mixed_14_3

I0430 14:38:39.975833 15443 solver.cpp:280] Solving mixed_lstm
I0430 14:38:39.975847 15443 solver.cpp:281] Learning Rate Policy: fixed
I0430 14:38:40.358908 15443 solver.cpp:229] Iteration 0, loss = 5.42065
I0430 14:38:40.358966 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.510638
I0430 14:38:40.358986 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 14:38:40.358999 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0430 14:38:40.359011 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 14:38:40.359024 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0430 14:38:40.359035 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0430 14:38:40.359047 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 14:38:40.359093 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 14:38:40.359107 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 14:38:40.359119 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0430 14:38:40.359132 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 14:38:40.359143 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 14:38:40.359154 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 14:38:40.359165 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 14:38:40.359176 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 14:38:40.359187 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 14:38:40.359203 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 14:38:40.359215 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 14:38:40.359226 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 14:38:40.359237 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 14:38:40.359248 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 14:38:40.359259 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 14:38:40.359271 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 14:38:40.359282 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.846591
I0430 14:38:40.359293 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.787234
I0430 14:38:40.359311 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.10707 (* 0.3 = 0.632121 loss)
I0430 14:38:40.359326 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.624383 (* 0.3 = 0.187315 loss)
I0430 14:38:40.359340 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.00493 (* 0.0272727 = 0.0274073 loss)
I0430 14:38:40.359354 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.30379 (* 0.0272727 = 0.0628306 loss)
I0430 14:38:40.359369 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 3.1408 (* 0.0272727 = 0.0856581 loss)
I0430 14:38:40.359382 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.52694 (* 0.0272727 = 0.0689166 loss)
I0430 14:38:40.359395 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.45131 (* 0.0272727 = 0.0668539 loss)
I0430 14:38:40.359410 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.7552 (* 0.0272727 = 0.047869 loss)
I0430 14:38:40.359422 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.810831 (* 0.0272727 = 0.0221136 loss)
I0430 14:38:40.359436 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.239466 (* 0.0272727 = 0.0065309 loss)
I0430 14:38:40.359450 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.00827495 (* 0.0272727 = 0.000225681 loss)
I0430 14:38:40.359482 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00490117 (* 0.0272727 = 0.000133668 loss)
I0430 14:38:40.359499 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00317545 (* 0.0272727 = 8.66031e-05 loss)
I0430 14:38:40.359513 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00246912 (* 0.0272727 = 6.73395e-05 loss)
I0430 14:38:40.359527 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00315794 (* 0.0272727 = 8.61258e-05 loss)
I0430 14:38:40.359541 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00215697 (* 0.0272727 = 5.88265e-05 loss)
I0430 14:38:40.359555 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00157216 (* 0.0272727 = 4.2877e-05 loss)
I0430 14:38:40.359568 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.002219 (* 0.0272727 = 6.05181e-05 loss)
I0430 14:38:40.359582 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00198532 (* 0.0272727 = 5.4145e-05 loss)
I0430 14:38:40.359596 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00166345 (* 0.0272727 = 4.53667e-05 loss)
I0430 14:38:40.359623 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00161378 (* 0.0272727 = 4.40122e-05 loss)
I0430 14:38:40.359638 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000557906 (* 0.0272727 = 1.52156e-05 loss)
I0430 14:38:40.359652 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000467645 (* 0.0272727 = 1.2754e-05 loss)
I0430 14:38:40.359665 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000178285 (* 0.0272727 = 4.86233e-06 loss)
I0430 14:38:40.359678 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.531915
I0430 14:38:40.359689 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 14:38:40.359701 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 14:38:40.359714 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.25
