I believe in Theano world, "dot" is only meant for vector-to-vector dot
products. Where as in Numpy world, it as an overloaded operator/function
that is sometimes a matrix-vector product, a matrix-matrix product or a
vector-vector product depending on the input.
On Tue, Oct 2, 2018 at 7:40 PM DL_user <stehu...@gmail.com> wrote:
> I would think z=np.dot(x,y) is more meaningful but anyway apparently dot()
> has different meanings in Theano's world.
>
> thanks
>
> On Tuesday, October 2, 2018 at 3:59:14 PM UTC-7, Buruk Aregawi wrote:
>>
>> It seems that np.dot is interpreting this as the more standard A*x where
>> A is a 2x3 matrix and x is a 3 dimensional vector. Where as theano is
>> interpreting it is X*A where X is a 3x1 matrix and A is a 2x3 matrix.
>> If you do
>> z = T.dot(x,y)
>> instead of
>> z = np.dot(x,y)
>> they will both work the same and the theano function will interpret it as
>> the standard A*x.
>>
>> On Tuesday, October 2, 2018 at 5:55:10 PM UTC-4, DL_user wrote:
>>>
>>> Why do these two functions have different outputs, even both of them
>>> defined from numpy's dot() function:
>>>
>>> x = T.dmatrix('x')
>>>
>>> y = T.dvector('y')
>>>
>>> z = np.dot(x,y)
>>>
>>> f = theano.function([x,y],z)
>>>
>>> f([[1,2,3],[4,5,6]],[7,8,9])
>>> Out[31]:
>>> array([[ 7., 16., 27.],
>>> [28., 40., 54.]])
>>>
>>> np.dot([[1,2,3],[4,5,6]],[7,8,9])
>>> Out[32]: array([ 50, 122])
>>>
>> --
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