caffe_log.txt

I0320 14:39:22.460748  8660 solver.cpp:219] Iteration 0 (-6.57593e+33 iter/s, 0.322088s/250 iters), loss = 0.901091
I0320 14:39:22.460778  8660 solver.cpp:238]     Train net output #0: loss1 = 0.693134 (* 0.3 = 0.20794 loss)
I0320 14:39:22.460798  8660 solver.cpp:238]     Train net output #1: loss2 = 0.693151 (* 1 = 0.693151 loss)
I0320 14:39:22.460814  8660 sgd_solver.cpp:105] Iteration 0, lr = 0.0001
I0320 14:40:56.452066  8660 solver.cpp:219] Iteration 250 (2.65983 iter/s, 93.9909s/250 iters), loss = 0.133513
I0320 14:40:56.452141  8660 solver.cpp:238]     Train net output #0: loss1 = 0.119867 (* 0.3 = 0.0359602 loss)
I0320 14:40:56.452162  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0975531 (* 1 = 0.0975531 loss)
I0320 14:40:56.452167  8660 sgd_solver.cpp:105] Iteration 250, lr = 0.0001
I0320 14:42:58.538004  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_500.caffemodel
I0320 14:42:58.732300  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_500.solverstate
I0320 14:42:58.995854  8660 solver.cpp:219] Iteration 500 (2.0401 iter/s, 122.543s/250 iters), loss = 0.196389
I0320 14:42:58.995885  8660 solver.cpp:238]     Train net output #0: loss1 = 0.172398 (* 0.3 = 0.0517195 loss)
I0320 14:42:58.995905  8660 solver.cpp:238]     Train net output #1: loss2 = 0.14467 (* 1 = 0.14467 loss)
I0320 14:42:58.995910  8660 sgd_solver.cpp:105] Iteration 500, lr = 0.0001
I0320 14:44:54.118072  8660 solver.cpp:219] Iteration 750 (2.17162 iter/s, 115.121s/250 iters), loss = 0.214478
I0320 14:44:54.118132  8660 solver.cpp:238]     Train net output #0: loss1 = 0.171585 (* 0.3 = 0.0514756 loss)
I0320 14:44:54.118141  8660 solver.cpp:238]     Train net output #1: loss2 = 0.163003 (* 1 = 0.163003 loss)
I0320 14:44:54.118146  8660 sgd_solver.cpp:105] Iteration 750, lr = 0.0001
I0320 14:46:31.545164  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_1000.caffemodel
I0320 14:46:31.732240  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_1000.solverstate
I0320 14:46:31.994148  8660 solver.cpp:219] Iteration 1000 (2.55427 iter/s, 97.8754s/250 iters), loss = 0.0636998
I0320 14:46:31.994179  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0688252 (* 0.3 = 0.0206476 loss)
I0320 14:46:31.994199  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0430523 (* 1 = 0.0430523 loss)
I0320 14:46:31.994207  8660 sgd_solver.cpp:105] Iteration 1000, lr = 0.0001
I0320 14:48:27.806548  8660 solver.cpp:219] Iteration 1250 (2.15868 iter/s, 115.812s/250 iters), loss = 0.103568
I0320 14:48:27.806610  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0975634 (* 0.3 = 0.029269 loss)
I0320 14:48:27.806618  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0742987 (* 1 = 0.0742987 loss)
I0320 14:48:27.806624  8660 sgd_solver.cpp:105] Iteration 1250, lr = 0.0001
I0320 14:50:21.811064  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_1500.caffemodel
I0320 14:50:21.997051  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_1500.solverstate
I0320 14:50:22.302403  8660 solver.cpp:219] Iteration 1500 (2.1835 iter/s, 114.495s/250 iters), loss = 0.168361
I0320 14:50:22.302433  8660 solver.cpp:238]     Train net output #0: loss1 = 0.131816 (* 0.3 = 0.0395447 loss)
I0320 14:50:22.302453  8660 solver.cpp:238]     Train net output #1: loss2 = 0.128817 (* 1 = 0.128817 loss)
I0320 14:50:22.302459  8660 sgd_solver.cpp:105] Iteration 1500, lr = 0.0001
I0320 14:52:00.561578  8660 solver.cpp:219] Iteration 1750 (2.54431 iter/s, 98.2584s/250 iters), loss = 0.0988486
I0320 14:52:00.561626  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0847277 (* 0.3 = 0.0254183 loss)
I0320 14:52:00.561647  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0734305 (* 1 = 0.0734305 loss)
I0320 14:52:00.561653  8660 sgd_solver.cpp:105] Iteration 1750, lr = 0.0001
I0320 14:53:52.533936  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_2000.caffemodel
I0320 14:53:52.719159  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_2000.solverstate
I0320 14:53:52.983052  8660 solver.cpp:219] Iteration 2000 (2.22379 iter/s, 112.421s/250 iters), loss = 0.136109
I0320 14:53:52.983079  8660 solver.cpp:238]     Train net output #0: loss1 = 0.110293 (* 0.3 = 0.0330879 loss)
I0320 14:53:52.983085  8660 solver.cpp:238]     Train net output #1: loss2 = 0.103021 (* 1 = 0.103021 loss)
I0320 14:53:52.983090  8660 sgd_solver.cpp:105] Iteration 2000, lr = 0.0001
I0320 14:55:39.330852  8660 solver.cpp:219] Iteration 2250 (2.3508 iter/s, 106.347s/250 iters), loss = 0.189567
I0320 14:55:39.331012  8660 solver.cpp:238]     Train net output #0: loss1 = 0.163043 (* 0.3 = 0.0489129 loss)
I0320 14:55:39.331020  8660 solver.cpp:238]     Train net output #1: loss2 = 0.140655 (* 1 = 0.140655 loss)
I0320 14:55:39.331027  8660 sgd_solver.cpp:105] Iteration 2250, lr = 0.0001
I0320 14:57:44.294443  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_2500.caffemodel
I0320 14:57:44.494257  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_2500.solverstate
I0320 14:57:44.754773  8660 solver.cpp:219] Iteration 2500 (1.99326 iter/s, 125.423s/250 iters), loss = 0.119829
I0320 14:57:44.754807  8660 solver.cpp:238]     Train net output #0: loss1 = 0.108856 (* 0.3 = 0.0326569 loss)
I0320 14:57:44.754828  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0871725 (* 1 = 0.0871725 loss)
I0320 14:57:44.754835  8660 sgd_solver.cpp:105] Iteration 2500, lr = 0.0001
I0320 14:59:29.422793  8660 solver.cpp:219] Iteration 2750 (2.38852 iter/s, 104.667s/250 iters), loss = 0.0390435
I0320 14:59:29.422924  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0360377 (* 0.3 = 0.0108113 loss)
I0320 14:59:29.422946  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0282323 (* 1 = 0.0282323 loss)
I0320 14:59:29.422951  8660 sgd_solver.cpp:105] Iteration 2750, lr = 0.0001
I0320 15:01:13.495165  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_3000.caffemodel
