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\n\u901a\u8fc7\u8fd9\u4e9b\u7814\u7a76\u5185\u5bb9，\u671f\u671b\u80fd\u591f\u5f00\u53d1\u51fa\u4e00\u4e2a\u65e2\u9ad8\u6548\u53c8\u51c6\u786e\u7684\u7535\u5b50\u90ae\u7bb1\u5206\u7c7b\u7cfb\u7edf，\u4e3a\u7528\u6237\u8282\u7701\u65f6\u95f4，\u63d0\u9ad8\u901a\u4fe1\u6548\u7387\u3002\u540c\u65f6，\u8be5\u7cfb\u7edf\u7684\u7814\u7a76\u4e5f\u5c06\u4e3a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u53c2\u8003\u548c\u501f\u9274\u3002<\/p>\n

Jieba
\nJieba\u662f\u4e00\u6b3e\u975e\u5e38\u6d41\u884c\u7684\u4e2d\u6587\u5206\u8bcd\u5de5\u5177，\u5b83\u57fa\u4e8ePython\u8bed\u8a00\u5f00\u53d1，\u652f\u6301\u591a\u79cd\u5206\u8bcd\u6a21\u5f0f，\u5305\u62ec\u7cbe\u786e\u6a21\u5f0f\u3001\u5168\u6a21\u5f0f\u3001\u641c\u7d22\u5f15\u64ce\u6a21\u5f0f\u7b49，\u80fd\u591f\u6ee1\u8db3\u4e0d\u540c\u573a\u666f\u4e0b\u7684\u4e2d\u6587\u5206\u8bcd\u9700\u6c42\u3002Jieba\u7684\u5206\u8bcd\u6548\u679c\u4f18\u5f02，\u5e7f\u6cdb\u5e94\u7528\u4e8e\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df，\u5982\u6587\u672c\u6316\u6398\u3001\u60c5\u611f\u5206\u6790\u3001\u673a\u5668\u7ffb\u8bd1\u7b49\u3002
\nJieba\u7684\u5206\u8bcd\u539f\u7406\u4e3b\u8981\u57fa\u4e8eTrie\u6811\u548c\u56fe\u626b\u63cf\u7b97\u6cd5\u3002Trie\u6811\u662f\u4e00\u79cd\u7528\u4e8e\u5feb\u901f\u68c0\u7d22\u7684\u6811\u5f62\u6570\u636e\u7ed3\u6784，Jieba\u5229\u7528Trie\u6811\u6765\u5b58\u50a8\u5b57\u5178\u4e2d\u7684\u8bcd\u6c47，\u4ece\u800c\u5b9e\u73b0\u5feb\u901f\u5339\u914d\u3002\u56fe\u626b\u63cf\u7b97\u6cd5\u5219\u7528\u4e8e\u5904\u7406\u4e2d\u6587\u4e2d\u7684\u6b67\u4e49\u5207\u5206\u95ee\u9898，\u901a\u8fc7\u6784\u5efa\u4e00\u4e2a\u8bcd\u56fe，\u7136\u540e\u5728\u8bcd\u56fe\u4e2d\u5bfb\u627e\u6700\u4f18\u8def\u5f84\u6765\u5b9e\u73b0\u5206\u8bcd\u3002
\n\u9664\u4e86\u57fa\u672c\u7684\u5206\u8bcd\u529f\u80fd\u5916，Jieba\u8fd8\u63d0\u4f9b\u4e86\u8bcd\u6027\u6807\u6ce8\u3001\u5173\u952e\u8bcd\u63d0\u53d6\u3001\u81ea\u5b9a\u4e49\u8bcd\u5178\u7b49\u529f\u80fd\u3002\u8bcd\u6027\u6807\u6ce8\u53ef\u4ee5\u5bf9\u5206\u8bcd\u7ed3\u679c\u8fdb\u884c\u8bcd\u6027\u5206\u7c7b，\u5982\u540d\u8bcd\u3001\u52a8\u8bcd\u3001\u5f62\u5bb9\u8bcd\u7b49，\u8fd9\u5bf9\u4e8e\u7406\u89e3\u6587\u672c\u7684\u8bed\u6cd5\u7ed3\u6784\u548c\u8bed\u4e49\u4fe1\u606f\u975e\u5e38\u6709\u5e2e\u52a9\u3002\u5173\u952e\u8bcd\u63d0\u53d6\u5219\u53ef\u4ee5\u4ece\u6587\u672c\u4e2d\u63d0\u53d6\u51fa\u91cd\u8981\u7684\u8bcd\u6c47，\u7528\u4e8e\u6587\u672c\u6458\u8981\u3001\u4fe1\u606f\u68c0\u7d22\u7b49\u5e94\u7528\u3002\u81ea\u5b9a\u4e49\u8bcd\u5178\u529f\u80fd\u5141\u8bb8\u7528\u6237\u6dfb\u52a0\u81ea\u5df1\u7684\u8bcd\u6c47\u5230Jieba\u7684\u5b57\u5178\u4e2d，\u4ece\u800c\u63d0\u9ad8\u5206\u8bcd\u7684\u51c6\u786e\u6027\u548c\u9002\u5e94\u6027\u3002
\nJieba\u7684\u5b89\u88c5\u548c\u4f7f\u7528\u975e\u5e38\u7b80\u5355，\u53ef\u4ee5\u901a\u8fc7pip\u547d\u4ee4\u8f7b\u677e\u5b89\u88c5，\u5e76\u4e14\u53ea\u9700\u8981\u51e0\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u5b9e\u73b0\u57fa\u672c\u7684\u5206\u8bcd\u529f\u80fd\u3002Jieba\u7684\u6587\u6863\u4e5f\u975e\u5e38\u4e30\u5bcc，\u63d0\u4f9b\u4e86\u8be6\u7ec6\u7684API\u8bf4\u660e\u548c\u4f7f\u7528\u793a\u4f8b，\u65b9\u4fbf\u7528\u6237\u5feb\u901f\u4e0a\u624b\u3002
\n\u603b\u7684\u6765\u8bf4，Jieba\u662f\u4e00\u6b3e\u529f\u80fd\u5f3a\u5927\u3001\u6613\u4e8e\u4f7f\u7528\u7684\u4e2d\u6587\u5206\u8bcd\u5de5\u5177，\u5b83\u4e3a\u4e2d\u6587\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u7814\u7a76\u548c\u5e94\u7528\u63d0\u4f9b\u4e86\u91cd\u8981\u7684\u652f\u6301\u3002<\/p>\n

