MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(seed) .l2(0.0005) .weightInit(WeightInit.XAVIER) .updater(new Nesterovs.Builder().learningRate(.01).build()) .biasUpdater(new Nesterovs.Builder().learningRate(0.02).build()) .list() .layer(0, new ConvolutionLayer.Builder(5, 5)
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(12345) .activation(Activation.LEAKYRELU) .weightInit(WeightInit.XAVIER) .updater(new Nesterovs(0.02))// To configure: .updater(Nesterovs.builder().momentum(0.9).build()) .l2(1e-4) .list() .layer(0, new DenseLayer.Builder().nIn(28 * 28).nOut(500).build()) .layer(1, new DenseLayer.Builder().nIn(500).nOut(100).build())
ComputationGraphConfiguration config = new NeuralNetConfiguration.Builder() .weightInit(WeightInit.RELU) .activation(Activation.LEAKYRELU) .updater(Updater.ADADELTA) .convolutionMode(ConvolutionMode.Same) .regularization(true).dropOut(0.2) .learningRate(learnRate) .graphBuilder() .addInputs("input") .addLayer("cnn3", new ConvolutionLayer.Builder()
public ComputationGraphConfiguration conf() { int embeddingSize = 128; ComputationGraphConfiguration.GraphBuilder graph = new NeuralNetConfiguration.Builder().seed(seed) .iterations(iterations).activation(Activation.IDENTITY) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new Adam(0.1, 0.9, 0.999, 0.01)).weightInit(WeightInit.RELU).regularization(true) .l2(5e-5).learningRate(0.1).miniBatch(true).convolutionMode(ConvolutionMode.Same) .graphBuilder();
public ComputationGraphConfiguration.GraphBuilder graphBuilder(String input) { ComputationGraphConfiguration.GraphBuilder graph = new NeuralNetConfiguration.Builder().seed(seed) .iterations(iterations).activation(Activation.RELU) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new RmsProp(0.1, 0.96, 0.001)).weightInit(WeightInit.DISTRIBUTION) .dist(new NormalDistribution(0.0, 0.5)).regularization(true).l2(5e-5).miniBatch(true) .convolutionMode(ConvolutionMode.Truncate).graphBuilder();
DataSetIterator testIter = new RecordReaderDataSetIterator(rrTest,batchSize,0,2); MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(seed) .updater(new Nesterovs(learningRate, 0.9)) .list()
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(seed) .updater(new Nesterovs(learningRate, 0.9)) .list() .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER)
config = new NeuralNetConfiguration.Builder() .weightInit(WeightInit.DISTRIBUTION) .dist(new NormalDistribution(0.0, 0.01)) .activation(Activation.RELU) .updater(new Nesterovs(new StepSchedule(ScheduleType.ITERATION, 1e-2, 0.1, 100000), 0.9)) .biasUpdater(new Nesterovs(new StepSchedule(ScheduleType.ITERATION, 2e-2, 0.1, 100000), 0.9)) .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) // normalize to prevent vanishing or exploding gradients .l2(5 * 1e-4) .list() .layer(new ConvolutionLayer.Builder(11,11) .nIn(channels)
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(seed) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .weightInit(WeightInit.XAVIER) .updater(new Sgd(learningRate)) .list() .layer(0, new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(seed) .iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .learningRate(learningRate) .updater(Updater.NESTEROVS).momentum(0.9) .list() .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER)
public NeuralNetConfiguration.Builder appliedNeuralNetConfigurationBuilder() { NeuralNetConfiguration.Builder confBuilder = new NeuralNetConfiguration.Builder(); if (activationFn != null) confBuilder.setActivationFn(activationFn); if (weightInit != null) confBuilder.setWeightInit(weightInit); if (biasInit != null) confBuilder.setBiasInit(biasInit); if (dist != null) confBuilder.setDist(dist); if (learningRate != null) { confBuilder.setLearningRate(learningRate); confBuilder.setBiasLearningRate(biasLearningRate); if (learningRateSchedule != null) confBuilder.setLearningRateSchedule(learningRateSchedule); confBuilder.setL1(l1); if (l2 != null) confBuilder.setL2(l2); if (l1Bias != null) confBuilder.setL1Bias(l1Bias); if (l2Bias != null) confBuilder.setL2Bias(l2Bias); if (dropOut != null) confBuilder.setDropOut(dropOut); if (iUpdater != null) confBuilder.updater(iUpdater); if (updater != null)
