Hi all,
I am wondering what loss to use for a specific application.
I am trying to predict some binary image. For example, given some inputs a simple two layer neural net with ReLU activations after each layer outputs some 2x2 matrix [[0.01, 0.9], [0.1, 0.2]]. This prediction is compared to a ground truth 2x2 image like [[0, 1], [1, 1]] and the networks task is to get as close as possible.
I am currently using BCE loss with logits over the prediction and the ground truth, however I was wondering if I should be using the plain BCE loss instead (no logits)? If neither of these are suitable, should I be using CrossEntropyLoss? Not quite sure what the difference between these is in this context.
Any help is appreciated - thanks in advance