Tensorflow\u4ecb\u7ecd
\nTensorFlow\u662f\u4e00\u4e2a\u7531Google\u5f00\u53d1\u7684\u5f00\u6e90\u673a\u5668\u5b66\u4e60\u6846\u67b6，\u5b83\u652f\u6301\u591a\u79cd\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u6784\u5efa\u548c\u8bad\u7ec3，\u5305\u62ec\u5377\u79ef\u795e\u7ecf\u7f51\u7edc（CNN）\u3001\u5faa\u73af\u795e\u7ecf\u7f51\u7edc（RNN）\u7b49\u3002\u5728\u57fa\u4e8ePython\u7684\u7535\u5b50\u90ae\u7bb1\u5206\u7c7b\u4efb\u52a1\u4e2d，TensorFlow\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7075\u6d3b\u3001\u9ad8\u6548\u7684\u5e73\u53f0，\u7528\u4e8e\u6784\u5efa\u548c\u8bad\u7ec3\u6587\u672c\u5206\u7c7b\u6a21\u578b\u3002
\nTensorFlow\u7684\u6838\u5fc3\u7279\u70b9\u662f\u5b83\u7684\u8ba1\u7b97\u56fe（Graph）\u548c\u4f1a\u8bdd（Session）\u673a\u5236\u3002\u8ba1\u7b97\u56fe\u662f\u4e00\u4e2a\u7531\u8282\u70b9\u548c\u8fb9\u7ec4\u6210\u7684\u56fe\u7ed3\u6784，\u8282\u70b9\u8868\u793a\u6570\u5b66\u8fd0\u7b97，\u8fb9\u8868\u793a\u6570\u636e\u6d41\u3002\u4f1a\u8bdd\u5219\u8d1f\u8d23\u6267\u884c\u8ba1\u7b97\u56fe\u4e2d\u7684\u8fd0\u7b97\u3002\u8fd9\u79cd\u8bbe\u8ba1\u4f7f\u5f97TensorFlow\u80fd\u591f\u6709\u6548\u5730\u7ba1\u7406\u8ba1\u7b97\u8d44\u6e90，\u652f\u6301\u5206\u5e03\u5f0f\u8ba1\u7b97\u548cGPU\u52a0\u901f，\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u8bad\u7ec3\u6548\u7387\u3002
\n\u5728\u7535\u5b50\u90ae\u7bb1\u5206\u7c7b\u4efb\u52a1\u4e2d，TensorFlow\u53ef\u4ee5\u7528\u6765\u6784\u5efaTextCNN\u6a21\u578b\u3002TextCNN\u6a21\u578b\u901a\u8fc7\u8bcd\u5d4c\u5165\u5c42\u5c06\u6587\u672c\u8f6c\u6362\u4e3a\u6570\u503c\u5411\u91cf，\u7136\u540e\u5229\u7528\u5377\u79ef\u5c42\u548c\u6c60\u5316\u5c42\u63d0\u53d6\u7279\u5f81，\u6700\u540e\u901a\u8fc7\u5168\u8fde\u63a5\u5c42\u548cSoftmax\u6fc0\u6d3b\u51fd\u6570\u8fdb\u884c\u5206\u7c7b\u3002TensorFlow\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684API，\u652f\u6301\u7528\u6237\u81ea\u5b9a\u4e49\u6a21\u578b\u7ed3\u6784\u548c\u8bad\u7ec3\u8fc7\u7a0b，\u540c\u65f6\u4e5f\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9884\u8bad\u7ec3\u6a21\u578b\u548c\u4f18\u5316\u7b97\u6cd5，\u65b9\u4fbf\u7528\u6237\u5feb\u901f\u642d\u5efa\u548c\u8bad\u7ec3\u6a21\u578b\u3002
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\u6570\u636e\u96c6\u6765\u6e90
