

In [1]:

    
# http://deeplearning.net/software/theano/tutorial/index.html
from theano import *
import theano.tensor as T



In [2]:

    
x = T.dscalar('x')
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 [5]:

    
type(x)









    Out[5]:





theano.tensor.var.TensorVariable



In [7]:

    
x.type









    Out[7]:





TensorType(float64, scalar)



In [8]:

    
T.dscalar









    Out[8]:





TensorType(float64, scalar)



In [10]:

    
x.type is T.dscalar









    Out[10]:





True



In [12]:

    
print pp(z)









    



(x + y)



In [14]:

    
z.eval({x : 16.3, y : 12.1})









    Out[14]:





array(28.4)



In [15]:

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



In [17]:

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









    Out[17]:





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

The following types are available:

byte: bscalar, bvector, bmatrix, brow, bcol, btensor3, btensor4
16-bit integers: wscalar, wvector, wmatrix, wrow, wcol, wtensor3, wtensor4
32-bit integers: iscalar, ivector, imatrix, irow, icol, itensor3, itensor4
64-bit integers: lscalar, lvector, lmatrix, lrow, lcol, ltensor3, ltensor4
float: fscalar, fvector, fmatrix, frow, fcol, ftensor3, ftensor4
double: dscalar, dvector, dmatrix, drow, dcol, dtensor3, dtensor4
complex: cscalar, cvector, cmatrix, crow, ccol, ctensor3, ctensor4



In [18]:

    
a = T.vector()



In [19]:

    
out = a + a ** 10



In [20]:

    
f = function([a], out)



In [22]:

    
print f([0, 1, 2])









    



[    0.     2.  1026.]



In [23]:

    
b = T.vector()



In [24]:

    
out = a**2 + b**2 + 2*a*b



In [27]:

    
f = function([a,b], out)



In [28]:

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









    Out[28]:





array([ 25.,  49.])