diff --git a/java-wowa-training.iml b/java-wowa-training.iml
index 08c09f61200f2f2388e08bc4ac406e511612c382..07a0b767d4bdc777f3b37cf6f213126ed7f88d11 100644
--- a/java-wowa-training.iml
+++ b/java-wowa-training.iml
@@ -32,6 +32,97 @@
<SOURCES />
</library>
</orderEntry>
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+ <orderEntry type="library" name="Maven: commons-io:commons-io:2.4" level="project" />
+ <orderEntry type="library" name="Maven: org.slf4j:slf4j-api:1.7.7" level="project" />
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+ <orderEntry type="library" name="Maven: org.nd4j:jackson:0.9.1" level="project" />
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+ <orderEntry type="library" name="Maven: org.freemarker:freemarker:2.3.23" level="project" />
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+ <orderEntry type="library" name="Maven: org.javassist:javassist:3.19.0-GA" level="project" />
+ <orderEntry type="library" name="Maven: com.google.code.findbugs:annotations:2.0.1" level="project" />
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+ <orderEntry type="library" name="Maven: commons-codec:commons-codec:1.10" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter-api:5.3.1" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.apiguardian:apiguardian-api:1.0.0" level="project" />
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diff --git a/src/main/java/be/cylab/java/wowa/training/MainDL4J.java b/src/main/java/be/cylab/java/wowa/training/MainDL4J.java
index d8a5b18a6ac2110b44ccec1bc938a8e6f14e6400..727407e9b63db631aba74d0f3b7ed58796a50189 100644
--- a/src/main/java/be/cylab/java/wowa/training/MainDL4J.java
+++ b/src/main/java/be/cylab/java/wowa/training/MainDL4J.java
@@ -43,7 +43,7 @@ public final class MainDL4J {
if (args[3].matches("RELU")) {
activation_function = Activation.RELU;
- } else if (args[3].matches("SIGMOID")) {
+ } else if (args[3].matches("SIGMOID")) {
activation_function = Activation.SIGMOID;
} else if (args[3].matches("TANH")) {
activation_function = Activation.TANH;
@@ -52,16 +52,14 @@ public final class MainDL4J {
"Not correct activation function");
}
- LearningNeuralNetwork lnn
- = new LearningNeuralNetwork(file_name,
- learning_rate,
- optimization_algorithm,
- activation_function);
- for (int neurons_number = 5; neurons_number < 50; neurons_number++) {
- System.out.println("Number of neurons in hidden layer : "
- + neurons_number);
- lnn.learning(neurons_number);
- }
-
+ NeuralNetwork nn = new NeuralNetwork(20, learning_rate,
+ optimization_algorithm, activation_function);
+ nn.run("./ressources/webshell_data.json",
+ "./ressources/webshell_expected.json");
+ /*
+ NeuralNetwork nn = new NeuralNetwork(20, learning_rate,
+ optimization_algorithm, activation_function);
+ nn.runCSV("webshell_data.csv");
+ */
}
}
diff --git a/src/main/java/be/cylab/java/wowa/training/LearningNeuralNetwork.java b/src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java
similarity index 54%
rename from src/main/java/be/cylab/java/wowa/training/LearningNeuralNetwork.java
rename to src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java
index 7328913d5b67cd2845395633e1ffff9b547b04eb..6a6339aaf5a52063437ce77d6563823050923663 100644
--- a/src/main/java/be/cylab/java/wowa/training/LearningNeuralNetwork.java
+++ b/src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java
@@ -5,6 +5,7 @@ import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.datavec.api.util.ClassPathResource;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
+import org.deeplearning4j.datasets.iterator.DoublesDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.eval.ROC;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
@@ -22,14 +23,17 @@ import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize;
import org.nd4j.linalg.lossfunctions.LossFunctions;
+import org.nd4j.linalg.primitives.Pair;
import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
/**
* Class to test neural network learning.
*/
-public final class LearningNeuralNetwork {
+public final class NeuralNetwork {
/**
* Class count.
@@ -41,47 +45,28 @@ public final class LearningNeuralNetwork {
public static final int FEATURES_COUNT = 5;
- private DataSet training_data;
- private DataSet testing_data;
+ private int neurons_number;
private double learning_rate;
private OptimizationAlgorithm algorithm;
private Activation activation_function;
/**
- * Constructor.
