31 lines
965 B
Python
31 lines
965 B
Python
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import torch
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import torch.nn as nn
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# Create a similar student class where we return a tuple. We do not apply pooling after flattening.
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class ModifiedLightNNCosine(nn.Module):
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def __init__(self, num_classes=10):
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super(ModifiedLightNNCosine, self).__init__()
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self.features = nn.Sequential(
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nn.Conv2d(3, 16, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=2),
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nn.Conv2d(16, 16, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=2),
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)
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self.classifier = nn.Sequential(
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nn.Linear(1024, 256),
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nn.ReLU(),
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nn.Dropout(0.1),
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nn.Linear(256, num_classes)
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)
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def forward(self, x):
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x = self.features(x)
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flattened_conv_output = torch.flatten(x, 1)
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x = self.classifier(flattened_conv_output)
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return x
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Model = ModifiedLightNNCosine
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