Minor fixes

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

@@ -2,6 +2,7 @@ package com.naaturel.ANN;
 
 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;
@@ -18,12 +19,12 @@ public class Main {
 
     public static void main(String[] args){
 
-        int nbrClass = 3;
+        int nbrClass = 1;
 
         DataSet dataset = new DatasetExtractor()
-                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_4_14.csv", nbrClass);
+                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_2_9.csv", nbrClass);
 
-        int[] neuronPerLayer = new int[]{27, dataset.getNbrLabels()};
+        int[] neuronPerLayer = new int[]{10, 5, 5, dataset.getNbrLabels()};
         int nbrInput = dataset.getNbrInputs();
 
         List<Layer> layers = new ArrayList<>();
@@ -51,7 +52,7 @@ public class Main {
         FullyConnectedNetwork network = new FullyConnectedNetwork(layers.toArray(new Layer[0]));
 
         Trainer trainer = new GradientBackpropagationTraining();
-        trainer.train(0.0001F, 15000, network, dataset);
+        trainer.train(0.0005F, 15000, network, dataset);
 
         GraphVisualizer visualizer = new GraphVisualizer();
 
@@ -60,13 +61,13 @@ public class Main {
             visualizer.addPoint("Label " + label.getFirst(), entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
         }
 
-        float min = -5F;
+        float min = 0F;
-        float max = 5F;
+        float max = 15F;
-        float step = 0.025F;
+        float step = 0.03F;
         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();
-                float predSeries = prediction > 0.5F ? 1 : 0;
+                float predSeries = prediction > 0.5F ? 1 : -1;
                 visualizer.addPoint(Float.toString(predSeries), x, y);
             }
         }

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 <= 0.00F || ctx.epoch > epoch)
+                .stopCondition(ctx -> ctx.globalLoss <= 0.001F || ctx.epoch > epoch)
                 .beforeEpoch(ctx -> {
                     ctx.globalLoss = 0.0F;
                 })
                 .afterEpoch(ctx -> ctx.globalLoss /= dataset.size())
-                .withVerbose(true, epoch/10)
+                .withVerbose(true,epoch/10)
                 .withTimeMeasurement(true)
                 .run(context);
     }

src/main/resources/assets/table_2_9.csv

@@ -19,3 +19,10 @@
 4,7,-1
 4,9,1
 4,10,1
+2,6,-1
+7,7,-1
+5,9,1
+9,10,1
+7,1,-1
+5,0,1
+9,5,1