public class GradientBackwardsMarker extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
Modifier and Type | Field and Description |
---|---|
static String |
OP_NAME |
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
---|
GradientBackwardsMarker() |
GradientBackwardsMarker(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
Modifier and Type | Method and Description |
---|---|
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> i_v)
The actual implementation for automatic differentiation.
|
int |
getNumOutputs() |
String |
onnxName()
The opName of this function in onnx
|
String |
opName()
The opName of this operation
|
int |
opNum()
The number of the op (mainly for old legacy XYZ ops
like
Op ) |
String |
tensorflowName()
The opName of this function tensorflow
|
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, opHash, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNull
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public static final String OP_NAME
public GradientBackwardsMarker(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2)
public GradientBackwardsMarker()
public int opNum()
DifferentialFunction
Op
)opNum
in class DynamicCustomOp
public String opName()
opName
in interface CustomOp
opName
in class DynamicCustomOp
public String onnxName()
DifferentialFunction
onnxName
in class DynamicCustomOp
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DynamicCustomOp
public List<SDVariable> doDiff(List<SDVariable> i_v)
DifferentialFunction
doDiff
in class DynamicCustomOp
public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes)
DifferentialFunction
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.calculateOutputDataTypes
in class DifferentialFunction
dataTypes
- The data types of the inputspublic List<LongShapeDescriptor> calculateOutputShape()
DifferentialFunction
calculateOutputShape
in interface CustomOp
calculateOutputShape
in class DynamicCustomOp
public int getNumOutputs()
getNumOutputs
in class DifferentialFunction
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