Minor fixes

SciML · Nov 21, 2023 · 683ac1b · 683ac1b
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Showing 9 changed files with 15 additions and 17 deletions.
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -5,7 +5,6 @@ ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 DataDeps = "124859b0-ceae-595e-8997-d05f6a7a8dfe"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DiffEqFlux = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
-DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
@@ -43,7 +42,6 @@ ComponentArrays = "0.13, 0.14, 0.15"
 DataDeps = "0.7"
 DataFrames = "1"
 DiffEqFlux = "3"
-DifferentialEquations = "7.6.0"
 Distances = "0.10.7"
 Distributions = "0.25.78"
 Documenter = "1"

diff --git a/docs/src/examples/GPUs.md b/docs/src/examples/GPUs.md
@@ -8,7 +8,7 @@ For a detailed discussion on how GPUs need to be setup refer to
 [Lux Docs](https://lux.csail.mit.edu/stable/manual/gpu_management).
 
 ```julia
-using DifferentialEquations, Lux, LuxCUDA, SciMLSensitivity, ComponentArrays
+using OrdinaryDiffEq, Lux, LuxCUDA, SciMLSensitivity, ComponentArrays
 using Random
 rng = Random.default_rng()
 

diff --git a/docs/src/examples/augmented_neural_ode.md b/docs/src/examples/augmented_neural_ode.md
@@ -3,7 +3,7 @@
 ## Copy-Pasteable Code
 
 ```@example augneuralode_cp
-using DiffEqFlux, DifferentialEquations, Statistics, LinearAlgebra, Plots, LuxCUDA, Random
+using DiffEqFlux, OrdinaryDiffEq, Statistics, LinearAlgebra, Plots, LuxCUDA, Random
 using MLUtils, ComponentArrays
 using Optimization, OptimizationOptimisers, IterTools
 

diff --git a/docs/src/examples/collocation.md b/docs/src/examples/collocation.md
@@ -96,7 +96,7 @@ us to get an estimate of the approximate noiseless dynamics:
 
 ```@example collocation
 using ComponentArrays,
- Lux, DiffEqFlux, Optimization, OptimizationOptimisers, DifferentialEquations, Plots
+ Lux, DiffEqFlux, Optimization, OptimizationOptimisers, OrdinaryDiffEq, Plots
 
 using Random
 rng = Random.default_rng()

diff --git a/docs/src/examples/hamiltonian_nn.md b/docs/src/examples/hamiltonian_nn.md
@@ -92,7 +92,7 @@ dataloader = ncycle(((selectdim(data, 2, ((i - 1) * B + 1):(min(i * B, size(data
 We parameterize the HamiltonianNN with a small MultiLayered Perceptron. HNNs are trained by optimizing the gradients of the Neural Network. Zygote currently doesn't support nesting itself, so we will be using ForwardDiff in the training loop to compute the gradients of the HNN Layer for Optimization.
 
 ```@example hamiltonian
-hnn = HamiltonianNN(Chain(Dense(2 => 64, relu), Dense(64 => 1)); ad - AutoZygote())
+hnn = HamiltonianNN(Chain(Dense(2 => 64, relu), Dense(64 => 1)); ad = AutoZygote())
 ps, st = Lux.setup(Random.default_rng(), hnn)
 ps_c = ps |> ComponentArray
 

diff --git a/docs/src/examples/multiple_shooting.md b/docs/src/examples/multiple_shooting.md
@@ -24,7 +24,7 @@ The following is a working demo, using Multiple Shooting:
 
 ```julia
 using ComponentArrays,
- Lux, DiffEqFlux, Optimization, OptimizationPolyalgorithms, DifferentialEquations, Plots
+ Lux, DiffEqFlux, Optimization, OptimizationPolyalgorithms, OrdinaryDiffEq, Plots
 using DiffEqFlux: group_ranges
 
 using Random

diff --git a/docs/src/examples/neural_ode.md b/docs/src/examples/neural_ode.md
@@ -12,8 +12,8 @@ Before getting to the explanation, here's some code to start with. We will
 follow a full explanation of the definition and training process:
 
