diff --git a/Experiments/experiments.py b/Experiments/experiments.py
index bab104ebe8ca2ad37556acb98939c91d76ba9c7c..e9d9f0894c35aa116e1a5aa5a787614df9859574 100644
--- a/Experiments/experiments.py
+++ b/Experiments/experiments.py
@@ -2,11 +2,17 @@ from utils.load_model import load
from viz.visualize import Visualizer as Viz
import matplotlib.pyplot as plt
from dataloader.dataloaders import *
+from VAE_model.models import VAE
+import os
+import torch
-def viz_reconstruction(path, expe_name, batch):
+def viz_reconstruction(model, path, expe_name, batch):
+
+ file_path = os.path.join(path, expe_name, 'checkpoints', 'last')
+ checkpoint = torch.load(file_path, map_location=torch.device('cpu'))
+ model.load_state_dict(checkpoint['model_states']['model'])
- model = load(path, expe_name)
viz_chairs = Viz(model)
viz_chairs.save_images = False
@@ -24,6 +30,8 @@ _, dataloader_chairs = get_chairs_dataloader(batch_size=32)
for batch_chairs, labels_chairs in dataloader_chairs:
break
+# torch.load('data/batch_chairs.pt')
+
path_to_model_folder_chairs = '../trained_models/rendered_chairs/'
expe_name_1 = 'VAE_bs_64'
expe_name_2 = 'VAE_bs_256'
@@ -32,5 +40,9 @@ expe_name_4 = 'beta_VAE_bs_256'
list_expe = [expe_name_1, expe_name_2, expe_name_3, expe_name_4]
+img_size = (3, 64, 64)
+latent_spec = {"cont": 10}
+model = VAE(img_size, latent_spec=latent_spec)
+
for i in list_expe:
- viz_reconstruction(path_to_model_folder_chairs, i, batch_chairs)
+ viz_reconstruction(model, path_to_model_folder_chairs, i, batch_chairs)
diff --git a/OAR.2066986.stderr b/OAR.2066986.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..0bb03bf57906dfc4031825f7fa552cc35aec1353
--- /dev/null
+++ b/OAR.2066986.stderr
@@ -0,0 +1,9 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py:26: UserWarning:
+ There is an imbalance between your GPUs. You may want to exclude GPU 1 which
+ has less than 75% of the memory or cores of GPU 0. You can do so by setting
+ the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES
+ environment variable.
+ warnings.warn(imbalance_warn.format(device_ids[min_pos], device_ids[max_pos]))
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+ warnings.warn(warning.format(ret))
+## OAR [2020-06-24 03:51:26] Job 2066986 KILLED ##
diff --git a/OAR.2066986.stdout b/OAR.2066986.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..d44df1f0e4627fcfca61ce3f83ffa302f875433e
--- /dev/null
+++ b/OAR.2066986.stdout
@@ -0,0 +1,1553 @@
+Namespace(batch_size=256, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_256', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/beta_VAE_bs_256
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+Tesla K40c
+Tesla K20m
+DataParallel(
+ (module): VAE(
+ (img_to_last_conv): Sequential(
+ (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (1): ReLU()
+ (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (3): ReLU()
+ (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (5): ReLU()
+ (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (7): ReLU()
+ )
+ (last_conv_to_continuous_features): Sequential(
+ (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+ (1): ReLU()
+ )
+ (features_to_hidden_continue): Sequential(
+ (0): Linear(in_features=256, out_features=20, bias=True)
+ (1): ReLU()
+ )
+ (latent_to_features): Sequential(
+ (0): Linear(in_features=10, out_features=256, bias=True)
+ (1): ReLU()
+ )
+ (features_to_img): Sequential(
+ (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+ (1): ReLU()
+ (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (3): ReLU()
+ (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (5): ReLU()
+ (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (7): ReLU()
+ (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (9): Sigmoid()
+ )
+ )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last (iter 4)'
+0/69092 Loss: 211.825
+12800/69092 Loss: 211.804
+25600/69092 Loss: 206.202
+38400/69092 Loss: 202.006
+51200/69092 Loss: 200.623
+64000/69092 Loss: 197.778
+Training time 0:03:33.530106
+Epoch: 1 Average loss: 203.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 5)
+0/69092 Loss: 194.363
+12800/69092 Loss: 193.513
+25600/69092 Loss: 190.886
+38400/69092 Loss: 189.957
+51200/69092 Loss: 188.767
+64000/69092 Loss: 186.445
+Training time 0:03:35.633651
+Epoch: 2 Average loss: 189.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 6)
+0/69092 Loss: 191.560
+12800/69092 Loss: 186.911
+25600/69092 Loss: 186.547
+38400/69092 Loss: 187.360
+51200/69092 Loss: 186.168
+64000/69092 Loss: 184.956
+Training time 0:03:34.640039
+Epoch: 3 Average loss: 186.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 7)
+0/69092 Loss: 188.289
+12800/69092 Loss: 182.612
+25600/69092 Loss: 181.754
+38400/69092 Loss: 177.558
+51200/69092 Loss: 178.641
+64000/69092 Loss: 178.958
+Training time 0:03:34.619088
+Epoch: 4 Average loss: 179.99
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 8)
+0/69092 Loss: 176.130
+12800/69092 Loss: 179.349
+25600/69092 Loss: 175.271
+38400/69092 Loss: 175.914
+51200/69092 Loss: 176.593
+64000/69092 Loss: 175.448
+Training time 0:03:34.581288
+Epoch: 5 Average loss: 176.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 9)
+0/69092 Loss: 173.057
+12800/69092 Loss: 174.321
+25600/69092 Loss: 173.044
+38400/69092 Loss: 172.272
+51200/69092 Loss: 173.259
+64000/69092 Loss: 170.322
+Training time 0:03:34.404385
+Epoch: 6 Average loss: 172.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 10)
+0/69092 Loss: 166.655
+12800/69092 Loss: 171.300
+25600/69092 Loss: 170.458
+38400/69092 Loss: 170.805
+51200/69092 Loss: 170.232
+64000/69092 Loss: 171.220
+Training time 0:03:34.964220
+Epoch: 7 Average loss: 170.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 11)
+0/69092 Loss: 178.479
+12800/69092 Loss: 170.169
+25600/69092 Loss: 169.556
+38400/69092 Loss: 171.901
+51200/69092 Loss: 168.732
+64000/69092 Loss: 169.240
+Training time 0:03:33.828026
+Epoch: 8 Average loss: 169.95
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 12)
+0/69092 Loss: 172.547
+12800/69092 Loss: 169.826
+25600/69092 Loss: 170.576
+38400/69092 Loss: 169.827
+51200/69092 Loss: 168.754
+64000/69092 Loss: 168.303
+Training time 0:03:34.457471
+Epoch: 9 Average loss: 169.32
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 13)
+0/69092 Loss: 167.148
+12800/69092 Loss: 168.280
+25600/69092 Loss: 168.913
+38400/69092 Loss: 170.169
+51200/69092 Loss: 168.747
+64000/69092 Loss: 168.010
+Training time 0:03:34.634507
+Epoch: 10 Average loss: 168.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 14)
+0/69092 Loss: 177.245
+12800/69092 Loss: 169.183
+25600/69092 Loss: 169.266
+38400/69092 Loss: 167.712
+51200/69092 Loss: 167.721
+64000/69092 Loss: 168.102
+Training time 0:03:34.876272
+Epoch: 11 Average loss: 168.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 15)
+0/69092 Loss: 176.615
+12800/69092 Loss: 167.388
+25600/69092 Loss: 168.006
+38400/69092 Loss: 167.276
+51200/69092 Loss: 168.548
+64000/69092 Loss: 166.197
+Training time 0:03:34.165047
+Epoch: 12 Average loss: 167.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 16)
+0/69092 Loss: 172.005
+12800/69092 Loss: 167.224
+25600/69092 Loss: 168.013
+38400/69092 Loss: 169.018
+51200/69092 Loss: 166.406
+64000/69092 Loss: 166.123
+Training time 0:03:34.981962
+Epoch: 13 Average loss: 167.29
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 17)
+0/69092 Loss: 168.780
+12800/69092 Loss: 168.028
+25600/69092 Loss: 166.477
+38400/69092 Loss: 166.556
+51200/69092 Loss: 166.215
+64000/69092 Loss: 168.803
+Training time 0:03:34.777682
+Epoch: 14 Average loss: 167.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 18)
+0/69092 Loss: 166.192
+12800/69092 Loss: 166.410
+25600/69092 Loss: 167.980
+38400/69092 Loss: 165.740
+51200/69092 Loss: 166.931
+64000/69092 Loss: 165.295
+Training time 0:03:33.907632
+Epoch: 15 Average loss: 166.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 19)
+0/69092 Loss: 168.910
+12800/69092 Loss: 166.387
+25600/69092 Loss: 168.154
+38400/69092 Loss: 167.979
+51200/69092 Loss: 165.611
+64000/69092 Loss: 164.256
+Training time 0:03:34.308635
+Epoch: 16 Average loss: 166.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 20)
+0/69092 Loss: 153.952
+12800/69092 Loss: 166.799
+25600/69092 Loss: 166.583
+38400/69092 Loss: 166.714
+51200/69092 Loss: 164.663
+64000/69092 Loss: 165.421
+Training time 0:03:34.306057
+Epoch: 17 Average loss: 166.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 21)
+0/69092 Loss: 179.877
+12800/69092 Loss: 167.473
+25600/69092 Loss: 166.346
+38400/69092 Loss: 164.610
+51200/69092 Loss: 165.784
+64000/69092 Loss: 165.548
+Training time 0:03:34.278194
+Epoch: 18 Average loss: 165.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 22)
+0/69092 Loss: 152.548
+12800/69092 Loss: 164.441
+25600/69092 Loss: 166.865
+38400/69092 Loss: 164.547
+51200/69092 Loss: 166.574
+64000/69092 Loss: 166.189
+Training time 0:03:34.177869
+Epoch: 19 Average loss: 165.65
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 23)
+0/69092 Loss: 169.217
+12800/69092 Loss: 166.643
+25600/69092 Loss: 166.234
+38400/69092 Loss: 164.382
+51200/69092 Loss: 164.328
+64000/69092 Loss: 165.078
+Training time 0:03:34.422146
+Epoch: 20 Average loss: 165.53
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 24)
+0/69092 Loss: 161.289
+12800/69092 Loss: 165.853
+25600/69092 Loss: 165.618
+38400/69092 Loss: 164.688
+51200/69092 Loss: 165.000
+64000/69092 Loss: 165.459
+Training time 0:03:34.461971
+Epoch: 21 Average loss: 165.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 25)
+0/69092 Loss: 159.510
+12800/69092 Loss: 165.153
+25600/69092 Loss: 165.337
+38400/69092 Loss: 164.817
+51200/69092 Loss: 165.308
+64000/69092 Loss: 165.527
+Training time 0:03:33.570288
+Epoch: 22 Average loss: 165.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 26)
+0/69092 Loss: 170.139
+12800/69092 Loss: 164.568
+25600/69092 Loss: 164.903
+38400/69092 Loss: 164.993
+51200/69092 Loss: 167.294
+64000/69092 Loss: 164.163
+Training time 0:03:34.137382
+Epoch: 23 Average loss: 165.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 27)
+0/69092 Loss: 160.074
+12800/69092 Loss: 164.802
+25600/69092 Loss: 165.156
+38400/69092 Loss: 165.485
+51200/69092 Loss: 165.304
+64000/69092 Loss: 164.874
+Training time 0:03:34.155758
+Epoch: 24 Average loss: 165.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 28)
+0/69092 Loss: 159.392
+12800/69092 Loss: 165.178
+25600/69092 Loss: 164.090
+38400/69092 Loss: 166.206
+51200/69092 Loss: 164.525
+64000/69092 Loss: 164.707
+Training time 0:03:34.617919
+Epoch: 25 Average loss: 164.87
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 29)
+0/69092 Loss: 171.862
+12800/69092 Loss: 163.451
+25600/69092 Loss: 165.349
+38400/69092 Loss: 166.284
+51200/69092 Loss: 164.517
+64000/69092 Loss: 164.092
+Training time 0:03:34.527260
+Epoch: 26 Average loss: 164.91
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 30)
+0/69092 Loss: 158.628
+12800/69092 Loss: 165.301
+25600/69092 Loss: 164.939
+38400/69092 Loss: 163.872
+51200/69092 Loss: 164.439
+64000/69092 Loss: 165.157
+Training time 0:03:35.035772
+Epoch: 27 Average loss: 164.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 31)
+0/69092 Loss: 159.360
+12800/69092 Loss: 165.523
+25600/69092 Loss: 164.674
+38400/69092 Loss: 164.216
+51200/69092 Loss: 163.631
+64000/69092 Loss: 165.648
+Training time 0:03:34.900889
+Epoch: 28 Average loss: 164.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 32)
+0/69092 Loss: 157.986
+12800/69092 Loss: 164.944
+25600/69092 Loss: 164.624
+38400/69092 Loss: 163.472
+51200/69092 Loss: 164.677
+64000/69092 Loss: 165.441
+Training time 0:03:34.285686
+Epoch: 29 Average loss: 164.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 33)
+0/69092 Loss: 161.244
+12800/69092 Loss: 163.373
+25600/69092 Loss: 165.776
+38400/69092 Loss: 164.253
+51200/69092 Loss: 164.947
+64000/69092 Loss: 164.238
+Training time 0:03:34.892902
+Epoch: 30 Average loss: 164.40
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 34)
+0/69092 Loss: 163.053
+12800/69092 Loss: 165.200
+25600/69092 Loss: 164.347
+38400/69092 Loss: 164.216
+51200/69092 Loss: 163.448
+64000/69092 Loss: 164.677
+Training time 0:03:34.057974
+Epoch: 31 Average loss: 164.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 35)
+0/69092 Loss: 156.522
+12800/69092 Loss: 163.038
+25600/69092 Loss: 164.385
+38400/69092 Loss: 164.930
+51200/69092 Loss: 163.186
+64000/69092 Loss: 164.172
+Training time 0:03:34.517277
+Epoch: 32 Average loss: 164.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 36)
+0/69092 Loss: 167.346
+12800/69092 Loss: 164.662
+25600/69092 Loss: 163.356
+38400/69092 Loss: 163.878
+51200/69092 Loss: 164.450
+64000/69092 Loss: 164.437
+Training time 0:03:34.484296
+Epoch: 33 Average loss: 164.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 37)
+0/69092 Loss: 150.947
+12800/69092 Loss: 164.719
+25600/69092 Loss: 162.579
+38400/69092 Loss: 164.336
+51200/69092 Loss: 165.475
+64000/69092 Loss: 163.452
+Training time 0:03:34.858968
+Epoch: 34 Average loss: 164.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 38)
+0/69092 Loss: 168.195
+12800/69092 Loss: 164.326
+25600/69092 Loss: 164.275
+38400/69092 Loss: 164.487
+51200/69092 Loss: 164.394
+64000/69092 Loss: 163.267
+Training time 0:03:34.799991
+Epoch: 35 Average loss: 164.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 39)
+0/69092 Loss: 162.548
+12800/69092 Loss: 164.372
+25600/69092 Loss: 164.171
+38400/69092 Loss: 163.765
+51200/69092 Loss: 163.489
+64000/69092 Loss: 163.863
+Training time 0:03:34.533021
+Epoch: 36 Average loss: 164.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 40)
+0/69092 Loss: 175.309
+12800/69092 Loss: 163.169
+25600/69092 Loss: 164.087
+38400/69092 Loss: 163.781
+51200/69092 Loss: 162.098
+64000/69092 Loss: 164.361
+Training time 0:03:34.473704
+Epoch: 37 Average loss: 163.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 41)
+0/69092 Loss: 165.228
+12800/69092 Loss: 163.195
+25600/69092 Loss: 164.960
+38400/69092 Loss: 164.452
+51200/69092 Loss: 164.154
+64000/69092 Loss: 162.288
+Training time 0:03:34.369595
+Epoch: 38 Average loss: 163.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 42)
+0/69092 Loss: 171.521
+12800/69092 Loss: 162.591
+25600/69092 Loss: 163.128
+38400/69092 Loss: 165.067
+51200/69092 Loss: 163.874
+64000/69092 Loss: 163.911
+Training time 0:03:34.312838
+Epoch: 39 Average loss: 163.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 43)
+0/69092 Loss: 155.946
+12800/69092 Loss: 163.299
+25600/69092 Loss: 162.675
+38400/69092 Loss: 165.110
+51200/69092 Loss: 164.525
+64000/69092 Loss: 164.077
+Training time 0:03:34.585969
+Epoch: 40 Average loss: 163.81
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 44)
+0/69092 Loss: 153.399
+12800/69092 Loss: 163.496
+25600/69092 Loss: 163.969
+38400/69092 Loss: 165.591
+51200/69092 Loss: 164.615
+64000/69092 Loss: 161.441
+Training time 0:03:34.709067
+Epoch: 41 Average loss: 163.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 45)
+0/69092 Loss: 156.591
+12800/69092 Loss: 163.524
+25600/69092 Loss: 162.621
+38400/69092 Loss: 164.014
+51200/69092 Loss: 164.112
+64000/69092 Loss: 164.345
+Training time 0:03:35.252902
+Epoch: 42 Average loss: 163.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 46)
+0/69092 Loss: 163.608
+12800/69092 Loss: 163.123
+25600/69092 Loss: 162.704
+38400/69092 Loss: 163.056
+51200/69092 Loss: 164.623
+64000/69092 Loss: 164.656
+Training time 0:03:34.662832
+Epoch: 43 Average loss: 163.69
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 47)
+0/69092 Loss: 153.614
+12800/69092 Loss: 163.977
+25600/69092 Loss: 163.717
+38400/69092 Loss: 163.211
+51200/69092 Loss: 163.595
+64000/69092 Loss: 162.397
+Training time 0:03:34.460078
+Epoch: 44 Average loss: 163.41
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 48)
+0/69092 Loss: 156.672
+12800/69092 Loss: 164.010
+25600/69092 Loss: 164.568
+38400/69092 Loss: 163.828
+51200/69092 Loss: 161.124
+64000/69092 Loss: 162.826
+Training time 0:03:34.715757
+Epoch: 45 Average loss: 163.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 49)
+0/69092 Loss: 170.756
+12800/69092 Loss: 163.939
+25600/69092 Loss: 163.215
+38400/69092 Loss: 163.414
+51200/69092 Loss: 163.922
+64000/69092 Loss: 163.683
+Training time 0:03:35.627656
+Epoch: 46 Average loss: 163.62
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 50)
+0/69092 Loss: 164.595
+12800/69092 Loss: 163.255
+25600/69092 Loss: 162.672
+38400/69092 Loss: 164.189
+51200/69092 Loss: 163.686
+64000/69092 Loss: 162.669
+Training time 0:03:34.370078
+Epoch: 47 Average loss: 163.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 51)
+0/69092 Loss: 165.749
+12800/69092 Loss: 162.771
+25600/69092 Loss: 163.035
+38400/69092 Loss: 164.054
+51200/69092 Loss: 161.917
+64000/69092 Loss: 162.585
+Training time 0:03:34.667025
+Epoch: 48 Average loss: 163.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 52)
+0/69092 Loss: 154.571
+12800/69092 Loss: 163.939
+25600/69092 Loss: 163.544
+38400/69092 Loss: 163.970
+51200/69092 Loss: 161.575
+64000/69092 Loss: 163.866
+Training time 0:03:34.864267
+Epoch: 49 Average loss: 163.40
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 53)
+0/69092 Loss: 166.004
+12800/69092 Loss: 163.521
+25600/69092 Loss: 162.988
+38400/69092 Loss: 163.130
+51200/69092 Loss: 163.261
+64000/69092 Loss: 163.026
+Training time 0:03:34.138736
+Epoch: 50 Average loss: 163.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 54)
+0/69092 Loss: 156.938
+12800/69092 Loss: 163.198
+25600/69092 Loss: 163.394
+38400/69092 Loss: 163.262
+51200/69092 Loss: 162.025
+64000/69092 Loss: 163.956
+Training time 0:03:34.763237
+Epoch: 51 Average loss: 163.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 55)
+0/69092 Loss: 164.332
+12800/69092 Loss: 163.878
+25600/69092 Loss: 163.516
+38400/69092 Loss: 163.663
+51200/69092 Loss: 162.682
+64000/69092 Loss: 161.136
+Training time 0:03:35.382783
+Epoch: 52 Average loss: 163.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 56)
+0/69092 Loss: 155.649
+12800/69092 Loss: 163.492
+25600/69092 Loss: 161.749
+38400/69092 Loss: 164.039
+51200/69092 Loss: 162.869
+64000/69092 Loss: 163.493
+Training time 0:03:35.305348
+Epoch: 53 Average loss: 163.03
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 57)
+0/69092 Loss: 157.357
+12800/69092 Loss: 162.833
+25600/69092 Loss: 163.294
+38400/69092 Loss: 162.755
+51200/69092 Loss: 162.863
+64000/69092 Loss: 163.724
+Training time 0:03:34.340976
+Epoch: 54 Average loss: 163.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 58)
+0/69092 Loss: 151.717
+12800/69092 Loss: 163.086
+25600/69092 Loss: 162.400
+38400/69092 Loss: 164.084
+51200/69092 Loss: 162.815
+64000/69092 Loss: 163.041
+Training time 0:03:35.284810
+Epoch: 55 Average loss: 163.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 59)
+0/69092 Loss: 158.464
+12800/69092 Loss: 163.570
+25600/69092 Loss: 161.727
+38400/69092 Loss: 164.416
+51200/69092 Loss: 162.037
+64000/69092 Loss: 163.618
+Training time 0:03:35.450159
+Epoch: 56 Average loss: 162.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 60)
+0/69092 Loss: 163.640
+12800/69092 Loss: 161.432
+25600/69092 Loss: 162.910
+38400/69092 Loss: 162.904
+51200/69092 Loss: 163.806
+64000/69092 Loss: 162.356
+Training time 0:03:35.267777
+Epoch: 57 Average loss: 162.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 61)
+0/69092 Loss: 161.019
+12800/69092 Loss: 163.341
+25600/69092 Loss: 162.614
+38400/69092 Loss: 162.124
+51200/69092 Loss: 163.792
+64000/69092 Loss: 162.896
+Training time 0:03:34.874771
+Epoch: 58 Average loss: 162.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 62)
+0/69092 Loss: 166.624
+12800/69092 Loss: 161.990
+25600/69092 Loss: 164.154
+38400/69092 Loss: 161.882
+51200/69092 Loss: 163.737
+64000/69092 Loss: 163.187
+Training time 0:03:34.613702
+Epoch: 59 Average loss: 162.95
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 63)
+0/69092 Loss: 154.731
+12800/69092 Loss: 162.162
+25600/69092 Loss: 164.492
+38400/69092 Loss: 161.770
+51200/69092 Loss: 162.852
+64000/69092 Loss: 163.725
+Training time 0:03:34.790103
+Epoch: 60 Average loss: 162.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 64)
+0/69092 Loss: 162.959
+12800/69092 Loss: 162.458
+25600/69092 Loss: 163.497
+38400/69092 Loss: 163.245
+51200/69092 Loss: 162.263
+64000/69092 Loss: 162.196
+Training time 0:03:34.640282
+Epoch: 61 Average loss: 162.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 65)
+0/69092 Loss: 149.759
+12800/69092 Loss: 162.575
+25600/69092 Loss: 163.300
+38400/69092 Loss: 162.595
+51200/69092 Loss: 162.451
+64000/69092 Loss: 163.141
+Training time 0:03:34.993601
+Epoch: 62 Average loss: 162.87
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 66)
+0/69092 Loss: 167.817
+12800/69092 Loss: 163.591
+25600/69092 Loss: 161.586
+38400/69092 Loss: 162.502
+51200/69092 Loss: 163.298
+64000/69092 Loss: 162.439
+Training time 0:03:34.761672
+Epoch: 63 Average loss: 162.86
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 67)
+0/69092 Loss: 162.536
+12800/69092 Loss: 163.860
+25600/69092 Loss: 161.999
+38400/69092 Loss: 162.851
+51200/69092 Loss: 161.707
+64000/69092 Loss: 161.781
+Training time 0:03:34.965002
+Epoch: 64 Average loss: 162.63
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 68)
+0/69092 Loss: 179.664
+12800/69092 Loss: 161.629
+25600/69092 Loss: 161.899
+38400/69092 Loss: 163.334
+51200/69092 Loss: 162.383
+64000/69092 Loss: 163.327
+Training time 0:03:34.568599
+Epoch: 65 Average loss: 162.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 69)
+0/69092 Loss: 173.059
+12800/69092 Loss: 161.902
+25600/69092 Loss: 162.708
+38400/69092 Loss: 162.622
+51200/69092 Loss: 161.650
+64000/69092 Loss: 162.824
+Training time 0:03:35.257544
+Epoch: 66 Average loss: 162.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 70)
+0/69092 Loss: 143.647
+12800/69092 Loss: 161.864
+25600/69092 Loss: 164.030
+38400/69092 Loss: 162.059
+51200/69092 Loss: 162.619
+64000/69092 Loss: 162.301
+Training time 0:03:35.838210
+Epoch: 67 Average loss: 162.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 71)
+0/69092 Loss: 166.080
+12800/69092 Loss: 162.964
+25600/69092 Loss: 163.455
+38400/69092 Loss: 162.464
+51200/69092 Loss: 162.000
+64000/69092 Loss: 161.847
+Training time 0:03:34.850876
+Epoch: 68 Average loss: 162.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 72)
+0/69092 Loss: 165.306
+12800/69092 Loss: 160.963
+25600/69092 Loss: 162.906
+38400/69092 Loss: 162.016
+51200/69092 Loss: 162.826
+64000/69092 Loss: 162.230
+Training time 0:03:35.222198
+Epoch: 69 Average loss: 162.24
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 73)
+0/69092 Loss: 159.279
+12800/69092 Loss: 161.766
+25600/69092 Loss: 162.684
+38400/69092 Loss: 162.991
+51200/69092 Loss: 163.154
+64000/69092 Loss: 161.012
+Training time 0:03:34.964302
+Epoch: 70 Average loss: 162.46
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 74)
+0/69092 Loss: 154.284
+12800/69092 Loss: 161.469
+25600/69092 Loss: 162.418
+38400/69092 Loss: 163.576
+51200/69092 Loss: 162.523
+64000/69092 Loss: 162.598
+Training time 0:03:34.914750
+Epoch: 71 Average loss: 162.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 75)
+0/69092 Loss: 167.185
+12800/69092 Loss: 160.845
+25600/69092 Loss: 164.084
+38400/69092 Loss: 161.897
+51200/69092 Loss: 162.055
+64000/69092 Loss: 163.471
+Training time 0:03:35.329838
+Epoch: 72 Average loss: 162.44
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 76)
+0/69092 Loss: 162.192
+12800/69092 Loss: 162.530
+25600/69092 Loss: 162.514
+38400/69092 Loss: 163.070
+51200/69092 Loss: 163.560
+64000/69092 Loss: 160.817
+Training time 0:03:35.328085
+Epoch: 73 Average loss: 162.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 77)
+0/69092 Loss: 166.539
+12800/69092 Loss: 162.278
+25600/69092 Loss: 163.629
+38400/69092 Loss: 162.288
+51200/69092 Loss: 160.127
+64000/69092 Loss: 162.499
+Training time 0:03:35.936274
+Epoch: 74 Average loss: 162.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 78)
+0/69092 Loss: 172.523
+12800/69092 Loss: 162.526
+25600/69092 Loss: 161.003
+38400/69092 Loss: 161.351
+51200/69092 Loss: 163.644
+64000/69092 Loss: 162.010
+Training time 0:03:34.917218
+Epoch: 75 Average loss: 162.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 79)
+0/69092 Loss: 160.719
+12800/69092 Loss: 161.441
+25600/69092 Loss: 163.190
+38400/69092 Loss: 161.822
+51200/69092 Loss: 162.764
+64000/69092 Loss: 162.811
+Training time 0:03:34.825778
+Epoch: 76 Average loss: 162.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 80)
+0/69092 Loss: 155.755
+12800/69092 Loss: 162.279
+25600/69092 Loss: 162.457
+38400/69092 Loss: 161.739
+51200/69092 Loss: 162.629
+64000/69092 Loss: 161.704
+Training time 0:03:34.994539
+Epoch: 77 Average loss: 162.20
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 81)
+0/69092 Loss: 167.212
+12800/69092 Loss: 161.920
+25600/69092 Loss: 162.322
+38400/69092 Loss: 163.715
+51200/69092 Loss: 162.922
+64000/69092 Loss: 161.310
+Training time 0:03:35.098604
+Epoch: 78 Average loss: 162.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 82)
+0/69092 Loss: 151.279
+12800/69092 Loss: 163.096
+25600/69092 Loss: 161.743
+38400/69092 Loss: 162.718
+51200/69092 Loss: 161.715
+64000/69092 Loss: 161.252
+Training time 0:03:34.378245
+Epoch: 79 Average loss: 162.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 83)
+0/69092 Loss: 171.928
+12800/69092 Loss: 161.661
+25600/69092 Loss: 162.137
+38400/69092 Loss: 162.428
+51200/69092 Loss: 162.382
+64000/69092 Loss: 162.189
+Training time 0:03:35.523365
+Epoch: 80 Average loss: 162.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 84)
+0/69092 Loss: 162.969
+12800/69092 Loss: 161.660
+25600/69092 Loss: 162.341
+38400/69092 Loss: 162.861
+51200/69092 Loss: 162.088
+64000/69092 Loss: 160.830
+Training time 0:03:35.828830
+Epoch: 81 Average loss: 162.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 85)
+0/69092 Loss: 163.799
+12800/69092 Loss: 160.779
+25600/69092 Loss: 161.781
+38400/69092 Loss: 162.490
+51200/69092 Loss: 162.095
+64000/69092 Loss: 162.997
+Training time 0:03:35.324407
+Epoch: 82 Average loss: 162.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 86)
+0/69092 Loss: 161.640
+12800/69092 Loss: 161.039
+25600/69092 Loss: 160.883
+38400/69092 Loss: 163.249
+51200/69092 Loss: 162.210
+64000/69092 Loss: 162.516
+Training time 0:03:35.899620
+Epoch: 83 Average loss: 162.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 87)
+0/69092 Loss: 158.307
+12800/69092 Loss: 161.081
+25600/69092 Loss: 162.671
+38400/69092 Loss: 163.349
+51200/69092 Loss: 161.665
+64000/69092 Loss: 162.282
+Training time 0:03:35.265596
+Epoch: 84 Average loss: 162.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 88)
+0/69092 Loss: 164.806
+12800/69092 Loss: 162.299
+25600/69092 Loss: 162.815
+38400/69092 Loss: 162.283
+51200/69092 Loss: 162.029
+64000/69092 Loss: 161.074
+Training time 0:03:35.331087
+Epoch: 85 Average loss: 162.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 89)
+0/69092 Loss: 155.805
+12800/69092 Loss: 160.191
+25600/69092 Loss: 162.544
+38400/69092 Loss: 162.671
+51200/69092 Loss: 162.905
+64000/69092 Loss: 161.941
+Training time 0:03:34.833442
+Epoch: 86 Average loss: 162.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 90)
+0/69092 Loss: 155.322
+12800/69092 Loss: 162.365
+25600/69092 Loss: 160.966
+38400/69092 Loss: 162.498
+51200/69092 Loss: 161.492
+64000/69092 Loss: 162.268
+Training time 0:03:35.194527
+Epoch: 87 Average loss: 161.91
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 91)
+0/69092 Loss: 147.863
+12800/69092 Loss: 161.758
+25600/69092 Loss: 163.217
+38400/69092 Loss: 160.943
+51200/69092 Loss: 161.846
+64000/69092 Loss: 163.150
+Training time 0:03:35.543712
+Epoch: 88 Average loss: 161.97
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 92)
+0/69092 Loss: 156.224
+12800/69092 Loss: 161.407
+25600/69092 Loss: 163.099
+38400/69092 Loss: 160.885
+51200/69092 Loss: 163.510
+64000/69092 Loss: 160.672
+Training time 0:03:34.976525
+Epoch: 89 Average loss: 162.03
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 93)
+0/69092 Loss: 163.257
+12800/69092 Loss: 160.499
+25600/69092 Loss: 161.542
+38400/69092 Loss: 161.733
+51200/69092 Loss: 162.542
+64000/69092 Loss: 162.036
+Training time 0:03:35.251829
+Epoch: 90 Average loss: 161.62
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 94)
+0/69092 Loss: 154.922
+12800/69092 Loss: 162.931
+25600/69092 Loss: 161.596
+38400/69092 Loss: 161.078
+51200/69092 Loss: 161.143
+64000/69092 Loss: 162.084
+Training time 0:03:34.964534
+Epoch: 91 Average loss: 161.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 95)
+0/69092 Loss: 160.102
+12800/69092 Loss: 160.236
+25600/69092 Loss: 160.102
+38400/69092 Loss: 163.086
+51200/69092 Loss: 161.250
+64000/69092 Loss: 161.530
+Training time 0:03:35.274464
+Epoch: 92 Average loss: 161.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 96)
+0/69092 Loss: 166.773
+12800/69092 Loss: 161.029
+25600/69092 Loss: 160.234
+38400/69092 Loss: 161.051
+51200/69092 Loss: 159.310
+64000/69092 Loss: 162.371
+Training time 0:03:34.579357
+Epoch: 93 Average loss: 160.69
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 97)
+0/69092 Loss: 156.949
+12800/69092 Loss: 160.754
+25600/69092 Loss: 161.179
+38400/69092 Loss: 160.530
+51200/69092 Loss: 160.001
+64000/69092 Loss: 160.276
+Training time 0:03:35.364384
+Epoch: 94 Average loss: 160.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 98)
+0/69092 Loss: 159.017
+12800/69092 Loss: 160.227
+25600/69092 Loss: 160.172
+38400/69092 Loss: 161.081
+51200/69092 Loss: 160.087
+64000/69092 Loss: 159.089
+Training time 0:03:35.051431
+Epoch: 95 Average loss: 160.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 99)
+0/69092 Loss: 158.123
+12800/69092 Loss: 161.822
+25600/69092 Loss: 161.200
+38400/69092 Loss: 159.726
+51200/69092 Loss: 159.801
+64000/69092 Loss: 159.733
+Training time 0:03:34.861642
+Epoch: 96 Average loss: 160.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 100)
+0/69092 Loss: 159.461
+12800/69092 Loss: 161.147
+25600/69092 Loss: 159.821
+38400/69092 Loss: 160.349
+51200/69092 Loss: 159.651
+64000/69092 Loss: 159.265
+Training time 0:03:34.392620
+Epoch: 97 Average loss: 160.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 101)
+0/69092 Loss: 162.921
+12800/69092 Loss: 160.283
+25600/69092 Loss: 160.020
+38400/69092 Loss: 159.135
+51200/69092 Loss: 160.403
+64000/69092 Loss: 159.789
+Training time 0:03:34.944138
+Epoch: 98 Average loss: 160.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 102)
+0/69092 Loss: 157.861
+12800/69092 Loss: 160.768
+25600/69092 Loss: 160.062
+38400/69092 Loss: 159.811
+51200/69092 Loss: 159.629
+64000/69092 Loss: 159.130
+Training time 0:03:35.133845
+Epoch: 99 Average loss: 159.78
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 103)
+0/69092 Loss: 158.712
