@Override public ComputationGraphConfiguration deserialize(JsonParser jp, DeserializationContext ctxt) throws IOException, JsonProcessingException { ComputationGraphConfiguration conf = (ComputationGraphConfiguration) 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 List<Layer> layerList = new ArrayList<>(); Map<String, GraphVertex> vertices = conf.getVertices(); for (Map.Entry<String, GraphVertex> entry : vertices.entrySet()) { if (entry.getValue() instanceof LayerVertex) { LayerVertex lv = (LayerVertex) entry.getValue(); layerList.add(lv.getLayerConf().getLayer()); } } Layer[] layers = layerList.toArray(new Layer[layerList.size()]); handleUpdaterBackwardCompatibility(layers); return conf; } }
Map<String, GraphVertex> vertices = config.getVertices(); Map<String, List<String>> vertexInputs = config.getVertexInputs(); List<String> networkInputs = config.getNetworkInputs();
Map<String, GraphVertex> vertices = config.getVertices(); Map<String, List<String>> vertexInputs = config.getVertexInputs(); List<String> networkInputs = config.getNetworkInputs();
Map<String, GraphVertex> vertices = config.getVertices(); Map<String, List<String>> vertexInputs = config.getVertexInputs(); List<String> networkInputs = config.getNetworkInputs();
Map<String, org.deeplearning4j.nn.conf.graph.GraphVertex> nodeMap = configuration.getVertices(); List<String> networkInputNames = configuration.getNetworkInputs(); int numVertices = networkInputNames.size() + configuration.getVertices().size(); int[] out = new int[numVertices]; int outCounter = 0;
Map<String, GraphVertex> vertices = conf.getVertices(); if (vertices.containsKey(vertexName) && vertices.get(vertexName) instanceof LayerVertex) { LayerVertex lv = (LayerVertex) vertices.get(vertexName); layerType = "Input"; } else { GraphVertex gv = conf.getVertices().get(vertexName); if (gv != null) { layerType = gv.getClass().getSimpleName();
Map<String, GraphVertex> vertices = conf.getVertices(); if (vertices.containsKey(vertexName) && vertices.get(vertexName) instanceof LayerVertex) { LayerVertex lv = (LayerVertex) vertices.get(vertexName); layerType = "Input"; } else { GraphVertex gv = conf.getVertices().get(vertexName); if (gv != null) { layerType = gv.getClass().getSimpleName();
LayerVertex lv = (LayerVertex) origConfig.getVertices().get(layerName); String[] lvInputs = origConfig.getVertexInputs().get(layerName).toArray(new String[0]); editedConfigBuilder.addLayer(layerName, layerImpl, lv.getPreProcessor(), lvInputs); lv = (LayerVertex) origConfig.getVertices().get(fanoutVertexName); lvInputs = origConfig.getVertexInputs().get(fanoutVertexName).toArray(new String[0]); editedConfigBuilder.addLayer(fanoutVertexName, layerImpl, lv.getPreProcessor(), lvInputs);
Map<String, GraphVertex> vertices = conf.getVertices(); if (vertices.containsKey(vertexName) && vertices.get(vertexName) instanceof LayerVertex) { LayerVertex lv = (LayerVertex) vertices.get(vertexName); layerType = "Input"; } else { GraphVertex gv = conf.getVertices().get(vertexName); if (gv != null) { layerType = gv.getClass().getSimpleName();
numParamsForVertex[i] = 0; //No parameters for input vertices Map<String, org.deeplearning4j.nn.conf.graph.GraphVertex> configVertexMap = configuration.getVertices(); for (Map.Entry<String, org.deeplearning4j.nn.conf.graph.GraphVertex> nodeEntry : configVertexMap .entrySet()) {
Map<String, org.deeplearning4j.nn.conf.graph.GraphVertex> configVertexMap = configuration.getVertices(); this.vertices = new GraphVertex[networkInputNames.size() + configuration.getVertices().size()];
for (String vertexName : graph.getConfiguration().getVertices().keySet()) { GraphVertex gv = graph.getConfiguration().getVertices().get(vertexName); if (!(gv instanceof LayerVertex)) continue;
public GraphBuilder(ComputationGraphConfiguration newConf, NeuralNetConfiguration.Builder globalConfiguration) { ComputationGraphConfiguration clonedConf = newConf.clone(); this.vertices = clonedConf.getVertices(); this.vertexInputs = clonedConf.getVertexInputs(); this.networkInputs = clonedConf.getNetworkInputs(); this.networkOutputs = clonedConf.getNetworkOutputs(); this.pretrain = clonedConf.isPretrain(); this.backprop = clonedConf.isBackprop(); this.backpropType = clonedConf.getBackpropType(); this.tbpttFwdLength = clonedConf.getTbpttFwdLength(); this.tbpttBackLength = clonedConf.getTbpttBackLength(); this.globalConfiguration = globalConfiguration; //this.getGlobalConfiguration().setSeed(clonedConf.getDefaultConfiguration().getSeed()); }
Map<String, GraphVertex> vertexMap = conf.getVertices(); JsonNode vertices = null; for (Map.Entry<String, GraphVertex> entry : vertexMap.entrySet()) {
LayerVertex currLayerVertex = (LayerVertex) newConfig.getVertices().get(layerName); Layer origLayerConf = currLayerVertex.getLayerConf().getLayer(); Layer newLayerConf = new org.deeplearning4j.nn.conf.layers.misc.FrozenLayer(origLayerConf);