https://github.com/sparkier/luna
Luna is inspired by Lucid, a framework for Feature Visualization. However, Luna is built on Tensorflow 2, and thus supports modern models and deep learning techniques.
https://github.com/sparkier/luna
artificial-intelligence deep-learning machine-learning tensorflow visualization
Last synced: 4 months ago
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Luna is inspired by Lucid, a framework for Feature Visualization. However, Luna is built on Tensorflow 2, and thus supports modern models and deep learning techniques.
- Host: GitHub
- URL: https://github.com/sparkier/luna
- Owner: Sparkier
- License: mit
- Created: 2021-01-11T11:43:55.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-08-17T08:27:42.000Z (almost 4 years ago)
- Last Synced: 2025-04-13T13:12:10.301Z (about 1 year ago)
- Topics: artificial-intelligence, deep-learning, machine-learning, tensorflow, visualization
- Language: Python
- Homepage: http://a13x.io/luna/
- Size: 48.1 MB
- Stars: 11
- Watchers: 3
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
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README
# Luna
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.