{"id":998412,"date":"2024-12-27T09:33:05","date_gmt":"2024-12-27T01:33:05","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/998412.html"},"modified":"2024-12-27T09:33:07","modified_gmt":"2024-12-27T01:33:07","slug":"%e5%a6%82%e4%bd%95%e6%99%ba%e8%83%bd%e8%af%86%e5%88%ab%e5%9b%be%e7%89%87python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/998412.html","title":{"rendered":"\u5982\u4f55\u667a\u80fd\u8bc6\u522b\u56fe\u7247python"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25073839\/3878cb60-1e95-42d7-a6ff-2e1a8d1daf8a.webp\" alt=\"\u5982\u4f55\u667a\u80fd\u8bc6\u522b\u56fe\u7247python\" \/><\/p>\n<p><p> <strong>\u667a\u80fd\u8bc6\u522b\u56fe\u7247\u5728Python\u4e2d\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3001\u56fe\u50cf\u5904\u7406\u5e93\u3001\u9884\u8bad\u7ec3\u6a21\u578b\u7b49\u6765\u5b9e\u73b0\u3002\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff08\u5982\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff0cCNN\uff09\u3001\u56fe\u50cf\u5904\u7406\u5e93\uff08\u5982OpenCV\uff09\u548c\u9884\u8bad\u7ec3\u6a21\u578b\uff08\u5982TensorFlow\u7684Inception\u6216PyTorch\u7684ResNet\uff09\u662f\u5b9e\u73b0\u56fe\u50cf\u8bc6\u522b\u7684\u5173\u952e\u3002<\/strong>\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u662f\u56fe\u50cf\u8bc6\u522b\u7684\u6838\u5fc3\uff0c\u56e0\u4e3a\u5b83\u4eec\u80fd\u591f\u5b66\u4e60\u548c\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u590d\u6742\u7279\u5f81\u3002\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u5c24\u5176\u64c5\u957f\u5904\u7406\u56fe\u50cf\u6570\u636e\uff0c\u56e0\u4e3a\u5b83\u4eec\u53ef\u4ee5\u6709\u6548\u5730\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u7a7a\u95f4\u5c42\u6b21\u7ed3\u6784\u3002\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u4f60\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u81ea\u5b9a\u4e49\u7684\u56fe\u50cf\u8bc6\u522b\u6a21\u578b\uff0c\u6216\u5229\u7528\u9884\u8bad\u7ec3\u6a21\u578b\u8fdb\u884c\u8fc1\u79fb\u5b66\u4e60\uff0c\u4ee5\u5b9e\u73b0\u9ad8\u6548\u7684\u56fe\u50cf\u5206\u7c7b\u548c\u8bc6\u522b\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u4e0e\u5377\u79ef\u795e\u7ecf\u7f51\u7edc<\/p>\n<\/p>\n<p><p>\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u662f\u7528\u4e8e\u56fe\u50cf\u8bc6\u522b\u7684\u6700\u5e38\u7528\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u4e4b\u4e00\u3002\u5b83\u4eec\u901a\u8fc7\u5377\u79ef\u5c42\u3001\u6c60\u5316\u5c42\u548c\u5168\u8fde\u63a5\u5c42\u6765\u81ea\u52a8\u5b66\u4e60\u56fe\u50cf\u7279\u5f81\u3002CNN\u7684\u7ed3\u6784\u4f7f\u5f97\u5b83\u4eec\u975e\u5e38\u9002\u5408\u4e8e\u5904\u7406\u56fe\u50cf\u6570\u636e\uff0c\u56e0\u4e3a\u5b83\u4eec\u80fd\u591f\u6355\u6349\u56fe\u50cf\u4e2d\u7684\u7a7a\u95f4\u5c42\u6b21\u4fe1\u606f\u3002<\/p>\n<\/p>\n<ol>\n<li>\u5377\u79ef\u5c42\u548c\u6c60\u5316\u5c42<\/li>\n<\/ol>\n<p><p>\u5377\u79ef\u5c42\u662fCNN\u7684\u6838\u5fc3\u7ec4\u4ef6\uff0c\u901a\u8fc7\u5bf9\u8f93\u5165\u56fe\u50cf\u5e94\u7528\u4e00\u7ec4\u6ee4\u6ce2\u5668\uff08\u6216\u5377\u79ef\u6838\uff09\u6765\u63d0\u53d6\u7279\u5f81\u3002\u6ee4\u6ce2\u5668\u5728\u8f93\u5165\u56fe\u50cf\u4e0a\u6ed1\u52a8\uff0c\u751f\u6210\u7279\u5f81\u56fe\uff0c\u6bcf\u4e2a\u7279\u5f81\u56fe\u4ee3\u8868\u8f93\u5165\u56fe\u50cf\u7684