PyTorch-MNIST-Example/ConvolutionalNetwork.py at main · pinae/PyTorch-MNIST-Example · GitHub

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from torch import nn


class ConvolutionalNetwork(nn.Module):
    def __init__(self):
        super(ConvolutionalNetwork, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(
                in_channels=1,
                out_channels=16,
                kernel_size=5,
                stride=1,
                padding=2,
            ),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(16, 32, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(2),
        )
        # fully connected layer, output 10 classes
        self.out = nn.Linear(32 * 7 * 7, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
        x = x.view(x.size(0), -1)
        output = self.out(x)
        return output


class DeepConvolutionalNetwork(nn.Module):
    def __init__(self):
        super(DeepConvolutionalNetwork, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(
                in_channels=1,
                out_channels=32,
                kernel_size=5,
                stride=1,
                padding=2,
            ),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(32, 64, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(2),
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(64, 64, 5, 1, 2),
            nn.ReLU(),
            nn.Conv2d(64, 64, 5, 1, 2),
            nn.ReLU(),
            nn.Conv2d(64, 64, 5, 1, 2),
            nn.ReLU(),
            nn.Conv2d(64, 64, 5, 1, 2),
            nn.ReLU(),
        )
        # fully connected layer, output 10 classes
        self.out = nn.Linear(64 * 7 * 7, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
        x = x.view(x.size(0), -1)
        output = self.out(x)
        return output