Skip to main content

scikit-learn compatible wrappers for neural net libraries, and other utilities.

Project description

nolearn contains a number of wrappers around existing neural network libraries, along with a few machine learning utility modules. Most functionality is written to be compatible with the the excellent scikit-learn library.

View the documentation here.

Change History

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.

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

nolearn-0.5b1.tar.gz (31.0 kB view hashes)

Uploaded source

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