tf.sets.difference

Aliases:

• tf.contrib.metrics.set_difference
• tf.sets.difference
• tf.sets.set_difference

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

Defined in tensorflow/python/ops/sets_impl.py.

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

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.set_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.