public class BroadcastGradientArgs extends BaseBroadcastOp
dimension
dimensionz, extraArgz, x, xVertexId, y, yVertexId, z, zVertexId
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
Constructor and Description |
---|
BroadcastGradientArgs() |
BroadcastGradientArgs(INDArray x,
INDArray y,
INDArray z,
int... dimension) |
BroadcastGradientArgs(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastGradientArgs(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastGradientArgs(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
Modifier and Type | Method and Description |
---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> dataTypes)
Calculate the data types for the output arrays.
|
List<SDVariable> |
doDiff(List<SDVariable> f1)
The actual implementation for automatic differentiation.
|
String |
opName()
The name of the op
|
int |
opNum()
The number of the op (mainly for old legacy XYZ ops
like
Op ) |
calculateOutputShape, getDimension, getOpType, initFromOnnx, initFromTensorFlow, opType, setDimension, validateDataTypes
clearArrays, defineDimensions, dimensions, equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getFinalResult, getInputArgument, getNumOutputs, getOpType, hashCode, onnxName, outputVariables, setX, setY, setZ, tensorflowName, toCustomOp, toString, x, y, z
arg, arg, argNames, args, attributeAdaptersForFunction, calculateOutputShape, configFieldName, diff, dup, getValue, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariables, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
dimensions
clearArrays, extraArgs, extraArgsBuff, extraArgsDataBuff, setExtraArgs, setX, setY, setZ, toCustomOp, x, y, z
public BroadcastGradientArgs(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, int[] dimension)
public BroadcastGradientArgs(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace, int[] dimension)
public BroadcastGradientArgs(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, int[] dimension, Object[] extraArgs)
public BroadcastGradientArgs()
public int opNum()
DifferentialFunction
Op
)opNum
in interface Op
opNum
in class DifferentialFunction
public String opName()
DifferentialFunction
opName
in interface Op
opName
in class DifferentialFunction
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 inputspublic List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunction
doDiff
in class DifferentialFunction
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