PyTorch实战:Seq2seq模型完成机器翻译任务(详细注释版)

简要

本博客以《PyTorch自然语言处理入门与实战》第九章的Seq2seq模型处理英译中翻译任务作为基础,给出自己的理解及代码的详细注释。

数据预处理

在数据预处理中,用到了train.tags.zh-en.en和train.tags.zh-en.zh两个文件,第一个文件包含英译中任务的英文内容,第二个文件包含英译中任务的中文内容(数据集下载链接)。

两个文件来自IWSLT 2015数据集(提供了一些TED演讲的多种语言和英语之间的翻译)。

代码如下:

# step1: 从文件所包含的众多信息中筛选出演讲内容
fen = open('train.tags.zh-en.en', encoding='utf8')  # 演讲稿的英文内容
fzh = open('train.tags.zh-en.zh', encoding='utf8')  # 演讲稿的中文内容
en_zh = []
while True:
    lz = fzh.readline()  # 每次执行依次读取中文文件中的一行内容
    le = fen.readline()  #  每次执行依次读取英文文件中的一行内容
    # 判断是否读完文件
    if not lz:
        assert not le  # 如果读完,两个文件的结果都应该是空行  not lz == not le == True
        break
    lz, le = lz.strip(), le.strip()  # 返回删除首尾空白字符(换行符也能删除)的字符串副本
    # 筛选出需要的演讲内容部分
    if lz.startswith(''):
        assert le.startswith('')
        lz = fzh.readline()
        le = fen.readline()
        # 关键词部分
        assert lz.startswith('')
        assert le.startswith('')
        lz = fzh.readline()
        le = fen.readline()
        # 演讲人部分
        assert lz.startswith('')
        assert le.startswith('')
        lz = fzh.readline()
        le = fen.readline()
        # 演讲 ID
        assert lz.startswith('')
        assert le.startswith('')
        lz = fzh.readline()
        le = fen.readline()
        # 标题部分
        assert lz.startswith(''</span><span class="token punctuation">)</span>
        <span class="token keyword">assert</span> le<span class="token punctuation">.</span>startswith<span class="token punctuation">(</span><span class="token string">'<title>'</span><span class="token punctuation">)</span>
        lz <span class="token operator">=</span> fzh<span class="token punctuation">.</span>readline<span class="token punctuation">(</span><span class="token punctuation">)</span>
        le <span class="token operator">=</span> fen<span class="token punctuation">.</span>readline<span class="token punctuation">(</span><span class="token punctuation">)</span>
        <span class="token comment"># 描述部分</span>
        <span class="token keyword">assert</span> lz<span class="token punctuation">.</span>startswith<span class="token punctuation">(</span><span class="token string">'<description>'</span><span class="token punctuation">)</span>
        <span class="token keyword">assert</span> le<span class="token punctuation">.</span>startswith<span class="token punctuation">(</span><span class="token string">'<description>'</span><span class="token punctuation">)</span>
    <span class="token keyword">else</span><span class="token punctuation">:</span>  <span class="token comment"># 演讲内容部分</span>
        <span class="token keyword">if</span> <span class="token keyword">not</span> lz<span class="token punctuation">:</span>
            <span class="token keyword">assert</span> <span class="token keyword">not</span> le
            <span class="token keyword">break</span>
    <span class="token comment"># step2: 定位到演讲内容部分后,进行分词</span>
    <span class="token comment"># 对于中文内容, 我们把每个字当成一个词,因此list(lz)就实现分词</span>
    <span class="token comment"># 对于英文内容,我们把每个单词当成一个词,因此用空格字符“ ”进行分词</span>
        new_le <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
        <span class="token keyword">for</span> w <span class="token keyword">in</span> le<span class="token punctuation">.</span>split<span class="token punctuation">(</span><span class="token string">' '</span><span class="token punctuation">)</span><span class="token punctuation">:</span>  <span class="token comment"># 英文内容按照空格字符进行分词</span>
            <span class="token comment"># 按照空格进行分词后,某些单词后面会跟着标点符号 "." 和 “,”</span>
            w <span class="token operator">=</span> w<span class="token punctuation">.</span>replace<span class="token punctuation">(</span><span class="token string">'.'</span><span class="token punctuation">,</span> <span class="token string">''</span><span class="token punctuation">)</span><span class="token punctuation">.</span>replace<span class="token punctuation">(</span><span class="token string">','</span><span class="token punctuation">,</span> <span class="token string">''</span><span class="token punctuation">)</span>  <span class="token comment"># 去掉跟单词连着的标点符号</span>
            w <span class="token operator">=</span> w<span class="token punctuation">.</span>lower<span class="token punctuation">(</span><span class="token punctuation">)</span>  <span class="token comment"># 统一单词大小写</span>
            <span class="token keyword">if</span> w<span class="token punctuation">:</span>
                new_le<span class="token punctuation">.</span>append<span class="token punctuation">(</span>w<span class="token punctuation">)</span>
        en_zh<span class="token punctuation">.</span>append<span class="token punctuation">(</span><span class="token punctuation">[</span>new_le<span class="token punctuation">,</span> <span class="token builtin">list</span><span class="token punctuation">(</span>lz<span class="token punctuation">)</span><span class="token punctuation">]</span><span class="token punctuation">)</span> 


<span class="token comment"># step3 分别统计中英文内容数据中出现的词的数量</span>
<span class="token keyword">from</span> tqdm <span class="token keyword">import</span> tqdm  <span class="token comment"># 利用进度条直观展示处理进度</span>
en_words <span class="token operator">=</span> <span class="token builtin">set</span><span class="token punctuation">(</span><span class="token punctuation">)</span>  <span class="token comment"># 初始化集合对象  自动去重</span>
zh_words <span class="token operator">=</span> <span class="token builtin">set</span><span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token keyword">for</span> s <span class="token keyword">in</span> tqdm<span class="token punctuation">(</span>en_zh<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">for</span> w <span class="token keyword">in</span> s<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">:</span> <span class="token comment"># 统计英文</span>
        w <span class="token operator">=</span> w<span class="token punctuation">.</span>replace<span class="token punctuation">(</span><span class="token string">'.'</span><span class="token punctuation">,</span> <span class="token string">''</span><span class="token punctuation">)</span><span class="token punctuation">.</span>replace<span class="token punctuation">(</span><span class="token string">','</span><span class="token punctuation">,</span> <span class="token string">''</span><span class="token punctuation">)</span><span class="token punctuation">.</span>lower<span class="token punctuation">(</span><span class="token punctuation">)</span>
        <span class="token keyword">if</span> w<span class="token punctuation">:</span>
            en_words<span class="token punctuation">.</span>add<span class="token punctuation">(</span>w<span class="token punctuation">)</span>
    <span class="token keyword">for</span> w <span class="token keyword">in</span> s<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">:</span> <span class="token comment"># 统计中文</span>
        <span class="token keyword">if</span> w<span class="token punctuation">:</span>
            zh_words<span class="token punctuation">.</span>add<span class="token punctuation">(</span>w<span class="token punctuation">)</span>
            
<span class="token comment"># step4 将集合对象转换为列表对象后,添加三个标识符'<sos>', '<eos>', '<pad>'</span>
<span class="token comment"># sos ---> start of sentence 句子开头  	索引:0</span>
<span class="token comment"># eos ---> end of sentence	 句子结尾	索引:1</span>
<span class="token comment"># pad ---> 填充标识符	索引:2</span>
en_wl <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token string">'<sos>'</span><span class="token punctuation">,</span> <span class="token string">'<eos>'</span><span class="token punctuation">,</span> <span class="token string">'<pad>'</span><span class="token punctuation">]</span> <span class="token operator">+</span> <span class="token builtin">list</span><span class="token punctuation">(</span>en_words<span class="token punctuation">)</span>
zh_wl <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token string">'<sos>'</span><span class="token punctuation">,</span> <span class="token string">'<eos>'</span><span class="token punctuation">,</span> <span class="token string">'<pad>'</span><span class="token punctuation">]</span> <span class="token operator">+</span> <span class="token builtin">list</span><span class="token punctuation">(</span>zh_words<span class="token punctuation">)</span>
pad_id <span class="token operator">=</span> <span class="token number">2</span>

<span class="token comment"># step5 利用字典对象存储词和索引的对应关系</span>
en2id <span class="token operator">=</span> <span class="token punctuation">{</span><span class="token punctuation">}</span>
zh2id <span class="token operator">=</span> <span class="token punctuation">{</span><span class="token punctuation">}</span>
<span class="token keyword">for</span> i<span class="token punctuation">,</span> w <span class="token keyword">in</span> <span class="token builtin">enumerate</span><span class="token punctuation">(</span>en_wl<span class="token punctuation">)</span><span class="token punctuation">:</span>  <span class="token comment"># 遍历枚举类型对象实现此功能</span>
    en2id<span class="token punctuation">[</span>w<span class="token punctuation">]</span> <span class="token operator">=</span> i
<span class="token keyword">for</span> i<span class="token punctuation">,</span> w <span class="token keyword">in</span> <span class="token builtin">enumerate</span><span class="token punctuation">(</span>zh_wl<span class="token punctuation">)</span><span class="token punctuation">:</span>
    zh2id<span class="token punctuation">[</span>w<span class="token punctuation">]</span> <span class="token operator">=</span> i            
    
