Refactor handling of model creation

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

@@ -40,22 +40,15 @@ public class Main {
         DataSet dataset = new DatasetExtractor().extract(datasetPath, nbrClass);
         int nbrInput = dataset.getNbrInputs();
 
-        ModelSnapshot snapshot;
+        ModelSnapshot snapshot = new ModelSnapshot();
 
-        Model network;
+        if(newModel) {
-        if(newModel){
+            Trainer trainer = new GradientBackpropagationTraining(modelParameters, nbrInput);
-            network = createNetwork(modelParameters, nbrInput);
+            trainer.train(learningRate, maxEpoch, dataset);
-            snapshot = new ModelSnapshot(network);
+            trainer.saveModel(snapshot, modelPath);
-            System.out.println("Parameters: " + network.synCount());
-            Trainer trainer = new GradientBackpropagationTraining();
-            trainer.train(learningRate, maxEpoch, network, dataset);
-            snapshot.saveToFile(modelPath);
-        } else {
-            snapshot = new ModelSnapshot();
-            snapshot.loadFromFile(modelPath);
-            network = snapshot.getModel();
         }
 
+        Model network = snapshot.loadFromFile(modelPath);
         plotGraph(dataset, network);
 
         new ModelVisualizer(network)
@@ -63,33 +56,7 @@ public class Main {
                 .display();
     }
 
-    private static FullyConnectedNetwork createNetwork(int[] neuronPerLayer, int nbrInput){
-        int neuronId = 0;
-        List<Layer> layers = new ArrayList<>();
-        for (int i = 0; i < neuronPerLayer.length; i++){
 
-            List<Neuron> neurons = new ArrayList<>();
-            for (int j = 0; j < neuronPerLayer[i]; j++){
-
-                int nbrSyn = i == 0 ? nbrInput: neuronPerLayer[i-1];
-
-                List<Synapse> syns = new ArrayList<>();
-                for (int k=0; k < nbrSyn; k++){
-                    syns.add(new Synapse(new Input(0), new Weight()));
-                }
-
-                Bias bias =  new Bias(new Weight());
-
-                Neuron n = new Neuron(neuronId, syns.toArray(new Synapse[0]), bias, new TanH());
-                neurons.add(n);
-                neuronId++;
-            }
-            Layer layer = new Layer(neurons.toArray(new Neuron[0]));
-            layers.add(layer);
-        }
-
-        return new FullyConnectedNetwork(layers.toArray(new Layer[0]));
-    }
 
     private static void plotGraph(DataSet dataset, Model network){
 

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

@@ -1,7 +1,9 @@
 package com.naaturel.ANN.domain.abstraction;
 
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
+import com.naaturel.ANN.infrastructure.persistence.ModelSnapshot;
 
 public interface Trainer {
-    void train(float learningRate, int epoch, Model model, DataSet dataset);
+    void train(float learningRate, int epoch, DataSet dataset);
+    void saveModel(ModelSnapshot snapshot, String path);
 }

src/main/java/com/naaturel/ANN/domain/model/helpers/ModelCreator.java

@@ -0,0 +1,39 @@
+package com.naaturel.ANN.domain.model.helpers;
+
+import com.naaturel.ANN.domain.abstraction.Model;
+import com.naaturel.ANN.domain.model.neuron.*;
+import com.naaturel.ANN.implementation.multiLayers.TanH;
+
+import java.util.ArrayList;
+import java.util.List;
+
+public class ModelCreator {
+
+    public static Model createModel(int[] neuronPerLayer, int nbrInput){
+        int neuronId = 0;
+        List<Layer> layers = new ArrayList<>();
+        for (int i = 0; i < neuronPerLayer.length; i++){
+
+            List<Neuron> neurons = new ArrayList<>();
+            for (int j = 0; j < neuronPerLayer[i]; j++){
+
+                int nbrSyn = i == 0 ? nbrInput: neuronPerLayer[i-1];
+
+                List<Synapse> syns = new ArrayList<>();
+                for (int k=0; k < nbrSyn; k++){
+                    syns.add(new Synapse(new Input(0), new Weight()));
+                }
+
+                Bias bias =  new Bias(new Weight());
+
+                Neuron n = new Neuron(neuronId, syns.toArray(new Synapse[0]), bias, new TanH());
+                neurons.add(n);
+                neuronId++;
+            }
+            Layer layer = new Layer(neurons.toArray(new Neuron[0]));
+            layers.add(layer);
+        }
+
+        return new FullyConnectedNetwork(layers.toArray(new Layer[0]));
+    }
+}

