Announcing Theano 1.0.2
This is a maintenance release of Theano, version 1.0.2, with no new features, but some important bug fixes.
Upgrading to Theano 1.0.2 is recommended for everyone. For those using the bleeding edge version in the git repository, we encourage you to update to the rel-1.0.2 tag.
Highlights (since 1.0.1):
- Theano should work under PyPy now (this is experimental).
- Update for cuDNN 7.1 RNN API changes.
- Fix for a crash related to mixed dtypes with cuDNN convolutions.
- MAGMA should work in more cases without manual config.
- Handle reductions with non-default accumulator dtype better on the GPU.
- Improvements to the test suite so that it fails less often due to
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).
Machine Learning Tutorial with Theano on Deep Architectures:
I would like to thank all contributors of Theano. Since release 1.0.1, many people have helped, notably (in alphabetical order):
- Arnaud Bergeron
- Desiree Vogt-Lee
- Frederic Bastien
- Garming Sam
- Jon Haygood
- Jordan Melendez
- Pascal Lamblin
- Simon Lefrancois
- Steven Bocco
- Vincent Dumoulin
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