This repository contains code to implement a Generative Adversarial Network (GAN) for generating images of shirts from the Fashion MNIST dataset. The GAN consists of a generator that creates new images from random noise and a discriminator that distinguishes between real and fake images. The generator and discriminator are trained simultaneously in a zero-sum game until the generator produces realistic images.
- Introduction
- Requirements
- Dataset
- Preprocessing
- GAN Architecture
- Training
- Generating Images
- Creating GIF
- Usage
- Conclusion
Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. GANs consist of two neural networks, the generator and the discriminator, which compete against each other in a zero-sum game. The generator creates new data instances, while the discriminator evaluates them for authenticity.
This project demonstrates the implementation of a GAN to generate images of shirts from the Fashion MNIST dataset using TensorFlow and Keras.
To run the code, you'll need the following libraries:
- numpy
- pandas
- matplotlib
- seaborn
- tensorflow
- keras
- imageio
You can install the required libraries using the following command:
pip install numpy pandas matplotlib seaborn tensorflow keras imageio