In [1]:

    

from theano import *
import theano.tensor as T


    

In [2]:

    

# make a function
# scalar
from theano import function # function is compiled by C
x = T.dscalar('x') # set Variable (symbol)
y = T.dscalar('y')
z = x + y
f = function([x,y], z)


    

In [3]:

    

f(2,3)


    

    
Out[3]:





array(5.0)

In [4]:

    

f(16.3, 12.1)


    

    
Out[4]:





array(28.4)

In [6]:

    

type(x)
type(y)


    

    
Out[6]:





theano.tensor.var.TensorVariable

In [7]:

    

from theano import pp
print pp(z)


    

    




(x + y)

In [8]:

    

# make a function
# matrix
x = T.dmatrix('x')
y = T.dmatrix('y')
z = x + y
f = function([x,y], z)


    

In [9]:

    

f([[1,2], [3,4]], [[10,20], [30,40]])


    

    
Out[9]:





array([[ 11.,  22.],
       [ 33.,  44.]])

In [10]:

    

# make a funtion
# broadcasting
x = T.dmatrix('x')
y = T.dscalar('y')
z = x * y
f = function([x,y], z)


    

In [11]:

    

f([[1,2],[3,4]], 5)


    

    
Out[11]:





array([[  5.,  10.],
       [ 15.,  20.]])

In [12]:

    

# exercise
a = theano.tensor.vector() # declare variable
b = theano.tensor.vector()
out = a**2 + b**2 + 2*a*b                # build symbolic expression
f = theano.function([a, b], out)   # compile function
print(f([0, 1, 2], [2,3,4]))


    

    




[  4.  16.  36.]

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