Sequential

lamp.nn.Sequential
See theSequential companion object
case class Sequential[A, M <: GenericModule[A, A]](members: M & GenericModule[A, A]*) extends GenericModule[A, A]

Attributes

Companion
object
Graph
Supertypes
trait Serializable
trait Product
trait Equals
trait GenericModule[A, A]
class Object
trait Matchable
class Any
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Members list

Value members

Concrete methods

def forward[S : Sc](x: A): A

The implementation of the function.

The implementation of the function.

In addition of x it can also use all the `state to compute its value.

Attributes

override def state: Seq[(Constant, PTag)]

List of optimizable, or non-optimizable, but stateful parameters

List of optimizable, or non-optimizable, but stateful parameters

Stateful means that the state is carried over the repeated forward calls.

Attributes

Definition Classes

Inherited methods

def apply[S : Sc](a: A): B

Alias of forward

Alias of forward

Attributes

Inherited from:
GenericModule
final def gradients(loss: Variable, zeroGrad: Boolean): Seq[Option[STen]]

Computes the gradient of loss with respect to the parameters.

Computes the gradient of loss with respect to the parameters.

Attributes

Inherited from:
GenericModule
final def learnableParameters: Long

Returns the total number of optimizable parameters.

Returns the total number of optimizable parameters.

Attributes

Inherited from:
GenericModule
final def parameters: Seq[(Constant, PTag)]

Returns the state variables which need gradient computation.

Returns the state variables which need gradient computation.

Attributes

Inherited from:
GenericModule
def productElementNames: Iterator[String]

Attributes

Inherited from:
Product
def productIterator: Iterator[Any]

Attributes

Inherited from:
Product
final def zeroGrad(): Unit

Attributes

Inherited from:
GenericModule