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.
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