Add model visualization

src/main/java/com/naaturel/ANN/Main.java

@@ -1,23 +1,19 @@
 package com.naaturel.ANN;
 
 import com.naaturel.ANN.domain.abstraction.Model;
-import com.naaturel.ANN.domain.abstraction.Network;
 import com.naaturel.ANN.domain.model.neuron.Neuron;
 import com.naaturel.ANN.domain.abstraction.Trainer;
-import com.naaturel.ANN.implementation.gradientDescent.Linear;
-import com.naaturel.ANN.implementation.multiLayers.Sigmoid;
 import com.naaturel.ANN.implementation.multiLayers.TanH;
 import com.naaturel.ANN.implementation.training.GradientBackpropagationTraining;
 import com.naaturel.ANN.infrastructure.config.ConfigDto;
 import com.naaturel.ANN.infrastructure.config.ConfigLoader;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
-import com.naaturel.ANN.infrastructure.dataset.DataSetEntry;
 import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
 import com.naaturel.ANN.domain.model.neuron.*;
-import com.naaturel.ANN.infrastructure.graph.GraphVisualizer;
+import com.naaturel.ANN.infrastructure.visualization.GraphVisualizer;
 import com.naaturel.ANN.infrastructure.persistence.ModelSnapshot;
+import com.naaturel.ANN.infrastructure.visualization.ModelVisualizer;
 
-import java.io.Console;
 import java.util.*;
 
 public class Main {
@@ -53,6 +49,10 @@ public class Main {
             network = snapshot.getModel();
         }
         //plotGraph(dataset, network);
+
+        new ModelVisualizer(network)
+                .withWeights(true)
+                .display();
     }
 
     private static FullyConnectedNetwork createNetwork(int[] neuronPerLayer, int nbrInput){
@@ -86,16 +86,16 @@ public class Main {
     private static void plotGraph(DataSet dataset, Model network){
         GraphVisualizer visualizer = new GraphVisualizer();
 
-        for (DataSetEntry entry : dataset) {
+        /*for (DataSetEntry entry : dataset) {
             List<Float> label = dataset.getLabelsAsFloat(entry);
             label.forEach(l -> {
                 visualizer.addPoint("Label " + l,
                         entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
             });
-        }
+        }*/
 
-        float min = -5F;
-        float max = 5F;
+        float min = -50F;
+        float max = 50F;
         float step = 0.03F;
         for (float x = min; x < max; x+=step){
             for (float y = min; y < max; y+=step){