tf.sets.difference

TensorFlow 1 version View source on GitHub

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.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.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),
  # ])

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.

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.

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.