Training the simGAN network

Now that we've built the infrastructure, we can develop the training methodology in the train script. In this section, we'll also create the run python and shell scripts that will be used for running everything in the Docker environment.

Getting ready

We're almost at the end! So, make sure you have every one of the following directories and files in your $HOME directory:

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

How to do it...

The training script will read in data, process the data for input into the networks, and then train the simGAN model.

Initialization

Take the following steps to initialize the training class and the basic functionality needed to train the models:

 

 

1. Create a train.py file and place the following imports...