\u7279\u5f81\u9009\u62e9<\/strong>\uff1a\u903b\u8f91\u56de\u5f52\u5bf9\u4e8e\u4e0d\u76f8\u5173\u7684\u7279\u5f81\u548c\u7279\u5f81\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u6bd4\u8f83\u654f\u611f\u3002<\/li>\n<\/ol>\n\u901a\u8fc7\u8fd9\u4e2a\u7ae0\u8282\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u903b\u8f91\u56de\u5f52\u5728\u7b80\u6d01\u6027\u548c\u89e3\u91ca\u6027\u65b9\u9762\u6709\u7740\u663e\u8457\u7684\u4f18\u70b9\uff0c\u4f46\u540c\u65f6\u4e5f\u5b58\u5728\u4e00\u5b9a\u7684\u5c40\u9650\u6027\u3002<\/p>\n
\n\u4e09\u3001\u6570\u5b66\u539f\u7406<\/h1>\n
![\"img\"](\"https:\/\/lemon-guess.oss-cn-hangzhou.aliyuncs.com\/img\/v2-4cb19a6060dcda5e8c121c7350cb396b_1440w.jpg\")
<\/p>\n
\u7406\u89e3\u903b\u8f91\u56de\u5f52\u80cc\u540e\u7684\u6570\u5b66\u539f\u7406\u662f\u638c\u63e1\u8fd9\u4e00\u7b97\u6cd5\u7684\u5173\u952e\u3002\u8fd9\u90e8\u5206\u5c06\u6df1\u5165\u89e3\u6790\u903b\u8f91\u56de\u5f52\u7684\u6570\u5b66\u7ed3\u6784\uff0c\u5305\u62ec\u6982\u7387\u6a21\u578b\u3001\u635f\u5931\u51fd\u6570\u4f18\u5316\u548c\u7279\u5f81\u9009\u62e9\u3002<\/p>\n
\u6982\u7387\u6a21\u578b<\/h2>\n
![\"img\"](\"https:\/\/lemon-guess.oss-cn-hangzhou.aliyuncs.com\/img\/v2-584812f0655963787239c727c4ed036e_1440w.jpg\")
<\/p>\n
\u4e3e\u4f8b\uff1a\u4fe1\u7528\u5361\u4ea4\u6613<\/h3>\n
\u60f3\u8c61\u4f60\u6b63\u5728\u5f00\u53d1\u4e00\u4e2a\u7528\u4e8e\u68c0\u6d4b\u4fe1\u7528\u5361\u6b3a\u8bc8\u4ea4\u6613\u7684\u6a21\u578b\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c(X) \u53ef\u80fd\u5305\u62ec\u4ea4\u6613\u91d1\u989d\u3001\u5730\u70b9\u3001\u65f6\u95f4\u7b49\u7279\u5f81\uff0c\u6a21\u578b\u4f1a\u8f93\u51fa\u8fd9\u7b14\u4ea4\u6613\u662f\u6b3a\u8bc8\u4ea4\u6613\u7684\u6982\u7387\u3002<\/p>\n
\u635f\u5931\u51fd\u6570\u4e0e\u6700\u5927\u4f3c\u7136\u4f30\u8ba1<\/h2>\n
\u6700\u5e38\u7528\u4e8e\u903b\u8f91\u56de\u5f52\u7684\u635f\u5931\u51fd\u6570\u662f\u4ea4\u53c9\u71b5\u635f\u5931\u3002\u8fd9\u5176\u5b9e\u662f\u6700\u5927\u4f3c\u7136\u4f30\u8ba1\uff08MLE\uff09\u5728\u903b\u8f91\u56de\u5f52\u4e2d\u7684\u5177\u4f53\u5e94\u7528\u3002<\/p>\n
![\"img\"](\"https:\/\/lemon-guess.oss-cn-hangzhou.aliyuncs.com\/img\/v2-2fac6eb9755b9988e73483336010334d_1440w.jpg\")
<\/p>\n
\u4e3e\u4f8b\uff1a\u7535\u5b50\u90ae\u4ef6\u5206\u7c7b<\/h3>\n
