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Java MnistDataFetcher.NUM_EXAMPLES属性代码示例

本文整理汇总了Java中org.deeplearning4j.datasets.fetchers.MnistDataFetcher.NUM_EXAMPLES属性的典型用法代码示例。如果您正苦于以下问题：Java MnistDataFetcher.NUM_EXAMPLES属性的具体用法？Java MnistDataFetcher.NUM_EXAMPLES怎么用？Java MnistDataFetcher.NUM_EXAMPLES使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类org.deeplearning4j.datasets.fetchers.MnistDataFetcher的用法示例。


在下文中一共展示了MnistDataFetcher.NUM_EXAMPLES属性的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。

示例1: main

public static void main(String[] args) throws Exception {
    final int numRows = 28;
    final int numColumns = 28;
    int seed = 123;
    int numSamples = MnistDataFetcher.NUM_EXAMPLES;
    int batchSize = 1000;
    int iterations = 1;
    int listenerFreq = iterations/5;

    log.info("Load data....");
    DataSetIterator iter = new MnistDataSetIterator(batchSize,numSamples,true);

    log.info("Build model....");
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(seed)
            .iterations(iterations)
            .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
            .list(8)
            .layer(0, new RBM.Builder().nIn(numRows * numColumns).nOut(2000).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
            .layer(1, new RBM.Builder().nIn(2000).nOut(1000).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
            .layer(2, new RBM.Builder().nIn(1000).nOut(500).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
            .layer(3, new RBM.Builder().nIn(500).nOut(30).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
            .layer(4, new RBM.Builder().nIn(30).nOut(500).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build()) 
            .layer(5, new RBM.Builder().nIn(500).nOut(1000).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
            .layer(6, new RBM.Builder().nIn(1000).nOut(2000).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
            .layer(7, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.SIGMOID).nIn(2000).nOut(numRows*numColumns).build())
            .pretrain(true).backprop(true)
            .build();

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

    model.setListeners(new ScoreIterationListener(listenerFreq));

    log.info("Train model....");
    while(iter.hasNext()) {
        DataSet next = iter.next();
        model.fit(new DataSet(next.getFeatureMatrix(),next.getFeatureMatrix()));
    }
}
开发者ID:PacktPublishing，项目名称:Deep-Learning-with-Hadoop，代码行数:40，代码来源:DeepAutoEncoder.java

示例2: DeepAutoEncoderExample

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();
    }
}
开发者ID:PacktPublishing，项目名称:Machine-Learning-End-to-Endguide-for-Java-developers，代码行数:54，代码来源:DeepAutoEncoderExample.java

示例3: main

public static void main(String[] args) throws Exception {
    final int numRows = 28;
    final int numColumns = 28;
    int seed = 123;
    int numSamples = MnistDataFetcher.NUM_EXAMPLES;
    int batchSize = 1000;
    int iterations = 1;
    int listenerFreq = iterations/5;

    log.info("Load data....");
    DataSetIterator iter = new MnistDataSetIterator(batchSize,numSamples,true);

    log.info("Build model....");
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(seed)
            .iterations(iterations)
            .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
            .list(10)
            .layer(0, new RBM.Builder().nIn(numRows * numColumns).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(numRows*numColumns).build())
            .pretrain(true).backprop(true)
            .build();

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

    model.setListeners(Arrays.asList((IterationListener) new ScoreIterationListener(listenerFreq)));

    log.info("Train model....");
    while(iter.hasNext()) {
        DataSet next = iter.next();
        model.fit(new DataSet(next.getFeatureMatrix(),next.getFeatureMatrix()));
    }
}
开发者ID:PacktPublishing，项目名称:Java-Data-Science-Cookbook，代码行数:42，代码来源:DeepAutoEncoderExample.java

示例4: main

public static void main(String[] args) throws Exception {
    final int numRows = 28;
    final int numColumns = 28;
    int seed = 123;
    int numSamples = MnistDataFetcher.NUM_EXAMPLES;
    int batchSize = 1000;
    int iterations = 1;
    int listenerFreq = iterations/5;

    log.info("Load data....");
    DataSetIterator iter = new MnistDataSetIterator(batchSize,numSamples,true);

    log.info("Build model....");
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(seed)
            .iterations(iterations)
            .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
            .list()
            .layer(0, new RBM.Builder().nIn(numRows * numColumns).nOut(1000).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
            .layer(1, new RBM.Builder().nIn(1000).nOut(500).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
            .layer(2, new RBM.Builder().nIn(500).nOut(250).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
            .layer(3, new RBM.Builder().nIn(250).nOut(100).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
            .layer(4, new RBM.Builder().nIn(100).nOut(30).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())  
            .layer(5, new RBM.Builder().nIn(30).nOut(100).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())  
            .layer(6, new RBM.Builder().nIn(100).nOut(250).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
            .layer(7, new RBM.Builder().nIn(250).nOut(500).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
            .layer(8, new RBM.Builder().nIn(500).nOut(1000).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
            .layer(9, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.SIGMOID).nIn(1000).nOut(numRows*numColumns).build())
            .pretrain(true).backprop(true)
            .build();

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

    model.setListeners(new ScoreIterationListener(listenerFreq));

    log.info("Train model....");
    while(iter.hasNext()) {
        DataSet next = iter.next();
        model.fit(new DataSet(next.getFeatureMatrix(),next.getFeatureMatrix()));
    }


}
开发者ID:PacktPublishing，项目名称:Deep-Learning-with-Hadoop，代码行数:44，代码来源:DBN.java

示例5: MnistManager

/**
 * Constructs an instance managing the two given data files. Supports
 * <code>NULL</code> value for one of the arguments in case reading only one
 * of the files (images and labels) is required.
 *
 * @param imagesFile
 *            Can be <code>NULL</code>. In that case all future operations
 *            using that file will fail.
 * @param labelsFile
 *            Can be <code>NULL</code>. In that case all future operations
 *            using that file will fail.
 * @throws IOException
 */
public MnistManager(String imagesFile, String labelsFile, boolean train) throws IOException {
    this(imagesFile, labelsFile, train ? MnistDataFetcher.NUM_EXAMPLES : MnistDataFetcher.NUM_EXAMPLES_TEST);
}
开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:16，代码来源:MnistManager.java

示例6: MnistDataSetIterator

/** Constructor to get the full MNIST data set (either test or train sets) without binarization (i.e., just normalization
 * into range of 0 to 1), with shuffling based on a random seed.
 * @param batchSize
 * @param train
 * @throws IOException
 */
public MnistDataSetIterator(int batchSize, boolean train, int seed) throws IOException {
    this(batchSize, (train ? MnistDataFetcher.NUM_EXAMPLES : MnistDataFetcher.NUM_EXAMPLES_TEST), false, train,
                    true, seed);
}
开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:10，代码来源:MnistDataSetIterator.java


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