Regarding binary Classification, I have two question,


Lets say, I only care about one class, If my model gives one output (using sigmoid) rather than two (using softmax), will it be a better idea. If so, could you please explain the reason?


For one output, what loss function should I use? It seems that the PyTorch implementation of cross-entropy is not suitable for this. Because, according to the official document, as target  I have to provide the indices of the correct classes and for one output that is not possible. What should I do in this case? Is there any option I am missing?