TensorFlow 1 version | View source on GitHub |
Computes the Levenshtein distance between sequences.
tf.edit_distance(
hypothesis, truth, normalize=True, name='edit_distance'
)
This operation takes variable-length sequences (hypothesis
and truth
),
each provided as a SparseTensor
, and computes the Levenshtein distance.
You can normalize the edit distance by length of truth
by setting
normalize
to true.
For example, given the following input:
# 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values:
# (0,0) = ["a"]
# (1,0) = ["b"]
hypothesis = tf.SparseTensor(
[[0, 0, 0],
[1, 0, 0]],
["a", "b"],
(2, 1, 1))
# 'truth' is a tensor of shape `[2, 2]` with variable-length values:
# (0,0) = []
# (0,1) = ["a"]
# (1,0) = ["b", "c"]
# (1,1) = ["a"]
truth = tf.SparseTensor(
[[0, 1, 0],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0]],
["a", "b", "c", "a"],
(2, 2, 2))
normalize = True
This operation would return the following:
# 'output' is a tensor of shape `[2, 2]` with edit distances normalized
# by 'truth' lengths.
output ==> [[inf, 1.0], # (0,0): no truth, (0,1): no hypothesis
[0.5, 1.0]] # (1,0): addition, (1,1): no hypothesis
Args | |
---|---|
hypothesis
|
A SparseTensor containing hypothesis sequences.
|
truth
|
A SparseTensor containing truth sequences.
|
normalize
|
A bool . If True , normalizes the Levenshtein distance by
length of truth.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A dense Tensor with rank R - 1 , where R is the rank of the
SparseTensor inputs hypothesis and truth .
|
Raises | |
---|---|
TypeError
|
If either hypothesis or truth are not a SparseTensor .
|