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| I0401 10:07:22.297281 31447 solver.cpp:280] Solving mixed_lstm | |
| I0401 10:07:22.297292 31447 solver.cpp:281] Learning Rate Policy: fixed | |
| I0401 10:07:22.643712 31447 solver.cpp:229] Iteration 0, loss = 14.0021 | |
| I0401 10:07:22.643754 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0401 10:07:22.643769 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0 | |
| I0401 10:07:22.643782 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.0238095 | |
| I0401 10:07:22.643798 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 4.45928 (* 0.3 = 1.33778 loss) | |
| I0401 10:07:22.643812 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 4.36399 (* 0.3 = 1.3092 loss) | |
| I0401 10:07:22.643826 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0 | |
| I0401 10:07:22.643837 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0 | |
| I0401 10:07:22.643873 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.0714286 | |
| I0401 10:07:22.643890 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 4.29269 (* 0.3 = 1.28781 loss) | |
| I0401 10:07:22.643904 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 4.42637 (* 0.3 = 1.32791 loss) | |
| I0401 10:07:22.643916 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0 | |
| I0401 10:07:22.643928 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0 | |
| I0401 10:07:22.643939 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0 | |
| I0401 10:07:22.643952 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 4.39482 (* 1 = 4.39482 loss) | |
| I0401 10:07:22.643965 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 4.34453 (* 1 = 4.34453 loss) | |
| I0401 10:07:22.643977 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:07:22.643990 31447 solver.cpp:245] Train net output #16: total_confidence = 2.29857e-33 | |
| I0401 10:07:22.644007 31447 sgd_solver.cpp:106] Iteration 0, lr = 0.05 | |
| I0401 10:07:22.661419 31447 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.5701 > 30) by scale factor 0.950265 | |
| I0401 10:09:39.937007 31447 solver.cpp:229] Iteration 500, loss = 8.4845 | |
| I0401 10:09:39.937325 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0769231 | |
| I0401 10:09:39.937345 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.744318 | |
| I0401 10:09:39.937358 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.128205 | |
| I0401 10:09:39.937374 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.66361 (* 0.3 = 1.09908 loss) | |
| I0401 10:09:39.937389 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.14997 (* 0.3 = 0.344991 loss) | |
| I0401 10:09:39.937402 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0512821 | |
| I0401 10:09:39.937414 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.784091 | |
| I0401 10:09:39.937427 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.102564 | |
| I0401 10:09:39.937440 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.7524 (* 0.3 = 1.12572 loss) | |
| I0401 10:09:39.937453 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.955194 (* 0.3 = 0.286558 loss) | |
| I0401 10:09:39.937466 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.025641 | |
| I0401 10:09:39.937479 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.767045 | |
| I0401 10:09:39.937490 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.205128 | |
| I0401 10:09:39.937504 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.60386 (* 1 = 3.60386 loss) | |
| I0401 10:09:39.937520 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.02063 (* 1 = 1.02063 loss) | |
| I0401 10:09:39.937532 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:09:39.937544 31447 solver.cpp:245] Train net output #16: total_confidence = 6.91831e-08 | |
| I0401 10:09:39.937556 31447 sgd_solver.cpp:106] Iteration 500, lr = 0.05 | |
| I0401 10:11:55.218194 31447 solver.cpp:229] Iteration 1000, loss = 8.0588 | |
| I0401 10:11:55.218322 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0175439 | |
| I0401 10:11:55.218343 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.664773 | |
| I0401 10:11:55.218355 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.0701754 | |
| I0401 10:11:55.218371 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.81747 (* 0.3 = 1.14524 loss) | |
| I0401 10:11:55.218385 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.48454 (* 0.3 = 0.445362 loss) | |
| I0401 10:11:55.218399 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0175439 | |
| I0401 10:11:55.218411 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.676136 | |
| I0401 10:11:55.218422 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.0877193 | |
| I0401 10:11:55.218436 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.85042 (* 0.3 = 1.15513 loss) | |
| I0401 10:11:55.218451 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.40503 (* 0.3 = 0.421509 loss) | |
| I0401 10:11:55.218462 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0175439 | |
| I0401 10:11:55.218474 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.681818 | |
| I0401 10:11:55.218487 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.0701754 | |
| I0401 10:11:55.218500 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.8703 (* 1 = 3.8703 loss) | |
| I0401 10:11:55.218514 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.34249 (* 1 = 1.34249 loss) | |
| I0401 10:11:55.218529 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:11:55.218541 31447 solver.cpp:245] Train net output #16: total_confidence = 2.87411e-07 | |
| I0401 10:11:55.218554 31447 sgd_solver.cpp:106] Iteration 1000, lr = 0.05 | |
| I0401 10:14:09.062777 31447 solver.cpp:229] Iteration 1500, loss = 7.90593 | |
| I0401 10:14:09.063005 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0444444 | |
| I0401 10:14:09.063025 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.755682 | |
| I0401 10:14:09.063038 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.133333 | |
| I0401 10:14:09.063056 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.93499 (* 0.3 = 1.1805 loss) | |
| I0401 10:14:09.063071 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.22782 (* 0.3 = 0.368346 loss) | |
| I0401 10:14:09.063084 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0444444 | |
| I0401 10:14:09.063097 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.755682 | |
| I0401 10:14:09.063108 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.111111 | |
| I0401 10:14:09.063122 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.89466 (* 0.3 = 1.1684 loss) | |
| I0401 10:14:09.063136 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.10339 (* 0.3 = 0.331017 loss) | |
| I0401 10:14:09.063148 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0222222 | |
| I0401 10:14:09.063160 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.744318 | |
| I0401 10:14:09.063172 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.0888889 | |
| I0401 10:14:09.063186 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.91233 (* 1 = 3.91233 loss) | |
| I0401 10:14:09.063199 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.13475 (* 1 = 1.13475 loss) | |
| I0401 10:14:09.063211 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:14:09.063223 31447 solver.cpp:245] Train net output #16: total_confidence = 1.08004e-06 | |
| I0401 10:14:09.063235 31447 sgd_solver.cpp:106] Iteration 1500, lr = 0.05 | |
| I0401 10:16:21.920889 31447 solver.cpp:229] Iteration 2000, loss = 7.7953 | |
| I0401 10:16:21.921196 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0731707 | |
| I0401 10:16:21.921218 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 10:16:21.921231 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.219512 | |
| I0401 10:16:21.921247 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.53857 (* 0.3 = 1.06157 loss) | |
| I0401 10:16:21.921262 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.17712 (* 0.3 = 0.353136 loss) | |
| I0401 10:16:21.921274 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0487805 | |
| I0401 10:16:21.921286 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.744318 | |
| I0401 10:16:21.921298 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.121951 | |
| I0401 10:16:21.921311 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.65119 (* 0.3 = 1.09536 loss) | |
| I0401 10:16:21.921325 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.17547 (* 0.3 = 0.35264 loss) | |
| I0401 10:16:21.921337 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0731707 | |
| I0401 10:16:21.921350 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.767045 | |
| I0401 10:16:21.921361 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.170732 | |
| I0401 10:16:21.921375 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.68009 (* 1 = 3.68009 loss) | |
| I0401 10:16:21.921388 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.03867 (* 1 = 1.03867 loss) | |
| I0401 10:16:21.921401 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:16:21.921412 31447 solver.cpp:245] Train net output #16: total_confidence = 2.44957e-06 | |
| I0401 10:16:21.921424 31447 sgd_solver.cpp:106] Iteration 2000, lr = 0.05 | |
| I0401 10:18:34.086995 31447 solver.cpp:229] Iteration 2500, loss = 7.75007 | |
| I0401 10:18:34.087103 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0652174 | |
| I0401 10:18:34.087128 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.732955 | |
| I0401 10:18:34.087141 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.173913 | |
| I0401 10:18:34.087157 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.62505 (* 0.3 = 1.08752 loss) | |
| I0401 10:18:34.087172 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.1446 (* 0.3 = 0.34338 loss) | |
| I0401 10:18:34.087185 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0652174 | |
| I0401 10:18:34.087198 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.738636 | |
| I0401 10:18:34.087210 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.152174 | |
| I0401 10:18:34.087224 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.59562 (* 0.3 = 1.07869 loss) | |
| I0401 10:18:34.087237 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.21445 (* 0.3 = 0.364335 loss) | |
| I0401 10:18:34.087249 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0652174 | |
| I0401 10:18:34.087261 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.75 | |
| I0401 10:18:34.087273 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.195652 | |
| I0401 10:18:34.087290 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.5994 (* 1 = 3.5994 loss) | |
| I0401 10:18:34.087303 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.05716 (* 1 = 1.05716 loss) | |
| I0401 10:18:34.087316 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:18:34.087327 31447 solver.cpp:245] Train net output #16: total_confidence = 2.92561e-05 | |
| I0401 10:18:34.087339 31447 sgd_solver.cpp:106] Iteration 2500, lr = 0.05 | |
| I0401 10:20:45.572913 31447 solver.cpp:229] Iteration 3000, loss = 7.67646 | |
| I0401 10:20:45.573036 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0208333 | |
| I0401 10:20:45.573056 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.630682 | |
| I0401 10:20:45.573070 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.125 | |
| I0401 10:20:45.573086 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.88973 (* 0.3 = 1.16692 loss) | |
| I0401 10:20:45.573107 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.49621 (* 0.3 = 0.448862 loss) | |
| I0401 10:20:45.573132 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0 | |
| I0401 10:20:45.573148 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.630682 | |
| I0401 10:20:45.573160 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.0833333 | |
| I0401 10:20:45.573175 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.89172 (* 0.3 = 1.16752 loss) | |
| I0401 10:20:45.573189 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.46019 (* 0.3 = 0.438058 loss) | |
| I0401 10:20:45.573202 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0208333 | |
| I0401 10:20:45.573215 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.715909 | |
| I0401 10:20:45.573225 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.0833333 | |
| I0401 10:20:45.573240 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.92089 (* 1 = 3.92089 loss) | |
| I0401 10:20:45.573252 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.25091 (* 1 = 1.25091 loss) | |
| I0401 10:20:45.573264 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:20:45.573276 31447 solver.cpp:245] Train net output #16: total_confidence = 4.56838e-07 | |
| I0401 10:20:45.573288 31447 sgd_solver.cpp:106] Iteration 3000, lr = 0.05 | |
| I0401 10:22:56.938113 31447 solver.cpp:229] Iteration 3500, loss = 7.49258 | |
| I0401 10:22:56.938215 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0769231 | |
| I0401 10:22:56.938233 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.727273 | |
| I0401 10:22:56.938249 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.230769 | |
| I0401 10:22:56.938266 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.46954 (* 0.3 = 1.04086 loss) | |
| I0401 10:22:56.938282 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.10675 (* 0.3 = 0.332025 loss) | |
| I0401 10:22:56.938293 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0769231 | |
| I0401 10:22:56.938307 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.721591 | |
| I0401 10:22:56.938318 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.153846 | |
| I0401 10:22:56.938331 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.58776 (* 0.3 = 1.07633 loss) | |
| I0401 10:22:56.938345 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.15081 (* 0.3 = 0.345243 loss) | |
| I0401 10:22:56.938357 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0384615 | |
| I0401 10:22:56.938369 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.704545 | |
| I0401 10:22:56.938381 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.115385 | |
| I0401 10:22:56.938395 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.62136 (* 1 = 3.62136 loss) | |
| I0401 10:22:56.938408 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.19916 (* 1 = 1.19916 loss) | |
| I0401 10:22:56.938421 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:22:56.938432 31447 solver.cpp:245] Train net output #16: total_confidence = 1.78431e-06 | |
| I0401 10:22:56.938444 31447 sgd_solver.cpp:106] Iteration 3500, lr = 0.05 | |
| I0401 10:25:08.040726 31447 solver.cpp:229] Iteration 4000, loss = 7.43541 | |
| I0401 10:25:08.040976 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.04 | |
| I0401 10:25:08.040995 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.727273 | |
| I0401 10:25:08.041008 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.14 | |
| I0401 10:25:08.041024 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.52256 (* 0.3 = 1.05677 loss) | |
| I0401 10:25:08.041039 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.1191 (* 0.3 = 0.33573 loss) | |
| I0401 10:25:08.041069 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.02 | |
| I0401 10:25:08.041082 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.721591 | |
| I0401 10:25:08.041095 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.1 | |
| I0401 10:25:08.041110 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.58161 (* 0.3 = 1.07448 loss) | |
| I0401 10:25:08.041123 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.12284 (* 0.3 = 0.336852 loss) | |
| I0401 10:25:08.041136 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.04 | |
| I0401 10:25:08.041147 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.727273 | |
| I0401 10:25:08.041159 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.16 | |
| I0401 10:25:08.041172 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.38369 (* 1 = 3.38369 loss) | |
| I0401 10:25:08.041187 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.02775 (* 1 = 1.02775 loss) | |
| I0401 10:25:08.041198 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:25:08.041209 31447 solver.cpp:245] Train net output #16: total_confidence = 1.86441e-05 | |
| I0401 10:25:08.041223 31447 sgd_solver.cpp:106] Iteration 4000, lr = 0.05 | |
| I0401 10:27:18.562579 31447 solver.cpp:229] Iteration 4500, loss = 7.33296 | |
| I0401 10:27:18.562686 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0681818 | |
| I0401 10:27:18.562706 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.767045 | |
| I0401 10:27:18.562718 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.227273 | |
| I0401 10:27:18.562734 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.2244 (* 0.3 = 0.96732 loss) | |
| I0401 10:27:18.562749 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.921885 (* 0.3 = 0.276565 loss) | |
| I0401 10:27:18.562762 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0909091 | |
| I0401 10:27:18.562774 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.767045 | |
