public static ComputationGraphConfiguration getConf() { ComputationGraphConfiguration.GraphBuilder builder = new NeuralNetConfiguration.Builder() .seed(12345) .updater(new Adam(0.01)) .weightInit(WeightInit.RELU) .graphBuilder() .addInputs("in"); String[] poolNames = new String[ngramFilters.length]; int i = 0; for (int ngram : ngramFilters) { String filterName = String.format("ngram%d", ngram); poolNames[i] = String.format("pool%d", ngram); builder = builder.addLayer(filterName, new Convolution1DLayer.Builder() .nOut(numFilters) .kernelSize(ngram) .activation(Activation.RELU) .build(), "in") .addLayer(poolNames[i], new GlobalPoolingLayer.Builder(PoolingType.MAX).build(), filterName); i++; } return builder.addVertex("concat", new MergeVertex(), poolNames) .addLayer("predict", new DenseLayer.Builder().nOut(numClasses).dropOut(dropoutRetain) .activation(Activation.SOFTMAX).build(), "concat") .addLayer("loss", new LossLayer.Builder(LossFunctions.LossFunction.MCXENT).build(), "predict") .setOutputs("loss") .setInputTypes(InputType.recurrent(W2V_VECTOR_SIZE, 1000)) .build(); } }
.learningRate(learnRate) .graphBuilder() .addInputs("input") .addLayer("cnn3", new ConvolutionLayer.Builder() .kernelSize(3,vectorSize)
graphBuilder.addInputs(inputLayerNameArray);
.weightInit(WeightInit.XAVIER).graphBuilder().addInputs("input") .addLayer("dense1", new DenseLayer.Builder().nIn(numInputs).nOut(nOut0) .weightInit(WEIGHT_INIT)
.weightInit(WeightInit.XAVIER).graphBuilder().addInputs("input") .addLayer("dense1", new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WEIGHT_INIT)
.activation("softmax").weightInit(WEIGHT_INIT).learningRateDecayPolicy(learningRatePolicy) .nIn(numIn) .nOut(domainDescriptor.getNumOutputs("softmaxGenotype")[0]).build(), lastDenseLayerName).addInputs(); break; case DISTINCT_ALLELES:
throws InvalidNetworkArchitectureException, InvalidLayerConfigurationException { String currentInput = "input"; gb.addInputs(currentInput);
graph.addInputs("input").setInputTypes(InputType.convolutional(inputShape[2], inputShape[1], inputShape[0]))
.weightInit(WeightInit.XAVIER).regularization(true).l2(2e-4).graphBuilder(); graph.addInputs("input").addLayer("cnn1", conv7x7(inputShape[0], 64, 0.2), "input") .addLayer("max1", new SubsamplingLayer.Builder(new int[] {3, 3}, new int[] {2, 2},
builder.addInputs(currentInput);
.activation("softmax").weightInit(WEIGHT_INIT).learningRateDecayPolicy(learningRatePolicy) .nIn(numIn) .nOut(domainDescriptor.getNumOutputs("numDistinctAlleles")[0]).build(), lastDenseLayerName).addInputs();
gb.addInputs(currentInput);
@Override public GraphConfiguration getValue(double[] values) { //Create ComputationGraphConfiguration... NeuralNetConfiguration.Builder builder = randomGlobalConf(values); ComputationGraphConfiguration.GraphBuilder graphBuilder = builder.graphBuilder(); graphBuilder.addInputs(this.networkInputs); graphBuilder.setOutputs(this.networkOutputs); if (inputTypes != null) graphBuilder.setInputTypes(inputTypes.getValue(values)); //Build/add our layers and vertices: for (LayerConf c : layerSpaces) { org.deeplearning4j.nn.conf.layers.Layer l = c.layerSpace.getValue(values); graphBuilder.addLayer(c.getLayerName(), l, c.getInputs()); } for (VertexConf gv : vertices) { graphBuilder.addVertex(gv.getVertexName(), gv.getGraphVertex(), gv.getInputs()); } if (backprop != null) graphBuilder.backprop(backprop.getValue(values)); if (pretrain != null) graphBuilder.pretrain(pretrain.getValue(values)); if (backpropType != null) graphBuilder.backpropType(backpropType.getValue(values)); if (tbpttFwdLength != null) graphBuilder.tBPTTForwardLength(tbpttFwdLength.getValue(values)); if (tbpttBwdLength != null) graphBuilder.tBPTTBackwardLength(tbpttBwdLength.getValue(values)); ComputationGraphConfiguration configuration = graphBuilder.build(); return new GraphConfiguration(configuration, earlyStoppingConfiguration, numEpochs); }