</code></pre> 
  <h3>运行结果</h3> 
  <p><a href="http://img.e-com-net.com/image/info8/bea31a59818c47eb97e1db6df07b3ab6.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/bea31a59818c47eb97e1db6df07b3ab6.jpg" alt="运行结果" width="650" height="77"></a></p> 
  <h2>随机划分训练集和测试集</h2> 
  <p>使用80%数据作为训练集,20%数据作为测试集,代码如下:</p> 
  <pre><code class="prism language-python"><span class="token keyword">import</span> random

random<span class="token punctuation">.</span>shuffle<span class="token punctuation">(</span>en_zh<span class="token punctuation">)</span>  <span class="token comment"># 随机打乱全部数据</span>
train_num <span class="token operator">=</span> <span class="token builtin">len</span><span class="token punctuation">(</span>en_zh<span class="token punctuation">)</span> <span class="token operator">*</span> <span class="token number">0.8</span>
train_set <span class="token operator">=</span> en_zh<span class="token punctuation">[</span><span class="token punctuation">:</span>train_num<span class="token punctuation">]</span>  <span class="token comment"># 8成用于训练</span>
dev_set <span class="token operator">=</span> en_zh<span class="token punctuation">[</span>train_num<span class="token punctuation">:</span><span class="token punctuation">]</span>  <span class="token comment"># 2成用于测试</span>
</code></pre> 
  <h2>创建训练集和测试集</h2> 
  <p>利用<code>torch.utils.data.dataset</code>和<code>torch.utils.data.DataLoader</code>创建训练集和测试集以及它们的数据加载器</p> 
  <h3>torch.utils.data.dataset(存放中英文内容,为遍历数据做准备)</h3> 
  <pre><code class="prism language-python"><span class="token keyword">import</span> torch

batch_size <span class="token operator">=</span> <span class="token number">16</span>
data_workers <span class="token operator">=</span> <span class="token number">0</span>  <span class="token comment"># 子进程数 </span>

<span class="token keyword">class</span> <span class="token class-name">MyDataSet</span><span class="token punctuation">(</span>torch<span class="token punctuation">.</span>utils<span class="token punctuation">.</span>data<span class="token punctuation">.</span>Dataset<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">def</span> <span class="token function">__init__</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> examples<span class="token punctuation">)</span><span class="token punctuation">:</span>
        self<span class="token punctuation">.</span>examples <span class="token operator">=</span> examples

    <span class="token keyword">def</span> <span class="token function">__len__</span><span class="token punctuation">(</span>self<span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token keyword">return</span> <span class="token builtin">len</span><span class="token punctuation">(</span>self<span class="token punctuation">.</span>examples<span class="token punctuation">)</span>

    <span class="token keyword">def</span> <span class="token function">__getitem__</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> index<span class="token punctuation">)</span><span class="token punctuation">:</span>
        example <span class="token operator">=</span> self<span class="token punctuation">.</span>examples<span class="token punctuation">[</span>index<span class="token punctuation">]</span>
        s1 <span class="token operator">=</span> example<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span>
        s2 <span class="token operator">=</span> example<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span>
        l1 <span class="token operator">=</span> <span class="token builtin">len</span><span class="token punctuation">(</span>s1<span class="token punctuation">)</span>
        l2 <span class="token operator">=</span> <span class="token builtin">len</span><span class="token punctuation">(</span>s2<span class="token punctuation">)</span>
        <span class="token keyword">return</span> s1<span class="token punctuation">,</span> l1<span class="token punctuation">,</span> s2<span class="token punctuation">,</span> l2<span class="token punctuation">,</span> index  <span class="token comment"># 英文句子  英文句子长度  中文句子  中文句子长度 当前数据在数据集中的索引</span>

<span class="token comment"># batch_size = 16 是全局变量</span>
<span class="token keyword">def</span> <span class="token function">the_collate_fn</span><span class="token punctuation">(</span>batch<span class="token punctuation">)</span><span class="token punctuation">:</span>
    src <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span> <span class="token operator">*</span> batch_size<span class="token punctuation">]</span>  <span class="token comment"># src ---> source 缩写   该任务中 源句子指的是英文句子  # 每个样本的开头都是0(起始标识符的编码)</span>
    tar <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span> <span class="token operator">*</span> batch_size<span class="token punctuation">]</span>  <span class="token comment"># tar ---> target 缩写           目标句子指的是中文句子</span>
    src_max_l <span class="token operator">=</span> <span class="token number">0</span>  <span class="token comment"># 初始化英文句子最大长度  方便计算需要填充的个数</span>
    <span class="token keyword">for</span> b <span class="token keyword">in</span> batch<span class="token punctuation">:</span> <span class="token comment"># 每个batch的数据有五个信息 分别是: 英文句子  英文句子长度  中文句子  中文句子长度 当前数据在数据集中的索引</span>
        src_max_l <span class="token operator">=</span> <span class="token builtin">max</span><span class="token punctuation">(</span>src_max_l<span class="token punctuation">,</span> b<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span>  <span class="token comment"># b[1] 即英文句子的长度</span>
    tar_max_l <span class="token operator">=</span> <span class="token number">0</span>
    <span class="token keyword">for</span> b <span class="token keyword">in</span> batch<span class="token punctuation">:</span>
        tar_max_l <span class="token operator">=</span> <span class="token builtin">max</span><span class="token punctuation">(</span>tar_max_l<span class="token punctuation">,</span> b<span class="token punctuation">[</span><span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">)</span>  <span class="token comment"># b[3] 即中文句子的长度</span>
    <span class="token keyword">for</span> i <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>src_max_l<span class="token punctuation">)</span><span class="token punctuation">:</span>
        l <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
        <span class="token keyword">for</span> x <span class="token keyword">in</span> batch<span class="token punctuation">:</span>
            <span class="token keyword">if</span> i <span class="token operator"><</span> x<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">:</span>
                l<span class="token punctuation">.</span>append<span class="token punctuation">(</span>en2id<span class="token punctuation">[</span>x<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">[</span>i<span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
            <span class="token keyword">else</span><span class="token punctuation">:</span>
                l<span class="token punctuation">.</span>append<span class="token punctuation">(</span>pad_id<span class="token punctuation">)</span>  <span class="token comment"># 如果句子长度小于最大句子长度,进行填充</span>
        src<span class="token punctuation">.</span>append<span class="token punctuation">(</span>l<span class="token punctuation">)</span>
        <span class="token comment"># l记录的是每个句子的第 i 个词  有多少个句子? batch size个,因此len(l) == batch_size == 句子的数量</span>
        <span class="token comment"># src记录的是每个 l  总共多少个l? src_max_l个,因此len(src) == src_max_l == 句子的最大长度</span>
        <span class="token comment"># len(src) == 句子的最大长度    len(src[0]) == 句子的数量</span>
        <span class="token comment"># [len(src), len(src[0])] ==> [src len, batch size]</span>

    <span class="token keyword">for</span> i <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>tar_max_l<span class="token punctuation">)</span><span class="token punctuation">:</span>  <span class="token comment"># 注释参考上面</span>
        l <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
        <span class="token keyword">for</span> x <span class="token keyword">in</span> batch<span class="token punctuation">:</span>
            <span class="token keyword">if</span> i <span class="token operator"><</span> x<span class="token punctuation">[</span><span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">:</span>
                l<span class="token punctuation">.</span>append<span class="token punctuation">(</span>zh2id<span class="token punctuation">[</span>x<span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">]</span><span class="token punctuation">[</span>i<span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
            <span class="token keyword">else</span><span class="token punctuation">:</span>
                l<span class="token punctuation">.</span>append<span class="token punctuation">(</span>pad_id<span class="token punctuation">)</span>  <span class="token comment"># 如果句子长度小于最大句子长度,进行填充</span>
        tar<span class="token punctuation">.</span>append<span class="token punctuation">(</span>l<span class="token punctuation">)</span>
    indexs <span class="token operator">=</span> <span class="token punctuation">[</span>b<span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">]</span> <span class="token keyword">for</span> b <span class="token keyword">in</span> batch<span class="token punctuation">]</span>  <span class="token comment"># b[4] 记录的是 当前数据在数据集中的索引</span>
    src<span class="token punctuation">.</span>append<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span> <span class="token operator">*</span> batch_size<span class="token punctuation">)</span>  <span class="token comment"># 终止标识符的编码为1 所以src和tar在句子的最后把终止符加上</span>
    tar<span class="token punctuation">.</span>append<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span> <span class="token operator">*</span> batch_size<span class="token punctuation">)</span>
    s1 <span class="token operator">=</span> torch<span class="token punctuation">.</span>LongTensor<span class="token punctuation">(</span>src<span class="token punctuation">)</span> 
    s2 <span class="token operator">=</span> torch<span class="token punctuation">.</span>LongTensor<span class="token punctuation">(</span>tar<span class="token punctuation">)</span>
    <span class="token keyword">return</span> s1<span class="token punctuation">,</span> s2<span class="token punctuation">,</span> indexs
</code></pre> 
  <pre><code class="prism language-python"><span class="token comment"># 构建训练集 </span>
train_dataset <span class="token operator">=</span> MyDataSet<span class="token punctuation">(</span>train_set<span class="token punctuation">)</span>
dev_dataset <span class="token operator">=</span> MyDataSet<span class="token punctuation">(</span>dev_set<span class="token punctuation">)</span>
</code></pre> 
  <h3>torch.utils.data.DataLoader</h3> 
  <pre><code class="prism language-python"><span class="token comment"># 定义训练集数据加载器和验证集数据加载器</span>
train_data_loader <span class="token operator">=</span> torch<span class="token punctuation">.</span>utils<span class="token punctuation">.</span>data<span class="token punctuation">.</span>DataLoader<span class="token punctuation">(</span>
    train_dataset<span class="token punctuation">,</span>
    batch_size<span class="token operator">=</span>batch_size<span class="token punctuation">,</span>
    shuffle<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">,</span>
    num_workers<span class="token operator">=</span>data_workers<span class="token punctuation">,</span>
    collate_fn<span class="token operator">=</span>the_collate_fn<span class="token punctuation">,</span>
<span class="token punctuation">)</span>

dev_data_loader <span class="token operator">=</span> torch<span class="token punctuation">.</span>utils<span class="token punctuation">.</span>data<span class="token punctuation">.</span>DataLoader<span class="token punctuation">(</span>
    dev_dataset<span class="token punctuation">,</span>
    batch_size<span class="token operator">=</span>batch_size<span class="token punctuation">,</span>
    shuffle<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">,</span>
    num_workers<span class="token operator">=</span>data_workers<span class="token punctuation">,</span>
    collate_fn<span class="token operator">=</span>the_collate_fn<span class="token punctuation">,</span>
<span class="token punctuation">)</span>
</code></pre> 
  <h2>定义Seq2Seq模型</h2> 
  <p>代码如下:</p> 
  <pre><code class="prism language-python"><span class="token keyword">import</span> torch<span class="token punctuation">.</span>nn <span class="token keyword">as</span> nn


