TinyAI ：全栈式轻量级 AI 框架

// 创建数组的多种方式NdArray a = NdArray.of(newfloat[][]{{1, 2}, {3, 4}});     // 从二维数组创建NdArray b = NdArray.zeros(Shape.of(2, 3));                 // 创建2x3的零矩阵NdArray c = NdArray.randn(Shape.of(100, 50));              // 创建随机正态分布矩阵
// 丰富的数学运算NdArray result = a.add(b)           // 矩阵加法                 .mul(c)            // 对应元素相乘                 .dot(d)            // 矩阵乘法                 .sigmoid()         // Sigmoid激活函数                 .transpose();      // 转置

// 构建一个简单的计算图Variable x = new Variable(NdArray.of(2.0f), "x");Variable y = new Variable(NdArray.of(3.0f), "y");
// 正向传播：构建计算图Variable z = x.mul(y).add(x.squ());  // z = x*y + x²
// 反向传播：自动计算梯度z.backward();
System.out.println("dz/dx = " + x.getGrad().getNumber());  // 输出：dz/dx = 7.0System.out.println("dz/dy = " + y.getGrad().getNumber());  // 输出：dz/dy = 2.0

public voidbackward(){    if (!requireGrad) return;        // 初始化梯度为1（链式法则的起点）    if (Objects.isNull(grad)) {        setGrad(NdArray.ones(this.getValue().getShape()));    }        Function creator = this.creator;    if (creator != null) {        Variable[] inputs = creator.getInputs();        List<NdArray> grads = creator.backward(grad);  // 计算输入的梯度                // 递归计算每个输入变量的梯度        for (int i = 0; i < inputs.length; i++) {            Variable input = inputs[i];            // 梯度累积：支持变量被多次使用的情况            if (input.getGrad() != null) {                input.setGrad(input.getGrad().add(grads.get(i)));            } else {                input.setGrad(grads.get(i));            }            input.backward();  // 递归调用        }    }}

// Layer：最基础的计算单元public abstract classLayer {    protected Map<String, Variable> parameters = new HashMap<>();        public abstract Variable layerForward(Variable... inputs);        // 参数管理    protectedvoidaddParameter(String name, NdArray value){        parameters.put(name, new Variable(value, name));    }}
// Block：Layer的组合容器public abstract classBlock {    protected List<Layer> layers = new ArrayList<>();        public abstract Variable blockForward(Variable... inputs);        // 支持嵌套组合    publicvoidaddBlock(Block subBlock){        // 将子Block的Layer添加到当前Block    }}

// 构建一个多层感知机MlpBlock mlp = new MlpBlock("classifier", 784, newint[]{128, 64, 10});
// 构建一个完整的神经网络SequentialBlock network = new SequentialBlock("mnist_net");network.addLayer(new FlattenLayer("flatten"))           // 展平层       .addLayer(new LinearLayer("fc1", 784, 128))      // 全连接层1       .addLayer(new ReluLayer("relu1"))                // ReLU激活       .addLayer(new LinearLayer("fc2", 128, 64))       // 全连接层2       .addLayer(new ReluLayer("relu2"))                // ReLU激活       .addLayer(new LinearLayer("fc3", 64, 10))        // 输出层       .addLayer(new SoftmaxLayer("softmax"));          // Softmax

public class TransformerBlockextendsBlock {    private MultiHeadAttentionLayer attention;    private FeedForwardLayer feedForward;    private LayerNormalizationLayer norm1, norm2;        @Override    public Variable blockForward(Variable... inputs){        Variable input = inputs[0];                // Self-Attention + 残差连接        Variable attnOut = norm1.layerForward(input);        attnOut = attention.layerForward(attnOut, attnOut, attnOut);        Variable residual1 = input.add(attnOut);                // Feed-Forward + 残差连接        Variable ffOut = norm2.layerForward(residual1);        ffOut = feedForward.layerForward(ffOut);        return residual1.add(ffOut);    }}

