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src/layers/conv.jl

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@@ -42,7 +42,7 @@ and a batch of 50 would be a `100×100×3×50` array.
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This has `N=2` spatial dimensions, and needs a kernel size like `(5,5)`,
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a 2-tuple of integers.
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For `N` dimensional data, this layer expects as input an array
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To take convolutions along `N` dimensions, this layer expects as input an array
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with `ndims(x) == N+2`, where `size(x,N+1) == in` is the number of input channels,
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and the last dimension is `size(x,N+2) == batches`.
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Then:

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