e7a02b6293100e21ef15870fd0f9069beaae5290,train.py,,,#,21

Before Change

        batch_size = data.size(0)
        running_batch_sizes += batch_size
        real_label = Variable(torch.ones(batch_size))
        fake_label = Variable(torch.zeros(batch_size))

        ////////////////////////////////////////////////////////
        // (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
        //////////////////////////////////////////////////////

After Change

        g_loss = generator_criterion(fake_out, fake_img, real_img)
        running_g_loss += g_loss.data[0] * batch_size
        d_loss = - torch.mean(torch.log(real_out) + torch.log(1 - fake_out))
        running_d_loss += d_loss.data[0] * batch_size
        running_fake_scores += fake_scores

        train_bar.set_description(desc="[%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f"

Instances