{"id":479160,"date":"2023-08-09T10:31:59","date_gmt":"2023-08-09T10:31:59","guid":{"rendered":""},"modified":"2023-09-05T11:18:19","modified_gmt":"2023-09-05T11:18:19","slug":"stochastic-gradient-descent","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/stochastic-gradient-descent\/","title":{"rendered":"\u968f\u673a\u68af\u5ea6\u4e0b\u964d"},"content":{"rendered":"<p>\u968f\u673a\u68af\u5ea6\u4e0b\u964d (SGD) \u662f\u4e00\u79cd\u6d41\u884c\u7684\u4f18\u5316\u7b97\u6cd5\uff0c\u5e7f\u6cdb\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u3002\u5b83\u5728\u8bad\u7ec3\u5404\u79cd\u5e94\u7528\u7684\u6a21\u578b\u4e2d\u8d77\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\uff0c\u5305\u62ec\u56fe\u50cf\u8bc6\u522b\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u548c\u63a8\u8350\u7cfb\u7edf\u3002SGD \u662f\u68af\u5ea6\u4e0b\u964d\u7b97\u6cd5\u7684\u6269\u5c55\uff0c\u65e8\u5728\u901a\u8fc7\u57fa\u4e8e\u8bad\u7ec3\u6570\u636e\u7684\u5c0f\u5b50\u96c6\uff08\u79f0\u4e3a\u5c0f\u6279\u91cf\uff09\u8fed\u4ee3\u66f4\u65b0\u6a21\u578b\u53c2\u6570\u6765\u6709\u6548\u5730\u627e\u5230\u6a21\u578b\u7684\u6700\u4f73\u53c2\u6570\u3002<\/p>\n<h2>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7684\u8d77\u6e90\u5386\u53f2\u53ca\u5176\u9996\u6b21\u63d0\u53ca<\/h2>\n<p>\u968f\u673a\u4f18\u5316\u7684\u6982\u5ff5\u53ef\u4ee5\u8ffd\u6eaf\u5230 20 \u4e16\u7eaa 50 \u5e74\u4ee3\u65e9\u671f\uff0c\u5f53\u65f6\u7814\u7a76\u4eba\u5458\u6b63\u5728\u63a2\u7d22\u4e0d\u540c\u7684\u4f18\u5316\u6280\u672f\u3002\u7136\u800c\uff0c\u5728\u673a\u5668\u5b66\u4e60\u7684\u80cc\u666f\u4e0b\uff0c\u7b2c\u4e00\u6b21\u63d0\u5230\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u53ef\u4ee5\u8ffd\u6eaf\u5230 20 \u4e16\u7eaa 60 \u5e74\u4ee3\u3002\u8fd9\u4e2a\u60f3\u6cd5\u5728 20 \u4e16\u7eaa 80 \u5e74\u4ee3\u548c 90 \u5e74\u4ee3\u5f00\u59cb\u6d41\u884c\uff0c\u5f53\u65f6\u5b83\u88ab\u8bc1\u660e\u53ef\u4ee5\u6709\u6548\u5730\u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc\u548c\u5176\u4ed6\u590d\u6742\u6a21\u578b\u3002<\/p>\n<h2>\u6709\u5173\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7684\u8be6\u7ec6\u4fe1\u606f<\/h2>\n<p>SGD \u662f\u4e00\u79cd\u8fed\u4ee3\u4f18\u5316\u7b97\u6cd5\uff0c\u65e8\u5728\u901a\u8fc7\u8c03\u6574\u6a21\u578b\u7684\u53c2\u6570\u6765\u6700\u5c0f\u5316\u635f\u5931\u51fd\u6570\u3002\u4e0e\u4f7f\u7528\u6574\u4e2a\u8bad\u7ec3\u6570\u636e\u96c6\uff08\u6279\u91cf\u68af\u5ea6\u4e0b\u964d\uff09\u8ba1\u7b97\u68af\u5ea6\u7684\u4f20\u7edf\u68af\u5ea6\u4e0b\u964d\u4e0d\u540c\uff0cSGD \u4f1a\u968f\u673a\u62bd\u53d6\u4e00\u5c0f\u6279\u6570\u636e\u70b9\uff0c\u5e76\u6839\u636e\u5728\u6b64\u5c0f\u6279\u6570\u636e\u70b9\u4e0a\u8ba1\u7b97\u7684\u635f\u5931\u51fd\u6570\u7684\u68af\u5ea6\u6765\u66f4\u65b0\u53c2\u6570\u3002<\/p>\n<p>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7b97\u6cd5\u7684\u5173\u952e\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<ol>\n<li>\u968f\u673a\u521d\u59cb\u5316\u6a21\u578b\u53c2\u6570\u3002<\/li>\n<li>\u968