{"id":1111427,"date":"2025-01-08T17:29:15","date_gmt":"2025-01-08T09:29:15","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1111427.html"},"modified":"2025-01-08T17:29:17","modified_gmt":"2025-01-08T09:29:17","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%88%b6%e4%bd%9c%e5%9b%be%e7%89%87%e8%af%86%e5%88%ab%e7%b3%bb%e7%bb%9f","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1111427.html","title":{"rendered":"\u5982\u4f55\u7528python\u5236\u4f5c\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25073909\/d19f78bf-8671-41ff-b8dc-2abf5f6f1ac6.webp\" alt=\"\u5982\u4f55\u7528python\u5236\u4f5c\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\" \/><\/p>\n<p><p> <strong>\u5982\u4f55\u7528Python\u5236\u4f5c\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf<\/strong><\/p>\n<\/p>\n<p><p>\u7528Python\u5236\u4f5c\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u7684\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\uff1a<strong>\u9009\u62e9\u5408\u9002\u7684\u6846\u67b6\u548c\u5e93\u3001\u83b7\u53d6\u548c\u5904\u7406\u6570\u636e\u3001\u6784\u5efa\u548c\u8bad\u7ec3\u6a21\u578b\u3001\u8fdb\u884c\u9884\u6d4b\u548c\u8bc4\u4f30\u3001\u90e8\u7f72\u548c\u4f18\u5316\u7cfb\u7edf<\/strong>\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u5e76\u5bf9\u5176\u4e2d\u7684\u201c\u6784\u5efa\u548c\u8bad\u7ec3\u6a21\u578b\u201d\u8fdb\u884c\u8be6\u7ec6\u63cf\u8ff0\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u9009\u62e9\u5408\u9002\u7684\u6846\u67b6\u548c\u5e93<\/h3>\n<\/p>\n<p><p>\u5728\u5f00\u59cb\u6784\u5efa\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u4e4b\u524d\uff0c\u9009\u62e9\u5408\u9002\u7684\u6846\u67b6\u548c\u5e93\u662f\u81f3\u5173\u91cd\u8981\u7684\u3002Python\u6709\u8bb8\u591a\u5f3a\u5927\u7684\u5de5\u5177\u53ef\u4ee5\u5e2e\u52a9\u4f60\u5b8c\u6210\u8fd9\u4e00\u4efb\u52a1\u3002\u6700\u5e38\u7528\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u5305\u62ecTensorFlow\u3001Keras\u548cPyTorch\u3002\u6bcf\u4e2a\u6846\u67b6\u90fd\u6709\u5176\u4f18\u7f3a\u70b9\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>TensorFlow<\/strong>\uff1a\u7531Google\u5f00\u53d1\uff0c\u529f\u80fd\u5f3a\u5927\u4e14\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5de5\u4e1a\u754c\u3002\u9002\u7528\u4e8e\u590d\u6742\u7684\u6a21\u578b\u548c\u5927\u89c4\u6a21\u7684\u8bad\u7ec3\u4efb\u52a1\u3002<\/li>\n<li><strong>Keras<\/strong>\uff1a\u4e00\u4e2a\u9ad8\u7ea7\u795e\u7ecf\u7f51\u7edcAPI\uff0c\u8fd0\u884c\u5728TensorFlow\u4e4b\u4e0a\u3002\u6613\u4e8e\u4f7f\u7528\u548c\u5feb\u901f\u5f00\u53d1\uff0c\u9002\u5408\u521d\u5b66\u8005\u548c\u5feb\u901f\u539f\u578b\u8bbe\u8ba1\u3002<\/li>\n<li><strong>PyTorch<\/strong>\uff1a\u7531Facebook\u5f00\u53d1\uff0c\u52a8\u6001\u8ba1\u7b97\u56fe\u548c\u6613\u4e8e\u8c03\u8bd5\u7684\u7279\u6027\u4f7f\u5176\u5728\u7814\u7a76\u548c\u5f00\u53d1\u4e2d\u975e\u5e38\u53d7\u6b22\u8fce\u3002<\/li>\n<\/ul>\n<p><h3>\u4e8c\u3001\u83b7\u53d6\u548c\u5904\u7406\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u6570\u636e\u662f\u4efb\u4f55<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u9879\u76ee\u7684\u6838\u5fc3\u3002\u4e3a\u4e86\u6784\u5efa\u4e00\u4e2a\u6210\u529f\u7684\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\uff0c\u4f60\u9700\u8981\u5927\u91cf\u7684\u6807\u6ce8\u6570\u636e\u3002\u6570\u636e\u96c6\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u51e0\u79cd\u65b9\u5f0f\u83b7\u53