3243317597e602fe8b869662e04dccf76cddfbd5,dcgan/main.py,,,#,162

Before Change

        // train with fake
        noise.data.resize_(batch_size, nz, 1, 1)
        noise.data.normal_(0, 1)
        fake = netG(noise)
        input.data.copy_(fake.data)
        label.data.fill_(fake_label)
        output = netD(input)
        errD_fake = criterion(output, label)

After Change

        // train with fake
        noise.data.resize_(batch_size, nz, 1, 1)
        noise.data.normal_(0, 1)
        fake = netG(noise).detach()
        label.data.fill_(fake_label)
        output = netD(fake)
        errD_fake = criterion(output, label)
        errD_fake.backward()
        D_G_z1 = output.data.mean()
        errD = errD_real + errD_fake
        optimizerD.step()

        ////////////////////////////////////////////////////////

Instances