preflt.nn

一个外国人写的人脸检测程序

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net = train2(net, {face;nface}, 1000, 'trainscg');
TRAINSCG-calcgrad, Epoch 0/1000, MSE 0.0706625/0, Gradient 0.0408157/1e-006
TRAINSCG-calcgrad, Epoch 25/1000, MSE 0.0252248/0, Gradient 0.00524303/1e-006
TRAINSCG-calcgrad, Epoch 50/1000, MSE 0.0208643/0, Gradient 0.00410831/1e-006
TRAINSCG-calcgrad, Epoch 75/1000, MSE 0.0199327/0, Gradient 0.00245891/1e-006
TRAINSCG-calcgrad, Epoch 100/1000, MSE 0.0183846/0, Gradient 0.00243653/1e-006
TRAINSCG-calcgrad, Epoch 125/1000, MSE 0.0167635/0, Gradient 0.00413765/1e-006
TRAINSCG-calcgrad, Epoch 150/1000, MSE 0.0156056/0, Gradient 0.00222181/1e-006
TRAINSCG-calcgrad, Epoch 175/1000, MSE 0.0145851/0, Gradient 0.00154122/1e-006
TRAINSCG-calcgrad, Epoch 200/1000, MSE 0.0138646/0, Gradient 0.00187787/1e-006
TRAINSCG-calcgrad, Epoch 225/1000, MSE 0.0130431/0, Gradient 0.00110514/1e-006
TRAINSCG-calcgrad, Epoch 250/1000, MSE 0.0124017/0, Gradient 0.000794686/1e-006
TRAINSCG-calcgrad, Epoch 275/1000, MSE 0.0119851/0, Gradient 0.000686196/1e-006
TRAINSCG-calcgrad, Epoch 300/1000, MSE 0.0115745/0, Gradient 0.000538587/1e-006
TRAINSCG-calcgrad, Epoch 325/1000, MSE 0.0110115/0, Gradient 0.000619825/1e-006
TRAINSCG-calcgrad, Epoch 350/1000, MSE 0.0106856/0, Gradient 0.000397777/1e-006
TRAINSCG-calcgrad, Epoch 375/1000, MSE 0.0103944/0, Gradient 0.000335075/1e-006
TRAINSCG-calcgrad, Epoch 400/1000, MSE 0.0101617/0, Gradient 0.000491891/1e-006
TRAINSCG-calcgrad, Epoch 425/1000, MSE 0.00997477/0, Gradient 0.00054992/1e-006
TRAINSCG-calcgrad, Epoch 450/1000, MSE 0.00982742/0, Gradient 0.000277635/1e-006
TRAINSCG-calcgrad, Epoch 475/1000, MSE 0.00973586/0, Gradient 0.000246542/1e-006
TRAINSCG-calcgrad, Epoch 500/1000, MSE 0.00964214/0, Gradient 0.000430392/1e-006
TRAINSCG-calcgrad, Epoch 525/1000, MSE 0.00955025/0, Gradient 0.000208358/1e-006
TRAINSCG-calcgrad, Epoch 550/1000, MSE 0.00946008/0, Gradient 0.000259309/1e-006
TRAINSCG-calcgrad, Epoch 575/1000, MSE 0.00935917/0, Gradient 0.000293115/1e-006
TRAINSCG-calcgrad, Epoch 600/1000, MSE 0.00928761/0, Gradient 0.000297855/1e-006
TRAINSCG-calcgrad, Epoch 625/1000, MSE 0.00922528/0, Gradient 0.000220927/1e-006
TRAINSCG-calcgrad, Epoch 650/1000, MSE 0.0091606/0, Gradient 0.000237646/1e-006
TRAINSCG-calcgrad, Epoch 675/1000, MSE 0.0091048/0, Gradient 0.000301604/1e-006
TRAINSCG-calcgrad, Epoch 700/1000, MSE 0.00903629/0, Gradient 0.000338506/1e-006
TRAINSCG-calcgrad, Epoch 725/1000, MSE 0.00897432/0, Gradient 0.000207054/1e-006
TRAINSCG-calcgrad, Epoch 750/1000, MSE 0.00892802/0, Gradient 0.000189826/1e-006
TRAINSCG-calcgrad, Epoch 775/1000, MSE 0.00888097/0, Gradient 0.000197304/1e-006
TRAINSCG-calcgrad, Epoch 800/1000, MSE 0.00882134/0, Gradient 0.00015089/1e-006
TRAINSCG-calcgrad, Epoch 825/1000, MSE 0.00875718/0, Gradient 0.000262308/1e-006
TRAINSCG-calcgrad, Epoch 850/1000, MSE 0.00870468/0, Gradient 0.000160878/1e-006
TRAINSCG-calcgrad, Epoch 875/1000, MSE 0.00865585/0, Gradient 0.000154301/1e-006
TRAINSCG-calcgrad, Epoch 900/1000, MSE 0.00860733/0, Gradient 0.000194127/1e-006
TRAINSCG-calcgrad, Epoch 925/1000, MSE 0.00853522/0, Gradient 0.000360248/1e-006
TRAINSCG-calcgrad, Epoch 950/1000, MSE 0.00847012/0, Gradient 0.000149492/1e-006
TRAINSCG-calcgrad, Epoch 975/1000, MSE 0.00841884/0, Gradient 0.00017306/1e-006
TRAINSCG-calcgrad, Epoch 1000/1000, MSE 0.0083688/0, Gradient 0.000173782/1e-006
TRAINSCG, Maximum epoch reached, performance goal was not met.

elapsed time: 18.441611 min
 train set: 1788 17853
  se: 95.08
  sp: 99.16
  pp: 91.89
  np: 99.51
  ac: 98.79
  er: 0.008369