Hi,
I am new to Pytorch so sorry in advance if this is a trivial question. I would simply like to rescale the nn.MSELoss() in the spirit of rescaled_MSE() = C*MSELoss(). I tried to define a tensor C=torch.FloatTensor(1), C[0]= “some number”, but this gives an error because the multiplication does not accept the MSELoss as argument.
How can I rescale the nn.MSELoss() without constructing a loss function myself?
I know that this amounts to rescaling the gradients/learning rate, but I would like to fix the scale of the loss function to separate it conceptrually from the learning rate scale.
Thank you, any comments are appreciated.
You could just rescale the loss. Currently it looks like you try to rescale a function, which is probably the workflow in static frameworks (e.g. Theano).
Try the following:
criterion = nn.MSELoss()
scale = torch.tensor([2.0])
# your training procedure
loss = criterion(output, target)
loss = loss * scale
loss.backward()
...
Great! Thanks @ptrblck for your suggestion. The only change is that I need to use
scale = torch.FloatTensor([2.0])
Otherwise it works.
If you are using the current stable release, you don’t have to change this line.
Have a look at the website for install instructions.
There are a lot of improvements and nice features! 