DeepLIFT (Deep Learning Important FeaTures)
Algorithms for computing importance scores in deep neural networks.
Implements the methods in “Learning Important Features Through Propagating Activation Differences” by Shrikumar, Greenside & Kundaje, as well as other commonly-used methods such as gradients, guided backprop and integrated gradients. See https://github.com/kundajelab/deeplift for documentation and FAQ.
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.