{"id":86980,"date":"2026-07-29T15:16:34","date_gmt":"2026-07-29T07:16:34","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/86980.html"},"modified":"2026-07-29T15:16:34","modified_gmt":"2026-07-29T07:16:34","slug":"%e6%8e%8c%e6%8f%a1%e5%a4%a7%e6%a8%a1%e5%9e%8b%e7%bb%93%e6%9e%84%ef%bc%9atransformer%e6%9e%b6%e6%9e%84%e6%b7%b1%e5%ba%a6%e8%a7%a3%e6%9e%90","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/86980.html","title":{"rendered":"\u638c\u63e1\u5927\u6a21\u578b\u7ed3\u6784\uff1aTransformer\u67b6\u6784\u6df1\u5ea6\u89e3\u6790"},"content":{"rendered":"

\u638c\u63e1\u5927\u6a21\u578b\u7ed3\u6784：Transformer\u67b6\u6784\u6df1\u5ea6\u89e3\u6790<\/h2>\n

\u4e00\u3001\u5f15\u8a00：\u4e3a\u4ec0\u4e48Transformer\u6210\u4e3a\u5927\u6a21\u578b\u7684\u57fa\u77f3<\/h3>\n

2017\u5e74，Google\u5728\u8bba\u6587\u300aAttention Is All You Need\u300b\u4e2d\u63d0\u51fa\u4e86Transformer\u67b6\u6784\u3002\u8fd9\u4e00\u67b6\u6784\u5f7b\u5e95\u6539\u53d8\u4e86\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u4f9d\u8d56RNN\/CNN\u7684\u8303\u5f0f，\u51ed\u501f\u5176\u9ad8\u6548\u7684\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\u548c\u5bf9\u957f\u5e8f\u5217\u7684\u5f3a\u5927\u5efa\u6a21\u80fd\u529b，\u8fc5\u901f\u6210\u4e3a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u6838\u5fc3\u67b6\u6784，\u5e76\u9010\u6b65\u6269\u5c55\u81f3\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u8bed\u97f3\u8bc6\u522b\u7b49\u9886\u57df\u3002<\/p>\n

Transformer\u7684\u6838\u5fc3\u7a81\u7834\u5728\u4e8e\u6452\u5f03\u4f20\u7edf\u5faa\u73af\u795e\u7ecf\u7f51\u7edc（RNN）\u7684\u5e8f\u5217\u4f9d\u8d56\u7ed3\u6784，\u91c7\u7528\u5168\u6ce8\u610f\u529b\u673a\u5236\u5b9e\u73b0\u5e76\u884c\u8ba1\u7b97\u3002\u5728Transformer\u4e4b\u524d，RNN\u53ca\u5176\u53d8\u4f53（\u5982LSTM\u3001GRU）\u662f\u5e8f\u5217\u5efa\u6a21\u7684\u4e3b\u6d41\u65b9\u6848，\u4f46RNN\u5b58\u5728\u4e24\u5927\u7f3a\u9677：\u4e00\u662f\u9700\u8981\u6309\u65f6\u95f4\u6b65\u9010\u4e2a\u5904\u7406\u8f93\u5165，\u65e0\u6cd5\u5e76\u884c\u5316，\u5bfc\u81f4\u8bad\u7ec3\u6548\u7387\u4f4e\u4e0b；\u4e8c\u662f\u968f\u7740\u5e8f\u5217\u957f\u5ea6\u589e\u52a0，\u68af\u5ea6\u6d88\u5931\u6216\u7206\u70b8\u95ee\u9898\u4f1a\u524a\u5f31\u6a21\u578b\u5bf9\u8fdc\u8ddd\u79bb\u4fe1\u606f\u7684\u6355\u6349\u80fd\u529b\u3002Transformer\u901a\u8fc7\u81ea\u6ce8\u610f\u529b\u673a\u5236\u76f4\u63a5\u8ba1\u7b97\u5e8f\u5217\u4e2d\u4efb\u610f\u4f4d\u7f6e\u7684\u5173\u8054，\u65e0\u9700\u9012\u5f52，\u5f7b\u5e95\u89e3\u51b3\u4e86\u8fd9\u4e9b\u95ee\u9898\u3002<\/p>\n

\u672c\u6587\u5c06\u6df1\u5165\u89e3\u6790Transformer\u7684\u6838\u5fc3\u67b6\u6784，\u4ece\u81ea\u6ce8\u610f\u529b\u673a\u5236\u3001\u591a\u5934\u6ce8\u610f\u529b\u3001\u4f4d\u7f6e\u7f16\u7801\u5230\u6b8b\u5dee\u8fde\u63a5\u4e0e\u5c42\u5f52\u4e00\u5316，\u7ed3\u5408PyTorch\u4ee3\u7801\u5b9e\u73b0\u4e0e\u5b9e\u9a8c\u9a8c\u8bc1，\u5e2e\u52a9\u5f00\u53d1\u8005\u5168\u9762\u638c\u63e1\u8fd9\u4e00\u73b0\u4ee3\u5927\u6a21\u578b\u7684\u57fa\u77f3\u6280\u672f\u3002<\/p>\n

\n

\u4e8c\u3001Transformer\u6574\u4f53\u67b6\u6784\u6982\u89c8<\/h3>\n

Transformer\u91c7\u7528\u7ecf\u5178\u7684\u7f16\u7801\u5668-\u89e3\u7801\u5668（Encoder-Decoder） \u7ed3\u6784，\u4e24\u8005\u5747\u7531N\u4e2a\u76f8\u540c\u5c42\u5806\u53e0\u800c\u6210（\u539f\u59cb\u8bba\u6587\u4e2dN=6）\u3002\u8fd9\u79cd\u5206\u5c42\u8bbe\u8ba1\u901a\u8fc7\u9010\u6b65\u62bd\u8c61\u7279\u5f81，\u5b9e\u73b0\u4e86\u5bf9\u8f93\u5165\u5e8f\u5217\u7684\u6df1\u5ea6\u7406\u89e3\u4e0e\u751f\u6210\u3002<\/p>\n

2.1 \u7f16\u7801\u5668（Encoder）<\/h4>\n

\u7f16\u7801\u5668\u8d1f\u8d23\u5c06\u8f93\u5165\u5e8f\u5217\u6620\u5c04\u4e3a\u9ad8\u7ef4\u8bed\u4e49\u8868\u793a\u3002\u6bcf\u4e2a\u7f16\u7801\u5668\u5c42\u5305\u542b\u4e24\u4e2a\u6838\u5fc3\u5b50\u5c42：<\/p>\n