<span class="token keyword">class</span> <span class="token class-name">Encoder</span><span class="token punctuation">(</span>nn<span class="token punctuation">.</span>Module<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">def</span> <span class="token function">__init__</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> input_dim<span class="token punctuation">,</span> emb_dim<span class="token punctuation">,</span> hid_dim<span class="token punctuation">,</span> n_layers<span class="token punctuation">,</span> dropout<span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token builtin">super</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">.</span>__init__<span class="token punctuation">(</span><span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>hid_dim <span class="token operator">=</span> hid_dim
        self<span class="token punctuation">.</span>n_layers <span class="token operator">=</span> n_layers
        self<span class="token punctuation">.</span>embedding <span class="token operator">=</span> nn<span class="token punctuation">.</span>Embedding<span class="token punctuation">(</span>input_dim<span class="token punctuation">,</span> emb_dim<span class="token punctuation">)</span>  <span class="token comment"># 词嵌入</span>
        self<span class="token punctuation">.</span>rnn <span class="token operator">=</span> nn<span class="token punctuation">.</span>LSTM<span class="token punctuation">(</span>emb_dim<span class="token punctuation">,</span> hid_dim<span class="token punctuation">,</span> n_layers<span class="token punctuation">,</span> dropout<span class="token operator">=</span>dropout<span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>dropout <span class="token operator">=</span> nn<span class="token punctuation">.</span>Dropout<span class="token punctuation">(</span>dropout<span class="token punctuation">)</span>

    <span class="token keyword">def</span> <span class="token function">forward</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> src<span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token comment"># src = (src len, batch size)</span>
        embedded <span class="token operator">=</span> self<span class="token punctuation">.</span>dropout<span class="token punctuation">(</span>self<span class="token punctuation">.</span>embedding<span class="token punctuation">(</span>src<span class="token punctuation">)</span><span class="token punctuation">)</span>
        <span class="token comment"># embedded = (src len, batch size, emb dim)</span>
        outputs<span class="token punctuation">,</span> <span class="token punctuation">(</span>hidden<span class="token punctuation">,</span> cell<span class="token punctuation">)</span> <span class="token operator">=</span> self<span class="token punctuation">.</span>rnn<span class="token punctuation">(</span>embedded<span class="token punctuation">)</span>
        <span class="token comment"># outputs = (src len, batch size, hid dim * n directions)</span>
        <span class="token comment"># hidden = (n layers * n directions, batch size, hid dim)</span>
        <span class="token comment"># cell = (n layers * n directions, batch size, hid dim)</span>
        <span class="token comment"># rnn的输出总是来自顶部的隐藏层</span>
        <span class="token keyword">return</span> hidden<span class="token punctuation">,</span> cell


<span class="token keyword">class</span> <span class="token class-name">Decoder</span><span class="token punctuation">(</span>nn<span class="token punctuation">.</span>Module<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">def</span> <span class="token function">__init__</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> output_dim<span class="token punctuation">,</span> emb_dim<span class="token punctuation">,</span> hid_dim<span class="token punctuation">,</span> n_layers<span class="token punctuation">,</span> dropout<span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token builtin">super</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">.</span>__init__<span class="token punctuation">(</span><span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>output_dim <span class="token operator">=</span> output_dim
        self<span class="token punctuation">.</span>hid_dim <span class="token operator">=</span> hid_dim
        self<span class="token punctuation">.</span>n_layers <span class="token operator">=</span> n_layers
        self<span class="token punctuation">.</span>embedding <span class="token operator">=</span> nn<span class="token punctuation">.</span>Embedding<span class="token punctuation">(</span>output_dim<span class="token punctuation">,</span> emb_dim<span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>rnn <span class="token operator">=</span> nn<span class="token punctuation">.</span>LSTM<span class="token punctuation">(</span>emb_dim<span class="token punctuation">,</span> hid_dim<span class="token punctuation">,</span> n_layers<span class="token punctuation">,</span> dropout<span class="token operator">=</span>dropout<span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>fc_out <span class="token operator">=</span> nn<span class="token punctuation">.</span>Linear<span class="token punctuation">(</span>hid_dim<span class="token punctuation">,</span> output_dim<span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>dropout <span class="token operator">=</span> nn<span class="token punctuation">.</span>Dropout<span class="token punctuation">(</span>dropout<span class="token punctuation">)</span>

    <span class="token keyword">def</span> <span class="token function">forward</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> <span class="token builtin">input</span><span class="token punctuation">,</span> hidden<span class="token punctuation">,</span> cell<span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token comment"># 各输入的形状</span>
        <span class="token comment"># input = (batch size)</span>
        <span class="token comment"># hidden = (n layers * n directions, batch size, hid dim)</span>
        <span class="token comment"># cell = (n layers * n directions, batch size, hid dim)</span>

        <span class="token comment"># LSTM是单向的  ==> n directions == 1</span>
        <span class="token comment"># hidden = (n layers, batch size, hid dim)</span>
        <span class="token comment"># cell = (n layers, batch size, hid dim)</span>

        <span class="token builtin">input</span> <span class="token operator">=</span> <span class="token builtin">input</span><span class="token punctuation">.</span>unsqueeze<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">)</span>  <span class="token comment"># (batch size)  --> [1, batch size)</span>

        embedded <span class="token operator">=</span> self<span class="token punctuation">.</span>dropout<span class="token punctuation">(</span>self<span class="token punctuation">.</span>embedding<span class="token punctuation">(</span><span class="token builtin">input</span><span class="token punctuation">)</span><span class="token punctuation">)</span>  <span class="token comment"># (1, batch size, emb dim)</span>

        output<span class="token punctuation">,</span> <span class="token punctuation">(</span>hidden<span class="token punctuation">,</span> cell<span class="token punctuation">)</span> <span class="token operator">=</span> self<span class="token punctuation">.</span>rnn<span class="token punctuation">(</span>embedded<span class="token punctuation">,</span> <span class="token punctuation">(</span>hidden<span class="token punctuation">,</span> cell<span class="token punctuation">)</span><span class="token punctuation">)</span>
        <span class="token comment"># LSTM理论上的输出形状</span>
        <span class="token comment"># output = (seq len, batch size, hid dim * n directions)</span>
        <span class="token comment"># hidden = (n layers * n directions, batch size, hid dim)</span>
        <span class="token comment"># cell = (n layers * n directions, batch size, hid dim)</span>

        <span class="token comment"># 解码器中的序列长度 seq len == 1</span>
        <span class="token comment"># 解码器的LSTM是单向的 n directions == 1 则实际上</span>
        <span class="token comment"># output = (1, batch size, hid dim)</span>
        <span class="token comment"># hidden = (n layers, batch size, hid dim)</span>
        <span class="token comment"># cell = (n layers, batch size, hid dim)</span>

        prediction <span class="token operator">=</span> self<span class="token punctuation">.</span>fc_out<span class="token punctuation">(</span>output<span class="token punctuation">.</span>squeeze<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">)</span><span class="token punctuation">)</span>

        <span class="token comment"># prediction = (batch size, output dim)</span>

        <span class="token keyword">return</span> prediction<span class="token punctuation">,</span> hidden<span class="token punctuation">,</span> cell


<span class="token keyword">class</span> <span class="token class-name">Seq2Seq</span><span class="token punctuation">(</span>nn<span class="token punctuation">.</span>Module<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">def</span> <span class="token function">__init__</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> input_word_count<span class="token punctuation">,</span> output_word_count<span class="token punctuation">,</span> encode_dim<span class="token punctuation">,</span> decode_dim<span class="token punctuation">,</span> hidden_dim<span class="token punctuation">,</span> n_layers<span class="token punctuation">,</span>
                 encode_dropout<span class="token punctuation">,</span> decode_dropout<span class="token punctuation">,</span> device<span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token triple-quoted-string string">"""

        :param input_word_count:    英文词表的长度     34737
        :param output_word_count:   中文词表的长度     4015
        :param encode_dim:          编码器的词嵌入维度
        :param decode_dim:          解码器的词嵌入维度
        :param hidden_dim:          LSTM的隐藏层维度
        :param n_layers:            采用n层LSTM
        :param encode_dropout:      编码器的dropout概率
        :param decode_dropout:      编码器的dropout概率
        :param device:              cuda / cpu
        """</span>
        <span class="token builtin">super</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">.</span>__init__<span class="token punctuation">(</span><span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>encoder <span class="token operator">=</span> Encoder<span class="token punctuation">(</span>input_word_count<span class="token punctuation">,</span> encode_dim<span class="token punctuation">,</span> hidden_dim<span class="token punctuation">,</span> n_layers<span class="token punctuation">,</span> encode_dropout<span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>decoder <span class="token operator">=</span> Decoder<span class="token punctuation">(</span>output_word_count<span class="token punctuation">,</span> decode_dim<span class="token punctuation">,</span> hidden_dim<span class="token punctuation">,</span> n_layers<span class="token punctuation">,</span> decode_dropout<span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>device <span class="token operator">=</span> device

    <span class="token keyword">def</span> <span class="token function">forward</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> src<span class="token punctuation">,</span> trg<span class="token punctuation">,</span> teacher_forcing_ratio<span class="token operator">=</span><span class="token number">0.5</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token comment"># src = (src len, batch size)</span>
        <span class="token comment"># trg = (trg len, batch size)</span>
        <span class="token comment"># teacher_forcing_ratio 定义使用Teacher Forcing的比例</span>
        <span class="token comment"># 例如 if teacher_forcing_ratio is 0.75 we use ground-truth inputs 75% of the time</span>
        batch_size <span class="token operator">=</span> trg<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span>
        trg_len <span class="token operator">=</span> trg<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span>
        trg_vocab_size <span class="token operator">=</span> self<span class="token punctuation">.</span>decoder<span class="token punctuation">.</span>output_dim  <span class="token comment"># 实际上就是中文词表的长度</span>
        <span class="token comment"># 初始化保存解码器输出的Tensor</span>
        outputs <span class="token operator">=</span> torch<span class="token punctuation">.</span>zeros<span class="token punctuation">(</span>trg_len<span class="token punctuation">,</span> batch_size<span class="token punctuation">,</span> trg_vocab_size<span class="token punctuation">)</span><span class="token punctuation">.</span>to<span class="token punctuation">(</span>self<span class="token punctuation">.</span>device<span class="token punctuation">)</span>

