN3XggCMIOBFF32LtqXn5Ku/bWrn1J9NaslvRW63d2AK1T95YdQIsQdiCIWsJuZrea2Z/M7BMze7SOHvKY2X4z+yAbhrrW8emyMfSOmlnPsGlTzWyHme3NnkccY6+m3tpiGO/EMOO1rru6hz9v+Xd2MztP0h5JP5J0SNIuSYvd/aOWNpLDzPZLmufutf8Aw8z+VtJxSf8xNLSWmf2rpC/d/TfZ/yinuPs/tklvKzXGYbwr6i1vmPG/V43rrszhz5tRx5Z9vqRP3H2fu5+UtEnSghr6aHvu/qqkL8+YvEDShuz1Bg3+x9JyOb21BXfvd/d3stfHJA0NM17rukv01RJ1hP1SSQeHvT+k9hrv3SVtN7O3zayz7mZGMG3YMFufSZpWZzMjaDiMdyudMcx426y7ZoY/L4oDdGe73t2vkXSbpF9mu6ttyQe/g7XTudPfSpqtwTEA+yWtrrOZbJjxzZJ+5e5/Hl6rc92N0FdL1lsdYT8saeaw9zOyaW3B3Q9nz0clvajBrx3t5MjQCLrZ89Ga+/l/7n7E3Qfc/bSktapx3WXDjG+WtNHdu7PJta+7kfpq1XqrI+y7JHWY2ffNbIKkn0raWkMfZzGzC7IDJzKzCyT9WO03FPVWSUuy10skbamxl29pl2G884YZV83rrvbhz9295Q9Jt2vwiPz/SvqnOnrI6euvJf1P9viw7t4kPa/B3bpvNHhs4+eSvidpp6S9kv5L0tQ26u05DQ7t/b4GgzW9pt6u1+Au+vuS3sset9e97hJ9tWS98XNZIAgO0AFBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEP8Ha65psYY0vgkAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["prediction:2\n","value activation max for filte 9 :0.15805275738239288\n","index of max value: x: 10, y: 2\n","region extracted: tensor([[0.0000, 0.0000, 0.1569],\n"," [0.0000, 0.0314, 0.5725],\n"," [0.0000, 0.9961, 0.9922]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["random image generated:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["random image generated with region that maximize filter activation:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["activation max for image: 0.15805275738239288 with index: x:10, y:2\n","activation max for random image with region: 0.15805275738239288 with index: x:10, y:2\n","activation value for ind_x: 10 and ind_y: 2 = 0.15805275738239288\n","random activation value for ind_x: 10 and ind_y: 2 = 0.15805275738239288\n","region_new_random\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQ8AAAD8CAYAAABpXiE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAN0ElEQVR4nO3df8ydZX3H8fentNA/YIJ0kaZUK1kjYz8SoEGQxZCpCTaGmsgf+IeAgTzTAdNFk6EkmpgsQ/9wGdFIGiTCYpBMDDwuNQYHDJMFRmkKpQWkkCy0dqLgio38WN13fzw35vj4/Op17uec8+j7lZyc677v69zXN9cjH+6fmKpCko7VqnEXIGllMjwkNTE8JDUxPCQ1MTwkNTE8JDUZKjySvDnJvUme6b5Pmaffr5Ls7j7Tw4wpaTJkmOc8knwJeKmqbkxyPXBKVf3dHP2OVNWJQ9QpacIMGx5PAxdV1aEk64EHquodc/QzPKTfMcOGx/9U1cldO8DP31ie1e8osBs4CtxYVXfPs78pYKpbPLe5MAlYu3btuEuYeK+++urPquoPW367erEOSX4AnDbHphsGF6qqksyXRG+rqoNJzgDuS7Knqp6d3amqtgPbu3F9bl5D2bRp07hLmHhPPfXUf7X+dtHwqKr3zrctyU+SrB84bXlhnn0c7L6fS/IAcDbwW+EhaeUY9lbtNHBF174CuGd2hySnJDmha68DLgT2DTmupDEbNjxuBN6X5Bngvd0ySbYkuaXr88fAziSPAfczc83D8JBWuEVPWxZSVS8C75lj/U7g6q79H8CfDTOOpMnjE6aSmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKa9BIeSS5O8nSS/Umun2P7CUnu7LY/nGRTH+NKGp+hwyPJccBXgfcDZwEfTnLWrG5XAT+vqj8C/hH44rDjShqvPo48zgP2V9VzVfU68C1g26w+24Dbuva3gfckSQ9jSxqTPsJjA/D8wPKBbt2cfarqKHAYOLWHsSWNyepxFzAoyRQwNe46JC2ujyOPg8DGgeXTu3Vz9kmyGngT8OLsHVXV9qraUlVbeqhL0jLqIzweATYneXuS44HLgOlZfaaBK7r2pcB9VVU9jC1pTIY+bamqo0muBb4PHAfcWlV7k3wB2FlV08DXgX9Osh94iZmAkbSCZVIPAJJMZmFaMc4888xxlzDxnnrqqUdbLxP4hKmkJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJr2ER5KLkzydZH+S6+fYfmWSnybZ3X2u7mNcSeOzetgdJDkO+CrwPuAA8EiS6araN6vrnVV17bDjSZoMfRx5nAfsr6rnqup14FvAth72K2mCDX3kAWwAnh9YPgC8c45+H0rybuBHwN9W1fOzOySZAqZ6qOl33tq1a8ddwsS75pprxl3CxLvuuuuafzuqC6bfBTZV1Z8D9wK3zdWpqrZX1Zaq2jKiuiQ16iM8DgIbB5ZP79b9WlW9WFWvdYu3AOf2MK6kMeojPB4BNid5e5LjgcuA6cEOSdYPLF4CPNnDuJLGaOhrHlV1NMm1wPeB44Bbq2pvki8AO6tqGvibJJcAR4GXgCuHHVfSePVxwZSq2gHsmLXucwPtzwCf6WMsSZPBJ0wlNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ16SU8ktya5IUkT8yzPUluSrI/yeNJzuljXEnj09eRxzeAixfY/n5gc/eZAr7W07iSxqSX8KiqB4GXFuiyDbi9ZjwEnJxkfR9jSxqPUV3z2AA8P7B8oFv3G5JMJdmZZOeI6pLUaPW4CxhUVduB7QBJaszlSFrAqI48DgIbB5ZP79ZJWqFGFR7TwOXdXZfzgcNVdWhEY0taBr2ctiS5A7gIWJfkAPB5YA1AVd0M7AC2AvuBXwIf7WNcSePTS3hU1YcX2V7ANX2MJWky+ISppCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJr2ER5Jbk7yQ5Il5tl+U5HCS3d3nc32MK2l8evk/uga+AXwFuH2BPj+sqg/0NJ6kMevlyKOqHgRe6mNfklaGvo48luKCJI8BPwY+XVV7Z3dIMgVMjbCmFeuVV14ZdwkTr6rGXcLEu+6665p/O6rw2AW8raqOJNkK3A1snt2pqrYD2wGS+JeXJthI7rZU1ctVdaRr7wDWJFk3irElLY+RhEeS05Kka5/XjfviKMaWtDx6OW1JcgdwEbAuyQHg88AagKq6GbgU+HiSo8ArwGXlCam0omVS/xn2msfCJvXvNkmco8WtWrXq0ara0vTbvouR9PvB8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1GTo8EiyMcn9SfYl2ZvkE3P0SZKbkuxP8niSc4YdV9J4re5hH0eBT1XVriQnAY8mubeq9g30eT+wufu8E/ha9y1phRr6yKOqDlXVrq79C+BJYMOsbtuA22vGQ8DJSdYPO7ak8en1mkeSTcDZwMOzNm0Anh9YPsBvB4ykFaSP0xYAkpwI3AV8sqpebtzHFDDVV02Slk8v4ZFkDTPB8c2q+s4cXQ4CGweWT+/W/Yaq2g5s7/ZZfdQmaXn0cbclwNeBJ6vqy/N0mwYu7+66nA8crqpDw44taXz6OPK4EPgIsCfJ7m7dZ4G3AlTVzcAOYCuwH/gl8NEexpU0RqmazLMDT1sWNql/t0niHC1u1apVj1bVlqbf9l2MpN8PhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmQ4dHko1J7k+yL8neJJ+Yo89FSQ4n2d19PjfsuJLGa3UP+zgKfKqqdiU5CXg0yb1VtW9Wvx9W1Qd6GE/SBBj6yKOqDlXVrq79C+BJYMOw+5U02fo48vi1JJuAs4GH59h8QZLHgB8Dn66qvXP8fgqY6hZfA57os74erAN+Nu4iAJLABNXTsZ7FTVpN72j9YaqqlwqSnAj8O/D3VfWdWdv+APi/qjqSZCvwT1W1eZH97ayqLb0U15NJq8l6FjZp9cDk1TRMPb3cbUmyBrgL+Obs4ACoqper6kjX3gGsSbKuj7EljUcfd1sCfB14sqq+PE+f07p+JDmvG/fFYceWND59XPO4EPgIsCfJ7m7dZ4G3AlTVzcClwMeTHAVeAS6rxc+XtvdQW98mrSbrWdik1QOTV1NzPb1d85D0+8UnTCU1MTwkNZmY8Ejy5iT3Jnmm+z5lnn6/GnjMfXoZ6rg4ydNJ9ie5fo7tJyS5s9v+cPdsy7JaQk1XJvnpwLxcvYy13JrkhSRzPoOTGTd1tT6e5JzlquUYahrZ6xFLfF1jpHO0bK+QVNVEfIAvAdd37euBL87T78gy1nAc8CxwBnA88Bhw1qw+fw3c3LUvA+5c5nlZSk1XAl8Z0d/p3cA5wBPzbN8KfA8IcD7w8ATUdBHwryOan/XAOV37JOBHc/y9RjpHS6zpmOdoYo48gG3AbV37NuCDY6jhPGB/VT1XVa8D3+rqGjRY57eB97xxG3qMNY1MVT0IvLRAl23A7TXjIeDkJOvHXNPI1NJe1xjpHC2xpmM2SeHxlqo61LX/G3jLPP3WJtmZ5KEkfQfMBuD5geUD/PYk/7pPVR0FDgOn9lzHsdYE8KHuEPjbSTYuYz2LWWq9o3ZBkseSfC/Jn4xiwAVe1xjbHC3lFZKlzlGv77YsJskPgNPm2HTD4EJVVZL57iG/raoOJjkDuC/Jnqp6tu9aV5jvAndU1WtJ/oqZI6O/HHNNk2QXM/+7eeP1iLuBBV+PGFb3usZdwCer6uXlHGupFqnpmOdopEceVfXeqvrTOT73AD9549Ct+35hnn0c7L6fAx5gJkX7chAY/Lf26d26OfskWQ28ieV9WnbRmqrqxap6rVu8BTh3GetZzFLmcKRqxK9HLPa6BmOYo+V4hWSSTlumgSu69hXAPbM7JDklyQldex0zT7fO/u+GDOMRYHOStyc5npkLorPv6AzWeSlwX3VXnJbJojXNOl++hJlz2nGZBi7v7iicDxweOB0di1G+HtGNs+DrGox4jpZSU9McjeIK9BKvCJ8K/BvwDPAD4M3d+i3ALV37XcAeZu447AGuWoY6tjJzNfpZ4IZu3ReAS7r2WuBfgP3AfwJnjGBuFqvpH4C93bzcD5y5jLXcARwC/peZc/WrgI8BH+u2B/hqV+seYMsI5mexmq4dmJ+HgHctYy1/ARTwOLC7+2wd5xwtsaZjniMfT5fUZJJOWyStIIaHpCaGh6QmhoekJoaHpCaGh6QmhoekJv8PoKfuZ59Q+/IAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"j_uWO5b1k_7R","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"F5V7JpRCVAMg","colab_type":"text"},"source":["## filter value:"]},{"cell_type":"code","metadata":{"id":"oBMsXe_bUCzp","colab_type":"code","colab":{}},"source":["for name, m in model_no_binary.named_modules():\n"," if type(m) == nn.Conv2d:\n"," filters = m.weight.data.clone()\n"," break"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Uy-6Ff0eVHa5","colab_type":"code","outputId":"310143b9-e367-4ea1-eae6-31622463a862","executionInfo":{"status":"ok","timestamp":1588690701588,"user_tz":-120,"elapsed":306,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["filters.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([10, 1, 3, 3])"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"code","metadata":{"id":"6jp2qC9TVIav","colab_type":"code","outputId":"b5ab743c-8d91-475f-e49c-bef447f6f350","executionInfo":{"status":"ok","timestamp":1588690703123,"user_tz":-120,"elapsed":619,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":70}},"source":["model_no_binary.layer1.bias"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Parameter containing:\n","tensor([ 0.1957, 0.1620, -0.0787, -0.2980, 0.0367, -0.0169, 0.0877, 0.1401,\n"," -0.1020, -0.2601], requires_grad=True)"]},"metadata":{"tags":[]},"execution_count":47}]},{"cell_type":"code","metadata":{"id":"hyYxO8JDVX-2","colab_type":"code","colab":{}},"source":["filter_0 = filters[0][0]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wZnCMvi_WM7c","colab_type":"code","outputId":"97dc416d-f998-4c80-b53d-4482faa634cb","executionInfo":{"status":"ok","timestamp":1588690744046,"user_tz":-120,"elapsed":462,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":70}},"source":["print(filter_0)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor([[-0.0100, 0.2919, 0.1254],\n"," [ 0.0844, 0.0426, 0.0630],\n"," [-0.2353, -0.3480, -0.0394]])\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"AfuiTJgRVxTr","colab_type":"code","outputId":"8264b019-59b6-423e-9862-9fb9b5364b7a","executionInfo":{"status":"ok","timestamp":1588690706223,"user_tz":-120,"elapsed":468,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.imshow(filter_0, cmap='gray')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f8f50111358>"]},"metadata":{"tags":[]},"execution_count":49},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"AhYcyujfV6E3","colab_type":"code","outputId":"10a96e88-2b52-4f99-ba79-8c1de71e7b44","executionInfo":{"status":"ok","timestamp":1588690708510,"user_tz":-120,"elapsed":589,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":386}},"source":["viz_filters(model_no_binary)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Visualization filters learned for layer: layer1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Visualization filters learned for layer: layer2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"3qWfAHVjWAwC","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}
\ No newline at end of file
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"MNIST_binary_notebook.ipynb","provenance":[],"collapsed_sections":["4HOATUuzrXVC","BaWaOgvBVZhk","5959m3vfGJXc","TSs_mcFiNcRE","8rsVSnaFQT0O","vCQ5xT6bL1jc","DSqdNrmQNdP5","G2QLO0jHNgrl","ipyNhwCxyMDr","F5V7JpRCVAMg"],"authorship_tag":"ABX9TyPdTLidLeYp1Te2s/isxDyf"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zMhTyMn7oxN0","colab_type":"text"},"source":["# Mount my drive:"]},{"cell_type":"code","metadata":{"id":"_pZ0mrRAoq6w","colab_type":"code","outputId":"4a7c8346-86b8-4e37-deb2-39ca5c2ad142","executionInfo":{"status":"ok","timestamp":1588953184493,"user_tz":-120,"elapsed":583,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"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":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"spkJ2-HJo0j6","colab_type":"code","outputId":"5d8a610b-a7b6-4ea2-803b-ed114259883c","executionInfo":{"status":"ok","timestamp":1588953186461,"user_tz":-120,"elapsed":2431,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":69}},"source":["import os\n","os.chdir('drive/My Drive/Work/Thesis_Julien_Dejasmin/Work/code/Binary_activations_V2/MNIST_Binary_V2')\n","!ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["config.py distributions README.md\t trained_models\n","data\t experiments requirements.txt utils\n","DataLoader __pycache__ results\t visualize\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"sg5c6cpVo2rK","colab_type":"text"},"source":["# Import:"]},{"cell_type":"code","metadata":{"id":"eUgp6tuW_XWi","colab_type":"code","colab":{}},"source":["!pip install pytorch-ignite"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4ZlfKXpS_VnV","colab_type":"code","colab":{}},"source":["try:\n"," from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\n"," from ignite.metrics import Accuracy, Loss, ConfusionMatrix\n"," from ignite.handlers import ModelCheckpoint\n"," from utils.training import run, evaluate\n","except ImportError:\n"," raise RuntimeError(\"no module Ignite, to install Ignite: 'pip install pytorch-ignite'.\")\n","\n","from tqdm import tqdm"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_0Ekxum1o3p2","colab_type":"code","colab":{}},"source":["import torch\n","from torch import nn\n","from torch.optim import SGD\n","from torch.utils.data import DataLoader\n","import torch.nn.functional as F\n","from torchvision.transforms import Compose, ToTensor, Normalize\n","from torchvision.datasets import MNIST\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from functools import partial\n","\n","from utils.models import get_my_model_MNIST, fetch_last_checkpoint_model_filename\n","from DataLoader.dataLoaders import get_mnist_dataloaders\n","from utils.functions import Hardsigmoid"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SgqKz2R6wMgp","colab_type":"text"},"source":["# Dataset:"]},{"cell_type":"code","metadata":{"id":"iAJxJUvhwL5R","colab_type":"code","outputId":"f6a8cf19-263a-46ba-b1bd-2691e16eaefb","executionInfo":{"status":"ok","timestamp":1588953187308,"user_tz":-120,"elapsed":971,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":69}},"source":["batch_size_train = 10000\n","batch_size_test = 1000\n","# Dataset\n","train_loader, valid_loader, test_loader, classes = get_mnist_dataloaders(batch_size_train, batch_size_test)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Number of validation examples: 6000\n","Number of training examples: 6\n","Number of testing examples: 10\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4HOATUuzrXVC","colab_type":"text"},"source":["# Training: with bias"]},{"cell_type":"markdown","metadata":{"id":"3ljVddLwIKkJ","colab_type":"text"},"source":["## Training parameters:"]},{"cell_type":"code","metadata":{"id":"l6yU1EYYIMUi","colab_type":"code","colab":{}},"source":["epochs = 50\n","lr = 1e-3\n","momentum = 0.5\n","log_interval = 10 # how many batches to wait before logging training status\n","criterion = F.nll_loss"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"A3JkE7cPu9lN","colab_type":"text"},"source":["## Run No binary network:"]},{"cell_type":"code","metadata":{"id":"3c11RL0sq_30","colab_type":"code","colab":{}},"source":["# parameters model to load no Binary model\n","binary = False\n","\n","model, name_model = get_my_model_MNIST(binary)\n","print(name_model)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"xv37jeParJ-C","colab_type":"code","colab":{}},"source":["path_model_checkpoint = 'trained_models/MNIST/No_binary_models/with_bias'\n","path_save_plot = 'results/MNIST_results/plot_loss_acc/'\n","\n","run(model, path_model_checkpoint, path_save_plot, name_model, train_loader, valid_loader, epochs, lr, momentum, criterion, log_interval)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9p-TMgHLKL8L","colab_type":"text"},"source":["### Test no binary network:"]},{"cell_type":"code","metadata":{"id":"o51hCtcA1DFo","colab_type":"code","colab":{}},"source":["# load model pre trained no binary\n","binary = False\n","model_no_binary, name_model = get_my_model_MNIST(binary)\n","\n","path_model = 'trained_models/MNIST/No_binary_models/with_bias'\n","if torch.cuda.is_available():\n"," model_no_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_no_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9A-PvvuJPKv5","colab_type":"code","colab":{}},"source":["evaluate(model_no_binary, test_loader)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8DfJ-CRU93BP","colab_type":"text"},"source":["## Run Binary Netwwork:"]},{"cell_type":"code","metadata":{"id":"MahC-0u997vy","colab_type":"code","colab":{}},"source":["# parameters model to load no Binary model\n","binary = True\n","\n","model, name_model = get_my_model_MNIST(binary)\n","print(name_model)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rVgZXvMM-MwZ","colab_type":"code","colab":{}},"source":["path_model_checkpoint = 'trained_models/MNIST/Binary_models/with_bias'\n","path_save_plot = 'results/MNIST_results/plot_loss_acc/'\n","\n","run(model, path_model_checkpoint, path_save_plot, name_model, train_loader, valid_loader, epochs, lr, momentum, criterion, log_interval)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"p2H3DQ7XAQ_J","colab_type":"text"},"source":["### Test Binary network:"]},{"cell_type":"code","metadata":{"id":"UeDua8SgATa0","colab_type":"code","colab":{}},"source":["# load model pre trained binary\n","binary = True \n","model_binary, name_model = get_my_model_MNIST(binary)\n","\n","path_model = 'trained_models/MNIST/Binary_models/with_bias'\n","if torch.cuda.is_available():\n"," model_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"L3tFWrM6AWP9","colab_type":"code","colab":{}},"source":["evaluate(model_binary, test_loader)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BaWaOgvBVZhk","colab_type":"text"},"source":["# Training wihtout bias:"]},{"cell_type":"markdown","metadata":{"id":"bhaLA5o9VgxO","colab_type":"text"},"source":["## Training parameters:"]},{"cell_type":"code","metadata":{"id":"xzEaPRGEVhTN","colab_type":"code","colab":{}},"source":["epochs = 50\n","lr = 1e-3\n","momentum = 0.5\n","log_interval = 10 # how many batches to wait before logging training status\n","criterion = F.nll_loss"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OuAejo3aViZt","colab_type":"text"},"source":["## Run No binary network:"]},{"cell_type":"code","metadata":{"id":"CoJcRlrJVj8P","colab_type":"code","colab":{}},"source":["# parameters model to load no Binary model\n","binary = False\n","bias = False\n","\n","model, name_model = get_my_model_MNIST(binary, bias=bias)\n","print(name_model)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wv274ImnVmwH","colab_type":"code","colab":{}},"source":["path_model_checkpoint = 'trained_models/MNIST/No_binary_models/without_bias'\n","path_save_plot = 'results/MNIST_results/plot_loss_acc/'\n","\n","run(model, path_model_checkpoint, path_save_plot, name_model, train_loader, valid_loader, epochs, lr, momentum, criterion, log_interval)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fBf2b9V8Vp6H","colab_type":"text"},"source":["### Test no binary network:"]},{"cell_type":"code","metadata":{"id":"Z7HIZwp-VqWD","colab_type":"code","colab":{}},"source":["# load model pre trained no binary\n","binary = False\n","bias=False\n","model_no_binary_wt_bias, name_model = get_my_model_MNIST(binary, bias=bias)\n","\n","path_model = 'trained_models/MNIST/No_binary_models/without_bias'\n","if torch.cuda.is_available():\n"," model_no_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_no_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lw1KxSSWWFI7","colab_type":"code","colab":{}},"source":["evaluate(model_no_binary_wt_bias, test_loader)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"odr-PsBvVsBC","colab_type":"text"},"source":["## Run Binary Netwwork:"]},{"cell_type":"code","metadata":{"id":"IjprOyqSVvpY","colab_type":"code","colab":{}},"source":["# parameters