public class StridedSliceBp extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
Constructor and Description |
---|
StridedSliceBp() |
StridedSliceBp(SameDiff sameDiff,
@NonNull SDVariable in,
@NonNull SDVariable grad,
@NonNull long[] begin,
@NonNull long[] end,
@NonNull long[] strides,
int beginMask,
int endMask,
int ellipsisMask,
int newAxisMask,
int shrinkAxisMask) |
StridedSliceBp(SameDiff sameDiff,
@NonNull SDVariable in,
@NonNull SDVariable grad,
@NonNull SDVariable begin,
@NonNull SDVariable end,
@NonNull SDVariable strides,
int beginMask,
int endMask,
int ellipsisMask,
int newAxisMask,
int shrinkAxisMask) |
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.
|
String |
opName()
This method returns op opName as string
|
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, 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, wrapOrNull
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, getNumOutputs, 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 StridedSliceBp()
public StridedSliceBp(SameDiff sameDiff, @NonNull @NonNull SDVariable in, @NonNull @NonNull SDVariable grad, @NonNull @NonNull long[] begin, @NonNull @NonNull long[] end, @NonNull @NonNull long[] strides, int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask)
public StridedSliceBp(SameDiff sameDiff, @NonNull @NonNull SDVariable in, @NonNull @NonNull SDVariable grad, @NonNull @NonNull SDVariable begin, @NonNull @NonNull SDVariable end, @NonNull @NonNull SDVariable strides, int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask)
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public void assertValidForExecution()
CustomOp
ND4JIllegalStateException
assertValidForExecution
in interface CustomOp
assertValidForExecution
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 inputsCopyright © 2021. All rights reserved.