Now that we've assembled both the generator and the discriminator, we need to assemble a third model that is the stack of both models together that we can use for training the generator given the discriminator loss.

To do that we can just create a new model, this time using the previous models as layers in the new model, as shown in the following code:

discriminator = build_discriminator(img_shape=(28, 28, 1))
generator = build_generator()

z = Input(shape=(100,))
img = generator(z)
discriminator.trainable = False
real = discriminator(img)
combined = Model(z, real)

Notice that we're setting the discriminator's training attribute to False before building the model. This means that for this model we will not be updating the weights of the discriminator during backpropagation. As we mentioned in the Stacked training section, we will freeze these weights and only move the generator weights with the stack. The discriminator will be trained separately.

Now that all the models are built, they need to be compiled, as shown in the following code:

gen_optimizer = Adam(lr=0.0002, beta_1=0.5)
disc_optimizer = Adam(lr=0.0002, beta_1=0.5)

discriminator.compile(loss='binary_crossentropy',
                           optimizer=disc_optimizer,
                           metrics=['accuracy'])

generator.compile(loss='binary_crossentropy', optimizer=gen_optimizer)

combined.compile(loss='binary_crossentropy', optimizer=gen_optimizer)

If you'll notice, we're creating two custom Adam optimizers. This is because many times we will want to change the learning rate for only the discriminator or generator, slowing one or the other down so that we end up with a stable GAN where neither is overpowering the other. You'll also notice that I'm using beta_1 = 0.5. This is a recommendation from the original DCGAN paper that I've carried forward and also had success with. A learning rate of 0.0002 is a good place to start as well, and was found in the original DCGAN paper.