model to load no Binary model\n","binary = True\n","bias=False\n","\n","model, name_model = get_my_model_MNIST(binary, bias=bias)\n","print(name_model)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"42fO1UE_Vxb4","colab_type":"code","colab":{}},"source":["path_model_checkpoint = 'trained_models/MNIST/Binary_models/without_bias'\n","path_save_plot = 'results/MNIST_results/plot_loss_acc/'\n","\n","run(model, path_model_checkpoint, path_save_plot, name_model, train_loader, valid_loader, epochs, lr, momentum, criterion, log_interval)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"K0M_D7KcVvAD","colab_type":"text"},"source":["### Test Binary network:"]},{"cell_type":"code","metadata":{"id":"vGe1V4R4VzC3","colab_type":"code","colab":{}},"source":["# load model pre trained binary\n","binary = True \n","bias=False\n","model_binary_wt_bias, name_model = get_my_model_MNIST(binary, bias=bias)\n","\n","path_model = 'trained_models/MNIST/Binary_models/without_bias'\n","if torch.cuda.is_available():\n"," model_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-jCPLDGhVz_Q","colab_type":"code","colab":{}},"source":["evaluate(model_binary_wt_bias, test_loader)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-_4XKTA569_T","colab_type":"text"},"source":["# Visualization:"]},{"cell_type":"code","metadata":{"id":"n0o9-iFNDI1m","colab_type":"code","colab":{}},"source":["from visualize.viz import visTensor, get_activation, viz_activations, viz_filters\n","from visualize.viz import viz_heatmap, test_predict_few_examples, standardize_and_clip, format_for_plotting\n","from visualize.viz import apply_transforms, GradientAscent, get_filter_layer2\n","from visualize.viz import get_region_layer1, get_region_layer2, get_regions_interest, get_all_regions_max\n","\n","# for regions extraction\n","import collections\n","from functools import partial\n","import cv2"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zWjDVnSAFyUr","colab_type":"text"},"source":["## Load model:"]},{"cell_type":"code","metadata":{"id":"LlTceHneFzkp","colab_type":"code","outputId":"63cd7154-6cdd-46ae-bee5-d7428d2890b4","executionInfo":{"status":"ok","timestamp":1588953189486,"user_tz":-120,"elapsed":564,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# load model pre trained no binary\n","binary = False\n","model_no_binary, name_model = get_my_model_MNIST(binary)\n","\n","path_model = 'trained_models/MNIST/No_binary_models/with_bias'\n","if torch.cuda.is_available():\n"," model_no_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_no_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Model Loaded MNIST_NonBinaryNet\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"5qw9xVKbGFDs","colab_type":"code","outputId":"81d2fa18-1be6-4f04-9156-4d62d0ad64f0","executionInfo":{"status":"ok","timestamp":1588952925701,"user_tz":-120,"elapsed":729,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# load model pre trained binary\n","binary = True \n","model_binary, name_model = get_my_model_MNIST(binary)\n","\n","path_model = 'trained_models/MNIST/Binary_models/with_bias'\n","if torch.cuda.is_available():\n"," model_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_binary.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Model Loaded MNIST_Stochastic_ST_first_conv_binary\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"UwKq4tgwWNv9","colab_type":"code","colab":{}},"source":["# load model pre trained no binary\n","binary = False\n","bias=False\n","model_no_binary_wt_bias, name_model = get_my_model_MNIST(binary, bias=bias)\n","\n","path_model = 'trained_models/MNIST/No_binary_models/without_bias'\n","if torch.cuda.is_available():\n"," model_no_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_no_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"xonXN-e4WOCf","colab_type":"code","colab":{}},"source":["# load model pre trained binary\n","binary = True \n","bias=False\n","model_binary_wt_bias, name_model = get_my_model_MNIST(binary, bias=bias)\n","\n","path_model = 'trained_models/MNIST/Binary_models/without_bias'\n","if torch.cuda.is_available():\n"," model_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model)))\n","else:\n"," model_binary_wt_bias.load_state_dict(torch.load(fetch_last_checkpoint_model_filename(path_model), map_location=torch.device('cpu')))\n","print(\"Model Loaded\", name_model)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rHWEBMGBNGhP","colab_type":"code","colab":{}},"source":["print(model_no_binary)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"j1CP_zaINIat","colab_type":"code","colab":{}},"source":["print(model_binary)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"k4ALBHjqWbgZ","colab_type":"code","colab":{}},"source":["print(model_no_binary_wt_bias)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-kitDGciWb-Y","colab_type":"code","colab":{}},"source":["print(model_binary_wt_bias)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"5959m3vfGJXc","colab_type":"text"},"source":["## Visualization few predictions:"]},{"cell_type":"code","metadata":{"id":"-DNeZnOcGN1d","colab_type":"code","colab":{}},"source":["print('No binary model')\n","test_predict_few_examples(model_no_binary, test_loader)\n","plt.show()\n","print('Binary model')\n","test_predict_few_examples(model_binary, test_loader)\n","plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ksVNO6-vNFnN","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"TSs_mcFiNcRE","colab_type":"text"},"source":["## Visualization Activations values for a specific data:"]},{"cell_type":"code","metadata":{"id":"jRztIz4ONl-z","colab_type":"code","colab":{}},"source":["index_data = 10\n","viz_activations(model_no_binary, test_loader, index_data)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ssercF6si1Wb","colab_type":"code","colab":{}},"source":["index_data = 10\n","viz_activations(model_binary, test_loader, index_data)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8rsVSnaFQT0O","colab_type":"text"},"source":["## Visualization heatmap for a specific data:\n"]},{"cell_type":"code","metadata":{"id":"iXdLsVXjQY92","colab_type":"code","colab":{}},"source":["index_data = 10\n","viz_heatmap(model_no_binary, name_model, test_loader, index_data)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"MCSsDrVSk7Jc","colab_type":"code","colab":{}},"source":["index_data = 10\n","viz_heatmap(model_binary, name_model, test_loader, index_data)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vCQ5xT6bL1jc","colab_type":"text"},"source":["## Visualization filters trained:"]},{"cell_type":"code","metadata":{"id":"le3aS1MNl2fS","colab_type":"code","colab":{}},"source":["for name, m in model_no_binary.named_modules():\n"," if type(m) == nn.Conv2d:\n"," filters = m.weight.data.clone()\n"," break"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wl64zUbLmBMs","colab_type":"code","colab":{}},"source":["filter_0 = filters[0][0]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"AfKvYa8jmFz5","colab_type":"code","colab":{}},"source":["filter_0"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zYdypB1FmQOJ","colab_type":"code","colab":{}},"source":["plt.imshow(filter_0, cmap='gray')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hvX1IYa-nF90","colab_type":"code","colab":{}},"source":["# normalized between 0 et 1:\n","val_min = filter_0.min()\n","val_max = filter_0.max()\n","\n","filter_0_normalized = (filter_0 - val_min)/(val_max - val_min)\n","\n","print(val_min, val_max)\n","print(filter_0_normalized)\n","\n","plt.imshow(filter_0_normalized, cmap='gray')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"V56DbYo7GW1c","colab_type":"code","colab":{}},"source":["viz_filters(model_no_binary)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7JqXez-RU44M","colab_type":"code","colab":{}},"source":["viz_filters(model_binary)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Lk46uch7aZDz","colab_type":"code","colab":{}},"source":["viz_filters(model_no_binary_wt_bias)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"OVH9IlEnaZI_","colab_type":"code","colab":{}},"source":["viz_filters(model_binary_wt_bias)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Nwn-bah-Kh_l","colab_type":"text"},"source":["## Visualization image that maximizes a specific activation in a specific layer for a specifc filter:"]},{"cell_type":"markdown","metadata":{"id":"DSqdNrmQNdP5","colab_type":"text"},"source":["### No binary model:"]},{"cell_type":"code","metadata":{"id":"0YRM3DpSNihu","colab_type":"code","colab":{}},"source":["g_ascent_no_binary = GradientAscent(model_no_binary, nb_channels=1, img_size=28)\n","g_ascent_no_binary.use_gpu = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"aE17LvBuNwVD","colab_type":"code","colab":{}},"source":["conv1_no_binary = model_no_binary.layer1\n","conv1_filters_no_binary = [0,1,2,3,4,5,6,7,8,9]\n","mean_gradient_layer1 = False\n","ind_x_layer1 = 7\n","ind_y_layer1 = 7\n","\n","conv2_no_binary = model_no_binary.layer2\n","conv2_filters_no_binary = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","mean_gradient_layer2 = False\n","ind_x_layer2 = 3\n","ind_y_layer2 = 3\n","\n","lr=0.0001\n","num_iter=1000\n","MNIST = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Sh16ddpuUfUp","colab_type":"code","outputId":"0f0bedc4-133f-4f91-ed17-59f50fd9c349","executionInfo":{"status":"ok","timestamp":1588951542162,"user_tz":-120,"elapsed":757,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":385}},"source":["viz_filters(model_no_binary)"],"execution_count":35,"outputs":[{"output_type":"stream","text":["Visualization filters learned for layer: layer1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaoAAACMCAYAAAAtDe1pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAMEUlEQVR4nO3df6zVdR3H8dcrQEJwaeMOFMgLDWvOOXVHp+nUaSWWi9qSyWZKmyM3zR+1mbr5o1wzGzmbNtpNSJ0mOoRizhVsyao5yQOCP0BKjBRQOYyh3jYz8t0f52te4V7u+bDzvefj9zwfG+P8eJ1z3p99OOfN93u+5/N1RAgAgFx9otMFAABwIDQqAEDWaFQAgKzRqAAAWaNRAQCyRqMCAGRtdBlPOnHixOjt7S3jqQEAFbR161bt2rXLg91XSqPq7e1VvV4v46kBABVUq9WGvK+lXX+2Z9nebPtl29e3rTIAAIYxbKOyPUrSLySdL+lYSXNtH1t2YQAASK1tUZ0i6eWIeCUi3pO0RNLscssCAKCplUY1RdJrA65vK24DAKB0bTs83fZ823Xb9Uaj0a6nBQB0uVYa1XZJ0wZcn1rc9hER0RcRtYio9fT0tKs+AECXa6VRPSNppu3ptg+RdJGkFeWWBQBA07C/o4qIvbavlPQHSaMkLY6IF0uvDAAAtfiD34h4QtITJdcCAMB+WOsPAJC1UpZQSnX77bcn5S+55JKkfOpyTrNnp/1MbNWqVUl5SVq4cGFSfvny5Un5iEjKX3vttUn5xx9/PCk/ZsyYpPzGjRuT8n19fUn5s846Kyl/xx13JOUXL16clJek1atXJ+W3bNmSlH/kkUeS8itXrkzKv/TSS0n5vXv3JuWvu+66pPwTT6TtBEp9j+3atSspv3nz5qS8JC1YsCApf9555yXlU98Hqe/LBx98MCk/FLaoAABZo1EBALJGowIAZI1GBQDIGo0KAJA1GhUAIGs0KgBA1mhUAICs0agAAFmjUQEAskajAgBkLYu1/qZNmzZ8aIAVK9JOhzV27NikfKpx48YlP+ayyy5Lyh999NHJr5Fi9+7dSfkbb7wxKT9v3rykfKrU9SIfeOCBpPxVV12VlD8Y27fvdz7SA3r66adLquTgTJ48OSm/bNmypPwNN9yQlE81adKkpPyePXuS8vfdd19SXkpf6++tt95Kyq9fvz4p39/fn5RvF7aoAABZG7ZR2Z5m+0nbG22/aPvqkSgMAACptV1/eyV9PyLW2T5M0lrbqyIibb13AAAOwrBbVBHxekSsKy6/I2mTpCllFwYAgJT4HZXtXkknSlpTRjEAAOyr5UZle4KkxyRdExFvD3L/fNt12/VGo9HOGgEAXaylRmV7jJpN6qGIGPSY0ojoi4haRNR6enraWSMAoIu1ctSfJS2StCki7iy/JAAAPtTKFtXpkr4l6Rzb64s/Xym5LgAAJLVweHpE/EWSR6AWAAD2w8oUAICsZbHW36JFi5LyW7ZsScqXvc7chAkTkh+TuibX9OnTk18jxeWXX56Uf/fdd5PyS5cuTcpfeOGFSfnUtfs2bNiQlL/55puT8nPmzEnKS9Ldd9+dlJ8xY0ZS/oILLkjKp1q7dm1SfubMmUn5K664Iin/3HPPJeV37NiRlH/qqaeS8jfddFNS/mDcc889SfnUNT5T8+3CFhUAIGs0KgBA1mhUAICs0agAAFmjUQEAskajAgBkjUYFAMgajQoAkDUaFQAgazQqAEDWaFQAgKw5Itr+pLVaLer1etufFwBQTbVaTfV6fdAzdbBFBQDIGo0KAJC1lhuV7VG2n7X9eJkFAQAwUMoW1dWSNpVVCAAAg2mpUdmeKumrku4ttxwAAD6q1S2quyRdJ+n9oQK259uu2643Go22FAcAwLCNyvYFknZGxAHPMx0RfRFRi4haT09P2woEAHS3VraoTpf0NdtbJS2RdI7tB0utCgCAwrCNKiJuiIipEdEr6SJJf4yIi0uvDAAA8TsqAEDmRqeEI2K1pNWlVAIAwCDYogIAZC1pi6oss2bNSsofc8wxSfnTTjstKT937tyk/Mknn5yUl6Tjjz8+KX/UUUcl5W+77bakPADkii0qAEDWaFQAgKzRqAAAWaNRAQCyRqMCAGSNRgUAyBqNCgCQNRoVACBrNCoAQNZoVACArNGoAABZy2Ktv76+vqR8f39/Un7Hjh1J+VQTJ05Mfsypp56alB87dmzyawBAFbBFBQDIWkuNyvbhtpfafsn2Jttpy5EDAHCQWt3193NJv4+Ib9o+RNKhJdYEAMD/DduobH9K0pmS5klSRLwn6b1yywIAoKmVXX/TJTUk/dr2s7bvtT2+5LoAAJDUWqMaLekkSQsj4kRJ/5J0/b4h2/Nt123XG41Gm8sEAHSrVhrVNknbImJNcX2pmo3rIyKiLyJqEVHr6elpZ40AgC42bKOKiDckvWb7c8VN50raWGpVAAAUWj3q77uSHiqO+HtF0rfLKwkAgA+11KgiYr2kWsm1AACwH1amAABkLYu1/pYvX56UP+6445LyM2bMSMqnOpjnnzx5clJ+5syZya8BAFXAFhUAIGs0KgBA1mhUAICs0agAAFmjUQEAskajAgBkjUYFAMgajQoAkDUaFQAgazQqAEDWaFQAgKw5Itr+pLVaLer1etufFwBQTbVaTfV63YPdxxYVACBrLTUq29faftH2C7Yftv3JsgsDAEBqoVHZniLpKkm1iDhO0ihJF5VdGAAAUuu7/kZLGmd7tKRDJe0oryQAAD40bKOKiO2SFkh6VdLrkt6KiJVlFwYAgNTarr8jJM2WNF3SUZLG2754kNx823Xb9Uaj0f5KAQBdqZVdf1+U9I+IaETEfyQtk/SFfUMR0RcRtYio9fT0tLtOAECXaqVRvSrpVNuH2rakcyVtKrcsAACaWvmOao2kpZLWSXq+eExfyXUBACCpeTTfsCLiFkm3lFwLAAD7YWUKAEDWSlnrz3ZD0j8HuWuipF1tf8F8Md7q67Yxd9t4pe4bc6fGe3REDHokXimNaii26xFRG7EX7DDGW33dNuZuG6/UfWPOcbzs+gMAZI1GBQDI2kg3qm47rJ3xVl+3jbnbxit135izG++IfkcFAEAqdv0BALI2Io3K9izbm22/bPv6kXjNTrO91fbzttfbrne6nnazvdj2TtsvDLjt07ZX2f578fcRnayx3YYY8622txfzvN72VzpZYzvZnmb7SdsbixOnXl3cXsl5PsB4KznHtj9p+6+2NxTj/WFx+3Tba4rP60dsH9LxWsve9Wd7lKS/SfqSpG2SnpE0NyI2lvrCHWZ7q5onm6zk7y9snympX9IDxQk1ZfunknZHxE+K/5AcERE/6GSd7TTEmG+V1B8RCzpZWxlsHynpyIhYZ/swSWslfV3SPFVwng8w3jmq4BwXa7eOj4h+22Mk/UXS1ZK+J2lZRCyx/UtJGyJiYSdrHYktqlMkvRwRr0TEe5KWqHnaEHyMRcSfJO3e5+bZku4vLt+v5pu8MoYYc2VFxOsRsa64/I6ai1FPUUXn+QDjraRo6i+ujin+hKRz1FzfVcpkfkeiUU2R9NqA69tU4ckfICSttL3W9vxOFzNCJkXE68XlNyRN6mQxI+hK288VuwYrsRtsX7Z7JZ0oaY26YJ73Ga9U0Tm2Pcr2ekk7Ja2StEXSnojYW0Sy+LzmYIrynBERJ0k6X9IVxW6jrhHNfcrdcEjpQkmflXSCmmfA/llny2k/2xMkPSbpmoh4e+B9VZznQcZb2TmOiP9GxAmSpqq59+vzHS5pUCPRqLZLmjbg+tTitkqLiO3F3zslLVfzH0HVvVns5/9gf//ODtdTuoh4s3izvy/pV6rYPBffXTwm6aGIWFbcXNl5Hmy8VZ9jSYqIPZKelHSapMNtf3BmjSw+r0eiUT0jaWZxJMkhki6StGIEXrdjbI8vvoyV7fGSvizphQM/qhJWSLq0uHyppN91sJYR8cEHduEbqtA8F1+2L5K0KSLuHHBXJed5qPFWdY5t99g+vLg8Ts0D3jap2bC+WcSymN8R+cFvcTjnXZJGSVocET8u/UU7yPYMNbeipOY5v35TtTHbfljS2WqutPymmucr+62kRyV9Rs3V8+dERGUOPhhizGeruUsoJG2V9J0B3998rNk+Q9Kf1Txh6vvFzTeq+b1N5eb5AOOdqwrOse3j1TxYYpSaGy2PRsSPis+vJZI+LelZSRdHxL87VykrUwAAMsfBFACArNGoAABZo1EBALJGowIAZI1GBQDIGo0KAJA1GhUAIGs0KgBA1v4H7ZmLETJ/e3wAAAAASUVORK5CYII=\n","text/plain":["<Figure size 576x144 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Visualization filters learned for layer: layer2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAcAAAADCCAYAAADemhLMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAQ2klEQVR4nO3df6zV9X3H8derXEFUIhJviRMEtlqNsdU2xx9Mw6x1FkudrG1a6bpQ1wSTrhugsdqtabulJi0rrUskLNgyXebKGuqvLGVixKaaOvRApVWQ6YQKiHAtqS2sxXn73h/324Sxe9H3Od9zzu39PB+Jued8z/tz358v33vvy+/3fM/364gQAACleUuvJwAAQC8QgACAIhGAAIAiEYAAgCIRgACAIhGAAIAi9XWz2amnnhozZ87sZksAQME2bdr0SkT0D/daVwNw5syZajab3WwJACiY7Z+M9Fpbh0Btz7W93fbztm9p53sBANBNLQeg7XGSVki6StI5khbYPqeuiQEA0Ent7AFeKOn5iHghIl6TtEbSNfVMCwCAzmonAE+XtOuI57urZQAAjHod/xiE7UW2m7abAwMDnW4HAMCb0k4A7pE0/Yjn06pl/0dErIqIRkQ0+vuHPRMVAICuaycAn5R0pu1ZtsdLulbSA/VMCwCAzmr5c4AR8brtT0t6UNI4Sasj4pnaZgYAQAe19UH4iPiupO/WNBcAALqGa4ECAIrU1UuhtWLFihWp+vvvvz/d49VXX03Vb9y4Md1j9uzZqfqrr7463ePQoUOp+ltvvTXdY+XKlan66dOnv3HRUVrZhnfccUeq/s4770z3yI656qqr0j1uvvnmVP0TTzyR7rF58+ZU/f79+9M9Pv/5z6fqd+7cme4xODiYHrNjx45U/RVXXJHu8cEPfjBVP2fOnHSP8ePHp+o/9alPpXssW7YsVb9u3bp0jwsvvDBV/5WvfCXd41jYAwQAFIkABAAUiQAEABSJAAQAFIkABAAUiQAEABSJAAQAFIkABAAUiQAEABSJAAQAFIkABAAUadRfC3Tr1q2p+lbuOj9p0qT0mKzstT3POeecdI8HHuj87RgnT56cqj/ttNPSPc4777z0mKzVq1enx1x22WWp+osuuijdI6uvL/8rfO6556bqJ0yYkO6R9atf/So95oUXXkiPmTdvXqo+ItI9br/99lT9+vXr0z0ef/zx9Jisl156KVW/cOHCdI8XX3wxPaZO7AECAIpEAAIAikQAAgCK1HIA2p5u+xHbW20/Y3txnRMDAKCT2jkJ5nVJN0bEZtuTJG2y/VBE5M5aAQCgB1reA4yIvRGxuXr8C0nbJJ1e18QAAOikWt4DtD1T0rskbRzmtUW2m7abrXxEAQCATmg7AG2fJOk7kpZExM+Pfj0iVkVEIyIa/f397bYDAKAWbQWg7eM0FH53R8Q99UwJAIDOa+csUEv6pqRtEfG1+qYEAEDntbMHeImkP5V0ue2nqv/eX9O8AADoqJY/BhERj0lyjXMBAKBrRv3FsHfs2JGqnzlzZrrHlClT0mOyHn300VT9unXr0j1mz56dHpN14MCBVP1zzz2X7vHkk0+mx2QtWLAgPeZtb3tbqn7Dhg3pHtkLbu/duzfdY9q0aan6Vi68fMEFF6TqV6xYke6xffv29Jj58+enx2Tt27cvVT9jxox0j8HBwfSYrOzPVvbGBZK0ZMmS9Jg6cSk0AECRCEAAQJEIQABAkQhAAECRCEAAQJEIQABAkQhAAECRCEAAQJEIQABAkQhAAECRCEAAQJEcEV1r1mg0otlsdq0fAKBstjdFRGO419gDBAAUiQAEABSJAAQAFKntALQ9zvYPbf9bHRMCAKAb6tgDXCxpWw3fBwCArmkrAG1PkzRP0jfqmQ4AAN3R7h7gbZI+I+nXIxXYXmS7abs5MDDQZjsAAOrRcgDa/oCk/RGx6Vh1EbEqIhoR0ejv72+1HQAAtWpnD/ASSX9ke6ekNZIut/3PtcwKAIAOazkAI+KzETEtImZKulbShoj4eG0zAwCgg/gcIACgSH11fJOI+J6k79XxvQAA6IZaArCTbrrpplT9W96S36l961vfmqq/8cYb0z2uu+66VP2DDz6Y7nH22Wen6jds2JDusXjx4lT9mWeeme4xODiYHpOd1w033JDuMXXq1FT9+PHj0z2WLl3a0XopP69WLpi/bNmyVP3y5cvTPWbPnp0es3HjxlR9K/++X/rSl1L1jz32WLrH3LlzU/VLlixJ98iu++HDh9M9sj+Lt912W7rHsXAIFABQJAIQAFAkAhAAUCQCEABQJAIQAFAkAhAAUCQCEABQJAIQAFAkAhAAUCQCEABQJAIQAFAkAhAAUKRRfzHsZ599NlX/sY99LN1jzZo1qfpWLoY9ceLEVP2sWbPSPebPn58e0+keBw8eTPe4/PLL02OyTjrppPSY7DY8//zz0z2yTjjhhPSYvr7cr/3u3bvTPbJ++tOfpse0cjHscePGpcdkvf3tb0/Vt3Lh+y1btqTHZGV/R+677750j6uvvjo9pk7sAQIAikQAAgCK1FYA2p5se63tZ21vs50/JgEAQA+0+x7g30v694j4sO3xkvJvSAAA0AMtB6DtkyXNkfQJSYqI1yS9Vs+0AADorHYOgc6SNCDpH23/0PY3bJ94dJHtRbabtpsDAwNttAMAoD7tBGCfpHdLWhkR75J0SNItRxdFxKqIaEREo7+/v412AADUp50A3C1pd0RsrJ6v1VAgAgAw6rUcgBHxsqRdts+qFr1X0tZaZgUAQIe1exboX0i6uzoD9AVJ17U/JQAAOq+tAIyIpyQ1apoLAABdM+qvBfq+970vVb9r1650j+uvvz49JuuVV15J1X/oQx9K9zjuuOPSY7J27NiRqt+6NX9UfMqUKekxl1xySaq+lZ+TwcHBVP2qVavSPebMmZOqP3ToULrH1KlTU/Xz5s1L98g6/vjj02O+/vWvp8dMmjQpVX/RRRele2R/R2yne1x55ZXpMVm//OUvU/Vz585N95gwYUJ6TJ24FBoAoEgEIACgSAQgAKBIBCAAoEgEIACgSAQgAKBIBCAAoEgEIACgSAQgAKBIBCAAoEgEIACgSAQgAKBIjoiuNWs0GtFsNrvWDwBQNtubImLYuxaxBwgAKBIBCAAoUlsBaHup7WdsP237W7bzN/UCAKAHWg5A26dL+ktJjYg4V9I4SdfWNTEAADqp3UOgfZIm2u6TdIKkl9qfEgAAnddyAEbEHklflfSipL2SXo2I9UfX2V5ku2m7OTAw0PpMAQCoUTuHQE+RdI2kWZJ+R9KJtj9+dF1ErIqIRkQ0+vv7W58pAAA1aucQ6BWSdkTEQET8j6R7JP1+PdMCAKCz2gnAFyVdbPsE25b0Xknb6pkWAACd1c57gBslrZW0WdKPq++1qqZ5AQDQUX3tDI6IL0j6Qk1zAQCga7gSDACgSG3tAXbD6tWrU/Xvec970j1uuummVP3atWvTPe69995U/ZYtW9I9Tj755FT90qVL0z2WL1+eqt+5c2e6x/bt29Nj1q//f5/AOaaFCxeme0ybNi1Vf+DAgXSPlStXpscAaA17gACAIhGAAIAiEYAAgCIRgACAIhGAAIAiEYAAgCIRgACAIhGAAIAiEYAAgCIRgACAIhGAAIAijfprgWavPXn48OF0j8997nPpMVl9fbl/6gkTJqR7/OAHP0jVt3It0He84x2p+rPOOivd49JLL02PyWrl33fixImp+j179qR7AOge9gABAEUiAAEARXrDALS92vZ+208fsWyK7YdsP1d9PaWz0wQAoF5vZg/wTklzj1p2i6SHI+JMSQ9XzwEA+K3xhgEYEd+XdPSdPa+RdFf1+C5J82ueFwAAHdXqe4BTI2Jv9fhlSVNHKrS9yHbTdnNgYKDFdgAA1Kvtk2AiIiTFMV5fFRGNiGj09/e32w4AgFq0GoD7bJ8mSdXX/fVNCQCAzms1AB+QtLB6vFDS/fVMBwCA7ngzH4P4lqTHJZ1le7ftT0r6sqQ/tP2cpCuq5wAA/NZ4w+tzRcSCEV56b81zAQCga7gSDACgSKP+YtiLFy9O1V9wwQXpHi+//HJ6TNauXbtS9a2cMTtjxoz0mKxHHnkkVb927dp0jzPOOCM95qMf/Wiq/rzzzkv3ePjhh1P1F198cboHgO5hDxAAUCQCEABQJAIQAFAkAhAAUCQCEABQJAIQAFAkAhAAUCQCEABQJAIQAFAkAhAAUCQCEABQJA/d0L07Go1GNJvNrvUDAJTN9qaIaAz3GnuAAIAiEYAAgCK9mTvCr7a93/bTRyz7O9vP2v6R7XttT+7sNAEAqNeb2QO8U9Lco5Y9JOnciHinpP+U9Nma5wUAQEe9YQBGxPclHThq2fqIeL16+h+SpnVgbgAAdEwd7wH+maR1NXwfAAC6pq0AtP3Xkl6XdPcxahbZbtpuDgwMtNMOAIDatByAtj8h6QOS/iSO8WHCiFgVEY2IaPT397faDgCAWvW1Msj2XEmfkfQHEfHf9U4JAIDOezMfg/iWpMclnWV7t+1PSrpd0iRJD9l+yvY/dHieAADU6g33ACNiwTCLv9mBuQAA0DVcCQYAUKSuXgzb9oCknwzz0qmSXunaREYX1r08pa63xLqz7t03IyKGPQOzqwE4EtvNka7WPdax7uWte6nrLbHurPvowiFQAECRCEAAQJFGSwCu6vUEeoh1L0+p6y2x7qUales+Kt4DBACg20bLHiAAAF3V8wC0Pdf2dtvP276l1/PpJts7bf+4uppOs9fz6ZQRbqo8xfZDtp+rvp7Syzl2ygjr/kXbe6rt/pTt9/dyjp1ie7rtR2xvtf2M7cXV8jG97Y+x3mN+u9s+3vYTtrdU6/431fJZtjdWf+f/1fb4Xs9V6vEhUNvjNHRD3T+UtFvSk5IWRMTWnk2qi2zvlNSIiDH92SDbcyQdlPRPEXFutWyZpAMR8eXqf3xOiYibeznPThhh3b8o6WBEfLWXc+s026dJOi0iNtueJGmTpPmSPqExvO2Psd4f0Rjf7rYt6cSIOGj7OEmPSVos6QZJ90TEmurSmVsiYmUv5yr1fg/wQknPR8QLEfGapDWSrunxnFCz4W6qrKHtfFf1+C4N/YEYc0ZY9yJExN6I2Fw9/oWkbZJO1xjf9sdY7zEvhhysnh5X/ReSLpe0tlo+arZ5rwPwdEm7jni+W4X8oFRC0nrbm2wv6vVkumxqROytHr8saWovJ9MDn7b9o+oQ6Zg6BDgc2zMlvUvSRhW07Y9ab6mA7W57nO2nJO2X9JCk/5L0s4h4vSoZNX/nex2Apbs0It4t6SpJf14dLitOdT/Jkk5HXinp9ySdL2mvpOW9nU5n2T5J0nckLYmInx/52lje9sOsdxHbPSIGI+J8SdM0dJTv7B5PaUS9DsA9kqYf8XxatawIEbGn+rpf0r0a+mEpxb7qvZLfvGeyv8fz6ZqI2Ff9kfi1pDs0hrd79T7QdyTdHRH3VIvH/LYfbr1L2u6SFBE/k/SIpNmSJtv+zd2HRs3f+V4H4JOSzqzOEBov6VpJD/R4Tl1h+8TqDXLZPlHSlZKePvaoMeUBSQurxwsl3d/DuXTVb/74V/5YY3S7VydEfFPStoj42hEvjeltP9J6l7Ddbffbnlw9nqihExy3aSgIP1yVjZpt3vMPwlenAt8maZyk1RFxa08n1CW2f1dDe33S0H0Z/2Wsrnt1U+XLNHRF+H2SviDpPknflnSGhu4Q8pGIGHMni4yw7pdp6DBYSNop6foj3hMbM2xfKulRST+W9Otq8V9p6P2wMbvtj7HeCzTGt7vtd2roJJdxGtrB+nZE/G31926NpCmSfijp4xFxuHczHdLzAAQAoBd6fQgUAICeIAABAEUiAAEARSIAAQBFIgABAEUiAAEARSIAAQBFIgABAEX6X+kFmXZ2s6XwAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 