{"id":34816,"date":"2025-05-01T06:02:46","date_gmt":"2025-04-30T22:02:46","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/34816.html"},"modified":"2025-05-01T06:02:46","modified_gmt":"2025-04-30T22:02:46","slug":"%e5%9f%ba%e4%ba%8e%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0-lstm-%e7%ae%97%e6%b3%95%e7%9a%84%e8%b1%86%e7%93%a3%e8%af%84%e8%ae%ba%e6%83%85%e6%84%9f%e5%88%86%e6%9e%90%e7%b3%bb%e7%bb%9f","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/34816.html","title":{"rendered":"\u57fa\u4e8e\u673a\u5668\u5b66\u4e60 LSTM \u7b97\u6cd5\u7684\u8c46\u74e3\u8bc4\u8bba\u60c5\u611f\u5206\u6790\u7cfb\u7edf"},"content":{"rendered":"

\u57fa\u4e8e\u673a\u5668\u5b66\u4e60 LSTM \u7b97\u6cd5\u7684\u8c46\u74e3\u8bc4\u8bba\u60c5\u611f\u5206\u6790\u7cfb\u7edf<\/h2>\n

\u535a\u4e3b\u4ecb\u7ecd：\u270c\u7a0b\u5e8f\u5458\u5f90\u5e08\u5144\u30017\u5e74\u5927\u5382\u7a0b\u5e8f\u5458\u7ecf\u5386\u3002\u5168\u7f51\u7c89\u4e1d12w+\u3001csdn\u535a\u5ba2\u4e13\u5bb6\u3001\u6398\u91d1\/\u534e\u4e3a\u4e91\/\u963f\u91cc\u4e91\/InfoQ\u7b49\u5e73\u53f0\u4f18\u8d28\u4f5c\u8005\u3001\u4e13\u6ce8\u4e8eJava\u6280\u672f\u9886\u57df\u548c\u6bd5\u4e1a\u9879\u76ee\u5b9e\u6218\u270c<\/p>\n

🍅\u6587\u672b\u83b7\u53d6\u6e90\u7801\u8054\u7cfb🍅<\/p>\n

👇🏻 \u7cbe\u5f69\u4e13\u680f\u63a8\u8350\u8ba2\u9605👇🏻 \u4e0d\u7136\u4e0b\u6b21\u627e\u4e0d\u5230\u54df<\/font><\/p>\n

2022-2024\u5e74\u6700\u5168\u7684\u8ba1\u7b97\u673a\u8f6f\u4ef6\u6bd5\u4e1a\u8bbe\u8ba1\u9009\u9898\u5927\u5168：1000\u4e2a\u70ed\u95e8\u9009\u9898\u63a8\u8350\u2705<\/p>\n

Java\u9879\u76ee\u7cbe\u54c1\u5b9e\u6218\u6848\u4f8b\u300a100\u5957\u300b<\/p>\n

Java\u5fae\u4fe1\u5c0f\u7a0b\u5e8f\u9879\u76ee\u5b9e\u6218\u300a100\u5957\u300b<\/p>\n

Python\u9879\u76ee\u5b9e\u6218\u300a100\u5957\u300b<\/p>\n

\u611f\u5174\u8da3\u7684\u53ef\u4ee5\u5148\u6536\u85cf\u8d77\u6765，\u8fd8\u6709\u5927\u5bb6\u5728\u6bd5\u8bbe\u9009\u9898，\u9879\u76ee\u4ee5\u53ca\u6587\u6863\u7f16\u5199\u7b49\u76f8\u5173\u95ee\u9898\u90fd\u53ef\u4ee5\u7ed9\u6211\u7559\u8a00\u54a8\u8be2，\u5e0c\u671b\u5e2e\u52a9\u66f4\u591a\u7684\u4eba<\/font><\/p>\n<\/p>\n