I0320 15:01:13.680974  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_3000.solverstate
I0320 15:01:13.943644  8660 solver.cpp:219] Iteration 3000 (2.39189 iter/s, 104.52s/250 iters), loss = 0.168349
I0320 15:01:13.943670  8660 solver.cpp:238]     Train net output #0: loss1 = 0.132356 (* 0.3 = 0.0397067 loss)
I0320 15:01:13.943676  8660 solver.cpp:238]     Train net output #1: loss2 = 0.128642 (* 1 = 0.128642 loss)
I0320 15:01:13.943696  8660 sgd_solver.cpp:105] Iteration 3000, lr = 0.0001
I0320 15:02:59.868567  8660 solver.cpp:219] Iteration 3250 (2.36018 iter/s, 105.924s/250 iters), loss = 0.15818
I0320 15:02:59.868726  8660 solver.cpp:238]     Train net output #0: loss1 = 0.13571 (* 0.3 = 0.0407129 loss)
I0320 15:02:59.868734  8660 solver.cpp:238]     Train net output #1: loss2 = 0.117467 (* 1 = 0.117467 loss)
I0320 15:02:59.868741  8660 sgd_solver.cpp:105] Iteration 3250, lr = 0.0001
I0320 15:04:45.611006  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_3500.caffemodel
I0320 15:04:45.797266  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_3500.solverstate
I0320 15:04:46.059311  8660 solver.cpp:219] Iteration 3500 (2.35428 iter/s, 106.19s/250 iters), loss = 0.0794251
I0320 15:04:46.059343  8660 solver.cpp:238]     Train net output #0: loss1 = 0.063437 (* 0.3 = 0.0190311 loss)
I0320 15:04:46.059363  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0603942 (* 1 = 0.0603942 loss)
I0320 15:04:46.059368  8660 sgd_solver.cpp:105] Iteration 3500, lr = 0.0001
I0320 15:06:41.588568  8660 solver.cpp:219] Iteration 3750 (2.16397 iter/s, 115.528s/250 iters), loss = 0.024885
I0320 15:06:41.588632  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0268489 (* 0.3 = 0.00805466 loss)
I0320 15:06:41.588641  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0168305 (* 1 = 0.0168305 loss)
I0320 15:06:41.588646  8660 sgd_solver.cpp:105] Iteration 3750, lr = 0.0001
I0320 15:08:27.386817  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_4000.caffemodel
I0320 15:08:27.571789  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_4000.solverstate
I0320 15:08:27.833307  8660 solver.cpp:219] Iteration 4000 (2.35308 iter/s, 106.244s/250 iters), loss = 0.0590761
I0320 15:08:27.833334  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0408715 (* 0.3 = 0.0122614 loss)
I0320 15:08:27.833354  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0468148 (* 1 = 0.0468148 loss)
I0320 15:08:27.833360  8660 sgd_solver.cpp:105] Iteration 4000, lr = 0.0001
I0320 15:10:13.062584  8660 solver.cpp:219] Iteration 4250 (2.37578 iter/s, 105.228s/250 iters), loss = 0.0416112
I0320 15:10:13.062634  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0322575 (* 0.3 = 0.00967725 loss)
I0320 15:10:13.062656  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0319341 (* 1 = 0.0319341 loss)
I0320 15:10:13.062661  8660 sgd_solver.cpp:105] Iteration 4250, lr = 0.0001
I0320 15:11:50.426448  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_4500.caffemodel
I0320 15:11:50.613603  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_4500.solverstate
I0320 15:11:50.876730  8660 solver.cpp:219] Iteration 4500 (2.55592 iter/s, 97.8122s/250 iters), loss = 0.175392
I0320 15:11:50.876758  8660 solver.cpp:238]     Train net output #0: loss1 = 0.131334 (* 0.3 = 0.0394001 loss)
I0320 15:11:50.876778  8660 solver.cpp:238]     Train net output #1: loss2 = 0.135992 (* 1 = 0.135992 loss)
I0320 15:11:50.876785  8660 sgd_solver.cpp:105] Iteration 4500, lr = 0.0001
I0320 15:13:37.791244  8660 solver.cpp:219] Iteration 4750 (2.33836 iter/s, 106.913s/250 iters), loss = 0.122099
I0320 15:13:37.791432  8660 solver.cpp:238]     Train net output #0: loss1 = 0.129181 (* 0.3 = 0.0387543 loss)
I0320 15:13:37.791442  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0833454 (* 1 = 0.0833454 loss)
I0320 15:13:37.791447  8660 sgd_solver.cpp:105] Iteration 4750, lr = 0.0001
I0320 15:15:32.981083  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_5000.caffemodel
I0320 15:15:33.178998  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_5000.solverstate
I0320 15:15:33.442401  8660 solver.cpp:219] Iteration 5000 (2.16171 iter/s, 115.649s/250 iters), loss = 0.0323473
I0320 15:15:33.442445  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0299938 (* 0.3 = 0.00899815 loss)
I0320 15:15:33.442451  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0233493 (* 1 = 0.0233493 loss)
I0320 15:15:33.442457  8660 sgd_solver.cpp:105] Iteration 5000, lr = 0.0001
I0320 15:17:26.805774  8660 solver.cpp:219] Iteration 5250 (2.20533 iter/s, 113.362s/250 iters), loss = 0.151089
I0320 15:17:26.805824  8660 solver.cpp:238]     Train net output #0: loss1 = 0.104227 (* 0.3 = 0.0312681 loss)
I0320 15:17:26.805845  8660 solver.cpp:238]     Train net output #1: loss2 = 0.119822 (* 1 = 0.119822 loss)
I0320 15:17:26.805850  8660 sgd_solver.cpp:105] Iteration 5250, lr = 0.0001
I0320 15:19:29.502621  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_5500.caffemodel
I0320 15:19:29.690469  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_5500.solverstate
I0320 15:19:29.952426  8660 solver.cpp:219] Iteration 5500 (2.03013 iter/s, 123.145s/250 iters), loss = 0.0328786
I0320 15:19:29.952453  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0293932 (* 0.3 = 0.00881797 loss)
I0320 15:19:29.952474  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0240607 (* 1 = 0.0240607 loss)
I0320 15:19:29.952479  8660 sgd_solver.cpp:105] Iteration 5500, lr = 0.0001
I0320 15:21:16.545523  8660 solver.cpp:219] Iteration 5750 (2.3454 iter/s, 106.592s/250 iters), loss = 0.07115
I0320 15:21:16.545680  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0545106 (* 0.3 = 0.0163532 loss)
I0320 15:21:16.545691  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0547969 (* 1 = 0.0547969 loss)
I0320 15:21:16.545696  8660 sgd_solver.cpp:105] Iteration 5750, lr = 0.0001
I0320 15:22:53.719756  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_6000.caffemodel
