Generating MNIST images using a Keras GAN
We've worked with MNIST before, but this time we will be generating new MNIST like images with a GAN. It can take a very long time to train a GAN; however, this problem is small enough that it can be run on most laptops in a few hours, which makes it a great example. Later we will expand this example to CIFAR-10 images.
The network architecture that I'm using here has been found by, and optimized by, many folks, including the authors of the DCGAN paper and people like Erik Linder-Norén, who's excellent collection of GAN implementations called Keras GAN (https://github.com/eriklindernoren/Keras-GAN) served as the basis of the code I used here. If you're wondering how I came to the architecture choices I used here, these are the giants whose shoulders I'm attempting to stand upon.
Loading the dataset
The MNIST dataset consists of 60,000 hand-drawn numbers, 0 to 9. Keras provides us with a built-in loader that splits it into 50,000 training images and...
