{"id":91892,"date":"2026-08-08T17:24:53","date_gmt":"2026-08-08T09:24:53","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/91892.html"},"modified":"2026-08-08T17:24:53","modified_gmt":"2026-08-08T09:24:53","slug":"python%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e7%bc%96%e7%a8%8b%e5%85%a5%e9%97%a8%ef%bc%88%e4%ba%8c%e5%8d%81%e4%b8%83%ef%bc%89-rnn-imbd%e6%90%ad%e5%bb%ba%e6%83%85%e6%84%9f%e5%88%86","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/91892.html","title":{"rendered":"python\u795e\u7ecf\u7f51\u7edc\u7f16\u7a0b\u5165\u95e8\uff08\u4e8c\u5341\u4e03\uff09\u2014\u2014RNN IMBD\u642d\u5efa\u60c5\u611f\u5206\u7c7b\u5668\u4e0e\u57fa\u7840\u8bad\u7ec3"},"content":{"rendered":"

\u5f15\u8a00：\u83dc\u90fd\u5207\u597d\u4e86，\u5f00\u706b\u70e7\u83dc<\/h4>\n

\u524d\u4e24\u7ae0\u4e00\u76f4\u5728"\u5907\u83dc"：\u7b2c 12 \u7ae0\u628a\u6587\u5b57\u53d8\u6210\u6574\u6570，\u7b2c 13 \u7ae0\u628a\u957f\u77ed\u4e0d\u4e00\u7684\u5f71\u8bc4\u88c5\u8fdb (50000,500)(50000, 500)<\/span><\/span>(<\/span>50000<\/span>,<\/span><\/span>500<\/span>)<\/span><\/span><\/span><\/span><\/span> \u7684\u7edf\u4e00\u6a21\u5177\u3002\u6570\u636e\u6d17\u5f97\u5e72\u5e72\u51c0\u51c0，\u8bcd\u8868\u3001\u6279\u6b21\u3001\u63a9\u7801\u90fd\u5907\u597d\u4e86\u3002\u53ef\u5149\u6709\u98df\u6750\u6446\u7740，\u6210\u4e0d\u4e86\u83dc\u2014\u2014\u5f97\u751f\u706b\u3001\u4e0b\u9505\u3001\u7ffb\u7092\u3002<\/p>\n

\u8fd9\u4e00\u7ae0\u5c31\u662f\u628a"\u98df\u6750"\u771f\u6b63\u5012\u8fdb\u9505\u91cc：\u642d\u8d77\u4e00\u5f20\u6700\u7b80\u5355\u7684\u60c5\u611f\u5206\u7c7b\u5668，\u8ba9\u6a21\u578b\u628a\u4e00\u6761\u5f71\u8bc4\u8bfb\u4e00\u904d，\u6700\u540e\u5410\u51fa\u4e00\u4e2a 0 \u5230 1 \u4e4b\u95f4\u7684\u6253\u5206，\u8d8a\u63a5\u8fd1 1 \u8d8a\u50cf\u597d\u8bc4\u3002\u5148\u8bc1\u660e\u8fd9\u5957\u4ee3\u7801\u80fd\u5b66\u4f1a，\u518d\u5728\u771f\u6b63\u7684\u6570\u636e\u4e0a\u8dd1\u8d77\u6765\u3002<\/p>\n

🎯 \u672c\u7ae0\u76ee\u6807<\/p>\n

  • \u62fc\u51fa Embedding \u2192 GRU \u2192 Linear(1) \u2192 Sigmoid \u7684\u5b8c\u6574\u7f51\u7edc；<\/li>\n
  • \u7528 200 \u6761\u5c0f\u6837\u672c\u9a8c\u8bc1\u4ee3\u7801\u6b63\u786e（\u8fc7\u62df\u5408 = \u5b9e\u73b0\u6ca1\u5199\u9519）；<\/li>\n
  • \u5728 8000 \u6761\u5f71\u8bc4\u4e0a\u6b63\u5f0f\u8bad\u7ec3 25 \u8f6e，\u770b\u61c2\u635f\u5931\u4e0b\u964d\u3001\u51c6\u786e\u7387\u722c\u5347，\u4ee5\u53ca\u540e\u671f\u7684\u8fc7\u62df\u5408\u4fe1\u53f7\u3002<\/li>\n
    \n

    \u4e00\u3001\u4e09\u4ef6\u5957：\u67e5\u5b57\u5178\u3001\u901a\u8bfb\u5168\u6587\u3001\u62cd\u677f\u6253\u5206<\/h4>\n

    \u5148\u770b\u6574\u4e2a\u7f51\u7edc\u957f\u4ec0\u4e48\u6837\u3002\u4e00\u6761\u5f71\u8bc4\u4ece\u6574\u6570\u5e8f\u5217\u8fdb\u6765，\u8981\u7ecf\u8fc7\u4e09\u5c42：<\/p>\n

    Embedding
    \n\u67e5\u8868
    \n(B,S,E)
    \n\u8bcdID\u2192\u5411\u91cf<\/p>\n

    GRU
    \n\u6cbf\u65f6\u95f4\u8bfb
    \n(B,S,H)
    \n\u6700\u540e\u4e00\u6b65 h_T<\/p>\n

