Lux.jl Docs

Appearance

0.7k

Trusted by

Scientific Computing

SciML.ai

Machine Learning

Training a Simple LSTM ​

In this tutorial we will go over using a recurrent neural network to classify clockwise and anticlockwise spirals. By the end of this tutorial you will be able to:

  1. Create custom Lux models.

  2. Become familiar with the Lux recurrent neural network API.

  3. Training using Optimisers.jl and Zygote.jl.

Package Imports ​

Note: If you wish to use AutoZygote() for automatic differentiation, add Zygote to your project dependencies and include using Zygote.

julia
using ADTypes, Lux, JLD2, MLUtils, Optimisers, Printf, Reactant, Random

Dataset ​

We will use MLUtils to generate 500 (noisy) clockwise and 500 (noisy) anticlockwise spirals. Using this data we will create a MLUtils.DataLoader. Our dataloader will give us sequences of size 2 × seq_len × batch_size and we need to predict a binary value whether the sequence is clockwise or anticlockwise.

julia
function create_dataset(; dataset_size=1000, sequence_length=50)
    # Create the spirals
    data = [MLUtils.Datasets.make_spiral(sequence_length) for _ in 1:dataset_size]
    # Get the labels
    labels = vcat(repeat([0.0f0], dataset_size ÷ 2), repeat([1.0f0], dataset_size ÷ 2))
    clockwise_spirals = [
        reshape(d[1][:, 1:sequence_length], :, sequence_length, 1) for
        d in data[1:(dataset_size ÷ 2)]
    ]
    anticlockwise_spirals = [
        reshape(d[1][:, (sequence_length + 1):end], :, sequence_length, 1) for
        d in data[((dataset_size ÷ 2) + 1):end]
    ]
    x_data = Float32.(cat(clockwise_spirals..., anticlockwise_spirals...; dims=3))
    return x_data, labels
end

function get_dataloaders(; dataset_size=1000, sequence_length=50)
    x_data, labels = create_dataset(; dataset_size, sequence_length)
    # Split the dataset
    (x_train, y_train), (x_val, y_val) = splitobs((x_data, labels); at=0.8, shuffle=true)
    # Create DataLoaders
    return (
        # Use DataLoader to automatically minibatch and shuffle the data
        DataLoader(
            collect.((x_train, y_train)); batchsize=128, shuffle=true, partial=false
        ),
        # Don't shuffle the validation data
        DataLoader(collect.((x_val, y_val)); batchsize=128, shuffle=false, partial=false),
    )
end

Creating a Classifier ​

We will be extending the Lux.AbstractLuxContainerLayer type for our custom model since it will contain a LSTM block and a classifier head.

We pass the field names lstm_cell and classifier to the type to ensure that the parameters and states are automatically populated and we don't have to define Lux.initialparameters and Lux.initialstates.

To understand more about container layers, please look at Container Layer.

julia
struct SpiralClassifier{L,C} <: AbstractLuxContainerLayer{(:lstm_cell, :classifier)}
    lstm_cell::L
    classifier::C
end

We won't define the model from scratch but rather use the Lux.LSTMCell and Lux.Dense.

julia
function SpiralClassifier(in_dims, hidden_dims, out_dims)
    return SpiralClassifier(
        LSTMCell(in_dims => hidden_dims), Dense(hidden_dims => out_dims, sigmoid)
    )
end

We can use default Lux blocks – Recurrence(LSTMCell(in_dims => hidden_dims) – instead of defining the following. But let's still do it for the sake of it.

