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RobAltenaRobAltena
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transferlearning
Signed-off-by: Robert Altena <[email protected]>
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‎dl4j-examples/src/main/java/org/deeplearning4j/examples/transferlearning/vgg16/EditAtBottleneckOthersFrozen.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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package org.deeplearning4j.examples.transferlearning.vgg16;
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import org.deeplearning4j.eval.Evaluation;
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import org.deeplearning4j.examples.transferlearning.vgg16.dataHelpers.FlowerDataSetIterator;
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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.weights.WeightInit;
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import org.deeplearning4j.zoo.ZooModel;
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import org.deeplearning4j.zoo.model.VGG16;
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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.learning.config.Nesterovs;

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/transferlearning/vgg16/EditLastLayerOthersFrozen.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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package org.deeplearning4j.examples.transferlearning.vgg16;
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import org.deeplearning4j.eval.Evaluation;
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import org.deeplearning4j.examples.transferlearning.vgg16.dataHelpers.FlowerDataSetIterator;
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import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.graph.ComputationGraph;
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import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
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import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
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import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration;
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import org.deeplearning4j.nn.transferlearning.TransferLearning;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.zoo.ZooModel;
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import org.deeplearning4j.zoo.model.VGG16;
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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.learning.config.Nesterovs;
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private static final int batchSize = 15;
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private static final String featureExtractionLayer = "fc2";
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public static void main(String [] args) throws UnsupportedKerasConfigurationException, IOException, InvalidKerasConfigurationException {
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public static void main(String [] args) throws IOException {
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//Import vgg
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//Note that the model imported does not have an output layer (check printed summary)
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.addLayer("predictions",
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new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
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.nIn(4096).nOut(numClasses)
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.weightInit(WeightInit.DISTRIBUTION)
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.dist(new NormalDistribution(0,0.2*(2.0/(4096+numClasses)))) //This weight init dist gave better results than Xavier
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.weightInit(new NormalDistribution(0,0.2*(2.0/(4096+numClasses)))) //This weight init dist gave better results than Xavier
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.activation(Activation.SOFTMAX).build(),
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"fc2")
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.build();

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/transferlearning/vgg16/FineTuneFromBlockFour.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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package org.deeplearning4j.examples.transferlearning.vgg16;
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import org.deeplearning4j.eval.Evaluation;
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import org.deeplearning4j.examples.transferlearning.vgg16.dataHelpers.FlowerDataSetIterator;
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import org.deeplearning4j.nn.graph.ComputationGraph;
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import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration;
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import org.deeplearning4j.nn.transferlearning.TransferLearning;
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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.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.learning.config.Sgd;
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import org.slf4j.Logger;
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* Finetuning like this is usually done with a low learning rate and a simple SGD optimizer
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* @author susaneraly on 3/6/17.
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*/
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@SuppressWarnings("DuplicatedCode")
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public class FineTuneFromBlockFour {
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private static final Logger log = org.slf4j.LoggerFactory.getLogger(FineTuneFromBlockFour.class);
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‎dl4j-examples/src/main/java/org/deeplearning4j/examples/transferlearning/vgg16/FitFromFeaturized.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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package org.deeplearning4j.examples.transferlearning.vgg16;
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import org.deeplearning4j.eval.Evaluation;
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import org.deeplearning4j.examples.transferlearning.vgg16.dataHelpers.FeaturizedPreSave;
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import org.deeplearning4j.examples.transferlearning.vgg16.dataHelpers.FlowerDataSetIteratorFeaturized;
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import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
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import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration;
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import org.deeplearning4j.nn.transferlearning.TransferLearning;
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import org.deeplearning4j.nn.transferlearning.TransferLearningHelper;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.zoo.ZooModel;
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import org.deeplearning4j.zoo.model.VGG16;
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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.learning.config.Nesterovs;
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* Since the helper avoids the forward pass through the frozen layers we save on computation time when running multiple epochs.
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* In this manner, users can iterate quickly tweaking learning rates, weight initialization etc` to settle on a model that gives good results.
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*/
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@SuppressWarnings("DuplicatedCode")
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public class FitFromFeaturized {
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private static final Logger log = org.slf4j.LoggerFactory.getLogger(FitFromFeaturized.class);
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public static final String featureExtractionLayer = FeaturizedPreSave.featurizeExtractionLayer;
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private static final String featureExtractionLayer = FeaturizedPreSave.featurizeExtractionLayer;
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protected static final long seed = 12345;
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protected static final int numClasses = 5;
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protected static final int nEpochs = 3;
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.addLayer("predictions",
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new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
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.nIn(4096).nOut(numClasses)
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.weightInit(WeightInit.DISTRIBUTION)
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.dist(new NormalDistribution(0,0.2*(2.0/(4096+numClasses)))) //This weight init dist gave better results than Xavier
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.weightInit(new NormalDistribution(0,0.2*(2.0/(4096+numClasses)))) //This weight init dist gave better results than Xavier
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.activation(Activation.SOFTMAX).build(),
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"fc2")
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.build();

