0368a2665b52b843785ffe0b18b3154fd32539b0,pro_gan_pytorch/Losses.py,CondLSGAN_SIGMOID,gen_loss,#CondLSGAN_SIGMOID#,388

Before Change

    def gen_loss(self, _, fake_samps, labels, height, alpha):
        from torch.nn.functional import sigmoid
        scores = th.mean(sigmoid(self.dis(fake_samps, labels, height, alpha)))
        return 0.5 * ((scores - 1) ** 2)


class CondHingeGAN(ConditionalGANLoss):

After Change

    def gen_loss(self, _, fake_samps, labels, height, alpha):
        from torch.nn.functional import sigmoid
        scores = sigmoid(self.dis(fake_samps, labels, height, alpha))
        return 0.5 * (th.mean((scores - 1) ** 2))


class CondHingeGAN(ConditionalGANLoss):

Instances