# Python – tensorflow.math.unsorted_segment_sqrt_n()

• Last Updated : 16 Jun, 2020

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

unsorted_segment_sqrt_n() is used to find the sum of segments divided by sqrt(N).

Syntax: tensorflow.math.unsorted_segment_sqrt_n( data, segment_ids, num_segments, name )

Parameter:

• data: It is a tensor. Allowed dtypes are floating point or complex.
• segment_ids: It’s 1-D tensor with sorted values. It’s size should be equal to  size of first dimension of data. It represents number of distinct segment IDs. Allowed dtypes are int32 and int64.
• num_segments: It is a Tensor. Allowed dtypes are int32 and int64.
• name(optional): It defines the name for the operation.

Return: It returns a tensor of dtype as x.

Example 1:

## Python3

 `# importing the library ` `import` `tensorflow as tf ` ` `  `# Initializing the input tensor ` `data ``=` `tf.constant([``1``, ``2``, ``3``], dtype ``=` `tf.float64) ` `segment_ids ``=` `tf.constant([``2``, ``2``, ``2``]) ` ` `  `# Printing the input tensor ` `print``(``'data: '``, data) ` `print``(``'segment_ids: '``, segment_ids) ` ` `  `# Calculating result ` `res ``=` `tf.math.unsorted_segment_sqrt_n(data, segment_ids, tf.constant(``3``)) ` ` `  `# Printing the result ` `print``(``'Result: '``, res) `

Output:

```data:  tf.Tensor([1. 2. 3.], shape=(3, ), dtype=float64)
segment_ids:  tf.Tensor([2 2 2], shape=(3, ), dtype=int32)
Result:  tf.Tensor([0.         0.         3.46410162], shape=(3, ), dtype=float64)

```

Example 2:

## Python3

 `# importing the library ` `import` `tensorflow as tf ` ` `  `# Initializing the input tensor ` `data ``=` `tf.constant([[``1``, ``2``, ``3``], [``4``, ``5``, ``6``], [``7``, ``8``, ``9``]], dtype ``=` `tf.float64) ` `segment_ids ``=` `tf.constant([``0``, ``0``, ``2``]) ` ` `  `# Printing the input tensor ` `print``(``'data: '``, data) ` `print``(``'segment_ids: '``, segment_ids) ` ` `  `# Calculating result ` `res ``=` `tf.math.unsorted_segment_sqrt_n(data, segment_ids, tf.constant(``3``)) ` ` `  `# Printing the result ` `print``(``'Result: '``, res) `

Output:

```data:  tf.Tensor(
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]], shape=(3, 3), dtype=float64)
segment_ids:  tf.Tensor([0 0 2], shape=(3, ), dtype=int32)
Result:  tf.Tensor(
[[3.53553391 4.94974747 6.36396103]
[0.         0.         0.        ]
[7.         8.         9.        ]], shape=(3, 3), dtype=float64)
```

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