public class LstmLayerextendsLayer {    @Override    public Variable layerForward(Variable... inputs){        Variable x = inputs[0];        Variable h = inputs[1];  // 隐藏状态        Variable c = inputs[2];  // 细胞状态                // 遗忘门        Variable f = sigmoid(linear(concat(x, h), Wf).add(bf));        // 输入门        Variable i = sigmoid(linear(concat(x, h), Wi).add(bi));        // 候选值        Variable g = tanh(linear(concat(x, h), Wg).add(bg));        // 输出门        Variable o = sigmoid(linear(concat(x, h), Wo).add(bo));                // 更新细胞状态和隐藏状态        Variable newC = f.mul(c).add(i.mul(g));        Variable newH = o.mul(tanh(newC));                return newH;    }}

// 创建数据集DataSet trainData = new ArrayDataset(trainX, trainY);
// 构建模型Model model = new Model("mnist_classifier", mlpBlock);
// 配置训练器（支持并行训练）Trainer trainer = new Trainer(    epochs: 100,                          // 训练轮数    monitor: new TrainingMonitor(),       // 训练监控器    evaluator: new AccuracyEvaluator(),   // 评估器    useParallel: true,                    // 启用并行训练    threadCount: 4                        // 线程数);
// 初始化训练器trainer.init(trainData, model,             new MeanSquaredErrorLoss(),    // 损失函数            new SgdOptimizer(0.01f));      // 优化器
// 开始训练（一键式训练）trainer.train(showTrainingCurve: true);

public voidtrain(boolean showCurve){    for (int epoch = 0; epoch < epochs; epoch++) {        // 1. 设置模型为训练模式        model.setTraining(true);                // 2. 批次训练        for (DataBatch batch : dataSet.getBatches()) {            // 2.1 前向传播            Variable prediction = model.forward(batch.getInputs());                        // 2.2 计算损失            Variable loss = lossFunction.forward(prediction, batch.getTargets());                        // 2.3 清空梯度            model.clearGradients();                        // 2.4 反向传播            loss.backward();                        // 2.5 参数更新            optimizer.step(model.getParameters());                        // 2.6 记录训练信息            monitor.recordTrainingStep(loss.getValue().getNumber());        }                // 3. 模型评估        if (epoch % 10 == 0) {            float accuracy = evaluator.evaluate(model, validationData);            monitor.recordEpoch(epoch, accuracy);        }    }        // 4. 可视化训练曲线    if (showCurve) {        monitor.plotTrainingCurve();    }}

public class ParallelTrainer {    private ExecutorService executorService;    private int threadCount;        public voidparallelTrainBatch(List<DataBatch> batches){        // 创建线程池        executorService = Executors.newFixedThreadPool(threadCount);                // 将批次分配给不同线程        List<Future<TrainingResult>> futures = new ArrayList<>();        for (DataBatch batch : batches) {            Future<TrainingResult> future = executorService.submit(() -> {                // 每个线程独立训练一个批次                return trainSingleBatch(batch);            });            futures.add(future);        }                // 收集训练结果并聚合梯度        List<Map<String, NdArray>> gradients = new ArrayList<>();        for (Future<TrainingResult> future : futures) {            TrainingResult result = future.get();            gradients.add(result.getGradients());        }                // 梯度聚合和参数更新        Map<String, NdArray> aggregatedGrads = aggregateGradients(gradients);        optimizer.step(aggregatedGrads);    }}

public class GPT1ModelextendsModel {    private TokenEmbedding tokenEmbedding;    private PositionalEncoding posEncoding;    private List<TransformerBlock> transformerBlocks;    private LayerNormalizationLayer finalNorm;    private LinearLayer outputProjection;        @Override    public Variable forward(Variable... inputs){        Variable tokens = inputs[0];                // 1. Token嵌入 + 位置编码        Variable embedded = tokenEmbedding.forward(tokens);        Variable positioned = posEncoding.forward(embedded);                // 2. 多层Transformer块        Variable hidden = positioned;        for (TransformerBlock block : transformerBlocks) {            hidden = block.blockForward(hidden);        }                // 3. 最终归一化和输出投影        hidden = finalNorm.layerForward(hidden);        return outputProjection.layerForward(hidden);    }}

