[SciPy-User] Announcing Theano 1.0.1

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[SciPy-User] Announcing Theano 1.0.1

Steven Bocco

Announcing Theano 1.0.1

This is a maintenance release of Theano, version 1.0.1, with no new features, but some important bug fixes.

Upgrading to Theano 1.0.1 is recommended for everyone. For those using the bleeding edge version in the git repository, we encourage you to update to the rel-1.0.1 tag.

What's New

Highlights (since 1.0.0):

  • Fixed compilation and improved float16 support for topK on GPU
    • NB: topK support on GPU is experimental and may not work for large input sizes on certain GPUs
  • Fixed cuDNN reductions when axes to reduce have size 1
  • Attempted to prevent re-initialization of the GPU in a child process
  • Fixed support for temporary paths with spaces in Theano initialization
  • Spell check pass on the documentation

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


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 much faster than on a CPU.
  • 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).


About Theano:


Theano-related projects:


About NumPy:


About SciPy:


Machine Learning Tutorial with Theano on Deep Architectures:



I would like to thank all contributors of Theano. Since release 1.0.0, many people have helped, notably (in alphabetical order):

  • Arnaud Bergeron
  • Edward Betts
  • Frederic Bastien
  • Sam Johnson
  • Simon Lefrancois
  • Steven Bocco

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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