case class Embedding(weights: Constant) extends Module with Product with Serializable
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.
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Alias of forward
Alias of forward
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The implementation of the function.
The implementation of the function.
In addition of
xit can also use all thestate to compute its value.- Definition Classes
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- final def gradients(loss: Variable, zeroGrad: Boolean = true): Seq[Option[STen]]
Computes the gradient of loss with respect to the parameters.
Computes the gradient of loss with respect to the parameters.
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Returns the total number of optimizable parameters.
Returns the total number of optimizable parameters.
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Returns the state variables which need gradient computation.
Returns the state variables which need gradient computation.
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- def productElementNames: Iterator[String]
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- val state: List[(Constant, Weights.type)]
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.
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- val weights: Constant
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