f37d0f2da815d3674dbba5ef1d8711357348c94f,implementations/wgan/wgan.py,,,#,88

Before Change

        optimizer_G.zero_grad()

        // Sample noise as generator input
        z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))

        // Generate a batch of images
        gen_imgs = generator(z)

After Change

            // Generate a batch of images
            fake_imgs = generator(z)
            // Adversarial loss
            loss_D = -torch.mean(discriminator(real_imgs)) + torch.mean(discriminator(fake_imgs))

            loss_D.backward()
            optimizer_D.step()

Instances