Metadata-Version: 1.1
Name: DeepCL
Version: 5.5.0
Summary: python wrapper for DeepCL deep convolutional neural network library for OpenCL
Home-page: https://github.com/hughperkins/DeepCL
Author: Hugh Perkins
Author-email: hughperkins@gmail.com
License: MPL
Description: DeepCL Python wrappers
        ======================
        
        Python wrapper for `DeepCL <https://github.com/hughperkins/DeepCL>`__
        
        To install from pip
        ===================
        
        .. code:: bash
        
            pip install DeepCL 
        
        -  related pypi page: https://pypi.python.org/pypi/DeepCL
        
        How to use
        ==========
        
        See
        `test\_deepcl.py <https://github.com/hughperkins/DeepCL/blob/master/python/test_deepcl.py>`__
        for an example of:
        
        -  creating a network, with several layers
        -  loading mnist data
        -  training the network using a higher-level interface (``NetLearner``)
        
        For examples of using lower-level entrypoints, see
        `test\_lowlevel.py <https://github.com/hughperkins/DeepCL/blob/master/python/test_lowlevel.py>`__:
        
        -  creating layers directly
        -  running epochs and forward/backprop directly
        
        For example of using q-learning, see
        `test\_qlearning.py <https://github.com/hughperkins/DeepCL/blob/master/python/test_qlearning.py>`__.
        
        To build from source
        ====================
        
        Pre-requisites:
        ---------------
        
        -  on Windows:
        -  Python 2.7 or Python 3.4
        -  A compiler:
        
           -  Python 2.7 build: need `Visual Studio 2008 for Python
              2.7 <http://www.microsoft.com/en-us/download/details.aspx?id=44266>`__
              from Microsoft
           -  Python 3.4 build: need Visual Studio 2010, eg `Visual C++ 2010
              Express <https://www.visualstudio.com/downloads/download-visual-studio-vs#DownloadFamilies_4>`__
        
        -  on linux:
        -  Python 2.7 or Python 3.4
        -  g++, supporting c++0x, eg 4.4 or higher
        
        To build:
        ---------
        
        .. code:: bash
        
            cd python
            python setup.py build_ext -i
        
        Then, you can run from this directory, by making sure to add it to the
        path, eg:
        
        ::
        
            PYTHONPATH=. python test_lowlevel.py /my/mnist/data/dir 
        
        To install:
        -----------
        
        .. code:: bash
        
            cd python
            python setup.py install
        
        Notes on how the wrapper works
        ------------------------------
        
        -  `cDeepCL.pxd <https://github.com/hughperkins/DeepCL/blob/master/python/cDeepCL.pxd>`__
           contains the definitions of the underlying DeepCL c++ libraries
           classes
        -  `PyDeepCL.pyx <https://github.com/hughperkins/DeepCL/blob/master/python/PyDeepCL.pyx>`__
           contains Cython wrapper classes around the underlying c++ classes
        -  `setup.py <https://github.com/hughperkins/DeepCL/blob/master/python/setup.py>`__
           is a setup file for compiling the ``PyDeepCL.pyx`` Cython file
        
        to run unit-tests
        -----------------
        
        From the python directory:
        
        .. code:: bash
        
            nosetests -sv
        
        Development builds
        ------------------
        
        -  If you want to modify the sourcecode, you'll need to re-run cython,
           so you'll need cython:
        
           ::
        
               pip install cython
        
        -  If you want to update this readme, you might want to re-generate the
           README.rst, so you'll need pypandoc:
        
           ::
        
               pip install pypandoc
        
        -  (note that pypandoc depends on pandoc)
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
