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Updates Flux and Optimisers dependencies #12

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Dec 19, 2024
Merged

Updates Flux and Optimisers dependencies #12

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ef6b8ac
lets go
pat-alt Dec 19, 2024
0132995
updated docs thing
pat-alt Dec 19, 2024
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2 changes: 2 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -4,6 +4,8 @@ All notable changes to this project will be documented in this file.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## Version [1.0.3] - 2024-12-19

## Version [1.0.2] - 2024-11-13

### Added
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6 changes: 3 additions & 3 deletions Project.toml
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@@ -1,7 +1,7 @@
name = "EnergySamplers"
uuid = "f446124b-5d5e-4171-a6dd-a1d99768d3ce"
authors = ["Patrick Altmeyer and contributors"]
version = "1.0.2"
version = "1.0.3"

[deps]
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
Expand All @@ -16,9 +16,9 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
Aqua = "0.8"
CategoricalArrays = "0.10"
Distributions = "0.25"
Flux = "0.14"
Flux = "0.14, 0.15, 0.16"
MLUtils = "0.4"
Optimisers = "0.3"
Optimisers = "0.3, 0.4"
StatsBase = "0.33, 0.34"
Tables = "1.12"
Test = "1"
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22 changes: 11 additions & 11 deletions docs/src/_intro.qmd
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Expand Up @@ -55,8 +55,8 @@ for feature in numerics
rescale!(test[!, feature], μ, σ; obsdim=1)
end

prep_X(x) = gpu(Matrix(x)')
prep_y(y) = gpu(reshape(y, 1, :))
prep_X(x) = Matrix(x)'
prep_y(y) = reshape(y, 1, :)
train_X, test_X = prep_X.((train[:, features], test[:, features]))
train_y, test_y = prep_y.((train[:, target], test[:, target]))
train_set = Flux.DataLoader((train_X, train_y); batchsize=100, shuffle=false)
Expand All @@ -68,22 +68,22 @@ Finally, we create a small helper function that runs the training loop for a giv
function train_logreg(; steps::Int=1000, opt=Flux.Descent(2))
Random.seed!(1)

paramvec(θ) = reduce(hcat, cpu(θ))
model = gpu(Dense(length(features), 1, sigmoid))
paramvec(θ) = reduce(hcat, θ)
model = Dense(length(features), 1, sigmoid)
θ = Flux.params(model)
θ₀ = paramvec(θ)

predict(x; thres=0.5) = model(x) .> thres
accuracy(x, y) = mean(cpu(predict(x)) .== cpu(y))
accuracy(x, y) = mean(predict(x) .== y)

loss(yhat, y) = Flux.binarycrossentropy(yhat, y)
avg_loss(yhat, y) = mean(loss(yhat, y))
trainloss() = avg_loss(model(train_X), train_y)
testloss() = avg_loss(model(test_X), test_y)

trainlosses = [cpu(trainloss()); zeros(steps)]
testlosses = [cpu(testloss()); zeros(steps)]
weights = [cpu(θ₀); zeros(steps, length(θ₀))]
trainlosses = [trainloss(); zeros(steps)]
testlosses = [testloss(); zeros(steps)]
weights = [θ₀; zeros(steps, length(θ₀))]

opt_state = Flux.setup(opt, model)

Expand All @@ -102,9 +102,9 @@ function train_logreg(; steps::Int=1000, opt=Flux.Descent(2))
end

# Bookkeeping
weights[t + 1, :] = cpu(paramvec(θ))
trainlosses[t + 1] = cpu(trainloss())
testlosses[t + 1] = cpu(testloss())
weights[t + 1, :] = paramvec(θ)
trainlosses[t + 1] = trainloss()
testlosses[t + 1] = testloss()
end

println("Final parameters are $(paramvec(θ))")
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