\u6587\u7ae0\u76ee\u5f55<\/h4>\n
    \n
  • \u57fa\u4e8e\u673a\u5668\u5b66\u4e60 LSTM \u7b97\u6cd5\u7684\u8c46\u74e3\u8bc4\u8bba\u60c5\u611f\u5206\u6790\u7cfb\u7edf<\/li>\n
  • \n
      \n
    • 1 \u7b80\u4ecb<\/li>\n
    • 2 \u6280\u672f\u6808<\/li>\n
    • 3 \u7cfb\u7edf\u67b6\u6784<\/li>\n
    • 4 \u5177\u4f53\u5b9e\u73b0<\/li>\n
    • \n
        \n
      • 4.1 \u722c\u866b\u6a21\u5757：\u81ea\u52a8\u6293\u8bc4\u8bba<\/li>\n
      • 4.2 \u6587\u672c\u9884\u5904\u7406<\/li>\n
      • 4.3 \u6a21\u578b\u8bad\u7ec3（\u6838\u5fc3：LSTM）<\/li>\n
      • 4.4 \u540e\u7aef\u96c6\u6210<\/li>\n
      • 4.5 \u53ef\u89c6\u5316\u5c55\u793a<\/li>\n<\/ul>\n<\/li>\n
      • 5 \u6548\u679c\u5c55\u793a<\/li>\n
      • 6 \u6e90\u7801\u83b7\u53d6：<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

        1 \u7b80\u4ecb<\/h3>\n

        \u57fa\u4e8e\u673a\u5668\u5b66\u4e60 LSTM \u7b97\u6cd5\u7684\u8c46\u74e3\u8bc4\u8bba\u60c5\u611f\u5206\u6790\u7cfb\u7edf，\u8be5\u7cfb\u7edf\u5728\u539f\u672c\u7684\u57fa\u7840\u4e4b\u4e0a\u8fdb\u884c\u4f18\u5316\u3002<\/p>\n

      • \u652f\u6301\u4e86\u673a\u5668\u5b66\u4e60 lstm \u7b97\u6cd5，\u8fdb\u884c\u60c5\u611f\u5206\u6790<\/li>\n
      • \u652f\u6301\u4e86\u7528\u6237\u624b\u52a8\u8f93\u5165\u6587\u672c\u8fdb\u884c\u60c5\u611f\u5206\u6790<\/li>\n

        \"\u8c46\u74e3\u7535\u5f71\"<\/p>\n

        \u54b1\u4eec\u8fd9\u5957\u7cfb\u7edf\u662f\u7528 Python + Flask \u642d\u7684，\u7528 LSTM（\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc）\u6a21\u578b\u6765\u505a\u8c46\u74e3\u5f71\u8bc4\u7684\u60c5\u611f\u5206\u6790\u3002\u5b83\u80fd\u81ea\u52a8\u722c\u53d6\u8c46\u74e3\u4e0a\u7684\u6700\u65b0\u8bc4\u8bba，\u7ed9\u4f60\u7b97\u51fa\u597d\u8bc4\u7387\u3001\u5dee\u8bc4\u7387，\u8fd8\u80fd\u628a\u5206\u6790\u7ed3\u679c\u5b9e\u65f6\u53ef\u89c6\u5316，\u652f\u6301\u997c\u56fe\u3001\u6761\u5f62\u56fe\u7b49\u591a\u79cd\u56fe\u8868\u5c55\u793a\u3002\u6574\u4e2a\u9879\u76ee\u542b\u6e90\u7801\u3001\u5f00\u53d1\u73af\u5883\u914d\u7f6e\u3001\u5b89\u88c5\u6559\u7a0b，\u529f\u80fd\u5b8c\u6574，\u7279\u522b\u9002\u5408\u62ff\u53bb\u505a\u6bd5\u4e1a\u8bbe\u8ba1\u3001\u8bfe\u7a0b\u8bbe\u8ba1\u6216\u6570\u636e\u5e93\u5927\u4f5c\u4e1a\u3002<\/p>\n

        \u7b80\u5355\u6765\u8bf4，\u8fd9\u4e2a\u7cfb\u7edf\u5206\u4e09\u5927\u5757：<\/p>\n

      • \u722c\u866b\u6a21\u5757：\u81ea\u52a8\u6293\u53d6\u8c46\u74e3\u7535\u5f71\u7684\u8bc4\u8bba\u6570\u636e；<\/li>\n
      • \u60c5\u611f\u5206\u6790\u6a21\u5757：\u7528 LSTM \u5bf9\u8bc4\u8bba\u505a\u6b63\u8d1f\u9762\u5206\u7c7b；<\/li>\n
      • \u53ef\u89c6\u5316\u5c55\u793a：\u7528\u524d\u7aef\u9875\u9762\u628a\u7ed3\u679c\u7528\u56fe\u8868\u5c55\u793a\u51fa\u6765\u3002<\/li>\n

