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https://github.com/maet3608/nuts-ml

Flow-based data pre-processing for deep learning
https://github.com/maet3608/nuts-ml

data-preprocessing data-science deep-learning deep-learning-framework deep-learning-library

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Flow-based data pre-processing for deep learning

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README

          

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

- `Introduction `_
- `Installation `_
- `Tutorial `_
- `Documentation `_
- `Examples `_
- `Github `_

**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 `_,
which is described `here `_.

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

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.. 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

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:target: https://github.com/maet3608/nuts-ml/issues

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