How to work with training data

As with every architecture we train throughout this book, understanding the structure of the data and the development environment is important to overall success. So, in this section, we'll set up the development environment and download the data inside the Docker container.

Getting ready

You'll need to create a folder at the $HOME directory level of your Linux machine with the following directory structure (which can be checked using the tree function):

├── docker
│   ├── build.sh
│   ├── clean.sh
│   ├── Dockerfile
│   └── kaggle.json
├── out
├── README.md
├── run.sh
└── src

How to do it...

In this chapter, we're going to introduce the Kaggle API so we can grab the necessary data for the SimGAN training architecture. Using the Kaggle API will require you to set up a Kaggle account and get API token access.

Kaggle and its API

Kaggle.com is a popular online site that holds machine learning (ML) competitions and discussions. Kaggle also supplies an API for accessing...