public class LayerNorm extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
axis, bArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
---|
LayerNorm(INDArray input,
INDArray gain,
boolean channelsFirst,
int... dimensions) |
LayerNorm(INDArray input,
INDArray gain,
INDArray result,
boolean channelsFirst,
int... dimensions) |
LayerNorm(INDArray input,
INDArray gain,
INDArray bias,
INDArray result,
boolean channelsFirst,
int... dimensions) |
LayerNorm(SameDiff sameDiff,
SDVariable input,
SDVariable gain,
boolean channelsFirst,
int... dimensions) |
LayerNorm(SameDiff sameDiff,
SDVariable input,
SDVariable gain,
SDVariable bias,
boolean channelsFirst,
int... dimensions) |
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> gradient)
The actual implementation for automatic differentiation.
|
int |
numOutputArguments() |
String |
onnxName()
The opName of this function in onnx
|
String |
opName()
This method returns op opName as string
|
void |
setDimensions(int[] dimensions) |
String |
tensorflowName()
The opName of this function tensorflow
|
addBArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, clearArrays, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, initFromOnnx, initFromTensorFlow, inputArguments, numBArguments, numIArguments, numInputArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, f, getNumOutputs, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputVariable, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public LayerNorm(@NonNull SameDiff sameDiff, @NonNull SDVariable input, @NonNull SDVariable gain, SDVariable bias, boolean channelsFirst, int... dimensions)
public LayerNorm(SameDiff sameDiff, SDVariable input, SDVariable gain, boolean channelsFirst, int... dimensions)
public LayerNorm(INDArray input, INDArray gain, INDArray bias, INDArray result, boolean channelsFirst, int... dimensions)
public LayerNorm(@NonNull INDArray input, @NonNull INDArray gain, boolean channelsFirst, int... dimensions)
public void setDimensions(int[] dimensions)
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DynamicCustomOp
public String onnxName()
DifferentialFunction
onnxName
in class DynamicCustomOp
public List<SDVariable> doDiff(List<SDVariable> gradient)
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 int numOutputArguments()
numOutputArguments
in interface CustomOp
numOutputArguments
in class DynamicCustomOp
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