tensorflow::ops::EditDistance

#include <array_ops.h>

Computes the (possibly normalized) Levenshtein Edit Distance.

Summary

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:

Arguments:

  • scope: A Scope object
  • hypothesis_indices: The indices of the hypothesis list SparseTensor. This is an N x R int64 matrix.
  • hypothesis_values: The values of the hypothesis list SparseTensor. This is an N-length vector.
  • hypothesis_shape: The shape of the hypothesis list SparseTensor. This is an R-length vector.
  • truth_indices: The indices of the truth list SparseTensor. This is an M x R int64 matrix.
  • truth_values: The values of the truth list SparseTensor. This is an M-length vector.
  • truth_shape: truth indices, vector.

Optional attributes (see Attrs):

  • normalize: boolean (if true, edit distances are normalized by length of truth).

The output is:

Returns:

  • Output: A dense float tensor with rank R - 1.

For the example input:

// hypothesis represents a 2x1 matrix with variable-length values:
//   (0,0) = ["a"]
//   (1,0) = ["b"]
hypothesis_indices = [[0, 0, 0],
                      [1, 0, 0]]
hypothesis_values = ["a", "b"]
hypothesis_shape = [2, 1, 1]

// truth represents a 2x2 matrix with variable-length values:
//   (0,0) = []
//   (0,1) = ["a"]
//   (1,0) = ["b", "c"]
//   (1,1) = ["a"]
truth_indices = [[0, 1, 0],
                 [1, 0, 0],
                 [1, 0, 1],
                 [1, 1, 0]]
truth_values = ["a", "b", "c", "a"]
truth_shape = [2, 2, 2]
normalize = true

The output will be:

// output is a 2x2 matrix 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  

Constructors and Destructors

EditDistance(const ::tensorflow::Scope & scope, ::tensorflow::Input hypothesis_indices, ::tensorflow::Input hypothesis_values, ::tensorflow::Input hypothesis_shape, ::tensorflow::Input truth_indices, ::tensorflow::Input truth_values, ::tensorflow::Input truth_shape)
EditDistance(const ::tensorflow::Scope & scope, ::tensorflow::Input hypothesis_indices, ::tensorflow::Input hypothesis_values, ::tensorflow::Input hypothesis_shape, ::tensorflow::Input truth_indices, ::tensorflow::Input truth_values, ::tensorflow::Input truth_shape, const EditDistance::Attrs & attrs)

Public attributes

output

Public functions

node() const
::tensorflow::Node *
operator::tensorflow::Input() const
operator::tensorflow::Output() const

Public static functions

Normalize(bool x)

Structs

tensorflow::ops::EditDistance::Attrs

Optional attribute setters for EditDistance.

Public attributes

output

::tensorflow::Output output

Public functions

EditDistance

 EditDistance(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input hypothesis_indices,
  ::tensorflow::Input hypothesis_values,
  ::tensorflow::Input hypothesis_shape,
  ::tensorflow::Input truth_indices,
  ::tensorflow::Input truth_values,
  ::tensorflow::Input truth_shape
)

EditDistance

 EditDistance(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input hypothesis_indices,
  ::tensorflow::Input hypothesis_values,
  ::tensorflow::Input hypothesis_shape,
  ::tensorflow::Input truth_indices,
  ::tensorflow::Input truth_values,
  ::tensorflow::Input truth_shape,
  const EditDistance::Attrs & attrs
)

node

::tensorflow::Node * node() const 

operator::tensorflow::Input

 operator::tensorflow::Input() const 

operator::tensorflow::Output

 operator::tensorflow::Output() const 

Public static functions

Normalize

Attrs Normalize(
  bool x
)