+12800/69092 Loss: 161.059
+25600/69092 Loss: 159.315
+38400/69092 Loss: 158.331
+51200/69092 Loss: 160.059
+64000/69092 Loss: 159.669
+Training time 0:03:34.706540
+Epoch: 100 Average loss: 159.69
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 104)
+0/69092 Loss: 155.913
+12800/69092 Loss: 159.568
+25600/69092 Loss: 158.244
+38400/69092 Loss: 160.843
+51200/69092 Loss: 160.379
+64000/69092 Loss: 158.854
+Training time 0:03:35.405917
+Epoch: 101 Average loss: 159.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 105)
+0/69092 Loss: 167.682
+12800/69092 Loss: 158.469
+25600/69092 Loss: 159.905
+38400/69092 Loss: 159.218
+51200/69092 Loss: 159.482
+64000/69092 Loss: 160.674
+Training time 0:03:35.620727
+Epoch: 102 Average loss: 159.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 106)
+0/69092 Loss: 171.435
+12800/69092 Loss: 159.276
+25600/69092 Loss: 161.324
+38400/69092 Loss: 158.919
+51200/69092 Loss: 159.018
+64000/69092 Loss: 157.720
+Training time 0:03:35.123399
+Epoch: 103 Average loss: 159.50
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 107)
+0/69092 Loss: 157.325
+12800/69092 Loss: 160.057
+25600/69092 Loss: 159.965
+38400/69092 Loss: 158.745
+51200/69092 Loss: 158.629
+64000/69092 Loss: 160.076
+Training time 0:03:35.501608
+Epoch: 104 Average loss: 159.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 108)
+0/69092 Loss: 157.509
+12800/69092 Loss: 160.053
+25600/69092 Loss: 158.607
+38400/69092 Loss: 159.661
+51200/69092 Loss: 160.847
+64000/69092 Loss: 158.248
+Training time 0:03:35.400709
+Epoch: 105 Average loss: 159.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 109)
+0/69092 Loss: 163.613
+12800/69092 Loss: 158.970
+25600/69092 Loss: 161.421
+38400/69092 Loss: 160.211
+51200/69092 Loss: 158.902
+64000/69092 Loss: 156.983
+Training time 0:03:36.255216
+Epoch: 106 Average loss: 159.40
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 110)
+0/69092 Loss: 155.686
+12800/69092 Loss: 160.493
+25600/69092 Loss: 160.184
+38400/69092 Loss: 159.047
+51200/69092 Loss: 158.804
+64000/69092 Loss: 158.537
+Training time 0:03:35.555878
+Epoch: 107 Average loss: 159.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 111)
+0/69092 Loss: 156.330
+12800/69092 Loss: 159.314
+25600/69092 Loss: 159.905
+38400/69092 Loss: 159.925
+51200/69092 Loss: 159.359
+64000/69092 Loss: 157.967
+Training time 0:03:35.554763
+Epoch: 108 Average loss: 159.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 112)
+0/69092 Loss: 155.420
+12800/69092 Loss: 159.249
+25600/69092 Loss: 161.278
+38400/69092 Loss: 159.389
+51200/69092 Loss: 157.854
+64000/69092 Loss: 159.796
+Training time 0:03:35.583739
+Epoch: 109 Average loss: 159.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 113)
+0/69092 Loss: 159.001
+12800/69092 Loss: 160.341
+25600/69092 Loss: 159.930
+38400/69092 Loss: 159.423
+51200/69092 Loss: 156.880
+64000/69092 Loss: 159.597
+Training time 0:03:35.430130
+Epoch: 110 Average loss: 159.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 114)
+0/69092 Loss: 158.623
+12800/69092 Loss: 158.642
+25600/69092 Loss: 158.103
+38400/69092 Loss: 160.518
+51200/69092 Loss: 159.409
+64000/69092 Loss: 159.840
+Training time 0:03:34.558461
+Epoch: 111 Average loss: 159.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 115)
+0/69092 Loss: 152.833
+12800/69092 Loss: 159.505
+25600/69092 Loss: 159.212
+38400/69092 Loss: 158.390
+51200/69092 Loss: 159.647
+64000/69092 Loss: 158.710
+Training time 0:03:34.888443
+Epoch: 112 Average loss: 159.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 116)
+0/69092 Loss: 155.050
+12800/69092 Loss: 158.368
+25600/69092 Loss: 158.714
+38400/69092 Loss: 158.764
+51200/69092 Loss: 159.854
+64000/69092 Loss: 159.695
+Training time 0:03:34.671602
+Epoch: 113 Average loss: 158.90
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 117)
+0/69092 Loss: 163.357
+12800/69092 Loss: 158.739
+25600/69092 Loss: 159.252
+38400/69092 Loss: 158.969
+51200/69092 Loss: 159.156
+64000/69092 Loss: 157.985
+Training time 0:03:34.661148
+Epoch: 114 Average loss: 158.96
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 118)
+0/69092 Loss: 162.348
+12800/69092 Loss: 159.469
+25600/69092 Loss: 157.832
+38400/69092 Loss: 159.314
+51200/69092 Loss: 157.827
+64000/69092 Loss: 159.283
+Training time 0:03:35.230302
+Epoch: 115 Average loss: 158.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 119)
+0/69092 Loss: 162.101
+12800/69092 Loss: 159.002
+25600/69092 Loss: 158.509
+38400/69092 Loss: 159.518
+51200/69092 Loss: 159.064
+64000/69092 Loss: 158.847
+Training time 0:03:35.700575
+Epoch: 116 Average loss: 158.84
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 120)
+0/69092 Loss: 154.467
+12800/69092 Loss: 160.387
+25600/69092 Loss: 157.670
+38400/69092 Loss: 159.577
+51200/69092 Loss: 159.122
+64000/69092 Loss: 157.489
+Training time 0:03:34.765808
+Epoch: 117 Average loss: 158.96
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 121)
+0/69092 Loss: 158.054
+12800/69092 Loss: 158.487
+25600/69092 Loss: 159.933
+38400/69092 Loss: 158.417
+51200/69092 Loss: 158.789
+64000/69092 Loss: 158.287
+Training time 0:03:34.685419
+Epoch: 118 Average loss: 158.90
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 122)
+0/69092 Loss: 157.949
+12800/69092 Loss: 158.047
+25600/69092 Loss: 159.524
+38400/69092 Loss: 158.612
+51200/69092 Loss: 158.843
+64000/69092 Loss: 158.874
+Training time 0:03:35.283052
+Epoch: 119 Average loss: 158.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 123)
+0/69092 Loss: 158.028
+12800/69092 Loss: 158.785
+25600/69092 Loss: 159.106
+38400/69092 Loss: 158.322
+51200/69092 Loss: 159.148
+64000/69092 Loss: 158.622
+Training time 0:03:35.123444
+Epoch: 120 Average loss: 158.91
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 124)
+0/69092 Loss: 153.756
+12800/69092 Loss: 158.229
+25600/69092 Loss: 157.864
+38400/69092 Loss: 158.555
+51200/69092 Loss: 158.647
+64000/69092 Loss: 159.850
+Training time 0:03:35.094660
+Epoch: 121 Average loss: 158.70
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 125)
+0/69092 Loss: 143.921
+12800/69092 Loss: 159.522
+25600/69092 Loss: 158.944
+38400/69092 Loss: 159.566
+51200/69092 Loss: 157.765
+64000/69092 Loss: 158.544
+Training time 0:03:35.945210
+Epoch: 122 Average loss: 158.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 126)
+0/69092 Loss: 168.748
+12800/69092 Loss: 156.964
+25600/69092 Loss: 158.709
+38400/69092 Loss: 158.935
+51200/69092 Loss: 158.750
+64000/69092 Loss: 158.742
+Training time 0:03:35.022846
+Epoch: 123 Average loss: 158.57
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 127)
+0/69092 Loss: 174.862
+12800/69092 Loss: 159.569
+25600/69092 Loss: 157.994
+38400/69092 Loss: 159.820
+51200/69092 Loss: 159.512
+64000/69092 Loss: 156.537
+Training time 0:03:36.069587
+Epoch: 124 Average loss: 158.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 128)
+0/69092 Loss: 157.101
+12800/69092 Loss: 158.661
+25600/69092 Loss: 158.794
+38400/69092 Loss: 158.160
+51200/69092 Loss: 158.458
+64000/69092 Loss: 158.821
+Training time 0:03:34.841007
+Epoch: 125 Average loss: 158.81
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 129)
+0/69092 Loss: 152.880
+12800/69092 Loss: 158.483
+25600/69092 Loss: 159.524
+38400/69092 Loss: 158.460
+51200/69092 Loss: 158.845
+64000/69092 Loss: 158.793
+Training time 0:03:35.971434
+Epoch: 126 Average loss: 158.69
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 130)
+0/69092 Loss: 154.705
+12800/69092 Loss: 159.422
+25600/69092 Loss: 157.914
+38400/69092 Loss: 158.562
+51200/69092 Loss: 158.664
+64000/69092 Loss: 158.733
+Training time 0:03:35.689516
+Epoch: 127 Average loss: 158.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 131)
+0/69092 Loss: 160.352
+12800/69092 Loss: 158.191
+25600/69092 Loss: 158.584
+38400/69092 Loss: 157.742
+51200/69092 Loss: 159.398
+64000/69092 Loss: 159.173
+Training time 0:03:35.079701
+Epoch: 128 Average loss: 158.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 132)
+0/69092 Loss: 162.502
+12800/69092 Loss: 157.818
+25600/69092 Loss: 157.109
+38400/69092 Loss: 159.595
+51200/69092 Loss: 158.628
+64000/69092 Loss: 159.182
+Training time 0:03:35.702826
+Epoch: 129 Average loss: 158.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 133)
+0/69092 Loss: 157.088
+12800/69092 Loss: 158.229
+25600/69092 Loss: 157.869
+38400/69092 Loss: 159.662
+51200/69092 Loss: 158.721
+64000/69092 Loss: 157.514
+Training time 0:03:35.305305
+Epoch: 130 Average loss: 158.46
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 134)
+0/69092 Loss: 159.425
+12800/69092 Loss: 158.244
+25600/69092 Loss: 159.269
+38400/69092 Loss: 158.749
+51200/69092 Loss: 157.334
+64000/69092 Loss: 158.863
+Training time 0:03:35.265594
+Epoch: 131 Average loss: 158.43
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 135)
+0/69092 Loss: 155.062
+12800/69092 Loss: 159.819
+25600/69092 Loss: 157.603
+38400/69092 Loss: 156.517
+51200/69092 Loss: 158.284
+64000/69092 Loss: 158.365
+Training time 0:03:35.083675
+Epoch: 132 Average loss: 158.22
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 136)
+0/69092 Loss: 159.165
+12800/69092 Loss: 158.364
+25600/69092 Loss: 158.555
+38400/69092 Loss: 157.526
+51200/69092 Loss: 157.675
+64000/69092 Loss: 159.562
+Training time 0:03:34.917954
+Epoch: 133 Average loss: 158.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 137)
+0/69092 Loss: 164.086
+12800/69092 Loss: 158.136
+25600/69092 Loss: 158.652
+38400/69092 Loss: 158.086
+51200/69092 Loss: 158.328
+64000/69092 Loss: 158.661
+Training time 0:03:36.238460
+Epoch: 134 Average loss: 158.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 138)
+0/69092 Loss: 160.262
+12800/69092 Loss: 158.737
+25600/69092 Loss: 157.851
+38400/69092 Loss: 158.741
+51200/69092 Loss: 159.681
+64000/69092 Loss: 157.209
+Training time 0:03:35.637006
+Epoch: 135 Average loss: 158.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 139)
+0/69092 Loss: 153.405
+12800/69092 Loss: 158.161
+25600/69092 Loss: 157.899
+38400/69092 Loss: 158.128
+51200/69092 Loss: 158.549
+64000/69092 Loss: 158.466
+Training time 0:03:35.008490
+Epoch: 136 Average loss: 158.32
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 140)
+0/69092 Loss: 163.934
+12800/69092 Loss: 158.955
+25600/69092 Loss: 157.057
+38400/69092 Loss: 158.497
+51200/69092 Loss: 157.707
+64000/69092 Loss: 158.849
+Training time 0:03:35.428050
+Epoch: 137 Average loss: 158.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 141)
+0/69092 Loss: 155.505
+12800/69092 Loss: 157.641
+25600/69092 Loss: 158.311
+38400/69092 Loss: 158.298
+51200/69092 Loss: 159.948
+64000/69092 Loss: 159.012
+Training time 0:03:35.474795
+Epoch: 138 Average loss: 158.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 142)
+0/69092 Loss: 157.240
+12800/69092 Loss: 157.478
+25600/69092 Loss: 157.290
+38400/69092 Loss: 159.291
+51200/69092 Loss: 159.086
+64000/69092 Loss: 157.686
+Training time 0:03:34.563481
+Epoch: 139 Average loss: 158.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 143)
+0/69092 Loss: 163.161
+12800/69092 Loss: 158.980
+25600/69092 Loss: 158.207
+38400/69092 Loss: 157.897
+51200/69092 Loss: 158.807
+64000/69092 Loss: 157.534
+Training time 0:03:35.396170
+Epoch: 140 Average loss: 158.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 144)
+0/69092 Loss: 158.131
+12800/69092 Loss: 158.350
+25600/69092 Loss: 159.413
+38400/69092 Loss: 157.611
+51200/69092 Loss: 157.664
+64000/69092 Loss: 158.728
+Training time 0:03:35.857792
+Epoch: 141 Average loss: 158.33
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 145)
+0/69092 Loss: 157.857
+12800/69092 Loss: 156.593
+25600/69092 Loss: 158.148
+38400/69092 Loss: 158.734
+51200/69092 Loss: 157.102
+64000/69092 Loss: 160.617
+Training time 0:03:35.646313
+Epoch: 142 Average loss: 158.24
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 146)
+0/69092 Loss: 156.539
+12800/69092 Loss: 157.214
+25600/69092 Loss: 157.716
+38400/69092 Loss: 158.455
+51200/69092 Loss: 159.153
+64000/69092 Loss: 158.133
+Training time 0:03:35.056588
+Epoch: 143 Average loss: 158.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 147)
+0/69092 Loss: 158.767
+12800/69092 Loss: 158.426
+25600/69092 Loss: 158.740
+38400/69092 Loss: 158.653
+51200/69092 Loss: 157.037
+64000/69092 Loss: 158.165
+Training time 0:03:35.533263
+Epoch: 144 Average loss: 158.28
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 148)
+0/69092 Loss: 168.808
+12800/69092 Loss: 158.690
+25600/69092 Loss: 158.282
+38400/69092 Loss: 158.720
+51200/69092 Loss: 158.501
+64000/69092 Loss: 156.006
+Training time 0:03:35.591309
+Epoch: 145 Average loss: 158.29
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 149)
+0/69092 Loss: 154.829
+12800/69092 Loss: 157.699
+25600/69092 Loss: 158.313
+38400/69092 Loss: 158.132
+51200/69092 Loss: 159.645
+64000/69092 Loss: 157.362
+Training time 0:03:34.651382
+Epoch: 146 Average loss: 158.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 150)
+0/69092 Loss: 159.604
+12800/69092 Loss: 158.903
+25600/69092 Loss: 158.258
+38400/69092 Loss: 156.677
+51200/69092 Loss: 158.281
+64000/69092 Loss: 158.580
+Training time 0:03:36.077990
+Epoch: 147 Average loss: 158.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 151)
+0/69092 Loss: 152.812
+12800/69092 Loss: 158.197
+25600/69092 Loss: 158.080
+38400/69092 Loss: 157.186
+51200/69092 Loss: 159.838
+64000/69092 Loss: 156.717
+Training time 0:03:35.545069
+Epoch: 148 Average loss: 158.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 152)
+0/69092 Loss: 149.683
+12800/69092 Loss: 159.115
+25600/69092 Loss: 157.753
+38400/69092 Loss: 157.867
+51200/69092 Loss: 158.096
+64000/69092 Loss: 156.865
+Training time 0:03:35.144237
+Epoch: 149 Average loss: 158.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 153)
+0/69092 Loss: 156.870
+12800/69092 Loss: 158.312
+25600/69092 Loss: 158.026
+38400/69092 Loss: 157.353
+51200/69092 Loss: 158.157
+64000/69092 Loss: 158.827
+Training time 0:03:34.748605
+Epoch: 150 Average loss: 158.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 154)
+0/69092 Loss: 151.995
+12800/69092 Loss: 159.054
+25600/69092 Loss: 157.990
+38400/69092 Loss: 158.319
+51200/69092 Loss: 157.961
+64000/69092 Loss: 157.131
+Training time 0:03:35.971841
+Epoch: 151 Average loss: 158.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 155)
+0/69092 Loss: 157.716
+12800/69092 Loss: 158.155
+25600/69092 Loss: 157.841
+38400/69092 Loss: 158.173
+51200/69092 Loss: 157.463
+64000/69092 Loss: 158.897
+Training time 0:03:35.695476
+Epoch: 152 Average loss: 158.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 156)
+0/69092 Loss: 158.047
+12800/69092 Loss: 156.737
+25600/69092 Loss: 159.180
+38400/69092 Loss: 158.536
+51200/69092 Loss: 157.577
+64000/69092 Loss: 158.394
+Training time 0:03:35.084412
+Epoch: 153 Average loss: 158.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 157)
+0/69092 Loss: 153.462
+12800/69092 Loss: 159.247
+25600/69092 Loss: 155.980
+38400/69092 Loss: 157.080
+51200/69092 Loss: 158.520
+64000/69092 Loss: 158.175
+Training time 0:03:35.337585
+Epoch: 154 Average loss: 157.86
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 158)
+0/69092 Loss: 161.419
+12800/69092 Loss: 157.782
+25600/69092 Loss: 158.716
+38400/69092 Loss: 158.169
+51200/69092 Loss: 158.782
+64000/69092 Loss: 156.570
+Training time 0:03:35.909995
+Epoch: 155 Average loss: 158.07
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 159)
+0/69092 Loss: 153.223
+12800/69092 Loss: 158.127
+25600/69092 Loss: 157.457
+38400/69092 Loss: 158.450
+51200/69092 Loss: 158.295
+64000/69092 Loss: 157.931
+Training time 0:03:36.134499
+Epoch: 156 Average loss: 158.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 160)
+0/69092 Loss: 162.210
+12800/69092 Loss: 158.300
+25600/69092 Loss: 157.865
+38400/69092 Loss: 158.634
+51200/69092 Loss: 158.413
+64000/69092 Loss: 156.988
+Training time 0:03:35.206750
+Epoch: 157 Average loss: 157.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 161)
+0/69092 Loss: 161.032
+12800/69092 Loss: 158.088
+25600/69092 Loss: 157.736
+38400/69092 Loss: 157.373
+51200/69092 Loss: 158.703
+64000/69092 Loss: 158.100
+Training time 0:03:35.589710
+Epoch: 158 Average loss: 158.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 162)
+0/69092 Loss: 153.675
+12800/69092 Loss: 158.432
+25600/69092 Loss: 158.292
+38400/69092 Loss: 157.321
+51200/69092 Loss: 157.387
+64000/69092 Loss: 158.348
+Training time 0:03:35.281603
+Epoch: 159 Average loss: 157.84
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 163)
+0/69092 Loss: 155.890
+12800/69092 Loss: 158.047
+25600/69092 Loss: 158.035
+38400/69092 Loss: 158.387
+51200/69092 Loss: 158.192
+64000/69092 Loss: 157.601
+Training time 0:03:35.345290
+Epoch: 160 Average loss: 158.03
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 164)
+0/69092 Loss: 162.835
+12800/69092 Loss: 157.335
+25600/69092 Loss: 157.960
+38400/69092 Loss: 158.461
+51200/69092 Loss: 158.302
+64000/69092 Loss: 157.646
+Training time 0:03:36.107215
+Epoch: 161 Average loss: 157.91
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 165)
+0/69092 Loss: 156.025
+12800/69092 Loss: 156.943
+25600/69092 Loss: 157.574
+38400/69092 Loss: 158.075
+51200/69092 Loss: 158.509
+64000/69092 Loss: 159.194
+Training time 0:03:36.127311
+Epoch: 162 Average loss: 157.96
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 166)
+0/69092 Loss: 151.681
+12800/69092 Loss: 158.233
+25600/69092 Loss: 156.736
+38400/69092 Loss: 158.012
+51200/69092 Loss: 158.785
+64000/69092 Loss: 157.544
+Training time 0:03:35.675035
+Epoch: 163 Average loss: 157.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 167)
+0/69092 Loss: 149.342
+12800/69092 Loss: 157.504
+25600/69092 Loss: 157.447
+38400/69092 Loss: 159.814
+51200/69092 Loss: 156.905
+64000/69092 Loss: 158.121
+Training time 0:03:34.849772
+Epoch: 164 Average loss: 157.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 168)
+0/69092 Loss: 158.956
+12800/69092 Loss: 158.108
+25600/69092 Loss: 158.557
+38400/69092 Loss: 157.314
+51200/69092 Loss: 156.850
+64000/69092 Loss: 157.646
+Training time 0:03:35.625526
+Epoch: 165 Average loss: 157.77
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 169)
+0/69092 Loss: 157.486
+12800/69092 Loss: 157.619
+25600/69092 Loss: 158.104
+38400/69092 Loss: 159.780
+51200/69092 Loss: 157.374
+64000/69092 Loss: 157.813
+Training time 0:03:35.019341
+Epoch: 166 Average loss: 158.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 170)
+0/69092 Loss: 152.670
+12800/69092 Loss: 158.740
+25600/69092 Loss: 157.840
+38400/69092 Loss: 158.200
+51200/69092 Loss: 159.229
+64000/69092 Loss: 157.155
+Training time 0:03:34.361037
+Epoch: 167 Average loss: 157.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last' (iter 171)
+0/69092 Loss: 151.640
+12800/69092 Loss: 157.037
+25600/69092 Loss: 158.233
diff --git a/OAR.2066987.stderr b/OAR.2066987.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..01f042ddbf3b684dce92eb842907f4494d809311
--- /dev/null
+++ b/OAR.2066987.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+ warnings.warn(warning.format(ret))
+## OAR [2020-06-24 03:51:26] Job 2066987 KILLED ##
diff --git a/OAR.2066987.stdout b/OAR.2066987.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..e0026953c1132b772ac79dc80a441f853c1fa15f
--- /dev/null
+++ b/OAR.2066987.stdout
@@ -0,0 +1,7588 @@
+Namespace(batch_size=64, beta=4, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='beta_VAE_bs_64', gpu_devices=[0, 1], is_beta_VAE=True, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/beta_VAE_bs_64
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+Tesla K80
+Tesla K80
+DataParallel(
+ (module): VAE(
+ (img_to_last_conv): Sequential(
+ (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (1): ReLU()
+ (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (3): ReLU()
+ (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (5): ReLU()
+ (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (7): ReLU()
+ )
+ (last_conv_to_continuous_features): Sequential(
+ (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+ (1): ReLU()
+ )
+ (features_to_hidden_continue): Sequential(
+ (0): Linear(in_features=256, out_features=20, bias=True)
+ (1): ReLU()
+ )
+ (latent_to_features): Sequential(
+ (0): Linear(in_features=10, out_features=256, bias=True)
+ (1): ReLU()
+ )
+ (features_to_img): Sequential(
+ (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+ (1): ReLU()
+ (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (3): ReLU()
+ (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (5): ReLU()
+ (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (7): ReLU()
+ (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (9): Sigmoid()
+ )
+ )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> no checkpoint found at 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last'
+0/69092 Loss: 2996.283
+3200/69092 Loss: 2849.192
+6400/69092 Loss: 969.503
+9600/69092 Loss: 533.346
+12800/69092 Loss: 488.827
+16000/69092 Loss: 465.819
+19200/69092 Loss: 455.119
+22400/69092 Loss: 411.276
+25600/69092 Loss: 292.547
+28800/69092 Loss: 250.187
+32000/69092 Loss: 235.426
+35200/69092 Loss: 230.877
+38400/69092 Loss: 228.614
+41600/69092 Loss: 229.160
+44800/69092 Loss: 235.136
+48000/69092 Loss: 225.149
+51200/69092 Loss: 227.474
+54400/69092 Loss: 230.152
+57600/69092 Loss: 226.084
+60800/69092 Loss: 223.701
+64000/69092 Loss: 231.250
+67200/69092 Loss: 227.171
+Training time 0:04:11.450352
+Epoch: 1 Average loss: 447.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 1)
+0/69092 Loss: 251.797
+3200/69092 Loss: 225.635
+6400/69092 Loss: 228.129
+9600/69092 Loss: 223.246
+12800/69092 Loss: 214.800
+16000/69092 Loss: 216.244
+19200/69092 Loss: 213.488
+22400/69092 Loss: 204.465
+25600/69092 Loss: 210.539
+28800/69092 Loss: 208.756
+32000/69092 Loss: 211.511
+35200/69092 Loss: 201.741
+38400/69092 Loss: 208.114
+41600/69092 Loss: 203.864
+44800/69092 Loss: 207.909
+48000/69092 Loss: 200.300
+51200/69092 Loss: 202.518
+54400/69092 Loss: 202.010
+57600/69092 Loss: 199.430
+60800/69092 Loss: 199.646
+64000/69092 Loss: 191.796
+67200/69092 Loss: 192.559
+Training time 0:01:56.222555
+Epoch: 2 Average loss: 207.52
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 2)
+0/69092 Loss: 213.776
+3200/69092 Loss: 195.255
+6400/69092 Loss: 190.901
+9600/69092 Loss: 193.280
+12800/69092 Loss: 190.337
+16000/69092 Loss: 190.632
+19200/69092 Loss: 188.622
+22400/69092 Loss: 189.298
+25600/69092 Loss: 192.051
+28800/69092 Loss: 189.590
+32000/69092 Loss: 187.170
+35200/69092 Loss: 189.669
+38400/69092 Loss: 187.333
+41600/69092 Loss: 186.043
+44800/69092 Loss: 187.783
+48000/69092 Loss: 189.375
+51200/69092 Loss: 187.365
+54400/69092 Loss: 191.405
+57600/69092 Loss: 183.256
+60800/69092 Loss: 186.471
+64000/69092 Loss: 186.531
+67200/69092 Loss: 190.937
+Training time 0:01:56.276983
+Epoch: 3 Average loss: 189.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 3)
+0/69092 Loss: 216.677
+3200/69092 Loss: 187.545
+6400/69092 Loss: 189.138
+9600/69092 Loss: 188.047
+12800/69092 Loss: 184.307
+16000/69092 Loss: 188.439
+19200/69092 Loss: 186.454
+22400/69092 Loss: 188.196
+25600/69092 Loss: 184.643
+28800/69092 Loss: 187.148
+32000/69092 Loss: 184.818
+35200/69092 Loss: 184.574
+38400/69092 Loss: 185.366
+41600/69092 Loss: 192.044
+44800/69092 Loss: 186.806
+48000/69092 Loss: 188.283
+51200/69092 Loss: 183.873
+54400/69092 Loss: 188.628
+57600/69092 Loss: 186.013
+60800/69092 Loss: 187.894
+64000/69092 Loss: 186.695
+67200/69092 Loss: 184.645
+Training time 0:01:58.635203
+Epoch: 4 Average loss: 186.84
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 4)
+0/69092 Loss: 203.150
+3200/69092 Loss: 188.230
+6400/69092 Loss: 184.638
+9600/69092 Loss: 186.948
+12800/69092 Loss: 188.693
+16000/69092 Loss: 183.433
+19200/69092 Loss: 187.675
+22400/69092 Loss: 187.232
+25600/69092 Loss: 187.306
+28800/69092 Loss: 187.523
+32000/69092 Loss: 186.648
+35200/69092 Loss: 186.452
+38400/69092 Loss: 183.993
+41600/69092 Loss: 185.320
+44800/69092 Loss: 182.017
+48000/69092 Loss: 183.917
+51200/69092 Loss: 186.099
+54400/69092 Loss: 179.476
+57600/69092 Loss: 182.079
+60800/69092 Loss: 179.817
+64000/69092 Loss: 181.986
+67200/69092 Loss: 177.421
+Training time 0:01:57.159992
+Epoch: 5 Average loss: 184.44
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 5)
+0/69092 Loss: 201.415
+3200/69092 Loss: 180.093
+6400/69092 Loss: 179.419
+9600/69092 Loss: 178.953
+12800/69092 Loss: 178.537
+16000/69092 Loss: 181.504
+19200/69092 Loss: 179.501
+22400/69092 Loss: 179.450
+25600/69092 Loss: 176.027
+28800/69092 Loss: 178.539
+32000/69092 Loss: 176.076
+35200/69092 Loss: 175.172
+38400/69092 Loss: 177.610
+41600/69092 Loss: 177.594
+44800/69092 Loss: 176.750
+48000/69092 Loss: 177.454
+51200/69092 Loss: 176.812
+54400/69092 Loss: 181.063
+57600/69092 Loss: 176.534
+60800/69092 Loss: 177.212
+64000/69092 Loss: 173.409
+67200/69092 Loss: 175.427
+Training time 0:01:58.295935
+Epoch: 6 Average loss: 177.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 6)
+0/69092 Loss: 181.005
+3200/69092 Loss: 173.445
+6400/69092 Loss: 178.272
+9600/69092 Loss: 174.963
+12800/69092 Loss: 177.643
+16000/69092 Loss: 175.353
+19200/69092 Loss: 176.767
+22400/69092 Loss: 172.433
+25600/69092 Loss: 175.713
+28800/69092 Loss: 172.440
+32000/69092 Loss: 172.158
+35200/69092 Loss: 172.938
+38400/69092 Loss: 173.011
+41600/69092 Loss: 173.479
+44800/69092 Loss: 173.250
+48000/69092 Loss: 174.012
+51200/69092 Loss: 174.165
+54400/69092 Loss: 170.919
+57600/69092 Loss: 174.869
+60800/69092 Loss: 172.601
+64000/69092 Loss: 173.367
+67200/69092 Loss: 170.740
+Training time 0:01:56.841558
+Epoch: 7 Average loss: 173.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 7)
+0/69092 Loss: 155.570
+3200/69092 Loss: 171.951
+6400/69092 Loss: 172.733
+9600/69092 Loss: 173.404
+12800/69092 Loss: 175.665
+16000/69092 Loss: 169.749
+19200/69092 Loss: 168.763
+22400/69092 Loss: 168.842
+25600/69092 Loss: 167.193
+28800/69092 Loss: 168.480
+32000/69092 Loss: 172.957
+35200/69092 Loss: 167.509
+38400/69092 Loss: 170.859
+41600/69092 Loss: 169.670
+44800/69092 Loss: 169.974
+48000/69092 Loss: 170.610
+51200/69092 Loss: 169.231
+54400/69092 Loss: 169.917
+57600/69092 Loss: 166.675
+60800/69092 Loss: 169.919
+64000/69092 Loss: 168.816
+67200/69092 Loss: 167.087
+Training time 0:01:57.474977
+Epoch: 8 Average loss: 169.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 8)
+0/69092 Loss: 152.314
+3200/69092 Loss: 169.040
+6400/69092 Loss: 165.803
+9600/69092 Loss: 167.887
+12800/69092 Loss: 170.555
+16000/69092 Loss: 168.071
+19200/69092 Loss: 168.366
+22400/69092 Loss: 170.014
+25600/69092 Loss: 165.270
+28800/69092 Loss: 169.082
+32000/69092 Loss: 168.066
+35200/69092 Loss: 167.370
+38400/69092 Loss: 167.869
+41600/69092 Loss: 165.092
+44800/69092 Loss: 168.710
+48000/69092 Loss: 167.832
+51200/69092 Loss: 169.236
+54400/69092 Loss: 169.679
+57600/69092 Loss: 165.386
+60800/69092 Loss: 166.058
+64000/69092 Loss: 165.404
+67200/69092 Loss: 167.973
+Training time 0:01:58.003875
+Epoch: 9 Average loss: 167.62
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 9)
+0/69092 Loss: 186.743
+3200/69092 Loss: 166.642
+6400/69092 Loss: 165.061
+9600/69092 Loss: 165.308
+12800/69092 Loss: 168.703
+16000/69092 Loss: 168.617
+19200/69092 Loss: 170.108
+22400/69092 Loss: 167.263
+25600/69092 Loss: 165.110
+28800/69092 Loss: 167.110
+32000/69092 Loss: 168.740
+35200/69092 Loss: 167.422
+38400/69092 Loss: 166.927
+41600/69092 Loss: 165.685
+44800/69092 Loss: 166.346
+48000/69092 Loss: 167.761
+51200/69092 Loss: 165.619
+54400/69092 Loss: 162.744
+57600/69092 Loss: 167.680
+60800/69092 Loss: 166.785
+64000/69092 Loss: 166.229
+67200/69092 Loss: 164.606
+Training time 0:01:57.541170
+Epoch: 10 Average loss: 166.70
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 10)
+0/69092 Loss: 174.037
+3200/69092 Loss: 168.532
+6400/69092 Loss: 164.939
+9600/69092 Loss: 168.164
+12800/69092 Loss: 167.037
+16000/69092 Loss: 167.490
+19200/69092 Loss: 164.382
+22400/69092 Loss: 162.732
+25600/69092 Loss: 164.682
+28800/69092 Loss: 166.368
+32000/69092 Loss: 168.401
+35200/69092 Loss: 165.558
+38400/69092 Loss: 163.828
+41600/69092 Loss: 162.653
+44800/69092 Loss: 164.643
+48000/69092 Loss: 163.984
+51200/69092 Loss: 163.990
+54400/69092 Loss: 163.986
+57600/69092 Loss: 162.717
+60800/69092 Loss: 164.781
+64000/69092 Loss: 163.207
+67200/69092 Loss: 165.003
+Training time 0:01:56.971581
+Epoch: 11 Average loss: 164.96
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 11)
+0/69092 Loss: 166.630
+3200/69092 Loss: 164.078
+6400/69092 Loss: 163.281
+9600/69092 Loss: 161.550
+12800/69092 Loss: 162.644
+16000/69092 Loss: 165.764
+19200/69092 Loss: 161.325
+22400/69092 Loss: 164.922
+25600/69092 Loss: 162.244
+28800/69092 Loss: 161.489
+32000/69092 Loss: 163.428
+35200/69092 Loss: 160.567
+38400/69092 Loss: 161.385
+41600/69092 Loss: 165.746
+44800/69092 Loss: 161.974
+48000/69092 Loss: 163.419
+51200/69092 Loss: 162.336
+54400/69092 Loss: 161.887
+57600/69092 Loss: 163.420
+60800/69092 Loss: 161.745
+64000/69092 Loss: 165.138
+67200/69092 Loss: 165.768
+Training time 0:01:57.907880
+Epoch: 12 Average loss: 163.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 12)
+0/69092 Loss: 152.506
+3200/69092 Loss: 162.750
+6400/69092 Loss: 161.690
+9600/69092 Loss: 159.541
+12800/69092 Loss: 162.265
+16000/69092 Loss: 163.082
+19200/69092 Loss: 163.886
+22400/69092 Loss: 162.987
+25600/69092 Loss: 162.344
+28800/69092 Loss: 160.054
+32000/69092 Loss: 165.178
+35200/69092 Loss: 160.814
+38400/69092 Loss: 160.135
+41600/69092 Loss: 163.047
+44800/69092 Loss: 163.450
+48000/69092 Loss: 162.674
+51200/69092 Loss: 163.462
+54400/69092 Loss: 161.420
+57600/69092 Loss: 162.183
+60800/69092 Loss: 159.362
+64000/69092 Loss: 162.698
+67200/69092 Loss: 163.860
+Training time 0:01:57.682821
+Epoch: 13 Average loss: 162.29
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 13)
+0/69092 Loss: 151.394
+3200/69092 Loss: 160.503
+6400/69092 Loss: 162.126
+9600/69092 Loss: 161.237
+12800/69092 Loss: 161.298
+16000/69092 Loss: 161.677
+19200/69092 Loss: 161.436
+22400/69092 Loss: 158.760
+25600/69092 Loss: 163.906
+28800/69092 Loss: 158.558
+32000/69092 Loss: 162.262
+35200/69092 Loss: 161.091
+38400/69092 Loss: 162.268
+41600/69092 Loss: 166.815
+44800/69092 Loss: 161.044
+48000/69092 Loss: 161.988
+51200/69092 Loss: 165.298
+54400/69092 Loss: 165.238
+57600/69092 Loss: 163.105
+60800/69092 Loss: 163.867
+64000/69092 Loss: 158.813
+67200/69092 Loss: 163.015
+Training time 0:01:57.568042
+Epoch: 14 Average loss: 162.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 14)
+0/69092 Loss: 138.507
+3200/69092 Loss: 161.731
+6400/69092 Loss: 164.068
+9600/69092 Loss: 161.188
+12800/69092 Loss: 159.349
+16000/69092 Loss: 162.151
+19200/69092 Loss: 160.767
+22400/69092 Loss: 160.923
+25600/69092 Loss: 162.628
+28800/69092 Loss: 162.037
