GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability
Project description
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability
Disclaimer
This is code associated with the paper "GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability," published in the NeurIPS Workshop on Differential Geometry meets Deep Learning 2020.
Preliminaries
Our package is designed to run in Python 3.6 and pip version 20.2.4....
pip install -r requirements.txt
Usage
To use our package...
>>> from GENNI import genni_vis
>>> plot = genni_vis(mesh, V, X, Y, eigenpairs)
>>> plot.optimize()
python demo_dragon.py --help
usage: demo_dragon.py [-h] [--num-eigenpairs NUM_EIGENPAIRS] [--seed SEED]
[--output-dir OUTPUT_DIR]
[--eigenpairs-file EIGENPAIRS_FILE] [--mayavi]
[--num-samples NUM_SAMPLES]
optional arguments:
-h, --help show this help message and exit
--num-eigenpairs NUM_EIGENPAIRS
Number of eigenpairs to use. Default is 500
--seed SEED Random seed
--output-dir OUTPUT_DIR
Output directory to save .pvd files to. Default is
./output
--eigenpairs-file EIGENPAIRS_FILE
.npy file with precomputed eigenpairs
--mayavi Render results to .png with Mayavi
--num-samples NUM_SAMPLES
Number of random samples to generate
How saving is done:
Results are expected to saved in specific locations. If this code is not used to create equivalences classes, but the plotting functions want to be used, we advise to follow the structure laied out in get_grid.py and simply use the methods in interpolation.py which are agnostic to the saved locations.
Run experiment.py to produce elements in equivalence classes
To check if the elements converged to elements in the equivalence class, run stats_plotting.
Run the griding code to produce a set of elements in a subspace spanned by elements that were found.
Subset the set by elements wiht loss less than some epsilon and choose appropriate plotting mechanism.
Reproducing the paper
- How to reproduce figures
- How to reproduce values
Citing
If you use GENNI anywhere in your work, please cite use using
@article{2020,
title={GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability},
author={},
booktitle={},
year={2020}
}
TODO LIST
- Licence (MIT)
- Documentation
- Github actions
- Contributing
- Pull request / Issues templates
- Put on PyPI
- Make environment?
- CI
- Make package conform to PEP and packaging standards
Project details
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