{"id":1881,"date":"2025-02-06T07:25:00","date_gmt":"2025-02-05T23:25:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=1881"},"modified":"2025-01-09T13:47:35","modified_gmt":"2025-01-09T05:47:35","slug":"%e7%94%a8python%e7%8e%a9%e8%bd%ac%e4%ba%a4%e6%98%93%ef%bc%9a10%e7%a7%8d%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e8%b6%85%e5%ae%9e%e7%94%a8%e6%96%b9%e6%b3%95","status":"publish","type":"post","link":"https:\/\/www.laoyulaoyu.com\/index.php\/2025\/02\/06\/%e7%94%a8python%e7%8e%a9%e8%bd%ac%e4%ba%a4%e6%98%93%ef%bc%9a10%e7%a7%8d%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e8%b6%85%e5%ae%9e%e7%94%a8%e6%96%b9%e6%b3%95\/","title":{"rendered":"\u7528Python\u73a9\u8f6c\u4ea4\u6613\uff1a10\u79cd\u673a\u5668\u5b66\u4e60\u8d85\u5b9e\u7528\u65b9\u6cd5"},"content":{"rendered":"\n

\u4f5c\u8005\uff1a\u8001\u4f59\u635e\u9c7c<\/a><\/p>\n\n\n\n

\u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005\u3002<\/mark><\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\n
\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u4f5c\u4e3a\u4e00\u540d\u667a\u80fd\u91d1\u878d\u4ece\u4e1a\u8005\uff0c\u6211\u6df1\u77e5\u673a\u5668\u5b66\u4e60\u5728\u4ea4\u6613\u4e2d\u7684\u5de8\u5927\u6f5c\u529b\u3002\u672c\u6587\u5c06\u5e26\u60a8\u63a2\u7d22\u5982\u4f55\u4f7f\u7528Python\u4e2d\u768410\u79cd\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u6280\u672f\uff0c\u63d0\u5347\u60a8\u7684\u4ea4\u6613\u7b56\u7565\u548c\u6536\u76ca<\/mark>\u3002\u4ece\u6570\u636e\u9884\u5904\u7406\u5230\u6a21\u578b\u90e8\u7f72\uff0c\u4ece\u9884\u6d4b\u80a1\u4ef7\u5230\u98ce\u9669\u7ba1\u7406\uff0c\u6db5\u76d6\u60c5\u611f\u5206\u6790\u3001\u8d8b\u52bf\u9884\u6d4b\u7b49\u3002\u6bcf\u4e2a\u6b65\u9aa4\u90fd\u914d\u6709\u8be6\u7ec6\u7684\u4ee3\u7801\u793a\u4f8b\u548c\u5b9e\u9645\u6848\u4f8b\uff0c\u6db5\u76d6\u4e86\u4ece\u5165\u95e8\u5230\u8fdb\u9636\u7684\u65b9\u65b9\u9762\u9762\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u673a\u5668\u5b66\u4e60\u6b63\u5728\u5f7b\u5e95\u6539\u53d8\u4ea4\u6613\u9886\u57df\uff0c\u65e0\u8bba\u662f\u9884\u6d4b\u80a1\u4ef7\u8d70\u52bf\uff0c\u8fd8\u662f\u5206\u6790\u5e02\u573a\u60c5\u7eea\uff0c\u5b83\u90fd\u80fd\u5e2e\u52a9\u4ea4\u6613\u8005\u505a\u51fa\u66f4\u5feb\u901f\u3001\u66f4\u667a\u80fd\u3001\u66f4\u57fa\u4e8e\u6570\u636e\u7684\u51b3\u7b56\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u6df1\u5165\u63a2\u8ba8\u5982\u4f55\u7528 Python \u5b9e\u73b0\u673a\u5668\u5b66\u4e60\u7684 10 \u79cd\u5f3a\u5927\u5e94\u7528\uff0c\u5e76\u4e3a\u5927\u5bb6\u63d0\u4f9b\u5b9e\u7528\u7684\u793a\u4f8b\u548c\u5de5\u5177\uff0c\u52a9\u60a8\u8f7b\u677e\u4e0a\u624b\u3002<\/p>\n\n\n\n
