Open In App

Python – tensorflow.math.segment_min()

Improve
Improve
Like Article
Like
Save
Share
Report

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

segment_min() is used to find the minimum element in segments of a tensor.

Syntax: tensorflow.math.segment_min(  data, segment_ids, 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. 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.segment_min(data, segment_ids)
  
# 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 1], 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]], dtype = 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.segment_min(data, segment_ids)
  
# 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(
[[1.  2.  3. ]
 [0.  0.  0. ]
 [7.  8.  9. ]], shape=(3, 3), dtype=float64)


Last Updated : 12 Jun, 2020
Like Article
Save Article
Previous
Next
Share your thoughts in the comments
Similar Reads