List of usage examples for org.deeplearning4j.util ModelSerializer restoreMultiLayerNetwork
public static MultiLayerNetwork restoreMultiLayerNetwork(@NonNull String path, boolean loadUpdater) throws IOException
From source file:org.audiveris.omrdataset.train.Training.java
License:Open Source License
/** * Perform the training of the neural network. * <p>/*from w ww . j a v a 2s .c o m*/ * Before training is launched, if the network model exists on disk it is reloaded, otherwise a * brand new one is created. * * @throws Exception in case of IO problem or interruption */ public void process() throws Exception { Files.createDirectories(MISTAKES_PATH); int nChannels = 1; // Number of input channels int batchSize = 64; // Batch size int nEpochs = 1; //3; //10; //2; // Number of training epochs int iterations = 1; // 2; //10; // Number of training iterations int seed = 123; // // Pixel norms NormalizerStandardize normalizer = NormalizerSerializer.getDefault().restore(PIXELS_PATH.toFile()); // Get the dataset using the record reader. CSVRecordReader handles loading/parsing int labelIndex = CONTEXT_WIDTH * CONTEXT_HEIGHT; // format: all cells then label int numLinesToSkip = 1; // Because of header comment line String delimiter = ","; RecordReader trainRecordReader = new CSVRecordReader(numLinesToSkip, delimiter); trainRecordReader.initialize(new FileSplit(FEATURES_PATH.toFile())); logger.info("Getting dataset from {} ...", FEATURES_PATH); RecordReaderDataSetIterator trainIter = new RecordReaderDataSetIterator(trainRecordReader, batchSize, labelIndex, numClasses, -1); trainIter.setCollectMetaData(true); //Instruct the iterator to collect metadata, and store it in the DataSet objects RecordReader testRecordReader = new CSVRecordReader(numLinesToSkip, delimiter); testRecordReader.initialize(new FileSplit(FEATURES_PATH.toFile())); RecordReaderDataSetIterator testIter = new RecordReaderDataSetIterator(testRecordReader, batchSize, labelIndex, numClasses, -1); testIter.setCollectMetaData(true); //Instruct the iterator to collect metadata, and store it in the DataSet objects // Normalization DataSetPreProcessor preProcessor = new MyPreProcessor(normalizer); trainIter.setPreProcessor(preProcessor); testIter.setPreProcessor(preProcessor); if (false) { System.out.println("\n +++++ Test Set Examples MetaData +++++"); while (testIter.hasNext()) { DataSet ds = testIter.next(); List<RecordMetaData> testMetaData = ds.getExampleMetaData(RecordMetaData.class); for (RecordMetaData recordMetaData : testMetaData) { System.out.println(recordMetaData.getLocation()); } } testIter.reset(); } final MultiLayerNetwork model; if (Files.exists(MODEL_PATH)) { model = ModelSerializer.restoreMultiLayerNetwork(MODEL_PATH.toFile(), false); logger.info("Model restored from {}", MODEL_PATH.toAbsolutePath()); } else { logger.info("Building model from scratch"); MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder() // .seed(seed) // .iterations(iterations) // .regularization(true) // .l2(0.0005) // .learningRate(.002) // HB: was .01 initially //.biasLearningRate(0.02) //.learningRateDecayPolicy(LearningRatePolicy.Inverse).lrPolicyDecayRate(0.001).lrPolicyPower(0.75) .weightInit(WeightInit.XAVIER) // .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) // .updater(Updater.NESTEROVS).momentum(0.9) // .list() // .layer(0, new ConvolutionLayer.Builder(5, 5) // .name("C0") // .nIn(nChannels) // .stride(1, 1) // .nOut(20) // .activation(Activation.IDENTITY) // .build()) // .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) // .name("S1") // .kernelSize(2, 2) // .stride(2, 2) // .build()) // .layer(2, new ConvolutionLayer.Builder(5, 5) // .name("C2") // .stride(1, 1) // .nOut(50) // .activation(Activation.IDENTITY) // .build()) // .layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) // .name("S3") // .kernelSize(2, 2) // .stride(2, 2) // .build()) // .layer(4, new DenseLayer.Builder() // .name("D4") // .nOut(500) // .activation(Activation.RELU) // .build()) // .