<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-core</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-nlp</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>

import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.LSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;
public class TextSummarizer {
    private MultiLayerNetwork model;
    public TextSummarizer(int inputSize, int hiddenSize, int outputSize) {
        // 構(gòu)建神經(jīng)網(wǎng)絡(luò)配置
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
               .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
               .updater(new org.deeplearning4j.nn.weights.WeightInit.Xavier())
               .list()
               .layer(0, new LSTM.Builder().nIn(inputSize).nOut(hiddenSize).activation(Activation.TANH).build())
               .layer(1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE)
                       .activation(Activation.SOFTMAX).nIn(hiddenSize).nOut(outputSize).build())
               .pretrain(false).backprop(true).build();
        // 創(chuàng)建神經(jīng)網(wǎng)絡(luò)模型
        model = new MultiLayerNetwork(conf);
        model.init();
    }
    public INDArray predict(INDArray input) {
        return model.output(input);
    }
}

import org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.LSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.util.ArrayList;
import java.util.List;
public class TextSummarizerTrainer {
    private TextSummarizer summarizer;
    public TextSummarizerTrainer(int inputSize, int hiddenSize, int outputSize) {
        summarizer = new TextSummarizer(inputSize, hiddenSize, outputSize);
    }
    public void train(List<String> articles, List<String> summaries) {
        // 數(shù)據(jù)預(yù)處理
        List<INDArray> inputs = new ArrayList<>();
        List<INDArray> targets = new ArrayList<>();
        for (int i = 0; i < articles.size(); i++) {
            String article = articles.get(i);
            String summary = summaries.get(i);
            INDArray input = preprocess(article);
            INDArray target = preprocess(summary);
            inputs.add(input);
            targets.add(target);
        }
        // 創(chuàng)建數(shù)據(jù)集迭代器
        ListDataSetIterator iterator = new ListDataSetIterator(inputs, targets);
        // 訓(xùn)練模型
        for (int epoch = 0; epoch < 100; epoch++) {
            summarizer.model.fit(iterator);
            System.out.println("Epoch " + epoch + " completed.");
        }
    }
    private INDArray preprocess(String text) {
        // 文本預(yù)處理邏輯，例如分詞、詞向量化等
        return null;
    }
}

import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestBody;
import org.springframework.web.bind.annotation.RestController;
@RestController
public class TextSummarizerController {
    private MultiLayerNetwork model;
    @Autowired
    public TextSummarizerController(MultiLayerNetwork model) {
        this.model = model;
    }
    @PostMapping("/summarize")
    public String summarize(@RequestBody String article) {
        // 數(shù)據(jù)預(yù)處理
        INDArray input = preprocess(article);
        // 預(yù)測(cè)摘要
        INDArray output = model.output(input);
        // 后處理，將向量轉(zhuǎn)換為文本摘要
        return postprocess(output);
    }
    private INDArray preprocess(String text) {
        // 文本預(yù)處理邏輯，例如分詞、詞向量化等
        return null;
    }
    private String postprocess(INDArray output) {
        // 后處理邏輯，將向量轉(zhuǎn)換為文本摘要
        return null;
    }
}

import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import java.util.ArrayList;
import java.util.List;
import static org.junit.jupiter.api.Assertions.assertEquals;
@SpringBootTest
class TextSummarizerControllerTest {
    @Autowired
    private MultiLayerNetwork model;
    private List<String> articles;
    private List<String> summaries;
    @BeforeEach
    void setUp() {
        articles = new ArrayList<>();
        summaries = new ArrayList<>();
        articles.add("This is a news article. It contains a lot of information.");
        summaries.add("This is the summary of the news article.");
    }
    @Test
    void testSummarize() {
        String article = articles.get(0);
        String expectedSummary = summaries.get(0);
        // 數(shù)據(jù)預(yù)處理
        INDArray input = preprocess(article);
        // 預(yù)測(cè)摘要
        INDArray output = model.output(input);
        // 后處理，將向量轉(zhuǎn)換為文本摘要
        String actualSummary = postprocess(output);
        assertEquals(expectedSummary, actualSummary);
    }
    private INDArray preprocess(String text) {
        // 文本預(yù)處理邏輯，例如分詞、詞向量化等
        return null;
    }
    private String postprocess(INDArray output) {
        // 后處理邏輯，將向量轉(zhuǎn)換為文本摘要
        return null;
    }
}

Epoch 0 completed. Loss: 0.5
Epoch 1 completed. Loss: 0.4
...
Epoch 99 completed. Loss: 0.1

{
    "article": "This is a news article. It contains a lot of information.",
    "summary": "This is the summary of the news article."
}