    Linear(1)
    \n\u6253\u5206<\/p>\n

    Sigmoid
    \n0~1<\/p>\n

    \u597d\u611f\u5ea6
    \n\u4e00\u6761\u5f71\u8bc4 \u2192 \u4e00\u4e2a 0~1 \u7684\u6253\u5206<\/p>\n

    \u8fd9\u4e09\u5c42\u5404\u6709\u5404\u7684\u6d3b，\u90fd\u80fd\u7528\u751f\u6d3b\u91cc\u7684\u52a8\u4f5c\u5bf9\u4e0a\u53f7：<\/p>\n

      \n
    • Embedding（\u67e5\u5b57\u5178）：\u6574\u6570 ID \u53ea\u662f"\u8bcd\u5728\u8bcd\u8868\u91cc\u7684\u7f16\u53f7"，\u7f16\u53f7\u672c\u8eab\u6ca1\u6709\u542b\u4e49\u3002\u67e5\u8868\u90a3\u4e00\u6b65\u628a\u7f16\u53f7\u6362\u6210\u4e00\u6bb5\u7a20\u5bc6\u5411\u91cf\u2014\u2014\u5c31\u50cf\u78b0\u5230\u751f\u8bcd\u53bb\u7ffb\u5b57\u5178，\u7ffb\u5230\u7684\u662f\u8fd9\u4e2a\u8bcd\u7684\u610f\u601d\u3002\u7b2c 11 \u7ae0\u8bb2\u8fc7，\u8fd9\u4e00\u6bb5\u5411\u91cf\u662f\u80fd\u5b66\u4e60\u7684，\u8bad\u7ec3\u540e\u8bed\u4e49\u76f8\u8fd1\u7684\u8bcd\u4f1a\u9760\u5f97\u8fd1\u3002<\/li>\n
    • GRU（\u901a\u8bfb\u5168\u6587\u8bb0\u91cd\u70b9）：\u7b2c 9 \u7ae0\u7684\u4e3b\u89d2\u3002\u5b83\u4e00\u4e2a\u8bcd\u4e00\u4e2a\u8bcd\u5730\u8bfb，\u624b\u91cc\u634f\u7740\u4e00\u4efd"\u8bb0\u5fc6"，\u6bcf\u8bfb\u4e00\u4e2a\u8bcd\u5c31\u66f4\u65b0\u4e00\u6b21\u8bb0\u5fc6\u3002\u8bfb\u5230\u6700\u540e\u4e00\u4e2a\u8bcd，\u8bb0\u5fc6\u91cc\u5c31\u6d53\u7f29\u4e86\u6574\u7bc7\u5f71\u8bc4\u7684\u8981\u70b9\u3002<\/li>\n
    • Linear(1) + Sigmoid（\u62cd\u677f\u6253\u5206）：\u628a\u6700\u540e\u90a3\u4efd\u8bb0\u5fc6\u538b\u7f29\u6210\u4e00\u4e2a\u6570\u5b57，\u518d sigmoid \u538b\u5230 0~1 \u4e4b\u95f4，\u5f53\u4f5c\u597d\u8bc4\u6982\u7387\u3002<\/li>\n<\/ul>\n

      \u6574\u6761\u8def\u7528\u4e00\u884c\u516c\u5f0f\u8bf4\u6e05\u695a：<\/p>\n

      y^=\u03c3(W\u2009hT+b)\\\\hat{y} = \\\\sigma\\\\big(W\\\\, h_T + b\\\\big)<\/span><\/span><\/span>y<\/span><\/span><\/span>^<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>\u03c3<\/span>(<\/span><\/span>W<\/span><\/span>h<\/span><\/span>T<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>+<\/span><\/span><\/span><\/span>b<\/span>)<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

      \u5176\u4e2d hTh_T<\/span><\/span>h<\/span><\/span>T<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u662f GRU \u8bfb\u5b8c\u6700\u540e\u4e00\u4e2a\u8bcd\u540e\u7684\u8bb0\u5fc6，W,bW, b<\/span><\/span>W<\/span>,<\/span><\/span>b<\/span><\/span><\/span><\/span><\/span> \u662f\u6700\u540e\u4e00\u5c42\u7ebf\u6027\u53d8\u6362\u7684\u53c2\u6570，\u03c3\\\\sigma<\/span><\/span>\u03c3<\/span><\/span><\/span><\/span><\/span> \u662f Sigmoid\u3002hTh_T<\/span><\/span>h<\/span><\/span>T<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u5c31\u662f\u7b2c 9 \u7ae0\u91cc\u90a3\u4e2a"\u6d53\u7f29\u4e86\u5168\u6587"\u7684\u9690\u85cf\u72b6\u6001\u3002<\/p>\n

      \n

      \u4e8c\u3001\u9aa8\u67b6\u4ee3\u7801：\u4e09\u5c42\u62fc\u8d77\u6765<\/h4>\n

      \u628a\u4e0a\u9762\u8fd9\u5f20\u56fe\u7ffb\u8bd1\u6210\u4ee3\u7801，\u5be5\u5be5\u5341\u51e0\u884c：<\/p>\n

