Example usage for org.deeplearning4j.nn.conf.layers SubsamplingLayer.Builder SubsamplingLayer.Builder

Introduction

In this page you can find the example usage for org.deeplearning4j.nn.conf.layers SubsamplingLayer.Builder SubsamplingLayer.Builder.

Prototype

public Builder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType) 

Source Link

Usage

From source file:com.heatonresearch.aifh.examples.ann.LearnDigitsConv.java

License:Apache License

/**
 * The main method./*from ww  w.ja v  a  2s . c  o  m*/
 * @param args Not used.
 */
public static void main(String[] args) {
    try {
        int seed = 43;
        double learningRate = 1e-2;
        int nEpochs = 50;
        int batchSize = 500;
        int channels = 1;

        // Setup training data.
        System.out.println("Please wait, reading MNIST training data.");
        String dir = System.getProperty("user.dir");
        MNISTReader trainingReader = MNIST.loadMNIST(dir, true);
        MNISTReader validationReader = MNIST.loadMNIST(dir, false);

        DataSet trainingSet = trainingReader.getData();
        DataSet validationSet = validationReader.getData();

        DataSetIterator trainSetIterator = new ListDataSetIterator(trainingSet.asList(), batchSize);
        DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(),
                validationReader.getNumRows());

        System.out.println("Training set size: " + trainingReader.getNumImages());
        System.out.println("Validation set size: " + validationReader.getNumImages());

        int numOutputs = 10;

        // Create neural network.
        MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed).iterations(1)
                .regularization(true).l2(0.0005).learningRate(0.01).weightInit(WeightInit.XAVIER)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.NESTEROVS)
                .momentum(0.9).list(4)
                .layer(0,
                        new ConvolutionLayer.Builder(5, 5).nIn(channels).stride(1, 1).nOut(20).dropOut(0.5)
                                .activation("relu").build())
                .layer(1,
                        new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2)
                                .stride(2, 2).build())
                .layer(2, new DenseLayer.Builder().activation("relu").nOut(500).build())
                .layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(10)
                        .activation("softmax").build())
                .backprop(true).pretrain(false);

        new ConvolutionLayerSetup(builder, 28, 28, 1);
        MultiLayerConfiguration conf = builder.build();

        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
        model.setListeners(new ScoreIterationListener(1));

        // Define when we want to stop training.
        EarlyStoppingModelSaver saver = new InMemoryModelSaver();
        EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder()
                //.epochTerminationConditions(new MaxEpochsTerminationCondition(10))
                .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(5))
                .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score
                .modelSaver(saver).build();
        EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator);

        // Train and display result.
        EarlyStoppingResult result = trainer.fit();
        System.out.println("Termination reason: " + result.getTerminationReason());
        System.out.println("Termination details: " + result.getTerminationDetails());
        System.out.println("Total epochs: " + result.getTotalEpochs());
        System.out.println("Best epoch number: " + result.getBestModelEpoch());
        System.out.println("Score at best epoch: " + result.getBestModelScore());

        model = saver.getBestModel();

        // Evaluate
        Evaluation eval = new Evaluation(numOutputs);
        validationSetIterator.reset();

        for (int i = 0; i < validationSet.numExamples(); i++) {
            DataSet t = validationSet.get(i);
            INDArray features = t.getFeatureMatrix();
            INDArray labels = t.getLabels();
            INDArray predicted = model.output(features, false);
            eval.eval(labels, predicted);
        }

        //Print the evaluation statistics
        System.out.println(eval.stats());
    } catch (Exception ex) {
        ex.printStackTrace();
    }

}

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