Just a regular commit

src/main/java/com/naaturel/ANN/domain/model/neuron/Network.java

@@ -6,6 +6,9 @@ import java.util.ArrayList;
 import java.util.List;
 import java.util.function.Consumer;
 
+/**
+ * Represents a fully connected neural network
+ */
 public class Network implements Model {
 
     private final List<Layer> layers;
@@ -16,11 +19,12 @@ public class Network implements Model {
 
     @Override
     public List<Float> predict(List<Input> inputs) {
-        List<Input> currentLayerOutput = new ArrayList<>(inputs);
+        List<Input> previousLayerOutput = new ArrayList<>(inputs);
         for(Layer layer : this.layers){
-            currentLayerOutput = layer.predict(currentLayerOutput).stream().map(Input::new).toList();
+            List<Float> currentLayerOutput = layer.predict(previousLayerOutput);
+            previousLayerOutput = currentLayerOutput.stream().map(Input::new).toList();
         }
-        return currentLayerOutput.stream().map(Input::getValue).toList();
+        return previousLayerOutput.stream().map(Input::getValue).toList();
     }
 
     @Override

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

@@ -0,0 +1,6 @@
+package com.naaturel.ANN.implementation.multiLayers;
+
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
+
+public class GradientBackpropagationContext extends TrainingContext {
+}

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

@@ -0,0 +1,17 @@
+package com.naaturel.ANN.implementation.multiLayers;
+
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+
+public class GradientBackpropagationStrategy implements AlgorithmStrategy {
+
+    private GradientBackpropagationContext context;
+
+    public GradientBackpropagationStrategy(GradientBackpropagationContext context) {
+        this.context = context;
+    }
+
+    @Override
+    public void apply() {
+
+    }
+}

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

@@ -2,11 +2,35 @@ package com.naaturel.ANN.implementation.training;
 
 import com.naaturel.ANN.domain.abstraction.Model;
 import com.naaturel.ANN.domain.abstraction.Trainer;
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
+import com.naaturel.ANN.domain.abstraction.TrainingStep;
+import com.naaturel.ANN.domain.model.training.TrainingPipeline;
+import com.naaturel.ANN.implementation.gradientDescent.GradientDescentTrainingContext;
+import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStrategy;
+import com.naaturel.ANN.implementation.training.steps.DeltaStep;
+import com.naaturel.ANN.implementation.training.steps.PredictionStep;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
 
+import java.util.List;
+
+
 public class GradientBackpropagationTraining implements Trainer {
     @Override
     public void train(Model model, DataSet dataset) {
+        TrainingContext context = new GradientDescentTrainingContext();
+        context.dataset = dataset;
+        context.model = model;
+        context.learningRate = 0.0008F;
+
+        List<TrainingStep> steps = List.of(
+                new PredictionStep(new SimplePredictionStrategy(context)),
+                new DeltaStep()
+        );
+
+        new TrainingPipeline(steps)
+                .stopCondition(ctx -> false)
+                .withVerbose(true)
+                .run(context);
 
     }
 }

src/main/java/com/naaturel/ANN/implementation/training/steps/PredictionStep.java

@@ -10,9 +10,9 @@ import java.util.List;
 
 public class PredictionStep implements TrainingStep {
 
-    private final SimplePredictionStrategy strategy;
+    private final AlgorithmStrategy strategy;
 
-    public PredictionStep(SimplePredictionStrategy strategy) {
+    public PredictionStep(AlgorithmStrategy strategy) {
         this.strategy = strategy;
     }