+32000/69092 Loss: 162.783
+35200/69092 Loss: 161.348
+38400/69092 Loss: 163.619
+41600/69092 Loss: 164.794
+44800/69092 Loss: 161.011
+48000/69092 Loss: 164.298
+51200/69092 Loss: 162.475
+54400/69092 Loss: 158.875
+57600/69092 Loss: 160.008
+60800/69092 Loss: 161.022
+64000/69092 Loss: 158.871
+67200/69092 Loss: 161.856
+Training time 0:01:57.727705
+Epoch: 15 Average loss: 161.58
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 15)
+0/69092 Loss: 145.301
+3200/69092 Loss: 163.730
+6400/69092 Loss: 159.274
+9600/69092 Loss: 160.533
+12800/69092 Loss: 163.232
+16000/69092 Loss: 160.052
+19200/69092 Loss: 158.232
+22400/69092 Loss: 161.700
+25600/69092 Loss: 161.574
+28800/69092 Loss: 159.018
+32000/69092 Loss: 163.837
+35200/69092 Loss: 161.704
+38400/69092 Loss: 161.651
+41600/69092 Loss: 158.541
+44800/69092 Loss: 162.820
+48000/69092 Loss: 162.896
+51200/69092 Loss: 158.152
+54400/69092 Loss: 160.193
+57600/69092 Loss: 160.873
+60800/69092 Loss: 162.326
+64000/69092 Loss: 164.243
+67200/69092 Loss: 160.145
+Training time 0:01:57.861897
+Epoch: 16 Average loss: 161.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 16)
+0/69092 Loss: 155.339
+3200/69092 Loss: 162.152
+6400/69092 Loss: 161.102
+9600/69092 Loss: 159.391
+12800/69092 Loss: 161.266
+16000/69092 Loss: 161.011
+19200/69092 Loss: 160.102
+22400/69092 Loss: 160.218
+25600/69092 Loss: 160.872
+28800/69092 Loss: 160.790
+32000/69092 Loss: 159.368
+35200/69092 Loss: 162.241
+38400/69092 Loss: 160.620
+41600/69092 Loss: 160.933
+44800/69092 Loss: 158.249
+48000/69092 Loss: 160.275
+51200/69092 Loss: 161.236
+54400/69092 Loss: 162.588
+57600/69092 Loss: 164.950
+60800/69092 Loss: 161.210
+64000/69092 Loss: 162.227
+67200/69092 Loss: 161.003
+Training time 0:01:57.713720
+Epoch: 17 Average loss: 161.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 17)
+0/69092 Loss: 148.239
+3200/69092 Loss: 159.891
+6400/69092 Loss: 164.055
+9600/69092 Loss: 159.331
+12800/69092 Loss: 161.076
+16000/69092 Loss: 158.351
+19200/69092 Loss: 158.842
+22400/69092 Loss: 162.516
+25600/69092 Loss: 164.261
+28800/69092 Loss: 160.496
+32000/69092 Loss: 162.196
+35200/69092 Loss: 161.130
+38400/69092 Loss: 163.890
+41600/69092 Loss: 158.830
+44800/69092 Loss: 158.579
+48000/69092 Loss: 157.500
+51200/69092 Loss: 159.903
+54400/69092 Loss: 158.277
+57600/69092 Loss: 157.346
+60800/69092 Loss: 161.180
+64000/69092 Loss: 159.906
+67200/69092 Loss: 163.404
+Training time 0:01:56.465208
+Epoch: 18 Average loss: 160.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 18)
+0/69092 Loss: 159.095
+3200/69092 Loss: 164.018
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+Training time 0:01:57.294461
+Epoch: 19 Average loss: 160.37
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 19)
+0/69092 Loss: 166.383
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+Training time 0:01:56.904126
+Epoch: 20 Average loss: 159.91
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 20)
+0/69092 Loss: 171.773
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+Training time 0:01:56.811086
+Epoch: 21 Average loss: 159.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 21)
+0/69092 Loss: 172.026
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+Training time 0:01:57.005308
+Epoch: 22 Average loss: 159.40
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 22)
+0/69092 Loss: 162.527
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+Training time 0:01:56.138479
+Epoch: 23 Average loss: 159.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 23)
+0/69092 Loss: 159.601
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+64000/69092 Loss: 161.840
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+Training time 0:01:57.433571
+Epoch: 24 Average loss: 159.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 24)
+0/69092 Loss: 150.259
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+Training time 0:01:56.686785
+Epoch: 25 Average loss: 158.97
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 25)
+0/69092 Loss: 175.847
+3200/69092 Loss: 157.577
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+Training time 0:01:57.091565
+Epoch: 26 Average loss: 158.62
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 26)
+0/69092 Loss: 174.317
+3200/69092 Loss: 158.828
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+67200/69092 Loss: 157.710
+Training time 0:01:57.573170
+Epoch: 27 Average loss: 158.77
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 27)
+0/69092 Loss: 169.950
+3200/69092 Loss: 160.448
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+Training time 0:01:57.898555
+Epoch: 28 Average loss: 158.56
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 28)
+0/69092 Loss: 180.791
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+64000/69092 Loss: 157.580
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+Training time 0:01:57.589396
+Epoch: 29 Average loss: 158.44
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 29)
+0/69092 Loss: 154.473
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+6400/69092 Loss: 157.999
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+64000/69092 Loss: 156.272
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+Training time 0:01:57.963856
+Epoch: 30 Average loss: 158.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 30)
+0/69092 Loss: 149.043
+3200/69092 Loss: 156.477
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+64000/69092 Loss: 157.999
+67200/69092 Loss: 156.369
+Training time 0:01:57.846660
+Epoch: 31 Average loss: 158.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 31)
+0/69092 Loss: 166.044
+3200/69092 Loss: 157.123
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+64000/69092 Loss: 161.310
+67200/69092 Loss: 157.672
+Training time 0:01:57.086052
+Epoch: 32 Average loss: 158.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 32)
+0/69092 Loss: 141.886
+3200/69092 Loss: 159.636
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+67200/69092 Loss: 156.970
+Training time 0:01:56.965621
+Epoch: 33 Average loss: 158.03
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 33)
+0/69092 Loss: 149.203
+3200/69092 Loss: 159.589
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+Training time 0:01:57.468334
+Epoch: 34 Average loss: 158.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 34)
+0/69092 Loss: 155.562
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+Training time 0:01:57.655810
+Epoch: 35 Average loss: 158.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 35)
+0/69092 Loss: 168.523
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+64000/69092 Loss: 155.990
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+Training time 0:01:57.653648
+Epoch: 36 Average loss: 157.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 36)
+0/69092 Loss: 146.145
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+Training time 0:01:56.633660
+Epoch: 37 Average loss: 158.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 37)
+0/69092 Loss: 159.461
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+Training time 0:01:56.737842
+Epoch: 38 Average loss: 157.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 38)
+0/69092 Loss: 147.399
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+Training time 0:01:57.367414
+Epoch: 39 Average loss: 157.93
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 39)
+0/69092 Loss: 164.177
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+Training time 0:01:56.571606
+Epoch: 40 Average loss: 157.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 40)
+0/69092 Loss: 163.158
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+Training time 0:01:57.496448
+Epoch: 41 Average loss: 157.63
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 41)
+0/69092 Loss: 142.771
+3200/69092 Loss: 157.839
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+64000/69092 Loss: 155.546
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+Training time 0:01:56.132207
+Epoch: 42 Average loss: 157.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 42)
+0/69092 Loss: 148.719
+3200/69092 Loss: 157.723
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+64000/69092 Loss: 156.933
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+Training time 0:01:55.631859
+Epoch: 43 Average loss: 157.74
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 43)
+0/69092 Loss: 148.240
+3200/69092 Loss: 158.520
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+Training time 0:01:57.376562
+Epoch: 44 Average loss: 157.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 44)
+0/69092 Loss: 183.489
+3200/69092 Loss: 158.599
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+Training time 0:01:57.126096
+Epoch: 45 Average loss: 157.46
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 45)
+0/69092 Loss: 134.899
+3200/69092 Loss: 155.808
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+64000/69092 Loss: 155.883
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+Training time 0:01:56.797464
+Epoch: 46 Average loss: 157.27
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 46)
+0/69092 Loss: 149.035
+3200/69092 Loss: 156.858
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+67200/69092 Loss: 155.473
+Training time 0:01:58.057136
+Epoch: 47 Average loss: 157.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 47)
+0/69092 Loss: 156.587
+3200/69092 Loss: 157.950
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+64000/69092 Loss: 157.118
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+Training time 0:01:58.795023
+Epoch: 48 Average loss: 157.39
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 48)
+0/69092 Loss: 165.872
+3200/69092 Loss: 157.354
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+64000/69092 Loss: 157.252
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+Training time 0:01:56.342746
+Epoch: 49 Average loss: 157.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 49)
+0/69092 Loss: 160.775
+3200/69092 Loss: 155.519
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+64000/69092 Loss: 157.450
+67200/69092 Loss: 161.151
+Training time 0:01:57.673239
+Epoch: 50 Average loss: 157.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 50)
+0/69092 Loss: 151.309
+3200/69092 Loss: 154.663
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+64000/69092 Loss: 157.056
+67200/69092 Loss: 157.738
+Training time 0:01:57.105160
+Epoch: 51 Average loss: 157.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 51)
+0/69092 Loss: 156.573
+3200/69092 Loss: 159.189
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+64000/69092 Loss: 157.889
+67200/69092 Loss: 156.688
+Training time 0:01:58.536600
+Epoch: 52 Average loss: 156.83
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 52)
+0/69092 Loss: 151.972
+3200/69092 Loss: 157.845
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+64000/69092 Loss: 156.755
+67200/69092 Loss: 154.428
+Training time 0:01:57.886704
+Epoch: 53 Average loss: 157.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 53)
+0/69092 Loss: 174.818
+3200/69092 Loss: 154.922
+6400/69092 Loss: 156.329
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+64000/69092 Loss: 157.022
+67200/69092 Loss: 156.841
+Training time 0:01:57.631492
+Epoch: 54 Average loss: 156.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 54)
+0/69092 Loss: 161.799
+3200/69092 Loss: 158.768
+6400/69092 Loss: 155.949
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+64000/69092 Loss: 157.700
+67200/69092 Loss: 157.772
+Training time 0:01:57.196255
+Epoch: 55 Average loss: 157.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 55)
+0/69092 Loss: 171.154
+3200/69092 Loss: 153.401
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+67200/69092 Loss: 159.281
+Training time 0:01:57.246343
+Epoch: 56 Average loss: 156.81
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 56)
+0/69092 Loss: 161.665
+3200/69092 Loss: 159.070
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+64000/69092 Loss: 154.616
+67200/69092 Loss: 156.717
+Training time 0:01:57.070016
+Epoch: 57 Average loss: 156.90
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 57)
+0/69092 Loss: 154.231
+3200/69092 Loss: 158.923
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+64000/69092 Loss: 156.288
+67200/69092 Loss: 154.829
+Training time 0:01:56.619470
+Epoch: 58 Average loss: 156.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 58)
+0/69092 Loss: 158.747
+3200/69092 Loss: 156.800
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+64000/69092 Loss: 156.935
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+Training time 0:01:57.085682
+Epoch: 59 Average loss: 156.87
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 59)
+0/69092 Loss: 146.586
+3200/69092 Loss: 158.287
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+64000/69092 Loss: 159.004
+67200/69092 Loss: 158.058
+Training time 0:01:57.663232
+Epoch: 60 Average loss: 156.85
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 60)
+0/69092 Loss: 166.009
+3200/69092 Loss: 156.702
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+67200/69092 Loss: 158.808
+Training time 0:01:56.126829
+Epoch: 61 Average loss: 156.76
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 61)
+0/69092 Loss: 149.690
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+64000/69092 Loss: 157.972
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+Training time 0:01:55.724764
+Epoch: 62 Average loss: 156.81
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 62)
+0/69092 Loss: 130.425
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+67200/69092 Loss: 154.854
+Training time 0:01:56.869917
+Epoch: 63 Average loss: 156.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 63)
+0/69092 Loss: 168.278
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+67200/69092 Loss: 155.685
+Training time 0:01:56.637123
+Epoch: 64 Average loss: 156.81
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 64)
+0/69092 Loss: 179.476
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+64000/69092 Loss: 157.397
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+Training time 0:01:58.626638
+Epoch: 65 Average loss: 156.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 65)
+0/69092 Loss: 176.773
+3200/69092 Loss: 156.387
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+64000/69092 Loss: 159.062
+67200/69092 Loss: 157.388
+Training time 0:01:57.428312
+Epoch: 66 Average loss: 156.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 66)
+0/69092 Loss: 174.267
+3200/69092 Loss: 156.316
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+67200/69092 Loss: 155.924
+Training time 0:01:58.339228
+Epoch: 67 Average loss: 156.70
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 67)
+0/69092 Loss: 140.377
+3200/69092 Loss: 154.747
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+67200/69092 Loss: 154.496
+Training time 0:01:56.978773
+Epoch: 68 Average loss: 156.65
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 68)
+0/69092 Loss: 158.141
+3200/69092 Loss: 159.387
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+64000/69092 Loss: 159.013
+67200/69092 Loss: 156.078
+Training time 0:01:56.638653
+Epoch: 69 Average loss: 156.74
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 69)
+0/69092 Loss: 158.210
+3200/69092 Loss: 154.090
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+Training time 0:01:57.764959
+Epoch: 70 Average loss: 156.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 70)
+0/69092 Loss: 171.868
+3200/69092 Loss: 154.971
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+Training time 0:01:57.575431
+Epoch: 71 Average loss: 156.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 71)
+0/69092 Loss: 165.875
+3200/69092 Loss: 155.211
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+Training time 0:01:57.928112
+Epoch: 72 Average loss: 156.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 72)
+0/69092 Loss: 149.009
+3200/69092 Loss: 157.306
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+64000/69092 Loss: 157.771
+67200/69092 Loss: 154.503
+Training time 0:01:58.927985
+Epoch: 73 Average loss: 156.45
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 73)
+0/69092 Loss: 162.335
+3200/69092 Loss: 156.992
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+64000/69092 Loss: 155.921
+67200/69092 Loss: 158.465
+Training time 0:01:57.424789
+Epoch: 74 Average loss: 156.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 74)
+0/69092 Loss: 163.777
+3200/69092 Loss: 157.036
+6400/69092 Loss: 156.859
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+64000/69092 Loss: 158.049
+67200/69092 Loss: 155.451
+Training time 0:01:58.770586
+Epoch: 75 Average loss: 156.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 75)
+0/69092 Loss: 153.101
+3200/69092 Loss: 155.568
+6400/69092 Loss: 157.207
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+64000/69092 Loss: 154.556
+67200/69092 Loss: 156.564
+Training time 0:01:57.292697
+Epoch: 76 Average loss: 156.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 76)
+0/69092 Loss: 145.325
+3200/69092 Loss: 155.806
+6400/69092 Loss: 159.306
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+64000/69092 Loss: 156.278
+67200/69092 Loss: 157.242
+Training time 0:01:57.657184
+Epoch: 77 Average loss: 156.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 77)
+0/69092 Loss: 146.821
+3200/69092 Loss: 155.702
+6400/69092 Loss: 157.032
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+64000/69092 Loss: 155.960
+67200/69092 Loss: 159.255
+Training time 0:01:58.393952
+Epoch: 78 Average loss: 156.32
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 78)
+0/69092 Loss: 156.952
+3200/69092 Loss: 154.559
+6400/69092 Loss: 156.841
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+64000/69092 Loss: 157.854
+67200/69092 Loss: 157.608
+Training time 0:01:57.321396
+Epoch: 79 Average loss: 156.33
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 79)
+0/69092 Loss: 158.649
+3200/69092 Loss: 156.310
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+64000/69092 Loss: 155.501
+67200/69092 Loss: 154.854
+Training time 0:01:57.033627
+Epoch: 80 Average loss: 156.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 80)
+0/69092 Loss: 153.145
+3200/69092 Loss: 157.788
+6400/69092 Loss: 155.704
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+64000/69092 Loss: 156.993
+67200/69092 Loss: 155.297
+Training time 0:01:57.857139
+Epoch: 81 Average loss: 156.47
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 81)
+0/69092 Loss: 154.239
+3200/69092 Loss: 155.129
+6400/69092 Loss: 158.601
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+64000/69092 Loss: 156.259
+67200/69092 Loss: 156.098
+Training time 0:01:57.681612
+Epoch: 82 Average loss: 156.31
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 82)
+0/69092 Loss: 147.194
+3200/69092 Loss: 155.118
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+64000/69092 Loss: 159.009
+67200/69092 Loss: 157.212
+Training time 0:01:56.507123
+Epoch: 83 Average loss: 156.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 83)
+0/69092 Loss: 175.445
+3200/69092 Loss: 156.969
+6400/69092 Loss: 157.031
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+64000/69092 Loss: 155.874
+67200/69092 Loss: 154.274
+Training time 0:01:57.244601
+Epoch: 84 Average loss: 156.41
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 84)
+0/69092 Loss: 164.614
+3200/69092 Loss: 157.171
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+67200/69092 Loss: 152.564
+Training time 0:01:57.380581
+Epoch: 85 Average loss: 156.19
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 85)
+0/69092 Loss: 149.693
+3200/69092 Loss: 157.577
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+64000/69092 Loss: 158.885
+67200/69092 Loss: 155.049
+Training time 0:01:58.151490
+Epoch: 86 Average loss: 156.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 86)
+0/69092 Loss: 166.214
+3200/69092 Loss: 155.207
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+67200/69092 Loss: 157.277
+Training time 0:01:57.070829
+Epoch: 87 Average loss: 156.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 87)
+0/69092 Loss: 159.401
+3200/69092 Loss: 155.259
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+64000/69092 Loss: 156.764
+67200/69092 Loss: 154.235
+Training time 0:01:57.391573
+Epoch: 88 Average loss: 156.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 88)
+0/69092 Loss: 170.257
+3200/69092 Loss: 155.170
+6400/69092 Loss: 154.292
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+67200/69092 Loss: 156.647
+Training time 0:01:57.305374
+Epoch: 89 Average loss: 156.09
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 89)
+0/69092 Loss: 166.612
+3200/69092 Loss: 158.085
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+64000/69092 Loss: 154.917
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+Training time 0:01:57.986733
+Epoch: 90 Average loss: 156.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 90)
+0/69092 Loss: 161.165
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+Training time 0:01:57.421474
+Epoch: 91 Average loss: 156.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 91)
+0/69092 Loss: 151.802
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+Training time 0:01:58.422619
+Epoch: 92 Average loss: 156.14
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 92)
+0/69092 Loss: 171.456
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+Training time 0:01:58.731457
+Epoch: 93 Average loss: 156.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 93)
+0/69092 Loss: 142.786
+3200/69092 Loss: 153.297
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+Training time 0:01:57.963713
+Epoch: 94 Average loss: 156.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 94)
+0/69092 Loss: 146.263
+3200/69092 Loss: 155.555
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+Training time 0:01:57.965091
+Epoch: 95 Average loss: 156.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 95)
+0/69092 Loss: 155.656
+3200/69092 Loss: 157.595
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+64000/69092 Loss: 156.543
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+Training time 0:01:58.809980
+Epoch: 96 Average loss: 156.34
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 96)
+0/69092 Loss: 143.069
+3200/69092 Loss: 156.347
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+Training time 0:01:57.791352
+Epoch: 97 Average loss: 155.96
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 97)
+0/69092 Loss: 149.637
+3200/69092 Loss: 155.549
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+64000/69092 Loss: 157.863
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+Training time 0:01:58.207927
+Epoch: 98 Average loss: 156.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 98)
+0/69092 Loss: 163.931
+3200/69092 Loss: 158.819
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+64000/69092 Loss: 153.272
+67200/69092 Loss: 158.289
+Training time 0:01:57.142832
+Epoch: 99 Average loss: 156.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 99)
+0/69092 Loss: 174.706
+3200/69092 Loss: 157.421
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+64000/69092 Loss: 159.171
+67200/69092 Loss: 154.491
+Training time 0:01:58.077721
+Epoch: 100 Average loss: 156.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 100)
+0/69092 Loss: 177.416
+3200/69092 Loss: 157.837
+6400/69092 Loss: 156.611
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+64000/69092 Loss: 156.362
+67200/69092 Loss: 155.231
+Training time 0:01:57.508650
+Epoch: 101 Average loss: 156.11
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 101)
+0/69092 Loss: 155.446
+3200/69092 Loss: 154.474
+6400/69092 Loss: 153.953
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+64000/69092 Loss: 155.515
+67200/69092 Loss: 156.003
+Training time 0:01:57.582020
+Epoch: 102 Average loss: 155.84
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 102)
+0/69092 Loss: 159.455
+3200/69092 Loss: 155.785
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+64000/69092 Loss: 155.967
+67200/69092 Loss: 153.019
+Training time 0:01:58.641232
+Epoch: 103 Average loss: 156.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 103)
+0/69092 Loss: 158.399
+3200/69092 Loss: 155.141
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+64000/69092 Loss: 157.746
+67200/69092 Loss: 154.849
+Training time 0:01:56.475910
+Epoch: 104 Average loss: 156.03
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 104)
+0/69092 Loss: 161.428
+3200/69092 Loss: 156.067
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+64000/69092 Loss: 156.383
+67200/69092 Loss: 154.772
+Training time 0:01:57.094854
+Epoch: 105 Average loss: 155.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 105)
+0/69092 Loss: 150.408
+3200/69092 Loss: 157.610
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+64000/69092 Loss: 157.451
+67200/69092 Loss: 154.696
+Training time 0:01:57.905391
+Epoch: 106 Average loss: 155.97
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 106)
+0/69092 Loss: 147.812
+3200/69092 Loss: 158.075
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+64000/69092 Loss: 154.585
+67200/69092 Loss: 155.212
+Training time 0:01:56.307127
+Epoch: 107 Average loss: 155.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 107)
+0/69092 Loss: 154.253
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+64000/69092 Loss: 158.875
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+Training time 0:01:57.121476
+Epoch: 108 Average loss: 156.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 108)
+0/69092 Loss: 137.626
+3200/69092 Loss: 155.626
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+64000/69092 Loss: 154.637
+67200/69092 Loss: 155.397
+Training time 0:01:57.346284
+Epoch: 109 Average loss: 155.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 109)
+0/69092 Loss: 149.738
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+Training time 0:01:57.531353
+Epoch: 110 Average loss: 155.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 110)
+0/69092 Loss: 170.792
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+Training time 0:01:56.946783
+Epoch: 111 Average loss: 155.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 111)
+0/69092 Loss: 150.964
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+67200/69092 Loss: 156.427
+Training time 0:01:57.189807
+Epoch: 112 Average loss: 155.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 112)
+0/69092 Loss: 175.846
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+Training time 0:01:57.976793
+Epoch: 113 Average loss: 155.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 113)
+0/69092 Loss: 174.227
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+Training time 0:01:58.318560
+Epoch: 114 Average loss: 155.34
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 114)
+0/69092 Loss: 168.966
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+Training time 0:01:57.300875
+Epoch: 115 Average loss: 155.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 115)
+0/69092 Loss: 153.426
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+Training time 0:01:57.025523
+Epoch: 116 Average loss: 155.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 116)
+0/69092 Loss: 148.078
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+Training time 0:01:58.664741
+Epoch: 117 Average loss: 155.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 117)
+0/69092 Loss: 171.513
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+Training time 0:01:58.273350
+Epoch: 118 Average loss: 154.97
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 118)
+0/69092 Loss: 150.001
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+Training time 0:01:58.633698
+Epoch: 119 Average loss: 154.83
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 119)
+0/69092 Loss: 160.933
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+Training time 0:01:58.076896
+Epoch: 120 Average loss: 154.87
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 120)
+0/69092 Loss: 132.012
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+Training time 0:01:57.341197
+Epoch: 121 Average loss: 154.90
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 121)
+0/69092 Loss: 147.670
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+Training time 0:01:58.555985
+Epoch: 122 Average loss: 154.70
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 122)
+0/69092 Loss: 149.961
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+Training time 0:01:58.459415
+Epoch: 123 Average loss: 154.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 123)
+0/69092 Loss: 143.996
+3200/69092 Loss: 154.933
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+Training time 0:01:57.505004
+Epoch: 124 Average loss: 154.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 124)
+0/69092 Loss: 149.688
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+Training time 0:01:57.617767
+Epoch: 125 Average loss: 154.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 125)
+0/69092 Loss: 174.120
+3200/69092 Loss: 154.710
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+Training time 0:01:57.489586
+Epoch: 126 Average loss: 154.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 126)
+0/69092 Loss: 161.715
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+Training time 0:01:58.320576
+Epoch: 127 Average loss: 154.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 127)
+0/69092 Loss: 149.820
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+Training time 0:01:58.128835
+Epoch: 128 Average loss: 154.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 128)
+0/69092 Loss: 154.357
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+Training time 0:01:58.853265
+Epoch: 129 Average loss: 154.32
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 129)
+0/69092 Loss: 159.408
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+Training time 0:01:59.618850
+Epoch: 130 Average loss: 154.32
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 130)
+0/69092 Loss: 155.935
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+Training time 0:01:57.487546
+Epoch: 131 Average loss: 154.45
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 131)
+0/69092 Loss: 145.728
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+Training time 0:01:57.605448
+Epoch: 132 Average loss: 154.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 132)
+0/69092 Loss: 147.528
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+Training time 0:01:58.702622
+Epoch: 133 Average loss: 154.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 133)
+0/69092 Loss: 159.723
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+Training time 0:01:57.000587
+Epoch: 134 Average loss: 154.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 134)
+0/69092 Loss: 156.543
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+Training time 0:01:57.662060
