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| I0506 00:21:17.931069 15760 solver.cpp:280] Solving mixed_lstm | |
| I0506 00:21:17.931088 15760 solver.cpp:281] Learning Rate Policy: fixed | |
| I0506 00:21:18.209065 15760 solver.cpp:229] Iteration 0, loss = 27.5695 | |
| I0506 00:21:18.209156 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0506 00:21:18.209175 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:21:18.209188 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:21:18.209200 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:21:18.209211 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 00:21:18.209223 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 00:21:18.209236 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0 | |
| I0506 00:21:18.209247 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.125 | |
| I0506 00:21:18.209264 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0 | |
| I0506 00:21:18.209276 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0 | |
| I0506 00:21:18.209288 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0 | |
| I0506 00:21:18.209300 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0 | |
| I0506 00:21:18.209311 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0 | |
| I0506 00:21:18.209322 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0 | |
| I0506 00:21:18.209333 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 0 | |
| I0506 00:21:18.209352 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 0 | |
| I0506 00:21:18.209372 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 0 | |
| I0506 00:21:18.209391 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 0 | |
| I0506 00:21:18.209410 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 0 | |
| I0506 00:21:18.209422 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 0 | |
| I0506 00:21:18.209434 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 0 | |
| I0506 00:21:18.209445 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 0 | |
| I0506 00:21:18.209456 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 0 | |
| I0506 00:21:18.209467 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0 | |
| I0506 00:21:18.209487 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0 | |
| I0506 00:21:18.209506 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 4.31111 (* 0.3 = 1.29333 loss) | |
| I0506 00:21:18.209522 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 4.35963 (* 0.3 = 1.30789 loss) | |
| I0506 00:21:18.209568 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 4.27506 (* 0.0272727 = 0.116593 loss) | |
| I0506 00:21:18.209583 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 4.31833 (* 0.0272727 = 0.117773 loss) | |
| I0506 00:21:18.209596 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.30486 (* 0.0272727 = 0.117405 loss) | |
| I0506 00:21:18.209610 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 4.31247 (* 0.0272727 = 0.117613 loss) | |
| I0506 00:21:18.209625 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 4.26995 (* 0.0272727 = 0.116453 loss) | |
| I0506 00:21:18.209638 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 4.32896 (* 0.0272727 = 0.118062 loss) | |
| I0506 00:21:18.209652 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 4.26778 (* 0.0272727 = 0.116394 loss) | |
| I0506 00:21:18.209667 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 4.31872 (* 0.0272727 = 0.117783 loss) | |
| I0506 00:21:18.209686 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 4.26878 (* 0.0272727 = 0.116421 loss) | |
| I0506 00:21:18.209707 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 4.35593 (* 0.0272727 = 0.118798 loss) | |
| I0506 00:21:18.209729 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 4.37412 (* 0.0272727 = 0.119294 loss) | |
| I0506 00:21:18.209750 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 4.31432 (* 0.0272727 = 0.117663 loss) | |
| I0506 00:21:18.209764 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 4.30842 (* 0.0272727 = 0.117502 loss) | |
| I0506 00:21:18.209777 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 4.34458 (* 0.0272727 = 0.118489 loss) | |
| I0506 00:21:18.209791 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 4.28856 (* 0.0272727 = 0.116961 loss) | |
| I0506 00:21:18.209815 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 4.3757 (* 0.0272727 = 0.119337 loss) | |
| I0506 00:21:18.209830 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 4.34243 (* 0.0272727 = 0.11843 loss) | |
| I0506 00:21:18.209846 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 4.31728 (* 0.0272727 = 0.117744 loss) | |
| I0506 00:21:18.209858 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 4.28913 (* 0.0272727 = 0.116976 loss) | |
| I0506 00:21:18.209872 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 4.37894 (* 0.0272727 = 0.119426 loss) | |
| I0506 00:21:18.209885 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 4.34273 (* 0.0272727 = 0.118438 loss) | |
| I0506 00:21:18.209899 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 4.28937 (* 0.0272727 = 0.116983 loss) | |
| I0506 00:21:18.209911 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:21:18.209923 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:21:18.209935 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 00:21:18.209946 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:21:18.209964 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 00:21:18.209975 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0 | |
| I0506 00:21:18.209986 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0 | |
| I0506 00:21:18.209997 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.25 | |
| I0506 00:21:18.210010 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0 | |
| I0506 00:21:18.210029 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0 | |
| I0506 00:21:18.210041 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0 | |
| I0506 00:21:18.210052 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0 | |
| I0506 00:21:18.210062 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0 | |
| I0506 00:21:18.210074 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0 | |
| I0506 00:21:18.210085 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 0 | |
| I0506 00:21:18.210109 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 0 | |
| I0506 00:21:18.210122 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 0 | |
| I0506 00:21:18.210134 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 0 | |
| I0506 00:21:18.210145 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 0 | |
| I0506 00:21:18.210156 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 0 | |
| I0506 00:21:18.210167 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 0 | |
| I0506 00:21:18.210178 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 0 | |
| I0506 00:21:18.210189 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 0 | |
| I0506 00:21:18.210201 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0 | |
| I0506 00:21:18.210211 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.0357143 | |
| I0506 00:21:18.210225 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 4.30228 (* 0.3 = 1.29068 loss) | |
| I0506 00:21:18.210239 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 4.29985 (* 0.3 = 1.28996 loss) | |
| I0506 00:21:18.210253 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 4.29906 (* 0.0272727 = 0.117247 loss) | |
| I0506 00:21:18.210268 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 4.29953 (* 0.0272727 = 0.11726 loss) | |
| I0506 00:21:18.210281 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 4.30392 (* 0.0272727 = 0.11738 loss) | |
| I0506 00:21:18.210294 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 4.31178 (* 0.0272727 = 0.117594 loss) | |
| I0506 00:21:18.210311 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 4.30406 (* 0.0272727 = 0.117383 loss) | |
| I0506 00:21:18.210326 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 4.30301 (* 0.0272727 = 0.117355 loss) | |
| I0506 00:21:18.210340 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 4.29 (* 0.0272727 = 0.117 loss) | |
| I0506 00:21:18.210353 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 4.30608 (* 0.0272727 = 0.117438 loss) | |
| I0506 00:21:18.210367 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 4.30373 (* 0.0272727 = 0.117375 loss) | |
| I0506 00:21:18.210381 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 4.3076 (* 0.0272727 = 0.11748 loss) | |
| I0506 00:21:18.210396 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 4.31231 (* 0.0272727 = 0.117608 loss) | |
| I0506 00:21:18.210408 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 4.3022 (* 0.0272727 = 0.117333 loss) | |
| I0506 00:21:18.210422 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 4.29722 (* 0.0272727 = 0.117197 loss) | |
| I0506 00:21:18.210435 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 4.30905 (* 0.0272727 = 0.11752 loss) | |
| I0506 00:21:18.210449 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 4.31153 (* 0.0272727 = 0.117587 loss) | |
| I0506 00:21:18.210464 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 4.31299 (* 0.0272727 = 0.117627 loss) | |
| I0506 00:21:18.210476 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 4.2969 (* 0.0272727 = 0.117188 loss) | |
| I0506 00:21:18.210490 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 4.29871 (* 0.0272727 = 0.117238 loss) | |
| I0506 00:21:18.210505 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 4.30277 (* 0.0272727 = 0.117348 loss) | |
| I0506 00:21:18.210518 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 4.31366 (* 0.0272727 = 0.117645 loss) | |
| I0506 00:21:18.210531 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 4.30117 (* 0.0272727 = 0.117305 loss) | |
| I0506 00:21:18.210544 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 4.30819 (* 0.0272727 = 0.117496 loss) | |
| I0506 00:21:18.210556 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0 | |
| I0506 00:21:18.210575 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:21:18.210597 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:21:18.210610 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 00:21:18.210630 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0 | |
| I0506 00:21:18.210642 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0 | |
| I0506 00:21:18.210650 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0 | |
| I0506 00:21:18.210657 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0 | |
| I0506 00:21:18.210664 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0 | |
| I0506 00:21:18.210680 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.5 | |
| I0506 00:21:18.210700 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0 | |
| I0506 00:21:18.210711 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0 | |
| I0506 00:21:18.210737 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0 | |
| I0506 00:21:18.210752 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0 | |
| I0506 00:21:18.210764 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 0.5 | |
| I0506 00:21:18.210777 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 0.25 | |
| I0506 00:21:18.210796 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 0 | |
| I0506 00:21:18.210808 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 0 | |
| I0506 00:21:18.210819 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 0 | |
| I0506 00:21:18.210829 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 0 | |
| I0506 00:21:18.210840 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 0 | |
| I0506 00:21:18.210851 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 0.25 | |
| I0506 00:21:18.210867 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 0 | |
| I0506 00:21:18.210880 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0 | |
| I0506 00:21:18.210891 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.0357143 | |
| I0506 00:21:18.210906 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 4.30345 (* 1 = 4.30345 loss) | |
| I0506 00:21:18.210919 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 4.30733 (* 1 = 4.30733 loss) | |
| I0506 00:21:18.210932 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 4.30537 (* 0.0909091 = 0.391397 loss) | |
| I0506 00:21:18.210947 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 4.30413 (* 0.0909091 = 0.391285 loss) | |
| I0506 00:21:18.210965 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 4.30106 (* 0.0909091 = 0.391006 loss) | |
| I0506 00:21:18.210979 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 4.30585 (* 0.0909091 = 0.391441 loss) | |
| I0506 00:21:18.210993 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 4.3096 (* 0.0909091 = 0.391781 loss) | |
| I0506 00:21:18.211005 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 4.30686 (* 0.0909091 = 0.391533 loss) | |
| I0506 00:21:18.211019 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 4.3029 (* 0.0909091 = 0.391173 loss) | |
| I0506 00:21:18.211037 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 4.30354 (* 0.0909091 = 0.391231 loss) | |
| I0506 00:21:18.211051 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 4.29065 (* 0.0909091 = 0.390059 loss) | |
| I0506 00:21:18.211064 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 4.30462 (* 0.0909091 = 0.391329 loss) | |
| I0506 00:21:18.211078 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 4.30367 (* 0.0909091 = 0.391242 loss) | |
| I0506 00:21:18.211091 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 4.30754 (* 0.0909091 = 0.391594 loss) | |
| I0506 00:21:18.211105 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 4.30465 (* 0.0909091 = 0.391332 loss) | |
| I0506 00:21:18.211118 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 4.28587 (* 0.0909091 = 0.389624 loss) | |
| I0506 00:21:18.211143 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 4.2886 (* 0.0909091 = 0.389873 loss) | |
| I0506 00:21:18.211158 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 4.30016 (* 0.0909091 = 0.390924 loss) | |
| I0506 00:21:18.211172 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 4.30987 (* 0.0909091 = 0.391807 loss) | |
| I0506 00:21:18.211186 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 4.29836 (* 0.0909091 = 0.39076 loss) | |
| I0506 00:21:18.211199 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 4.30481 (* 0.0909091 = 0.391347 loss) | |
| I0506 00:21:18.211213 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 4.30033 (* 0.0909091 = 0.39094 loss) | |
| I0506 00:21:18.211226 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 4.294 (* 0.0909091 = 0.390363 loss) | |
| I0506 00:21:18.211241 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 4.30833 (* 0.0909091 = 0.391667 loss) | |
| I0506 00:21:18.211252 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:21:18.211263 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:21:18.211274 15760 solver.cpp:245] Train net output #149: total_confidence = 1.07311e-41 | |
| I0506 00:21:18.211287 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 1.19166e-41 | |
| I0506 00:21:18.211308 15760 sgd_solver.cpp:106] Iteration 0, lr = 0.001 | |
| I0506 00:23:05.545322 15760 solver.cpp:229] Iteration 500, loss = 16.3829 | |
| I0506 00:23:05.545789 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0506 00:23:05.545810 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:23:05.545824 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:23:05.545835 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:23:05.545846 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 00:23:05.545858 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 00:23:05.545871 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 00:23:05.545886 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 00:23:05.545898 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.625 | |
| I0506 00:23:05.545910 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 00:23:05.545922 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 00:23:05.545934 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 00:23:05.545946 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 00:23:05.545958 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:23:05.545969 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:23:05.545981 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:23:05.545994 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:23:05.546005 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:23:05.546016 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:23:05.546028 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:23:05.546039 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:23:05.546051 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:23:05.546062 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:23:05.546074 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.693182 | |
| I0506 00:23:05.546087 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.111111 | |
| I0506 00:23:05.546103 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 4.56627 (* 0.3 = 1.36988 loss) | |
| I0506 00:23:05.546125 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.89838 (* 0.3 = 0.569514 loss) | |
| I0506 00:23:05.546140 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 4.87167 (* 0.0272727 = 0.132864 loss) | |
| I0506 00:23:05.546154 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 4.69127 (* 0.0272727 = 0.127944 loss) | |
| I0506 00:23:05.546169 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.39758 (* 0.0272727 = 0.119934 loss) | |
| I0506 00:23:05.546182 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 4.14132 (* 0.0272727 = 0.112945 loss) | |
| I0506 00:23:05.546195 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 4.16997 (* 0.0272727 = 0.113726 loss) | |
| I0506 00:23:05.546210 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.39378 (* 0.0272727 = 0.0925576 loss) | |
| I0506 00:23:05.546222 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 3.21706 (* 0.0272727 = 0.0877381 loss) | |
| I0506 00:23:05.546236 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 2.01055 (* 0.0272727 = 0.054833 loss) | |
| I0506 00:23:05.546250 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.874339 (* 0.0272727 = 0.0238456 loss) | |
| I0506 00:23:05.546264 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 1.03425 (* 0.0272727 = 0.0282069 loss) | |
| I0506 00:23:05.546277 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.963172 (* 0.0272727 = 0.0262683 loss) | |
| I0506 00:23:05.546291 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.887575 (* 0.0272727 = 0.0242066 loss) | |
| I0506 00:23:05.546306 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0540829 (* 0.0272727 = 0.00147499 loss) | |
| I0506 00:23:05.546339 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0535799 (* 0.0272727 = 0.00146127 loss) | |
| I0506 00:23:05.546355 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0688035 (* 0.0272727 = 0.00187646 loss) | |
| I0506 00:23:05.546368 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0584937 (* 0.0272727 = 0.00159528 loss) | |
| I0506 00:23:05.546381 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0604571 (* 0.0272727 = 0.00164883 loss) | |
| I0506 00:23:05.546396 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0340607 (* 0.0272727 = 0.000928929 loss) | |
| I0506 00:23:05.546409 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0290498 (* 0.0272727 = 0.000792267 loss) | |
| I0506 00:23:05.546423 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0359117 (* 0.0272727 = 0.000979409 loss) | |
| I0506 00:23:05.546437 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0465619 (* 0.0272727 = 0.00126987 loss) | |
| I0506 00:23:05.546452 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.029777 (* 0.0272727 = 0.0008121 loss) | |
| I0506 00:23:05.546464 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:23:05.546481 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 00:23:05.546504 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:23:05.546525 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:23:05.546538 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 00:23:05.546550 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 00:23:05.546561 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 00:23:05.546572 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 00:23:05.546584 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.625 | |
| I0506 00:23:05.546596 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 00:23:05.546608 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 00:23:05.546619 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 00:23:05.546633 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 00:23:05.546645 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:23:05.546658 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:23:05.546669 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:23:05.546680 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:23:05.546691 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:23:05.546702 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:23:05.546722 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:23:05.546735 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:23:05.546746 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:23:05.546757 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:23:05.546768 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.693182 | |
| I0506 00:23:05.546780 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.0185185 | |
| I0506 00:23:05.546794 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 4.46598 (* 0.3 = 1.33979 loss) | |
| I0506 00:23:05.546813 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.9682 (* 0.3 = 0.59046 loss) | |
| I0506 00:23:05.546828 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 4.35242 (* 0.0272727 = 0.118702 loss) | |
| I0506 00:23:05.546841 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 4.92942 (* 0.0272727 = 0.134439 loss) | |
| I0506 00:23:05.546867 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 5.21635 (* 0.0272727 = 0.142264 loss) | |
| I0506 00:23:05.546882 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 4.18059 (* 0.0272727 = 0.114016 loss) | |
| I0506 00:23:05.546896 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 4.10592 (* 0.0272727 = 0.11198 loss) | |
| I0506 00:23:05.546911 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 4.10223 (* 0.0272727 = 0.111879 loss) | |
| I0506 00:23:05.546923 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 3.87705 (* 0.0272727 = 0.105738 loss) | |
| I0506 00:23:05.546941 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 2.42071 (* 0.0272727 = 0.0660193 loss) | |
| I0506 00:23:05.546955 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.804887 (* 0.0272727 = 0.0219515 loss) | |
| I0506 00:23:05.546969 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.902877 (* 0.0272727 = 0.0246239 loss) | |
| I0506 00:23:05.546983 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.845956 (* 0.0272727 = 0.0230715 loss) | |
| I0506 00:23:05.546998 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 1.01545 (* 0.0272727 = 0.027694 loss) | |
| I0506 00:23:05.547011 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0807468 (* 0.0272727 = 0.00220219 loss) | |
| I0506 00:23:05.547025 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0782937 (* 0.0272727 = 0.00213528 loss) | |
| I0506 00:23:05.547039 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0928551 (* 0.0272727 = 0.00253241 loss) | |
| I0506 00:23:05.547054 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0373838 (* 0.0272727 = 0.00101956 loss) | |
| I0506 00:23:05.547066 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0381566 (* 0.0272727 = 0.00104063 loss) | |
| I0506 00:23:05.547080 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0533634 (* 0.0272727 = 0.00145537 loss) | |
| I0506 00:23:05.547094 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0300339 (* 0.0272727 = 0.000819106 loss) | |
| I0506 00:23:05.547107 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0274469 (* 0.0272727 = 0.000748553 loss) | |
| I0506 00:23:05.547122 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0792853 (* 0.0272727 = 0.00216233 loss) | |
| I0506 00:23:05.547135 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0315272 (* 0.0272727 = 0.000859833 loss) | |
| I0506 00:23:05.547147 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0 | |
| I0506 00:23:05.547158 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 00:23:05.547170 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:23:05.547183 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:23:05.547194 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 00:23:05.547205 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.125 | |
| I0506 00:23:05.547217 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 00:23:05.547230 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 00:23:05.547241 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.625 | |
| I0506 00:23:05.547252 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 00:23:05.547265 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 00:23:05.547276 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 00:23:05.547287 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 00:23:05.547299 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:23:05.547310 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:23:05.547322 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:23:05.547333 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:23:05.547354 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:23:05.547368 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:23:05.547379 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:23:05.547389 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:23:05.547400 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:23:05.547412 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:23:05.547423 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.693182 | |
| I0506 00:23:05.547435 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.037037 | |
| I0506 00:23:05.547449 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 4.38273 (* 1 = 4.38273 loss) | |
| I0506 00:23:05.547463 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.53588 (* 1 = 1.53588 loss) | |
| I0506 00:23:05.547477 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 4.0189 (* 0.0909091 = 0.365355 loss) | |
| I0506 00:23:05.547490 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 4.11131 (* 0.0909091 = 0.373756 loss) | |
| I0506 00:23:05.547504 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 4.03544 (* 0.0909091 = 0.366858 loss) | |
| I0506 00:23:05.547518 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.84076 (* 0.0909091 = 0.34916 loss) | |
| I0506 00:23:05.547531 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 4.0625 (* 0.0909091 = 0.369318 loss) | |
| I0506 00:23:05.547544 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 3.62582 (* 0.0909091 = 0.32962 loss) | |
| I0506 00:23:05.547559 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.90576 (* 0.0909091 = 0.26416 loss) | |
| I0506 00:23:05.547572 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 2.45031 (* 0.0909091 = 0.222756 loss) | |
| I0506 00:23:05.547585 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.859863 (* 0.0909091 = 0.0781693 loss) | |
| I0506 00:23:05.547598 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.808726 (* 0.0909091 = 0.0735206 loss) | |
| I0506 00:23:05.547612 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.74866 (* 0.0909091 = 0.06806 loss) | |
| I0506 00:23:05.547626 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.707002 (* 0.0909091 = 0.0642729 loss) | |
| I0506 00:23:05.547639 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0328685 (* 0.0909091 = 0.00298805 loss) | |
| I0506 00:23:05.547654 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0287749 (* 0.0909091 = 0.0026159 loss) | |
| I0506 00:23:05.547668 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0201702 (* 0.0909091 = 0.00183365 loss) | |
| I0506 00:23:05.547683 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.011708 (* 0.0909091 = 0.00106436 loss) | |
| I0506 00:23:05.547696 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00883825 (* 0.0909091 = 0.000803478 loss) | |
| I0506 00:23:05.547710 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00536457 (* 0.0909091 = 0.000487688 loss) | |
| I0506 00:23:05.547724 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00964439 (* 0.0909091 = 0.000876762 loss) | |
| I0506 00:23:05.547737 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00593543 (* 0.0909091 = 0.000539584 loss) | |
| I0506 00:23:05.547751 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.0042607 (* 0.0909091 = 0.000387336 loss) | |
| I0506 00:23:05.547765 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00615515 (* 0.0909091 = 0.000559559 loss) | |
| I0506 00:23:05.547776 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:23:05.547788 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:23:05.547799 15760 solver.cpp:245] Train net output #149: total_confidence = 1.75176e-08 | |
| I0506 00:23:05.547821 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 2.24725e-05 | |
| I0506 00:23:05.547835 15760 sgd_solver.cpp:106] Iteration 500, lr = 0.001 | |
| I0506 00:24:52.640451 15760 solver.cpp:229] Iteration 1000, loss = 13.3096 | |
| I0506 00:24:52.640609 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0506 00:24:52.640630 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:24:52.640642 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:24:52.640655 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 00:24:52.640666 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 00:24:52.640678 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 00:24:52.640689 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.25 | |
| I0506 00:24:52.640702 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 00:24:52.640713 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 00:24:52.640725 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 00:24:52.640738 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 00:24:52.640749 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 00:24:52.640760 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 00:24:52.640772 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0.875 | |
| I0506 00:24:52.640784 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 0.875 | |
| I0506 00:24:52.640796 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:24:52.640808 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:24:52.640820 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:24:52.640832 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:24:52.640843 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:24:52.640856 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:24:52.640866 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:24:52.640882 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:24:52.640893 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.681818 | |
| I0506 00:24:52.640905 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.0714286 | |
| I0506 00:24:52.640923 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 4.09987 (* 0.3 = 1.22996 loss) | |
| I0506 00:24:52.640945 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.77853 (* 0.3 = 0.533559 loss) | |
| I0506 00:24:52.640960 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.74897 (* 0.0272727 = 0.102245 loss) | |
| I0506 00:24:52.640974 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 4.52189 (* 0.0272727 = 0.123324 loss) | |
| I0506 00:24:52.640988 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.21062 (* 0.0272727 = 0.114835 loss) | |
| I0506 00:24:52.641002 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.9198 (* 0.0272727 = 0.106904 loss) | |
| I0506 00:24:52.641016 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.52713 (* 0.0272727 = 0.0961944 loss) | |
| I0506 00:24:52.641031 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.72529 (* 0.0272727 = 0.101599 loss) | |
| I0506 00:24:52.641043 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 3.3599 (* 0.0272727 = 0.0916337 loss) | |
| I0506 00:24:52.641057 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.18466 (* 0.0272727 = 0.032309 loss) | |
| I0506 00:24:52.641070 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.35041 (* 0.0272727 = 0.0368293 loss) | |
| I0506 00:24:52.641084 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 1.24964 (* 0.0272727 = 0.0340812 loss) | |
| I0506 00:24:52.641098 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 1.22607 (* 0.0272727 = 0.0334383 loss) | |
| I0506 00:24:52.641111 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 1.37914 (* 0.0272727 = 0.037613 loss) | |
| I0506 00:24:52.641144 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 1.22804 (* 0.0272727 = 0.033492 loss) | |
| I0506 00:24:52.641180 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 1.44944 (* 0.0272727 = 0.0395303 loss) | |
| I0506 00:24:52.641196 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0695762 (* 0.0272727 = 0.00189753 loss) | |
| I0506 00:24:52.641211 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0420125 (* 0.0272727 = 0.0011458 loss) | |
| I0506 00:24:52.641225 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0312006 (* 0.0272727 = 0.000850926 loss) | |
| I0506 00:24:52.641238 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0325085 (* 0.0272727 = 0.000886596 loss) | |
| I0506 00:24:52.641252 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0374326 (* 0.0272727 = 0.00102089 loss) | |
| I0506 00:24:52.641266 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0392999 (* 0.0272727 = 0.00107182 loss) | |
| I0506 00:24:52.641280 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0385019 (* 0.0272727 = 0.00105005 loss) | |
| I0506 00:24:52.641294 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0372033 (* 0.0272727 = 0.00101464 loss) | |
| I0506 00:24:52.641306 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:24:52.641317 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:24:52.641330 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:24:52.641340 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:24:52.641351 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 00:24:52.641362 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0 | |
| I0506 00:24:52.641373 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.25 | |
| I0506 00:24:52.641386 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 00:24:52.641396 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 00:24:52.641408 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 00:24:52.641419 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 00:24:52.641432 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 00:24:52.641443 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 00:24:52.641454 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0.875 | |
| I0506 00:24:52.641466 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 0.875 | |
| I0506 00:24:52.641479 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:24:52.641485 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:24:52.641494 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:24:52.641500 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:24:52.641512 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:24:52.641525 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:24:52.641535 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:24:52.641547 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:24:52.641564 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.681818 | |
| I0506 00:24:52.641577 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.160714 | |
| I0506 00:24:52.641592 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 4.00529 (* 0.3 = 1.20159 loss) | |
| I0506 00:24:52.641607 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.76381 (* 0.3 = 0.529142 loss) | |
| I0506 00:24:52.641623 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 4.29422 (* 0.0272727 = 0.117115 loss) | |
| I0506 00:24:52.641638 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 4.04607 (* 0.0272727 = 0.110347 loss) | |
| I0506 00:24:52.641652 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 4.31008 (* 0.0272727 = 0.117548 loss) | |
| I0506 00:24:52.641677 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.98193 (* 0.0272727 = 0.108598 loss) | |
| I0506 00:24:52.641692 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 4.43462 (* 0.0272727 = 0.120944 loss) | |
| I0506 00:24:52.641711 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.84968 (* 0.0272727 = 0.104991 loss) | |
| I0506 00:24:52.641726 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.7958 (* 0.0272727 = 0.0762491 loss) | |
| I0506 00:24:52.641739 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.96776 (* 0.0272727 = 0.0263934 loss) | |
| I0506 00:24:52.641752 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.01108 (* 0.0272727 = 0.0275749 loss) | |
| I0506 00:24:52.641767 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 1.01574 (* 0.0272727 = 0.0277021 loss) | |
| I0506 00:24:52.641779 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.999028 (* 0.0272727 = 0.0272462 loss) | |
| I0506 00:24:52.641793 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 1.18247 (* 0.0272727 = 0.0322492 loss) | |
| I0506 00:24:52.641806 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.787272 (* 0.0272727 = 0.0214711 loss) | |
| I0506 00:24:52.641820 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 1.06647 (* 0.0272727 = 0.0290856 loss) | |
| I0506 00:24:52.641834 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0389922 (* 0.0272727 = 0.00106342 loss) | |
| I0506 00:24:52.641847 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0151379 (* 0.0272727 = 0.000412852 loss) | |
| I0506 00:24:52.641861 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0169646 (* 0.0272727 = 0.00046267 loss) | |
| I0506 00:24:52.641875 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0195236 (* 0.0272727 = 0.000532462 loss) | |
| I0506 00:24:52.641888 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0115629 (* 0.0272727 = 0.000315351 loss) | |
| I0506 00:24:52.641902 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0199931 (* 0.0272727 = 0.000545266 loss) | |
| I0506 00:24:52.641916 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0198533 (* 0.0272727 = 0.000541453 loss) | |
| I0506 00:24:52.641932 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0276728 (* 0.0272727 = 0.000754714 loss) | |
| I0506 00:24:52.641945 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0535714 | |
| I0506 00:24:52.641957 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:24:52.641968 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:24:52.641979 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:24:52.641990 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0 | |
| I0506 00:24:52.642001 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.125 | |
| I0506 00:24:52.642014 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 00:24:52.642024 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 00:24:52.642036 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 00:24:52.642047 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 00:24:52.642060 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 00:24:52.642071 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 00:24:52.642082 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 00:24:52.642093 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0.875 | |
| I0506 00:24:52.642105 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 0.875 | |
| I0506 00:24:52.642117 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:24:52.642128 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:24:52.642149 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:24:52.642163 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:24:52.642174 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:24:52.642184 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:24:52.642196 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:24:52.642207 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:24:52.642218 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.698864 | |
| I0506 00:24:52.642230 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.125 | |
| I0506 00:24:52.642243 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.8648 (* 1 = 3.8648 loss) | |
| I0506 00:24:52.642256 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.47553 (* 1 = 1.47553 loss) | |
| I0506 00:24:52.642271 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.60432 (* 0.0909091 = 0.327666 loss) | |
| I0506 00:24:52.642284 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.73804 (* 0.0909091 = 0.339822 loss) | |
| I0506 00:24:52.642297 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 4.18159 (* 0.0909091 = 0.380145 loss) | |
| I0506 00:24:52.642312 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.61211 (* 0.0909091 = 0.328374 loss) | |
| I0506 00:24:52.642324 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.73111 (* 0.0909091 = 0.339192 loss) | |
| I0506 00:24:52.642338 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 3.55194 (* 0.0909091 = 0.322904 loss) | |
| I0506 00:24:52.642351 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.42822 (* 0.0909091 = 0.220748 loss) | |
| I0506 00:24:52.642364 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.892041 (* 0.0909091 = 0.0810946 loss) | |
| I0506 00:24:52.642379 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.780486 (* 0.0909091 = 0.0709532 loss) | |
| I0506 00:24:52.642391 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.878631 (* 0.0909091 = 0.0798756 loss) | |
| I0506 00:24:52.642405 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 1.03265 (* 0.0909091 = 0.0938774 loss) | |
| I0506 00:24:52.642418 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 1.11645 (* 0.0909091 = 0.101496 loss) | |
| I0506 00:24:52.642436 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 1.053 (* 0.0909091 = 0.0957275 loss) | |
| I0506 00:24:52.642462 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 1.17701 (* 0.0909091 = 0.107001 loss) | |
| I0506 00:24:52.642482 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00800252 (* 0.0909091 = 0.000727502 loss) | |
| I0506 00:24:52.642495 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00469739 (* 0.0909091 = 0.000427036 loss) | |
| I0506 00:24:52.642510 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00443574 (* 0.0909091 = 0.000403249 loss) | |
| I0506 00:24:52.642524 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00316208 (* 0.0909091 = 0.000287462 loss) | |
| I0506 00:24:52.642537 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00269565 (* 0.0909091 = 0.000245059 loss) | |
| I0506 00:24:52.642551 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00220258 (* 0.0909091 = 0.000200235 loss) | |
| I0506 00:24:52.642565 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00232497 (* 0.0909091 = 0.000211361 loss) | |
| I0506 00:24:52.642578 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00257904 (* 0.0909091 = 0.000234458 loss) | |
| I0506 00:24:52.642590 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:24:52.642601 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:24:52.642612 15760 solver.cpp:245] Train net output #149: total_confidence = 4.59729e-09 | |
| I0506 00:24:52.642635 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 5.06196e-06 | |
| I0506 00:24:52.642650 15760 sgd_solver.cpp:106] Iteration 1000, lr = 0.001 | |
| I0506 00:26:39.756435 15760 solver.cpp:229] Iteration 1500, loss = 13.0559 | |
| I0506 00:26:39.756608 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.025641 | |
| I0506 00:26:39.756628 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:26:39.756641 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 00:26:39.756654 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:26:39.756665 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 00:26:39.756677 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.625 | |
| I0506 00:26:39.756690 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.75 | |
| I0506 00:26:39.756701 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.75 | |
| I0506 00:26:39.756713 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 00:26:39.756724 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 00:26:39.756736 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:26:39.756747 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:26:39.756759 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:26:39.756770 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:26:39.756783 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:26:39.756793 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:26:39.756805 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:26:39.756817 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:26:39.756829 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:26:39.756840 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:26:39.756852 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:26:39.756863 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:26:39.756878 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:26:39.756891 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.784091 | |
| I0506 00:26:39.756902 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.128205 | |
| I0506 00:26:39.756919 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.98222 (* 0.3 = 1.19466 loss) | |
| I0506 00:26:39.756933 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.2733 (* 0.3 = 0.381991 loss) | |
| I0506 00:26:39.756947 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 4.17234 (* 0.0272727 = 0.113791 loss) | |
| I0506 00:26:39.756963 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.98602 (* 0.0272727 = 0.10871 loss) | |
| I0506 00:26:39.756975 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.07679 (* 0.0272727 = 0.111185 loss) | |
| I0506 00:26:39.756989 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.99092 (* 0.0272727 = 0.108843 loss) | |
| I0506 00:26:39.757004 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.49714 (* 0.0272727 = 0.0681039 loss) | |
| I0506 00:26:39.757016 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 1.5788 (* 0.0272727 = 0.0430582 loss) | |
| I0506 00:26:39.757030 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.61359 (* 0.0272727 = 0.044007 loss) | |
| I0506 00:26:39.757043 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.35327 (* 0.0272727 = 0.0369075 loss) | |
| I0506 00:26:39.757058 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.139368 (* 0.0272727 = 0.00380094 loss) | |
| I0506 00:26:39.757072 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.127891 (* 0.0272727 = 0.00348793 loss) | |
| I0506 00:26:39.757086 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.146241 (* 0.0272727 = 0.0039884 loss) | |
| I0506 00:26:39.757100 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.110602 (* 0.0272727 = 0.00301642 loss) | |
| I0506 00:26:39.757114 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.129875 (* 0.0272727 = 0.00354205 loss) | |
| I0506 00:26:39.757164 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0834726 (* 0.0272727 = 0.00227652 loss) | |
| I0506 00:26:39.757180 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0744574 (* 0.0272727 = 0.00203066 loss) | |
| I0506 00:26:39.757194 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0653196 (* 0.0272727 = 0.00178144 loss) | |
| I0506 00:26:39.757208 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0443507 (* 0.0272727 = 0.00120956 loss) | |
| I0506 00:26:39.757222 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0473176 (* 0.0272727 = 0.00129048 loss) | |
| I0506 00:26:39.757236 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0426922 (* 0.0272727 = 0.00116433 loss) | |
| I0506 00:26:39.757249 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0366324 (* 0.0272727 = 0.000999064 loss) | |
| I0506 00:26:39.757263 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.049832 (* 0.0272727 = 0.00135906 loss) | |
| I0506 00:26:39.757277 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.051196 (* 0.0272727 = 0.00139626 loss) | |
| I0506 00:26:39.757289 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:26:39.757302 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 00:26:39.757313 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:26:39.757324 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:26:39.757335 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 00:26:39.757347 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.625 | |
| I0506 00:26:39.757359 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.75 | |
| I0506 00:26:39.757371 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 00:26:39.757382 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 00:26:39.757393 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 00:26:39.757405 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:26:39.757416 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:26:39.757427 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:26:39.757438 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:26:39.757449 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:26:39.757462 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:26:39.757472 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:26:39.757484 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:26:39.757495 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:26:39.757508 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:26:39.757519 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:26:39.757530 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:26:39.757541 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:26:39.757552 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.778409 | |
| I0506 00:26:39.757565 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.0769231 | |
| I0506 00:26:39.757585 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 4.04705 (* 0.3 = 1.21411 loss) | |
| I0506 00:26:39.757614 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.36421 (* 0.3 = 0.409264 loss) | |
| I0506 00:26:39.757637 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 4.02083 (* 0.0272727 = 0.109659 loss) | |
| I0506 00:26:39.757652 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 4.00307 (* 0.0272727 = 0.109175 loss) | |
| I0506 00:26:39.757665 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.92114 (* 0.0272727 = 0.10694 loss) | |
| I0506 00:26:39.757693 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.95336 (* 0.0272727 = 0.107819 loss) | |
| I0506 00:26:39.757707 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.81371 (* 0.0272727 = 0.0767374 loss) | |
| I0506 00:26:39.757721 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.26678 (* 0.0272727 = 0.0618214 loss) | |
| I0506 00:26:39.757735 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.55512 (* 0.0272727 = 0.0424123 loss) | |
| I0506 00:26:39.757748 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.59181 (* 0.0272727 = 0.043413 loss) | |
| I0506 00:26:39.757762 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.231361 (* 0.0272727 = 0.00630983 loss) | |
| I0506 00:26:39.757776 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.135195 (* 0.0272727 = 0.00368713 loss) | |
| I0506 00:26:39.757789 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.121192 (* 0.0272727 = 0.00330524 loss) | |
| I0506 00:26:39.757803 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.104785 (* 0.0272727 = 0.00285777 loss) | |
| I0506 00:26:39.757817 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.158118 (* 0.0272727 = 0.00431231 loss) | |
| I0506 00:26:39.757832 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.100016 (* 0.0272727 = 0.0027277 loss) | |
| I0506 00:26:39.757845 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0704425 (* 0.0272727 = 0.00192116 loss) | |
| I0506 00:26:39.757859 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0454905 (* 0.0272727 = 0.00124065 loss) | |
| I0506 00:26:39.757874 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0427882 (* 0.0272727 = 0.00116695 loss) | |
| I0506 00:26:39.757887 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0650557 (* 0.0272727 = 0.00177425 loss) | |
| I0506 00:26:39.757901 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.041326 (* 0.0272727 = 0.00112707 loss) | |
| I0506 00:26:39.757915 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0323184 (* 0.0272727 = 0.00088141 loss) | |
| I0506 00:26:39.757931 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0558574 (* 0.0272727 = 0.00152338 loss) | |
| I0506 00:26:39.757946 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0533589 (* 0.0272727 = 0.00145524 loss) | |
| I0506 00:26:39.757958 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0 | |
| I0506 00:26:39.757971 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:26:39.757982 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:26:39.757992 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:26:39.758004 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 00:26:39.758015 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.625 | |
| I0506 00:26:39.758028 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.75 | |
| I0506 00:26:39.758038 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 00:26:39.758049 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 00:26:39.758061 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 00:26:39.758074 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:26:39.758085 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:26:39.758095 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:26:39.758106 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:26:39.758117 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:26:39.758129 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:26:39.758141 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:26:39.758152 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:26:39.758173 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:26:39.758186 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:26:39.758198 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:26:39.758208 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:26:39.758220 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:26:39.758231 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.744318 | |
| I0506 00:26:39.758242 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.0512821 | |
| I0506 00:26:39.758256 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.71762 (* 1 = 3.71762 loss) | |
| I0506 00:26:39.758270 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.20591 (* 1 = 1.20591 loss) | |
| I0506 00:26:39.758283 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.84848 (* 0.0909091 = 0.349862 loss) | |
| I0506 00:26:39.758297 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.74599 (* 0.0909091 = 0.340544 loss) | |
| I0506 00:26:39.758311 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.96269 (* 0.0909091 = 0.360244 loss) | |
| I0506 00:26:39.758324 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.44116 (* 0.0909091 = 0.312833 loss) | |
| I0506 00:26:39.758338 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.14927 (* 0.0909091 = 0.195388 loss) | |
| I0506 00:26:39.758352 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.46511 (* 0.0909091 = 0.133192 loss) | |
| I0506 00:26:39.758364 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.20192 (* 0.0909091 = 0.109266 loss) | |
| I0506 00:26:39.758378 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.34568 (* 0.0909091 = 0.122334 loss) | |
| I0506 00:26:39.758393 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.0982478 (* 0.0909091 = 0.00893162 loss) | |
| I0506 00:26:39.758406 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0791269 (* 0.0909091 = 0.00719336 loss) | |
| I0506 00:26:39.758419 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0715274 (* 0.0909091 = 0.00650249 loss) | |
| I0506 00:26:39.758433 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0571588 (* 0.0909091 = 0.00519625 loss) | |
| I0506 00:26:39.758447 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0405384 (* 0.0909091 = 0.00368531 loss) | |
| I0506 00:26:39.758461 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0327443 (* 0.0909091 = 0.00297675 loss) | |
| I0506 00:26:39.758474 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.027085 (* 0.0909091 = 0.00246228 loss) | |
| I0506 00:26:39.758488 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0170948 (* 0.0909091 = 0.00155407 loss) | |
| I0506 00:26:39.758502 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00911789 (* 0.0909091 = 0.000828899 loss) | |
| I0506 00:26:39.758515 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0114029 (* 0.0909091 = 0.00103663 loss) | |
| I0506 00:26:39.758529 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00997418 (* 0.0909091 = 0.000906744 loss) | |
| I0506 00:26:39.758543 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00685661 (* 0.0909091 = 0.000623329 loss) | |
| I0506 00:26:39.758556 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00704779 (* 0.0909091 = 0.000640708 loss) | |
| I0506 00:26:39.758570 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00887051 (* 0.0909091 = 0.00080641 loss) | |
| I0506 00:26:39.758582 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:26:39.758594 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:26:39.758605 15760 solver.cpp:245] Train net output #149: total_confidence = 2.01951e-08 | |
| I0506 00:26:39.758615 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 2.88337e-05 | |
| I0506 00:26:39.758638 15760 sgd_solver.cpp:106] Iteration 1500, lr = 0.001 | |
| I0506 00:28:26.846534 15760 solver.cpp:229] Iteration 2000, loss = 12.9355 | |
| I0506 00:28:26.846665 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0506 00:28:26.846686 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:28:26.846698 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:28:26.846710 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:28:26.846721 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 00:28:26.846735 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 00:28:26.846745 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.25 | |
| I0506 00:28:26.846757 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 00:28:26.846770 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 00:28:26.846781 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 00:28:26.846793 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:28:26.846804 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:28:26.846817 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:28:26.846827 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:28:26.846839 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:28:26.846850 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:28:26.846869 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:28:26.846885 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:28:26.846897 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:28:26.846909 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:28:26.846920 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:28:26.846931 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:28:26.846942 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:28:26.846953 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.710227 | |
| I0506 00:28:26.846966 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.0588235 | |
| I0506 00:28:26.846982 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 4.58497 (* 0.3 = 1.37549 loss) | |
| I0506 00:28:26.846997 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.66613 (* 0.3 = 0.49984 loss) | |
| I0506 00:28:26.847010 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 4.25839 (* 0.0272727 = 0.116138 loss) | |
| I0506 00:28:26.847023 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 4.05794 (* 0.0272727 = 0.110671 loss) | |
| I0506 00:28:26.847038 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.22501 (* 0.0272727 = 0.115228 loss) | |
| I0506 00:28:26.847050 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.99626 (* 0.0272727 = 0.108989 loss) | |
| I0506 00:28:26.847064 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 4.50855 (* 0.0272727 = 0.12296 loss) | |
| I0506 00:28:26.847077 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.77043 (* 0.0272727 = 0.10283 loss) | |
| I0506 00:28:26.847090 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.85657 (* 0.0272727 = 0.0779064 loss) | |
| I0506 00:28:26.847105 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.77322 (* 0.0272727 = 0.0483606 loss) | |
| I0506 00:28:26.847118 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.06149 (* 0.0272727 = 0.0289497 loss) | |
| I0506 00:28:26.847132 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0835307 (* 0.0272727 = 0.00227811 loss) | |
| I0506 00:28:26.847147 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0625388 (* 0.0272727 = 0.0017056 loss) | |
| I0506 00:28:26.847162 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0419291 (* 0.0272727 = 0.00114352 loss) | |
| I0506 00:28:26.847180 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0543077 (* 0.0272727 = 0.00148112 loss) | |
| I0506 00:28:26.847213 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0316339 (* 0.0272727 = 0.000862741 loss) | |
| I0506 00:28:26.847229 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0301841 (* 0.0272727 = 0.000823203 loss) | |
| I0506 00:28:26.847242 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0259126 (* 0.0272727 = 0.000706706 loss) | |
| I0506 00:28:26.847256 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0161531 (* 0.0272727 = 0.000440539 loss) | |
| I0506 00:28:26.847270 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0375606 (* 0.0272727 = 0.00102438 loss) | |
| I0506 00:28:26.847283 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0132868 (* 0.0272727 = 0.000362368 loss) | |
| I0506 00:28:26.847297 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0164362 (* 0.0272727 = 0.000448261 loss) | |
| I0506 00:28:26.847311 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.016404 (* 0.0272727 = 0.00044738 loss) | |
| I0506 00:28:26.847324 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0144294 (* 0.0272727 = 0.000393529 loss) | |
| I0506 00:28:26.847337 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:28:26.847348 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:28:26.847359 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:28:26.847370 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:28:26.847381 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 00:28:26.847393 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 00:28:26.847404 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.25 | |
| I0506 00:28:26.847415 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 00:28:26.847426 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 00:28:26.847437 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 00:28:26.847450 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:28:26.847460 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:28:26.847471 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:28:26.847482 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:28:26.847492 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:28:26.847503 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:28:26.847514 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:28:26.847525 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:28:26.847537 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:28:26.847548 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:28:26.847558 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:28:26.847569 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:28:26.847580 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:28:26.847591 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.710227 | |
| I0506 00:28:26.847602 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.0196078 | |
| I0506 00:28:26.847616 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 4.75247 (* 0.3 = 1.42574 loss) | |
| I0506 00:28:26.847630 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.5897 (* 0.3 = 0.476909 loss) | |
| I0506 00:28:26.847642 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 4.30272 (* 0.0272727 = 0.117347 loss) | |
| I0506 00:28:26.847656 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 4.10631 (* 0.0272727 = 0.11199 loss) | |
| I0506 00:28:26.847669 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 4.15888 (* 0.0272727 = 0.113424 loss) | |
| I0506 00:28:26.847698 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 4.01243 (* 0.0272727 = 0.10943 loss) | |
| I0506 00:28:26.847714 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 4.58324 (* 0.0272727 = 0.124997 loss) | |
| I0506 00:28:26.847728 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.64666 (* 0.0272727 = 0.0994544 loss) | |
| I0506 00:28:26.847741 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 3.07906 (* 0.0272727 = 0.0839743 loss) | |
| I0506 00:28:26.847754 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.50057 (* 0.0272727 = 0.0409247 loss) | |
| I0506 00:28:26.847769 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.05007 (* 0.0272727 = 0.0286382 loss) | |
| I0506 00:28:26.847781 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0978743 (* 0.0272727 = 0.0026693 loss) | |
| I0506 00:28:26.847795 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0688154 (* 0.0272727 = 0.00187678 loss) | |
| I0506 00:28:26.847808 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0788556 (* 0.0272727 = 0.00215061 loss) | |
| I0506 00:28:26.847822 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0471807 (* 0.0272727 = 0.00128675 loss) | |
| I0506 00:28:26.847836 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0561733 (* 0.0272727 = 0.001532 loss) | |
| I0506 00:28:26.847851 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0381294 (* 0.0272727 = 0.00103989 loss) | |
| I0506 00:28:26.847863 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0364693 (* 0.0272727 = 0.000994617 loss) | |
| I0506 00:28:26.847877 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0223451 (* 0.0272727 = 0.000609412 loss) | |
| I0506 00:28:26.847892 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0204029 (* 0.0272727 = 0.000556443 loss) | |
| I0506 00:28:26.847904 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0276189 (* 0.0272727 = 0.000753243 loss) | |
| I0506 00:28:26.847918 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0186221 (* 0.0272727 = 0.000507875 loss) | |
| I0506 00:28:26.847935 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0113354 (* 0.0272727 = 0.000309147 loss) | |
| I0506 00:28:26.847949 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0148253 (* 0.0272727 = 0.000404328 loss) | |
| I0506 00:28:26.847961 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0196078 | |
| I0506 00:28:26.847973 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:28:26.847985 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.125 | |
| I0506 00:28:26.847996 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:28:26.848007 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 00:28:26.848018 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.125 | |
| I0506 00:28:26.848029 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 00:28:26.848040 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 00:28:26.848052 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 00:28:26.848063 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 00:28:26.848074 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:28:26.848085 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:28:26.848096 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:28:26.848107 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:28:26.848119 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:28:26.848130 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:28:26.848137 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:28:26.848145 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:28:26.848166 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:28:26.848179 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:28:26.848191 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:28:26.848201 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:28:26.848212 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:28:26.848223 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.715909 | |
| I0506 00:28:26.848234 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.0784314 | |
| I0506 00:28:26.848248 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 4.56817 (* 1 = 4.56817 loss) | |
| I0506 00:28:26.848260 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.41079 (* 1 = 1.41079 loss) | |
| I0506 00:28:26.848274 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 4.14843 (* 0.0909091 = 0.37713 loss) | |
| I0506 00:28:26.848287 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.89591 (* 0.0909091 = 0.354174 loss) | |
| I0506 00:28:26.848300 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 4.32905 (* 0.0909091 = 0.39355 loss) | |
| I0506 00:28:26.848314 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 4.07549 (* 0.0909091 = 0.370499 loss) | |
| I0506 00:28:26.848326 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 4.20437 (* 0.0909091 = 0.382216 loss) | |
| I0506 00:28:26.848340 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 3.7577 (* 0.0909091 = 0.341609 loss) | |
| I0506 00:28:26.848352 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.89969 (* 0.0909091 = 0.263608 loss) | |
| I0506 00:28:26.848366 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.57 (* 0.0909091 = 0.142728 loss) | |
| I0506 00:28:26.848379 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.831506 (* 0.0909091 = 0.0755915 loss) | |
| I0506 00:28:26.848392 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.13764 (* 0.0909091 = 0.0125127 loss) | |
| I0506 00:28:26.848405 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.096592 (* 0.0909091 = 0.0087811 loss) | |
| I0506 00:28:26.848418 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.076536 (* 0.0909091 = 0.00695782 loss) | |
| I0506 00:28:26.848433 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0663221 (* 0.0909091 = 0.00602928 loss) | |
| I0506 00:28:26.848445 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0578812 (* 0.0909091 = 0.00526193 loss) | |
| I0506 00:28:26.848459 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0412424 (* 0.0909091 = 0.00374931 loss) | |
| I0506 00:28:26.848471 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0260153 (* 0.0909091 = 0.00236502 loss) | |
| I0506 00:28:26.848485 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0133953 (* 0.0909091 = 0.00121775 loss) | |
| I0506 00:28:26.848498 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0105016 (* 0.0909091 = 0.000954689 loss) | |
| I0506 00:28:26.848511 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0116866 (* 0.0909091 = 0.00106242 loss) | |
| I0506 00:28:26.848526 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00820997 (* 0.0909091 = 0.000746361 loss) | |
| I0506 00:28:26.848538 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00875113 (* 0.0909091 = 0.000795557 loss) | |
| I0506 00:28:26.848551 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00843555 (* 0.0909091 = 0.000766868 loss) | |
| I0506 00:28:26.848563 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:28:26.848573 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:28:26.848584 15760 solver.cpp:245] Train net output #149: total_confidence = 4.16044e-07 | |
| I0506 00:28:26.848597 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 4.35956e-06 | |
| I0506 00:28:26.848619 15760 sgd_solver.cpp:106] Iteration 2000, lr = 0.001 | |
| I0506 00:30:13.732159 15760 solver.cpp:229] Iteration 2500, loss = 12.7455 | |
| I0506 00:30:13.732319 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.025 | |
| I0506 00:30:13.732339 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:30:13.732353 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 00:30:13.732367 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:30:13.732378 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 00:30:13.732389 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.5 | |
| I0506 00:30:13.732401 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.625 | |
| I0506 00:30:13.732414 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.75 | |
| I0506 00:30:13.732425 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 00:30:13.732437 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 00:30:13.732450 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:30:13.732461 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:30:13.732472 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:30:13.732484 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:30:13.732496 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:30:13.732507 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:30:13.732518 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:30:13.732530 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:30:13.732542 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:30:13.732554 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:30:13.732565 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:30:13.732578 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:30:13.732589 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:30:13.732600 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.772727 | |
| I0506 00:30:13.732612 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.025 | |
| I0506 00:30:13.732628 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.97107 (* 0.3 = 1.19132 loss) | |
| I0506 00:30:13.732642 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.42762 (* 0.3 = 0.428285 loss) | |
| I0506 00:30:13.732656 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 4.16652 (* 0.0272727 = 0.113632 loss) | |
| I0506 00:30:13.732671 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 4.07344 (* 0.0272727 = 0.111094 loss) | |
| I0506 00:30:13.732684 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.84805 (* 0.0272727 = 0.104947 loss) | |
| I0506 00:30:13.732698 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.29731 (* 0.0272727 = 0.0899265 loss) | |
| I0506 00:30:13.732712 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.91242 (* 0.0272727 = 0.0794295 loss) | |
| I0506 00:30:13.732727 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.17943 (* 0.0272727 = 0.0594389 loss) | |
| I0506 00:30:13.732739 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.5637 (* 0.0272727 = 0.0426464 loss) | |
| I0506 00:30:13.732753 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.967264 (* 0.0272727 = 0.0263799 loss) | |
| I0506 00:30:13.732767 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.86543 (* 0.0272727 = 0.0236026 loss) | |
| I0506 00:30:13.732781 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.111245 (* 0.0272727 = 0.00303396 loss) | |
| I0506 00:30:13.732795 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.143507 (* 0.0272727 = 0.00391384 loss) | |
| I0506 00:30:13.732810 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.10044 (* 0.0272727 = 0.00273927 loss) | |
| I0506 00:30:13.732823 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0798597 (* 0.0272727 = 0.00217799 loss) | |
| I0506 00:30:13.732857 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0651934 (* 0.0272727 = 0.001778 loss) | |
| I0506 00:30:13.732872 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.056648 (* 0.0272727 = 0.00154495 loss) | |
| I0506 00:30:13.732890 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0550949 (* 0.0272727 = 0.00150259 loss) | |
| I0506 00:30:13.732905 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0502941 (* 0.0272727 = 0.00137166 loss) | |
| I0506 00:30:13.732919 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.040641 (* 0.0272727 = 0.00110839 loss) | |
| I0506 00:30:13.732933 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0217395 (* 0.0272727 = 0.000592896 loss) | |
| I0506 00:30:13.732947 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0229452 (* 0.0272727 = 0.000625779 loss) | |
| I0506 00:30:13.732961 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0396954 (* 0.0272727 = 0.0010826 loss) | |
| I0506 00:30:13.732976 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0391358 (* 0.0272727 = 0.00106734 loss) | |
| I0506 00:30:13.732988 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.025 | |
| I0506 00:30:13.733000 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:30:13.733012 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:30:13.733024 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.25 | |
| I0506 00:30:13.733036 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.375 | |
| I0506 00:30:13.733047 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.5 | |
| I0506 00:30:13.733059 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.625 | |
| I0506 00:30:13.733070 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 00:30:13.733083 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 00:30:13.733093 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 00:30:13.733104 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:30:13.733116 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:30:13.733149 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:30:13.733160 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:30:13.733172 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:30:13.733185 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:30:13.733192 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:30:13.733199 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:30:13.733212 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:30:13.733224 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:30:13.733237 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:30:13.733247 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:30:13.733258 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:30:13.733270 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.772727 | |
| I0506 00:30:13.733283 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.075 | |
| I0506 00:30:13.733296 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.91285 (* 0.3 = 1.17386 loss) | |
| I0506 00:30:13.733310 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.36105 (* 0.3 = 0.408314 loss) | |
| I0506 00:30:13.733326 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.87384 (* 0.0272727 = 0.10565 loss) | |
| I0506 00:30:13.733341 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.84855 (* 0.0272727 = 0.10496 loss) | |
| I0506 00:30:13.733355 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.67644 (* 0.0272727 = 0.100267 loss) | |
| I0506 00:30:13.733381 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.86126 (* 0.0272727 = 0.105307 loss) | |
| I0506 00:30:13.733397 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.75571 (* 0.0272727 = 0.0751558 loss) | |
| I0506 00:30:13.733410 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.17223 (* 0.0272727 = 0.0592426 loss) | |
| I0506 00:30:13.733423 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.5886 (* 0.0272727 = 0.0433255 loss) | |
| I0506 00:30:13.733438 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.995935 (* 0.0272727 = 0.0271619 loss) | |
| I0506 00:30:13.733450 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.981473 (* 0.0272727 = 0.0267674 loss) | |
| I0506 00:30:13.733465 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.154178 (* 0.0272727 = 0.00420485 loss) | |
| I0506 00:30:13.733479 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.141413 (* 0.0272727 = 0.00385673 loss) | |
| I0506 00:30:13.733492 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0947652 (* 0.0272727 = 0.0025845 loss) | |
| I0506 00:30:13.733506 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0992798 (* 0.0272727 = 0.00270763 loss) | |
| I0506 00:30:13.733520 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0811393 (* 0.0272727 = 0.00221289 loss) | |
| I0506 00:30:13.733535 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0978261 (* 0.0272727 = 0.00266798 loss) | |
| I0506 00:30:13.733547 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.048119 (* 0.0272727 = 0.00131234 loss) | |
| I0506 00:30:13.733561 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0602379 (* 0.0272727 = 0.00164285 loss) | |
| I0506 00:30:13.733574 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0339662 (* 0.0272727 = 0.000926352 loss) | |
| I0506 00:30:13.733588 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0462804 (* 0.0272727 = 0.00126219 loss) | |
| I0506 00:30:13.733603 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.064329 (* 0.0272727 = 0.00175443 loss) | |
| I0506 00:30:13.733615 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0476948 (* 0.0272727 = 0.00130077 loss) | |
| I0506 00:30:13.733629 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0361015 (* 0.0272727 = 0.000984586 loss) | |
| I0506 00:30:13.733641 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.05 | |
| I0506 00:30:13.733654 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 00:30:13.733665 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.125 | |
| I0506 00:30:13.733676 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:30:13.733687 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.375 | |
| I0506 00:30:13.733698 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.5 | |
| I0506 00:30:13.733711 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.625 | |
| I0506 00:30:13.733722 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 00:30:13.733733 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 00:30:13.733746 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 00:30:13.733757 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:30:13.733768 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:30:13.733779 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:30:13.733791 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:30:13.733803 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:30:13.733814 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:30:13.733825 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:30:13.733836 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:30:13.733857 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:30:13.733870 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:30:13.733881 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:30:13.733892 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:30:13.733903 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:30:13.733916 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.761364 | |
| I0506 00:30:13.733929 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.15 | |
| I0506 00:30:13.733943 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.80217 (* 1 = 3.80217 loss) | |
| I0506 00:30:13.733958 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.13843 (* 1 = 1.13843 loss) | |
| I0506 00:30:13.733970 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.66058 (* 0.0909091 = 0.33278 loss) | |
| I0506 00:30:13.733985 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.53179 (* 0.0909091 = 0.321072 loss) | |
| I0506 00:30:13.733999 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.69976 (* 0.0909091 = 0.336342 loss) | |
| I0506 00:30:13.734012 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.35831 (* 0.0909091 = 0.305301 loss) | |
| I0506 00:30:13.734025 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.85152 (* 0.0909091 = 0.259229 loss) | |
| I0506 00:30:13.734040 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.25952 (* 0.0909091 = 0.205411 loss) | |
| I0506 00:30:13.734052 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.50918 (* 0.0909091 = 0.137198 loss) | |
| I0506 00:30:13.734066 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.784802 (* 0.0909091 = 0.0713456 loss) | |
| I0506 00:30:13.734079 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.815925 (* 0.0909091 = 0.074175 loss) | |
| I0506 00:30:13.734094 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0512112 (* 0.0909091 = 0.00465557 loss) | |
| I0506 00:30:13.734107 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0463037 (* 0.0909091 = 0.00420943 loss) | |
| I0506 00:30:13.734122 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0283051 (* 0.0909091 = 0.00257319 loss) | |
| I0506 00:30:13.734135 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0227992 (* 0.0909091 = 0.00207265 loss) | |
| I0506 00:30:13.734148 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.020244 (* 0.0909091 = 0.00184036 loss) | |
| I0506 00:30:13.734163 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0116791 (* 0.0909091 = 0.00106173 loss) | |
| I0506 00:30:13.734175 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00587467 (* 0.0909091 = 0.000534061 loss) | |
| I0506 00:30:13.734189 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00324904 (* 0.0909091 = 0.000295368 loss) | |
| I0506 00:30:13.734202 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00193283 (* 0.0909091 = 0.000175712 loss) | |
| I0506 00:30:13.734217 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00204906 (* 0.0909091 = 0.000186279 loss) | |
| I0506 00:30:13.734230 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00177996 (* 0.0909091 = 0.000161815 loss) | |
| I0506 00:30:13.734244 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00160649 (* 0.0909091 = 0.000146044 loss) | |
| I0506 00:30:13.734258 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00166082 (* 0.0909091 = 0.000150984 loss) | |
| I0506 00:30:13.734271 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:30:13.734282 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:30:13.734292 15760 solver.cpp:245] Train net output #149: total_confidence = 2.48768e-07 | |
| I0506 00:30:13.734314 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 2.7664e-06 | |
| I0506 00:30:13.734329 15760 sgd_solver.cpp:106] Iteration 2500, lr = 0.001 | |
| I0506 00:32:00.868652 15760 solver.cpp:229] Iteration 3000, loss = 12.6213 | |
| I0506 00:32:00.868798 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0506 00:32:00.868818 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:32:00.868830 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:32:00.868842 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:32:00.868854 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 00:32:00.868866 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 00:32:00.868881 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 00:32:00.868893 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 00:32:00.868906 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 00:32:00.868918 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 00:32:00.868929 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 00:32:00.868942 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 00:32:00.868954 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 00:32:00.868966 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:32:00.868978 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:32:00.868990 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:32:00.869002 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:32:00.869014 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:32:00.869026 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:32:00.869038 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:32:00.869050 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:32:00.869061 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:32:00.869073 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:32:00.869084 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.710227 | |
| I0506 00:32:00.869096 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.0980392 | |
| I0506 00:32:00.869113 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.84904 (* 0.3 = 1.15471 loss) | |
| I0506 00:32:00.869143 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.31798 (* 0.3 = 0.395393 loss) | |
| I0506 00:32:00.869158 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.90403 (* 0.0272727 = 0.106474 loss) | |
| I0506 00:32:00.869171 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.59418 (* 0.0272727 = 0.0980231 loss) | |
| I0506 00:32:00.869185 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.19533 (* 0.0272727 = 0.114418 loss) | |
| I0506 00:32:00.869199 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.87634 (* 0.0272727 = 0.105718 loss) | |
| I0506 00:32:00.869213 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.12591 (* 0.0272727 = 0.085252 loss) | |
| I0506 00:32:00.869227 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.24565 (* 0.0272727 = 0.0885178 loss) | |
| I0506 00:32:00.869241 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.70176 (* 0.0272727 = 0.0736844 loss) | |
| I0506 00:32:00.869254 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.43939 (* 0.0272727 = 0.0392562 loss) | |
| I0506 00:32:00.869268 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.15144 (* 0.0272727 = 0.0314028 loss) | |
| I0506 00:32:00.869282 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.93976 (* 0.0272727 = 0.0256298 loss) | |
| I0506 00:32:00.869295 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.962259 (* 0.0272727 = 0.0262434 loss) | |
| I0506 00:32:00.869309 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 1.31591 (* 0.0272727 = 0.0358883 loss) | |
| I0506 00:32:00.869323 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.120271 (* 0.0272727 = 0.00328011 loss) | |
| I0506 00:32:00.869357 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0549085 (* 0.0272727 = 0.00149751 loss) | |
| I0506 00:32:00.869374 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0327472 (* 0.0272727 = 0.000893105 loss) | |
| I0506 00:32:00.869387 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0485638 (* 0.0272727 = 0.00132447 loss) | |
| I0506 00:32:00.869401 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0356977 (* 0.0272727 = 0.000973573 loss) | |
| I0506 00:32:00.869415 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0356044 (* 0.0272727 = 0.000971029 loss) | |
| I0506 00:32:00.869429 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0351435 (* 0.0272727 = 0.000958459 loss) | |
| I0506 00:32:00.869443 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.025078 (* 0.0272727 = 0.000683945 loss) | |
| I0506 00:32:00.869457 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0217307 (* 0.0272727 = 0.000592656 loss) | |
| I0506 00:32:00.869472 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0540666 (* 0.0272727 = 0.00147454 loss) | |
| I0506 00:32:00.869484 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0196078 | |
| I0506 00:32:00.869496 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 00:32:00.869509 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:32:00.869520 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 00:32:00.869532 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 00:32:00.869544 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 00:32:00.869556 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 00:32:00.869568 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 00:32:00.869580 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 00:32:00.869591 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 00:32:00.869603 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 00:32:00.869616 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 00:32:00.869626 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 00:32:00.869638 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:32:00.869649 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:32:00.869662 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:32:00.869673 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:32:00.869683 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:32:00.869695 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:32:00.869706 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:32:00.869717 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:32:00.869729 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:32:00.869740 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:32:00.869752 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.715909 | |
| I0506 00:32:00.869763 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.0588235 | |
| I0506 00:32:00.869777 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 4.02809 (* 0.3 = 1.20843 loss) | |
| I0506 00:32:00.869791 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.31618 (* 0.3 = 0.394853 loss) | |
| I0506 00:32:00.869806 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.59261 (* 0.0272727 = 0.0979802 loss) | |
| I0506 00:32:00.869823 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.82711 (* 0.0272727 = 0.104376 loss) | |
| I0506 00:32:00.869849 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.79382 (* 0.0272727 = 0.103468 loss) | |
| I0506 00:32:00.869863 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.50746 (* 0.0272727 = 0.0956581 loss) | |
| I0506 00:32:00.869877 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.43224 (* 0.0272727 = 0.0936066 loss) | |
| I0506 00:32:00.869891 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.05017 (* 0.0272727 = 0.0831865 loss) | |
| I0506 00:32:00.869905 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.85447 (* 0.0272727 = 0.0778493 loss) | |
| I0506 00:32:00.869918 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.58285 (* 0.0272727 = 0.0431688 loss) | |
| I0506 00:32:00.869935 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.868676 (* 0.0272727 = 0.0236912 loss) | |
| I0506 00:32:00.869949 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.973399 (* 0.0272727 = 0.0265472 loss) | |
| I0506 00:32:00.869963 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.960614 (* 0.0272727 = 0.0261986 loss) | |
| I0506 00:32:00.869977 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.957529 (* 0.0272727 = 0.0261144 loss) | |
| I0506 00:32:00.869992 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0834575 (* 0.0272727 = 0.00227611 loss) | |
| I0506 00:32:00.870004 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.104621 (* 0.0272727 = 0.00285331 loss) | |
| I0506 00:32:00.870018 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.074547 (* 0.0272727 = 0.0020331 loss) | |
| I0506 00:32:00.870033 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0563175 (* 0.0272727 = 0.00153593 loss) | |
| I0506 00:32:00.870048 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0383428 (* 0.0272727 = 0.00104571 loss) | |
| I0506 00:32:00.870061 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0426646 (* 0.0272727 = 0.00116358 loss) | |
| I0506 00:32:00.870074 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0521135 (* 0.0272727 = 0.00142128 loss) | |
| I0506 00:32:00.870088 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0372527 (* 0.0272727 = 0.00101598 loss) | |
| I0506 00:32:00.870102 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0429663 (* 0.0272727 = 0.00117181 loss) | |
| I0506 00:32:00.870117 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0331418 (* 0.0272727 = 0.000903868 loss) | |
| I0506 00:32:00.870129 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0 | |
| I0506 00:32:00.870141 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:32:00.870152 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:32:00.870163 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:32:00.870175 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 00:32:00.870187 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.375 | |
| I0506 00:32:00.870198 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 00:32:00.870209 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 00:32:00.870221 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 00:32:00.870232 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 00:32:00.870244 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 00:32:00.870255 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 00:32:00.870267 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 00:32:00.870280 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:32:00.870290 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:32:00.870301 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:32:00.870313 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:32:00.870334 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:32:00.870347 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:32:00.870358 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:32:00.870370 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:32:00.870381 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:32:00.870393 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:32:00.870404 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.704545 | |
| I0506 00:32:00.870416 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.176471 | |
| I0506 00:32:00.870430 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.72663 (* 1 = 3.72663 loss) | |
| I0506 00:32:00.870443 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.24754 (* 1 = 1.24754 loss) | |
| I0506 00:32:00.870457 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.58997 (* 0.0909091 = 0.326361 loss) | |
| I0506 00:32:00.870471 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.62278 (* 0.0909091 = 0.329344 loss) | |
| I0506 00:32:00.870484 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.68129 (* 0.0909091 = 0.334663 loss) | |
| I0506 00:32:00.870498 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.34572 (* 0.0909091 = 0.304156 loss) | |
| I0506 00:32:00.870512 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.89505 (* 0.0909091 = 0.263186 loss) | |
| I0506 00:32:00.870525 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.81631 (* 0.0909091 = 0.256028 loss) | |
| I0506 00:32:00.870538 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.56669 (* 0.0909091 = 0.233336 loss) | |
| I0506 00:32:00.870553 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.23629 (* 0.0909091 = 0.11239 loss) | |
| I0506 00:32:00.870565 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.925374 (* 0.0909091 = 0.0841249 loss) | |
| I0506 00:32:00.870579 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.757541 (* 0.0909091 = 0.0688674 loss) | |
| I0506 00:32:00.870592 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.683039 (* 0.0909091 = 0.0620945 loss) | |
| I0506 00:32:00.870606 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.786719 (* 0.0909091 = 0.0715199 loss) | |
| I0506 00:32:00.870620 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0839267 (* 0.0909091 = 0.0076297 loss) | |
| I0506 00:32:00.870633 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0585565 (* 0.0909091 = 0.00532331 loss) | |
| I0506 00:32:00.870647 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0358712 (* 0.0909091 = 0.00326102 loss) | |
| I0506 00:32:00.870661 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.028012 (* 0.0909091 = 0.00254654 loss) | |
| I0506 00:32:00.870674 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0201999 (* 0.0909091 = 0.00183635 loss) | |
| I0506 00:32:00.870688 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0173667 (* 0.0909091 = 0.00157879 loss) | |
| I0506 00:32:00.870702 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0127893 (* 0.0909091 = 0.00116266 loss) | |
| I0506 00:32:00.870715 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.0111715 (* 0.0909091 = 0.00101559 loss) | |
| I0506 00:32:00.870729 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.013716 (* 0.0909091 = 0.00124691 loss) | |
| I0506 00:32:00.870743 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00916522 (* 0.0909091 = 0.000833202 loss) | |
| I0506 00:32:00.870755 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:32:00.870766 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:32:00.870777 15760 solver.cpp:245] Train net output #149: total_confidence = 2.28998e-07 | |
| I0506 00:32:00.870798 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 7.81395e-06 | |
| I0506 00:32:00.870813 15760 sgd_solver.cpp:106] Iteration 3000, lr = 0.001 | |
| I0506 00:33:47.828197 15760 solver.cpp:229] Iteration 3500, loss = 12.3009 | |
| I0506 00:33:47.828315 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0506 00:33:47.828335 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:33:47.828346 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:33:47.828358 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:33:47.828371 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 00:33:47.828382 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 00:33:47.828393 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.625 | |
| I0506 00:33:47.828405 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.75 | |
| I0506 00:33:47.828418 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 00:33:47.828429 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 00:33:47.828441 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 00:33:47.828454 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 00:33:47.828464 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 00:33:47.828476 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0.875 | |
| I0506 00:33:47.828487 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:33:47.828500 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:33:47.828512 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:33:47.828534 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:33:47.828548 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:33:47.828559 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:33:47.828570 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:33:47.828583 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:33:47.828593 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:33:47.828604 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.727273 | |
