Digit Generation using Generative Adversarial Networks (GANs)

Example Output

This is a digit generation project that employs Generative Adversarial Networks (GANs) to generate realistic handwritten digits. The project is implemented in a Jupyter Notebook, making it easy to run and experiment with.

Table of Contents

• Introduction
• Setup
• Network Architecture
• Usage
• Dataset
• Model Architecture
• Results
• Contributing

Introduction

Generative Adversarial Networks (GANs) are a powerful class of generative models that can learn to generate data similar to a given dataset. In this project, we focus on training a GAN to generate handwritten digits, specifically using the famous MNIST dataset, which contains images of handwritten digits from 0 to 9.

The GAN architecture consists of a generator and a discriminator, which are trained in an adversarial manner. The generator aims to generate realistic digits, while the discriminator tries to differentiate between real and generated digits. Through this adversarial training, the generator improves its ability to create more convincing digits.

Setup

To run this project, you'll need the following environment:

1. Python >= 3.8
2. Jupyter Notebook
3. Required libraries: TensorFlow, Keras, NumPy, Matplotlib, etc.

You can install the necessary libraries using pip:

pip install tensorflow keras numpy matplotlib

Network Architecture

Usage

1. Clone this repository to your local machine.
2. Open the Jupyter Notebook Copy_of_Build_a_Generative_Adversarial_Network.ipynb.
3. Follow the instructions provided in the notebook to execute each code cell.

The Jupyter Notebook contains detailed explanations for each step of the digit generation process, making it easy to understand the workflow.

Dataset

This project uses the MNIST dataset, which is a widely-used dataset for handwritten digit recognition. The MNIST dataset consists of 28x28 grayscale images of handwritten digits from 0 to 9.

The dataset is readily available in popular machine learning libraries like TensorFlow and Keras, making it easy to load and use for training the GAN.

Model Architecture

The GAN architecture in this project consists of a generator and a discriminator.

1. Generator: The generator takes random noise as input and generates synthetic digit images. It starts by producing random noise and gradually learns to map this noise to more realistic digits as the training progresses.

2. Discriminator: The discriminator takes images as input and predicts whether they are real (from the MNIST dataset) or fake (generated by the generator). It learns to distinguish between real and generated digits.

During training, the generator and discriminator play a minimax game, where the generator aims to generate more realistic digits to fool the discriminator, while the discriminator strives to become better at distinguishing real from fake digits. This adversarial training process leads to the generation of increasingly realistic digits.

Results

The results of the digit generation process will be displayed in the Jupyter Notebook. You will see a series of generated digit images as the model trains over epochs. The quality of the generated digits should improve as the GAN learns to create more convincing representations.

We will also plot the training progress, showing the generator and discriminator losses over time.

Contributing

Contributions to this project are welcome. If you find any issues or want to add improvements, please create a pull request, and we'll be happy to review it.

Thank You