I0430 14:38:40.359724 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 14:38:40.359736 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0430 14:38:40.359747 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 14:38:40.359760 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0430 14:38:40.359771 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 14:38:40.359782 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0430 14:38:40.359793 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 14:38:40.359804 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 14:38:40.359815 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 14:38:40.359827 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 14:38:40.359838 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 14:38:40.359849 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 14:38:40.359860 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 14:38:40.359871 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 14:38:40.359882 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 14:38:40.359894 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 14:38:40.359905 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 14:38:40.359916 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 14:38:40.359927 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 14:38:40.359938 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.863636
I0430 14:38:40.359951 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.808511
I0430 14:38:40.359963 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.95857 (* 0.3 = 0.587572 loss)
I0430 14:38:40.359977 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.568896 (* 0.3 = 0.170669 loss)
I0430 14:38:40.359992 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.763864 (* 0.0272727 = 0.0208326 loss)
I0430 14:38:40.360004 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.58349 (* 0.0272727 = 0.043186 loss)
I0430 14:38:40.360018 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.56793 (* 0.0272727 = 0.0700345 loss)
I0430 14:38:40.360034 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.62179 (* 0.0272727 = 0.0442306 loss)
I0430 14:38:40.360049 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.5723 (* 0.0272727 = 0.0701536 loss)
I0430 14:38:40.360062 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 2.43584 (* 0.0272727 = 0.0664319 loss)
I0430 14:38:40.360075 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.55724 (* 0.0272727 = 0.0151974 loss)
I0430 14:38:40.360100 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.459152 (* 0.0272727 = 0.0125223 loss)
I0430 14:38:40.360115 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0134022 (* 0.0272727 = 0.000365514 loss)
I0430 14:38:40.360128 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00744476 (* 0.0272727 = 0.000203039 loss)
I0430 14:38:40.360141 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00733836 (* 0.0272727 = 0.000200137 loss)
I0430 14:38:40.360155 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00443522 (* 0.0272727 = 0.000120961 loss)
I0430 14:38:40.360169 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00378394 (* 0.0272727 = 0.000103199 loss)
I0430 14:38:40.360183 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00348188 (* 0.0272727 = 9.49602e-05 loss)
I0430 14:38:40.360196 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00335253 (* 0.0272727 = 9.14327e-05 loss)
I0430 14:38:40.360210 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00349473 (* 0.0272727 = 9.53108e-05 loss)
I0430 14:38:40.360224 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0017921 (* 0.0272727 = 4.88756e-05 loss)
I0430 14:38:40.360237 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00162231 (* 0.0272727 = 4.42449e-05 loss)
I0430 14:38:40.360255 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00161274 (* 0.0272727 = 4.39838e-05 loss)
I0430 14:38:40.360270 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.0018384 (* 0.0272727 = 5.01383e-05 loss)
I0430 14:38:40.360282 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00134879 (* 0.0272727 = 3.67853e-05 loss)
I0430 14:38:40.360296 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.00150581 (* 0.0272727 = 4.10675e-05 loss)
I0430 14:38:40.360308 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.787234
I0430 14:38:40.360319 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 14:38:40.360332 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 14:38:40.360342 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0430 14:38:40.360354 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 14:38:40.360365 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 14:38:40.360376 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0430 14:38:40.360388 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 14:38:40.360399 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0430 14:38:40.360410 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 14:38:40.360422 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 14:38:40.360433 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 14:38:40.360445 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 14:38:40.360456 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 14:38:40.360467 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 14:38:40.360484 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 14:38:40.360492 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 14:38:40.360504 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 14:38:40.360515 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 14:38:40.360527 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 14:38:40.360538 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 14:38:40.360548 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 14:38:40.360559 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 14:38:40.360570 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.9375
