Package: nolearn
nolearncontains a number of wrappers and abstractions around
existing neural network libraries, most notablyLasagne, along with a few machine learning
utility modules.  All code is written to be compatible withscikit-learn.Notenolearn 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work (namely with library versions that are outdated at this point).If you’re looking for an alternative tonolearn.lasagne, a library
that integrates neural networks with scikit-learn, then take a look
atskorch, which wraps
PyTorch for scikit-learn.InstallationWe recommend usingvenv(when using Python 3)
orvirtualenv(Python 2) to install nolearn.nolearn comes with a list of known good versions of dependencies that
we test with inrequirements.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.gitDocumentationIf you’re looking for how to usenolearn.lasagne, then there’s two
introductory tutorials that you can choose from:Using convolutional neural nets to detect facial keypoints tutorialwithcodeTraining convolutional neural networks with nolearnFor specifics around classes and functions out of thelasagnepackage, such as layers, updates, and nonlinearities, you’ll want to
look at theLasagne project’s documentation.nolearn.lasagnecomes with anumber of teststhat demonstrate some of the more advanced features, such as networks
with merge layers, and networks with multiple inputs.nolearn’s own documentationis somewhat out of date at this point.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:Oliver Dürr’sConvolutional Neural Nets II Hands OnwithcodeRoelof Pieters’ presentationPython for Image Understandingcomes with nolearn.lasagne code examplesBenjamin Bossan’sOtto Group Product Classification Challenge
using nolearn/lasagneKaggle’sinstructions on how to set up an AWS GPU instance to run
nolearn.lasagneand the facial keypoint detection tutorialAn example convolutional autoencoderWinners of the saliency prediction task in the 2015LSUN Challengehave published theirlasagne/nolearn-based code.The winners of the 2nd place in theKaggle Diabetic Retinopathy Detection
challengehave
published theirlasagne/nolearn-based code.The winner of the 2nd place in theKaggle Right Whale Recognition
challengehas
published hislasagne/nolearn-based code.SupportIf you’re seeing a bug with nolearn, please submit a bug report to thenolearn issue tracker.
Make sure to include information such as:how to reproduce the error: show us how to trigger the bug using a
minimal examplewhat versions you are using: include the Git revision and/or version
of nolearn (and possibly Lasagne) that you’re usingPlease 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 theLasagne issue trackerinstead.There’s currently no user mailing list for nolearn.  However, if you
have a question related to Lasagne, you might want to try theLasagne
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.CitationCitations are welcome:Daniel Nouri. 2014.nolearn: scikit-learn compatible neural
network libraryhttps://github.com/dnouri/nolearnLicenseSee theLICENSE.txtfile for license rights and
limitations (MIT).Change History0.6.1 - 2019-11-05See Github for a list of changes between this release and the last:https://github.com/dnouri/nolearn/pulls?q=is%3Apr+is%3Aclosed0.6.0 - 2016-08-27Thanks to @BenjaminBossan, @cancan101, @DanChianucci who greatly
contributed to this release.lasagne: Many improvements to the nolearn.lasagne interface.  Some
of the more important changes:Add basic support for multiple outputshttps://github.com/dnouri/nolearn/pull/278Extra scores can now be computed as part of Theano computation
graphhttps://github.com/dnouri/nolearn/pull/261Fix excessive memory usage in batch iterator when using shufflehttps://github.com/dnouri/nolearn/pull/238Add visualization code for saliency mapshttps://github.com/dnouri/nolearn/pull/223Add method for convenient access of network’s intermediate layer
outputhttps://github.com/dnouri/nolearn/pull/196Allow gradients to be scaled per layerhttps://github.com/dnouri/nolearn/pull/195Add shuffling to BatchIteratorhttps://github.com/dnouri/nolearn/pull/193Add l1 and l2 regularization to default objectivehttps://github.com/dnouri/nolearn/pull/169Add RememberBestWeights handler: restores best weights after
traininghttps://github.com/dnouri/nolearn/pull/155Passing Lasagne layer instances to ‘layers’ parameter of NeuralNet
is now possiblehttps://github.com/dnouri/nolearn/pull/146Add feature visualization functions plot_loss, plot_weights,
plot_activity, and plot_occlusion.  The latter shows for image
samples, which part of the images are crucial for the predictionhttps://github.com/dnouri/nolearn/pull/74Add SaveWeights handler that saves weights to disk every n epochshttps://github.com/dnouri/nolearn/pull/91In verbose mode, print out more detailed layer information before
starting with traininghttps://github.com/dnouri/nolearn/pull/85List of NeuralNet’s ‘layers’ parameter may now be formatted to
contain ‘(layer_factory, layer_kwargs)’ tupleshttps://github.com/dnouri/nolearn/pull/73dbn: Added back module dbn because there’s a few online articles
referencing it.  Works with Python 2 only.Removed deprecated modules.  Also deprecate grid_search module.0.5 - 2015-01-22Deprecated modules console, dataset, dbn, and model.lasagne: Added scikit-learn compatible wrapper around theLasagneneural network library for building simple feed-forward networks.0.5b1 - 2014-08-09overfeat: Add OverFeat-based feature extractor.caffe: Add feature extractor based on ImageNet-pretrained nets found
in caffe.0.4 - 2014-01-15cache: Use joblib’snumpy_pickleinstead ofcPickleto persist.0.3.1 - 2013-11-18convnet: Addcenter_onlyandclassify_directoptions.0.3 - 2013-11-02convnet: Add scikit-learn estimator based on Jia and Donahue’sDeCAF.dbn: Change default args ofuse_re_lu=Trueandnesterov=True.0.2 - 2013-03-03dbn: Add parameterslearn_rate_decaysandlearn_rate_minimums,
which allow for decreasing the learning after each epoch of
fine-tuning.dbn: Allow-1as the value of the input and output layers of the
neural network.  The shapes ofXandywill then be used to
determine those.dbn: Add support for processing sparse input data matrices.dbn: Improve miserable speed ofDBN.predict_proba.0.2b1 - 2012-12-30Added a scikit-learn estimator based on George Dahl’sgdbninnolearn.dbn.