  • \u591a\u5934\u81ea\u6ce8\u610f\u529b\u673a\u5236：\u901a\u8fc7\u5e76\u884c\u8ba1\u7b97\u591a\u4e2a\u6ce8\u610f\u529b\u5934，\u6355\u6349\u8f93\u5165\u5e8f\u5217\u4e2d\u4e0d\u540c\u4f4d\u7f6e\u7684\u5173\u8054\u5173\u7cfb<\/li>\n
  • \u524d\u9988\u795e\u7ecf\u7f51\u7edc（FFN） ：\u5bf9\u6ce8\u610f\u529b\u8f93\u51fa\u8fdb\u884c\u975e\u7ebf\u6027\u53d8\u6362，\u589e\u5f3a\u6a21\u578b\u7684\u8868\u8fbe\u80fd\u529b<\/li>\n

    \u6bcf\u4e2a\u5b50\u5c42\u90fd\u914d\u6709\u6b8b\u5dee\u8fde\u63a5\u548c\u5c42\u5f52\u4e00\u5316，\u786e\u4fdd\u6df1\u5c42\u7f51\u7edc\u7684\u8bad\u7ec3\u7a33\u5b9a\u6027\u3002<\/p>\n

    2.2 \u89e3\u7801\u5668（Decoder）<\/h4>\n

    \u89e3\u7801\u5668\u6839\u636e\u7f16\u7801\u5668\u7684\u8f93\u51fa\u751f\u6210\u76ee\u6807\u5e8f\u5217\u3002\u89e3\u7801\u5668\u5728\u7f16\u7801\u5668\u57fa\u7840\u4e0a\u589e\u52a0\u4e86\u63a9\u7801\u591a\u5934\u6ce8\u610f\u529b，\u9632\u6b62\u751f\u6210\u65f6\u770b\u5230\u672a\u6765\u4fe1\u606f\u3002\u5176\u4e09\u5c42\u7ed3\u6784\u5305\u62ec：<\/p>\n

  • \u63a9\u7801\u81ea\u6ce8\u610f\u529b：\u4f7f\u7528\u4e09\u89d2\u63a9\u7801，\u4f7f\u6bcf\u4e2a\u4f4d\u7f6e\u4ec5\u80fd\u5173\u6ce8\u5df2\u751f\u6210\u7684\u90e8\u5206<\/li>\n
  • \u7f16\u7801\u5668-\u89e3\u7801\u5668\u6ce8\u610f\u529b：\u878d\u5408\u8f93\u5165\u5e8f\u5217\u7684\u5168\u5c40\u4fe1\u606f<\/li>\n
  • \u524d\u9988\u795e\u7ecf\u7f51\u7edc<\/li>\n
    \n

    \u4e09\u3001\u81ea\u6ce8\u610f\u529b\u673a\u5236：Transformer\u7684\u6838\u5fc3<\/h3>\n

    3.1 \u4e3a\u4ec0\u4e48\u9700\u8981\u81ea\u6ce8\u610f\u529b？<\/h4>\n

    \u81ea\u6ce8\u610f\u529b\u673a\u5236\u662fTransformer\u6700\u6838\u5fc3\u7684\u7ec4\u4ef6\u3002\u5176\u6838\u5fc3\u601d\u60f3\u662f\u901a\u8fc7\u8ba1\u7b97\u5e8f\u5217\u4e2d\u6bcf\u4e2a\u4f4d\u7f6e\u4e0e\u5176\u4ed6\u4f4d\u7f6e\u7684\u5173\u8054\u6743\u91cd，\u52a8\u6001\u8c03\u6574\u4e0d\u540c\u4f4d\u7f6e\u5bf9\u5f53\u524d\u4f4d\u7f6e\u8f93\u51fa\u7684\u8d21\u732e\u3002<\/p>\n

    \u7ed9\u5b9a\u8f93\u5165\u5e8f\u5217 \\\\(X \\\\in \\\\mathbb{R}^{n \\\\times d}\\\\)（\\\\(n\\\\)\u4e3a\u5e8f\u5217\u957f\u5ea6，\\\\(d\\\\)\u4e3a\u7279\u5f81\u7ef4\u5ea6），\u901a\u8fc7\u7ebf\u6027\u53d8\u6362\u751f\u6210\u67e5\u8be2（Query）\u3001\u952e（Key）\u3001\u503c（Value）：<\/p>\n

    Q=XWQ,K=XWK,V=XWVQ = XW^Q, \\\\quad K = XW^K, \\\\quad V = XW^V<\/span><\/span>Q<\/span><\/span>=<\/span><\/span><\/span><\/span>X<\/span>W<\/span><\/span>Q<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>,<\/span><\/span><\/span>K<\/span><\/span>=<\/span><\/span><\/span><\/span>X<\/span>W<\/span><\/span>K<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>,<\/span><\/span><\/span>V<\/span><\/span>=<\/span><\/span><\/span><\/span>X<\/span>W<\/span><\/span>V<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

    \u6ce8\u610f\u529b\u5206\u6570\u7684\u8ba1\u7b97\u516c\u5f0f\u4e3a：<\/p>\n

    Attention(Q,K,V)=softmax(QKTdk)V\\\\text{Attention}(Q, K, V) = \\\\text{softmax}\\\\left(\\\\frac{QK^T}{\\\\sqrt{d_k}}\\\\right)V<\/span><\/span>Attention<\/span><\/span>(<\/span>Q<\/span>,<\/span><\/span>K<\/span>,<\/span><\/span>V<\/span>)<\/span><\/span>=<\/span><\/span><\/span><\/span>softmax<\/span><\/span><\/span>(<\/span><\/span><\/span><\/span><\/span>d<\/span><\/span>k<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>Q<\/span>K<\/span><\/span>T<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span>V<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

    \u5176\u4e2d \\\\(\\\\sqrt{d_k}\\\\) \u4e3a\u7f29\u653e\u56e0\u5b50，\u9632\u6b62\u70b9\u79ef\u7ed3\u679c\u8fc7\u5927\u5bfc\u81f4softmax\u68af\u5ea6\u6d88\u5931\u3002<\/p>\n

