Good Afternoon,
I am wondering whether this is a suitable way to approach a problem or if I should consider alternatives (that I am unaware of) as well.
I have a relatively balanced classification problem involving 3 labels: 0, 1, and 2. I am interested, however, in paying a little more attention to getting the 0s right. My approach is, when using nn.CrossEntropyLoss, is to punish mistakes for the 0s more than 1 or 2 by:
weights = [2.0, 1.0, 1.0]
class_weights = torch.FloatTensor(weights).cuda()
criterion = nn.CrossEntropyLoss(weight=class_weights)

Is this is a reasonable strategy or are there other things that I might consider?
Thanks in advance for your time and consideration!