+Epoch: 135 Average loss: 154.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 135)
+0/69092 Loss: 159.106
+3200/69092 Loss: 153.990
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+64000/69092 Loss: 150.308
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+Training time 0:01:56.928577
+Epoch: 136 Average loss: 154.26
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 136)
+0/69092 Loss: 153.956
+3200/69092 Loss: 155.212
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+Training time 0:01:57.550661
+Epoch: 137 Average loss: 153.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 137)
+0/69092 Loss: 142.783
+3200/69092 Loss: 156.137
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+Training time 0:01:57.336705
+Epoch: 138 Average loss: 154.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 138)
+0/69092 Loss: 160.587
+3200/69092 Loss: 156.926
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+64000/69092 Loss: 152.417
+67200/69092 Loss: 153.493
+Training time 0:01:58.230480
+Epoch: 139 Average loss: 154.03
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 139)
+0/69092 Loss: 139.523
+3200/69092 Loss: 154.231
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+Training time 0:01:56.672850
+Epoch: 140 Average loss: 154.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 140)
+0/69092 Loss: 156.655
+3200/69092 Loss: 155.086
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+64000/69092 Loss: 152.991
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+Training time 0:01:58.026175
+Epoch: 141 Average loss: 153.86
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 141)
+0/69092 Loss: 160.142
+3200/69092 Loss: 153.358
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+64000/69092 Loss: 153.761
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+Training time 0:01:57.734306
+Epoch: 142 Average loss: 153.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 142)
+0/69092 Loss: 150.114
+3200/69092 Loss: 156.249
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+Training time 0:01:57.780932
+Epoch: 143 Average loss: 153.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 143)
+0/69092 Loss: 169.829
+3200/69092 Loss: 152.957
+6400/69092 Loss: 152.465
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+64000/69092 Loss: 152.200
+67200/69092 Loss: 153.992
+Training time 0:01:57.695235
+Epoch: 144 Average loss: 153.90
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 144)
+0/69092 Loss: 155.833
+3200/69092 Loss: 153.278
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+64000/69092 Loss: 154.794
+67200/69092 Loss: 154.613
+Training time 0:01:57.431273
+Epoch: 145 Average loss: 153.83
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 145)
+0/69092 Loss: 158.462
+3200/69092 Loss: 154.293
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+64000/69092 Loss: 156.301
+67200/69092 Loss: 153.371
+Training time 0:01:57.628707
+Epoch: 146 Average loss: 153.93
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 146)
+0/69092 Loss: 156.194
+3200/69092 Loss: 154.547
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+64000/69092 Loss: 152.733
+67200/69092 Loss: 151.789
+Training time 0:01:58.751913
+Epoch: 147 Average loss: 153.91
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 147)
+0/69092 Loss: 162.678
+3200/69092 Loss: 154.196
+6400/69092 Loss: 153.003
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+64000/69092 Loss: 156.925
+67200/69092 Loss: 151.513
+Training time 0:01:57.488062
+Epoch: 148 Average loss: 153.77
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 148)
+0/69092 Loss: 151.224
+3200/69092 Loss: 153.451
+6400/69092 Loss: 154.341
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+64000/69092 Loss: 152.221
+67200/69092 Loss: 153.810
+Training time 0:01:58.789382
+Epoch: 149 Average loss: 153.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 149)
+0/69092 Loss: 152.636
+3200/69092 Loss: 151.073
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+64000/69092 Loss: 156.101
+67200/69092 Loss: 154.313
+Training time 0:01:58.051617
+Epoch: 150 Average loss: 153.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 150)
+0/69092 Loss: 162.084
+3200/69092 Loss: 153.760
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+64000/69092 Loss: 150.369
+67200/69092 Loss: 155.480
+Training time 0:01:58.039805
+Epoch: 151 Average loss: 153.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 151)
+0/69092 Loss: 154.323
+3200/69092 Loss: 154.346
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+64000/69092 Loss: 153.178
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+Training time 0:01:57.533614
+Epoch: 152 Average loss: 154.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 152)
+0/69092 Loss: 160.166
+3200/69092 Loss: 155.509
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+67200/69092 Loss: 153.594
+Training time 0:01:57.634748
+Epoch: 153 Average loss: 154.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 153)
+0/69092 Loss: 140.068
+3200/69092 Loss: 155.298
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+Training time 0:01:58.139985
+Epoch: 154 Average loss: 153.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 154)
+0/69092 Loss: 156.653
+3200/69092 Loss: 152.359
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+Training time 0:01:57.368083
+Epoch: 155 Average loss: 153.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 155)
+0/69092 Loss: 167.637
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+67200/69092 Loss: 151.785
+Training time 0:01:57.368878
+Epoch: 156 Average loss: 153.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 156)
+0/69092 Loss: 151.414
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+Training time 0:01:57.937851
+Epoch: 157 Average loss: 153.69
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 157)
+0/69092 Loss: 155.203
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+64000/69092 Loss: 156.315
+67200/69092 Loss: 154.819
+Training time 0:01:56.487464
+Epoch: 158 Average loss: 153.59
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 158)
+0/69092 Loss: 138.796
+3200/69092 Loss: 150.871
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+67200/69092 Loss: 155.879
+Training time 0:01:56.763215
+Epoch: 159 Average loss: 153.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 159)
+0/69092 Loss: 135.210
+3200/69092 Loss: 153.410
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+Training time 0:01:57.123982
+Epoch: 160 Average loss: 153.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 160)
+0/69092 Loss: 160.980
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+Training time 0:01:57.824355
+Epoch: 161 Average loss: 153.73
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 161)
+0/69092 Loss: 154.934
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+Training time 0:01:57.259768
+Epoch: 162 Average loss: 153.68
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 162)
+0/69092 Loss: 165.094
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+Training time 0:01:57.292765
+Epoch: 163 Average loss: 153.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 163)
+0/69092 Loss: 148.269
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+Training time 0:01:57.148237
+Epoch: 164 Average loss: 153.40
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 164)
+0/69092 Loss: 151.540
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+Training time 0:01:58.221382
+Epoch: 165 Average loss: 153.58
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 165)
+0/69092 Loss: 171.343
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+Training time 0:01:57.881041
+Epoch: 166 Average loss: 153.55
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 166)
+0/69092 Loss: 133.887
+3200/69092 Loss: 153.197
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+Training time 0:02:00.223303
+Epoch: 167 Average loss: 153.58
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 167)
+0/69092 Loss: 149.147
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+Training time 0:01:59.050741
+Epoch: 168 Average loss: 153.47
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 168)
+0/69092 Loss: 144.126
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+64000/69092 Loss: 153.893
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+Training time 0:01:58.963661
+Epoch: 169 Average loss: 153.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 169)
+0/69092 Loss: 154.990
+3200/69092 Loss: 152.755
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+Training time 0:01:58.488707
+Epoch: 170 Average loss: 153.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 170)
+0/69092 Loss: 167.717
+3200/69092 Loss: 152.391
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+64000/69092 Loss: 155.423
+67200/69092 Loss: 152.926
+Training time 0:01:58.307055
+Epoch: 171 Average loss: 153.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 171)
+0/69092 Loss: 170.096
+3200/69092 Loss: 155.635
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+67200/69092 Loss: 154.415
+Training time 0:01:58.900402
+Epoch: 172 Average loss: 153.58
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 172)
+0/69092 Loss: 143.302
+3200/69092 Loss: 152.115
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+64000/69092 Loss: 152.712
+67200/69092 Loss: 154.420
+Training time 0:01:57.682425
+Epoch: 173 Average loss: 153.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 173)
+0/69092 Loss: 161.742
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+Training time 0:01:57.521824
+Epoch: 174 Average loss: 153.59
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 174)
+0/69092 Loss: 158.305
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+Training time 0:01:57.989732
+Epoch: 175 Average loss: 153.45
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 175)
+0/69092 Loss: 154.773
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+Training time 0:01:58.843046
+Epoch: 176 Average loss: 153.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 176)
+0/69092 Loss: 158.858
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+Training time 0:01:58.518576
+Epoch: 177 Average loss: 153.23
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 177)
+0/69092 Loss: 157.223
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+64000/69092 Loss: 152.790
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+Training time 0:01:57.289607
+Epoch: 178 Average loss: 153.53
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 178)
+0/69092 Loss: 145.029
+3200/69092 Loss: 152.700
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+Training time 0:01:58.109030
+Epoch: 179 Average loss: 153.47
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 179)
+0/69092 Loss: 132.821
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+67200/69092 Loss: 156.048
+Training time 0:01:57.815115
+Epoch: 180 Average loss: 153.48
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 180)
+0/69092 Loss: 173.967
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+Training time 0:01:57.631428
+Epoch: 181 Average loss: 153.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 181)
+0/69092 Loss: 160.243
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+64000/69092 Loss: 153.509
+67200/69092 Loss: 154.321
+Training time 0:01:57.180327
+Epoch: 182 Average loss: 153.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 182)
+0/69092 Loss: 136.811
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+64000/69092 Loss: 155.455
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+Training time 0:01:57.497684
+Epoch: 183 Average loss: 153.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 183)
+0/69092 Loss: 161.665
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+64000/69092 Loss: 151.734
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+Training time 0:01:57.514177
+Epoch: 184 Average loss: 153.38
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 184)
+0/69092 Loss: 144.798
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+Training time 0:01:56.780317
+Epoch: 185 Average loss: 153.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 185)
+0/69092 Loss: 153.127
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+64000/69092 Loss: 151.698
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+Training time 0:01:56.582316
+Epoch: 186 Average loss: 153.25
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 186)
+0/69092 Loss: 139.081
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+Training time 0:01:57.961633
+Epoch: 187 Average loss: 153.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 187)
+0/69092 Loss: 137.805
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+64000/69092 Loss: 153.789
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+Training time 0:01:57.402130
+Epoch: 188 Average loss: 153.35
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 188)
+0/69092 Loss: 157.948
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+Training time 0:01:56.221509
+Epoch: 189 Average loss: 153.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 189)
+0/69092 Loss: 153.654
+3200/69092 Loss: 156.417
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+67200/69092 Loss: 151.896
+Training time 0:01:57.703280
+Epoch: 190 Average loss: 153.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 190)
+0/69092 Loss: 144.723
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+67200/69092 Loss: 152.526
+Training time 0:01:57.576354
+Epoch: 191 Average loss: 153.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 191)
+0/69092 Loss: 164.213
+3200/69092 Loss: 151.961
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+67200/69092 Loss: 152.214
+Training time 0:01:57.872127
+Epoch: 192 Average loss: 153.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 192)
+0/69092 Loss: 153.189
+3200/69092 Loss: 155.156
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+67200/69092 Loss: 153.127
+Training time 0:01:57.083778
+Epoch: 193 Average loss: 153.49
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 193)
+0/69092 Loss: 154.344
+3200/69092 Loss: 153.425
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+64000/69092 Loss: 154.010
+67200/69092 Loss: 153.323
+Training time 0:01:58.359636
+Epoch: 194 Average loss: 153.30
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 194)
+0/69092 Loss: 146.915
+3200/69092 Loss: 152.609
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+64000/69092 Loss: 152.136
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+Training time 0:01:58.474192
+Epoch: 195 Average loss: 153.33
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 195)
+0/69092 Loss: 160.613
+3200/69092 Loss: 153.715
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+64000/69092 Loss: 153.222
+67200/69092 Loss: 153.562
+Training time 0:01:57.272817
+Epoch: 196 Average loss: 153.13
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 196)
+0/69092 Loss: 168.341
+3200/69092 Loss: 155.781
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+64000/69092 Loss: 152.720
+67200/69092 Loss: 155.200
+Training time 0:01:57.564049
+Epoch: 197 Average loss: 153.36
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 197)
+0/69092 Loss: 152.832
+3200/69092 Loss: 155.401
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+64000/69092 Loss: 152.644
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+Training time 0:01:57.663572
+Epoch: 201 Average loss: 153.17
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 201)
+0/69092 Loss: 154.454
+3200/69092 Loss: 155.674
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+64000/69092 Loss: 152.747
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+Training time 0:01:58.155480
+Epoch: 202 Average loss: 152.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 202)
+0/69092 Loss: 151.555
+3200/69092 Loss: 153.526
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+Training time 0:01:56.956451
+Epoch: 203 Average loss: 152.97
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 203)
+0/69092 Loss: 178.683
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+64000/69092 Loss: 154.347
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+Training time 0:01:56.859912
+Epoch: 204 Average loss: 153.16
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 204)
+0/69092 Loss: 138.625
+3200/69092 Loss: 152.850
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+64000/69092 Loss: 154.963
+67200/69092 Loss: 155.373
+Training time 0:01:59.365824
+Epoch: 205 Average loss: 153.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 205)
+0/69092 Loss: 147.201
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+64000/69092 Loss: 154.757
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+Training time 0:01:57.188512
+Epoch: 206 Average loss: 153.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 206)
+0/69092 Loss: 136.333
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+64000/69092 Loss: 153.330
+67200/69092 Loss: 151.401
+Training time 0:01:56.002772
+Epoch: 207 Average loss: 153.15
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 207)
+0/69092 Loss: 159.126
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+Training time 0:01:56.828773
+Epoch: 208 Average loss: 153.08
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 208)
+0/69092 Loss: 151.807
+3200/69092 Loss: 154.319
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+Training time 0:01:57.092329
+Epoch: 209 Average loss: 153.44
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 209)
+0/69092 Loss: 141.051
+3200/69092 Loss: 152.772
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+Training time 0:01:58.064314
+Epoch: 210 Average loss: 153.18
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 210)
+0/69092 Loss: 159.396
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+67200/69092 Loss: 152.980
+Training time 0:01:56.995772
+Epoch: 211 Average loss: 152.97
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 211)
+0/69092 Loss: 144.274
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+67200/69092 Loss: 152.952
+Training time 0:01:57.304691
+Epoch: 212 Average loss: 152.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 212)
+0/69092 Loss: 152.058
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+64000/69092 Loss: 153.789
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+Training time 0:01:57.575948
+Epoch: 213 Average loss: 153.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 213)
+0/69092 Loss: 139.377
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+Training time 0:01:57.480193
+Epoch: 214 Average loss: 153.12
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 214)
+0/69092 Loss: 162.559
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+Training time 0:01:58.489547
+Epoch: 215 Average loss: 153.29
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 215)
+0/69092 Loss: 142.518
+3200/69092 Loss: 152.601
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+64000/69092 Loss: 153.093
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+Training time 0:01:58.124137
+Epoch: 216 Average loss: 153.07
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 216)
+0/69092 Loss: 145.412
+3200/69092 Loss: 153.349
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+67200/69092 Loss: 151.924
+Training time 0:01:57.129648
+Epoch: 217 Average loss: 153.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 217)
+0/69092 Loss: 137.423
+3200/69092 Loss: 153.207
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+Training time 0:01:57.834001
+Epoch: 218 Average loss: 153.21
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 218)
+0/69092 Loss: 134.265
+3200/69092 Loss: 153.752
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+67200/69092 Loss: 153.247
+Training time 0:01:57.508754
+Epoch: 219 Average loss: 153.04
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 219)
+0/69092 Loss: 161.132
+3200/69092 Loss: 150.378
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+64000/69092 Loss: 151.246
+67200/69092 Loss: 153.691
+Training time 0:01:59.273445
+Epoch: 220 Average loss: 153.06
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 220)
+0/69092 Loss: 134.050
+3200/69092 Loss: 151.060
+6400/69092 Loss: 153.877
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+64000/69092 Loss: 153.873
+67200/69092 Loss: 151.987
+Training time 0:01:58.140186
+Epoch: 221 Average loss: 152.99
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 221)
+0/69092 Loss: 167.617
+3200/69092 Loss: 150.980
+6400/69092 Loss: 154.489
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+64000/69092 Loss: 154.685
+67200/69092 Loss: 150.734
+Training time 0:01:58.151980
+Epoch: 222 Average loss: 153.10
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 222)
+0/69092 Loss: 163.035
+3200/69092 Loss: 150.475
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+64000/69092 Loss: 153.166
+67200/69092 Loss: 154.064
+Training time 0:01:58.093341
+Epoch: 223 Average loss: 153.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 223)
+0/69092 Loss: 149.104
+3200/69092 Loss: 153.765
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+67200/69092 Loss: 153.445
+Training time 0:01:57.995336
+Epoch: 224 Average loss: 152.98
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 224)
+0/69092 Loss: 149.157
+3200/69092 Loss: 154.474
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+64000/69092 Loss: 151.943
+67200/69092 Loss: 149.966
+Training time 0:01:57.633472
+Epoch: 225 Average loss: 152.95
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 225)
+0/69092 Loss: 157.358
+3200/69092 Loss: 153.386
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+64000/69092 Loss: 152.688
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+Training time 0:01:58.981002
+Epoch: 226 Average loss: 152.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 226)
+0/69092 Loss: 168.753
+3200/69092 Loss: 153.817
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+67200/69092 Loss: 153.873
+Training time 0:01:57.918332
+Epoch: 227 Average loss: 152.90
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 227)
+0/69092 Loss: 145.500
+3200/69092 Loss: 150.818
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+67200/69092 Loss: 154.719
+Training time 0:01:57.894648
+Epoch: 228 Average loss: 153.07
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 228)
+0/69092 Loss: 180.293
+3200/69092 Loss: 153.891
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+Training time 0:01:57.319584
+Epoch: 229 Average loss: 153.05
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 229)
+0/69092 Loss: 142.525
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+Training time 0:01:57.791838
+Epoch: 230 Average loss: 153.00
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 230)
+0/69092 Loss: 154.642
+3200/69092 Loss: 151.589
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+Training time 0:01:57.312702
+Epoch: 231 Average loss: 152.97
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 231)
+0/69092 Loss: 140.259
+3200/69092 Loss: 154.425
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+67200/69092 Loss: 152.980
+Training time 0:01:56.487330
+Epoch: 232 Average loss: 152.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 232)
+0/69092 Loss: 171.943
+3200/69092 Loss: 154.085
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+Training time 0:01:57.974114
+Epoch: 233 Average loss: 153.07
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 233)
+0/69092 Loss: 146.546
+3200/69092 Loss: 151.793
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+Training time 0:01:56.738304
+Epoch: 234 Average loss: 152.94
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 234)
+0/69092 Loss: 147.142
+3200/69092 Loss: 154.610
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+Training time 0:01:56.618151
+Epoch: 235 Average loss: 152.87
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 235)
+0/69092 Loss: 140.823
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+Training time 0:01:57.025385
+Epoch: 236 Average loss: 152.92
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 236)
+0/69092 Loss: 150.785
+3200/69092 Loss: 153.572
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+64000/69092 Loss: 151.943
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+Training time 0:01:58.060189
+Epoch: 237 Average loss: 152.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 237)
+0/69092 Loss: 146.388
+3200/69092 Loss: 155.082
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+64000/69092 Loss: 152.992
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+Training time 0:01:58.970486
+Epoch: 238 Average loss: 152.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 238)
+0/69092 Loss: 149.429
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+Training time 0:01:58.623239
+Epoch: 239 Average loss: 152.99
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 239)
+0/69092 Loss: 167.180
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+64000/69092 Loss: 152.129
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+Training time 0:01:58.830728
+Epoch: 240 Average loss: 152.78
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 240)
+0/69092 Loss: 153.859
+3200/69092 Loss: 149.460
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+64000/69092 Loss: 152.422
+67200/69092 Loss: 154.780
+Training time 0:01:57.960246
+Epoch: 241 Average loss: 152.59
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 241)
+0/69092 Loss: 158.629
+3200/69092 Loss: 150.650
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+64000/69092 Loss: 153.345
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+Training time 0:01:58.264088
+Epoch: 242 Average loss: 152.88
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 242)
+0/69092 Loss: 137.126
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+64000/69092 Loss: 152.561
+67200/69092 Loss: 153.073
+Training time 0:01:57.771225
+Epoch: 243 Average loss: 152.90
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 243)
+0/69092 Loss: 153.165
+3200/69092 Loss: 151.722
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+64000/69092 Loss: 151.940
+67200/69092 Loss: 155.476
+Training time 0:01:57.331840
+Epoch: 244 Average loss: 153.02
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 244)
+0/69092 Loss: 163.873
+3200/69092 Loss: 151.982
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+64000/69092 Loss: 156.619
+67200/69092 Loss: 151.462
+Training time 0:01:57.832230
+Epoch: 245 Average loss: 152.87
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 245)
+0/69092 Loss: 148.779
+3200/69092 Loss: 152.686
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+64000/69092 Loss: 152.996
+67200/69092 Loss: 151.816
+Training time 0:01:58.694350
+Epoch: 246 Average loss: 152.79
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 246)
+0/69092 Loss: 140.414
+3200/69092 Loss: 151.577
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+64000/69092 Loss: 151.277
+67200/69092 Loss: 152.981
+Training time 0:01:58.011280
+Epoch: 247 Average loss: 153.01
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 247)
+0/69092 Loss: 152.841
+3200/69092 Loss: 151.889
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+64000/69092 Loss: 151.431
+67200/69092 Loss: 153.094
+Training time 0:01:57.768346
+Epoch: 248 Average loss: 152.81
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 248)
+0/69092 Loss: 166.012
+3200/69092 Loss: 153.240
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+64000/69092 Loss: 153.514
+67200/69092 Loss: 151.401
+Training time 0:01:58.015001
+Epoch: 249 Average loss: 152.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 249)
+0/69092 Loss: 171.326
+3200/69092 Loss: 150.472
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+67200/69092 Loss: 154.974
+Training time 0:01:58.922323
+Epoch: 250 Average loss: 152.78
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 250)
+0/69092 Loss: 138.233
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+Training time 0:01:58.782987
+Epoch: 251 Average loss: 152.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 251)
+0/69092 Loss: 137.886
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+Training time 0:01:57.211614
+Epoch: 252 Average loss: 152.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 252)
+0/69092 Loss: 152.495
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+67200/69092 Loss: 154.340
+Training time 0:01:58.622072
+Epoch: 253 Average loss: 152.59
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 253)
+0/69092 Loss: 154.265
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+67200/69092 Loss: 151.886
+Training time 0:01:57.786232
+Epoch: 254 Average loss: 152.65
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 254)
+0/69092 Loss: 164.848
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+64000/69092 Loss: 151.697
+67200/69092 Loss: 150.450
+Training time 0:01:57.186922
+Epoch: 255 Average loss: 152.77
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 255)
+0/69092 Loss: 159.468
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+64000/69092 Loss: 153.208
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+Training time 0:01:57.968381
+Epoch: 256 Average loss: 152.70
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 256)
+0/69092 Loss: 156.280
+3200/69092 Loss: 152.914
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+Training time 0:01:57.276543
+Epoch: 257 Average loss: 152.76
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 257)
+0/69092 Loss: 142.211
+3200/69092 Loss: 151.189
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+67200/69092 Loss: 152.065
+Training time 0:01:56.878804
+Epoch: 258 Average loss: 152.65
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 258)
+0/69092 Loss: 158.911
+3200/69092 Loss: 153.411
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+64000/69092 Loss: 151.809
+67200/69092 Loss: 154.798
+Training time 0:01:57.443749
+Epoch: 259 Average loss: 152.68
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 259)
+0/69092 Loss: 149.823
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+Training time 0:01:57.874040
+Epoch: 260 Average loss: 152.84
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 260)
+0/69092 Loss: 160.968
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+64000/69092 Loss: 150.971
+67200/69092 Loss: 150.893
+Training time 0:01:58.076517
+Epoch: 261 Average loss: 152.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 261)
+0/69092 Loss: 147.421
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+64000/69092 Loss: 152.706
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+Training time 0:01:57.302088
+Epoch: 262 Average loss: 152.69
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 262)
+0/69092 Loss: 142.283
+3200/69092 Loss: 154.785
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+64000/69092 Loss: 150.834
+67200/69092 Loss: 150.893
+Training time 0:01:57.486335
+Epoch: 263 Average loss: 152.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 263)
+0/69092 Loss: 154.675
