All learning used the same testinf dataset et learning dataset. Better to...

All learning used the same testinf dataset et learning dataset. Better to compare performance of hyper-parameters

src/main/java/be/cylab/java/wowa/training/LearningNeuralNetwork.java

@@ -24,15 +24,11 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 import java.io.IOException;
 
 /**
- * Class to test learning.
+ * Class to test neural network learning.
  */
 
 public final class LearningNeuralNetwork {
 
-    private LearningNeuralNetwork() {
-
-    }
-
     /**
      * Class count.
      */
@@ -43,72 +39,73 @@ public final class LearningNeuralNetwork {
     public static final int FEATURES_COUNT = 5;
 
 
-    /**
-     * Default constructor.
-     */
-
+    private DataSet training_data;
+    private DataSet testing_data;
 
     /**
-     * Test.
-     *
-     * @param neurons_number
+     * Constructor.
+     * @param file_name
      */
-    public static void learning(final int neurons_number) {
+    public LearningNeuralNetwork(final String file_name) {
         try (RecordReader record_reader = new CSVRecordReader(0, ',')) {
             record_reader.initialize(new FileSplit(
-                    new ClassPathResource("webshell_data.csv").getFile()
+                    new ClassPathResource(file_name).getFile()
             ));
             DataSetIterator iterator = new RecordReaderDataSetIterator(
                     record_reader, 12468, FEATURES_COUNT, CLASSES_COUNT);
             DataSet all_data = iterator.next();
-            for (int i = 5; i < 50; i++) {
-                System.out.println("Number of neurons in hidden layer : "
-                        + i);
-                all_data.shuffle();
-
-                DataNormalization normalizer = new NormalizerStandardize();
-                normalizer.fit(all_data);
-                normalizer.transform(all_data);
+            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 test_data = test_and_train.getTest();
+            SplitTestAndTrain test_and_train = all_data.splitTestAndTrain(0.80);
+            this.training_data = test_and_train.getTrain();
+            this.testing_data = test_and_train.getTest();
 
-                MultiLayerConfiguration configuration
-                        = new NeuralNetConfiguration.Builder()
-                        .iterations(1000)
-                        .activation(Activation.RELU)
-                        .weightInit(WeightInit.XAVIER)
-                        .learningRate(0.1)
-                        .regularization(true).l2(0.0001)
-                        .list()
-                        .layer(0, new DenseLayer.Builder().nIn(FEATURES_COUNT)
-                                .nOut(i).build())
-                        .layer(1, new DenseLayer.Builder().nIn(i)
-                                .nOut(i).build())
-                        .layer(2, new OutputLayer.Builder(LossFunctions
-                                .LossFunction.NEGATIVELOGLIKELIHOOD)
-                                .activation(Activation.SOFTMAX)
-                                .nIn(i)
-                                .nOut(CLASSES_COUNT).build())
-                        .backprop(true).pretrain(false)
-                        .build();
-
-                MultiLayerNetwork model = new MultiLayerNetwork(configuration);
-                model.init();
-                model.fit(training_data);
-
-                INDArray output = model.output((test_data.getFeatureMatrix()));
-
-                Evaluation eval = new Evaluation(CLASSES_COUNT);
-                eval.eval(test_data.getLabels(), output);
-                System.out.println(eval.stats());
-            }
         } catch (IOException e) {
             e.printStackTrace();
         } catch (InterruptedException e) {
             e.printStackTrace();
         }
     }
+
+    /**
+     * Method to build a simple neural network, train and test it.
+     *
+     * @param neurons_number
+     */
+    public void learning(final int neurons_number) {
+
+        MultiLayerConfiguration configuration
+                = new NeuralNetConfiguration.Builder()
+                .iterations(1000)
+                .activation(Activation.RELU)
+                .weightInit(WeightInit.XAVIER)
+                .learningRate(0.1)
+                .regularization(true).l2(0.0001)
+                .list()
+                .layer(0, new DenseLayer.Builder().nIn(FEATURES_COUNT)
+                        .nOut(neurons_number).build())
+                .layer(1, new DenseLayer.Builder().nIn(neurons_number)
+                        .nOut(neurons_number).build())
+                .layer(2, new OutputLayer.Builder(LossFunctions
+                        .LossFunction.NEGATIVELOGLIKELIHOOD)
+                        .activation(Activation.SOFTMAX)
+                        .nIn(neurons_number)
+                        .nOut(CLASSES_COUNT).build())
+                .backprop(true).pretrain(false)
+                .build();
+
+        MultiLayerNetwork model = new MultiLayerNetwork(configuration);
+        model.init();
+        model.fit(this.training_data);
+
+        INDArray output
+                = model.output((this.testing_data.getFeatureMatrix()));
+
+        Evaluation eval = new Evaluation(CLASSES_COUNT);
+        eval.eval(this.testing_data.getLabels(), output);
+        System.out.println(eval.stats());
+    }
 }

src/main/java/be/cylab/java/wowa/training/MainDL4J.java

@@ -18,8 +18,10 @@ public final class MainDL4J {
      */
     public static void main(final String[] args) {
 
+        LearningNeuralNetwork lnn
+                = new LearningNeuralNetwork("webshell_data.csv");
         for (int neurons_number = 5; neurons_number < 50; neurons_number++) {
-            LearningNeuralNetwork.learning(neurons_number);
+            lnn.learning(neurons_number);
         }
 
     }