org.deeplearning4j.ui.stats.StatsListener Java Examples

The following examples show how to use org.deeplearning4j.ui.stats.StatsListener. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: Main.java    From twse-captcha-solver-dl4j with MIT License 5 votes vote down vote up
public static void main(String[] args) throws Exception {
  long startTime = System.currentTimeMillis();
  logger.info("start up time: " + startTime);

  File modelDir = new File(modelDirPath);

  // create dir
  boolean hasDir = modelDir.exists() || modelDir.mkdirs();
  logger.info(modelPath);

  // create model
  ComputationGraph model = createModel();
  // monitor the model score
  UIServer uiServer = UIServer.getInstance();
  StatsStorage statsStorage = new InMemoryStatsStorage();
  uiServer.attach(statsStorage);

  model.setListeners(new ScoreIterationListener(36), new StatsListener(statsStorage));

  // construct the iterator
  MultiDataSetIterator trainMulIterator = new CaptchaSetIterator(batchSize, "train");
  MultiDataSetIterator testMulIterator = new CaptchaSetIterator(batchSize, "test");
  MultiDataSetIterator validateMulIterator = new CaptchaSetIterator(batchSize, "validate");
  // fit
  for (int i = 0; i < epochs; i++) {
    System.out.println("Epoch=====================" + i);
    model.fit(trainMulIterator);
  }
  ModelSerializer.writeModel(model, modelPath, true);
  long endTime = System.currentTimeMillis();
  System.out.println("=============run time=====================" + (endTime - startTime));

  System.out.println("=====eval model=====test==================");
  modelPredict(model, testMulIterator);

  System.out.println("=====eval model=====validate==================");
  modelPredict(model, validateMulIterator);
}
 
Example #2
Source File: CustomerRetentionPredictionExample.java    From Java-Deep-Learning-Cookbook with MIT License 4 votes vote down vote up
public static void main(String[] args) throws IOException, InterruptedException {

       final int labelIndex=11;
       final int batchSize=8;
       final int numClasses=2;
       final INDArray weightsArray = Nd4j.create(new double[]{0.57, 0.75});

       final RecordReader recordReader = generateReader(new ClassPathResource("Churn_Modelling.csv").getFile());
       final DataSetIterator dataSetIterator = new RecordReaderDataSetIterator.Builder(recordReader,batchSize)
                                                                .classification(labelIndex,numClasses)
                                                                .build();
       final DataNormalization dataNormalization = new NormalizerStandardize();
       dataNormalization.fit(dataSetIterator);
       dataSetIterator.setPreProcessor(dataNormalization);
       final DataSetIteratorSplitter dataSetIteratorSplitter = new DataSetIteratorSplitter(dataSetIterator,1250,0.8);

       log.info("Building Model------------------->>>>>>>>>");

        final MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
                                                                    .weightInit(WeightInit.RELU_UNIFORM)
                                                                    .updater(new Adam(0.015D))
                                                                    .list()
                                                                    .layer(new DenseLayer.Builder().nIn(11).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new DenseLayer.Builder().nIn(6).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new DenseLayer.Builder().nIn(6).nOut(4).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new OutputLayer.Builder(new LossMCXENT(weightsArray)).nIn(4).nOut(2).activation(Activation.SOFTMAX).build())
                                                                    .build();

        final UIServer uiServer = UIServer.getInstance();
        final StatsStorage statsStorage = new InMemoryStatsStorage();

        final MultiLayerNetwork multiLayerNetwork = new MultiLayerNetwork(configuration);
        multiLayerNetwork.init();
        multiLayerNetwork.setListeners(new ScoreIterationListener(100),
                                       new StatsListener(statsStorage));
        uiServer.attach(statsStorage);
        multiLayerNetwork.fit(dataSetIteratorSplitter.getTrainIterator(),100);

        final Evaluation evaluation =  multiLayerNetwork.evaluate(dataSetIteratorSplitter.getTestIterator(),Arrays.asList("0","1"));
        System.out.println(evaluation.stats());

        final File file = new File("model.zip");
        ModelSerializer.writeModel(multiLayerNetwork,file,true);
        ModelSerializer.addNormalizerToModel(file,dataNormalization);


