What does the .fc.in_feature mean?

Can anyone tell me what does the following code mean in the Transfer learning tutorial?

model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)

I can see that this code is use to adjuest the last fully connected layer to the ‘ant’ and ‘bee’ poblem. But I can’t find anything in the pytorch documents about .fc.in_feature, So what to do if I wants to transfer another pre-trained model, say .vgg19_bn, to another classification problem?

in_feature is the number of inputs for your linear layer:

# constructor of nn.Lienar
def __init__(self, in_features, out_features, bias=True):
    super(Linear, self).__init__()
    self.in_features = in_features # num inputs
    self.out_features = out_features # num outputs
    self.weight = Parameter(torch.Tensor(out_features, in_features))
    if bias:
        self.bias = Parameter(torch.Tensor(out_features))

You can still check in the sources: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/linear.py


Thank you very much! You helped a lot!

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model_ft is a type of <class ‘torchvision.models.resnet.ResNet’>.

model_ft.fc is a type of <class ‘torch.nn.modules.linear.Linear’>.