【SpingBoot】SpringBoot实现卷积神经模型代码

下面是一个基本的卷积神经网络（CNN）模型代码示例：

1. 导入依赖

    org.springframework.boot
    spring-boot-starter-web



    org.tensorflow
    tensorflow
    2.4.0

2.定义模型架构

public class CNNModel {
    private static final int IMAGE_WIDTH = 28;
    private static final int IMAGE_HEIGHT = 28;
    private static final int NUM_CLASSES = 10;

    private static final int NUM_FILTERS = 32;
    private static final int FILTER_SIZE = 3;
    private static final int STRIDE = 1;

    private static final int NUM_UNITS = 128;

    private Model model;

    public CNNModel() {
        model = Sequential.create();

        // 第一层卷积层
        model.add(Conv2d.create(NUM_FILTERS, FILTER_SIZE, FILTER_SIZE, 1, "same", Input.inputShape(IMAGE_HEIGHT, IMAGE_WIDTH, 1)));
        model.add(ReLU.create());
        model.add(MaxPool2d.create(2, 2, "valid"));

        // 第二层卷积层
        model.add(Conv2d.create(NUM_FILTERS, FILTER_SIZE, FILTER_SIZE, 1, "same"));
        model.add(ReLU.create());
        model.add(MaxPool2d.create(2, 2, "valid"));

        // 平坦层
        model.add(Flatten.create());

        // 全连接层
        model.add(Dense.create(NUM_UNITS));
        model.add(ReLU.create());

        // 输出层
        model.add(Dense.create(NUM_CLASSES));
        model.add(Softmax.create());
    }

    public Model getModel() {
        return model;
    }
}

3.加载数据集

public class DataLoader {
    private static final String TRAIN_IMAGES_FILE = "train-images-idx3-ubyte.gz";
    private static final String TRAIN_LABELS_FILE = "train-labels-idx1-ubyte.gz";
    private static final String TEST_IMAGES_FILE = "t10k-images-idx3-ubyte.gz";
    private static final String TEST_LABELS_FILE = "t10k-labels-idx1-ubyte.gz";

    private static final int IMAGE_WIDTH = 28;
    private static final int IMAGE_HEIGHT = 28;

    public static DataSet loadTrainData() throws IOException {
        String trainImagesPath = DataLoader.class.getClassLoader().getResource(TRAIN_IMAGES_FILE).getPath();
        String trainLabelsPath = DataLoader.class.getClassLoader().getResource(TRAIN_LABELS_FILE).getPath();

        MnistDataSetIterator trainIterator = new MnistDataSetIterator(128, true, 12345);
        return trainIterator.next();
    }

    public static DataSet loadTestData() throws IOException {
        String testImagesPath = DataLoader.class.getClassLoader().getResource(TEST_IMAGES_FILE).getPath();
        String testLabelsPath = DataLoader.class.getClassLoader().getResource(TEST_LABELS_FILE).getPath();

        MnistDataSetIterator testIterator = new MnistDataSetIterator(128, false, 12345);
        return testIterator.next();
    }

    public static void saveImage(String filePath, INDArray image) {
        BufferedImage bufferedImage = new BufferedImage(IMAGE_WIDTH, IMAGE_HEIGHT, BufferedImage.TYPE_BYTE_GRAY);
        for (int i = 0; i < IMAGE_HEIGHT; i++) {
            for (int j = 0; j < IMAGE_WIDTH; j++) {
                int pixelValue = (int) (255 * image.getDouble(i, j));
                bufferedImage.setRGB(j, i, new Color(pixelValue, pixelValue, pixelValue).getRGB());
            }
        }
        try {
            ImageIO.write(bufferedImage, "png", new File(filePath));
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

4.训练和评估模型

@RestController
public class ModelController {
    private static final String MODEL_FILE = "model/cnn.pb";

    private CNNModel cnnModel = new CNNModel();

    @PostMapping("/train")
    public ResponseEntity trainModel() throws IOException {
        DataSet trainData = DataLoader.loadTrainData();
        DataSet testData = DataLoader.loadTestData();

        // 数据预处理
        trainData.normalize();
        testData.normalize();

        // 构建计算图
        Model model = cnnModel.getModel();
        model.compile(new Adam(), new LossSparseCategoricalCrossEntropy(), new Metric[]{new Accuracy()});

        // 训练模型
        model.fit(trainData);

        // 评估模型
        Evaluation evaluation = model.evaluate(testData);
        System.out.println("Test loss: " + evaluation.getLoss());
        System.out.println("Test accuracy: " + evaluation.getAccuracy());

        // 保存模型
        model.save(new File(MODEL_FILE), true);

        return ResponseEntity.ok("Model trained and saved.");
    }

    @PostMapping("/predict")
    public ResponseEntity predictImage(@RequestParam("image") MultipartFile imageFile) throws IOException {
        // 读取图片
        BufferedImage bufferedImage = ImageIO.read(imageFile.getInputStream());
        INDArray image = Nd4j.create(bufferedImage.getHeight(), bufferedImage.getWidth());
        for (int i = 0; i < bufferedImage.getHeight(); i++) {
            for (int j = 0; j < bufferedImage.getWidth(); j++) {
                int pixelValue = new Color(bufferedImage.getRGB(j, i)).getRed();
                image.putScalar(i, j, pixelValue / 255.0);
            }
        }

        // 加载模型
        Model model = Model.importFrozenModel(new File(MODEL_FILE))
            .setInputNames("conv2d_input")
            .setOutputNames("Identity");

        // 预处理图片
        INDArray input = image.reshape(1, 28, 28, 1);
        input.divi(255.0);

        // 预测结果
        INDArray output = model.predict(input);
        int predictedLabel = Nd4j.argMax(output, 1).getInt(0);

        return ResponseEntity.ok("Predicted label: " + predictedLabel);
    }
}

5.Spring Boot应用程序

SpringBootApplication
public class Application {
    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }
}

这只是一个简单的卷积神经网络模型实现示例，实际上要构建一个准确的CNN模型需要更多的调整和优化。此外，还需要处理各种安全问题，例如防止对模型进行攻击、保证模型的隐私性等。因此，建议在实际应用中使用成熟的深度学习框架，而不是从头开始编写自己的模型代码。