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Examples

The aim of ChainPlots.jl is to provide a visualization of the topology of a neural network constructed with Flux.Chain. It does that by applying a Plots.jl recipe. The recipe is built by first constructing a MetaGraph.

Here, we consider some examples of building both a MetaGraph and some plots.

Load Packages

First we load some packages:

using Flux
using Plots
using Random
using Colors
using Cairo
using Compose
using Graphs
using MetaGraphs
using GraphPlot
[ Info: Precompiling Cairo [159f3aea-2a34-519c-b102-8c37f9878175]
[ Info: Precompiling Compose [a81c6b42-2e10-5240-aca2-a61377ecd94b]
[ Info: Precompiling GraphPlot [a2cc645c-3eea-5389-862e-a155d0052231]

and, of course, ChainPlots:

include("../src/ChainPlots.jl")

using .ChainPlots

We play with different themes for Plots.jl, so we collect them here:

themes = [
    :default
    :dark
    :ggplot2
    :juno
    :lime
    :orange
    :sand
    :solarized
    :solarized_light
    :wong
    :wong2
    :gruvbox_dark
    :gruvbox_light
]
13-element Vector{Symbol}:
 :default
 :dark
 :ggplot2
 :juno
 :lime
 :orange
 :sand
 :solarized
 :solarized_light
 :wong
 :wong2
 :gruvbox_dark
 :gruvbox_light

We start with the default one:

gr()
theme(:default)

Chain to MetaGraph

We first consider a neural network with several different types of layers and building a MetaGraph from it.

Here is the neural network:

nnr = Chain(Dense(2, 5, σ), RNN(5, 4, relu), LSTM(4, 4), GRU(4, 4), Dense(4, 3))
Chain(
  Dense(2, 5, σ),                       # 15 parameters
  Recur(
    RNNCell(5, 4, relu),                # 44 parameters
  ),
  Recur(
    LSTMCell(4, 4),                     # 152 parameters
  ),
  Recur(
    GRUCell(4, 4),                      # 112 parameters
  ),
  Dense(4, 3),                          # 15 parameters
)         # Total: 17 trainable arrays, 338 parameters,
          # plus 4 non-trainable, 16 parameters, summarysize 2.391 KiB.

The MetaGraph is built with the function ChainPlots.chaingraph:

mg_nnr = ChainPlots.chaingraph(nnr)
{22, 74} undirected Int64 metagraph with Float64 weights defined by :weight (default weight 1.0)

We can already see the number of vertices and edges in this graph.

We can also look at the first vertice for all the attributes it contains:

mg_nnr.vprops[1]
Dict{Symbol, Any} with 8 entries:
  :loc_y => 0.416667
  :neuron_color => :yellow
  :layer_number => 0
  :layer_type => :input_layer
  :layer_name => "input layer"
  :index_in_layer => (1,)
  :layer_center => 1.0
  :loc_x => 0.0

The two attributes locs_x and locs_y give the coordinates of each vertice, while neuron_color gives the color based on the type of layer, whether dense, recurrent, convolutional, etc.

We can get the list of those attributes for all the vertices and use them to plot the topology of the network with GraphPlot:

locs_x = [get_prop(mg_nnr, v, :loc_x) for v in vertices(mg_nnr)]
locs_y = [get_prop(mg_nnr, v, :loc_y) for v in vertices(mg_nnr)]
nodefillc = [parse(Colorant, get_prop(mg_nnr, v, :neuron_color)) for v in vertices(mg_nnr)]
plt = gplot(mg_nnr, locs_x, locs_y, nodefillc=nodefillc)

draw(PNG("img/mg_nnr.png", 600, 400), plt)

Now with Plots.jl

It is easier, however, to use a Plots.jl recipe, as we show here.

Single layer networks with fixed-size input

For illustrative purposes, we start with some simple, single-layer networks:

dl = Dense(2, 3)
plot(dl, title="$dl", titlefontsize=12)

savefig("img/dl.png")
rl = RNN(3, 5)
plot(rl, title="$rl", titlefontsize=12)

savefig("img/rl.png")
llstm = LSTM(4, 7)
plot(llstm, title="$llstm", titlefontsize=12)

savefig("img/llstm.png")
lgru = GRU(5, 7)
plot(lgru, title="$lgru", titlefontsize=12)

savefig("img/lgru.png")

Single-layer with variable input

Some layers accept input with varied size. In this case, we need to provide either an input, in the form of a Vector or Array, or the size of the input, in the form of a Tuple.

