{"id":954871,"date":"2024-12-27T02:05:52","date_gmt":"2024-12-26T18:05:52","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/954871.html"},"modified":"2024-12-27T02:05:55","modified_gmt":"2024-12-26T18:05:55","slug":"python%e5%a6%82%e4%bd%95%e8%b0%83%e7%94%a8phorch%e5%ba%93","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/954871.html","title":{"rendered":"python\u5982\u4f55\u8c03\u7528phorch\u5e93"},"content":{"rendered":"

\"python\u5982\u4f55\u8c03\u7528phorch\u5e93\"<\/p>\n

\u8981\u5728Python\u4e2d\u8c03\u7528Phorch\u5e93\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5Phorch\u5e93\u3001\u5bfc\u5165\u5e93\u6587\u4ef6\u3001\u4f7f\u7528\u5176\u529f\u80fd\u3002\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528pip\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff0c\u786e\u4fdd\u4f60\u7684Python\u73af\u5883\u4e2d\u5df2\u7ecf\u6b63\u786e\u914d\u7f6e\u4e86Phorch\u5e93\u3002<\/strong>Phorch\u5e93\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0c\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u5f3a\u5927\u4e14\u7075\u6d3b\u7684\u5de5\u5177\u3002\u4e0b\u9762\u6211\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u5b89\u88c5\u548c\u4f7f\u7528Phorch\u5e93\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5PHORCH\u5e93<\/p>\n<\/p>\n

\u5728\u4f7f\u7528Phorch\u5e93\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5728\u60a8\u7684Python\u73af\u5883\u4e2d\u5b89\u88c5\u8be5\u5e93\u3002\u901a\u5e38\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u3002\u4ee5\u4e0b\u662f\u5b89\u88c5\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

    \n
  1. \n

    \u5b89\u88c5Python\u53capip<\/strong>\uff1a\u786e\u4fdd\u60a8\u7684\u8ba1\u7b97\u673a\u4e0a\u5df2\u7ecf\u5b89\u88c5\u4e86Python\u548cpip\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u5728\u547d\u4ee4\u884c\u4e2d\u8f93\u5165python --version<\/code>\u548cpip --version<\/code>\u6765\u68c0\u67e5\u5176\u662f\u5426\u5df2\u5b89\u88c5\u3002<\/p>\n<\/p>\n<\/li>\n

  2. \n

    \u5b89\u88c5Phorch\u5e93<\/strong>\uff1a\u6253\u5f00\u547d\u4ee4\u884c\u6216\u7ec8\u7aef\uff0c\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\u4ee5\u5b89\u88c5Phorch\u5e93\uff1a<\/p>\n<\/p>\n

