/** * setup the local DBN instance based on conf params * * * */ @Override public void setup(Configuration conf) { log.info("Worker-Conf: " + conf.get(MULTI_LAYER_CONF)); this.batchSize = conf.getInt("org.deeplearning4j.batchSize", 10); this.numberClasses = conf.getInt("org.deeplearning4j.numberClasses", 2); this.numberFeatures = conf.getInt("org.deeplearning4j.features", 5); log.info("Classes: " + this.numberClasses + ", Features: " + this.numberFeatures); MultiLayerConfiguration conf2 = MultiLayerConfiguration.fromJson( conf.get(MULTI_LAYER_CONF)); multiLayerNetwork = new MultiLayerNetwork(conf2); }
/** * JSON model configuration passed in * If you are entering a MultiLayerConfiguration JSON, * your file name MUST contain '_multi'. * Otherwise, it will be processed as a regular * NeuralNetConfiguration * * Takes in JSON file path * Checks file path for indication of MultiLayer * Reads JSON file to string * Creates neural net configuration from string config * */ @Override public <E> E value(String value) throws Exception { Boolean isMultiLayer = value.contains("_multi"); String json = FileUtils.readFileToString(new File(value)); if (isMultiLayer) { return (E) MultiLayerConfiguration.fromJson(json); } else { return (E) NeuralNetConfiguration.fromJson(json); } }
public void loadConf(Resource confFile) { try { // Load network configuration from disk: MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(IOUtils.toString(confFile.getInputStream())); // Create a MultiLayerNetwork from the saved configuration and parameters //confFromJson.setTrainingWorkspaceMode(WorkspaceMode.SINGLE); //confFromJson.setInferenceWorkspaceMode(WorkspaceMode.SINGLE); net = new MultiLayerNetwork(confFromJson); net.init(); } catch (IOException ex) { log.error(ex.toString()); } }
/** * Initialize the network based on the configuration * * @param conf the configuration json * @param params the parameters */ public MultiLayerNetwork(String conf, INDArray params) { this(MultiLayerConfiguration.fromJson(conf)); init(); setParameters(params); }
@Override public void setup(Configuration conf) { MultiLayerConfiguration conf2 = MultiLayerConfiguration.fromJson(conf.get(MULTI_LAYER_CONF)); multiLayerNetwork = new MultiLayerNetwork(conf2); }
public void setLayerConfiguration(JsonNode conf) { if(conf != null) { String json = conf.toString(); if(json != null && !json.equals("null")) { net = new MultiLayerNetwork(MultiLayerConfiguration.fromJson(json)); net.init(); } } }
MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json); MultiLayerNetwork network = new MultiLayerNetwork(confFromJson); network.init(params, false);
public Model loadModel(String modelNamePrefix) throws IOException { Model model = null; String pathname = getPath(modelNamePrefix, "/%sModel.bin"); if (new File(pathname).exists()) { model = ModelSerializer.restoreMultiLayerNetwork(pathname); return model; } else { pathname = getPath(modelNamePrefix, "/%s-ComputationGraph.bin"); if (new File(pathname).exists()) { model = ModelSerializer.restoreComputationGraph(pathname); return model; } } MultiLayerNetwork net = null; if (!(new File(pathname).exists() || new File(getPath(modelNamePrefix, "/%sModelParams.bin")).exists())) { return null; } //Load parameters from disk: INDArray newParams; DataInputStream dis = new DataInputStream(new FileInputStream(getPath(modelNamePrefix, "/%sModelParams.bin"))); newParams = Nd4j.read(dis); //Load network configuration from disk: MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(FileUtils.readFileToString(new File(getPath(modelNamePrefix, "/%sModelConf.json")), Charset.defaultCharset())); //Create a MultiLayerNetwork from the saved configuration and parameters net = new MultiLayerNetwork(confFromJson); net.init(); net.setParameters(newParams); return net; }
return MultiLayerConfiguration.fromJson(input); } catch (Exception e) { log.warn("Tried multi layer config from json", e);
case NETWORK: String json = IOUtils.toString(new UnclosableInputStream(zipIn)); model = new MultiLayerNetwork(MultiLayerConfiguration.fromJson(json)); break; case WEIGHTS:
