The adversarial network is a combined network that uses the generator, the discriminator, and VGG19. In this section, we will create an adversarial network.

Perform the following steps to create an adversarial network:

1. Start by creating an input layer for the network:

input_low_resolution = Input(shape=(64, 64, 3))

The adversarial network will receive an image of a shape of (64, 64, 3), which is why we have created an input layer.

2. Next, generate fake high-resolution images using the generator network, as follows:

fake_hr_images = generator(input_low_resolution)

3. Next, extract the features of the fake images using the VGG19 network, as follows: 

fake_features = vgg(fake_hr_images)

4. Next, make the discriminator network non-trainable in the adversarial network:

discriminator.trainable = False

We are making the discriminator network non-trainable because we don't want to train the discriminator network while we train the generator network. 

5. Next, pass the fake images to the discriminator network:

output = discriminator(fake_hr_images)

6. Finally, create a Keras model, which will be our adversarial model:

model = Model(inputs=[input_low_resolution], outputs=[output, 
              fake_features])

7. Wrap the entire code for the adversarial model inside a Python function:

def build_adversarial_model(generator, discriminator, vgg):

    input_low_resolution = Input(shape=(64, 64, 3))

    fake_hr_images = generator(input_low_resolution)
    fake_features = vgg(fake_hr_images)

    discriminator.trainable = False

    output = discriminator(fake_hr_images)

    model = Model(inputs=[input_low_resolution],
                  outputs=[output, fake_features])

    for layer in model.layers:
        print(layer.name, layer.trainable)

    print(model.summary())
    return model

We have now successfully implemented the networks in Keras. Next, we will train the network on the dataset that we downloaded in the Data preparation section.