public class BatchMmul extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
Modifier and Type | Field and Description |
---|---|
protected int |
batchSize |
protected int |
K |
protected int |
M |
protected int |
N |
protected int |
transposeA |
protected int |
transposeB |
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
Constructor and Description |
---|
BatchMmul() |
BatchMmul(INDArray[] matricesA,
INDArray[] matricesB,
boolean transposeA,
boolean transposeB) |
BatchMmul(SameDiff sameDiff,
SDVariable[] matrices,
boolean transposeA,
boolean transposeB) |
BatchMmul(SameDiff sameDiff,
SDVariable[] matricesA,
SDVariable[] matricesB,
boolean transposeA,
boolean transposeB) |
Modifier and Type | Method and Description |
---|---|
void |
addArgs() |
List<DataType> |
calculateOutputDataTypes(List<DataType> dataTypes)
Calculate the data types for the output arrays.
|
List<SDVariable> |
doDiff(List<SDVariable> grads)
The actual implementation for automatic differentiation.
|
int |
getNumOutputs() |
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, 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
protected int transposeA
protected int transposeB
protected int batchSize
protected int M
protected int N
protected int K
public BatchMmul(SameDiff sameDiff, SDVariable[] matricesA, SDVariable[] matricesB, boolean transposeA, boolean transposeB)
public BatchMmul(SameDiff sameDiff, SDVariable[] matrices, boolean transposeA, boolean transposeB)
public BatchMmul(INDArray[] matricesA, INDArray[] matricesB, boolean transposeA, boolean transposeB)
public BatchMmul()
public int getNumOutputs()
getNumOutputs
in class DifferentialFunction
public void addArgs()
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public List<SDVariable> doDiff(List<SDVariable> grads)
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.