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

Class MaxPooling2D

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPooling2D

All Implemented Interfaces:

CustomOp

public class MaxPooling2D
extends DynamicCustomOp

Max Pooling2D operation

Nested Class Summary

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

DynamicCustomOp.DynamicCustomOpsBuilder

Field Summary

Fields

Modifier and Type	Field and Description
protected Pooling2DConfig	config

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
MaxPooling2D()
MaxPooling2D(INDArray input, INDArray output, @NonNull Pooling2DConfig config)
MaxPooling2D(INDArray input, @NonNull Pooling2DConfig config)
MaxPooling2D(SameDiff sameDiff, SDVariable input, Pooling2DConfig config)

Method Summary

Modifier and Type	Method and Description
List<DataType>	calculateOutputDataTypes(List<DataType> inputDataTypes) Calculate the data types for the output arrays.
String	configFieldName() Returns the name of the field to be used for looking up field names.
List<SDVariable>	doDiff(List<SDVariable> f1) The actual implementation for automatic differentiation.
String	getPoolingPrefix()
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
boolean	isConfigProperties() Returns true if the fields for this class should be looked up from a configuration class.
Map<String,Map<String,PropertyMapping>>	mappingsForFunction() Returns the mappings for a given function ( for tensorflow and onnx import mapping properties of this function).
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[]	tensorflowNames() 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, tensorflowName, toString, wrapFilterNull, wrapOrNull, wrapOrNull

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

arg, arg, argNames, args, attributeAdaptersForFunction, diff, dup, equals, getNumOutputs, getValue, hashCode, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor

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

Field Detail

config

protected Pooling2DConfig config

Constructor Detail

MaxPooling2D

public MaxPooling2D()

MaxPooling2D

public MaxPooling2D(SameDiff sameDiff,
                    SDVariable input,
                    Pooling2DConfig config)

MaxPooling2D

public MaxPooling2D(INDArray input,
                    INDArray output,
                    @NonNull
                    @NonNull Pooling2DConfig config)

MaxPooling2D

public MaxPooling2D(INDArray input,
                    @NonNull
                    @NonNull Pooling2DConfig config)

Method Detail

isConfigProperties

public boolean isConfigProperties()

Description copied from class: DifferentialFunction

Returns true if the fields for this class should be looked up from a configuration class.

Overrides:

isConfigProperties in class DifferentialFunction

Returns:

configFieldName

public String configFieldName()

Description copied from class: DifferentialFunction

Returns the name of the field to be used for looking up field names. This should be used in conjunction with DifferentialFunction.isConfigProperties() to facilitate mapping fields for model import.

Overrides:

configFieldName in class DifferentialFunction

Returns:

propertiesForFunction

public Map<String,Object> propertiesForFunction()

Description copied from class: DifferentialFunction

Returns the properties for a given function

Overrides:

propertiesForFunction in class DifferentialFunction

Returns:

getPoolingPrefix

public String getPoolingPrefix()

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:

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:

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

mappingsForFunction

public Map<String,Map<String,PropertyMapping>> mappingsForFunction()

Description copied from class: DifferentialFunction

Returns the mappings for a given function ( for tensorflow and onnx import mapping properties of this function). The mapping is indexed by field name. If the function has no properties, this returned map will be empty. Note that some functions have multiple names. This function returns a map indexed by each alias it has for a given name. These names include both onnx and tensorflow names (which might be 1 or more)

Overrides:

mappingsForFunction in class DifferentialFunction

Returns:

onnxName

public String onnxName()

Description copied from class: DifferentialFunction

The opName of this function in onnx

Overrides:

onnxName in class DynamicCustomOp

Returns:

tensorflowNames

public String[] tensorflowNames()

Description copied from class: DifferentialFunction

The opName of this function tensorflow

Overrides:

tensorflowNames in class DifferentialFunction

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