cnn.py

import torch.nn as nn
import torch.nn.functional as F


class CNN(nn.Module):
    def __init__(self, input_channels):
        super(CNN, self).__init__()
        self.input_channels = input_channels
        self.conv1 = nn.Conv2d(self.input_channels, 32, kernel_size=(3, 3))
        self.batchnorm1 = nn.BatchNorm2d(32)
        self.pool1 = nn.MaxPool2d(kernel_size=(3, 3))
        self.dropout1 = nn.Dropout(0.3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=(3, 3))
        self.batchnorm2 = nn.BatchNorm2d(64)
        self.pool2 = nn.MaxPool2d(kernel_size=(1, 3))
        self.conv3 = nn.Conv2d(64, 128, kernel_size=(3, 3))
        self.batchnorm3 = nn.BatchNorm2d(128)
        self.pool3 = nn.MaxPool2d(kernel_size=2)
        self.dropout2 = nn.Dropout(0.3)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc1 = nn.Linear(128, 256)
        self.fc2 = nn.Linear(256, 512)
        self.dropout3 = nn.Dropout(0.5)
        self.fc3 = nn.Linear(512, 10)
        self.apply(self._init_weights)

    def _init_weights(self, module):
        if isinstance(module, nn.Conv2d):
            nn.init.xavier_normal_(module.weight.data)
            if module.bias is not None:
                nn.init.constant_(module.bias.data, 0)

        elif isinstance(module, nn.BatchNorm2d):
            nn.init.constant_(module.weight.data, 1)
            nn.init.constant_(module.bias.data, 0)

        elif isinstance(module, nn.Linear):
            n = module.in_features
            y = 1.0 / n ** (1/2)
            module.weight.data.uniform_(-y, y)
            module.bias.data.fill_(0)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.batchnorm1(x)
        x = self.pool1(x)
        x = self.dropout1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.batchnorm2(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = F.relu(x)
        x = self.batchnorm3(x)
        x = self.pool3(x)
        x = self.dropout2(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout3(x)
        x = self.fc2(x)
        x = F.relu(x)
        x = self.dropout3(x)
        x = self.fc3(x)
        return x