From 447fd9d604891584eaa69082daf70646f04ab37f Mon Sep 17 00:00:00 2001 From: Mike Innes Date: Mon, 19 Aug 2019 15:30:59 +0100 Subject: [PATCH] conv docstring formatting --- src/layers/conv.jl | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/layers/conv.jl b/src/layers/conv.jl index b99c289f..4361a389 100644 --- a/src/layers/conv.jl +++ b/src/layers/conv.jl @@ -74,8 +74,10 @@ end Standard convolutional transpose layer. `size` should be a tuple like `(2, 2)`. `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 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`. """ struct ConvTranspose{N,M,F,A,V} @@ -138,11 +140,14 @@ end """ DepthwiseConv(size, in=>out) DepthwiseConv(size, in=>out, relu) + Depthwise convolutional layer. `size` should be a tuple like `(2, 2)`. `in` and `out` specify the number of input and output channels respectively. 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 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`. """ struct DepthwiseConv{N,M,F,A,V}