Packages

c

lamp.autograd

AvgPool2D

case class AvgPool2D(scope: Scope, input: Variable, kernelSize: Long, stride: Long, padding: Long) extends Op with Product with Serializable

2D avg pooling

input

batch x in_channels x h x w

Linear Supertypes
Serializable, Product, Equals, Op, AnyRef, Any
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Inherited
  1. AvgPool2D
  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 AvgPool2D(scope: Scope, input: Variable, kernelSize: Long, stride: Long, padding: Long)

    input

    batch x in_channels x h x w

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 batchSize: Long
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native() @IntrinsicCandidate()
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  9. val imageSize: Long
  10. val input: Variable
  11. val inputChannels: Long
  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  13. val kernelSize: Long
  14. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  16. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  17. val padding: Long
  18. 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
    AvgPool2DOp
    See also

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

  19. def productElementNames: Iterator[String]
    Definition Classes
    Product
  20. val scope: Scope
  21. val stride: Long
  22. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  23. val value: Variable

    The value of this operation

    The value of this operation

    Definition Classes
    AvgPool2DOp
  24. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  25. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  26. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])

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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