Abstract
This repository contains a python package of neural network interpretability techniques (interpretability) and a keras callback to easily compute and capture data related to these techniques (values metrics) during training. It was developed to be used with NREL's BUTTER Deep Learning Experimental Framework, but does not depend on this framework and may be useful to projects outside of this framework.
The vision for this codebase is to collect algorithms for explainable artificial intelligence (XAI) in a single framework that is easy to use, easy to read, and can be expand upon. Here, we package XAI algorithms into a module called "metrics", which are implemented as python functions. The return type of a metric is typically a dictionary holding data of multiple data types, such as real values and numpy matrices. Callbacks and any other connector code is provided as necessary in a separate module to make these metrics more easily usable. This project depends on Tensorflow's Keras API, although it would be nice to try and support multiple backends one day.
Related to: https://github.com/NREL/BUTTER-Empirical-Deep-Learning-Experimental-Framework
- Developers:
-
Perr-Sauer, Jordan [1] ; Tripp, Charles [1]
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Release Date:
- 2023-09-15
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE Laboratory Directed Research and Development (LDRD) ProgramPrimary Award/Contract Number:AC36-08GO28308
- Code ID:
- 113847
- Site Accession Number:
- NREL SWR-23-61
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Perr-Sauer, Jordan, and Tripp, Charles.
BUTTER-Clarifier [SWR-23-61].
Computer Software.
https://github.com/NREL/BUTTER-Clarifier.
USDOE Laboratory Directed Research and Development (LDRD) Program.
15 Sep. 2023.
Web.
doi:10.11578/dc.20240827.2.
Perr-Sauer, Jordan, & Tripp, Charles.
(2023, September 15).
BUTTER-Clarifier [SWR-23-61].
[Computer software].
https://github.com/NREL/BUTTER-Clarifier.
https://doi.org/10.11578/dc.20240827.2.
Perr-Sauer, Jordan, and Tripp, Charles.
"BUTTER-Clarifier [SWR-23-61]." Computer software.
September 15, 2023.
https://github.com/NREL/BUTTER-Clarifier.
https://doi.org/10.11578/dc.20240827.2.
@misc{
doecode_113847,
title = {BUTTER-Clarifier [SWR-23-61]},
author = {Perr-Sauer, Jordan and Tripp, Charles},
abstractNote = {This repository contains a python package of neural network interpretability techniques (interpretability) and a keras callback to easily compute and capture data related to these techniques (values metrics) during training. It was developed to be used with NREL's BUTTER Deep Learning Experimental Framework, but does not depend on this framework and may be useful to projects outside of this framework.
The vision for this codebase is to collect algorithms for explainable artificial intelligence (XAI) in a single framework that is easy to use, easy to read, and can be expand upon. Here, we package XAI algorithms into a module called "metrics", which are implemented as python functions. The return type of a metric is typically a dictionary holding data of multiple data types, such as real values and numpy matrices. Callbacks and any other connector code is provided as necessary in a separate module to make these metrics more easily usable. This project depends on Tensorflow's Keras API, although it would be nice to try and support multiple backends one day.
Related to: https://github.com/NREL/BUTTER-Empirical-Deep-Learning-Experimental-Framework},
doi = {10.11578/dc.20240827.2},
url = {https://doi.org/10.11578/dc.20240827.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240827.2}},
year = {2023},
month = {sep}
}