Springboot 整合DL4J 打造智能写作助手（文本天生） - qidao123.com技术社区-IT企服评测·应用市场

<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-core</artifactId>
<version>1.0.0-beta7</version>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-native</artifactId>
<version>1.0.0-beta7</version>
</dependency>

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

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@RestController
@RequestMapping("/api/writing-assistant")
public class WritingAssistantController {
@Autowired
private TextGenerationService textGenerationService;
@PostMapping("/generate")
public ResponseEntity<String> generateText(@RequestBody String input) {
String generatedText = textGenerationService.generate(input);
return ResponseEntity.ok(generatedText);
}
}

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@Service
public class TextGenerationService {
public String generate(String input) {
// 使用训练好的模型进行文本生成
// ...
return generatedText;
}
}

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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.layers.LSTM;
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.dataset.api.iterator.IteratorUtils;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.dataset.DataSet;
// 假设你已经有一个 DataSetIterator 用于训练
DataSetIterator trainingData = ...;
// 定义网络配置
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.updater(new Adam(0.001))
.list()
.layer(0, new LSTM.Builder().nIn(inputSize).nOut(hiddenSize)
.activation(Activation.TANH)
.build())
.layer(1, new OutputLayer.Builder()
.nIn(hiddenSize).nOut(outputSize)
.activation(Activation.SOFTMAX)
.build())
.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setListeners(new ScoreIterationListener(1)); // 输出每次迭代的分数
// 训练模型
for (int epoch = 0; epoch < numberOfEpochs; epoch++) {
model.fit(trainingData);
}

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File modelFile = new File("path/to/savedModel.zip");
model.save(modelFile);

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MultiLayerNetwork model = MultiLayerNetwork.load(modelFile, true);

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public String generateText(String seedText, int numWords) {
// 将 seedText 转换为模型输入格式
INDArray input = prepareInput(seedText);
StringBuilder output = new StringBuilder(seedText);
for (int i = 0; i < numWords; i++) {
// 获取模型的输出
INDArray outputProbabilities = model.output(input);
// 基于输出的概率选择下一个词
String nextWord = getNextWord(outputProbabilities);
// 更新输入用于生成下一个词（例如，仅保留最后 N 个词）
input = updateInput(input, nextWord);
output.append(" ").append(nextWord);
}
return output.toString();
}

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private INDArray prepareInput(String seedText, Map<String, Integer> wordIndexMap, int maxLength) {
// 将 seedText 分词
String[] words = seedText.split(" ");
int[] inputIndices = new int[maxLength];
for (int i = 0; i < maxLength; i++) {
if (i < words.length) {
Integer index = wordIndexMap.get(words[i]);
inputIndices[i] = index != null ? index : 0; // 默认0代表未知词
} else {
inputIndices[i] = 0; // 用0填充
}
}
// 转换成 INDArray 形式
return Nd4j.create(inputIndices);
}
private String getNextWord(INDArray outputProbabilities, Map<Integer, String> indexWordMap, double temperature) {
// 应用温度
for (int i = 0; i < outputProbabilities.length(); i++) {
double prob = outputProbabilities.getDouble(i);
prob = Math.pow(prob, 1.0 / temperature); // 增大概率差异
outputProbabilities.putScalar(i, prob);
}
// 归一化
outputProbabilities.divi(outputProbabilities.sumNumber());
// 选择下一个单词
int nextWordIndex = Nd4j.getExecutioner().execAndReturn(new org.nd4j.linalg.api.ops.impl.shape.ArgMax(outputProbabilities, 1)).getInt(0);
return indexWordMap.get(nextWordIndex);
}
private INDArray updateInput(INDArray input, String nextWord, Map<String, Integer> wordIndexMap, int maxLength) {
// 除去第一个元素，加入新生成的单词
int[] inputIndices = new int[maxLength];
for (int i = 1; i < maxLength; i++) {
inputIndices[i - 1] = (int) input.getInt(i);
}
inputIndices[maxLength - 1] = wordIndexMap.getOrDefault(nextWord, 0); // 新单词的索引
return Nd4j.create(inputIndices);
}
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.List;
// 读取文章并合并成字符串
public String readArticles(List<String> articlePaths) {
StringBuilder sb = new StringBuilder();
for (String path : articlePaths) {
try {
List<String> lines = Files.readAllLines(Paths.get(path));
for (String line : lines) {
sb.append(line).append("\n");
}
} catch (IOException e) {
e.printStackTrace();
}
}
return sb.toString();
}
import java.util.HashMap;
import java.util.Map;
// 假设已经给出完整的文本
String allText = readArticles(articlePaths);
String[] words = allText.split(" ");
Map<String, Integer> wordIndexMap = new HashMap<>();
Map<Integer, String> indexWordMap = new HashMap<>();
int index = 0;
// 创建词汇表
for (String word : words) {
if (!wordIndexMap.containsKey(word)) {
wordIndexMap.put(word, index);
indexWordMap.put(index++, word);
}
}

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