    3.2 \u81ea\u6ce8\u610f\u529b\u7684\u8ba1\u7b97\u6d41\u7a0b<\/h4>\n

    \u81ea\u6ce8\u610f\u529b\u7684\u8ba1\u7b97\u5206\u4e3a\u56db\u4e2a\u6b65\u9aa4：<\/p>\n

  • \u7ebf\u6027\u53d8\u6362：\u8f93\u5165\u5e8f\u5217\u901a\u8fc7\u6743\u91cd\u77e9\u9635\u751f\u6210Q\u3001K\u3001V<\/li>\n
  • \u6ce8\u610f\u529b\u5206\u6570\u8ba1\u7b97：\u8ba1\u7b97Q\u4e0eK\u7684\u8f6c\u7f6e\u7684\u70b9\u79ef，\u5f97\u5230\u6ce8\u610f\u529b\u5206\u6570\u77e9\u9635<\/li>\n
  • Softmax\u5f52\u4e00\u5316：\u5bf9\u6ce8\u610f\u529b\u5206\u6570\u8fdb\u884cSoftmax\u5f52\u4e00\u5316，\u5f97\u5230\u6743\u91cd\u77e9\u9635<\/li>\n
  • \u52a0\u6743\u6c42\u548c：\u5c06\u6743\u91cd\u77e9\u9635\u4e0eV\u76f8\u4e58，\u5f97\u5230\u52a0\u6743\u540e\u7684\u8f93\u51fa<\/li>\n

    3.3 PyTorch\u4ee3\u7801\u5b9e\u73b0<\/h4>\n

    \u4ee5\u4e0b\u662f\u81ea\u6ce8\u610f\u529b\u673a\u5236\u7684PyTorch\u5b9e\u73b0：<\/p>\n

    import<\/span> torch
    \nimport<\/span> torch.<\/span>nn as<\/span> nn
    \nimport<\/span> torch.<\/span>nn.<\/span>functional as<\/span> F<\/p>\n

    class<\/span> SelfAttention<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self,<\/span> embed_dim,<\/span> num_heads)<\/span>:<\/span>
    \n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span>
    \n self.<\/span>multihead_attn =<\/span> nn.<\/span>MultiheadAttention(<\/span>embed_dim,<\/span> num_heads)<\/span><\/p>\n

    def<\/span> forward<\/span>(<\/span>self,<\/span> x)<\/span>:<\/span>
    \n # x: (seq_len, batch_size, embed_dim)<\/span>
    \n attn_output,<\/span> _ =<\/span> self.<\/span>multihead_attn(<\/span>x,<\/span> x,<\/span> x)<\/span>
    \n return<\/span> attn_output<\/p>\n

    # \u4ece\u96f6\u5b9e\u73b0Scaled Dot-Product Attention<\/span>
    \nclass<\/span> ScaledDotProductAttention<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self,<\/span> d_k)<\/span>:<\/span>
    \n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span>
    \n self.<\/span>d_k =<\/span> d_k<\/p>\n

    def<\/span> forward<\/span>(<\/span>self,<\/span> Q,<\/span> K,<\/span> V,<\/span> mask=<\/span>None<\/span>)<\/span>:<\/span>
    \n # Q, K, V: (batch_size, num_heads, seq_len, d_k)<\/span>
    \n scores =<\/span> torch.<\/span>matmul(<\/span>Q,<\/span> K.<\/span>transpose(<\/span>–<\/span>2<\/span>,<\/span> –<\/span>1<\/span>)<\/span>)<\/span> \/<\/span> torch.<\/span>sqrt(<\/span>torch.<\/span>tensor(<\/span>self.<\/span>d_k,<\/span> dtype=<\/span>torch.<\/span>float32)<\/span>)<\/span><\/p>\n

    if<\/span> mask is<\/span> not<\/span> None<\/span>:<\/span>
    \n scores =<\/span> scores.<\/span>masked_fill(<\/span>mask ==<\/span> 0<\/span>,<\/span> –<\/span>1e9<\/span>)<\/span><\/p>\n

    attention_weights =<\/span> F.<\/span>softmax(<\/span>scores,<\/span> dim=<\/span>–<\/span>1<\/span>)<\/span>
    \n output =<\/span> torch.<\/span>matmul(<\/span>attention_weights,<\/span> V)<\/span>
    \n return<\/span> output,<\/span> attention_weights<\/p>\n

    \n

    \u56db\u3001\u591a\u5934\u6ce8\u610f\u529b：\u589e\u5f3a\u6a21\u578b\u8868\u8fbe\u80fd\u529b<\/h3>\n

    4.1 \u4e3a\u4ec0\u4e48\u9700\u8981\u591a\u5934？<\/h4>\n

    \u5355\u5934\u6ce8\u610f\u529b\u4ec5\u80fd\u5b66\u4e60\u4e00\u79cd\u6ce8\u610f\u529b\u6a21\u5f0f，\u53ef\u80fd\u5ffd\u7565\u5e8f\u5217\u4e2d\u7684\u591a\u5c42\u6b21\u8bed\u4e49\u4fe1\u606f（\u5982\u8bed\u6cd5\u3001\u8bed\u4e49\u3001\u4e0a\u4e0b\u6587）\u3002\u591a\u5934\u6ce8\u610f\u529b\u901a\u8fc7\u5c06Q\u3001K\u3001V\u62c6\u5206\u4e3a\u591a\u4e2a\u5b50\u7a7a\u95f4（\u5934），\u6bcf\u4e2a\u5934\u72ec\u7acb\u8ba1\u7b97\u6ce8\u610f\u529b，\u6700\u540e\u62fc\u63a5\u7ed3\u679c：<\/p>\n

    MultiHead(Q,K,V)=Concat(head1,\u2026,headh)WO\\\\text{MultiHead}(Q, K, V) = \\\\text{Concat}(\\\\text{head}_1, \\\\dots, \\\\text{head}_h)W^O<\/span><\/span>MultiHead<\/span><\/span>(<\/span>Q<\/span>,<\/span><\/span>K<\/span>,<\/span><\/span>V<\/span>)<\/span><\/span>=<\/span><\/span><\/span><\/span>Concat<\/span><\/span>(<\/span>head<\/span><\/span><\/span>1<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>,<\/span><\/span>\u2026<\/span><\/span>,<\/span><\/span>head<\/span><\/span><\/span>h<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span>W<\/span><\/span>O<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

