The Layer-wise Relevance Propagation (LRP) algorithm explains a classifer's prediction specific to a given data point by attributing relevance scores to important components of the input by using the topology of the learned model itself.
The LRP Toolbox provides simple and accessible stand-alone implementations of LRP for artificial neural networks supporting Matlab and python. The Toolbox realizes LRP functionality for the Caffe Deep Learning Framework as an extension of Caffe source code published in 10/2015.
The implementations for Matlab and python are intended as a sandbox or playground to familiarize the user to the LRP algorithm and thus are implemented with readability and transparency in mind. Models and data can be imported and exported using raw text formats, Matlab's .mat files and the .npy format for python/numpy.
To try out either the python-based MNIST demo, or the Caffe based ImageNet demo in your browser, click on the respective panels:
| MNIST | Images | Text |
|---|---|---|
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 |
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| A simple LRP demo based on neural networks that predict handwritten digits and were trained using the MNIST data set. | A more complex LRP demo based on a neural network implemented using Caffe. The neural network predicts the contents of the picture. | An LRP demo that explains classification on natural language. The neural network predicts the type of document. |
- update to python 3
- updated treatment of softmax and target class
- lrp_aware option for efficient calculation of multiple backward passes
- custom colormaps in render.py
- gpu support when installing cupy. this is an optional feature. without the cupy package, the python code will execute using the cpu/numpy.
- updated the installation config
- new recommended formula types 100, 102, 104
- new python wrapper to use lrp in pycaffe
- pycaffe demo file
- bugfixes
- singularity image for building a hassle-free OS-agnostic command line executable
- Convnets with Sum- and Maxpooling are now supported, including demo code.
- LRP-parameters can now be set for each layer individually
- w² and flat weight decomposition implemented.
- Minimal output versions implemented.
- Matthew Zeiler et al.'s Deconvolution, Karen Simonyan et al.'s Sensitivity Maps, and aspects of Grégoire Montavon et al.'s Deep Taylor Decomposition are implemented, alongside the flat weight decomposition for uniformly projecting relevance scores to a neuron's receptive field have been implemented.
- Various optimizations, refactoring, bits and pieces here and there.
Clone it from github!
After having obtained the toolbox code, data and models of choice, simply move into the subpackage folder of you choice -- matlab, python or caffe-master-lrp -- and execute the installation script (written for Ubuntu 14.04 or newer).
<obtain the toolbox>
cd lrp_toolbox/$yourChoice
bash install.sh
Make sure to at least skim through the installation scripts! For more details and instructions please refer to the manual.
When using (any part) of this toolbox, please cite our paper
@article{JMLR:v17:15-618,
author = {Sebastian Lapuschkin and Alexander Binder and Gr{{\'e}}goire Montavon and Klaus-Robert M{{{\"u}}}ller and Wojciech Samek},
title = {The LRP Toolbox for Artificial Neural Networks},
journal = {Journal of Machine Learning Research},
year = {2016},
volume = {17},
number = {114},
pages = {1-5},
url = {http://jmlr.org/papers/v17/15-618.html}
}
For further research and projects involving LRP, visit heatmapping.org
Also, consider paying https://github.com/albermax/innvestigate a visit! Next to LRP, iNNvestigate efficiently implements a hand full of additional DNN analysis methods and can boast with a >500-fold increase in computation speed when compared with our CPU-bound Caffe implementation!