    }
 
Example #3
Source File: CustomerRetentionPredictionExample.java    From Java-Deep-Learning-Cookbook with MIT License 4 votes vote down vote up
public static void main(String[] args) throws IOException, InterruptedException {

       final int labelIndex=11;
       final int batchSize=8;
       final int numClasses=2;
       final INDArray weightsArray = Nd4j.create(new double[]{0.57, 0.75});

       final RecordReader recordReader = generateReader(new ClassPathResource("Churn_Modelling.csv").getFile());
       final DataSetIterator dataSetIterator = new RecordReaderDataSetIterator.Builder(recordReader,batchSize)
                                                                .classification(labelIndex,numClasses)
                                                                .build();
       final DataNormalization dataNormalization = new NormalizerStandardize();
       dataNormalization.fit(dataSetIterator);
       dataSetIterator.setPreProcessor(dataNormalization);
       final DataSetIteratorSplitter dataSetIteratorSplitter = new DataSetIteratorSplitter(dataSetIterator,1250,0.8);

       log.info("Building Model------------------->>>>>>>>>");

        final MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
                                                                    .weightInit(WeightInit.RELU_UNIFORM)
                                                                    .updater(new Adam(0.015D))
                                                                    .list()
                                                                    .layer(new DenseLayer.Builder().nIn(11).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new DenseLayer.Builder().nIn(6).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new DenseLayer.Builder().nIn(6).nOut(4).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new OutputLayer.Builder(new LossMCXENT(weightsArray)).nIn(4).nOut(2).activation(Activation.SOFTMAX).build())
                                                                    .build();

        final UIServer uiServer = UIServer.getInstance();
        final StatsStorage statsStorage = new InMemoryStatsStorage();

        final MultiLayerNetwork multiLayerNetwork = new MultiLayerNetwork(configuration);
        multiLayerNetwork.init();
        multiLayerNetwork.setListeners(new ScoreIterationListener(100),
                                       new StatsListener(statsStorage));
        uiServer.attach(statsStorage);
        multiLayerNetwork.fit(dataSetIteratorSplitter.getTrainIterator(),100);

        final Evaluation evaluation =  multiLayerNetwork.evaluate(dataSetIteratorSplitter.getTestIterator(),Arrays.asList("0","1"));
        System.out.println(evaluation.stats());

        final File file = new File("model.zip");
        ModelSerializer.writeModel(multiLayerNetwork,file,true);
        ModelSerializer.addNormalizerToModel(file,dataNormalization);


    }
 
Example #4
Source File: MLPMnistUIExample.java    From dl4j-tutorials with MIT License 4 votes vote down vote up
public static void main(String[] args) throws IOException {
    //number of rows and columns in the input pictures
    final int numRows = 28;
    final int numColumns = 28;
    int outputNum = 10; // number of output classes
    int batchSize = 128; // batch size for each epoch
    int rngSeed = 123; // random number seed for reproducibility
    int numEpochs = 15; // number of epochs to perform
    int listenerFrequency = 1;


    //Get the DataSetIterators:
    DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, rngSeed);
    DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, rngSeed);


    log.info("Build model....");
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(rngSeed) //include a random seed for reproducibility
            // use stochastic gradient descent as an optimization algorithm
            .updater(new Nesterovs(0.006, 0.9))
            .l2(1e-4)
            .list()
            .layer(0, new DenseLayer.Builder() //create the first, input layer with xavier initialization
                    // batchSize, features
                    .nIn(numRows * numColumns)
                    .nOut(1000)
                    .activation(Activation.RELU)
                    .weightInit(WeightInit.XAVIER)
                    .build())
            .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) //create hidden layer
                    .nIn(1000)
                    .nOut(outputNum)
                    .activation(Activation.SOFTMAX)
                    .weightInit(WeightInit.XAVIER)
                    .build())
            .pretrain(false).backprop(true) //use backpropagation to adjust weights
            .build();

    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    //Initialize the user interface backend
    // 获取一个UI实例
    UIServer uiServer = UIServer.getInstance();

    //Configure where the network information (gradients, activations, score vs. time etc) is to be stored
    //Then add the StatsListener to collect this information from the network, as it trains
    // 训练的存储位置
    StatsStorage statsStorage = new InMemoryStatsStorage();             //Alternative: new FileStatsStorage(File) - see UIStorageExample

    //Attach the StatsStorage instance to the UI: this allows the contents of the StatsStorage to be visualized
    uiServer.attach(statsStorage);
    model.init();
    //print the score with every 1 iteration
    model.setListeners(new StatsListener(statsStorage, listenerFrequency)
            ,new ScoreIterationListener(1)
    );

    log.info("Train model....");
    for( int i=0; i<numEpochs; i++ ){
        model.fit(mnistTrain);
    }


    log.info("Evaluate model....");
    Evaluation eval = new Evaluation(outputNum); //create an evaluation object with 10 possible classes
    while(mnistTest.hasNext()){
        DataSet next = mnistTest.next();
        INDArray output = model.output(next.getFeatures(), false); //get the networks prediction
        eval.eval(next.getLabels(), output); //check the prediction against the true class
    }

    log.info(eval.stats());
    log.info("****************Example finished********************");
}