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Computes the Levenshtein distance between sequences.
tf.edit_distance(
hypothesis, truth, normalize=True, name='edit_distance'
)
This operation takes variablelength 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 atf.SparseTensor
of shape[2, 1, 1]
truth
is atf.SparseTensor
of shape[2, 2, 2]
hypothesis = tf.SparseTensor(
[[0, 0, 0],
[1, 0, 0]],
["a", "b"],
(2, 1, 1))
truth = tf.SparseTensor(
[[0, 1, 0],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0]],
["a", "b", "c", "a"],
(2, 2, 2))
tf.edit_distance(hypothesis, truth, normalize=True)
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[inf, 1. ],
[0.5, 1. ]], dtype=float32)>
The operation returns a dense Tensor of shape [2, 2]
with
edit distances normalized by truth
lengths.
For the following inputs,
# 'hypothesis' is a tensor of shape `[2, 1]` with variablelength values:
# (0,0) = ["a"]
# (1,0) = ["b"]
hypothesis = tf.sparse.SparseTensor(
[[0, 0, 0],
[1, 0, 0]],
["a", "b"],
(2, 1, 1))
# 'truth' is a tensor of shape `[2, 2]` with variablelength values:
# (0,0) = []
# (0,1) = ["a"]
# (1,0) = ["b", "c"]
# (1,1) = ["a"]
truth = tf.sparse.SparseTensor(
[[0, 1, 0],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0]],
["a", "b", "c", "a"],
(2, 2, 2))
normalize = True
# The output would be a dense Tensor of shape `(2,)`, with edit distances
normalized by 'truth' lengths.
# output => array([0., 0.5], dtype=float32)
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 .