f\u673a\u6253\u4e71\u8bad\u7ec3\u6570\u636e\u96c6\u3002<\/li>\n<li>\u5c06\u6570\u636e\u5206\u6210\u5c0f\u6279\u91cf\u3002<\/li>\n<li>\u5bf9\u4e8e\u6bcf\u4e2a\u5c0f\u6279\u91cf\uff0c\u8ba1\u7b97\u635f\u5931\u51fd\u6570\u5173\u4e8e\u53c2\u6570\u7684\u68af\u5ea6\u3002<\/li>\n<li>\u4f7f\u7528\u8ba1\u7b97\u7684\u68af\u5ea6\u548c\u5b66\u4e60\u7387\u66f4\u65b0\u6a21\u578b\u53c2\u6570\uff0c\u63a7\u5236\u66f4\u65b0\u7684\u6b65\u957f\u3002<\/li>\n<li>\u91cd\u590d\u8be5\u8fc7\u7a0b\u56fa\u5b9a\u6b21\u6570\u6216\u76f4\u5230\u6ee1\u8db3\u6536\u655b\u6807\u51c6\u3002<\/li>\n<\/ol>\n<h2>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7684\u5185\u90e8\u7ed3\u6784 - SGD \u7684\u5de5\u4f5c\u539f\u7406<\/h2>\n<p>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u80cc\u540e\u7684\u4e3b\u8981\u601d\u60f3\u662f\u901a\u8fc7\u4f7f\u7528\u5c0f\u6279\u91cf\u5728\u53c2\u6570\u66f4\u65b0\u4e2d\u5f15\u5165\u968f\u673a\u6027\u3002\u8fd9\u79cd\u968f\u673a\u6027\u901a\u5e38\u4f1a\u5bfc\u81f4\u66f4\u5feb\u7684\u6536\u655b\uff0c\u5e76\u6709\u52a9\u4e8e\u5728\u4f18\u5316\u8fc7\u7a0b\u4e2d\u6446\u8131\u5c40\u90e8\u6700\u5c0f\u503c\u3002\u7136\u800c\uff0c\u968f\u673a\u6027\u4e5f\u53ef\u80fd\u5bfc\u81f4\u4f18\u5316\u8fc7\u7a0b\u5728\u6700\u4f18\u89e3\u9644\u8fd1\u9707\u8361\u3002<\/p>\n<p>SGD \u5177\u6709\u8ba1\u7b97\u6548\u7387\uff0c\u5c24\u5176\u662f\u5bf9\u4e8e\u5927\u578b\u6570\u636e\u96c6\uff0c\u56e0\u4e3a\u5b83\u6bcf\u6b21\u8fed\u4ee3\u4ec5\u5904\u7406\u4e00\u5c0f\u90e8\u5206\u6570\u636e\u3002\u6b64\u5c5e\u6027\u4f7f\u5176\u80fd\u591f\u5904\u7406\u53ef\u80fd\u65e0\u6cd5\u5b8c\u5168\u88c5\u5165\u5185\u5b58\u7684\u6d77\u91cf\u6570\u636e\u96c6\u3002\u4f46\u662f\uff0c\u5c0f\u6279\u91cf\u91c7\u6837\u5f15\u5165\u7684\u566a\u58f0\u4f1a\u4f7f\u4f18\u5316\u8fc7\u7a0b\u53d8\u5f97\u5608\u6742\uff0c\u4ece\u800c\u5bfc\u81f4\u8bad\u7ec3\u671f\u95f4\u635f\u5931\u51fd\u6570\u51fa\u73b0\u6ce2\u52a8\u3002<\/p>\n<p>\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u5df2\u7ecf\u63d0\u51fa\u4e86\u51e0\u79cd SGD \u53d8\u4f53\uff0c\u4f8b\u5982\uff1a<\/p>\n<ul>\n<li><strong>\u5c0f\u6279\u91cf\u68af\u5ea6\u4e0b\u964d<\/strong>\uff1a\u5b83\u5728\u6bcf\u6b21\u8fed\u4ee3\u4e2d\u4f7f\u7528\u4e00\u5c0f\u6279\u56fa\u5b9a\u5927\u5c0f\u7684\u6570\u636e\u70b9\uff0c\u5728\u6279\u91cf\u68af\u5ea6\u4e0b\u964d\u7684\u7a33\u5b9a\u6027\u548c SGD \u7684\u8ba1\u7b97\u6548\u7387\u4e4b\u95f4\u53d6\u5f97\u5e73\u8861\u3002<\/li>\n<li><strong>\u5728\u7ebf\u68af\u5ea6\u4e0b\u964d<\/strong>\uff1a\u5b83\u6bcf\u6b21\u5904\u7406\u4e00\u4e2a\u6570\u636e\u70b9\uff0c\u5e76\u5728\u6bcf\u4e2a\u6570\u636e\u70b9\u4e4b\u540e\u66f4\u65b0\u53c2\u6570\u3002\u8fd9\u79cd\u65b9\u6cd5\u53ef\u80fd\u975e\u5e38\u4e0d\u7a33\u5b9a\uff0c\u4f46\u5728\u5904\u7406\u6d41\u6570\u636e\u65f6\u5f88\u6709\u7528\u3002<\/li>\n<\/ul>\n<h2>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7684\u5173\u952e\u7279\u5f81\u5206\u6790<\/h2>\n<p>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7684\u4e3b\u8981\u7279\u5f81\u5305\u62ec\uff1a<\/p>\n<ol>\n<li><strong>\u6548\u7387<\/strong>\uff1aSGD \u5728\u6bcf\u6b21\u8fed\u4ee3\u4e2d\u4ec5\u5904\u7406\u4e00\u5c0f\u90e8\u5206\u6570\u636e\uff0c\u56e0\u6b64\u8ba1\u7b97\u6548\u7387\u8f83\u9ad8\uff0c\u5c24\u5176\u9002\u7528\u4e8e\u5927\u578b\u6570\u636e\u96c6\u3002<\/li>\n<li><strong>\u5185\u5b58\u53ef\u6269\u5c55\u6027<\/strong>\uff1a\u7531\u4e8e SGD \u4e0e\u5c0f\u6279\u91cf\u4e00\u8d77\u5de5\u4f5c\uff0c\u56e0\u6b64\u5b83\u53ef\u4ee5\u5904\u7406\u4e0d\u80fd\u5b8c\u5168\u653e\u5165\u5185\u5b58\u7684\u6570\u636e\u96c6\u3002<\/li>\n<li><strong>\u968f\u673a\u6027<\/strong>\uff1aSGD \u7684\u968f\u673a\u6027\u53ef\u4ee5\u5e2e\u52a9\u9003\u79bb\u5c40\u90e8\u6700\u5c0f\u503c\u5e76\u907f\u514d\u5728\u4f18\u5316\u8fc7\u7a0b\u4e2d\u9677\u5165\u505c\u6ede\u72b6\u6001\u3002<\/li>\n<li><strong>\u566a\u97f3<\/strong>\uff1a\u5c0f\u6279\u91cf\u91c7\u6837\u5f15\u5165\u7684\u968f\u673a\u6027\u4f1a\u5f15\u8d77\u635f\u5931\u51fd\u6570\u7684\u6ce2\u52a8\uff0c\u4f7f\u5f97\u4f18\u5316\u8fc7\u7a0b\u5145\u6ee1\u566a\u58f0\u3002<\/li>\n<\/ol>\n<h2>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7684\u7c7b\u578b<\/h2>\n<p>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u6709\u51e0\u79cd\u53d8\u4f53\uff0c\u6bcf\u79cd\u90fd\u6709\u81ea\u5df1\u7684\u7279\u70b9\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u7c7b\u578b\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u7c7b\u578b<\/th>\n<th>\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u5c0f\u6279\u91cf\u68af\u5ea6\u4e0b\u964d<\/td>\n<td>\u5728\u6bcf\u6b21\u8fed\u4ee3\u4e2d\u4f7f\u7528\u4e00\u5c0f\u6279\u56fa\u5b9a\u5927\u5c0f\u7684\u6570\u636e\u70b9\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u5728\u7ebf\u68af\u5ea6\u4e0b\u964d<\/td>\n<td>\u4e00\u6b21\u5904\u7406\u4e00\u4e2a\u6570\u636e\u70b9\uff0c\u5728\u6bcf\u4e2a\u6570\u636e\u70b9\u4e4b\u540e\u66f4\u65b0\u53c2\u6570\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u52a8\u91cf SGD<\/td>\n<td>\u7ed3\u5408\u52a8\u91cf\u6765\u5e73\u6ed1\u4f18\u5316\u8fc7\u7a0b\u5e76\u52a0\u901f\u6536\u655b\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u6d85\u65af\u6377\u7f57\u592b\u52a0\u901f\u68af\u5ea6 (NAG)<\/td>\n<td>\u52a8\u91cf SGD \u7684\u6269\u5c55\uff0c\u53ef\u4ee5\u8c03\u6574\u66f4\u65b0\u65b9\u5411\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u6027\u80fd\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u963f\u8fbe\u683c\u62c9\u5fb7<\/td>\n<td>\u6839\u636e\u5386\u53f2\u68af\u5ea6\u8c03\u6574\u6bcf\u4e2a\u53c2\u6570\u7684\u5b66\u4e60\u7387\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u5747\u65b9\u6839\u4f20\u64ad\u7b97\u6cd5<\/td>\n<td>\u4e0e Adagrad \u7c7b\u4f3c\uff0c\u4f46\u4f7f\u7528\u68af\u5ea6\u5e73\u65b9\u7684\u79fb\u52a8\u5e73\u5747\u503c\u6765\u8c03\u6574\u5b66\u4e60\u7387\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u4e9a\u5f53<\/td>\n<td>\u7ed3\u5408\u52a8\u91cf\u548c RMSprop \u7684\u4f18\u70b9\uff0c\u5b9e\u73b0\u66f4\u5feb\u7684\u6536\u655b\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848<\/h2>\n<p>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5404\u79cd\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0c\u5c24\u5176\u662f\u5728\u8bad\u7ec3\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u65f6\u3002\u7531\u4e8e\u5176\u6548\u7387\u548c\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u7684\u80fd\u529b\uff0c\u5b83\u5df2\u5728\u8bb8\u591a\u5e94\u7528\u4e2d\u53d6\u5f97\u6210\u529f\u3002\u7136\u800c\uff0c\u6709\u6548\u4f7f\u7528 SGD \u4e5f\u9762\u4e34\u6311\u6218\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u5b66\u4e60\u7387\u9009\u62e9<\/strong>\uff1a\u9009\u62e9\u5408\u9002\u7684\u5b66\u4e60\u7387\u5bf9\u4e8e SGD \u7684\u6536\u655b\u81f3\u5173\u91cd\u8981\u3002\u5b66\u4e60\u7387\u8fc7\u9ad8\u53ef\u80fd\u4f1a\u5bfc\u81f4\u4f18\u5316\u8fc7\u7a0b\u53d1\u6563\uff0c\u800c\u5b66\u4e60\u7387\u8fc7\u4f4e\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6536\u655b\u7f13\u6162\u3002\u5b66\u4e60\u7387\u8c03\u5ea6\u6216\u81ea\u9002\u5e94\u5b66\u4e60\u7387\u7b97\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u7f13\u89e3\u6b64\u95ee\u9898\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u566a\u97f3\u548c\u6ce2\u52a8<\/strong>\uff1aSGD \u7684\u968f\u673a\u6027\u4f1a\u5f15\u5165\u566a\u58f0\uff0c\u5bfc\u81f4\u8bad\u7ec3\u671f\u95f4\u635f\u5931\u51fd\u6570\u51fa\u73b0\u6ce2\u52a8\u3002\u8fd9\u4f7f\u5f97\u786e\u5b9a\u4f18\u5316\u8fc7\u7a0b\u662f\u771f\u6b63\u6536\u655b\u8fd8\u662f\u505c\u7559\u5728\u6b21\u4f18\u89e3\u51b3\u65b9\u6848\u4e2d\u53d8\u5f97\u56f0\u96be\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u7814\u7a76\u4eba\u5458\u901a\u5e38\u4f1a\u5728\u591a\u6b21\u8fd0\u884c\u4e2d\u76d1\u63a7\u635f\u5931\u51fd\u6570\uff0c\u6216\u8005\u6839\u636e\u9a8c\u8bc1\u6027\u80fd\u4f7f\u7528\u63d0\u524d\u505c\u6b62\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u68af\u5ea6\u6d88\u5931\u4e0e\u68af\u5ea6\u7206\u70b8<\/strong>\uff1a\u5728\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u4e2d\uff0c\u68af\u5ea6\u53ef\u80fd\u4f1a\u5728\u8bad\u7ec3\u671f\u95f4\u53d8\u5f97\u975e\u5e38\u5c0f\u6216\u7206\u70b8\uff0c\u4ece\u800c\u5f71\u54cd\u53c2\u6570\u66f4\u65b0\u3002\u68af\u5ea6\u88c1\u526a\u548c\u6279\u91cf\u5f52\u4e00\u5316\u7b49\u6280\u672f\u53ef\u4ee5\u5e2e\u52a9\u7a33\u5b9a\u4f18\u5316\u8fc7\u7a0b\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u978d\u70b9<\/strong>\uff1aSGD \u53ef\u80fd\u4f1a\u9677\u5165\u978d\u70b9\uff0c\u978d\u70b9\u662f\u635f\u5931\u51fd\u6570\u7684\u4e34\u754c\u70b9\uff0c\u5176\u4e2d\u67d0\u4e9b\u65b9\u5411\u5177\u6709\u6b63\u66f2\u7387\uff0c\u800c\u5176\u4ed6\u65b9\u5411\u5177\u6709\u8d1f\u66f2\u7387\u3002\u4f7f\u7528\u57fa\u4e8e\u52a8\u91cf\u7684 SGD \u53d8\u4f53\u53ef\u4ee5\u5e2e\u52a9\u66f4\u6709\u6548\u5730\u514b\u670d\u978d\u70b9\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u4e3b\u8981\u7279\u70b9\u53ca\u4e0e\u540c\u7c7b\u672f\u8bed\u7684\u5176\u4ed6\u6bd4\u8f83<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>\u968f\u673a\u68af\u5ea6\u4e0b\u964d (SGD)<\/th>\n<th>\u6279\u91cf\u68af\u5ea6\u4e0b\u964d<\/th>\n<th>\u5c0f\u6279\u91cf\u68af\u5ea6\u4e0b\u964d<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u6570\u636e\u5904\u7406<\/td>\n<td>\u4ece\u8bad\u7ec3\u6570\u636e\u4e2d\u968f\u673a\u62bd\u53d6\u5c0f\u6279\u91cf\u6837\u672c\u3002<\/td>\n<td>\u4e00\u6b21\u5904\u7406\u6574\u4e2a\u8bad\u7ec3\u6570\u636e\u96c6\u3002<\/td>\n<td>\u968f\u673a\u62bd\u6837\u5c0f\u6279\u91cf\uff0c\u8fd9\u662f SGD \u548c Batch GD \u4e4b\u95f4\u7684\u6298\u8877\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u8ba1\u7b97\u6548\u7387<\/td>\n<td>\u6548\u7387\u9ad8\uff0c\u56e0\u4e3a\u5b83\u53ea\u5904\u7406\u4e00\u5c0f\u90e8\u5206\u6570\u636e\u3002<\/td>\n<td>\u6548\u7387\u8f83\u4f4e\uff0c\u56e0\u4e3a\u5b83\u5904\u7406\u6574\u4e2a\u6570\u636e\u96c6\u3002<\/td>\n<td>\u6709\u6548\uff0c\u4f46\u4e0d\u5982\u7eaf SGD\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u6536\u655b\u6027\u8d28<\/td>\n<td>\u7531\u4e8e\u6446\u8131\u5c40\u90e8\u6700\u5c0f\u503c\uff0c\u53ef\u80fd\u4f1a\u6536\u655b\u5f97\u66f4\u5feb\u3002<\/td>\n<td>\u6536\u655b\u901f\u5ea6\u6162\u4f46\u66f4\u7a33\u5b9a\u3002<\/td>\n<td>\u6bd4 Batch GD \u6536\u655b\u901f\u5ea6\u66f4\u5feb\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u566a\u97f3<\/td>\n<td>\u5f15\u5165\u566a\u58f0\uff0c\u5bfc\u81f4\u635f\u5931\u51fd\u6570\u6ce2\u52a8\u3002<\/td>\n<td>\u7531\u4e8e\u4f7f\u7528\u5b8c\u6574\u6570\u636e\u96c6\u800c\u6ca1\u6709\u566a\u97f3\u3002<\/td>\n<td>\u5f15\u5165\u4e00\u4e9b\u566a\u97f3\uff0c\u4f46\u6bd4\u7eaf SGD \u8981\u5c11\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u76f8\u5173\u7684\u672a\u6765\u89c2\u70b9\u548c\u6280\u672f<\/h2>\n<p>\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u4ecd\u7136\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u57fa\u672c\u4f18\u5316\u7b97\u6cd5\uff0c\u9884\u8ba1\u672a\u6765\u5c06\u53d1\u6325\u91cd\u8981\u4f5c\u7528\u3002\u7814\u7a76\u4eba\u5458\u6b63\u5728\u4e0d\u65ad\u63a2\u7d22\u4fee\u6539\u548c\u6539\u8fdb\uff0c\u4ee5\u63d0\u9ad8\u5176\u6027\u80fd\u548c\u7a33\u5b9a\u6027\u3002\u4e00\u4e9b\u6f5c\u5728\u7684\u672a\u6765\u53d1\u5c55\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u81ea\u9002\u5e94\u5b66\u4e60\u7387<\/strong>\uff1a\u53ef\u4ee5\u5f00\u53d1\u66f4\u590d\u6742\u7684\u81ea\u9002\u5e94\u5b66\u4e60\u7387\u7b97\u6cd5\u6765\u6709\u6548\u5730\u5904\u7406\u66f4\u5e7f\u6cdb\u7684\u4f18\u5316\u95ee\u9898\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5e76\u884c\u5316<\/strong>\uff1a\u5e76\u884c\u5316 SGD \u4ee5\u5229\u7528\u591a\u4e2a\u5904\u7406\u5668\u6216\u5206\u5e03\u5f0f\u8ba1\u7b97\u7cfb\u7edf\u53ef\u4ee5\u663e\u8457\u52a0\u5feb\u5927\u89c4\u6a21\u6a21\u578b\u7684\u8bad\u7ec3\u65f6\u95f4\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u52a0\u901f\u6280\u672f<\/strong>\uff1a\u52a8\u91cf\u3001Nesterov \u52a0\u901f\u548c\u65b9\u5dee\u51cf\u5c11\u65b9\u6cd5\u7b49\u6280\u672f\u53ef\u80fd\u4f1a\u5f97\u5230\u8fdb\u4e00\u6b65\u6539\u8fdb\uff0c\u4ee5\u63d0\u9ad8\u6536\u655b\u901f\u5ea6\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5c06\u5176\u4e0e\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u5173\u8054<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\u5145\u5f53\u5ba2\u6237\u7aef\u548c\u4e92\u8054\u7f51\u4e0a\u5176\u4ed6\u670d\u52a1\u5668\u4e4b\u95f4\u7684\u4e2d\u4ecb\u3002\u867d\u7136\u5b83\u4eec\u4e0e\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u6ca1\u6709\u76f4\u63a5\u5173\u7cfb\uff0c\u4f46\u5b83\u4eec\u5728\u7279\u5b9a\u573a\u666f\u4e2d\u53ef\u80fd\u6709\u7528\u3002\u4f8b\u5982\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u6570\u636e\u9690\u79c1<\/strong>\uff1a\u5728\u654f\u611f\u6216\u4e13\u6709\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u673a\u5668\u5b66\u4e60\u6a21\u578b\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6765\u533f\u540d\u5316\u6570\u636e\uff0c\u4fdd\u62a4\u7528\u6237\u9690\u79c1\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8d1f\u8f7d\u5747\u8861<\/strong>\uff1a\u5728\u5206\u5e03\u5f0f\u673a\u5668\u5b66\u4e60\u7cfb\u7edf\u4e2d\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u534f\u52a9\u8d1f\u8f7d\u5e73\u8861\u5e76\u6709\u6548\u5730\u5206\u914d\u8ba1\u7b97\u5de5\u4f5c\u91cf\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u7f13\u5b58<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u7f13\u5b58\u7ecf\u5e38\u8bbf\u95ee\u7684\u8d44\u6e90\uff0c\u5305\u62ec\u5c0f\u6279\u91cf\u6570\u636e\uff0c\u8fd9\u53ef\u4ee5\u6539\u5584\u8bad\u7ec3\u671f\u95f4\u7684\u6570\u636e\u8bbf\u95ee\u65f6\u95f4\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<p>\u6709\u5173\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7684\u66f4\u591a\u4fe1\u606f\uff0c\u53ef\u4ee5\u53c2\u8003\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n<ol>\n<li><a href=\"http:\/\/cs231n.github.io\/optimization-1\/\" target=\"_new\" rel=\"noopener nofollow\">\u65af\u5766\u798f\u5927\u5b66 CS231n \u4f18\u5316\u65b9\u6cd5\u8bb2\u5ea7<\/a><\/li>\n<li><a href=\"https:\/\/www.deeplearningbook.org\/contents\/optimization.html\" target=\"_new\" rel=\"noopener nofollow\">\u6df1\u5ea6\u5b66\u4e60\u4e66\u7c4d \u2013 \u7b2c 8 \u7ae0\uff1a\u8bad\u7ec3\u6df1\u5ea6\u6a21\u578b\u7684\u4f18\u5316<\/a><\/li>\n<\/ol>\n<p>\u8bb0\u4f4f\u63a2\u7d22\u8fd9\u4e9b\u6765\u6e90\uff0c\u4ee5\u4fbf\u66f4\u6df1\u5165\u5730\u7406\u89e3\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7684\u6982\u5ff5\u548c\u5e94\u7528\u3002<\/p>","protected":false},"featured_media":470609,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479160","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Stochastic Gradient Descent: An In-depth Analysis<\/mark>","faq_items":[{"question":"What is Stochastic Gradient Descent (SGD)?","answer":"<p>Stochastic Gradient Descent (SGD) is an optimization algorithm used in machine learning and deep learning to find the optimal parameters of a model by iteratively updating them based on mini-batches of training data. It introduces randomness in the parameter updates, making it computationally efficient and capable of handling large datasets.