resnet_mnist

实验记录：0318 Resnet mnist

2020·0318·22:30


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    Layer (type)               Output Shape         Param #

================================================================ Conv2d-1 [-1, 64, 14, 14] 3,136 BatchNorm2d-2 [-1, 64, 14, 14] 128 ReLU-3 [-1, 64, 14, 14] 0 MaxPool2d-4 [-1, 64, 7, 7] 0 Conv2d-5 [-1, 64, 7, 7] 36,864 BatchNorm2d-6 [-1, 64, 7, 7] 128 ReLU-7 [-1, 64, 7, 7] 0 Conv2d-8 [-1, 64, 7, 7] 36,864 BatchNorm2d-9 [-1, 64, 7, 7] 128 ReLU-10 [-1, 64, 7, 7] 0 BasicBlock-11 [-1, 64, 7, 7] 0 Conv2d-12 [-1, 64, 7, 7] 36,864 BatchNorm2d-13 [-1, 64, 7, 7] 128 ReLU-14 [-1, 64, 7, 7] 0 Conv2d-15 [-1, 64, 7, 7] 36,864 BatchNorm2d-16 [-1, 64, 7, 7] 128 ReLU-17 [-1, 64, 7, 7] 0 BasicBlock-18 [-1, 64, 7, 7] 0 Conv2d-19 [-1, 128, 4, 4] 73,728 BatchNorm2d-20 [-1, 128, 4, 4] 256 ReLU-21 [-1, 128, 4, 4] 0 Conv2d-22 [-1, 128, 4, 4] 147,456 BatchNorm2d-23 [-1, 128, 4, 4] 256 Conv2d-24 [-1, 128, 4, 4] 8,192 BatchNorm2d-25 [-1, 128, 4, 4] 256 ReLU-26 [-1, 128, 4, 4] 0 BasicBlock-27 [-1, 128, 4, 4] 0 Conv2d-28 [-1, 128, 4, 4] 147,456 BatchNorm2d-29 [-1, 128, 4, 4] 256 ReLU-30 [-1, 128, 4, 4] 0 Conv2d-31 [-1, 128, 4, 4] 147,456 BatchNorm2d-32 [-1, 128, 4, 4] 256 ReLU-33 [-1, 128, 4, 4] 0 BasicBlock-34 [-1, 128, 4, 4] 0 Conv2d-35 [-1, 256, 2, 2] 294,912 BatchNorm2d-36 [-1, 256, 2, 2] 512 ReLU-37 [-1, 256, 2, 2] 0 Conv2d-38 [-1, 256, 2, 2] 589,824 BatchNorm2d-39 [-1, 256, 2, 2] 512 Conv2d-40 [-1, 256, 2, 2] 32,768 BatchNorm2d-41 [-1, 256, 2, 2] 512 ReLU-42 [-1, 256, 2, 2] 0 BasicBlock-43 [-1, 256, 2, 2] 0 Conv2d-44 [-1, 256, 2, 2] 589,824 BatchNorm2d-45 [-1, 256, 2, 2] 512 ReLU-46 [-1, 256, 2, 2] 0 Conv2d-47 [-1, 256, 2, 2] 589,824 BatchNorm2d-48 [-1, 256, 2, 2] 512 ReLU-49 [-1, 256, 2, 2] 0 BasicBlock-50 [-1, 256, 2, 2] 0 Conv2d-51 [-1, 512, 1, 1] 1,179,648 BatchNorm2d-52 [-1, 512, 1, 1] 1,024 ReLU-53 [-1, 512, 1, 1] 0 Conv2d-54 [-1, 512, 1, 1] 2,359,296 BatchNorm2d-55 [-1, 512, 1, 1] 1,024 Conv2d-56 [-1, 512, 1, 1] 131,072 BatchNorm2d-57 [-1, 512, 1, 1] 1,024 ReLU-58 [-1, 512, 1, 1] 0 BasicBlock-59 [-1, 512, 1, 1] 0 Conv2d-60 [-1, 512, 1, 1] 2,359,296 BatchNorm2d-61 [-1, 512, 1, 1] 1,024 ReLU-62 [-1, 512, 1, 1] 0 Conv2d-63 [-1, 512, 1, 1] 2,359,296 BatchNorm2d-64 [-1, 512, 1, 1] 1,024 ReLU-65 [-1, 512, 1, 1] 0 BasicBlock-66 [-1, 512, 1, 1] 0

Linear-67 [-1, 10] 5,130

Total params: 11,175,370 Trainable params: 11,175,370

Non-trainable params: 0

Input size (MB): 0.00 Forward/backward pass size (MB): 1.08 Params size (MB): 42.63

Estimated Total Size (MB): 43.72

Traceback (most recent call last): File "myresnet.py", line 170, in %(epoch+1, NUM_EPOCHS, batch_idx, NameError: name 'NUM_EPOCHS' is not defined (python36) [zhangkaiwang@m7-model-gpu15 resnet18]$ python myresnet.py Image batch dimensions: torch.Size([128, 1, 28, 28]) Image label dimensions: torch.Size([128]) Epoch: 1 | Batch index: 0 | Batch size: 128

Epoch: 2 | Batch index: 0 | Batch size: 128


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1
    Layer (type)               Output Shape         Param #

Linear-67 [-1, 10] 5,130

Total params: 11,175,370 Trainable params: 11,175,370

Non-trainable params: 0

Input size (MB): 0.00 Forward/backward pass size (MB): 1.08 Params size (MB): 42.63

Estimated Total Size (MB): 43.72