Announcing Theano 0.9.0
This is a release for a major version, with lots of new features, bug fixes, and some interface changes (deprecated or potentially misleading features were removed).
This release is the last major version that features the old GPU back-end (theano.sandbox.cuda, accessible through device=gpu*). All GPU users are encouraged to transition to the new GPU back-end, based on libgpuarray (theano.gpuarray, accessible through device=cuda*). For more information, see https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29 .
Upgrading to Theano 0.9.0 is recommended for everyone, but you should first make sure that your code does not raise deprecation warnings with Theano 0.8*. Otherwise either results can change, or warnings may have been turned into errors.
For those using the bleeding edge version in the git repository, we encourage you to update to the rel-0.9.0 tag.
Highlights (since 0.8.0):
Other more detailed changes:
Download and Install
You can download Theano from http://pypi.python.org/pypi/Theano
Installation instructions are available at http://deeplearning.net/software/theano/install.html
Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy. Theano features:
Theano has been powering large-scale computationally intensive scientific research since 2007, but it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal).
Machine Learning Tutorial with Theano on Deep Architectures:
I would like to thank all contributors of Theano. For this particular release, many people have helped, notably (in alphabetical order):
Also, thank you to all NumPy and Scipy developers as Theano builds on their strengths.
All questions/comments are always welcome on the Theano mailing-lists ( http://deeplearning.net/software/theano/#community )
SciPy-User mailing list
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