commit dc399629ba706979c574da22afc604869acd7723 Author: qwjyh Date: Fri Nov 22 21:30:46 2024 +0900 done: training a simple LSTM diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..87620ac --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.ipynb_checkpoints/ diff --git a/Manifest.toml b/Manifest.toml new file mode 100644 index 0000000..6f5d184 --- /dev/null +++ b/Manifest.toml @@ -0,0 +1,2709 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.11.1" +manifest_format = "2.0" +project_hash = "ab2197e930896350f4ba6508edd2db23590d6902" + +[[deps.ADTypes]] +git-tree-sha1 = "30bb95a372787af850addf28ac937f1be7b79173" +uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b" +version = "1.10.0" +weakdeps = ["ChainRulesCore", "ConstructionBase", "EnzymeCore"] + + [deps.ADTypes.extensions] + ADTypesChainRulesCoreExt = "ChainRulesCore" + ADTypesConstructionBaseExt = "ConstructionBase" + ADTypesEnzymeCoreExt = "EnzymeCore" + +[[deps.AbstractFFTs]] 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"b2108857-7c20-44ae-9111-449ecde12c47" +LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda" +MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" +Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba" +Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" +Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" diff --git a/notebooks/lux_LSTM.html b/notebooks/lux_LSTM.html new file mode 100644 index 0000000..cda1376 --- /dev/null +++ b/notebooks/lux_LSTM.html @@ -0,0 +1,17 @@ + + + + + + + +
\ No newline at end of file diff --git a/notebooks/lux_LSTM.jl b/notebooks/lux_LSTM.jl new file mode 100644 index 0000000..16ed6f7 --- /dev/null +++ b/notebooks/lux_LSTM.jl @@ -0,0 +1,2663 @@ +### A Pluto.jl notebook ### +# v0.20.3 + +using Markdown +using InteractiveUtils + +# ╔═╡ 4e77ccf2-a66a-11ef-29bd-bdbd19c10711 +using ADTypes, Lux, LuxCUDA, JLD2, MLUtils, Optimisers, Zygote, Printf, Random, Statistics, PlutoUI, CairoMakie + +# ╔═╡ 15c384f6-65df-4a24-b92b-d66845781452 +TableOfContents() + +# ╔═╡ c25119d0-b6d9-4f3e-ac8d-8a30524e6e41 +md""" +- [Training a Simple LSTM](https://lux.csail.mit.edu/stable/tutorials/beginner/3_SimpleRNN) +""" + +# ╔═╡ c12e62a9-713b-4fa5-9bf0-de3dffcc977c +md""" +# Main +""" + +# ╔═╡ 25189054-c156-41b3-899d-1b1babe7a602 +md""" +## Dataset +""" + +# ╔═╡ 89273d38-928f-4ad7-b492-111a364da0f3 +function get_dataloaders(; 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)) + # 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), + # Don't shuffle the validation data + DataLoader(collect.((x_val, y_val)); batchsize=128, shuffle=false)) +end + +# ╔═╡ d665db3a-ee49-482c-ae19-89be97664492 +get_dataloaders() + +# ╔═╡ da997aa3-b2ad-470b-bce2-c3f353d5064f +get_dataloaders() |> first |> first |> first |> (a -> a[:, :, 1]) |> (a -> plot(a[1, :], a[2, :])) + +# ╔═╡ e4cc2672-5bbc-4f01-a2b7-f764d763f0d5 +md""" +## Creating a Classifier +""" + +# ╔═╡ 51157a68-884e-440b-8589-885138cb75eb +begin + struct SpiralClassifier{L,C} <: Lux.AbstractLuxContainerLayer{(:lstm_cell, :classifier)} + lstm_cell::L + classifier::C + end + + function SpiralClassifier(in_dims, hidden_dims, out_dims) + return SpiralClassifier( + LSTMCell(in_dims => hidden_dims), + Dense(hidden_dims => out_dims, sigmoid), + ) + end + + function (s::SpiralClassifier)(x::AbstractArray{T,3}, ps::NamedTuple, st::NamedTuple) where {T} + local y, carry + # 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 +end + +# ╔═╡ 7f29c405-faf3-4ad6-871e-69bab5556ed1 +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 + +# ╔═╡ 8ec202f5-d1b7-442e-a2a4-13c61b70290b +md""" +## Defining Accuracy, Loss and Optimiser +""" + +# ╔═╡ 04864cb4-5c1a-4129-ba52-02b1494dedb6 +const lossfn = BinaryCrossEntropyLoss() + +# ╔═╡ 7d1aef48-98d2-4ae1-8a4a-219e6eb4b27b +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 + +# ╔═╡ c6c78324-0910-4aeb-bda4-4f1e954453c8 +matches(y_pred, y_true) = sum((y_pred .> 0.5) .== y_true) + +# ╔═╡ ac29db96-7902-4e98-8468-9e20520bebf7 +accuracy(y_pred, y_true) = matches(y_pred, y_true) / length(y_pred) + +# ╔═╡ fb1b044c-67ef-4e26-85c0-bda9029a3e22 +md""" +## Training the Model +""" + +# ╔═╡ 6b2584a2-3aca-40d7-a123-7fcd556a0c6b +function main(model_type) + dev = gpu_device() + + # Get the dataloaders + train_loader, val_loader = get_dataloaders() .|> dev + + # Create the model + model = model_type(2, 8, 1) + rng = Xoshiro(0) + ps, st = Lux.setup(rng, model) |> dev + + train_state = Training.TrainState(model, ps, st, Adam(0.01f0)) + + for epoch in 1:25 + # Train the model + for (x, y) in train_loader + (_, loss, _, train_state) = Training.single_train_step!( + AutoZygote(), lossfn, (x, y), train_state + ) + + @printf "Epoch [%3d]: Loss %4.5f\n" epoch loss + end + + # Validate the model + st_ = Lux.testmode(train_state.states) + for (x, y) in val_loader + yhat, st_ = model(x, train_state.parameters, st_) + loss = lossfn(yhat, y) + acc = accuracy(yhat, y) + @printf "Validation: Loss %4.5f Accuracy %4.5f\n" loss acc + end + end + + return (train_state.parameters, train_state.states) |> cpu_device() +end + +# ╔═╡ e090f9e1-80a7-4a4f-a1e3-05d294a20901 +ps_trained, st_trained = main(SpiralClassifier) + +# ╔═╡ e79ae982-26f4-40e2-ba4f-7ec938f09745 +main(SpiralClassifierCompact) + +# ╔═╡ ed14a94b-df23-42c6-92e8-f7e8f2548239 +md""" +# Misc +""" + +# ╔═╡ 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╠═4d20c68a-553b-4515-a5f1-49d5af9254ef +# ╟─00000000-0000-0000-0000-000000000001 +# ╟─00000000-0000-0000-0000-000000000002