 ```@example neuralode_cp
-using ComponentArrays, Lux, DiffEqFlux, DifferentialEquations, Optimization,
- OptimizationOptimJL, OptimizationOptimisers, Random, Plots
+using ComponentArrays, Lux, DiffEqFlux, OrdinaryDiffEq, Optimization, OptimizationOptimJL,
+ OptimizationOptimisers, Random, Plots
 
 rng = Random.default_rng()
 u0 = Float32[2.0; 0.0]
@@ -83,7 +83,7 @@ callback(result_neuralode2.u, loss_neuralode(result_neuralode2.u)...; doplot = t
 Let's get a time series array from a spiral ODE to train against.
 
 ```@example neuralode
-using ComponentArrays, Lux, DiffEqFlux, DifferentialEquations, Optimization,
+using ComponentArrays, Lux, DiffEqFlux, OrdinaryDiffEq, Optimization,
  OptimizationOptimJL, OptimizationOptimisers, Random, Plots
 
 rng = Random.default_rng()

diff --git a/docs/src/examples/normalizing_flows.md b/docs/src/examples/normalizing_flows.md
@@ -8,8 +8,8 @@ Before getting to the explanation, here's some code to start with. We will
 follow a full explanation of the definition and training process:
 
 ```@example cnf
-using ComponentArrays, DiffEqFlux, DifferentialEquations, Optimization,
- OptimizationOptimisers, OptimizationOptimJL, Distributions, Random
+using ComponentArrays, DiffEqFlux, OrdinaryDiffEq, Optimization, Distributions,
+ Random, OptimizationOptimisers, OptimizationOptimJL
 
 nn = Chain(Dense(1, 3, tanh), Dense(3, 1, tanh))
 tspan = (0.0f0, 10.0f0)
@@ -37,7 +37,7 @@ adtype = Optimization.AutoForwardDiff()
 optf = Optimization.OptimizationFunction((x, p) -> loss(x), adtype)
 optprob = Optimization.OptimizationProblem(optf, ps)
 
-res1 = Optimization.solve(optprob, Adam(0.1); maxiters = 20, callback = cb)
+res1 = Optimization.solve(optprob, Adam(0.01); maxiters = 20, callback = cb)
 
 optprob2 = Optimization.OptimizationProblem(optf, res1.u)
 res2 = Optimization.solve(optprob2, Optim.LBFGS(); allow_f_increases = false,
@@ -62,8 +62,8 @@ new_data = rand(ffjord_dist, 100)
 We can use DiffEqFlux.jl to define, train and output the densities computed by CNF layers. In the same way as a neural ODE, the layer takes a neural network that defines its derivative function (see [1] for a reference). A possible way to define a CNF layer, would be:
 
 ```@example cnf2
-using ComponentArray, DiffEqFlux, DifferentialEquations, Optimization,
- OptimizationOptimisers, OptimizationOptimJL, Distributions, Random
+using ComponentArrays, DiffEqFlux, OrdinaryDiffEq, Optimization, OptimizationOptimisers,
+ OptimizationOptimJL, Distributions, Random
 
 nn = Chain(Dense(1, 3, tanh), Dense(3, 1, tanh))
 tspan = (0.0f0, 10.0f0)
@@ -113,7 +113,7 @@ adtype = Optimization.AutoForwardDiff()
 optf = Optimization.OptimizationFunction((x, p) -> loss(x), adtype)
 optprob = Optimization.OptimizationProblem(optf, ps)
 
-res1 = Optimization.solve(optprob, Adam(0.1); maxiters = 20, callback = cb)
+res1 = Optimization.solve(optprob, Adam(0.01); maxiters = 20, callback = cb)
 ```
 
 We then complete the training using a different optimizer, starting from where `Adam` stopped.

diff --git a/docs/src/examples/tensor_layer.md b/docs/src/examples/tensor_layer.md
@@ -14,7 +14,7 @@ solvers in `DifferentialEquations`:
 
 ```@example tensor
 using ComponentArrays, DiffEqFlux, Optimization, OptimizationOptimisers,
- DifferentialEquations, LinearAlgebra, Random
+ OrdinaryDiffEq, LinearAlgebra, Random
 k, α, β, γ = 1, 0.1, 0.2, 0.3
 tspan = (0.0, 10.0)