.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)
.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)
.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)
.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))
.activation(Activation.RELU) .updater(new Nesterovs(new StepSchedule(ScheduleType.ITERATION, 0.1, 0.1, 100000), 0.9)) .biasUpdater(new Nesterovs(new StepSchedule(ScheduleType.ITERATION, 0.2, 0.1, 100000), 0.9))
.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)) .l2(aTraits.getL2())
.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)) .l2(aTraits.getL2())
/** * 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; }