Naaturel/ANN-framework
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Implement batch size

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Laurent
2026-03-31 22:52:03 +02:00
parent 5aca7b87e3
commit daba4f8420
8 changed files with 82 additions and 36 deletions
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39
src/main/java/com/naaturel/ANN/Main.java
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@@ -1,5 +1,7 @@
package com.naaturel.ANN;
import com.naaturel.ANN.domain.abstraction.Model;
import com.naaturel.ANN.domain.abstraction.Network;
import com.naaturel.ANN.domain.model.neuron.Neuron;
import com.naaturel.ANN.domain.abstraction.Trainer;
import com.naaturel.ANN.implementation.gradientDescent.Linear;
@@ -22,11 +24,21 @@ public class Main {
int nbrClass = 1;
DataSet dataset = new DatasetExtractor()
.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_2_9.csv", nbrClass);
.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_4_12.csv", nbrClass);
int[] neuronPerLayer = new int[]{10, 5, 5, dataset.getNbrLabels()};
int[] neuronPerLayer = new int[]{50, 50, 50, dataset.getNbrLabels()};
int nbrInput = dataset.getNbrInputs();
FullyConnectedNetwork network = createNetwork(neuronPerLayer, nbrInput);
Trainer trainer = new GradientBackpropagationTraining();
trainer.train(0.01F, 2000, network, dataset);
//plotGraph(dataset, network);
}
private static FullyConnectedNetwork createNetwork(int[] neuronPerLayer, int nbrInput){
List<Layer> layers = new ArrayList<>();
for (int i = 0; i < neuronPerLayer.length; i++){
@@ -49,26 +61,27 @@ public class Main {
layers.add(layer);
}
FullyConnectedNetwork network = new FullyConnectedNetwork(layers.toArray(new Layer[0]));
Trainer trainer = new GradientBackpropagationTraining();
trainer.train(0.0005F, 15000, network, dataset);
return new FullyConnectedNetwork(layers.toArray(new Layer[0]));
}
private static void plotGraph(DataSet dataset, Model network){
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());
label.forEach(l -> {
visualizer.addPoint("Label " + l,
entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
});
}
float min = 0F;
float max = 15F;
float step = 0.03F;
float min = -5F;
float max = 5F;
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 : -1;
visualizer.addPoint(Float.toString(predSeries), x, y);
List<Float> predictions = network.predict(List.of(new Input(x), new Input(y)));
visualizer.addPoint(Float.toString(Math.round(predictions.getFirst())), x, y);
}
}