NNClassifierModel

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final val batchSize: IntParam

Global batch size across the cluster.
Global batch size across the cluster.

Definition Classes
HasBatchSize
final def clear(param: Param[_]): NNClassifierModel.this.type

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def copy(extra: ParamMap): NNClassifierModel[T]

Definition Classes
NNClassifierModel → NNModel → DLTransformerBase → Model → Transformer → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
final def extractParamMap(): ParamMap

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes
Params
final val featuresCol: Param[String]

Definition Classes
HasFeaturesCol
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def get[T](param: Param[T]): Option[T]

Definition Classes
Params
def getBatchSize: Int

Definition Classes
HasBatchSize
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
def getParam(paramName: String): Param[Any]

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
def getSamplePreprocessing: Preprocessing[Any, Sample[T]]

Definition Classes
NNParams
def getThreshold: Double
def getVectorSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal]

Definition Classes
VectorCompatibility
final def hasDefault[T](param: Param[T]): Boolean

Definition Classes
Params
def hasParam(paramName: String): Boolean

Definition Classes
Params
def hasParent: Boolean

Definition Classes
Model
def hashCode(): Int

Definition Classes
AnyRef → Any
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
def internalTransform(dataFrame: DataFrame): DataFrame

Perform a prediction on featureCol, and write result to the predictionCol.
Perform a prediction on featureCol, and write result to the predictionCol.

Attributes
protected
Definition Classes
NNModel → DLTransformerBase
final def isDefined(param: Param[_]): Boolean

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def isSet(param: Param[_]): Boolean

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
val model: Module[T]

trained BigDL models to use in prediction.
trained BigDL models to use in prediction.

Definition Classes
NNClassifierModel → NNModel
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def outputToPrediction(output: Tensor[T]): Any

Attributes
protected
Definition Classes
NNClassifierModel → NNModel
lazy val params: Array[Param[_]]

Definition Classes
Params
var parent: Estimator[NNModel[T]]

Definition Classes
Model
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
final val samplePreprocessing: Param[Preprocessing[Any, Sample[T]]]

Definition Classes
NNParams
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final def set(paramPair: ParamPair[_]): NNClassifierModel.this.type

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): NNClassifierModel.this.type

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): NNClassifierModel.this.type

Definition Classes
Params
def setBatchSize(value: Int): NNClassifierModel.this.type

Set global batch size across the cluster.
Set global batch size across the cluster. Global batch size = Batch per thread * num of cores.

Definition Classes
NNModel
final def setDefault(paramPairs: ParamPair[_]*): NNClassifierModel.this.type

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): NNClassifierModel.this.type

Attributes
protected
Definition Classes
Params
def setFeaturesCol(featuresColName: String): NNClassifierModel.this.type

Definition Classes
NNModel
def setParent(parent: Estimator[NNModel[T]]): NNModel[T]

Definition Classes
Model
def setPredictionCol(value: String): NNClassifierModel.this.type

Definition Classes
NNModel
def setSamplePreprocessing[FF](value: Preprocessing[FF, Sample[T]]): NNClassifierModel.this.type

set Preprocessing.
set Preprocessing.

Definition Classes
NNModel
def setThreshold(value: Double): NNClassifierModel.this.type
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
final val threshold: DoubleParam

Param for threshold in binary classification prediction.
Param for threshold in binary classification prediction.
The threshold applies to the raw output of the model. If the output is greater than threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often.
Note: the param is different from the one in Spark ProbabilisticClassifier which is compared against estimated probability.
Default is 0.5.
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transform(dataset: Dataset[_]): DataFrame

Definition Classes
DLTransformerBase → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()
def transformSchema(schema: StructType): StructType

Definition Classes
NNClassifierModel → NNModel → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String

Definition Classes
NNClassifierModel → NNModel → Identifiable
def unwrapVectorAsNecessary(colType: DataType): (Row, Int) ⇒ Any

Attributes
protected
Definition Classes
NNParams
val validVectorTypes: Seq[UserDefinedType[_ >: Vector with Vector <: Serializable] { def sqlType: org.apache.spark.sql.types.StructType }]

Definition Classes
VectorCompatibility
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
def write: MLWriter

Definition Classes
NNModel → MLWritable

Related Docs: object NNClassifierModel | package nnframes

class NNClassifierModel[T] extends NNModel[T]

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final val batchSize: IntParam

final def clear(param: Param[_]): NNClassifierModel.this.type

def clone(): AnyRef

def copy(extra: ParamMap): NNClassifierModel[T]

def copyValues[T <: Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: Params](extra: ParamMap): T

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

final val featuresCol: Param[String]

def finalize(): Unit

final def get[T](param: Param[T]): Option[T]

def getBatchSize: Int

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

final def getFeaturesCol: String

final def getOrDefault[T](param: Param[T]): T

def getParam(paramName: String): Param[Any]

final def getPredictionCol: String

def getSamplePreprocessing: Preprocessing[Any, Sample[T]]

def getThreshold: Double

def getVectorSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal]

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

def internalTransform(dataFrame: DataFrame): DataFrame

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

val model: Module[T]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def outputToPrediction(output: Tensor[T]): Any

lazy val params: Array[Param[_]]

var parent: Estimator[NNModel[T]]

final val predictionCol: Param[String]

final val samplePreprocessing: Param[Preprocessing[Any, Sample[T]]]

def save(path: String): Unit

final def set(paramPair: ParamPair[_]): NNClassifierModel.this.type

final def set(param: String, value: Any): NNClassifierModel.this.type

final def set[T](param: Param[T], value: T): NNClassifierModel.this.type

def setBatchSize(value: Int): NNClassifierModel.this.type

final def setDefault(paramPairs: ParamPair[_]*): NNClassifierModel.this.type

final def setDefault[T](param: Param[T], value: T): NNClassifierModel.this.type

def setFeaturesCol(featuresColName: String): NNClassifierModel.this.type

def setParent(parent: Estimator[NNModel[T]]): NNModel[T]

def setPredictionCol(value: String): NNClassifierModel.this.type

def setSamplePreprocessing[FF](value: Preprocessing[FF, Sample[T]]): NNClassifierModel.this.type

def setThreshold(value: Double): NNClassifierModel.this.type

final def synchronized[T0](arg0: ⇒ T0): T0

final val threshold: DoubleParam