Now we can use a GAN approach to learn how to forge CIFAR-10 and create synthetic images that look real. Let's see the open source code (https://github.com/bstriner/keras-adversarial/blob/master/examples/example_gan_cifar10.py). Again, note that it uses the syntax of Keras 1.x, but it also runs on the top of Keras 2.x thanks to a convenient set of utility functions contained in legacy.py (https://github.com/bstriner/keras-adversarial/blob/master/keras_adversarial/legacy.py). First, the open source example imports a number of packages:
import matplotlib as mpl# This line allows mpl to run with no DISPLAY definedmpl.use('Agg')import pandas as pdimport numpy as npimport osfrom keras.layers import Dense, ...