        <span class="token comment"># 编码器的隐藏层输出将作为i解码器的第一个隐藏层输入</span>
        hidden<span class="token punctuation">,</span> cell <span class="token operator">=</span> self<span class="token punctuation">.</span>encoder<span class="token punctuation">(</span>src<span class="token punctuation">)</span>

        <span class="token comment"># 解码器的第一个输入应该是起始标识符<sos></span>
        <span class="token builtin">input</span> <span class="token operator">=</span> trg<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token punctuation">:</span><span class="token punctuation">]</span>  <span class="token comment"># 取trg的第“0”行所有列  “0”指的是索引</span>
        <span class="token comment"># 从the_collate_fn函数中可以看出trg的第“0”行全是0,也就是起始标识符对应的ID</span>

        <span class="token keyword">for</span> t <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> trg_len<span class="token punctuation">)</span><span class="token punctuation">:</span> <span class="token comment"># 从 trg的第"1"行开始遍历</span>
            <span class="token comment"># 解码器的输入包括:起始标识符的词嵌入input; 编码器输出的 hidden and cell states</span>
            <span class="token comment"># 解码器的输出包括:输出张量(predictions) and new hidden and cell states</span>
            output<span class="token punctuation">,</span> hidden<span class="token punctuation">,</span> cell <span class="token operator">=</span> self<span class="token punctuation">.</span>decoder<span class="token punctuation">(</span><span class="token builtin">input</span><span class="token punctuation">,</span> hidden<span class="token punctuation">,</span> cell<span class="token punctuation">)</span>

            <span class="token comment"># 保存每次预测结果于outputs</span>
            <span class="token comment"># outputs (trg_len, batch_size, trg_vocab_size)</span>
            <span class="token comment"># output  (batch size, trg_vocab_size)</span>
            outputs<span class="token punctuation">[</span>t<span class="token punctuation">]</span> <span class="token operator">=</span> output

            <span class="token comment"># 随机决定是否使用Teacher Forcing</span>
            teacher_force <span class="token operator">=</span> random<span class="token punctuation">.</span>random<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token operator"><</span> teacher_forcing_ratio

            <span class="token comment"># output  (batch size, trg_vocab_size)  沿dim=1取最大值索引</span>
            top1 <span class="token operator">=</span> output<span class="token punctuation">.</span>argmax<span class="token punctuation">(</span>dim<span class="token operator">=</span><span class="token number">1</span><span class="token punctuation">)</span>  <span class="token comment"># (batch size, )</span>

            <span class="token comment"># if teacher forcing, 以真实值作为下一个输入 否则 使用预测值</span>
            <span class="token builtin">input</span> <span class="token operator">=</span> trg<span class="token punctuation">[</span>t<span class="token punctuation">]</span> <span class="token keyword">if</span> teacher_force <span class="token keyword">else</span> top1

        <span class="token keyword">return</span> outputs


source_word_count <span class="token operator">=</span> <span class="token builtin">len</span><span class="token punctuation">(</span>en_wl<span class="token punctuation">)</span>  <span class="token comment"># 英文词表的长度     34737</span>
target_word_count <span class="token operator">=</span> <span class="token builtin">len</span><span class="token punctuation">(</span>zh_wl<span class="token punctuation">)</span>  <span class="token comment"># 中文词表的长度     4015</span>
encode_dim <span class="token operator">=</span> <span class="token number">256</span>    <span class="token comment"># 编码器的词嵌入维度</span>
decode_dim <span class="token operator">=</span> <span class="token number">256</span>    <span class="token comment"># 解码器的词嵌入维度</span>
hidden_dim <span class="token operator">=</span> <span class="token number">512</span>    <span class="token comment"># LSTM的隐藏层维度</span>
n_layers <span class="token operator">=</span> <span class="token number">2</span>        <span class="token comment"># 采用n层LSTM</span>
encode_dropout <span class="token operator">=</span> <span class="token number">0.5</span>    <span class="token comment"># 编码器的dropout概率</span>
decode_dropout <span class="token operator">=</span> <span class="token number">0.5</span>    <span class="token comment"># 编码器的dropout概率</span>
device <span class="token operator">=</span> torch<span class="token punctuation">.</span>device<span class="token punctuation">(</span><span class="token string">'cuda'</span><span class="token punctuation">)</span> <span class="token keyword">if</span> torch<span class="token punctuation">.</span>cuda<span class="token punctuation">.</span>is_available<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">else</span> torch<span class="token punctuation">.</span>device<span class="token punctuation">(</span><span class="token string">'cpu'</span><span class="token punctuation">)</span>  <span class="token comment"># GPU可用 用GPU</span>
<span class="token comment"># Seq2Seq模型实例化</span>
model <span class="token operator">=</span> Seq2Seq<span class="token punctuation">(</span>source_word_count<span class="token punctuation">,</span> target_word_count<span class="token punctuation">,</span> encode_dim<span class="token punctuation">,</span> decode_dim<span class="token punctuation">,</span> hidden_dim<span class="token punctuation">,</span> n_layers<span class="token punctuation">,</span> encode_dropout<span class="token punctuation">,</span>
                decode_dropout<span class="token punctuation">,</span> device<span class="token punctuation">)</span><span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span>

<span class="token comment"># 初始化模型参数</span>
<span class="token keyword">def</span> <span class="token function">init_weights</span><span class="token punctuation">(</span>m<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">for</span> name<span class="token punctuation">,</span> param <span class="token keyword">in</span> m<span class="token punctuation">.</span>named_parameters<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
        nn<span class="token punctuation">.</span>init<span class="token punctuation">.</span>uniform_<span class="token punctuation">(</span>param<span class="token punctuation">.</span>data<span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">0.08</span><span class="token punctuation">,</span> <span class="token number">0.08</span><span class="token punctuation">)</span>

model<span class="token punctuation">.</span><span class="token builtin">apply</span><span class="token punctuation">(</span>init_weights<span class="token punctuation">)</span>  

<span class="token comment"># 统计Seq2Seq模型中可训练的参数个数</span>
<span class="token keyword">def</span> <span class="token function">count_parameters</span><span class="token punctuation">(</span>model<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">return</span> <span class="token builtin">sum</span><span class="token punctuation">(</span>p<span class="token punctuation">.</span>numel<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">for</span> p <span class="token keyword">in</span> model<span class="token punctuation">.</span>parameters<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">if</span> p<span class="token punctuation">.</span>requires_grad<span class="token punctuation">)</span>

<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string-interpolation"><span class="token string">f'The model has </span><span class="token interpolation"><span class="token punctuation">{</span>count_parameters<span class="token punctuation">(</span>model<span class="token punctuation">)</span><span class="token punctuation">:</span><span class="token format-spec">,</span><span class="token punctuation">}</span></span><span class="token string"> trainable parameters'</span></span><span class="token punctuation">)</span>

<span class="token keyword">import</span> torch<span class="token punctuation">.</span>optim <span class="token keyword">as</span> optim

<span class="token comment"># 定义优化器</span>
optimizer <span class="token operator">=</span> optim<span class="token punctuation">.</span>Adam<span class="token punctuation">(</span>model<span class="token punctuation">.</span>parameters<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>

<span class="token comment"># 定义损失函数</span>
criterion <span class="token operator">=</span> nn<span class="token punctuation">.</span>CrossEntropyLoss<span class="token punctuation">(</span>ignore_index<span class="token operator">=</span>pad_id<span class="token punctuation">)</span><span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span>  <span class="token comment"># 忽略填充标识符的索引</span>
</code></pre> 
  <h2>模型的训练和评估</h2> 
  <pre><code class="prism language-python"><span class="token comment"># 训练策略</span>
<span class="token keyword">def</span> <span class="token function">train</span><span class="token punctuation">(</span>model<span class="token punctuation">,</span> iterator<span class="token punctuation">,</span> optimizer<span class="token punctuation">,</span> criterion<span class="token punctuation">,</span> clip<span class="token punctuation">)</span><span class="token punctuation">:</span>
    model<span class="token punctuation">.</span>train<span class="token punctuation">(</span><span class="token punctuation">)</span>  <span class="token comment"># 切换到训练模式</span>
    epoch_loss <span class="token operator">=</span> <span class="token number">0</span>

    <span class="token keyword">for</span> i<span class="token punctuation">,</span> batch <span class="token keyword">in</span> <span class="token builtin">enumerate</span><span class="token punctuation">(</span>iterator<span class="token punctuation">)</span><span class="token punctuation">:</span>
        src <span class="token operator">=</span> batch<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span>
        trg <span class="token operator">=</span> batch<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span>

        optimizer<span class="token punctuation">.</span>zero_grad<span class="token punctuation">(</span><span class="token punctuation">)</span>  <span class="token comment"># 梯度清零</span>

        output <span class="token operator">=</span> model<span class="token punctuation">(</span>src<span class="token punctuation">,</span> trg<span class="token punctuation">)</span>  <span class="token comment"># 前向传播</span>

        <span class="token comment"># trg = [trg len, batch size]</span>
        <span class="token comment"># output = [trg len, batch size, output dim]</span>

        output_dim <span class="token operator">=</span> output<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">]</span>

        output <span class="token operator">=</span> output<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">:</span><span class="token punctuation">]</span><span class="token punctuation">.</span>view<span class="token punctuation">(</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">,</span> output_dim<span class="token punctuation">)</span>
        trg <span class="token operator">=</span> trg<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">:</span><span class="token punctuation">]</span><span class="token punctuation">.</span>view<span class="token punctuation">(</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span>

