{"id":53772,"date":"2025-08-12T16:39:40","date_gmt":"2025-08-12T08:39:40","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/53772.html"},"modified":"2025-08-12T16:39:40","modified_gmt":"2025-08-12T08:39:40","slug":"%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0-%e5%8d%b7%e7%a7%af%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c-nin","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/53772.html","title":{"rendered":"\u6df1\u5ea6\u5b66\u4e60-\u5377\u79ef\u795e\u7ecf\u7f51\u7edc-NIN"},"content":{"rendered":"<p>\u7f51\u7edc\u7ed3\u6784\u662f\u5377\u79ef\u795e\u7ecf\u7f51\u7edc&#xff08;CNN&#xff09;\u53d1\u5c55\u7684\u5173\u952e\u3002\u5176\u4e2d&#xff0c;\u7f51\u7edc\u7ed3\u6784\u7684\u6539\u8fdb\u81f3\u5173\u91cd\u8981\u3002\u672c\u6587\u5c06\u4ecb\u7ecd\u4e00\u79cd\u5177\u6709\u521b\u65b0\u610f\u4e49\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u2014\u2014NIN&#xff08;Network in Network&#xff09;\u3002<\/p>\n<p>LeNet\u3001AlexNet\u548cVGG\u90fd\u6709\u4e00\u4e2a\u5171\u540c\u7684\u8bbe\u8ba1\u6a21\u5f0f&#xff1a;\u901a\u8fc7\u4e00\u7cfb\u5217\u7684\u5377\u79ef\u5c42\u4e0e\u6c47\u805a\u5c42\u6765\u63d0\u53d6\u7a7a\u95f4\u7ed3\u6784\u7279\u5f81&#xff1b;\u7136\u540e\u901a\u8fc7\u5168\u8fde\u63a5\u5c42\u5bf9\u7279\u5f81\u7684\u8868\u5f81\u8fdb\u884c\u5904\u7406\u3002<\/p>\n<p>AlexNet\u548cVGG\u5bf9LeNet\u7684\u6539\u8fdb\u4e3b\u8981\u5728\u4e8e\u5982\u4f55\u6269\u5927\u548c\u52a0\u6df1\u8fd9\u4e24\u4e2a\u6a21\u5757\u3002 \u6216\u8005&#xff0c;\u53ef\u4ee5\u60f3\u8c61\u5728\u8fd9\u4e2a\u8fc7\u7a0b\u7684\u65e9\u671f\u4f7f\u7528\u5168\u8fde\u63a5\u5c42\u3002\u7136\u800c&#xff0c;\u5982\u679c\u4f7f\u7528\u4e86\u5168\u8fde\u63a5\u5c42&#xff0c;\u53ef\u80fd\u4f1a\u5b8c\u5168\u653e\u5f03\u8868\u5f81\u7684\u7a7a\u95f4\u7ed3\u6784\u3002<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"498\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250812083937-689afdc964d98.png\" width=\"1043\" \/><\/p>\n<p>\u7f51\u7edc\u4e2d\u7684\u7f51\u7edcNiN \u63d0\u4f9b\u4e86\u4e00\u4e2a\u975e\u5e38\u7b80\u5355\u7684\u89e3\u51b3\u65b9\u6848&#xff1a;<span style=\"color:#fe2c24\">\u5728\u6bcf\u4e2a<\/span><span style=\"color:#fe2c24\">\u50cf\u7d20\u7684\u901a\u9053\u4e0a\u5206\u522b\u4f7f\u7528\u591a\u5c42\u611f\u77e5\u673a<\/span>\u3002<\/p>\n<\/p>\n<h3>1. \u7f51\u7edc\u7ed3\u6784\u7ec4\u6210<\/h3>\n<p>NiN\u5757 \u4f7f\u7528\u4e861\u00d71\u7684\u5377\u79ef\u5c42\u4ee3\u8868\u5168\u8fde\u63a5\u5c42\u3002<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"394\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250812083938-689afdca0c885.png\" width=\"1267\" \/><\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"733\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250812083938-689afdcaa4c68.png\" width=\"1036\" \/><\/p>\n<p>NIN\u7684\u6838\u5fc3\u521b\u65b0\u5728\u4e8e\u5f15\u5165\u4e86\u201c\u5fae\u7f51\u7edc\u201d&#xff08;Network in