+3200/69092 Loss: 151.951
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+64000/69092 Loss: 155.343
+67200/69092 Loss: 153.840
+Training time 0:01:59.114386
+Epoch: 264 Average loss: 152.89
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 264)
+0/69092 Loss: 139.263
+3200/69092 Loss: 154.954
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+64000/69092 Loss: 151.914
+67200/69092 Loss: 152.941
+Training time 0:01:57.698918
+Epoch: 265 Average loss: 152.84
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 265)
+0/69092 Loss: 155.013
+3200/69092 Loss: 151.811
+6400/69092 Loss: 155.869
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+64000/69092 Loss: 154.203
+67200/69092 Loss: 152.275
+Training time 0:01:57.469466
+Epoch: 266 Average loss: 152.77
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 266)
+0/69092 Loss: 141.726
+3200/69092 Loss: 155.509
+6400/69092 Loss: 152.069
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+64000/69092 Loss: 154.605
+67200/69092 Loss: 152.399
+Training time 0:01:58.696024
+Epoch: 267 Average loss: 152.82
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 267)
+0/69092 Loss: 162.807
+3200/69092 Loss: 152.459
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+64000/69092 Loss: 152.601
+67200/69092 Loss: 151.127
+Training time 0:01:58.643948
+Epoch: 268 Average loss: 152.59
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 268)
+0/69092 Loss: 146.007
+3200/69092 Loss: 152.665
+6400/69092 Loss: 148.254
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+64000/69092 Loss: 153.367
+67200/69092 Loss: 152.949
+Training time 0:01:57.551861
+Epoch: 269 Average loss: 152.80
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 269)
+0/69092 Loss: 149.141
+3200/69092 Loss: 151.763
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+57600/69092 Loss: 153.258
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+64000/69092 Loss: 152.301
+67200/69092 Loss: 151.229
+Training time 0:01:57.921661
+Epoch: 270 Average loss: 152.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 270)
+0/69092 Loss: 142.933
+3200/69092 Loss: 152.796
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+64000/69092 Loss: 154.389
+67200/69092 Loss: 154.353
+Training time 0:01:59.452528
+Epoch: 271 Average loss: 152.87
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 271)
+0/69092 Loss: 150.556
+3200/69092 Loss: 153.041
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+64000/69092 Loss: 152.066
+67200/69092 Loss: 151.505
+Training time 0:01:57.362015
+Epoch: 272 Average loss: 152.81
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 272)
+0/69092 Loss: 142.003
+3200/69092 Loss: 151.796
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+64000/69092 Loss: 153.881
+67200/69092 Loss: 152.031
+Training time 0:01:58.291481
+Epoch: 273 Average loss: 152.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 273)
+0/69092 Loss: 158.201
+3200/69092 Loss: 152.413
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+64000/69092 Loss: 151.277
+67200/69092 Loss: 150.896
+Training time 0:01:58.769421
+Epoch: 274 Average loss: 152.42
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 274)
+0/69092 Loss: 160.438
+3200/69092 Loss: 152.681
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+64000/69092 Loss: 152.197
+67200/69092 Loss: 153.990
+Training time 0:01:58.371440
+Epoch: 275 Average loss: 152.60
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 275)
+0/69092 Loss: 141.484
+3200/69092 Loss: 149.177
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+64000/69092 Loss: 152.869
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+Training time 0:01:58.752701
+Epoch: 276 Average loss: 152.78
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 276)
+0/69092 Loss: 161.542
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+67200/69092 Loss: 151.537
+Training time 0:01:57.784326
+Epoch: 277 Average loss: 152.69
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 277)
+0/69092 Loss: 149.280
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+Training time 0:01:57.859074
+Epoch: 278 Average loss: 152.62
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 278)
+0/69092 Loss: 156.299
+3200/69092 Loss: 151.066
+6400/69092 Loss: 151.783
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+64000/69092 Loss: 152.275
+67200/69092 Loss: 152.630
+Training time 0:01:58.990335
+Epoch: 279 Average loss: 152.60
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 279)
+0/69092 Loss: 143.356
+3200/69092 Loss: 152.061
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+Training time 0:01:58.308913
+Epoch: 280 Average loss: 152.63
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 280)
+0/69092 Loss: 142.265
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+Training time 0:01:58.651631
+Epoch: 281 Average loss: 152.60
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 281)
+0/69092 Loss: 151.869
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+Training time 0:01:57.497398
+Epoch: 282 Average loss: 152.66
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 282)
+0/69092 Loss: 165.902
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+Training time 0:01:59.049903
+Epoch: 283 Average loss: 152.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 283)
+0/69092 Loss: 174.708
+3200/69092 Loss: 151.921
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+Training time 0:01:57.222668
+Epoch: 284 Average loss: 152.33
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 284)
+0/69092 Loss: 148.232
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+Training time 0:01:56.426317
+Epoch: 285 Average loss: 152.61
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 285)
+0/69092 Loss: 154.183
+3200/69092 Loss: 150.867
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+64000/69092 Loss: 151.610
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+Training time 0:01:57.813196
+Epoch: 286 Average loss: 152.77
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 286)
+0/69092 Loss: 148.648
+3200/69092 Loss: 154.444
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+Training time 0:01:57.746516
+Epoch: 287 Average loss: 152.45
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 287)
+0/69092 Loss: 138.473
+3200/69092 Loss: 150.914
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+Training time 0:01:58.206529
+Epoch: 288 Average loss: 152.32
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 288)
+0/69092 Loss: 139.143
+3200/69092 Loss: 151.870
+6400/69092 Loss: 149.610
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+64000/69092 Loss: 154.021
+67200/69092 Loss: 155.655
+Training time 0:01:58.182080
+Epoch: 289 Average loss: 152.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 289)
+0/69092 Loss: 153.771
+3200/69092 Loss: 153.182
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+64000/69092 Loss: 153.491
+67200/69092 Loss: 153.766
+Training time 0:01:56.702625
+Epoch: 290 Average loss: 152.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 290)
+0/69092 Loss: 144.858
+3200/69092 Loss: 154.124
+6400/69092 Loss: 152.808
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+64000/69092 Loss: 150.302
+67200/69092 Loss: 152.367
+Training time 0:01:57.117872
+Epoch: 291 Average loss: 152.54
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 291)
+0/69092 Loss: 146.660
+3200/69092 Loss: 154.319
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+64000/69092 Loss: 150.303
+67200/69092 Loss: 154.587
+Training time 0:01:58.680452
+Epoch: 292 Average loss: 152.64
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 292)
+0/69092 Loss: 145.385
+3200/69092 Loss: 151.340
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+64000/69092 Loss: 153.636
+67200/69092 Loss: 153.198
+Training time 0:01:57.312848
+Epoch: 293 Average loss: 152.68
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 293)
+0/69092 Loss: 159.010
+3200/69092 Loss: 151.893
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+64000/69092 Loss: 154.047
+67200/69092 Loss: 154.118
+Training time 0:01:58.901837
+Epoch: 294 Average loss: 152.70
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 294)
+0/69092 Loss: 154.872
+3200/69092 Loss: 152.256
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+64000/69092 Loss: 155.805
+67200/69092 Loss: 153.871
+Training time 0:01:57.515136
+Epoch: 295 Average loss: 152.72
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 295)
+0/69092 Loss: 172.136
+3200/69092 Loss: 154.477
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+67200/69092 Loss: 151.667
+Training time 0:01:58.336184
+Epoch: 296 Average loss: 152.51
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 296)
+0/69092 Loss: 154.380
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+Training time 0:01:58.935444
+Epoch: 297 Average loss: 152.53
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 297)
+0/69092 Loss: 162.380
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+67200/69092 Loss: 152.781
+Training time 0:01:58.921640
+Epoch: 298 Average loss: 152.66
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 298)
+0/69092 Loss: 140.366
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+Training time 0:01:58.544115
+Epoch: 299 Average loss: 152.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 299)
+0/69092 Loss: 150.468
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+64000/69092 Loss: 153.845
+67200/69092 Loss: 150.960
+Training time 0:01:58.547261
+Epoch: 300 Average loss: 152.71
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 300)
+0/69092 Loss: 143.955
+3200/69092 Loss: 151.614
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+67200/69092 Loss: 151.593
+Training time 0:01:58.548118
+Epoch: 301 Average loss: 152.46
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 301)
+0/69092 Loss: 147.279
+3200/69092 Loss: 155.644
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+44800/69092 Loss: 150.427
+48000/69092 Loss: 152.403
+51200/69092 Loss: 153.461
+54400/69092 Loss: 152.818
+57600/69092 Loss: 153.848
+60800/69092 Loss: 151.459
+64000/69092 Loss: 153.510
+67200/69092 Loss: 153.262
+Training time 0:01:59.635448
+Epoch: 302 Average loss: 152.62
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 302)
+0/69092 Loss: 153.877
+3200/69092 Loss: 152.488
+6400/69092 Loss: 152.250
+9600/69092 Loss: 153.468
+12800/69092 Loss: 154.536
+16000/69092 Loss: 151.385
+19200/69092 Loss: 151.035
+22400/69092 Loss: 150.293
+25600/69092 Loss: 154.147
+28800/69092 Loss: 153.163
+32000/69092 Loss: 154.560
+35200/69092 Loss: 152.221
+38400/69092 Loss: 155.581
+41600/69092 Loss: 153.473
+44800/69092 Loss: 153.722
+48000/69092 Loss: 151.588
+51200/69092 Loss: 151.839
+54400/69092 Loss: 152.329
+57600/69092 Loss: 150.435
+60800/69092 Loss: 151.320
+64000/69092 Loss: 153.641
+67200/69092 Loss: 153.587
+Training time 0:01:58.953545
+Epoch: 303 Average loss: 152.75
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 303)
+0/69092 Loss: 150.752
+3200/69092 Loss: 154.228
+6400/69092 Loss: 151.872
+9600/69092 Loss: 151.435
+12800/69092 Loss: 153.457
+16000/69092 Loss: 155.695
+19200/69092 Loss: 154.470
+22400/69092 Loss: 153.321
+25600/69092 Loss: 153.885
+28800/69092 Loss: 152.395
+32000/69092 Loss: 151.585
+35200/69092 Loss: 151.974
+38400/69092 Loss: 151.704
+41600/69092 Loss: 154.140
+44800/69092 Loss: 152.625
+48000/69092 Loss: 151.551
+51200/69092 Loss: 153.556
+54400/69092 Loss: 153.088
+57600/69092 Loss: 150.098
+60800/69092 Loss: 150.307
+64000/69092 Loss: 152.213
+67200/69092 Loss: 153.466
+Training time 0:01:59.769055
+Epoch: 304 Average loss: 152.67
+=> saved checkpoint 'trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last' (iter 304)
+0/69092 Loss: 135.868
+3200/69092 Loss: 151.608
+6400/69092 Loss: 153.318
+9600/69092 Loss: 154.543
+12800/69092 Loss: 152.205
+16000/69092 Loss: 152.045
+19200/69092 Loss: 153.663
+22400/69092 Loss: 153.126
+25600/69092 Loss: 151.074
diff --git a/OAR.2066988.stderr b/OAR.2066988.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..e842733f51f7c87aabeda14f050c0fa2ff041395
--- /dev/null
+++ b/OAR.2066988.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+ warnings.warn(warning.format(ret))
+## OAR [2020-06-24 06:43:10] Job 2066988 KILLED ##
diff --git a/OAR.2066988.stdout b/OAR.2066988.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..42d8ca3af1e081f42c686dc71d29f58a989a07bb
--- /dev/null
+++ b/OAR.2066988.stdout
@@ -0,0 +1,1336 @@
+Namespace(batch_size=256, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_256', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_256
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+ (module): VAE(
+ (img_to_last_conv): Sequential(
+ (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (1): ReLU()
+ (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (3): ReLU()
+ (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (5): ReLU()
+ (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (7): ReLU()
+ )
+ (last_conv_to_continuous_features): Sequential(
+ (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+ (1): ReLU()
+ )
+ (features_to_hidden_continue): Sequential(
+ (0): Linear(in_features=256, out_features=20, bias=True)
+ (1): ReLU()
+ )
+ (latent_to_features): Sequential(
+ (0): Linear(in_features=10, out_features=256, bias=True)
+ (1): ReLU()
+ )
+ (features_to_img): Sequential(
+ (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+ (1): ReLU()
+ (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (3): ReLU()
+ (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (5): ReLU()
+ (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (7): ReLU()
+ (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (9): Sigmoid()
+ )
+ )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> no checkpoint found at 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last'
+0/69092 Loss: 2814.439
+12800/69092 Loss: 2606.417
+25600/69092 Loss: 812.792
+38400/69092 Loss: 524.628
+51200/69092 Loss: 410.449
+64000/69092 Loss: 288.235
+Training time 0:04:00.502541
+Epoch: 1 Average loss: 888.26
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 1)
+0/69092 Loss: 254.843
+12800/69092 Loss: 241.234
+25600/69092 Loss: 230.002
+38400/69092 Loss: 228.340
+51200/69092 Loss: 221.194
+64000/69092 Loss: 215.089
+Training time 0:03:58.901498
+Epoch: 2 Average loss: 226.36
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 2)
+0/69092 Loss: 208.402
+12800/69092 Loss: 212.035
+25600/69092 Loss: 210.421
+38400/69092 Loss: 210.173
+51200/69092 Loss: 207.824
+64000/69092 Loss: 205.581
+Training time 0:03:51.885673
+Epoch: 3 Average loss: 208.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 3)
+0/69092 Loss: 211.543
+12800/69092 Loss: 202.477
+25600/69092 Loss: 200.710
+38400/69092 Loss: 198.968
+51200/69092 Loss: 193.583
+64000/69092 Loss: 186.156
+Training time 0:03:57.600496
+Epoch: 4 Average loss: 195.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 4)
+0/69092 Loss: 173.161
+12800/69092 Loss: 173.079
+25600/69092 Loss: 166.273
+38400/69092 Loss: 165.127
+51200/69092 Loss: 162.785
+64000/69092 Loss: 159.291
+Training time 0:04:05.123858
+Epoch: 5 Average loss: 164.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 5)
+0/69092 Loss: 156.576
+12800/69092 Loss: 154.753
+25600/69092 Loss: 151.747
+38400/69092 Loss: 151.202
+51200/69092 Loss: 148.720
+64000/69092 Loss: 147.345
+Training time 0:04:05.650963
+Epoch: 6 Average loss: 150.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 6)
+0/69092 Loss: 147.825
+12800/69092 Loss: 144.725
+25600/69092 Loss: 143.537
+38400/69092 Loss: 143.139
+51200/69092 Loss: 141.119
+64000/69092 Loss: 141.689
+Training time 0:04:08.122079
+Epoch: 7 Average loss: 142.63
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 7)
+0/69092 Loss: 131.992
+12800/69092 Loss: 140.162
+25600/69092 Loss: 138.903
+38400/69092 Loss: 138.267
+51200/69092 Loss: 136.916
+64000/69092 Loss: 138.901
+Training time 0:03:54.331655
+Epoch: 8 Average loss: 138.51
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 8)
+0/69092 Loss: 140.047
+12800/69092 Loss: 136.032
+25600/69092 Loss: 136.670
+38400/69092 Loss: 135.414
+51200/69092 Loss: 135.785
+64000/69092 Loss: 135.055
+Training time 0:04:09.508379
+Epoch: 9 Average loss: 135.55
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 9)
+0/69092 Loss: 131.478
+12800/69092 Loss: 134.254
+25600/69092 Loss: 133.785
+38400/69092 Loss: 133.843
+51200/69092 Loss: 134.290
+64000/69092 Loss: 132.657
+Training time 0:04:13.579643
+Epoch: 10 Average loss: 133.88
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 10)
+0/69092 Loss: 129.377
+12800/69092 Loss: 133.059
+25600/69092 Loss: 132.677
+38400/69092 Loss: 132.448
+51200/69092 Loss: 131.756
+64000/69092 Loss: 132.828
+Training time 0:04:11.038189
+Epoch: 11 Average loss: 132.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 11)
+0/69092 Loss: 128.447
+12800/69092 Loss: 132.007
+25600/69092 Loss: 131.486
+38400/69092 Loss: 131.955
+51200/69092 Loss: 131.331
+64000/69092 Loss: 132.508
+Training time 0:04:05.952124
+Epoch: 12 Average loss: 131.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 12)
+0/69092 Loss: 127.952
+12800/69092 Loss: 131.422
+25600/69092 Loss: 130.121
+38400/69092 Loss: 130.560
+51200/69092 Loss: 130.675
+64000/69092 Loss: 130.489
+Training time 0:04:03.246605
+Epoch: 13 Average loss: 130.66
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 13)
+0/69092 Loss: 136.223
+12800/69092 Loss: 129.585
+25600/69092 Loss: 129.881
+38400/69092 Loss: 130.199
+51200/69092 Loss: 129.653
+64000/69092 Loss: 129.837
+Training time 0:03:58.965557
+Epoch: 14 Average loss: 129.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 14)
+0/69092 Loss: 126.331
+12800/69092 Loss: 129.435
+25600/69092 Loss: 128.785
+38400/69092 Loss: 128.843
+51200/69092 Loss: 130.551
+64000/69092 Loss: 128.547
+Training time 0:04:09.519483
+Epoch: 15 Average loss: 129.28
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 15)
+0/69092 Loss: 124.206
+12800/69092 Loss: 128.104
+25600/69092 Loss: 127.977
+38400/69092 Loss: 129.959
+51200/69092 Loss: 129.623
+64000/69092 Loss: 128.646
+Training time 0:04:06.651803
+Epoch: 16 Average loss: 128.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 16)
+0/69092 Loss: 123.285
+12800/69092 Loss: 129.276
+25600/69092 Loss: 128.834
+38400/69092 Loss: 128.149
+51200/69092 Loss: 128.007
+64000/69092 Loss: 127.012
+Training time 0:04:09.535457
+Epoch: 17 Average loss: 128.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 17)
+0/69092 Loss: 122.490
+12800/69092 Loss: 128.020
+25600/69092 Loss: 128.469
+38400/69092 Loss: 127.697
+51200/69092 Loss: 127.443
+64000/69092 Loss: 127.452
+Training time 0:04:15.114925
+Epoch: 18 Average loss: 127.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 18)
+0/69092 Loss: 125.483
+12800/69092 Loss: 127.551
+25600/69092 Loss: 127.873
+38400/69092 Loss: 127.776
+51200/69092 Loss: 127.685
+64000/69092 Loss: 126.057
+Training time 0:04:01.902371
+Epoch: 19 Average loss: 127.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 19)
+0/69092 Loss: 122.885
+12800/69092 Loss: 126.344
+25600/69092 Loss: 127.344
+38400/69092 Loss: 127.723
+51200/69092 Loss: 126.600
+64000/69092 Loss: 127.361
+Training time 0:04:00.677389
+Epoch: 20 Average loss: 127.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 20)
+0/69092 Loss: 131.818
+12800/69092 Loss: 126.634
+25600/69092 Loss: 125.596
+38400/69092 Loss: 126.447
+51200/69092 Loss: 127.722
+64000/69092 Loss: 125.743
+Training time 0:04:08.702550
+Epoch: 21 Average loss: 126.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 21)
+0/69092 Loss: 120.820
+12800/69092 Loss: 125.706
+25600/69092 Loss: 127.387
+38400/69092 Loss: 126.881
+51200/69092 Loss: 125.378
+64000/69092 Loss: 126.583
+Training time 0:04:03.607133
+Epoch: 22 Average loss: 126.26
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 22)
+0/69092 Loss: 123.223
+12800/69092 Loss: 125.898
+25600/69092 Loss: 125.606
+38400/69092 Loss: 125.715
+51200/69092 Loss: 126.186
+64000/69092 Loss: 126.409
+Training time 0:04:07.609030
+Epoch: 23 Average loss: 125.96
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 23)
+0/69092 Loss: 128.143
+12800/69092 Loss: 125.965
+25600/69092 Loss: 126.009
+38400/69092 Loss: 125.024
+51200/69092 Loss: 125.283
+64000/69092 Loss: 126.305
+Training time 0:04:00.812965
+Epoch: 24 Average loss: 125.70
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 24)
+0/69092 Loss: 125.396
+12800/69092 Loss: 125.719
+25600/69092 Loss: 126.159
+38400/69092 Loss: 124.345
+51200/69092 Loss: 124.650
+64000/69092 Loss: 125.511
+Training time 0:03:58.507761
+Epoch: 25 Average loss: 125.29
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 25)
+0/69092 Loss: 136.129
+12800/69092 Loss: 126.168
+25600/69092 Loss: 125.467
+38400/69092 Loss: 124.673
+51200/69092 Loss: 124.727
+64000/69092 Loss: 124.674
+Training time 0:04:06.371973
+Epoch: 26 Average loss: 125.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 26)
+0/69092 Loss: 119.995
+12800/69092 Loss: 125.162
+25600/69092 Loss: 124.900
+38400/69092 Loss: 124.906
+51200/69092 Loss: 124.934
+64000/69092 Loss: 124.702
+Training time 0:04:08.636811
+Epoch: 27 Average loss: 124.80
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 27)
+0/69092 Loss: 121.814
+12800/69092 Loss: 124.749
+25600/69092 Loss: 123.778
+38400/69092 Loss: 125.225
+51200/69092 Loss: 124.118
+64000/69092 Loss: 125.262
+Training time 0:04:07.395318
+Epoch: 28 Average loss: 124.58
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 28)
+0/69092 Loss: 122.369
+12800/69092 Loss: 124.628
+25600/69092 Loss: 124.176
+38400/69092 Loss: 124.751
+51200/69092 Loss: 123.993
+64000/69092 Loss: 124.350
+Training time 0:04:13.117962
+Epoch: 29 Average loss: 124.32
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 29)
+0/69092 Loss: 121.895
+12800/69092 Loss: 124.362
+25600/69092 Loss: 125.226
+38400/69092 Loss: 124.151
+51200/69092 Loss: 123.218
+64000/69092 Loss: 124.114
+Training time 0:04:00.434000
+Epoch: 30 Average loss: 124.12
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 30)
+0/69092 Loss: 115.194
+12800/69092 Loss: 124.568
+25600/69092 Loss: 123.711
+38400/69092 Loss: 122.897
+51200/69092 Loss: 124.132
+64000/69092 Loss: 123.650
+Training time 0:04:05.715337
+Epoch: 31 Average loss: 123.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 31)
+0/69092 Loss: 124.334
+12800/69092 Loss: 123.616
+25600/69092 Loss: 123.198
+38400/69092 Loss: 124.313
+51200/69092 Loss: 123.299
+64000/69092 Loss: 123.692
+Training time 0:04:08.544469
+Epoch: 32 Average loss: 123.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 32)
+0/69092 Loss: 127.550
+12800/69092 Loss: 123.950
+25600/69092 Loss: 123.163
+38400/69092 Loss: 123.634
+51200/69092 Loss: 122.978
+64000/69092 Loss: 124.027
+Training time 0:04:13.082735
+Epoch: 33 Average loss: 123.47
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 33)
+0/69092 Loss: 123.320
+12800/69092 Loss: 123.340
+25600/69092 Loss: 122.071
+38400/69092 Loss: 123.296
+51200/69092 Loss: 123.829
+64000/69092 Loss: 123.658
+Training time 0:04:15.650392
+Epoch: 34 Average loss: 123.40
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 34)
+0/69092 Loss: 128.216
+12800/69092 Loss: 122.262
+25600/69092 Loss: 123.458
+38400/69092 Loss: 123.642
+51200/69092 Loss: 123.133
+64000/69092 Loss: 123.217
+Training time 0:04:08.276315
+Epoch: 35 Average loss: 123.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 35)
+0/69092 Loss: 120.675
+12800/69092 Loss: 122.984
+25600/69092 Loss: 121.834
+38400/69092 Loss: 123.413
+51200/69092 Loss: 122.305
+64000/69092 Loss: 123.635
+Training time 0:04:03.058292
+Epoch: 36 Average loss: 122.89
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 36)
+0/69092 Loss: 118.445
+12800/69092 Loss: 123.013
+25600/69092 Loss: 122.418
+38400/69092 Loss: 122.231
+51200/69092 Loss: 123.021
+64000/69092 Loss: 122.842
+Training time 0:04:06.648961
+Epoch: 37 Average loss: 122.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 37)
+0/69092 Loss: 121.083
+12800/69092 Loss: 123.263
+25600/69092 Loss: 122.257
+38400/69092 Loss: 122.379
+51200/69092 Loss: 122.171
+64000/69092 Loss: 123.059
+Training time 0:04:15.049360
+Epoch: 38 Average loss: 122.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 38)
+0/69092 Loss: 123.324
+12800/69092 Loss: 122.454
+25600/69092 Loss: 121.804
+38400/69092 Loss: 122.938
+51200/69092 Loss: 122.913
+64000/69092 Loss: 122.085
+Training time 0:04:05.371204
+Epoch: 39 Average loss: 122.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 39)
+0/69092 Loss: 128.633
+12800/69092 Loss: 121.116
+25600/69092 Loss: 121.840
+38400/69092 Loss: 122.284
+51200/69092 Loss: 122.594
+64000/69092 Loss: 123.471
+Training time 0:04:11.828321
+Epoch: 40 Average loss: 122.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 40)
+0/69092 Loss: 118.211
+12800/69092 Loss: 122.969
+25600/69092 Loss: 123.368
+38400/69092 Loss: 121.575
+51200/69092 Loss: 122.347
+64000/69092 Loss: 121.758
+Training time 0:04:09.545590
+Epoch: 41 Average loss: 122.25
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 41)
+0/69092 Loss: 122.601
+12800/69092 Loss: 122.031
+25600/69092 Loss: 123.502
+38400/69092 Loss: 123.221
+51200/69092 Loss: 121.567
+64000/69092 Loss: 121.257
+Training time 0:04:03.271690
+Epoch: 42 Average loss: 122.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 42)
+0/69092 Loss: 127.457
+12800/69092 Loss: 121.780
+25600/69092 Loss: 121.304
+38400/69092 Loss: 123.179
+51200/69092 Loss: 121.032
+64000/69092 Loss: 122.156
+Training time 0:04:06.866565
+Epoch: 43 Average loss: 121.91
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 43)
+0/69092 Loss: 122.912
+12800/69092 Loss: 121.686
+25600/69092 Loss: 121.672
+38400/69092 Loss: 121.170
+51200/69092 Loss: 121.757
+64000/69092 Loss: 122.824
+Training time 0:04:05.681837
+Epoch: 44 Average loss: 121.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 44)
+0/69092 Loss: 135.368
+12800/69092 Loss: 121.265
+25600/69092 Loss: 121.626
+38400/69092 Loss: 121.669
+51200/69092 Loss: 122.190
+64000/69092 Loss: 121.709
+Training time 0:04:11.599922
+Epoch: 45 Average loss: 121.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 45)
+0/69092 Loss: 121.074
+12800/69092 Loss: 121.516
+25600/69092 Loss: 120.940
+38400/69092 Loss: 122.202
+51200/69092 Loss: 121.848
+64000/69092 Loss: 122.093
+Training time 0:04:15.838166
+Epoch: 46 Average loss: 121.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 46)
+0/69092 Loss: 126.527
+12800/69092 Loss: 121.600
+25600/69092 Loss: 121.211
+38400/69092 Loss: 122.194
+51200/69092 Loss: 121.122
+64000/69092 Loss: 121.598
+Training time 0:04:08.633490
+Epoch: 47 Average loss: 121.51
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 47)
+0/69092 Loss: 126.953
+12800/69092 Loss: 121.570
+25600/69092 Loss: 121.045
+38400/69092 Loss: 122.628
+51200/69092 Loss: 121.052
+64000/69092 Loss: 120.897
+Training time 0:03:58.102023
+Epoch: 48 Average loss: 121.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 48)
+0/69092 Loss: 124.567
+12800/69092 Loss: 121.877
+25600/69092 Loss: 120.928
+38400/69092 Loss: 121.011
+51200/69092 Loss: 121.311
+64000/69092 Loss: 121.826
+Training time 0:04:06.935042
+Epoch: 49 Average loss: 121.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 49)
+0/69092 Loss: 121.404
+12800/69092 Loss: 121.514
+25600/69092 Loss: 121.087
+38400/69092 Loss: 121.419
+51200/69092 Loss: 121.384
+64000/69092 Loss: 121.023
+Training time 0:04:15.166332
+Epoch: 50 Average loss: 121.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 50)
+0/69092 Loss: 126.006
+12800/69092 Loss: 120.948
+25600/69092 Loss: 121.760
+38400/69092 Loss: 121.779
+51200/69092 Loss: 120.402
+64000/69092 Loss: 120.823
+Training time 0:04:28.909935
+Epoch: 51 Average loss: 121.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 51)
+0/69092 Loss: 124.286
+12800/69092 Loss: 121.074
+25600/69092 Loss: 121.402
+38400/69092 Loss: 121.318
+51200/69092 Loss: 120.578
+64000/69092 Loss: 121.595
+Training time 0:04:18.253963
+Epoch: 52 Average loss: 121.13
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 52)
+0/69092 Loss: 120.751
+12800/69092 Loss: 121.077
+25600/69092 Loss: 121.218
+38400/69092 Loss: 120.888
+51200/69092 Loss: 120.694
+64000/69092 Loss: 120.242
+Training time 0:04:06.896767
+Epoch: 53 Average loss: 120.93
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 53)
+0/69092 Loss: 127.181
+12800/69092 Loss: 120.765
+25600/69092 Loss: 120.254
+38400/69092 Loss: 120.713
+51200/69092 Loss: 120.709
+64000/69092 Loss: 121.636
+Training time 0:04:06.321434
+Epoch: 54 Average loss: 120.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 54)
+0/69092 Loss: 125.942
+12800/69092 Loss: 121.474
+25600/69092 Loss: 120.848
+38400/69092 Loss: 120.489
+51200/69092 Loss: 121.334
+64000/69092 Loss: 119.914
+Training time 0:04:07.908865
+Epoch: 55 Average loss: 120.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 55)
+0/69092 Loss: 119.879
+12800/69092 Loss: 121.761
+25600/69092 Loss: 120.564
+38400/69092 Loss: 119.969
+51200/69092 Loss: 120.840
+64000/69092 Loss: 121.243
+Training time 0:04:24.374569
+Epoch: 56 Average loss: 120.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 56)
+0/69092 Loss: 122.110
+12800/69092 Loss: 120.853
+25600/69092 Loss: 121.248
+38400/69092 Loss: 118.912
+51200/69092 Loss: 120.841
+64000/69092 Loss: 121.255
+Training time 0:04:19.634250
+Epoch: 57 Average loss: 120.63
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 57)
+0/69092 Loss: 119.217
+12800/69092 Loss: 119.208
+25600/69092 Loss: 119.932
+38400/69092 Loss: 121.283
+51200/69092 Loss: 120.620
+64000/69092 Loss: 120.339
+Training time 0:04:20.572270
+Epoch: 58 Average loss: 120.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 58)
+0/69092 Loss: 118.004
+12800/69092 Loss: 121.649
+25600/69092 Loss: 120.544
+38400/69092 Loss: 120.965
+51200/69092 Loss: 118.516
+64000/69092 Loss: 120.499
+Training time 0:04:13.101164
+Epoch: 59 Average loss: 120.46
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 59)
+0/69092 Loss: 112.881
+12800/69092 Loss: 120.567
+25600/69092 Loss: 120.420
+38400/69092 Loss: 119.364
+51200/69092 Loss: 119.955
+64000/69092 Loss: 121.037
+Training time 0:04:10.750013
+Epoch: 60 Average loss: 120.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 60)
+0/69092 Loss: 122.250
+12800/69092 Loss: 119.708
+25600/69092 Loss: 120.189
+38400/69092 Loss: 120.499
+51200/69092 Loss: 120.354
+64000/69092 Loss: 120.335
+Training time 0:04:13.578982
+Epoch: 61 Average loss: 120.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 61)
+0/69092 Loss: 117.498
+12800/69092 Loss: 120.426
+25600/69092 Loss: 120.447
+38400/69092 Loss: 119.672
+51200/69092 Loss: 120.147
+64000/69092 Loss: 119.269
+Training time 0:04:20.939895
+Epoch: 62 Average loss: 119.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 62)
+0/69092 Loss: 125.455
+12800/69092 Loss: 119.791
+25600/69092 Loss: 120.792
+38400/69092 Loss: 119.489
+51200/69092 Loss: 120.414
+64000/69092 Loss: 119.984
