Zennit Documentation
Zennit (Zennit Explains Neural Networks in Torch) is a python framework using PyTorch to compute local attributions in the sense of eXplainable AI (XAI) with a focus on Layer-wise Relevance Propagation. It works by defining rules which are used to overwrite the gradient of PyTorch modules in PyTorch’s auto-differentiation engine. Rules are mapped to layers with composites, which contain directions to compute the attributions of a full model, which maps rules to modules based on their properties and context.
Zennit is available on PyPI and may be installed using:
$ pip install zennit
Contents
Indices and tables
Citing
If you find Zennit useful, why not cite our related paper [Anders et al., 2021]:
@article{anders2021software,
author = {Anders, Christopher J. and
Neumann, David and
Samek, Wojciech and
Müller, Klaus-Robert and
Lapuschkin, Sebastian},
title = {Software for Dataset-wide XAI: From Local Explanations to Global Insights with {Zennit}, {CoRelAy}, and {ViRelAy}},
journal = {CoRR},
volume = {abs/2106.13200},
year = {2021},
}