tf.raw_ops.EditDistance

Computes the (possibly normalized) Levenshtein Edit Distance.

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.