| I0506 00:33:47.828616 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.0625 | |
| I0506 00:33:47.828632 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 4.03322 (* 0.3 = 1.20997 loss) | |
| I0506 00:33:47.828646 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.43338 (* 0.3 = 0.430013 loss) | |
| I0506 00:33:47.828660 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.57503 (* 0.0272727 = 0.0975008 loss) | |
| I0506 00:33:47.828675 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.82702 (* 0.0272727 = 0.104373 loss) | |
| I0506 00:33:47.828687 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.82907 (* 0.0272727 = 0.104429 loss) | |
| I0506 00:33:47.828701 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.85485 (* 0.0272727 = 0.105132 loss) | |
| I0506 00:33:47.828714 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.05619 (* 0.0272727 = 0.0833507 loss) | |
| I0506 00:33:47.828728 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.16594 (* 0.0272727 = 0.059071 loss) | |
| I0506 00:33:47.828742 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.21279 (* 0.0272727 = 0.0330761 loss) | |
| I0506 00:33:47.828755 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.807349 (* 0.0272727 = 0.0220186 loss) | |
| I0506 00:33:47.828768 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.833934 (* 0.0272727 = 0.0227437 loss) | |
| I0506 00:33:47.828783 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.833415 (* 0.0272727 = 0.0227295 loss) | |
| I0506 00:33:47.828796 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.701894 (* 0.0272727 = 0.0191426 loss) | |
| I0506 00:33:47.828810 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.905934 (* 0.0272727 = 0.0247073 loss) | |
| I0506 00:33:47.828824 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.739747 (* 0.0272727 = 0.0201749 loss) | |
| I0506 00:33:47.828860 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0809727 (* 0.0272727 = 0.00220835 loss) | |
| I0506 00:33:47.828876 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0453824 (* 0.0272727 = 0.0012377 loss) | |
| I0506 00:33:47.828889 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0426141 (* 0.0272727 = 0.0011622 loss) | |
| I0506 00:33:47.828903 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0601587 (* 0.0272727 = 0.00164069 loss) | |
| I0506 00:33:47.828919 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0299801 (* 0.0272727 = 0.00081764 loss) | |
| I0506 00:33:47.828943 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0285102 (* 0.0272727 = 0.00077755 loss) | |
| I0506 00:33:47.828958 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0315874 (* 0.0272727 = 0.000861475 loss) | |
| I0506 00:33:47.828971 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0341306 (* 0.0272727 = 0.000930835 loss) | |
| I0506 00:33:47.828985 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0356923 (* 0.0272727 = 0.000973427 loss) | |
| I0506 00:33:47.828997 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:33:47.829010 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 00:33:47.829020 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:33:47.829032 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.25 | |
| I0506 00:33:47.829043 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 00:33:47.829054 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 00:33:47.829066 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.625 | |
| I0506 00:33:47.829077 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 00:33:47.829089 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 00:33:47.829114 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 00:33:47.829130 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 00:33:47.829143 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 00:33:47.829154 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 00:33:47.829164 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0.875 | |
| I0506 00:33:47.829176 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:33:47.829187 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:33:47.829198 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:33:47.829210 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:33:47.829221 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:33:47.829232 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:33:47.829243 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:33:47.829254 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:33:47.829265 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:33:47.829277 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.727273 | |
| I0506 00:33:47.829288 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.104167 | |
| I0506 00:33:47.829301 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 4.11886 (* 0.3 = 1.23566 loss) | |
| I0506 00:33:47.829315 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.46064 (* 0.3 = 0.438191 loss) | |
| I0506 00:33:47.829329 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.84576 (* 0.0272727 = 0.104884 loss) | |
| I0506 00:33:47.829342 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.81715 (* 0.0272727 = 0.104104 loss) | |
| I0506 00:33:47.829368 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.40583 (* 0.0272727 = 0.0928861 loss) | |
| I0506 00:33:47.829383 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.81701 (* 0.0272727 = 0.1041 loss) | |
| I0506 00:33:47.829397 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.88249 (* 0.0272727 = 0.0786133 loss) | |
| I0506 00:33:47.829411 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.29229 (* 0.0272727 = 0.0625169 loss) | |
| I0506 00:33:47.829424 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.42444 (* 0.0272727 = 0.0388483 loss) | |
| I0506 00:33:47.829437 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.704689 (* 0.0272727 = 0.0192188 loss) | |
| I0506 00:33:47.829450 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.699383 (* 0.0272727 = 0.0190741 loss) | |
| I0506 00:33:47.829464 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.683791 (* 0.0272727 = 0.0186488 loss) | |
| I0506 00:33:47.829478 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.818273 (* 0.0272727 = 0.0223165 loss) | |
| I0506 00:33:47.829495 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.632378 (* 0.0272727 = 0.0172467 loss) | |
| I0506 00:33:47.829522 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.661038 (* 0.0272727 = 0.0180283 loss) | |
| I0506 00:33:47.829541 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0930086 (* 0.0272727 = 0.0025366 loss) | |
| I0506 00:33:47.829556 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0516917 (* 0.0272727 = 0.00140977 loss) | |
| I0506 00:33:47.829571 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0634086 (* 0.0272727 = 0.00172933 loss) | |
| I0506 00:33:47.829584 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0313486 (* 0.0272727 = 0.000854961 loss) | |
| I0506 00:33:47.829597 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0220616 (* 0.0272727 = 0.000601679 loss) | |
| I0506 00:33:47.829612 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.046253 (* 0.0272727 = 0.00126145 loss) | |
| I0506 00:33:47.829625 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0208509 (* 0.0272727 = 0.00056866 loss) | |
| I0506 00:33:47.829639 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0347184 (* 0.0272727 = 0.000946866 loss) | |
| I0506 00:33:47.829655 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0293512 (* 0.0272727 = 0.000800487 loss) | |
| I0506 00:33:47.829679 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0208333 | |
| I0506 00:33:47.829695 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:33:47.829707 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:33:47.829718 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:33:47.829730 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0 | |
| I0506 00:33:47.829741 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.375 | |
| I0506 00:33:47.829752 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.625 | |
| I0506 00:33:47.829764 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 00:33:47.829776 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 00:33:47.829787 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 00:33:47.829798 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 00:33:47.829809 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 00:33:47.829820 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 00:33:47.829833 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0.875 | |
| I0506 00:33:47.829843 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:33:47.829855 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:33:47.829866 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:33:47.829887 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:33:47.829900 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:33:47.829916 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:33:47.829926 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:33:47.829937 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:33:47.829949 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:33:47.829960 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.710227 | |
| I0506 00:33:47.829972 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.125 | |
| I0506 00:33:47.829984 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.87512 (* 1 = 3.87512 loss) | |
| I0506 00:33:47.829998 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.36801 (* 1 = 1.36801 loss) | |
| I0506 00:33:47.830011 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.68714 (* 0.0909091 = 0.335195 loss) | |
| I0506 00:33:47.830025 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.4805 (* 0.0909091 = 0.316409 loss) | |
| I0506 00:33:47.830039 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.82876 (* 0.0909091 = 0.348069 loss) | |
| I0506 00:33:47.830051 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.76502 (* 0.0909091 = 0.342275 loss) | |
| I0506 00:33:47.830065 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.01714 (* 0.0909091 = 0.274285 loss) | |
| I0506 00:33:47.830078 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.35207 (* 0.0909091 = 0.213824 loss) | |
| I0506 00:33:47.830091 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.40258 (* 0.0909091 = 0.127507 loss) | |
| I0506 00:33:47.830106 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.546795 (* 0.0909091 = 0.0497087 loss) | |
| I0506 00:33:47.830118 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.623232 (* 0.0909091 = 0.0566575 loss) | |
| I0506 00:33:47.830132 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.593915 (* 0.0909091 = 0.0539922 loss) | |
| I0506 00:33:47.830145 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.672493 (* 0.0909091 = 0.0611357 loss) | |
| I0506 00:33:47.830163 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.576907 (* 0.0909091 = 0.0524461 loss) | |
| I0506 00:33:47.830178 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.684968 (* 0.0909091 = 0.0622699 loss) | |
| I0506 00:33:47.830188 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0262888 (* 0.0909091 = 0.00238989 loss) | |
| I0506 00:33:47.830204 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0241652 (* 0.0909091 = 0.00219683 loss) | |
| I0506 00:33:47.830224 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0161979 (* 0.0909091 = 0.00147254 loss) | |
| I0506 00:33:47.830245 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00708229 (* 0.0909091 = 0.000643845 loss) | |
| I0506 00:33:47.830258 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00532999 (* 0.0909091 = 0.000484545 loss) | |
| I0506 00:33:47.830271 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00583906 (* 0.0909091 = 0.000530824 loss) | |
| I0506 00:33:47.830286 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00496013 (* 0.0909091 = 0.000450921 loss) | |
| I0506 00:33:47.830299 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00555014 (* 0.0909091 = 0.000504558 loss) | |
| I0506 00:33:47.830312 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00337735 (* 0.0909091 = 0.000307032 loss) | |
| I0506 00:33:47.830324 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:33:47.830335 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:33:47.830346 15760 solver.cpp:245] Train net output #149: total_confidence = 8.86271e-08 | |
| I0506 00:33:47.830368 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 4.65825e-06 | |
| I0506 00:33:47.830382 15760 sgd_solver.cpp:106] Iteration 3500, lr = 0.001 | |
| I0506 00:35:34.941033 15760 solver.cpp:229] Iteration 4000, loss = 12.254 | |
| I0506 00:35:34.941159 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0506 00:35:34.941176 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:35:34.941190 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 00:35:34.941201 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 00:35:34.941213 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 00:35:34.941226 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.5 | |
| I0506 00:35:34.941236 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.625 | |
| I0506 00:35:34.941248 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.875 | |
| I0506 00:35:34.941260 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 1 | |
| I0506 00:35:34.941275 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 00:35:34.941287 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:35:34.941299 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:35:34.941310 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:35:34.941321 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:35:34.941334 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:35:34.941344 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:35:34.941355 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:35:34.941367 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:35:34.941378 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:35:34.941390 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:35:34.941401 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:35:34.941412 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:35:34.941423 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:35:34.941434 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.778409 | |
| I0506 00:35:34.941447 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.0769231 | |
| I0506 00:35:34.941470 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 4.07275 (* 0.3 = 1.22183 loss) | |
| I0506 00:35:34.941485 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.17727 (* 0.3 = 0.353182 loss) | |
| I0506 00:35:34.941499 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 4.41745 (* 0.0272727 = 0.120476 loss) | |
| I0506 00:35:34.941514 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.74258 (* 0.0272727 = 0.10207 loss) | |
| I0506 00:35:34.941526 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.63972 (* 0.0272727 = 0.099265 loss) | |
| I0506 00:35:34.941540 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 4.2451 (* 0.0272727 = 0.115775 loss) | |
| I0506 00:35:34.941553 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.76502 (* 0.0272727 = 0.0754097 loss) | |
| I0506 00:35:34.941567 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.1064 (* 0.0272727 = 0.0574473 loss) | |
| I0506 00:35:34.941581 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 0.775062 (* 0.0272727 = 0.0211381 loss) | |
| I0506 00:35:34.941594 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.209599 (* 0.0272727 = 0.00571634 loss) | |
| I0506 00:35:34.941608 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.178712 (* 0.0272727 = 0.00487397 loss) | |
| I0506 00:35:34.941622 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0996949 (* 0.0272727 = 0.00271895 loss) | |
| I0506 00:35:34.941635 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0834083 (* 0.0272727 = 0.00227477 loss) | |
| I0506 00:35:34.941649 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0612707 (* 0.0272727 = 0.00167102 loss) | |
| I0506 00:35:34.941663 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0505425 (* 0.0272727 = 0.00137843 loss) | |
| I0506 00:35:34.941695 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0714976 (* 0.0272727 = 0.00194993 loss) | |
| I0506 00:35:34.941710 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0460242 (* 0.0272727 = 0.00125521 loss) | |
| I0506 00:35:34.941725 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0419106 (* 0.0272727 = 0.00114301 loss) | |
| I0506 00:35:34.941738 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0173208 (* 0.0272727 = 0.000472385 loss) | |
| I0506 00:35:34.941751 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0201159 (* 0.0272727 = 0.000548615 loss) | |
| I0506 00:35:34.941766 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0226381 (* 0.0272727 = 0.000617404 loss) | |
| I0506 00:35:34.941779 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0135586 (* 0.0272727 = 0.00036978 loss) | |
| I0506 00:35:34.941793 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0166058 (* 0.0272727 = 0.000452886 loss) | |
| I0506 00:35:34.941807 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0187883 (* 0.0272727 = 0.000512407 loss) | |
| I0506 00:35:34.941818 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:35:34.941830 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:35:34.941841 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:35:34.941853 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:35:34.941864 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 00:35:34.941874 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.5 | |
| I0506 00:35:34.941890 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.625 | |
| I0506 00:35:34.941901 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.875 | |
| I0506 00:35:34.941913 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 1 | |
| I0506 00:35:34.941925 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 00:35:34.941936 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:35:34.941946 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:35:34.941958 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:35:34.941965 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:35:34.941972 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:35:34.941984 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:35:34.941997 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:35:34.942008 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:35:34.942018 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:35:34.942035 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:35:34.942047 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:35:34.942059 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:35:34.942070 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:35:34.942080 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.778409 | |
| I0506 00:35:34.942092 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.128205 | |
| I0506 00:35:34.942106 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.99793 (* 0.3 = 1.19938 loss) | |
| I0506 00:35:34.942119 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.22247 (* 0.3 = 0.366741 loss) | |
| I0506 00:35:34.942133 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 4.13107 (* 0.0272727 = 0.112666 loss) | |
| I0506 00:35:34.942147 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.48123 (* 0.0272727 = 0.0949426 loss) | |
| I0506 00:35:34.942160 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 4.18514 (* 0.0272727 = 0.11414 loss) | |
| I0506 00:35:34.942185 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.84139 (* 0.0272727 = 0.104765 loss) | |
| I0506 00:35:34.942200 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.09699 (* 0.0272727 = 0.0844634 loss) | |
| I0506 00:35:34.942214 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.26481 (* 0.0272727 = 0.0617675 loss) | |
| I0506 00:35:34.942227 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 0.837699 (* 0.0272727 = 0.0228463 loss) | |
| I0506 00:35:34.942241 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.25565 (* 0.0272727 = 0.00697226 loss) | |
| I0506 00:35:34.942255 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.159062 (* 0.0272727 = 0.00433806 loss) | |
| I0506 00:35:34.942268 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.114289 (* 0.0272727 = 0.00311697 loss) | |
| I0506 00:35:34.942282 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.14157 (* 0.0272727 = 0.00386101 loss) | |
| I0506 00:35:34.942296 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.122838 (* 0.0272727 = 0.00335011 loss) | |
| I0506 00:35:34.942309 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.114564 (* 0.0272727 = 0.00312448 loss) | |
| I0506 00:35:34.942327 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.118745 (* 0.0272727 = 0.00323849 loss) | |
| I0506 00:35:34.942342 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.086947 (* 0.0272727 = 0.00237128 loss) | |
| I0506 00:35:34.942355 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0545653 (* 0.0272727 = 0.00148814 loss) | |
| I0506 00:35:34.942369 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0568515 (* 0.0272727 = 0.00155049 loss) | |
| I0506 00:35:34.942383 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0425192 (* 0.0272727 = 0.00115961 loss) | |
| I0506 00:35:34.942395 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0560162 (* 0.0272727 = 0.00152771 loss) | |
| I0506 00:35:34.942409 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0476825 (* 0.0272727 = 0.00130043 loss) | |
| I0506 00:35:34.942422 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0433958 (* 0.0272727 = 0.00118352 loss) | |
| I0506 00:35:34.942436 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0276074 (* 0.0272727 = 0.000752928 loss) | |
| I0506 00:35:34.942447 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.128205 | |
| I0506 00:35:34.942459 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.25 | |
| I0506 00:35:34.942471 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:35:34.942482 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 00:35:34.942493 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 00:35:34.942504 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.5 | |
| I0506 00:35:34.942515 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.625 | |
| I0506 00:35:34.942528 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.875 | |
| I0506 00:35:34.942538 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 1 | |
| I0506 00:35:34.942549 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 00:35:34.942560 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:35:34.942571 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:35:34.942582 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:35:34.942594 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:35:34.942605 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:35:34.942616 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:35:34.942627 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:35:34.942637 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:35:34.942659 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:35:34.942672 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:35:34.942683 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:35:34.942694 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:35:34.942705 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:35:34.942716 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.806818 | |
| I0506 00:35:34.942728 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.230769 | |
| I0506 00:35:34.942741 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.6056 (* 1 = 3.6056 loss) | |
| I0506 00:35:34.942755 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.935333 (* 1 = 0.935333 loss) | |
| I0506 00:35:34.942769 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 4.11263 (* 0.0909091 = 0.373875 loss) | |
| I0506 00:35:34.942782 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.42022 (* 0.0909091 = 0.310929 loss) | |
| I0506 00:35:34.942795 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.79834 (* 0.0909091 = 0.345304 loss) | |
| I0506 00:35:34.942808 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.88476 (* 0.0909091 = 0.35316 loss) | |
| I0506 00:35:34.942822 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.7687 (* 0.0909091 = 0.2517 loss) | |
| I0506 00:35:34.942836 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.07317 (* 0.0909091 = 0.18847 loss) | |
| I0506 00:35:34.942848 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 0.757425 (* 0.0909091 = 0.0688568 loss) | |
| I0506 00:35:34.942862 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.193482 (* 0.0909091 = 0.0175893 loss) | |
| I0506 00:35:34.942876 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.111617 (* 0.0909091 = 0.010147 loss) | |
| I0506 00:35:34.942889 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.117132 (* 0.0909091 = 0.0106483 loss) | |
| I0506 00:35:34.942903 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0791824 (* 0.0909091 = 0.0071984 loss) | |
| I0506 00:35:34.942916 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0581582 (* 0.0909091 = 0.00528711 loss) | |
| I0506 00:35:34.942930 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0470163 (* 0.0909091 = 0.00427421 loss) | |
| I0506 00:35:34.942947 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.044675 (* 0.0909091 = 0.00406136 loss) | |
| I0506 00:35:34.942961 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0306138 (* 0.0909091 = 0.00278307 loss) | |
| I0506 00:35:34.942975 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0135754 (* 0.0909091 = 0.00123413 loss) | |
| I0506 00:35:34.942988 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0104848 (* 0.0909091 = 0.000953165 loss) | |
| I0506 00:35:34.943002 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00659729 (* 0.0909091 = 0.000599753 loss) | |
| I0506 00:35:34.943016 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00546778 (* 0.0909091 = 0.000497071 loss) | |
| I0506 00:35:34.943029 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00344047 (* 0.0909091 = 0.00031277 loss) | |
| I0506 00:35:34.943042 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00277346 (* 0.0909091 = 0.000252133 loss) | |
| I0506 00:35:34.943056 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.0026065 (* 0.0909091 = 0.000236955 loss) | |
| I0506 00:35:34.943068 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:35:34.943079 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:35:34.943090 15760 solver.cpp:245] Train net output #149: total_confidence = 1.56474e-05 | |
| I0506 00:35:34.943101 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 3.18569e-05 | |
| I0506 00:35:34.943123 15760 sgd_solver.cpp:106] Iteration 4000, lr = 0.001 | |
| I0506 00:37:22.028241 15760 solver.cpp:229] Iteration 4500, loss = 12.0236 | |
| I0506 00:37:22.028367 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0925926 | |
| I0506 00:37:22.028388 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:37:22.028400 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:37:22.028411 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:37:22.028424 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 00:37:22.028434 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 00:37:22.028446 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.125 | |
| I0506 00:37:22.028458 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.375 | |
| I0506 00:37:22.028470 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.5 | |
| I0506 00:37:22.028481 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 00:37:22.028493 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:37:22.028504 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:37:22.028517 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:37:22.028527 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:37:22.028538 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:37:22.028556 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:37:22.028574 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:37:22.028587 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:37:22.028599 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:37:22.028610 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:37:22.028621 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:37:22.028632 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:37:22.028643 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:37:22.028656 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.715909 | |
| I0506 00:37:22.028666 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.222222 | |
| I0506 00:37:22.028682 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.59925 (* 0.3 = 1.07978 loss) | |
| I0506 00:37:22.028697 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.33753 (* 0.3 = 0.40126 loss) | |
| I0506 00:37:22.028710 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.74171 (* 0.0272727 = 0.102047 loss) | |
| I0506 00:37:22.028724 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.39782 (* 0.0272727 = 0.0926677 loss) | |
| I0506 00:37:22.028738 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.8293 (* 0.0272727 = 0.104435 loss) | |
| I0506 00:37:22.028751 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.63609 (* 0.0272727 = 0.099166 loss) | |
| I0506 00:37:22.028765 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.85098 (* 0.0272727 = 0.105027 loss) | |
| I0506 00:37:22.028779 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.39209 (* 0.0272727 = 0.0925116 loss) | |
| I0506 00:37:22.028792 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 3.30596 (* 0.0272727 = 0.0901625 loss) | |
| I0506 00:37:22.028806 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 2.7968 (* 0.0272727 = 0.0762763 loss) | |
| I0506 00:37:22.028821 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.104119 (* 0.0272727 = 0.00283961 loss) | |
| I0506 00:37:22.028836 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0947401 (* 0.0272727 = 0.00258382 loss) | |
| I0506 00:37:22.028858 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.110597 (* 0.0272727 = 0.00301627 loss) | |
| I0506 00:37:22.028877 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0599865 (* 0.0272727 = 0.00163599 loss) | |
| I0506 00:37:22.028893 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0373423 (* 0.0272727 = 0.00101843 loss) | |
| I0506 00:37:22.028924 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.027426 (* 0.0272727 = 0.000747981 loss) | |
| I0506 00:37:22.028940 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0323856 (* 0.0272727 = 0.000883242 loss) | |
| I0506 00:37:22.028954 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0220958 (* 0.0272727 = 0.000602612 loss) | |
| I0506 00:37:22.028969 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0212059 (* 0.0272727 = 0.000578342 loss) | |
| I0506 00:37:22.028982 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0129363 (* 0.0272727 = 0.000352809 loss) | |
| I0506 00:37:22.028995 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0120446 (* 0.0272727 = 0.00032849 loss) | |
| I0506 00:37:22.029009 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0125592 (* 0.0272727 = 0.000342525 loss) | |
| I0506 00:37:22.029023 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0154013 (* 0.0272727 = 0.000420037 loss) | |
| I0506 00:37:22.029037 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0141938 (* 0.0272727 = 0.000387104 loss) | |
| I0506 00:37:22.029049 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.037037 | |
| I0506 00:37:22.029062 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:37:22.029073 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:37:22.029084 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.25 | |
| I0506 00:37:22.029096 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 00:37:22.029108 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.25 | |
| I0506 00:37:22.029134 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.125 | |
| I0506 00:37:22.029148 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.375 | |
| I0506 00:37:22.029160 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.5 | |
| I0506 00:37:22.029171 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 00:37:22.029182 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:37:22.029194 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:37:22.029206 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:37:22.029217 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:37:22.029227 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:37:22.029238 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:37:22.029249 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:37:22.029260 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:37:22.029271 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:37:22.029283 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:37:22.029294 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:37:22.029305 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:37:22.029316 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:37:22.029328 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.698864 | |
| I0506 00:37:22.029340 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.203704 | |
| I0506 00:37:22.029353 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.69776 (* 0.3 = 1.10933 loss) | |
| I0506 00:37:22.029367 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.49093 (* 0.3 = 0.447278 loss) | |
| I0506 00:37:22.029384 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.51469 (* 0.0272727 = 0.0958551 loss) | |
| I0506 00:37:22.029399 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.58438 (* 0.0272727 = 0.0977559 loss) | |
| I0506 00:37:22.029413 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.37438 (* 0.0272727 = 0.0920286 loss) | |
| I0506 00:37:22.029439 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.45409 (* 0.0272727 = 0.0942025 loss) | |
| I0506 00:37:22.029454 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.2429 (* 0.0272727 = 0.0884427 loss) | |
| I0506 00:37:22.029469 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.72559 (* 0.0272727 = 0.101607 loss) | |
| I0506 00:37:22.029481 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 3.43158 (* 0.0272727 = 0.0935885 loss) | |
| I0506 00:37:22.029495 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 2.95689 (* 0.0272727 = 0.0806426 loss) | |
| I0506 00:37:22.029508 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.195549 (* 0.0272727 = 0.00533315 loss) | |
| I0506 00:37:22.029522 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.159679 (* 0.0272727 = 0.00435489 loss) | |
| I0506 00:37:22.029536 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.220008 (* 0.0272727 = 0.00600021 loss) | |
| I0506 00:37:22.029549 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.116634 (* 0.0272727 = 0.00318093 loss) | |
| I0506 00:37:22.029563 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.165492 (* 0.0272727 = 0.00451342 loss) | |
| I0506 00:37:22.029577 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.134888 (* 0.0272727 = 0.00367875 loss) | |
| I0506 00:37:22.029590 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.107014 (* 0.0272727 = 0.00291857 loss) | |
| I0506 00:37:22.029604 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.118491 (* 0.0272727 = 0.00323158 loss) | |
| I0506 00:37:22.029618 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0990855 (* 0.0272727 = 0.00270233 loss) | |
| I0506 00:37:22.029631 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0800525 (* 0.0272727 = 0.00218325 loss) | |
| I0506 00:37:22.029644 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0804018 (* 0.0272727 = 0.00219278 loss) | |
| I0506 00:37:22.029659 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0537953 (* 0.0272727 = 0.00146714 loss) | |
| I0506 00:37:22.029672 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0602264 (* 0.0272727 = 0.00164254 loss) | |
| I0506 00:37:22.029685 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0400085 (* 0.0272727 = 0.00109114 loss) | |
| I0506 00:37:22.029697 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0555556 | |
| I0506 00:37:22.029709 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:37:22.029721 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:37:22.029732 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 00:37:22.029743 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 00:37:22.029755 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 00:37:22.029767 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 00:37:22.029778 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.375 | |
| I0506 00:37:22.029788 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.5 | |
| I0506 00:37:22.029800 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 00:37:22.029811 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:37:22.029826 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:37:22.029839 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:37:22.029850 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:37:22.029871 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:37:22.029884 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:37:22.029896 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:37:22.029906 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:37:22.029932 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:37:22.029945 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:37:22.029956 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:37:22.029968 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:37:22.029978 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:37:22.029990 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.698864 | |
| I0506 00:37:22.030001 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.203704 | |
| I0506 00:37:22.030014 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.33715 (* 1 = 3.33715 loss) | |
| I0506 00:37:22.030028 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.25807 (* 1 = 1.25807 loss) | |
| I0506 00:37:22.030041 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.45987 (* 0.0909091 = 0.314533 loss) | |
| I0506 00:37:22.030055 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.0107 (* 0.0909091 = 0.2737 loss) | |
| I0506 00:37:22.030068 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.29278 (* 0.0909091 = 0.299344 loss) | |
| I0506 00:37:22.030081 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.52 (* 0.0909091 = 0.32 loss) | |
| I0506 00:37:22.030094 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.08121 (* 0.0909091 = 0.28011 loss) | |
| I0506 00:37:22.030108 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 3.37688 (* 0.0909091 = 0.306989 loss) | |
| I0506 00:37:22.030122 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.92882 (* 0.0909091 = 0.266256 loss) | |
| I0506 00:37:22.030134 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 2.53484 (* 0.0909091 = 0.23044 loss) | |
| I0506 00:37:22.030148 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.261933 (* 0.0909091 = 0.0238121 loss) | |
| I0506 00:37:22.030160 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.148355 (* 0.0909091 = 0.0134868 loss) | |
| I0506 00:37:22.030174 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.140901 (* 0.0909091 = 0.0128091 loss) | |
| I0506 00:37:22.030187 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0798702 (* 0.0909091 = 0.00726093 loss) | |
| I0506 00:37:22.030201 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0587203 (* 0.0909091 = 0.00533821 loss) | |
| I0506 00:37:22.030215 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0351203 (* 0.0909091 = 0.00319275 loss) | |
| I0506 00:37:22.030228 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0237311 (* 0.0909091 = 0.00215737 loss) | |
| I0506 00:37:22.030242 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.011033 (* 0.0909091 = 0.001003 loss) | |
| I0506 00:37:22.030256 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00468164 (* 0.0909091 = 0.000425604 loss) | |
| I0506 00:37:22.030269 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00243345 (* 0.0909091 = 0.000221222 loss) | |
| I0506 00:37:22.030282 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00160373 (* 0.0909091 = 0.000145794 loss) | |
| I0506 00:37:22.030297 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00167546 (* 0.0909091 = 0.000152315 loss) | |
| I0506 00:37:22.030310 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00076029 (* 0.0909091 = 6.91172e-05 loss) | |
| I0506 00:37:22.030324 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000632408 (* 0.0909091 = 5.74916e-05 loss) | |
| I0506 00:37:22.030336 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:37:22.030347 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:37:22.030359 15760 solver.cpp:245] Train net output #149: total_confidence = 5.72235e-07 | |
| I0506 00:37:22.030370 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 1.71345e-06 | |
| I0506 00:37:22.030393 15760 sgd_solver.cpp:106] Iteration 4500, lr = 0.001 | |
| I0506 00:39:05.517979 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.2561 > 30) by scale factor 0.959812 | |
| I0506 00:39:08.933492 15760 solver.cpp:338] Iteration 5000, Testing net (#0) | |
| I0506 00:39:45.399798 15760 solver.cpp:393] Test loss: 11.2564 | |
| I0506 00:39:45.399894 15760 solver.cpp:406] Test net output #0: loss1/accuracy = 0.0268623 | |
| I0506 00:39:45.399914 15760 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.06 | |
| I0506 00:39:45.399927 15760 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.059 | |
| I0506 00:39:45.399940 15760 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.04 | |
| I0506 00:39:45.399950 15760 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.132 | |
| I0506 00:39:45.399962 15760 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.287 | |
| I0506 00:39:45.399974 15760 solver.cpp:406] Test net output #6: loss1/accuracy06 = 0.446 | |
| I0506 00:39:45.399986 15760 solver.cpp:406] Test net output #7: loss1/accuracy07 = 0.729 | |
| I0506 00:39:45.399996 15760 solver.cpp:406] Test net output #8: loss1/accuracy08 = 0.91 | |
| I0506 00:39:45.400012 15760 solver.cpp:406] Test net output #9: loss1/accuracy09 = 0.99 | |
| I0506 00:39:45.400032 15760 solver.cpp:406] Test net output #10: loss1/accuracy10 = 0.999 | |
| I0506 00:39:45.400053 15760 solver.cpp:406] Test net output #11: loss1/accuracy11 = 1 | |
| I0506 00:39:45.400068 15760 solver.cpp:406] Test net output #12: loss1/accuracy12 = 1 | |
| I0506 00:39:45.400079 15760 solver.cpp:406] Test net output #13: loss1/accuracy13 = 1 | |
| I0506 00:39:45.400089 15760 solver.cpp:406] Test net output #14: loss1/accuracy14 = 1 | |
| I0506 00:39:45.400101 15760 solver.cpp:406] Test net output #15: loss1/accuracy15 = 1 | |
| I0506 00:39:45.400112 15760 solver.cpp:406] Test net output #16: loss1/accuracy16 = 1 | |
| I0506 00:39:45.400123 15760 solver.cpp:406] Test net output #17: loss1/accuracy17 = 1 | |
| I0506 00:39:45.400135 15760 solver.cpp:406] Test net output #18: loss1/accuracy18 = 1 | |
| I0506 00:39:45.400146 15760 solver.cpp:406] Test net output #19: loss1/accuracy19 = 1 | |
| I0506 00:39:45.400156 15760 solver.cpp:406] Test net output #20: loss1/accuracy20 = 1 | |
| I0506 00:39:45.400167 15760 solver.cpp:406] Test net output #21: loss1/accuracy21 = 1 | |
| I0506 00:39:45.400178 15760 solver.cpp:406] Test net output #22: loss1/accuracy22 = 1 | |
| I0506 00:39:45.400189 15760 solver.cpp:406] Test net output #23: loss1/accuracy_incl_empty = 0.75541 | |
| I0506 00:39:45.400202 15760 solver.cpp:406] Test net output #24: loss1/accuracy_top3 = 0.116806 | |
| I0506 00:39:45.400218 15760 solver.cpp:406] Test net output #25: loss1/cross_entropy_loss = 4.54395 (* 0.3 = 1.36318 loss) | |
| I0506 00:39:45.400233 15760 solver.cpp:406] Test net output #26: loss1/cross_entropy_loss_incl_empty = 1.19011 (* 0.3 = 0.357032 loss) | |
| I0506 00:39:45.400246 15760 solver.cpp:406] Test net output #27: loss1/loss01 = 3.71166 (* 0.0272727 = 0.101227 loss) | |
| I0506 00:39:45.400260 15760 solver.cpp:406] Test net output #28: loss1/loss02 = 3.72333 (* 0.0272727 = 0.101545 loss) | |
| I0506 00:39:45.400274 15760 solver.cpp:406] Test net output #29: loss1/loss03 = 3.79118 (* 0.0272727 = 0.103396 loss) | |
| I0506 00:39:45.400286 15760 solver.cpp:406] Test net output #30: loss1/loss04 = 3.63281 (* 0.0272727 = 0.0990765 loss) | |
| I0506 00:39:45.400300 15760 solver.cpp:406] Test net output #31: loss1/loss05 = 3.26002 (* 0.0272727 = 0.0889097 loss) | |
| I0506 00:39:45.400313 15760 solver.cpp:406] Test net output #32: loss1/loss06 = 2.76716 (* 0.0272727 = 0.0754681 loss) | |
| I0506 00:39:45.400327 15760 solver.cpp:406] Test net output #33: loss1/loss07 = 1.65351 (* 0.0272727 = 0.0450957 loss) | |
| I0506 00:39:45.400341 15760 solver.cpp:406] Test net output #34: loss1/loss08 = 0.687023 (* 0.0272727 = 0.018737 loss) | |
| I0506 00:39:45.400354 15760 solver.cpp:406] Test net output #35: loss1/loss09 = 0.144178 (* 0.0272727 = 0.00393212 loss) | |
| I0506 00:39:45.400368 15760 solver.cpp:406] Test net output #36: loss1/loss10 = 0.0926076 (* 0.0272727 = 0.00252566 loss) | |
| I0506 00:39:45.400382 15760 solver.cpp:406] Test net output #37: loss1/loss11 = 0.0682623 (* 0.0272727 = 0.0018617 loss) | |
| I0506 00:39:45.400395 15760 solver.cpp:406] Test net output #38: loss1/loss12 = 0.059141 (* 0.0272727 = 0.00161294 loss) | |
| I0506 00:39:45.400409 15760 solver.cpp:406] Test net output #39: loss1/loss13 = 0.0473945 (* 0.0272727 = 0.00129258 loss) | |
| I0506 00:39:45.400442 15760 solver.cpp:406] Test net output #40: loss1/loss14 = 0.0463171 (* 0.0272727 = 0.00126319 loss) | |
| I0506 00:39:45.400457 15760 solver.cpp:406] Test net output #41: loss1/loss15 = 0.0362911 (* 0.0272727 = 0.000989756 loss) | |
| I0506 00:39:45.400471 15760 solver.cpp:406] Test net output #42: loss1/loss16 = 0.0266797 (* 0.0272727 = 0.000727628 loss) | |
| I0506 00:39:45.400485 15760 solver.cpp:406] Test net output #43: loss1/loss17 = 0.0210556 (* 0.0272727 = 0.000574242 loss) | |
| I0506 00:39:45.400498 15760 solver.cpp:406] Test net output #44: loss1/loss18 = 0.0180348 (* 0.0272727 = 0.000491858 loss) | |
| I0506 00:39:45.400511 15760 solver.cpp:406] Test net output #45: loss1/loss19 = 0.0173178 (* 0.0272727 = 0.000472303 loss) | |
| I0506 00:39:45.400526 15760 solver.cpp:406] Test net output #46: loss1/loss20 = 0.0181812 (* 0.0272727 = 0.000495852 loss) | |
| I0506 00:39:45.400538 15760 solver.cpp:406] Test net output #47: loss1/loss21 = 0.0161144 (* 0.0272727 = 0.000439483 loss) | |
| I0506 00:39:45.400552 15760 solver.cpp:406] Test net output #48: loss1/loss22 = 0.0163444 (* 0.0272727 = 0.000445757 loss) | |
| I0506 00:39:45.400563 15760 solver.cpp:406] Test net output #49: loss2/accuracy = 0.0239448 | |
| I0506 00:39:45.400575 15760 solver.cpp:406] Test net output #50: loss2/accuracy01 = 0.06 | |
| I0506 00:39:45.400586 15760 solver.cpp:406] Test net output #51: loss2/accuracy02 = 0.045 | |
| I0506 00:39:45.400598 15760 solver.cpp:406] Test net output #52: loss2/accuracy03 = 0.05 | |
| I0506 00:39:45.400609 15760 solver.cpp:406] Test net output #53: loss2/accuracy04 = 0.133 | |
| I0506 00:39:45.400620 15760 solver.cpp:406] Test net output #54: loss2/accuracy05 = 0.286 | |
| I0506 00:39:45.400631 15760 solver.cpp:406] Test net output #55: loss2/accuracy06 = 0.446 | |
| I0506 00:39:45.400642 15760 solver.cpp:406] Test net output #56: loss2/accuracy07 = 0.729 | |
| I0506 00:39:45.400653 15760 solver.cpp:406] Test net output #57: loss2/accuracy08 = 0.91 | |
| I0506 00:39:45.400665 15760 solver.cpp:406] Test net output #58: loss2/accuracy09 = 0.99 | |
| I0506 00:39:45.400679 15760 solver.cpp:406] Test net output #59: loss2/accuracy10 = 0.999 | |
| I0506 00:39:45.400692 15760 solver.cpp:406] Test net output #60: loss2/accuracy11 = 1 | |
| I0506 00:39:45.400703 15760 solver.cpp:406] Test net output #61: loss2/accuracy12 = 1 | |
| I0506 00:39:45.400714 15760 solver.cpp:406] Test net output #62: loss2/accuracy13 = 1 | |
| I0506 00:39:45.400725 15760 solver.cpp:406] Test net output #63: loss2/accuracy14 = 1 | |
| I0506 00:39:45.400737 15760 solver.cpp:406] Test net output #64: loss2/accuracy15 = 1 | |
| I0506 00:39:45.400748 15760 solver.cpp:406] Test net output #65: loss2/accuracy16 = 1 | |
| I0506 00:39:45.400758 15760 solver.cpp:406] Test net output #66: loss2/accuracy17 = 1 | |
| I0506 00:39:45.400769 15760 solver.cpp:406] Test net output #67: loss2/accuracy18 = 1 | |
| I0506 00:39:45.400780 15760 solver.cpp:406] Test net output #68: loss2/accuracy19 = 1 | |
| I0506 00:39:45.400791 15760 solver.cpp:406] Test net output #69: loss2/accuracy20 = 1 | |
| I0506 00:39:45.400802 15760 solver.cpp:406] Test net output #70: loss2/accuracy21 = 1 | |
| I0506 00:39:45.400813 15760 solver.cpp:406] Test net output #71: loss2/accuracy22 = 1 | |
| I0506 00:39:45.400825 15760 solver.cpp:406] Test net output #72: loss2/accuracy_incl_empty = 0.754638 | |
| I0506 00:39:45.400835 15760 solver.cpp:406] Test net output #73: loss2/accuracy_top3 = 0.122148 | |
| I0506 00:39:45.400849 15760 solver.cpp:406] Test net output #74: loss2/cross_entropy_loss = 4.53591 (* 0.3 = 1.36077 loss) | |
| I0506 00:39:45.400863 15760 solver.cpp:406] Test net output #75: loss2/cross_entropy_loss_incl_empty = 1.18647 (* 0.3 = 0.355942 loss) | |
| I0506 00:39:45.400876 15760 solver.cpp:406] Test net output #76: loss2/loss01 = 3.70351 (* 0.0272727 = 0.101005 loss) | |
| I0506 00:39:45.400890 15760 solver.cpp:406] Test net output #77: loss2/loss02 = 3.72907 (* 0.0272727 = 0.101702 loss) | |
| I0506 00:39:45.400903 15760 solver.cpp:406] Test net output #78: loss2/loss03 = 3.77849 (* 0.0272727 = 0.10305 loss) | |
| I0506 00:39:45.400930 15760 solver.cpp:406] Test net output #79: loss2/loss04 = 3.63279 (* 0.0272727 = 0.0990761 loss) | |
| I0506 00:39:45.400945 15760 solver.cpp:406] Test net output #80: loss2/loss05 = 3.27139 (* 0.0272727 = 0.0892196 loss) | |
| I0506 00:39:45.400959 15760 solver.cpp:406] Test net output #81: loss2/loss06 = 2.76122 (* 0.0272727 = 0.0753059 loss) | |
| I0506 00:39:45.400972 15760 solver.cpp:406] Test net output #82: loss2/loss07 = 1.65289 (* 0.0272727 = 0.0450789 loss) | |
| I0506 00:39:45.400985 15760 solver.cpp:406] Test net output #83: loss2/loss08 = 0.684667 (* 0.0272727 = 0.0186727 loss) | |
| I0506 00:39:45.401000 15760 solver.cpp:406] Test net output #84: loss2/loss09 = 0.141235 (* 0.0272727 = 0.00385185 loss) | |
| I0506 00:39:45.401012 15760 solver.cpp:406] Test net output #85: loss2/loss10 = 0.0736969 (* 0.0272727 = 0.00200991 loss) | |
| I0506 00:39:45.401026 15760 solver.cpp:406] Test net output #86: loss2/loss11 = 0.0569693 (* 0.0272727 = 0.00155371 loss) | |
| I0506 00:39:45.401039 15760 solver.cpp:406] Test net output #87: loss2/loss12 = 0.0519677 (* 0.0272727 = 0.0014173 loss) | |
| I0506 00:39:45.401052 15760 solver.cpp:406] Test net output #88: loss2/loss13 = 0.0398315 (* 0.0272727 = 0.00108631 loss) | |
| I0506 00:39:45.401065 15760 solver.cpp:406] Test net output #89: loss2/loss14 = 0.0360374 (* 0.0272727 = 0.000982839 loss) | |
| I0506 00:39:45.401079 15760 solver.cpp:406] Test net output #90: loss2/loss15 = 0.0306675 (* 0.0272727 = 0.000836386 loss) | |
| I0506 00:39:45.401093 15760 solver.cpp:406] Test net output #91: loss2/loss16 = 0.0208196 (* 0.0272727 = 0.000567807 loss) | |
| I0506 00:39:45.401105 15760 solver.cpp:406] Test net output #92: loss2/loss17 = 0.0157995 (* 0.0272727 = 0.000430894 loss) | |
| I0506 00:39:45.401135 15760 solver.cpp:406] Test net output #93: loss2/loss18 = 0.014277 (* 0.0272727 = 0.000389374 loss) | |
| I0506 00:39:45.401152 15760 solver.cpp:406] Test net output #94: loss2/loss19 = 0.0137202 (* 0.0272727 = 0.000374189 loss) | |
| I0506 00:39:45.401166 15760 solver.cpp:406] Test net output #95: loss2/loss20 = 0.0132576 (* 0.0272727 = 0.000361572 loss) | |
| I0506 00:39:45.401180 15760 solver.cpp:406] Test net output #96: loss2/loss21 = 0.0128908 (* 0.0272727 = 0.000351567 loss) | |
| I0506 00:39:45.401193 15760 solver.cpp:406] Test net output #97: loss2/loss22 = 0.0130262 (* 0.0272727 = 0.000355259 loss) | |
| I0506 00:39:45.401206 15760 solver.cpp:406] Test net output #98: loss3/accuracy = 0.0555345 | |
| I0506 00:39:45.401217 15760 solver.cpp:406] Test net output #99: loss3/accuracy01 = 0.09 | |
| I0506 00:39:45.401228 15760 solver.cpp:406] Test net output #100: loss3/accuracy02 = 0.083 | |
| I0506 00:39:45.401239 15760 solver.cpp:406] Test net output #101: loss3/accuracy03 = 0.055 | |
| I0506 00:39:45.401250 15760 solver.cpp:406] Test net output #102: loss3/accuracy04 = 0.143 | |
| I0506 00:39:45.401262 15760 solver.cpp:406] Test net output #103: loss3/accuracy05 = 0.284 | |
| I0506 00:39:45.401273 15760 solver.cpp:406] Test net output #104: loss3/accuracy06 = 0.446 | |
| I0506 00:39:45.401284 15760 solver.cpp:406] Test net output #105: loss3/accuracy07 = 0.729 | |
| I0506 00:39:45.401295 15760 solver.cpp:406] Test net output #106: loss3/accuracy08 = 0.91 | |
| I0506 00:39:45.401306 15760 solver.cpp:406] Test net output #107: loss3/accuracy09 = 0.99 | |
| I0506 00:39:45.401317 15760 solver.cpp:406] Test net output #108: loss3/accuracy10 = 0.999 | |
| I0506 00:39:45.401329 15760 solver.cpp:406] Test net output #109: loss3/accuracy11 = 1 | |
| I0506 00:39:45.401340 15760 solver.cpp:406] Test net output #110: loss3/accuracy12 = 1 | |
| I0506 00:39:45.401350 15760 solver.cpp:406] Test net output #111: loss3/accuracy13 = 1 | |
| I0506 00:39:45.401361 15760 solver.cpp:406] Test net output #112: loss3/accuracy14 = 1 | |
| I0506 00:39:45.401371 15760 solver.cpp:406] Test net output #113: loss3/accuracy15 = 1 | |
| I0506 00:39:45.401382 15760 solver.cpp:406] Test net output #114: loss3/accuracy16 = 1 | |
| I0506 00:39:45.401393 15760 solver.cpp:406] Test net output #115: loss3/accuracy17 = 1 | |
| I0506 00:39:45.401417 15760 solver.cpp:406] Test net output #116: loss3/accuracy18 = 1 | |
| I0506 00:39:45.401428 15760 solver.cpp:406] Test net output #117: loss3/accuracy19 = 1 | |
| I0506 00:39:45.401439 15760 solver.cpp:406] Test net output #118: loss3/accuracy20 = 1 | |
| I0506 00:39:45.401454 15760 solver.cpp:406] Test net output #119: loss3/accuracy21 = 1 | |
| I0506 00:39:45.401475 15760 solver.cpp:406] Test net output #120: loss3/accuracy22 = 1 | |
| I0506 00:39:45.401494 15760 solver.cpp:406] Test net output #121: loss3/accuracy_incl_empty = 0.758865 | |
| I0506 00:39:45.401506 15760 solver.cpp:406] Test net output #122: loss3/accuracy_top3 = 0.199386 | |
| I0506 00:39:45.401520 15760 solver.cpp:406] Test net output #123: loss3/cross_entropy_loss = 3.54302 (* 1 = 3.54302 loss) | |
| I0506 00:39:45.401533 15760 solver.cpp:406] Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.965883 (* 1 = 0.965883 loss) | |
| I0506 00:39:45.401547 15760 solver.cpp:406] Test net output #125: loss3/loss01 = 3.45983 (* 0.0909091 = 0.31453 loss) | |
| I0506 00:39:45.401561 15760 solver.cpp:406] Test net output #126: loss3/loss02 = 3.52331 (* 0.0909091 = 0.320301 loss) | |
| I0506 00:39:45.401573 15760 solver.cpp:406] Test net output #127: loss3/loss03 = 3.60732 (* 0.0909091 = 0.327938 loss) | |
| I0506 00:39:45.401587 15760 solver.cpp:406] Test net output #128: loss3/loss04 = 3.41953 (* 0.0909091 = 0.310866 loss) | |
| I0506 00:39:45.401599 15760 solver.cpp:406] Test net output #129: loss3/loss05 = 3.08296 (* 0.0909091 = 0.280269 loss) | |
| I0506 00:39:45.401612 15760 solver.cpp:406] Test net output #130: loss3/loss06 = 2.62448 (* 0.0909091 = 0.238589 loss) | |
| I0506 00:39:45.401625 15760 solver.cpp:406] Test net output #131: loss3/loss07 = 1.54298 (* 0.0909091 = 0.140271 loss) | |
| I0506 00:39:45.401638 15760 solver.cpp:406] Test net output #132: loss3/loss08 = 0.600824 (* 0.0909091 = 0.0546204 loss) | |
| I0506 00:39:45.401653 15760 solver.cpp:406] Test net output #133: loss3/loss09 = 0.0874537 (* 0.0909091 = 0.00795034 loss) | |
| I0506 00:39:45.401665 15760 solver.cpp:406] Test net output #134: loss3/loss10 = 0.0445834 (* 0.0909091 = 0.00405304 loss) | |
| I0506 00:39:45.401679 15760 solver.cpp:406] Test net output #135: loss3/loss11 = 0.0333765 (* 0.0909091 = 0.00303423 loss) | |
| I0506 00:39:45.401692 15760 solver.cpp:406] Test net output #136: loss3/loss12 = 0.027383 (* 0.0909091 = 0.00248936 loss) | |
| I0506 00:39:45.401705 15760 solver.cpp:406] Test net output #137: loss3/loss13 = 0.0236712 (* 0.0909091 = 0.00215193 loss) | |
| I0506 00:39:45.401718 15760 solver.cpp:406] Test net output #138: loss3/loss14 = 0.019458 (* 0.0909091 = 0.00176891 loss) | |
| I0506 00:39:45.401736 15760 solver.cpp:406] Test net output #139: loss3/loss15 = 0.0144217 (* 0.0909091 = 0.00131107 loss) | |
| I0506 00:39:45.401751 15760 solver.cpp:406] Test net output #140: loss3/loss16 = 0.0085763 (* 0.0909091 = 0.000779664 loss) | |
| I0506 00:39:45.401764 15760 solver.cpp:406] Test net output #141: loss3/loss17 = 0.00415313 (* 0.0909091 = 0.000377558 loss) | |
| I0506 00:39:45.401777 15760 solver.cpp:406] Test net output #142: loss3/loss18 = 0.00327481 (* 0.0909091 = 0.00029771 loss) | |
| I0506 00:39:45.401792 15760 solver.cpp:406] Test net output #143: loss3/loss19 = 0.00238702 (* 0.0909091 = 0.000217002 loss) | |
| I0506 00:39:45.401804 15760 solver.cpp:406] Test net output #144: loss3/loss20 = 0.00203339 (* 0.0909091 = 0.000184854 loss) | |
| I0506 00:39:45.401818 15760 solver.cpp:406] Test net output #145: loss3/loss21 = 0.0017253 (* 0.0909091 = 0.000156846 loss) | |
| I0506 00:39:45.401831 15760 solver.cpp:406] Test net output #146: loss3/loss22 = 0.00137777 (* 0.0909091 = 0.000125252 loss) | |
| I0506 00:39:45.401844 15760 solver.cpp:406] Test net output #147: total_accuracy = 0 | |
| I0506 00:39:45.401854 15760 solver.cpp:406] Test net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:39:45.401865 15760 solver.cpp:406] Test net output #149: total_confidence = 0.000108748 | |
| I0506 00:39:45.401876 15760 solver.cpp:406] Test net output #150: total_confidence_not_rec = 0.000126195 | |
| I0506 00:39:45.401895 15760 solver.cpp:338] Iteration 5000, Testing net (#1) | |
| I0506 00:40:22.368854 15760 solver.cpp:393] Test loss: 11.9666 | |
| I0506 00:40:22.368981 15760 solver.cpp:406] Test net output #0: loss1/accuracy = 0.0288351 | |
| I0506 00:40:22.369000 15760 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.065 | |
| I0506 00:40:22.369014 15760 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.06 | |
| I0506 00:40:22.369026 15760 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.034 | |
| I0506 00:40:22.369038 15760 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.124 | |
| I0506 00:40:22.369050 15760 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.25 | |
| I0506 00:40:22.369061 15760 solver.cpp:406] Test net output #6: loss1/accuracy06 = 0.407 | |
| I0506 00:40:22.369073 15760 solver.cpp:406] Test net output #7: loss1/accuracy07 = 0.633 | |
| I0506 00:40:22.369093 15760 solver.cpp:406] Test net output #8: loss1/accuracy08 = 0.792 | |
| I0506 00:40:22.369104 15760 solver.cpp:406] Test net output #9: loss1/accuracy09 = 0.884 | |
| I0506 00:40:22.369117 15760 solver.cpp:406] Test net output #10: loss1/accuracy10 = 0.909 | |
| I0506 00:40:22.369145 15760 solver.cpp:406] Test net output #11: loss1/accuracy11 = 0.924 | |
| I0506 00:40:22.369166 15760 solver.cpp:406] Test net output #12: loss1/accuracy12 = 0.935 | |
| I0506 00:40:22.369177 15760 solver.cpp:406] Test net output #13: loss1/accuracy13 = 0.948 | |
| I0506 00:40:22.369189 15760 solver.cpp:406] Test net output #14: loss1/accuracy14 = 0.959 | |
| I0506 00:40:22.369200 15760 solver.cpp:406] Test net output #15: loss1/accuracy15 = 0.969 | |
| I0506 00:40:22.369212 15760 solver.cpp:406] Test net output #16: loss1/accuracy16 = 0.978 | |
| I0506 00:40:22.369225 15760 solver.cpp:406] Test net output #17: loss1/accuracy17 = 0.989 | |
| I0506 00:40:22.369235 15760 solver.cpp:406] Test net output #18: loss1/accuracy18 = 0.996 | |
| I0506 00:40:22.369247 15760 solver.cpp:406] Test net output #19: loss1/accuracy19 = 0.997 | |
| I0506 00:40:22.369259 15760 solver.cpp:406] Test net output #20: loss1/accuracy20 = 0.999 | |
| I0506 00:40:22.369271 15760 solver.cpp:406] Test net output #21: loss1/accuracy21 = 1 | |
| I0506 00:40:22.369282 15760 solver.cpp:406] Test net output #22: loss1/accuracy22 = 1 | |
| I0506 00:40:22.369293 15760 solver.cpp:406] Test net output #23: loss1/accuracy_incl_empty = 0.720182 | |
| I0506 00:40:22.369305 15760 solver.cpp:406] Test net output #24: loss1/accuracy_top3 = 0.129125 | |
| I0506 00:40:22.369323 15760 solver.cpp:406] Test net output #25: loss1/cross_entropy_loss = 4.55856 (* 0.3 = 1.36757 loss) | |
| I0506 00:40:22.369336 15760 solver.cpp:406] Test net output #26: loss1/cross_entropy_loss_incl_empty = 1.38516 (* 0.3 = 0.415549 loss) | |
| I0506 00:40:22.369350 15760 solver.cpp:406] Test net output #27: loss1/loss01 = 3.66958 (* 0.0272727 = 0.10008 loss) | |
| I0506 00:40:22.369367 15760 solver.cpp:406] Test net output #28: loss1/loss02 = 3.68548 (* 0.0272727 = 0.100513 loss) | |
| I0506 00:40:22.369380 15760 solver.cpp:406] Test net output #29: loss1/loss03 = 3.76582 (* 0.0272727 = 0.102704 loss) | |
| I0506 00:40:22.369393 15760 solver.cpp:406] Test net output #30: loss1/loss04 = 3.63249 (* 0.0272727 = 0.0990679 loss) | |
| I0506 00:40:22.369406 15760 solver.cpp:406] Test net output #31: loss1/loss05 = 3.2862 (* 0.0272727 = 0.0896236 loss) | |
| I0506 00:40:22.369428 15760 solver.cpp:406] Test net output #32: loss1/loss06 = 2.83911 (* 0.0272727 = 0.0774302 loss) | |
| I0506 00:40:22.369441 15760 solver.cpp:406] Test net output #33: loss1/loss07 = 1.96588 (* 0.0272727 = 0.0536148 loss) | |
| I0506 00:40:22.369456 15760 solver.cpp:406] Test net output #34: loss1/loss08 = 1.19099 (* 0.0272727 = 0.0324815 loss) | |
| I0506 00:40:22.369468 15760 solver.cpp:406] Test net output #35: loss1/loss09 = 0.681106 (* 0.0272727 = 0.0185756 loss) | |
| I0506 00:40:22.369482 15760 solver.cpp:406] Test net output #36: loss1/loss10 = 0.577888 (* 0.0272727 = 0.0157606 loss) | |
| I0506 00:40:22.369494 15760 solver.cpp:406] Test net output #37: loss1/loss11 = 0.494281 (* 0.0272727 = 0.0134804 loss) | |
| I0506 00:40:22.369508 15760 solver.cpp:406] Test net output #38: loss1/loss12 = 0.443227 (* 0.0272727 = 0.012088 loss) | |
| I0506 00:40:22.369521 15760 solver.cpp:406] Test net output #39: loss1/loss13 = 0.370128 (* 0.0272727 = 0.0100944 loss) | |
| I0506 00:40:22.369554 15760 solver.cpp:406] Test net output #40: loss1/loss14 = 0.307017 (* 0.0272727 = 0.0083732 loss) | |
| I0506 00:40:22.369570 15760 solver.cpp:406] Test net output #41: loss1/loss15 = 0.238482 (* 0.0272727 = 0.00650405 loss) | |
| I0506 00:40:22.369583 15760 solver.cpp:406] Test net output #42: loss1/loss16 = 0.174826 (* 0.0272727 = 0.00476798 loss) | |
| I0506 00:40:22.369596 15760 solver.cpp:406] Test net output #43: loss1/loss17 = 0.0996975 (* 0.0272727 = 0.00271902 loss) | |
| I0506 00:40:22.369609 15760 solver.cpp:406] Test net output #44: loss1/loss18 = 0.0490802 (* 0.0272727 = 0.00133855 loss) | |
| I0506 00:40:22.369623 15760 solver.cpp:406] Test net output #45: loss1/loss19 = 0.0409047 (* 0.0272727 = 0.00111558 loss) | |
| I0506 00:40:22.369637 15760 solver.cpp:406] Test net output #46: loss1/loss20 = 0.0269477 (* 0.0272727 = 0.000734938 loss) | |
| I0506 00:40:22.369650 15760 solver.cpp:406] Test net output #47: loss1/loss21 = 0.0172787 (* 0.0272727 = 0.000471238 loss) | |
| I0506 00:40:22.369663 15760 solver.cpp:406] Test net output #48: loss1/loss22 = 0.0171458 (* 0.0272727 = 0.000467612 loss) | |
| I0506 00:40:22.369675 15760 solver.cpp:406] Test net output #49: loss2/accuracy = 0.0232811 | |
| I0506 00:40:22.369686 15760 solver.cpp:406] Test net output #50: loss2/accuracy01 = 0.065 | |
| I0506 00:40:22.369699 15760 solver.cpp:406] Test net output #51: loss2/accuracy02 = 0.064 | |
| I0506 00:40:22.369709 15760 solver.cpp:406] Test net output #52: loss2/accuracy03 = 0.039 | |
| I0506 00:40:22.369724 15760 solver.cpp:406] Test net output #53: loss2/accuracy04 = 0.117 | |
| I0506 00:40:22.369737 15760 solver.cpp:406] Test net output #54: loss2/accuracy05 = 0.25 | |
| I0506 00:40:22.369748 15760 solver.cpp:406] Test net output #55: loss2/accuracy06 = 0.407 | |
| I0506 00:40:22.369760 15760 solver.cpp:406] Test net output #56: loss2/accuracy07 = 0.633 | |
| I0506 00:40:22.369771 15760 solver.cpp:406] Test net output #57: loss2/accuracy08 = 0.792 | |
| I0506 00:40:22.369781 15760 solver.cpp:406] Test net output #58: loss2/accuracy09 = 0.884 | |
| I0506 00:40:22.369792 15760 solver.cpp:406] Test net output #59: loss2/accuracy10 = 0.909 | |
| I0506 00:40:22.369804 15760 solver.cpp:406] Test net output #60: loss2/accuracy11 = 0.924 | |
| I0506 00:40:22.369815 15760 solver.cpp:406] Test net output #61: loss2/accuracy12 = 0.935 | |
| I0506 00:40:22.369827 15760 solver.cpp:406] Test net output #62: loss2/accuracy13 = 0.948 | |
| I0506 00:40:22.369837 15760 solver.cpp:406] Test net output #63: loss2/accuracy14 = 0.959 | |
| I0506 00:40:22.369848 15760 solver.cpp:406] Test net output #64: loss2/accuracy15 = 0.969 | |
| I0506 00:40:22.369859 15760 solver.cpp:406] Test net output #65: loss2/accuracy16 = 0.978 | |
| I0506 00:40:22.369871 15760 solver.cpp:406] Test net output #66: loss2/accuracy17 = 0.989 | |
| I0506 00:40:22.369882 15760 solver.cpp:406] Test net output #67: loss2/accuracy18 = 0.996 | |
| I0506 00:40:22.369894 15760 solver.cpp:406] Test net output #68: loss2/accuracy19 = 0.997 | |
| I0506 00:40:22.369904 15760 solver.cpp:406] Test net output #69: loss2/accuracy20 = 0.999 | |
| I0506 00:40:22.369915 15760 solver.cpp:406] Test net output #70: loss2/accuracy21 = 1 | |
| I0506 00:40:22.369930 15760 solver.cpp:406] Test net output #71: loss2/accuracy22 = 1 | |
| I0506 00:40:22.369941 15760 solver.cpp:406] Test net output #72: loss2/accuracy_incl_empty = 0.719136 | |
| I0506 00:40:22.369953 15760 solver.cpp:406] Test net output #73: loss2/accuracy_top3 = 0.134291 | |
| I0506 00:40:22.369963 15760 solver.cpp:406] Test net output #74: loss2/cross_entropy_loss = 4.52435 (* 0.3 = 1.3573 loss) | |
| I0506 00:40:22.369973 15760 solver.cpp:406] Test net output #75: loss2/cross_entropy_loss_incl_empty = 1.37004 (* 0.3 = 0.411011 loss) | |
| I0506 00:40:22.369987 15760 solver.cpp:406] Test net output #76: loss2/loss01 = 3.66483 (* 0.0272727 = 0.0999499 loss) | |
| I0506 00:40:22.370002 15760 solver.cpp:406] Test net output #77: loss2/loss02 = 3.67993 (* 0.0272727 = 0.100362 loss) | |
| I0506 00:40:22.370025 15760 solver.cpp:406] Test net output #78: loss2/loss03 = 3.7498 (* 0.0272727 = 0.102267 loss) | |
| I0506 00:40:22.370044 15760 solver.cpp:406] Test net output #79: loss2/loss04 = 3.63579 (* 0.0272727 = 0.0991579 loss) | |
| I0506 00:40:22.370057 15760 solver.cpp:406] Test net output #80: loss2/loss05 = 3.29958 (* 0.0272727 = 0.0899887 loss) | |
| I0506 00:40:22.370070 15760 solver.cpp:406] Test net output #81: loss2/loss06 = 2.82668 (* 0.0272727 = 0.0770912 loss) | |
| I0506 00:40:22.370084 15760 solver.cpp:406] Test net output #82: loss2/loss07 = 1.97877 (* 0.0272727 = 0.0539664 loss) | |
| I0506 00:40:22.370096 15760 solver.cpp:406] Test net output #83: loss2/loss08 = 1.19632 (* 0.0272727 = 0.0326269 loss) | |
| I0506 00:40:22.370110 15760 solver.cpp:406] Test net output #84: loss2/loss09 = 0.689656 (* 0.0272727 = 0.0188088 loss) | |
| I0506 00:40:22.370122 15760 solver.cpp:406] Test net output #85: loss2/loss10 = 0.565355 (* 0.0272727 = 0.0154188 loss) | |
| I0506 00:40:22.370136 15760 solver.cpp:406] Test net output #86: loss2/loss11 = 0.49978 (* 0.0272727 = 0.0136304 loss) | |
| I0506 00:40:22.370148 15760 solver.cpp:406] Test net output #87: loss2/loss12 = 0.442456 (* 0.0272727 = 0.012067 loss) | |
| I0506 00:40:22.370162 15760 solver.cpp:406] Test net output #88: loss2/loss13 = 0.367251 (* 0.0272727 = 0.0100159 loss) | |
| I0506 00:40:22.370174 15760 solver.cpp:406] Test net output #89: loss2/loss14 = 0.301094 (* 0.0272727 = 0.00821165 loss) | |
| I0506 00:40:22.370187 15760 solver.cpp:406] Test net output #90: loss2/loss15 = 0.233142 (* 0.0272727 = 0.00635841 loss) | |
| I0506 00:40:22.370200 15760 solver.cpp:406] Test net output #91: loss2/loss16 = 0.17466 (* 0.0272727 = 0.00476346 loss) | |
| I0506 00:40:22.370213 15760 solver.cpp:406] Test net output #92: loss2/loss17 = 0.0960569 (* 0.0272727 = 0.00261973 loss) | |
| I0506 00:40:22.370226 15760 solver.cpp:406] Test net output #93: loss2/loss18 = 0.0445587 (* 0.0272727 = 0.00121524 loss) | |
| I0506 00:40:22.370239 15760 solver.cpp:406] Test net output #94: loss2/loss19 = 0.0368121 (* 0.0272727 = 0.00100397 loss) | |
| I0506 00:40:22.370252 15760 solver.cpp:406] Test net output #95: loss2/loss20 = 0.0223634 (* 0.0272727 = 0.000609911 loss) | |
| I0506 00:40:22.370265 15760 solver.cpp:406] Test net output #96: loss2/loss21 = 0.0139406 (* 0.0272727 = 0.000380199 loss) | |
| I0506 00:40:22.370278 15760 solver.cpp:406] Test net output #97: loss2/loss22 = 0.0134886 (* 0.0272727 = 0.000367872 loss) | |
| I0506 00:40:22.370290 15760 solver.cpp:406] Test net output #98: loss3/accuracy = 0.0544308 | |
| I0506 00:40:22.370301 15760 solver.cpp:406] Test net output #99: loss3/accuracy01 = 0.09 | |
| I0506 00:40:22.370312 15760 solver.cpp:406] Test net output #100: loss3/accuracy02 = 0.083 | |
| I0506 00:40:22.370323 15760 solver.cpp:406] Test net output #101: loss3/accuracy03 = 0.062 | |
| I0506 00:40:22.370334 15760 solver.cpp:406] Test net output #102: loss3/accuracy04 = 0.147 | |
| I0506 00:40:22.370345 15760 solver.cpp:406] Test net output #103: loss3/accuracy05 = 0.267 | |
| I0506 00:40:22.370357 15760 solver.cpp:406] Test net output #104: loss3/accuracy06 = 0.407 | |
| I0506 00:40:22.370368 15760 solver.cpp:406] Test net output #105: loss3/accuracy07 = 0.633 | |
| I0506 00:40:22.370378 15760 solver.cpp:406] Test net output #106: loss3/accuracy08 = 0.792 | |
| I0506 00:40:22.370389 15760 solver.cpp:406] Test net output #107: loss3/accuracy09 = 0.884 | |
| I0506 00:40:22.370400 15760 solver.cpp:406] Test net output #108: loss3/accuracy10 = 0.909 | |
| I0506 00:40:22.370411 15760 solver.cpp:406] Test net output #109: loss3/accuracy11 = 0.924 | |
| I0506 00:40:22.370422 15760 solver.cpp:406] Test net output #110: loss3/accuracy12 = 0.935 | |
| I0506 00:40:22.370432 15760 solver.cpp:406] Test net output #111: loss3/accuracy13 = 0.948 | |
| I0506 00:40:22.370443 15760 solver.cpp:406] Test net output #112: loss3/accuracy14 = 0.959 | |
| I0506 00:40:22.370455 15760 solver.cpp:406] Test net output #113: loss3/accuracy15 = 0.969 | |
| I0506 00:40:22.370466 15760 solver.cpp:406] Test net output #114: loss3/accuracy16 = 0.978 | |
| I0506 00:40:22.370486 15760 solver.cpp:406] Test net output #115: loss3/accuracy17 = 0.989 | |
| I0506 00:40:22.370498 15760 solver.cpp:406] Test net output #116: loss3/accuracy18 = 0.996 | |
| I0506 00:40:22.370509 15760 solver.cpp:406] Test net output #117: loss3/accuracy19 = 0.997 | |
| I0506 00:40:22.370520 15760 solver.cpp:406] Test net output #118: loss3/accuracy20 = 0.999 | |
| I0506 00:40:22.370532 15760 solver.cpp:406] Test net output #119: loss3/accuracy21 = 1 | |
| I0506 00:40:22.370543 15760 solver.cpp:406] Test net output #120: loss3/accuracy22 = 1 | |
| I0506 00:40:22.370554 15760 solver.cpp:406] Test net output #121: loss3/accuracy_incl_empty = 0.725 | |
| I0506 00:40:22.370565 15760 solver.cpp:406] Test net output #122: loss3/accuracy_top3 = 0.191833 | |
| I0506 00:40:22.370579 15760 solver.cpp:406] Test net output #123: loss3/cross_entropy_loss = 3.51349 (* 1 = 3.51349 loss) | |
| I0506 00:40:22.370591 15760 solver.cpp:406] Test net output #124: loss3/cross_entropy_loss_incl_empty = 1.09993 (* 1 = 1.09993 loss) | |
| I0506 00:40:22.370604 15760 solver.cpp:406] Test net output #125: loss3/loss01 = 3.38436 (* 0.0909091 = 0.307669 loss) | |
| I0506 00:40:22.370617 15760 solver.cpp:406] Test net output #126: loss3/loss02 = 3.45736 (* 0.0909091 = 0.314305 loss) | |
| I0506 00:40:22.370630 15760 solver.cpp:406] Test net output #127: loss3/loss03 = 3.54114 (* 0.0909091 = 0.321922 loss) | |
| I0506 00:40:22.370643 15760 solver.cpp:406] Test net output #128: loss3/loss04 = 3.39087 (* 0.0909091 = 0.308261 loss) | |
| I0506 00:40:22.370656 15760 solver.cpp:406] Test net output #129: loss3/loss05 = 3.04134 (* 0.0909091 = 0.276486 loss) | |
| I0506 00:40:22.370669 15760 solver.cpp:406] Test net output #130: loss3/loss06 = 2.63886 (* 0.0909091 = 0.239896 loss) | |
| I0506 00:40:22.370682 15760 solver.cpp:406] Test net output #131: loss3/loss07 = 1.82592 (* 0.0909091 = 0.165993 loss) | |
| I0506 00:40:22.370695 15760 solver.cpp:406] Test net output #132: loss3/loss08 = 1.06301 (* 0.0909091 = 0.096637 loss) | |
| I0506 00:40:22.370708 15760 solver.cpp:406] Test net output #133: loss3/loss09 = 0.563858 (* 0.0909091 = 0.0512598 loss) | |
| I0506 00:40:22.370721 15760 solver.cpp:406] Test net output #134: loss3/loss10 = 0.463761 (* 0.0909091 = 0.0421601 loss) | |
| I0506 00:40:22.370734 15760 solver.cpp:406] Test net output #135: loss3/loss11 = 0.412728 (* 0.0909091 = 0.0375207 loss) | |
| I0506 00:40:22.370748 15760 solver.cpp:406] Test net output #136: loss3/loss12 = 0.377764 (* 0.0909091 = 0.0343421 loss) | |
| I0506 00:40:22.370760 15760 solver.cpp:406] Test net output #137: loss3/loss13 = 0.315398 (* 0.0909091 = 0.0286725 loss) | |
| I0506 00:40:22.370779 15760 solver.cpp:406] Test net output #138: loss3/loss14 = 0.260061 (* 0.0909091 = 0.0236419 loss) | |
| I0506 00:40:22.370792 15760 solver.cpp:406] Test net output #139: loss3/loss15 = 0.206498 (* 0.0909091 = 0.0187726 loss) | |
| I0506 00:40:22.370805 15760 solver.cpp:406] Test net output #140: loss3/loss16 = 0.164701 (* 0.0909091 = 0.0149729 loss) | |
| I0506 00:40:22.370820 15760 solver.cpp:406] Test net output #141: loss3/loss17 = 0.0939701 (* 0.0909091 = 0.00854273 loss) | |
| I0506 00:40:22.370832 15760 solver.cpp:406] Test net output #142: loss3/loss18 = 0.0404725 (* 0.0909091 = 0.00367931 loss) | |
| I0506 00:40:22.370846 15760 solver.cpp:406] Test net output #143: loss3/loss19 = 0.0302107 (* 0.0909091 = 0.00274643 loss) | |
| I0506 00:40:22.370859 15760 solver.cpp:406] Test net output #144: loss3/loss20 = 0.0116014 (* 0.0909091 = 0.00105467 loss) | |
| I0506 00:40:22.370872 15760 solver.cpp:406] Test net output #145: loss3/loss21 = 0.00179832 (* 0.0909091 = 0.000163484 loss) | |
| I0506 00:40:22.370887 15760 solver.cpp:406] Test net output #146: loss3/loss22 = 0.00141692 (* 0.0909091 = 0.000128811 loss) | |
| I0506 00:40:22.370898 15760 solver.cpp:406] Test net output #147: total_accuracy = 0 | |
| I0506 00:40:22.370908 15760 solver.cpp:406] Test net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:40:22.370919 15760 solver.cpp:406] Test net output #149: total_confidence = 9.15408e-05 | |
| I0506 00:40:22.370940 15760 solver.cpp:406] Test net output #150: total_confidence_not_rec = 9.53168e-05 | |
| I0506 00:40:22.508584 15760 solver.cpp:229] Iteration 5000, loss = 11.6962 | |
| I0506 00:40:22.508651 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0506 00:40:22.508669 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:40:22.508682 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 00:40:22.508694 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 00:40:22.508707 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 00:40:22.508718 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 00:40:22.508729 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.25 | |
| I0506 00:40:22.508741 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 00:40:22.508754 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 00:40:22.508765 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 00:40:22.508776 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:40:22.508788 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:40:22.508800 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:40:22.508810 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:40:22.508822 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:40:22.508834 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:40:22.508846 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:40:22.508860 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:40:22.508872 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:40:22.508883 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:40:22.508894 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:40:22.508906 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:40:22.508918 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:40:22.508929 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.715909 | |
| I0506 00:40:22.508940 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.06 | |
| I0506 00:40:22.508956 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.74631 (* 0.3 = 1.12389 loss) | |
| I0506 00:40:22.508971 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.15464 (* 0.3 = 0.346392 loss) | |
| I0506 00:40:22.508985 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.69347 (* 0.0272727 = 0.100731 loss) | |
| I0506 00:40:22.508998 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.77612 (* 0.0272727 = 0.102985 loss) | |
| I0506 00:40:22.509012 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.29957 (* 0.0272727 = 0.0899884 loss) | |
| I0506 00:40:22.509026 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.83759 (* 0.0272727 = 0.104661 loss) | |
| I0506 00:40:22.509039 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.52195 (* 0.0272727 = 0.0960531 loss) | |
| I0506 00:40:22.509052 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.64183 (* 0.0272727 = 0.0993226 loss) | |
| I0506 00:40:22.509066 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 3.2276 (* 0.0272727 = 0.0880255 loss) | |
| I0506 00:40:22.509079 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.8893 (* 0.0272727 = 0.0242536 loss) | |
| I0506 00:40:22.509093 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.0266088 (* 0.0272727 = 0.000725696 loss) | |
| I0506 00:40:22.509116 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0282175 (* 0.0272727 = 0.000769569 loss) | |
| I0506 00:40:22.509147 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0315762 (* 0.0272727 = 0.000861169 loss) | |
| I0506 00:40:22.509201 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0242613 (* 0.0272727 = 0.000661672 loss) | |
| I0506 00:40:22.509215 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0227553 (* 0.0272727 = 0.000620599 loss) | |
| I0506 00:40:22.509229 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0172005 (* 0.0272727 = 0.000469105 loss) | |
| I0506 00:40:22.509243 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.019726 (* 0.0272727 = 0.000537981 loss) | |
| I0506 00:40:22.509256 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.00898979 (* 0.0272727 = 0.000245176 loss) | |
| I0506 00:40:22.509270 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00410569 (* 0.0272727 = 0.000111973 loss) | |
| I0506 00:40:22.509284 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00686188 (* 0.0272727 = 0.000187142 loss) | |
| I0506 00:40:22.509297 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0042966 (* 0.0272727 = 0.00011718 loss) | |
| I0506 00:40:22.509310 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00462673 (* 0.0272727 = 0.000126183 loss) | |
| I0506 00:40:22.509325 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00419469 (* 0.0272727 = 0.000114401 loss) | |
| I0506 00:40:22.509337 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00847785 (* 0.0272727 = 0.000231214 loss) | |
| I0506 00:40:22.509349 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.02 | |
| I0506 00:40:22.509361 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:40:22.509369 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:40:22.509377 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 00:40:22.509384 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 00:40:22.509397 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 00:40:22.509407 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.25 | |
| I0506 00:40:22.509419 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 00:40:22.509431 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 00:40:22.509443 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 00:40:22.509454 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:40:22.509465 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:40:22.509476 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:40:22.509487 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:40:22.509498 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:40:22.509510 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:40:22.509521 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:40:22.509531 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:40:22.509542 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:40:22.509553 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:40:22.509564 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:40:22.509575 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:40:22.509587 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:40:22.509598 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.721591 | |
| I0506 00:40:22.509609 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.1 | |
| I0506 00:40:22.509623 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.79315 (* 0.3 = 1.13795 loss) | |
| I0506 00:40:22.509636 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.1525 (* 0.3 = 0.345751 loss) | |
| I0506 00:40:22.509650 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 4.08041 (* 0.0272727 = 0.111284 loss) | |
| I0506 00:40:22.509682 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 4.09387 (* 0.0272727 = 0.111651 loss) | |
| I0506 00:40:22.509799 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.52455 (* 0.0272727 = 0.096124 loss) | |
| I0506 00:40:22.509820 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 4.08873 (* 0.0272727 = 0.111511 loss) | |
| I0506 00:40:22.509834 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.85967 (* 0.0272727 = 0.105264 loss) | |
| I0506 00:40:22.509847 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.12843 (* 0.0272727 = 0.0853207 loss) | |
| I0506 00:40:22.509861 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 3.00106 (* 0.0272727 = 0.0818472 loss) | |
| I0506 00:40:22.509882 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.16671 (* 0.0272727 = 0.0318194 loss) | |
| I0506 00:40:22.509908 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.0723691 (* 0.0272727 = 0.0019737 loss) | |
| I0506 00:40:22.509953 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0496581 (* 0.0272727 = 0.00135431 loss) | |
| I0506 00:40:22.509970 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0382053 (* 0.0272727 = 0.00104196 loss) | |
| I0506 00:40:22.509985 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0352455 (* 0.0272727 = 0.00096124 loss) | |
| I0506 00:40:22.509999 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0221103 (* 0.0272727 = 0.000603007 loss) | |
| I0506 00:40:22.510020 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0185505 (* 0.0272727 = 0.000505924 loss) | |
| I0506 00:40:22.510051 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0175232 (* 0.0272727 = 0.000477906 loss) | |
| I0506 00:40:22.510066 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0126316 (* 0.0272727 = 0.000344499 loss) | |
| I0506 00:40:22.510088 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0123698 (* 0.0272727 = 0.000337358 loss) | |
| I0506 00:40:22.510102 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00707177 (* 0.0272727 = 0.000192866 loss) | |
| I0506 00:40:22.510116 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00937575 (* 0.0272727 = 0.000255702 loss) | |
| I0506 00:40:22.510129 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00899948 (* 0.0272727 = 0.00024544 loss) | |
| I0506 00:40:22.510151 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00827024 (* 0.0272727 = 0.000225552 loss) | |
| I0506 00:40:22.510164 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00614708 (* 0.0272727 = 0.000167648 loss) | |
| I0506 00:40:22.510176 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0 | |
| I0506 00:40:22.510187 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:40:22.510208 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:40:22.510220 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:40:22.510231 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 00:40:22.510241 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.125 | |
| I0506 00:40:22.510253 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 00:40:22.510288 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 00:40:22.510301 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 00:40:22.510313 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 00:40:22.510332 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:40:22.510344 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:40:22.510355 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:40:22.510366 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:40:22.510377 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:40:22.510401 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:40:22.510413 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:40:22.510426 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:40:22.510437 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:40:22.510447 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:40:22.510458 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:40:22.510469 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:40:22.510480 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:40:22.510491 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.715909 | |
| I0506 00:40:22.510504 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.14 | |
| I0506 00:40:22.510516 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.58243 (* 1 = 3.58243 loss) | |
| I0506 00:40:22.510530 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.09348 (* 1 = 1.09348 loss) | |
| I0506 00:40:22.510545 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.82456 (* 0.0909091 = 0.347688 loss) | |
| I0506 00:40:22.510557 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.62655 (* 0.0909091 = 0.329686 loss) | |
| I0506 00:40:22.510571 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.43081 (* 0.0909091 = 0.311892 loss) | |
| I0506 00:40:22.510584 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.38981 (* 0.0909091 = 0.308165 loss) | |
| I0506 00:40:22.510597 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.20746 (* 0.0909091 = 0.291587 loss) | |
| I0506 00:40:22.510612 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 3.35644 (* 0.0909091 = 0.305131 loss) | |
| I0506 00:40:22.510624 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 3.11747 (* 0.0909091 = 0.283406 loss) | |
| I0506 00:40:22.510637 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.848095 (* 0.0909091 = 0.0770996 loss) | |
| I0506 00:40:22.510651 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.013667 (* 0.0909091 = 0.00124245 loss) | |
| I0506 00:40:22.510665 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0177721 (* 0.0909091 = 0.00161564 loss) | |
| I0506 00:40:22.510679 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0125938 (* 0.0909091 = 0.00114489 loss) | |
| I0506 00:40:22.510692 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0117343 (* 0.0909091 = 0.00106676 loss) | |
| I0506 00:40:22.510705 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00927592 (* 0.0909091 = 0.000843265 loss) | |
| I0506 00:40:22.510718 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00850808 (* 0.0909091 = 0.000773462 loss) | |
| I0506 00:40:22.510732 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00558495 (* 0.0909091 = 0.000507723 loss) | |
| I0506 00:40:22.510746 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00375123 (* 0.0909091 = 0.000341021 loss) | |
| I0506 00:40:22.510764 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00158938 (* 0.0909091 = 0.000144489 loss) | |
| I0506 00:40:22.510779 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00106593 (* 0.0909091 = 9.69025e-05 loss) | |
| I0506 00:40:22.510793 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.000697817 (* 0.0909091 = 6.34379e-05 loss) | |
| I0506 00:40:22.510807 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000764633 (* 0.0909091 = 6.95121e-05 loss) | |
| I0506 00:40:22.510821 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000507627 (* 0.0909091 = 4.61479e-05 loss) | |
| I0506 00:40:22.510864 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000314227 (* 0.0909091 = 2.85661e-05 loss) | |
| I0506 00:40:22.510879 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:40:22.510910 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:40:22.510921 15760 solver.cpp:245] Train net output #149: total_confidence = 1.94226e-05 | |
| I0506 00:40:22.510933 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 6.34781e-05 | |
| I0506 00:40:22.510946 15760 sgd_solver.cpp:106] Iteration 5000, lr = 0.001 | |
| I0506 00:40:55.341235 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 35.6563 > 30) by scale factor 0.841367 | |
| I0506 00:41:28.324769 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 35.6717 > 30) by scale factor 0.841003 | |
| I0506 00:41:30.056152 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.9235 > 30) by scale factor 0.939747 | |
| I0506 00:42:10.325240 15760 solver.cpp:229] Iteration 5500, loss = 11.6692 | |
| I0506 00:42:10.325407 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0185185 | |
| I0506 00:42:10.325428 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:42:10.325441 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:42:10.325461 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:42:10.325474 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 00:42:10.325485 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 00:42:10.325497 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 00:42:10.325508 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 00:42:10.325521 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.625 | |
| I0506 00:42:10.325533 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.75 | |
| I0506 00:42:10.325546 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 00:42:10.325556 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 00:42:10.325568 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:42:10.325579 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:42:10.325592 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:42:10.325618 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:42:10.325630 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:42:10.325641 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:42:10.325652 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:42:10.325664 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:42:10.325680 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:42:10.325690 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:42:10.325701 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:42:10.325713 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.698864 | |
| I0506 00:42:10.325724 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.037037 | |
| I0506 00:42:10.325742 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.77336 (* 0.3 = 1.13201 loss) | |
| I0506 00:42:10.325757 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.3148 (* 0.3 = 0.39444 loss) | |
| I0506 00:42:10.325770 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.56202 (* 0.0272727 = 0.0971459 loss) | |
| I0506 00:42:10.325784 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.39636 (* 0.0272727 = 0.0926281 loss) | |
| I0506 00:42:10.325798 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.30526 (* 0.0272727 = 0.117416 loss) | |
| I0506 00:42:10.325811 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.88276 (* 0.0272727 = 0.105894 loss) | |
| I0506 00:42:10.325825 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.60972 (* 0.0272727 = 0.0984469 loss) | |
| I0506 00:42:10.325839 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.4883 (* 0.0272727 = 0.0678628 loss) | |
| I0506 00:42:10.325851 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.47182 (* 0.0272727 = 0.0674131 loss) | |
| I0506 00:42:10.325865 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 2.0965 (* 0.0272727 = 0.0571773 loss) | |
| I0506 00:42:10.325882 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.23953 (* 0.0272727 = 0.0338054 loss) | |
| I0506 00:42:10.325897 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.686877 (* 0.0272727 = 0.018733 loss) | |
| I0506 00:42:10.325911 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.872287 (* 0.0272727 = 0.0237896 loss) | |
| I0506 00:42:10.325925 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0845576 (* 0.0272727 = 0.00230612 loss) | |
| I0506 00:42:10.325939 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0786655 (* 0.0272727 = 0.00214542 loss) | |
| I0506 00:42:10.325979 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0570378 (* 0.0272727 = 0.00155558 loss) | |
| I0506 00:42:10.325995 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0439905 (* 0.0272727 = 0.00119974 loss) | |
| I0506 00:42:10.326009 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0248823 (* 0.0272727 = 0.000678609 loss) | |
| I0506 00:42:10.326027 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0120519 (* 0.0272727 = 0.000328687 loss) | |
| I0506 00:42:10.326042 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0114849 (* 0.0272727 = 0.000313225 loss) | |
| I0506 00:42:10.326056 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0184248 (* 0.0272727 = 0.000502494 loss) | |
| I0506 00:42:10.326078 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0263443 (* 0.0272727 = 0.000718481 loss) | |
| I0506 00:42:10.326092 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0112407 (* 0.0272727 = 0.000306566 loss) | |
| I0506 00:42:10.326105 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0216104 (* 0.0272727 = 0.000589375 loss) | |
| I0506 00:42:10.326117 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:42:10.326129 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:42:10.326140 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 00:42:10.326153 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:42:10.326164 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 00:42:10.326174 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 00:42:10.326190 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 00:42:10.326202 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 00:42:10.326213 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.625 | |
| I0506 00:42:10.326225 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.75 | |
| I0506 00:42:10.326236 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 00:42:10.326247 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 00:42:10.326264 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:42:10.326275 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:42:10.326287 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:42:10.326297 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:42:10.326308 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:42:10.326319 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:42:10.326330 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:42:10.326341 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:42:10.326354 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:42:10.326364 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:42:10.326375 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:42:10.326386 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.693182 | |
| I0506 00:42:10.326401 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.037037 | |
| I0506 00:42:10.326421 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.81862 (* 0.3 = 1.14559 loss) | |
| I0506 00:42:10.326436 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.38362 (* 0.3 = 0.415085 loss) | |
| I0506 00:42:10.326448 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.92069 (* 0.0272727 = 0.106928 loss) | |
| I0506 00:42:10.326467 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.43389 (* 0.0272727 = 0.0936516 loss) | |
| I0506 00:42:10.326493 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.86065 (* 0.0272727 = 0.10529 loss) | |
| I0506 00:42:10.326506 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.30845 (* 0.0272727 = 0.0902304 loss) | |
| I0506 00:42:10.326520 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.2967 (* 0.0272727 = 0.0899099 loss) | |
| I0506 00:42:10.326534 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.86538 (* 0.0272727 = 0.0781469 loss) | |
| I0506 00:42:10.326547 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.40633 (* 0.0272727 = 0.0656271 loss) | |
| I0506 00:42:10.326561 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.83471 (* 0.0272727 = 0.0500374 loss) | |
| I0506 00:42:10.326575 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.22599 (* 0.0272727 = 0.0334361 loss) | |
| I0506 00:42:10.326588 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.670039 (* 0.0272727 = 0.0182738 loss) | |
| I0506 00:42:10.326601 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.745505 (* 0.0272727 = 0.0203319 loss) | |
| I0506 00:42:10.326616 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0925337 (* 0.0272727 = 0.00252365 loss) | |
| I0506 00:42:10.326629 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0410184 (* 0.0272727 = 0.00111868 loss) | |
| I0506 00:42:10.326642 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.04672 (* 0.0272727 = 0.00127418 loss) | |
| I0506 00:42:10.326656 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.025204 (* 0.0272727 = 0.000687383 loss) | |
| I0506 00:42:10.326670 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0266322 (* 0.0272727 = 0.000726333 loss) | |
| I0506 00:42:10.326684 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0221192 (* 0.0272727 = 0.000603251 loss) | |
| I0506 00:42:10.326697 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0194918 (* 0.0272727 = 0.000531594 loss) | |
| I0506 00:42:10.326710 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0173857 (* 0.0272727 = 0.000474155 loss) | |
| I0506 00:42:10.326725 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0107142 (* 0.0272727 = 0.000292206 loss) | |
| I0506 00:42:10.326738 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0120979 (* 0.0272727 = 0.000329944 loss) | |
| I0506 00:42:10.326751 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0133348 (* 0.0272727 = 0.000363677 loss) | |
| I0506 00:42:10.326763 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.037037 | |
| I0506 00:42:10.326776 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 00:42:10.326786 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.125 | |
| I0506 00:42:10.326798 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 00:42:10.326809 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 00:42:10.326820 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.125 | |
| I0506 00:42:10.326833 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 00:42:10.326843 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 00:42:10.326854 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.625 | |
| I0506 00:42:10.326865 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.75 | |
| I0506 00:42:10.326877 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 00:42:10.326889 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 00:42:10.326900 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:42:10.326911 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:42:10.326922 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:42:10.326941 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:42:10.326952 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:42:10.326974 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:42:10.326987 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:42:10.326998 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:42:10.327008 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:42:10.327034 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:42:10.327046 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:42:10.327061 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.704545 | |
| I0506 00:42:10.327074 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.203704 | |
| I0506 00:42:10.327088 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.60833 (* 1 = 3.60833 loss) | |
| I0506 00:42:10.327102 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.22241 (* 1 = 1.22241 loss) | |
| I0506 00:42:10.327116 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.60434 (* 0.0909091 = 0.327668 loss) | |
| I0506 00:42:10.327128 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.57342 (* 0.0909091 = 0.324856 loss) | |
| I0506 00:42:10.327142 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.50442 (* 0.0909091 = 0.318584 loss) | |
| I0506 00:42:10.327162 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.09721 (* 0.0909091 = 0.281565 loss) | |
| I0506 00:42:10.327174 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.51152 (* 0.0909091 = 0.319229 loss) | |
| I0506 00:42:10.327188 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.73252 (* 0.0909091 = 0.248411 loss) | |
| I0506 00:42:10.327201 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.34001 (* 0.0909091 = 0.212728 loss) | |
| I0506 00:42:10.327214 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.70367 (* 0.0909091 = 0.154879 loss) | |
| I0506 00:42:10.327234 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 1.01027 (* 0.0909091 = 0.0918427 loss) | |
| I0506 00:42:10.327249 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.723234 (* 0.0909091 = 0.0657485 loss) | |
| I0506 00:42:10.327261 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.692879 (* 0.0909091 = 0.062989 loss) | |
