AutoEncoder

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. val W: IndexedSeq[ScalarMatrix]

   

   All weights of layers

   All weights of layers

   returns

   all weights of layers

   Definition Classes

   AutoEncoder → Network

5. def andThen[A](g: (ScalarMatrix) ⇒ A): (ScalarMatrix) ⇒ A

   

   Definition Classes

   Function1

   Annotations

   @unspecialized()

6. def apply(in: ScalarMatrix): ScalarMatrix

   

   Compute output of neural network with given input (without reconstruction) If drop-out is used, to average drop-out effect, we need to multiply output by presence probability.

   Compute output of neural network with given input (without reconstruction) If drop-out is used, to average drop-out effect, we need to multiply output by presence probability.

   in

   an input vector

   returns

   output of the vector

   Definition Classes

   AutoEncoder → Function1

7. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

8. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

9. def compose[A](g: (A) ⇒ ScalarMatrix): (A) ⇒ ScalarMatrix

   

   Definition Classes

   Function1

   Annotations

   @unspecialized()

10. val dW: IndexedSeq[ScalarMatrix]

    

    All accumulated delta weights of layers

    All accumulated delta weights of layers

    returns

    all accumulated delta weights

    Definition Classes

    AutoEncoder → Network

11. def decode(x: ScalarMatrix): ScalarMatrix

    

    Decode computation for training.

    Decode computation for training. If drop-out is used, we need to drop-out entry of input vector.

    x

    hidden values

    returns

    output matrix

12. def decode_!(err: ScalarMatrix): ScalarMatrix

    

    Backpropagation algorithm for decoding phrase

    Backpropagation algorithm for decoding phrase

    err

    backpropagated error from error function

13. def encode(x: ScalarMatrix): ScalarMatrix

    

    Encode computation for training.

    Encode computation for training. If drop-out is used, we need to drop-out entry of input vector.

    x

    input matrix

    returns

    hidden values

14. def encode_!(err: ScalarMatrix): ScalarMatrix

    

    Backpropagation algorithm for encoding phrase

    Backpropagation algorithm for encoding phrase

    err

    backpropagated error from error function

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

    

    Definition Classes

    AnyRef

16. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

17. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    

    Definition Classes

    AnyRef → Any

19. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

20. var input: Seq[ScalarMatrix]

    

    Collected input & output of each layer

    Collected input & output of each layer

    Attributes

    protected[kr.ac.kaist.ir.deep]

21. def into_:(x: ScalarMatrix): ScalarMatrix

    

    Forward computation for training.

    Forward computation for training. If drop-out is used, we need to drop-out entry of input vector.

    x

    input matrix

    returns

    output matrix

    Definition Classes

    AutoEncoder → Network

22. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

23. val layer: Reconstructable

    

    A reconstructable layer for this network

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

    

    Definition Classes

    AnyRef

25. final def notify(): Unit

    

    Definition Classes

    AnyRef

26. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

27. def of(x: ScalarMatrix): ScalarMatrix

    

    Sugar: Forward computation for validation.

    Sugar: Forward computation for validation. Calls apply(x)

    x

    input matrix

    returns

    output matrix

    Definition Classes

    AutoEncoder → Network

28. def reconstruct(x: ScalarMatrix): ScalarMatrix

    

    Reconstruct the given hidden value

    Reconstruct the given hidden value

    x

    hidden value to be reconstructed.

    returns

    reconstruction value.

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

    

    Definition Classes

    AnyRef

30. def toJSON: JsObject

    

    Serialize network to JSON

    Serialize network to JSON

    returns

    JsObject of this network

    Definition Classes

    AutoEncoder → Network

31. def toString(): String

    

    Definition Classes

    Function1 → AnyRef → Any

32. def updateBy(err: ScalarMatrix): ScalarMatrix

    

    Backpropagation algorithm

    Backpropagation algorithm

    err

    backpropagated error from error function

    Definition Classes

    AutoEncoder → Network

33. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )