{"id":35065,"date":"2025-05-02T08:05:55","date_gmt":"2025-05-02T00:05:55","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/35065.html"},"modified":"2025-05-02T08:05:55","modified_gmt":"2025-05-02T00:05:55","slug":"deepseek-%e6%9d%80%e5%85%a5%e8%bf%90%e7%bb%b4%e9%a2%86%e5%9f%9f%ef%bc%9a%e7%94%a8%e5%a4%a7%e6%a8%a1%e5%9e%8b%e9%a2%84%e6%b5%8b%e6%9c%8d%e5%8a%a1%e5%99%a8%e7%a1%ac%e4%bb%b6%e6%95%85%e9%9a%9c","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/35065.html","title":{"rendered":"DeepSeek \u6740\u5165\u8fd0\u7ef4\u9886\u57df\uff1a\u7528\u5927\u6a21\u578b\u9884\u6d4b\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c"},"content":{"rendered":"

\u5728\u6570\u5b57\u5316\u65f6\u4ee3\u7684\u6d6a\u6f6e\u4e2d，\u670d\u52a1\u5668\u4f5c\u4e3a\u4f01\u4e1a\u6838\u5fc3\u4e1a\u52a1\u7684\u627f\u8f7d\u57fa\u77f3，\u5176\u7a33\u5b9a\u6027\u548c\u53ef\u9760\u6027\u5173\u4e4e\u4f01\u4e1a\u7684\u751f\u6b7b\u5b58\u4ea1\u3002\u4efb\u4f55\u4e00\u6b21\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c，\u90fd\u53ef\u80fd\u5f15\u53d1\u4e1a\u52a1\u4e2d\u65ad\u3001\u6570\u636e\u4e22\u5931\u7b49\u4e25\u91cd\u540e\u679c，\u7ed9\u4f01\u4e1a\u5e26\u6765\u96be\u4ee5\u4f30\u91cf\u7684\u7ecf\u6d4e\u635f\u5931\u548c\u58f0\u8a89\u635f\u5bb3\u3002\u636e\u76f8\u5173\u6570\u636e\u663e\u793a，\u67d0\u4e92\u8054\u7f51\u5927\u5382\u7684\u6570\u636e\u4e2d\u5fc3\u5728 4 \u5e74\u5185\u53d1\u751f\u7684 29 \u4e07\u6b21\u786c\u4ef6\u6545\u969c\u4e2d，\u786c\u76d8\u6545\u969c\u5360\u6bd4\u9ad8\u8fbe 81.84%，\u800c\u6bcf\u6b21\u6545\u969c\u7684\u4fee\u590d\u65f6\u95f4\u5f80\u5f80\u4ee5\u5929\u8ba1\u7b97，\u8fd9\u5bf9\u4e1a\u52a1\u8fde\u7eed\u6027\u9020\u6210\u4e86\u6781\u5927\u7684\u5f71\u54cd\u3002\u4f20\u7edf\u7684\u670d\u52a1\u5668\u8fd0\u7ef4\u65b9\u5f0f\u4e3b\u8981\u4f9d\u8d56\u4eba\u5de5\u5de1\u68c0\u548c\u7b80\u5355\u7684\u9608\u503c\u544a\u8b66，\u4e0d\u4ec5\u6548\u7387\u4f4e\u4e0b，\u800c\u4e14\u96be\u4ee5\u63d0\u524d\u53d1\u73b0\u6f5c\u5728\u7684\u786c\u4ef6\u6545\u969c\u9690\u60a3\u3002\u56e0\u6b64，\u5982\u4f55\u7cbe\u51c6\u3001\u9ad8\u6548\u5730\u9884\u6d4b\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c，\u6210\u4e3a\u4e86\u8fd0\u7ef4\u9886\u57df\u4e9f\u5f85\u89e3\u51b3\u7684\u5173\u952e\u96be\u9898\u3002<\/p>\n

DeepSeek \u5927\u6a21\u578b\u6280\u672f\u89e3\u6790<\/h3>\n

DeepSeek \u5927\u8bed\u8a00\u6a21\u578b\u7cfb\u4ee5 Transformer \u67b6\u6784\u4e3a\u57fa\u7840，\u662f\u5317\u4eac\u6df1\u5ea6\u6c42\u7d22\u4eba\u5de5\u667a\u80fd\u57fa\u7840\u6280\u672f\u7814\u7a76\u6709\u9650\u516c\u53f8\u63a8\u51fa\u7684\u6df1\u5ea6\u5408\u6210\u670d\u52a1\u7b97\u6cd5，\u4e8e 2024 \u5e74 4 \u6708\u4e0a\u7ebf\u3002\u5b83\u901a\u8fc7\u6d77\u91cf\u8bed\u6599\u6570\u636e\u8fdb\u884c\u9884\u8bad\u7ec3，\u5e76\u7ecf\u8fc7\u76d1\u7763\u5fae\u8c03\u3001\u4eba\u7c7b\u53cd\u9988\u7684\u5f3a\u5316\u5b66\u4e60\u7b49\u8fdb\u884c\u5bf9\u9f50，\u6784\u5efa\u5f62\u6210\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc，\u5e76\u589e\u52a0\u5ba1\u6838\u3001\u8fc7\u6ee4\u7b49\u5b89\u5168\u673a\u5236\u3002\u5176\u5728\u6a21\u578b\u7b97\u6cd5\u548c\u5de5\u7a0b\u4f18\u5316\u65b9\u9762\u8fdb\u884c\u4e86\u7cfb\u7edf\u7ea7\u521b\u65b0，\u4f8b\u5982\u91c7\u7528\u6df7\u5408\u4e13\u5bb6\u6a21\u578b\u3001\u4f4e\u79e9\u6ce8\u610f\u529b\u673a\u5236\u7b49，\u4f7f\u5f97\u6a21\u578b\u5728\u6027\u80fd\u548c\u6548\u7387\u4e0a\u53d6\u5f97\u4e86\u663e\u8457\u7a81\u7834\u3002\u8fd9\u79cd\u5f3a\u5927\u7684\u6a21\u578b\u67b6\u6784\u548c\u521b\u65b0\u673a\u5236，\u4e3a\u5176\u5728\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u9884\u6d4b\u9886\u57df\u7684\u5e94\u7528\u5960\u5b9a\u4e86\u575a\u5b9e\u57fa\u7840\u3002<\/p>\n

