Simple GAN

Attempt at implementation of a simple GAN using Keras

Description

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

Programming language: Python

License: MIT License

Tags: Machine Learning     Artificial Intelligence     Deep Learning     Keras    

Latest version: v1.0.1

README

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
  • Documentation
  • Credits
  • Contribution
  • License (MIT)
  • Todo

Overview

[alt text](assets/mnist_gan.png "GAN network using the MNIST dataset")

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.

[alt text](assets/flow.jpg "High level flowchart")

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)

Documentation

Github Pages

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.

Todo

• Add view training(discriminator and generator) simultaneously using tensorboard
• Provision for Parallel data processing and multithreading
• Saving models to Protobuff files
• Using TfGraphDef and other things that could speed up training and inference