DLClassifier

Instance Constructors

new DLClassifier(model: Module[T], criterion: Criterion[T], featureSize: Array[Int], uid: String = ...)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

model
BigDL module to be optimized
criterion
BigDL criterion method
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[_]): DLClassifier.this.type

Attributes
protected
Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
DLClassifier → DLEstimator → DLEstimatorBase → Estimator → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
val criterion: Criterion[T]

BigDL criterion method
BigDL criterion method

Definition Classes
DLClassifier → DLEstimator
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final val endWhen: Param[Trigger]

When to stop the training, passed in a Trigger.
When to stop the training, passed in a Trigger. E.g. Trigger.maxIterations

Definition Classes
DLParams
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
val featureSize: Array[Int]

The size (Tensor dimensions) of the feature data.
The size (Tensor dimensions) of the feature data.

Definition Classes
DLClassifier → DLEstimator
final val featuresCol: Param[String]

Definition Classes
HasFeaturesCol
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit(dataFrame: DataFrame): DLModel[T]

Definition Classes
DLEstimatorBase → Estimator
def fit(dataset: DataFrame, paramMaps: Array[ParamMap]): Seq[DLModel[T]]

Definition Classes
Estimator
def fit(dataset: DataFrame, paramMap: ParamMap): DLModel[T]

Definition Classes
Estimator
def fit(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DLModel[T]

Definition Classes
Estimator
Annotations
@varargs()
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 getEndWhen: Trigger

Definition Classes
DLParams
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getLabelCol: String

Definition Classes
HasLabelCol
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 getTrainSummary: Option[TrainSummary]

Definition Classes
DLEstimator
def getValidationSummary: Option[ValidationSummary]

Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the validation data if validation data is set, which can be used for visualization via Tensorboard.
Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the validation data if validation data is set, which can be used for visualization via Tensorboard. Use setValidationSummary to enable validation logger. Then the log will be saved to logDir/appName/ as specified by the parameters of validationSummary.
Default: None

Definition Classes
DLEstimator
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 hashCode(): Int

Definition Classes
AnyRef → Any
def internalFit(dataFrame: DataFrame): DLModel[T]

Attributes
protected
Definition Classes
DLEstimator → DLEstimatorBase
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 labelCol: Param[String]

Definition Classes
HasLabelCol
val labelSize: Array[Int]

Definition Classes
DLEstimator
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: 50
Number of max Epoch for the training, an epoch refers to a traverse over the training data Default: 50

Definition Classes
DLParams
val model: Module[T]

BigDL module to be optimized
BigDL module to be optimized

Definition Classes
DLClassifier → DLEstimator
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
lazy val params: Array[Param[_]]

Definition Classes
Params
final val predictionCol: Param[String]

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

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

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

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

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

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

Attributes
protected
Definition Classes
Params
def setEndWhen(trigger: Trigger): DLClassifier.this.type

Definition Classes
DLEstimator
def setFeaturesCol(featuresColName: String): DLClassifier.this.type

Definition Classes
DLEstimator
def setLabelCol(labelColName: String): DLClassifier.this.type

Definition Classes
DLEstimator
def setLearningRate(value: Double): DLClassifier.this.type

Definition Classes
DLEstimator
def setLearningRateDecay(value: Double): DLClassifier.this.type

Definition Classes
DLEstimator
def setMaxEpoch(value: Int): DLClassifier.this.type

Definition Classes
DLEstimator
def setOptimMethod(value: OptimMethod[T]): DLClassifier.this.type

Definition Classes
DLEstimator
def setPredictionCol(value: String): DLClassifier.this.type

Definition Classes
DLEstimator
def setTrainSummary(value: TrainSummary): DLClassifier.this.type

Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the training data, which can be used for visualization via Tensorboard.
Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the training data, which can be used for visualization via Tensorboard. Use setTrainSummary to enable train logger. Then the log will be saved to logDir/appName/train as specified by the parameters of TrainSummary.
Default: Not enabled

Definition Classes
DLEstimator
def setValidation(trigger: Trigger, validationDF: DataFrame, vMethods: Array[ValidationMethod[T]], batchSize: Int): DLClassifier.this.type

Set a validate evaluation during training
Set a validate evaluation during training
trigger
how often to evaluation validation set
validationDF
validate data set
vMethods
a set of validation method ValidationMethod
batchSize
batch size for validation
returns
this optimizer

Definition Classes
DLEstimator
def setValidationSummary(value: ValidationSummary): DLClassifier.this.type

Enable validation Summary
Enable validation Summary

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transformSchema(schema: StructType): StructType

Definition Classes
DLClassifier → DLEstimator → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType

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

Definition Classes
DLClassifier → DLEstimator → 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(schema: StructType): Unit

Attributes
protected
Definition Classes
DLEstimator
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( ... )
def wrapBigDLModel(m: Module[T], featureSize: Array[Int]): DLClassifierModel[T]

sub classes can extend the method and return required model for different transform tasks
sub classes can extend the method and return required model for different transform tasks

Attributes
protected
Definition Classes
DLClassifier → DLEstimator

class DLClassifier[T] extends DLEstimator[T]

Instance Constructors

new DLClassifier(model: Module[T], criterion: Criterion[T], featureSize: Array[Int], uid: String = ...)(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[_]): DLClassifier.this.type

def clone(): AnyRef

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

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

val criterion: Criterion[T]

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

final val endWhen: Param[Trigger]

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

val featureSize: Array[Int]

final val featuresCol: Param[String]

def finalize(): Unit

def fit(dataFrame: DataFrame): DLModel[T]

def fit(dataset: DataFrame, paramMaps: Array[ParamMap]): Seq[DLModel[T]]

def fit(dataset: DataFrame, paramMap: ParamMap): DLModel[T]

def fit(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DLModel[T]

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 getEndWhen: Trigger

final def getFeaturesCol: String

final def getLabelCol: 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 getTrainSummary: Option[TrainSummary]

def getValidationSummary: Option[ValidationSummary]

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

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

def hasParam(paramName: String): Boolean

def hashCode(): Int

def internalFit(dataFrame: DataFrame): DLModel[T]

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

final def isInstanceOf[T0]: Boolean

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

def isTraceEnabled(): Boolean

final val labelCol: Param[String]

val labelSize: Array[Int]

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