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
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Public Methods
| Output<Float> |
asOutput()
Returns the symbolic handle of a tensor.
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| static <T> EditDistance | |
| static EditDistance.Options |
normalize(Boolean normalize)
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| Output<Float> |
output()
A dense float tensor with rank R - 1.
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Inherited Methods
Public Methods
public Output<Float> asOutput ()
Returns the symbolic handle of a 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<Long> hypothesisIndices, Operand<T> hypothesisValues, Operand<Long> hypothesisShape, Operand<Long> truthIndices, Operand<T> truthValues, Operand<Long> 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: |
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public Output<Float> 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