extraArgs, extraArgz, n, numProcessed, passThrough, x, xVertexId, y, yVertexId, z, zVertexId
dimensions, inPlace, sameDiff, scalarValue
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) |
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
|
equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getOpType, hashCode, init, initFromOnnx, initFromTensorFlow, isPassThrough, n, numProcessed, outputVariables, setN, setX, setY, setZ, toCustomOp, toString, x, y
arg, args, asProperties, attributeAdaptersForFunction, configFieldName, diff, dup, f, getValue, hasPlaceHolderInputs, isConfigProperties, larg, mappingsForFunction, onnxNames, outputVariables, rarg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
setExtraArgs
public RollAxis(SameDiff sameDiff, int axis)
public RollAxis(SameDiff sameDiff, SDVariable i_v, int axis)
public RollAxis(SameDiff sameDiff, SDVariable i_v, int[] shape, boolean inPlace, Object[] extraArgs, int axis)
public RollAxis()
public RollAxis(INDArray x)
public Map<String,Object> propertiesForFunction()
DifferentialFunction
propertiesForFunction
in class DifferentialFunction
public void exec(int... dimensions)
Op
public boolean isExecSpecial()
Op
isExecSpecial
in interface Op
isExecSpecial
in class BaseOp
public void exec()
Op
public List<int[]> calculateOutputShape()
DifferentialFunction
calculateOutputShape
in class ShapeOp
public int opNum()
DifferentialFunction
Op
)opNum
in interface Op
opNum
in class DifferentialFunction
public String opName()
DifferentialFunction
opName
in interface Op
opName
in class DifferentialFunction
public String onnxName()
DifferentialFunction
onnxName
in class DifferentialFunction
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DifferentialFunction
public List<SDVariable> doDiff(List<SDVariable> i_v)
DifferentialFunction
doDiff
in class DifferentialFunction
Copyright © 2018. All rights reserved.