The steps given in this section show how to train the discriminator network. This is a continuation of the last series of steps:

1. Generate fake high-resolution images using the generator network:

generated_high_resolution_images = 
          generator.predict(low_resolution_images)

2. Create a batch of real labels and fake labels:

    real_labels = np.ones((batch_size, 16, 16, 1))
    fake_labels = np.zeros((batch_size, 16, 16, 1))

3. Train the discriminator network on real images and real labels:

d_loss_real = discriminator.train_on_batch(high_resolution_images, 
   real_labels)

4. Train the discriminator on generated images and fake labels:

d_loss_fake = discriminator.train_on_batch(generated_high_resolution_images, fake_labels)

5. Finally, calculate the total discriminator loss:

    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

We have now added the code to train the discriminator network. Next, add the code to train the adversarial model, which trains the generator network.