Metadata-Version: 1.1
Name: nolearn
Version: 0.6.0
Summary: scikit-learn compatible neural network library
Home-page: https://github.com/dnouri/nolearn
Author: Daniel Nouri
Author-email: daniel.nouri@gmail.com
License: MIT
Description: *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/>`_.
        
        .. 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.
        
        To install the latest release of nolearn from the Python Package
        Index, do::
        
          pip install nolearn
        
        At the time of this writing, nolearn works with the latest versions of
        its dependencies, such as numpy, scipy, Theano, and Lasagne (the
        latter `from Git <https://github.com/Lasagne/Lasagne>`_).  But we also
        maintain a list of known good versions of dependencies that we support
        and test.  Should you run into hairy depdendency issues during
        installation or runtime, we recommend you try this same set of tested
        depdencencies instead::
        
          pip install -r https://github.com/dnouri/nolearn/tree/0.6.0/requirements.txt
          pip install nolearn
          
        If you want to install the latest development version of nolearn
        directly from Git, run::
        
          pip install -r https://raw.githubusercontent.com/dnouri/nolearn/master/requirements.txt
          pip install git+https://github.com/dnouri/nolearn.git@master#egg=nolearn==0.7.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
        ==============
        
        0.6.0 - 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`.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