<\/p>"},{"question":"How does Stochastic Gradient Descent work?","answer":"<p>SGD works by randomly sampling mini-batches of data from the training set and computing the gradient of the loss function with respect to the model parameters on these mini-batches. The parameters are then updated using the computed gradient and a learning rate, which controls the step size of the updates. This process is repeated iteratively until the convergence criteria are met.<\/p>"},{"question":"What are the key features of Stochastic Gradient Descent?","answer":"<p>The key features of SGD include its efficiency, memory scalability, and ability to escape local minima due to the randomness introduced by mini-batch sampling. However, it can also introduce noise in the optimization process, leading to fluctuations in the loss function during training.<\/p>"},{"question":"What types of Stochastic Gradient Descent exist?","answer":"<p>Several variants of Stochastic Gradient Descent have been developed, including:<\/p><ul><li>Mini-batch Gradient Descent: Uses a fixed-size batch of data points in each iteration.<\/li><li>Online Gradient Descent: Processes one data point at a time.<\/li><li>Momentum SGD: Incorporates momentum to accelerate convergence.<\/li><li>Nesterov Accelerated Gradient (NAG): Adjusts the update direction for better performance.<\/li><li>Adagrad and RMSprop: Adaptive learning rate algorithms.<\/li><li>Adam: Combines benefits of momentum and RMSprop for faster convergence.<\/li><\/ul>"},{"question":"How can Stochastic Gradient Descent be used, and what are the challenges?","answer":"<p>SGD is widely used in machine learning tasks, particularly in training deep neural networks. However, using SGD effectively comes with challenges, such as selecting an appropriate learning rate, dealing with noise and fluctuations, handling vanishing and exploding gradients, and addressing saddle points.<\/p>"},{"question":"What are the future perspectives of Stochastic Gradient Descent?","answer":"<p>In the future, researchers are expected to explore improvements in adaptive learning rates, parallelization, and acceleration techniques to further enhance the performance and stability of SGD in machine learning applications.<\/p>"},{"question":"How are proxy servers associated with Stochastic Gradient Descent?","answer":"<p>Proxy servers can be relevant in scenarios involving data privacy, load balancing in distributed systems, and caching frequently accessed resources like mini-batches during SGD training. They can complement the use of SGD in specific machine learning setups.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/479160","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/479160\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/470609"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=479160"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}