format

docs/src/examples/neural_gde.md

@@ -1,6 +1,7 @@
 # Neural Graph Differential Equations
 
 !!! warn
+
  This tutorial has not been ran or updated in awhile.
 
 This tutorial has been adapted from [here](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/blob/master/examples/neural_ode_cora.jl).
@@ -33,7 +34,7 @@ onecold(y) = map(argmax, eachcol(y))
 X = g.ndata.features
 y = onehotbatch(g.ndata.targets, classes) # a dense matrix is not the optimal, but we don't want to use Flux here
 
-Ã = normalized_adjacency(g, add_self_loops = true) |> device
+Ã = normalized_adjacency(g; add_self_loops = true) |> device
 
 (; train_mask, val_mask, test_mask) = g.ndata
 ytrain = y[:, train_mask]
@@ -70,9 +71,9 @@ initialstates(rng::AbstractRNG, d::ExplicitGCNConv) = (Ã = d.init_Ã(),)
 function ExplicitGCNConv(Ã, ch::Pair{Int, Int}, activation = identity;
  init_weight = glorot_normal, init_bias = zeros32)
  init_Ã = () -> copy(Ã)
- return ExplicitGCNConv{typeof(activation), typeof(init_Ã), typeof(init_weight),
- typeof(init_bias)}(first(ch), last(ch), activation,
- init_Ã, init_weight, init_bias)
+ return ExplicitGCNConv{
+ typeof(activation), typeof(init_Ã), typeof(init_weight), typeof(init_bias)}(
+ first(ch), last(ch), activation, init_Ã, init_weight, init_bias)
 end
 
 function (l::ExplicitGCNConv)(x::AbstractMatrix, ps, st::NamedTuple)
@@ -94,13 +95,11 @@ initialstates(rng::AbstractRNG, node::NeuralODE) = initialstates(rng, node.model
 gnn = Chain(ExplicitGCNConv(Ã, nhidden => nhidden, relu),
  ExplicitGCNConv(Ã, nhidden => nhidden, relu))
 
-node = NeuralODE(gnn, (0.0f0, 1.0f0), Tsit5(), save_everystep = false,
+node = NeuralODE(gnn, (0.0f0, 1.0f0), Tsit5(); save_everystep = false,
  reltol = 1e-3, abstol = 1e-3, save_start = false)
 
 model = Chain(ExplicitGCNConv(Ã, nin => nhidden, relu),
- node,
- diffeqsol_to_array,
- Dense(nhidden, nout))
+ node, diffeqsol_to_array, Dense(nhidden, nout))
 
 # Loss
 logitcrossentropy(ŷ, y) = mean(-sum(y .* logsoftmax(ŷ); dims = 1))
@@ -114,7 +113,7 @@ function eval_loss_accuracy(X, y, mask, model, ps, st)
  ŷ, _ = model(X, ps, st)
  l = logitcrossentropy(ŷ[:, mask], y[:, mask])
  acc = mean(onecold(ŷ[:, mask]) .== onecold(y[:, mask]))
- return (loss = round(l, digits = 4), acc = round(acc * 100, digits = 2))
+ return (loss = round(l; digits = 4), acc = round(acc * 100; digits = 2))
 end
 
 # Training
@@ -183,7 +182,7 @@ onecold(y) = map(argmax, eachcol(y))
 X = g.ndata.features
 y = onehotbatch(g.ndata.targets, classes) # a dense matrix is not the optimal, but we don't want to use Flux here
 
-Ã = normalized_adjacency(g, add_self_loops = true) |> device
+Ã = normalized_adjacency(g; add_self_loops = true) |> device
 ```
 
 ### Training Data
@@ -233,12 +232,8 @@ end
 
 function ExplicitGCNConv(Ã, ch::Pair{Int, Int}, activation = identity;
  init_weight = glorot_normal, init_bias = zeros32)
- return ExplicitGCNConv{typeof(activation), typeof(init_weight), typeof(init_bias)}(Ã,
- first(ch),
- last(ch),
- activation,
- init_weight,
- init_bias)
+ return ExplicitGCNConv{typeof(activation), typeof(init_weight), typeof(init_bias)}(
+ Ã, first(ch), last(ch), activation, init_weight, init_bias)
 end
 
 function (l::ExplicitGCNConv)(x::AbstractMatrix, ps, st::NamedTuple)
@@ -260,13 +255,11 @@ diffeqsol_to_array(x::ODESolution) = dropdims(Array(x); dims = 3)
 gnn = Chain(ExplicitGCNConv(Ã, nhidden => nhidden, relu),
  ExplicitGCNConv(Ã, nhidden => nhidden, relu))
 
-node = NeuralODE(gnn, (0.0f0, 1.0f0), Tsit5(), save_everystep = false,
+node = NeuralODE(gnn, (0.0f0, 1.0f0), Tsit5(); save_everystep = false,
  reltol = 1e-3, abstol = 1e-3, save_start = false)
 
 model = Chain(ExplicitGCNConv(Ã, nin => nhidden, relu),
- node,
- diffeqsol_to_array,
- Dense(nhidden, nout))
+ node, diffeqsol_to_array, Dense(nhidden, nout))
 ```
 
