Fixed benchmarking demo

docs/src/demos/benchmarking.md

@@ -81,6 +81,7 @@ opt = [OptimizationOptimisers.Adam(0.05), OptimizationOptimJL.BFGS()]
 optimization_args = [[:maxiters => 300], [:maxiters => 300]]
 
 # Run benchmark
+endpoint_check = (x) -> ≈([sin(x[1]), cos(x[1]), x[2]], [0, -1, 0], atol = 5e-3)
 cm, time = benchmark(
     open_loop_pendulum_dynamics,
     lb,
@@ -97,7 +98,8 @@ cm, time = benchmark(
     state_syms,
     parameter_syms,
     policy_search = true,
-    endpoint_check = (x) -> ≈([sin(x[1]), cos(x[1]), x[2]], [0, -1, 0], atol=5e-3),
+    endpoint_check,
+    classifier = (V, V̇, x) -> V̇ < zero(V̇) || endpoint_check(x),
     init_params = ps
 )
 ```
@@ -112,7 +114,6 @@ using NeuralPDE, NeuralLyapunov, Lux
 import Boltz.Layers: PeriodicEmbedding
 using Random, StableRNGs
 
-rng = StableRNG(0)
 Random.seed!(200)
 
 # Define dynamics and domain
@@ -142,7 +143,7 @@ chain = [Chain(
              PeriodicEmbedding([1], [2π]),
              Dense(3, dim_hidden, tanh),
              Dense(dim_hidden, dim_hidden, tanh),
-             Dense(dim_hidden, 1, use_bias = false)
+             Dense(dim_hidden, 1)
          ) for _ in 1:dim_output]
 ps = Lux.initialparameters(StableRNG(0), chain)
 
@@ -209,6 +210,7 @@ endpoint_check = (x) -> ≈([sin(x[1]), cos(x[1]), x[2]], [0, -1, 0], atol=5e-3)
     parameter_syms,
     policy_search = true,
     endpoint_check,
+    classifier = (V, V̇, x) -> V̇ < zero(V̇) || endpoint_check(x),
     verbose = true,
     init_params = ps
 );
@@ -238,7 +240,6 @@ all(endpoint_check.(endpoints) .== actual)
 ```
 
 Similarly, the `predicted` labels are the results of the neural Lyapunov classifier.
-In this case, we used the default classifier, which just checks for negative values of ``\dot{V}``.
 
 ```@example benchmarking
 classifier = (V, V̇, x) -> V̇ < zero(V̇) || endpoint_check(x)