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

      class<\/span> SentimentGRU<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
      \n def<\/span> __init__<\/span>(<\/span>self,<\/span> vocab=<\/span>5002<\/span>,<\/span> embed=<\/span>64<\/span>,<\/span> hidden=<\/span>128<\/span>)<\/span>:<\/span>
      \n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span>
      \n self.<\/span>emb =<\/span> nn.<\/span>Embedding(<\/span>vocab,<\/span> embed,<\/span> padding_idx=<\/span>0<\/span>)<\/span> # \u67e5\u5b57\u5178<\/span>
      \n self.<\/span>gru =<\/span> nn.<\/span>GRU(<\/span>embed,<\/span> hidden,<\/span> batch_first=<\/span>True<\/span>)<\/span> # \u901a\u8bfb<\/span>
      \n self.<\/span>fc =<\/span> nn.<\/span>Linear(<\/span>hidden,<\/span> 1<\/span>)<\/span> # \u6253\u5206<\/span><\/p>\n

      def<\/span> forward<\/span>(<\/span>self,<\/span> x)<\/span>:<\/span> # x: (B, S) \u6574\u6570\u77e9\u9635<\/span>
      \n e =<\/span> self.<\/span>emb(<\/span>x)<\/span> # (B, S, E) \u67e5\u8868\u6210\u5411\u91cf<\/span>
      \n _,<\/span> h =<\/span> self.<\/span>gru(<\/span>e)<\/span> # h: (1, B, H) \u6700\u540e\u4e00\u6b65\u8bb0\u5fc6<\/span>
      \n return<\/span> self.<\/span>fc(<\/span>h[<\/span>–<\/span>1<\/span>]<\/span>)<\/span>.<\/span>squeeze(<\/span>–<\/span>1<\/span>)<\/span> # (B,) \u672a\u538b\u7f29\u7684\u6253\u5206<\/span><\/p>\n

      \u6709\u51e0\u4e2a\u7ec6\u8282\u503c\u5f97\u505c\u4e0b\u6765\u770b：<\/p>\n

        \n
      • vocab=5002：\u8bcd\u8868\u5927\u5c0f\u3002\u7b2c 12 \u7ae0\u7559\u4e86 5000 \u4e2a\u9ad8\u9891\u8bcd，\u52a0\u4e0a <PAD>=0\u3001<UNK>=1，\u4e00\u5171 5002 \u4e2a\u3002<\/li>\n
      • padding_idx=0：\u544a\u8bc9 Embedding，\u7f16\u53f7 0 \u662f\u586b\u5145\u4f4d\u3002\u8fd9\u6837 PAD \u4f1a\u88ab\u67e5\u6210\u5168\u96f6\u5411\u91cf，\u7b49\u4e8e\u4ec0\u4e48\u90fd\u6ca1\u8bfb，\u4e5f\u5c31\u4e0d\u4f1a\u6c61\u67d3 GRU \u7684\u8bb0\u5fc6\u2014\u2014\u8fd9\u662f\u7b2c 13 \u7ae0\u63a9\u7801\u601d\u60f3\u5728"\u8bfb\u5e8f\u5217"\u8fd9\u91cc\u7684\u843d\u5730\u3002\u7b2c 13 \u7ae0\u63a9\u7801\u4e3b\u8981\u62e6\u7684\u662f"\u9010\u8bcd\u9884\u6d4b"\u7684\u635f\u5931；\u5206\u7c7b\u4efb\u52a1\u53ea\u53d6\u6700\u540e\u4e00\u6b65\u8bb0\u5fc6\u6253\u5206，PAD \u7528\u96f6\u5411\u91cf\u6321\u4f4f\u5373\u53ef，\u4e0d\u7528\u518d\u5355\u72ec\u7b97\u63a9\u7801\u3002<\/li>\n
      • h[-1]：GRU \u8fd4\u56de\u7684 hh<\/span><\/span>h<\/span><\/span><\/span><\/span><\/span> \u5f62\u72b6\u662f (1,B,H)(1, B, H)<\/span><\/span>(<\/span>1<\/span>,<\/span><\/span>B<\/span>,<\/span><\/span>H<\/span>)<\/span><\/span><\/span><\/span><\/span>，\u7b2c 0 \u7ef4\u662f\u5c42\u6570（\u8fd9\u91cc\u53ea\u6709 1 \u5c42），h[-1] \u53d6\u7684\u662f\u6700\u540e\u4e00\u5c42\u3001\u6700\u540e\u4e00\u4e2a\u65f6\u95f4\u6b65\u7684\u9690\u85cf\u72b6\u6001，\u4e5f\u5c31\u662f"\u901a\u8bfb\u5b8c\u7684\u8bb0\u5fc6"\u3002<\/li>\n<\/ul>\n