    \u5176\u4e2d \\\\(\\\\text{head}_i = \\\\text{Attention}(QW_i^Q, KW_i^K, VW_iV)\\\\)，\\\\(W_i<\/sup>Q, W_i^K, W_i^V\\\\)\u4e3a\u6bcf\u4e2a\u5934\u7684\u6295\u5f71\u77e9\u9635\u3002<\/p>\n

    \u591a\u5934\u6ce8\u610f\u529b\u7684\u4f18\u52bf\u5728\u4e8e：<\/p>\n

      \n
    • \u591a\u89c6\u89d2\u5efa\u6a21：\u4e0d\u540c\u5934\u53ef\u5173\u6ce8\u5e8f\u5217\u7684\u4e0d\u540c\u7279\u5f81（\u5982\u8bed\u6cd5\u7ed3\u6784\u3001\u5b9e\u4f53\u5173\u7cfb\u3001\u957f\u8ddd\u79bb\u4f9d\u8d56）<\/li>\n
    • \u53c2\u6570\u5171\u4eab：\u901a\u8fc7\u6743\u91cd\u5171\u4eab\u51cf\u5c11\u53c2\u6570\u91cf，\u907f\u514d\u8fc7\u62df\u5408<\/li>\n<\/ul>\n

      4.2 \u5b8c\u6574\u7684\u591a\u5934\u6ce8\u610f\u529b\u5b9e\u73b0<\/h4>\n

      class<\/span> MultiHeadAttention<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
      \n def<\/span> __init__<\/span>(<\/span>self,<\/span> d_model,<\/span> num_heads)<\/span>:<\/span>
      \n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span>
      \n assert<\/span> d_model %<\/span> num_heads ==<\/span> 0<\/span>,<\/span> "d_model must be divisible by num_heads"<\/span><\/p>\n

      self.<\/span>d_model =<\/span> d_model
      \n self.<\/span>num_heads =<\/span> num_heads
      \n self.<\/span>d_k =<\/span> d_model \/\/<\/span> num_heads<\/p>\n

      # \u7ebf\u6027\u53d8\u6362\u5c42<\/span>
      \n self.<\/span>W_q =<\/span> nn.<\/span>Linear(<\/span>d_model,<\/span> d_model)<\/span>
      \n self.<\/span>W_k =<\/span> nn.<\/span>Linear(<\/span>d_model,<\/span> d_model)<\/span>
      \n self.<\/span>W_v =<\/span> nn.<\/span>Linear(<\/span>d_model,<\/span> d_model)<\/span>
      \n self.<\/span>W_o =<\/span> nn.<\/span>Linear(<\/span>d_model,<\/span> d_model)<\/span><\/p>\n

      self.<\/span>attention =<\/span> ScaledDotProductAttention(<\/span>self.<\/span>d_k)<\/span><\/p>\n

      def<\/span> forward<\/span>(<\/span>self,<\/span> Q,<\/span> K,<\/span> V,<\/span> mask=<\/span>None<\/span>)<\/span>:<\/span>
      \n batch_size =<\/span> Q.<\/span>size(<\/span>0<\/span>)<\/span><\/p>\n

      # 1. \u7ebf\u6027\u53d8\u6362\u5e76\u62c6\u5206\u4e3a\u591a\u5934<\/span>
      \n Q =<\/span> self.<\/span>W_q(<\/span>Q)<\/span>.<\/span>view(<\/span>batch_size,<\/span> –<\/span>1<\/span>,<\/span> self.<\/span>num_heads,<\/span> self.<\/span>d_k)<\/span>.<\/span>transpose(<\/span>1<\/span>,<\/span> 2<\/span>)<\/span>
      \n K =<\/span> self.<\/span>W_k(<\/span>K)<\/span>.<\/span>view(<\/span>batch_size,<\/span> –<\/span>1<\/span>,<\/span> self.<\/span>num_heads,<\/span> self.<\/span>d_k)<\/span>.<\/span>transpose(<\/span>1<\/span>,<\/span> 2<\/span>)<\/span>
      \n V =<\/span> self.<\/span>W_v(<\/span>V)<\/span>.<\/span>view(<\/span>batch_size,<\/span> –<\/span>1<\/span>,<\/span> self.<\/span>num_heads,<\/span> self.<\/span>d_k)<\/span>.<\/span>transpose(<\/span>1<\/span>,<\/span> 2<\/span>)<\/span><\/p>\n

      # 2. \u5e94\u7528\u7f29\u653e\u70b9\u79ef\u6ce8\u610f\u529b<\/span>
      \n attn_output,<\/span> attn_weights =<\/span> self.<\/span>attention(<\/span>Q,<\/span> K,<\/span> V,<\/span> mask)<\/span><\/p>\n

      # 3. \u5408\u5e76\u591a\u5934<\/span>
      \n attn_output =<\/span> attn_output.<\/span>transpose(<\/span>1<\/span>,<\/span> 2<\/span>)<\/span>.<\/span>contiguous(<\/span>)<\/span>.<\/span>view(<\/span>batch_size,<\/span> –<\/span>1<\/span>,<\/span> self.<\/span>d_model)<\/span><\/p>\n

      # 4. \u6700\u7ec8\u7ebf\u6027\u53d8\u6362<\/span>
      \n output =<\/span> self.<\/span>W_o(<\/span>attn_output)<\/span>
      \n return<\/span> output<\/p>\n

      \n

      \u4e94\u3001\u4f4d\u7f6e\u7f16\u7801：\u5f25\u8865\u5e8f\u5217\u987a\u5e8f\u4fe1\u606f<\/h3>\n

      5.1 \u4e3a\u4ec0\u4e48\u9700\u8981\u4f4d\u7f6e\u7f16\u7801？<\/h4>\n

      \u81ea\u6ce8\u610f\u529b\u673a\u5236\u672c\u8eab\u662f\u4f4d\u7f6e\u65e0\u5173\u7684\u2014\u2014\u4ea4\u6362\u5e8f\u5217\u4e2d\u4e24\u4e2a\u5143\u7d20\u7684\u4f4d\u7f6e，\u6ce8\u610f\u529b\u7ed3\u679c\u4e0d\u53d8\u3002\u4e3a\u5f15\u5165\u5e8f\u5217\u987a\u5e8f\u4fe1\u606f，\u5fc5\u987b\u663e\u5f0f\u7f16\u7801\u4f4d\u7f6e\u3002<\/p>\n

