tf.sets.intersection

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

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),   # ])

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.

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 intersections.