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CHANGE: all image and label tensors must be provided as numpy tensors now, array.array no longer valid input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-11.1.0-py3.4-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1fa2e0c78ef0d3d4ece84d5cd6afa5fac548c5e4a703a7cd2a5ca8605fe1dc28 |
|
MD5 | 4c5715621725f8eb0e74b35d9176e35c |
|
BLAKE2b-256 | 1a59441796e40ca36d1510bbfd24ef58690c0ff91995549ccc24f83a86f39ee9 |
Hashes for DeepCL-11.1.0-py3.4-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 53bb7b8227e6cbda22bc125c1dc261760e6f317d7d8f54e016179d02d9caf05b |
|
MD5 | 4b421a835bc547be04802f02a9fb9ce3 |
|
BLAKE2b-256 | 81d793e0b4c87775aa7772192b13a2cbe6bcf4b3b209296a40fa9ecbf8604f71 |
Hashes for DeepCL-11.1.0-py2.7-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7d94d0079b89d2977fc0ce5bd1a158b67b08ad81ab25448bf16464779ee49049 |
|
MD5 | f6aaf96fa5dc01b7a60a20c6aaa642b1 |
|
BLAKE2b-256 | 98eb9f99e43b75e1f60f9b5135b82286b94d441811c5067aae70ef7da1a7735e |
Hashes for DeepCL-11.1.0-py2.7-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 846234ea607d5a2a872cb48a0f0e4a57be674fcffe5cfce6c70102d453e8d30e |
|
MD5 | b17e04822e0c89d86457eab9bd308cd0 |
|
BLAKE2b-256 | 5ff2b33c74d1b4ba28e07efe5e730a25f9914cc75d6d7ab2ebcab85aba0703d3 |