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) // .name("O5") // .nOut(numClasses) // .activation(Activation.SOFTMAX) // .build()) // .setInputType(InputType.convolutionalFlat(CONTEXT_HEIGHT, CONTEXT_WIDTH, 1)); MultiLayerConfiguration conf = builder.build(); model = new MultiLayerNetwork(conf); model.init(); } // Prepare monitoring UIServer uiServer = null; try { if (true) { //Initialize the user interface backend uiServer = UIServer.getInstance(); //Configure where the network information (gradients, score vs. time etc) is to be stored. Here: store in memory. StatsStorage statsStorage = new InMemoryStatsStorage(); //Alternative: new FileStatsStorage(File), for saving and loading later //Attach the StatsStorage instance to the UI: this allows the contents of the StatsStorage to be visualized uiServer.attach(statsStorage); //Then add the StatsListener to collect this information from the network, as it trains model.setListeners(new StatsListener(statsStorage), new ScoreIterationListener(10)); } else { model.setListeners(new ScoreIterationListener(10)); } logger.info("Training model..."); for (int epoch = 1; epoch <= nEpochs; epoch++) { Path epochFolder = Main.cli.mistakes ? MISTAKES_PATH.resolve("epoch#" + epoch) : null; long start = System.currentTimeMillis(); model.fit(trainIter); long stop = System.currentTimeMillis(); double dur = stop - start; logger.info(String.format("*** End epoch#%d, time: %.0f sec", epoch, dur / 1000)); // Save model ModelSerializer.writeModel(model, MODEL_PATH.toFile(), false); ModelSerializer.addNormalizerToModel(MODEL_PATH.toFile(), normalizer); logger.info("Model+normalizer stored as {}", MODEL_PATH.toAbsolutePath()); // // logger.info("Evaluating model..."); // // Evaluation eval = new Evaluation(OmrShapes.NAMES); // // while (testIter.hasNext()) { // DataSet ds = testIter.next(); // List<RecordMetaData> testMetaData = ds.getExampleMetaData(RecordMetaData.class); // INDArray output = model.output(ds.getFeatureMatrix(), false); // eval.eval(ds.getLabels(), output, testMetaData); // } // // System.out.println(eval.stats()); // testIter.reset(); // // //Get a list of prediction errors, from the Evaluation object // //Prediction errors like this are only available after calling iterator.setCollectMetaData(true) // List<Prediction> mistakes = eval.getPredictionErrors(); // logger.info("Epoch#{} Prediction Errors: {}", epoch, mistakes.size()); // // //We can also load a subset of the data, to a DataSet object: // //Here we load the raw data: // List<RecordMetaData> predictionErrorMetaData = new ArrayList<RecordMetaData>(); // // for (Prediction p : mistakes) { // predictionErrorMetaData.add(p.getRecordMetaData(RecordMetaData.class)); // } // // List<Record> predictionErrorRawData = testRecordReader.loadFromMetaData( // predictionErrorMetaData); // // for (int ie = 0; ie < mistakes.size(); ie++) { // Prediction p = mistakes.get(ie); // List<Writable> rawData = predictionErrorRawData.get(ie).getRecord(); // saveMistake(p, rawData, epochFolder); // } // // // // To avoid long useless sessions... // if (mistakes.isEmpty()) { // logger.info("No mistakes left, training stopped."); // // break; // } } } finally { // Stop monitoring if (uiServer != null) { uiServer.stop(); } } logger.info("****************Example finished********************"); }
From source file:org.knime.ext.dl4j.base.util.DLModelPortObjectUtils.java
License:Open Source License
