|View source on GitHub|
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 )
maxlength are not given, returns a vector with length
tf.reduce_max(arr) + 1 if
arr is non-empty, and length 0 otherwise.
weights are non-None, then index
i of the output stores the sum of the
weights at each index where the corresponding value in
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 = 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
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
arrthat are equal or greater than
maxlength, ensuring that the output has length at most
weightsis None, determines the type of the output bins.
name: A name scope for the associated operations (optional).
A vector with the same dtype as
weights or the given
dtype. The bin
InvalidArgumentError if negative values are provided as an input.