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/transferlearning/vgg16/dataHelpers/FeaturizedPreSave.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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package org.deeplearning4j.examples.transferlearning.vgg16.dataHelpers;
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import org.deeplearning4j.nn.graph.ComputationGraph;
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import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
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import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
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import org.deeplearning4j.nn.transferlearning.TransferLearningHelper;
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import org.deeplearning4j.zoo.ZooModel;
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import org.deeplearning4j.zoo.model.VGG16;
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protected static final int batchSize = 15;
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public static final String featurizeExtractionLayer = "fc2";
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public static void main(String [] args) throws UnsupportedKerasConfigurationException, IOException, InvalidKerasConfigurationException {
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public static void main(String [] args) throws IOException {
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//import org.deeplearning4j.transferlearning.vgg16 and print summary
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log.info("\n\nLoading org.deeplearning4j.transferlearning.vgg16...\n\n");
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log.info("Finished pre saving featurized test and train data");
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}
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public static void saveToDisk(DataSet currentFeaturized, int iterNum, boolean isTrain) {
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private static void saveToDisk(DataSet currentFeaturized, int iterNum, boolean isTrain) {
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File fileFolder = isTrain ? new File("trainFolder"): new File("testFolder");
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if (iterNum == 0) {
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fileFolder.mkdirs();

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/transferlearning/vgg16/dataHelpers/FlowerDataSetIterator.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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}
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public static void setup(int batchSizeArg, int trainPerc) throws IOException {
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public static void setup(int batchSizeArg, int trainPerc) {
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try {
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downloadAndUntar();
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} catch (IOException e) {
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return iter;
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}
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public static void downloadAndUntar() throws IOException {
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private static void downloadAndUntar() throws IOException {
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File rootFile = new File(DATA_DIR);
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if (!rootFile.exists()) {
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rootFile.mkdir();

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/transferlearning/vgg16/dataHelpers/FlowerDataSetIteratorFeaturized.java

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package org.deeplearning4j.examples.transferlearning.vgg16.dataHelpers;
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import org.deeplearning4j.datasets.iterator.AsyncDataSetIterator;
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import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
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import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
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import org.nd4j.linalg.dataset.AsyncDataSetIterator;
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import org.nd4j.linalg.dataset.ExistingMiniBatchDataSetIterator;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.slf4j.Logger;
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public class FlowerDataSetIteratorFeaturized {
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private static final Logger log = org.slf4j.LoggerFactory.getLogger(FlowerDataSetIteratorFeaturized.class);
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static String featureExtractorLayer = FeaturizedPreSave.featurizeExtractionLayer;
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public static void setup(String featureExtractorLayerArg) {
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featureExtractorLayer = featureExtractorLayerArg;
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}
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private static String featureExtractorLayer = FeaturizedPreSave.featurizeExtractionLayer;
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public static DataSetIterator trainIterator() throws UnsupportedKerasConfigurationException, IOException, InvalidKerasConfigurationException {
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runFeaturize();

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