- *
- * @param file_name
+ * @param neurons_number
+ * @param learning_rate
+ * @param algorithm
+ * @param activation_function
*/
- public LearningNeuralNetwork(final String file_name,
- final double learning_rate,
- final OptimizationAlgorithm algorithm,
- final Activation activation_function
+ public NeuralNetwork(final int neurons_number,
+ final double learning_rate,
+ final OptimizationAlgorithm algorithm,
+ final Activation activation_function
) {
- try (RecordReader record_reader = new CSVRecordReader(0, ',')) {
- record_reader.initialize(new FileSplit(
- new ClassPathResource(file_name).getFile()
- ));
- DataSetIterator iterator = new RecordReaderDataSetIterator(
- record_reader, 12468, FEATURES_COUNT, CLASSES_COUNT);
- DataSet all_data = iterator.next();
- all_data.shuffle();
- DataNormalization normalizer = new NormalizerStandardize();
- normalizer.fit(all_data);
- normalizer.transform(all_data);
-
- SplitTestAndTrain test_and_train = all_data.splitTestAndTrain(0.90);
- this.training_data = test_and_train.getTrain();
- this.testing_data = test_and_train.getTest();
- } catch (IOException e) {
- e.printStackTrace();
- } catch (InterruptedException e) {
- e.printStackTrace();
- }
this.learning_rate = learning_rate;
this.algorithm = algorithm;
this.activation_function = activation_function;
+ this.neurons_number = neurons_number;
}
/**
@@ -89,7 +74,10 @@ public final class LearningNeuralNetwork {
*
* @param neurons_number
*/
- public void learning(final int neurons_number) {
+ MultiLayerNetwork learning(
+ final int neurons_number,
+ final DataSet learning,
+ final DataSet testing) {
MultiLayerConfiguration configuration
= new NeuralNetConfiguration.Builder()
@@ -115,16 +103,100 @@ public final class LearningNeuralNetwork {
MultiLayerNetwork model = new MultiLayerNetwork(configuration);
model.init();
- model.fit(this.training_data);
+ model.fit(learning);
INDArray output
- = model.output((this.testing_data.getFeatureMatrix()));
+ = model.output((testing.getFeatureMatrix()));
Evaluation eval = new Evaluation(CLASSES_COUNT);
- eval.eval(this.testing_data.getLabels(), output);
+ eval.eval(testing.getLabels(), output);
System.out.println(eval.stats());
ROC roc = new ROC(CLASSES_COUNT);
- roc.eval(this.testing_data.getLabels(), output);
+ roc.eval(testing.getLabels(), output);
System.out.println(roc.stats());
+ return model;
+ }
+
+ /**
+ * @param data
+ * @param expected
+ * @return
+ */
+ public MultiLayerNetwork run(
+ final List<List<Double>> data,
+ final List<Double> expected) {
+ if (data.size() != expected.size()) {
+ throw new IllegalArgumentException(
+ "Data and Expected must have the same size");
+ }
+ List<Pair<double[], double[]>> pair = new ArrayList<>();
+ for (int i = 0; i < expected.size(); i++) {
+ double[] da = new double[data.get(i).size()];
+ double[] ex = {1.0, 0.0};
+ for (int j = 0; j < data.get(i).size(); j++) {
+ da[j] = data.get(i).get(j);
+ }
+ Pair<double[], double[]> p = new Pair<>();
+ p.setKey(da);
+ p.setValue(ex);
+ pair.add(p);
+ }
+ DataSetIterator doubles_dataset = new DoublesDataSetIterator(pair,
+ 12468);
+ DataSet all_data = doubles_dataset.next();
+ all_data.shuffle();
+ DataNormalization normalizer = new NormalizerStandardize();
+ normalizer.fit(all_data);
+ normalizer.transform(all_data);
+ SplitTestAndTrain test_train = all_data.splitTestAndTrain(0.8);
+ DataSet training_data = test_train.getTrain();
+ DataSet testing_data = test_train.getTest();
+ return this.learning(neurons_number, training_data, testing_data);
+
+ }
+
+ /**
+ * @param data_filename
+ * @param expected_filename
+ * @return
+ */
+ public MultiLayerNetwork run(
+ final String data_filename,
+ final String expected_filename) {
+ List<List<Double>> data
+ = Utils.convertJsonToDataForTrainer(data_filename);
+ List<Double> expected
+ = Utils.convertJsonToExpectedForTrainer(expected_filename);
+ return this.run(data, expected);
+ }
+
+ /**
+ * @param filename
+ * @return
+ */
+ public MultiLayerNetwork runCSV(final String filename) {
+ try (RecordReader record_reader = new CSVRecordReader(0, ',')) {
+ record_reader.initialize(new FileSplit(
+ new ClassPathResource(filename).getFile()
+ ));
+ DataSetIterator iterator = new RecordReaderDataSetIterator(
+ record_reader, 12468, FEATURES_COUNT, CLASSES_COUNT);
+ DataSet all_data = iterator.next();
+ all_data.shuffle();
+ DataNormalization normalizer = new NormalizerStandardize();
+ normalizer.fit(all_data);
+ normalizer.transform(all_data);
+
+ SplitTestAndTrain test_and_train = all_data.splitTestAndTrain(0.80);
+ DataSet training_data = test_and_train.getTrain();
+ DataSet testing_data = test_and_train.getTest();
+ return learning(neurons_number, training_data, testing_data);
+
+ } catch (IOException e) {
+ e.printStackTrace();
+ } catch (InterruptedException e) {
+ e.printStackTrace();
+ }
+ return null;
}
}