Reworked synapses data structure

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

@@ -26,7 +26,7 @@ public class Main {
         DataSet dataset = new DatasetExtractor()
                 .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_4_12.csv", nbrClass);
 
-        int[] neuronPerLayer = new int[]{10, 5, 10, dataset.getNbrLabels()};
+        int[] neuronPerLayer = new int[]{100, 100, 50, dataset.getNbrLabels()};
         int nbrInput = dataset.getNbrInputs();
 
         FullyConnectedNetwork network = createNetwork(neuronPerLayer, nbrInput);
@@ -34,7 +34,7 @@ public class Main {
         System.out.println(network.synCount());
 
         Trainer trainer = new GradientBackpropagationTraining();
-        trainer.train(0.01F, 2000, network, dataset);
+        trainer.train(0.001F, 2000, network, dataset);
 
         plotGraph(dataset, network);
     }

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

@@ -12,7 +12,7 @@ public interface Model {
     int neuronCount();
     int indexInLayerOf(Neuron n);
     void forEachNeuron(Consumer<Neuron> consumer);
-    void forEachSynapse(Consumer<Synapse> consumer);
+    //void forEachSynapse(Consumer<Synapse> consumer);
     void forEachOutputNeurons(Consumer<Neuron> consumer);
     void forEachNeuronConnectedTo(Neuron n, Consumer<Neuron> consumer);
     List<Float> predict(List<Input> inputs);

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

@@ -51,13 +51,6 @@ public class FullyConnectedNetwork implements Model {
         return res;
     }
 
-    @Override
-    public void forEachSynapse(Consumer<Synapse> consumer) {
-        for(Layer l : this.layers){
-            l.forEachSynapse(consumer);
-        }
-    }
-
     @Override
     public void forEachNeuron(Consumer<Neuron> consumer) {
         for(Layer l : this.layers){

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

@@ -54,12 +54,12 @@ public class Layer implements Model {
         }
     }
 
-    @Override
+    /*@Override
     public void forEachSynapse(Consumer<Synapse> consumer) {
         for (Neuron n : this.neurons){
             n.forEachSynapse(consumer);
         }
-    }
+    }*/
 
     @Override
     public void forEachOutputNeurons(Consumer<Neuron> consumer) {

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

@@ -7,40 +7,37 @@ import java.util.function.Consumer;
 
 public class Neuron implements Model {
 
-    protected int id;
+    private final int id;
-    protected final Synapse[] synapses;
+    private float output;
-    protected Bias bias;
+    private final float[] weights;
-    protected ActivationFunction activationFunction;
+    private final float[] inputs;
-    protected Float output;
+    private final ActivationFunction activationFunction;
-    protected Float weightedSum;
-    protected final float[] weights;
-    protected final float[] inputs;
 
     public Neuron(int id, Synapse[] synapses, Bias bias, ActivationFunction func){
         this.id = id;
-        this.synapses = synapses;
-        this.bias = bias;
         this.activationFunction = func;
-        this.output = null;
-        this.weightedSum = null;
 
-        weights = new float[synapses.length];
+        weights = new float[synapses.length+1]; //takes the bias into account
-        inputs = new float[synapses.length];
+        inputs = new float[synapses.length+1]; //takes the bias into account
+
+        weights[0] = bias.getWeight();
+        inputs[0] = bias.getInput();
+        for (int i = 0; i < synapses.length; i++){
+            weights[i+1] = synapses[i].getWeight();
+            inputs[i+1] = synapses[i].getInput();
+        }
     }
 
-    public void updateBias(Weight weight) {
+    public void setWeight(int index, float value) {
-        this.bias.setWeight(weight.getValue());
+        this.weights[index] = value;
     }
 
-    public void updateWeight(int index, Weight weight) {
+    public float getWeight(int index) {
-        this.synapses[index].setWeight(weight.getValue());
+        return this.weights[index];
     }
 
-    protected void setInputs(List<Input> inputs){
+    public float getInput(int index) {
-        for(int i = 0;  i < inputs.size() && i < synapses.length; i++){
+        return this.inputs[index];
-            Synapse syn = this.synapses[i];
-            syn.setInput(inputs.get(i));
-        }
     }
 
     public ActivationFunction getActivationFunction(){
@@ -51,21 +48,13 @@ public class Neuron implements Model {
         return this.output;
     }
 