Network&#xff09;\u7684\u6982\u5ff5\u3002\u5177\u4f53\u6765\u8bf4&#xff0c;\u6bcf\u4e2a\u5377\u79ef\u5c42\u540e\u9762\u90fd\u8ddf\u968f\u7740\u4e00\u4e2a1\u00d71\u5377\u79ef\u6838\u7684\u5fae\u578b\u7f51\u7edc&#xff0c;\u7528\u4e8e\u5bf9\u7279\u5f81\u56fe\u8fdb\u884c\u9010\u50cf\u7d20\u7684\u7279\u5f81\u53d8\u6362&#xff0c;\u589e\u52a0\u4e86\u7f51\u7edc\u7684\u975e\u7ebf\u6027\u8868\u8fbe\u80fd\u529b\u3002\u8fd9\u79cd\u8bbe\u8ba1\u4f7f\u5f97\u6a21\u578b\u80fd\u591f\u66f4\u6df1\u5165\u5730\u6316\u6398\u548c\u5229\u7528\u7279\u5f81\u4fe1\u606f\u3002<\/p>\n<p>\u4e0e\u4f20\u7edf\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u76f8\u6bd4&#xff0c;NIN\u7684\u7ed3\u6784\u8bbe\u8ba1\u66f4\u52a0\u7d27\u51d1&#xff0c;\u80fd\u591f\u4ee5\u8f83\u5c11\u7684\u53c2\u6570\u5b9e\u73b0\u66f4\u597d\u7684\u6027\u80fd\u3002\u5176\u4e3b\u8981\u7279\u70b9\u5982\u4e0b&#xff1a;<\/p>\n<li>\n<p>1\u00d71\u5377\u79ef\u5c42\u7684\u5e94\u7528&#xff1a;\u901a\u8fc71\u00d71\u5377\u79ef\u6838\u5bf9\u7279\u5f81\u56fe\u8fdb\u884c\u9010\u50cf\u7d20\u7684\u7279\u5f81\u53d8\u6362&#xff0c;\u589e\u52a0\u6a21\u578b\u7684\u975e\u7ebf\u6027\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n<\/li>\n<li>\n<p>\u5168\u5c40\u5e73\u5747\u6c60\u5316\u5c42&#xff1a;\u5728\u6700\u540e\u4e00\u5c42\u4f7f\u7528\u5168\u5c40\u5e73\u5747\u6c60\u5316\u5c42\u4ee3\u66ff\u5168\u8fde\u63a5\u5c42&#xff0c;\u964d\u4f4e\u53c2\u6570\u6570\u91cf&#xff0c;\u51cf\u5c11\u8fc7\u62df\u5408\u98ce\u9669&#xff0c;\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/li>\n<li>\n<p>\u7f51\u7edc\u6df1\u5ea6\u7684\u4f18\u5316&#xff1a;NIN\u901a\u8fc7\u589e\u52a0\u7f51\u7edc\u7684\u6df1\u5ea6\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u7279\u5f81\u63d0\u53d6\u80fd\u529b&#xff0c;\u540c\u65f6\u4fdd\u6301\u4e86\u6a21\u578b\u7684\u7b80\u6d01\u6027\u3002<\/p>\n<\/li>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"733\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250812083939-689afdcb3f262.png\" width=\"872\" \/><br \/>\n\u5bf9\u6bd4 VGG \u548c NIN \u4ee5\u53ca\u4ed6\u4eec\u7684\u67b6\u6784\u7684\u4e0d\u540c<\/p>\n<h3>2. \u4ee3\u7801\u5b9e\u73b0<\/h3>\n<p>\u4ee3\u7801\u6539\u7f16\u81ea\u300a\u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60\u300b<\/p>\n<p>import torch<br \/>\nfrom torch import nn<br \/>\nfrom d2l import torch as d2l<\/p>\n<p>def nin_block(in_channels, out_channels, kernel_size, strides, padding):<br \/>\n    return nn.Sequential(<br \/>\n        nn.Conv2d(in_channels, out_channels, kernel_size, strides, padding),<br \/>\n        nn.ReLU(),<br \/>\n        nn.Conv2d(out_channels, out_channels, kernel_size&#061;1), nn.ReLU(),<br \/>\n        nn.Conv2d(out_channels, out_channels, kernel_size&#061;1), nn.ReLU())<\/p>\n<p>net &#061; nn.Sequential(<br \/>\n    nin_block(1, 96, kernel_size&#061;11, strides&#061;4, padding&#061;0),<br \/>\n    nn.MaxPool2d(3, stride&#061;2),<br \/>\n    nin_block(96, 256, kernel_size&#061;5, strides&#061;1, padding&#061;2),<br \/>\n    nn.MaxPool2d(3, stride&#061;2),<br \/>\n    nin_block(256, 384, kernel_size&#061;3, strides&#061;1, padding&#061;1),<br \/>\n    nn.MaxPool2d(3, stride&#061;2),<br \/>\n    nn.Dropout(0.5),<br \/>\n    # \u6807\u7b7e\u7c7b\u522b\u6570\u662f10<br \/>\n    nin_block(384, 10, kernel_size&#061;3, strides&#061;1, padding&#061;1),<br \/>\n    nn.AdaptiveAvgPool2d((1, 1)),<br \/>\n    # \u5c06\u56db\u7ef4\u7684\u8f93\u51fa\u8f6c\u6210\u4e8c\u7ef4\u7684\u8f93\u51fa&#xff0c;\u5176\u5f62\u72b6\u4e3a(\u6279\u91cf\u5927\u5c0f,10)<br \/>\n    nn.Flatten())<\/p>\n<p>X &#061; torch.rand(size&#061;(1, 1, 224, 224))<br \/>\nfor layer in net:<br \/>\n    X &#061; layer(X)<br \/>\n    print(layer.