Original author(s) Montreal Institute for Learning Algorithms (MILA), University of Montreal PyMC Development Team 2007; 17 years ago 2.19.0[1] / 9 March 2024; 16 days ago .mw-parser-output .plainlist ol,.mw-parser-output .plainlist ul{line-height:inherit;list-style:none;margin:0;padding:0}.mw-parser-output .plainlist ol li,.mw-parser-output .plainlist ul li{margin-bottom:0} Python, CUDA Linux, macOS, Windows Machine learning library The 3-Clause BSD License pytensor.readthedocs.io/en/latest/

Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones.[2] In Theano, computations are expressed using a NumPy-esque syntax and compiled to run efficiently on either CPU or GPU architectures.

## History

Theano is an open source project[3] primarily developed by the Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal.[4]

The name of the software references the ancient philosopher Theano, long associated with the development of the golden mean.

On 28 September 2017, Pascal Lamblin posted a message from Yoshua Bengio, Head of MILA: major development would cease after the 1.0 release due to competing offerings by strong industrial players.[5] Theano 1.0.0 was then released on 15 November 2017.[6]

On 17 May 2018, Chris Fonnesbeck wrote on behalf of the PyMC development team[7] that the PyMC developers will officially assume control of Theano maintenance once the MILA developerment team steps down. On 29 January 2021, they started using the name Aesara for their fork of Theano.[8]

On 29 Nov 2022, the PyMC development team announced that the PyMC developers will fork the Aesara project under the name PyTensor.[9]

## Sample code

The following code is the original Theano's example. It defines a computational graph with 2 scalars a and b of type double and an operation between them (addition) and then creates a Python function f that does the actual computation.[10]

```import theano
from theano import tensor

# Declare two symbolic floating-point scalars
a = tensor.dscalar()
b = tensor.dscalar()

# Create a simple expression
c = a + b

# Convert the expression into a callable object that takes (a, b)
# values as input and computes a value for c
f = theano.function([a, b], c)

# Bind 1.5 to 'a', 2.5 to 'b', and evaluate 'c'
assert 4.0 == f(1.5, 2.5)
```