I0320 15:22:53.907472  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_6000.solverstate
I0320 15:22:54.169785  8660 solver.cpp:219] Iteration 6000 (2.56087 iter/s, 97.6229s/250 iters), loss = 0.0506827
I0320 15:22:54.169838  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0271124 (* 0.3 = 0.00813373 loss)
I0320 15:22:54.169859  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0425492 (* 1 = 0.0425492 loss)
I0320 15:22:54.169867  8660 sgd_solver.cpp:105] Iteration 6000, lr = 0.0001
I0320 15:25:07.275828  8660 solver.cpp:219] Iteration 6250 (1.87822 iter/s, 133.104s/250 iters), loss = 0.228438
I0320 15:25:07.275892  8660 solver.cpp:238]     Train net output #0: loss1 = 0.177147 (* 0.3 = 0.0531441 loss)
I0320 15:25:07.275899  8660 solver.cpp:238]     Train net output #1: loss2 = 0.175294 (* 1 = 0.175294 loss)
I0320 15:25:07.275904  8660 sgd_solver.cpp:105] Iteration 6250, lr = 0.0001
I0320 15:26:52.741569  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_6500.caffemodel
I0320 15:26:52.928666  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_6500.solverstate
I0320 15:26:53.192320  8660 solver.cpp:219] Iteration 6500 (2.36038 iter/s, 105.915s/250 iters), loss = 0.0346454
I0320 15:26:53.192363  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0339637 (* 0.3 = 0.0101891 loss)
I0320 15:26:53.192368  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0244564 (* 1 = 0.0244564 loss)
I0320 15:26:53.192376  8660 sgd_solver.cpp:105] Iteration 6500, lr = 0.0001
I0320 15:28:31.327363  8660 solver.cpp:219] Iteration 6750 (2.54754 iter/s, 98.1339s/250 iters), loss = 0.0888975
I0320 15:28:31.327512  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0633339 (* 0.3 = 0.0190002 loss)
I0320 15:28:31.327522  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0698975 (* 1 = 0.0698975 loss)
I0320 15:28:31.327527  8660 sgd_solver.cpp:105] Iteration 6750, lr = 0.0001
I0320 15:30:15.106427  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_7000.caffemodel
I0320 15:30:15.293828  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_7000.solverstate
I0320 15:30:15.611948  8660 solver.cpp:219] Iteration 7000 (2.39732 iter/s, 104.283s/250 iters), loss = 0.0555131
I0320 15:30:15.611980  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0467004 (* 0.3 = 0.0140101 loss)
I0320 15:30:15.611986  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0415031 (* 1 = 0.0415031 loss)
I0320 15:30:15.612005  8660 sgd_solver.cpp:105] Iteration 7000, lr = 0.0001
I0320 15:32:07.765674  8660 solver.cpp:219] Iteration 7250 (2.22911 iter/s, 112.152s/250 iters), loss = 0.16715
I0320 15:32:07.765723  8660 solver.cpp:238]     Train net output #0: loss1 = 0.117516 (* 0.3 = 0.0352548 loss)
I0320 15:32:07.765743  8660 solver.cpp:238]     Train net output #1: loss2 = 0.131895 (* 1 = 0.131895 loss)
I0320 15:32:07.765748  8660 sgd_solver.cpp:105] Iteration 7250, lr = 0.0001
I0320 15:34:11.548018  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_7500.caffemodel
I0320 15:34:11.735795  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_7500.solverstate
I0320 15:34:11.998136  8660 solver.cpp:219] Iteration 7500 (2.01238 iter/s, 124.231s/250 iters), loss = 0.0519847
I0320 15:34:11.998183  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0456833 (* 0.3 = 0.013705 loss)
I0320 15:34:11.998189  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0382799 (* 1 = 0.0382799 loss)
I0320 15:34:11.998195  8660 sgd_solver.cpp:105] Iteration 7500, lr = 0.0001
I0320 15:35:49.500708  8660 solver.cpp:219] Iteration 7750 (2.56406 iter/s, 97.5015s/250 iters), loss = 0.0490289
I0320 15:35:49.500756  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0458159 (* 0.3 = 0.0137448 loss)
I0320 15:35:49.500777  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0352843 (* 1 = 0.0352843 loss)
I0320 15:35:49.500784  8660 sgd_solver.cpp:105] Iteration 7750, lr = 0.0001
I0320 15:37:40.407963  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_8000.caffemodel
I0320 15:37:40.596401  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_8000.solverstate
I0320 15:37:40.860996  8660 solver.cpp:219] Iteration 8000 (2.24499 iter/s, 111.359s/250 iters), loss = 0.0554023
I0320 15:37:40.861027  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0440226 (* 0.3 = 0.0132068 loss)
I0320 15:37:40.861047  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0421957 (* 1 = 0.0421957 loss)
I0320 15:37:40.861052  8660 sgd_solver.cpp:105] Iteration 8000, lr = 0.0001
I0320 15:39:35.687068  8660 solver.cpp:219] Iteration 8250 (2.17723 iter/s, 114.825s/250 iters), loss = 0.0167441
I0320 15:39:35.687211  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0173484 (* 0.3 = 0.00520452 loss)
I0320 15:39:35.687233  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0115397 (* 1 = 0.0115397 loss)
I0320 15:39:35.687239  8660 sgd_solver.cpp:105] Iteration 8250, lr = 0.0001
I0320 15:41:20.632143  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_8500.caffemodel
I0320 15:41:20.818895  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_8500.solverstate
I0320 15:41:21.081765  8660 solver.cpp:219] Iteration 8500 (2.37206 iter/s, 105.394s/250 iters), loss = 0.0590669
I0320 15:41:21.081797  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0418142 (* 0.3 = 0.0125443 loss)
I0320 15:41:21.081817  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0465227 (* 1 = 0.0465227 loss)
I0320 15:41:21.081821  8660 sgd_solver.cpp:105] Iteration 8500, lr = 0.0001
I0320 15:43:24.566627  8660 solver.cpp:219] Iteration 8750 (2.02456 iter/s, 123.484s/250 iters), loss = 0.023846
I0320 15:43:24.566798  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0228341 (* 0.3 = 0.00685024 loss)
I0320 15:43:24.566807  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0169958 (* 1 = 0.0169958 loss)
I0320 15:43:24.566828  8660 sgd_solver.cpp:105] Iteration 8750, lr = 0.0001
