Example usage for org.deeplearning4j.nn.multilayer MultiLayerNetwork setParameters

Introduction

In this page you can find the example usage for org.deeplearning4j.nn.multilayer MultiLayerNetwork setParameters.

Prototype

public void setParameters(INDArray params) 

Source Link

Document

See #setParams(INDArray)

Usage

From source file:examples.cnn.NetworkTrainer.java

License:Apache License

public void train(JavaRDD<DataSet> train, JavaRDD<DataSet> test) {

    int batchSize = 12 * cores;
    int lrCount = 0;
    double bestAccuracy = Double.MIN_VALUE;

    double learningRate = initialLearningRate;

    int trainCount = Long.valueOf(train.count()).intValue();
    log.info("Number of training images {}", trainCount);
    log.info("Number of test images {}", test.count());

    MultiLayerNetwork net = new MultiLayerNetwork(
            model.apply(learningRate, width, height, channels, numLabels));
    net.init();//from ww  w . j a va2 s  . c o  m

    Map<Integer, Double> acc = new HashMap<>();
    for (int i = 0; i < epochs; i++) {

        SparkDl4jMultiLayer sparkNetwork = networkToSparkNetwork.apply(net);
        final MultiLayerNetwork nn = sparkNetwork.fitDataSet(train, batchSize, trainCount, cores);
        log.info("Epoch {} completed", i);

        JavaPairRDD<Object, Object> predictionsAndLabels = test.mapToPair(
                ds -> new Tuple2<>(label(nn.output(ds.getFeatureMatrix(), false)), label(ds.getLabels())));
        MulticlassMetrics metrics = new MulticlassMetrics(predictionsAndLabels.rdd());
        double accuracy = 1.0 * predictionsAndLabels.filter(x -> x._1.equals(x._2)).count() / test.count();
        log.info("Epoch {} accuracy {} ", i, accuracy);
        acc.put(i, accuracy);
        predictionsAndLabels.take(10).forEach(t -> log.info("predicted {}, label {}", t._1, t._2));
        log.info("confusionMatrix {}", metrics.confusionMatrix());

        INDArray params = nn.params();
        if (accuracy > bestAccuracy) {
            bestAccuracy = accuracy;
            try {
                ModelSerializer.writeModel(nn, new File(workingDir, Double.toString(accuracy)), false);
            } catch (IOException e) {
                log.error("Error writing trained model", e);
            }
            lrCount = 0;
        } else {

            if (++lrCount % stepDecayTreshold == 0) {
                learningRate *= learningRateDecayFactor;
            }
            if (lrCount >= resetLearningRateThreshold) {
                lrCount = 0;
                learningRate = initialLearningRate;
            }
            if (learningRate < minimumLearningRate) {
                lrCount = 0;
                learningRate = initialLearningRate;
            }
            if (bestAccuracy - accuracy > downgradeAccuracyThreshold) {
                params = ModelLoader.load(workingDir, bestAccuracy);
            }
        }
        net = new MultiLayerNetwork(model.apply(learningRate, width, height, channels, numLabels));
        net.init();
        net.setParameters(params);
        log.info("Learning rate {} for epoch {}", learningRate, i + 1);
    }
    log.info("Training completed");

}

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From source file:vectorizer.ModelSerializer.java

public static MultiLayerNetwork restoreMultiLayerNetwork(File file) throws IOException {
    ZipFile zipFile = new ZipFile(file);

    boolean gotConfig = false;
    boolean gotCoefficients = false;
    boolean gotUpdater = false;

    String json = "";
    INDArray params = null;/*from w  w w.  jav  a  2s .c  o m*/
    Updater updater = null;

    ZipEntry config = zipFile.getEntry("configuration.json");
    if (config != null) {
        //restoring configuration

        InputStream stream = zipFile.getInputStream(config);
        BufferedReader reader = new BufferedReader(new InputStreamReader(stream));
        String line = "";
        StringBuilder js = new StringBuilder();
        while ((line = reader.readLine()) != null) {
            js.append(line).append("\n");
        }
        json = js.toString();

        reader.close();
        stream.close();
        gotConfig = true;
    }

    ZipEntry coefficients = zipFile.getEntry("coefficients.bin");
    if (coefficients != null) {
        InputStream stream = zipFile.getInputStream(coefficients);
        DataInputStream dis = new DataInputStream(stream);
        params = Nd4j.read(dis);

        dis.close();
        gotCoefficients = true;
    }

    ZipEntry updaters = zipFile.getEntry("updater.bin");
    if (updaters != null) {
        InputStream stream = zipFile.getInputStream(updaters);
        ObjectInputStream ois = new ObjectInputStream(stream);

        try {
            updater = (Updater) ois.readObject();
        } catch (ClassNotFoundException e) {
            throw new RuntimeException(e);
        }

        gotUpdater = true;
    }

    zipFile.close();

    if (gotConfig && gotCoefficients) {
        MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
        MultiLayerNetwork network = new MultiLayerNetwork(confFromJson);
        network.init();
        network.setParameters(params);

        if (gotUpdater && updater != null) {
            network.setUpdater(updater);
        }
        return network;
    } else
        throw new IllegalStateException("Model wasnt found within file: gotConfig: [" + gotConfig
                + "], gotCoefficients: [" + gotCoefficients + "], gotUpdater: [" + gotUpdater + "]");
}

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