Example usage for org.deeplearning4j.nn.conf MultiLayerConfiguration toJson

Introduction

In this page you can find the example usage for org.deeplearning4j.nn.conf MultiLayerConfiguration toJson.

Prototype

public String toJson() 

Source Link

Usage

From source file:weka.classifiers.functions.Dl4jMlpClassifier.java

License:Open Source License

/**
 * The method used to train the classifier.
 *
 * @param data set of instances serving as training data
 * @throws Exception if something goes wrong in the training process
 *//*  w  w w .  j a v  a2s .co m*/
@Override
public void buildClassifier(Instances data) throws Exception {
    ClassLoader orig = Thread.currentThread().getContextClassLoader();
    try {
        Thread.currentThread().setContextClassLoader(this.getClass().getClassLoader());
        // Can classifier handle the data?
        getCapabilities().testWithFail(data);

        // Check basic network structure
        if (m_layers.length == 0) {
            throw new Exception("No layers have been added!");
        }
        if (!(m_layers[m_layers.length - 1] instanceof OutputLayer)) {
            throw new Exception("Last layer in network must be an output layer!");
        }

        // Remove instances with missing class and check that instances and
        // predictor attributes remain.
        data = new Instances(data);
        data.deleteWithMissingClass();
        m_zeroR = null;
        if (data.numInstances() == 0 || data.numAttributes() < 2) {
            m_zeroR = new ZeroR();
            m_zeroR.buildClassifier(data);
            return;
        }

        // Replace missing values
        m_replaceMissing = new ReplaceMissingValues();
        m_replaceMissing.setInputFormat(data);
        data = Filter.useFilter(data, m_replaceMissing);

        // Retrieve two different class values used to determine filter
        // transformation
        double y0 = data.instance(0).classValue();
        int index = 1;
        while (index < data.numInstances() && data.instance(index).classValue() == y0) {
            index++;
        }
        if (index == data.numInstances()) {
            // degenerate case, all class values are equal
            // we don't want to deal with this, too much hassle
            throw new Exception("All class values are the same. At least two class values should be different");
        }
        double y1 = data.instance(index).classValue();

        // Replace nominal attributes by binary numeric attributes.
        m_nominalToBinary = new NominalToBinary();
        m_nominalToBinary.setInputFormat(data);
        data = Filter.useFilter(data, m_nominalToBinary);

        // Standardize or normalize (as requested), including the class
        if (m_standardizeInsteadOfNormalize) {
            m_normalize = new Standardize();
            m_normalize.setOptions(new String[] { "-unset-class-temporarily" });
        } else {
            m_normalize = new Normalize();
        }
        m_normalize.setInputFormat(data);
        data = Filter.useFilter(data, m_normalize);

        double z0 = data.instance(0).classValue();
        double z1 = data.instance(index).classValue();
        m_x1 = (y0 - y1) / (z0 - z1); // no division by zero, since y0 != y1
                                      // guaranteed => z0 != z1 ???
        m_x0 = (y0 - m_x1 * z0); // = y1 - m_x1 * z1

        // Randomize the data, just in case
        Random rand = new Random(getSeed());
        data.randomize(rand);

        // Initialize random number generator for construction of network
        NeuralNetConfiguration.Builder builder = new NeuralNetConfiguration.Builder();
        if (getOptimizationAlgorithm() == null) {
            builder.setOptimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT);
        } else {
            builder.setOptimizationAlgo(getOptimizationAlgorithm());
        }
        builder.setSeed(rand.nextInt());

        // Construct the mlp configuration
        ListBuilder ip = builder.list(getLayers());
        int numInputAttributes = getDataSetIterator().getNumAttributes(data);

        // Connect up the layers appropriately
        for (int x = 0; x < m_layers.length; x++) {

            // Is this the first hidden layer?
            if (x == 0) {
                setNumIncoming(m_layers[x], numInputAttributes);
            } else {
                setNumIncoming(m_layers[x], getNumUnits(m_layers[x - 1]));
            }

            // Is this the output layer?
            if (x == m_layers.length - 1) {
                ((OutputLayer) m_layers[x]).setNOut(data.numClasses());
            }
            ip = ip.layer(x, m_layers[x]);
        }

        // If we have a convolutional network
        if (getDataSetIterator() instanceof ImageDataSetIterator) {
            ImageDataSetIterator idsi = (ImageDataSetIterator) getDataSetIterator();
            ip.setInputType(
                    InputType.convolutionalFlat(idsi.getWidth(), idsi.getHeight(), idsi.getNumChannels()));
        } else if (getDataSetIterator() instanceof ConvolutionalInstancesIterator) {
            ConvolutionalInstancesIterator cii = (ConvolutionalInstancesIterator) getDataSetIterator();
            ip.setInputType(InputType.convolutionalFlat(cii.getWidth(), cii.getHeight(), cii.getNumChannels()));
        }

        ip = ip.pretrain(false).backprop(true);

        MultiLayerConfiguration conf = ip.build();

        if (getDebug()) {
            System.err.println(conf.toJson());
        }

        // build the network
        m_model = new MultiLayerNetwork(conf);
        m_model.init();

        if (getDebug()) {
            System.err.println(m_model.conf().toYaml());
        }

        ArrayList<IterationListener> listeners = new ArrayList<IterationListener>();
        listeners.add(
                new ScoreIterationListener(data.numInstances() / getDataSetIterator().getTrainBatchSize()));

        // if the log file doesn't point to a directory, set up the listener
        if (getLogFile() != null && !getLogFile().isDirectory()) {
            int numMiniBatches = (int) Math
                    .ceil(((double) data.numInstances()) / ((double) getDataSetIterator().getTrainBatchSize()));
            listeners.add(new FileIterationListener(getLogFile().getAbsolutePath(), numMiniBatches));
        }

        m_model.setListeners(listeners);

        // Abusing the MultipleEpochsIterator because it splits the data into
        // batches
        DataSetIterator iter = getDataSetIterator().getIterator(data, getSeed());
        for (int i = 0; i < getNumEpochs(); i++) {
            m_model.fit(iter); // Note that this calls the reset() method of the
                               // iterator
            if (getDebug()) {
                m_log.info("*** Completed epoch {} ***", i + 1);
            }
            iter.reset();
        }
    } finally {
        Thread.currentThread().setContextClassLoader(orig);
    }
}

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