Bayesian Neural Network

We borrow this tutorial from the official Turing Docs. We will show how the explicit parameterization of Lux enables first-class composability with packages which expect flattened out parameter vectors.

We will use Turing.jl with Lux.jl to implement implementing a classification algorithm. Lets start by importing the relevant libraries.

# Import libraries

using Lux, Turing, CairoMakie, Random, Tracker, Functors, LinearAlgebra

# Sampling progress
Turing.setprogress!(true);

[ Info: [Turing]: progress logging is enabled globally
[ Info: [AdvancedVI]: global PROGRESS is set as true

Generating data

Our goal here is to use a Bayesian neural network to classify points in an artificial dataset. The code below generates data points arranged in a box-like pattern and displays a graph of the dataset we'll be working with.

# Number of points to generate
N = 80
M = round(Int, N / 4)
rng = Random.default_rng()
Random.seed!(rng, 1234)

# Generate artificial data
x1s = rand(rng, Float32, M) * 4.5f0;
x2s = rand(rng, Float32, M) * 4.5f0;
xt1s = Array([[x1s[i] + 0.5f0; x2s[i] + 0.5f0] for i in 1:M])
x1s = rand(rng, Float32, M) * 4.5f0;
x2s = rand(rng, Float32, M) * 4.5f0;
append!(xt1s, Array([[x1s[i] - 5.0f0; x2s[i] - 5.0f0] for i in 1:M]))

x1s = rand(rng, Float32, M) * 4.5f0;
x2s = rand(rng, Float32, M) * 4.5f0;
xt0s = Array([[x1s[i] + 0.5f0; x2s[i] - 5.0f0] for i in 1:M])
x1s = rand(rng, Float32, M) * 4.5f0;
x2s = rand(rng, Float32, M) * 4.5f0;
append!(xt0s, Array([[x1s[i] - 5.0f0; x2s[i] + 0.5f0] for i in 1:M]))

# Store all the data for later
xs = [xt1s; xt0s]
ts = [ones(2 * M); zeros(2 * M)]

# Plot data points

function plot_data()
    x1 = first.(xt1s)
    y1 = last.(xt1s)
    x2 = first.(xt0s)
    y2 = last.(xt0s)

    fig = Figure()
    ax = CairoMakie.Axis(fig[1, 1]; xlabel="x", ylabel="y")

    scatter!(ax, x1, y1; markersize=16, color=:red, strokecolor=:black, strokewidth=2)
    scatter!(ax, x2, y2; markersize=16, color=:blue, strokecolor=:black, strokewidth=2)

    return fig
end

plot_data()

Building the Neural Network

The next step is to define a feedforward neural network where we express our parameters as distributions, and not single points as with traditional neural networks. For this we will use Dense to define liner layers and compose them via Chain, both are neural network primitives from Lux. The network nn we will create will have two hidden layers with tanh activations and one output layer with sigmoid activation, as shown below.

The nn is an instance that acts as a function and can take data, parameters and current state as inputs and output predictions. We will define distributions on the neural network parameters.

# Construct a neural network using Lux
nn = Chain(Dense(2 => 3, tanh), Dense(3 => 2, tanh), Dense(2 => 1, sigmoid))

# Initialize the model weights and state
ps, st = Lux.setup(rng, nn)

Lux.parameterlength(nn) # number of paraemters in NN

20

The probabilistic model specification below creates a parameters variable, which has IID normal variables. The parameters represents all parameters of our neural net (weights and biases).

# Create a regularization term and a Gaussian prior variance term.
alpha = 0.09
sig = sqrt(1.0 / alpha)

3.3333333333333335

Construct named tuple from a sampled parameter vector. We could also use ComponentArrays here and simply broadcast to avoid doing this. But let's do it this way to avoid dependencies.

function vector_to_parameters(ps_new::AbstractVector, ps::NamedTuple)
    @assert length(ps_new) == Lux.parameterlength(ps)
    i = 1
    function get_ps(x)
        z = reshape(view(ps_new, i:(i + length(x) - 1)), size(x))
        i += length(x)
        return z
    end
    return fmap(get_ps, ps)
end

vector_to_parameters (generic function with 1 method)

To interface with external libraries it is often desirable to use the StatefulLuxLayer to automatically handle the neural network states.

const model = StatefulLuxLayer(nn, st)

# Specify the probabilistic model.
@model function bayes_nn(xs, ts)
    # Sample the parameters
    nparameters = Lux.parameterlength(nn)
    parameters ~ MvNormal(zeros(nparameters), Diagonal(abs2.(sig .* ones(nparameters))))

    # Forward NN to make predictions
    preds = Lux.apply(model, xs, vector_to_parameters(parameters, ps))

    # Observe each prediction.
    for i in eachindex(ts)
        ts[i] ~ Bernoulli(preds[i])
    end
end

bayes_nn (generic function with 2 methods)

Inference can now be performed by calling sample. We use the HMC sampler here.