 ## Training Configuration
@@ -287,7 +280,7 @@ function eval_loss_accuracy(X, y, mask, model, ps, st)
  ŷ, _ = model(X, ps, st)
  l = logitcrossentropy(ŷ[:, mask], y[:, mask])
  acc = mean(onecold(ŷ[:, mask]) .== onecold(y[:, mask]))
- return (loss = round(l, digits = 4), acc = round(acc * 100, digits = 2))
+ return (loss = round(l; digits = 4), acc = round(acc * 100; digits = 2))
 end
 ```
 

docs/src/examples/physical_constraints.md

@@ -10,8 +10,7 @@ terms must add to one. An example of this is as follows:
 
 ```@example dae
 using SciMLSensitivity
-using Lux, ComponentArrays, Optimization, OptimizationOptimJL,
- OrdinaryDiffEq, Plots
+using Lux, ComponentArrays, Optimization, OptimizationOptimJL, OrdinaryDiffEq, Plots
 
 using Random
 rng = Random.default_rng()
@@ -33,17 +32,16 @@ M = [1.0 0 0
 tspan = (0.0, 1.0)
 p = [0.04, 3e7, 1e4]
 
-stiff_func = ODEFunction(f!, mass_matrix = M)
+stiff_func = ODEFunction(f!; mass_matrix = M)
 prob_stiff = ODEProblem(stiff_func, u₀, tspan, p)
-sol_stiff = solve(prob_stiff, Rodas5(), saveat = 0.1)
+sol_stiff = solve(prob_stiff, Rodas5(); saveat = 0.1)
 
-nn_dudt2 = Lux.Chain(Lux.Dense(3, 64, tanh),
- Lux.Dense(64, 2))
+nn_dudt2 = Lux.Chain(Lux.Dense(3, 64, tanh), Lux.Dense(64, 2))
 
 pinit, st = Lux.setup(rng, nn_dudt2)
 
 model_stiff_ndae = NeuralODEMM(nn_dudt2, (u, p, t) -> [u[1] + u[2] + u[3] - 1],
- tspan, M, Rodas5(autodiff = false), saveat = 0.1)
+ tspan, M, Rodas5(; autodiff = false); saveat = 0.1)
 
 function predict_stiff_ndae(p)
  return model_stiff_ndae(u₀, p, st)[1]
@@ -65,7 +63,7 @@ l1 = first(loss_stiff_ndae(ComponentArray(pinit)))
 adtype = Optimization.AutoZygote()
 optf = Optimization.OptimizationFunction((x, p) -> loss_stiff_ndae(x), adtype)
 optprob = Optimization.OptimizationProblem(optf, ComponentArray(pinit))
-result_stiff = Optimization.solve(optprob, OptimizationOptimJL.BFGS(), maxiters = 100)
+result_stiff = Optimization.solve(optprob, OptimizationOptimJL.BFGS(); maxiters = 100)
 ```
 
 ## Step-by-Step Description
@@ -74,8 +72,7 @@ result_stiff = Optimization.solve(optprob, OptimizationOptimJL.BFGS(), maxiters
 
 ```@example dae2
 using SciMLSensitivity
-using Lux, ComponentArrays, Optimization, OptimizationOptimJL,
- OrdinaryDiffEq, Plots
+using Lux, ComponentArrays, Optimization, OptimizationOptimJL, OrdinaryDiffEq, Plots
 
 using Random
 rng = Random.default_rng()
@@ -123,9 +120,9 @@ We define and solve our ODE problem to generate the “labeled” data which wil
 train our Neural Network.
 
 ```@example dae2
-stiff_func = ODEFunction(f!, mass_matrix = M)
+stiff_func = ODEFunction(f!; mass_matrix = M)
 prob_stiff = ODEProblem(stiff_func, u₀, tspan, p)
-sol_stiff = solve(prob_stiff, Rodas5(), saveat = 0.1)
+sol_stiff = solve(prob_stiff, Rodas5(); saveat = 0.1)
 ```
 
 Because this is a DAE, we need to make sure to use a **compatible solver**.
@@ -138,13 +135,12 @@ is more suited to SciML applications (similarly for `Lux.Dense`). The input to o
 will be the initial conditions fed in as `u₀`.
 
 ```@example dae2
-nn_dudt2 = Lux.Chain(Lux.Dense(3, 64, tanh),
- Lux.Dense(64, 2))
+nn_dudt2 = Lux.Chain(Lux.Dense(3, 64, tanh), Lux.Dense(64, 2))
 
 pinit, st = Lux.setup(rng, nn_dudt2)
 
 model_stiff_ndae = NeuralODEMM(nn_dudt2, (u, p, t) -> [u[1] + u[2] + u[3] - 1],
- tspan, M, Rodas5(autodiff = false), saveat = 0.1)
+ tspan, M, Rodas5(; autodiff = false); saveat = 0.1)
 model_stiff_ndae(u₀, ComponentArray(pinit), st)
 ```
 
@@ -210,5 +206,5 @@ Finally, training with `Optimization.solve` by passing: *loss function*, *model
 adtype = Optimization.AutoZygote()
 optf = Optimization.OptimizationFunction((x, p) -> loss_stiff_ndae(x), adtype)
 optprob = Optimization.OptimizationProblem(optf, ComponentArray(pinit))
-result_stiff = Optimization.solve(optprob, OptimizationOptimJL.BFGS(), maxiters = 100)
+result_stiff = Optimization.solve(optprob, OptimizationOptimJL.BFGS(); maxiters = 100)
 ```