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scikit-learn compatible neural network library

Project description

nolearn contains a number of wrappers and abstractions around existing neural network libraries, most notably Lasagne, along with a few machine learning utility modules. All code is written to be compatible with scikit-learn.

Note

nolearn is currently unmaintained. However, if you follow the installation instructions, you should still be able to get it to work (namely with library versions that are outdated at this point).

If you’re looking for an alternative to nolearn.lasagne, a library that integrates neural networks with scikit-learn, then take a look at skorch, which wraps PyTorch for scikit-learn.

https://travis-ci.org/dnouri/nolearn.svg?branch=master

Installation

We recommend using venv (when using Python 3) or virtualenv (Python 2) to install nolearn.

nolearn comes with a list of known good versions of dependencies that we test with in requirements.txt. To install the latest version of nolearn from Git along with these known good dependencies, run these two commands:

pip install -r https://raw.githubusercontent.com/dnouri/nolearn/master/requirements.txt
pip install git+https://github.com/dnouri/nolearn.git

Documentation

If you’re looking for how to use nolearn.lasagne, then there’s two introductory tutorials that you can choose from:

For specifics around classes and functions out of the lasagne package, such as layers, updates, and nonlinearities, you’ll want to look at the Lasagne project’s documentation.

nolearn.lasagne comes with a number of tests that demonstrate some of the more advanced features, such as networks with merge layers, and networks with multiple inputs.

nolearn’s own documentation is somewhat out of date at this point. But there’s more resources online.

Finally, there’s a few presentations and examples from around the web. Note that some of these might need a specific version of nolearn and Lasange to run:

Support

If you’re seeing a bug with nolearn, please submit a bug report to the nolearn issue tracker. Make sure to include information such as:

  • how to reproduce the error: show us how to trigger the bug using a minimal example
  • what versions you are using: include the Git revision and/or version of nolearn (and possibly Lasagne) that you’re using

Please also make sure to search the issue tracker to see if your issue has been encountered before or fixed.

If you believe that you’re seeing an issue with Lasagne, which is a different software project, please use the Lasagne issue tracker instead.

There’s currently no user mailing list for nolearn. However, if you have a question related to Lasagne, you might want to try the Lasagne users list, or use Stack Overflow. Please refrain from contacting the authors for non-commercial support requests directly; public forums are the right place for these.

Citation

Citations are welcome:

Daniel Nouri. 2014. nolearn: scikit-learn compatible neural network library https://github.com/dnouri/nolearn

License

See the LICENSE.txt file for license rights and limitations (MIT).

Change History

0.6.1 - 2019-11-05

See Github for a list of changes between this release and the last: https://github.com/dnouri/nolearn/pulls?q=is%3Apr+is%3Aclosed

0.6.0 - 2016-08-27

Thanks to @BenjaminBossan, @cancan101, @DanChianucci who greatly contributed to this release.

0.5 - 2015-01-22

  • Deprecated modules console, dataset, dbn, and model.
  • lasagne: Added scikit-learn compatible wrapper around the Lasagne neural network library for building simple feed-forward networks.

0.5b1 - 2014-08-09

  • overfeat: Add OverFeat-based feature extractor.
  • caffe: Add feature extractor based on ImageNet-pretrained nets found in caffe.

0.4 - 2014-01-15

  • cache: Use joblib’s numpy_pickle instead of cPickle to persist.

0.3.1 - 2013-11-18

  • convnet: Add center_only and classify_direct options.

0.3 - 2013-11-02

  • convnet: Add scikit-learn estimator based on Jia and Donahue’s DeCAF.
  • dbn: Change default args of use_re_lu=True and nesterov=True.

0.2 - 2013-03-03

  • dbn: Add parameters learn_rate_decays and learn_rate_minimums, which allow for decreasing the learning after each epoch of fine-tuning.
  • dbn: Allow -1 as the value of the input and output layers of the neural network. The shapes of X and y will then be used to determine those.
  • dbn: Add support for processing sparse input data matrices.
  • dbn: Improve miserable speed of DBN.predict_proba.

0.2b1 - 2012-12-30

  • Added a scikit-learn estimator based on George Dahl’s gdbn in nolearn.dbn.

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