import theano
from theano import tensor

import numpy

# Two scalar symbolic variables
a = tensor.scalar()
b = tensor.scalar()

# Definition of output symbolic variable
c = a * b
# Definition of the function computing it
fprop = theano.function([a,b], [c])

# Initialize numerical variables
a_val = numpy.array(12.)
b_val = numpy.array(2.)
print 'a_val =', a_val
print 'b_val =', b_val

# Numerical value of output is returned by the call to "fprop"
c_val = fprop(a_val, b_val)
print 'c_val =', c_val


# Definition of simple update (increment by one)
new_b = b + 1
update = theano.function([b], [new_b])

# New numerical value of b is returned by the call to "update"
b_val = update(b_val)
print 'new b_val =', b_val
# We can use the new value in "fprop"
c_val = fprop(a_val, b_val)
print 'c_val =', c_val


# Definition of in-place update (increment by one)
re_new_b = tensor.add_inplace(b, 1.)
re_update = theano.function([b], [re_new_b])

# "re_update" can be used the same way as "update"
b_val = re_update(b_val)
print 'new b_val =', b_val
# We can use the new value in "fprop"
c_val = fprop(a_val, b_val)
print 'c_val =', c_val

# It is not necessary to keep the return value when the update is done in place
re_update(b_val)
print 'new b_val =', b_val
c_val = fprop(a_val, b_val)
print 'c_val =', c_val