{"id":997577,"date":"2024-12-27T09:25:23","date_gmt":"2024-12-27T01:25:23","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/997577.html"},"modified":"2024-12-27T09:25:28","modified_gmt":"2024-12-27T01:25:28","slug":"%e5%a6%82%e4%bd%95%e6%8a%8a%e6%a8%a1%e5%9e%8b%e5%bc%95%e5%85%a5python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/997577.html","title":{"rendered":"\u5982\u4f55\u628a\u6a21\u578b\u5f15\u5165python"},"content":{"rendered":"

\"\u5982\u4f55\u628a\u6a21\u578b\u5f15\u5165python\"<\/p>\n

\u5c06\u6a21\u578b\u5f15\u5165Python\u7684\u8fc7\u7a0b\u5305\u62ec\u51e0\u4e2a\u5173\u952e\u6b65\u9aa4\uff1a\u9009\u62e9\u5408\u9002\u7684\u673a\u5668\u5b66\u4e60<\/a>\u5e93\u3001\u51c6\u5907\u6570\u636e\u3001\u52a0\u8f7d\u548c\u5904\u7406\u6a21\u578b\u3001\u8fdb\u884c\u9884\u6d4b\u6216\u8bc4\u4f30\u3002<\/strong>\u9996\u5148\uff0c\u9009\u62e9\u4e00\u4e2a\u9002\u5408\u60a8\u9700\u6c42\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u5982TensorFlow\u3001PyTorch\u6216Scikit-learn\u3002\u7136\u540e\uff0c\u51c6\u5907\u597d\u7528\u4e8e\u6a21\u578b\u8bad\u7ec3\u6216\u9884\u6d4b\u7684\u6570\u636e\u3002\u63a5\u4e0b\u6765\uff0c\u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b\u6216\u4ece\u5934\u5f00\u59cb\u8bad\u7ec3\u6a21\u578b\uff0c\u5e76\u786e\u4fdd\u6a21\u578b\u7684\u8f93\u5165\u683c\u5f0f\u4e0e\u6570\u636e\u4e00\u81f4\u3002\u6700\u540e\uff0c\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u6216\u8fdb\u884c\u6027\u80fd\u8bc4\u4f30\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u8fd9\u4e9b\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u9009\u62e9\u5408\u9002\u7684\u673a\u5668\u5b66\u4e60\u5e93<\/p>\n<\/p>\n

\u9009\u62e9\u5408\u9002\u7684\u673a\u5668\u5b66\u4e60\u5e93\u662f\u6a21\u578b\u5f15\u5165Python\u7684\u7b2c\u4e00\u6b65\u3002\u5e02\u573a\u4e0a\u6709\u8bb8\u591a\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u5404\u81ea\u6709\u4e0d\u540c\u7684\u7279\u70b9\u548c\u9002\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    TensorFlow\u548cKeras<\/strong><\/p>\n<\/p>\n

    TensorFlow\u662f\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u5f00\u6e90\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff0c\u7531Google\u5f00\u53d1\u3002\u5b83\u7684\u4f18\u52bf\u5728\u4e8e\u5f3a\u5927\u7684\u652f\u6301\u548c\u5e7f\u6cdb\u7684\u793e\u533a\u8d44\u6e90\u3002Keras\u662f\u4e00\u4e2a\u9ad8\u7ea7\u7684\u795e\u7ecf\u7f51\u7edcAPI\uff0c\u80fd\u591f\u5728TensorFlow\u4e4b\u4e0a\u8fd0\u884c\uff0c\u4f7f\u5f97\u590d\u6742\u6a21\u578b\u7684\u6784\u5efa\u53d8\u5f97\u66f4\u52a0\u7b80\u5355\u548c\u76f4\u89c2\u3002\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\uff0c\u5c24\u5176\u662f\u9700\u8981\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u7684\u4efb\u52a1\uff0cTensorFlow\u548cKeras\u662f\u975e\u5e38\u4e0d\u9519\u7684\u9009\u62e9\u3002<\/p>\n<\/p>\n<\/li>\n

