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How to run the train.py program:
/home/workspace/ImageClassifier# python train.py --arch vgg19 --epochs 20 --batch_size 64 --gpu --lr .001 --dropout .05
How to run the predict.py program:
python predict.py vgg19checkpoint.pth --r
or
python predict.py vgg19_checkpoint.pth --r --top_k 5 --category_names cat_to_name.json --accuracy --gpu
05/13/2019 03:29:15 AM train logging started
05/13/2019 03:29:15 AM architecture: vgg19 checkpoint: vgg19_checkpoint.pth
05/13/2019 03:29:22 AM Data load_and_transforms completed-------------------------
05/13/2019 03:29:46 AM architecture: vgg19 input_size:= 25088 output_size:= 102 hidden_layers: [4096, 4096]
05/13/2019 03:29:46 AM batch_size: = 64 epochs: = 20 dropout: = 0.05 learning_rate= 0.001
05/13/2019 03:29:47 AM learning_rate: = 0.001 hidden_layers: = [4096, 4096] batch_size: = 64 checkpoint: vgg19_checkpoint.pth
05/13/2019 03:29:47 AM architecture: vgg19 Number of epochs: 20
05/13/2019 03:29:47 AM Training Starting-----
05/13/2019 03:31:18 AM Epoch: 1/20 | Training Loss: 2.493 Validation Loss: 1.561 Validation Accuracy: 58.620 Steps: 40
05/13/2019 03:32:46 AM Epoch: 1/20 | Training Loss: 1.032 Validation Loss: 0.878 Validation Accuracy: 75.764 Steps: 80
05/13/2019 03:34:11 AM Epoch: 2/20 | Training Loss: 1.354 Validation Loss: 0.805 Validation Accuracy: 76.779 Steps: 120
05/13/2019 03:35:35 AM Epoch: 2/20 | Training Loss: 1.153 Validation Loss: 0.654 Validation Accuracy: 81.683 Steps: 160
05/13/2019 03:36:58 AM Epoch: 2/20 | Training Loss: 0.813 Validation Loss: 0.658 Validation Accuracy: 80.726 Steps: 200
05/13/2019 03:38:21 AM Epoch: 3/20 | Training Loss: 1.097 Validation Loss: 0.543 Validation Accuracy: 83.058 Steps: 240
05/13/2019 03:39:45 AM Epoch: 3/20 | Training Loss: 0.774 Validation Loss: 0.497 Validation Accuracy: 85.293 Steps: 280
05/13/2019 03:41:07 AM Epoch: 4/20 | Training Loss: 0.625 Validation Loss: 0.576 Validation Accuracy: 83.163 Steps: 320
05/13/2019 03:42:31 AM Epoch: 4/20 | Training Loss: 0.613 Validation Loss: 0.521 Validation Accuracy: 84.466 Steps: 360
05/13/2019 03:43:54 AM Epoch: 4/20 | Training Loss: 0.622 Validation Loss: 0.431 Validation Accuracy: 87.572 Steps: 400
05/13/2019 03:45:16 AM Epoch: 5/20 | Training Loss: 0.742 Validation Loss: 0.434 Validation Accuracy: 87.524 Steps: 440
05/13/2019 03:46:39 AM Epoch: 5/20 | Training Loss: 1.069 Validation Loss: 0.481 Validation Accuracy: 85.788 Steps: 480
05/13/2019 03:48:03 AM Epoch: 6/20 | Training Loss: 0.451 Validation Loss: 0.439 Validation Accuracy: 85.962 Steps: 520
05/13/2019 03:49:26 AM Epoch: 6/20 | Training Loss: 0.598 Validation Loss: 0.443 Validation Accuracy: 87.091 Steps: 560
05/13/2019 03:50:49 AM Epoch: 6/20 | Training Loss: 0.521 Validation Loss: 0.444 Validation Accuracy: 87.726 Steps: 600
05/13/2019 03:52:12 AM Epoch: 7/20 | Training Loss: 0.507 Validation Loss: 0.456 Validation Accuracy: 86.731 Steps: 640
05/13/2019 03:53:34 AM Epoch: 7/20 | Training Loss: 0.714 Validation Loss: 0.424 Validation Accuracy: 87.692 Steps: 680
05/13/2019 03:54:58 AM Epoch: 7/20 | Training Loss: 0.617 Validation Loss: 0.501 Validation Accuracy: 86.216 Steps: 720
05/13/2019 03:56:19 AM Epoch: 8/20 | Training Loss: 0.607 Validation Loss: 0.496 Validation Accuracy: 86.168 Steps: 760
