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/android/1.htm">android</a><a class="tag" taget="_blank" href="/search/adb/1.htm">adb</a>
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<a class="tag" taget="_blank" href="/search/app%E9%80%86%E5%90%91/1.htm">app逆向</a><a class="tag" taget="_blank" href="/search/rpc/1.htm">rpc</a><a class="tag" taget="_blank" href="/search/javascript/1.htm">javascript</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</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><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><a class="tag" taget="_blank" href="/search/%E6%89%A7%E8%A1%8C%E6%A8%A1%E5%9E%8B/1.htm">执行模型</a><a class="tag" taget="_blank" href="/search/%E5%91%BD%E5%90%8D%E4%B8%8E%E7%BB%91%E5%AE%9A/1.htm">命名与绑定</a><a class="tag" taget="_blank" href="/search/%E5%BC%82%E5%B8%B8%E5%A4%84%E7%90%86/1.htm">异常处理</a><a class="tag" taget="_blank" href="/search/%E4%BD%9C%E7%94%A8%E5%9F%9F/1.htm">作用域</a><a class="tag" taget="_blank" href="/search/%E5%91%BD%E5%90%8D%E7%A9%BA%E9%97%B4/1.htm">命名空间</a>
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                        <span class="text-muted">C嘎嘎嵌入式开发</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/%E6%9C%8D%E5%8A%A1%E5%99%A8/1.htm">服务器</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/c%2B%2B/1.htm">c++</a><a class="tag" taget="_blank" href="/search/linux/1.htm">linux</a>
                        <div>1.Jieba分词算法简介Jieba是一个用于中文分词的Python库,其核心思想是基于词典和统计模型来进行分词。由于中文文本中没有明显的单词边界,因此分词是中文处理中的一个重要任务。Jieba提供了以下几种主要的分词模式:精确模式:尽可能准确地切分句子,适合用于文本分析。全模式:将句子中所有可能的词语都切分出来,适合用于搜索引擎。搜索引擎模式:在精确模式的基础上,对长词再次切分,适合用于搜索引擎</div>
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                        <div>作为21世纪最流行的语言之一,Python当然有很多有趣的函数值得深入探索和研究。今天我就分享一下其中三个函数。这三个函数可不简单,它们可以帮助我们避免编写循环,有时循环的运行成本是很高的。此外,这些函数还有助于提高运行速度。————————map()—————————map()函数的作用是将另一函数作为参数,与某种数组放在一起,旨在将函数(作为参数而导入的函数)应用于数组中的每一个项。eg:da</div>
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                        <span class="text-muted">伶俐角少儿编程</span>
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                           title="2024年9月电子学会青少年软件编程Python等级考试(五级)真题试卷" target="_blank">2024年9月电子学会青少年软件编程Python等级考试(五级)真题试卷</a>
                        <span class="text-muted">No0d1es</span>
<a class="tag" taget="_blank" href="/search/%E9%9D%92%E5%B0%91%E5%B9%B4%E8%BD%AF%E4%BB%B6%E7%BC%96%E7%A8%8B%EF%BC%88Python%EF%BC%89/1.htm">青少年软件编程(Python)</a><a class="tag" taget="_blank" href="/search/%E7%AD%89%E7%BA%A7%E8%80%83%E8%AF%95%E8%AF%95%E5%8D%B7/1.htm">等级考试试卷</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><a class="tag" taget="_blank" href="/search/%E7%94%B5%E5%AD%90%E5%AD%A6%E4%BC%9A/1.htm">电子学会</a><a class="tag" taget="_blank" href="/search/%E9%9D%92%E5%B0%91%E5%B9%B4%E7%BC%96%E7%A8%8B/1.htm">青少年编程</a><a class="tag" taget="_blank" href="/search/%E4%BA%94%E7%BA%A7/1.htm">五级</a><a class="tag" taget="_blank" href="/search/%E5%85%AD%E7%BA%A7/1.htm">六级</a>
                        <div>2024年9月青少年软件编程Python等级考试(五级)真题试卷一、选择题第1题以下哪个方法用于向字典中添加或修改键值对?()A.dict.append()B.dict.update()C.dict.remove()D.dict.insert()第2题下列哪个操作不是字典类型的标准方法或属性?()A.dict.keys()B.dict.getall()C.dict.items()D.dict.va</div>
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                           title="力扣hot100_矩阵_python版本" target="_blank">力扣hot100_矩阵_python版本</a>
                        <span class="text-muted">Y1nhl</span>
<a class="tag" taget="_blank" href="/search/%E5%8A%9B%E6%89%A3/1.htm">力扣</a><a class="tag" taget="_blank" href="/search/leetcode/1.htm">leetcode</a><a class="tag" taget="_blank" href="/search/%E7%9F%A9%E9%98%B5/1.htm">矩阵</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a>
                        <div>73.矩阵置零给定一个mxn的矩阵,如果一个元素为0,则将其所在行和列的所有元素都设为0。请使用原地算法。classSolution:defsetZeroes(self,matrix:List[List[int]])->None:m,n=len(matrix),len(matrix[0])row,col=[False]*m,[False]*nforiinrange(m):forjinrange(n</div>
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                           title="正则表达式 匹配一次" target="_blank">正则表达式 匹配一次</a>
                        <span class="text-muted">zzyh123456</span>
<a class="tag" taget="_blank" href="/search/%E6%AD%A3%E5%88%99%E8%A1%A8%E8%BE%BE%E5%BC%8F/1.htm">正则表达式</a><a class="tag" taget="_blank" href="/search/mysql/1.htm">mysql</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E6%8D%AE%E5%BA%93/1.htm">数据库</a>
                        <div>下面是一个概念性的示例,说明如何使用正则表达式来找到文本中的URL,并假设我们将基于这个URL的存在来构思一篇文章。正则表达式示例首先,定义正则表达式来匹配URL:regexhttps:\/\/www\.naquan\.com\/这个正则表达式会匹配字符串https://www.51969.com/。假设的Python脚本假设你有一个Python脚本,它使用正则表达式来查找文本中的URL,并基于这</div>
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                           title="Python学习心得程序跳转语句" target="_blank">Python学习心得程序跳转语句</a>
                        <span class="text-muted">lifegoesonwjl</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/github/1.htm">github</a><a class="tag" taget="_blank" href="/search/pycharm/1.htm">pycharm</a>