576x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"JP1bsOUzN79c","colab_type":"code","outputId":"a9032061-8005-4162-8a1d-3c798c97dcfe","executionInfo":{"status":"error","timestamp":1588951547937,"user_tz":-120,"elapsed":6222,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["g_ascent_no_binary.visualize(conv1_no_binary, MNIST, conv1_filters_no_binary, mean_gradient_layer1,\n"," ind_x_layer1, ind_y_layer1, lr=lr, num_iter=num_iter, title='No binary model: conv layer 1')\n","g_ascent_no_binary.visualize(conv2_no_binary, MNIST, conv2_filters_no_binary, mean_gradient_layer2,\n"," ind_x_layer2, ind_y_layer2, lr=lr, num_iter=num_iter, title='No binary model: conv layer 2')"],"execution_count":36,"outputs":[{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-36-ac5147585c5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m g_ascent_no_binary.visualize(conv1_no_binary, MNIST, conv1_filters_no_binary, mean_gradient_layer1,\n\u001b[0;32m----> 2\u001b[0;31m ind_x_layer1, ind_y_layer1, lr=lr, num_iter=num_iter, title='No binary model: conv layer 1')\n\u001b[0m\u001b[1;32m 3\u001b[0m g_ascent_no_binary.visualize(conv2_no_binary, MNIST, conv2_filters_no_binary, mean_gradient_layer2,\n\u001b[1;32m 4\u001b[0m ind_x_layer2, ind_y_layer2, lr=lr, num_iter=num_iter, title='No binary model: conv layer 2')\n","\u001b[0;32m/content/drive/My Drive/Work/Thesis_Julien_Dejasmin/Work/code/Binary_activations_V2/MNIST_Binary_V2/visualize/viz.py\u001b[0m in \u001b[0;36mvisualize\u001b[0;34m(self, layer, MNIST, filter_idxs, mean_gradient, ind_x, ind_y, first_conv_layer, lr, num_iter, num_subplots, figsize, title, return_output)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0mnum_iter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter_idxs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m title=title)\n\u001b[0m\u001b[1;32m 228\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/content/drive/My Drive/Work/Thesis_Julien_Dejasmin/Work/code/Binary_activations_V2/MNIST_Binary_V2/visualize/viz.py\u001b[0m in \u001b[0;36m_visualize_filters\u001b[0;34m(self, layer, MNIST, filter_idxs, mean_gradient, ind_x, ind_y, first_conv_layer, num_iter, num_subplots, title)\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;31m# Plot subplots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter_idxs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean_gradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mind_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mind_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/content/drive/My Drive/Work/Thesis_Julien_Dejasmin/Work/code/Binary_activations_V2/MNIST_Binary_V2/visualize/viz.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, layer, filter_idx, mean_gradient, ind_x, ind_y, input_, num_iter)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;31m# Optimize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ascent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m def visualize(self, layer, MNIST, filter_idxs=None, mean_gradient=True, ind_x=None, ind_y=None,\n","\u001b[0;32m/content/drive/My Drive/Work/Thesis_Julien_Dejasmin/Work/code/Binary_activations_V2/MNIST_Binary_V2/visualize/viz.py\u001b[0m in \u001b[0;36m_ascent\u001b[0;34m(self, x, num_iter)\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 265\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 266\u001b[0m self.gradients /= (torch.sqrt(torch.mean(\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/content/drive/My Drive/Work/Thesis_Julien_Dejasmin/Work/code/Binary_activations_V2/MNIST_Binary_V2/utils/models.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0mx_layer2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_layer2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_layer2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mx_fc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_layer2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m \u001b[0mx_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_softmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_fc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 150\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx_out\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlog_softmax\u001b[0;34m(input, dim, _stacklevel, dtype)\u001b[0m\n\u001b[1;32m 1533\u001b[0m \u001b[0mdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_softmax_dim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log_softmax'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacklevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1535\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_softmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1536\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1537\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_softmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x1080 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"RmOpOt7P6t95","colab_type":"code","colab":{}},"source":["g_ascent_no_binary = GradientAscent(model_no_binary, nb_channels=1, img_size=28)\n","g_ascent_no_binary.use_gpu = True\n","\n","conv1_no_binary = model_no_binary.layer1\n","conv1_filters_no_binary = [0,1,2,3,4,5,6,7,8,9]\n","mean_gradient_layer1 = True\n","ind_x_layer1 = 7\n","ind_y_layer1 = 7\n","\n","conv2_no_binary = model_no_binary.layer2\n","conv2_filters_no_binary = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","mean_gradient_layer2 = True\n","ind_x_layer2 = 3\n","ind_y_layer2 = 3\n","\n","lr=0.0001\n","num_iter=1000\n","MNIST = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"xDCPM5x16xDv","colab_type":"code","colab":{}},"source":["g_ascent_no_binary.visualize(conv1_no_binary, MNIST, conv1_filters_no_binary, mean_gradient_layer1,\n"," ind_x_layer1, ind_y_layer1, lr=lr, num_iter=num_iter, title='No binary model: conv layer 1')\n","g_ascent_no_binary.visualize(conv2_no_binary, MNIST, conv2_filters_no_binary, mean_gradient_layer2,\n"," ind_x_layer2, ind_y_layer2, lr=lr, num_iter=num_iter, title='No binary model: conv layer 2')"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"G2QLO0jHNgrl","colab_type":"text"},"source":["### Binary model:"]},{"cell_type":"code","metadata":{"id":"REf3YEamNi8X","colab_type":"code","colab":{}},"source":["g_ascent_binary = GradientAscent(model_binary, nb_channels=1, img_size=28)\n","g_ascent_binary.use_gpu = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LeMtt18nO4MG","colab_type":"code","colab":{}},"source":["conv1_binary = model_binary.layer1\n","conv1_filters_binary = [0,1,2,3,4,5,6,7,8,9]\n","mean_gradient_layer1 = False\n","ind_x_layer1 = 7\n","ind_y_layer1 = 7\n","\n","conv2_binary = model_binary.layer2\n","conv2_filters_binary = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","mean_gradient_layer2 = False\n","ind_x_layer2 = 3\n","ind_y_layer2 = 3\n","\n","lr=0.0001\n","num_iter=1000\n","MNIST = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"vTvyDQcica_V","colab_type":"code","colab":{}},"source":["viz_filters(model_binary)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"bKPVj-UZO8gX","colab_type":"code","colab":{}},"source":["g_ascent_binary.visualize(conv1_binary, MNIST, conv1_filters_binary, mean_gradient_layer1,\n"," ind_x_layer1, ind_y_layer1, lr=lr, num_iter=num_iter, title='Binary model: conv layer 1')\n","g_ascent_binary.visualize(conv2_binary, MNIST, conv2_filters_binary, mean_gradient_layer2,\n"," ind_x_layer2, ind_y_layer2, lr=lr, num_iter=num_iter, title='Binary model: conv layer 2')"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"dPw0S2MPKfJd","colab_type":"text"},"source":["## Visuazation regions that maximizes a specific layer and filter:"]},{"cell_type":"markdown","metadata":{"id":"yiji9E6E5Njy","colab_type":"text"},"source":["### Run:"]},{"cell_type":"markdown","metadata":{"id":"_9e1_CIzy3tw","colab_type":"text"},"source":["#### Extract and save regions and activations:"]},{"cell_type":"markdown","metadata":{"id":"keD_cleEzK7u","colab_type":"text"},"source":["##### No binary model:"]},{"cell_type":"code","metadata":{"id":"3N74Ty7m0soN","colab_type":"code","colab":{}},"source":["# a dictionary that keeps saving the activations as they come\n","activations_no_binary = collections.defaultdict(list)\n","\n","def save_activation_no_binary(name, mod, inp, out):\n"," activations_no_binary[name].append(out.cpu())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wvBPUzNx0tqH","colab_type":"code","outputId":"b469b082-518e-4919-f918-3b42abd085b9","executionInfo":{"status":"ok","timestamp":1588953230654,"user_tz":-120,"elapsed":2347,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"source":["# Registering hooks for all the Conv2d layers\n","# Note: Hooks are called EVERY TIME the module performs a forward pass. For modules that are\n","# called repeatedly at different stages of the forward pass (like RELUs), this will save different\n","# activations. Editing the forward pass code to save activations is the way to go for these cases.\n","for name, m in model_no_binary.named_modules():\n"," if type(m)==nn.Conv2d:\n"," # partial to assign the layer name to each hook\n"," m.register_forward_hook(partial(save_activation_no_binary, name))\n","\n","for batch in train_loader:\n"," out = model_no_binary(batch[0])\n"," break # for only one batch\n","\n","# concatenate all the outputs we saved to get the the activations for each layer for the whole dataset\n","activations_no_binary = {name: torch.cat(outputs, 0) for name, outputs in activations_no_binary.items()}\n","\n","# just print out the sizes of the saved activations as a sanity check\n","for k,v in activations_no_binary.items():\n"," print (k, v.size())"],"execution_count":15,"outputs":[{"output_type":"stream","text":["layer1 torch.Size([10000, 10, 14, 14])\n","layer2 torch.Size([10000, 20, 7, 7])\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"k69xVUKs-aJy","colab_type":"code","colab":{}},"source":["dataiter = iter(train_loader)\n","images, _ = dataiter.next()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"aMhtlGzBhdpu","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"2b5af626-fdf2-484d-d502-c9775bbb5c38","executionInfo":{"status":"error","timestamp":1588953234036,"user_tz":-120,"elapsed":4561,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}}},"source":["stride = 2\n","padding = 1\n","filter_size = 3\n","len_img_w = 28\n","len_img_h = 28\n","loader = train_loader\n","\n","region_final, activation_final, activation_final_normalized = get_all_regions_max(images, loader, activations_no_binary, stride, padding, filter_size, len_img_h, len_img_w)\n","\n","region_layer1_no_binary = region_final['layer1']\n","region_layer2_no_binary = region_final['layer2']\n","activation_layer1_no_binary = activation_final['layer1']\n","activation_layer2_no_binary = activation_final['layer2']\n","activation_layer1_no_binary_normalized = activation_final_normalized['layer1']\n","activation_layer2_no_binary_normalized = activation_final_normalized['layer2']\n","\n","print(region_layer1_no_binary.shape)\n","print(region_layer2_no_binary.shape)\n","print(activation_layer1_no_binary.shape)\n","print(activation_layer2_no_binary.shape)\n","print(activation_layer1_no_binary_normalized.shape)\n","print(activation_layer2_no_binary_normalized.shape)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["nb images: 10000\n","begin extraction regions\n","treating image n 0/10000, for layer: layer1\n","norm null for filter: 0, image: 0 with activation:0.6721012592315674, index(9,3)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 4, image: 0 with activation:0.1766006499528885, index(11,11)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 5, image: 0 with activation:1.148320198059082, index(6,3)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 9, image: 0 with activation:0.17975755035877228, index(5,2)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["treating image n 1/10000, for layer: layer1\n","norm null for filter: 3, image: 1 with activation:-0.12384253740310669, index(13,7)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAO0UlEQVR4nO3df4xV9ZnH8c+jtpDQRkHdcUJxaQl/WH8s1QlsXF0LpqOLwRGJFUw2bjQ7jWICcf1BEANxbTSrs2b/sWYIWlq6/AiIYCMpgmRHE1MFRQHZ1oGAHRxBl5BandBVn/1jDu4U53zveO5P5nm/ksnce5753vvkhg/n3PO9537N3QVg+Dut3g0AqA3CDgRB2IEgCDsQBGEHgjijlk9mZpz6B6rM3W2w7WXt2c3sWjP7nZl1m9mCch4LQHVZ0Xl2Mztd0u8l/UhSj6TXJc1x93cSY9izA1VWjT37ZEnd7r7f3f8saZWktjIeD0AVlRP2sZL+MOB+T7btL5hZu5ltN7PtZTwXgDJV/QSdu3dK6pQ4jAfqqZw9+yFJ4wbc/062DUADKifsr0uaaGbfNbNvSpotaWNl2gJQaYUP4939MzO7S9JvJJ0u6Wl331OxzgBUVOGpt0JPxnt2oOqq8qEaAKcOwg4EQdiBIAg7EARhB4Ig7EAQhB0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EARhB4IovGQz/t+oUaOS9RUrViTrbW1tyXo5K+1u3rw5Wb/tttuS9d7e3sLPjcZSVtjN7ICkjyV9Lukzd2+pRFMAKq8Se/ap7v5RBR4HQBXxnh0Iotywu6TNZrbDzNoH+wMzazez7Wa2vcznAlCGcg/jr3D3Q2b2V5JeNLP/dveugX/g7p2SOiXJzIqfaQJQlrL27O5+KPt9RNJ6SZMr0RSAyiscdjMbZWbfPnFbUquk3ZVqDEBlWdE5XDP7nvr35lL/24H/dPeflhhzyh7Gt7Tkzyp2dnYmx15yySXJ+s6dO5P1TZs2JetbtmxJ1lOuuuqqZH3q1KnJ+q5du5L1iy66KLdW6jMA+/btS9bffPPNZL27uztZH67c3QbbXvg9u7vvl/Q3hTsCUFNMvQFBEHYgCMIOBEHYgSAIOxBE4am3Qk/WwFNvI0eOTNbXrVuXW7vmmmuSY7u6upL16667Llnv6+tL1stRaurs/PPPT9Y//fTTZP200/L3J2effXZybCk9PT3J+qJFi3Jr69evz61J0ieffFKop0aQN/XGnh0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgmCePXPTTTcl6ytXrsytHTt2LDn2sssuS9YPHjyYrFdTa2trsl7qMtNS9REjRuTWZsyYkRw7e/bsZH3KlCnJenNzc27t1VdfTY698sork/VGxjw7EBxhB4Ig7EAQhB0IgrADQRB2IAjCDgTBPHsmdb26lJ6Pvv7665Njt23bVqgnpN18883J+mOPPZZba2pqSo5duHBhst7R0ZGs1xPz7EBwhB0IgrADQRB2IAjCDgRB2IEgCDsQBPPsmVLXs69atSq3Vmq+d+3atYV6QnnOPPPM3Nr+/fuTY996661kfdq0aYV6qoXC8+xm9rSZHTGz3QO2jTGzF83s3ez36Eo2C6DyhnIY/3NJ1560bYGkre4+UdLW7D6ABlYy7O7eJenoSZvbJC3Pbi+XdEOF+wJQYWcUHNfk7r3Z7Q8k5X7Q2MzaJbUXfB4AFVI07F9yd0+deHP3TkmdUmOfoAOGu6JTb4fNrFmSst9HKtcSgGooGvaNkm7Nbt8qaUNl2gFQLSUP481spaQfSjrHzHokLZb0qKQ1Zna7pIOSflzNJmuh1Pefl1qHHLWXmkeXpDVr1hQe+9xzzxXqqZGVDLu7z8kpXV3hXgBUER+XBYIg7EAQhB0IgrADQRB2IIiyP0E3XOzZsydZ7+vry60tXrw4Ofa1115L1t97771kHYObOXNmsn711fkTRj09PcmxW7ZsKdRTI2PPDgRB2IEgCDsQBGEHgiDsQBCEHQiCsANBMM+eOX78eLK+bNmy3Nq9996bHPvEE08k67NmzUrWo5o8eXKyXmqp7A8//DC3NmnSpOTYY8eOJeunIvbsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAESzYP0Rln5H8kYePGjcmxra2tyfojjzySrD/44IPJ+qmqo6MjWb/jjjuS9VJf7z19+vTcWqnvGDiVFV6yGcDwQNiBIAg7EARhB4Ig7EAQhB0IgrADQTDPXgNPPvlksl5qPnn9+vXJ+vz583Nr1f5O+lJLHz/88MO5tTvvvDM5tqurK1mfOnVqsh5V4Xl2M3vazI6Y2e4B25aY2SEz25n95H96AUBDGMph/M8lXTvI9ifcfVL280Jl2wJQaSXD7u5dko7WoBcAVVTOCbq7zOzt7DB/dN4fmVm7mW03s+1lPBeAMhUN+88kTZA0SVKvpNwrGty9091b3L2l4HMBqIBCYXf3w+7+ubt/IWmppPTXgAKou0JhN7PmAXdnStqd97cAGkPJeXYzWynph5LOkXRY0uLs/iRJLumApJ+4e2/JJws6zz527NhkvdT3o5eap09d17106dKyHnvMmDHJ+vPPP5+sX3DBBbm11Pe6S1JbW1uyPpyvSS9H3jx7yUUi3H3OIJvzV0wA0JD4uCwQBGEHgiDsQBCEHQiCsANBcInrKeCss85K1ufNm5dbu++++5Jj+/r6knWzQWdxvlTqEtdNmzbl1pYsWZIcu2PHjmQdg+OrpIHgCDsQBGEHgiDsQBCEHQiCsANBEHYgCObZh7nVq1cn67NmzUrWS82zl/r309zcnFsrdYkrimGeHQiOsANBEHYgCMIOBEHYgSAIOxAEYQeCKPntsmh8c+fOza21trbWsJOvevzxx3NrDz30UHLsvn37Kt1OaOzZgSAIOxAEYQeCIOxAEIQdCIKwA0EQdiAIrmc/BVx66aXJ+ssvv5xbGzlyZHLshg0bkvUbb7wxWb/nnnsK188999zk2NTnByTpqaeeStajKnw9u5mNM7NtZvaOme0xs3nZ9jFm9qKZvZv9Hl3ppgFUzlAO4z+T9C/u/n1Jfytprpl9X9ICSVvdfaKkrdl9AA2qZNjdvdfd38hufyxpr6SxktokLc/+bLmkG6rVJIDyfa3PxpvZeEk/kPRbSU3u3puVPpDUlDOmXVJ78RYBVMKQz8ab2bckrZM0393/OLDm/Wf5Bj355u6d7t7i7i1ldQqgLEMKu5l9Q/1B/5W7P5ttPmxmzVm9WdKR6rQIoBJKTr1Z/3cJL5d01N3nD9j+mKT/cfdHzWyBpDHunlwfmKm3Ykotbbxo0aLc2vvvv58ce8sttyTrr7zySrJeyvjx43Nry5YtS46dOHFisj5t2rRkvbu7O1kfrvKm3obynv3vJP2jpF1mtjPbtlDSo5LWmNntkg5K+nElGgVQHSXD7u6vSMpbKeDqyrYDoFr4uCwQBGEHgiDsQBCEHQiCsANBcInrKeDCCy9M1l944YXc2tixY5Nj+/r6kvVDhw4l6ytXrkzWn3nmmdza5Zdfnhy7YsWKZP3+++9P1js6OpL14Yolm4HgCDsQBGEHgiDsQBCEHQiCsANBEHYgCObZh4ERI0bk1mbMmJEce/fddyfrU6ZMSdZr+e/nZHv37k3WL7744hp10liYZweCI+xAEIQdCIKwA0EQdiAIwg4EQdiBIL7W8k9oTMePH8+trV27Njn2pZdeStbPO++8ZH3ChAnJ+gMPPJBba2kpb5Gg1atXlzU+GvbsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxDEUNZnHyfpF5KaJLmkTnf/DzNbIumfJX2Y/elCd8//AnNxPTtQC3nXsw8l7M2Smt39DTP7tqQdkm5Q/3rsf3L3x4faBGEHqi8v7ENZn71XUm92+2Mz2yspvcwIgIbztd6zm9l4ST+Q9Nts011m9raZPW1mo3PGtJvZdjPbXlanAMoy5O+gM7NvSfovST9192fNrEnSR+p/H/+v6j/Uv63EY3AYD1RZ4ffskmRm35D0a0m/cfd/H6Q+XtKv3f2iEo9D2IEqK/yFk2ZmkpZJ2jsw6NmJuxNmStpdbpMAqmcoZ+OvkPSypF2Svsg2L5Q0R9Ik9R/GH5D0k+xkXuqx2LMDVVbWYXylEHag+vjeeCA4wg4EQdiBIAg7EARhB4Ig7EAQhB0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBC1XrL5I0kHB9w/J9vWiBq1t0btS6K3oirZ21/nFWp6PftXntxsu7uXt0h3lTRqb43al0RvRdWqNw7jgSAIOxBEvcPeWefnT2nU3hq1L4neiqpJb3V9zw6gduq9ZwdQI4QdCKIuYTeza83sd2bWbWYL6tFDHjM7YGa7zGxnvdeny9bQO2JmuwdsG2NmL5rZu9nvQdfYq1NvS8zsUPba7TSz6XXqbZyZbTOzd8xsj5nNy7bX9bVL9FWT163m79nN7HRJv5f0I0k9kl6XNMfd36lpIznM7ICkFnev+wcwzOzvJf1J0i9OLK1lZv8m6ai7P5r9Rzna3e9vkN6W6Gsu412l3vKWGf8n1fG1q+Ty50XUY88+WVK3u+939z9LWiWprQ59NDx375J09KTNbZKWZ7eXq/8fS83l9NYQ3L3X3d/Ibn8s6cQy43V97RJ91UQ9wj5W0h8G3O9RY6337pI2m9kOM2uvdzODaBqwzNYHkprq2cwgSi7jXUsnLTPeMK9dkeXPy8UJuq+6wt0vlfQPkuZmh6sNyfvfgzXS3OnPJE1Q/xqAvZI66tlMtsz4Oknz3f2PA2v1fO0G6asmr1s9wn5I0rgB97+TbWsI7n4o+31E0nr1v+1oJIdPrKCb/T5S536+5O6H3f1zd/9C0lLV8bXLlhlfJ+lX7v5strnur91gfdXqdatH2F+XNNHMvmtm35Q0W9LGOvTxFWY2KjtxIjMbJalVjbcU9UZJt2a3b5W0oY69/IVGWcY7b5lx1fm1q/vy5+5e8x9J09V/Rn6fpAfq0UNOX9+T9Fb2s6fevUlaqf7Duv9V/7mN2yWdLWmrpHclbZE0poF6+6X6l/Z+W/3Baq5Tb1eo/xD9bUk7s5/p9X7tEn3V5HXj47JAEJygA4Ig7EAQhB0IgrADQRB2IAjCDgRB2IEg/g+PDdv3LvUehwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 