case class Seq2SeqWithAttention[S0, S1, M0 <: Module, M1 <: StatefulModule2[Variable, Variable, S0, S1], M2 <: StatefulModule[Variable, Variable, S1]](destinationEmbedding: M0 with Module, encoder: M1 with StatefulModule2[Variable, Variable, S0, S1], decoder: M2 with StatefulModule[Variable, Variable, S1], padToken: Long)(stateToKey: (S1) => Variable) extends StatefulModule2[(Variable, Variable), Variable, S0, S1] with Product with Serializable
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- new Seq2SeqWithAttention(destinationEmbedding: M0 with Module, encoder: M1 with StatefulModule2[Variable, Variable, S0, S1], decoder: M2 with StatefulModule[Variable, Variable, S1], padToken: Long)(stateToKey: (S1) => Variable)
Value Members
- final def !=(arg0: Any): Boolean
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- final def ##: Int
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- final def ==(arg0: Any): Boolean
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- def apply[S](a: ((Variable, Variable), S0))(implicit arg0: Sc[S]): (Variable, S1)
Alias of forward
Alias of forward
- Definition Classes
- GenericModule
- final def asInstanceOf[T0]: T0
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- Any
- def attentionDecoder(keyValue: Variable, source: Variable): AttentionDecoder[S1, M2 with StatefulModule[Variable, Variable, S1], M0 with Module]
- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @IntrinsicCandidate()
- val decoder: M2 with StatefulModule[Variable, Variable, S1]
- val destinationEmbedding: M0 with Module
- val encoder: M1 with StatefulModule2[Variable, Variable, S0, S1]
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def forward[S](x: ((Variable, Variable), S0))(implicit arg0: Sc[S]): (Variable, S1)
The implementation of the function.
The implementation of the function.
In addition of
x
it can also use all thestate to compute its value.
- Definition Classes
- Seq2SeqWithAttention → GenericModule
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
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- @native() @IntrinsicCandidate()
- 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.
- Definition Classes
- GenericModule
- final def isInstanceOf[T0]: Boolean
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- final def learnableParameters: Long
Returns the total number of optimizable parameters.
Returns the total number of optimizable parameters.
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- GenericModule
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- @native() @IntrinsicCandidate()
- final def notifyAll(): Unit
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- @native() @IntrinsicCandidate()
- val padToken: Long
- final def parameters: Seq[(Constant, PTag)]
Returns the state variables which need gradient computation.
Returns the state variables which need gradient computation.
- Definition Classes
- GenericModule
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- 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.
- Definition Classes
- Seq2SeqWithAttention → GenericModule
- val stateToKey: (S1) => Variable
- final def synchronized[T0](arg0: => T0): T0
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- final def wait(arg0: Long, arg1: Int): Unit
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- final def wait(): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def zeroGrad(): Unit
- Definition Classes
- GenericModule
Deprecated Value Members
- def finalize(): Unit
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- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated