Python - tensorflow.math.count_nonzero()

The tensorflow.math.count_nonzero() function is used to count the number of non-zero elements in a tensor. It is a part of the TensorFlow library, which is widely used for numerical computations and machine learning tasks.

Syntax

tensorflow.math.count_nonzero(
    input_tensor,
    axis=None,
    keepdims=None,
    dtype=tf.dtypes.int64,
    name=None
)

Parameters

The count_nonzero() function takes the following parameters:

• input_tensor: A tensor containing values for which non-zero elements need to be counted.
• axis: (Optional) The dimensions along which the non-zero counts are calculated. If not specified, all elements are considered.
• keepdims: (Optional) A boolean value indicating whether to keep the reduced dimensions with length 1. If not specified, it defaults to None, which implies keeping the reduced dimensions.
• dtype: (Optional) The data type of the output tensor. Defaults to tf.dtypes.int64.
• name: (Optional) A string name for the operation.

Returns

The function returns a tensor with the count of non-zero elements along the specified axis or in the whole tensor.

Example

Consider the following example:

import tensorflow as tf

# Create a tensor
tensor = tf.constant([[0, 1, 0], [2, 0, 3], [0, 4, 5]])

# Count non-zero elements in the tensor
count = tf.math.count_nonzero(tensor)

# Print the result
print(count)

Output:

<tf.Tensor: shape=(), dtype=int64, numpy=5>

In this example, we created a tensor and used tensorflow.math.count_nonzero() to count the non-zero elements in the tensor. The output, 5, indicates that there are five non-zero elements in the tensor.

Conclusion

The tensorflow.math.count_nonzero() function is a useful tool for counting non-zero elements in a tensor. It provides flexibility with options to specify the axis and keep or reduce specific dimensions. This function is commonly used in various TensorFlow applications, especially in scenarios where counting non-zero elements is required.