  2. \n

    PyTorch<\/strong><\/p>\n<\/p>\n

    PyTorch\u662fFacebook\u5f00\u53d1\u7684\u53e6\u4e00\u4e2a\u6df1\u53d7\u6b22\u8fce\u7684\u5f00\u6e90\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u3002\u5b83\u7684\u52a8\u6001\u8ba1\u7b97\u56fe\u4f7f\u5f97\u6a21\u578b\u6784\u5efa\u548c\u8c03\u8bd5\u66f4\u52a0\u7075\u6d3b\u548c\u76f4\u89c2\u3002PyTorch\u5728\u7814\u7a76\u793e\u533a\u4e2d\u975e\u5e38\u53d7\u6b22\u8fce\uff0c\u5c24\u5176\u662f\u5728\u9700\u8981\u5feb\u901f\u539f\u578b\u8bbe\u8ba1\u548c\u5b9e\u9a8c\u7684\u60c5\u51b5\u4e0b\u3002<\/p>\n<\/p>\n<\/li>\n

  3. \n

    Scikit-learn<\/strong><\/p>\n<\/p>\n

    Scikit-learn\u662f\u4e00\u4e2a\u7528\u4e8e\u7b80\u5355\u548c\u4e2d\u7b49\u590d\u6742\u5ea6\u7684\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u7684\u5e93\uff0c\u7279\u522b\u9002\u5408\u4e8e\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u6a21\u578b\u7684\u5b9e\u73b0\uff0c\u5982\u56de\u5f52\u3001\u5206\u7c7b\u548c\u805a\u7c7b\u3002\u5b83\u7684API\u7b80\u5355\u6613\u7528\uff0c\u975e\u5e38\u9002\u5408\u521d\u5b66\u8005\u548c\u4e2d\u5c0f\u578b\u9879\u76ee\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

    \u4e8c\u3001\u51c6\u5907\u6570\u636e<\/p>\n<\/p>\n

    \u5f15\u5165\u6a21\u578b\u7684\u4e0b\u4e00\u6b65\u662f\u51c6\u5907\u6570\u636e\u3002\u8fd9\u4e00\u6b65\u9aa4\u5bf9\u4e8e\u6a21\u578b\u7684\u8868\u73b0\u81f3\u5173\u91cd\u8981\u3002\u6570\u636e\u51c6\u5907\u7684\u8fc7\u7a0b\u5305\u62ec\u6570\u636e\u6536\u96c6\u3001\u6e05\u6d17\u3001\u9884\u5904\u7406\u548c\u5206\u5272\u3002<\/p>\n<\/p>\n

      \n
    1. \n

      \u6570\u636e\u6536\u96c6<\/strong><\/p>\n<\/p>\n

      \u6570\u636e\u6536\u96c6\u662f\u6570\u636e\u51c6\u5907\u7684\u7b2c\u4e00\u6b65\uff0c\u60a8\u53ef\u4ee5\u4ece\u516c\u5f00\u6570\u636e\u96c6\u3001\u6570\u636e\u5e93\u6216\u5b9e\u65f6\u6570\u636e\u6d41\u4e2d\u83b7\u53d6\u6570\u636e\u3002\u786e\u4fdd\u6570\u636e\u8d28\u91cf\u9ad8\u4e14\u4e0e\u6a21\u578b\u4efb\u52a1\u76f8\u5173\u3002<\/p>\n<\/p>\n<\/li>\n

    2. \n

      \u6570\u636e\u6e05\u6d17<\/strong><\/p>\n<\/p>\n

      \u6570\u636e\u6e05\u6d17\u6d89\u53ca\u53bb\u9664\u6216\u4fee\u6b63\u6570\u636e\u4e2d\u7684\u9519\u8bef\u548c\u5f02\u5e38\u503c\u3002\u5e38\u89c1\u7684\u6e05\u6d17\u65b9\u6cd5\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u53bb\u9664\u91cd\u590d\u6570\u636e\u548c\u4fee\u6b63\u683c\u5f0f\u9519\u8bef\u3002<\/p>\n<\/p>\n<\/li>\n

    3. \n

      \u6570\u636e\u9884\u5904\u7406<\/strong><\/p>\n<\/p>\n

      \u6570\u636e\u9884\u5904\u7406\u5305\u62ec\u5bf9\u6570\u636e\u8fdb\u884c\u6807\u51c6\u5316\u3001\u5f52\u4e00\u5316\u548c\u7279\u5f81\u5de5\u7a0b\u3002\u6807\u51c6\u5316\u548c\u5f52\u4e00\u5316\u80fd\u591f\u4f7f\u5f97\u4e0d\u540c\u7279\u5f81\u7684\u6570\u636e\u5c3a\u5ea6\u4e00\u81f4\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u8bad\u7ec3\u6548\u679c\u3002\u7279\u5f81\u5de5\u7a0b\u5219\u662f\u4ece\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u7528\u7684\u7279\u5f81\uff0c\u4ee5\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u8fdb\u884c\u5b66\u4e60\u3002<\/p>\n<\/p>\n<\/li>\n