\u5728\u8fd0\u7ef4\u573a\u666f\u4e2d，DeepSeek \u5927\u6a21\u578b\u80fd\u591f\u5c55\u73b0\u51fa\u591a\u65b9\u9762\u7684\u72ec\u7279\u4f18\u52bf\u3002\u9996\u5148，\u5b83\u5177\u5907\u8d85\u5f3a\u7684\u5b66\u4e60\u80fd\u529b，\u80fd\u591f\u5feb\u901f\u5438\u6536\u548c\u7406\u89e3\u5927\u91cf\u590d\u6742\u7684\u8fd0\u7ef4\u6570\u636e，\u5305\u62ec\u670d\u52a1\u5668\u786c\u4ef6\u7684\u5386\u53f2\u8fd0\u884c\u6570\u636e\u3001\u6545\u969c\u6848\u4f8b\u6570\u636e\u3001\u7cfb\u7edf\u65e5\u5fd7\u6570\u636e\u7b49\u3002\u5176\u6b21，\u5176\u5f3a\u5927\u7684\u63a8\u7406\u80fd\u529b\u53ef\u4ee5\u6839\u636e\u5df2\u6709\u7684\u6570\u636e\u6a21\u5f0f\u548c\u89c4\u5f8b，\u5bf9\u670d\u52a1\u5668\u786c\u4ef6\u7684\u672a\u6765\u72b6\u6001\u8fdb\u884c\u5408\u7406\u63a8\u65ad，\u51c6\u786e\u9884\u6d4b\u6f5c\u5728\u7684\u6545\u969c\u98ce\u9669\u3002\u6b64\u5916，DeepSeek \u5927\u6a21\u578b\u8fd8\u62e5\u6709\u826f\u597d\u7684\u6cdb\u5316\u80fd\u529b，\u80fd\u591f\u9002\u5e94\u4e0d\u540c\u54c1\u724c\u3001\u578b\u53f7\u670d\u52a1\u5668\u786c\u4ef6\u7684\u591a\u6837\u6027\u548c\u590d\u6742\u6027，\u63d0\u4f9b\u901a\u7528\u4e14\u7cbe\u51c6\u7684\u6545\u969c\u9884\u6d4b\u670d\u52a1\u3002<\/p>\n

\u6570\u636e\u6536\u96c6\u4e0e\u9884\u5904\u7406<\/h3>\n

\u8981\u5b9e\u73b0\u57fa\u4e8e DeepSeek \u5927\u6a21\u578b\u7684\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u9884\u6d4b，\u5168\u9762\u3001\u51c6\u786e\u7684\u6570\u636e\u6536\u96c6\u662f\u7b2c\u4e00\u6b65\u3002\u670d\u52a1\u5668\u786c\u4ef6\u5305\u542b\u4f17\u591a\u5173\u952e\u7ec4\u4ef6，\u5982 CPU\u3001\u786c\u76d8\u3001\u5185\u5b58\u7b49，\u6bcf\u4e2a\u7ec4\u4ef6\u90fd\u6709\u4e00\u7cfb\u5217\u4e0e\u4e4b\u76f8\u5173\u7684\u91cd\u8981\u6307\u6807\u3002\u4ee5\u786c\u76d8\u4e3a\u4f8b，\u9700\u8981\u6536\u96c6\u786c\u76d8\u7684\u8bfb\u5199\u901f\u7387\u3001\u6e29\u5ea6\u3001\u9519\u8bef\u7387\u7b49\u6307\u6807；\u5bf9\u4e8e CPU，\u5176\u4f7f\u7528\u7387\u3001\u6838\u5fc3\u6e29\u5ea6\u3001\u8d1f\u8f7d\u7b49\u6570\u636e\u81f3\u5173\u91cd\u8981；\u5185\u5b58\u5219\u9700\u5173\u6ce8\u4f7f\u7528\u7387\u3001\u8bfb\u5199\u5ef6\u8fdf\u7b49\u4fe1\u606f\u3002\u8fd9\u4e9b\u6570\u636e\u80fd\u591f\u76f4\u89c2\u53cd\u6620\u786c\u4ef6\u7684\u8fd0\u884c\u72b6\u6001，\u662f\u8fdb\u884c\u6545\u969c\u9884\u6d4b\u7684\u57fa\u7840\u3002<\/p>\n

