{"id":1174496,"date":"2025-01-15T17:18:54","date_gmt":"2025-01-15T09:18:54","guid":{"rendered":""},"modified":"2025-01-15T17:19:01","modified_gmt":"2025-01-15T09:19:01","slug":"%e5%a6%82%e4%bd%95%e7%9c%8b%e6%87%82python%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1174496.html","title":{"rendered":"\u5982\u4f55\u770b\u61c2python\u795e\u7ecf\u7f51\u7edc"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/26080000\/c3c89d51-ccf4-4248-aef6-82ce63ca0235.webp\" alt=\"\u5982\u4f55\u770b\u61c2python\u795e\u7ecf\u7f51\u7edc\" \/><\/p>\n<p><p> \u770b\u61c2Python\u795e\u7ecf\u7f51\u7edc\u7684\u5173\u952e\u5728\u4e8e\u7406\u89e3\u795e\u7ecf\u7f51\u7edc\u7684\u57fa\u672c\u6982\u5ff5\u3001\u638c\u63e1Python\u7f16\u7a0b\u57fa\u7840\u3001\u719f\u6089\u5e38\u7528\u7684\u795e\u7ecf\u7f51\u7edc\u6846\u67b6\uff08\u5982TensorFlow\u3001Keras\u3001PyTorch\u7b49\uff09\uff0c\u4ee5\u53ca\u4e86\u89e3\u5982\u4f55\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\u548c\u8bc4\u4f30\u3002<strong>\u638c\u63e1\u795e\u7ecf\u7f51\u7edc\u57fa\u672c\u6982\u5ff5\u3001\u719f\u6089Python\u7f16\u7a0b\u57fa\u7840\u3001\u4e86\u89e3\u5e38\u7528\u795e\u7ecf\u7f51\u7edc\u6846\u67b6\u3001\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3\u548c\u8bc4\u4f30<\/strong>\u3002\u5176\u4e2d\uff0c\u638c\u63e1\u795e\u7ecf\u7f51\u7edc\u57fa\u672c\u6982\u5ff5\u662f\u6700\u91cd\u8981\u7684\uff0c\u56e0\u4e3a\u8fd9\u662f\u7406\u89e3\u548c\u6784\u5efa\u795e\u7ecf\u7f51\u7edc\u7684\u57fa\u7840\u3002<\/p>\n<\/p>\n<p><p>\u638c\u63e1\u795e\u7ecf\u7f51\u7edc\u57fa\u672c\u6982\u5ff5\u5305\u62ec\u4e86\u89e3\u795e\u7ecf\u5143\u3001\u5c42\u3001\u6fc0\u6d3b\u51fd\u6570\u3001\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u7b97\u6cd5\u7b49\u3002\u795e\u7ecf\u5143\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u57fa\u672c\u5355\u5143\uff0c\u6bcf\u4e2a\u795e\u7ecf\u5143\u63a5\u6536\u8f93\u5165\u4fe1\u53f7\uff0c\u901a\u8fc7\u6fc0\u6d3b\u51fd\u6570\u8fdb\u884c\u5904\u7406\uff0c\u7136\u540e\u8f93\u51fa\u4fe1\u53f7\u3002\u5c42\u662f\u7531\u591a\u4e2a\u795e\u7ecf\u5143\u7ec4\u6210\u7684\uff0c\u795e\u7ecf\u7f51\u7edc\u901a\u5e38\u7531\u8f93\u5165\u5c42\u3001\u9690\u85cf\u5c42\u548c\u8f93\u51fa\u5c42\u6784\u6210\u3002\u6fc0\u6d3b\u51fd\u6570\u7528\u4e8e\u5f15\u5165\u975e\u7ebf\u6027\uff0c\u5e38\u89c1\u7684\u6709ReLU\u3001Sigmoid\u548cTanh\u7b49\u3002\u635f\u5931\u51fd\u6570\u7528\u4e8e\u8861\u91cf\u6a21\u578b\u9884\u6d4b\u4e0e\u5b9e\u9645\u7ed3\u679c\u4e4b\u95f4\u7684\u5dee\u8ddd\uff0c\u4f18\u5316\u7b97\u6cd5\u5219\u7528\u4e8e\u8c03\u6574\u6a21\u578b\u53c2\u6570\u4ee5\u6700\u5c0f\u5316\u635f\u5931\u51fd\u6570\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u638c\u63e1\u795e\u7ecf\u7f51\u7edc\u57fa\u672c