# summit-notebooks

**Repository Path**: xlk0101/summit-notebooks

## Basic Information

- **Project Name**: summit-notebooks
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-20
- **Last Updated**: 2021-10-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Summit Notebooks

Summit is an interactive system that scalably and systematically summarizes and visualizes what features a deep learning model has learned and how those features interact to make predictions.
This repository contains the python notebooks used to generate the data used in the [Summit visualization][summit].

For the main Summit repo, go to [https://github.com/fredhohman/summit][summit].


### Main notebooks:

* [`activation-matrices.ipynb`](activation-matrices.ipynb): generate aggregated activation matrices
* [`influence.py`](activation-matrices.ipynb): generate aggregated influence matrices
* [`activation-matrices-to-json.ipynb`](activation-matrices-to-json.ipynb): combine activation matrices per class into json format
* [`attribution-graph.ipynb`](dag.ipynb): generating class attribution graphs
* [`feature-vis-sprite-to-images.ipynb`](feature-vis-sprite-to-images.ipynb): split feature visualization sprites to single images

### Experimental notebooks:

* [`top-channels-used-per-layer.ipynb`](top-channels-used-per-layer.ipynb): analysis for determining which channels were used the most by all classes for all layers


## Live Demo

For a live demo, visit: [fredhohman.com/summit][demo]


## Resources

We used the following ImageNet metadata:

* [https://github.com/google/inception/blob/master/synsets.txt](https://github.com/google/inception/blob/master/synsets.txt)
* [https://gist.github.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57](https://gist.github.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57)
* [https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a)


## License

MIT License. See [`LICENSE.md`](LICENSE.md).


## Citation

```
@article{hohman2020summit,
  title={Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations},
  author={Hohman, Fred and Park, Haekyu and Robinson, Caleb and Chau, Duen Horng},
  journal={IEEE Transactions on Visualization and Computer Graphics (TVCG)},
  year={2020},
  publisher={IEEE},
  url={https://fredhohman.com/summit/}
}
```


## Contact

For questions or support [open an issue][issues] or contact [Fred Hohman][fred].

[summit]: https://github.com/fredhohman/summit
[fred]: https://fredhohman.com
[demo]: https://fredhohman.com/summit/
[issues]: https://github.com/fredhohman/summit-notebooks/issues