/** * Loads a {@link DLModelPortObject} from the specified {@link ZipInputStream}. Supports both deserialization of old * and new format./* w ww . j a v a2s . com*/ * * @param inStream the stream to load from * @return the loaded {@link DLModelPortObject} * @throws IOException */ @SuppressWarnings("resource") public static DLModelPortObject loadPortFromZip(final ZipInputStream inStream) throws IOException { final List<Layer> layers = new ArrayList<>(); //old model format INDArray mln_params = null; MultiLayerConfiguration mln_config = null; org.deeplearning4j.nn.api.Updater updater = null; //new model format boolean mlnLoaded = false; boolean cgLoaded = false; MultiLayerNetwork mlnFromModelSerializer = null; ComputationGraph cgFromModelSerializer = null; ZipEntry entry; while ((entry = inStream.getNextEntry()) != null) { // read layers if (entry.getName().matches("layer[0123456789]+")) { final String read = readStringFromZipStream(inStream); Layer l = NeuralNetConfiguration.fromJson(read).getLayer(); if (l instanceof BaseLayer) { BaseLayer bl = (BaseLayer) l; /* Compatibility issue between dl4j 0.6 and 0.8 due to API change. Activations changed from * Strings to an interface. Therefore, if a model was saved with 0.6 the corresponding member * of the layer object will contain null after 'NeuralNetConfiguration.fromJson'. Old method to * retrieve String representation of the activation function was removed. Therefore, we parse * the old activation from the json ourself and map it to the new Activation. */ if (bl.getActivationFn() == null) { Optional<Activation> layerActivation = DL4JVersionUtils.parseLayerActivationFromJson(read); if (layerActivation.isPresent()) { bl.setActivationFn(layerActivation.get().getActivationFunction()); } } } layers.add(l); // directly read MultiLayerNetwork, new format } else if (entry.getName().matches("mln_model")) { //stream must not be closed, ModelSerializer tries to close the stream CloseShieldInputStream shieldIs = new CloseShieldInputStream(inStream); mlnFromModelSerializer = ModelSerializer.restoreMultiLayerNetwork(shieldIs, true); mlnLoaded = true; // directly read MultiLayerNetwork, new format } else if (entry.getName().matches("cg_model")) { //stream must not be closed, ModelSerializer tries to close the stream CloseShieldInputStream shieldIs = new CloseShieldInputStream(inStream); cgFromModelSerializer = ModelSerializer.restoreComputationGraph(shieldIs, true); cgLoaded = true; // read MultilayerNetworkConfig, old format } else if (entry.getName().matches("mln_config")) { final String read = readStringFromZipStream(inStream); mln_config = MultiLayerConfiguration.fromJson(read.toString()); // read params, old format } else if (entry.getName().matches("mln_params")) { try { mln_params = Nd4j.read(inStream); } catch (Exception e) { throw new IOException("Could not load network parameters. Please re-execute the Node.", e); } // read updater, old format } else if (entry.getName().matches("mln_updater")) { // stream must not be closed, even if an exception is thrown, because the wrapped stream must stay open final IgnoreIDObjectInputStream ois = new IgnoreIDObjectInputStream(inStream); try { updater = (org.deeplearning4j.nn.api.Updater) ois.readObject(); } catch (final ClassNotFoundException e) { throw new IOException("Problem with updater loading: " + e.getMessage(), e); } } } if (mlnLoaded) { assert (!cgLoaded); return new DLModelPortObject(layers, mlnFromModelSerializer, null); } else if (cgLoaded) { assert (!mlnLoaded); return new DLModelPortObject(layers, cgFromModelSerializer, null); } else { return new DLModelPortObject(layers, buildMln(mln_config, updater, mln_params), null); } }
From source file:weka.classifiers.functions.Dl4jMlpClassifier.java
License:Open Source License
/** * Custom deserialization method// w ww .j a v a2s. co m * * @param ois the object input stream * @throws ClassNotFoundException * @throws IOException */ private void readObject(ObjectInputStream ois) throws ClassNotFoundException, IOException { // default deserialization ois.defaultReadObject(); // restore the model if (m_replaceMissing != null) { ClassLoader origLoader = Thread.currentThread().getContextClassLoader(); try { Thread.currentThread().setContextClassLoader(this.getClass().getClassLoader()); m_model = ModelSerializer.restoreMultiLayerNetwork(ois, false); } finally { Thread.currentThread().setContextClassLoader(origLoader); } } }