model.py

import torch
import torch.nn as nn

class CatDogClassifier(nn.Module):
    def __init__(self):
        super(CatDogClassifier, self).__init__()
        
        # Convolutional layers
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        
        # Pooling and activation
        self.pool = nn.MaxPool2d(2, 2)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.5)
        
        # Fully connected layers
        self.fc1 = nn.Linear(128 * 28 * 28, 512)
        self.fc2 = nn.Linear(512, 128)
        self.fc3 = nn.Linear(128, 2)
        
    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.pool(x)
        x = self.relu(self.conv2(x))
        x = self.pool(x)
        x = self.relu(self.conv3(x))
        x = self.pool(x)
        x = x.view(x.size(0), -1)
        x = self.dropout(self.relu(self.fc1(x)))
        x = self.dropout(self.relu(self.fc2(x)))
        x = self.fc3(x)
        return x