Packages

case class Conv1D(scope: Scope, input: Variable, weight: Variable, bias: Variable, stride: Long, padding: Long, dilation: Long, groups: Long) extends Op with Product with Serializable

1D convolution

input

batch x in_channels x L

weight

out_channels x in_channels x kernel_size

bias

out_channels

returns

Variable with Tensor of size batch x out_channels x L' (length depends on stride/padding/dilation)

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Serializable, Serializable, Product, Equals, Op, AnyRef, Any
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Instance Constructors

  1. new Conv1D(scope: Scope, input: Variable, weight: Variable, bias: Variable, stride: Long, padding: Long, dilation: Long, groups: Long)

    input

    batch x in_channels x L

    weight

    out_channels x in_channels x kernel_size

    bias

    out_channels

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. val bias: Variable
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. val dilation: Long
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  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 groups: Long
  13. val imageSize: Long
  14. val input: Variable
  15. val inputChannels: Long
  16. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  17. val kernelSize: Long
  18. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  20. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  21. val outChannels: Long
  22. val padding: Long
  23. 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
    Conv1D → Op
    See also

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

  24. val scope: Scope
  25. val stride: Long
  26. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  27. val value: Variable

    The value of this operation

    The value of this operation

    Definition Classes
    Conv1D → Op
  28. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  31. val weight: Variable

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Op

Inherited from AnyRef

Inherited from Any

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