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Graph generator and Plot recipes of the topology of FluxML/Flux.jl's neural networks composed with Flux.Chain.


It implements a plot recipe for Flux.Chain using the recipe tool from JuliaPlots/RecipesBase.jl.

It first generates a MetaGraph.jl from the Flux.Chain and then apply a plot recipe based on the generated metagraph.


The aim is to obtain a pictorial representations for all types of layers implemented with Flux.Chain and in a way similar to the representations given in the following links:

Current state

At the moment, the recipe has been tested with most of the layers in Flux.jl/Basic Layers, as well as with a number of "functional" layers (e.g. x³ = x -> x .^ 3, dx = x -> x[2:end] - x[1:end-1]), and with all activation functions in Flux/NNlib.

There is, however, only partial support for multidimensional layers (convolutional and pooling layers, as well as data with multiple batches) in the sense that only 1d and 2d views are available, and with the 2d visualization not being that great, yet. But hopefully soon there will be a proper multidimensional visualization for them. Batches are collapsed into a single lot.

How it works

There is a distinction between networks starting with a layer with fixed-size input (Dense and Recurrent) and networks starting with a layer with variable-size input (Convolutional, Pooling, and functional).

In the former case, just passing a network m = Chain(...) to plot works, e.g. plot(m). In the latter case, one needs to pass along an initial input inp, or input size inpsz = size(inp), as the second argument, like plot(m, inp) or plot(m, inpsz), so that the plot recipe can properly figure out the size of each layer.

Any other argument for plot is accepted, like plot(m, inp, title="Convolutional network with $(length(m)) layers", titlefont = 12)

One can also obtain a metagraph with mg = ChainPlots.chaingraph(m) or mg = ChainPlots.chaingraph(m, a). The current attributes can be seen in the docstring for chaingraph.


There are several examples in the Literated file examples/build/ (the source file is in examples/examples.jl, with all the plots saved to the folder examples/img).

Here is a little taste of it.

In all the examples below, one needs Flux, ChainPlots and Plots, while for the graph, one needs Graphs and MetaGraphs. One can also display the metagraph using GraphPlot, for which one also needs Cairo and Compose.

Dense and Recurrent layers

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

Variable-input layers

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> dx(x) = x[2:end]-x[1:end-1]
dx (generic function with 1 method)

julia> (x) = x.^3
 (generic function with 1 method)

julia> nna = Chain(Dense(2,5,σ), dx, RNN(4,6,relu), , LSTM(6,4), GRU(4,4), Dense(4,3))
Chain(Dense(2, 5, σ), dx, Recur(RNNCell(4, 6, relu)), , Recur(LSTMCell(6, 4)), Recur(GRUCell(4, 4)), Dense(4, 3))

julia> plot(nna, title="$nna", titlefontsize=7)

nna plot

julia> nnx = Chain(, dx, LSTM(5,10), Dense(10,5))
Chain(, dx, Recur(LSTMCell(5, 10)), Dense(10, 5))

julia> input_data = rand(6);

julia> plot(nnx, input_data, title="$nnx", titlefontsize=9)

nnx plot

Convolutional networks

A neural network with a one-dimensional convolutional layer:

julia> reshape6x1x1(a) = reshape(a, 6,  1, 1)
reshape6x1x1 (generic function with 1 method)

julia> slice(a) = a[:,1,1]
slice (generic function with 1 method)

julia> nnrs = Chain(, Dense(3,6), reshape6x1x1, Conv((2,), 1=>1), slice, Dense(5,4))
Chain(, Dense(3, 6), reshape6x1x1, Conv((2,), 1=>1), slice, Dense(5, 4))

julia> plot(nnrs, Float32.(rand(3)), title="$nnrs", titlefontsize=9)

nnrs plot

Now with a two-dimensional convolution:

julia> reshape4x4x1x1(a) = reshape(a, 4, 4, 1, 1)
reshape4x4x1x1 (generic function with 1 method)