I0320 15:45:27.078243  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_9000.caffemodel
I0320 15:45:27.265426  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_9000.solverstate
I0320 15:45:27.529323  8660 solver.cpp:219] Iteration 9000 (2.03315 iter/s, 122.962s/250 iters), loss = 0.0574504
I0320 15:45:27.529353  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0491437 (* 0.3 = 0.0147431 loss)
I0320 15:45:27.529373  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0427073 (* 1 = 0.0427073 loss)
I0320 15:45:27.529381  8660 sgd_solver.cpp:105] Iteration 9000, lr = 0.0001
I0320 15:47:05.652360  8660 solver.cpp:219] Iteration 9250 (2.54782 iter/s, 98.1229s/250 iters), loss = 0.0488792
I0320 15:47:05.652408  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0488736 (* 0.3 = 0.0146621 loss)
I0320 15:47:05.652429  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0342172 (* 1 = 0.0342172 loss)
I0320 15:47:05.652433  8660 sgd_solver.cpp:105] Iteration 9250, lr = 0.0001
I0320 15:48:52.426908  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_9500.caffemodel
I0320 15:48:52.614338  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_9500.solverstate
I0320 15:48:52.876337  8660 solver.cpp:219] Iteration 9500 (2.33157 iter/s, 107.224s/250 iters), loss = 0.0716814
I0320 15:48:52.876368  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0551878 (* 0.3 = 0.0165563 loss)
I0320 15:48:52.876389  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0551251 (* 1 = 0.0551251 loss)
I0320 15:48:52.876394  8660 sgd_solver.cpp:105] Iteration 9500, lr = 0.0001
I0320 15:50:30.952338  8660 solver.cpp:219] Iteration 9750 (2.54905 iter/s, 98.0756s/250 iters), loss = 0.0734016
I0320 15:50:30.952389  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0676564 (* 0.3 = 0.0202969 loss)
I0320 15:50:30.952409  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0531048 (* 1 = 0.0531048 loss)
I0320 15:50:30.952414  8660 sgd_solver.cpp:105] Iteration 9750, lr = 0.0001
I0320 15:52:42.866659  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_10000.caffemodel
I0320 15:52:43.066792  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_10000.solverstate
I0320 15:52:43.330834  8660 solver.cpp:219] Iteration 10000 (1.88853 iter/s, 132.378s/250 iters), loss = 0.041644
I0320 15:52:43.330865  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0439596 (* 0.3 = 0.0131879 loss)
I0320 15:52:43.330885  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0284562 (* 1 = 0.0284562 loss)
I0320 15:52:43.330890  8660 sgd_solver.cpp:105] Iteration 10000, lr = 0.0001
I0320 15:54:41.881640  8660 solver.cpp:219] Iteration 10250 (2.10881 iter/s, 118.55s/250 iters), loss = 0.0986199
I0320 15:54:41.882112  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0786911 (* 0.3 = 0.0236073 loss)
I0320 15:54:41.882123  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0750126 (* 1 = 0.0750126 loss)
I0320 15:54:41.882128  8660 sgd_solver.cpp:105] Iteration 10250, lr = 0.0001
I0320 15:56:27.000804  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_10500.caffemodel
I0320 15:56:27.188149  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_10500.solverstate
I0320 15:56:27.452320  8660 solver.cpp:219] Iteration 10500 (2.36811 iter/s, 105.57s/250 iters), loss = 0.140338
I0320 15:56:27.452363  8660 solver.cpp:238]     Train net output #0: loss1 = 0.137573 (* 0.3 = 0.041272 loss)
I0320 15:56:27.452370  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0990661 (* 1 = 0.0990661 loss)
I0320 15:56:27.452376  8660 sgd_solver.cpp:105] Iteration 10500, lr = 0.0001
I0320 15:58:05.493760  8660 solver.cpp:219] Iteration 10750 (2.54996 iter/s, 98.0408s/250 iters), loss = 0.0199037
I0320 15:58:05.493829  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0163691 (* 0.3 = 0.00491073 loss)
I0320 15:58:05.493850  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0149931 (* 1 = 0.0149931 loss)
I0320 15:58:05.493856  8660 sgd_solver.cpp:105] Iteration 10750, lr = 0.0001
I0320 15:59:51.825798  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_11000.caffemodel
I0320 15:59:52.014582  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_11000.solverstate
I0320 15:59:52.276759  8660 solver.cpp:219] Iteration 11000 (2.34121 iter/s, 106.782s/250 iters), loss = 0.0151178
I0320 15:59:52.276790  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0173248 (* 0.3 = 0.00519743 loss)
I0320 15:59:52.276811  8660 solver.cpp:238]     Train net output #1: loss2 = 0.00992039 (* 1 = 0.00992039 loss)
I0320 15:59:52.276815  8660 sgd_solver.cpp:105] Iteration 11000, lr = 0.0001
I0320 16:01:41.512071  8660 solver.cpp:219] Iteration 11250 (2.28865 iter/s, 109.235s/250 iters), loss = 0.0310647
I0320 16:01:41.512224  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0243637 (* 0.3 = 0.00730912 loss)
I0320 16:01:41.512234  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0237556 (* 1 = 0.0237556 loss)
I0320 16:01:41.512240  8660 sgd_solver.cpp:105] Iteration 11250, lr = 0.0001
I0320 16:03:36.413206  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_11500.caffemodel
I0320 16:03:36.601552  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_11500.solverstate
I0320 16:03:36.864590  8660 solver.cpp:219] Iteration 11500 (2.16729 iter/s, 115.352s/250 iters), loss = 0.0386754
I0320 16:03:36.864619  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0428879 (* 0.3 = 0.0128664 loss)
I0320 16:03:36.864640  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0258091 (* 1 = 0.0258091 loss)
I0320 16:03:36.864645  8660 sgd_solver.cpp:105] Iteration 11500, lr = 0.0001
I0320 16:05:22.927242  8660 solver.cpp:219] Iteration 11750 (2.35712 iter/s, 106.062s/250 iters), loss = 0.0911799
I0320 16:05:22.927291  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0590382 (* 0.3 = 0.0177114 loss)
I0320 16:05:22.927312  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0734685 (* 1 = 0.0734685 loss)
I0320 16:05:22.927319  8660 sgd_solver.cpp:105] Iteration 11750, lr = 0.0001