\u6536\u96c6\u6570\u636e\u7684\u6765\u6e90\u4e5f\u662f\u591a\u6837\u7684\u3002\u670d\u52a1\u5668\u7ba1\u7406\u7cfb\u7edf（\u5982 IPMI，Intelligent Platform Management Interface）\u53ef\u4ee5\u5b9e\u65f6\u91c7\u96c6\u786c\u4ef6\u7684\u5404\u79cd\u72b6\u6001\u4fe1\u606f；\u7cfb\u7edf\u65e5\u5fd7\u5219\u8be6\u7ec6\u8bb0\u5f55\u4e86\u670d\u52a1\u5668\u8fd0\u884c\u8fc7\u7a0b\u4e2d\u7684\u5404\u7c7b\u4e8b\u4ef6\u548c\u62a5\u9519\u4fe1\u606f；\u76d1\u63a7\u8f6f\u4ef6\u80fd\u591f\u6301\u7eed\u76d1\u6d4b\u786c\u4ef6\u6027\u80fd\u6307\u6807，\u5e76\u5c06\u6570\u636e\u5b58\u50a8\u5728\u6570\u636e\u5e93\u4e2d\u3002\u901a\u8fc7\u6574\u5408\u8fd9\u4e9b\u4e0d\u540c\u6765\u6e90\u7684\u6570\u636e，\u80fd\u591f\u6784\u5efa\u8d77\u4e00\u4e2a\u5168\u9762\u3001\u4e30\u5bcc\u7684\u670d\u52a1\u5668\u786c\u4ef6\u6570\u636e\u96c6\u3002<\/p>\n

\u5728\u5b9e\u9645\u6536\u96c6\u8fc7\u7a0b\u4e2d，\u91c7\u7528\u811a\u672c\u8bed\u8a00 Python \u7f16\u5199\u6570\u636e\u91c7\u96c6\u7a0b\u5e8f\u662f\u4e00\u79cd\u9ad8\u6548\u7684\u65b9\u5f0f\u3002\u4f8b\u5982，\u5229\u7528psutil\u5e93\u53ef\u4ee5\u8f7b\u677e\u83b7\u53d6 CPU\u3001\u5185\u5b58\u7b49\u786c\u4ef6\u4fe1\u606f\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b：<\/p>\n

import psutil<\/p>\n

# \u83b7\u53d6CPU\u4f7f\u7528\u7387
\ncpu_percent = psutil.cpu_percent(interval=1)
\nprint(f"\u5f53\u524dCPU\u4f7f\u7528\u7387: {cpu_percent}%")<\/p>\n

# \u83b7\u53d6\u5185\u5b58\u4fe1\u606f
\nmemory = psutil.virtual_memory()
\nprint(f"\u603b\u5185\u5b58: {memory.total \/ (1024 * 1024 * 1024):.2f}GB")
\nprint(f"\u5df2\u4f7f\u7528\u5185\u5b58: {memory.used \/ (1024 * 1024 * 1024):.2f}GB")
\nprint(f"\u5185\u5b58\u4f7f\u7528\u7387: {memory.percent}%") <\/p>\n

\u6570\u636e\u6536\u96c6\u5b8c\u6210\u540e，\u9884\u5904\u7406\u5de5\u4f5c\u81f3\u5173\u91cd\u8981\u3002\u7531\u4e8e\u539f\u59cb\u6570\u636e\u4e2d\u5f80\u5f80\u5b58\u5728\u566a\u58f0\u3001\u7f3a\u5931\u503c\u548c\u5f02\u5e38\u503c\u7b49\u95ee\u9898，\u8fd9\u4e9b\u95ee\u9898\u4f1a\u4e25\u91cd\u5f71\u54cd\u6a21\u578b\u7684\u8bad\u7ec3\u6548\u679c\u548c\u9884\u6d4b\u51c6\u786e\u6027\u3002\u5bf9\u4e8e\u7f3a\u5931\u503c，\u53ef\u4ee5\u91c7\u7528\u5747\u503c\u586b\u5145\u3001\u4e2d\u4f4d\u6570\u586b\u5145\u6216\u57fa\u4e8e\u6a21\u578b\u9884\u6d4b\u586b\u5145\u7b49\u65b9\u6cd5\u3002\u4f8b\u5982，\u4f7f\u7528pandas\u5e93\u8fdb\u884c\u5747\u503c\u586b\u5145\u7684\u4ee3\u7801\u5982\u4e0b：<\/p>\n

import pandas as pd<\/p>\n

data = pd.read_csv('server_data.csv')
\ndata.fillna(data.mean(), inplace=True) <\/p>\n

\u5bf9\u4e8e\u5f02\u5e38\u503c，\u53ef\u901a\u8fc7\u7bb1\u7ebf\u56fe\u5206\u6790\u30013\u03c3 \u539f\u5219\u7b49\u65b9\u6cd5\u8fdb\u884c\u8bc6\u522b\u548c\u5904\u7406\u3002\u4ee5 3\u03c3 \u539f\u5219\u4e3a\u4f8b，\u4ee3\u7801\u5982\u4e0b：<\/p>\n

import numpy as np<\/p>\n

def remove_outliers(data, column):
\n mean = np.mean(data[column])
\n std = np.std(data[column])
\n lower_bound = mean – 3 * std
\n upper_bound = mean + 3 * std
\n return data[(data[column] >= lower_bound) & (data[column] <= upper_bound)] <\/p>\n

\u901a\u8fc7\u6570\u636e\u6e05\u6d17，\u80fd\u591f\u63d0\u9ad8\u6570\u636e\u7684\u8d28\u91cf\u548c\u53ef\u9760\u6027，\u4e3a\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u63d0\u4f9b\u575a\u5b9e\u7684\u6570\u636e\u57fa\u7840\u3002\u540c\u65f6，\u8fdb\u884c\u7279\u5f81\u5de5\u7a0b，\u63d0\u53d6\u4e0e\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u76f8\u5173\u7684\u6709\u6548\u7279\u5f81，\u5982\u901a\u8fc7\u7edf\u8ba1\u7279\u5f81\u3001\u65f6\u57df\u7279\u5f81\u3001\u9891\u57df\u7279\u5f81\u7b49\u65b9\u6cd5，\u8fdb\u4e00\u6b65\u4f18\u5316\u6570\u636e，\u63d0\u5347\u6a21\u578b\u5bf9\u6545\u969c\u6a21\u5f0f\u7684\u8bc6\u522b\u80fd\u529b\u3002<\/p>\n

