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""" |
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Contains Pytorch model code instantiate a TinyVGG model. |
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""" |
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import torch |
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from torch import nn |
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class TinyVGG(nn.Module): |
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""" |
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Creates the TinyVGG architecture |
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""" |
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def __init__(self, input_shape: int, hidden_units: int, output_shape: int) -> None: |
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super().__init__() |
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self.conv_block_1 = nn.Sequential( |
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nn.Conv2d(in_channels=input_shape, |
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out_channels=hidden_units, |
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kernel_size=3, |
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stride=1, |
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padding=0), |
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nn.ReLU(), |
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nn.Conv2d(in_channels=hidden_units, |
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out_channels=hidden_units, |
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kernel_size=3, |
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stride=1, |
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padding=0), |
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nn.ReLU(), |
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nn.MaxPool2d(kernel_size=2, |
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stride=2) |
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) |
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self.conv_block_2=nn.Sequential( |
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nn.Conv2d(in_channels=hidden_units, |
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out_channels=hidden_units, |
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kernel_size=3, |
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padding=0), |
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nn.ReLU(), |
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nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=0), |
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nn.ReLU(), |
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nn.MaxPool2d(kernel_size=2, |
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stride=2) |
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) |
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self.classifier=nn.Sequential( |
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nn.Flatten(), |
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nn.Linear(in_features=hidden_units*13*13, |
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out_features=output_shape) |
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) |
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def forward(self, x: torch.Tensor): |
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x=self.conv_block_1(x) |
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x=self.conv_block_2(x) |
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x=self.classifier(x) |
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return x |
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