case class TransformerEncoder(blocks: Seq[TransformerEncoderBlock]) extends GenericModule[(Variable, STen), Variable] with Product with Serializable
TransformerEncoder module
Input is (data, tokens)
where
data
is (batch, num tokens, in dimension), double tensor
tokens
is (batch,num tokens) long tensor.
Output is (bach, num tokens, out dimension)
The sole purpose of tokens
is to carry over the padding
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- new TransformerEncoder(blocks: Seq[TransformerEncoderBlock])
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def
apply[S](a: (Variable, STen))(implicit arg0: Sc[S]): Variable
Alias of forward
Alias of forward
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asInstanceOf[T0]: T0
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- val blocks: Seq[TransformerEncoderBlock]
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forward[S](x: (Variable, STen))(implicit arg0: Sc[S]): Variable
The implementation of the function.
The implementation of the function.
In addition of
x
it can also use all thestate to compute its value.
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- TransformerEncoder → GenericModule
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def
getClass(): Class[_]
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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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learnableParameters: Long
Returns the total number of optimizable parameters.
Returns the total number of optimizable parameters.
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def
parameters: Seq[(Constant, PTag)]
Returns the state variables which need gradient computation.
Returns the state variables which need gradient computation.
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def
state: List[(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.
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- TransformerEncoder → GenericModule
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