@Override public Collection<?> getCollection(){ return neuralLayer.getNeurons(); } });
/** * Create an instance of {@link NeuralLayer } * */ public NeuralLayer createNeuralLayer() { return new NeuralLayer(); }
NeuralLayer neuralLayer = new NeuralLayer(); .setId(String.valueOf(layer) + "/" + String.valueOf(column + 1)); neuralLayer.addNeurons(neuron); .setActivationFunction(NeuralNetwork.ActivationFunction.IDENTITY) .setNormalizationMethod(NeuralNetwork.NormalizationMethod.SOFTMAX); entities = neuralLayer.getNeurons();
NeuralLayer hiddenNeuralLayer = new NeuralLayer(); .setId("hidden/" + String.valueOf(row + 1)); hiddenNeuralLayer.addNeurons(neuron); entities = hiddenNeuralLayer.getNeurons(); NeuralLayer outputNeuralLayer = new NeuralLayer(); .setId("output/" + String.valueOf(column + 1)); outputNeuralLayer.addNeurons(neuron); entities = outputNeuralLayer.getNeurons();
if(nHidden > 0){ NeuralLayer neuralLayer = encodeNeuralLayer("hidden", nHidden, entities, wts, offset) .setActivationFunction(NeuralNetwork.ActivationFunction.LOGISTIC); entities = neuralLayer.getNeurons(); entities = neuralLayer.getNeurons(); entities = neuralLayer.getNeurons(); neuralLayer.setNormalizationMethod(NeuralNetwork.NormalizationMethod.SOFTMAX); entities = neuralLayer.getNeurons(); } else
@Override public VisitorAction accept(Visitor visitor) { VisitorAction status = visitor.visit(this); if (status == VisitorAction.CONTINUE) { visitor.pushParent(this); if ((status == VisitorAction.CONTINUE)&&hasExtensions()) { status = org.dmg.pmml.PMMLObject.traverse(visitor, getExtensions()); } if ((status == VisitorAction.CONTINUE)&&hasNeurons()) { status = org.dmg.pmml.PMMLObject.traverse(visitor, getNeurons()); } visitor.popParent(); } if (status == VisitorAction.TERMINATE) { return VisitorAction.TERMINATE; } return VisitorAction.CONTINUE; }
static private NeuralLayer encodeNeuralLayer(String prefix, int n, List<? extends Entity> entities, RDoubleVector wts, int offset){ NeuralLayer neuralLayer = new NeuralLayer(); for(int i = 0; i < n; i++){ List<Double> weights = (wts.getValues()).subList(offset + 1, offset + (entities.size() + 1)); Double bias = wts.getValue(offset); Neuron neuron = NeuralNetworkUtil.createNeuron(entities, weights, bias) .setId(prefix + "/" + String.valueOf(i + 1)); neuralLayer.addNeurons(neuron); offset += (entities.size() + 1); } return neuralLayer; } }
NeuralNetwork.ActivationFunction activationFunction = neuralLayer.getActivationFunction(); if(activationFunction == null){ locatable = neuralNetwork; Double threshold = neuralLayer.getThreshold(); if(threshold == null){ threshold = neuralNetwork.getThreshold(); List<Neuron> neurons = neuralLayer.getNeurons(); for(int i = 0; i < neurons.size(); i++){ Neuron neuron = neurons.get(i); NeuralNetwork.NormalizationMethod normalizationMethod = neuralLayer.getNormalizationMethod(); if(normalizationMethod == null){ locatable = neuralNetwork;
@Override public VisitorAction visit(NeuralLayer neuralLayer){ NeuralNetwork.ActivationFunction activationFunction = neuralLayer.getActivationFunction(); if(activationFunction != null){ switch(activationFunction){ case RADIAL_BASIS: report(new UnsupportedAttributeException(neuralLayer, activationFunction)); break; default: break; } } return super.visit(neuralLayer); }
int columns = shape[1]; NeuralLayer neuralLayer = new NeuralLayer(); .setId(String.valueOf(layer + 1) + "/" + String.valueOf(column + 1)); neuralLayer.addNeurons(neuron); entities = neuralLayer.getNeurons(); neuralLayer.setActivationFunction(NeuralNetwork.ActivationFunction.IDENTITY); entities = neuralLayer.getNeurons(); } else neuralLayer.setNormalizationMethod(NeuralNetwork.NormalizationMethod.SOFTMAX); } else
@Override public VisitorAction accept(Visitor visitor) { VisitorAction status = visitor.visit(this); if (status == VisitorAction.CONTINUE) { visitor.pushParent(this); if ((status == VisitorAction.CONTINUE)&&hasExtensions()) { status = org.dmg.pmml.PMMLObject.traverse(visitor, getExtensions()); } if ((status == VisitorAction.CONTINUE)&&hasNeurons()) { status = org.dmg.pmml.PMMLObject.traverse(visitor, getNeurons()); } visitor.popParent(); } if (status == VisitorAction.TERMINATE) { return VisitorAction.TERMINATE; } return VisitorAction.CONTINUE; }
boolean hidden = (i < (weights.size() - 1)); NeuralLayer neuralLayer = new NeuralLayer(); neuralLayer.setActivationFunction(activationFunction); .setId(id); neuralLayer.addNeurons(neuron); entities = neuralLayer.getNeurons();
public NeuralLayer addNeurons(Neuron... neurons) { getNeurons().addAll(Arrays.asList(neurons)); return this; }
/** * Create an instance of {@link NeuralLayer } * */ public NeuralLayer createNeuralLayer() { return new NeuralLayer(); }
public NeuralLayer addNeurons(Neuron... neurons) { getNeurons().addAll(Arrays.asList(neurons)); return this; }
@Override public BiMap<String, Entity> load(NeuralNetwork neuralNetwork){ ImmutableBiMap.Builder<String, Entity> builder = new ImmutableBiMap.Builder<>(); AtomicInteger index = new AtomicInteger(1); NeuralInputs neuralInputs = neuralNetwork.getNeuralInputs(); for(NeuralInput neuralInput : neuralInputs){ builder = EntityUtil.put(neuralInput, index, builder); } List<NeuralLayer> neuralLayers = neuralNetwork.getNeuralLayers(); for(NeuralLayer neuralLayer : neuralLayers){ List<Neuron> neurons = neuralLayer.getNeurons(); for(int i = 0; i < neurons.size(); i++){ Neuron neuron = neurons.get(i); builder = EntityUtil.put(neuron, index, builder); } } return builder.build(); } });