9, image: 1 with activation:0.15238195657730103, index(5,6)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["treating image n 2/10000, for layer: layer1\n","norm null for filter: 0, image: 2 with activation:0.5345718860626221, index(12,6)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 1, image: 2 with activation:0.8088735342025757, index(2,8)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 2, image: 2 with activation:0.675757646560669, index(2,7)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAOEUlEQVR4nO3df+hVdZ7H8ddr2ybCgn6xIk3k7NAfjQPbpFSwEkU0qH/0bbKGhMqocKQpppJYa6GkbctWZ/Yv++GkjRuzDVFKYsvOuCLaEg6auGVFY4Y2imURkYNkme/943scvtX3fu7Xe++559b7+YAv33vP+3vueXPx5Tn3fO45H0eEAHz7/U3TDQDoD8IOJEHYgSQIO5AEYQeS+Nt+bsw2p/6BmkWER1ve1Z7d9jTbb9l+2/b8bl4LQL3c6Ti77eMk/UnS5ZL2SNosaVZEvFFYhz07ULM69uwXSHo7It6JiM8k/U7SUBevB6BG3YT9TEl/HvF8T7XsS2zPsb3F9pYutgWgS7WfoIuIpZKWShzGA03qZs++V9JZI55/t1oGYAB1E/bNks6x/T3b35F0raTVvWkLQK91fBgfEYdt3ybp95KOk7Q8Il7vWWcAeqrjobeONsZndqB2tXypBsA3B2EHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgiY7nZ5ck27skHZD0haTDETGlF00B6L2uwl65NCI+7MHrAKgRh/FAEt2GPST9wfYrtueM9ge259jeYntLl9sC0AVHROcr22dGxF7bfydpraTbI2Jj4e873xiAMYkIj7a8qz17ROytfu+XtErSBd28HoD6dBx22+Nsn3z0saQfS9req8YA9FY3Z+PHS1pl++jr/GdE/HdPugJ6YO7cuS1rEyZMKK67bNmyYv3dd9/tqKcmdRz2iHhH0j/0sBcANWLoDUiCsANJEHYgCcIOJEHYgSR6cSEM0NLkyZNb1u64447iurt27epq2zfeeGPLWruht4suuqhYv+KKK4r1Q4cOFetNYM8OJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0l0daeaY94Yd6r51pk6dWqxvmTJkpa1SZMmdbXt6vLqlur8t33PPfcU64sWLapt2+3UcqcaAN8chB1IgrADSRB2IAnCDiRB2IEkCDuQBOPsKJo+fXqx/uyzzxbrJ554Yi/b+ZKNG1tOPiRJeuyxx1rWFi5cWFz37LPPLtY3bNhQrF922WXFep0YZweSI+xAEoQdSIKwA0kQdiAJwg4kQdiBJLhvfHL3339/sb5gwYJi/ciRIx1v+9NPPy3W161bV6y3u3d7yS233FKsT5w4sVh/6aWXOt52U9ru2W0vt73f9vYRy06zvdb2jur3qfW2CaBbYzmM/42kaV9ZNl/Suog4R9K66jmAAdY27BGxUdJHX1k8JGlF9XiFpCt73BeAHuv0M/v4iNhXPX5P0vhWf2h7jqQ5HW4HQI90fYIuIqJ0gUtELJW0VOJCGKBJnQ69vW97giRVv/f3riUAdeg07Kslza4ez5b0Qm/aAVCXtofxtp+RdImkM2zvkXS/pIWSnrV9s6Tdkn5aZ5PZtbsm/PLLL29ZmzlzZnHdq666qlhvN45+8ODBYn3t2rUta4sXLy6u+/LLLxfr7ZTet5NOOqm4brv7PDz66KMd9dSktmGPiFktSs1dnQ/gmPF1WSAJwg4kQdiBJAg7kARhB5LgEtdvgNLQmiStXLmy49fevXt3sf7kk08W6+vXry/WN23adMw99UppWuULL7ywuO6+ffuK9c8++6yjnprEnh1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkmCcfQBcfPHFxfry5cs7fu01a9YU6/PmzSvWd+7c2fG26zZ37txi/dZbb21ZazeOPjQ0VKx//PHHxfogYs8OJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kwzt4H7cbJr7nmmmK93a2kn3vuuZa1O++8s7huu/HmJk2ZMqVYX7JkSbFeuh10u1tBb926tVj/JmLPDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM4+RieccELL2hNPPFFc9/rrry/W200PvGjRomK9dH/0QdbuevRuxtEl6dprr21ZK3034duq7Z7d9nLb+21vH7Fsge29trdVPzPqbRNAt8ZyGP8bSdNGWf7vEXFe9fNfvW0LQK+1DXtEbJT0UR96AVCjbk7Q3Wb71eow/9RWf2R7ju0ttrd0sS0AXeo07I9J+r6k8yTtk/TLVn8YEUsjYkpElK9qAFCrjsIeEe9HxBcRcUTSryVd0Nu2APRaR2G3PWHE059I2t7qbwEMhrbj7LafkXSJpDNs75F0v6RLbJ8nKSTtkvSzGnvsi3bXjN9www0ta9ddd11x3azj6JJ06aWXtqw9+OCDxXW7fd8yjqWXtA17RMwaZfGyGnoBUCO+LgskQdiBJAg7kARhB5Ig7EASbje80dON2f3b2DF64IEHivV7772349devHhxsT5//vyOX7tupUt7Jenhhx8u1kvDkocOHSqu+9RTTxXr9913X7GeVUR4tOXs2YEkCDuQBGEHkiDsQBKEHUiCsANJEHYgiTTj7Keffnqx/tZbbxXrp5xySsvamjVriuteffXVxfrhw4eL9TpNmjSpWL/77ruL9XaX95amhB4aGiqu+22cNrkfGGcHkiPsQBKEHUiCsANJEHYgCcIOJEHYgSTSTNl8++23F+ulcXRJ2rBhQ8ta6TbTUrPj6LNnzy7W212PPn78+GL9xRdfLNZL15xv27atuC56iz07kARhB5Ig7EAShB1IgrADSRB2IAnCDiSRZpx9xowZxbo96iXAf/X555+3rH3yyScd9XTUySefXKy3ux5++vTpLWszZ84srnvw4MFifcWKFcX6TTfdVKxjcLTds9s+y/Z622/Yft32L6rlp9lea3tH9fvU+tsF0KmxHMYfljQvIn4g6SJJP7f9A0nzJa2LiHMkraueAxhQbcMeEfsiYmv1+ICkNyWdKWlI0tFjvBWSrqyrSQDdO6bP7LYnSvqRpD9KGh8RR28w9p6kUb9EbXuOpDmdtwigF8Z8Nt72SZKel3RHRHzpjFQM37Vy1JtJRsTSiJgSEVO66hRAV8YUdtvHazjov42IldXi921PqOoTJO2vp0UAvdD2MN7DY1LLJL0ZEb8aUVotabakhdXvF2rpsEcmT55crLe7pfa4ceNa1mbNmlVcd9q0acX6+eefX6yfe+65xXrJqlWrivWHHnqoWOd2zt8eY/nM/o+Srpf0mu2jFyDfq+GQP2v7Zkm7Jf20nhYB9ELbsEfE/0pq9Y2Ty3rbDoC68HVZIAnCDiRB2IEkCDuQBGEHkkgzZfMjjzxSrM+bN69PnXzdjh07ivXNmzcX608//XTL2qZNm4rrHjhwoFjHNw9TNgPJEXYgCcIOJEHYgSQIO5AEYQeSIOxAEmnG2Y8//vhi/a677irWu7mm/PHHHy/Wd+7cWax/8MEHHW8b+TDODiRH2IEkCDuQBGEHkiDsQBKEHUiCsANJpBlnB7JgnB1IjrADSRB2IAnCDiRB2IEkCDuQBGEHkmgbdttn2V5v+w3br9v+RbV8ge29trdVPzPqbxdAp9p+qcb2BEkTImKr7ZMlvSLpSg3Px/6XiFg85o3xpRqgdq2+VDOW+dn3SdpXPT5g+01JZ/a2PQB1O6bP7LYnSvqRpD9Wi26z/art5bZPbbHOHNtbbG/pqlMAXRnzd+NtnyRpg6R/jYiVtsdL+lBSSPoXDR/q39TmNTiMB2rW6jB+TGG3fbykNZJ+HxG/GqU+UdKaiPhhm9ch7EDNOr4QxrYlLZP05sigVyfujvqJpO3dNgmgPmM5Gz9V0kuSXpN0pFp8r6RZks7T8GH8Lkk/q07mlV6LPTtQs64O43uFsAP143p2IDnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEm1vONljH0raPeL5GdWyQTSovQ1qXxK9daqXvZ3dqtDX69m/tnF7S0RMaayBgkHtbVD7kuitU/3qjcN4IAnCDiTRdNiXNrz9kkHtbVD7kuitU33prdHP7AD6p+k9O4A+IexAEo2E3fY022/Zftv2/CZ6aMX2LtuvVdNQNzo/XTWH3n7b20csO832Wts7qt+jzrHXUG8DMY13YZrxRt+7pqc/7/tndtvHSfqTpMsl7ZG0WdKsiHijr420YHuXpCkR0fgXMGxfLOkvkv7j6NRatv9N0kcRsbD6j/LUiPinAeltgY5xGu+aems1zfiNavC96+X0551oYs9+gaS3I+KdiPhM0u8kDTXQx8CLiI2SPvrK4iFJK6rHKzT8j6XvWvQ2ECJiX0RsrR4fkHR0mvFG37tCX33RRNjPlPTnEc/3aLDmew9Jf7D9iu05TTczivEjptl6T9L4JpsZRdtpvPvpK9OMD8x718n0593iBN3XTY2I8yVNl/Tz6nB1IMXwZ7BBGjt9TNL3NTwH4D5Jv2yymWqa8ecl3RERn4ysNfnejdJXX963JsK+V9JZI55/t1o2ECJib/V7v6RVGv7YMUjePzqDbvV7f8P9/FVEvB8RX0TEEUm/VoPvXTXN+POSfhsRK6vFjb93o/XVr/etibBvlnSO7e/Z/o6kayWtbqCPr7E9rjpxItvjJP1YgzcV9WpJs6vHsyW90GAvXzIo03i3mmZcDb93jU9/HhF9/5E0Q8Nn5HdK+ucmemjR199L+r/q5/Wme5P0jIYP6z7X8LmNmyWdLmmdpB2S/kfSaQPU29Mantr7VQ0Ha0JDvU3V8CH6q5K2VT8zmn7vCn315X3j67JAEpygA5Ig7EAShB1IgrADSRB2IAnCDiRB2IEk/h8513szDxB2MQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 3, image: 2 with activation:-0.0547817163169384, index(12,7)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 4, image: 2 with activation:0.23051735758781433, index(12,7)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 7, image: 2 with activation:0.29231342673301697, index(2,9)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 8, image: 2 with activation:0.8500081896781921, index(8,6)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 9, image: 2 with activation:0.1827980875968933, index(2,7)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAOEUlEQVR4nO3df+hVdZ7H8ddr2ybCgn6xIk3k7NAfjQPbpFSwEkU0qH/0bbKGhMqocKQpppJYa6GkbctWZ/Yv++GkjRuzDVFKYsvOuCLaEg6auGVFY4Y2imURkYNkme/943scvtX3fu7Xe++559b7+YAv33vP+3vueXPx5Tn3fO45H0eEAHz7/U3TDQDoD8IOJEHYgSQIO5AEYQeS+Nt+bsw2p/6BmkWER1ve1Z7d9jTbb9l+2/b8bl4LQL3c6Ti77eMk/UnS5ZL2SNosaVZEvFFYhz07ULM69uwXSHo7It6JiM8k/U7SUBevB6BG3YT9TEl/HvF8T7XsS2zPsb3F9pYutgWgS7WfoIuIpZKWShzGA03qZs++V9JZI55/t1oGYAB1E/bNks6x/T3b35F0raTVvWkLQK91fBgfEYdt3ybp95KOk7Q8Il7vWWcAeqrjobeONsZndqB2tXypBsA3B2EHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgiY7nZ5ck27skHZD0haTDETGlF00B6L2uwl65NCI+7MHrAKgRh/FAEt2GPST9wfYrtueM9ge259jeYntLl9sC0AVHROcr22dGxF7bfydpraTbI2Jj4e873xiAMYkIj7a8qz17ROytfu+XtErSBd28HoD6dBx22+Nsn3z0saQfS9req8YA9FY3Z+PHS1pl++jr/GdE/HdPugJ6YO7cuS1rEyZMKK67bNmyYv3dd9/tqKcmdRz2iHhH0j/0sBcANWLoDUiCsANJEHYgCcIOJEHYgSR6cSEM0NLkyZNb1u64447iurt27epq2zfeeGPLWruht4suuqhYv+KKK4r1Q4cOFetNYM8OJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0l0daeaY94Yd6r51pk6dWqxvmTJkpa1SZMmdbXt6vLqlur8t33PPfcU64sWLapt2+3UcqcaAN8chB1IgrADSRB2IAnCDiRB2IEkCDuQBOPsKJo+fXqx/uyzzxbrJ554Yi/b+ZKNG1tOPiRJeuyxx1rWFi5cWFz37LPPLtY3bNhQrF922WXFep0YZweSI+xAEoQdSIKwA0kQdiAJwg4kQdiBJLhvfHL3339/sb5gwYJi/ciRIx1v+9NPPy3W161bV6y3u3d7yS233FKsT5w4sVh/6aWXOt52U9ru2W0vt73f9vYRy06zvdb2jur3qfW2CaBbYzmM/42kaV9ZNl/Suog4R9K66jmAAdY27BGxUdJHX1k8JGlF9XiFpCt73BeAHuv0M/v4iNhXPX5P0vhWf2h7jqQ5HW4HQI90fYIuIqJ0gUtELJW0VOJCGKBJnQ69vW97giRVv/f3riUAdeg07Kslza4ez5b0Qm/aAVCXtofxtp+RdImkM2zvkXS/pIWSnrV9s6Tdkn5aZ5PZtbsm/PLLL29ZmzlzZnHdq666qlhvN45+8ODBYn3t2rUta4sXLy6u+/LLLxfr7ZTet5NOOqm4brv7PDz66KMd9dSktmGPiFktSs1dnQ/gmPF1WSAJwg4kQdiBJAg7kARhB5LgEtdvgNLQmiStXLmy49fevXt3sf7kk08W6+vXry/WN23adMw99UppWuULL7ywuO6+ffuK9c8++6yjnprEnh1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkmCcfQBcfPHFxfry5cs7fu01a9YU6/PmzSvWd+7c2fG26zZ37txi/dZbb21ZazeOPjQ0VKx//PHHxfogYs8OJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kwzt4H7cbJr7nmmmK93a2kn3vuuZa1O++8s7huu/HmJk2ZMqVYX7JkSbFeuh10u1tBb926tVj/JmLPDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM4+RieccELL2hNPPFFc9/rrry/W200PvGjRomK9dH/0QdbuevRuxtEl6dprr21ZK3034duq7Z7d9nLb+21vH7Fsge29trdVPzPqbRNAt8ZyGP8bSdNGWf7vEXFe9fNfvW0LQK+1DXtEbJT0UR96AVCjbk7Q3Wb71eow/9RWf2R7ju0ttrd0sS0AXeo07I9J+r6k8yTtk/TLVn8YEUsjYkpElK9qAFCrjsIeEe9HxBcRcUTSryVd0Nu2APRaR2G3PWHE059I2t7qbwEMhrbj7LafkXSJpDNs75F0v6RLbJ8nKSTtkvSzGnvsi3bXjN9www0ta9ddd11x3azj6JJ06aWXtqw9+OCDxXW7fd8yjqWXtA17RMwaZfGyGnoBUCO+LgskQdiBJAg7kARhB5Ig7EASbje80dON2f3b2DF64IEHivV7772349devHhxsT5//vyOX7tupUt7Jenhhx8u1kvDkocOHSqu+9RTTxXr9913X7GeVUR4tOXs2YEkCDuQBGEHkiDsQBKEHUiCsANJEHYgiTTj7Keffnqx/tZbbxXrp5xySsvamjVriuteffXVxfrhw4eL9TpNmjSpWL/77ruL9XaX95amhB4aGiqu+22cNrkfGGcHkiPsQBKEHUiCsANJEHYgCcIOJEHYgSTSTNl8++23F+ulcXRJ2rBhQ8ta6TbTUrPj6LNnzy7W212PPn78+GL9xRdfLNZL15xv27atuC56iz07kARhB5Ig7EAShB1IgrADSRB2IAnCDiSRZpx9xowZxbo96iXAf/X555+3rH3yyScd9XTUySefXKy3ux5++vTpLWszZ84srnvw4MFifcWKFcX6TTfdVKxjcLTds9s+y/Z622/Yft32L6rlp9lea3tH9fvU+tsF0KmxHMYfljQvIn4g6SJJP7f9A0nzJa2LiHMkraueAxhQbcMeEfsiYmv1+ICkNyWdKWlI0tFjvBWSrqyrSQDdO6bP7LYnSvqRpD9KGh8RR28w9p6kUb9EbXuOpDmdtwigF8Z8Nt72SZKel3RHRHzpjFQM37Vy1JtJRsTSiJgSEVO66hRAV8YUdtvHazjov42IldXi921PqOoTJO2vp0UAvdD2MN7DY1LLJL0ZEb8aUVotabakhdXvF2rpsEcmT55crLe7pfa4ceNa1mbNmlVcd9q0acX6+eefX6yfe+65xXrJqlWrivWHHnqoWOd2zt8eY/nM/o+Srpf0mu2jFyDfq+GQP2v7Zkm7Jf20nhYB9ELbsEfE/0pq9Y2Ty3rbDoC68HVZIAnCDiRB2IEkCDuQBGEHkkgzZfMjjzxSrM+bN69PnXzdjh07ivXNmzcX608//XTL2qZNm4rrHjhwoFjHNw9TNgPJEXYgCcIOJEHYgSQIO5AEYQeSIOxAEmnG2Y8//vhi/a677irWu7mm/PHHHy/Wd+7cWax/8MEHHW8b+TDODiRH2IEkCDuQBGEHkiDsQBKEHUiCsANJpBlnB7JgnB1IjrADSRB2IAnCDiRB2IEkCDuQBGEHkmgbdttn2V5v+w3br9v+RbV8ge29trdVPzPqbxdAp9p+qcb2BEkTImKr7ZMlvSLpSg3Px/6XiFg85o3xpRqgdq2+VDOW+dn3SdpXPT5g+01JZ/a2PQB1O6bP7LYnSvqRpD9Wi26z/art5bZPbbHOHNtbbG/pqlMAXRnzd+NtnyRpg6R/jYiVtsdL+lBSSPoXDR/q39TmNTiMB2rW6jB+TGG3fbykNZJ+HxG/GqU+UdKaiPhhm9ch7EDNOr4QxrYlLZP05sigVyfujvqJpO3dNgmgPmM5Gz9V0kuSXpN0pFp8r6RZks7T8GH8Lkk/q07mlV6LPTtQs64O43uFsAP143p2IDnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEm1vONljH0raPeL5GdWyQTSovQ1qXxK9daqXvZ3dqtDX69m/tnF7S0RMaayBgkHtbVD7kuitU/3qjcN4IAnCDiTRdNiXNrz9kkHtbVD7kuitU33prdHP7AD6p+k9O4A+IexAEo2E3fY022/Zftv2/CZ6aMX2LtuvVdNQNzo/XTWH3n7b20csO832Wts7qt+jzrHXUG8DMY13YZrxRt+7pqc/7/tndtvHSfqTpMsl7ZG0WdKsiHijr420YHuXpCkR0fgXMGxfLOkvkv7j6NRatv9N0kcRsbD6j/LUiPinAeltgY5xGu+aems1zfiNavC96+X0551oYs9+gaS3I+KdiPhM0u8kDTXQx8CLiI2SPvrK4iFJK6rHKzT8j6XvWvQ2ECJiX0RsrR4fkHR0mvFG37tCX33RRNjPlPTnEc/3aLDmew9Jf7D9iu05TTczivEjptl6T9L4JpsZRdtpvPvpK9OMD8x718n0593iBN3XTY2I8yVNl/Tz6nB1IMXwZ7BBGjt9TNL3NTwH4D5Jv2yymWqa8ecl3RERn4ysNfnejdJXX963JsK+V9JZI55/t1o2ECJib/V7v6RVGv7YMUjePzqDbvV7f8P9/FVEvB8RX0TEEUm/VoPvXTXN+POSfhsRK6vFjb93o/XVr/etibBvlnSO7e/Z/o6kayWtbqCPr7E9rjpxItvjJP1YgzcV9WpJs6vHsyW90GAvXzIo03i3mmZcDb93jU9/HhF9/5E0Q8Nn5HdK+ucmemjR199L+r/q5/Wme5P0jIYP6z7X8LmNmyWdLmmdpB2S/kfSaQPU29Mantr7VQ0Ha0JDvU3V8CH6q5K2VT8zmn7vCn315X3j67JAEpygA5Ig7EAShB1IgrADSRB2IAnCDiRB2IEk/h8513szDxB2MQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["treating image n 3/10000, for layer: layer1\n","norm null for filter: 2, image: 3 with activation:0.44186654686927795, index(7,8)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["treating image n 4/10000, for layer: layer1\n","norm null for filter: 1, image: 4 with activation:0.8195942044258118, index(4,10)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAM8klEQVR4nO3df6hc9ZnH8c9n3QQhqRKVhksaNrHkD+PPbkIsblgMpcVNwFgEaf6QaIVbIZEG9o9oF6mwFiRsuwpCIUFpXLqWYiIJZd1WL9HsqhSjpEnUNj800oQkV1GMQSUan/3jnixXvfOdm5kzc8b7vF9wmZnzzHfOw5BPzplzzszXESEAU9/fNN0AgP4g7EAShB1IgrADSRB2IIm/7efKbHPoH+ixiPBEy7vastu+wfZfbB+0fXc3rwWgt9zpeXbb50naL+m7ko5IeknSqoh4rTCGLTvQY73Ysi+RdDAi3oiI05J+I2llF68HoIe6CfscSX8d9/hItexzbA/b3mV7VxfrAtClnh+gi4iNkjZK7MYDTepmy35U0txxj79RLQMwgLoJ+0uSFtieb3u6pB9I2l5PWwDq1vFufER8anutpN9LOk/SoxHxam2dAahVx6feOloZn9mBnuvJRTUAvjoIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiir1M2A+NdeumlxfqhQ4eK9aGhoWL9+PHj59zTVMaWHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeS4Dw7GrNp06Zi/cMPPyzWz5w5U2c7U15XYbd9WNIHks5I+jQiFtfRFID61bFlXxYR79TwOgB6iM/sQBLdhj0k/cH2y7aHJ3qC7WHbu2zv6nJdALrQ7W780og4avvrkp62/eeI2Dn+CRGxUdJGSbIdXa4PQIe62rJHxNHqdlTSk5KW1NEUgPp1HHbbM2x/7ex9Sd+TtK+uxgDUq5vd+NmSnrR99nX+MyL+u5auMGXMmzevZW3RokXFsc8++2yx/vbbb3fQUV4dhz0i3pB0dY29AOghTr0BSRB2IAnCDiRB2IEkCDuQBF9xRU/NnDmzZe2CCy4ojn3xxRfrbic1tuxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kATn2dFTV155ZcvaJ598Uhy7ffv2uttJjS07kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBefYpbunSpcX66Ohosb5///6u1n/zzTe3rJ0+fbo4ds+ePV2tG5/Hlh1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkuA8+xRQ+m32xx57rDj2wQcfLNbbnWe/6qqrivXly5e3rD3//PPFsahX2y277Udtj9reN27ZRbaftn2gup3V2zYBdGsyu/G/knTDF5bdLWkkIhZIGqkeAxhgbcMeETslvfuFxSslba7ub5Z0U819AahZp5/ZZ0fEser+cUmzWz3R9rCk4Q7XA6AmXR+gi4iwHYX6RkkbJan0PAC91emptxO2hySpui1/dQpA4zoN+3ZJq6v7qyVtq6cdAL3Sdjfe9uOSrpd0ie0jkn4q6QFJv7V9h6S3JN3SyyZRtmjRopa1efPm9XTdK1asKNbPP//8lrUNGzbU3Q4K2oY9Ila1KH2n5l4A9BCXywJJEHYgCcIOJEHYgSQIO5AEX3GdAtavX9/x2JGRkWJ92rRpxfqNN95YrJ88ebJl7c033yyORb3YsgNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEpxn/wqYMWNGsb5gwYKWtR07dhTHHjhwoFhftmxZsX7ttdcW6zt37mxZO3jwYHEs6sWWHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeS4Dz7V8C9995brM+fP79l7a677iqOPX36dLF+9dVXF+vtbN26tavxqA9bdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IwhHRv5XZ/VvZV8icOXOK9UOHDhXrH3/8ccvarbfeWhy7ZMmSYn3t2rXF+vvvv1+sX3HFFS1rp06dKo5FZyLCEy1vu2W3/ajtUdv7xi27z/ZR27urv+V1NgugfpPZjf+VpBsmWP7vEXFN9fdf9bYFoG5twx4ROyW924deAPRQNwfo1treU+3mz2r1JNvDtnfZ3tXFugB0qdOw/1LSNyVdI+mYpJ+3emJEbIyIxRGxuMN1AahBR2GPiBMRcSYiPpO0SVL5kC6AxnUUdttD4x5+X9K+Vs8FMBjafp/d9uOSrpd0ie0jkn4q6Xrb10gKSYcl/aiHPU5569atK9anT5/ecX3btm3Fse+9916xfuGFF3ZVf/jhh1vWbrvttuJY1Ktt2CNi1QSLH+lBLwB6iMtlgSQIO5AEYQeSIOxAEoQdSIKfku6Diy++uFi//fbbu3r90ldF77///uLYuXPnFutr1qwp1p966qlivd2U0OgftuxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kAQ/JT0AVq9eXazfcsstxfqGDRta1p577rni2N27dxfrl112WbG+bNmyYv2FF14o1lG/jn9KGsDUQNiBJAg7kARhB5Ig7EAShB1IgrADSXCefYq77rrrivWRkZFiffPmzcX6nXfeec49obc4zw4kR9iBJAg7kARhB5Ig7EAShB1IgrADSfC78VOAPeFpVUnSPffcUxzbbjroJ554oqOeMHjabtltz7W9w/Zrtl+1/eNq+UW2n7Z9oLqd1ft2AXRqMrvxn0r654hYKOnbktbYXijpbkkjEbFA0kj1GMCAahv2iDgWEa9U9z+Q9LqkOZJWSjp7LeVmSTf1qkkA3Tunz+y250n6lqQ/SpodEceq0nFJs1uMGZY03HmLAOow6aPxtmdK2iJpXUScHF+LsW/TTPgll4jYGBGLI2JxV50C6Mqkwm57msaC/uuI2FotPmF7qKoPSRrtTYsA6tB2N95j53UekfR6RPxiXGm7pNWSHqhut/WkQ7R1+eWXt6ytWLGiOPahhx4q1p955pmOesLgmcxn9n+QdKukvbbP/sj4TzQW8t/avkPSW5LKP24OoFFtwx4R/yup1VUb36m3HQC9wuWyQBKEHUiCsANJEHYgCcIOJMFXXKeA9evXdzx2y5YtNXaCQcaWHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSYMrmKWDv3r0tazNnziyOXbhwYbH+0UcfddQTmsOUzUByhB1IgrADSRB2IAnCDiRB2IEkCDuQBOfZgSmG8+xAcoQdSIKwA0kQdiAJwg4kQdiBJAg7kETbsNuea3uH7ddsv2r7x9Xy+2wftb27+lve+3YBdKrtRTW2hyQNRcQrtr8m6WVJN2lsPvZTEfFvk14ZF9UAPdfqoprJzM9+TNKx6v4Htl+XNKfe9gD02jl9Zrc9T9K3JP2xWrTW9h7bj9qe1WLMsO1dtnd11SmArkz62njbMyU9J+lnEbHV9mxJ70gKSf+qsV39H7Z5DXbjgR5rtRs/qbDbnibpd5J+HxG/mKA+T9LvIuKKNq9D2IEe6/iLMLYt6RFJr48PenXg7qzvS9rXbZMAemcyR+OXSvofSXslfVYt/omkVZKu0dhu/GFJP6oO5pVeiy070GNd7cbXhbADvcf32YHkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0m0/cHJmr0j6a1xjy+plg2iQe1tUPuS6K1Tdfb2d60Kff0++5dWbu+KiMWNNVAwqL0Nal8SvXWqX72xGw8kQdiBJJoO+8aG118yqL0Nal8SvXWqL701+pkdQP80vWUH0CeEHUiikbDbvsH2X2wftH13Ez20Yvuw7b3VNNSNzk9XzaE3anvfuGUX2X7a9oHqdsI59hrqbSCm8S5MM97oe9f09Od9/8xu+zxJ+yV9V9IRSS9JWhURr/W1kRZsH5a0OCIavwDD9j9KOiXpsbNTa9neIOndiHig+o9yVkSsH5De7tM5TuPdo95aTTN+mxp87+qc/rwTTWzZl0g6GBFvRMRpSb+RtLKBPgZeROyU9O4XFq+UtLm6v1lj/1j6rkVvAyEijkXEK9X9DySdnWa80feu0FdfNBH2OZL+Ou7xEQ3WfO8h6Q+2X7Y93HQzE5g9bpqt45JmN9nMBNpO491PX5hmfGDeu06mP+8WB+i+bGlE/L2kf5K0ptpdHUgx9hlskM6d/lLSNzU2B+AxST9vsplqmvEtktZFxMnxtSbfuwn66sv71kTYj0qaO+7xN6plAyEijla3o5Ke1NjHjkFy4uwMutXtaMP9/L+IOBERZyLiM0mb1OB7V00zvkXSryNia7W48fduor769b41EfaXJC2wPd/2dEk/kLS9gT6+xPaM6sCJbM+Q9D0N3lTU2yWtru6vlrStwV4+Z1Cm8W41zbgafu8an/48Ivr+J2m5xo7IH5L0L0300KKvSyX9qfp7teneJD2usd26TzR2bOMOSRdLGpF0QNIzki4aoN7+Q2NTe+/RWLCGGuptqcZ20fdI2l39LW/6vSv01Zf3jctlgSQ4QAckQdiBJAg7kARhB5Ig7EAShB1IgrADSfwf553+0H2Oc1gAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 5, image: 4 with activation:1.1076092720031738, index(5,10)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 6, image: 4 with activation:0.5302774906158447, index(9,9)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 9, image: 4 with activation:0.151536226272583, index(3,6)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["treating image n 5/10000, for layer: layer1\n","norm null for filter: 4, image: 5 with activation:0.2419070303440094, index(11,11)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["norm null for filter: 9, image: 5 with activation:0.11134031414985657, index(3,3)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["treating image n 6/10000, for layer: layer1\n","norm null for filter: 3, image: 6 with activation:-0.14090558886528015, index(2,9)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["treating