\u5047\u8bbe\u4f60\u6b63\u5728\u6784\u5efa\u4e00\u4e2a\u7535\u5b50\u90ae\u4ef6\u5206\u7c7b\u5668\u6765\u533a\u5206\u5783\u573e\u90ae\u4ef6\u548c\u6b63\u5e38\u90ae\u4ef6\u3002\u4f7f\u7528\u4ea4\u53c9\u71b5\u635f\u5931\u51fd\u6570\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7\u6700\u5927\u5316\u4f3c\u7136\u51fd\u6570\u6765\u201c\u6559\u201d\u6a21\u578b\u5982\u4f55\u66f4\u51c6\u786e\u5730\u8fdb\u884c\u5206\u7c7b\u3002<\/p>\n
\u68af\u5ea6\u4e0b\u964d\u4f18\u5316<\/h2>\n
![\"img\"](\"https:\/\/lemon-guess.oss-cn-hangzhou.aliyuncs.com\/img\/v2-b3864e551c07406fc31d2e24e2662d8d_1440w.jpg\")
<\/p>\n
\u4e3e\u4f8b\uff1a\u80a1\u7968\u4ef7\u683c\u9884\u6d4b<\/h3>\n
\u867d\u7136\u903b\u8f91\u56de\u5f52\u901a\u5e38\u4e0d\u7528\u4e8e\u56de\u5f52\u95ee\u9898\uff0c\u4f46\u68af\u5ea6\u4e0b\u964d\u7684\u4f18\u5316\u7b97\u6cd5\u5728\u5f88\u591a\u5176\u4ed6\u7c7b\u578b\u7684\u95ee\u9898\u4e2d\u4e5f\u662f\u901a\u7528\u7684\u3002\u4f8b\u5982\uff0c\u5728\u9884\u6d4b\u80a1\u7968\u4ef7\u683c\u65f6\uff0c\u540c\u6837\u53ef\u4ee5\u4f7f\u7528\u68af\u5ea6\u4e0b\u964d\u6765\u4f18\u5316\u6a21\u578b\u53c2\u6570\u3002<\/p>\n
\u7279\u5f81\u9009\u62e9\u4e0e\u6b63\u5219\u5316<\/h2>\n
\u7279\u5f81\u9009\u62e9\u5728\u903b\u8f91\u56de\u5f52\u4e2d\u975e\u5e38\u91cd\u8981\uff0c\u56e0\u4e3a\u4e0d\u76f8\u5173\u6216\u5197\u4f59\u7684\u7279\u5f81\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6a21\u578b\u6027\u80fd\u4e0b\u964d\u3002\u6b63\u5219\u5316\u662f\u4e00\u79cd\u7528\u4e8e\u9632\u6b62\u8fc7\u62df\u5408\u7684\u6280\u672f\uff0c\u5e38\u89c1\u7684\u6b63\u5219\u5316\u65b9\u6cd5\u5305\u62ec L1 \u6b63\u5219\u5316\u548c L2 \u6b63\u5219\u5316\u3002<\/p>\n
![\"img\"](\"https:\/\/lemon-guess.oss-cn-hangzhou.aliyuncs.com\/img\/v2-d8ec8e77a2e0e448ff4a531afaa8ecd6_1440w.jpg\")
<\/p>\n
\u4e3e\u4f8b\uff1a\u623f\u4ef7\u9884\u6d4b<\/h3>\n
\u5728\u623f\u4ef7\u9884\u6d4b\u6a21\u578b\u4e2d\uff0c\u53ef\u80fd\u6709\u5f88\u591a\u76f8\u5173\u548c\u4e0d\u76f8\u5173\u7684\u7279\u5f81\uff0c\u5982\u9762\u79ef\u3001\u5730\u6bb5\u3001\u5468\u56f4\u5b66\u6821\u6570\u91cf\u7b49\u3002\u901a\u8fc7\u4f7f\u7528\u6b63\u5219\u5316\uff0c\u4f60\u53ef\u4ee5\u786e\u4fdd\u6a21\u578b\u5728\u62df\u5408\u8fd9\u4e9b\u7279\u5f81\u65f6\u4e0d\u4f1a\u8fc7\u4e8e\u590d\u6742\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n