+Training time 0:04:19.158055
+Epoch: 63 Average loss: 120.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 63)
+0/69092 Loss: 117.290
+12800/69092 Loss: 119.601
+25600/69092 Loss: 120.635
+38400/69092 Loss: 119.446
+51200/69092 Loss: 119.494
+64000/69092 Loss: 120.180
+Training time 0:04:19.186761
+Epoch: 64 Average loss: 119.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 64)
+0/69092 Loss: 120.642
+12800/69092 Loss: 119.806
+25600/69092 Loss: 120.017
+38400/69092 Loss: 120.107
+51200/69092 Loss: 120.154
+64000/69092 Loss: 119.791
+Training time 0:04:17.913614
+Epoch: 65 Average loss: 119.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 65)
+0/69092 Loss: 119.794
+12800/69092 Loss: 118.851
+25600/69092 Loss: 120.739
+38400/69092 Loss: 120.140
+51200/69092 Loss: 120.621
+64000/69092 Loss: 118.912
+Training time 0:04:07.989220
+Epoch: 66 Average loss: 119.93
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 66)
+0/69092 Loss: 121.311
+12800/69092 Loss: 121.100
+25600/69092 Loss: 118.866
+38400/69092 Loss: 119.869
+51200/69092 Loss: 119.210
+64000/69092 Loss: 119.637
+Training time 0:04:04.809304
+Epoch: 67 Average loss: 119.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 67)
+0/69092 Loss: 117.776
+12800/69092 Loss: 119.768
+25600/69092 Loss: 119.897
+38400/69092 Loss: 119.051
+51200/69092 Loss: 119.888
+64000/69092 Loss: 119.118
+Training time 0:04:05.612033
+Epoch: 68 Average loss: 119.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 68)
+0/69092 Loss: 114.908
+12800/69092 Loss: 119.558
+25600/69092 Loss: 120.312
+38400/69092 Loss: 119.431
+51200/69092 Loss: 118.943
+64000/69092 Loss: 119.501
+Training time 0:04:18.977186
+Epoch: 69 Average loss: 119.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 69)
+0/69092 Loss: 122.569
+12800/69092 Loss: 118.864
+25600/69092 Loss: 119.829
+38400/69092 Loss: 119.767
+51200/69092 Loss: 119.599
+64000/69092 Loss: 119.105
+Training time 0:04:11.458453
+Epoch: 70 Average loss: 119.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 70)
+0/69092 Loss: 125.237
+12800/69092 Loss: 119.324
+25600/69092 Loss: 119.526
+38400/69092 Loss: 119.403
+51200/69092 Loss: 119.721
+64000/69092 Loss: 119.557
+Training time 0:04:15.062350
+Epoch: 71 Average loss: 119.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 71)
+0/69092 Loss: 117.608
+12800/69092 Loss: 119.207
+25600/69092 Loss: 119.193
+38400/69092 Loss: 119.453
+51200/69092 Loss: 119.239
+64000/69092 Loss: 119.616
+Training time 0:04:05.494584
+Epoch: 72 Average loss: 119.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 72)
+0/69092 Loss: 113.774
+12800/69092 Loss: 119.703
+25600/69092 Loss: 120.271
+38400/69092 Loss: 118.884
+51200/69092 Loss: 118.561
+64000/69092 Loss: 119.390
+Training time 0:04:04.810459
+Epoch: 73 Average loss: 119.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 73)
+0/69092 Loss: 121.206
+12800/69092 Loss: 119.420
+25600/69092 Loss: 119.455
+38400/69092 Loss: 119.734
+51200/69092 Loss: 118.942
+64000/69092 Loss: 118.688
+Training time 0:04:03.597461
+Epoch: 74 Average loss: 119.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 74)
+0/69092 Loss: 123.219
+12800/69092 Loss: 118.896
+25600/69092 Loss: 119.816
+38400/69092 Loss: 117.844
+51200/69092 Loss: 119.564
+64000/69092 Loss: 119.447
+Training time 0:04:11.406864
+Epoch: 75 Average loss: 119.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 75)
+0/69092 Loss: 116.660
+12800/69092 Loss: 118.787
+25600/69092 Loss: 118.336
+38400/69092 Loss: 119.625
+51200/69092 Loss: 119.877
+64000/69092 Loss: 119.186
+Training time 0:04:24.342829
+Epoch: 76 Average loss: 119.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 76)
+0/69092 Loss: 119.502
+12800/69092 Loss: 117.959
+25600/69092 Loss: 118.876
+38400/69092 Loss: 119.940
+51200/69092 Loss: 118.478
+64000/69092 Loss: 119.287
+Training time 0:04:07.572541
+Epoch: 77 Average loss: 119.02
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 77)
+0/69092 Loss: 112.084
+12800/69092 Loss: 117.931
+25600/69092 Loss: 118.700
+38400/69092 Loss: 119.683
+51200/69092 Loss: 118.883
+64000/69092 Loss: 118.919
+Training time 0:04:03.865588
+Epoch: 78 Average loss: 118.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 78)
+0/69092 Loss: 117.714
+12800/69092 Loss: 119.233
+25600/69092 Loss: 118.715
+38400/69092 Loss: 118.166
+51200/69092 Loss: 118.807
+64000/69092 Loss: 119.992
+Training time 0:04:05.190180
+Epoch: 79 Average loss: 119.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 79)
+0/69092 Loss: 117.169
+12800/69092 Loss: 117.980
+25600/69092 Loss: 118.887
+38400/69092 Loss: 118.441
+51200/69092 Loss: 118.812
+64000/69092 Loss: 119.412
+Training time 0:04:02.204515
+Epoch: 80 Average loss: 118.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 80)
+0/69092 Loss: 118.471
+12800/69092 Loss: 118.986
+25600/69092 Loss: 117.801
+38400/69092 Loss: 118.665
+51200/69092 Loss: 119.535
+64000/69092 Loss: 119.216
+Training time 0:04:14.209794
+Epoch: 81 Average loss: 118.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 81)
+0/69092 Loss: 113.233
+12800/69092 Loss: 118.796
+25600/69092 Loss: 120.057
+38400/69092 Loss: 118.499
+51200/69092 Loss: 118.816
+64000/69092 Loss: 117.538
+Training time 0:04:15.709971
+Epoch: 82 Average loss: 118.69
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 82)
+0/69092 Loss: 114.715
+12800/69092 Loss: 118.884
+25600/69092 Loss: 118.494
+38400/69092 Loss: 118.577
+51200/69092 Loss: 118.888
+64000/69092 Loss: 118.886
+Training time 0:04:11.655027
+Epoch: 83 Average loss: 118.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 83)
+0/69092 Loss: 114.184
+12800/69092 Loss: 118.511
+25600/69092 Loss: 119.265
+38400/69092 Loss: 118.358
+51200/69092 Loss: 119.018
+64000/69092 Loss: 118.696
+Training time 0:04:03.034456
+Epoch: 84 Average loss: 118.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 84)
+0/69092 Loss: 119.023
+12800/69092 Loss: 118.235
+25600/69092 Loss: 118.429
+38400/69092 Loss: 117.985
+51200/69092 Loss: 118.307
+64000/69092 Loss: 119.246
+Training time 0:04:07.248343
+Epoch: 85 Average loss: 118.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 85)
+0/69092 Loss: 121.420
+12800/69092 Loss: 118.604
+25600/69092 Loss: 118.223
+38400/69092 Loss: 118.012
+51200/69092 Loss: 118.248
+64000/69092 Loss: 119.041
+Training time 0:04:09.127944
+Epoch: 86 Average loss: 118.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 86)
+0/69092 Loss: 119.622
+12800/69092 Loss: 118.327
+25600/69092 Loss: 119.245
+38400/69092 Loss: 118.592
+51200/69092 Loss: 117.328
+64000/69092 Loss: 118.681
+Training time 0:04:19.338612
+Epoch: 87 Average loss: 118.44
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 87)
+0/69092 Loss: 111.174
+12800/69092 Loss: 118.593
+25600/69092 Loss: 118.148
+38400/69092 Loss: 118.586
+51200/69092 Loss: 118.532
+64000/69092 Loss: 118.767
+Training time 0:04:15.535839
+Epoch: 88 Average loss: 118.37
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 88)
+0/69092 Loss: 116.705
+12800/69092 Loss: 118.352
+25600/69092 Loss: 118.533
+38400/69092 Loss: 118.169
+51200/69092 Loss: 118.406
+64000/69092 Loss: 118.343
+Training time 0:04:20.266325
+Epoch: 89 Average loss: 118.38
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 89)
+0/69092 Loss: 115.584
+12800/69092 Loss: 117.936
+25600/69092 Loss: 118.092
+38400/69092 Loss: 118.414
+51200/69092 Loss: 118.575
+64000/69092 Loss: 117.829
+Training time 0:04:17.261602
+Epoch: 90 Average loss: 118.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 90)
+0/69092 Loss: 117.601
+12800/69092 Loss: 118.211
+25600/69092 Loss: 118.045
+38400/69092 Loss: 117.952
+51200/69092 Loss: 118.657
+64000/69092 Loss: 118.763
+Training time 0:04:10.787288
+Epoch: 91 Average loss: 118.30
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 91)
+0/69092 Loss: 111.534
+12800/69092 Loss: 117.956
+25600/69092 Loss: 118.221
+38400/69092 Loss: 118.217
+51200/69092 Loss: 117.930
+64000/69092 Loss: 118.685
+Training time 0:04:05.952103
+Epoch: 92 Average loss: 118.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 92)
+0/69092 Loss: 115.097
+12800/69092 Loss: 118.364
+25600/69092 Loss: 118.456
+38400/69092 Loss: 117.776
+51200/69092 Loss: 118.108
+64000/69092 Loss: 118.348
+Training time 0:04:14.091436
+Epoch: 93 Average loss: 118.24
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 93)
+0/69092 Loss: 117.903
+12800/69092 Loss: 118.141
+25600/69092 Loss: 117.958
+38400/69092 Loss: 118.750
+51200/69092 Loss: 116.999
+64000/69092 Loss: 119.126
+Training time 0:04:19.666973
+Epoch: 94 Average loss: 118.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 94)
+0/69092 Loss: 122.937
+12800/69092 Loss: 118.038
+25600/69092 Loss: 117.933
+38400/69092 Loss: 117.870
+51200/69092 Loss: 117.970
+64000/69092 Loss: 118.466
+Training time 0:04:27.989384
+Epoch: 95 Average loss: 118.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 95)
+0/69092 Loss: 113.251
+12800/69092 Loss: 117.549
+25600/69092 Loss: 118.034
+38400/69092 Loss: 118.375
+51200/69092 Loss: 117.229
+64000/69092 Loss: 118.759
+Training time 0:04:23.592842
+Epoch: 96 Average loss: 117.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 96)
+0/69092 Loss: 125.196
+12800/69092 Loss: 118.374
+25600/69092 Loss: 118.202
+38400/69092 Loss: 118.131
+51200/69092 Loss: 117.639
+64000/69092 Loss: 117.563
+Training time 0:04:07.757856
+Epoch: 97 Average loss: 117.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 97)
+0/69092 Loss: 116.827
+12800/69092 Loss: 118.167
+25600/69092 Loss: 117.874
+38400/69092 Loss: 118.108
+51200/69092 Loss: 117.770
+64000/69092 Loss: 117.935
+Training time 0:04:07.602487
+Epoch: 98 Average loss: 117.88
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 98)
+0/69092 Loss: 116.395
+12800/69092 Loss: 118.537
+25600/69092 Loss: 116.787
+38400/69092 Loss: 117.802
+51200/69092 Loss: 118.060
+64000/69092 Loss: 117.920
+Training time 0:04:04.077916
+Epoch: 99 Average loss: 117.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 99)
+0/69092 Loss: 120.129
+12800/69092 Loss: 117.395
+25600/69092 Loss: 118.096
+38400/69092 Loss: 118.253
+51200/69092 Loss: 118.095
+64000/69092 Loss: 117.124
+Training time 0:04:14.157682
+Epoch: 100 Average loss: 117.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 100)
+0/69092 Loss: 115.819
+12800/69092 Loss: 117.516
+25600/69092 Loss: 117.688
+38400/69092 Loss: 117.822
+51200/69092 Loss: 117.359
+64000/69092 Loss: 118.094
+Training time 0:04:19.060029
+Epoch: 101 Average loss: 117.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 101)
+0/69092 Loss: 117.652
+12800/69092 Loss: 117.546
+25600/69092 Loss: 117.209
+38400/69092 Loss: 118.417
+51200/69092 Loss: 117.194
+64000/69092 Loss: 117.343
+Training time 0:04:27.360355
+Epoch: 102 Average loss: 117.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 102)
+0/69092 Loss: 119.372
+12800/69092 Loss: 117.161
+25600/69092 Loss: 117.579
+38400/69092 Loss: 118.675
+51200/69092 Loss: 118.077
+64000/69092 Loss: 116.353
+Training time 0:04:21.829024
+Epoch: 103 Average loss: 117.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 103)
+0/69092 Loss: 122.145
+12800/69092 Loss: 117.681
+25600/69092 Loss: 117.564
+38400/69092 Loss: 117.854
+51200/69092 Loss: 117.792
+64000/69092 Loss: 117.095
+Training time 0:04:10.505366
+Epoch: 104 Average loss: 117.66
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 104)
+0/69092 Loss: 119.037
+12800/69092 Loss: 117.433
+25600/69092 Loss: 117.319
+38400/69092 Loss: 118.096
+51200/69092 Loss: 118.451
+64000/69092 Loss: 117.593
+Training time 0:04:13.309748
+Epoch: 105 Average loss: 117.70
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 105)
+0/69092 Loss: 115.671
+12800/69092 Loss: 118.765
+25600/69092 Loss: 117.530
+38400/69092 Loss: 116.610
+51200/69092 Loss: 117.387
+64000/69092 Loss: 118.102
+Training time 0:04:10.225632
+Epoch: 106 Average loss: 117.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 106)
+0/69092 Loss: 115.970
+12800/69092 Loss: 117.271
+25600/69092 Loss: 118.336
+38400/69092 Loss: 116.654
+51200/69092 Loss: 116.908
+64000/69092 Loss: 118.475
+Training time 0:04:08.311380
+Epoch: 107 Average loss: 117.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 107)
+0/69092 Loss: 118.297
+12800/69092 Loss: 118.045
+25600/69092 Loss: 117.715
+38400/69092 Loss: 116.549
+51200/69092 Loss: 117.337
+64000/69092 Loss: 118.109
+Training time 0:04:18.767566
+Epoch: 108 Average loss: 117.52
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 108)
+0/69092 Loss: 115.676
+12800/69092 Loss: 118.016
+25600/69092 Loss: 116.507
+38400/69092 Loss: 117.254
+51200/69092 Loss: 117.718
+64000/69092 Loss: 118.072
+Training time 0:04:27.749704
+Epoch: 109 Average loss: 117.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 109)
+0/69092 Loss: 118.938
+12800/69092 Loss: 116.951
+25600/69092 Loss: 117.728
+38400/69092 Loss: 118.054
+51200/69092 Loss: 117.318
+64000/69092 Loss: 117.840
+Training time 0:04:26.627106
+Epoch: 110 Average loss: 117.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 110)
+0/69092 Loss: 121.937
+12800/69092 Loss: 118.140
+25600/69092 Loss: 117.377
+38400/69092 Loss: 117.915
+51200/69092 Loss: 117.499
+64000/69092 Loss: 116.375
+Training time 0:04:12.037837
+Epoch: 111 Average loss: 117.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 111)
+0/69092 Loss: 113.947
+12800/69092 Loss: 115.591
+25600/69092 Loss: 117.857
+38400/69092 Loss: 117.139
+51200/69092 Loss: 117.992
+64000/69092 Loss: 117.336
+Training time 0:04:07.543581
+Epoch: 112 Average loss: 117.20
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 112)
+0/69092 Loss: 117.774
+12800/69092 Loss: 117.031
+25600/69092 Loss: 116.865
+38400/69092 Loss: 117.537
+51200/69092 Loss: 117.092
+64000/69092 Loss: 117.203
+Training time 0:04:06.793473
+Epoch: 113 Average loss: 117.34
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 113)
+0/69092 Loss: 122.003
+12800/69092 Loss: 117.281
+25600/69092 Loss: 116.915
+38400/69092 Loss: 117.246
+51200/69092 Loss: 117.098
+64000/69092 Loss: 117.323
+Training time 0:04:18.110371
+Epoch: 114 Average loss: 117.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 114)
+0/69092 Loss: 113.728
+12800/69092 Loss: 117.338
+25600/69092 Loss: 117.415
+38400/69092 Loss: 117.887
+51200/69092 Loss: 116.883
+64000/69092 Loss: 116.655
+Training time 0:04:23.082706
+Epoch: 115 Average loss: 117.17
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 115)
+0/69092 Loss: 113.460
+12800/69092 Loss: 116.962
+25600/69092 Loss: 117.133
+38400/69092 Loss: 117.736
+51200/69092 Loss: 116.920
+64000/69092 Loss: 117.195
+Training time 0:04:17.946739
+Epoch: 116 Average loss: 117.18
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 116)
+0/69092 Loss: 122.396
+12800/69092 Loss: 117.253
+25600/69092 Loss: 117.988
+38400/69092 Loss: 116.718
+51200/69092 Loss: 116.569
+64000/69092 Loss: 117.342
+Training time 0:04:09.487106
+Epoch: 117 Average loss: 117.17
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 117)
+0/69092 Loss: 120.599
+12800/69092 Loss: 118.029
+25600/69092 Loss: 116.844
+38400/69092 Loss: 116.261
+51200/69092 Loss: 117.148
+64000/69092 Loss: 116.991
+Training time 0:04:06.126837
+Epoch: 118 Average loss: 117.11
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 118)
+0/69092 Loss: 113.447
+12800/69092 Loss: 117.673
+25600/69092 Loss: 116.305
+38400/69092 Loss: 117.318
+51200/69092 Loss: 117.771
+64000/69092 Loss: 116.620
+Training time 0:04:06.952300
+Epoch: 119 Average loss: 117.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 119)
+0/69092 Loss: 116.694
+12800/69092 Loss: 117.533
+25600/69092 Loss: 117.339
+38400/69092 Loss: 116.549
+51200/69092 Loss: 116.852
+64000/69092 Loss: 117.144
+Training time 0:04:18.524811
+Epoch: 120 Average loss: 117.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 120)
+0/69092 Loss: 121.263
+12800/69092 Loss: 117.623
+25600/69092 Loss: 116.567
+38400/69092 Loss: 117.494
+51200/69092 Loss: 116.391
+64000/69092 Loss: 116.778
+Training time 0:04:26.026426
+Epoch: 121 Average loss: 116.96
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 121)
+0/69092 Loss: 119.814
+12800/69092 Loss: 117.488
+25600/69092 Loss: 116.667
+38400/69092 Loss: 116.857
+51200/69092 Loss: 116.277
+64000/69092 Loss: 116.752
+Training time 0:04:21.240310
+Epoch: 122 Average loss: 116.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 122)
+0/69092 Loss: 114.083
+12800/69092 Loss: 117.017
+25600/69092 Loss: 116.481
+38400/69092 Loss: 115.768
+51200/69092 Loss: 117.344
+64000/69092 Loss: 118.059
+Training time 0:04:21.731750
+Epoch: 123 Average loss: 116.92
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 123)
+0/69092 Loss: 115.396
+12800/69092 Loss: 116.999
+25600/69092 Loss: 117.044
+38400/69092 Loss: 116.922
+51200/69092 Loss: 116.681
+64000/69092 Loss: 117.204
+Training time 0:04:13.355516
+Epoch: 124 Average loss: 116.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 124)
+0/69092 Loss: 114.354
+12800/69092 Loss: 116.758
+25600/69092 Loss: 117.844
+38400/69092 Loss: 116.776
+51200/69092 Loss: 116.723
+64000/69092 Loss: 116.333
+Training time 0:04:11.060562
+Epoch: 125 Average loss: 116.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 125)
+0/69092 Loss: 112.850
+12800/69092 Loss: 116.547
+25600/69092 Loss: 117.430
+38400/69092 Loss: 116.559
+51200/69092 Loss: 116.838
+64000/69092 Loss: 117.391
+Training time 0:04:11.748621
+Epoch: 126 Average loss: 116.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 126)
+0/69092 Loss: 122.874
+12800/69092 Loss: 115.833
+25600/69092 Loss: 116.628
+38400/69092 Loss: 117.270
+51200/69092 Loss: 116.436
+64000/69092 Loss: 116.940
+Training time 0:04:12.916334
+Epoch: 127 Average loss: 116.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 127)
+0/69092 Loss: 120.091
+12800/69092 Loss: 116.054
+25600/69092 Loss: 116.758
+38400/69092 Loss: 116.714
+51200/69092 Loss: 116.977
+64000/69092 Loss: 117.389
+Training time 0:04:19.136137
+Epoch: 128 Average loss: 116.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 128)
+0/69092 Loss: 120.740
+12800/69092 Loss: 117.049
+25600/69092 Loss: 117.354
+38400/69092 Loss: 116.619
+51200/69092 Loss: 116.406
+64000/69092 Loss: 116.879
+Training time 0:04:14.033693
+Epoch: 129 Average loss: 116.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 129)
+0/69092 Loss: 115.168
+12800/69092 Loss: 117.192
+25600/69092 Loss: 115.843
+38400/69092 Loss: 116.705
+51200/69092 Loss: 116.972
+64000/69092 Loss: 117.077
+Training time 0:04:12.246856
+Epoch: 130 Average loss: 116.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 130)
+0/69092 Loss: 112.333
+12800/69092 Loss: 116.003
+25600/69092 Loss: 117.295
+38400/69092 Loss: 116.347
+51200/69092 Loss: 117.229
+64000/69092 Loss: 117.151
+Training time 0:04:09.115987
+Epoch: 131 Average loss: 116.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 131)
+0/69092 Loss: 111.468
+12800/69092 Loss: 117.271
+25600/69092 Loss: 116.352
+38400/69092 Loss: 116.958
+51200/69092 Loss: 116.093
+64000/69092 Loss: 116.560
+Training time 0:04:06.089068
+Epoch: 132 Average loss: 116.62
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 132)
+0/69092 Loss: 122.202
+12800/69092 Loss: 116.612
+25600/69092 Loss: 116.396
+38400/69092 Loss: 116.750
+51200/69092 Loss: 117.098
+64000/69092 Loss: 116.652
+Training time 0:04:12.456707
+Epoch: 133 Average loss: 116.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 133)
+0/69092 Loss: 115.194
+12800/69092 Loss: 115.900
+25600/69092 Loss: 117.075
+38400/69092 Loss: 116.256
+51200/69092 Loss: 116.497
+64000/69092 Loss: 116.524
+Training time 0:04:19.269725
+Epoch: 134 Average loss: 116.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 134)
+0/69092 Loss: 117.595
+12800/69092 Loss: 116.274
+25600/69092 Loss: 116.078
+38400/69092 Loss: 116.369
+51200/69092 Loss: 116.956
+64000/69092 Loss: 116.922
+Training time 0:04:17.374130
+Epoch: 135 Average loss: 116.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 135)
+0/69092 Loss: 118.568
+12800/69092 Loss: 116.903
+25600/69092 Loss: 116.886
+38400/69092 Loss: 116.347
+51200/69092 Loss: 116.076
+64000/69092 Loss: 116.188
+Training time 0:04:24.367970
+Epoch: 136 Average loss: 116.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 136)
+0/69092 Loss: 111.307
+12800/69092 Loss: 116.186
+25600/69092 Loss: 116.851
+38400/69092 Loss: 116.367
+51200/69092 Loss: 117.102
+64000/69092 Loss: 116.760
+Training time 0:04:08.283412
+Epoch: 137 Average loss: 116.63
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 137)
+0/69092 Loss: 120.235
+12800/69092 Loss: 116.380
+25600/69092 Loss: 116.760
+38400/69092 Loss: 116.232
+51200/69092 Loss: 116.531
+64000/69092 Loss: 116.034
+Training time 0:04:11.104504
+Epoch: 138 Average loss: 116.44
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 138)
+0/69092 Loss: 118.149
+12800/69092 Loss: 116.341
+25600/69092 Loss: 116.744
+38400/69092 Loss: 116.054
+51200/69092 Loss: 116.238
+64000/69092 Loss: 116.862
+Training time 0:04:04.615421
+Epoch: 139 Average loss: 116.47
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 139)
+0/69092 Loss: 116.938
+12800/69092 Loss: 116.694
+25600/69092 Loss: 116.568
+38400/69092 Loss: 116.814
+51200/69092 Loss: 115.828
+64000/69092 Loss: 116.409
+Training time 0:04:13.500231
+Epoch: 140 Average loss: 116.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 140)
+0/69092 Loss: 120.534
+12800/69092 Loss: 116.519
+25600/69092 Loss: 116.369
+38400/69092 Loss: 115.763
+51200/69092 Loss: 116.506
+64000/69092 Loss: 116.365
+Training time 0:04:16.106278
+Epoch: 141 Average loss: 116.28
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 141)
+0/69092 Loss: 119.007
+12800/69092 Loss: 116.184
+25600/69092 Loss: 117.124
+38400/69092 Loss: 115.399
+51200/69092 Loss: 115.922
+64000/69092 Loss: 116.029
+Training time 0:04:20.728542
+Epoch: 142 Average loss: 116.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 142)
+0/69092 Loss: 112.271
+12800/69092 Loss: 116.729
+25600/69092 Loss: 115.879
+38400/69092 Loss: 117.016
+51200/69092 Loss: 116.293
+64000/69092 Loss: 116.261
+Training time 0:04:14.548097
+Epoch: 143 Average loss: 116.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_256/checkpoints/last' (iter 143)
+0/69092 Loss: 119.247
+12800/69092 Loss: 116.008
diff --git a/OAR.2066989.stderr b/OAR.2066989.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..234323fdd0c0cd9b7dadab71ac41d247f46869b1
--- /dev/null
+++ b/OAR.2066989.stderr
@@ -0,0 +1,3 @@
+/data1/home/julien.dejasmin/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
+ warnings.warn(warning.format(ret))
+## OAR [2020-06-24 07:00:02] Job 2066989 KILLED ##
diff --git a/OAR.2066989.stdout b/OAR.2066989.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..d530a05bea3bf17252ed2c1e75fa5b2dfa98a56e
--- /dev/null
+++ b/OAR.2066989.stdout
@@ -0,0 +1,3460 @@
+Namespace(batch_size=64, beta=None, ckpt_dir='checkpoints', ckpt_name='last', cont_capacity=None, dataset='rendered_chairs', disc_capacity=None, epochs=400, experiment_name='VAE_bs_64', gpu_devices=[0, 1], is_beta_VAE=False, latent_name='', latent_spec_cont=10, latent_spec_disc=None, load_expe_name='', load_model_checkpoint=False, lr=0.0001, num_worker=4, print_loss_every=50, record_loss_every=50, save_model=True, save_reconstruction_image=False, save_step=1, verbose=True)
+creare new diretory experiment: rendered_chairs/VAE_bs_64
+load dataset: rendered_chairs, with: 69120 train images of shape: (3, 64, 64)
+use 2 gpu who named:
+GeForce RTX 2080 Ti
+GeForce RTX 2080 Ti
+DataParallel(
+ (module): VAE(
+ (img_to_last_conv): Sequential(
+ (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (1): ReLU()
+ (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (3): ReLU()
+ (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (5): ReLU()
+ (6): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (7): ReLU()
+ )
+ (last_conv_to_continuous_features): Sequential(
+ (0): Conv2d(64, 256, kernel_size=(4, 4), stride=(1, 1))
+ (1): ReLU()
+ )
+ (features_to_hidden_continue): Sequential(
+ (0): Linear(in_features=256, out_features=20, bias=True)
+ (1): ReLU()
+ )
+ (latent_to_features): Sequential(
+ (0): Linear(in_features=10, out_features=256, bias=True)
+ (1): ReLU()
+ )
+ (features_to_img): Sequential(
+ (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(1, 1))
+ (1): ReLU()
+ (2): ConvTranspose2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (3): ReLU()
+ (4): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (5): ReLU()
+ (6): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (7): ReLU()
+ (8): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+ (9): Sigmoid()
+ )
+ )
+)
+The number of parameters of model is 765335
+don't use continuous capacity
+=> loaded checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last (iter 3)'
+0/69092 Loss: 168.710
+3200/69092 Loss: 151.090
+6400/69092 Loss: 149.554
+9600/69092 Loss: 147.097
+12800/69092 Loss: 149.283
+16000/69092 Loss: 147.124
+19200/69092 Loss: 148.296
+22400/69092 Loss: 151.390
+25600/69092 Loss: 149.540
+28800/69092 Loss: 148.538
+32000/69092 Loss: 146.801
+35200/69092 Loss: 146.608
+38400/69092 Loss: 147.409
+41600/69092 Loss: 141.844
+44800/69092 Loss: 148.554
+48000/69092 Loss: 149.620
+51200/69092 Loss: 146.300
+54400/69092 Loss: 150.148
+57600/69092 Loss: 146.875
+60800/69092 Loss: 143.943
+64000/69092 Loss: 147.783
+67200/69092 Loss: 147.213
+Training time 0:04:24.323380
+Epoch: 1 Average loss: 147.75
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 4)
+0/69092 Loss: 158.819
+3200/69092 Loss: 145.455
+6400/69092 Loss: 146.130
+9600/69092 Loss: 144.380
+12800/69092 Loss: 144.843
+16000/69092 Loss: 146.080
+19200/69092 Loss: 145.421
+22400/69092 Loss: 149.645
+25600/69092 Loss: 148.440
+28800/69092 Loss: 144.370
+32000/69092 Loss: 147.816
+35200/69092 Loss: 146.294
+38400/69092 Loss: 145.632
+41600/69092 Loss: 141.891
+44800/69092 Loss: 143.450
+48000/69092 Loss: 143.778
+51200/69092 Loss: 145.786
+54400/69092 Loss: 143.362
+57600/69092 Loss: 147.704
+60800/69092 Loss: 147.121
+64000/69092 Loss: 145.282
+67200/69092 Loss: 145.587
+Training time 0:04:20.176991
+Epoch: 2 Average loss: 145.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 5)
+0/69092 Loss: 134.195
+3200/69092 Loss: 148.996
+6400/69092 Loss: 142.480
+9600/69092 Loss: 141.951
+12800/69092 Loss: 144.894
+16000/69092 Loss: 142.587
+19200/69092 Loss: 143.194
+22400/69092 Loss: 144.316
+25600/69092 Loss: 142.355
+28800/69092 Loss: 145.405
+32000/69092 Loss: 142.902
+35200/69092 Loss: 146.418
+38400/69092 Loss: 144.190
+41600/69092 Loss: 143.508
+44800/69092 Loss: 142.121
+48000/69092 Loss: 144.655
+51200/69092 Loss: 143.216
+54400/69092 Loss: 145.905
+57600/69092 Loss: 144.233
+60800/69092 Loss: 143.225
+64000/69092 Loss: 143.747
+67200/69092 Loss: 144.372
+Training time 0:04:21.847400
+Epoch: 3 Average loss: 144.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 6)
+0/69092 Loss: 158.546
+3200/69092 Loss: 143.724
+6400/69092 Loss: 143.463
+9600/69092 Loss: 143.337
+12800/69092 Loss: 144.160
+16000/69092 Loss: 144.717
+19200/69092 Loss: 142.720
+22400/69092 Loss: 141.418
+25600/69092 Loss: 142.358
+28800/69092 Loss: 143.985
+32000/69092 Loss: 141.719
+35200/69092 Loss: 140.359
+38400/69092 Loss: 142.268
+41600/69092 Loss: 143.411
+44800/69092 Loss: 144.112
+48000/69092 Loss: 142.263
+51200/69092 Loss: 142.091
+54400/69092 Loss: 144.641
+57600/69092 Loss: 142.175
+60800/69092 Loss: 141.229
+64000/69092 Loss: 140.349
+67200/69092 Loss: 140.640
+Training time 0:04:28.338593
+Epoch: 4 Average loss: 142.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 7)
+0/69092 Loss: 142.651
+3200/69092 Loss: 140.940
+6400/69092 Loss: 141.995
+9600/69092 Loss: 143.163
+12800/69092 Loss: 139.080
+16000/69092 Loss: 142.381
+19200/69092 Loss: 139.068
+22400/69092 Loss: 142.262
+25600/69092 Loss: 142.301
+28800/69092 Loss: 144.625
+32000/69092 Loss: 143.733
+35200/69092 Loss: 139.129
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+57600/69092 Loss: 143.115
+60800/69092 Loss: 142.930
+64000/69092 Loss: 139.570
+67200/69092 Loss: 143.483
+Training time 0:04:32.850446
+Epoch: 5 Average loss: 141.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 8)
+0/69092 Loss: 148.280
+3200/69092 Loss: 139.345
+6400/69092 Loss: 140.230
+9600/69092 Loss: 143.982
+12800/69092 Loss: 139.087
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+60800/69092 Loss: 141.552
+64000/69092 Loss: 140.871
+67200/69092 Loss: 141.383
+Training time 0:04:28.358295
+Epoch: 6 Average loss: 140.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 9)
+0/69092 Loss: 151.288
+3200/69092 Loss: 139.086
+6400/69092 Loss: 139.084
+9600/69092 Loss: 142.941
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+57600/69092 Loss: 139.065
+60800/69092 Loss: 141.348
+64000/69092 Loss: 139.424
+67200/69092 Loss: 137.497
+Training time 0:04:20.120376
+Epoch: 7 Average loss: 139.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 10)
+0/69092 Loss: 141.026
+3200/69092 Loss: 139.577
+6400/69092 Loss: 136.711
+9600/69092 Loss: 141.149
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+64000/69092 Loss: 137.397
+67200/69092 Loss: 142.087
+Training time 0:04:18.076296
+Epoch: 8 Average loss: 138.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 11)
+0/69092 Loss: 142.430
+3200/69092 Loss: 135.373
+6400/69092 Loss: 136.746
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+57600/69092 Loss: 138.901
+60800/69092 Loss: 135.466
+64000/69092 Loss: 137.750
+67200/69092 Loss: 135.623
+Training time 0:04:24.652008
+Epoch: 9 Average loss: 137.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 12)
+0/69092 Loss: 133.767
+3200/69092 Loss: 132.615
+6400/69092 Loss: 135.810
+9600/69092 Loss: 136.655
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+57600/69092 Loss: 134.247
+60800/69092 Loss: 133.764
+64000/69092 Loss: 132.814
+67200/69092 Loss: 132.712
+Training time 0:04:27.168263
+Epoch: 10 Average loss: 135.18
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 13)
+0/69092 Loss: 133.262
+3200/69092 Loss: 132.734
+6400/69092 Loss: 132.997
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+60800/69092 Loss: 130.160
+64000/69092 Loss: 131.685
+67200/69092 Loss: 127.816
+Training time 0:04:24.848130
+Epoch: 11 Average loss: 132.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 14)
+0/69092 Loss: 124.336
+3200/69092 Loss: 129.374
+6400/69092 Loss: 131.302
+9600/69092 Loss: 129.214
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+64000/69092 Loss: 128.923
+67200/69092 Loss: 128.455
+Training time 0:04:25.437912
+Epoch: 12 Average loss: 129.40
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 15)
+0/69092 Loss: 134.263
+3200/69092 Loss: 127.368
+6400/69092 Loss: 129.353
+9600/69092 Loss: 129.035
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+64000/69092 Loss: 127.095
+67200/69092 Loss: 127.681
+Training time 0:04:32.032921
+Epoch: 13 Average loss: 127.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 16)
+0/69092 Loss: 131.333
+3200/69092 Loss: 126.908
