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tf.sets.difference

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

tf.sets.difference(
    a, b, aminusb=True, 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])

# np.array([[{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_difference` is applied to each aligned pair of sets.
tf.sets.difference(a, b)

# The result will be equivalent to either of:
#
# np.array([[{2}, {3}], [{}, {}]])
#
# collections.OrderedDict([
#     ((0, 0, 0), 2),
#     ((0, 1, 0), 3),
# ])
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.
aminusb Whether to subtract b from a, vs vice versa.
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 differences.
Raises
TypeError If inputs are invalid types, or if a and b have different types.
ValueError If a is sparse and b is dense.
errors_impl.InvalidArgumentError If the shapes of a and b do not match in any dimension other than the last dimension.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/sets/difference