tf.sets.union

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Compute set union of elements in last dimension of a and b.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.sets.set_union, tf.compat.v1.sets.union

tf.sets.union(
    a, b, validate_indices=True
)

All but the last dimension of a and b must match.

Example:

import tensorflow as tf
import collections

# [[{1, 2}, {3}], [{4}, {5, 6}]]
a = collections.OrderedDict([
    ((0, 0, 0), 1),
    ((0, 0, 1), 2),
    ((0, 1, 0), 3),
    ((1, 0, 0), 4),
    ((1, 1, 0), 5),
    ((1, 1, 1), 6),
])
a = tf.sparse.SparseTensor(list(a.keys()), list(a.values()),
                           dense_shape=[2, 2, 2])

# [[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]
b = collections.OrderedDict([
    ((0, 0, 0), 1),
    ((0, 0, 1), 3),
    ((0, 1, 0), 2),
    ((1, 0, 0), 4),
    ((1, 0, 1), 5),
    ((1, 1, 0), 5),
    ((1, 1, 1), 6),
    ((1, 1, 2), 7),
    ((1, 1, 3), 8),
])
b = tf.sparse.SparseTensor(list(b.keys()), list(b.values()),
                           dense_shape=[2, 2, 4])

# `set_union` is applied to each aligned pair of sets.
tf.sets.union(a, b)

# The result will be a equivalent to either of:
#
# np.array([[{1, 2, 3}, {2, 3}], [{4, 5}, {5, 6, 7, 8}]])
#
# collections.OrderedDict([
#     ((0, 0, 0), 1),
#     ((0, 0, 1), 2),
#     ((0, 0, 2), 3),
#     ((0, 1, 0), 2),
#     ((0, 1, 1), 3),
#     ((1, 0, 0), 4),
#     ((1, 0, 1), 5),
#     ((1, 1, 0), 5),
#     ((1, 1, 1), 6),
#     ((1, 1, 2), 7),
#     ((1, 1, 3), 8),
# ])

Args
a	Tensor or SparseTensor of the same type as b. If sparse, indices must be sorted in row-major order.
b	Tensor or SparseTensor of the same type as a. If sparse, indices must be sorted in row-major order.
validate_indices	Whether to validate the order and range of sparse indices in a and b.

Returns
A SparseTensor whose shape is the same rank as a and b, and all but the last dimension the same. Elements along the last dimension contain the unions.