A collection of infrastructure and tools for research in neural network interpretability.

repo name	tensorflow/lucid
repo link	https://github.com/tensorflow/lucid
homepage
language	Jupyter Notebook
size (curr.)	147267 kB
stars (curr.)	3450
created	2018-01-25
license	Apache License 2.0

Lucid

Lucid is a collection of infrastructure and tools for research in neural network interpretability.

We’re not currently supporting tensorflow 2!

Lucid is research code, not production code. We provide no guarantee it will work for your use case. Lucid is maintained by volunteers who are unable to provide significant technical support.

• 📓 Notebooks – Get started without any setup!
• 📚 Reading – Learn more about visualizing neural nets.
• 💬 Community – Want to get involved? Please reach out!
• 🔧 Additional Information – Licensing, code style, etc.
• 🔬 Start Doing Research! – Want to get involved? We’re trying to research openly!
• 📦 Visualize your own model – How to import your own model for visualization

Notebooks

Start visualizing neural networks with no setup. The following notebooks run right from your browser, thanks to Colaboratory. It’s a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud.

You can run the notebooks on your local machine, too. Clone the repository and find them in the notebooks subfolder. You will need to run a local instance of the Jupyter notebook environment to execute them.

Tutorial Notebooks

Feature Visualization Notebooks

Notebooks corresponding to the Feature Visualization article

Building Blocks Notebooks

Notebooks corresponding to the Building Blocks of Interpretability article

Differentiable Image Parameterizations Notebooks

Notebooks corresponding to the Differentiable Image Parameterizations article

Activation Atlas Notebooks

Notebooks corresponding to the Activation Atlas article

Miscellaneous Notebooks

Recomended Reading

• Feature Visualization
• The Building Blocks of Interpretability
• Using Artificial Intelligence to Augment Human Intelligence
• Visualizing Representations: Deep Learning and Human Beings
• Differentiable Image Parameterizations
• Activation Atlas

Related Talks

• Lessons from a year of Distill ML Research (Shan Carter, OpenVisConf)
• Machine Learning for Visualization (Ian Johnson, OpenVisConf)

Community

We’re in #proj-lucid on the Distill slack (join link).

We’d love to see more people doing research in this space!

Additional Information

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.

Special consideration for TensorFlow dependency

Lucid requires tensorflow, but does not explicitly depend on it in setup.py. Due to the way tensorflow is packaged and some deficiencies in how pip handles dependencies, specifying either the GPU or the non-GPU version of tensorflow will conflict with the version of tensorflow your already may have installed.

If you don’t want to add your own dependency on tensorflow, you can specify which tensorflow version you want lucid to install by selecting from extras_require like so: lucid[tf] or lucid[tf_gpu].

In actual practice, we recommend you use your already installed version of tensorflow.