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.
Highlights (since 1.0.0):
Fixed compilation and improved float16 support for topK on GPUNB: 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 1Attempted to prevent re-initialization of the GPU in a child processFixed support for temporary paths with spaces in Theano initializationSpell check pass on the documentation
You can download Theano from http://pypi.python.org/pypi/Theano
Installation instructions are available at
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 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).
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 BergeronEdward BettsFrederic BastienSam JohnsonSimon LefrancoisSteven Bocco
Also, thank you to all NumPy and Scipy developers as Theano builds on
All questions/comments are always welcome on the Theano
mailing-lists ( http://deeplearning.net/software/theano/#community )