# Perform inference.
N = 5000
ch = sample(bayes_nn(reduce(hcat, xs), ts), HMC(0.05, 4; adtype=AutoTracker()), N)

Chains MCMC chain (5000×30×1 Array{Float64, 3}):

Iterations        = 1:1:5000
Number of chains  = 1
Samples per chain = 5000
Wall duration     = 24.7 seconds
Compute duration  = 24.7 seconds
parameters        = parameters[1], parameters[2], parameters[3], parameters[4], parameters[5], parameters[6], parameters[7], parameters[8], parameters[9], parameters[10], parameters[11], parameters[12], parameters[13], parameters[14], parameters[15], parameters[16], parameters[17], parameters[18], parameters[19], parameters[20]
internals         = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, numerical_error, step_size, nom_step_size

Summary Statistics
      parameters      mean       std      mcse   ess_bulk   ess_tail      rhat   ess_per_sec
          Symbol   Float64   Float64   Float64    Float64    Float64   Float64       Float64

   parameters[1]   -0.5133    1.8835    0.5330    13.1888    25.0326    1.4853        0.5339
   parameters[2]   -5.3361    2.4104    0.6377    15.5139    36.9602    1.0376        0.6281
   parameters[3]    0.3151    0.6835    0.1441    27.6358    50.3442    1.1216        1.1188
   parameters[4]    1.9624    3.7648    1.1478    11.5629    25.5317    2.0118        0.4681
   parameters[5]   -0.0914    0.6013    0.0858    52.3129    54.6520    1.1223        2.1178
   parameters[6]    4.9348    2.3882    0.6909    12.4651    22.5599    1.8704        0.5046
   parameters[7]   -1.6494    2.7707    0.8306    11.9136    35.7353    1.9059        0.4823
   parameters[8]   -0.3367    1.5561    0.3957    15.3027    35.5527    1.2732        0.6195
   parameters[9]   -0.6247    1.7715    0.4439    16.1582    41.0067    1.0886        0.6542
  parameters[10]   -0.0485    2.6496    0.7711    12.0002    18.2390    1.6173        0.4858
  parameters[11]   -2.7777    2.4100    0.6937    13.2493    54.0054    1.2589        0.5364
  parameters[12]    1.7397    2.7286    0.8144    12.5350    30.6674    1.3093        0.5075
  parameters[13]   -1.3065    3.0122    0.9212    11.3718    29.5307    1.8415        0.4604
  parameters[14]    2.1359    1.7145    0.4376    16.0168    29.9135    1.1185        0.6484
  parameters[15]   -2.2917    1.1075    0.2143    27.1032    53.9717    1.0339        1.0973
  parameters[16]   -1.8241    1.7783    0.4754    14.8255    44.7465    1.2380        0.6002
  parameters[17]   -2.9541    1.0941    0.1964    32.4857    49.3634    1.0550        1.3152
  parameters[18]   -2.9283    3.1083    0.9424    13.4337    49.1159    1.4880        0.5439
  parameters[19]   -5.8506    1.2702    0.1981    41.3283    64.0949    1.0032        1.6731
  parameters[20]   -3.6909    1.8615    0.5266    13.1481    52.3750    1.6064        0.5323

Quantiles
      parameters      2.5%     25.0%     50.0%     75.0%     97.5%
          Symbol   Float64   Float64   Float64   Float64   Float64

   parameters[1]   -2.9584   -2.1586   -0.3884    0.5613    4.2684
   parameters[2]   -9.8741   -7.2592   -4.9623   -3.2715   -1.7035
   parameters[3]   -0.6888   -0.1073    0.1721    0.5713    2.2193
   parameters[4]   -5.7669   -1.4891    3.0249    5.1736    7.8110
   parameters[5]   -1.2037   -0.4726   -0.1166    0.2270    1.2686
   parameters[6]    1.5300    2.9517    4.4678    6.7396   10.5049
   parameters[7]   -5.8618   -4.2435   -1.3364    0.6954    3.0399
   parameters[8]   -3.2162   -1.3171   -0.3432    0.4455    3.4264
   parameters[9]   -4.2319   -1.6828   -0.5051    0.6070    2.9411
  parameters[10]   -6.1276   -1.8893    0.2035    1.6808    4.6669
  parameters[11]   -6.8391   -4.9499   -2.5672   -0.5826    1.1874
  parameters[12]   -3.4081   -0.4600    2.2126    3.9338    5.8529
  parameters[13]   -6.1325   -3.7644   -2.1160    1.6571    3.6084
  parameters[14]   -1.1205    1.0660    1.9677    3.1250    5.5755
  parameters[15]   -4.6287   -3.0619   -2.1911   -1.5340   -0.1136
  parameters[16]   -5.3259   -3.0340   -1.9008   -0.9440    1.7702
  parameters[17]   -5.6472   -3.5470   -2.8160   -2.2108   -1.0283
  parameters[18]   -6.7277   -5.2922   -4.2915    0.7602    2.7335
  parameters[19]   -8.5012   -6.5957   -5.8670   -5.0328   -3.3450
  parameters[20]   -6.7333   -5.1577   -3.8273   -2.2208   -0.3195

Now we extract the parameter samples from the sampled chain as θ (this is of size 5000 x 20 where 5000 is the number of iterations and 20 is the number of parameters). We'll use these primarily to determine how good our model's classifier is.

