Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.

## Project description

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 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).

## Release Notes

Theano 0.7 (26th of March, 2015)

We recommand to everyone to upgrade to this version.

Highlights:
• 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

## Project details

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