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conv docstring formatting

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Mike Innes 2019-08-19 15:30:59 +01:00
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commit 447fd9d604
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src/layers/conv.jl
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@ -74,8 +74,10 @@ end
Standard convolutional transpose layer. `size` should be a tuple like `(2, 2)`. Standard convolutional transpose layer. `size` should be a tuple like `(2, 2)`.
`in` and `out` specify the number of input and output channels respectively. `in` and `out` specify the number of input and output channels respectively.
Data should be stored in WHCN order. In other words, a 100×100 RGB image would Data should be stored in WHCN order. In other words, a 100×100 RGB image would
be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array. be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """
struct ConvTranspose{N,M,F,A,V} struct ConvTranspose{N,M,F,A,V}
@ -138,11 +140,14 @@ end
""" """
DepthwiseConv(size, in=>out) DepthwiseConv(size, in=>out)
DepthwiseConv(size, in=>out, relu) DepthwiseConv(size, in=>out, relu)
Depthwise convolutional layer. `size` should be a tuple like `(2, 2)`. Depthwise convolutional layer. `size` should be a tuple like `(2, 2)`.
`in` and `out` specify the number of input and output channels respectively. `in` and `out` specify the number of input and output channels respectively.
Note that `out` must be an integer multiple of `in`. Note that `out` must be an integer multiple of `in`.
Data should be stored in WHCN order. In other words, a 100×100 RGB image would Data should be stored in WHCN order. In other words, a 100×100 RGB image would
be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array. be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """
struct DepthwiseConv{N,M,F,A,V} struct DepthwiseConv{N,M,F,A,V}