\u57fa\u4e8e DeepSeek \u7684\u6545\u969c\u9884\u6d4b\u6a21\u578b\u6784\u5efa<\/h3>\n

\u5728\u6784\u5efa\u6545\u969c\u9884\u6d4b\u6a21\u578b\u65f6，\u4ee5 DeepSeek \u5927\u6a21\u578b\u4e3a\u6838\u5fc3，\u7ed3\u5408\u6df1\u5ea6\u5b66\u4e60\u67b6\u6784\u8fdb\u884c\u8bbe\u8ba1\u3002\u53ef\u4ee5\u91c7\u7528\u5faa\u73af\u795e\u7ecf\u7f51\u7edc（RNN）\u53ca\u5176\u53d8\u4f53\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc（LSTM）\u6765\u5904\u7406\u5177\u6709\u65f6\u5e8f\u7279\u6027\u7684\u670d\u52a1\u5668\u786c\u4ef6\u6570\u636e\u3002LSTM \u80fd\u591f\u6709\u6548\u6355\u6349\u6570\u636e\u4e2d\u7684\u957f\u671f\u4f9d\u8d56\u5173\u7cfb，\u5bf9\u4e8e\u9884\u6d4b\u786c\u4ef6\u6545\u969c\u7684\u53d1\u5c55\u8d8b\u52bf\u5177\u6709\u72ec\u7279\u4f18\u52bf\u3002\u5c06\u7ecf\u8fc7\u9884\u5904\u7406\u7684\u6570\u636e\u8f93\u5165\u5230\u7531 LSTM \u548c DeepSeek \u5927\u6a21\u578b\u7ec4\u6210\u7684\u590d\u5408\u6a21\u578b\u4e2d\u3002DeepSeek \u5927\u6a21\u578b\u9996\u5148\u5bf9\u6570\u636e\u8fdb\u884c\u8bed\u4e49\u7406\u89e3\u548c\u7279\u5f81\u63d0\u53d6，\u6316\u6398\u6570\u636e\u4e2d\u9690\u85cf\u7684\u590d\u6742\u6a21\u5f0f\u548c\u5173\u8054\u4fe1\u606f\u3002\u4f8b\u5982，\u5b83\u53ef\u4ee5\u5206\u6790\u7cfb\u7edf\u65e5\u5fd7\u4e2d\u7684\u6587\u672c\u4fe1\u606f，\u7406\u89e3\u5176\u4e2d\u6240\u8868\u8fbe\u7684\u786c\u4ef6\u8fd0\u884c\u72b6\u6001\u548c\u6f5c\u5728\u95ee\u9898\u3002\u7136\u540e，LSTM \u7f51\u7edc\u5bf9\u8fd9\u4e9b\u7279\u5f81\u8fdb\u884c\u65f6\u5e8f\u5efa\u6a21，\u5b66\u4e60\u786c\u4ef6\u72b6\u6001\u968f\u65f6\u95f4\u7684\u53d8\u5316\u89c4\u5f8b\u3002\u901a\u8fc7\u4e24\u8005\u7684\u534f\u540c\u5de5\u4f5c，\u5b9e\u73b0\u5bf9\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u7684\u7cbe\u51c6\u9884\u6d4b\u3002<\/p>\n

\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u91c7\u7528\u76d1\u7763\u5b66\u4e60\u7684\u65b9\u5f0f，\u4ee5\u5386\u53f2\u786c\u4ef6\u6545\u969c\u6570\u636e\u4f5c\u4e3a\u8bad\u7ec3\u6837\u672c，\u5176\u4e2d\u6545\u969c\u53d1\u751f\u7684\u65f6\u95f4\u548c\u7c7b\u578b\u4f5c\u4e3a\u6807\u7b7e\u3002\u4f7f\u7528\u5747\u65b9\u8bef\u5dee（MSE，Mean Squared Error）\u7b49\u635f\u5931\u51fd\u6570\u6765\u8861\u91cf\u6a21\u578b\u9884\u6d4b\u7ed3\u679c\u4e0e\u771f\u5b9e\u6807\u7b7e\u4e4b\u95f4\u7684\u5dee\u5f02\u3002\u901a\u8fc7\u53cd\u5411\u4f20\u64ad\u7b97\u6cd5，\u4e0d\u65ad\u8c03\u6574\u6a21\u578b\u7684\u53c2\u6570，\u4f7f\u5f97\u635f\u5931\u51fd\u6570\u503c\u6700\u5c0f\u5316，\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u51c6\u786e\u6027\u3002\u540c\u65f6，\u4e3a\u4e86\u9632\u6b62\u6a21\u578b\u8fc7\u62df\u5408，\u53ef\u91c7\u7528\u6b63\u5219\u5316\u6280\u672f，\u5982 L1 \u548c L2 \u6b63\u5219\u5316，\u5bf9\u6a21\u578b\u7684\u53c2\u6570\u8fdb\u884c\u7ea6\u675f\u3002\u6b64\u5916，\u8fd8\u53ef\u4ee5\u4f7f\u7528 Dropout \u65b9\u6cd5，\u968f\u673a\u4e22\u5f03\u90e8\u5206\u795e\u7ecf\u5143，\u51cf\u5c11\u795e\u7ecf\u5143\u4e4b\u95f4\u7684\u5171\u9002\u5e94\u6027，\u589e\u5f3a\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b（\u4ee5 Keras \u6846\u67b6\u4e3a\u4f8b）：<\/p>\n

from keras.models import Sequential
\nfrom keras.layers import LSTM, Dense
\nfrom keras.regularizers import l2<\/p>\n

