DigitGenerator-GAN
Introduction
This repository contains code for creating A Generative Adversarial Network (GAN) project designed to generate realistic fake handwritten digits, trained on the MNIST dataset.The MNIST dataset is a well-known collection of 70,000 images of handwritten digits, commonly used for training various image processing systems. By leveraging the power of GANs, this project aims to create high-quality synthetic handwritten digits that closely resemble those found in the MNIST dataset.
Key Features
- Generative Adversarial Network Architecture: Utilizes a GAN framework comprising a generator and a discriminator, where the generator creates fake digit images and the discriminator evaluates their authenticity.
- MNIST Dataset: Trained on the MNIST dataset to ensure the generated digits are representative of a wide variety of handwriting styles.
- High-Quality Synthetic Digits: Produces realistic, high-quality images of handwritten digits that can be used for various applications, including data augmentation, digit recognition research, and artistic purposes.
- Training and Evaluation: Includes scripts for training the GAN, monitoring its performance, and evaluating the quality of the generated images.
Objectives
- Generate Realistic Handwritten Digits: Develop a model capable of producing high-fidelity handwritten digits that are indistinguishable from real ones.
- Enhance Data Augmentation: Provide additional synthetic data for training other machine learning models, improving their robustness and accuracy.
- Explore GAN Capabilities: Investigate the potential of GANs in generating high-quality images and contributing to advancements in the field of generative models.
Usage
Running the Colab Notebook
To run the Colab notebook efficiently, it is recommended to use a GPU. Follow these steps:
- Open the notebook in Google Colab.
- Go to
Runtime > Change runtime type.
- Under
Hardware accelerator, select GPU and click Save.
- Click
Connect in the upper right corner and select Connect to hosted runtime.
- Execute each cell in the notebook sequentially.
Dependencies
- TensorFlow
- TensorFlow GAN
- TensorFlow Datasets
- Matplotlib
- NumPy
Training the GAN
- Execute the notebook cells to set up the input pipeline, build the generator and discriminator networks, and train the GAN.
- Monitor the training progress and evaluate the generated images.
Note
- This repository provides a simplified implementation of a GAN for educational purposes.
- Adjustments and optimizations may be required for real-world applications.
Contributing
Contributions are welcome! Please fork the repository and submit pull requests for any enhancements or bug fixes.
License
This project is licensed under the MIT License.
