Class BroadcastAddOp
- java.lang.Object
-
- org.nd4j.autodiff.functions.DifferentialFunction
-
- org.nd4j.linalg.api.ops.BaseOp
-
- org.nd4j.linalg.api.ops.BaseBroadcastOp
-
- org.nd4j.linalg.api.ops.impl.broadcast.BroadcastAddOp
-
- All Implemented Interfaces:
BroadcastOp
,Op
public class BroadcastAddOp extends BaseBroadcastOp
-
-
Field Summary
-
Fields inherited from class org.nd4j.linalg.api.ops.BaseBroadcastOp
dimension
-
Fields inherited from class org.nd4j.linalg.api.ops.BaseOp
dimensionz, extraArgz, x, xVertexId, y, yVertexId, z, zVertexId
-
Fields inherited from class org.nd4j.autodiff.functions.DifferentialFunction
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
-
-
Constructor Summary
Constructors Constructor Description BroadcastAddOp()
BroadcastAddOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace, int[] dimension)
BroadcastAddOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, int[] dimension)
BroadcastAddOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, int[] dimension, Object[] extraArgs)
BroadcastAddOp(INDArray x, INDArray y, INDArray z, int... dimension)
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description List<SDVariable>
doDiff(List<SDVariable> f1)
The actual implementation for automatic differentiation.String
opName()
The name of the opint
opNum()
The number of the op (mainly for old legacy XYZ ops likeOp
)-
Methods inherited from class org.nd4j.linalg.api.ops.BaseBroadcastOp
calculateOutputShape, getDimension, getOpType, initFromOnnx, initFromTensorFlow, opType, setDimension, validateDataTypes
-
Methods inherited from class org.nd4j.linalg.api.ops.BaseOp
clearArrays, computeVariables, defineDimensions, dimensions, equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getFinalResult, getInputArgument, getNumOutputs, getOpType, hashCode, onnxName, outputVariables, setX, setY, setZ, tensorflowName, toCustomOp, toString, x, y, z
-
Methods inherited from class org.nd4j.autodiff.functions.DifferentialFunction
arg, arg, argNames, args, attributeAdaptersForFunction, calculateOutputDataTypes, calculateOutputShape, configFieldName, configureWithSameDiff, diff, dup, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getStringFromProperty, getValue, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariables, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
-
Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
-
Methods inherited from interface org.nd4j.linalg.api.ops.BroadcastOp
dimensions
-
Methods inherited from interface org.nd4j.linalg.api.ops.Op
clearArrays, extraArgs, extraArgsBuff, extraArgsDataBuff, setExtraArgs, setX, setY, setZ, toCustomOp, x, y, z
-
-
-
-
Constructor Detail
-
BroadcastAddOp
public BroadcastAddOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, int[] dimension)
-
BroadcastAddOp
public BroadcastAddOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace, int[] dimension)
-
BroadcastAddOp
public BroadcastAddOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, int[] dimension, Object[] extraArgs)
-
BroadcastAddOp
public BroadcastAddOp()
-
-
Method Detail
-
opNum
public int opNum()
Description copied from class:DifferentialFunction
The number of the op (mainly for old legacy XYZ ops likeOp
)- Specified by:
opNum
in interfaceOp
- Overrides:
opNum
in classDifferentialFunction
- Returns:
-
opName
public String opName()
Description copied from class:DifferentialFunction
The name of the op- Specified by:
opName
in interfaceOp
- Overrides:
opName
in classDifferentialFunction
- Returns:
- the opName of this operation
-
doDiff
public List<SDVariable> doDiff(List<SDVariable> f1)
Description copied from class:DifferentialFunction
The actual implementation for automatic differentiation.- Specified by:
doDiff
in classDifferentialFunction
- Returns:
-
-