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Merged
ablaom merged 1 commit into from
Nov 4, 2026
Merged

Some doc fixes, improvements #77

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7 changes: 4 additions & 3 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,8 +1,9 @@
# IterationControl.jl

| Linux | Coverage |
| :-----------: | :------: |
| [![Build status](https://github.com/JuliaAI/IterationControl.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/IterationControl.jl/actions)| [![codecov.io](http://codecov.io/github/JuliaAI/IterationControl.jl/coverage.svg?branch=master)](http://codecov.io/github/JuliaAI/IterationControl.jl?branch=master) |
[![Build status](https://github.com/JuliaAI/IterationControl.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/IterationControl.jl/actions)
[![codecov.io](http://codecov.io/github/JuliaAI/IterationControl.jl/coverage.svg?branch=master)](http://codecov.io/github/JuliaAI/IterationControl.jl?branch=master)
[![Docs](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliaai.github.io/IterationControl.jl/dev/)
[![Docs](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliaai.github.io/IterationControl.jl/stable/)

A lightweight package for controlling iterative algorithms, with a
view to training and optimizing machine learning models.
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4 changes: 2 additions & 2 deletions docs/src/controls.md
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Expand Up @@ -15,8 +15,8 @@ summary, with some advanced options omitted.
| [`NumberLimit(n=100)`](@ref) | Stop after `n` applications of the control | `train!` | yes | |
| [`NumberSinceBest(n=6)`](@ref) | Stop when best loss occurred `n` control applications ago | `train!` | yes | |
| [`WithNumberDo(f=identity)`](@ref) | Call `f(n + 1)` where `n` is the number of complete control cycles so far | `train!` | yes | |
| [`WithLossDo(f=x->@info("loss: $x"))`](@ref) | Call `f(loss)` where `loss` is the current loss | `train!`, `loss` | yes | |
| [`WithTrainingLossesDo(f=v->@info(v))`](@ref) | Call `f(v)` where `v` is the current batch of training losses | `train!`, `training_losses` | yes | |
| [`WithLossDo(f=identity)`](@ref) | Call `f(loss)` where `loss` is the current loss | `train!`, `loss` | yes | |
| [`WithTrainingLossesDo(f=identity)`](@ref) | Call `f(v)` where `v` is the current batch of training losses | `train!`, `training_losses` | yes | |
| [`InvalidValue()`](@ref) | Stop when `NaN`, `Inf` or `-Inf` loss/training loss encountered | `train!` | yes | |
| [`Threshold(value=0.0)`](@ref) | Stop when `loss < value` | `train!`, `loss` | yes | |
| [`GL(alpha=2.0)`](@ref) | Stop after "Generalization Loss" exceeds `alpha` | `train!`, `loss` | yes | ``GL_α`` |
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