simple_cnn.py

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

class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        # Convolutional layers
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)  # 28x28 -> 28x28
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)  # 28x28 -> 28x28
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)  # 28x28 -> 14x14

        self.bn1 = nn.BatchNorm2d(32)
        self.bn2 = nn.BatchNorm2d(64)

        # Fully connected layers
        self.fc1 = nn.Linear(64 * 14 * 14, 128)
        self.dropout = nn.Dropout(0.5)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))  # Apply first convolution and ReLU
        x = self.pool(F.relu(self.bn2(self.conv2(x))))  # Apply second convolution, ReLU, and pooling
        x = torch.flatten(x, 1) # Flatten the feature maps
        x = F.relu(self.fc1(x))  # Fully connected layer with ReLU
        x = self.dropout(x)
        x = self.fc2(x)  # Output layer
        return x