Each of these building blocks is fairly unique, but the generator is arguably the most important concept to understand. Ultimately, the generator will produce the images or output that we see after this entire training process is complete. When we talk about training GANs, it refers directly to training the generator. As we mentioned in a previous section, the discriminator will need to train for a few epochs prior to beginning the training process in most architectures or it would never complete training.

For each of these sections, it is important to understand the structure of the code we'll start building through the course of this book. In each chapter, we're going to define classes for each of the components. The generator will need to have three main functions within the class:

The loss function will define a custom loss function in training the model (if needed for that particular implementation). The buildModel function will construct the actual model of the given neural network. Specific training sequences for a model will go inside this class though we'll likely not use the internal training methods for anything but the discriminator.