One correction above for posterity. The Theano version you posted, f =
theano.function([x,y], np.dot(x, y)), does two vector-vector dot products.
So it interprets your input matrix as two separate vectors and separately
does a dot of each of them with the input vector, y. Also, note that the
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
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
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
