Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.
Project 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 up to 140x faster than on a CPU (support for float32 only).
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).
Release Notes
Theano 0.10.0beta4 (16th of October, 2017)
This release contains new features, improvements and bug fixes to prepare the upcoming release candidate.
We recommend that every developer updates to this version.
- Highlights:
Announcing that MILA will stop developing Theano
Bug fixes, crash fixes, warning improvements and documentation updates
A total of 70 people contributed to this release since 0.9.0, see list below.
- 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
- Commiters since 0.9.0:
Frederic Bastien
João Victor Tozatti Risso
Arnaud Bergeron
Steven Bocco
Mohammed Affan
amrithasuresh
Pascal Lamblin
Reyhane Askari
Alexander Matyasko
Shawn Tan
Simon Lefrancois
Adam Becker
Vikram
Gijs van Tulder
Faruk Ahmed
Thomas George
erakra
Andrei Costinescu
Boris Fomitchev
Zhouhan LIN
Aleksandar Botev
jhelie
xiaoqie
Tegan Maharaj
Matt Graham
Cesar Laurent
Gabe Schwartz
Juan Camilo Gamboa Higuera
Tim Cooijmans
Anirudh Goyal
Saizheng Zhang
Yikang Shen
vipulraheja
Florian Bordes
Sina Honari
Chiheb Trabelsi
Shubh Vachher
Daren Eiri
Joseph Paul Cohen
Laurent Dinh
Mohamed Ishmael Diwan Belghazi
Jeff Donahue
Ramana Subramanyam
Bogdan Budescu
Dzmitry Bahdanau
Ghislain Antony Vaillant
Jan Schlüter
Nan Jiang
Xavier Bouthillier
fo40225
mrTsjolder
wyjw
Aarni Koskela
Adam Geitgey
Adrian Keet
Adrian Seyboldt
Anmol Sahoo
Chong Wu
Holger Kohr
Jayanth Koushik
Lilian Besson
Lv Tao
Michael Manukyan
Murugesh Marvel
NALEPA
Zotov Yuriy
dareneiri
lrast
morrme
naitonium
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