        <span class="token comment"># trg = [(trg len - 1) * batch size]</span>
        <span class="token comment"># output = [(trg len - 1) * batch size, output dim]</span>

        loss <span class="token operator">=</span> criterion<span class="token punctuation">(</span>output<span class="token punctuation">,</span> trg<span class="token punctuation">)</span> <span class="token comment"># 计算损失</span>

        loss<span class="token punctuation">.</span>backward<span class="token punctuation">(</span><span class="token punctuation">)</span>  <span class="token comment"># 反向传播</span>

        torch<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>utils<span class="token punctuation">.</span>clip_grad_norm_<span class="token punctuation">(</span>model<span class="token punctuation">.</span>parameters<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span> clip<span class="token punctuation">)</span>

        optimizer<span class="token punctuation">.</span>step<span class="token punctuation">(</span><span class="token punctuation">)</span>  <span class="token comment"># 更新参数</span>

        epoch_loss <span class="token operator">+=</span> loss<span class="token punctuation">.</span>item<span class="token punctuation">(</span><span class="token punctuation">)</span>

    <span class="token keyword">return</span> epoch_loss <span class="token operator">/</span> <span class="token builtin">len</span><span class="token punctuation">(</span>iterator<span class="token punctuation">)</span>

<span class="token comment"># 验证策略</span>
<span class="token keyword">def</span> <span class="token function">evaluate</span><span class="token punctuation">(</span>model<span class="token punctuation">,</span> iterator<span class="token punctuation">,</span> criterion<span class="token punctuation">)</span><span class="token punctuation">:</span>
    model<span class="token punctuation">.</span><span class="token builtin">eval</span><span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token comment"># 切换到验证模式</span>

    epoch_loss <span class="token operator">=</span> <span class="token number">0</span>

    <span class="token keyword">with</span> torch<span class="token punctuation">.</span>no_grad<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">:</span>  <span class="token comment"># 不计算梯度</span>
        <span class="token keyword">for</span> i<span class="token punctuation">,</span> batch <span class="token keyword">in</span> <span class="token builtin">enumerate</span><span class="token punctuation">(</span>iterator<span class="token punctuation">)</span><span class="token punctuation">:</span>
            src <span class="token operator">=</span> batch<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span>
            trg <span class="token operator">=</span> batch<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span>

            output <span class="token operator">=</span> model<span class="token punctuation">(</span>src<span class="token punctuation">,</span> trg<span class="token punctuation">,</span> teacher_forcing_ratio<span class="token operator">=</span><span class="token number">0</span><span class="token punctuation">)</span>  <span class="token comment"># 验证时禁用Teacher Forcing</span>

            <span class="token comment"># trg = [trg len, batch size]</span>
            <span class="token comment"># output = [trg len, batch size, output dim]</span>

            output_dim <span class="token operator">=</span> output<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">]</span>

            output <span class="token operator">=</span> output<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">:</span><span class="token punctuation">]</span><span class="token punctuation">.</span>view<span class="token punctuation">(</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">,</span> output_dim<span class="token punctuation">)</span>
            trg <span class="token operator">=</span> trg<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">:</span><span class="token punctuation">]</span><span class="token punctuation">.</span>view<span class="token punctuation">(</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span>

            <span class="token comment"># trg = [(trg len - 1) * batch size]</span>
            <span class="token comment"># output = [(trg len - 1) * batch size, output dim]</span>

            loss <span class="token operator">=</span> criterion<span class="token punctuation">(</span>output<span class="token punctuation">,</span> trg<span class="token punctuation">)</span>

            epoch_loss <span class="token operator">+=</span> loss<span class="token punctuation">.</span>item<span class="token punctuation">(</span><span class="token punctuation">)</span>

    <span class="token keyword">return</span> epoch_loss <span class="token operator">/</span> <span class="token builtin">len</span><span class="token punctuation">(</span>iterator<span class="token punctuation">)</span>

<span class="token comment"># 记录每个epoch的用时</span>
<span class="token keyword">def</span> <span class="token function">epoch_time</span><span class="token punctuation">(</span>start_time<span class="token punctuation">,</span> end_time<span class="token punctuation">)</span><span class="token punctuation">:</span>
    elapsed_time <span class="token operator">=</span> end_time <span class="token operator">-</span> start_time
    elapsed_mins <span class="token operator">=</span> <span class="token builtin">int</span><span class="token punctuation">(</span>elapsed_time <span class="token operator">/</span> <span class="token number">60</span><span class="token punctuation">)</span>
    elapsed_secs <span class="token operator">=</span> <span class="token builtin">int</span><span class="token punctuation">(</span>elapsed_time <span class="token operator">-</span> <span class="token punctuation">(</span>elapsed_mins <span class="token operator">*</span> <span class="token number">60</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
    <span class="token keyword">return</span> elapsed_mins<span class="token punctuation">,</span> elapsed_secs


<span class="token keyword">import</span> math
<span class="token keyword">import</span> time

N_EPOCHS <span class="token operator">=</span> <span class="token number">10</span>  <span class="token comment"># 训练轮次</span>
CLIP <span class="token operator">=</span> <span class="token number">1</span>

best_valid_loss <span class="token operator">=</span> <span class="token builtin">float</span><span class="token punctuation">(</span><span class="token string">'inf'</span><span class="token punctuation">)</span>

<span class="token comment"># 开始训练</span>
<span class="token keyword">for</span> epoch <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>N_EPOCHS<span class="token punctuation">)</span><span class="token punctuation">:</span>

    start_time <span class="token operator">=</span> time<span class="token punctuation">.</span>time<span class="token punctuation">(</span><span class="token punctuation">)</span>

    train_loss <span class="token operator">=</span> train<span class="token punctuation">(</span>model<span class="token punctuation">,</span> train_data_loader<span class="token punctuation">,</span> optimizer<span class="token punctuation">,</span> criterion<span class="token punctuation">,</span> CLIP<span class="token punctuation">)</span>
    <span class="token comment"># 每训练一个轮次,测试一次</span>
    valid_loss <span class="token operator">=</span> evaluate<span class="token punctuation">(</span>model<span class="token punctuation">,</span> dev_data_loader<span class="token punctuation">,</span> criterion<span class="token punctuation">)</span>

    end_time <span class="token operator">=</span> time<span class="token punctuation">.</span>time<span class="token punctuation">(</span><span class="token punctuation">)</span>

    epoch_mins<span class="token punctuation">,</span> epoch_secs <span class="token operator">=</span> epoch_time<span class="token punctuation">(</span>start_time<span class="token punctuation">,</span> end_time<span class="token punctuation">)</span>

    <span class="token keyword">if</span> valid_loss <span class="token operator"><</span> best_valid_loss<span class="token punctuation">:</span> <span class="token comment"># 保存最优模型(验证loss阶段性最低时)</span>
        best_valid_loss <span class="token operator">=</span> valid_loss
        torch<span class="token punctuation">.</span>save<span class="token punctuation">(</span>model<span class="token punctuation">.</span>state_dict<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token string">'best_model.pth'</span><span class="token punctuation">)</span>
	
