public class LogNormalDistribution extends BaseRandomOp
dataType, shape
dimensionz, extraArgz, x, xVertexId, y, yVertexId, z, zVertexId
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
Constructor and Description |
---|
LogNormalDistribution() |
LogNormalDistribution(double mean,
double stddev,
DataType datatype,
long... shape) |
LogNormalDistribution(@NonNull INDArray z)
This op fills Z with random values within -1.0..0..1.0
|
LogNormalDistribution(@NonNull INDArray z,
double stddev)
This op fills Z with random values within stddev..0..stddev
|
LogNormalDistribution(@NonNull INDArray z,
double mean,
double stddev)
This op fills Z with random values within stddev..mean..stddev boundaries
|
LogNormalDistribution(@NonNull INDArray z,
@NonNull INDArray means,
double stddev) |
LogNormalDistribution(SameDiff sd,
double mean,
double stdev,
DataType dataType,
long... shape) |
LogNormalDistribution(SameDiff sd,
double mean,
double stdev,
long... shape) |
Modifier and Type | Method and Description |
---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.
|
List<LongShapeDescriptor> |
calculateOutputShape()
Calculate the output shape for this op
|
List<LongShapeDescriptor> |
calculateOutputShape(OpContext oc) |
List<SDVariable> |
doDiff(List<SDVariable> f1)
The actual implementation for automatic differentiation.
|
boolean |
isTripleArgRngOp() |
String |
onnxName()
The opName of this function in onnx
|
String |
opName()
The name of the op
|
int |
opNum()
The number of the op (mainly for old legacy XYZ ops
like
Op ) |
void |
setZ(INDArray z)
set z (the solution ndarray)
|
String |
tensorflowName()
The opName of this function tensorflow
|
isInPlace, opType
clearArrays, defineDimensions, dimensions, equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getFinalResult, getInputArgument, getNumOutputs, getOpType, hashCode, initFromOnnx, initFromTensorFlow, outputVariables, setX, setY, toCustomOp, toString, x, y, z
arg, arg, argNames, args, attributeAdaptersForFunction, 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
clearArrays, extraArgs, extraArgsBuff, extraArgsDataBuff, setExtraArgs, setX, setY, toCustomOp, x, y, z
public LogNormalDistribution()
public LogNormalDistribution(SameDiff sd, double mean, double stdev, long... shape)
public LogNormalDistribution(SameDiff sd, double mean, double stdev, DataType dataType, long... shape)
public LogNormalDistribution(double mean, double stddev, DataType datatype, long... shape)
public LogNormalDistribution(@NonNull @NonNull INDArray z, double mean, double stddev)
z
- mean
- stddev
- public LogNormalDistribution(@NonNull @NonNull INDArray z, @NonNull @NonNull INDArray means, double stddev)
public LogNormalDistribution(@NonNull @NonNull INDArray z)
z
- public LogNormalDistribution(@NonNull @NonNull INDArray z, double stddev)
z
- public String onnxName()
DifferentialFunction
public String tensorflowName()
DifferentialFunction
tensorflowName
in class BaseOp
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 void setZ(INDArray z)
Op
public List<LongShapeDescriptor> calculateOutputShape(OpContext oc)
calculateOutputShape
in class DifferentialFunction
public List<LongShapeDescriptor> calculateOutputShape()
DifferentialFunction
calculateOutputShape
in class BaseRandomOp
public List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunction
doDiff
in class DifferentialFunction
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes)
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 BaseRandomOp
inputDataTypes
- The data types of the inputspublic boolean isTripleArgRngOp()
isTripleArgRngOp
in class BaseRandomOp
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