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.
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Release files for deeplift 0.6.13.0
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| File | Size | Uploaded | |
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Release files / deeplift-0.6.13.0.tar.gz
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twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0.post20200714 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3
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