public class GPT2ModelextendsGPT1Model {    // GPT-2相对于GPT-1的主要改进：    // 1. 更大的模型参数（1.5B）    // 2. 更多的注意力头和层数    // 3. 改进的初始化策略        public static GPT2Model createMediumModel(){        GPT2Config config = GPT2Config.builder()            .vocabSize(50257)            .hiddenSize(1024)            .numLayers(24)            .numHeads(16)            .maxPositionEmbeddings(1024)            .build();                returnnew GPT2Model(config);    }}

public class GPT3ModelextendsGPT2Model {    @Override    protected MultiHeadAttentionLayer createAttentionLayer(GPT3Config config){        // GPT-3引入稀疏注意力机制        returnnew SparseMultiHeadAttentionLayer(            config.getHiddenSize(),            config.getNumHeads(),            config.getAttentionPatterns()  // 稀疏注意力模式        );    }}

public class Qwen3ModelextendsModel {    @Override    public Variable forward(Variable... inputs){        Variable tokens = inputs[0];                // 1. 嵌入层        Variable embedded = tokenEmbedding.forward(tokens);                // 2. 多个Decoder块（集成了现代技术）        Variable hidden = embedded;        for (Qwen3DecoderBlock block : decoderBlocks) {            hidden = block.blockForward(hidden);        }                // 3. RMS归一化（替代LayerNorm）        hidden = rmsNorm.layerForward(hidden);                return outputProjection.layerForward(hidden);    }}
public class Qwen3DecoderBlockextendsBlock {    private Qwen3AttentionBlock attention;    // 集成GQA和RoPE    private Qwen3MLPBlock mlp;               // 集成SwiGLU激活    private RMSNormLayer preAttnNorm;    private RMSNormLayer preMlpNorm;        @Override    public Variable blockForward(Variable... inputs){        Variable input = inputs[0];                // 预归一化 + 注意力 + 残差连接        Variable normed1 = preAttnNorm.layerForward(input);        Variable attnOut = attention.blockForward(normed1);        Variable residual1 = input.add(attnOut);                // 预归一化 + MLP + 残差连接        Variable normed2 = preMlpNorm.layerForward(residual1);        Variable mlpOut = mlp.blockForward(normed2);        return residual1.add(mlpOut);    }}

public class RotaryPositionalEmbeddingLayerextendsLayer {    @Override    public Variable layerForward(Variable... inputs){        Variable x = inputs[0];        int seqLen = x.getValue().getShape().get(1);        int dim = x.getValue().getShape().get(2);                // 计算旋转角度        NdArray freqs = computeFrequencies(dim, seqLen);                // 应用旋转变换        return applyRotaryEmbedding(x, freqs);    }}

public class GroupedQueryAttentionextendsLayer {    private int numHeads;    private int numKeyValueHeads;  // KV头数少于Q头数        @Override    public Variable layerForward(Variable... inputs){        // Q、K、V投影，但K和V共享参数组        Variable q = queryProjection.layerForward(inputs[0]);        Variable k = keyProjection.layerForward(inputs[0]);        Variable v = valueProjection.layerForward(inputs[0]);                // 重复K和V以匹配Q的头数        k = repeatKVHeads(k);        v = repeatKVHeads(v);                return computeAttention(q, k, v);    }}

// 基础智能体：具备基本的感知和行动能力public abstract classBaseAgent {    protected String name;    protected String systemPrompt;    protected Memory memory;    protected ToolRegistry toolRegistry;        public abstract AgentResponse processMessage(String message);        protected Object performTask(AgentTask task) throws Exception {        // 任务执行的基本流程        return null;    }}