	<span class="token comment"># 打印相关指标</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string-interpolation"><span class="token string">f'Epoch: </span><span class="token interpolation"><span class="token punctuation">{</span>epoch <span class="token operator">+</span> <span class="token number">1</span><span class="token punctuation">:</span><span class="token format-spec">02</span><span class="token punctuation">}</span></span><span class="token string"> | Time: </span><span class="token interpolation"><span class="token punctuation">{</span>epoch_mins<span class="token punctuation">}</span></span><span class="token string">m </span><span class="token interpolation"><span class="token punctuation">{</span>epoch_secs<span class="token punctuation">}</span></span><span class="token string">s'</span></span><span class="token punctuation">)</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string-interpolation"><span class="token string">f'\tTrain Loss: </span><span class="token interpolation"><span class="token punctuation">{</span>train_loss<span class="token punctuation">:</span><span class="token format-spec">.3f</span><span class="token punctuation">}</span></span><span class="token string"> | Train PPL: </span><span class="token interpolation"><span class="token punctuation">{</span>math<span class="token punctuation">.</span>exp<span class="token punctuation">(</span>train_loss<span class="token punctuation">)</span><span class="token punctuation">:</span><span class="token format-spec">7.3f</span><span class="token punctuation">}</span></span><span class="token string">'</span></span><span class="token punctuation">)</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string-interpolation"><span class="token string">f'\t Val. Loss: </span><span class="token interpolation"><span class="token punctuation">{</span>valid_loss<span class="token punctuation">:</span><span class="token format-spec">.3f</span><span class="token punctuation">}</span></span><span class="token string"> |  Val. PPL: </span><span class="token interpolation"><span class="token punctuation">{</span>math<span class="token punctuation">.</span>exp<span class="token punctuation">(</span>valid_loss<span class="token punctuation">)</span><span class="token punctuation">:</span><span class="token format-spec">7.3f</span><span class="token punctuation">}</span></span><span class="token string">'</span></span><span class="token punctuation">)</span>
</code></pre> 
  <h1>如果你有任何疑问或者更好的建议,欢迎评论区留言~</h1> 
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<a class="tag" taget="_blank" href="/search/neo4j/1.htm">neo4j</a><a class="tag" taget="_blank" href="/search/neo4j/1.htm">neo4j</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E6%8D%AE%E5%BA%93/1.htm">数据库</a><a class="tag" taget="_blank" href="/search/%E7%AE%97%E6%B3%95/1.htm">算法</a><a class="tag" taget="_blank" href="/search/%E5%9B%BE%E6%95%B0%E6%8D%AE%E5%BA%93/1.htm">图数据库</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E6%BA%90/1.htm">开源</a>
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                        <span class="text-muted">36度道</span>
<a class="tag" taget="_blank" href="/search/python%E7%B3%BB%E5%88%97%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/1.htm">python系列学习笔记</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a>
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                        <span class="text-muted">36度道</span>
<a class="tag" taget="_blank" href="/search/python%E7%B3%BB%E5%88%97%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/1.htm">python系列学习笔记</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a>
                        <div>functools模块提供了一系列用于处理函数的工具。其中,像partial可以创建一个新的可调用对象,这个对象固定了原函数的部分参数,有点像给函数穿上了“参数防护服”;reduce能对一个序列进行累积计算,就好比是一个勤劳的小会计,按顺序把序列里的数加起来或者做其他运算;wraps主要用于装饰器,它能帮助装饰器函数保留被装饰函数的元信息,比如函数名、文档字符串等,让被装饰函数“表里如一”。底层原</div>
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                        <span class="text-muted">进击的雷神</span>
<a class="tag" taget="_blank" href="/search/prompt/1.htm">prompt</a>
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<a class="tag" taget="_blank" href="/search/HTML5/1.htm">HTML5</a><a class="tag" taget="_blank" href="/search/1024%E7%A8%8B%E5%BA%8F%E5%91%98%E8%8A%82/1.htm">1024程序员节</a>
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<a class="tag" taget="_blank" href="/search/Python/1.htm">Python</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a>
                        <div>一、十进制小数转换成二进制小数【问题描述】编写程序,输入十进制小数(只考虑正数),把它转换为以字符串形式存储的二进制小数,输出该二进制小数字符串。对于转换得到的二进制小数,小数点后最多保留10位。小数点后不足10位,则输出这些位,尾部不补0;小数点后超出10位,则直接舍弃超出部分。【输入形式】十进制浮点小数【输出形式】对应输入小数的二进制小数字符串。若整数部分或者小数部分为0,则输出0。比如输入0</div>
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                        <span class="text-muted">ZengDerby</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/flask/1.htm">flask</a><a class="tag" taget="_blank" href="/search/fastapi/1.htm">fastapi</a><a class="tag" taget="_blank" href="/search/django/1.htm">django</a>
                        <div>如果您需要构建大型的、功能丰富的应用程序,Django可能是一个很好的选择。如果您需要更灵活的框架,可以选择Flask来定制开发。而对于追求极致性能和高并发处理的项目,FastAPI可能是一个更加理想的选择。优缺点Flask在小型项目或微服务理想的选择。Flask灵活且轻量,非常适合快速开发小型应用。Flask是一个非常灵活的框架,它允许您根据项目需求进行定制。您可以根据需要选择合适的插件和扩展。</div>
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                        <span class="text-muted">IT技术土狗</span>
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                        <span class="text-muted">cbxjsdg</span>
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                        <span class="text-muted">全世界最帅的男人</span>
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                        <div>Python是一种非常流行的编程语言,也是许多接口自动化测试框架的首选语言。下面是一个简单的接口自动化测试框架的思路:1.安装必要的库和工具:在Python中,我们可以使用requests库来发送HTTP请求,使用unittest库来编写测试用例,使用HTMLTestRunner库来生成测试报告。此外,我们还需要安装一个代码编辑器,如PyCharm或VSCode。2.创建测试用例:编写测试用例是接</div>
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                        <span class="text-muted">花落同学</span>
<a class="tag" taget="_blank" href="/search/Python%E8%87%AA%E5%8A%A8%E5%8C%96%E4%BB%8E%E5%85%A5%E9%97%A8%E5%88%B0%E6%94%BE%E5%BC%83/1.htm">Python自动化从入门到放弃</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E8%87%AA%E5%8A%A8%E5%8C%96/1.htm">自动化</a>
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<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E7%BD%91%E7%BB%9C/1.htm">网络</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E6%8D%AE%E5%BA%93/1.htm">数据库</a>
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<a class="tag" taget="_blank" href="/search/Python/1.htm">Python</a><a class="tag" taget="_blank" href="/search/%E7%AC%94%E8%AE%B0/1.htm">笔记</a><a class="tag" taget="_blank" href="/search/Python/1.htm">Python</a><a class="tag" taget="_blank" href="/search/%E7%AE%97%E6%B3%95/1.htm">算法</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E8%AF%AD%E8%A8%80/1.htm">开发语言</a>
                        <div>在机器学习领域,Stacking是一种高级的集成学习方法,它通过将多个基本模型的预测结果作为新的特征输入到一个元模型中,从而提高整体模型的性能和鲁棒性。本文将深入介绍Stacking的原理、实现方式以及如何在Python中应用。什么是Stacking?Stacking,又称为堆叠泛化(StackedGeneralization),是一种模型集成方法,与Bagging和Boosting不同,它并不直</div>
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                        <span class="text-muted">音乐学家方大刚</span>
<a class="tag" taget="_blank" href="/search/Python/1.htm">Python</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0/1.htm">集成学习</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E8%AF%AD%E8%A8%80/1.htm">开发语言</a>
                        <div>我们都找到天使了说好了心事不能偷藏着什么都一起做幸福得没话说把坏脾气变成了好沟通我们都找到天使了约好了负责对方的快乐阳光下的山坡你素描的以后怎么抄袭我脑袋想的薛凯琪《找到天使了》在机器学习中,单一模型的性能可能会受到其局限性和数据的影响。为了解决这个问题,我们可以使用集成学习(EnsembleLearning)方法。集成学习通过结合多个基模型的预测结果,来提高整体模型的准确性和稳健性。Stacki</div>
                    </li>
                    <li><a href="/article/1903340060036624384.htm"
                           title="minimind2学习:(1)训练" target="_blank">minimind2学习:(1)训练</a>
                        <span class="text-muted">溯源006</span>
<a class="tag" taget="_blank" href="/search/minimind%E5%AD%A6%E4%B9%A0/1.htm">minimind学习</a><a class="tag" taget="_blank" href="/search/%E5%AD%A6%E4%B9%A0/1.htm">学习</a><a class="tag" taget="_blank" href="/search/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/1.htm">深度学习</a><a class="tag" taget="_blank" href="/search/%E7%94%9F%E6%88%90%E6%A8%A1%E5%9E%8B/1.htm">生成模型</a>
                        <div>1、数据下载参考:https://github.com/jingyaogong/minimind/tree/master2、预训练训练6个epochspythontrain_pretrain.py--epochs6训练过程:LLM总参数量:25.830百万Epoch:[1/6](0/11040)loss:8.940lr:0.000550000000epoch_Time:106.0min:Epoch</div>
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                    <li><a href="/article/1903339429766950912.htm"
                           title="使用Seaborn库中的`violinplot`函数绘制水平小提琴图(Violin Plot)是一种常见的数据可视化方法" target="_blank">使用Seaborn库中的`violinplot`函数绘制水平小提琴图(Violin Plot)是一种常见的数据可视化方法</a>
                        <span class="text-muted">code_welike</span>
<a class="tag" taget="_blank" href="/search/%E4%BF%A1%E6%81%AF%E5%8F%AF%E8%A7%86%E5%8C%96/1.htm">信息可视化</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/1.htm">数据分析</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98/1.htm">数据挖掘</a><a class="tag" taget="_blank" href="/search/Python/1.htm">Python</a>
                        <div>使用Seaborn库中的violinplot函数绘制水平小提琴图(ViolinPlot)是一种常见的数据可视化方法。水平小提琴图可以展示数据的分布特征,并可以对比不同组别之间的差异。本文将介绍如何使用Python和Seaborn库绘制水平小提琴图,并提供相应的源代码示例。首先,我们需要确保已经安装了Seaborn库。可以使用以下命令在Python中安装Seaborn:pipinstallseabo</div>
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                    <li><a href="/article/1903339173968932864.htm"
                           title="Stacking算法:集成学习的终极武器" target="_blank">Stacking算法:集成学习的终极武器</a>
                        <span class="text-muted">civilpy</span>
<a class="tag" taget="_blank" href="/search/%E7%AE%97%E6%B3%95/1.htm">算法</a><a class="tag" taget="_blank" href="/search/%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0/1.htm">集成学习</a><a class="tag" taget="_blank" href="/search/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/1.htm">机器学习</a>
                        <div>Stacking算法:集成学习的终极武器在机器学习的竞技场中,集成学习方法以其卓越的性能而闻名。其中,Stacking(堆叠泛化)作为一种高级集成技术,更是被誉为“集成学习的终极武器”。本文将带你深入了解Stacking算法的原理和实现,并提供一些实战技巧和最佳实践。1.Stacking算法原理探秘Stacking算法的核心思想是训练多个不同的基模型,并将它们的预测结果作为新模型的输入特征,以此来</div>
                    </li>
                                <li><a href="/article/54.htm"
                                       title="JAVA中的Enum" target="_blank">JAVA中的Enum</a>
                                    <span class="text-muted">周凡杨</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/enum/1.htm">enum</a><a class="tag" taget="_blank" href="/search/%E6%9E%9A%E4%B8%BE/1.htm">枚举</a>
                                    <div>Enum是计算机编程语言中的一种数据类型---枚举类型。 在实际问题中,有些变量的取值被限定在一个有限的范围内。       例如,一个星期内只有七天 我们通常这样实现上面的定义: 
public String monday;
public String tuesday;
public String wensday;
public String thursday</div>
                                </li>
                                <li><a href="/article/181.htm"
                                       title="赶集网mysql开发36条军规" target="_blank">赶集网mysql开发36条军规</a>
                                    <span class="text-muted">Bill_chen</span>
<a class="tag" taget="_blank" href="/search/mysql/1.htm">mysql</a><a class="tag" taget="_blank" href="/search/%E4%B8%9A%E5%8A%A1%E6%9E%B6%E6%9E%84%E8%AE%BE%E8%AE%A1/1.htm">业务架构设计</a><a class="tag" taget="_blank" href="/search/mysql%E8%B0%83%E4%BC%98/1.htm">mysql调优</a><a class="tag" taget="_blank" href="/search/mysql%E6%80%A7%E8%83%BD%E4%BC%98%E5%8C%96/1.htm">mysql性能优化</a>
                                    <div>(一)核心军规   (1)不在数据库做运算      cpu计算务必移至业务层;   (2)控制单表数据量      int型不超过1000w,含char则不超过500w;      合理分表;      限制单库表数量在300以内;   (3)控制列数量      字段少而精,字段数建议在20以内</div>
                                </li>
                                <li><a href="/article/308.htm"
                                       title="Shell test命令" target="_blank">Shell test命令</a>
                                    <span class="text-muted">daizj</span>
<a class="tag" taget="_blank" href="/search/shell/1.htm">shell</a><a class="tag" taget="_blank" href="/search/%E5%AD%97%E7%AC%A6%E4%B8%B2/1.htm">字符串</a><a class="tag" taget="_blank" href="/search/test/1.htm">test</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E5%AD%97/1.htm">数字</a><a class="tag" taget="_blank" href="/search/%E6%96%87%E4%BB%B6%E6%AF%94%E8%BE%83/1.htm">文件比较</a>
                                    <div>Shell test命令 
Shell中的 test 命令用于检查某个条件是否成立,它可以进行数值、字符和文件三个方面的测试。  数值测试    参数 说明   -eq 等于则为真   -ne 不等于则为真   -gt 大于则为真   -ge 大于等于则为真   -lt 小于则为真   -le 小于等于则为真    
实例演示: 
num1=100
num2=100if test $[num1]</div>
                                </li>
                                <li><a href="/article/435.htm"
                                       title="XFire框架实现WebService(二)" target="_blank">XFire框架实现WebService(二)</a>
                                    <span class="text-muted">周凡杨</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/webservice/1.htm">webservice</a>
                                    <div>   有了XFire框架实现WebService(一),就可以继续开发WebService的简单应用。 
Webservice的服务端(WEB工程): 
两个java bean类: 
Course.java 
   package cn.com.bean; 
public class Course { 
    private </div>
                                </li>
                                <li><a href="/article/562.htm"
                                       title="重绘之画图板" target="_blank">重绘之画图板</a>
                                    <span class="text-muted">朱辉辉33</span>
<a class="tag" taget="_blank" href="/search/%E7%94%BB%E5%9B%BE%E6%9D%BF/1.htm">画图板</a>
                                    <div>       上次博客讲的五子棋重绘比较简单,因为只要在重写系统重绘方法paint()时加入棋盘和棋子的绘制。这次我想说说画图板的重绘。 
       画图板重绘难在需要重绘的类型很多,比如说里面有矩形,园,直线之类的,所以我们要想办法将里面的图形加入一个队列中,这样在重绘时就</div>
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                                <li><a href="/article/689.htm"
                                       title="Java的IO流" target="_blank">Java的IO流</a>
                                    <span class="text-muted">西蜀石兰</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a>
                                    <div>刚学Java的IO流时,被各种inputStream流弄的很迷糊,看老罗视频时说想象成插在文件上的一根管道,当初听时觉得自己很明白,可到自己用时,有不知道怎么代码了。。。 
每当遇到这种问题时,我习惯性的从头开始理逻辑,会问自己一些很简单的问题,把这些简单的问题想明白了,再看代码时才不会迷糊。 
 
