Package org.nd4j.linalg.api.ops.impl.loss

Class WeightedCrossEntropyLoss

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

org.nd4j.linalg.api.ops.impl.loss.BaseLoss

org.nd4j.linalg.api.ops.impl.loss.WeightedCrossEntropyLoss

All Implemented Interfaces:

CustomOp

public class WeightedCrossEntropyLoss
extends BaseLoss

Nested Class Summary

Nested classes/interfaces inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp

DynamicCustomOp.DynamicCustomOpsBuilder

Field Summary

Fields inherited from class org.nd4j.linalg.api.ops.impl.loss.BaseLoss

lossReduce

Fields inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp

axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, sArguments, tArguments

Fields inherited from class org.nd4j.autodiff.functions.DifferentialFunction

dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor	Description
WeightedCrossEntropyLoss()
WeightedCrossEntropyLoss(@NonNull LossReduce lossReduce, @NonNull INDArray predictions, INDArray weights, @NonNull INDArray labels)
WeightedCrossEntropyLoss(@NonNull SameDiff sameDiff, @NonNull LossReduce lossReduce, @NonNull SDVariable predictions, SDVariable weights, @NonNull SDVariable labels)
WeightedCrossEntropyLoss(SameDiff sd, SDVariable targets, SDVariable inputs, SDVariable weights)
WeightedCrossEntropyLoss(INDArray targets, INDArray inputs, INDArray weights)

Method Summary

Modifier and Type	Method	Description
List<DataType>	calculateOutputDataTypes(List<DataType> inputDataTypes)	Calculate the data types for the output arrays.
String	onnxName()	The opName of this function in onnx
String	opName()	This method returns op opName as string
Op.Type	opType()	The type of the op
String	tensorflowName()	The opName of this function tensorflow

Methods inherited from class org.nd4j.linalg.api.ops.impl.loss.BaseLoss

addArgs, getWeights, getWeights

Methods inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp

addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addOutputsToOp, addSArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, computeArrays, configureFromArguments, dArgs, doDiff, generateFake, generateFake, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getSArgument, getTArgument, getValue, iArgs, initFromOnnx, initFromTensorFlow, inputArguments, mappingsForFunction, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, opHash, opNum, outputArguments, outputVariables, outputVariables, propertiesForFunction, removeIArgument, removeInputArgument, removeOutputArgument, removeSArgument, removeTArgument, sArgs, setInputArgument, setInputArguments, setOutputArgument, setPropertiesForFunction, setValueFor, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNull

Methods inherited from class org.nd4j.autodiff.functions.DifferentialFunction

arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, configureWithSameDiff, diff, dup, equals, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getNumOutputs, getStringFromProperty, hashCode, isConfigProperties, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, tensorflowNames

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.nd4j.linalg.api.ops.CustomOp

isInplaceCall

Constructor Detail

WeightedCrossEntropyLoss

public WeightedCrossEntropyLoss(@NonNull
                                @NonNull SameDiff sameDiff,
                                @NonNull
                                @NonNull LossReduce lossReduce,
                                @NonNull
                                @NonNull SDVariable predictions,
                                SDVariable weights,
                                @NonNull
                                @NonNull SDVariable labels)

WeightedCrossEntropyLoss

public WeightedCrossEntropyLoss(@NonNull
                                @NonNull LossReduce lossReduce,
                                @NonNull
                                @NonNull INDArray predictions,
                                INDArray weights,
                                @NonNull
                                @NonNull INDArray labels)

WeightedCrossEntropyLoss

public WeightedCrossEntropyLoss()

WeightedCrossEntropyLoss

public WeightedCrossEntropyLoss(SameDiff sd,
                                SDVariable targets,
                                SDVariable inputs,
                                SDVariable weights)

WeightedCrossEntropyLoss

public WeightedCrossEntropyLoss(INDArray targets,
                                INDArray inputs,
                                INDArray weights)

Method Detail

opName

public String opName()

Description copied from class: DynamicCustomOp

This method returns op opName as string

Specified by:

opName in interface CustomOp

Specified by:

opName in class BaseLoss

Returns:

onnxName

public String onnxName()

Description copied from class: DifferentialFunction

The opName of this function in onnx

Overrides:

onnxName in class DynamicCustomOp

Returns:

tensorflowName

public String tensorflowName()

Description copied from class: DifferentialFunction

The opName of this function tensorflow

Overrides:

tensorflowName in class DynamicCustomOp

Returns:

opType

public Op.Type opType()

Description copied from class: DifferentialFunction

The type of the op

Overrides:

opType in class DynamicCustomOp

Returns:

calculateOutputDataTypes

public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes)

Description copied from class: DifferentialFunction

Calculate the data types for the output arrays. Though datatypes can also be inferred from DifferentialFunction.calculateOutputShape(), this method differs in that it does not require the input arrays to be populated. This is important as it allows us to do greedy datatype inference for the entire net - even if arrays are not available.

Overrides:

calculateOutputDataTypes in class BaseLoss

Parameters:

inputDataTypes - The data types of the inputs

Returns:

The data types of the outputs