1.\u5f3a\u5316\u5b66\u4e60 – \u667a\u80fd\u6295\u8d44\u7ec4\u5408\u7ba1\u7406<\/strong><\/h2>\n\n\n\n
\u6211\u4eec\u53ef\u4ee5\u60f3\u8c61\u4e00\u4e0b\u673a\u5668\u4eba\u5b66\u4e60\u8d70\u8def\u7684\u60c5\u666f–\u5b83\u901a\u8fc7\u5c1d\u8bd5\u4e0d\u540c\u7684\u52a8\u4f5c\u5e76\u5728\u6210\u529f\u540e\u83b7\u5f97\u5956\u52b1\u6765\u63d0\u9ad8\u81ea\u5df1\u7684\u80fd\u529b\u3002\u540c\u6837\uff0c\u5f3a\u5316\u5b66\u4e60\uff08RL\uff09\u901a\u8fc7\u4ece\u5956\u52b1\u4e2d\u5b66\u4e60\u6765\u4f18\u5316\u6295\u8d44\u7ec4\u5408\u7ba1\u7406\u3002<\/p>\n\n\n\n
1.1 \u5e94\u7528\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
\u6bd4\u5982\u7528 RL agent \u5c06 70% \u7684\u8d44\u91d1\u5206\u914d\u7ed9\u79d1\u6280\u80a1\uff0c30% \u5206\u914d\u7ed9\u533b\u7597\u4fdd\u5065\u80a1\u3002\u5982\u679c\u6295\u8d44\u7ec4\u5408\u589e\u957f\u4e86 5%\uff0c\u6a21\u578b\u5c31\u4f1a\u5c06\u6b64\u89c6\u4e3a “\u5956\u52b1”\uff0c\u5e76\u8c03\u6574\u7b56\u7565\u4ee5\u8fdb\u4e00\u6b65\u63d0\u9ad8\u4e1a\u7ee9\u3002<\/p>\n\n\n\n
1.2 Python \u5de5\u5177<\/strong><\/h3>\n\n\n\n
\u4f7f\u7528 Stable Baselines3 \u7684 A2C \u6a21\u578b\u5efa\u7acb\u57fa\u4e8e RL \u7684\u4ea4\u6613\u7cfb\u7edf\u3002<\/p>\n\n\n\n
#1. Reinforcement Learning\ndef reinforcement_learning (portfolio):\nmodel\nA2C( 'MlpPolicy', 'CartPole-v1', verbose=1)\nmodel.learn (total_timesteps=10000)\nobs = portfolio.reset()\nfor i in range(1000):\naction, _states model.predict(obs, deterministic=True) obs, reward, done, info = portfolio.step(action)\nif done:\nobs portfolio.reset()<\/code><\/pre>\n\n\n\n
2. Support Vector Machines (SVM) – \u8d8b\u52bf\u9884\u6d4b<\/strong><\/h2>\n\n\n\n
\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u5c31\u50cf\u662f\u4e00\u4f4d\u806a\u660e\u7684\u5206\u6790\u5e08\uff0c\u5b83\u80fd\u6839\u636e\u5386\u53f2\u6570\u636e\uff0c\u5728\u5373\u5c06\u4e0a\u6da8\u548c\u4e0b\u8dcc\u7684\u80a1\u7968\u4e4b\u95f4\u753b\u51fa\u4e00\u6761\u6e05\u6670\u7684\u201c\u5206\u754c\u7ebf\u201d\uff0c\u5e2e\u52a9\u4ea4\u6613\u8005\u66f4\u597d\u5730\u9884\u6d4b\u5e02\u573a\u8d8b\u52bf\u3002<\/p>\n\n\n\n
2.1 \u5e94\u7528\u793a\u4f8b<\/strong><\/strong><\/h3>\n\n\n\n
\u5206\u6790\u8fc7\u53bb\u7684\u4ef7\u683c\u3001\u4ea4\u6613\u91cf\u548c\u8d8b\u52bf\uff0c\u6bd4\u5982\u9884\u6d4b\u7279\u65af\u62c9\u4e0b\u5468\u7684\u80a1\u4ef7\u4f1a\u4e0a\u6da8\u8fd8\u662f\u4e0b\u8dcc\u3002<\/p>\n\n\n\n
2.2 Python \u5de5\u5177<\/strong><\/h3>\n\n\n\n
SciKit Learn \u7684 svm.SVC \u7c7b\u662f\u6784\u5efa SVM \u6a21\u578b\u7684\u5b8c\u7f8e\u5de5\u5177\u3002<\/p>\n\n\n\n