\n\u5728\u57fa\u4e8ePython\u7684\u7535\u5b50\u90ae\u7bb1\u5206\u7c7b\u4efb\u52a1\u4e2d，\u6570\u636e\u96c6\u7684\u6784\u5efa\u662f\u6a21\u578b\u8bad\u7ec3\u7684\u57fa\u7840\u3002\u901a\u8fc7\u5c06\u5783\u573e\u90ae\u4ef6\u4e0e\u6b63\u5e38\u90ae\u4ef6\u7684\u6587\u672c\u6570\u636e\u5408\u5e76，\u6784\u5efa\u4e86\u4e00\u4e2a\u7efc\u5408\u7684\u6570\u636e\u96c6\u3002\u9996\u5148，\u4f7f\u7528Tokenizer\u5bf9\u6587\u672c\u6570\u636e\u8fdb\u884c\u8bcd\u6c47\u8868\u7684\u6784\u5efa，\u8fd9\u4e00\u6b65\u9aa4\u5c06\u6587\u672c\u4e2d\u7684\u5355\u8bcd\u8f6c\u6362\u4e3a\u6570\u5b57\u7d22\u5f15，\u5f62\u6210\u8bcd\u6c47\u8868\u3002\u63a5\u7740，\u5229\u7528texts_to_sequences()\u65b9\u6cd5\u5c06\u6587\u672c\u6570\u636e\u8f6c\u5316\u4e3a\u6570\u5b57\u5e8f\u5217，\u6bcf\u4e2a\u5355\u8bcd\u5bf9\u5e94\u8bcd\u6c47\u8868\u4e2d\u7684\u4e00\u4e2a\u6570\u5b57\u3002\u7531\u4e8e\u90ae\u4ef6\u7684\u957f\u5ea6\u4e0d\u4e00，\u4f7f\u7528pad_sequences()\u5bf9\u5e8f\u5217\u8fdb\u884c\u586b\u5145，\u786e\u4fdd\u6bcf\u4e2a\u5e8f\u5217\u5177\u6709\u76f8\u540c\u7684\u957f\u5ea6，\u4ee5\u4fbf\u4e8e\u540e\u7eed\u7684\u6a21\u578b\u5904\u7406\u3002
\n\u5177\u4f53\u5b9e\u73b0\u4e2d，\u5b9a\u4e49\u4e86\u4e00\u4e2a\u540d\u4e3aserialization_numeralization\u7684\u51fd\u6570，\u8be5\u51fd\u6570\u63a5\u53d7\u5783\u573e\u90ae\u4ef6\u548c\u6b63\u5e38\u90ae\u4ef6\u7684\u6570\u636e\u4f5c\u4e3a\u8f93\u5165，\u5e76\u8fd4\u56de\u586b\u5145\u540e\u7684\u6570\u5b57\u5e8f\u5217\u3002\u5728\u51fd\u6570\u5185\u90e8，\u9996\u5148\u5c06\u5783\u573e\u90ae\u4ef6\u548c\u6b63\u5e38\u90ae\u4ef6\u5206\u522b\u8f6c\u6362\u4e3a\u6570\u5b57\u5e8f\u5217，\u7136\u540e\u5bf9\u5e8f\u5217\u8fdb\u884c\u586b\u5145，\u4f7f\u5176\u957f\u5ea6\u4e00\u81f4\u3002\u586b\u5145\u540e\u7684\u5e8f\u5217\u5c06\u4f5c\u4e3a\u6a21\u578b\u7684\u8f93\u5165\u6570\u636e，\u7528\u4e8e\u8bad\u7ec3\u548c\u6d4b\u8bd5\u3002
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\n\u603b\u4e4b，\u901a\u8fc7\u5c06\u5783\u573e\u90ae\u4ef6\u4e0e\u6b63\u5e38\u90ae\u4ef6\u7684\u6587\u672c\u6570\u636e\u5408\u5e76，\u5e76\u4f7f\u7528Tokenizer\u548cpad_sequences\u7b49\u65b9\u6cd5\u8fdb\u884c\u6570\u636e\u8f6c\u6362\u548c\u586b\u5145，\u6784\u5efa\u4e86\u4e00\u4e2a\u9002\u5408\u6a21\u578b\u8bad\u7ec3\u7684\u6570\u636e\u96c6\u3002\u8fd9\u4e00\u8fc7\u7a0b\u4e0d\u4ec5\u786e\u4fdd\u4e86\u6570\u636e\u7684\u4e00\u81f4\u6027\u548c\u53ef\u5904\u7406\u6027，\u8fd8\u4e3a\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u548c\u4f18\u5316\u63d0\u4f9b\u4e86\u575a\u5b9e\u7684\u57fa\u7840\u3002<\/p>\n

