z.Lam - 3 months ago 24

Python Question

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)

a+b

And it outputs:

`array([0,1,2],`

[1,2,3],

[2,3,4]]

Now i would like use theano to do the same things so as to speed up the code.

My code is shown as follow:

`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([[0],[1],[2]])#

a1=theano.shared(numpy.asarray(a), broadcastable =(True,False))

b1 = theano.shared(numpy.asarray(b),broadcastable=(False, True))

c = alpha_matrix_compute(a1,b1)

But it outputs

`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?')`

I am confused, why would it happens?

BTW, will it be faster using theano with GPU than using numpy?

Answer

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([[0],[1],[2]])#
#b = a.reshape(a.shape[1],a.shape[0])
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.