tf.edit_distance(hypothesis, truth, normalize=True, name='edit_distance')

tf.edit_distance(hypothesis, truth, normalize=True, name='edit_distance')

See the guide: Math > Sequence Comparison and Indexing

Computes the Levenshtein distance between sequences.

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.

Defined in tensorflow/python/ops/array_ops.py.