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Java example source code file (ModelSerializer.java)

This example Java source code file (ModelSerializer.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

bufferedreader, computationgraph, datainputstream, file, indarray, ioexception, model, multilayernetwork, objectinputstream, string, stringbuilder, updater, zip, zipentry, zipfile

The ModelSerializer.java Java example source code

package org.deeplearning4j.util;

import lombok.NonNull;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.api.Updater;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.layers.RBM;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.heartbeat.reports.Task;

import java.io.*;
import java.util.zip.ZipEntry;
import java.util.zip.ZipFile;
import java.util.zip.ZipInputStream;
import java.util.zip.ZipOutputStream;

/**
 * Utility class suited to save/restore neural net models
 *
 * @author raver119@gmail.com
 */
public class ModelSerializer {

    /**
     * Write a model to a file
     * @param model the model to write
     * @param file the file to write to
     * @param saveUpdater whether to save the updater or not
     * @throws IOException
     */
    public static void writeModel(@NonNull Model model, @NonNull File file, boolean saveUpdater) throws IOException {
        try(BufferedOutputStream stream = new BufferedOutputStream(new FileOutputStream(file))){
            writeModel(model, stream, saveUpdater);
        }
    }

    /**
     * Write a model to a file path
     * @param model the model to write
     * @param path the path to write to
     * @param saveUpdater whether to save the updater
     *                    or not
     * @throws IOException
     */
    public static void writeModel(@NonNull Model model, @NonNull String path, boolean saveUpdater) throws IOException {
        try(BufferedOutputStream stream = new BufferedOutputStream(new FileOutputStream(path))){
            writeModel(model, stream, saveUpdater);
        }
    }

    /**
     * Write a model to an output stream
     * @param model the model to save
     * @param stream the output stream to write to
     * @param saveUpdater whether to save the updater for the model or not
     * @throws IOException
     */
    public static void writeModel(@NonNull Model model, @NonNull OutputStream stream, boolean saveUpdater) throws IOException {
        ZipOutputStream zipfile = new ZipOutputStream(stream);

        // save json first
        String json = "";
        if (model instanceof MultiLayerNetwork) {
            json = ((MultiLayerNetwork) model).getLayerWiseConfigurations().toJson();
        } else if (model instanceof ComputationGraph) {
            json = ((ComputationGraph) model).getConfiguration().toJson();
        }

        ZipEntry config = new ZipEntry("configuration.json");
        zipfile.putNextEntry(config);

        writeEntry(new ByteArrayInputStream(json.getBytes()), zipfile);

        ZipEntry coefficients = new ZipEntry("coefficients.bin");
        zipfile.putNextEntry(coefficients);

        ByteArrayOutputStream bos = new ByteArrayOutputStream();
        DataOutputStream dos = new DataOutputStream(bos);
        Nd4j.write(model.params(), dos);
        dos.flush();
        dos.close();

        InputStream inputStream = new ByteArrayInputStream(bos.toByteArray());
        writeEntry(inputStream, zipfile);

        if (saveUpdater) {
            ZipEntry updater = new ZipEntry("updater.bin");
            zipfile.putNextEntry(updater);


            bos = new ByteArrayOutputStream();
            ObjectOutputStream oos = new ObjectOutputStream(bos);
            if (model instanceof  MultiLayerNetwork) {
                oos.writeObject(((MultiLayerNetwork) model).getUpdater());
            } else if (model instanceof ComputationGraph) {
                oos.writeObject(((ComputationGraph) model).getUpdater());
            }
            oos.flush();
            oos.close();

            inputStream = new ByteArrayInputStream(bos.toByteArray());
            writeEntry(inputStream, zipfile);
        }

        zipfile.flush();
        zipfile.close();
    }


    private static void writeEntry(InputStream inputStream, ZipOutputStream zipStream) throws IOException {
        byte[] bytes = new byte[1024];
        int bytesRead;
        while ((bytesRead = inputStream.read(bytes)) != -1) {
            zipStream.write(bytes, 0, bytesRead);
        }
    }

    /**
     * Load a multi layer network
     * from a file
     * @param file the file to load from
     * @return the loaded multi layer network
     * @throws IOException
     */
    public static MultiLayerNetwork restoreMultiLayerNetwork(@NonNull File file) throws IOException {
        ZipFile zipFile = new ZipFile(file);

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

        String json = "";
        INDArray params = null;
        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(params, false);


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


    /**
     * Load a multi layer network
     * from a file
     * @param is the inputstream to load from
     * @return the loaded multi layer network
     * @throws IOException
     */
    public static MultiLayerNetwork restoreMultiLayerNetwork(@NonNull InputStream is) throws IOException {
        ZipInputStream zipFile = new ZipInputStream(is);

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

        String json = "";
        INDArray params = null;
        Updater updater = null;


        ZipEntry entry;
        while((entry = zipFile.getNextEntry()) != null) {
            switch (entry.getName()) {
                case "configuration.json":
                    DataInputStream dis = new DataInputStream(zipFile);
                    params = Nd4j.read(dis);
                    gotCoefficients = true;
                    break;
                case "coefficients.bin":
                    DataInputStream dis2 = new DataInputStream(zipFile);
                    params = Nd4j.read(dis2);
                    gotCoefficients = true;
                    break;
                case "updater.bin":
                    ObjectInputStream ois = new ObjectInputStream(zipFile);

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

                    gotUpdater = true;
                    break;

            }

            zipFile.closeEntry();

        }


        zipFile.close();

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

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

    /**
     *
     * @param path
     * @return
     * @throws IOException
     */
    public static MultiLayerNetwork restoreMultiLayerNetwork(@NonNull String path) throws IOException {
        return restoreMultiLayerNetwork(new File(path));
    }

