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model.py

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+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