public class Concat extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilderaxis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, sArguments, tArgumentsdimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue| Constructor and Description |
|---|
Concat() |
Concat(INDArray[] arrays,
int concatDimension) |
Concat(int concatDimension,
INDArray... arrays) |
Concat(SameDiff sameDiff,
int concatDimension,
SDVariable... inputs) |
Concat(SameDiff sameDiff,
SDVariable[] inputs,
int concatDimension) |
| Modifier and Type | Method and Description |
|---|---|
void |
assertValidForExecution()
Asserts a valid state for execution,
otherwise throws an
ND4JIllegalStateException |
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 |
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
|
Map<String,Object> |
propertiesForFunction()
Returns the properties for a given function
|
String |
tensorflowName()
The opName of this function tensorflow
|
String[] |
tensorflowNames()
The opName of this function tensorflow
|
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addSArgument, addTArgument, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, computeArrays, configureFromArguments, dArgs, generateFake, generateFake, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getSArgument, getTArgument, getValue, iArgs, initFromOnnx, inputArguments, mappingsForFunction, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, opHash, opNum, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeSArgument, removeTArgument, sArgs, setInputArgument, setInputArguments, setOutputArgument, setPropertiesForFunction, setValueFor, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNullarg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, configureWithSameDiff, diff, dup, equals, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getNumOutputs, getStringFromProperty, hashCode, isConfigProperties, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceIdclone, finalize, getClass, notify, notifyAll, wait, wait, waitisInplaceCallpublic Concat()
public Concat(int concatDimension,
INDArray... arrays)
public Concat(INDArray[] arrays, int concatDimension)
public Concat(SameDiff sameDiff, SDVariable[] inputs, int concatDimension)
public Concat(SameDiff sameDiff, int concatDimension, SDVariable... inputs)
public String opName()
DynamicCustomOpopName in interface CustomOpopName in class DynamicCustomOppublic void assertValidForExecution()
CustomOpND4JIllegalStateExceptionassertValidForExecution in interface CustomOpassertValidForExecution in class DynamicCustomOppublic void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunctionNodeDefinitFromTensorFlow in class DynamicCustomOppublic Map<String,Object> propertiesForFunction()
DifferentialFunctionpropertiesForFunction in class DynamicCustomOppublic String onnxName()
DifferentialFunctiononnxName in class DynamicCustomOppublic String tensorflowName()
DifferentialFunctiontensorflowName in class DynamicCustomOppublic String[] tensorflowNames()
DifferentialFunctiontensorflowNames in class DifferentialFunctionpublic Op.Type opType()
DifferentialFunctionopType in class DynamicCustomOppublic List<SDVariable> doDiff(List<SDVariable> i_v)
DifferentialFunctiondoDiff in class DynamicCustomOppublic List<DataType> calculateOutputDataTypes(List<DataType> dataTypes)
DifferentialFunctionDifferentialFunction.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.calculateOutputDataTypes in class DifferentialFunctiondataTypes - The data types of the inputsCopyright © 2022. All rights reserved.