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#BAEL-18260 Downgrade deeplearning4j to version 0.9.1, the latest non beta
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Lines changed: 15 additions & 20 deletions

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‎deeplearning4j/pom.xml‎

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@@ -48,7 +48,7 @@
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</dependencies>
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<properties>
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<dl4j.version>1.0.0-beta5</dl4j.version>
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<dl4j.version>0.9.1</dl4j.version> <!-- Latest non beta version -->
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</properties>
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</project>

‎deeplearning4j/src/main/java/com/baeldung/deeplearning4j/IrisClassifier.java‎

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@@ -4,15 +4,14 @@
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import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
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import org.datavec.api.split.FileSplit;
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import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
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import org.deeplearning4j.eval.Evaluation;
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import org.deeplearning4j.nn.conf.BackpropType;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.conf.layers.DenseLayer;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.util.NetworkUtils;
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import org.nd4j.evaluation.classification.Evaluation;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.DataSet;
@@ -51,11 +50,11 @@ public static void main(String[] args) throws IOException, InterruptedException
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DataSet testData = testAndTrain.getTest();
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MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
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.maxNumLineSearchIterations(1000)
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.iterations(1000)
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.activation(Activation.TANH)
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.weightInit(WeightInit.XAVIER)
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//.regularization(true)
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.l2(0.0001)
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.regularization(true)
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.learningRate(0.1).l2(0.0001)
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.list()
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.layer(0, new DenseLayer.Builder().nIn(FEATURES_COUNT).nOut(3)
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.build())
@@ -64,12 +63,11 @@ public static void main(String[] args) throws IOException, InterruptedException
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.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
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.activation(Activation.SOFTMAX)
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.nIn(3).nOut(CLASSES_COUNT).build())
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.backpropType(BackpropType.Standard)//.pretrain(false)
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.backpropType(BackpropType.Standard).pretrain(false)
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.build();
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MultiLayerNetwork model = new MultiLayerNetwork(configuration);
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model.init();
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NetworkUtils.setLearningRate(model, 0.1);
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model.fit(trainingData);
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INDArray output = model.output(testData.getFeatures());

‎deeplearning4j/src/main/java/com/baeldung/logreg/MnistClassifier.java‎

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@@ -10,6 +10,7 @@
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import org.datavec.image.loader.NativeImageLoader;
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import org.datavec.image.recordreader.ImageRecordReader;
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import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
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import org.deeplearning4j.eval.Evaluation;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.conf.inputs.InputType;
@@ -21,15 +22,12 @@
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
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import org.deeplearning4j.util.ModelSerializer;
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import org.nd4j.evaluation.classification.Evaluation;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
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import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
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import org.nd4j.linalg.learning.config.Nesterovs;
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import org.nd4j.linalg.lossfunctions.LossFunctions;
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import org.nd4j.linalg.schedule.MapSchedule;
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import org.nd4j.linalg.schedule.ScheduleType;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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@@ -71,8 +69,7 @@ public static void main(String[] args) throws Exception {
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String localFilePath = basePath + "mnist_png.tar.gz";
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File file = new File(localFilePath);
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if (!file.exists()) {
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file.getParentFile()
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.mkdirs();
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file.getParentFile().mkdirs();
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Utils.downloadAndSave(dataUrl, file);
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Utils.extractTarArchive(file, basePath);
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}
@@ -135,15 +132,15 @@ public static void main(String[] args) throws Exception {
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.build();
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final MultiLayerConfiguration config = new NeuralNetConfiguration.Builder().seed(seed)
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.l2(0.0005) // ridge regression value
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.updater(new Nesterovs(new MapSchedule(ScheduleType.ITERATION, learningRateSchedule)))
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.updater(new Nesterovs()) //TODO new MapSchedule(ScheduleType.ITERATION, learningRateSchedule)
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.weightInit(WeightInit.XAVIER)
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.list()
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.layer(layer1)
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.layer(layer2)
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.layer(layer3)
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.layer(layer2)
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.layer(layer4)
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.layer(layer5)
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.layer(0, layer1)
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.layer(1, layer2)
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.layer(2, layer3)
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.layer(3, layer2)
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.layer(4, layer4)
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.layer(5, layer5)
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.setInputType(InputType.convolutionalFlat(height, width, channels))
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.build();
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