#2. Support Vector Machines\ndef support_vector_machine (train_data, train_labels, test_data):\nclf svm.SVC ()\nclf.fit(train_data, train_labels) predictions = clf.predict(test_data) return predictions<\/code><\/pre>\n\n\n\n
3.\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09–\u60c5\u611f\u5206\u6790<\/strong><\/h2>\n\n\n\n
\u4f60\u662f\u5426\u597d\u5947\u793e\u4ea4\u5a92\u4f53\u5bf9\u4f60\u6700\u5173\u6ce8\u7684\u80a1\u7968\u662f\u5982\u4f55\u8bc4\u4ef7\u7684\uff1f\u501f\u52a9\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\uff0c\u6211\u4eec\u53ef\u4ee5\u4ece\u6d77\u91cf\u6587\u672c\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u4ef7\u503c\u7684\u89c1\u89e3\uff0c\u6bd4\u5982\u5e02\u573a\u8206\u8bba\u548c\u60c5\u7eea\u503e\u5411\u3002<\/p>\n\n\n\n
3.1 \u5e94\u7528\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u6a21\u578b\u80fd\u591f\u5feb\u901f\u5206\u6790\u6570\u4e07\u6761\u5173\u4e8e\u82f9\u679c\u516c\u53f8\u7684\u63a8\u6587\uff0c\u7cbe\u51c6\u5224\u65ad\u5e02\u573a\u60c5\u7eea\u662f\u79ef\u6781\u8fd8\u662f\u6d88\u6781\uff0c\u4e3a\u4ea4\u6613\u8005\u63d0\u4f9b\u6709\u529b\u7684\u51b3\u7b56\u652f\u6301\u3002<\/p>\n\n\n\n
3.2 Python \u5de5\u5177<\/strong><\/h3>\n\n\n\n
\u4f7f\u7528 NLTK \u7684 SentimentIntensityAnalyzer \u4ece\u65b0\u95fb\u6216\u793e\u4ea4\u5a92\u4f53\u6570\u636e\u4e2d\u5224\u65ad\u60c5\u611f\u3002<\/p>\n\n\n\n
# 3. Natural Language Processing\ndef analyze_sentiment (text_data): nltk.download ('vader_lexicon')\nsid Sentiment IntensityAnalyzer ()\nsentiment = sid.polarity_scores (text_data)\nreturn sentiment<\/code><\/pre>\n\n\n\n
4.\u968f\u673a\u68ee\u6797\uff08Random Forests\uff09 – \u80a1\u4ef7\u9884\u6d4b<\/strong><\/h2>\n\n\n\n
\u968f\u673a\u68ee\u6797\uff08Random Forests\uff09\u5c31\u50cf\u4e00\u7fa4\u667a\u6167\u7684\u51b3\u7b56\u8005\uff0c\u901a\u8fc7\u6295\u7968\u9009\u51fa\u6700\u4f73\u7ed3\u679c\u3002\u5b83\u7ed3\u5408\u4e86\u591a\u68f5\u51b3\u7b56\u6811\u7684\u529b\u91cf\uff0c\u80fd\u591f\u505a\u51fa\u9ad8\u5ea6\u7cbe\u51c6\u7684\u9884\u6d4b\uff0c\u7279\u522b\u9002\u5408\u5904\u7406\u590d\u6742\u7684\u4ea4\u6613\u6570\u636e\u3002<\/p>\n\n\n\n
4.1 \u5e94\u7528\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
\u6839\u636e\u76c8\u5229\u62a5\u544a\u3001\u65b0\u95fb\u62a5\u9053\u548c\u6280\u672f\u6307\u6807\u9884\u6d4b\u4e9a\u9a6c\u900a\uff08Amazon\uff09\u7684\u80a1\u7968\u662f\u5426\u4f1a\u4e0a\u6da8\u3002<\/p>\n\n\n\n
4.2 Python \u5de5\u5177<\/strong><\/h3>\n\n\n\n
\u5c1d\u8bd5\u4f7f\u7528 SciKit Learn \u7684 RandomForestRegressor \u8fdb\u884c\u7a33\u5065\u9884\u6d4b\u3002<\/p>\n\n\n\n