@Override public ComputationGraph createComputationalGraph(DomainDescriptor domainDescriptor) { LearningRatePolicy learningRatePolicy = LearningRatePolicy.Poly; layerAssembler.setLearningRatePolicy(learningRatePolicy); layerAssembler.initializeBuilder(); int numInputs = domainDescriptor.getNumInputs("input")[0]; int numHiddenNodes = domainDescriptor.getNumHiddenNodes("firstDense"); ComputationGraphConfiguration.GraphBuilder build = layerAssembler.assemble(numInputs, numHiddenNodes, numLayers); int numIn = layerAssembler.getNumOutputs(); WeightInit WEIGHT_INIT = WeightInit.XAVIER; String lastDenseLayerName = layerAssembler.lastLayerName(); build.addLayer("softmaxGenotype", new OutputLayer.Builder( domainDescriptor.getOutputLoss("softmaxGenotype")) .weightInit(WEIGHT_INIT) .activation("softmax").weightInit(WEIGHT_INIT).learningRateDecayPolicy(learningRatePolicy) .nIn(numIn) .nOut(domainDescriptor.getNumOutputs("softmaxGenotype")[0]).build(), lastDenseLayerName).addInputs(); appendMetaDataLayer(domainDescriptor, learningRatePolicy, build, numIn, WEIGHT_INIT, lastDenseLayerName); appendIsVariantLayer(domainDescriptor, learningRatePolicy, build, numIn, WEIGHT_INIT, lastDenseLayerName); ComputationGraphConfiguration conf = build .setOutputs(outputNames) .build(); return new ComputationGraph(conf); }
public void initializeBuilder(String... inputNames) { if (inputNames.length == 0) { inputNames = new String[]{"input"}; } NeuralNetConfiguration.Builder graphBuilder = new NeuralNetConfiguration.Builder() .seed(args().seed) .iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .learningRate(args().learningRate) .updater(Updater.ADAGRAD) .epsilon(BUILDER_EPSILON) .lrPolicyDecayRate(0.5) .weightInit(WEIGHT_INIT); if (args().regularizationRate != null) { graphBuilder.l2(args().regularizationRate); graphBuilder.regularization(args().regularizationRate != null); } if (args().dropoutRate != null) { graphBuilder.dropOut(args().dropoutRate); graphBuilder.setUseDropConnect(true); } modelCapacity=args().modelCapacity; reductionRate=args().reductionRate; build = graphBuilder.graphBuilder().addInputs(inputNames); }
@Override public ComputationGraph init() { int embeddingSize = 128; ComputationGraphConfiguration.GraphBuilder graph = graphBuilder("input1"); graph.addInputs("input1").setInputTypes(InputType.convolutional(inputShape[2], inputShape[1], inputShape[0])) // Logits .addLayer("bottleneck", new DenseLayer.Builder().nIn(5376).nOut(embeddingSize).build(), "avgpool") // Embeddings .addVertex("embeddings", new L2NormalizeVertex(new int[] {1}, 1e-10), "bottleneck") // Output .addLayer("outputLayer", new CenterLossOutputLayer.Builder() .lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .activation(Activation.SOFTMAX).alpha(0.9).lambda(1e-4) .nIn(embeddingSize).nOut(numClasses).build(), "embeddings") .setOutputs("outputLayer").backprop(true).pretrain(false); ComputationGraphConfiguration conf = graph.build(); ComputationGraph model = new ComputationGraph(conf); model.init(); return model; }
public static void main(String[] args) throws Exception { //Define a simple ComputationGraph: ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder() .weightInit(WeightInit.XAVIER) .updater(new Nesterovs(0.01, 0.9)) .graphBuilder() .addInputs("in") .addLayer("layer0", new DenseLayer.Builder().nIn(4).nOut(3).activation(Activation.TANH).build(), "in") .addLayer("layer1", new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).activation(Activation.SOFTMAX).nIn(3).nOut(3).build(), "layer0") .setOutputs("layer1") .backprop(true).pretrain(false).build(); ComputationGraph net = new ComputationGraph(conf); net.init(); //Save the model File locationToSave = new File("model/MyComputationGraph.zip"); //Where to save the network. Note: the file is in .zip format - can be opened externally boolean saveUpdater = true; //Updater: i.e., the state for Momentum, RMSProp, Adagrad etc. Save this if you want to train your network more in the future ModelSerializer.writeModel(net, locationToSave, saveUpdater); //Load the model ComputationGraph restored = ModelSerializer.restoreComputationGraph(locationToSave); System.out.println("Saved and loaded parameters are equal: " + net.params().equals(restored.params())); System.out.println("Saved and loaded configurations are equal: " + net.getConfiguration().equals(restored.getConfiguration())); }
/** * Default layer setup: Create sequential layer network defined by the order of the layer list * * @param gb GraphBuilder object */ protected void makeDefaultLayerSetup(GraphBuilder gb) { String currentInput = "input"; gb.addInputs(currentInput); // Collect layers for (Layer layer : layers) { String lName = layer.getLayerName(); gb.addLayer(lName, layer.getBackend().clone(), currentInput); currentInput = lName; } gb.setOutputs(currentInput); }
public GraphBuilder addInputs(String... inputNames) { editedConfigBuilder.addInputs(inputNames); return this; }
.weightInit(WeightInit.RELU) .graphBuilder() .addInputs("input") .setOutputs("output");