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)
