Enhancing Restricted Boltzmann Machines Reconstructability Through Meta-Heuristic Optimization

This repository holds all the necessary code to run the very-same experiments described in the paper "Enhancing Restricted Boltzmann Machines Reconstructability Through Meta-Heuristic Optimization".

References

If you use our work to fulfill any of your needs, please cite us:

Structure

utils
- loader.py: Utility to load datasets and split them into training, validation and testing sets;
- objects.py: Wraps objects instantiation for command line usage;
- optimizer.py: Wraps the optimization task into a single method;
- target.py: Implements the objective functions to be optimized.

Package Guidelines

Installation

Install all the pre-needed requirements using:

pip install -r requirements.txt

Data configuration

In order to run the experiments, you can use torchvision to load pre-implemented datasets.

Usage

Model Training

The first step is to pre-train an RBM architecture. To accomplish such a step, one needs to use the following script:

python rbm_training.py -h

Note that -h invokes the script helper, which assists users in employing the appropriate parameters.

Model Optimization

After conducting the training task, one needs to optimize the weights over the validation set. Please, use the following script to accomplish such a procedure:

python rbm_optimization.py -h

Model Evaluation

Finally, it is now possible to evaluate a model using the testing set. Please, use the following script to accomplish such a procedure:

python rbm_evaluation.py -h

Analyze Optimization Convergence (Optional)

Additionally, one can gather the optimization history files and input them to a script that analyzes its convergence and produces a plot that compares how each optimization technique has performed during its procedure. Please, use such a script as follows:

python analyze_optimization_convergence.py -h

Bash Script

Instead of invoking every script to conduct the experiments, it is also possible to use the provided shell script, as follows:

./pipeline.sh

Such a script will conduct every step needed to accomplish the experimentation used throughout this paper. Furthermore, one can change any input argument that is defined in the script.

Support

We know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository.

gugarosa/rbm_tuning