Let’s say we have a logistic regression model that takes num_features and outputs num_classes. Traditionally, num_classes is equal to one.
Now if I want to weight my loss function, I would make something like this:
F.binary_cross_entropy(probas, target, weight=torch.tensor([2]))
Where my weights were calculated by:
# n samples / n_classes * bincount
((183473+47987) / (2 * np.array([183473, 47987])))
[0.63074419 2.41213088] # [0, 1]

My question is, the weight in the loss function, as I can only give it one value, which class is it for, 0 or 1?
target, in our data set, contains either zeros or ones, like a traditional binary regression problem.