        \u642d\u5efa\u6d41\u7a0b：\u5148\u5199\u722c\u866b\u62ff\u8bc4\u8bba，\u5b58\u5230 MySQL；\u7136\u540e\u7528 Python \u505a\u6587\u672c\u9884\u5904\u7406，\u8bad\u7ec3 LSTM \u6a21\u578b；\u6700\u540e\u5728 Flask Web \u7aef\u8c03\u7528\u6a21\u578b，\u62ff\u5230\u6bcf\u6761\u8bc4\u8bba\u7684\u60c5\u611f\u9884\u6d4b，\u518d\u7528 ECharts／Matplotlib \u8f93\u51fa\u6f02\u4eae\u56fe\u8868\u3002\u8dd1\u8d77\u6765\u4ee5\u540e，\u53ea\u8981\u8f93\u5165\u60f3\u5206\u6790\u7684\u7535\u5f71，\u5c31\u80fd\u79d2\u51fa\u60c5\u611f\u7edf\u8ba1\u548c\u8d8b\u52bf\u56fe，\u5e2e\u4f60\u5feb\u901f\u4e86\u89e3\u89c2\u4f17\u5bf9\u7535\u5f71\u7684\u771f\u5b9e\u611f\u53d7\u3002<\/p>\n[video(video-P439PEYV-1697528973620)(type-bilibili)(url=https:\/\/player.bilibili.com\/player.html?aid=704762195)(image=https:\/\/i-blog.csdnimg.cn\/blog_migrate\/c7d97361c3bef18379dfe031f1be636f.jpeg)(title-\u57fa\u4e8ePython flask\u7684\u8c46\u74e3\u7535\u5f71\u5206\u6790\u53ef\u89c6\u5316\u7cfb\u7edf)]\n

        \n

        2 \u6280\u672f\u6808<\/h3>\n
          \n
        • \u5f00\u53d1\u8bed\u8a00：Python 3.8+<\/li>\n
        • \u540e\u7aef\u6846\u67b6：Flask<\/li>\n
        • \u6df1\u5ea6\u5b66\u4e60：TensorFlow \/ Pytorch（LSTM）<\/li>\n
        • \u524d\u7aef\u5c55\u793a：HTML + ECharts \u6216 Matplotlib<\/li>\n
        • \u6570\u636e\u5e93：MySQL<\/li>\n
        • \u722c\u866b：Requests + BeautifulSoup \/ Selenium（\u52a8\u6001\u52a0\u8f7d）<\/li>\n
        • \u5f00\u53d1\u5de5\u5177：PyCharm<\/li>\n<\/ul>\n
          \n

          3 \u7cfb\u7edf\u67b6\u6784<\/h3>\n

          \u6574\u4e2a\u7cfb\u7edf\u91c7\u7528 B\/S \u67b6\u6784，\u5206\u4e3a\u4ee5\u4e0b\u51e0\u5c42：<\/p>\n

        • \n

          \u6570\u636e\u91c7\u96c6\u5c42<\/p>\n

            \n
          • \u81ea\u52a8\u5316\u722c\u866b：\u901a\u8fc7 Requests \u6216 Selenium \u83b7\u53d6\u8c46\u74e3\u7535\u5f71\u7684\u8bc4\u8bba JSON \u6216\u9875\u9762 HTML；<\/li>\n
          • \u6570\u636e\u9884\u5904\u7406：\u6e05\u6d17\u3001\u53bb\u91cd\u3001\u5206\u8bcd\u3001\u505c\u7528\u8bcd\u8fc7\u6ee4；<\/li>\n<\/ul>\n<\/li>\n
          • \n

            \u6a21\u578b\u8bad\u7ec3\u5c42<\/p>\n

              \n
            • \u6587\u672c\u5411\u91cf\u5316：\u7528 Tokenizer + Embedding \u5c42\u628a\u8bc4\u8bba\u8f6c\u6210\u5411\u91cf\u5e8f\u5217；<\/li>\n
            • LSTM \u6a21\u578b：\u6784\u5efa 2 \u5c42 LSTM，\u6700\u540e\u63a5\u5168\u8fde\u63a5\u548c Softmax，\u5b9e\u73b0\u4e8c\u5206\u7c7b；<\/li>\n
            • \u6a21\u578b\u4f18\u5316：\u91c7\u7528\u4ea4\u53c9\u71b5\u635f\u5931\u3001Adam \u4f18\u5316\u5668，\u5e76\u8bbe\u7f6e\u65e9\u505c（EarlyStopping）；<\/li>\n<\/ul>\n<\/li>\n
            • \n

