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 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 libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([7, 2, 3, 9, 5], dtype = tf.float64)# Printing the input tensorprint('a: ', a)# Calculating resultres = tf.math.top_k(a)# Printing the resultprint('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 libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([[7, 2, 3], [ 9, 5, 7]], dtype = tf.float64)# Printing the input tensorprint('a: ', a)# Calculating resultres = tf.math.top_k(a, k = 2)# Printing the resultprint('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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