Theano

Developer(s)	Montreal Institute for Learning Algorithms (MILA), University of Montreal
Initial release	2007; 16 years ago (2007)
Final release	1.0.5[1] / 27 July 2020; 2 years ago (27 July 2020)
Repository	github.com/Theano/Theano
Written in	Python, CUDA
Platform	Linux, macOS, Windows
Type	Machine learning library
License	The 3-Clause BSD License
Website	www.deeplearning.net/software/theano

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.

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 they step down. On 29 January 2021, they started using the name Aesara for their fork of Theano.^[8]

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.^[9]

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)