torch/torch7

Hello everybody,

I’m new to Torch and I’m using it for my master thesis project.
I am trying to integrate to an existing implementation a personalized loss function like below :

sum_loss = lambda * coral_loss + classification_loss

where coral_loss is an unsupervised loss that measures the “distance” between a source and a target batch.
Basically I want to minimize the classification loss on the source data while jointly minimizing this distance between source and target data distributions.

In PyTorch that is very easy since I can just call sum_loss.backward() to compute the gradients. However in Torch there are predefined Criterions in which I can only call backward(input,target) to compute the gradients of the loss function associated to that specific criterion.

How can I implement the new loss function and backpropagate to compute the gradients?

This is the loss that I would like to implement in Torch :

def CORAL(source, target):

   d = source.data.shape[1]

   # source covariance
   xm = torch.mean(source, 0, keepdim=True) - source
   xc = xm.t() @ xm

   # target covariance
   xmt = torch.mean(target, 0, keepdim=True) - target
   xct = xmt.t() @ xmt

   # frobenius norm between source and target
   loss = torch.mean(torch.mul((xc - xct), (xc - xct)))
   loss = loss/(4*d*d)

   return loss