diff --git a/src/main/java/com/naaturel/ANN/Main.java b/src/main/java/com/naaturel/ANN/Main.java
index 723da24..58de78f 100644
--- a/src/main/java/com/naaturel/ANN/Main.java
+++ b/src/main/java/com/naaturel/ANN/Main.java
@@ -11,6 +11,7 @@ import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
 import com.naaturel.ANN.domain.model.neuron.*;
 import com.naaturel.ANN.infrastructure.graph.GraphVisualizer;
 
+import java.io.Console;
 import java.util.*;
 
 public class Main {
@@ -40,19 +41,19 @@ public class Main {
 
                 Bias bias =  new Bias(new Weight());
 
-                Neuron n = new Neuron(syns, bias, new TanH());
+                Neuron n = new Neuron(syns.toArray(new Synapse[0]), bias, new TanH());
                 neurons.add(n);
             }
-            Layer layer = new Layer(neurons);
+            Layer layer = new Layer(neurons.toArray(new Neuron[0]));
             layers.add(layer);
         }
 
-        FullyConnectedNetwork network = new FullyConnectedNetwork(layers);
+        FullyConnectedNetwork network = new FullyConnectedNetwork(layers.toArray(new Layer[0]));
 
         Trainer trainer = new GradientBackpropagationTraining();
-        trainer.train(0.001F, 1000, network, dataset);
+        trainer.train(0.01F, 5000, network, dataset);
 
-        /*GraphVisualizer visualizer = new GraphVisualizer();
+        GraphVisualizer visualizer = new GraphVisualizer();
 
         for (DataSetEntry entry : dataset) {
             List<Float> label = dataset.getLabelsAsFloat(entry);
@@ -71,7 +72,7 @@ public class Main {
         }
 
 
-        visualizer.buildScatterGraph();*/
+        visualizer.buildScatterGraph();
     }
 
 }
diff --git a/src/main/java/com/naaturel/ANN/domain/model/neuron/FullyConnectedNetwork.java b/src/main/java/com/naaturel/ANN/domain/model/neuron/FullyConnectedNetwork.java
index f2a63b8..dfd51ef 100644
--- a/src/main/java/com/naaturel/ANN/domain/model/neuron/FullyConnectedNetwork.java
+++ b/src/main/java/com/naaturel/ANN/domain/model/neuron/FullyConnectedNetwork.java
@@ -14,10 +14,10 @@ import java.util.function.Consumer;
  */
 public class FullyConnectedNetwork implements Model {
 
-    private final List<Layer> layers;
+    private final Layer[] layers;
     private final Map<Neuron, List<Neuron>> connectionMap;
     private final Map<Neuron, Integer> layerIndexByNeuron;
-    public FullyConnectedNetwork(List<Layer> layers) {
+    public FullyConnectedNetwork(Layer[] layers) {
         this.layers = layers;
         this.connectionMap = this.createConnectionMap();
         this.layerIndexByNeuron = this.createNeuronIndex();
@@ -53,17 +53,22 @@ public class FullyConnectedNetwork implements Model {
 
     @Override
     public void forEachSynapse(Consumer<Synapse> consumer) {
-        this.layers.forEach(layer -> layer.forEachSynapse(consumer));
+        for(Layer l : this.layers){
+            l.forEachSynapse(consumer);
+        }
     }
 
     @Override
     public void forEachNeuron(Consumer<Neuron> consumer) {
-        this.layers.forEach(layer -> layer.forEachNeuron(consumer));
+        for(Layer l : this.layers){
+            l.forEachNeuron(consumer);
+        }
     }
 
     @Override
     public void forEachOutputNeurons(Consumer<Neuron> consumer) {
-        this.layers.getLast().forEachNeuron(consumer);
+        int lastIndex = this.layers.length-1;
+        this.layers[lastIndex].forEachNeuron(consumer);
     }
 
     @Override
@@ -74,16 +79,16 @@ public class FullyConnectedNetwork implements Model {
     @Override
     public int indexInLayerOf(Neuron n) {
         int layerIndex = this.layerIndexByNeuron.get(n);
-        return this.layers.get(layerIndex).indexInLayerOf(n);
+        return this.layers[layerIndex].indexInLayerOf(n);
     }
 
     private Map<Neuron, List<Neuron>> createConnectionMap() {
         Map<Neuron, List<Neuron>> res = new HashMap<>();
 
-        for (int i = 0; i < this.layers.size() - 1; i++) {
+        for (int i = 0; i < this.layers.length - 1; i++) {
             List<Neuron> nextLayerNeurons = new ArrayList<>();
-            this.layers.get(i + 1).forEachNeuron(nextLayerNeurons::add);
-            this.layers.get(i).forEachNeuron(n -> res.put(n, nextLayerNeurons));
+            this.layers[i + 1].forEachNeuron(nextLayerNeurons::add);
+            this.layers[i].forEachNeuron(n -> res.put(n, nextLayerNeurons));
         }
         return res;
     }
@@ -91,10 +96,10 @@ public class FullyConnectedNetwork implements Model {
     private Map<Neuron, Integer> createNeuronIndex() {
         Map<Neuron, Integer> res = new HashMap<>();
         AtomicInteger index = new AtomicInteger(0);
-        this.layers.forEach(l -> {
+        for(Layer l : this.layers){
             l.forEachNeuron(n -> res.put(n, index.get()));
             index.incrementAndGet();
-        });
+        }
         return res;
     }
 }
diff --git a/src/main/java/com/naaturel/ANN/domain/model/neuron/Layer.java b/src/main/java/com/naaturel/ANN/domain/model/neuron/Layer.java
index 36c487d..3d3039f 100644
--- a/src/main/java/com/naaturel/ANN/domain/model/neuron/Layer.java
+++ b/src/main/java/com/naaturel/ANN/domain/model/neuron/Layer.java
@@ -10,10 +10,10 @@ import java.util.function.Consumer;
 
 public class Layer implements Model {
 
-    private final List<Neuron> neurons;
+    private final Neuron[] neurons;
     private final Map<Neuron, Integer> neuronIndex;
 
