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 two papers:
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!
See our Theano Citation Policy for details.
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)
The appropriate venue for seeking help depends on the kind of question you have.
Please do take some time to search for similar questions that were asked and answered in the past. If you find something similar that doesn’t fully answer your question, it can be helpful to say something like “I found X but it doesn’t address facet Y” and link to the previous discussion.
When asking questions on StackOverflow, please use the theano tag, so your question can be found, and follow StackOverflow’s guidance on asking questions. Consider also using the python and numpy tags, especially if you are unsure which library your problem relates to.
It’s often helpful to include the following details with your question:
Spending the time to create a minimal specific example of a problem is likely to get you to an answer quicker than posting something quickly that has too much irrelevant detail or is too vague. A minimal example may take you a bit more time to create but the first response is more likely to be the answer you need than, rather than a frustrated request for clarification.
We were all newbies to Theano once and, as the community expands, there is a constant stream of new Theano users looking for help. Perhaps you asked a question when you were first starting out? Now you can pay it forward by helping others. It’s also a good way to reinforce your own Theano knowledge.
Often it’s easiest to answer a question directly but sometimes it may be better to refer people to a good answer that was provided in the past. Pointing people to relevant sections in the documentation, or to a Theano tutorial, can also be helpful.