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Python – tensorflow.math.unsorted_segment_prod()

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  • Last Updated : 16 Jun, 2020
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TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

unsorted_segment_prod() is used to find the product of segments.

Syntax: tensorflow.math.unsorted_segment_prod( 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_prod(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([1 1 6], shape=(3, ), dtype=int32)




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_prod(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(
[[ 4 10 18]
 [ 1  1  1]
 [ 7  8  9]], shape=(3, 3), dtype=int32)

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