[Numpy-discussion] Theano 0.6 released
nouiz at nouiz.org
Tue Dec 3 14:50:37 EST 2013
We recommend that everybody update to this version.
Highlights (since 0.6rc5):
* Last release with support for Python 2.4 and 2.5.
* We will try to release more frequently.
* Fix crash/installation problems.
* Use less memory for conv3d2d.
0.6rc4 skipped for a technical reason.
Highlights (since 0.6rc3):
* Python 3.3 compatibility with buildbot test for it.
* Full advanced indexing support.
* Better Windows 64 bit support.
* New profiler.
* Better error messages that help debugging.
* Better support for newer NumPy versions (remove useless warning/crash).
* Faster optimization/compilation for big graph.
* Move in Theano the Conv3d2d implementation.
* Better SymPy/Theano bridge: Make an Theano op from SymPy expression
and use SymPy c code generator.
* Bug fixes.
Change from 0.6rc5:
* Fix crash when specifing march in cxxflags Theano flag. (Frederic
B., reported by FiReTiTi)
* code cleanup (Jorg Bornschein)
* Fix Canopy installation on windows when it was installed for all
* Fix Theano tests due to a scipy change. (Frederic B.)
* Work around bug introduced in scipy dev 0.14. (Frederic B.)
* Fix Theano tests following bugfix in SciPy. (Frederic B., reported
by Ziyuan Lin)
* Add Theano flag cublas.lib (Misha Denil)
* Make conv3d2d work more inplace (so less memory usage) (Frederic
B., repoted by Jean-Philippe Ouellet)
See https://pypi.python.org/pypi/Theano for more details.
Download and Install
You can download Theano from http://pypi.python.org/pypi/Theano
Installation instructions are available at
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
* tight integration with NumPy: a similar interface to NumPy's.
numpy.ndarrays are also used internally in Theano-compiled functions.
* transparent use of a GPU: perform data-intensive computations up to
140x faster than on a CPU (support for float32 only).
* efficient symbolic differentiation: Theano can compute derivatives
for functions of one or many inputs.
* speed and stability optimizations: avoid nasty bugs when computing
expressions such as log(1+ exp(x)) for large values of x.
* dynamic C code generation: evaluate expressions faster.
* extensive unit-testing and self-verification: includes tools for
detecting and diagnosing bugs and/or potential problems.
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 (since 0.5), many people have helped, notably:
Nicolas Bouchard +
Jeremiah Lowin +
Eric Larsen +
John Salvatier +
Vivek Kulkarni +
Yann N. Dauphin
Ludwig Schmidt-Hackenberg +
Gabe Schwartz +
Rami Al-Rfou' +
Sigurd Spieckermann +
Steven Pigeon +
Bogdan Budescu +
Jey Kottalam +
Mehdi Mirza +
Alexander Belopolsky +
Ethan Buchman +
Nicolas Pinto +
Sina Honari +
Ben McCann +
Ilya Dyachenko +
Jan Schlüter +
Jorg Bornschein +
Micky Latowicki +
Yaroslav Halchenko +
Eric Hunsberger +
Amir Elaguizy +
Hannes Schulz +
Huy Nguyen +
Ilan Schnell +
Misha Denil +
Robert Kern +
Sebastian Berg +
Vincent Dumoulin +
Wei Li +
A total of 51 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
Also, thank you to all NumPy and Scipy developers as Theano builds on
All questions/comments are always welcome on the Theano
mailing-lists ( http://deeplearning.net/software/theano/#community )
More information about the NumPy-Discussion