Randomized opt networks with PyTorch
Project description
pyperch
Getting Started
About
Pyperch is a neural network weight optimization package developed to support students taking Georgia Tech’s graduate machine learning course CS7641. Three random optimization algorithms—randomized hill climbing, simulated annealing, and a genetic algorithm—can be used as drop-in replacements for traditional gradient-based optimizers using PyTorch.
Install
pip install pyperch.
pip install pyperch
Examples
For starter code and examples, see
Contributing
Pull requests are welcome.
- Fork pyperch.
- Create a branch (
git checkout -b branch_name
) - Commit changes (
git commit -m "Comments"
) - Push to branch (
git push origin branch_name
) - Open a pull request
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pyperch-0.1.3.tar.gz
(733.5 kB
view details)
File details
Details for the file pyperch-0.1.3.tar.gz
.
File metadata
- Download URL: pyperch-0.1.3.tar.gz
- Upload date:
- Size: 733.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.8.0 colorama/0.4.4 importlib-metadata/4.6.4 keyring/23.5.0 pkginfo/1.8.2 readme-renderer/34.0 requests-toolbelt/0.9.1 requests/2.25.1 rfc3986/1.5.0 tqdm/4.66.1 urllib3/1.26.5 CPython/3.10.12
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6e131b8f8cfbb7ca5dab949fc14526274c7151ee84c7a62a2c8380d95ed1798d |
|
MD5 | 4962d4116f62e1c2496a0c42302766e8 |
|
BLAKE2b-256 | 206476f1ab61159d90256b2d393fe6b511099774313ba0cce4df04cb93946e5f |