    4. \n

      \u6570\u636e\u5206\u5272<\/strong><\/p>\n<\/p>\n

      \u901a\u5e38\u4f1a\u5c06\u6570\u636e\u5206\u4e3a\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u4ee5\u4fbf\u6a21\u578b\u7684\u8bad\u7ec3\u3001\u8c03\u53c2\u548c\u8bc4\u4f30\u3002\u5e38\u89c1\u7684\u5206\u5272\u6bd4\u4f8b\u662f70:20:10\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

      \u4e09\u3001\u52a0\u8f7d\u548c\u5904\u7406\u6a21\u578b<\/p>\n<\/p>\n

      \u6839\u636e\u6240\u9009\u62e9\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u52a0\u8f7d\u548c\u5904\u7406\u6a21\u578b\u7684\u6b65\u9aa4\u4f1a\u6709\u6240\u4e0d\u540c\u3002<\/p>\n<\/p>\n

        \n
      1. \n

        \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b<\/strong><\/p>\n<\/p>\n

        \u5f88\u591a\u60c5\u51b5\u4e0b\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u6a21\u578b\uff0c\u4f8b\u5982\u5728\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1\u4e2d\u4f7f\u7528VGG\u3001ResNet\u7b49\u9884\u8bad\u7ec3\u6a21\u578b\u3002\u8fd9\u4e9b\u6a21\u578b\u901a\u5e38\u5df2\u7ecf\u5728\u5927\u89c4\u6a21\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u4e86\u8bad\u7ec3\uff0c\u80fd\u591f\u6709\u6548\u5730\u5e94\u7528\u4e8e\u76f8\u4f3c\u7684\u4efb\u52a1\u3002\u4f7f\u7528\u9884\u8bad\u7ec3\u6a21\u578b\u80fd\u591f\u8282\u7701\u8bad\u7ec3\u65f6\u95f4\u548c\u8ba1\u7b97\u8d44\u6e90\u3002<\/p>\n<\/p>\n<\/li>\n

      2. \n

        \u4ece\u5934\u5f00\u59cb\u8bad\u7ec3\u6a21\u578b<\/strong><\/p>\n<\/p>\n

        \u5982\u679c\u9884\u8bad\u7ec3\u6a21\u578b\u4e0d\u80fd\u6ee1\u8db3\u9700\u6c42\uff0c\u60a8\u53ef\u4ee5\u4ece\u5934\u5f00\u59cb\u8bad\u7ec3\u6a21\u578b\u3002\u4e3a\u6b64\uff0c\u60a8\u9700\u8981\u5b9a\u4e49\u6a21\u578b\u67b6\u6784\u3001\u9009\u62e9\u5408\u9002\u7684\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u5668\uff0c\u5e76\u8fdb\u884c\u8bad\u7ec3\u3002<\/p>\n<\/p>\n<\/li>\n

      3. \n

        \u5904\u7406\u6a21\u578b\u8f93\u5165<\/strong><\/p>\n<\/p>\n

        \u786e\u4fdd\u6a21\u578b\u7684\u8f93\u5165\u683c\u5f0f\u4e0e\u6570\u636e\u4e00\u81f4\u662f\u5173\u952e\u7684\u4e00\u6b65\u3002\u901a\u5e38\u9700\u8981\u5bf9\u8f93\u5165\u6570\u636e\u8fdb\u884c\u5904\u7406\uff0c\u4ee5\u5339\u914d\u6a21\u578b\u7684\u8f93\u5165\u8981\u6c42\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u56fe\u50cf\u6570\u636e\uff0c\u53ef\u80fd\u9700\u8981\u8c03\u6574\u56fe\u50cf\u7684\u5c3a\u5bf8\u548c\u901a\u9053\u987a\u5e8f\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

        \u56db\u3001\u8fdb\u884c\u9884\u6d4b\u6216\u8bc4\u4f30<\/p>\n<\/p>\n

        \u6700\u540e\u4e00\u6b65\u662f\u4f7f\u7528\u52a0\u8f7d\u6216\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u6216\u8bc4\u4f30\u3002<\/p>\n<\/p>\n

