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https://github.com/ro-jefferson/entropy_dnn
Code for project on relative entropy in deep neural networks
https://github.com/ro-jefferson/entropy_dnn
Last synced: 11 days ago
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Code for project on relative entropy in deep neural networks
- Host: GitHub
- URL: https://github.com/ro-jefferson/entropy_dnn
- Owner: ro-jefferson
- License: mit
- Created: 2021-07-14T12:05:31.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-11T16:10:35.000Z (about 1 year ago)
- Last Synced: 2024-08-01T16:46:57.108Z (3 months ago)
- Language: Jupyter Notebook
- Size: 985 KB
- Stars: 6
- Watchers: 1
- Forks: 3
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# entropy_dnn
Code for project on relative entropy in deep neural networks; corresponding paper available at https://arxiv.org/abs/2107.06898. Files include:* `Gaussian_Feedforward.ipynb` -- jupyter notebook that creates and trains feedforward random networks used in the analysis; data written in HDF5 format.
* `Gaussian_Feedforward_Analysis.ipynb` -- jupyter notebook that reads HDF5 files created by `Gaussian_Feedforward.ipynb` and performs analysis (e.g., computes correlation length, generates plots).
* `Relative_Entropy.ipynb` -- jupyter notebook that reads HDF5 files created by `Gaussian_Feedforward.ipynb` and computes the relative entropy or Kullback-Leibler (KL) divergence as a function of depth.
It is recommended to open the `.ipynb` files with jupyter so that LaTeX expressions in markdown cells are rendered correctly.