Theano is a python library and optimizing compiler for manipulating and evaluating expressions, especially matrix-valued ones.
What does Theano do that Python and numpy do not?
- *execution speed optimizations*: Theano can use `g++` to compile parts your expression graph into native machine code, which runs much faster than python.
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- *symbolic differentiation*: Theano can convert a symbolic graph build symbolic graphs for computing gradients.
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- *stability optimizations*: Theano can recognize numerically unstable expressions and compute them with more stable algorithms.
Here's a very simple example of how to use Theano. It doesn't show off many of Theano's features, but it illustrates concretely what Theano is.
.. code-block:: python
import theano
from theano import tensor
a = tensor.fscalar() # declare a symbolic floating-point scalar.
b = tensor.fscalar() # declare a symbolic floating-point scalar.
c = a + b # create a simple expression
f = theano.function([a,b], [c]) # convert the expression into a callable object
# that takes (a,b) values as input and computes a value for c
assert 4.0 == f(1.5, 2.5) # bind 1.5 to 'a', 2.5 to 'b', and evaluate 'c'
Theano is not a programming language in the normal sense because you write a program in Python that builds expressions for Theano. Still it is like a programming language in the sense that to use theano, you have to
- declare variables (``a,b``) and give their types
- build expressions for how to put those variables together
- compile expression graphs to functions in order to use them for computation.
It is good to think of ``theano.function`` as the interface to a compiler
which builds a callable object from a purely symbolic graph; one of
theano's most important features is that ``theano.function`` can
optimize a graph, and even compile some or all of it into native machine
instructions if you pass ``linker='c'`` as a keyword argument.
