org.nd4j.linalg.api.ops.impl.transforms

Class LogSoftMax

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.BaseOp

org.nd4j.linalg.api.ops.BaseTransformOp

org.nd4j.linalg.api.ops.impl.transforms.LogSoftMax

All Implemented Interfaces:

Op, TransformOp

public class LogSoftMax
extends BaseTransformOp

Log(softmax(X))

Author:

Alex Black

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.BaseOp

extraArgs, extraArgz, n, numProcessed, passThrough, x, xVertexId, y, yVertexId, z, zVertexId

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

dimensions, inPlace, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor and Description
LogSoftMax()
LogSoftMax(INDArray x)
LogSoftMax(INDArray x, INDArray z)
LogSoftMax(INDArray x, INDArray y, INDArray z)
LogSoftMax(INDArray x, INDArray y, INDArray z, long n)
LogSoftMax(INDArray x, INDArray z, long n)
LogSoftMax(SameDiff sameDiff, SDVariable i_v, boolean inPlace)
LogSoftMax(SameDiff sameDiff, SDVariable i_v, int[] shape, boolean inPlace, Object[] extraArgs)
LogSoftMax(SameDiff sameDiff, SDVariable i_v, Object[] extraArgs)

Method Summary

Modifier and Type	Method and Description
List<SDVariable>	doDiff(List<SDVariable> i_v) The actual implementation for automatic differentiation.
void	exec() Execute the op if its pass through (not needed most of the time)
void	exec(int... dimensions) Exec along each dimension
boolean	isExecSpecial() Whether the executioner needs to do a special call or not
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.BaseTransformOp

calculateOutputShape, opType, z

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

equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getOpType, hashCode, init, initFromOnnx, initFromTensorFlow, isPassThrough, n, numProcessed, outputVariables, setN, setX, setY, setZ, toCustomOp, toString, x, y

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

arg, args, asProperties, attributeAdaptersForFunction, configFieldName, diff, dup, f, getValue, hasPlaceHolderInputs, isConfigProperties, larg, mappingsForFunction, onnxNames, outputVariables, propertiesForFunction, rarg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, 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, init, isPassThrough, n, numProcessed, setExtraArgs, setN, setX, setY, setZ, toCustomOp, x, y

Constructor Detail

LogSoftMax

public LogSoftMax(SameDiff sameDiff,
                  SDVariable i_v,
                  boolean inPlace)

LogSoftMax

public LogSoftMax(SameDiff sameDiff,
                  SDVariable i_v,
                  int[] shape,
                  boolean inPlace,
                  Object[] extraArgs)

LogSoftMax

public LogSoftMax(SameDiff sameDiff,
                  SDVariable i_v,
                  Object[] extraArgs)

LogSoftMax

public LogSoftMax()

LogSoftMax

public LogSoftMax(INDArray x,
                  INDArray z)

LogSoftMax

public LogSoftMax(INDArray x,
                  INDArray z,
                  long n)

LogSoftMax

public LogSoftMax(INDArray x,
                  INDArray y,
                  INDArray z,
                  long n)

LogSoftMax

public LogSoftMax(INDArray x,
                  INDArray y,
                  INDArray z)

LogSoftMax

public LogSoftMax(INDArray x)

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:

exec

public void exec()

Description copied from interface: Op

Execute the op if its pass through (not needed most of the time)

Specified by:

exec in interface Op

Overrides:

exec in class BaseOp

isExecSpecial

public boolean isExecSpecial()

Description copied from interface: Op

Whether the executioner needs to do a special call or not

Specified by:

isExecSpecial in interface Op

Overrides:

isExecSpecial in class BaseOp

Returns:

true if the executioner needs to do a special call or not false otherwise

exec

public void exec(int... dimensions)

Description copied from interface: Op

Exec along each dimension

Specified by:

exec in interface Op

Overrides:

exec in class BaseOp

Parameters:

dimensions - the dimensions to execute on

doDiff

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

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Specified by:

doDiff in class DifferentialFunction

Returns: