Packages

c

lamp.autograd

BatchNorm

case class BatchNorm(scope: Scope, input: Variable, weight: Variable, bias: Variable, runningMean: STen, runningVar: STen, training: Boolean, momentum: Double, eps: Double) extends Op with Product with Serializable

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Serializable, Serializable, Product, Equals, Op, AnyRef, Any
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  1. BatchNorm
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. Op
  7. AnyRef
  8. Any
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Visibility
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Instance Constructors

  1. new BatchNorm(scope: Scope, input: Variable, weight: Variable, bias: Variable, runningMean: STen, runningVar: STen, training: Boolean, momentum: Double, eps: Double)

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. val bias: Variable
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  7. val eps: Double
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. val expectedShape: List[Long]
  10. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  12. val input: Variable
  13. val input_flattened: Tensor
  14. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  15. val momentum: Double
  16. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  18. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  19. val output: Tensor
  20. val output_reshaped: Tensor
  21. val params: List[(Variable, (STen, STen) ⇒ Unit)]

    Implementation of the backward pass

    Implementation of the backward pass

    A list of input variables paired up with an anonymous function computing the respective partial derivative. With the notation in the documentation of the trait lamp.autograd.Op: dy/dw2 => dy/dw2 * dw2/dw1. The first argument of the anonymous function is the incoming partial derivative (dy/dw2), the second argument is the output tensor into which the result (dy/dw2 * dw2/dw1) is accumulated (added).

    If the operation does not support computing the partial derivative for some of its arguments, then do not include that argument in this list.

    Definition Classes
    BatchNorm → Op
    See also

    The documentation on the trait lamp.autograd.Op for more details and example.

  22. val runningMean: STen
  23. val runningVar: STen
  24. val saveInvstd: Tensor
  25. val saveMean: Tensor
  26. val scope: Scope
  27. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  28. val training: Boolean
  29. val value: Variable

    The value of this operation

    The value of this operation

    Definition Classes
    BatchNorm → Op
  30. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  31. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  32. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  33. val weight: Variable

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Op

Inherited from AnyRef

Inherited from Any

Ungrouped