| I0506 00:42:10.327275 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0835956 (* 0.0909091 = 0.0075996 loss) | |
| I0506 00:42:10.327288 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0433524 (* 0.0909091 = 0.00394112 loss) | |
| I0506 00:42:10.327301 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0347878 (* 0.0909091 = 0.00316253 loss) | |
| I0506 00:42:10.327314 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0245039 (* 0.0909091 = 0.00222763 loss) | |
| I0506 00:42:10.327328 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0188591 (* 0.0909091 = 0.00171446 loss) | |
| I0506 00:42:10.327342 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0060941 (* 0.0909091 = 0.000554009 loss) | |
| I0506 00:42:10.327355 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00378667 (* 0.0909091 = 0.000344243 loss) | |
| I0506 00:42:10.327368 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00265264 (* 0.0909091 = 0.000241149 loss) | |
| I0506 00:42:10.327383 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00186553 (* 0.0909091 = 0.000169593 loss) | |
| I0506 00:42:10.327396 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00106786 (* 0.0909091 = 9.70784e-05 loss) | |
| I0506 00:42:10.327409 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00141573 (* 0.0909091 = 0.000128703 loss) | |
| I0506 00:42:10.327421 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:42:10.327432 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:42:10.327445 15760 solver.cpp:245] Train net output #149: total_confidence = 1.23584e-05 | |
| I0506 00:42:10.327471 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000267152 | |
| I0506 00:42:10.327486 15760 sgd_solver.cpp:106] Iteration 5500, lr = 0.001 | |
| I0506 00:43:11.496431 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.0541 > 30) by scale factor 0.966056 | |
| I0506 00:43:57.778695 15760 solver.cpp:229] Iteration 6000, loss = 11.4742 | |
| I0506 00:43:57.778812 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0810811 | |
| I0506 00:43:57.778831 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:43:57.778847 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:43:57.778861 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:43:57.778872 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.375 | |
| I0506 00:43:57.778883 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.5 | |
| I0506 00:43:57.778894 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.625 | |
| I0506 00:43:57.778906 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.875 | |
| I0506 00:43:57.778918 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 1 | |
| I0506 00:43:57.778929 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 00:43:57.778941 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:43:57.778955 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:43:57.778967 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:43:57.778980 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:43:57.778990 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:43:57.779002 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:43:57.779013 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:43:57.779026 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:43:57.779037 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:43:57.779048 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:43:57.779059 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:43:57.779072 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:43:57.779083 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:43:57.779093 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.778409 | |
| I0506 00:43:57.779105 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.162162 | |
| I0506 00:43:57.779121 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.58998 (* 0.3 = 1.07699 loss) | |
| I0506 00:43:57.779135 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.1054 (* 0.3 = 0.331621 loss) | |
| I0506 00:43:57.779150 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.57522 (* 0.0272727 = 0.097506 loss) | |
| I0506 00:43:57.779163 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.8534 (* 0.0272727 = 0.105093 loss) | |
| I0506 00:43:57.779177 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.50678 (* 0.0272727 = 0.0956395 loss) | |
| I0506 00:43:57.779191 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.14382 (* 0.0272727 = 0.0857404 loss) | |
| I0506 00:43:57.779204 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.71145 (* 0.0272727 = 0.0739487 loss) | |
| I0506 00:43:57.779217 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 1.96934 (* 0.0272727 = 0.0537092 loss) | |
| I0506 00:43:57.779232 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.11507 (* 0.0272727 = 0.030411 loss) | |
| I0506 00:43:57.779244 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.276354 (* 0.0272727 = 0.00753694 loss) | |
| I0506 00:43:57.779258 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.093735 (* 0.0272727 = 0.00255641 loss) | |
| I0506 00:43:57.779273 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0847181 (* 0.0272727 = 0.00231049 loss) | |
| I0506 00:43:57.779286 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0555037 (* 0.0272727 = 0.00151374 loss) | |
| I0506 00:43:57.779299 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0480605 (* 0.0272727 = 0.00131074 loss) | |
| I0506 00:43:57.779314 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0379006 (* 0.0272727 = 0.00103365 loss) | |
| I0506 00:43:57.779345 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0324523 (* 0.0272727 = 0.000885063 loss) | |
| I0506 00:43:57.779361 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0267234 (* 0.0272727 = 0.00072882 loss) | |
| I0506 00:43:57.779374 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0162839 (* 0.0272727 = 0.000444107 loss) | |
| I0506 00:43:57.779388 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0136602 (* 0.0272727 = 0.00037255 loss) | |
| I0506 00:43:57.779402 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0137741 (* 0.0272727 = 0.000375656 loss) | |
| I0506 00:43:57.779417 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00698953 (* 0.0272727 = 0.000190623 loss) | |
| I0506 00:43:57.779429 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00791223 (* 0.0272727 = 0.000215788 loss) | |
| I0506 00:43:57.779443 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00826333 (* 0.0272727 = 0.000225363 loss) | |
| I0506 00:43:57.779458 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00856035 (* 0.0272727 = 0.000233464 loss) | |
| I0506 00:43:57.779469 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0540541 | |
| I0506 00:43:57.779481 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.25 | |
| I0506 00:43:57.779494 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:43:57.779505 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 00:43:57.779515 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 00:43:57.779527 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.5 | |
| I0506 00:43:57.779538 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.75 | |
| I0506 00:43:57.779549 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.875 | |
| I0506 00:43:57.779561 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 1 | |
| I0506 00:43:57.779572 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 00:43:57.779583 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:43:57.779594 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:43:57.779606 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:43:57.779618 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:43:57.779629 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:43:57.779636 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:43:57.779644 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:43:57.779655 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:43:57.779666 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:43:57.779677 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:43:57.779688 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:43:57.779700 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:43:57.779711 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:43:57.779721 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.778409 | |
| I0506 00:43:57.779732 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.0810811 | |
| I0506 00:43:57.779747 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.73444 (* 0.3 = 1.12033 loss) | |
| I0506 00:43:57.779760 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.11469 (* 0.3 = 0.334408 loss) | |
| I0506 00:43:57.779774 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.21466 (* 0.0272727 = 0.0876726 loss) | |
| I0506 00:43:57.779788 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.79458 (* 0.0272727 = 0.103489 loss) | |
| I0506 00:43:57.779811 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.40568 (* 0.0272727 = 0.0928823 loss) | |
| I0506 00:43:57.779826 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.48387 (* 0.0272727 = 0.0950148 loss) | |
| I0506 00:43:57.779840 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.76113 (* 0.0272727 = 0.0753034 loss) | |
| I0506 00:43:57.779853 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 1.97485 (* 0.0272727 = 0.0538596 loss) | |
| I0506 00:43:57.779867 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.01428 (* 0.0272727 = 0.0276621 loss) | |
| I0506 00:43:57.779881 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.259536 (* 0.0272727 = 0.00707824 loss) | |
| I0506 00:43:57.779898 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.126123 (* 0.0272727 = 0.00343972 loss) | |
| I0506 00:43:57.779912 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0669654 (* 0.0272727 = 0.00182633 loss) | |
| I0506 00:43:57.779925 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0838407 (* 0.0272727 = 0.00228656 loss) | |
| I0506 00:43:57.779939 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0422779 (* 0.0272727 = 0.00115303 loss) | |
| I0506 00:43:57.779953 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0469876 (* 0.0272727 = 0.00128148 loss) | |
| I0506 00:43:57.779966 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0212913 (* 0.0272727 = 0.000580672 loss) | |
| I0506 00:43:57.779980 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.017784 (* 0.0272727 = 0.000485017 loss) | |
| I0506 00:43:57.779994 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0197501 (* 0.0272727 = 0.000538638 loss) | |
| I0506 00:43:57.780010 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00840394 (* 0.0272727 = 0.000229198 loss) | |
| I0506 00:43:57.780025 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00729914 (* 0.0272727 = 0.000199067 loss) | |
| I0506 00:43:57.780038 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00674832 (* 0.0272727 = 0.000184045 loss) | |
| I0506 00:43:57.780052 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00774124 (* 0.0272727 = 0.000211125 loss) | |
| I0506 00:43:57.780066 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.008188 (* 0.0272727 = 0.000223309 loss) | |
| I0506 00:43:57.780079 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00797718 (* 0.0272727 = 0.00021756 loss) | |
| I0506 00:43:57.780092 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.135135 | |
| I0506 00:43:57.780102 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.25 | |
| I0506 00:43:57.780114 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.125 | |
| I0506 00:43:57.780125 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:43:57.780136 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 00:43:57.780148 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.5 | |
| I0506 00:43:57.780160 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.75 | |
| I0506 00:43:57.780171 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.875 | |
| I0506 00:43:57.780182 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 1 | |
| I0506 00:43:57.780194 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 00:43:57.780205 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:43:57.780216 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:43:57.780227 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:43:57.780238 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:43:57.780249 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:43:57.780261 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:43:57.780272 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:43:57.780292 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:43:57.780305 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:43:57.780318 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:43:57.780328 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:43:57.780339 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:43:57.780350 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:43:57.780361 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.806818 | |
| I0506 00:43:57.780374 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.297297 | |
| I0506 00:43:57.780386 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.2338 (* 1 = 3.2338 loss) | |
| I0506 00:43:57.780400 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.839843 (* 1 = 0.839843 loss) | |
| I0506 00:43:57.780413 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.31735 (* 0.0909091 = 0.301577 loss) | |
| I0506 00:43:57.780427 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.74163 (* 0.0909091 = 0.340148 loss) | |
| I0506 00:43:57.780441 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.51769 (* 0.0909091 = 0.31979 loss) | |
| I0506 00:43:57.780453 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.48129 (* 0.0909091 = 0.316481 loss) | |
| I0506 00:43:57.780467 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.26178 (* 0.0909091 = 0.205616 loss) | |
| I0506 00:43:57.780480 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.46365 (* 0.0909091 = 0.133059 loss) | |
| I0506 00:43:57.780493 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 0.833514 (* 0.0909091 = 0.075774 loss) | |
| I0506 00:43:57.780506 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.187623 (* 0.0909091 = 0.0170566 loss) | |
| I0506 00:43:57.780520 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.0427189 (* 0.0909091 = 0.00388354 loss) | |
| I0506 00:43:57.780534 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0395283 (* 0.0909091 = 0.00359348 loss) | |
| I0506 00:43:57.780547 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.02843 (* 0.0909091 = 0.00258454 loss) | |
| I0506 00:43:57.780560 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0341646 (* 0.0909091 = 0.00310587 loss) | |
| I0506 00:43:57.780575 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0278406 (* 0.0909091 = 0.00253097 loss) | |
| I0506 00:43:57.780587 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0309432 (* 0.0909091 = 0.00281302 loss) | |
| I0506 00:43:57.780601 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0182998 (* 0.0909091 = 0.00166362 loss) | |
| I0506 00:43:57.780614 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0153835 (* 0.0909091 = 0.0013985 loss) | |
| I0506 00:43:57.780628 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00769423 (* 0.0909091 = 0.000699475 loss) | |
| I0506 00:43:57.780642 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00646255 (* 0.0909091 = 0.000587504 loss) | |
| I0506 00:43:57.780655 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00301443 (* 0.0909091 = 0.000274039 loss) | |
| I0506 00:43:57.780668 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00282702 (* 0.0909091 = 0.000257002 loss) | |
| I0506 00:43:57.780683 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00210064 (* 0.0909091 = 0.000190967 loss) | |
| I0506 00:43:57.780696 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00228041 (* 0.0909091 = 0.00020731 loss) | |
| I0506 00:43:57.780707 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:43:57.780719 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:43:57.780730 15760 solver.cpp:245] Train net output #149: total_confidence = 9.13916e-06 | |
| I0506 00:43:57.780750 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000977473 | |
| I0506 00:43:57.780764 15760 sgd_solver.cpp:106] Iteration 6000, lr = 0.001 | |
| I0506 00:45:45.000713 15760 solver.cpp:229] Iteration 6500, loss = 11.1994 | |
| I0506 00:45:45.000830 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0465116 | |
| I0506 00:45:45.000850 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.125 | |
| I0506 00:45:45.000866 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:45:45.000880 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 00:45:45.000891 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 00:45:45.000903 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 00:45:45.000915 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 00:45:45.000926 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.75 | |
| I0506 00:45:45.000937 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 00:45:45.000949 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 00:45:45.000962 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:45:45.000972 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:45:45.000984 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:45:45.000995 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:45:45.001006 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:45:45.001018 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:45:45.001029 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:45:45.001041 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:45:45.001052 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:45:45.001071 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:45:45.001085 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:45:45.001108 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:45:45.001123 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:45:45.001135 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.75 | |
| I0506 00:45:45.001147 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.139535 | |
| I0506 00:45:45.001163 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.6682 (* 0.3 = 1.10046 loss) | |
| I0506 00:45:45.001178 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.1275 (* 0.3 = 0.338249 loss) | |
| I0506 00:45:45.001191 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 4.18083 (* 0.0272727 = 0.114023 loss) | |
| I0506 00:45:45.001205 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.74471 (* 0.0272727 = 0.102128 loss) | |
| I0506 00:45:45.001219 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.85873 (* 0.0272727 = 0.105238 loss) | |
| I0506 00:45:45.001232 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.26453 (* 0.0272727 = 0.0890325 loss) | |
| I0506 00:45:45.001245 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.79206 (* 0.0272727 = 0.0761472 loss) | |
| I0506 00:45:45.001258 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.13847 (* 0.0272727 = 0.0855945 loss) | |
| I0506 00:45:45.001272 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.32448 (* 0.0272727 = 0.0361222 loss) | |
| I0506 00:45:45.001286 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.841896 (* 0.0272727 = 0.0229608 loss) | |
| I0506 00:45:45.001299 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.0763734 (* 0.0272727 = 0.00208291 loss) | |
| I0506 00:45:45.001313 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0634932 (* 0.0272727 = 0.00173163 loss) | |
| I0506 00:45:45.001327 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0335026 (* 0.0272727 = 0.000913707 loss) | |
| I0506 00:45:45.001341 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0475581 (* 0.0272727 = 0.00129704 loss) | |
| I0506 00:45:45.001354 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0164225 (* 0.0272727 = 0.000447887 loss) | |
| I0506 00:45:45.001386 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.02132 (* 0.0272727 = 0.000581456 loss) | |
| I0506 00:45:45.001402 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.019064 (* 0.0272727 = 0.000519927 loss) | |
| I0506 00:45:45.001415 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0210501 (* 0.0272727 = 0.000574093 loss) | |
| I0506 00:45:45.001430 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00979053 (* 0.0272727 = 0.000267014 loss) | |
| I0506 00:45:45.001444 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00880097 (* 0.0272727 = 0.000240026 loss) | |
| I0506 00:45:45.001457 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00541412 (* 0.0272727 = 0.000147658 loss) | |
| I0506 00:45:45.001471 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00782166 (* 0.0272727 = 0.000213318 loss) | |
| I0506 00:45:45.001485 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00853513 (* 0.0272727 = 0.000232776 loss) | |
| I0506 00:45:45.001505 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00675797 (* 0.0272727 = 0.000184308 loss) | |
| I0506 00:45:45.001518 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0465116 | |
| I0506 00:45:45.001530 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:45:45.001541 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:45:45.001554 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:45:45.001564 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 00:45:45.001575 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 00:45:45.001586 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 00:45:45.001597 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 00:45:45.001610 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 00:45:45.001621 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 00:45:45.001631 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:45:45.001642 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:45:45.001653 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:45:45.001664 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:45:45.001675 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:45:45.001687 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:45:45.001698 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:45:45.001708 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:45:45.001719 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:45:45.001730 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:45:45.001741 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:45:45.001752 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:45:45.001763 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:45:45.001775 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.710227 | |
| I0506 00:45:45.001785 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.162791 | |
| I0506 00:45:45.001799 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.64507 (* 0.3 = 1.09352 loss) | |
| I0506 00:45:45.001812 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.27133 (* 0.3 = 0.381399 loss) | |
| I0506 00:45:45.001827 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.79537 (* 0.0272727 = 0.10351 loss) | |
| I0506 00:45:45.001839 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.69101 (* 0.0272727 = 0.100664 loss) | |
| I0506 00:45:45.001865 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.77383 (* 0.0272727 = 0.102923 loss) | |
| I0506 00:45:45.001879 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.39625 (* 0.0272727 = 0.0926251 loss) | |
| I0506 00:45:45.001893 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.23554 (* 0.0272727 = 0.088242 loss) | |
| I0506 00:45:45.001906 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.70087 (* 0.0272727 = 0.0736602 loss) | |
| I0506 00:45:45.001924 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.4751 (* 0.0272727 = 0.0402301 loss) | |
| I0506 00:45:45.001937 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.772073 (* 0.0272727 = 0.0210565 loss) | |
| I0506 00:45:45.001951 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.0706693 (* 0.0272727 = 0.00192734 loss) | |
| I0506 00:45:45.001965 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0754229 (* 0.0272727 = 0.00205699 loss) | |
| I0506 00:45:45.001978 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0509024 (* 0.0272727 = 0.00138825 loss) | |
| I0506 00:45:45.001991 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0297053 (* 0.0272727 = 0.000810145 loss) | |
| I0506 00:45:45.002005 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0193039 (* 0.0272727 = 0.000526469 loss) | |
| I0506 00:45:45.002019 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0510748 (* 0.0272727 = 0.00139295 loss) | |
| I0506 00:45:45.002032 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0104123 (* 0.0272727 = 0.000283971 loss) | |
| I0506 00:45:45.002046 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.00818533 (* 0.0272727 = 0.000223236 loss) | |
| I0506 00:45:45.002059 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00870661 (* 0.0272727 = 0.000237453 loss) | |
| I0506 00:45:45.002074 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0118304 (* 0.0272727 = 0.000322647 loss) | |
| I0506 00:45:45.002086 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00960166 (* 0.0272727 = 0.000261863 loss) | |
| I0506 00:45:45.002100 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00477042 (* 0.0272727 = 0.000130102 loss) | |
| I0506 00:45:45.002113 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0117106 (* 0.0272727 = 0.00031938 loss) | |
| I0506 00:45:45.002127 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00616618 (* 0.0272727 = 0.000168168 loss) | |
| I0506 00:45:45.002140 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0697674 | |
| I0506 00:45:45.002154 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 00:45:45.002167 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:45:45.002178 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:45:45.002189 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 00:45:45.002202 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 00:45:45.002213 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 00:45:45.002223 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 00:45:45.002235 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 00:45:45.002246 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 00:45:45.002259 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:45:45.002269 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:45:45.002280 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:45:45.002291 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:45:45.002302 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:45:45.002313 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:45:45.002324 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:45:45.002346 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:45:45.002359 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:45:45.002370 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:45:45.002382 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:45:45.002393 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:45:45.002403 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:45:45.002415 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.727273 | |
| I0506 00:45:45.002426 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.162791 | |
| I0506 00:45:45.002440 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.58659 (* 1 = 3.58659 loss) | |
| I0506 00:45:45.002454 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.18822 (* 1 = 1.18822 loss) | |
| I0506 00:45:45.002467 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.51287 (* 0.0909091 = 0.319352 loss) | |
| I0506 00:45:45.002480 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.87003 (* 0.0909091 = 0.351821 loss) | |
| I0506 00:45:45.002493 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.58333 (* 0.0909091 = 0.325758 loss) | |
| I0506 00:45:45.002507 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.79548 (* 0.0909091 = 0.254134 loss) | |
| I0506 00:45:45.002521 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.809 (* 0.0909091 = 0.255363 loss) | |
| I0506 00:45:45.002533 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.94261 (* 0.0909091 = 0.26751 loss) | |
| I0506 00:45:45.002547 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.17783 (* 0.0909091 = 0.107075 loss) | |
| I0506 00:45:45.002560 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.909811 (* 0.0909091 = 0.0827101 loss) | |
| I0506 00:45:45.002574 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.0672546 (* 0.0909091 = 0.00611406 loss) | |
| I0506 00:45:45.002588 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0164414 (* 0.0909091 = 0.00149467 loss) | |
| I0506 00:45:45.002601 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.010662 (* 0.0909091 = 0.000969271 loss) | |
| I0506 00:45:45.002614 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.00811591 (* 0.0909091 = 0.00073781 loss) | |
| I0506 00:45:45.002629 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00618807 (* 0.0909091 = 0.000562552 loss) | |
| I0506 00:45:45.002641 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00521302 (* 0.0909091 = 0.000473911 loss) | |
| I0506 00:45:45.002657 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00405041 (* 0.0909091 = 0.000368219 loss) | |
| I0506 00:45:45.002686 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0035341 (* 0.0909091 = 0.000321282 loss) | |
| I0506 00:45:45.002708 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00129096 (* 0.0909091 = 0.00011736 loss) | |
| I0506 00:45:45.002723 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0013747 (* 0.0909091 = 0.000124973 loss) | |
| I0506 00:45:45.002737 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.000755556 (* 0.0909091 = 6.86869e-05 loss) | |
| I0506 00:45:45.002751 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000612622 (* 0.0909091 = 5.56929e-05 loss) | |
| I0506 00:45:45.002769 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000273954 (* 0.0909091 = 2.49049e-05 loss) | |
| I0506 00:45:45.002784 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000226074 (* 0.0909091 = 2.05522e-05 loss) | |
| I0506 00:45:45.002795 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:45:45.002807 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:45:45.002818 15760 solver.cpp:245] Train net output #149: total_confidence = 9.51512e-06 | |
| I0506 00:45:45.002852 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 7.14106e-05 | |
| I0506 00:45:45.002867 15760 sgd_solver.cpp:106] Iteration 6500, lr = 0.001 | |
| I0506 00:47:30.430546 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 35.2518 > 30) by scale factor 0.851019 | |
| I0506 00:47:32.301420 15760 solver.cpp:229] Iteration 7000, loss = 11.1788 | |
| I0506 00:47:32.301496 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0714286 | |
| I0506 00:47:32.301514 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:47:32.301527 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:47:32.301538 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:47:32.301550 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 00:47:32.301563 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 00:47:32.301574 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.5 | |
| I0506 00:47:32.301585 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.75 | |
| I0506 00:47:32.301597 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 00:47:32.301609 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 00:47:32.301621 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:47:32.301632 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:47:32.301643 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:47:32.301656 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:47:32.301666 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:47:32.301678 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:47:32.301690 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:47:32.301702 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:47:32.301713 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:47:32.301724 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:47:32.301736 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:47:32.301748 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:47:32.301759 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:47:32.301770 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.767045 | |
| I0506 00:47:32.301781 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.166667 | |
| I0506 00:47:32.301798 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.59231 (* 0.3 = 1.07769 loss) | |
| I0506 00:47:32.301812 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.05857 (* 0.3 = 0.31757 loss) | |
| I0506 00:47:32.301826 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.47736 (* 0.0272727 = 0.0948371 loss) | |
| I0506 00:47:32.301839 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.56611 (* 0.0272727 = 0.0972577 loss) | |
| I0506 00:47:32.301853 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.39115 (* 0.0272727 = 0.0924859 loss) | |
| I0506 00:47:32.301867 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.38379 (* 0.0272727 = 0.0922852 loss) | |
| I0506 00:47:32.301883 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.66915 (* 0.0272727 = 0.0727949 loss) | |
| I0506 00:47:32.301898 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.37804 (* 0.0272727 = 0.0648556 loss) | |
| I0506 00:47:32.301913 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.44082 (* 0.0272727 = 0.0392951 loss) | |
| I0506 00:47:32.301925 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.723215 (* 0.0272727 = 0.019724 loss) | |
| I0506 00:47:32.301940 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.0423823 (* 0.0272727 = 0.00115588 loss) | |
| I0506 00:47:32.301954 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0432371 (* 0.0272727 = 0.00117919 loss) | |
| I0506 00:47:32.301967 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0213231 (* 0.0272727 = 0.000581539 loss) | |
| I0506 00:47:32.301981 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0431183 (* 0.0272727 = 0.00117595 loss) | |
| I0506 00:47:32.302027 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0276027 (* 0.0272727 = 0.000752801 loss) | |
| I0506 00:47:32.302043 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0199374 (* 0.0272727 = 0.000543746 loss) | |
| I0506 00:47:32.302057 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0130468 (* 0.0272727 = 0.000355821 loss) | |
| I0506 00:47:32.302070 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0113107 (* 0.0272727 = 0.000308474 loss) | |
| I0506 00:47:32.302084 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00645166 (* 0.0272727 = 0.000175954 loss) | |
| I0506 00:47:32.302098 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00602656 (* 0.0272727 = 0.000164361 loss) | |
| I0506 00:47:32.302111 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00877267 (* 0.0272727 = 0.000239255 loss) | |
| I0506 00:47:32.302125 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00747345 (* 0.0272727 = 0.000203821 loss) | |
| I0506 00:47:32.302139 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00679367 (* 0.0272727 = 0.000185282 loss) | |
| I0506 00:47:32.302152 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00621222 (* 0.0272727 = 0.000169424 loss) | |
| I0506 00:47:32.302165 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:47:32.302176 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:47:32.302187 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 00:47:32.302199 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 00:47:32.302211 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 00:47:32.302222 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 00:47:32.302233 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.5 | |
| I0506 00:47:32.302245 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 00:47:32.302256 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 00:47:32.302268 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 00:47:32.302279 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:47:32.302291 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:47:32.302304 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:47:32.302315 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:47:32.302326 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:47:32.302338 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:47:32.302350 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:47:32.302361 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:47:32.302371 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:47:32.302382 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:47:32.302393 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:47:32.302404 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:47:32.302415 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:47:32.302426 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.761364 | |
| I0506 00:47:32.302438 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.047619 | |
| I0506 00:47:32.302453 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.59567 (* 0.3 = 1.0787 loss) | |
| I0506 00:47:32.302465 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.953823 (* 0.3 = 0.286147 loss) | |
| I0506 00:47:32.302479 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.35704 (* 0.0272727 = 0.0915558 loss) | |
| I0506 00:47:32.302494 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.52751 (* 0.0272727 = 0.096205 loss) | |
| I0506 00:47:32.302517 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.05925 (* 0.0272727 = 0.0834342 loss) | |
| I0506 00:47:32.302536 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.15636 (* 0.0272727 = 0.0860826 loss) | |
| I0506 00:47:32.302551 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.66404 (* 0.0272727 = 0.0726556 loss) | |
| I0506 00:47:32.302564 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.27067 (* 0.0272727 = 0.0619273 loss) | |
| I0506 00:47:32.302577 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.62327 (* 0.0272727 = 0.0442709 loss) | |
| I0506 00:47:32.302592 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.63151 (* 0.0272727 = 0.017223 loss) | |
| I0506 00:47:32.302605 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.0274872 (* 0.0272727 = 0.000749651 loss) | |
| I0506 00:47:32.302619 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0207883 (* 0.0272727 = 0.000566954 loss) | |
| I0506 00:47:32.302633 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0115262 (* 0.0272727 = 0.00031435 loss) | |
| I0506 00:47:32.302646 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.00993835 (* 0.0272727 = 0.000271046 loss) | |
| I0506 00:47:32.302660 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.00785574 (* 0.0272727 = 0.000214247 loss) | |
| I0506 00:47:32.302673 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0137687 (* 0.0272727 = 0.000375509 loss) | |
| I0506 00:47:32.302687 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.00712391 (* 0.0272727 = 0.000194288 loss) | |
| I0506 00:47:32.302701 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.00391947 (* 0.0272727 = 0.000106895 loss) | |
| I0506 00:47:32.302714 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00551265 (* 0.0272727 = 0.000150345 loss) | |
| I0506 00:47:32.302728 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00457584 (* 0.0272727 = 0.000124796 loss) | |
| I0506 00:47:32.302742 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00168615 (* 0.0272727 = 4.59858e-05 loss) | |
| I0506 00:47:32.302755 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00218954 (* 0.0272727 = 5.97147e-05 loss) | |
| I0506 00:47:32.302769 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00285072 (* 0.0272727 = 7.77469e-05 loss) | |
| I0506 00:47:32.302783 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00357659 (* 0.0272727 = 9.75433e-05 loss) | |
| I0506 00:47:32.302795 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.047619 | |
| I0506 00:47:32.302806 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 00:47:32.302819 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:47:32.302830 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:47:32.302841 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.375 | |
| I0506 00:47:32.302853 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.375 | |
| I0506 00:47:32.302865 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.5 | |
| I0506 00:47:32.302875 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 00:47:32.302887 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 00:47:32.302899 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 00:47:32.302911 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:47:32.302922 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:47:32.302937 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:47:32.302948 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:47:32.302959 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:47:32.302970 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:47:32.302992 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:47:32.303004 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:47:32.303016 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:47:32.303027 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:47:32.303040 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:47:32.303050 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:47:32.303061 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:47:32.303072 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.772727 | |
| I0506 00:47:32.303084 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.238095 | |
| I0506 00:47:32.303098 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.00077 (* 1 = 3.00077 loss) | |
| I0506 00:47:32.303112 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.774452 (* 1 = 0.774452 loss) | |
| I0506 00:47:32.303125 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.41843 (* 0.0909091 = 0.310766 loss) | |
| I0506 00:47:32.303139 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.27046 (* 0.0909091 = 0.297315 loss) | |
| I0506 00:47:32.303153 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.10266 (* 0.0909091 = 0.28206 loss) | |
| I0506 00:47:32.303166 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.52203 (* 0.0909091 = 0.229275 loss) | |
| I0506 00:47:32.303179 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.37464 (* 0.0909091 = 0.215876 loss) | |
| I0506 00:47:32.303194 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.27004 (* 0.0909091 = 0.206367 loss) | |
| I0506 00:47:32.303206 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.10162 (* 0.0909091 = 0.100147 loss) | |
| I0506 00:47:32.303220 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.573018 (* 0.0909091 = 0.0520925 loss) | |
| I0506 00:47:32.303234 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.00854008 (* 0.0909091 = 0.000776371 loss) | |
| I0506 00:47:32.303248 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.00664499 (* 0.0909091 = 0.00060409 loss) | |
| I0506 00:47:32.303261 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.00779798 (* 0.0909091 = 0.000708907 loss) | |
| I0506 00:47:32.303275 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.00664851 (* 0.0909091 = 0.00060441 loss) | |
| I0506 00:47:32.303289 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00575081 (* 0.0909091 = 0.0005228 loss) | |
| I0506 00:47:32.303303 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00447502 (* 0.0909091 = 0.00040682 loss) | |
| I0506 00:47:32.303316 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00525202 (* 0.0909091 = 0.000477457 loss) | |
| I0506 00:47:32.303329 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00362054 (* 0.0909091 = 0.00032914 loss) | |
| I0506 00:47:32.303344 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00193277 (* 0.0909091 = 0.000175706 loss) | |
| I0506 00:47:32.303357 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00170616 (* 0.0909091 = 0.000155105 loss) | |
| I0506 00:47:32.303371 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00170603 (* 0.0909091 = 0.000155094 loss) | |
| I0506 00:47:32.303385 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.0008655 (* 0.0909091 = 7.86818e-05 loss) | |
| I0506 00:47:32.303400 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00115464 (* 0.0909091 = 0.000104967 loss) | |
| I0506 00:47:32.303412 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000437421 (* 0.0909091 = 3.97655e-05 loss) | |
| I0506 00:47:32.303424 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:47:32.303436 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:47:32.303457 15760 solver.cpp:245] Train net output #149: total_confidence = 7.54778e-05 | |
| I0506 00:47:32.303469 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000720442 | |
| I0506 00:47:32.303483 15760 sgd_solver.cpp:106] Iteration 7000, lr = 0.001 | |
| I0506 00:48:15.384912 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 33.2339 > 30) by scale factor 0.902692 | |
| I0506 00:48:49.846098 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 34.5061 > 30) by scale factor 0.869412 | |
| I0506 00:48:56.059046 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.0624 > 30) by scale factor 0.997925 | |
| I0506 00:48:57.992563 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 34.0538 > 30) by scale factor 0.880959 | |
| I0506 00:49:19.556100 15760 solver.cpp:229] Iteration 7500, loss = 11.0525 | |
| I0506 00:49:19.556170 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0506 00:49:19.556190 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:49:19.556201 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:49:19.556213 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:49:19.556224 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 00:49:19.556236 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 00:49:19.556248 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 00:49:19.556260 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 1 | |
| I0506 00:49:19.556272 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 1 | |
| I0506 00:49:19.556284 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 00:49:19.556295 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:49:19.556308 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:49:19.556318 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:49:19.556330 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:49:19.556342 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:49:19.556354 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:49:19.556365 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:49:19.556376 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:49:19.556388 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:49:19.556399 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:49:19.556411 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:49:19.556422 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:49:19.556434 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:49:19.556445 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.75 | |
| I0506 00:49:19.556457 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.0681818 | |
| I0506 00:49:19.556473 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.78844 (* 0.3 = 1.13653 loss) | |
| I0506 00:49:19.556488 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.03195 (* 0.3 = 0.309584 loss) | |
| I0506 00:49:19.556502 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.57242 (* 0.0272727 = 0.0974298 loss) | |
| I0506 00:49:19.556517 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 4.06833 (* 0.0272727 = 0.110955 loss) | |
| I0506 00:49:19.556530 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.26139 (* 0.0272727 = 0.11622 loss) | |
| I0506 00:49:19.556545 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.92224 (* 0.0272727 = 0.10697 loss) | |
| I0506 00:49:19.556558 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.84452 (* 0.0272727 = 0.104851 loss) | |
| I0506 00:49:19.556571 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.49767 (* 0.0272727 = 0.0681184 loss) | |
| I0506 00:49:19.556586 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 0.259671 (* 0.0272727 = 0.00708193 loss) | |
| I0506 00:49:19.556599 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.0537082 (* 0.0272727 = 0.00146477 loss) | |
| I0506 00:49:19.556614 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.0276215 (* 0.0272727 = 0.000753313 loss) | |
| I0506 00:49:19.556628 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0178089 (* 0.0272727 = 0.000485698 loss) | |
| I0506 00:49:19.556680 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0214321 (* 0.0272727 = 0.000584513 loss) | |
| I0506 00:49:19.556696 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0217227 (* 0.0272727 = 0.000592438 loss) | |
| I0506 00:49:19.556710 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.00804599 (* 0.0272727 = 0.000219436 loss) | |
| I0506 00:49:19.556723 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0240869 (* 0.0272727 = 0.000656915 loss) | |
| I0506 00:49:19.556737 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0255653 (* 0.0272727 = 0.000697235 loss) | |
| I0506 00:49:19.556751 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0208639 (* 0.0272727 = 0.000569014 loss) | |
| I0506 00:49:19.556766 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0212314 (* 0.0272727 = 0.000579037 loss) | |
| I0506 00:49:19.556779 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0223436 (* 0.0272727 = 0.000609372 loss) | |
| I0506 00:49:19.556793 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0166979 (* 0.0272727 = 0.000455398 loss) | |
| I0506 00:49:19.556807 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00866406 (* 0.0272727 = 0.000236292 loss) | |
| I0506 00:49:19.556821 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0124183 (* 0.0272727 = 0.00033868 loss) | |
| I0506 00:49:19.556835 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0124096 (* 0.0272727 = 0.000338443 loss) | |
| I0506 00:49:19.556848 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:49:19.556864 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 00:49:19.556875 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:49:19.556887 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:49:19.556900 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 00:49:19.556910 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 00:49:19.556922 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 00:49:19.556934 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 1 | |
| I0506 00:49:19.556946 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 1 | |
| I0506 00:49:19.556957 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 00:49:19.556967 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:49:19.556979 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:49:19.556990 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:49:19.557001 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:49:19.557013 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:49:19.557024 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:49:19.557035 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:49:19.557047 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:49:19.557059 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:49:19.557070 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:49:19.557081 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:49:19.557092 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:49:19.557103 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:49:19.557116 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.744318 | |
| I0506 00:49:19.557144 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.0681818 | |
| I0506 00:49:19.557160 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.86831 (* 0.3 = 1.16049 loss) | |
| I0506 00:49:19.557174 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.13594 (* 0.3 = 0.340782 loss) | |
| I0506 00:49:19.557201 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.44783 (* 0.0272727 = 0.0940316 loss) | |
| I0506 00:49:19.557216 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.95692 (* 0.0272727 = 0.107916 loss) | |
| I0506 00:49:19.557230 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 4.33617 (* 0.0272727 = 0.118259 loss) | |
| I0506 00:49:19.557245 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 4.20719 (* 0.0272727 = 0.114742 loss) | |
| I0506 00:49:19.557257 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.97049 (* 0.0272727 = 0.108286 loss) | |
| I0506 00:49:19.557271 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.91222 (* 0.0272727 = 0.0794242 loss) | |
| I0506 00:49:19.557284 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 0.284202 (* 0.0272727 = 0.00775097 loss) | |
| I0506 00:49:19.557298 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.0997996 (* 0.0272727 = 0.00272181 loss) | |
| I0506 00:49:19.557312 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.0244524 (* 0.0272727 = 0.000666885 loss) | |
| I0506 00:49:19.557327 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0208433 (* 0.0272727 = 0.000568453 loss) | |
| I0506 00:49:19.557340 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0200295 (* 0.0272727 = 0.00054626 loss) | |
| I0506 00:49:19.557354 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0174618 (* 0.0272727 = 0.000476232 loss) | |
| I0506 00:49:19.557368 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0343247 (* 0.0272727 = 0.000936129 loss) | |
| I0506 00:49:19.557382 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0167289 (* 0.0272727 = 0.000456244 loss) | |
| I0506 00:49:19.557395 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0162993 (* 0.0272727 = 0.000444526 loss) | |
| I0506 00:49:19.557410 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.00941945 (* 0.0272727 = 0.000256894 loss) | |
| I0506 00:49:19.557422 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00570159 (* 0.0272727 = 0.000155498 loss) | |
| I0506 00:49:19.557436 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00922217 (* 0.0272727 = 0.000251514 loss) | |
| I0506 00:49:19.557451 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00979037 (* 0.0272727 = 0.00026701 loss) | |
| I0506 00:49:19.557464 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00751942 (* 0.0272727 = 0.000205075 loss) | |
| I0506 00:49:19.557477 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00429059 (* 0.0272727 = 0.000117016 loss) | |
| I0506 00:49:19.557492 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00765562 (* 0.0272727 = 0.00020879 loss) | |
| I0506 00:49:19.557503 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0227273 | |
| I0506 00:49:19.557515 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:49:19.557528 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:49:19.557538 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:49:19.557550 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0 | |
| I0506 00:49:19.557561 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.125 | |
| I0506 00:49:19.557574 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 00:49:19.557585 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 1 | |
| I0506 00:49:19.557595 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 1 | |
| I0506 00:49:19.557607 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 00:49:19.557618 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:49:19.557629 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:49:19.557641 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:49:19.557652 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:49:19.557673 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:49:19.557685 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:49:19.557698 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:49:19.557708 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:49:19.557720 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:49:19.557728 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:49:19.557735 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:49:19.557742 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:49:19.557754 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:49:19.557765 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.744318 | |
| I0506 00:49:19.557777 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.113636 | |
| I0506 00:49:19.557791 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.1283 (* 1 = 3.1283 loss) | |
| I0506 00:49:19.557804 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.901818 (* 1 = 0.901818 loss) | |
| I0506 00:49:19.557818 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.24575 (* 0.0909091 = 0.295068 loss) | |
| I0506 00:49:19.557832 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.11775 (* 0.0909091 = 0.283432 loss) | |
| I0506 00:49:19.557845 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.05924 (* 0.0909091 = 0.278113 loss) | |
| I0506 00:49:19.557858 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.35864 (* 0.0909091 = 0.305331 loss) | |
| I0506 00:49:19.557873 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.28031 (* 0.0909091 = 0.29821 loss) | |
| I0506 00:49:19.557885 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.60311 (* 0.0909091 = 0.236647 loss) | |
| I0506 00:49:19.557899 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 0.176115 (* 0.0909091 = 0.0160105 loss) | |
| I0506 00:49:19.557917 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.0541397 (* 0.0909091 = 0.00492179 loss) | |
| I0506 00:49:19.557931 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.00243154 (* 0.0909091 = 0.000221049 loss) | |
| I0506 00:49:19.557945 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.00145145 (* 0.0909091 = 0.00013195 loss) | |
| I0506 00:49:19.557958 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.00160723 (* 0.0909091 = 0.000146112 loss) | |
| I0506 00:49:19.557971 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.00144612 (* 0.0909091 = 0.000131465 loss) | |
| I0506 00:49:19.557986 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00170219 (* 0.0909091 = 0.000154745 loss) | |
| I0506 00:49:19.557998 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00184661 (* 0.0909091 = 0.000167873 loss) | |
| I0506 00:49:19.558012 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00185095 (* 0.0909091 = 0.000168268 loss) | |
| I0506 00:49:19.558025 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00111364 (* 0.0909091 = 0.00010124 loss) | |
| I0506 00:49:19.558039 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.000780794 (* 0.0909091 = 7.09813e-05 loss) | |
| I0506 00:49:19.558053 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00107941 (* 0.0909091 = 9.81286e-05 loss) | |
| I0506 00:49:19.558068 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.000637976 (* 0.0909091 = 5.79978e-05 loss) | |
| I0506 00:49:19.558080 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000588501 (* 0.0909091 = 5.35001e-05 loss) | |
| I0506 00:49:19.558094 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00030625 (* 0.0909091 = 2.78409e-05 loss) | |
| I0506 00:49:19.558107 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000251835 (* 0.0909091 = 2.28941e-05 loss) | |
| I0506 00:49:19.558130 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:49:19.558141 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:49:19.558152 15760 solver.cpp:245] Train net output #149: total_confidence = 1.26082e-06 | |
| I0506 00:49:19.558164 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000213499 | |
| I0506 00:49:19.558182 15760 sgd_solver.cpp:106] Iteration 7500, lr = 0.001 | |
| I0506 00:50:10.922317 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 32.8727 > 30) by scale factor 0.912612 | |
| I0506 00:50:17.552284 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 33.5504 > 30) by scale factor 0.894178 | |
| I0506 00:51:06.867019 15760 solver.cpp:229] Iteration 8000, loss = 10.9567 | |
| I0506 00:51:06.867143 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0769231 | |
| I0506 00:51:06.867163 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:51:06.867177 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:51:06.867187 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 00:51:06.867199 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 00:51:06.867211 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 00:51:06.867223 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.25 | |
| I0506 00:51:06.867235 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.625 | |
| I0506 00:51:06.867246 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 00:51:06.867259 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 00:51:06.867270 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 00:51:06.867282 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 00:51:06.867295 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:51:06.867305 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:51:06.867317 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:51:06.867328 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:51:06.867341 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:51:06.867352 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:51:06.867363 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:51:06.867374 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:51:06.867385 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:51:06.867398 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:51:06.867408 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:51:06.867420 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.704545 | |
| I0506 00:51:06.867431 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.173077 | |
| I0506 00:51:06.867449 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.87185 (* 0.3 = 1.16156 loss) | |
| I0506 00:51:06.867462 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.37123 (* 0.3 = 0.411368 loss) | |
| I0506 00:51:06.867476 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.69045 (* 0.0272727 = 0.100649 loss) | |
| I0506 00:51:06.867491 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 4.56885 (* 0.0272727 = 0.124605 loss) | |
| I0506 00:51:06.867504 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.33216 (* 0.0272727 = 0.11815 loss) | |
| I0506 00:51:06.867518 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 4.32714 (* 0.0272727 = 0.118013 loss) | |
| I0506 00:51:06.867532 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.93024 (* 0.0272727 = 0.107188 loss) | |
| I0506 00:51:06.867545 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.26351 (* 0.0272727 = 0.0890048 loss) | |
| I0506 00:51:06.867559 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.47051 (* 0.0272727 = 0.0401049 loss) | |
| I0506 00:51:06.867573 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.631927 (* 0.0272727 = 0.0172344 loss) | |
| I0506 00:51:06.867586 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.636753 (* 0.0272727 = 0.017366 loss) | |
| I0506 00:51:06.867600 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.51555 (* 0.0272727 = 0.0140605 loss) | |
| I0506 00:51:06.867614 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.780591 (* 0.0272727 = 0.0212889 loss) | |
| I0506 00:51:06.867629 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.112546 (* 0.0272727 = 0.00306945 loss) | |
| I0506 00:51:06.867642 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.143977 (* 0.0272727 = 0.00392666 loss) | |
| I0506 00:51:06.867674 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.113084 (* 0.0272727 = 0.00308411 loss) | |
| I0506 00:51:06.867689 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0631169 (* 0.0272727 = 0.00172137 loss) | |
| I0506 00:51:06.867703 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0589932 (* 0.0272727 = 0.00160891 loss) | |
| I0506 00:51:06.867717 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0338548 (* 0.0272727 = 0.000923314 loss) | |
| I0506 00:51:06.867732 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.030605 (* 0.0272727 = 0.000834681 loss) | |
| I0506 00:51:06.867745 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0187678 (* 0.0272727 = 0.000511848 loss) | |
| I0506 00:51:06.867759 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.03252 (* 0.0272727 = 0.000886908 loss) | |
| I0506 00:51:06.867772 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0313437 (* 0.0272727 = 0.000854828 loss) | |
| I0506 00:51:06.867786 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0218382 (* 0.0272727 = 0.000595589 loss) | |
| I0506 00:51:06.867799 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0576923 | |
| I0506 00:51:06.867810 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:51:06.867821 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:51:06.867832 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:51:06.867843 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 00:51:06.867854 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0 | |
| I0506 00:51:06.867866 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.25 | |
| I0506 00:51:06.867882 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.625 | |
| I0506 00:51:06.867889 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 00:51:06.867902 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 00:51:06.867914 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 00:51:06.867925 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 00:51:06.867936 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:51:06.867949 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:51:06.867959 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:51:06.867970 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:51:06.867981 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:51:06.867992 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:51:06.868003 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:51:06.868015 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:51:06.868026 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:51:06.868036 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:51:06.868047 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:51:06.868058 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.710227 | |
| I0506 00:51:06.868070 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.0961538 | |
| I0506 00:51:06.868083 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.70956 (* 0.3 = 1.11287 loss) | |
| I0506 00:51:06.868098 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.30027 (* 0.3 = 0.390082 loss) | |
| I0506 00:51:06.868111 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.5944 (* 0.0272727 = 0.098029 loss) | |
| I0506 00:51:06.868125 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 4.03767 (* 0.0272727 = 0.110118 loss) | |
| I0506 00:51:06.868149 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 4.46828 (* 0.0272727 = 0.121862 loss) | |
| I0506 00:51:06.868168 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.78126 (* 0.0272727 = 0.103125 loss) | |
| I0506 00:51:06.868182 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 4.04408 (* 0.0272727 = 0.110293 loss) | |
| I0506 00:51:06.868196 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.1447 (* 0.0272727 = 0.0857646 loss) | |
| I0506 00:51:06.868209 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.70878 (* 0.0272727 = 0.0466032 loss) | |
| I0506 00:51:06.868222 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.748104 (* 0.0272727 = 0.0204028 loss) | |
| I0506 00:51:06.868237 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.895461 (* 0.0272727 = 0.0244217 loss) | |
| I0506 00:51:06.868249 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.675607 (* 0.0272727 = 0.0184256 loss) | |
| I0506 00:51:06.868263 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.765761 (* 0.0272727 = 0.0208844 loss) | |
| I0506 00:51:06.868276 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.186477 (* 0.0272727 = 0.00508574 loss) | |
| I0506 00:51:06.868290 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.136768 (* 0.0272727 = 0.00373004 loss) | |
| I0506 00:51:06.868304 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.123565 (* 0.0272727 = 0.00336995 loss) | |
| I0506 00:51:06.868316 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0776778 (* 0.0272727 = 0.00211849 loss) | |
| I0506 00:51:06.868330 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0737898 (* 0.0272727 = 0.00201245 loss) | |
| I0506 00:51:06.868343 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0485096 (* 0.0272727 = 0.00132299 loss) | |
| I0506 00:51:06.868357 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0445539 (* 0.0272727 = 0.00121511 loss) | |
| I0506 00:51:06.868371 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0354874 (* 0.0272727 = 0.000967839 loss) | |
| I0506 00:51:06.868383 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0257701 (* 0.0272727 = 0.00070282 loss) | |
| I0506 00:51:06.868397 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0452421 (* 0.0272727 = 0.00123387 loss) | |
| I0506 00:51:06.868410 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0272014 (* 0.0272727 = 0.000741855 loss) | |
| I0506 00:51:06.868422 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0961538 | |
| I0506 00:51:06.868434 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:51:06.868445 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:51:06.868456 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:51:06.868468 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 00:51:06.868479 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.125 | |
| I0506 00:51:06.868490 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 00:51:06.868501 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.625 | |
| I0506 00:51:06.868512 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 00:51:06.868525 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 00:51:06.868535 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 00:51:06.868546 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 00:51:06.868557 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:51:06.868568 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:51:06.868579 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:51:06.868590 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:51:06.868602 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:51:06.868623 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:51:06.868635 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:51:06.868646 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:51:06.868657 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:51:06.868669 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:51:06.868680 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:51:06.868690 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.732955 | |
| I0506 00:51:06.868702 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.192308 | |
| I0506 00:51:06.868716 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.47772 (* 1 = 3.47772 loss) | |
| I0506 00:51:06.868729 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.18621 (* 1 = 1.18621 loss) | |
| I0506 00:51:06.868742 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.17397 (* 0.0909091 = 0.288542 loss) | |
| I0506 00:51:06.868755 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.72822 (* 0.0909091 = 0.338929 loss) | |
| I0506 00:51:06.868768 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 4.10211 (* 0.0909091 = 0.37292 loss) | |
| I0506 00:51:06.868782 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.63457 (* 0.0909091 = 0.330416 loss) | |
| I0506 00:51:06.868795 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.14555 (* 0.0909091 = 0.285959 loss) | |
| I0506 00:51:06.868808 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.81035 (* 0.0909091 = 0.255486 loss) | |
| I0506 00:51:06.868823 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.17026 (* 0.0909091 = 0.106387 loss) | |
| I0506 00:51:06.868835 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.691949 (* 0.0909091 = 0.0629045 loss) | |
| I0506 00:51:06.868849 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.675912 (* 0.0909091 = 0.0614465 loss) | |
| I0506 00:51:06.868862 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.462631 (* 0.0909091 = 0.0420574 loss) | |
| I0506 00:51:06.868875 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.584726 (* 0.0909091 = 0.0531569 loss) | |
| I0506 00:51:06.868888 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.202504 (* 0.0909091 = 0.0184095 loss) | |
| I0506 00:51:06.868901 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.132688 (* 0.0909091 = 0.0120625 loss) | |
| I0506 00:51:06.868916 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.117889 (* 0.0909091 = 0.0107172 loss) | |
| I0506 00:51:06.868932 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0785487 (* 0.0909091 = 0.00714079 loss) | |
| I0506 00:51:06.868947 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0540603 (* 0.0909091 = 0.00491457 loss) | |
| I0506 00:51:06.868960 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0197566 (* 0.0909091 = 0.00179606 loss) | |
| I0506 00:51:06.868974 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0152736 (* 0.0909091 = 0.0013885 loss) | |
| I0506 00:51:06.868988 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00864989 (* 0.0909091 = 0.000786354 loss) | |
| I0506 00:51:06.869002 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00638144 (* 0.0909091 = 0.000580131 loss) | |
| I0506 00:51:06.869016 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00631912 (* 0.0909091 = 0.000574465 loss) | |
| I0506 00:51:06.869029 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00538587 (* 0.0909091 = 0.000489625 loss) | |
| I0506 00:51:06.869041 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:51:06.869052 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:51:06.869063 15760 solver.cpp:245] Train net output #149: total_confidence = 2.33091e-06 | |
| I0506 00:51:06.869086 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 1.61887e-05 | |
| I0506 00:51:06.869099 15760 sgd_solver.cpp:106] Iteration 8000, lr = 0.001 | |
| I0506 00:51:48.267562 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 33.9585 > 30) by scale factor 0.883431 | |
| I0506 00:52:48.641942 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 52.5746 > 30) by scale factor 0.570618 | |
| I0506 00:52:54.127571 15760 solver.cpp:229] Iteration 8500, loss = 10.7516 | |
| I0506 00:52:54.127640 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0425532 | |
| I0506 00:52:54.127657 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.125 | |
| I0506 00:52:54.127671 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:52:54.127684 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:52:54.127696 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 00:52:54.127708 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.25 | |
| I0506 00:52:54.127719 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.25 | |
| I0506 00:52:54.127732 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 00:52:54.127743 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 00:52:54.127755 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 00:52:54.127768 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:52:54.127779 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:52:54.127790 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:52:54.127802 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:52:54.127813 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:52:54.127825 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:52:54.127837 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:52:54.127849 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:52:54.127861 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:52:54.127873 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:52:54.127884 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:52:54.127899 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:52:54.127912 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:52:54.127923 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.744318 | |
| I0506 00:52:54.127935 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.191489 | |
| I0506 00:52:54.127951 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.43153 (* 0.3 = 1.02946 loss) | |
| I0506 00:52:54.127966 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.985312 (* 0.3 = 0.295594 loss) | |
| I0506 00:52:54.127980 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.31198 (* 0.0272727 = 0.0903268 loss) | |
| I0506 00:52:54.127995 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.47487 (* 0.0272727 = 0.0947692 loss) | |
| I0506 00:52:54.128008 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.42879 (* 0.0272727 = 0.0935124 loss) | |
| I0506 00:52:54.128021 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.2696 (* 0.0272727 = 0.0891709 loss) | |
| I0506 00:52:54.128036 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.40413 (* 0.0272727 = 0.0928398 loss) | |
| I0506 00:52:54.128049 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.19551 (* 0.0272727 = 0.0871504 loss) | |
| I0506 00:52:54.128062 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.5863 (* 0.0272727 = 0.0705355 loss) | |
| I0506 00:52:54.128077 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.897786 (* 0.0272727 = 0.0244851 loss) | |
| I0506 00:52:54.128092 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.060299 (* 0.0272727 = 0.00164452 loss) | |
| I0506 00:52:54.128105 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0427422 (* 0.0272727 = 0.0011657 loss) | |
| I0506 00:52:54.128120 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0327025 (* 0.0272727 = 0.000891887 loss) | |
| I0506 00:52:54.128134 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0445189 (* 0.0272727 = 0.00121415 loss) | |
| I0506 00:52:54.128178 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0211145 (* 0.0272727 = 0.000575851 loss) | |
| I0506 00:52:54.128193 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0263237 (* 0.0272727 = 0.000717918 loss) | |
| I0506 00:52:54.128208 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.022586 (* 0.0272727 = 0.000615981 loss) | |
| I0506 00:52:54.128222 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0136389 (* 0.0272727 = 0.00037197 loss) | |
| I0506 00:52:54.128235 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0080343 (* 0.0272727 = 0.000219117 loss) | |
| I0506 00:52:54.128249 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00893449 (* 0.0272727 = 0.000243668 loss) | |
| I0506 00:52:54.128263 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00656172 (* 0.0272727 = 0.000178956 loss) | |
| I0506 00:52:54.128276 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00715192 (* 0.0272727 = 0.000195052 loss) | |
| I0506 00:52:54.128290 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00572702 (* 0.0272727 = 0.000156191 loss) | |
| I0506 00:52:54.128304 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00445453 (* 0.0272727 = 0.000121487 loss) | |
| I0506 00:52:54.128315 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0212766 | |
| I0506 00:52:54.128329 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 00:52:54.128340 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:52:54.128350 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 00:52:54.128362 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 00:52:54.128374 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.25 | |
| I0506 00:52:54.128386 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.25 | |
| I0506 00:52:54.128399 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 00:52:54.128407 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 00:52:54.128414 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 00:52:54.128427 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:52:54.128437 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:52:54.128453 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:52:54.128464 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:52:54.128475 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:52:54.128487 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:52:54.128499 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:52:54.128509 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:52:54.128520 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:52:54.128531 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:52:54.128542 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:52:54.128554 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:52:54.128566 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:52:54.128576 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.732955 | |
| I0506 00:52:54.128588 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.191489 | |
| I0506 00:52:54.128602 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.47952 (* 0.3 = 1.04386 loss) | |
| I0506 00:52:54.128615 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.02473 (* 0.3 = 0.30742 loss) | |
| I0506 00:52:54.128629 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.50966 (* 0.0272727 = 0.095718 loss) | |
| I0506 00:52:54.128643 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.38732 (* 0.0272727 = 0.0923816 loss) | |
| I0506 00:52:54.128667 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.40303 (* 0.0272727 = 0.09281 loss) | |
| I0506 00:52:54.128682 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.36017 (* 0.0272727 = 0.091641 loss) | |
| I0506 00:52:54.128695 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.2076 (* 0.0272727 = 0.0874801 loss) | |
| I0506 00:52:54.128710 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.24515 (* 0.0272727 = 0.0885041 loss) | |
| I0506 00:52:54.128723 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.49506 (* 0.0272727 = 0.0680471 loss) | |
| I0506 00:52:54.128736 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.734285 (* 0.0272727 = 0.020026 loss) | |
| I0506 00:52:54.128751 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.186903 (* 0.0272727 = 0.00509736 loss) | |
| I0506 00:52:54.128764 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.138347 (* 0.0272727 = 0.00377311 loss) | |
| I0506 00:52:54.128779 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.11638 (* 0.0272727 = 0.003174 loss) | |
| I0506 00:52:54.128793 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0712567 (* 0.0272727 = 0.00194337 loss) | |
| I0506 00:52:54.128806 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0693711 (* 0.0272727 = 0.00189194 loss) | |
| I0506 00:52:54.128819 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0453671 (* 0.0272727 = 0.00123728 loss) | |
| I0506 00:52:54.128834 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0509122 (* 0.0272727 = 0.00138851 loss) | |
| I0506 00:52:54.128846 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0225156 (* 0.0272727 = 0.000614061 loss) | |
| I0506 00:52:54.128860 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0268744 (* 0.0272727 = 0.000732939 loss) | |
| I0506 00:52:54.128873 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0158127 (* 0.0272727 = 0.000431256 loss) | |
| I0506 00:52:54.128886 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0156798 (* 0.0272727 = 0.000427632 loss) | |
| I0506 00:52:54.128901 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00753018 (* 0.0272727 = 0.000205369 loss) | |
| I0506 00:52:54.128913 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0142717 (* 0.0272727 = 0.000389228 loss) | |
| I0506 00:52:54.128927 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.013533 (* 0.0272727 = 0.000369081 loss) | |
| I0506 00:52:54.128939 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0638298 | |
| I0506 00:52:54.128954 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:52:54.128967 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.25 | |
| I0506 00:52:54.128978 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:52:54.128989 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 00:52:54.129000 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 00:52:54.129012 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 00:52:54.129024 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 00:52:54.129035 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 00:52:54.129046 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 00:52:54.129057 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:52:54.129070 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:52:54.129081 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:52:54.129091 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:52:54.129103 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:52:54.129114 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:52:54.129153 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:52:54.129166 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:52:54.129179 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:52:54.129189 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:52:54.129200 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:52:54.129211 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:52:54.129223 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:52:54.129235 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.738636 | |
| I0506 00:52:54.129245 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.319149 | |
| I0506 00:52:54.129259 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.91032 (* 1 = 2.91032 loss) | |
| I0506 00:52:54.129273 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.855718 (* 1 = 0.855718 loss) | |
| I0506 00:52:54.129287 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.50177 (* 0.0909091 = 0.227434 loss) | |
| I0506 00:52:54.129300 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.67085 (* 0.0909091 = 0.242805 loss) | |
| I0506 00:52:54.129314 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.17149 (* 0.0909091 = 0.288317 loss) | |
| I0506 00:52:54.129328 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.84966 (* 0.0909091 = 0.25906 loss) | |
| I0506 00:52:54.129340 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.8197 (* 0.0909091 = 0.256336 loss) | |
| I0506 00:52:54.129354 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.61965 (* 0.0909091 = 0.23815 loss) | |
| I0506 00:52:54.129367 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.33116 (* 0.0909091 = 0.211923 loss) | |
| I0506 00:52:54.129381 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.651906 (* 0.0909091 = 0.0592642 loss) | |
| I0506 00:52:54.129395 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.0358951 (* 0.0909091 = 0.00326319 loss) | |
| I0506 00:52:54.129410 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0286475 (* 0.0909091 = 0.00260432 loss) | |
| I0506 00:52:54.129422 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0185314 (* 0.0909091 = 0.00168467 loss) | |
| I0506 00:52:54.129436 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.00979562 (* 0.0909091 = 0.000890511 loss) | |
| I0506 00:52:54.129451 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0124579 (* 0.0909091 = 0.00113254 loss) | |
| I0506 00:52:54.129463 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00877838 (* 0.0909091 = 0.000798034 loss) | |
| I0506 00:52:54.129477 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00585565 (* 0.0909091 = 0.000532332 loss) | |
| I0506 00:52:54.129492 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00448248 (* 0.0909091 = 0.000407498 loss) | |
| I0506 00:52:54.129509 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0020351 (* 0.0909091 = 0.000185009 loss) | |
| I0506 00:52:54.129523 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0012843 (* 0.0909091 = 0.000116754 loss) | |
| I0506 00:52:54.129539 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00092513 (* 0.0909091 = 8.41027e-05 loss) | |
| I0506 00:52:54.129552 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000515956 (* 0.0909091 = 4.69051e-05 loss) | |
| I0506 00:52:54.129566 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000470173 (* 0.0909091 = 4.2743e-05 loss) | |
| I0506 00:52:54.129580 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000365104 (* 0.0909091 = 3.31913e-05 loss) | |
| I0506 00:52:54.129592 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:52:54.129603 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:52:54.129624 15760 solver.cpp:245] Train net output #149: total_confidence = 2.81988e-05 | |
| I0506 00:52:54.129637 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000177664 | |
| I0506 00:52:54.129650 15760 sgd_solver.cpp:106] Iteration 8500, lr = 0.001 | |
| I0506 00:53:27.546367 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 39.4644 > 30) by scale factor 0.760178 | |
| I0506 00:53:49.387737 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.3106 > 30) by scale factor 0.958142 | |
| I0506 00:54:41.152658 15760 solver.cpp:229] Iteration 9000, loss = 10.752 | |
| I0506 00:54:41.152775 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0526316 | |
| I0506 00:54:41.152796 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.25 | |
| I0506 00:54:41.152808 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:54:41.152820 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:54:41.152832 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 00:54:41.152843 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 00:54:41.152856 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.75 | |
| I0506 00:54:41.152868 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.875 | |
| I0506 00:54:41.152884 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 1 | |
| I0506 00:54:41.152895 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 00:54:41.152907 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 00:54:41.152920 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 00:54:41.152930 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:54:41.152945 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:54:41.152966 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:54:41.152981 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:54:41.152992 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:54:41.153003 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:54:41.153015 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:54:41.153026 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:54:41.153038 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:54:41.153049 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:54:41.153060 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:54:41.153072 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.789773 | |
| I0506 00:54:41.153084 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.210526 | |
| I0506 00:54:41.153100 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 4.27533 (* 0.3 = 1.2826 loss) | |
| I0506 00:54:41.153115 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.08086 (* 0.3 = 0.324257 loss) | |
| I0506 00:54:41.153144 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 4.62632 (* 0.0272727 = 0.126172 loss) | |
| I0506 00:54:41.153159 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 4.18196 (* 0.0272727 = 0.114053 loss) | |
| I0506 00:54:41.153173 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.13183 (* 0.0272727 = 0.112686 loss) | |
| I0506 00:54:41.153187 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 4.02435 (* 0.0272727 = 0.109755 loss) | |
| I0506 00:54:41.153201 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.1247 (* 0.0272727 = 0.0852191 loss) | |
| I0506 00:54:41.153216 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.16426 (* 0.0272727 = 0.0590252 loss) | |
| I0506 00:54:41.153230 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.50662 (* 0.0272727 = 0.0410897 loss) | |
| I0506 00:54:41.153254 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.519745 (* 0.0272727 = 0.0141749 loss) | |
| I0506 00:54:41.153270 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.273945 (* 0.0272727 = 0.00747123 loss) | |
| I0506 00:54:41.153285 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.234559 (* 0.0272727 = 0.00639706 loss) | |
| I0506 00:54:41.153298 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.216941 (* 0.0272727 = 0.00591656 loss) | |
| I0506 00:54:41.153312 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.175811 (* 0.0272727 = 0.00479484 loss) | |
| I0506 00:54:41.153326 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.139038 (* 0.0272727 = 0.00379194 loss) | |
| I0506 00:54:41.153357 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0938709 (* 0.0272727 = 0.00256011 loss) | |
| I0506 00:54:41.153373 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0841886 (* 0.0272727 = 0.00229605 loss) | |
| I0506 00:54:41.153388 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.101304 (* 0.0272727 = 0.00276284 loss) | |
| I0506 00:54:41.153400 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0947164 (* 0.0272727 = 0.00258318 loss) | |
| I0506 00:54:41.153414 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0940587 (* 0.0272727 = 0.00256524 loss) | |
| I0506 00:54:41.153429 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0533823 (* 0.0272727 = 0.00145588 loss) | |
| I0506 00:54:41.153442 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0550308 (* 0.0272727 = 0.00150084 loss) | |
| I0506 00:54:41.153455 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0464855 (* 0.0272727 = 0.00126779 loss) | |
| I0506 00:54:41.153470 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0605824 (* 0.0272727 = 0.00165225 loss) | |
| I0506 00:54:41.153481 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 00:54:41.153493 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:54:41.153506 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:54:41.153517 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.25 | |
| I0506 00:54:41.153528 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 00:54:41.153539 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 00:54:41.153551 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.75 | |
| I0506 00:54:41.153563 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.875 | |
| I0506 00:54:41.153574 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 1 | |
| I0506 00:54:41.153585 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 00:54:41.153596 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 00:54:41.153609 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 00:54:41.153620 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:54:41.153630 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:54:41.153641 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:54:41.153652 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:54:41.153664 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:54:41.153676 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:54:41.153687 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:54:41.153697 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:54:41.153709 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:54:41.153720 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:54:41.153730 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:54:41.153743 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.784091 | |
| I0506 00:54:41.153753 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.131579 | |
| I0506 00:54:41.153767 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 4.28226 (* 0.3 = 1.28468 loss) | |
| I0506 00:54:41.153780 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.00142 (* 0.3 = 0.300425 loss) | |
| I0506 00:54:41.153800 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 4.24048 (* 0.0272727 = 0.115649 loss) | |
| I0506 00:54:41.153815 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 4.22889 (* 0.0272727 = 0.115333 loss) | |
| I0506 00:54:41.153827 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.55691 (* 0.0272727 = 0.0970067 loss) | |
| I0506 00:54:41.153852 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.94358 (* 0.0272727 = 0.107552 loss) | |
| I0506 00:54:41.153867 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.08009 (* 0.0272727 = 0.0840025 loss) | |
| I0506 00:54:41.153882 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.12773 (* 0.0272727 = 0.058029 loss) | |
| I0506 00:54:41.153894 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.50334 (* 0.0272727 = 0.0410001 loss) | |
| I0506 00:54:41.153908 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.584234 (* 0.0272727 = 0.0159337 loss) | |
| I0506 00:54:41.153923 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.317162 (* 0.0272727 = 0.00864986 loss) | |
| I0506 00:54:41.153940 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.239492 (* 0.0272727 = 0.00653161 loss) | |
| I0506 00:54:41.153954 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.230786 (* 0.0272727 = 0.00629416 loss) | |
| I0506 00:54:41.153969 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.154348 (* 0.0272727 = 0.00420949 loss) | |
| I0506 00:54:41.153981 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.135166 (* 0.0272727 = 0.00368635 loss) | |
| I0506 00:54:41.153995 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.117124 (* 0.0272727 = 0.0031943 loss) | |
| I0506 00:54:41.154009 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.108009 (* 0.0272727 = 0.00294569 loss) | |
| I0506 00:54:41.154023 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0706505 (* 0.0272727 = 0.00192683 loss) | |
| I0506 00:54:41.154037 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0611958 (* 0.0272727 = 0.00166898 loss) | |
| I0506 00:54:41.154050 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0622615 (* 0.0272727 = 0.00169804 loss) | |
| I0506 00:54:41.154064 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.031097 (* 0.0272727 = 0.0008481 loss) | |
| I0506 00:54:41.154078 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0490017 (* 0.0272727 = 0.00133641 loss) | |
| I0506 00:54:41.154093 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0398314 (* 0.0272727 = 0.00108631 loss) | |
| I0506 00:54:41.154106 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0418978 (* 0.0272727 = 0.00114267 loss) | |
| I0506 00:54:41.154114 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0789474 | |
| I0506 00:54:41.154122 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.25 | |
| I0506 00:54:41.154136 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.25 | |
| I0506 00:54:41.154155 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:54:41.154171 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 00:54:41.154183 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.5 | |
| I0506 00:54:41.154196 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.625 | |
| I0506 00:54:41.154207 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.875 | |
| I0506 00:54:41.154218 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 1 | |
| I0506 00:54:41.154229 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 00:54:41.154242 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 00:54:41.154253 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 00:54:41.154263 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:54:41.154274 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:54:41.154285 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:54:41.154296 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:54:41.154306 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:54:41.154319 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:54:41.154340 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:54:41.154353 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:54:41.154364 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:54:41.154376 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:54:41.154386 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:54:41.154397 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.789773 | |
| I0506 00:54:41.154408 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.131579 | |
| I0506 00:54:41.154422 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 4.31501 (* 1 = 4.31501 loss) | |
| I0506 00:54:41.154435 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.06915 (* 1 = 1.06915 loss) | |
| I0506 00:54:41.154449 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.8728 (* 0.0909091 = 0.352073 loss) | |
| I0506 00:54:41.154463 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.54477 (* 0.0909091 = 0.322252 loss) | |
| I0506 00:54:41.154475 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.62764 (* 0.0909091 = 0.329786 loss) | |
| I0506 00:54:41.154489 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.84213 (* 0.0909091 = 0.349284 loss) | |
| I0506 00:54:41.154501 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.86781 (* 0.0909091 = 0.26071 loss) | |
| I0506 00:54:41.154515 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.82583 (* 0.0909091 = 0.165985 loss) | |
| I0506 00:54:41.154527 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.43346 (* 0.0909091 = 0.130314 loss) | |
| I0506 00:54:41.154541 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.318811 (* 0.0909091 = 0.0289828 loss) | |
| I0506 00:54:41.154554 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.111809 (* 0.0909091 = 0.0101644 loss) | |
| I0506 00:54:41.154568 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0856162 (* 0.0909091 = 0.00778329 loss) | |
| I0506 00:54:41.154582 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0682379 (* 0.0909091 = 0.00620345 loss) | |
| I0506 00:54:41.154595 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.049676 (* 0.0909091 = 0.004516 loss) | |
| I0506 00:54:41.154609 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0561225 (* 0.0909091 = 0.00510204 loss) | |
| I0506 00:54:41.154623 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0480109 (* 0.0909091 = 0.00436463 loss) | |
