I would like to use different final activations for different loss functions that train the same model. In code that would mean:
model = MyModel() criterion1 = nn.BCELoss() criterion2 = nn.L1Loss() out1 = model(input) out2 = torch.sigmoid(out1) loss = criterion1(out1, target1) + criterion2(out2, target2) loss.backward() optimizer.step()
Is this possible and if yes, how? Thank you!
In this minimal example is might seem to make no sense, but in my code it is a GAN that uses multiple loss functions to train the generator if that helps…