| I0401 10:27:18.562786 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.227273 | |
| I0401 10:27:18.562800 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.20662 (* 0.3 = 0.961985 loss) | |
| I0401 10:27:18.562814 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.96095 (* 0.3 = 0.288285 loss) | |
| I0401 10:27:18.562826 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.159091 | |
| I0401 10:27:18.562839 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.772727 | |
| I0401 10:27:18.562850 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.227273 | |
| I0401 10:27:18.562865 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.12757 (* 1 = 3.12757 loss) | |
| I0401 10:27:18.562878 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.937485 (* 1 = 0.937485 loss) | |
| I0401 10:27:18.562891 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:27:18.562902 31447 solver.cpp:245] Train net output #16: total_confidence = 2.79196e-07 | |
| I0401 10:27:18.562914 31447 sgd_solver.cpp:106] Iteration 4500, lr = 0.05 | |
| I0401 10:29:28.883522 31447 solver.cpp:338] Iteration 5000, Testing net (#0) | |
| I0401 10:30:01.564424 31447 solver.cpp:393] Test loss: 9.02601 | |
| I0401 10:30:01.564527 31447 solver.cpp:406] Test net output #0: loss1/accuracy = 0.118462 | |
| I0401 10:30:01.564548 31447 solver.cpp:406] Test net output #1: loss1/accuracy_incl_empty = 0.644409 | |
| I0401 10:30:01.564560 31447 solver.cpp:406] Test net output #2: loss1/accuracy_top3 = 0.302396 | |
| I0401 10:30:01.564576 31447 solver.cpp:406] Test net output #3: loss1/cross_entropy_loss = 3.37803 (* 0.3 = 1.01341 loss) | |
| I0401 10:30:01.564591 31447 solver.cpp:406] Test net output #4: loss1/cross_entropy_loss_incl_empty = 1.51148 (* 0.3 = 0.453443 loss) | |
| I0401 10:30:01.564604 31447 solver.cpp:406] Test net output #5: loss2/accuracy = 0.0659536 | |
| I0401 10:30:01.564615 31447 solver.cpp:406] Test net output #6: loss2/accuracy_incl_empty = 0.535773 | |
| I0401 10:30:01.564627 31447 solver.cpp:406] Test net output #7: loss2/accuracy_top3 = 0.180392 | |
| I0401 10:30:01.564640 31447 solver.cpp:406] Test net output #8: loss2/cross_entropy_loss = 3.55198 (* 0.3 = 1.06559 loss) | |
| I0401 10:30:01.564654 31447 solver.cpp:406] Test net output #9: loss2/cross_entropy_loss_incl_empty = 1.87488 (* 0.3 = 0.562464 loss) | |
| I0401 10:30:01.564666 31447 solver.cpp:406] Test net output #10: loss3/accuracy = 0.0961015 | |
| I0401 10:30:01.564678 31447 solver.cpp:406] Test net output #11: loss3/accuracy_incl_empty = 0.469636 | |
| I0401 10:30:01.564689 31447 solver.cpp:406] Test net output #12: loss3/accuracy_top3 = 0.219661 | |
| I0401 10:30:01.564703 31447 solver.cpp:406] Test net output #13: loss3/cross_entropy_loss = 3.44299 (* 1 = 3.44299 loss) | |
| I0401 10:30:01.564716 31447 solver.cpp:406] Test net output #14: loss3/cross_entropy_loss_incl_empty = 2.48812 (* 1 = 2.48812 loss) | |
| I0401 10:30:01.564728 31447 solver.cpp:406] Test net output #15: total_accuracy = 0 | |
| I0401 10:30:01.564740 31447 solver.cpp:406] Test net output #16: total_confidence = 2.92926e-05 | |
| I0401 10:30:01.715370 31447 solver.cpp:229] Iteration 5000, loss = 7.30369 | |
| I0401 10:30:01.715409 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0425532 | |
| I0401 10:30:01.715425 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.732955 | |
| I0401 10:30:01.715438 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.191489 | |
| I0401 10:30:01.715453 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.70196 (* 0.3 = 1.11059 loss) | |
| I0401 10:30:01.715467 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.19505 (* 0.3 = 0.358515 loss) | |
| I0401 10:30:01.715479 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0425532 | |
| I0401 10:30:01.715492 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.744318 | |
| I0401 10:30:01.715503 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.170213 | |
| I0401 10:30:01.715517 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.79743 (* 0.3 = 1.13923 loss) | |
| I0401 10:30:01.715531 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.1537 (* 0.3 = 0.34611 loss) | |
| I0401 10:30:01.715543 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0425532 | |
| I0401 10:30:01.715555 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.744318 | |
| I0401 10:30:01.715566 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.148936 | |
| I0401 10:30:01.715580 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.66647 (* 1 = 3.66647 loss) | |
| I0401 10:30:01.715593 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.08638 (* 1 = 1.08638 loss) | |
| I0401 10:30:01.715605 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:30:01.715617 31447 solver.cpp:245] Train net output #16: total_confidence = 3.11934e-07 | |
| I0401 10:30:01.715631 31447 sgd_solver.cpp:106] Iteration 5000, lr = 0.05 | |
| I0401 10:32:12.165473 31447 solver.cpp:229] Iteration 5500, loss = 7.26118 | |
| I0401 10:32:12.165568 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0401 10:32:12.165586 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.710227 | |
| I0401 10:32:12.165599 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.106383 | |
| I0401 10:32:12.165616 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.35702 (* 0.3 = 1.00711 loss) | |
| I0401 10:32:12.165632 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.07803 (* 0.3 = 0.32341 loss) | |
| I0401 10:32:12.165643 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0212766 | |
| I0401 10:32:12.165657 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.727273 | |
| I0401 10:32:12.165673 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.170213 | |
| I0401 10:32:12.165686 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.51953 (* 0.3 = 1.05586 loss) | |
| I0401 10:32:12.165700 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.1613 (* 0.3 = 0.348391 loss) | |
| I0401 10:32:12.165712 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0425532 | |
| I0401 10:32:12.165724 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.732955 | |
| I0401 10:32:12.165736 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.191489 | |
| I0401 10:32:12.165750 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.48118 (* 1 = 3.48118 loss) | |
| I0401 10:32:12.165765 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.04803 (* 1 = 1.04803 loss) | |
| I0401 10:32:12.165776 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:32:12.165787 31447 solver.cpp:245] Train net output #16: total_confidence = 2.18508e-05 | |
| I0401 10:32:12.165801 31447 sgd_solver.cpp:106] Iteration 5500, lr = 0.05 | |
| I0401 10:34:22.652707 31447 solver.cpp:229] Iteration 6000, loss = 7.18585 | |
| I0401 10:34:22.652801 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0465116 | |
| I0401 10:34:22.652819 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 10:34:22.652832 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.325581 | |
| I0401 10:34:22.652848 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.10021 (* 0.3 = 0.930064 loss) | |
| I0401 10:34:22.652863 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.921475 (* 0.3 = 0.276443 loss) | |
| I0401 10:34:22.652879 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0930233 | |
| I0401 10:34:22.652892 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.767045 | |
| I0401 10:34:22.652904 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.209302 | |
| I0401 10:34:22.652918 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.06814 (* 0.3 = 0.920443 loss) | |
| I0401 10:34:22.652940 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.979367 (* 0.3 = 0.29381 loss) | |
| I0401 10:34:22.652963 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0465116 | |
| I0401 10:34:22.652982 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.767045 | |
| I0401 10:34:22.652995 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.325581 | |
| I0401 10:34:22.653009 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.96385 (* 1 = 2.96385 loss) | |
| I0401 10:34:22.653024 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.854304 (* 1 = 0.854304 loss) | |
| I0401 10:34:22.653036 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:34:22.653066 31447 solver.cpp:245] Train net output #16: total_confidence = 6.47205e-07 | |
| I0401 10:34:22.653081 31447 sgd_solver.cpp:106] Iteration 6000, lr = 0.05 | |
| I0401 10:36:32.591294 31447 solver.cpp:229] Iteration 6500, loss = 7.2004 | |
| I0401 10:36:32.591548 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0192308 | |
| I0401 10:36:32.591579 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.710227 | |
| I0401 10:36:32.591604 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.230769 | |
| I0401 10:36:32.591631 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.43788 (* 0.3 = 1.03136 loss) | |
| I0401 10:36:32.591661 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.07174 (* 0.3 = 0.321521 loss) | |
| I0401 10:36:32.591687 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0384615 | |
| I0401 10:36:32.591711 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.715909 | |
| I0401 10:36:32.591734 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.192308 | |
| I0401 10:36:32.591759 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.49427 (* 0.3 = 1.04828 loss) | |
| I0401 10:36:32.591786 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.11501 (* 0.3 = 0.334503 loss) | |
| I0401 10:36:32.591809 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0384615 | |
| I0401 10:36:32.591830 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.715909 | |
| I0401 10:36:32.591851 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.211538 | |
| I0401 10:36:32.591876 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.3657 (* 1 = 3.3657 loss) | |
| I0401 10:36:32.591902 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.03682 (* 1 = 1.03682 loss) | |
| I0401 10:36:32.591924 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:36:32.591944 31447 solver.cpp:245] Train net output #16: total_confidence = 8.7927e-05 | |
| I0401 10:36:32.591966 31447 sgd_solver.cpp:106] Iteration 6500, lr = 0.05 | |
| I0401 10:38:42.548524 31447 solver.cpp:229] Iteration 7000, loss = 7.15174 | |
| I0401 10:38:42.548632 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0625 | |
| I0401 10:38:42.548652 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.732955 | |
| I0401 10:38:42.548666 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.166667 | |
| I0401 10:38:42.548681 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.42472 (* 0.3 = 1.02742 loss) | |
| I0401 10:38:42.548696 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.10329 (* 0.3 = 0.330987 loss) | |
| I0401 10:38:42.548708 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0416667 | |
| I0401 10:38:42.548722 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.732955 | |
| I0401 10:38:42.548733 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.104167 | |
| I0401 10:38:42.548746 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.66769 (* 0.3 = 1.10031 loss) | |
| I0401 10:38:42.548760 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.19331 (* 0.3 = 0.357993 loss) | |
| I0401 10:38:42.548773 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0416667 | |
| I0401 10:38:42.548785 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.732955 | |
| I0401 10:38:42.548796 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.0833333 | |
| I0401 10:38:42.548811 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.42921 (* 1 = 3.42921 loss) | |
| I0401 10:38:42.548825 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.05694 (* 1 = 1.05694 loss) | |
| I0401 10:38:42.548836 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:38:42.548848 31447 solver.cpp:245] Train net output #16: total_confidence = 9.98726e-06 | |
| I0401 10:38:42.548861 31447 sgd_solver.cpp:106] Iteration 7000, lr = 0.05 | |
| I0401 10:40:52.497665 31447 solver.cpp:229] Iteration 7500, loss = 7.14815 | |
| I0401 10:40:52.497799 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0384615 | |
| I0401 10:40:52.497822 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.704545 | |
| I0401 10:40:52.497833 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.153846 | |
| I0401 10:40:52.497849 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.61527 (* 0.3 = 1.08458 loss) | |
| I0401 10:40:52.497864 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.18834 (* 0.3 = 0.356503 loss) | |
| I0401 10:40:52.497877 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0576923 | |
| I0401 10:40:52.497890 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.721591 | |
| I0401 10:40:52.497902 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.0961538 | |
| I0401 10:40:52.497916 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.78424 (* 0.3 = 1.13527 loss) | |
| I0401 10:40:52.497931 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.23429 (* 0.3 = 0.370288 loss) | |
| I0401 10:40:52.497942 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0576923 | |
| I0401 10:40:52.497954 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.721591 | |
| I0401 10:40:52.497967 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.134615 | |
| I0401 10:40:52.497979 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.55559 (* 1 = 3.55559 loss) | |
| I0401 10:40:52.497993 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.1219 (* 1 = 1.1219 loss) | |
| I0401 10:40:52.498005 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:40:52.498018 31447 solver.cpp:245] Train net output #16: total_confidence = 6.70406e-08 | |
| I0401 10:40:52.498030 31447 sgd_solver.cpp:106] Iteration 7500, lr = 0.05 | |
| I0401 10:43:02.361325 31447 solver.cpp:229] Iteration 8000, loss = 7.09206 | |
| I0401 10:43:02.361461 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0243902 | |
| I0401 10:43:02.361482 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 10:43:02.361495 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.243902 | |
| I0401 10:43:02.361511 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.15362 (* 0.3 = 0.946087 loss) | |
| I0401 10:43:02.361529 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.952513 (* 0.3 = 0.285754 loss) | |
| I0401 10:43:02.361542 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0243902 | |
| I0401 10:43:02.361556 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.755682 | |
| I0401 10:43:02.361567 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.268293 | |
| I0401 10:43:02.361582 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.16177 (* 0.3 = 0.94853 loss) | |
| I0401 10:43:02.361595 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.919329 (* 0.3 = 0.275799 loss) | |
| I0401 10:43:02.361608 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0 | |
| I0401 10:43:02.361620 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.727273 | |
| I0401 10:43:02.361632 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.219512 | |
| I0401 10:43:02.361646 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.07003 (* 1 = 3.07003 loss) | |
| I0401 10:43:02.361661 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.933359 (* 1 = 0.933359 loss) | |
| I0401 10:43:02.361673 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:43:02.361685 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000368991 | |
| I0401 10:43:02.361699 31447 sgd_solver.cpp:106] Iteration 8000, lr = 0.05 | |
| I0401 10:45:11.915096 31447 solver.cpp:229] Iteration 8500, loss = 7.0311 | |
| I0401 10:45:11.915381 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.130435 | |
| I0401 10:45:11.915403 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.761364 | |
| I0401 10:45:11.915416 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.26087 | |
| I0401 10:45:11.915432 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.20153 (* 0.3 = 0.960458 loss) | |
| I0401 10:45:11.915447 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.00703 (* 0.3 = 0.302108 loss) | |
| I0401 10:45:11.915460 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0869565 | |
| I0401 10:45:11.915473 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.755682 | |
| I0401 10:45:11.915485 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.26087 | |
| I0401 10:45:11.915498 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.15529 (* 0.3 = 0.946587 loss) | |
| I0401 10:45:11.915513 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.987235 (* 0.3 = 0.29617 loss) | |
| I0401 10:45:11.915527 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.130435 | |
| I0401 10:45:11.915539 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.755682 | |
| I0401 10:45:11.915551 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.304348 | |
| I0401 10:45:11.915565 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.07865 (* 1 = 3.07865 loss) | |
