@Operator public final class EditDistance extends PrimitiveOp implements Operand<Float>
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:
Modifier and Type | Class and Description |
---|---|
static class |
EditDistance.Options
Optional attributes for
EditDistance |
operation
Modifier and Type | Method and Description |
---|---|
Output<Float> |
asOutput()
Returns the symbolic handle of a tensor.
|
static <T> EditDistance |
create(Scope scope,
Operand<Long> hypothesisIndices,
Operand<T> hypothesisValues,
Operand<Long> hypothesisShape,
Operand<Long> truthIndices,
Operand<T> truthValues,
Operand<Long> truthShape,
EditDistance.Options... options)
Factory method to create a class wrapping a new EditDistance operation.
|
static EditDistance.Options |
normalize(Boolean normalize) |
Output<Float> |
output()
A dense float tensor with rank R - 1.
|
equals, hashCode, op, toString
public static <T> EditDistance create(Scope scope, Operand<Long> hypothesisIndices, Operand<T> hypothesisValues, Operand<Long> hypothesisShape, Operand<Long> truthIndices, Operand<T> truthValues, Operand<Long> truthShape, EditDistance.Options... options)
scope
- current scopehypothesisIndices
- 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 valuespublic static EditDistance.Options normalize(Boolean normalize)
normalize
- boolean (if true, edit distances are normalized by length of truth).
The output is:
public Output<Float> output()
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
public Output<Float> asOutput()
Operand
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.
asOutput
in interface Operand<Float>
OperationBuilder.addInput(Output)
Copyright © 2022. All rights reserved.