22 lines
812 B
Python
22 lines
812 B
Python
import torch.nn as nn
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import torch.optim as optim
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# Define CNN Model
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class CatDogClassifier(nn.Module):
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def __init__(self, img_size=128):
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super(CatDogClassifier, self).__init__()
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self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
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self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
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self.pool = nn.MaxPool2d(2, 2)
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self.fc1 = nn.Linear(64 * (img_size//4) * (img_size//4), 128) # Flatten size depends on image size
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self.fc2 = nn.Linear(128, 2) # Output 2 classes (cat or dog)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = self.pool(self.relu(self.conv1(x)))
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x = self.pool(self.relu(self.conv2(x)))
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x = x.view(x.size(0), -1) # Flatten
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x = self.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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