tf.sets.intersection

Compute set intersection 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.intersection, tf.compat.v1.sets.set_intersection

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

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

Example:

import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor:
# a = np.array([[{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])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]])
b = collections.OrderedDict([
    ((0, 0, 0), 1),
    ((1, 0, 0), 4),
    ((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])

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

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