\u67d0\u4e2a\u7279\u5f81\u3002\u6c60\u5316\u5c42\u901a\u5e38\u8ddf\u968f\u5377\u79ef\u5c42\uff0c\u51cf\u5c11\u7279\u5f81\u56fe\u7684\u7ef4\u5ea6\uff0c\u540c\u65f6\u4fdd\u7559\u91cd\u8981\u4fe1\u606f\u3002\u8fd9\u79cd\u5904\u7406\u65b9\u5f0f\u80fd\u591f\u63d0\u9ad8\u6a21\u578b\u7684\u8ba1\u7b97\u6548\u7387\uff0c\u5e76\u51cf\u5c11\u8fc7\u62df\u5408\u7684\u98ce\u9669\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li>\u5168\u8fde\u63a5\u5c42\u548c\u5206\u7c7b<\/li>\n<\/ol>\n<p><p>\u5728\u7ecf\u8fc7\u591a\u4e2a\u5377\u79ef\u5c42\u548c\u6c60\u5316\u5c42\u540e\uff0cCNN\u901a\u5e38\u4f1a\u4f7f\u7528\u4e00\u5230\u591a\u4e2a\u5168\u8fde\u63a5\u5c42\u6765\u8fdb\u884c\u5206\u7c7b\u3002\u8fd9\u4e9b\u5c42\u5c06\u7279\u5f81\u56fe\u5c55\u5e73\u6210\u4e00\u7ef4\u5411\u91cf\uff0c\u5e76\u901a\u8fc7\u4e00\u4e2a\u6216\u591a\u4e2a\u5168\u8fde\u63a5\u5c42\u8fdb\u884c\u5904\u7406\uff0c\u6700\u540e\u901a\u8fc7softmax\u6216sigmoid\u7b49\u6fc0\u6d3b\u51fd\u6570\u8f93\u51fa\u5206\u7c7b\u7ed3\u679c\u3002\u5168\u8fde\u63a5\u5c42\u7684\u8f93\u51fa\u5373\u662f\u6a21\u578b\u5bf9\u8f93\u5165\u56fe\u50cf\u7684\u9884\u6d4b\u7c7b\u522b\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001\u56fe\u50cf\u5904\u7406\u5e93\u4e0e\u6570\u636e\u9884\u5904\u7406<\/p>\n<\/p>\n<p><p>\u5728\u8fdb\u884c\u56fe\u50cf\u8bc6\u522b\u4e4b\u524d\uff0c\u56fe\u50cf\u7684\u9884\u5904\u7406\u662f\u4e00\u4e2a\u91cd\u8981\u6b65\u9aa4\u3002Python\u4e2d\u6709\u8bb8\u591a\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982OpenCV\u548cPIL\uff08Python Imaging Library\uff09\uff0c\u53ef\u4ee5\u5e2e\u52a9\u8fdb\u884c\u56fe\u50cf\u7684\u9884\u5904\u7406\u548c\u589e\u5f3a\u3002<\/p>\n<\/p>\n<ol>\n<li>OpenCV\u7684\u4f7f\u7528<\/li>\n<\/ol>\n<p><p>OpenCV\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u652f\u6301\u591a\u79cd\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u5982\u56fe\u50cf\u8bfb\u53d6\u3001\u7f29\u653e\u3001\u65cb\u8f6c\u548c\u8f6c\u6362\u989c\u8272\u7a7a\u95f4\u7b49\u3002\u5728\u56fe\u50cf\u8bc6\u522b\u4efb\u52a1\u4e2d\uff0cOpenCV\u53ef\u4ee5\u7528\u4e8e\u56fe\u50cf\u9884\u5904\u7406\uff0c\u4f8b\u5982\u8c03\u6574\u56fe\u50cf\u5927\u5c0f\u3001\u53bb\u9664\u566a\u58f0\u3001\u589e\u52a0\u5bf9\u6bd4\u5ea6\u7b49\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u8bc6\u522b\u7cbe\u5ea6\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li>\u6570\u636e\u589e\u5f3a\u6280\u672f<\/li>\n<\/ol>\n<p><p>\u6570\u636e\u589e\u5f3a\u6280\u672f\u901a\u8fc7\u5bf9\u8bad\u7ec3\u6570\u636e\u8fdb\u884c\u968f\u673a\u53d8\u6362\uff08\u5982\u65cb\u8f6c\u3001\u7ffb\u8f6c\u3001\u7f29\u653e\u7b49\uff09\u6765\u589e\u52a0\u6570\u636e\u96c6\u7684\u591a\u6837\u6027\uff0c\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u6cdb\u5316\u3002Python\u4e2d\u7684Keras\u5e93\u63d0\u4f9b\u4e86ImageDataGenerator\u7c7b\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u6570\u636e\u589e\u5f3a\u3002\u901a\u8fc7\u6570\u636e\u589e\u5f3a\uff0c\u6a21\u578b\u53ef\u4ee5\u66f4\u597d\u5730\u9002\u5e94\u4e0d\u540c\u7684\u56fe\u50cf\u53d8\u4f53\uff0c\u63d0\u9ad8\u8bc6\u522b\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001\u9884\u8bad\u7ec3\u6a21\u578b\u4e0e\u8fc1\u79fb\u5b66\u4e60<\/p>\n<\/p>\n<p><p>\u5728\u5904\u7406\u590d\u6742\u7684\u56fe\u50cf\u8bc6\u522b\u