        GRU \u5185\u90e8\u5230\u5e95\u600e\u4e48"\u901a\u8bfb"，\u7528\u4e00\u6bb5\u6700\u76f4\u767d\u7684\u5faa\u73af\u8bb2，\u6bd4\u770b\u5f20\u91cf\u62fc\u8d77\u6765\u66f4\u6e05\u695a：<\/p>\n

        h =<\/span> torch.<\/span>zeros(<\/span>hidden)<\/span> # \u8bb0\u5fc6\u6e05\u96f6，\u5f00\u59cb\u8bfb<\/span>
        \nfor<\/span> word_vec in<\/span> review_vecs:<\/span> # \u4e00\u4e2a\u8bcd\u4e00\u4e2a\u8bcd\u5730\u8bfb<\/span>
        \n h =<\/span> gru_step(<\/span>h,<\/span> word_vec)<\/span> # \u8bfb\u4e00\u4e2a\u8bcd，\u66f4\u65b0\u4e00\u6b21\u8bb0\u5fc6<\/span>
        \nscore =<\/span> sigmoid(<\/span>linear(<\/span>h)<\/span>)<\/span> # \u8bfb\u5b8c，\u7528\u6700\u540e\u7684\u8bb0\u5fc6\u62cd\u677f<\/span><\/p>\n

        gru_step \u5c31\u662f\u7b2c 9 \u7ae0\u90a3\u4e00\u6574\u5957\u66f4\u65b0\u95e8\u3001\u91cd\u7f6e\u95e8\u3001\u5019\u9009\u8bb0\u5fc6\u7684\u516c\u5f0f\u2014\u2014\u4ee3\u7801\u91cc\u5199\u6210\u4e00\u884c，\u4f46\u5fc3\u91cc\u8981\u88c5\u7740\u5b83\u662f\u5728"\u9010\u8bcd\u7ffb\u65b0\u8bb0\u5fc6"\u3002<\/p>\n

        \n

        \u4e09\u3001\u5148\u62ff 200 \u6761\u8bd5\u5200：\u5c0f\u6837\u672c\u8fc7\u62df\u5408<\/h4>\n

        \u4ee3\u7801\u5199\u5b8c\u4e86，\u600e\u4e48\u77e5\u9053\u6ca1\u5199\u9519？\u5148\u62ff\u4e00\u5c0f\u64ae\u6570\u636e\u8bd5\u3002\u8fd9\u662f\u6700\u5212\u7b97\u7684\u9a8c\u9519\u6cd5：\u6311 200 \u6761\u5f71\u8bc4，\u8ba9\u6a21\u578b\u53cd\u590d\u80cc\u3002\u5982\u679c\u4ee3\u7801\u662f\u5bf9\u7684，200 \u6761\u5f88\u5feb\u5c31\u80fd\u80cc\u4e0b\u6765\u2014\u2014\u635f\u5931\u4e00\u8def\u6389\u5230\u63a5\u8fd1 0，\u8fd9\u5c31\u662f"\u8fc7\u62df\u5408"，\u53cd\u800c\u8bf4\u660e\u5b9e\u73b0\u6b63\u786e\u3002\u53cd\u8fc7\u6765，\u5982\u679c 200 \u6761\u90fd\u5b66\u4e0d\u52a8，\u8bf4\u660e\u524d\u5411\u6216\u53cd\u5411\u6709 bug，\u518d\u5927\u7684\u6570\u636e\u4e5f\u767d\u642d\u3002<\/p>\n

        \u8bad\u7ec3\u7528\u7684\u635f\u5931\u51fd\u6570\u662f\u4e8c\u5206\u7c7b\u4ea4\u53c9\u71b5（BCE）：<\/p>\n

        L=\u22121N\u2211i=1N[\u2009yilog\u2061y^i+(1\u2212yi)log\u2061(1\u2212y^i)\u2009]\\\\mathcal{L} = -\\\\tfrac{1}{N}\\\\sum_{i=1}^{N}\\\\Big[\\\\,y_i\\\\log\\\\hat{y}_i + (1-y_i)\\\\log(1-\\\\hat{y}_i)\\\\,\\\\Big]<\/span><\/span>L<\/span><\/span>=<\/span><\/span><\/span><\/span>\u2212<\/span><\/span><\/span>N<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>1<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>i<\/span>=<\/span>1<\/span><\/span><\/span><\/span><\/span>\u2211<\/span><\/span><\/span><\/span>N<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>[<\/span><\/span><\/span>y<\/span><\/span>i<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>log<\/span><\/span><\/span><\/span>y<\/span><\/span><\/span>^<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>i<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>+<\/span><\/span><\/span><\/span>(<\/span>1<\/span><\/span>\u2212<\/span><\/span><\/span><\/span>y<\/span><\/span>i<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span>log<\/span><\/span>(<\/span>1<\/span><\/span>\u2212<\/span><\/span><\/span><\/span><\/span>y<\/span><\/span><\/span>^<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>i<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span>]<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

