EditDistance

public final class EditDistance

Computes the (possibly normalized) Levenshtein Edit Distance.

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:

Nested Classes

class EditDistance.Options Optional attributes for EditDistance  

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

Output<TFloat32>
asOutput()
Returns the symbolic handle of the tensor.
static <T extends TType> EditDistance
create(Scope scope, Operand<TInt64> hypothesisIndices, Operand<T> hypothesisValues, Operand<TInt64> hypothesisShape, Operand<TInt64> truthIndices, Operand<T> truthValues, Operand<TInt64> truthShape, Options... options)
Factory method to create a class wrapping a new EditDistance operation.
static EditDistance.Options
normalize(Boolean normalize)
Output<TFloat32>
output()
A dense float tensor with rank R - 1.

Inherited Methods

org.tensorflow.op.RawOp
final boolean
equals(Object obj)
final int
hashCode()
Operation
op()
Return this unit of computation as a single Operation.
final String
toString()
boolean
equals(Object arg0)
final Class<?>
getClass()
int
hashCode()
final void
notify()
final void
notifyAll()
String
toString()
final void
wait(long arg0, int arg1)
final void
wait(long arg0)
final void
wait()
org.tensorflow.op.Op
abstract ExecutionEnvironment
env()
Return the execution environment this op was created in.
abstract Operation
op()
Return this unit of computation as a single Operation.
org.tensorflow.Operand
abstract Output<TFloat32>
asOutput()
Returns the symbolic handle of the tensor.
abstract TFloat32
asTensor()
Returns the tensor at this operand.
abstract Shape
shape()
Returns the (possibly partially known) shape of the tensor referred to by the Output of this operand.
abstract Class<TFloat32>
type()
Returns the tensor type of this operand
org.tensorflow.ndarray.Shaped
abstract int
rank()
abstract Shape
shape()
abstract long
size()
Computes and returns the total size of this container, in number of values.

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "EditDistance"

Public Methods

public Output<TFloat32> asOutput ()

Returns the symbolic handle of the tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static EditDistance create (Scope scope, Operand<TInt64> hypothesisIndices, Operand<T> hypothesisValues, Operand<TInt64> hypothesisShape, Operand<TInt64> truthIndices, Operand<T> truthValues, Operand<TInt64> truthShape, Options... options)

Factory method to create a class wrapping a new EditDistance operation.

Parameters
scope current scope
hypothesisIndices The indices of the hypothesis list SparseTensor. This is an N x R int64 matrix.
hypothesisValues The values of the hypothesis list SparseTensor. This is an N-length vector.
hypothesisShape The shape of the hypothesis list SparseTensor. This is an R-length vector.
truthIndices The indices of the truth list SparseTensor. This is an M x R int64 matrix.
truthValues The values of the truth list SparseTensor. This is an M-length vector.
truthShape truth indices, vector.
options carries optional attributes values
Returns
  • a new instance of EditDistance

public static EditDistance.Options normalize (Boolean normalize)

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

The output is:

public Output<TFloat32> 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