Integrated Gradients

Python implementation of integrated gradients [1]. The algorithm "explains" a prediction of a Keras-based deep learning model by approximating Aumann–Shapley values for the input features. These values allocate the difference between the model prediction for a reference value (all zeros by default) and the prediction for the current sample among the input features. TensorFlow version is implemented now!

Usage

Using Integrated_Gradients is very easy. There is no need to modify your Keras model.
Here is a minimal working example on UCI Iris data.

Build your own Keras model and train it. Make sure to complie it!

from IntegratedGradients import *
from keras.layers import Dense
from keras.layers.core import Activation

X = np.array([[float(j) for j in i.rstrip().split(",")[:-1]] for i in open("iris.data").readlines()][:-1])
Y = np.array([0 for i in range(100)] + [1 for i in range(50)])

model = Sequential([
    Dense(1, input_dim=4),
    Activation('sigmoid'),
])
model.compile(optimizer='sgd', loss='binary_crossentropy')
model.fit(X, Y, epochs=300, batch_size=10, validation_split=0.2, verbose=0)

Wrap it with an integrated_gradients instance.

ig = integrated_gradients(model)

Call explain() with a sample to explain.

ig.explain(X[0])
==> array([-0.25757075, -0.24014562,  0.12732635,  0.00960122])

Features

supports both Sequential() and Model() instances.
supports both TensorFlow and Theano backends.
works on models with multiple outputs.
works on models with mulitple input branches.

Example notebooks

More thorough example can be found here.
There is also an example of running this on VGG16 model.
If your network has multiple input sources (branches), you can take a look at this.

MNIST example

We trained a simple CNN model (1 conv layer and 1 dense layer) on the MNIST imagesets. Here are some results of running integrated_gradients on the trained model and explaining some samples.

References

Sundararajan, Mukund, Ankur Taly, and Qiqi Yan. "Axiomatic Attribution for Deep Networks." arXiv preprint arXiv:1703.01365 (2017).