Variable x = Variable.of(ndarray);
Variable y = x.relu().linear(128).softmax();
Loss loss = CrossEntropyLoss.of(y, target);
loss.backward();

public class LinearLayer extends Layer {
    @Override
    public Variable layerForward(Variable input) {
        Variable result = input.matmul(this.weight);
        if (bias != null) result = result.add(bias);
        return result;
    }
}

NdArray array = new NdArray(data);           // 数值计算
Variable var = new Variable(array, true);    // 自动微分
Model model = new GPT2Model("gpt2");         // 大语言模型
EmbodiedAgent agent = new EmbodiedAgent();   // 具身智能体

import io.leavesfly.tinyai.agent.AdvancedAgent;

// 创建智能体
AdvancedAgent agent = new AdvancedAgent("我的AI助手", "你是一个专业AI助手");

// 添加知识库
agent.addKnowledge("TinyAI是一个纯Java实现的AI框架", "tinyai_intro");

// 对话交互
String answer = agent.processMessage("什么是TinyAI?");
System.out.println(answer);

import io.leavesfly.tinyai.ml.*;
import io.leavesfly.tinyai.nnet.v1.block.MlpBlock;

// 构建模型
MlpBlock mlp = new MlpBlock("mnist", 784, new int[]{256, 128, 10});
Model model = new Model("classifier", mlp);

// 准备数据
DataSet dataset = new ArrayDataset(trainX, trainY);

// 配置训练
Trainer trainer = new Trainer(
    100,                              // epochs
    new Monitor(),                    // 训练监控
    new AccuracyEval(),               // 评估器
    true,                             // 启用并行
    4                                 // 4线程
);

// 开始训练
trainer.init(dataset, model, new SoftmaxCrossEntropyLoss(), new Adam(0.001));
trainer.train(true);

// 保存模型
model.save("model.bin");

import io.leavesfly.tinyai.gpt2.GPT2Model;
import io.leavesfly.tinyai.deepseek.rx.DeepSeekR1Model;

// GPT-2 文本生成
GPT2Model gpt2=GPT2Model.createSmallModel("gpt2-small");
        List<Integer> tokens=Arrays.asList(100,200,300);
        List<Integer> generated=gpt2.generateText(tokens,50);

// DeepSeek R1 推理
        DeepSeekR1Model r1=new DeepSeekR1Model("deepseek-r1");
        DeepSeekR1Model.ReasoningResult result=r1.performReasoning(inputIds);
        System.out.println("推理步骤: "+result.numSteps);
        System.out.println("置信度: "+result.averageConfidence);

import io.leavesfly.tinyai.embodied.*;

// 自动驾驶智能体
EnvironmentConfig config = EnvironmentConfig.createHighwayConfig();
EmbodiedAgent agent = new EmbodiedAgent(config);

// 运行一个回合
Episode episode = agent.runEpisode(200);
System.out.println("总奖励: " + episode.getTotalReward());
System.out.println("步数: " + episode.getLength());
System.out.println("平均奖励: " + episode.getAverageReward());

import io.leavesfly.tinyai.banana.*;

// 创建 Banana 模型
BananaModel banana = BananaModel.create("banana-tiny", "tiny");

// 文本生成图像
Variable textInput = new Variable(textTokens);  // 文本描述
Variable generatedImage = banana.generateImage(textInput);

System.out.println("生成图像尺寸: " + generatedImage.getValue().getShape());
// 输出: [batch, 3, 256, 256]