Fashion MNIST Generative Adversarial Network

This project is a simple implementation of a Generative Adversarial Network (GAN) using the Fashion MNIST dataset. The GAN is implemented using Tensorflow and trained on the Fashion MNIST dataset. The GAN is trained to generate new images of clothing items that are similar to the images in the Fashion MNIST dataset.

Installation

The requirements of the project are listed in the requirements.txt file. To install the requirements, run the following command:

1. Create a virtual environment and activate it:

python -m venv venv
source venv/bin/activate

2. Install the requirements:

pip install -r requirements.txt

Usage

All the code for the project is in the main.ipynb notebook. Architectures of the generator and discriminator are located in separate directories called generator and discriminator respectively. Code for training the GAN is in the notebook.

Results

The GAN was trained for 10000 epochs on the Fashion MNIST dataset with 32 batch size. The results of the training are:

• After 500 epochs

• After 1000 epochs

• After 5000 epochs

• After 9500 epochs