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

@@ -3,6 +3,7 @@ package com.naaturel.ANN.implementation.training;
 import com.naaturel.ANN.domain.abstraction.AlgorithmStep;
 import com.naaturel.ANN.domain.abstraction.Model;
 import com.naaturel.ANN.domain.abstraction.Trainer;
+import com.naaturel.ANN.domain.model.helpers.ModelCreator;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
 import com.naaturel.ANN.domain.model.training.TrainingPipeline;
 import com.naaturel.ANN.implementation.adaline.AdalineTrainingContext;
@@ -11,6 +12,7 @@ import com.naaturel.ANN.implementation.simplePerceptron.SimpleCorrectionStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimpleDeltaStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimpleErrorRegistrationStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStep;
+import com.naaturel.ANN.infrastructure.persistence.ModelSnapshot;
 import com.naaturel.ANN.infrastructure.visualization.GraphVisualizer;
 
 import java.util.List;
@@ -18,12 +20,14 @@ import java.util.List;
 
 public class AdalineTraining implements Trainer {
 
-    public AdalineTraining(){
+    private Model model;
 
+    public AdalineTraining(int[] neurons, int nbrInputs){
+        model = ModelCreator.createModel(neurons, nbrInputs);
     }
 
     @Override
-    public void train(float learningRate, int epoch, Model model, DataSet dataset) {
+    public void train(float learningRate, int epoch, DataSet dataset) {
         AdalineTrainingContext context = new AdalineTrainingContext(model, dataset);
         context.learningRate = learningRate;
 
@@ -45,6 +49,15 @@ public class AdalineTraining implements Trainer {
                 .run(context);
     }
 
+    @Override
+    public void saveModel(ModelSnapshot snapshot, String path) {
+        try {
+            snapshot.saveToFile(model, path);
+        } catch (Exception e) {
+            throw new RuntimeException(e);
+        }
+    }
+
     /*public void train(Neuron n, float learningRate, DataSet dataSet) {
         int epoch = 1;
         int maxEpoch = 202;

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

@@ -3,19 +3,30 @@ package com.naaturel.ANN.implementation.training;
 import com.naaturel.ANN.domain.abstraction.AlgorithmStep;
 import com.naaturel.ANN.domain.abstraction.Model;
 import com.naaturel.ANN.domain.abstraction.Trainer;
+import com.naaturel.ANN.domain.model.helpers.ModelCreator;
 import com.naaturel.ANN.domain.model.training.TrainingPipeline;
 import com.naaturel.ANN.implementation.gradientDescent.SquareLossStep;
-import com.naaturel.ANN.implementation.multiLayers.BackpropagationCorrectionStep;
+import com.naaturel.ANN.implementation.multiLayers.*;
-import com.naaturel.ANN.implementation.multiLayers.GradientBackpropagationContext;
-import com.naaturel.ANN.implementation.multiLayers.ErrorSignalStep;
-import com.naaturel.ANN.implementation.multiLayers.OutputLayerErrorStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStep;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
+import com.naaturel.ANN.infrastructure.persistence.ModelSnapshot;
+
 import java.util.List;
 
 public class GradientBackpropagationTraining implements Trainer {
+
+    private Model model;
+
+    public GradientBackpropagationTraining(int[] neurons, int nbrInputs){
+        model = ModelCreator.createModel(neurons, nbrInputs);
+    }
+
+    public Model getModel() {
+        return model;
+    }
+
     @Override
-    public void train(float learningRate, int epoch, Model model, DataSet dataset) {
+    public void train(float learningRate, int epoch, DataSet dataset) {
         GradientBackpropagationContext context =
                 new GradientBackpropagationContext(model, dataset, learningRate, 10);
 
@@ -39,4 +50,13 @@ public class GradientBackpropagationTraining implements Trainer {
                 .withTimeMeasurement(true)
                 .run(context);
     }
+
+    @Override
+    public void saveModel(ModelSnapshot snapshot, String path) {
+        try {
+            snapshot.saveToFile(model, path);
+        } catch (Exception e) {
+            throw new RuntimeException(e);
+        }
+    }
 }

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

@@ -3,6 +3,7 @@ package com.naaturel.ANN.implementation.training;
 import com.naaturel.ANN.domain.abstraction.AlgorithmStep;
 import com.naaturel.ANN.domain.abstraction.Model;
 import com.naaturel.ANN.domain.abstraction.Trainer;
+import com.naaturel.ANN.domain.model.helpers.ModelCreator;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
 import com.naaturel.ANN.implementation.gradientDescent.GradientDescentErrorStrategy;
 import com.naaturel.ANN.implementation.gradientDescent.GradientDescentTrainingContext;
@@ -11,6 +12,7 @@ import com.naaturel.ANN.implementation.gradientDescent.GradientDescentCorrection
 import com.naaturel.ANN.implementation.gradientDescent.SquareLossStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimpleDeltaStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStep;
+import com.naaturel.ANN.infrastructure.persistence.ModelSnapshot;
 import com.naaturel.ANN.infrastructure.visualization.GraphVisualizer;
 
 import java.util.ArrayList;
@@ -18,12 +20,14 @@ import java.util.List;
 
 public class GradientDescentTraining implements Trainer {
 
-    public GradientDescentTraining(){
+    private Model model;
 
