public class Cast extends BaseDynamicTransformOp
DynamicCustomOp.DynamicCustomOpsBuilder
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
Constructor and Description |
---|
Cast() |
Cast(@NonNull INDArray arg,
@NonNull DataType dataType) |
Cast(SameDiff sameDiff,
SDVariable arg,
@NonNull DataType dst) |
Modifier and Type | Method and Description |
---|---|
protected void |
addArgs() |
Map<String,Map<String,AttributeAdapter>> |
attributeAdaptersForFunction()
Returns the
AttributeAdapter s for each of the
possible ops for import (typically tensorflow and onnx)
See AttributeAdapter for more information on what the
adapter does. |
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 |
Map<String,Map<String,PropertyMapping>> |
mappingsForFunction()
Returns the mappings for a given function (
for tensorflow and onnx import mapping properties
of this function).
|
String |
opName()
This method returns op opName as string
|
void |
setValueFor(Field target,
Object value)
Set the value for this function.
|
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, onnxName, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNull
arg, arg, argNames, args, configFieldName, diff, dup, equals, getNumOutputs, getValue, hashCode, isConfigProperties, larg, onnxNames, outputs, outputVariable, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public Cast()
public Cast(SameDiff sameDiff, SDVariable arg, @NonNull @NonNull DataType dst)
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunction
NodeDef
initFromTensorFlow
in class DynamicCustomOp
protected void addArgs()
public Map<String,Map<String,AttributeAdapter>> attributeAdaptersForFunction()
DifferentialFunction
AttributeAdapter
s for each of the
possible ops for import (typically tensorflow and onnx)
See AttributeAdapter
for more information on what the
adapter does.
Similar to DifferentialFunction.mappingsForFunction()
, the returned map
contains a AttributeAdapter
for each field name
when one is present. (It is optional for one to exist)_attributeAdaptersForFunction
in class DifferentialFunction
public Map<String,Map<String,PropertyMapping>> mappingsForFunction()
DifferentialFunction
mappingsForFunction
in class DifferentialFunction
public void setValueFor(Field target, Object value)
DifferentialFunction
ND4JIllegalStateException
will be thrown.setValueFor
in class DifferentialFunction
target
- the target fieldvalue
- the value to setpublic String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DynamicCustomOp
public List<SDVariable> doDiff(List<SDVariable> i_v)
DifferentialFunction
doDiff
in class DynamicCustomOp
public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes)
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 BaseDynamicTransformOp
dataTypes
- The data types of the inputsCopyright © 2021. All rights reserved.