private Triple<MultiLayerConfiguration, ComputationGraphConfiguration, NeuralNetConfiguration> getConfig() { boolean noData = currentSessionID == null; StatsStorage ss = (noData ? null : knownSessionIDs.get(currentSessionID)); List<Persistable> allStatic = (noData ? Collections.EMPTY_LIST : ss.getAllStaticInfos(currentSessionID, StatsListener.TYPE_ID)); if (allStatic.size() == 0) return null; StatsInitializationReport p = (StatsInitializationReport) allStatic.get(0); String modelClass = p.getModelClassName(); String config = p.getModelConfigJson(); if (modelClass.endsWith("MultiLayerNetwork")) { MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(config); return new Triple<>(conf, null, null); } else if (modelClass.endsWith("ComputationGraph")) { ComputationGraphConfiguration conf = ComputationGraphConfiguration.fromJson(config); return new Triple<>(null, conf, null); } else { try { NeuralNetConfiguration layer = NeuralNetConfiguration.mapper().readValue(config, NeuralNetConfiguration.class); return new Triple<>(null, null, layer); } catch (Exception e) { e.printStackTrace(); } } return null; }
private Triple<MultiLayerConfiguration, ComputationGraphConfiguration, NeuralNetConfiguration> getConfig() { boolean noData = currentSessionID == null; StatsStorage ss = (noData ? null : knownSessionIDs.get(currentSessionID)); List<Persistable> allStatic = (noData ? Collections.EMPTY_LIST : ss.getAllStaticInfos(currentSessionID, StatsListener.TYPE_ID)); if (allStatic.size() == 0) return null; StatsInitializationReport p = (StatsInitializationReport) allStatic.get(0); String modelClass = p.getModelClassName(); String config = p.getModelConfigJson(); if (modelClass.endsWith("MultiLayerNetwork")) { MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(config); return new Triple<>(conf, null, null); } else if (modelClass.endsWith("ComputationGraph")) { ComputationGraphConfiguration conf = ComputationGraphConfiguration.fromJson(config); return new Triple<>(null, conf, null); } else { try { NeuralNetConfiguration layer = NeuralNetConfiguration.mapper().readValue(config, NeuralNetConfiguration.class); return new Triple<>(null, null, layer); } catch (Exception e) { e.printStackTrace(); } } return null; }
private Triple<MultiLayerConfiguration, ComputationGraphConfiguration, NeuralNetConfiguration> getConfig() { boolean noData = currentSessionID == null; StatsStorage ss = (noData ? null : knownSessionIDs.get(currentSessionID)); List<Persistable> allStatic = (noData ? Collections.EMPTY_LIST : ss.getAllStaticInfos(currentSessionID, StatsListener.TYPE_ID)); if (allStatic.size() == 0) return null; StatsInitializationReport p = (StatsInitializationReport) allStatic.get(0); String modelClass = p.getModelClassName(); String config = p.getModelConfigJson(); if (modelClass.endsWith("MultiLayerNetwork")) { MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(config); return new Triple<>(conf, null, null); } else if (modelClass.endsWith("ComputationGraph")) { ComputationGraphConfiguration conf = ComputationGraphConfiguration.fromJson(config); return new Triple<>(null, conf, null); } else { try { NeuralNetConfiguration layer = NeuralNetConfiguration.mapper().readValue(config, NeuralNetConfiguration.class); return new Triple<>(null, null, layer); } catch (Exception e) { e.printStackTrace(); } } return null; }
MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(FileUtils.readFileToString(new File(modelPath))); FeedForwardLayer outputLayer = (FeedForwardLayer) conf.getConf(conf.getConfs().size() - 1).getLayer();
NeuralNetConfiguration nnc = null; if (modelClass.endsWith("MultiLayerNetwork")) { MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(configJson); int confIdx = layerIdx - 1; //-1 because of input if (confIdx >= 0) {
NeuralNetConfiguration nnc = null; if (modelClass.endsWith("MultiLayerNetwork")) { MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(configJson); int confIdx = layerIdx - 1; //-1 because of input if (confIdx >= 0) {
NeuralNetConfiguration nnc = null; if (modelClass.endsWith("MultiLayerNetwork")) { MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(configJson); int confIdx = layerIdx - 1; //-1 because of input if (confIdx >= 0) {
this.replicatedModel = new MultiLayerNetwork(MultiLayerConfiguration.fromJson( ((MultiLayerNetwork) protoModel).getLayerWiseConfigurations().toJson())); this.replicatedModel.init();
this.replicatedModel = new MultiLayerNetwork(MultiLayerConfiguration.fromJson( ((MultiLayerNetwork) protoModel).getLayerWiseConfigurations().toJson())); this.replicatedModel.init();
MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson( ((MultiLayerNetwork) originalModel).getLayerWiseConfigurations().toJson()); conf.setTrainingWorkspaceMode(workspaceMode);