# 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)
```

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