# \u6784\u5efa\u6a21\u578b
\nmodel = Sequential()
\nmodel.add(LSTM(units=64, input_shape=(time_steps, num_features), kernel_regularizer=l2(0.01)))
\nmodel.add(Dense(1, activation='linear'))<\/p>\n

# \u7f16\u8bd1\u6a21\u578b
\nmodel.compile(optimizer='adam', loss='mse')<\/p>\n

# \u8bad\u7ec3\u6a21\u578b
\nmodel.fit(X_train, y_train, epochs=50, batch_size=32) <\/p>\n

\u5176\u4e2d，time_steps\u8868\u793a\u65f6\u95f4\u6b65\u957f，\u5373\u8f93\u5165\u6570\u636e\u7684\u65f6\u95f4\u5e8f\u5217\u957f\u5ea6；num_features\u8868\u793a\u7279\u5f81\u6570\u91cf，\u5373\u7ecf\u8fc7\u9884\u5904\u7406\u540e\u7684\u6570\u636e\u7279\u5f81\u7ef4\u5ea6；X_train\u548cy_train\u5206\u522b\u662f\u8bad\u7ec3\u6570\u636e\u548c\u5bf9\u5e94\u7684\u6807\u7b7e\u3002\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u9700\u8981\u4e0d\u65ad\u8c03\u6574\u6a21\u578b\u7684\u8d85\u53c2\u6570，\u5982 LSTM \u5355\u5143\u6570\u91cf\u3001\u6b63\u5219\u5316\u7cfb\u6570\u3001\u5b66\u4e60\u7387\u7b49，\u4ee5\u627e\u5230\u6700\u4f18\u7684\u6a21\u578b\u914d\u7f6e，\u63d0\u9ad8\u6545\u969c\u9884\u6d4b\u7684\u7cbe\u5ea6\u548c\u53ef\u9760\u6027\u3002<\/p>\n

\u6a21\u578b\u8bad\u7ec3\u4e0e\u4f18\u5316<\/h3>\n

\u4e3a\u4e86\u4f7f\u6a21\u578b\u80fd\u591f\u66f4\u597d\u5730\u5b66\u4e60\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u7684\u6a21\u5f0f\u548c\u89c4\u5f8b，\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u8bad\u7ec3\u7b97\u6cd5\u3002Adam \u4f18\u5316\u7b97\u6cd5\u662f\u4e00\u79cd\u5e38\u7528\u7684\u81ea\u9002\u5e94\u5b66\u4e60\u7387\u7b97\u6cd5，\u5b83\u7ed3\u5408\u4e86 Adagrad \u548c RMSProp \u7b97\u6cd5\u7684\u4f18\u70b9，\u80fd\u591f\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u81ea\u52a8\u8c03\u6574\u5b66\u4e60\u7387，\u52a0\u5feb\u6a21\u578b\u7684\u6536\u655b\u901f\u5ea6\u3002\u5728\u4f7f\u7528 Adam \u4f18\u5316\u7b97\u6cd5\u65f6，\u9700\u8981\u8bbe\u7f6e\u5408\u9002\u7684\u8d85\u53c2\u6570，\u5982\u5b66\u4e60\u7387\u3001\u03b21 \u548c \u03b22 \u7b49\u3002\u901a\u5e38，\u5b66\u4e60\u7387\u53ef\u4ee5\u8bbe\u7f6e\u4e3a 0.001，\u03b21 \u8bbe\u7f6e\u4e3a 0.9，\u03b22 \u8bbe\u7f6e\u4e3a 0.999\u3002\u4ee3\u7801\u793a\u4f8b\u5982\u4e0b：<\/p>\n

from keras.optimizers import Adam<\/p>\n

optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999)
\nmodel.compile(optimizer=optimizer, loss='mse') <\/p>\n

\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u4e3a\u4e86\u9632\u6b62\u6a21\u578b\u8fc7\u62df\u5408，\u53ef\u4ee5\u91c7\u7528\u591a\u79cd\u7b56\u7565\u3002\u9664\u4e86\u524d\u9762\u63d0\u5230\u7684\u6b63\u5219\u5316\u548c Dropout \u65b9\u6cd5\u5916，\u8fd8\u53ef\u4ee5\u4f7f\u7528\u65e9\u505c\u6cd5（Early Stopping）\u3002\u65e9\u505c\u6cd5\u901a\u8fc7\u76d1\u63a7\u6a21\u578b\u5728\u9a8c\u8bc1\u96c6\u4e0a\u7684\u6027\u80fd\u6307\u6807（\u5982\u635f\u5931\u503c），\u5f53\u6307\u6807\u5728\u4e00\u5b9a\u7684\u8bad\u7ec3\u8f6e\u6570\u5185\u4e0d\u518d\u63d0\u5347\u65f6，\u505c\u6b62\u8bad\u7ec3，\u4ee5\u907f\u514d\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u8fc7\u62df\u5408\u3002\u5728 Keras \u4e2d，\u53ef\u4ee5\u4f7f\u7528EarlyStopping\u56de\u8c03\u51fd\u6570\u6765\u5b9e\u73b0\u65e9\u505c\u6cd5\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b：<\/p>\n

from keras.callbacks import EarlyStopping<\/p>\n

early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
\nmodel.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_val, y_val), callbacks=[early_stopping]) <\/p>\n

