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 1.0.1 (6th of December, 2017)

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

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

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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