tf.sets.difference
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. |