Now we need to define the behavior of the Classifier when it is invoked.

julia
function (s::SpiralClassifier)(
    x::AbstractArray{T,3}, ps::NamedTuple, st::NamedTuple
) where {T}
    # First we will have to run the sequence through the LSTM Cell
    # The first call to LSTM Cell will create the initial hidden state
    # See that the parameters and states are automatically populated into a field called
    # `lstm_cell` We use `eachslice` to get the elements in the sequence without copying,
    # and `Iterators.peel` to split out the first element for LSTM initialization.
    x_init, x_rest = Iterators.peel(LuxOps.eachslice(x, Val(2)))
    (y, carry), st_lstm = s.lstm_cell(x_init, ps.lstm_cell, st.lstm_cell)
    # Now that we have the hidden state and memory in `carry` we will pass the input and
    # `carry` jointly
    for x in x_rest
        (y, carry), st_lstm = s.lstm_cell((x, carry), ps.lstm_cell, st_lstm)
    end
    # After running through the sequence we will pass the output through the classifier
    y, st_classifier = s.classifier(y, ps.classifier, st.classifier)
    # Finally remember to create the updated state
    st = merge(st, (classifier=st_classifier, lstm_cell=st_lstm))
    return vec(y), st
end

Using the @compact API ​

We can also define the model using the Lux.@compact API, which is a more concise way of defining models. This macro automatically handles the boilerplate code for you and as such we recommend this way of defining custom layers

julia
function SpiralClassifierCompact(in_dims, hidden_dims, out_dims)
    return @compact(;
        lstm_cell=LSTMCell(in_dims => hidden_dims),
        classifier=Dense(hidden_dims => out_dims, sigmoid)
    ) do x::AbstractArray{T,3} where {T}
        x_init, x_rest = Iterators.peel(LuxOps.eachslice(x, Val(2)))
        y, carry = lstm_cell(x_init)
        for x in x_rest
            y, carry = lstm_cell((x, carry))
        end
        @return vec(classifier(y))
    end
end

Defining Accuracy, Loss and Optimiser ​

Now let's define the binary cross-entropy loss. Typically it is recommended to use logitbinarycrossentropy since it is more numerically stable, but for the sake of simplicity we will use binarycrossentropy.

julia
const lossfn = BinaryCrossEntropyLoss()

function compute_loss(model, ps, st, (x, y))
    ŷ, st_ = model(x, ps, st)
    loss = lossfn(ŷ, y)
    return loss, st_, (; y_pred=ŷ)
end

matches(y_pred, y_true) = sum((y_pred .> 0.5f0) .== y_true)
accuracy(y_pred, y_true) = matches(y_pred, y_true) / length(y_pred)

Training the Model ​

julia
function main(model_type)
    dev = reactant_device()
    cdev = cpu_device()

    # Get the dataloaders
    train_loader, val_loader = get_dataloaders() |> dev

    # Create the model
    model = model_type(2, 8, 1)
    ps, st = Lux.setup(Random.default_rng(), model) |> dev

    train_state = Training.TrainState(model, ps, st, Adam(0.01f0))
    model_compiled = if dev isa ReactantDevice
        @compile model(first(train_loader)[1], ps, Lux.testmode(st))
    else
        model
    end
    ad = dev isa ReactantDevice ? AutoReactant() : AutoZygote()

    for epoch in 1:25
        # Train the model
        total_loss = 0.0f0
        total_samples = 0
        for (x, y) in train_loader
            (_, loss, _, train_state) = Training.single_train_step!(
                ad, lossfn, (x, y), train_state
            )
            total_loss += loss * length(y)
            total_samples += length(y)
        end
        @printf("Epoch [%3d]: Loss %4.5f\n", epoch, total_loss / total_samples)

        # Validate the model
        total_acc = 0.0f0
        total_loss = 0.0f0
        total_samples = 0

        st_ = Lux.testmode(train_state.states)
        for (x, y) in val_loader
            ŷ, st_ = model_compiled(x, train_state.parameters, st_)
            ŷ, y = cdev(ŷ), cdev(y)
            total_acc += accuracy(ŷ, y) * length(y)
            total_loss += lossfn(ŷ, y) * length(y)
            total_samples += length(y)
        end

