Metadata-Version: 1.1
Name: scikit-neuralnetwork
Version: 0.7
Summary: Deep neural networks without the learning cliff! A wrapper library compatible with scikit-learn.
Home-page: https://github.com/aigamedev/scikit-neuralnetwork
Author: UNKNOWN
Author-email: UNKNOWN
License: BSD 3-clause license
Description: :orphan:
        
        scikit-neuralnetwork
        ====================
        
        Deep neural network implementation without the learning cliff!  This library implements multi-layer perceptrons as a wrapper for the powerful ``Lasagne`` library that's compatible with ``scikit-learn`` for a more user-friendly and Pythonic interface.
        
        **NOTE**: This project is possible thanks to the `nucl.ai Conference <http://nucl.ai/>`_ on **July 18-20**. Join us in **Vienna**!
        
        |Documentation Status| |Code Coverage| |License Type| |Project Stars|
        
        ----
        
        Features
        --------
        
        Thanks to the underlying ``Lasagne`` implementation, this library supports the following neural network features, which are exposed in an intuitive and `well documented <http://scikit-neuralnetwork.readthedocs.org/>`_ API:
        
        * **Activation Functions —**
            * Nonlinear: ``Sigmoid``, ``Tanh``, ``Rectifier``.
            * Linear: ``Linear``, ``Gaussian``, ``Softmax``.
        * **Layer Types —** ``Convolution`` (greyscale and color, 2D), ``Dense`` (standard, 1D).
        * **Learning Rules —** ``sgd``, ``momentum``, ``nesterov``, ``adadelta``, ``adagrad``, ``rmsprop``.
        * **Regularization —** ``L1``, ``L2`` and ``dropout``.
        * **Dataset Formats —** ``numpy.ndarray``, ``scipy.sparse``, coming soon: iterators.
        
        If a feature you need is missing, consider opening a `GitHub Issue <https://github.com/aigamedev/scikit-neuralnetwork/issues>`_ with a detailed explanation about the use case and we'll see what we can do.
        
        
        Installation
        ------------
        
        To download and setup the latest official release, you can do so from PYPI directly::
        
            > pip install scikit-neuralnetwork
        
        This will install a copy of `Lasagne` too as a dependency. We recommend you use a virtual environment for Python.
        
        Then, you can run the tests using ``nosetests -v sknn``, and other samples or benchmarks are available in the ``examples/`` folder.
        
        
        Getting Started
        ---------------
        
        The library supports both regressors (to estimate continuous outputs from inputs) and classifiers (to predict labels from features).  This is the ``sklearn``-compatible API:
        
        .. code:: python
        
            from sknn.mlp import Classifier, Layer
        
            nn = Classifier(
                layers=[
                    Layer("Rectifier", units=100),
                    Layer("Linear")],
                learning_rate=0.02,
                n_iter=10)
            nn.fit(X_train, y_train)
        
            y_valid = nn.predict(X_valid)
        
            score = nn.score(X_test, y_test)
        
        The `generated documentation <http://scikit-neuralnetwork.readthedocs.org/>`_ as a standalone page where you can find more information about parameters, as well as examples in the `User Guide <http://scikit-neuralnetwork.readthedocs.org/en/latest/guide.html>`_.
        
        ----
        
        |Documentation Status| |Code Coverage| |License Type| |Project Stars|
        
        .. |Documentation Status| image:: https://readthedocs.org/projects/scikit-neuralnetwork/badge/?version=stable
            :target: http://scikit-neuralnetwork.readthedocs.org/en/stable/
        
        .. |Code Coverage| image:: https://coveralls.io/repos/aigamedev/scikit-neuralnetwork/badge.svg?branch=master
            :target: https://coveralls.io/r/aigamedev/scikit-neuralnetwork?branch=master
        
        .. |License Type| image:: https://img.shields.io/badge/license-New%20BSD-blue.svg
            :target: https://github.com/aigamedev/scikit-neuralnetwork/blob/master/LICENSE
        
        .. |Project Stars| image:: https://img.shields.io/github/stars/aigamedev/scikit-neuralnetwork.svg
            :target: https://github.com/aigamedev/scikit-neuralnetwork/stargazers    
        
Keywords: deep learning,neural networks
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
