Class/Object

io.github.mandar2812.dynaml.models.neuralnets

GenericFFNeuralNet

Related Docs: object GenericFFNeuralNet | package neuralnets

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class GenericFFNeuralNet[Data, LayerP, I] extends NeuralNet[Data, Seq[NeuralLayer[LayerP, I, I]], I, I, NeuralStack[LayerP, I]]

Base class for implementations of feed-forward neural network models.

Data

The type of the training data.

LayerP

The type of the layer parameters i.e. weights/connections etc.

I

The type of the input features, output features and layer activations

Linear Supertypes
NeuralNet[Data, Seq[NeuralLayer[LayerP, I, I]], I, I, NeuralStack[LayerP, I]], ParameterizedLearner[Data, NeuralStack[LayerP, I], I, I, Stream[(I, I)]], Model[Data, I, I], AnyRef, Any
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Inherited
  1. GenericFFNeuralNet
  2. NeuralNet
  3. ParameterizedLearner
  4. Model
  5. AnyRef
  6. Any
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Visibility
  1. Public
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Instance Constructors

  1. new GenericFFNeuralNet(trainingAlgorithm: GradBasedBackPropagation[LayerP, I], data: Data, trans: DataPipe[Data, Stream[(I, I)]], layerInitializer: RandomVariable[Seq[LayerP]])

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  4. def _neuralStack: NeuralStack[LayerP, I]

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    Definition Classes
    NeuralNet

  5. val activations: Seq[Activation[I]]

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  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  8. def data: Data

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    Definition Classes
    Model

  9. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  10. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  11. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  12. val g: Data

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    The training data

    The training data

    Attributes
    protected
    Definition Classes
    GenericFFNeuralNet → Model

  13. val generator: RandomVariable[Seq[LayerP]]

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    Attributes
    protected

  14. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  15. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  16. def initParams(): NeuralStack[LayerP, I]

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    Definition Classes
    GenericFFNeuralNet → ParameterizedLearner

  17. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  18. def learn(): Unit

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    Learn the parameters of the model.

    Learn the parameters of the model.

    Definition Classes
    GenericFFNeuralNet → ParameterizedLearner

  19. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  20. final def notify(): Unit

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    Definition Classes
    AnyRef

  21. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  22. val numPoints: Int

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    Definition Classes
    GenericFFNeuralNet → NeuralNet

  23. val num_hidden_layers: Int

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  24. val num_layers: Int

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  25. val optimizer: GradBasedBackPropagation[LayerP, I]

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    Attributes
    protected
    Definition Classes
    GenericFFNeuralNet → ParameterizedLearner

  26. def parameters(): NeuralStack[LayerP, I]

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    Get the value of the parameters of the model.

    Get the value of the parameters of the model.

    Definition Classes
    ParameterizedLearner

  27. var params: NeuralStack[LayerP, I]

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    Attributes
    protected
    Definition Classes
    GenericFFNeuralNet → ParameterizedLearner

  28. def predict(point: I): I

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    Predict the value of the target variable given a point.

    Predict the value of the target variable given a point.

    Definition Classes
    NeuralNet → Model

  29. def setBatchFraction(f: Double): GenericFFNeuralNet.this.type

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    Definition Classes
    ParameterizedLearner

  30. def setLearningRate(alpha: Double): GenericFFNeuralNet.this.type

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    Definition Classes
    ParameterizedLearner

  31. def setMaxIterations(i: Int): GenericFFNeuralNet.this.type

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    Definition Classes
    ParameterizedLearner

  32. def setRegParam(r: Double): GenericFFNeuralNet.this.type

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    Definition Classes
    ParameterizedLearner

  33. val stackFactory: NeuralStackFactory[LayerP, I]

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

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    Definition Classes
    AnyRef

  35. def toString(): String

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    Definition Classes
    AnyRef → Any

  36. val transform: DataPipe[Data, Stream[(I, I)]]

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    Definition Classes
    GenericFFNeuralNet → NeuralNet

  37. def updateParameters(param: NeuralStack[LayerP, I]): Unit

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    Definition Classes
    ParameterizedLearner

  38. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  40. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from NeuralNet[Data, Seq[NeuralLayer[LayerP, I, I]], I, I, NeuralStack[LayerP, I]]

Inherited from ParameterizedLearner[Data, NeuralStack[LayerP, I], I, I, Stream[(I, I)]]

Inherited from Model[Data, I, I]

Inherited from AnyRef

Inherited from Any

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