Reimplement Adaline

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

@@ -8,6 +8,7 @@ import com.naaturel.ANN.domain.model.neuron.*;
 import com.naaturel.ANN.implementation.gradientDescent.Linear;
 import com.naaturel.ANN.implementation.simplePerceptron.Heaviside;
 import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
+import com.naaturel.ANN.implementation.training.AdalineTraining;
 import com.naaturel.ANN.implementation.training.GradientDescentTraining;
 import com.naaturel.ANN.implementation.training.SimpleTraining;
 
@@ -21,7 +22,7 @@ public class Main {
                 .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_2_9.csv");
 
         DataSet andDataset = new DatasetExtractor()
-                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and.csv");
+                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and-gradient.csv");
 
         List<Synapse> syns = new ArrayList<>();
         syns.add(new Synapse(new Input(0), new Weight(0)));
@@ -29,12 +30,12 @@ public class Main {
 
         Bias bias =  new Bias(new Weight(0));
 
-        Neuron neuron = new SimplePerceptron(syns, bias, new Heaviside());
+        Neuron neuron = new SimplePerceptron(syns, bias, new Linear());
         Layer layer = new Layer(List.of(neuron));
         Network network = new Network(List.of(layer));
 
-        Trainer trainer = new SimpleTraining();
+        Trainer trainer = new AdalineTraining();
-        trainer.train(network, andDataset);
+        trainer.train(network, dataset);
 
     }
 }

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

@@ -4,6 +4,7 @@ import com.naaturel.ANN.domain.abstraction.TrainingContext;
 import com.naaturel.ANN.domain.abstraction.TrainingStep;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
 
+import java.sql.Time;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.function.Consumer;
@@ -52,11 +53,23 @@ public class TrainingPipeline {
     }
 
     public void run(TrainingContext ctx) {
+
+        long start = this.timeMeasurement ? System.currentTimeMillis() : 0;
+
         do {
             this.beforeEpoch.accept(ctx);
             this.executeSteps(ctx);
             this.afterEpoch.accept(ctx);
+            if(this.verbose) {
+                System.out.printf("[Global error] : %f\n", ctx.globalLoss);
+            }
         } while (!this.stopCondition.test(ctx));
+
+        if(this.timeMeasurement) {
+            long end = System.currentTimeMillis();
+            System.out.printf("[Training finished in %.3fs]", (end-start)/1000.0);
+        }
+
     }
 
     private void executeSteps(TrainingContext ctx){
@@ -74,9 +87,6 @@ public class TrainingPipeline {
                 System.out.printf("loss : %.5f\n", ctx.localLoss);
             }
         }
-        if(this.verbose) {
-            System.out.printf("[Global error] : %.2f\n", ctx.globalLoss);
-        }
         ctx.epoch += 1;
     }
 }

src/main/java/com/naaturel/ANN/implementation/adaline/AdalineTrainingContext.java

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

src/main/java/com/naaturel/ANN/implementation/gradientDescent/GradientDescentErrorStrategy.java

@@ -22,5 +22,6 @@ public class GradientDescentErrorStrategy implements AlgorithmStrategy {
             context.correctorTerms.set(i.get(), corrector);
             i.incrementAndGet();
         });
+        context.globalLoss += context.localLoss;
     }
 }

src/main/java/com/naaturel/ANN/implementation/gradientDescent/SquareLossStrategy.java

@@ -1,19 +1,19 @@
 package com.naaturel.ANN.implementation.gradientDescent;
 
 import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
 import com.naaturel.ANN.implementation.simplePerceptron.SimpleTrainingContext;
 
 public class SquareLossStrategy implements AlgorithmStrategy {
 
-    private final GradientDescentTrainingContext context;
+    private final TrainingContext context;
 
-    public SquareLossStrategy(GradientDescentTrainingContext context) {
+    public SquareLossStrategy(TrainingContext context) {
         this.context = context;
     }
 
     @Override
     public void apply() {
         this.context.localLoss = (float)Math.pow(this.context.delta, 2)/2;
-        this.context.globalLoss += context.localLoss;
     }
 }

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

@@ -1,13 +1,14 @@
 package com.naaturel.ANN.implementation.simplePerceptron;
 
 import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
 
 
 public class SimpleCorrectionStrategy implements AlgorithmStrategy {
 
-    private final SimpleTrainingContext context;
+    private final TrainingContext context;
 
-    public SimpleCorrectionStrategy(SimpleTrainingContext context) {
+    public SimpleCorrectionStrategy(TrainingContext context) {
         this.context = context;
     }
 

src/main/java/com/naaturel/ANN/implementation/simplePerceptron/SimpleErrorRegistrationStrategy.java

