Lucid is a collection of infrastructure and tools for research in neural network interpretability.
In particular, it provides state of the art implementations of feature visualization techniques, and flexible abstractions that make it very easy to explore new research directions.
Notebooks corresponding to the Building Blocks of Interpretability article
- Feaure Visualization
- The Building Blocks of Interpretability
- Using Artiﬁcial Intelligence to Augment Human Intelligence
- Visualizing Representations: Deep Learning and Human Beings
License and Disclaimer
You may use this software under the Apache 2.0 License. See LICENSE.
This project is research code. It is not an official Google product.
tox to run the test suite on all supported environments.
To run tests only for a specific module, pass a folder to
To run tests only in a specific environment, pass the environment's identifier
tox -e py27.
After adding dependencies to
setup.py, run tox with the
--recreate flag to
update the environments' dependencies.