Generative AI Model From Scratch with Python

This project implements a Generative Adversarial Network (GAN) from scratch to generate handwritten digits using the MNIST dataset.

Generative Adversarial Networks (GANs)

Generator: Generates new data samples. Discriminator: Evaluates whether a given data sample is real (from the training data) or fake (generated by the generator). The two networks are trained together in a zero-sum game: the generator tries to fool the discriminator, while the discriminator aims to accurately distinguish real from fake data. A GAN consists of the following key components: Noise Vector: A random input vector fed into the generator. Generator: A neural network that transforms the noise vector into a data sample. Discriminator: A neural network that classifies input data as real or fake.

Project Structure

• src/: Contains the main implementation code
• outputs/: Stores generated images during training
• notebooks/: Contains exploratory analysis (if any)

Requirements

• Python 3.6+
• Keras
• NumPy
• Matplotlib

Installation

pip install -r requirements.txt

## Usage
To train the model:
python src/train.py
## Results
The model generates handwritten digits after training. Sample outputs are stored in the outputs/ directory.