              \u540e\u7aef\u670d\u52a1\u5c42<\/p>\n

                \n
              • Flask \u5de5\u7a0b：\u63d0\u4f9b API \u63a5\u53e3，\u63a5\u6536\u7535\u5f71 ID \u6216\u540d\u5b57，\u8fd4\u56de\u60c5\u611f\u5206\u6790\u7ed3\u679c JSON；<\/li>\n
              • \u6570\u636e\u5e93\u64cd\u4f5c：\u4f7f\u7528 PyMySQL \u6216 SQLAlchemy \u5b58\u53d6\u8bc4\u8bba\u548c\u5206\u6790\u7ed3\u679c；<\/li>\n<\/ul>\n<\/li>\n
              • \n

                \u53ef\u89c6\u5316\u5c55\u793a\u5c42<\/p>\n

                  \n
                • Web \u9875\u9762：HTML + ECharts \u6216\u540e\u53f0 Matplotlib，\u5c06\u60c5\u611f\u7edf\u8ba1\u7528\u997c\u56fe\u3001\u6298\u7ebf\u56fe\u3001\u8bcd\u4e91\u7b49\u5f62\u5f0f\u5c55\u793a；<\/li>\n
                • \u5b9e\u65f6\u5237\u65b0：\u5f02\u6b65\u8bf7\u6c42\u63a5\u53e3，\u52a8\u6001\u66f4\u65b0\u56fe\u8868\u3002<\/li>\n<\/ul>\n<\/li>\n
                  \n

                  4 \u5177\u4f53\u5b9e\u73b0<\/h3>\n

                  4.1 \u722c\u866b\u6a21\u5757：\u81ea\u52a8\u6293\u8bc4\u8bba<\/h4>\n
                • \u5206\u6790\u76ee\u6807\n
                    \n
                  • \u8c46\u74e3\u7535\u5f71\u8bc4\u8bba\u9875\u91c7\u7528\u52a8\u6001\u52a0\u8f7d\u548c\u5206\u9875，\u9700\u8981\u5148\u83b7\u53d6\u7535\u5f71\u8bc4\u8bba\u7684 API \u63a5\u53e3\u6216 AJAX JSON \u6570\u636e；<\/li>\n<\/ul>\n<\/li>\n
                  • \u6a21\u62df\u8bf7\u6c42\n
                      \n
                    • \u6dfb\u52a0 User-Agent \u548c Cookies，\u907f\u514d\u88ab\u5c01；<\/li>\n
                    • \u5bf9\u8bc4\u8bba\u5206\u9875\u52a0\u968f\u673a\u5ef6\u65f6（time.sleep(random.gauss(1,0.3))）\u6a21\u62df\u4eba\u5de5\u8bbf\u95ee；<\/li>\n<\/ul>\n<\/li>\n
                    • \u5b57\u6bb5\u5065\u58ee\u6027\n
                        \n
                      • \u5bf9\u7f3a\u5931\u5b57\u6bb5（\u8bc4\u8bba\u65f6\u95f4\u3001\u7528\u6237\u540d\u3001\u8bc4\u5206）\u505a\u7a7a\u503c\u5224\u65ad，\u672a\u83b7\u53d6\u5230\u5c31\u8d4b None，\u4fdd\u8bc1\u722c\u866b\u4e0d\u505c；<\/li>\n<\/ul>\n<\/li>\n
                      • \u5b58\u5165\u6570\u636e\u5e93\n
                          \n
                        • \u5efa\u7acb comments \u8868：id, movie_id, user, rating, content, comment_time；<\/li>\n
                        • \u6279\u91cf\u63d2\u5165，\u964d\u4f4e\u6570\u636e\u5e93\u5f00\u9500\u3002<\/li>\n<\/ul>\n<\/li>\n

