tnx
if I do soft-max and one-hot, the final loss is a vector with length equal to the classes count
and in this case I cant do backwards because it works on single value:
def weighted_mse_loss(input,target,weights):
    out = (input-target)**2
    out = out * weights.expand_as(out)
    loss = out.sum(0)
    return loss