Pytorch implementation of Block Neural Autoregressive Flow based on our paper:
De Cao Nicola, Titov Ivan and Aziz Wilker, Block Neural Autoregressive Flow (2019)
python>=3.6(it will probably work on older versions but I have not tested on them)pytorch>=1.0.0
Optional for visualization and plotting: numpy, matplotlib and tensorboardX
- bnaf.py: Implementation of Block Neural Normalzing Flow.
- toy2d.py: Experiments of 2d toy task (density estimation and energy matching).
- density_estimation.py: Experiments on density estimation on real datasets.
- optim: A custom extension of
torch.optim.Adamandtorch.optim.Adamaxwith Polyak averaging. A custom extension oftorch.optim.lr_scheduler.ReduceLROnPlateauwith callbacks. - data: Data classes to handle the real datasets.
Below, example commands are given for running experiments.
Run the following command to download the datasets:
./download_datasets.sh
This example runs density estimation on the 8 Gaussians dataset using 1 flow of BNAF with 2 layers and 100 hidden units (50 * 2 since the data dimensionality is 2).
python toy2d.py --dataset 8gaussians \ # which dataset to use
--experiment density2d \ # which experiment to run
--flows 1 \ # BNAF flows to concatenate
--layers 2 \ # layers for each flow of BNAF
--hidden_dim 50 \ # hidden units per dimension for each hidden layer
--save # save the model after training
--savefig # save the density plot on disk
This example runs energy matching on the t4 function using 1 flow of BNAF with 2 layers and 100 hidden units (50 * 2 since the data dimensionality is 2).
python toy2d.py --dataset t4 \ # which dataset to use
--experiment energy2d \ # which experiment to run
--flows 1 \ # BNAF flows to concatenate
--layers 2 \ # layers for each flow of BNAF
--hidden_dim 50 \ # hidden units per dimension for each hidden layer
--save # save the model after training
--savefig # save the density plot on disk
This example runs density estimation on the MINIBOONE dataset using 5 flows of BNAF with 0 layers.
python density_estimation.py --dataset miniboone \ # which dataset to use
--flows 5 \ # BNAF flows to concatenate
--layers 0 \ # layers for each flow of BNAF
--hidden_dim 10 \ # hidden units per dimension for each hidden layer
--save # save the model after training
De Cao Nicola, Titov Ivan, Aziz Wilker,
Block Neural Autoregressive Flow,
35th Conference on Uncertainty in Artificial Intelligence (UAI19) (2019).
BibTeX format:
@article{bnaf19,
title={Block Neural Autoregressive Flow},
author={De Cao, Nicola and
Titov, Ivan and
Aziz, Wilker},
journal={35th Conference on Uncertainty in Artificial Intelligence (UAI19)},
year={2019}
}
For questions and comments, feel free to contact Nicola De Cao.
MIT