Java MnistDataSetIterator.hasNext方法代码示例

import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; //导入方法依赖的package包/类
/**
 * @param args
 */
public static void main(String[] args) throws Exception {
	MnistDataSetIterator iter = new MnistDataSetIterator(60,60000);
	@SuppressWarnings("unchecked")
	ObjectInputStream ois = new ObjectInputStream(new FileInputStream(args[0]));
	
	BasePretrainNetwork network = (BasePretrainNetwork) ois.readObject();
	
	
	DataSet test = null;
	while(iter.hasNext()) {
		INDArray reconstructed = network.transform(test.getFeatureMatrix());
		for(int i = 0; i < test.numExamples(); i++) {
			INDArray draw1 = test.get(i).getFeatureMatrix().mul(255);
			INDArray reconstructed2 = reconstructed.getRow(i);
			INDArray draw2 = Sampling.binomial(reconstructed2, 1, new MersenneTwister(123)).mul(255);

			DrawReconstruction d = new DrawReconstruction(draw1);
			d.title = "REAL";
			d.draw();
			DrawReconstruction d2 = new DrawReconstruction(draw2,100,100);
			d2.title = "TEST";
			d2.draw();
			Thread.sleep(10000);
			d.frame.dispose();
			d2.frame.dispose();
		}
	}
	
	
}
 

import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; //导入方法依赖的package包/类
/**
 * @param args
 */
public static void main(String[] args) throws Exception {
    MnistDataSetIterator iter = new MnistDataSetIterator(60, 60000);
    @SuppressWarnings("unchecked")
    ObjectInputStream ois = new ObjectInputStream(new FileInputStream(args[0]));

    BasePretrainNetwork network = (BasePretrainNetwork) ois.readObject();
    try {
        ois.close();
    } catch (IOException e) {
    }

    DataSet test = null;
    while (iter.hasNext()) {
        test = iter.next();
        INDArray reconstructed = network.activate(test.getFeatureMatrix());
        for (int i = 0; i < test.numExamples(); i++) {
            INDArray draw1 = test.get(i).getFeatureMatrix().mul(255);
            INDArray reconstructed2 = reconstructed.getRow(i);
            INDArray draw2 = Nd4j.getDistributions().createBinomial(1, reconstructed2)
                            .sample(reconstructed2.shape()).mul(255);

            DrawReconstruction d = new DrawReconstruction(draw1);
            d.title = "REAL";
            d.draw();
            DrawReconstruction d2 = new DrawReconstruction(draw2, 100, 100);
            d2.title = "TEST";
            d2.draw();
            Thread.sleep(10000);
            d.frame.dispose();
            d2.frame.dispose();
        }
    }


}
 

import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; //导入方法依赖的package包/类
public DeepAutoEncoderExample() {
    try {
        int seed = 123;
        int numberOfIterations = 1;
        iterator = new MnistDataSetIterator(1000, MnistDataFetcher.NUM_EXAMPLES, true);
        
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .iterations(numberOfIterations)
                .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
                .list()
                .layer(0, new RBM.Builder().nIn(numberOfRows * numberOfColumns)
                        .nOut(1000)
                        .lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
                .layer(1, new RBM.Builder().nIn(1000).nOut(500)
                        .lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
                .layer(2, new RBM.Builder().nIn(500).nOut(250)
                        .lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
                .layer(3, new RBM.Builder().nIn(250).nOut(100)
                        .lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
                .layer(4, new RBM.Builder().nIn(100).nOut(30)
                        .lossFunction(LossFunctions.LossFunction.RMSE_XENT).build()) //encoding stops
                .layer(5, new RBM.Builder().nIn(30).nOut(100)
                        .lossFunction(LossFunctions.LossFunction.RMSE_XENT).build()) //decoding starts
                .layer(6, new RBM.Builder().nIn(100).nOut(250)
                        .lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
                .layer(7, new RBM.Builder().nIn(250).nOut(500)
                        .lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
                .layer(8, new RBM.Builder().nIn(500).nOut(1000)
                        .lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
                .layer(9, new OutputLayer.Builder(
                                LossFunctions.LossFunction.RMSE_XENT).nIn(1000)
                        .nOut(numberOfRows * numberOfColumns).build())
                .pretrain(true).backprop(true)
                .build();

        model = new MultiLayerNetwork(conf);
        model.init();

        model.setListeners(Collections.singletonList(
                (IterationListener) new ScoreIterationListener()));

        while (iterator.hasNext()) {
            DataSet dataSet = iterator.next();
            model.fit(new DataSet(dataSet.getFeatureMatrix(),
                    dataSet.getFeatureMatrix()));
        }

        modelFile = new File("savedModel");
        ModelSerializer.writeModel(model, modelFile, true);
    } catch (IOException ex) {
        ex.printStackTrace();
    }
}