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CHANGE: all image and label tensors must be provided as numpy tensors now, array.array no longer valid input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-10.1.0-py3.4-win-amd64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 591bfe98a108841a57852074d393bde34c4ba50559141680aa6b2eeb75bc9ccb |
|
MD5 | 82b5425e67adfba0396982cb81fda87e |
|
BLAKE2b-256 | e8d6e5e32ee815b90a7223ed82ec8023a1853de182b500ff8bf2891f4e8ad1de |
Hashes for DeepCL-10.1.0-py3.4-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 10730dd8d77ea305df465aac15511ec4763790b0b8fae37f1fab8f7bc560d0a5 |
|
MD5 | d2c80ce2e628e394c056efa8a8c23a00 |
|
BLAKE2b-256 | 605fed313df9494f65379c7dbbf712b0ce1eaed83e0ef1d3826bbc05e7abbf10 |
Hashes for DeepCL-10.1.0-py3.4-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 04b76497193d69f6c1005ee16d3c3f9cd4a9c1c4b5a5821993a3ce49f3a2ed9d |
|
MD5 | 99ea8f12e8cd9c4ba58385788258d145 |
|
BLAKE2b-256 | e2692634a248c91de932d3b15dcac2ca5cca883309229b53dfeec96fb5d2040a |
Hashes for DeepCL-10.1.0-py2.7-win-amd64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | abcd66c51f5c2f9f2f9eee064b7575970058e2020f57cf47649526f456792ade |
|
MD5 | 99eba415832fe59bcb5aa43c6894984c |
|
BLAKE2b-256 | 35912246dd71fa1714700996c2905dc8d8e8d897f5638c73add4a1d2a17e1e6a |
Hashes for DeepCL-10.1.0-py2.7-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 07517a06ab967bf4eaac0137f951bda23c88ab154600b3599de6bf4645d09c5c |
|
MD5 | 591e1cde3bfff72b2ac8802b14659e2d |
|
BLAKE2b-256 | d0ac373c8d7f732534727218e5006f54d2b8fd95fdf8708148fdf7dada01d51f |
Hashes for DeepCL-10.1.0-py2.7-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 49588510ba83d103a5baa7260e531a1efaf864cecd2fc647bc62a6858a470afe |
|
MD5 | bc85cf05ee2c76788aa1a143d29337f6 |
|
BLAKE2b-256 | 8b1f771c097d69639dc696c43569f1091c63c8411266819f4726a627e98f1016 |