I0320 16:07:07.347621  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_12000.caffemodel
I0320 16:07:07.533644  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_12000.solverstate
I0320 16:07:07.796973  8660 solver.cpp:219] Iteration 12000 (2.38393 iter/s, 104.869s/250 iters), loss = 0.0937317
I0320 16:07:07.797001  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0652112 (* 0.3 = 0.0195634 loss)
I0320 16:07:07.797022  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0741683 (* 1 = 0.0741683 loss)
I0320 16:07:07.797026  8660 sgd_solver.cpp:105] Iteration 12000, lr = 0.0001
I0320 16:09:01.461864  8660 solver.cpp:219] Iteration 12250 (2.19947 iter/s, 113.664s/250 iters), loss = 0.0758073
I0320 16:09:01.461910  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0509623 (* 0.3 = 0.0152887 loss)
I0320 16:09:01.461931  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0605186 (* 1 = 0.0605186 loss)
I0320 16:09:01.461935  8660 sgd_solver.cpp:105] Iteration 12250, lr = 0.0001
I0320 16:11:14.010860  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_12500.caffemodel
I0320 16:11:14.224155  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_12500.solverstate
I0320 16:11:14.489136  8660 solver.cpp:219] Iteration 12500 (1.87933 iter/s, 133.026s/250 iters), loss = 0.025361
I0320 16:11:14.489182  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0272198 (* 0.3 = 0.00816595 loss)
I0320 16:11:14.489189  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0171952 (* 1 = 0.0171952 loss)
I0320 16:11:14.489197  8660 sgd_solver.cpp:105] Iteration 12500, lr = 0.0001
I0320 16:13:06.801807  8660 solver.cpp:219] Iteration 12750 (2.22595 iter/s, 112.312s/250 iters), loss = 0.0406086
I0320 16:13:06.801858  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0258244 (* 0.3 = 0.00774731 loss)
I0320 16:13:06.801879  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0328614 (* 1 = 0.0328614 loss)
I0320 16:13:06.801885  8660 sgd_solver.cpp:105] Iteration 12750, lr = 0.0001
I0320 16:14:50.780978  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_13000.caffemodel
I0320 16:14:50.969106  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_13000.solverstate
I0320 16:14:51.232558  8660 solver.cpp:219] Iteration 13000 (2.39395 iter/s, 104.43s/250 iters), loss = 0.115294
I0320 16:14:51.232584  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0647345 (* 0.3 = 0.0194203 loss)
I0320 16:14:51.232591  8660 solver.cpp:238]     Train net output #1: loss2 = 0.095874 (* 1 = 0.095874 loss)
I0320 16:14:51.232610  8660 sgd_solver.cpp:105] Iteration 13000, lr = 0.0001
I0320 16:16:29.003845  8660 solver.cpp:219] Iteration 13250 (2.55701 iter/s, 97.7705s/250 iters), loss = 0.13866
I0320 16:16:29.003998  8660 solver.cpp:238]     Train net output #0: loss1 = 0.101997 (* 0.3 = 0.0305991 loss)
I0320 16:16:29.004009  8660 solver.cpp:238]     Train net output #1: loss2 = 0.108061 (* 1 = 0.108061 loss)
I0320 16:16:29.004015  8660 sgd_solver.cpp:105] Iteration 13250, lr = 0.0001
I0320 16:18:21.758936  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_13500.caffemodel
I0320 16:18:21.946588  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_13500.solverstate
I0320 16:18:22.209416  8660 solver.cpp:219] Iteration 13500 (2.20839 iter/s, 113.204s/250 iters), loss = 0.0144826
I0320 16:18:22.209444  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0160236 (* 0.3 = 0.00480709 loss)
I0320 16:18:22.209450  8660 solver.cpp:238]     Train net output #1: loss2 = 0.00967554 (* 1 = 0.00967554 loss)
I0320 16:18:22.209470  8660 sgd_solver.cpp:105] Iteration 13500, lr = 0.0001
I0320 16:20:11.966859  8660 solver.cpp:219] Iteration 13750 (2.27779 iter/s, 109.756s/250 iters), loss = 0.041231
I0320 16:20:11.966920  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0353376 (* 0.3 = 0.0106013 loss)
I0320 16:20:11.966928  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0306298 (* 1 = 0.0306298 loss)
I0320 16:20:11.966933  8660 sgd_solver.cpp:105] Iteration 13750, lr = 0.0001
I0320 16:21:58.708315  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_14000.caffemodel
I0320 16:21:58.896278  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_14000.solverstate
I0320 16:21:59.159425  8660 solver.cpp:219] Iteration 14000 (2.33229 iter/s, 107.191s/250 iters), loss = 0.0571887
I0320 16:21:59.159457  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0410621 (* 0.3 = 0.0123186 loss)
I0320 16:21:59.159477  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0448701 (* 1 = 0.0448701 loss)
I0320 16:21:59.159482  8660 sgd_solver.cpp:105] Iteration 14000, lr = 0.0001
I0320 16:23:44.323015  8660 solver.cpp:219] Iteration 14250 (2.37728 iter/s, 105.162s/250 iters), loss = 0.0347948
I0320 16:23:44.323155  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0273463 (* 0.3 = 0.00820388 loss)
I0320 16:23:44.323179  8660 solver.cpp:238]     Train net output #1: loss2 = 0.026591 (* 1 = 0.026591 loss)
I0320 16:23:44.323184  8660 sgd_solver.cpp:105] Iteration 14250, lr = 0.0001
I0320 16:25:30.662310  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_14500.caffemodel
I0320 16:25:30.850910  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_14500.solverstate
I0320 16:25:31.115025  8660 solver.cpp:219] Iteration 14500 (2.34103 iter/s, 106.79s/250 iters), loss = 0.142961
I0320 16:25:31.115053  8660 solver.cpp:238]     Train net output #0: loss1 = 0.110516 (* 0.3 = 0.0331548 loss)
I0320 16:25:31.115073  8660 solver.cpp:238]     Train net output #1: loss2 = 0.109807 (* 1 = 0.109807 loss)
I0320 16:25:31.115078  8660 sgd_solver.cpp:105] Iteration 14500, lr = 0.0001
I0320 16:27:16.705945  8660 solver.cpp:219] Iteration 14750 (2.36766 iter/s, 105.59s/250 iters), loss = 0.0283191
I0320 16:27:16.705992  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0281096 (* 0.3 = 0.00843287 loss)
I0320 16:27:16.706013  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0198863 (* 1 = 0.0198863 loss)
I0320 16:27:16.706019  8660 sgd_solver.cpp:105] Iteration 14750, lr = 0.0001
I0320 16:29:00.930589  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_15000.caffemodel
I0320 16:29:01.130162  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_15000.solverstate
I0320 16:29:01.393637  8660 solver.cpp:219] Iteration 15000 (2.38809 iter/s, 104.686s/250 iters), loss = 0.0445361
I0320 16:29:01.393664  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0361786 (* 0.3 = 0.0108536 loss)
I0320 16:29:01.393685  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0336825 (* 1 = 0.0336825 loss)
I0320 16:29:01.393690  8660 sgd_solver.cpp:105] Iteration 15000, lr = 0.0001
I0320 16:30:39.804167  8660 solver.cpp:219] Iteration 15250 (2.54041 iter/s, 98.4093s/250 iters), loss = 0.0216282
I0320 16:30:39.804216  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0167607 (* 0.3 = 0.00502822 loss)
I0320 16:30:39.804237  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0165999 (* 1 = 0.0165999 loss)
I0320 16:30:39.804241  8660 sgd_solver.cpp:105] Iteration 15250, lr = 0.0001
I0320 16:32:23.912160  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_15500.caffemodel
I0320 16:32:24.099608  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_15500.solverstate
I0320 16:32:24.362900  8660 solver.cpp:219] Iteration 15500 (2.39103 iter/s, 104.557s/250 iters), loss = 0.0592931
I0320 16:32:24.362931  8660 solver.cpp:238]     Train net output #0: loss1 = 0.047149 (* 0.3 = 0.0141447 loss)
I0320 16:32:24.362951  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0451484 (* 1 = 0.0451484 loss)
I0320 16:32:24.362956  8660 sgd_solver.cpp:105] Iteration 15500, lr = 0.0001
I0320 16:34:10.378954  8660 solver.cpp:219] Iteration 15750 (2.35816 iter/s, 106.015s/250 iters), loss = 0.0305034
I0320 16:34:10.379096  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0254216 (* 0.3 = 0.00762648 loss)
I0320 16:34:10.379119  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0228769 (* 1 = 0.0228769 loss)
I0320 16:34:10.379124  8660 sgd_solver.cpp:105] Iteration 15750, lr = 0.0001
I0320 16:35:54.811903  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_16000.caffemodel
I0320 16:35:54.998677  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_16000.solverstate
I0320 16:35:55.261534  8660 solver.cpp:219] Iteration 16000 (2.38365 iter/s, 104.881s/250 iters), loss = 0.0201045
I0320 16:35:55.261566  8660 solver.cpp:238]     Train net output #0: loss1 = 0.018576 (* 0.3 = 0.00557281 loss)
I0320 16:35:55.261572  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0145317 (* 1 = 0.0145317 loss)
I0320 16:35:55.261591  8660 sgd_solver.cpp:105] Iteration 16000, lr = 0.0001
I0320 16:37:34.217835  8660 solver.cpp:219] Iteration 16250 (2.5264 iter/s, 98.9552s/250 iters), loss = 0.0701102
I0320 16:37:34.217902  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0603396 (* 0.3 = 0.0181019 loss)
I0320 16:37:34.217924  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0520083 (* 1 = 0.0520083 loss)
I0320 16:37:34.217931  8660 sgd_solver.cpp:105] Iteration 16250, lr = 0.0001
I0320 16:39:18.164846  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_16500.caffemodel
I0320 16:39:18.353013  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_16500.solverstate
I0320 16:39:18.615941  8660 solver.cpp:219] Iteration 16500 (2.39471 iter/s, 104.397s/250 iters), loss = 0.115759
I0320 16:39:18.615968  8660 solver.cpp:238]     Train net output #0: loss1 = 0.113478 (* 0.3 = 0.0340434 loss)
I0320 16:39:18.615988  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0817157 (* 1 = 0.0817157 loss)
I0320 16:39:18.615993  8660 sgd_solver.cpp:105] Iteration 16500, lr = 0.0001
I0320 16:41:03.286749  8660 solver.cpp:219] Iteration 16750 (2.38847 iter/s, 104.67s/250 iters), loss = 0.00803516
I0320 16:41:03.286811  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0092554 (* 0.3 = 0.00277662 loss)
I0320 16:41:03.286820  8660 solver.cpp:238]     Train net output #1: loss2 = 0.00525855 (* 1 = 0.00525855 loss)
I0320 16:41:03.286825  8660 sgd_solver.cpp:105] Iteration 16750, lr = 0.0001
I0320 16:42:47.608502  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_17000.caffemodel
I0320 16:42:47.796193  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_17000.solverstate
I0320 16:42:48.060544  8660 solver.cpp:219] Iteration 17000 (2.38612 iter/s, 104.773s/250 iters), loss = 0.038466
I0320 16:42:48.060571  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0391683 (* 0.3 = 0.0117505 loss)
I0320 16:42:48.060592  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0267155 (* 1 = 0.0267155 loss)
I0320 16:42:48.060597  8660 sgd_solver.cpp:105] Iteration 17000, lr = 0.0001
I0320 16:44:42.226050  8660 solver.cpp:219] Iteration 17250 (2.18983 iter/s, 114.164s/250 iters), loss = 0.0264153
I0320 16:44:42.226209  8660 solver.cpp:238]     Train net output #0: loss1 = 0.027277 (* 0.3 = 0.0081831 loss)
I0320 16:44:42.226219  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0182322 (* 1 = 0.0182322 loss)
I0320 16:44:42.226224  8660 sgd_solver.cpp:105] Iteration 17250, lr = 0.0001
I0320 16:46:41.199741  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_17500.caffemodel
I0320 16:46:41.386503  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_17500.solverstate
I0320 16:46:41.698820  8660 solver.cpp:219] Iteration 17500 (2.09255 iter/s, 119.471s/250 iters), loss = 0.129282
I0320 16:46:41.698853  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0975092 (* 0.3 = 0.0292527 loss)
I0320 16:46:41.698874  8660 solver.cpp:238]     Train net output #1: loss2 = 0.10003 (* 1 = 0.10003 loss)
I0320 16:46:41.698880  8660 sgd_solver.cpp:105] Iteration 17500, lr = 0.0001
I0320 16:48:27.365780  8660 solver.cpp:219] Iteration 17750 (2.36595 iter/s, 105.666s/250 iters), loss = 0.045789
I0320 16:48:27.365936  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0410803 (* 0.3 = 0.0123241 loss)
