Metadata-Version: 2.1
Name: nutsml
Version: 1.1.0
Summary: Flow-based data pre-processing for Machine Learning
Home-page: https://maet3608.github.io/nuts-ml
Author: Stefan Maetschke
Author-email: stefan.maetschke@gmail.com
License: Apache Software License (http://www.apache.org/licenses/LICENSE-2.0)
Download-URL: https://github.com/maet3608/nuts-ml
Keywords: machine learning,deep learning,image processing
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Natural Language :: English
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/x-rst
Requires-Dist: nutsflow (>=1.0.37)
Requires-Dist: pandas (>0.21.0)
Requires-Dist: six (>=1.10.0)
Requires-Dist: pyyaml (>=3.12)
Requires-Dist: xlrd (>=1.0.0)
Requires-Dist: dplython (>=0.0.7)
Requires-Dist: scipy (>=0.17.0)
Requires-Dist: pillow (>=3.0.0)
Requires-Dist: scikit-image (>=0.12.3)


.. image:: pics/nutsml_logo.gif
   :align: center

- `Introduction <https://maet3608.github.io/nuts-ml/introduction.html>`_
- `Installation <https://maet3608.github.io/nuts-ml/installation.html>`_
- `Tutorial <https://maet3608.github.io/nuts-ml/tutorial/introduction.html>`_
- `Documentation <https://maet3608.github.io/nuts-ml/>`_
- `Examples <https://github.com/maet3608/nuts-ml/tree/master/nutsml/examples>`_
- `Github <https://github.com/maet3608/nuts-ml>`_

**nuts-ml** is a data pre-processing library for GPU based deep learning
that provides common pre-processing functions as independent, reusable units. 
These so called 'nuts' can be freely arranged to build data flows that 
are efficient, easy to read and modify.

The following example gives a taste of a **nuts-ml** data-flow that
trains a network on image data and prints training loss and accuracy

.. code:: python

   (train_samples >> Stratify(1) >> read_image >> transform >> augment >> 
      Shuffle(100) >> build_batch >> network.train() >>  
      Print('train loss:{} acc:{}') >> Consume())

**nuts-ml** is based on `nuts-flow <https://github.com/maet3608/nuts-flow>`_,
which is described `here <https://maet3608.github.io/nuts-flow/>`_.

.. image:: https://badge.fury.io/py/nutsml.svg
   :target: https://badge.fury.io/py/nutsml

.. image:: https://img.shields.io/pypi/pyversions/nutsml.svg
   :target: https://pypi.python.org/pypi/nutsml/

.. image:: https://travis-ci.org/maet3608/nuts-ml.svg?branch=master
   :target: https://travis-ci.org/maet3608/nuts-ml

.. image:: https://coveralls.io/repos/github/maet3608/nuts-ml/badge.png?branch=master
   :target: https://coveralls.io/github/maet3608/nuts-ml?branch=master

.. image:: https://img.shields.io/github/issues/maet3608/nuts-ml.svg
   :target: https://github.com/maet3608/nuts-ml/issues

.. image:: https://img.shields.io/badge/license-Apache%202-blue.svg
   :target: https://github.com/maet3608/nuts-ml/blob/master/LICENSE