__class__.__name__,&#039;output shape:\\\\t&#039;, X.shape)<\/p>\n<p>\u8f93\u51fa&#xff1a;<br \/>\nSequential output shape:     torch.Size([1, 96, 54, 54])<br \/>\nMaxPool2d output shape:      torch.Size([1, 96, 26, 26])<br \/>\nSequential output shape:     torch.Size([1, 256, 26, 26])<br \/>\nMaxPool2d output shape:      torch.Size([1, 256, 12, 12])<br \/>\nSequential output shape:     torch.Size([1, 384, 12, 12])<br \/>\nMaxPool2d output shape:      torch.Size([1, 384, 5, 5])<br \/>\nDropout output shape:        torch.Size([1, 384, 5, 5])<br \/>\nSequential output shape:     torch.Size([1, 10, 5, 5])<br \/>\nAdaptiveAvgPool2d output shape:      torch.Size([1, 10, 1, 1])<br \/>\nFlatten output shape:        torch.Size([1, 10])<\/p>\n<p>lr, num_epochs, batch_size &#061; 0.1, 10, 128<br \/>\ntrain_iter, test_iter &#061; d2l.load_data_fashion_mnist(batch_size, resize&#061;224)<br \/>\nd2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())<\/p>\n<p>\u8f93\u51fa&#xff1a;<br \/>\nloss 0.563, train acc 0.786, test acc 0.790<br \/>\n3087.6 examples\/sec on cuda:0<\/p>\n<\/p>\n<h3>3. NIN\u7684\u5f71\u54cd<\/h3>\n<p>NIN\u5728\u591a\u4e2a\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1\u4e2d\u8868\u73b0\u51fa\u4e86\u663e\u8457\u7684\u6027\u80fd\u63d0\u5347\u3002\u901a\u8fc7\u5728ImageNet\u6570\u636e\u96c6\u4e0a\u7684\u5b9e\u9a8c\u9a8c\u8bc1&#xff0c;NIN\u5728Top-5\u9519\u8bef\u7387\u4e0a\u8fbe\u5230\u4e86\u8f83\u4f4e\u7684\u6c34\u5e73&#xff0c;\u8bc1\u660e\u4e86\u5176\u5728\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1\u4e2d\u7684\u6709\u6548\u6027\u3002\u6b64\u5916&#xff0c;NIN\u7684\u7ed3\u6784\u8bbe\u8ba1\u4f7f\u5176\u9002\u7528\u4e8e\u5404\u79cd\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1&#xff0c;\u5305\u62ec\u76ee\u6807\u68c0\u6d4b\u548c\u56fe\u50cf\u5206\u5272\u7b49\u3002<\/p>\n<p>\u4e0e\u5176\u4ed6\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\u76f8\u6bd4&#xff0c;NIN\u5728\u7f51\u7edc\u7ed3\u6784\u548c\u6027\u80fd\u4e0a\u5177\u6709\u4ee5\u4e0b\u4f18\u52bf&#xff1a;<\/p>\n<li>\n<p>\u66f4\u5c11\u7684\u53c2\u6570&#xff1a;NIN\u901a\u8fc7\u51cf\u5c11\u5168\u8fde\u63a5\u5c42\u7684\u6570\u91cf&#xff0c;\u663e\u8457\u964d\u4f4e\u4e86\u6a21\u578b\u7684\u53c2\u6570\u6570\u91cf\u3002<\/p>\n<\/li>\n<li>\n<p>\u66f4\u9ad8\u7684\u6548\u7387&#xff1a;\u7531\u4e8e\u53c2\u6570\u6570\u91cf\u7684\u51cf\u5c11&#xff0c;NIN\u5728\u8bad\u7ec3\u548c\u63a8\u7406\u8fc7\u7a0b\u4e2d\u66f4\u52a0\u9ad8\u6548\u3002<\/p>\n<\/li>\n<li>\n<p>\u66f4\u597d\u7684\u6cdb\u5316\u80fd\u529b&#xff1a;\u5168\u5c40\u5e73\u5747\u6c60\u5316\u5c42\u7684\u4f7f\u7528\u63d0\u9ad8\u4e86\u6a21\u578b\u5bf9\u65b0\u6570\u636e\u7684\u9002\u5e94\u80fd\u529b\u3002<\/p>\n<\/li>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"371\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250812083939-689afdcbb14d5.png\" width=\"884\" \/><\/p>\n<li>\n<p>NiN \u4f7f\u7528\u7531\u4e00\u4e2a\u5377\u79ef\u5c42\u548c\u591a\u4e2a1\u00d71\u5377\u79ef\u5c42\u7ec4\u6210\u7684\u5757\u3002<\/p>\n<\/li>\n<li>\n<p>NiN\u5757 \u53ef\u4ee5\u5728\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u4e2d\u4f7f\u7528&#xff0c;\u4ee5\u5141\u8bb8\u66f4\u591a\u7684\u6bcf\u50cf\u7d20\u975e\u7ebf\u6027\u3002<\/p>\n<\/li>\n<li>\n<p>NiN\u53bb\u9664\u4e86\u5bb9\u6613\u9020\u6210\u8fc7\u62df\u5408\u7684\u5168\u8fde\u63a5\u5c42&#xff0c;\u5c06\u5b83\u4eec\u66ff\u6362\u4e3a\u5168\u5c40\u5e73\u5747\u6c47\u805a\u5c42&#xff08;\u5373\u5728\u6240\u6709\u4f4d\u7f6e\u4e0a\u8fdb\u884c\u6c42\u548c&#xff09;\u3002\u8be5\u6c47\u805a\u5c42\u901a\u9053\u6570\u91cf\u4e3a\u6240\u9700\u7684\u8f93\u51fa\u6570\u91cf&#xff08;\u4f8b\u5982&#xff0c;Fashion-MNIST\u7684\u8f93\u51fa\u4e3a10&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p>\u79fb\u9664\u5168\u8fde\u63a5\u5c42\u53ef\u51cf\u5c11\u8fc7\u62df\u5408&#xff0c;\u540c\u65f6\u663e\u8457\u51cf\u5c11NiN\u7684\u53c2\u6570\u3002<\/p>\n<\/li>\n<li>\n<p>NiN\u7684\u8bbe\u8ba1\u5f71\u54cd\u4e86\u8bb8\u591a\u540e\u7eed\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u8bbe\u8ba1\u3002<\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb234\u6b21\uff0c\u70b9\u8d5e8\u6b21\uff0c\u6536\u85cf2\u6b21\u3002\u7f51\u7edc\u7ed3\u6784\u662f\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u53d1\u5c55\u7684\u5173\u952e\u3002\u5176\u4e2d\uff0c\u7f51\u7edc\u7ed3\u6784\u7684\u6539\u8fdb\u81f3\u5173\u91cd\u8981\u3002\u672c\u6587\u5c06\u4ecb\u7ecd\u4e00\u79cd\u5177\u6709\u521b\u65b0\u610f\u4e49\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u2014\u2014NIN\uff08Network in Network\uff09\u3002AlexNet\u548cVGG\u5bf9LeNet\u7684\u6539\u8fdb\u4e3b\u8981\u5728\u4e8e\u5982\u4f55\u6269\u5927\u548c\u52a0\u6df1\u8fd9\u4e24\u4e2a\u6a21\u5757\u3002\u6216\u8005\uff0c\u53ef\u4ee5\u60f3\u8c61\u5728\u8fd9\u4e2a\u8fc7\u7a0b\u7684\u65e9\u671f\u4f7f\u7528\u5168\u8fde\u63a5\u5c42\u3002\u7136\u800c\uff0c\u5728\u6bcf\u4e2aNIN\u7684\u6838\u5fc3\u521b\u65b0\u5728\u4e8e\u5f15\u5165\u4e86\u201c\u5fae\u7f51\u7edc\u201d\uff08Network in Network\uff09\u7684\u6982\u5ff5\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u6bcf\u4e2a\u5377\u79ef\u5c42\u540e\u9762\u90fd\u8ddf\u968f\u7740\u4e00\u4e2a1\u00d71\u5377\u79ef\u6838\u7684\u5fae\u578b\u7f51\u7edc\uff0c\u7528\u4e8e\u5bf9\u7279\u5f81\u56fe\u8fdb\u884c\u9010\u50cf\u7d20\u7684\u7279\u5f81\u53d8\u6362\uff0c\u589e\u52a0\u4e86\u7f51\u7edc\u7684\u975e\u7ebf\u6027\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n","protected":false},"author":2,"featured_media":53767,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[2394,50,86],"topic":[],"class_list":["post-53772","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-cnn","tag-50","tag-86"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - 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