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
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CHANGE: all image and label tensors must be provided as numpy tensors now, array.array no longer valid input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-9.0.3-py3.4-win-amd64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9e10eb8ce81ecb8ebad4b5ac8edd95abb951f3f9f927106c84f70fdfd444b1f5 |
|
MD5 | b4a3ef0bf805a7832278e394d72ceb70 |
|
BLAKE2b-256 | 02a22f64db7595657e5797b13aeea82808483cfd044c79dc8c05047e00f742ff |
Hashes for DeepCL-9.0.3-py3.4-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 54b0adb5d526a12ce71afbc9abd99eeb0eb888871c3a15bca0ece5d573b656b4 |
|
MD5 | d23d5a04b3b83075c6980520a6091074 |
|
BLAKE2b-256 | d3d5347bab63b7554be080c2fa0c92e35481016b1602d8bb16fa8660f5682008 |
Hashes for DeepCL-9.0.3-py3.4-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | dac078b438f10af143ebe9dc2936cbd564b52c3e2e2704b57b43b5ce7d7df422 |
|
MD5 | 2052b1b238e09e80050e293e4a358cc6 |
|
BLAKE2b-256 | aa8f7095337351fb18d02a6a9050c59c921b9f067274be630f35b66a3f9815b8 |
Hashes for DeepCL-9.0.3-py2.7-win-amd64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 732c469b688858823610f2ff858cb5da470d27948e415910dd43356f832150c7 |
|
MD5 | 6dec995d2d61ba8a501cc69b1d461517 |
|
BLAKE2b-256 | fbe2173df3da853ae77248c88ff9c95312e93675c11d7004ac12a59c3a02e646 |
Hashes for DeepCL-9.0.3-py2.7-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9dc36c3c0b2b935a30f34d4dae4f4d6c427ba07683b89ec90916296664d0e48b |
|
MD5 | bde4100f6b47d08224c92af98c5ba7ef |
|
BLAKE2b-256 | 15a4acd82bb72074462819471f7b0d37caf984fcc0361cfacfc10783c4813021 |
Hashes for DeepCL-9.0.3-py2.7-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 34aea4af4e621b25d9cf53c4901552f0ba5b9e3d832df57326ddfda466e6cb26 |
|
MD5 | 2f4e77dff60798ccd72c733d67669bf0 |
|
BLAKE2b-256 | 9e4182e4652fc5667d3b65bfbeec41acd04d5ebddbbc075888fc5f1d632f1125 |