org.nd4j.linalg.api.ops.impl.shape

Class RollAxis

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.BaseOp

org.nd4j.linalg.api.ops.ShapeOp

org.nd4j.linalg.api.ops.impl.shape.RollAxis

All Implemented Interfaces:

Op

public class RollAxis
extends ShapeOp

Transpose function

Author:

Adam Gibson

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
RollAxis()
RollAxis(INDArray x)
RollAxis(INDArray x, INDArray z)
RollAxis(INDArray x, INDArray y, INDArray z, long n)
RollAxis(INDArray x, INDArray z, long n)
RollAxis(SameDiff sameDiff, int axis)
RollAxis(SameDiff sameDiff, SDVariable i_v, int axis)
RollAxis(SameDiff sameDiff, SDVariable i_v, int[] shape, boolean inPlace, Object[] extraArgs, int axis)

Method Summary

Modifier and Type	Method and Description
List<int[]>	calculateOutputShape() Calculate the output shape for this op
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)
Map<String,Object>	propertiesForFunction() Returns the properties for a given function
String	tensorflowName() The opName of this function tensorflow
INDArray	z() The resulting ndarray

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

opType

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, 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

setExtraArgs

Constructor Detail

RollAxis

public RollAxis(SameDiff sameDiff,
                int axis)

RollAxis

public RollAxis(SameDiff sameDiff,
                SDVariable i_v,
                int axis)

RollAxis

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

RollAxis

public RollAxis()

RollAxis

public RollAxis(INDArray x,
                INDArray z)

RollAxis

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

RollAxis

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

RollAxis

public RollAxis(INDArray x)

Method Detail

propertiesForFunction

public Map<String,Object> propertiesForFunction()

Description copied from class: DifferentialFunction

Returns the properties for a given function

Overrides:

propertiesForFunction in class DifferentialFunction

Returns:

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

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()

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

calculateOutputShape

public List<int[]> calculateOutputShape()

Description copied from class: DifferentialFunction

Calculate the output shape for this op

Overrides:

calculateOutputShape in class ShapeOp

Returns:

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:

z

public INDArray z()

Description copied from interface: Op

The resulting ndarray

Specified by:

z in interface Op

Overrides:

z in class BaseOp

Returns:

the resulting ndarray

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: