In this example, we're using layer sizes that are appropriate for generating a 28 x 28 grayscale image, which is exactly what we will be doing later in our MNIST example. The arithmetic of generators can be a little tricky if you haven't worked with one before, so we will cover that as we walk through each layer. The following figure shows the architecture:
The input to the generator is just a 100 x 1 vector of randomness that we will call a noise vector. GANs tend to work best when this noise vector is generated from a normal distribution.
The first layer of the network is dense and fully connected. It provides us with ...
