Hi all, my objective is to have the model prioritize class 1 and 2.
I’m currently doing the following:
weights = torch.tensor([5., 10., 10., 5., 5.]) class_weights = torch.FloatTensor(weights).cuda() criterion_weighted = nn.CrossEntropyLoss(weight=class_weights)
I’d like to know by how much is the model currently prioritizing those classes. Are the class weights relative to the sum of them? (5/35), (10/35)?