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, Product, Equals, Op, AnyRef, Any
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  1. BatchNorm
  2. Serializable
  3. Product
  4. Equals
  5. Op
  6. AnyRef
  7. Any
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Visibility
  1. Public
  2. Protected

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(classOf[java.lang.CloneNotSupportedException]) @native() @IntrinsicCandidate()
  7. val eps: Double
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. val expectedShape: List[Long]
  10. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  11. val input: Variable
  12. val input_flattened: Tensor
  13. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  14. val momentum: Double
  15. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  17. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  18. val output: Tensor
  19. val output_reshaped: Tensor
  20. 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
    BatchNormOp
    See also

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

  21. def productElementNames: Iterator[String]
    Definition Classes
    Product
  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
    BatchNormOp
  30. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  31. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  32. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  33. val weight: Variable

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Op

Inherited from AnyRef

Inherited from Any

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