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' or 'Captum' for Python.
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 RevealCancel rule
Gradient-based methods:
Vanilla Gradient, including Gradient\(\times\)Input
Smoothed gradients (SmoothGrad), including SmoothGrad\(\times\)Input
Integrated gradients (IntegratedGradient)
Expected gradients (ExpectedGradient)
Model-agnostic methods:
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).
Useful links: