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fix doc
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‎docs/Project.toml‎

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[deps]
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DecisionFocusedLearningAlgorithms = "46d52364-bc3b-4fac-a992-eb1d3ef2de15"
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DecisionFocusedLearningBenchmarks = "2fbe496a-299b-4c81-bab5-c44dfc55cf20"
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
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Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
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MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
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Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"

‎docs/make.jl‎

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using DecisionFocusedLearningAlgorithms
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using Documenter
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using Literate
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DocMeta.setdocmeta!(
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DecisionFocusedLearningAlgorithms,
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:DocTestSetup,
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:(using DecisionFocusedLearningAlgorithms);
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:(begin
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using DecisionFocusedLearningAlgorithms
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using DecisionFocusedLearningBenchmarks
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using Flux
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using MLUtils
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using Plots
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end),
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recursive=true,
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)
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# Generate markdown files from tutorial scripts
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tutorial_dir = joinpath(@__DIR__, "src", "tutorials")
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tutorial_files = filter(f -> endswith(f, ".jl"), readdir(tutorial_dir))
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include_tutorial = true
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if include_tutorial
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for file in tutorial_files
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filepath = joinpath(tutorial_dir, file)
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Literate.markdown(filepath, md_dir; documenter=true, execute=false)
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end
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# Convert .jl tutorial files to markdown
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for file in tutorial_files
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filepath = joinpath(tutorial_dir, file)
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Literate.markdown(filepath, tutorial_dir; documenter=true, execute=false)
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end
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# Get list of generated markdown files for the docs
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md_tutorial_files = [
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"tutorials/" * replace(file, ".jl" => ".md") for file in tutorial_files
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]
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makedocs(;
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modules=[DecisionFocusedLearningAlgorithms],
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authors="Members of JuliaDecisionFocusedLearning and contributors",
@@ -28,7 +39,7 @@ makedocs(;
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edit_link="main",
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assets=String[],
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),
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pages=["Home" => "index.md", "Tutorials" => include_tutorial ? md_tutorial_files : []],
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pages=["Home" => "index.md", "Tutorials" => md_tutorial_files],
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)
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deploydocs(;

‎docs/src/tutorials/tutorial.jl‎

Lines changed: 47 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -13,35 +13,65 @@ train_instances, validation_instances, test_instances = splitobs(
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model = generate_statistical_model(b; seed=0)
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maximizer = generate_maximizer(b)
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compute_gap(b, test_instances, model, maximizer)
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metrics_callbacks = (;
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:time => (model, maximizer, epoch) -> (epoch_time = time()),
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:gap => (;
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:val =>
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(model, maximizer, epoch) ->
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(gap = compute_gap(b, validation_instances, model, maximizer)),
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:test =>
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(model, maximizer, epoch) ->
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(gap = compute_gap(b, test_instances, model, maximizer)),
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),
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# Compute initial gap
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initial_gap = compute_gap(b, test_instances, model, maximizer)
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println("Initial test gap: $initial_gap")
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# Configure the training algorithm
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algorithm = PerturbedImitationAlgorithm(;
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nb_samples=10, ε=0.1, threaded=true, seed=0
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)
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# Define metrics to track during training
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validation_loss_metric = FYLLossMetric(validation_instances, :validation_loss)
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# Validation gap metric
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val_gap_metric = FunctionMetric(:val_gap, validation_instances) do ctx, data
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compute_gap(b, data, ctx.model, ctx.maximizer)
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end
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# Test gap metric
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test_gap_metric = FunctionMetric(:test_gap, test_instances) do ctx, data
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compute_gap(b, data, ctx.model, ctx.maximizer)
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end
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# Combine metrics
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metrics = (validation_loss_metric, val_gap_metric, test_gap_metric)
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# Train the model
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fyl_model = deepcopy(model)
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log = fyl_train_model!(
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history = train_policy!(
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algorithm,
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fyl_model,
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maximizer,
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train_instances,
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validation_instances;
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epochs=100,
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metrics_callbacks,
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metrics=metrics,
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)
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log[:gap]
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# Plot validation and test gaps
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val_gap_epochs, val_gap_values = get(history, :val_gap)
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test_gap_epochs, test_gap_values = get(history, :test_gap)
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plot(
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[log[:gap].val, log[:gap].test];
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[val_gap_epochs, test_gap_epochs],
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[val_gap_values, test_gap_values];
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labels=["Val Gap" "Test Gap"],
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xlabel="Epoch",
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ylabel="Gap",
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title="Gap Evolution During Training",
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)
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# Plot validation loss
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train_loss_epochs, train_loss_values = get(history, :training_loss)
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val_loss_epochs, val_loss_values = get(history, :validation_loss)
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70+
plot(
71+
[train_loss_epochs, val_loss_epochs],
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[train_loss_values, val_loss_values];
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labels=["Training Loss" "Validation Loss"],
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xlabel="Epoch",
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ylabel="Loss",
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title="Loss Evolution During Training",
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)
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plot(log[:validation_loss])

‎docs/src/tutorials/tutorial.md‎

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```@meta
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EditURL = "tutorial.jl"
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```
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Tutorial
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````@example tutorial
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using DecisionFocusedLearningAlgorithms
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using DecisionFocusedLearningBenchmarks
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using MLUtils: splitobs
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using Plots
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b = ArgmaxBenchmark()
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dataset = generate_dataset(b, 100)
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train_instances, validation_instances, test_instances = splitobs(
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dataset; at=(0.3, 0.3, 0.4)
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)
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model = generate_statistical_model(b; seed=0)
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maximizer = generate_maximizer(b)
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````
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Compute initial gap
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````@example tutorial
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initial_gap = compute_gap(b, test_instances, model, maximizer)
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println("Initial test gap: $initial_gap")
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````
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Configure the training algorithm
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32+
````@example tutorial
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algorithm = PerturbedImitationAlgorithm(;
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nb_samples=10, ε=0.1, threaded=true, seed=0
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)
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````
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Define metrics to track during training
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40+
````@example tutorial
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validation_loss_metric = FYLLossMetric(validation_instances, :validation_loss)
42+
````
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44+
Validation gap metric
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46+
````@example tutorial
47+
val_gap_metric = FunctionMetric(:val_gap, validation_instances) do ctx, data
48+
compute_gap(b, data, ctx.model, ctx.maximizer)
49+
end
50+
````
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52+
Test gap metric
53+
54+
````@example tutorial
55+
test_gap_metric = FunctionMetric(:test_gap, test_instances) do ctx, data
56+
compute_gap(b, data, ctx.model, ctx.maximizer)
57+
end
58+
````
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60+
Combine metrics
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62+
````@example tutorial
63+
metrics = (validation_loss_metric, val_gap_metric, test_gap_metric)
64+
````
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66+
Train the model
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68+
````@example tutorial
69+
fyl_model = deepcopy(model)
70+
history = train_policy!(
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algorithm,
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fyl_model,
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maximizer,
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train_instances,
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validation_instances;
76+
epochs=100,
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metrics=metrics,
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)
79+
````
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81+
Plot validation and test gaps
82+
83+
````@example tutorial
84+
val_gap_epochs, val_gap_values = get(history, :val_gap)
85+
test_gap_epochs, test_gap_values = get(history, :test_gap)
86+
87+
plot(
88+
[val_gap_epochs, test_gap_epochs],
89+
[val_gap_values, test_gap_values];
90+
labels=["Val Gap" "Test Gap"],
91+
xlabel="Epoch",
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ylabel="Gap",
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title="Gap Evolution During Training",
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)
95+
````
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Plot validation loss
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99+
````@example tutorial
100+
train_loss_epochs, train_loss_values = get(history, :training_loss)
101+
val_loss_epochs, val_loss_values = get(history, :validation_loss)
102+
103+
plot(
104+
[train_loss_epochs, val_loss_epochs],
105+
[train_loss_values, val_loss_values];
106+
labels=["Training Loss" "Validation Loss"],
107+
xlabel="Epoch",
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ylabel="Loss",
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title="Loss Evolution During Training",
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)
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````
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---
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*This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*
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‎src/DecisionFocusedLearningAlgorithms.jl‎

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@@ -19,8 +19,8 @@ include("metrics/accumulators.jl")
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include("metrics/function_metric.jl")
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include("metrics/periodic.jl")
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include("fyl.jl")
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include("dagger.jl")
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include("algorithms/fyl.jl")
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include("algorithms/dagger.jl")
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export TrainingContext
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src/dagger.jl renamed to src/algorithms/dagger.jl

File renamed without changes.

src/fyl.jl renamed to src/algorithms/fyl.jl

File renamed without changes.

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