| I0401 10:45:11.915578 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.964204 (* 1 = 0.964204 loss) | |
| I0401 10:45:11.915591 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:45:11.915602 31447 solver.cpp:245] Train net output #16: total_confidence = 1.40189e-06 | |
| I0401 10:45:11.915616 31447 sgd_solver.cpp:106] Iteration 8500, lr = 0.05 | |
| I0401 10:47:21.321585 31447 solver.cpp:229] Iteration 9000, loss = 7.01921 | |
| I0401 10:47:21.321686 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.075 | |
| I0401 10:47:21.321707 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.755682 | |
| I0401 10:47:21.321719 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.225 | |
| I0401 10:47:21.321735 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.16561 (* 0.3 = 0.949683 loss) | |
| I0401 10:47:21.321750 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.93509 (* 0.3 = 0.280527 loss) | |
| I0401 10:47:21.321763 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.05 | |
| I0401 10:47:21.321775 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.755682 | |
| I0401 10:47:21.321786 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.225 | |
| I0401 10:47:21.321800 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.07341 (* 0.3 = 0.922023 loss) | |
| I0401 10:47:21.321815 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.977897 (* 0.3 = 0.293369 loss) | |
| I0401 10:47:21.321826 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.075 | |
| I0401 10:47:21.321838 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.761364 | |
| I0401 10:47:21.321851 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.175 | |
| I0401 10:47:21.321864 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.09824 (* 1 = 3.09824 loss) | |
| I0401 10:47:21.321878 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.885343 (* 1 = 0.885343 loss) | |
| I0401 10:47:21.321890 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:47:21.321902 31447 solver.cpp:245] Train net output #16: total_confidence = 3.73739e-05 | |
| I0401 10:47:21.321914 31447 sgd_solver.cpp:106] Iteration 9000, lr = 0.05 | |
| I0401 10:49:30.846891 31447 solver.cpp:229] Iteration 9500, loss = 7.03755 | |
| I0401 10:49:30.847019 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0243902 | |
| I0401 10:49:30.847040 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.744318 | |
| I0401 10:49:30.847054 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.170732 | |
| I0401 10:49:30.847069 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.14395 (* 0.3 = 0.943185 loss) | |
| I0401 10:49:30.847084 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.965905 (* 0.3 = 0.289772 loss) | |
| I0401 10:49:30.847096 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0243902 | |
| I0401 10:49:30.847110 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.744318 | |
| I0401 10:49:30.847121 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.146341 | |
| I0401 10:49:30.847134 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.22086 (* 0.3 = 0.966258 loss) | |
| I0401 10:49:30.847148 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.988799 (* 0.3 = 0.29664 loss) | |
| I0401 10:49:30.847160 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0243902 | |
| I0401 10:49:30.847172 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.738636 | |
| I0401 10:49:30.847184 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.219512 | |
| I0401 10:49:30.847198 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.09102 (* 1 = 3.09102 loss) | |
| I0401 10:49:30.847213 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.932101 (* 1 = 0.932101 loss) | |
| I0401 10:49:30.847224 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:49:30.847236 31447 solver.cpp:245] Train net output #16: total_confidence = 1.88155e-05 | |
| I0401 10:49:30.847255 31447 sgd_solver.cpp:106] Iteration 9500, lr = 0.05 | |
| I0401 10:51:40.304813 31447 solver.cpp:338] Iteration 10000, Testing net (#0) | |
| I0401 10:52:10.095230 31447 solver.cpp:393] Test loss: 6.57555 | |
| I0401 10:52:10.095278 31447 solver.cpp:406] Test net output #0: loss1/accuracy = 0.126468 | |
| I0401 10:52:10.095295 31447 solver.cpp:406] Test net output #1: loss1/accuracy_incl_empty = 0.771726 | |
| I0401 10:52:10.095309 31447 solver.cpp:406] Test net output #2: loss1/accuracy_top3 = 0.320689 | |
| I0401 10:52:10.095324 31447 solver.cpp:406] Test net output #3: loss1/cross_entropy_loss = 3.09905 (* 0.3 = 0.929716 loss) | |
| I0401 10:52:10.095338 31447 solver.cpp:406] Test net output #4: loss1/cross_entropy_loss_incl_empty = 0.874265 (* 0.3 = 0.26228 loss) | |
| I0401 10:52:10.095350 31447 solver.cpp:406] Test net output #5: loss2/accuracy = 0.14872 | |
| I0401 10:52:10.095362 31447 solver.cpp:406] Test net output #6: loss2/accuracy_incl_empty = 0.698909 | |
| I0401 10:52:10.095374 31447 solver.cpp:406] Test net output #7: loss2/accuracy_top3 = 0.304892 | |
| I0401 10:52:10.095387 31447 solver.cpp:406] Test net output #8: loss2/cross_entropy_loss = 3.13778 (* 0.3 = 0.941333 loss) | |
| I0401 10:52:10.095402 31447 solver.cpp:406] Test net output #9: loss2/cross_entropy_loss_incl_empty = 1.2205 (* 0.3 = 0.366151 loss) | |
| I0401 10:52:10.095412 31447 solver.cpp:406] Test net output #10: loss3/accuracy = 0.162881 | |
| I0401 10:52:10.095424 31447 solver.cpp:406] Test net output #11: loss3/accuracy_incl_empty = 0.726591 | |
| I0401 10:52:10.095437 31447 solver.cpp:406] Test net output #12: loss3/accuracy_top3 = 0.337139 | |
| I0401 10:52:10.095449 31447 solver.cpp:406] Test net output #13: loss3/cross_entropy_loss = 2.93699 (* 1 = 2.93699 loss) | |
| I0401 10:52:10.095463 31447 solver.cpp:406] Test net output #14: loss3/cross_entropy_loss_incl_empty = 1.13908 (* 1 = 1.13908 loss) | |
| I0401 10:52:10.095474 31447 solver.cpp:406] Test net output #15: total_accuracy = 0 | |
| I0401 10:52:10.095486 31447 solver.cpp:406] Test net output #16: total_confidence = 0.000124669 | |
| I0401 10:52:10.247308 31447 solver.cpp:229] Iteration 10000, loss = 7.02116 | |
| I0401 10:52:10.247370 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0638298 | |
| I0401 10:52:10.247387 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.732955 | |
| I0401 10:52:10.247401 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.170213 | |
| I0401 10:52:10.247418 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.41513 (* 0.3 = 1.02454 loss) | |
| I0401 10:52:10.247432 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.03599 (* 0.3 = 0.310798 loss) | |
| I0401 10:52:10.247445 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0425532 | |
| I0401 10:52:10.247457 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.727273 | |
| I0401 10:52:10.247473 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.170213 | |
| I0401 10:52:10.247488 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.34853 (* 0.3 = 1.00456 loss) | |
| I0401 10:52:10.247501 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.06553 (* 0.3 = 0.31966 loss) | |
| I0401 10:52:10.247514 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0212766 | |
| I0401 10:52:10.247526 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.727273 | |
| I0401 10:52:10.247539 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.191489 | |
| I0401 10:52:10.247552 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.22606 (* 1 = 3.22606 loss) | |
| I0401 10:52:10.247566 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.979342 (* 1 = 0.979342 loss) | |
| I0401 10:52:10.247578 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:52:10.247591 31447 solver.cpp:245] Train net output #16: total_confidence = 4.34381e-07 | |
| I0401 10:52:10.247604 31447 sgd_solver.cpp:106] Iteration 10000, lr = 0.05 | |
| I0401 10:54:19.732379 31447 solver.cpp:229] Iteration 10500, loss = 6.94567 | |
| I0401 10:54:19.732507 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0769231 | |
| I0401 10:54:19.732527 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.778409 | |
| I0401 10:54:19.732539 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.153846 | |
| I0401 10:54:19.732555 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.14397 (* 0.3 = 0.943192 loss) | |
| I0401 10:54:19.732570 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.852547 (* 0.3 = 0.255764 loss) | |
| I0401 10:54:19.732583 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.102564 | |
| I0401 10:54:19.732595 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.784091 | |
| I0401 10:54:19.732607 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.205128 | |
| I0401 10:54:19.732621 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.09603 (* 0.3 = 0.928808 loss) | |
| I0401 10:54:19.732635 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.855776 (* 0.3 = 0.256733 loss) | |
| I0401 10:54:19.732647 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.025641 | |
| I0401 10:54:19.732659 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.761364 | |
| I0401 10:54:19.732671 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.128205 | |
| I0401 10:54:19.732686 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.07572 (* 1 = 3.07572 loss) | |
| I0401 10:54:19.732698 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.835178 (* 1 = 0.835178 loss) | |
| I0401 10:54:19.732710 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:54:19.732722 31447 solver.cpp:245] Train net output #16: total_confidence = 8.12738e-05 | |
| I0401 10:54:19.732735 31447 sgd_solver.cpp:106] Iteration 10500, lr = 0.05 | |
| I0401 10:56:29.687058 31447 solver.cpp:229] Iteration 11000, loss = 6.9215 | |
| I0401 10:56:29.687325 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.06 | |
| I0401 10:56:29.687345 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.721591 | |
| I0401 10:56:29.687358 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.24 | |
| I0401 10:56:29.687374 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.38849 (* 0.3 = 1.01655 loss) | |
| I0401 10:56:29.687389 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.07288 (* 0.3 = 0.321863 loss) | |
| I0401 10:56:29.687402 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.12 | |
| I0401 10:56:29.687414 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.727273 | |
| I0401 10:56:29.687427 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.28 | |
| I0401 10:56:29.687440 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.36495 (* 0.3 = 1.00949 loss) | |
| I0401 10:56:29.687453 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.18375 (* 0.3 = 0.355126 loss) | |
| I0401 10:56:29.687465 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.06 | |
| I0401 10:56:29.687479 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.727273 | |
| I0401 10:56:29.687490 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.16 | |
| I0401 10:56:29.687504 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.23563 (* 1 = 3.23563 loss) | |
| I0401 10:56:29.687520 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.987894 (* 1 = 0.987894 loss) | |
| I0401 10:56:29.687532 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:56:29.687544 31447 solver.cpp:245] Train net output #16: total_confidence = 5.01888e-05 | |
| I0401 10:56:29.687556 31447 sgd_solver.cpp:106] Iteration 11000, lr = 0.05 | |
| I0401 10:58:39.200718 31447 solver.cpp:229] Iteration 11500, loss = 6.90679 | |
| I0401 10:58:39.200850 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0909091 | |
| I0401 10:58:39.200870 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.744318 | |
| I0401 10:58:39.200883 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.272727 | |
| I0401 10:58:39.200901 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.14622 (* 0.3 = 0.943866 loss) | |
| I0401 10:58:39.200916 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.965346 (* 0.3 = 0.289604 loss) | |
| I0401 10:58:39.200927 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0681818 | |
| I0401 10:58:39.200940 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.767045 | |
| I0401 10:58:39.200953 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.204545 | |
| I0401 10:58:39.200965 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.23767 (* 0.3 = 0.9713 loss) | |
| I0401 10:58:39.200980 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.854302 (* 0.3 = 0.256291 loss) | |
| I0401 10:58:39.200992 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0681818 | |
| I0401 10:58:39.201004 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.744318 | |
| I0401 10:58:39.201016 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.25 | |
| I0401 10:58:39.201030 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.19202 (* 1 = 3.19202 loss) | |
| I0401 10:58:39.201057 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.91941 (* 1 = 0.91941 loss) | |
| I0401 10:58:39.201072 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 10:58:39.201084 31447 solver.cpp:245] Train net output #16: total_confidence = 6.28907e-05 | |
| I0401 10:58:39.201097 31447 sgd_solver.cpp:106] Iteration 11500, lr = 0.05 | |
| I0401 11:00:48.513089 31447 solver.cpp:229] Iteration 12000, loss = 6.85376 | |
| I0401 11:00:48.513221 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.025 | |
| I0401 11:00:48.513242 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.761364 | |
| I0401 11:00:48.513253 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.25 | |
| I0401 11:00:48.513269 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.34758 (* 0.3 = 1.00427 loss) | |
| I0401 11:00:48.513284 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.915381 (* 0.3 = 0.274614 loss) | |
| I0401 11:00:48.513298 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.05 | |
| I0401 11:00:48.513310 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.778409 | |
| I0401 11:00:48.513324 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.25 | |
| I0401 11:00:48.513336 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.18698 (* 0.3 = 0.956094 loss) | |
| I0401 11:00:48.513350 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.847171 (* 0.3 = 0.254151 loss) | |
| I0401 11:00:48.513362 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.175 | |
| I0401 11:00:48.513375 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.801136 | |
| I0401 11:00:48.513386 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.25 | |
| I0401 11:00:48.513401 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.04066 (* 1 = 3.04066 loss) | |
| I0401 11:00:48.513414 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.790594 (* 1 = 0.790594 loss) | |
| I0401 11:00:48.513427 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:00:48.513437 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000456405 | |
| I0401 11:00:48.513450 31447 sgd_solver.cpp:106] Iteration 12000, lr = 0.05 | |
| I0401 11:02:57.806831 31447 solver.cpp:229] Iteration 12500, loss = 6.88715 | |
| I0401 11:02:57.806936 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0869565 | |
| I0401 11:02:57.806956 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 11:02:57.806969 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.217391 | |
| I0401 11:02:57.806984 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.33544 (* 0.3 = 1.00063 loss) | |
| I0401 11:02:57.807000 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.979513 (* 0.3 = 0.293854 loss) | |
| I0401 11:02:57.807013 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0869565 | |
| I0401 11:02:57.807024 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.755682 | |
| I0401 11:02:57.807036 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.195652 | |
| I0401 11:02:57.807049 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.28666 (* 0.3 = 0.985997 loss) | |
| I0401 11:02:57.807063 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.943889 (* 0.3 = 0.283167 loss) | |
| I0401 11:02:57.807076 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0652174 | |
| I0401 11:02:57.807088 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.75 | |
| I0401 11:02:57.807101 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.282609 | |
| I0401 11:02:57.807114 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.2252 (* 1 = 3.2252 loss) | |
| I0401 11:02:57.807128 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.898052 (* 1 = 0.898052 loss) | |
| I0401 11:02:57.807140 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:02:57.807157 31447 solver.cpp:245] Train net output #16: total_confidence = 3.47409e-05 | |
| I0401 11:02:57.807176 31447 sgd_solver.cpp:106] Iteration 12500, lr = 0.05 | |
| I0401 11:05:07.275419 31447 solver.cpp:229] Iteration 13000, loss = 6.83265 | |
| I0401 11:05:07.275686 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.179487 | |
| I0401 11:05:07.275707 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.795455 | |
| I0401 11:05:07.275719 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.25641 | |