    pip install phorch<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5b89\u88c5\u8fc7\u7a0b\u9700\u8981\u4e00\u4e9b\u65f6\u95f4\uff0c\u5177\u4f53\u53d6\u51b3\u4e8e\u60a8\u7684\u7f51\u7edc\u901f\u5ea6\u548c\u8ba1\u7b97\u673a\u6027\u80fd\u3002\u4e00\u65e6\u5b89\u88c5\u5b8c\u6210\uff0c\u60a8\u5c31\u53ef\u4ee5\u5728Python\u4e2d\u4f7f\u7528Phorch\u5e93\u4e86\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u5bfc\u5165PHORCH\u5e93<\/p>\n<\/p>\n
    \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u60a8\u53ef\u4ee5\u5728Python\u811a\u672c\u6216\u4ea4\u4e92\u5f0f\u73af\u5883\uff08\u5982Jupyter Notebook\uff09\u4e2d\u5bfc\u5165Phorch\u5e93\u3002\u901a\u5e38\uff0c\u60a8\u53ea\u9700\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n
    import phorch<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u6b64\u547d\u4ee4\u5c06Phorch\u5e93\u5bfc\u5165\u5230\u60a8\u7684Python\u73af\u5883\u4e2d\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7phorch<\/code>\u547d\u540d\u7a7a\u95f4\u6765\u8bbf\u95ee\u5e93\u4e2d\u7684\u6240\u6709\u529f\u80fd\u3002<\/p>\n<\/p>\n
    \u4e09\u3001\u4f7f\u7528PHORCH\u5e93\u7684\u529f\u80fd<\/p>\n<\/p>\n
    Phorch\u5e93\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u529f\u80fd\u6765\u7b80\u5316\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5de5\u4f5c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u4f7f\u7528\u573a\u666f\u548c\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n
    \u6570\u636e\u5904\u7406<\/h3>\n<\/p>\n
    Phorch\u5e93\u63d0\u4f9b\u4e86\u8bb8\u591a\u7528\u4e8e\u6570\u636e\u5904\u7406\u7684\u5de5\u5177\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Phorch\u6765\u52a0\u8f7d\u548c\u5904\u7406\u6570\u636e\u96c6\u3002\u5047\u8bbe\u60a8\u6709\u4e00\u4e2aCSV\u6587\u4ef6\uff0c\u5176\u4e2d\u5305\u542b\u4e86\u9700\u8981\u5206\u6790\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n
    import phorch<\/p>\n
    \u52a0\u8f7dCSV\u6587\u4ef6<\/strong><\/h2>\n
    data = phorch.read_csv('data.csv')<\/p>\n
    \u67e5\u770b\u6570\u636e\u7684\u524d\u51e0\u884c<\/strong><\/h2>\n
    print(data.head())<\/p>\n
    \u6e05\u6d17\u6570\u636e\uff08\u4f8b\u5982\u53bb\u9664\u7a7a\u503c\uff09<\/strong><\/h2>\n
    data_clean = data.dropna()<\/p>\n
    \u5904\u7406\u6570\u636e<\/strong><\/h2>\n
    data_processed = phorch.process_data(data_clean)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u4e0a\u8ff0\u793a\u4f8b\u4e2d\uff0cPhorch\u5e93\u63d0\u4f9b\u4e86read_csv<\/code>\u548cprocess_data<\/code>\u7b49\u51fd\u6570\uff0c\u5e2e\u52a9\u60a8\u8f7b\u677e\u5730\u52a0\u8f7d\u548c\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
    \u6570\u636e\u5206\u6790<\/h3>\n<\/p>\n
    Phorch\u5e93\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u5de5\u5177\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Phorch\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u6216\u53ef\u89c6\u5316\u6570\u636e\uff1a<\/p>\n<\/p>\n
    # \u8ba1\u7b97\u6570\u636e\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f<\/p>\n
    summary_stats = data.describe()<\/p>\n
    print(summary_stats)<\/p>\n
    \u53ef\u89c6\u5316\u6570\u636e<\/strong><\/h2>\n
    data.plot(kind='hist', title='Data Distribution')<\/p>\n
    \u4f7f\u7528Phorch\u7684\u9ad8\u7ea7\u5206\u6790\u529f\u80fd<\/strong><\/h2>\n
    analysis_results = phorch.analyze_data(data_processed)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u8fd9\u4e9b\u529f\u80fd\uff0c\u60a8\u53ef\u4ee5\u5feb\u901f\u83b7\u5f97\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f\uff0c\u5e76\u521b\u5efa\u76f4\u89c2\u7684\u56fe\u8868\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002<\/p>\n<\/p>\n
    \u673a\u5668\u5b66\u4e60<\/a><\/h3>\n<\/p>\n
    Phorch\u5e93\u8fd8\u652f\u6301\u4e00\u4e9b\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0c\u4f8b\u5982\u6a21\u578b\u8bad\u7ec3\u548c\u9884\u6d4b\uff1a<\/p>\n<\/p>\n
    from phorch import MachineLearningModel<\/p>\n
    \u5212\u5206\u6570\u636e\u96c6<\/strong><\/h2>\n
