public class SConv2D extends Conv2D
DynamicCustomOp.DynamicCustomOpsBuilder
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
---|
SConv2D() |
SConv2D(INDArray[] inputs,
INDArray[] outputs,
Conv2DConfig config) |
SConv2D(@NonNull INDArray layerInput,
@NonNull INDArray depthWeights,
INDArray pointWeights,
@NonNull Conv2DConfig Conv2DConfig) |
SConv2D(INDArray layerInput,
INDArray depthWeights,
INDArray pointWeights,
INDArray bias,
Conv2DConfig config) |
SConv2D(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv2DConfig conv2DConfig) |
SConv2D(@NonNull SameDiff sameDiff,
@NonNull SDVariable layerInput,
@NonNull SDVariable depthWeights,
SDVariable pointWeights,
SDVariable bias,
@NonNull Conv2DConfig conv2DConfig) |
Modifier and Type | Method and Description |
---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.
|
String |
configFieldName()
Returns the name of the field to be used for looking up field names.
|
List<SDVariable> |
doDiff(List<SDVariable> f1)
The actual implementation for automatic differentiation.
|
long[] |
iArgs() |
boolean |
isConfigProperties()
Returns true if the fields for this class should be looked up from a configuration class.
|
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
|
String[] |
tensorflowNames()
The opName of this function tensorflow
|
addArgs, attributeAdaptersForFunction, getValue, initConfig, initFromOnnx, initFromTensorFlow, mappingsForFunction, propertiesForFunction
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNull
arg, arg, argNames, args, diff, dup, equals, getNumOutputs, hashCode, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public SConv2D(SameDiff sameDiff, SDVariable[] inputFunctions, Conv2DConfig conv2DConfig)
public SConv2D(@NonNull @NonNull SameDiff sameDiff, @NonNull @NonNull SDVariable layerInput, @NonNull @NonNull SDVariable depthWeights, SDVariable pointWeights, SDVariable bias, @NonNull @NonNull Conv2DConfig conv2DConfig)
public SConv2D(INDArray[] inputs, INDArray[] outputs, Conv2DConfig config)
public SConv2D(@NonNull @NonNull INDArray layerInput, @NonNull @NonNull INDArray depthWeights, INDArray pointWeights, @NonNull @NonNull Conv2DConfig Conv2DConfig)
public SConv2D(INDArray layerInput, INDArray depthWeights, INDArray pointWeights, INDArray bias, Conv2DConfig config)
public SConv2D()
public String opName()
DynamicCustomOp
public List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunction
public long[] iArgs()
public boolean isConfigProperties()
DifferentialFunction
isConfigProperties
in class Conv2D
public String configFieldName()
DifferentialFunction
DifferentialFunction.isConfigProperties()
to facilitate mapping fields for model import.configFieldName
in class Conv2D
public String[] tensorflowNames()
DifferentialFunction
tensorflowNames
in class Conv2D
public String onnxName()
DifferentialFunction
public String tensorflowName()
DifferentialFunction
tensorflowName
in class Conv2D
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 Conv2D
inputDataTypes
- The data types of the inputsCopyright © 2020. All rights reserved.