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diff --git a/artifacts/results/mnist_architectures.jls b/artifacts/results/mnist_architectures.jls
index 3b5e1755cb6ab5c7c2ef31eb799ed1ff27020615..ff591e713aa432da892c1adead65f15de1c253b7 100644
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diff --git a/artifacts/results/mnist_model_performance.csv b/artifacts/results/mnist_model_performance.csv
index 6fcf69754ab576fde6c523972f2249f1b16aeb15..c61e7d1849d84f1250982e1555348ad85433b9f5 100644
--- a/artifacts/results/mnist_model_performance.csv
+++ b/artifacts/results/mnist_model_performance.csv
@@ -1,5 +1,5 @@
 acc,precision,f1score,mod_name,dataname
-0.8973,0.8966409231713075,0.8961958418891548,JEM Ensemble,MNIST
-0.9103,0.912803491500963,0.9091350554105558,MLP,MNIST
-0.9304,0.9296691361848305,0.9296246536238468,MLP Ensemble,MNIST
-0.8399,0.8454738316353376,0.8378058821466077,JEM,MNIST
+0.9154,0.9156058593092286,0.9144154048006502,JEM Ensemble,MNIST
+0.9651,0.9649131785895403,0.9647206151544168,MLP,MNIST
+0.9745,0.9743410428044881,0.9743191770867725,MLP Ensemble,MNIST
+0.8533999999999999,0.8673648922304751,0.8529767185660582,JEM,MNIST
diff --git a/artifacts/results/mnist_model_performance.jls b/artifacts/results/mnist_model_performance.jls
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diff --git a/artifacts/results/mnist_models.jls b/artifacts/results/mnist_models.jls
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diff --git a/artifacts/results/mnist_vae.jls b/artifacts/results/mnist_vae.jls
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diff --git a/artifacts/results/mnist_vae_weak.jls b/artifacts/results/mnist_vae_weak.jls
index ebd07d30a83332c40c823b8399ca1760ef72fb0f..c0d6c2a6eb82fa5d9dfc10d86de3aaca234bf441 100644
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diff --git a/notebooks/mnist.qmd b/notebooks/mnist.qmd
index c3774a14083b13774be4e6da99c8b4025e6c2d18..bc4da42932630634e0381dec321c57f7e098abe5 100644
--- a/notebooks/mnist.qmd
+++ b/notebooks/mnist.qmd
@@ -164,11 +164,11 @@ end
 
 ```{julia}
 # Hyper:
-_retrain = false
-_regen = false
+_retrain = true
+_regen = true
 
 # Data:
-n_obs = 10000
+n_obs = nothing
 counterfactual_data = load_mnist(n_obs)
 counterfactual_data.X = pre_process.(counterfactual_data.X)
 counterfactual_data.generative_model = vae
@@ -185,7 +185,7 @@ First, let's create a couple of image classifier architectures:
 
 ```{julia}
 # Model parameters:
-epochs = 10
+epochs = 25
 batch_size = minimum([Int(round(n_obs/10)), 128])
 n_hidden = 128
 activation = Flux.swish
@@ -207,7 +207,7 @@ sampler = ConditionalSampler(
     input_size=(input_dim,), 
     batch_size=10,
 )
-α = [1.0,1.0,1e-2]      # penalty strengths
+α = [1.0,1.0,25e-3]      # penalty strengths
 ```
 
 ```{julia}
@@ -496,7 +496,7 @@ end
 # Final model:
 lenet = NeuralNetworkClassifier(
     builder=LeNetBuilder(5, 6, 16),
-    epochs=epochs,
+    epochs=50,
     batch_size=batch_size,
     finaliser=_finaliser,
     loss=_loss,