AI写作在AI人工智能领域的发展机遇

    graph TD  
    A[规则引擎时代] --> B[基于模板的生成]  
    B --> C[统计模型时代]  
    C --> D[隐马尔可夫模型(HMM)]  
    C --> E[统计机器翻译(SMT)]  
    D --> F[深度学习时代]  
    E --> F  
    F --> G[循环神经网络(RNN)]  
    F --> H[长短期记忆网络(LSTM)]  
    G --> I[Transformer架构(2017)]  
    H --> I  
    I --> J[GPT系列(2018-2023)]  
    I --> K[PaLM/LLaMA系列]  
    
    
    mermaid
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/9wgarBOSHehZVM7zn2PJ6dKfAYIX.png)

    import torch  
    import torch.nn as nn  
    import math  
    
    class MultiHeadAttention(nn.Module):  
    def __init__(self, d_model, n_heads):  
        super().__init__()  
        self.d_model = d_model  
        self.n_heads = n_heads  
        self.head_dim = d_model // n_heads  
        
        self.q_proj = nn.Linear(d_model, d_model)  
        self.k_proj = nn.Linear(d_model, d_model)  
        self.v_proj = nn.Linear(d_model, d_model)  
        self.out_proj = nn.Linear(d_model, d_model)  
    
    def forward(self, q, k, v, mask=None):  
        batch_size = q.size(0)  
        # 投影并拆分多头：[batch_size, seq_len, d_model] -> [batch_size, n_heads, seq_len, head_dim]  
        q = self.q_proj(q).view(batch_size, -1, self.n_heads, self.head_dim).transpose(1, 2)  
        k = self.k_proj(k).view(batch_size, -1, self.n_heads, self.head_dim).transpose(1, 2)  
        v = self.v_proj(v).view(batch_size, -1, self.n_heads, self.head_dim).transpose(1, 2)  
        
        # 计算注意力分数：[batch_size, n_heads, seq_len, seq_len]  
        scores = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)  
        if mask is not None:  
            scores = scores.masked_fill(mask == 0, -1e9)  
        attention = nn.functional.softmax(scores, dim=-1)  
        
        # 加权求和：[batch_size, n_heads, seq_len, head_dim] -> [batch_size, seq_len, d_model]  
        out = (attention @ v).transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)  
        return self.out_proj(out)  
    
    class TransformerEncoderLayer(nn.Module):  
    def __init__(self, d_model, n_heads, dim_feedforward=2048):  
        super().__init__()  
        self.self_attn = MultiHeadAttention(d_model, n_heads)  
        self.feed_forward = nn.Sequential(  
            nn.Linear(d_model, dim_feedforward),  
            nn.ReLU(),  
            nn.Linear(dim_feedforward, d_model)  
        )  
        self.norm1 = nn.LayerNorm(d_model)  
        self.norm2 = nn.LayerNorm(d_model)  
        self.dropout = nn.Dropout(0.1)  
    
    def forward(self, src, mask=None):  
        src2 = self.self_attn(src, src, src, mask)  
        src = src + self.dropout(src2)  
        src = self.norm1(src)  
        src2 = self.feed_forward(src)  
        src = src + self.dropout(src2)  
        src = self.norm2(src)  
        return src  
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/LCSvP7WqMyAshwcJ5XOxQ96zdg4e.png)

    pip install torch==2.0.1 transformers==4.28.1 datasets==2.13.1 flask==2.3.2  
    
    
    bash

    from datasets import load_dataset  
    from transformers import GPT2Tokenizer  
    
    dataset = load_dataset("csv", data_files="product_descriptions.csv")  
    tokenizer = GPT2Tokenizer.from_pretrained("gpt2", padding_side="left")  
    tokenizer.pad_token = tokenizer.eos_token  # 设置填充令牌  
    
    def preprocess_function(examples):  
    prompts = ["生成产品描述：" + title for title in examples["title"]]  
    targets = examples["description"]  
    inputs = tokenizer(prompts, max_length=128, truncation=True, padding="max_length", return_tensors="pt")  
    with tokenizer.as_target_tokenizer():  
        labels = tokenizer(targets, max_length=512, truncation=True, padding="max_length", return_tensors="pt")  
    inputs["labels"] = labels["input_ids"]  
    return inputs  
    
    tokenized_dataset = dataset.map(preprocess_function, batched=True)  
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/1fhBEJPqSVIl2jTUmDsYaOpctvM5.png)

    from transformers import GPT2LMHeadModel, TrainingArguments, Trainer  
    
    model = GPT2LMHeadModel.from_pretrained("gpt2")  
    training_args = TrainingArguments(  
    output_dir="gpt2-product-designer",  
    overwrite_output_dir=True,  
    num_train_epochs=3,  
    per_device_train_batch_size=8,  
    per_device_eval_batch_size=8,  
    warmup_steps=500,  
    weight_decay=0.01,  
    logging_dir="./logs",  
    save_strategy="no",  
    )  
    
    trainer = Trainer(  
    model=model,  
    args=training_args,  
    train_dataset=tokenized_dataset["train"],  
    eval_dataset=tokenized_dataset["test"],  
    )  
    
    trainer.train()  
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/igULoW3qVzZhprfKJ0Aws5cSn6Md.png)

    from flask import Flask, request, jsonify  
    
    app = Flask(__name__)  
    
    @app.route("/generate", methods=["POST"])  
    def generate_description():  
    title = request.json["title"]  
    prompt = f"生成产品描述：{title}"  
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")  
    outputs = model.generate(  
        inputs["input_ids"],  
        max_length=512,  
        num_beams=5,  
        temperature=0.7,  
        repetition_penalty=1.2,  
    )  
    description = tokenizer.decode(outputs[0], skip_special_tokens=True)  
    return jsonify({"description": description})  
    
    if __name__ == "__main__":  
    app.run(host="0.0.0.0", port=5000)  
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/qUGyo4rYlKxIfvVAguDwNa6HR2BJ.png)

 * 加入示例提示：

    输入："无线降噪耳机"  
    输出："【XX品牌】主动降噪蓝牙耳机，搭载ANC芯片，深度消除环境噪音。13mm大动圈单元带来Hi-Fi音质，单次续航6小时，搭配充电盒总续航24小时。支持蓝牙5.3快速连接，智能触控操作，佩戴舒适无压力。"  
    
    

 * `temperature=0.7`：平衡创造性与确定性
 * `num_beams=5`：集束搜索提升输出质量