        @printf(
            "Validation:\tLoss %4.5f\tAccuracy %4.5f\n",
            total_loss / total_samples,
            total_acc / total_samples
        )
    end

    return (train_state.parameters, train_state.states) |> cdev
end

ps_trained, st_trained = main(SpiralClassifier)
┌ Warning: `replicate` doesn't work for `TaskLocalRNG`. Returning the same `TaskLocalRNG`.
└ @ LuxCore ~/work/Lux.jl/Lux.jl/lib/LuxCore/src/LuxCore.jl:18
Epoch [  1]: Loss 0.66542
Validation:	Loss 0.58600	Accuracy 1.00000
Epoch [  2]: Loss 0.54916
Validation:	Loss 0.48532	Accuracy 1.00000
Epoch [  3]: Loss 0.45214
Validation:	Loss 0.39267	Accuracy 1.00000
Epoch [  4]: Loss 0.36055
Validation:	Loss 0.30246	Accuracy 1.00000
Epoch [  5]: Loss 0.27154
Validation:	Loss 0.21883	Accuracy 1.00000
Epoch [  6]: Loss 0.19208
Validation:	Loss 0.14930	Accuracy 1.00000
Epoch [  7]: Loss 0.12941
Validation:	Loss 0.09960	Accuracy 1.00000
Epoch [  8]: Loss 0.08676
Validation:	Loss 0.06817	Accuracy 1.00000
Epoch [  9]: Loss 0.06044
Validation:	Loss 0.04918	Accuracy 1.00000
Epoch [ 10]: Loss 0.04483
Validation:	Loss 0.03803	Accuracy 1.00000
Epoch [ 11]: Loss 0.03531
Validation:	Loss 0.03081	Accuracy 1.00000
Epoch [ 12]: Loss 0.02910
Validation:	Loss 0.02584	Accuracy 1.00000
Epoch [ 13]: Loss 0.02467
Validation:	Loss 0.02229	Accuracy 1.00000
Epoch [ 14]: Loss 0.02140
Validation:	Loss 0.01956	Accuracy 1.00000
Epoch [ 15]: Loss 0.01898
Validation:	Loss 0.01741	Accuracy 1.00000
Epoch [ 16]: Loss 0.01690
Validation:	Loss 0.01566	Accuracy 1.00000
Epoch [ 17]: Loss 0.01528
Validation:	Loss 0.01421	Accuracy 1.00000
Epoch [ 18]: Loss 0.01388
Validation:	Loss 0.01298	Accuracy 1.00000
Epoch [ 19]: Loss 0.01269
Validation:	Loss 0.01194	Accuracy 1.00000
Epoch [ 20]: Loss 0.01172
Validation:	Loss 0.01102	Accuracy 1.00000
Epoch [ 21]: Loss 0.01084
Validation:	Loss 0.01022	Accuracy 1.00000
Epoch [ 22]: Loss 0.01004
Validation:	Loss 0.00950	Accuracy 1.00000
Epoch [ 23]: Loss 0.00934
Validation:	Loss 0.00885	Accuracy 1.00000
Epoch [ 24]: Loss 0.00868
Validation:	Loss 0.00826	Accuracy 1.00000
Epoch [ 25]: Loss 0.00812
Validation:	Loss 0.00773	Accuracy 1.00000

We can also train the compact model with the exact same code!

