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c

scalation.analytics

RecurrentNeuralNetLayer

class RecurrentNeuralNetLayer extends AnyRef

The RecurrentNeuralNetLayer is a 3-layer where x denotes the input, 'y 'denotes the output and 's' is the intermediate/hidden value. We have 'St = Activate (U dot x(t) + W dot x(t-1))' and 'y(t) = softmax(V dot St)'.

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Instance Constructors

  1. new RecurrentNeuralNetLayer()

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. var add: VectoD
  5. final def asInstanceOf[T0]: T0
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  6. def backward(x: VectoD, prev_s: VectoD, u: MatriD, w: MatriD, v: MatriD, diff_s: VectoD, dmulv: VectoD): (VectoD, MatriD, MatriD, MatriD)

    Calculate the derivate regarding to prev_s , U, W, V by backward of each unit

    Calculate the derivate regarding to prev_s , U, W, V by backward of each unit

    x

    the input data

    prev_s

    record the previous hidden layer value

    u

    parameter for input x

    w

    parameter for hidden layer z

    v

    parameter for output

    diff_s

    diff_s = ds(t+1)/ ds (t)

    dmulv

    dl/dmulv where l is the loss, mulv = V dot s

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  10. def finalize(): Unit
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  11. def forward(x: VectoD, prev_s: VectoD, u: MatriD, w: MatriD, v: MatriD): Unit

    Forward the x into the RecurrentNeuralNet layer.

    Forward the x into the RecurrentNeuralNet layer. We have St = Activate (U dot x(t) + W dot x(t-1)) y(t) = softmax(V dot St)

    x

    the input data

    prev_s

    record the previous hidden layer value

    u

    parameter for input x

    w

    parameter for hidden layer z

    v

    parameter for output

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  14. final def isInstanceOf[T0]: Boolean
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  15. var mulu: VectoD
  16. var mulv: VectoD
  17. var mulw: VectoD
  18. final def ne(arg0: AnyRef): Boolean
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  19. final def notify(): Unit
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  20. final def notifyAll(): Unit
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  21. var s: VectoD
  22. final def synchronized[T0](arg0: ⇒ T0): T0
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