                          4.2 \u6587\u672c\u9884\u5904\u7406<\/h4>\n
                        • \u6e05\u6d17\n
                            \n
                          • \u53bb\u6389 HTML \u6807\u7b7e\u3001\u94fe\u63a5\u3001\u7279\u6b8a\u5b57\u7b26；<\/li>\n
                          • \u7528\u6b63\u5219\u53bb\u9664\u591a\u4f59\u7a7a\u683c\u548c\u6362\u884c；<\/li>\n<\/ul>\n<\/li>\n
                          • \u5206\u8bcd & \u505c\u7528\u8bcd\n
                              \n
                            • \u4e2d\u6587\u8bc4\u8bba\u7528 jieba \u5206\u8bcd；<\/li>\n
                            • \u52a0\u8f7d\u505c\u7528\u8bcd\u8868，\u8fc7\u6ee4\u9ad8\u9891\u65e0\u610f\u4e49\u8bcd；<\/li>\n<\/ul>\n<\/li>\n
                            • \u5e8f\u5217\u5316\n
                                \n
                              • \u7528 Keras \u7684 Tokenizer \u5efa\u7acb\u8bcd\u5178，\u9650\u5236\u6700\u5927\u8bcd\u6c47\u91cf（\u6bd4\u5982 10000）；<\/li>\n
                              • \u628a\u8bc4\u8bba\u8f6c\u6362\u6210\u56fa\u5b9a\u957f\u5ea6\u7684\u6574\u6570\u5e8f\u5217，\u8d85\u957f\u622a\u65ad，\u8fc7\u77ed\u8865 0；<\/li>\n<\/ul>\n<\/li>\n

                                4.3 \u6a21\u578b\u8bad\u7ec3（\u6838\u5fc3：LSTM）<\/h4>\n

                                from<\/span> tensorflow.<\/span>keras.<\/span>models import<\/span> Sequential
                                \nfrom<\/span> tensorflow.<\/span>keras.<\/span>layers import<\/span> Embedding,<\/span> LSTM,<\/span> Dense,<\/span> Dropout
                                \nfrom<\/span> tensorflow.<\/span>keras.<\/span>callbacks import<\/span> EarlyStopping,<\/span> ModelCheckpoint<\/p>\n

                                model =<\/span> Sequential(<\/span>)<\/span>
                                \nmodel.<\/span>add(<\/span>Embedding(<\/span>input_dim=<\/span>10000<\/span>,<\/span> output_dim=<\/span>128<\/span>,<\/span> input_length=<\/span>max_len)<\/span>)<\/span>
                                \nmodel.<\/span>add(<\/span>LSTM(<\/span>64<\/span>,<\/span> return_sequences=<\/span>True<\/span>)<\/span>)<\/span>
                                \nmodel.<\/span>add(<\/span>Dropout(<\/span>0.5<\/span>)<\/span>)<\/span>
                                \nmodel.<\/span>add(<\/span>LSTM(<\/span>32<\/span>)<\/span>)<\/span>
                                \nmodel.<\/span>add(<\/span>Dropout(<\/span>0.5<\/span>)<\/span>)<\/span>
                                \nmodel.<\/span>add(<\/span>Dense(<\/span>2<\/span>,<\/span> activation=<\/span>'softmax'<\/span>)<\/span>)<\/span><\/p>\n

                                model.<\/span>compile<\/span>(<\/span>loss=<\/span>'categorical_crossentropy'<\/span>,<\/span>
                                \n optimizer=<\/span>'adam'<\/span>,<\/span>
                                \n metrics=<\/span>[<\/span>'accuracy'<\/span>]<\/span>)<\/span><\/p>\n

                                callbacks =<\/span> [<\/span>
                                \n EarlyStopping(<\/span>monitor=<\/span>'val_loss'<\/span>,<\/span> patience=<\/span>3<\/span>)<\/span>,<\/span>
                                \n ModelCheckpoint(<\/span>'best_lstm.h5'<\/span>,<\/span> save_best_only=<\/span>True<\/span>)<\/span>
                                \n]<\/span><\/p>\n

                                history =<\/span> model.<\/span>fit(<\/span>X_train,<\/span> y_train,<\/span>
                                \n validation_data=<\/span>(<\/span>X_val,<\/span> y_val)<\/span>,<\/span>
                                \n epochs=<\/span>10<\/span>,<\/span>
                                \n batch_size=<\/span>64<\/span>,<\/span>
                                \n callbacks=<\/span>callbacks)<\/span><\/p>\n

