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!

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.

1. 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)

2. Wrap it with an integrated_gradients instance.

ig = integrated_gradients(model)

3. Call explain() with a sample to explain.

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

• 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.

• 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.

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.

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

Email me at hiranumn at cs dot washington dot edu for questions.