{"id":261,"date":"2019-03-21T03:55:25","date_gmt":"2019-03-21T03:55:25","guid":{"rendered":""},"modified":"2020-09-29T15:15:49","modified_gmt":"2020-09-29T07:15:49","slug":"%e8%bd%ac-%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%ef%bc%9a%e6%8d%9f%e5%a4%b1%e5%87%bd%e6%95%b0","status":"publish","type":"post","link":"http:\/\/weizn.net\/?p=261","title":{"rendered":"[\u8f6c] \u6df1\u5ea6\u5b66\u4e60\uff1a\u635f\u5931\u51fd\u6570"},"content":{"rendered":"

\u6b22\u8fce\u8f6c\u8f7d\uff0c\u4f5c\u8005\uff1aLing\uff0c\u6ce8\u660e\u51fa\u5904\uff1a<\/span>\u6df1\u5ea6\u5b66\u4e60\uff1a\u539f\u7406\u7b80\u660e\u6559\u7a0b09-\u6df1\u5ea6\u5b66\u4e60:\u635f\u5931\u51fd\u6570<\/a><\/span><\/p>\n

\u635f\u5931\u51fd\u6570<\/span>\uff1a\u53c8\u79f0Objective\u51fd\u6570\u6216\u8005\u79f0Cost\u51fd\u6570\uff0c\u4e3b\u8981\u662f\u7528\u4e8e\u8861\u91cf\u9884\u6d4b\u503c\u4e0e\u771f\u5b9e\u503c\u7684\u5dee\u5f02\u60c5\u51b5\u3002<\/span><\/p>\n

1\uff09\u5982\u679c\u9884\u6d4b\u7ed3\u679c\u548c\u771f\u5b9e\u7ed3\u679c\u8d8a\u63a5\u8fd1\uff0c\u635f\u5931\u51fd\u6570\u503c\u8d8a\u5c0f\uff0c\u9884\u6d4b\u7ed3\u679c\u548c\u771f\u5b9e\u7ed3\u679c\u8d8a\u4e0d\u540c\uff0c\u635f\u5931\u51fd\u6570\u503c\u8d8a\u5927<\/span><\/p>\n

2\uff09\u901a\u8fc7BP\u7b97\u6cd5\uff0c\u6839\u636e\u635f\u5931\u51fd\u6570\uff0c\u53ef\u4ee5\u4e0d\u65ad\u4f18\u5316\u795e\u7ecf\u7f51\u7edc\u5404\u79cd\u53c2\u6570<\/span><\/p>\n

\u6846\u67b6\u5e38\u89c1\u635f\u5931\u51fd\u6570\u6709<\/span>\uff1a\u57fa\u672c\u6240\u6709\u635f\u5931\u51fd\u6570\u90fd\u662f\u8f93\u5165\u9884\u6d4b\u503c\u4e0e\u771f\u5b9e\u503c<\/span><\/p>\n

mean_squared_error<\/span><\/p>\n

mean_absolute_error<\/span><\/p>\n

mean_absolute_percentage_error<\/span><\/p>\n

mean_squared_logarithmic_error<\/span><\/p>\n

squared_hinge<\/span><\/p>\n

hinge<\/span><\/p>\n

categorical_hinge<\/span><\/p>\n

logcosh<\/span><\/p>\n

binary_crossentropy<\/span><\/p>\n

categorical_crossentropy<\/span><\/p>\n

sparse_categorical_crossentropy<\/span><\/p>\n

kullback_leibler_divergence<\/span><\/p>\n

poisson<\/span><\/p>\n

cosine_proximity<\/span><\/p>\n

mean_squared_error<\/span>\uff1a<\/span><\/p>\n

\u516c\u5f0f<\/span>\uff1a<\/span><\/p>\n

\"dl_09_001-1\"<\/p>\n

\u7279\u70b9<\/span>\uff1a<\/span><\/p>\n

1\uff09\u5e38\u7528\u4e8e\u56de\u5f52\u95ee\u9898<\/span><\/p>\n

2\uff09MSE\u5747\u65b9\u8bef\u5dee\u635f\u5931<\/span><\/p>\n

\u00a0<\/span><\/p>\n

mean_absolute_error<\/span>\uff1a<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_002-1\"<\/p>\n

\u7279\u70b9<\/span>\uff1a<\/span><\/p>\n

1\uff09MAE\u5e73\u5747\u7edd\u5bf9\u503c\u8bef\u5dee\u635f\u5931<\/span><\/p>\n

mean_absolute_percentage_error\uff1a<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_003-1\"<\/p>\n

\u7279\u70b9\uff1a<\/span><\/p>\n

1\uff09MAPE\u5e73\u5747\u7edd\u5bf9\u767e\u5206\u8bef\u5dee<\/span><\/p>\n

mean_squared_logarithmic_error\uff1a<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_004-1\"<\/p>\n