    /**
     *
     * @param path
     * @return
     * @throws IOException
     */
    public static ComputationGraph restoreComputationGraph(@NonNull String path) throws IOException {
        return restoreComputationGraph(new File(path));
    }


    /**
     * Load a computation graph from a file
     * @param is the inputstream to get the computation graph from
     * @return the loaded computation graph
     *
     * @throws IOException
     */
    public static ComputationGraph restoreComputationGraph(@NonNull InputStream is) throws IOException {
        ZipInputStream zis = new ZipInputStream(is);
        boolean gotConfig = false;
        boolean gotCoefficients = false;
        boolean gotUpdater = false;

        String json = "";
        INDArray params = null;
        ComputationGraphUpdater updater = null;
        BufferedReader reader = new BufferedReader(new InputStreamReader(zis));

        ZipEntry entry;
        while((entry = zis.getNextEntry()) != null) {
            switch(entry.getName()) {
                case "configuration.json":
                    String line;
                    StringBuilder js = new StringBuilder();
                    while ((line = reader.readLine()) != null) {
                        js.append(line).append("\n");
                    }
                    json = js.toString();

                    gotConfig = true;
                    break;
                case "coefficients.bin":
                    DataInputStream dis = new DataInputStream(zis);
                    params = Nd4j.read(dis);

                    gotCoefficients = true;
                    break;
                case "updater.bin":
                    ObjectInputStream ois = new ObjectInputStream(zis);

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

                    gotUpdater = true;
            }

            zis.closeEntry();
        }

        if (gotConfig && gotCoefficients) {
            ComputationGraphConfiguration confFromJson = ComputationGraphConfiguration.fromJson(json);
            ComputationGraph cg = new ComputationGraph(confFromJson);
            cg.init(params, false);

            if (gotUpdater && updater != null) {
                cg.setUpdater(updater);
            }

            zis.close();

            return cg;
        }
        else {
            zis.close();
            throw new IllegalStateException("Model wasnt found within file: gotConfig: [" + gotConfig + "], gotCoefficients: [" + gotCoefficients + "], gotUpdater: [" + gotUpdater + "]");
        }
    }

    /**
     * Load a computation graph from a file
     * @param file the file to get the computation graph from
     * @return the loaded computation graph
     *
     * @throws IOException
     */
    public static ComputationGraph restoreComputationGraph(@NonNull File file) throws IOException {
        ZipFile zipFile = new ZipFile(file);

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

        String json = "";
        INDArray params = null;
        ComputationGraphUpdater 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 = (ComputationGraphUpdater) ois.readObject();
            } catch (ClassNotFoundException e) {
                throw new RuntimeException(e);
            }

            gotUpdater = true;
        }


        zipFile.close();

        if (gotConfig && gotCoefficients) {
            ComputationGraphConfiguration confFromJson = ComputationGraphConfiguration.fromJson(json);
            ComputationGraph cg = new ComputationGraph(confFromJson);
            cg.init(params, false);


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

    /**
     *
     * @param model
     * @return
     */
    public static Task taskByModel(Model model) {
        Task task = new Task();
        try {
            task.setArchitectureType(Task.ArchitectureType.RECURRENT);
            if (model instanceof ComputationGraph) {
                task.setNetworkType(Task.NetworkType.ComputationalGraph);
                ComputationGraph network = (ComputationGraph) model;
                try {
                    if (network.getLayers() != null && network.getLayers().length > 0) {
                        for (Layer layer : network.getLayers()) {
                            if (layer instanceof RBM || layer instanceof org.deeplearning4j.nn.layers.feedforward.rbm.RBM) {
                                task.setArchitectureType(Task.ArchitectureType.RBM);
                                break;
                            }
                            if (layer.type().equals(Layer.Type.CONVOLUTIONAL)) {
                                task.setArchitectureType(Task.ArchitectureType.CONVOLUTION);
                                break;
                            } else if (layer.type().equals(Layer.Type.RECURRENT) || layer.type().equals(Layer.Type.RECURSIVE)) {
                                task.setArchitectureType(Task.ArchitectureType.RECURRENT);
                                break;
                            }
                        }
                    } else task.setArchitectureType(Task.ArchitectureType.UNKNOWN);
                } catch (Exception e) {
                    ; // do nothing here
                }
            } else if (model instanceof MultiLayerNetwork) {
                task.setNetworkType(Task.NetworkType.MultilayerNetwork);
                MultiLayerNetwork network = (MultiLayerNetwork) model;
                try {
                    if (network.getLayers() != null && network.getLayers().length > 0) {
                        for (Layer layer : network.getLayers()) {
                            if (layer instanceof RBM || layer instanceof org.deeplearning4j.nn.layers.feedforward.rbm.RBM) {
                                task.setArchitectureType(Task.ArchitectureType.RBM);
                                break;
                            }
                            if (layer.type().equals(Layer.Type.CONVOLUTIONAL)) {
                                task.setArchitectureType(Task.ArchitectureType.CONVOLUTION);
                                break;
                            } else if (layer.type().equals(Layer.Type.RECURRENT) || layer.type().equals(Layer.Type.RECURSIVE)) {
                                task.setArchitectureType(Task.ArchitectureType.RECURRENT);
                                break;
                            }
                        }
                    } else task.setArchitectureType(Task.ArchitectureType.UNKNOWN);
                } catch (Exception e) {
                    ; // do nothing here
                }
            }
            return task;
        } catch (Exception e) {
            task.setArchitectureType(Task.ArchitectureType.UNKNOWN);
            task.setNetworkType(Task.NetworkType.DenseNetwork);
            return task;
        }
    }
}

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