python wrapper for DeepCL deep convolutional neural network library for OpenCL
Project description
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 3.4
A compiler:
Python 2.7 build: need Visual Studio 2008 for Python 2.7 from Microsoft
Python 3.4 build: need Visual Studio 2010, eg Visual C++ 2010 Express
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
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CHANGE: all image and label tensors must be provided as numpy tensors now, array.array no longer valid input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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for DeepCL-11.0.0alpha5-py3.4-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1fbe07fdcf33c0d0c9312e99189a83df4446992ef01039c3f8718794a537a354 |
|
MD5 | daf47b9efb7898ce3763f7986f2ded1c |
|
BLAKE2b-256 | b512bf08be73f9600a0c43db9e8ad4392920f12787305413ae33f4591101ad13 |
Hashes for DeepCL-11.0.0alpha5-py3.4-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8689c32d4eadc784c5da171e231b392e72a4b6c3dd6a09b71fa3f7888da718f6 |
|
MD5 | 21255bd02135eb179cc158ea00c0724e |
|
BLAKE2b-256 | a71f01b936de1d3f8bafa784266f0874698b87a1236c6803304b6a932f43777d |
Hashes for DeepCL-11.0.0alpha5-py2.7-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5ae801889806e6df2d6471398ec0095aa1c9811b2b3facd6f1d67cbb42dac424 |
|
MD5 | efb6c9abc0809aab5a8ddda46686bfad |
|
BLAKE2b-256 | 6d4f4075396447f4c5102f6a1936c93cfe76c4bbc8a5140f3257091577d0e589 |
Hashes for DeepCL-11.0.0alpha5-py2.7-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6dcb37b209520c0ce4e2762b8ed5030c2e582c38d827bf9b39008080b4a479b8 |
|
MD5 | f59eaf8f516d96de4163195c13df2838 |
|
BLAKE2b-256 | a57284d675a6b65fcec675b0d94457d09f51dde5273b34cc84129b166a210611 |