Metadata-Version: 2.1
Name: simplegan
Version: 0.2.6
Summary: Framework to ease training of generative models based on TensorFlow
Home-page: https://github.com/grohith327
Author: Rohith Gandhi G
Author-email: grohith327@gmail.com
License: MIT
Description: # SimpleGAN
        **Framework to ease training of generative models**
        [![License](http://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat)](LICENSE)
        
        SimpleGAN is a framework based on [TensorFlow](https://www.tensorflow.org/) to make training of generative models easier. SimpleGAN provides high level APIs with customizability options to user which allows them to train a generative model with few lines of code.
        
        ### Requirements
        Make sure you have the following packages installed
        * [tensorflow](https://www.tensorflow.org/install)
        * [tqdm](https://github.com/tqdm/tqdm#latest-pypi-stable-release)
        * [imagio](https://pypi.org/project/imageio/)
        * [opencv](https://pypi.org/project/opencv-python/)
        * [tensorflow-datasets](https://www.tensorflow.org/datasets/overview#installation)
        ### Installation
        Latest stable release:
        ```bash
          $ pip install simplegan
        ```
        Latest Development release:
        ```bash
          $ pip install git+https://github.com/grohith327/EasyGAN.git
        ```
        ### Getting Started
        ```python
        >>> from simplegan.gan import DCGAN
        >>> gan = DCGAN()
        >>> train_ds = gan.load_data(use_mnist = True)
        >>> samples = gan.get_sample(train_ds, n_samples = 5)
        >>> gan.fit(train_ds = train_ds)
        >>> generated_samples = gan.generate_samples(n_samples = 5)
        ```
        To have a look at more examples, check [here](examples)
        ### Provided models
        * Autoencoders
            * Vanilla Autoencoder
            * Convolutional Autoencoder
            * Variational Autoencoder [[Paper](https://arxiv.org/abs/1312.6114)]
            * Vector Quantized - Variational Autoencoder [[Paper](https://arxiv.org/abs/1711.00937)]
        * Generative Adversarial Networks(GANs)
            * Vanilla GAN [[Paper](https://arxiv.org/abs/1406.2661)]
            * DCGAN [[Paper](https://arxiv.org/abs/1511.06434)]
            * WGAN [[Paper](https://arxiv.org/abs/1701.07875)]
            * CGAN [[Paper](https://arxiv.org/abs/1411.1784)]
            * InfoGAN [[Paper](https://arxiv.org/abs/1606.03657)]
            * Pix2Pix [[Paper](https://arxiv.org/abs/1611.07004)]
            * CycleGAN [[Paper](https://arxiv.org/abs/1703.10593)]
        
        ### Contributing
        We appreciate all contributions. If you are planning to perform bug-fixes, add new features or models, please file an issue and discuss before making a pull request.
        ### Contributors 
        * [Rohith Gandhi](https://github.com/grohith327)
        * [Prem Kumar](https://github.com/Prem-kumar27)
        
Keywords: GAN,Computer Vision,Deep Learning,TensorFlow,Generative Models,Neural Networks,AI
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
