In [2]:

    

import numpy as np
import theano.tensor as T
import theano

#activation
x = T.dmatrix('x')
s = 1 / (1 + T.exp(-x)) #np.exp()
logistic = theano.function([x], s) 
print logistic([[0,1], [2,3]])


    

    




[[ 0.5         0.73105858]
 [ 0.88079708  0.95257413]]

In [15]:

    

#mulitipy outputs
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])

x1, x2, x3 = f(np.ones((2,2)), np.arange(4).reshape((2,2)))
print x1, x2 ,x3


    

    




[[ 1.  0.]
 [-1. -2.]] [[ 1.  0.]
 [ 1.  2.]] [[ 1.  0.]
 [ 1.  4.]]

In [18]:

    

#name for a function
x, y, w = T.dscalars('x','y','w')
z = (x+y)*w
f = theano.function([x, theano.In(y, value=1), theano.In(w, value=2, name='weights')], z)

print f(23)


    

    




48.0