Minor changes

config.json

@@ -1,14 +1,14 @@
 {
   "model": {
     "new": true,
-    "parameters": [2, 4, 2, 1],
+    "parameters": [5, 2, 3],
-    "path": "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/snapshots/best-snapshot.json"
+    "path": "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/snapshots/snapshot-test.json"
   },
   "training" : {
     "learning_rate" : 0.0003,
     "max_epoch" : 5000
   },
   "dataset" : {
-    "path" : "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/LangageDesSignes/data_formatted.csv"
+    "path" : "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_3_1.csv"
   }
 }

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

@@ -8,6 +8,7 @@ import com.naaturel.ANN.implementation.training.GradientBackpropagationTraining;
 import com.naaturel.ANN.infrastructure.config.ConfigDto;
 import com.naaturel.ANN.infrastructure.config.ConfigLoader;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
+import com.naaturel.ANN.infrastructure.dataset.DataSetEntry;
 import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
 import com.naaturel.ANN.domain.model.neuron.*;
 import com.naaturel.ANN.infrastructure.visualization.GraphVisualizer;
@@ -20,6 +21,8 @@ public class Main {
 
     public static void main(String[] args) throws Exception {
 
+        Scanner sc = new Scanner(System.in);
+
         ConfigDto config = ConfigLoader.load("C:/Users/Laurent/Desktop/ANN-framework/config.json");
 
         boolean newModel = config.getModelProperty("new", Boolean.class);
@@ -29,7 +32,12 @@ public class Main {
         float learningRate = config.getTrainingProperty("learning_rate", Double.class).floatValue();
         String datasetPath = config.getDatasetProperty("path", String.class);
 
-        int nbrClass = 5;
+        System.out.printf("How many classes ? [%d] : ",  modelParameters[modelParameters.length-1]);
+        String input = sc.nextLine().trim();
+        int nbrClass = input.isEmpty() ? modelParameters[modelParameters.length-1] : Integer.parseInt(input);
+        System.out.println();
+
+
         DataSet dataset = new DatasetExtractor().extract(datasetPath, nbrClass);
         int nbrInput = dataset.getNbrInputs();
 
@@ -48,7 +56,8 @@ public class Main {
             snapshot.loadFromFile(modelPath);
             network = snapshot.getModel();
         }
-        //plotGraph(dataset, network);
+
+        plotGraph(dataset, network);
 
         new ModelVisualizer(network)
                 .withWeights(true)
@@ -84,23 +93,35 @@ public class Main {
     }
 
     private static void plotGraph(DataSet dataset, Model network){
+
+        if(dataset.getNbrInputs() != 2) return;
+
         GraphVisualizer visualizer = new GraphVisualizer();
+        float min = -10F;
+        float max = 10F;
+        float step = Math.abs(max - min)/300;
 
-        /*for (DataSetEntry entry : dataset) {
+        //plot labels
+        for (DataSetEntry entry : dataset) {
             List<Float> label = dataset.getLabelsAsFloat(entry);
-            label.forEach(l -> {
+            int expectedClass = 0;
-                visualizer.addPoint("Label " + l,
+            for (int i = 1; i < label.size(); i++) {
+                if (label.get(i) > label.get(expectedClass)) expectedClass = i;
+            }
+            visualizer.addPoint("Label " + expectedClass,
                     entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
-            });
+        }
-        }*/
 
-        float min = -50F;
+        //plot predictions
-        float max = 50F;
-        float step = 0.03F;
         for (float x = min; x < max; x+=step){
             for (float y = min; y < max; y+=step){
-                float[] predictions = network.predict(new float[]{x, y});
+                List<Float> predictions = new ArrayList<>();
-                visualizer.addPoint(Float.toString(Math.round(predictions[0])), x, y);
+
+                for (float p : network.predict(new float[]{x, y})) predictions.add(p);
+                float highest = Collections.max(predictions);
+                int predictedClass = predictions.indexOf(highest);
+
+                visualizer.addPoint("Predict " + predictedClass, x, y);
             }
         }
 

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

@@ -84,7 +84,7 @@ public class TrainingPipeline {
             System.out.printf("[Training finished in %.3fs]\n", (end-start)/1000.0);
         }
         System.out.printf("[Final global error] : %f\n", ctx.globalLoss);
-        //if(this.visualization) this.visualize(ctx);
+        System.out.println("Accuracy: " +  accuracyCheck(ctx) + "%");
     }
 
     private void executeSteps(TrainingContext ctx){
@@ -107,26 +107,28 @@ public class TrainingPipeline {
         }
     }
 
-    /*private void visualize(TrainingContext ctx){
+    private float accuracyCheck(TrainingContext ctx){
-        AtomicInteger neuronIndex = new AtomicInteger(0);
+        int correct = 0;
-        ctx.model.forEachNeuron(n -> {
+        for (DataSetEntry entry : ctx.dataset) {
-            List<Float> weights = new ArrayList<>();
+            float[] predictions = ctx.model.predict(entry.getDataAsFloat());
-            n.forEachSynapse(syn -> weights.add(syn.getWeight()));
+            List<Float> labels = ctx.dataset.getLabelsAsFloat(entry);
 
-            float b  = weights.get(0);
+            // argmax of predictions
-            float w1 = weights.get(1);
+            int predictedClass = 0;
-            float w2 = weights.get(2);
+            for (int i = 1; i < predictions.length; i++) {
-
+                if (predictions[i] > predictions[predictedClass]) predictedClass = i;
-            this.visualizer.addEquation("boundary_" + neuronIndex.getAndIncrement(), w1, w2, b, -3, 3);
+            }
-        });
+
-        int i = 0;
+            // argmax of labels
-        for(DataSetEntry entry : ctx.dataset){
+            int expectedClass = 0;
-            List<Input> inputs = entry.getData();
+            for (int i = 1; i < labels.size(); i++) {
-            this.visualizer.addPoint("p"+i, inputs.get(0).getValue(), inputs.get(1).getValue());
+                if (labels.get(i) > labels.get(expectedClass)) expectedClass = i;
-            this.visualizer.addPoint("p"+i, inputs.get(0).getValue()+0.01F, inputs.get(1).getValue()+0.01F);
+            }
-            i++;
+
+            if (predictedClass == expectedClass) correct++;
+        }
+
+        return 100f * correct / ctx.dataset.size();
     }
-        this.visualizer.buildLineGraph();
-    }*/
 
 }

src/main/java/com/naaturel/ANN/infrastructure/dataset/DataSetEntry.java

@@ -17,6 +17,16 @@ public class DataSetEntry implements Iterable<Input> {
     }
 
 
+    public float[] getDataAsFloat() {
+        float[] res =  new float[data.size()];
+        for(int i = 0; i < data.size(); i++){
+            res[i] = data.get(i).getValue();
+        }
+        return res;
+    }
+
+
+
     @Override
     public int hashCode() {
         return Objects.hash(this.data);