public class Range extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder, DynamicCustomOp.SameDiffBuilder
Modifier and Type | Field and Description |
---|---|
static DataType |
DEFAULT_DTYPE |
axis, bArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
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Range() |
Range(SameDiff sd,
double from,
double to,
double step,
DataType dataType) |
Modifier and Type | Method and Description |
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List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.
|
List<LongShapeDescriptor> |
calculateOutputShape()
Calculate the output shape for this op
|
List<SDVariable> |
doDiff(List<SDVariable> f1)
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
|
int |
opNum()
The number of the op (mainly for old legacy XYZ ops
like
Op ) |
Op.Type |
opType()
The type of the op
|
String |
tensorflowName()
The opName of this function tensorflow
|
addBArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, initFromOnnx, inputArguments, numBArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, sameDiffBuilder, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, f, getNumOutputs, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputVariable, outputVariablesNames, propertiesForFunction, rarg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public static final DataType DEFAULT_DTYPE
public int opNum()
DifferentialFunction
Op
)opNum
in class DynamicCustomOp
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public String onnxName()
DifferentialFunction
onnxName
in class DynamicCustomOp
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DynamicCustomOp
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunction
NodeDef
initFromTensorFlow
in class DynamicCustomOp
public List<LongShapeDescriptor> calculateOutputShape()
DifferentialFunction
calculateOutputShape
in interface CustomOp
calculateOutputShape
in class DynamicCustomOp
public List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunction
doDiff
in class DynamicCustomOp
public Op.Type opType()
DifferentialFunction
opType
in class DynamicCustomOp
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 inputsCopyright © 2019. All rights reserved.