case class RNN(weightXh: Constant, weightHh: Constant, biasH: Constant) extends StatefulModule[Variable, Variable, Option[Variable]] with Product with Serializable
Inputs of size (sequence length * batch * in dim) Outputs of size (sequence length * batch * hidden dim)
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Alias of forward
Alias of forward
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- val biasH: Constant
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forward[S](a: (Variable, Option[Variable]))(implicit arg0: Sc[S]): (Variable, Some[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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- RNN → GenericModule
- def forward1[S](x: Variable, state: Option[Variable])(implicit arg0: Sc[S]): (Variable, Some[Variable])
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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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- val hiddenSize: Long
- val inputSize: Long
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def
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, LeafTag with Product with Serializable)]
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 weightHh: Constant
- val weightXh: Constant
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def
zeroGrad(): Unit
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- GenericModule