def serialization_numeralization<\/span>(<\/span>spam_data,<\/span> normal_data)<\/span>:<\/span>
\n x_train_spam =<\/span> tokenizer.<\/span>texts_to_sequences<\/span>(<\/span>spam_data)<\/span>
\n x_train_normal =<\/span> tokenizer.<\/span>texts_to_sequences<\/span>(<\/span>normal_data)<\/span>
\n x_train_spam_pad =<\/span> pad_sequences<\/span>(<\/span>x_train_spam,<\/span> max_sequence_length)<\/span>
\n x_train_normal_pad =<\/span> pad_sequences<\/span>(<\/span>x_train_normal,<\/span> max_sequence_length)<\/span><\/p>\n

\u4e8c\u3001\u6587\u6863\u4ecb\u7ecd<\/h3>\n

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

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

\u4e09\u3001\u9879\u76ee\u8bbe\u8ba1\u4e0e\u5b9e\u73b0<\/h3>\n

\u6280\u672f\u6808\u9009\u578b
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"\u6587\u672c\u957f\u5ea6\u5206\u5e03
\n\u6587\u672c\u957f\u5ea6\u5206\u5e03\u7684\u5206\u6790\u3002\u901a\u8fc7\u7edf\u8ba1\u5783\u573e\u90ae\u4ef6\u548c\u6b63\u5e38\u90ae\u4ef6\u4e2d\u5355\u8bcd\u6570\u91cf\u7684\u5206\u5e03，\u5e76\u7ed8\u5236\u76f4\u65b9\u56fe，\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdf\u5230\u4e24\u7c7b\u90ae\u4ef6\u5728\u6587\u672c\u957f\u5ea6\u4e0a\u7684\u5dee\u5f02\u3002\u4f8b\u5982，\u5783\u573e\u90ae\u4ef6\u53ef\u80fd\u503e\u5411\u4e8e\u4f7f\u7528\u66f4\u77ed\u7684\u6587\u672c，\u4ee5\u4fbf\u5feb\u901f\u4f20\u8fbe\u4fe1\u606f\u5e76\u907f\u514d\u88ab\u5bdf\u89c9；\u800c\u6b63\u5e38\u90ae\u4ef6\u5219\u53ef\u80fd\u5305\u542b\u66f4\u591a\u7684\u7ec6\u8282\u548c\u4e0a\u4e0b\u6587\u4fe1\u606f，\u56e0\u6b64\u6587\u672c\u957f\u5ea6\u53ef\u80fd\u66f4\u957f\u3002\u901a\u8fc7\u7ed8\u5236\u76f4\u65b9\u56fe，\u53ef\u4ee5\u6e05\u6670\u5730\u770b\u5230\u8fd9\u79cd\u5dee\u5f02，\u5e76\u636e\u6b64\u5bf9\u6a21\u578b\u8fdb\u884c\u8c03\u6574\u3002\u4f8b\u5982，\u5982\u679c\u53d1\u73b0\u5783\u573e\u90ae\u4ef6\u7684\u6587\u672c\u957f\u5ea6\u666e\u904d\u8f83\u77ed，\u53ef\u4ee5\u5728\u6a21\u578b\u4e2d\u52a0\u5165\u5bf9\u6587\u672c\u957f\u5ea6\u7684\u8003\u8651，\u4f7f\u5176\u5728\u5206\u7c7b\u65f6\u66f4\u52a0\u5173\u6ce8\u8fd9\u4e00\u7279\u5f81\u3002\u6b64\u5916，\u6587\u672c\u957f\u5ea6\u5206\u5e03\u7684\u5206\u6790\u8fd8\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u4e00\u4e9b\u5f02\u5e38\u503c，\u8fd9\u4e9b\u5f02\u5e38\u503c\u53ef\u80fd\u662f\u7531\u4e8e\u6570\u636e\u6536\u96c6\u6216\u9884\u5904\u7406\u8fc7\u7a0b\u4e2d\u7684\u9519\u8bef\u5bfc\u81f4\u7684，\u9700\u8981\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u68c0\u67e5\u548c\u5904\u7406\u3002\u901a\u8fc7\u5206\u6790\u6587\u672c\u957f\u5ea6\u5206\u5e03，\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u7279\u5f81，\u4e3a\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u548c\u4f18\u5316\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u53c2\u8003\u4fe1\u606f\u3002\u4f8b\u5982，\u5982\u679c\u53d1\u73b0\u5783\u573e\u90ae\u4ef6\u7684\u6587\u672c\u957f\u5ea6\u666e\u904d\u8f83\u77ed，\u53ef\u4ee5\u5728\u6a21\u578b\u4e2d\u52a0\u5165\u5bf9\u6587\u672c\u957f\u5ea6\u7684\u8003\u8651，\u4f7f\u5176\u5728\u5206\u7c7b\u65f6\u66f4\u52a0\u5173\u6ce8\u8fd9\u4e00\u7279\u5f81\u3002\u6b64\u5916，\u6587\u672c\u957f\u5ea6\u5206\u5e03\u7684\u5206\u6790\u8fd8\u53ef\u4ee5\u8bc6\u522b\u4e00\u4e9b\u5f02\u5e38\u503c，\u8fd9\u4e9b\u5f02\u5e38\u503c\u53ef\u80fd\u662f\u7531\u4e8e\u6570\u636e\u6536\u96c6\u6216\u9884\u5904\u7406\u8fc7\u7a0b\u4e2d\u7684\u9519\u8bef\u5bfc\u81f4\u7684，\u9700\u8981\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u68c0\u67e5\u548c\u5904\u7406\u3002\u901a\u8fc7\u5206\u6790\u6587\u672c\u957f\u5ea6\u5206\u5e03，\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u7279\u5f81，\u4e3a\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u548c\u4f18\u5316\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u53c2\u8003\u4fe1\u606f\u3002<\/p>\n

