Metadata-Version: 2.1
Name: normflowpy
Version: 0.2.0
Summary: A Normalizing flow package using PyTorch
Home-page: UNKNOWN
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: torch
Requires-Dist: numpy

# NormFlowPy
This repository contains the implementation of various types of normalizing flow/ invertible neural networks. In addition, we provide a simple API run, train, and implement new types of normalizing flows.
We have implemented the following layers:
* Affine Coupling
* Invertible 1x1 
* Neural Spline Flow and Cubic Flow
and others. Note, that so implementation's based on other Github repositories and this would be stated in each file.
# Installation
```
pip install normflowpy
```
# Code Examples 
We have provide only a single example at this stage please see [moons notebook](https://github.com/haihabi/NormFlowPy/blob/main/examples/moons_glow_example.ipynb)
# Contribution & Problems

We welcomes contributions from anyone and if you find a bug or have a question, please create a GitHub issue.

# Refernces

[1] Kingma, Durk P., and Prafulla Dhariwal. "Glow: Generative flow with invertible 1x1 convolutions." Advances in neural information processing systems 31 (2018).

[2] Laurent Dinh, David Krueger, and Yoshua Bengio. NICE: nonlinear independent components estimation. In Yoshua Bengio and Yann LeCun, editors, 3rd International Conference on
Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Workshop Track Proceedings, 2015.

[3] Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and RaduTimofte.Srflow:  Learning  the  super-resolution  space  withnormalizing flow. InEuropean Conference on Computer Vision,pages 715–732. Springer, 2020.

[4]  Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Densityestimation  using  real  nvp.arXiv preprint arXiv:1605.08803,2016.

[5] Conor Durkan, Artur Bekasov, Iain Murray, and George Papa-makarios. Cubic-spline flows.arXiv preprint arXiv:1906.02145,2019



