case class Conv2D(scope: Scope, input: Variable, weight: Variable, bias: Variable, stride: Long, padding: Long, dilation: Long, groups: Long) extends Op with Product with Serializable
2D convolution
- input
batch x in_channels x height x width
- weight
out_channels x in_channels x kernel_size x kernel_size
- bias
out_channels
- returns
Variable with Tensor of size batch x out_channels x L' (length depends on stride/padding/dilation)
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- val batchSize: Long
- val bias: Variable
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- val dilation: Long
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- val groups: Long
- val imageHeight: Long
- val imageWidth: Long
- val input: Variable
- val inputChannels: Long
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- val kernelSize: Long
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- val outChannels: Long
- val padding: Long
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val
params: List[(Variable, (STen, STen) ⇒ Unit)]
Implementation of the backward pass
Implementation of the backward pass
A list of input variables paired up with an anonymous function computing the respective partial derivative. With the notation in the documentation of the trait lamp.autograd.Op:
dy/dw2 => dy/dw2 * dw2/dw1. The first argument of the anonymous function is the incoming partial derivative (dy/dw2), the second argument is the output tensor into which the result (dy/dw2 * dw2/dw1) is accumulated (added).If the operation does not support computing the partial derivative for some of its arguments, then do not include that argument in this list.
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The documentation on the trait lamp.autograd.Op for more details and example.
- val scope: Scope
- val stride: Long
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value: Variable
The value of this operation
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- val weight: Variable