\u6982\u5ff5<\/h3>\n<\/p>\n<p><h4>1\u3001\u795e\u7ecf\u5143\u548c\u5c42<\/h4>\n<\/p>\n<p><p>\u795e\u7ecf\u5143\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u57fa\u672c\u6784\u6210\u5355\u5143\uff0c\u6a21\u62df\u4e86\u751f\u7269\u795e\u7ecf\u5143\u7684\u5de5\u4f5c\u65b9\u5f0f\u3002\u6bcf\u4e2a\u795e\u7ecf\u5143\u63a5\u6536\u8f93\u5165\uff0c\u8fdb\u884c\u52a0\u6743\u6c42\u548c\uff0c\u7136\u540e\u901a\u8fc7\u4e00\u4e2a\u6fc0\u6d3b\u51fd\u6570\u8f93\u51fa\u7ed3\u679c\u3002\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\u7531\u591a\u4e2a\u5c42\u7ec4\u6210\uff0c\u5305\u62ec\u8f93\u5165\u5c42\u3001\u9690\u85cf\u5c42\u548c\u8f93\u51fa\u5c42\u3002<\/p>\n<\/p>\n<p><p>\u8f93\u5165\u5c42\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u7b2c\u4e00\u5c42\uff0c\u76f4\u63a5\u63a5\u6536\u6570\u636e\u8f93\u5165\u3002\u9690\u85cf\u5c42\u4f4d\u4e8e\u8f93\u5165\u5c42\u548c\u8f93\u51fa\u5c42\u4e4b\u95f4\uff0c\u8fdb\u884c\u7279\u5f81\u63d0\u53d6\u548c\u5904\u7406\u3002\u8f93\u51fa\u5c42\u662f\u6700\u540e\u4e00\u5c42\uff0c\u8f93\u51fa\u6700\u7ec8\u7684\u9884\u6d4b\u7ed3\u679c\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u6fc0\u6d3b\u51fd\u6570<\/h4>\n<\/p>\n<p><p>\u6fc0\u6d3b\u51fd\u6570\u5f15\u5165\u975e\u7ebf\u6027\uff0c\u4f7f\u795e\u7ecf\u7f51\u7edc\u80fd\u591f\u5904\u7406\u590d\u6742\u7684\u975e\u7ebf\u6027\u95ee\u9898\u3002\u5e38\u89c1\u7684\u6fc0\u6d3b\u51fd\u6570\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>ReLU\uff08Rectified Linear Unit\uff09<\/strong>\uff1a\u8f93\u51fa\u8f93\u5165\u503c\u7684\u6b63\u90e8\u5206\uff0c\u8d1f\u90e8\u5206\u7f6e\u4e3a\u96f6\uff0c\u8ba1\u7b97\u7b80\u5355\u4e14\u6536\u655b\u5feb\u3002<\/li>\n<li><strong>Sigmoid<\/strong>\uff1a\u8f93\u51fa\u8303\u56f4\u57280\u52301\u4e4b\u95f4\uff0c\u9002\u7528\u4e8e\u4e8c\u5206\u7c7b\u95ee\u9898\uff0c\u4f46\u5728\u68af\u5ea6\u6d88\u5931\u95ee\u9898\u4e0a\u8868\u73b0\u4e0d\u4f73\u3002<\/li>\n<li><strong>Tanh\uff08\u53cc\u66f2\u6b63\u5207\u51fd\u6570\uff09<\/strong>\uff1a\u8f93\u51fa\u8303\u56f4\u5728-1\u52301\u4e4b\u95f4\uff0c\u76f8\u6bd4Sigmoid\u5728\u4e2d\u5fc3\u5bf9\u79f0\u6027\u4e0a\u6709\u6240\u6539\u8fdb\uff0c\u4f46\u4ecd\u5b58\u5728\u68af\u5ea6\u6d88\u5931\u95ee\u9898\u3002<\/li>\n<\/ul>\n<p><h4>3\u3001\u635f\u5931\u51fd\u6570<\/h4>\n<\/p>\n<p><p>\u635f\u5931\u51fd\u6570\u7528\u4e8e\u8861\u91cf\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b\u503c\u4e0e\u771f\u5b9e\u503c\u4e4b\u95f4\u7684\u5dee\u8ddd\uff0c\u5e38\u89c1\u7684\u635f\u5931\u51fd\u6570\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09<\/strong>\uff1a\u7528\u4e8e\u56de\u5f52\u95ee\u9898\uff0c\u8ba1\u7b97\u9884\u6d4b\u503c\u4e0e\u771f\u5b9e\u503c\u4e4b\u95f4\u7684\u5e73\u65b9\u8bef\u5dee\u7684\u5e73\u5747\u503c\u3002<\/li>\n<li><strong>\u4ea4\u53c9\u71b5\u635f\u5931\uff08Cross-Entropy