# 4. Random Forests\ndef random_forest (train_data, train_labels, test_data):\nregressor RandomForestRegressor (n_estimators=20, random_state=0) regressor.fit (train_data, train_labels)\npredictions = regressor.predict(test_data)\nreturn predictions<\/code><\/pre>\n\n\n\n
5.\u805a\u7c7b\u7b97\u6cd5 – \u6295\u8d44\u7ec4\u5408\u591a\u6837\u5316<\/strong><\/h2>\n\n\n\n
\u805a\u7c7b\u6709\u52a9\u4e8e\u5c06\u76f8\u4f3c\u7684\u80a1\u7968\u5f52\u4e3a\u4e00\u7c7b\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u660e\u667a\u7684\u5206\u6563\u6295\u8d44\uff0c\u964d\u4f4e\u98ce\u9669\u3002<\/p>\n\n\n\n
5.1 \u5e94\u7528\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
\u6839\u636e\u884c\u4e1a\u3001\u89c4\u6a21\u548c\u4e1a\u7ee9\u5c06\u80a1\u7968\u5206\u7ec4\u3002\u4e00\u4e2a\u591a\u5143\u5316\u7684\u6295\u8d44\u7ec4\u5408\u53ef\u80fd\u4f1a\u6709\u79d1\u6280\u3001\u80fd\u6e90\u548c\u91d1\u878d\u7b49\u7c7b\u522b\u7684\u80a1\u7968\u3002<\/p>\n\n\n\n
5.2 Python \u5de5\u5177<\/strong><\/h3>\n\n\n\n
SciKit Learn \u7684 KMeans \u7c7b\u53ef\u8f7b\u677e\u5bf9\u80a1\u7968\u8fdb\u884c\u805a\u7c7b\u3002<\/p>\n\n\n\n
# 5. Clustering Algorithms\ndef clustering (data, num_clusters):\nkmeans = KMeans (n_clusters=num_clusters) kmeans.fit(data)\nlabels = kmeans.predict(data)\nreturn labels<\/code><\/pre>\n\n\n\n
6.\u68af\u5ea6\u589e\u5f3a\uff08Gradient Boosting\uff09- \u6539\u8fdb\u4ea4\u6613\u4fe1\u53f7<\/strong><\/h2>\n\n\n\n
\u68af\u5ea6\u589e\u5f3a\u5c06\u591a\u79cd\u7b56\u7565\u7684\u9884\u6d4b\u7ed3\u679c\u7ed3\u5408\u8d77\u6765\uff0c\u4ee5\u51cf\u5c11\u504f\u5dee\u5e76\u63d0\u9ad8\u51c6\u786e\u6027\u3002<\/p>\n\n\n\n
6.1 \u5e94\u7528\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
\u63d0\u5347\u5404\u79cd\u6307\u6807\uff08\u5982\u79fb\u52a8\u5e73\u5747\u7ebf\u548c\u5e03\u6797\u7ebf\uff09\u7684\u9884\u6d4b\uff0c\u751f\u6210\u66f4\u51c6\u786e\u7684\u4e70\u5165\u6216\u5356\u51fa\u4fe1\u53f7\u3002<\/p>\n\n\n\n
6.2 Python \u5de5\u5177<\/strong><\/h3>\n\n\n\n
SciKit Learn \u7684\u68af\u5ea6\u63d0\u5347\u56de\u5f52\u5668\uff08GradientBoostingRegressor\uff09\u53ef\u901a\u8fc7\u51cf\u5c11\u8bef\u5dee\u6765\u589e\u5f3a\u9884\u6d4b\u6548\u679c\u3002<\/p>\n\n\n\n
# 6. Gradient Boosting\ndef gradient_boosting (train_data, train_labels, test_data):\ngbr Gradient BoostingRegressor (n_estimators=100, learning_rate=0.1) gbr.fit(train_data, train_labels)\npredictions = gbr.predict(test_data)\nreturn predictions<\/code><\/pre>\n\n\n\n
7.\u6df1\u5ea6\u5b66\u4e60\uff08RNN\uff09–\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b<\/strong><\/h2>\n\n\n\n
