https://github.com/gugarosa/rbm_tuning
📄 Official implementation regarding the paper "Enhancing Restricted Boltzmann Machines Reconstructability Through Meta-Heuristic Optimization".
https://github.com/gugarosa/rbm_tuning
fine-tuning implementation machine-learning meta-heuristic optimization paper rbm
Last synced: 4 months ago
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📄 Official implementation regarding the paper "Enhancing Restricted Boltzmann Machines Reconstructability Through Meta-Heuristic Optimization".
- Host: GitHub
- URL: https://github.com/gugarosa/rbm_tuning
- Owner: gugarosa
- License: gpl-3.0
- Created: 2020-11-04T15:54:40.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-10-05T12:17:14.000Z (over 2 years ago)
- Last Synced: 2024-10-18T07:40:00.305Z (9 months ago)
- Topics: fine-tuning, implementation, machine-learning, meta-heuristic, optimization, paper, rbm
- Language: Python
- Homepage:
- Size: 58.6 KB
- Stars: 0
- Watchers: 5
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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:
```Python
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
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
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
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
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:
```Bash
./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.
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