I0320 16:48:27.365947  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0334649 (* 1 = 0.0334649 loss)
I0320 16:48:27.365952  8660 sgd_solver.cpp:105] Iteration 17750, lr = 0.0001
I0320 16:50:22.101233  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_18000.caffemodel
I0320 16:50:22.289037  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_18000.solverstate
I0320 16:50:22.551883  8660 solver.cpp:219] Iteration 18000 (2.17042 iter/s, 115.185s/250 iters), loss = 0.057926
I0320 16:50:22.551915  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0480529 (* 0.3 = 0.0144159 loss)
I0320 16:50:22.551935  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0435102 (* 1 = 0.0435102 loss)
I0320 16:50:22.551940  8660 sgd_solver.cpp:105] Iteration 18000, lr = 0.0001
I0320 16:52:00.478417  8660 solver.cpp:219] Iteration 18250 (2.55296 iter/s, 97.9256s/250 iters), loss = 0.0210491
I0320 16:52:00.478487  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0202579 (* 0.3 = 0.00607736 loss)
I0320 16:52:00.478508  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0149718 (* 1 = 0.0149718 loss)
I0320 16:52:00.478514  8660 sgd_solver.cpp:105] Iteration 18250, lr = 0.0001
I0320 16:53:44.284078  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_18500.caffemodel
I0320 16:53:44.471097  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_18500.solverstate
I0320 16:53:44.735255  8660 solver.cpp:219] Iteration 18500 (2.39795 iter/s, 104.256s/250 iters), loss = 0.0777822
I0320 16:53:44.735306  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0529839 (* 0.3 = 0.0158952 loss)
I0320 16:53:44.735313  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0618871 (* 1 = 0.0618871 loss)
I0320 16:53:44.735318  8660 sgd_solver.cpp:105] Iteration 18500, lr = 0.0001
I0320 16:55:47.900426  8660 solver.cpp:219] Iteration 18750 (2.02982 iter/s, 123.164s/250 iters), loss = 0.0774224
I0320 16:55:47.900477  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0490758 (* 0.3 = 0.0147227 loss)
I0320 16:55:47.900498  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0626997 (* 1 = 0.0626997 loss)
I0320 16:55:47.900504  8660 sgd_solver.cpp:105] Iteration 18750, lr = 0.0001
I0320 16:57:25.337448  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_19000.caffemodel
I0320 16:57:25.525547  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_19000.solverstate
I0320 16:57:25.788143  8660 solver.cpp:219] Iteration 19000 (2.55397 iter/s, 97.8867s/250 iters), loss = 0.0226965
I0320 16:57:25.788172  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0292466 (* 0.3 = 0.00877397 loss)
I0320 16:57:25.788192  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0139225 (* 1 = 0.0139225 loss)
I0320 16:57:25.788198  8660 sgd_solver.cpp:105] Iteration 19000, lr = 0.0001
I0320 16:59:11.438735  8660 solver.cpp:219] Iteration 19250 (2.36631 iter/s, 105.65s/250 iters), loss = 0.102652
I0320 16:59:11.438784  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0869889 (* 0.3 = 0.0260967 loss)
I0320 16:59:11.438804  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0765557 (* 1 = 0.0765557 loss)
I0320 16:59:11.438812  8660 sgd_solver.cpp:105] Iteration 19250, lr = 0.0001
I0320 17:00:54.706084  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_19500.caffemodel
I0320 17:00:54.893934  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_19500.solverstate
I0320 17:00:55.158069  8660 solver.cpp:219] Iteration 19500 (2.41038 iter/s, 103.718s/250 iters), loss = 0.0196821
I0320 17:00:55.158099  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0199941 (* 0.3 = 0.00599824 loss)
I0320 17:00:55.158120  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0136839 (* 1 = 0.0136839 loss)
I0320 17:00:55.158125  8660 sgd_solver.cpp:105] Iteration 19500, lr = 0.0001
I0320 17:02:42.276094  8660 solver.cpp:219] Iteration 19750 (2.3339 iter/s, 107.117s/250 iters), loss = 0.0393437
I0320 17:02:42.276145  8660 solver.cpp:238]     Train net output #0: loss1 = 0.028986 (* 0.3 = 0.00869579 loss)
I0320 17:02:42.276166  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0306479 (* 1 = 0.0306479 loss)
I0320 17:02:42.276172  8660 sgd_solver.cpp:105] Iteration 19750, lr = 0.0001
I0320 17:04:44.734506  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_20000.caffemodel
I0320 17:04:44.922827  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_20000.solverstate
I0320 17:04:45.186285  8660 solver.cpp:219] Iteration 20000 (2.03403 iter/s, 122.909s/250 iters), loss = 0.0489709
I0320 17:04:45.186318  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0358658 (* 0.3 = 0.0107597 loss)
I0320 17:04:45.186338  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0382112 (* 1 = 0.0382112 loss)
I0320 17:04:45.186343  8660 sgd_solver.cpp:105] Iteration 20000, lr = 2e-05
I0320 17:06:39.487000  8660 solver.cpp:219] Iteration 20250 (2.18723 iter/s, 114.3s/250 iters), loss = 0.0105672
I0320 17:06:39.487069  8660 solver.cpp:238]     Train net output #0: loss1 = 0.00670937 (* 0.3 = 0.00201281 loss)
I0320 17:06:39.487090  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0085544 (* 1 = 0.0085544 loss)
I0320 17:06:39.487097  8660 sgd_solver.cpp:105] Iteration 20250, lr = 2e-05
I0320 17:08:24.937078  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_20500.caffemodel
I0320 17:08:25.123971  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_20500.solverstate
I0320 17:08:25.387178  8660 solver.cpp:219] Iteration 20500 (2.36074 iter/s, 105.899s/250 iters), loss = 0.0179724
I0320 17:08:25.387210  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0188956 (* 0.3 = 0.00566868 loss)
I0320 17:08:25.387230  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0123037 (* 1 = 0.0123037 loss)
I0320 17:08:25.387236  8660 sgd_solver.cpp:105] Iteration 20500, lr = 2e-05
I0320 17:10:10.363008  8660 solver.cpp:219] Iteration 20750 (2.38152 iter/s, 104.975s/250 iters), loss = 0.100157
