Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 

Working neural network model templates in pytorch.

This code was developed to help me turn neural network literature into practical knowledge and techniques for using neural networks on actual problems. This repository was created to share the networks and notes I made while training the networks with others.

This repository contains:

(1) Simple, self-contained, and working neural networks

Models are self contained, clearly coded and commented, and come with unit tests in an effort to provide a codebase that can be used to quickly start using the models on your specific problem.

(2) Notes about what I learned while training the networks.

Literature is often missing tricks, caveats, and why hyperparameters were chosen. Notes on challenges and intricacies learned while training the networks are included for each model.

Due to limitations on embedding equations into markdown on GitHub, notes are published at www.sciencesundires.com/sundries

Networks Implemented

vanilla GANs

Linear and convolutional generative adversarial networks (GAN).

Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley,
    Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative
    Adversarial Nets.” In Advances in Neural Information Processing Systems,
    2672–2680.http://papers.nips.cc/paper/5423-generative-adversarial-nets.

Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation
    learning with deep convolutional generative adversarial networks. ArXiv
    Prepr. ArXiv151106434.

To Install

git clone git@github.com:cottersci/ModelTests.git
pipenv install

Training depends on my pytorch_utils package, which is installed by pipenv.

Other useful pytorch links

About

Working neural network model templates in pytorch.

Resources

License

Releases

No releases published

Packages

No packages published

Languages