Set neural network on specific branch

pom.xml

@@ -98,17 +98,6 @@
             <version>0.0.3</version>
         </dependency>
 
-        <dependency>
-            <groupId>org.nd4j</groupId>
-            <artifactId>nd4j-native-platform</artifactId>
-            <version>0.9.1</version>
-        </dependency>
-
-        <dependency>
-            <groupId>org.deeplearning4j</groupId>
-            <artifactId>deeplearning4j-core</artifactId>
-            <version>0.9.1</version>
-        </dependency>
 
     </dependencies>
 

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

-package be.cylab.java.wowa.training;
-
-import org.datavec.api.records.reader.RecordReader;
-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.eval.Evaluation;
-import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.layers.DenseLayer;
-import org.deeplearning4j.nn.conf.layers.OutputLayer;
-import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
-import org.deeplearning4j.nn.weights.WeightInit;
-import org.nd4j.linalg.activations.Activation;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.dataset.DataSet;
-import org.nd4j.linalg.dataset.SplitTestAndTrain;
-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 java.io.IOException;
-
-/**
- * Class for learn in neuronal network.
- */
-public final class MainDL4J {
-    /**
-     * Default constructor.
-     */
-    private MainDL4J() {
-
-    }
-
-    /**
-     * Class count.
-     */
-    public static final int CLASSES_COUNT = 2;
-    /**
-     * Features count.
-     */
-    public static final int FEATURES_COUNT = 5;
-
-    /**
-     * Main class for deep-learning.
-     *
-     * @param args
-     */
-    public static void main(final String[] args) {
-
-
-        try (RecordReader record_reader = new CSVRecordReader(0, ',')) {
-            record_reader.initialize(new FileSplit(
-                    new ClassPathResource("webshell_data.csv").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(0.75);
-            DataSet training_data = test_and_train.getTrain();
-            DataSet test_data = test_and_train.getTest();
-
-            MultiLayerConfiguration configuration
-                    = new NeuralNetConfiguration.Builder()
-                    .iterations(1000)
-                    .activation(Activation.TANH)
-                    .weightInit(WeightInit.XAVIER)
-                    .learningRate(0.1)
-                    .regularization(true).l2(0.0001)
-                    .list()
-                    .layer(0, new DenseLayer.Builder().nIn(FEATURES_COUNT).nOut(10).build())
-                    .layer(1, new DenseLayer.Builder().nIn(10).nOut(10).build())
-                    .layer(2, new OutputLayer.Builder(
-                            LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
-                            .activation(Activation.SOFTMAX)
-                            .nIn(10).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();
-        }
-    }
-}