public class UniformDistribution extends BaseRandomOp
shape
dimensionz, extraArgs, extraArgz, x, xVertexId, y, yVertexId, z, zVertexId
dimensions, inPlace, sameDiff, scalarValue
Constructor and Description |
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UniformDistribution() |
UniformDistribution(INDArray z)
This op fills Z with random values within 0...1
|
UniformDistribution(INDArray z,
double to)
This op fills Z with random values within 0...to
|
UniformDistribution(INDArray z,
double from,
double to)
This op fills Z with random values within from...to boundaries
|
UniformDistribution(SameDiff sd,
double from,
double to,
long[] shape) |
Modifier and Type | Method and Description |
---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.
|
List<SDVariable> |
doDiff(List<SDVariable> f1)
The actual implementation for automatic differentiation.
|
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 ) |
String |
tensorflowName()
The opName of this function tensorflow
|
calculateOutputShape, opType
defineDimensions, dimensions, equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getFinalResult, getNumOutputs, getOpType, hashCode, initFromOnnx, initFromTensorFlow, outputVariables, setX, setY, setZ, toCustomOp, toString, x, y, z
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, f, getValue, isConfigProperties, larg, mappingsForFunction, onnxNames, outputVariable, outputVariables, outputVariablesNames, propertiesForFunction, rarg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
extraArgs, extraArgsBuff, extraArgsDataBuff, setExtraArgs, setX, setY, setZ, toCustomOp, x, y, z
public UniformDistribution()
public UniformDistribution(SameDiff sd, double from, double to, long[] shape)
public UniformDistribution(@NonNull INDArray z, double from, double to)
z
- from
- to
- public UniformDistribution(@NonNull INDArray z)
z
- public UniformDistribution(@NonNull INDArray z, double to)
z
- 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 String onnxName()
DifferentialFunction
onnxName
in class DifferentialFunction
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DifferentialFunction
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 inputsCopyright © 2019. All rights reserved.