| I0401 11:05:07.275734 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.24876 (* 0.3 = 0.974629 loss) | |
| I0401 11:05:07.275749 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.909586 (* 0.3 = 0.272876 loss) | |
| I0401 11:05:07.275763 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0512821 | |
| I0401 11:05:07.275775 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.778409 | |
| I0401 11:05:07.275787 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.25641 | |
| I0401 11:05:07.275801 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.2703 (* 0.3 = 0.981089 loss) | |
| I0401 11:05:07.275815 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.906939 (* 0.3 = 0.272082 loss) | |
| I0401 11:05:07.275827 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.102564 | |
| I0401 11:05:07.275840 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.784091 | |
| I0401 11:05:07.275851 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.230769 | |
| I0401 11:05:07.275866 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.2242 (* 1 = 3.2242 loss) | |
| I0401 11:05:07.275879 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.853905 (* 1 = 0.853905 loss) | |
| I0401 11:05:07.275892 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:05:07.275903 31447 solver.cpp:245] Train net output #16: total_confidence = 5.20698e-05 | |
| I0401 11:05:07.275915 31447 sgd_solver.cpp:106] Iteration 13000, lr = 0.05 | |
| I0401 11:07:16.563278 31447 solver.cpp:229] Iteration 13500, loss = 6.85462 | |
| I0401 11:07:16.563386 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0401 11:07:16.563405 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.738636 | |
| I0401 11:07:16.563418 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.0909091 | |
| I0401 11:07:16.563434 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.86068 (* 0.3 = 1.1582 loss) | |
| I0401 11:07:16.563449 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.06975 (* 0.3 = 0.320924 loss) | |
| I0401 11:07:16.563462 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0 | |
| I0401 11:07:16.563474 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.721591 | |
| I0401 11:07:16.563485 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.136364 | |
| I0401 11:07:16.563499 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.63054 (* 0.3 = 1.08916 loss) | |
| I0401 11:07:16.563513 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.07492 (* 0.3 = 0.322477 loss) | |
| I0401 11:07:16.563529 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0681818 | |
| I0401 11:07:16.563541 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.744318 | |
| I0401 11:07:16.563554 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.136364 | |
| I0401 11:07:16.563567 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.5212 (* 1 = 3.5212 loss) | |
| I0401 11:07:16.563580 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.01434 (* 1 = 1.01434 loss) | |
| I0401 11:07:16.563592 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:07:16.563604 31447 solver.cpp:245] Train net output #16: total_confidence = 5.51353e-05 | |
| I0401 11:07:16.563617 31447 sgd_solver.cpp:106] Iteration 13500, lr = 0.05 | |
| I0401 11:09:26.566402 31447 solver.cpp:229] Iteration 14000, loss = 6.82927 | |
| I0401 11:09:26.566529 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0401 11:09:26.566550 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.698864 | |
| I0401 11:09:26.566562 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.0961538 | |
| I0401 11:09:26.566578 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.22745 (* 0.3 = 0.968236 loss) | |
| I0401 11:09:26.566593 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.06493 (* 0.3 = 0.31948 loss) | |
| I0401 11:09:26.566606 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0384615 | |
| I0401 11:09:26.566618 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.710227 | |
| I0401 11:09:26.566630 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.153846 | |
| I0401 11:09:26.566644 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.225 (* 0.3 = 0.967499 loss) | |
| I0401 11:09:26.566658 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.02976 (* 0.3 = 0.308928 loss) | |
| I0401 11:09:26.566670 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0 | |
| I0401 11:09:26.566684 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.704545 | |
| I0401 11:09:26.566695 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.134615 | |
| I0401 11:09:26.566709 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.04002 (* 1 = 3.04002 loss) | |
| I0401 11:09:26.566722 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.954043 (* 1 = 0.954043 loss) | |
| I0401 11:09:26.566735 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:09:26.566746 31447 solver.cpp:245] Train net output #16: total_confidence = 3.75094e-05 | |
| I0401 11:09:26.566758 31447 sgd_solver.cpp:106] Iteration 14000, lr = 0.05 | |
| I0401 11:11:35.906218 31447 solver.cpp:229] Iteration 14500, loss = 6.77427 | |
| I0401 11:11:35.906322 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0434783 | |
| I0401 11:11:35.906342 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.738636 | |
| I0401 11:11:35.906353 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.130435 | |
| I0401 11:11:35.906369 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.20046 (* 0.3 = 0.960137 loss) | |
| I0401 11:11:35.906385 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.94099 (* 0.3 = 0.282297 loss) | |
| I0401 11:11:35.906397 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0434783 | |
| I0401 11:11:35.906411 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.75 | |
| I0401 11:11:35.906424 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.108696 | |
| I0401 11:11:35.906437 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.1636 (* 0.3 = 0.949079 loss) | |
| I0401 11:11:35.906451 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.939333 (* 0.3 = 0.2818 loss) | |
| I0401 11:11:35.906463 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0217391 | |
| I0401 11:11:35.906476 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.744318 | |
| I0401 11:11:35.906487 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.195652 | |
| I0401 11:11:35.906500 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.1174 (* 1 = 3.1174 loss) | |
| I0401 11:11:35.906514 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.904541 (* 1 = 0.904541 loss) | |
| I0401 11:11:35.906529 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:11:35.906541 31447 solver.cpp:245] Train net output #16: total_confidence = 8.23459e-07 | |
| I0401 11:11:35.906554 31447 sgd_solver.cpp:106] Iteration 14500, lr = 0.05 | |
| I0401 11:13:44.887145 31447 solver.cpp:338] Iteration 15000, Testing net (#0) | |
| I0401 11:14:14.671084 31447 solver.cpp:393] Test loss: 6.84012 | |
| I0401 11:14:14.671133 31447 solver.cpp:406] Test net output #0: loss1/accuracy = 0.118761 | |
| I0401 11:14:14.671159 31447 solver.cpp:406] Test net output #1: loss1/accuracy_incl_empty = 0.772909 | |
| I0401 11:14:14.671185 31447 solver.cpp:406] Test net output #2: loss1/accuracy_top3 = 0.286252 | |
| I0401 11:14:14.671211 31447 solver.cpp:406] Test net output #3: loss1/cross_entropy_loss = 3.10582 (* 0.3 = 0.931747 loss) | |
| I0401 11:14:14.671241 31447 solver.cpp:406] Test net output #4: loss1/cross_entropy_loss_incl_empty = 0.863085 (* 0.3 = 0.258925 loss) | |
| I0401 11:14:14.671264 31447 solver.cpp:406] Test net output #5: loss2/accuracy = 0.128375 | |
| I0401 11:14:14.671286 31447 solver.cpp:406] Test net output #6: loss2/accuracy_incl_empty = 0.684364 | |
| I0401 11:14:14.671306 31447 solver.cpp:406] Test net output #7: loss2/accuracy_top3 = 0.299695 | |
| I0401 11:14:14.671332 31447 solver.cpp:406] Test net output #8: loss2/cross_entropy_loss = 3.15802 (* 0.3 = 0.947407 loss) | |
| I0401 11:14:14.671357 31447 solver.cpp:406] Test net output #9: loss2/cross_entropy_loss_incl_empty = 1.76949 (* 0.3 = 0.530847 loss) | |
| I0401 11:14:14.671378 31447 solver.cpp:406] Test net output #10: loss3/accuracy = 0.113399 | |
| I0401 11:14:14.671399 31447 solver.cpp:406] Test net output #11: loss3/accuracy_incl_empty = 0.721227 | |
| I0401 11:14:14.671421 31447 solver.cpp:406] Test net output #12: loss3/accuracy_top3 = 0.299622 | |
| I0401 11:14:14.671445 31447 solver.cpp:406] Test net output #13: loss3/cross_entropy_loss = 2.92447 (* 1 = 2.92447 loss) | |
| I0401 11:14:14.671473 31447 solver.cpp:406] Test net output #14: loss3/cross_entropy_loss_incl_empty = 1.24673 (* 1 = 1.24673 loss) | |
| I0401 11:14:14.671497 31447 solver.cpp:406] Test net output #15: total_accuracy = 0.001 | |
| I0401 11:14:14.671524 31447 solver.cpp:406] Test net output #16: total_confidence = 5.43999e-05 | |
| I0401 11:14:14.822613 31447 solver.cpp:229] Iteration 15000, loss = 6.85827 | |
| I0401 11:14:14.822656 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.04 | |
| I0401 11:14:14.822685 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.727273 | |
| I0401 11:14:14.822713 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.16 | |
| I0401 11:14:14.822742 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.30188 (* 0.3 = 0.990564 loss) | |
| I0401 11:14:14.822772 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.01173 (* 0.3 = 0.303519 loss) | |
| I0401 11:14:14.822798 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.08 | |
| I0401 11:14:14.822820 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.738636 | |
| I0401 11:14:14.822844 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.22 | |
| I0401 11:14:14.822870 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.36035 (* 0.3 = 1.0081 loss) | |
| I0401 11:14:14.822896 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.04453 (* 0.3 = 0.313359 loss) | |
| I0401 11:14:14.822916 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.06 | |
| I0401 11:14:14.822938 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.727273 | |
| I0401 11:14:14.822960 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.16 | |
| I0401 11:14:14.822985 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.37809 (* 1 = 3.37809 loss) | |
| I0401 11:14:14.823010 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.01996 (* 1 = 1.01996 loss) | |
| I0401 11:14:14.823032 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:14:14.823056 31447 solver.cpp:245] Train net output #16: total_confidence = 4.29245e-07 | |
| I0401 11:14:14.823081 31447 sgd_solver.cpp:106] Iteration 15000, lr = 0.05 | |
| I0401 11:16:23.843647 31447 solver.cpp:229] Iteration 15500, loss = 6.82119 | |
| I0401 11:16:23.843914 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.133333 | |
| I0401 11:16:23.843935 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.778409 | |
| I0401 11:16:23.843946 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.288889 | |
| I0401 11:16:23.843962 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 2.98122 (* 0.3 = 0.894365 loss) | |
| I0401 11:16:23.843977 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.81619 (* 0.3 = 0.244857 loss) | |
| I0401 11:16:23.843989 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.133333 | |
| I0401 11:16:23.844002 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.772727 | |
| I0401 11:16:23.844014 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.266667 | |
| I0401 11:16:23.844028 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.06237 (* 0.3 = 0.918712 loss) | |
| I0401 11:16:23.844043 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.898177 (* 0.3 = 0.269453 loss) | |
| I0401 11:16:23.844054 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0888889 | |
| I0401 11:16:23.844066 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.767045 | |
| I0401 11:16:23.844077 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.288889 | |
| I0401 11:16:23.844091 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.96414 (* 1 = 2.96414 loss) | |
| I0401 11:16:23.844105 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.793952 (* 1 = 0.793952 loss) | |
| I0401 11:16:23.844117 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:16:23.844130 31447 solver.cpp:245] Train net output #16: total_confidence = 1.50311e-05 | |
| I0401 11:16:23.844141 31447 sgd_solver.cpp:106] Iteration 15500, lr = 0.05 | |
| I0401 11:18:33.145603 31447 solver.cpp:229] Iteration 16000, loss = 6.81961 | |
| I0401 11:18:33.145755 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.104167 | |
| I0401 11:18:33.145776 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.738636 | |
| I0401 11:18:33.145789 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.3125 | |
| I0401 11:18:33.145807 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 2.96312 (* 0.3 = 0.888937 loss) | |
| I0401 11:18:33.145822 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.923139 (* 0.3 = 0.276942 loss) | |
| I0401 11:18:33.145833 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0416667 | |
| I0401 11:18:33.145846 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.727273 | |
| I0401 11:18:33.145859 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.166667 | |
| I0401 11:18:33.145889 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.14594 (* 0.3 = 0.943781 loss) | |
| I0401 11:18:33.145907 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.974801 (* 0.3 = 0.29244 loss) | |
| I0401 11:18:33.145920 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0416667 | |
| I0401 11:18:33.145932 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.732955 | |
| I0401 11:18:33.145944 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.145833 | |
| I0401 11:18:33.145958 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.01636 (* 1 = 3.01636 loss) | |
| I0401 11:18:33.145972 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.900213 (* 1 = 0.900213 loss) | |
| I0401 11:18:33.145984 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:18:33.145997 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000253748 | |
| I0401 11:18:33.146009 31447 sgd_solver.cpp:106] Iteration 16000, lr = 0.05 | |
| I0401 11:20:42.105851 31447 solver.cpp:229] Iteration 16500, loss = 6.74981 | |
| I0401 11:20:42.106003 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0454545 | |
| I0401 11:20:42.106024 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.738636 | |
| I0401 11:20:42.106036 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.113636 | |
| I0401 11:20:42.106052 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.32432 (* 0.3 = 0.997295 loss) | |
| I0401 11:20:42.106067 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.988179 (* 0.3 = 0.296454 loss) | |
| I0401 11:20:42.106081 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0454545 | |
| I0401 11:20:42.106092 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.744318 | |
| I0401 11:20:42.106104 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.181818 | |
| I0401 11:20:42.106117 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.19633 (* 0.3 = 0.958898 loss) | |
| I0401 11:20:42.106133 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.977196 (* 0.3 = 0.293159 loss) | |
| I0401 11:20:42.106144 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0681818 | |
| I0401 11:20:42.106158 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.75 | |
| I0401 11:20:42.106169 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.181818 | |
| I0401 11:20:42.106184 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.25947 (* 1 = 3.25947 loss) | |
| I0401 11:20:42.106197 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.944047 (* 1 = 0.944047 loss) | |
| I0401 11:20:42.106209 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:20:42.106221 31447 solver.cpp:245] Train net output #16: total_confidence = 8.56118e-07 | |
| I0401 11:20:42.106235 31447 sgd_solver.cpp:106] Iteration 16500, lr = 0.05 | |
| I0401 11:22:51.174625 31447 solver.cpp:229] Iteration 17000, loss = 6.73016 | |
| I0401 11:22:51.174767 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0784314 | |
| I0401 11:22:51.174787 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.732955 | |
| I0401 11:22:51.174800 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.176471 | |
| I0401 11:22:51.174818 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.23278 (* 0.3 = 0.969834 loss) | |
| I0401 11:22:51.174832 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.978248 (* 0.3 = 0.293474 loss) | |
| I0401 11:22:51.174845 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0392157 | |
| I0401 11:22:51.174857 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.721591 | |
| I0401 11:22:51.174870 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.196078 | |
| I0401 11:22:51.174883 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.22145 (* 0.3 = 0.966434 loss) | |
| I0401 11:22:51.174897 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.989039 (* 0.3 = 0.296712 loss) | |
