Parsing the data – is our data unique?

Data is the lifeblood of these algorithms. If you take nothing else away from this book, please learn this lesson. In this recipe, we'll read each of the files in an array, resize them for learning, and save them into an easy-to-access compressed format.

 

 

Getting ready

First, let's perform a sanity check on our directory structure to make sure that we have all the right pieces; it should look as follows:

DCGAN
├── data
│   └── README.md
├── docker
│   ├── build.sh
│   ├── clean.sh
│   └── Dockerfile
├── README.md
├── scripts
│   └── create_data.sh
└── src
    ├── save_to_npy.py

You should notice the new folder, src, along with a new file, save_to_npy.py. The following recipe will focus on this Python file and how to run it to save data.

How to do it...

1. First, create the save_to_npy.py file and add the following lines to import the necessary dependencies and point to the python3 interpreter:

#!/usr/bin/env python3
from PIL import Image
import numpy as np
import...