cf65784f7297dca491436112b9a5689ecd7533ec,acgan/acgan.py,ACGAN,train,#ACGAN#,121

Before Change

            valid = np.ones((batch_size, 1))
            // Generator wants discriminator to label the generated images as the intended
            // digits
            sampled_labels = np.random.randint(0, 10, batch_size).reshape(-1, 1)

            // Train the generator
            g_loss = self.combined.train_on_batch([noise, sampled_labels], [valid, sampled_labels])

            // Plot the progress
            print ("%d [D loss: %f, acc.: %.2f%%, op_acc: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[3], 100*d_loss[4], g_loss[0]))

After Change

            // The labels of the digits that the generator tries to create an
            // image representation of
            sampled_labels = np.random.randint(0, 10, (batch_size, 1))

            // Generate a half batch of new images
            gen_imgs = self.generator.predict([noise, sampled_labels])

            // Image labels. 0-9 if image is valid or 10 if it is generated (fake)
            img_labels = y_train[idx]
            fake_labels = 10 * np.ones(img_labels.shape)

            // Train the discriminator
            d_loss_real = self.discriminator.train_on_batch(imgs, [valid, img_labels])
            d_loss_fake = self.discriminator.train_on_batch(gen_imgs, [fake, fake_labels])
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

            // ---------------------
            //  Train Generator
            // ---------------------

            // Train the generator
            g_loss = self.combined.train_on_batch([noise, sampled_labels], [valid, sampled_labels])

            // Plot the progress
            print ("%d [D loss: %f, acc.: %.2f%%, op_acc: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[3], 100*d_loss[4], g_loss[0]))

Instances