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@ -22,6 +22,7 @@ package org.nd4j.linalg.api.ops.impl.loss;
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import org.nd4j.autodiff.samediff.SDVariable;
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import org.nd4j.autodiff.samediff.SameDiff;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.api.ops.DynamicCustomOp;
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import org.nd4j.linalg.api.ops.impl.loss.bp.CtcLossBp;
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@ -30,6 +31,11 @@ import java.util.List;
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public class CtcLoss extends DynamicCustomOp {
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public CtcLoss(INDArray targetLabels, INDArray logitInputs, INDArray targetLabelLengths, INDArray logitInputLengths) {
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super(new INDArray[]{targetLabels,logitInputs,targetLabelLengths,logitInputLengths},null);
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}
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public CtcLoss(SameDiff sameDiff, SDVariable targetLabels,SDVariable logitInputs,SDVariable targetLabelLengths,SDVariable logitInputLengths){
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super(sameDiff,new SDVariable[]{targetLabels,logitInputs,targetLabelLengths,logitInputLengths});
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}
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@ -40,7 +40,7 @@ public class HingeLoss extends BaseLoss {
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this(sameDiff, lossReduce, predictions, weights, labels);
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}
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public HingeLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce){
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public HingeLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce) {
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super(lossReduce, predictions, weights, labels);
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}
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@ -419,6 +419,16 @@ val checkNumerics = TensorflowMappingProcess(
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tensorMappingRules = listOf(mappingNDArrayInputs(mutableMapOf("input" to "tensor")))
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)
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val ctcLoss = TensorflowMappingProcess(
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opName = "ctc_loss",
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inputFrameworkOpName = "CTCLoss",
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opMappingRegistry = tensorflowOpRegistry,
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attributeMappingRules = listOf(),
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tensorMappingRules = listOf(mappingNDArrayInputs(mutableMapOf("logitInput" to "inputs","targetLabels" to "labels_values",
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"targetLabelLengths" to "labels_indices","logitInputLengths" to "sequence_length")))
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)
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//only exists in tf2, tf-java can't run it
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val checkNumericsV2 = TensorflowMappingProcess(
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@ -10307,6 +10307,41 @@ mappings {
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inputFrameworkOpName: "UniqueWithCounts"
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}
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}
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mappings {
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frameworkName: "tensorflow"
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opName: "ctc_loss"
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inputFrameworkOpName: "CTCLoss"
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rule {
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ruleName: "ndarraymapping"
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functionName: "ndarraymapping"
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inputTensorName: "inputs"
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inputTensorName: "labels_values"
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inputTensorName: "labels_indices"
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inputTensorName: "sequence_length"
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outputTensorName: "logitInput"
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outputTensorName: "targetLabels"
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outputTensorName: "targetLabelLengths"
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outputTensorName: "logitInputLengths"
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inputToOutput {
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key: "logitInput"
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value: "inputs"
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}
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inputToOutput {
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key: "targetLabels"
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value: "labels_values"
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}
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inputToOutput {
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key: "targetLabelLengths"
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value: "labels_indices"
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}
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inputToOutput {
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key: "logitInputLengths"
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value: "sequence_length"
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}
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ruleType: "tensor"
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inputFrameworkOpName: "CTCLoss"
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}
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}
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mappings {
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frameworkName: "tensorflow"
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opName: "randomuniform"
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