Understanding GANs

To start implementing GANs, we need to. It is hard to determine the quality of examples produced by GANs. A lower loss value doesn't always represent better quality. Often, for images, the only way to determine the quality is by visually inspecting the generated examples. We can than determine whether the generated images are realistic enough, more or less like a simple Turing test. In the following recipe, we will introduce GANs by using the well-known MNIST dataset and the Keras framework.

How to do it...

1. We start by importing the necessary libraries, as follows:

import numpy as np
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Activation, Flatten, Reshape
from...