# Element wise multiplication of two list that are tf.Tensor in tensorflow

## Issue

What is the fastest way to do an element-wise multiplication between a tensor and an array in Tensorflow 2?

For example, if the tensor `T` (of type tf.Tensor) is:

``````[[0, 1],
[2, 3]]
``````

and we have an array `a` (of type np.array):

``````[0, 1, 2]
``````

I wand to have:

``````[[[0, 0],
[0, 0]],

[[0, 1],
[2, 3]],

[[0, 2],
[4, 6]]]
``````

as output.

## Solution

What you describe is the outer product of two tensors. This can be expressed simply using Tensorflow’s broadcasting rules.

``````import numpy as np
import tensorflow as tf

t = tf.constant([[0, 1],[2, 3]])
a = np.array([0, 1, 2])

# (2,2) x (3,1,1) produces the desired shape of (3,2,2)
result = t * a.reshape((-1, 1, 1))
# Alternatively: result = t * a[:, np.newaxis, np.newaxis]

print(result)
``````

results in

``````<tf.Tensor: shape=(3, 2, 2), dtype=int32, numpy=
array([[[0, 0],
[0, 0]],

[[0, 1],
[2, 3]],

[[0, 2],
[4, 6]]], dtype=int32)>
``````