Naaturel/ANN-framework
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LEMLIJN Clément
2026-05-12 11:44:58 +02:00
parent 2f37d72efd
commit 657f6c2894
7 changed files with 368 additions and 31 deletions
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35
README.md Normal file
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@@ -0,0 +1,35 @@
```json
{
"model": {
"new": true,
"parameters": [10, 5, 1], // [input neurons, hidden neurons, output neuron]
"path": "C:/HEPL/RNA2/ANN-framework/src/main/resources/snapshots/snapshot-test.json"
},
"training" : {
"learning_rate" : 0.002,
"max_epoch" : 5000
},
"dataset" : {
"path" : "C:/HEPL/RNA2/ANN-framework/src/main/resources/assets/and.csv"
}
}
```
2
```
{
"model": {
"new": true,
"parameters": [1],
"path": "C:/HEPL/RNA2/ANN-framework/src/main/resources/snapshots/snapshot-test.json"
},
"training" : {
"learning_rate" : 0.002,
"max_epoch" : 5000
},
"dataset" : {
"path" : "C:/HEPL/RNA2/ANN-framework/src/main/resources/assets/and.csv"
}
}
```

10
config.json
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@@ -1,14 +1,14 @@
{
"model": {
"new": true,
"parameters": [10, 5, 1],
"path": "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/snapshots/snapshot-test.json"
"new": false,
"parameters": [10, 10, 5],
"path": "C:/HEPL/RNA2/ANN-framework/src/main/resources/snapshots/snapshot-learning.json"
},
"training" : {
"learning_rate" : 0.002,
"learning_rate" : 0.003,
"max_epoch" : 5000
},
"dataset" : {
"path" : "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_2_10.csv"
"path" : "C:/HEPL/RNA2/ANN-framework/src/main/resources/assets/LangageDesSignes/data_formatted_validation.csv"
}
}

28
src/main/java/com/naaturel/ANN/Main.java
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@@ -2,6 +2,7 @@ package com.naaturel.ANN;
import com.naaturel.ANN.domain.abstraction.Model;
import com.naaturel.ANN.domain.abstraction.Trainer;
import com.naaturel.ANN.domain.abstraction.TrainingContext;
import com.naaturel.ANN.implementation.training.AdalineTraining;
import com.naaturel.ANN.implementation.training.GradientBackpropagationTraining;
import com.naaturel.ANN.implementation.training.GradientDescentTraining;
@@ -65,19 +66,44 @@ public class Main {
}
Model network = snapshot.loadFromFile(modelPath);
accuracyCheck(dataset, network);
plotGraph(dataset, network);
new ModelVisualizer(network)
.withWeights(true)
.display();
}
private static void accuracyCheck(DataSet dataset, Model model){
int correct = 0;
for (DataSetEntry entry : dataset) {
float[] predictions = model.predict(entry.getDataAsFloat());
List<Float> labels = dataset.getLabelsAsFloat(entry);
// argmax of predictions
int predictedClass = 0;
for (int i = 1; i < predictions.length; i++) {
if (predictions[i] > predictions[predictedClass]) predictedClass = i;
}
// argmax of labels
int expectedClass = 0;
for (int i = 1; i < labels.size(); i++) {
if (labels.get(i) > labels.get(expectedClass)) expectedClass = i;
}
if (predictedClass == expectedClass) correct++;
}
float x = 100f * correct / dataset.size();
System.out.println("Accuracy: " + x + "%");
}
private static void plotGraph(DataSet dataset, Model network){
if(dataset.getNbrInputs() != 2) return;
GraphVisualizer visualizer = new GraphVisualizer();
float min = 0F;
float min = -15F;
float max = 15F;
float step = Math.abs(max - min)/300;

24
src/main/java/com/naaturel/ANN/domain/model/training/TrainingPipeline.java
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@@ -84,7 +84,6 @@ 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);
System.out.println("Accuracy: " + accuracyCheck(ctx) + "%");
}
private void executeSteps(TrainingContext ctx){
@@ -107,28 +106,5 @@ public class TrainingPipeline {
}
}
private float accuracyCheck(TrainingContext ctx){
int correct = 0;
for (DataSetEntry entry : ctx.dataset) {
float[] predictions = ctx.model.predict(entry.getDataAsFloat());
List<Float> labels = ctx.dataset.getLabelsAsFloat(entry);
// argmax of predictions
int predictedClass = 0;
for (int i = 1; i < predictions.length; i++) {
if (predictions[i] > predictions[predictedClass]) predictedClass = i;
}
// argmax of labels
int expectedClass = 0;
for (int i = 1; i < labels.size(); i++) {
if (labels.get(i) > labels.get(expectedClass)) expectedClass = i;
}
if (predictedClass == expectedClass) correct++;
}
return 100f * correct / ctx.dataset.size();
}
}

2
src/main/java/com/naaturel/ANN/implementation/training/GradientDescentTraining.java
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@@ -50,7 +50,7 @@ public class GradientDescentTraining implements Trainer {
context.globalLoss /= context.dataset.size();
new GradientDescentCorrectionStrategy(context).run();
})
//.withVerbose(true)
.withVerbose(true, 5)
.withTimeMeasurement(true)
.withVisualization(true, new GraphVisualizer())
.run(context);

250
src/main/resources/assets/LangageDesSignes/data_formatted_learning.csv Normal file
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@@ -0,0 +1,250 @@
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