https://github.com/hyperopt/hyperopt-nnet
Hyperparameter optimization for neural networks
https://github.com/hyperopt/hyperopt-nnet
Last synced: 11 months ago
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Hyperparameter optimization for neural networks
- Host: GitHub
- URL: https://github.com/hyperopt/hyperopt-nnet
- Owner: hyperopt
- License: bsd-3-clause
- Created: 2013-07-05T16:24:51.000Z (almost 13 years ago)
- Default Branch: master
- Last Pushed: 2013-10-08T18:26:04.000Z (over 12 years ago)
- Last Synced: 2025-06-08T10:41:04.812Z (about 1 year ago)
- Language: Python
- Size: 527 KB
- Stars: 48
- Watchers: 7
- Forks: 11
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
hyperopt-nnet
=============
This package provides a
[hyperopt](http://jaberg.github.io/hyperopt)-compatible neural network
implementation.
Currently, it can be used to tune neural network hyperparameters for data sets
provided as [skdata](http://jaberg.github.io/skdata) protocols.
See the `./examples` subdirectory for sample training scripts (e.g. `nips2011_nnet.py`)
and a plotting script (`plot_trials.py`).
The `hpnnet.nips2011` file implements the search parameterization used in
Bergstra, Bardenet, Bengio, and Kegl ("[Algorithms for Hyper-parameter
Optimization](http://books.nips.cc/papers/files/nips24/NIPS2011_1385.pdf)") from NIPS 2011.
Dependencies
------------
* NumPy
* Sklearn
* Theano
* Skdata (github master, not PyPI)
* Hyperopt (github master, not PyPI)
* matplotlib (for plotting)
* IPython (for parallel search, option 1)
* MongoDB (for parallel search, option 2)