Class

io.github.mandar2812.dynaml.models.neuralnets

GenericNeuralStack

Related Doc: package neuralnets

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class GenericNeuralStack[P, I, Layer <: NeuralLayer[P, I, I], T[L] <: Traversable[L] with GenTraversableLike[L, T[L]]] extends NeuralGraph[T[Layer], I, I]

Self Type
GenericNeuralStack[P, I, Layer, T]
Linear Supertypes
NeuralGraph[T[Layer], I, I], AnyRef, Any
Known Subclasses
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  1. GenericNeuralStack
  2. NeuralGraph
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Instance Constructors

  1. new GenericNeuralStack(elements: T[Layer])

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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
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  3. def ++[L <: NeuralLayer[P, I, I], G[L1] <: Traversable[L1] with GenTraversableLike[L1, G[L1]]](otherStack: GenericNeuralStack[P, I, L, G]): GenericNeuralStack[P, I, NeuralLayer[P, I, I], T]

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    Append another computation stack to the end of the current one.

  4. def :+(computationLayer: NeuralLayer[P, I, I]): GenericNeuralStack[P, I, NeuralLayer[P, I, I], T]

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    Append a single computation layer to the stack.

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

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    Definition Classes
    AnyRef → Any
  6. def _layers: T[Layer]

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  7. def apply(r: Range): GenericNeuralStack[P, I, Layer, T]

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    Slice the stack according to a range.

  8. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  9. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    @throws( ... )
  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  13. val forwardPass: (I) ⇒ I

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    Do a forward pass through the network outputting only the output layer activations.

    Do a forward pass through the network outputting only the output layer activations.

    Definition Classes
    GenericNeuralStackNeuralGraph
  14. def forwardPassBatch[G <: Traversable[I]](d: G): G

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    Batch version of forwardPass()

  15. def forwardPropagate(x: I): T[I]

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    Do a forward pass through the network outputting all the intermediate.

    Do a forward pass through the network outputting all the intermediate. layer activations.

  16. def forwardPropagateBatch[G <: Traversable[I]](d: G): T[G]

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    Batch version of forwardPropagate()

  17. val g: T[Layer]

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    Attributes
    protected
    Definition Classes
    GenericNeuralStackNeuralGraph
  18. final def getClass(): Class[_]

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  19. def hashCode(): Int

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  20. final def isInstanceOf[T0]: Boolean

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

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  22. final def notify(): Unit

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  23. final def notifyAll(): Unit

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

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  25. def toString(): String

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  26. final def wait(): Unit

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    @throws( ... )
  27. final def wait(arg0: Long, arg1: Int): Unit

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

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Inherited from NeuralGraph[T[Layer], I, I]

Inherited from AnyRef

Inherited from Any

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