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.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size deeplift-0.6.13.0.tar.gz (30.8 kB)||File type Source||Python version None||Upload date||Hashes View|