public class ZerosLike extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder| Modifier and Type | Field and Description |
|---|---|
protected DataType |
outputType |
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArgumentsdimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue| Constructor and Description |
|---|
ZerosLike(INDArray in) |
ZerosLike(INDArray in,
INDArray out) |
ZerosLike(INDArray in,
INDArray out,
DataType dataType) |
ZerosLike(SameDiff sameDiff,
SDVariable input) |
ZerosLike(String name,
SameDiff sameDiff,
SDVariable input) |
ZerosLike(String name,
SameDiff sameDiff,
SDVariable input,
boolean inPlace) |
ZerosLike(String name,
SameDiff sameDiff,
SDVariable input,
boolean inPlace,
DataType dataType) |
ZerosLike(String name,
SameDiff sameDiff,
SDVariable input,
DataType dataType) |
| 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_v)
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 |
onnxName()
The opName of this function in onnx
|
String |
opName()
This method returns op opName as string
|
String |
tensorflowName()
The opName of this function tensorflow
|
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, initFromOnnx, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNullarg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, getNumOutputs, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNamesclone, finalize, getClass, notify, notifyAll, wait, wait, waitisInplaceCallprotected DataType outputType
public ZerosLike(SameDiff sameDiff, SDVariable input)
public ZerosLike(String name, SameDiff sameDiff, SDVariable input)
public ZerosLike(String name, SameDiff sameDiff, SDVariable input, DataType dataType)
public ZerosLike(String name, SameDiff sameDiff, SDVariable input, boolean inPlace)
public ZerosLike(String name, SameDiff sameDiff, SDVariable input, boolean inPlace, DataType dataType)
public ZerosLike(INDArray in)
public String opName()
DynamicCustomOpopName in interface CustomOpopName in class DynamicCustomOppublic String onnxName()
DifferentialFunctiononnxName in class DynamicCustomOppublic String tensorflowName()
DifferentialFunctiontensorflowName in class DynamicCustomOppublic void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunctionNodeDefinitFromTensorFlow in class DynamicCustomOppublic List<SDVariable> doDiff(List<SDVariable> i_v)
DifferentialFunctiondoDiff in class DynamicCustomOppublic List<DataType> calculateOutputDataTypes(List<DataType> dataTypes)
DifferentialFunctionDifferentialFunction.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 DifferentialFunctiondataTypes - The data types of the inputsCopyright © 2021. All rights reserved.