julia> nnrs2d = Chain(, Dense(4,16), reshape4x4x1x1, Conv((2,2), 1=>1), slice)
Chain(, Dense(4, 16), reshape4x4x1x1, Conv((2, 2), 1=>1), slice)

julia> plot(nnrs2d, Float32.(rand(4)), title="$nnrs2d", titlefontsize=9)

nnrs2d plot

With convolutional and pooling layers:

julia> nncp = Chain(
           Conv((3, 3), 1=>2, pad=(1,1), bias=false),
           Conv((3, 3), 2=>4, pad=SamePad(), relu),
           Conv((3, 3), 4=>4, relu),
  Conv((3, 3), 1 => 2, pad=1, bias=false),  # 18 parameters
  MaxPool((2, 2)),
  Conv((3, 3), 2 => 4, relu, pad=1),    # 76 parameters
  AdaptiveMaxPool((4, 4)),
  Conv((3, 3), 4 => 4, relu),           # 148 parameters
)                   # 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

From Chain to MetaGraph

With ChainPlots.chaingraph() we can convert a Flux.Chain to a MetaGraph.

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))

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

julia> get_prop(mg_nnr, 1, :layer_type)

julia> get_prop(mg_nnr, 3, :layer_type)
Dense(2, 5, σ)

julia> get_prop(mg_nnr, 7, :index_in_layer)

julia> first(edges(mg_nnr)).src

julia> first(edges(mg_nnr)).dst

julia> outdegree(mg_nnr, 12)

julia> get_prop.(Ref(mg_nnr), 15, [:loc_x, :loc_y])
2-element Vector{Real}:

Visualizing the MetaGraph

We may visualize the generated MetaGraph with JuliaGraphs/GraphPlot.jl. We use the attributes :loc_x, :loc_y, and :neuron_color to properly position and color every neuron.

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))

julia> mg_nnr = ChainPlots.chaingraph(nnr)
{22, 65} undirected Int64 metagraph with Float64 weights defined by :weight (default weight 1.0)

julia> locs_x = [get_prop(mg_nnr, v, :loc_x) for v in vertices(mg_nnr)]
22-element Vector{Float64}:

julia> locs_y = [get_prop(mg_nnr, v, :loc_y) for v in vertices(mg_nnr)]
22-element Vector{Float64}:

julia> nodefillc = [parse(Colorant, get_prop(mg_nnr, v, :neuron_color)) for v in vertices(mg_nnr)]
22-element Array{RGB{N0f8},1} with eltype RGB{FixedPointNumbers.N0f8}:

julia> draw(PNG("img/mg_nnr.png", 600, 400), gplot(mg_nnr, locs_x, locs_y, nodefillc=nodefillc))

And here is the result.

mg_nnr plot


There is a lot to be done:

  • Add Documentation.
  • Proper visualization for multidimensional layers.
  • Optimization of the plot recipe (large networks - with hundreds of neurons - take too long, and sometimes plotting seem to hang, but building just the graph works fine).
  • Add other plotting options (e.g. not annotate the plot with the type of the layer; only use circles as markers since they are accepted by all the backends).
  • Improve coverage.
  • Make it work across different backends.
  • Make sure it works with all types of layers in Flux.jl.

Once it is in a more polished state, this package might be transfered to the FluxML organization.


All the above works fine with the GR backend for Plots.jl. There are many Plots backends, however, which have some issue:

  • Get Warning: pyplot() backend does not have :rtriangle and seems not to scale properly.

  • Get Error: plotly() and plotlyjs() do not support custom shapes.

  • hdf5() works partially. Neurons are not showing up. On the other hand, despite saying in Plots's page that it is currently missing support for SeriesAnnotations, this seems to be working, since SeriesAnnotations is used to display the type/activation function of each layer.

  • unicodeplots() does not accept custom shapes, nor :rtriangle. Should choose from: [:none, :auto, :circle].

  • Have not tried others.