conv docs

docs/src/models/layers.md

@@ -5,6 +5,7 @@ These core layers form the foundation of almost all neural networks.
 ```@docs
 Chain
 Dense
+Conv2D
 ```
 
 ## Recurrent Layers

src/layers/conv.jl

@@ -1,3 +1,15 @@
+"""
+    Conv2D(size, in=>out)
+    Conv2d(size, in=>out, relu)
+
+Standard convolutional 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 HWCN 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` and `stride`.
+"""
 struct Conv2D{F,A}
   σ::F
   weight::A