Python – tensorflow.math.unsorted_segment_mean()

• 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_mean() is used to find the mean of segments.

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

Parameter:

• data: It is a tensor. Allowed dtypes  are float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
• 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``]) ` `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_mean(data, segment_ids, tf.constant(``3``)) ` ` `  `# Printing the result ` `print``(``'Result: '``, res) `

Output:

```data:  tf.Tensor([1 2 3], shape=(3, ), dtype=int32)
segment_ids:  tf.Tensor([2 2 2], shape=(3, ), dtype=int32)
Result:  tf.Tensor([0. 0. 2.], 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``]]) ` `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_mean(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=int32)
segment_ids:  tf.Tensor([0 0 2], shape=(3, ), dtype=int32)
Result:  tf.Tensor(
[[2.5 3.5 4.5]
[0.  0.  0. ]
[7.  8.  9. ]], shape=(3, 3), dtype=float64)
```

My Personal Notes arrow_drop_up
Related Articles