diff --git a/src/main/java/net/echo/brain4j/activation/Activations.java b/src/main/java/net/echo/brain4j/activation/Activations.java
index 10022d4..05b2c13 100644
--- a/src/main/java/net/echo/brain4j/activation/Activations.java
+++ b/src/main/java/net/echo/brain4j/activation/Activations.java
@@ -6,6 +6,7 @@ public enum Activations {
 
     LINEAR(new LinearActivation()),
     RELU(new ReLUActivation()),
+    GELU(new GELUActivation()),
     LEAKY_RELU(new LeakyReLUActivation()),
     SIGMOID(new SigmoidActivation()),
     SOFTMAX(new SoftmaxActivation()),
diff --git a/src/main/java/net/echo/brain4j/activation/impl/GELUActivation.java b/src/main/java/net/echo/brain4j/activation/impl/GELUActivation.java
index 19d072f..fe5cffa 100644
--- a/src/main/java/net/echo/brain4j/activation/impl/GELUActivation.java
+++ b/src/main/java/net/echo/brain4j/activation/impl/GELUActivation.java
@@ -26,7 +26,7 @@ public double getDerivative(double input) {
     @Override
     public void apply(List<Neuron> neurons) {
         for (Neuron neuron : neurons) {
-            neuron.setValue(activate(neuron.getValue())); // Applicare GELU al valore di ciascun neurone
+            neuron.setValue(activate(neuron.getValue()));
         }
     }
 }
diff --git a/src/main/java/net/echo/brain4j/layer/impl/LayerNorm.java b/src/main/java/net/echo/brain4j/layer/impl/LayerNorm.java
index f7071cd..580d9e3 100644
--- a/src/main/java/net/echo/brain4j/layer/impl/LayerNorm.java
+++ b/src/main/java/net/echo/brain4j/layer/impl/LayerNorm.java
@@ -35,16 +35,20 @@ public void applyFunction(Layer previous) {
         }
     }
 
-    public void normalize(Vector input) {
+    public Vector normalize(Vector input) {
         double mean = input.mean();
         double variance = input.variance(mean);
 
+        double denominator = Math.sqrt(variance + epsilon);
+
         for (int i = 0; i < input.size(); i++) {
             double value = input.get(i);
-            double normalized = (value - mean) / Math.sqrt(variance + epsilon);
+            double normalized = (value - mean) / denominator;
 
             input.set(i, normalized);
         }
+
+        return input;
     }
 
     private double calculateMean(List<Neuron> inputs) {
diff --git a/src/main/java/net/echo/brain4j/model/Model.java b/src/main/java/net/echo/brain4j/model/Model.java
index 685def2..4690e6f 100644
--- a/src/main/java/net/echo/brain4j/model/Model.java
+++ b/src/main/java/net/echo/brain4j/model/Model.java
@@ -166,6 +166,7 @@ public Vector predict(Vector input) {
         }
 
         Layer outputLayer = layers.get(layers.size() - 1);
+
         double[] output = new double[outputLayer.getNeurons().size()];
 
         for (int i = 0; i < output.length; i++) {
diff --git a/src/main/java/net/echo/brain4j/nlp/model/Transformer.java b/src/main/java/net/echo/brain4j/nlp/model/Transformer.java
index c3b3553..4af0c6e 100644
--- a/src/main/java/net/echo/brain4j/nlp/model/Transformer.java
+++ b/src/main/java/net/echo/brain4j/nlp/model/Transformer.java
@@ -38,6 +38,11 @@ public List<Vector> transform(List<Vector> embeddings) {
             }
         }
 
+        for (Vector vector : resulting) {
+            System.out.println("Resulting");
+            System.out.println(vector);
+        }
+
         List<Vector> concatEmbeddings = new ArrayList<>(resulting);
 
         for (Vector embedding : resulting) {
diff --git a/src/main/java/net/echo/brain4j/nlp/model/layers/TransformerEncoder.java b/src/main/java/net/echo/brain4j/nlp/model/layers/TransformerEncoder.java
index 72fcdb9..e149fdb 100644
--- a/src/main/java/net/echo/brain4j/nlp/model/layers/TransformerEncoder.java
+++ b/src/main/java/net/echo/brain4j/nlp/model/layers/TransformerEncoder.java
@@ -23,7 +23,7 @@ public TransformerEncoder(int numHeads, int contextSize, int dimension, double t
         this.attention = new MultiHeadAttention(numHeads, contextSize, dimension, temperature);
         this.feedForward = new Model(
                 new DenseLayer(dimension, Activations.LINEAR),
-                new DenseLayer(4 * dimension, Activations.RELU),
+                new DenseLayer(4 * dimension, Activations.GELU),
                 new DenseLayer(dimension, Activations.LINEAR)
         );
     }
@@ -47,14 +47,18 @@ public List<Vector> transform(List<Vector> embeddings) {
 
         for (Vector vector : embeddings) {
             Vector embedding = Vector.of(vector.toArray());
-            normalizer.normalize(embedding);
+            embedding = normalizer.normalize(embedding);
 
             Vector attended = attention.attend(embedding.toArray());
-            normalizer.normalize(attended);
+            attended = normalizer.normalize(attended);
 
+            System.out.println("Attended");
+            System.out.println(attended);
             Vector result = feedForward.predict(attended);
-            normalizer.normalize(result);
+            result = normalizer.normalize(result);
 
+            System.out.println("Result");
+            System.out.println(result);
             resulting.add(result);
         }
 
diff --git a/src/main/java/net/echo/brain4j/utils/Vector.java b/src/main/java/net/echo/brain4j/utils/Vector.java
index 10bcaf9..98b2c0a 100644
--- a/src/main/java/net/echo/brain4j/utils/Vector.java
+++ b/src/main/java/net/echo/brain4j/utils/Vector.java
@@ -16,7 +16,7 @@ private Vector(double... data) {
     }
 
     public static Vector of(double... data) {
-        return new Vector(data);
+        return new Vector(Arrays.copyOf(data, data.length));
     }
 
     public static Vector random(int size) {
diff --git a/src/test/java/antiswear/ToxicCommentClassification.java b/src/test/java/antiswear/ToxicCommentClassification.java
index 44c2ed8..9925507 100644
--- a/src/test/java/antiswear/ToxicCommentClassification.java
+++ b/src/test/java/antiswear/ToxicCommentClassification.java
@@ -41,14 +41,9 @@ public static void main(String[] args) {
         String phrase = "the pen is on the table";
         var embeddings = getEmbeddings(vectors, phrase);
 
-        for (var embed : embeddings) {
-            System.out.println(embed);
-        }
-
         List<Vector> output = transformer.transform(embeddings);
 
         for (Vector vector : output) {
-            System.out.println("------------------------------");
             System.out.println(vector);
         }
     }