tf.math.bessel

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)>

Args
`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.

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.

TensorFlow

tf.math / bessel_i1e

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tf.math / bessel_i1e

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