DynamicCustomOp.DynamicCustomOpsBuilderaxis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArgumentsdimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue| Constructor and Description |
|---|
Conv2DDerivative() |
Conv2DDerivative(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv2DConfig config) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.
|
List<SDVariable> |
doDiff(List<SDVariable> f1)
The actual implementation for automatic differentiation.
|
int |
getNumOutputs() |
String |
onnxName()
The opName of this function in onnx
|
String |
opName()
This method returns op opName as string
|
String |
tensorflowName()
The opName of this function tensorflow
|
String[] |
tensorflowNames()
The opName of this function tensorflow
|
addArgs, attributeAdaptersForFunction, configFieldName, getValue, iArgs, initConfig, initFromOnnx, initFromTensorFlow, isConfigProperties, mappingsForFunction, propertiesForFunctionaddBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNullarg, arg, argNames, args, diff, dup, equals, hashCode, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueForclone, finalize, getClass, notify, notifyAll, wait, wait, waitisInplaceCallpublic Conv2DDerivative(SameDiff sameDiff, SDVariable[] inputFunctions, Conv2DConfig config)
public Conv2DDerivative()
public String onnxName()
DifferentialFunctionpublic String tensorflowName()
DifferentialFunctiontensorflowName in class Conv2Dpublic String[] tensorflowNames()
DifferentialFunctiontensorflowNames in class Conv2Dpublic String opName()
DynamicCustomOppublic List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunctionpublic 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 Conv2DinputDataTypes - The data types of the inputsCopyright © 2021. All rights reserved.