julia
ps_trained2, st_trained2 = main(SpiralClassifierCompact)
┌ Warning: `replicate` doesn't work for `TaskLocalRNG`. Returning the same `TaskLocalRNG`.
└ @ LuxCore ~/work/Lux.jl/Lux.jl/lib/LuxCore/src/LuxCore.jl:18
Epoch [  1]: Loss 0.79162
Validation:	Loss 0.70687	Accuracy 0.52344
Epoch [  2]: Loss 0.66215
Validation:	Loss 0.59054	Accuracy 0.90625
Epoch [  3]: Loss 0.55249
Validation:	Loss 0.48464	Accuracy 1.00000
Epoch [  4]: Loss 0.44685
Validation:	Loss 0.38201	Accuracy 1.00000
Epoch [  5]: Loss 0.34736
Validation:	Loss 0.29338	Accuracy 1.00000
Epoch [  6]: Loss 0.26724
Validation:	Loss 0.22457	Accuracy 1.00000
Epoch [  7]: Loss 0.20342
Validation:	Loss 0.16954	Accuracy 1.00000
Epoch [  8]: Loss 0.15195
Validation:	Loss 0.12521	Accuracy 1.00000
Epoch [  9]: Loss 0.11194
Validation:	Loss 0.08959	Accuracy 1.00000
Epoch [ 10]: Loss 0.08033
Validation:	Loss 0.06608	Accuracy 1.00000
Epoch [ 11]: Loss 0.05962
Validation:	Loss 0.05055	Accuracy 1.00000
Epoch [ 12]: Loss 0.04616
Validation:	Loss 0.04007	Accuracy 1.00000
Epoch [ 13]: Loss 0.03708
Validation:	Loss 0.03296	Accuracy 1.00000
Epoch [ 14]: Loss 0.03075
Validation:	Loss 0.02761	Accuracy 1.00000
Epoch [ 15]: Loss 0.02585
Validation:	Loss 0.02325	Accuracy 1.00000
Epoch [ 16]: Loss 0.02159
Validation:	Loss 0.01931	Accuracy 1.00000
Epoch [ 17]: Loss 0.01796
Validation:	Loss 0.01637	Accuracy 1.00000
Epoch [ 18]: Loss 0.01548
Validation:	Loss 0.01444	Accuracy 1.00000
Epoch [ 19]: Loss 0.01384
Validation:	Loss 0.01311	Accuracy 1.00000
Epoch [ 20]: Loss 0.01265
Validation:	Loss 0.01204	Accuracy 1.00000
Epoch [ 21]: Loss 0.01167
Validation:	Loss 0.01114	Accuracy 1.00000
Epoch [ 22]: Loss 0.01083
Validation:	Loss 0.01037	Accuracy 1.00000
Epoch [ 23]: Loss 0.01010
Validation:	Loss 0.00968	Accuracy 1.00000
Epoch [ 24]: Loss 0.00945
Validation:	Loss 0.00907	Accuracy 1.00000
Epoch [ 25]: Loss 0.00886
Validation:	Loss 0.00851	Accuracy 1.00000

Saving the Model ​

We can save the model using JLD2 (and any other serialization library of your choice) Note that we transfer the model to CPU before saving. Additionally, we recommend that you don't save the model struct and only save the parameters and states.

julia
@save "trained_model.jld2" ps_trained st_trained

Let's try loading the model

julia
@load "trained_model.jld2" ps_trained st_trained
2-element Vector{Symbol}:
 :ps_trained
 :st_trained

Appendix ​

julia
using InteractiveUtils
InteractiveUtils.versioninfo()

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

    if @isdefined(AMDGPU) && MLDataDevices.functional(AMDGPUDevice)
        println()
        AMDGPU.versioninfo()
    end
end
Julia Version 1.12.6
Commit 15346901f00 (2026-04-09 19:20 UTC)
Build Info:
  Official https://julialang.org release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 4 × Intel(R) Xeon(R) Platinum 8370C CPU @ 2.80GHz
  WORD_SIZE: 64
  LLVM: libLLVM-18.1.7 (ORCJIT, icelake-server)
  GC: Built with stock GC
Threads: 4 default, 1 interactive, 4 GC (on 4 virtual cores)
Environment:
  JULIA_DEBUG = Literate
  LD_LIBRARY_PATH = 
  JULIA_NUM_THREADS = 4
  JULIA_CPU_HARD_MEMORY_LIMIT = 100%
  JULIA_PKG_PRECOMPILE_AUTO = 0

This page was generated using Literate.jl.