TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
unsorted_segment_min() is used to find the minimum element in segments of Tensor.
Syntax: tensorflow.math.unsorted_segment_min( 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_min(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([2147483647 2147483647 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]]) 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_min(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( [[ 1 2 3] [2147483647 2147483647 2147483647] [ 7 8 9]], shape=(3, 3), dtype=int32)
