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

Class Moments

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

org.nd4j.linalg.api.ops.impl.reduce.Moments

All Implemented Interfaces:

CustomOp

public class Moments
extends DynamicCustomOp

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.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
Moments()
Moments(@NonNull INDArray input, boolean keepDims, int... dimensions)
Moments(@NonNull INDArray input, int... dimensions)
Moments(SameDiff sameDiff, SDVariable input)
Moments(SameDiff sameDiff, SDVariable input, int[] axes)
Moments(SameDiff sd, SDVariable input, int[] axes, boolean keepDims)
Moments(SameDiff sd, SDVariable input, SDVariable axes, boolean keepDims)
Moments(INDArray input, int[] axes, boolean keepDims)
Moments(INDArray input, INDArray axes, boolean keepDims)
Moments(INDArray in, INDArray outMean, INDArray outStd, int... axes)

Method Summary

Modifier and Type	Method	Description
protected void	addArgs()
List<DataType>	calculateOutputDataTypes(List<DataType> dataTypes)	Calculate the data types for the output arrays.
List<SDVariable>	doDiff(List<SDVariable> grad)	The actual implementation for automatic differentiation.
void	initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)	Initialize the function from the given NodeDef
String	opName()	This method returns op opName as string
Map<String,Object>	propertiesForFunction()	Returns the properties for a given function

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, generateFake, generateFake, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getSArgument, getTArgument, getValue, iArgs, initFromOnnx, inputArguments, mappingsForFunction, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, onnxName, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeSArgument, removeTArgument, sArgs, setInputArgument, setInputArguments, setOutputArgument, setPropertiesForFunction, setValueFor, tArgs, tensorflowName, 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

Moments

public Moments()

Moments

public Moments(@NonNull
               @NonNull INDArray input,
               boolean keepDims,
               int... dimensions)

Moments

public Moments(@NonNull
               @NonNull INDArray input,
               int... dimensions)

Moments

public Moments(SameDiff sameDiff,
               SDVariable input)

Moments

public Moments(SameDiff sameDiff,
               SDVariable input,
               int[] axes)

Moments

public Moments(INDArray in,
               INDArray outMean,
               INDArray outStd,
               int... axes)

Moments

public Moments(INDArray input,
               int[] axes,
               boolean keepDims)

Moments

public Moments(INDArray input,
               INDArray axes,
               boolean keepDims)

Moments

public Moments(SameDiff sd,
               SDVariable input,
               int[] axes,
               boolean keepDims)

Moments

public Moments(SameDiff sd,
               SDVariable input,
               SDVariable axes,
               boolean keepDims)

Method Detail

initFromTensorFlow

public void initFromTensorFlow(NodeDef nodeDef,
                               SameDiff initWith,
                               Map<String,AttrValue> attributesForNode,
                               GraphDef graph)

Description copied from class: DifferentialFunction

Initialize the function from the given NodeDef

Overrides:

initFromTensorFlow in class DynamicCustomOp

opName

public String opName()

Description copied from class: DynamicCustomOp

This method returns op opName as string

Specified by:

opName in interface CustomOp

Overrides:

opName in class DynamicCustomOp

Returns:

doDiff

public List<SDVariable> doDiff(List<SDVariable> grad)

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Overrides:

doDiff in class DynamicCustomOp

Returns:

calculateOutputDataTypes

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

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 DifferentialFunction

Parameters:

dataTypes - The data types of the inputs

Returns:

The data types of the outputs

propertiesForFunction

public Map<String,Object> propertiesForFunction()

Description copied from class: DifferentialFunction

Returns the properties for a given function

Overrides:

propertiesForFunction in class DynamicCustomOp

Returns:

addArgs

protected void addArgs()