| I0506 00:54:41.154636 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0422868 (* 0.0909091 = 0.00384425 loss) | |
| I0506 00:54:41.154649 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0403587 (* 0.0909091 = 0.00366897 loss) | |
| I0506 00:54:41.154664 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0237668 (* 0.0909091 = 0.00216062 loss) | |
| I0506 00:54:41.154677 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0220263 (* 0.0909091 = 0.00200239 loss) | |
| I0506 00:54:41.154690 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0155641 (* 0.0909091 = 0.00141491 loss) | |
| I0506 00:54:41.154705 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.0130077 (* 0.0909091 = 0.00118252 loss) | |
| I0506 00:54:41.154718 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.0135548 (* 0.0909091 = 0.00123226 loss) | |
| I0506 00:54:41.154731 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00859373 (* 0.0909091 = 0.000781248 loss) | |
| I0506 00:54:41.154743 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:54:41.154754 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:54:41.154765 15760 solver.cpp:245] Train net output #149: total_confidence = 2.10098e-05 | |
| I0506 00:54:41.154777 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 7.04996e-05 | |
| I0506 00:54:41.154798 15760 sgd_solver.cpp:106] Iteration 9000, lr = 0.001 | |
| I0506 00:56:28.480145 15760 solver.cpp:229] Iteration 9500, loss = 10.6672 | |
| I0506 00:56:28.480332 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0333333 | |
| I0506 00:56:28.480355 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:56:28.480370 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 00:56:28.480381 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:56:28.480392 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 00:56:28.480404 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0 | |
| I0506 00:56:28.480417 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 00:56:28.480428 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.625 | |
| I0506 00:56:28.480440 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 00:56:28.480453 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.75 | |
| I0506 00:56:28.480464 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.75 | |
| I0506 00:56:28.480476 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.75 | |
| I0506 00:56:28.480489 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 00:56:28.480500 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0.875 | |
| I0506 00:56:28.480512 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 0.875 | |
| I0506 00:56:28.480525 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 0.875 | |
| I0506 00:56:28.480536 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:56:28.480548 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:56:28.480561 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:56:28.480572 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:56:28.480584 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:56:28.480595 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:56:28.480607 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:56:28.480618 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.670455 | |
| I0506 00:56:28.480630 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.183333 | |
| I0506 00:56:28.480648 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.74583 (* 0.3 = 1.12375 loss) | |
| I0506 00:56:28.480661 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.46244 (* 0.3 = 0.438732 loss) | |
| I0506 00:56:28.480675 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 4.01048 (* 0.0272727 = 0.109377 loss) | |
| I0506 00:56:28.480690 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.84825 (* 0.0272727 = 0.104952 loss) | |
| I0506 00:56:28.480703 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.20106 (* 0.0272727 = 0.114574 loss) | |
| I0506 00:56:28.480717 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.90272 (* 0.0272727 = 0.106438 loss) | |
| I0506 00:56:28.480731 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 4.51813 (* 0.0272727 = 0.123222 loss) | |
| I0506 00:56:28.480744 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.88911 (* 0.0272727 = 0.0787938 loss) | |
| I0506 00:56:28.480758 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.41712 (* 0.0272727 = 0.0659214 loss) | |
| I0506 00:56:28.480772 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.903247 (* 0.0272727 = 0.024634 loss) | |
| I0506 00:56:28.480787 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.30256 (* 0.0272727 = 0.0355244 loss) | |
| I0506 00:56:28.480799 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 1.31265 (* 0.0272727 = 0.0357995 loss) | |
| I0506 00:56:28.480813 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 1.57128 (* 0.0272727 = 0.042853 loss) | |
| I0506 00:56:28.480828 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.615826 (* 0.0272727 = 0.0167953 loss) | |
| I0506 00:56:28.480841 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.808543 (* 0.0272727 = 0.0220512 loss) | |
| I0506 00:56:28.480870 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.695929 (* 0.0272727 = 0.0189799 loss) | |
| I0506 00:56:28.480890 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.656389 (* 0.0272727 = 0.0179015 loss) | |
| I0506 00:56:28.480904 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0405641 (* 0.0272727 = 0.0011063 loss) | |
| I0506 00:56:28.480919 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0449023 (* 0.0272727 = 0.00122461 loss) | |
| I0506 00:56:28.480933 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0268221 (* 0.0272727 = 0.000731511 loss) | |
| I0506 00:56:28.480952 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.019339 (* 0.0272727 = 0.000527427 loss) | |
| I0506 00:56:28.480967 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.016643 (* 0.0272727 = 0.0004539 loss) | |
| I0506 00:56:28.480980 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0176316 (* 0.0272727 = 0.000480862 loss) | |
| I0506 00:56:28.480994 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0269962 (* 0.0272727 = 0.00073626 loss) | |
| I0506 00:56:28.481006 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0833333 | |
| I0506 00:56:28.481019 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 00:56:28.481030 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 00:56:28.481042 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:56:28.481053 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 00:56:28.481065 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0 | |
| I0506 00:56:28.481076 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 00:56:28.481087 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.625 | |
| I0506 00:56:28.481098 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 00:56:28.481111 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.75 | |
| I0506 00:56:28.481143 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.75 | |
| I0506 00:56:28.481158 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.75 | |
| I0506 00:56:28.481169 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 00:56:28.481181 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0.875 | |
| I0506 00:56:28.481194 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 0.875 | |
| I0506 00:56:28.481205 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 0.875 | |
| I0506 00:56:28.481216 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:56:28.481228 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:56:28.481240 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:56:28.481251 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:56:28.481262 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:56:28.481273 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:56:28.481284 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:56:28.481295 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.6875 | |
| I0506 00:56:28.481307 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.183333 | |
| I0506 00:56:28.481322 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.80969 (* 0.3 = 1.14291 loss) | |
| I0506 00:56:28.481341 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.47767 (* 0.3 = 0.443302 loss) | |
| I0506 00:56:28.481356 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 4.00865 (* 0.0272727 = 0.109327 loss) | |
| I0506 00:56:28.481369 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.95256 (* 0.0272727 = 0.107797 loss) | |
| I0506 00:56:28.481396 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.94329 (* 0.0272727 = 0.107544 loss) | |
| I0506 00:56:28.481411 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 4.5078 (* 0.0272727 = 0.12294 loss) | |
| I0506 00:56:28.481426 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 4.71646 (* 0.0272727 = 0.128631 loss) | |
| I0506 00:56:28.481438 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.89074 (* 0.0272727 = 0.0788385 loss) | |
| I0506 00:56:28.481452 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.01815 (* 0.0272727 = 0.0550405 loss) | |
| I0506 00:56:28.481465 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.905816 (* 0.0272727 = 0.0247041 loss) | |
| I0506 00:56:28.481479 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.44898 (* 0.0272727 = 0.0395176 loss) | |
| I0506 00:56:28.481492 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 1.0937 (* 0.0272727 = 0.0298282 loss) | |
| I0506 00:56:28.481505 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 1.3646 (* 0.0272727 = 0.0372163 loss) | |
| I0506 00:56:28.481519 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.730006 (* 0.0272727 = 0.0199092 loss) | |
| I0506 00:56:28.481533 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.823092 (* 0.0272727 = 0.022448 loss) | |
| I0506 00:56:28.481546 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.690946 (* 0.0272727 = 0.018844 loss) | |
| I0506 00:56:28.481559 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.809502 (* 0.0272727 = 0.0220773 loss) | |
| I0506 00:56:28.481573 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0350865 (* 0.0272727 = 0.000956903 loss) | |
| I0506 00:56:28.481586 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0249166 (* 0.0272727 = 0.000679544 loss) | |
| I0506 00:56:28.481600 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0343365 (* 0.0272727 = 0.000936449 loss) | |
| I0506 00:56:28.481614 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0174892 (* 0.0272727 = 0.000476979 loss) | |
| I0506 00:56:28.481627 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0201106 (* 0.0272727 = 0.000548472 loss) | |
| I0506 00:56:28.481642 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0221855 (* 0.0272727 = 0.000605058 loss) | |
| I0506 00:56:28.481654 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0241259 (* 0.0272727 = 0.000657979 loss) | |
| I0506 00:56:28.481667 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.05 | |
| I0506 00:56:28.481678 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 00:56:28.481690 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:56:28.481701 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:56:28.481712 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0 | |
| I0506 00:56:28.481724 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0 | |
| I0506 00:56:28.481735 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 00:56:28.481746 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.625 | |
| I0506 00:56:28.481757 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 00:56:28.481770 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.75 | |
| I0506 00:56:28.481781 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.75 | |
| I0506 00:56:28.481792 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.75 | |
| I0506 00:56:28.481803 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 00:56:28.481814 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0.875 | |
| I0506 00:56:28.481827 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 0.875 | |
| I0506 00:56:28.481837 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 0.875 | |
| I0506 00:56:28.481848 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:56:28.481869 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:56:28.481883 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:56:28.481894 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:56:28.481905 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:56:28.481916 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:56:28.481930 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:56:28.481942 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.664773 | |
| I0506 00:56:28.481955 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.183333 | |
| I0506 00:56:28.481968 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.52624 (* 1 = 3.52624 loss) | |
| I0506 00:56:28.481981 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.36497 (* 1 = 1.36497 loss) | |
| I0506 00:56:28.481995 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.63245 (* 0.0909091 = 0.330223 loss) | |
| I0506 00:56:28.482008 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.48691 (* 0.0909091 = 0.316992 loss) | |
| I0506 00:56:28.482023 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.76068 (* 0.0909091 = 0.34188 loss) | |
| I0506 00:56:28.482035 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.61723 (* 0.0909091 = 0.328839 loss) | |
| I0506 00:56:28.482048 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.56412 (* 0.0909091 = 0.324011 loss) | |
| I0506 00:56:28.482062 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.45318 (* 0.0909091 = 0.223017 loss) | |
| I0506 00:56:28.482075 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.041 (* 0.0909091 = 0.185545 loss) | |
| I0506 00:56:28.482089 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.768896 (* 0.0909091 = 0.0698996 loss) | |
| I0506 00:56:28.482102 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 1.06431 (* 0.0909091 = 0.096755 loss) | |
| I0506 00:56:28.482115 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.892999 (* 0.0909091 = 0.0811817 loss) | |
| I0506 00:56:28.482130 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 1.06739 (* 0.0909091 = 0.0970357 loss) | |
| I0506 00:56:28.482142 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.388528 (* 0.0909091 = 0.0353208 loss) | |
| I0506 00:56:28.482156 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.598845 (* 0.0909091 = 0.0544404 loss) | |
| I0506 00:56:28.482167 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.461171 (* 0.0909091 = 0.0419246 loss) | |
| I0506 00:56:28.482182 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.402219 (* 0.0909091 = 0.0365653 loss) | |
| I0506 00:56:28.482197 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0779824 (* 0.0909091 = 0.00708931 loss) | |
| I0506 00:56:28.482210 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0384997 (* 0.0909091 = 0.00349997 loss) | |
| I0506 00:56:28.482223 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0197891 (* 0.0909091 = 0.00179901 loss) | |
| I0506 00:56:28.482237 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0157673 (* 0.0909091 = 0.00143339 loss) | |
| I0506 00:56:28.482250 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00989705 (* 0.0909091 = 0.000899732 loss) | |
| I0506 00:56:28.482264 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00632072 (* 0.0909091 = 0.000574611 loss) | |
| I0506 00:56:28.482277 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00946749 (* 0.0909091 = 0.000860681 loss) | |
| I0506 00:56:28.482290 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:56:28.482300 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:56:28.482311 15760 solver.cpp:245] Train net output #149: total_confidence = 5.63842e-07 | |
| I0506 00:56:28.482332 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 8.64962e-06 | |
| I0506 00:56:28.482347 15760 sgd_solver.cpp:106] Iteration 9500, lr = 0.001 | |
| I0506 00:57:15.484561 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 40.6454 > 30) by scale factor 0.738091 | |
| I0506 00:57:28.308847 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 40.9365 > 30) by scale factor 0.732843 | |
| I0506 00:58:15.612496 15760 solver.cpp:456] Snapshotting to binary proto file /mnt/snapshots/mixed_lstm15_bn_iter_10000.caffemodel | |
| I0506 00:58:16.070243 15760 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /mnt/snapshots/mixed_lstm15_bn_iter_10000.solverstate | |
| I0506 00:58:16.283087 15760 solver.cpp:338] Iteration 10000, Testing net (#0) | |
| I0506 00:58:52.263614 15760 solver.cpp:393] Test loss: 9.96764 | |
| I0506 00:58:52.263741 15760 solver.cpp:406] Test net output #0: loss1/accuracy = 0.0488992 | |
| I0506 00:58:52.263770 15760 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.1 | |
| I0506 00:58:52.263793 15760 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.083 | |
| I0506 00:58:52.263811 15760 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.056 | |
| I0506 00:58:52.263823 15760 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.157 | |
| I0506 00:58:52.263835 15760 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.286 | |
| I0506 00:58:52.263846 15760 solver.cpp:406] Test net output #6: loss1/accuracy06 = 0.449 | |
| I0506 00:58:52.263859 15760 solver.cpp:406] Test net output #7: loss1/accuracy07 = 0.726 | |
| I0506 00:58:52.263870 15760 solver.cpp:406] Test net output #8: loss1/accuracy08 = 0.91 | |
| I0506 00:58:52.263885 15760 solver.cpp:406] Test net output #9: loss1/accuracy09 = 0.99 | |
| I0506 00:58:52.263896 15760 solver.cpp:406] Test net output #10: loss1/accuracy10 = 0.999 | |
| I0506 00:58:52.263908 15760 solver.cpp:406] Test net output #11: loss1/accuracy11 = 1 | |
| I0506 00:58:52.263919 15760 solver.cpp:406] Test net output #12: loss1/accuracy12 = 1 | |
| I0506 00:58:52.263931 15760 solver.cpp:406] Test net output #13: loss1/accuracy13 = 1 | |
| I0506 00:58:52.263942 15760 solver.cpp:406] Test net output #14: loss1/accuracy14 = 1 | |
| I0506 00:58:52.263952 15760 solver.cpp:406] Test net output #15: loss1/accuracy15 = 1 | |
| I0506 00:58:52.263963 15760 solver.cpp:406] Test net output #16: loss1/accuracy16 = 1 | |
| I0506 00:58:52.263975 15760 solver.cpp:406] Test net output #17: loss1/accuracy17 = 1 | |
| I0506 00:58:52.263986 15760 solver.cpp:406] Test net output #18: loss1/accuracy18 = 1 | |
| I0506 00:58:52.263998 15760 solver.cpp:406] Test net output #19: loss1/accuracy19 = 1 | |
| I0506 00:58:52.264008 15760 solver.cpp:406] Test net output #20: loss1/accuracy20 = 1 | |
| I0506 00:58:52.264019 15760 solver.cpp:406] Test net output #21: loss1/accuracy21 = 1 | |
| I0506 00:58:52.264031 15760 solver.cpp:406] Test net output #22: loss1/accuracy22 = 1 | |
| I0506 00:58:52.264042 15760 solver.cpp:406] Test net output #23: loss1/accuracy_incl_empty = 0.759683 | |
| I0506 00:58:52.264055 15760 solver.cpp:406] Test net output #24: loss1/accuracy_top3 = 0.186877 | |
| I0506 00:58:52.264070 15760 solver.cpp:406] Test net output #25: loss1/cross_entropy_loss = 3.90343 (* 0.3 = 1.17103 loss) | |
| I0506 00:58:52.264083 15760 solver.cpp:406] Test net output #26: loss1/cross_entropy_loss_incl_empty = 1.03026 (* 0.3 = 0.309078 loss) | |
| I0506 00:58:52.264097 15760 solver.cpp:406] Test net output #27: loss1/loss01 = 3.45167 (* 0.0272727 = 0.0941364 loss) | |
| I0506 00:58:52.264111 15760 solver.cpp:406] Test net output #28: loss1/loss02 = 3.52966 (* 0.0272727 = 0.0962633 loss) | |
| I0506 00:58:52.264124 15760 solver.cpp:406] Test net output #29: loss1/loss03 = 3.63291 (* 0.0272727 = 0.0990795 loss) | |
| I0506 00:58:52.264137 15760 solver.cpp:406] Test net output #30: loss1/loss04 = 3.42171 (* 0.0272727 = 0.0933195 loss) | |
| I0506 00:58:52.264150 15760 solver.cpp:406] Test net output #31: loss1/loss05 = 3.03831 (* 0.0272727 = 0.0828629 loss) | |
| I0506 00:58:52.264163 15760 solver.cpp:406] Test net output #32: loss1/loss06 = 2.56934 (* 0.0272727 = 0.0700728 loss) | |
| I0506 00:58:52.264178 15760 solver.cpp:406] Test net output #33: loss1/loss07 = 1.58666 (* 0.0272727 = 0.0432726 loss) | |
| I0506 00:58:52.264190 15760 solver.cpp:406] Test net output #34: loss1/loss08 = 0.667311 (* 0.0272727 = 0.0181994 loss) | |
| I0506 00:58:52.264204 15760 solver.cpp:406] Test net output #35: loss1/loss09 = 0.186906 (* 0.0272727 = 0.00509744 loss) | |
| I0506 00:58:52.264217 15760 solver.cpp:406] Test net output #36: loss1/loss10 = 0.136748 (* 0.0272727 = 0.00372948 loss) | |
| I0506 00:58:52.264231 15760 solver.cpp:406] Test net output #37: loss1/loss11 = 0.114558 (* 0.0272727 = 0.00312431 loss) | |
| I0506 00:58:52.264245 15760 solver.cpp:406] Test net output #38: loss1/loss12 = 0.0855736 (* 0.0272727 = 0.00233382 loss) | |
| I0506 00:58:52.264258 15760 solver.cpp:406] Test net output #39: loss1/loss13 = 0.0771584 (* 0.0272727 = 0.00210432 loss) | |
| I0506 00:58:52.264525 15760 solver.cpp:406] Test net output #40: loss1/loss14 = 0.0596202 (* 0.0272727 = 0.00162601 loss) | |
| I0506 00:58:52.264549 15760 solver.cpp:406] Test net output #41: loss1/loss15 = 0.0483694 (* 0.0272727 = 0.00131917 loss) | |
| I0506 00:58:52.264564 15760 solver.cpp:406] Test net output #42: loss1/loss16 = 0.0361562 (* 0.0272727 = 0.000986078 loss) | |
| I0506 00:58:52.264577 15760 solver.cpp:406] Test net output #43: loss1/loss17 = 0.0283995 (* 0.0272727 = 0.000774533 loss) | |
| I0506 00:58:52.264592 15760 solver.cpp:406] Test net output #44: loss1/loss18 = 0.0258818 (* 0.0272727 = 0.000705868 loss) | |
| I0506 00:58:52.264606 15760 solver.cpp:406] Test net output #45: loss1/loss19 = 0.0178287 (* 0.0272727 = 0.000486237 loss) | |
| I0506 00:58:52.264621 15760 solver.cpp:406] Test net output #46: loss1/loss20 = 0.022501 (* 0.0272727 = 0.000613663 loss) | |
| I0506 00:58:52.264634 15760 solver.cpp:406] Test net output #47: loss1/loss21 = 0.0205788 (* 0.0272727 = 0.000561241 loss) | |
| I0506 00:58:52.264649 15760 solver.cpp:406] Test net output #48: loss1/loss22 = 0.0182358 (* 0.0272727 = 0.000497341 loss) | |
| I0506 00:58:52.264662 15760 solver.cpp:406] Test net output #49: loss2/accuracy = 0.0534036 | |
| I0506 00:58:52.264673 15760 solver.cpp:406] Test net output #50: loss2/accuracy01 = 0.107 | |
| I0506 00:58:52.264685 15760 solver.cpp:406] Test net output #51: loss2/accuracy02 = 0.096 | |
| I0506 00:58:52.264696 15760 solver.cpp:406] Test net output #52: loss2/accuracy03 = 0.079 | |
| I0506 00:58:52.264708 15760 solver.cpp:406] Test net output #53: loss2/accuracy04 = 0.154 | |
| I0506 00:58:52.264719 15760 solver.cpp:406] Test net output #54: loss2/accuracy05 = 0.279 | |
| I0506 00:58:52.264731 15760 solver.cpp:406] Test net output #55: loss2/accuracy06 = 0.45 | |
| I0506 00:58:52.264742 15760 solver.cpp:406] Test net output #56: loss2/accuracy07 = 0.726 | |
| I0506 00:58:52.264755 15760 solver.cpp:406] Test net output #57: loss2/accuracy08 = 0.91 | |
| I0506 00:58:52.264765 15760 solver.cpp:406] Test net output #58: loss2/accuracy09 = 0.99 | |
| I0506 00:58:52.264776 15760 solver.cpp:406] Test net output #59: loss2/accuracy10 = 0.999 | |
| I0506 00:58:52.264788 15760 solver.cpp:406] Test net output #60: loss2/accuracy11 = 1 | |
| I0506 00:58:52.264799 15760 solver.cpp:406] Test net output #61: loss2/accuracy12 = 1 | |
| I0506 00:58:52.264811 15760 solver.cpp:406] Test net output #62: loss2/accuracy13 = 1 | |
| I0506 00:58:52.264822 15760 solver.cpp:406] Test net output #63: loss2/accuracy14 = 1 | |
| I0506 00:58:52.264832 15760 solver.cpp:406] Test net output #64: loss2/accuracy15 = 1 | |
| I0506 00:58:52.264843 15760 solver.cpp:406] Test net output #65: loss2/accuracy16 = 1 | |
| I0506 00:58:52.264854 15760 solver.cpp:406] Test net output #66: loss2/accuracy17 = 1 | |
| I0506 00:58:52.264865 15760 solver.cpp:406] Test net output #67: loss2/accuracy18 = 1 | |
| I0506 00:58:52.264879 15760 solver.cpp:406] Test net output #68: loss2/accuracy19 = 1 | |
| I0506 00:58:52.264891 15760 solver.cpp:406] Test net output #69: loss2/accuracy20 = 1 | |
| I0506 00:58:52.264902 15760 solver.cpp:406] Test net output #70: loss2/accuracy21 = 1 | |
| I0506 00:58:52.264912 15760 solver.cpp:406] Test net output #71: loss2/accuracy22 = 1 | |
| I0506 00:58:52.264924 15760 solver.cpp:406] Test net output #72: loss2/accuracy_incl_empty = 0.760683 | |
| I0506 00:58:52.264935 15760 solver.cpp:406] Test net output #73: loss2/accuracy_top3 = 0.200686 | |
| I0506 00:58:52.264950 15760 solver.cpp:406] Test net output #74: loss2/cross_entropy_loss = 3.77821 (* 0.3 = 1.13346 loss) | |
| I0506 00:58:52.264962 15760 solver.cpp:406] Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.990767 (* 0.3 = 0.29723 loss) | |
| I0506 00:58:52.264976 15760 solver.cpp:406] Test net output #76: loss2/loss01 = 3.3838 (* 0.0272727 = 0.0922853 loss) | |
| I0506 00:58:52.265002 15760 solver.cpp:406] Test net output #77: loss2/loss02 = 3.43939 (* 0.0272727 = 0.0938016 loss) | |
| I0506 00:58:52.265019 15760 solver.cpp:406] Test net output #78: loss2/loss03 = 3.5303 (* 0.0272727 = 0.0962809 loss) | |
| I0506 00:58:52.265048 15760 solver.cpp:406] Test net output #79: loss2/loss04 = 3.36451 (* 0.0272727 = 0.0917593 loss) | |
| I0506 00:58:52.265063 15760 solver.cpp:406] Test net output #80: loss2/loss05 = 3.01219 (* 0.0272727 = 0.0821507 loss) | |
| I0506 00:58:52.265076 15760 solver.cpp:406] Test net output #81: loss2/loss06 = 2.56015 (* 0.0272727 = 0.0698222 loss) | |
| I0506 00:58:52.265089 15760 solver.cpp:406] Test net output #82: loss2/loss07 = 1.58619 (* 0.0272727 = 0.0432596 loss) | |
| I0506 00:58:52.265103 15760 solver.cpp:406] Test net output #83: loss2/loss08 = 0.688661 (* 0.0272727 = 0.0187817 loss) | |
| I0506 00:58:52.265138 15760 solver.cpp:406] Test net output #84: loss2/loss09 = 0.204663 (* 0.0272727 = 0.00558171 loss) | |
| I0506 00:58:52.265156 15760 solver.cpp:406] Test net output #85: loss2/loss10 = 0.157159 (* 0.0272727 = 0.00428615 loss) | |
| I0506 00:58:52.265171 15760 solver.cpp:406] Test net output #86: loss2/loss11 = 0.122111 (* 0.0272727 = 0.00333031 loss) | |
| I0506 00:58:52.265184 15760 solver.cpp:406] Test net output #87: loss2/loss12 = 0.101523 (* 0.0272727 = 0.0027688 loss) | |
| I0506 00:58:52.265197 15760 solver.cpp:406] Test net output #88: loss2/loss13 = 0.0858127 (* 0.0272727 = 0.00234035 loss) | |
| I0506 00:58:52.265211 15760 solver.cpp:406] Test net output #89: loss2/loss14 = 0.0683536 (* 0.0272727 = 0.00186419 loss) | |
| I0506 00:58:52.265224 15760 solver.cpp:406] Test net output #90: loss2/loss15 = 0.0609621 (* 0.0272727 = 0.0016626 loss) | |
| I0506 00:58:52.265239 15760 solver.cpp:406] Test net output #91: loss2/loss16 = 0.0431074 (* 0.0272727 = 0.00117566 loss) | |
| I0506 00:58:52.265251 15760 solver.cpp:406] Test net output #92: loss2/loss17 = 0.0269514 (* 0.0272727 = 0.000735037 loss) | |
| I0506 00:58:52.265265 15760 solver.cpp:406] Test net output #93: loss2/loss18 = 0.0257418 (* 0.0272727 = 0.000702048 loss) | |
| I0506 00:58:52.265278 15760 solver.cpp:406] Test net output #94: loss2/loss19 = 0.0222686 (* 0.0272727 = 0.000607326 loss) | |
| I0506 00:58:52.265292 15760 solver.cpp:406] Test net output #95: loss2/loss20 = 0.021895 (* 0.0272727 = 0.000597136 loss) | |
| I0506 00:58:52.265305 15760 solver.cpp:406] Test net output #96: loss2/loss21 = 0.0203247 (* 0.0272727 = 0.00055431 loss) | |
| I0506 00:58:52.265319 15760 solver.cpp:406] Test net output #97: loss2/loss22 = 0.0206635 (* 0.0272727 = 0.00056355 loss) | |
| I0506 00:58:52.265331 15760 solver.cpp:406] Test net output #98: loss3/accuracy = 0.0808052 | |
| I0506 00:58:52.265342 15760 solver.cpp:406] Test net output #99: loss3/accuracy01 = 0.133 | |
| I0506 00:58:52.265353 15760 solver.cpp:406] Test net output #100: loss3/accuracy02 = 0.099 | |
| I0506 00:58:52.265365 15760 solver.cpp:406] Test net output #101: loss3/accuracy03 = 0.082 | |
| I0506 00:58:52.265377 15760 solver.cpp:406] Test net output #102: loss3/accuracy04 = 0.172 | |
| I0506 00:58:52.265388 15760 solver.cpp:406] Test net output #103: loss3/accuracy05 = 0.306 | |
| I0506 00:58:52.265398 15760 solver.cpp:406] Test net output #104: loss3/accuracy06 = 0.445 | |
| I0506 00:58:52.265410 15760 solver.cpp:406] Test net output #105: loss3/accuracy07 = 0.726 | |
| I0506 00:58:52.265421 15760 solver.cpp:406] Test net output #106: loss3/accuracy08 = 0.91 | |
| I0506 00:58:52.265432 15760 solver.cpp:406] Test net output #107: loss3/accuracy09 = 0.99 | |
| I0506 00:58:52.265444 15760 solver.cpp:406] Test net output #108: loss3/accuracy10 = 0.999 | |
| I0506 00:58:52.265455 15760 solver.cpp:406] Test net output #109: loss3/accuracy11 = 1 | |
| I0506 00:58:52.265466 15760 solver.cpp:406] Test net output #110: loss3/accuracy12 = 1 | |
| I0506 00:58:52.265477 15760 solver.cpp:406] Test net output #111: loss3/accuracy13 = 1 | |
| I0506 00:58:52.265488 15760 solver.cpp:406] Test net output #112: loss3/accuracy14 = 1 | |
| I0506 00:58:52.265502 15760 solver.cpp:406] Test net output #113: loss3/accuracy15 = 1 | |
| I0506 00:58:52.265514 15760 solver.cpp:406] Test net output #114: loss3/accuracy16 = 1 | |
| I0506 00:58:52.265525 15760 solver.cpp:406] Test net output #115: loss3/accuracy17 = 1 | |
| I0506 00:58:52.265548 15760 solver.cpp:406] Test net output #116: loss3/accuracy18 = 1 | |
| I0506 00:58:52.265561 15760 solver.cpp:406] Test net output #117: loss3/accuracy19 = 1 | |
| I0506 00:58:52.265573 15760 solver.cpp:406] Test net output #118: loss3/accuracy20 = 1 | |
| I0506 00:58:52.265583 15760 solver.cpp:406] Test net output #119: loss3/accuracy21 = 1 | |
| I0506 00:58:52.265599 15760 solver.cpp:406] Test net output #120: loss3/accuracy22 = 1 | |
| I0506 00:58:52.265619 15760 solver.cpp:406] Test net output #121: loss3/accuracy_incl_empty = 0.762501 | |
| I0506 00:58:52.265635 15760 solver.cpp:406] Test net output #122: loss3/accuracy_top3 = 0.235607 | |
| I0506 00:58:52.265648 15760 solver.cpp:406] Test net output #123: loss3/cross_entropy_loss = 3.14806 (* 1 = 3.14806 loss) | |
| I0506 00:58:52.265662 15760 solver.cpp:406] Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.875052 (* 1 = 0.875052 loss) | |
| I0506 00:58:52.265676 15760 solver.cpp:406] Test net output #125: loss3/loss01 = 2.97803 (* 0.0909091 = 0.27073 loss) | |
| I0506 00:58:52.265689 15760 solver.cpp:406] Test net output #126: loss3/loss02 = 3.10629 (* 0.0909091 = 0.28239 loss) | |
| I0506 00:58:52.265702 15760 solver.cpp:406] Test net output #127: loss3/loss03 = 3.21792 (* 0.0909091 = 0.292538 loss) | |
| I0506 00:58:52.265715 15760 solver.cpp:406] Test net output #128: loss3/loss04 = 2.99786 (* 0.0909091 = 0.272532 loss) | |
| I0506 00:58:52.265728 15760 solver.cpp:406] Test net output #129: loss3/loss05 = 2.63807 (* 0.0909091 = 0.239824 loss) | |
| I0506 00:58:52.265741 15760 solver.cpp:406] Test net output #130: loss3/loss06 = 2.25463 (* 0.0909091 = 0.204967 loss) | |
| I0506 00:58:52.265754 15760 solver.cpp:406] Test net output #131: loss3/loss07 = 1.37475 (* 0.0909091 = 0.124977 loss) | |
| I0506 00:58:52.265768 15760 solver.cpp:406] Test net output #132: loss3/loss08 = 0.577035 (* 0.0909091 = 0.0524577 loss) | |
| I0506 00:58:52.265780 15760 solver.cpp:406] Test net output #133: loss3/loss09 = 0.137019 (* 0.0909091 = 0.0124562 loss) | |
| I0506 00:58:52.265794 15760 solver.cpp:406] Test net output #134: loss3/loss10 = 0.0942238 (* 0.0909091 = 0.0085658 loss) | |
| I0506 00:58:52.265808 15760 solver.cpp:406] Test net output #135: loss3/loss11 = 0.07254 (* 0.0909091 = 0.00659455 loss) | |
| I0506 00:58:52.265822 15760 solver.cpp:406] Test net output #136: loss3/loss12 = 0.0617776 (* 0.0909091 = 0.00561614 loss) | |
| I0506 00:58:52.265831 15760 solver.cpp:406] Test net output #137: loss3/loss13 = 0.0572792 (* 0.0909091 = 0.0052072 loss) | |
| I0506 00:58:52.265841 15760 solver.cpp:406] Test net output #138: loss3/loss14 = 0.0452412 (* 0.0909091 = 0.00411283 loss) | |
| I0506 00:58:52.265856 15760 solver.cpp:406] Test net output #139: loss3/loss15 = 0.0383829 (* 0.0909091 = 0.00348935 loss) | |
| I0506 00:58:52.265869 15760 solver.cpp:406] Test net output #140: loss3/loss16 = 0.031392 (* 0.0909091 = 0.00285381 loss) | |
| I0506 00:58:52.265882 15760 solver.cpp:406] Test net output #141: loss3/loss17 = 0.021861 (* 0.0909091 = 0.00198736 loss) | |
| I0506 00:58:52.265895 15760 solver.cpp:406] Test net output #142: loss3/loss18 = 0.0182784 (* 0.0909091 = 0.00166167 loss) | |
| I0506 00:58:52.265908 15760 solver.cpp:406] Test net output #143: loss3/loss19 = 0.0146292 (* 0.0909091 = 0.00132993 loss) | |
| I0506 00:58:52.265923 15760 solver.cpp:406] Test net output #144: loss3/loss20 = 0.0141077 (* 0.0909091 = 0.00128252 loss) | |
| I0506 00:58:52.265938 15760 solver.cpp:406] Test net output #145: loss3/loss21 = 0.0118563 (* 0.0909091 = 0.00107785 loss) | |
| I0506 00:58:52.265952 15760 solver.cpp:406] Test net output #146: loss3/loss22 = 0.0110283 (* 0.0909091 = 0.00100257 loss) | |
| I0506 00:58:52.265964 15760 solver.cpp:406] Test net output #147: total_accuracy = 0 | |
| I0506 00:58:52.265974 15760 solver.cpp:406] Test net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:58:52.265985 15760 solver.cpp:406] Test net output #149: total_confidence = 0.000378326 | |
| I0506 00:58:52.265996 15760 solver.cpp:406] Test net output #150: total_confidence_not_rec = 0.000485832 | |
| I0506 00:58:52.266019 15760 solver.cpp:338] Iteration 10000, Testing net (#1) | |
| I0506 00:59:28.166398 15760 solver.cpp:393] Test loss: 10.5642 | |
| I0506 00:59:28.166514 15760 solver.cpp:406] Test net output #0: loss1/accuracy = 0.0550997 | |
| I0506 00:59:28.166534 15760 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.098 | |
| I0506 00:59:28.166548 15760 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.088 | |
| I0506 00:59:28.166559 15760 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.066 | |
| I0506 00:59:28.166571 15760 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.152 | |
| I0506 00:59:28.166582 15760 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.281 | |
| I0506 00:59:28.166595 15760 solver.cpp:406] Test net output #6: loss1/accuracy06 = 0.404 | |
| I0506 00:59:28.166606 15760 solver.cpp:406] Test net output #7: loss1/accuracy07 = 0.633 | |
| I0506 00:59:28.166618 15760 solver.cpp:406] Test net output #8: loss1/accuracy08 = 0.792 | |
| I0506 00:59:28.166630 15760 solver.cpp:406] Test net output #9: loss1/accuracy09 = 0.885 | |
| I0506 00:59:28.166642 15760 solver.cpp:406] Test net output #10: loss1/accuracy10 = 0.911 | |
| I0506 00:59:28.166653 15760 solver.cpp:406] Test net output #11: loss1/accuracy11 = 0.924 | |
| I0506 00:59:28.166666 15760 solver.cpp:406] Test net output #12: loss1/accuracy12 = 0.936 | |
| I0506 00:59:28.166676 15760 solver.cpp:406] Test net output #13: loss1/accuracy13 = 0.949 | |
| I0506 00:59:28.166688 15760 solver.cpp:406] Test net output #14: loss1/accuracy14 = 0.96 | |
| I0506 00:59:28.166700 15760 solver.cpp:406] Test net output #15: loss1/accuracy15 = 0.97 | |
| I0506 00:59:28.166712 15760 solver.cpp:406] Test net output #16: loss1/accuracy16 = 0.979 | |
| I0506 00:59:28.166731 15760 solver.cpp:406] Test net output #17: loss1/accuracy17 = 0.988 | |
| I0506 00:59:28.166743 15760 solver.cpp:406] Test net output #18: loss1/accuracy18 = 0.996 | |
| I0506 00:59:28.166755 15760 solver.cpp:406] Test net output #19: loss1/accuracy19 = 0.997 | |
| I0506 00:59:28.166767 15760 solver.cpp:406] Test net output #20: loss1/accuracy20 = 0.999 | |
| I0506 00:59:28.166779 15760 solver.cpp:406] Test net output #21: loss1/accuracy21 = 1 | |
| I0506 00:59:28.166790 15760 solver.cpp:406] Test net output #22: loss1/accuracy22 = 1 | |
| I0506 00:59:28.166801 15760 solver.cpp:406] Test net output #23: loss1/accuracy_incl_empty = 0.727683 | |
| I0506 00:59:28.166812 15760 solver.cpp:406] Test net output #24: loss1/accuracy_top3 = 0.190298 | |
| I0506 00:59:28.166828 15760 solver.cpp:406] Test net output #25: loss1/cross_entropy_loss = 3.84116 (* 0.3 = 1.15235 loss) | |
| I0506 00:59:28.166842 15760 solver.cpp:406] Test net output #26: loss1/cross_entropy_loss_incl_empty = 1.15297 (* 0.3 = 0.345892 loss) | |
| I0506 00:59:28.166857 15760 solver.cpp:406] Test net output #27: loss1/loss01 = 3.43357 (* 0.0272727 = 0.0936429 loss) | |
| I0506 00:59:28.166869 15760 solver.cpp:406] Test net output #28: loss1/loss02 = 3.4968 (* 0.0272727 = 0.0953672 loss) | |
| I0506 00:59:28.166887 15760 solver.cpp:406] Test net output #29: loss1/loss03 = 3.60228 (* 0.0272727 = 0.0982441 loss) | |
| I0506 00:59:28.166901 15760 solver.cpp:406] Test net output #30: loss1/loss04 = 3.42712 (* 0.0272727 = 0.093467 loss) | |
| I0506 00:59:28.166914 15760 solver.cpp:406] Test net output #31: loss1/loss05 = 3.04571 (* 0.0272727 = 0.0830648 loss) | |
| I0506 00:59:28.166929 15760 solver.cpp:406] Test net output #32: loss1/loss06 = 2.67468 (* 0.0272727 = 0.0729457 loss) | |
| I0506 00:59:28.166941 15760 solver.cpp:406] Test net output #33: loss1/loss07 = 1.85455 (* 0.0272727 = 0.0505786 loss) | |
| I0506 00:59:28.166954 15760 solver.cpp:406] Test net output #34: loss1/loss08 = 1.07447 (* 0.0272727 = 0.0293038 loss) | |
| I0506 00:59:28.166968 15760 solver.cpp:406] Test net output #35: loss1/loss09 = 0.602951 (* 0.0272727 = 0.0164441 loss) | |
| I0506 00:59:28.166981 15760 solver.cpp:406] Test net output #36: loss1/loss10 = 0.51623 (* 0.0272727 = 0.014079 loss) | |
| I0506 00:59:28.166995 15760 solver.cpp:406] Test net output #37: loss1/loss11 = 0.453426 (* 0.0272727 = 0.0123662 loss) | |
| I0506 00:59:28.167008 15760 solver.cpp:406] Test net output #38: loss1/loss12 = 0.393031 (* 0.0272727 = 0.010719 loss) | |
| I0506 00:59:28.167021 15760 solver.cpp:406] Test net output #39: loss1/loss13 = 0.332644 (* 0.0272727 = 0.0090721 loss) | |
| I0506 00:59:28.167055 15760 solver.cpp:406] Test net output #40: loss1/loss14 = 0.274013 (* 0.0272727 = 0.00747309 loss) | |
| I0506 00:59:28.167070 15760 solver.cpp:406] Test net output #41: loss1/loss15 = 0.213513 (* 0.0272727 = 0.00582309 loss) | |
| I0506 00:59:28.167089 15760 solver.cpp:406] Test net output #42: loss1/loss16 = 0.158498 (* 0.0272727 = 0.00432266 loss) | |
| I0506 00:59:28.167104 15760 solver.cpp:406] Test net output #43: loss1/loss17 = 0.102948 (* 0.0272727 = 0.00280767 loss) | |
| I0506 00:59:28.167119 15760 solver.cpp:406] Test net output #44: loss1/loss18 = 0.0520784 (* 0.0272727 = 0.00142032 loss) | |
| I0506 00:59:28.167131 15760 solver.cpp:406] Test net output #45: loss1/loss19 = 0.0395593 (* 0.0272727 = 0.00107889 loss) | |
| I0506 00:59:28.167145 15760 solver.cpp:406] Test net output #46: loss1/loss20 = 0.0274063 (* 0.0272727 = 0.000747445 loss) | |
| I0506 00:59:28.167158 15760 solver.cpp:406] Test net output #47: loss1/loss21 = 0.018311 (* 0.0272727 = 0.000499391 loss) | |
| I0506 00:59:28.167171 15760 solver.cpp:406] Test net output #48: loss1/loss22 = 0.0163301 (* 0.0272727 = 0.000445366 loss) | |
| I0506 00:59:28.167183 15760 solver.cpp:406] Test net output #49: loss2/accuracy = 0.0575927 | |
| I0506 00:59:28.167196 15760 solver.cpp:406] Test net output #50: loss2/accuracy01 = 0.112 | |
| I0506 00:59:28.167207 15760 solver.cpp:406] Test net output #51: loss2/accuracy02 = 0.093 | |
| I0506 00:59:28.167218 15760 solver.cpp:406] Test net output #52: loss2/accuracy03 = 0.077 | |
| I0506 00:59:28.167229 15760 solver.cpp:406] Test net output #53: loss2/accuracy04 = 0.155 | |
| I0506 00:59:28.167240 15760 solver.cpp:406] Test net output #54: loss2/accuracy05 = 0.282 | |
| I0506 00:59:28.167251 15760 solver.cpp:406] Test net output #55: loss2/accuracy06 = 0.405 | |
| I0506 00:59:28.167263 15760 solver.cpp:406] Test net output #56: loss2/accuracy07 = 0.633 | |
| I0506 00:59:28.167274 15760 solver.cpp:406] Test net output #57: loss2/accuracy08 = 0.792 | |
| I0506 00:59:28.167285 15760 solver.cpp:406] Test net output #58: loss2/accuracy09 = 0.885 | |
| I0506 00:59:28.167296 15760 solver.cpp:406] Test net output #59: loss2/accuracy10 = 0.911 | |
| I0506 00:59:28.167309 15760 solver.cpp:406] Test net output #60: loss2/accuracy11 = 0.924 | |
| I0506 00:59:28.167323 15760 solver.cpp:406] Test net output #61: loss2/accuracy12 = 0.936 | |
| I0506 00:59:28.167335 15760 solver.cpp:406] Test net output #62: loss2/accuracy13 = 0.949 | |
| I0506 00:59:28.167347 15760 solver.cpp:406] Test net output #63: loss2/accuracy14 = 0.96 | |
| I0506 00:59:28.167358 15760 solver.cpp:406] Test net output #64: loss2/accuracy15 = 0.97 | |
| I0506 00:59:28.167369 15760 solver.cpp:406] Test net output #65: loss2/accuracy16 = 0.979 | |
| I0506 00:59:28.167381 15760 solver.cpp:406] Test net output #66: loss2/accuracy17 = 0.988 | |
| I0506 00:59:28.167392 15760 solver.cpp:406] Test net output #67: loss2/accuracy18 = 0.996 | |
| I0506 00:59:28.167403 15760 solver.cpp:406] Test net output #68: loss2/accuracy19 = 0.997 | |
| I0506 00:59:28.167414 15760 solver.cpp:406] Test net output #69: loss2/accuracy20 = 0.999 | |
| I0506 00:59:28.167426 15760 solver.cpp:406] Test net output #70: loss2/accuracy21 = 1 | |
| I0506 00:59:28.167438 15760 solver.cpp:406] Test net output #71: loss2/accuracy22 = 1 | |
| I0506 00:59:28.167448 15760 solver.cpp:406] Test net output #72: loss2/accuracy_incl_empty = 0.728364 | |
| I0506 00:59:28.167459 15760 solver.cpp:406] Test net output #73: loss2/accuracy_top3 = 0.207221 | |
| I0506 00:59:28.167472 15760 solver.cpp:406] Test net output #74: loss2/cross_entropy_loss = 3.72239 (* 0.3 = 1.11672 loss) | |
| I0506 00:59:28.167485 15760 solver.cpp:406] Test net output #75: loss2/cross_entropy_loss_incl_empty = 1.11036 (* 0.3 = 0.333108 loss) | |
| I0506 00:59:28.167500 15760 solver.cpp:406] Test net output #76: loss2/loss01 = 3.36031 (* 0.0272727 = 0.0916449 loss) | |
| I0506 00:59:28.167512 15760 solver.cpp:406] Test net output #77: loss2/loss02 = 3.41069 (* 0.0272727 = 0.0930188 loss) | |
| I0506 00:59:28.167537 15760 solver.cpp:406] Test net output #78: loss2/loss03 = 3.49728 (* 0.0272727 = 0.0953802 loss) | |
| I0506 00:59:28.167552 15760 solver.cpp:406] Test net output #79: loss2/loss04 = 3.37254 (* 0.0272727 = 0.0919784 loss) | |
| I0506 00:59:28.167565 15760 solver.cpp:406] Test net output #80: loss2/loss05 = 3.01337 (* 0.0272727 = 0.0821827 loss) | |
| I0506 00:59:28.167578 15760 solver.cpp:406] Test net output #81: loss2/loss06 = 2.65986 (* 0.0272727 = 0.0725416 loss) | |
| I0506 00:59:28.167592 15760 solver.cpp:406] Test net output #82: loss2/loss07 = 1.85173 (* 0.0272727 = 0.0505017 loss) | |
| I0506 00:59:28.167604 15760 solver.cpp:406] Test net output #83: loss2/loss08 = 1.08712 (* 0.0272727 = 0.0296488 loss) | |
| I0506 00:59:28.167618 15760 solver.cpp:406] Test net output #84: loss2/loss09 = 0.622052 (* 0.0272727 = 0.016965 loss) | |
| I0506 00:59:28.167630 15760 solver.cpp:406] Test net output #85: loss2/loss10 = 0.523458 (* 0.0272727 = 0.0142761 loss) | |
| I0506 00:59:28.167644 15760 solver.cpp:406] Test net output #86: loss2/loss11 = 0.454983 (* 0.0272727 = 0.0124086 loss) | |
| I0506 00:59:28.167657 15760 solver.cpp:406] Test net output #87: loss2/loss12 = 0.404688 (* 0.0272727 = 0.011037 loss) | |
| I0506 00:59:28.167670 15760 solver.cpp:406] Test net output #88: loss2/loss13 = 0.332892 (* 0.0272727 = 0.00907887 loss) | |
| I0506 00:59:28.167685 15760 solver.cpp:406] Test net output #89: loss2/loss14 = 0.277845 (* 0.0272727 = 0.0075776 loss) | |
| I0506 00:59:28.167697 15760 solver.cpp:406] Test net output #90: loss2/loss15 = 0.219961 (* 0.0272727 = 0.00599894 loss) | |
| I0506 00:59:28.167711 15760 solver.cpp:406] Test net output #91: loss2/loss16 = 0.167297 (* 0.0272727 = 0.00456265 loss) | |
| I0506 00:59:28.167724 15760 solver.cpp:406] Test net output #92: loss2/loss17 = 0.104143 (* 0.0272727 = 0.00284026 loss) | |
| I0506 00:59:28.167737 15760 solver.cpp:406] Test net output #93: loss2/loss18 = 0.0511249 (* 0.0272727 = 0.00139431 loss) | |
| I0506 00:59:28.167752 15760 solver.cpp:406] Test net output #94: loss2/loss19 = 0.0430014 (* 0.0272727 = 0.00117276 loss) | |
| I0506 00:59:28.167764 15760 solver.cpp:406] Test net output #95: loss2/loss20 = 0.0277939 (* 0.0272727 = 0.000758016 loss) | |
| I0506 00:59:28.167778 15760 solver.cpp:406] Test net output #96: loss2/loss21 = 0.0205771 (* 0.0272727 = 0.000561193 loss) | |
| I0506 00:59:28.167790 15760 solver.cpp:406] Test net output #97: loss2/loss22 = 0.0196306 (* 0.0272727 = 0.000535381 loss) | |
| I0506 00:59:28.167803 15760 solver.cpp:406] Test net output #98: loss3/accuracy = 0.0831391 | |
| I0506 00:59:28.167814 15760 solver.cpp:406] Test net output #99: loss3/accuracy01 = 0.121 | |
| I0506 00:59:28.167824 15760 solver.cpp:406] Test net output #100: loss3/accuracy02 = 0.102 | |
| I0506 00:59:28.167835 15760 solver.cpp:406] Test net output #101: loss3/accuracy03 = 0.103 | |
| I0506 00:59:28.167846 15760 solver.cpp:406] Test net output #102: loss3/accuracy04 = 0.179 | |
| I0506 00:59:28.167858 15760 solver.cpp:406] Test net output #103: loss3/accuracy05 = 0.322 | |
| I0506 00:59:28.167870 15760 solver.cpp:406] Test net output #104: loss3/accuracy06 = 0.424 | |
| I0506 00:59:28.167881 15760 solver.cpp:406] Test net output #105: loss3/accuracy07 = 0.636 | |
| I0506 00:59:28.167891 15760 solver.cpp:406] Test net output #106: loss3/accuracy08 = 0.796 | |
| I0506 00:59:28.167902 15760 solver.cpp:406] Test net output #107: loss3/accuracy09 = 0.888 | |
| I0506 00:59:28.167913 15760 solver.cpp:406] Test net output #108: loss3/accuracy10 = 0.911 | |
| I0506 00:59:28.167927 15760 solver.cpp:406] Test net output #109: loss3/accuracy11 = 0.924 | |
| I0506 00:59:28.167939 15760 solver.cpp:406] Test net output #110: loss3/accuracy12 = 0.936 | |
| I0506 00:59:28.167951 15760 solver.cpp:406] Test net output #111: loss3/accuracy13 = 0.949 | |
| I0506 00:59:28.167963 15760 solver.cpp:406] Test net output #112: loss3/accuracy14 = 0.96 | |
| I0506 00:59:28.167973 15760 solver.cpp:406] Test net output #113: loss3/accuracy15 = 0.97 | |
| I0506 00:59:28.167984 15760 solver.cpp:406] Test net output #114: loss3/accuracy16 = 0.979 | |
| I0506 00:59:28.168006 15760 solver.cpp:406] Test net output #115: loss3/accuracy17 = 0.988 | |
| I0506 00:59:28.168018 15760 solver.cpp:406] Test net output #116: loss3/accuracy18 = 0.996 | |
| I0506 00:59:28.168030 15760 solver.cpp:406] Test net output #117: loss3/accuracy19 = 0.997 | |
| I0506 00:59:28.168041 15760 solver.cpp:406] Test net output #118: loss3/accuracy20 = 0.999 | |
| I0506 00:59:28.168052 15760 solver.cpp:406] Test net output #119: loss3/accuracy21 = 1 | |
| I0506 00:59:28.168063 15760 solver.cpp:406] Test net output #120: loss3/accuracy22 = 1 | |
| I0506 00:59:28.168074 15760 solver.cpp:406] Test net output #121: loss3/accuracy_incl_empty = 0.731547 | |
| I0506 00:59:28.168086 15760 solver.cpp:406] Test net output #122: loss3/accuracy_top3 = 0.241565 | |
| I0506 00:59:28.168099 15760 solver.cpp:406] Test net output #123: loss3/cross_entropy_loss = 3.16321 (* 1 = 3.16321 loss) | |
| I0506 00:59:28.168112 15760 solver.cpp:406] Test net output #124: loss3/cross_entropy_loss_incl_empty = 1.00049 (* 1 = 1.00049 loss) | |
| I0506 00:59:28.168125 15760 solver.cpp:406] Test net output #125: loss3/loss01 = 2.9826 (* 0.0909091 = 0.271145 loss) | |
| I0506 00:59:28.168138 15760 solver.cpp:406] Test net output #126: loss3/loss02 = 3.10081 (* 0.0909091 = 0.281891 loss) | |
| I0506 00:59:28.168151 15760 solver.cpp:406] Test net output #127: loss3/loss03 = 3.19002 (* 0.0909091 = 0.290002 loss) | |
| I0506 00:59:28.168164 15760 solver.cpp:406] Test net output #128: loss3/loss04 = 3.02565 (* 0.0909091 = 0.275059 loss) | |
| I0506 00:59:28.168177 15760 solver.cpp:406] Test net output #129: loss3/loss05 = 2.63974 (* 0.0909091 = 0.239976 loss) | |
| I0506 00:59:28.168190 15760 solver.cpp:406] Test net output #130: loss3/loss06 = 2.34779 (* 0.0909091 = 0.213436 loss) | |
| I0506 00:59:28.168203 15760 solver.cpp:406] Test net output #131: loss3/loss07 = 1.64185 (* 0.0909091 = 0.149259 loss) | |
| I0506 00:59:28.168216 15760 solver.cpp:406] Test net output #132: loss3/loss08 = 0.953071 (* 0.0909091 = 0.0866428 loss) | |
| I0506 00:59:28.168231 15760 solver.cpp:406] Test net output #133: loss3/loss09 = 0.510933 (* 0.0909091 = 0.0464485 loss) | |
| I0506 00:59:28.168243 15760 solver.cpp:406] Test net output #134: loss3/loss10 = 0.421544 (* 0.0909091 = 0.0383222 loss) | |
| I0506 00:59:28.168256 15760 solver.cpp:406] Test net output #135: loss3/loss11 = 0.373685 (* 0.0909091 = 0.0339714 loss) | |
| I0506 00:59:28.168270 15760 solver.cpp:406] Test net output #136: loss3/loss12 = 0.3338 (* 0.0909091 = 0.0303455 loss) | |
| I0506 00:59:28.168279 15760 solver.cpp:406] Test net output #137: loss3/loss13 = 0.282813 (* 0.0909091 = 0.0257103 loss) | |
| I0506 00:59:28.168288 15760 solver.cpp:406] Test net output #138: loss3/loss14 = 0.2333 (* 0.0909091 = 0.0212091 loss) | |
| I0506 00:59:28.168303 15760 solver.cpp:406] Test net output #139: loss3/loss15 = 0.185442 (* 0.0909091 = 0.0168584 loss) | |
| I0506 00:59:28.168315 15760 solver.cpp:406] Test net output #140: loss3/loss16 = 0.144675 (* 0.0909091 = 0.0131523 loss) | |
| I0506 00:59:28.168335 15760 solver.cpp:406] Test net output #141: loss3/loss17 = 0.0929149 (* 0.0909091 = 0.00844681 loss) | |
| I0506 00:59:28.168354 15760 solver.cpp:406] Test net output #142: loss3/loss18 = 0.0453755 (* 0.0909091 = 0.00412505 loss) | |
| I0506 00:59:28.168372 15760 solver.cpp:406] Test net output #143: loss3/loss19 = 0.0338278 (* 0.0909091 = 0.00307526 loss) | |
| I0506 00:59:28.168386 15760 solver.cpp:406] Test net output #144: loss3/loss20 = 0.019295 (* 0.0909091 = 0.00175409 loss) | |
| I0506 00:59:28.168401 15760 solver.cpp:406] Test net output #145: loss3/loss21 = 0.00955671 (* 0.0909091 = 0.000868792 loss) | |
| I0506 00:59:28.168413 15760 solver.cpp:406] Test net output #146: loss3/loss22 = 0.00820083 (* 0.0909091 = 0.00074553 loss) | |
| I0506 00:59:28.168424 15760 solver.cpp:406] Test net output #147: total_accuracy = 0 | |
| I0506 00:59:28.168436 15760 solver.cpp:406] Test net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:59:28.168447 15760 solver.cpp:406] Test net output #149: total_confidence = 0.000421244 | |
| I0506 00:59:28.168468 15760 solver.cpp:406] Test net output #150: total_confidence_not_rec = 0.000417029 | |
| I0506 00:59:28.304327 15760 solver.cpp:229] Iteration 10000, loss = 10.6356 | |
| I0506 00:59:28.304385 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0506 00:59:28.304404 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 00:59:28.304416 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 00:59:28.304428 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 00:59:28.304440 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 00:59:28.304452 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 00:59:28.304463 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.5 | |
| I0506 00:59:28.304476 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.75 | |
| I0506 00:59:28.304487 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 00:59:28.304500 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.75 | |
| I0506 00:59:28.304512 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 00:59:28.304524 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 00:59:28.304536 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 00:59:28.304548 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 00:59:28.304559 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 00:59:28.304571 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 00:59:28.304582 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 00:59:28.304594 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 00:59:28.304605 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 00:59:28.304617 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 00:59:28.304628 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 00:59:28.304641 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 00:59:28.304651 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 00:59:28.304663 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.721591 | |
| I0506 00:59:28.304674 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.133333 | |
| I0506 00:59:28.304690 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.303 (* 0.3 = 0.9909 loss) | |
| I0506 00:59:28.304704 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.17134 (* 0.3 = 0.351402 loss) | |
| I0506 00:59:28.304718 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.77251 (* 0.0272727 = 0.102887 loss) | |
| I0506 00:59:28.304733 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.19829 (* 0.0272727 = 0.0872261 loss) | |
| I0506 00:59:28.304746 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.09528 (* 0.0272727 = 0.111689 loss) | |
| I0506 00:59:28.304760 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.13438 (* 0.0272727 = 0.0854831 loss) | |
| I0506 00:59:28.304774 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.42107 (* 0.0272727 = 0.0660292 loss) | |
| I0506 00:59:28.304787 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.0744 (* 0.0272727 = 0.0565745 loss) | |
| I0506 00:59:28.304805 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.50918 (* 0.0272727 = 0.0411596 loss) | |
| I0506 00:59:28.304821 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.06871 (* 0.0272727 = 0.0291465 loss) | |
| I0506 00:59:28.304834 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.00841 (* 0.0272727 = 0.0275021 loss) | |
| I0506 00:59:28.304848 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.621779 (* 0.0272727 = 0.0169576 loss) | |
| I0506 00:59:28.304862 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.842215 (* 0.0272727 = 0.0229695 loss) | |
| I0506 00:59:28.304906 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.296551 (* 0.0272727 = 0.00808774 loss) | |
| I0506 00:59:28.304921 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.307914 (* 0.0272727 = 0.00839764 loss) | |
| I0506 00:59:28.304936 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.193028 (* 0.0272727 = 0.0052644 loss) | |
| I0506 00:59:28.304950 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.238225 (* 0.0272727 = 0.00649705 loss) | |
| I0506 00:59:28.304965 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.195258 (* 0.0272727 = 0.00532521 loss) | |
| I0506 00:59:28.304978 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.122558 (* 0.0272727 = 0.0033425 loss) | |
| I0506 00:59:28.304992 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.070545 (* 0.0272727 = 0.00192395 loss) | |
| I0506 00:59:28.305006 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0639804 (* 0.0272727 = 0.00174492 loss) | |
| I0506 00:59:28.305021 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0658903 (* 0.0272727 = 0.00179701 loss) | |
| I0506 00:59:28.305033 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0711849 (* 0.0272727 = 0.00194141 loss) | |
| I0506 00:59:28.305047 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0481119 (* 0.0272727 = 0.00131214 loss) | |
| I0506 00:59:28.305059 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0444444 | |
| I0506 00:59:28.305071 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 00:59:28.305083 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.25 | |
| I0506 00:59:28.305095 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 00:59:28.305106 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 00:59:28.305131 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.5 | |
| I0506 00:59:28.305146 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.5 | |
| I0506 00:59:28.305158 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 00:59:28.305171 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 00:59:28.305181 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.75 | |
| I0506 00:59:28.305193 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 00:59:28.305205 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 00:59:28.305217 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 00:59:28.305228 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 00:59:28.305239 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 00:59:28.305250 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 00:59:28.305261 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 00:59:28.305272 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 00:59:28.305284 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 00:59:28.305295 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 00:59:28.305306 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 00:59:28.305318 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 00:59:28.305330 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 00:59:28.305341 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.732955 | |
| I0506 00:59:28.305352 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.266667 | |
| I0506 00:59:28.305366 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.19583 (* 0.3 = 0.958749 loss) | |
| I0506 00:59:28.305379 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.0916 (* 0.3 = 0.327479 loss) | |
| I0506 00:59:28.305393 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.70144 (* 0.0272727 = 0.100948 loss) | |
| I0506 00:59:28.305420 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.27555 (* 0.0272727 = 0.0893331 loss) | |
| I0506 00:59:28.305434 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.82602 (* 0.0272727 = 0.104346 loss) | |
| I0506 00:59:28.305449 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.29162 (* 0.0272727 = 0.0897715 loss) | |
| I0506 00:59:28.305462 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.28982 (* 0.0272727 = 0.0624496 loss) | |
| I0506 00:59:28.305476 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.6296 (* 0.0272727 = 0.0717163 loss) | |
| I0506 00:59:28.305490 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.27947 (* 0.0272727 = 0.0348946 loss) | |
| I0506 00:59:28.305502 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.919719 (* 0.0272727 = 0.0250833 loss) | |
| I0506 00:59:28.305516 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.14309 (* 0.0272727 = 0.0311753 loss) | |
| I0506 00:59:28.305529 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.465673 (* 0.0272727 = 0.0127002 loss) | |
| I0506 00:59:28.305543 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.647262 (* 0.0272727 = 0.0176526 loss) | |
| I0506 00:59:28.305557 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.170321 (* 0.0272727 = 0.00464512 loss) | |
| I0506 00:59:28.305570 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.170956 (* 0.0272727 = 0.00466242 loss) | |
| I0506 00:59:28.305584 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.17076 (* 0.0272727 = 0.00465709 loss) | |
| I0506 00:59:28.305598 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.112699 (* 0.0272727 = 0.00307361 loss) | |
| I0506 00:59:28.305611 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0604654 (* 0.0272727 = 0.00164906 loss) | |
| I0506 00:59:28.305625 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0283562 (* 0.0272727 = 0.000773351 loss) | |
| I0506 00:59:28.305639 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.037568 (* 0.0272727 = 0.00102458 loss) | |
| I0506 00:59:28.305652 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.03604 (* 0.0272727 = 0.00098291 loss) | |
| I0506 00:59:28.305666 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0240135 (* 0.0272727 = 0.000654915 loss) | |
| I0506 00:59:28.305680 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0303727 (* 0.0272727 = 0.000828347 loss) | |
| I0506 00:59:28.305693 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0347186 (* 0.0272727 = 0.000946872 loss) | |
| I0506 00:59:28.305706 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0222222 | |
| I0506 00:59:28.305718 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 00:59:28.305729 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 00:59:28.305740 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 00:59:28.305752 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 00:59:28.305763 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.375 | |
| I0506 00:59:28.305775 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.5 | |
| I0506 00:59:28.305788 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 00:59:28.305799 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 00:59:28.305809 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.75 | |
| I0506 00:59:28.305821 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 00:59:28.305833 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 00:59:28.305845 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 00:59:28.305861 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 00:59:28.305873 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 00:59:28.305891 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 00:59:28.305905 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 00:59:28.305917 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 00:59:28.305929 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 00:59:28.305943 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 00:59:28.305954 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 00:59:28.305965 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 00:59:28.305977 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 00:59:28.305989 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.727273 | |
| I0506 00:59:28.305999 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.244444 | |
| I0506 00:59:28.306013 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.07061 (* 1 = 3.07061 loss) | |
| I0506 00:59:28.306027 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.01035 (* 1 = 1.01035 loss) | |
| I0506 00:59:28.306041 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.25298 (* 0.0909091 = 0.295726 loss) | |
| I0506 00:59:28.306054 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.94275 (* 0.0909091 = 0.267523 loss) | |
| I0506 00:59:28.306067 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.45129 (* 0.0909091 = 0.313754 loss) | |
| I0506 00:59:28.306082 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.87947 (* 0.0909091 = 0.26177 loss) | |
| I0506 00:59:28.306094 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.36098 (* 0.0909091 = 0.214634 loss) | |
| I0506 00:59:28.306107 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.32278 (* 0.0909091 = 0.211162 loss) | |
| I0506 00:59:28.306120 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.16318 (* 0.0909091 = 0.105744 loss) | |
| I0506 00:59:28.306134 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.97473 (* 0.0909091 = 0.0886118 loss) | |
| I0506 00:59:28.306148 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.909218 (* 0.0909091 = 0.0826562 loss) | |
| I0506 00:59:28.306160 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.528028 (* 0.0909091 = 0.0480026 loss) | |
| I0506 00:59:28.306174 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.641086 (* 0.0909091 = 0.0582806 loss) | |
| I0506 00:59:28.306187 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.35663 (* 0.0909091 = 0.0324209 loss) | |
| I0506 00:59:28.306201 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.19882 (* 0.0909091 = 0.0180746 loss) | |
| I0506 00:59:28.306215 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.141375 (* 0.0909091 = 0.0128522 loss) | |
| I0506 00:59:28.306228 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.101308 (* 0.0909091 = 0.00920985 loss) | |
| I0506 00:59:28.306241 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0795631 (* 0.0909091 = 0.00723301 loss) | |
| I0506 00:59:28.306255 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0343418 (* 0.0909091 = 0.00312198 loss) | |
| I0506 00:59:28.306269 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.019265 (* 0.0909091 = 0.00175136 loss) | |
| I0506 00:59:28.306282 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0128025 (* 0.0909091 = 0.00116386 loss) | |
| I0506 00:59:28.306296 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.0114918 (* 0.0909091 = 0.00104471 loss) | |
| I0506 00:59:28.306309 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00954312 (* 0.0909091 = 0.000867556 loss) | |
| I0506 00:59:28.306324 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00870823 (* 0.0909091 = 0.000791657 loss) | |
| I0506 00:59:28.306335 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 00:59:28.306346 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 00:59:28.306368 15760 solver.cpp:245] Train net output #149: total_confidence = 0.00015581 | |
| I0506 00:59:28.306381 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.00075208 | |
| I0506 00:59:28.306394 15760 sgd_solver.cpp:106] Iteration 10000, lr = 0.001 | |
| I0506 00:59:53.840303 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 33.4995 > 30) by scale factor 0.895535 | |
| I0506 01:00:16.419589 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.5951 > 30) by scale factor 0.949514 | |
| I0506 01:01:15.483536 15760 solver.cpp:229] Iteration 10500, loss = 10.5483 | |
| I0506 01:01:15.483670 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.118644 | |
| I0506 01:01:15.483693 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.125 | |
| I0506 01:01:15.483706 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.25 | |
| I0506 01:01:15.483718 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 01:01:15.483731 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 01:01:15.483742 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.25 | |
| I0506 01:01:15.483754 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 01:01:15.483767 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 01:01:15.483778 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.625 | |
| I0506 01:01:15.483790 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.75 | |
| I0506 01:01:15.483803 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 01:01:15.483815 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 01:01:15.483827 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 01:01:15.483839 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0.875 | |
| I0506 01:01:15.483850 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 0.875 | |
| I0506 01:01:15.483862 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 0.875 | |
| I0506 01:01:15.483876 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 0.875 | |
| I0506 01:01:15.483899 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 0.875 | |
| I0506 01:01:15.483917 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:01:15.483933 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:01:15.483945 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:01:15.483958 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:01:15.483969 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:01:15.483980 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.704545 | |
| I0506 01:01:15.483992 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.271186 | |
| I0506 01:01:15.484009 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.27133 (* 0.3 = 0.981398 loss) | |
| I0506 01:01:15.484024 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.23294 (* 0.3 = 0.369881 loss) | |
| I0506 01:01:15.484037 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.55605 (* 0.0272727 = 0.0969833 loss) | |
| I0506 01:01:15.484051 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.0148 (* 0.0272727 = 0.0822217 loss) | |
| I0506 01:01:15.484066 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.29544 (* 0.0272727 = 0.0898757 loss) | |
| I0506 01:01:15.484079 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.289 (* 0.0272727 = 0.0897 loss) | |
| I0506 01:01:15.484093 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.94715 (* 0.0272727 = 0.0803768 loss) | |
| I0506 01:01:15.484107 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.74355 (* 0.0272727 = 0.0748241 loss) | |
| I0506 01:01:15.484122 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.7012 (* 0.0272727 = 0.073669 loss) | |
| I0506 01:01:15.484135 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.77319 (* 0.0272727 = 0.0483597 loss) | |
| I0506 01:01:15.484149 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.31711 (* 0.0272727 = 0.0359211 loss) | |
| I0506 01:01:15.484163 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.557158 (* 0.0272727 = 0.0151952 loss) | |
| I0506 01:01:15.484177 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.767127 (* 0.0272727 = 0.0209216 loss) | |
| I0506 01:01:15.484191 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.650533 (* 0.0272727 = 0.0177418 loss) | |
| I0506 01:01:15.484223 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.692635 (* 0.0272727 = 0.0188901 loss) | |
| I0506 01:01:15.484238 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.706198 (* 0.0272727 = 0.0192599 loss) | |
| I0506 01:01:15.484252 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.942836 (* 0.0272727 = 0.0257137 loss) | |
| I0506 01:01:15.484267 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.849106 (* 0.0272727 = 0.0231574 loss) | |
| I0506 01:01:15.484282 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 1.04101 (* 0.0272727 = 0.0283911 loss) | |
| I0506 01:01:15.484297 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00598681 (* 0.0272727 = 0.000163277 loss) | |
| I0506 01:01:15.484310 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00630412 (* 0.0272727 = 0.000171931 loss) | |
| I0506 01:01:15.484324 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00370788 (* 0.0272727 = 0.000101124 loss) | |
| I0506 01:01:15.484338 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0033013 (* 0.0272727 = 9.00354e-05 loss) | |
| I0506 01:01:15.484354 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00447863 (* 0.0272727 = 0.000122145 loss) | |
| I0506 01:01:15.484365 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0847458 | |
| I0506 01:01:15.484377 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 01:01:15.484390 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:01:15.484401 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.25 | |
| I0506 01:01:15.484413 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:01:15.484424 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 01:01:15.484436 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 01:01:15.484448 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 01:01:15.484460 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.625 | |
| I0506 01:01:15.484472 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.75 | |
| I0506 01:01:15.484483 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 01:01:15.484495 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 01:01:15.484508 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 01:01:15.484519 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0.875 | |
| I0506 01:01:15.484530 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 0.875 | |
| I0506 01:01:15.484542 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 0.875 | |
| I0506 01:01:15.484555 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 0.875 | |
| I0506 01:01:15.484565 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 0.875 | |
| I0506 01:01:15.484577 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:01:15.484589 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:01:15.484601 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:01:15.484612 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:01:15.484623 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:01:15.484635 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.693182 | |
| I0506 01:01:15.484647 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.254237 | |
| I0506 01:01:15.484660 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.26079 (* 0.3 = 0.978236 loss) | |
| I0506 01:01:15.484674 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.16264 (* 0.3 = 0.348791 loss) | |
| I0506 01:01:15.484694 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.1686 (* 0.0272727 = 0.0864165 loss) | |
| I0506 01:01:15.484707 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 2.87479 (* 0.0272727 = 0.0784034 loss) | |
| I0506 01:01:15.484732 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.32295 (* 0.0272727 = 0.090626 loss) | |
| I0506 01:01:15.484747 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.46813 (* 0.0272727 = 0.0945854 loss) | |
| I0506 01:01:15.484761 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.48118 (* 0.0272727 = 0.0949413 loss) | |
| I0506 01:01:15.484776 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.74473 (* 0.0272727 = 0.0748562 loss) | |
| I0506 01:01:15.484789 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.78209 (* 0.0272727 = 0.0758752 loss) | |
| I0506 01:01:15.484802 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.67586 (* 0.0272727 = 0.0457052 loss) | |
| I0506 01:01:15.484817 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.09762 (* 0.0272727 = 0.0299352 loss) | |
| I0506 01:01:15.484830 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.621731 (* 0.0272727 = 0.0169563 loss) | |
| I0506 01:01:15.484844 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.649121 (* 0.0272727 = 0.0177033 loss) | |
| I0506 01:01:15.484858 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.875334 (* 0.0272727 = 0.0238728 loss) | |
| I0506 01:01:15.484871 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.593328 (* 0.0272727 = 0.0161817 loss) | |
| I0506 01:01:15.484885 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.6046 (* 0.0272727 = 0.0164891 loss) | |
| I0506 01:01:15.484899 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.834738 (* 0.0272727 = 0.0227656 loss) | |
| I0506 01:01:15.484912 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.959198 (* 0.0272727 = 0.0261599 loss) | |
| I0506 01:01:15.484928 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 1.03078 (* 0.0272727 = 0.0281123 loss) | |
| I0506 01:01:15.484943 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0186311 (* 0.0272727 = 0.000508121 loss) | |
| I0506 01:01:15.484958 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0219622 (* 0.0272727 = 0.00059897 loss) | |
| I0506 01:01:15.484972 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0196076 (* 0.0272727 = 0.000534752 loss) | |
| I0506 01:01:15.484985 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0179337 (* 0.0272727 = 0.000489102 loss) | |
| I0506 01:01:15.484999 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0140727 (* 0.0272727 = 0.000383802 loss) | |
| I0506 01:01:15.485011 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.135593 | |
| I0506 01:01:15.485023 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:01:15.485035 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.25 | |
| I0506 01:01:15.485046 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.25 | |
| I0506 01:01:15.485059 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 01:01:15.485070 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.375 | |
| I0506 01:01:15.485081 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 01:01:15.485093 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 01:01:15.485105 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.625 | |
| I0506 01:01:15.485128 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.75 | |
| I0506 01:01:15.485144 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 01:01:15.485157 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 01:01:15.485168 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 01:01:15.485180 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0.875 | |
| I0506 01:01:15.485191 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 0.875 | |
| I0506 01:01:15.485203 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 0.875 | |
| I0506 01:01:15.485226 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 0.875 | |
| I0506 01:01:15.485239 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 0.875 | |
| I0506 01:01:15.485251 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:01:15.485263 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:01:15.485275 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:01:15.485286 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:01:15.485298 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:01:15.485309 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.710227 | |
| I0506 01:01:15.485321 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.355932 | |
| I0506 01:01:15.485335 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.03903 (* 1 = 3.03903 loss) | |
| I0506 01:01:15.485349 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.07905 (* 1 = 1.07905 loss) | |
| I0506 01:01:15.485363 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.40619 (* 0.0909091 = 0.309653 loss) | |
| I0506 01:01:15.485378 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.55486 (* 0.0909091 = 0.23226 loss) | |
| I0506 01:01:15.485390 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.99117 (* 0.0909091 = 0.271925 loss) | |
| I0506 01:01:15.485404 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.87439 (* 0.0909091 = 0.261308 loss) | |
| I0506 01:01:15.485417 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.21721 (* 0.0909091 = 0.201565 loss) | |
| I0506 01:01:15.485431 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.28533 (* 0.0909091 = 0.207757 loss) | |
| I0506 01:01:15.485445 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.33544 (* 0.0909091 = 0.212312 loss) | |
| I0506 01:01:15.485458 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.27318 (* 0.0909091 = 0.115744 loss) | |
| I0506 01:01:15.485472 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.771533 (* 0.0909091 = 0.0701394 loss) | |
| I0506 01:01:15.485486 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.433976 (* 0.0909091 = 0.0394524 loss) | |
| I0506 01:01:15.485499 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.505805 (* 0.0909091 = 0.0459823 loss) | |
| I0506 01:01:15.485517 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.531173 (* 0.0909091 = 0.0482884 loss) | |
| I0506 01:01:15.485532 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.398535 (* 0.0909091 = 0.0362305 loss) | |
| I0506 01:01:15.485546 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.559081 (* 0.0909091 = 0.0508255 loss) | |
| I0506 01:01:15.485559 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.865604 (* 0.0909091 = 0.0786913 loss) | |
| I0506 01:01:15.485574 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.708214 (* 0.0909091 = 0.0643831 loss) | |
| I0506 01:01:15.485586 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.91132 (* 0.0909091 = 0.0828473 loss) | |
| I0506 01:01:15.485600 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00361443 (* 0.0909091 = 0.000328584 loss) | |
| I0506 01:01:15.485615 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00239349 (* 0.0909091 = 0.00021759 loss) | |
| I0506 01:01:15.485628 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00245557 (* 0.0909091 = 0.000223234 loss) | |
| I0506 01:01:15.485641 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00142854 (* 0.0909091 = 0.000129867 loss) | |
| I0506 01:01:15.485656 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00157136 (* 0.0909091 = 0.000142851 loss) | |
| I0506 01:01:15.485667 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:01:15.485678 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:01:15.485689 15760 solver.cpp:245] Train net output #149: total_confidence = 7.32735e-05 | |
| I0506 01:01:15.485710 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000747179 | |
| I0506 01:01:15.485724 15760 sgd_solver.cpp:106] Iteration 10500, lr = 0.001 | |
| I0506 01:02:19.659266 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 32.3831 > 30) by scale factor 0.926409 | |
| I0506 01:03:02.706852 15760 solver.cpp:229] Iteration 11000, loss = 10.4638 | |
| I0506 01:03:02.706982 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0384615 | |
| I0506 01:03:02.707003 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.25 | |
| I0506 01:03:02.707016 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.25 | |
| I0506 01:03:02.707028 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:03:02.707041 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 01:03:02.707052 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 01:03:02.707064 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.25 | |
| I0506 01:03:02.707077 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 01:03:02.707088 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 01:03:02.707100 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 01:03:02.707114 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:03:02.707125 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:03:02.707137 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:03:02.707149 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:03:02.707160 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:03:02.707172 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:03:02.707185 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:03:02.707196 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:03:02.707207 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:03:02.707219 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:03:02.707231 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:03:02.707242 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:03:02.707253 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:03:02.707265 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.710227 | |
| I0506 01:03:02.707278 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.173077 | |
| I0506 01:03:02.707293 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.36283 (* 0.3 = 1.00885 loss) | |
| I0506 01:03:02.707307 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.18883 (* 0.3 = 0.35665 loss) | |
| I0506 01:03:02.707321 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 2.90145 (* 0.0272727 = 0.0791305 loss) | |
| I0506 01:03:02.707335 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.31946 (* 0.0272727 = 0.0905306 loss) | |
| I0506 01:03:02.707350 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.42457 (* 0.0272727 = 0.0933974 loss) | |
| I0506 01:03:02.707363 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.77122 (* 0.0272727 = 0.102851 loss) | |
| I0506 01:03:02.707377 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.52534 (* 0.0272727 = 0.0961455 loss) | |
| I0506 01:03:02.707391 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.48366 (* 0.0272727 = 0.0950089 loss) | |
| I0506 01:03:02.707404 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.805 (* 0.0272727 = 0.0764999 loss) | |
| I0506 01:03:02.707418 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.7063 (* 0.0272727 = 0.0465354 loss) | |
| I0506 01:03:02.707432 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.486952 (* 0.0272727 = 0.0132805 loss) | |
| I0506 01:03:02.707448 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.105908 (* 0.0272727 = 0.00288839 loss) | |
| I0506 01:03:02.707461 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.113852 (* 0.0272727 = 0.00310506 loss) | |
| I0506 01:03:02.707475 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0713797 (* 0.0272727 = 0.00194672 loss) | |
| I0506 01:03:02.707489 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.050942 (* 0.0272727 = 0.00138933 loss) | |
| I0506 01:03:02.707522 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.05799 (* 0.0272727 = 0.00158155 loss) | |
| I0506 01:03:02.707537 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0362265 (* 0.0272727 = 0.000987994 loss) | |
| I0506 01:03:02.707552 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.019965 (* 0.0272727 = 0.000544501 loss) | |
| I0506 01:03:02.707566 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00948847 (* 0.0272727 = 0.000258777 loss) | |
| I0506 01:03:02.707581 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00668053 (* 0.0272727 = 0.000182196 loss) | |
| I0506 01:03:02.707594 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00599524 (* 0.0272727 = 0.000163506 loss) | |
| I0506 01:03:02.707608 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00617411 (* 0.0272727 = 0.000168385 loss) | |
| I0506 01:03:02.707623 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00284744 (* 0.0272727 = 7.76576e-05 loss) | |
| I0506 01:03:02.707636 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00629933 (* 0.0272727 = 0.0001718 loss) | |
| I0506 01:03:02.707648 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0576923 | |
| I0506 01:03:02.707661 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.5 | |
| I0506 01:03:02.707674 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:03:02.707685 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.25 | |
| I0506 01:03:02.707696 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:03:02.707708 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 01:03:02.707720 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.25 | |
| I0506 01:03:02.707731 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 01:03:02.707743 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 01:03:02.707756 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 01:03:02.707767 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:03:02.707778 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:03:02.707789 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:03:02.707800 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:03:02.707811 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:03:02.707823 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:03:02.707834 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:03:02.707845 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:03:02.707856 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:03:02.707869 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:03:02.707883 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:03:02.707895 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:03:02.707906 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:03:02.707918 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.693182 | |
| I0506 01:03:02.707931 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.173077 | |
| I0506 01:03:02.707943 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.22953 (* 0.3 = 0.96886 loss) | |
| I0506 01:03:02.707957 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.25325 (* 0.3 = 0.375975 loss) | |
| I0506 01:03:02.707972 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 2.33298 (* 0.0272727 = 0.0636266 loss) | |
| I0506 01:03:02.707985 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.48282 (* 0.0272727 = 0.0949861 loss) | |
| I0506 01:03:02.708014 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.36083 (* 0.0272727 = 0.091659 loss) | |
| I0506 01:03:02.708030 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.78153 (* 0.0272727 = 0.103133 loss) | |
| I0506 01:03:02.708044 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.3376 (* 0.0272727 = 0.0910255 loss) | |
| I0506 01:03:02.708057 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.7032 (* 0.0272727 = 0.100996 loss) | |
| I0506 01:03:02.708071 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.50497 (* 0.0272727 = 0.0683173 loss) | |
| I0506 01:03:02.708086 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.55227 (* 0.0272727 = 0.0423347 loss) | |
| I0506 01:03:02.708099 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.505009 (* 0.0272727 = 0.013773 loss) | |
| I0506 01:03:02.708113 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.106214 (* 0.0272727 = 0.00289675 loss) | |
| I0506 01:03:02.708127 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0810997 (* 0.0272727 = 0.00221181 loss) | |
| I0506 01:03:02.708142 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0536257 (* 0.0272727 = 0.00146252 loss) | |
| I0506 01:03:02.708155 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0256581 (* 0.0272727 = 0.000699766 loss) | |
| I0506 01:03:02.708168 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0263094 (* 0.0272727 = 0.000717528 loss) | |
| I0506 01:03:02.708183 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.028855 (* 0.0272727 = 0.000786956 loss) | |
| I0506 01:03:02.708196 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0124511 (* 0.0272727 = 0.000339576 loss) | |
| I0506 01:03:02.708210 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0142428 (* 0.0272727 = 0.00038844 loss) | |
| I0506 01:03:02.708225 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00872765 (* 0.0272727 = 0.000238027 loss) | |
| I0506 01:03:02.708238 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00559468 (* 0.0272727 = 0.000152582 loss) | |
| I0506 01:03:02.708252 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00299132 (* 0.0272727 = 8.15816e-05 loss) | |
| I0506 01:03:02.708266 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00267565 (* 0.0272727 = 7.29723e-05 loss) | |
| I0506 01:03:02.708279 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00370963 (* 0.0272727 = 0.000101172 loss) | |
| I0506 01:03:02.708292 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.134615 | |
| I0506 01:03:02.708303 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:03:02.708314 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:03:02.708326 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:03:02.708338 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:03:02.708349 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.125 | |
| I0506 01:03:02.708361 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.125 | |
| I0506 01:03:02.708372 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 01:03:02.708384 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 01:03:02.708395 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 01:03:02.708407 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:03:02.708418 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:03:02.708430 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:03:02.708441 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:03:02.708451 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:03:02.708463 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:03:02.708474 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:03:02.708495 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:03:02.708508 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:03:02.708520 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:03:02.708531 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:03:02.708542 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:03:02.708554 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:03:02.708565 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.732955 | |
| I0506 01:03:02.708576 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.25 | |
| I0506 01:03:02.708590 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.07338 (* 1 = 3.07338 loss) | |
| I0506 01:03:02.708603 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.0299 (* 1 = 1.0299 loss) | |
| I0506 01:03:02.708617 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.25936 (* 0.0909091 = 0.205396 loss) | |
| I0506 01:03:02.708631 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.11415 (* 0.0909091 = 0.283105 loss) | |
| I0506 01:03:02.708644 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.02855 (* 0.0909091 = 0.275322 loss) | |
| I0506 01:03:02.708658 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.52432 (* 0.0909091 = 0.320393 loss) | |
| I0506 01:03:02.708672 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.06209 (* 0.0909091 = 0.278371 loss) | |
| I0506 01:03:02.708691 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 3.02361 (* 0.0909091 = 0.274874 loss) | |
| I0506 01:03:02.708701 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.29617 (* 0.0909091 = 0.208742 loss) | |
| I0506 01:03:02.708715 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.22708 (* 0.0909091 = 0.111553 loss) | |
| I0506 01:03:02.708729 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.450954 (* 0.0909091 = 0.0409958 loss) | |
| I0506 01:03:02.708742 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0716993 (* 0.0909091 = 0.00651812 loss) | |
| I0506 01:03:02.708755 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0493934 (* 0.0909091 = 0.00449031 loss) | |
| I0506 01:03:02.708770 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0217353 (* 0.0909091 = 0.00197594 loss) | |
| I0506 01:03:02.708783 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0154308 (* 0.0909091 = 0.0014028 loss) | |
| I0506 01:03:02.708796 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00819239 (* 0.0909091 = 0.000744763 loss) | |
| I0506 01:03:02.708811 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00275524 (* 0.0909091 = 0.000250476 loss) | |
| I0506 01:03:02.708823 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00245053 (* 0.0909091 = 0.000222776 loss) | |
| I0506 01:03:02.708837 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.000512455 (* 0.0909091 = 4.65868e-05 loss) | |
| I0506 01:03:02.708852 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.000455629 (* 0.0909091 = 4.14208e-05 loss) | |
| I0506 01:03:02.708864 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.000244467 (* 0.0909091 = 2.22243e-05 loss) | |
| I0506 01:03:02.708878 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 9.52487e-05 (* 0.0909091 = 8.65897e-06 loss) | |
| I0506 01:03:02.708892 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 8.20351e-05 (* 0.0909091 = 7.45774e-06 loss) | |
| I0506 01:03:02.708905 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000168938 (* 0.0909091 = 1.5358e-05 loss) | |
| I0506 01:03:02.708917 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:03:02.708931 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:03:02.708943 15760 solver.cpp:245] Train net output #149: total_confidence = 8.2572e-07 | |
| I0506 01:03:02.708966 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 7.11067e-06 | |
| I0506 01:03:02.708979 15760 sgd_solver.cpp:106] Iteration 11000, lr = 0.001 | |
| I0506 01:03:20.575698 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 35.0641 > 30) by scale factor 0.855575 | |
| I0506 01:04:49.923508 15760 solver.cpp:229] Iteration 11500, loss = 10.4523 | |
| I0506 01:04:49.923650 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0208333 | |
| I0506 01:04:49.923671 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.125 | |
| I0506 01:04:49.923684 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:04:49.923696 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 01:04:49.923708 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 01:04:49.923720 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:04:49.923732 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.625 | |
| I0506 01:04:49.923744 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.75 | |
| I0506 01:04:49.923755 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 01:04:49.923768 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 01:04:49.923779 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 01:04:49.923791 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 01:04:49.923804 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 01:04:49.923815 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0.875 | |
| I0506 01:04:49.923827 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 0.875 | |
| I0506 01:04:49.923840 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 0.875 | |
| I0506 01:04:49.923852 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:04:49.923864 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:04:49.923882 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:04:49.923893 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:04:49.923905 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:04:49.923918 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:04:49.923928 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:04:49.923940 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.732955 | |
| I0506 01:04:49.923952 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.208333 | |
| I0506 01:04:49.923969 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.25259 (* 0.3 = 0.975776 loss) | |
| I0506 01:04:49.923991 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.00478 (* 0.3 = 0.301433 loss) | |
| I0506 01:04:49.924006 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.30956 (* 0.0272727 = 0.0902608 loss) | |
| I0506 01:04:49.924021 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.36205 (* 0.0272727 = 0.0916922 loss) | |
| I0506 01:04:49.924034 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.6784 (* 0.0272727 = 0.10032 loss) | |
| I0506 01:04:49.924048 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 2.97611 (* 0.0272727 = 0.0811667 loss) | |
| I0506 01:04:49.924062 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.29914 (* 0.0272727 = 0.0627037 loss) | |
| I0506 01:04:49.924077 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 1.75434 (* 0.0272727 = 0.0478456 loss) | |
| I0506 01:04:49.924089 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.40739 (* 0.0272727 = 0.0383835 loss) | |
| I0506 01:04:49.924103 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.916214 (* 0.0272727 = 0.0249877 loss) | |
| I0506 01:04:49.924116 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.472512 (* 0.0272727 = 0.0128867 loss) | |
| I0506 01:04:49.924130 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.507841 (* 0.0272727 = 0.0138502 loss) | |
| I0506 01:04:49.924144 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.566653 (* 0.0272727 = 0.0154542 loss) | |
| I0506 01:04:49.924159 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.628099 (* 0.0272727 = 0.01713 loss) | |
| I0506 01:04:49.924190 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.58875 (* 0.0272727 = 0.0160568 loss) | |
| I0506 01:04:49.924206 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.610776 (* 0.0272727 = 0.0166575 loss) | |
| I0506 01:04:49.924219 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.507857 (* 0.0272727 = 0.0138507 loss) | |
| I0506 01:04:49.924234 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0432954 (* 0.0272727 = 0.00118078 loss) | |
| I0506 01:04:49.924249 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0331715 (* 0.0272727 = 0.000904676 loss) | |
| I0506 01:04:49.924263 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0287202 (* 0.0272727 = 0.000783279 loss) | |
| I0506 01:04:49.924278 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0123468 (* 0.0272727 = 0.000336731 loss) | |
| I0506 01:04:49.924291 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0110719 (* 0.0272727 = 0.00030196 loss) | |
| I0506 01:04:49.924305 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00888394 (* 0.0272727 = 0.000242289 loss) | |
| I0506 01:04:49.924319 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00831943 (* 0.0272727 = 0.000226894 loss) | |
| I0506 01:04:49.924331 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.104167 | |
| I0506 01:04:49.924343 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 01:04:49.924355 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 01:04:49.924367 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:04:49.924379 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 01:04:49.924391 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.5 | |
| I0506 01:04:49.924402 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.625 | |
| I0506 01:04:49.924414 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 01:04:49.924427 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 01:04:49.924437 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 01:04:49.924449 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 01:04:49.924460 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 01:04:49.924473 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 01:04:49.924484 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0.875 | |
| I0506 01:04:49.924495 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 0.875 | |
| I0506 01:04:49.924506 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 0.875 | |
| I0506 01:04:49.924518 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:04:49.924530 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:04:49.924542 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:04:49.924551 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:04:49.924564 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:04:49.924576 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:04:49.924587 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:04:49.924599 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.75 | |
| I0506 01:04:49.924617 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.25 | |
| I0506 01:04:49.924633 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.30082 (* 0.3 = 0.990246 loss) | |
| I0506 01:04:49.924646 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.00934 (* 0.3 = 0.302803 loss) | |
| I0506 01:04:49.924664 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.25958 (* 0.0272727 = 0.0888977 loss) | |
| I0506 01:04:49.924679 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.15247 (* 0.0272727 = 0.0859763 loss) | |
| I0506 01:04:49.924705 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.74091 (* 0.0272727 = 0.102025 loss) | |
| I0506 01:04:49.924720 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 2.82792 (* 0.0272727 = 0.0771252 loss) | |
| I0506 01:04:49.924733 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.36506 (* 0.0272727 = 0.0645017 loss) | |
| I0506 01:04:49.924747 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 1.78531 (* 0.0272727 = 0.0486902 loss) | |
| I0506 01:04:49.924762 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.11509 (* 0.0272727 = 0.0304115 loss) | |
| I0506 01:04:49.924774 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.832551 (* 0.0272727 = 0.0227059 loss) | |
| I0506 01:04:49.924788 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.565274 (* 0.0272727 = 0.0154166 loss) | |
| I0506 01:04:49.924803 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.501374 (* 0.0272727 = 0.0136738 loss) | |
| I0506 01:04:49.924816 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.65265 (* 0.0272727 = 0.0177995 loss) | |
| I0506 01:04:49.924829 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.713095 (* 0.0272727 = 0.0194481 loss) | |
| I0506 01:04:49.924844 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.58373 (* 0.0272727 = 0.0159199 loss) | |
| I0506 01:04:49.924856 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.657769 (* 0.0272727 = 0.0179392 loss) | |