\u7279\u70b9<\/span>\uff1a<\/span><\/p>\n

1\uff09MSLE\u5747\u65b9\u5bf9\u6570\u635f\u5931(mse\u7684\u6539\u8fdb)<\/span><\/p>\n

2\uff09MSLE\u76f8\u6bd4\u4e0eMSE\u7684\u6539\u8fdb:\u5982\u679c\u60f3\u8981\u9884\u6d4b\u7684\u503c\u8303\u56f4\u5f88\u5927,MSE\u4f1a\u53d7\u5230\u4e00\u4e9b\u5927\u7684\u503c\u7684\u5f15\u5bfc,\u5373\u4f7f\u5c0f\u7684\u503c\u9884\u6d4b\u51c6\u4e5f\u4e0d\u884c.\u5047\u8bbe\u5982:<\/span><\/p>\n

y_true:[1,2,3,100]<\/span><\/p>\n

y_1:[1,2,3,110]<\/span><\/p>\n

y_2:[2,3,4,100]<\/span><\/p>\n

MSE\u8ba1\u7b97\u7ed3\u679c(y_1:100,y_2:3)\u4f1a\u8ba4\u4e3ay_2\u4f18\u4e8ey_1.<\/span><\/p>\n

MSLE\u8ba1\u7b97\u7ed3\u679c(\u6709_1:0.047,0.27)\u901a\u8fc7\u9884\u5148\u5c06\u6240\u6709\u503c\u53d6log\u7f13\u89e3\u4e86\u8fd9\u4e00\u60c5\u51b5,\u4f1a\u8ba4\u4e3ay_1\u4f18\u4e8ey_2.\u8fd9\u6bd4\u8f83\u5408\u7406.<\/span><\/p>\n

\u00a0<\/span><\/p>\n

squared_hinge\uff1a<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_005-1\"<\/p>\n

hinge\uff1a<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_006-1\"<\/p>\n

\u00a0<\/span><\/p>\n

categorical_hinge\uff1a<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_007-1\"<\/p>\n

\u00a0<\/span><\/p>\n

logcosh\uff1a<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_008-1\"<\/p>\n

\u7279\u70b9<\/span>\uff1a<\/span><\/p>\n

1\uff09\u4ece\u56fe\u4e2d\u5927\u81f4\u53ef\u4ee5\u770b\u51fa\u6765\u5982\u679cy_pred\u4e0ey_true\u5dee\u5f02\u8d8a\u5c0f\u5219\u503c\u8d8a\u5c0f.<\/span><\/p>\n

\"dl_09_009-1\"<\/p>\n

2\uff09\u611f\u89c9\u7528\u5f97\u4e0d\u591a<\/span><\/p>\n

\u00a0<\/span><\/p>\n

binary_crossentropy\uff1a\uff08\u6ce8\u610fkeras\u4e2d\u662f\u6982\u7387\uff0c\u9700\u8981\u5148\u8f6c\u6210logits\uff0c\u4e5f\u5c31\u662fw*x\u6ca1\u6709\u7ecf\u8fc7sigmoid\u4e4b\u524d\u7684\u503c\uff09<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

L\u00a0\u00a0\u00a0 = y * -log(sigmoid(z)) + (1 \u2013 y) * -log(1 \u2013 sigmoid(z))<\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0 = y * -log(1 \/ (1 + exp(-z))) + (1 \u2013 y) * -log(exp(-z) \/ (1 + exp(-z)))<\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0 = y * log(1 + exp(-z)) + (1 -y) * (-log(exp(-z)) + log(1 + exp(-z)))<\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0 = y * log(1 + exp(-z)) + (1 \u2013 y) * (z + log(1 + exp(-z))<\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0 = (1 \u2013 y) * z + log(1 + exp(-z))<\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0 = z \u2013 z * y + log(1 + exp(-z))<\/span><\/p>\n

\u5176\u4e2dz=w*x\uff0c\u5373\u6ca1\u6709\u505asigmoid\u4e4b\u524d\u7684\u503c\uff0c\u5728tensorflow\u4e2d\u79f0\u4e3alogits<\/span><\/p>\n

\u5982\u679c\u662f\u505a\u4e86sigmod\u4e4b\u540e\u7684\u503c\u4e3a\uff1a<\/span><\/p>\n

\"dl_09_010-1\"<\/p>\n

\u672c\u6587\u6807\u51c6\u5199\u6cd5\uff1a<\/span><\/p>\n

\"dl_09_011-1\"<\/p>\n

\u7279\u70b9\uff1a<\/span><\/p>\n

1\uff09\u4e3b\u8981\u7528\u4e8e\u4e8c\u5206\u7c7b<\/span><\/p>\n

2\uff09\u914d\u5408sigmod\u7528<\/span><\/p>\n

\u00a0<\/span><\/p>\n

categorical_crossentropy:<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_012-1\"<\/p>\n