4efb\u52a1\u65f6\uff0c\u4f7f\u7528\u9884\u8bad\u7ec3\u6a21\u578b\u662f\u4e00\u79cd\u6709\u6548\u7684\u7b56\u7565\u3002\u9884\u8bad\u7ec3\u6a21\u578b\u662f\u5728\u5927\u578b\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u597d\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u5982TensorFlow\u7684Inception\u3001VGG\u3001ResNet\u7b49\u3002\u901a\u8fc7\u8fc1\u79fb\u5b66\u4e60\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528\u8fd9\u4e9b\u6a21\u578b\u7684\u9884\u8bad\u7ec3\u6743\u91cd\uff0c\u5feb\u901f\u5b9e\u73b0\u9ad8\u6027\u80fd\u7684\u56fe\u50cf\u8bc6\u522b\u3002<\/p>\n<\/p>\n<ol>\n<li>TensorFlow\u4e0eKeras<\/li>\n<\/ol>\n<p><p>TensorFlow\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u652f\u6301\u591a\u79cd\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u6784\u5efa\u548c\u8bad\u7ec3\u3002Keras\u662fTensorFlow\u7684\u9ad8\u7ea7API\uff0c\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u63a5\u53e3\u6765\u6784\u5efa\u548c\u8bad\u7ec3\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3002\u5728Keras\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u5e76\u901a\u8fc7\u8fc1\u79fb\u5b66\u4e60\u5bf9\u7279\u5b9a\u4efb\u52a1\u8fdb\u884c\u5fae\u8c03\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li>PyTorch\u4e0etorchvision<\/li>\n<\/ol>\n<p><p>PyTorch\u662f\u53e6\u4e00\u4e2a\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u4ee5\u5176\u7075\u6d3b\u6027\u548c\u52a8\u6001\u8ba1\u7b97\u56fe\u800c\u95fb\u540d\u3002torchvision\u662fPyTorch\u7684\u4e00\u4e2a\u5b50\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u9884\u8bad\u7ec3\u6a21\u578b\u548c\u6570\u636e\u589e\u5f3a\u5de5\u5177\u3002\u5728\u4f7f\u7528PyTorch\u8fdb\u884c\u56fe\u50cf\u8bc6\u522b\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u52a0\u8f7dtorchvision\u4e2d\u7684\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u5e76\u8fdb\u884c\u8fc1\u79fb\u5b66\u4e60\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001\u5b9e\u6218\u6848\u4f8b\uff1a\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1<\/p>\n<\/p>\n<p><p>\u5728\u4e86\u89e3\u4e86\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3001\u56fe\u50cf\u5904\u7406\u5e93\u548c\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u57fa\u672c\u6982\u5ff5\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u7ed3\u5408\u8fd9\u4e9b\u5de5\u5177\uff0c\u5b8c\u6210\u4e00\u4e2a\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u5b9e\u6218\u6848\u4f8b\uff0c\u6f14\u793a\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u56fe\u50cf\u8bc6\u522b\u3002<\/p>\n<\/p>\n<ol>\n<li>\u6570\u636e\u96c6\u51c6\u5907<\/li>\n<\/ol>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u4e00\u4e2a\u56fe\u50cf\u5206\u7c7b\u6570\u636e\u96c6\u3002\u53ef\u4ee5\u9009\u62e9\u5e38\u7528\u7684\u516c\u5f00\u6570\u636e\u96c6\uff0c\u5982CIFAR-10\u3001MNIST\u7b49\uff0c\u6216\u8005\u4f7f\u7528\u81ea\u5df1\u7684\u6570\u636e\u96c6\u3002\u6570\u636e\u96c6\u9700\u8981\u5206\u4e3a\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u4ee5\u4fbf\u4e8e\u6a21\u578b\u7684\u8bad\u7ec3\u548c\u8bc4\u4f30\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li>\u6a21\u578b\u6784\u5efa\u4e0e\u8bad\u7ec3<\/li>\n<\/ol>\n<p><p>\u5728\u6570\u636e\u96c6\u51c6\u5907\u597d\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u9009\u62e9\u4f7f\u7528Keras\u6216PyTorch\u6784\u5efa\u4e00\u4e2a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u3002\u5bf9\u4e8e\u521d\u5b66\u8005\uff0c\u4f7f\u7528Keras\u7684Sequential\u6a21\u578b\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u901a\u8fc7\u5806\u53e0\u591a\u4e2a\u5c42\u6765\u5feb\u901f\u6784\u5efa\u6a21\u578b\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from