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Pre-release

This release is a pre-release and may not be stable for production use.

DeepCL Python wrappers

Python wrapper for DeepCL

Pre-requisites

You must have first installed and activated DeepCL native libraries, see Build.md

To install from pip

pip install --pre --upgrade DeepCL

How to use

See 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)

The same example, using numpy arrays: test_deepcl_numpy.py

For examples of using lower-level entrypoints, see test_lowlevel.py:

  • creating layers directly

  • running epochs and forward/backprop directly

  • note that you need numpy installed to run this example

For example of using q-learning, see test_qlearning.py.

To install from source

Pre-requisites:

  • on Windows:

  • Python 2.7 or Python 3.4

  • A compiler:

  • on linux:

  • Python 2.7 or Python 3.4

  • g++, supporting c++0x, eg 4.4 or higher

  • have first already built the native libraries, see Build.md

  • have activated the native library installation, ie called dist/bin/activate.sh, or dist/bin/activate.bat

To install:

cd python
python setup.py install

Changes

  • 25 July 2016:

  • added RandomSingleton class, to set the seed for weights initialization

  • added xor.py example

Metadata

Release files for DeepCL 8.4.0alpha4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for DeepCL 8.4.0alpha4
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Table of built distributions (wheels) for DeepCL 8.4.0alpha4
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