Executing the training session

We finally initiate the training session with the respective arguments. You will notice the tqdm module displaying a percentage bar indicating the number of processed batches per epoch. At the end of the epoch, you will be able to visualize a 4 x 4 grid (shown next) of samples generated from the GAN network. And there you have it, now you know how to implement a GAN in Keras. On a side note, it can be very beneficial to have tensorflow-gpu along with CUDA set up, if you're running the code on a local machine with access to a GPU. We ran this code for 200 epochs, yet it would not be uncommon to let it run for thousands of epochs, given the resources and time. Ideally, the longer the two networks battle, the better the results should get. Yet, this may not always be the case, and hence, such attempts may also require careful monitoring of the...