| I0506 01:04:49.924870 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.671718 (* 0.0272727 = 0.0183196 loss) | |
| I0506 01:04:49.924885 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0279034 (* 0.0272727 = 0.000761001 loss) | |
| I0506 01:04:49.924898 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0188801 (* 0.0272727 = 0.000514911 loss) | |
| I0506 01:04:49.924911 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0198359 (* 0.0272727 = 0.000540978 loss) | |
| I0506 01:04:49.924928 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.010802 (* 0.0272727 = 0.000294601 loss) | |
| I0506 01:04:49.924943 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00715719 (* 0.0272727 = 0.000195196 loss) | |
| I0506 01:04:49.924957 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00549443 (* 0.0272727 = 0.000149848 loss) | |
| I0506 01:04:49.924970 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00781407 (* 0.0272727 = 0.000213111 loss) | |
| I0506 01:04:49.924983 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0625 | |
| I0506 01:04:49.924994 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 01:04:49.925006 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.25 | |
| I0506 01:04:49.925017 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 01:04:49.925029 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 01:04:49.925040 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.375 | |
| I0506 01:04:49.925052 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.625 | |
| I0506 01:04:49.925063 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 01:04:49.925076 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 01:04:49.925086 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 01:04:49.925098 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 01:04:49.925109 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 01:04:49.925138 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 01:04:49.925153 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0.875 | |
| I0506 01:04:49.925164 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 0.875 | |
| I0506 01:04:49.925176 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 0.875 | |
| I0506 01:04:49.925189 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:04:49.925211 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:04:49.925225 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:04:49.925236 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:04:49.925247 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:04:49.925258 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:04:49.925271 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:04:49.925282 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.738636 | |
| I0506 01:04:49.925293 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.270833 | |
| I0506 01:04:49.925307 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.12362 (* 1 = 3.12362 loss) | |
| I0506 01:04:49.925321 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.903029 (* 1 = 0.903029 loss) | |
| I0506 01:04:49.925335 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.14302 (* 0.0909091 = 0.285729 loss) | |
| I0506 01:04:49.925348 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.80875 (* 0.0909091 = 0.255341 loss) | |
| I0506 01:04:49.925362 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.31556 (* 0.0909091 = 0.301415 loss) | |
| I0506 01:04:49.925375 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.54021 (* 0.0909091 = 0.230928 loss) | |
| I0506 01:04:49.925390 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.14028 (* 0.0909091 = 0.194571 loss) | |
| I0506 01:04:49.925403 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.66334 (* 0.0909091 = 0.151213 loss) | |
| I0506 01:04:49.925416 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.00642 (* 0.0909091 = 0.0914927 loss) | |
| I0506 01:04:49.925431 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.609401 (* 0.0909091 = 0.0554001 loss) | |
| I0506 01:04:49.925444 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.431853 (* 0.0909091 = 0.0392594 loss) | |
| I0506 01:04:49.925457 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.361581 (* 0.0909091 = 0.032871 loss) | |
| I0506 01:04:49.925472 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.669643 (* 0.0909091 = 0.0608767 loss) | |
| I0506 01:04:49.925485 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.676325 (* 0.0909091 = 0.0614841 loss) | |
| I0506 01:04:49.925498 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.549119 (* 0.0909091 = 0.0499199 loss) | |
| I0506 01:04:49.925513 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.412904 (* 0.0909091 = 0.0375367 loss) | |
| I0506 01:04:49.925525 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.392425 (* 0.0909091 = 0.035675 loss) | |
| I0506 01:04:49.925539 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0735802 (* 0.0909091 = 0.00668911 loss) | |
| I0506 01:04:49.925554 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0612064 (* 0.0909091 = 0.00556422 loss) | |
| I0506 01:04:49.925567 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.029667 (* 0.0909091 = 0.002697 loss) | |
| I0506 01:04:49.925581 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0114395 (* 0.0909091 = 0.00103995 loss) | |
| I0506 01:04:49.925595 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00967834 (* 0.0909091 = 0.000879849 loss) | |
| I0506 01:04:49.925608 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00606467 (* 0.0909091 = 0.000551333 loss) | |
| I0506 01:04:49.925622 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00399857 (* 0.0909091 = 0.000363507 loss) | |
| I0506 01:04:49.925634 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:04:49.925645 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:04:49.925657 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000490988 | |
| I0506 01:04:49.925678 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000741576 | |
| I0506 01:04:49.925693 15760 sgd_solver.cpp:106] Iteration 11500, lr = 0.001 | |
| I0506 01:05:21.972265 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 47.5154 > 30) by scale factor 0.631375 | |
| I0506 01:06:30.842111 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 36.7563 > 30) by scale factor 0.816187 | |
| I0506 01:06:35.330862 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.0768 > 30) by scale factor 0.997446 | |
| I0506 01:06:37.308562 15760 solver.cpp:229] Iteration 12000, loss = 10.3015 | |
| I0506 01:06:37.308627 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0625 | |
| I0506 01:06:37.308646 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.375 | |
| I0506 01:06:37.308660 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:06:37.308671 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 01:06:37.308682 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 01:06:37.308694 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 01:06:37.308706 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.25 | |
| I0506 01:06:37.308718 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.625 | |
| I0506 01:06:37.308730 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 1 | |
| I0506 01:06:37.308743 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 01:06:37.308753 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:06:37.308765 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:06:37.308776 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:06:37.308789 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:06:37.308800 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:06:37.308818 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:06:37.308830 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:06:37.308843 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:06:37.308854 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:06:37.308866 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:06:37.308877 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:06:37.308889 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:06:37.308908 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:06:37.308920 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.738636 | |
| I0506 01:06:37.308933 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.208333 | |
| I0506 01:06:37.308948 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.33432 (* 0.3 = 1.0003 loss) | |
| I0506 01:06:37.308962 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.05465 (* 0.3 = 0.316395 loss) | |
| I0506 01:06:37.308976 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.40606 (* 0.0272727 = 0.0928927 loss) | |
| I0506 01:06:37.308990 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.32203 (* 0.0272727 = 0.0906007 loss) | |
| I0506 01:06:37.309005 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.76209 (* 0.0272727 = 0.102603 loss) | |
| I0506 01:06:37.309018 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.9733 (* 0.0272727 = 0.108363 loss) | |
| I0506 01:06:37.309031 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.51999 (* 0.0272727 = 0.0959996 loss) | |
| I0506 01:06:37.309046 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.55716 (* 0.0272727 = 0.0970133 loss) | |
| I0506 01:06:37.309058 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.35234 (* 0.0272727 = 0.0641547 loss) | |
| I0506 01:06:37.309072 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.28462 (* 0.0272727 = 0.00776238 loss) | |
| I0506 01:06:37.309087 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.212122 (* 0.0272727 = 0.00578514 loss) | |
| I0506 01:06:37.309101 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.100715 (* 0.0272727 = 0.00274677 loss) | |
| I0506 01:06:37.309114 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.104065 (* 0.0272727 = 0.00283815 loss) | |
| I0506 01:06:37.309175 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0633845 (* 0.0272727 = 0.00172867 loss) | |
| I0506 01:06:37.309191 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.058751 (* 0.0272727 = 0.0016023 loss) | |
| I0506 01:06:37.309206 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0433146 (* 0.0272727 = 0.00118131 loss) | |
| I0506 01:06:37.309219 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0222198 (* 0.0272727 = 0.000605995 loss) | |
| I0506 01:06:37.309233 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0179303 (* 0.0272727 = 0.000489008 loss) | |
| I0506 01:06:37.309247 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00814499 (* 0.0272727 = 0.000222136 loss) | |
| I0506 01:06:37.309262 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0112573 (* 0.0272727 = 0.000307016 loss) | |
| I0506 01:06:37.309275 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00863001 (* 0.0272727 = 0.000235364 loss) | |
| I0506 01:06:37.309289 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00559227 (* 0.0272727 = 0.000152516 loss) | |
| I0506 01:06:37.309303 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0061799 (* 0.0272727 = 0.000168543 loss) | |
| I0506 01:06:37.309317 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00416933 (* 0.0272727 = 0.000113709 loss) | |
| I0506 01:06:37.309329 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.125 | |
| I0506 01:06:37.309340 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 01:06:37.309352 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 01:06:37.309365 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:06:37.309376 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:06:37.309387 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.25 | |
| I0506 01:06:37.309396 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 01:06:37.309403 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.625 | |
| I0506 01:06:37.309415 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 1 | |
| I0506 01:06:37.309427 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 01:06:37.309438 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:06:37.309449 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:06:37.309468 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:06:37.309479 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:06:37.309491 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:06:37.309502 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:06:37.309514 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:06:37.309525 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:06:37.309535 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:06:37.309546 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:06:37.309558 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:06:37.309569 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:06:37.309581 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:06:37.309592 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.761364 | |
| I0506 01:06:37.309603 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.291667 | |
| I0506 01:06:37.309617 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.265 (* 0.3 = 0.979499 loss) | |
| I0506 01:06:37.309631 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.978868 (* 0.3 = 0.29366 loss) | |
| I0506 01:06:37.309645 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.6756 (* 0.0272727 = 0.100244 loss) | |
| I0506 01:06:37.309671 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.40544 (* 0.0272727 = 0.0928756 loss) | |
| I0506 01:06:37.309686 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.65614 (* 0.0272727 = 0.0997128 loss) | |
| I0506 01:06:37.309700 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.57142 (* 0.0272727 = 0.0974024 loss) | |
| I0506 01:06:37.309713 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.12843 (* 0.0272727 = 0.0853207 loss) | |
| I0506 01:06:37.309727 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.20829 (* 0.0272727 = 0.0874988 loss) | |
| I0506 01:06:37.309741 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.29678 (* 0.0272727 = 0.0626396 loss) | |
| I0506 01:06:37.309756 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.176046 (* 0.0272727 = 0.00480124 loss) | |
| I0506 01:06:37.309769 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.135367 (* 0.0272727 = 0.00369182 loss) | |
| I0506 01:06:37.309782 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0449097 (* 0.0272727 = 0.00122481 loss) | |
| I0506 01:06:37.309797 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0228787 (* 0.0272727 = 0.000623964 loss) | |
| I0506 01:06:37.309810 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0326295 (* 0.0272727 = 0.000889896 loss) | |
| I0506 01:06:37.309824 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0373717 (* 0.0272727 = 0.00101923 loss) | |
| I0506 01:06:37.309839 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0109434 (* 0.0272727 = 0.000298455 loss) | |
| I0506 01:06:37.309851 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0140702 (* 0.0272727 = 0.000383732 loss) | |
| I0506 01:06:37.309870 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0124313 (* 0.0272727 = 0.000339035 loss) | |
| I0506 01:06:37.309885 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00905197 (* 0.0272727 = 0.000246872 loss) | |
| I0506 01:06:37.309898 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00837656 (* 0.0272727 = 0.000228452 loss) | |
| I0506 01:06:37.309912 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00606906 (* 0.0272727 = 0.00016552 loss) | |
| I0506 01:06:37.309926 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00706285 (* 0.0272727 = 0.000192623 loss) | |
| I0506 01:06:37.309940 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00581072 (* 0.0272727 = 0.000158474 loss) | |
| I0506 01:06:37.309954 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00417125 (* 0.0272727 = 0.000113761 loss) | |
| I0506 01:06:37.309967 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0416667 | |
| I0506 01:06:37.309978 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 01:06:37.309989 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:06:37.310000 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:06:37.310012 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 01:06:37.310024 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.125 | |
| I0506 01:06:37.310034 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 01:06:37.310046 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.625 | |
| I0506 01:06:37.310057 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 1 | |
| I0506 01:06:37.310070 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 01:06:37.310081 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:06:37.310091 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:06:37.310102 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:06:37.310113 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:06:37.310125 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:06:37.310147 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:06:37.310159 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:06:37.310170 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:06:37.310183 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:06:37.310194 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:06:37.310205 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:06:37.310216 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:06:37.310227 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:06:37.310238 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.732955 | |
| I0506 01:06:37.310250 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.229167 | |
| I0506 01:06:37.310263 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.98158 (* 1 = 2.98158 loss) | |
| I0506 01:06:37.310277 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.899339 (* 1 = 0.899339 loss) | |
| I0506 01:06:37.310291 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.07966 (* 0.0909091 = 0.279969 loss) | |
| I0506 01:06:37.310304 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.28932 (* 0.0909091 = 0.299029 loss) | |
| I0506 01:06:37.310317 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.15286 (* 0.0909091 = 0.286624 loss) | |
| I0506 01:06:37.310331 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.19431 (* 0.0909091 = 0.290392 loss) | |
| I0506 01:06:37.310344 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.68118 (* 0.0909091 = 0.243744 loss) | |
| I0506 01:06:37.310358 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.52793 (* 0.0909091 = 0.229812 loss) | |
| I0506 01:06:37.310371 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.7011 (* 0.0909091 = 0.154646 loss) | |
| I0506 01:06:37.310385 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.137951 (* 0.0909091 = 0.012541 loss) | |
| I0506 01:06:37.310398 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.0577708 (* 0.0909091 = 0.00525189 loss) | |
| I0506 01:06:37.310412 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0237347 (* 0.0909091 = 0.0021577 loss) | |
| I0506 01:06:37.310426 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.00681033 (* 0.0909091 = 0.000619121 loss) | |
| I0506 01:06:37.310441 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0037311 (* 0.0909091 = 0.000339191 loss) | |
| I0506 01:06:37.310454 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00319353 (* 0.0909091 = 0.000290321 loss) | |
| I0506 01:06:37.310467 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00437012 (* 0.0909091 = 0.000397284 loss) | |
| I0506 01:06:37.310482 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00475531 (* 0.0909091 = 0.000432301 loss) | |
| I0506 01:06:37.310495 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00240352 (* 0.0909091 = 0.000218502 loss) | |
| I0506 01:06:37.310509 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00163277 (* 0.0909091 = 0.000148434 loss) | |
| I0506 01:06:37.310523 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00154774 (* 0.0909091 = 0.000140704 loss) | |
| I0506 01:06:37.310536 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.000717736 (* 0.0909091 = 6.52487e-05 loss) | |
| I0506 01:06:37.310550 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000817442 (* 0.0909091 = 7.43129e-05 loss) | |
| I0506 01:06:37.310564 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000807638 (* 0.0909091 = 7.34217e-05 loss) | |
| I0506 01:06:37.310578 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.0007146 (* 0.0909091 = 6.49637e-05 loss) | |
| I0506 01:06:37.310590 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:06:37.310611 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:06:37.310622 15760 solver.cpp:245] Train net output #149: total_confidence = 2.16203e-05 | |
| I0506 01:06:37.310634 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 9.1891e-05 | |
| I0506 01:06:37.310647 15760 sgd_solver.cpp:106] Iteration 12000, lr = 0.001 | |
| I0506 01:06:44.426525 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 32.5373 > 30) by scale factor 0.922018 | |
| I0506 01:06:55.568876 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 37.9125 > 30) by scale factor 0.791296 | |
| I0506 01:07:31.619340 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.129 > 30) by scale factor 0.995718 | |
| I0506 01:07:34.632398 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 33.5592 > 30) by scale factor 0.893942 | |
| I0506 01:08:01.907382 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.0591 > 30) by scale factor 0.965901 | |
| I0506 01:08:24.633149 15760 solver.cpp:229] Iteration 12500, loss = 10.3307 | |
| I0506 01:08:24.633213 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.08 | |
| I0506 01:08:24.633230 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 01:08:24.633244 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 01:08:24.633255 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:08:24.633267 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 01:08:24.633280 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:08:24.633291 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 01:08:24.633302 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 01:08:24.633316 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.625 | |
| I0506 01:08:24.633327 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.75 | |
| I0506 01:08:24.633340 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:08:24.633352 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:08:24.633363 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:08:24.633374 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:08:24.633386 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:08:24.633397 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:08:24.633409 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:08:24.633421 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:08:24.633432 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:08:24.633445 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:08:24.633465 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:08:24.633476 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:08:24.633488 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:08:24.633499 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.721591 | |
| I0506 01:08:24.633512 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.24 | |
| I0506 01:08:24.633527 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.28076 (* 0.3 = 0.98423 loss) | |
| I0506 01:08:24.633543 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.16126 (* 0.3 = 0.348378 loss) | |
| I0506 01:08:24.633555 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.38701 (* 0.0272727 = 0.0923729 loss) | |
| I0506 01:08:24.633569 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.76501 (* 0.0272727 = 0.102682 loss) | |
| I0506 01:08:24.633584 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 4.18069 (* 0.0272727 = 0.114019 loss) | |
| I0506 01:08:24.633597 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.25799 (* 0.0272727 = 0.0888544 loss) | |
| I0506 01:08:24.633611 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.67981 (* 0.0272727 = 0.0730857 loss) | |
| I0506 01:08:24.633625 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.59701 (* 0.0272727 = 0.0708276 loss) | |
| I0506 01:08:24.633638 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.33479 (* 0.0272727 = 0.0636761 loss) | |
| I0506 01:08:24.633652 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 2.14751 (* 0.0272727 = 0.0585684 loss) | |
| I0506 01:08:24.633666 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.45953 (* 0.0272727 = 0.0398055 loss) | |
| I0506 01:08:24.633679 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.216417 (* 0.0272727 = 0.00590229 loss) | |
| I0506 01:08:24.633693 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.162257 (* 0.0272727 = 0.00442518 loss) | |
| I0506 01:08:24.633708 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0965052 (* 0.0272727 = 0.00263196 loss) | |
| I0506 01:08:24.633752 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0801705 (* 0.0272727 = 0.00218647 loss) | |
| I0506 01:08:24.633769 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0657624 (* 0.0272727 = 0.00179352 loss) | |
| I0506 01:08:24.633782 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0375755 (* 0.0272727 = 0.00102479 loss) | |
| I0506 01:08:24.633796 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0179099 (* 0.0272727 = 0.000488451 loss) | |
| I0506 01:08:24.633817 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0221905 (* 0.0272727 = 0.000605196 loss) | |
| I0506 01:08:24.633832 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00476622 (* 0.0272727 = 0.000129988 loss) | |
| I0506 01:08:24.633846 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0102008 (* 0.0272727 = 0.000278205 loss) | |
| I0506 01:08:24.633860 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00531479 (* 0.0272727 = 0.000144949 loss) | |
| I0506 01:08:24.633873 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00434108 (* 0.0272727 = 0.000118393 loss) | |
| I0506 01:08:24.633891 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0032883 (* 0.0272727 = 8.96808e-05 loss) | |
| I0506 01:08:24.633904 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.02 | |
| I0506 01:08:24.633918 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 01:08:24.633929 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.25 | |
| I0506 01:08:24.633940 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:08:24.633952 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:08:24.633963 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 01:08:24.633975 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 01:08:24.633986 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 01:08:24.633997 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 01:08:24.634009 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.75 | |
| I0506 01:08:24.634030 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:08:24.634050 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:08:24.634063 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:08:24.634075 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:08:24.634086 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:08:24.634098 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:08:24.634109 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:08:24.634120 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:08:24.634131 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:08:24.634142 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:08:24.634155 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:08:24.634166 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:08:24.634176 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:08:24.634187 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.681818 | |
| I0506 01:08:24.634199 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.16 | |
| I0506 01:08:24.634212 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.32224 (* 0.3 = 0.996672 loss) | |
| I0506 01:08:24.634227 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.25783 (* 0.3 = 0.377348 loss) | |
| I0506 01:08:24.634240 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.47466 (* 0.0272727 = 0.0947634 loss) | |
| I0506 01:08:24.634254 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.07151 (* 0.0272727 = 0.0837685 loss) | |
| I0506 01:08:24.634280 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.57248 (* 0.0272727 = 0.0974312 loss) | |
| I0506 01:08:24.634295 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.6985 (* 0.0272727 = 0.100868 loss) | |
| I0506 01:08:24.634308 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.50879 (* 0.0272727 = 0.0684215 loss) | |
| I0506 01:08:24.634322 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.41572 (* 0.0272727 = 0.0658834 loss) | |
| I0506 01:08:24.634336 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.02353 (* 0.0272727 = 0.0551871 loss) | |
| I0506 01:08:24.634349 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.67112 (* 0.0272727 = 0.0455759 loss) | |
| I0506 01:08:24.634363 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.39346 (* 0.0272727 = 0.0380034 loss) | |
| I0506 01:08:24.634377 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.397091 (* 0.0272727 = 0.0108298 loss) | |
| I0506 01:08:24.634392 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.179715 (* 0.0272727 = 0.00490131 loss) | |
| I0506 01:08:24.634405 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.222558 (* 0.0272727 = 0.00606976 loss) | |
| I0506 01:08:24.634418 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.140311 (* 0.0272727 = 0.00382665 loss) | |
| I0506 01:08:24.634433 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0848269 (* 0.0272727 = 0.00231346 loss) | |
| I0506 01:08:24.634446 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0706339 (* 0.0272727 = 0.00192638 loss) | |
| I0506 01:08:24.634460 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0258409 (* 0.0272727 = 0.000704751 loss) | |
| I0506 01:08:24.634474 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0131587 (* 0.0272727 = 0.000358874 loss) | |
| I0506 01:08:24.634487 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0137824 (* 0.0272727 = 0.000375885 loss) | |
| I0506 01:08:24.634501 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00651639 (* 0.0272727 = 0.00017772 loss) | |
| I0506 01:08:24.634516 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0104886 (* 0.0272727 = 0.000286054 loss) | |
| I0506 01:08:24.634528 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0131744 (* 0.0272727 = 0.000359301 loss) | |
| I0506 01:08:24.634542 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0105068 (* 0.0272727 = 0.00028655 loss) | |
| I0506 01:08:24.634554 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.1 | |
| I0506 01:08:24.634567 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:08:24.634577 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.25 | |
| I0506 01:08:24.634589 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:08:24.634600 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:08:24.634613 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 01:08:24.634624 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 01:08:24.634634 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 01:08:24.634645 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.625 | |
| I0506 01:08:24.634657 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.75 | |
| I0506 01:08:24.634668 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:08:24.634680 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:08:24.634690 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:08:24.634702 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:08:24.634713 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:08:24.634724 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:08:24.634735 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:08:24.634757 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:08:24.634769 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:08:24.634780 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:08:24.634791 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:08:24.634802 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:08:24.634814 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:08:24.634824 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.727273 | |
| I0506 01:08:24.634836 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.32 | |
| I0506 01:08:24.634850 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.11879 (* 1 = 3.11879 loss) | |
| I0506 01:08:24.634863 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.05873 (* 1 = 1.05873 loss) | |
| I0506 01:08:24.634873 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.99311 (* 0.0909091 = 0.272101 loss) | |
| I0506 01:08:24.634887 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.13939 (* 0.0909091 = 0.285399 loss) | |
| I0506 01:08:24.634902 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.08555 (* 0.0909091 = 0.280504 loss) | |
| I0506 01:08:24.634914 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.79411 (* 0.0909091 = 0.25401 loss) | |
| I0506 01:08:24.634927 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.76 (* 0.0909091 = 0.250909 loss) | |
| I0506 01:08:24.634946 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.31418 (* 0.0909091 = 0.21038 loss) | |
| I0506 01:08:24.634968 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.67582 (* 0.0909091 = 0.152347 loss) | |
| I0506 01:08:24.634987 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.40122 (* 0.0909091 = 0.127384 loss) | |
| I0506 01:08:24.635001 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 1.3908 (* 0.0909091 = 0.126436 loss) | |
| I0506 01:08:24.635015 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.254715 (* 0.0909091 = 0.0231559 loss) | |
| I0506 01:08:24.635028 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.178407 (* 0.0909091 = 0.0162188 loss) | |
| I0506 01:08:24.635042 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0836328 (* 0.0909091 = 0.00760298 loss) | |
| I0506 01:08:24.635056 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0828859 (* 0.0909091 = 0.00753509 loss) | |
| I0506 01:08:24.635069 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0505152 (* 0.0909091 = 0.00459229 loss) | |
| I0506 01:08:24.635083 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0262974 (* 0.0909091 = 0.00239068 loss) | |
| I0506 01:08:24.635097 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0246523 (* 0.0909091 = 0.00224112 loss) | |
| I0506 01:08:24.635110 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0128676 (* 0.0909091 = 0.00116979 loss) | |
| I0506 01:08:24.635124 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00776033 (* 0.0909091 = 0.000705485 loss) | |
| I0506 01:08:24.635138 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0036484 (* 0.0909091 = 0.000331673 loss) | |
| I0506 01:08:24.635151 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.0014328 (* 0.0909091 = 0.000130254 loss) | |
| I0506 01:08:24.635165 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000516041 (* 0.0909091 = 4.69128e-05 loss) | |
| I0506 01:08:24.635179 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000200503 (* 0.0909091 = 1.82275e-05 loss) | |
| I0506 01:08:24.635191 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:08:24.635202 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:08:24.635213 15760 solver.cpp:245] Train net output #149: total_confidence = 8.77779e-05 | |
| I0506 01:08:24.635234 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000276919 | |
| I0506 01:08:24.635249 15760 sgd_solver.cpp:106] Iteration 12500, lr = 0.001 | |
| I0506 01:08:47.420627 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 33.5332 > 30) by scale factor 0.894634 | |
| I0506 01:09:36.976449 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 34.38 > 30) by scale factor 0.872602 | |
| I0506 01:09:49.866714 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 53.2879 > 30) by scale factor 0.562979 | |
| I0506 01:10:04.725905 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.0608 > 30) by scale factor 0.997977 | |
| I0506 01:10:12.517460 15760 solver.cpp:229] Iteration 13000, loss = 10.2157 | |
| I0506 01:10:12.517675 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0357143 | |
| I0506 01:10:12.517712 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 01:10:12.517731 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.25 | |
| I0506 01:10:12.517745 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 01:10:12.517757 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 01:10:12.517770 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.5 | |
| I0506 01:10:12.517788 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.5 | |
| I0506 01:10:12.517801 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 01:10:12.517812 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.625 | |
| I0506 01:10:12.517824 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.625 | |
| I0506 01:10:12.517837 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 01:10:12.517849 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 01:10:12.517860 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 01:10:12.517875 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0.875 | |
| I0506 01:10:12.517889 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:10:12.517901 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:10:12.517913 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:10:12.517925 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:10:12.517936 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:10:12.517948 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:10:12.517961 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:10:12.517972 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:10:12.517984 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:10:12.517997 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.693182 | |
| I0506 01:10:12.518008 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.160714 | |
| I0506 01:10:12.518024 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.3727 (* 0.3 = 1.01181 loss) | |
| I0506 01:10:12.518038 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.14547 (* 0.3 = 0.34364 loss) | |
| I0506 01:10:12.518054 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.49963 (* 0.0272727 = 0.0954445 loss) | |
| I0506 01:10:12.518066 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.26925 (* 0.0272727 = 0.0891613 loss) | |
| I0506 01:10:12.518080 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.40375 (* 0.0272727 = 0.0928296 loss) | |
| I0506 01:10:12.518095 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.1232 (* 0.0272727 = 0.0851781 loss) | |
| I0506 01:10:12.518108 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.15812 (* 0.0272727 = 0.0588577 loss) | |
| I0506 01:10:12.518121 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.56126 (* 0.0272727 = 0.0698525 loss) | |
| I0506 01:10:12.518136 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.17122 (* 0.0272727 = 0.0592151 loss) | |
| I0506 01:10:12.518149 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.43488 (* 0.0272727 = 0.039133 loss) | |
| I0506 01:10:12.518163 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.56377 (* 0.0272727 = 0.0426484 loss) | |
| I0506 01:10:12.518177 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.568061 (* 0.0272727 = 0.0154926 loss) | |
| I0506 01:10:12.518192 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.777873 (* 0.0272727 = 0.0212147 loss) | |
| I0506 01:10:12.518204 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.523339 (* 0.0272727 = 0.0142729 loss) | |
| I0506 01:10:12.518218 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.64375 (* 0.0272727 = 0.0175568 loss) | |
| I0506 01:10:12.518400 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0912989 (* 0.0272727 = 0.00248997 loss) | |
| I0506 01:10:12.518419 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0253375 (* 0.0272727 = 0.000691024 loss) | |
| I0506 01:10:12.518442 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0224558 (* 0.0272727 = 0.000612432 loss) | |
| I0506 01:10:12.518476 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00506593 (* 0.0272727 = 0.000138162 loss) | |
| I0506 01:10:12.518498 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00373114 (* 0.0272727 = 0.000101758 loss) | |
| I0506 01:10:12.518514 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.011854 (* 0.0272727 = 0.000323291 loss) | |
| I0506 01:10:12.518528 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00297973 (* 0.0272727 = 8.12654e-05 loss) | |
| I0506 01:10:12.518543 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00365449 (* 0.0272727 = 9.96678e-05 loss) | |
| I0506 01:10:12.518556 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00317327 (* 0.0272727 = 8.65437e-05 loss) | |
| I0506 01:10:12.518568 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0 | |
| I0506 01:10:12.518581 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 01:10:12.518592 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:10:12.518612 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.25 | |
| I0506 01:10:12.518625 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:10:12.518635 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.5 | |
| I0506 01:10:12.518647 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 01:10:12.518659 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 01:10:12.518671 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.625 | |
| I0506 01:10:12.518682 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.625 | |
| I0506 01:10:12.518694 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 01:10:12.518707 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 01:10:12.518718 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 01:10:12.518730 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0.875 | |
| I0506 01:10:12.518743 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:10:12.518754 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:10:12.518765 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:10:12.518776 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:10:12.518789 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:10:12.518800 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:10:12.518811 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:10:12.518822 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:10:12.518833 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:10:12.518846 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.681818 | |
| I0506 01:10:12.518857 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.142857 | |
| I0506 01:10:12.518870 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.35831 (* 0.3 = 1.00749 loss) | |
| I0506 01:10:12.518888 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.16586 (* 0.3 = 0.349757 loss) | |
| I0506 01:10:12.518903 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.58595 (* 0.0272727 = 0.0977987 loss) | |
| I0506 01:10:12.518918 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.5601 (* 0.0272727 = 0.0970938 loss) | |
| I0506 01:10:12.518944 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.19653 (* 0.0272727 = 0.0871782 loss) | |
| I0506 01:10:12.518959 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 4.00132 (* 0.0272727 = 0.109127 loss) | |
| I0506 01:10:12.518973 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 1.95664 (* 0.0272727 = 0.0533629 loss) | |
| I0506 01:10:12.518996 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.583 (* 0.0272727 = 0.0704454 loss) | |
| I0506 01:10:12.519013 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.86355 (* 0.0272727 = 0.0508242 loss) | |
| I0506 01:10:12.519028 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.26932 (* 0.0272727 = 0.0346179 loss) | |
| I0506 01:10:12.519042 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.84542 (* 0.0272727 = 0.0503296 loss) | |
| I0506 01:10:12.519060 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.618624 (* 0.0272727 = 0.0168716 loss) | |
| I0506 01:10:12.519074 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.542232 (* 0.0272727 = 0.0147882 loss) | |
| I0506 01:10:12.519088 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.458524 (* 0.0272727 = 0.0125052 loss) | |
| I0506 01:10:12.519101 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.72497 (* 0.0272727 = 0.0197719 loss) | |
| I0506 01:10:12.519115 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0564237 (* 0.0272727 = 0.00153883 loss) | |
| I0506 01:10:12.519129 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0100467 (* 0.0272727 = 0.000274001 loss) | |
| I0506 01:10:12.519143 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0124112 (* 0.0272727 = 0.000338487 loss) | |
| I0506 01:10:12.519157 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00286671 (* 0.0272727 = 7.81829e-05 loss) | |
| I0506 01:10:12.519171 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00168184 (* 0.0272727 = 4.58683e-05 loss) | |
| I0506 01:10:12.519186 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00150142 (* 0.0272727 = 4.09478e-05 loss) | |
| I0506 01:10:12.519201 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00118066 (* 0.0272727 = 3.21998e-05 loss) | |
| I0506 01:10:12.519214 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.000862421 (* 0.0272727 = 2.35206e-05 loss) | |
| I0506 01:10:12.519229 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00118918 (* 0.0272727 = 3.24322e-05 loss) | |
| I0506 01:10:12.519242 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0714286 | |
| I0506 01:10:12.519253 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 01:10:12.519265 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:10:12.519278 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 01:10:12.519289 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:10:12.519301 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.5 | |
| I0506 01:10:12.519314 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 01:10:12.519325 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 01:10:12.519336 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.625 | |
| I0506 01:10:12.519348 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.625 | |
| I0506 01:10:12.519361 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 01:10:12.519371 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 01:10:12.519383 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 01:10:12.519395 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0.875 | |
| I0506 01:10:12.519410 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:10:12.519423 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:10:12.519435 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:10:12.519457 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:10:12.519470 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:10:12.519482 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:10:12.519490 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:10:12.519498 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:10:12.519506 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:10:12.519520 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.704545 | |
| I0506 01:10:12.519532 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.214286 | |
| I0506 01:10:12.519546 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.21378 (* 1 = 3.21378 loss) | |
| I0506 01:10:12.519567 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.11266 (* 1 = 1.11266 loss) | |
| I0506 01:10:12.519579 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.99447 (* 0.0909091 = 0.272224 loss) | |
| I0506 01:10:12.519593 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.26806 (* 0.0909091 = 0.297097 loss) | |
| I0506 01:10:12.519606 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.90881 (* 0.0909091 = 0.264437 loss) | |
| I0506 01:10:12.519620 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.15341 (* 0.0909091 = 0.286673 loss) | |
| I0506 01:10:12.519640 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 1.76352 (* 0.0909091 = 0.16032 loss) | |
| I0506 01:10:12.519654 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.28891 (* 0.0909091 = 0.208082 loss) | |
| I0506 01:10:12.519667 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.82664 (* 0.0909091 = 0.166058 loss) | |
| I0506 01:10:12.519680 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.31262 (* 0.0909091 = 0.119329 loss) | |
| I0506 01:10:12.519695 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 1.75423 (* 0.0909091 = 0.159475 loss) | |
| I0506 01:10:12.519707 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.520176 (* 0.0909091 = 0.0472887 loss) | |
| I0506 01:10:12.519721 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.558078 (* 0.0909091 = 0.0507344 loss) | |
| I0506 01:10:12.519734 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.538366 (* 0.0909091 = 0.0489423 loss) | |
| I0506 01:10:12.519748 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.625333 (* 0.0909091 = 0.0568485 loss) | |
| I0506 01:10:12.519762 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0624547 (* 0.0909091 = 0.0056777 loss) | |
| I0506 01:10:12.519775 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0194882 (* 0.0909091 = 0.00177165 loss) | |
| I0506 01:10:12.519789 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00930948 (* 0.0909091 = 0.000846316 loss) | |
| I0506 01:10:12.519804 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00805821 (* 0.0909091 = 0.000732565 loss) | |
| I0506 01:10:12.519816 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00322805 (* 0.0909091 = 0.000293459 loss) | |
| I0506 01:10:12.519830 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0023985 (* 0.0909091 = 0.000218045 loss) | |
| I0506 01:10:12.519843 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00179379 (* 0.0909091 = 0.000163072 loss) | |
| I0506 01:10:12.519857 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000596851 (* 0.0909091 = 5.42592e-05 loss) | |
| I0506 01:10:12.519871 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000275066 (* 0.0909091 = 2.5006e-05 loss) | |
| I0506 01:10:12.519883 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:10:12.519894 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:10:12.519906 15760 solver.cpp:245] Train net output #149: total_confidence = 2.13964e-05 | |
| I0506 01:10:12.519930 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 8.1941e-05 | |
| I0506 01:10:12.519947 15760 sgd_solver.cpp:106] Iteration 13000, lr = 0.001 | |
| I0506 01:10:52.888744 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 37.1151 > 30) by scale factor 0.808296 | |
| I0506 01:11:14.626556 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 56.7327 > 30) by scale factor 0.528795 | |
| I0506 01:11:41.191826 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 32.2491 > 30) by scale factor 0.93026 | |
| I0506 01:12:00.263945 15760 solver.cpp:229] Iteration 13500, loss = 10.1872 | |
| I0506 01:12:00.264014 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.128205 | |
| I0506 01:12:00.264030 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.125 | |
| I0506 01:12:00.264047 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 01:12:00.264060 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:12:00.264072 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 01:12:00.264084 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.5 | |
| I0506 01:12:00.264096 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.625 | |
| I0506 01:12:00.264108 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.75 | |
| I0506 01:12:00.264119 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 1 | |
| I0506 01:12:00.264132 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 01:12:00.264142 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:12:00.264154 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:12:00.264166 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:12:00.264178 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:12:00.264189 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:12:00.264200 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:12:00.264212 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:12:00.264225 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:12:00.264235 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:12:00.264247 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:12:00.264258 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:12:00.264271 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:12:00.264281 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:12:00.264293 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.806818 | |
| I0506 01:12:00.264304 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.205128 | |
| I0506 01:12:00.264322 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.29026 (* 0.3 = 0.987078 loss) | |
| I0506 01:12:00.264335 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.8184 (* 0.3 = 0.24552 loss) | |
| I0506 01:12:00.264358 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.03156 (* 0.0272727 = 0.0826789 loss) | |
| I0506 01:12:00.264374 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.61096 (* 0.0272727 = 0.0984808 loss) | |
| I0506 01:12:00.264387 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.62234 (* 0.0272727 = 0.098791 loss) | |
| I0506 01:12:00.264400 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.55539 (* 0.0272727 = 0.0969651 loss) | |
| I0506 01:12:00.264415 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.20658 (* 0.0272727 = 0.0601795 loss) | |
| I0506 01:12:00.264428 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 1.99262 (* 0.0272727 = 0.0543443 loss) | |
| I0506 01:12:00.264442 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.15218 (* 0.0272727 = 0.031423 loss) | |
| I0506 01:12:00.264456 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.148992 (* 0.0272727 = 0.00406341 loss) | |
| I0506 01:12:00.264470 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.0792642 (* 0.0272727 = 0.00216175 loss) | |
| I0506 01:12:00.264484 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0480067 (* 0.0272727 = 0.00130927 loss) | |
| I0506 01:12:00.264498 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0326268 (* 0.0272727 = 0.000889821 loss) | |
| I0506 01:12:00.264513 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0384926 (* 0.0272727 = 0.0010498 loss) | |
| I0506 01:12:00.264556 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0275469 (* 0.0272727 = 0.000751279 loss) | |
| I0506 01:12:00.264571 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0288686 (* 0.0272727 = 0.000787325 loss) | |
| I0506 01:12:00.264585 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0203367 (* 0.0272727 = 0.000554636 loss) | |
| I0506 01:12:00.264600 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0205238 (* 0.0272727 = 0.000559741 loss) | |
| I0506 01:12:00.264613 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0120362 (* 0.0272727 = 0.00032826 loss) | |
| I0506 01:12:00.264627 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0120819 (* 0.0272727 = 0.000329505 loss) | |
| I0506 01:12:00.264642 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00940583 (* 0.0272727 = 0.000256523 loss) | |
| I0506 01:12:00.264655 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.012492 (* 0.0272727 = 0.000340691 loss) | |
| I0506 01:12:00.264669 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0130575 (* 0.0272727 = 0.000356113 loss) | |
| I0506 01:12:00.264683 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00789107 (* 0.0272727 = 0.000215211 loss) | |
| I0506 01:12:00.264695 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0512821 | |
| I0506 01:12:00.264708 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.25 | |
| I0506 01:12:00.264719 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:12:00.264730 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:12:00.264742 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 01:12:00.264753 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.5 | |
| I0506 01:12:00.264765 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.625 | |
| I0506 01:12:00.264775 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 01:12:00.264787 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 1 | |
| I0506 01:12:00.264798 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 01:12:00.264809 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:12:00.264821 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:12:00.264832 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:12:00.264842 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:12:00.264858 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:12:00.264869 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:12:00.264881 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:12:00.264892 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:12:00.264900 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:12:00.264907 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:12:00.264919 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:12:00.264930 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:12:00.264942 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:12:00.264953 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.778409 | |
| I0506 01:12:00.264971 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.179487 | |
| I0506 01:12:00.264986 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.23658 (* 0.3 = 0.970975 loss) | |
| I0506 01:12:00.264999 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.810961 (* 0.3 = 0.243288 loss) | |
| I0506 01:12:00.265013 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.26388 (* 0.0272727 = 0.089015 loss) | |
| I0506 01:12:00.265027 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.65537 (* 0.0272727 = 0.0996919 loss) | |
| I0506 01:12:00.265053 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.69845 (* 0.0272727 = 0.100867 loss) | |
| I0506 01:12:00.265067 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.55433 (* 0.0272727 = 0.0969362 loss) | |
| I0506 01:12:00.265080 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.22202 (* 0.0272727 = 0.0606005 loss) | |
| I0506 01:12:00.265115 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 1.76985 (* 0.0272727 = 0.0482687 loss) | |
| I0506 01:12:00.265131 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 0.975666 (* 0.0272727 = 0.0266091 loss) | |
| I0506 01:12:00.265146 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.128465 (* 0.0272727 = 0.00350358 loss) | |
| I0506 01:12:00.265161 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.0378701 (* 0.0272727 = 0.00103282 loss) | |
| I0506 01:12:00.265174 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0261739 (* 0.0272727 = 0.000713833 loss) | |
| I0506 01:12:00.265188 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0140477 (* 0.0272727 = 0.000383118 loss) | |
| I0506 01:12:00.265202 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0129944 (* 0.0272727 = 0.000354393 loss) | |
| I0506 01:12:00.265215 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.015936 (* 0.0272727 = 0.000434618 loss) | |
| I0506 01:12:00.265229 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.00958437 (* 0.0272727 = 0.000261392 loss) | |
| I0506 01:12:00.265244 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0186364 (* 0.0272727 = 0.000508266 loss) | |
| I0506 01:12:00.265256 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0117355 (* 0.0272727 = 0.000320058 loss) | |
| I0506 01:12:00.265270 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.012361 (* 0.0272727 = 0.000337119 loss) | |
| I0506 01:12:00.265283 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0389757 (* 0.0272727 = 0.00106297 loss) | |
| I0506 01:12:00.265297 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00643344 (* 0.0272727 = 0.000175458 loss) | |
| I0506 01:12:00.265311 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0334483 (* 0.0272727 = 0.000912227 loss) | |
| I0506 01:12:00.265324 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0109932 (* 0.0272727 = 0.000299814 loss) | |
| I0506 01:12:00.265337 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0248216 (* 0.0272727 = 0.000676953 loss) | |
| I0506 01:12:00.265349 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.102564 | |
| I0506 01:12:00.265360 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.25 | |
| I0506 01:12:00.265372 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:12:00.265383 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:12:00.265395 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 01:12:00.265406 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.625 | |
| I0506 01:12:00.265418 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.75 | |
| I0506 01:12:00.265429 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 01:12:00.265439 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 1 | |
| I0506 01:12:00.265450 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 01:12:00.265462 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:12:00.265473 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:12:00.265485 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:12:00.265496 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:12:00.265506 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:12:00.265517 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:12:00.265540 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:12:00.265552 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:12:00.265564 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:12:00.265575 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:12:00.265586 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:12:00.265597 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:12:00.265609 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:12:00.265619 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.795455 | |
| I0506 01:12:00.265630 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.230769 | |
| I0506 01:12:00.265645 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.99007 (* 1 = 2.99007 loss) | |
| I0506 01:12:00.265658 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.74796 (* 1 = 0.74796 loss) | |
| I0506 01:12:00.265672 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.75477 (* 0.0909091 = 0.250434 loss) | |
| I0506 01:12:00.265686 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.07956 (* 0.0909091 = 0.27996 loss) | |
| I0506 01:12:00.265698 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.36443 (* 0.0909091 = 0.305857 loss) | |
| I0506 01:12:00.265712 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.34752 (* 0.0909091 = 0.30432 loss) | |
| I0506 01:12:00.265725 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 1.66298 (* 0.0909091 = 0.15118 loss) | |
| I0506 01:12:00.265739 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.2915 (* 0.0909091 = 0.117409 loss) | |
| I0506 01:12:00.265753 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 0.904418 (* 0.0909091 = 0.0822199 loss) | |
| I0506 01:12:00.265765 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.180925 (* 0.0909091 = 0.0164478 loss) | |
| I0506 01:12:00.265779 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.0179006 (* 0.0909091 = 0.00162733 loss) | |
| I0506 01:12:00.265794 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0132754 (* 0.0909091 = 0.00120686 loss) | |
| I0506 01:12:00.265807 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.00942598 (* 0.0909091 = 0.000856907 loss) | |
| I0506 01:12:00.265820 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.00812456 (* 0.0909091 = 0.000738596 loss) | |
| I0506 01:12:00.265835 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00845734 (* 0.0909091 = 0.000768849 loss) | |
| I0506 01:12:00.265847 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00978049 (* 0.0909091 = 0.000889136 loss) | |
| I0506 01:12:00.265861 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00507534 (* 0.0909091 = 0.000461394 loss) | |
| I0506 01:12:00.265875 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00725744 (* 0.0909091 = 0.000659768 loss) | |
| I0506 01:12:00.265889 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00443012 (* 0.0909091 = 0.000402738 loss) | |
| I0506 01:12:00.265905 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00384406 (* 0.0909091 = 0.00034946 loss) | |
| I0506 01:12:00.265920 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00329056 (* 0.0909091 = 0.000299142 loss) | |
| I0506 01:12:00.265934 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00335404 (* 0.0909091 = 0.000304913 loss) | |
| I0506 01:12:00.265947 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00272079 (* 0.0909091 = 0.000247344 loss) | |
| I0506 01:12:00.265961 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00186497 (* 0.0909091 = 0.000169543 loss) | |
| I0506 01:12:00.265974 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:12:00.265985 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:12:00.266005 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000459506 | |
| I0506 01:12:00.266018 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.00110744 | |
| I0506 01:12:00.266032 15760 sgd_solver.cpp:106] Iteration 13500, lr = 0.001 | |
| I0506 01:13:25.868455 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 38.6551 > 30) by scale factor 0.776094 | |
| I0506 01:13:47.521690 15760 solver.cpp:229] Iteration 14000, loss = 10.1379 | |
| I0506 01:13:47.521754 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.130435 | |
| I0506 01:13:47.521773 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.125 | |
| I0506 01:13:47.521786 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:13:47.521798 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.375 | |
| I0506 01:13:47.521809 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 01:13:47.521821 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:13:47.521833 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.5 | |
| I0506 01:13:47.521847 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.625 | |
| I0506 01:13:47.521858 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 01:13:47.521872 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 01:13:47.521886 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 01:13:47.521898 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:13:47.521910 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:13:47.521922 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:13:47.521936 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:13:47.521950 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:13:47.521960 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:13:47.521972 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:13:47.521984 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:13:47.521996 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:13:47.522007 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:13:47.522018 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:13:47.522030 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:13:47.522042 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.761364 | |
| I0506 01:13:47.522053 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.369565 | |
| I0506 01:13:47.522068 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 2.97803 (* 0.3 = 0.89341 loss) | |
| I0506 01:13:47.522083 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.00445 (* 0.3 = 0.301334 loss) | |
| I0506 01:13:47.522097 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.3012 (* 0.0272727 = 0.0900328 loss) | |
| I0506 01:13:47.522111 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 2.86598 (* 0.0272727 = 0.0781632 loss) | |
| I0506 01:13:47.522125 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.07876 (* 0.0272727 = 0.0839661 loss) | |
| I0506 01:13:47.522140 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 2.64763 (* 0.0272727 = 0.0722081 loss) | |
| I0506 01:13:47.522153 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.30138 (* 0.0272727 = 0.0627648 loss) | |
| I0506 01:13:47.522166 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 1.93026 (* 0.0272727 = 0.0526436 loss) | |
| I0506 01:13:47.522181 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.36061 (* 0.0272727 = 0.0371076 loss) | |
| I0506 01:13:47.522193 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.754575 (* 0.0272727 = 0.0205793 loss) | |
| I0506 01:13:47.522207 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.743939 (* 0.0272727 = 0.0202892 loss) | |
| I0506 01:13:47.522222 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.63626 (* 0.0272727 = 0.0173525 loss) | |
| I0506 01:13:47.522235 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0957874 (* 0.0272727 = 0.00261238 loss) | |
| I0506 01:13:47.522249 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0661391 (* 0.0272727 = 0.00180379 loss) | |
| I0506 01:13:47.522295 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0477949 (* 0.0272727 = 0.0013035 loss) | |
| I0506 01:13:47.522311 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.03091 (* 0.0272727 = 0.000843001 loss) | |
| I0506 01:13:47.522325 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0221597 (* 0.0272727 = 0.000604356 loss) | |
| I0506 01:13:47.522341 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.014267 (* 0.0272727 = 0.0003891 loss) | |
| I0506 01:13:47.522354 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0115984 (* 0.0272727 = 0.000316319 loss) | |
| I0506 01:13:47.522367 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0122472 (* 0.0272727 = 0.000334014 loss) | |
| I0506 01:13:47.522382 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00506716 (* 0.0272727 = 0.000138195 loss) | |
| I0506 01:13:47.522395 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00842909 (* 0.0272727 = 0.000229884 loss) | |
| I0506 01:13:47.522408 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00839747 (* 0.0272727 = 0.000229022 loss) | |
| I0506 01:13:47.522423 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0065175 (* 0.0272727 = 0.00017775 loss) | |
| I0506 01:13:47.522434 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.108696 | |
| I0506 01:13:47.522446 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.25 | |
| I0506 01:13:47.522459 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.375 | |
| I0506 01:13:47.522470 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:13:47.522482 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 01:13:47.522493 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 01:13:47.522505 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.5 | |
| I0506 01:13:47.522517 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.625 | |
| I0506 01:13:47.522528 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 01:13:47.522541 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 01:13:47.522552 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 01:13:47.522563 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:13:47.522575 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:13:47.522586 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:13:47.522598 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:13:47.522609 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:13:47.522620 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:13:47.522631 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:13:47.522644 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:13:47.522655 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:13:47.522666 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:13:47.522677 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:13:47.522689 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:13:47.522701 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.755682 | |
| I0506 01:13:47.522711 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.347826 | |
| I0506 01:13:47.522725 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 2.98626 (* 0.3 = 0.895877 loss) | |
| I0506 01:13:47.522739 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.92827 (* 0.3 = 0.278481 loss) | |
| I0506 01:13:47.522753 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.18989 (* 0.0272727 = 0.0869969 loss) | |
| I0506 01:13:47.522766 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 2.7356 (* 0.0272727 = 0.0746073 loss) | |
| I0506 01:13:47.522790 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.66572 (* 0.0272727 = 0.0999741 loss) | |
| I0506 01:13:47.522805 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 2.85147 (* 0.0272727 = 0.0777674 loss) | |
| I0506 01:13:47.522819 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.36399 (* 0.0272727 = 0.0644726 loss) | |
| I0506 01:13:47.522832 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.24724 (* 0.0272727 = 0.0612882 loss) | |
| I0506 01:13:47.522846 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.88441 (* 0.0272727 = 0.0513929 loss) | |
| I0506 01:13:47.522860 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.928529 (* 0.0272727 = 0.0253235 loss) | |
| I0506 01:13:47.522873 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.767638 (* 0.0272727 = 0.0209356 loss) | |
| I0506 01:13:47.522886 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.52933 (* 0.0272727 = 0.0144363 loss) | |
| I0506 01:13:47.522897 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0556491 (* 0.0272727 = 0.0015177 loss) | |
| I0506 01:13:47.522913 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0656989 (* 0.0272727 = 0.00179179 loss) | |
| I0506 01:13:47.522930 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0627824 (* 0.0272727 = 0.00171225 loss) | |
| I0506 01:13:47.522945 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0198789 (* 0.0272727 = 0.000542153 loss) | |
| I0506 01:13:47.522959 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0284077 (* 0.0272727 = 0.000774756 loss) | |
| I0506 01:13:47.522974 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0101408 (* 0.0272727 = 0.000276566 loss) | |
| I0506 01:13:47.522990 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0160885 (* 0.0272727 = 0.000438776 loss) | |
| I0506 01:13:47.523005 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00902091 (* 0.0272727 = 0.000246025 loss) | |
| I0506 01:13:47.523018 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00520205 (* 0.0272727 = 0.000141874 loss) | |
| I0506 01:13:47.523032 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00560155 (* 0.0272727 = 0.00015277 loss) | |
| I0506 01:13:47.523046 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00532253 (* 0.0272727 = 0.00014516 loss) | |
| I0506 01:13:47.523059 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00504309 (* 0.0272727 = 0.000137539 loss) | |
| I0506 01:13:47.523072 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0869565 | |
| I0506 01:13:47.523083 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.25 | |
| I0506 01:13:47.523094 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.125 | |
| I0506 01:13:47.523105 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 01:13:47.523118 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 01:13:47.523128 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.375 | |
| I0506 01:13:47.523139 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.5 | |
| I0506 01:13:47.523150 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.625 | |
| I0506 01:13:47.523161 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 01:13:47.523174 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 01:13:47.523185 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 01:13:47.523195 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:13:47.523206 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:13:47.523217 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:13:47.523228 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:13:47.523239 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:13:47.523262 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:13:47.523273 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:13:47.523285 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:13:47.523296 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:13:47.523308 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:13:47.523319 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:13:47.523329 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:13:47.523340 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.755682 | |
| I0506 01:13:47.523352 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.391304 | |
| I0506 01:13:47.523365 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.75523 (* 1 = 2.75523 loss) | |
| I0506 01:13:47.523380 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.808721 (* 1 = 0.808721 loss) | |
| I0506 01:13:47.523392 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.71116 (* 0.0909091 = 0.246469 loss) | |
| I0506 01:13:47.523406 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.39447 (* 0.0909091 = 0.21768 loss) | |
| I0506 01:13:47.523419 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.07068 (* 0.0909091 = 0.279153 loss) | |
| I0506 01:13:47.523433 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.23634 (* 0.0909091 = 0.203304 loss) | |
| I0506 01:13:47.523447 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 1.84821 (* 0.0909091 = 0.168019 loss) | |
| I0506 01:13:47.523459 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.67811 (* 0.0909091 = 0.152556 loss) | |
| I0506 01:13:47.523473 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.23269 (* 0.0909091 = 0.112063 loss) | |
| I0506 01:13:47.523486 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.729383 (* 0.0909091 = 0.0663075 loss) | |
| I0506 01:13:47.523500 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.556753 (* 0.0909091 = 0.0506139 loss) | |
| I0506 01:13:47.523514 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.473569 (* 0.0909091 = 0.0430517 loss) | |
| I0506 01:13:47.523526 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.168734 (* 0.0909091 = 0.0153394 loss) | |
| I0506 01:13:47.523540 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.13414 (* 0.0909091 = 0.0121946 loss) | |
| I0506 01:13:47.523553 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0574514 (* 0.0909091 = 0.00522286 loss) | |
| I0506 01:13:47.523567 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0399175 (* 0.0909091 = 0.00362886 loss) | |
| I0506 01:13:47.523581 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0205918 (* 0.0909091 = 0.00187198 loss) | |
| I0506 01:13:47.523594 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0129964 (* 0.0909091 = 0.00118149 loss) | |
| I0506 01:13:47.523608 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00545606 (* 0.0909091 = 0.000496006 loss) | |
| I0506 01:13:47.523622 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00573461 (* 0.0909091 = 0.000521328 loss) | |
| I0506 01:13:47.523635 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00281748 (* 0.0909091 = 0.000256134 loss) | |
| I0506 01:13:47.523649 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000925993 (* 0.0909091 = 8.41812e-05 loss) | |
| I0506 01:13:47.523663 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00137911 (* 0.0909091 = 0.000125373 loss) | |
| I0506 01:13:47.523676 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000760699 (* 0.0909091 = 6.91545e-05 loss) | |
| I0506 01:13:47.523689 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:13:47.523700 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:13:47.523720 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000513099 | |
| I0506 01:13:47.523733 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.00227681 | |
| I0506 01:13:47.523747 15760 sgd_solver.cpp:106] Iteration 14000, lr = 0.001 | |
| I0506 01:13:54.205555 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.205 > 30) by scale factor 0.993213 | |
| I0506 01:14:10.259616 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 53.6161 > 30) by scale factor 0.559533 | |
| I0506 01:14:11.775970 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 38.7052 > 30) by scale factor 0.775089 | |
| I0506 01:15:34.706714 15760 solver.cpp:229] Iteration 14500, loss = 10.1471 | |
| I0506 01:15:34.706827 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0909091 | |
| I0506 01:15:34.706850 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 01:15:34.706861 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:15:34.706876 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 01:15:34.706889 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 01:15:34.706902 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0 | |
| I0506 01:15:34.706912 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.125 | |
| I0506 01:15:34.706924 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 01:15:34.706936 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 01:15:34.706948 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 01:15:34.706959 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 01:15:34.706971 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:15:34.706982 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:15:34.706993 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:15:34.707005 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:15:34.707015 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:15:34.707027 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:15:34.707038 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:15:34.707051 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:15:34.707062 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:15:34.707072 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:15:34.707084 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:15:34.707095 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:15:34.707106 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.715909 | |
| I0506 01:15:34.707118 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.327273 | |
| I0506 01:15:34.707134 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.13687 (* 0.3 = 0.941061 loss) | |
| I0506 01:15:34.707147 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.05194 (* 0.3 = 0.315583 loss) | |
| I0506 01:15:34.707161 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 2.82961 (* 0.0272727 = 0.0771712 loss) | |
| I0506 01:15:34.707175 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.22008 (* 0.0272727 = 0.0878203 loss) | |
| I0506 01:15:34.707190 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.76604 (* 0.0272727 = 0.10271 loss) | |
| I0506 01:15:34.707202 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.68135 (* 0.0272727 = 0.1004 loss) | |
| I0506 01:15:34.707216 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.86663 (* 0.0272727 = 0.105454 loss) | |
| I0506 01:15:34.707231 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.22887 (* 0.0272727 = 0.0880601 loss) | |
| I0506 01:15:34.707244 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.09255 (* 0.0272727 = 0.0570697 loss) | |
| I0506 01:15:34.707258 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.83195 (* 0.0272727 = 0.0499622 loss) | |
| I0506 01:15:34.707272 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.651819 (* 0.0272727 = 0.0177769 loss) | |
| I0506 01:15:34.707285 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.844457 (* 0.0272727 = 0.0230306 loss) | |
| I0506 01:15:34.707299 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0500671 (* 0.0272727 = 0.00136547 loss) | |
| I0506 01:15:34.707314 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0688767 (* 0.0272727 = 0.00187846 loss) | |
| I0506 01:15:34.707327 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0371768 (* 0.0272727 = 0.00101391 loss) | |
| I0506 01:15:34.707360 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0269354 (* 0.0272727 = 0.000734603 loss) | |
| I0506 01:15:34.707375 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0297297 (* 0.0272727 = 0.00081081 loss) | |
| I0506 01:15:34.707389 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0213514 (* 0.0272727 = 0.00058231 loss) | |
| I0506 01:15:34.707402 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0176239 (* 0.0272727 = 0.000480651 loss) | |
| I0506 01:15:34.707417 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0103552 (* 0.0272727 = 0.000282414 loss) | |
| I0506 01:15:34.707430 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.01404 (* 0.0272727 = 0.000382909 loss) | |
| I0506 01:15:34.707444 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00743621 (* 0.0272727 = 0.000202806 loss) | |
| I0506 01:15:34.707458 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0115278 (* 0.0272727 = 0.000314394 loss) | |
| I0506 01:15:34.707473 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00793353 (* 0.0272727 = 0.000216369 loss) | |
| I0506 01:15:34.707484 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0909091 | |
| I0506 01:15:34.707496 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.25 | |
| I0506 01:15:34.707509 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:15:34.707520 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:15:34.707531 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 01:15:34.707542 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 01:15:34.707553 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.125 | |
| I0506 01:15:34.707566 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 01:15:34.707576 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 01:15:34.707587 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 01:15:34.707599 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 01:15:34.707610 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:15:34.707622 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:15:34.707633 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:15:34.707643 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:15:34.707654 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:15:34.707666 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:15:34.707677 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:15:34.707689 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:15:34.707700 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:15:34.707710 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:15:34.707721 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:15:34.707732 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:15:34.707743 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.715909 | |
| I0506 01:15:34.707754 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.272727 | |
| I0506 01:15:34.707768 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.1986 (* 0.3 = 0.959581 loss) | |
| I0506 01:15:34.707782 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.06007 (* 0.3 = 0.318021 loss) | |
| I0506 01:15:34.707797 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.31494 (* 0.0272727 = 0.0904075 loss) | |
| I0506 01:15:34.707810 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.26341 (* 0.0272727 = 0.0890022 loss) | |
| I0506 01:15:34.707839 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.45696 (* 0.0272727 = 0.0942807 loss) | |
| I0506 01:15:34.707872 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.48927 (* 0.0272727 = 0.095162 loss) | |
| I0506 01:15:34.707890 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.51656 (* 0.0272727 = 0.0959062 loss) | |
| I0506 01:15:34.707906 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.29819 (* 0.0272727 = 0.0899505 loss) | |
| I0506 01:15:34.707918 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.12806 (* 0.0272727 = 0.058038 loss) | |
| I0506 01:15:34.707936 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.6017 (* 0.0272727 = 0.0436827 loss) | |
| I0506 01:15:34.707949 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.460615 (* 0.0272727 = 0.0125622 loss) | |
| I0506 01:15:34.707963 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.704828 (* 0.0272727 = 0.0192226 loss) | |
| I0506 01:15:34.707978 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0693191 (* 0.0272727 = 0.00189052 loss) | |
| I0506 01:15:34.707991 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0439532 (* 0.0272727 = 0.00119872 loss) | |
| I0506 01:15:34.708005 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0354576 (* 0.0272727 = 0.000967025 loss) | |
| I0506 01:15:34.708019 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0217756 (* 0.0272727 = 0.00059388 loss) | |
| I0506 01:15:34.708032 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0215318 (* 0.0272727 = 0.000587231 loss) | |
| I0506 01:15:34.708046 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0191593 (* 0.0272727 = 0.000522525 loss) | |
| I0506 01:15:34.708060 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00968549 (* 0.0272727 = 0.00026415 loss) | |
| I0506 01:15:34.708073 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0156878 (* 0.0272727 = 0.000427848 loss) | |
| I0506 01:15:34.708087 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00863561 (* 0.0272727 = 0.000235517 loss) | |
| I0506 01:15:34.708097 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00538772 (* 0.0272727 = 0.000146938 loss) | |
| I0506 01:15:34.708106 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0043156 (* 0.0272727 = 0.000117698 loss) | |
| I0506 01:15:34.708122 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00816693 (* 0.0272727 = 0.000222735 loss) | |
| I0506 01:15:34.708133 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.109091 | |
| I0506 01:15:34.708145 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:15:34.708158 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:15:34.708168 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:15:34.708180 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0 | |
| I0506 01:15:34.708191 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0 | |
| I0506 01:15:34.708202 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 01:15:34.708214 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 01:15:34.708225 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 01:15:34.708236 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 01:15:34.708247 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 01:15:34.708259 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:15:34.708271 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:15:34.708281 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:15:34.708292 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:15:34.708303 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:15:34.708314 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:15:34.708336 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:15:34.708349 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:15:34.708360 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:15:34.708371 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:15:34.708382 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:15:34.708395 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:15:34.708405 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.715909 | |
| I0506 01:15:34.708416 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.218182 | |
| I0506 01:15:34.708431 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.95003 (* 1 = 2.95003 loss) | |
| I0506 01:15:34.708443 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.969615 (* 1 = 0.969615 loss) | |
| I0506 01:15:34.708457 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.77837 (* 0.0909091 = 0.252579 loss) | |
| I0506 01:15:34.708470 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.00608 (* 0.0909091 = 0.27328 loss) | |
| I0506 01:15:34.708484 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.28518 (* 0.0909091 = 0.298653 loss) | |
| I0506 01:15:34.708498 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.34226 (* 0.0909091 = 0.303841 loss) | |
| I0506 01:15:34.708511 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.26479 (* 0.0909091 = 0.296799 loss) | |
| I0506 01:15:34.708524 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.90251 (* 0.0909091 = 0.263864 loss) | |
| I0506 01:15:34.708537 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.66449 (* 0.0909091 = 0.151317 loss) | |
| I0506 01:15:34.708550 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.13884 (* 0.0909091 = 0.103531 loss) | |
| I0506 01:15:34.708564 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.453446 (* 0.0909091 = 0.0412224 loss) | |
| I0506 01:15:34.708577 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.538978 (* 0.0909091 = 0.048998 loss) | |
| I0506 01:15:34.708591 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.00492277 (* 0.0909091 = 0.000447525 loss) | |
| I0506 01:15:34.708605 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0100114 (* 0.0909091 = 0.00091013 loss) | |
| I0506 01:15:34.708618 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00538368 (* 0.0909091 = 0.000489426 loss) | |
| I0506 01:15:34.708631 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00470833 (* 0.0909091 = 0.00042803 loss) | |
| I0506 01:15:34.708645 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00311807 (* 0.0909091 = 0.000283461 loss) | |
| I0506 01:15:34.708659 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00493102 (* 0.0909091 = 0.000448274 loss) | |
| I0506 01:15:34.708673 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00168771 (* 0.0909091 = 0.000153428 loss) | |
| I0506 01:15:34.708686 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00183872 (* 0.0909091 = 0.000167156 loss) | |
| I0506 01:15:34.708700 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.000734499 (* 0.0909091 = 6.67727e-05 loss) | |
| I0506 01:15:34.708714 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00067965 (* 0.0909091 = 6.17864e-05 loss) | |
| I0506 01:15:34.708729 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000349837 (* 0.0909091 = 3.18034e-05 loss) | |
| I0506 01:15:34.708741 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000503578 (* 0.0909091 = 4.57799e-05 loss) | |
| I0506 01:15:34.708753 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:15:34.708765 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:15:34.708776 15760 solver.cpp:245] Train net output #149: total_confidence = 3.93226e-07 | |
| I0506 01:15:34.708796 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 1.91091e-06 | |
| I0506 01:15:34.708811 15760 sgd_solver.cpp:106] Iteration 14500, lr = 0.001 | |
| I0506 01:17:21.871377 15760 solver.cpp:338] Iteration 15000, Testing net (#0) | |
| I0506 01:17:58.382797 15760 solver.cpp:393] Test loss: 9.46736 | |
| I0506 01:17:58.382943 15760 solver.cpp:406] Test net output #0: loss1/accuracy = 0.064083 | |
| I0506 01:17:58.382964 15760 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.117 | |
| I0506 01:17:58.382978 15760 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.092 | |
| I0506 01:17:58.382990 15760 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.072 | |
| I0506 01:17:58.383003 15760 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.181 | |
| I0506 01:17:58.383015 15760 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.292 | |
| I0506 01:17:58.383026 15760 solver.cpp:406] Test net output #6: loss1/accuracy06 = 0.445 | |
| I0506 01:17:58.383038 15760 solver.cpp:406] Test net output #7: loss1/accuracy07 = 0.727 | |
| I0506 01:17:58.383049 15760 solver.cpp:406] Test net output #8: loss1/accuracy08 = 0.908 | |
| I0506 01:17:58.383060 15760 solver.cpp:406] Test net output #9: loss1/accuracy09 = 0.99 | |
| I0506 01:17:58.383071 15760 solver.cpp:406] Test net output #10: loss1/accuracy10 = 0.999 | |
| I0506 01:17:58.383082 15760 solver.cpp:406] Test net output #11: loss1/accuracy11 = 1 | |
| I0506 01:17:58.383095 15760 solver.cpp:406] Test net output #12: loss1/accuracy12 = 1 | |
| I0506 01:17:58.383105 15760 solver.cpp:406] Test net output #13: loss1/accuracy13 = 1 | |
| I0506 01:17:58.383116 15760 solver.cpp:406] Test net output #14: loss1/accuracy14 = 1 | |
| I0506 01:17:58.383128 15760 solver.cpp:406] Test net output #15: loss1/accuracy15 = 1 | |
| I0506 01:17:58.383139 15760 solver.cpp:406] Test net output #16: loss1/accuracy16 = 1 | |
| I0506 01:17:58.383152 15760 solver.cpp:406] Test net output #17: loss1/accuracy17 = 1 | |
| I0506 01:17:58.383162 15760 solver.cpp:406] Test net output #18: loss1/accuracy18 = 1 | |
| I0506 01:17:58.383173 15760 solver.cpp:406] Test net output #19: loss1/accuracy19 = 1 | |
| I0506 01:17:58.383184 15760 solver.cpp:406] Test net output #20: loss1/accuracy20 = 1 | |
| I0506 01:17:58.383195 15760 solver.cpp:406] Test net output #21: loss1/accuracy21 = 1 | |
| I0506 01:17:58.383206 15760 solver.cpp:406] Test net output #22: loss1/accuracy22 = 1 | |
| I0506 01:17:58.383218 15760 solver.cpp:406] Test net output #23: loss1/accuracy_incl_empty = 0.763638 | |
| I0506 01:17:58.383229 15760 solver.cpp:406] Test net output #24: loss1/accuracy_top3 = 0.227932 | |
| I0506 01:17:58.383245 15760 solver.cpp:406] Test net output #25: loss1/cross_entropy_loss = 3.74962 (* 0.3 = 1.12489 loss) | |
| I0506 01:17:58.383260 15760 solver.cpp:406] Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.977147 (* 0.3 = 0.293144 loss) | |
| I0506 01:17:58.383273 15760 solver.cpp:406] Test net output #27: loss1/loss01 = 3.15864 (* 0.0272727 = 0.0861447 loss) | |
| I0506 01:17:58.383286 15760 solver.cpp:406] Test net output #28: loss1/loss02 = 3.29323 (* 0.0272727 = 0.0898153 loss) | |
| I0506 01:17:58.383299 15760 solver.cpp:406] Test net output #29: loss1/loss03 = 3.40568 (* 0.0272727 = 0.0928823 loss) | |
| I0506 01:17:58.383312 15760 solver.cpp:406] Test net output #30: loss1/loss04 = 3.20364 (* 0.0272727 = 0.0873721 loss) | |
| I0506 01:17:58.383327 15760 solver.cpp:406] Test net output #31: loss1/loss05 = 2.81908 (* 0.0272727 = 0.0768841 loss) | |
| I0506 01:17:58.383339 15760 solver.cpp:406] Test net output #32: loss1/loss06 = 2.38377 (* 0.0272727 = 0.0650119 loss) | |
| I0506 01:17:58.383352 15760 solver.cpp:406] Test net output #33: loss1/loss07 = 1.40049 (* 0.0272727 = 0.0381953 loss) | |
| I0506 01:17:58.383365 15760 solver.cpp:406] Test net output #34: loss1/loss08 = 0.539722 (* 0.0272727 = 0.0147197 loss) | |
| I0506 01:17:58.383379 15760 solver.cpp:406] Test net output #35: loss1/loss09 = 0.0798035 (* 0.0272727 = 0.00217646 loss) | |
| I0506 01:17:58.383393 15760 solver.cpp:406] Test net output #36: loss1/loss10 = 0.0302285 (* 0.0272727 = 0.000824414 loss) | |
| I0506 01:17:58.383406 15760 solver.cpp:406] Test net output #37: loss1/loss11 = 0.020605 (* 0.0272727 = 0.000561954 loss) | |
| I0506 01:17:58.383420 15760 solver.cpp:406] Test net output #38: loss1/loss12 = 0.0147296 (* 0.0272727 = 0.000401717 loss) | |
| I0506 01:17:58.383433 15760 solver.cpp:406] Test net output #39: loss1/loss13 = 0.0120589 (* 0.0272727 = 0.000328879 loss) | |
| I0506 01:17:58.383467 15760 solver.cpp:406] Test net output #40: loss1/loss14 = 0.00989585 (* 0.0272727 = 0.000269887 loss) | |
| I0506 01:17:58.383482 15760 solver.cpp:406] Test net output #41: loss1/loss15 = 0.00806179 (* 0.0272727 = 0.000219867 loss) | |
| I0506 01:17:58.383496 15760 solver.cpp:406] Test net output #42: loss1/loss16 = 0.0057349 (* 0.0272727 = 0.000156406 loss) | |
| I0506 01:17:58.383509 15760 solver.cpp:406] Test net output #43: loss1/loss17 = 0.00459388 (* 0.0272727 = 0.000125288 loss) | |
| I0506 01:17:58.383523 15760 solver.cpp:406] Test net output #44: loss1/loss18 = 0.00354053 (* 0.0272727 = 9.656e-05 loss) | |
| I0506 01:17:58.383538 15760 solver.cpp:406] Test net output #45: loss1/loss19 = 0.00299916 (* 0.0272727 = 8.17953e-05 loss) | |
| I0506 01:17:58.383551 15760 solver.cpp:406] Test net output #46: loss1/loss20 = 0.00346931 (* 0.0272727 = 9.46177e-05 loss) | |
| I0506 01:17:58.383565 15760 solver.cpp:406] Test net output #47: loss1/loss21 = 0.00316964 (* 0.0272727 = 8.64448e-05 loss) | |
| I0506 01:17:58.383579 15760 solver.cpp:406] Test net output #48: loss1/loss22 = 0.00307601 (* 0.0272727 = 8.38911e-05 loss) | |
| I0506 01:17:58.383590 15760 solver.cpp:406] Test net output #49: loss2/accuracy = 0.0610802 | |
| I0506 01:17:58.383602 15760 solver.cpp:406] Test net output #50: loss2/accuracy01 = 0.109 | |
| I0506 01:17:58.383613 15760 solver.cpp:406] Test net output #51: loss2/accuracy02 = 0.096 | |
| I0506 01:17:58.383625 15760 solver.cpp:406] Test net output #52: loss2/accuracy03 = 0.079 | |
| I0506 01:17:58.383636 15760 solver.cpp:406] Test net output #53: loss2/accuracy04 = 0.172 | |
| I0506 01:17:58.383646 15760 solver.cpp:406] Test net output #54: loss2/accuracy05 = 0.289 | |
| I0506 01:17:58.383657 15760 solver.cpp:406] Test net output #55: loss2/accuracy06 = 0.444 | |
| I0506 01:17:58.383669 15760 solver.cpp:406] Test net output #56: loss2/accuracy07 = 0.727 | |
| I0506 01:17:58.383680 15760 solver.cpp:406] Test net output #57: loss2/accuracy08 = 0.908 | |
| I0506 01:17:58.383692 15760 solver.cpp:406] Test net output #58: loss2/accuracy09 = 0.99 | |
| I0506 01:17:58.383702 15760 solver.cpp:406] Test net output #59: loss2/accuracy10 = 0.999 | |
| I0506 01:17:58.383713 15760 solver.cpp:406] Test net output #60: loss2/accuracy11 = 1 | |
| I0506 01:17:58.383723 15760 solver.cpp:406] Test net output #61: loss2/accuracy12 = 1 | |
| I0506 01:17:58.383734 15760 solver.cpp:406] Test net output #62: loss2/accuracy13 = 1 | |
| I0506 01:17:58.383745 15760 solver.cpp:406] Test net output #63: loss2/accuracy14 = 1 | |
| I0506 01:17:58.383756 15760 solver.cpp:406] Test net output #64: loss2/accuracy15 = 1 | |
| I0506 01:17:58.383767 15760 solver.cpp:406] Test net output #65: loss2/accuracy16 = 1 | |
| I0506 01:17:58.383779 15760 solver.cpp:406] Test net output #66: loss2/accuracy17 = 1 | |
| I0506 01:17:58.383786 15760 solver.cpp:406] Test net output #67: loss2/accuracy18 = 1 | |
| I0506 01:17:58.383793 15760 solver.cpp:406] Test net output #68: loss2/accuracy19 = 1 | |
| I0506 01:17:58.383805 15760 solver.cpp:406] Test net output #69: loss2/accuracy20 = 1 | |
| I0506 01:17:58.383816 15760 solver.cpp:406] Test net output #70: loss2/accuracy21 = 1 | |
| I0506 01:17:58.383826 15760 solver.cpp:406] Test net output #71: loss2/accuracy22 = 1 | |
| I0506 01:17:58.383837 15760 solver.cpp:406] Test net output #72: loss2/accuracy_incl_empty = 0.76282 | |
| I0506 01:17:58.383848 15760 solver.cpp:406] Test net output #73: loss2/accuracy_top3 = 0.230079 | |
| I0506 01:17:58.383862 15760 solver.cpp:406] Test net output #74: loss2/cross_entropy_loss = 3.72993 (* 0.3 = 1.11898 loss) | |
| I0506 01:17:58.383878 15760 solver.cpp:406] Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.967423 (* 0.3 = 0.290227 loss) | |
| I0506 01:17:58.383893 15760 solver.cpp:406] Test net output #76: loss2/loss01 = 3.08892 (* 0.0272727 = 0.0842433 loss) | |
| I0506 01:17:58.383906 15760 solver.cpp:406] Test net output #77: loss2/loss02 = 3.20182 (* 0.0272727 = 0.0873224 loss) | |
| I0506 01:17:58.383919 15760 solver.cpp:406] Test net output #78: loss2/loss03 = 3.30703 (* 0.0272727 = 0.0901918 loss) | |
| I0506 01:17:58.383944 15760 solver.cpp:406] Test net output #79: loss2/loss04 = 3.12759 (* 0.0272727 = 0.0852979 loss) | |
| I0506 01:17:58.383963 15760 solver.cpp:406] Test net output #80: loss2/loss05 = 2.75173 (* 0.0272727 = 0.0750472 loss) | |
| I0506 01:17:58.383977 15760 solver.cpp:406] Test net output #81: loss2/loss06 = 2.34594 (* 0.0272727 = 0.0639803 loss) | |
| I0506 01:17:58.383991 15760 solver.cpp:406] Test net output #82: loss2/loss07 = 1.37992 (* 0.0272727 = 0.0376341 loss) | |
| I0506 01:17:58.384003 15760 solver.cpp:406] Test net output #83: loss2/loss08 = 0.541194 (* 0.0272727 = 0.0147598 loss) | |
| I0506 01:17:58.384017 15760 solver.cpp:406] Test net output #84: loss2/loss09 = 0.0707106 (* 0.0272727 = 0.00192847 loss) | |
| I0506 01:17:58.384030 15760 solver.cpp:406] Test net output #85: loss2/loss10 = 0.0256099 (* 0.0272727 = 0.000698452 loss) | |
| I0506 01:17:58.384044 15760 solver.cpp:406] Test net output #86: loss2/loss11 = 0.0151536 (* 0.0272727 = 0.000413281 loss) | |
| I0506 01:17:58.384057 15760 solver.cpp:406] Test net output #87: loss2/loss12 = 0.0113996 (* 0.0272727 = 0.000310898 loss) | |
| I0506 01:17:58.384070 15760 solver.cpp:406] Test net output #88: loss2/loss13 = 0.00836933 (* 0.0272727 = 0.000228255 loss) | |
| I0506 01:17:58.384084 15760 solver.cpp:406] Test net output #89: loss2/loss14 = 0.00724379 (* 0.0272727 = 0.000197558 loss) | |
| I0506 01:17:58.384098 15760 solver.cpp:406] Test net output #90: loss2/loss15 = 0.00558723 (* 0.0272727 = 0.000152379 loss) | |
| I0506 01:17:58.384111 15760 solver.cpp:406] Test net output #91: loss2/loss16 = 0.00342645 (* 0.0272727 = 9.34487e-05 loss) | |
| I0506 01:17:58.384124 15760 solver.cpp:406] Test net output #92: loss2/loss17 = 0.00220963 (* 0.0272727 = 6.02627e-05 loss) | |
| I0506 01:17:58.384137 15760 solver.cpp:406] Test net output #93: loss2/loss18 = 0.00207886 (* 0.0272727 = 5.66961e-05 loss) | |
| I0506 01:17:58.384151 15760 solver.cpp:406] Test net output #94: loss2/loss19 = 0.0017325 (* 0.0272727 = 4.725e-05 loss) | |
| I0506 01:17:58.384165 15760 solver.cpp:406] Test net output #95: loss2/loss20 = 0.00173066 (* 0.0272727 = 4.71998e-05 loss) | |
| I0506 01:17:58.384178 15760 solver.cpp:406] Test net output #96: loss2/loss21 = 0.00148852 (* 0.0272727 = 4.05961e-05 loss) | |
| I0506 01:17:58.384191 15760 solver.cpp:406] Test net output #97: loss2/loss22 = 0.00165501 (* 0.0272727 = 4.51367e-05 loss) | |
| I0506 01:17:58.384203 15760 solver.cpp:406] Test net output #98: loss3/accuracy = 0.0857385 | |
| I0506 01:17:58.384214 15760 solver.cpp:406] Test net output #99: loss3/accuracy01 = 0.136 | |
| I0506 01:17:58.384225 15760 solver.cpp:406] Test net output #100: loss3/accuracy02 = 0.111 | |
| I0506 01:17:58.384238 15760 solver.cpp:406] Test net output #101: loss3/accuracy03 = 0.093 | |
| I0506 01:17:58.384248 15760 solver.cpp:406] Test net output #102: loss3/accuracy04 = 0.191 | |
| I0506 01:17:58.384258 15760 solver.cpp:406] Test net output #103: loss3/accuracy05 = 0.316 | |
| I0506 01:17:58.384269 15760 solver.cpp:406] Test net output #104: loss3/accuracy06 = 0.449 | |
| I0506 01:17:58.384281 15760 solver.cpp:406] Test net output #105: loss3/accuracy07 = 0.728 | |
| I0506 01:17:58.384291 15760 solver.cpp:406] Test net output #106: loss3/accuracy08 = 0.908 | |
| I0506 01:17:58.384304 15760 solver.cpp:406] Test net output #107: loss3/accuracy09 = 0.99 | |
| I0506 01:17:58.384315 15760 solver.cpp:406] Test net output #108: loss3/accuracy10 = 0.998 | |
| I0506 01:17:58.384325 15760 solver.cpp:406] Test net output #109: loss3/accuracy11 = 0.999 | |
| I0506 01:17:58.384336 15760 solver.cpp:406] Test net output #110: loss3/accuracy12 = 1 | |
| I0506 01:17:58.384347 15760 solver.cpp:406] Test net output #111: loss3/accuracy13 = 1 | |
| I0506 01:17:58.384358 15760 solver.cpp:406] Test net output #112: loss3/accuracy14 = 1 | |
| I0506 01:17:58.384369 15760 solver.cpp:406] Test net output #113: loss3/accuracy15 = 1 | |
| I0506 01:17:58.384379 15760 solver.cpp:406] Test net output #114: loss3/accuracy16 = 1 | |
| I0506 01:17:58.384400 15760 solver.cpp:406] Test net output #115: loss3/accuracy17 = 1 | |
| I0506 01:17:58.384413 15760 solver.cpp:406] Test net output #116: loss3/accuracy18 = 1 | |
| I0506 01:17:58.384424 15760 solver.cpp:406] Test net output #117: loss3/accuracy19 = 1 | |
| I0506 01:17:58.384435 15760 solver.cpp:406] Test net output #118: loss3/accuracy20 = 1 | |
| I0506 01:17:58.384446 15760 solver.cpp:406] Test net output #119: loss3/accuracy21 = 1 | |
| I0506 01:17:58.384456 15760 solver.cpp:406] Test net output #120: loss3/accuracy22 = 1 | |
| I0506 01:17:58.384467 15760 solver.cpp:406] Test net output #121: loss3/accuracy_incl_empty = 0.766092 | |
| I0506 01:17:58.384479 15760 solver.cpp:406] Test net output #122: loss3/accuracy_top3 = 0.256556 | |
| I0506 01:17:58.384491 15760 solver.cpp:406] Test net output #123: loss3/cross_entropy_loss = 3.06403 (* 1 = 3.06403 loss) | |
| I0506 01:17:58.384505 15760 solver.cpp:406] Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.821588 (* 1 = 0.821588 loss) | |
| I0506 01:17:58.384517 15760 solver.cpp:406] Test net output #125: loss3/loss01 = 2.82631 (* 0.0909091 = 0.256937 loss) | |
| I0506 01:17:58.384531 15760 solver.cpp:406] Test net output #126: loss3/loss02 = 2.99119 (* 0.0909091 = 0.271926 loss) | |
| I0506 01:17:58.384543 15760 solver.cpp:406] Test net output #127: loss3/loss03 = 3.10522 (* 0.0909091 = 0.282292 loss) | |
| I0506 01:17:58.384557 15760 solver.cpp:406] Test net output #128: loss3/loss04 = 2.87108 (* 0.0909091 = 0.261007 loss) | |
| I0506 01:17:58.384569 15760 solver.cpp:406] Test net output #129: loss3/loss05 = 2.47231 (* 0.0909091 = 0.224755 loss) | |
| I0506 01:17:58.384582 15760 solver.cpp:406] Test net output #130: loss3/loss06 = 2.11961 (* 0.0909091 = 0.192692 loss) | |
| I0506 01:17:58.384595 15760 solver.cpp:406] Test net output #131: loss3/loss07 = 1.1951 (* 0.0909091 = 0.108645 loss) | |
| I0506 01:17:58.384608 15760 solver.cpp:406] Test net output #132: loss3/loss08 = 0.463354 (* 0.0909091 = 0.0421231 loss) | |
| I0506 01:17:58.384621 15760 solver.cpp:406] Test net output #133: loss3/loss09 = 0.0706262 (* 0.0909091 = 0.00642056 loss) | |
| I0506 01:17:58.384635 15760 solver.cpp:406] Test net output #134: loss3/loss10 = 0.0261324 (* 0.0909091 = 0.00237567 loss) | |
| I0506 01:17:58.384649 15760 solver.cpp:406] Test net output #135: loss3/loss11 = 0.0156067 (* 0.0909091 = 0.00141879 loss) | |
| I0506 01:17:58.384661 15760 solver.cpp:406] Test net output #136: loss3/loss12 = 0.0110937 (* 0.0909091 = 0.00100852 loss) | |
| I0506 01:17:58.384675 15760 solver.cpp:406] Test net output #137: loss3/loss13 = 0.00949553 (* 0.0909091 = 0.00086323 loss) | |
| I0506 01:17:58.384687 15760 solver.cpp:406] Test net output #138: loss3/loss14 = 0.00829717 (* 0.0909091 = 0.000754288 loss) | |
| I0506 01:17:58.384701 15760 solver.cpp:406] Test net output #139: loss3/loss15 = 0.00691062 (* 0.0909091 = 0.000628238 loss) | |
| I0506 01:17:58.384714 15760 solver.cpp:406] Test net output #140: loss3/loss16 = 0.00454091 (* 0.0909091 = 0.00041281 loss) | |
| I0506 01:17:58.384727 15760 solver.cpp:406] Test net output #141: loss3/loss17 = 0.00260643 (* 0.0909091 = 0.000236949 loss) | |
| I0506 01:17:58.384742 15760 solver.cpp:406] Test net output #142: loss3/loss18 = 0.00225509 (* 0.0909091 = 0.000205008 loss) | |
| I0506 01:17:58.384754 15760 solver.cpp:406] Test net output #143: loss3/loss19 = 0.00156607 (* 0.0909091 = 0.00014237 loss) | |
| I0506 01:17:58.384768 15760 solver.cpp:406] Test net output #144: loss3/loss20 = 0.00139005 (* 0.0909091 = 0.000126369 loss) | |
| I0506 01:17:58.384781 15760 solver.cpp:406] Test net output #145: loss3/loss21 = 0.00120476 (* 0.0909091 = 0.000109524 loss) | |
| I0506 01:17:58.384795 15760 solver.cpp:406] Test net output #146: loss3/loss22 = 0.00109171 (* 0.0909091 = 9.92465e-05 loss) | |
| I0506 01:17:58.384807 15760 solver.cpp:406] Test net output #147: total_accuracy = 0 | |
| I0506 01:17:58.384819 15760 solver.cpp:406] Test net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:17:58.384829 15760 solver.cpp:406] Test net output #149: total_confidence = 0.000688981 | |
| I0506 01:17:58.384840 15760 solver.cpp:406] Test net output #150: total_confidence_not_rec = 0.00127372 | |
| I0506 01:17:58.384862 15760 solver.cpp:338] Iteration 15000, Testing net (#1) | |
| I0506 01:18:34.738880 15760 solver.cpp:393] Test loss: 10.0826 | |
| I0506 01:18:34.739002 15760 solver.cpp:406] Test net output #0: loss1/accuracy = 0.0642857 | |
| I0506 01:18:34.739022 15760 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.104 | |
| I0506 01:18:34.739035 15760 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.084 | |
| I0506 01:18:34.739048 15760 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.1 | |
| I0506 01:18:34.739059 15760 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.188 | |
| I0506 01:18:34.739070 15760 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.296 | |
| I0506 01:18:34.739083 15760 solver.cpp:406] Test net output #6: loss1/accuracy06 = 0.414 | |
| I0506 01:18:34.739094 15760 solver.cpp:406] Test net output #7: loss1/accuracy07 = 0.638 | |
| I0506 01:18:34.739104 15760 solver.cpp:406] Test net output #8: loss1/accuracy08 = 0.794 | |
| I0506 01:18:34.739115 15760 solver.cpp:406] Test net output #9: loss1/accuracy09 = 0.887 | |
| I0506 01:18:34.739127 15760 solver.cpp:406] Test net output #10: loss1/accuracy10 = 0.91 | |
| I0506 01:18:34.739138 15760 solver.cpp:406] Test net output #11: loss1/accuracy11 = 0.924 | |
| I0506 01:18:34.739150 15760 solver.cpp:406] Test net output #12: loss1/accuracy12 = 0.934 | |
| I0506 01:18:34.739161 15760 solver.cpp:406] Test net output #13: loss1/accuracy13 = 0.948 | |
| I0506 01:18:34.739172 15760 solver.cpp:406] Test net output #14: loss1/accuracy14 = 0.959 | |
| I0506 01:18:34.739183 15760 solver.cpp:406] Test net output #15: loss1/accuracy15 = 0.97 | |
| I0506 01:18:34.739194 15760 solver.cpp:406] Test net output #16: loss1/accuracy16 = 0.98 | |
| I0506 01:18:34.739207 15760 solver.cpp:406] Test net output #17: loss1/accuracy17 = 0.99 | |
| I0506 01:18:34.739217 15760 solver.cpp:406] Test net output #18: loss1/accuracy18 = 0.996 | |
| I0506 01:18:34.739229 15760 solver.cpp:406] Test net output #19: loss1/accuracy19 = 0.997 | |
| I0506 01:18:34.739240 15760 solver.cpp:406] Test net output #20: loss1/accuracy20 = 0.999 | |
| I0506 01:18:34.739251 15760 solver.cpp:406] Test net output #21: loss1/accuracy21 = 1 | |
| I0506 01:18:34.739264 15760 solver.cpp:406] Test net output #22: loss1/accuracy22 = 1 | |
| I0506 01:18:34.739274 15760 solver.cpp:406] Test net output #23: loss1/accuracy_incl_empty = 0.730228 | |
| I0506 01:18:34.739286 15760 solver.cpp:406] Test net output #24: loss1/accuracy_top3 = 0.225088 | |
| I0506 01:18:34.739301 15760 solver.cpp:406] Test net output #25: loss1/cross_entropy_loss = 3.72146 (* 0.3 = 1.11644 loss) | |
| I0506 01:18:34.739318 15760 solver.cpp:406] Test net output #26: loss1/cross_entropy_loss_incl_empty = 1.10789 (* 0.3 = 0.332367 loss) | |
| I0506 01:18:34.739341 15760 solver.cpp:406] Test net output #27: loss1/loss01 = 3.13767 (* 0.0272727 = 0.0855729 loss) | |
| I0506 01:18:34.739357 15760 solver.cpp:406] Test net output #28: loss1/loss02 = 3.26956 (* 0.0272727 = 0.0891698 loss) | |
| I0506 01:18:34.739370 15760 solver.cpp:406] Test net output #29: loss1/loss03 = 3.34981 (* 0.0272727 = 0.0913585 loss) | |
| I0506 01:18:34.739384 15760 solver.cpp:406] Test net output #30: loss1/loss04 = 3.18527 (* 0.0272727 = 0.0868711 loss) | |
| I0506 01:18:34.739398 15760 solver.cpp:406] Test net output #31: loss1/loss05 = 2.8337 (* 0.0272727 = 0.0772828 loss) | |
| I0506 01:18:34.739410 15760 solver.cpp:406] Test net output #32: loss1/loss06 = 2.44116 (* 0.0272727 = 0.0665772 loss) | |
| I0506 01:18:34.739423 15760 solver.cpp:406] Test net output #33: loss1/loss07 = 1.64405 (* 0.0272727 = 0.0448377 loss) | |
| I0506 01:18:34.739436 15760 solver.cpp:406] Test net output #34: loss1/loss08 = 0.95023 (* 0.0272727 = 0.0259154 loss) | |
| I0506 01:18:34.739449 15760 solver.cpp:406] Test net output #35: loss1/loss09 = 0.49676 (* 0.0272727 = 0.013548 loss) | |
| I0506 01:18:34.739462 15760 solver.cpp:406] Test net output #36: loss1/loss10 = 0.416388 (* 0.0272727 = 0.011356 loss) | |
| I0506 01:18:34.739476 15760 solver.cpp:406] Test net output #37: loss1/loss11 = 0.36524 (* 0.0272727 = 0.00996108 loss) | |
| I0506 01:18:34.739490 15760 solver.cpp:406] Test net output #38: loss1/loss12 = 0.339022 (* 0.0272727 = 0.00924604 loss) | |
| I0506 01:18:34.739503 15760 solver.cpp:406] Test net output #39: loss1/loss13 = 0.279407 (* 0.0272727 = 0.00762018 loss) | |
| I0506 01:18:34.739537 15760 solver.cpp:406] Test net output #40: loss1/loss14 = 0.23614 (* 0.0272727 = 0.00644018 loss) | |
| I0506 01:18:34.739552 15760 solver.cpp:406] Test net output #41: loss1/loss15 = 0.178836 (* 0.0272727 = 0.00487736 loss) | |
| I0506 01:18:34.739564 15760 solver.cpp:406] Test net output #42: loss1/loss16 = 0.13203 (* 0.0272727 = 0.00360082 loss) | |
| I0506 01:18:34.739578 15760 solver.cpp:406] Test net output #43: loss1/loss17 = 0.0786726 (* 0.0272727 = 0.00214562 loss) | |
| I0506 01:18:34.739591 15760 solver.cpp:406] Test net output #44: loss1/loss18 = 0.0365071 (* 0.0272727 = 0.000995647 loss) | |
| I0506 01:18:34.739604 15760 solver.cpp:406] Test net output #45: loss1/loss19 = 0.0288934 (* 0.0272727 = 0.000788002 loss) | |
| I0506 01:18:34.739619 15760 solver.cpp:406] Test net output #46: loss1/loss20 = 0.011237 (* 0.0272727 = 0.000306463 loss) | |
| I0506 01:18:34.739631 15760 solver.cpp:406] Test net output #47: loss1/loss21 = 0.00260023 (* 0.0272727 = 7.09155e-05 loss) | |
| I0506 01:18:34.739645 15760 solver.cpp:406] Test net output #48: loss1/loss22 = 0.00254123 (* 0.0272727 = 6.93062e-05 loss) | |
| I0506 01:18:34.739657 15760 solver.cpp:406] Test net output #49: loss2/accuracy = 0.0593548 | |
| I0506 01:18:34.739668 15760 solver.cpp:406] Test net output #50: loss2/accuracy01 = 0.112 | |
| I0506 01:18:34.739680 15760 solver.cpp:406] Test net output #51: loss2/accuracy02 = 0.097 | |
| I0506 01:18:34.739691 15760 solver.cpp:406] Test net output #52: loss2/accuracy03 = 0.085 | |
| I0506 01:18:34.739702 15760 solver.cpp:406] Test net output #53: loss2/accuracy04 = 0.193 | |
| I0506 01:18:34.739713 15760 solver.cpp:406] Test net output #54: loss2/accuracy05 = 0.289 | |
| I0506 01:18:34.739724 15760 solver.cpp:406] Test net output #55: loss2/accuracy06 = 0.414 | |
| I0506 01:18:34.739735 15760 solver.cpp:406] Test net output #56: loss2/accuracy07 = 0.638 | |
| I0506 01:18:34.739747 15760 solver.cpp:406] Test net output #57: loss2/accuracy08 = 0.796 | |
| I0506 01:18:34.739758 15760 solver.cpp:406] Test net output #58: loss2/accuracy09 = 0.887 | |
| I0506 01:18:34.739771 15760 solver.cpp:406] Test net output #59: loss2/accuracy10 = 0.91 | |
| I0506 01:18:34.739784 15760 solver.cpp:406] Test net output #60: loss2/accuracy11 = 0.924 | |
| I0506 01:18:34.739795 15760 solver.cpp:406] Test net output #61: loss2/accuracy12 = 0.934 | |
| I0506 01:18:34.739806 15760 solver.cpp:406] Test net output #62: loss2/accuracy13 = 0.948 | |
| I0506 01:18:34.739819 15760 solver.cpp:406] Test net output #63: loss2/accuracy14 = 0.959 | |
| I0506 01:18:34.739830 15760 solver.cpp:406] Test net output #64: loss2/accuracy15 = 0.97 | |
| I0506 01:18:34.739840 15760 solver.cpp:406] Test net output #65: loss2/accuracy16 = 0.98 | |
| I0506 01:18:34.739852 15760 solver.cpp:406] Test net output #66: loss2/accuracy17 = 0.99 | |
| I0506 01:18:34.739863 15760 solver.cpp:406] Test net output #67: loss2/accuracy18 = 0.996 | |
| I0506 01:18:34.739874 15760 solver.cpp:406] Test net output #68: loss2/accuracy19 = 0.997 | |
| I0506 01:18:34.739886 15760 solver.cpp:406] Test net output #69: loss2/accuracy20 = 0.999 | |
| I0506 01:18:34.739897 15760 solver.cpp:406] Test net output #70: loss2/accuracy21 = 1 | |
| I0506 01:18:34.739907 15760 solver.cpp:406] Test net output #71: loss2/accuracy22 = 1 | |
| I0506 01:18:34.739918 15760 solver.cpp:406] Test net output #72: loss2/accuracy_incl_empty = 0.72991 | |
| I0506 01:18:34.739933 15760 solver.cpp:406] Test net output #73: loss2/accuracy_top3 = 0.231621 | |
| I0506 01:18:34.739946 15760 solver.cpp:406] Test net output #74: loss2/cross_entropy_loss = 3.71242 (* 0.3 = 1.11373 loss) | |
| I0506 01:18:34.739960 15760 solver.cpp:406] Test net output #75: loss2/cross_entropy_loss_incl_empty = 1.09671 (* 0.3 = 0.329012 loss) | |
| I0506 01:18:34.739977 15760 solver.cpp:406] Test net output #76: loss2/loss01 = 3.07392 (* 0.0272727 = 0.0838341 loss) | |
| I0506 01:18:34.739987 15760 solver.cpp:406] Test net output #77: loss2/loss02 = 3.19504 (* 0.0272727 = 0.0871375 loss) | |
| I0506 01:18:34.740018 15760 solver.cpp:406] Test net output #78: loss2/loss03 = 3.26694 (* 0.0272727 = 0.0890983 loss) | |
| I0506 01:18:34.740037 15760 solver.cpp:406] Test net output #79: loss2/loss04 = 3.11242 (* 0.0272727 = 0.0848842 loss) | |
| I0506 01:18:34.740051 15760 solver.cpp:406] Test net output #80: loss2/loss05 = 2.78209 (* 0.0272727 = 0.0758753 loss) | |
| I0506 01:18:34.740063 15760 solver.cpp:406] Test net output #81: loss2/loss06 = 2.40416 (* 0.0272727 = 0.0655679 loss) | |
| I0506 01:18:34.740077 15760 solver.cpp:406] Test net output #82: loss2/loss07 = 1.63179 (* 0.0272727 = 0.0445034 loss) | |
| I0506 01:18:34.740089 15760 solver.cpp:406] Test net output #83: loss2/loss08 = 0.945957 (* 0.0272727 = 0.0257988 loss) | |
| I0506 01:18:34.740103 15760 solver.cpp:406] Test net output #84: loss2/loss09 = 0.488492 (* 0.0272727 = 0.0133225 loss) | |
| I0506 01:18:34.740115 15760 solver.cpp:406] Test net output #85: loss2/loss10 = 0.40967 (* 0.0272727 = 0.0111728 loss) | |
| I0506 01:18:34.740129 15760 solver.cpp:406] Test net output #86: loss2/loss11 = 0.364839 (* 0.0272727 = 0.00995015 loss) | |
| I0506 01:18:34.740142 15760 solver.cpp:406] Test net output #87: loss2/loss12 = 0.337633 (* 0.0272727 = 0.00920816 loss) | |
| I0506 01:18:34.740155 15760 solver.cpp:406] Test net output #88: loss2/loss13 = 0.277557 (* 0.0272727 = 0.00756975 loss) | |
| I0506 01:18:34.740170 15760 solver.cpp:406] Test net output #89: loss2/loss14 = 0.237736 (* 0.0272727 = 0.00648371 loss) | |
| I0506 01:18:34.740182 15760 solver.cpp:406] Test net output #90: loss2/loss15 = 0.179616 (* 0.0272727 = 0.00489863 loss) | |
| I0506 01:18:34.740195 15760 solver.cpp:406] Test net output #91: loss2/loss16 = 0.13734 (* 0.0272727 = 0.00374563 loss) | |
| I0506 01:18:34.740208 15760 solver.cpp:406] Test net output #92: loss2/loss17 = 0.0826075 (* 0.0272727 = 0.00225293 loss) | |
| I0506 01:18:34.740221 15760 solver.cpp:406] Test net output #93: loss2/loss18 = 0.0336986 (* 0.0272727 = 0.000919052 loss) | |
| I0506 01:18:34.740236 15760 solver.cpp:406] Test net output #94: loss2/loss19 = 0.027736 (* 0.0272727 = 0.000756436 loss) | |
| I0506 01:18:34.740248 15760 solver.cpp:406] Test net output #95: loss2/loss20 = 0.0102872 (* 0.0272727 = 0.000280561 loss) | |
| I0506 01:18:34.740262 15760 solver.cpp:406] Test net output #96: loss2/loss21 = 0.00170133 (* 0.0272727 = 4.64e-05 loss) | |
| I0506 01:18:34.740274 15760 solver.cpp:406] Test net output #97: loss2/loss22 = 0.00165171 (* 0.0272727 = 4.50467e-05 loss) | |
| I0506 01:18:34.740286 15760 solver.cpp:406] Test net output #98: loss3/accuracy = 0.081825 | |
| I0506 01:18:34.740298 15760 solver.cpp:406] Test net output #99: loss3/accuracy01 = 0.138 | |
| I0506 01:18:34.740309 15760 solver.cpp:406] Test net output #100: loss3/accuracy02 = 0.115 | |
| I0506 01:18:34.740319 15760 solver.cpp:406] Test net output #101: loss3/accuracy03 = 0.091 | |
| I0506 01:18:34.740330 15760 solver.cpp:406] Test net output #102: loss3/accuracy04 = 0.194 | |
| I0506 01:18:34.740341 15760 solver.cpp:406] Test net output #103: loss3/accuracy05 = 0.317 | |
| I0506 01:18:34.740352 15760 solver.cpp:406] Test net output #104: loss3/accuracy06 = 0.441 | |
| I0506 01:18:34.740363 15760 solver.cpp:406] Test net output #105: loss3/accuracy07 = 0.642 | |
| I0506 01:18:34.740375 15760 solver.cpp:406] Test net output #106: loss3/accuracy08 = 0.799 | |
| I0506 01:18:34.740386 15760 solver.cpp:406] Test net output #107: loss3/accuracy09 = 0.893 | |
| I0506 01:18:34.740396 15760 solver.cpp:406] Test net output #108: loss3/accuracy10 = 0.913 | |
| I0506 01:18:34.740407 15760 solver.cpp:406] Test net output #109: loss3/accuracy11 = 0.924 | |
| I0506 01:18:34.740418 15760 solver.cpp:406] Test net output #110: loss3/accuracy12 = 0.934 | |
| I0506 01:18:34.740429 15760 solver.cpp:406] Test net output #111: loss3/accuracy13 = 0.948 | |
| I0506 01:18:34.740440 15760 solver.cpp:406] Test net output #112: loss3/accuracy14 = 0.959 | |
| I0506 01:18:34.740452 15760 solver.cpp:406] Test net output #113: loss3/accuracy15 = 0.97 | |
| I0506 01:18:34.740461 15760 solver.cpp:406] Test net output #114: loss3/accuracy16 = 0.98 | |
| I0506 01:18:34.740483 15760 solver.cpp:406] Test net output #115: loss3/accuracy17 = 0.99 | |
| I0506 01:18:34.740495 15760 solver.cpp:406] Test net output #116: loss3/accuracy18 = 0.996 | |
| I0506 01:18:34.740507 15760 solver.cpp:406] Test net output #117: loss3/accuracy19 = 0.997 | |
| I0506 01:18:34.740519 15760 solver.cpp:406] Test net output #118: loss3/accuracy20 = 0.999 | |
| I0506 01:18:34.740530 15760 solver.cpp:406] Test net output #119: loss3/accuracy21 = 1 | |
| I0506 01:18:34.740540 15760 solver.cpp:406] Test net output #120: loss3/accuracy22 = 1 | |
| I0506 01:18:34.740550 15760 solver.cpp:406] Test net output #121: loss3/accuracy_incl_empty = 0.7345 | |
| I0506 01:18:34.740561 15760 solver.cpp:406] Test net output #122: loss3/accuracy_top3 = 0.261215 | |
| I0506 01:18:34.740576 15760 solver.cpp:406] Test net output #123: loss3/cross_entropy_loss = 3.07178 (* 1 = 3.07178 loss) | |
| I0506 01:18:34.740588 15760 solver.cpp:406] Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.940189 (* 1 = 0.940189 loss) | |
| I0506 01:18:34.740602 15760 solver.cpp:406] Test net output #125: loss3/loss01 = 2.84173 (* 0.0909091 = 0.258339 loss) | |
| I0506 01:18:34.740614 15760 solver.cpp:406] Test net output #126: loss3/loss02 = 2.99783 (* 0.0909091 = 0.27253 loss) | |
| I0506 01:18:34.740628 15760 solver.cpp:406] Test net output #127: loss3/loss03 = 3.07917 (* 0.0909091 = 0.279925 loss) | |
| I0506 01:18:34.740640 15760 solver.cpp:406] Test net output #128: loss3/loss04 = 2.91793 (* 0.0909091 = 0.265266 loss) | |
| I0506 01:18:34.740653 15760 solver.cpp:406] Test net output #129: loss3/loss05 = 2.52294 (* 0.0909091 = 0.229359 loss) | |
| I0506 01:18:34.740666 15760 solver.cpp:406] Test net output #130: loss3/loss06 = 2.16318 (* 0.0909091 = 0.196653 loss) | |
| I0506 01:18:34.740679 15760 solver.cpp:406] Test net output #131: loss3/loss07 = 1.44195 (* 0.0909091 = 0.131087 loss) | |
| I0506 01:18:34.740692 15760 solver.cpp:406] Test net output #132: loss3/loss08 = 0.850479 (* 0.0909091 = 0.0773163 loss) | |
| I0506 01:18:34.740706 15760 solver.cpp:406] Test net output #133: loss3/loss09 = 0.439606 (* 0.0909091 = 0.0399641 loss) | |
| I0506 01:18:34.740720 15760 solver.cpp:406] Test net output #134: loss3/loss10 = 0.357882 (* 0.0909091 = 0.0325348 loss) | |
| I0506 01:18:34.740732 15760 solver.cpp:406] Test net output #135: loss3/loss11 = 0.311207 (* 0.0909091 = 0.0282915 loss) | |
| I0506 01:18:34.740746 15760 solver.cpp:406] Test net output #136: loss3/loss12 = 0.289679 (* 0.0909091 = 0.0263345 loss) | |
| I0506 01:18:34.740758 15760 solver.cpp:406] Test net output #137: loss3/loss13 = 0.232066 (* 0.0909091 = 0.0210969 loss) | |
| I0506 01:18:34.740772 15760 solver.cpp:406] Test net output #138: loss3/loss14 = 0.195612 (* 0.0909091 = 0.0177829 loss) | |
| I0506 01:18:34.740785 15760 solver.cpp:406] Test net output #139: loss3/loss15 = 0.149774 (* 0.0909091 = 0.0136158 loss) | |
| I0506 01:18:34.740798 15760 solver.cpp:406] Test net output #140: loss3/loss16 = 0.115048 (* 0.0909091 = 0.0104589 loss) | |
| I0506 01:18:34.740811 15760 solver.cpp:406] Test net output #141: loss3/loss17 = 0.0694714 (* 0.0909091 = 0.00631558 loss) | |
| I0506 01:18:34.740829 15760 solver.cpp:406] Test net output #142: loss3/loss18 = 0.0333526 (* 0.0909091 = 0.00303205 loss) | |
| I0506 01:18:34.740844 15760 solver.cpp:406] Test net output #143: loss3/loss19 = 0.0256266 (* 0.0909091 = 0.00232969 loss) | |
| I0506 01:18:34.740856 15760 solver.cpp:406] Test net output #144: loss3/loss20 = 0.00869203 (* 0.0909091 = 0.000790184 loss) | |
| I0506 01:18:34.740870 15760 solver.cpp:406] Test net output #145: loss3/loss21 = 0.00104604 (* 0.0909091 = 9.50944e-05 loss) | |
| I0506 01:18:34.740885 15760 solver.cpp:406] Test net output #146: loss3/loss22 = 0.000635208 (* 0.0909091 = 5.77462e-05 loss) | |
| I0506 01:18:34.740895 15760 solver.cpp:406] Test net output #147: total_accuracy = 0 | |
| I0506 01:18:34.740906 15760 solver.cpp:406] Test net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:18:34.740917 15760 solver.cpp:406] Test net output #149: total_confidence = 0.000799994 | |
| I0506 01:18:34.740938 15760 solver.cpp:406] Test net output #150: total_confidence_not_rec = 0.00130653 | |
| I0506 01:18:34.877380 15760 solver.cpp:229] Iteration 15000, loss = 10.1085 | |
| I0506 01:18:34.877436 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.025 | |
| I0506 01:18:34.877454 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 01:18:34.877466 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:18:34.877478 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 01:18:34.877490 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 01:18:34.877506 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:18:34.877519 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.5 | |
| I0506 01:18:34.877532 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 1 | |
| I0506 01:18:34.877544 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 1 | |
| I0506 01:18:34.877557 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 01:18:34.877568 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:18:34.877580 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:18:34.877591 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:18:34.877604 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:18:34.877614 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:18:34.877626 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:18:34.877638 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:18:34.877650 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:18:34.877662 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:18:34.877673 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:18:34.877686 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:18:34.877696 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:18:34.877708 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:18:34.877719 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.772727 | |
| I0506 01:18:34.877732 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.125 | |
| I0506 01:18:34.877748 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.71756 (* 0.3 = 1.11527 loss) | |
| I0506 01:18:34.877761 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.963816 (* 0.3 = 0.289145 loss) | |
| I0506 01:18:34.877775 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.87316 (* 0.0272727 = 0.105632 loss) | |
| I0506 01:18:34.877789 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.61535 (* 0.0272727 = 0.0986003 loss) | |
| I0506 01:18:34.877802 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.56355 (* 0.0272727 = 0.0971877 loss) | |
| I0506 01:18:34.877816 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.18504 (* 0.0272727 = 0.0868647 loss) | |
| I0506 01:18:34.877830 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.87338 (* 0.0272727 = 0.078365 loss) | |
| I0506 01:18:34.877843 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.53873 (* 0.0272727 = 0.069238 loss) | |
| I0506 01:18:34.877857 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 0.157052 (* 0.0272727 = 0.00428324 loss) | |
| I0506 01:18:34.877871 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.0487337 (* 0.0272727 = 0.0013291 loss) | |
| I0506 01:18:34.877887 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.0302051 (* 0.0272727 = 0.000823777 loss) | |
| I0506 01:18:34.877900 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0205137 (* 0.0272727 = 0.000559463 loss) | |
| I0506 01:18:34.877918 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0152702 (* 0.0272727 = 0.000416461 loss) | |
| I0506 01:18:34.877961 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0102163 (* 0.0272727 = 0.000278625 loss) | |
| I0506 01:18:34.877976 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.00797908 (* 0.0272727 = 0.000217611 loss) | |
| I0506 01:18:34.877990 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.00660342 (* 0.0272727 = 0.000180093 loss) | |
| I0506 01:18:34.878005 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.00706615 (* 0.0272727 = 0.000192713 loss) | |
| I0506 01:18:34.878018 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0063529 (* 0.0272727 = 0.000173261 loss) | |
| I0506 01:18:34.878032 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00324002 (* 0.0272727 = 8.83641e-05 loss) | |
| I0506 01:18:34.878046 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00122857 (* 0.0272727 = 3.35066e-05 loss) | |
| I0506 01:18:34.878059 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00253774 (* 0.0272727 = 6.9211e-05 loss) | |
| I0506 01:18:34.878073 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00240224 (* 0.0272727 = 6.55157e-05 loss) | |
| I0506 01:18:34.878087 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.000803981 (* 0.0272727 = 2.19268e-05 loss) | |
| I0506 01:18:34.878100 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.000907273 (* 0.0272727 = 2.47438e-05 loss) | |
| I0506 01:18:34.878113 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.025 | |
| I0506 01:18:34.878125 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 01:18:34.878136 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 01:18:34.878149 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:18:34.878160 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:18:34.878171 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 01:18:34.878183 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.5 | |
| I0506 01:18:34.878195 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 1 | |
| I0506 01:18:34.878206 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 1 | |
| I0506 01:18:34.878217 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 01:18:34.878228 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:18:34.878240 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:18:34.878252 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:18:34.878262 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:18:34.878273 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:18:34.878285 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:18:34.878296 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:18:34.878307 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:18:34.878319 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:18:34.878330 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:18:34.878341 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:18:34.878353 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:18:34.878365 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:18:34.878376 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.778409 | |
| I0506 01:18:34.878387 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.1 | |
| I0506 01:18:34.878401 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.62474 (* 0.3 = 1.08742 loss) | |
| I0506 01:18:34.878414 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.88559 (* 0.3 = 0.265677 loss) | |
| I0506 01:18:34.878428 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.68353 (* 0.0272727 = 0.10046 loss) | |
| I0506 01:18:34.878453 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.5538 (* 0.0272727 = 0.0969219 loss) | |
| I0506 01:18:34.878466 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.82261 (* 0.0272727 = 0.104253 loss) | |
| I0506 01:18:34.878480 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.55625 (* 0.0272727 = 0.0969887 loss) | |
| I0506 01:18:34.878494 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.86324 (* 0.0272727 = 0.0780884 loss) | |
| I0506 01:18:34.878507 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.39896 (* 0.0272727 = 0.0654261 loss) | |
| I0506 01:18:34.878521 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 0.134045 (* 0.0272727 = 0.00365577 loss) | |
| I0506 01:18:34.878535 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.0501041 (* 0.0272727 = 0.00136648 loss) | |
| I0506 01:18:34.878548 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.00912019 (* 0.0272727 = 0.000248733 loss) | |
| I0506 01:18:34.878567 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0139905 (* 0.0272727 = 0.000381558 loss) | |
| I0506 01:18:34.878582 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.00842633 (* 0.0272727 = 0.000229809 loss) | |
| I0506 01:18:34.878595 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0138354 (* 0.0272727 = 0.000377328 loss) | |
| I0506 01:18:34.878609 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.00810878 (* 0.0272727 = 0.000221149 loss) | |
| I0506 01:18:34.878623 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.00586413 (* 0.0272727 = 0.000159931 loss) | |
| I0506 01:18:34.878638 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.00505185 (* 0.0272727 = 0.000137778 loss) | |
| I0506 01:18:34.878650 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.00277365 (* 0.0272727 = 7.5645e-05 loss) | |
| I0506 01:18:34.878664 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00213047 (* 0.0272727 = 5.81038e-05 loss) | |
| I0506 01:18:34.878679 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0020606 (* 0.0272727 = 5.61982e-05 loss) | |
| I0506 01:18:34.878692 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00109202 (* 0.0272727 = 2.97823e-05 loss) | |
| I0506 01:18:34.878706 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.000672109 (* 0.0272727 = 1.83303e-05 loss) | |
| I0506 01:18:34.878720 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00118826 (* 0.0272727 = 3.24072e-05 loss) | |
| I0506 01:18:34.878733 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.000796421 (* 0.0272727 = 2.17206e-05 loss) | |
| I0506 01:18:34.878746 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.05 | |
| I0506 01:18:34.878756 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 01:18:34.878768 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.125 | |
| I0506 01:18:34.878779 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 01:18:34.878792 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:18:34.878803 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.375 | |
| I0506 01:18:34.878813 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.5 | |
| I0506 01:18:34.878825 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 1 | |
| I0506 01:18:34.878836 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 1 | |
| I0506 01:18:34.878847 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 01:18:34.878859 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:18:34.878870 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:18:34.878882 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:18:34.878893 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:18:34.878901 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:18:34.878918 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:18:34.878931 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:18:34.878943 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:18:34.878957 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:18:34.878969 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:18:34.878981 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:18:34.878993 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:18:34.879004 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:18:34.879014 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.784091 | |
| I0506 01:18:34.879026 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.175 | |
| I0506 01:18:34.879040 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.43985 (* 1 = 3.43985 loss) | |
| I0506 01:18:34.879053 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.824498 (* 1 = 0.824498 loss) | |
| I0506 01:18:34.879066 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.50474 (* 0.0909091 = 0.318613 loss) | |
| I0506 01:18:34.879081 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.466 (* 0.0909091 = 0.315091 loss) | |
| I0506 01:18:34.879093 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.84658 (* 0.0909091 = 0.349689 loss) | |
| I0506 01:18:34.879107 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.06476 (* 0.0909091 = 0.278615 loss) | |
| I0506 01:18:34.879120 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.71465 (* 0.0909091 = 0.246786 loss) | |
| I0506 01:18:34.879133 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.02498 (* 0.0909091 = 0.184089 loss) | |
| I0506 01:18:34.879148 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 0.0592063 (* 0.0909091 = 0.00538239 loss) | |
| I0506 01:18:34.879160 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.0386987 (* 0.0909091 = 0.00351806 loss) | |
| I0506 01:18:34.879174 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.0363173 (* 0.0909091 = 0.00330157 loss) | |
| I0506 01:18:34.879187 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0360351 (* 0.0909091 = 0.00327592 loss) | |
| I0506 01:18:34.879201 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0316278 (* 0.0909091 = 0.00287525 loss) | |
| I0506 01:18:34.879215 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0190793 (* 0.0909091 = 0.00173448 loss) | |
| I0506 01:18:34.879228 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.033115 (* 0.0909091 = 0.00301046 loss) | |
| I0506 01:18:34.879241 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0245007 (* 0.0909091 = 0.00222734 loss) | |
| I0506 01:18:34.879254 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0149691 (* 0.0909091 = 0.00136083 loss) | |
| I0506 01:18:34.879268 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0144147 (* 0.0909091 = 0.00131043 loss) | |
| I0506 01:18:34.879281 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00605087 (* 0.0909091 = 0.000550079 loss) | |
| I0506 01:18:34.879295 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00368396 (* 0.0909091 = 0.000334906 loss) | |
| I0506 01:18:34.879309 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00407637 (* 0.0909091 = 0.000370579 loss) | |
| I0506 01:18:34.879323 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00311162 (* 0.0909091 = 0.000282874 loss) | |
| I0506 01:18:34.879336 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00273891 (* 0.0909091 = 0.000248992 loss) | |
| I0506 01:18:34.879349 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00181369 (* 0.0909091 = 0.000164881 loss) | |
| I0506 01:18:34.879361 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:18:34.879381 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:18:34.879395 15760 solver.cpp:245] Train net output #149: total_confidence = 8.95553e-05 | |
| I0506 01:18:34.879406 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000371555 | |
| I0506 01:18:34.879420 15760 sgd_solver.cpp:106] Iteration 15000, lr = 0.001 | |
| I0506 01:18:50.323886 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 37.3662 > 30) by scale factor 0.802864 | |
| I0506 01:19:07.091051 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 35.7328 > 30) by scale factor 0.839566 | |
| I0506 01:19:32.420841 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.491 > 30) by scale factor 0.983898 | |
| I0506 01:20:22.258218 15760 solver.cpp:229] Iteration 15500, loss = 10.0185 | |
| I0506 01:20:22.258338 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.097561 | |
| I0506 01:20:22.258360 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 01:20:22.258373 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.25 | |
| I0506 01:20:22.258384 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.375 | |
| I0506 01:20:22.258396 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 01:20:22.258409 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:20:22.258420 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.625 | |
| I0506 01:20:22.258432 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.875 | |
| I0506 01:20:22.258445 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 01:20:22.258456 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 01:20:22.258467 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:20:22.258478 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:20:22.258491 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:20:22.258502 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:20:22.258512 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:20:22.258524 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:20:22.258536 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:20:22.258548 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:20:22.258559 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:20:22.258570 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:20:22.258582 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:20:22.258594 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:20:22.258605 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:20:22.258615 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.778409 | |
| I0506 01:20:22.258627 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.317073 | |
| I0506 01:20:22.258643 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 2.89251 (* 0.3 = 0.867752 loss) | |
| I0506 01:20:22.258657 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.831932 (* 0.3 = 0.24958 loss) | |
| I0506 01:20:22.258671 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 2.57678 (* 0.0272727 = 0.0702759 loss) | |
| I0506 01:20:22.258685 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.53107 (* 0.0272727 = 0.0963019 loss) | |
| I0506 01:20:22.258698 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 2.97432 (* 0.0272727 = 0.0811178 loss) | |
| I0506 01:20:22.258713 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.62921 (* 0.0272727 = 0.0989785 loss) | |
| I0506 01:20:22.258725 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.48679 (* 0.0272727 = 0.0678215 loss) | |
| I0506 01:20:22.258739 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.18862 (* 0.0272727 = 0.0596895 loss) | |
| I0506 01:20:22.258752 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.07173 (* 0.0272727 = 0.0292291 loss) | |
| I0506 01:20:22.258766 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.994714 (* 0.0272727 = 0.0271286 loss) | |
| I0506 01:20:22.258780 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.00521614 (* 0.0272727 = 0.000142258 loss) | |
| I0506 01:20:22.258795 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.00396978 (* 0.0272727 = 0.000108267 loss) | |
| I0506 01:20:22.258808 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.00335319 (* 0.0272727 = 9.14505e-05 loss) | |
| I0506 01:20:22.258822 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.00282798 (* 0.0272727 = 7.71267e-05 loss) | |
| I0506 01:20:22.258854 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.00135551 (* 0.0272727 = 3.69684e-05 loss) | |
| I0506 01:20:22.258870 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.00118944 (* 0.0272727 = 3.24392e-05 loss) | |
| I0506 01:20:22.258888 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.00159376 (* 0.0272727 = 4.34663e-05 loss) | |
| I0506 01:20:22.258903 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.00126895 (* 0.0272727 = 3.46077e-05 loss) | |
| I0506 01:20:22.258918 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.000901664 (* 0.0272727 = 2.45908e-05 loss) | |
| I0506 01:20:22.258931 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.000429478 (* 0.0272727 = 1.1713e-05 loss) | |
| I0506 01:20:22.258945 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.000731044 (* 0.0272727 = 1.99376e-05 loss) | |
| I0506 01:20:22.258958 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.000712979 (* 0.0272727 = 1.94449e-05 loss) | |
| I0506 01:20:22.258972 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.000401409 (* 0.0272727 = 1.09475e-05 loss) | |
| I0506 01:20:22.258985 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.000517541 (* 0.0272727 = 1.41148e-05 loss) | |
| I0506 01:20:22.258997 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.146341 | |
| I0506 01:20:22.259009 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 01:20:22.259021 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 01:20:22.259033 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.25 | |
| I0506 01:20:22.259044 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 01:20:22.259055 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 01:20:22.259068 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.625 | |
| I0506 01:20:22.259078 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.875 | |
| I0506 01:20:22.259089 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 01:20:22.259101 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 01:20:22.259112 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:20:22.259124 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:20:22.259135 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:20:22.259145 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:20:22.259156 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:20:22.259167 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:20:22.259178 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:20:22.259189 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:20:22.259201 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:20:22.259212 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:20:22.259223 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:20:22.259234 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:20:22.259245 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:20:22.259256 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.761364 | |
| I0506 01:20:22.259268 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.292683 | |
| I0506 01:20:22.259281 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 2.87106 (* 0.3 = 0.861319 loss) | |
| I0506 01:20:22.259294 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.921219 (* 0.3 = 0.276366 loss) | |
| I0506 01:20:22.259308 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 2.37139 (* 0.0272727 = 0.0646744 loss) | |
| I0506 01:20:22.259322 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.30616 (* 0.0272727 = 0.0901681 loss) | |
| I0506 01:20:22.259347 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 2.66005 (* 0.0272727 = 0.0725469 loss) | |
| I0506 01:20:22.259366 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.37073 (* 0.0272727 = 0.0919291 loss) | |
| I0506 01:20:22.259380 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.33933 (* 0.0272727 = 0.0638 loss) | |
| I0506 01:20:22.259393 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 1.51877 (* 0.0272727 = 0.041421 loss) | |
| I0506 01:20:22.259407 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 0.496957 (* 0.0272727 = 0.0135534 loss) | |
| I0506 01:20:22.259420 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.863992 (* 0.0272727 = 0.0235634 loss) | |
| I0506 01:20:22.259434 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.00527777 (* 0.0272727 = 0.000143939 loss) | |
| I0506 01:20:22.259448 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.00424488 (* 0.0272727 = 0.00011577 loss) | |
| I0506 01:20:22.259461 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.00194418 (* 0.0272727 = 5.3023e-05 loss) | |
| I0506 01:20:22.259475 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.00143735 (* 0.0272727 = 3.92003e-05 loss) | |
| I0506 01:20:22.259488 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.00133623 (* 0.0272727 = 3.64428e-05 loss) | |
| I0506 01:20:22.259502 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.001927 (* 0.0272727 = 5.25544e-05 loss) | |
| I0506 01:20:22.259516 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.00376584 (* 0.0272727 = 0.000102705 loss) | |
| I0506 01:20:22.259529 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.000297865 (* 0.0272727 = 8.1236e-06 loss) | |
| I0506 01:20:22.259543 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.000251314 (* 0.0272727 = 6.85403e-06 loss) | |
| I0506 01:20:22.259557 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.000592849 (* 0.0272727 = 1.61686e-05 loss) | |
| I0506 01:20:22.259570 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.000873099 (* 0.0272727 = 2.38118e-05 loss) | |
| I0506 01:20:22.259584 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.000245385 (* 0.0272727 = 6.69232e-06 loss) | |
| I0506 01:20:22.259598 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.000416082 (* 0.0272727 = 1.13477e-05 loss) | |
| I0506 01:20:22.259613 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.000439665 (* 0.0272727 = 1.19909e-05 loss) | |
| I0506 01:20:22.259624 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.121951 | |
| I0506 01:20:22.259635 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.25 | |
| I0506 01:20:22.259647 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:20:22.259659 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 01:20:22.259670 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0 | |
| I0506 01:20:22.259680 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.5 | |
| I0506 01:20:22.259692 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.625 | |
| I0506 01:20:22.259703 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.875 | |
| I0506 01:20:22.259716 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 01:20:22.259727 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 01:20:22.259737 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:20:22.259748 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:20:22.259759 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:20:22.259770 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:20:22.259783 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:20:22.259793 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:20:22.259814 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:20:22.259826 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:20:22.259838 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:20:22.259850 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:20:22.259860 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:20:22.259871 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:20:22.259881 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:20:22.259892 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.778409 | |
| I0506 01:20:22.259904 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.390244 | |
| I0506 01:20:22.259917 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.70269 (* 1 = 2.70269 loss) | |
| I0506 01:20:22.259934 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.775542 (* 1 = 0.775542 loss) | |
| I0506 01:20:22.259949 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.20519 (* 0.0909091 = 0.200472 loss) | |
| I0506 01:20:22.259963 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.07664 (* 0.0909091 = 0.279695 loss) | |
| I0506 01:20:22.259976 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.92698 (* 0.0909091 = 0.266089 loss) | |
| I0506 01:20:22.259989 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.94695 (* 0.0909091 = 0.267904 loss) | |
| I0506 01:20:22.260002 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 1.74954 (* 0.0909091 = 0.159049 loss) | |
| I0506 01:20:22.260016 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.42449 (* 0.0909091 = 0.129499 loss) | |
| I0506 01:20:22.260026 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 0.563892 (* 0.0909091 = 0.0512629 loss) | |
| I0506 01:20:22.260035 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.637516 (* 0.0909091 = 0.057956 loss) | |
| I0506 01:20:22.260049 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.000987547 (* 0.0909091 = 8.9777e-05 loss) | |
| I0506 01:20:22.260063 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.00042451 (* 0.0909091 = 3.85918e-05 loss) | |
| I0506 01:20:22.260076 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.000255228 (* 0.0909091 = 2.32026e-05 loss) | |
| I0506 01:20:22.260089 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.000254594 (* 0.0909091 = 2.31449e-05 loss) | |
| I0506 01:20:22.260103 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.000174291 (* 0.0909091 = 1.58447e-05 loss) | |
| I0506 01:20:22.260116 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.000225521 (* 0.0909091 = 2.05019e-05 loss) | |
| I0506 01:20:22.260130 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00017171 (* 0.0909091 = 1.561e-05 loss) | |
| I0506 01:20:22.260143 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.000210939 (* 0.0909091 = 1.91763e-05 loss) | |
| I0506 01:20:22.260157 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.000152743 (* 0.0909091 = 1.38857e-05 loss) | |
| I0506 01:20:22.260170 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.000250773 (* 0.0909091 = 2.27975e-05 loss) | |
| I0506 01:20:22.260185 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 9.55157e-05 (* 0.0909091 = 8.68325e-06 loss) | |
| I0506 01:20:22.260197 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 7.73192e-05 (* 0.0909091 = 7.02902e-06 loss) | |
| I0506 01:20:22.260211 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 8.03297e-05 (* 0.0909091 = 7.3027e-06 loss) | |
| I0506 01:20:22.260224 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 7.62979e-05 (* 0.0909091 = 6.93617e-06 loss) | |
| I0506 01:20:22.260236 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:20:22.260247 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:20:22.260267 15760 solver.cpp:245] Train net output #149: total_confidence = 7.34426e-06 | |
| I0506 01:20:22.260282 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000165033 | |
| I0506 01:20:22.260293 15760 sgd_solver.cpp:106] Iteration 15500, lr = 0.001 | |
| I0506 01:20:28.526556 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.369 > 30) by scale factor 0.956358 | |
| I0506 01:22:09.616576 15760 solver.cpp:229] Iteration 16000, loss = 10.0155 | |
| I0506 01:22:09.616736 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0588235 | |
| I0506 01:22:09.616757 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.125 | |
| I0506 01:22:09.616771 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:22:09.616782 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:22:09.616794 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 01:22:09.616807 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.25 | |
| I0506 01:22:09.616818 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 01:22:09.616830 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 01:22:09.616842 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.5 | |
| I0506 01:22:09.616854 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.5 | |
| I0506 01:22:09.616865 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.5 | |
| I0506 01:22:09.616880 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.625 | |
| I0506 01:22:09.616894 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.625 | |
| I0506 01:22:09.616905 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0.75 | |
| I0506 01:22:09.616917 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 0.875 | |
| I0506 01:22:09.616930 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 0.875 | |
| I0506 01:22:09.616941 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 0.875 | |
| I0506 01:22:09.616953 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:22:09.616966 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:22:09.616977 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:22:09.616988 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:22:09.616999 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:22:09.617012 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:22:09.617022 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.585227 | |
| I0506 01:22:09.617034 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.235294 | |
| I0506 01:22:09.617051 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.30075 (* 0.3 = 0.990225 loss) | |
| I0506 01:22:09.617066 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.58378 (* 0.3 = 0.475135 loss) | |
| I0506 01:22:09.617080 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.29737 (* 0.0272727 = 0.0899282 loss) | |
| I0506 01:22:09.617094 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.55503 (* 0.0272727 = 0.0969554 loss) | |
| I0506 01:22:09.617108 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.55646 (* 0.0272727 = 0.0969944 loss) | |
| I0506 01:22:09.617138 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 2.97025 (* 0.0272727 = 0.0810069 loss) | |
| I0506 01:22:09.617154 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.30811 (* 0.0272727 = 0.0902213 loss) | |
| I0506 01:22:09.617169 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.08169 (* 0.0272727 = 0.0567733 loss) | |
| I0506 01:22:09.617182 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.10424 (* 0.0272727 = 0.0573884 loss) | |
| I0506 01:22:09.617197 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.99202 (* 0.0272727 = 0.0543277 loss) | |
| I0506 01:22:09.617209 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.62224 (* 0.0272727 = 0.0442429 loss) | |
| I0506 01:22:09.617223 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 1.78602 (* 0.0272727 = 0.0487096 loss) | |
| I0506 01:22:09.617236 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 1.53565 (* 0.0272727 = 0.0418814 loss) | |
| I0506 01:22:09.617250 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 1.72693 (* 0.0272727 = 0.0470982 loss) | |
| I0506 01:22:09.617283 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 1.25572 (* 0.0272727 = 0.0342469 loss) | |
| I0506 01:22:09.617298 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.650294 (* 0.0272727 = 0.0177353 loss) | |
| I0506 01:22:09.617312 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.709703 (* 0.0272727 = 0.0193555 loss) | |
| I0506 01:22:09.617326 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.59788 (* 0.0272727 = 0.0163058 loss) | |
| I0506 01:22:09.617341 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0541845 (* 0.0272727 = 0.00147776 loss) | |
| I0506 01:22:09.617355 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0444428 (* 0.0272727 = 0.00121208 loss) | |
| I0506 01:22:09.617369 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.023739 (* 0.0272727 = 0.000647428 loss) | |
| I0506 01:22:09.617383 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0389436 (* 0.0272727 = 0.0010621 loss) | |
| I0506 01:22:09.617398 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0277357 (* 0.0272727 = 0.000756428 loss) | |
| I0506 01:22:09.617411 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0218852 (* 0.0272727 = 0.000596868 loss) | |
| I0506 01:22:09.617424 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0441176 | |
| I0506 01:22:09.617435 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 01:22:09.617447 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 01:22:09.617460 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 01:22:09.617470 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 01:22:09.617482 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.25 | |
| I0506 01:22:09.617493 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.5 | |
| I0506 01:22:09.617506 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 01:22:09.617516 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.5 | |
| I0506 01:22:09.617528 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.5 | |
| I0506 01:22:09.617539 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.5 | |
| I0506 01:22:09.617552 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.625 | |
| I0506 01:22:09.617563 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.625 | |
| I0506 01:22:09.617573 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0.75 | |
| I0506 01:22:09.617585 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 0.875 | |
| I0506 01:22:09.617597 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 0.875 | |
| I0506 01:22:09.617609 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 0.875 | |
| I0506 01:22:09.617622 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:22:09.617635 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:22:09.617645 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:22:09.617657 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:22:09.617669 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:22:09.617681 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:22:09.617691 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.630682 | |
| I0506 01:22:09.617703 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.220588 | |
| I0506 01:22:09.617717 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.17769 (* 0.3 = 0.953307 loss) | |
| I0506 01:22:09.617730 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.34911 (* 0.3 = 0.404734 loss) | |
| I0506 01:22:09.617748 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.19893 (* 0.0272727 = 0.0872436 loss) | |
| I0506 01:22:09.617763 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.18344 (* 0.0272727 = 0.0868211 loss) | |
| I0506 01:22:09.617787 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.22784 (* 0.0272727 = 0.0880321 loss) | |
| I0506 01:22:09.617802 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.3002 (* 0.0272727 = 0.0900055 loss) | |
| I0506 01:22:09.617816 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.01413 (* 0.0272727 = 0.0822035 loss) | |
| I0506 01:22:09.617830 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 1.99928 (* 0.0272727 = 0.0545258 loss) | |
| I0506 01:22:09.617843 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.18563 (* 0.0272727 = 0.059608 loss) | |
| I0506 01:22:09.617857 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.80454 (* 0.0272727 = 0.0492148 loss) | |
| I0506 01:22:09.617871 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.53695 (* 0.0272727 = 0.0419169 loss) | |
| I0506 01:22:09.617884 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 2.03682 (* 0.0272727 = 0.0555495 loss) | |
| I0506 01:22:09.617897 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 1.83435 (* 0.0272727 = 0.0500278 loss) | |
| I0506 01:22:09.617911 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 1.81815 (* 0.0272727 = 0.049586 loss) | |
| I0506 01:22:09.617928 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 1.43049 (* 0.0272727 = 0.0390133 loss) | |
| I0506 01:22:09.617943 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.555064 (* 0.0272727 = 0.0151381 loss) | |
| I0506 01:22:09.617956 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.731932 (* 0.0272727 = 0.0199618 loss) | |
| I0506 01:22:09.617970 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.607337 (* 0.0272727 = 0.0165637 loss) | |
| I0506 01:22:09.617983 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0624634 (* 0.0272727 = 0.00170355 loss) | |
| I0506 01:22:09.617997 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0532133 (* 0.0272727 = 0.00145127 loss) | |
| I0506 01:22:09.618011 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0298245 (* 0.0272727 = 0.000813396 loss) | |
| I0506 01:22:09.618024 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0263644 (* 0.0272727 = 0.00071903 loss) | |
| I0506 01:22:09.618038 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0368105 (* 0.0272727 = 0.00100392 loss) | |
| I0506 01:22:09.618052 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0143828 (* 0.0272727 = 0.000392259 loss) | |
| I0506 01:22:09.618063 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.117647 | |
| I0506 01:22:09.618075 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:22:09.618088 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:22:09.618098 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 01:22:09.618109 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.5 | |
| I0506 01:22:09.618121 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 01:22:09.618134 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.5 | |
| I0506 01:22:09.618144 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 01:22:09.618155 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.5 | |
| I0506 01:22:09.618167 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.625 | |
| I0506 01:22:09.618180 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.5 | |
| I0506 01:22:09.618191 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.625 | |
| I0506 01:22:09.618201 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.625 | |
| I0506 01:22:09.618213 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0.75 | |
| I0506 01:22:09.618226 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 0.875 | |
| I0506 01:22:09.618237 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 0.875 | |
| I0506 01:22:09.618247 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 0.875 | |
| I0506 01:22:09.618268 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:22:09.618281 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:22:09.618294 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:22:09.618305 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:22:09.618316 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:22:09.618327 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:22:09.618338 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.659091 | |
| I0506 01:22:09.618350 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.279412 | |
| I0506 01:22:09.618363 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.94411 (* 1 = 2.94411 loss) | |
| I0506 01:22:09.618377 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.22606 (* 1 = 1.22606 loss) | |
| I0506 01:22:09.618391 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.88686 (* 0.0909091 = 0.262442 loss) | |
| I0506 01:22:09.618404 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.12062 (* 0.0909091 = 0.283693 loss) | |
| I0506 01:22:09.618418 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.07351 (* 0.0909091 = 0.27941 loss) | |
| I0506 01:22:09.618432 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.64689 (* 0.0909091 = 0.240626 loss) | |
| I0506 01:22:09.618444 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.76248 (* 0.0909091 = 0.251134 loss) | |
| I0506 01:22:09.618458 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.88796 (* 0.0909091 = 0.171633 loss) | |
| I0506 01:22:09.618471 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.0011 (* 0.0909091 = 0.181918 loss) | |
| I0506 01:22:09.618485 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.72197 (* 0.0909091 = 0.156543 loss) | |
| I0506 01:22:09.618499 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 1.56017 (* 0.0909091 = 0.141834 loss) | |
| I0506 01:22:09.618512 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 1.74588 (* 0.0909091 = 0.158717 loss) | |
| I0506 01:22:09.618525 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 1.39196 (* 0.0909091 = 0.126542 loss) | |
| I0506 01:22:09.618538 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 1.32776 (* 0.0909091 = 0.120706 loss) | |
| I0506 01:22:09.618552 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 1.11253 (* 0.0909091 = 0.101139 loss) | |
| I0506 01:22:09.618566 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.408071 (* 0.0909091 = 0.0370974 loss) | |
| I0506 01:22:09.618578 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.45507 (* 0.0909091 = 0.04137 loss) | |
| I0506 01:22:09.618592 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.47877 (* 0.0909091 = 0.0435246 loss) | |
| I0506 01:22:09.618605 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0191025 (* 0.0909091 = 0.00173659 loss) | |
| I0506 01:22:09.618619 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0154845 (* 0.0909091 = 0.00140768 loss) | |
| I0506 01:22:09.618633 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0139276 (* 0.0909091 = 0.00126615 loss) | |
| I0506 01:22:09.618646 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.0144877 (* 0.0909091 = 0.00131706 loss) | |
| I0506 01:22:09.618660 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00595587 (* 0.0909091 = 0.000541443 loss) | |
| I0506 01:22:09.618674 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.0041569 (* 0.0909091 = 0.0003779 loss) | |
| I0506 01:22:09.618686 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:22:09.618698 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:22:09.618710 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000200075 | |
| I0506 01:22:09.618729 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000509643 | |
| I0506 01:22:09.618744 15760 sgd_solver.cpp:106] Iteration 16000, lr = 0.001 | |
| I0506 01:22:52.101835 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 45.928 > 30) by scale factor 0.653196 | |
| I0506 01:23:56.735894 15760 solver.cpp:229] Iteration 16500, loss = 9.94345 | |
| I0506 01:23:56.736032 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.108696 | |
| I0506 01:23:56.736053 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.25 | |
| I0506 01:23:56.736066 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:23:56.736078 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 01:23:56.736090 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 01:23:56.736104 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:23:56.736114 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 01:23:56.736126 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.625 | |
| I0506 01:23:56.736138 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 01:23:56.736151 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 01:23:56.736162 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 01:23:56.736174 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:23:56.736186 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:23:56.736198 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:23:56.736210 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:23:56.736222 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:23:56.736233 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:23:56.736245 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:23:56.736258 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:23:56.736268 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:23:56.736280 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:23:56.736292 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:23:56.736304 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:23:56.736315 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.732955 | |
| I0506 01:23:56.736326 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.217391 | |
| I0506 01:23:56.736343 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 2.95162 (* 0.3 = 0.885485 loss) | |
| I0506 01:23:56.736357 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.00708 (* 0.3 = 0.302125 loss) | |
| I0506 01:23:56.736371 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 2.68253 (* 0.0272727 = 0.0731598 loss) | |
| I0506 01:23:56.736385 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 2.70056 (* 0.0272727 = 0.0736517 loss) | |
| I0506 01:23:56.736398 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.42388 (* 0.0272727 = 0.0933785 loss) | |
| I0506 01:23:56.736413 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 2.80842 (* 0.0272727 = 0.0765933 loss) | |
| I0506 01:23:56.736425 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.3907 (* 0.0272727 = 0.065201 loss) | |
| I0506 01:23:56.736439 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.65103 (* 0.0272727 = 0.0723008 loss) | |
| I0506 01:23:56.736452 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.96038 (* 0.0272727 = 0.053465 loss) | |
| I0506 01:23:56.736466 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.863598 (* 0.0272727 = 0.0235527 loss) | |
| I0506 01:23:56.736480 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.381467 (* 0.0272727 = 0.0104036 loss) | |
| I0506 01:23:56.736493 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.594235 (* 0.0272727 = 0.0162064 loss) | |
| I0506 01:23:56.736508 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0420926 (* 0.0272727 = 0.00114798 loss) | |
| I0506 01:23:56.736522 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0222665 (* 0.0272727 = 0.000607269 loss) | |
| I0506 01:23:56.736555 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0186127 (* 0.0272727 = 0.00050762 loss) | |
| I0506 01:23:56.736570 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0114274 (* 0.0272727 = 0.000311657 loss) | |
| I0506 01:23:56.736585 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0104528 (* 0.0272727 = 0.000285076 loss) | |
| I0506 01:23:56.736599 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.00780933 (* 0.0272727 = 0.000212982 loss) | |
| I0506 01:23:56.736613 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00562703 (* 0.0272727 = 0.000153464 loss) | |
| I0506 01:23:56.736626 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00611188 (* 0.0272727 = 0.000166688 loss) | |
| I0506 01:23:56.736640 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00314545 (* 0.0272727 = 8.57851e-05 loss) | |
| I0506 01:23:56.736654 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00338058 (* 0.0272727 = 9.21977e-05 loss) | |
| I0506 01:23:56.736668 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00204477 (* 0.0272727 = 5.57664e-05 loss) | |
| I0506 01:23:56.736682 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00446146 (* 0.0272727 = 0.000121676 loss) | |
| I0506 01:23:56.736695 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.130435 | |
| I0506 01:23:56.736706 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.375 | |
| I0506 01:23:56.736714 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:23:56.736722 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 01:23:56.736735 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 01:23:56.736747 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 01:23:56.736758 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 01:23:56.736770 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.625 | |
| I0506 01:23:56.736781 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 01:23:56.736793 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 01:23:56.736804 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 01:23:56.736816 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:23:56.736827 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:23:56.736838 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:23:56.736850 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:23:56.736860 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:23:56.736871 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:23:56.736886 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:23:56.736898 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:23:56.736910 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:23:56.736922 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:23:56.736932 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:23:56.736944 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:23:56.736955 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.761364 | |
| I0506 01:23:56.736968 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.23913 | |
| I0506 01:23:56.736980 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 2.98506 (* 0.3 = 0.895518 loss) | |
| I0506 01:23:56.736994 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.91561 (* 0.3 = 0.274683 loss) | |
| I0506 01:23:56.737009 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 2.44143 (* 0.0272727 = 0.0665846 loss) | |
| I0506 01:23:56.737021 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.17463 (* 0.0272727 = 0.0865808 loss) | |
| I0506 01:23:56.737051 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.24347 (* 0.0272727 = 0.0884583 loss) | |
| I0506 01:23:56.737066 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.07812 (* 0.0272727 = 0.0839488 loss) | |
| I0506 01:23:56.737081 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.20907 (* 0.0272727 = 0.0602474 loss) | |
| I0506 01:23:56.737094 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.24959 (* 0.0272727 = 0.0613525 loss) | |
| I0506 01:23:56.737108 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.82522 (* 0.0272727 = 0.0497788 loss) | |
| I0506 01:23:56.737136 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.656586 (* 0.0272727 = 0.0179069 loss) | |
| I0506 01:23:56.737154 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.522952 (* 0.0272727 = 0.0142623 loss) | |
| I0506 01:23:56.737167 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.520556 (* 0.0272727 = 0.014197 loss) | |
| I0506 01:23:56.737181 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0377954 (* 0.0272727 = 0.00103078 loss) | |
| I0506 01:23:56.737195 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0202517 (* 0.0272727 = 0.000552319 loss) | |
| I0506 01:23:56.737208 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0151163 (* 0.0272727 = 0.000412262 loss) | |
| I0506 01:23:56.737222 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0101104 (* 0.0272727 = 0.000275737 loss) | |
| I0506 01:23:56.737236 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.00799029 (* 0.0272727 = 0.000217917 loss) | |
| I0506 01:23:56.737249 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.00551868 (* 0.0272727 = 0.000150509 loss) | |
| I0506 01:23:56.737263 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0017718 (* 0.0272727 = 4.83219e-05 loss) | |
| I0506 01:23:56.737278 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00290701 (* 0.0272727 = 7.92821e-05 loss) | |
| I0506 01:23:56.737293 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00313903 (* 0.0272727 = 8.56098e-05 loss) | |
| I0506 01:23:56.737305 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00285319 (* 0.0272727 = 7.78142e-05 loss) | |
| I0506 01:23:56.737319 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00745389 (* 0.0272727 = 0.000203288 loss) | |
| I0506 01:23:56.737334 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00342281 (* 0.0272727 = 9.33494e-05 loss) | |
| I0506 01:23:56.737345 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.173913 | |
| I0506 01:23:56.737356 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.25 | |
| I0506 01:23:56.737368 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:23:56.737380 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.25 | |
| I0506 01:23:56.737390 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 01:23:56.737401 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.5 | |
| I0506 01:23:56.737413 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 01:23:56.737424 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.5 | |
| I0506 01:23:56.737437 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 01:23:56.737448 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 01:23:56.737459 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 01:23:56.737471 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:23:56.737483 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:23:56.737493 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:23:56.737504 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:23:56.737515 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:23:56.737526 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:23:56.737550 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:23:56.737562 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:23:56.737573 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:23:56.737586 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:23:56.737596 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:23:56.737607 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:23:56.737618 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.778409 | |
| I0506 01:23:56.737630 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.347826 | |
| I0506 01:23:56.737643 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.75113 (* 1 = 2.75113 loss) | |
| I0506 01:23:56.737658 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.794765 (* 1 = 0.794765 loss) | |
| I0506 01:23:56.737670 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.42228 (* 0.0909091 = 0.220207 loss) | |
| I0506 01:23:56.737684 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.67373 (* 0.0909091 = 0.243066 loss) | |
| I0506 01:23:56.737697 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.82387 (* 0.0909091 = 0.256716 loss) | |
| I0506 01:23:56.737710 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.75818 (* 0.0909091 = 0.250744 loss) | |
| I0506 01:23:56.737725 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 1.82405 (* 0.0909091 = 0.165822 loss) | |
| I0506 01:23:56.737737 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.82471 (* 0.0909091 = 0.165883 loss) | |
| I0506 01:23:56.737751 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.4616 (* 0.0909091 = 0.132872 loss) | |
| I0506 01:23:56.737766 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.765494 (* 0.0909091 = 0.0695904 loss) | |
| I0506 01:23:56.737778 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.397768 (* 0.0909091 = 0.0361607 loss) | |
| I0506 01:23:56.737792 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.481252 (* 0.0909091 = 0.0437501 loss) | |
| I0506 01:23:56.737805 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0145984 (* 0.0909091 = 0.00132713 loss) | |
| I0506 01:23:56.737819 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0152466 (* 0.0909091 = 0.00138605 loss) | |
| I0506 01:23:56.737833 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0120426 (* 0.0909091 = 0.00109478 loss) | |
| I0506 01:23:56.737846 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00606218 (* 0.0909091 = 0.000551107 loss) | |
| I0506 01:23:56.737860 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00498137 (* 0.0909091 = 0.000452851 loss) | |
| I0506 01:23:56.737874 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00547663 (* 0.0909091 = 0.000497876 loss) | |
| I0506 01:23:56.737887 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00312826 (* 0.0909091 = 0.000284387 loss) | |
| I0506 01:23:56.737900 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00147396 (* 0.0909091 = 0.000133997 loss) | |
| I0506 01:23:56.737915 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00139995 (* 0.0909091 = 0.000127269 loss) | |
| I0506 01:23:56.737931 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00112971 (* 0.0909091 = 0.000102701 loss) | |
| I0506 01:23:56.737946 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000831991 (* 0.0909091 = 7.56355e-05 loss) | |
| I0506 01:23:56.737960 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000570404 (* 0.0909091 = 5.18549e-05 loss) | |
| I0506 01:23:56.737972 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:23:56.737983 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:23:56.738004 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000534827 | |
| I0506 01:23:56.738018 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000870216 | |
| I0506 01:23:56.738030 15760 sgd_solver.cpp:106] Iteration 16500, lr = 0.001 | |
| I0506 01:25:43.991717 15760 solver.cpp:229] Iteration 17000, loss = 9.93592 | |
| I0506 01:25:43.991858 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0677966 | |
| I0506 01:25:43.991880 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.25 | |
| I0506 01:25:43.991894 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:25:43.991907 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:25:43.991919 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 01:25:43.991930 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 01:25:43.991943 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 01:25:43.991955 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.625 | |
| I0506 01:25:43.991967 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.625 | |
| I0506 01:25:43.991979 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 01:25:43.991991 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 01:25:43.992003 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 01:25:43.992015 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 01:25:43.992034 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0.875 | |
| I0506 01:25:43.992046 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 0.875 | |
| I0506 01:25:43.992058 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 0.875 | |
| I0506 01:25:43.992071 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 0.875 | |
| I0506 01:25:43.992084 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:25:43.992094 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:25:43.992106 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:25:43.992117 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:25:43.992130 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:25:43.992141 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:25:43.992151 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.6875 | |
| I0506 01:25:43.992163 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.254237 | |
| I0506 01:25:43.992179 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.26894 (* 0.3 = 0.980682 loss) | |
| I0506 01:25:43.992193 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.17943 (* 0.3 = 0.353828 loss) | |
| I0506 01:25:43.992208 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.21518 (* 0.0272727 = 0.0876867 loss) | |
| I0506 01:25:43.992221 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.36198 (* 0.0272727 = 0.0916903 loss) | |
| I0506 01:25:43.992235 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.73144 (* 0.0272727 = 0.101766 loss) | |
| I0506 01:25:43.992249 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.43644 (* 0.0272727 = 0.0937212 loss) | |
| I0506 01:25:43.992262 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.12808 (* 0.0272727 = 0.0853114 loss) | |
| I0506 01:25:43.992276 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.72989 (* 0.0272727 = 0.0744517 loss) | |
| I0506 01:25:43.992290 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.86329 (* 0.0272727 = 0.0508169 loss) | |
| I0506 01:25:43.992310 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.74905 (* 0.0272727 = 0.0477013 loss) | |
| I0506 01:25:43.992323 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.72083 (* 0.0272727 = 0.019659 loss) | |
| I0506 01:25:43.992337 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.763149 (* 0.0272727 = 0.0208132 loss) | |
| I0506 01:25:43.992350 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.792021 (* 0.0272727 = 0.0216006 loss) | |
| I0506 01:25:43.992364 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.676367 (* 0.0272727 = 0.0184464 loss) | |
| I0506 01:25:43.992396 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.65164 (* 0.0272727 = 0.017772 loss) | |
| I0506 01:25:43.992411 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.595811 (* 0.0272727 = 0.0162494 loss) | |
| I0506 01:25:43.992425 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.850738 (* 0.0272727 = 0.0232019 loss) | |
| I0506 01:25:43.992439 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.782456 (* 0.0272727 = 0.0213397 loss) | |
| I0506 01:25:43.992454 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0295341 (* 0.0272727 = 0.000805475 loss) | |
| I0506 01:25:43.992466 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0175755 (* 0.0272727 = 0.000479331 loss) | |
| I0506 01:25:43.992480 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0120356 (* 0.0272727 = 0.000328245 loss) | |
| I0506 01:25:43.992494 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0195462 (* 0.0272727 = 0.000533079 loss) | |
| I0506 01:25:43.992508 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0130612 (* 0.0272727 = 0.000356214 loss) | |
| I0506 01:25:43.992522 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0115032 (* 0.0272727 = 0.000313724 loss) | |
| I0506 01:25:43.992533 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0338983 | |
| I0506 01:25:43.992545 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 01:25:43.992558 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:25:43.992568 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 01:25:43.992580 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 01:25:43.992591 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 01:25:43.992604 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 01:25:43.992615 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.625 | |
| I0506 01:25:43.992627 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.625 | |
| I0506 01:25:43.992638 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.75 | |
| I0506 01:25:43.992650 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 01:25:43.992662 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 01:25:43.992673 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 01:25:43.992686 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0.875 | |
| I0506 01:25:43.992697 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 0.875 | |
| I0506 01:25:43.992708 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 0.875 | |
| I0506 01:25:43.992720 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 0.875 | |
| I0506 01:25:43.992733 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:25:43.992743 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:25:43.992754 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:25:43.992765 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:25:43.992776 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:25:43.992789 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:25:43.992799 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.659091 | |
| I0506 01:25:43.992811 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.254237 | |
| I0506 01:25:43.992825 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.3268 (* 0.3 = 0.998039 loss) | |
| I0506 01:25:43.992838 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.27955 (* 0.3 = 0.383866 loss) | |
| I0506 01:25:43.992851 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.06026 (* 0.0272727 = 0.0834616 loss) | |
| I0506 01:25:43.992866 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.49767 (* 0.0272727 = 0.095391 loss) | |
| I0506 01:25:43.992893 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.32879 (* 0.0272727 = 0.0907852 loss) | |
| I0506 01:25:43.992909 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.58122 (* 0.0272727 = 0.0976696 loss) | |
| I0506 01:25:43.992923 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.72277 (* 0.0272727 = 0.0742575 loss) | |
| I0506 01:25:43.992940 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.81279 (* 0.0272727 = 0.0767124 loss) | |
| I0506 01:25:43.992954 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.7841 (* 0.0272727 = 0.0486572 loss) | |
| I0506 01:25:43.992967 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 2.2305 (* 0.0272727 = 0.0608318 loss) | |
| I0506 01:25:43.992980 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.861565 (* 0.0272727 = 0.0234972 loss) | |
| I0506 01:25:43.992995 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.762614 (* 0.0272727 = 0.0207986 loss) | |
| I0506 01:25:43.993007 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.695635 (* 0.0272727 = 0.0189719 loss) | |
| I0506 01:25:43.993021 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.911969 (* 0.0272727 = 0.0248719 loss) | |
| I0506 01:25:43.993034 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.920529 (* 0.0272727 = 0.0251053 loss) | |
| I0506 01:25:43.993048 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.693535 (* 0.0272727 = 0.0189146 loss) | |
| I0506 01:25:43.993062 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.964285 (* 0.0272727 = 0.0262987 loss) | |
| I0506 01:25:43.993074 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.958382 (* 0.0272727 = 0.0261377 loss) | |
| I0506 01:25:43.993089 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.031243 (* 0.0272727 = 0.000852082 loss) | |
| I0506 01:25:43.993103 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0175685 (* 0.0272727 = 0.00047914 loss) | |
| I0506 01:25:43.993113 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0204817 (* 0.0272727 = 0.000558592 loss) | |
| I0506 01:25:43.993141 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0204225 (* 0.0272727 = 0.000556977 loss) | |
| I0506 01:25:43.993157 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.013855 (* 0.0272727 = 0.000377864 loss) | |
| I0506 01:25:43.993170 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0174338 (* 0.0272727 = 0.000475467 loss) | |
| I0506 01:25:43.993190 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.118644 | |
| I0506 01:25:43.993202 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:25:43.993214 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:25:43.993226 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:25:43.993237 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:25:43.993248 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 01:25:43.993260 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 01:25:43.993271 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.625 | |
| I0506 01:25:43.993283 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.625 | |
| I0506 01:25:43.993294 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.75 | |
| I0506 01:25:43.993305 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 01:25:43.993316 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 01:25:43.993327 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 01:25:43.993340 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0.875 | |
| I0506 01:25:43.993350 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 0.875 | |
| I0506 01:25:43.993361 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 0.875 | |
| I0506 01:25:43.993372 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 0.875 | |
| I0506 01:25:43.993394 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:25:43.993407 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:25:43.993418 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:25:43.993429 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:25:43.993440 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:25:43.993453 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:25:43.993463 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.681818 | |
| I0506 01:25:43.993474 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.38983 | |
| I0506 01:25:43.993487 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.97575 (* 1 = 2.97575 loss) | |
| I0506 01:25:43.993500 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.15731 (* 1 = 1.15731 loss) | |
| I0506 01:25:43.993515 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.89693 (* 0.0909091 = 0.263357 loss) | |
| I0506 01:25:43.993527 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.09933 (* 0.0909091 = 0.281757 loss) | |
| I0506 01:25:43.993541 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.56477 (* 0.0909091 = 0.32407 loss) | |
| I0506 01:25:43.993553 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.20304 (* 0.0909091 = 0.291185 loss) | |
| I0506 01:25:43.993566 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.58208 (* 0.0909091 = 0.234735 loss) | |
| I0506 01:25:43.993579 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.35369 (* 0.0909091 = 0.213971 loss) | |
| I0506 01:25:43.993593 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.72875 (* 0.0909091 = 0.157159 loss) | |
| I0506 01:25:43.993607 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.56987 (* 0.0909091 = 0.142715 loss) | |
| I0506 01:25:43.993619 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.516857 (* 0.0909091 = 0.046987 loss) | |
| I0506 01:25:43.993633 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.565294 (* 0.0909091 = 0.0513904 loss) | |
| I0506 01:25:43.993646 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.52204 (* 0.0909091 = 0.0474582 loss) | |
| I0506 01:25:43.993659 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.586505 (* 0.0909091 = 0.0533186 loss) | |
| I0506 01:25:43.993674 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.458763 (* 0.0909091 = 0.0417057 loss) | |
| I0506 01:25:43.993686 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.515217 (* 0.0909091 = 0.0468379 loss) | |
| I0506 01:25:43.993700 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.502176 (* 0.0909091 = 0.0456524 loss) | |
| I0506 01:25:43.993712 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.624175 (* 0.0909091 = 0.0567432 loss) | |
| I0506 01:25:43.993726 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0138602 (* 0.0909091 = 0.00126001 loss) | |
| I0506 01:25:43.993741 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0191622 (* 0.0909091 = 0.00174201 loss) | |
| I0506 01:25:43.993753 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0130351 (* 0.0909091 = 0.00118501 loss) | |
| I0506 01:25:43.993767 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00799869 (* 0.0909091 = 0.000727154 loss) | |
| I0506 01:25:43.993780 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00593357 (* 0.0909091 = 0.000539415 loss) | |
| I0506 01:25:43.993794 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00304111 (* 0.0909091 = 0.000276465 loss) | |
| I0506 01:25:43.993806 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:25:43.993818 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:25:43.993829 15760 solver.cpp:245] Train net output #149: total_confidence = 2.62953e-05 | |
| I0506 01:25:43.993849 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000133172 | |
| I0506 01:25:43.993863 15760 sgd_solver.cpp:106] Iteration 17000, lr = 0.001 | |
| I0506 01:27:31.295233 15760 solver.cpp:229] Iteration 17500, loss = 9.92399 | |
| I0506 01:27:31.295379 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.148936 | |
| I0506 01:27:31.295400 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.125 | |
| I0506 01:27:31.295413 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:27:31.295425 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:27:31.295438 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 01:27:31.295449 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:27:31.295461 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 01:27:31.295474 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 01:27:31.295485 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.625 | |
| I0506 01:27:31.295497 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 01:27:31.295509 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:27:31.295521 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:27:31.295532 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:27:31.295544 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:27:31.295555 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:27:31.295567 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:27:31.295579 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:27:31.295591 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:27:31.295603 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:27:31.295615 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:27:31.295626 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:27:31.295639 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:27:31.295650 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:27:31.295661 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.715909 | |
| I0506 01:27:31.295673 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.234043 | |
| I0506 01:27:31.295691 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.28264 (* 0.3 = 0.984792 loss) | |
| I0506 01:27:31.295704 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.31676 (* 0.3 = 0.395028 loss) | |
| I0506 01:27:31.295727 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.37559 (* 0.0272727 = 0.0920615 loss) | |
| I0506 01:27:31.295742 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.15503 (* 0.0272727 = 0.0860463 loss) | |
| I0506 01:27:31.295755 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.58466 (* 0.0272727 = 0.0977635 loss) | |
| I0506 01:27:31.295769 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.55164 (* 0.0272727 = 0.0968628 loss) | |
| I0506 01:27:31.295783 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.95932 (* 0.0272727 = 0.0807087 loss) | |
| I0506 01:27:31.295797 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.62397 (* 0.0272727 = 0.0715628 loss) | |
| I0506 01:27:31.295810 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.96144 (* 0.0272727 = 0.0534938 loss) | |
| I0506 01:27:31.295825 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.24654 (* 0.0272727 = 0.0339965 loss) | |
| I0506 01:27:31.295837 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.818099 (* 0.0272727 = 0.0223118 loss) | |
| I0506 01:27:31.295852 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.333367 (* 0.0272727 = 0.00909184 loss) | |