// 高级智能体：具备学习和推理能力public class AdvancedAgentextendsBaseAgent {    private KnowledgeBase knowledgeBase;    private ReasoningEngine reasoningEngine;        @Override    public AgentResponse processMessage(String message){        // 1. 理解用户意图        Intent intent = intentRecognition.analyze(message);                // 2. 检索相关知识        List<Knowledge> relevantKnowledge = knowledgeBase.retrieve(intent);                // 3. 推理和生成回答        String response = reasoningEngine.generateResponse(intent, relevantKnowledge);                // 4. 更新记忆        memory.store(new Conversation(message, response));                returnnew AgentResponse(response);    }}

public class SelfEvolvingAgentextendsAdvancedAgent {    private ExperienceBuffer experienceBuffer;    private StrategyOptimizer strategyOptimizer;    private KnowledgeGraphBuilder knowledgeGraphBuilder;        @Override    public TaskResult processTask(String taskName, TaskContext context){        // 1. 记录任务开始状态        TaskSnapshot snapshot = captureTaskSnapshot(taskName, context);                // 2. 执行任务        TaskResult result = super.processTask(taskName, context);                // 3. 记录经验        Experience experience = new Experience(snapshot, result);        experienceBuffer.add(experience);                // 4. 触发学习（如果需要）        if (shouldTriggerLearning()) {            selfEvolve();        }                return result;    }        public voidselfEvolve(){        // 1. 经验分析        List<Experience> recentExperiences = experienceBuffer.getRecentExperiences();        PerformanceAnalysis analysis = analyzePerformance(recentExperiences);                // 2. 策略优化        if (analysis.hasImprovementOpportunity()) {            Strategy newStrategy = strategyOptimizer.optimize(analysis);            updateStrategy(newStrategy);        }                // 3. 知识图谱更新        List<KnowledgeNode> newNodes = extractKnowledgeFromExperiences(recentExperiences);        knowledgeGraphBuilder.updateGraph(newNodes);                // 4. 能力提升        enhanceCapabilities(analysis);    }}

public class RAGSystem {    private VectorDatabase vectorDB;    private TextEncoder textEncoder;    private DocumentProcessor documentProcessor;        public String generateAnswer(String question, List<Document> documents){        // 1. 文档预处理和向量化        for (Document doc : documents) {            List<TextChunk> chunks = documentProcessor.chunkDocument(doc);            for (TextChunk chunk : chunks) {                NdArray embedding = textEncoder.encode(chunk.getText());                vectorDB.store(chunk.getId(), embedding, chunk);            }        }                // 2. 问题向量化        NdArray questionEmbedding = textEncoder.encode(question);                // 3. 相似度检索        List<RetrievalResult> relevantChunks = vectorDB.similaritySearch(            questionEmbedding, topK: 5);                // 4. 上下文构建        String context = buildContext(relevantChunks);                // 5. 生成回答        String prompt = String.format(            "基于以下上下文回答问题：\n上下文：%s\n问题：%s\n回答：",             context, question);                return textGenerator.generate(prompt);    }}

// 每个类都有明确的单一职责public class LinearLayerextendsLayer {        // 只负责线性变换public class ReluLayerextendsLayer {          // 只负责ReLU激活public class SoftmaxLayerextendsLayer {       // 只负责Softmax计算

// 对扩展开放，对修改封闭public abstract classLayer {    // 基础功能稳定不变    public final Variable forward(Variable... inputs){        return layerForward(inputs);  // 委托给子类实现    }        // 扩展点：子类可以实现自己的计算逻辑    protected abstract Variable layerForward(Variable... inputs);}

// 高层模块不依赖低层模块，都依赖抽象public class Trainer {    private LossFunction lossFunction;      // 依赖抽象接口    private Optimizer optimizer;            // 依赖抽象接口    private Evaluator evaluator;            // 依赖抽象接口        // 通过依赖注入获得具体实现    public voidinit(DataSet dataSet, Model model,                     LossFunction loss, Optimizer opt) {        this.lossFunction = loss;        this.optimizer = opt;    }}

public class SequentialBlockextendsBlock {    private List<Layer> layers = new ArrayList<>();        public SequentialBlock addLayer(Layer layer){        layers.add(layer);        returnthis;  // 支持链式调用    }        @Override    public Variable blockForward(Variable... inputs){        Variable output = inputs[0];        for (Layer layer : layers) {            output = layer.layerForward(output);  // 逐层前向传播        }        return output;    }}

