gan_training_fit.py

for epoch in range(epoch_num):
  for index in range(batches):
    # [Batch Preparation]

    # Generate fake inputs
    generated_images = g.predict(x=image_blur_batch, batch_size=batch_size)
    
    # Train multiple times discriminator on real and fake inputs
    for _ in range(critic_updates):
        d_loss_real = d.train_on_batch(image_full_batch, output_true_batch)
        d_loss_fake = d.train_on_batch(generated_images, output_false_batch)
        d_loss = 0.5 * np.add(d_loss_fake, d_loss_real)

    d.trainable = False
    # Train generator only on discriminator's decision and generated images
    d_on_g_loss = d_on_g.train_on_batch(image_blur_batch, [image_full_batch, output_true_batch])

    d.trainable = True