Skip to main content

High-Performance implementation of an Extreme Learning Machine

Project description

High Performance toolbox for Extreme Learning Machines.
--------

Extreme learning machines (ELM) are a particular kind of Artificial Neural Networks,
which solve classification and regression problems. Their performance is comparable
to a classical Multilayer Perceptron trained with Error Back-Propagation algorithm,
but the training time is up to 6 orders of magnitude smaller. (yes, a million times!)

ELMs are suitable for processing huge datasets and dealing with Big Data,
and this toolbox is created as their fastest and most scalable implementation.

Documentation is available here: http://hpelm.readthedocs.org,
it uses Numpydocs.

NEW: Parallel HP-ELM tutorial! See the documentation: http://hpelm.readthedocs.org

Highlights:
- Efficient matrix math implementation without bottlenecks
- Efficient data storage (HDF5 file format)
- Data size not limited by the available memory
- GPU accelerated computations (if you have one)
- Regularization and model selection (for in-memory models)

Main classes:
- hpelm.ELM for in-memory computations (dataset fits into RAM)
- hpelm.HPELM for out-of-memory computations (dataset on disk in HDF5 format)

Example usage::
>>> from hpelm import ELM
>>> elm = ELM(X.shape[1], T.shape[1])
>>> elm.add_neurons(20, "sigm")
>>> elm.add_neurons(10, "rbf_l2")
>>> elm.train(X, T, "LOO")
>>> Y = elm.predict(X)

If you use the toolbox, cite our open access paper "High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications" in IEEE Access.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7140733&newsearch=true&queryText=High%20Performance%20Extreme%20Learning%20Machines

@ARTICLE{7140733,
author={Akusok, A. and Bj\"{o}rk, K.-M. and Miche, Y. and Lendasse, A.},
journal={Access, IEEE},
title={High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications},
year={2015},
volume={3},
pages={1011-1025},
doi={10.1109/ACCESS.2015.2450498},
ISSN={2169-3536},
month={},}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

hpelm-1.0.10-py3-none-any.whl (50.0 kB view details)

Uploaded Python 3

File details

Details for the file hpelm-1.0.10-py3-none-any.whl.

File metadata

File hashes

Hashes for hpelm-1.0.10-py3-none-any.whl
Algorithm Hash digest
SHA256 f3dcf173f0aeb71613c60abce4492ea4d005cfa25f8f871f9aadc6846388ecfb
MD5 8bc2512751b129372424464348ad83df
BLAKE2b-256 a29ae68e1fd4ec6388979737be537d05b34626840b353e5db6e05951286614c0

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page