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

Class TensorMmul

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

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

All Implemented Interfaces:

CustomOp

public class TensorMmul
extends DynamicCustomOp

TensorMmul

Author:

Adam Gibson

Nested Class Summary

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

DynamicCustomOp.DynamicCustomOpsBuilder

Field Summary

Fields

Modifier and Type	Field	Description
protected boolean	addedEdges
protected MMulTranspose	mMulTranspose

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

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

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

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

Constructor Summary

Constructors

Constructor	Description
TensorMmul(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, int[][] dimensions)
TensorMmul(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, int[][] dimensions, MMulTranspose mMulTranspose)
TensorMmul(SameDiff sameDiff, SDVariable x, SDVariable y, int[] dimensionsX, int[] dimensionsY, boolean transposeX, boolean transposeY, boolean transposeZ)
TensorMmul(INDArray x, INDArray y, int[][] axes)
TensorMmul(INDArray x, INDArray y, int[] dimensionsX, int[] dimensionsY, boolean transposeX, boolean transposeY, boolean transposeZ)
TensorMmul(INDArray x, INDArray y, INDArray z, int[][] axes)	Initialize with the given input, pairwise transform, result, and number of elements

Method Summary

Modifier and Type	Method	Description
List<DataType>	calculateOutputDataTypes(List<DataType> inputDataTypes)	Calculate the data types for the output arrays.
void	configureFromArguments()	This allows a custom op to configure relevant fields from its arguments.
List<SDVariable>	doDiff(List<SDVariable> gradients)	The actual implementation for automatic differentiation.
boolean	equals(Object o)
int	hashCode()
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
Op.Type	opType()	The type of the op
void	setPropertiesForFunction(Map<String,Object> properties)

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

addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addOutputsToOp, addSArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, computeArrays, dArgs, generateFake, generateFake, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getSArgument, getTArgument, getValue, iArgs, inputArguments, mappingsForFunction, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, opHash, opNum, outputArguments, outputVariables, outputVariables, propertiesForFunction, removeIArgument, removeInputArgument, removeOutputArgument, removeSArgument, removeTArgument, sArgs, setInputArgument, setInputArguments, setOutputArgument, setValueFor, tArgs, tensorflowName, toString, wrapFilterNull, wrapOrNull, wrapOrNull

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

arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, configureWithSameDiff, diff, dup, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getNumOutputs, getStringFromProperty, isConfigProperties, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, 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

Field Detail

addedEdges

protected boolean addedEdges

mMulTranspose

protected MMulTranspose mMulTranspose

Constructor Detail

TensorMmul

public TensorMmul(INDArray x,
                  INDArray y,
                  int[][] axes)

TensorMmul

public TensorMmul(INDArray x,
                  INDArray y,
                  INDArray z,
                  int[][] axes)

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

Parameters:

x - the input

y - the pairwise transform

z - the result

TensorMmul

public TensorMmul(INDArray x,
                  INDArray y,
                  int[] dimensionsX,
                  int[] dimensionsY,
                  boolean transposeX,
                  boolean transposeY,
                  boolean transposeZ)

TensorMmul

public TensorMmul(SameDiff sameDiff,
                  SDVariable i_v1,
                  SDVariable i_v2,
                  int[][] dimensions)

TensorMmul

public TensorMmul(SameDiff sameDiff,
                  SDVariable i_v1,
                  SDVariable i_v2,
                  int[][] dimensions,
                  MMulTranspose mMulTranspose)

TensorMmul

public TensorMmul(SameDiff sameDiff,
                  SDVariable x,
                  SDVariable y,
                  int[] dimensionsX,
                  int[] dimensionsY,
                  boolean transposeX,
                  boolean transposeY,
                  boolean transposeZ)

Method Detail

doDiff

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

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Overrides:

doDiff in class DynamicCustomOp

Returns:

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:

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

equals

public boolean equals(Object o)

Overrides:

equals in class DifferentialFunction

hashCode

public int hashCode()

Overrides:

hashCode in class DifferentialFunction

configureFromArguments

public void configureFromArguments()

Description copied from interface: CustomOp

This allows a custom op to configure relevant fields from its arguments. This is needed when ops are created via reflection for things like model import.

Specified by:

configureFromArguments in interface CustomOp

Overrides:

configureFromArguments in class DynamicCustomOp

setPropertiesForFunction

public void setPropertiesForFunction(Map<String,Object> properties)

Overrides:

setPropertiesForFunction in class DynamicCustomOp

opType

public Op.Type opType()

Description copied from class: DifferentialFunction

The type of the op

Overrides:

opType 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:

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