From 17cff89b44ffad141ceefe3c1cea75b2471ea70f Mon Sep 17 00:00:00 2001
From: Laurent <naaturel@noreply.git.naaturel.be>
Date: Sat, 28 Mar 2026 13:19:36 +0100
Subject: [PATCH] Implement gradient backpropagation stub

---
 src/main/java/com/naaturel/ANN/Main.java       |  7 +++++--
 .../implementation/multiLayers/Sigmoid.java    | 18 ++++++++++++++++++
 .../ANN/implementation/multiLayers/TanH.java   | 17 +++++++++++++++++
 .../GradientBackpropagationTraining.java       | 12 ++++++++++++
 .../training/GradientDescentTraining.java      |  2 +-
 5 files changed, 53 insertions(+), 3 deletions(-)
 create mode 100644 src/main/java/com/naaturel/ANN/implementation/multiLayers/Sigmoid.java
 create mode 100644 src/main/java/com/naaturel/ANN/implementation/multiLayers/TanH.java
 create mode 100644 src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java

diff --git a/src/main/java/com/naaturel/ANN/Main.java b/src/main/java/com/naaturel/ANN/Main.java
index d53a1ab..d1735b0 100644
--- a/src/main/java/com/naaturel/ANN/Main.java
+++ b/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);
 
     }
diff --git a/src/main/java/com/naaturel/ANN/implementation/multiLayers/Sigmoid.java b/src/main/java/com/naaturel/ANN/implementation/multiLayers/Sigmoid.java
new file mode 100644
index 0000000..01149cd
--- /dev/null
+++ b/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())));
+    }
+}
diff --git a/src/main/java/com/naaturel/ANN/implementation/multiLayers/TanH.java b/src/main/java/com/naaturel/ANN/implementation/multiLayers/TanH.java
new file mode 100644
index 0000000..a3b8508
--- /dev/null
+++ b/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);
+    }
+
+}
diff --git a/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java b/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java
new file mode 100644
index 0000000..45452df
--- /dev/null
+++ b/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) {
+
+    }
+}
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 f6a08d8..bbcf03f 100644
--- a/src/main/java/com/naaturel/ANN/implementation/training/GradientDescentTraining.java
+++ b/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);