在Java技术栈中实现人工智能和机器学习

import weka.core.Instances;
import weka.core.converters.ConverterUtils;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Normalize;

public class DataPreprocessing {
    public static void main(String[] args) throws Exception {
        // 读取数据文件
        Instances data = ConverterUtils.DataSource.read("data.arff");

        // 使用Normalize过滤器进行数据归一化
        Normalize normalize = new Normalize();
        normalize.setInputFormat(data);
        data = Filter.useFilter(data, normalize);

        // 输出预处理后的数据
        System.out.println(data);
    }
}

import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.RBM;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.lossfunctions.LossFunctions;

public class NeuralNetwork {
    public static void main(String[] args) throws Exception {
        int numRows = 28;
        int numColumns = 28;
        int outputNum = 10;
        int batchSize = 64;
        int rngSeed = 123;
        int numEpochs = 15;
        double learningRate = 0.0015;

        // 获取训练和测试数据
        MnistDataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, rngSeed);
        MnistDataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, rngSeed);

        // 构建神经网络模型
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(rngSeed)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .iterations(1)
                .learningRate(learningRate)
                .list()
                .layer(0, new DenseLayer.Builder()
                        .nIn(numRows * numColumns)
                        .nOut(500)
                        .activation(Activation.RELU)
                        .weightInit(org.deeplearning4j.nn.weights.WeightInit.XAVIER)
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nIn(500)
                        .nOut(outputNum)
                        .activation(Activation.SOFTMAX)
                        .weightInit(org.deeplearning4j.nn.weights.WeightInit.XAVIER)
                        .build())
                .pretrain(false).backprop(true)
                .build();

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

        // 模型训练
        model.setListeners(new ScoreIterationListener(10));
        for (int i = 0; i < numEpochs; i++) {
            model.fit(mnistTrain);
        }

        // 模型评估
        DataSet testData = mnistTest.next();
        int prediction = model.predict(testData.getFeatures());
        int actual = testData.getLabels().argMax(1).getInt(0);
        System.out.println("Prediction: " + prediction);
        System.out.println("Actual: " + actual);
    }
}