How can I extract my fc layer ’output or my self.layer3 ’output as my picture feature？

1

This is my codes， and Iwant to extract the data of 128 dimensions in fc，and the output of layer3 of CNN，How can I do it ？Thank you！

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3,stride=1,padding=1),  # b, 16, 34, 34
            nn.BatchNorm2d(16),
            nn.ReLU(inplace=True))

        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=3,stride=1,padding=1),  # b, 32, 34, 34
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)  #b, 32, 17, 17
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3,stride=1,padding=1),  #b, 64, 17, 17
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True))

        self.fc = nn.Sequential(
            nn.Linear(64 * 17 * 17, 1024),
            nn.ReLU(inplace=True),
            nn.Linear(1024, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128,4))
        
        self.drop = nn.Dropout(p=0.5)

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

model=CNN()
print(model)
optimizer = torch.optim.Adam(model.parameters())
loss_func = torch.nn.CrossEntropyLoss()
2

go through the thread to learn how to do it.

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