Janis Fluri, Nathanaël Perraudin, Michaël Defferrard
This is an implementation of DeepSphere using TensorFlow 2.x.
Code:
arxiv:1810.12186
.arxiv:2012.15000
.Papers:
Clone this repository.
git clone https://github.com/deepsphere/deepsphere-cosmo-tf2.git
cd deepsphere-cosmo-tf2
Install the dependencies.
pip install -r requirements.txt
Note: the code has been developed and tested with Python 3.6. It does not work on Python 2.7!
Install the package.
pip install -e .
(Optional) Test the installation.
pytest tests
Play with the Jupyter notebooks.
jupyter notebook
The below notebooks contain examples and experiments to play with the model.
The content of this repository is released under the terms of the MIT license.\ Please consider citing our papers if you find it useful.
@article{deepsphere_cosmo,
title = {{DeepSphere}: Efficient spherical Convolutional Neural Network with {HEALPix} sampling for cosmological applications},
author = {Perraudin, Nathana\"el and Defferrard, Micha\"el and Kacprzak, Tomasz and Sgier, Raphael},
journal = {Astronomy and Computing},
volume = {27},
pages = {130-146},
year = {2019},
month = apr,
publisher = {Elsevier BV},
issn = {2213-1337},
doi = {10.1016/j.ascom.2019.03.004},
archiveprefix = {arXiv},
eprint = {1810.12186},
url = {https://arxiv.org/abs/1810.12186},
}
@inproceedings{deepsphere_rlgm,
title = {{DeepSphere}: towards an equivariant graph-based spherical {CNN}},
author = {Defferrard, Micha\"el and Perraudin, Nathana\"el and Kacprzak, Tomasz and Sgier, Raphael},
booktitle = {ICLR Workshop on Representation Learning on Graphs and Manifolds},
year = {2019},
archiveprefix = {arXiv},
eprint = {1904.05146},
url = {https://arxiv.org/abs/1904.05146},
}
@inproceedings{deepsphere_iclr,
title = {{DeepSphere}: a graph-based spherical {CNN}},
author = {Defferrard, Michaël and Milani, Martino and Gusset, Frédérick and Perraudin, Nathanaël},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2020},
url = {https://openreview.net/forum?id=B1e3OlStPB},
}