Xplique (pronounced \ɛks.plik\) is a Python toolkit dedicated to explainability, currently based on Tensorflow. The goal of this library is to gather the state of the art of Explainable AI to help you understand your complex neural network models.

The library is composed of several modules, the Attributions Methods module implements various methods (e.g Saliency, Grad-CAM, Integrated-Gradients...), with explanations, examples and links to official papers. The Feature Visualization module allows to see how neural networks build their understanding of images by finding inputs that maximize neurons, channels, layers or compositions of these elements. The Concepts module allows you to extract human concepts from a model and to test their usefulness with respect to a class. Finally, the Metrics module covers the current metrics used in explainability. Used in conjunction with the Attribution Methods module, it allows you to test the different methods or evaluate the explanations of a model.

Quick Start

Xplique requires a version of python higher than 3.6 and several libraries including Tensorflow and Numpy. Installation can be done using Pypi:

pip install xplique

Now that Xplique is installed, here are 4 basic examples of what you can do with the available modules.

Attributions Methods

let's start with a simple example, by computing Grad-CAM for several images (or a complete dataset) on a trained model.

from xplique.attributions import GradCAM

# load images, labels and model
# ...

explainer = GradCAM(model)
explanations = explainer.explain(images, labels)
# or just `explainer(images, labels)`

Attributions Metrics

In order to measure if the explanations provided by our method are faithful (it reflects well the functioning of the model) we can use a fidelity metric such as Deletion

from xplique.attributions import GradCAM
from xplique.metrics import Deletion

# load images, labels and model
# ...

explainer = GradCAM(model)
explanations = explainer(inputs, labels)
metric = Deletion(model, inputs, labels)

score_grad_cam = metric(explanations)

Concepts Extraction

Concerning the concept-based methods, we can for example extract a concept vector from a layer of a model. In order to do this, we use two datasets, one containing inputs containing the concept: positive_samples, the other containing other entries which do not contain the concept: negative_samples.

from xplique.concepts import Cav

# load a model, samples that contain a concept
# (positive) and samples who don't (negative)
# ...

extractor = Cav(model, 'mixed3')
concept_vector = extractor(positive_samples,

Feature Visualization

Finally, in order to find an image that maximizes a neuron and at the same time a layer, we build two objectives that we combine together. We then call the optimizer which returns our images

from xplique.features_visualizations import Objective
from xplique.features_visualizations import optimize

# load a model...

neuron_obj = Objective.neuron(model, "logits", 200)
channel_obj = Objective.layer(model, "mixed3", 10)

obj = neuron_obj + 2.0 * channel_obj
images = optimize(obj)


We propose some Hands-on tutorials to get familiar with the library and its api:

  • Attribution Methods: Getting started
Open In Colab
  • Attribution Methods: Tabular data and Regression
Open In Colab
  • Attribution Methods: Metrics
Open In Colab
  • Concepts Methods: Testing with Concept Activation Vectors
Open In Colab
  • Feature Visualization: Getting started
Open In Colab

You can find a certain number of other practical tutorials just here. This section is actively developed and more contents will be included. We will try to cover all the possible usage of the library, feel free to contact us if you have any suggestions or recommandations towards tutorials you would like to see.


This library was started as a side-project by Thomas FEL who is doing a thesis on explainability.


The package is released under MIT license.

GitHub - deel-ai/xplique: ? Xplique is a Neural Networks Explainability Toolbox
? Xplique is a Neural Networks Explainability Toolbox - GitHub - deel-ai/xplique: ? Xplique is a Neural Networks Explainability Toolbox