Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:
Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal).
You can watch a quick (20 minute) introduction to Theano given as a talk at SciPy 2010 via streaming (or downloaded) video:
Transparent GPU Computing With Theano. James Bergstra, SciPy 2010, June 30, 2010.
Theano is now available on PyPI, and can be installed via easy_install Theano, pip install Theano or by downloading and unpacking the tarball and typing python setup.py install.
Those interested in bleeding-edge features should obtain the latest development version, available via:
git clone git://github.com/Theano/Theano.git
You can then place the checkout directory on your $PYTHONPATH or use python setup.py develop to install a .pth into your site-packages directory, so that when you pull updates via Git, they will be automatically reflected the “installed” version. For more information about installation and configuration, see installing Theano.
If you use Theano for academic research, you are highly encouraged (though not required) to cite the following paper:
Theano is primarily developed by academics, and so citations matter a lot to us. As an added benefit, you increase Theano’s exposure and potential user (and developer) base, which is to the benefit of all users of Theano. Thanks in advance!
Roughly in order of what you’ll want to check out:
You can download the latest PDF documentation, rather than reading it online.
Check out how Theano can be used for Machine Learning: Deep Learning Tutorials.
Theano was featured at SciPy 2010.
“Thank YOU for correcting it so quickly. I wish all packages I worked with would have such an active maintenance - this is as good as it gets :-)”
(theano-users, Aug 2, 2010)