Thanks - very useful. Small questions:


why no_grad. Is there  reason to freeze the weights?
Note that I count trainable parameters before and after making the change and get the same answer in both cases.


what are these two lines for - test?
x = torch.randn(10, 4, 224, 224)
output = model(x)