https://github.com/deven96/simple_gan

Attempt at implementation of a simple GAN using Keras
https://github.com/deven96/simple_gan

Last synced: about 1 month ago
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Attempt at implementation of a simple GAN using Keras

• Host: GitHub
• URL: https://github.com/deven96/simple_gan
• Owner: deven96
• License: mit
• Created: 2018-12-20T01:20:28.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-08-21T01:05:54.000Z (over 5 years ago)
• Last Synced: 2024-10-13T00:41:56.066Z (about 1 month ago)
• Language: Python
• Homepage:
• Size: 19.1 MB
• Stars: 7
• Watchers: 3
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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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.

[![Build Status](https://travis-ci.org/deven96/Simple_GAN.svg?branch=master)](https://travis-ci.com/deven96/Simple_GAN)[![PyPI version](https://badge.fury.io/py/Adversarials.svg)](https://badge.fury.io/py/Adversarials)![Quality Gate](https://sonarcloud.io/api/project_badges/measure?project=deven96_Simple_GAN&metric=alert_status)

- [Simple GAN](#simple-gan)

  - [Overview](#overview)

  - [Flow Chart](#flow-chart)

  - [Installation](#installation)

  - [Example](#example)

  - [Documentation](#documentation)

  - [Credits](#credits)

  - [Contribution](#contribution)

  - [License (MIT)](#license-mit)

  - [Todo](#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

```bash

    pip install adversarials

```

## Example 

```python

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](https://deven96.github.io/Simple_GAN)

## Credits

- [Understanding Generative Adversarial Networks](https://towardsdatascience.com/understanding-generative-adversarial-networks-4dafc963f2ef) - Noaki Shibuya

- [Github Keras Gan](https://github.com/osh/KerasGAN)

- [Simple gan](https://github.com/daymos/simple_keras_GAN/blob/master/gan.py)

## Contribution

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

Please keep a link to the [original repository](https://github.com/deven96/Simple_GAN). If you have made a fork with substantial modifications that you feel may be useful, then please [open a new issue on GitHub](https://github.com/deven96/Simple_GAN/issues) with a link and short description.

## License (MIT)

This project is opened under the [MIT 2.0 License](https://github.com/deven96/Simple_GAN/blob/master/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