      5.2 \u6b63\u5f26\u4f4d\u7f6e\u7f16\u7801<\/h4>\n

      Transformer\u91c7\u7528\u6b63\u5f26\u51fd\u6570\u751f\u6210\u4f4d\u7f6e\u7f16\u7801：<\/p>\n

      PE(pos,2i)=sin\u2061(pos100002i\/dmodel)PE_{(pos, 2i)} = \\\\sin\\\\left(\\\\frac{pos}{10000^{2i\/d_{model}}}\\\\right)<\/span><\/span>P<\/span>E<\/span><\/span>(<\/span>p<\/span>os<\/span>,<\/span>2<\/span>i<\/span>)<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>sin<\/span><\/span>(<\/span><\/span><\/span><\/span>1000<\/span>0<\/span><\/span>2<\/span>i<\/span>\/<\/span>d<\/span><\/span>m<\/span>o<\/span>d<\/span>e<\/span>l<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>p<\/span>os<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

      PE(pos,2i+1)=cos\u2061(pos100002i\/dmodel)PE_{(pos, 2i+1)} = \\\\cos\\\\left(\\\\frac{pos}{10000^{2i\/d_{model}}}\\\\right)<\/span><\/span>P<\/span>E<\/span><\/span>(<\/span>p<\/span>os<\/span>,<\/span>2<\/span>i<\/span>+<\/span>1<\/span>)<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>cos<\/span><\/span>(<\/span><\/span><\/span><\/span>1000<\/span>0<\/span><\/span>2<\/span>i<\/span>\/<\/span>d<\/span><\/span>m<\/span>o<\/span>d<\/span>e<\/span>l<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>p<\/span>os<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

      \u5176\u4e2d \\\\(pos\\\\) \u4e3a\u4f4d\u7f6e，\\\\(i\\\\) \u4e3a\u7ef4\u5ea6\u7d22\u5f15\u3002<\/p>\n

      \u8fd9\u79cd\u7f16\u7801\u65b9\u5f0f\u7684\u4f18\u52bf\u5728\u4e8e：<\/p>\n

        \n
      • \u56fa\u5b9a\u516c\u5f0f：\u4e0d\u9700\u8981\u901a\u8fc7\u8bad\u7ec3\u6765\u5b66\u4e60，\u53ef\u4ee5\u76f4\u63a5\u8ba1\u7b97\u51fa\u4efb\u610f\u4f4d\u7f6e\u7684\u7f16\u7801<\/li>\n
      • \u6cdb\u5316\u80fd\u529b：\u7406\u8bba\u4e0a\u53ef\u4ee5\u5904\u7406\u6bd4\u8bad\u7ec3\u65f6\u89c1\u8fc7\u7684\u5e8f\u5217\u66f4\u957f\u7684\u5e8f\u5217<\/li>\n<\/ul>\n

        5.3 \u4f4d\u7f6e\u7f16\u7801\u7684\u4ee3\u7801\u5b9e\u73b0<\/h4>\n

        class<\/span> PositionalEncoding<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
        \n def<\/span> __init__<\/span>(<\/span>self,<\/span> d_model,<\/span> max_seq_len=<\/span>5000<\/span>)<\/span>:<\/span>
        \n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span><\/p>\n

        # \u521b\u5efa\u4f4d\u7f6e\u7f16\u7801\u77e9\u9635<\/span>
        \n pe =<\/span> torch.<\/span>zeros(<\/span>max_seq_len,<\/span> d_model)<\/span>
        \n position =<\/span> torch.<\/span>arange(<\/span>0<\/span>,<\/span> max_seq_len,<\/span> dtype=<\/span>torch.<\/span>float32)<\/span>.<\/span>unsqueeze(<\/span>1<\/span>)<\/span><\/p>\n

        # \u8ba1\u7b97\u5206\u6bcd\u9879<\/span>
        \n div_term =<\/span> torch.<\/span>exp(<\/span>torch.<\/span>arange(<\/span>0<\/span>,<\/span> d_model,<\/span> 2<\/span>)<\/span>.<\/span>float<\/span>(<\/span>)<\/span> *<\/span> (<\/span>–<\/span>math.<\/span>log(<\/span>10000.0<\/span>)<\/span> \/<\/span> d_model)<\/span>)<\/span><\/p>\n

        # \u5e94\u7528\u6b63\u5f26\u548c\u4f59\u5f26<\/span>
        \n pe[<\/span>:<\/span>,<\/span> 0<\/span>:<\/span>:<\/span>2<\/span>]<\/span> =<\/span> torch.<\/span>sin(<\/span>position *<\/span> div_term)<\/span>
        \n pe[<\/span>:<\/span>,<\/span> 1<\/span>:<\/span>:<\/span>2<\/span>]<\/span> =<\/span> torch.<\/span>cos(<\/span>position *<\/span> div_term)<\/span><\/p>\n

        pe =<\/span> pe.<\/span>unsqueeze(<\/span>0<\/span>)<\/span> # (1, max_seq_len, d_model)<\/span>
        \n self.<\/span>register_buffer(<\/span>'pe'<\/span>,<\/span> pe)<\/span><\/p>\n

        def<\/span> forward<\/span>(<\/span>self,<\/span> x)<\/span>:<\/span>
        \n # x: (batch_size, seq_len, d_model)<\/span>
        \n return<\/span> x +<\/span> self.<\/span>pe[<\/span>:<\/span>,<\/span> :<\/span>x.<\/span>size(<\/span>1<\/span>)<\/span>,<\/span> :<\/span>]<\/span><\/p>\n

        \n

        \u516d\u3001\u6b8b\u5dee\u8fde\u63a5\u4e0e\u5c42\u5f52\u4e00\u5316<\/h3>\n

        6.1 \u6b8b\u5dee\u8fde\u63a5<\/h4>\n

        \u6b8b\u5dee\u8fde\u63a5\u901a\u8fc7\u5c06\u8f93\u5165\u76f4\u63a5\u52a0\u5230\u8f93\u51fa\u4e0a，\u7f13\u89e3\u6df1\u5c42\u7f51\u7edc\u7684\u68af\u5ea6\u6d88\u5931\u95ee\u9898\u3002\u5728\u6bcf\u4e2a\u5b50\u5c42\u4e2d：<\/p>\n

        output=LayerNorm(x+Sublayer(x))\\\\text{output} = \\\\text{LayerNorm}(x + \\\\text{Sublayer}(x))<\/span><\/span>output<\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>LayerNorm<\/span><\/span>(<\/span>x<\/span><\/span>+<\/span><\/span><\/span><\/span>Sublayer<\/span><\/span>(<\/span>x<\/span>))<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

