

In [1]:

    
import theano.tensor as T
from theano import function









    



Couldn't import dot_parser, loading of dot files will not be possible.






    



Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 30 days
Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 30 days



In [13]:

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

print f(2, 3)
print f(16.3, 12.1)
print type(x)
print x.type
print T.dscalar
print x.type is T.dscalar









    



5.0
28.4
<class 'theano.tensor.var.TensorVariable'>
TensorType(float64, scalar)
TensorType(float64, scalar)
True

Logistic Regression



In [15]:

    
import theano
import theano.tensor as T









    Out[15]:





array([[ 0.5       ,  0.73105858],
       [ 0.26894142,  0.11920292]])



In [18]:

    
# Logisitic Function
x = T.dmatrix('x')
s = 1 / (1 + T.exp(-x))
logistic = theano.function([x], s)

# test
logistic([[0,1],[-1,-2]])









    Out[18]:





array([[ 0.5       ,  0.73105858],
       [ 0.26894142,  0.11920292]])



In [19]:

    
# Alternate formulation
s2 = (1 + T.tanh(x / 2)) / 2
logistic2 = theano.function([x], s2)

# test
logistic2([[0,1],[-1,-2]])









    Out[19]:





array([[ 0.5       ,  0.73105858],
       [ 0.26894142,  0.11920292]])



In [21]:

    
# Computing more than one thing at the same time
a, b = T.dmatrices('a','b')
diff = a - b
abs_diff = abs(diff)
diff_squared = diff**2
f = theano.function([a,b], [diff, abs_diff, diff_squared])



In [22]:

    
f([[1, 1], [1, 1]],[[0, 1], [2, 3]])









    Out[22]:





[array([[ 1.,  0.],
        [-1., -2.]]), array([[ 1.,  0.],
        [ 1.,  2.]]), array([[ 1.,  0.],
        [ 1.,  4.]])]



In [23]:

    
# Setting a Default Value for an argument
from theano import Param
from theano import function
x, y = T.dscalars('x', 'y')
z = x + y
f = function([x, Param(y, default=1)], z)

print f(33)

print f(33,2)



In [25]:

    
x, y, w = T.dscalars('x','y','w')
z = (x + y) * w
f = function([x, Param(y, default=1), Param(w,default=2, name='w_by_name')], z)

print f(33)
print f(33, 2)
print f(33, 0, 1)
print f(33, w_by_name=1)
print f(33, w_by_name=1, y=0)



In [26]:

    
# Using shared variables
from theano import shared
state = shared(0)
inc = T.iscalar('inc')
accumulator = function([inc], state, updates=[(state, state+inc)])



In [27]:

    
state.get_value()









    Out[27]:





array(0)



In [30]:

    
accumulator(1)









    Out[30]:





array(0)



In [ ]: