Generating LSTM attributions with integrated gradients
We first learned about integrated gradients (IG) in Chapter 8, Visualizing Convolutional Neural Networks. Unlike the other gradient-based attribution methods studied in that chapter, path-integrated gradients is not contingent on convolutional layers, nor is it limited to classification problems. In fact, since it computes the gradients of the output concerning the inputs averaged along the path, the input and output could be anything! It is common to use integrated gradients with CNNs and Recurrent Neural Networks (RNNs), like the one we are interpreting in this chapter. Frankly, when you see an IG LSTM example online, it has an embedding layer and is an NLP classifier, but IG could be used very effectively for LSTMs that even process sounds or genetic data!
The integrated gradient explainer and the explainers that we will use moving forward can access any part of the traffic dataset. First, let's create a generator for...
