CRAN/E | innsight

innsight

Get the Insights of Your Neural Network

Installation

About

Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004) doi:10.1016/j.ecolmodel.2004.03.013, layer-wise relevance propagation ('LRP') described by Bach et al. (2015) doi:10.1371/journal.pone.0130140, deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) and gradient-based methods like 'SmoothGrad' described by Smilkov et al. (2017) , 'Gradient x Input' described by Baehrens et al. (2009) or 'Vanilla Gradient'.

bips-hb.github.io/innsight/
github.com/bips-hb/innsight/
Bug report File report

Key Metrics

Version 0.3.0
R ≥ 3.5.0
Published 2023-12-21 126 days ago
Needs compilation? no
License MIT
License File
CRAN checks innsight results
Language en-US

Downloads

Yesterday 14 -78%
Last 7 days 167 -15%
Last 30 days 729 +2%
Last 90 days 2.183 +9%
Last 365 days 7.876 +68%

Maintainer

Maintainer

Niklas Koenen

niklas.koenen@gmail.com

Authors

Niklas Koenen

aut / cre

Raphael Baudeu

ctb

Material

README
NEWS
Reference manual
Package source

Vignettes

Example 1: Iris dataset with torch
Example 2: Penguin dataset with torch and luz
In-depth explanation
Introduction to innsight

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

innsight archive

Depends

R ≥ 3.5.0

Imports

checkmate
cli
ggplot2
methods
R6
torch

Suggests

covr
fastshap
GGally
grid
gridExtra
gtable
keras
knitr
lime
luz
neuralnet
palmerpenguins
plotly
rmarkdown
ranger
spelling
tensorflow
testthat ≥ 3.0.0