From abc5fc7a14170bdc40fa598d41932b8c9d5ef5d1 Mon Sep 17 00:00:00 2001 From: Nicholas Bauer Date: Tue, 19 Mar 2024 08:20:33 -0400 Subject: [PATCH] Fix dead link in docs --- docs/src/training/optimisers.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/src/training/optimisers.md b/docs/src/training/optimisers.md index 6b8a80c25e..fc4e38eebe 100644 --- a/docs/src/training/optimisers.md +++ b/docs/src/training/optimisers.md @@ -76,7 +76,7 @@ Flux.Optimise.Optimiser ## Scheduling Optimisers -In practice, it is fairly common to schedule the learning rate of an optimiser to obtain faster convergence. There are a variety of popular scheduling policies, and you can find implementations of them in [ParameterSchedulers.jl](http://fluxml.ai/ParameterSchedulers.jl/dev/README.html). The documentation for ParameterSchedulers.jl provides a more detailed overview of the different scheduling policies, and how to use them with Flux optimisers. Below, we provide a brief snippet illustrating a [cosine annealing](https://arxiv.org/pdf/1608.03983.pdf) schedule with a momentum optimiser. +In practice, it is fairly common to schedule the learning rate of an optimiser to obtain faster convergence. There are a variety of popular scheduling policies, and you can find implementations of them in [ParameterSchedulers.jl](http://fluxml.ai/ParameterSchedulers.jl/stable). The documentation for ParameterSchedulers.jl provides a more detailed overview of the different scheduling policies, and how to use them with Flux optimisers. Below, we provide a brief snippet illustrating a [cosine annealing](https://arxiv.org/pdf/1608.03983.pdf) schedule with a momentum optimiser. First, we import ParameterSchedulers.jl and initialize a cosine annealing schedule to vary the learning rate between `1e-4` and `1e-2` every 10 steps. We also create a new [`Momentum`](@ref) optimiser. ```julia