1D WASSERSTEIN STATISTICAL DISTANCE LOSSES IN PYTORCH

 

Introduction:

This repository is created to provide a Pytorch Wasserstein Statistical Loss solution for a pair of 1D weight distributions.

 

How To:

All core functions of this repository are created in pytorch_stats_loss.py. To introduce the related Pytorch losses, just add this file into your project and import it at your wish.
A group of dependent examples of related functionalities could be found in stats_loss_testing_file.py.
Pytorch_Statistical_Losses_Combined.py makes a combination of the loss functions and their examples, and provides a "one click and run" program for the convinence of interested users.
pytorch_stats_loss.py should be regarded as the center file of this project.

 

Points of Background Information:

Statistial Distances for 1D weight distributions
Inspired by Scipy.Stats Statistial Distances for 1D distributions
Pytorch Version, supporting Autograd to make a valid Loss for deep learning
Supposing Inputs are Groups of Same-Length Weight Vectors
Instead of (Points, Weight), full-length Weight Vectors are taken as Inputs
Losses are built up based on the result of CDF calculations

 

If you want to know more:

Check Scipy.Stats module for more background knowledge.
Check Pytorch to know more about deep learning.