\u901a\u8fc7\u672c\u7ae0\u7684\u8ba8\u8bba\uff0c\u6211\u4eec\u4e0d\u4ec5\u6df1\u5165\u4e86\u89e3\u4e86\u903b\u8f91\u56de\u5f52\u7684\u6570\u5b66\u57fa\u7840\uff0c\u8fd8\u901a\u8fc7\u5177\u4f53\u7684\u4f8b\u5b50\u548c\u5e94\u7528\u573a\u666f\uff0c\u8ba9\u8fd9\u4e9b\u770b\u4f3c\u590d\u6742\u7684\u6570\u5b66\u6982\u5ff5\u66f4\u52a0\u8d34\u8fd1\u5b9e\u9645\uff0c\u6613\u4e8e\u7406\u89e3\u3002\u8fd9\u6709\u52a9\u4e8e\u6211\u4eec\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u66f4\u52a0\u7075\u6d3b\u5730\u4f7f\u7528\u903b\u8f91\u56de\u5f52\uff0c\u4ee5\u89e3\u51b3\u5404\u79cd\u5206\u7c7b\u95ee\u9898\u3002<\/p>\n
\n\u56db\u3001\u5b9e\u6218\u6848\u4f8b<\/h1>\n
![\"img\"](\"https:\/\/lemon-guess.oss-cn-hangzhou.aliyuncs.com\/img\/v2-705494ab13e82976d5fe83a7f9925341_1440w.jpg\")
<\/p>\n
\u5b9e\u6218\u662f\u5b66\u4e60\u903b\u8f91\u56de\u5f52\u7684\u6700\u4f73\u65b9\u5f0f\u3002\u5728\u8fd9\u4e00\u90e8\u5206\uff0c\u6211\u4eec\u5c06\u4f7f\u7528Python\u548cPyTorch\u5e93\u6765\u5b9e\u73b0\u4e00\u4e2a\u5b8c\u6574\u7684\u903b\u8f91\u56de\u5f52\u6a21\u578b\u3002\u6211\u4eec\u5c06\u4f7f\u7528\u7ecf\u5178\u7684\u9e22\u5c3e\u82b1\uff08Iris\uff09\u6570\u636e\u96c6\uff0c\u8be5\u6570\u636e\u96c6\u5305\u62ec\u56db\u4e2a\u7279\u5f81\uff1a\u843c\u7247\u957f\u5ea6\u3001\u843c\u7247\u5bbd\u5ea6\u3001\u82b1\u74e3\u957f\u5ea6\u3001\u82b1\u74e3\u5bbd\u5ea6\uff0c\u4ee5\u53ca\u4e00\u4e2a\u6807\u7b7e\uff0c\u7528\u4e8e\u533a\u5206\u4e09\u79cd\u4e0d\u540c\u7c7b\u578b\u7684\u9e22\u5c3e\u82b1\u3002<\/p>\n
\u6570\u636e\u51c6\u5907<\/h2>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u52a0\u8f7d\u548c\u51c6\u5907\u6570\u636e\u3002<\/p>\n
# \u5bfc\u5165\u6240\u9700\u5e93\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\n\n# \u52a0\u8f7d\u6570\u636e\niris = load_iris()\nX, y = iris.data, iris.target\n\n# \u7531\u4e8e\u903b\u8f91\u56de\u5f52\u662f\u4e8c\u5206\u7c7b\u6a21\u578b\uff0c\u6211\u4eec\u53ea\u53d6\u5176\u4e2d\u4e24\u7c7b\u6570\u636e\nX, y = X[y != 2], y[y != 2]\n\n# \u6570\u636e\u5206\u5272\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \u8f6c\u6362\u4e3aPyTorch\u5f20\u91cf\nX_train = torch.FloatTensor(X_train)\nX_test = torch.FloatTensor(X_test)\ny_train = torch.LongTensor(y_train)\ny_test = torch.LongTensor(y_test)<\/code><\/pre>\n\u6a21\u578b\u6784\u5efa<\/h2>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5b9a\u4e49\u903b\u8f91\u56de\u5f52\u6a21\u578b\u3002<\/p>\n
class LogisticRegression(nn.Module):\n def __init__(self, input_dim):\n super(LogisticRegression, self).__init__()\n self.linear = nn.Linear(input_dim, 1)\n self.sigmoid = nn.Sigmoid()\n\n def forward(self, x):\n return self.sigmoid(self.linear(x))<\/code><\/pre>\n\u6a21\u578b\u8bad\u7ec3<\/h2>\n
