case class Linear(weights: Constant, bias: Option[Constant]) extends Module with Product with Serializable
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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
x
it can also use all thestate to compute its value.
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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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def
learnableParameters: Long
Returns the total number of optimizable parameters.
Returns the total number of optimizable parameters.
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parameters: Seq[(Constant, PTag)]
Returns the state variables which need gradient computation.
Returns the state variables which need gradient computation.
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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.
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wait(arg0: Long): Unit
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- val weights: Constant