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.
The requirements of the project are listed in the requirements.txt file. To install the requirements, run the
following command:
- Create a virtual environment and activate it:
python -m venv venv
source venv/bin/activate- Install the requirements:
pip install -r requirements.txtAll 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.
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
