org.nd4j.linalg.api.ops.random.impl

Class AlphaDropOut

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.BaseOp

org.nd4j.linalg.api.ops.random.BaseRandomOp

org.nd4j.linalg.api.ops.random.impl.AlphaDropOut

All Implemented Interfaces:

Op, RandomOp

public class AlphaDropOut
extends BaseRandomOp

AlphaDropOut implementation as Op

Author:

raver119@gmail.com

Nested Class Summary

Nested classes/interfaces inherited from interface org.nd4j.linalg.api.ops.Op

Op.Type

Field Summary

Fields inherited from class org.nd4j.linalg.api.ops.random.BaseRandomOp

shape

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

dimensionz, extraArgz, x, xVertexId, y, yVertexId, z, zVertexId

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

dimensions, extraArgs, inPlace, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor and Description
AlphaDropOut()
AlphaDropOut(INDArray x, double p, double alpha, double alphaPrime, double beta)
AlphaDropOut(INDArray x, INDArray z, double p, double alpha, double alphaPrime, double beta)

Method Summary

Modifier and Type	Method and Description
List<SDVariable>	doDiff(List<SDVariable> f1) The actual implementation for automatic differentiation.
String	onnxName() The opName of this function in onnx
String	opName() The name of the op
int	opNum() The number of the op (mainly for old legacy XYZ ops like Op)
String	tensorflowName() The opName of this function tensorflow

Methods inherited from class org.nd4j.linalg.api.ops.random.BaseRandomOp

calculateOutputDataTypes, calculateOutputShape, isInPlace, opType

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

defineDimensions, dimensions, equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getFinalResult, getNumOutputs, getOpType, hashCode, initFromOnnx, initFromTensorFlow, outputVariables, setX, setY, setZ, toCustomOp, toString, x, y, z

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

arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, f, getValue, isConfigProperties, larg, mappingsForFunction, onnxNames, outputVariable, outputVariables, outputVariablesNames, propertiesForFunction, rarg, replaceArg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setPropertiesForFunction, setValueFor, 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.Op

extraArgs, extraArgsBuff, extraArgsDataBuff, setExtraArgs, setX, setY, setZ, toCustomOp, x, y, z

Constructor Detail

AlphaDropOut

public AlphaDropOut()

AlphaDropOut

public AlphaDropOut(@NonNull
                    INDArray x,
                    double p,
                    double alpha,
                    double alphaPrime,
                    double beta)

AlphaDropOut

public AlphaDropOut(@NonNull
                    INDArray x,
                    @NonNull
                    INDArray z,
                    double p,
                    double alpha,
                    double alphaPrime,
                    double beta)

Method Detail

opNum

public int opNum()

Description copied from class: DifferentialFunction

The number of the op (mainly for old legacy XYZ ops like Op)

Specified by:

opNum in interface Op

Overrides:

opNum in class DifferentialFunction

Returns:

opName

public String opName()

Description copied from class: DifferentialFunction

The name of the op

Specified by:

opName in interface Op

Overrides:

opName in class DifferentialFunction

Returns:

the opName of this operation

onnxName

public String onnxName()

Description copied from class: DifferentialFunction

The opName of this function in onnx

Specified by:

onnxName in class DifferentialFunction

Returns:

tensorflowName

public String tensorflowName()

Description copied from class: DifferentialFunction

The opName of this function tensorflow

Specified by:

tensorflowName in class DifferentialFunction

Returns:

doDiff

public List<SDVariable> doDiff(List<SDVariable> f1)

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Specified by:

doDiff in class DifferentialFunction

Returns: