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A toolbox to iNNvestigate neural networks' predictions!
| Date | Stars |
|---|---|
| 2026-07-31 | 1308 |
| 2026-08-04 | 1308 |
| 2026-08-05 | 1308 |
| 2026-08-06 | 1308 |
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<div align="center">
<a href="https://github.com/albermax/innvestigate/">
<img
src="docs/assets/logo.svg"
alt="iNNvestigate Logo"
height="100"
/>
</a>
<br />
<p>
<h1>
<b>
iNNvestigate neural networks!
</b>
</h1>
[](https://innvestigate.readthedocs.io/en/latest/)
[](https://github.com/albermax/innvestigate/actions/workflows/ci.yml)
[](https://twitter.com/intent/tweet?text=iNNvestigate%20neural%20networks!&url=https://github.com/albermax/innvestigate&hashtags=iNNvestigate,artificialintelligence,machinelearning,deeplearning,datascience)
[](https://pypi.org/project/innvestigate/)
[](https://github.com/albermax/innvestigate/tags)
[](https://github.com/albermax/innvestigate/blob/master/LICENSE)
[](https://github.com/psf/black)
[](https://badge.fury.io/py/innvestigate)
[](https://github.com/albermax/innvestigate)
</p>
</div>
## Table of contents
* [Introduction](#introduction)
* [Installation](#installation)
* [Usage and Examples](#usage-and-examples)
* [More documentation](#more-documentation)
* [Contributing](#contributing)
* [Releases](#releases)
## Introduction
In the recent years neural networks furthered the state of the art in many domains like, e.g., object detection and speech recognition.
Despite the success neural networks are typically still treated as black boxes. Their internal workings are not fully understood and the basis for their predictions is unclear.
In the attempt to understand neural networks better several methods were proposed, e.g., Saliency, Deconvnet, GuidedBackprop, SmoothGrad, IntegratedGradients, LRP, PatternNet and PatternAttribution.
Due to the lack of a reference implementations comparing them is a major effort.
This library addresses this by providing a common interface and out-of-the-box implementation for many analysis methods.
Our goal is to make analyzing neural networks' predictions easy!
### If you use this code please star the repository and cite the following paper:
[Alber, M., Lapuschkin, S., Seegerer, P., Hägele, M., Schütt, K. T., Montavon, G., Samek, W., Müller, K. R., Dähne, S., & Kindermans, P. J. (2019). **iNNvestigate neural networks!** Journal of Machine Learning Research, 20.](https://jmlr.org/papers/v20/18-540.html)
```
@article{JMLR:v20:18-540,
author = {Maximilian Alber and Sebastian Lapuschkin and Philipp Seegerer and Miriam H{{\"a}}gele and Kristof T. Sch{{\"u}}tt and Gr{{\'e}}goire Montavon and Wojciech Samek and Klaus-Robert M{{\"u}}ller and Sven D{{\"a}}hne and Pieter-Jan Kindermans},
title = {iNNvestigate Neural Networks!},
journal = {Journal of Machine Learning Research},
year = {2019},
volume = {20},
number = {93},
pages = {1-8},
url = {http://jmlr.org/papers/v20/18-540.html}
}
```
## Installation
iNNvestigate is based on Keras and TensorFlow 2 and can be installed with the following commands:
```bash
pip install innvestigate
```
**Please note that iNNvestigate currently requires disabling TF2's eager execution.**
To use the example scripts and notebooks one additionally needs to install the package matplotlib:
```bash
pip install matplotlib
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fff7cac2761ca345, llm:Repository description: 'A toolbox to iNNvestigate neural networks' predictions!'. Known project innvestigate provides interpretability methods (Layer-wise relevance propagation, saliency, gradient-based) for neural networks in Python (Keras/Theano historically).
matched fp:fff7cac2761ca345, llm:Repository description: 'A toolbox to iNNvestigate neural networks' predictions!'. Known project innvestigate provides interpretability methods (Layer-wise relevance propagation, saliency, gradient-based) for neural networks in Python (Keras/Theano historically).