=============================
Announcing Theano 0.9.0beta1
=============================
This is a beta release for a major version, with lots of new
features, bug fixes, and some interface changes (deprecated or
potentially misleading features were removed).
The upgrade is recommended for developers who want to help test and
report bugs, or want to use new features now.
For those using the bleeding edge version in the git repository,
we encourage you to update to the `0.9.0beta1` tag.
What's New
----------
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.
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)
Download and Install
--------------------
You can download Theano from http://pypi.python.org/pypi/Theano
Installation instructions are available at
http://deeplearning.net/software/theano/install.html
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).
Resources
---------
About Theano:
http://deeplearning.net/software/theano/
Theano-related projects:
http://github.com/Theano/Theano/wiki/Related-projects
About NumPy:
http://numpy.scipy.org/
About SciPy:
http://www.scipy.org/
Machine Learning Tutorial with Theano on Deep Architectures:
http://deeplearning.net/tutorial/
Acknowledgments
---------------
I would like to thank all contributors of Theano. For this particular
release, many people have helped, notably (in alphabetical order):
- Alexander Matyasko
- Alexandre de Brebisson
- Amjad Almahairi
- Andrés Gottlieb
- Arnaud Bergeron
- Ben Poole
- Benjamin Scellier
- Bhavishya Pohani
- Bryn Keller
- Caglar
- Carl Thomé
- Cesar Laurent
- Chiheb Trabelsi
- Chinnadhurai Sankar
- Christos Tsirigotis
- Ciyong Chen
- Evelyn Mitchell
- Faruk Ahmed
- Fei Wang
- Fei Zhan
- Francesco Visin
- Frederic Bastien
- Fábio Perez
- Gennadiy Tupitsin
- Gijs van Tulder
- Gilles Louppe
- Gokula Krishnan
- Greg Ciccarelli
- Harm de Vries
- He
- Huan Zhang
- Ilya Kulikov
- Iulian Vlad Serban
- Jakub Sygnowski
- Jan Schlüter
- Jesse Livezey
- Jonas Degrave
- Kaixhin
- Karthik Karanth
- Kelvin Xu
- Kevin Keraudren
- Kirill Bobyrev
- Kumar Krishna Agrawal
- Kv Manohar
- Liwei Cai
- Maltimore
- Marc-Alexandre Cote
- Marco
- Marius F. Killinger
- Mathieu Germain
- Matt Graham
- Maxim Kochurov
- Mikhail Korobov
- Mohammad Pezeshki
- Morgan Stuart
- Nan Rosemary Ke
- Neil
- Nicolas Ballas
- Nizar Assaf
- Olivier Mastropietro
- Ozan Çağlayan
- Pascal Lamblin
- Pierre Luc Carrier
- RadhikaG
- Ramana Subramanyam
- Ray Donnelly
- Reyhane Askari
- Rithesh Kumar
- Rizky Luthfianto
- Robin Millette
- Roman Ring
- Ruslana Makovetsky
- Saizheng Zhang
- Samira Ebrahimi Kahou
- Samira Shabanian
- Sander Dieleman
- Sebastin Santy
- Shawn Tan
- Simon Lefrancois
- Sina Honari
- Steven Bocco
- Taesup (TS) Kim
- Thomas George
- Tim Cooijmans
- Tim Gasper
- Vincent Dumoulin
- Vincent Michalski
- Vitaliy Kurlin
- Wazeer Zulfikar
- Wojciech Głogowski
- Xavier Bouthillier
- Yang Zhang
- Yann N. Dauphin
- Yaroslav Ganin
- Ying Zhang
- Zhouhan LIN
- gw0 [http://gw.tnode.com/]
- happygds
- hexahedria
- hsintone
- jakirkham
- joncrall
- khaotik
- mockingjamie
- p
- root
- superantichrist
- texot
- theano-bot
- tillahoffmann
- wazeerzulfikar
- you-n-g
Also, thank you to all NumPy and Scipy developers as Theano builds on
their strengths.
All questions/comments are always welcome on the Theano
mailing-lists ( http://deeplearning.net/software/theano/#community )
--
Steven Bocco
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