Metadata-Version: 2.1
Name: keras-adf
Version: 19.1.0
Summary: Assumed Density Filtering (ADF) Probabilistic Networks
Home-page: https://github.com/jmaces/keras-adf
Author: Jan Maces
Author-email: janmaces@gmail.com
Maintainer: Jan Maces
Maintainer-email: janmaces@gmail.com
License: MIT
Description: ============================================================================
        ``keras-adf``: Assumed Density Filtering (ADF) Probabilistic Neural Networks
        ============================================================================
        
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        .. teaser-start
        
        ``keras-adf`` provides implementations for probabilistic
        `Tensorflow <https://www.tensorflow.org/>`_/`Keras <https://keras.io/>`_ neural network layers,
        which are based on assumed density filtering.
        Assumed density filtering (ADF) is a general concept from Bayesian inference, but in the case of feed-forward neural networks that we consider here
        it is a way to approximately propagate a random distribution through the neural network.
        
        The layers in this package have the same names and arguments as their corresponding
        Keras version. We use Gaussian distributions for our ADF approximations, which are
        described by their means and (co-)variances. So unlike the standard Keras layers,
        each ``keras-adf`` layer takes two inputs and produces two outputs (one for the means
        and one for the (co-)variances).
        
        .. teaser-end
        
        
        .. example
        
        ``keras-adf`` layers can be used exactly like the corresponding `Keras <https://keras.io/>`_
        layers within a Keras model. However, as mentioned above, ADF layers take two inputs and produce two outputs
        instead of one, so it is not possible to simply mix ADF and standard layers within the same model.
        
        .. code-block:: python
        
            from tensorflow.keras import Input, Model
            from kerasadf.layers import Dense
        
            in_mean = Input((10,))
            in_var = Input((10,))
            out_mean, out_var  = Dense(10, activation="relu")([in_mean, in_var])
            model = Model([in_mean, in_var], [out_mean, out_var])
        
        The `Overview <https://keras-adf.readthedocs.io/en/latest/overview.html>`_ and
        `Examples <https://keras-adf.readthedocs.io/en/latest/examples.html>`_ sections
        of our documentation provide more realistic and complete examples.
        
        .. project-info-start
        
        Project Information
        ===================
        
        ``keras-adf`` is released under the `MIT license <https://github.com/jmaces/keras-adf/blob/master/LICENSE>`_,
        its documentation lives at `Read the Docs <https://keras-adf.readthedocs.io/en/latest/>`_,
        the code on `GitHub <https://github.com/jmaces/keras-adf>`_,
        and the latest release can be found on `PyPI <https://pypi.org/project/keras-adf/>`_.
        It’s tested on Python 2.7 and 3.4+.
        
        If you'd like to contribute to ``keras-adf`` you're most welcome.
        We have written a `short guide <https://github.com/jmaces/keras-adf/blob/master/.github/CONTRIBUTING.rst>`_ to help you get you started!
        
        .. project-info-end
        
        
        .. literature-start
        
        Further Reading
        ===============
        
        Additional information on the algorithmic aspects of ``keras-adf`` can be found
        in the following works:
        
        
        - Jochen Gast, Stefan Roth,
          "Lightweight Probabilistic Deep Networks",
          2018
        - Jan Macdonald, Stephan Wäldchen, Sascha Hauch, Gitta Kutyniok,
          "A Rate-Distortion Framework for Explaining Neural Network Decisions",
          2019
        
        .. literature-end
        
        
        Acknowledgments
        ===============
        
        During the setup of this project we were heavily influenced and inspired by
        the works of `Hynek Schlawack <https://hynek.me/>`_ and in particular his
        `attrs <https://www.attrs.org/en/stable/>`_ package and blog posts on
        `testing and packaing <https://hynek.me/articles/testing-packaging/>`_
        and `deploying to PyPI <https://hynek.me/articles/sharing-your-labor-of-love-pypi-quick-and-dirty/>`_.
        Thank you for sharing your experiences and insights.
        
Keywords: tensorflow,keras,probabilistic-models,deep-learning,deep-neural-networks,machine-learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*
Description-Content-Type: text/x-rst
Provides-Extra: docs
Provides-Extra: tests
Provides-Extra: dev
