model.py

from torch import nn


class DropoutNet(nn.Module):
    def __init__(self):
        super(DropoutNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.Dropout2d(0.1))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.Dropout2d(0.1))

        self.layer3 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Dropout2d(0.1))
        self.layer4 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Dropout2d(0.1))
        self.layer5 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Dropout2d(0.1))
        self.fc = nn.Sequential(
            nn.Linear(256*28*28, 256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, 16),
            nn.ReLU(),
            nn.Linear(16, 4)
            )

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x