Metadata-Version: 2.1
Name: luna-fviz
Version: 0.3.0
Summary: Feature Visualization with Tensorflow 2
Home-page: https://github.com/Sparkier/luna
Author: Alex Bäuerle
Author-email: alex.baeuerle@uni-ulm.de
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.md

# Luna

<img src="https://raw.githubusercontent.com/Sparkier/luna/master/luna.png" alt="icon showing a stylized moon" width="100"/>

Inspired by [Lucid](https://github.com/tensorflow/lucid), **Luna** is a Feature Visualization package for Tensorflow.
While Lucid does not support Tensorflow 2, **Luna** was built with Tensorflow 2 at its core.

**Luna is under active development. It is research code and not production-ready.**

To learn how to use Luna and for insights into its API, see our [documentation](http://a13x.io/luna/).

## Contributing

We greatly appreciate any effort to improve **Luna**.
If you want to contribute to its development, see our [contribution guidelines](./CONTRIBUTING.md).

## Recomended Reading

- [Feature Visualization](https://distill.pub/2017/feature-visualization/)
- [The Building Blocks of Interpretability](https://distill.pub/2018/building-blocks/)
- [Using Artiﬁcial Intelligence to Augment Human Intelligence](https://distill.pub/2017/aia/)
- [Visualizing Representations: Deep Learning and Human Beings](http://colah.github.io/posts/2015-01-Visualizing-Representations/)
- [Differentiable Image Parameterizations](https://distill.pub/2018/differentiable-parameterizations/)
- [Activation Atlas](https://distill.pub/2019/activation-atlas/)

## Related Talks

- [Lessons from a year of Distill ML Research](https://www.youtube.com/watch?v=jlZsgUZaIyY) (Shan Carter, OpenVisConf)
- [Machine Learning for Visualization](https://www.youtube.com/watch?v=6n-kCYn0zxU) (Ian Johnson, OpenVisConf)

## Community

While we admire their work, we have no affiliation with the Lucid authors or project. Nonetheless, if you are interested in research like this, the Distill slack ([join link](http://slack.distill.pub)) might be a good place for you to get to know other people in this area.

On the awesome Distill slack, Lucid has its own `#proj-lucid` channel, where general questions about the technology are discussed.
