Class UnsortedSegmentSum
- java.lang.Object
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- org.nd4j.autodiff.functions.DifferentialFunction
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- org.nd4j.linalg.api.ops.DynamicCustomOp
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- org.nd4j.linalg.api.ops.impl.transforms.segment.UnsortedSegmentSum
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- All Implemented Interfaces:
CustomOp
public class UnsortedSegmentSum extends DynamicCustomOp
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Nested Class Summary
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Nested classes/interfaces inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
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Field Summary
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Fields inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, sArguments, tArguments
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Fields inherited from class org.nd4j.autodiff.functions.DifferentialFunction
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
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Constructor Summary
Constructors Constructor Description UnsortedSegmentSum(SameDiff sameDiff, SDVariable data, SDVariable segmentIds, int numSegments)
UnsortedSegmentSum(SameDiff sd, SDVariable data, SDVariable segmentIds, SDVariable numSegments)
UnsortedSegmentSum(INDArray data, INDArray segmentIds, int numSegments)
UnsortedSegmentSum(INDArray data, INDArray segmentIds, INDArray numSegments)
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description List<DataType>
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.void
configureFromArguments()
This allows a custom op to configure relevant fields from its arguments.List<SDVariable>
doDiff(List<SDVariable> gradients)
The actual implementation for automatic differentiation.String
opName()
This method returns op opName as stringvoid
setPropertiesForFunction(Map<String,Object> properties)
String
tensorflowName()
The opName of this function tensorflow-
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, dArgs, generateFake, generateFake, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getSArgument, getTArgument, getValue, iArgs, initFromOnnx, initFromTensorFlow, inputArguments, mappingsForFunction, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, onnxName, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, propertiesForFunction, removeIArgument, removeInputArgument, removeOutputArgument, removeSArgument, removeTArgument, sArgs, setInputArgument, setInputArguments, setOutputArgument, setValueFor, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNull
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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
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Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
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Methods inherited from interface org.nd4j.linalg.api.ops.CustomOp
isInplaceCall
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Constructor Detail
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UnsortedSegmentSum
public UnsortedSegmentSum(SameDiff sameDiff, SDVariable data, SDVariable segmentIds, int numSegments)
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UnsortedSegmentSum
public UnsortedSegmentSum(SameDiff sd, SDVariable data, SDVariable segmentIds, SDVariable numSegments)
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Method Detail
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opName
public String opName()
Description copied from class:DynamicCustomOp
This method returns op opName as string- Specified by:
opName
in interfaceCustomOp
- Overrides:
opName
in classDynamicCustomOp
- Returns:
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tensorflowName
public String tensorflowName()
Description copied from class:DifferentialFunction
The opName of this function tensorflow- Overrides:
tensorflowName
in classDynamicCustomOp
- Returns:
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doDiff
public List<SDVariable> doDiff(List<SDVariable> gradients)
Description copied from class:DifferentialFunction
The actual implementation for automatic differentiation.- Overrides:
doDiff
in classDynamicCustomOp
- Returns:
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configureFromArguments
public void configureFromArguments()
Description copied from interface:CustomOp
This allows a custom op to configure relevant fields from its arguments. This is needed when ops are created via reflection for things like model import.- Specified by:
configureFromArguments
in interfaceCustomOp
- Overrides:
configureFromArguments
in classDynamicCustomOp
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setPropertiesForFunction
public void setPropertiesForFunction(Map<String,Object> properties)
- Overrides:
setPropertiesForFunction
in classDynamicCustomOp
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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 fromDifferentialFunction.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 classDifferentialFunction
- Parameters:
inputDataTypes
- The data types of the inputs- Returns:
- The data types of the outputs
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