Python tf.sets.difference用法及代码示例

计算 a 和 b 的最后一维元素的集合差。

用法

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

除了a 和b 的最后一个维度之外的所有维度都必须匹配。

例子：

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),
  # ])