05/13/2019 03:57:42 AM Epoch: 8/20 | Training Loss: 0.489 Validation Loss: 0.481 Validation Accuracy: 85.668 Steps: 800
05/13/2019 03:59:05 AM Epoch: 9/20 | Training Loss: 0.403 Validation Loss: 0.434 Validation Accuracy: 88.293 Steps: 840
05/13/2019 04:00:28 AM Epoch: 9/20 | Training Loss: 0.985 Validation Loss: 0.388 Validation Accuracy: 90.010 Steps: 880
05/13/2019 04:01:51 AM Epoch: 9/20 | Training Loss: 0.404 Validation Loss: 0.422 Validation Accuracy: 88.053 Steps: 920
05/13/2019 04:03:14 AM Epoch: 10/20 | Training Loss: 0.622 Validation Loss: 0.421 Validation Accuracy: 89.255 Steps: 960
05/13/2019 04:04:37 AM Epoch: 10/20 | Training Loss: 0.575 Validation Loss: 0.391 Validation Accuracy: 87.572 Steps: 1000
05/13/2019 04:06:00 AM Epoch: 11/20 | Training Loss: 0.707 Validation Loss: 0.430 Validation Accuracy: 89.495 Steps: 1040
05/13/2019 04:07:23 AM Epoch: 11/20 | Training Loss: 0.437 Validation Loss: 0.466 Validation Accuracy: 87.452 Steps: 1080
05/13/2019 04:08:46 AM Epoch: 11/20 | Training Loss: 0.935 Validation Loss: 0.488 Validation Accuracy: 88.173 Steps: 1120
05/13/2019 04:10:09 AM Epoch: 12/20 | Training Loss: 0.781 Validation Loss: 0.433 Validation Accuracy: 88.740 Steps: 1160
05/13/2019 04:11:33 AM Epoch: 12/20 | Training Loss: 0.481 Validation Loss: 0.423 Validation Accuracy: 87.986 Steps: 1200
05/13/2019 04:12:55 AM Epoch: 13/20 | Training Loss: 0.544 Validation Loss: 0.533 Validation Accuracy: 86.837 Steps: 1240
05/13/2019 04:14:19 AM Epoch: 13/20 | Training Loss: 0.329 Validation Loss: 0.483 Validation Accuracy: 87.572 Steps: 1280
05/13/2019 04:15:42 AM Epoch: 13/20 | Training Loss: 0.511 Validation Loss: 0.532 Validation Accuracy: 87.144 Steps: 1320
05/13/2019 04:17:04 AM Epoch: 14/20 | Training Loss: 0.343 Validation Loss: 0.496 Validation Accuracy: 88.279 Steps: 1360
05/13/2019 04:18:28 AM Epoch: 14/20 | Training Loss: 0.483 Validation Loss: 0.482 Validation Accuracy: 88.260 Steps: 1400
05/13/2019 04:19:52 AM Epoch: 14/20 | Training Loss: 0.404 Validation Loss: 0.446 Validation Accuracy: 89.428 Steps: 1440
05/13/2019 04:21:14 AM Epoch: 15/20 | Training Loss: 0.499 Validation Loss: 0.448 Validation Accuracy: 89.135 Steps: 1480
05/13/2019 04:22:38 AM Epoch: 15/20 | Training Loss: 0.338 Validation Loss: 0.491 Validation Accuracy: 89.101 Steps: 1520
05/13/2019 04:25:24 AM Epoch: 16/20 | Training Loss: 0.781 Validation Loss: 0.539 Validation Accuracy: 88.399 Steps: 1600
05/13/2019 04:26:48 AM Epoch: 16/20 | Training Loss: 0.251 Validation Loss: 0.522 Validation Accuracy: 87.812 Steps: 1640
05/13/2019 04:28:10 AM Epoch: 17/20 | Training Loss: 0.652 Validation Loss: 0.449 Validation Accuracy: 89.562 Steps: 1680
05/13/2019 04:29:33 AM Epoch: 17/20 | Training Loss: 0.676 Validation Loss: 0.434 Validation Accuracy: 89.668 Steps: 1720
05/13/2019 04:30:56 AM Epoch: 18/20 | Training Loss: 0.319 Validation Loss: 0.455 Validation Accuracy: 90.490 Steps: 1760
05/13/2019 04:32:19 AM Epoch: 18/20 | Training Loss: 0.495 Validation Loss: 0.471 Validation Accuracy: 89.769 Steps: 1800
05/13/2019 04:33:42 AM Epoch: 18/20 | Training Loss: 0.795 Validation Loss: 0.552 Validation Accuracy: 87.091 Steps: 1840
05/13/2019 04:35:05 AM Epoch: 19/20 | Training Loss: 0.660 Validation Loss: 0.506 Validation Accuracy: 88.654 Steps: 1880
05/13/2019 04:36:28 AM Epoch: 19/20 | Training Loss: 0.537 Validation Loss: 0.589 Validation Accuracy: 87.572 Steps: 1920
