标签:int Builder deeplearning4j lrs build new net
1.概述
一个基于java实现的深度学习框架,用于深度学习神经网络的搭建和模型训练。
2.demo
public class Demo { public static void main(String[] args) throws Exception { int height = 28; int width = 28; int channels = 1; // 这里有没有复杂的识别,没有分成红绿蓝三个通道 int outputNum = 10; // 有十个数字,所以输出为10 int batchSize = 54;//每次迭代取54张小批量来训练,可以查阅神经网络的mini batch相关优化,也就是小批量求平均梯度 int nEpochs = 1;//整个样本集只训练一次 int iterations = 1; int seed = 1234; Random randNumGen = new Random(seed); File trainData = new File(basePath + "/mnist_png/training"); FileSplit trainSplit = new FileSplit(trainData, NativeImageLoader.ALLOWED_FORMATS, randNumGen); ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator(); //以父级目录名作为分类的标签名 ImageRecordReader trainRR = new ImageRecordReader(height, width, channels, labelMaker);//构造图片读取类 trainRR.initialize(trainSplit); DataSetIterator trainIter = new RecordReaderDataSetIterator(trainRR, batchSize, 1, outputNum); // 把像素值区间 0-255 压缩到0-1 区间 DataNormalization scaler = new ImagePreProcessingScaler(0, 1); scaler.fit(trainIter); trainIter.setPreProcessor(scaler); // 向量化测试集 File testData = new File(basePath + "/mnist_png/testing"); FileSplit testSplit = new FileSplit(testData, NativeImageLoader.ALLOWED_FORMATS, randNumGen); ImageRecordReader testRR = new ImageRecordReader(height, width, channels, labelMaker); testRR.initialize(testSplit); DataSetIterator testIter = new RecordReaderDataSetIterator(testRR, batchSize, 1, outputNum); testIter.setPreProcessor(scaler); Map<Integer, Double> lrs = new HashMap<>(); lrs.put(0, 0.06); lrs.put(200, 0.05); lrs.put(600, 0.028); lrs.put(800, 0.0060); lrs.put(1000, 0.001); MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(seed) .iterations(iterations) .regularization(true).l2(0.0005) .learningRate(.01) .learningRateDecayPolicy(LearningRatePolicy.Schedule) .learningRateSchedule(lrSchedule) .weightInit(WeightInit.XAVIER) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(Updater.NESTEROVS) .list() .layer(0, new ConvolutionLayer.Builder(5, 5) .nIn(channels) .stride(1, 1) .nOut(20) .activation(Activation.IDENTITY) .build()) .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) .kernelSize(2, 2) .stride(2, 2) .build()) .layer(2, new ConvolutionLayer.Builder(5, 5) .stride(1, 1) .nOut(50) .activation(Activation.IDENTITY) .build()) .layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) .kernelSize(2, 2) .stride(2, 2) .build()) .layer(4, new DenseLayer.Builder().activation(Activation.RELU) .nOut(500).build()) .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .nOut(outputNum) .activation(Activation.SOFTMAX) .build()) .setInputType(InputType.convolutionalFlat(28, 28, 1)) .backprop(true).pretrain(false).build(); MultiLayerNetwork net = new MultiLayerNetwork(conf); net.init(); net.setListeners(new ScoreIterationListener(10)); log.debug("Total num of params: {}", net.numParams()); // 评估测试集 for (int i = 0; i < nEpochs; i++) { net.fit(trainIter); Evaluation eval = net.evaluate(testIter); log.info(eval.stats()); trainIter.reset(); testIter.reset(); } ModelSerializer.writeModel(net, new File(basePath + "/minist-model.zip"), true); } }
标签:int,Builder,deeplearning4j,lrs,build,new,net 来源: https://www.cnblogs.com/yangyang12138/p/13649367.html
本站声明: 1. iCode9 技术分享网(下文简称本站)提供的所有内容,仅供技术学习、探讨和分享; 2. 关于本站的所有留言、评论、转载及引用,纯属内容发起人的个人观点,与本站观点和立场无关; 3. 关于本站的所有言论和文字,纯属内容发起人的个人观点,与本站观点和立场无关; 4. 本站文章均是网友提供,不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属;如您发现该文章侵犯了您的权益,可联系我们第一时间进行删除; 5. 本站为非盈利性的个人网站,所有内容不会用来进行牟利,也不会利用任何形式的广告来间接获益,纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。