Loss\uff09<\/strong>\uff1a\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\uff0c\u8861\u91cf\u9884\u6d4b\u6982\u7387\u5206\u5e03\u4e0e\u771f\u5b9e\u5206\u5e03\u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/li>\n<\/ul>\n<p><h4>4\u3001\u4f18\u5316\u7b97\u6cd5<\/h4>\n<\/p>\n<p><p>\u4f18\u5316\u7b97\u6cd5\u7528\u4e8e\u8c03\u6574\u795e\u7ecf\u7f51\u7edc\u7684\u53c2\u6570\uff08\u6743\u91cd\u548c\u504f\u7f6e\uff09\uff0c\u4f7f\u635f\u5931\u51fd\u6570\u6700\u5c0f\u5316\u3002\u5e38\u89c1\u7684\u4f18\u5316\u7b97\u6cd5\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u68af\u5ea6\u4e0b\u964d\u6cd5\uff08Gradient Descent\uff09<\/strong>\uff1a\u901a\u8fc7\u8ba1\u7b97\u635f\u5931\u51fd\u6570\u76f8\u5bf9\u4e8e\u53c2\u6570\u7684\u68af\u5ea6\uff0c\u6cbf\u7740\u68af\u5ea6\u7684\u53cd\u65b9\u5411\u8c03\u6574\u53c2\u6570\u3002<\/li>\n<li><strong>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u6cd5\uff08SGD\uff09<\/strong>\uff1a\u6bcf\u6b21\u4ec5\u4f7f\u7528\u4e00\u4e2a\u6837\u672c\u66f4\u65b0\u53c2\u6570\uff0c\u8ba1\u7b97\u6548\u7387\u66f4\u9ad8\uff0c\u4f46\u6536\u655b\u901f\u5ea6\u8f83\u6162\u3002<\/li>\n<li><strong>Adam<\/strong>\uff1a\u7ed3\u5408\u4e86\u52a8\u91cf\u6cd5\u548cRMSprop\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3\u4efb\u52a1\u3002<\/li>\n<\/ul>\n<p><h3>\u4e8c\u3001\u719f\u6089Python\u7f16\u7a0b\u57fa\u7840<\/h3>\n<\/p>\n<p><h4>1\u3001Python\u57fa\u7840\u77e5\u8bc6<\/h4>\n<\/p>\n<p><p>Python\u662f\u4e00\u79cd\u9ad8\u7ea7\u7f16\u7a0b\u8bed\u8a00\uff0c\u5177\u6709\u7b80\u6d01\u3001\u6613\u8bfb\u7684\u8bed\u6cd5\uff0c\u9002\u5408\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u4efb\u52a1\u3002\u5b66\u4e60Python\u7f16\u7a0b\u57fa\u7840\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u6570\u636e\u7c7b\u578b<\/strong>\uff1a\u5982\u6574\u6570\u3001\u6d6e\u70b9\u6570\u3001\u5b57\u7b26\u4e32\u3001\u5217\u8868\u3001\u5b57\u5178\u7b49\u3002<\/li>\n<li><strong>\u63a7\u5236\u7ed3\u6784<\/strong>\uff1a\u5982\u6761\u4ef6\u8bed\u53e5\uff08if-else\uff09\u3001\u5faa\u73af\u8bed\u53e5\uff08for\u3001while\uff09\u7b49\u3002<\/li>\n<li><strong>\u51fd\u6570\u548c\u6a21\u5757<\/strong>\uff1a\u5b9a\u4e49\u548c\u4f7f\u7528\u51fd\u6570\uff0c\u5bfc\u5165\u548c\u4f7f\u7528\u6a21\u5757\u3002<\/li>\n<\/ul>\n<p><h4>2\u3001NumPy\u548cPandas<\/h4>\n<\/p>\n<p><p>NumPy\u548cPandas\u662fPython\u4e2d\u5e38\u7528\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u719f\u7ec3\u638c\u63e1\u5b83\u4eec\u5bf9\u4e8e\u5904\u7406\u6570\u636e\u548c\u6784\u5efa\u795e\u7ecf\u7f51\u7edc\u975e\u5e38\u91cd\u8981\u3002<\/p>\n<\/p>\n<ul>\n<li><strong>NumPy<\/strong>\uff1a\u63d0\u4f9b\u