public class Conv1DDerivative extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder| Modifier and Type | Field and Description |
|---|---|
protected Conv1DConfig |
config |
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArgumentsdimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue| Constructor and Description |
|---|
Conv1DDerivative(INDArray[] inputs,
INDArray[] outputs,
Conv1DConfig config) |
Conv1DDerivative(@NonNull INDArray input,
@NonNull INDArray weights,
INDArray bias,
@NonNull INDArray gradOut,
INDArray output,
@NonNull Conv1DConfig config) |
Conv1DDerivative(@NonNull SameDiff sameDiff,
@NonNull SDVariable[] inputs,
@NonNull Conv1DConfig config) |
Conv1DDerivative(@NonNull SameDiff sd,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
SDVariable gradOut,
@NonNull Conv1DConfig config) |
| Modifier and Type | Method and Description |
|---|---|
protected void |
addArgs() |
List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.
|
String |
configFieldName()
Returns the name of the field to be used for looking up field names.
|
int |
getNumOutputs() |
Object |
getValue(Field property)
Get the value for a given property
for this function
|
long[] |
iArgs() |
boolean |
isConfigProperties()
Returns true if the fields for this class should be looked up from a configuration class.
|
String |
opName()
This method returns op opName as string
|
Map<String,Object> |
propertiesForFunction()
Returns the properties for a given function
|
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, doDiff, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, 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, wrapOrNullarg, arg, argNames, args, attributeAdaptersForFunction, diff, dup, equals, hashCode, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNamesclone, finalize, getClass, notify, notifyAll, wait, wait, waitisInplaceCallprotected Conv1DConfig config
public Conv1DDerivative(@NonNull
@NonNull SameDiff sameDiff,
@NonNull
@NonNull SDVariable[] inputs,
@NonNull
@NonNull Conv1DConfig config)
public Conv1DDerivative(@NonNull
@NonNull SameDiff sd,
@NonNull
@NonNull SDVariable input,
@NonNull
@NonNull SDVariable weights,
SDVariable bias,
SDVariable gradOut,
@NonNull
@NonNull Conv1DConfig config)
public Conv1DDerivative(INDArray[] inputs, INDArray[] outputs, Conv1DConfig config)
protected void addArgs()
public long[] iArgs()
iArgs in interface CustomOpiArgs in class DynamicCustomOppublic Object getValue(Field property)
DifferentialFunctiongetValue in class DifferentialFunctionproperty - the property to getpublic Map<String,Object> propertiesForFunction()
DifferentialFunctionpropertiesForFunction in class DifferentialFunctionpublic boolean isConfigProperties()
DifferentialFunctionisConfigProperties in class DifferentialFunctionpublic String configFieldName()
DifferentialFunctionDifferentialFunction.isConfigProperties()
to facilitate mapping fields for model import.configFieldName in class DifferentialFunctionpublic String opName()
DynamicCustomOpopName in interface CustomOpopName in class DynamicCustomOppublic int getNumOutputs()
getNumOutputs in class DifferentialFunctionpublic List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes)
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 DifferentialFunctioninputDataTypes - The data types of the inputsCopyright © 2021. All rights reserved.