@@ -1,12 +1,13 @@
 package com.naaturel.ANN.implementation.simplePerceptron;
 
 import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
 
 public class SimpleErrorRegistrationStrategy implements AlgorithmStrategy {
 
-    private final SimpleTrainingContext context;
+    private final TrainingContext context;
 
-    public SimpleErrorRegistrationStrategy(SimpleTrainingContext context) {
+    public SimpleErrorRegistrationStrategy(TrainingContext context) {
         this.context = context;
     }
 

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

@@ -1,21 +1,65 @@
 package com.naaturel.ANN.implementation.training;
 
+import com.naaturel.ANN.domain.abstraction.Model;
 import com.naaturel.ANN.domain.abstraction.Neuron;
 import com.naaturel.ANN.domain.abstraction.Trainer;
+import com.naaturel.ANN.domain.abstraction.TrainingStep;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
 import com.naaturel.ANN.domain.model.neuron.Input;
 import com.naaturel.ANN.domain.model.neuron.Synapse;
 import com.naaturel.ANN.domain.model.neuron.Weight;
+import com.naaturel.ANN.domain.model.training.TrainingPipeline;
+import com.naaturel.ANN.implementation.adaline.AdalineTrainingContext;
+import com.naaturel.ANN.implementation.gradientDescent.GradientDescentCorrectionStrategy;
+import com.naaturel.ANN.implementation.gradientDescent.GradientDescentErrorStrategy;
+import com.naaturel.ANN.implementation.gradientDescent.GradientDescentTrainingContext;
+import com.naaturel.ANN.implementation.gradientDescent.SquareLossStrategy;
+import com.naaturel.ANN.implementation.simplePerceptron.SimpleCorrectionStrategy;
+import com.naaturel.ANN.implementation.simplePerceptron.SimpleDeltaStrategy;
+import com.naaturel.ANN.implementation.simplePerceptron.SimpleErrorRegistrationStrategy;
+import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStrategy;
+import com.naaturel.ANN.implementation.training.steps.*;
+
+import java.util.ArrayList;
+import java.util.List;
 
 
-/*public class AdalineTraining implements Trainer {
+public class AdalineTraining implements Trainer {
 
     public AdalineTraining(){
 
     }
 
-    public void train(Neuron n, float learningRate, DataSet dataSet) {
+    @Override
+    public void train(Model model, DataSet dataset) {
+        AdalineTrainingContext context = new AdalineTrainingContext();
+        context.dataset = dataset;
+        context.model = model;
+        context.learningRate = 0.003F;
+
+        List<TrainingStep> steps = List.of(
+                new PredictionStep(new SimplePredictionStrategy(context)),
+                new DeltaStep(new SimpleDeltaStrategy(context)),
+                new LossStep(new SquareLossStrategy(context)),
+                new ErrorRegistrationStep(new SimpleErrorRegistrationStrategy(context)),
+                new WeightCorrectionStep(new SimpleCorrectionStrategy(context))
+        );
+
+        new TrainingPipeline(steps)
+                .stopCondition(ctx -> ctx.globalLoss <= 0.125F || ctx.epoch > 10000)
+                .beforeEpoch(ctx -> {
+                    ctx.globalLoss = 0.0F;
+                })
+                .afterEpoch(ctx -> {
+                    ctx.globalLoss /= context.dataset.size();
+                })
+                .withVerbose(true)
+                .withTimeMeasurement(true)
+                .run(context);
+    }
+
+    /*public void train(Neuron n, float learningRate, DataSet dataSet) {
         int epoch = 1;
         int maxEpoch = 202;
         float errorThreshold = 0.0F;
@@ -76,6 +120,6 @@ import com.naaturel.ANN.domain.model.neuron.Weight;
 
     private float calculateLoss(float delta){
         return (float) Math.pow(delta, 2)/2;
-    }
+    }*/
 
-}*/
+}

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

@@ -27,6 +27,7 @@ public class GradientDescentTraining implements Trainer {
         GradientDescentTrainingContext context = new GradientDescentTrainingContext();
         context.dataset = dataset;
         context.model = model;
+        context.learningRate = 0.0011F;
         context.correctorTerms =  new ArrayList<>();
 
