Designing the generator module

Now comes the fun part. We will be implementing a Deep Convolutional Generative Adversarial Network (DCGAN). We start with the first part of the DCGAN: the generator network. The generator network will essentially learn to recreate realistic car images, by transforming a sample from some normal probability distribution, representing a latent space.

We will again use the functional API to defile our model, nesting it in a function with three different arguments. The first argument, latent_dim, refers to the dimension of the input data randomly sampled from a normal distribution. The leaky_alpha argument simply refers to the alpha parameter provided to the LeakyRelu activation function used throughout the network. Finally, the argument init_stddev simply refers to the standard deviation with which to initialize the random weights of the network, used...