Java框架作为人工智能平台的优势

import org.tensorflow.keras.datasets.mnist;
import org.tensorflow.keras.models;
import org.tensorflow.keras.layers;
import org.tensorflow.keras.utils.to_categorical;

public class ImageClassifier {

    public static void main(String[] args) {
        // 载入 MNIST 数据集
        (trainX, trainY), (testX, testY) = mnist.load_data();

        // 预处理数据
        trainX = trainX.astype("float32") / 255;
        testX = testX.astype("float32") / 255;
        trainY = to_categorical(trainY, 10);
        testY = to_categorical(testY, 10);

        // 构建模型
        Sequential model = new Sequential();
        model.add(new Flatten(input_shape=(28, 28)));
        model.add(new Dense(128, activation="relu"));
        model.add(new Dense(10, activation="softmax"));

        // 编译模型
        model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]);

        // 训练模型
        model.fit(trainX, trainY, epochs=5);

        // 评估模型
        loss, acc = model.evaluate(testX, testY);
        System.out.println("Test accuracy: " + acc);
    }
}