// 优化器策略public interface Optimizer {    voidstep(Map<String, Variable> parameters);}
public class SgdOptimizerimplementsOptimizer {    publicvoidstep(Map<String, Variable> parameters){        // SGD优化策略    }}
public class AdamOptimizerimplementsOptimizer {    publicvoidstep(Map<String, Variable> parameters){        // Adam优化策略    }}

public class TrainingMonitor {    private List<TrainingListener> listeners = new ArrayList<>();        public voidaddListener(TrainingListener listener){        listeners.add(listener);    }        public voidnotifyEpochComplete(int epoch, float loss, float accuracy){        for (TrainingListener listener : listeners) {            listener.onEpochComplete(epoch, loss, accuracy);        }    }}

public class NdArrayCpuimplementsNdArray {    private float[] data;    private Shape shape;    private boolean isView = false;  // 标记是否为视图（共享数据）        // 避免不必要的数据拷贝    public NdArray reshape(Shape newShape){        if (newShape.size() != shape.size()) {            throw new IllegalArgumentException("Shape size mismatch");        }                NdArrayCpu result = new NdArrayCpu();        result.data = this.data;      // 共享底层数据        result.shape = newShape;        result.isView = true;         // 标记为视图        return result;    }}

public class Variable {    publicvoidunChainBackward(){        // 切断计算图，释放不需要的引用        Function creatorFunc = creator;        if (creatorFunc != null) {            Variable[] xs = creatorFunc.getInputs();            unChain();  // 清除当前节点的creator引用            for (Variable x : xs) {                x.unChainBackward();  // 递归切断            }        }    }}

public NdArray dot(NdArray other){    if (!isMatrix() || !other.isMatrix()) {        thrownew IllegalArgumentException(            String.format("Matrix multiplication requires 2D arrays. " +                         "Got shapes: %s and %s",                          this.getShape(), other.getShape()));    }        if (this.getShape().get(1) != other.getShape().get(0)) {        thrownew IllegalArgumentException(            String.format("Matrix dimensions mismatch for multiplication: " +                         "(%d x %d) * (%d x %d)",                          this.getShape().get(0), this.getShape().get(1),                         other.getShape().get(0), other.getShape().get(1)));    }        return dotImpl(other);}

public class Variable {    private String name;  // 变量名称，便于调试        @Override    public String toString(){        return String.format("Variable(name='%s', shape=%s, requireGrad=%s)",                            name, value.getShape(), requireGrad);    }}

📈 训练进度展示Epoch 1/50:  Loss=2.156, Accuracy=23.4% ████▒▒▒▒▒▒Epoch 10/50: Loss=0.845, Accuracy=75.6% ████████▒▒Epoch 25/50: Loss=0.234, Accuracy=89.3% █████████▒Epoch 50/50: Loss=0.089, Accuracy=97.3% ██████████
🎯 最终测试准确率: 97.3%

public class IntelligentCustomerService {    public staticvoidmain(String[] args){        // 1. 创建RAG系统        RAGSystem ragSystem = new RAGSystem();                // 2. 加载企业知识库        List<Document> knowledgeBase = Arrays.asList(            new Document("产品说明书", loadProductDocs()),            new Document("常见问题", loadFAQs()),            new Document("服务流程", loadServiceProcesses())        );                // 3. 创建智能客服Agent        AdvancedAgent customerServiceAgent = new AdvancedAgent(            "智能客服小助手",             "你是一个专业的客服助手，能够基于企业知识库回答用户问题"        );                // 4. 集成RAG能力        customerServiceAgent.addTool("knowledge_search",             (query) -> ragSystem.generateAnswer(query, knowledgeBase));                // 5. 处理客户咨询        Scanner scanner = new Scanner(System.in);        System.out.println("智能客服系统启动，请输入您的问题：");                while (true) {            String userInput = scanner.nextLine();            if ("退出".equals(userInput)) break;                        AgentResponse response = customerServiceAgent.processMessage(userInput);            System.out.println("客服助手：" + response.getMessage());        }    }}

