Metadata-Version: 1.1
Name: muffnn
Version: 2.3.2
Summary: Multilayer Feed-Forward Neural Network (MuFFNN) models with TensorFlow and scikit-learn
Home-page: https://github.com/civisanalytics/muffnn
Author: Civis Analytics, Inc.
Author-email: opensource@civisanalytics.com
License: BSD-3
Description: muffnn
        ======
        
        |Travis| |PyPI|
        
        .. |Travis| image:: https://img.shields.io/travis/civisanalytics/muffnn/master.svg
           :alt: Build status
           :target: https://travis-ci.org/civisanalytics/muffnn
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/muffnn.svg
           :target: https://pypi.org/project/muffnn/
           :alt: Latest version on PyPI
        
        `scikit-learn <http://scikit-learn.org>`__-compatible neural network
        models implemented in `TensorFlow <https://www.tensorflow.org/>`__
        
        Installation
        ============
        
        This package currently supports Python 3.6 and 3.7.
        
        Installation with ``pip`` is recommended:
        
        .. code:: bash
        
            pip install muffnn
        
        You can install the dependencies via:
        
        .. code:: bash
        
            pip install -r requirements.txt
        
        If you have trouble installing TensorFlow, see `this
        page <https://www.tensorflow.org/install/>`__ for more details.
        
        For development, a few additional dependencies are needed:
        
        .. code:: bash
        
            pip install -r dev-requirements.txt
        
        Usage
        =====
        
        Each estimator in the code follows the scikit-learn API. Thus usage
        follows the scikit-learn conventions:
        
        .. code:: python
        
            from muffnn import MLPClassifier
        
            X, y = load_some_data()
        
            mlp = MLPClassifier()
            mlp.fit(X, y)
        
            X_new = load_some_unlabeled_data()
            y_pred = mlp.predict(X_new)
        
        Further, serialization of the TensorFlow graph and data is handled
        automatically when the object is pickled:
        
        .. code:: python
        
            import pickle
        
            with open('est.pkl', 'wb') as fp:
                pickle.dump(est, fp)
        
        Contributing
        ============
        
        See ``CONTIBUTING.md`` for information about contributing to this
        project.
        
        License
        =======
        
        BSD-3
        
        See ``LICENSE.txt`` for details.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3 :: Only
