tf.raw_ops.UniqueWithCountsV2

Finds unique elements along an axis of a tensor.

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Compat aliases for migration

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tf.compat.v1.raw_ops.UniqueWithCountsV2

tf.raw_ops.UniqueWithCountsV2(
    x, axis, out_idx=tf.dtypes.int32, name=None
)

This operation either returns a tensor y containing unique elements along the axis of a tensor. The returned unique elements is sorted in the same order as they occur along axis in x. This operation also returns a tensor idx and a tensor count that are the same size as the number of the elements in x along the axis dimension. The idx contains the index in the unique output y and the count contains the count in the unique output y. In other words, for an 1-D tensor x with `axis = None:

y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]

For example:

# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx, count = unique_with_counts(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
count ==> [2, 1, 3, 1, 2]

For an 2-D tensor x with axis = 0:

# tensor 'x' is [[1, 0, 0],
#                [1, 0, 0],
#                [2, 0, 0]]
y, idx, count = unique_with_counts(x, axis=0)
y ==> [[1, 0, 0],
       [2, 0, 0]]
idx ==> [0, 0, 1]
count ==> [2, 1]

For an 2-D tensor x with axis = 1:

# tensor 'x' is [[1, 0, 0],
#                [1, 0, 0],
#                [2, 0, 0]]
y, idx, count = unique_with_counts(x, axis=1)
y ==> [[1, 0],
       [1, 0],
       [2, 0]]
idx ==> [0, 1, 1]
count ==> [1, 2]