org.nd4j.linalg.api.ops.impl.reduce.floating

Class SquaredNorm

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.BaseOp

org.nd4j.linalg.api.ops.BaseReduceOp

org.nd4j.linalg.api.ops.BaseReduceFloatOp

org.nd4j.linalg.api.ops.impl.reduce.floating.SquaredNorm

All Implemented Interfaces:

Op, ReduceFloatOp, ReduceOp

public class SquaredNorm
extends BaseReduceFloatOp

Squared norm (sum_i x_i^2) reduction operation

Author:

Alex Black

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.BaseReduceOp

isComplex, isEmptyReduce, keepDims

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

dimensionz, extraArgz, x, xVertexId, y, yVertexId, z, zVertexId

Fields inherited from class org.nd4j.autodiff.functions.DifferentialFunction

dimensions, extraArgs, inPlace, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor and Description
SquaredNorm()
SquaredNorm(INDArray input, INDArray output, boolean keepDims, int... dimensions)
SquaredNorm(SameDiff sameDiff, SDVariable input, boolean keepDims, int... dimensions)

Method Summary

Modifier and Type	Method and Description
List<SDVariable>	doDiff(List<SDVariable> grad) The actual implementation for automatic differentiation.
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

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

calculateOutputDataTypes, calculateOutputShape, getOpType, opType, resultType, validateDataTypes

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

hasReductionIndices, initFromOnnx, initFromTensorFlow, isComplexAccumulation, isKeepDims, noOp, setDimensions

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

defineDimensions, dimensions, equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getFinalResult, getNumOutputs, getOpType, hashCode, outputVariables, setX, setY, setZ, toCustomOp, toString, x, y, z

Methods inherited from class org.nd4j.autodiff.functions.DifferentialFunction

arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, f, getValue, isConfigProperties, larg, mappingsForFunction, onnxNames, outputVariable, outputVariables, outputVariablesNames, propertiesForFunction, rarg, replaceArg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setPropertiesForFunction, 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.ReduceOp

dimensions, getFinalResult, isComplexAccumulation, isKeepDims, noOp, setDimensions

Methods inherited from interface org.nd4j.linalg.api.ops.Op

extraArgs, extraArgsBuff, extraArgsDataBuff, setExtraArgs, setX, setY, setZ, toCustomOp, x, y, z

Constructor Detail

SquaredNorm

public SquaredNorm(SameDiff sameDiff,
                   SDVariable input,
                   boolean keepDims,
                   int... dimensions)

SquaredNorm

public SquaredNorm(INDArray input,
                   INDArray output,
                   boolean keepDims,
                   int... dimensions)

SquaredNorm

public SquaredNorm()

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

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:

doDiff

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

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Specified by:

doDiff in class DifferentialFunction

Returns: