org.nd4j.linalg.api.ops.impl.layers.convolution

Class BatchNorm

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

org.nd4j.linalg.api.ops.impl.layers.convolution.BatchNorm

All Implemented Interfaces:

CustomOp

Direct Known Subclasses:

BatchNormDerivative

public class BatchNorm
extends DynamicCustomOp

Nested Class Summary

Nested classes/interfaces inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp

DynamicCustomOp.DynamicCustomOpsBuilder

Field Summary

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

axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments

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

dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor and Description
BatchNorm(INDArray input, INDArray mean, INDArray variance, INDArray gamma, INDArray beta, double epsilon, int... axis)
BatchNorm(SameDiff sameDiff, SDVariable[] inputFunctions, INDArray[] inputArrays, INDArray[] outputArrays, boolean inPlace, boolean applyGamma, boolean applyBeta, double epsilon, int[] axis)
BatchNorm(SameDiff sameDiff, SDVariable input, SDVariable mean, SDVariable variance, SDVariable gamma, SDVariable beta, double epsilon, int[] axis)

Method Summary

Modifier and Type	Method and Description
void	addArgs()
List<DataType>	calculateOutputDataTypes(List<DataType> inputDataTypes) Calculate the data types for the output arrays.
List<SDVariable>	doDiff(List<SDVariable> f1) The actual implementation for automatic differentiation.
void	initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map<String,Onnx.AttributeProto> attributesForNode, Onnx.GraphProto graph) Iniitialize the function from the given Onnx.NodeProto
void	initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph) Initialize the function from the given NodeDef
String	onnxName() The opName of this function in onnx
String	opName() This method returns op opName as string
Map<String,Object>	propertiesForFunction() Returns the properties for a given function
String	tensorflowName() The opName of this function tensorflow

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

addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNull

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

arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, getNumOutputs, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, 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.CustomOp

isInplaceCall

Constructor Detail

BatchNorm

public BatchNorm(SameDiff sameDiff,
                 SDVariable[] inputFunctions,
                 INDArray[] inputArrays,
                 INDArray[] outputArrays,
                 boolean inPlace,
                 boolean applyGamma,
                 boolean applyBeta,
                 double epsilon,
                 int[] axis)

BatchNorm

public BatchNorm(SameDiff sameDiff,
                 SDVariable input,
                 SDVariable mean,
                 SDVariable variance,
                 SDVariable gamma,
                 SDVariable beta,
                 double epsilon,
                 int[] axis)

BatchNorm

public BatchNorm(INDArray input,
                 INDArray mean,
                 INDArray variance,
                 INDArray gamma,
                 INDArray beta,
                 double epsilon,
                 int... axis)

Method Detail

addArgs

public void addArgs()

propertiesForFunction

public Map<String,Object> propertiesForFunction()

Description copied from class: DifferentialFunction

Returns the properties for a given function

Overrides:

propertiesForFunction in class DifferentialFunction

Returns:

initFromTensorFlow

public void initFromTensorFlow(NodeDef nodeDef,
                               SameDiff initWith,
                               Map<String,AttrValue> attributesForNode,
                               GraphDef graph)

Description copied from class: DifferentialFunction

Initialize the function from the given NodeDef

Overrides:

initFromTensorFlow in class DynamicCustomOp

initFromOnnx

public void initFromOnnx(Onnx.NodeProto node,
                         SameDiff initWith,
                         Map<String,Onnx.AttributeProto> attributesForNode,
                         Onnx.GraphProto graph)

Description copied from class: DifferentialFunction

Iniitialize the function from the given Onnx.NodeProto

Overrides:

initFromOnnx in class DynamicCustomOp

opName

public String opName()

Description copied from class: DynamicCustomOp

This method returns op opName as string

Specified by:

opName in interface CustomOp

Overrides:

opName in class DynamicCustomOp

Returns:

onnxName

public String onnxName()

Description copied from class: DifferentialFunction

The opName of this function in onnx

Overrides:

onnxName in class DynamicCustomOp

Returns:

tensorflowName

public String tensorflowName()

Description copied from class: DifferentialFunction

The opName of this function tensorflow

Overrides:

tensorflowName in class DynamicCustomOp

Returns:

doDiff

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

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Overrides:

doDiff in class DynamicCustomOp

Returns:

calculateOutputDataTypes

public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes)

Description copied from class: DifferentialFunction

Calculate the data types for the output arrays. Though datatypes can also be inferred from DifferentialFunction.calculateOutputShape(), this method differs in that it does not require the input arrays to be populated. This is important as it allows us to do greedy datatype inference for the entire net - even if arrays are not available.

Overrides:

calculateOutputDataTypes in class DifferentialFunction

Parameters:

inputDataTypes - The data types of the inputs

Returns:

The data types of the outputs