                                  \n
                                • Embedding：\u628a\u6bcf\u4e2a\u8bcd\u7f16\u7801\u6210 128 \u7ef4\u5411\u91cf；<\/li>\n
                                • \u4e24\u5c42 LSTM：\u7b2c\u4e00\u5c42\u8fd4\u56de\u5e8f\u5217，\u7b2c\u4e8c\u5c42\u8f93\u51fa\u6700\u7ec8\u72b6\u6001；<\/li>\n
                                • Dropout：\u9632\u6b62\u8fc7\u62df\u5408；<\/li>\n
                                • EarlyStopping + ModelCheckpoint：\u81ea\u52a8\u4fdd\u5b58\u6700\u4f18\u6a21\u578b\u3002<\/li>\n<\/ul>\n

                                  4.4 \u540e\u7aef\u96c6\u6210<\/h4>\n

                                  from<\/span> flask import<\/span> Flask,<\/span> request,<\/span> jsonify
                                  \nfrom<\/span> tensorflow.<\/span>keras.<\/span>models import<\/span> load_model
                                  \nfrom<\/span> utils import<\/span> preprocess_text # \u5305\u542b\u524d\u9762\u8bb2\u7684\u5206\u8bcd\u3001\u5e8f\u5217\u5316<\/span><\/p>\n

                                  app =<\/span> Flask(<\/span>__name__)<\/span>
                                  \nlstm_model =<\/span> load_model(<\/span>'best_lstm.h5'<\/span>)<\/span><\/p>\n

                                  @app.<\/span>route<\/span>(<\/span>'\/api\/sentiment'<\/span>,<\/span> methods=<\/span>[<\/span>'POST'<\/span>]<\/span>)<\/span>
                                  \ndef<\/span> sentiment<\/span>(<\/span>)<\/span>:<\/span>
                                  \n data =<\/span> request.<\/span>json
                                  \n content =<\/span> data.<\/span>get(<\/span>'content'<\/span>,<\/span> ''<\/span>)<\/span>
                                  \n seq =<\/span> preprocess_text(<\/span>content)<\/span> # \u5206\u8bcd\u3001\u5e8f\u5217\u5316<\/span>
                                  \n pred =<\/span> lstm_model.<\/span>predict(<\/span>seq)<\/span>[<\/span>0<\/span>]<\/span>
                                  \n label =<\/span> '\u6b63\u9762'<\/span> if<\/span> pred[<\/span>1<\/span>]<\/span> ><\/span> pred[<\/span>0<\/span>]<\/span> else<\/span> '\u8d1f\u9762'<\/span>
                                  \n prob =<\/span> float<\/span>(<\/span>pred.<\/span>max<\/span>(<\/span>)<\/span>)<\/span>
                                  \n return<\/span> jsonify(<\/span>{<\/span>'label'<\/span>:<\/span> label,<\/span> 'probability'<\/span>:<\/span> prob}<\/span>)<\/span><\/p>\n

                                  if<\/span> __name__ ==<\/span> '__main__'<\/span>:<\/span>
                                  \n app.<\/span>run(<\/span>debug=<\/span>True<\/span>)<\/span><\/p>\n

                                    \n
                                  • \u63a5\u53e3 \/api\/sentiment \u63a5\u6536\u539f\u59cb\u8bc4\u8bba，\u8fd4\u56de\u60c5\u611f\u6807\u7b7e\u548c\u6982\u7387；<\/li>\n
                                  • \u524d\u7aef\u7528 AJAX \u8c03\u7528\u8fd9\u4e2a\u63a5\u53e3，\u5b9e\u65f6\u83b7\u53d6\u5206\u6790\u7ed3\u679c\u3002<\/li>\n<\/ul>\n

                                    4.5 \u53ef\u89c6\u5316\u5c55\u793a<\/h4>\n

                                    \u7cfb\u7edf\u9875\u9762\u5305\u542b\u4e0b\u5217\u56fe\u8868：<\/p>\n

                                      \n
                                    • \u8bc4\u8bba\u60c5\u611f\u997c\u56fe：\u5c55\u793a\u6b63\u3001\u8d1f\u9762\u8bc4\u8bba\u7684\u5360\u6bd4；<\/li>\n
                                    • \u60c5\u611f\u8d8b\u52bf\u6298\u7ebf\u56fe：\u6309\u5929\u6216\u5c0f\u65f6\u7edf\u8ba1\u8bc4\u8bba\u60c5\u611f\u53d8\u5316；<\/li>\n
                                    • \u5173\u952e\u8bcd\u8bcd\u4e91：\u6b63\u9762\u3001\u8d1f\u9762\u8bc4\u8bba\u5206\u522b\u751f\u6210\u8bcd\u4e91，\u76f4\u89c2\u4e86\u89e3\u70ed\u95e8\u8bcd；<\/li>\n<\/ul>\n

