org.nd4j.linalg.api.ops.impl.accum

Class Max

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.BaseOp

org.nd4j.linalg.api.ops.BaseAccumulation

org.nd4j.linalg.api.ops.impl.accum.Max

All Implemented Interfaces:

Accumulation, Op

public class Max
extends BaseAccumulation

Calculate the max over a vector

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.BaseAccumulation

finalResult, isComplex, keepDims, newFormat

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
Max()
Max(INDArray x)
Max(INDArray x, INDArray y)
Max(INDArray x, INDArray y, INDArray z, long n) Initialize with the given input, pairwise transform, result, and number of elements
Max(INDArray x, INDArray y, long n)
Max(SameDiff sameDiff, SDVariable i_v, int[] dimensions)
Max(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions)

Method Summary

Modifier and Type	Method and Description
List<SDVariable>	doDiff(List<SDVariable> i_v1) The actual implementation for automatic differentiation.
Op.Type	getOpType()
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)
String	tensorflowName() The opName of this function tensorflow
double	zeroDouble() Initial value (used to initialize the accumulation op)
float	zeroFloat() Initial value (used to initialize the accumulation op)
float	zeroHalf() Initial value for half

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

calculateOutputShape, getFinalResult, hasReductionIndices, initFromOnnx, initFromTensorFlow, isComplexAccumulation, isKeepDims, noOp, opType, setFinalResult

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

equals, exec, exec, extraArgs, extraArgsBuff, extraArgsDataBuff, getOpType, hashCode, init, isExecSpecial, isPassThrough, n, numProcessed, outputVariables, setN, setX, setY, setZ, toCustomOp, toString, x, y, z

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

exec, exec, extraArgs, extraArgsBuff, extraArgsDataBuff, init, isExecSpecial, isPassThrough, n, numProcessed, setExtraArgs, setN, setX, setY, setZ, toCustomOp, x, y, z

Constructor Detail

Max

public Max(SameDiff sameDiff,
           SDVariable i_v,
           int[] dimensions)

Max

public Max(SameDiff sameDiff,
           SDVariable i_v,
           SDVariable i_v2,
           int[] dimensions)

Max

public Max()

Max

public Max(INDArray x,
           INDArray y,
           long n)

Max

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

Initialize with the given input, pairwise transform, result, and number of elements

Parameters:

x - the input

y - the pairwise transform

z - the result

n - the number of elements

Max

public Max(INDArray x)

Max

public Max(INDArray x,
           INDArray y)

Method Detail

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

zeroDouble

public double zeroDouble()

Description copied from interface: Accumulation

Initial value (used to initialize the accumulation op)

Specified by:

zeroDouble in interface Accumulation

Overrides:

zeroDouble in class BaseAccumulation

Returns:

the initial value

zeroHalf

public float zeroHalf()

Description copied from interface: Accumulation

Initial value for half

Specified by:

zeroHalf in interface Accumulation

Overrides:

zeroHalf in class BaseAccumulation

Returns:

zeroFloat

public float zeroFloat()

Description copied from interface: Accumulation

Initial value (used to initialize the accumulation op)

Specified by:

zeroFloat in interface Accumulation

Overrides:

zeroFloat in class BaseAccumulation

doDiff

public List<SDVariable> doDiff(List<SDVariable> i_v1)

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Specified by:

doDiff in class DifferentialFunction

Returns:

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:

getOpType

public Op.Type getOpType()