diff --git a/src/main/java/com/naaturel/ANN/Main.java b/src/main/java/com/naaturel/ANN/Main.java
index 58de78f..1871013 100644
--- a/src/main/java/com/naaturel/ANN/Main.java
+++ b/src/main/java/com/naaturel/ANN/Main.java
@@ -23,7 +23,7 @@ public class Main {
         DataSet dataset = new DatasetExtractor()
                 .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_4_14.csv", nbrClass);
 
-        int[] neuronPerLayer = new int[]{3, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 37, dataset.getNbrLabels()};
+        int[] neuronPerLayer = new int[]{27, dataset.getNbrLabels()};
         int nbrInput = dataset.getNbrInputs();
 
         List<Layer> layers = new ArrayList<>();
@@ -51,7 +51,7 @@ public class Main {
         FullyConnectedNetwork network = new FullyConnectedNetwork(layers.toArray(new Layer[0]));
 
         Trainer trainer = new GradientBackpropagationTraining();
-        trainer.train(0.01F, 5000, network, dataset);
+        trainer.train(0.0001F, 15000, network, dataset);
 
         GraphVisualizer visualizer = new GraphVisualizer();
 
@@ -60,9 +60,9 @@ public class Main {
             visualizer.addPoint("Label " + label.getFirst(), entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
         }
 
-        float min = -3F;
-        float max = 2F;
-        float step = 0.01F;
+        float min = -5F;
+        float max = 5F;
+        float step = 0.025F;
         for (float x = min; x < max; x+=step){
             for (float y = min; y < max; y+=step){
                 float prediction = network.predict(List.of(new Input(x), new Input(y))).getFirst();
@@ -71,8 +71,7 @@ public class Main {
             }
         }
 
-
-        visualizer.buildScatterGraph();
+        visualizer.buildScatterGraph((int)min-1, (int)max+1);
     }
 
 }
diff --git a/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java b/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java
index 2383b95..3c443b1 100644
--- a/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java
+++ b/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java
@@ -35,7 +35,7 @@ public class GradientBackpropagationTraining implements Trainer {
                     ctx.globalLoss = 0.0F;
                 })
                 .afterEpoch(ctx -> ctx.globalLoss /= dataset.size())
-                .withVerbose(false, epoch/10)
+                .withVerbose(true, epoch/10)
                 .withTimeMeasurement(true)
                 .run(context);
     }
diff --git a/src/main/java/com/naaturel/ANN/infrastructure/graph/GraphVisualizer.java b/src/main/java/com/naaturel/ANN/infrastructure/graph/GraphVisualizer.java
index cec7d14..9314764 100644
--- a/src/main/java/com/naaturel/ANN/infrastructure/graph/GraphVisualizer.java
+++ b/src/main/java/com/naaturel/ANN/infrastructure/graph/GraphVisualizer.java
@@ -42,13 +42,13 @@ public class GraphVisualizer {
     }
 
 
-    public void buildScatterGraph(){
+    public void buildScatterGraph(int lower, int upper){
         JFreeChart chart = ChartFactory.createScatterPlot(
                 "Predictions", "X", "Y", dataset
         );
         XYPlot plot = chart.getXYPlot();
-        plot.getDomainAxis().setRange(-2, 2);
-        plot.getRangeAxis().setRange(-2, 2);
+        plot.getDomainAxis().setRange(lower, upper);
+        plot.getRangeAxis().setRange(lower, upper);
 
         JFrame frame = new JFrame("Predictions");
         frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);