Is there standard Hinge Loss in Pytorch?

Looking through the documentation, I was not able to find the standard binary classification hinge loss function, like the one defined on wikipedia page:

l(y) = max( 0, 1 - t*y) where t E {-1, 1}

Is this loss implemented?

Like for doing a MCSVM. I’m not sure was looking for that the other day myself too but didn’t see one. Let me know if you find please. Was gonna do a more thorough check later but would save me the time😁

They have the MultiMarginLoss and MultilabelMarginLoss. But the one in particular you looking for is MarginRankingLoss and suits your needs


Did you find the implementation of this loss in Pytorch? Although i think it should be easier to implement this

May be you could do something like this

class MyHingeLoss(torch.nn.Module):

    def __init__(self):
        super(MyHingeLoss, self).__init__()

    def forward(self, output, target):

        hinge_loss = 1 - torch.mul(output, target)
        hinge_loss[hinge_loss < 0] = 0
        return hinge_loss