public class StockPredictionSystem {    public staticvoidmain(String[] args){        // 1. 构建LSTM网络        SequentialBlock lstm = new SequentialBlock("stock_predictor");        lstm.addLayer(new LstmLayer("lstm1", 10, 50))      // 输入10个特征，隐藏50维            .addLayer(new DropoutLayer("dropout1", 0.2f))            .addLayer(new LstmLayer("lstm2", 50, 25))       // 第二层LSTM            .addLayer(new DropoutLayer("dropout2", 0.2f))            .addLayer(new LinearLayer("output", 25, 1))     // 输出层预测价格            .addLayer(new LinearLayer("final", 1, 1));      // 最终输出                Model model = new Model("stock_predictor", lstm);                // 2. 准备时间序列数据        TimeSeriesDataSet stockData = new TimeSeriesDataSet(            loadStockData("AAPL", "2020-01-01", "2023-12-31"),            sequenceLength: 30,  // 使用30天的历史数据预测下一天            features: Arrays.asList("open", "high", "low", "close", "volume",                                    "ma5", "ma20", "rsi", "macd", "volume_ma")        );                // 3. 训练模型        Trainer trainer = new Trainer(100, new TrainingMonitor(),                                     new MSEEvaluator());        trainer.init(stockData, model,                     new MeanSquaredErrorLoss(),                     new AdamOptimizer(0.001f));        trainer.train(true);                // 4. 预测未来价格        Variable prediction = model.forward(stockData.getLastSequence());        float predictedPrice = prediction.getValue().getNumber().floatValue();                System.out.printf("预测明日股价: $%.2f\n", predictedPrice);    }}

public class NdArrayPool {    privatestaticfinal Map<Shape, Queue<NdArrayCpu>> pool = new ConcurrentHashMap<>();        public static NdArrayCpu acquire(Shape shape){        Queue<NdArrayCpu> queue = pool.computeIfAbsent(shape,             k -> new ConcurrentLinkedQueue<>());                NdArrayCpu array = queue.poll();        if (array == null) {            array = new NdArrayCpu(shape);        }        returnarray;    }        public staticvoidrelease(NdArrayCpu array){        // 清零数据并返回池中        Arrays.fill(array.getData(), 0.0f);        Queue<NdArrayCpu> queue = pool.get(array.getShape());        if (queue != null) {            queue.offer(array);        }    }}

public class BatchProcessor {    public static NdArray batchMatMul(List<NdArray> matrices1,                                      List<NdArray> matrices2) {        // 将多个矩阵乘法合并为一次批量操作        NdArray batch1 = NdArray.stack(matrices1, axis: 0);        NdArray batch2 = NdArray.stack(matrices2, axis: 0);                return batch1.batchDot(batch2);  // 批量矩阵乘法，充分利用并行性    }}

// ✅ 好的做法：层次清晰，易于理解和调试public class GoodModelDesign {    public Model createModel(){        // 特征提取器        Block featureExtractor = new SequentialBlock("feature_extractor")            .addLayer(new LinearLayer("fe1", 784, 512))            .addLayer(new BatchNormalizationLayer("bn1", 512))            .addLayer(new ReluLayer("relu1"))            .addLayer(new DropoutLayer("dropout1", 0.3f));                // 分类器        Block classifier = new SequentialBlock("classifier")            .addLayer(new LinearLayer("cls1", 512, 256))            .addLayer(new ReluLayer("relu2"))            .addLayer(new LinearLayer("cls2", 256, 10))            .addLayer(new SoftmaxLayer("softmax"));                // 组合模型        SequentialBlock fullModel = new SequentialBlock("full_model")            .addBlock(featureExtractor)            .addBlock(classifier);                returnnew Model("mnist_advanced", fullModel);    }}
// ❌ 不好的做法：所有层混在一起，难以理解和修改public class BadModelDesign {    public Model createModel(){        SequentialBlock model = new SequentialBlock("model");        model.addLayer(new LinearLayer("l1", 784, 512))             .addLayer(new BatchNormalizationLayer("b1", 512))             .addLayer(new ReluLayer("r1"))             .addLayer(new DropoutLayer("d1", 0.3f))             .addLayer(new LinearLayer("l2", 512, 256))             .addLayer(new ReluLayer("r2"))             .addLayer(new LinearLayer("l3", 256, 10))             .addLayer(new SoftmaxLayer("s1"));                returnnew Model("mnist_bad", model);    }}

