Code implementation – GAN

The Generative Adversarial Model, or GAN, is at the heart of adversarial training architecture. In fact, this model is different only in the fact that we use custom loss functions in our compile step. Let's take a look at how it's implemented.

 

 

Getting ready

This section will fill out the core of the base classes and functionality we need to have for training the simGAN architecture. The following files, and structure, should be included in your current directory:

├── data
├── docker
│   ├── build.sh
│   ├── clean.sh
│   ├── Dockerfile
│   └── kaggle.json
├── out
├── README.md
├── run.sh
└── src
    ├── discriminator.py
    ├── gan.py
    ├── generator.py
    ├── loss.py

How to do it...

The GAN model is vastly simplified in comparison to the building of the generator and discriminator. Essentially, this class will put the generator and discriminator into adversarial training along with the custom loss functions.

Take the following steps:

1. Use the python3 interpreter and...