Hey there👋🏼! Welcome to my Generative Adversarial Neural Network (GAN) project! This project aims to provide a deeper understanding of generative models and how they work by implementing a GAN using TensorFlow Keras. Generative models have gained significant popularity in recent years due to their ability to generate new and realistic data. GANs, in particular, have shown remarkable results in generating images, audio, and even text.
In this project, I have implemented a GAN architecture using TensorFlow Keras to generate fashion images. I utilize the FashionMNIST dataset from TensorFlow, which consists of 60,000 grayscale images of 10 different fashion categories. The GAN architecture comprises the Generator and Discriminator models, which are trained in an adversarial manner.
The GAN network architecture involves the design and implementation of the Generator and Discriminator models. The Generator is responsible for generating synthetic fashion images that closely resemble the real fashion images in the dataset. The Discriminator, on the other hand, learns to differentiate between real fashion images and the synthetic images generated by the Generator.