        6.2 \u5c42\u5f52\u4e00\u5316<\/h4>\n

        \u5c42\u5f52\u4e00\u5316\u5bf9\u6bcf\u4e2a\u6837\u672c\u7684\u7279\u5f81\u8fdb\u884c\u5f52\u4e00\u5316，\u7a33\u5b9a\u8bad\u7ec3\u8fc7\u7a0b\u3002<\/p>\n

        6.3 \u5b8c\u6574\u7684\u7f16\u7801\u5668\u5c42\u5b9e\u73b0<\/h4>\n

        class<\/span> EncoderLayer<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
        \n def<\/span> __init__<\/span>(<\/span>self,<\/span> d_model,<\/span> num_heads,<\/span> d_ff,<\/span> dropout=<\/span>0.1<\/span>)<\/span>:<\/span>
        \n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span>
        \n self.<\/span>self_attn =<\/span> MultiHeadAttention(<\/span>d_model,<\/span> num_heads)<\/span>
        \n self.<\/span>feed_forward =<\/span> nn.<\/span>Sequential(<\/span>
        \n nn.<\/span>Linear(<\/span>d_model,<\/span> d_ff)<\/span>,<\/span>
        \n nn.<\/span>ReLU(<\/span>)<\/span>,<\/span>
        \n nn.<\/span>Linear(<\/span>d_ff,<\/span> d_model)<\/span>
        \n )<\/span>
        \n self.<\/span>norm1 =<\/span> nn.<\/span>LayerNorm(<\/span>d_model)<\/span>
        \n self.<\/span>norm2 =<\/span> nn.<\/span>LayerNorm(<\/span>d_model)<\/span>
        \n self.<\/span>dropout =<\/span> nn.<\/span>Dropout(<\/span>dropout)<\/span><\/p>\n

        def<\/span> forward<\/span>(<\/span>self,<\/span> x,<\/span> mask=<\/span>None<\/span>)<\/span>:<\/span>
        \n # \u81ea\u6ce8\u610f\u529b + \u6b8b\u5dee\u8fde\u63a5 + \u5c42\u5f52\u4e00\u5316<\/span>
        \n attn_output =<\/span> self.<\/span>self_attn(<\/span>x,<\/span> x,<\/span> x,<\/span> mask)<\/span>
        \n x =<\/span> self.<\/span>norm1(<\/span>x +<\/span> self.<\/span>dropout(<\/span>attn_output)<\/span>)<\/span><\/p>\n

        # \u524d\u9988\u7f51\u7edc + \u6b8b\u5dee\u8fde\u63a5 + \u5c42\u5f52\u4e00\u5316<\/span>
        \n ff_output =<\/span> self.<\/span>feed_forward(<\/span>x)<\/span>
        \n x =<\/span> self.<\/span>norm2(<\/span>x +<\/span> self.<\/span>dropout(<\/span>ff_output)<\/span>)<\/span><\/p>\n

        return<\/span> x<\/p>\n

        \n

        \u4e03\u3001\u5b8c\u6574\u7684Transformer\u6a21\u578b<\/h3>\n

        class<\/span> Transformer<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
        \n def<\/span> __init__<\/span>(<\/span>self,<\/span> src_vocab_size,<\/span> tgt_vocab_size,<\/span> d_model=<\/span>512<\/span>,<\/span> num_heads=<\/span>8<\/span>,<\/span>
        \n num_layers=<\/span>6<\/span>,<\/span> d_ff=<\/span>2048<\/span>,<\/span> max_seq_len=<\/span>5000<\/span>,<\/span> dropout=<\/span>0.1<\/span>)<\/span>:<\/span>
        \n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span><\/p>\n

        self.<\/span>encoder_embedding =<\/span> nn.<\/span>Embedding(<\/span>src_vocab_size,<\/span> d_model)<\/span>
        \n self.<\/span>decoder_embedding =<\/span> nn.<\/span>Embedding(<\/span>tgt_vocab_size,<\/span> d_model)<\/span>
        \n self.<\/span>positional_encoding =<\/span> PositionalEncoding(<\/span>d_model,<\/span> max_seq_len)<\/span><\/p>\n

        self.<\/span>encoder_layers =<\/span> nn.<\/span>ModuleList(<\/span>[<\/span>
        \n EncoderLayer(<\/span>d_model,<\/span> num_heads,<\/span> d_ff,<\/span> dropout)<\/span> for<\/span> _ in<\/span> range<\/span>(<\/span>num_layers)<\/span>
        \n ]<\/span>)<\/span>
        \n self.<\/span>decoder_layers =<\/span> nn.<\/span>ModuleList(<\/span>[<\/span>
        \n DecoderLayer(<\/span>d_model,<\/span> num_heads,<\/span> d_ff,<\/span> dropout)<\/span> for<\/span> _ in<\/span> range<\/span>(<\/span>num_layers)<\/span>
        \n ]<\/span>)<\/span><\/p>\n

        self.<\/span>fc_out =<\/span> nn.<\/span>Linear(<\/span>d_model,<\/span> tgt_vocab_size)<\/span>
        \n self.<\/span>dropout =<\/span> nn.<\/span>Dropout(<\/span>dropout)<\/span>
        \n self.<\/span>d_model =<\/span> d_model<\/p>\n

        def<\/span> forward<\/span>(<\/span>self,<\/span> src,<\/span> tgt,<\/span> src_mask=<\/span>None<\/span>,<\/span> tgt_mask=<\/span>None<\/span>)<\/span>:<\/span>
        \n # \u7f16\u7801\u5668<\/span>
        \n src_emb =<\/span> self.<\/span>dropout(<\/span>self.<\/span>positional_encoding(<\/span>self.<\/span>encoder_embedding(<\/span>src)<\/span> *<\/span> math.<\/span>sqrt(<\/span>self.<\/span>d_model)<\/span>)<\/span>)<\/span>
        \n for<\/span> layer in<\/span> self.<\/span>encoder_layers:<\/span>
        \n src_emb =<\/span> layer(<\/span>src_emb,<\/span> src_mask)<\/span><\/p>\n