| I0506 01:27:31.295866 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.282768 (* 0.0272727 = 0.00771185 loss) | |
| I0506 01:27:31.295883 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.218396 (* 0.0272727 = 0.00595626 loss) | |
| I0506 01:27:31.295898 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.125067 (* 0.0272727 = 0.00341092 loss) | |
| I0506 01:27:31.295933 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.103732 (* 0.0272727 = 0.00282906 loss) | |
| I0506 01:27:31.295948 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0887034 (* 0.0272727 = 0.00241918 loss) | |
| I0506 01:27:31.295961 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0417917 (* 0.0272727 = 0.00113977 loss) | |
| I0506 01:27:31.295975 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0316902 (* 0.0272727 = 0.000864277 loss) | |
| I0506 01:27:31.295989 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0206065 (* 0.0272727 = 0.000561995 loss) | |
| I0506 01:27:31.296003 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0132933 (* 0.0272727 = 0.000362544 loss) | |
| I0506 01:27:31.296017 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0172034 (* 0.0272727 = 0.000469184 loss) | |
| I0506 01:27:31.296030 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.012377 (* 0.0272727 = 0.000337553 loss) | |
| I0506 01:27:31.296044 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0212143 (* 0.0272727 = 0.000578572 loss) | |
| I0506 01:27:31.296056 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.148936 | |
| I0506 01:27:31.296068 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 01:27:31.296080 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 01:27:31.296092 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 01:27:31.296104 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 01:27:31.296115 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.25 | |
| I0506 01:27:31.296128 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 01:27:31.296139 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.625 | |
| I0506 01:27:31.296150 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 01:27:31.296161 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.75 | |
| I0506 01:27:31.296174 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:27:31.296185 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:27:31.296196 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:27:31.296207 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:27:31.296218 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:27:31.296231 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:27:31.296241 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:27:31.296252 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:27:31.296263 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:27:31.296275 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:27:31.296286 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:27:31.296298 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:27:31.296309 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:27:31.296317 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.704545 | |
| I0506 01:27:31.296325 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.276596 | |
| I0506 01:27:31.296335 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.19658 (* 0.3 = 0.958975 loss) | |
| I0506 01:27:31.296352 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.35875 (* 0.3 = 0.407626 loss) | |
| I0506 01:27:31.296366 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.0947 (* 0.0272727 = 0.0844009 loss) | |
| I0506 01:27:31.296380 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.46317 (* 0.0272727 = 0.0944502 loss) | |
| I0506 01:27:31.296416 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.15938 (* 0.0272727 = 0.0861648 loss) | |
| I0506 01:27:31.296430 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.7172 (* 0.0272727 = 0.101378 loss) | |
| I0506 01:27:31.296444 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.12332 (* 0.0272727 = 0.0851815 loss) | |
| I0506 01:27:31.296458 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.7075 (* 0.0272727 = 0.0738408 loss) | |
| I0506 01:27:31.296473 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.8352 (* 0.0272727 = 0.050051 loss) | |
| I0506 01:27:31.296485 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.13909 (* 0.0272727 = 0.0310662 loss) | |
| I0506 01:27:31.296499 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.04301 (* 0.0272727 = 0.0284457 loss) | |
| I0506 01:27:31.296514 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.302543 (* 0.0272727 = 0.00825116 loss) | |
| I0506 01:27:31.296526 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.256308 (* 0.0272727 = 0.00699022 loss) | |
| I0506 01:27:31.296540 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.175673 (* 0.0272727 = 0.00479108 loss) | |
| I0506 01:27:31.296555 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.11875 (* 0.0272727 = 0.00323864 loss) | |
| I0506 01:27:31.296567 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0579148 (* 0.0272727 = 0.00157949 loss) | |
| I0506 01:27:31.296581 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0405887 (* 0.0272727 = 0.00110696 loss) | |
| I0506 01:27:31.296596 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0204879 (* 0.0272727 = 0.000558762 loss) | |
| I0506 01:27:31.296608 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00865761 (* 0.0272727 = 0.000236117 loss) | |
| I0506 01:27:31.296622 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00808234 (* 0.0272727 = 0.000220427 loss) | |
| I0506 01:27:31.296635 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00488437 (* 0.0272727 = 0.00013321 loss) | |
| I0506 01:27:31.296649 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00532533 (* 0.0272727 = 0.000145236 loss) | |
| I0506 01:27:31.296663 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00468999 (* 0.0272727 = 0.000127909 loss) | |
| I0506 01:27:31.296676 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0041603 (* 0.0272727 = 0.000113463 loss) | |
| I0506 01:27:31.296689 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.12766 | |
| I0506 01:27:31.296700 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:27:31.296711 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:27:31.296722 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.375 | |
| I0506 01:27:31.296735 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:27:31.296746 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 01:27:31.296756 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.5 | |
| I0506 01:27:31.296768 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.625 | |
| I0506 01:27:31.296779 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 01:27:31.296790 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 01:27:31.296802 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:27:31.296813 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:27:31.296824 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:27:31.296835 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:27:31.296847 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:27:31.296859 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:27:31.296869 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:27:31.296890 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:27:31.296903 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:27:31.296916 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:27:31.296929 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:27:31.296941 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:27:31.296953 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:27:31.296964 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.732955 | |
| I0506 01:27:31.296977 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.276596 | |
| I0506 01:27:31.296990 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.07685 (* 1 = 3.07685 loss) | |
| I0506 01:27:31.297003 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.03974 (* 1 = 1.03974 loss) | |
| I0506 01:27:31.297018 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.80935 (* 0.0909091 = 0.255395 loss) | |
| I0506 01:27:31.297030 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.02275 (* 0.0909091 = 0.274795 loss) | |
| I0506 01:27:31.297044 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.80305 (* 0.0909091 = 0.254823 loss) | |
| I0506 01:27:31.297057 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.21661 (* 0.0909091 = 0.292419 loss) | |
| I0506 01:27:31.297071 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.66864 (* 0.0909091 = 0.242603 loss) | |
| I0506 01:27:31.297085 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.21453 (* 0.0909091 = 0.201321 loss) | |
| I0506 01:27:31.297097 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.63165 (* 0.0909091 = 0.148332 loss) | |
| I0506 01:27:31.297111 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.979778 (* 0.0909091 = 0.0890708 loss) | |
| I0506 01:27:31.297143 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 1.17348 (* 0.0909091 = 0.10668 loss) | |
| I0506 01:27:31.297159 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.16045 (* 0.0909091 = 0.0145864 loss) | |
| I0506 01:27:31.297173 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.183888 (* 0.0909091 = 0.0167171 loss) | |
| I0506 01:27:31.297186 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0860582 (* 0.0909091 = 0.00782348 loss) | |
| I0506 01:27:31.297200 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0438154 (* 0.0909091 = 0.00398322 loss) | |
| I0506 01:27:31.297214 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0478049 (* 0.0909091 = 0.0043459 loss) | |
| I0506 01:27:31.297227 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0165837 (* 0.0909091 = 0.00150761 loss) | |
| I0506 01:27:31.297241 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00926764 (* 0.0909091 = 0.000842513 loss) | |
| I0506 01:27:31.297255 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00228216 (* 0.0909091 = 0.000207469 loss) | |
| I0506 01:27:31.297269 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00182974 (* 0.0909091 = 0.00016634 loss) | |
| I0506 01:27:31.297283 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00107091 (* 0.0909091 = 9.73553e-05 loss) | |
| I0506 01:27:31.297297 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000705886 (* 0.0909091 = 6.41715e-05 loss) | |
| I0506 01:27:31.297312 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000643947 (* 0.0909091 = 5.85407e-05 loss) | |
| I0506 01:27:31.297324 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00041101 (* 0.0909091 = 3.73646e-05 loss) | |
| I0506 01:27:31.297336 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:27:31.297348 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:27:31.297358 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000256744 | |
| I0506 01:27:31.297381 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000820921 | |
| I0506 01:27:31.297395 15760 sgd_solver.cpp:106] Iteration 17500, lr = 0.001 | |
| I0506 01:29:01.606556 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 34.5051 > 30) by scale factor 0.869437 | |
| I0506 01:29:18.691413 15760 solver.cpp:229] Iteration 18000, loss = 9.84151 | |
| I0506 01:29:18.691473 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.102564 | |
| I0506 01:29:18.691489 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.25 | |
| I0506 01:29:18.691503 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:29:18.691514 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:29:18.691526 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 01:29:18.691539 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.75 | |
| I0506 01:29:18.691550 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.625 | |
| I0506 01:29:18.691563 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.75 | |
| I0506 01:29:18.691576 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 1 | |
| I0506 01:29:18.691587 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 01:29:18.691598 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:29:18.691611 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:29:18.691622 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:29:18.691633 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:29:18.691645 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:29:18.691658 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:29:18.691669 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:29:18.691681 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:29:18.691694 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:29:18.691705 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:29:18.691716 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:29:18.691728 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:29:18.691740 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:29:18.691751 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.795455 | |
| I0506 01:29:18.691763 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.333333 | |
| I0506 01:29:18.691779 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 2.904 (* 0.3 = 0.871201 loss) | |
| I0506 01:29:18.691794 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.745958 (* 0.3 = 0.223787 loss) | |
| I0506 01:29:18.691808 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 2.72143 (* 0.0272727 = 0.0742207 loss) | |
| I0506 01:29:18.691823 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.06869 (* 0.0272727 = 0.0836914 loss) | |
| I0506 01:29:18.691839 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.29165 (* 0.0272727 = 0.0897723 loss) | |
| I0506 01:29:18.691854 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.32526 (* 0.0272727 = 0.0906888 loss) | |
| I0506 01:29:18.691867 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 1.66562 (* 0.0272727 = 0.0454259 loss) | |
| I0506 01:29:18.691881 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 1.49849 (* 0.0272727 = 0.040868 loss) | |
| I0506 01:29:18.691895 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.2468 (* 0.0272727 = 0.0340037 loss) | |
| I0506 01:29:18.691910 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.157636 (* 0.0272727 = 0.00429915 loss) | |
| I0506 01:29:18.691923 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.0265715 (* 0.0272727 = 0.000724678 loss) | |
| I0506 01:29:18.691937 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0322698 (* 0.0272727 = 0.000880085 loss) | |
| I0506 01:29:18.691951 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0143936 (* 0.0272727 = 0.000392553 loss) | |
| I0506 01:29:18.691967 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0142723 (* 0.0272727 = 0.000389244 loss) | |
| I0506 01:29:18.692013 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0112115 (* 0.0272727 = 0.000305769 loss) | |
| I0506 01:29:18.692029 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.00858928 (* 0.0272727 = 0.000234253 loss) | |
| I0506 01:29:18.692044 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.00661772 (* 0.0272727 = 0.000180483 loss) | |
| I0506 01:29:18.692057 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.00611934 (* 0.0272727 = 0.000166891 loss) | |
| I0506 01:29:18.692071 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00367219 (* 0.0272727 = 0.000100151 loss) | |
| I0506 01:29:18.692085 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00307575 (* 0.0272727 = 8.38842e-05 loss) | |
| I0506 01:29:18.692100 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00503934 (* 0.0272727 = 0.000137436 loss) | |
| I0506 01:29:18.692113 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00204089 (* 0.0272727 = 5.56606e-05 loss) | |
| I0506 01:29:18.692126 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00224598 (* 0.0272727 = 6.12541e-05 loss) | |
| I0506 01:29:18.692140 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00179553 (* 0.0272727 = 4.89691e-05 loss) | |
| I0506 01:29:18.692153 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.102564 | |
| I0506 01:29:18.692165 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.25 | |
| I0506 01:29:18.692178 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:29:18.692189 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 01:29:18.692200 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:29:18.692212 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.625 | |
| I0506 01:29:18.692224 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.625 | |
| I0506 01:29:18.692236 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 01:29:18.692247 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 1 | |
| I0506 01:29:18.692260 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 01:29:18.692270 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:29:18.692286 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:29:18.692298 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:29:18.692309 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:29:18.692322 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:29:18.692332 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:29:18.692344 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:29:18.692355 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:29:18.692366 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:29:18.692378 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:29:18.692389 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:29:18.692401 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:29:18.692412 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:29:18.692425 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.778409 | |
| I0506 01:29:18.692435 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.230769 | |
| I0506 01:29:18.692450 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 2.88019 (* 0.3 = 0.864056 loss) | |
| I0506 01:29:18.692463 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.757471 (* 0.3 = 0.227241 loss) | |
| I0506 01:29:18.692477 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 2.61931 (* 0.0272727 = 0.0714358 loss) | |
| I0506 01:29:18.692492 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.13169 (* 0.0272727 = 0.0854099 loss) | |
| I0506 01:29:18.692517 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 2.8764 (* 0.0272727 = 0.0784472 loss) | |
| I0506 01:29:18.692530 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.15442 (* 0.0272727 = 0.0860296 loss) | |
| I0506 01:29:18.692544 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 1.52634 (* 0.0272727 = 0.0416275 loss) | |
| I0506 01:29:18.692559 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 1.36576 (* 0.0272727 = 0.0372481 loss) | |
| I0506 01:29:18.692571 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.14483 (* 0.0272727 = 0.0312225 loss) | |
| I0506 01:29:18.692585 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.0348468 (* 0.0272727 = 0.000950367 loss) | |
| I0506 01:29:18.692600 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.00318891 (* 0.0272727 = 8.69704e-05 loss) | |
| I0506 01:29:18.692613 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0020861 (* 0.0272727 = 5.68935e-05 loss) | |
| I0506 01:29:18.692627 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.000919052 (* 0.0272727 = 2.50651e-05 loss) | |
| I0506 01:29:18.692641 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.000775888 (* 0.0272727 = 2.11606e-05 loss) | |
| I0506 01:29:18.692654 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.00062808 (* 0.0272727 = 1.71295e-05 loss) | |
| I0506 01:29:18.692668 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.000446156 (* 0.0272727 = 1.21679e-05 loss) | |
| I0506 01:29:18.692682 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.000765015 (* 0.0272727 = 2.08641e-05 loss) | |
| I0506 01:29:18.692695 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.000873215 (* 0.0272727 = 2.38149e-05 loss) | |
| I0506 01:29:18.692709 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.000243824 (* 0.0272727 = 6.64976e-06 loss) | |
| I0506 01:29:18.692723 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.000120272 (* 0.0272727 = 3.28014e-06 loss) | |
| I0506 01:29:18.692737 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.000203077 (* 0.0272727 = 5.53847e-06 loss) | |
| I0506 01:29:18.692751 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.000128699 (* 0.0272727 = 3.50997e-06 loss) | |
| I0506 01:29:18.692765 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.000362223 (* 0.0272727 = 9.8788e-06 loss) | |
| I0506 01:29:18.692778 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 5.3572e-05 (* 0.0272727 = 1.46105e-06 loss) | |
| I0506 01:29:18.692790 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.128205 | |
| I0506 01:29:18.692802 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:29:18.692814 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.125 | |
| I0506 01:29:18.692826 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.25 | |
| I0506 01:29:18.692837 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:29:18.692848 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.75 | |
| I0506 01:29:18.692859 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.625 | |
| I0506 01:29:18.692870 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 01:29:18.692883 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 1 | |
| I0506 01:29:18.692898 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 01:29:18.692909 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:29:18.692920 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:29:18.692931 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:29:18.692942 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:29:18.692953 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:29:18.692965 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:29:18.692986 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:29:18.692998 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:29:18.693009 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:29:18.693020 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:29:18.693032 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:29:18.693043 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:29:18.693054 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:29:18.693065 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.795455 | |
| I0506 01:29:18.693078 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.410256 | |
| I0506 01:29:18.693091 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.78117 (* 1 = 2.78117 loss) | |
| I0506 01:29:18.693101 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.693835 (* 1 = 0.693835 loss) | |
| I0506 01:29:18.693115 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.56413 (* 0.0909091 = 0.233103 loss) | |
| I0506 01:29:18.693145 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.61322 (* 0.0909091 = 0.237565 loss) | |
| I0506 01:29:18.693158 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.571 (* 0.0909091 = 0.233727 loss) | |
| I0506 01:29:18.693172 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.95808 (* 0.0909091 = 0.268917 loss) | |
| I0506 01:29:18.693186 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 1.07135 (* 0.0909091 = 0.0973955 loss) | |
| I0506 01:29:18.693198 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.37143 (* 0.0909091 = 0.124675 loss) | |
| I0506 01:29:18.693212 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.03956 (* 0.0909091 = 0.0945052 loss) | |
| I0506 01:29:18.693227 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.0834733 (* 0.0909091 = 0.00758848 loss) | |
| I0506 01:29:18.693239 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.00435222 (* 0.0909091 = 0.000395657 loss) | |
| I0506 01:29:18.693253 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.00304929 (* 0.0909091 = 0.000277209 loss) | |
| I0506 01:29:18.693266 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.00237025 (* 0.0909091 = 0.000215477 loss) | |
| I0506 01:29:18.693279 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.00196659 (* 0.0909091 = 0.000178781 loss) | |
| I0506 01:29:18.693294 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00142652 (* 0.0909091 = 0.000129683 loss) | |
| I0506 01:29:18.693306 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0015665 (* 0.0909091 = 0.000142409 loss) | |
| I0506 01:29:18.693320 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.000896199 (* 0.0909091 = 8.14727e-05 loss) | |
| I0506 01:29:18.693337 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00101171 (* 0.0909091 = 9.19733e-05 loss) | |
| I0506 01:29:18.693351 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.000667271 (* 0.0909091 = 6.0661e-05 loss) | |
| I0506 01:29:18.693366 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.000944585 (* 0.0909091 = 8.58713e-05 loss) | |
| I0506 01:29:18.693379 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.000604601 (* 0.0909091 = 5.49638e-05 loss) | |
| I0506 01:29:18.693392 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000727181 (* 0.0909091 = 6.61074e-05 loss) | |
| I0506 01:29:18.693406 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000259394 (* 0.0909091 = 2.35813e-05 loss) | |
| I0506 01:29:18.693419 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000264789 (* 0.0909091 = 2.40717e-05 loss) | |
| I0506 01:29:18.693431 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:29:18.693454 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:29:18.693472 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000242945 | |
| I0506 01:29:18.693496 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000672208 | |
| I0506 01:29:18.693514 15760 sgd_solver.cpp:106] Iteration 18000, lr = 0.001 | |
| I0506 01:30:07.061231 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.7386 > 30) by scale factor 0.975973 | |
| I0506 01:31:05.965481 15760 solver.cpp:229] Iteration 18500, loss = 9.96795 | |
| I0506 01:31:05.965617 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0943396 | |
| I0506 01:31:05.965638 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 01:31:05.965651 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:31:05.965663 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:31:05.965675 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 01:31:05.965687 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.25 | |
| I0506 01:31:05.965699 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.25 | |
| I0506 01:31:05.965710 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.625 | |
| I0506 01:31:05.965723 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 01:31:05.965734 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 01:31:05.965746 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 01:31:05.965759 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 01:31:05.965770 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 01:31:05.965782 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:31:05.965793 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:31:05.965806 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:31:05.965816 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:31:05.965828 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:31:05.965840 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:31:05.965852 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:31:05.965862 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:31:05.965878 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:31:05.965889 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:31:05.965910 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.727273 | |
| I0506 01:31:05.965936 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.207547 | |
| I0506 01:31:05.965955 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.07283 (* 0.3 = 0.921848 loss) | |
| I0506 01:31:05.965968 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.968718 (* 0.3 = 0.290615 loss) | |
| I0506 01:31:05.965982 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.09016 (* 0.0272727 = 0.0842771 loss) | |
| I0506 01:31:05.965996 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.06972 (* 0.0272727 = 0.0837197 loss) | |
| I0506 01:31:05.966009 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.21976 (* 0.0272727 = 0.0878115 loss) | |
| I0506 01:31:05.966023 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 2.86161 (* 0.0272727 = 0.0780438 loss) | |
| I0506 01:31:05.966037 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.646 (* 0.0272727 = 0.0721637 loss) | |
| I0506 01:31:05.966050 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.73375 (* 0.0272727 = 0.0745567 loss) | |
| I0506 01:31:05.966064 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.15323 (* 0.0272727 = 0.0587244 loss) | |
| I0506 01:31:05.966078 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.770748 (* 0.0272727 = 0.0210204 loss) | |
| I0506 01:31:05.966091 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.667598 (* 0.0272727 = 0.0182072 loss) | |
| I0506 01:31:05.966105 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.567386 (* 0.0272727 = 0.0154742 loss) | |
| I0506 01:31:05.966119 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.638171 (* 0.0272727 = 0.0174047 loss) | |
| I0506 01:31:05.966132 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.518874 (* 0.0272727 = 0.0141511 loss) | |
| I0506 01:31:05.966146 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.20874 (* 0.0272727 = 0.00569292 loss) | |
| I0506 01:31:05.966181 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.145455 (* 0.0272727 = 0.00396696 loss) | |
| I0506 01:31:05.966195 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.132394 (* 0.0272727 = 0.00361075 loss) | |
| I0506 01:31:05.966210 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0941058 (* 0.0272727 = 0.00256652 loss) | |
| I0506 01:31:05.966224 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0479438 (* 0.0272727 = 0.00130756 loss) | |
| I0506 01:31:05.966238 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0213675 (* 0.0272727 = 0.000582749 loss) | |
| I0506 01:31:05.966253 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0170765 (* 0.0272727 = 0.000465723 loss) | |
| I0506 01:31:05.966265 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0143635 (* 0.0272727 = 0.000391733 loss) | |
| I0506 01:31:05.966279 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0118474 (* 0.0272727 = 0.00032311 loss) | |
| I0506 01:31:05.966294 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0138069 (* 0.0272727 = 0.000376552 loss) | |
| I0506 01:31:05.966305 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0943396 | |
| I0506 01:31:05.966317 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 01:31:05.966330 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 01:31:05.966341 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:31:05.966351 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:31:05.966363 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 01:31:05.966374 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.125 | |
| I0506 01:31:05.966387 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 01:31:05.966398 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 01:31:05.966409 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 01:31:05.966421 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 01:31:05.966429 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 01:31:05.966437 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 01:31:05.966450 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:31:05.966471 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:31:05.966485 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:31:05.966505 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:31:05.966517 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:31:05.966528 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:31:05.966541 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:31:05.966552 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:31:05.966562 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:31:05.966574 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:31:05.966585 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.721591 | |
| I0506 01:31:05.966598 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.226415 | |
| I0506 01:31:05.966611 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.11192 (* 0.3 = 0.933577 loss) | |
| I0506 01:31:05.966629 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.04694 (* 0.3 = 0.314083 loss) | |
| I0506 01:31:05.966645 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.0313 (* 0.0272727 = 0.0826719 loss) | |
| I0506 01:31:05.966658 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 2.95098 (* 0.0272727 = 0.0804814 loss) | |
| I0506 01:31:05.966684 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.30574 (* 0.0272727 = 0.0901565 loss) | |
| I0506 01:31:05.966699 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.07955 (* 0.0272727 = 0.0839877 loss) | |
| I0506 01:31:05.966713 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.07402 (* 0.0272727 = 0.0838369 loss) | |
| I0506 01:31:05.966727 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.87024 (* 0.0272727 = 0.0782793 loss) | |
| I0506 01:31:05.966740 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.3266 (* 0.0272727 = 0.0634528 loss) | |
| I0506 01:31:05.966753 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.696118 (* 0.0272727 = 0.018985 loss) | |
| I0506 01:31:05.966768 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.577623 (* 0.0272727 = 0.0157533 loss) | |
| I0506 01:31:05.966781 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.690363 (* 0.0272727 = 0.0188281 loss) | |
| I0506 01:31:05.966795 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.512631 (* 0.0272727 = 0.0139808 loss) | |
| I0506 01:31:05.966809 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.451186 (* 0.0272727 = 0.0123051 loss) | |
| I0506 01:31:05.966822 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.207339 (* 0.0272727 = 0.00565469 loss) | |
| I0506 01:31:05.966837 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.126093 (* 0.0272727 = 0.0034389 loss) | |
| I0506 01:31:05.966850 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0598614 (* 0.0272727 = 0.00163258 loss) | |
| I0506 01:31:05.966864 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0788703 (* 0.0272727 = 0.00215101 loss) | |
| I0506 01:31:05.966878 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0350391 (* 0.0272727 = 0.000955613 loss) | |
| I0506 01:31:05.966892 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0125406 (* 0.0272727 = 0.000342017 loss) | |
| I0506 01:31:05.966905 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0151844 (* 0.0272727 = 0.000414119 loss) | |
| I0506 01:31:05.966919 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0235293 (* 0.0272727 = 0.000641709 loss) | |
| I0506 01:31:05.966935 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0168701 (* 0.0272727 = 0.000460093 loss) | |
| I0506 01:31:05.966950 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0114119 (* 0.0272727 = 0.000311235 loss) | |
| I0506 01:31:05.966963 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0377358 | |
| I0506 01:31:05.966974 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 01:31:05.966985 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:31:05.966996 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:31:05.967008 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 01:31:05.967020 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 01:31:05.967031 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 01:31:05.967042 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.625 | |
| I0506 01:31:05.967053 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 01:31:05.967066 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 01:31:05.967077 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 01:31:05.967087 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 01:31:05.967099 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 01:31:05.967110 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:31:05.967123 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:31:05.967133 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:31:05.967144 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:31:05.967164 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:31:05.967177 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:31:05.967190 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:31:05.967200 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:31:05.967211 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:31:05.967222 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:31:05.967233 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.698864 | |
| I0506 01:31:05.967245 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.207547 | |
| I0506 01:31:05.967258 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.92021 (* 1 = 2.92021 loss) | |
| I0506 01:31:05.967272 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.998333 (* 1 = 0.998333 loss) | |
| I0506 01:31:05.967286 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.7211 (* 0.0909091 = 0.247373 loss) | |
| I0506 01:31:05.967300 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.87189 (* 0.0909091 = 0.261081 loss) | |
| I0506 01:31:05.967314 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.77382 (* 0.0909091 = 0.252165 loss) | |
| I0506 01:31:05.967326 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.77372 (* 0.0909091 = 0.252156 loss) | |
| I0506 01:31:05.967340 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.50811 (* 0.0909091 = 0.22801 loss) | |
| I0506 01:31:05.967353 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.60796 (* 0.0909091 = 0.237087 loss) | |
| I0506 01:31:05.967366 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.05242 (* 0.0909091 = 0.186583 loss) | |
| I0506 01:31:05.967381 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.732637 (* 0.0909091 = 0.0666034 loss) | |
| I0506 01:31:05.967394 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.526012 (* 0.0909091 = 0.0478193 loss) | |
| I0506 01:31:05.967407 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.510588 (* 0.0909091 = 0.0464171 loss) | |
| I0506 01:31:05.967420 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.525153 (* 0.0909091 = 0.0477412 loss) | |
| I0506 01:31:05.967434 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.48706 (* 0.0909091 = 0.0442782 loss) | |
| I0506 01:31:05.967447 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0296824 (* 0.0909091 = 0.0026984 loss) | |
| I0506 01:31:05.967461 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0197288 (* 0.0909091 = 0.00179353 loss) | |
| I0506 01:31:05.967475 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0160996 (* 0.0909091 = 0.0014636 loss) | |
| I0506 01:31:05.967489 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0106057 (* 0.0909091 = 0.000964159 loss) | |
| I0506 01:31:05.967504 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00620007 (* 0.0909091 = 0.000563643 loss) | |
| I0506 01:31:05.967517 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00350373 (* 0.0909091 = 0.000318521 loss) | |
| I0506 01:31:05.967530 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00299023 (* 0.0909091 = 0.00027184 loss) | |
| I0506 01:31:05.967545 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00188039 (* 0.0909091 = 0.000170944 loss) | |
| I0506 01:31:05.967557 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00119178 (* 0.0909091 = 0.000108344 loss) | |
| I0506 01:31:05.967571 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000466759 (* 0.0909091 = 4.24326e-05 loss) | |
| I0506 01:31:05.967583 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:31:05.967594 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:31:05.967605 15760 solver.cpp:245] Train net output #149: total_confidence = 9.8025e-05 | |
| I0506 01:31:05.967625 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000730898 | |
| I0506 01:31:05.967639 15760 sgd_solver.cpp:106] Iteration 18500, lr = 0.001 | |
| I0506 01:31:26.194779 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 46.9968 > 30) by scale factor 0.638341 | |
| I0506 01:31:55.444499 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 44.7393 > 30) by scale factor 0.670551 | |
| I0506 01:32:53.348167 15760 solver.cpp:229] Iteration 19000, loss = 10.0253 | |
| I0506 01:32:53.348289 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.03125 | |
| I0506 01:32:53.348309 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.25 | |
| I0506 01:32:53.348322 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 01:32:53.348335 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:32:53.348346 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.25 | |
| I0506 01:32:53.348358 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.25 | |
| I0506 01:32:53.348369 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.25 | |
| I0506 01:32:53.348381 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.5 | |
| I0506 01:32:53.348393 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 01:32:53.348405 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.75 | |
| I0506 01:32:53.348417 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.75 | |
| I0506 01:32:53.348428 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 01:32:53.348440 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 01:32:53.348453 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0.875 | |
| I0506 01:32:53.348464 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 0.875 | |
| I0506 01:32:53.348476 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 0.875 | |
| I0506 01:32:53.348489 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 0.875 | |
| I0506 01:32:53.348500 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 0.875 | |
| I0506 01:32:53.348512 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 0.875 | |
| I0506 01:32:53.348525 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 0.875 | |
| I0506 01:32:53.348536 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 0.875 | |
| I0506 01:32:53.348548 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 0.875 | |
| I0506 01:32:53.348559 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:32:53.348572 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.647727 | |
| I0506 01:32:53.348583 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.28125 | |
| I0506 01:32:53.348608 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.27787 (* 0.3 = 0.98336 loss) | |
| I0506 01:32:53.348623 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.25076 (* 0.3 = 0.375228 loss) | |
| I0506 01:32:53.348637 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.3567 (* 0.0272727 = 0.0915463 loss) | |
| I0506 01:32:53.348650 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.58198 (* 0.0272727 = 0.0976903 loss) | |
| I0506 01:32:53.348664 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.13647 (* 0.0272727 = 0.08554 loss) | |
| I0506 01:32:53.348678 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 2.81663 (* 0.0272727 = 0.0768173 loss) | |
| I0506 01:32:53.348692 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.76021 (* 0.0272727 = 0.0752784 loss) | |
| I0506 01:32:53.348706 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.06974 (* 0.0272727 = 0.0837202 loss) | |
| I0506 01:32:53.348719 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.95295 (* 0.0272727 = 0.0532623 loss) | |
| I0506 01:32:53.348733 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.8757 (* 0.0272727 = 0.0238827 loss) | |
| I0506 01:32:53.348747 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 1.01478 (* 0.0272727 = 0.0276758 loss) | |
| I0506 01:32:53.348760 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 1.2079 (* 0.0272727 = 0.0329428 loss) | |
| I0506 01:32:53.348774 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.587191 (* 0.0272727 = 0.0160143 loss) | |
| I0506 01:32:53.348788 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.502086 (* 0.0272727 = 0.0136932 loss) | |
| I0506 01:32:53.348820 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.495775 (* 0.0272727 = 0.0135211 loss) | |
| I0506 01:32:53.348835 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.480941 (* 0.0272727 = 0.0131166 loss) | |
| I0506 01:32:53.348850 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.492761 (* 0.0272727 = 0.0134389 loss) | |
| I0506 01:32:53.348863 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.696535 (* 0.0272727 = 0.0189964 loss) | |
| I0506 01:32:53.348876 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.685813 (* 0.0272727 = 0.018704 loss) | |
| I0506 01:32:53.348891 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.769912 (* 0.0272727 = 0.0209976 loss) | |
| I0506 01:32:53.348904 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.78856 (* 0.0272727 = 0.0215062 loss) | |
| I0506 01:32:53.348917 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.836307 (* 0.0272727 = 0.0228084 loss) | |
| I0506 01:32:53.348932 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.80239 (* 0.0272727 = 0.0218834 loss) | |
| I0506 01:32:53.348947 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0181417 (* 0.0272727 = 0.000494774 loss) | |
| I0506 01:32:53.348961 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.046875 | |
| I0506 01:32:53.348974 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 01:32:53.348986 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 01:32:53.349001 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 01:32:53.349022 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:32:53.349036 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.25 | |
| I0506 01:32:53.349047 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.25 | |
| I0506 01:32:53.349059 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.5 | |
| I0506 01:32:53.349071 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 01:32:53.349083 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.75 | |
| I0506 01:32:53.349095 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.75 | |
| I0506 01:32:53.349103 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 01:32:53.349112 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 01:32:53.349138 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0.875 | |
| I0506 01:32:53.349153 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 0.875 | |
| I0506 01:32:53.349164 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 0.875 | |
| I0506 01:32:53.349182 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 0.875 | |
| I0506 01:32:53.349195 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 0.875 | |
| I0506 01:32:53.349207 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 0.875 | |
| I0506 01:32:53.349220 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 0.875 | |
| I0506 01:32:53.349231 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 0.875 | |
| I0506 01:32:53.349251 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 0.875 | |
| I0506 01:32:53.349266 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:32:53.349277 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.653409 | |
| I0506 01:32:53.349289 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.265625 | |
| I0506 01:32:53.349303 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.23589 (* 0.3 = 0.970767 loss) | |
| I0506 01:32:53.349318 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.20758 (* 0.3 = 0.362273 loss) | |
| I0506 01:32:53.349335 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.29988 (* 0.0272727 = 0.0899967 loss) | |
| I0506 01:32:53.349350 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 2.87484 (* 0.0272727 = 0.0784047 loss) | |
| I0506 01:32:53.349377 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 2.98521 (* 0.0272727 = 0.0814148 loss) | |
| I0506 01:32:53.349392 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.18563 (* 0.0272727 = 0.0868807 loss) | |
| I0506 01:32:53.349406 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.87786 (* 0.0272727 = 0.0784872 loss) | |
| I0506 01:32:53.349419 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.20318 (* 0.0272727 = 0.0873595 loss) | |
| I0506 01:32:53.349433 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.04121 (* 0.0272727 = 0.0556694 loss) | |
| I0506 01:32:53.349447 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.883892 (* 0.0272727 = 0.0241062 loss) | |
| I0506 01:32:53.349460 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 1.21122 (* 0.0272727 = 0.0330333 loss) | |
| I0506 01:32:53.349473 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 1.31744 (* 0.0272727 = 0.0359303 loss) | |
| I0506 01:32:53.349488 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.69748 (* 0.0272727 = 0.0190222 loss) | |
| I0506 01:32:53.349500 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.61417 (* 0.0272727 = 0.0167501 loss) | |
| I0506 01:32:53.349514 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.677156 (* 0.0272727 = 0.0184679 loss) | |
| I0506 01:32:53.349527 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.529993 (* 0.0272727 = 0.0144543 loss) | |
| I0506 01:32:53.349541 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.746355 (* 0.0272727 = 0.0203551 loss) | |
| I0506 01:32:53.349555 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.764952 (* 0.0272727 = 0.0208623 loss) | |
| I0506 01:32:53.349568 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.928908 (* 0.0272727 = 0.0253338 loss) | |
| I0506 01:32:53.349581 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.999314 (* 0.0272727 = 0.027254 loss) | |
| I0506 01:32:53.349594 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 1.0122 (* 0.0272727 = 0.0276056 loss) | |
| I0506 01:32:53.349608 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 1.15832 (* 0.0272727 = 0.0315906 loss) | |
| I0506 01:32:53.349622 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 1.1474 (* 0.0272727 = 0.0312928 loss) | |
| I0506 01:32:53.349635 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00194434 (* 0.0272727 = 5.30275e-05 loss) | |
| I0506 01:32:53.349647 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.078125 | |
| I0506 01:32:53.349658 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.25 | |
| I0506 01:32:53.349670 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.25 | |
| I0506 01:32:53.349681 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 01:32:53.349692 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:32:53.349704 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 01:32:53.349715 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 01:32:53.349726 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.625 | |
| I0506 01:32:53.349746 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 01:32:53.349766 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.75 | |
| I0506 01:32:53.349781 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.75 | |
| I0506 01:32:53.349792 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 01:32:53.349803 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 01:32:53.349815 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0.875 | |
| I0506 01:32:53.349827 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 0.875 | |
| I0506 01:32:53.349838 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 0.875 | |
| I0506 01:32:53.349849 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 0.875 | |
| I0506 01:32:53.349871 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 0.875 | |
| I0506 01:32:53.349884 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 0.875 | |
| I0506 01:32:53.349896 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 0.875 | |
| I0506 01:32:53.349907 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 0.875 | |
| I0506 01:32:53.349920 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 0.875 | |
| I0506 01:32:53.349931 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:32:53.349941 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.664773 | |
| I0506 01:32:53.349953 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.265625 | |
| I0506 01:32:53.349967 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.09378 (* 1 = 3.09378 loss) | |
| I0506 01:32:53.349980 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.14793 (* 1 = 1.14793 loss) | |
| I0506 01:32:53.349993 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.90204 (* 0.0909091 = 0.263822 loss) | |
| I0506 01:32:53.350010 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.77895 (* 0.0909091 = 0.252632 loss) | |
| I0506 01:32:53.350024 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.64331 (* 0.0909091 = 0.240301 loss) | |
| I0506 01:32:53.350039 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.65081 (* 0.0909091 = 0.240982 loss) | |
| I0506 01:32:53.350051 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.71967 (* 0.0909091 = 0.247243 loss) | |
| I0506 01:32:53.350064 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.87335 (* 0.0909091 = 0.261214 loss) | |
| I0506 01:32:53.350078 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.8812 (* 0.0909091 = 0.171018 loss) | |
| I0506 01:32:53.350091 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.657989 (* 0.0909091 = 0.0598171 loss) | |
| I0506 01:32:53.350105 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 1.06833 (* 0.0909091 = 0.0971209 loss) | |
| I0506 01:32:53.350118 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 1.14041 (* 0.0909091 = 0.103674 loss) | |
| I0506 01:32:53.350131 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.552255 (* 0.0909091 = 0.050205 loss) | |
| I0506 01:32:53.350145 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.46742 (* 0.0909091 = 0.0424927 loss) | |
| I0506 01:32:53.350158 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.453183 (* 0.0909091 = 0.0411985 loss) | |
| I0506 01:32:53.350172 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.418611 (* 0.0909091 = 0.0380556 loss) | |
| I0506 01:32:53.350186 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.610126 (* 0.0909091 = 0.055466 loss) | |
| I0506 01:32:53.350199 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.535275 (* 0.0909091 = 0.0486614 loss) | |
| I0506 01:32:53.350214 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.447987 (* 0.0909091 = 0.0407261 loss) | |
| I0506 01:32:53.350227 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.645515 (* 0.0909091 = 0.0586832 loss) | |
| I0506 01:32:53.350240 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.723813 (* 0.0909091 = 0.0658012 loss) | |
| I0506 01:32:53.350255 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.739833 (* 0.0909091 = 0.0672575 loss) | |
| I0506 01:32:53.350267 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.903198 (* 0.0909091 = 0.0821089 loss) | |
| I0506 01:32:53.350281 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00332593 (* 0.0909091 = 0.000302357 loss) | |
| I0506 01:32:53.350294 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:32:53.350306 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:32:53.350317 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000143586 | |
| I0506 01:32:53.350337 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000505558 | |
| I0506 01:32:53.350353 15760 sgd_solver.cpp:106] Iteration 19000, lr = 0.001 | |
| I0506 01:34:40.817276 15760 solver.cpp:229] Iteration 19500, loss = 9.90241 | |
| I0506 01:34:40.817433 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0816327 | |
| I0506 01:34:40.817453 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 01:34:40.817466 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:34:40.817479 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 01:34:40.817490 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 01:34:40.817502 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0506 01:34:40.817514 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.125 | |
| I0506 01:34:40.817525 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.625 | |
| I0506 01:34:40.817538 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 1 | |
| I0506 01:34:40.817549 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 01:34:40.817561 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:34:40.817572 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:34:40.817584 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:34:40.817595 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:34:40.817606 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:34:40.817618 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:34:40.817630 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:34:40.817641 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:34:40.817652 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:34:40.817663 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:34:40.817675 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:34:40.817687 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:34:40.817698 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:34:40.817708 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.732955 | |
| I0506 01:34:40.817720 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.204082 | |
| I0506 01:34:40.817736 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.11162 (* 0.3 = 0.933486 loss) | |
| I0506 01:34:40.817750 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.981451 (* 0.3 = 0.294435 loss) | |
| I0506 01:34:40.817764 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.21496 (* 0.0272727 = 0.0876808 loss) | |
| I0506 01:34:40.817778 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.01258 (* 0.0272727 = 0.0821612 loss) | |
| I0506 01:34:40.817791 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.11137 (* 0.0272727 = 0.0848554 loss) | |
| I0506 01:34:40.817806 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.55032 (* 0.0272727 = 0.0968269 loss) | |
| I0506 01:34:40.817819 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.38588 (* 0.0272727 = 0.0923421 loss) | |
| I0506 01:34:40.817832 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.44171 (* 0.0272727 = 0.0938648 loss) | |
| I0506 01:34:40.817847 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.92601 (* 0.0272727 = 0.0525274 loss) | |
| I0506 01:34:40.817860 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.120298 (* 0.0272727 = 0.00328087 loss) | |
| I0506 01:34:40.817876 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.0415266 (* 0.0272727 = 0.00113254 loss) | |
| I0506 01:34:40.817891 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0244901 (* 0.0272727 = 0.000667911 loss) | |
| I0506 01:34:40.817906 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0384318 (* 0.0272727 = 0.00104814 loss) | |
| I0506 01:34:40.817920 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0171975 (* 0.0272727 = 0.000469023 loss) | |
| I0506 01:34:40.817934 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0179786 (* 0.0272727 = 0.000490326 loss) | |
| I0506 01:34:40.817961 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0197122 (* 0.0272727 = 0.000537605 loss) | |
| I0506 01:34:40.817976 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0183153 (* 0.0272727 = 0.000499507 loss) | |
| I0506 01:34:40.817991 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0252694 (* 0.0272727 = 0.000689165 loss) | |
| I0506 01:34:40.818004 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0257167 (* 0.0272727 = 0.000701364 loss) | |
| I0506 01:34:40.818034 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.020058 (* 0.0272727 = 0.000547035 loss) | |
| I0506 01:34:40.818053 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0134699 (* 0.0272727 = 0.00036736 loss) | |
| I0506 01:34:40.818066 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0249889 (* 0.0272727 = 0.000681516 loss) | |
| I0506 01:34:40.818080 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0113371 (* 0.0272727 = 0.000309194 loss) | |
| I0506 01:34:40.818094 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0139992 (* 0.0272727 = 0.000381796 loss) | |
| I0506 01:34:40.818106 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0612245 | |
| I0506 01:34:40.818117 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 01:34:40.818130 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:34:40.818141 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:34:40.818152 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:34:40.818164 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 01:34:40.818176 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.125 | |
| I0506 01:34:40.818186 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.625 | |
| I0506 01:34:40.818198 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 1 | |
| I0506 01:34:40.818209 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 01:34:40.818220 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:34:40.818233 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:34:40.818243 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:34:40.818254 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:34:40.818265 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:34:40.818276 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:34:40.818287 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:34:40.818298 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:34:40.818310 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:34:40.818321 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:34:40.818332 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:34:40.818343 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:34:40.818354 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:34:40.818366 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.732955 | |
| I0506 01:34:40.818377 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.183673 | |
| I0506 01:34:40.818390 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 2.97176 (* 0.3 = 0.891527 loss) | |
| I0506 01:34:40.818404 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.910871 (* 0.3 = 0.273261 loss) | |
| I0506 01:34:40.818418 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.01163 (* 0.0272727 = 0.0821354 loss) | |
| I0506 01:34:40.818431 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 2.98883 (* 0.0272727 = 0.0815135 loss) | |
| I0506 01:34:40.818460 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.16004 (* 0.0272727 = 0.086183 loss) | |
| I0506 01:34:40.818475 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.12614 (* 0.0272727 = 0.0852582 loss) | |
| I0506 01:34:40.818490 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.08725 (* 0.0272727 = 0.0841976 loss) | |
| I0506 01:34:40.818503 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.36097 (* 0.0272727 = 0.0916628 loss) | |
| I0506 01:34:40.818516 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.78533 (* 0.0272727 = 0.0486909 loss) | |
| I0506 01:34:40.818531 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.0909473 (* 0.0272727 = 0.00248038 loss) | |
| I0506 01:34:40.818543 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.0314063 (* 0.0272727 = 0.000856534 loss) | |
| I0506 01:34:40.818557 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0179284 (* 0.0272727 = 0.000488956 loss) | |
| I0506 01:34:40.818572 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.00566096 (* 0.0272727 = 0.00015439 loss) | |
| I0506 01:34:40.818584 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0110704 (* 0.0272727 = 0.00030192 loss) | |
| I0506 01:34:40.818598 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.00606628 (* 0.0272727 = 0.000165444 loss) | |
| I0506 01:34:40.818613 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.00645816 (* 0.0272727 = 0.000176132 loss) | |
| I0506 01:34:40.818625 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.00504201 (* 0.0272727 = 0.000137509 loss) | |
| I0506 01:34:40.818639 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.00348962 (* 0.0272727 = 9.51716e-05 loss) | |
| I0506 01:34:40.818653 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00537771 (* 0.0272727 = 0.000146665 loss) | |
| I0506 01:34:40.818667 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00289354 (* 0.0272727 = 7.89148e-05 loss) | |
| I0506 01:34:40.818681 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00461429 (* 0.0272727 = 0.000125844 loss) | |
| I0506 01:34:40.818694 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00402077 (* 0.0272727 = 0.000109657 loss) | |
| I0506 01:34:40.818708 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00359085 (* 0.0272727 = 9.79324e-05 loss) | |
| I0506 01:34:40.818722 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00202584 (* 0.0272727 = 5.52503e-05 loss) | |
| I0506 01:34:40.818733 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0816327 | |
| I0506 01:34:40.818745 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 01:34:40.818756 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:34:40.818768 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 01:34:40.818779 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:34:40.818790 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.5 | |
| I0506 01:34:40.818802 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 01:34:40.818814 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.625 | |
| I0506 01:34:40.818825 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 1 | |
| I0506 01:34:40.818835 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 01:34:40.818847 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:34:40.818858 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:34:40.818868 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:34:40.818879 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:34:40.818892 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:34:40.818902 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:34:40.818912 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:34:40.818936 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:34:40.818950 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:34:40.818961 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:34:40.818974 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:34:40.818984 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:34:40.818995 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:34:40.819006 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.738636 | |
| I0506 01:34:40.819018 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.285714 | |
| I0506 01:34:40.819031 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.84167 (* 1 = 2.84167 loss) | |
| I0506 01:34:40.819046 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.875722 (* 1 = 0.875722 loss) | |
| I0506 01:34:40.819059 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.71501 (* 0.0909091 = 0.246819 loss) | |
| I0506 01:34:40.819072 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.09289 (* 0.0909091 = 0.281172 loss) | |
| I0506 01:34:40.819087 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.10776 (* 0.0909091 = 0.282523 loss) | |
| I0506 01:34:40.819099 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.99168 (* 0.0909091 = 0.271971 loss) | |
| I0506 01:34:40.819113 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.36154 (* 0.0909091 = 0.214685 loss) | |
| I0506 01:34:40.819126 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.67615 (* 0.0909091 = 0.243286 loss) | |
| I0506 01:34:40.819139 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.44542 (* 0.0909091 = 0.131401 loss) | |
| I0506 01:34:40.819154 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.12107 (* 0.0909091 = 0.0110063 loss) | |
| I0506 01:34:40.819166 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.0129517 (* 0.0909091 = 0.00117743 loss) | |
| I0506 01:34:40.819180 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.00357616 (* 0.0909091 = 0.000325105 loss) | |
| I0506 01:34:40.819195 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.00113624 (* 0.0909091 = 0.000103294 loss) | |
| I0506 01:34:40.819207 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.00103679 (* 0.0909091 = 9.42536e-05 loss) | |
| I0506 01:34:40.819221 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00126625 (* 0.0909091 = 0.000115114 loss) | |
| I0506 01:34:40.819236 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.000979683 (* 0.0909091 = 8.90621e-05 loss) | |
| I0506 01:34:40.819249 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.000544621 (* 0.0909091 = 4.9511e-05 loss) | |
| I0506 01:34:40.819263 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.000792618 (* 0.0909091 = 7.20562e-05 loss) | |
| I0506 01:34:40.819277 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.000665278 (* 0.0909091 = 6.04798e-05 loss) | |
| I0506 01:34:40.819290 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.000459807 (* 0.0909091 = 4.18006e-05 loss) | |
| I0506 01:34:40.819300 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.000718965 (* 0.0909091 = 6.53605e-05 loss) | |
| I0506 01:34:40.819310 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000453162 (* 0.0909091 = 4.11965e-05 loss) | |
| I0506 01:34:40.819324 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000433362 (* 0.0909091 = 3.93965e-05 loss) | |
| I0506 01:34:40.819337 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000266776 (* 0.0909091 = 2.42523e-05 loss) | |
| I0506 01:34:40.819350 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:34:40.819360 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:34:40.819371 15760 solver.cpp:245] Train net output #149: total_confidence = 4.72565e-07 | |
| I0506 01:34:40.819392 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 7.43332e-06 | |
| I0506 01:34:40.819406 15760 sgd_solver.cpp:106] Iteration 19500, lr = 0.001 | |
| I0506 01:35:11.597625 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 32.4421 > 30) by scale factor 0.924723 | |
| I0506 01:36:27.951376 15760 solver.cpp:456] Snapshotting to binary proto file /mnt/snapshots/mixed_lstm15_bn_iter_20000.caffemodel | |
| I0506 01:36:28.399567 15760 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /mnt/snapshots/mixed_lstm15_bn_iter_20000.solverstate | |
| I0506 01:36:28.615901 15760 solver.cpp:338] Iteration 20000, Testing net (#0) | |
| I0506 01:37:04.665987 15760 solver.cpp:393] Test loss: 9.183 | |
| I0506 01:37:04.666117 15760 solver.cpp:406] Test net output #0: loss1/accuracy = 0.0663389 | |
| I0506 01:37:04.666137 15760 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.124 | |
| I0506 01:37:04.666152 15760 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.098 | |
| I0506 01:37:04.666163 15760 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.079 | |
| I0506 01:37:04.666175 15760 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.195 | |
| I0506 01:37:04.666187 15760 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.299 | |
| I0506 01:37:04.666198 15760 solver.cpp:406] Test net output #6: loss1/accuracy06 = 0.445 | |
| I0506 01:37:04.666209 15760 solver.cpp:406] Test net output #7: loss1/accuracy07 = 0.726 | |
| I0506 01:37:04.666220 15760 solver.cpp:406] Test net output #8: loss1/accuracy08 = 0.908 | |
| I0506 01:37:04.666232 15760 solver.cpp:406] Test net output #9: loss1/accuracy09 = 0.99 | |
| I0506 01:37:04.666244 15760 solver.cpp:406] Test net output #10: loss1/accuracy10 = 0.999 | |
| I0506 01:37:04.666254 15760 solver.cpp:406] Test net output #11: loss1/accuracy11 = 1 | |
| I0506 01:37:04.666266 15760 solver.cpp:406] Test net output #12: loss1/accuracy12 = 1 | |
| I0506 01:37:04.666277 15760 solver.cpp:406] Test net output #13: loss1/accuracy13 = 1 | |
| I0506 01:37:04.666288 15760 solver.cpp:406] Test net output #14: loss1/accuracy14 = 1 | |
| I0506 01:37:04.666301 15760 solver.cpp:406] Test net output #15: loss1/accuracy15 = 1 | |
| I0506 01:37:04.666311 15760 solver.cpp:406] Test net output #16: loss1/accuracy16 = 1 | |
| I0506 01:37:04.666323 15760 solver.cpp:406] Test net output #17: loss1/accuracy17 = 1 | |
| I0506 01:37:04.666334 15760 solver.cpp:406] Test net output #18: loss1/accuracy18 = 1 | |
| I0506 01:37:04.666345 15760 solver.cpp:406] Test net output #19: loss1/accuracy19 = 1 | |
| I0506 01:37:04.666357 15760 solver.cpp:406] Test net output #20: loss1/accuracy20 = 1 | |
| I0506 01:37:04.666368 15760 solver.cpp:406] Test net output #21: loss1/accuracy21 = 1 | |
| I0506 01:37:04.666379 15760 solver.cpp:406] Test net output #22: loss1/accuracy22 = 1 | |
| I0506 01:37:04.666390 15760 solver.cpp:406] Test net output #23: loss1/accuracy_incl_empty = 0.76382 | |
| I0506 01:37:04.666402 15760 solver.cpp:406] Test net output #24: loss1/accuracy_top3 = 0.230643 | |
| I0506 01:37:04.666425 15760 solver.cpp:406] Test net output #25: loss1/cross_entropy_loss = 3.58719 (* 0.3 = 1.07616 loss) | |
| I0506 01:37:04.666441 15760 solver.cpp:406] Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.935042 (* 0.3 = 0.280513 loss) | |
| I0506 01:37:04.666455 15760 solver.cpp:406] Test net output #27: loss1/loss01 = 3.01397 (* 0.0272727 = 0.0821991 loss) | |
| I0506 01:37:04.666468 15760 solver.cpp:406] Test net output #28: loss1/loss02 = 3.13801 (* 0.0272727 = 0.0855821 loss) | |
| I0506 01:37:04.666482 15760 solver.cpp:406] Test net output #29: loss1/loss03 = 3.25163 (* 0.0272727 = 0.0886809 loss) | |
| I0506 01:37:04.666496 15760 solver.cpp:406] Test net output #30: loss1/loss04 = 3.03274 (* 0.0272727 = 0.082711 loss) | |
| I0506 01:37:04.666508 15760 solver.cpp:406] Test net output #31: loss1/loss05 = 2.65562 (* 0.0272727 = 0.0724259 loss) | |
| I0506 01:37:04.666522 15760 solver.cpp:406] Test net output #32: loss1/loss06 = 2.25847 (* 0.0272727 = 0.0615945 loss) | |
| I0506 01:37:04.666534 15760 solver.cpp:406] Test net output #33: loss1/loss07 = 1.32846 (* 0.0272727 = 0.0362306 loss) | |
| I0506 01:37:04.666548 15760 solver.cpp:406] Test net output #34: loss1/loss08 = 0.517447 (* 0.0272727 = 0.0141122 loss) | |
| I0506 01:37:04.666561 15760 solver.cpp:406] Test net output #35: loss1/loss09 = 0.0766959 (* 0.0272727 = 0.00209171 loss) | |
| I0506 01:37:04.666575 15760 solver.cpp:406] Test net output #36: loss1/loss10 = 0.0337174 (* 0.0272727 = 0.000919565 loss) | |
| I0506 01:37:04.666589 15760 solver.cpp:406] Test net output #37: loss1/loss11 = 0.0238053 (* 0.0272727 = 0.000649237 loss) | |
| I0506 01:37:04.666602 15760 solver.cpp:406] Test net output #38: loss1/loss12 = 0.0172065 (* 0.0272727 = 0.000469268 loss) | |
| I0506 01:37:04.666616 15760 solver.cpp:406] Test net output #39: loss1/loss13 = 0.0133634 (* 0.0272727 = 0.000364456 loss) | |
| I0506 01:37:04.666651 15760 solver.cpp:406] Test net output #40: loss1/loss14 = 0.0106183 (* 0.0272727 = 0.000289589 loss) | |
| I0506 01:37:04.666666 15760 solver.cpp:406] Test net output #41: loss1/loss15 = 0.0077444 (* 0.0272727 = 0.000211211 loss) | |
| I0506 01:37:04.666679 15760 solver.cpp:406] Test net output #42: loss1/loss16 = 0.00491199 (* 0.0272727 = 0.000133963 loss) | |
| I0506 01:37:04.666693 15760 solver.cpp:406] Test net output #43: loss1/loss17 = 0.00294261 (* 0.0272727 = 8.02531e-05 loss) | |
| I0506 01:37:04.666707 15760 solver.cpp:406] Test net output #44: loss1/loss18 = 0.00268327 (* 0.0272727 = 7.318e-05 loss) | |
| I0506 01:37:04.666720 15760 solver.cpp:406] Test net output #45: loss1/loss19 = 0.00235571 (* 0.0272727 = 6.42466e-05 loss) | |
| I0506 01:37:04.666733 15760 solver.cpp:406] Test net output #46: loss1/loss20 = 0.00211555 (* 0.0272727 = 5.76969e-05 loss) | |
| I0506 01:37:04.666748 15760 solver.cpp:406] Test net output #47: loss1/loss21 = 0.00202896 (* 0.0272727 = 5.53351e-05 loss) | |
| I0506 01:37:04.666760 15760 solver.cpp:406] Test net output #48: loss1/loss22 = 0.00195844 (* 0.0272727 = 5.34119e-05 loss) | |
| I0506 01:37:04.666772 15760 solver.cpp:406] Test net output #49: loss2/accuracy = 0.0679004 | |
| I0506 01:37:04.666784 15760 solver.cpp:406] Test net output #50: loss2/accuracy01 = 0.132 | |
| I0506 01:37:04.666795 15760 solver.cpp:406] Test net output #51: loss2/accuracy02 = 0.095 | |
| I0506 01:37:04.666806 15760 solver.cpp:406] Test net output #52: loss2/accuracy03 = 0.092 | |
| I0506 01:37:04.666817 15760 solver.cpp:406] Test net output #53: loss2/accuracy04 = 0.183 | |
| I0506 01:37:04.666828 15760 solver.cpp:406] Test net output #54: loss2/accuracy05 = 0.317 | |
| I0506 01:37:04.666841 15760 solver.cpp:406] Test net output #55: loss2/accuracy06 = 0.441 | |
| I0506 01:37:04.666851 15760 solver.cpp:406] Test net output #56: loss2/accuracy07 = 0.724 | |
| I0506 01:37:04.666862 15760 solver.cpp:406] Test net output #57: loss2/accuracy08 = 0.907 | |
| I0506 01:37:04.666877 15760 solver.cpp:406] Test net output #58: loss2/accuracy09 = 0.99 | |
| I0506 01:37:04.666888 15760 solver.cpp:406] Test net output #59: loss2/accuracy10 = 0.999 | |
| I0506 01:37:04.666900 15760 solver.cpp:406] Test net output #60: loss2/accuracy11 = 1 | |
| I0506 01:37:04.666911 15760 solver.cpp:406] Test net output #61: loss2/accuracy12 = 1 | |
| I0506 01:37:04.666923 15760 solver.cpp:406] Test net output #62: loss2/accuracy13 = 1 | |
| I0506 01:37:04.666934 15760 solver.cpp:406] Test net output #63: loss2/accuracy14 = 1 | |
| I0506 01:37:04.666945 15760 solver.cpp:406] Test net output #64: loss2/accuracy15 = 1 | |
| I0506 01:37:04.666952 15760 solver.cpp:406] Test net output #65: loss2/accuracy16 = 1 | |
| I0506 01:37:04.666960 15760 solver.cpp:406] Test net output #66: loss2/accuracy17 = 1 | |
| I0506 01:37:04.666971 15760 solver.cpp:406] Test net output #67: loss2/accuracy18 = 1 | |
| I0506 01:37:04.666982 15760 solver.cpp:406] Test net output #68: loss2/accuracy19 = 1 | |
| I0506 01:37:04.666993 15760 solver.cpp:406] Test net output #69: loss2/accuracy20 = 1 | |
| I0506 01:37:04.667004 15760 solver.cpp:406] Test net output #70: loss2/accuracy21 = 1 | |
| I0506 01:37:04.667016 15760 solver.cpp:406] Test net output #71: loss2/accuracy22 = 1 | |
| I0506 01:37:04.667032 15760 solver.cpp:406] Test net output #72: loss2/accuracy_incl_empty = 0.763729 | |
| I0506 01:37:04.667045 15760 solver.cpp:406] Test net output #73: loss2/accuracy_top3 = 0.234064 | |
| I0506 01:37:04.667058 15760 solver.cpp:406] Test net output #74: loss2/cross_entropy_loss = 3.5501 (* 0.3 = 1.06503 loss) | |
| I0506 01:37:04.667073 15760 solver.cpp:406] Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.925855 (* 0.3 = 0.277757 loss) | |
| I0506 01:37:04.667086 15760 solver.cpp:406] Test net output #76: loss2/loss01 = 2.96445 (* 0.0272727 = 0.0808488 loss) | |
| I0506 01:37:04.667099 15760 solver.cpp:406] Test net output #77: loss2/loss02 = 3.08507 (* 0.0272727 = 0.0841383 loss) | |
| I0506 01:37:04.667112 15760 solver.cpp:406] Test net output #78: loss2/loss03 = 3.20373 (* 0.0272727 = 0.0873745 loss) | |
| I0506 01:37:04.667142 15760 solver.cpp:406] Test net output #79: loss2/loss04 = 2.98155 (* 0.0272727 = 0.081315 loss) | |
| I0506 01:37:04.667157 15760 solver.cpp:406] Test net output #80: loss2/loss05 = 2.6168 (* 0.0272727 = 0.0713671 loss) | |
| I0506 01:37:04.667171 15760 solver.cpp:406] Test net output #81: loss2/loss06 = 2.22296 (* 0.0272727 = 0.0606262 loss) | |
| I0506 01:37:04.667184 15760 solver.cpp:406] Test net output #82: loss2/loss07 = 1.30553 (* 0.0272727 = 0.0356053 loss) | |
| I0506 01:37:04.667197 15760 solver.cpp:406] Test net output #83: loss2/loss08 = 0.513095 (* 0.0272727 = 0.0139935 loss) | |
| I0506 01:37:04.667210 15760 solver.cpp:406] Test net output #84: loss2/loss09 = 0.0788435 (* 0.0272727 = 0.00215028 loss) | |
| I0506 01:37:04.667224 15760 solver.cpp:406] Test net output #85: loss2/loss10 = 0.0345731 (* 0.0272727 = 0.000942903 loss) | |
| I0506 01:37:04.667237 15760 solver.cpp:406] Test net output #86: loss2/loss11 = 0.0245209 (* 0.0272727 = 0.000668751 loss) | |
| I0506 01:37:04.667250 15760 solver.cpp:406] Test net output #87: loss2/loss12 = 0.0178599 (* 0.0272727 = 0.000487087 loss) | |
| I0506 01:37:04.667264 15760 solver.cpp:406] Test net output #88: loss2/loss13 = 0.0157482 (* 0.0272727 = 0.000429497 loss) | |
| I0506 01:37:04.667278 15760 solver.cpp:406] Test net output #89: loss2/loss14 = 0.011043 (* 0.0272727 = 0.000301174 loss) | |
| I0506 01:37:04.667290 15760 solver.cpp:406] Test net output #90: loss2/loss15 = 0.00894737 (* 0.0272727 = 0.000244019 loss) | |
| I0506 01:37:04.667304 15760 solver.cpp:406] Test net output #91: loss2/loss16 = 0.00562217 (* 0.0272727 = 0.000153332 loss) | |
| I0506 01:37:04.667316 15760 solver.cpp:406] Test net output #92: loss2/loss17 = 0.00353906 (* 0.0272727 = 9.65197e-05 loss) | |
| I0506 01:37:04.667330 15760 solver.cpp:406] Test net output #93: loss2/loss18 = 0.00295099 (* 0.0272727 = 8.04815e-05 loss) | |
| I0506 01:37:04.667343 15760 solver.cpp:406] Test net output #94: loss2/loss19 = 0.00247464 (* 0.0272727 = 6.74903e-05 loss) | |
| I0506 01:37:04.667356 15760 solver.cpp:406] Test net output #95: loss2/loss20 = 0.00242008 (* 0.0272727 = 6.60022e-05 loss) | |
| I0506 01:37:04.667369 15760 solver.cpp:406] Test net output #96: loss2/loss21 = 0.00220097 (* 0.0272727 = 6.00264e-05 loss) | |
| I0506 01:37:04.667382 15760 solver.cpp:406] Test net output #97: loss2/loss22 = 0.00220788 (* 0.0272727 = 6.0215e-05 loss) | |
| I0506 01:37:04.667393 15760 solver.cpp:406] Test net output #98: loss3/accuracy = 0.101381 | |
| I0506 01:37:04.667405 15760 solver.cpp:406] Test net output #99: loss3/accuracy01 = 0.145 | |
| I0506 01:37:04.667417 15760 solver.cpp:406] Test net output #100: loss3/accuracy02 = 0.117 | |
| I0506 01:37:04.667428 15760 solver.cpp:406] Test net output #101: loss3/accuracy03 = 0.087 | |
| I0506 01:37:04.667438 15760 solver.cpp:406] Test net output #102: loss3/accuracy04 = 0.192 | |
| I0506 01:37:04.667449 15760 solver.cpp:406] Test net output #103: loss3/accuracy05 = 0.327 | |
| I0506 01:37:04.667459 15760 solver.cpp:406] Test net output #104: loss3/accuracy06 = 0.443 | |
| I0506 01:37:04.667471 15760 solver.cpp:406] Test net output #105: loss3/accuracy07 = 0.723 | |
| I0506 01:37:04.667481 15760 solver.cpp:406] Test net output #106: loss3/accuracy08 = 0.904 | |
| I0506 01:37:04.667493 15760 solver.cpp:406] Test net output #107: loss3/accuracy09 = 0.988 | |
| I0506 01:37:04.667505 15760 solver.cpp:406] Test net output #108: loss3/accuracy10 = 0.997 | |
| I0506 01:37:04.667515 15760 solver.cpp:406] Test net output #109: loss3/accuracy11 = 0.999 | |
| I0506 01:37:04.667526 15760 solver.cpp:406] Test net output #110: loss3/accuracy12 = 0.999 | |
| I0506 01:37:04.667537 15760 solver.cpp:406] Test net output #111: loss3/accuracy13 = 0.999 | |
| I0506 01:37:04.667548 15760 solver.cpp:406] Test net output #112: loss3/accuracy14 = 0.999 | |
| I0506 01:37:04.667559 15760 solver.cpp:406] Test net output #113: loss3/accuracy15 = 1 | |
| I0506 01:37:04.667569 15760 solver.cpp:406] Test net output #114: loss3/accuracy16 = 1 | |
| I0506 01:37:04.667592 15760 solver.cpp:406] Test net output #115: loss3/accuracy17 = 1 | |
| I0506 01:37:04.667603 15760 solver.cpp:406] Test net output #116: loss3/accuracy18 = 1 | |
| I0506 01:37:04.667614 15760 solver.cpp:406] Test net output #117: loss3/accuracy19 = 1 | |
| I0506 01:37:04.667625 15760 solver.cpp:406] Test net output #118: loss3/accuracy20 = 1 | |
| I0506 01:37:04.667635 15760 solver.cpp:406] Test net output #119: loss3/accuracy21 = 1 | |
| I0506 01:37:04.667646 15760 solver.cpp:406] Test net output #120: loss3/accuracy22 = 1 | |
| I0506 01:37:04.667657 15760 solver.cpp:406] Test net output #121: loss3/accuracy_incl_empty = 0.766502 | |
| I0506 01:37:04.667668 15760 solver.cpp:406] Test net output #122: loss3/accuracy_top3 = 0.267058 | |
| I0506 01:37:04.667682 15760 solver.cpp:406] Test net output #123: loss3/cross_entropy_loss = 2.98996 (* 1 = 2.98996 loss) | |
| I0506 01:37:04.667695 15760 solver.cpp:406] Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.822966 (* 1 = 0.822966 loss) | |
| I0506 01:37:04.667708 15760 solver.cpp:406] Test net output #125: loss3/loss01 = 2.7696 (* 0.0909091 = 0.251782 loss) | |
| I0506 01:37:04.667721 15760 solver.cpp:406] Test net output #126: loss3/loss02 = 2.90462 (* 0.0909091 = 0.264056 loss) | |
| I0506 01:37:04.667734 15760 solver.cpp:406] Test net output #127: loss3/loss03 = 3.03938 (* 0.0909091 = 0.276307 loss) | |
| I0506 01:37:04.667747 15760 solver.cpp:406] Test net output #128: loss3/loss04 = 2.80102 (* 0.0909091 = 0.254638 loss) | |
| I0506 01:37:04.667760 15760 solver.cpp:406] Test net output #129: loss3/loss05 = 2.41963 (* 0.0909091 = 0.219966 loss) | |
| I0506 01:37:04.667773 15760 solver.cpp:406] Test net output #130: loss3/loss06 = 2.06338 (* 0.0909091 = 0.18758 loss) | |
| I0506 01:37:04.667786 15760 solver.cpp:406] Test net output #131: loss3/loss07 = 1.1725 (* 0.0909091 = 0.106591 loss) | |
| I0506 01:37:04.667799 15760 solver.cpp:406] Test net output #132: loss3/loss08 = 0.456325 (* 0.0909091 = 0.0414841 loss) | |
| I0506 01:37:04.667812 15760 solver.cpp:406] Test net output #133: loss3/loss09 = 0.0789594 (* 0.0909091 = 0.00717813 loss) | |
| I0506 01:37:04.667826 15760 solver.cpp:406] Test net output #134: loss3/loss10 = 0.0324539 (* 0.0909091 = 0.00295036 loss) | |
| I0506 01:37:04.667840 15760 solver.cpp:406] Test net output #135: loss3/loss11 = 0.0220152 (* 0.0909091 = 0.00200139 loss) | |
| I0506 01:37:04.667852 15760 solver.cpp:406] Test net output #136: loss3/loss12 = 0.0159475 (* 0.0909091 = 0.00144977 loss) | |
| I0506 01:37:04.667865 15760 solver.cpp:406] Test net output #137: loss3/loss13 = 0.0126234 (* 0.0909091 = 0.00114758 loss) | |
| I0506 01:37:04.667878 15760 solver.cpp:406] Test net output #138: loss3/loss14 = 0.00924947 (* 0.0909091 = 0.000840861 loss) | |
| I0506 01:37:04.667892 15760 solver.cpp:406] Test net output #139: loss3/loss15 = 0.00703806 (* 0.0909091 = 0.000639824 loss) | |
| I0506 01:37:04.667906 15760 solver.cpp:406] Test net output #140: loss3/loss16 = 0.00516888 (* 0.0909091 = 0.000469898 loss) | |
| I0506 01:37:04.667918 15760 solver.cpp:406] Test net output #141: loss3/loss17 = 0.00292683 (* 0.0909091 = 0.000266075 loss) | |
| I0506 01:37:04.667935 15760 solver.cpp:406] Test net output #142: loss3/loss18 = 0.00307141 (* 0.0909091 = 0.000279219 loss) | |
| I0506 01:37:04.667949 15760 solver.cpp:406] Test net output #143: loss3/loss19 = 0.00288985 (* 0.0909091 = 0.000262714 loss) | |
| I0506 01:37:04.667963 15760 solver.cpp:406] Test net output #144: loss3/loss20 = 0.0024279 (* 0.0909091 = 0.000220718 loss) | |
| I0506 01:37:04.667976 15760 solver.cpp:406] Test net output #145: loss3/loss21 = 0.00208389 (* 0.0909091 = 0.000189445 loss) | |
| I0506 01:37:04.667989 15760 solver.cpp:406] Test net output #146: loss3/loss22 = 0.00207489 (* 0.0909091 = 0.000188626 loss) | |
| I0506 01:37:04.668000 15760 solver.cpp:406] Test net output #147: total_accuracy = 0 | |
| I0506 01:37:04.668011 15760 solver.cpp:406] Test net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:37:04.668022 15760 solver.cpp:406] Test net output #149: total_confidence = 0.00032937 | |
| I0506 01:37:04.668043 15760 solver.cpp:406] Test net output #150: total_confidence_not_rec = 0.000532796 | |
| I0506 01:37:04.668057 15760 solver.cpp:338] Iteration 20000, Testing net (#1) | |
| I0506 01:37:40.583534 15760 solver.cpp:393] Test loss: 9.78443 | |
| I0506 01:37:40.583657 15760 solver.cpp:406] Test net output #0: loss1/accuracy = 0.0609514 | |
| I0506 01:37:40.583678 15760 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.124 | |
| I0506 01:37:40.583690 15760 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.094 | |
| I0506 01:37:40.583703 15760 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.098 | |
| I0506 01:37:40.583714 15760 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.184 | |
| I0506 01:37:40.583726 15760 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.31 | |
| I0506 01:37:40.583739 15760 solver.cpp:406] Test net output #6: loss1/accuracy06 = 0.423 | |
| I0506 01:37:40.583750 15760 solver.cpp:406] Test net output #7: loss1/accuracy07 = 0.634 | |
| I0506 01:37:40.583761 15760 solver.cpp:406] Test net output #8: loss1/accuracy08 = 0.799 | |
| I0506 01:37:40.583772 15760 solver.cpp:406] Test net output #9: loss1/accuracy09 = 0.886 | |
| I0506 01:37:40.583784 15760 solver.cpp:406] Test net output #10: loss1/accuracy10 = 0.907 | |
| I0506 01:37:40.583796 15760 solver.cpp:406] Test net output #11: loss1/accuracy11 = 0.923 | |
| I0506 01:37:40.583806 15760 solver.cpp:406] Test net output #12: loss1/accuracy12 = 0.936 | |
| I0506 01:37:40.583818 15760 solver.cpp:406] Test net output #13: loss1/accuracy13 = 0.948 | |
| I0506 01:37:40.583829 15760 solver.cpp:406] Test net output #14: loss1/accuracy14 = 0.96 | |
| I0506 01:37:40.583840 15760 solver.cpp:406] Test net output #15: loss1/accuracy15 = 0.97 | |
| I0506 01:37:40.583853 15760 solver.cpp:406] Test net output #16: loss1/accuracy16 = 0.979 | |
| I0506 01:37:40.583863 15760 solver.cpp:406] Test net output #17: loss1/accuracy17 = 0.989 | |
| I0506 01:37:40.583878 15760 solver.cpp:406] Test net output #18: loss1/accuracy18 = 0.996 | |
| I0506 01:37:40.583890 15760 solver.cpp:406] Test net output #19: loss1/accuracy19 = 0.997 | |
| I0506 01:37:40.583901 15760 solver.cpp:406] Test net output #20: loss1/accuracy20 = 0.999 | |
| I0506 01:37:40.583912 15760 solver.cpp:406] Test net output #21: loss1/accuracy21 = 1 | |
| I0506 01:37:40.583925 15760 solver.cpp:406] Test net output #22: loss1/accuracy22 = 1 | |
| I0506 01:37:40.583936 15760 solver.cpp:406] Test net output #23: loss1/accuracy_incl_empty = 0.729364 | |
| I0506 01:37:40.583947 15760 solver.cpp:406] Test net output #24: loss1/accuracy_top3 = 0.234498 | |
| I0506 01:37:40.583963 15760 solver.cpp:406] Test net output #25: loss1/cross_entropy_loss = 3.57385 (* 0.3 = 1.07216 loss) | |
| I0506 01:37:40.583977 15760 solver.cpp:406] Test net output #26: loss1/cross_entropy_loss_incl_empty = 1.0628 (* 0.3 = 0.318841 loss) | |
| I0506 01:37:40.583992 15760 solver.cpp:406] Test net output #27: loss1/loss01 = 3.01882 (* 0.0272727 = 0.0823315 loss) | |
| I0506 01:37:40.584004 15760 solver.cpp:406] Test net output #28: loss1/loss02 = 3.14569 (* 0.0272727 = 0.0857915 loss) | |
| I0506 01:37:40.584018 15760 solver.cpp:406] Test net output #29: loss1/loss03 = 3.21529 (* 0.0272727 = 0.0876897 loss) | |
| I0506 01:37:40.584031 15760 solver.cpp:406] Test net output #30: loss1/loss04 = 3.06394 (* 0.0272727 = 0.083562 loss) | |
| I0506 01:37:40.584044 15760 solver.cpp:406] Test net output #31: loss1/loss05 = 2.6697 (* 0.0272727 = 0.07281 loss) | |
| I0506 01:37:40.584059 15760 solver.cpp:406] Test net output #32: loss1/loss06 = 2.34564 (* 0.0272727 = 0.063972 loss) | |
| I0506 01:37:40.584071 15760 solver.cpp:406] Test net output #33: loss1/loss07 = 1.59404 (* 0.0272727 = 0.0434737 loss) | |
| I0506 01:37:40.584084 15760 solver.cpp:406] Test net output #34: loss1/loss08 = 0.913763 (* 0.0272727 = 0.0249208 loss) | |
| I0506 01:37:40.584098 15760 solver.cpp:406] Test net output #35: loss1/loss09 = 0.488729 (* 0.0272727 = 0.013329 loss) | |
| I0506 01:37:40.584111 15760 solver.cpp:406] Test net output #36: loss1/loss10 = 0.397615 (* 0.0272727 = 0.010844 loss) | |
| I0506 01:37:40.584125 15760 solver.cpp:406] Test net output #37: loss1/loss11 = 0.339222 (* 0.0272727 = 0.00925151 loss) | |
| I0506 01:37:40.584138 15760 solver.cpp:406] Test net output #38: loss1/loss12 = 0.30214 (* 0.0272727 = 0.00824018 loss) | |
| I0506 01:37:40.584152 15760 solver.cpp:406] Test net output #39: loss1/loss13 = 0.259943 (* 0.0272727 = 0.00708934 loss) | |
| I0506 01:37:40.586251 15760 solver.cpp:406] Test net output #40: loss1/loss14 = 0.213387 (* 0.0272727 = 0.00581964 loss) | |
| I0506 01:37:40.586267 15760 solver.cpp:406] Test net output #41: loss1/loss15 = 0.165606 (* 0.0272727 = 0.00451652 loss) | |
| I0506 01:37:40.586282 15760 solver.cpp:406] Test net output #42: loss1/loss16 = 0.12858 (* 0.0272727 = 0.00350674 loss) | |
| I0506 01:37:40.586294 15760 solver.cpp:406] Test net output #43: loss1/loss17 = 0.0814644 (* 0.0272727 = 0.00222176 loss) | |
| I0506 01:37:40.586308 15760 solver.cpp:406] Test net output #44: loss1/loss18 = 0.035316 (* 0.0272727 = 0.000963163 loss) | |
| I0506 01:37:40.586323 15760 solver.cpp:406] Test net output #45: loss1/loss19 = 0.0273294 (* 0.0272727 = 0.000745347 loss) | |
| I0506 01:37:40.586336 15760 solver.cpp:406] Test net output #46: loss1/loss20 = 0.0102829 (* 0.0272727 = 0.000280442 loss) | |
| I0506 01:37:40.586349 15760 solver.cpp:406] Test net output #47: loss1/loss21 = 0.00247842 (* 0.0272727 = 6.75932e-05 loss) | |
| I0506 01:37:40.586364 15760 solver.cpp:406] Test net output #48: loss1/loss22 = 0.00225595 (* 0.0272727 = 6.15258e-05 loss) | |
| I0506 01:37:40.586375 15760 solver.cpp:406] Test net output #49: loss2/accuracy = 0.0690248 | |
| I0506 01:37:40.586386 15760 solver.cpp:406] Test net output #50: loss2/accuracy01 = 0.123 | |
| I0506 01:37:40.586398 15760 solver.cpp:406] Test net output #51: loss2/accuracy02 = 0.12 | |
| I0506 01:37:40.586410 15760 solver.cpp:406] Test net output #52: loss2/accuracy03 = 0.107 | |
| I0506 01:37:40.586421 15760 solver.cpp:406] Test net output #53: loss2/accuracy04 = 0.18 | |
| I0506 01:37:40.586432 15760 solver.cpp:406] Test net output #54: loss2/accuracy05 = 0.311 | |
| I0506 01:37:40.586443 15760 solver.cpp:406] Test net output #55: loss2/accuracy06 = 0.418 | |
| I0506 01:37:40.586454 15760 solver.cpp:406] Test net output #56: loss2/accuracy07 = 0.634 | |
| I0506 01:37:40.586467 15760 solver.cpp:406] Test net output #57: loss2/accuracy08 = 0.8 | |
| I0506 01:37:40.586477 15760 solver.cpp:406] Test net output #58: loss2/accuracy09 = 0.887 | |
| I0506 01:37:40.586488 15760 solver.cpp:406] Test net output #59: loss2/accuracy10 = 0.907 | |
| I0506 01:37:40.586499 15760 solver.cpp:406] Test net output #60: loss2/accuracy11 = 0.923 | |
| I0506 01:37:40.586510 15760 solver.cpp:406] Test net output #61: loss2/accuracy12 = 0.936 | |
| I0506 01:37:40.586521 15760 solver.cpp:406] Test net output #62: loss2/accuracy13 = 0.948 | |
| I0506 01:37:40.586534 15760 solver.cpp:406] Test net output #63: loss2/accuracy14 = 0.96 | |
| I0506 01:37:40.586544 15760 solver.cpp:406] Test net output #64: loss2/accuracy15 = 0.97 | |
| I0506 01:37:40.586556 15760 solver.cpp:406] Test net output #65: loss2/accuracy16 = 0.979 | |
| I0506 01:37:40.586567 15760 solver.cpp:406] Test net output #66: loss2/accuracy17 = 0.989 | |
| I0506 01:37:40.586578 15760 solver.cpp:406] Test net output #67: loss2/accuracy18 = 0.996 | |
| I0506 01:37:40.586591 15760 solver.cpp:406] Test net output #68: loss2/accuracy19 = 0.997 | |
| I0506 01:37:40.586601 15760 solver.cpp:406] Test net output #69: loss2/accuracy20 = 0.999 | |
| I0506 01:37:40.586612 15760 solver.cpp:406] Test net output #70: loss2/accuracy21 = 1 | |
| I0506 01:37:40.586623 15760 solver.cpp:406] Test net output #71: loss2/accuracy22 = 1 | |
| I0506 01:37:40.586634 15760 solver.cpp:406] Test net output #72: loss2/accuracy_incl_empty = 0.731501 | |
| I0506 01:37:40.586645 15760 solver.cpp:406] Test net output #73: loss2/accuracy_top3 = 0.23616 | |
| I0506 01:37:40.586659 15760 solver.cpp:406] Test net output #74: loss2/cross_entropy_loss = 3.5317 (* 0.3 = 1.05951 loss) | |
| I0506 01:37:40.586673 15760 solver.cpp:406] Test net output #75: loss2/cross_entropy_loss_incl_empty = 1.04604 (* 0.3 = 0.313813 loss) | |
| I0506 01:37:40.586685 15760 solver.cpp:406] Test net output #76: loss2/loss01 = 2.97754 (* 0.0272727 = 0.0812055 loss) | |
| I0506 01:37:40.586699 15760 solver.cpp:406] Test net output #77: loss2/loss02 = 3.10165 (* 0.0272727 = 0.0845905 loss) | |
| I0506 01:37:40.586725 15760 solver.cpp:406] Test net output #78: loss2/loss03 = 3.15782 (* 0.0272727 = 0.0861224 loss) | |
| I0506 01:37:40.586740 15760 solver.cpp:406] Test net output #79: loss2/loss04 = 3.01433 (* 0.0272727 = 0.0822089 loss) | |
| I0506 01:37:40.586766 15760 solver.cpp:406] Test net output #80: loss2/loss05 = 2.63497 (* 0.0272727 = 0.0718628 loss) | |
| I0506 01:37:40.586783 15760 solver.cpp:406] Test net output #81: loss2/loss06 = 2.32954 (* 0.0272727 = 0.0635329 loss) | |
| I0506 01:37:40.586796 15760 solver.cpp:406] Test net output #82: loss2/loss07 = 1.58228 (* 0.0272727 = 0.043153 loss) | |
| I0506 01:37:40.586810 15760 solver.cpp:406] Test net output #83: loss2/loss08 = 0.897453 (* 0.0272727 = 0.024476 loss) | |
| I0506 01:37:40.586823 15760 solver.cpp:406] Test net output #84: loss2/loss09 = 0.48924 (* 0.0272727 = 0.0133429 loss) | |
| I0506 01:37:40.586838 15760 solver.cpp:406] Test net output #85: loss2/loss10 = 0.386867 (* 0.0272727 = 0.0105509 loss) | |
| I0506 01:37:40.586850 15760 solver.cpp:406] Test net output #86: loss2/loss11 = 0.332958 (* 0.0272727 = 0.00908067 loss) | |
| I0506 01:37:40.586864 15760 solver.cpp:406] Test net output #87: loss2/loss12 = 0.300982 (* 0.0272727 = 0.0082086 loss) | |
| I0506 01:37:40.586881 15760 solver.cpp:406] Test net output #88: loss2/loss13 = 0.250874 (* 0.0272727 = 0.00684201 loss) | |
| I0506 01:37:40.586895 15760 solver.cpp:406] Test net output #89: loss2/loss14 = 0.21091 (* 0.0272727 = 0.00575209 loss) | |
| I0506 01:37:40.586908 15760 solver.cpp:406] Test net output #90: loss2/loss15 = 0.163571 (* 0.0272727 = 0.00446102 loss) | |
| I0506 01:37:40.586922 15760 solver.cpp:406] Test net output #91: loss2/loss16 = 0.129346 (* 0.0272727 = 0.00352763 loss) | |
| I0506 01:37:40.586935 15760 solver.cpp:406] Test net output #92: loss2/loss17 = 0.0790065 (* 0.0272727 = 0.00215472 loss) | |
| I0506 01:37:40.586949 15760 solver.cpp:406] Test net output #93: loss2/loss18 = 0.0324925 (* 0.0272727 = 0.000886159 loss) | |
| I0506 01:37:40.586962 15760 solver.cpp:406] Test net output #94: loss2/loss19 = 0.0262327 (* 0.0272727 = 0.000715438 loss) | |
| I0506 01:37:40.586976 15760 solver.cpp:406] Test net output #95: loss2/loss20 = 0.0116936 (* 0.0272727 = 0.000318916 loss) | |
| I0506 01:37:40.586989 15760 solver.cpp:406] Test net output #96: loss2/loss21 = 0.00318833 (* 0.0272727 = 8.69546e-05 loss) | |
| I0506 01:37:40.587002 15760 solver.cpp:406] Test net output #97: loss2/loss22 = 0.00293142 (* 0.0272727 = 7.99478e-05 loss) | |
| I0506 01:37:40.587014 15760 solver.cpp:406] Test net output #98: loss3/accuracy = 0.0987928 | |
| I0506 01:37:40.587025 15760 solver.cpp:406] Test net output #99: loss3/accuracy01 = 0.137 | |
| I0506 01:37:40.587038 15760 solver.cpp:406] Test net output #100: loss3/accuracy02 = 0.107 | |
| I0506 01:37:40.587049 15760 solver.cpp:406] Test net output #101: loss3/accuracy03 = 0.099 | |
| I0506 01:37:40.587059 15760 solver.cpp:406] Test net output #102: loss3/accuracy04 = 0.178 | |
| I0506 01:37:40.587071 15760 solver.cpp:406] Test net output #103: loss3/accuracy05 = 0.335 | |
| I0506 01:37:40.587081 15760 solver.cpp:406] Test net output #104: loss3/accuracy06 = 0.434 | |
| I0506 01:37:40.587093 15760 solver.cpp:406] Test net output #105: loss3/accuracy07 = 0.635 | |
| I0506 01:37:40.587105 15760 solver.cpp:406] Test net output #106: loss3/accuracy08 = 0.807 | |
| I0506 01:37:40.587116 15760 solver.cpp:406] Test net output #107: loss3/accuracy09 = 0.893 | |
| I0506 01:37:40.587126 15760 solver.cpp:406] Test net output #108: loss3/accuracy10 = 0.912 | |
| I0506 01:37:40.587137 15760 solver.cpp:406] Test net output #109: loss3/accuracy11 = 0.925 | |
| I0506 01:37:40.587148 15760 solver.cpp:406] Test net output #110: loss3/accuracy12 = 0.935 | |
| I0506 01:37:40.587159 15760 solver.cpp:406] Test net output #111: loss3/accuracy13 = 0.949 | |
| I0506 01:37:40.587170 15760 solver.cpp:406] Test net output #112: loss3/accuracy14 = 0.96 | |
| I0506 01:37:40.587182 15760 solver.cpp:406] Test net output #113: loss3/accuracy15 = 0.97 | |
| I0506 01:37:40.587193 15760 solver.cpp:406] Test net output #114: loss3/accuracy16 = 0.979 | |
| I0506 01:37:40.587214 15760 solver.cpp:406] Test net output #115: loss3/accuracy17 = 0.989 | |
| I0506 01:37:40.587227 15760 solver.cpp:406] Test net output #116: loss3/accuracy18 = 0.996 | |
| I0506 01:37:40.587239 15760 solver.cpp:406] Test net output #117: loss3/accuracy19 = 0.997 | |
| I0506 01:37:40.587255 15760 solver.cpp:406] Test net output #118: loss3/accuracy20 = 0.999 | |
| I0506 01:37:40.587266 15760 solver.cpp:406] Test net output #119: loss3/accuracy21 = 1 | |
| I0506 01:37:40.587278 15760 solver.cpp:406] Test net output #120: loss3/accuracy22 = 1 | |
| I0506 01:37:40.587286 15760 solver.cpp:406] Test net output #121: loss3/accuracy_incl_empty = 0.735364 | |
| I0506 01:37:40.587297 15760 solver.cpp:406] Test net output #122: loss3/accuracy_top3 = 0.267207 | |
| I0506 01:37:40.587311 15760 solver.cpp:406] Test net output #123: loss3/cross_entropy_loss = 3.00465 (* 1 = 3.00465 loss) | |
| I0506 01:37:40.587324 15760 solver.cpp:406] Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.940135 (* 1 = 0.940135 loss) | |
| I0506 01:37:40.587338 15760 solver.cpp:406] Test net output #125: loss3/loss01 = 2.78026 (* 0.0909091 = 0.25275 loss) | |
| I0506 01:37:40.587352 15760 solver.cpp:406] Test net output #126: loss3/loss02 = 2.93738 (* 0.0909091 = 0.267035 loss) | |
| I0506 01:37:40.587364 15760 solver.cpp:406] Test net output #127: loss3/loss03 = 3.00608 (* 0.0909091 = 0.27328 loss) | |
| I0506 01:37:40.587378 15760 solver.cpp:406] Test net output #128: loss3/loss04 = 2.839 (* 0.0909091 = 0.258091 loss) | |
| I0506 01:37:40.587391 15760 solver.cpp:406] Test net output #129: loss3/loss05 = 2.42774 (* 0.0909091 = 0.220704 loss) | |
| I0506 01:37:40.587404 15760 solver.cpp:406] Test net output #130: loss3/loss06 = 2.14946 (* 0.0909091 = 0.195405 loss) | |
| I0506 01:37:40.587417 15760 solver.cpp:406] Test net output #131: loss3/loss07 = 1.42402 (* 0.0909091 = 0.129457 loss) | |
| I0506 01:37:40.587431 15760 solver.cpp:406] Test net output #132: loss3/loss08 = 0.799061 (* 0.0909091 = 0.0726419 loss) | |
| I0506 01:37:40.587445 15760 solver.cpp:406] Test net output #133: loss3/loss09 = 0.436925 (* 0.0909091 = 0.0397205 loss) | |
| I0506 01:37:40.587458 15760 solver.cpp:406] Test net output #134: loss3/loss10 = 0.341707 (* 0.0909091 = 0.0310643 loss) | |
| I0506 01:37:40.587471 15760 solver.cpp:406] Test net output #135: loss3/loss11 = 0.289849 (* 0.0909091 = 0.0263499 loss) | |
| I0506 01:37:40.587486 15760 solver.cpp:406] Test net output #136: loss3/loss12 = 0.266267 (* 0.0909091 = 0.0242061 loss) | |
| I0506 01:37:40.587498 15760 solver.cpp:406] Test net output #137: loss3/loss13 = 0.219769 (* 0.0909091 = 0.019979 loss) | |
| I0506 01:37:40.587512 15760 solver.cpp:406] Test net output #138: loss3/loss14 = 0.175977 (* 0.0909091 = 0.0159979 loss) | |
| I0506 01:37:40.587525 15760 solver.cpp:406] Test net output #139: loss3/loss15 = 0.136791 (* 0.0909091 = 0.0124355 loss) | |
