Hello,
I am working on a problem where I am using two loss functions together i.e.
Total_loss = cross_entropy_loss + custom_ loss
And then Total_ loss.backward().
The value of Cross entropy loss for a training of say 20 epochs, reaches to ~0.7 from 2.4. It converges faster till approx. 8th epoch. Thereafter very low decrement.
While the value of custom loss decreases almost exponentially/linearly over entire training, starting from 3.2 to -10 (negative) approx.
That means after certain epoch model focuses mainly on custom loss and almost ignores cross entropy loss!
So, what can I do to make the model treat both losses equally and give them dual importance.?
I am currently multiplying a small number to custom loss,but it doesn’t seem to work.