org.nd4j.linalg.api.ops.impl.shape

Class Eye

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

org.nd4j.linalg.api.ops.impl.shape.Eye

All Implemented Interfaces:

CustomOp

public class Eye
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 and Description
static DataType	DEFAULT_DTYPE

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
Eye()
Eye(@NonNull INDArray rows)
Eye(@NonNull INDArray rows, @NonNull INDArray columns)
Eye(int rows)
Eye(int numRows, int numCols)
Eye(int numRows, int numCols, DataType dataType)
Eye(int numRows, int numCols, DataType dataType, int[] batchDimension)
Eye(SameDiff sameDiff, int numRows)
Eye(SameDiff sameDiff, int numRows, int numCols)
Eye(SameDiff sameDiff, int numRows, int numCols, DataType dataType)
Eye(SameDiff sameDiff, int numRows, int numCols, DataType dataType, int[] batchDimension)
Eye(SameDiff sameDiff, SDVariable numRows)
Eye(SameDiff sameDiff, SDVariable numRows, SDVariable numCols)
Eye(SameDiff sameDiff, SDVariable numRows, SDVariable numCols, DataType dataType, int[] batchDimension)
Eye(SameDiff sameDiff, SDVariable numRows, SDVariable numCols, SDVariable batch_shape)

Method Summary

Modifier and Type	Method and Description
protected void	addArgs()
List<DataType>	calculateOutputDataTypes(List<DataType> dataTypes) Calculate the data types for the output arrays.
List<LongShapeDescriptor>	calculateOutputShape() Calculate the output shape for this op
List<SDVariable>	doDiff(List<SDVariable> outGrad) The actual implementation for automatic differentiation.
String	opName() This method returns op opName as string

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

addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, clearArrays, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, initFromOnnx, initFromTensorFlow, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, onnxName, 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, configFieldName, diff, dup, equals, getNumOutputs, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, propertiesForFunction, 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

Field Detail

DEFAULT_DTYPE

public static final DataType DEFAULT_DTYPE

Constructor Detail

Eye

public Eye()

Eye

public Eye(@NonNull
           @NonNull INDArray rows)

Eye

public Eye(@NonNull
           @NonNull INDArray rows,
           @NonNull
           @NonNull INDArray columns)

Eye

public Eye(int rows)

Eye

public Eye(SameDiff sameDiff,
           SDVariable numRows)

Eye

public Eye(SameDiff sameDiff,
           SDVariable numRows,
           SDVariable numCols)

Eye

public Eye(SameDiff sameDiff,
           SDVariable numRows,
           SDVariable numCols,
           SDVariable batch_shape)

Eye

public Eye(SameDiff sameDiff,
           int numRows)

Eye

public Eye(SameDiff sameDiff,
           int numRows,
           int numCols)

Eye

public Eye(SameDiff sameDiff,
           int numRows,
           int numCols,
           DataType dataType)

Eye

public Eye(int numRows,
           int numCols,
           DataType dataType,
           int[] batchDimension)

Eye

public Eye(int numRows,
           int numCols)

Eye

public Eye(int numRows,
           int numCols,
           DataType dataType)

Eye

public Eye(SameDiff sameDiff,
           int numRows,
           int numCols,
           DataType dataType,
           int[] batchDimension)

Eye

public Eye(SameDiff sameDiff,
           SDVariable numRows,
           SDVariable numCols,
           DataType dataType,
           int[] batchDimension)

Method Detail

addArgs

protected void addArgs()

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:

calculateOutputShape

public List<LongShapeDescriptor> 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:

List of output shape descriptors

doDiff

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

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Overrides:

doDiff 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