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 0.9.0beta1 (24th of January, 2017)
This release contains a lot of bug fixes and improvements + new features, to prepare the upcoming release candidate.
- Highlight:
Many computation and compilation speed up
More numerical stability by default for some graph
Jenkins (gpu tests run on PR in addition to daily buildbot)
Better handling of corner cases for theano functions and graph optimizations
More graph optimization (faster execution and smaller graph, so more readable)
Less c code compilation
Better Python 3.5 support
Better numpy 1.12 support
Support newer Mac and Windows version
Conda packages for Mac, Linux and Windows
Theano scripts now works on Windows
scan with checkpoint (trade off between speed and memory usage, useful for long sequences)
Added a bool dtype
New back-end: - float16 storage - better mapping between theano device number and nvidia-smi number, using the PCI bus ID of graphic cards - More pooling support on GPU when cuDNN isn’t there. - ignore_border=False is now implemented for pooling.
A total of 112 people contributed to this release, see the list at the bottom.
- Interface changes:
New pooling interface
Pooling parameters can change at run time
When converting empty list/tuple, now we use floatX dtype
The MRG random generator now try to infer the broadcast pattern of its output
Move softsign out of sandbox to theano.tensor.nnet.softsign
Roll make the shift be modulo the size of the axis we roll on
Merge CumsumOp/CumprodOp into CumOp
round() default to the same as NumPy: half_to_even.
- Convolution updates:
Multi-cores convolution and pooling on CPU
New abstract 3d convolution interface similar to the 2d convolution interface
Dilated convolution
- GPU:
cuDNN: support versoin 5.1 and wrap batch normalization (2d and 3d) and RNN functions
Multiple-GPU, synchrone update (via platoon, use NCCL)
GpuAdvancedSubtensor in new back-end
Gemv(matrix-vector product) speed up for special shape
Support for MaxAndArgMax for some axis combination
Support for solve (using cusolver), erfinv and erfcinv
cublas gemv workaround when we reduce on an axis with a dimensions size of 0
Warn user that some cuDNN algorithms may produce unexpected results in certain environments for convolution backward filter operations.
- New features:
Add gradient of solve, tensorinv (CPU), tensorsolve (CPU) searchsorted (CPU)
Add Multinomial Without Replacement
conv3d2d support full and half mode (REMOVE?)
Add DownsampleFactorMaxGradGrad.grad
Allow partial evaluation of compiled function
More Rop support
Indexing support ellipsis: a[…, 3], a[1,…,3]
Added theano.tensor.{tensor5,dtensor5, …}
compiledir_format support device
Added new Theano flag cmodule.age_thresh_use
- Others:
Speed up argmax only on gpu (without also needing the max)
A few unfrequent bugfix
More stack trace in error message
Speed up cholesky grad
log(sum(exp(…))) now get stability optimized
- Other more detailed changes:
Allow more then one output to be an destructive inplace
Add flag profiling.ignore_first_call, useful to profile the new gpu back-end
Doc/error message fixes/updates
More support of negative axis
Added the keepdims parameter to the norm function
Crash fixes
Make scan gradient more deterministic
Add support for space in path on Windows
remove ProfileMode (use Theano flag profile=True instead)
- Committers since 0.8.0:
Frederic Bastien
Arnaud Bergeron
Pascal Lamblin
Ramana Subramanyam
Simon Lefrancois
Steven Bocco
Gijs van Tulder
Cesar Laurent
Chiheb Trabelsi
Chinnadhurai Sankar
Mohammad Pezeshki
Reyhane Askari
Alexander Matyasko
Alexandre de Brebisson
Nan Rosemary Ke
Pierre Luc Carrier
Mathieu Germain
Olivier Mastropietro
khaotik
Saizheng Zhang
Thomas George
Iulian Vlad Serban
Benjamin Scellier
Francesco Visin
Caglar
Harm de Vries
Samira Shabanian
Jakub Sygnowski
Samira Ebrahimi Kahou
Mikhail Korobov
Faruk Ahmed
Fei Wang
Jan Schlüter
Kv Manohar
Jesse Livezey
Kelvin Xu
Matt Graham
Ruslana Makovetsky
Sina Honari
Bryn Keller
Ciyong Chen
Nicolas Ballas
Vitaliy Kurlin
Zhouhan LIN
Gokula Krishnan
Kumar Krishna Agrawal
Ozan Çağlayan
Vincent Michalski
Ray Donnelly
Tim Cooijmans
Vincent Dumoulin
happygds
mockingjamie
Amjad Almahairi
Christos Tsirigotis
Ilya Kulikov
RadhikaG
Taesup (TS) Kim
Ying Zhang
Karthik Karanth
Kirill Bobyrev
Yang Zhang
Yaroslav Ganin
theano-bot
Liwei Cai
Morgan Stuart
Tim Gasper
Xavier Bouthillier
p
texot
Andrés Gottlieb
Ben Poole
Bhavishya Pohani
Carl Thomé
Evelyn Mitchell
Fei Zhan
Fábio Perez
Gennadiy Tupitsin
Gilles Louppe
Greg Ciccarelli
He
Huan Zhang
Jonas Degrave
Kaixhin
Kevin Keraudren
Maltimore
Marc-Alexandre Cote
Marco
Marius F. Killinger
Maxim Kochurov
Neil
Nizar Assaf
Rithesh Kumar
Rizky Luthfianto
Robin Millette
Roman Ring
Sander Dieleman
Sebastin Santy
Shawn Tan
Wazeer Zulfikar
Wojciech Głogowski
Yann N. Dauphin
gw0 [http://gw.tnode.com/]
hexahedria
hsintone
jakirkham
joncrall
root
superantichrist
tillahoffmann
wazeerzulfikar
you-n-g
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