CNN_Class.py

import torch.nn as nn

class CNN(nn.Module):
    def __init__(self, dropout_rate=0.25):
        super(CNN, self).__init__()
        # Initial number of channels increased
        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        
        self.relu = nn.LeakyReLU(0.1)  # Changed to LeakyReLU
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.dropout = nn.Dropout(dropout_rate)
        
        # Adjusted fully connected layers
        self.fc1 = nn.Linear(256 * 3 * 3, 512)
        self.bn4 = nn.BatchNorm1d(512)
        self.fc2 = nn.Linear(512, 10)
    
    def forward(self, x):
        # First conv block
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.dropout(x)
        
        # Second conv block
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.dropout(x)
        
        # Third conv block
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.dropout(x)
        
        # Flatten and FC layers
        x = x.view(-1, 256 * 3 * 3)
        x = self.fc1(x)
        x = self.bn4(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.fc2(x)
        return x