Related Articles
Python – tensorflow.math.multiply()
• Last Updated : 10 Jun, 2020

TensorFlow is open-source python library designed by Google to develop Machine Learning models and deep learning  neural networks. multiply() is used to find element wise x*y. It supports broadcasting.

Syntax: tf.math.multiply(x, y, name)

Parameter:

• x: It’s the input tensor. Allowed dtype for this tensor are bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
• y: It’s the input tensor of same dtype as x.
• name(optional): It’s defines the name for the operation.

Returns:
It returns a tensor of same dtype as x.

Example 1:

## Python3

 `# Importing the library``import` `tensorflow as tf`` ` `# Initializing the input tensor``a ``=` `tf.constant([.``2``, .``5``, .``7``, ``1``], dtype ``=` `tf.float64)``b ``=` `tf.constant([.``1``, .``3``, ``1``, ``5``], dtype ``=` `tf.float64)`` ` `# Printing the input tensor``print``(``'a: '``, a)``print``(``'b: '``, b)`` ` `# Calculating result``res ``=` `tf.math.multiply(x ``=` `a, y ``=` `b)`` ` `# Printing the result``print``(``'Result: '``, res)`

Output:

```a:  tf.Tensor([0.2 0.5 0.7 1. ], shape=(4, ), dtype=float64)
b:  tf.Tensor([0.1 0.3 1.  5. ], shape=(4, ), dtype=float64)
Result:  tf.Tensor([0.02 0.15 0.7  5.  ], shape=(4, ), dtype=float64)

```

Example 2: Complex number multiplication

## Python3

 `# importing the library``import` `tensorflow as tf`` ` `# Initializing the input tensor``a ``=` `tf.constant([``-``2` `+` `3j``, ``-``5` `+` `4j``, ``7` `+` `2j``, ``1` `+` `7j``], dtype ``=` `tf.complex128)``b ``=` `tf.constant([``-``1` `+` `2j``, ``-``6` `+` `8j``, ``8` `+` `2j``, ``0` `+` `1j``], dtype ``=` `tf.complex128)`` ` `# Printing the input tensor``print``(``'a: '``, a)``print``(``'b: '``, b)`` ` `# Calculating result``res ``=` `tf.math.multiply(x ``=` `a, y ``=` `b)`` ` `# Printing the result``print``(``'Result: '``, res)`

Output:

```a:  tf.Tensor([-2.+3.j -5.+4.j  7.+2.j  1.+7.j], shape=(4, ), dtype=complex128)
b:  tf.Tensor([-1.+2.j -6.+8.j  8.+2.j  0.+1.j], shape=(4, ), dtype=complex128)
Result:  tf.Tensor([-4. -7.j -2.-64.j 52.+30.j -7. +1.j], shape=(4, ), dtype=complex128)
```

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up