public class Eye 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 |
---|
Eye() |
Eye(SameDiff sameDiff,
int numRows) |
Eye(SameDiff sameDiff,
int numRows,
int numCols) |
Eye(SameDiff sameDiff,
int numRows,
int numCols,
DataType dataType) |
Eye(SameDiff sameDiff,
int numRows,
int numCols,
DataType dataType,
int[] batchDimension) |
Eye(SameDiff sameDiff,
SDVariable numRows) |
Eye(SameDiff sameDiff,
SDVariable numRows,
SDVariable numCols) |
Eye(SameDiff sameDiff,
SDVariable numRows,
SDVariable numCols,
SDVariable batch_shape) |
Modifier and Type | Method and Description |
---|---|
protected void |
addArgs() |
List<DataType> |
calculateOutputDataTypes(List<DataType> dataTypes)
Calculate the data types for the output arrays.
|
List<LongShapeDescriptor> |
calculateOutputShape()
Calculate the output shape for this op
|
List<SDVariable> |
doDiff(List<SDVariable> outGrad)
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
|
addBArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, initFromOnnx, initFromTensorFlow, inputArguments, numBArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, opNum, opType, 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 Eye()
public Eye(SameDiff sameDiff, SDVariable numRows)
public Eye(SameDiff sameDiff, SDVariable numRows, SDVariable numCols)
public Eye(SameDiff sameDiff, SDVariable numRows, SDVariable numCols, SDVariable batch_shape)
public Eye(SameDiff sameDiff, int numRows)
public Eye(SameDiff sameDiff, int numRows, int numCols)
protected void addArgs()
public String onnxName()
DifferentialFunction
onnxName
in class DynamicCustomOp
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DynamicCustomOp
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public List<LongShapeDescriptor> calculateOutputShape()
DifferentialFunction
calculateOutputShape
in interface CustomOp
calculateOutputShape
in class DynamicCustomOp
public List<SDVariable> doDiff(List<SDVariable> outGrad)
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 DifferentialFunction
dataTypes
- The data types of the inputsCopyright © 2019. All rights reserved.