No, the mean loss is used in the backward pass and thus to calculate the gradients. You are usually using the mean, since the sum of the loss would e.g. depend on the batch size and you would have to adapt the learning rate based on the batch size


No, haven’t heard it before and I would assume your model overfits to the majority class. Is this behavior reproducible, i.e. are you seeing the same “label preference” using different seeds?