| I0401 11:22:51.174909 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0784314 | |
| I0401 11:22:51.174921 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.732955 | |
| I0401 11:22:51.174933 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.27451 | |
| I0401 11:22:51.174947 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.04248 (* 1 = 3.04248 loss) | |
| I0401 11:22:51.174960 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.922856 (* 1 = 0.922856 loss) | |
| I0401 11:22:51.174973 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:22:51.174985 31447 solver.cpp:245] Train net output #16: total_confidence = 1.95092e-06 | |
| I0401 11:22:51.174998 31447 sgd_solver.cpp:106] Iteration 17000, lr = 0.05 | |
| I0401 11:25:00.126704 31447 solver.cpp:229] Iteration 17500, loss = 6.70327 | |
| I0401 11:25:00.126842 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0416667 | |
| I0401 11:25:00.126863 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.732955 | |
| I0401 11:25:00.126875 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.229167 | |
| I0401 11:25:00.126891 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.39643 (* 0.3 = 1.01893 loss) | |
| I0401 11:25:00.126906 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.00564 (* 0.3 = 0.301693 loss) | |
| I0401 11:25:00.126919 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0625 | |
| I0401 11:25:00.126931 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.738636 | |
| I0401 11:25:00.126943 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.1875 | |
| I0401 11:25:00.126956 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.44461 (* 0.3 = 1.03338 loss) | |
| I0401 11:25:00.126971 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.01599 (* 0.3 = 0.304798 loss) | |
| I0401 11:25:00.126982 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0208333 | |
| I0401 11:25:00.126994 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.727273 | |
| I0401 11:25:00.127007 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.208333 | |
| I0401 11:25:00.127020 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.42022 (* 1 = 3.42022 loss) | |
| I0401 11:25:00.127034 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.00408 (* 1 = 1.00408 loss) | |
| I0401 11:25:00.127046 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:25:00.127058 31447 solver.cpp:245] Train net output #16: total_confidence = 2.78401e-06 | |
| I0401 11:25:00.127069 31447 sgd_solver.cpp:106] Iteration 17500, lr = 0.05 | |
| I0401 11:27:09.260237 31447 solver.cpp:229] Iteration 18000, loss = 6.69793 | |
| I0401 11:27:09.260470 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.177778 | |
| I0401 11:27:09.260489 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.778409 | |
| I0401 11:27:09.260502 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.355556 | |
| I0401 11:27:09.260519 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 2.83541 (* 0.3 = 0.850624 loss) | |
| I0401 11:27:09.260532 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.806087 (* 0.3 = 0.241826 loss) | |
| I0401 11:27:09.260545 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.177778 | |
| I0401 11:27:09.260557 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.778409 | |
| I0401 11:27:09.260570 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.355556 | |
| I0401 11:27:09.260582 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 2.87698 (* 0.3 = 0.863093 loss) | |
| I0401 11:27:09.260596 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.843269 (* 0.3 = 0.252981 loss) | |
| I0401 11:27:09.260608 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.222222 | |
| I0401 11:27:09.260620 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.801136 | |
| I0401 11:27:09.260632 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.4 | |
| I0401 11:27:09.260646 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.65981 (* 1 = 2.65981 loss) | |
| I0401 11:27:09.260660 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.716114 (* 1 = 0.716114 loss) | |
| I0401 11:27:09.260673 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:27:09.260684 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000395087 | |
| I0401 11:27:09.260696 31447 sgd_solver.cpp:106] Iteration 18000, lr = 0.05 | |
| I0401 11:29:18.279605 31447 solver.cpp:229] Iteration 18500, loss = 6.73159 | |
| I0401 11:29:18.279780 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.119048 | |
| I0401 11:29:18.279803 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.789773 | |
| I0401 11:29:18.279814 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.285714 | |
| I0401 11:29:18.279830 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 2.86986 (* 0.3 = 0.860957 loss) | |
| I0401 11:29:18.279845 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.758748 (* 0.3 = 0.227624 loss) | |
| I0401 11:29:18.279858 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.119048 | |
| I0401 11:29:18.279871 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.784091 | |
| I0401 11:29:18.279882 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.309524 | |
| I0401 11:29:18.279896 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 2.88253 (* 0.3 = 0.864759 loss) | |
| I0401 11:29:18.279911 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.78807 (* 0.3 = 0.236421 loss) | |
| I0401 11:29:18.279922 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.142857 | |
| I0401 11:29:18.279934 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.795455 | |
| I0401 11:29:18.279947 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.261905 | |
| I0401 11:29:18.279960 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.78921 (* 1 = 2.78921 loss) | |
| I0401 11:29:18.279974 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.741273 (* 1 = 0.741273 loss) | |
| I0401 11:29:18.279985 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:29:18.279997 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000326672 | |
| I0401 11:29:18.280009 31447 sgd_solver.cpp:106] Iteration 18500, lr = 0.05 | |
| I0401 11:31:27.447751 31447 solver.cpp:229] Iteration 19000, loss = 6.65687 | |
| I0401 11:31:27.447888 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.1 | |
| I0401 11:31:27.447909 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.744318 | |
| I0401 11:31:27.447921 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.28 | |
| I0401 11:31:27.447938 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.08886 (* 0.3 = 0.926657 loss) | |
| I0401 11:31:27.447952 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.924297 (* 0.3 = 0.277289 loss) | |
| I0401 11:31:27.447965 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.06 | |
| I0401 11:31:27.447978 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.732955 | |
| I0401 11:31:27.447989 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.22 | |
| I0401 11:31:27.448004 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.11446 (* 0.3 = 0.934339 loss) | |
| I0401 11:31:27.448016 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.945824 (* 0.3 = 0.283747 loss) | |
| I0401 11:31:27.448029 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.14 | |
| I0401 11:31:27.448040 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.755682 | |
| I0401 11:31:27.448052 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.28 | |
| I0401 11:31:27.448065 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.04928 (* 1 = 3.04928 loss) | |
| I0401 11:31:27.448088 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.923117 (* 1 = 0.923117 loss) | |
| I0401 11:31:27.448110 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:31:27.448124 31447 solver.cpp:245] Train net output #16: total_confidence = 3.15767e-06 | |
| I0401 11:31:27.448137 31447 sgd_solver.cpp:106] Iteration 19000, lr = 0.05 | |
| I0401 11:33:36.669929 31447 solver.cpp:229] Iteration 19500, loss = 6.69879 | |
| I0401 11:33:36.670070 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0638298 | |
| I0401 11:33:36.670090 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 11:33:36.670104 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.191489 | |
| I0401 11:33:36.670120 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.00373 (* 0.3 = 0.901119 loss) | |
| I0401 11:33:36.670137 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.829349 (* 0.3 = 0.248805 loss) | |
| I0401 11:33:36.670161 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0425532 | |
| I0401 11:33:36.670182 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.744318 | |
| I0401 11:33:36.670197 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.12766 | |
| I0401 11:33:36.670210 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.14126 (* 0.3 = 0.942379 loss) | |
| I0401 11:33:36.670224 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.893111 (* 0.3 = 0.267933 loss) | |
| I0401 11:33:36.670238 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0425532 | |
| I0401 11:33:36.670249 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.744318 | |
| I0401 11:33:36.670260 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.212766 | |
| I0401 11:33:36.670274 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.94746 (* 1 = 2.94746 loss) | |
| I0401 11:33:36.670289 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.816489 (* 1 = 0.816489 loss) | |
| I0401 11:33:36.670300 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:33:36.670312 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000106236 | |
| I0401 11:33:36.670325 31447 sgd_solver.cpp:106] Iteration 19500, lr = 0.05 | |
| I0401 11:35:45.458858 31447 solver.cpp:338] Iteration 20000, Testing net (#0) | |
| I0401 11:36:15.263394 31447 solver.cpp:393] Test loss: 6.39369 | |
| I0401 11:36:15.263442 31447 solver.cpp:406] Test net output #0: loss1/accuracy = 0.098063 | |
| I0401 11:36:15.263458 31447 solver.cpp:406] Test net output #1: loss1/accuracy_incl_empty = 0.774227 | |
| I0401 11:36:15.263471 31447 solver.cpp:406] Test net output #2: loss1/accuracy_top3 = 0.271838 | |
| I0401 11:36:15.263489 31447 solver.cpp:406] Test net output #3: loss1/cross_entropy_loss = 3.06198 (* 0.3 = 0.918593 loss) | |
| I0401 11:36:15.263504 31447 solver.cpp:406] Test net output #4: loss1/cross_entropy_loss_incl_empty = 0.829713 (* 0.3 = 0.248914 loss) | |
| I0401 11:36:15.263515 31447 solver.cpp:406] Test net output #5: loss2/accuracy = 0.0911015 | |
| I0401 11:36:15.263531 31447 solver.cpp:406] Test net output #6: loss2/accuracy_incl_empty = 0.753181 | |
| I0401 11:36:15.263543 31447 solver.cpp:406] Test net output #7: loss2/accuracy_top3 = 0.249839 | |
| I0401 11:36:15.263557 31447 solver.cpp:406] Test net output #8: loss2/cross_entropy_loss = 3.15339 (* 0.3 = 0.946017 loss) | |
| I0401 11:36:15.263571 31447 solver.cpp:406] Test net output #9: loss2/cross_entropy_loss_incl_empty = 1.08421 (* 0.3 = 0.325263 loss) | |
| I0401 11:36:15.263582 31447 solver.cpp:406] Test net output #10: loss3/accuracy = 0.133775 | |
| I0401 11:36:15.263595 31447 solver.cpp:406] Test net output #11: loss3/accuracy_incl_empty = 0.782272 | |
| I0401 11:36:15.263607 31447 solver.cpp:406] Test net output #12: loss3/accuracy_top3 = 0.27498 | |
| I0401 11:36:15.263620 31447 solver.cpp:406] Test net output #13: loss3/cross_entropy_loss = 3.0959 (* 1 = 3.0959 loss) | |
| I0401 11:36:15.263634 31447 solver.cpp:406] Test net output #14: loss3/cross_entropy_loss_incl_empty = 0.859001 (* 1 = 0.859001 loss) | |
| I0401 11:36:15.263646 31447 solver.cpp:406] Test net output #15: total_accuracy = 0 | |
| I0401 11:36:15.263659 31447 solver.cpp:406] Test net output #16: total_confidence = 6.27805e-05 | |
| I0401 11:36:15.414266 31447 solver.cpp:229] Iteration 20000, loss = 6.65628 | |
| I0401 11:36:15.414304 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0465116 | |
| I0401 11:36:15.414322 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 11:36:15.414335 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.186047 | |
| I0401 11:36:15.414350 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.9841 (* 0.3 = 1.19523 loss) | |
| I0401 11:36:15.414366 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.11508 (* 0.3 = 0.334525 loss) | |
| I0401 11:36:15.414378 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0465116 | |
| I0401 11:36:15.414391 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.761364 | |
| I0401 11:36:15.414402 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.186047 | |
| I0401 11:36:15.414417 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 4.02688 (* 0.3 = 1.20806 loss) | |
| I0401 11:36:15.414430 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.09183 (* 0.3 = 0.327548 loss) | |
| I0401 11:36:15.414443 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0 | |
| I0401 11:36:15.414454 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.744318 | |
| I0401 11:36:15.414465 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.139535 | |
| I0401 11:36:15.414479 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.91993 (* 1 = 3.91993 loss) | |
| I0401 11:36:15.414494 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.07095 (* 1 = 1.07095 loss) | |
| I0401 11:36:15.414505 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:36:15.414516 31447 solver.cpp:245] Train net output #16: total_confidence = 8.89328e-05 | |
| I0401 11:36:15.414530 31447 sgd_solver.cpp:106] Iteration 20000, lr = 0.05 | |
| I0401 11:38:24.265202 31447 solver.cpp:229] Iteration 20500, loss = 6.66704 | |
| I0401 11:38:24.265409 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0487805 | |
| I0401 11:38:24.265434 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.761364 | |
| I0401 11:38:24.265445 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.219512 | |
| I0401 11:38:24.265462 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.11617 (* 0.3 = 0.93485 loss) | |
| I0401 11:38:24.265477 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.838418 (* 0.3 = 0.251525 loss) | |
| I0401 11:38:24.265489 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0487805 | |
| I0401 11:38:24.265501 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.772727 | |
| I0401 11:38:24.265513 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.219512 | |
| I0401 11:38:24.265530 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.25693 (* 0.3 = 0.977078 loss) | |
| I0401 11:38:24.265544 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.878504 (* 0.3 = 0.263551 loss) | |
| I0401 11:38:24.265558 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.146341 | |
| I0401 11:38:24.265569 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.784091 | |
| I0401 11:38:24.265581 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.268293 | |
| I0401 11:38:24.265595 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.9533 (* 1 = 2.9533 loss) | |
| I0401 11:38:24.265609 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.853877 (* 1 = 0.853877 loss) | |
| I0401 11:38:24.265620 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:38:24.265632 31447 solver.cpp:245] Train net output #16: total_confidence = 3.5811e-05 | |
| I0401 11:38:24.265645 31447 sgd_solver.cpp:106] Iteration 20500, lr = 0.05 | |
| I0401 11:40:33.254006 31447 solver.cpp:229] Iteration 21000, loss = 6.63513 | |
| I0401 11:40:33.254151 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.075 | |
| I0401 11:40:33.254173 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.778409 | |
| I0401 11:40:33.254185 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.125 | |
| I0401 11:40:33.254201 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.55838 (* 0.3 = 1.06752 loss) | |
| I0401 11:40:33.254216 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.908712 (* 0.3 = 0.272614 loss) | |
| I0401 11:40:33.254228 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.05 | |
| I0401 11:40:33.254240 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.784091 | |
| I0401 11:40:33.254252 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.175 | |
| I0401 11:40:33.254266 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.59513 (* 0.3 = 1.07854 loss) | |
| I0401 11:40:33.254279 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.885734 (* 0.3 = 0.26572 loss) | |
| I0401 11:40:33.254292 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.075 | |
| I0401 11:40:33.254304 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.789773 | |
| I0401 11:40:33.254315 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.15 | |
| I0401 11:40:33.254329 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.53802 (* 1 = 3.53802 loss) | |
| I0401 11:40:33.254343 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.870339 (* 1 = 0.870339 loss) | |
| I0401 11:40:33.254354 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:40:33.254365 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000355387 | |
| I0401 11:40:33.254379 31447 sgd_solver.cpp:106] Iteration 21000, lr = 0.05 | |
