From f0c5a347a7e81429b4c4e28303a1a049927bf810 Mon Sep 17 00:00:00 2001
From: Sathvik Bhagavan <sathvik.bhagavan@juliahub.com>
Date: Thu, 21 Mar 2024 14:38:02 +0000
Subject: [PATCH] fixup! test: make verbose = false for NNODE tests

---
 test/NNODE_tests.jl | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/test/NNODE_tests.jl b/test/NNODE_tests.jl
index 1d219ab05..8d2717161 100644
--- a/test/NNODE_tests.jl
+++ b/test/NNODE_tests.jl
@@ -9,6 +9,7 @@ Random.seed!(100)
 
 @testset "Scalar" begin
     # Run a solve on scalars
+    println("Scalar")
     linear = (u, p, t) -> cos(2pi * t)
     tspan = (0.0f0, 1.0f0)
     u0 = 0.0f0
@@ -36,6 +37,7 @@ end
 
 @testset "Vector" begin
     # Run a solve on vectors
+    println("Vector")
     linear = (u, p, t) -> [cos(2pi * t)]
     tspan = (0.0f0, 1.0f0)
     u0 = [0.0f0]
@@ -59,6 +61,7 @@ end
 end
 
 @testset "Example 1" begin
+    println("Example 1")
     linear = (u, p, t) -> @. t^3 + 2 * t + (t^2) * ((1 + 3 * (t^2)) / (1 + t + (t^3))) -
                             u * (t + ((1 + 3 * (t^2)) / (1 + t + t^3)))
     linear_analytic = (u0, p, t) -> [exp(-(t^2) / 2) / (1 + t + t^3) + t^2]
@@ -92,6 +95,7 @@ end
 end
 
 @testset "Example 2" begin
+    println("Example 2")
     linear = (u, p, t) -> -u / 5 + exp(-t / 5) .* cos(t)
     linear_analytic = (u0, p, t) -> exp(-t / 5) * (u0 + sin(t))
     prob = ODEProblem(ODEFunction(linear, analytic = linear_analytic), 0.0f0, (0.0f0, 1.0f0))
@@ -128,6 +132,7 @@ end
 end
 
 @testset "Example 3" begin
+    println("Example 3")
     linear = (u, p, t) -> [cos(2pi * t), sin(2pi * t)]
     tspan = (0.0f0, 1.0f0)
     u0 = [0.0f0, -1.0f0 / 2pi]
@@ -146,6 +151,7 @@ end
 
 @testset "Training Strategies" begin
     @testset "WeightedIntervalTraining" begin
+        println("WeightedIntervalTraining")
         function f(u, p, t)
             [p[1] * u[1] - p[2] * u[1] * u[2], -p[3] * u[2] + p[4] * u[1] * u[2]]
         end
@@ -162,7 +168,7 @@ end
         points = 200
         alg = NNODE(chain, opt, autodiff = false,
                             strategy = NeuralPDE.WeightedIntervalTraining(weights, points))
-        sol = solve(prob_oop, alg, verbose = false, maxiters = 100000, saveat = 0.01)
+        sol = solve(prob_oop, alg, verbose = true, maxiters = 5000, saveat = 0.01)
         @test abs(mean(sol) - mean(true_sol)) < 0.2
     end
 
@@ -176,6 +182,7 @@ end
     u_analytical(x) = (1 / (2pi)) .* sin.(2pi .* x)
 
     @testset "GridTraining" begin
+        println("GridTraining")
         luxchain = Lux.Chain(Lux.Dense(1, 5, Lux.σ), Lux.Dense(5, 1))
         (u_, t_) = (u_analytical(ts), ts)
         function additional_loss(phi, θ)
@@ -188,6 +195,7 @@ end
     end
 
     @testset "QuadratureTraining" begin
+        println("QuadratureTraining")
         luxchain = Lux.Chain(Lux.Dense(1, 5, Lux.σ), Lux.Dense(5, 1))
         (u_, t_) = (u_analytical(ts), ts)
         function additional_loss(phi, θ)
@@ -199,6 +207,7 @@ end
     end
 
     @testset "StochasticTraining" begin
+        println("StochasticTraining")
         luxchain = Lux.Chain(Lux.Dense(1, 5, Lux.σ), Lux.Dense(5, 1))
         (u_, t_) = (u_analytical(ts), ts)
         function additional_loss(phi, θ)
@@ -212,6 +221,7 @@ end
 end
 
 @testset "Parameter Estimation" begin
+    println("Parameter Estimation")
     function lorenz(u, p, t)
         return [p[1]*(u[2]-u[1]),
                 u[1]*(p[2]-u[3])-u[2],
@@ -241,6 +251,7 @@ end
 end
 
 @testset "Translating from Flux" begin
+    println("Translating from Flux")
     linear = (u, p, t) -> cos(2pi * t)
     linear_analytic = (u, p, t) -> (1 / (2pi)) * sin(2pi * t)
     tspan = (0.0, 1.0)