                                      \u7528 ECharts \u5b9e\u73b0，\u4ee3\u7801\u793a\u4f8b（\u997c\u56fe）：<\/p>\n

                                      var<\/span> chart =<\/span> echarts.<\/span>init<\/span>(<\/span>document.<\/span>getElementById<\/span>(<\/span>'pieChart'<\/span>)<\/span>)<\/span>;<\/span>
                                      \nchart.<\/span>setOption<\/span>(<\/span>{<\/span>
                                      \n title:<\/span> {<\/span> text:<\/span> '\u60c5\u611f\u5360\u6bd4'<\/span> }<\/span>,<\/span>
                                      \n tooltip:<\/span> {<\/span> trigger:<\/span> 'item'<\/span> }<\/span>,<\/span>
                                      \n series:<\/span> [<\/span>{<\/span>
                                      \n name:<\/span> '\u60c5\u611f'<\/span>,<\/span>
                                      \n type:<\/span> 'pie'<\/span>,<\/span>
                                      \n radius:<\/span> '50%'<\/span>,<\/span>
                                      \n data:<\/span> [<\/span>
                                      \n {<\/span> value:<\/span> posCount,<\/span> name:<\/span> '\u6b63\u9762'<\/span> }<\/span>,<\/span>
                                      \n {<\/span> value:<\/span> negCount,<\/span> name:<\/span> '\u8d1f\u9762'<\/span> }<\/span>
                                      \n ]<\/span>
                                      \n }<\/span>]<\/span>
                                      \n}<\/span>)<\/span>;<\/span><\/p>\n

                                      \n

                                      5 \u6548\u679c\u5c55\u793a<\/h3>\n

                                      \u4e0b\u56fe\u5c55\u793a\u4e86\u7cfb\u7edf\u5bf9\u300a\u8096\u7533\u514b\u7684\u6551\u8d4e\u300b\u5f71\u8bc4\u7684\u60c5\u611f\u5206\u6790\u7ed3\u679c\u3002<\/p>\n

                                      \"\u7535\u5f71\u5bfc\u6f14\" \"\u7535\u5f71\u7c7b\u578b\"<\/p>\n

                                      \u4e0a\u56fe\u4e3a\u8bc4\u8bba\u60c5\u611f\u997c\u56fe\u548c\u8d8b\u52bf\u6761\u5f62\u56fe\u793a\u4f8b\u3002<\/p>\n

                                      \"\u7535\u5f71\u5217\u8868\" \"\u7535\u5f71\u8bc4\u5206\"<\/p>\n

                                      \u9875\u9762\u4e0a\u8fd8\u6709\u8bcd\u4e91\u548c\u8bc4\u8bba\u5217\u8868，\u65b9\u4fbf\u6df1\u5165\u67e5\u770b\u6bcf\u6761\u8bc4\u8bba\u5185\u5bb9\u3002<\/p>\n

                                      \n

                                      6 \u6e90\u7801\u83b7\u53d6：<\/h3>\n

                                      \u5927\u5bb6\u70b9\u8d5e\u3001\u6536\u85cf\u3001\u5173\u6ce8\u3001\u8bc4\u8bba\u5566 \u3001\u67e5\u770b👇🏻\u83b7\u53d6\u8054\u7cfb\u65b9\u5f0f👇🏻<\/p>\n

                                      👇🏻 \u7cbe\u5f69\u4e13\u680f\u63a8\u8350\u8ba2\u9605👇🏻 \u4e0d\u7136\u4e0b\u6b21\u627e\u4e0d\u5230\u54df<\/font><\/p>\n

                                      2022-2024\u5e74\u6700\u5168\u7684\u8ba1\u7b97\u673a\u8f6f\u4ef6\u6bd5\u4e1a\u8bbe\u8ba1\u9009\u9898\u5927\u5168：1000\u4e2a\u70ed\u95e8\u9009\u9898\u63a8\u8350\u2705<\/p>\n