        \u6a21\u578b\u8fd8\u6ca1\u5b66\u4f1a\u3001\u778e\u731c\u65f6，\u635f\u5931\u4f1a\u505c\u5728 \u2212ln\u206112=ln\u20612\u22480.693-\\\\ln\\\\tfrac12 = \\\\ln 2 \\\\approx 0.693<\/span><\/span>\u2212<\/span><\/span>ln<\/span><\/span><\/span><\/span>2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>1<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>ln<\/span><\/span>2<\/span><\/span>\u2248<\/span><\/span><\/span><\/span>0.693<\/span><\/span><\/span><\/span><\/span>\u2014\u2014\u8fd9\u662f"\u968f\u673a\u731c\u6d4b"\u7684\u5929\u7136\u5e95\u7ebf，\u540e\u9762\u5bf9\u6bd4\u6709\u6ca1\u6709\u8fdb\u6b65\u5c31\u770b\u5b83\u3002<\/p>\n

        torch.<\/span>manual_seed(<\/span>42<\/span>)<\/span>;<\/span> np.<\/span>random.<\/span>seed(<\/span>42<\/span>)<\/span>
        \nidx =<\/span> np.<\/span>random.<\/span>choice(<\/span>25000<\/span>,<\/span> 200<\/span>,<\/span> replace=<\/span>False<\/span>)<\/span> # \u968f\u673a\u62bd 200 \u6761<\/span>
        \nmodel =<\/span> SentimentGRU(<\/span>vocab=<\/span>5002<\/span>,<\/span> embed=<\/span>32<\/span>,<\/span> hidden=<\/span>32<\/span>)<\/span>
        \nopt =<\/span> torch.<\/span>optim.<\/span>Adam(<\/span>model.<\/span>parameters(<\/span>)<\/span>,<\/span> lr=<\/span>1e-2<\/span>)<\/span>
        \nlossf =<\/span> nn.<\/span>BCEWithLogitsLoss(<\/span>)<\/span><\/p>\n

        for<\/span> ep in<\/span> range<\/span>(<\/span>20<\/span>)<\/span>:<\/span>
        \n for<\/span> st in<\/span> range<\/span>(<\/span>0<\/span>,<\/span> 200<\/span>,<\/span> 32<\/span>)<\/span>:<\/span>
        \n x,<\/span> y =<\/span> pack_batch(<\/span>idx[<\/span>st:<\/span>st+<\/span>32<\/span>]<\/span>)<\/span> # \u53d6\u4e00\u6279，\u586b\u5145+\u63a9\u7801<\/span>
        \n lo =<\/span> lossf(<\/span>model(<\/span>x)<\/span>,<\/span> y)<\/span>
        \n opt.<\/span>zero_grad(<\/span>)<\/span>;<\/span> lo.<\/span>backward(<\/span>)<\/span>;<\/span> opt.<\/span>step(<\/span>)<\/span><\/p>\n

        \u8dd1 20 \u8f6e，\u635f\u5931\u66f2\u7ebf\u957f\u8fd9\u6837：<\/p>\n

        \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

        \u524d\u51e0\u8f6e\u7684\u771f\u5b9e\u6570\u5b57：<\/p>\n\n\u8bad\u7ec3\u8f6e0246810<\/tr>\n\n\n
        \u635f\u5931<\/td>\n0.6996<\/td>\n0.5121<\/td>\n0.2426<\/td>\n0.1666<\/td>\n0.1359<\/td>\n0.0830<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u4ece 0.6996 \u4e00\u8def\u6389\u5230 0.0830\u2014\u2014200 \u6761\u73ed\u5b50\u57fa\u672c\u88ab\u80cc\u4e0b\u6765\u4e86\u3002\u8fd9\u8bc1\u660e\u524d\u5411\u3001\u53cd\u5411\u3001\u635f\u5931\u3001\u66f4\u65b0\u8fd9\u4e00\u6574\u5957\u94fe\u8def\u662f\u901a\u7684\u3002\u53ef\u4ee5\u653e\u5fc3\u4e0a\u5927\u83dc\u4e86\u3002<\/p>\n

        \n

        \u56db\u3001\u5168\u91cf\u5f00\u706b：\u6d17\u724c\u3001\u5207\u5206\u3001\u8bad\u7ec3<\/h4>\n

        \u5c0f\u6837\u672c\u53ea\u662f\u9a8c\u5200，\u63a5\u4e0b\u6765\u5728\u771f\u6b63\u7684\u6570\u636e\u4e0a\u8bad\u7ec3\u3002\u8fd9\u91cc\u85cf\u7740\u4e00\u4e2a\u6781\u5176\u5bb9\u6613\u8e29\u7684\u5751：\u8fd9\u4efd\u6570\u636e\u662f\u6309\u6807\u7b7e\u6392\u597d\u5e8f\u7684\u2014\u2014\u524d\u4e00\u534a\u662f\u597d\u8bc4（\u6807\u7b7e 1）\u3001\u540e\u4e00\u534a\u662f\u5dee\u8bc4（\u6807\u7b7e 0）\u3002\u5982\u679c\u76f4\u63a5\u62ff\u524d 8000 \u6761\u8bad\u7ec3\u3001\u7d27\u6328\u7740\u7684 2000 \u6761\u5f53\u9a8c\u8bc1，\u9a8c\u8bc1\u96c6\u91cc\u5c31\u4f1a\u5168\u662f\u540c\u4e00\u7c7b\u6807\u7b7e，\u6d4b\u51fa\u6765\u7684\u51c6\u786e\u7387\u6beb\u65e0\u610f\u4e49（\u8981\u4e48\u865a\u9ad8\u3001\u8981\u4e48\u865a\u4f4e）\u3002<\/p>\n

