From 0c52e8a9bc38bc358abb0198e31c62cddf2ca6c4 Mon Sep 17 00:00:00 2001
From: Alex <croix.alexandre@gmail.com>
Date: Fri, 7 Jun 2019 10:56:28 +0200
Subject: [PATCH] Set neural network on specific branch
---
pom.xml | 11 --
.../be/cylab/java/wowa/training/MainDL4J.java | 103 ------------------
2 files changed, 114 deletions(-)
delete mode 100644 src/main/java/be/cylab/java/wowa/training/MainDL4J.java
diff --git a/pom.xml b/pom.xml
index 7db5371..9bbf15c 100644
--- a/pom.xml
+++ b/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>
diff --git a/src/main/java/be/cylab/java/wowa/training/MainDL4J.java b/src/main/java/be/cylab/java/wowa/training/MainDL4J.java
deleted file mode 100644
index 3209094..0000000
--- a/src/main/java/be/cylab/java/wowa/training/MainDL4J.java
+++ /dev/null
@@ -1,103 +0,0 @@
-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();
- }
- }
-}
--
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