Naaturel/ANN-framework
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Change signature of train method

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Laurent
2026-03-30 18:28:21 +02:00
parent aed78fe9d2
commit ada01d350b
11 changed files with 100 additions and 26 deletions
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37
src/main/java/com/naaturel/ANN/Main.java
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@@ -6,8 +6,10 @@ import com.naaturel.ANN.implementation.multiLayers.Sigmoid;
import com.naaturel.ANN.implementation.multiLayers.TanH;
import com.naaturel.ANN.implementation.training.GradientBackpropagationTraining;
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.graph.GraphVisualizer;
import java.util.*;
@@ -15,13 +17,13 @@ public class Main {
public static void main(String[] args){
int nbrInput = 25;
int nbrClass = 4;
int[] neuronPerLayer = new int[]{10, nbrClass};
int nbrClass = 1;
DataSet dataset = new DatasetExtractor()
.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_3_5.csv", nbrClass);
.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_4_12.csv", nbrClass);
int[] neuronPerLayer = new int[]{10, dataset.getNbrLabels()};
int nbrInput = dataset.getNbrInputs();
List<Layer> layers = new ArrayList<>();
for (int i = 0; i < neuronPerLayer.length; i++){
@@ -38,7 +40,7 @@ public class Main {
Bias bias = new Bias(new Weight());
Neuron n = new Neuron(syns, bias, new TanH());
Neuron n = new Neuron(syns, bias, new Sigmoid(2));
neurons.add(n);
}
Layer layer = new Layer(neurons);
@@ -48,7 +50,28 @@ public class Main {
FullyConnectedNetwork network = new FullyConnectedNetwork(layers);
Trainer trainer = new GradientBackpropagationTraining();
trainer.train(network, dataset);
trainer.train(0.5F, network, dataset);
/*GraphVisualizer visualizer = new GraphVisualizer();
for (DataSetEntry entry : dataset) {
List<Float> label = dataset.getLabelsAsFloat(entry);
visualizer.addPoint("Label " + label.getFirst(), entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
}
float min = -2F;
float max = 2F;
float step = 0.01F;
for (float x = min; x < max; x+=step){
for (float y = min; y < max; y+=step){
float prediction = network.predict(List.of(new Input(x), new Input(y))).getFirst();
float predSeries = prediction > 0.5F ? 1 : 0;
visualizer.addPoint(Float.toString(predSeries), x, y);
}
}
visualizer.buildScatterGraph();*/
}
}