\u73b0\u5728\u6211\u4eec\u53ef\u4ee5\u5f00\u59cb\u8bad\u7ec3\u6a21\u578b\u3002<\/p>\n
# \u521d\u59cb\u5316\u6a21\u578b\u3001\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u5668\nmodel = LogisticRegression(X_train.shape[1])\ncriterion = nn.BCELoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\n# \u8bad\u7ec3\u6a21\u578b\nfor epoch in range(1000):\n model.train()\n optimizer.zero_grad()\n\n # \u524d\u5411\u4f20\u64ad\n outputs = model(X_train).squeeze()\n loss = criterion(outputs, y_train.float())\n\n # \u53cd\u5411\u4f20\u64ad\u548c\u4f18\u5316\n loss.backward()\n optimizer.step()\n\n if (epoch + 1) % 100 == 0:\n print(f'Epoch [{epoch+1}\/1000], Loss: {loss.item()}')<\/code><\/pre>\n\u6a21\u578b\u8bc4\u4f30<\/h2>\n
\u6700\u540e\uff0c\u6211\u4eec\u7528\u6d4b\u8bd5\u96c6\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n
# \u6d4b\u8bd5\u6a21\u578b\nmodel.eval()\nwith torch.no_grad():\n test_outputs = model(X_test).squeeze()\n test_outputs = (test_outputs > 0.5).long()\n accuracy = (test_outputs == y_test).float().mean()\n print(f'Accuracy: {accuracy.item()}')<\/code><\/pre>\n\u5728\u8fd9\u4e2a\u5b9e\u6218\u6848\u4f8b\u4e2d\uff0c\u6211\u4eec\u5b8c\u6574\u5730\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528PyTorch\u6765\u6784\u5efa\u3001\u8bad\u7ec3\u548c\u8bc4\u4f30\u4e00\u4e2a\u903b\u8f91\u56de\u5f52\u6a21\u578b\u3002\u6211\u4eec\u4f7f\u7528\u4e86\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\uff0c\u4f46\u8fd9\u4e2a\u6846\u67b6\u53ef\u4ee5\u65b9\u4fbf\u5730\u5e94\u7528\u5230\u5176\u4ed6\u4e8c\u5206\u7c7b\u95ee\u9898\u4e0a\u3002<\/p>\n
\n\u4e94\u3001\u603b\u7ed3<\/h1>\n
\u5728\u672c\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5168\u9762\u3001\u6df1\u5165\u5730\u63a2\u8ba8\u4e86\u903b\u8f91\u56de\u5f52\u8fd9\u4e00\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002\u4ece\u57fa\u7840\u6982\u5ff5\u5230\u6570\u5b66\u539f\u7406\uff0c\u518d\u5230\u5b9e\u6218\u5e94\u7528\u3002<\/p>\n