\u7279\u70b9<\/span>\uff1a<\/span><\/p>\n

1\uff09\u5176\u4e2dn\u4e3a\u6837\u672c\u6570\uff0cc\u4e3alabel\u7c7b\u522b\uff0c\u4e3b\u8981\u9002\u7528\u4e8eone-hot<\/span><\/p>\n

2\uff09\u4e00\u5b9a\u914d\u5408softmax<\/span><\/p>\n

3\uff09\u9002\u7528\u4e8e\u591a\u5206\u7c7b\u95ee\u9898<\/span><\/p>\n

4\uff09\u5fc5\u987b\u662fone-hot\u7c7b\u578b\u7684vector\u4f5c\u4e3ay\u503c<\/span><\/p>\n

\u00a0<\/span><\/p>\n

sparse_categorical_crossentropy\uff1a<\/span><\/p>\n

\u7279\u70b9\uff1a<\/span><\/p>\n

1\uff09\u548ccategorical_crossentropy\u4e00\u6837\uff0c\u4e0d\u8fc7\u53ef\u4ee5\u63a5\u53d7\u975eone-hot\u7684vector\u4f5c\u4e3ay\u503c<\/span><\/p>\n

2\uff09\u914d\u5408softmax\u7528<\/span><\/p>\n

3\uff09\u53ef\u7528\u4e8e\u7a00\u758f\u591a\u5206\u7c7b\u95ee\u9898<\/span><\/p>\n

\u00a0<\/span><\/p>\n

kullback_leibler_divergence\uff1a<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_013-1\"<\/p>\n

\u7279\u70b9\uff1a<\/span><\/p>\n

1\uff09\u4e24\u4e2a\u6982\u7387\u5206\u5e03P\u548cQ\u5dee\u522b\u7684\u975e\u5bf9\u79f0\u6027\u7684\u5ea6\u91cf. \u5178\u578b\u60c5\u51b5\u4e0b\uff0cP\u8868\u793a\u6570\u636e\u7684\u771f\u5b9e\u5206\u5e03\uff0cQ\u8868\u793a\u6570\u636e\u7684\u7406\u8bba\u5206\u5e03\uff0c\u6a21\u578b\u5206\u5e03\uff0c\u6216P\u7684\u8fd1\u4f3c\u5206\u5e03\u3002\u5982\u679c\u4e24\u8005\u53ef\u4ee5\u4e92\u76f8\u66ff\u4ee3\uff0c\u503c\u8d8a\u5c0f\uff0c\u5426\u5219\u8d8a\u5927\u3002<\/span><\/p>\n

2\uff09KL\u6563\u5ea6<\/span><\/p>\n

\u00a0<\/span><\/p>\n

\u00a0<\/span><\/p>\n

poisson\uff1a<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_014-1\"<\/p>\n

\u00a0<\/span><\/p>\n

cosine_proximity\uff1a<\/span><\/p>\n

\u516c\u5f0f\uff1a<\/span><\/p>\n

\"dl_09_015-1\"<\/p>\n

\u00a0<\/span><\/p>\n

\u603b\u7ed3\uff1a<\/span><\/p>\n

1\uff09\u56de\u5f52\u95ee\u9898\uff0c\u7ecf\u5e38\u7528\uff1a<\/span><\/p>\n

mean_squared_error<\/span><\/p>\n

mean_absolute_error<\/span><\/p>\n

mean_absolute_percentage_error<\/span><\/p>\n

mean_squared_logarithmic_error<\/span><\/p>\n

squared_hinge<\/span><\/p>\n

hinge<\/span><\/p>\n

categorical_hinge<\/span><\/p>\n

logcosh<\/span><\/p>\n

kullback_leibler_divergence<\/span><\/p>\n

poisson<\/span><\/p>\n

cosine_proximity<\/span><\/p>\n

2\uff09\u4e8c\u5206\u7c7b\u95ee\u9898\u5e38\u7528\uff1a<\/span><\/p>\n

binary_crossentropy<\/span><\/p>\n

3\uff09\u591a\u5206\u7c7b\u95ee\u9898\u5e38\u7528\uff1a<\/span><\/p>\n

categorical_crossentropy\uff1aone-hot\u7684y<\/span><\/p>\n

sparse_categorical_crossentropy\uff1a\u975eone-hot\u7684y<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

\u635f\u5931\u51fd\u6570\uff1a\u53c8\u79f0Objective\u51fd\u6570\u6216\u8005\u79f0Cost\u51fd\u6570\uff0c\u4e3b\u8981\u662f\u7528\u4e8e\u8861\u91cf\u9884\u6d4b\u503c\u4e0e\u771f\u5b9e\u503c\u7684\u5dee\u5f02\u60c5\u51b5\u3002<\/p>\n

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