Fix docstrings for different conv layers

Fix docstrings for different conv layers

Author: Saransh-cpp

src/layers/basic.jl

@@ -68,8 +68,6 @@ function applychain(layers::AbstractVector, x)  # type-unstable path, helps comp
   x
 end
 
-# Overrides Base.getindex to obtain a `Chain` with selected layers
-# from a `Chain` passed to it.
 Base.getindex(c::Chain, i::AbstractArray) = Chain(c.layers[i])
 Base.getindex(c::Chain{<:NamedTuple}, i::AbstractArray) =
   Chain(NamedTuple{keys(c)[i]}(Tuple(c.layers)[i]))
@@ -79,7 +77,6 @@ function Base.show(io::IO, c::Chain)
   print(io, ")")
 end
 
-# supporting methods for the overriden `Base.show` function
 _show_layers(io, layers::Tuple) = join(io, layers, ", ")
 _show_layers(io, layers::NamedTuple) = join(io, ["$k = $v" for (k, v) in pairs(layers)], ", ")
 _show_layers(io, layers::AbstractVector) = (print(io, "["); join(io, layers, ", "); print(io, "]"))

src/layers/conv.jl

@@ -213,9 +213,9 @@ Note that `pad=SamePad()` here tries to ensure `size(output,d) == size(x,d) * st
 Parameters are controlled by additional keywords, with defaults
 `init=glorot_uniform` and `bias=true`.
 
-ConvTranspose layer can also be constructed with given weights and biases.
-While constructung a ConvTranspose layer by providing weights and biases, the layer accepts the
-same keywords (and has the same defaults) as the `ConvTranspose((4,4), 3 => 7, relu)` method.
+ConvTranspose layer can also be manually constructed with passing in weights and
+biases. This constructor accepts the same keywords (and has the same defaults) as the
+`ConvTranspose((4,4), 3 => 7, relu)` method.
 
 See also [`Conv`](@ref) for more detailed description of keywords.
 
@@ -352,9 +352,9 @@ specifying the size of the convolutional kernel;
 Parameters are controlled by additional keywords, with defaults
 `init=glorot_uniform` and `bias=true`.
 
-CrossCor layer can also be constructed with given weights and biases.
-While constructung a CrossCor layer by providing weights and biases, the layer accepts the
-same keywords (and has the same defaults) as the `CrossCor((4,4), 3 => 7, relu)` method.
+CrossCor layer can also be manually constructed with passing in weights and
+biases. This constructor accepts the layer accepts the same keywords (and has
+the same defaults) as the `CrossCor((4,4), 3 => 7, relu)` method.
 
 See also [`Conv`](@ref) for more detailed description of keywords.