+6400/69092 Loss: 127.240
+9600/69092 Loss: 125.892
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+57600/69092 Loss: 127.925
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+64000/69092 Loss: 125.735
+67200/69092 Loss: 129.284
+Training time 0:04:26.086614
+Epoch: 14 Average loss: 126.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 17)
+0/69092 Loss: 133.018
+3200/69092 Loss: 124.639
+6400/69092 Loss: 127.398
+9600/69092 Loss: 128.027
+12800/69092 Loss: 127.694
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+57600/69092 Loss: 128.311
+60800/69092 Loss: 128.544
+64000/69092 Loss: 125.838
+67200/69092 Loss: 125.219
+Training time 0:04:26.177394
+Epoch: 15 Average loss: 126.43
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 18)
+0/69092 Loss: 129.000
+3200/69092 Loss: 126.913
+6400/69092 Loss: 126.775
+9600/69092 Loss: 125.272
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+25600/69092 Loss: 126.913
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+48000/69092 Loss: 125.397
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+57600/69092 Loss: 125.470
+60800/69092 Loss: 125.073
+64000/69092 Loss: 123.041
+67200/69092 Loss: 125.522
+Training time 0:04:17.666420
+Epoch: 16 Average loss: 125.19
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 19)
+0/69092 Loss: 130.460
+3200/69092 Loss: 125.000
+6400/69092 Loss: 122.856
+9600/69092 Loss: 124.084
+12800/69092 Loss: 123.778
+16000/69092 Loss: 122.321
+19200/69092 Loss: 124.423
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+25600/69092 Loss: 124.913
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+48000/69092 Loss: 120.418
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+57600/69092 Loss: 123.103
+60800/69092 Loss: 122.097
+64000/69092 Loss: 122.104
+67200/69092 Loss: 126.776
+Training time 0:04:17.206166
+Epoch: 17 Average loss: 123.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 20)
+0/69092 Loss: 125.709
+3200/69092 Loss: 122.481
+6400/69092 Loss: 124.142
+9600/69092 Loss: 125.479
+12800/69092 Loss: 122.792
+16000/69092 Loss: 122.145
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+25600/69092 Loss: 123.891
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+32000/69092 Loss: 124.310
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+57600/69092 Loss: 122.085
+60800/69092 Loss: 122.979
+64000/69092 Loss: 122.467
+67200/69092 Loss: 125.233
+Training time 0:04:21.616950
+Epoch: 18 Average loss: 122.98
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 21)
+0/69092 Loss: 131.789
+3200/69092 Loss: 122.443
+6400/69092 Loss: 122.318
+9600/69092 Loss: 122.900
+12800/69092 Loss: 122.974
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+19200/69092 Loss: 120.611
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+57600/69092 Loss: 121.043
+60800/69092 Loss: 122.161
+64000/69092 Loss: 123.240
+67200/69092 Loss: 122.289
+Training time 0:04:23.710109
+Epoch: 19 Average loss: 122.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 22)
+0/69092 Loss: 132.011
+3200/69092 Loss: 122.395
+6400/69092 Loss: 122.687
+9600/69092 Loss: 119.839
+12800/69092 Loss: 120.750
+16000/69092 Loss: 121.405
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+57600/69092 Loss: 123.741
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+64000/69092 Loss: 123.181
+67200/69092 Loss: 119.608
+Training time 0:04:22.895842
+Epoch: 20 Average loss: 121.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 23)
+0/69092 Loss: 119.060
+3200/69092 Loss: 124.563
+6400/69092 Loss: 119.417
+9600/69092 Loss: 120.945
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+16000/69092 Loss: 118.880
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+25600/69092 Loss: 120.804
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+41600/69092 Loss: 120.950
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+60800/69092 Loss: 121.478
+64000/69092 Loss: 121.191
+67200/69092 Loss: 119.711
+Training time 0:04:25.586193
+Epoch: 21 Average loss: 121.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 24)
+0/69092 Loss: 147.141
+3200/69092 Loss: 122.941
+6400/69092 Loss: 121.454
+9600/69092 Loss: 121.708
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+19200/69092 Loss: 120.991
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+57600/69092 Loss: 121.708
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+64000/69092 Loss: 119.001
+67200/69092 Loss: 120.973
+Training time 0:04:25.966213
+Epoch: 22 Average loss: 121.14
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 25)
+0/69092 Loss: 137.482
+3200/69092 Loss: 122.287
+6400/69092 Loss: 122.076
+9600/69092 Loss: 121.104
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+64000/69092 Loss: 121.307
+67200/69092 Loss: 120.479
+Training time 0:04:26.382108
+Epoch: 23 Average loss: 120.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 26)
+0/69092 Loss: 118.231
+3200/69092 Loss: 120.758
+6400/69092 Loss: 122.165
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+64000/69092 Loss: 119.563
+67200/69092 Loss: 116.662
+Training time 0:04:25.570850
+Epoch: 24 Average loss: 120.61
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 27)
+0/69092 Loss: 108.304
+3200/69092 Loss: 119.500
+6400/69092 Loss: 122.795
+9600/69092 Loss: 122.464
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+57600/69092 Loss: 120.914
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+64000/69092 Loss: 118.564
+67200/69092 Loss: 119.804
+Training time 0:04:18.949771
+Epoch: 25 Average loss: 120.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 28)
+0/69092 Loss: 118.364
+3200/69092 Loss: 120.370
+6400/69092 Loss: 120.641
+9600/69092 Loss: 119.070
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+57600/69092 Loss: 120.210
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+64000/69092 Loss: 119.213
+67200/69092 Loss: 119.887
+Training time 0:04:25.713850
+Epoch: 26 Average loss: 120.20
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 29)
+0/69092 Loss: 118.972
+3200/69092 Loss: 121.785
+6400/69092 Loss: 120.323
+9600/69092 Loss: 120.396
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+64000/69092 Loss: 119.094
+67200/69092 Loss: 121.274
+Training time 0:04:32.947963
+Epoch: 27 Average loss: 120.08
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 30)
+0/69092 Loss: 105.711
+3200/69092 Loss: 120.707
+6400/69092 Loss: 119.270
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+57600/69092 Loss: 118.897
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+64000/69092 Loss: 119.202
+67200/69092 Loss: 118.477
+Training time 0:04:38.239052
+Epoch: 28 Average loss: 119.79
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 31)
+0/69092 Loss: 125.045
+3200/69092 Loss: 119.077
+6400/69092 Loss: 121.288
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+57600/69092 Loss: 120.759
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+64000/69092 Loss: 119.713
+67200/69092 Loss: 119.882
+Training time 0:04:30.982903
+Epoch: 29 Average loss: 119.67
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 32)
+0/69092 Loss: 116.535
+3200/69092 Loss: 120.467
+6400/69092 Loss: 120.213
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+64000/69092 Loss: 120.850
+67200/69092 Loss: 119.152
+Training time 0:04:32.173868
+Epoch: 30 Average loss: 119.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 33)
+0/69092 Loss: 116.206
+3200/69092 Loss: 120.007
+6400/69092 Loss: 120.306
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+64000/69092 Loss: 118.875
+67200/69092 Loss: 118.516
+Training time 0:04:29.045180
+Epoch: 31 Average loss: 119.29
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 34)
+0/69092 Loss: 117.266
+3200/69092 Loss: 118.274
+6400/69092 Loss: 117.234
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+64000/69092 Loss: 118.560
+67200/69092 Loss: 119.088
+Training time 0:04:29.912910
+Epoch: 32 Average loss: 119.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 35)
+0/69092 Loss: 123.715
+3200/69092 Loss: 119.440
+6400/69092 Loss: 118.955
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+64000/69092 Loss: 120.255
+67200/69092 Loss: 118.699
+Training time 0:04:33.274534
+Epoch: 33 Average loss: 119.11
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 36)
+0/69092 Loss: 118.849
+3200/69092 Loss: 120.621
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+64000/69092 Loss: 118.783
+67200/69092 Loss: 120.760
+Training time 0:04:27.301002
+Epoch: 34 Average loss: 119.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 37)
+0/69092 Loss: 117.403
+3200/69092 Loss: 119.066
+6400/69092 Loss: 118.569
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+64000/69092 Loss: 120.180
+67200/69092 Loss: 117.345
+Training time 0:04:26.693822
+Epoch: 35 Average loss: 118.71
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 38)
+0/69092 Loss: 126.114
+3200/69092 Loss: 120.061
+6400/69092 Loss: 117.689
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+64000/69092 Loss: 119.610
+67200/69092 Loss: 119.881
+Training time 0:04:28.605144
+Epoch: 36 Average loss: 118.68
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 39)
+0/69092 Loss: 116.029
+3200/69092 Loss: 119.409
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+64000/69092 Loss: 117.346
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+Training time 0:04:26.394531
+Epoch: 37 Average loss: 118.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 40)
+0/69092 Loss: 131.857
+3200/69092 Loss: 119.845
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+64000/69092 Loss: 119.288
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+Training time 0:04:28.231272
+Epoch: 38 Average loss: 118.76
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 41)
+0/69092 Loss: 104.315
+3200/69092 Loss: 118.207
+6400/69092 Loss: 118.797
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+64000/69092 Loss: 120.259
+67200/69092 Loss: 117.853
+Training time 0:04:36.544689
+Epoch: 39 Average loss: 118.36
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 42)
+0/69092 Loss: 115.324
+3200/69092 Loss: 118.398
+6400/69092 Loss: 119.789
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+57600/69092 Loss: 118.737
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+64000/69092 Loss: 116.808
+67200/69092 Loss: 117.400
+Training time 0:04:33.331351
+Epoch: 40 Average loss: 118.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 43)
+0/69092 Loss: 118.771
+3200/69092 Loss: 116.662
+6400/69092 Loss: 117.344
+9600/69092 Loss: 117.850
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+64000/69092 Loss: 118.007
+67200/69092 Loss: 118.956
+Training time 0:04:29.097078
+Epoch: 41 Average loss: 118.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 44)
+0/69092 Loss: 121.785
+3200/69092 Loss: 118.478
+6400/69092 Loss: 119.252
+9600/69092 Loss: 116.702
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+57600/69092 Loss: 118.281
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+64000/69092 Loss: 118.264
+67200/69092 Loss: 118.857
+Training time 0:04:34.029356
+Epoch: 42 Average loss: 118.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 45)
+0/69092 Loss: 123.996
+3200/69092 Loss: 116.337
+6400/69092 Loss: 115.972
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+64000/69092 Loss: 119.213
+67200/69092 Loss: 119.978
+Training time 0:04:14.785945
+Epoch: 43 Average loss: 117.97
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 46)
+0/69092 Loss: 106.408
+3200/69092 Loss: 116.920
+6400/69092 Loss: 118.497
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+64000/69092 Loss: 117.965
+67200/69092 Loss: 115.849
+Training time 0:04:21.044989
+Epoch: 44 Average loss: 117.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 47)
+0/69092 Loss: 120.892
+3200/69092 Loss: 118.694
+6400/69092 Loss: 118.532
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+64000/69092 Loss: 118.261
+67200/69092 Loss: 115.889
+Training time 0:04:12.758008
+Epoch: 45 Average loss: 117.74
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 48)
+0/69092 Loss: 118.087
+3200/69092 Loss: 117.098
+6400/69092 Loss: 118.501
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+19200/69092 Loss: 117.987
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+64000/69092 Loss: 118.153
+67200/69092 Loss: 117.880
+Training time 0:04:20.462099
+Epoch: 46 Average loss: 117.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 49)
+0/69092 Loss: 125.376
+3200/69092 Loss: 118.748
+6400/69092 Loss: 116.575
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+64000/69092 Loss: 116.628
+67200/69092 Loss: 118.540
+Training time 0:04:20.977065
+Epoch: 47 Average loss: 117.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 50)
+0/69092 Loss: 116.650
+3200/69092 Loss: 117.711
+6400/69092 Loss: 117.896
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+64000/69092 Loss: 116.324
+67200/69092 Loss: 117.485
+Training time 0:04:32.062968
+Epoch: 48 Average loss: 117.64
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 51)
+0/69092 Loss: 109.422
+3200/69092 Loss: 118.779
+6400/69092 Loss: 114.385
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+64000/69092 Loss: 117.278
+67200/69092 Loss: 117.851
+Training time 0:04:23.378010
+Epoch: 49 Average loss: 117.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 52)
+0/69092 Loss: 134.673
+3200/69092 Loss: 116.821
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+64000/69092 Loss: 117.501
+67200/69092 Loss: 117.372
+Training time 0:04:20.237584
+Epoch: 50 Average loss: 117.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 53)
+0/69092 Loss: 111.840
+3200/69092 Loss: 116.925
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+64000/69092 Loss: 118.638
+67200/69092 Loss: 119.231
+Training time 0:04:22.076445
+Epoch: 51 Average loss: 117.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 54)
+0/69092 Loss: 112.129
+3200/69092 Loss: 117.937
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+64000/69092 Loss: 117.797
+67200/69092 Loss: 117.296
+Training time 0:04:18.183982
+Epoch: 52 Average loss: 117.44
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 55)
+0/69092 Loss: 109.696
+3200/69092 Loss: 118.624
+6400/69092 Loss: 116.392
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+64000/69092 Loss: 117.791
+67200/69092 Loss: 117.873
+Training time 0:04:28.247742
+Epoch: 53 Average loss: 117.04
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 56)
+0/69092 Loss: 119.751
+3200/69092 Loss: 118.410
+6400/69092 Loss: 116.364
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+64000/69092 Loss: 115.422
+67200/69092 Loss: 118.018
+Training time 0:04:33.892244
+Epoch: 54 Average loss: 117.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 57)
+0/69092 Loss: 122.561
+3200/69092 Loss: 117.605
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+64000/69092 Loss: 117.863
+67200/69092 Loss: 118.023
+Training time 0:04:26.042149
+Epoch: 55 Average loss: 117.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 58)
+0/69092 Loss: 114.835
+3200/69092 Loss: 116.415
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+64000/69092 Loss: 116.443
+67200/69092 Loss: 116.465
+Training time 0:04:28.371721
+Epoch: 56 Average loss: 116.85
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 59)
+0/69092 Loss: 116.169
+3200/69092 Loss: 117.142
+6400/69092 Loss: 115.437
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+64000/69092 Loss: 117.079
+67200/69092 Loss: 116.638
+Training time 0:04:26.000514
+Epoch: 57 Average loss: 116.88
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 60)
+0/69092 Loss: 110.920
+3200/69092 Loss: 114.819
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+64000/69092 Loss: 115.175
+67200/69092 Loss: 115.445
+Training time 0:04:20.411956
+Epoch: 58 Average loss: 116.94
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 61)
+0/69092 Loss: 123.952
+3200/69092 Loss: 115.271
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+64000/69092 Loss: 118.402
+67200/69092 Loss: 119.948
+Training time 0:04:14.535964
+Epoch: 59 Average loss: 116.89
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 62)
+0/69092 Loss: 100.414
+3200/69092 Loss: 115.779
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+64000/69092 Loss: 117.807
+67200/69092 Loss: 117.300
+Training time 0:04:16.028042
+Epoch: 60 Average loss: 116.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 63)
+0/69092 Loss: 115.661
+3200/69092 Loss: 116.403
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+64000/69092 Loss: 117.599
+67200/69092 Loss: 116.314
+Training time 0:04:18.673967
+Epoch: 61 Average loss: 116.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 64)
+0/69092 Loss: 116.701
+3200/69092 Loss: 115.635
+6400/69092 Loss: 115.918
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+64000/69092 Loss: 115.270
+67200/69092 Loss: 116.514
+Training time 0:04:21.632812
+Epoch: 62 Average loss: 116.60
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 65)
+0/69092 Loss: 107.765
+3200/69092 Loss: 115.992
+6400/69092 Loss: 114.613
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+64000/69092 Loss: 116.337
+67200/69092 Loss: 116.728
+Training time 0:04:21.771630
+Epoch: 63 Average loss: 116.49
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 66)
+0/69092 Loss: 112.972
+3200/69092 Loss: 116.920
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+64000/69092 Loss: 118.028
+67200/69092 Loss: 116.265
+Training time 0:04:21.300752
+Epoch: 64 Average loss: 116.53
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 67)
+0/69092 Loss: 127.194
+3200/69092 Loss: 115.406
+6400/69092 Loss: 116.160
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+64000/69092 Loss: 117.099
+67200/69092 Loss: 117.352
+Training time 0:04:19.523237
+Epoch: 65 Average loss: 116.47
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 68)
+0/69092 Loss: 130.036
+3200/69092 Loss: 116.596
+6400/69092 Loss: 115.924
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+57600/69092 Loss: 114.892
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+64000/69092 Loss: 118.491
+67200/69092 Loss: 117.641
+Training time 0:04:13.537568
+Epoch: 66 Average loss: 116.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 69)
+0/69092 Loss: 131.276
+3200/69092 Loss: 116.788
+6400/69092 Loss: 115.669
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+64000/69092 Loss: 117.088
+67200/69092 Loss: 118.403
+Training time 0:04:15.694500
+Epoch: 67 Average loss: 116.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 70)
+0/69092 Loss: 116.447
+3200/69092 Loss: 117.105
+6400/69092 Loss: 114.875
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+64000/69092 Loss: 117.407
+67200/69092 Loss: 117.801
+Training time 0:04:23.256883
+Epoch: 68 Average loss: 116.39
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 71)
+0/69092 Loss: 109.427
+3200/69092 Loss: 115.791
+6400/69092 Loss: 117.890
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+64000/69092 Loss: 116.069
+67200/69092 Loss: 115.233
+Training time 0:04:14.798986
+Epoch: 69 Average loss: 116.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 72)
+0/69092 Loss: 115.002
+3200/69092 Loss: 117.518
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+64000/69092 Loss: 115.752
+67200/69092 Loss: 116.489
+Training time 0:04:19.510293
+Epoch: 70 Average loss: 116.12
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 73)
+0/69092 Loss: 134.101
+3200/69092 Loss: 117.654
+6400/69092 Loss: 115.580
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+64000/69092 Loss: 117.044
+67200/69092 Loss: 115.881
+Training time 0:04:21.271139
+Epoch: 71 Average loss: 116.21
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 74)
+0/69092 Loss: 110.241
+3200/69092 Loss: 115.037
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+64000/69092 Loss: 115.862
+67200/69092 Loss: 116.257
+Training time 0:04:20.527632
+Epoch: 72 Average loss: 116.07
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 75)
+0/69092 Loss: 117.426
+3200/69092 Loss: 114.095
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+64000/69092 Loss: 116.421
+67200/69092 Loss: 117.095
+Training time 0:04:14.108358
+Epoch: 73 Average loss: 116.01
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 76)
+0/69092 Loss: 120.607
+3200/69092 Loss: 115.399
+6400/69092 Loss: 114.639
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+57600/69092 Loss: 119.706
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+64000/69092 Loss: 117.580
+67200/69092 Loss: 115.744
+Training time 0:04:18.325905
+Epoch: 74 Average loss: 115.99
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 77)
+0/69092 Loss: 118.108
+3200/69092 Loss: 115.449
+6400/69092 Loss: 117.178
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+64000/69092 Loss: 116.446
+67200/69092 Loss: 114.254
+Training time 0:04:11.717487
+Epoch: 75 Average loss: 115.98
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 78)
+0/69092 Loss: 110.195
+3200/69092 Loss: 116.650
+6400/69092 Loss: 116.428
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+64000/69092 Loss: 115.741
+67200/69092 Loss: 115.039
+Training time 0:04:20.126153
+Epoch: 76 Average loss: 115.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 79)
+0/69092 Loss: 115.282
+3200/69092 Loss: 115.687
+6400/69092 Loss: 116.064
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+64000/69092 Loss: 115.782
+67200/69092 Loss: 114.948
+Training time 0:04:26.667052
+Epoch: 77 Average loss: 115.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 80)
+0/69092 Loss: 113.016
+3200/69092 Loss: 116.797
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+64000/69092 Loss: 116.509
+67200/69092 Loss: 116.613
+Training time 0:04:27.455867
+Epoch: 78 Average loss: 115.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 81)
+0/69092 Loss: 116.266
+3200/69092 Loss: 116.131
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+64000/69092 Loss: 118.061
+67200/69092 Loss: 113.611
+Training time 0:04:25.561758
+Epoch: 79 Average loss: 115.93
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 82)
+0/69092 Loss: 105.227
+3200/69092 Loss: 116.596
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+64000/69092 Loss: 115.588
+67200/69092 Loss: 115.213
+Training time 0:04:21.035061
+Epoch: 80 Average loss: 115.73
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 83)
+0/69092 Loss: 116.023
+3200/69092 Loss: 114.750
+6400/69092 Loss: 115.606
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+64000/69092 Loss: 115.104
+67200/69092 Loss: 115.512
+Training time 0:04:27.606268
+Epoch: 81 Average loss: 115.63
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 84)
+0/69092 Loss: 109.763
+3200/69092 Loss: 114.796
+6400/69092 Loss: 114.820
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+64000/69092 Loss: 115.743
+67200/69092 Loss: 114.532
+Training time 0:04:21.981919
+Epoch: 82 Average loss: 115.55
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 85)
+0/69092 Loss: 102.658
+3200/69092 Loss: 115.777
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+67200/69092 Loss: 115.187
+Training time 0:04:15.339523
+Epoch: 83 Average loss: 115.65
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 86)
+0/69092 Loss: 106.183
+3200/69092 Loss: 115.604
+6400/69092 Loss: 114.901
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+64000/69092 Loss: 115.958
+67200/69092 Loss: 114.216
+Training time 0:04:24.070150
+Epoch: 84 Average loss: 115.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 87)
+0/69092 Loss: 109.772
+3200/69092 Loss: 114.663
+6400/69092 Loss: 116.979
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+64000/69092 Loss: 113.235
+67200/69092 Loss: 116.336
+Training time 0:04:27.389755
+Epoch: 85 Average loss: 115.56
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 88)
+0/69092 Loss: 114.321
+3200/69092 Loss: 114.351
+6400/69092 Loss: 115.291
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+64000/69092 Loss: 116.776
+67200/69092 Loss: 115.444
+Training time 0:04:35.227404
+Epoch: 86 Average loss: 115.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 89)
+0/69092 Loss: 110.096
+3200/69092 Loss: 115.085
+6400/69092 Loss: 115.375
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+64000/69092 Loss: 114.703
+67200/69092 Loss: 116.976
+Training time 0:04:32.743549
+Epoch: 87 Average loss: 115.41
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 90)
+0/69092 Loss: 119.462
+3200/69092 Loss: 115.533
+6400/69092 Loss: 117.001
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+57600/69092 Loss: 116.559
+60800/69092 Loss: 114.095
+64000/69092 Loss: 114.392
+67200/69092 Loss: 114.071
+Training time 0:04:25.855823
+Epoch: 88 Average loss: 115.29
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 91)
+0/69092 Loss: 112.185
+3200/69092 Loss: 115.037
+6400/69092 Loss: 114.506
+9600/69092 Loss: 115.579
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+57600/69092 Loss: 114.616
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+64000/69092 Loss: 115.626
+67200/69092 Loss: 114.778
+Training time 0:04:18.054649
+Epoch: 89 Average loss: 115.32
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 92)
+0/69092 Loss: 117.381
+3200/69092 Loss: 116.538
+6400/69092 Loss: 114.395
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+19200/69092 Loss: 115.942
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+57600/69092 Loss: 114.440
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+64000/69092 Loss: 116.254
+67200/69092 Loss: 115.486
+Training time 0:04:11.502557
+Epoch: 90 Average loss: 115.44
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 93)
+0/69092 Loss: 109.876
+3200/69092 Loss: 114.018
+6400/69092 Loss: 112.307
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+12800/69092 Loss: 116.645
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+57600/69092 Loss: 116.659
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+64000/69092 Loss: 115.627
+67200/69092 Loss: 115.893
+Training time 0:04:23.661572
+Epoch: 91 Average loss: 115.23
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 94)
+0/69092 Loss: 109.875
+3200/69092 Loss: 116.904
+6400/69092 Loss: 114.350
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+64000/69092 Loss: 115.288
+67200/69092 Loss: 116.407
+Training time 0:04:18.970973
+Epoch: 92 Average loss: 115.28
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 95)
+0/69092 Loss: 107.112
+3200/69092 Loss: 116.615
+6400/69092 Loss: 115.904
+9600/69092 Loss: 115.694
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+57600/69092 Loss: 115.897
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+64000/69092 Loss: 115.341
+67200/69092 Loss: 113.812
+Training time 0:04:34.910466
+Epoch: 93 Average loss: 115.33
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 96)
+0/69092 Loss: 111.090
+3200/69092 Loss: 113.802
+6400/69092 Loss: 113.778
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+64000/69092 Loss: 115.140
+67200/69092 Loss: 114.921
+Training time 0:04:29.937189
+Epoch: 94 Average loss: 115.17
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 97)
+0/69092 Loss: 104.309
+3200/69092 Loss: 114.970
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+64000/69092 Loss: 115.104
+67200/69092 Loss: 115.522
+Training time 0:04:32.566710
+Epoch: 95 Average loss: 115.10
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 98)
+0/69092 Loss: 103.548
+3200/69092 Loss: 114.404
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+64000/69092 Loss: 114.218
+67200/69092 Loss: 115.762
+Training time 0:04:30.297416
+Epoch: 96 Average loss: 115.27
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 99)
+0/69092 Loss: 103.183
+3200/69092 Loss: 114.371
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+64000/69092 Loss: 114.572
+67200/69092 Loss: 113.786
+Training time 0:04:14.350046
+Epoch: 97 Average loss: 114.96
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 100)
+0/69092 Loss: 109.205
+3200/69092 Loss: 114.470
+6400/69092 Loss: 115.518
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+64000/69092 Loss: 114.032
+67200/69092 Loss: 115.695
+Training time 0:04:16.813142
+Epoch: 98 Average loss: 115.06
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 101)
+0/69092 Loss: 102.324
+3200/69092 Loss: 117.226
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+57600/69092 Loss: 114.983
+60800/69092 Loss: 113.886
+64000/69092 Loss: 115.446
+67200/69092 Loss: 116.695
+Training time 0:04:16.901688
+Epoch: 99 Average loss: 115.03
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 102)
+0/69092 Loss: 101.450
+3200/69092 Loss: 114.254
+6400/69092 Loss: 114.502
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+64000/69092 Loss: 116.074
+67200/69092 Loss: 113.280
+Training time 0:04:30.717629
+Epoch: 100 Average loss: 115.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 103)
+0/69092 Loss: 110.561
+3200/69092 Loss: 114.694
+6400/69092 Loss: 115.096
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+64000/69092 Loss: 114.837
+67200/69092 Loss: 113.714
+Training time 0:04:33.068145
+Epoch: 101 Average loss: 114.95
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 104)
+0/69092 Loss: 111.877
+3200/69092 Loss: 113.528
+6400/69092 Loss: 116.126
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+64000/69092 Loss: 113.442
+67200/69092 Loss: 114.790
+Training time 0:04:26.031708
+Epoch: 102 Average loss: 114.77
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 105)
+0/69092 Loss: 123.542
+3200/69092 Loss: 115.210
+6400/69092 Loss: 114.367
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+64000/69092 Loss: 114.436
+67200/69092 Loss: 112.579
+Training time 0:04:25.144287
+Epoch: 103 Average loss: 114.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 106)
+0/69092 Loss: 105.940
+3200/69092 Loss: 115.154
+6400/69092 Loss: 115.266
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+64000/69092 Loss: 115.321
+67200/69092 Loss: 114.775
+Training time 0:04:26.405936
+Epoch: 104 Average loss: 114.83
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 107)
+0/69092 Loss: 105.310
+3200/69092 Loss: 114.426
+6400/69092 Loss: 113.549
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+64000/69092 Loss: 115.615
+67200/69092 Loss: 115.623
+Training time 0:04:30.908597
+Epoch: 105 Average loss: 114.84
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 108)
+0/69092 Loss: 116.573
+3200/69092 Loss: 113.115
+6400/69092 Loss: 116.044
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+64000/69092 Loss: 113.984
+67200/69092 Loss: 114.659
+Training time 0:04:22.615178
+Epoch: 106 Average loss: 114.72
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 109)
+0/69092 Loss: 116.218
+3200/69092 Loss: 116.439
+6400/69092 Loss: 114.330
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+64000/69092 Loss: 114.589
+67200/69092 Loss: 113.700
+Training time 0:04:15.871955
+Epoch: 107 Average loss: 114.78
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 110)
+0/69092 Loss: 110.384
+3200/69092 Loss: 115.819
+6400/69092 Loss: 115.273
