Python – tensorflow.math.segment_min()

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。

segment_min()用于查找张量段中的最小元素。

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.

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.segment_min(data, segment_ids)
  
# 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([0 0 1], 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]], 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)

输出：

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)