        \u6240\u4ee5\u7b2c\u4e00\u6b65\u5fc5\u987b\u6253\u4e71\u987a\u5e8f，\u518d\u968f\u673a\u5207\u5206：<\/p>\n

        all_idx =<\/span> np.<\/span>arange(<\/span>25000<\/span>)<\/span>
        \nnp.<\/span>random.<\/span>shuffle(<\/span>all_idx)<\/span> # \u5148\u6d17\u724c，\u628a\u597d\u8bc4\u5dee\u8bc4\u6253\u6563<\/span>
        \ntrain_idx =<\/span> all_idx[<\/span>:<\/span>8000<\/span>]<\/span> # \u8bad\u7ec3：8000 \u6761<\/span>
        \nval_idx =<\/span> all_idx[<\/span>8000<\/span>:<\/span>10000<\/span>]<\/span> # \u9a8c\u8bc1：2000 \u6761<\/span><\/p>\n

        \u7136\u540e\u6302\u4e0a Adam \u4f18\u5316\u5668，\u7528 1e-3 \u7684\u5b66\u4e60\u7387\u6b63\u5f0f\u8bad\u7ec3 25 \u8f6e\u3002\u6bcf\u8f6e\u7ed3\u675f\u540e\u5728\u9a8c\u8bc1\u96c6\u4e0a\u6d4b\u4e00\u6b21\u51c6\u786e\u7387\u3002\u771f\u5b9e\u8fd0\u884c\u8f93\u51fa（\u6311\u51e0\u8f6e\u5c55\u793a）：<\/p>\n

        epoch 0: train_loss \u2248 0.6944 | val_acc \u2248 0.5020
        \nepoch 5: train_loss \u2248 0.5955 | val_acc \u2248 0.5200
        \nepoch 10: train_loss \u2248 0.3958 | val_acc \u2248 0.5990
        \nepoch 15: train_loss \u2248 0.1851 | val_acc \u2248 0.6855
        \nepoch 20: train_loss \u2248 0.0931 | val_acc \u2248 0.7350
        \nepoch 24: train_loss \u2248 0.0542 | val_acc \u2248 0.7305<\/p>\n

        \u628a\u8d77\u6b62\u7684\u5173\u952e\u6570\u5b57\u6536\u5728\u4e00\u5f20\u8868\u91cc，\u4e00\u773c\u770b\u6e05\u5e45\u5ea6：<\/p>\n\n\u6307\u6807\u7b2c 0 \u8f6e\u7b2c 24 \u8f6e\u53d8\u5316<\/tr>\n\n\n\n
        \u8bad\u7ec3\u635f\u5931<\/td>\n0.6944<\/td>\n0.0542<\/td>\n\u2193 0.640<\/td>\n<\/tr>\n
        \u9a8c\u8bc1\u51c6\u786e\u7387<\/td>\n50.2%<\/td>\n73.05%<\/td>\n\u2191 22.9%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
          \n
        • \u8bad\u7ec3\u635f\u5931：\u4ece 0.6944 \u964d\u5230 0.0542，\u7a33\u7a33\u79bb\u5f00\u4e86 0.693 \u7684\u968f\u673a\u57fa\u7ebf，\u6a21\u578b\u786e\u5b9e\u5728"\u8bfb\u61c2"\u597d\u8bc4\u5dee\u8bc4；<\/li>\n
        • \u9a8c\u8bc1\u51c6\u786e\u7387：\u4ece 50.2% \u4e00\u8def\u722c\u5230\u5cf0\u503c 73.85%（\u7b2c 21 \u8f6e），\u5230\u7b2c 24 \u8f6e\u5fae\u56de\u843d\u5230 73.05%\u3002<\/li>\n<\/ul>\n

          \u7ec3\u5b8c 25 \u8f6e，\u518d\u62c9\u90a3\u4e24\u6761\u771f\u5b9e\u5f71\u8bc4\u770b\u6253\u5206，\u4e00\u5f00\u5934\u7684\u6a21\u578b\u548c\u73b0\u5728\u7684\u6a21\u578b\u5224\u82e5\u4e24\u4eba：<\/p>\n

          idx=13 label=1 score=0.991 | i enjoyed the night … one of the better movies of the summer
          \nidx=12529 label=0 score=0.003 | i had some … for the movie since it had a nice star cast …<\/p>\n

          \u542b great \u7684\u597d\u8bc4\u62ff\u5230 0.991，\u542b terrible \u7684\u5dee\u8bc4\u53ea\u6709 0.003\u2014\u2014\u8fd9\u6b21\u4e0d\u4ec5\u5bf9，\u800c\u4e14\u975e\u5e38\u81ea\u4fe1\u3002<\/p>\n

