simple_cnn.py

import torch
import torch.nn as nn

class SimpleCNN(nn.Module):
    def __init__(self, num_classes):
        super(SimpleCNN, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(64),  # Batch normalization
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(128),  # Batch normalization
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.classifier = nn.Sequential(
            nn.Linear(128 * 56 * 56, 512),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),  # Dropout with 50% probability
            nn.Linear(512, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)  # Flatten the tensor
        x = self.classifier(x)
        return x