I0430 14:38:40.360592 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.808511
I0430 14:38:40.360607 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.73575 (* 1 = 1.73575 loss)
I0430 14:38:40.360620 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.492345 (* 1 = 0.492345 loss)
I0430 14:38:40.360635 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.737604 (* 0.0909091 = 0.0670549 loss)
I0430 14:38:40.360647 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 1.64843 (* 0.0909091 = 0.149857 loss)
I0430 14:38:40.360661 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.67359 (* 0.0909091 = 0.152144 loss)
I0430 14:38:40.360674 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.56455 (* 0.0909091 = 0.142232 loss)
I0430 14:38:40.360687 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 2.30365 (* 0.0909091 = 0.209423 loss)
I0430 14:38:40.360700 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.43782 (* 0.0909091 = 0.130711 loss)
I0430 14:38:40.360713 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.229591 (* 0.0909091 = 0.0208719 loss)
I0430 14:38:40.360728 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.0338439 (* 0.0909091 = 0.00307672 loss)
I0430 14:38:40.360740 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.012127 (* 0.0909091 = 0.00110246 loss)
I0430 14:38:40.360754 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00925085 (* 0.0909091 = 0.000840986 loss)
I0430 14:38:40.360767 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00959856 (* 0.0909091 = 0.000872596 loss)
I0430 14:38:40.360781 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00672952 (* 0.0909091 = 0.000611775 loss)
I0430 14:38:40.360795 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00507921 (* 0.0909091 = 0.000461747 loss)
I0430 14:38:40.360808 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00433375 (* 0.0909091 = 0.000393977 loss)
I0430 14:38:40.360821 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00411059 (* 0.0909091 = 0.00037369 loss)
I0430 14:38:40.360836 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00333872 (* 0.0909091 = 0.00030352 loss)
I0430 14:38:40.360848 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0026606 (* 0.0909091 = 0.000241872 loss)
I0430 14:38:40.360862 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00242034 (* 0.0909091 = 0.000220031 loss)
I0430 14:38:40.360875 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.0026502 (* 0.0909091 = 0.000240927 loss)
I0430 14:38:40.360888 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00221298 (* 0.0909091 = 0.00020118 loss)
I0430 14:38:40.360901 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00218935 (* 0.0909091 = 0.000199032 loss)
I0430 14:38:40.360914 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.00221471 (* 0.0909091 = 0.000201337 loss)
I0430 14:38:40.360926 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0430 14:38:40.360937 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0430 14:38:40.360949 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.618523
I0430 14:38:40.360960 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.629324
I0430 14:38:40.360980 15443 sgd_solver.cpp:106] Iteration 0, lr = 0.001
I0430 14:40:40.554004 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.4951 > 30) by scale factor 0.983765
I0430 14:40:57.197389 15443 solver.cpp:229] Iteration 500, loss = 3.74038
I0430 14:40:57.197470 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.413043
I0430 14:40:57.197489 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 14:40:57.197500 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0430 14:40:57.197513 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 14:40:57.197525 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0430 14:40:57.197536 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 14:40:57.197548 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 14:40:57.197561 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0430 14:40:57.197574 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 14:40:57.197587 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0430 14:40:57.197599 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 14:40:57.197610 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 14:40:57.197623 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 14:40:57.197634 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 14:40:57.197645 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 14:40:57.197657 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 14:40:57.197669 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 14:40:57.197680 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 14:40:57.197691 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 14:40:57.197703 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 14:40:57.197715 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 14:40:57.197726 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 14:40:57.197738 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 14:40:57.197749 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.835227
I0430 14:40:57.197762 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.717391
I0430 14:40:57.197777 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.65073 (* 0.3 = 0.495218 loss)
I0430 14:40:57.197791 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.464944 (* 0.3 = 0.139483 loss)
I0430 14:40:57.197806 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.762655 (* 0.0272727 = 0.0207997 loss)