\u5176\u4e2d，monitor\u53c2\u6570\u6307\u5b9a\u76d1\u63a7\u7684\u6307\u6807\u4e3a\u9a8c\u8bc1\u96c6\u4e0a\u7684\u635f\u5931\u503c（val_loss）；patience\u53c2\u6570\u8868\u793a\u5728\u6307\u6807\u4e0d\u518d\u63d0\u5347\u7684\u60c5\u51b5\u4e0b，\u7ee7\u7eed\u8bad\u7ec3\u7684\u8f6e\u6570；restore_best_weights\u53c2\u6570\u8868\u793a\u5728\u8bad\u7ec3\u7ed3\u675f\u65f6，\u6062\u590d\u6a21\u578b\u5728\u9a8c\u8bc1\u96c6\u4e0a\u8868\u73b0\u6700\u597d\u65f6\u7684\u6743\u91cd\u3002<\/p>\n

\u6b64\u5916，\u4e3a\u4e86\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b，\u8fd8\u53ef\u4ee5\u91c7\u7528\u6570\u636e\u589e\u5f3a\u7684\u65b9\u6cd5\u3002\u5bf9\u4e8e\u670d\u52a1\u5668\u786c\u4ef6\u6570\u636e，\u53ef\u4ee5\u901a\u8fc7\u5bf9\u6570\u636e\u8fdb\u884c\u5e73\u79fb\u3001\u7f29\u653e\u3001\u566a\u58f0\u6dfb\u52a0\u7b49\u64cd\u4f5c，\u751f\u6210\u66f4\u591a\u7684\u8bad\u7ec3\u6837\u672c，\u4ece\u800c\u589e\u52a0\u6570\u636e\u7684\u591a\u6837\u6027，\u4f7f\u6a21\u578b\u80fd\u591f\u5b66\u4e60\u5230\u66f4\u5e7f\u6cdb\u7684\u6545\u969c\u6a21\u5f0f\u3002\u4f8b\u5982，\u5bf9\u786c\u76d8\u6e29\u5ea6\u6570\u636e\u8fdb\u884c\u8f7b\u5fae\u7684\u968f\u673a\u566a\u58f0\u6dfb\u52a0：<\/p>\n

import numpy as np<\/p>\n

def add_noise(data, noise_level=0.05):
\n noise = np.random.normal(0, noise_level, data.shape)
\n return data + noise <\/p>\n

\u901a\u8fc7\u7efc\u5408\u8fd0\u7528\u8fd9\u4e9b\u8bad\u7ec3\u7b97\u6cd5\u548c\u4f18\u5316\u7b56\u7565，\u53ef\u4ee5\u6709\u6548\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd，\u4f7f\u5176\u5728\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u9884\u6d4b\u4efb\u52a1\u4e2d\u8868\u73b0\u66f4\u52a0\u51fa\u8272\u3002\u5728\u8bad\u7ec3\u5b8c\u6210\u540e，\u8fd8\u9700\u8981\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30，\u4f7f\u7528\u6d4b\u8bd5\u96c6\u6570\u636e\u8ba1\u7b97\u6a21\u578b\u7684\u51c6\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1 \u503c\u7b49\u6307\u6807，\u5168\u9762\u8bc4\u4f30\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b，\u5e76\u6839\u636e\u8bc4\u4f30\u7ed3\u679c\u8fdb\u4e00\u6b65\u8c03\u6574\u548c\u4f18\u5316\u6a21\u578b\u3002<\/p>\n

\u9884\u6d4b\u7ed3\u679c\u5c55\u793a\u4e0e\u5e94\u7528<\/h3>\n

\u5f53\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\u5e76\u901a\u8fc7\u8bc4\u4f30\u540e，\u5c31\u53ef\u4ee5\u5c06\u5176\u5e94\u7528\u4e8e\u5b9e\u9645\u7684\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u9884\u6d4b\u4e2d\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d，\u901a\u8fc7\u53ef\u89c6\u5316\u7684\u65b9\u5f0f\u5c55\u793a\u9884\u6d4b\u7ed3\u679c\u80fd\u591f\u8ba9\u8fd0\u7ef4\u4eba\u5458\u66f4\u76f4\u89c2\u5730\u4e86\u89e3\u670d\u52a1\u5668\u786c\u4ef6\u7684\u5065\u5eb7\u72b6\u6001\u548c\u6f5c\u5728\u6545\u969c\u98ce\u9669\u3002\u53ef\u4ee5\u4f7f\u7528 Python \u7684matplotlib\u5e93\u548cseaborn\u5e93\u6765\u521b\u5efa\u76f4\u89c2\u7684\u56fe\u8868\u3002\u4f8b\u5982，\u7ed8\u5236\u670d\u52a1\u5668\u786c\u76d8\u672a\u6765\u4e00\u5468\u5185\u7684\u6545\u969c\u6982\u7387\u53d8\u5316\u66f2\u7ebf，\u4ee3\u7801\u5982\u4e0b：<\/p>\n

import matplotlib.pyplot as plt
\nimport numpy as np<\/p>\n

# \u5047\u8bbe\u5df2\u7ecf\u5f97\u5230\u672a\u6765\u4e00\u5468\u7684\u6545\u969c\u6982\u7387\u9884\u6d4b\u7ed3\u679c
\npredicted_probs = np.array([0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4])
\ndays = np.arange(1, 8)<\/p>\n

plt.plot(days, predicted_probs, marker='o')
\nplt.xlabel('\u5929\u6570')
\nplt.ylabel('\u6545\u969c\u6982\u7387')
\nplt.title('\u670d\u52a1\u5668\u786c\u76d8\u672a\u6765\u4e00\u5468\u6545\u969c\u6982\u7387\u9884\u6d4b')
\nplt.grid(True)
\nplt.show() <\/p>\n

