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Invertible Gaussian Reparameterization: Revisting the Gumbel-Softmax

This repo contains a TensorFlow 2.0 implementation of the Invertible Gaussian Reparameterization.


Abstract
The Gumbel-Softmax is a continuous distribution over the simplex that is often used as a relaxation of discrete distributions. Because it can be readily interpreted and easily reparameterized, it enjoys widespread use. We propose a modular and more flexible family of reparameterizable distributions where Gaussian noise is transformed into a one-hot approximation through an invertible function. This invertible function is composed of a modified softmax and can incorporate diverse transformations that serve different specific purposes. For example, the stick-breaking procedure allows us to extend the reparameterization trick to distributions with countably infinite support, thus enabling the use of our distribution along nonparametric models, or normalizing flows let us increase the flexibility of the distribution. Our construction enjoys theoretical advantages over the Gumbel-Softmax, such as closed form KL, and significantly outperforms it in a variety of experiments.

Overview

The goal of this documentation is to clarify the structure of the repo and to provide a guide to replicate the results from the paper. To avoid reading all details, I recommend that you find the files that are linked to the experiment that you want to run (see Replicating Figures / Tables section) and only then check the information about the folder of interest in the (General Information section).

Requirements

Briefly, the requirements for the project are Python >= 3.6 and to pip install the the packages needed (which will fetch all the dependencies as well). Make sure that you have the latest pip version. This repo was develop using TensorFlow 2.0.1, but it runs for 2.1.0 as well. The only package that requires a specific version is TensorFlow Probability. Moreover, we also added a Singularity definition file igr_singularity.def if you want to create an image to run the project in a HPC cluster (it will only require that the host has the 10.0 CUDA drivers available). In summary you could run the following in you terminal to ensure that the installation works (after adding the repo to your $PYTHONPATH export PYTHONPATH=$PYTHONPATH:"path/to/igr")

python3 -m pip install --update pip
python3 -m pip install -r requirements.txt
cd ./igr/
mkdir Log
python3 vae_experiments/experiments_grid.py

from wherever you cloned the repo. It should successfully run an experiment with an small sample that should take 5 - 10 seconds.

General Information

Structure of the Repository

  • Log: is the directory where the outputs from the experiments are logged and saved (weights of the NNs). The contents of this directory are disposable, move the results that you want to save into the Results directory for further analysis (see below).
  • Models: contains the training functions (train_vae.py, SOPOptimizer,py), the optimizer classes (OptVAE, SOPOptimizer), and the neural network architectures (VAENet.py SOP.py) for both the VAE and for the SOP experiments.
  • Results: this directory serves two purposes. First, it holds all the figures created by the scripts in the repo and then it contains the outputs from the experiments that are used as input for some figures / tables.
  • Tests: contains various tests for relevant classes in the repo. The name indicates which class is being tested.
  • Utils: has key functions / classes used throughout the repo: Distributions.py contains the GS and IGR samplers, MinimizeEmpiricalLoss.py contains the optimizer class for learning the parameters that approximate a discrete distribution, general.py contains the functions used for approximating discrete distributions, for evaluating simplex proximity and for initializing the distribution's parameters, load_data.py contains all the utils needed to load the data, and viz_vae.py contains the functions to plot the performance over time.
  • approximations: contains all the scripts to approximate discrete distributions and to learn the IGR priors (more details in the Replicating section).
  • vae_experiments: contains all the scripts to run the VAE experiments to replicate the paper results (more details in the Replicating section).
  • structure_output_prediction: contains all the scripts to run the SOP experiments (more details in the Replicating section).

Variables Explanation

All the scripts and saving references assume that you are running the files from the root folder of the repo (as in the first test to check that the installation was correct). In other words, from the terminal run the scripts as

python3 path/to/script.py

or set your IDE to run files from the root location. Throughout the code, the discrete tensors have the following shape (batch_n, categories_n, sample_size, num_of_vars). Where batch_n is the batch size used at each iteration (selected by the user), categories_n is the dimension of each of the discrete variables, sample_size is the number of samples taken during the reparameterization trick and num_of_vars the number of discrete variables that the model has. Finally, below is a list that expands on the name of variables and hyperparameters that appear frequently in different experiment scripts.

Name Type and Value (Example) Description
dataset_name <str> ('mnist') The name of the dataset to run the model.
model_type <str> ('ExpGSDis') The name of the model to use. Look at ./Models/train_vae.py for all the model options.
temp <float> (0.25) The value of the temperature hyperparameter.
sample_size <int> (1) The number of samples that are taken from the noise distribution at each iteration.
n_required <int> ('10') The number of categories needed for each discrete variable (identical to categories_n.
num_of_discrete_param <int> (1) The number of parameters for the discrete variables. Alpha for GS
mu and sigma for IGR
num_of_discrete_var <int> (2) The number of discrete variables in the model.
num_of_norm_param <int> (0) The number of parameters for the continuous variables.
num_of_norm_var <int> (0) The number of continuous variables in the model.
latent_norm_n <int> (0) The dimensionality of the continuous variables in the model.
architecture <str> ('dense') The neural network architecture employed. All the options are in ./Models/VAENet.py.
learning_rate <float> (0.001) The learning rate used when training.
batch_n <int> (64) The batch size taken per iteration.
epochs <int> (100) The number of epochs used when training.
run_jv <bool> (False) Whether to run the JointVAE model or not.
gamma <int> (100) The penalty coefficient in the JointVAE loss.
cont_c_linspace <tuple> ((0., 5., 25000)) The lower bound, upper bound, and how many iters to get from lower to upper.
disc_c_linspace <tuple> ((0., 5., 25000)) The lower bound, upper bound, and how many iters to get from lower to upper.
check_every <int> (1) How often (in terms of epochs) the test loss is evaluated.
run_with_sample <bool> (True) Test the experiment with a small sample.
num_of_repetitions <int> (1) Determine how many times to run the experiment (useful for doing CV).
truncation_options <str> ('quantile') The statistical procedure to determine the categories needed in a given batch.
threshold <float> (0.99) The precision parameter for the IGR-SB.
prior_file <str> ('./Results/mu_xi_unif_10_IGR_SB_Finite.pkl') Location of the prior parameters file.
run_closed_form_kl <bool> (True) Whether to use the Gaussian close form KL (only available to the IGR).
width_height <tuple> ((14, 28, 1)) The size of the images for the SOP experiment.
iter_per_epoch <int> (937) The number of iterations per epoch in SOP (the VAE experiments infer this).

Implementation Nuances

The IGR-SB extends the reparameterization trick to distributions with an countably infinite support. However, to implement the IGR-SB with finite resources, there is a threshold imposed on the stick breaking procedure that determines a finite number of categories that are needed for a proper estimation. Additionally, the number of categories has a maximum that is set as a reference (this is called latent_discrete_n in the hyper dictionary). This maximum can be moved accordingly to fit the problem. For example, for CelebA we set a maximum number of categories to 50 but the thresholding procedure ended up selecting 20-30. Moving the maximum beyond 50 would have resulted in waste of memory allocated but would have not yield any quantitative difference. However, setting the maximum to 10 would have truncated the stick-breaking procedure too soon and would have resulted in a loss of performance. To avoid this situation, we recommend monitoring if the threshold is met. If not, then increasing the maximum would be needed.

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