README.md

DeepCL Python wrappers

Python wrapper for DeepCL

Pre-requisites

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

To install from pip

pip install --upgrade DeepCL

• related pypi page: https://pypi.python.org/pypi/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)

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

• creating layers directly
• running epochs and forward/backprop directly

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

To install from source

Pre-requisites:

• on Windows:
  • Python 2.7 or Python 3.5
  • A compiler:
    • Python 2.7 build: need Visual Studio 2008 for Python 2.7 from Microsoft
    • Python 3.5 build: need Visual Studio 2015, (https://www.visualstudio.com/downloads)
• on linux:
  • Python 2.7 or Python 3.4/3.5
  • g++, supporting c++11, eg 4.6 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
• numpy installed

To install:

cd python
python setup.py install

Changes

• 30 July 2016:
  • Added net.getNetdef(). Note that this is only an approximate representation of the network
• 29 July 2016:
  • New feature: can provide image tensor as 4d tensor now ,instead of 1d tensor (1d tensor ok too)
  • CHANGE: all image and label tensors must be provided as numpy tensors now, array.array no longer valid input
  • bug fix: qlearning works again :-)
• 25 July 2016:
  • added RandomSingleton class, to set the seed for weights initialization
  • added xor.py example