cf65784f7297dca491436112b9a5689ecd7533ec,bgan/bgan.py,BGAN,train,#BGAN#,100

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)

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):

Instances