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import torch.nn as nn |
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import torch.nn.functional as F |
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# CNN architecture definition |
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class Net(nn.Module): |
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def __init__(self): |
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super(Net, self).__init__() |
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# convolutional layer |
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self.conv1 = nn.Conv2d(3, 16, 3, padding=1) |
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# max pooling layer |
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self.pool = nn.MaxPool2d(2, 2) |
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def forward(self, x): |
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# add sequence of convolutional and max pooling layers |
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x = self.pool(F.relu(self.conv1(x))) |
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return x |