Description:

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:

* tight integration with numpy – Use numpy.ndarray in Theano-compiled functions.
* transparent use of a GPU – perform data-intensive computations much faster than on a CPU.
* symbolic differentiation – Let Theano do your derivatives.
* speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
* dynamic C code generation – Evaluate expressions faster.
* extensive unit-testing and self-verification – Detect and diagnose many types of mistake.

Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal).

Theano has been used primarily to implement large-scale deep learning algorithms. To see how, see the Deep Learning Tutorials (http://www.deeplearning.net/tutorial/)

Changes to previous version:

Theano 1.0.2 (23rd of May, 2018)

This is a maintenance release of Theano, version 1.0.2, with no new features, but some important bug fixes.

We recommend that everybody update to this version.

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 random chance.

A total of 6 people contributed to this release since 1.0.1:

• Frederic Bastien
• Steven Bocco
• Jon Haygood
• Arnaud Bergeron
• Jordan Melendez
• Desiree Vogt-Lee
• Garming Sam
• Pascal Lamblin
• Vincent Dumoulin
• Glexin
• Simon Lefrancois

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Macosx, Windows

Data Formats: Agnostic

Tags: Python, Cuda, Gpu, Symbolic Differentiation, Numpy

Archive: download here

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