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https://github.com/VincentStimper/hmc-hyperparameter-tuning
Framework to tune the hyperparameters of Hamiltonian Monte Carlo in an automated fashion
https://github.com/VincentStimper/hmc-hyperparameter-tuning
Last synced: 3 months ago
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Framework to tune the hyperparameters of Hamiltonian Monte Carlo in an automated fashion
- Host: GitHub
- URL: https://github.com/VincentStimper/hmc-hyperparameter-tuning
- Owner: VincentStimper
- License: mit
- Created: 2020-10-01T17:36:31.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-08-10T11:54:39.000Z (over 2 years ago)
- Last Synced: 2024-07-04T00:56:43.640Z (4 months ago)
- Language: Python
- Homepage: https://proceedings.mlr.press/v139/campbell21a.html
- Size: 201 KB
- Stars: 3
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Tuning hyperparameters of Hamiltonian Monte Carlo
This is the code used to do the experiments of the article [A Gradient Based Strategy for Hamiltonian Monte Carlo
Hyperparameter Optimization](https://proceedings.mlr.press/v139/campbell21a.html).## Installation instructions
### 2D toy examples
These experiments were done with numpy, scipy, and autograd, so these packages have to be installed.
### Variational Autoencoder
The framework TensorFlow 1.15 was used here. For installation instruction, see https://www.tensorflow.org/install.
### 1D experiments and molecular configuration sampling
We used the framework PyTorch 1.6 for these experiments, see https://pytorch.org/get-started/locally/ for installation
instructions. The experiments involving alanine dipeptide require [OpenMM](https://openmm.org/) to be
installed, which can be done via [conda](https://anaconda.org/conda-forge/openmm). The other dependencies
can be installed via```
pip install -r requirements.txt
```The scripts for running the experiments are in the `molecular-configurations` directory. Each experiment can be
reproduced using the respective configuration file in `molecular-configurations/config`.