          \n
        1. \n

          \u8fdb\u884c\u9884\u6d4b<\/strong><\/p>\n<\/p>\n

          \u5728\u8fdb\u884c\u9884\u6d4b\u65f6\uff0c\u5c06\u65b0\u7684\u6570\u636e\u8f93\u5165\u6a21\u578b\uff0c\u5f97\u5230\u9884\u6d4b\u7ed3\u679c\u3002\u6839\u636e\u4efb\u52a1\u7684\u4e0d\u540c\uff0c\u9884\u6d4b\u7ed3\u679c\u53ef\u80fd\u662f\u5206\u7c7b\u6807\u7b7e\u3001\u56de\u5f52\u503c\u7b49\u3002<\/p>\n<\/p>\n<\/li>\n

        2. \n

          \u6a21\u578b\u8bc4\u4f30<\/strong><\/p>\n<\/p>\n

          \u5728\u6a21\u578b\u8bc4\u4f30\u9636\u6bb5\uff0c\u4f7f\u7528\u6d4b\u8bd5\u96c6\u5bf9\u6a21\u578b\u8fdb\u884c\u6027\u80fd\u8bc4\u4f30\u3002\u5e38\u7528\u7684\u8bc4\u4f30\u6307\u6807\u5305\u62ec\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u548cF1\u5206\u6570\u7b49\u3002\u6839\u636e\u8bc4\u4f30\u7ed3\u679c\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u5bf9\u6a21\u578b\u8fdb\u884c\u8c03\u53c2\u6216\u91cd\u65b0\u8bad\u7ec3\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

          \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u60a8\u53ef\u4ee5\u6210\u529f\u5730\u5c06\u6a21\u578b\u5f15\u5165Python\uff0c\u5e76\u5e94\u7528\u4e8e\u5b9e\u9645\u4efb\u52a1\u3002\u6a21\u578b\u5f15\u5165\u7684\u8fc7\u7a0b\u9700\u8981\u4e0d\u65ad\u5730\u8c03\u6574\u548c\u4f18\u5316\uff0c\u4ee5\u786e\u4fdd\u6a21\u578b\u7684\u6027\u80fd\u8fbe\u5230\u9884\u671f\u76ee\u6807\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u826f\u597d\u7684\u6570\u636e\u51c6\u5907\u548c\u6a21\u578b\u9009\u62e9\u662f\u6210\u529f\u7684\u5173\u952e\u56e0\u7d20\u3002<\/p>\n<\/p>\n

          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n

          \u5982\u4f55\u5c06\u673a\u5668\u5b66\u4e60\u6a21\u578b\u52a0\u8f7d\u5230Python\u4e2d\uff1f<\/strong>
          \u8981\u5728Python\u4e2d\u52a0\u8f7d\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u901a\u5e38\u4f7f\u7528\u50cfjoblib<\/code>\u6216pickle<\/code>\u8fd9\u6837\u7684\u5e93\u3002\u5bf9\u4e8e\u4f7f\u7528scikit-learn<\/code>\u8bad\u7ec3\u7684\u6a21\u578b\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528joblib.load('model.pkl')<\/code>\u6765\u5b9e\u73b0\u52a0\u8f7d\u3002\u786e\u4fdd\u5c06\u6a21\u578b\u6587\u4ef6\u4fdd\u5b58\u5728\u6307\u5b9a\u8def\u5f84\u4e0b\u3002\u5bf9\u4e8eTensorFlow\u6216Keras\u6a21\u578b\uff0c\u53ef\u4ee5\u4f7f\u7528tf.keras.models.load_model('model.h5')<\/code>\u6765\u52a0\u8f7d\u3002<\/p>\n

          \u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u6a21\u578b\u7684\u8f93\u5165\u6570\u636e\uff1f<\/strong>
          \u5728\u5c06\u6a21\u578b\u5f15\u5165Python\u5e76\u8fdb\u884c\u9884\u6d4b\u4e4b\u524d\uff0c\u8f93\u5165\u6570\u636e\u9700\u8981\u7ecf\u8fc7\u9002\u5f53\u7684\u9884\u5904\u7406\u3002\u8fd9\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u9009\u62e9\u548c\u6807\u51c6\u5316\u7b49\u6b65\u9aa4\u3002\u786e\u4fdd\u8f93\u5165\u6570\u636e\u7684\u683c\u5f0f\u4e0e\u8bad\u7ec3\u6a21\u578b\u65f6\u7684\u683c\u5f0f\u4e00\u81f4\uff0c\u901a\u5e38\u662f\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\u6216DataFrame\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u5904\u7406\u6570\u636e\uff0c\u4f7f\u5176\u7b26\u5408\u6a21\u578b\u7684\u8981\u6c42\u3002<\/p>\n

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