We recommend reading our blog for an introduction to hyperbolic neural networks. Other related material can be accessed here.
- Prerequisites:
python3.5, Tensorflow 1.8, numpy, pickle, logging
- Generate the 3d MLR figure from our paper.
python3.5 viz_mlr.py
- Run the code to reproduce results from Table 1. Example of command that runs hyperbolic GRUs + one hyperbolic fully connected layer + hyperbolic MLR to embed each pair of sentences from the PREFIX10 dataset (assuming the location of this dataset is in the same directory as the source code):
CUDA_VISIBLE_DEVICES='' python3.5 hyp_rnn.py --base_name='' --dataset='PRFX10' --inputs_geom='hyp' --word_dim=5 --word_init_avg_norm=0.001 --cell_type='gru' --cell_non_lin='id' --sent_geom='hyp' --bias_geom='hyp' --ffnn_geom='hyp' --ffnn_non_lin='id' --additional_features='dsq' --dropout=1.0 --before_mlr_dim=5 --mlr_geom='hyp' --reg_beta=0.0 --hyp_opt='rsgd' --lr_ffnn=0.01 --lr_words=0.1 --burnin='n' --proj_eps=1e-5 --batch_size=64 --root_path=./
The data needed in this code lives in the *_dataset folders and was generated as follows:
-
SNLI data was put in a binary format using the file
binarize_snli_dataset.py
and the original SNLI dataset -
the PREFIX dataset was generated using the file
prefix_dataset.py
If you find this code useful for your research, please cite the following paper in your publication:
@inproceedings{ganea2018hyperbolic,
title={Hyperbolic neural networks},
author={Ganea, Octavian and B{\'e}cigneul, Gary and Hofmann, Thomas},
booktitle={Advances in neural information processing systems},
pages={5345--5355},
year={2018}
}