DLClassifierModel

Instance Constructors

new DLClassifierModel(model: Module[T], featureSize: Array[Int], uid: String = "DLClassifierModel")(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

model
BigDL module to be optimized
featureSize
The size (Tensor dimensions) of the feature data.

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def !=(arg0: Any): Boolean

Definition Classes
Any
final def ##(): Int

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

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

Definition Classes
AnyRef
final def ==(arg0: Any): Boolean

Definition Classes
Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final val batchSize: Param[Int]

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

Attributes
protected
Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
DLModel → 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
def getConvertFunc(colType: DataType): (Row, Int) ⇒ Seq[AnyVal]

Get conversion function to extract data from original DataFrame Default: 0
Get conversion function to extract data from original DataFrame Default: 0

Attributes
protected
Definition Classes
DLParams
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
def getFeatureSize: Array[Int]

Definition Classes
DLModel
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
def getLearningRate: Double

Definition Classes
DLParams
def getLearningRateDecay: Double

Definition Classes
DLParams
def getMaxEpoch: Int

Definition Classes
DLParams
def getOptimMethod: OptimMethod[T]

Definition Classes
DLParams
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 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 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
DLModel → 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
final val learningRate: DoubleParam

learning rate for the optimizer in the DLEstimator.
learning rate for the optimizer in the DLEstimator. Default: 0.001

Definition Classes
DLParams
final val learningRateDecay: DoubleParam

learning rate decay for each iteration.
learning rate decay for each iteration. Default: 0

Definition Classes
DLParams
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
final val maxEpoch: IntParam

number of max Epoch for the training, an epoch refers to a traverse over the training data Default: 100
number of max Epoch for the training, an epoch refers to a traverse over the training data Default: 100

Definition Classes
DLParams
val model: Module[T]

BigDL module to be optimized
BigDL module to be optimized

Definition Classes
DLClassifierModel → DLModel
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final val optimMethod: Param[OptimMethod[T]]

optimization method to be used.
optimization method to be used. BigDL supports many optimization methods like Adam, SGD and LBFGS. Refer to package com.intel.analytics.bigdl.optim for all the options. Default: SGD

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

Attributes
protected
Definition Classes
DLClassifierModel → DLModel
lazy val params: Array[Param[_]]

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

Definition Classes
Model
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
final def set(paramPair: ParamPair[_]): DLClassifierModel.this.type

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

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

Attributes
protected
Definition Classes
Params
def setBatchSize(value: Int): DLClassifierModel.this.type

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

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

Attributes
protected
Definition Classes
Params
def setFeatureSize(value: Array[Int]): DLClassifierModel.this.type

Definition Classes
DLModel
def setFeaturesCol(featuresColName: String): DLClassifierModel.this.type

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

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

Definition Classes
DLModel
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transform(dataFrame: DataFrame): DataFrame

Definition Classes
DLTransformerBase → Transformer
def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame

Definition Classes
Transformer
def transform(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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

Definition Classes
DLClassifierModel → DLModel → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType

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

Definition Classes
DLClassifierModel → DLModel → Identifiable
val validVectorTypes: Seq[VectorUDT]

Definition Classes
VectorCompatibility
def validateDataType(schema: StructType, colName: String): Unit

Validate if feature and label columns are of supported data types.
Validate if feature and label columns are of supported data types. Default: 0

Attributes
protected
Definition Classes
DLParams
def validateParams(): Unit

Definition Classes
Params
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( ... )

class DLClassifierModel[T] extends DLModel[T]

Instance Constructors

new DLClassifierModel(model: Module[T], featureSize: Array[Int], uid: String = "DLClassifierModel")(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final val batchSize: Param[Int]

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

def clone(): AnyRef

def copy(extra: ParamMap): DLModel[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[_]

def getConvertFunc(colType: DataType): (Row, Int) ⇒ Seq[AnyVal]

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

def getFeatureSize: Array[Int]

final def getFeaturesCol: String

def getLearningRate: Double

def getLearningRateDecay: Double

def getMaxEpoch: Int

def getOptimMethod: OptimMethod[T]

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

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

final def getPredictionCol: String

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 internalTransform(dataFrame: DataFrame): DataFrame

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

final def isInstanceOf[T0]: Boolean

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

def isTraceEnabled(): Boolean

final val learningRate: DoubleParam

final val learningRateDecay: DoubleParam

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

final val maxEpoch: IntParam

val model: Module[T]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final val optimMethod: Param[OptimMethod[T]]

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

lazy val params: Array[Param[_]]

var parent: Estimator[DLModel[T]]

final val predictionCol: Param[String]

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

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

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

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

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

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