        # \u89e3\u7801\u5668<\/span>
        \n tgt_emb =<\/span> self.<\/span>dropout(<\/span>self.<\/span>positional_encoding(<\/span>self.<\/span>decoder_embedding(<\/span>tgt)<\/span> *<\/span> math.<\/span>sqrt(<\/span>self.<\/span>d_model)<\/span>)<\/span>)<\/span>
        \n for<\/span> layer in<\/span> self.<\/span>decoder_layers:<\/span>
        \n tgt_emb =<\/span> layer(<\/span>tgt_emb,<\/span> src_emb,<\/span> src_mask,<\/span> tgt_mask)<\/span><\/p>\n

        # \u8f93\u51fa<\/span>
        \n output =<\/span> self.<\/span>fc_out(<\/span>tgt_emb)<\/span>
        \n return<\/span> output<\/p>\n

        \n

        \u516b\u3001\u5b9e\u9a8c\u9a8c\u8bc1\u4e0e\u6027\u80fd\u5206\u6790<\/h3>\n

        8.1 \u673a\u5668\u7ffb\u8bd1\u4efb\u52a1\u9a8c\u8bc1<\/h4>\n

        \u5728\u539f\u59cb\u8bba\u6587\u4e2d，\u4f5c\u8005\u901a\u8fc7\u642d\u5efa\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668\u54046\u5c42\u3001\u603b\u517112\u5c42\u7684Transformer，\u5728\u673a\u5668\u7ffb\u8bd1\u4efb\u52a1\u4e2d\u53d6\u5f97\u4e86BLEU\u503c\u7684\u65b0\u9ad8\u3002<\/p>\n

        \u540e\u7eed\u7814\u7a76\u8fdb\u4e00\u6b65\u9a8c\u8bc1\u4e86Transformer\u7684\u4f18\u52bf\u3002\u4ee5\u82f1\u6cd5\u7ffb\u8bd1\u4efb\u52a1\u4e3a\u4f8b，Transformer\u5728BLEU\u8bc4\u5206\u4e0a\u8f83RNN\u6a21\u578b\u63d0\u534715%\u4ee5\u4e0a，\u4e14\u8bad\u7ec3\u901f\u5ea6\u63d0\u9ad83\u500d\u3002\u5728\u4f4e\u8d44\u6e90\u8bed\u8a00\u7684\u795e\u7ecf\u673a\u5668\u7ffb\u8bd1\u4efb\u52a1\u4e2d，Transformer\u6a21\u578b\u5728\u7ffb\u8bd1\u51c6\u786e\u7387（BLEU-4）\u4e0a\u6301\u7eed\u4f18\u4e8e\u57fa\u4e8e\u6ce8\u610f\u529b\u7684RNN\u6a21\u578b\u3002<\/p>\n

        \u5728\u5b9e\u9645\u541e\u5410\u91cf\u6d4b\u8bd5\u4e2d，\u5f53batch size\u63d0\u53473\u500d\u65f6，Transformer\u7684\u541e\u5410\u91cf\u589e\u52a02.4\u500d\u3002\u5728\u7ffb\u8bd1\u901f\u5ea6\u65b9\u9762，Transformer\u6bcf\u5206\u949f\u53ef\u7ffb\u8bd1350\u4e2a\u53e5\u5b50，\u800cRNN\u4ec5\u80fd\u7ffb\u8bd1250\u4e2a\u3002<\/p>\n

        8.2 \u6ce8\u610f\u529b\u53ef\u89c6\u5316\u9a8c\u8bc1<\/h4>\n

        \u901a\u8fc7\u53ef\u89c6\u5316\u6ce8\u610f\u529b\u6743\u91cd，\u53ef\u4ee5\u76f4\u89c2\u5730\u9a8c\u8bc1Transformer\u7684\u5b66\u4e60\u6548\u679c\u3002\u6ce8\u610f\u529b\u70ed\u56fe\u5c55\u793a\u4e86\u6a21\u578b\u5728\u5904\u7406\u8f93\u5165\u65f6\u201c\u5173\u6ce8\u201d\u7684\u4f4d\u7f6e\u2014\u2014\u6743\u91cd\u8d8a\u9ad8\u8868\u793a\u8be5\u4f4d\u7f6e\u5bf9\u5f53\u524d\u8f93\u51fa\u7684\u8d21\u732e\u8d8a\u5927\u3002<\/p>\n

        import<\/span> matplotlib.<\/span>pyplot as<\/span> plt
        \nimport<\/span> seaborn as<\/span> sns<\/p>\n

        def<\/span> visualize_attention<\/span>(<\/span>attention_weights,<\/span> tokens)<\/span>:<\/span>
        \n """
        \n \u53ef\u89c6\u5316\u6ce8\u610f\u529b\u6743\u91cd
        \n attention_weights: (num_heads, seq_len, seq_len)
        \n tokens: \u8f93\u5165token\u5217\u8868
        \n """<\/span>
        \n fig,<\/span> axes =<\/span> plt.<\/span>subplots(<\/span>1<\/span>,<\/span> attention_weights.<\/span>shape[<\/span>0<\/span>]<\/span>,<\/span> figsize=<\/span>(<\/span>20<\/span>,<\/span> 4<\/span>)<\/span>)<\/span><\/p>\n

        for<\/span> i,<\/span> ax in<\/span> enumerate<\/span>(<\/span>axes)<\/span>:<\/span>
        \n sns.<\/span>heatmap(<\/span>attention_weights[<\/span>i]<\/span>.<\/span>detach(<\/span>)<\/span>.<\/span>numpy(<\/span>)<\/span>,<\/span>
        \n xticklabels=<\/span>tokens,<\/span> yticklabels=<\/span>tokens,<\/span>
        \n ax=<\/span>ax,<\/span> cmap=<\/span>'Blues'<\/span>,<\/span> cbar=<\/span>False<\/span>)<\/span>
        \n ax.<\/span>set_title(<\/span>f'Head <\/span>{<\/span>i+<\/span>1<\/span>}<\/span><\/span>'<\/span><\/span>)<\/span>
        \n ax.<\/span>set_xlabel(<\/span>'Key'<\/span>)<\/span>
        \n ax.<\/span>set_ylabel(<\/span>'Query'<\/span>)<\/span><\/p>\n

        plt.<\/span>tight_layout(<\/span>)<\/span>
        \n plt.<\/span>savefig(<\/span>'attention_heads.png'<\/span>)<\/span>
        \n plt.<\/span>show(<\/span>)<\/span><\/p>\n

