.learningRate(.001) .weightInit(WeightInit.XAVIER) .miniBatch(true) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.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();
.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();
builder.maxNumLineSearchIterations(maxNumLineSearchIterations.getValue(values)); if (miniBatch != null) builder.miniBatch(miniBatch.getValue(values)); if (minimize != null) builder.minimize(minimize.getValue(values));
.dropOut(0.5).l2(5 * 1e-4).miniBatch(false) .list().layer(0, new ConvolutionLayer.Builder(new int[] {11, 11}, new int[] {4, 4},
.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).l1(1e-7).l2(5e-5).miniBatch(true) .convolutionMode(ConvolutionMode.Truncate).graphBuilder();
/** * Deliver access to the internal builder * * @return NeuralNetworkConfiguration */ public org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder builder() { Builder builder = new Builder(); // Set dist to null if Disabled was chosen as dl4j backend defaults to null builder .l1(l1) .l2(l2) .optimizationAlgo(optimizationAlgo) .seed(seed) .weightInit(weightInit) .dist(dist.getBackend()) .biasInit(biasInit) .updater(updater.getBackend()) .biasUpdater(biasUpdater.getBackend()) .dropOut(dropout.getBackend()) .miniBatch(miniBatch) .minimize(minimize) .weightNoise(weightNoise.getBackend()) .gradientNormalization(gradientNormalization.getBackend()) .gradientNormalizationThreshold(gradientNormalizationThreshold) .inferenceWorkspaceMode(inferenceWorkspaceMode) .trainingWorkspaceMode(trainingWorkspaceMode); builder.setPretrain(pretrain); return builder; }