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

Class Entropy

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.Entropy

All Implemented Interfaces:

Op, ReduceFloatOp, ReduceOp

public class Entropy
extends BaseReduceFloatOp

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, ownName, ownNameSetWithDefault, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor and Description
Entropy()
Entropy(INDArray x, INDArray z, int... dimensions)
Entropy(INDArray x, int... dimensions)
Entropy(SameDiff sameDiff, SDVariable i_v, int[] dimensions)

Method Summary

Modifier and Type	Method and Description
List<SDVariable>	doDiff(List<SDVariable> f1) The actual implementation for automatic differentiation.
Op.Type	getOpType()
static List<SDVariable>	grad(SameDiff sd, SDVariable arg, SDVariable grad, int[] dimensions)
String	opName() The name of the op
int	opNum() The number of the op (mainly for old legacy XYZ ops like Op)

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

calculateOutputDataTypes, calculateOutputShape, calculateOutputShape, opType, resultType, 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

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

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

arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, getValue, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariables, outputVariablesNames, propertiesForFunction, rarg, replaceArg, 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

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

Constructor Detail

Entropy

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

Entropy

public Entropy()

Entropy

public Entropy(INDArray x,
               INDArray z,
               int... dimensions)

Entropy

public Entropy(INDArray x,
               int... dimensions)

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

getOpType

public Op.Type getOpType()

Specified by:

getOpType in interface ReduceOp

Overrides:

getOpType in class BaseReduceFloatOp

doDiff

public List<SDVariable> doDiff(List<SDVariable> f1)

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Specified by:

doDiff in class DifferentialFunction

Returns:

grad

public static List<SDVariable> grad(SameDiff sd,
                                    SDVariable arg,
                                    SDVariable grad,
                                    int[] dimensions)