accessing_tensor_values.py

# Ensure python 3 forward compatibility
from __future__ import print_function

import numpy as np
import matplotlib.pyplot as plt
import theano
# By convention, the tensor submodule is loaded as T
import theano.tensor as T


# The theano.tensor submodule has various primitive symbolic variable types.
# Here, we're defining a scalar (0-d) variable.
# The argument gives the variable its name.
foo = T.scalar('foo')
# Now, we can define another variable bar which is just foo squared.
bar = foo**2
# It will also be a theano variable.
print(type(bar))
print(bar.type)
# Using theano's pp (pretty print) function, we see that 
# bar is defined symbolically as the square of foo
print(theano.pp(bar))


# We can't compute anything with foo and bar yet.
# We need to define a theano function first.
# The first argument of theano.function defines the inputs to the function.
# Note that bar relies on foo, so foo is an input to this function.
# theano.function will compile code for computing values of bar given values of foo
f = theano.function([foo], bar)
print(f(3))


# Alternatively, in some cases you can use a symbolic variable's eval method.
# This can be more convenient than defining a function.
# The eval method takes a dictionary where the keys are theano variables and the values are values for those variables.
print(bar.eval({foo: 3}))

# We can also use Python functions to construct Theano variables.
# It seems pedantic here, but can make syntax cleaner for more complicated examples.
def square(x):
    return x**2

bar = square(foo)
print(bar.eval({foo: 3}))