Python – tensorflow.math.unsorted_segment_max()

TensorFlow 是由 Google 设计的开源Python库，用于开发机器学习模型和深度学习神经网络。

unsorted_segment_max()用于查找张量段中的最大元素。

Syntax: tensorflow.math.unsorted_segment_max(  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. 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.

示例 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_max(data, segment_ids, tf.constant(3))
  
# Printing the result
print('Result: ', res)

输出：

data:  tf.Tensor([1 2 3], shape=(3, ), dtype=int32)
segment_ids:  tf.Tensor([2 2 2], shape=(3, ), dtype=int32)
Result:  tf.Tensor([-2147483648 -2147483648           3], shape=(3, ), dtype=int32)

示例 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_max(data, segment_ids, tf.constant(3))
  
# Printing the result
print('Result: ', res)

输出：

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           5           6]
 [-2147483648 -2147483648 -2147483648]
 [          7           8           9]], shape=(3, 3), dtype=int32)