05/13/2019 04:37:50 AM Epoch: 20/20 | Training Loss: 0.378 Validation Loss: 0.513 Validation Accuracy: 88.688 Steps: 1960
05/13/2019 04:39:14 AM Epoch: 20/20 | Training Loss: 0.293 Validation Loss: 0.483 Validation Accuracy: 88.087 Steps: 2000
05/13/2019 04:40:37 AM Epoch: 20/20 | Training Loss: 0.557 Validation Loss: 0.541 Validation Accuracy: 87.144 Steps: 2040
05/13/2019 04:41:07 AM Training Completed-----
05/13/2019 04:48:31 AM train logging started
05/13/2019 04:48:31 AM architecture: vgg19 checkpoint: vgg19_checkpoint.pth
05/13/2019 04:48:35 AM Data load_and_transforms completed-------------------------
05/13/2019 04:48:37 AM architecture: vgg19 input_size:= 25088 output_size:= 102 hidden_layers: [4096, 4096]
05/13/2019 04:48:37 AM batch_size: = 64 epochs: = 20 dropout: = 0.05 learning_rate= 0.001
05/13/2019 04:48:38 AM learning_rate: = 0.001 hidden_layers: = [4096, 4096] batch_size: = 64 checkpoint: vgg19_checkpoint.pth
05/13/2019 04:48:38 AM architecture: vgg19 Number of epochs: 20
05/13/2019 04:48:38 AM Training Starting-----
05/13/2019 04:53:45 AM Epoch: 1/1 | Training Loss: 1.613 Validation Loss: 1.098 Validation Accuracy: 69.688 Steps: 80
05/13/2019 04:54:20 AM Training Completed-----
05/13/2019 04:54:23 AM checkpoint saved----- vgg19 VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(inplace)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(inplace)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace)
(16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(17): ReLU(inplace)
(18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(inplace)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(inplace)
(23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): ReLU(inplace)
(25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(26): ReLU(inplace)
(27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(inplace)
(30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(31): ReLU(inplace)
(32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(33): ReLU(inplace)
(34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(35): ReLU(inplace)
(36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(fc1): Linear(in_features=25088, out_features=4096, bias=True)
(relu): ReLU()
(dropout): Dropout(p=0.05)
(fc2): Linear(in_features=4096, out_features=102, bias=True)
(output): LogSoftmax()
)
) vgg19_checkpoint.pth 1
05/13/2019 04:54:23 AM Training time_elapsed: 3m 26s
05/13/2019 04:57:04 AM train logging started
05/13/2019 04:57:04 AM architecture: densenet121 checkpoint: densenet121_checkpoint.pth
05/13/2019 04:57:09 AM Data load_and_transforms completed-------------------------
05/13/2019 04:57:11 AM architecture: densenet121 input_size:= 1024 output_size:= 102 hidden_layers: [4096, 4096]
05/13/2019 04:57:11 AM batch_size: = 64 epochs: = 10 dropout: = 0.05 learning_rate= 0.001
05/13/2019 04:57:12 AM learning_rate: = 0.001 hidden_layers: = [4096, 4096] batch_size: = 64 checkpoint: densenet121_checkpoint.pth
05/13/2019 04:57:12 AM architecture: densenet121 Number of epochs: 10
05/13/2019 04:57:12 AM Training Starting-----
05/13/2019 04:58:11 AM Epoch: 1/10 | Training Loss: 1.861 Validation Loss: 1.698 Validation Accuracy: 58.183 Steps: 40
05/13/2019 04:59:10 AM Epoch: 1/10 | Training Loss: 1.027 Validation Loss: 0.747 Validation Accuracy: 81.394 Steps: 80
