Simple GAN Implementation in Python

This project demonstrates a simple implementation of a Generative Adversarial Network (GAN) in Python using a basic architecture. The GAN is trained to generate synthetic data/images, and the results showcase the evolution of generated outputs as training progresses.

Features

• Simple GAN architecture: A generator and discriminator implemented from scratch.
• Trains on a basic dataset and outputs generated samples.
• Customizable hyperparameters for learning rate, epochs, etc.

Prerequisites

• Python 3.8+
• Libraries: TensorFlow, NumPy, Matplotlib (see requirements.txt for details)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/GAN-Project.git
   cd GAN-Project