TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
count_nonzero() is used to count the number of non zero elements in a Tensor.
Syntax: tf.math.count_nonzero( input, axis, keepdim, dtype, name)
Parameters:
- input: It’s a Tensor that need to be reduced.
- axis(optional): It defines the axis along which input need to be reduced. Allowed range for this is [-rank(input), rank(input)). If no value is given then default is none i.e. input will be reduced along all axis.
- keepdim(optional): If it is true, it will retain the reduced dimensions with length 1.
- dtype(optional): It defines the output dtype. Default if int32.
- name(optional): It defines the name for the operation.
Returns:
It returns a tensor that contains the number of non-zero values.
Example 1:
Python3
# importing the libraryimport tensorflow as tf# initializing the inputa = tf.constant([1,0,2,5,0], dtype = tf.int32) # 3 non-zero # Printing the inputprint("Input: ",a)# Counting non-zerores = tf.math.count_nonzero(a)# Printing the resultprint("No of non-zero elements: ",res) |
Output:
Input: tf.Tensor([1 0 2 5 0], shape=(5,), dtype=int32) No of non-zero elements: tf.Tensor(3, shape=(), dtype=int64)
Example 2: When input tensor is of type string, “” is considered as empty string. ” ” is non zero.
Python3
# importing the libraryimport tensorflow as tf# initializing the inputa = tf.constant([""," ","a","b"]) # 3 non-zero # Printing the inputprint("Input: ",a)# Counting non-zerores = tf.math.count_nonzero(a)# Printing the resultprint("No of non-zero elements: ",res) |
Output:
Input: tf.Tensor([b'' b' ' b'a' b'b'], shape=(4,), dtype=string) No of non-zero elements: tf.Tensor(3, shape=(), dtype=int64)
