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            "Operating System :: Microsoft :: Windows",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
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        "description": ".. image:: https://travis-ci.org/drasmuss/hessianfree.svg?branch=master\n  :target: https://travis-ci.org/drasmuss/hessianfree\n  :alt: Travis-CI build status\n  \n.. image:: https://ci.appveyor.com/api/projects/status/mytincgjvboav37o/branch/master?svg=true\n  :target: https://ci.appveyor.com/project/drasmuss/hessianfree\n  :alt: AppVeyor build status\n    \n.. image:: https://img.shields.io/pypi/v/hessianfree.svg\n  :target: https://pypi.python.org/pypi/hessianfree\n  :alt: Latest PyPI version\n\n.. image:: https://img.shields.io/pypi/dm/hessianfree.svg\n  :target: https://pypi.python.org/pypi/hessianfree\n  :alt: Number of PyPI downloads\n\n*******************************************\nHessian-free optimization for deep networks\n*******************************************\n\nSetup\n=====\n\nQuick start\n-----------\n\nInstall the package via:\n\n.. code-block:: bash\n\n    pip install hessianfree\n    \nTo make sure things are working, open a python interpreter and enter:\n\n.. code-block:: python\n    \n    import hessianfree as hf\n    hf.demos.xor()\n    \nA simple xor training example will run, at the end of which it will display\nthe target and actual outputs from the network.\n\n\nDeveloper install\n-----------------\n\nUse this if you want to track the latest changes from the repository:\n\n.. code-block:: bash\n\n    git clone https://github.com/drasmuss/hessianfree.git\n    cd hessianfree\n    python setup.py develop --user\n\nRequirements\n------------\n\n* python 2.7 or 3.5\n* numpy 1.9.2\n* matplotlib 1.3.1\n* optional: scipy 0.15.1, pycuda 2015.1.3, scikit-cuda 0.5.1, pytest 2.7.0\n\n(older versions may work, but are untested)\n\nFeatures\n========\n\nAll the standard features of Hessian-free optimization from Martens (2010) and \nMartens and Sutskever (2011) are implemented (Gauss-Newton approximation, early \ntermination, CG backtracking, Tikhonov damping, structural damping, etc.).  In \naddition, the code has been designed to make it easy to customize the network \nyou want to train, without having to modify the internal computations of the \noptimization process.\n\n* Works for feedforward and recurrent deep networks (or mixtures of the two)\n* Standard nonlinearities built in (e.g., logistic, tanh, ReLU, softmax), and \n  support for custom nonlinearities\n* Standard loss functions (squared error, cross entropy, sparsity constraints), \n  and support for custom loss functions\n* Various weight initialization methods (although Hessian-free optimization \n  doesn't usually require much tweaking)\n* Customizable connectivity between layers (e.g., skip connections)\n* Efficient implementation, taking advantage of things like activity caching\n* Optional GPU acceleration if PyCUDA and scikit-cuda are installed\n* Gradient checking (and Gauss-Newton matrix checking) implemented to help with \n  debugging\n* Inputs can be predefined or generated dynamically by some other system (like \n  an environmental simulation)\n* Different optimizers can be swapped out for comparison (e.g., Hessian-free \n  versus SGD)\n\nDocumentation\n=============\n\nView the documentation at http://pythonhosted.org/hessianfree/\n\nIn addition, examples illustrating the main features of the code can be found\nin ``demos.py``.",
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