public static <F, S, T> Triple<F, S,T> of(F first, S second, T third) { return new Triple<>(first, second, third); } }
private TrainModuleUtils.GraphInfo getGraphInfo() { Triple<MultiLayerConfiguration, ComputationGraphConfiguration, NeuralNetConfiguration> conf = getConfig(); if (conf == null) { return null; } if (conf.getFirst() != null) { return TrainModuleUtils.buildGraphInfo(conf.getFirst()); } else if (conf.getSecond() != null) { return TrainModuleUtils.buildGraphInfo(conf.getSecond()); } else if (conf.getThird() != null) { return TrainModuleUtils.buildGraphInfo(conf.getThird()); } else { return null; } }
String origName = entry.getKey(); multiGradientKey = String.valueOf(numLayers - 1) + "_" + origName; gradientList.addLast(new Triple<>(multiGradientKey, entry.getValue(), currPair.getFirst().flatteningOrderForVariable(origName))); String origName = entry.getKey(); multiGradientKey = String.valueOf(j) + "_" + origName; tempList.addFirst(new Triple<>(multiGradientKey, entry.getValue(), currPair.getFirst().flatteningOrderForVariable(origName))); gradient.setGradientFor(triple.getFirst(), triple.getSecond(), triple.getThird());
private TrainModuleUtils.GraphInfo getGraphInfo() { Triple<MultiLayerConfiguration, ComputationGraphConfiguration, NeuralNetConfiguration> conf = getConfig(); if (conf == null) { return null; } if (conf.getFirst() != null) { return TrainModuleUtils.buildGraphInfo(conf.getFirst()); } else if (conf.getSecond() != null) { return TrainModuleUtils.buildGraphInfo(conf.getSecond()); } else if (conf.getThird() != null) { return TrainModuleUtils.buildGraphInfo(conf.getThird()); } else { return null; } }
private TrainModuleUtils.GraphInfo getGraphInfo() { Triple<MultiLayerConfiguration, ComputationGraphConfiguration, NeuralNetConfiguration> conf = getConfig(); if (conf == null) { return null; } if (conf.getFirst() != null) { return TrainModuleUtils.buildGraphInfo(conf.getFirst()); } else if (conf.getSecond() != null) { return TrainModuleUtils.buildGraphInfo(conf.getSecond()); } else if (conf.getThird() != null) { return TrainModuleUtils.buildGraphInfo(conf.getThird()); } else { return null; } }
public static <F, S, T> Triple<F, S,T> tripleOf(F first, S second, T third) { return new Triple<>(first, second, third); }
@Override public MultiDataSet next() { if(!hasNext()) throw new NoSuchElementException("No next element"); INDArray[] f = new INDArray[features.size()]; INDArray[] l = new INDArray[labels.size()]; for( int i=0; i<f.length; i++ ){ Triple<long[], Character, Values> t = features.get(i); f[i] = generate(t.getFirst(), t.getSecond(), t.getThird()); } for( int i=0; i<l.length; i++ ){ Triple<long[], Character, Values> t = labels.get(i); l[i] = generate(t.getFirst(), t.getSecond(), t.getThird()); } position++; MultiDataSet mds = new org.nd4j.linalg.dataset.MultiDataSet(f,l); if(preProcessor != null) preProcessor.preProcess(mds); return mds; }
public static <F, S, T> Triple<F, S,T> of(F first, S second, T third) { return new Triple<>(first, second, third); } }
if (conf.getFirst() != null) mt = ModelType.MLN; else if (conf.getSecond() != null) mt = ModelType.CG; else activationMap.put("iterCount", activationsData.getFirst()); activationMap.put("mean", activationsData.getSecond()); activationMap.put("stdev", activationsData.getThird()); result.put("activations", activationMap);
public static <F, S, T> Triple<F, S,T> tripleOf(F first, S second, T third) { return new Triple<>(first, second, third); }
if (conf.getFirst() != null) mt = ModelType.MLN; else if (conf.getSecond() != null) mt = ModelType.CG; else activationMap.put("iterCount", activationsData.getFirst()); activationMap.put("mean", activationsData.getSecond()); activationMap.put("stdev", activationsData.getThird()); result.put("activations", activationMap);
/** * Add a new features array to the iterator * @param shape Shape of the features * @param order Order ('c' or 'f') for the array * @param values Values to fill the array with */ public Builder addFeatures(long[] shape, char order, Values values){ features.add(new Triple<>(shape, order, values)); return this; }
if (conf.getFirst() != null) mt = ModelType.MLN; else if (conf.getSecond() != null) mt = ModelType.CG; else activationMap.put("iterCount", activationsData.getFirst()); activationMap.put("mean", activationsData.getSecond()); activationMap.put("stdev", activationsData.getThird()); result.put("activations", activationMap);
/** * Add a new labels array to the iterator * @param shape Shape of the features * @param order Order ('c' or 'f') for the array * @param values Values to fill the array with */ public Builder addLabels(long[] shape, char order, Values values){ labels.add(new Triple<>(shape, order, values)); return this; }
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; }
return new Triple<>(iterCounts, mean, stdev);
return new Triple<>(iterCounts, mean, stdev);