Trained on Fashion_MNIST dataset using Keras Sequential API and Tensorflow gradient tape training loop
Generative Adversarial Networks have two components:
A Generator and Discriminator which are trained simultaneosuly using adversarial crafting process.
A Generator network : It behaves as an artist trying to generate images without any knowledge about the true and learns by interacting with the Discriminator.
A Discriminator network : It behaves as an art critic trying to determine whether a given image is real or fake. It uses the output of generator as training data
The training for generator began by giving input as some random noise and after 60 epochs it starts to generate images that resemble images from the Fashion MNIST dataset.
