Class Rank
- java.lang.Object
-
- org.nd4j.autodiff.functions.DifferentialFunction
-
- org.nd4j.linalg.api.ops.DynamicCustomOp
-
- org.nd4j.linalg.api.ops.impl.shape.Rank
-
- All Implemented Interfaces:
CustomOp
public class Rank extends DynamicCustomOp
-
-
Nested Class Summary
-
Nested classes/interfaces inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
-
-
Field Summary
-
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 Rank()
Rank(SameDiff sameDiff, SDVariable input)
Rank(SameDiff sameDiff, SDVariable input, boolean inPlace)
Rank(INDArray indArray)
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description List<DataType>
calculateOutputDataTypes(List<DataType> dataTypes)
Calculate the data types for the output arrays.List<SDVariable>
doDiff(List<SDVariable> i_v)
The actual implementation for automatic differentiation.void
initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map<String,Onnx.AttributeProto> attributesForNode, Onnx.GraphProto graph)
Iniitialize the function from the givenOnnx.NodeProto
String
onnxName()
The opName of this function in onnxString
opName()
This method returns op opName as stringString
tensorflowName()
The opName of this function tensorflow-
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, configureFromArguments, dArgs, generateFake, generateFake, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getSArgument, getTArgument, getValue, iArgs, initFromTensorFlow, inputArguments, mappingsForFunction, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, propertiesForFunction, removeIArgument, removeInputArgument, removeOutputArgument, removeSArgument, removeTArgument, sArgs, setInputArgument, setInputArguments, setOutputArgument, setPropertiesForFunction, setValueFor, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNull
-
Methods inherited from class org.nd4j.autodiff.functions.DifferentialFunction
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, configureWithSameDiff, diff, dup, equals, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getNumOutputs, getStringFromProperty, hashCode, 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
-
-
-
-
Constructor Detail
-
Rank
public Rank()
-
Rank
public Rank(SameDiff sameDiff, SDVariable input)
-
Rank
public Rank(SameDiff sameDiff, SDVariable input, boolean inPlace)
-
Rank
public Rank(INDArray indArray)
-
-
Method Detail
-
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 givenOnnx.NodeProto
- Overrides:
initFromOnnx
in classDynamicCustomOp
-
opName
public String opName()
Description copied from class:DynamicCustomOp
This method returns op opName as string- Specified by:
opName
in interfaceCustomOp
- Overrides:
opName
in classDynamicCustomOp
- Returns:
-
onnxName
public String onnxName()
Description copied from class:DifferentialFunction
The opName of this function in onnx- Overrides:
onnxName
in classDynamicCustomOp
- Returns:
-
tensorflowName
public String tensorflowName()
Description copied from class:DifferentialFunction
The opName of this function tensorflow- Overrides:
tensorflowName
in classDynamicCustomOp
- Returns:
-
doDiff
public List<SDVariable> doDiff(List<SDVariable> i_v)
Description copied from class:DifferentialFunction
The actual implementation for automatic differentiation.- Overrides:
doDiff
in classDynamicCustomOp
- 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 fromDifferentialFunction.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 classDifferentialFunction
- Parameters:
dataTypes
- The data types of the inputs- Returns:
- The data types of the outputs
-
-