# tf.edit_distance

<br />

|---|---|
| [![](https://www.tensorflow.org/images/tf_logo_32px.png) TensorFlow 1 version](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/edit_distance) | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/python/ops/array_ops.py#L3022-L3098) |

Computes the Levenshtein distance between sequences.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.edit_distance`](https://www.tensorflow.org/api_docs/python/tf/edit_distance)

<br />

    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

<br />

<br />

<br />

| ## 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). |

<br />

<br />

<br />

<br />

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

<br />

<br />

<br />

<br />

| ## Raises ||
|---|---|
| `TypeError` | If either `hypothesis` or `truth` are not a `SparseTensor`. |

<br />