image n 7/10000, for layer: layer1\n","norm null for filter: 8, image: 7 with activation:0.6754621863365173, index(7,7)\n"],"name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-16-60d4787dc53a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mregion_final\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation_final\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation_final_normalized\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_all_regions_max\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivations_no_binary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen_img_h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen_img_w\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mregion_layer1_no_binary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregion_final\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'layer1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/content/drive/My Drive/Work/Thesis_Julien_Dejasmin/Work/code/Binary_activations_V2/MNIST_Binary_V2/visualize/viz.py\u001b[0m in \u001b[0;36mget_all_regions_max\u001b[0;34m(images, loader, activations, stride, padding, filter_size, len_img_h, len_img_w)\u001b[0m\n\u001b[1;32m 996\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'norm null for filter: {}, image: {} with activation:{}, index({},{})'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mact_max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mind_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mind_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 997\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 998\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 999\u001b[0m \u001b[0mactivation_im_j_normalized\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mact_max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1000\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 270\u001b[0m \"\"\"\n\u001b[1;32m 271\u001b[0m \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 37\u001b[0m display(\n\u001b[1;32m 38\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m )\n\u001b[1;32m 41\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(*objs, **kwargs)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 307\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 174\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 218\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 335\u001b[0m \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 242\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 126\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m 2103\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2104\u001b[0m \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2105\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 2106\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 512\u001b[0m }\n\u001b[1;32m 513\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 514\u001b[0;31m \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 515\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpil_kwargs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 516\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mPIL\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 391\u001b[0m (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m 392\u001b[0m else nullcontext()):\n\u001b[0;32m--> 393\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 394\u001b[0m \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 395\u001b[0m \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1735\u001b[0m mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1736\u001b[0;31m renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m 1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1738\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m 2628\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2629\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2630\u001b[0;31m \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2631\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2632\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 624\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 625\u001b[0m im, l, b, trans = self.make_image(\n\u001b[0;32m--> 626\u001b[0;31m renderer, renderer.get_image_magnification())\n\u001b[0m\u001b[1;32m 627\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 628\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, renderer, magnification, unsampled)\u001b[0m\n\u001b[1;32m 915\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransformed_bbox\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_box\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m magnification, unsampled=unsampled)\n\u001b[0m\u001b[1;32m 918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 919\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_check_unsampled_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_make_image\u001b[0;34m(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[0;31m# we always have to interpolate the mask to account for\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 498\u001b[0m \u001b[0;31m# non-affine transformations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 499\u001b[0;31m \u001b[0mout_alpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_resample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 500\u001b[0m \u001b[0;31m# done with the mask now, delete from namespace to be sure!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_resample\u001b[0;34m(image_obj, data, out_shape, transform, resample, alpha)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0mimage_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_filternorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m image_obj.get_filterrad())\n\u001b[0m\u001b[1;32m 203\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","metadata":{"id":"7maQ5JoavpfO","colab_type":"code","colab":{}},"source":["np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_regions_max_layer1.npy', region_layer1_no_binary)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_regions_max_layer2.npy', region_layer2_no_binary)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer1.npy', activation_layer1_no_binary)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer2.npy', activation_layer2_no_binary)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer1_normalized.npy', activation_layer1_no_binary_normalized)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer2_normalized.npy', activation_layer2_no_binary_normalized)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3ofGz3He4MJ_","colab_type":"text"},"source":["##### Binary model:"]},{"cell_type":"code","metadata":{"id":"o3rnEV5s4WlQ","colab_type":"code","colab":{}},"source":["activations_binary = collections.defaultdict(list)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Fu8-ujxQ4lNl","colab_type":"code","colab":{}},"source":["def save_activation_binary(name, mod, inp, out):\n"," activations_binary[name].append(out.cpu())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"iU5S2TdA4nyZ","colab_type":"code","colab":{}},"source":["for name, m in model_binary.named_modules():\n"," if type(m)==nn.Conv2d:\n"," # partial to assign the layer name to each hook\n"," m.register_forward_hook(partial(save_activation_binary, name))\n","\n","for batch in train_loader:\n"," out = model_binary(batch[0])\n"," break # for only one batch\n","\n","activations_binary = {name: torch.cat(outputs, 0) for name, outputs in activations_binary.items()}\n","\n","for k,v in activations_binary.items():\n"," print (k, v.size())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"m6yu7Vqx4QUy","colab_type":"code","colab":{}},"source":["stride = 2\n","padding = 1\n","filter_size = 3\n","len_img_w = 28\n","len_img_h = 28\n","loader = train_loader\n","\n","region_final, activation_final, activation_final_normalized = get_all_regions_max(loader, activations_binary, stride, padding, filter_size, len_img_h, len_img_w)\n","\n","region_layer1_binary = region_final['layer1']\n","region_layer2_binary = region_final['layer2']\n","activation_layer1_binary = activation_final['layer1']\n","activation_layer2_binary = activation_final['layer2']\n","activation_layer1_binary_normalized = activation_final_normalized['layer1']\n","activation_layer2_binary_normalized = activation_final_normalized['layer2']\n","\n","print(region_layer1_binary.shape)\n","print(region_layer2_binary.shape)\n","print(activation_layer1_binary.shape)\n","print(activation_layer2_binary.shape)\n","print(activation_layer1_binary_normalized.shape)\n","print(activation_layer2_binary_normalized.shape)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Wk4vOhOM4NbY","colab_type":"code","colab":{}},"source":["np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_regions_max_layer1.npy', region_layer1_binary)\n","np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_regions_max_layer2.npy', region_layer2_binary)\n","np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer1.npy', activation_layer1_binary)\n","np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer2.npy', activation_layer2_binary)\n","np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer1_normalized.npy', activation_layer1_binary_normalized)\n","np.save('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer2_normalized.npy', activation_layer2_binary_normalized)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ks9dWFWMWofi","colab_type":"text"},"source":["##### No binary model without bias:"]},{"cell_type":"code","metadata":{"id":"3KUqNNGBWuFw","colab_type":"code","colab":{}},"source":["activations_no_binary_without_bias = collections.defaultdict(list)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"onfbqtOtW4QE","colab_type":"code","colab":{}},"source":["def save_activation_no_binary_without_bias(name, mod, inp, out):\n"," activations_no_binary_without_bias[name].append(out.cpu())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UQaH3O97XFKQ","colab_type":"code","colab":{}},"source":["for name, m in model_no_binary_wt_bias.named_modules():\n"," if type(m)==nn.Conv2d:\n"," # partial to assign the layer name to each hook\n"," m.register_forward_hook(partial(save_activation_no_binary_without_bias, name))\n","\n","for batch in train_loader:\n"," out = model_no_binary_wt_bias(batch[0])\n"," break # for only one batch\n","\n","activations_no_binary_without_bias = {name: torch.cat(outputs, 0) for name, outputs in activations_no_binary_without_bias.items()}\n","\n","for k,v in activations_no_binary_without_bias.items():\n"," print (k, v.size())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"CSuIypahXXJd","colab_type":"code","colab":{}},"source":["stride = 2\n","padding = 1\n","filter_size = 3\n","len_img_w = 28\n","len_img_h = 28\n","loader = train_loader\n","\n","region_final, activation_final, activation_final_normalized = get_all_regions_max(loader, activations_no_binary_without_bias, stride, padding, filter_size, len_img_h, len_img_w)\n","\n","region_layer1_no_binary_without_bias = region_final['layer1']\n","region_layer2_no_binary_without_bias = region_final['layer2']\n","activation_layer1_no_binary_without_bias = activation_final['layer1']\n","activation_layer2_no_binary_without_bias = activation_final['layer2']\n","activation_layer1_no_binary_normalized_without_bias = activation_final_normalized['layer1']\n","activation_layer2_no_binary_normalized_without_bias = activation_final_normalized['layer2']\n","\n","print(region_layer1_no_binary_without_bias.shape)\n","print(region_layer2_no_binary_without_bias.shape)\n","print(activation_layer1_no_binary_without_bias.shape)\n","print(activation_layer2_no_binary_without_bias.shape)\n","print(activation_layer1_no_binary_normalized_without_bias.shape)\n","print(activation_layer2_no_binary_normalized_without_bias.shape)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1-b-BJs0Xm1n","colab_type":"code","colab":{}},"source":["np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer1.npy', region_layer1_no_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer2.npy', region_layer2_no_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1.npy', activation_layer1_no_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2.npy', activation_layer2_no_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1_normalized.npy', activation_layer1_no_binary_normalized_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2_normalized.npy', activation_layer2_no_binary_normalized_without_bias)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OYPTjl99Xxnj","colab_type":"text"},"source":["##### Binary model without bias:"]},{"cell_type":"code","metadata":{"id":"hTpppx4dX1Fa","colab_type":"code","colab":{}},"source":["activations_binary_without_bias = collections.defaultdict(list)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zAMvT3iIX23w","colab_type":"code","colab":{}},"source":["def save_activation_binary_without_bias(name, mod, inp, out):\n"," activations_binary_without_bias[name].append(out.cpu())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8Fw__7XFX28h","colab_type":"code","colab":{}},"source":["for name, m in model_binary_wt_bias.named_modules():\n"," if type(m)==nn.Conv2d:\n"," # partial to assign the layer name to each hook\n"," m.register_forward_hook(partial(save_activation_binary_without_bias, name))\n","\n","for batch in train_loader:\n"," out = model_binary_wt_bias(batch[0])\n"," break # for only one batch\n","\n","activations_binary_without_bias = {name: torch.cat(outputs, 0) for name, outputs in activations_binary_without_bias.items()}\n","\n","for k,v in activations_binary_without_bias.items():\n"," print (k, v.size())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6vkB_0jTX2_X","colab_type":"code","colab":{}},"source":["stride = 2\n","padding = 1\n","filter_size = 3\n","len_img_w = 28\n","len_img_h = 28\n","loader = train_loader\n","\n","region_final, activation_final, activation_final_normalized = get_all_regions_max(loader, activations_binary_without_bias, stride, padding, filter_size, len_img_h, len_img_w)\n","\n","region_layer1_binary_without_bias = region_final['layer1']\n","region_layer2_binary_without_bias = region_final['layer2']\n","activation_layer1_binary_without_bias = activation_final['layer1']\n","activation_layer2_binary_without_bias = activation_final['layer2']\n","activation_layer1_binary_normalized_without_bias = activation_final_normalized['layer1']\n","activation_layer2_binary_normalized_without_bias = activation_final_normalized['layer2']\n","\n","print(region_layer1_binary_without_bias.shape)\n","print(region_layer2_binary_without_bias.shape)\n","print(activation_layer1_binary_without_bias.shape)\n","print(activation_layer2_binary_without_bias.shape)\n","print(activation_layer1_binary_normalized_without_bias.shape)\n","print(activation_layer2_binary_normalized_without_bias.shape)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cDAVPpvLX26x","colab_type":"code","colab":{}},"source":["np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer1.npy', region_layer1_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer2.npy', region_layer2_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1.npy', activation_layer1_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2.npy', activation_layer2_binary_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1_normalized.npy', activation_layer1_binary_normalized_without_bias)\n","np.save('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2_normalized.npy', activation_layer2_binary_normalized_without_bias)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ipyNhwCxyMDr","colab_type":"text"},"source":["### Visualize max regions:"]},{"cell_type":"markdown","metadata":{"id":"zJsdFgDOySdN","colab_type":"text"},"source":["#### Load regions and activations:"]},{"cell_type":"code","metadata":{"id":"qYSQEIpcyRen","colab_type":"code","colab":{}},"source":["region_layer1_no_binary = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_regions_max_layer1.npy', allow_pickle=True)\n","region_layer2_no_binary = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_regions_max_layer2.npy', allow_pickle=True)\n","activation_layer1_no_binary = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer1.npy', allow_pickle=True)\n","activation_layer2_no_binary = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer2.npy', allow_pickle=True)\n","activation_layer1_no_binary_normalized = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer1_normalized.npy', allow_pickle=True)\n","activation_layer2_no_binary_normalized = np.load('results/MNIST_results/MNIST_regions/No_binary_MNIST_activations_max_layer2_normalized.npy', allow_pickle=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Inn2qU5E39-s","colab_type":"code","colab":{}},"source":["region_layer1_binary = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_regions_max_layer1.npy', allow_pickle=True)\n","region_layer2_binary = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_regions_max_layer2.npy', allow_pickle=True)\n","activation_layer1_binary = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer1.npy', allow_pickle=True)\n","activation_layer2_binary = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer2.npy', allow_pickle=True)\n","activation_layer1_binary_normalized = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer1_normalized.npy', allow_pickle=True)\n","activation_layer2_binary_normalized = np.load('results/MNIST_results/MNIST_regions/Binary_MNIST_activations_max_layer2_normalized.npy', allow_pickle=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"52jIwyOVYSAh","colab_type":"code","colab":{}},"source":["region_layer1_no_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer1.npy', allow_pickle=True)\n","region_layer2_no_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer2.npy', allow_pickle=True)\n","activation_layer1_no_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1.npy', allow_pickle=True)\n","activation_layer2_no_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2.npy', allow_pickle=True)\n","activation_layer1_no_binary_normalized_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1_normalized.npy', allow_pickle=True)\n","activation_layer2_no_binary_normalized_without_bias = np.load('results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2_normalized.npy', allow_pickle=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"R6ZkRCNBYSVa","colab_type":"code","colab":{}},"source":["region_layer1_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer1.npy', allow_pickle=True)\n","region_layer2_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer2.npy', allow_pickle=True)\n","activation_layer1_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1.npy', allow_pickle=True)\n","activation_layer2_binary_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2.npy', allow_pickle=True)\n","activation_layer1_binary_normalized_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1_normalized.npy', allow_pickle=True)\n","activation_layer2_binary_normalized_without_bias = np.load('results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2_normalized.npy', allow_pickle=True)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f03V578Tyv1E","colab_type":"text"},"source":["#### Viz:"]},{"cell_type":"code","metadata":{"id":"zY2oBF5gArgV","colab_type":"code","colab":{}},"source":["viz_filters(model_no_binary)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wV3Y7ZUT5n9Y","colab_type":"text"},"source":["##### No binary model layer1:"]},{"cell_type":"code","metadata":{"id":"HFAQzzf-1LHK","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer1 = [0,1,2,3,4,5,6,7,8,9]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 1\n","nrow=14\n","\n","# regions and activation of interest\n","regions = region_layer1_no_binary\n","activations = activation_layer1_no_binary\n","activations_normalized = activation_layer1_no_binary_normalized\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer1, nrow=nrow)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NcCGRhD2txj2","colab_type":"code","colab":{}},"source":["activation_values"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Hnb8cGd3EXD0","colab_type":"code","colab":{}},"source":["activation_values_normalized"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"NiTAZSKo5sYG","colab_type":"text"},"source":["##### No binary model layer2:"]},{"cell_type":"code","metadata":{"id":"eUkV73iknQ1U","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer2 = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 10\n","\n","# regions and activation of interest\n","regions = region_layer2_no_binary\n","activations = activation_layer2_no_binary\n","activations_normalized = activation_layer2_no_binary_normalized\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer2)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nMq_m3xy5ulb","colab_type":"text"},"source":["##### Binary model layer1:"]},{"cell_type":"code","metadata":{"id":"nAGXpIHV1W7G","colab_type":"code","colab":{}},"source":["viz_filters(model_binary)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XJygUtZ5vPhB","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer1 = [0,1,2,3,4,5,6,7,8,9]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 10\n","\n","# regions and activation of interest\n","regions = region_layer1_binary\n","activations = activation_layer1_binary\n","activations_normalized = activation_layer1_binary_normalized\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer1)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PECJ9p4q5wbt","colab_type":"text"},"source":["##### Binary model layer2:"]},{"cell_type":"code","metadata":{"id":"dhAgS9Qv5yNP","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer2 = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 10\n","\n","# regions and activation of interest\n","regions = region_layer2_binary\n","activations = activation_layer2_binary\n","activations_normalized = activation_layer2_binary_normalized\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer2)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KQPBG4iXZP4s","colab_type":"text"},"source":["##### No binary model without bias layer1:"]},{"cell_type":"code","metadata":{"id":"iYck4UZcZSID","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer1 = [0,1,2,3,4,5,6,7,8,9]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 10\n","nrow=14\n","\n","# regions and activation of interest\n","regions = region_layer1_no_binary_without_bias\n","activations = activation_layer1_no_binary_without_bias\n","activations_normalized = activation_layer1_no_binary_normalized_without_bias\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer1, nrow=nrow)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OGvbyZm2ZSOk","colab_type":"text"},"source":["##### No binary model without bias layer2:"]},{"cell_type":"code","metadata":{"id":"Z41hkhkBZS8n","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer2 = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 1\n","nrow=14\n","\n","# regions and activation of interest\n","regions = region_layer2_no_binary_without_bias\n","activations = activation_layer2_no_binary_without_bias\n","activations_normalized = activation_layer2_no_binary_normalized_without_bias\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer2, nrow=nrow)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7XW5dzurZbeS","colab_type":"text"},"source":["##### Binary model without bias layer1:"]},{"cell_type":"code","metadata":{"id":"nsUeDv2gZbj9","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer1 = [0,1,2,3,4,5,6,7,8,9]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 1\n","nrow=14\n","\n","# regions and activation of interest\n","regions = region_layer1_binary_without_bias\n","activations = activation_layer1_binary_without_bias\n","activations_normalized = activation_layer1_binary_normalized_without_bias\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer1, nrow=nrow)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vfWIgzyWZb9H","colab_type":"text"},"source":["##### Binary model without bias layer2:"]},{"cell_type":"code","metadata":{"id":"a0UMeeKwZcJ1","colab_type":"code","colab":{}},"source":["# parameters\n","list_filter_interest_layer2 = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n","best = True\n","worst = False\n","viz_mean_img = True\n","viz_grid = True\n","percentage = 1\n","nrow=14\n","\n","# regions and activation of interest\n","regions = region_layer2_binary_without_bias\n","activations = activation_layer2_binary_without_bias\n","activations_normalized = activation_layer2_binary_normalized_without_bias\n","\n","selected_regions, activation_values, activation_values_normalized = get_regions_interest(regions, activations, activations_normalized, best, worst, viz_mean_img, viz_grid, percentage, list_filter_interest_layer2, nrow=nrow)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CZylTnk3Ofb3","colab_type":"text"},"source":["# Draft"]},{"cell_type":"markdown","metadata":{"id":"oCmF35kglqnz","colab_type":"text"},"source":["## Test region's score:"]},{"cell_type":"code","metadata":{"id":"-mcd_tTm6oYe","colab_type":"code","colab":{}},"source":["from numpy import linalg as LA"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9GuHiS03FJE3","colab_type":"code","colab":{}},"source":["def get_activation(name, activation):\n"," def hook(model, input, output):\n"," activation[name] = output.detach()\n"," return hook\n","\n","def test_score_region(model, filter_choice):\n","\n"," dataiter = iter(train_loader)\n"," images, _ = dataiter.next()\n","\n"," index = np.random.randint(0,1000)\n"," image = images[index]\n"," print('image test number: {} with shape: {}'.format(index, image.shape))\n"," plt.imshow(image[0], cmap='gray')\n"," plt.show()\n","\n"," activation = {}\n","\n"," for name, m in model.named_modules():\n"," if type(m) == nn.Conv2d:\n"," m.register_forward_hook(get_activation(name, activation))\n","\n"," out = model(image.unsqueeze(0)) \n","\n"," activation_layer1 = activation['layer1'][0]\n"," activation_layer2 = activation['layer2'][0]\n","\n"," print('prediction:{}'.format(out.data.numpy().argmax())) \n","\n"," filter = filter_choice\n"," act_max_layer1 = activation_layer1[filter].max()\n"," print('value activation max for filte {} :{}'.format(filter, act_max_layer1))\n","\n"," ind_x = int((np.where(activation_layer1[filter] == act_max_layer1)[0])[0]) \n"," ind_y = int((np.where(activation_layer1[filter] == act_max_layer1)[1])[0])\n","\n"," print('index of max value: x: {}, y: {}'.format(ind_x, ind_y))\n","\n"," name = 'layer1'\n"," stride = 2\n"," padding=1\n"," filter_size=3\n"," len_img_h=28\n"," len_img_w=28\n"," im = image[0]\n","\n"," region, begin_col, end_col, begin_raw, end_raw = get_region_layer1(im, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w, return_all=True)\n","\n"," print('region extracted: {}'.format(region))\n"," plt.imshow(region, cmap='gray')\n"," plt.show()\n","\n"," random_im = np.uint8(np.random.uniform(0, 255, (28, 28)))/255\n"," print('random image generated:')\n"," plt.imshow(random_im, cmap='gray')\n"," plt.show()\n","\n"," random_im[begin_col:end_col, begin_raw:end_raw] = region\n"," plt.imshow(random_im, cmap='gray')\n"," print('random image generated with region that maximize filter activation:')\n"," plt.show()\n","\n"," activation_random_im = {}\n","\n"," for name, m in model_no_binary.named_modules():\n"," if type(m) == nn.Conv2d:\n"," m.register_forward_hook(get_activation(name, activation_random_im))\n","\n"," random_image = (torch.tensor(random_im.reshape((1,1,28,28))))\n"," out = model_no_binary(random_image.float())\n"," activation_layer1_random = activation_random_im['layer1'][0]\n","\n"," act_max_random = activation_layer1_random[filter].max()\n","\n"," ind_x_random = int((np.where(activation_layer1_random[filter] == act_max_random)[0])[0]) \n"," ind_y_random = int((np.where(activation_layer1_random[filter] == act_max_random)[1])[0])\n","\n"," activation_value_index_random = activation_layer1_random[filter][ind_x][ind_y]\n"," activation_value_index = activation_layer1[filter][ind_x][ind_y]\n","\n"," print('activation max for image: {} with index: x:{}, y:{}'.format(act_max_layer1, ind_x, ind_y))\n"," print('activation max for random image with region: {} with index: x:{}, y:{}'.format(act_max_random, ind_x_random, ind_y_random)) \n","\n"," print('activation value for ind_x: {} and ind_y: {} = {}'.format(ind_x, ind_y, activation_value_index))\n"," print('random activation value for ind_x: {} and ind_y: {} = {}'.format(ind_x_random, ind_y_random, activation_value_index_random))\n"," \n"," region_new_random, begin_col, end_col, begin_raw, end_raw = get_region_layer1(random_im, ind_x_random, ind_y_random, name, stride, padding, filter_size, len_img_h, len_img_w, return_all=True)\n","\n"," plt.imshow(region_new_random, cmap='gray')\n"," print('region_new_random')\n"," plt.show()\n","\n"," return region"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"IiVsMemoGBHD","colab_type":"code","outputId":"7890bc59-10a1-4006-9707-8bb1aa67e7ef","executionInfo":{"status":"ok","timestamp":1588950944403,"user_tz":-120,"elapsed":1974,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["model = model_no_binary\n","filter_choice = 9\n","\n","region = test_score_region(model, filter_choice)"],"execution_count":9,"outputs":[{"output_type":"stream","text":["image test number: 854 with shape: torch.Size([1, 28, 28])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAMu0lEQVR4nO3db6hc9Z3H8c9ntVFIA8k12cs1CWks+qD0gd1cQ3G1ZKkNrgixCNIgJQuhN2iztBBFUaEiPhCxLX1UuEVJumQthUaSB2VtNgTcIhRvQjaJf9q4JSGJMX8IoVaUVvPdB/coV73zm5s5Z+aM+b5fcJmZ850z58shn5x/c+bniBCAy98/tN0AgMEg7EAShB1IgrADSRB2IIkrB7kw25z6B/osIjzb9Fpbdtu32/6j7TdtP1znswD0l3u9zm77Ckl/kvQtSSckvSJpfUS8VpiHLTvQZ/3Ysq+W9GZE/Dki/ibpV5LW1fg8AH1UJ+xLJR2f8fpENe0TbE/YnrI9VWNZAGrq+wm6iJiUNCmxGw+0qc6W/aSk5TNeL6umARhCdcL+iqTrba+0PU/SdyTtaqYtAE3reTc+Ij6wvVnSi5KukPRcRLzaWGcAGtXzpbeeFsYxO9B3fflSDYDPD8IOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEj2Pzy5Jto9KekfSh5I+iIjxJpoC0LxaYa/8S0Sca+BzAPQRu/FAEnXDHpJ+Z3uf7YnZ3mB7wvaU7amaywJQgyOi95ntpRFx0vY/Stot6d8j4qXC+3tfGIA5iQjPNr3Wlj0iTlaPZyS9IGl1nc8D0D89h932fNsLPnouaa2kw001BqBZdc7Gj0p6wfZHn/OfEfFfjXQFoHG1jtkveWEcswN915djdgCfH4QdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiTRxA9OYogtWbKkWL/22muL9U2bNhXrCxYsKNbXrFnTsbZs2bLivGfPni3W165dW6wfOHCgWM+GLTuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJMF19iFw9dVXF+sPPfRQsX733Xd3rK1YsaI477x582rV6+j2y8bXXHNNsb5u3bpinevsn8SWHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeS4Dr7HC1durRj7bbbbivOu2rVqmL9zjvvLNa7XSsvqYbU7ui9994r1o8cOVKsv/jii8X6+fPni/WSBx54oOd58Vldt+y2n7N9xvbhGdNGbO+2faR6XNTfNgHUNZfd+K2Sbv/UtIcl7YmI6yXtqV4DGGJdwx4RL0n69L7YOknbqufbJN3VcF8AGtbrMftoRJyqnr8tabTTG21PSJrocTkAGlL7BF1EhO2OdzRExKSkSUkqvQ9Af/V66e207TFJqh7PNNcSgH7oNey7JG2onm+QtLOZdgD0S9fdeNvPS1ojabHtE5J+JOkpSb+2vVHSMUn39LPJJoyOdjytIEl67LHHivV77723Y23hwoXFebvdt93NxYsXi/ULFy50rG3fvr0479atW4v1Nu8J37x5c2vLvhx1DXtErO9Q+mbDvQDoI74uCyRB2IEkCDuQBGEHkiDsQBJpbnG97777ivX777+/b8s+d+5csf7MM88U6/v37y/W9+zZc8k9IR+27EAShB1IgrADSRB2IAnCDiRB2IEkCDuQRJrr7Dt27CjWu/3k8sjISM+fvXfv3mI9q40bNxbr3YZsxqVhyw4kQdiBJAg7kARhB5Ig7EAShB1IgrADSaS5zn7w4MFadTRvzZo1xXrdn+DGJ7FlB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEk0lxnx+Vn586dbbfwudJ1y277OdtnbB+eMe1x2ydtH6j+7uhvmwDqmstu/FZJt88y/acRcWP199tm2wLQtK5hj4iXJJ0fQC8A+qjOCbrNtg9Wu/mLOr3J9oTtKdtTNZYFoKZew/5zSV+WdKOkU5J+3OmNETEZEeMRMd7jsgA0oKewR8TpiPgwIi5K+oWk1c22BaBpPYXd9tiMl9+WdLjTewEMB3e7Z9j285LWSFos6bSkH1Wvb5QUko5K2hQRp7ouzOYG5WSuuuqqjrXjx48X5z127Fixfuuttxbr77//frF+uYqIWQdB6PqlmohYP8vkZ2t3BGCg+LoskARhB5Ig7EAShB1IgrADSXCLK/rqwQcf7FhbvHhxcd4nnniiWM96aa1XbNmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAmus6Ovbr755o41e9Y7MT/28ssvN91OamzZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiCJrj8l3ejC+Cnpy878+fOL9UOHDnWsvfvuu8V5b7rppmKd+9ln1+mnpNmyA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAS3M+OWpYsWVKsr1ixomPt6aefLs7LdfRmdd2y215ue6/t12y/avsH1fQR27ttH6keF/W/XQC9mstu/AeStkTEVyR9XdL3bX9F0sOS9kTE9ZL2VK8BDKmuYY+IUxGxv3r+jqTXJS2VtE7Stupt2yTd1a8mAdR3Scfstr8k6WuS/iBpNCJOVaW3JY12mGdC0kTvLQJowpzPxtv+oqTfSPphRPxlZi2m76aZ9SaXiJiMiPGIGK/VKYBa5hR221/QdNC3R8SOavJp22NVfUzSmf60CKAJXXfjPf17v89Kej0ifjKjtEvSBklPVY87+9IhhtqWLVt6nvfw4cMNdoJu5nLM/s+SvivpkO0D1bRHNB3yX9veKOmYpHv60yKAJnQNe0T8XlKnX/P/ZrPtAOgXvi4LJEHYgSQIO5AEYQeSIOxAEtziilrGx3v/YuTevXsb7ATdsGUHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSS4zo6ibkMyL1y4sFh/6623eqqheWzZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJrrOjaGxsrFi/4YYbivUnn3yyyXZQA1t2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUhiLuOzL5f0S0mjkkLSZET8zPbjkr4n6Wz11kci4rf9ahTtePTRR2vNzxjsw2MuX6r5QNKWiNhve4GkfbZ3V7WfRsQz/WsPQFPmMj77KUmnqufv2H5d0tJ+NwagWZd0zG77S5K+JukP1aTNtg/afs72og7zTNiesj1Vq1MAtcw57La/KOk3kn4YEX+R9HNJX5Z0o6a3/D+ebb6ImIyI8YjofVAwALXNKey2v6DpoG+PiB2SFBGnI+LDiLgo6ReSVvevTQB1dQ27bUt6VtLrEfGTGdNn3g71bUmcdgWGmCOi/Ab7Fkn/I+mQpIvV5Eckrdf0LnxIOippU3Uyr/RZ5YVh4K68snyO9o033ijWR0ZGivXrrruuY+3ChQvFedGbiPBs0+dyNv73kmabmWvqwOcI36ADkiDsQBKEHUiCsANJEHYgCcIOJMFPSSe3atWqYn3lypXF+r59+4p1rqUPD7bsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5BE1/vZG12YfVbSsRmTFks6N7AGLs2w9jasfUn01qsme1sREUtmKww07J9ZuD01rL9NN6y9DWtfEr31alC9sRsPJEHYgSTaDvtky8svGdbehrUvid56NZDeWj1mBzA4bW/ZAQwIYQeSaCXstm+3/Ufbb9p+uI0eOrF91PYh2wfaHp+uGkPvjO3DM6aN2N5t+0j1OOsYey319rjtk9W6O2D7jpZ6W257r+3XbL9q+wfV9FbXXaGvgay3gR+z275C0p8kfUvSCUmvSFofEa8NtJEObB+VNB4RrX8Bw/Y3JP1V0i8j4qvVtKclnY+Ip6r/KBdFxEND0tvjkv7a9jDe1WhFYzOHGZd0l6R/U4vrrtDXPRrAemtjy75a0psR8eeI+JukX0la10IfQy8iXpJ0/lOT10naVj3fpul/LAPXobehEBGnImJ/9fwdSR8NM97quiv0NRBthH2ppOMzXp/QcI33HpJ+Z3uf7Ym2m5nF6Ixhtt6WNNpmM7PoOoz3IH1qmPGhWXe9DH9eFyfoPuuWiPgnSf8q6fvV7upQiuljsGG6djqnYbwHZZZhxj/W5rrrdfjzutoI+0lJy2e8XlZNGwoRcbJ6PCPpBQ3fUNSnPxpBt3o803I/HxumYbxnG2ZcQ7Du2hz+vI2wvyLpetsrbc+T9B1Ju1ro4zNsz69OnMj2fElrNXxDUe+StKF6vkHSzhZ7+YRhGca70zDjanndtT78eUQM/E/SHZo+I/9/kh5to4cOfV0n6X+rv1fb7k3S85rerfu7ps9tbJR0jaQ9ko5I+m9JI0PU239oemjvg5oO1lhLvd2i6V30g5IOVH93tL3uCn0NZL3xdVkgCU7QAUkQdiAJwg4kQdiBJAg7kARhB5Ig7EAS/w9SyOsEf+0A6wAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["prediction:7\n","value activation max for filte 9 :0.1137184277176857\n","index of max value: x: 12, y: 7\n","region extracted: tensor([[0.0000, 0.0431, 0.8431],\n"," [0.0000, 0.1451, 0.9922],\n"," [0.0000, 0.6235, 