| I0401 11:42:42.116894 31447 solver.cpp:229] Iteration 21500, loss = 6.63652 | |
| I0401 11:42:42.117063 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.12 | |
| I0401 11:42:42.117085 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 11:42:42.117099 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.32 | |
| I0401 11:42:42.117115 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.24581 (* 0.3 = 0.973743 loss) | |
| I0401 11:42:42.117130 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.960261 (* 0.3 = 0.288078 loss) | |
| I0401 11:42:42.117142 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.06 | |
| I0401 11:42:42.117156 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.732955 | |
| I0401 11:42:42.117167 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.24 | |
| I0401 11:42:42.117182 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.1975 (* 0.3 = 0.959251 loss) | |
| I0401 11:42:42.117195 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.953304 (* 0.3 = 0.285991 loss) | |
| I0401 11:42:42.117208 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.14 | |
| I0401 11:42:42.117219 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.75 | |
| I0401 11:42:42.117231 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.36 | |
| I0401 11:42:42.117245 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.0343 (* 1 = 3.0343 loss) | |
| I0401 11:42:42.117259 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.915558 (* 1 = 0.915558 loss) | |
| I0401 11:42:42.117270 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:42:42.117282 31447 solver.cpp:245] Train net output #16: total_confidence = 0.00044572 | |
| I0401 11:42:42.117295 31447 sgd_solver.cpp:106] Iteration 21500, lr = 0.05 | |
| I0401 11:44:51.109897 31447 solver.cpp:229] Iteration 22000, loss = 6.61003 | |
| I0401 11:44:51.110036 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.113208 | |
| I0401 11:44:51.110057 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.727273 | |
| I0401 11:44:51.110069 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.320755 | |
| I0401 11:44:51.110085 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.09746 (* 0.3 = 0.929237 loss) | |
| I0401 11:44:51.110100 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.0229 (* 0.3 = 0.306871 loss) | |
| I0401 11:44:51.110113 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.113208 | |
| I0401 11:44:51.110126 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.721591 | |
| I0401 11:44:51.110138 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.283019 | |
| I0401 11:44:51.110152 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.21551 (* 0.3 = 0.964654 loss) | |
| I0401 11:44:51.110165 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.05798 (* 0.3 = 0.317395 loss) | |
| I0401 11:44:51.110178 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.132075 | |
| I0401 11:44:51.110190 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.732955 | |
| I0401 11:44:51.110203 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.245283 | |
| I0401 11:44:51.110216 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.14618 (* 1 = 3.14618 loss) | |
| I0401 11:44:51.110229 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.01148 (* 1 = 1.01148 loss) | |
| I0401 11:44:51.110241 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:44:51.110254 31447 solver.cpp:245] Train net output #16: total_confidence = 2.77906e-06 | |
| I0401 11:44:51.110266 31447 sgd_solver.cpp:106] Iteration 22000, lr = 0.05 | |
| I0401 11:45:25.753068 31447 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 32.3642 > 30) by scale factor 0.92695 | |
| I0401 11:46:59.733364 31447 solver.cpp:229] Iteration 22500, loss = 6.67448 | |
| I0401 11:46:59.733480 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.148936 | |
| I0401 11:46:59.733500 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.772727 | |
| I0401 11:46:59.733513 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.319149 | |
| I0401 11:46:59.733532 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 2.91955 (* 0.3 = 0.875866 loss) | |
| I0401 11:46:59.733547 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.83516 (* 0.3 = 0.250548 loss) | |
| I0401 11:46:59.733561 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.106383 | |
| I0401 11:46:59.733573 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.755682 | |
| I0401 11:46:59.733585 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.340426 | |
| I0401 11:46:59.733599 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 2.93853 (* 0.3 = 0.88156 loss) | |
| I0401 11:46:59.733613 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.85109 (* 0.3 = 0.255327 loss) | |
| I0401 11:46:59.733625 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.148936 | |
| I0401 11:46:59.733639 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.767045 | |
| I0401 11:46:59.733649 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.297872 | |
| I0401 11:46:59.733664 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.96957 (* 1 = 2.96957 loss) | |
| I0401 11:46:59.733677 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.83541 (* 1 = 0.83541 loss) | |
| I0401 11:46:59.733690 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:46:59.733702 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000370114 | |
| I0401 11:46:59.733716 31447 sgd_solver.cpp:106] Iteration 22500, lr = 0.05 | |
| I0401 11:49:08.537348 31447 solver.cpp:229] Iteration 23000, loss = 6.59504 | |
| I0401 11:49:08.537547 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0754717 | |
| I0401 11:49:08.537571 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.721591 | |
| I0401 11:49:08.537585 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.320755 | |
| I0401 11:49:08.537600 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.04535 (* 0.3 = 0.913605 loss) | |
| I0401 11:49:08.537616 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.981499 (* 0.3 = 0.29445 loss) | |
| I0401 11:49:08.537628 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.150943 | |
| I0401 11:49:08.537642 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.744318 | |
| I0401 11:49:08.537653 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.301887 | |
| I0401 11:49:08.537667 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.14842 (* 0.3 = 0.944525 loss) | |
| I0401 11:49:08.537680 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.991788 (* 0.3 = 0.297537 loss) | |
| I0401 11:49:08.537693 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.132075 | |
| I0401 11:49:08.537705 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.738636 | |
| I0401 11:49:08.537717 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.339623 | |
| I0401 11:49:08.537731 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.00633 (* 1 = 3.00633 loss) | |
| I0401 11:49:08.537746 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.974402 (* 1 = 0.974402 loss) | |
| I0401 11:49:08.537758 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:49:08.537770 31447 solver.cpp:245] Train net output #16: total_confidence = 7.14575e-06 | |
| I0401 11:49:08.537783 31447 sgd_solver.cpp:106] Iteration 23000, lr = 0.05 | |
| I0401 11:51:17.113847 31447 solver.cpp:229] Iteration 23500, loss = 6.63031 | |
| I0401 11:51:17.113973 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0465116 | |
| I0401 11:51:17.113993 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 11:51:17.114006 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.162791 | |
| I0401 11:51:17.114022 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.15369 (* 0.3 = 0.946106 loss) | |
| I0401 11:51:17.114037 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.893401 (* 0.3 = 0.26802 loss) | |
| I0401 11:51:17.114050 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.116279 | |
| I0401 11:51:17.114063 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.778409 | |
| I0401 11:51:17.114074 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.232558 | |
| I0401 11:51:17.114089 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.07963 (* 0.3 = 0.923888 loss) | |
| I0401 11:51:17.114102 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.855814 (* 0.3 = 0.256744 loss) | |
| I0401 11:51:17.114114 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.116279 | |
| I0401 11:51:17.114126 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.778409 | |
| I0401 11:51:17.114138 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.325581 | |
| I0401 11:51:17.114152 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.01565 (* 1 = 3.01565 loss) | |
| I0401 11:51:17.114166 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.825555 (* 1 = 0.825555 loss) | |
| I0401 11:51:17.114178 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:51:17.114189 31447 solver.cpp:245] Train net output #16: total_confidence = 7.00245e-05 | |
| I0401 11:51:17.114202 31447 sgd_solver.cpp:106] Iteration 23500, lr = 0.05 | |
| I0401 11:53:25.960703 31447 solver.cpp:229] Iteration 24000, loss = 6.57825 | |
| I0401 11:53:25.960840 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0434783 | |
| I0401 11:53:25.960862 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.732955 | |
| I0401 11:53:25.960875 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.173913 | |
| I0401 11:53:25.960891 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.27406 (* 0.3 = 0.982218 loss) | |
| I0401 11:53:25.960906 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.944766 (* 0.3 = 0.28343 loss) | |
| I0401 11:53:25.960918 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0869565 | |
| I0401 11:53:25.960932 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.75 | |
| I0401 11:53:25.960943 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.195652 | |
| I0401 11:53:25.960958 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.32958 (* 0.3 = 0.998874 loss) | |
| I0401 11:53:25.960971 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.0089 (* 0.3 = 0.302671 loss) | |
| I0401 11:53:25.960983 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.108696 | |
| I0401 11:53:25.960995 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.761364 | |
| I0401 11:53:25.961007 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.26087 | |
| I0401 11:53:25.961021 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.23961 (* 1 = 3.23961 loss) | |
| I0401 11:53:25.961035 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.932254 (* 1 = 0.932254 loss) | |
| I0401 11:53:25.961064 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:53:25.961078 31447 solver.cpp:245] Train net output #16: total_confidence = 3.12191e-06 | |
| I0401 11:53:25.961091 31447 sgd_solver.cpp:106] Iteration 24000, lr = 0.05 | |
| I0401 11:55:34.697960 31447 solver.cpp:229] Iteration 24500, loss = 6.6139 | |
| I0401 11:55:34.698185 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.104167 | |
| I0401 11:55:34.698204 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.738636 | |
| I0401 11:55:34.698216 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.208333 | |
| I0401 11:55:34.698232 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 2.97209 (* 0.3 = 0.891627 loss) | |
| I0401 11:55:34.698247 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.93015 (* 0.3 = 0.279045 loss) | |
| I0401 11:55:34.698259 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.125 | |
| I0401 11:55:34.698271 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.75 | |
| I0401 11:55:34.698283 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.208333 | |
| I0401 11:55:34.698297 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 2.91862 (* 0.3 = 0.875585 loss) | |
| I0401 11:55:34.698312 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.898243 (* 0.3 = 0.269473 loss) | |
| I0401 11:55:34.698323 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0833333 | |
| I0401 11:55:34.698335 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.738636 | |
| I0401 11:55:34.698348 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.3125 | |
| I0401 11:55:34.698361 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.78063 (* 1 = 2.78063 loss) | |
| I0401 11:55:34.698375 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.857674 (* 1 = 0.857674 loss) | |
| I0401 11:55:34.698387 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:55:34.698400 31447 solver.cpp:245] Train net output #16: total_confidence = 7.07007e-06 | |
| I0401 11:55:34.698411 31447 sgd_solver.cpp:106] Iteration 24500, lr = 0.05 | |
| I0401 11:57:43.288077 31447 solver.cpp:338] Iteration 25000, Testing net (#0) | |
| I0401 11:58:13.080390 31447 solver.cpp:393] Test loss: 6.43145 | |
| I0401 11:58:13.080440 31447 solver.cpp:406] Test net output #0: loss1/accuracy = 0.0890714 | |
| I0401 11:58:13.080457 31447 solver.cpp:406] Test net output #1: loss1/accuracy_incl_empty = 0.767272 | |
| I0401 11:58:13.080471 31447 solver.cpp:406] Test net output #2: loss1/accuracy_top3 = 0.259474 | |
| I0401 11:58:13.080487 31447 solver.cpp:406] Test net output #3: loss1/cross_entropy_loss = 3.15469 (* 0.3 = 0.946407 loss) | |
| I0401 11:58:13.080502 31447 solver.cpp:406] Test net output #4: loss1/cross_entropy_loss_incl_empty = 0.868826 (* 0.3 = 0.260648 loss) | |
| I0401 11:58:13.080514 31447 solver.cpp:406] Test net output #5: loss2/accuracy = 0.121075 | |
| I0401 11:58:13.080530 31447 solver.cpp:406] Test net output #6: loss2/accuracy_incl_empty = 0.749908 | |
| I0401 11:58:13.080543 31447 solver.cpp:406] Test net output #7: loss2/accuracy_top3 = 0.28881 | |
| I0401 11:58:13.080556 31447 solver.cpp:406] Test net output #8: loss2/cross_entropy_loss = 3.02058 (* 0.3 = 0.906173 loss) | |
| I0401 11:58:13.080570 31447 solver.cpp:406] Test net output #9: loss2/cross_entropy_loss_incl_empty = 1.11001 (* 0.3 = 0.333004 loss) | |
| I0401 11:58:13.080582 31447 solver.cpp:406] Test net output #10: loss3/accuracy = 0.104328 | |
| I0401 11:58:13.080595 31447 solver.cpp:406] Test net output #11: loss3/accuracy_incl_empty = 0.763363 | |
| I0401 11:58:13.080606 31447 solver.cpp:406] Test net output #12: loss3/accuracy_top3 = 0.273946 | |
| I0401 11:58:13.080621 31447 solver.cpp:406] Test net output #13: loss3/cross_entropy_loss = 3.08522 (* 1 = 3.08522 loss) | |
| I0401 11:58:13.080633 31447 solver.cpp:406] Test net output #14: loss3/cross_entropy_loss_incl_empty = 0.899988 (* 1 = 0.899988 loss) | |
| I0401 11:58:13.080646 31447 solver.cpp:406] Test net output #15: total_accuracy = 0 | |
| I0401 11:58:13.080657 31447 solver.cpp:406] Test net output #16: total_confidence = 4.79027e-05 | |
| I0401 11:58:13.231775 31447 solver.cpp:229] Iteration 25000, loss = 6.52742 | |
| I0401 11:58:13.231822 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0555556 | |
| I0401 11:58:13.231838 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.710227 | |
| I0401 11:58:13.231851 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.314815 | |
| I0401 11:58:13.231866 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.03851 (* 0.3 = 0.911553 loss) | |
| I0401 11:58:13.231881 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.976507 (* 0.3 = 0.292952 loss) | |
| I0401 11:58:13.231894 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0925926 | |
| I0401 11:58:13.231907 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.721591 | |
| I0401 11:58:13.231920 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.333333 | |
| I0401 11:58:13.231932 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.06801 (* 0.3 = 0.920402 loss) | |
| I0401 11:58:13.231946 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.983609 (* 0.3 = 0.295083 loss) | |
| I0401 11:58:13.231958 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.111111 | |
| I0401 11:58:13.231973 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.727273 | |
| I0401 11:58:13.231986 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.296296 | |
| I0401 11:58:13.232000 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.99004 (* 1 = 2.99004 loss) | |
| I0401 11:58:13.232014 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.938092 (* 1 = 0.938092 loss) | |
| I0401 11:58:13.232026 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 11:58:13.232038 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000142136 | |
| I0401 11:58:13.232051 31447 sgd_solver.cpp:106] Iteration 25000, lr = 0.05 | |
| I0401 12:00:22.037478 31447 solver.cpp:229] Iteration 25500, loss = 6.59794 | |
| I0401 12:00:22.037598 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0666667 | |
| I0401 12:00:22.037628 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 12:00:22.037652 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.244444 | |
| I0401 12:00:22.037679 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 2.99816 (* 0.3 = 0.899448 loss) | |
| I0401 12:00:22.037706 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.862841 (* 0.3 = 0.258852 loss) | |
