Simple GAN

This is my attempt to make a wrapper class for a GAN in keras which can be used to abstract the whole architecture process.

• Simple GAN
  • Overview
  • Flow Chart
  • Installation
  • Example
  • Credits
  • Contribution
  • License (MIT)

Overview

Flow Chart

Setting up a Generative Adversarial Network involves having a discriminator and a generator working in tandem, with the ultimate goal being that the generator can come up with samples that are indistinguishable from valid samples by the discriminator.

Installation

    pip install adversarials

Example

import numpy as np
from keras.datasets import mnist

from adversarials.core import Log
from adversarials import SimpleGAN

if __name__ == '__main__':
    (X_train, _), (_, _) = mnist.load_data()

    # Rescale -1 to 1
    X_train = (X_train.astype(np.float32) - 127.5) / 127.5
    X_train = np.expand_dims(X_train, axis=3)

    Log.info('X_train.shape = {}'.format(X_train.shape))

    gan = SimpleGAN(save_to_dir="./assets/images",
    save_interval=20)
    gan.train(X_train, epochs=40)

Credits

• Understanding Generative Adversarial Networks - Noaki Shibuya
• Github Keras Gan
• Simple gan

Contribution

You are very welcome to modify and use them in your own projects.

Please keep a link to the original repository. If you have made a fork with substantial modifications that you feel may be useful, then please open a new issue on GitHub with a link and short description.

License (MIT)

This project is opened under the MIT 2.0 License which allows very broad use for both academic and commercial purposes.

A few of the images used for demonstration purposes may be under copyright. These images are included under the "fair usage" laws.