================= keras Explainable

.. image:: https://github.com/lucasdavid/keras-explainable/actions/workflows/ci.yml/badge.svg?branch=release :alt: Travis build status :target: https://github.com/lucasdavid/keras-explainable/actions/workflows/ci.yml

.. image:: https://img.shields.io/badge/docs-0.0.2-blue :alt: Documentation status :target: https://lucasdavid.github.io/keras-explainable

Efficient explaining AI algorithms for Keras models.

.. image:: _static/images/cover.jpg :alt: Examples of explaining methods employed to explain outputs from various example images.

Installation

.. code-block:: shell

pip install tensorflow pip install git+https://github.com/lucasdavid/keras-explainable.git

Usage

This example illustrate how to explain predictions of a Convolutional Neural Network (CNN) using Grad-CAM. This can be easily achieved with the following example:

.. code-block:: python

import keras_explainable as ke

model = tf.keras.applications.ResNet50V2(...) model = ke.inspection.expose(model)

scores, cams = ke.gradcam(model, x, y, batch_size=32)

Implemented Explaining Methods

.. table:: :widths: auto :align: left

=========================== ========= ====================================================== ================== Method Kind Description Reference
=========================== ========= ====================================================== ================== Gradient Back-propagation gradient Computes the gradient of the output activation unit docs <https://lucasdavid.github.io/keras-explainable/api/keras_explainable.methods.html#keras_explainable.methods.gradient.gradients>_ being explained with respect to each unit in the input paper <https://arxiv.org/abs/1312.6034>_ signal. Full-Gradient gradient Adds the individual contributions of each bias factor docs <https://lucasdavid.github.io/keras-explainable/api/keras_explainable.methods.html#keras_explainable.methods.gradient.full_gradients>_ in the model to the extracted gradient, forming the paper <https://arxiv.org/abs/1905.00780>_ "full gradient" representation. CAM CAM Creates class-specific maps by linearly combining the docs <https://lucasdavid.github.io/keras-explainable/api/keras_explainable.methods.html#keras_explainable.methods.cams.cam>_ activation maps advent from the last convolutional paper <https://arxiv.org/abs/1512.04150>_ layer, scaled by their contributions to the unit of interest. Grad-CAM CAM Linear combination of activation maps, weighted by docs <https://lucasdavid.github.io/keras-explainable/api/keras_explainable.methods.html#keras_explainable.methods.cams.gradcam>_ the gradient of the output unit with respect to the paper <https://arxiv.org/abs/1610.02391>_ maps themselves. Grad-CAM++ CAM Weights pixels in the activation maps in order to docs <https://lucasdavid.github.io/keras-explainable/api/keras_explainable.methods.html#keras_explainable.methods.cams.gradcampp>_ counterbalance, resulting in similar activation paper <https://arxiv.org/abs/1710.11063>_ intensity over multiple instances of objects. Score-CAM CAM Combines activation maps considering their docs <https://lucasdavid.github.io/keras-explainable/api/keras_explainable.methods.html#keras_explainable.methods.cams.scorecam>_ contribution towards activation, when used to mask paper <https://arxiv.org/abs/1910.01279>_ Activation maps are used to mask the input signal, which is feed-forwarded and activation intensity is computed for the new . Maps are combined weighted by their relative activation retention. SmoothGrad Meta Consecutive applications of an AI explaining method, docs <https://lucasdavid.github.io/keras-explainable/api/keras_explainable.methods.html#keras_explainable.methods.meta.smooth>_ adding Gaussian noise to the input signal each time. paper <https://arxiv.org/abs/1706.03825>_ TTA Meta Consecutive applications of an AI explaining method, docs <https://lucasdavid.github.io/keras-explainable/api/keras_explainable.methods.html#keras_explainable.methods.meta.tta>_ applying augmentation to the input signal each time. paper <https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0197-0/>_ =========================== ========= ====================================================== ==================