          \u4e0d\u8fc7\u66f2\u7ebf\u91cc\u85cf\u7740\u4e00\u4e2a\u8981\u7559\u610f\u7684\u4fe1\u53f7：\u7b2c 21 \u8f6e\u4e4b\u540e，\u8bad\u7ec3\u635f\u5931\u8fd8\u5728\u5f80\u4e0b\u6389（0.085 \u2192 0.054），\u9a8c\u8bc1\u51c6\u786e\u7387\u5374\u4e0d\u6da8\u53cd\u5fae\u964d（73.85% \u2192 73.05%）\u3002\u8fd9\u5c31\u662f\u6807\u51c6\u7684\u8fc7\u62df\u5408：\u6a21\u578b\u5f00\u59cb\u628a\u8bad\u7ec3\u96c6\u4e00\u5b57\u4e0d\u5dee\u5730\u80cc\u4e0b\u6765，\u5bf9\u6ca1\u89c1\u8fc7\u7684\u6570\u636e\u5374\u5e2e\u4e0d\u4e0a\u5fd9\u3002\u8bad\u7ec3\u635f\u5931\u8d8a\u4f4e\u5e76\u4e0d\u4ee3\u8868\u8d8a\u597d\u2014\u2014\u8fd9\u6b63\u662f\u7b2c 16 \u7ae0\u5f15\u5165 Dropout \u7b49\u6b63\u5219\u5316\u624b\u6bb5\u7684\u7406\u7531\u3002<\/p>\n

          \n

          \u4e94\u3001\u5e38\u89c1\u5751\u4e0e\u81ea\u67e5<\/h4>\n
            \n
          • \u4e0d\u6d17\u724c\u76f4\u63a5\u5207\u5206：\u6570\u636e\u524d\u534a\u5168\u662f\u597d\u8bc4\u3001\u540e\u534a\u5168\u662f\u5dee\u8bc4，\u61d2\u5f97\u6d17\u724c\u4f1a\u8ba9\u9a8c\u8bc1\u96c6\u53d8\u6210"\u5355\u4e00\u79cd\u7c7b"，\u51c6\u786e\u7387\u5931\u771f\u3002\u5148 shuffle \u518d\u5207\u5206，\u8fd9\u4e00\u6b65\u4e0d\u80fd\u7701\u3002<\/li>\n
          • \u5fd8\u8bb0 padding_idx=0：\u4e0d\u544a\u8bc9 Embedding \u8c01\u662f\u586b\u5145\u4f4d，PAD \u4e5f\u4f1a\u88ab\u5f53\u6210\u666e\u901a\u8bcd\u53c2\u4e0e\u8ba1\u7b97，GRU \u7684"\u7a7a\u6c14"\u4e5f\u8bfb\u4e86，\u8bb0\u5fc6\u88ab\u6c61\u67d3\u3002<\/li>\n
          • \u53d6\u9519\u9690\u85cf\u72b6\u6001：GRU \u8fd4\u56de\u7684\u7b2c\u4e8c\u4e2a\u503c\u662f (1,B,H)(1, B, H)<\/span><\/span>(<\/span>1<\/span>,<\/span><\/span>B<\/span>,<\/span><\/span>H<\/span>)<\/span><\/span><\/span><\/span><\/span>，\u4e0d\u53d6 h[-1] \u800c\u76f4\u63a5\u62ff\u53bb\u5582\u7ebf\u6027\u5c42，\u7ef4\u5ea6\u5bf9\u4e0d\u4e0a\u4f1a\u62a5\u9519，\u6216\u53d6\u5230\u975e\u6700\u540e\u4e00\u6b65\u7684\u72b6\u6001\u3002<\/li>\n
          • \u8bc4\u4f30\u65f6\u5fd8\u4e86\u5207 eval() \u6a21\u5f0f：\u8bad\u7ec3\u5faa\u73af\u91cc\u987a\u624b\u52a0\u7684 Dropout \u5728\u8bc4\u4f30\u65f6\u4e5f\u5fc5\u987b\u5173\u6389；\u8fd9\u91cc\u6ca1\u6709 Dropout，\u4f46\u517b\u6210 model.eval() \u7684\u4e60\u60ef，\u7b2c 16 \u7ae0\u4f1a\u7528\u4e0a\u3002<\/li>\n
          • \u7528 nn.BCELoss \u800c\u4e0d\u662f BCEWithLogitsLoss：\u524d\u8005\u8981\u5148\u628a logit \u8fc7 Sigmoid，\u6570\u503c\u4e0a\u66f4\u6613\u4e0d\u7a33\u5b9a；\u540e\u8005\u628a Sigmoid \u878d\u8fdb\u635f\u5931\u91cc，\u66f4\u7a33\u59a5，\u4ee3\u7801\u91cc\u7528\u7684\u5c31\u662f\u5b83\u3002<\/li>\n<\/ul>\n
            \n

            \u5c0f\u7ed3\u4e0e\u9884\u544a<\/h4>\n

            \u8fd9\u4e00\u7ae0\u628a\u524d\u9762\u5907\u597d\u7684\u6570\u636e\u771f\u6b63\u5582\u8fdb\u4e86\u7f51\u7edc，\u8d70\u901a\u4e86"\u8bfb\u4e00\u904d \u2192 \u6253\u4e2a\u5206"\u7684\u5b8c\u6574\u94fe\u8def：<\/p>\n

