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PyPI package version GitHub package version License: BSD-2 Black

Python TensorFlow package version

Different explanation methods on ImageNet.

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Table of contents

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.

@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:

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:

pip install matplotlib

The library's tests can be executed via pytest. The easiest way to do reproducible development on iNNvestigate is to install all dev dependencies via Poetry:

git clone https://github.com/albermax/innvestigate.git
cd innvestigate

poetry install
poetry run pytest

Usage and Examples

The iNNvestigate library contains implementations for the following methods:

The intention behind iNNvestigate is to make it easy to use analysis methods, but it is not to explain the underlying concepts and assumptions. Please, read the according publication(s) when using a certain method and when publishing please cite the according paper(s) (as well as the iNNvestigate paper). Thank you!

All the available methods have in common that they try to analyze the output of a specific neuron with respect to input to the neural network. Typically one analyses the neuron with the largest activation in the output layer. For example, given a Keras model, one can create a 'gradient' analyzer:

import tensorflow as tf
import innvestigate
tf.compat.v1.disable_eager_execution()

model = create_keras_model()

analyzer = innvestigate.create_analyzer("gradient", model)

and analyze the influence of the neural network's input on the output neuron by:

analysis = analyzer.analyze(inputs)

To analyze a neuron with the index i, one can use the following scheme:

analyzer = innvestigate.create_analyzer("gradient",
                                        model,
					neuron_selection_mode="index")
analysis = analyzer.analyze(inputs, i)

Let's look at an example (code) with VGG16 and this image:

Input image.

import tensorflow as tf
import tensorflow.keras.applications.vgg16 as vgg16
tf.compat.v1.disable_eager_execution()

import innvestigate

# Get model
model, preprocess = vgg16.VGG16(), vgg16.preprocess_input
# Strip softmax layer
model = innvestigate.model_wo_softmax(model)

# Create analyzer
analyzer = innvestigate.create_analyzer("deep_taylor", model)

# Add batch axis and preprocess
x = preprocess(image[None])
# Apply analyzer w.r.t. maximum activated output-neuron
a = analyzer.analyze(x)

# Aggregate along color channels and normalize to [-1, 1]
a = a.sum(axis=np.argmax(np.asarray(a.shape) == 3))
a /= np.max(np.abs(a))
# Plot
plt.imshow(a[0], cmap="seismic", clim=(-1, 1))

Analysis image.

Tutorials

In the directory examples one can find different examples as Python scripts and as Jupyter notebooks:

To use the ImageNet examples please download the example images first (script).

More documentation

... can be found here:

Contributing

If you would like to contribute or add your analysis method please open an issue or submit a pull request.

Releases

Can be found here.

Acknowledgements

Adrian Hill acknowledges support by the Federal Ministry of Education and Research (BMBF) for the Berlin Institute for the Foundations of Learning and Data (BIFOLD) (01IS18037A).