\n- \u6982\u7387\u6a21\u578b\u7684\u5e7f\u6cdb\u5e94\u7528<\/strong>\uff1a\u903b\u8f91\u56de\u5f52\u901a\u5e38\u88ab\u8ba4\u4e3a\u53ea\u9002\u7528\u4e8e\u4e8c\u5206\u7c7b\u95ee\u9898\uff0c\u4f46\u5176\u5b9e\uff0c\u5b83\u53ef\u4ee5\u4f5c\u4e3a\u4e00\u4e2a\u6982\u7387\u6a21\u578b\u7528\u4e8e\u591a\u4e2a\u9886\u57df\u3002\u4f8b\u5982\uff0c\u5728\u63a8\u8350\u7cfb\u7edf\u4e2d\uff0c\u903b\u8f91\u56de\u5f52\u53ef\u4ee5\u7528\u4e8e\u4f30\u7b97\u7528\u6237\u70b9\u51fb\u67d0\u4e2a\u4ea7\u54c1\u7684\u6982\u7387\u3002<\/li>\n
- \u635f\u5931\u51fd\u6570\u4e0e\u4f18\u5316\u7684\u590d\u6742\u6027<\/strong>\uff1a\u867d\u7136\u903b\u8f91\u56de\u5f52\u672c\u8eab\u662f\u4e00\u4e2a\u7ebf\u6027\u6a21\u578b\uff0c\u4f46\u8981\u4f18\u5316\u5b83\u6d89\u53ca\u5230\u7684\u6570\u5b66\u5374\u5e76\u4e0d\u7b80\u5355\u3002\u8fd9\u53cd\u6620\u4e86\u4e00\u4e2a\u666e\u904d\u73b0\u8c61\uff1a\u5373\u4f7f\u662f\u6700\u57fa\u7840\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u4e5f\u53ef\u4ee5\u4e0e\u590d\u6742\u7684\u6570\u5b66\u7ed3\u6784\u76f8\u8fde\u63a5\u3002<\/li>\n
- \u7279\u5f81\u9009\u62e9\u4e0e\u6b63\u5219\u5316<\/strong>\uff1a\u5728\u73b0\u5b9e\u4e16\u754c\u7684\u6570\u636e\u79d1\u5b66\u9879\u76ee\u4e2d\uff0c\u7279\u5f81\u9009\u62e9\u548c\u6b63\u5219\u5316\u5f80\u5f80\u6bd4\u6a21\u578b\u9009\u62e9\u66f4\u4e3a\u5173\u952e\u3002\u4e00\u4e2a\u597d\u7684\u7279\u5f81\u5de5\u7a0b\u548c\u6b63\u5219\u5316\u7b56\u7565\u53ef\u4ee5\u663e\u8457\u63d0\u5347\u6a21\u578b\u6027\u80fd\u3002<\/li>\n
- \u903b\u8f91\u56de\u5f52\u4e0e\u6df1\u5ea6\u5b66\u4e60<\/strong>\uff1a\u5c3d\u7ba1\u6df1\u5ea6\u5b66\u4e60\u5728\u8bb8\u591a\u4efb\u52a1\u4e0a\u8868\u73b0\u51fa\u8272\uff0c\u4f46\u4e0d\u5e94\u5ffd\u89c6\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u4ef7\u503c\u3002\u903b\u8f91\u56de\u5f52\u5728\u8ba1\u7b97\u8d44\u6e90\u6709\u9650\u6216\u6570\u636e\u96c6\u8f83\u5c0f\u7684\u573a\u666f\u4e2d\uff0c\u5f80\u5f80\u80fd\u66f4\u5feb\u5730\u8fbe\u5230\u4ee4\u4eba\u6ee1\u610f\u7684\u6027\u80fd\u3002<\/li>\n<\/ol>\n
\u901a\u8fc7\u6df1\u5165\u5206\u6790\u548c\u5b9e\u6218\u5e94\u7528\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u903b\u8f91\u56de\u5f52\u5e76\u4e0d\u662f\u4e00\u4e2a\u201c\u7b80\u5355\u201d\u7684\u7b97\u6cd5\uff0c\u800c\u662f\u4e00\u4e2a\u65e2\u5b9e\u7528\u53c8\u6df1\u523b\u7684\u5de5\u5177\uff0c\u5176\u80cc\u540e\u8574\u85cf\u7740\u4e30\u5bcc\u7684\u6570\u5b66\u539f\u7406\u548c\u5b9e\u9645\u5e94\u7528\u6f5c\u529b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
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