From 462c69fcfbc0260081404a625dbbbb93ec3e1249 Mon Sep 17 00:00:00 2001
From: Ricardo Rosa <rmsrosa@gmail.com>
Date: Mon, 1 May 2023 09:01:29 -0300
Subject: [PATCH] 4 backticks avoid vscode seemingly parsing error

---
 README.md | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/README.md b/README.md
index 543f5ed..cf51cc4 100644
--- a/README.md
+++ b/README.md
@@ -45,12 +45,12 @@ In all the examples below, one needs `Flux`, `ChainPlots` and `Plots`, while for
 
 ### Dense and Recurrent layers
 
-```julia
+````julia
 julia> nnr = Chain(Dense(2,5,σ),RNN(5,4,relu), LSTM(4,4), GRU(4,4), Dense(4,3))
 Chain(Dense(2, 5, σ), Recur(RNNCell(5, 4, relu)), Recur(LSTMCell(4, 4)), Dense(4, 3))
 
 julia> plot(nnr, title="With theme default", titlefontsize=10)
-```
+````
 
 ![nnr_default plot](examples/img/nnr_default.png)
 
@@ -58,7 +58,7 @@ julia> plot(nnr, title="With theme default", titlefontsize=10)
 
 Variable-input functional layers are also accepted. If given as the first layer, then an initial input must be provided, otherwise, the input data is not needed. Here are two examples, illustrating each case.
 
-```julia
+````julia
 julia> dx(x) = x[2:end]-x[1:end-1]
 dx (generic function with 1 method)
 
@@ -69,18 +69,18 @@ julia> nna = Chain(Dense(2,5,σ), dx, RNN(4,6,relu), x³, LSTM(6,4), GRU(4,4), D
 Chain(Dense(2, 5, σ), dx, Recur(RNNCell(4, 6, relu)), x³, Recur(LSTMCell(6, 4)), Recur(GRUCell(4, 4)), Dense(4, 3))
 
 julia> plot(nna, title="$nna", titlefontsize=7)
-```
+````
 
 ![nna plot](examples/img/nna.png)
 
-```julia
+````julia
 julia> nnx = Chain(x³, dx, LSTM(5,10), Dense(10,5))
 Chain(x³, dx, Recur(LSTMCell(5, 10)), Dense(10, 5))
 
 julia> input_data = rand(6);
 
 julia> plot(nnx, input_data, title="$nnx", titlefontsize=9)
-```
+````
 
 ![nnx plot](examples/img/nnx.png)
 
@@ -88,7 +88,7 @@ julia> plot(nnx, input_data, title="$nnx", titlefontsize=9)
 
 A neural network with a one-dimensional convolutional layer:
 
-```julia
+````julia
 julia> reshape6x1x1(a) = reshape(a, 6,  1, 1)
 reshape6x1x1 (generic function with 1 method)
 
@@ -99,13 +99,13 @@ julia> nnrs = Chain(x³, Dense(3,6), reshape6x1x1, Conv((2,), 1=>1), slice, Dens
 Chain(x³, Dense(3, 6), reshape6x1x1, Conv((2,), 1=>1), slice, Dense(5, 4))
 
 julia> plot(nnrs, Float32.(rand(3)), title="$nnrs", titlefontsize=9)
-```
+````
 
 ![nnrs plot](examples/img/nnrs.png)
 
 Now with a two-dimensional convolution:
 
-```julia
+````julia
 julia> reshape4x4x1x1(a) = reshape(a, 4, 4, 1, 1)
 reshape4x4x1x1 (generic function with 1 method)
 
@@ -113,13 +113,13 @@ julia> nnrs2d = Chain(x³, Dense(4,16), reshape4x4x1x1, Conv((2,2), 1=>1), slice
 Chain(x³, Dense(4, 16), reshape4x4x1x1, Conv((2, 2), 1=>1), slice)
 
 julia> plot(nnrs2d, Float32.(rand(4)), title="$nnrs2d", titlefontsize=9)
-```
+````
 
 ![nnrs2d plot](examples/img/nnrs2d.png)
 
 With convolutional and pooling layers:
 
-```julia
+````julia
 julia> nncp = Chain(
            Conv((3, 3), 1=>2, pad=(1,1), bias=false),
            MaxPool((2,2)),
@@ -138,7 +138,7 @@ Chain(
 )                   # Total: 5 arrays, 242 parameters, 2.047 KiB.
 
 julia> plot(nncp, (16, 16, 1, 1), title="Chain with convolutional and pooling layers", titlefontsize=10)
-```
+````
 
 ![nncp plot](examples/img/nncp.png)
 
@@ -146,7 +146,7 @@ julia> plot(nncp, (16, 16, 1, 1), title="Chain with convolutional and pooling la
 
 With `ChainPlots.chaingraph()` we can convert a `Flux.Chain` to a `MetaGraph`.
 
-```julia
+````julia
 julia> nnr = Chain(Dense(2,5,σ),RNN(5,4,relu), LSTM(4,4), GRU(4,4), Dense(4,3))
 Chain(Dense(2, 5, σ), Recur(RNNCell(5, 4, relu)), Recur(LSTMCell(4, 4)), Recur(GRUCell(4, 4)), Dense(4, 3))
 
@@ -175,13 +175,13 @@ julia> get_prop.(Ref(mg_nnr), 15, [:loc_x, :loc_y])
 2-element Vector{Real}:
  3.0
  0.75
-```
+````
 
 ### Visualizing the MetaGraph
 
 We may visualize the generated MetaGraph with [JuliaGraphs/GraphPlot.jl](https://github.com/JuliaGraphs/GraphPlot.jl). We use the attributes `:loc_x`, `:loc_y`, and `:neuron_color` to properly position and color every neuron.
 
-```julia
+````julia
 julia> nnr = Chain(Dense(2,5,σ),RNN(5,4,relu), LSTM(4,4), GRU(4,4), Dense(4,3))
 Chain(Dense(2, 5, σ), Recur(RNNCell(5, 4, relu)), Recur(LSTMCell(4, 4)), Recur(GRUCell(4, 4)), Dense(4, 3))
 
@@ -222,7 +222,7 @@ julia> nodefillc = [parse(Colorant, get_prop(mg_nnr, v, :neuron_color)) for v in
  RGB{N0f8}(0.565,0.933,0.565)
 
 julia> draw(PNG("img/mg_nnr.png", 600, 400), gplot(mg_nnr, locs_x, locs_y, nodefillc=nodefillc))
-```
+````
 
 And here is the result.