-    public float getWeight(int index){
-        return this.synapses[index].getWeight();
-    }
-
-    public float getWeightedSum(){
-        return this.weightedSum;
-    }
-
     public float calculateWeightedSum() {
-        this.weightedSum = 0F;
+        int count = synCount();
-        this.weightedSum += this.bias.getWeight() * this.bias.getInput();
+        float weightedSum = 0F;
-        for(Synapse syn : this.synapses){
+        for (int i = 0; i < count; i++){
-            this.weightedSum += syn.getWeight() * syn.getInput();
+            weightedSum += weights[i] * inputs[i];
         }
-        return this.weightedSum;
+        return weightedSum;
     }
 
     public int getId(){
@@ -74,7 +63,7 @@ public class Neuron implements Model {
 
     @Override
     public int synCount() {
-        return this.synapses.length+1; //take the bias into account
+        return this.weights.length;
     }
 
     @Override
@@ -99,14 +88,6 @@ public class Neuron implements Model {
         consumer.accept(this);
     }
 
-    @Override
-    public void forEachSynapse(Consumer<Synapse> consumer) {
-        consumer.accept(this.bias);
-        for (Synapse syn : this.synapses){
-            consumer.accept(syn);
-        }
-    }
-
     @Override
     public void forEachOutputNeurons(Consumer<Neuron> consumer) {
         consumer.accept(this);
@@ -116,4 +97,11 @@ public class Neuron implements Model {
     public void forEachNeuronConnectedTo(Neuron n, Consumer<Neuron> consumer) {
         throw new UnsupportedOperationException("Neurons have no connection with themselves");
     }
+
+    private void setInputs(List<Input> values){
+        for(int i = 0;  i < values.size(); i++){
+            inputs[i+1] = values.get(i).getValue();
+        }
+    }
+
 }

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

@@ -86,7 +86,7 @@ public class TrainingPipeline {
             System.out.printf("[Training finished in %.3fs]", (end-start)/1000.0);
         }
 
-        if(this.visualization) this.visualize(ctx);
+        //if(this.visualization) this.visualize(ctx);
     }
 
     private void executeSteps(TrainingContext ctx){
@@ -109,7 +109,7 @@ public class TrainingPipeline {
         }
     }
 
-    private void visualize(TrainingContext ctx){
+    /*private void visualize(TrainingContext ctx){
         AtomicInteger neuronIndex = new AtomicInteger(0);
         ctx.model.forEachNeuron(n -> {
             List<Float> weights = new ArrayList<>();
@@ -129,6 +129,6 @@ public class TrainingPipeline {
             i++;
         }
         this.visualizer.buildLineGraph();
-    }
+    }*/
 
 }

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

@@ -14,12 +14,14 @@ public class GradientDescentCorrectionStrategy implements AlgorithmStep {
 
     @Override
     public void run() {
-        AtomicInteger i = new AtomicInteger(0);
+        int[] globalSynIndex = {0};
-        context.model.forEachSynapse(syn -> {
+        context.model.forEachNeuron(n -> {
-            float corrector = context.correctorTerms.get(i.get());
+            for(int i = 0; i < n.synCount(); i++){
-            float c = syn.getWeight() + corrector;
+                float corrector = context.correctorTerms.get(globalSynIndex[0]);
-            syn.setWeight(c);
+                float c = n.getWeight(i) + corrector;
-            i.incrementAndGet();
+                n.setWeight(i, c);
+                globalSynIndex[0]++;
+            }
         });
     }
 }

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

@@ -22,13 +22,12 @@ public class GradientDescentErrorStrategy implements AlgorithmStep {
         context.model.forEachNeuron(neuron -> {
             float correspondingDelta = context.deltas[neuronIndex.get()];
 
-            neuron.forEachSynapse(syn -> {
+            for(int i = 0; i < neuron.synCount(); i++){
                 float corrector = context.correctorTerms.get(synIndex.get());
-                corrector += context.learningRate * correspondingDelta * syn.getInput();
+                corrector += context.learningRate * correspondingDelta * neuron.getInput(i);
                 context.correctorTerms.set(synIndex.get(), corrector);
                 synIndex.incrementAndGet();
-            });
+            }
-
             neuronIndex.incrementAndGet();
         });
 

