tf.edit_distance

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 = 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)

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).

A dense Tensor with rank R - 1, where R is the rank of the SparseTensor inputs hypothesis and truth.

TypeError If either hypothesis or truth are not a SparseTensor.