Generative Adversarial Network(GAN)

Implemented DCGAN which is a direct extension of the GAN.

Getting Started
Install
Execute The Project
Features Released
Upcoming Features
Maintainers
Contributes
Disclaimer
References
License

Getting Started

These instructions will get you a copy of the project up and running on your development machine for contributing to development, testing purposes.

Network Architecture of DCGAN

Python Version

Minimum python version should have 3.x.x or upper

Install

A step by step series of examples that tell you have to get a development env running

How do I get set up?

If you would like to used Virtualenv Install the virtualenv using this command(If you have not installed virtualenv yet.)

$ [sudo] pip install virtualenv

Learn more to visit Virtualenv, User Guide

Create a directory using the following command from your terminal

$ [sudo] mkdir GAN

Switch to GAN directory

$ cd GAN

git clone

$ git clone git@github.com:porimol/GAN.git .

Afterthen, create virtual env file by the following command from your terminal

$ virtualenv -p python3 .venv

If you create virtual env file successfully on your development machine then run this command

$ source .venv/bin/activate

Installing the requirements using the following commands

$ pip install -r requirements.txt

How to run this project

$ cd GAN
$ python dcgan.py

Features Released

Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks(DCGAN)

Upcoming Features

Deploy demo using Flask

Maintainer

Porimol Chandro
Linkedin Profile

Contributors

Contributing

See the list of contributors who participated in this project.

How to become a contributor

If you want to contribute to GAN and make it better, your help is very welcome. You can make constructive, helpful bug reports, feature requests and the noblest of all contributions. If like to contribute in a good way, then follow the following guidelines.

How to make a clean pull request

Create a personal fork on Github.
Clone the fork on your local machine.(Your remote repo on Github is called origin
Add the original repository as a remote called upstream.
If you created your fork a while ago be sure to pull upstream changes into your local repository.
Create a new branch to work on! Branch from dev.
Implement/fix your feature, comment your code.
Follow GAN's code style, including indentation(4 spaces).
Write or adapt tests as needed.
Add or change the documentation as needed.
Push your branch to your fork on Github, the remote origin.
From your fork open a pull request to the dev branch.
Once the pull request is approved and merged, please pull the changes from upstream to your local repo and delete your extra branch(es).

Disclaimer

This repository is not ready as production grade and it is being implemented in the contributor's free time, and as such, may contain minor errors in regards to some portion of the code.

Inspired By

PyTorch DCGAN Tutorial

References

[1] Radford, A., Metz, L. and Chintala, S., 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
[2] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y., 2014. Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).

License

The MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

porimol / GAN