\u8bcd\u9891\u7edf\u8ba1
\n\u8bcd\u9891\u7edf\u8ba1\u7684\u5206\u6790\u3002\u901a\u8fc7\u7edf\u8ba1\u5e76\u53ef\u89c6\u5316\u5783\u573e\u90ae\u4ef6\u548c\u6b63\u5e38\u90ae\u4ef6\u4e2d\u6700\u5e38\u89c1\u7684\u8bcd\u6c47，\u53ef\u4ee5\u6df1\u5165\u4e86\u89e3\u90ae\u4ef6\u5185\u5bb9\u7684\u7279\u70b9\u3002\u4f8b\u5982，\u5783\u573e\u90ae\u4ef6\u53ef\u80fd\u9891\u7e41\u4f7f\u7528\u4e00\u4e9b\u7279\u5b9a\u7684\u8bcd\u6c47，\u5982\u201c\u514d\u8d39\u201d\u3001\u201c\u7acb\u5373\u8d2d\u4e70\u201d\u7b49，\u4ee5\u5438\u5f15\u6536\u4ef6\u4eba\u7684\u6ce8\u610f；\u800c\u6b63\u5e38\u90ae\u4ef6\u5219\u53ef\u80fd\u4f7f\u7528\u66f4\u52a0\u591a\u6837\u5316\u548c\u590d\u6742\u7684\u8bcd\u6c47\u3002\u901a\u8fc7\u53ef\u89c6\u5316\u8fd9\u4e9b\u8bcd\u6c47\u7684\u9891\u7387，\u53ef\u4ee5\u53d1\u73b0\u4e00\u4e9b\u6709\u8da3\u7684\u73b0\u8c61\u548c\u89c4\u5f8b\u3002\u4f8b\u5982，\u67d0\u4e9b\u8bcd\u6c47\u53ef\u80fd\u53ea\u5728\u5783\u573e\u90ae\u4ef6\u4e2d\u51fa\u73b0，\u800c\u5728\u6b63\u5e38\u90ae\u4ef6\u4e2d\u51e0\u4e4e\u4e0d\u51fa\u73b0，\u8fd9\u4e9b\u8bcd\u6c47\u5c31\u53ef\u4ee5\u4f5c\u4e3a\u533a\u5206\u4e24\u7c7b\u90ae\u4ef6\u7684\u91cd\u8981\u7279\u5f81\u3002\u6b64\u5916，\u8bcd\u9891\u7edf\u8ba1\u8fd8\u53ef\u4ee5\u8bc6\u522b\u4e00\u4e9b\u5e38\u89c1\u7684\u505c\u7528\u8bcd，\u8fd9\u4e9b\u8bcd\u5728\u4e0d\u540c\u7c7b\u578b\u7684\u90ae\u4ef6\u4e2d\u90fd\u9891\u7e41\u51fa\u73b0，\u4f46\u5bf9\u5206\u7c7b\u4efb\u52a1\u5e76\u6ca1\u6709\u592a\u5927\u7684\u5e2e\u52a9，\u56e0\u6b64\u5728\u9884\u5904\u7406\u9636\u6bb5\u53ef\u4ee5\u8003\u8651\u53bb\u9664\u8fd9\u4e9b\u505c\u7528\u8bcd，\u4ee5\u51cf\u5c11\u566a\u58f0，\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002\u901a\u8fc7\u8bcd\u9891\u7edf\u8ba1\u7684\u5206\u6790，\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u90ae\u4ef6\u5185\u5bb9\u7684\u7279\u70b9，\u4e3a\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u548c\u4f18\u5316\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u53c2\u8003\u4fe1\u606f\u3002\u4f8b\u5982，\u5982\u679c\u53d1\u73b0\u67d0\u4e9b\u8bcd\u6c47\u5728\u5