9ad8\u6548\u7684\u591a\u7ef4\u6570\u7ec4\u64cd\u4f5c\uff0c\u5e38\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u548c\u77e9\u9635\u8fd0\u7b97\u3002<\/li>\n<li><strong>Pandas<\/strong>\uff1a\u63d0\u4f9b\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u5e38\u7528\u4e8e\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\u3002<\/li>\n<\/ul>\n<p><h3>\u4e09\u3001\u4e86\u89e3\u5e38\u7528\u795e\u7ecf\u7f51\u7edc\u6846\u67b6<\/h3>\n<\/p>\n<p><h4>1\u3001TensorFlow<\/h4>\n<\/p>\n<p><p>TensorFlow\u662f\u7531Google\u5f00\u53d1\u7684\u5f00\u6e90\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u5177\u6709\u9ad8\u5ea6\u7684\u7075\u6d3b\u6027\u548c\u6269\u5c55\u6027\u3002\u5176\u6838\u5fc3\u7ec4\u4ef6\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u5f20\u91cf\uff08Tensor\uff09<\/strong>\uff1a\u591a\u7ef4\u6570\u7ec4\uff0c\u662fTensorFlow\u7684\u57fa\u672c\u6570\u636e\u7ed3\u6784\u3002<\/li>\n<li><strong>\u8ba1\u7b97\u56fe\uff08Computational Graph\uff09<\/strong>\uff1a\u8868\u793a\u8ba1\u7b97\u8fc7\u7a0b\u7684\u6709\u5411\u56fe\uff0c\u6bcf\u4e2a\u8282\u70b9\u8868\u793a\u64cd\u4f5c\uff0c\u6bcf\u6761\u8fb9\u8868\u793a\u6570\u636e\u6d41\u3002<\/li>\n<\/ul>\n<p><p>\u4f7f\u7528TensorFlow\u53ef\u4ee5\u6784\u5efa\u3001\u8bad\u7ec3\u548c\u90e8\u7f72\u5404\u79cd\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002<\/p>\n<\/p>\n<p><h4>2\u3001Keras<\/h4>\n<\/p>\n<p><p>Keras\u662f\u4e00\u4e2a\u9ad8\u7ea7\u795e\u7ecf\u7f51\u7edcAPI\uff0c\u57fa\u4e8eTensorFlow\u6784\u5efa\uff0c\u63d0\u4f9b\u7b80\u6d01\u6613\u7528\u7684\u63a5\u53e3\uff0c\u9002\u5408\u5feb\u901f\u6784\u5efa\u548c\u5b9e\u9a8c\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002\u5176\u6838\u5fc3\u7ec4\u4ef6\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u6a21\u578b\uff08Model\uff09<\/strong>\uff1a\u795e\u7ecf\u7f51\u7edc\u7684\u5bb9\u5668\uff0c\u53ef\u4ee5\u901a\u8fc7Sequential\u6216Functional API\u6784\u5efa\u3002<\/li>\n<li><strong>\u5c42\uff08Layer\uff09<\/strong>\uff1a\u795e\u7ecf\u7f51\u7edc\u7684\u57fa\u672c\u6784\u6210\u5355\u5143\uff0c\u5982Dense\u3001Conv2D\u3001LSTM\u7b49\u3002<\/li>\n<\/ul>\n<p><h4>3\u3001PyTorch<\/h4>\n<\/p>\n<p><p>PyTorch\u662f\u7531Facebook\u5f00\u53d1\u7684\u5f00\u6e90\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u4ee5\u52a8\u6001\u8ba1\u7b97\u56fe\u548c\u7075\u6d3b\u7684\u63a5\u53e3\u8457\u79f0\u3002\u5176\u6838\u5fc3\u7ec4\u4ef6\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u5f20\u91cf\uff08Tensor\uff09<\/strong>\uff1a\u591a\u7ef4\u6570\u7ec4\uff0c\u4e0eNumPy\u517c\u5bb9\uff0c\u652f\u6301\u81ea\u52a8\u5fae\u5206\u3002<\/li>\n<li><strong>\u6a21\u5757\uff08Module\uff09<\/strong>\uff1a\u795e\u7ecf\u7f51\u7edc\u7684\u5bb9\u5668\uff0c\u5305\u542