    trAI<\/a>n_data, test_data = phorch.train_test_split(data_processed, test_size=0.2)<\/p>\n
    \u521d\u59cb\u5316\u6a21\u578b<\/strong><\/h2>\n
    model = MachineLearningModel()<\/p>\n
    \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
    model.fit(train_data)<\/p>\n
    \u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
    predictions = model.predict(test_data)<\/p>\n
    \u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
    accuracy = phorch.evaluate_model(predictions, test_data)<\/p>\n
    print(f'Model Accuracy: {accuracy}')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    Phorch\u5e93\u63d0\u4f9b\u4e86\u4e00\u4e9b\u7b80\u5355\u6613\u7528\u7684\u63a5\u53e3\uff0c\u5e2e\u52a9\u60a8\u5feb\u901f\u5b9e\u73b0\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u3002<\/p>\n<\/p>\n
    \u56db\u3001PHORCH\u5e93\u7684\u4f18\u52bf<\/p>\n<\/p>\n
    Phorch\u5e93\u5728\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u65b9\u9762\u5177\u6709\u591a\u79cd\u4f18\u52bf\uff0c\u4ee5\u4e0b\u662f\u5176\u4e2d\u4e00\u4e9b\u5173\u952e\u70b9\uff1a<\/p>\n<\/p>\n
      \n
    1. \n
      \u6613\u4e8e\u4f7f\u7528<\/strong>\uff1aPhorch\u5e93\u63d0\u4f9b\u4e86\u7b80\u5355\u76f4\u89c2\u7684API\u63a5\u53e3\uff0c\u5373\u4f7f\u662f\u521d\u5b66\u8005\u4e5f\u53ef\u4ee5\u5feb\u901f\u4e0a\u624b\u3002<\/p>\n<\/p>\n<\/li>\n
    2. \n
      \u529f\u80fd\u5f3a\u5927<\/strong>\uff1aPhorch\u5e93\u5185\u7f6e\u4e86\u8bb8\u591a\u5b9e\u7528\u7684\u5de5\u5177\u548c\u51fd\u6570\uff0c\u6db5\u76d6\u4e86\u4ece\u6570\u636e\u52a0\u8f7d\u3001\u6e05\u6d17\u5230\u5206\u6790\u3001\u53ef\u89c6\u5316\u7684\u5404\u4e2a\u73af\u8282\u3002<\/p>\n<\/p>\n<\/li>\n
    3. \n
      \u9ad8\u6548\u6027<\/strong>\uff1aPhorch\u5e93\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u8868\u73b0\u826f\u597d\uff0c\u5e76\u4e14\u5176\u5e95\u5c42\u5b9e\u73b0\u7ecf\u8fc7\u4f18\u5316\uff0c\u786e\u4fdd\u4e86\u8f83\u9ad8\u7684\u6267\u884c\u6548\u7387\u3002<\/p>\n<\/p>\n<\/li>\n
    4. \n
      \u53ef\u6269\u5c55\u6027<\/strong>\uff1a\u60a8\u53ef\u4ee5\u6839\u636e\u9700\u8981\u6269\u5c55Phorch\u5e93\u7684\u529f\u80fd\uff0c\u6dfb\u52a0\u81ea\u5b9a\u4e49\u7684\u6570\u636e\u5904\u7406\u6216\u5206\u6790\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e94\u3001PHORCH\u5e93\u7684\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n
      Phorch\u5e93\u5e7f\u6cdb\u5e94\u7528\u4e8e\u591a\u4e2a\u9886\u57df\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
        \n
      1. \n
        \u91d1\u878d\u6570\u636e\u5206\u6790<\/strong>\uff1aPhorch\u5e93\u53ef\u4ee5\u7528\u4e8e\u5206\u6790\u80a1\u7968\u3001\u503a\u5238\u7b49\u91d1\u878d\u6570\u636e\uff0c\u5e2e\u52a9\u6295\u8d44\u8005\u505a\u51fa\u66f4\u660e\u667a\u7684\u51b3\u7b56\u3002<\/p>\n<\/p>\n<\/li>\n
      2. \n
        \u5e02\u573a\u8c03\u7814<\/strong>\uff1a\u4f01\u4e1a\u53ef\u4ee5\u4f7f\u7528Phorch\u5e93\u6765\u5206\u6790\u6d88\u8d39\u8005\u884c\u4e3a\u548c\u5e02\u573a\u8d8b\u52bf\uff0c\u4ece\u800c\u4f18\u5316\u8425\u9500\u7b56\u7565\u3002<\/p>\n<\/p>\n<\/li>\n
      3. \n
        \u79d1\u7814\u6570\u636e\u5904\u7406<\/strong>\uff1a\u7814\u7a76\u4eba\u5458\u53ef\u4ee5\u5229\u7528Phorch\u5e93\u5904\u7406\u5b9e\u9a8c\u6570\u636e\uff0c\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n<\/li>\n
      4. \n
        \u6559\u80b2\u9886\u57df<\/strong>\uff1aPhorch\u5e93\u53ef\u4ee5\u7528\u4e8e\u6559\u80b2\u6570\u636e\u7684\u5206\u6790\uff0c\u5e2e\u52a9\u6559\u80b2\u5de5\u4f5c\u8005\u4e86\u89e3\u5b66\u751f\u7684\u5b66\u4e60\u60c5\u51b5\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
        \u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
