Fix BNN doc to work with Mooncake

tutorials/03-bayesian-neural-network/index.qmd

@@ -37,18 +37,18 @@ rng = Random.default_rng()
 Random.seed!(rng, 1234)
 
 # Generate artificial data
-x1s = rand(rng, Float32, M) * 4.5f0;
-x2s = rand(rng, Float32, M) * 4.5f0;
+x1s = rand(rng, M) * 4.5f0;
+x2s = rand(rng, M) * 4.5f0;
 xt1s = Array([[x1s[i] + 0.5f0; x2s[i] + 0.5f0] for i in 1:M])
-x1s = rand(rng, Float32, M) * 4.5f0;
-x2s = rand(rng, Float32, M) * 4.5f0;
+x1s = rand(rng, M) * 4.5f0;
+x2s = rand(rng, M) * 4.5f0;
 append!(xt1s, Array([[x1s[i] - 5.0f0; x2s[i] - 5.0f0] for i in 1:M]))
 
-x1s = rand(rng, Float32, M) * 4.5f0;
-x2s = rand(rng, Float32, M) * 4.5f0;
+x1s = rand(rng, M) * 4.5f0;
+x2s = rand(rng, M) * 4.5f0;
 xt0s = Array([[x1s[i] + 0.5f0; x2s[i] - 5.0f0] for i in 1:M])
-x1s = rand(rng, Float32, M) * 4.5f0;
-x2s = rand(rng, Float32, M) * 4.5f0;
+x1s = rand(rng, M) * 4.5f0;
+x2s = rand(rng, M) * 4.5f0;
 append!(xt0s, Array([[x1s[i] - 5.0f0; x2s[i] + 0.5f0] for i in 1:M]))
 
 # Store all the data for later
@@ -189,7 +189,7 @@ const nn = StatefulLuxLayer{true}(nn_initial, nothing, st)
     parameters ~ MvNormal(zeros(nparameters), Diagonal(abs2.(sigma .* ones(nparameters))))
 
     # Forward NN to make predictions
-    preds = Lux.apply(nn, xs, vector_to_parameters(parameters, ps))
+    preds = Lux.apply(nn, xs, f64(vector_to_parameters(parameters, ps)))
 
     # Observe each prediction.
     for i in eachindex(ts)