Python – Numpy operator for multiple external products

import numpy as np
mat1 = np.random.rand(2,3)
mat2 = np.random.rand(2,5)

I want to get a 2x3x5 tensor where each layer is a 3×5 outer product obtained by multiplying the 3×1 transpose row of mat by the 1×5 row of mat2.

Can it be done with numpy matmul?

You can simply use > broadcasting np.newaxis/none After expanding their size-

mat1[...,None]*mat2[:,None]

This will be the highest performance because there is no need for sum-reduction to guarantee service from np.einsum or np.matmul

If you also want to drag in< a href="https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.matmul.html" rel="noreferrer noopener nofollow">np.matmul , it is the same as Broadcast is basically the same:

np.matmul(mat1[...,None],mat2[:,None])

與 np.einsum , If you’re familiar with its string notation, it may look cleaner than the others

np.einsum('ij,ik->ijk',mat1,mat2)
#          23,25->235  (to explain einsum's string notation using axes lens)