torch_pso
Particle Swarm Optimization implemented using PyTorch Optimizer API
Science Score: 67.0%
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Repository
Particle Swarm Optimization implemented using PyTorch Optimizer API
Basic Info
- Host: GitHub
- Owner: qthequartermasterman
- License: mit
- Language: Python
- Default Branch: master
- Size: 113 KB
Statistics
- Stars: 17
- Watchers: 2
- Forks: 1
- Open Issues: 10
- Releases: 6
Metadata Files
README.md
Torch PSO
Particle Swarm Optimization is an optimization technique that iteratively attempts to improve a list of candidate solutions. Each candidate solution is called a "particle", and collectively they are called a "swarm". In each step of the optimization, each particle moves in a random directly while simultaneously being pulled towards the other particles in the swarm. A simple introduction to the algorithm can be found on its Wikipedia article.
This package implements the Particle Swarm Optimization using the PyTorch Optimizer API, making it compatible with most pre-existing Torch training loops.
Installation
To install Torch PSO using PyPI, run the following command:
$ pip install torch-pso
Getting Started
To use the ParticleSwarmOptimizer, simply import it, and use it as with any other PyTorch Optimizer. Hyperparameters of the optimizer can also be specified. In practice, most PyTorch tutorials could be used to create a use-case, simply substituting the ParticleSwarmOptimizer for any other optimizer. A simplified use-case can be seen below, which trains a simple neural network to match its output to a target.
```python import torch from torch.nn import Sequential, Linear, MSELoss from torch_pso import ParticleSwarmOptimizer
net = Sequential(Linear(10,100), Linear(100,100), Linear(100,10)) optim = ParticleSwarmOptimizer(net.parameters(), inertialweight=0.5, numparticles=100, maxparamvalue=1, minparamvalue=-1) criterion = MSELoss() target = torch.rand((10,)).round()
x = torch.rand((10,)) for _ in range(100):
def closure():
# Clear any grads from before the optimization step, since we will be changing the parameters
optim.zero_grad()
return criterion(net(x), target)
optim.step(closure)
print('Prediciton', net(x))
print('Target ', target)
```
Citation
To cite this work in a paper use the following citation:
bibtex
@software{Sansom_Torch_PSO_2022,
author = {Sansom, Andrew P.},
doi = {10.5281/zenodo.6982304},
month = {8},
title = {{Torch PSO}},
url = {https://github.com/qthequartermasterman/torch_pso},
year = {2022}
}
Owner
- Name: Andrew Sansom
- Login: qthequartermasterman
- Kind: user
- Company: Protopia AI
- Repositories: 4
- Profile: https://github.com/qthequartermasterman
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Sansom" given-names: "Andrew P." orcid: "https://orcid.org/0000-0002-2276-7224" title: "Torch PSO" doi: 10.5281/zenodo.6982304 date-released: 2022-08-10 url: "https://github.com/qthequartermasterman/torch_pso"
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Top Authors
Issue Authors
- qthequartermasterman (10)
- Simply-Adi (2)
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- qthequartermasterman (9)
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Dependencies
- torch *
- actions/checkout v3 composite
- actions/setup-python v3 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite