Java MnistDataFetcher类代码示例

OGeek|极客世界-中国程序员成长平台 › 门户 › 编程› Java›Java编程经验

原作者: [db:作者] 来自: [db:来源] 收藏邀请

本文整理汇总了Java中org.deeplearning4j.datasets.fetchers.MnistDataFetcher类的典型用法代码示例。如果您正苦于以下问题：Java MnistDataFetcher类的具体用法？Java MnistDataFetcher怎么用？Java MnistDataFetcher使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。

MnistDataFetcher类属于org.deeplearning4j.datasets.fetchers包，在下文中一共展示了MnistDataFetcher类的10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，您的评价将有助于我们的系统推荐出更棒的Java代码示例。

示例1: testAutoEncoder

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher; //导入依赖的package包/类
@Test
public void testAutoEncoder() throws Exception {

    MnistDataFetcher fetcher = new MnistDataFetcher(true);
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).updater(new Sgd(0.1))
                    .layer(new org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder().nIn(784).nOut(600)
                                    .corruptionLevel(0.6)
                                    .lossFunction(LossFunctions.LossFunction.RECONSTRUCTION_CROSSENTROPY).build())
                    .build();


    fetcher.fetch(100);
    DataSet d2 = fetcher.next();

    INDArray input = d2.getFeatureMatrix();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    AutoEncoder da = (AutoEncoder) conf.getLayer().instantiate(conf,
                    Arrays.<IterationListener>asList(new ScoreIterationListener(1)), 0, params, true);
    assertEquals(da.params(), da.params());
    assertEquals(471784, da.params().length());
    da.setParams(da.params());
    da.setBackpropGradientsViewArray(Nd4j.create(1, params.length()));
    da.fit(input);
}

开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:27，代码来源:AutoEncoderTest.java

示例2: testBackProp

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher; //导入依赖的package包/类
@Test
public void testBackProp() throws Exception {
    MnistDataFetcher fetcher = new MnistDataFetcher(true);
    //        LayerFactory layerFactory = LayerFactories.getFactory(new org.deeplearning4j.nn.conf.layers.AutoEncoder());
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
                    .updater(new Sgd(0.1))
                    .layer(new org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder().nIn(784).nOut(600)
                                    .corruptionLevel(0.6)
                                    .lossFunction(LossFunctions.LossFunction.RECONSTRUCTION_CROSSENTROPY).build())
                    .build();

    fetcher.fetch(100);
    DataSet d2 = fetcher.next();

    INDArray input = d2.getFeatureMatrix();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    AutoEncoder da = (AutoEncoder) conf.getLayer().instantiate(conf, null, 0, params, true);
    Gradient g = new DefaultGradient();
    g.gradientForVariable().put(DefaultParamInitializer.WEIGHT_KEY, da.decode(da.activate(input)).sub(input));
}

开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:23，代码来源:AutoEncoderTest.java

示例3: main

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher; //导入依赖的package包/类
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:41，代码来源:DeepAutoEncoder.java

示例4: mnist

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher; //导入依赖的package包/类
public static DataSet mnist(int num) {
    try {
        MnistDataFetcher fetcher = new MnistDataFetcher();
        fetcher.fetch(num);
        return fetcher.next();
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
}

开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:10，代码来源:DataSets.java

示例5: DeepAutoEncoderExample

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher; //导入依赖的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();
    }
}

开发者ID:PacktPublishing，项目名称:Machine-Learning-End-to-Endguide-for-Java-developers，代码行数:55，代码来源:DeepAutoEncoderExample.java

示例6: main

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher; //导入依赖的package包/类
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:43，代码来源:DeepAutoEncoderExample.java

示例7: main

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher; //导入依赖的package包/类
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:45，代码来源:DBN.java

示例8: MnistManager

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher; //导入依赖的package包/类
/**
 * 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:17，代码来源:MnistManager.java

示例9: MnistDataSetIterator

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher; //导入依赖的package包/类
/** 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:11，代码来源:MnistDataSetIterator.java

示例10: RawMnistDataSetIterator

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher; //导入依赖的package包/类
public RawMnistDataSetIterator(int batch, int numExamples) throws IOException {
    super(batch, numExamples, new MnistDataFetcher(false));

}

开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:5，代码来源:RawMnistDataSetIterator.java

注：本文中的org.deeplearning4j.datasets.fetchers.MnistDataFetcher类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

鲜花

握手

雷人

路过

鸡蛋

该文章已有0人参与评论

请发表评论

全部评论

专题导读

More+

10-27 六六分期app的软件客服如何联系？(六六分期

11-06 可心卡盟:win10系统火狐flash插件崩溃怎么

11-06 亲亲特价:怎么删除回收站图标

11-06 济南大学虚拟社区:鲁大师节能降温的具体办

11-06 xlueops.exe:无线网络安装向导

11-06 女斗合众国:win7系统cf与主机连接不稳定怎

11-06 0xc000022-[cf烟雾头]cf怎么调烟雾头

11-06 qizideyouhuo:应用程序无法正常启动0xc0000

11-06 ipz-185:win7系统vcf文件怎么打开

11-06 傻哥蹦迪:win10系统s4怎么打开usb调试

11-06 八神浩树gtaste:回收站清空了怎么恢复

11-06 妖尾之黑色守护:win10系统电脑没有1440x900

11-06 校园至尊魔王小说:win7系统浏览网页时字体

11-06 女斗合众国:win10系统访问共享文件夹提示请

11-06 tokyo hot n0654:恢复win7系统默认字体一招

11-06 雨酷仙境:设置win7系统转移临时文件夹腾出

11-06 阿穆纳伊之杖:win7系统开始菜单在右边还原

11-06 tunespotting:win10系统火狐flash插件总是

11-06 甘尔葛分析师：计谋网站seo关键词暴涨有什

11-06 蔡贵霖: 计谋网站seo关键词暴涨有什么秘密

11-06 博益网首页:ao3网页版进入不了解决方法

11-06 漏斗子专栏: 网站数据分析小白易懂精华篇

11-06 见证双虹怎么做:win7系统开启telnet命令的

11-06 颾狐蝶蜋:系统资源不足无法完成请求的服务

11-06 国光中学校歌:提交网站到alexa查询详细步骤

11-06 西安有情天:静态网页和动态网页的区别

11-06 红木雅尚斋:外部链接构造对网站的好处

11-06 前官礼遇：防止域名劫持–增强域安全性的10

11-06 密传二转答案: 中文分词算法有哪些

11-06 金泉家园邮编:百度快照劫持的表现及应对方

Java UpdateRequestHandler类代码示例发布时间：2022-05-23

Java OverrideOnDemand类代码示例发布时间：2022-05-23

剪的笔顺,诠释剪的笔画,认识剪的部首

1 六六分期app的软件客服如何联系？(六六分期

六六分期app的软件客服如何联系？不知道吗？加qq群【895510560】即可！标题：六六分期

阅读：18135|2023-10-27

2 可心卡盟:win10系统火狐flash插件崩溃怎么

今天小编告诉大家如何处理win10系统火狐flash插件总是崩溃的问题，可能很多用户都不知

阅读：9630|2022-11-06

3 亲亲特价:怎么删除回收站图标

今天小编告诉大家如何对win10系统删除桌面回收站图标进行设置，可能很多用户都不知道

阅读：8161|2022-11-06

4 济南大学虚拟社区:鲁大师节能降温的具体办

今天小编告诉大家如何对win10系统电脑设置节能降温的设置方法，想必大家都遇到过需要

阅读：8538|2022-11-06

5 xlueops.exe:无线网络安装向导

我们在使用xp系统的过程中,经常需要对xp系统无线网络安装向导设置进行设置，可能很多

阅读：8439|2022-11-06

6 女斗合众国:win7系统cf与主机连接不稳定怎

今天小编告诉大家如何处理win7系统玩cf老是与主机连接不稳定的问题，可能很多用户都不

阅读：9358|2022-11-06

7 0xc000022-[cf烟雾头]cf怎么调烟雾头

电脑对日常生活的重要性小编就不多说了，可是一旦碰到win7系统设置cf烟雾头的问题，很

阅读：8408|2022-11-06

8 qizideyouhuo:应用程序无法正常启动0xc0000

我们在日常使用电脑的时候，有的小伙伴们可能在打开应用的时候会遇见提示应用程序无法

阅读：7845|2022-11-06

9 ipz-185:win7系统vcf文件怎么打开

今天小编告诉大家如何对win7系统打开vcf文件进行设置，可能很多用户都不知道怎么对win

阅读：8393|2022-11-06

10 傻哥蹦迪:win10系统s4怎么打开usb调试

今天小编告诉大家如何对win10系统s4开启USB调试模式进行设置，可能很多用户都不知道怎

阅读：7387|2022-11-06

客服电话

电子邮件

Java MnistDataFetcher类代码示例

示例1: testAutoEncoder

示例2: testBackProp

示例3: main

示例4: mnist

示例5: DeepAutoEncoderExample

示例6: main

示例7: main

示例8: MnistManager

示例9: MnistDataSetIterator

示例10: RawMnistDataSetIterator

请发表评论

全部评论

上一篇：

下一篇：

dustinvtran/ml-videos: A collection of v

delphi线程使用

ravikumar001/maven

更的笔顺,体会更的笔画,理会更的部首

ceejbot/LOUDBOT: AUTOMATED SHOUTING FOR

剪的笔顺,诠释剪的笔画,认识剪的部首

六六分期app的软件客服如何联系？(六六分期

florent37/ViewAnimator: A fluent Android

florent37/Shrine-MaterialDesign2: implem

CVE-2020-36276

SimpleSoftwareIO/simple-sms: Send and re

关于我们

产品与服务

解决方案

139-2527-9053