+    public GradientDescentTraining(int[] neurons, int nbrInputs){
+        model = ModelCreator.createModel(neurons, nbrInputs);
     }
 
     @Override
-    public void train(float learningRate, int epoch, Model model, DataSet dataset) {
+    public void train(float learningRate, int epoch, DataSet dataset) {
         GradientDescentTrainingContext context = new GradientDescentTrainingContext(model, dataset);
         context.learningRate = learningRate;
         context.correctorTerms =  new ArrayList<>();
@@ -55,6 +59,15 @@ public class GradientDescentTraining implements Trainer {
             .run(context);
     }
 
+    @Override
+    public void saveModel(ModelSnapshot snapshot, String path) {
+        try {
+            snapshot.saveToFile(model, path);
+        } catch (Exception e) {
+            throw new RuntimeException(e);
+        }
+    }
+
     /*public void train(Neuron n, float learningRate, DataSet dataSet) {
         int epoch = 1;
         int maxEpoch = 402;

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

@@ -3,20 +3,24 @@ package com.naaturel.ANN.implementation.training;
 import com.naaturel.ANN.domain.abstraction.AlgorithmStep;
 import com.naaturel.ANN.domain.abstraction.Model;
 import com.naaturel.ANN.domain.abstraction.Trainer;
+import com.naaturel.ANN.domain.model.helpers.ModelCreator;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
 import com.naaturel.ANN.implementation.simplePerceptron.*;
 import com.naaturel.ANN.domain.model.training.TrainingPipeline;
+import com.naaturel.ANN.infrastructure.persistence.ModelSnapshot;
 
 import java.util.List;
 
 public class SimpleTraining implements Trainer {
 
-    public SimpleTraining() {
+    private Model model;
 
+    public SimpleTraining(int[] neurons, int nbrInputs){
+        model = ModelCreator.createModel(neurons, nbrInputs);
     }
 
     @Override
-    public void train(float learningRate, int epoch, Model model, DataSet dataset) {
+    public void train(float learningRate, int epoch, DataSet dataset) {
         SimpleTrainingContext context = new SimpleTrainingContext(model, dataset);
         context.dataset = dataset;
         context.model = model;
@@ -38,6 +42,15 @@ public class SimpleTraining implements Trainer {
                 .run(context);
     }
 
+    @Override
+    public void saveModel(ModelSnapshot snapshot, String path) {
+        try {
+            snapshot.saveToFile(model, path);
+        } catch (Exception e) {
+            throw new RuntimeException(e);
+        }
+    }
+
     /*public void train(Neuron n, float learningRate, DataSet dataSet) {
         int epoch = 1;
         int errorCount;

src/main/java/com/naaturel/ANN/infrastructure/persistence/ModelSnapshot.java

@@ -15,23 +15,13 @@ import java.util.Map;
 
 public class ModelSnapshot {
 
-    private Model model;
     private final ObjectMapper mapper;
 
     public ModelSnapshot(){
-        this(null);
-    }
-
-    public ModelSnapshot(Model model){
-        this.model = model;
         mapper = new ObjectMapper();
     }
 
-    public Model getModel() {
+    public void saveToFile(Model model, String path) throws Exception {
-        return model;
-    }
-
-    public void saveToFile(String path) throws Exception {
 
         ArrayNode root = mapper.createArrayNode();
         model.forEachNeuron(n -> {
@@ -52,7 +42,7 @@ public class ModelSnapshot {
         mapper.writerWithDefaultPrettyPrinter().writeValue(new File(path), root);
     }
 
-    public void loadFromFile(String path) throws Exception {
+    public Model loadFromFile(String path) throws Exception {
         ArrayNode root = (ArrayNode) mapper.readTree(new File(path));
 
         Map<Integer, List<Neuron>> neuronsByLayer = new LinkedHashMap<>();
@@ -76,6 +66,6 @@ public class ModelSnapshot {
                 .map(neurons -> new Layer(neurons.toArray(new Neuron[0])))
                 .toArray(Layer[]::new);
 
-        this.model = new FullyConnectedNetwork(layers);
+        return new FullyConnectedNetwork(layers);
     }
 }