innsight is an R package that interprets the behavior and explains individual predictions of modern neural networks. Many methods for explaining individual predictions already exist, but hardly any of them are implemented or available in R. Most of these so-called 'Feature Attribution' methods are only implemented in Python and thus difficult to access or use for the R community. In this sense, the package innsight provides a common interface for various methods for the interpretability of neural networks and can therefore be considered as an R analogue to 'iNNvestigate' for Python.

Details

This package implements several model-specific interpretability (Feature Attribution) methods based on neural networks in R, e.g.,

  • Layer-wise Relevance Propagation (LRP)

    • Including propagation rules: \(\epsilon\)-rule and \(\alpha\)-\(\beta\)-rule

  • Deep Learning Important Features (DeepLift)

    • Including propagation rules for non-linearities: rescale rule and reveal-cancel rule

  • Gradient-based methods:

    • Vanilla Gradient, including 'Gradient x Input'

    • Smoothed gradients (SmoothGrad), including 'SmoothGrad x Input'

  • ConnectionWeights

The package innsight aims to be as flexible as possible and independent of a specific deep learning package in which the passed network has been learned. Basically, a Neural Network of the libraries torch::nn_sequential, keras::keras_model_sequential, keras::keras_model and neuralnet::neuralnet can be passed to the main building block Converter, which converts and stores the passed model as a torch model (ConvertedModel) with special insights needed for interpretation. It is also possible to pass an arbitrary net in form of a named list (see details in Converter).

Author

Maintainer: Niklas Koenen niklas.koenen@gmail.com (ORCID)

Other contributors: