cf65784f7297dca491436112b9a5689ecd7533ec,lsgan/lsgan.py,LSGAN,train,#LSGAN#,93

Before Change

        X_train = (X_train.astype(np.float32) - 127.5) / 127.5
        X_train = np.expand_dims(X_train, axis=3)

        half_batch = int(batch_size / 2)

        for epoch in range(epochs):

            // ---------------------
            //  Train Discriminator
            // ---------------------

            // Select a random half batch of images
            idx = np.random.randint(0, X_train.shape[0], half_batch)
            imgs = X_train[idx]

            noise = np.random.normal(0, 1, (half_batch, self.latent_dim))

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

            // Train the discriminator
            d_loss_real = self.discriminator.train_on_batch(imgs, np.ones((half_batch, 1)))
            d_loss_fake = self.discriminator.train_on_batch(gen_imgs, np.zeros((half_batch, 1)))
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)


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

            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))

            // The generator wants the discriminator to label the generated samples
            // as valid (ones)
            valid_y = np.array([1] * batch_size)

            // Train the generator
            g_loss = self.combined.train_on_batch(noise, valid_y)

            // Plot the progress
            print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))

After Change

        X_train = np.expand_dims(X_train, axis=3)

        // Adversarial ground truths
        valid = np.ones((batch_size, 1))
        fake = np.zeros((batch_size, 1))

        for epoch in range(epochs):

            // ---------------------
            //  Train Discriminator
            // ---------------------

            // Select a random batch of images
            idx = np.random.randint(0, X_train.shape[0], batch_size)
            imgs = X_train[idx]

            // Sample noise as generator input
            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))

            // Generate a batch of new images
            gen_imgs = self.generator.predict(noise)

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


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

            g_loss = self.combined.train_on_batch(noise, valid)

            // Plot the progress
            print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))

Instances