/** * Get the number of layers in the network * * @return the number of layers in the network */ public int getnLayers() { return layerWiseConfigurations.getConfs().size(); }
/** * Prints the configuration */ public void printConfiguration() { StringBuilder sb = new StringBuilder(); int count = 0; for (NeuralNetConfiguration conf : getLayerWiseConfigurations().getConfs()) { sb.append(" Layer " + count++ + " conf " + conf); } log.info(sb.toString()); }
public static GraphInfo buildGraphInfo(MultiLayerConfiguration config) { List<String> vertexNames = new ArrayList<>(); List<String> originalVertexName = new ArrayList<>(); List<String> layerTypes = new ArrayList<>(); List<List<Integer>> layerInputs = new ArrayList<>(); List<Map<String, String>> layerInfo = new ArrayList<>(); vertexNames.add("Input"); originalVertexName.add(null); layerTypes.add("Input"); layerInputs.add(Collections.emptyList()); layerInfo.add(Collections.emptyMap()); List<NeuralNetConfiguration> list = config.getConfs(); int layerIdx = 1; for (NeuralNetConfiguration c : list) { Layer layer = c.getLayer(); String layerName = layer.getLayerName(); if (layerName == null) layerName = "layer" + layerIdx; vertexNames.add(layerName); originalVertexName.add(String.valueOf(layerIdx - 1)); String layerType = c.getLayer().getClass().getSimpleName().replaceAll("Layer$", ""); layerTypes.add(layerType); layerInputs.add(Collections.singletonList(layerIdx - 1)); layerIdx++; //Extract layer info Map<String, String> map = getLayerInfo(c, layer); layerInfo.add(map); } return new GraphInfo(vertexNames, layerTypes, layerInputs, layerInfo, originalVertexName); }
public static GraphInfo buildGraphInfo(MultiLayerConfiguration config) { List<String> vertexNames = new ArrayList<>(); List<String> originalVertexName = new ArrayList<>(); List<String> layerTypes = new ArrayList<>(); List<List<Integer>> layerInputs = new ArrayList<>(); List<Map<String, String>> layerInfo = new ArrayList<>(); vertexNames.add("Input"); originalVertexName.add(null); layerTypes.add("Input"); layerInputs.add(Collections.emptyList()); layerInfo.add(Collections.emptyMap()); List<NeuralNetConfiguration> list = config.getConfs(); int layerIdx = 1; for (NeuralNetConfiguration c : list) { Layer layer = c.getLayer(); String layerName = layer.getLayerName(); if (layerName == null) layerName = "layer" + layerIdx; vertexNames.add(layerName); originalVertexName.add(String.valueOf(layerIdx - 1)); String layerType = c.getLayer().getClass().getSimpleName().replaceAll("Layer$", ""); layerTypes.add(layerType); layerInputs.add(Collections.singletonList(layerIdx - 1)); layerIdx++; //Extract layer info Map<String, String> map = getLayerInfo(c, layer); layerInfo.add(map); } return new GraphInfo(vertexNames, layerTypes, layerInputs, layerInfo, originalVertexName); }
public static GraphInfo buildGraphInfo(MultiLayerConfiguration config) { List<String> vertexNames = new ArrayList<>(); List<String> originalVertexName = new ArrayList<>(); List<String> layerTypes = new ArrayList<>(); List<List<Integer>> layerInputs = new ArrayList<>(); List<Map<String, String>> layerInfo = new ArrayList<>(); vertexNames.add("Input"); originalVertexName.add(null); layerTypes.add("Input"); layerInputs.add(Collections.emptyList()); layerInfo.add(Collections.emptyMap()); List<NeuralNetConfiguration> list = config.getConfs(); int layerIdx = 1; for (NeuralNetConfiguration c : list) { Layer layer = c.getLayer(); String layerName = layer.getLayerName(); if (layerName == null) layerName = "layer" + layerIdx; vertexNames.add(layerName); originalVertexName.add(String.valueOf(layerIdx - 1)); String layerType = c.getLayer().getClass().getSimpleName().replaceAll("Layer$", ""); layerTypes.add(layerType); layerInputs.add(Collections.singletonList(layerIdx - 1)); layerIdx++; //Extract layer info Map<String, String> map = getLayerInfo(c, layer); layerInfo.add(map); } return new GraphInfo(vertexNames, layerTypes, layerInputs, layerInfo, originalVertexName); }
private void fineTuneConfigurationBuild() { for (int i = 0; i < origConf.getConfs().size(); i++) { NeuralNetConfiguration layerConf; if (finetuneConfiguration != null) { NeuralNetConfiguration nnc = origConf.getConf(i).clone(); finetuneConfiguration.applyToNeuralNetConfiguration(nnc); layerConf = nnc; } else { layerConf = origConf.getConf(i).clone(); } editedConfs.add(layerConf); } }
@Override public MultiLayerConfiguration deserialize(JsonParser jp, DeserializationContext ctxt) throws IOException, JsonProcessingException { MultiLayerConfiguration conf = (MultiLayerConfiguration) defaultDeserializer.deserialize(jp, ctxt); //Updater configuration changed after 0.8.0 release //Previously: enumerations and fields. Now: classes //Here, we manually create the appropriate Updater instances, if the IUpdater field is empty Layer[] layers = new Layer[conf.getConfs().size()]; for (int i = 0; i < layers.length; i++) { layers[i] = conf.getConf(i).getLayer(); } handleUpdaterBackwardCompatibility(layers); return conf; } }
try { MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(FileUtils.readFileToString(new File(modelPath))); FeedForwardLayer outputLayer = (FeedForwardLayer) conf.getConf(conf.getConfs().size() - 1).getLayer();
for (NeuralNetConfiguration n : mln.getLayerWiseConfigurations().getConfs()) { if (n.getLayer() instanceof BaseLayer) { BaseLayer bl = (BaseLayer) n.getLayer();
for (NeuralNetConfiguration nnc : conf.getConfs()) { Layer l = nnc.getLayer(); if (l instanceof BaseOutputLayer && ((BaseOutputLayer) l).getLossFn() == null) {
.inferenceWorkspaceMode(Preferences.WORKSPACE_MODE) .graphBuilder(); List<NeuralNetConfiguration> confs = mlc.getConfs();