case class Conv2D(weights: Constant, bias: Constant, stride: Long, padding: Long, dilation: Long, groups: Long) extends Module with Product with Serializable
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Alias of forward
Alias of forward
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- val bias: Constant
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- val dilation: Long
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The implementation of the function.
The implementation of the function.
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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 groups: Long
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learnableParameters: Long
Returns the total number of optimizable parameters.
Returns the total number of optimizable parameters.
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- val padding: Long
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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: 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 stride: Long
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- val weights: Constant