keras Explainable¶

Efficient explaining AI algorithms for Keras models.

Examples of explaining methods employed to explain outputs from various example images.

Installation¶

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:

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¶

Method	Kind	Description	Reference
Gradient Back-propagation	gradient	Computes the gradient of the output activation unit being explained with respect to each unit in the input signal.	docs paper
Full-Gradient	gradient	Adds the individual contributions of each bias factor in the model to the extracted gradient, forming the “full gradient” representation.	docs paper
CAM	CAM	Creates class-specific maps by linearly combining the activation maps advent from the last convolutional layer, scaled by their contributions to the unit of interest.	docs paper
Grad-CAM	CAM	Linear combination of activation maps, weighted by the gradient of the output unit with respect to the maps themselves.	docs paper
Grad-CAM++	CAM	Weights pixels in the activation maps in order to counterbalance, resulting in similar activation intensity over multiple instances of objects.	docs paper
Score-CAM	CAM	Combines activation maps considering their contribution towards activation, when used to mask 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.	docs paper
SmoothGrad	Meta	Consecutive applications of an AI explaining method, adding Gaussian noise to the input signal each time.	docs paper
TTA	Meta	Consecutive applications of an AI explaining method, applying augmentation to the input signal each time.	docs paper

Contents¶

Indices and tables¶

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