| I0401 12:00:22.037729 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0666667 | |
| I0401 12:00:22.037752 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.755682 | |
| I0401 12:00:22.037775 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.244444 | |
| I0401 12:00:22.037799 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.06565 (* 0.3 = 0.919695 loss) | |
| I0401 12:00:22.037824 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.865275 (* 0.3 = 0.259583 loss) | |
| I0401 12:00:22.037845 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0444444 | |
| I0401 12:00:22.037866 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.755682 | |
| I0401 12:00:22.037886 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.311111 | |
| I0401 12:00:22.037911 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.94134 (* 1 = 2.94134 loss) | |
| I0401 12:00:22.037940 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.800866 (* 1 = 0.800866 loss) | |
| I0401 12:00:22.037962 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:00:22.037983 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000297451 | |
| I0401 12:00:22.038008 31447 sgd_solver.cpp:106] Iteration 25500, lr = 0.05 | |
| I0401 12:02:30.872120 31447 solver.cpp:229] Iteration 26000, loss = 6.51017 | |
| I0401 12:02:30.872227 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0454545 | |
| I0401 12:02:30.872246 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.755682 | |
| I0401 12:02:30.872259 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.227273 | |
| I0401 12:02:30.872277 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.52927 (* 0.3 = 1.05878 loss) | |
| I0401 12:02:30.872290 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.958184 (* 0.3 = 0.287455 loss) | |
| I0401 12:02:30.872303 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.113636 | |
| I0401 12:02:30.872316 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.772727 | |
| I0401 12:02:30.872328 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.25 | |
| I0401 12:02:30.872342 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.43135 (* 0.3 = 1.02941 loss) | |
| I0401 12:02:30.872356 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.925246 (* 0.3 = 0.277574 loss) | |
| I0401 12:02:30.872369 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0909091 | |
| I0401 12:02:30.872380 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.761364 | |
| I0401 12:02:30.872392 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.318182 | |
| I0401 12:02:30.872406 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.26078 (* 1 = 3.26078 loss) | |
| I0401 12:02:30.872421 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.901761 (* 1 = 0.901761 loss) | |
| I0401 12:02:30.872432 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:02:30.872444 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000210276 | |
| I0401 12:02:30.872457 31447 sgd_solver.cpp:106] Iteration 26000, lr = 0.05 | |
| I0401 12:04:39.562217 31447 solver.cpp:229] Iteration 26500, loss = 6.54396 | |
| I0401 12:04:39.562364 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0212766 | |
| I0401 12:04:39.562384 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.727273 | |
| I0401 12:04:39.562397 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.12766 | |
| I0401 12:04:39.562413 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.56762 (* 0.3 = 1.07029 loss) | |
| I0401 12:04:39.562427 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.0436 (* 0.3 = 0.313079 loss) | |
| I0401 12:04:39.562440 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0 | |
| I0401 12:04:39.562453 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.727273 | |
| I0401 12:04:39.562464 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.0638298 | |
| I0401 12:04:39.562479 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.48735 (* 0.3 = 1.04621 loss) | |
| I0401 12:04:39.562492 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.00444 (* 0.3 = 0.301333 loss) | |
| I0401 12:04:39.562505 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0425532 | |
| I0401 12:04:39.562520 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.721591 | |
| I0401 12:04:39.562531 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.106383 | |
| I0401 12:04:39.562546 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.50577 (* 1 = 3.50577 loss) | |
| I0401 12:04:39.562559 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.0171 (* 1 = 1.0171 loss) | |
| I0401 12:04:39.562572 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:04:39.562583 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000150378 | |
| I0401 12:04:39.562595 31447 sgd_solver.cpp:106] Iteration 26500, lr = 0.05 | |
| I0401 12:06:48.221434 31447 solver.cpp:229] Iteration 27000, loss = 6.49055 | |
| I0401 12:06:48.221654 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.137255 | |
| I0401 12:06:48.221673 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.738636 | |
| I0401 12:06:48.221685 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.313726 | |
| I0401 12:06:48.221701 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.21603 (* 0.3 = 0.964808 loss) | |
| I0401 12:06:48.221716 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.01668 (* 0.3 = 0.305004 loss) | |
| I0401 12:06:48.221729 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.137255 | |
| I0401 12:06:48.221741 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.738636 | |
| I0401 12:06:48.221753 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.294118 | |
| I0401 12:06:48.221766 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.22599 (* 0.3 = 0.967796 loss) | |
| I0401 12:06:48.221781 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.012 (* 0.3 = 0.303599 loss) | |
| I0401 12:06:48.221792 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.156863 | |
| I0401 12:06:48.221804 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.75 | |
| I0401 12:06:48.221817 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.294118 | |
| I0401 12:06:48.221830 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.13421 (* 1 = 3.13421 loss) | |
| I0401 12:06:48.221844 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.972799 (* 1 = 0.972799 loss) | |
| I0401 12:06:48.221856 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:06:48.221868 31447 solver.cpp:245] Train net output #16: total_confidence = 3.88315e-06 | |
| I0401 12:06:48.221880 31447 sgd_solver.cpp:106] Iteration 27000, lr = 0.05 | |
| I0401 12:08:56.875216 31447 solver.cpp:229] Iteration 27500, loss = 6.50224 | |
| I0401 12:08:56.875329 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0925926 | |
| I0401 12:08:56.875346 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.715909 | |
| I0401 12:08:56.875360 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.259259 | |
| I0401 12:08:56.875375 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.28408 (* 0.3 = 0.985223 loss) | |
| I0401 12:08:56.875390 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.07307 (* 0.3 = 0.321921 loss) | |
| I0401 12:08:56.875402 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.037037 | |
| I0401 12:08:56.875416 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.698864 | |
| I0401 12:08:56.875427 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.185185 | |
| I0401 12:08:56.875440 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.4522 (* 0.3 = 1.03566 loss) | |
| I0401 12:08:56.875454 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.10234 (* 0.3 = 0.330701 loss) | |
| I0401 12:08:56.875466 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0555556 | |
| I0401 12:08:56.875479 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.710227 | |
| I0401 12:08:56.875491 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.333333 | |
| I0401 12:08:56.875504 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.22587 (* 1 = 3.22587 loss) | |
| I0401 12:08:56.875519 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.01592 (* 1 = 1.01592 loss) | |
| I0401 12:08:56.875530 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:08:56.875542 31447 solver.cpp:245] Train net output #16: total_confidence = 2.75131e-05 | |
| I0401 12:08:56.875555 31447 sgd_solver.cpp:106] Iteration 27500, lr = 0.05 | |
| I0401 12:11:05.622807 31447 solver.cpp:229] Iteration 28000, loss = 6.48189 | |
| I0401 12:11:05.622934 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0652174 | |
| I0401 12:11:05.622956 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.738636 | |
| I0401 12:11:05.622968 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.326087 | |
| I0401 12:11:05.622984 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.33793 (* 0.3 = 1.00138 loss) | |
| I0401 12:11:05.622999 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 1.00457 (* 0.3 = 0.301371 loss) | |
| I0401 12:11:05.623011 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0217391 | |
| I0401 12:11:05.623024 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.721591 | |
| I0401 12:11:05.623039 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.326087 | |
| I0401 12:11:05.623054 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.45397 (* 0.3 = 1.03619 loss) | |
| I0401 12:11:05.623069 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.05839 (* 0.3 = 0.317518 loss) | |
| I0401 12:11:05.623080 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.152174 | |
| I0401 12:11:05.623092 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.761364 | |
| I0401 12:11:05.623105 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.26087 | |
| I0401 12:11:05.623118 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.46469 (* 1 = 3.46469 loss) | |
| I0401 12:11:05.623133 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 1.0023 (* 1 = 1.0023 loss) | |
| I0401 12:11:05.623145 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:11:05.623157 31447 solver.cpp:245] Train net output #16: total_confidence = 1.63722e-05 | |
| I0401 12:11:05.623169 31447 sgd_solver.cpp:106] Iteration 28000, lr = 0.05 | |
| I0401 12:13:14.218230 31447 solver.cpp:229] Iteration 28500, loss = 6.47152 | |
| I0401 12:13:14.218367 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0681818 | |
| I0401 12:13:14.218387 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 12:13:14.218400 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.181818 | |
| I0401 12:13:14.218416 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.39742 (* 0.3 = 1.01923 loss) | |
| I0401 12:13:14.218431 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.934821 (* 0.3 = 0.280446 loss) | |
| I0401 12:13:14.218443 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0454545 | |
| I0401 12:13:14.218456 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.744318 | |
| I0401 12:13:14.218467 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.181818 | |
| I0401 12:13:14.218482 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.48612 (* 0.3 = 1.04584 loss) | |
| I0401 12:13:14.218495 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.958105 (* 0.3 = 0.287431 loss) | |
| I0401 12:13:14.218508 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0681818 | |
| I0401 12:13:14.218523 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.75 | |
| I0401 12:13:14.218535 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.204545 | |
| I0401 12:13:14.218549 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.18759 (* 1 = 3.18759 loss) | |
| I0401 12:13:14.218564 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.886208 (* 1 = 0.886208 loss) | |
| I0401 12:13:14.218575 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:13:14.218587 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000172292 | |
| I0401 12:13:14.218600 31447 sgd_solver.cpp:106] Iteration 28500, lr = 0.05 | |
| I0401 12:15:22.888389 31447 solver.cpp:229] Iteration 29000, loss = 6.44567 | |
| I0401 12:15:22.888665 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0666667 | |
| I0401 12:15:22.888686 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.755682 | |
| I0401 12:15:22.888700 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.155556 | |
| I0401 12:15:22.888715 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.25209 (* 0.3 = 0.975626 loss) | |
| I0401 12:15:22.888730 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.919658 (* 0.3 = 0.275897 loss) | |
| I0401 12:15:22.888742 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0666667 | |
| I0401 12:15:22.888754 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.75 | |
| I0401 12:15:22.888767 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.222222 | |
| I0401 12:15:22.888780 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.22341 (* 0.3 = 0.967022 loss) | |
| I0401 12:15:22.888794 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.918481 (* 0.3 = 0.275544 loss) | |
| I0401 12:15:22.888806 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0888889 | |
| I0401 12:15:22.888818 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.75 | |
| I0401 12:15:22.888830 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.244444 | |
| I0401 12:15:22.888844 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.21877 (* 1 = 3.21877 loss) | |
| I0401 12:15:22.888859 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.932457 (* 1 = 0.932457 loss) | |
| I0401 12:15:22.888870 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:15:22.888882 31447 solver.cpp:245] Train net output #16: total_confidence = 3.11775e-05 | |
| I0401 12:15:22.888895 31447 sgd_solver.cpp:106] Iteration 29000, lr = 0.05 | |
| I0401 12:17:31.653494 31447 solver.cpp:229] Iteration 29500, loss = 6.49534 | |
| I0401 12:17:31.653614 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.113636 | |
| I0401 12:17:31.653633 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.761364 | |
| I0401 12:17:31.653646 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.159091 | |
| I0401 12:17:31.653661 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.37656 (* 0.3 = 1.01297 loss) | |
| I0401 12:17:31.653676 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.97184 (* 0.3 = 0.291552 loss) | |
| I0401 12:17:31.653689 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0909091 | |
| I0401 12:17:31.653702 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.761364 | |
| I0401 12:17:31.653714 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.181818 | |
| I0401 12:17:31.653728 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.3382 (* 0.3 = 1.00146 loss) | |
| I0401 12:17:31.653743 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.94534 (* 0.3 = 0.283602 loss) | |
| I0401 12:17:31.653754 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.113636 | |
| I0401 12:17:31.653767 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.767045 | |
| I0401 12:17:31.653779 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.181818 | |
| I0401 12:17:31.653792 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.28948 (* 1 = 3.28948 loss) | |
| I0401 12:17:31.653806 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.91977 (* 1 = 0.91977 loss) | |
| I0401 12:17:31.653818 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:17:31.653831 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000120836 | |
| I0401 12:17:31.653842 31447 sgd_solver.cpp:106] Iteration 29500, lr = 0.05 | |
| I0401 12:19:40.093232 31447 solver.cpp:338] Iteration 30000, Testing net (#0) | |
| I0401 12:20:09.885484 31447 solver.cpp:393] Test loss: 6.05536 | |
| I0401 12:20:09.885530 31447 solver.cpp:406] Test net output #0: loss1/accuracy = 0.113754 | |
| I0401 12:20:09.885547 31447 solver.cpp:406] Test net output #1: loss1/accuracy_incl_empty = 0.775273 | |
| I0401 12:20:09.885560 31447 solver.cpp:406] Test net output #2: loss1/accuracy_top3 = 0.305335 | |
| I0401 12:20:09.885576 31447 solver.cpp:406] Test net output #3: loss1/cross_entropy_loss = 2.95625 (* 0.3 = 0.886876 loss) | |
| I0401 12:20:09.885591 31447 solver.cpp:406] Test net output #4: loss1/cross_entropy_loss_incl_empty = 0.792714 (* 0.3 = 0.237814 loss) | |
| I0401 12:20:09.885602 31447 solver.cpp:406] Test net output #5: loss2/accuracy = 0.119265 | |
| I0401 12:20:09.885614 31447 solver.cpp:406] Test net output #6: loss2/accuracy_incl_empty = 0.755636 | |
| I0401 12:20:09.885625 31447 solver.cpp:406] Test net output #7: loss2/accuracy_top3 = 0.31066 | |
| I0401 12:20:09.885639 31447 solver.cpp:406] Test net output #8: loss2/cross_entropy_loss = 2.90411 (* 0.3 = 0.871232 loss) | |
| I0401 12:20:09.885653 31447 solver.cpp:406] Test net output #9: loss2/cross_entropy_loss_incl_empty = 1.04512 (* 0.3 = 0.313537 loss) | |
| I0401 12:20:09.885664 31447 solver.cpp:406] Test net output #10: loss3/accuracy = 0.101335 | |
| I0401 12:20:09.885678 31447 solver.cpp:406] Test net output #11: loss3/accuracy_incl_empty = 0.761363 | |
| I0401 12:20:09.885689 31447 solver.cpp:406] Test net output #12: loss3/accuracy_top3 = 0.292665 | |