                                      Java\u9879\u76ee\u7cbe\u54c1\u5b9e\u6218\u6848\u4f8b\u300a100\u5957\u300b<\/p>\n

                                      Java\u5fae\u4fe1\u5c0f\u7a0b\u5e8f\u9879\u76ee\u5b9e\u6218\u300a100\u5957\u300b<\/p>\n

                                      Python\u9879\u76ee\u5b9e\u6218\u300a100\u5957\u300b<\/p>\n

                                      \u611f\u5174\u8da3\u7684\u53ef\u4ee5\u5148\u6536\u85cf\u8d77\u6765，\u8fd8\u6709\u5927\u5bb6\u5728\u6bd5\u8bbe\u9009\u9898，\u9879\u76ee\u4ee5\u53ca\u6587\u6863\u7f16\u5199\u7b49\u76f8\u5173\u95ee\u9898\u90fd\u53ef\u4ee5\u7ed9\u6211\u7559\u8a00\u54a8\u8be2，\u5e0c\u671b\u5e2e\u52a9\u66f4\u591a\u7684\u4eba<\/font><\/p>\n","protected":false},"excerpt":{"rendered":"

                                      \u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb1.2k\u6b21\uff0c\u70b9\u8d5e13\u6b21\uff0c\u6536\u85cf20\u6b21\u3002\u57fa\u4e8e\u673a\u5668\u5b66\u4e60 LSTM \u7b97\u6cd5\u7684\u8c46\u74e3\u8bc4\u8bba\u60c5\u611f\u5206\u6790\u7cfb\u7edf\uff0c\u8be5\u7cfb\u7edf\u5728\u539f\u672c\u7684\u57fa\u7840\u4e4b\u4e0a\u8fdb\u884c\u4f18\u5316\u3002\u652f\u6301\u4e86\u673a\u5668\u5b66\u4e60 lstm \u7b97\u6cd5\uff0c\u8fdb\u884c\u60c5\u611f\u5206\u6790\u652f\u6301\u4e86\u7528\u6237\u624b\u52a8\u8f93\u5165\u6587\u672c\u8fdb\u884c\u60c5\u611f\u5206\u6790\u54b1\u4eec\u8fd9\u5957\u7cfb\u7edf\u662f\u7528 Python + Flask \u642d\u7684\uff0c\u7528 LSTM\uff08\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc\uff09\u6a21\u578b\u6765\u505a\u8c46\u74e3\u5f71\u8bc4\u7684\u60c5\u611f\u5206\u6790\u3002\u5b83\u80fd\u81ea\u52a8\u722c\u53d6\u8c46\u74e3\u4e0a\u7684\u6700\u65b0\u8bc4\u8bba\uff0c\u7ed9\u4f60\u7b97\u51fa\u597d\u8bc4\u7387\u3001\u5dee\u8bc4\u7387\uff0c\u8fd8\u80fd\u628a\u5206\u6790\u7ed3\u679c\u5b9e\u65f6\u53ef\u89c6\u5316\uff0c\u652f\u6301\u997c\u56fe\u3001\u6761\u5f62\u56fe\u7b49\u591a\u79cd\u56fe\u8868\u5c55\u793a\u3002\u6574\u4e2a\u9879\u76ee\u542b\u6e90\u7801\u3001\u5f00\u53d1\u73af\u5883\u914d\u7f6e\u3001\u5b89\u88c5\u6559\u7a0b\uff0c\u529f\u80fd\u5b8c\u6574\uff0c\u7279\u522b\u9002\u5408\u62ff\u53bb\u505a\u6bd5\u4e1a\u8bbe\u8ba1\u3001\u8bfe\u7a0b\u8bbe\u8ba1\u6216\u6570\u636e\u5e93\u5927\u4f5c\u4e1a\u3002\u722c\u866b\u6a21\u5757\u3002<\/p>\n","protected":false},"author":2,"featured_media":34811,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[2951,2950,207,2953,427,2952],"topic":[],"class_list":["post-34816","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-lstm","tag-python-","tag-207","tag-2953","tag-427","tag-2952"],"yoast_head":"\n\u57fa\u4e8e\u673a\u5668\u5b66\u4e60 LSTM \u7b97\u6cd5\u7684\u8c46\u74e3\u8bc4\u8bba\u60c5\u611f\u5206\u6790\u7cfb\u7edf - 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