AffineLayerModel

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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. def apply(layer: AffineLayer, weights: DenseVector[Double], random: Random): AffineLayerModel

   

   Creates a model of Affine layer

   Creates a model of Affine layer

   layer

   layer properties

   weights

   vector for weights initialization

   random

   random number generator

   returns

   model of Affine layer

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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

   

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8. def equals(arg0: Any): Boolean

   

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9. def finalize(): Unit

   

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

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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

    

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14. final def notify(): Unit

    

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15. final def notifyAll(): Unit

    

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16. def randomWeights(numIn: Int, numOut: Int, weights: DenseVector[Double], random: Random): Unit

    

    Initialize weights randomly in the interval.

    Initialize weights randomly in the interval. Uses [Bottou-88] heuristic [-a/sqrt(in); a/sqrt(in)], where a is chosen in such a way that the weight variance corresponds to the points to the maximal curvature of the activation function (which is approximately 2.38 for a standard sigmoid).

    numIn

    number of inputs

    numOut

    number of outputs

    weights

    vector for weights initialization

    random

    random number generator

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

    

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18. def toString(): String

    

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19. final def wait(): Unit

    

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

    

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

    

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