d6\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u516c\u5f00\u6570\u636e\u96c6<\/strong>\uff1a\u4f8b\u5982CIFAR-10\u3001ImageNet\u548cMNIST\u7b49\u3002<\/li>\n<li><strong>\u81ea\u5b9a\u4e49\u6570\u636e\u96c6<\/strong>\uff1a\u81ea\u5df1\u6536\u96c6\u548c\u6807\u6ce8\u56fe\u7247\u6570\u636e\u3002<\/li>\n<li><strong>\u6570\u636e\u589e\u5f3a<\/strong>\uff1a\u901a\u8fc7\u65cb\u8f6c\u3001\u7ffb\u8f6c\u3001\u7f29\u653e\u7b49\u65b9\u6cd5\u589e\u52a0\u73b0\u6709\u6570\u636e\u96c6\u7684\u591a\u6837\u6027\u3002<\/li>\n<\/ul>\n<p><p>\u5728\u83b7\u53d6\u6570\u636e\u540e\uff0c\u6570\u636e\u9884\u5904\u7406\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\u4e00\u6b65\u3002\u5305\u62ec\u56fe\u50cf\u7684\u5c3a\u5bf8\u8c03\u6574\u3001\u5f52\u4e00\u5316\u5904\u7406\u548c\u6570\u636e\u96c6\u7684\u5212\u5206\uff08\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\uff09\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u6784\u5efa\u548c\u8bad\u7ec3\u6a21\u578b<\/h3>\n<\/p>\n<p><p>\u8fd9\u662f\u6574\u4e2a\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u7684\u6838\u5fc3\u90e8\u5206\u3002\u8be6\u7ec6\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<ol>\n<li><strong>\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u67b6\u6784<\/strong>\uff1a\u5e38\u7528\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u67b6\u6784\u5305\u62ecLeNet\u3001AlexNet\u3001VGG\u3001ResNet\u7b49\u3002\u53ef\u4ee5\u6839\u636e\u4efb\u52a1\u7684\u590d\u6742\u6027\u548c\u6570\u636e\u89c4\u6a21\u9009\u62e9\u5408\u9002\u7684\u67b6\u6784\u3002<\/li>\n<li><strong>\u5b9a\u4e49\u6a21\u578b<\/strong>\uff1a\u4f7f\u7528\u6846\u67b6\u63d0\u4f9b\u7684API\u5b9a\u4e49\u6a21\u578b\u7ed3\u6784\u3002\u4ee5Keras\u4e3a\u4f8b\uff1a<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">from keras.models import Sequential<\/p>\n<p>from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense<\/p>\n<p>model = Sequential([<\/p>\n<p>    Conv2D(32, (3, 3), activation=&#39;relu&#39;, input_shape=(64, 64, 3)),<\/p>\n<p>    MaxPooling2D(pool_size=(2, 2)),<\/p>\n<p>    Flatten(),<\/p>\n<p>    Dense(128, activation=&#39;relu&#39;),<\/p>\n<p>    Dense(10, activation=&#39;softmax&#39;)<\/p>\n<p>])<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"3\">\n<li><strong>\u7f16\u8bd1\u6a21\u578b<\/strong>\uff1a\u9009\u62e9\u4f18\u5316\u5668\u3001\u635f\u5931\u51fd\u6570\u548c\u8bc4\u4f30\u6307\u6807\u3002<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">model.compile(optimizer=&#39;adam&#39;, loss=&#39;categorical_crossentropy&#39;, metrics=[&#39;accuracy&#39;])<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"4\">\n<li><strong>\u8bad\u7ec3\u6a21\u578b<\/strong>\uff1a\u4f7f\u7528\u8bad\u7ec3\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\uff0c\u5e76\u5728\u9a8c\u8bc1\u96c6\u4e0a\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">model.fit(tr<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>n_images, train_labels, epochs=10, validation_data=(val_images, val_labels))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"5\">\n<li><strong>\u6a21\u578b\u8bc4\u4f30<\/strong>\uff1a\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8bc4\u4f30\u6a21\u578b\u7684\u6700\u7ec8\u6027\u80fd\u3002<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">loss, accuracy = model.evaluate(test_images, test_labels)<\/p>\n<p>print(f&#39;Test accuracy: {accuracy}&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u8fdb\u884c\u9884\u6d4b\u548c\u8bc4\u4f30<\/h3>\n<\/p>\n<p><p>\u5728\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u5b83\u8fdb\u884c\u56fe\u7247\u8bc6\u522b\u3002\u5c06\u65b0\u56fe\u50cf\u8f93\u5165\u6a21\u578b\uff0c\u5f97\u5230\u9884\u6d4b\u7ed3\u679c\u3002\u901a\u8fc7\u6df7\u6dc6\u77e9\u9635\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u548cF1\u5206\u6570\u7b49\u6307\u6807\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u90e8\u7f72\u548c\u4f18\u5316\u7cfb\u7edf<\/h3>\n<\/p>\n<p><p>\u5728\u6a21\u578b\u8fbe\u5230\u6ee1\u610f\u7684\u6027\u80fd\u540e\uff0c\u5c06\u5176\u90e8\u7f72\u5230\u5b9e\u9645\u5e94\u7528\u4e2d\u3002\u53ef\u4ee5\u5c06\u6a21\u578b\u4fdd\u5b58\u4e3a\u6587\u4ef6\u5e76\u52a0\u8f7d\u5230\u751f\u4ea7\u73af\u5883\u4e2d\u8fdb\u884c\u9884\u6d4b\u3002\u5e38\u7528\u7684\u90e8\u7f72\u65b9\u6cd5\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>Web\u670d\u52a1<\/strong>\uff1a\u5c06\u6a21\u578b\u5c01\u88c5\u6210API\uff0c\u901a\u8fc7HTTP\u8bf7\u6c42\u8fdb\u884c\u9884\u6d4b\u3002<\/li>\n<li><strong>\u79fb\u52a8\u5e94\u7528<\/strong>\uff1a\u5c06\u6a21\u578b\u8f6c\u6362\u4e3a\u79fb\u52a8\u8bbe\u5907\u652f\u6301\u7684\u683c\u5f0f\uff08\u5982TensorFlow Lite\uff09\u5e76\u5d4c\u5165\u5e94\u7528\u4e2d\u3002<\/li>\n<li><strong>\u5d4c\u5165\u5f0f\u7cfb\u7edf<\/strong>\uff1a\u5c06\u6a21\u578b\u90e8\u7f72\u5230\u8fb9\u7f18\u8bbe\u5907\u4e0a\uff0c\u63d0\u9ad8\u54cd\u5e94\u901f\u5ea6\u548c\u8282\u7701\u5e26\u5bbd\u3002<\/li>\n<\/ul>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u4e0d\u65ad\u4f18\u5316\u6a21\u578b\u548c\u7cfb\u7edf\u6027\u80fd\uff0c\u5305\u62ec\u6a21\u578b\u538b\u7f29\u3001\u91cf\u5316\u3001\u526a\u679d\u7b49\u6280\u672f\u3002<\/p>\n<\/p>\n<p><h3>\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u6784\u5efa\u4e00\u4e2a\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u6d89\u53ca\u591a\u4e2a\u6b65\u9aa4\u548c\u6280\u672f\uff0c\u6bcf\u4e00\u6b65\u90fd\u9700\u8981\u4ed4\u7ec6\u8bbe\u8ba1\u548c\u8c03\u8bd5\u3002\u901a\u8fc7\u9009\u62e9\u5408\u9002\u7684\u6846\u67b6\u548c\u5e93\u3001\u83b7\u53d6\u548c\u5904\u7406\u6570\u636e\u3001\u6784\u5efa\u548c\u8bad\u7ec3\u6a21\u578b\u3001\u8fdb\u884c\u9884\u6d4b\u548c\u8bc4\u4f30\u3001\u90e8\u7f72\u548c\u4f18\u5316\u7cfb\u7edf\uff0c\u4f60\u53ef\u4ee5\u6210\u529f\u5730\u521b\u5efa\u4e00\u4e2a\u9ad8\u6548\u7684\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u795d\u4f60\u5728\u5b9e\u73b0\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u7684\u8fc7\u7a0b\u4e2d\u53d6\u5f97\u6210\u529f\uff01<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u9009\u62e9\u9002\u5408\u7684Python\u5e93\u8fdb\u884c\u56fe\u7247\u8bc6\u522b\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u6709\u591a\u4e2a\u6d41\u884c\u7684\u5e93\u53ef\u4ee5\u7528\u4e8e\u56fe\u7247\u8bc6\u522b\uff0c\u6bd4\u5982TensorFlow\u3001Keras\u3001OpenCV\u548cPyTorch\u7b49\u3002TensorFlow\u548cKeras\u901a\u5e38\u9002\u7528\u4e8e\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\uff0c\u5177\u6709\u5f3a\u5927\u7684\u6a21\u578b\u8bad\u7ec3\u548c\u9884\u6d4b\u529f\u80fd\u3002OpenCV\u5219\u66f4\u9002\u5408\u4f20\u7edf\u7684\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\uff0c\u9002\u5408\u5feb\u901f\u5904\u7406\u548c\u5206\u6790\u56fe\u50cf\u3002\u5982\u679c\u4f60\u662f\u521d\u5b66\u8005\uff0cKeras\u53ef\u80fd\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\uff0c\u56e0\u4e3a\u5b83\u7684API\u8bbe\u8ba1\u8f83\u4e3a\u7b80\u6d01\uff0c\u6613\u4e8e\u4e0a\u624b\u3002<\/p>\n<p><strong>\u5236\u4f5c\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u9700\u8981\u54ea\u4e9b\u6570\u636e\u96c6\uff1f<\/strong><br \/>\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u96c6\u662f\u6210\u529f\u8bad\u7ec3\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u7684\u5173\u952e\u3002\u5e38\u7528\u7684\u6570\u636e\u96c6\u5305\u62ecCIFAR-10\u3001ImageNet\u548cMNIST\u7b49\u3002\u8fd9\u4e9b\u6570\u636e\u96c6\u6db5\u76d6\u4e86\u4e0d\u540c\u7c7b\u522b\u548c\u6570\u91cf\u7684\u56fe\u50cf\uff0c\u9002\u5408\u8fdb\u884c\u5404\u79cd\u8bc6\u522b\u4efb\u52a1\u3002\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u786e\u4fdd\u6570\u636e\u96c6\u7684\u8d28\u91cf\u548c\u591a\u6837\u6027\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u6cdb\u5316\u80fd\u529b\u3002\u6b64\u5916\uff0c\u4e5f\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u6570\u636e\u589e\u5f3a\u6280\u672f\uff0c\u901a\u8fc7\u5bf9\u73b0\u6709\u6570\u636e\u8fdb\u884c\u53d8\u6362\uff0c\u589e\u52a0\u6570\u636e\u96c6\u7684\u591a\u6837\u6027\u3002<\/p>\n<p><strong>\u5982\u4f55\u63d0\u9ad8\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u7684\u51c6\u786e\u6027\uff1f<\/strong><br \/>\u63d0\u9ad8\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u51c6\u786e\u6027\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u3002\u9996\u5148\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528\u66f4\u590d\u6742\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u4f8b\u5982\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\uff0c\u5e76\u8fdb\u884c\u8d85\u53c2\u6570\u8c03\u6574\u4ee5\u4f18\u5316\u6027\u80fd\u3002\u5176\u6b21\uff0c\u91c7\u7528\u8fc1\u79fb\u5b66\u4e60\u6280\u672f\uff0c\u5229\u7528\u9884\u8bad\u7ec3\u6a21\u578b\u8fdb\u884c\u5fae\u8c03\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u51c6\u786e\u6027\u3002\u6b64\u5916\uff0c\u96c6\u6210\u5b66\u4e60\u65b9\u6cd5\u5982\u6a21\u578b\u878d\u5408\uff0c\u4e5f\u53ef\u4ee5\u901a\u8fc7\u7ed3\u5408\u591a\u4e2a\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\u6765\u63d0\u5347\u6574\u4f53\u6027\u80fd\u3002\u6700\u540e\uff0c\u786e\u4fdd\u6a21\u578b\u5728\u4e0d\u540c\u7684\u6d4b\u8bd5\u96c6\u4e0a\u8fdb\u884c\u9a8c\u8bc1\uff0c\u4ee5\u907f\u514d\u8fc7\u62df\u5408\u73b0\u8c61\u7684\u53d1\u751f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u7528Python\u5236\u4f5c\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf \u7528Python\u5236\u4f5c\u56fe\u7247\u8bc6\u522b\u7cfb\u7edf\u7684\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\uff1a\u9009\u62e9\u5408\u9002\u7684\u6846\u67b6\u548c\u5e93\u3001\u83b7\u53d6\u548c\u5904 [&hellip;]","protected":false},"author":3,"featured_media":1111433,"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\/1111427"}],"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=1111427"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1111427\/revisions"}],"predecessor-version":[{"id":1111436,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1111427\/revisions\/1111436"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1111433"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1111427"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1111427"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1111427"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}