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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>