PyTorch Description of Convolutional Neural Networks

CIFAR-Networks.txt

P4MNetC(
(conv1): P4MConvZ2()
(conv2): P4MConvP4M()
(conv3): P4MConvP4M()
(fc1): Linear(in_features=48, out_features=32, bias=True)
(fc2): Linear(in_features=32, out_features=10, bias=True)
)
P4NetC(
(conv1): P4ConvZ2()
(conv2): P4ConvP4()
(conv3): P4ConvP4()
(fc1): Linear(in_features=1024, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=64, bias=True)
(fc3): Linear(in_features=64, out_features=10, bias=True)
(dropout): Dropout(p=0.2, inplace=False)
)
ConvNetC(
(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv1): Conv2d(3, 12, kernel_size=(3, 3), stride=(1, 1))
(conv2): Conv2d(12, 24, kernel_size=(3, 3), stride=(1, 1))
(conv3): Conv2d(24, 32, kernel_size=(3, 3), stride=(1, 1))
(conv4): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))
(fc1): Linear(in_features=576, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=128, bias=True)
(fc3): Linear(in_features=128, out_features=10, bias=True)

MNIST-Networks.txt

P4MNet(
  (conv1): P4MConvZ2()
  (conv2): P4MConvP4M()
  (conv3): P4MConvP4M()
  (fc1): Linear(in_features=32, out_features=16, bias=True)
  (fc2): Linear(in_features=16, out_features=10, bias=True)
)
P4Net(
  (conv1): P4ConvZ2()
  (conv2): P4ConvP4()
  (conv3): P4ConvP4()
  (fc1): Linear(in_features=64, out_features=16, bias=True)
  (fc2): Linear(in_features=16, out_features=10, bias=True)
)
ConvNet(
  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv1): Conv2d(1, 6, kernel_size=(3, 3), stride=(1, 1))
  (conv2): Conv2d(6, 16, kernel_size=(3, 3), stride=(1, 1))
  (fc1): Linear(in_features=576, out_features=184, bias=True)
  (fc2): Linear(in_features=184, out_features=64, bias=True)
  (fc3): Linear(in_features=64, out_features=10, bias=True)
)