        8.3 \u5173\u952e\u6027\u80fd\u6570\u636e\u603b\u7ed3<\/h4>\n\n\u6307\u6807TransformerRNN\/LSTM<\/tr>\n\n\n\n\n\n\n
        BLEU\u8bc4\u5206（\u82f1\u6cd5\u7ffb\u8bd1）<\/td>\n\u57fa\u51c6+15%<\/td>\n\u57fa\u51c6<\/td>\n<\/tr>\n
        \u8bad\u7ec3\u901f\u5ea6<\/td>\n3\u500d<\/td>\n1\u500d<\/td>\n<\/tr>\n
        \u7ffb\u8bd1\u541e\u5410\u91cf（\u53e5\/\u5206\u949f）<\/td>\n350<\/td>\n250<\/td>\n<\/tr>\n
        \u5185\u5b58\u6d88\u8017<\/td>\n1.93\u500d<\/td>\n1\u500d<\/td>\n<\/tr>\n
        \u957f\u8ddd\u79bb\u4f9d\u8d56\u6355\u6349<\/td>\n\u2705 \u5168\u5c40<\/td>\n\u274c \u6613\u4e22\u5931<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
        \n

        \u4e5d\u3001\u603b\u7ed3<\/h3>\n

        Transformer\u67b6\u6784\u7684\u6210\u529f\u6e90\u4e8e\u5176\u6838\u5fc3\u8bbe\u8ba1\u7684\u534f\u540c\u4f5c\u7528：<\/p>\n

      • \u81ea\u6ce8\u610f\u529b\u673a\u5236：\u5b9e\u73b0\u4e86\u5168\u5c40\u4fe1\u606f\u6355\u6349\u548c\u5e76\u884c\u8ba1\u7b97，\u5f7b\u5e95\u89e3\u51b3\u4e86RNN\u7684\u5e8f\u5217\u4f9d\u8d56\u548c\u957f\u8ddd\u79bb\u4f9d\u8d56\u95ee\u9898<\/li>\n
      • \u591a\u5934\u6ce8\u610f\u529b：\u901a\u8fc7\u591a\u89c6\u89d2\u5efa\u6a21，\u6355\u6349\u4e0d\u540c\u5c42\u6b21\u7684\u8bed\u4e49\u7279\u5f81，\u589e\u5f3a\u4e86\u6a21\u578b\u7684\u8868\u8fbe\u80fd\u529b<\/li>\n
      • \u4f4d\u7f6e\u7f16\u7801：\u5f25\u8865\u4e86\u81ea\u6ce8\u610f\u529b\u673a\u5236\u7684\u4f4d\u7f6e\u4fe1\u606f\u7f3a\u5931，\u4f7f\u6a21\u578b\u80fd\u591f\u611f\u77e5\u5e8f\u5217\u987a\u5e8f<\/li>\n
      • \u6b8b\u5dee\u8fde\u63a5\u4e0e\u5c42\u5f52\u4e00\u5316：\u4fdd\u969c\u4e86\u6df1\u5c42\u7f51\u7edc\u7684\u8bad\u7ec3\u7a33\u5b9a\u6027<\/li>\n

        \u4ece2017\u5e74\u81f3\u4eca，Transformer\u5df2\u4ece\u673a\u5668\u7ffb\u8bd1\u4efb\u52a1\u6269\u5c55\u5230BERT\u3001GPT\u7b49\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u6a21\u578b，\u6210\u4e3a\u73b0\u4ee3\u5927\u8bed\u8a00\u6a21\u578b（LLM）\u7684\u7edd\u5bf9\u57fa\u77f3\u3002\u638c\u63e1Transformer\u67b6\u6784，\u4e0d\u4ec5\u662f\u7406\u89e3\u5f53\u524d\u5927\u6a21\u578b\u6280\u672f\u7684\u5fc5\u7ecf\u4e4b\u8def，\u66f4\u662f\u53c2\u4e0eAI\u7b97\u6cd5\u5e94\u7528\u5f00\u53d1\u4e0e\u4f18\u5316\u7684\u6838\u5fc3\u80fd\u529b\u3002<\/p>\n

        \u6b63\u5982\u8bba\u6587\u6807\u9898\u6240\u8a00\u2014\u2014\u201cAttention Is All You Need\u201d ，\u6ce8\u610f\u529b\u673a\u5236\u5c31\u662fTransformer\u7684\u7075\u9b42\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

        \u638c\u63e1\u5927\u6a21\u578b\u7ed3\u6784：Transformer\u67b6\u6784\u6df1\u5ea6\u89e3\u6790
        \n\u4e00\u3001\u5f15\u8a00：\u4e3a\u4ec0\u4e48Transformer\u6210\u4e3a\u5927\u6a21\u578b\u7684\u57fa\u77f3
        \n2017\u5e74，Google\u5728\u8bba\u6587\u300aAttention Is All You Need\u300b\u4e2d\u63d0\u51fa\u4e86Transformer\u67b6\u6784\u3002\u8fd9\u4e00\u67b6\u6784\u5f7b\u5e95\u6539\u53d8\u4e86\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u4f9d\u8d56RNN\/CNN\u7684\u8303\u5f0f，\u51ed\u501f\u5176\u9ad8\u6548\u7684\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\u548c\u5bf9\u957f\u5e8f\u5217\u7684\u5f3a\u5927\u5efa\u6a21\u80fd\u529b，\u8fc5\u901f\u6210\u4e3a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u6838\u5fc3\u67b6\u6784，\u5e76\u9010\u6b65\u6269\u5c55\u81f3\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u8bed<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[841,50,86],"topic":[],"class_list":["post-86980","post","type-post","status-publish","format-standard","hentry","category-server","tag-transformer","tag-50","tag-86"],"yoast_head":"\n\u638c\u63e1\u5927\u6a21\u578b\u7ed3\u6784\uff1aTransformer\u67b6\u6784\u6df1\u5ea6\u89e3\u6790 - 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