\u6df1\u5ea6\u5b66\u4e60\uff0c\u5c24\u5176\u662f\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\uff08RNN\uff09\uff0c\u975e\u5e38\u9002\u5408\u5206\u6790\u8fde\u7eed\u6570\u636e\uff0c\u6bd4\u5982\u968f\u65f6\u95f4\u53d8\u5316\u7684\u80a1\u7968\u4ef7\u683c\u3002<\/p>\n\n\n\n
7.1 \u5e94\u7528\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u6bd4\u7279\u5e01\u8fc7\u53bb\u7684\u8868\u73b0\u9884\u6d4b\u5176\u672a\u6765\u7684\u4ef7\u683c\u3002\u4e0e\u4f20\u7edf\u6a21\u578b\u76f8\u6bd4\uff0cRNN \u80fd\u66f4\u597d\u5730\u5904\u7406\u968f\u65f6\u95f4\u53d8\u5316\u7684\u6a21\u5f0f\u3002<\/p>\n\n\n\n
7.2 Python \u5de5\u5177<\/strong><\/h3>\n\n\n\n
Keras \u5e8f\u5217\u6a21\u578b\u4e2d\u7684 LSTM \u5c42\u975e\u5e38\u9002\u5408\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u3002<\/p>\n\n\n\n
# 7. Deep Learning (RNNs)\ndef recurrent_neural_network (train_data, train_labels, test_data):\nmodel\nSequential()\nmodel.add(LSTM(50, return_sequences=True, input_shape=(train_data.shape[1], 1))) model.add(LSTM(50, return_sequences=False))\nmodel.add(Dense (25))\nmodel.add(Dense (1))\nmodel.compile (optimizer=' adam', loss='mean_squared_error') model.fit(train_data, train_labels, batch_size=1, epochs=1)\npredictions = model.predict(test_data)\nreturn predictions<\/code><\/pre>\n\n\n\n
8.\u5f02\u5e38\u68c0\u6d4b – \u98ce\u9669\u7ba1\u7406<\/strong><\/h2>\n\n\n\n
\u53d1\u73b0\u5f02\u5e38\u60c5\u51b5\uff08\u5982\u4ef7\u683c\u7a81\u7136\u4e0b\u8dcc\u6216\u98d9\u5347\uff09\u6709\u52a9\u4e8e\u4ea4\u6613\u8005\u907f\u514d\u635f\u5931\u5e76\u8bc6\u522b\u5e02\u573a\u64cd\u7eb5\u884c\u4e3a\u3002<\/p>\n\n\n\n
8.1 \u5e94\u7528\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
\u672c\u4f8b\u4e2d\u6211\u4eec\u6765\u68c0\u6d4b GameStop \u80a1\u7968\u7684\u5f02\u5e38\u4ea4\u6613\u6216\u4ef7\u683c\u7a81\u7136\u4e0b\u8dcc\uff0c\u8fd9\u8868\u660e\u53ef\u80fd\u5b58\u5728\u5e02\u573a\u64cd\u7eb5\u884c\u4e3a\u3002<\/p>\n\n\n\n
8.2 Python \u5de5\u5177<\/strong><\/h3>\n\n\n\n
SciKit Learn \u7684 IsolationForest \u53ef\u6709\u6548\u8bc6\u522b\u4ea4\u6613\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u60c5\u51b5\u3002<\/p>\n\n\n\n
# 8. Anomaly Detection\ndef anomaly detection (data):\nclf IsolationForest (contamination=0.01)\npreds = clf.fit_predict (data)\nreturn preds<\/code><\/pre>\n\n\n\n
9.\u51b3\u7b56\u6811–\u7b80\u5316\u6295\u8d44\u51b3\u7b56<\/strong><\/h2>\n\n\n\n
\u51b3\u7b56\u6811\uff08Decision Trees\uff09\u5c06\u590d\u6742\u7684\u51b3\u7b56\u5206\u89e3\u6210\u7b80\u5355\u7684\u6b65\u9aa4–\u5c31\u50cf\u6d41\u7a0b\u56fe\u4e00\u6837–\u6765\u51b3\u5b9a\u662f\u4e70\u5165\u3001\u5356\u51fa\u8fd8\u662f\u6301\u6709\u3002<\/p>\n\n\n\n
9.1 \u5e94\u7528\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
\u901a\u8fc7\u5206\u6790\u76c8\u5229\u589e\u957f\u548c\u4f30\u503c\u7b49\u5173\u952e\u56e0\u7d20\u6765\u8bc4\u4f30\u80a1\u7968\u6f5c\u529b\u3002\u5982\u679c\u76c8\u5229\u8868\u73b0\u5f3a\u52b2\uff0c\u679c\u65ad\u4e70\u5165\uff1b\u5982\u679c\u8868\u73b0\u4e0d\u4f73\uff0c\u5219\u9009\u62e9\u6301\u6709\u89c2\u671b\u3002<\/p>\n\n\n\n
9.2 Python \u5de5\u5177<\/strong><\/h3>\n\n\n\n
SciKit Learn \u7684\u51b3\u7b56\u6811\u5206\u7c7b\u5668\u8ba9\u51b3\u7b56\u6811\u7684\u6784\u5efa\u53d8\u5f97\u7b80\u5355\u3002<\/p>\n\n\n\n
# 9. Decision Trees
def decision_tree (train_data, train_labels, test_data):
clf clf
tree. DecisionTreeClassifier()
clf.fit (train_data, train_labels) predictions = clf.predict(test_data) return predictions<\/pre>\n\n\n\n
10.\u795e\u7ecf\u7f51\u7edc–\u6a21\u5f0f\u8bc6\u522b<\/strong><\/h2>\n\n\n\n
\u795e\u7ecf\u7f51\u7edc\uff08Neural networks\uff09\u6a21\u4eff\u4eba\u8111\uff0c\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u6a21\u5f0f\uff0c\u4ece\u800c\u505a\u51fa\u9884\u6d4b\u3002\u5b83\u4eec\u5728\u8bc6\u522b\u80a1\u7968\u4ef7\u683c\u8d8b\u52bf\u65b9\u9762\u975e\u5e38\u5f3a\u5927\u3002<\/p>\n\n\n\n
10.1 \u5e94\u7528\u793a\u4f8b<\/strong><\/h3>\n\n\n\n
\u5206\u6790\u5386\u53f2\u6570\u636e\uff0c\u6839\u636e\u5934\u80a9\u5f62\u6001\u7b49\u6280\u672f\u5f62\u6001\u9884\u6d4b\u5fae\u8f6f\u80a1\u4ef7\u662f\u5426\u4f1a\u4e0a\u6da8\u3002<\/p>\n\n\n\n
10.2 Python \u5de5\u5177<\/strong><\/h3>\n\n\n\n
\u4f7f\u7528 TensorFlow \u6216 PyTorch \u4ece\u96f6\u5f00\u59cb\u6784\u5efa\u795e\u7ecf\u7f51\u7edc\u3002<\/p>\n\n\n\n
# 10. Neural Networks\ndef neural network (train_data, train_labels, test_data):\nmodel\nSequential()\nmodel.add(Dense (12, input_dim=8, activation='relu'))\nmodel.add(Dense (8, activation='relu'))\nmodel.add(Dense (1, activation='sigmoid'))\nmodel.compile (loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(train_data, train_labels, epochs=150, batch_size=10)\npredictions = model.predict(test_data)\nreturn predictions<\/code><\/pre>\n\n\n\n
\u89c2\u70b9\u603b\u7ed3<\/strong><\/h2>\n\n\n\n
\u673a\u5668\u5b66\u4e60\u4e0d\u518d\u662f\u6570\u636e\u79d1\u5b66\u5bb6\u7684\u4e13\u5c5e\u5de5\u5177\u3002\u5982\u4eca\uff0c\u6295\u8d44\u8005\u4e5f\u80fd\u501f\u52a9\u5b83\u7684\u5f3a\u5927\u80fd\u529b\uff0c\u505a\u51fa\u66f4\u7cbe\u51c6\u7684\u51b3\u7b56\u3002\u65e0\u8bba\u662f\u5206\u6790\u5e02\u573a\u60c5\u7eea\u3001\u9884\u6d4b\u4ef7\u683c\u8d70\u52bf\uff0c\u8fd8\u662f\u68c0\u6d4b\u5f02\u5e38\u6570\u636e\uff0cPython \u7684\u5404\u79cd\u5e93\u90fd\u80fd\u8ba9\u8fd9\u4e00\u5207\u53d8\u5f97\u7b80\u5355\u6613\u884c\u3002<\/p>\n\n\n\n