public class TrainingBestPractices {    public voidtrainModel(){        // ✅ 使用学习率调度        LearningRateScheduler scheduler = new CosineAnnealingScheduler(            initialLR: 0.01f, minLR: 0.001f, maxEpochs: 100);                // ✅ 使用早停机制        EarlyStopping earlyStopping = new EarlyStopping(            patience: 10, minDelta: 0.001f);                // ✅ 使用检查点保存        ModelCheckpoint checkpoint = new ModelCheckpoint(            "best_model.json", saveOnlyBest: true);                Trainer trainer = new Trainer(100, new TrainingMonitor(),                                     new AccuracyEvaluator());        trainer.addCallback(scheduler)               .addCallback(earlyStopping)               .addCallback(checkpoint);                trainer.train(true);    }}

// 计划支持GPU加速public interface NdArray {    NdArray toGPU();         // 数据迁移到GPU    NdArray toCPU();         // 数据迁移回CPU    DeviceType getDevice();  // 获取当前设备类型}
// 支持分布式训练public class DistributedTrainerextendsTrainer {    private List<TrainingNode> nodes;        public voiddistributedTrain(){        // AllReduce梯度聚合        // 参数同步        // 负载均衡    }}

public class ModelQuantization {    public Model quantizeToInt8(Model model){        // 将Float32模型量化为Int8        // 减少模型大小和推理时间    }        public Model pruneModel(Model model, float sparsity){        // 模型剪枝，移除不重要的连接        // 保持精度的同时减少计算量    }}

// 计算机视觉模型public class VisionModels {    public static Model createResNet50(){ /* ... */ }    public static Model createViT(){ /* ... */ }    public static Model createYOLOv8(){ /* ... */ }}
// 自然语言处理模型public classNLPModels {    public static Model createBERT(){ /* ... */ }    public static Model createT5(){ /* ... */ }    public static Model createLLaMA(){ /* ... */ }}

# TinyAI CLI工具tinyai create-project my-ai-app --template=chatbottinyai train --config=training.yaml --data=dataset/tinyai deploy --model=best_model.json --endpoint=/api/predicttinyai benchmark --model=my_model.json --dataset=test_data/

// 支持第三方插件public interface TinyAIPlugin {    String getName();    String getVersion();    voidinitialize(TinyAIContext context);    voidshutdown();}
// 插件管理器public class PluginManager {    publicvoidloadPlugin(String pluginPath){ /* ... */ }    publicvoidunloadPlugin(String pluginName){ /* ... */ }    public List<TinyAIPlugin> getLoadedPlugins(){ /* ... */ }}

public class InteractiveLearning {    public voiddemonstrateBackpropagation(){        // 可视化反向传播过程        Variable x = new Variable(NdArray.of(2.0f), "输入x");        Variable w = new Variable(NdArray.of(3.0f), "权重w");        Variable y = x.mul(w).add(x.squ());  // y = w*x + x²                // 显示计算图        ComputationGraphVisualizer.display(y);                // 逐步展示反向传播        y.backward();        StepByStepVisualizer.showBackpropagation(y);    }}

Level 1: 基础概念 → 多维数组、基本运算Level 2: 自动微分 → 计算图、梯度计算Level 3: 神经网络 → 层、块、网络构建Level 4: 训练过程 → 优化器、损失函数Level 5: 高级模型 → Transformer、LSTMLevel 6: 智能体系统 → RAG、多智能体协作