Fix weights correction

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

@@ -68,8 +68,9 @@ public class Main {
         Network network = new Network(List.of(layer));
 
         TrainingContext context = new TrainingContext();
-        context.dataset = dataSet;
+        context.dataset = orDataSet;
         context.model = network;
+        context.learningRate = 0.3F;
 
         List<TrainingStep> steps = List.of(
                 new PredictionStep(),
@@ -81,8 +82,8 @@ public class Main {
 
         TrainingPipeline pipeline = new TrainingPipeline(steps);
         pipeline
-                .stopCondition(ctx -> ctx.globalLoss == 0 && ctx.epoch >= 1000)
+                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 100)
-                .afterEpoch(ctx -> ctx.globalLoss = 0)
+                .beforeEpoch(ctx -> ctx.globalLoss = 0)
                 .withVerbose(true)
                 .run(context);
 

src/main/java/com/naaturel/ANN/domain/abstraction/Trainable.java

@@ -9,6 +9,6 @@ import java.util.function.Consumer;
 public interface Trainable {
     List<Float> predict(List<Input> inputs);
 
-    void forEachSynapse(Consumer<Synapse> consumer);
+    void applyOnSynapses(Consumer<Synapse> consumer);
 
 }

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

@@ -1,6 +1,5 @@
 package com.naaturel.ANN.domain.model.neuron;
 
-import com.naaturel.ANN.domain.abstraction.CorrectionStrategy;
 import com.naaturel.ANN.domain.abstraction.Neuron;
 import com.naaturel.ANN.domain.abstraction.Trainable;
 
@@ -27,7 +26,7 @@ public class Layer implements Trainable {
     }
 
     @Override
-    public void forEachSynapse(Consumer<Synapse> consumer) {
+    public void applyOnSynapses(Consumer<Synapse> consumer) {
-        this.neurons.forEach(neuron -> neuron.forEachSynapse(consumer));
+        this.neurons.forEach(neuron -> neuron.applyOnSynapses(consumer));
     }
 }

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

@@ -25,7 +25,7 @@ public class Network implements Trainable {
     }
 
     @Override
-    public void forEachSynapse(Consumer<Synapse> consumer) {
+    public void applyOnSynapses(Consumer<Synapse> consumer) {
-        this.layers.forEach(layer -> layer.forEachSynapse(consumer));
+        this.layers.forEach(layer -> layer.applyOnSynapses(consumer));
     }
 }

src/main/java/com/naaturel/ANN/domain/model/training/TrainingContext.java

@@ -3,11 +3,13 @@ package com.naaturel.ANN.domain.model.training;
 import com.naaturel.ANN.domain.abstraction.Trainable;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
+import com.naaturel.ANN.domain.model.dataset.Label;
 
 public class TrainingContext {
     public Trainable model;
     public DataSet dataset;
     public DataSetEntry currentEntry;
+    public Label currentLabel;
 
     public float prediction;
     public float delta;

src/main/java/com/naaturel/ANN/domain/model/training/TrainingPipeline.java

@@ -11,7 +11,8 @@ import java.util.function.Predicate;
 public class TrainingPipeline {
 
     private final List<TrainingStep> steps;
-    private Consumer<TrainingContext> afterAll;
+    private Consumer<TrainingContext> beforeEpoch;
+    private Consumer<TrainingContext> afterEpoch;
     private Predicate<TrainingContext> stopCondition;
 
     private boolean verbose;
@@ -19,6 +20,9 @@ public class TrainingPipeline {
 
     public TrainingPipeline(List<TrainingStep> steps) {
         this.steps = new ArrayList<>(steps);
+        this.stopCondition = (ctx) -> false;
+        this.beforeEpoch = (context -> {});
+        this.afterEpoch = (context -> {});
     }
 
     public TrainingPipeline stopCondition(Predicate<TrainingContext> predicate) {
@@ -26,8 +30,13 @@ public class TrainingPipeline {
         return this;
     }
 
+    public TrainingPipeline beforeEpoch(Consumer<TrainingContext> consumer) {
+        this.beforeEpoch = consumer;
+        return this;
+    }
+
     public TrainingPipeline afterEpoch(Consumer<TrainingContext> consumer) {
-        this.afterAll = consumer;
+        this.afterEpoch = consumer;
         return this;
     }
 
@@ -43,25 +52,28 @@ public class TrainingPipeline {
 
     public void run(TrainingContext ctx) {
         do {
+            this.beforeEpoch.accept(ctx);
             this.executeSteps(ctx);
-            if(this.afterAll != null) {
+            this.afterEpoch.accept(ctx);
-                this.afterAll.accept(ctx);
-            }
         } while (!this.stopCondition.test(ctx));
     }
 
     private void executeSteps(TrainingContext ctx){
-        for (DataSetEntry sample : ctx.dataset) {
+        for (DataSetEntry entry : ctx.dataset) {
-            ctx.currentEntry = sample;
+            ctx.currentEntry = entry;
+            ctx.currentLabel = ctx.dataset.getLabel(entry);
             for (TrainingStep step : steps) {
                 step.run(ctx);
-                if(this.verbose) {
-                    System.out.printf("Epoch : %d, ", ctx.epoch);
-                    System.out.printf("predicted : %.2f, ", ctx.prediction);
-                    System.out.printf("expected : %.2f, ", ctx.dataset.getLabel(ctx.currentEntry).getValue());
-                    System.out.printf("delta : %.2f\n", ctx.delta);
-                }
             }
+            if(this.verbose) {
+                System.out.printf("Epoch : %d, ", ctx.epoch);
+                System.out.printf("predicted : %.2f, ", ctx.prediction);
+                System.out.printf("expected : %.2f, ", ctx.currentLabel.getValue());
+                System.out.printf("delta : %.2f\n", ctx.delta);
+            }
+        }
+        if(this.verbose) {
+            System.out.printf("[Global error] : %.2f\n", ctx.globalLoss);
         }
         ctx.epoch += 1;
     }

src/main/java/com/naaturel/ANN/implementation/correction/GradientDescentCorrectionStrategy.java

@@ -0,0 +1,13 @@
+package com.naaturel.ANN.implementation.correction;
+
+import com.naaturel.ANN.domain.abstraction.CorrectionStrategy;
+import com.naaturel.ANN.domain.model.training.TrainingContext;
+
+public class GradientDescentCorrectionStrategy implements CorrectionStrategy {
+
+    @Override
+    public void apply(TrainingContext context) {
+
+    }
+
+}

src/main/java/com/naaturel/ANN/implementation/correction/SimpleCorrectionStrategy.java

@@ -7,7 +7,9 @@ public class SimpleCorrectionStrategy implements CorrectionStrategy {
 
     @Override
     public void apply(TrainingContext context) {
-        context.model.forEachSynapse(syn -> {
+        if(context.currentLabel.getValue() == context.prediction) return ;
+
+        context.model.applyOnSynapses(syn -> {
             float currentW = syn.getWeight();
             float currentInput  = syn.getInput();
             float newValue = currentW + (context.learningRate * context.delta * currentInput);

src/main/java/com/naaturel/ANN/implementation/neuron/SimplePerceptron.java

@@ -25,7 +25,8 @@ public class SimplePerceptron extends Neuron {
     }
 
     @Override
-    public void forEachSynapse(Consumer<Synapse> consumer) {
+    public void applyOnSynapses(Consumer<Synapse> consumer) {
+        consumer.accept(this.bias);
         this.synapses.forEach(consumer);
     }