Metadata-Version: 2.0
Name: darkon
Version: 0.0.1
Summary: hack your deep learning performance 
Home-page: https://github.com/darkonhub/darkon
Author: Neosapience, Inc.
Author-email: UNKNOWN
License: Apache License 2.0
Download-URL: https://github.com/darkonhub/darkon/tarball/0.0.1
Keywords: AI,ML,DL,deep learning,machine learning,debugging,hack,performance,tuning,tensorflow,tf
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Debuggers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: numpy
Requires-Dist: tensorflow (>=1.3.0)

**Darkon: Performance hacking for your AI model**

**Darkon** is an open source software library for improving and
debugging deep learning models. People think that deep neural network is
a black-box that requires only large dataset and expect learning
algorithms returns well-performing models. However, trained models often
fail in real world usages, and it is difficult to fix such failure due
to the black-box nature of deep neural networks.

**Darkon** will gradually provide performance hacking features easily
applicable to your existing projects based on following technologies. -
Dataset inspection/filtering - Continual learning - Meta/transfer
learning - Hyper parameter optimization - Network architecture search

In this first release, we provide influence function calculation feature
easily applicable to any Tensorflow models. Influence score can be used
for filtering bad training samples that affects test performance
negatively. It can be used for prioritize potential mislabeled examples
to be annotated, and debugging distribution mismatch between train and
test samples.

More features will be released soon. Please keep your eyes on **Darkon**

Dependencies
------------

-  `Tensorflow>=1.3.0 <https://github.com/tensorflow/tensorflow>`__

Installation
------------

.. code:: bash

    pip install darkon

Examples / Getting Started
--------------------------

-  `Examples <https://github.com/darkonhub/darkon-examples>`__
-  Documentation: [STRIKEOUT:will be soon]

Communication
-------------

-  `Issues <https://github.com/darkonhub/darkon/issues>`__: report
   issues, bugs, and request new features
-  `Pull request <https://github.com/darkonhub/darkon/pulls>`__
-  News: [STRIKEOUT:link twitter account]
-  Discuss: [STRIKEOUT:gitter]
-  Email: darkon@neosapience.com

Authors
-------

`Neosapience Inc. <http://www.neosapience.com>`__

License
-------

**Apache License v2.0**

References
----------

[1] Pang Wei Koh and Percy Liang "`Understanding Black-box Predictions
via Influence Functions <https://arxiv.org/abs/1703.04730>`__" ICML2017]


