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).

.. _NumPy: http://numpy.scipy.org/

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Release Notes

Theano 1.0.5 (27th of July 2020)

This is a maintenance release of Theano, version 1.0.5, with no
new features, but some deprecation fixes.

We recommend that everybody update to this version.

Highlights (since 1.0.4):

Theano is now compatible with Python 3.9
Fixed many deprecation warnings

A total of 13 people contributed to this release since 1.0.4:

1fish2
Frederic Bastien
Rebecca Palmer
Miro Hrončok
Dan Foreman-Mackey
Adrian Seyboldt
abergeron
Tim Gates
Tim Odonnell
Robert P. Goldman
Duc Nguyen
Igor Varfolomeev
Thomas Wiecki

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Theano

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