# tf.sets.difference

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.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),
# ])
``````

`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.