I have two matrices which have shape (1,3) and (3,1)
And i want to add them and output a matrix (3,3)
In numpy, it works like this:
import numpy as np
a = np.array([0,1,2])
b = a.reshape(3,1)
label_vec1 = T.imatrix('label_vector')
label_vec2 = T.imatrix('label_vector')
alpha_matrix = T.add(label_vec1, label_vec2)
alpha_matrix_compute = theano.function([label_vec1,label_vec2],alpha_matrix)
a = numpy.array([[0,1,2]])
b = numpy.array([,,])#
a1=theano.shared(numpy.asarray(a), broadcastable =(True,False))
b1 = theano.shared(numpy.asarray(b),broadcastable=(False, True))
c = alpha_matrix_compute(a1,b1)
TypeError: ('Bad input argument to theano function at index 0(0-based)', 'Expected an array-like object, but found a Variable: maybe you are trying to call a function on a (possibly shared) variable instead of a numeric array?')
After serval hours searching and reading, i found an answer here.
When define a numpy array to shared variable, it becomes symbolic variable and are not numeric array anymore.
To compute with shared variables, the code should be modified as follow:
a = numpy.array([[0,1,2]]) b = numpy.array([,,])# #b = a.reshape(a.shape,a.shape) a1=theano.shared(numpy.asarray(a), broadcastable =(True,False), borrow =True) b1 = theano.shared(numpy.asarray(b),broadcastable=(False, True),borrow = True) alpha_matrix = T.add(a1, b1) alpha_matrix_compute = theano.function(, alpha_matrix) s_t_1 = timeit.default_timer() for i in range(10000): c = alpha_matrix_compute() e_t_1 = timeit.default_timer() for i in range(10000): c = numpy.add(a,b) e_t_2 = timeit.default_timer() print('t1:',e_t_1-s_t_1) print('t2:',e_t_2-e_t_1)
Also, I compared the time consuming of broadcastable matrix add using theano and numpy. The result is
t1: 0.25077449798077067 t2: 0.022930744192201424
It seems that numpy is faster. In fact, the data transfer between GPU and CPU took a lot of time. That is the reason why t2 is much smaller that t1.