How to build a GAN using TensorFlow 2

To illustrate the implementation of a GAN using Python, we will use the DCGAN example discussed earlier in this section to synthesize images from the Fashion-MNIST dataset that we first encountered in Chapter 13, Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning.

See the notebook deep_convolutional_generative_adversarial_network for implementation details and references.

Building the generator network

Both generator and discriminator use a deep CNN architecture along the lines illustrated in Figure 20.1, but with fewer layers. The generator uses a fully connected input layer, followed by three convolutional layers, as defined in the following build_generator() function, which returns a Keras model instance:

def build_generator():
    return Sequential([Dense(7 * 7 * 256, 
                             use_bias=False,
                             input_shape=(100,), 
                             name=&apos...