Python tensorflow.math.top_k()用法及代碼示例

TensorFlow是Google設計的開源Python庫，用於開發機器學習模型和深度學習神經網絡。

top_k()用於查找最後一個維度的前k個最大條目(矩陣的每一行)。

Syntax: tensorflow.math.top_k(input, k, sorted, name)

參數：

• input:It’s the input Tensor with 1 or more dimensions.
• k(optional):It’s is 0-D tensor with default value 0.
• sorted(optional):If it’s set to true returned elements will be sorted. Default is True.
• name(optional):It defines the name for the operation.

返回：

• values:  k largest elements along each last dimensional slice.
• indices:indices of values within the last dimension of input.

範例1：

# importing the library 
import tensorflow as tf 
  
# Initializing the input tensor 
a = tf.constant([7, 2, 3, 9, 5], dtype = tf.float64) 
  
# Printing the input tensor 
print('a:', a) 
  
# Calculating result 
res = tf.math.top_k(a) 
  
# Printing the result 
print('Result:', res)

輸出：

a: tf.Tensor([7. 2. 3. 9. 5.], shape=(5, ), dtype=float64)
Result: TopKV2(values=<tf.Tensor:shape=(1, ), dtype=float64, numpy=array([9.])>, 
    indices=<tf.Tensor:shape=(1, ), dtype=int32, numpy=array([3], dtype=int32)>)
    
    
    

範例2：

# importing the library 
import tensorflow as tf 
  
# Initializing the input tensor 
a = tf.constant([[7, 2, 3], [ 9, 5, 7]], dtype = tf.float64) 
  
# Printing the input tensor 
print('a:', a) 
  
# Calculating result 
res = tf.math.top_k(a, k = 2) 
  
# Printing the result 
print('Result:', res)

輸出：

a: tf.Tensor(
[[7. 2. 3.]
 [9. 5. 7.]], shape=(2, 3), dtype=float64)
Result: TopKV2(values=<tf.Tensor:shape=(2, 2), dtype=float64, numpy=
array([[7., 3.],
       [9., 7.]])>, indices=<tf.Tensor:shape=(2, 2), dtype=int32, numpy=
array([[0, 2],
       [0, 2]], dtype=int32)>)