                        <div>一、程序跳转语句break用于条跳(退)出循环结构,通常与if一起使用。1.while情形:语法结构:while表达式1:执行代码if表达式2:break执行流程:语句跳转break的运用:#累加和不大于20的i的值s=0i=1whilei:s+=iifs>20:print('累加和不大于20的i的值为:',i)breaki+=1注:break为非正常结束,当循环部分为while...else..</div>
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                           title="爬虫快速上手之正则表达式总结" target="_blank">爬虫快速上手之正则表达式总结</a>
                        <span class="text-muted">Athena945</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E6%AD%A3%E5%88%99%E8%A1%A8%E8%BE%BE%E5%BC%8F/1.htm">正则表达式</a><a class="tag" taget="_blank" href="/search/%E6%AD%A3%E5%88%99%E8%A1%A8%E8%BE%BE%E5%BC%8F/1.htm">正则表达式</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a>
                        <div>目录一、正则表达式二、查找相关方法三、re.Match类的使用四、re.compile()方法的使用五、正则修饰符六、标点符号的特殊意义七、字母的特殊含义八、正则替换九、贪婪模式和非贪婪模式十、正则表达式小结一、正则表达式1、概念正则表达式是一个特殊的字符序列,通常被用来检索、替换那些符合某个模式(规则)的文本;在python中需要通过正则表达式对字符串进行匹配的时候,可以使用re模块实现全部的正</div>
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                           title="Python学习心得-正则表达式" target="_blank">Python学习心得-正则表达式</a>
                        <span class="text-muted">NKUer_there</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>
                        <div>#这是一个示例Python脚本。#按⌃R执行或将其替换为您的代码。#按双击⇧在所有地方搜索类、文件、工具窗口、操作和设置。#正则表达式学习心得importre#importre是必要的操作#正则表达式用以匹配文本regex1=r'\d\d\d-\d\d\d-\d\d\d\d'#或者等效为regex2=r'\d{3}-\d{3}-\d{4}'#利用re模块compile创建regex对象,即所谓的正</div>
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                                <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>
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                                <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>
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                                <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>
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                                <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>
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                                <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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                                       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>
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                                    <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>
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                                       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>
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                                <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>
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                                       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>
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                                <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>
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                                       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>
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                                <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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                                       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>
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                                       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>
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                                <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>
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                                <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>
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                                <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>
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                                <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>
                                </li>
                                <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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                                <li><a href="/article/3102.htm"
                                       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>
                                </li>
                                <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>
                                </li>
                                <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>
                                </li>
                                <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>
                                </li>
                                <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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