I0320 17:10:10.363059  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0776349 (* 0.3 = 0.0232905 loss)
I0320 17:10:10.363080  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0768666 (* 1 = 0.0768666 loss)
I0320 17:10:10.363085  8660 sgd_solver.cpp:105] Iteration 20750, lr = 2e-05
I0320 17:12:03.182446  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_21000.caffemodel
I0320 17:12:03.370235  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_21000.solverstate
I0320 17:12:03.651702  8660 solver.cpp:219] Iteration 21000 (2.20677 iter/s, 113.288s/250 iters), loss = 0.0941481
I0320 17:12:03.651734  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0723637 (* 0.3 = 0.0217091 loss)
I0320 17:12:03.651756  8660 solver.cpp:238]     Train net output #1: loss2 = 0.072439 (* 1 = 0.072439 loss)
I0320 17:12:03.651762  8660 sgd_solver.cpp:105] Iteration 21000, lr = 2e-05
I0320 17:14:01.247369  8660 solver.cpp:219] Iteration 21250 (2.12595 iter/s, 117.595s/250 iters), loss = 0.0685704
I0320 17:14:01.247416  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0615222 (* 0.3 = 0.0184567 loss)
I0320 17:14:01.247423  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0501138 (* 1 = 0.0501138 loss)
I0320 17:14:01.247442  8660 sgd_solver.cpp:105] Iteration 21250, lr = 2e-05
I0320 17:15:47.596416  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_21500.caffemodel
I0320 17:15:47.783129  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_21500.solverstate
I0320 17:15:48.046540  8660 solver.cpp:219] Iteration 21500 (2.34087 iter/s, 106.798s/250 iters), loss = 0.0869684
I0320 17:15:48.046586  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0785267 (* 0.3 = 0.023558 loss)
I0320 17:15:48.046591  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0634104 (* 1 = 0.0634104 loss)
I0320 17:15:48.046597  8660 sgd_solver.cpp:105] Iteration 21500, lr = 2e-05
I0320 17:17:26.015758  8660 solver.cpp:219] Iteration 21750 (2.55185 iter/s, 97.9683s/250 iters), loss = 0.208667
I0320 17:17:26.015914  8660 solver.cpp:238]     Train net output #0: loss1 = 0.157107 (* 0.3 = 0.0471321 loss)
I0320 17:17:26.015924  8660 solver.cpp:238]     Train net output #1: loss2 = 0.161535 (* 1 = 0.161535 loss)
I0320 17:17:26.015929  8660 sgd_solver.cpp:105] Iteration 21750, lr = 2e-05
I0320 17:19:09.826639  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_22000.caffemodel
I0320 17:19:10.014731  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_22000.solverstate
I0320 17:19:10.277467  8660 solver.cpp:219] Iteration 22000 (2.39784 iter/s, 104.261s/250 iters), loss = 0.0125122
I0320 17:19:10.277498  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0106181 (* 0.3 = 0.00318543 loss)
I0320 17:19:10.277518  8660 solver.cpp:238]     Train net output #1: loss2 = 0.00932678 (* 1 = 0.00932678 loss)
I0320 17:19:10.277523  8660 sgd_solver.cpp:105] Iteration 22000, lr = 2e-05
I0320 17:21:04.042026  8660 solver.cpp:219] Iteration 22250 (2.19754 iter/s, 113.763s/250 iters), loss = 0.0153104
I0320 17:21:04.042073  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0171079 (* 0.3 = 0.00513238 loss)
I0320 17:21:04.042093  8660 solver.cpp:238]     Train net output #1: loss2 = 0.010178 (* 1 = 0.010178 loss)
I0320 17:21:04.042101  8660 sgd_solver.cpp:105] Iteration 22250, lr = 2e-05
I0320 17:23:10.297602  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_22500.caffemodel
I0320 17:23:10.499886  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_22500.solverstate
I0320 17:23:10.765874  8660 solver.cpp:219] Iteration 22500 (1.97281 iter/s, 126.723s/250 iters), loss = 0.124889
I0320 17:23:10.765903  8660 solver.cpp:238]     Train net output #0: loss1 = 0.108843 (* 0.3 = 0.0326528 loss)
I0320 17:23:10.765924  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0922361 (* 1 = 0.0922361 loss)
I0320 17:23:10.765928  8660 sgd_solver.cpp:105] Iteration 22500, lr = 2e-05
I0320 17:25:03.791049  8660 solver.cpp:219] Iteration 22750 (2.21192 iter/s, 113.024s/250 iters), loss = 0.0127795
I0320 17:25:03.791602  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0113423 (* 0.3 = 0.0034027 loss)
I0320 17:25:03.791611  8660 solver.cpp:238]     Train net output #1: loss2 = 0.00937679 (* 1 = 0.00937679 loss)
I0320 17:25:03.791615  8660 sgd_solver.cpp:105] Iteration 22750, lr = 2e-05
I0320 17:26:56.523061  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_23000.caffemodel
I0320 17:26:56.709435  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_23000.solverstate
I0320 17:26:56.973320  8660 solver.cpp:219] Iteration 23000 (2.20886 iter/s, 113.181s/250 iters), loss = 0.0309158
I0320 17:26:56.973351  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0281955 (* 0.3 = 0.00845866 loss)
I0320 17:26:56.973371  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0224572 (* 1 = 0.0224572 loss)
I0320 17:26:56.973376  8660 sgd_solver.cpp:105] Iteration 23000, lr = 2e-05
I0320 17:28:41.514343  8660 solver.cpp:219] Iteration 23250 (2.39141 iter/s, 104.541s/250 iters), loss = 0.0539727
I0320 17:28:41.514506  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0415872 (* 0.3 = 0.0124762 loss)
I0320 17:28:41.514516  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0414965 (* 1 = 0.0414965 loss)
I0320 17:28:41.514520  8660 sgd_solver.cpp:105] Iteration 23250, lr = 2e-05
I0320 17:30:19.608342  8660 solver.cpp:448] Snapshotting to binary proto file snapshots/sale_iter_23500.caffemodel
I0320 17:30:19.795714  8660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshots/sale_iter_23500.solverstate
I0320 17:30:20.058218  8660 solver.cpp:219] Iteration 23500 (2.53695 iter/s, 98.5434s/250 iters), loss = 0.0282598
I0320 17:30:20.058249  8660 solver.cpp:238]     Train net output #0: loss1 = 0.0354683 (* 0.3 = 0.0106405 loss)
I0320 17:30:20.058269  8660 solver.cpp:238]     Train net output #1: loss2 = 0.0176193 (* 1 = 0.0176193 loss)
I0320 17:30:20.058274  8660 sgd_solver.cpp:105] Iteration 23500, lr = 2e-05