Metadata-Version: 1.1
Name: Adversarials
Version: 1.0.1
Summary: easy wrapper for initializing several GAN networks in keras
Home-page: https://github.com/deven96/Simple_GAN
Author: Domnan Diretnan, Victor Afolabi
Author-email: diretnandomnan@gmail.com, javafolabi@gmail.com
License: MIT
Description-Content-Type: text/markdown
Description: # 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)
        
        - [Simple GAN](#simple-gan)
          - [Overview](#overview)
          - [Flow Chart](#flow-chart)
          - [Installation](#installation)
          - [Example](#example)
          - [Documentation](#documentation)
          - [Credits](#credits)
          - [Contribution](#contribution)
          - [License (MIT)](#license-mit)
        
        ## 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.
        
Keywords: keras GAN GANs networks adversarial
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
