tf.math.bincount

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

tf.math.bincount(
    arr,
    weights=None,
    minlength=None,
    maxlength=None,
    dtype=tf.dtypes.int32,
    name=None,
    axis=None,
    binary_output=False
)

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.

Bin-counting on a certain axis

This example takes a 2 dimensional input and returns a Tensor with bincounting on each sample.

data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)
tf.math.bincount(data, axis=-1)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
  array([[1, 1, 1, 1],
         [2, 1, 1, 0]], dtype=int32)>

Bin-counting with binary_output

This example gives binary output instead of counting the occurrence.

data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)
tf.math.bincount(data, axis=-1, binary_output=True)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
  array([[1, 1, 1, 1],
         [1, 1, 1, 0]], dtype=int32)>

Missing zeros in SparseTensor

Note that missing zeros (implict zeros) in SparseTensor are NOT counted. This supports cases such as 0 in the values tensor indicates that index/id 0is present and a missing zero indicates that no index/id is present.

If counting missing zeros is desired, there are workarounds. For the axis=0 case, the number of missing zeros can computed by subtracting the number of elements in the SparseTensor's values tensor from the number of elements in the dense shape, and this difference can be added to the first element of the output of bincount. For all cases, the SparseTensor can be converted to a dense Tensor with tf.sparse.to_dense before calling tf.math.bincount.

arr A Tensor, RaggedTensor, or SparseTensor whose values should be counted. These tensors must have a rank of 2 if axis=-1.
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).
axis The axis to slice over. Axes at and below axis will be flattened before bin counting. Currently, only 0, and -1 are supported. If None, all axes will be flattened (identical to passing 0).
binary_output If True, this op will output 1 instead of the number of times a token appears (equivalent to one_hot + reduce_any instead of one_hot + reduce_add). Defaults to False.

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

InvalidArgumentError if negative values are provided as an input.