I0430 14:40:57.197821 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.982337 (* 0.0272727 = 0.026791 loss)
I0430 14:40:57.197834 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.89024 (* 0.0272727 = 0.0515519 loss)
I0430 14:40:57.197849 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.18594 (* 0.0272727 = 0.0596166 loss)
I0430 14:40:57.197861 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.60494 (* 0.0272727 = 0.043771 loss)
I0430 14:40:57.197875 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.906324 (* 0.0272727 = 0.0247179 loss)
I0430 14:40:57.197890 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.380641 (* 0.0272727 = 0.0103811 loss)
I0430 14:40:57.197902 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.66793 (* 0.0272727 = 0.0182163 loss)
I0430 14:40:57.197917 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.00446605 (* 0.0272727 = 0.000121801 loss)
I0430 14:40:57.197932 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 5.46341e-05 (* 0.0272727 = 1.49002e-06 loss)
I0430 14:40:57.197945 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 5.34957e-06 (* 0.0272727 = 1.45897e-07 loss)
I0430 14:40:57.197959 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 8.64269e-07 (* 0.0272727 = 2.3571e-08 loss)
I0430 14:40:57.198015 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 2.38419e-07 (* 0.0272727 = 6.50233e-09 loss)
I0430 14:40:57.198030 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 2.98023e-07 (* 0.0272727 = 8.12791e-09 loss)
I0430 14:40:57.198045 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 7.45058e-08 (* 0.0272727 = 2.03198e-09 loss)
I0430 14:40:57.198058 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 5.96047e-08 (* 0.0272727 = 1.62558e-09 loss)
I0430 14:40:57.198072 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0 (* 0.0272727 = 0 loss)
I0430 14:40:57.198086 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0 (* 0.0272727 = 0 loss)
I0430 14:40:57.198099 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0 (* 0.0272727 = 0 loss)
I0430 14:40:57.198113 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0 (* 0.0272727 = 0 loss)
I0430 14:40:57.198127 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 7.45058e-08 (* 0.0272727 = 2.03198e-09 loss)
I0430 14:40:57.198142 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 2.98023e-08 (* 0.0272727 = 8.12791e-10 loss)
I0430 14:40:57.198153 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.608696
I0430 14:40:57.198165 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.5
I0430 14:40:57.198176 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 14:40:57.198189 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 14:40:57.198200 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 14:40:57.198211 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 14:40:57.198222 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 14:40:57.198235 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 14:40:57.198246 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0430 14:40:57.198257 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0430 14:40:57.198269 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 14:40:57.198281 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 14:40:57.198292 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 14:40:57.198300 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 14:40:57.198307 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 14:40:57.198320 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 14:40:57.198331 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 14:40:57.198343 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 14:40:57.198354 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 14:40:57.198366 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 14:40:57.198377 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 14:40:57.198388 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 14:40:57.198400 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 14:40:57.198411 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.892045
I0430 14:40:57.198422 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.804348
I0430 14:40:57.198436 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.44366 (* 0.3 = 0.433099 loss)
I0430 14:40:57.198454 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.395966 (* 0.3 = 0.11879 loss)
I0430 14:40:57.198468 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.859796 (* 0.0272727 = 0.023449 loss)
I0430 14:40:57.198482 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.603161 (* 0.0272727 = 0.0164498 loss)
I0430 14:40:57.198508 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.59974 (* 0.0272727 = 0.0436293 loss)
I0430 14:40:57.198523 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.94885 (* 0.0272727 = 0.0531503 loss)
I0430 14:40:57.198536 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.65548 (* 0.0272727 = 0.0451495 loss)
I0430 14:40:57.198550 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.754833 (* 0.0272727 = 0.0205864 loss)
I0430 14:40:57.198565 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.420079 (* 0.0272727 = 0.0114567 loss)
I0430 14:40:57.198578 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.105491 (* 0.0272727 = 0.00287703 loss)
I0430 14:40:57.198592 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0102671 (* 0.0272727 = 0.000280012 loss)
