Generative Adversarial Network (GAN)

This project implements a Generative Adversarial Network (GAN) using PyTorch. The GAN is trained to generate synthetic data by learning the underlying distribution of a dataset.

Features

• Generator and Discriminator Models: Implements standard architectures for GAN components.
• Configurable Parameters: Includes adjustable batch size, noise dimension, learning rate, and training epochs.
• Training Process: Demonstrates the training loop with loss visualization for both generator and discriminator.

Requirements

• Python 3.7+
• PyTorch
• NumPy
• Matplotlib
• tqdm

How to Run

1. Clone the repository:

   git clone <repository-url>