nolearn 0.6.1
scikit-learn compatible neural network library
License: MIT

*nolearn* contains a number of wrappers and abstractions around
existing neural network libraries, most notably `Lasagne
<http://lasagne.readthedocs.org/>`_, along with a few machine learning
utility modules.  All code is written to be compatible with
`scikit-learn <http://scikit-learn.org/>`_.

.. 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  work (namely with library versions that are outdated at this point).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 <https://github.com/skorch-dev/skorch>`_, which wraps
  PyTorch for scikit-learn.


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

Installation
============

We recommend using `venv
<https://docs.python.org/3/library/venv.html>`_ (when using Python 3)
or `virtualenv
<http://www.dabapps.com/blog/introduction-to-pip-and-virtualenv-python/>`_
(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:

- `Using convolutional neural nets to detect facial keypoints tutorial
  <http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/>`_
  with `code <https://github.com/dnouri/kfkd-tutorial>`__

- `Training convolutional neural networks with nolearn
  <http://nbviewer.ipython.org/github/dnouri/nolearn/blob/master/docs/notebooks/CNN_tutorial.ipynb>`_
  
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
<http://lasagne.readthedocs.org/>`_.

*nolearn.lasagne* comes with a `number of tests
<https://github.com/dnouri/nolearn/tree/master/nolearn/lasagne/tests>`__
that demonstrate some of the more advanced features, such as networks
with merge layers, and networks with multiple inputs.

`nolearn's own documentation <http://packages.python.org/nolearn/>`__
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:

- Oliver Dürr's `Convolutional Neural Nets II Hands On
  <https://home.zhaw.ch/~dueo/bbs/files/ConvNets_24_April.pdf>`_ with
  `code <https://github.com/oduerr/dl_tutorial/tree/master/lasagne>`__

- Roelof Pieters' presentation `Python for Image Understanding
  <http://www.slideshare.net/roelofp/python-for-image-understanding-deep-learning-with-convolutional-neural-nets>`_
  comes with nolearn.lasagne code examples

- Benjamin Bossan's `Otto Group Product Classification Challenge
  using nolearn/lasagne
  <https://github.com/ottogroup/kaggle/blob/master/Otto_Group_Competition.ipynb>`_

- Kaggle's `instructions on how to set up an AWS GPU instance to run
  nolearn.lasagne
  <https://www.kaggle.com/c/facial-keypoints-detection/details/deep-learning-tutorial>`_
  and the facial keypoint detection tutorial

- `An example convolutional autoencoder
  <https://github.com/mikesj-public/convolutional_autoencoder/blob/master/mnist_conv_autoencode.ipynb>`_

- Winners of the saliency prediction task in the 2015 `LSUN Challenge
  <http://lsun.cs.princeton.edu/>`_ have published their
  `lasagne/nolearn-based code
  <https://imatge.upc.edu/web/resources/end-end-convolutional-networks-saliency-prediction-software>`__.

- The winners of the 2nd place in the `Kaggle Diabetic Retinopathy Detection
  challenge <https://www.kaggle.com/c/diabetic-retinopathy-detection>`_ have
  published their `lasagne/nolearn-based code
  <https://github.com/sveitser/kaggle_diabetic>`__.

- The winner of the 2nd place in the `Kaggle Right Whale Recognition
  challenge <https://www.kaggle.com/c/noaa-right-whale-recognition>`_ has
  published his `lasagne/nolearn-based code
  <https://github.com/felixlaumon/kaggle-right-whale>`__.

Support
=======

If you're seeing a bug with nolearn, please submit a bug report to the
`nolearn issue tracker <https://github.com/dnouri/nolearn/issues>`_.
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
<https://github.com/Lasagne/Lasagne/issues>`_ 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 <https://groups.google.com/d/forum/lasagne-users>`_, 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 <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.

- lasagne: Many improvements to the nolearn.lasagne interface.  Some
  of the more important changes:

  - Add basic support for multiple outputs
    https://github.com/dnouri/nolearn/pull/278

  - Extra scores can now be computed as part of Theano computation
    graph
    https://github.com/dnouri/nolearn/pull/261

  - Fix excessive memory usage in batch iterator when using shuffle
    https://github.com/dnouri/nolearn/pull/238

  - Add visualization code for saliency maps
    https://github.com/dnouri/nolearn/pull/223

  - Add method for convenient access of network's intermediate layer
    output
    https://github.com/dnouri/nolearn/pull/196

  - Allow gradients to be scaled per layer
    https://github.com/dnouri/nolearn/pull/195

  - Add shuffling to BatchIterator
    https://github.com/dnouri/nolearn/pull/193

  - Add l1 and l2 regularization to default objective
    https://github.com/dnouri/nolearn/pull/169

  - Add RememberBestWeights handler: restores best weights after
    training
    https://github.com/dnouri/nolearn/pull/155

  - Passing Lasagne layer instances to 'layers' parameter of NeuralNet
    is now possible
    https://github.com/dnouri/nolearn/pull/146

  - Add 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 prediction
    https://github.com/dnouri/nolearn/pull/74

  - Add SaveWeights handler that saves weights to disk every n epochs
    https://github.com/dnouri/nolearn/pull/91

  - In verbose mode, print out more detailed layer information before
    starting with training
    https://github.com/dnouri/nolearn/pull/85

  - List of NeuralNet's 'layers' parameter may now be formatted to
    contain '(layer_factory, layer_kwargs)' tuples
    https://github.com/dnouri/nolearn/pull/73

- dbn: 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 - 2015-01-22
----------------

- 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`.