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

Class Mmul

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

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

All Implemented Interfaces:

CustomOp

public class Mmul
extends DynamicCustomOp

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 double	alpha
protected double	beta
protected MMulTranspose	mt

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
Mmul()
Mmul(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2)
Mmul(SameDiff sameDiff, SDVariable x, SDVariable y, boolean transposeX, boolean transposeY, boolean transposeZ)
Mmul(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, MMulTranspose mt)
Mmul(INDArray x, INDArray y)
Mmul(INDArray x, INDArray y, boolean transposeX, boolean transposeY, boolean transposeZ)
Mmul(INDArray x, INDArray y, double alpha, double beta)
Mmul(INDArray x, INDArray y, double alpha, double beta, boolean transposeX, boolean transposeY, boolean transposeZ)
Mmul(INDArray x, INDArray y, INDArray z, double alpha, double beta, MMulTranspose mt)
Mmul(INDArray x, INDArray y, INDArray z, MMulTranspose mt)

Method Summary

Modifier and Type	Method	Description
List<DataType>	calculateOutputDataTypes(List<DataType> dataTypes)	Calculate the data types for the output arrays.
String	configFieldName()	Returns the name of the field to be used for looking up field names.
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.
Object	getValue(Field property)	Get the value for a given property for this function
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
void	setPropertiesForFunction(Map<String,Object> properties)
String[]	tensorflowNames()	The opName of this function tensorflow
long[]	transposeShapeArray(long[] shape)	For a 2D matrix of shape (M, N) we return (N, M).

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, iArgs, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, 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, configureWithSameDiff, diff, dup, equals, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getNumOutputs, getStringFromProperty, hashCode, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId

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

mt

protected MMulTranspose mt

alpha

protected double alpha

beta

protected double beta

Constructor Detail

Mmul

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

Parameters:

sameDiff -

i_v1 -

i_v2 -

mt -

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,
            double alpha,
            double beta,
            MMulTranspose mt)

Mmul

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

Parameters:

x -

y -

z -

Mmul

public Mmul(INDArray x,
            INDArray y,
            boolean transposeX,
            boolean transposeY,
            boolean transposeZ)

Mmul

public Mmul(INDArray x,
            INDArray y,
            double alpha,
            double beta,
            boolean transposeX,
            boolean transposeY,
            boolean transposeZ)

Mmul

public Mmul(INDArray x,
            INDArray y,
            double alpha,
            double beta)

Mmul

public Mmul(INDArray x,
            INDArray y)

Mmul

public Mmul(SameDiff sameDiff,
            SDVariable x,
            SDVariable y,
            boolean transposeX,
            boolean transposeY,
            boolean transposeZ)

Mmul

public Mmul()

Method Detail

getValue

public Object getValue(Field property)

Description copied from class: DifferentialFunction

Get the value for a given property for this function

Overrides:

getValue in class DynamicCustomOp

Parameters:

property - the property to get

Returns:

the value for the function if it exists

propertiesForFunction

public Map<String,Object> propertiesForFunction()

Description copied from class: DifferentialFunction

Returns the properties for a given function

Overrides:

propertiesForFunction in class DynamicCustomOp

Returns:

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

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:

setPropertiesForFunction

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

Overrides:

setPropertiesForFunction in class DynamicCustomOp

transposeShapeArray

public long[] transposeShapeArray(long[] shape)

For a 2D matrix of shape (M, N) we return (N, M). For a 3D matrix with leading mini-batch dimension (mb, M, N) we return (mb, N, M)

Parameters:

shape - input shape array

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:

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

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:

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 DynamicCustomOp

Returns:

calculateOutputDataTypes

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

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:

dataTypes - The data types of the inputs

Returns:

The data types of the outputs