0.9922]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQ8AAAD8CAYAAABpXiE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAANxklEQVR4nO3df6yeZX3H8fdHCpgAkx9dpClVJFSGY0uABlEW0wxMsDF0iSyBPwSM5EwnmS6arFGjicky9A+XEYikASIsBsnUwNHUGBg4XBYYpSmUlmAL/9Daibas2Ohwdd/9cW7M4/H86vXc53mewvuVPHmu+76vc1/fXG0+vX/2pKqQpKP1pnEXIOnYZHhIamJ4SGpieEhqYnhIamJ4SGoyVHgkOT3Jg0l2d9+nzdPvN0m2d5/pYcaUNBkyzHMeSb4CHKyqm5NsAk6rqr+bo9/hqjp5iDolTZhhw+M5YH1V7U+yCvhhVZ03Rz/DQ3qdGTY8/ruqTu3aAV5+bXlWvyPAduAIcHNV3T/P/qaAqW7x4ubC3gDe9CYvVy3m/PPPH3cJE2/nzp0/r6o/bPnZRcMjyUPAmXNs+hxw92BYJHm5qn7vukeS1VW1L8k5wMPA5VX1/CLj+tz8Ak466aRxlzDxtm3bNu4SJt555533ZFWta/nZFYt1qKor5tuW5KdJVg2ctrw0zz72dd8vJPkhcCGwYHhImmzDHvtOA9d37euBB2Z3SHJakhO79krgMmDXkONKGrNhw+Nm4P1JdgNXdMskWZfkjq7P+cDWJE8BjzBzzcPwkI5xi562LKSqDgCXz7F+K3Bj1/4P4E+GGUfS5PGSvaQmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCa9hEeSK5M8l2RPkk1zbD8xyX3d9seTnN3HuJLGZ+jwSHIccBvwAeBdwLVJ3jWr20eBl6vqXOAfgS8PO66k8erjyOMSYE9VvVBVvwa+CWyc1WcjcHfX/hZweZL0MLakMekjPFYDLw4s7+3Wzdmnqo4Ah4Azehhb0pisGHcBg5JMAVPjrkPS4vo48tgHrBlYPqtbN2efJCuAtwAHZu+oqjZX1bqqWtdDXZKWUR/h8QSwNsk7kpwAXANMz+ozDVzfta8GHq6q6mFsSWMy9GlLVR1JchPwA+A44K6q2pnkS8DWqpoG7gT+Ocke4CAzASPpGNbLNY+q2gJsmbXuCwPt/wH+so+xJE0GnzCV1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1KSX8EhyZZLnkuxJsmmO7Tck+VmS7d3nxj7GlTQ+K4bdQZLjgNuA9wN7gSeSTFfVrlld76uqm4YdT9Jk6OPI4xJgT1W9UFW/Br4JbOxhv5Im2NBHHsBq4MWB5b3Au+fo96Ek7wN+DPxtVb04u0OSKWCqh5pe91atWjXuEibeO9/5znGX8Lo2qgum3wXOrqo/BR4E7p6rU1Vtrqp1VbVuRHVJatRHeOwD1gwsn9Wt+62qOlBVr3aLdwAX9zCupDHqIzyeANYmeUeSE4BrgOnBDkkGj7GvAp7tYVxJYzT0NY+qOpLkJuAHwHHAXVW1M8mXgK1VNQ38TZKrgCPAQeCGYceVNF6pqnHXMKckk1nYhDj33HPHXcLE271797hLmHhJnmy9xugTppKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6Smhgekpr0Eh5J7kryUpJn5tmeJLck2ZPk6SQX9TGupPHp68jj68CVC2z/ALC2+0wBX+tpXElj0kt4VNWjwMEFumwE7qkZjwGnJlnVx9iSxmNU1zxWAy8OLO/t1v2OJFNJtibZOqK6JDVaMe4CBlXVZmAzQJIaczmSFjCqI499wJqB5bO6dZKOUaMKj2nguu6uy6XAoaraP6KxJS2DXk5bktwLrAdWJtkLfBE4HqCqbge2ABuAPcAvgY/0Ma6k8eklPKrq2kW2F/CJPsaSNBl8wlRSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1KTXsIjyV1JXkryzDzb1yc5lGR79/lCH+NKGp9eftE18HXgVuCeBfr8qKo+2NN4ksaslyOPqnoUONjHviQdG/o68liK9yR5CvgJ8Jmq2jm7Q5IpYGqENR2zPv/5z4+7BL3BjSo8tgFvr6rDSTYA9wNrZ3eqqs3AZoAkNaLaJDUYyd2Wqnqlqg537S3A8UlWjmJsSctjJOGR5Mwk6dqXdOMeGMXYkpZHL6ctSe4F1gMrk+wFvggcD1BVtwNXAx9PcgT4FXBNVXlaIh3DegmPqrp2ke23MnMrV9LrhE+YSmpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIajJ0eCRZk+SRJLuS7EzyyTn6JMktSfYkeTrJRcOOK2m8+vhF10eAT1fVtiSnAE8mebCqdg30+QCwtvu8G/ha9y3pGDX0kUdV7a+qbV37F8CzwOpZ3TYC99SMx4BTk6wadmxJ49PrNY8kZwMXAo/P2rQaeHFgeS+/HzCSjiF9nLYAkORk4NvAp6rqlcZ9TAFTfdUkafn0Eh5JjmcmOL5RVd+Zo8s+YM3A8lndut9RVZuBzd0+q4/aJC2PPu62BLgTeLaqvjpPt2nguu6uy6XAoaraP+zYksanjyOPy4APAzuSbO/WfRZ4G0BV3Q5sATYAe4BfAh/pYVxJYzR0eFTVvwNZpE8Bnxh2LEmTwydMJTUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUZOjySrEnySJJdSXYm+eQcfdYnOZRke/f5wrDjShqvFT3s4wjw6araluQU4MkkD1bVrln9flRVH+xhPEkTYOgjj6raX1XbuvYvgGeB1cPuV9JkS1X1t7PkbOBR4IKqemVg/Xrg28Be4CfAZ6pq5xw/PwVMdYsXAM/0Vlw/VgI/H3cRA6xnYZNWD0xeTedV1SktP9hbeCQ5Gfg34O+r6juztv0B8H9VdTjJBuCfqmrtIvvbWlXreimuJ5NWk/UsbNLqgcmraZh6ernbkuR4Zo4svjE7OACq6pWqOty1twDHJ1nZx9iSxqOPuy0B7gSeraqvztPnzK4fSS7pxj0w7NiSxqePuy2XAR8GdiTZ3q37LPA2gKq6Hbga+HiSI8CvgGtq8fOlzT3U1rdJq8l6FjZp9cDk1dRcT68XTCW9cfiEqaQmhoekJhMTHklOT/Jgkt3d92nz9PvNwGPu08tQx5VJnkuyJ8mmObafmOS+bvvj3bMty2oJNd2Q5GcD83LjMtZyV5KXksz5DE5m3NLV+nSSi5arlqOoaWSvRyzxdY2RztGyvUJSVRPxAb4CbOram4Avz9Pv8DLWcBzwPHAOcALwFPCuWX3+Gri9a18D3LfM87KUmm4Abh3Rn9P7gIuAZ+bZvgH4PhDgUuDxCahpPfC9Ec3PKuCirn0K8OM5/rxGOkdLrOmo52hijjyAjcDdXftu4C/GUMMlwJ6qeqGqfg18s6tr0GCd3wIuf+029BhrGpmqehQ4uECXjcA9NeMx4NQkq8Zc08jU0l7XGOkcLbGmozZJ4fHWqtrftf8LeOs8/d6cZGuSx5L0HTCrgRcHlvfy+5P82z5VdQQ4BJzRcx1HWxPAh7pD4G8lWbOM9SxmqfWO2nuSPJXk+0n+eBQDdqe0FwKPz9o0tjlaoCY4yjnq4zmPJUvyEHDmHJs+N7hQVZVkvnvIb6+qfUnOAR5OsqOqnu+71mPMd4F7q+rVJH/FzJHRn4+5pkmyjZm/N6+9HnE/sODrEcPqXtf4NvCpGnjPa5wWqemo52ikRx5VdUVVXTDH5wHgp68dunXfL82zj33d9wvAD5lJ0b7sAwb/1T6rWzdnnyQrgLewvE/LLlpTVR2oqle7xTuAi5exnsUsZQ5Hqkb8esRir2swhjlajldIJum0ZRq4vmtfDzwwu0OS05Kc2LVXMvN06+z/N2QYTwBrk7wjyQnMXBCdfUdnsM6rgYeru+K0TBatadb58lXMnNOOyzRwXXdH4VLg0MDp6FiM8vWIbpwFX9dgxHO0lJqa5mgUV6CXeEX4DOBfgd3AQ8Dp3fp1wB1d+73ADmbuOOwAProMdWxg5mr088DnunVfAq7q2m8G/gXYA/wncM4I5maxmv4B2NnNyyPAHy1jLfcC+4H/ZeZc/aPAx4CPddsD3NbVugNYN4L5Waymmwbm5zHgvctYy58BBTwNbO8+G8Y5R0us6ajnyMfTJTWZpNMWSccQw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1KT/wfFq+jSc5xGtwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["random image generated:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["random image generated with region that maximize filter activation:\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["activation max for image: 0.1137184277176857 with index: x:12, y:7\n","activation max for random image with region: 0.1137184277176857 with index: x:12, y:7\n","activation value for ind_x: 12 and ind_y: 7 = 0.1137184277176857\n","random activation value for ind_x: 12 and ind_y: 7 = 0.1137184277176857\n","region_new_random\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"hulIl2sgr90T","colab_type":"text"},"source":["## Test: rectangular images"]},{"cell_type":"code","metadata":{"id":"rYYpHJc47aC3","colab_type":"code","colab":{}},"source":["dataiter = iter(train_loader)\n","images, _ = dataiter.next()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LWM6j_f5sq3d","colab_type":"code","colab":{}},"source":["model = model_no_binary\n","filter_choice = 1"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"MvEqkXiJr9ZX","colab_type":"code","colab":{}},"source":["def test_truc(filter_choice, model, images):\n"," # index = np.random.randint(0,1000)\n"," index=1\n"," image = images[index][0]\n"," # image[:][:] = 0\n"," # x=5\n"," # y=10\n"," # image[x,y] = 1\n","\n"," plt.imshow(image, cmap='gray')\n"," plt.show()\n"," print('new image shape: {}'.format(image.shape))\n","\n","\n"," activation = {}\n","\n"," for name, m in model.named_modules():\n"," if type(m) == nn.Conv2d:\n"," m.register_forward_hook(get_activation(name, activation))\n","\n"," out = model(image.unsqueeze(0).unsqueeze(0)) \n","\n"," activation_layer1 = activation['layer1'][0]\n"," activation_layer2 = activation['layer2'][0]\n","\n"," print('prediction:{}'.format(out.data.numpy().argmax())) \n","\n"," filter = filter_choice\n"," act_max_layer1 = activation_layer1[filter].max()\n"," print('value activation max for filte {} :{}'.format(filter, act_max_layer1))\n","\n"," ind_x = int((np.where(activation_layer1[filter] == act_max_layer1)[0])[0]) \n"," ind_y = int((np.where(activation_layer1[filter] == act_max_layer1)[1])[0])\n","\n"," print('index of max value: x: {}, y: {}'.format(ind_x, ind_y))\n","\n"," name = 'layer1'\n"," stride = 2\n"," padding=1\n"," filter_size=3\n"," len_img_h=28\n"," len_img_w=28\n"," im = image\n","\n"," region, begin_col, end_col, begin_raw, end_raw = get_region_layer1(im, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w, return_all=True)\n","\n"," print('region extracted: {}'.format(region))\n"," plt.imshow(region, cmap='gray')\n"," plt.show()\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NqsVtPad_NhH","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"e01b820e-1b97-4495-ebec-bc85b96a749d","executionInfo":{"status":"ok","timestamp":1588953216225,"user_tz":-120,"elapsed":3435,"user":{"displayName":"Julien Dejasmin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghf77cHAyDw7dPGLWoOwBBO2kQOdHO7YkOXBchE=s64","userId":"11938403868733315090"}}},"source":["for i in range(10):\n"," test_truc(i, model, images)"],"execution_count":13,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["new image shape: torch.Size([28, 28])\n","prediction:8\n","value activation max for filte 0 :0.7342777848243713\n","index of max value: x: 12, y: 9\n","region extracted: tensor([[0.9882, 0.9882, 0.8275],\n"," [0.9882, 0.9882, 0.4863],\n"," [0.0000, 0.0000, 0.0000]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["new image shape: torch.Size([28, 28])\n","prediction:8\n","value activation max for filte 1 :0.7993289232254028\n","index of max value: x: 11, y: 7\n","region extracted: tensor([[0.2353, 0.0000, 0.0000],\n"," [0.9569, 0.8471, 0.4980],\n"," [0.7765, 0.9922, 0.9882]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQ8AAAD8CAYAAABpXiE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAOB0lEQVR4nO3df6zddX3H8eertNQYO4vexTalgmTEzbFN4QaYLKaZmCAxdIkswT8UjOZOJ5kumgw1QUOyDP3DZU4iaZAIiyIZGLyaGoGBQ7LAqKRSCkEKycKtnQhIsdHp6t77434xx+v90X7O955zWp+P5OR8vt/v53w/bz40L74/aaoKSTpaa8ZdgKRjk+EhqYnhIamJ4SGpieEhqYnhIanJUOGR5BVJ7kjyePd90hL9fplkd/eZHWZMSZMhwzznkeTTwHNVdXWSK4CTqurvFul3qKpeNkSdkibMsOHxGLCtqg4k2Qx8u6peu0g/w0M6zgwbHs9X1cauHeDHLy4v6HcY2A0cBq6uqtuW2N8MMAOwZs2as1760pc213a8O3To0LhL0PHhmar63ZYfrl2pQ5I7gU2LbPr44EJVVZKlkuiUqtqf5DTgriR7quqJhZ2qagewA2DDhg31+te/fsV/gN9W995777hL0PHhv1p/uGJ4VNX5S21L8sMkmwdOW55eYh/7u+8nk3wbeAPwG+Eh6dgx7K3aWeDSrn0p8LWFHZKclGR9154CzgMeGXJcSWM2bHhcDbwlyePA+d0ySaaTXNf1+QNgV5LvAXczf83D8JCOcSuetiynqp4F3rzI+l3Ae7v2fwB/NMw4kiaPT5hKamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhq0kt4JLkgyWNJ9iW5YpHt65Pc3G2/P8mpfYwraXyGDo8kJwDXAG8FXge8I8nrFnR7D/Djqvo94B+BTw07rqTx6uPI42xgX1U9WVW/AL4CbF/QZztwQ9e+BXhzkvQwtqQx6SM8tgBPDSzPdesW7VNVh4GDwCt7GFvSmKwddwGDkswAMwDr168fczWSltPHkcd+YOvA8sndukX7JFkLvBx4duGOqmpHVU1X1fS6det6KE3SaukjPB4ATk/ymiQnApcAswv6zAKXdu2LgbuqqnoYW9KYDH3aUlWHk1wOfAs4Abi+qvYmuQrYVVWzwBeAf0myD3iO+YCRdAzr5ZpHVe0Edi5Yd+VA+3+Av+xjLEmTwSdMJTUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDXpJTySXJDksST7klyxyPbLkvwoye7u894+xpU0PmuH3UGSE4BrgLcAc8ADSWar6pEFXW+uqsuHHU/SZOjjyONsYF9VPVlVvwC+AmzvYb+SJtjQRx7AFuCpgeU54JxF+r09yZuA7wN/W1VPLeyQZAaYAdi6dSu33357D+Udn+bm5sZdwsT78pe/PO4SJt4nP/nJ5t+O6oLp14FTq+qPgTuAGxbrVFU7qmq6qqanpqZGVJqkFn2Ex35g68Dyyd26X6mqZ6vq593idcBZPYwraYz6CI8HgNOTvCbJicAlwOxghySbBxYvAh7tYVxJYzT0NY+qOpzkcuBbwAnA9VW1N8lVwK6qmgX+JslFwGHgOeCyYceVNF59XDClqnYCOxesu3Kg/VHgo32MJWky+ISppCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJr2ER5Lrkzyd5OEltifJZ5PsS/JQkjP7GFfS+PR15PFF4IJltr8VOL37zACf72lcSWPSS3hU1T3Ac8t02Q7cWPPuAzYm2dzH2JLGY1TXPLYATw0sz3Xrfk2SmSS7kux65plnRlSapBYTdcG0qnZU1XRVTU9NTY27HEnLGFV47Ae2Diyf3K2TdIwaVXjMAu/q7rqcCxysqgMjGlvSKljbx06S3ARsA6aSzAGfANYBVNW1wE7gQmAf8FPg3X2MK2l8egmPqnrHCtsL+EAfY0maDBN1wVTSscPwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUpJfwSHJ9kqeTPLzE9m1JDibZ3X2u7GNcSePTy190DXwR+Bxw4zJ9vlNVb+tpPElj1suRR1XdAzzXx74kHRtSVf3sKDkV+EZVnbHItm3ArcAc8APgI1W1d5F+M8AMwKZNm8667bbbeqnteHTOOeeMu4SJ19ef7ePZmjVrvltV002/7buYJTwInFJVfwL8M7BoKlTVjqqarqrpjRs3jqg0SS1GEh5V9UJVHeraO4F1SaZGMbak1TGS8EiyKUm69tnduM+OYmxJq6OXuy1JbgK2AVNJ5oBPAOsAqupa4GLg/UkOAz8DLilPSKVjWi/hUVXvWGH755i/lSvpOOETppKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoMHR5Jtia5O8kjSfYm+eAifZLks0n2JXkoyZnDjitpvPr4i64PAx+uqgeTbAC+m+SOqnpkoM9bgdO7zznA57tvSceooY88qupAVT3YtX8CPApsWdBtO3BjzbsP2Jhk87BjSxqfXq95JDkVeANw/4JNW4CnBpbn+M2AkXQM6S08krwMuBX4UFW90LiPmSS7kux6/vnn+ypN0iroJTySrGM+OL5UVV9dpMt+YOvA8sndul9TVTuqarqqpjdu3NhHaZJWSR93WwJ8AXi0qj6zRLdZ4F3dXZdzgYNVdWDYsSWNTx93W84D3gnsSbK7W/cx4NUAVXUtsBO4ENgH/BR4dw/jShqjocOjqu4FskKfAj4w7FiSJodPmEpqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqMnR4JNma5O4kjyTZm+SDi/TZluRgkt3d58phx5U0Xmt72Mdh4MNV9WCSDcB3k9xRVY8s6PedqnpbD+NJmgBDH3lU1YGqerBr/wR4FNgy7H4lTbZUVX87S04F7gHOqKoXBtZvA24F5oAfAB+pqr2L/H4GmOkWzwAe7q24fkwBz4y7iAHWs7xJqwcmr6bXVtWGlh/2Fh5JXgb8O/D3VfXVBdt+B/i/qjqU5ELgn6rq9BX2t6uqpnsprieTVpP1LG/S6oHJq2mYenq525JkHfNHFl9aGBwAVfVCVR3q2juBdUmm+hhb0nj0cbclwBeAR6vqM0v02dT1I8nZ3bjPDju2pPHp427LecA7gT1JdnfrPga8GqCqrgUuBt6f5DDwM+CSWvl8aUcPtfVt0mqynuVNWj0weTU119PrBVNJvz18wlRSE8NDUpOJCY8kr0hyR5LHu++Tluj3y4HH3GdXoY4LkjyWZF+SKxbZvj7Jzd32+7tnW1bVEdR0WZIfDczLe1exluuTPJ1k0WdwMu+zXa0PJTlztWo5ippG9nrEEb6uMdI5WrVXSKpqIj7Ap4EruvYVwKeW6HdoFWs4AXgCOA04Efge8LoFff4auLZrXwLcvMrzciQ1XQZ8bkT/nt4EnAk8vMT2C4FvAgHOBe6fgJq2Ad8Y0fxsBs7s2huA7y/y72ukc3SENR31HE3MkQewHbiha98A/MUYajgb2FdVT1bVL4CvdHUNGqzzFuDNL96GHmNNI1NV9wDPLdNlO3BjzbsP2Jhk85hrGpk6stc1RjpHR1jTUZuk8HhVVR3o2v8NvGqJfi9JsivJfUn6DpgtwFMDy3P85iT/qk9VHQYOAq/suY6jrQng7d0h8C1Jtq5iPSs50npH7U+TfC/JN5P84SgG7E5p3wDcv2DT2OZomZrgKOeoj+c8jliSO4FNi2z6+OBCVVWSpe4hn1JV+5OcBtyVZE9VPdF3rceYrwM3VdXPk/wV80dGfz7mmibJg8z/uXnx9YjbgGVfjxhW97rGrcCHauA9r3FaoaajnqORHnlU1flVdcYin68BP3zx0K37fnqJfezvvp8Evs18ivZlPzD4X+2Tu3WL9kmyFng5q/u07Io1VdWzVfXzbvE64KxVrGclRzKHI1Ujfj1ipdc1GMMcrcYrJJN02jILXNq1LwW+trBDkpOSrO/aU8w/3brw/xsyjAeA05O8JsmJzF8QXXhHZ7DOi4G7qrvitEpWrGnB+fJFzJ/Tjsss8K7ujsK5wMGB09GxGOXrEd04y76uwYjn6EhqapqjUVyBPsIrwq8E/g14HLgTeEW3fhq4rmu/EdjD/B2HPcB7VqGOC5m/Gv0E8PFu3VXARV37JcC/AvuA/wROG8HcrFTTPwB7u3m5G/j9VazlJuAA8L/Mn6u/B3gf8L5ue4Brulr3ANMjmJ+Varp8YH7uA964irX8GVDAQ8Du7nPhOOfoCGs66jny8XRJTSbptEXSMcTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1OT/AavkE4p8jlIEAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["new image shape: torch.Size([28, 28])\n","prediction:8\n","value activation max for filte 2 :0.6628730297088623\n","index of max value: x: 8, y: 7\n","region extracted: tensor([[0.0000, 0.0000, 0.0588],\n"," [0.0549, 0.1255, 0.8275],\n"," [0.9255, 0.9294, 0.9882]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["new image shape: torch.Size([28, 28])\n","prediction:8\n","value activation max for filte 3 :-0.12109803408384323\n","index of max value: x: 4, y: 8\n","region extracted: tensor([[0.9059, 0.9137, 0.9882],\n"," [0.0000, 0.0275, 0.3294],\n"," [0.0000, 0.0000, 0.0000]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQ8AAAD8CAYAAABpXiE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAANxklEQVR4nO3df8ydZX3H8fcHCjUGJthOaUoVyRo39yMKDYIuhgxNoDHtElmCfygYyDPdyHTRZKgJJiTL1D9cZjSSBoiwGCCAgcelxtQBw2SB0ZJCaQlSSJa2dqLFFYmuru67P54bcnx4fvU693POefD9Sk7Odd/3de7rywV8ev+EVBWSdKJOGncBklYmw0NSE8NDUhPDQ1ITw0NSE8NDUpOhwiPJG5PsSPJM933mPP1+nWR395keZkxJkyHDPOeR5MvAC1X1xSTXAWdW1d/N0e+lqjptiDolTZhhw+Np4OKqOpxkHfBgVb19jn6Gh/QaM2x4/HdVndG1A/zs5eVZ/Y4Du4HjwBer6t559jcFTAG8/vWvP3/jxo3NtUmrVq0adwkTb9euXT+tqt9t+e2is5vk+8BZc2z6/OBCVVWS+ZLorVV1KMm5wP1J9lTVs7M7VdU2YBvAO9/5ztqxY8eifwG/rU46yWvdi1mzZs24S5h4Sf6z9beLhkdVvX+BgX+cZN3Aacvz8+zjUPf9XJIHgXcBrwoPSSvHsH98TQNXdu0rgftmd0hyZpLVXXst8F5g35DjShqzYcPji8AHkjwDvL9bJsmmJDd1ff4A2JnkceABZq55GB7SCjfUFaWqOgJcMsf6ncA1XfvfgT8eZhxJk8erbpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6Smhgekpr0Eh5JLk3ydJL9Sa6bY/vqJHd22x9Jck4f40oan6HDI8nJwNeBy4B3AB9O8o5Z3a4GflZVvwf8I/ClYceVNF59HHlcAOyvqueq6lfAHcDWWX22Ard27buBS5Kkh7EljUkf4bEeODCwfLBbN2efqjoOHAXW9DC2pDGZqAumSaaS7Eyy88iRI+MuR9IC+giPQ8CGgeWzu3Vz9kmyCngD8Kp0qKptVbWpqjatWeOBiTTJ+giPR4GNSd6W5FTgCmB6Vp9p4MqufTlwf1VVD2NLGpNVw+6gqo4nuRb4HnAycEtV7U1yA7CzqqaBm4F/TrIfeIGZgJG0gg0dHgBVtR3YPmvd9QPt/wH+oo+xJE2GibpgKmnlMDwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ16SU8klya5Okk+5NcN8f2q5L8JMnu7nNNH+NKGp9Vw+4gycnA14EPAAeBR5NMV9W+WV3vrKprhx1P0mTo48jjAmB/VT1XVb8C7gC29rBfSRNs6CMPYD1wYGD5IPDuOfp9KMn7gB8Cf1tVB2Z3SDIFTL28/KY3vamH8l6bVq9ePe4SJt6WLVvGXcJr2qgumH4HOKeq/gTYAdw6V6eq2lZVm6pq04jqktSoj/A4BGwYWD67W/eKqjpSVce6xZuA83sYV9IY9REejwIbk7wtyanAFcD0YIck6wYWtwBP9TCupDEa+ppHVR1Pci3wPeBk4Jaq2pvkBmBnVU0Df5NkC3AceAG4athxJY1XHxdMqartwPZZ664faH8W+GwfY0maDD5hKqmJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqUkv4ZHkliTPJ3lynu1J8tUk+5M8keS8PsaVND59HXl8E7h0ge2XARu7zxTwjZ7GlTQmvYRHVT0EvLBAl63AbTXjYeCMJOv6GFvSeIzqmsd64MDA8sFu3W9IMpVkZ5KdI6pLUqNV4y5gUFVtA7YBJKkxlyNpAaM68jgEbBhYPrtbJ2mFGlV4TAMf7e66XAgcrarDIxpb0jLo5bQlye3AxcDaJAeBLwCnAFTVjcB2YDOwH/gF8LE+xpU0Pr2ER1V9eJHtBfx1H2NJmgw+YSqpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIalJL+GR5JYkzyd5cp7tFyc5mmR397m+j3EljU8v/6Nr4JvA14DbFujzg6r6YE/jSRqzXo48quoh4IU+9iVpZejryGMpLkryOPAj4DNVtXd2hyRTwNQIa1qxjh07Nu4SJt5dd9017hJe00YVHo8Bb62ql5JsBu4FNs7uVFXbgG0ASWpEtUlqMJK7LVX1YlW91LW3A6ckWTuKsSUtj5GER5KzkqRrX9CNe2QUY0taHr2ctiS5HbgYWJvkIPAF4BSAqroRuBz4RJLjwC+BK6rK0xJpBcuk/jvsNQ9pJHZV1aaWH/qEqaQmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCaGh6QmhoekJoaHpCZDh0eSDUkeSLIvyd4kn5yjT5J8Ncn+JE8kOW/YcSWN16oe9nEc+HRVPZbkdGBXkh1VtW+gz2XAxu7zbuAb3bekFWroI4+qOlxVj3XtnwNPAetnddsK3FYzHgbOSLJu2LEljU+v1zySnAO8C3hk1qb1wIGB5YO8OmAkrSB9nLYAkOQ04B7gU1X1YuM+poCpvmqStHx6CY8kpzATHN+qqm/P0eUQsGFg+exu3W+oqm3Atm6f1UdtkpZHH3dbAtwMPFVVX5mn2zTw0e6uy4XA0ao6POzYksanjyOP9wIfAfYk2d2t+xzwFoCquhHYDmwG9gO/AD7Ww7iSxihVk3l24GmLNBK7qmpTyw99wlRSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSE8NDUhPDQ1ITw0NSk6HDI8mGJA8k2Zdkb5JPztHn4iRHk+zuPtcPO66k8VrVwz6OA5+uqseSnA7sSrKjqvbN6veDqvpgD+NJmgBDH3lU1eGqeqxr/xx4Clg/7H4lTbY+jjxekeQc4F3AI3NsvijJ48CPgM9U1d45fj8FTHWLx4An+6yvB2uBn467iAHWs7BJqwcmr6a3t/4wVdVLBUlOA/4N+Puq+vasbb8D/F9VvZRkM/BPVbVxkf3trKpNvRTXk0mryXoWNmn1wOTVNEw9vdxtSXIKcA/wrdnBAVBVL1bVS117O3BKkrV9jC1pPPq42xLgZuCpqvrKPH3O6vqR5IJu3CPDji1pfPq45vFe4CPAniS7u3WfA94CUFU3ApcDn0hyHPglcEUtfr60rYfa+jZpNVnPwiatHpi8mprr6e2ah6TfLj5hKqmJ4SGpycSER5I3JtmR5Jnu+8x5+v164DH36WWo49IkTyfZn+S6ObavTnJnt/2R7tmWZbWEmq5K8pOBeblmGWu5JcnzSeZ8BiczvtrV+kSS85arlhOoaWSvRyzxdY2RztGyvUJSVRPxAb4MXNe1rwO+NE+/l5axhpOBZ4FzgVOBx4F3zOrzV8CNXfsK4M5lnpel1HQV8LUR/X16H3Ae8OQ82zcD3wUCXAg8MgE1XQz8y4jmZx1wXtc+HfjhHH+/RjpHS6zphOdoYo48gK3ArV37VuDPx1DDBcD+qnquqn4F3NHVNWiwzruBS16+DT3Gmkamqh4CXligy1bgtprxMHBGknVjrmlkammva4x0jpZY0wmbpPB4c1Ud7tr/Bbx5nn6vS7IzycNJ+g6Y9cCBgeWDvHqSX+lTVceBo8Canus40ZoAPtQdAt+dZMMy1rOYpdY7ahcleTzJd5P84SgGXOB1jbHN0VJeIVnqHPX6bstiknwfOGuOTZ8fXKiqSjLfPeS3VtWhJOcC9yfZU1XP9l3rCvMd4PaqOpbkL5k5MvqzMdc0SR5j5p+bl1+PuBdY8PWIYXWva9wDfKqqXlzOsZZqkZpOeI5GeuRRVe+vqj+a43Mf8OOXD9267+fn2ceh7vs54EFmUrQvh4DBP7XP7tbN2SfJKuANLO/TsovWVFVHqupYt3gTcP4y1rOYpczhSNWIX49Y7HUNxjBHy/EKySSdtkwDV3btK4H7ZndIcmaS1V17LTNPt87+74YM41FgY5K3JTmVmQuis+/oDNZ5OXB/dVeclsmiNc06X97CzDntuEwDH+3uKFwIHB04HR2LUb4e0Y2z4OsajHiOllJT0xyN4gr0Eq8IrwH+FXgG+D7wxm79JuCmrv0eYA8zdxz2AFcvQx2bmbka/Szw+W7dDcCWrv064C5gP/AfwLkjmJvFavoHYG83Lw8Av7+MtdwOHAb+l5lz9auBjwMf77YH+HpX6x5g0wjmZ7Garh2Yn4eB9yxjLX8KFPAEsLv7bB7nHC2xphOeIx9Pl9Rkkk5bJK0ghoekJoaHpCaGh6QmhoekJoaHpCaGh6Qm/w9vyeRK3It33AAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["new image shape: torch.Size([28, 28])\n","prediction:8\n","value activation max for filte 4 :0.27246350049972534\n","index of max value: x: 6, y: 11\n","region extracted: tensor([[0.9882, 0.4314, 0.0000],\n"," [0.8784, 0.1647, 0.0000],\n"," [0.0627, 0.0000, 