Merge pull request #12 from JuliaTrustworthyAI/update-deps

Updates Flux and Optimisers dependencies

CHANGELOG.md

@@ -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

Project.toml

@@ -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"
@@ -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"

docs/src/_intro.qmd

@@ -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)
@@ -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)
 
@@ -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))")