Announcing Theano 0.10.0beta4

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Steven Bocco

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Oct 17, 2017, 10:29:00 AM10/17/17

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Announcing Theano 0.10.0beta4

This is a beta release for a major version, with new features and bug fixes.

The upgrade is recommended for developers who want to help test and report bugs, or want to use new features now.

For those using the bleeding edge version in the git repository, we encourage you to update to the rel-0.10.0beta4 tag.

What's New

Highlights:

Announcing that MILA will stop developing Theano
Bug fixes, crash fixes, warning improvements and documentation updates

Interface changes:

Generalized AllocDiag for any non-scalar input

Convolution updates:

Implemented fractional bilinear upsampling

cuDNN (GPU):

Disallowed float16 precision for convolution gradients
Fixed memory alignment detection
Added profiling in C debug mode (with theano flag cmodule.debug=True)

New features:

Implemented truncated normal distribution with box-muller transform
Added L_op() overriding option for OpFromGraph
Added NumPy C-API based fallback implementation for [sd]gemv_ and [sd]dot_

Other more detailed changes:

Improved stack trace follow-up for GPU optimizations
Fixed gradient error for elemwise minimum and maximum when compared values are the same
Fixed gradient for ARange
Removed ViewOp subclass during optimization

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:

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. 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
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 )