DenoisingAutoEncodeLayer

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

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

   

   Definition Classes

   AnyRef → Any

4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. def backward(delta: Vector[Double], acts: ActivationStack, model: Model): (Vector[Double], Weight, Bias)

   

   Calculate the delta of this iteration.

   Calculate the delta of this iteration. The input of the layer in forward phase can be restored from ActivationStack. It returns the delta of input layer of this layer and the delta of coefficient and intercept parameter.

   Definition Classes

   DenoisingAutoEncodeLayer → Layer

6. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

7. def corrupt(input: Vector[Double]): Vector[Double]

   

   Attributes

   protected

8. val corruptionLevel: Double

   

9. def createTmpLayer(): PretrainLayer

   

   Returns the form for creating tmp model used while pretraining The layer used as prototype for creating tmp model.

   Returns the form for creating tmp model used while pretraining The layer used as prototype for creating tmp model. Only necessary fields are input size, output size and id.

   returns

   A new pretrain layer that is reversed output and input. It is used mainly for keeping bias value while pretraining.

   Definition Classes

   DenoisingAutoEncodeLayer → PretrainLayer

10. def decode(input: Vector[Double], model: Model, tmpModel: Model): (Vector[Double], Vector[Double])

    

    Decode hidden layer value to visible layer

    Decode hidden layer value to visible layer

    Definition Classes

    DenoisingAutoEncodeLayer → PretrainLayer

11. def encode(input: Vector[Double], model: Model, tmpModel: Model): (Vector[Double], Vector[Double])

    

    Encode the input to hidden layer

    Encode the input to hidden layer

    Definition Classes

    DenoisingAutoEncodeLayer → PretrainLayer

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

    

    Definition Classes

    AnyRef

13. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

14. def error(label: Vector[Double], prediction: Vector[Double]): Vector[Double]

    

    Calculate the error of output layer between label data and prediction.

    Calculate the error of output layer between label data and prediction.

    Attributes

    protected

    Definition Classes

    DenoisingAutoEncodeLayer → PretrainLayer

15. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

16. def forward(acts: ActivationStack, model: Model): Vector[Double]

    

    Calculate the output corresponding given input.

    Calculate the output corresponding given input. Input is given as a top of ActivationStack.

    returns

    The output tuple of the layer. First value of the tuple represents the raw output, the second is applied activation function of the layer.

    Definition Classes

    DenoisingAutoEncodeLayer → Layer

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

    

    Definition Classes

    AnyRef → Any

18. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

19. var id: String

    

    Definition Classes

    DenoisingAutoEncodeLayer → Layer

20. val inputSize: Int

    

    Definition Classes

    DenoisingAutoEncodeLayer → Layer

21. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

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

    

    Definition Classes

    AnyRef

23. final def notify(): Unit

    

    Definition Classes

    AnyRef

24. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

25. val outputSize: Int

    

    Definition Classes

    DenoisingAutoEncodeLayer → Layer

26. def pretrain(acts: ActivationStack, model: Model, tmpModel: Model): ((Weight, Bias), (Weight, Bias), Double)

    

    Pretraining with input data to the layer.

    Pretraining with input data to the layer. We can assume AutoEncoder can inherit this class. It returns the gradient of weight and bias of the layer.

    returns

    (dWeight1, dBias1): Hidden Layer Gradient (dWeight2, dBias2): Visible Layer Gradient (Hidden Layer Gradient, Visible Layer Gradient)

    Definition Classes

    PretrainLayer

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

    

    Definition Classes

    AnyRef

28. def toString(): String

    

    Definition Classes

    Layer → AnyRef → Any

29. def validateParamShapes(weight: Matrix[Double], bias: Vector[Double]): Unit

    

    Attributes

    protected

    Definition Classes

    ShapeValidator

30. def vizWeight(outputPath: String, model: Model): Unit

    

    Definition Classes

    Visualizable

31. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )