Code for reproducing results in "A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning".
$ docker build . -t <image-name>
$ docker run --runtime nvidia --rm -it \
-v $HOME/data:/root/data \
-v $PWD:/work -w /work \
<image-name> bash
train Hyperbolic VAE with synthetic dataset:
$ python3 -m scripts.train --recipe-path recipes/mlp_synthetic.yml \
--p-z nagano <experiment-name>
train Hyperbolic VAE with MNIST dataset:
$ python3 -m scripts.train --p-z nagano <experiment-name>
train Hyperbolic VAE with Breakout dataset (you have to place the dataset for explored trajectories of pretrained agent in Breakout to $HOME/data/breakout/state_samples
):
$ python3 -m scripts.train --recipe-path recipes/cnn_breakout.yml \
--p-z nagano <experiment-name>
train Hyperbolic word embedding model with WordNet dataset:
$ python3 -m scripts.train_embedding --p-z nagano <experiment-name>