DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKs (DCGANs)

We already learn about GANs in GANs section. We will learn about Deep Convolutional Generative Adversarial Networks in this section. This tutorial shows how to use a Deep Convolutional Generative Adversarial Network to generate images of handwritten digits (DCGAN). The code is written in Keras Sequential API with a training loop tf.GradientTape.

As we know that Generative Adversarial Networks (GANs) are one of the most interesting ideas in computer science today. Two models are trained simultaneously by an adversarial process. A generator ("the artist") learns to create images that look real, while a discriminator ("the art critic") learns to tell real images apart from fakes. During training, the generator progressively becomes better at creating images that look real, while the discriminator becomes better at telling them apart. The process reaches equilibrium when the discriminator can no longer distinguish real images from fakes.

Full Code Of Deep Convolutional Generative Adversarial Network (DCGAN) using the Keras Sequential API

This notebook demonstrates this process on the MNIST dataset. The following animation shows a series of images produced by the generator as it was trained for 50 epochs. The images begin as random noise, and increasingly resemble hand written digits over time.