Implement gradient backpropagation stub

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

@@ -2,6 +2,9 @@ package com.naaturel.ANN;
 
 import com.naaturel.ANN.domain.model.neuron.Neuron;
 import com.naaturel.ANN.domain.abstraction.Trainer;
+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.dataset.DataSet;
 import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
 import com.naaturel.ANN.domain.model.neuron.*;
@@ -35,7 +38,7 @@ public class Main {
 
                 Bias bias =  new Bias(new Weight(0));
 
-                Neuron n = new Neuron(syns, bias, new Linear());
+                Neuron n = new Neuron(syns, bias, new Sigmoid(1));
                 neurons.add(n);
             }
             Layer layer = new Layer(neurons);
@@ -43,7 +46,7 @@ public class Main {
         }
         Network network = new Network(layers);
 
-        Trainer trainer = new GradientDescentTraining();
+        Trainer trainer = new GradientBackpropagationTraining();
         trainer.train(network, dataset);
 
     }

src/main/java/com/naaturel/ANN/implementation/multiLayers/Sigmoid.java

@@ -0,0 +1,18 @@
+package com.naaturel.ANN.implementation.multiLayers;
+
+import com.naaturel.ANN.domain.abstraction.ActivationFunction;
+import com.naaturel.ANN.domain.model.neuron.Neuron;
+
+public class Sigmoid implements ActivationFunction {
+
+    private float steepness;
+
+    public Sigmoid(float steepness) {
+        this.steepness = steepness;
+    }
+
+    @Override
+    public float accept(Neuron n) {
+        return (float) (1.0/(1.0 + Math.exp(-steepness * n.calculateWeightedSum())));
+    }
+}

src/main/java/com/naaturel/ANN/implementation/multiLayers/TanH.java

@@ -0,0 +1,17 @@
+package com.naaturel.ANN.implementation.multiLayers;
+
+import com.naaturel.ANN.domain.abstraction.ActivationFunction;
+import com.naaturel.ANN.domain.model.neuron.Neuron;
+
+public class TanH implements ActivationFunction {
+
+    @Override
+    public float accept(Neuron n) {
+        //For educational purpose. Math.tanh() could have been used here
+        float weightedSum = n.calculateWeightedSum();
+        double exp = Math.exp(weightedSum);
+        double res = (exp-(1/exp))/(exp+(1/exp));
+        return (float)(res);
+    }
+
+}

src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java

@@ -0,0 +1,12 @@
+package com.naaturel.ANN.implementation.training;
+
+import com.naaturel.ANN.domain.abstraction.Model;
+import com.naaturel.ANN.domain.abstraction.Trainer;
+import com.naaturel.ANN.infrastructure.dataset.DataSet;
+
+public class GradientBackpropagationTraining implements Trainer {
+    @Override
+    public void train(Model model, DataSet dataset) {
+
+    }
+}

src/main/java/com/naaturel/ANN/implementation/training/GradientDescentTraining.java

@@ -50,7 +50,7 @@ public class GradientDescentTraining implements Trainer {
                 context.globalLoss /= context.dataset.size();
                 new GradientDescentCorrectionStrategy(context).apply();
             })
-            .withVerbose(true)
+            //.withVerbose(true)
             .withTimeMeasurement(true)
             .withVisualization(true, new GraphVisualizer())
             .run(context);