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Jeremie Tanguay

Alumni

Publications

Theano: A Python framework for fast computation of mathematical expressions
Rami Al-Rfou
Amjad Almahairi
Christof Angermueller
Frédéric Bastien
Justin Bayer
Anatoly Belikov
Alexander Belopolsky
Josh Bleecher Snyder
Pierre-Luc Carrier
Paul Christiano
Myriam Côté
Yann N. Dauphin
Julien Demouth
Sander Dieleman
Ziye Fan
Mathieu Germain
Matt Graham
Balázs Hidasi
Arjun Jain
Kai Jia
Mikhail Korobov
Vivek Kulkarni
Pascal Lamblin
Eric Larsen
Sean Lee
Simon Lefrancois
Jesse A. Livezey
Cory Lorenz
Jeremiah Lowin
Qianli Ma
Robert T. McGibbon
Mehdi Mirza
Alberto Orlandi
Christopher Pal
Colin Raffel
Daniel Renshaw
Matthew Rocklin
Adriana Romero
Markus Roth
Peter Sadowski
John Salvatier
Jan Schlüter
John Schulman
Gabriel Schwartz
Iulian Vlad Serban
Samira Shabanian
Sigurd Spieckermann
S. Ramana Subramanyam
Gijs van Tulder
Sebastian Urban
Dustin J. Webb
Matthew Willson
Lijun Xue
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (voir plus)ntly. 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.