Computes the Levenshtein distance between sequences.

# Aliases:

• tf.compat.v1.edit_distance
• tf.compat.v2.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 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.