Embedding

lamp.nn.Embedding
See theEmbedding companion object
case class Embedding(weights: Constant) extends Module

Learnable mapping from classes to dense vectors. Equivalent to L * W where L is the n x C one-hot encoded matrix of the classes * is matrix multiplication W is the C x dim dense matrix. W is learnable. L is never computed directly. C is the number of classes. n is the size of the batch.

Input is a long tensor with values in [0,C-1]. Input shape is arbitrary, (). Output shape is ( x D) where D is the embedding dimension.

Attributes

Companion
object
Graph
Supertypes
trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any
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Members list

Value members

Concrete methods

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

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

Inherited methods

def apply[S : Sc](a: Variable): 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]

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Inherited from:
Product
final def zeroGrad(): Unit

Attributes

Inherited from:
GenericModule

Concrete fields

val 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