James Bergstra
Université de Montréal

Olivier Breuleux
Université de Montréal

Frédéric Bastien
Université de Montréal

Pascal Lamblin
Université de Montréal

Razvan Pascanu
Université de Montréal

Guillaume Desjardins
Université de Montréal

Joseph Turian
Université de Montréal

David Warde-Farley
Université de Montréal

Yoshua Bengio
Université de Montréal

Abstract

Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPy's syntax with the speed of optimized native machine language. The user composes mathematical expressions in a high-level description that mimics NumPy's syntax and semantics, while being statically typed and functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learning algorithms implemented with Theano are from to faster than competitive alternatives (including those implemented with C/C++, NumPy/SciPy and MATLAB) when compiled for the CPU and between and faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.

Keywords

GPU, CUDA, machine learning, optimization, compiler, NumPy

DOI

10.25080/Majora-92bf1922-003

Bibtex entry

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Copyright © 2010 James Bergstra et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.