org.nd4j.linalg.api.ops.impl.accum

Class Mmul

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

org.nd4j.linalg.api.ops.impl.accum.Mmul

All Implemented Interfaces:

CustomOp

public class Mmul
extends DynamicCustomOp

Matrix multiplication/dot product

Author:

Adam Gibson

Nested Class Summary

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

DynamicCustomOp.DynamicCustomOpsBuilder, DynamicCustomOp.SameDiffBuilder

Field Summary

Fields

Modifier and Type	Field and Description
protected MMulTranspose	mMulTranspose

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

inplaceCall, outputVariables

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

dimensions, extraArgs, inPlace, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor and Description
Mmul()
Mmul(INDArray x, INDArray y, INDArray z, MMulTranspose mMulTranspose)
Mmul(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2)
Mmul(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, MMulTranspose mMulTranspose)

Method Summary

Modifier and Type	Method and Description
List<int[]>	calculateOutputShape() Calculate the output shape for this op
List<SDVariable>	doDiff(List<SDVariable> i_v1) The actual implementation for automatic differentiation.
boolean	equals(Object o)
int	hashCode()
void	initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map<String,OnnxProto3.AttributeProto> attributesForNode, OnnxProto3.GraphProto graph) Iniitialize the function from the given OnnxProto3.NodeProto
void	initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph) Initialize the function from the given NodeDef
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
String	tensorflowName() The opName of this function tensorflow

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

addIArgument, addInputArgument, addOutputArgument, addTArgument, asProperties, assertValidForExecution, builder, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, inputArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, populateInputsAndOutputsFromSameDiff, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, sameDiffBuilder, setInputArgument, setOutputArgument, tArgs, toString, updateInputsFromSameDiff

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

arg, args, attributeAdaptersForFunction, configFieldName, diff, dup, f, getValue, hasPlaceHolderInputs, isConfigProperties, larg, onnxNames, propertiesForFunction, rarg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, 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

Field Detail

mMulTranspose

protected MMulTranspose mMulTranspose

Constructor Detail

Mmul

public Mmul(SameDiff sameDiff,
            SDVariable i_v1,
            SDVariable i_v2,
            MMulTranspose mMulTranspose)

Parameters:

sameDiff -

i_v1 -

i_v2 -

mMulTranspose -

Mmul

public Mmul(SameDiff sameDiff,
            SDVariable i_v1,
            SDVariable i_v2)

Parameters:

sameDiff -

i_v1 -

i_v2 -

Mmul

public Mmul(INDArray x,
            INDArray y,
            INDArray z,
            MMulTranspose mMulTranspose)

Parameters:

x -

y -

z -

Mmul

public Mmul()

Method Detail

calculateOutputShape

public List<int[]> calculateOutputShape()

Description copied from class: DifferentialFunction

Calculate the output shape for this op

Specified by:

calculateOutputShape in interface CustomOp

Overrides:

calculateOutputShape 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:

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(OnnxProto3.NodeProto node,
                         SameDiff initWith,
                         Map<String,OnnxProto3.AttributeProto> attributesForNode,
                         OnnxProto3.GraphProto graph)

Description copied from class: DifferentialFunction

Iniitialize the function from the given OnnxProto3.NodeProto

Overrides:

initFromOnnx in class DynamicCustomOp

doDiff

public List<SDVariable> doDiff(List<SDVariable> i_v1)

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Overrides:

doDiff in class DynamicCustomOp

Returns:

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:

equals

public boolean equals(Object o)

Overrides:

equals in class DifferentialFunction

hashCode

public int hashCode()

Overrides:

hashCode in class DifferentialFunction