MNIST GAN (Generative Adversarial Network)

This project implements a Generative Adversarial Network (GAN) to generate handwritten digits similar to those in the MNIST dataset. The GAN comprises two main components: a generator that creates new images and a discriminator that evaluates their authenticity. The objective is for the generator to produce images that are indistinguishable from real handwritten digits.

Dataset

• The project uses the MNIST dataset, which is built into TensorFlow.

Steps

The process involves the following steps:

1. Loading the MNIST dataset and preprocessing the images.
2. Defining the generator and discriminator models.
3. Utilizing a custom training loop to train the GAN over a specified number of epochs.
4. During training, the generator and discriminator compete against each other, leading to improved performance over time.
5. Generating digits using the trained model.

Results

Here is an example of an image generated after 500 epochs:

Increasing the number of epochs may yield more realistic digits.