public class ManhattanDistance extends BaseReduce3Op
| Modifier and Type | Field and Description |
|---|---|
static String |
OP_NAME |
dimensionVariable, isComplex, isEmptyReduce, keepDimsdimensionz, extraArgz, x, xVertexId, y, yVertexId, z, zVertexIddimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue| Constructor and Description |
|---|
ManhattanDistance() |
ManhattanDistance(INDArray x,
INDArray y,
boolean keepDims,
boolean isComplex,
int[] dimensions) |
ManhattanDistance(INDArray x,
INDArray y,
boolean allDistances,
int... dimensions) |
ManhattanDistance(INDArray x,
INDArray y,
INDArray z) |
ManhattanDistance(INDArray x,
INDArray y,
INDArray z,
boolean keepDims,
boolean allDistances,
int... dimensions) |
ManhattanDistance(INDArray x,
INDArray y,
INDArray z,
boolean allDistances,
int... dimensions) |
ManhattanDistance(INDArray x,
INDArray y,
INDArray z,
int... dimensions) |
ManhattanDistance(INDArray x,
INDArray y,
int... dimensions) |
ManhattanDistance(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
ManhattanDistance(SameDiff sameDiff,
SDVariable i_v,
SDVariable dimensions) |
ManhattanDistance(SameDiff sd,
SDVariable x,
SDVariable y,
boolean keepDims,
boolean isComplex,
int[] dimensions) |
ManhattanDistance(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int... dimensions) |
ManhattanDistance(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
SDVariable dimensions) |
| Modifier and Type | Method and Description |
|---|---|
List<SDVariable> |
doDiff(List<SDVariable> i_v1)
The actual implementation for automatic differentiation.
|
String |
opName()
The name of the op
|
int |
opNum()
The number of the op (mainly for old legacy XYZ ops
like
Op) |
calculateOutputDataTypes, getOpType, onnxName, opType, resultType, tensorflowNamecalculateOutputShape, calculateOutputShape, resultType, validateDataTypesconfigureWithSameDiff, hasReductionIndices, initFromOnnx, initFromTensorFlow, isComplexAccumulation, isKeepDims, noOp, setDimensions, setPropertiesForFunctionclearArrays, computeVariables, defineDimensions, dimensions, equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getFinalResult, getInputArgument, getNumOutputs, getOpType, hashCode, outputVariables, setX, setY, setZ, toCustomOp, toString, x, y, zarg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getStringFromProperty, getValue, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariables, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setValueFor, tensorflowNamesclone, finalize, getClass, notify, notifyAll, wait, wait, waitdimensions, getFinalResult, isComplexAccumulation, isKeepDims, noOp, setDimensionsclearArrays, extraArgs, extraArgsBuff, extraArgsDataBuff, setExtraArgs, setX, setY, setZ, toCustomOp, x, y, zpublic static final String OP_NAME
public ManhattanDistance(SameDiff sameDiff, SDVariable i_v, int[] dimensions)
public ManhattanDistance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int... dimensions)
public ManhattanDistance(SameDiff sameDiff, SDVariable i_v, SDVariable dimensions)
public ManhattanDistance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions)
public ManhattanDistance()
public ManhattanDistance(INDArray x, INDArray y, boolean allDistances, int... dimensions)
public ManhattanDistance(INDArray x, INDArray y, INDArray z, boolean allDistances, int... dimensions)
public ManhattanDistance(INDArray x, INDArray y, INDArray z, boolean keepDims, boolean allDistances, int... dimensions)
public ManhattanDistance(SameDiff sd, SDVariable x, SDVariable y, boolean keepDims, boolean isComplex, int[] dimensions)
public int opNum()
DifferentialFunctionOp)opNum in interface OpopNum in class DifferentialFunctionpublic String opName()
DifferentialFunctionopName in interface OpopName in class DifferentialFunctionpublic List<SDVariable> doDiff(List<SDVariable> i_v1)
DifferentialFunctiondoDiff in class DifferentialFunctionCopyright © 2022. All rights reserved.