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Python – tensorflow.math.top_k()

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  • Last Updated : 16 Jun, 2020
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TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

top_k()  is used to find top k largest entries for the last dimension(along each row for matrices).

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

Parameter:

  • 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.

Returns:

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

Example 1:

Python3




# 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)


Output:

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)>)
    
    
    

Example 2:

Python3




# 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)


Output:

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)>)

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