b\u53c2\u6570\u548c\u5b50\u6a21\u5757\u3002<\/li>\n<\/ul>\n<p><p>\u4f7f\u7528PyTorch\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6a21\u578b\u5b9a\u4e49\u3001\u8bad\u7ec3\u548c\u8c03\u8bd5\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3\u548c\u8bc4\u4f30<\/h3>\n<\/p>\n<p><h4>1\u3001\u6570\u636e\u9884\u5904\u7406<\/h4>\n<\/p>\n<p><p>\u6570\u636e\u9884\u5904\u7406\u662f\u6a21\u578b\u8bad\u7ec3\u7684\u91cd\u8981\u6b65\u9aa4\uff0c\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u63d0\u53d6\u548c\u6807\u51c6\u5316\u7b49\u3002\u5e38\u89c1\u7684\u6570\u636e\u9884\u5904\u7406\u65b9\u6cd5\u6709\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u7f3a\u5931\u503c\u5904\u7406<\/strong>\uff1a\u586b\u8865\u6216\u5220\u9664\u7f3a\u5931\u6570\u636e\u3002<\/li>\n<li><strong>\u7279\u5f81\u63d0\u53d6<\/strong>\uff1a\u4ece\u539f\u59cb\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u7528\u7684\u7279\u5f81\uff0c\u5982\u6587\u672c\u7279\u5f81\u63d0\u53d6\u3001\u56fe\u50cf\u7279\u5f81\u63d0\u53d6\u7b49\u3002<\/li>\n<li><strong>\u6807\u51c6\u5316<\/strong>\uff1a\u5c06\u6570\u636e\u7f29\u653e\u5230\u76f8\u540c\u7684\u8303\u56f4\uff0c\u5982\u5f52\u4e00\u5316\u3001\u6807\u51c6\u5316\u7b49\u3002<\/li>\n<\/ul>\n<p><h4>2\u3001\u6a21\u578b\u8bad\u7ec3<\/h4>\n<\/p>\n<p><p>\u6a21\u578b\u8bad\u7ec3\u662f\u6307\u901a\u8fc7\u4f18\u5316\u7b97\u6cd5\u8c03\u6574\u6a21\u578b\u53c2\u6570\uff0c\u4f7f\u635f\u5931\u51fd\u6570\u6700\u5c0f\u5316\u7684\u8fc7\u7a0b\u3002\u5e38\u89c1\u7684\u8bad\u7ec3\u6b65\u9aa4\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u6570\u636e\u96c6\u5212\u5206<\/strong>\uff1a\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u3002<\/li>\n<li><strong>\u6a21\u578b\u5b9a\u4e49<\/strong>\uff1a\u6784\u5efa\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff0c\u5305\u62ec\u8f93\u5165\u5c42\u3001\u9690\u85cf\u5c42\u548c\u8f93\u51fa\u5c42\u3002<\/li>\n<li><strong>\u53c2\u6570\u521d\u59cb\u5316<\/strong>\uff1a\u521d\u59cb\u5316\u6a21\u578b\u53c2\u6570\uff0c\u5982\u6743\u91cd\u548c\u504f\u7f6e\u3002<\/li>\n<li><strong>\u524d\u5411\u4f20\u64ad<\/strong>\uff1a\u8ba1\u7b97\u6a21\u578b\u7684\u8f93\u51fa\u548c\u635f\u5931\u51fd\u6570\u503c\u3002<\/li>\n<li><strong>\u53cd\u5411\u4f20\u64ad<\/strong>\uff1a\u8ba1\u7b97\u635f\u5931\u51fd\u6570\u76f8\u5bf9\u4e8e\u53c2\u6570\u7684\u68af\u5ea6\uff0c\u66f4\u65b0\u53c2\u6570\u3002<\/li>\n<li><strong>\u8fed\u4ee3\u8bad\u7ec3<\/strong>\uff1a\u901a\u8fc7\u591a\u6b21\u8fed\u4ee3\uff0c\u9010\u6b65\u4f18\u5316\u6a21\u578b\u53c2\u6570\u3002<\/li>\n<\/ul>\n<p><h4>3\u3001\u6a21\u578b\u8bc4\u4f30<\/h4>\n<\/p>\n<p><p>\u6a21\u578b\u8bc4\u4f30\u662f\u8861\u91cf\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u6b65\u9aa4\uff0c\u5e38\u89c1\u7684\u8bc4\u4f30\u6307\u6807\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u51c6\u786e\u7387\uff08Accuracy\uff09<\/strong>\uff1a\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\uff0c\u8861\u91cf\u6a21\u578b\u9884\u6d4b\u6b63\u786e\u7684\u6837\u672c\u6bd4\u4f8b\u3002<\/li>\n<li><strong>\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09<\/strong>\uff1a\u7528\u4e8e\u56de\u5f52\u95ee\u9898\uff0c\u8861\u91cf\u9884\u6d4b\u503c\u4e0e\u771f\u5b9e\u503c\u4e4b\u95f4\u7684\u8bef\u5dee\u3002<\/li>\n<li><strong>F1-score<\/strong>\uff1a\u7efc\u5408\u8003\u8651\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\uff0c\u9002\u7528\u4e8e\u4e0d\u5e73\u8861\u6570\u636e\u96c6\u3002<\/li>\n<\/ul>\n<p><p>\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u7b49\u65b9\u6cd5\uff0c\u53ef\u4ee5\u66f4\u5168\u9762\u5730\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u548c\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u6df1\u5165\u7406\u89e3\u795e\u7ecf\u7f51\u7edc\u7684\u9ad8\u7ea7\u6982\u5ff5<\/h3>\n<\/p>\n<p><h4>1\u3001\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09<\/h4>\n<\/p>\n<p><p>\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08Convolutional Neural Network\uff0cCNN\uff09\u662f\u4e00\u79cd\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u56fe\u50cf\u6570\u636e\u7684\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u3002\u5176\u6838\u5fc3\u7ec4\u4ef6\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u5377\u79ef\u5c42\uff08Convolutional Layer\uff09<\/strong>\uff1a\u901a\u8fc7\u5377\u79ef\u64cd\u4f5c\u63d0\u53d6\u56fe\u50cf\u7684\u5c40\u90e8\u7279\u5f81\u3002<\/li>\n<li><strong>\u6c60\u5316\u5c42\uff08Pooling Layer\uff09<\/strong>\uff1a\u901a\u8fc7\u4e0b\u91c7\u6837\u64cd\u4f5c\u51cf\u5c0f\u7279\u5f81\u56fe\u7684\u5c3a\u5bf8\uff0c\u4fdd\u7559\u91cd\u8981\u7279\u5f81\u3002<\/li>\n<li><strong>\u5168\u8fde\u63a5\u5c42\uff08Fully Connected Layer\uff09<\/strong>\uff1a\u5c06\u63d0\u53d6\u7684\u7279\u5f81\u6620\u5c04\u5230\u8f93\u51fa\u7a7a\u95f4\u3002<\/li>\n<\/ul>\n<p><p>CNN\u5728\u56fe\u50cf\u5206\u7c7b\u3001\u76ee\u6807\u68c0\u6d4b\u548c\u56fe\u50cf\u5206\u5272\u7b49\u4efb\u52a1\u4e2d\u8868\u73b0\u4f18\u5f02\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\uff08RNN\uff09<\/h4>\n<\/p>\n<p><p>\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\uff08Recurrent Neural Network\uff0cRNN\uff09\u662f\u4e00\u79cd\u9002\u7528\u4e8e\u5904\u7406\u5e8f\u5217\u6570\u636e\u7684\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u3002\u5176\u6838\u5fc3\u7ec4\u4ef6\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u5faa\u73af\u5355\u5143\uff08Recurrent 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