Pooling2D.Divisor, Pooling2D.Pooling2DType
DynamicCustomOp.DynamicCustomOpsBuilder
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
---|
Pooling2DDerivative() |
Pooling2DDerivative(@NonNull INDArray input,
@NonNull INDArray grad,
INDArray output,
Pooling2DConfig config) |
Pooling2DDerivative(SameDiff sameDiff,
SDVariable[] inputs,
Pooling2DConfig config) |
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> f1)
The actual implementation for automatic differentiation.
|
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
|
configFieldName, getPoolingPrefix, iArgs, initFromOnnx, initFromTensorFlow, isConfigProperties, 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, attributeAdaptersForFunction, diff, dup, equals, getNumOutputs, getValue, hashCode, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public Pooling2DDerivative(SameDiff sameDiff, SDVariable[] inputs, Pooling2DConfig config)
public Pooling2DDerivative(@NonNull @NonNull INDArray input, @NonNull @NonNull INDArray grad, INDArray output, Pooling2DConfig config)
public Pooling2DDerivative()
public String opName()
DynamicCustomOp
public String onnxName()
DifferentialFunction
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DynamicCustomOp
public List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunction
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 Pooling2D
inputDataTypes
- The data types of the inputsCopyright © 2020. All rights reserved.