Good is somewhat subjective, when you're building a deep neural network to create images. Let's take a look at a few examples of the training process, so you can see for yourself how the GAN begins to learn to generate MNIST.
Here's the network at the very first batch of the very first epoch. Clearly, the generator doesn't really know anything about generating MNIST at this point; it's just noise, as shown in the following image:
But just 50 batches in, something is happening, as you can see from the following image:
And after 200 batches of epoch 0 we can almost see numbers, as you can see from the following image:
And here's our generator after one full epoch. I think these generated numbers look pretty good, and I can see how the discriminator might be fooled by them. At this point, we could probably continue to improve a little bit, but it looks like our GAN has worked as the computer is generating some pretty convincing MNIST digits, as shown in the following image:
While most of the code will be the same, before we close out the chapter let's look at one more example, using color images.