public class DeConv3D extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder| Modifier and Type | Field and Description |
|---|---|
protected DeConv3DConfig |
config |
axis, bArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArgumentsdimensions, extraArgs, inPlace, sameDiff, scalarValue| Constructor and Description |
|---|
DeConv3D(INDArray[] inputs,
INDArray[] outputs,
DeConv3DConfig config) |
DeConv3D(INDArray input,
INDArray weights,
INDArray bias,
INDArray output,
DeConv3DConfig config) |
DeConv3D(SameDiff sameDiff,
SDVariable input,
SDVariable weights,
SDVariable bias,
DeConv3DConfig config) |
| 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.
|
Object |
getValue(Field property)
Get the value for a given property
for this function
|
long[] |
iArgs() |
void |
initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph)
Initialize the function from the given
NodeDef |
boolean |
isConfigProperties()
Returns true if the fields for this class should be looked up from a configuration class.
|
String |
opName()
This method returns op opName as string
|
Map<String,Object> |
propertiesForFunction()
Returns the properties for a given function
|
addBArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, initFromOnnx, inputArguments, numBArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, onnxName, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, tensorflowName, toString, wrapFilterNull, wrapOrNullarg, arg, argNames, args, attributeAdaptersForFunction, diff, dup, equals, f, getNumOutputs, hashCode, larg, mappingsForFunction, onnxNames, outputVariable, outputVariablesNames, rarg, replaceArg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNamesclone, finalize, getClass, notify, notifyAll, wait, wait, waitisInplaceCallprotected DeConv3DConfig config
public DeConv3D(SameDiff sameDiff, @NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, @NonNull DeConv3DConfig config)
public DeConv3D(INDArray[] inputs, INDArray[] outputs, DeConv3DConfig config)
public DeConv3D(@NonNull
INDArray input,
@NonNull
INDArray weights,
INDArray bias,
INDArray output,
@NonNull
DeConv3DConfig config)
public long[] iArgs()
iArgs in interface CustomOpiArgs in class DynamicCustomOppublic Map<String,Object> propertiesForFunction()
DifferentialFunctionpropertiesForFunction in class DifferentialFunctionpublic boolean isConfigProperties()
DifferentialFunctionisConfigProperties in class DifferentialFunctionpublic String configFieldName()
DifferentialFunctionDifferentialFunction.isConfigProperties()
to facilitate mapping fields for model import.configFieldName in class DifferentialFunctionpublic Object getValue(Field property)
DifferentialFunctiongetValue in class DifferentialFunctionproperty - the property to getpublic void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunctionNodeDefinitFromTensorFlow in class DynamicCustomOppublic String opName()
DynamicCustomOpopName in interface CustomOpopName in class DynamicCustomOppublic List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunctiondoDiff in class DynamicCustomOppublic List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes)
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 DifferentialFunctioninputDataTypes - The data types of the inputsCopyright © 2019. All rights reserved.