Add parameters for neural network learning

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

@@ -6,6 +6,7 @@ import org.datavec.api.split.FileSplit;
 import org.datavec.api.util.ClassPathResource;
 import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
 import org.deeplearning4j.eval.Evaluation;
+import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
@@ -41,12 +42,20 @@ public final class LearningNeuralNetwork {
 
     private DataSet training_data;
     private DataSet testing_data;
+    private double learning_rate;
+    private OptimizationAlgorithm algorithm;
+    private Activation activation_function;
+
 
     /**
      * Constructor.
      * @param file_name
      */
-    public LearningNeuralNetwork(final String file_name) {
+    public LearningNeuralNetwork(final String file_name,
+                                 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()
@@ -68,6 +77,9 @@ public final class LearningNeuralNetwork {
         } catch (InterruptedException e) {
             e.printStackTrace();
         }
+        this.learning_rate = learning_rate;
+        this.algorithm = algorithm;
+        this.activation_function = activation_function;
     }
 
     /**
@@ -80,9 +92,11 @@ public final class LearningNeuralNetwork {
         MultiLayerConfiguration configuration
                 = new NeuralNetConfiguration.Builder()
                 .iterations(1000)
-                .activation(Activation.RELU)
+                .activation(this.activation_function)
+                .optimizationAlgo(
+                        this.algorithm)
                 .weightInit(WeightInit.XAVIER)
-                .learningRate(0.1)
+                .learningRate(this.learning_rate)
                 .regularization(true).l2(0.0001)
                 .list()
                 .layer(0, new DenseLayer.Builder().nIn(FEATURES_COUNT)

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

 package be.cylab.java.wowa.training;
 
+import org.deeplearning4j.nn.api.OptimizationAlgorithm;
+import org.nd4j.linalg.activations.Activation;
+
 /**
  * Class for learn in neuronal network.
  */
@@ -18,8 +21,42 @@ public final class MainDL4J {
      */
     public static void main(final String[] args) {
 
+        String file_name = args[0];
+        double learning_rate = Double.parseDouble(args[1]);
+        OptimizationAlgorithm optimization_algorithm;
+        Activation activation_function;
+
+        if (args[2] == "CONJUGATE_GRADIENT") {
+            optimization_algorithm = OptimizationAlgorithm.CONJUGATE_GRADIENT;
+        } else if (args[2] == "LBFGS") {
+            optimization_algorithm = OptimizationAlgorithm.LBFGS;
+        } else if (args[2] == "LINE_GRADIENT_DESCENT") {
+            optimization_algorithm
+                    = OptimizationAlgorithm.LINE_GRADIENT_DESCENT;
+        } else if (args[2] == "STOCHASTIC_GRADIENT_DESCENT") {
+            optimization_algorithm
+                    = OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT;
+        } else {
+            throw new IllegalArgumentException(
+                    "Not correct optimization algorithm");
+        }
+
+        if (args[3] == "RELU") {
+            activation_function = Activation.RELU;
+        } else if  (args[3] == "SIGMOID") {
+            activation_function = Activation.SIGMOID;
+        } else if (args[3] == "TANH") {
+            activation_function = Activation.TANH;
+        } else {
+            throw new IllegalArgumentException(
+                    "Not correct activation function");
+        }
+
         LearningNeuralNetwork lnn
-                = new LearningNeuralNetwork("webshell_data.csv");
+                = 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);