Naaturel/ANN-framework
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Laurent
2026-05-09 16:44:12 +02:00
parent b253fb74ee
commit dd664efa92
4 changed files with 68 additions and 35 deletions
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47
src/main/java/com/naaturel/ANN/Main.java
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@@ -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 -> {
visualizer.addPoint("Label " + l,
entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
});
}*/
int expectedClass = 0;
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;
float max = 50F;
float step = 0.03F;
//plot predictions
for (float x = min; x < max; x+=step){
for (float y = min; y < max; y+=step){
float[] predictions = network.predict(new float[]{x, y});
visualizer.addPoint(Float.toString(Math.round(predictions[0])), x, y);
List<Float> predictions = new ArrayList<>();
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);
}
}