From the story of faking a ticket to an event, the idea of GANs seems to be very intuitive. So to get a clear understanding of how GANs work and how to implement them, we are going to demonstrate a simple implementation of a GAN on the MNIST dataset.
First, we need to build the core of the GAN network, which is comprised of two major components: the generator and the discriminator. As we said, the generator will try to imagine or fake data samples from a specific probability distribution; the discriminator, which has access to and sees the actual data samples, will judge whether the generator's output has any flaws in the design or it's very close to the original data samples. Similar to the scenario of the event, the whole purpose of the generator is to try to convince the discriminator that the generated image is from the real dataset...
