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)
    lstm_cell = LSTMCell(in_dims => hidden_dims)
    classifier = Dense(hidden_dims => out_dims, sigmoid)
    return @compact(; lstm_cell, classifier) 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 ? AutoEnzyme() : 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 0.58885
Validation:	Loss 0.49334	Accuracy 0.96875
Epoch [  2]: Loss 0.47016
Validation:	Loss 0.40542	Accuracy 1.00000
Epoch [  3]: Loss 0.38202
Validation:	Loss 0.32570	Accuracy 1.00000
Epoch [  4]: Loss 0.30518
Validation:	Loss 0.25052	Accuracy 1.00000
Epoch [  5]: Loss 0.22850
Validation:	Loss 0.18812	Accuracy 1.00000
Epoch [  6]: Loss 0.17281
Validation:	Loss 0.14390	Accuracy 1.00000
Epoch [  7]: Loss 0.13229
Validation:	Loss 0.11088	Accuracy 1.00000
Epoch [  8]: Loss 0.10230
Validation:	Loss 0.08679	Accuracy 1.00000
Epoch [  9]: Loss 0.08068
Validation:	Loss 0.06939	Accuracy 1.00000
Epoch [ 10]: Loss 0.06480
Validation:	Loss 0.05613	Accuracy 1.00000
Epoch [ 11]: Loss 0.05242
Validation:	Loss 0.04520	Accuracy 1.00000
Epoch [ 12]: Loss 0.04169
Validation:	Loss 0.03600	Accuracy 1.00000
Epoch [ 13]: Loss 0.03317
Validation:	Loss 0.02925	Accuracy 1.00000
Epoch [ 14]: Loss 0.02698
Validation:	Loss 0.02422	Accuracy 1.00000
Epoch [ 15]: Loss 0.02248
Validation:	Loss 0.02057	Accuracy 1.00000
Epoch [ 16]: Loss 0.01918
Validation:	Loss 0.01775	Accuracy 1.00000
Epoch [ 17]: Loss 0.01658
Validation:	Loss 0.01543	Accuracy 1.00000
Epoch [ 18]: Loss 0.01448
Validation:	Loss 0.01365	Accuracy 1.00000
Epoch [ 19]: Loss 0.01292
Validation:	Loss 0.01231	Accuracy 1.00000
Epoch [ 20]: Loss 0.01174
Validation:	Loss 0.01125	Accuracy 1.00000
Epoch [ 21]: Loss 0.01075
Validation:	Loss 0.01036	Accuracy 1.00000
Epoch [ 22]: Loss 0.00993
Validation:	Loss 0.00960	Accuracy 1.00000
Epoch [ 23]: Loss 0.00924
Validation:	Loss 0.00894	Accuracy 1.00000
Epoch [ 24]: Loss 0.00860
Validation:	Loss 0.00836	Accuracy 1.00000
Epoch [ 25]: Loss 0.00806
Validation:	Loss 0.00784	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 0.50287
Validation:	Loss 0.40875	Accuracy 1.00000
Epoch [  2]: Loss 0.37375
Validation:	Loss 0.31609	Accuracy 1.00000
Epoch [  3]: Loss 0.28381
Validation:	Loss 0.23291	Accuracy 1.00000
Epoch [  4]: Loss 0.20631
Validation:	Loss 0.16998	Accuracy 1.00000
Epoch [  5]: Loss 0.15135
Validation:	Loss 0.12787	Accuracy 1.00000
Epoch [  6]: Loss 0.11325
Validation:	Loss 0.09596	Accuracy 1.00000
Epoch [  7]: Loss 0.08399
Validation:	Loss 0.07084	Accuracy 1.00000
Epoch [  8]: Loss 0.06123
Validation:	Loss 0.05113	Accuracy 1.00000
Epoch [  9]: Loss 0.04433
Validation:	Loss 0.03767	Accuracy 1.00000
Epoch [ 10]: Loss 0.03299
Validation:	Loss 0.02869	Accuracy 1.00000
Epoch [ 11]: Loss 0.02556
Validation:	Loss 0.02289	Accuracy 1.00000
Epoch [ 12]: Loss 0.02067
Validation:	Loss 0.01900	Accuracy 1.00000
Epoch [ 13]: Loss 0.01746
Validation:	Loss 0.01632	Accuracy 1.00000
Epoch [ 14]: Loss 0.01518
Validation:	Loss 0.01438	Accuracy 1.00000
Epoch [ 15]: Loss 0.01338
Validation:	Loss 0.01290	Accuracy 1.00000
Epoch [ 16]: Loss 0.01215
Validation:	Loss 0.01172	Accuracy 1.00000
Epoch [ 17]: Loss 0.01108
Validation:	Loss 0.01076	Accuracy 1.00000
Epoch [ 18]: Loss 0.01021
Validation:	Loss 0.00995	Accuracy 1.00000
Epoch [ 19]: Loss 0.00942
Validation:	Loss 0.00925	Accuracy 1.00000
Epoch [ 20]: Loss 0.00884
Validation:	Loss 0.00864	Accuracy 1.00000
Epoch [ 21]: Loss 0.00823
Validation:	Loss 0.00811	Accuracy 1.00000
Epoch [ 22]: Loss 0.00777
Validation:	Loss 0.00763	Accuracy 1.00000
Epoch [ 23]: Loss 0.00732
Validation:	Loss 0.00720	Accuracy 1.00000
Epoch [ 24]: Loss 0.00692
Validation:	Loss 0.00681	Accuracy 1.00000
Epoch [ 25]: Loss 0.00654
Validation:	Loss 0.00646	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 1.11.8
Commit cf1da5e20e3 (2025-11-06 17:49 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 4 × AMD EPYC 7763 64-Core Processor
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 4 default, 0 interactive, 2 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.