Hello,
I am trying to train a model with one input and two outputs, where each of the two outputs is computed with separate convolutional and dense layers within an architecture. Any suggestions? I have the model coded up, but was curious how I ensure the loss of one output does not impact the gradients of the other branch? Is there a way to specify when I do loss.backward? Do I need two separate loss functions?
To clarify, the input image is immediately passed through two separate branches, each consisting a simple CNN architecture, ending with two classification predictions (two different classification questions respectively).
Thanks!