05/13/2019 05:00:09 AM Epoch: 2/10 | Training Loss: 1.130 Validation Loss: 0.616 Validation Accuracy: 84.106 Steps: 120
05/13/2019 05:01:08 AM Epoch: 2/10 | Training Loss: 0.846 Validation Loss: 0.449 Validation Accuracy: 88.567 Steps: 160
05/13/2019 05:02:08 AM Epoch: 2/10 | Training Loss: 0.662 Validation Loss: 0.353 Validation Accuracy: 90.077 Steps: 200
05/13/2019 05:03:06 AM Epoch: 3/10 | Training Loss: 0.663 Validation Loss: 0.386 Validation Accuracy: 89.341 Steps: 240
05/13/2019 05:04:06 AM Epoch: 3/10 | Training Loss: 0.868 Validation Loss: 0.311 Validation Accuracy: 92.827 Steps: 280
05/13/2019 05:05:04 AM Epoch: 4/10 | Training Loss: 0.321 Validation Loss: 0.346 Validation Accuracy: 90.197 Steps: 320
05/13/2019 05:06:04 AM Epoch: 4/10 | Training Loss: 0.416 Validation Loss: 0.296 Validation Accuracy: 92.601 Steps: 360
05/13/2019 05:07:03 AM Epoch: 4/10 | Training Loss: 0.360 Validation Loss: 0.276 Validation Accuracy: 92.620 Steps: 400
05/13/2019 05:08:02 AM Epoch: 5/10 | Training Loss: 0.491 Validation Loss: 0.297 Validation Accuracy: 92.226 Steps: 440
05/13/2019 05:09:01 AM Epoch: 5/10 | Training Loss: 0.501 Validation Loss: 0.277 Validation Accuracy: 92.654 Steps: 480
05/13/2019 05:09:59 AM Epoch: 6/10 | Training Loss: 0.420 Validation Loss: 0.320 Validation Accuracy: 91.486 Steps: 520
05/13/2019 05:10:58 AM Epoch: 6/10 | Training Loss: 0.526 Validation Loss: 0.302 Validation Accuracy: 91.418 Steps: 560
05/13/2019 05:11:57 AM Epoch: 6/10 | Training Loss: 0.306 Validation Loss: 0.288 Validation Accuracy: 92.620 Steps: 600
05/13/2019 05:12:56 AM Epoch: 7/10 | Training Loss: 0.630 Validation Loss: 0.279 Validation Accuracy: 92.740 Steps: 640
05/13/2019 05:13:55 AM Epoch: 7/10 | Training Loss: 0.438 Validation Loss: 0.295 Validation Accuracy: 92.038 Steps: 680
05/13/2019 05:14:55 AM Epoch: 7/10 | Training Loss: 0.418 Validation Loss: 0.239 Validation Accuracy: 93.769 Steps: 720
05/13/2019 05:15:53 AM Epoch: 8/10 | Training Loss: 0.521 Validation Loss: 0.252 Validation Accuracy: 92.688 Steps: 760
05/13/2019 05:16:53 AM Epoch: 8/10 | Training Loss: 0.357 Validation Loss: 0.220 Validation Accuracy: 94.663 Steps: 800
05/13/2019 05:17:52 AM Epoch: 9/10 | Training Loss: 0.351 Validation Loss: 0.276 Validation Accuracy: 92.995 Steps: 840
05/13/2019 05:18:52 AM Epoch: 9/10 | Training Loss: 0.413 Validation Loss: 0.265 Validation Accuracy: 92.327 Steps: 880
05/13/2019 05:19:51 AM Epoch: 9/10 | Training Loss: 0.727 Validation Loss: 0.253 Validation Accuracy: 92.875 Steps: 920
05/13/2019 05:20:50 AM Epoch: 10/10 | Training Loss: 0.392 Validation Loss: 0.276 Validation Accuracy: 92.346 Steps: 960
05/13/2019 05:21:49 AM Epoch: 10/10 | Training Loss: 0.270 Validation Loss: 0.244 Validation Accuracy: 93.976 Steps: 1000
05/13/2019 05:22:22 AM Training Completed-----
05/13/2019 05:22:22 AM checkpoint saved----- densenet121 DenseNet(
(features): Sequential(
(conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu0): ReLU(inplace)
(pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(denseblock1): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition1): _Transition(
(norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock2): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition2): _Transition(
(norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock3): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer13): _DenseLayer(
(norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer14): _DenseLayer(
(norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer15): _DenseLayer(
(norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer16): _DenseLayer(
(norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer17): _DenseLayer(
(norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer18): _DenseLayer(
(norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer19): _DenseLayer(
(norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer20): _DenseLayer(
(norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer21): _DenseLayer(
(norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer22): _DenseLayer(
(norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer23): _DenseLayer(
(norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer24): _DenseLayer(
(norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition3): _Transition(
(norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock4): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer13): _DenseLayer(
(norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer14): _DenseLayer(
(norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer15): _DenseLayer(
(norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer16): _DenseLayer(
(norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(classifier): Sequential(
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(relu): ReLU()
(dropout): Dropout(p=0.05)
(fc2): Linear(in_features=4096, out_features=102, bias=True)
(output): LogSoftmax()
)
) densenet121_checkpoint.pth 10
05/13/2019 05:22:22 AM Training time_elapsed: 25m 11s
05/13/2019 05:28:47 AM train logging started
05/13/2019 05:28:47 AM architecture: alexnet checkpoint: alexnet_checkpoint.pth
05/13/2019 05:28:51 AM Data load_and_transforms completed-------------------------
05/13/2019 05:28:56 AM architecture: alexnet input_size:= 9216 output_size:= 102 hidden_layers: [4096, 4096]
05/13/2019 05:28:56 AM batch_size: = 64 epochs: = 5 dropout: = 0.04 learning_rate= 0.001
05/13/2019 05:28:56 AM learning_rate: = 0.001 hidden_layers: = [4096, 4096] batch_size: = 64 checkpoint: alexnet_checkpoint.pth
05/13/2019 05:28:56 AM architecture: alexnet Number of epochs: 5
05/13/2019 05:28:56 AM Training Starting-----
05/13/2019 05:29:37 AM Epoch: 1/5 | Training Loss: 2.281 Validation Loss: 1.667 Validation Accuracy: 59.505 Steps: 40
05/13/2019 05:30:17 AM Epoch: 1/5 | Training Loss: 1.826 Validation Loss: 1.156 Validation Accuracy: 70.736 Steps: 80
05/13/2019 05:30:57 AM Epoch: 2/5 | Training Loss: 1.144 Validation Loss: 0.763 Validation Accuracy: 80.038 Steps: 120
05/13/2019 05:31:37 AM Epoch: 2/5 | Training Loss: 1.215 Validation Loss: 0.776 Validation Accuracy: 77.240 Steps: 160
05/13/2019 05:32:17 AM Epoch: 2/5 | Training Loss: 1.173 Validation Loss: 0.682 Validation Accuracy: 81.942 Steps: 200
05/13/2019 05:32:57 AM Epoch: 3/5 | Training Loss: 0.839 Validation Loss: 0.719 Validation Accuracy: 80.933 Steps: 240
05/13/2019 05:33:38 AM Epoch: 3/5 | Training Loss: 1.032 Validation Loss: 0.659 Validation Accuracy: 82.716 Steps: 280
05/13/2019 05:34:18 AM Epoch: 4/5 | Training Loss: 1.110 Validation Loss: 0.672 Validation Accuracy: 82.476 Steps: 320