lvar = Conv((2,), 1 => 1)
plot(lvar, rand(5, 1, 1))

savefig("img/lvar5.png")
plot(lvar, (8, 1, 1))

savefig("img/lvar8.png")
nnc = Conv((3,3), 1=>2)
plot(nnc, (6, 5, 1, 1), title="$nnc", titlefontsize=10)

savefig("img/nnc.png")

Multilayer networks

nnd = Chain(Dense(2, 5), Dense(5, 7, σ), Dense(7, 2, relu), Dense(2, 3))
plot(nnd, title="$nnd", titlefontsize=10, xaxis=nothing)

savefig("img/nnd.png")
nnr = Chain(Dense(2, 5, σ), RNN(5, 4, relu), LSTM(4, 4), GRU(4, 4), Dense(4, 3))
plot(nnr, title="$nnr", titlefontsize=7)

savefig("img/nnr.png")
x³(x) = x .^ 3
dx(x) = x[2:end] - x[1:end-1]
nna = Chain(Dense(2, 5, σ), dx, RNN(4, 6, relu), x³, LSTM(6, 4), GRU(4, 4), Dense(4, 3))
plot(nna, title="$nna", titlefontsize=7)

savefig("img/nna.png")
nnx = Chain(x³, dx, LSTM(5, 10), Dense(10, 5))
input_data = rand(6)
plot(nnx, input_data, title="$nnx", titlefontsize=9)

savefig("img/nnx.png")

or just passing the dimensions:

nnx = Chain(x³, dx, LSTM(5, 10), Dense(10, 5))
plot(nnx, (6,), title="$nnx", titlefontsize=9)

savefig("img/nnx_ldim.png")
nnrlwide = Chain(Dense(5, 8), RNN(8, 20), LSTM(20, 10), Dense(10, 7))
plot(nnrlwide, title="$nnrlwide", titlefontsize=9)

savefig("img/nnrlwide.png")
reshape6x1x1(a) = reshape(a, 6, 1, 1)
nnrs = Chain(x³, Dense(3, 6), reshape6x1x1, Conv((2,), 1 => 1), vec, Dense(5, 4))
plot(nnrs, rand(Float32, 3), title="$nnrs", titlefontsize=9)

savefig("img/nnrs.png")
N = 4
reshapeNxNx1x1(a) = reshape(a, N, N, 1, 1)
nnrs2d = Chain(x³, Dense(4, N^2), reshapeNxNx1x1, Conv((2, 2), 1 => 1), vec)
plot(nnrs2d, (4,), title="$nnrs2d", titlefontsize=9)

savefig("img/nnrs2d.png")
nncg = Chain(Conv((3,3), 1=>4, leakyrelu, pad = 1),GroupNorm(4,2))
plot(nncg, (6,6,1,1), title="$nncg", titlefontsize=10)

savefig("img/nncg.png")
nncp = Chain(
    Conv((3, 3), 1=>2, pad=(1,1), bias=false),
    MaxPool((2,2)),
    Conv((3, 3), 2=>4, pad=SamePad(), relu),
    AdaptiveMaxPool((4,4)),
    Conv((3, 3), 4=>4, relu),
    GlobalMaxPool()
)
plot(nncp, (16, 16, 1, 1), title="Chain with convolutional and pooling layers", titlefontsize=10)

savefig("img/nncp.png")
hdf5()
plot(nnr, title="$nnr with HDF5", titlefontsize=7)
Plots.hdf5plot_write("img/nnrhdf5.hdf5")
gr()
plthdf5_read = Plots.hdf5plot_read("img/nnrhdf5.hdf5")

Plotting with different themes

gr()
plts = []
for t in themes
    theme(t)
    try
        push!(plts, plot(nnr, title="With theme $t", titlefontsize=10, show=true))
        display(plts[end])
        #
        savefig("img/nnr_$t.png")
    catch err
        @warn "Error in chain plot with theme $t: $err"
    end
end

Back to :default theme.

theme(:default)

Plotting with extra arguments

One is free to use (most of) other arguments accepted by Plots.jl. For instance, the following plot is used for the repo's social preview:

plot(nnr, title="ChainPlots.jl", topmargin=6mm, bottommargin=4mm, size=(720,380), titlefont=(42, "Bookman Demi"))

savefig("img/nnr_chainplots_socialpreview.png")

Some attributes, like annotationfontsize and markersize, however, which are explicitly set in the Plots, recipe cannot be currently changed, but there are plans to make this more flexible.

This page was generated using Literate.jl, with Literate.markdown("examples.jl", "build", execute = true, flavor = Literate.CommonMarkFlavor(), postprocess = s -> replace(s, r"(?!\")!\[\]\(img\/" => "![](../img/"), credit=false).