IO流作用是什么? 
答:实现对文件的读写,这里的文件是广义的; 
 
Java如何实现程序到文件</div>
                                </li>
                                <li><a href="/article/816.htm"
                                       title="No matching PlatformTransactionManager bean found for qualifier 'add' - neither" target="_blank">No matching PlatformTransactionManager bean found for qualifier 'add' - neither</a>
                                    <span class="text-muted">林鹤霄</span>

                                    <div>java.lang.IllegalStateException: No matching PlatformTransactionManager bean found for qualifier 'add' - neither qualifier match nor bean name match! 
  
网上找了好多的资料没能解决,后来发现:项目中使用的是xml配置的方式配置事务,但是</div>
                                </li>
                                <li><a href="/article/943.htm"
                                       title="Row size too large (> 8126). Changing some columns to TEXT or BLOB" target="_blank">Row size too large (> 8126). Changing some columns to TEXT or BLOB</a>
                                    <span class="text-muted">aigo</span>
<a class="tag" taget="_blank" href="/search/column/1.htm">column</a>
                                    <div>原文:http://stackoverflow.com/questions/15585602/change-limit-for-mysql-row-size-too-large 
  
异常信息: 
Row size too large (> 8126). Changing some columns to TEXT or BLOB or using ROW_FORMAT=DYNAM</div>
                                </li>
                                <li><a href="/article/1070.htm"
                                       title="JS 格式化时间" target="_blank">JS 格式化时间</a>
                                    <span class="text-muted">alxw4616</span>
<a class="tag" taget="_blank" href="/search/JavaScript/1.htm">JavaScript</a>
                                    <div>/**
 * 格式化时间 2013/6/13 by 半仙 alxw4616@msn.com
 * 需要 pad 函数
 * 接收可用的时间值.
 * 返回替换时间占位符后的字符串
 *
 * 时间占位符:年 Y 月 M 日 D 小时 h 分 m 秒 s 重复次数表示占位数
 * 如 YYYY 4占4位 YY 占2位<p></p>
 * MM DD hh mm</div>
                                </li>
                                <li><a href="/article/1197.htm"
                                       title="队列中数据的移除问题" target="_blank">队列中数据的移除问题</a>
                                    <span class="text-muted">百合不是茶</span>
<a class="tag" taget="_blank" href="/search/%E9%98%9F%E5%88%97%E7%A7%BB%E9%99%A4/1.htm">队列移除</a>
                                    <div>  
   队列的移除一般都是使用的remov();都可以移除的,但是在昨天做线程移除的时候出现了点问题,没有将遍历出来的全部移除,  代码如下; 
  
   // 
package com.Thread0715.com;

import java.util.ArrayList;

public class Threa</div>
                                </li>
                                <li><a href="/article/1324.htm"
                                       title="Runnable接口使用实例" target="_blank">Runnable接口使用实例</a>
                                    <span class="text-muted">bijian1013</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/thread/1.htm">thread</a><a class="tag" taget="_blank" href="/search/Runnable/1.htm">Runnable</a><a class="tag" taget="_blank" href="/search/java%E5%A4%9A%E7%BA%BF%E7%A8%8B/1.htm">java多线程</a>
                                    <div>Runnable接口 
a.       该接口只有一个方法:public void run(); 
b.       实现该接口的类必须覆盖该run方法 
c.       实现了Runnable接口的类并不具有任何天</div>
                                </li>
                                <li><a href="/article/1451.htm"
                                       title="oracle里的extend详解" target="_blank">oracle里的extend详解</a>
                                    <span class="text-muted">bijian1013</span>
<a class="tag" taget="_blank" href="/search/oracle/1.htm">oracle</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E6%8D%AE%E5%BA%93/1.htm">数据库</a><a class="tag" taget="_blank" href="/search/extend/1.htm">extend</a>
                                    <div>扩展已知的数组空间,例: 
DECLARE
  TYPE CourseList IS TABLE OF VARCHAR2(10);
  courses CourseList;
BEGIN
  --   初始化数组元素,大小为3
  courses := CourseList('Biol   4412 ', 'Psyc   3112 ', 'Anth   3001 ');
  --   </div>
                                </li>
                                <li><a href="/article/1578.htm"
                                       title="【httpclient】httpclient发送表单POST请求" target="_blank">【httpclient】httpclient发送表单POST请求</a>
                                    <span class="text-muted">bit1129</span>
<a class="tag" taget="_blank" href="/search/httpclient/1.htm">httpclient</a>
                                    <div>浏览器Form Post请求 
浏览器可以通过提交表单的方式向服务器发起POST请求,这种形式的POST请求不同于一般的POST请求 
1. 一般的POST请求,将请求数据放置于请求体中,服务器端以二进制流的方式读取数据,HttpServletRequest.getInputStream()。这种方式的请求可以处理任意数据形式的POST请求,比如请求数据是字符串或者是二进制数据 
2. Form </div>
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                                <li><a href="/article/1705.htm"
                                       title="【Hive十三】Hive读写Avro格式的数据" target="_blank">【Hive十三】Hive读写Avro格式的数据</a>
                                    <span class="text-muted">bit1129</span>
<a class="tag" taget="_blank" href="/search/hive/1.htm">hive</a>
                                    <div> 1. 原始数据 
hive> select * from word; 
OK
1	MSN  
10	QQ  
100	Gtalk  
1000	Skype  
  
  
 2. 创建avro格式的数据表 
  
hive> CREATE TABLE avro_table(age INT, name STRING)STORE</div>
                                </li>
                                <li><a href="/article/1832.htm"
                                       title="nginx+lua+redis自动识别封解禁频繁访问IP" target="_blank">nginx+lua+redis自动识别封解禁频繁访问IP</a>
                                    <span class="text-muted">ronin47</span>

                                    <div>在站点遇到攻击且无明显攻击特征,造成站点访问慢,nginx不断返回502等错误时,可利用nginx+lua+redis实现在指定的时间段 内,若单IP的请求量达到指定的数量后对该IP进行封禁,nginx返回403禁止访问。利用redis的expire命令设置封禁IP的过期时间达到在 指定的封禁时间后实行自动解封的目的。 
一、安装环境: 
 
 CentOS x64 release 6.4(Fin</div>
                                </li>
                                <li><a href="/article/1959.htm"
                                       title="java-二叉树的遍历-先序、中序、后序(递归和非递归)、层次遍历" target="_blank">java-二叉树的遍历-先序、中序、后序(递归和非递归)、层次遍历</a>
                                    <span class="text-muted">bylijinnan</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a>
                                    <div>
import java.util.LinkedList;
import java.util.List;
import java.util.Stack;


public class BinTreeTraverse {
	//private int[] array={ 1, 2, 3, 4, 5, 6, 7, 8, 9 };
	private int[] array={ 10,6,</div>
                                </li>
                                <li><a href="/article/2086.htm"
                                       title="Spring源码学习-XML 配置方式的IoC容器启动过程分析" target="_blank">Spring源码学习-XML 配置方式的IoC容器启动过程分析</a>
                                    <span class="text-muted">bylijinnan</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/spring/1.htm">spring</a><a class="tag" taget="_blank" href="/search/IOC/1.htm">IOC</a>
                                    <div>以FileSystemXmlApplicationContext为例,把Spring IoC容器的初始化流程走一遍: 
 

ApplicationContext context = new FileSystemXmlApplicationContext
            ("C:/Users/ZARA/workspace/HelloSpring/src/Beans.xml&q</div>
                                </li>
                                <li><a href="/article/2213.htm"
                                       title="[科研与项目]民营企业请慎重参与军事科技工程" target="_blank">[科研与项目]民营企业请慎重参与军事科技工程</a>
                                    <span class="text-muted">comsci</span>
<a class="tag" taget="_blank" href="/search/%E4%BC%81%E4%B8%9A/1.htm">企业</a>
                                    <div> 
 
