如何在Java中构建一个基本的神经网络？

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.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.api.iterator.DataSetIterator;
import org.nd4j.linalg.lossfunctions.LossFunctions;

public class BasicNeuralNetwork {
    public static void main(String[] args) throws Exception {
        // 设置神经网络的配置
        int numInput = 784; // 输入层神经元数量
        int numHidden = 1000; // 隐藏层神经元数量
        int numOutput = 10; // 输出层神经元数量
        double learningRate = 0.001; // 学习率
        int batchSize = 64; // 批处理大小
        int numEpochs = 10; // 迭代次数

        // 构建神经网络结构
        MultiLayerConfiguration config = new NeuralNetConfiguration.Builder()
                .seed(123)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .iterations(1)
                .learningRate(learningRate)
                .updater(org.deeplearning4j.nn.conf.Updater.NESTEROVS)
                .list()
                .layer(0, new DenseLayer.Builder()
                        .nIn(numInput)
                        .nOut(numHidden)
                        .activation(Activation.RELU)
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nIn(numHidden)
                        .nOut(numOutput)
                        .activation(Activation.SOFTMAX)
                        .build())
                .pretrain(false)
                .backprop(true)
                .build();

        // 创建神经网络模型
        MultiLayerNetwork model = new MultiLayerNetwork(config);
        model.init();

        // 加载MNIST数据集
        DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 123);
        DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, 123);

        // 训练神经网络
        model.setListeners(new ScoreIterationListener(10));
        for (int i = 0; i < numEpochs; i++) {
            model.fit(mnistTrain);
        }

        // 评估神经网络性能
        Evaluation eval = model.evaluate(mnistTest);
        System.out.println(eval.stats());
    }
}