783\u573e\u90ae\u4ef6\u4e2d\u9891\u7e41\u51fa\u73b0，\u800c\u5728\u6b63\u5e38\u90ae\u4ef6\u4e2d\u5f88\u5c11\u51fa\u73b0，\u53ef\u4ee5\u5728\u6a21\u578b\u4e2d\u52a0\u5165\u5bf9\u8fd9\u4e9b\u8bcd\u6c47\u7684\u5173\u6ce8，\u4f7f\u5176\u5728\u5206\u7c7b\u65f6\u66f4\u52a0\u5173\u6ce8\u8fd9\u4e9b\u7279\u5f81\u3002\u6b64\u5916，\u8bcd\u9891\u7edf\u8ba1\u8fd8\u53ef\u4ee5\u8bc6\u522b\u4e00\u4e9b\u5e38\u89c1\u7684\u505c\u7528\u8bcd，\u8fd9\u4e9b\u8bcd\u5728\u4e0d\u540c\u7c7b\u578b\u7684\u90ae\u4ef6\u4e2d\u90fd\u9891\u7e41\u51fa\u73b0，\u4f46\u5bf9\u5206\u7c7b\u4efb\u52a1\u5e76\u6ca1\u6709\u592a\u5927\u7684\u5e2e\u52a9，\u56e0\u6b64\u5728\u9884\u5904\u7406\u9636\u6bb5\u53ef\u4ee5\u8003\u8651\u53bb\u9664\u8fd9\u4e9b\u505c\u7528\u8bcd，\u4ee5\u51cf\u5c11\u566a\u58f0，\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002\u901a\u8fc7\u8bcd\u9891\u7edf\u8ba1\u7684\u5206\u6790，\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u90ae\u4ef6\u5185\u5bb9\u7684\u7279\u70b9，\u4e3a\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u548c\u4f18\u5316\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u53c2\u8003\u4fe1\u606f\u3002
\n\u7efc\u4e0a\u6240\u8ff0，\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790（EDA）\u5728\u57fa\u4e8ePython\u7684\u7535\u5b50\u90ae\u7bb1\u5206\u7c7b\u4efb\u52a1\u4e2d\u53d1\u6325\u7740\u91cd\u8981\u4f5c\u7528\u3002\u901a\u8fc7\u5206\u6790\u6587\u672c\u957f\u5ea6\u5206\u5e03\u548c\u8bcd\u9891\u7b49\u7279\u5f81，\u53ef\u4ee5\u6df1\u5165\u4e86\u89e3\u6570\u636e\u7684\u7279\u70b9\u548c\u89c4\u5f8b，\u4e3a\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u548c\u4f18\u5316\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u53c2\u8003\u4fe1\u606f\u3002\u8fd9\u4e9b\u5206\u6790\u4e0d\u4ec5\u53ef\u4ee5\u5e2e\u52a9\u8bbe\u8ba1\u51fa\u66f4\u6709\u6548\u7684\u5206\u7c7b\u6a21\u578b，\u8fd8\u53ef\u4ee5\u63d0\u9ad8\u5bf9\u6570\u636e\u7684\u8d28\u91cf\u548c\u53ef\u9760\u6027\u7684\u4fe1\u5fc3，\u4ece\u800c\u786e\u4fdd\u6700\u7ec8\u7684\u5206\u7c7b\u7ed3\u679c\u65e2\u51c6\u786e\u53c8\u53ef\u9760\u3002<\/p>\n

