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Theano: A Python framework for fast computation of mathematical expressions

9 May 2016
The Theano Development Team
Rami Al-Rfou
Guillaume Alain
Amjad Almahairi
Christof Angermüller
Dzmitry Bahdanau
Nicolas Ballas
Frédéric Bastien
Justin Bayer
A. Belikov
A. Belopolsky
Yoshua Bengio
Arnaud Bergeron
James Bergstra
Valentin Bisson
Josh Bleecher Snyder
Nicolas Bouchard
Nicolas Boulanger-Lewandowski
Xavier Bouthillier
A. D. Brébisson
Olivier Breuleux
P. Carrier
Kyunghyun Cho
J. Chorowski
Paul Christiano
Tim Cooijmans
Marc-Alexandre Côté
Myriam Côté
Aaron Courville
Yann N. Dauphin
Olivier Delalleau
Julien Demouth
Guillaume Desjardins
Sander Dieleman
Laurent Dinh
Mélanie Ducoffe
Vincent Dumoulin
Samira Ebrahimi Kahou
D. Erhan
Ziye Fan
Orhan Firat
M. Germain
Xavier Glorot
Ian Goodfellow
M. Graham
Çağlar Gülçehre
P. Hamel
Iban Harlouchet
J. Heng
Balázs Hidasi
S. Honari
Arjun Jain
Sébastien Jean
Kai Jia
Mikhail Korobov
Vivek Kulkarni
Alex Lamb
Pascal Lamblin
Eric Larsen
César Laurent
S. Lee
S. Lefrançois
S. Lemieux
Nicholas Léonard
Zhouhan Lin
J. Livezey
C. Lorenz
J. Lowin
Qianli Ma
Pierre-Antoine Manzagol
Olivier Mastropietro
R. McGibbon
Roland Memisevic
B. V. Merrienboer
Vincent Michalski
M. Berk Mirza
A. Orlandi
C. Pal
Razvan Pascanu
Mohammad Pezeshki
Colin Raffel
D. Renshaw
M. Rocklin
Adriana Romero
Markus Roth
Peter Sadowski
J. Salvatier
F. Savard
Jan Schluter
John Schulman
Gabriel Schwartz
Iulian Serban
Dmitriy Serdyuk
Samira Shabanian
Étienne Simon
Sigurd Spieckermann
S. Subramanyam
Jakub Sygnowski
Jérémie Tanguay
Gijs van Tulder
Joseph P. Turian
Sebastian Urban
Pascal Vincent
Francesco Visin
H. D. Vries
David Warde-Farley
Dustin J. Webb
Matthew Willson
Kelvin Xu
Lijun Xue
Li Yao
Saizheng Zhang
Ying Zhang
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Abstract

Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.

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