Metadata-Version: 1.1
Name: Theano
Version: 0.9.0b1
Summary: Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.
Home-page: http://deeplearning.net/software/theano/
Author: LISA laboratory, University of Montreal
Author-email: theano-dev@googlegroups.com
License: BSD
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).
        
        .. _NumPy: http://numpy.scipy.org/
        
        
        =============
        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
        
Keywords: theano math numerical symbolic blas numpy gpu autodiff differentiation
Platform: Windows
Platform: Linux
Platform: Solaris
Platform: Mac OS-X
Platform: Unix
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Code Generators
Classifier: Topic :: Software Development :: Compilers
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
