googlebot:

self.weights = Variable(self.weights.data, requires_grad=True)


I tried this and print the weights. It seems that they are not training, and stayed constant. Even when I load the check points, it does not ask for the values of those parameters across the layers.
For this one
self.weights = nn.Parameter(torch.ones(1) * weights)
is there a way that I impose the loader to ignore asking for the values in the case of loading from a check point with conventional nn.ReLU activation?