keras.models import Sequential<\/p>\n<p>from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense<\/p>\n<p>model = Sequential([<\/p>\n<p>    Conv2D(32, (3, 3), activation=&#39;relu&#39;, input_shape=(32, 32, 3)),<\/p>\n<p>    MaxPooling2D(pool_size=(2, 2)),<\/p>\n<p>    Flatten(),<\/p>\n<p>    Dense(128, activation=&#39;relu&#39;),<\/p>\n<p>    Dense(10, activation=&#39;softmax&#39;)<\/p>\n<p>])<\/p>\n<p>model.compile(optimizer=&#39;adam&#39;, loss=&#39;categorical_crossentropy&#39;, metrics=[&#39;accuracy&#39;])<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"3\">\n<li>\u6a21\u578b\u8bc4\u4f30\u4e0e\u4f18\u5316<\/li>\n<\/ol>\n<p><p>\u5728\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u9700\u8981\u4f7f\u7528\u9a8c\u8bc1\u96c6\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30\u3002\u901a\u8fc7\u89c2\u5bdf\u6a21\u578b\u7684\u51c6\u786e\u7387\u548c\u635f\u5931\u503c\uff0c\u53ef\u4ee5\u5224\u65ad\u6a21\u578b\u7684\u6027\u80fd\u3002\u5982\u679c\u6a21\u578b\u7684\u51c6\u786e\u7387\u4e0d\u591f\u9ad8\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u8c03\u6574\u6a21\u578b\u7684\u7ed3\u6784\u3001\u4f18\u5316\u5668\u6216\u5b66\u4e60\u7387\uff0c\u6216\u8005\u589e\u52a0\u6570\u636e\u589e\u5f3a\u6280\u672f\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/p>\n<p><p>\u4e94\u3001\u672a\u6765\u53d1\u5c55\u65b9\u5411\u4e0e\u6311\u6218<\/p>\n<\/p>\n<p><p>\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u7684\u53d1\u5c55\uff0c\u56fe\u50cf\u8bc6\u522b\u7684\u5e94\u7528\u53d8\u5f97\u8d8a\u6765\u8d8a\u5e7f\u6cdb\u3002\u7136\u800c\uff0c\u56fe\u50cf\u8bc6\u522b\u4ecd\u7136\u9762\u4e34\u8bb8\u591a\u6311\u6218\uff0c\u5982\u5904\u7406\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u3001\u5e94\u5bf9\u4e0d\u540c\u5149\u7167\u6761\u4ef6\u4e0b\u7684\u56fe\u50cf\u8bc6\u522b\u7b49\u3002\u672a\u6765\uff0c\u968f\u7740\u66f4\u5f3a\u5927\u7684\u8ba1\u7b97\u8d44\u6e90\u548c\u66f4\u5148\u8fdb\u7684\u7b97\u6cd5\u7684\u51fa\u73b0\uff0c\u56fe\u50cf\u8bc6\u522b\u6280\u672f\u5c06\u7ee7\u7eed\u53d1\u5c55\uff0c\u5e76\u5728\u5404\u4e2a\u9886\u57df\u53d1\u6325\u66f4\u5927\u7684\u4f5c\u7528\u3002<\/p>\n<\/p>\n<ol>\n<li>\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u5904\u7406<\/li>\n<\/ol>\n<p><p>\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u901a\u5e38\u5305\u542b\u66f4\u591a\u7684\u7ec6\u8282\u4fe1\u606f\uff0c\u4f46\u4e5f\u5bf9\u6a21\u578b\u7684\u8ba1\u7b97\u80fd\u529b\u63d0\u51fa\u4e86\u66f4\u9ad8\u7684\u8981\u6c42\u3002\u672a\u6765\u7684\u7814\u7a76\u53ef\u80fd\u4f1a\u96c6\u4e2d\u5728\u5982\u4f55\u4f18\u5316\u6a21\u578b\u7ed3\u6784\uff0c\u4ee5\u5728\u5904\u7406\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u65f6\u4fdd\u6301\u9ad8\u6548\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n<ol 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