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Fix more cross-references
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Saransh-cpp committed Sep 5, 2022
1 parent b44e11c commit d844d00
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3 changes: 3 additions & 0 deletions docs/src/api/reference.md
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Expand Up @@ -9,6 +9,9 @@ Modules = [Metalhead]
```

```@docs
Metalhead.create_classifier
Metalhead.squeeze_excite
Metalhead.LayerScale
Metalhead.DropBlock
Metalhead.StochasticDepth
```
2 changes: 1 addition & 1 deletion docs/src/tutorials/quickstart.md
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Expand Up @@ -4,7 +4,7 @@
using Flux, Metalhead
```

Using a model from Metalhead is as simple as selecting a model from the table of [available models](@ref). For example, below we use the pre-trained ResNet-18 model.
Using a model from Metalhead is as simple as selecting a model from the table of [available models](@ref API-Reference). For example, below we use the pre-trained ResNet-18 model.
```julia
using Flux, Metalhead

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2 changes: 1 addition & 1 deletion src/convnets/builders/mbconv.jl
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Expand Up @@ -6,7 +6,7 @@
Creates a block builder for `irblockfn` within a given stage.
Note that this function is not intended to be called directly, but instead passed to
[`mbconv_stage_builder`](@ref) which will return a builder over all stages.
[`Metalhead.mbconv_stage_builder`](@ref) which will return a builder over all stages.
Users wanting to provide a custom inverted residual block type can extend this
function by defining `invresbuilder(::typeof(my_block), ...)`.
"""
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4 changes: 0 additions & 4 deletions src/utilities.jl
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Expand Up @@ -15,8 +15,6 @@ end
Convenience function for applying an activation function to the output after
summing up the input arrays. Useful as the `connection` argument for the block
function in [`resnet`](@ref).
See also [`reluadd`](@ref).
"""
addact(activation = relu, xs...) = activation(sum(xs))

Expand All @@ -26,8 +24,6 @@ addact(activation = relu, xs...) = activation(sum(xs))
Convenience function for adding input arrays after applying an activation
function to them. Useful as the `connection` argument for the block function in
[`resnet`](@ref).
See also [`addrelu`](@ref).
"""
actadd(activation = relu, xs...) = sum(activation.(x) for x in xs)

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