         List<TrainingStep> steps = List.of(
@@ -37,17 +38,19 @@ public class GradientDescentTraining implements Trainer {
         );
 
         new TrainingPipeline(steps)
-            .stopCondition(ctx -> ctx.globalLoss <= 0.125F || ctx.epoch > 100)
+            .stopCondition(ctx -> ctx.globalLoss <= 0.08F || ctx.epoch > 5000)
             .beforeEpoch(ctx -> {
                 GradientDescentTrainingContext gdCtx = (GradientDescentTrainingContext) ctx;
                 gdCtx.globalLoss = 0.0F;
                 gdCtx.correctorTerms.clear();
-                for (int i = 0; i < ctx.model.synCount(); i++){
+                gdCtx.model.forEachSynapse(syn -> gdCtx.correctorTerms.add(0F));
-                    gdCtx.correctorTerms.add(0F);
+            })
-                }
+            .afterEpoch(ctx -> {
+                context.globalLoss /= context.dataset.size();
+                new GradientDescentCorrectionStrategy(context).apply();
             })
-            .afterEpoch(ctx -> new GradientDescentCorrectionStrategy(context).apply())
             .withVerbose(true)
+            .withTimeMeasurement(true)
             .run(context);
     }
 

src/test/java/adaline/AdalineTest.java

@@ -0,0 +1,93 @@
+package adaline;
+
+
+import com.naaturel.ANN.domain.abstraction.Neuron;
+import com.naaturel.ANN.domain.abstraction.TrainingStep;
+import com.naaturel.ANN.domain.model.dataset.DataSet;
+import com.naaturel.ANN.domain.model.dataset.DatasetExtractor;
+import com.naaturel.ANN.domain.model.neuron.*;
+import com.naaturel.ANN.domain.model.training.TrainingPipeline;
+import com.naaturel.ANN.implementation.adaline.AdalineTrainingContext;
+import com.naaturel.ANN.implementation.gradientDescent.*;
+import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
+import com.naaturel.ANN.implementation.simplePerceptron.SimpleCorrectionStrategy;
+import com.naaturel.ANN.implementation.simplePerceptron.SimpleDeltaStrategy;
+import com.naaturel.ANN.implementation.simplePerceptron.SimpleErrorRegistrationStrategy;
+import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStrategy;
+import com.naaturel.ANN.implementation.training.steps.*;
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+
+public class AdalineTest {
+
+    private DataSet dataset;
+    private AdalineTrainingContext context;
+
+    private List<Synapse> synapses;
+    private Bias bias;
+    private Network network;
+
+    private TrainingPipeline pipeline;
+
+    @BeforeEach
+    public void init(){
+        dataset = new DatasetExtractor()
+                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and-gradient.csv");
+
+        List<Synapse> syns = new ArrayList<>();
+        syns.add(new Synapse(new Input(0), new Weight(0)));
+        syns.add(new Synapse(new Input(0), new Weight(0)));
+
+        bias =  new Bias(new Weight(0));
+
+        Neuron neuron = new SimplePerceptron(syns, bias, new Linear());
+        Layer layer = new Layer(List.of(neuron));
+        network = new Network(List.of(layer));
+
+        context = new AdalineTrainingContext();
+        context.dataset = dataset;
+        context.model = network;
+
+        List<TrainingStep> steps = List.of(
+                new PredictionStep(new SimplePredictionStrategy(context)),
+                new DeltaStep(new SimpleDeltaStrategy(context)),
+                new LossStep(new SquareLossStrategy(context)),
+                new ErrorRegistrationStep(new SimpleErrorRegistrationStrategy(context)),
+                new WeightCorrectionStep(new SimpleCorrectionStrategy(context))
+        );
+
+        pipeline = new TrainingPipeline(steps)
+                .stopCondition(ctx -> ctx.globalLoss <= 0.1329F || ctx.epoch > 10000)
+                .beforeEpoch(ctx -> {
+                    ctx.globalLoss = 0.0F;
+                });
+    }
+
+    @Test
+    public void test_the_whole_algorithm(){
+
+        List<Float> expectedGlobalLosses = List.of(
+                0.501522F,
+                0.498601F
+        );
+
+        context.learningRate = 0.03F;
+        pipeline.afterEpoch(ctx -> {
+            ctx.globalLoss /= context.dataset.size();
+
+            int index = ctx.epoch-1;
+            if(index >= expectedGlobalLosses.size()) return;
+
+            //assertEquals(expectedGlobalLosses.get(index), context.globalLoss, 0.00001f);
+        });
+
+        pipeline.run(context);
+        assertEquals(214, context.epoch);
+    }
+}
+

src/test/java/gradientDescent/GradientDescentTest.java

@@ -49,7 +49,7 @@ public class GradientDescentTest {
         context = new GradientDescentTrainingContext();
         context.dataset = dataset;
         context.model = network;
-        context.correctorTerms =  new ArrayList<>();
+        context.correctorTerms = new ArrayList<>();
 
         List<TrainingStep> steps = List.of(
                 new PredictionStep(new SimplePredictionStrategy(context)),
@@ -92,7 +92,9 @@ public class GradientDescentTest {
             assertEquals(expectedGlobalLosses.get(index), context.globalLoss, 0.00001f);
         });
 
-        pipeline.run(context);
+        pipeline
+                .withVerbose(true)
+                .run(context);
         assertEquals(67, context.epoch);
     }
 }