tf.raw_ops.EditDistance

Computes the (possibly normalized) Levenshtein Edit Distance.

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tf.compat.v1.raw_ops.EditDistance

tf.raw_ops.EditDistance(
    hypothesis_indices,
    hypothesis_values,
    hypothesis_shape,
    truth_indices,
    truth_values,
    truth_shape,
    normalize=True,
    name=None
)

The inputs are variable-length sequences provided by SparseTensors (hypothesis_indices, hypothesis_values, hypothesis_shape) and (truth_indices, truth_values, truth_shape).

The inputs are:

Args
hypothesis_indices	A Tensor of type int64. The indices of the hypothesis list SparseTensor. This is an N x R int64 matrix.
hypothesis_values	A Tensor. The values of the hypothesis list SparseTensor. This is an N-length vector.
hypothesis_shape	A Tensor of type int64. The shape of the hypothesis list SparseTensor. This is an R-length vector.
truth_indices	A Tensor of type int64. The indices of the truth list SparseTensor. This is an M x R int64 matrix.
truth_values	A Tensor. Must have the same type as hypothesis_values. The values of the truth list SparseTensor. This is an M-length vector.
truth_shape	A Tensor of type int64. truth indices, vector.
normalize	An optional bool. Defaults to True. boolean (if true, edit distances are normalized by length of truth). The output is:
name	A name for the operation (optional).

Returns
A Tensor of type float32.