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

@@ -21,11 +21,11 @@ public class BackpropagationCorrectionStep implements AlgorithmStep {
         int[] synIndex = {0};
         context.model.forEachNeuron(n -> {
             float signal = context.errorSignals[n.getId()];
-            n.forEachSynapse(syn -> {
+            for (int i = 0; i < n.synCount(); i++){
-                inputs[synIndex[0]]  = syn.getInput();
+                inputs[synIndex[0]] = n.getInput(i);
                 signals[synIndex[0]] = signal;
                 synIndex[0]++;
-            });
+            }
         });
 
         float lr = context.learningRate;
@@ -44,13 +44,13 @@ public class BackpropagationCorrectionStep implements AlgorithmStep {
     }
 
     private void syncWeights() {
-        int[] i = {0};
+        int[] synIndex = {0};
         context.model.forEachNeuron(n -> {
-            n.forEachSynapse(syn -> {
+            for (int i = 0; i < n.synCount(); i++) {
-                syn.setWeight(syn.getWeight() + context.correctionBuffer[i[0]]);
+                n.setWeight(i, n.getWeight(i) + context.correctionBuffer[synIndex[0]]);
-                context.correctionBuffer[i[0]] = 0f;
+                context.correctionBuffer[synIndex[0]] = 0f;
-                i[0]++;
+                synIndex[0]++;
-            });
+            }
         });
     }
 }

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

@@ -13,7 +13,7 @@ public class OutputLayerErrorStep implements AlgorithmStep {
 
     public OutputLayerErrorStep(GradientBackpropagationContext context){
         this.context = context;
-        this.expectations = new float[context.model.neuronCount()];
+        this.expectations = new float[context.dataset.getNbrLabels()];
     }
 
     @Override

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

@@ -18,17 +18,16 @@ public class SimpleCorrectionStep implements AlgorithmStep {
     public void run() {
         if(context.expectations.equals(context.predictions)) return;
         AtomicInteger neuronIndex = new AtomicInteger(0);
-        AtomicInteger synIndex = new AtomicInteger(0);
 
         context.model.forEachNeuron(neuron -> {
             float correspondingDelta = context.deltas[neuronIndex.get()];
-            neuron.forEachSynapse(syn -> {
+
-                float currentW = syn.getWeight();
+            for(int i = 0; i < neuron.synCount(); i++){
-                float currentInput  = syn.getInput();
+                float currentW = neuron.getWeight(i);
+                float currentInput  = neuron.getInput(i);
                 float newValue = currentW + (context.learningRate * correspondingDelta * currentInput);
-                syn.setWeight(newValue);
+                neuron.setWeight(i, newValue);
-                synIndex.incrementAndGet();
+            }
-            });
             neuronIndex.incrementAndGet();
         });
     }

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

@@ -17,7 +17,7 @@ public class GradientBackpropagationTraining implements Trainer {
     @Override
     public void train(float learningRate, int epoch, Model model, DataSet dataset) {
         GradientBackpropagationContext context =
-                new GradientBackpropagationContext(model, dataset, learningRate, dataset.size()/3);
+                new GradientBackpropagationContext(model, dataset, learningRate, dataset.size());
 
         List<AlgorithmStep> steps = List.of(
                 new SimplePredictionStep(context),
@@ -35,7 +35,7 @@ public class GradientBackpropagationTraining implements Trainer {
                 .afterEpoch(ctx -> {
                     ctx.globalLoss /= dataset.size();
                 })
-                .withVerbose(true,epoch/10)
+                .withVerbose(false,epoch/10)
                 .withTimeMeasurement(true)
                 .run(context);
     }

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

@@ -41,7 +41,9 @@ public class GradientDescentTraining implements Trainer {
                 GradientDescentTrainingContext gdCtx = (GradientDescentTrainingContext) ctx;
                 gdCtx.globalLoss = 0.0F;
                 gdCtx.correctorTerms.clear();
-                gdCtx.model.forEachSynapse(syn -> gdCtx.correctorTerms.add(0F));
+                for(int i = 0; i < gdCtx.model.synCount(); i++){
+                    gdCtx.correctorTerms.add(0F);
+                }
             })
             .afterEpoch(ctx -> {
                 context.globalLoss /= context.dataset.size();