dot and inner returns the same result for 1D arrays.
However, for 2D arrays (matrices), dot gives the matrix product, whereas inner gives the sum-product over the last axis.
References
inner vs. dot
inner document
dot document
what is sum product?
sum product of A and B is given by numpy.inner(A,B) as follows
A's row i is element-wise multiplied by B's row j and the values are added to get the (i,j) element
[[x1*y1+x2*y2, x1*y3+x2*y4], [x3*y1+x4*y2, x3*y3+x4*y4]]
However, for 2D arrays (matrices), dot gives the matrix product, whereas inner gives the sum-product over the last axis.
References
inner vs. dot
inner document
dot document
what is sum product?
A = [[x1, x2], [x3, x4]]
B = [[y1, y2], [y3, y4]]
sum product of A and B is given by numpy.inner(A,B) as follows
A's row i is element-wise multiplied by B's row j and the values are added to get the (i,j) element
[[x1*y1+x2*y2, x1*y3+x2*y4], [x3*y1+x4*y2, x3*y3+x4*y4]]
