Move to the existing tutorials section

FluxML · Oct 27, 2022 · 0350e03 · 0350e03
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Showing 8 changed files with 12 additions and 15 deletions.
diff --git a/docs/make.jl b/docs/make.jl
@@ -15,9 +15,6 @@ makedocs(
             "Fitting a Line" => "getting_started/overview.md",
             "Gradients and Layers" => "getting_started/basics.md",
         ],
-        "Tutorials" => [
-            "Linear Regression" => "tutorials/linear_regression.md",
-        ],
         "Building Models" => [
             "Built-in Layers 📚" => "models/layers.md",
             "Recurrence" => "models/recurrence.md",
@@ -44,11 +41,12 @@ makedocs(
              "Flat vs. Nested 📚" => "destructure.md",
              "Functors.jl 📚 (`fmap`, ...)" => "models/functors.md",
          ],
+         "Tutorials" => [
+            "Linear Regression" => "tutorials/linear_regression.md",
+            "Custom Layers" => "tutorials/advanced.md",  # TODO move freezing to Training
+         ],
          "Performance Tips" => "performance.md",
          "Flux's Ecosystem" => "ecosystem.md",
-         "Tutorials" => [  # TODO, maybe
-             "Custom Layers" => "models/advanced.md",  # TODO move freezing to Training
-         ],
     ],
     format = Documenter.HTML(
         sidebar_sitename = false,

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -16,9 +16,9 @@ Other closely associated packages, also installed automatically, include [Zygote
 
 ## Learning Flux
 
-The [quick start](models/quickstart.md) page trains a simple neural network.
+The [quick start](getting_started/quickstart.md) page trains a simple neural network.
 
-This rest of this documentation provides a from-scratch introduction to Flux's take on models and how they work, starting with [fitting a line](models/overview.md). Once you understand these docs, congratulations, you also understand [Flux's source code](https://github.com/FluxML/Flux.jl), which is intended to be concise, legible and a good reference for more advanced concepts.
+This rest of this documentation provides a from-scratch introduction to Flux's take on models and how they work, starting with [fitting a line](getting_started/overview.md). Once you understand these docs, congratulations, you also understand [Flux's source code](https://github.com/FluxML/Flux.jl), which is intended to be concise, legible and a good reference for more advanced concepts.
 
 Sections with 📚 contain API listings. The same text is avalable at the Julia prompt, by typing for example `?gpu`.
 

diff --git a/docs/src/models/activation.md b/docs/src/models/activation.md
@@ -1,5 +1,4 @@
-
-# Activation Functions from NNlib.jl
+# [Activation Functions from NNlib.jl](@id man-activations)
 
 These non-linearities used between layers of your model are exported by the [NNlib](https://github.com/FluxML/NNlib.jl) package.
 

diff --git a/docs/src/models/functors.md b/docs/src/models/functors.md
@@ -4,7 +4,7 @@ Flux models are deeply nested structures, and [Functors.jl](https://github.com/F
 
 New layers should be annotated using the `Functors.@functor` macro. This will enable [`params`](@ref Flux.params) to see the parameters inside, and [`gpu`](@ref) to move them to the GPU.
 
-`Functors.jl` has its own [notes on basic usage](https://fluxml.ai/Functors.jl/stable/#Basic-Usage-and-Implementation) for more details. Additionally, the [Advanced Model Building and Customisation](../models/advanced.md) page covers the use cases of `Functors` in greater details.
+`Functors.jl` has its own [notes on basic usage](https://fluxml.ai/Functors.jl/stable/#Basic-Usage-and-Implementation) for more details. Additionally, the [Advanced Model Building and Customisation](../tutorials/advanced.md) page covers the use cases of `Functors` in greater details.
 
 ```@docs
 Functors.@functor

diff --git a/docs/src/training/optimisers.md b/docs/src/training/optimisers.md
@@ -4,7 +4,7 @@ CurrentModule = Flux
 
 # Optimisers
 
-Consider a [simple linear regression](../getting_started/linear_regression.md). We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters `W` and `b`.
+Consider a [simple linear regression](../tutorials/linear_regression.md). We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters `W` and `b`.
 
 ```julia
 using Flux

diff --git a/docs/src/training/training.md b/docs/src/training/training.md
@@ -36,7 +36,7 @@ Flux.Optimise.train!
 ```
 
 There are plenty of examples in the [model zoo](https://github.com/FluxML/model-zoo), and
-more information can be found on [Custom Training Loops](../models/advanced.md).
+more information can be found on [Custom Training Loops](../tutorials/advanced.md).
 
 ## Loss Functions
 
@@ -68,7 +68,7 @@ The model to be trained must have a set of tracked parameters that are used to c
 
 Such an object contains a reference to the model's parameters, not a copy, such that after their training, the model behaves according to their updated values.
 
-Handling all the parameters on a layer by layer basis is explained in the [Layer Helpers](../getting_started/basics.md) section. Also, for freezing model parameters, see the [Advanced Usage Guide](../models/advanced.md).
+Handling all the parameters on a layer by layer basis is explained in the [Layer Helpers](../getting_started/basics.md) section. Also, for freezing model parameters, see the [Advanced Usage Guide](../tutorials/advanced.md).
 
 ```@docs
 Flux.params

diff --git a/docs/src/models/advanced.md → docs/src/tutorials/advanced.md b/docs/src/models/advanced.md → docs/src/tutorials/advanced.md
diff --git a/src/losses/functions.jl b/src/losses/functions.jl
@@ -273,7 +273,7 @@ Return the binary cross-entropy loss, computed as
 
     agg(@.(-y * log(ŷ + ϵ) - (1 - y) * log(1 - ŷ + ϵ)))
 
-Where typically, the prediction `ŷ` is given by the output of a [sigmoid](@ref Activation-Functions-from-NNlib.jl) activation.
+Where typically, the prediction `ŷ` is given by the output of a [sigmoid](@ref man-activations) activation.
 The `ϵ` term is included to avoid infinity. Using [`logitbinarycrossentropy`](@ref) is recomended
 over `binarycrossentropy` for numerical stability.