This is an implementation of deep exponential families in MXNet/Gluon. DEFs are described in https://arxiv.org/abs/1411.2581
I found it much easier to implement this in an imperative / dynamic graph library like mxnet than in autodifferentiation libraries that only support static computation graphs.
Currently the code only implements a point-mass distributions for the weights and biases of each layer in the DEF (these parameters are learned using variational expectation-maximization). It should be straightforward to extend this to other distributions.
The gradients are computed with either the score function estimator or the pathwise (reparameterization trick) estimator. For score function gradient estimators, we use the optimal control variate scaling described in black box variational inference.
The code takes lots of inspiration from the official deep exponential families codebase and the gluon examples in mxnet.
Train a Poisson deep exponential family model on a large collection of science articles (in the LDA-C format):
PYTHONPATH=. python experiments/poisson_gaussian_deep_exp_fam_text.py
This periodically prints out the latent factors (dimensions of the latent variable), and the weight associated with each. For example, a dimension captures documents about DNA:
0.246 fig
-0.358 dna
-0.366 protein
-0.372 cells
-0.430 cell
-0.722 gene
-0.970 binding
-1.010 two
-1.026 sequence
-1.100 proteins
To train a Poisson deep exponential family model on the MNIST dataset:
PYTHONPATH=. python experiments/poisson_gaussian_deep_exp_fam_mnist.py
Also see examples in tests/ folder.
Install requirements with anaconda:
conda env create -f environment.yml
source activate deep_exp_fam
Run PYTHONPATH=. pytest for unit tests and mypy $(find . -name '*.py') for static type-checking.
- figure out a cleaner way to do per-sample gradients -- bug tracker: apache/mxnet#7987 (right now, parameters are repeated in deep_exp_fam.DeepExponentialFamilyModel class and require annoying processing)
- add support for priors on the weights