\u901a\u8fc7\u8fd9\u6837\u7684\u56fe\u8868，\u8fd0\u7ef4\u4eba\u5458\u53ef\u4ee5\u6e05\u6670\u5730\u770b\u5230\u786c\u76d8\u6545\u969c\u6982\u7387\u968f\u65f6\u95f4\u7684\u53d8\u5316\u8d8b\u52bf\u3002\u5982\u679c\u6545\u969c\u6982\u7387\u8d85\u8fc7\u4e86\u8bbe\u5b9a\u7684\u9608\u503c（\u4f8b\u5982 0.3），\u7cfb\u7edf\u4f1a\u7acb\u5373\u89e6\u53d1\u9884\u8b66\u673a\u5236\u3002\u9884\u8b66\u4fe1\u606f\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u53d1\u9001\u7ed9\u8fd0\u7ef4\u4eba\u5458，\u5982\u77ed\u4fe1\u3001\u90ae\u4ef6\u6216\u5373\u65f6\u901a\u8baf\u5de5\u5177\u3002\u4ee5\u53d1\u9001\u90ae\u4ef6\u4e3a\u4f8b，\u53ef\u4ee5\u4f7f\u7528 Python \u7684smtplib\u5e93\u548cemail\u5e93\u6765\u5b9e\u73b0\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b：<\/p>\n

import smtplib
\nfrom email.mime.text import MIMEText<\/p>\n

# \u90ae\u4ef6\u914d\u7f6e
\nsender_email = "your_email@example.com"
\nreceiver_email = "admin@example.com"
\npassword = "your_email_password"
\nmessage = MIMEText("\u670d\u52a1\u5668\u786c\u76d8\u6545\u969c\u6982\u7387\u8d85\u8fc7\u9608\u503c，\u8bf7\u53ca\u65f6\u68c0\u67e5！")
\nmessage['Subject'] = "\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u9884\u8b66"
\nmessage['From'] = sender_email
\nmessage['To'] = receiver_email<\/p>\n

# \u53d1\u9001\u90ae\u4ef6
\nwith smtplib.SMTP('smtp.example.com', 587) as server:
\n server.starttls()
\n server.login(sender_email, password)
\n server.sendmail(sender_email, receiver_email, message.as_string()) <\/p>\n

\u5f53\u8fd0\u7ef4\u4eba\u5458\u6536\u5230\u9884\u8b66\u4fe1\u606f\u540e，\u53ef\u4ee5\u6839\u636e\u9884\u6d4b\u7ed3\u679c\u53ca\u65f6\u91c7\u53d6\u76f8\u5e94\u7684\u63aa\u65bd\u3002\u5bf9\u4e8e\u6545\u969c\u6982\u7387\u8f83\u9ad8\u7684\u670d\u52a1\u5668\u786c\u4ef6，\u63d0\u524d\u5b89\u6392\u7ef4\u4fee\u4eba\u5458\u8fdb\u884c\u68c0\u4fee\u548c\u66f4\u6362，\u907f\u514d\u6545\u969c\u53d1\u751f\u5bfc\u81f4\u4e1a\u52a1\u4e2d\u65ad\u3002\u540c\u65f6，\u8fd0\u7ef4\u4eba\u5458\u8fd8\u53ef\u4ee5\u7ed3\u5408\u9884\u6d4b\u7ed3\u679c，\u4f18\u5316\u670d\u52a1\u5668\u7684\u8fd0\u7ef4\u8ba1\u5212，\u5408\u7406\u5b89\u6392\u8d44\u6e90，\u63d0\u9ad8\u8fd0\u7ef4\u6548\u7387\u3002\u4f8b\u5982，\u6839\u636e\u4e0d\u540c\u670d\u52a1\u5668\u786c\u4ef6\u7684\u6545\u969c\u98ce\u9669\u7b49\u7ea7，\u5236\u5b9a\u5dee\u5f02\u5316\u7684\u5de1\u68c0\u8ba1\u5212，\u5bf9\u9ad8\u98ce\u9669\u8bbe\u5907\u8fdb\u884c\u66f4\u9891\u7e41\u7684\u68c0\u67e5\u548c\u7ef4\u62a4\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f，\u5b9e\u73b0\u4ece\u4f20\u7edf\u7684\u88ab\u52a8\u5f0f\u8fd0\u7ef4\u5411\u4e3b\u52a8\u5f0f\u8fd0\u7ef4\u7684\u8f6c\u53d8，\u5927\u5927\u63d0\u5347\u670d\u52a1\u5668\u7684\u53ef\u9760\u6027\u548c\u7a33\u5b9a\u6027，\u4e3a\u4f01\u4e1a\u4e1a\u52a1\u7684\u6301\u7eed\u7a33\u5b9a\u8fd0\u884c\u63d0\u4f9b\u6709\u529b\u4fdd\u969c\u3002<\/p>\n

\u603b\u7ed3\u4e0e\u5c55\u671b<\/h3>\n

\u901a\u8fc7\u5c06 DeepSeek \u5927\u6a21\u578b\u5f15\u5165\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u9884\u6d4b\u9886\u57df，\u5229\u7528\u5176\u5f3a\u5927\u7684\u5b66\u4e60\u548c\u63a8\u7406\u80fd\u529b，\u7ed3\u5408\u5168\u9762\u7684\u6570\u636e\u6536\u96c6\u4e0e\u9884\u5904\u7406\u3001\u7cbe\u5fc3\u6784\u5efa\u7684\u9884\u6d4b\u6a21\u578b\u4ee5\u53ca\u6709\u6548\u7684\u8bad\u7ec3\u4f18\u5316\u7b56\u7565，\u80fd\u591f\u5b9e\u73b0\u5bf9\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u7684\u7cbe\u51c6\u9884\u6d4b\u3002\u8fd9\u79cd\u521b\u65b0\u7684\u8fd0\u7ef4\u65b9\u5f0f，\u4e3a\u4f01\u4e1a\u5e26\u6765\u4e86\u8bf8\u591a\u663e\u8457\u4f18\u52bf\u3002\u5b83\u4e0d\u4ec5\u80fd\u591f\u63d0\u524d\u53d1\u73b0\u6f5c\u5728\u7684\u786c\u4ef6\u6545\u969c\u9690\u60a3，\u907f\u514d\u4e1a\u52a1\u4e2d\u65ad，\u51cf\u5c11\u7ecf\u6d4e\u635f\u5931\u548c\u58f0\u8a89\u635f\u5bb3，\u8fd8\u80fd\u901a\u8fc7\u4f18\u5316\u8fd0\u7ef4\u8ba1\u5212，\u5408\u7406\u914d\u7f6e\u8d44\u6e90，\u964d\u4f4e\u8fd0\u7ef4\u6210\u672c，\u63d0\u9ad8\u8fd0\u7ef4\u6548\u7387\u3002<\/p>\n

\u968f\u7740\u4eba\u5de5\u667a\u80fd\u6280\u672f\u7684\u4e0d\u65ad\u53d1\u5c55，\u672a\u6765\u5728\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u9884\u6d4b\u9886\u57df，\u57fa\u4e8e\u5927\u6a21\u578b\u7684\u9884\u6d4b\u6280\u672f\u6709\u671b\u53d6\u5f97\u66f4\u5927\u7684\u7a81\u7834\u3002\u4e00\u65b9\u9762，\u5927\u6a21\u578b\u7684\u6027\u80fd\u5c06\u4e0d\u65ad\u63d0\u5347，\u80fd\u591f\u66f4\u51c6\u786e\u5730\u6355\u6349\u670d\u52a1\u5668\u786c\u4ef6\u8fd0\u884c\u6570\u636e\u4e2d\u7684\u590d\u6742\u6a21\u5f0f\u548c\u7ec6\u5fae\u53d8\u5316，\u8fdb\u4e00\u6b65\u63d0\u9ad8\u6545\u969c\u9884\u6d4b\u7684\u7cbe\u5ea6\u548c\u53ef\u9760\u6027\u3002\u53e6\u4e00\u65b9\u9762，\u968f\u7740\u7269\u8054\u7f51\u6280\u672f\u7684\u666e\u53ca，\u670d\u52a1\u5668\u786c\u4ef6\u5c06\u4ea7\u751f\u66f4\u591a\u7ef4\u5ea6\u3001\u66f4\u9ad8\u9891\u7387\u7684\u6570\u636e，\u901a\u8fc7\u5bf9\u8fd9\u4e9b\u6d77\u91cf\u6570\u636e\u7684\u6df1\u5165\u6316\u6398\u548c\u5206\u6790，\u5927\u6a21\u578b\u5c06\u80fd\u591f\u63d0\u4f9b\u66f4\u5168\u9762\u3001\u66f4\u4e2a\u6027\u5316\u7684\u6545\u969c\u9884\u6d4b\u670d\u52a1\u3002\u540c\u65f6，\u4e0e\u5176\u4ed6\u65b0\u5174\u6280\u672f\u5982\u8fb9\u7f18\u8ba1\u7b97\u3001\u533a\u5757\u94fe\u7b49\u7684\u878d\u5408，\u4e5f\u5c06\u4e3a\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u9884\u6d4b\u5e26\u6765\u65b0\u7684\u673a\u9047\u548c\u53d1\u5c55\u65b9\u5411\u3002\u4f8b\u5982，\u5229\u7528\u8fb9\u7f18\u8ba1\u7b97\u6280\u672f，\u53ef\u4ee5\u5728\u9760\u8fd1\u670d\u52a1\u5668\u786c\u4ef6\u7684\u8fb9\u7f18\u8282\u70b9\u5b9e\u65f6\u5904\u7406\u548c\u5206\u6790\u6570\u636e，\u51cf\u5c11\u6570\u636e\u4f20\u8f93\u5ef6\u8fdf，\u5b9e\u73b0\u66f4\u5feb\u901f\u7684\u6545\u969c\u9884\u6d4b\u548c\u54cd\u5e94；\u533a\u5757\u94fe\u6280\u672f\u5219\u53ef\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u5b89\u5168\u6027\u548c\u53ef\u4fe1\u5ea6，\u4e3a\u6545\u969c\u9884\u6d4b\u6a21\u578b\u7684\u8bad\u7ec3\u548c\u5e94\u7528\u63d0\u4f9b\u53ef\u9760\u7684\u6570\u636e\u57fa\u7840\u3002<\/p>\n

\u76f8\u4fe1\u5728\u4e0d\u4e45\u7684\u5c06\u6765，\u57fa\u4e8e\u5927\u6a21\u578b\u7684\u670d\u52a1\u5668\u786c\u4ef6\u6545\u969c\u9884\u6d4b\u6280\u672f\u5c06\u5728\u8fd0\u7ef4\u9886\u57df\u5f97\u5230\u66f4\u5e7f\u6cdb\u7684\u5e94\u7528，\u6210\u4e3a\u4fdd\u969c\u4f01\u4e1a\u6570\u5b57\u5316\u57fa\u7840\u8bbe\u65bd\u7a33\u5b9a\u8fd0\u884c\u7684\u6838\u5fc3\u6280\u672f\u4e4b\u4e00，\u63a8\u52a8\u8fd0\u7ef4\u884c\u4e1a\u5411\u667a\u80fd\u5316\u3001\u9ad8\u6548\u5316\u7684\u65b9\u5411\u8fc8\u8fdb\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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