In [1]:

    

import numpy as np


    

In [2]:

    

np.asarray([[1., 2], [3, 4], [5, 6]])


    

    
Out[2]:





array([[ 1.,  2.],
       [ 3.,  4.],
       [ 5.,  6.]])

In [4]:

    

np.asarray([[1., 2], [3, 4], [5, 6]]).shape


    

    
Out[4]:





(3L, 2L)

In [5]:

    

np.asarray([[1. ,2], [3, 4], [5, 6]])[2, 0]


    

    
Out[5]:





5.0

In [6]:

    

a = np.asarray([1.0, 2.0, 3.0])
b = 2.0
a*b


    

    
Out[6]:





array([ 2.,  4.,  6.])

In [7]:

    

import theano.tensor as T
from theano import function
x = T.dscalar('x')
y = T.dscalar('y')
z = x + y
f = function([x, y], z)


    

In [9]:

    

f(2, 3)


    

    
Out[9]:





array(5.0)

In [10]:

    

f(16.3, 12.1)


    

    
Out[10]:





array(28.4)

In [11]:

    

from theano import pp
print pp(z)


    

    




(x + y)

In [12]:

    

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


    

In [13]:

    

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


    

    
Out[13]:





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

In [15]:

    

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


    

    
Out[15]:





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

In [20]:

    

a = T.vector()
out = a + a ** 10
f = function([a], out)
print f([0, 1, 2])


    

    




[    0.     2.  1026.]

In [28]:

    

a = T.vector()
b = T.vector()
out = a ** 2 + b ** 2 + 2*a*b
f = function([a, b], out)
print f([1, 2], [4 ,5])


    

    




[ 25.  49.]

In [ ]: