Cleaning the Todos (#13)

* Cleaning the Todos --------- Co-authored-by: tmigot <[email protected]>
JuliaSmoothOptimizers · Jul 12, 2023 · d2cf7b9 · d2cf7b9
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diff --git a/README.md b/README.md
@@ -4,7 +4,6 @@
 [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://JuliaSmoothOptimizers.github.io/FluxNLPModels.jl/dev)
 [![Build Status](https://github.com/JuliaSmoothOptimizers/FluxNLPModels.jl/workflows/CI/badge.svg)](https://github.com/JuliaSmoothOptimizers/FluxNLPModels.jl/actions)
 [![Codecov](https://codecov.io/gh/JuliaSmoothOptimizers/FluxNLPModels.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/JuliaSmoothOptimizers/FluxNLPModels.jl) 
-<!-- TODO check the links -->
 
 This package serves as an NLPModels interface to the [Flux.jl](https://github.com/FluxML/Flux.jl) deep learning framework. It enables seamless integration between Flux's neural network architectures and NLPModels' optimization tools for natural language processing tasks.
 
@@ -13,8 +12,7 @@ This package serves as an NLPModels interface to the [Flux.jl](https://github.co
 To use FluxNLPModels, add the package in the Julia package manager:
 
 ```julia
-# pkg> add FluxNLPModels
-pkg> add https://github.com/JuliaSmoothOptimizers/FluxNLPModels.jl.git
+pkg> add FluxNLPModels
 ```
 
 ## How to Use

diff --git a/docs/make.jl b/docs/make.jl
@@ -1,4 +1,3 @@
-#TODO redo this section
 using Documenter, FluxNLPModels
 
 makedocs(

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -1,19 +1,18 @@
-#TODO redo this section 
 # FluxNLPModels.jl
 
 ## Compatibility
 Julia ≥ 1.6.
 
 ## How to install
-TODO: this section needs work since our package is not yet register
+
 This module can be installed with the following command:
 ```julia
-# pkg> add FluxNLPModels
-# pkg> test FluxNLPModels
+pkg> add FluxNLPModels
 ```
 
 ## Synopsis
-FluxNLPModels exposes neural network models as optimization problems conforming to the NLPModels.jl API. FluxNLPModels is an interface between [Flux.jl](https://github.com/FluxML/Flux.jl)'s classification neural networks and [NLPModels.jl](https://github.com/JuliaSmoothOptimizers/NLPModels.jl.git).
+
+FluxNLPModels exposes neural network models as optimization problems conforming to the [NLPModels API](https://github.com/JuliaSmoothOptimizers/NLPModels.jl). FluxNLPModels is an interface between [Flux.jl](https://github.com/FluxML/Flux.jl)'s classification neural networks and [NLPModels.jl](https://github.com/JuliaSmoothOptimizers/NLPModels.jl).
 
 A `FluxNLPModel` gives the user access to:
 - The values of the neural network variables/weights `w`;
@@ -25,12 +24,7 @@ In addition, it provides tools to:
 - Retrieve the current minibatch ;
 - Measure the neural network's loss at the current `w`.
 
-## How to use
-Check the [tutorial](https://jso.dev/FluxNLPModels.jl/dev/tutorial/).
-
 # Bug reports and discussions
 
-If you think you found a bug, feel free to open an [issue](https://github.com/JuliaSmoothOptimizers/FluxNLPModels.jl/issues). TODO: add repo link
-Focused suggestions and requests can also be opened as issues. Before opening a pull request, please start an issue or a discussion on the topic.
+If you encounter any bugs or have suggestions for improvement, please open an [issue](https://github.com/JuliaSmoothOptimizers/FluxNLPModels.jl/issues). For general questions or discussions related to this repository and the [JuliaSmoothOptimizers](https://github.com/JuliaSmoothOptimizers) organization, feel free to start a discussion [here](https://github.com/JuliaSmoothOptimizers/Organization/discussions).
 
-If you have a question that is not suited for a bug report, feel free to start a discussion [here](#TODO). This forum is for general discussion about this repository and the [JuliaSmoothOptimizers](https://github.com/JuliaSmoothOptimizers). Questions about any of our packages are welcome.
diff --git a/src/FluxNLPModels.jl b/src/FluxNLPModels.jl
@@ -32,9 +32,9 @@ mutable struct FluxNLPModel{T, S, C <: Chain, F <: Function} <: AbstractFluxNLPM
   chain::C
   counters::Counters
   loss_f::F
-  size_minibatch::Int #TODO remove this 
-  training_minibatch_iterator #TODO remove this, right now we pass the data
-  test_minibatch_iterator #TODO remove this 
+  size_minibatch::Int 
+  training_minibatch_iterator 
+  test_minibatch_iterator
   current_training_minibatch
   current_test_minibatch
   rebuild # this is used to create the rebuild of flat function 

diff --git a/src/utils.jl b/src/utils.jl
@@ -3,7 +3,7 @@
 
 Sets the vaiables and rebuild the chain
 """
-function set_vars!(nlp::AbstractFluxNLPModel{T, S}, new_w::AbstractVector{T}) where {T <: Number, S} #TODO test T 
+function set_vars!(nlp::AbstractFluxNLPModel{T, S}, new_w::AbstractVector{T}) where {T <: Number, S} 
   nlp.w .= new_w
   nlp.chain = nlp.rebuild(nlp.w)
 end

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -1,7 +1,7 @@
 using Test
 using FluxNLPModels
 using CUDA, Flux, NLPModels
-using Flux.Data: DataLoader #TODO update this
+using Flux.Data: DataLoader
 using Flux: onehotbatch, onecold, @epochs
 using Flux.Losses: logitcrossentropy
 using Base: @kwdef
@@ -24,7 +24,7 @@ function getdata(args)
   # One-hot-encode the labels
   ytrain, ytest = onehotbatch(ytrain, 0:9), onehotbatch(ytest, 0:9)
 
-  # Create DataLoaders (mini-batch iterators) #TODO it is passed down
+  # Create DataLoaders (mini-batch iterators) 
   train_loader = DataLoader((xtrain, ytrain), batchsize = args.batchsize, shuffle = true)
   test_loader = DataLoader((xtest, ytest), batchsize = args.batchsize)
 
@@ -44,7 +44,7 @@ end
 
 args = Args() # collect options in a struct for convenience
 
-device = cpu #TODO should we test on GPU?
+device = cpu
 
 @testset "FluxNLPModels tests" begin