Hi, I am using pytorch right now and there is one problem I encountered.
I previously used torch7 framework, and when I use any criterion, I can explicitly get the df_do.
Basically the back-propagated error to the network.
I am wondering can I obtain that in pytorch?
I need to modify that during training.
When I see the example, the code is like this:
     optimizer.zero_grad()
     output = model(data)
     loss = criterion(output, target)
     loss.backward()
     optimizer.step()

So all the network is back-propagated when I call loss.backward() ?