        Phorch\u5e93\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u4e14\u6613\u4e8e\u4f7f\u7528\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5de5\u5177\u3002\u901a\u8fc7\u6b63\u786e\u5b89\u88c5\u548c\u4f7f\u7528Phorch\u5e93\uff0c\u60a8\u53ef\u4ee5\u66f4\u9ad8\u6548\u5730\u5904\u7406\u5404\u79cd\u6570\u636e\u4efb\u52a1\u3002\u65e0\u8bba\u662f\u6570\u636e\u5904\u7406\u3001\u5206\u6790\u8fd8\u662f\u673a\u5668\u5b66\u4e60\uff0cPhorch\u5e93\u90fd\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u5b9e\u7528\u7684\u529f\u80fd\uff0c\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u5229\u7528\u6570\u636e\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u548c\u9886\u57df\uff0c\u5145\u5206\u53d1\u6325Phorch\u5e93\u7684\u4f18\u52bf\uff0c\u5b9e\u73b0\u66f4\u9ad8\u6548\u7684\u6570\u636e\u5206\u6790\u548c\u51b3\u7b56\u652f\u6301\u3002<\/p>\n<\/p>\n
        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
         \u5982\u4f55\u5b89\u88c5PyTorch\u5e93\u4ee5\u4fbf\u5728Python\u4e2d\u4f7f\u7528\uff1f<\/strong>
        \u8981\u5728Python\u4e2d\u8c03\u7528PyTorch\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528pip\u6216conda\u8fdb\u884c\u5b89\u88c5\u3002\u5728\u7ec8\u7aef\u6216\u547d\u4ee4\u63d0\u793a\u7b26\u4e2d\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\uff1a  <\/p>\n
        pip install torch torchvision torchaudio\n<\/code><\/pre>\n
        \u6216\u4f7f\u7528conda\uff1a  <\/p>\n
        conda install pytorch torchvision torchaudio -c pytorch\n<\/code><\/pre>\n
        \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165PyTorch\u5e93\u6765\u5f00\u59cb\u4f7f\u7528\uff0c\u65b9\u6cd5\u662f\uff1a  <\/p>\n
        import torch\n<\/code><\/pre>\n
        \u5728PyTorch\u4e2d\u5982\u4f55\u521b\u5efa\u5f20\u91cf\uff1f<\/strong>
        \u5728PyTorch\u4e2d\uff0c\u5f20\u91cf\u662f\u5176\u6838\u5fc3\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8eNumPy\u7684\u6570\u7ec4\u3002\u53ef\u4ee5\u4f7f\u7528torch.tensor()<\/code>\u51fd\u6570\u521b\u5efa\u5f20\u91cf\u3002\u4f8b\u5982\uff1a  <\/p>\n
        import torch\n\n# \u521b\u5efa\u4e00\u4e2a\u4e00\u7ef4\u5f20\u91cf\na = torch.tensor([1, 2, 3])\n\n# \u521b\u5efa\u4e00\u4e2a\u4e8c\u7ef4\u5f20\u91cf\nb = torch.tensor([[1, 2], [3, 4]])\n<\/code><\/pre>\n
        \u6b64\u5916\uff0cPyTorch\u8fd8\u63d0\u4f9b\u4e86\u591a\u79cd\u51fd\u6570\u6765\u521b\u5efa\u4e0d\u540c\u7c7b\u578b\u7684\u5f20\u91cf\uff0c\u5982torch.zeros()<\/code>\u548ctorch.ones()<\/code>\u7b49\u3002<\/p>\n
        \u5982\u4f55\u5728PyTorch\u4e2d\u8fdb\u884c\u57fa\u672c\u7684\u6570\u5b66\u8fd0\u7b97\uff1f<\/strong>
        PyTorch\u652f\u6301\u591a\u79cd\u6570\u5b66\u8fd0\u7b97\uff0c\u53ef\u4ee5\u5728\u5f20\u91cf\u4e0a\u76f4\u63a5\u8fdb\u884c\u3002\u5e38\u7528\u7684\u64cd\u4f5c\u5305\u62ec\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u548c\u9664\u6cd5\u7b49\u3002\u4f8b\u5982\uff1a  <\/p>\n
        import torch\n\n# \u521b\u5efa\u4e24\u4e2a\u5f20\u91cf\nx = torch.tensor([1, 2, 3])\ny = torch.tensor([4, 5, 6])\n\n# \u5f20\u91cf\u52a0\u6cd5\nresult_add = x + y\n\n# \u5f20\u91cf\u4e58\u6cd5\nresult_mul = x * y\n<\/code><\/pre>\n
        \u8fd9\u4e9b\u8fd0\u7b97\u4e0d\u4ec5\u7b80\u5355\u6613\u7528\uff0c\u800c\u4e14\u53ef\u4ee5\u5728GPU\u4e0a\u8fdb\u884c\u52a0\u901f\uff0c\u63d0\u5347\u8ba1\u7b97\u6548\u7387\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5728Python\u4e2d\u8c03\u7528Phorch\u5e93\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5Phorch\u5e93\u3001\u5bfc\u5165\u5e93\u6587\u4ef6\u3001\u4f7f\u7528\u5176\u529f\u80fd\u3002\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528pip\u547d […]","protected":false},"author":3,"featured_media":954878,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/954871"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=954871"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/954871\/revisions"}],"predecessor-version":[{"id":954880,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/954871\/revisions\/954880"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/954878"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=954871"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=954871"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=954871"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}