# Extract all weight and bias parameters.
θ = MCMCChains.group(ch, :parameters).value;

Prediction Visualization

# A helper to run the nn through data `x` using parameters `θ`
nn_forward(x, θ) = model(x, vector_to_parameters(θ, ps))

# Plot the data we have.
fig = plot_data()

# Find the index that provided the highest log posterior in the chain.
_, i = findmax(ch[:lp])

# Extract the max row value from i.
i = i.I[1]

# Plot the posterior distribution with a contour plot
x1_range = collect(range(-6; stop=6, length=25))
x2_range = collect(range(-6; stop=6, length=25))
Z = [nn_forward([x1, x2], θ[i, :])[1] for x1 in x1_range, x2 in x2_range]
contour!(x1_range, x2_range, Z; linewidth=3, colormap=:seaborn_bright)
fig

The contour plot above shows that the MAP method is not too bad at classifying our data. Now we can visualize our predictions.

$$p(\tilde{x}|X,\alpha )={\int }_{\theta }p(\tilde{x}|\theta )p(\theta |X,\alpha )\approx \sum _{\theta \sim p(\theta |X,\alpha )}{f}_{\theta }(\tilde{x})$$

The nn_predict function takes the average predicted value from a network parameterized by weights drawn from the MCMC chain.

# Return the average predicted value across multiple weights.
nn_predict(x, θ, num) = mean([first(nn_forward(x, view(θ, i, :))) for i in 1:10:num])

nn_predict (generic function with 1 method)

Next, we use the nn_predict function to predict the value at a sample of points where the x1 and x2 coordinates range between -6 and 6. As we can see below, we still have a satisfactory fit to our data, and more importantly, we can also see where the neural network is uncertain about its predictions much easier–-those regions between cluster boundaries.

Plot the average prediction.

fig = plot_data()

n_end = 1500
x1_range = collect(range(-6; stop=6, length=25))
x2_range = collect(range(-6; stop=6, length=25))
Z = [nn_predict([x1, x2], θ, n_end)[1] for x1 in x1_range, x2 in x2_range]
contour!(x1_range, x2_range, Z; linewidth=3, colormap=:seaborn_bright)
fig

Suppose we are interested in how the predictive power of our Bayesian neural network evolved between samples. In that case, the following graph displays an animation of the contour plot generated from the network weights in samples 1 to 5,000.

fig = plot_data()
Z = [first(nn_forward([x1, x2], θ[1, :])) for x1 in x1_range, x2 in x2_range]
c = contour!(x1_range, x2_range, Z; linewidth=3, colormap=:seaborn_bright)
record(fig, "results.gif", 1:250:size(θ, 1)) do i
    fig.current_axis[].title = "Iteration: $i"
    Z = [first(nn_forward([x1, x2], θ[i, :])) for x1 in x1_range, x2 in x2_range]
    c[3] = Z
    return fig
end

"results.gif"

Appendix

using InteractiveUtils
InteractiveUtils.versioninfo()

if @isdefined(LuxCUDA) && CUDA.functional()
    println()
    CUDA.versioninfo()
end

if @isdefined(LuxAMDGPU) && LuxAMDGPU.functional()
    println()
    AMDGPU.versioninfo()
end

Julia Version 1.10.3
Commit 0b4590a5507 (2024-04-30 10:59 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 48 × AMD EPYC 7402 24-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver2)
Threads: 8 default, 0 interactive, 4 GC (on 2 virtual cores)
Environment:
  JULIA_CPU_THREADS = 2
  JULIA_AMDGPU_LOGGING_ENABLED = true
  JULIA_DEPOT_PATH = /root/.cache/julia-buildkite-plugin/depots/01872db4-8c79-43af-ab7d-12abac4f24f6
  LD_LIBRARY_PATH = /usr/local/nvidia/lib:/usr/local/nvidia/lib64
  JULIA_PKG_SERVER = 
  JULIA_NUM_THREADS = 8
  JULIA_CUDA_HARD_MEMORY_LIMIT = 100%
  JULIA_PKG_PRECOMPILE_AUTO = 0
  JULIA_DEBUG = Literate

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