What's New ---------- 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 users: Raingo * 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 http://deeplearning.net/software/theano/install.html Description ----------- 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: * 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). Resources --------- About Theano: http://deeplearning.net/software/theano/ Theano-related projects: http://github.com/Theano/Theano/wiki/Related-projects About NumPy: http://numpy.scipy.org/ About SciPy: http://www.scipy.org/ Machine Learning Tutorial with Theano on Deep Architectures: http://deeplearning.net/tutorial/ Acknowledgments --------------- I would like to thank all contributors of Theano. For this particular release (since 0.5), many people have helped, notably: Frederic Bastien Pascal Lamblin Ian Goodfellow Olivier Delalleau Razvan Pascanu abalkin Arnaud Bergeron Nicolas Bouchard + Jeremiah Lowin + Matthew Rocklin Eric Larsen + James Bergstra David Warde-Farley John Salvatier + Vivek Kulkarni + Yann N. Dauphin Ludwig Schmidt-Hackenberg + Gabe Schwartz + Rami Al-Rfou' + Guillaume Desjardins Caglar + Sigurd Spieckermann + Steven Pigeon + Bogdan Budescu + Jey Kottalam + Mehdi Mirza + Alexander Belopolsky + Ethan Buchman + Jason Yosinski Nicolas Pinto + Sina Honari + Ben McCann + Graham Taylor Hani Almousli Ilya Dyachenko + Jan Schlüter + Jorg Bornschein + Micky Latowicki + Yaroslav Halchenko + Eric Hunsberger + Amir Elaguizy + Hannes Schulz + Huy Nguyen + Ilan Schnell + Li Yao Misha Denil + Robert Kern + Sebastian Berg + Vincent Dumoulin + Wei Li + XterNalz + 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 their strengths. All questions/comments are always welcome on the Theano mailing-lists ( http://deeplearning.net/software/theano/#community )
participants (1)
-
Frédéric Bastien