Class NormMax
- java.lang.Object
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- org.nd4j.autodiff.functions.DifferentialFunction
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- org.nd4j.linalg.api.ops.BaseOp
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- org.nd4j.linalg.api.ops.BaseReduceOp
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- org.nd4j.linalg.api.ops.BaseReduceFloatOp
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- org.nd4j.linalg.api.ops.impl.reduce.floating.NormMax
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- All Implemented Interfaces:
Op,ReduceFloatOp,ReduceOp
public class NormMax extends BaseReduceFloatOp
The max absolute value- Author:
- Adam Gibson
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Field Summary
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Fields inherited from class org.nd4j.linalg.api.ops.BaseReduceOp
dimensionVariable, isComplex, isEmptyReduce, keepDims
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Fields inherited from class org.nd4j.linalg.api.ops.BaseOp
dimensionz, extraArgz, x, xVertexId, y, yVertexId, z, zVertexId
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Fields inherited from class org.nd4j.autodiff.functions.DifferentialFunction
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
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Constructor Summary
Constructors Constructor Description NormMax()NormMax(SameDiff sameDiff, SDVariable i_v, boolean keepDims, int[] dimensions)NormMax(SameDiff sameDiff, SDVariable i_v, boolean keepDims, SDVariable dimensions)NormMax(SameDiff sameDiff, SDVariable input, int... dimensions)NormMax(SameDiff sameDiff, SDVariable input, int[] dimensions, boolean keepDims)NormMax(SameDiff sameDiff, SDVariable input, SDVariable dimensions)NormMax(SameDiff sameDiff, SDVariable input, SDVariable dimensions, boolean keepDims)NormMax(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions)NormMax(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions)NormMax(INDArray x, boolean keepDims, int... dimensions)NormMax(INDArray x, int... dimensions)NormMax(INDArray in, int[] dimensions, boolean keepDims)NormMax(INDArray in, INDArray indArray, boolean keepDims)NormMax(INDArray input, INDArray output, boolean keepDims, int... dimensions)NormMax(INDArray x, INDArray z, int... dimensions)NormMax(INDArray x, INDArray y, INDArray z, boolean keepDims, int... dimensions)NormMax(INDArray x, INDArray y, INDArray z, int... dimensions)
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description List<SDVariable>doDiff(List<SDVariable> grad)The actual implementation for automatic differentiation.INDArraynoOp()Returns the no op version of the input Basically when a reduce can't happen (eg: sum(0) on a row vector) you have a no op state for a given reduction.StringonnxName()The opName of this function in onnxStringopName()The name of the opintopNum()The number of the op (mainly for old legacy XYZ ops likeOp)StringtensorflowName()The opName of this function tensorflow-
Methods inherited from class org.nd4j.linalg.api.ops.BaseReduceFloatOp
calculateOutputDataTypes, calculateOutputShape, calculateOutputShape, getOpType, opType, resultType, resultType, validateDataTypes
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Methods inherited from class org.nd4j.linalg.api.ops.BaseReduceOp
configureWithSameDiff, hasReductionIndices, initFromOnnx, initFromTensorFlow, isComplexAccumulation, isKeepDims, setDimensions, setPropertiesForFunction
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Methods inherited from class org.nd4j.linalg.api.ops.BaseOp
clearArrays, computeVariables, defineDimensions, dimensions, equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getFinalResult, getInputArgument, getNumOutputs, getOpType, hashCode, outputVariables, setX, setY, setZ, toCustomOp, toString, x, y, z
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Methods inherited from class org.nd4j.autodiff.functions.DifferentialFunction
arg, 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, tensorflowNames
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Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
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Methods inherited from interface org.nd4j.linalg.api.ops.Op
clearArrays, extraArgs, extraArgsBuff, extraArgsDataBuff, setExtraArgs, setX, setY, setZ, toCustomOp, x, y, z
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Methods inherited from interface org.nd4j.linalg.api.ops.ReduceOp
dimensions, getFinalResult, isComplexAccumulation, isKeepDims, setDimensions
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Constructor Detail
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NormMax
public NormMax(SameDiff sameDiff, SDVariable i_v, boolean keepDims, int[] dimensions)
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NormMax
public NormMax(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions)
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NormMax
public NormMax(SameDiff sameDiff, SDVariable input, int[] dimensions, boolean keepDims)
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NormMax
public NormMax(SameDiff sameDiff, SDVariable input, int... dimensions)
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NormMax
public NormMax(SameDiff sameDiff, SDVariable i_v, boolean keepDims, SDVariable dimensions)
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NormMax
public NormMax(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions)
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NormMax
public NormMax(SameDiff sameDiff, SDVariable input, SDVariable dimensions, boolean keepDims)
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NormMax
public NormMax(SameDiff sameDiff, SDVariable input, SDVariable dimensions)
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NormMax
public NormMax()
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NormMax
public NormMax(INDArray x, int... dimensions)
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NormMax
public NormMax(INDArray x, boolean keepDims, int... dimensions)
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NormMax
public NormMax(INDArray in, int[] dimensions, boolean keepDims)
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Method Detail
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noOp
public INDArray noOp()
Description copied from interface:ReduceOpReturns the no op version of the input Basically when a reduce can't happen (eg: sum(0) on a row vector) you have a no op state for a given reduction. For most accumulations, this should return x but certain transformations should return say: the absolute value- Specified by:
noOpin interfaceReduceOp- Overrides:
noOpin classBaseReduceOp- Returns:
- the no op version of the input
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opNum
public int opNum()
Description copied from class:DifferentialFunctionThe number of the op (mainly for old legacy XYZ ops likeOp)- Specified by:
opNumin interfaceOp- Overrides:
opNumin classDifferentialFunction- Returns:
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opName
public String opName()
Description copied from class:DifferentialFunctionThe name of the op- Specified by:
opNamein interfaceOp- Overrides:
opNamein classDifferentialFunction- Returns:
- the opName of this operation
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doDiff
public List<SDVariable> doDiff(List<SDVariable> grad)
Description copied from class:DifferentialFunctionThe actual implementation for automatic differentiation.- Specified by:
doDiffin classDifferentialFunction- Returns:
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onnxName
public String onnxName()
Description copied from class:DifferentialFunctionThe opName of this function in onnx
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tensorflowName
public String tensorflowName()
Description copied from class:DifferentialFunctionThe opName of this function tensorflow- Overrides:
tensorflowNamein classBaseOp- Returns:
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