| I0401 12:20:09.885702 31447 solver.cpp:406] Test net output #13: loss3/cross_entropy_loss = 2.91806 (* 1 = 2.91806 loss) | |
| I0401 12:20:09.885716 31447 solver.cpp:406] Test net output #14: loss3/cross_entropy_loss_incl_empty = 0.82784 (* 1 = 0.82784 loss) | |
| I0401 12:20:09.885727 31447 solver.cpp:406] Test net output #15: total_accuracy = 0.001 | |
| I0401 12:20:09.885740 31447 solver.cpp:406] Test net output #16: total_confidence = 7.48164e-05 | |
| I0401 12:20:10.035734 31447 solver.cpp:229] Iteration 30000, loss = 6.43498 | |
| I0401 12:20:10.035771 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.146341 | |
| I0401 12:20:10.035789 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.784091 | |
| I0401 12:20:10.035800 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.341463 | |
| I0401 12:20:10.035815 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 2.69068 (* 0.3 = 0.807204 loss) | |
| I0401 12:20:10.035830 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.781104 (* 0.3 = 0.234331 loss) | |
| I0401 12:20:10.035842 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.219512 | |
| I0401 12:20:10.035854 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.806818 | |
| I0401 12:20:10.035866 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.341463 | |
| I0401 12:20:10.035879 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 2.78882 (* 0.3 = 0.836645 loss) | |
| I0401 12:20:10.035894 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.756817 (* 0.3 = 0.227045 loss) | |
| I0401 12:20:10.035907 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.219512 | |
| I0401 12:20:10.035918 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.789773 | |
| I0401 12:20:10.035929 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.365854 | |
| I0401 12:20:10.035943 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.66896 (* 1 = 2.66896 loss) | |
| I0401 12:20:10.035956 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.750371 (* 1 = 0.750371 loss) | |
| I0401 12:20:10.035969 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:20:10.035980 31447 solver.cpp:245] Train net output #16: total_confidence = 7.89306e-05 | |
| I0401 12:20:10.035994 31447 sgd_solver.cpp:106] Iteration 30000, lr = 0.05 | |
| I0401 12:22:18.529975 31447 solver.cpp:229] Iteration 30500, loss = 6.44151 | |
| I0401 12:22:18.530268 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0401 12:22:18.530287 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.744318 | |
| I0401 12:22:18.530302 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.205128 | |
| I0401 12:22:18.530318 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.54741 (* 0.3 = 1.06422 loss) | |
| I0401 12:22:18.530331 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.985062 (* 0.3 = 0.295519 loss) | |
| I0401 12:22:18.530344 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0512821 | |
| I0401 12:22:18.530357 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.761364 | |
| I0401 12:22:18.530369 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.205128 | |
| I0401 12:22:18.530382 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.51971 (* 0.3 = 1.05591 loss) | |
| I0401 12:22:18.530396 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.921328 (* 0.3 = 0.276398 loss) | |
| I0401 12:22:18.530408 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.025641 | |
| I0401 12:22:18.530421 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.772727 | |
| I0401 12:22:18.530432 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.205128 | |
| I0401 12:22:18.530447 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.48923 (* 1 = 3.48923 loss) | |
| I0401 12:22:18.530459 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.887271 (* 1 = 0.887271 loss) | |
| I0401 12:22:18.530472 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:22:18.530483 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000516864 | |
| I0401 12:22:18.530494 31447 sgd_solver.cpp:106] Iteration 30500, lr = 0.05 | |
| I0401 12:24:27.604528 31447 solver.cpp:229] Iteration 31000, loss = 6.46405 | |
| I0401 12:24:27.604660 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0714286 | |
| I0401 12:24:27.604681 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.704545 | |
| I0401 12:24:27.604694 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.160714 | |
| I0401 12:24:27.604710 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.03611 (* 0.3 = 0.910834 loss) | |
| I0401 12:24:27.604725 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.995256 (* 0.3 = 0.298577 loss) | |
| I0401 12:24:27.604737 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0535714 | |
| I0401 12:24:27.604751 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.698864 | |
| I0401 12:24:27.604763 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.214286 | |
| I0401 12:24:27.604776 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.31575 (* 0.3 = 0.994724 loss) | |
| I0401 12:24:27.604790 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.08351 (* 0.3 = 0.325052 loss) | |
| I0401 12:24:27.604802 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.107143 | |
| I0401 12:24:27.604815 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.710227 | |
| I0401 12:24:27.604827 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.285714 | |
| I0401 12:24:27.604841 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.91462 (* 1 = 2.91462 loss) | |
| I0401 12:24:27.604854 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.974486 (* 1 = 0.974486 loss) | |
| I0401 12:24:27.604866 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:24:27.604878 31447 solver.cpp:245] Train net output #16: total_confidence = 3.00539e-06 | |
| I0401 12:24:27.604892 31447 sgd_solver.cpp:106] Iteration 31000, lr = 0.05 | |
| I0401 12:26:36.183753 31447 solver.cpp:229] Iteration 31500, loss = 6.48811 | |
| I0401 12:26:36.183893 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0888889 | |
| I0401 12:26:36.183914 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.75 | |
| I0401 12:26:36.183928 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.266667 | |
| I0401 12:26:36.183943 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.00181 (* 0.3 = 0.900544 loss) | |
| I0401 12:26:36.183957 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.894487 (* 0.3 = 0.268346 loss) | |
| I0401 12:26:36.183970 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0222222 | |
| I0401 12:26:36.183984 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.744318 | |
| I0401 12:26:36.183995 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.244444 | |
| I0401 12:26:36.184008 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.10696 (* 0.3 = 0.932088 loss) | |
| I0401 12:26:36.184022 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.880856 (* 0.3 = 0.264257 loss) | |
| I0401 12:26:36.184034 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.155556 | |
| I0401 12:26:36.184046 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.772727 | |
| I0401 12:26:36.184058 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.333333 | |
| I0401 12:26:36.184072 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.90838 (* 1 = 2.90838 loss) | |
| I0401 12:26:36.184087 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.840072 (* 1 = 0.840072 loss) | |
| I0401 12:26:36.184099 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:26:36.184111 31447 solver.cpp:245] Train net output #16: total_confidence = 5.26969e-05 | |
| I0401 12:26:36.184123 31447 sgd_solver.cpp:106] Iteration 31500, lr = 0.05 | |
| I0401 12:28:44.702633 31447 solver.cpp:229] Iteration 32000, loss = 6.41531 | |
| I0401 12:28:44.702738 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0952381 | |
| I0401 12:28:44.702756 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.784091 | |
| I0401 12:28:44.702769 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.333333 | |
| I0401 12:28:44.702786 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 2.84963 (* 0.3 = 0.854888 loss) | |
| I0401 12:28:44.702801 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.749025 (* 0.3 = 0.224707 loss) | |
| I0401 12:28:44.702814 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.119048 | |
| I0401 12:28:44.702827 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.784091 | |
| I0401 12:28:44.702839 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.357143 | |
| I0401 12:28:44.702852 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 2.74947 (* 0.3 = 0.824841 loss) | |
| I0401 12:28:44.702867 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.728653 (* 0.3 = 0.218596 loss) | |
| I0401 12:28:44.702878 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.190476 | |
| I0401 12:28:44.702891 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.801136 | |
| I0401 12:28:44.702903 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.404762 | |
| I0401 12:28:44.702918 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 2.5501 (* 1 = 2.5501 loss) | |
| I0401 12:28:44.702931 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.66136 (* 1 = 0.66136 loss) | |
| I0401 12:28:44.702942 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:28:44.702955 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000207781 | |
| I0401 12:28:44.702966 31447 sgd_solver.cpp:106] Iteration 32000, lr = 0.05 | |
| I0401 12:30:53.237467 31447 solver.cpp:229] Iteration 32500, loss = 6.39665 | |
| I0401 12:30:53.237756 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.145833 | |
| I0401 12:30:53.237777 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.761364 | |
| I0401 12:30:53.237789 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.291667 | |
| I0401 12:30:53.237805 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.22404 (* 0.3 = 0.967211 loss) | |
| I0401 12:30:53.237820 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.91608 (* 0.3 = 0.274824 loss) | |
| I0401 12:30:53.237833 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.145833 | |
| I0401 12:30:53.237845 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.767045 | |
| I0401 12:30:53.237856 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.333333 | |
| I0401 12:30:53.237870 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.21609 (* 0.3 = 0.964826 loss) | |
| I0401 12:30:53.237884 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.909068 (* 0.3 = 0.27272 loss) | |
| I0401 12:30:53.237896 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.145833 | |
| I0401 12:30:53.237908 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.761364 | |
| I0401 12:30:53.237920 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.291667 | |
| I0401 12:30:53.237933 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.14969 (* 1 = 3.14969 loss) | |
| I0401 12:30:53.237947 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.900579 (* 1 = 0.900579 loss) | |
| I0401 12:30:53.237959 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:30:53.237972 31447 solver.cpp:245] Train net output #16: total_confidence = 0.000361797 | |
| I0401 12:30:53.237983 31447 sgd_solver.cpp:106] Iteration 32500, lr = 0.05 | |
| I0401 12:32:46.487531 31447 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 73.3525 > 30) by scale factor 0.408984 | |
| I0401 12:33:01.815485 31447 solver.cpp:229] Iteration 33000, loss = 6.32743 | |
| I0401 12:33:01.815536 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.106383 | |
| I0401 12:33:01.815553 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.744318 | |
| I0401 12:33:01.815565 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.191489 | |
| I0401 12:33:01.815582 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.15648 (* 0.3 = 0.946944 loss) | |
| I0401 12:33:01.815596 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.97896 (* 0.3 = 0.293688 loss) | |
| I0401 12:33:01.815608 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0638298 | |
| I0401 12:33:01.815621 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.727273 | |
| I0401 12:33:01.815634 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.148936 | |
| I0401 12:33:01.815647 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.26131 (* 0.3 = 0.978393 loss) | |
| I0401 12:33:01.815660 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 1.01987 (* 0.3 = 0.305962 loss) | |
| I0401 12:33:01.815672 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.0851064 | |
| I0401 12:33:01.815685 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.738636 | |
| I0401 12:33:01.815696 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.234043 | |
| I0401 12:33:01.815711 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.25063 (* 1 = 3.25063 loss) | |
| I0401 12:33:01.815724 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.959643 (* 1 = 0.959643 loss) | |
| I0401 12:33:01.815737 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:33:01.815747 31447 solver.cpp:245] Train net output #16: total_confidence = 8.50269e-05 | |
| I0401 12:33:01.815760 31447 sgd_solver.cpp:106] Iteration 33000, lr = 0.05 | |
| I0401 12:35:10.599174 31447 solver.cpp:229] Iteration 33500, loss = 6.37951 | |
| I0401 12:35:10.599308 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0888889 | |
| I0401 12:35:10.599339 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.732955 | |
| I0401 12:35:10.599362 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.2 | |
| I0401 12:35:10.599391 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.15642 (* 0.3 = 0.946925 loss) | |
| I0401 12:35:10.599416 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.994403 (* 0.3 = 0.298321 loss) | |
| I0401 12:35:10.599439 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0888889 | |
| I0401 12:35:10.599462 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.738636 | |
| I0401 12:35:10.599484 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.155556 | |
| I0401 12:35:10.599509 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.1555 (* 0.3 = 0.946649 loss) | |
| I0401 12:35:10.599539 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.99859 (* 0.3 = 0.299577 loss) | |
| I0401 12:35:10.599560 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0.111111 | |
| I0401 12:35:10.599583 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.75 | |
| I0401 12:35:10.599606 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.288889 | |
| I0401 12:35:10.599630 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.05897 (* 1 = 3.05897 loss) | |
| I0401 12:35:10.599654 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.959419 (* 1 = 0.959419 loss) | |
| I0401 12:35:10.599675 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:35:10.599694 31447 solver.cpp:245] Train net output #16: total_confidence = 1.86374e-05 | |
| I0401 12:35:10.599715 31447 sgd_solver.cpp:106] Iteration 33500, lr = 0.05 | |
| I0401 12:37:19.171293 31447 solver.cpp:229] Iteration 34000, loss = 6.36775 | |
| I0401 12:37:19.171429 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0714286 | |
| I0401 12:37:19.171449 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.761364 | |
| I0401 12:37:19.171463 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.142857 | |
| I0401 12:37:19.171478 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 3.16401 (* 0.3 = 0.949202 loss) | |
| I0401 12:37:19.171494 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 0.896118 (* 0.3 = 0.268836 loss) | |
| I0401 12:37:19.171505 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0.0238095 | |
| I0401 12:37:19.171521 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0.732955 | |
| I0401 12:37:19.171535 31447 solver.cpp:245] Train net output #7: loss2/accuracy_top3 = 0.0952381 | |
| I0401 12:37:19.171548 31447 solver.cpp:245] Train net output #8: loss2/cross_entropy_loss = 3.20621 (* 0.3 = 0.961862 loss) | |
| I0401 12:37:19.171562 31447 solver.cpp:245] Train net output #9: loss2/cross_entropy_loss_incl_empty = 0.962478 (* 0.3 = 0.288743 loss) | |
| I0401 12:37:19.171574 31447 solver.cpp:245] Train net output #10: loss3/accuracy = 0 | |
| I0401 12:37:19.171587 31447 solver.cpp:245] Train net output #11: loss3/accuracy_incl_empty = 0.738636 | |
| I0401 12:37:19.171599 31447 solver.cpp:245] Train net output #12: loss3/accuracy_top3 = 0.119048 | |
| I0401 12:37:19.171612 31447 solver.cpp:245] Train net output #13: loss3/cross_entropy_loss = 3.14425 (* 1 = 3.14425 loss) | |
| I0401 12:37:19.171627 31447 solver.cpp:245] Train net output #14: loss3/cross_entropy_loss_incl_empty = 0.902724 (* 1 = 0.902724 loss) | |
| I0401 12:37:19.171638 31447 solver.cpp:245] Train net output #15: total_accuracy = 0 | |
| I0401 12:37:19.171649 31447 solver.cpp:245] Train net output #16: total_confidence = 5.508e-05 | |
| I0401 12:37:19.171663 31447 sgd_solver.cpp:106] Iteration 34000, lr = 0.05 | |
| I0401 12:39:27.747115 31447 solver.cpp:229] Iteration 34500, loss = 6.32832 |
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