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
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)
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:
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
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
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-8.5.4alpha2-py3.4-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | d9ea08a6b515aaad02d9909ceaec0553ac6f225cc52c671ce83a98485a00e410 |
|
MD5 | f4144fdd600be3baeca46d1f13825ae4 |
|
BLAKE2b-256 | 18b2f934ac99be8a25ce8e9f8f842e32c8c45cb0e87d5f0db1f45a7b28408c00 |
Hashes for DeepCL-8.5.4alpha2-py3.4-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4082037f9e28728d3c26394ccb824cfb4c1566bc6b6792be4dd564d2347fde88 |
|
MD5 | d8953f2312ea631a55dce6c169e6b3ad |
|
BLAKE2b-256 | 69c4be7def8504b331d4bba9700512b456d8c69d99be682ef7626a2c4f20e462 |
Hashes for DeepCL-8.5.4alpha2-py2.7-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | da9574bb07fe66c15d3a5c898114f25ac187b68d3c2bf72306f6c19724efef53 |
|
MD5 | 427ae895c7aadb079f59fa0005a1b8b6 |
|
BLAKE2b-256 | d98548c0e221b566699cbbc008d3a65bff66ec140632ea6599e381d35484c725 |
Hashes for DeepCL-8.5.4alpha2-py2.7-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | ba39ddec98d18e03c6ecc5ef9e1c2e9ae03099f37c1172eda8164e91209c6b17 |
|
MD5 | eaa5b998ab888936cce7cc29df3b1f11 |
|
BLAKE2b-256 | 9083e262b476edae222f07eaf5becd609a40894fdbf5983bef37cdcf70749742 |