Sorry Smth, I am some months learning Pytorch and  I have a question related to this topic:
Given a net(), with some convolution layers, I can get the gradients easily:
g1 = net.conv1.weight.grad
for instance, for a `conv1 = conv1 = nn.Conv2d(64, 128, kernel_size=5, padding=2), g1 has a shape  torch.Size([128, 64, 5, 5]).
How can I use these gradients in order to get the gradient vector to do other computations (for instance, compute orthogonal vectors, dot product, normalize them but  in the context of vectors).  Can I assume the kernel_size as the vector-dim ?
Thanks in advance.