public class LSTMBlockCell extends DynamicCustomOp
LSTMLayer
.LSTMLayer
for 'full time series" op - this lstmBlockCell op is used internally at C++ level in LSTMLayer.LSTMWeights
.LSTMConfiguration
LSTMConfiguration
DynamicCustomOp.DynamicCustomOpsBuilder
axis, bArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
---|
LSTMBlockCell() |
LSTMBlockCell(SameDiff sameDiff,
SDVariable x,
SDVariable cLast,
SDVariable yLast,
LSTMWeights weights,
LSTMConfiguration configuration) |
Modifier and Type | Method and Description |
---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.
|
List<SDVariable> |
doDiff(List<SDVariable> grads)
The actual implementation for automatic differentiation.
|
void |
initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph)
Initialize the function from the given
NodeDef |
String |
opName()
This method returns op opName as string
|
Map<String,Object> |
propertiesForFunction()
Returns the properties for a given function
|
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, inputArguments, numBArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, onnxName, 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, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public LSTMBlockCell()
public LSTMBlockCell(SameDiff sameDiff, SDVariable x, SDVariable cLast, SDVariable yLast, LSTMWeights weights, LSTMConfiguration configuration)
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes)
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
inputDataTypes
- The data types of the inputspublic List<SDVariable> doDiff(List<SDVariable> grads)
DifferentialFunction
doDiff
in class DynamicCustomOp
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunction
NodeDef
initFromTensorFlow
in class DynamicCustomOp
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public Map<String,Object> propertiesForFunction()
DifferentialFunction
propertiesForFunction
in class DifferentialFunction
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DynamicCustomOp
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