tf.edit_distance  |  TensorFlow v2.16.1 (original) (raw)

tf.edit_distance

Stay organized with collections Save and categorize content based on your preferences.

Computes the Levenshtein distance between sequences.

View aliases

Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.edit_distance

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 settingnormalize to true.

For example:

Given the following input,

• hypothesis is a tf.SparseTensor of shape [2, 1, 1]
• truth is a tf.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 variable-length 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 variable-length 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)