| I0506 01:37:40.587538 15760 solver.cpp:406] Test net output #140: loss3/loss16 = 0.107394 (* 0.0909091 = 0.0097631 loss) | |
| I0506 01:37:40.587551 15760 solver.cpp:406] Test net output #141: loss3/loss17 = 0.0640844 (* 0.0909091 = 0.00582585 loss) | |
| I0506 01:37:40.587565 15760 solver.cpp:406] Test net output #142: loss3/loss18 = 0.0305451 (* 0.0909091 = 0.00277683 loss) | |
| I0506 01:37:40.587579 15760 solver.cpp:406] Test net output #143: loss3/loss19 = 0.0229486 (* 0.0909091 = 0.00208624 loss) | |
| I0506 01:37:40.587591 15760 solver.cpp:406] Test net output #144: loss3/loss20 = 0.00961045 (* 0.0909091 = 0.000873677 loss) | |
| I0506 01:37:40.587604 15760 solver.cpp:406] Test net output #145: loss3/loss21 = 0.00154461 (* 0.0909091 = 0.000140419 loss) | |
| I0506 01:37:40.587618 15760 solver.cpp:406] Test net output #146: loss3/loss22 = 0.000991224 (* 0.0909091 = 9.01113e-05 loss) | |
| I0506 01:37:40.587630 15760 solver.cpp:406] Test net output #147: total_accuracy = 0.001 | |
| I0506 01:37:40.587641 15760 solver.cpp:406] Test net output #148: total_accuracy_not_rec = 0.002 | |
| I0506 01:37:40.587651 15760 solver.cpp:406] Test net output #149: total_confidence = 0.000329749 | |
| I0506 01:37:40.587672 15760 solver.cpp:406] Test net output #150: total_confidence_not_rec = 0.000419286 | |
| I0506 01:37:40.722921 15760 solver.cpp:229] Iteration 20000, loss = 9.77093 | |
| I0506 01:37:40.722982 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0212766 | |
| I0506 01:37:40.723001 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.25 | |
| I0506 01:37:40.723013 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 01:37:40.723026 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.25 | |
| I0506 01:37:40.723037 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 01:37:40.723049 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:37:40.723062 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.375 | |
| I0506 01:37:40.723073 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.625 | |
| I0506 01:37:40.723085 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 01:37:40.723098 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 01:37:40.723109 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:37:40.723121 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:37:40.723132 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:37:40.723145 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:37:40.723157 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:37:40.723170 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:37:40.723181 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:37:40.723193 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:37:40.723204 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:37:40.723217 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:37:40.723228 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:37:40.723239 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:37:40.723251 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:37:40.723263 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.738636 | |
| I0506 01:37:40.723274 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.12766 | |
| I0506 01:37:40.723289 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.15 (* 0.3 = 0.945 loss) | |
| I0506 01:37:40.723304 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.893099 (* 0.3 = 0.26793 loss) | |
| I0506 01:37:40.723317 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 2.84196 (* 0.0272727 = 0.0775079 loss) | |
| I0506 01:37:40.723331 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.2981 (* 0.0272727 = 0.0899481 loss) | |
| I0506 01:37:40.723345 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 2.91562 (* 0.0272727 = 0.079517 loss) | |
| I0506 01:37:40.723359 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 2.97304 (* 0.0272727 = 0.0810829 loss) | |
| I0506 01:37:40.723373 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.90835 (* 0.0272727 = 0.0793187 loss) | |
| I0506 01:37:40.723387 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.86407 (* 0.0272727 = 0.078111 loss) | |
| I0506 01:37:40.723400 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.79675 (* 0.0272727 = 0.0490024 loss) | |
| I0506 01:37:40.723414 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.55213 (* 0.0272727 = 0.0423307 loss) | |
| I0506 01:37:40.723428 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.078158 (* 0.0272727 = 0.00213158 loss) | |
| I0506 01:37:40.723443 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0585067 (* 0.0272727 = 0.00159564 loss) | |
| I0506 01:37:40.723458 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0310441 (* 0.0272727 = 0.000846657 loss) | |
| I0506 01:37:40.723497 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0197639 (* 0.0272727 = 0.000539016 loss) | |
| I0506 01:37:40.723512 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0192038 (* 0.0272727 = 0.000523741 loss) | |
| I0506 01:37:40.723526 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0135224 (* 0.0272727 = 0.000368793 loss) | |
| I0506 01:37:40.723546 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0134936 (* 0.0272727 = 0.000368008 loss) | |
| I0506 01:37:40.723559 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.00926495 (* 0.0272727 = 0.00025268 loss) | |
| I0506 01:37:40.723573 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00451709 (* 0.0272727 = 0.000123193 loss) | |
| I0506 01:37:40.723587 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00634533 (* 0.0272727 = 0.000173054 loss) | |
| I0506 01:37:40.723601 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00415965 (* 0.0272727 = 0.000113445 loss) | |
| I0506 01:37:40.723615 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00547367 (* 0.0272727 = 0.000149282 loss) | |
| I0506 01:37:40.723629 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00699848 (* 0.0272727 = 0.000190868 loss) | |
| I0506 01:37:40.723644 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00353863 (* 0.0272727 = 9.6508e-05 loss) | |
| I0506 01:37:40.723655 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0638298 | |
| I0506 01:37:40.723669 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 01:37:40.723680 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:37:40.723692 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:37:40.723703 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:37:40.723716 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.25 | |
| I0506 01:37:40.723726 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.375 | |
| I0506 01:37:40.723738 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.625 | |
| I0506 01:37:40.723750 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 01:37:40.723762 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 01:37:40.723773 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:37:40.723785 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:37:40.723796 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:37:40.723808 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:37:40.723819 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:37:40.723830 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:37:40.723841 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:37:40.723853 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:37:40.723865 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:37:40.723875 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:37:40.723887 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:37:40.723898 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:37:40.723911 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:37:40.723922 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.744318 | |
| I0506 01:37:40.723933 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.148936 | |
| I0506 01:37:40.723950 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.20816 (* 0.3 = 0.962447 loss) | |
| I0506 01:37:40.723965 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.943109 (* 0.3 = 0.282933 loss) | |
| I0506 01:37:40.723979 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.1195 (* 0.0272727 = 0.0850774 loss) | |
| I0506 01:37:40.724004 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.47104 (* 0.0272727 = 0.0946647 loss) | |
| I0506 01:37:40.724019 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.27869 (* 0.0272727 = 0.0894189 loss) | |
| I0506 01:37:40.724032 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 2.9131 (* 0.0272727 = 0.0794481 loss) | |
| I0506 01:37:40.724045 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.36797 (* 0.0272727 = 0.0918537 loss) | |
| I0506 01:37:40.724059 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.26908 (* 0.0272727 = 0.0618839 loss) | |
| I0506 01:37:40.724073 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.13989 (* 0.0272727 = 0.0583606 loss) | |
| I0506 01:37:40.724086 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.65436 (* 0.0272727 = 0.0451189 loss) | |
| I0506 01:37:40.724100 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.139 (* 0.0272727 = 0.00379091 loss) | |
| I0506 01:37:40.724114 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0911022 (* 0.0272727 = 0.00248461 loss) | |
| I0506 01:37:40.724128 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0705299 (* 0.0272727 = 0.00192354 loss) | |
| I0506 01:37:40.724143 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0440521 (* 0.0272727 = 0.00120142 loss) | |
| I0506 01:37:40.724156 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0403902 (* 0.0272727 = 0.00110155 loss) | |
| I0506 01:37:40.724169 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0318146 (* 0.0272727 = 0.000867672 loss) | |
| I0506 01:37:40.724184 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0207052 (* 0.0272727 = 0.000564689 loss) | |
| I0506 01:37:40.724196 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0228364 (* 0.0272727 = 0.00062281 loss) | |
| I0506 01:37:40.724210 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00891846 (* 0.0272727 = 0.000243231 loss) | |
| I0506 01:37:40.724225 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0092248 (* 0.0272727 = 0.000251586 loss) | |
| I0506 01:37:40.724237 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00521517 (* 0.0272727 = 0.000142232 loss) | |
| I0506 01:37:40.724251 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00449229 (* 0.0272727 = 0.000122517 loss) | |
| I0506 01:37:40.724266 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00599615 (* 0.0272727 = 0.000163531 loss) | |
| I0506 01:37:40.724279 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00461278 (* 0.0272727 = 0.000125803 loss) | |
| I0506 01:37:40.724292 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0851064 | |
| I0506 01:37:40.724303 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:37:40.724315 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.125 | |
| I0506 01:37:40.724326 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:37:40.724337 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:37:40.724349 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 01:37:40.724360 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.375 | |
| I0506 01:37:40.724372 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.625 | |
| I0506 01:37:40.724385 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 01:37:40.724396 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 01:37:40.724407 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:37:40.724418 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:37:40.724431 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:37:40.724442 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:37:40.724453 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:37:40.724473 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:37:40.724486 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:37:40.724498 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:37:40.724509 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:37:40.724520 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:37:40.724531 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:37:40.724542 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:37:40.724555 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:37:40.724565 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.744318 | |
| I0506 01:37:40.724577 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.191489 | |
| I0506 01:37:40.724594 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.96439 (* 1 = 2.96439 loss) | |
| I0506 01:37:40.724609 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.870427 (* 1 = 0.870427 loss) | |
| I0506 01:37:40.724622 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.69713 (* 0.0909091 = 0.245194 loss) | |
| I0506 01:37:40.724637 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.18306 (* 0.0909091 = 0.289369 loss) | |
| I0506 01:37:40.724650 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.90056 (* 0.0909091 = 0.263687 loss) | |
| I0506 01:37:40.724663 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 2.73037 (* 0.0909091 = 0.248216 loss) | |
| I0506 01:37:40.724678 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.45187 (* 0.0909091 = 0.222897 loss) | |
| I0506 01:37:40.724690 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.13643 (* 0.0909091 = 0.19422 loss) | |
| I0506 01:37:40.724704 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.69823 (* 0.0909091 = 0.154384 loss) | |
| I0506 01:37:40.724717 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.34639 (* 0.0909091 = 0.122399 loss) | |
| I0506 01:37:40.724731 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.0389435 (* 0.0909091 = 0.00354032 loss) | |
| I0506 01:37:40.724745 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0232874 (* 0.0909091 = 0.00211704 loss) | |
| I0506 01:37:40.724758 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0105036 (* 0.0909091 = 0.00095487 loss) | |
| I0506 01:37:40.724772 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.00579453 (* 0.0909091 = 0.000526776 loss) | |
| I0506 01:37:40.724786 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00510447 (* 0.0909091 = 0.000464042 loss) | |
| I0506 01:37:40.724799 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00389442 (* 0.0909091 = 0.000354038 loss) | |
| I0506 01:37:40.724813 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00294994 (* 0.0909091 = 0.000268176 loss) | |
| I0506 01:37:40.724828 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00285326 (* 0.0909091 = 0.000259387 loss) | |
| I0506 01:37:40.724840 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00229571 (* 0.0909091 = 0.000208701 loss) | |
| I0506 01:37:40.724854 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00166039 (* 0.0909091 = 0.000150945 loss) | |
| I0506 01:37:40.724867 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00159168 (* 0.0909091 = 0.000144699 loss) | |
| I0506 01:37:40.724881 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000832416 (* 0.0909091 = 7.56742e-05 loss) | |
| I0506 01:37:40.724895 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000467003 (* 0.0909091 = 4.24548e-05 loss) | |
| I0506 01:37:40.724910 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000140476 (* 0.0909091 = 1.27705e-05 loss) | |
| I0506 01:37:40.724921 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:37:40.724942 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:37:40.724954 15760 solver.cpp:245] Train net output #149: total_confidence = 4.80083e-05 | |
| I0506 01:37:40.724967 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000170264 | |
| I0506 01:37:40.724979 15760 sgd_solver.cpp:106] Iteration 20000, lr = 0.001 | |
| I0506 01:37:52.347738 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 41.0424 > 30) by scale factor 0.730951 | |
| I0506 01:38:12.056915 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 52.3044 > 30) by scale factor 0.573566 | |
| I0506 01:39:27.162685 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 74.7365 > 30) by scale factor 0.40141 | |
| I0506 01:39:27.961073 15760 solver.cpp:229] Iteration 20500, loss = 9.83709 | |
| I0506 01:39:27.961163 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0217391 | |
| I0506 01:39:27.961182 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 01:39:27.961194 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0506 01:39:27.961207 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:39:27.961220 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 01:39:27.961231 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.5 | |
| I0506 01:39:27.961243 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.625 | |
| I0506 01:39:27.961256 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.875 | |
| I0506 01:39:27.961267 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 01:39:27.961283 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 01:39:27.961297 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.875 | |
| I0506 01:39:27.961308 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 0.875 | |
| I0506 01:39:27.961320 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 0.875 | |
| I0506 01:39:27.961333 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 0.875 | |
| I0506 01:39:27.961344 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:39:27.961356 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:39:27.961369 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:39:27.961380 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:39:27.961392 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:39:27.961403 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:39:27.961416 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:39:27.961427 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:39:27.961439 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:39:27.961450 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.744318 | |
| I0506 01:39:27.961462 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.23913 | |
| I0506 01:39:27.961479 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.13336 (* 0.3 = 0.940008 loss) | |
| I0506 01:39:27.961493 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.912462 (* 0.3 = 0.273739 loss) | |
| I0506 01:39:27.961508 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.24785 (* 0.0272727 = 0.0885777 loss) | |
| I0506 01:39:27.961521 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.94835 (* 0.0272727 = 0.107682 loss) | |
| I0506 01:39:27.961535 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.44199 (* 0.0272727 = 0.0938725 loss) | |
| I0506 01:39:27.961549 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.49649 (* 0.0272727 = 0.0953589 loss) | |
| I0506 01:39:27.961562 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.16445 (* 0.0272727 = 0.0590306 loss) | |
| I0506 01:39:27.961575 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 1.55406 (* 0.0272727 = 0.0423836 loss) | |
| I0506 01:39:27.961590 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 0.688783 (* 0.0272727 = 0.018785 loss) | |
| I0506 01:39:27.961603 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.457752 (* 0.0272727 = 0.0124841 loss) | |
| I0506 01:39:27.961616 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.456465 (* 0.0272727 = 0.012449 loss) | |
| I0506 01:39:27.961630 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.5713 (* 0.0272727 = 0.0155809 loss) | |
| I0506 01:39:27.961643 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.504697 (* 0.0272727 = 0.0137645 loss) | |
| I0506 01:39:27.961658 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.48136 (* 0.0272727 = 0.013128 loss) | |
| I0506 01:39:27.961709 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.438532 (* 0.0272727 = 0.01196 loss) | |
| I0506 01:39:27.961724 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.111103 (* 0.0272727 = 0.00303008 loss) | |
| I0506 01:39:27.961738 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0646384 (* 0.0272727 = 0.00176287 loss) | |
| I0506 01:39:27.961752 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0303005 (* 0.0272727 = 0.000826378 loss) | |
| I0506 01:39:27.961766 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0164644 (* 0.0272727 = 0.000449028 loss) | |
| I0506 01:39:27.961781 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0055464 (* 0.0272727 = 0.000151265 loss) | |
| I0506 01:39:27.961793 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00827488 (* 0.0272727 = 0.000225678 loss) | |
| I0506 01:39:27.961807 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00531508 (* 0.0272727 = 0.000144957 loss) | |
| I0506 01:39:27.961822 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00651926 (* 0.0272727 = 0.000177798 loss) | |
| I0506 01:39:27.961834 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00330143 (* 0.0272727 = 9.00391e-05 loss) | |
| I0506 01:39:27.961846 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0217391 | |
| I0506 01:39:27.961858 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 01:39:27.961870 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:39:27.961881 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:39:27.961896 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:39:27.961908 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.5 | |
| I0506 01:39:27.961920 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.625 | |
| I0506 01:39:27.961932 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.875 | |
| I0506 01:39:27.961943 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 01:39:27.961956 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 01:39:27.961967 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.875 | |
| I0506 01:39:27.961979 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 0.875 | |
| I0506 01:39:27.961990 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 0.875 | |
| I0506 01:39:27.962002 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 0.875 | |
| I0506 01:39:27.962014 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:39:27.962025 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:39:27.962038 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:39:27.962049 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:39:27.962059 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:39:27.962071 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:39:27.962082 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:39:27.962093 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:39:27.962105 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:39:27.962116 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.738636 | |
| I0506 01:39:27.962128 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.26087 | |
| I0506 01:39:27.962142 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.10693 (* 0.3 = 0.932079 loss) | |
| I0506 01:39:27.962157 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.918064 (* 0.3 = 0.275419 loss) | |
| I0506 01:39:27.962170 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 2.99024 (* 0.0272727 = 0.081552 loss) | |
| I0506 01:39:27.962183 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.66492 (* 0.0272727 = 0.0999523 loss) | |
| I0506 01:39:27.962208 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.43211 (* 0.0272727 = 0.0936029 loss) | |
| I0506 01:39:27.962224 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.2673 (* 0.0272727 = 0.0891083 loss) | |
| I0506 01:39:27.962237 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.06973 (* 0.0272727 = 0.0564472 loss) | |
| I0506 01:39:27.962251 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 1.6978 (* 0.0272727 = 0.0463036 loss) | |
| I0506 01:39:27.962265 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 0.983129 (* 0.0272727 = 0.0268126 loss) | |
| I0506 01:39:27.962275 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.549641 (* 0.0272727 = 0.0149902 loss) | |
| I0506 01:39:27.962285 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.396262 (* 0.0272727 = 0.0108071 loss) | |
| I0506 01:39:27.962301 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.527998 (* 0.0272727 = 0.0144 loss) | |
| I0506 01:39:27.962314 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.56302 (* 0.0272727 = 0.0153551 loss) | |
| I0506 01:39:27.962328 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.721282 (* 0.0272727 = 0.0196713 loss) | |
| I0506 01:39:27.962347 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.555074 (* 0.0272727 = 0.0151384 loss) | |
| I0506 01:39:27.962362 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.034205 (* 0.0272727 = 0.000932865 loss) | |
| I0506 01:39:27.962375 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0542686 (* 0.0272727 = 0.00148005 loss) | |
| I0506 01:39:27.962389 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0232908 (* 0.0272727 = 0.000635203 loss) | |
| I0506 01:39:27.962404 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0147022 (* 0.0272727 = 0.000400968 loss) | |
| I0506 01:39:27.962416 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00889032 (* 0.0272727 = 0.000242463 loss) | |
| I0506 01:39:27.962430 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00555505 (* 0.0272727 = 0.000151501 loss) | |
| I0506 01:39:27.962445 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00594274 (* 0.0272727 = 0.000162075 loss) | |
| I0506 01:39:27.962457 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00628782 (* 0.0272727 = 0.000171486 loss) | |
| I0506 01:39:27.962471 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00785678 (* 0.0272727 = 0.000214276 loss) | |
| I0506 01:39:27.962483 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.108696 | |
| I0506 01:39:27.962496 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:39:27.962507 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0.125 | |
| I0506 01:39:27.962519 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:39:27.962530 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.375 | |
| I0506 01:39:27.962543 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.625 | |
| I0506 01:39:27.962553 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.625 | |
| I0506 01:39:27.962565 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.875 | |
| I0506 01:39:27.962576 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 01:39:27.962589 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 01:39:27.962599 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.875 | |
| I0506 01:39:27.962610 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 0.875 | |
| I0506 01:39:27.962622 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 0.875 | |
| I0506 01:39:27.962633 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 0.875 | |
| I0506 01:39:27.962644 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:39:27.962656 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:39:27.962677 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:39:27.962690 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:39:27.962702 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:39:27.962713 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:39:27.962724 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:39:27.962735 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:39:27.962748 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:39:27.962759 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.767045 | |
| I0506 01:39:27.962769 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.282609 | |
| I0506 01:39:27.962784 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.96292 (* 1 = 2.96292 loss) | |
| I0506 01:39:27.962797 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.852065 (* 1 = 0.852065 loss) | |
| I0506 01:39:27.962811 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.77656 (* 0.0909091 = 0.252414 loss) | |
| I0506 01:39:27.962826 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.17273 (* 0.0909091 = 0.28843 loss) | |
| I0506 01:39:27.962839 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.2019 (* 0.0909091 = 0.291082 loss) | |
| I0506 01:39:27.962852 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.02828 (* 0.0909091 = 0.275299 loss) | |
| I0506 01:39:27.962867 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 1.46116 (* 0.0909091 = 0.132832 loss) | |
| I0506 01:39:27.962879 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.33182 (* 0.0909091 = 0.121075 loss) | |
| I0506 01:39:27.962893 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 0.728917 (* 0.0909091 = 0.0662652 loss) | |
| I0506 01:39:27.962908 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.393579 (* 0.0909091 = 0.0357799 loss) | |
| I0506 01:39:27.962920 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.312316 (* 0.0909091 = 0.0283923 loss) | |
| I0506 01:39:27.962934 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.390745 (* 0.0909091 = 0.0355223 loss) | |
| I0506 01:39:27.962951 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.32169 (* 0.0909091 = 0.0292446 loss) | |
| I0506 01:39:27.962965 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.563257 (* 0.0909091 = 0.0512052 loss) | |
| I0506 01:39:27.962978 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.254756 (* 0.0909091 = 0.0231596 loss) | |
| I0506 01:39:27.962992 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0384251 (* 0.0909091 = 0.00349319 loss) | |
| I0506 01:39:27.963006 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.017981 (* 0.0909091 = 0.00163464 loss) | |
| I0506 01:39:27.963021 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00736314 (* 0.0909091 = 0.000669377 loss) | |
| I0506 01:39:27.963034 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0100271 (* 0.0909091 = 0.000911555 loss) | |
| I0506 01:39:27.963048 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00286412 (* 0.0909091 = 0.000260374 loss) | |
| I0506 01:39:27.963062 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00125714 (* 0.0909091 = 0.000114285 loss) | |
| I0506 01:39:27.963075 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00285668 (* 0.0909091 = 0.000259699 loss) | |
| I0506 01:39:27.963088 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00139374 (* 0.0909091 = 0.000126703 loss) | |
| I0506 01:39:27.963102 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.00123529 (* 0.0909091 = 0.000112299 loss) | |
| I0506 01:39:27.963114 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:39:27.963125 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:39:27.963146 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000211799 | |
| I0506 01:39:27.963160 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000575102 | |
| I0506 01:39:27.963173 15760 sgd_solver.cpp:106] Iteration 20500, lr = 0.001 | |
| I0506 01:40:19.486096 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.8864 > 30) by scale factor 0.971302 | |
| I0506 01:40:41.117801 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.1351 > 30) by scale factor 0.963543 | |
| I0506 01:40:44.964016 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 41.1935 > 30) by scale factor 0.728271 | |
| I0506 01:41:15.301951 15760 solver.cpp:229] Iteration 21000, loss = 9.86575 | |
| I0506 01:41:15.302078 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.075 | |
| I0506 01:41:15.302100 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 01:41:15.302114 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:41:15.302125 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 01:41:15.302137 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.375 | |
| I0506 01:41:15.302150 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:41:15.302175 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.5 | |
| I0506 01:41:15.302198 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.75 | |
| I0506 01:41:15.302212 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 1 | |
| I0506 01:41:15.302224 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 01:41:15.302237 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:41:15.302255 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:41:15.302266 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:41:15.302278 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:41:15.302289 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:41:15.302301 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:41:15.302314 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:41:15.302325 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:41:15.302336 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:41:15.302366 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:41:15.302377 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:41:15.302389 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:41:15.302400 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:41:15.302412 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.789773 | |
| I0506 01:41:15.302429 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.275 | |
| I0506 01:41:15.302445 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 2.95773 (* 0.3 = 0.887319 loss) | |
| I0506 01:41:15.302459 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.771874 (* 0.3 = 0.231562 loss) | |
| I0506 01:41:15.302474 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.23963 (* 0.0272727 = 0.0883537 loss) | |
| I0506 01:41:15.302487 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.05856 (* 0.0272727 = 0.0834154 loss) | |
| I0506 01:41:15.302501 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 2.98152 (* 0.0272727 = 0.0813141 loss) | |
| I0506 01:41:15.302515 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 2.126 (* 0.0272727 = 0.0579818 loss) | |
| I0506 01:41:15.302528 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.43531 (* 0.0272727 = 0.0664176 loss) | |
| I0506 01:41:15.302542 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.15865 (* 0.0272727 = 0.0588723 loss) | |
| I0506 01:41:15.302556 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 1.17099 (* 0.0272727 = 0.0319361 loss) | |
| I0506 01:41:15.302569 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.0796253 (* 0.0272727 = 0.0021716 loss) | |
| I0506 01:41:15.302583 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.0364238 (* 0.0272727 = 0.000993377 loss) | |
| I0506 01:41:15.302597 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.019524 (* 0.0272727 = 0.000532472 loss) | |
| I0506 01:41:15.302611 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0192795 (* 0.0272727 = 0.000525805 loss) | |
| I0506 01:41:15.302624 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0203192 (* 0.0272727 = 0.00055416 loss) | |
| I0506 01:41:15.302639 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0218101 (* 0.0272727 = 0.00059482 loss) | |
| I0506 01:41:15.302672 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0305868 (* 0.0272727 = 0.000834185 loss) | |
| I0506 01:41:15.302687 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0279883 (* 0.0272727 = 0.000763316 loss) | |
| I0506 01:41:15.302702 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.01604 (* 0.0272727 = 0.000437454 loss) | |
| I0506 01:41:15.302716 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0168131 (* 0.0272727 = 0.00045854 loss) | |
| I0506 01:41:15.302745 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0292577 (* 0.0272727 = 0.000797938 loss) | |
| I0506 01:41:15.302759 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0173683 (* 0.0272727 = 0.00047368 loss) | |
| I0506 01:41:15.302773 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0154449 (* 0.0272727 = 0.000421224 loss) | |
| I0506 01:41:15.302786 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00739256 (* 0.0272727 = 0.000201615 loss) | |
| I0506 01:41:15.302800 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0223862 (* 0.0272727 = 0.000610532 loss) | |
| I0506 01:41:15.302812 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.175 | |
| I0506 01:41:15.302825 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 01:41:15.302836 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0.125 | |
| I0506 01:41:15.302848 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 01:41:15.302860 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.5 | |
| I0506 01:41:15.302871 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 01:41:15.302886 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.5 | |
| I0506 01:41:15.302898 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 01:41:15.302911 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 1 | |
| I0506 01:41:15.302922 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 01:41:15.302932 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:41:15.302943 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:41:15.302955 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:41:15.302966 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:41:15.302978 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:41:15.302989 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:41:15.302999 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:41:15.303010 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:41:15.303021 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:41:15.303032 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:41:15.303043 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:41:15.303055 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:41:15.303066 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:41:15.303077 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.801136 | |
| I0506 01:41:15.303092 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.325 | |
| I0506 01:41:15.303107 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 2.84484 (* 0.3 = 0.853451 loss) | |
| I0506 01:41:15.303120 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.76457 (* 0.3 = 0.229371 loss) | |
| I0506 01:41:15.303134 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 2.61084 (* 0.0272727 = 0.0712046 loss) | |
| I0506 01:41:15.303148 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 2.66361 (* 0.0272727 = 0.0726439 loss) | |
| I0506 01:41:15.303174 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 2.80314 (* 0.0272727 = 0.0764493 loss) | |
| I0506 01:41:15.303189 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 2.24684 (* 0.0272727 = 0.0612774 loss) | |
| I0506 01:41:15.303201 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.10996 (* 0.0272727 = 0.0575445 loss) | |
| I0506 01:41:15.303215 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 2.12213 (* 0.0272727 = 0.0578763 loss) | |
| I0506 01:41:15.303228 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.30065 (* 0.0272727 = 0.0354723 loss) | |
| I0506 01:41:15.303244 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.0410543 (* 0.0272727 = 0.00111966 loss) | |
| I0506 01:41:15.303257 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.0228115 (* 0.0272727 = 0.000622132 loss) | |
| I0506 01:41:15.303272 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0111743 (* 0.0272727 = 0.000304754 loss) | |
| I0506 01:41:15.303284 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0108795 (* 0.0272727 = 0.000296713 loss) | |
| I0506 01:41:15.303298 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.00339825 (* 0.0272727 = 9.26797e-05 loss) | |
| I0506 01:41:15.303313 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.00643621 (* 0.0272727 = 0.000175533 loss) | |
| I0506 01:41:15.303325 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.00939647 (* 0.0272727 = 0.000256267 loss) | |
| I0506 01:41:15.303339 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0103929 (* 0.0272727 = 0.000283442 loss) | |
| I0506 01:41:15.303354 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.00616458 (* 0.0272727 = 0.000168125 loss) | |
| I0506 01:41:15.303366 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00569456 (* 0.0272727 = 0.000155306 loss) | |
| I0506 01:41:15.303380 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00289212 (* 0.0272727 = 7.8876e-05 loss) | |
| I0506 01:41:15.303395 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00547476 (* 0.0272727 = 0.000149311 loss) | |
| I0506 01:41:15.303407 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00389556 (* 0.0272727 = 0.000106243 loss) | |
| I0506 01:41:15.303421 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0186206 (* 0.0272727 = 0.000507834 loss) | |
| I0506 01:41:15.303434 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0137633 (* 0.0272727 = 0.000375364 loss) | |
| I0506 01:41:15.303447 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.05 | |
| I0506 01:41:15.303458 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.375 | |
| I0506 01:41:15.303470 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:41:15.303481 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:41:15.303493 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.5 | |
| I0506 01:41:15.303503 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.375 | |
| I0506 01:41:15.303515 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.5 | |
| I0506 01:41:15.303526 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 01:41:15.303537 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 1 | |
| I0506 01:41:15.303550 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 01:41:15.303560 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:41:15.303571 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:41:15.303582 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:41:15.303593 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:41:15.303606 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:41:15.303617 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:41:15.303627 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:41:15.303648 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:41:15.303660 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:41:15.303671 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:41:15.303683 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:41:15.303694 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:41:15.303705 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:41:15.303716 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.784091 | |
| I0506 01:41:15.303727 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.3 | |
| I0506 01:41:15.303740 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.74939 (* 1 = 2.74939 loss) | |
| I0506 01:41:15.303755 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.68415 (* 1 = 0.68415 loss) | |
| I0506 01:41:15.303767 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.27775 (* 0.0909091 = 0.207068 loss) | |
| I0506 01:41:15.303781 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.47034 (* 0.0909091 = 0.224577 loss) | |
| I0506 01:41:15.303794 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.63664 (* 0.0909091 = 0.239694 loss) | |
| I0506 01:41:15.303807 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 1.8383 (* 0.0909091 = 0.167118 loss) | |
| I0506 01:41:15.303822 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.12734 (* 0.0909091 = 0.193394 loss) | |
| I0506 01:41:15.303834 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.72095 (* 0.0909091 = 0.15645 loss) | |
| I0506 01:41:15.303848 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 1.00963 (* 0.0909091 = 0.0917849 loss) | |
| I0506 01:41:15.303858 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.0326375 (* 0.0909091 = 0.00296704 loss) | |
| I0506 01:41:15.303867 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.00646006 (* 0.0909091 = 0.000587278 loss) | |
| I0506 01:41:15.303881 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.000933191 (* 0.0909091 = 8.48355e-05 loss) | |
| I0506 01:41:15.303895 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.000264759 (* 0.0909091 = 2.4069e-05 loss) | |
| I0506 01:41:15.303908 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.000203655 (* 0.0909091 = 1.85141e-05 loss) | |
| I0506 01:41:15.303922 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.000253192 (* 0.0909091 = 2.30175e-05 loss) | |
| I0506 01:41:15.303954 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.000245214 (* 0.0909091 = 2.22922e-05 loss) | |
| I0506 01:41:15.303974 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.000176224 (* 0.0909091 = 1.60204e-05 loss) | |
| I0506 01:41:15.303988 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.000161661 (* 0.0909091 = 1.46965e-05 loss) | |
| I0506 01:41:15.304003 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.000389183 (* 0.0909091 = 3.53803e-05 loss) | |
| I0506 01:41:15.304020 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.000298864 (* 0.0909091 = 2.71695e-05 loss) | |
| I0506 01:41:15.304034 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00030509 (* 0.0909091 = 2.77355e-05 loss) | |
| I0506 01:41:15.304047 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.000376152 (* 0.0909091 = 3.41956e-05 loss) | |
| I0506 01:41:15.304061 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000183993 (* 0.0909091 = 1.67266e-05 loss) | |
| I0506 01:41:15.304075 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000338962 (* 0.0909091 = 3.08148e-05 loss) | |
| I0506 01:41:15.304086 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:41:15.304097 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:41:15.304108 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000668356 | |
| I0506 01:41:15.304129 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.00225692 | |
| I0506 01:41:15.304148 15760 sgd_solver.cpp:106] Iteration 21000, lr = 0.001 | |
| I0506 01:42:46.655160 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 35.2722 > 30) by scale factor 0.850529 | |
| I0506 01:42:55.474550 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.947 > 30) by scale factor 0.969398 | |
| I0506 01:43:03.180140 15760 solver.cpp:229] Iteration 21500, loss = 9.81008 | |
| I0506 01:43:03.180199 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.097561 | |
| I0506 01:43:03.180217 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0506 01:43:03.180229 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:43:03.180241 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.125 | |
| I0506 01:43:03.180260 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 01:43:03.180272 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:43:03.180284 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.75 | |
| I0506 01:43:03.180296 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.875 | |
| I0506 01:43:03.180308 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 01:43:03.180320 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 01:43:03.180335 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:43:03.180346 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:43:03.180357 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:43:03.180368 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:43:03.180380 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:43:03.180395 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:43:03.180418 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:43:03.180434 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:43:03.180464 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:43:03.180476 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:43:03.180487 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:43:03.180500 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:43:03.180510 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:43:03.180531 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.778409 | |
| I0506 01:43:03.180541 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.341463 | |
| I0506 01:43:03.180557 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 2.9943 (* 0.3 = 0.898291 loss) | |
| I0506 01:43:03.180572 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.82332 (* 0.3 = 0.246996 loss) | |
| I0506 01:43:03.180585 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.44943 (* 0.0272727 = 0.0940754 loss) | |
| I0506 01:43:03.180599 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.22774 (* 0.0272727 = 0.0880294 loss) | |
| I0506 01:43:03.180613 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.49019 (* 0.0272727 = 0.0951869 loss) | |
| I0506 01:43:03.180626 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.45204 (* 0.0272727 = 0.0941466 loss) | |
| I0506 01:43:03.180640 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.20408 (* 0.0272727 = 0.0601112 loss) | |
| I0506 01:43:03.180654 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 1.68988 (* 0.0272727 = 0.0460876 loss) | |
| I0506 01:43:03.180667 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 0.628578 (* 0.0272727 = 0.017143 loss) | |
| I0506 01:43:03.180681 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.5888 (* 0.0272727 = 0.0160582 loss) | |
| I0506 01:43:03.180696 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.0437735 (* 0.0272727 = 0.00119382 loss) | |
| I0506 01:43:03.180711 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0343864 (* 0.0272727 = 0.000937811 loss) | |
| I0506 01:43:03.180734 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.02737 (* 0.0272727 = 0.000746454 loss) | |
| I0506 01:43:03.180797 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0169131 (* 0.0272727 = 0.000461266 loss) | |
| I0506 01:43:03.180814 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.011516 (* 0.0272727 = 0.000314072 loss) | |
| I0506 01:43:03.180826 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0108572 (* 0.0272727 = 0.000296105 loss) | |
| I0506 01:43:03.180850 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0122271 (* 0.0272727 = 0.000333466 loss) | |
| I0506 01:43:03.180865 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.00651319 (* 0.0272727 = 0.000177633 loss) | |
| I0506 01:43:03.180877 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00539903 (* 0.0272727 = 0.000147246 loss) | |
| I0506 01:43:03.180892 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00361601 (* 0.0272727 = 9.86185e-05 loss) | |
| I0506 01:43:03.180907 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00346559 (* 0.0272727 = 9.45161e-05 loss) | |
| I0506 01:43:03.180920 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.00517004 (* 0.0272727 = 0.000141001 loss) | |
| I0506 01:43:03.180933 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00183858 (* 0.0272727 = 5.01432e-05 loss) | |
| I0506 01:43:03.180948 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00275225 (* 0.0272727 = 7.50613e-05 loss) | |
| I0506 01:43:03.180959 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.146341 | |
| I0506 01:43:03.180971 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0 | |
| I0506 01:43:03.180982 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:43:03.180994 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.375 | |
| I0506 01:43:03.181005 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0 | |
| I0506 01:43:03.181016 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 01:43:03.181028 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.75 | |
| I0506 01:43:03.181040 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.875 | |
| I0506 01:43:03.181051 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 01:43:03.181062 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 01:43:03.181073 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:43:03.181085 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:43:03.181097 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:43:03.181107 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:43:03.181131 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:43:03.181146 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:43:03.181159 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:43:03.181169 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:43:03.181180 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:43:03.181191 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:43:03.181203 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:43:03.181215 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:43:03.181226 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:43:03.181236 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.795455 | |
| I0506 01:43:03.181248 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.341463 | |
| I0506 01:43:03.181265 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 2.98186 (* 0.3 = 0.894558 loss) | |
| I0506 01:43:03.181279 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.858501 (* 0.3 = 0.25755 loss) | |
| I0506 01:43:03.181293 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.74026 (* 0.0272727 = 0.102007 loss) | |
| I0506 01:43:03.181319 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 2.81876 (* 0.0272727 = 0.0768753 loss) | |
| I0506 01:43:03.181336 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 2.97142 (* 0.0272727 = 0.0810388 loss) | |
| I0506 01:43:03.181350 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.33204 (* 0.0272727 = 0.0908737 loss) | |
| I0506 01:43:03.181363 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.44306 (* 0.0272727 = 0.0666288 loss) | |
| I0506 01:43:03.181377 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 1.60262 (* 0.0272727 = 0.0437079 loss) | |
| I0506 01:43:03.181391 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 0.564649 (* 0.0272727 = 0.0153995 loss) | |
| I0506 01:43:03.181403 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.56775 (* 0.0272727 = 0.0154841 loss) | |
| I0506 01:43:03.181417 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.119758 (* 0.0272727 = 0.00326613 loss) | |
| I0506 01:43:03.181432 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.056474 (* 0.0272727 = 0.0015402 loss) | |
| I0506 01:43:03.181448 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.036609 (* 0.0272727 = 0.000998428 loss) | |
| I0506 01:43:03.181463 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0408065 (* 0.0272727 = 0.0011129 loss) | |
| I0506 01:43:03.181478 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0179319 (* 0.0272727 = 0.000489052 loss) | |
| I0506 01:43:03.181491 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0164141 (* 0.0272727 = 0.000447657 loss) | |
| I0506 01:43:03.181505 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0140822 (* 0.0272727 = 0.000384059 loss) | |
| I0506 01:43:03.181519 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.00987764 (* 0.0272727 = 0.00026939 loss) | |
| I0506 01:43:03.181532 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00924986 (* 0.0272727 = 0.000252269 loss) | |
| I0506 01:43:03.181545 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00371083 (* 0.0272727 = 0.000101204 loss) | |
| I0506 01:43:03.181560 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00379429 (* 0.0272727 = 0.000103481 loss) | |
| I0506 01:43:03.181573 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00248551 (* 0.0272727 = 6.77866e-05 loss) | |
| I0506 01:43:03.181586 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00236915 (* 0.0272727 = 6.46131e-05 loss) | |
| I0506 01:43:03.181601 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00305078 (* 0.0272727 = 8.3203e-05 loss) | |
| I0506 01:43:03.181612 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0487805 | |
| I0506 01:43:03.181623 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 01:43:03.181635 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:43:03.181646 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:43:03.181658 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0 | |
| I0506 01:43:03.181668 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.5 | |
| I0506 01:43:03.181680 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.75 | |
| I0506 01:43:03.181691 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.875 | |
| I0506 01:43:03.181702 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.875 | |
| I0506 01:43:03.181715 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 1 | |
| I0506 01:43:03.181726 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:43:03.181737 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:43:03.181753 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:43:03.181761 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:43:03.181774 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:43:03.181805 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:43:03.181826 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:43:03.181843 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:43:03.181859 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:43:03.181870 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:43:03.181881 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:43:03.181903 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:43:03.181915 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:43:03.181926 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.767045 | |
| I0506 01:43:03.181938 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.268293 | |
| I0506 01:43:03.181951 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 2.92624 (* 1 = 2.92624 loss) | |
| I0506 01:43:03.181967 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.767335 (* 1 = 0.767335 loss) | |
| I0506 01:43:03.181993 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 3.36705 (* 0.0909091 = 0.306096 loss) | |
| I0506 01:43:03.182014 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 2.87342 (* 0.0909091 = 0.26122 loss) | |
| I0506 01:43:03.182029 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.80997 (* 0.0909091 = 0.255452 loss) | |
| I0506 01:43:03.182042 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.08379 (* 0.0909091 = 0.280344 loss) | |
| I0506 01:43:03.182055 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.00038 (* 0.0909091 = 0.181852 loss) | |
| I0506 01:43:03.182068 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 1.34176 (* 0.0909091 = 0.121979 loss) | |
| I0506 01:43:03.182087 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 0.601749 (* 0.0909091 = 0.0547045 loss) | |
| I0506 01:43:03.182101 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.531281 (* 0.0909091 = 0.0482983 loss) | |
| I0506 01:43:03.182114 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.0717573 (* 0.0909091 = 0.00652339 loss) | |
| I0506 01:43:03.182128 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.0278614 (* 0.0909091 = 0.00253286 loss) | |
| I0506 01:43:03.182142 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.0138232 (* 0.0909091 = 0.00125665 loss) | |
| I0506 01:43:03.182154 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.00661395 (* 0.0909091 = 0.000601268 loss) | |
| I0506 01:43:03.182168 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.00534203 (* 0.0909091 = 0.000485639 loss) | |
| I0506 01:43:03.182181 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.00587642 (* 0.0909091 = 0.00053422 loss) | |
| I0506 01:43:03.182194 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.00382448 (* 0.0909091 = 0.00034768 loss) | |
| I0506 01:43:03.182207 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.00331293 (* 0.0909091 = 0.000301175 loss) | |
| I0506 01:43:03.182226 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.00337847 (* 0.0909091 = 0.000307134 loss) | |
| I0506 01:43:03.182240 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00195728 (* 0.0909091 = 0.000177934 loss) | |
| I0506 01:43:03.182252 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00165112 (* 0.0909091 = 0.000150102 loss) | |
| I0506 01:43:03.182266 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00120936 (* 0.0909091 = 0.000109942 loss) | |
| I0506 01:43:03.182279 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.000927079 (* 0.0909091 = 8.42799e-05 loss) | |
| I0506 01:43:03.182301 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000644712 (* 0.0909091 = 5.86102e-05 loss) | |
| I0506 01:43:03.182312 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:43:03.182334 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:43:03.182348 15760 solver.cpp:245] Train net output #149: total_confidence = 0.000163064 | |
| I0506 01:43:03.182359 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000596317 | |
| I0506 01:43:03.182371 15760 sgd_solver.cpp:106] Iteration 21500, lr = 0.001 | |
| I0506 01:43:03.667918 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 34.8996 > 30) by scale factor 0.859609 | |
| I0506 01:43:36.488999 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 45.7652 > 30) by scale factor 0.655519 | |
| I0506 01:44:50.815320 15760 solver.cpp:229] Iteration 22000, loss = 9.86653 | |
| I0506 01:44:50.815454 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.04 | |
| I0506 01:44:50.815474 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.125 | |
| I0506 01:44:50.815488 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:44:50.815500 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.25 | |
| I0506 01:44:50.815512 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.125 | |
| I0506 01:44:50.815523 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.25 | |
| I0506 01:44:50.815536 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.125 | |
| I0506 01:44:50.815547 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.875 | |
| I0506 01:44:50.815559 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 01:44:50.815572 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.75 | |
| I0506 01:44:50.815583 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 0.75 | |
| I0506 01:44:50.815596 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:44:50.815608 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:44:50.815619 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:44:50.815631 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:44:50.815644 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:44:50.815656 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:44:50.815668 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:44:50.815680 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:44:50.815691 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:44:50.815703 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:44:50.815717 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:44:50.815738 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:44:50.815752 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.664773 | |
| I0506 01:44:50.815763 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.22 | |
| I0506 01:44:50.815779 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.18583 (* 0.3 = 0.955748 loss) | |
| I0506 01:44:50.815793 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.27078 (* 0.3 = 0.381234 loss) | |
| I0506 01:44:50.815809 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 3.47707 (* 0.0272727 = 0.0948292 loss) | |
| I0506 01:44:50.815822 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.97722 (* 0.0272727 = 0.10847 loss) | |
| I0506 01:44:50.815836 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.08723 (* 0.0272727 = 0.0841971 loss) | |
| I0506 01:44:50.815850 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.15651 (* 0.0272727 = 0.0860868 loss) | |
| I0506 01:44:50.815865 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.36026 (* 0.0272727 = 0.0916435 loss) | |
| I0506 01:44:50.815881 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 3.81015 (* 0.0272727 = 0.103913 loss) | |
| I0506 01:44:50.815896 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 0.800744 (* 0.0272727 = 0.0218385 loss) | |
| I0506 01:44:50.815910 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.961825 (* 0.0272727 = 0.0262316 loss) | |
| I0506 01:44:50.815924 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.795964 (* 0.0272727 = 0.0217081 loss) | |
| I0506 01:44:50.815938 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.959404 (* 0.0272727 = 0.0261656 loss) | |
| I0506 01:44:50.815951 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.546226 (* 0.0272727 = 0.0148971 loss) | |
| I0506 01:44:50.815965 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.436588 (* 0.0272727 = 0.0119069 loss) | |
| I0506 01:44:50.815979 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.381201 (* 0.0272727 = 0.0103964 loss) | |
| I0506 01:44:50.816011 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.248311 (* 0.0272727 = 0.00677211 loss) | |
| I0506 01:44:50.816026 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.255183 (* 0.0272727 = 0.00695952 loss) | |
| I0506 01:44:50.816040 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.110768 (* 0.0272727 = 0.00302094 loss) | |
| I0506 01:44:50.816054 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0431397 (* 0.0272727 = 0.00117654 loss) | |
| I0506 01:44:50.816069 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0354935 (* 0.0272727 = 0.000968004 loss) | |
| I0506 01:44:50.816083 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0388912 (* 0.0272727 = 0.00106067 loss) | |
| I0506 01:44:50.816097 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0132568 (* 0.0272727 = 0.00036155 loss) | |
| I0506 01:44:50.816112 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.0144871 (* 0.0272727 = 0.000395103 loss) | |
| I0506 01:44:50.816125 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.0133985 (* 0.0272727 = 0.000365414 loss) | |
| I0506 01:44:50.816138 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.08 | |
| I0506 01:44:50.816149 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 01:44:50.816161 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:44:50.816174 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:44:50.816185 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:44:50.816196 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.25 | |
| I0506 01:44:50.816207 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.25 | |
| I0506 01:44:50.816220 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.75 | |
| I0506 01:44:50.816231 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 01:44:50.816244 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.75 | |
| I0506 01:44:50.816256 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 0.75 | |
| I0506 01:44:50.816270 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:44:50.816290 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:44:50.816303 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:44:50.816314 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:44:50.816325 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:44:50.816337 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:44:50.816349 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:44:50.816359 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:44:50.816371 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:44:50.816382 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:44:50.816393 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:44:50.816404 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:44:50.816416 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.710227 | |
| I0506 01:44:50.816428 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.24 | |
| I0506 01:44:50.816442 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.22309 (* 0.3 = 0.966926 loss) | |
| I0506 01:44:50.816457 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.14089 (* 0.3 = 0.342266 loss) | |
| I0506 01:44:50.816475 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 3.13866 (* 0.0272727 = 0.0855999 loss) | |
| I0506 01:44:50.816489 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.82128 (* 0.0272727 = 0.104217 loss) | |
| I0506 01:44:50.816504 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.26285 (* 0.0272727 = 0.0889869 loss) | |
| I0506 01:44:50.816529 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 2.99559 (* 0.0272727 = 0.081698 loss) | |
| I0506 01:44:50.816545 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.78013 (* 0.0272727 = 0.0758216 loss) | |
| I0506 01:44:50.816558 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.62302 (* 0.0272727 = 0.0988096 loss) | |
| I0506 01:44:50.816571 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 1.0807 (* 0.0272727 = 0.0294737 loss) | |
| I0506 01:44:50.816586 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.03257 (* 0.0272727 = 0.0281611 loss) | |
| I0506 01:44:50.816598 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.751611 (* 0.0272727 = 0.0204985 loss) | |
| I0506 01:44:50.816612 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.958176 (* 0.0272727 = 0.0261321 loss) | |
| I0506 01:44:50.816627 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.225473 (* 0.0272727 = 0.00614927 loss) | |
| I0506 01:44:50.816639 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.229921 (* 0.0272727 = 0.00627057 loss) | |
| I0506 01:44:50.816653 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.137808 (* 0.0272727 = 0.00375841 loss) | |
| I0506 01:44:50.816668 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0896624 (* 0.0272727 = 0.00244534 loss) | |
| I0506 01:44:50.816680 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.088586 (* 0.0272727 = 0.00241598 loss) | |
| I0506 01:44:50.816694 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0222397 (* 0.0272727 = 0.000606536 loss) | |
| I0506 01:44:50.816709 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0138269 (* 0.0272727 = 0.000377097 loss) | |
| I0506 01:44:50.816721 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00528553 (* 0.0272727 = 0.000144151 loss) | |
| I0506 01:44:50.816735 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00677551 (* 0.0272727 = 0.000184787 loss) | |
| I0506 01:44:50.816750 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00602799 (* 0.0272727 = 0.0001644 loss) | |
| I0506 01:44:50.816763 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00387946 (* 0.0272727 = 0.000105804 loss) | |
| I0506 01:44:50.816777 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00272527 (* 0.0272727 = 7.43255e-05 loss) | |
| I0506 01:44:50.816789 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.08 | |
| I0506 01:44:50.816802 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0 | |
| I0506 01:44:50.816812 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:44:50.816823 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0.125 | |
| I0506 01:44:50.816835 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 01:44:50.816846 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.25 | |
| I0506 01:44:50.816859 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.125 | |
| I0506 01:44:50.816869 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.75 | |
| I0506 01:44:50.816880 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 01:44:50.816892 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.75 | |
| I0506 01:44:50.816903 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 0.75 | |
| I0506 01:44:50.816915 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:44:50.816928 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:44:50.816941 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:44:50.816952 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:44:50.816963 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:44:50.816975 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:44:50.816997 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:44:50.817009 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:44:50.817020 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:44:50.817031 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:44:50.817044 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:44:50.817054 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:44:50.817065 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.698864 | |
| I0506 01:44:50.817077 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.32 | |
| I0506 01:44:50.817091 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.20712 (* 1 = 3.20712 loss) | |
| I0506 01:44:50.817104 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.16596 (* 1 = 1.16596 loss) | |
| I0506 01:44:50.817133 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.93878 (* 0.0909091 = 0.267162 loss) | |
| I0506 01:44:50.817152 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.60395 (* 0.0909091 = 0.327632 loss) | |
| I0506 01:44:50.817165 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 2.97407 (* 0.0909091 = 0.27037 loss) | |
| I0506 01:44:50.817178 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.12489 (* 0.0909091 = 0.284081 loss) | |
| I0506 01:44:50.817191 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 3.18527 (* 0.0909091 = 0.28957 loss) | |
| I0506 01:44:50.817205 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 3.50159 (* 0.0909091 = 0.318327 loss) | |
| I0506 01:44:50.817219 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 0.94783 (* 0.0909091 = 0.0861664 loss) | |
| I0506 01:44:50.817232 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 0.952377 (* 0.0909091 = 0.0865797 loss) | |
| I0506 01:44:50.817246 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.816229 (* 0.0909091 = 0.0742026 loss) | |
| I0506 01:44:50.817260 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.85622 (* 0.0909091 = 0.0778382 loss) | |
| I0506 01:44:50.817272 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.318728 (* 0.0909091 = 0.0289752 loss) | |
| I0506 01:44:50.817286 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.177642 (* 0.0909091 = 0.0161493 loss) | |
| I0506 01:44:50.817299 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.13148 (* 0.0909091 = 0.0119527 loss) | |
| I0506 01:44:50.817313 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0484561 (* 0.0909091 = 0.0044051 loss) | |
| I0506 01:44:50.817327 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0312042 (* 0.0909091 = 0.00283675 loss) | |
| I0506 01:44:50.817342 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0195665 (* 0.0909091 = 0.00177877 loss) | |
| I0506 01:44:50.817355 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0108038 (* 0.0909091 = 0.000982165 loss) | |
| I0506 01:44:50.817368 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.00893583 (* 0.0909091 = 0.000812348 loss) | |
| I0506 01:44:50.817383 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.00385931 (* 0.0909091 = 0.000350846 loss) | |
| I0506 01:44:50.817395 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.00153983 (* 0.0909091 = 0.000139984 loss) | |
| I0506 01:44:50.817409 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.00116841 (* 0.0909091 = 0.000106219 loss) | |
| I0506 01:44:50.817423 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.000203797 (* 0.0909091 = 1.8527e-05 loss) | |
| I0506 01:44:50.817435 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:44:50.817446 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:44:50.817457 15760 solver.cpp:245] Train net output #149: total_confidence = 1.40205e-05 | |
| I0506 01:44:50.817481 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 0.000125186 | |
| I0506 01:44:50.817495 15760 sgd_solver.cpp:106] Iteration 22000, lr = 0.001 | |
| I0506 01:44:56.303001 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 48.6849 > 30) by scale factor 0.616208 | |
| I0506 01:46:06.321776 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 32.7024 > 30) by scale factor 0.917364 | |
| I0506 01:46:25.498239 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 115.621 > 30) by scale factor 0.259468 | |
| I0506 01:46:38.783668 15760 solver.cpp:229] Iteration 22500, loss = 9.73912 | |
| I0506 01:46:38.783789 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0384615 | |
| I0506 01:46:38.783810 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.125 | |
| I0506 01:46:38.783823 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:46:38.783836 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.25 | |
| I0506 01:46:38.783849 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0506 01:46:38.783860 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.25 | |
| I0506 01:46:38.783872 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.25 | |
| I0506 01:46:38.783887 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.375 | |
| I0506 01:46:38.783900 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.75 | |
| I0506 01:46:38.783911 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 0.875 | |
| I0506 01:46:38.783923 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:46:38.783936 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:46:38.783946 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:46:38.783958 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:46:38.783969 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:46:38.783982 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:46:38.783993 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:46:38.784004 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:46:38.784016 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:46:38.784027 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:46:38.784039 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:46:38.784051 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:46:38.784062 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:46:38.784075 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.704545 | |
| I0506 01:46:38.784086 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.230769 | |
| I0506 01:46:38.784102 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 3.35521 (* 0.3 = 1.00656 loss) | |
| I0506 01:46:38.784116 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.12008 (* 0.3 = 0.336023 loss) | |
| I0506 01:46:38.784131 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 2.78806 (* 0.0272727 = 0.0760379 loss) | |
| I0506 01:46:38.784144 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 3.16936 (* 0.0272727 = 0.0864372 loss) | |
| I0506 01:46:38.784158 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.58785 (* 0.0272727 = 0.0978505 loss) | |
| I0506 01:46:38.784173 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 3.40414 (* 0.0272727 = 0.0928401 loss) | |
| I0506 01:46:38.784186 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 3.2479 (* 0.0272727 = 0.0885791 loss) | |
| I0506 01:46:38.784199 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 2.94696 (* 0.0272727 = 0.0803716 loss) | |
| I0506 01:46:38.784214 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 2.9754 (* 0.0272727 = 0.0811472 loss) | |
| I0506 01:46:38.784227 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 1.41444 (* 0.0272727 = 0.0385756 loss) | |
| I0506 01:46:38.784240 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.59087 (* 0.0272727 = 0.0161146 loss) | |
| I0506 01:46:38.784255 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.207092 (* 0.0272727 = 0.00564797 loss) | |
| I0506 01:46:38.784268 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.113819 (* 0.0272727 = 0.00310415 loss) | |
| I0506 01:46:38.784282 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.122696 (* 0.0272727 = 0.00334626 loss) | |
| I0506 01:46:38.784296 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.103288 (* 0.0272727 = 0.00281695 loss) | |
| I0506 01:46:38.784330 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.094634 (* 0.0272727 = 0.00258093 loss) | |
| I0506 01:46:38.784346 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0952354 (* 0.0272727 = 0.00259733 loss) | |
| I0506 01:46:38.784360 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0416378 (* 0.0272727 = 0.00113558 loss) | |
| I0506 01:46:38.784374 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.0200568 (* 0.0272727 = 0.000547002 loss) | |
| I0506 01:46:38.784389 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.0107033 (* 0.0272727 = 0.000291909 loss) | |
| I0506 01:46:38.784411 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.00994478 (* 0.0272727 = 0.000271221 loss) | |
| I0506 01:46:38.784425 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0127035 (* 0.0272727 = 0.000346458 loss) | |
| I0506 01:46:38.784440 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00429126 (* 0.0272727 = 0.000117034 loss) | |
| I0506 01:46:38.784453 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00554698 (* 0.0272727 = 0.000151281 loss) | |
| I0506 01:46:38.784471 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0576923 | |
| I0506 01:46:38.784483 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 01:46:38.784495 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:46:38.784508 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0 | |
| I0506 01:46:38.784519 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.125 | |
| I0506 01:46:38.784531 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.125 | |
| I0506 01:46:38.784543 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.125 | |
| I0506 01:46:38.784554 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.375 | |
| I0506 01:46:38.784565 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.75 | |
| I0506 01:46:38.784577 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 0.875 | |
| I0506 01:46:38.784590 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:46:38.784600 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:46:38.784612 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:46:38.784623 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:46:38.784634 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:46:38.784646 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:46:38.784657 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:46:38.784668 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:46:38.784679 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:46:38.784692 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:46:38.784703 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:46:38.784713 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:46:38.784725 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:46:38.784736 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.715909 | |
| I0506 01:46:38.784745 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.134615 | |
| I0506 01:46:38.784754 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 3.29507 (* 0.3 = 0.988522 loss) | |
| I0506 01:46:38.784770 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.05158 (* 0.3 = 0.315475 loss) | |
| I0506 01:46:38.784783 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 2.97221 (* 0.0272727 = 0.0810604 loss) | |
| I0506 01:46:38.784797 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.26853 (* 0.0272727 = 0.0891417 loss) | |
| I0506 01:46:38.784827 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.56099 (* 0.0272727 = 0.097118 loss) | |
| I0506 01:46:38.784843 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 3.16008 (* 0.0272727 = 0.0861839 loss) | |
| I0506 01:46:38.784857 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 3.47074 (* 0.0272727 = 0.0946565 loss) | |
| I0506 01:46:38.784870 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 3.09895 (* 0.0272727 = 0.0845169 loss) | |
| I0506 01:46:38.784884 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 2.76171 (* 0.0272727 = 0.0753193 loss) | |
| I0506 01:46:38.784898 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 1.30861 (* 0.0272727 = 0.0356895 loss) | |
| I0506 01:46:38.784911 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.601531 (* 0.0272727 = 0.0164054 loss) | |
| I0506 01:46:38.784927 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0931629 (* 0.0272727 = 0.00254081 loss) | |
| I0506 01:46:38.784942 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0773417 (* 0.0272727 = 0.00210932 loss) | |
| I0506 01:46:38.784956 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0767233 (* 0.0272727 = 0.00209245 loss) | |
| I0506 01:46:38.784970 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0904881 (* 0.0272727 = 0.00246786 loss) | |
| I0506 01:46:38.784983 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0469979 (* 0.0272727 = 0.00128176 loss) | |
| I0506 01:46:38.784997 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0313515 (* 0.0272727 = 0.000855041 loss) | |
| I0506 01:46:38.785012 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0451407 (* 0.0272727 = 0.00123111 loss) | |
| I0506 01:46:38.785024 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.0362241 (* 0.0272727 = 0.000987929 loss) | |
| I0506 01:46:38.785038 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.0245386 (* 0.0272727 = 0.000669234 loss) | |
| I0506 01:46:38.785053 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.0315892 (* 0.0272727 = 0.000861524 loss) | |
| I0506 01:46:38.785065 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.0083862 (* 0.0272727 = 0.000228715 loss) | |
| I0506 01:46:38.785079 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.0106735 (* 0.0272727 = 0.000291096 loss) | |
| I0506 01:46:38.785092 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.0148642 (* 0.0272727 = 0.000405388 loss) | |
| I0506 01:46:38.785104 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.0769231 | |
| I0506 01:46:38.785116 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.25 | |
| I0506 01:46:38.785148 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:46:38.785161 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:46:38.785172 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.125 | |
| I0506 01:46:38.785183 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = 0.125 | |
| I0506 01:46:38.785195 15760 solver.cpp:245] Train net output #104: loss3/accuracy06 = 0.25 | |
| I0506 01:46:38.785207 15760 solver.cpp:245] Train net output #105: loss3/accuracy07 = 0.375 | |
| I0506 01:46:38.785218 15760 solver.cpp:245] Train net output #106: loss3/accuracy08 = 0.75 | |
| I0506 01:46:38.785230 15760 solver.cpp:245] Train net output #107: loss3/accuracy09 = 0.875 | |
| I0506 01:46:38.785243 15760 solver.cpp:245] Train net output #108: loss3/accuracy10 = 1 | |
| I0506 01:46:38.785254 15760 solver.cpp:245] Train net output #109: loss3/accuracy11 = 1 | |
| I0506 01:46:38.785265 15760 solver.cpp:245] Train net output #110: loss3/accuracy12 = 1 | |
| I0506 01:46:38.785276 15760 solver.cpp:245] Train net output #111: loss3/accuracy13 = 1 | |
| I0506 01:46:38.785289 15760 solver.cpp:245] Train net output #112: loss3/accuracy14 = 1 | |
| I0506 01:46:38.785300 15760 solver.cpp:245] Train net output #113: loss3/accuracy15 = 1 | |
| I0506 01:46:38.785310 15760 solver.cpp:245] Train net output #114: loss3/accuracy16 = 1 | |
| I0506 01:46:38.785333 15760 solver.cpp:245] Train net output #115: loss3/accuracy17 = 1 | |
| I0506 01:46:38.785346 15760 solver.cpp:245] Train net output #116: loss3/accuracy18 = 1 | |
| I0506 01:46:38.785358 15760 solver.cpp:245] Train net output #117: loss3/accuracy19 = 1 | |
| I0506 01:46:38.785370 15760 solver.cpp:245] Train net output #118: loss3/accuracy20 = 1 | |
| I0506 01:46:38.785382 15760 solver.cpp:245] Train net output #119: loss3/accuracy21 = 1 | |
| I0506 01:46:38.785392 15760 solver.cpp:245] Train net output #120: loss3/accuracy22 = 1 | |
| I0506 01:46:38.785410 15760 solver.cpp:245] Train net output #121: loss3/accuracy_incl_empty = 0.710227 | |
| I0506 01:46:38.785423 15760 solver.cpp:245] Train net output #122: loss3/accuracy_top3 = 0.211538 | |
| I0506 01:46:38.785436 15760 solver.cpp:245] Train net output #123: loss3/cross_entropy_loss = 3.18365 (* 1 = 3.18365 loss) | |
| I0506 01:46:38.785450 15760 solver.cpp:245] Train net output #124: loss3/cross_entropy_loss_incl_empty = 1.06677 (* 1 = 1.06677 loss) | |
| I0506 01:46:38.785464 15760 solver.cpp:245] Train net output #125: loss3/loss01 = 2.70547 (* 0.0909091 = 0.245952 loss) | |
| I0506 01:46:38.785485 15760 solver.cpp:245] Train net output #126: loss3/loss02 = 3.2042 (* 0.0909091 = 0.291291 loss) | |
| I0506 01:46:38.785498 15760 solver.cpp:245] Train net output #127: loss3/loss03 = 3.39919 (* 0.0909091 = 0.309017 loss) | |
| I0506 01:46:38.785511 15760 solver.cpp:245] Train net output #128: loss3/loss04 = 3.34516 (* 0.0909091 = 0.304106 loss) | |
| I0506 01:46:38.785526 15760 solver.cpp:245] Train net output #129: loss3/loss05 = 2.96546 (* 0.0909091 = 0.269587 loss) | |
| I0506 01:46:38.785539 15760 solver.cpp:245] Train net output #130: loss3/loss06 = 2.90205 (* 0.0909091 = 0.263823 loss) | |
| I0506 01:46:38.785552 15760 solver.cpp:245] Train net output #131: loss3/loss07 = 2.47631 (* 0.0909091 = 0.225119 loss) | |
| I0506 01:46:38.785567 15760 solver.cpp:245] Train net output #132: loss3/loss08 = 1.04033 (* 0.0909091 = 0.0945753 loss) | |
| I0506 01:46:38.785580 15760 solver.cpp:245] Train net output #133: loss3/loss09 = 0.552452 (* 0.0909091 = 0.0502229 loss) | |
| I0506 01:46:38.785593 15760 solver.cpp:245] Train net output #134: loss3/loss10 = 0.102388 (* 0.0909091 = 0.009308 loss) | |
| I0506 01:46:38.785607 15760 solver.cpp:245] Train net output #135: loss3/loss11 = 0.081909 (* 0.0909091 = 0.00744627 loss) | |
| I0506 01:46:38.785621 15760 solver.cpp:245] Train net output #136: loss3/loss12 = 0.0810213 (* 0.0909091 = 0.00736558 loss) | |
| I0506 01:46:38.785635 15760 solver.cpp:245] Train net output #137: loss3/loss13 = 0.0786511 (* 0.0909091 = 0.0071501 loss) | |
| I0506 01:46:38.785648 15760 solver.cpp:245] Train net output #138: loss3/loss14 = 0.0770173 (* 0.0909091 = 0.00700157 loss) | |
| I0506 01:46:38.785661 15760 solver.cpp:245] Train net output #139: loss3/loss15 = 0.0631821 (* 0.0909091 = 0.00574383 loss) | |
| I0506 01:46:38.785676 15760 solver.cpp:245] Train net output #140: loss3/loss16 = 0.0478215 (* 0.0909091 = 0.00434741 loss) | |
| I0506 01:46:38.785689 15760 solver.cpp:245] Train net output #141: loss3/loss17 = 0.0373629 (* 0.0909091 = 0.00339663 loss) | |
| I0506 01:46:38.785702 15760 solver.cpp:245] Train net output #142: loss3/loss18 = 0.0231246 (* 0.0909091 = 0.00210224 loss) | |
| I0506 01:46:38.785717 15760 solver.cpp:245] Train net output #143: loss3/loss19 = 0.0314262 (* 0.0909091 = 0.00285693 loss) | |
| I0506 01:46:38.785729 15760 solver.cpp:245] Train net output #144: loss3/loss20 = 0.0254509 (* 0.0909091 = 0.00231372 loss) | |
| I0506 01:46:38.785743 15760 solver.cpp:245] Train net output #145: loss3/loss21 = 0.0187005 (* 0.0909091 = 0.00170004 loss) | |
| I0506 01:46:38.785756 15760 solver.cpp:245] Train net output #146: loss3/loss22 = 0.0221344 (* 0.0909091 = 0.00201222 loss) | |
| I0506 01:46:38.785768 15760 solver.cpp:245] Train net output #147: total_accuracy = 0 | |
| I0506 01:46:38.785779 15760 solver.cpp:245] Train net output #148: total_accuracy_not_rec = 0 | |
| I0506 01:46:38.785791 15760 solver.cpp:245] Train net output #149: total_confidence = 2.38985e-07 | |
| I0506 01:46:38.785812 15760 solver.cpp:245] Train net output #150: total_confidence_not_rec = 2.18112e-06 | |
| I0506 01:46:38.785826 15760 sgd_solver.cpp:106] Iteration 22500, lr = 0.001 | |
| I0506 01:46:55.903178 15760 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.6205 > 30) by scale factor 0.979736 | |
| I0506 01:48:27.095963 15760 solver.cpp:229] Iteration 23000, loss = 9.80171 | |
| I0506 01:48:27.096104 15760 solver.cpp:245] Train net output #0: loss1/accuracy = 0.102564 | |
| I0506 01:48:27.096127 15760 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.25 | |
| I0506 01:48:27.096139 15760 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.125 | |
| I0506 01:48:27.096151 15760 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0506 01:48:27.096163 15760 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.375 | |
| I0506 01:48:27.096175 15760 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.375 | |
| I0506 01:48:27.096187 15760 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.75 | |
| I0506 01:48:27.096199 15760 solver.cpp:245] Train net output #7: loss1/accuracy07 = 0.875 | |
| I0506 01:48:27.096210 15760 solver.cpp:245] Train net output #8: loss1/accuracy08 = 0.875 | |
| I0506 01:48:27.096223 15760 solver.cpp:245] Train net output #9: loss1/accuracy09 = 1 | |
| I0506 01:48:27.096235 15760 solver.cpp:245] Train net output #10: loss1/accuracy10 = 1 | |
| I0506 01:48:27.096246 15760 solver.cpp:245] Train net output #11: loss1/accuracy11 = 1 | |
| I0506 01:48:27.096258 15760 solver.cpp:245] Train net output #12: loss1/accuracy12 = 1 | |
| I0506 01:48:27.096269 15760 solver.cpp:245] Train net output #13: loss1/accuracy13 = 1 | |
| I0506 01:48:27.096282 15760 solver.cpp:245] Train net output #14: loss1/accuracy14 = 1 | |
| I0506 01:48:27.096293 15760 solver.cpp:245] Train net output #15: loss1/accuracy15 = 1 | |
| I0506 01:48:27.096305 15760 solver.cpp:245] Train net output #16: loss1/accuracy16 = 1 | |
| I0506 01:48:27.096318 15760 solver.cpp:245] Train net output #17: loss1/accuracy17 = 1 | |
| I0506 01:48:27.096328 15760 solver.cpp:245] Train net output #18: loss1/accuracy18 = 1 | |
| I0506 01:48:27.096340 15760 solver.cpp:245] Train net output #19: loss1/accuracy19 = 1 | |
| I0506 01:48:27.096351 15760 solver.cpp:245] Train net output #20: loss1/accuracy20 = 1 | |
| I0506 01:48:27.096364 15760 solver.cpp:245] Train net output #21: loss1/accuracy21 = 1 | |
| I0506 01:48:27.096374 15760 solver.cpp:245] Train net output #22: loss1/accuracy22 = 1 | |
| I0506 01:48:27.096386 15760 solver.cpp:245] Train net output #23: loss1/accuracy_incl_empty = 0.784091 | |
| I0506 01:48:27.096397 15760 solver.cpp:245] Train net output #24: loss1/accuracy_top3 = 0.205128 | |
| I0506 01:48:27.096415 15760 solver.cpp:245] Train net output #25: loss1/cross_entropy_loss = 2.85816 (* 0.3 = 0.857448 loss) | |
| I0506 01:48:27.096428 15760 solver.cpp:245] Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.839456 (* 0.3 = 0.251837 loss) | |
| I0506 01:48:27.096443 15760 solver.cpp:245] Train net output #27: loss1/loss01 = 2.63258 (* 0.0272727 = 0.0717977 loss) | |
| I0506 01:48:27.096457 15760 solver.cpp:245] Train net output #28: loss1/loss02 = 2.96322 (* 0.0272727 = 0.080815 loss) | |
| I0506 01:48:27.096470 15760 solver.cpp:245] Train net output #29: loss1/loss03 = 3.20713 (* 0.0272727 = 0.0874672 loss) | |
| I0506 01:48:27.096484 15760 solver.cpp:245] Train net output #30: loss1/loss04 = 2.66459 (* 0.0272727 = 0.0726707 loss) | |
| I0506 01:48:27.096498 15760 solver.cpp:245] Train net output #31: loss1/loss05 = 2.1767 (* 0.0272727 = 0.0593646 loss) | |
| I0506 01:48:27.096511 15760 solver.cpp:245] Train net output #32: loss1/loss06 = 1.57929 (* 0.0272727 = 0.0430716 loss) | |
| I0506 01:48:27.096525 15760 solver.cpp:245] Train net output #33: loss1/loss07 = 0.751464 (* 0.0272727 = 0.0204945 loss) | |
| I0506 01:48:27.096539 15760 solver.cpp:245] Train net output #34: loss1/loss08 = 0.901534 (* 0.0272727 = 0.0245873 loss) | |
| I0506 01:48:27.096554 15760 solver.cpp:245] Train net output #35: loss1/loss09 = 0.109988 (* 0.0272727 = 0.00299968 loss) | |
| I0506 01:48:27.096567 15760 solver.cpp:245] Train net output #36: loss1/loss10 = 0.0588343 (* 0.0272727 = 0.00160457 loss) | |
| I0506 01:48:27.096580 15760 solver.cpp:245] Train net output #37: loss1/loss11 = 0.0595061 (* 0.0272727 = 0.00162289 loss) | |
| I0506 01:48:27.096603 15760 solver.cpp:245] Train net output #38: loss1/loss12 = 0.0459467 (* 0.0272727 = 0.00125309 loss) | |
| I0506 01:48:27.096617 15760 solver.cpp:245] Train net output #39: loss1/loss13 = 0.0361056 (* 0.0272727 = 0.000984698 loss) | |
| I0506 01:48:27.096650 15760 solver.cpp:245] Train net output #40: loss1/loss14 = 0.0275801 (* 0.0272727 = 0.000752185 loss) | |
| I0506 01:48:27.096665 15760 solver.cpp:245] Train net output #41: loss1/loss15 = 0.0237966 (* 0.0272727 = 0.000648998 loss) | |
| I0506 01:48:27.096679 15760 solver.cpp:245] Train net output #42: loss1/loss16 = 0.0194347 (* 0.0272727 = 0.000530038 loss) | |
| I0506 01:48:27.096693 15760 solver.cpp:245] Train net output #43: loss1/loss17 = 0.00678812 (* 0.0272727 = 0.00018513 loss) | |
| I0506 01:48:27.096707 15760 solver.cpp:245] Train net output #44: loss1/loss18 = 0.00651316 (* 0.0272727 = 0.000177632 loss) | |
| I0506 01:48:27.096720 15760 solver.cpp:245] Train net output #45: loss1/loss19 = 0.0102387 (* 0.0272727 = 0.000279237 loss) | |
| I0506 01:48:27.096735 15760 solver.cpp:245] Train net output #46: loss1/loss20 = 0.0061118 (* 0.0272727 = 0.000166685 loss) | |
| I0506 01:48:27.096747 15760 solver.cpp:245] Train net output #47: loss1/loss21 = 0.00419701 (* 0.0272727 = 0.000114464 loss) | |
| I0506 01:48:27.096761 15760 solver.cpp:245] Train net output #48: loss1/loss22 = 0.00496545 (* 0.0272727 = 0.000135421 loss) | |
| I0506 01:48:27.096773 15760 solver.cpp:245] Train net output #49: loss2/accuracy = 0.0512821 | |
| I0506 01:48:27.096786 15760 solver.cpp:245] Train net output #50: loss2/accuracy01 = 0.125 | |
| I0506 01:48:27.096806 15760 solver.cpp:245] Train net output #51: loss2/accuracy02 = 0 | |
| I0506 01:48:27.096817 15760 solver.cpp:245] Train net output #52: loss2/accuracy03 = 0.125 | |
| I0506 01:48:27.096828 15760 solver.cpp:245] Train net output #53: loss2/accuracy04 = 0.25 | |
| I0506 01:48:27.096839 15760 solver.cpp:245] Train net output #54: loss2/accuracy05 = 0.375 | |
| I0506 01:48:27.096848 15760 solver.cpp:245] Train net output #55: loss2/accuracy06 = 0.75 | |
| I0506 01:48:27.096855 15760 solver.cpp:245] Train net output #56: loss2/accuracy07 = 0.875 | |
| I0506 01:48:27.096879 15760 solver.cpp:245] Train net output #57: loss2/accuracy08 = 0.875 | |
| I0506 01:48:27.096891 15760 solver.cpp:245] Train net output #58: loss2/accuracy09 = 1 | |
| I0506 01:48:27.096904 15760 solver.cpp:245] Train net output #59: loss2/accuracy10 = 1 | |
| I0506 01:48:27.096915 15760 solver.cpp:245] Train net output #60: loss2/accuracy11 = 1 | |
| I0506 01:48:27.096925 15760 solver.cpp:245] Train net output #61: loss2/accuracy12 = 1 | |
| I0506 01:48:27.096936 15760 solver.cpp:245] Train net output #62: loss2/accuracy13 = 1 | |
| I0506 01:48:27.096947 15760 solver.cpp:245] Train net output #63: loss2/accuracy14 = 1 | |
| I0506 01:48:27.096959 15760 solver.cpp:245] Train net output #64: loss2/accuracy15 = 1 | |
| I0506 01:48:27.096971 15760 solver.cpp:245] Train net output #65: loss2/accuracy16 = 1 | |
| I0506 01:48:27.096982 15760 solver.cpp:245] Train net output #66: loss2/accuracy17 = 1 | |
| I0506 01:48:27.096992 15760 solver.cpp:245] Train net output #67: loss2/accuracy18 = 1 | |
| I0506 01:48:27.097003 15760 solver.cpp:245] Train net output #68: loss2/accuracy19 = 1 | |
| I0506 01:48:27.097015 15760 solver.cpp:245] Train net output #69: loss2/accuracy20 = 1 | |
| I0506 01:48:27.097026 15760 solver.cpp:245] Train net output #70: loss2/accuracy21 = 1 | |
| I0506 01:48:27.097038 15760 solver.cpp:245] Train net output #71: loss2/accuracy22 = 1 | |
| I0506 01:48:27.097048 15760 solver.cpp:245] Train net output #72: loss2/accuracy_incl_empty = 0.761364 | |
| I0506 01:48:27.097060 15760 solver.cpp:245] Train net output #73: loss2/accuracy_top3 = 0.282051 | |
| I0506 01:48:27.097074 15760 solver.cpp:245] Train net output #74: loss2/cross_entropy_loss = 2.82071 (* 0.3 = 0.846214 loss) | |
| I0506 01:48:27.097087 15760 solver.cpp:245] Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.798828 (* 0.3 = 0.239648 loss) | |
| I0506 01:48:27.097101 15760 solver.cpp:245] Train net output #76: loss2/loss01 = 2.54673 (* 0.0272727 = 0.0694562 loss) | |
| I0506 01:48:27.097128 15760 solver.cpp:245] Train net output #77: loss2/loss02 = 3.18762 (* 0.0272727 = 0.086935 loss) | |
| I0506 01:48:27.097158 15760 solver.cpp:245] Train net output #78: loss2/loss03 = 3.24409 (* 0.0272727 = 0.0884752 loss) | |
| I0506 01:48:27.097173 15760 solver.cpp:245] Train net output #79: loss2/loss04 = 2.88141 (* 0.0272727 = 0.0785839 loss) | |
| I0506 01:48:27.097187 15760 solver.cpp:245] Train net output #80: loss2/loss05 = 2.41184 (* 0.0272727 = 0.0657776 loss) | |
| I0506 01:48:27.097200 15760 solver.cpp:245] Train net output #81: loss2/loss06 = 1.47967 (* 0.0272727 = 0.0403545 loss) | |
| I0506 01:48:27.097214 15760 solver.cpp:245] Train net output #82: loss2/loss07 = 0.74732 (* 0.0272727 = 0.0203814 loss) | |
| I0506 01:48:27.097228 15760 solver.cpp:245] Train net output #83: loss2/loss08 = 0.790997 (* 0.0272727 = 0.0215726 loss) | |
| I0506 01:48:27.097241 15760 solver.cpp:245] Train net output #84: loss2/loss09 = 0.0640428 (* 0.0272727 = 0.00174662 loss) | |
| I0506 01:48:27.097255 15760 solver.cpp:245] Train net output #85: loss2/loss10 = 0.0313644 (* 0.0272727 = 0.000855392 loss) | |
| I0506 01:48:27.097270 15760 solver.cpp:245] Train net output #86: loss2/loss11 = 0.0158407 (* 0.0272727 = 0.00043202 loss) | |
| I0506 01:48:27.097282 15760 solver.cpp:245] Train net output #87: loss2/loss12 = 0.0247166 (* 0.0272727 = 0.000674088 loss) | |
| I0506 01:48:27.097296 15760 solver.cpp:245] Train net output #88: loss2/loss13 = 0.0220872 (* 0.0272727 = 0.000602377 loss) | |
| I0506 01:48:27.097311 15760 solver.cpp:245] Train net output #89: loss2/loss14 = 0.0165353 (* 0.0272727 = 0.000450962 loss) | |
| I0506 01:48:27.097323 15760 solver.cpp:245] Train net output #90: loss2/loss15 = 0.0112308 (* 0.0272727 = 0.000306293 loss) | |
| I0506 01:48:27.097337 15760 solver.cpp:245] Train net output #91: loss2/loss16 = 0.0096345 (* 0.0272727 = 0.000262759 loss) | |
| I0506 01:48:27.097352 15760 solver.cpp:245] Train net output #92: loss2/loss17 = 0.00466891 (* 0.0272727 = 0.000127334 loss) | |
| I0506 01:48:27.097364 15760 solver.cpp:245] Train net output #93: loss2/loss18 = 0.00339534 (* 0.0272727 = 9.26003e-05 loss) | |
| I0506 01:48:27.097378 15760 solver.cpp:245] Train net output #94: loss2/loss19 = 0.00353045 (* 0.0272727 = 9.62849e-05 loss) | |
| I0506 01:48:27.097393 15760 solver.cpp:245] Train net output #95: loss2/loss20 = 0.00182685 (* 0.0272727 = 4.98233e-05 loss) | |
| I0506 01:48:27.097406 15760 solver.cpp:245] Train net output #96: loss2/loss21 = 0.00178632 (* 0.0272727 = 4.87177e-05 loss) | |
| I0506 01:48:27.097419 15760 solver.cpp:245] Train net output #97: loss2/loss22 = 0.00256699 (* 0.0272727 = 7.00089e-05 loss) | |
| I0506 01:48:27.097431 15760 solver.cpp:245] Train net output #98: loss3/accuracy = 0.153846 | |
| I0506 01:48:27.097443 15760 solver.cpp:245] Train net output #99: loss3/accuracy01 = 0.125 | |
| I0506 01:48:27.097455 15760 solver.cpp:245] Train net output #100: loss3/accuracy02 = 0 | |
| I0506 01:48:27.097466 15760 solver.cpp:245] Train net output #101: loss3/accuracy03 = 0 | |
| I0506 01:48:27.097477 15760 solver.cpp:245] Train net output #102: loss3/accuracy04 = 0.25 | |
| I0506 01:48:27.097489 15760 solver.cpp:245] Train net output #103: loss3/accuracy05 = |
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