Numpy: how to compute this product between two arrays with different shapes?

Question

I am sorry that the title of my question may sound vague, since I do not know the exact name of such operation.

Given a tensor A (N×M×M) and a one-dimension array b (N), I would like to get another tensor B (N×M×M) such that each item (M×M) in B is the multiplication between A and b .

A possible but ugly solution is to flatten(reshape) A firstly, ie, converting A into a 2D array, then apply a dot operation, and finally reshape back.

Is there any standard/simple operation in numpy to achieve this?

For example,

A = np.ones(12).reshape(3, 2, 2)
b = np.array([2, 3, 4])

The expected B is

[[[2, 2],
  [2, 2]],
 [[3, 3],
  [3, 3]],
 [[4, 4],
  [4, 4]]]

Answer 1

What you are looking for is broadcasting ; in two words, reshape your array b with the value 1 in some dimensions in order to get more control on what will happen; the total number of elements in b will remain unchanged but you may choose how the array will behave during the arithmetic operation:

A*b.reshape((3,1,1))