Adapting Convolutional Restricted Boltzmann Machines Through Evolutionary Optimization

This repository holds all the necessary code to run the very-same experiments described in the paper "Adapting Convolutional Restricted Boltzmann Machines Through Evolutionary 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 and the internal downloader to load pre-implemented datasets. If necessary, one can also download non-torchvision datasets and put them in the data/ folder.

Usage

CRBM Optimization

The first step is to optimize a Convolutional RBM architecture. To accomplish such a step, one needs to use the following script:

python crbm_optimization.py -h

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

CRBM Optimization using Genetic Programming

Alternatively, one can use Genetic Programming to optimize the architecture, as follows:

python crbm_tree_optimization.py -h

CRBM Evaluation

After conducting the optimization task, one needs to evaluate the best parameters over the testing set. Please, use the following script to accomplish such a procedure:

python crbm_evaluation.py -h

Note that this script evaluates the network on both reconstruction and classification tasks.

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/crbm_tuning