public abstract class BaseReduceBoolOp extends BaseReduceOp implements ReduceBoolOp
isComplex, isEmptyReduce, keepDimsdimensionz, extraArgz, x, xVertexId, y, yVertexId, z, zVertexIddimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue| Modifier | Constructor and Description |
|---|---|
protected |
BaseReduceBoolOp() |
|
BaseReduceBoolOp(INDArray x,
INDArray z,
boolean keepDims,
int[] dimensions) |
|
BaseReduceBoolOp(INDArray x,
INDArray z,
int... dimensions) |
|
BaseReduceBoolOp(INDArray x,
int... dimensions) |
protected |
BaseReduceBoolOp(SameDiff sameDiff,
SDVariable input,
int... dimensions) |
protected |
BaseReduceBoolOp(SameDiff sameDiff,
SDVariable input,
int[] dimensions,
boolean keepDims) |
|
BaseReduceBoolOp(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
| Modifier and Type | Method and Description |
|---|---|
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<LongShapeDescriptor> |
calculateOutputShape(OpContext oc) |
abstract boolean |
emptyValue() |
Op.Type |
getOpType() |
Op.Type |
opType()
The type of the op
|
DataType |
resultType()
This method returns datatype for result array wrt given inputs
|
DataType |
resultType(OpContext oc) |
boolean |
validateDataTypes(OpContext oc) |
hasReductionIndices, initFromOnnx, initFromTensorFlow, isComplexAccumulation, isKeepDims, noOp, setDimensionsclearArrays, defineDimensions, dimensions, equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getFinalResult, getInputArgument, getNumOutputs, getOpType, hashCode, onnxName, outputVariables, setX, setY, setZ, tensorflowName, toCustomOp, toString, x, y, zarg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, doDiff, dup, getValue, isConfigProperties, larg, mappingsForFunction, onnxNames, opName, opNum, outputs, outputVariable, outputVariables, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNamesclone, finalize, getClass, notify, notifyAll, wait, wait, waitdimensions, getFinalResult, isComplexAccumulation, isKeepDims, noOp, setDimensionsclearArrays, extraArgs, extraArgsBuff, extraArgsDataBuff, opName, opNum, setExtraArgs, setX, setY, setZ, toCustomOp, x, y, zpublic BaseReduceBoolOp(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions)
protected BaseReduceBoolOp(SameDiff sameDiff, SDVariable input, int[] dimensions, boolean keepDims)
protected BaseReduceBoolOp(SameDiff sameDiff, SDVariable input, int... dimensions)
public BaseReduceBoolOp(INDArray x, INDArray z, boolean keepDims, int[] dimensions)
public BaseReduceBoolOp(INDArray x, int... dimensions)
protected BaseReduceBoolOp()
public Op.Type opType()
DifferentialFunctionopType in class DifferentialFunctionpublic DataType resultType()
ReduceOpresultType in interface ReduceOppublic DataType resultType(OpContext oc)
resultType in interface ReduceOppublic boolean validateDataTypes(OpContext oc)
validateDataTypes in interface ReduceOppublic List<LongShapeDescriptor> calculateOutputShape()
DifferentialFunctioncalculateOutputShape in class BaseReduceOppublic List<LongShapeDescriptor> calculateOutputShape(OpContext oc)
calculateOutputShape in class DifferentialFunctionpublic List<DataType> calculateOutputDataTypes(List<DataType> dataTypes)
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 DifferentialFunctiondataTypes - The data types of the inputspublic abstract boolean emptyValue()
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