Announcing Theano 1.0.0rc1
Steven Bocco
Announcing Theano 1.0.0rc1
This is a release candidate for a major version, with lots of new features, bug fixes, and some interface changes (deprecated or potentially misleading features were removed).
The upgrade is recommended for developers who want to help test and report bugs, or want to use new features now. If you have updated to 0.10.0beta4, you are highly encouraged to update to 1.0.0rc1.
For those using the bleeding edge version in the git repository, we encourage you to update to the rel-1.0.0rc1 tag.
What's New
- Highlights:
- Make sure MKL uses GNU OpenMP
- NB: Matrix dot product (gemm) with mkl from conda could return wrong results in some cases. We have reported the problem upstream and we have a work around that raises an error with information about how to fix it.
- Optimized SUM(x^2), SUM(ABS(X)) and MAX(ABS(X)) operations with cuDNN reductions
- Added Python scripts to help test cuDNN convolutions
- Fixed invalid casts and index overflows in theano.tensor.signal.pool
- Make sure MKL uses GNU OpenMP
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 much faster than on a CPU.
- 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:
About SciPy:
Machine Learning Tutorial with Theano on Deep Architectures:
Acknowledgments
I would like to thank all contributors of Theano. Since release 0.9.0, many people have helped, notably (in alphabetical order):
- Aarni Koskela
- Adam Becker
- Adam Geitgey
- Adrian Keet
- Adrian Seyboldt
- Aleksandar Botev
- Alexander Matyasko
- amrithasuresh
- Andrei Costinescu
- Anirudh Goyal
- Anmol Sahoo
- Arnaud Bergeron
- Bogdan Budescu
- Boris Fomitchev
- Cesar Laurent
- Chiheb Trabelsi
- Chong Wu
- Daren Eiri
- dareneiri
- Dzmitry Bahdanau
- erakra
- Faruk Ahmed
- Florian Bordes
- fo40225
- Frederic Bastien
- Gabe Schwartz
- Ghislain Antony Vaillant
- Gijs van Tulder
- Holger Kohr
- Jan Schlüter
- Jayanth Koushik
- Jeff Donahue
- jhelie
- João Victor Tozatti Risso
- Joseph Paul Cohen
- Juan Camilo Gamboa Higuera
- Laurent Dinh
- Lilian Besson
- lrast
- Lv Tao
- Matt Graham
- Michael Manukyan
- Mohamed Ishmael Diwan Belghazi
- Mohammed Affan
- morrme
- mrTsjolder
- Murugesh Marvel
- naitonium
- NALEPA
- Nan Jiang
- Pascal Lamblin
- Ramana Subramanyam
- Rebecca N. Palmer
- Reyhane Askari
- Saizheng Zhang
- Shawn Tan
- Shubh Vachher
- Simon Lefrancois
- Sina Honari
- Steven Bocco
- Tegan Maharaj
- Thomas George
- Tim Cooijmans
- Vikram
- vipulraheja
- wyjw
- Xavier Bouthillier
- xiaoqie
- Yikang Shen
- Zhouhan LIN
- Zotov Yuriy
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 )
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