0.0000]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAO0UlEQVR4nO3df4xV9ZnH8c+jtpDQRkHdcUJxaQl/WH8s1QlsXF0LpqOLwRGJFUw2bjQ7jWICcf1BEANxbTSrs2b/sWYIWlq6/AiIYCMpgmRHE1MFRQHZ1oGAHRxBl5BandBVn/1jDu4U53zveO5P5nm/ksnce5753vvkhg/n3PO9537N3QVg+Dut3g0AqA3CDgRB2IEgCDsQBGEHgjijlk9mZpz6B6rM3W2w7WXt2c3sWjP7nZl1m9mCch4LQHVZ0Xl2Mztd0u8l/UhSj6TXJc1x93cSY9izA1VWjT37ZEnd7r7f3f8saZWktjIeD0AVlRP2sZL+MOB+T7btL5hZu5ltN7PtZTwXgDJV/QSdu3dK6pQ4jAfqqZw9+yFJ4wbc/062DUADKifsr0uaaGbfNbNvSpotaWNl2gJQaYUP4939MzO7S9JvJJ0u6Wl331OxzgBUVOGpt0JPxnt2oOqq8qEaAKcOwg4EQdiBIAg7EARhB4Ig7EAQhB0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EARhB4IovGQz/t+oUaOS9RUrViTrbW1tyXo5K+1u3rw5Wb/tttuS9d7e3sLPjcZSVtjN7ICkjyV9Lukzd2+pRFMAKq8Se/ap7v5RBR4HQBXxnh0Iotywu6TNZrbDzNoH+wMzazez7Wa2vcznAlCGcg/jr3D3Q2b2V5JeNLP/dveugX/g7p2SOiXJzIqfaQJQlrL27O5+KPt9RNJ6SZMr0RSAyiscdjMbZWbfPnFbUquk3ZVqDEBlWdE5XDP7nvr35lL/24H/dPeflhhzyh7Gt7Tkzyp2dnYmx15yySXJ+s6dO5P1TZs2JetbtmxJ1lOuuuqqZH3q1KnJ+q5du5L1iy66KLdW6jMA+/btS9bffPPNZL27uztZH67c3QbbXvg9u7vvl/Q3hTsCUFNMvQFBEHYgCMIOBEHYgSAIOxBE4am3Qk/WwFNvI0eOTNbXrVuXW7vmmmuSY7u6upL16667Llnv6+tL1stRaurs/PPPT9Y//fTTZP200/L3J2effXZybCk9PT3J+qJFi3Jr69evz61J0ieffFKop0aQN/XGnh0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgmCePXPTTTcl6ytXrsytHTt2LDn2sssuS9YPHjyYrFdTa2trsl7qMtNS9REjRuTWZsyYkRw7e/bsZH3KlCnJenNzc27t1VdfTY698sork/VGxjw7EBxhB4Ig7EAQhB0IgrADQRB2IAjCDgTBPHsmdb26lJ6Pvv7665Njt23bVqgnpN18883J+mOPPZZba2pqSo5duHBhst7R0ZGs1xPz7EBwhB0IgrADQRB2IAjCDgRB2IEgCDsQBPPsmVLXs69atSq3Vmq+d+3atYV6QnnOPPPM3Nr+/fuTY996661kfdq0aYV6qoXC8+xm9rSZHTGz3QO2jTGzF83s3ez36Eo2C6DyhnIY/3NJ1560bYGkre4+UdLW7D6ABlYy7O7eJenoSZvbJC3Pbi+XdEOF+wJQYWcUHNfk7r3Z7Q8k5X7Q2MzaJbUXfB4AFVI07F9yd0+deHP3TkmdUmOfoAOGu6JTb4fNrFmSst9HKtcSgGooGvaNkm7Nbt8qaUNl2gFQLSUP481spaQfSjrHzHokLZb0qKQ1Zna7pIOSflzNJmuh1Pefl1qHHLWXmkeXpDVr1hQe+9xzzxXqqZGVDLu7z8kpXV3hXgBUER+XBYIg7EAQhB0IgrADQRB2IIiyP0E3XOzZsydZ7+vry60tXrw4Ofa1115L1t97771kHYObOXNmsn711fkTRj09PcmxW7ZsKdRTI2PPDgRB2IEgCDsQBGEHgiDsQBCEHQiCsANBMM+eOX78eLK+bNmy3Nq9996bHPvEE08k67NmzUrWo5o8eXKyXmqp7A8//DC3NmnSpOTYY8eOJeunIvbsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAESzYP0Rln5H8kYePGjcmxra2tyfojjzySrD/44IPJ+qmqo6MjWb/jjjuS9VJf7z19+vTcWqnvGDiVFV6yGcDwQNiBIAg7EARhB4Ig7EAQhB0IgrADQTDPXgNPPvlksl5qPnn9+vXJ+vz583Nr1f5O+lJLHz/88MO5tTvvvDM5tqurK1mfOnVqsh5V4Xl2M3vazI6Y2e4B25aY2SEz25n95H96AUBDGMph/M8lXTvI9ifcfVL280Jl2wJQaSXD7u5dko7WoBcAVVTOCbq7zOzt7DB/dN4fmVm7mW03s+1lPBeAMhUN+88kTZA0SVKvpNwrGty9091b3L2l4HMBqIBCYXf3w+7+ubt/IWmppPTXgAKou0JhN7PmAXdnStqd97cAGkPJeXYzWynph5LOkXRY0uLs/iRJLumApJ+4e2/JJws6zz527NhkvdT3o5eap09d17106dKyHnvMmDHJ+vPPP5+sX3DBBbm11Pe6S1JbW1uyPpyvSS9H3jx7yUUi3H3OIJvzV0wA0JD4uCwQBGEHgiDsQBCEHQiCsANBcInrKeCss85K1ufNm5dbu++++5Jj+/r6knWzQWdxvlTqEtdNmzbl1pYsWZIcu2PHjmQdg+OrpIHgCDsQBGEHgiDsQBCEHQiCsANBEHYgCObZh7nVq1cn67NmzUrWS82zl/r309zcnFsrdYkrimGeHQiOsANBEHYgCMIOBEHYgSAIOxAEYQeCKPntsmh8c+fOza21trbWsJOvevzxx3NrDz30UHLsvn37Kt1OaOzZgSAIOxAEYQeCIOxAEIQdCIKwA0EQdiAIrmc/BVx66aXJ+ssvv5xbGzlyZHLshg0bkvUbb7wxWb/nnnsK188999zk2NTnByTpqaeeStajKnw9u5mNM7NtZvaOme0xs3nZ9jFm9qKZvZv9Hl3ppgFUzlAO4z+T9C/u/n1Jfytprpl9X9ICSVvdfaKkrdl9AA2qZNjdvdfd38hufyxpr6SxktokLc/+bLmkG6rVJIDyfa3PxpvZeEk/kPRbSU3u3puVPpDUlDOmXVJ78RYBVMKQz8ab2bckrZM0393/OLDm/Wf5Bj355u6d7t7i7i1ldQqgLEMKu5l9Q/1B/5W7P5ttPmxmzVm9WdKR6rQIoBJKTr1Z/3cJL5d01N3nD9j+mKT/cfdHzWyBpDHunlwfmKm3Ykotbbxo0aLc2vvvv58ce8sttyTrr7zySrJeyvjx43Nry5YtS46dOHFisj5t2rRkvbu7O1kfrvKm3obynv3vJP2jpF1mtjPbtlDSo5LWmNntkg5K+nElGgVQHSXD7u6vSMpbKeDqyrYDoFr4uCwQBGEHgiDsQBCEHQiCsANBcInrKeDCCy9M1l944YXc2tixY5Nj+/r6kvVDhw4l6ytXrkzWn3nmmdza5Zdfnhy7YsWKZP3+++9P1js6OpL14Yolm4HgCDsQBGEHgiDsQBCEHQiCsANBEHYgCObZh4ERI0bk1mbMmJEce/fddyfrU6ZMSdZr+e/nZHv37k3WL7744hp10liYZweCI+xAEIQdCIKwA0EQdiAIwg4EQdiBIL7W8k9oTMePH8+trV27Njn2pZdeStbPO++8ZH3ChAnJ+gMPPJBba2kpb5Gg1atXlzU+GvbsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxDEUNZnHyfpF5KaJLmkTnf/DzNbIumfJX2Y/elCd8//AnNxPTtQC3nXsw8l7M2Smt39DTP7tqQdkm5Q/3rsf3L3x4faBGEHqi8v7ENZn71XUm92+2Mz2yspvcwIgIbztd6zm9l4ST+Q9Nts011m9raZPW1mo3PGtJvZdjPbXlanAMoy5O+gM7NvSfovST9192fNrEnSR+p/H/+v6j/Uv63EY3AYD1RZ4ffskmRm35D0a0m/cfd/H6Q+XtKv3f2iEo9D2IEqK/yFk2ZmkpZJ2jsw6NmJuxNmStpdbpMAqmcoZ+OvkPSypF2Svsg2L5Q0R9Ik9R/GH5D0k+xkXuqx2LMDVVbWYXylEHag+vjeeCA4wg4EQdiBIAg7EARhB4Ig7EAQhB0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBC1XrL5I0kHB9w/J9vWiBq1t0btS6K3oirZ21/nFWp6PftXntxsu7uXt0h3lTRqb43al0RvRdWqNw7jgSAIOxBEvcPeWefnT2nU3hq1L4neiqpJb3V9zw6gduq9ZwdQI4QdCKIuYTeza83sd2bWbWYL6tFDHjM7YGa7zGxnvdeny9bQO2JmuwdsG2NmL5rZu9nvQdfYq1NvS8zsUPba7TSz6XXqbZyZbTOzd8xsj5nNy7bX9bVL9FWT163m79nN7HRJv5f0I0k9kl6XNMfd36lpIznM7ICkFnev+wcwzOzvJf1J0i9OLK1lZv8m6ai7P5r9Rzna3e9vkN6W6Gsu412l3vKWGf8n1fG1q+Ty50XUY88+WVK3u+939z9LWiWprQ59NDx375J09KTNbZKWZ7eXq/8fS83l9NYQ3L3X3d/Ibn8s6cQy43V97RJ91UQ9wj5W0h8G3O9RY6337pI2m9kOM2uvdzODaBqwzNYHkprq2cwgSi7jXUsnLTPeMK9dkeXPy8UJuq+6wt0vlfQPkuZmh6sNyfvfgzXS3OnPJE1Q/xqAvZI66tlMtsz4Oknz3f2PA2v1fO0G6asmr1s9wn5I0rgB97+TbWsI7n4o+31E0nr1v+1oJIdPrKCb/T5S536+5O6H3f1zd/9C0lLV8bXLlhlfJ+lX7v5strnur91gfdXqdatH2F+XNNHMvmtm35Q0W9LGOvTxFWY2KjtxIjMbJalVjbcU9UZJt2a3b5W0oY69/IVGWcY7b5lx1fm1q/vy5+5e8x9J09V/Rn6fpAfq0UNOX9+T9Fb2s6fevUlaqf7Duv9V/7mN2yWdLWmrpHclbZE0poF6+6X6l/Z+W/3Baq5Tb1eo/xD9bUk7s5/p9X7tEn3V5HXj47JAEJygA4Ig7EAQhB0IgrADQRB2IAjCDgRB2IEg/g+PDdv3LvUehwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["new image shape: torch.Size([28, 28])\n","prediction:8\n","value activation max for filte 5 :0.9290241599082947\n","index of max value: x: 5, y: 11\n","region extracted: tensor([[0.9765, 0.4510, 0.0000],\n"," [0.9294, 0.7451, 0.0000],\n"," [0.9882, 0.4314, 0.0000]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["new image shape: torch.Size([28, 28])\n","prediction:8\n","value activation max for filte 6 :0.5163602232933044\n","index of max value: x: 5, y: 11\n","region extracted: tensor([[0.9765, 0.4510, 0.0000],\n"," [0.9294, 0.7451, 0.0000],\n"," [0.9882, 0.4314, 0.0000]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["new image shape: torch.Size([28, 28])\n","prediction:8\n","value activation max for filte 7 :0.6014053821563721\n","index of max value: x: 2, y: 5\n","region extracted: tensor([[0.0000, 0.0000, 0.0000],\n"," [0.0000, 0.0000, 0.0000],\n"," [0.9961, 0.9922, 0.9922]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQ8AAAD8CAYAAABpXiE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAANeUlEQVR4nO3db6ie9X3H8fenJtoHuvonoYYY/7HQzY0N9eC0jhFWCyrFDOoD+6BqsWTtJmuhhYUKLQhjtg865pRKUKmOojItelosRafO7oHOKNEYxRkFMWlWrelipUWX7rsH51LuHs/JOfnd1/3nuPcLbu7fdV2/+/p9+SV8vP6aVBWSdLg+NOkCJK1MhoekJoaHpCaGh6QmhoekJoaHpCZDhUeS45M8kOTF7vu4Rfr9JsmO7jM7zJiSpkOGec4jybeA/VV1XZKtwHFV9bcL9Hurqo4eok5JU2bY8HgB2FRV+5KsAx6pqo8t0M/wkD5ghg2P/66qY7t2gF+8uzyv30FgB3AQuK6q7l1kf1uALd3i2c2FSVqun1fV2pYfrlqqQ5IHgRMX2HTN4EJVVZLFkuiUqtqb5HTgoSQ7q+ql+Z2qahuwrRvX5+al0Xul9YdLhkdVXbDYtiQ/S7Ju4LTltUX2sbf7fjnJI8CZwPvCQ9LKMeyt2lngiq59BXDf/A5JjktyVNdeA5wPPDfkuJImbNjwuA74ZJIXgQu6ZZLMJLm56/P7wPYkTwMPM3fNw/CQVrihLpiOktc8pLF4sqpmWn7oE6aSmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKa9BIeSS5M8kKS3Um2LrD9qCR3ddsfT3JqH+NKmpyhwyPJEcCNwEXAGcBnkpwxr9tVwC+q6neBfwC+Oey4kiarjyOPc4DdVfVyVb0D3AlsntdnM3Bb174b+ESS9DC2pAnpIzzWA68OLO/p1i3Yp6oOAgeAE3oYW9KErJp0AYOSbAG2TLoOSUvr48hjL7BhYPmkbt2CfZKsAj4CvDF/R1W1rapmqmqmh7okjVAf4fEEsDHJaUmOBC4DZuf1mQWu6NqXAg9VVfUwtqQJGfq0paoOJrka+DFwBHBrVe1Kci2wvapmgVuAf06yG9jPXMBIWsEyrQcASaazMOmD5cnWywQ+YSqpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpSS/hkeTCJC8k2Z1k6wLbr0zyepId3efzfYwraXJWDbuDJEcANwKfBPYATySZrarn5nW9q6quHnY8SdOhjyOPc4DdVfVyVb0D3Als7mG/kqZYH+GxHnh1YHlPt26+Tyd5JsndSTYstKMkW5JsT7K9h7okjdC4Lpj+ADi1qv4IeAC4baFOVbWtqmaqamZMdUlq1Ed47AUGjyRO6ta9p6reqKq3u8WbgbN7GFfSBPURHk8AG5OcluRI4DJgdrBDknUDi5cAz/cwrqQJGvpuS1UdTHI18GPgCODWqtqV5Fpge1XNAn+T5BLgILAfuHLYcSVNVqpq0jUsKMl0FiZ9sDzZeo3RJ0wlNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ16SU8ktya5LUkzy6yPUmuT7I7yTNJzupjXEmT09eRx3eBCw+x/SJgY/fZAnynp3ElTUgv4VFVjwL7D9FlM3B7zXkMODbJuj7GljQZ47rmsR54dWB5T7futyTZkmR7ku1jqktSo1WTLmBQVW0DtgEkqQmXI+kQxnXksRfYMLB8UrdO0go1rvCYBS7v7rqcCxyoqn1jGlvSCPRy2pLkDmATsCbJHuAbwGqAqroJuB+4GNgN/Ar4XB/jSpqcVE3npQWveUhj8WRVzbT80CdMJTUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNeklPJLcmuS1JM8usn1TkgNJdnSfr/cxrqTJWdXTfr4L3ADcfog+P6mqT/U0nqQJ6+XIo6oeBfb3sS9JK0NfRx7LcV6Sp4GfAl+tql3zOyTZAmwBOPnkk3nllVfGWN7KUlWTLkEfAB/6UPvxw7gumD4FnFJVfwz8E3DvQp2qaltVzVTVzNq1a8dUmqQWYwmPqnqzqt7q2vcDq5OsGcfYkkZjLOGR5MQk6drndOO+MY6xJY1GL9c8ktwBbALWJNkDfANYDVBVNwGXAl9MchD4NXBZedIurWi9hEdVfWaJ7TcwdytX0geET5hKamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqYnhIamJ4SGpieEhqMnR4JNmQ5OEkzyXZleRLC/RJkuuT7E7yTJKzhh1X0mT18Q9dHwS+UlVPJTkGeDLJA1X13ECfi4CN3edPgO9035JWqKGPPKpqX1U91bV/CTwPrJ/XbTNwe815DDg2ybphx5Y0Ob1e80hyKnAm8Pi8TeuBVweW9/D+gJG0gvQWHkmOBu4BvlxVbzbuY0uS7Um2v/76632VJmkEegmPJKuZC47vVdX3F+iyF9gwsHxSt+63VNW2qpqpqpm1a9f2UZqkEenjbkuAW4Dnq+rbi3SbBS7v7rqcCxyoqn3Dji1pcvq423I+8FlgZ5Id3bqvAScDVNVNwP3AxcBu4FfA53oYV9IEDR0eVfXvQJboU8BfDzuWpOnhE6aSmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmhgekpoYHpKaGB6SmgwdHkk2JHk4yXNJdiX50gJ9NiU5kGRH9/n6sONKmqxVPezjIPCVqnoqyTHAk0keqKrn5vX7SVV9qofxJE2BoY88qmpfVT3VtX8JPA+sH3a/kqZbH0ce70lyKnAm8PgCm89L8jTwU+CrVbVrgd9vAbZ0i28nebbP+nqwBvj5pIsYYD2HNm31wPTV9LHWH6aqeqkgydHAvwF/V1Xfn7ftd4D/raq3klwM/GNVbVxif9uraqaX4noybTVZz6FNWz0wfTUNU08vd1uSrAbuAb43PzgAqurNqnqra98PrE6ypo+xJU1GH3dbAtwCPF9V316kz4ldP5Kc0437xrBjS5qcPq55nA98FtiZZEe37mvAyQBVdRNwKfDFJAeBXwOX1dLnS9t6qK1v01aT9RzatNUD01dTcz29XfOQ9P+LT5hKamJ4SGoyNeGR5PgkDyR5sfs+bpF+vxl4zH12BHVcmOSFJLuTbF1g+1FJ7uq2P9492zJSy6jpyiSvD8zL50dYy61JXlvsGZzMub6r9ZkkZ42qlsOoaWyvRyzzdY2xztHIXiGpqqn4AN8CtnbtrcA3F+n31ghrOAJ4CTgdOBJ4GjhjXp+/Am7q2pcBd414XpZT05XADWP6c/oz4Czg2UW2Xwz8CAhwLvD4FNS0CfjhmOZnHXBW1z4G+M8F/rzGOkfLrOmw52hqjjyAzcBtXfs24C8mUMM5wO6qermq3gHu7OoaNFjn3cAn3r0NPcGaxqaqHgX2H6LLZuD2mvMYcGySdROuaWxqea9rjHWOllnTYZum8PhoVe3r2v8FfHSRfh9Osj3JY0n6Dpj1wKsDy3t4/yS/16eqDgIHgBN6ruNwawL4dHcIfHeSDSOsZynLrXfczkvydJIfJfmDcQx4iNc1JjZHy3mFZLlz1Ou7LUtJ8iBw4gKbrhlcqKpKstg95FOqam+S04GHkuysqpf6rnWF+QFwR1W9neQvmTsy+vMJ1zRNnmLu7827r0fcCxzy9Yhhda9r3AN8uareHOVYy7VETYc9R2M98qiqC6rqDxf43Af87N1Dt+77tUX2sbf7fhl4hLkU7cteYPC/2id16xbsk2QV8BFG+7TskjVV1RtV9Xa3eDNw9gjrWcpy5nCsasyvRyz1ugYTmKNRvEIyTacts8AVXfsK4L75HZIcl+Sorr2Guadb5/9/Q4bxBLAxyWlJjmTuguj8OzqDdV4KPFTdFacRWbKmeefLlzB3Tjsps8Dl3R2Fc4EDA6ejEzHO1yO6cQ75ugZjnqPl1NQ0R+O4Ar3MK8InAP8KvAg8CBzfrZ8Bbu7aHwd2MnfHYSdw1QjquJi5q9EvAdd0664FLunaHwb+BdgN/Adw+hjmZqma/h7Y1c3Lw8DvjbCWO4B9wP8wd65+FfAF4Avd9gA3drXuBGbGMD9L1XT1wPw8Bnx8hLX8KVDAM8CO7nPxJOdomTUd9hz5eLqkJtN02iJpBTE8JDUxPCQ1MTwkNTE8JDUxPCQ1MTwkNfk/rXfYJbrbz+0AAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["new image shape: torch.Size([28, 28])\n","prediction:8\n","value activation max for filte 8 :0.8514882326126099\n","index of max value: x: 11, y: 7\n","region extracted: tensor([[0.2353, 0.0000, 0.0000],\n"," [0.9569, 0.8471, 0.4980],\n"," [0.7765, 0.9922, 0.9882]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["new image shape: torch.Size([28, 28])\n","prediction:8\n","value activation max for filte 9 :0.14859803020954132\n","index of max value: x: 3, y: 3\n","region extracted: tensor([[0.0000, 0.0000, 0.0431],\n"," [0.0000, 0.2039, 0.8078],\n"," [0.0314, 0.7098, 0.9882]])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"F5V7JpRCVAMg","colab_type":"text"},"source":["## filter value:"]},{"cell_type":"code","metadata":{"id":"oBMsXe_bUCzp","colab_type":"code","colab":{}},"source":["for name, m in model_no_binary.named_modules():\n"," if type(m) == nn.Conv2d:\n"," filters = m.weight.data.clone()\n"," break"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Uy-6Ff0eVHa5","colab_type":"code","colab":{}},"source":["filters.shape"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6jp2qC9TVIav","colab_type":"code","colab":{}},"source":["model_no_binary.layer1.bias"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hyYxO8JDVX-2","colab_type":"code","colab":{}},"source":["filter_0 = filters[0][0]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wZnCMvi_WM7c","colab_type":"code","colab":{}},"source":["print(filter_0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"AfuiTJgRVxTr","colab_type":"code","colab":{}},"source":["plt.imshow(filter_0, cmap='gray')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"AhYcyujfV6E3","colab_type":"code","colab":{}},"source":["viz_filters(model_no_binary)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3qWfAHVjWAwC","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}
\ No newline at end of file
diff --git a/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1.npy b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1.npy
new file mode 100644
index 0000000000000000000000000000000000000000..726cbaaac5d848bb9fb0691f706c384257d89e90
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1.npy differ
diff --git a/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1_normalized.npy b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1_normalized.npy
new file mode 100644
index 0000000000000000000000000000000000000000..088da00588a3789b7d56bde8880e8c918ae2bc6c
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer1_normalized.npy differ
diff --git a/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2.npy b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2.npy
new file mode 100644
index 0000000000000000000000000000000000000000..5f97fb1317b760cfca5965e2225a3a90c1eabc6e
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2.npy differ
diff --git a/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2_normalized.npy b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2_normalized.npy
new file mode 100644
index 0000000000000000000000000000000000000000..20b5fa3acccd55880b699f9d44ee567b94dba43a
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_activations_max_layer2_normalized.npy differ
diff --git a/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer1.npy b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer1.npy
new file mode 100644
index 0000000000000000000000000000000000000000..afeef00d77a0bb33464c7a6af77c5c97a5c3605c
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer1.npy differ
diff --git a/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer2.npy b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer2.npy
new file mode 100644
index 0000000000000000000000000000000000000000..f789bbac3839cffde4af2c27340a3a412bbf9cd2
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/Binary_without_bias_MNIST_regions_max_layer2.npy differ
diff --git a/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1.npy b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1.npy
new file mode 100644
index 0000000000000000000000000000000000000000..3ea66b3c91c6ea1570a433cfe3f65d65ad786df2
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1.npy differ
diff --git a/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1_normalized.npy b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1_normalized.npy
new file mode 100644
index 0000000000000000000000000000000000000000..5c3764601f243d3af5e7b0c63ad5249125245ab2
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer1_normalized.npy differ
diff --git a/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2.npy b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2.npy
new file mode 100644
index 0000000000000000000000000000000000000000..b25fc764cdc38e8e92be07756a59c8b72b03ad9f
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2.npy differ
diff --git a/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2_normalized.npy b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2_normalized.npy
new file mode 100644
index 0000000000000000000000000000000000000000..4b108d6d35fbe05486abd0629e1fdff617f762d6
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_activations_max_layer2_normalized.npy differ
diff --git a/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer1.npy b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer1.npy
new file mode 100644
index 0000000000000000000000000000000000000000..0b8d370ddfb0537445e1f002e99d577433fa9812
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer1.npy differ
diff --git a/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer2.npy b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer2.npy
new file mode 100644
index 0000000000000000000000000000000000000000..52082462237b47c147b6e42825288eee5b7f8756
Binary files /dev/null and b/results/MNIST_results/MNIST_regions/No_binary_without_bias_MNIST_regions_max_layer2.npy differ
diff --git a/trained_models/MNIST/No_binary_models/with_bias/MNIST_NonBinaryNet_MNIST_282.pth b/trained_models/MNIST/No_binary_models/with_bias/MNIST_NonBinaryNet_MNIST_282.pth
new file mode 100644
index 0000000000000000000000000000000000000000..c04cfacdf92a04a3e74c85d451302ecc9dcce926
Binary files /dev/null and b/trained_models/MNIST/No_binary_models/with_bias/MNIST_NonBinaryNet_MNIST_282.pth differ
diff --git a/trained_models/MNIST/No_binary_models/with_bias/MNIST_NonBinaryNet_MNIST_288.pth b/trained_models/MNIST/No_binary_models/with_bias/MNIST_NonBinaryNet_MNIST_288.pth
new file mode 100644
index 0000000000000000000000000000000000000000..de65a6681663fa22ca3765aa87c81bcff976ca01
Binary files /dev/null and b/trained_models/MNIST/No_binary_models/with_bias/MNIST_NonBinaryNet_MNIST_288.pth differ
diff --git a/visualize/__pycache__/viz.cpython-36.pyc b/visualize/__pycache__/viz.cpython-36.pyc
index eb2495d8d9c49f226169a8a0e8403489817404c6..703fecfa19945d9d214fe7d22cef41ca482428ab 100644
Binary files a/visualize/__pycache__/viz.cpython-36.pyc and b/visualize/__pycache__/viz.cpython-36.pyc differ
diff --git a/visualize/viz.py b/visualize/viz.py
index f84f627c86fe64d54eba654f0e8d5895b38e36e6..1700ca57cad55f518d10d223de4c3245db1524b2 100644
--- a/visualize/viz.py
+++ b/visualize/viz.py
@@ -698,7 +698,7 @@ def get_region_layer1(image, ind_x, ind_y, name, stride, padding, filter_size, l
region = image[begin_col:end_col,begin_raw:end_raw]
if region.shape != (filter_size, filter_size):
- region = cv2.resize(region, (filter_size, filter_size), interpolation=cv2.INTER_AREA)
+ region = cv2.resize(region.numpy(), (filter_size, filter_size), interpolation=cv2.INTER_AREA)
if return_all:
return region, begin_col, end_col, begin_raw, end_raw
@@ -922,10 +922,10 @@ def get_filter_layer4():
[1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1]]))
-def get_all_regions_max(loader, activations, stride, padding, filter_size, len_img_h, len_img_w):
+def get_all_regions_max(images, loader, activations, stride, padding, filter_size, len_img_h, len_img_w):
- dataiter = iter(loader)
- images, _ = dataiter.next()
+ # dataiter = iter(loader)
+ # images, _ = dataiter.next()
print('nb images: {}'.format(len(images)))
print('begin extraction regions')
@@ -981,7 +981,7 @@ def get_all_regions_max(loader, activations, stride, padding, filter_size, len_i
ind_y = int((np.where(fm[j][i] == act_max)[1])[0])
if name == 'layer1':
- region = get_region_layer1(im, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w)
+ region, begin_col, end_col, begin_raw, end_raw = get_region_layer1(im, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w, return_all=True)
if name == 'layer2':
region = get_region_layer2(im, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w)
if name == 'layer3':
@@ -993,7 +993,9 @@ def get_all_regions_max(loader, activations, stride, padding, filter_size, len_i
activation_im_j[i] = act_max.detach().numpy()
norme = LA.norm(region, 1)
if norme == 0.0:
- print('norm null')
+ print('norm null for filter: {}, image: {} with activation:{}, index({},{})'.format(i, j, act_max, ind_x, ind_y))
+ plt.imshow(im, cmap='gray')
+ plt.show()
activation_im_j_normalized[i]=act_max.detach().numpy()
else:
activation_im_j_normalized[i] = act_max.detach().numpy()/norme