diff --git a/src/main/java/com/naaturel/ANN/Main.java b/src/main/java/com/naaturel/ANN/Main.java
index 8f52b09..c6a5562 100644
--- a/src/main/java/com/naaturel/ANN/Main.java
+++ b/src/main/java/com/naaturel/ANN/Main.java
@@ -38,7 +38,7 @@ public class Main {
         Layer layer = new Layer(neurons);
         Network network = new Network(List.of(layer));
 
-        Trainer trainer = new AdalineTraining();
+        Trainer trainer = new GradientDescentTraining();
         trainer.train(network, dataset);
 
     }
diff --git a/src/main/java/com/naaturel/ANN/domain/model/training/TrainingPipeline.java b/src/main/java/com/naaturel/ANN/domain/model/training/TrainingPipeline.java
index fc8aa1a..5e8865d 100644
--- a/src/main/java/com/naaturel/ANN/domain/model/training/TrainingPipeline.java
+++ b/src/main/java/com/naaturel/ANN/domain/model/training/TrainingPipeline.java
@@ -81,25 +81,7 @@ public class TrainingPipeline {
             System.out.printf("[Training finished in %.3fs]", (end-start)/1000.0);
         }
 
-        AtomicInteger neuronIndex = new AtomicInteger(0);
-        ctx.model.forEachNeuron(n -> {
-            List<Float> weights = new ArrayList<>();
-            n.forEachSynapse(syn -> weights.add(syn.getWeight()));
-
-            float b  = weights.get(0);
-            float w1 = weights.get(1);
-            float w2 = weights.get(2);
-
-            this.visualizer.addEquation("boundary_" + neuronIndex.getAndIncrement(), w1, w2, b, -3, 3);
-        });
-        int i = 0;
-        for(DataSetEntry entry : ctx.dataset){
-            List<Input> inputs = entry.getData();
-            this.visualizer.addPoint("p"+i, inputs.get(0).getValue(), inputs.get(1).getValue());
-            this.visualizer.addPoint("p"+i, inputs.get(0).getValue()+0.01F, inputs.get(1).getValue()+0.01F);
-            i++;
-        }
-        this.visualizer.build();
+        if(this.visualization) this.visualize(ctx);
     }
 
     private void executeSteps(TrainingContext ctx){
@@ -123,4 +105,26 @@ public class TrainingPipeline {
         ctx.epoch += 1;
     }
 
+    private void visualize(TrainingContext ctx){
+        AtomicInteger neuronIndex = new AtomicInteger(0);
+        ctx.model.forEachNeuron(n -> {
+            List<Float> weights = new ArrayList<>();
+            n.forEachSynapse(syn -> weights.add(syn.getWeight()));
+
+            float b  = weights.get(0);
+            float w1 = weights.get(1);
+            float w2 = weights.get(2);
+
+            this.visualizer.addEquation("boundary_" + neuronIndex.getAndIncrement(), w1, w2, b, -3, 3);
+        });
+        int i = 0;
+        for(DataSetEntry entry : ctx.dataset){
+            List<Input> inputs = entry.getData();
+            this.visualizer.addPoint("p"+i, inputs.get(0).getValue(), inputs.get(1).getValue());
+            this.visualizer.addPoint("p"+i, inputs.get(0).getValue()+0.01F, inputs.get(1).getValue()+0.01F);
+            i++;
+        }
+        this.visualizer.build();
+    }
+
 }
diff --git a/src/main/java/com/naaturel/ANN/implementation/training/GradientDescentTraining.java b/src/main/java/com/naaturel/ANN/implementation/training/GradientDescentTraining.java
index 2d98ccb..d40ab28 100644
--- a/src/main/java/com/naaturel/ANN/implementation/training/GradientDescentTraining.java
+++ b/src/main/java/com/naaturel/ANN/implementation/training/GradientDescentTraining.java
@@ -12,6 +12,7 @@ import com.naaturel.ANN.implementation.gradientDescent.SquareLossStrategy;
 import com.naaturel.ANN.implementation.simplePerceptron.SimpleDeltaStrategy;
 import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStrategy;
 import com.naaturel.ANN.implementation.training.steps.*;
+import com.naaturel.ANN.infrastructure.graph.GraphVisualizer;
 
 import java.util.ArrayList;
 import java.util.List;
@@ -27,7 +28,7 @@ public class GradientDescentTraining implements Trainer {
         GradientDescentTrainingContext context = new GradientDescentTrainingContext();
         context.dataset = dataset;
         context.model = model;
-        context.learningRate = 0.0011F;
+        context.learningRate = 0.0005F;
         context.correctorTerms =  new ArrayList<>();
 
         List<TrainingStep> steps = List.of(
@@ -38,7 +39,7 @@ public class GradientDescentTraining implements Trainer {
         );
 
         new TrainingPipeline(steps)
-            .stopCondition(ctx -> ctx.globalLoss <= 0.08F || ctx.epoch > 500)
+            .stopCondition(ctx -> ctx.globalLoss <= 0.00F || ctx.epoch > 50000)
             .beforeEpoch(ctx -> {
                 GradientDescentTrainingContext gdCtx = (GradientDescentTrainingContext) ctx;
                 gdCtx.globalLoss = 0.0F;
@@ -49,8 +50,9 @@ public class GradientDescentTraining implements Trainer {
                 context.globalLoss /= context.dataset.size();
                 new GradientDescentCorrectionStrategy(context).apply();
             })
-            .withVerbose(true)
+            //.withVerbose(true)
             .withTimeMeasurement(true)
+            .withVisualization(false, new GraphVisualizer())
             .run(context);
     }