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

Class TanhDerivative

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.BaseGradientOp

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

All Implemented Interfaces:

GradientOp, Op, TransformOp

public class TanhDerivative
extends BaseGradientOp

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
TanhDerivative()
TanhDerivative(INDArray x)
TanhDerivative(INDArray x, INDArray z)
TanhDerivative(INDArray x, INDArray y, INDArray z)
TanhDerivative(INDArray x, INDArray z, long n)
TanhDerivative(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2)
TanhDerivative(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace)

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
String	onnxName() The opName of this function in onnx
String	opName() The opName of this operation
int	opNum() An op number
String	tensorflowName() The opName of this function tensorflow

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

isExecSpecial, isPassThrough, wrt

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, 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, n, numProcessed, setExtraArgs, setN, setX, setY, setZ, toCustomOp, x, y, z

Constructor Detail

TanhDerivative

public TanhDerivative(SameDiff sameDiff,
                      SDVariable i_v1,
                      SDVariable i_v2)

TanhDerivative

public TanhDerivative(SameDiff sameDiff,
                      SDVariable i_v1,
                      SDVariable i_v2,
                      boolean inPlace)

TanhDerivative

public TanhDerivative(INDArray x,
                      INDArray z)

TanhDerivative

public TanhDerivative()

TanhDerivative

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

TanhDerivative

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

TanhDerivative

public TanhDerivative(INDArray x)

Method Detail

opNum

public int opNum()

An op number

Specified by:

opNum in interface Op

Overrides:

opNum in class DifferentialFunction

Returns:

opName

public String opName()

The opName of this operation

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

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: