I am slightly confused by the shape of the gradient after the backward pass on a VGG16 Network.
The input dimension of the tensor is  [1, 3, 224, 224] , but on backward pass the gradient is  [1 , 64, 224, 224] , which corresponds to the output after the first convolution layer in the network.
Shouldn’t the gradient be the same as the size of the input?