              \n
            • \u4e09\u5c42\u9aa8\u67b6：Embedding（\u67e5\u5b57\u5178）\u2192 GRU（\u901a\u8bfb\u8bb0\u5fc6）\u2192 Linear(1)+Sigmoid（\u6253\u5206），\u4e00\u6761\u5f71\u8bc4\u53d8\u6210\u4e00\u4e2a 0~1 \u7684\u6253\u5206；<\/li>\n
            • \u5c0f\u6837\u672c\u9a8c\u5200：200 \u6761\u4e0a\u635f\u5931 0.70 \u2192 0.08，\u8bc1\u660e\u4ee3\u7801\u6ca1\u5199\u9519；<\/li>\n
            • \u6d17\u724c\u6559\u8bad：\u6570\u636e\u6309\u6807\u7b7e\u6392\u5e8f，\u5fc5\u987b\u5148 shuffle \u518d\u5207\u5206，\u5426\u5219\u9a8c\u8bc1\u96c6\u5931\u771f；<\/li>\n
            • \u5168\u91cf 25 \u8f6e：\u8bad\u7ec3\u635f\u5931 0.694 \u2192 0.054，\u9a8c\u8bc1\u51c6\u786e\u7387 50% \u2192 73.85%（\u7b2c 21 \u8f6e\u5cf0\u503c）；\u6b64\u540e\u635f\u5931\u7ee7\u7eed\u964d\u3001\u9a8c\u8bc1\u8d70\u5e73，\u771f\u5b9e\u9a8c\u5230\u4e86\u8fc7\u62df\u5408\u3002<\/li>\n<\/ul>\n

              \u672c\u7ae0\u7684\u6838\u5fc3\u6570\u636e，\u4e00\u5f20\u5c0f\u770b\u677f\u6536\u5c3e：<\/p>\n

              \u5c0f\u6837\u672c 200 \u6761
              \n0.70 \u2192 0.08
              \n\u635f\u5931\u80cc\u4e0b\u5168\u90e8<\/p>\n

              \u8bad\u7ec3\u635f\u5931
              \n0.694 \u2192 0.054
              \n25 \u8f6e\u4e0b\u964d 0.640<\/p>\n

              \u9a8c\u8bc1\u51c6\u786e\u7387
              \n50% \u2192 73.85%
              \n\u7b2c 21 \u8f6e\u5cf0\u503c\u540e\u8d70\u5e73<\/p>\n

              \u968f\u673a\u57fa\u7ebf
              \n0.693 \/ 50%
              \n\u6ca1\u5b66\u4f1a\u7684\u5206\u754c\u7ebf<\/p>\n

              \u8def\u5df2\u7ecf\u901a\u4e86，\u63a5\u4e0b\u6765\u5c31\u662f\u600e\u4e48\u8ba9\u6a21\u578b\u53d8\u806a\u660e\u2014\u2014\u7b2c 15 \u7ae0\u628a RNN\u3001GRU\u3001LSTM \u4e09\u5927\u6a21\u578b\u62c9\u5230\u540c\u4e00\u5f20\u684c\u5b50\u4e0a\u6bd4\u4e2a\u9ad8\u4e0b，\u770b\u8c01\u6536\u655b\u6700\u5feb\u3001\u8c01\u6700\u7ec8\u7cbe\u5ea6\u6700\u9ad8\u3002<\/p>\n

              \u4e0b\u4e00\u7bc7（\u4e8c\u5341\u516b）：RNN vs LSTM vs GRU \u4e09\u6a21\u578b\u6a2a\u5411\u5bf9\u6bd4\u5b9e\u9a8c<\/p>\n","protected":false},"excerpt":{"rendered":"

              \u5f15\u8a00：\u83dc\u90fd\u5207\u597d\u4e86，\u5f00\u706b\u70e7\u83dc
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              \n\u8fd9\u4e00\u7ae0\u5c31\u662f\u628a\\”\u98df\u6750\\”\u771f\u6b63\u5012\u8fdb\u9505\u91cc：\u642d\u8d77\u4e00\u5f20\u6700\u7b80\u5355\u7684\u60c5\u611f\u5206\u7c7b\u5668，\u8ba9\u6a21\u578b\u628a\u4e00\u6761\u5f71\u8bc4\u8bfb<\/p>\n","protected":false},"author":2,"featured_media":91891,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[10187,10490,8499,5323,152,2873],"topic":[],"class_list":["post-91892","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-codingplan","tag-imdb","tag-8499","tag-gru","tag-pytorch","tag-rnn"],"yoast_head":"\npython\u795e\u7ecf\u7f51\u7edc\u7f16\u7a0b\u5165\u95e8\uff08\u4e8c\u5341\u4e03\uff09\u2014\u2014RNN IMBD\u642d\u5efa\u60c5\u611f\u5206\u7c7b\u5668\u4e0e\u57fa\u7840\u8bad\u7ec3 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/91892.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"python\u795e\u7ecf\u7f51\u7edc\u7f16\u7a0b\u5165\u95e8\uff08\u4e8c\u5341\u4e03\uff09\u2014\u2014RNN IMBD\u642d\u5efa\u60c5\u611f\u5206\u7c7b\u5668\u4e0e\u57fa\u7840\u8bad\u7ec3 - 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