[Numpy-discussion] Announcing Theano 0.7

Pascal Lamblin lamblinp at iro.umontreal.ca
Fri Mar 27 12:13:22 EDT 2015

 Announcing Theano 0.7

This is a release for a major version, with lots of new
features, bug fixes, and some interface changes (deprecated or
potentially misleading features were removed).

Upgrading to Theano 0.7 is recommended for everyone, but you should
first make sure that your code does not raise deprecation warnings with
the version you are currently using.

For those using the bleeding edge version in the git repository, we
encourage you to update to the `rel-0.7` tag.

What's New

 * Integration of CuDNN for 2D convolutions and pooling on supported GPUs
 * Too many optimizations and new features to count
 * Various fixes and improvements to scan
 * Better support for GPU on Windows
 * On Mac OS X, clang is used by default
 * Many crash fixes
 * Some bug fixes as well

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

 * 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).


About Theano:

Related projects:

About NumPy:

About SciPy:

Machine Learning Tutorial with Theano on Deep Architectures:


I would like to thank all contributors of Theano. For this particular
release, many people have helped, and to list them all would be

I would also like to thank users who submitted bug reports.

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 )


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