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


Indices and tables


If you find Zennit useful, why not cite our related paper [Anders et al., 2021]:

      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},