-    public Layer(List<Neuron> neurons) {
+    public Layer(Neuron[] neurons) {
         this.neurons = neurons;
         this.neuronIndex = createNeuronIndex();
     }
@@ -39,7 +39,7 @@ public class Layer implements Model {
 
     @Override
     public int neuronCount() {
-        return this.neurons.size();
+        return this.neurons.length;
     }
 
     @Override
@@ -49,17 +49,21 @@ public class Layer implements Model {
 
     @Override
     public void forEachNeuron(Consumer<Neuron> consumer) {
-        this.neurons.forEach(consumer);
+        for (Neuron n : this.neurons){
+            consumer.accept(n);
+        }
     }
 
     @Override
     public void forEachSynapse(Consumer<Synapse> consumer) {
-        this.neurons.forEach(neuron -> neuron.forEachSynapse(consumer));
+        for (Neuron n : this.neurons){
+            n.forEachSynapse(consumer);
+        }
     }
 
     @Override
     public void forEachOutputNeurons(Consumer<Neuron> consumer) {
-        this.neurons.forEach(consumer);
+        this.forEachNeuron(consumer);
     }
 
     @Override
@@ -70,7 +74,7 @@ public class Layer implements Model {
     private Map<Neuron, Integer> createNeuronIndex() {
         Map<Neuron, Integer> res = new HashMap<>();
         int[] index = {0};
-        this.neurons.forEach(n -> {
+        this.forEachNeuron(n -> {
             res.put(n, index[0]++);
         });
         return res;
diff --git a/src/main/java/com/naaturel/ANN/domain/model/neuron/Neuron.java b/src/main/java/com/naaturel/ANN/domain/model/neuron/Neuron.java
index 1ca4327..587febc 100644
--- a/src/main/java/com/naaturel/ANN/domain/model/neuron/Neuron.java
+++ b/src/main/java/com/naaturel/ANN/domain/model/neuron/Neuron.java
@@ -7,13 +7,13 @@ import java.util.function.Consumer;
 
 public class Neuron implements Model {
 
-    protected List<Synapse> synapses;
+    protected Synapse[] synapses;
     protected Bias bias;
     protected ActivationFunction activationFunction;
     protected Float output;
     protected Float weightedSum;
 
-    public Neuron(List<Synapse> synapses, Bias bias, ActivationFunction func){
+    public Neuron(Synapse[] synapses, Bias bias, ActivationFunction func){
         this.synapses = synapses;
         this.bias = bias;
         this.activationFunction = func;
@@ -26,12 +26,12 @@ public class Neuron implements Model {
     }
 
     public void updateWeight(int index, Weight weight) {
-        this.synapses.get(index).setWeight(weight.getValue());
+        this.synapses[index].setWeight(weight.getValue());
     }
 
     protected void setInputs(List<Input> inputs){
-        for(int i = 0;  i < inputs.size() && i < synapses.size(); i++){
-            Synapse syn = this.synapses.get(i);
+        for(int i = 0;  i < inputs.size() && i < synapses.length; i++){
+            Synapse syn = this.synapses[i];
             syn.setInput(inputs.get(i));
         }
     }
@@ -45,7 +45,7 @@ public class Neuron implements Model {
     }
 
     public float getWeight(int index){
-        return this.synapses.get(index).getWeight();
+        return this.synapses[index].getWeight();
     }
 
     public float getWeightedSum(){
@@ -63,7 +63,7 @@ public class Neuron implements Model {
 
     @Override
     public int synCount() {
-        return this.synapses.size()+1; //take the bias into account
+        return this.synapses.length+1; //take the bias into account
     }
 
     @Override
@@ -91,7 +91,9 @@ public class Neuron implements Model {
     @Override
     public void forEachSynapse(Consumer<Synapse> consumer) {
         consumer.accept(this.bias);
-        this.synapses.forEach(consumer);
+        for (Synapse syn : this.synapses){
+            consumer.accept(syn);
+        }
     }
 
     @Override
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 833ae01..2383b95 100644
--- a/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java
+++ b/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java
@@ -30,12 +30,12 @@ public class GradientBackpropagationTraining implements Trainer {
         );
 
         new TrainingPipeline(steps)
-                .stopCondition(ctx -> ctx.globalLoss <= 0.0001F || ctx.epoch > epoch)
+                .stopCondition(ctx -> ctx.globalLoss <= 0.00F || ctx.epoch > epoch)
                 .beforeEpoch(ctx -> {
                     ctx.globalLoss = 0.0F;
                 })
                 .afterEpoch(ctx -> ctx.globalLoss /= dataset.size())
-                .withVerbose(true, 100)
+                .withVerbose(false, epoch/10)
                 .withTimeMeasurement(true)
                 .run(context);
     }