     军事科研工程和项目 并非要用最先进,最时髦的技术,而是要做到“万无一失” 
 
   而民营科技企业在搞科技创新工程的时候,往往考虑的是技术的先进性,而对先进技术带来的风险考虑得不够,在今天提倡军民融合发展的大环境下,这种“万无一失”和“时髦性”的矛盾会日益凸显。。。。。。所以请大家在参与任何重大的军事和政府项目之前,对</div>
                                </li>
                                <li><a href="/article/2340.htm"
                                       title="spring 定时器-两种方式" target="_blank">spring 定时器-两种方式</a>
                                    <span class="text-muted">cuityang</span>
<a class="tag" taget="_blank" href="/search/spring/1.htm">spring</a><a class="tag" taget="_blank" href="/search/quartz/1.htm">quartz</a><a class="tag" taget="_blank" href="/search/%E5%AE%9A%E6%97%B6%E5%99%A8/1.htm">定时器</a>
                                    <div>方式一: 
间隔一定时间 运行 
 
<bean id="updateSessionIdTask" class="com.yang.iprms.common.UpdateSessionTask" autowire="byName" /> 
 
 <bean id="updateSessionIdSchedule</div>
                                </li>
                                <li><a href="/article/2467.htm"
                                       title="简述一下关于BroadView站点的相关设计" target="_blank">简述一下关于BroadView站点的相关设计</a>
                                    <span class="text-muted">damoqiongqiu</span>
<a class="tag" taget="_blank" href="/search/view/1.htm">view</a>
                                    <div>终于弄上线了,累趴,戳这里http://www.broadview.com.cn 
  
简述一下相关的技术点 
  
前端:jQuery+BootStrap3.2+HandleBars,全站Ajax(貌似对SEO的影响很大啊!怎么破?),用Grunt对全部JS做了压缩处理,对部分JS和CSS做了合并(模块间存在很多依赖,全部合并比较繁琐,待完善)。 
  
后端:U</div>
                                </li>
                                <li><a href="/article/2594.htm"
                                       title="运维 PHP问题汇总" target="_blank">运维 PHP问题汇总</a>
                                    <span class="text-muted">dcj3sjt126com</span>
<a class="tag" taget="_blank" href="/search/windows2003/1.htm">windows2003</a>
                                    <div>1、Dede(织梦)发表文章时,内容自动添加关键字显示空白页 
解决方法: 
后台>系统>系统基本参数>核心设置>关键字替换(是/否),这里选择“是”。 
后台>系统>系统基本参数>其他选项>自动提取关键字,这里选择“是”。 
  
2、解决PHP168超级管理员上传图片提示你的空间不足 
网站是用PHP168做的,反映使用管理员在后台无法</div>
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                                <li><a href="/article/2721.htm"
                                       title="mac 下 安装php扩展 - mcrypt" target="_blank">mac 下 安装php扩展 - mcrypt</a>
                                    <span class="text-muted">dcj3sjt126com</span>
<a class="tag" taget="_blank" href="/search/PHP/1.htm">PHP</a>
                                    <div>MCrypt是一个功能强大的加密算法扩展库,它包括有22种算法,phpMyAdmin依赖这个PHP扩展,具体如下: 
 
  
  下载并解压libmcrypt-2.5.8.tar.gz。 
  在终端执行如下命令:  tar zxvf libmcrypt-2.5.8.tar.gz cd libmcrypt-2.5.8/ ./configure --disable-posix-threads --</div>
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                                <li><a href="/article/2848.htm"
                                       title="MongoDB更新文档 [四]" target="_blank">MongoDB更新文档 [四]</a>
                                    <span class="text-muted">eksliang</span>
<a class="tag" taget="_blank" href="/search/mongodb/1.htm">mongodb</a><a class="tag" taget="_blank" href="/search/Mongodb%E6%9B%B4%E6%96%B0%E6%96%87%E6%A1%A3/1.htm">Mongodb更新文档</a>
                                    <div>MongoDB更新文档 
转载请出自出处:http://eksliang.iteye.com/blog/2174104 
MongoDB对文档的CURD,前面的博客简单介绍了,但是对文档更新篇幅比较大,所以这里单独拿出来。 
语法结构如下: 
db.collection.update( criteria, objNew, upsert, multi) 
参数含义    参数   </div>
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                                <li><a href="/article/2975.htm"
                                       title="Linux下的解压,移除,复制,查看tomcat命令" target="_blank">Linux下的解压,移除,复制,查看tomcat命令</a>
                                    <span class="text-muted">y806839048</span>
<a class="tag" taget="_blank" href="/search/tomcat/1.htm">tomcat</a>
                                    <div>重复myeclipse生成webservice有问题删除以前的,干净 
 
 1、先切换到:cd usr/local/tomcat5/logs 
 
2、tail -f catalina.out 
 
3、这样运行时就可以实时查看运行日志了 
 
 
 
 
Ctrl+c 是退出tail命令。 
 有问题不明的先注掉 
   cp /opt/tomcat-6.0.44/webapps/g</div>
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                                       title="Spring之使用事务缘由(3-XML实现)" target="_blank">Spring之使用事务缘由(3-XML实现)</a>
                                    <span class="text-muted">ihuning</span>
<a class="tag" taget="_blank" href="/search/spring/1.htm">spring</a>
                                    <div>  
用事务通知声明式地管理事务 
  
事务管理是一种横切关注点。为了在 Spring 2.x 中启用声明式事务管理,可以通过 tx Schema 中定义的 <tx:advice> 元素声明事务通知,为此必须事先将这个 Schema 定义添加到 <beans> 根元素中去。声明了事务通知后,就需要将它与切入点关联起来。由于事务通知是在 <aop:</div>
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                                <li><a href="/article/3229.htm"
                                       title="GCD使用经验与技巧浅谈" target="_blank">GCD使用经验与技巧浅谈</a>
                                    <span class="text-muted">啸笑天</span>
<a class="tag" taget="_blank" href="/search/GC/1.htm">GC</a>
                                    <div>前言 
GCD(Grand Central Dispatch)可以说是Mac、iOS开发中的一大“利器”,本文就总结一些有关使用GCD的经验与技巧。 
dispatch_once_t必须是全局或static变量 
这一条算是“老生常谈”了,但我认为还是有必要强调一次,毕竟非全局或非static的dispatch_once_t变量在使用时会导致非常不好排查的bug,正确的如下:        1  </div>
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                                <li><a href="/article/3356.htm"
                                       title="linux(Ubuntu)下常用命令备忘录1" target="_blank">linux(Ubuntu)下常用命令备忘录1</a>
                                    <span class="text-muted">macroli</span>
<a class="tag" taget="_blank" href="/search/linux/1.htm">linux</a><a class="tag" taget="_blank" href="/search/%E5%B7%A5%E4%BD%9C/1.htm">工作</a><a class="tag" taget="_blank" href="/search/ubuntu/1.htm">ubuntu</a>
                                    <div>在使用下面的命令是可以通过--help来获取更多的信息1,查询当前目录文件列表:ls 
 
 ls命令默认状态下将按首字母升序列出你当前文件夹下面的所有内容,但这样直接运行所得到的信息也是比较少的,通常它可以结合以下这些参数运行以查询更多的信息:  
 ls / 显示/.下的所有文件和目录  
 ls -l 给出文件或者文件夹的详细信息 
 ls -a 显示所有文件,包括隐藏文</div>
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                                <li><a href="/article/3483.htm"
                                       title="nodejs同步操作mysql" target="_blank">nodejs同步操作mysql</a>
                                    <span class="text-muted">qiaolevip</span>
<a class="tag" taget="_blank" href="/search/%E5%AD%A6%E4%B9%A0%E6%B0%B8%E6%97%A0%E6%AD%A2%E5%A2%83/1.htm">学习永无止境</a><a class="tag" taget="_blank" href="/search/%E6%AF%8F%E5%A4%A9%E8%BF%9B%E6%AD%A5%E4%B8%80%E7%82%B9%E7%82%B9/1.htm">每天进步一点点</a><a class="tag" taget="_blank" href="/search/mysql/1.htm">mysql</a><a class="tag" taget="_blank" href="/search/nodejs/1.htm">nodejs</a>
                                    <div>// db-util.js
var mysql = require('mysql');
var pool = mysql.createPool({
  connectionLimit : 10,
  host: 'localhost',
  user: 'root',
  password: '',
  database: 'test',
  port: 3306
});

</div>
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                                <li><a href="/article/3610.htm"
                                       title="一起学Hive系列文章" target="_blank">一起学Hive系列文章</a>
                                    <span class="text-muted">superlxw1234</span>
<a class="tag" taget="_blank" href="/search/hive/1.htm">hive</a><a class="tag" taget="_blank" href="/search/Hive%E5%85%A5%E9%97%A8/1.htm">Hive入门</a>
                                    <div>  
[一起学Hive]系列文章 目录贴,入门Hive,持续更新中。 
  
[一起学Hive]之一—Hive概述,Hive是什么 
[一起学Hive]之二—Hive函数大全-完整版 
[一起学Hive]之三—Hive中的数据库(Database)和表(Table) 
[一起学Hive]之四-Hive的安装配置 
[一起学Hive]之五-Hive的视图和分区 
[一起学Hive</div>
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                                <li><a href="/article/3737.htm"
                                       title="Spring开发利器:Spring Tool Suite 3.7.0 发布" target="_blank">Spring开发利器:Spring Tool Suite 3.7.0 发布</a>
                                    <span class="text-muted">wiselyman</span>
<a class="tag" taget="_blank" href="/search/spring/1.htm">spring</a>
                                    <div>Spring Tool Suite(简称STS)是基于Eclipse,专门针对Spring开发者提供大量的便捷功能的优秀开发工具。 
  
在3.7.0版本主要做了如下的更新: 
  
 
 将eclipse版本更新至Eclipse Mars 4.5 GA 
 Spring Boot(JavaEE开发的颠覆者集大成者,推荐大家学习)的配置语言YAML编辑器的支持(包含自动提示,</div>
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