def<\/span> plot_metrics<\/span>(<\/span>history)<\/span>:<\/span>
\n metrics =<\/span> [<\/span>'accuracy',<\/span> 'loss',<\/span> 'precision',<\/span> 'recall',<\/span> 'f1_score',<\/span> 'auc']<\/span>
\n titles =<\/span> [<\/span>'Training and<\/span> Validation Accuracy',<\/span> 'Training and<\/span> Validation Loss',<\/span> 'Training and<\/span> Validation Precision',<\/span> 'Training and<\/span> Validation Recall',<\/span> 'Training and<\/span> Validation F1 Score',<\/span> 'Training and<\/span> Validation AUC']<\/span>
\n for<\/span> metric,<\/span> title in<\/span> zip<\/span>(<\/span>metrics,<\/span> titles)<\/span>:<\/span>
\n plt.<\/span>figure<\/span>(<\/span>figsize=<\/span>(<\/span>8<\/span>,<\/span> 6<\/span>)<\/span>)<\/span>
\n plt.<\/span>plot<\/span>(<\/span>history.<\/span>history[<\/span>metric]<\/span>,<\/span> label=<\/span>'Training ' +<\/span> metric)<\/span>
\n plt.<\/span>plot<\/span>(<\/span>history.<\/span>history[<\/span>'val_' +<\/span> metric]<\/span>,<\/span> label=<\/span>'Validation ' +<\/span> metric)<\/span>
\n plt.<\/span>savefig<\/span>(<\/span>f'.<\/span>\/<\/span>web\/<\/span>src\/<\/span>assets\/<\/span>algorithm\/<\/span>train\/<\/span>{<\/span>metric}<\/span>_plot.<\/span>png')<\/span>
\n plt.<\/span>close<\/span>(<\/span>)<\/span><\/p>\n

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

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

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

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
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