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+57600/69092 Loss: 113.777
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+64000/69092 Loss: 114.026
+67200/69092 Loss: 114.251
+Training time 0:04:15.986438
+Epoch: 108 Average loss: 114.82
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 111)
+0/69092 Loss: 110.926
+3200/69092 Loss: 115.009
+6400/69092 Loss: 113.864
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+64000/69092 Loss: 116.204
+67200/69092 Loss: 114.208
+Training time 0:04:20.591390
+Epoch: 109 Average loss: 114.50
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 112)
+0/69092 Loss: 115.884
+3200/69092 Loss: 117.003
+6400/69092 Loss: 116.712
+9600/69092 Loss: 114.350
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+57600/69092 Loss: 113.641
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+64000/69092 Loss: 113.758
+67200/69092 Loss: 113.087
+Training time 0:04:26.836611
+Epoch: 110 Average loss: 114.81
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 113)
+0/69092 Loss: 125.851
+3200/69092 Loss: 116.071
+6400/69092 Loss: 112.736
+9600/69092 Loss: 115.678
+12800/69092 Loss: 114.922
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+57600/69092 Loss: 115.297
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+64000/69092 Loss: 114.664
+67200/69092 Loss: 115.492
+Training time 0:04:35.629081
+Epoch: 111 Average loss: 114.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 114)
+0/69092 Loss: 104.279
+3200/69092 Loss: 115.154
+6400/69092 Loss: 113.715
+9600/69092 Loss: 113.930
+12800/69092 Loss: 114.900
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+19200/69092 Loss: 113.740
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+25600/69092 Loss: 114.678
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+57600/69092 Loss: 114.990
+60800/69092 Loss: 115.176
+64000/69092 Loss: 113.339
+67200/69092 Loss: 114.725
+Training time 0:04:29.687402
+Epoch: 112 Average loss: 114.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 115)
+0/69092 Loss: 102.917
+3200/69092 Loss: 114.706
+6400/69092 Loss: 115.422
+9600/69092 Loss: 116.626
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+35200/69092 Loss: 112.962
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+57600/69092 Loss: 114.777
+60800/69092 Loss: 114.589
+64000/69092 Loss: 114.345
+67200/69092 Loss: 114.994
+Training time 0:04:30.520088
+Epoch: 113 Average loss: 114.54
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 116)
+0/69092 Loss: 113.905
+3200/69092 Loss: 117.563
+6400/69092 Loss: 113.548
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+57600/69092 Loss: 114.396
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+64000/69092 Loss: 115.306
+67200/69092 Loss: 115.853
+Training time 0:04:29.241112
+Epoch: 114 Average loss: 114.59
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 117)
+0/69092 Loss: 104.253
+3200/69092 Loss: 114.970
+6400/69092 Loss: 113.504
+9600/69092 Loss: 114.310
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+64000/69092 Loss: 114.327
+67200/69092 Loss: 114.012
+Training time 0:04:27.381340
+Epoch: 115 Average loss: 114.45
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 118)
+0/69092 Loss: 110.148
+3200/69092 Loss: 114.791
+6400/69092 Loss: 115.172
+9600/69092 Loss: 114.780
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+19200/69092 Loss: 114.977
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+64000/69092 Loss: 113.563
+67200/69092 Loss: 113.980
+Training time 0:04:18.154084
+Epoch: 116 Average loss: 114.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 119)
+0/69092 Loss: 120.825
+3200/69092 Loss: 115.325
+6400/69092 Loss: 113.553
+9600/69092 Loss: 113.736
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+64000/69092 Loss: 114.951
+67200/69092 Loss: 116.248
+Training time 0:04:18.164247
+Epoch: 117 Average loss: 114.55
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 120)
+0/69092 Loss: 109.928
+3200/69092 Loss: 115.008
+6400/69092 Loss: 113.864
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+60800/69092 Loss: 113.893
+64000/69092 Loss: 114.535
+67200/69092 Loss: 111.640
+Training time 0:04:21.234648
+Epoch: 118 Average loss: 114.48
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 121)
+0/69092 Loss: 112.347
+3200/69092 Loss: 112.206
+6400/69092 Loss: 113.684
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+19200/69092 Loss: 114.991
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+64000/69092 Loss: 113.369
+67200/69092 Loss: 113.812
+Training time 0:04:21.509968
+Epoch: 119 Average loss: 114.42
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 122)
+0/69092 Loss: 110.610
+3200/69092 Loss: 114.091
+6400/69092 Loss: 114.390
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+64000/69092 Loss: 112.766
+67200/69092 Loss: 116.240
+Training time 0:04:26.174176
+Epoch: 120 Average loss: 114.31
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 123)
+0/69092 Loss: 108.468
+3200/69092 Loss: 113.823
+6400/69092 Loss: 113.926
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+57600/69092 Loss: 114.942
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+64000/69092 Loss: 115.730
+67200/69092 Loss: 113.697
+Training time 0:04:26.866437
+Epoch: 121 Average loss: 114.57
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 124)
+0/69092 Loss: 120.564
+3200/69092 Loss: 113.837
+6400/69092 Loss: 113.033
+9600/69092 Loss: 114.391
+12800/69092 Loss: 112.768
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+64000/69092 Loss: 115.693
+67200/69092 Loss: 112.606
+Training time 0:04:26.091665
+Epoch: 122 Average loss: 114.24
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 125)
+0/69092 Loss: 116.260
+3200/69092 Loss: 115.572
+6400/69092 Loss: 113.364
+9600/69092 Loss: 114.549
+12800/69092 Loss: 112.625
+16000/69092 Loss: 114.697
+19200/69092 Loss: 114.014
+22400/69092 Loss: 114.915
+25600/69092 Loss: 113.741
+28800/69092 Loss: 113.853
+32000/69092 Loss: 113.749
+35200/69092 Loss: 114.529
+38400/69092 Loss: 115.581
+41600/69092 Loss: 113.452
+44800/69092 Loss: 114.885
+48000/69092 Loss: 114.452
+51200/69092 Loss: 113.929
+54400/69092 Loss: 113.919
+57600/69092 Loss: 112.803
+60800/69092 Loss: 116.090
+64000/69092 Loss: 113.673
+67200/69092 Loss: 114.878
+Training time 0:04:20.195962
+Epoch: 123 Average loss: 114.29
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 126)
+0/69092 Loss: 102.904
+3200/69092 Loss: 114.950
+6400/69092 Loss: 114.285
+9600/69092 Loss: 113.059
+12800/69092 Loss: 114.756
+16000/69092 Loss: 114.081
+19200/69092 Loss: 116.571
+22400/69092 Loss: 115.653
+25600/69092 Loss: 114.392
+28800/69092 Loss: 112.085
+32000/69092 Loss: 115.196
+35200/69092 Loss: 115.576
+38400/69092 Loss: 112.741
+41600/69092 Loss: 114.079
+44800/69092 Loss: 112.967
+48000/69092 Loss: 114.430
+51200/69092 Loss: 113.740
+54400/69092 Loss: 112.241
+57600/69092 Loss: 115.875
+60800/69092 Loss: 115.158
+64000/69092 Loss: 113.297
+67200/69092 Loss: 113.612
+Training time 0:04:21.333011
+Epoch: 124 Average loss: 114.22
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 127)
+0/69092 Loss: 121.503
+3200/69092 Loss: 113.770
+6400/69092 Loss: 113.968
+9600/69092 Loss: 115.570
+12800/69092 Loss: 113.710
+16000/69092 Loss: 115.004
+19200/69092 Loss: 115.766
+22400/69092 Loss: 110.885
+25600/69092 Loss: 116.284
+28800/69092 Loss: 115.173
+32000/69092 Loss: 115.608
+35200/69092 Loss: 113.924
+38400/69092 Loss: 113.265
+41600/69092 Loss: 111.167
+44800/69092 Loss: 114.134
+48000/69092 Loss: 113.143
+51200/69092 Loss: 111.826
+54400/69092 Loss: 114.397
+57600/69092 Loss: 113.879
+60800/69092 Loss: 115.124
+64000/69092 Loss: 115.703
+67200/69092 Loss: 113.662
+Training time 0:04:15.476898
+Epoch: 125 Average loss: 114.16
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 128)
+0/69092 Loss: 126.295
+3200/69092 Loss: 116.617
+6400/69092 Loss: 114.015
+9600/69092 Loss: 113.958
+12800/69092 Loss: 113.577
+16000/69092 Loss: 115.547
+19200/69092 Loss: 113.954
+22400/69092 Loss: 113.498
+25600/69092 Loss: 115.607
+28800/69092 Loss: 111.954
+32000/69092 Loss: 113.496
+35200/69092 Loss: 113.677
+38400/69092 Loss: 114.765
+41600/69092 Loss: 115.236
+44800/69092 Loss: 114.410
+48000/69092 Loss: 114.308
+51200/69092 Loss: 111.934
+54400/69092 Loss: 113.136
+57600/69092 Loss: 115.013
+60800/69092 Loss: 114.129
+64000/69092 Loss: 113.522
+67200/69092 Loss: 113.718
+Training time 0:04:28.347039
+Epoch: 126 Average loss: 114.13
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 129)
+0/69092 Loss: 119.682
+3200/69092 Loss: 114.543
+6400/69092 Loss: 112.717
+9600/69092 Loss: 114.208
+12800/69092 Loss: 112.800
+16000/69092 Loss: 112.286
+19200/69092 Loss: 114.785
+22400/69092 Loss: 112.812
+25600/69092 Loss: 114.896
+28800/69092 Loss: 113.281
+32000/69092 Loss: 114.762
+35200/69092 Loss: 114.394
+38400/69092 Loss: 114.450
+41600/69092 Loss: 115.436
+44800/69092 Loss: 115.494
+48000/69092 Loss: 115.467
+51200/69092 Loss: 116.115
+54400/69092 Loss: 113.398
+57600/69092 Loss: 115.479
+60800/69092 Loss: 113.288
+64000/69092 Loss: 112.189
+67200/69092 Loss: 113.367
+Training time 0:04:24.226082
+Epoch: 127 Average loss: 114.09
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 130)
+0/69092 Loss: 108.846
+3200/69092 Loss: 113.563
+6400/69092 Loss: 113.164
+9600/69092 Loss: 113.377
+12800/69092 Loss: 113.232
+16000/69092 Loss: 115.017
+19200/69092 Loss: 114.418
+22400/69092 Loss: 113.556
+25600/69092 Loss: 113.549
+28800/69092 Loss: 114.477
+32000/69092 Loss: 113.487
+35200/69092 Loss: 115.755
+38400/69092 Loss: 113.114
+41600/69092 Loss: 112.750
+44800/69092 Loss: 114.507
+48000/69092 Loss: 113.253
+51200/69092 Loss: 114.938
+54400/69092 Loss: 114.313
+57600/69092 Loss: 113.574
+60800/69092 Loss: 115.895
+64000/69092 Loss: 112.618
+67200/69092 Loss: 115.091
+Training time 0:04:26.704741
+Epoch: 128 Average loss: 114.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 131)
+0/69092 Loss: 116.501
+3200/69092 Loss: 115.050
+6400/69092 Loss: 113.112
+9600/69092 Loss: 113.652
+12800/69092 Loss: 114.119
+16000/69092 Loss: 112.435
+19200/69092 Loss: 113.488
+22400/69092 Loss: 113.293
+25600/69092 Loss: 114.508
+28800/69092 Loss: 113.832
+32000/69092 Loss: 115.152
+35200/69092 Loss: 114.316
+38400/69092 Loss: 113.723
+41600/69092 Loss: 112.864
+44800/69092 Loss: 114.576
+48000/69092 Loss: 114.918
+51200/69092 Loss: 114.874
+54400/69092 Loss: 115.212
+57600/69092 Loss: 114.723
+60800/69092 Loss: 114.521
+64000/69092 Loss: 114.774
+67200/69092 Loss: 114.068
+Training time 0:04:24.372506
+Epoch: 129 Average loss: 114.13
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 132)
+0/69092 Loss: 115.373
+3200/69092 Loss: 112.991
+6400/69092 Loss: 115.433
+9600/69092 Loss: 114.669
+12800/69092 Loss: 114.506
+16000/69092 Loss: 113.683
+19200/69092 Loss: 113.663
+22400/69092 Loss: 113.430
+25600/69092 Loss: 114.815
+28800/69092 Loss: 112.627
+32000/69092 Loss: 115.622
+35200/69092 Loss: 112.702
+38400/69092 Loss: 112.591
+41600/69092 Loss: 115.371
+44800/69092 Loss: 115.227
+48000/69092 Loss: 114.349
+51200/69092 Loss: 115.599
+54400/69092 Loss: 114.853
+57600/69092 Loss: 113.642
+60800/69092 Loss: 112.977
+64000/69092 Loss: 112.816
+67200/69092 Loss: 115.588
+Training time 0:04:25.369487
+Epoch: 130 Average loss: 114.15
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 133)
+0/69092 Loss: 107.789
+3200/69092 Loss: 112.483
+6400/69092 Loss: 115.576
+9600/69092 Loss: 115.011
+12800/69092 Loss: 113.660
+16000/69092 Loss: 115.363
+19200/69092 Loss: 115.619
+22400/69092 Loss: 113.759
+25600/69092 Loss: 111.395
+28800/69092 Loss: 112.099
+32000/69092 Loss: 114.761
+35200/69092 Loss: 113.568
+38400/69092 Loss: 114.094
+41600/69092 Loss: 114.263
+44800/69092 Loss: 115.484
+48000/69092 Loss: 113.398
+51200/69092 Loss: 114.657
+54400/69092 Loss: 114.476
+57600/69092 Loss: 113.724
+60800/69092 Loss: 113.923
+64000/69092 Loss: 113.543
+67200/69092 Loss: 113.137
+Training time 0:04:23.321508
+Epoch: 131 Average loss: 114.05
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 134)
+0/69092 Loss: 123.004
+3200/69092 Loss: 114.564
+6400/69092 Loss: 113.602
+9600/69092 Loss: 113.447
+12800/69092 Loss: 113.275
+16000/69092 Loss: 114.056
+19200/69092 Loss: 114.602
+22400/69092 Loss: 114.670
+25600/69092 Loss: 113.045
+28800/69092 Loss: 113.388
+32000/69092 Loss: 113.867
+35200/69092 Loss: 115.408
+38400/69092 Loss: 114.099
+41600/69092 Loss: 113.591
+44800/69092 Loss: 113.610
+48000/69092 Loss: 113.645
+51200/69092 Loss: 113.597
+54400/69092 Loss: 113.680
+57600/69092 Loss: 114.309
+60800/69092 Loss: 113.205
+64000/69092 Loss: 113.916
+67200/69092 Loss: 117.059
+Training time 0:04:20.153807
+Epoch: 132 Average loss: 114.00
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 135)
+0/69092 Loss: 108.133
+3200/69092 Loss: 112.305
+6400/69092 Loss: 113.728
+9600/69092 Loss: 111.871
+12800/69092 Loss: 112.839
+16000/69092 Loss: 113.411
+19200/69092 Loss: 114.898
+22400/69092 Loss: 112.742
+25600/69092 Loss: 113.579
+28800/69092 Loss: 113.920
+32000/69092 Loss: 115.454
+35200/69092 Loss: 112.449
+38400/69092 Loss: 113.912
+41600/69092 Loss: 113.215
+44800/69092 Loss: 114.048
+48000/69092 Loss: 115.217
+51200/69092 Loss: 113.958
+54400/69092 Loss: 115.548
+57600/69092 Loss: 115.074
+60800/69092 Loss: 114.615
+64000/69092 Loss: 114.327
+67200/69092 Loss: 114.783
+Training time 0:04:13.651886
+Epoch: 133 Average loss: 113.98
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 136)
+0/69092 Loss: 112.990
+3200/69092 Loss: 112.850
+6400/69092 Loss: 111.781
+9600/69092 Loss: 114.440
+12800/69092 Loss: 113.950
+16000/69092 Loss: 113.776
+19200/69092 Loss: 116.008
+22400/69092 Loss: 114.935
+25600/69092 Loss: 113.381
+28800/69092 Loss: 115.506
+32000/69092 Loss: 113.739
+35200/69092 Loss: 115.350
+38400/69092 Loss: 113.398
+41600/69092 Loss: 112.783
+44800/69092 Loss: 114.051
+48000/69092 Loss: 113.729
+51200/69092 Loss: 111.615
+54400/69092 Loss: 112.875
+57600/69092 Loss: 113.566
+60800/69092 Loss: 112.446
+64000/69092 Loss: 115.136
+67200/69092 Loss: 113.092
+Training time 0:04:03.871304
+Epoch: 134 Average loss: 113.86
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 137)
+0/69092 Loss: 113.820
+3200/69092 Loss: 113.023
+6400/69092 Loss: 113.605
+9600/69092 Loss: 115.006
+12800/69092 Loss: 113.062
+16000/69092 Loss: 113.313
+19200/69092 Loss: 113.548
+22400/69092 Loss: 113.821
+25600/69092 Loss: 113.696
+28800/69092 Loss: 113.847
+32000/69092 Loss: 112.898
+35200/69092 Loss: 115.648
+38400/69092 Loss: 115.076
+41600/69092 Loss: 112.041
+44800/69092 Loss: 112.616
+48000/69092 Loss: 114.295
+51200/69092 Loss: 114.063
+54400/69092 Loss: 114.141
+57600/69092 Loss: 113.066
+60800/69092 Loss: 114.087
+64000/69092 Loss: 116.195
+67200/69092 Loss: 113.690
+Training time 0:04:07.117686
+Epoch: 135 Average loss: 113.87
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 138)
+0/69092 Loss: 117.427
+3200/69092 Loss: 114.269
+6400/69092 Loss: 115.480
+9600/69092 Loss: 112.917
+12800/69092 Loss: 114.796
+16000/69092 Loss: 113.584
+19200/69092 Loss: 113.838
+22400/69092 Loss: 114.711
+25600/69092 Loss: 112.946
+28800/69092 Loss: 112.712
+32000/69092 Loss: 113.911
+35200/69092 Loss: 113.644
+38400/69092 Loss: 115.387
+41600/69092 Loss: 114.737
+44800/69092 Loss: 113.171
+48000/69092 Loss: 113.791
+51200/69092 Loss: 113.560
+54400/69092 Loss: 113.901
+57600/69092 Loss: 115.148
+60800/69092 Loss: 112.369
+64000/69092 Loss: 112.151
+67200/69092 Loss: 115.114
+Training time 0:04:02.716749
+Epoch: 136 Average loss: 113.90
+=> saved checkpoint 'trained_models/rendered_chairs/VAE_bs_64/checkpoints/last' (iter 139)
+0/69092 Loss: 109.092
+3200/69092 Loss: 115.821
+6400/69092 Loss: 112.875
+9600/69092 Loss: 114.357
+12800/69092 Loss: 114.035
+16000/69092 Loss: 112.059
+19200/69092 Loss: 114.598
+22400/69092 Loss: 113.213
+25600/69092 Loss: 113.436
+28800/69092 Loss: 115.048
+32000/69092 Loss: 113.937
+35200/69092 Loss: 112.612
+38400/69092 Loss: 114.134
diff --git a/dataloader/dataloaders.py b/dataloader/dataloaders.py
index 5c557b75bb4332ad0fc62e03394ce5dffaba8fc7..4e2d5fc7000778b39a4ae5c20c10cfcfdf28cbba 100644
--- a/dataloader/dataloaders.py
+++ b/dataloader/dataloaders.py
@@ -104,7 +104,7 @@ def get_dsprites_dataloader(batch_size=128, path_to_data='../data/dSprites/dspri
return train_loader, test_loader
-def get_chairs_dataloader(num_worker=4, batch_size=128, path_to_data='data/rendered_chairs'):
+def get_chairs_dataloader(num_worker=4, batch_size=128, path_to_data='../data/rendered_chairs'):
"""
Chairs dataloader. Chairs are center cropped and resized to (64, 64).
"""
diff --git a/img_gif/rendered_chairs_VAE_bs_256.gif b/img_gif/rendered_chairs_VAE_bs_256.gif
new file mode 100644
index 0000000000000000000000000000000000000000..8c9e20e22a38163a921a71a76909049c820a06c6
Binary files /dev/null and b/img_gif/rendered_chairs_VAE_bs_256.gif differ
diff --git a/img_gif/rendered_chairs_VAE_bs_64.gif b/img_gif/rendered_chairs_VAE_bs_64.gif
new file mode 100644
index 0000000000000000000000000000000000000000..73e8c7107d2ec3c283ebd56ad5ebefe0b58561c6
Binary files /dev/null and b/img_gif/rendered_chairs_VAE_bs_64.gif differ
diff --git a/img_gif/rendered_chairs_beta_VAE_bs_256.gif b/img_gif/rendered_chairs_beta_VAE_bs_256.gif
new file mode 100644
index 0000000000000000000000000000000000000000..dd5cf8b274bff2de340db5b3af845b768135d800
Binary files /dev/null and b/img_gif/rendered_chairs_beta_VAE_bs_256.gif differ
diff --git a/img_gif/rendered_chairs_beta_VAE_bs_64.gif b/img_gif/rendered_chairs_beta_VAE_bs_64.gif
new file mode 100644
index 0000000000000000000000000000000000000000..f250b235a2626fe803a5ea601b21c03c2cbf2f98
Binary files /dev/null and b/img_gif/rendered_chairs_beta_VAE_bs_64.gif differ
diff --git a/main.py b/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e80eb9cff461e1ecd3de9c4927dac28acbb5b9e
--- /dev/null
+++ b/main.py
@@ -0,0 +1,159 @@
+"""
+Code from: https://github.com/Schlumberger/joint-vae
+https://github.com/1Konny/Beta-VAE
+"""
+
+# import sys
+# sys.path.append('')
+import os
+from dataloader.dataloaders import *
+import torch.nn as nn
+from VAE_model.models import VAE
+from torch import optim
+from viz.visualize import Visualizer
+from utils.training import Trainer, gpu_config
+import argparse
+import json
+
+
+def main(args):
+
+ # continue and discrete capacity
+ if args.cont_capacity is not None:
+ cont_capacity = [float(item) for item in args.cont_capacity.split(',')]
+ else:
+ cont_capacity = args.cont_capacity
+ if args.disc_capacity is not None:
+ disc_capacity = [float(item) for item in args.disc_capacity.split(',')]
+ else:
+ disc_capacity = args.disc_capacity
+
+ # latent_spec
+ latent_spec = {"cont": args.latent_spec_cont}
+
+ # number of classes and image size:
+ if args.dataset == 'mnist' or args.dataset == 'fashion_data':
+ nb_classes = 10
+ img_size = (1, 32, 32)
+ elif args.dataset == 'celeba_64':
+ nb_classes = None
+ img_size = (3, 64, 64)
+ elif args.dataset == 'rendered_chairs':
+ nb_classes = 1393
+ img_size = (3, 64, 64)
+ elif args.dataset == 'dSprites':
+ nb_classes = 6
+
+ # create and write a json file:
+ if not args.load_model_checkpoint:
+ print('creare new diretory experiment: {}/{}'.format(args.dataset, args.experiment_name))
+ ckpt_dir = os.path.join('trained_models', args.dataset, args.experiment_name, args.ckpt_dir)
+ if not os.path.exists(ckpt_dir):
+ print("create new directory: {}".format(ckpt_dir))
+ os.makedirs(ckpt_dir, exist_ok=True)
+
+ parameter = {'dataset': args.dataset, 'epochs': args.epochs, 'cont_capacity': args.cont_capacity,
+ 'disc_capacity': args.disc_capacity, 'record_loss_every': args.record_loss_every,
+ 'batch_size': args.batch_size, 'latent_spec_cont': args.latent_spec_cont,
+ 'experiment_name': args.experiment_name, 'print_loss_every': args.print_loss_every,
+ 'latent_spec_disc': args.latent_spec_disc, 'nb_classes': nb_classes}
+
+ # Save json parameters:
+ file_path = os.path.join('trained_models/', args.dataset, args.experiment_name, 'specs.json')
+ with open(file_path, 'w') as json_file:
+ json.dump(parameter, json_file)
+
+ # create model
+ model = VAE(img_size, latent_spec=latent_spec)
+ # load dataset
+ train_loader, test_loader, dataset_name = load_dataset(args.dataset, args.batch_size, num_worker=args.num_worker)
+
+ # Define model
+ model, use_gpu, device = gpu_config(model)
+
+ if args.verbose:
+ print(model)
+ num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+ print('The number of parameters of model is', num_params)
+
+ # Define optimizer and criterion
+ optimizer = optim.Adam(model.parameters(), lr=args.lr)
+ criterion = nn.CrossEntropyLoss()
+
+ # Define trainer
+ trainer = Trainer(model, device, optimizer, criterion, save_step=args.save_step, ckpt_dir=args.ckpt_dir,
+ ckpt_name=args.ckpt_name,
+ expe_name=args.experiment_name,
+ dataset=args.dataset,
+ cont_capacity=cont_capacity,
+ disc_capacity=disc_capacity,
+ is_beta=args.is_beta_VAE,
+ beta=args.beta)
+
+ # define visualizer
+ viz = Visualizer(model)
+
+ # Train model:
+ trainer.train(train_loader, args.epochs, save_training_gif=('../img_gif/' + dataset_name + '_' +
+ args.latent_name + args.experiment_name + '.gif', viz))
+
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description='VAE')
+ parser.add_argument('--batch-size', type=int, default=64, metavar='integer value',
+ help='input batch size for training (default: 64)')
+ parser.add_argument('--record-loss-every', type=int, default=50, metavar='integer value',
+ help='Record loss every (value)')
+ parser.add_argument('--print-loss-every', type=int, default=50, metavar='integer value',
+ help='Print loss every (value)')
+ parser.add_argument('--epochs', type=int, default=100, metavar='integer value',
+ help='number of epochs to train (default: 100)')
+ parser.add_argument('--lr', type=float, default=5e-4, metavar='value',
+ help='learning rate value')
+ parser.add_argument('--dataset', type=str, default=None, metavar='name',
+ help='Dataset Name')
+ parser.add_argument('--save-model', type=bool, default=True, metavar='bool',
+ help='Save model')
+ parser.add_argument('--save-reconstruction-image', type=bool, default=False, metavar='bool',
+ help='Save reconstruction image')
+ parser.add_argument('--latent_spec_cont', type=int, default=10, metavar='integer value',
+ help='Capacity of continue latent space')
+ parser.add_argument('--latent_spec_disc', type=list, default=None, metavar='integer list',
+ help='Capacity of discrete latent space')
+ parser.add_argument('--cont-capacity', type=str, default=None, metavar='integer tuple',
+ help='capacity of continuous channels')
+ parser.add_argument('--disc-capacity', type=str, default=None, metavar='integer tuple',
+ help='capacity of discrete channels')
+ parser.add_argument('--experiment-name', type=str, default='', metavar='name',
+ help='experiment name')
+ parser.add_argument('--latent-name', type=str, default='', metavar='name',
+ help='Latent space name')
+ parser.add_argument('--is-beta-VAE', type=bool, default=False, metavar='beta_VAE',
+ help='If use beta-VAE')
+ parser.add_argument('--beta', type=int, default=None, metavar='beta',
+ help='Beta value')
+ parser.add_argument("--gpu-devices", type=int, nargs='+', default=None, help="GPU devices available")
+ parser.add_argument("--load-model-checkpoint", type=bool, default=False, help="If we use a pre trained model")
+ parser.add_argument("--load-expe-name", type=str, default='', help="The name expe to loading")
+ parser.add_argument("--num-worker", type=int, default=4, help="num worker to dataloader")
+ parser.add_argument("--verbose", type=bool, default=True, help="To print details model")
+ parser.add_argument("--save-step", type=int, default=1, help="save model every step")
+ parser.add_argument('--ckpt_dir', default='checkpoints', type=str, help='checkpoint directory')
+ parser.add_argument('--ckpt_name', default='last', type=str,
+ help='load previous checkpoint. insert checkpoint filename')
+
+ args = parser.parse_args()
+
+ assert args.dataset in ['mnist', 'fashion_data', 'celeba_64', 'rendered_chairs', 'dSprites'], \
+ "The choisen dataset is not available. Please choose a dataset from the following: ['mnist', 'fashion_data', " \
+ "'celeba_64', 'rendered_chairs', 'dSprites'] "
+ if args.is_beta_VAE:
+ assert args.beta is not None, 'Beta is null or if you use Beta-VAe model, please enter a beta value'
+
+ print(parser.parse_args())
+
+ gpu_devices = ','.join([str(id) for id in args.gpu_devices])
+ os.environ["CUDA_VISIBLE_DEVICES"] = gpu_devices
+
+ main(args)
diff --git a/parameters_combinations/param_combinations_chairs.txt b/parameters_combinations/param_combinations_chairs.txt
index 3564326e4e7db5ddce4e8e13009f245304c9926b..e1228ff44808c90a06ca62bdef045378612d1926 100644
--- a/parameters_combinations/param_combinations_chairs.txt
+++ b/parameters_combinations/param_combinations_chairs.txt
@@ -1,4 +1,4 @@
--batch-size=256 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --is-beta-VAE=True --beta=4 --lr=1e-4 --experiment-name=beta_VAE_bs_256 --gpu-devices 0 1 --experiment-name=beta_VAE_bs_256
--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --is-beta-VAE=True --beta=4 --lr=1e-4 --experiment-name=beta_VAE_bs_64 --gpu-devices 0 1 --experiment-name=beta_VAE_bs_64
--batch-size=256 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --lr=1e-4 --experiment-name=VAE_bs_256 --gpu-devices 0 1 --experiment-name=VAE_bs_256
---batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --lr=1e-4 --experiment-name=VAE_bs_64 --gpu-devices 0 1 --experiment-name=VAE_bs_64
+--batch-size=64 --dataset=rendered_chairs --epochs=400 --latent_spec_cont=10 --lr=1e-4 --experiment-name=VAE_bs_64 --gpu-devices 0 1 --experiment-name=VAE_bs_64
\ No newline at end of file
diff --git a/reconstruction_im/charis_VAE_bs_256.png b/reconstruction_im/charis_VAE_bs_256.png
index 32d809c7d3f9f9333b1ab0a8a68a93a72f77dd9c..fe5203301e4e636df2121a0f510738e750f76734 100644
Binary files a/reconstruction_im/charis_VAE_bs_256.png and b/reconstruction_im/charis_VAE_bs_256.png differ
diff --git a/reconstruction_im/charis_VAE_bs_64.png b/reconstruction_im/charis_VAE_bs_64.png
index 4c083d463dc5433906f55a51dc67786d729b8b4f..f85ecdf45943cdb836202d75b590d16f8e084349 100644
Binary files a/reconstruction_im/charis_VAE_bs_64.png and b/reconstruction_im/charis_VAE_bs_64.png differ
diff --git a/reconstruction_im/charis_beta_VAE_bs_256.png b/reconstruction_im/charis_beta_VAE_bs_256.png
index 24f379186d26895de0526f53882cb84b53016c04..187679a81c1eccd22d6432a74f8c391cf90f136e 100644
Binary files a/reconstruction_im/charis_beta_VAE_bs_256.png and b/reconstruction_im/charis_beta_VAE_bs_256.png differ
diff --git a/reconstruction_im/charis_beta_VAE_bs_64.png b/reconstruction_im/charis_beta_VAE_bs_64.png
index c92eb0ce79ebd76b029eff0a1da84bf11e58f004..b559a570179e6deb1bd9dd398ba343144e5b97ba 100644
Binary files a/reconstruction_im/charis_beta_VAE_bs_64.png and b/reconstruction_im/charis_beta_VAE_bs_64.png differ
diff --git a/reconstruction_im/rendered_chairs_beta_VAE.png b/reconstruction_im/rendered_chairs_beta_VAE.png
deleted file mode 100644
index 669f39ee976079cef2b5775cb5afdcfb2073dec2..0000000000000000000000000000000000000000
Binary files a/reconstruction_im/rendered_chairs_beta_VAE.png and /dev/null differ
diff --git a/trained_models/rendered_chairs/VAE_bs_256/checkpoints/last b/trained_models/rendered_chairs/VAE_bs_256/checkpoints/last
new file mode 100644
index 0000000000000000000000000000000000000000..99f36b9f28d156f588c78a5e48e07ef9edb5c2f3
Binary files /dev/null and b/trained_models/rendered_chairs/VAE_bs_256/checkpoints/last differ
diff --git a/trained_models/rendered_chairs/VAE_bs_256/specs.json b/trained_models/rendered_chairs/VAE_bs_256/specs.json
new file mode 100644
index 0000000000000000000000000000000000000000..e5db16dfdc394df06c653399ea4b0f8ceb67a98a
--- /dev/null
+++ b/trained_models/rendered_chairs/VAE_bs_256/specs.json
@@ -0,0 +1 @@
+{"dataset": "rendered_chairs", "epochs": 400, "cont_capacity": null, "disc_capacity": null, "record_loss_every": 50, "batch_size": 256, "latent_spec_cont": 10, "experiment_name": "VAE_bs_256", "print_loss_every": 50, "latent_spec_disc": null, "nb_classes": 1393}
\ No newline at end of file
diff --git a/trained_models/rendered_chairs/VAE_bs_64/checkpoints/last b/trained_models/rendered_chairs/VAE_bs_64/checkpoints/last
new file mode 100644
index 0000000000000000000000000000000000000000..726fe5245fd727ef42b5cab3fce2df35c699360b
Binary files /dev/null and b/trained_models/rendered_chairs/VAE_bs_64/checkpoints/last differ
diff --git a/trained_models/rendered_chairs/VAE_bs_64/specs.json b/trained_models/rendered_chairs/VAE_bs_64/specs.json
new file mode 100644
index 0000000000000000000000000000000000000000..7a56498990531b3e8d7a20338898b1169c0c6ad0
--- /dev/null
+++ b/trained_models/rendered_chairs/VAE_bs_64/specs.json
@@ -0,0 +1 @@
+{"dataset": "rendered_chairs", "epochs": 400, "cont_capacity": null, "disc_capacity": null, "record_loss_every": 50, "batch_size": 64, "latent_spec_cont": 10, "experiment_name": "VAE_bs_64", "print_loss_every": 50, "latent_spec_disc": null, "nb_classes": 1393}
\ No newline at end of file
diff --git a/trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last b/trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last
new file mode 100644
index 0000000000000000000000000000000000000000..e5e9aeabf13e873880a78a5259aa91b7a380e44d
Binary files /dev/null and b/trained_models/rendered_chairs/beta_VAE_bs_256/checkpoints/last differ
diff --git a/trained_models/rendered_chairs/beta_VAE_bs_256/specs.json b/trained_models/rendered_chairs/beta_VAE_bs_256/specs.json
new file mode 100644
index 0000000000000000000000000000000000000000..63331fde7e7e21992aa7dbc092d180154810b71c
--- /dev/null
+++ b/trained_models/rendered_chairs/beta_VAE_bs_256/specs.json
@@ -0,0 +1 @@
+{"dataset": "rendered_chairs", "epochs": 400, "cont_capacity": null, "disc_capacity": null, "record_loss_every": 50, "batch_size": 256, "latent_spec_cont": 10, "experiment_name": "beta_VAE_bs_256", "print_loss_every": 50, "latent_spec_disc": null, "nb_classes": 1393}
\ No newline at end of file
diff --git a/trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last b/trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last
new file mode 100644
index 0000000000000000000000000000000000000000..f4622f965f57120a08b95ae017bcf9a12094de95
Binary files /dev/null and b/trained_models/rendered_chairs/beta_VAE_bs_64/checkpoints/last differ
diff --git a/trained_models/rendered_chairs/beta_VAE_bs_64/specs.json b/trained_models/rendered_chairs/beta_VAE_bs_64/specs.json
new file mode 100644
index 0000000000000000000000000000000000000000..c11acc1ccaf888effbbb2b16b457aba4895677b6
--- /dev/null
+++ b/trained_models/rendered_chairs/beta_VAE_bs_64/specs.json
@@ -0,0 +1 @@
+{"dataset": "rendered_chairs", "epochs": 400, "cont_capacity": null, "disc_capacity": null, "record_loss_every": 50, "batch_size": 64, "latent_spec_cont": 10, "experiment_name": "beta_VAE_bs_64", "print_loss_every": 50, "latent_spec_disc": null, "nb_classes": 1393}
\ No newline at end of file