Python - tensorflow.math.unsorted_segment_min()

Introduction

In TensorFlow, tensorflow.math.unsorted_segment_min() is a function that computes the minimum value in each segment of a tensor along a specified axis, where the segments are defined by an index tensor. This function helps to split a tensor into segments and find the minimum value within each segment.

Syntax

The syntax of tensorflow.math.unsorted_segment_min() function is:

tf.math.unsorted_segment_min(
    data,
    segment_ids,
    num_segments,
    name=None
)

Parameters

The function tensorflow.math.unsorted_segment_min() accepts the following parameters:

• data: The tensor from which minimum values are to be computed.
• segment_ids: An index tensor that defines the segments. This tensor should have the same size as the first dimension of the data tensor.
• num_segments: The number of segments. This parameter is used to specify the size of the output tensor.
• name (optional): A name for the operation.

Returns

The function returns a tensor containing the minimum values from each segment along the specified axis.

Example

import tensorflow as tf

data = tf.constant([3, 1, 4, 1, 5, 9, 2, 6])
segment_ids = tf.constant([0, 1, 0, 2, 2, 1, 0, 2])
num_segments = 3

result = tf.math.unsorted_segment_min(data, segment_ids, num_segments)

print(result)

Output:

tf.Tensor([3 1 2], shape=(3,), dtype=int32)

In this example, we have a data tensor [3, 1, 4, 1, 5, 9, 2, 6] and segment_ids [0, 1, 0, 2, 2, 1, 0, 2]. The num_segments parameter is set to 3. The function unsorted_segment_min() splits the data tensor into segments based on the segment_ids and computes the minimum value in each segment. The output tensor contains the minimum values [3, 1, 2] from each segment.