tf.compat.v2.math.bincount

Counts the number of occurrences of each value in an integer array.

tf.compat.v2.math.bincount(
    arr,
    weights=None,
    minlength=None,
    maxlength=None,
    dtype=tf.dtypes.int32,
    name=None
)

If minlength and maxlength are not given, returns a vector with length tf.reduce_max(arr) + 1 if arr is non-empty, and length 0 otherwise. If weights are non-None, then index i of the output stores the sum of the value in weights at each index where the corresponding value in arr is i.

values = tf.constant([1,1,2,3,2,4,4,5])
tf.math.bincount(values) #[0 2 2 1 2 1]

Vector length = Maximum element in vector values is 5. Adding 1, which is 6 will be the vector length.

Each bin value in the output indicates number of occurrences of the particular index. Here, index 1 in output has a value 2. This indicates value 1 occurs two times in values.

values = tf.constant([1,1,2,3,2,4,4,5])
weights = tf.constant([1,5,0,1,0,5,4,5])
tf.math.bincount(values, weights=weights) #[0 6 0 1 9 5]

Bin will be incremented by the corresponding weight instead of 1. Here, index 1 in output has a value 6. This is the summation of weights corresponding to the value in values.

Args:

arr: An int32 tensor of non-negative values.
weights: If non-None, must be the same shape as arr. For each value in arr, the bin will be incremented by the corresponding weight instead of 1.
minlength: If given, ensures the output has length at least minlength, padding with zeros at the end if necessary.
maxlength: If given, skips values in arr that are equal or greater than maxlength, ensuring that the output has length at most maxlength.
dtype: If weights is None, determines the type of the output bins.
name: A name scope for the associated operations (optional).

Returns:

A vector with the same dtype as weights or the given dtype. The bin values.

Raises:

InvalidArgumentError if negative values are provided as an input.