TopologyModel

Abstract Value Members

abstract def computeGradient(data: DenseMatrix[Double], target: DenseMatrix[Double], cumGradient: Vector, blockSize: Int): Double

Computes gradient for the network
Computes gradient for the network
data
input data
target
target output
cumGradient
cumulative gradient
blockSize
block size
returns
error
abstract def forward(data: DenseMatrix[Double], includeLastLayer: Boolean): Array[DenseMatrix[Double]]

Forward propagation
Forward propagation
data
input data
includeLastLayer
Include the last layer in the output. In MultilayerPerceptronClassifier, the last layer is always softmax; the last layer of outputs is needed for class predictions, but not for rawPrediction.
returns
array of outputs for each of the layers
abstract val layerModels: Array[LayerModel]

Array of layer models
abstract val layers: Array[Layer]

Array of layers
abstract def predict(features: Vector): Vector

Prediction of the model.
Prediction of the model. See ProbabilisticClassificationModel
features
input features
returns
prediction
abstract def predictRaw(features: Vector): Vector

Raw prediction of the model.
Raw prediction of the model. See ProbabilisticClassificationModel
features
input features
returns
raw prediction Note: This interface is only used for classification Model.
abstract def raw2ProbabilityInPlace(rawPrediction: Vector): Vector

Probability of the model.
Probability of the model. See ProbabilisticClassificationModel
rawPrediction
raw prediction vector
returns
probability Note: This interface is only used for classification Model.
abstract val weights: Vector

Concrete Value Members

final def !=(arg0: Any): Boolean

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Related Doc: package ann

trait TopologyModel extends Serializable

Abstract Value Members

abstract def computeGradient(data: DenseMatrix[Double], target: DenseMatrix[Double], cumGradient: Vector, blockSize: Int): Double

abstract def forward(data: DenseMatrix[Double], includeLastLayer: Boolean): Array[DenseMatrix[Double]]

abstract val layerModels: Array[LayerModel]

abstract val layers: Array[Layer]

abstract def predict(features: Vector): Vector

abstract def predictRaw(features: Vector): Vector

abstract def raw2ProbabilityInPlace(rawPrediction: Vector): Vector

abstract val weights: Vector

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

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Inherited from Serializable

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