public class TensorMmulBp extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
Constructor and Description |
---|
TensorMmulBp() |
TensorMmulBp(INDArray x,
INDArray y,
INDArray gradAtOutput,
INDArray dldx,
INDArray dldy,
int[][] axes) |
TensorMmulBp(INDArray x,
INDArray y,
INDArray gradAtOutput,
INDArray dldx,
INDArray dldy,
int[] axesX,
int[] axesY) |
TensorMmulBp(INDArray x,
INDArray y,
INDArray gradAtOutput,
int[][] axes) |
TensorMmulBp(INDArray x,
INDArray y,
INDArray gradAtOutput,
int[] axesX,
int[] axesY) |
TensorMmulBp(SameDiff samediff,
SDVariable x,
SDVariable y,
SDVariable gradAtOutput,
int[][] axes) |
TensorMmulBp(SameDiff samediff,
SDVariable x,
SDVariable y,
SDVariable gradAtOutput,
int[] axesX,
int[] axesY) |
Modifier and Type | Method and Description |
---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> dataTypes)
Calculate the data types for the output arrays.
|
List<SDVariable> |
doDiff(List<SDVariable> i_v1)
The actual implementation for automatic differentiation.
|
String |
opName()
This method returns op opName as string
|
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, 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 TensorMmulBp()
public TensorMmulBp(SameDiff samediff, SDVariable x, SDVariable y, SDVariable gradAtOutput, int[][] axes)
public TensorMmulBp(SameDiff samediff, SDVariable x, SDVariable y, SDVariable gradAtOutput, int[] axesX, int[] axesY)
public TensorMmulBp(INDArray x, INDArray y, INDArray gradAtOutput, int[] axesX, int[] axesY)
public TensorMmulBp(INDArray x, INDArray y, INDArray gradAtOutput, INDArray dldx, INDArray dldy, int[][] axes)
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public List<SDVariable> doDiff(List<SDVariable> i_v1)
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.