Dot product of tensor with a grid

[zml-dub]

How can I write the inner product between a tensor and locations on the grid?

I have in my simulation code a function $$r(x) = e^{- C \cdot x}$$, where $C: R^{k \times d}$ where $d$ is the dimensionality of the grid/state space.

So in my code I have the following:

C = math.random_normal(batch(neurons=3), channel(vector=['x','y']))

def U_f(xs): #   
    r = lambda xx:  C.vector['x']*xx.vector['x'] + C.vector['y']*xx.vector['y']     ##here I want to rewrite this as dot product 
    U = phi.math.exp( - r(xs) )     
    return U

DOMAIN = dict(  x=nx, y=ny, bounds=Box(x=(low_x, up_x), y=(low_y, up_y) ) )

g1 = lambda xs: U_f(xs)
Ut = CenteredGrid(g1, extrapolation.ZERO, **DOMAIN)

However I would like to express the dot product $C\cdot x$ so that I wouldn't have to alter my code when I change the dimensionality of the grid. Do you have any clue on this?

[holl-]

Hi, to compute the dot product along the vector dimension, you can write a.vector * b.vector:

U = phi.math.exp( - (xs.vector * C.vector))

Hope that helps!

[zml-dub]

Thank you!