lrSchedule.put(3000, 0.001); MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(seed) .l2(0.0005) .weightInit(WeightInit.XAVIER) .updater(new Nesterovs(0.01, 0.9)) .list() .layer(0, new ConvolutionLayer.Builder(5, 5)
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(seed) .updater(new Nesterovs(learningRate, 0.9)) .list() .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER)
throw new InvalidKerasConfigurationException( "Keras model class name " + this.className + " incompatible with ComputationGraph"); NeuralNetConfiguration.Builder modelBuilder = new NeuralNetConfiguration.Builder(); ComputationGraphConfiguration.GraphBuilder graphBuilder = modelBuilder.graphBuilder(); modelBuilder.setUseRegularization(true);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(140) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .weightInit(WeightInit.XAVIER) .updater(new Nesterovs(0.15, 0.9)) .list() .layer(0, new GravesLSTM.Builder().activation(Activation.TANH).nIn(numOfVariables).nOut(10) .build())
assert minimum > numOutputsIsMutated : "Too much reduction, not enough outputs: "; ComputationGraphConfiguration confBuilder = null; NeuralNetConfiguration.Builder graphBuilder = new NeuralNetConfiguration.Builder() .seed(args().seed) .iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .learningRate(args().learningRate).regularization(args().regularizationRate != null).l2(args().regularizationRate != null ? args().regularizationRate : 0) .updater(Updater.ADAGRAD); graphBuilder.dropOut(args().dropoutRate); graphBuilder.setUseDropConnect(true); graphBuilder.setUseRegularization(true); NeuralNetConfiguration.Builder graphConfiguration = graphBuilder.lrPolicyDecayRate(0.5) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1) .learningRate(args().learningRate) .seed(args().seed); if (args().regularizationRate != null) { graphConfiguration.regularization(args().regularizationRate != null); graphConfiguration.dropOut(args().dropoutRate); graphConfiguration.setUseDropConnect(true); .weightInit(WeightInit.XAVIER).graphBuilder().addInputs("input") .addLayer("dense1", new DenseLayer.Builder().nIn(numInputs).nOut(nOut0) .weightInit(WEIGHT_INIT)
"MultiLayeNetwork expects only 1 output (found " + this.outputLayerNames.size() + ")"); NeuralNetConfiguration.Builder modelBuilder = new NeuralNetConfiguration.Builder(); NeuralNetConfiguration.ListBuilder listBuilder = modelBuilder.list(); for (KerasLayer layer : this.layersOrdered) { if (layer.usesRegularization()) modelBuilder.setUseRegularization(true); if (layer.isLayer()) { int nbInbound = layer.getInboundLayerNames().size();
public MultiLayerConfiguration conf() { MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(Updater.NESTEROVS).activation(Activation.RELU) .trainingWorkspaceMode(workspaceMode).inferenceWorkspaceMode(workspaceMode) .list()
NeuralNetConfiguration.Builder builder = new NeuralNetConfiguration.Builder(); if (useDropConnect != null) builder.useDropConnect(useDropConnect.getValue(values)); if (iterations != null) builder.iterations(iterations.getValue(values)); if (seed != null) builder.seed(seed); if (optimizationAlgo != null) builder.optimizationAlgo(optimizationAlgo.getValue(values)); if (regularization != null) builder.regularization(regularization.getValue(values)); builder.activation(activationFunction.getValue(values)); if (biasInit != null) builder.biasInit(biasInit.getValue(values)); if (weightInit != null) builder.weightInit(weightInit.getValue(values)); if (dist != null) builder.dist(dist.getValue(values)); if (learningRate != null) builder.learningRate(learningRate.getValue(values)); if (biasLearningRate != null) builder.biasLearningRate(biasLearningRate.getValue(values)); if (learningRateAfter != null) builder.learningRateSchedule(learningRateAfter.getValue(values)); if (lrScoreBasedDecay != null) builder.learningRateScoreBasedDecayRate(lrScoreBasedDecay.getValue(values)); if (learningRateDecayPolicy != null) builder.learningRateDecayPolicy(learningRateDecayPolicy.getValue(values));
public MultiLayerConfiguration conf() { MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(Updater.NESTEROVS).activation(Activation.RELU) .trainingWorkspaceMode(workspaceMode).inferenceWorkspaceMode(workspaceMode) .list()