I0430 14:40:57.198606 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.0014454 (* 0.0272727 = 3.942e-05 loss)
I0430 14:40:57.198622 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.000142971 (* 0.0272727 = 3.8992e-06 loss)
I0430 14:40:57.198637 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 4.3137e-05 (* 0.0272727 = 1.17646e-06 loss)
I0430 14:40:57.198652 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 8.41936e-06 (* 0.0272727 = 2.29619e-07 loss)
I0430 14:40:57.198665 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 1.44542e-06 (* 0.0272727 = 3.94204e-08 loss)
I0430 14:40:57.198678 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 6.85454e-07 (* 0.0272727 = 1.86942e-08 loss)
I0430 14:40:57.198693 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 3.42727e-07 (* 0.0272727 = 9.3471e-09 loss)
I0430 14:40:57.198705 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 2.68221e-07 (* 0.0272727 = 7.31512e-09 loss)
I0430 14:40:57.198719 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 2.23518e-07 (* 0.0272727 = 6.09593e-09 loss)
I0430 14:40:57.198732 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 7.45058e-08 (* 0.0272727 = 2.03198e-09 loss)
I0430 14:40:57.198746 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 4.47035e-08 (* 0.0272727 = 1.21919e-09 loss)
I0430 14:40:57.198760 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 8.9407e-08 (* 0.0272727 = 2.43837e-09 loss)
I0430 14:40:57.198773 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 3.57628e-07 (* 0.0272727 = 9.7535e-09 loss)
I0430 14:40:57.198786 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.76087
I0430 14:40:57.198797 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 14:40:57.198809 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 14:40:57.198820 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0430 14:40:57.198832 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 14:40:57.198844 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 14:40:57.198855 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0430 14:40:57.198868 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 14:40:57.198879 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 14:40:57.198889 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 14:40:57.198901 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 14:40:57.198912 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 14:40:57.198923 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 14:40:57.198935 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 14:40:57.198946 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 14:40:57.198957 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 14:40:57.198981 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 14:40:57.198993 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 14:40:57.199004 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 14:40:57.199015 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 14:40:57.199026 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 14:40:57.199038 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 14:40:57.199049 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 14:40:57.199060 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.926136
I0430 14:40:57.199072 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.978261
I0430 14:40:57.199085 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.657399 (* 1 = 0.657399 loss)
I0430 14:40:57.199100 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.212654 (* 1 = 0.212654 loss)
I0430 14:40:57.199113 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.343859 (* 0.0909091 = 0.0312599 loss)
I0430 14:40:57.199127 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.32394 (* 0.0909091 = 0.0294491 loss)
I0430 14:40:57.199141 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.797681 (* 0.0909091 = 0.0725165 loss)
I0430 14:40:57.199154 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.93652 (* 0.0909091 = 0.0851382 loss)
I0430 14:40:57.199167 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.994242 (* 0.0909091 = 0.0903857 loss)
I0430 14:40:57.199182 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.264282 (* 0.0909091 = 0.0240257 loss)
I0430 14:40:57.199194 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.18269 (* 0.0909091 = 0.0166082 loss)
I0430 14:40:57.199208 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.330743 (* 0.0909091 = 0.0300675 loss)
I0430 14:40:57.199223 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.0181934 (* 0.0909091 = 0.00165394 loss)
I0430 14:40:57.199236 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00162493 (* 0.0909091 = 0.000147721 loss)
I0430 14:40:57.199249 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00034514 (* 0.0909091 = 3.13764e-05 loss)
I0430 14:40:57.199264 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.000151523 (* 0.0909091 = 1.37748e-05 loss)
I0430 14:40:57.199276 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 7.16815e-05 (* 0.0909091 = 6.5165e-06 loss)
I0430 14:40:57.199290 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 3.85217e-05 (* 0.0909091 = 3.50198e-06 loss)
I0430 14:40:57.199304 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 1.79564e-05 (* 0.0909091 = 1.6324e-06 loss)
I0430 14:40:57.199318 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 1.49612e-05 (* 0.0909091 = 1.36011e-06 loss)
I0430 14:40:57.199331 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 1.27856e-05 (* 0.0909091 = 1.16232e-06 loss)
I0430 14:40:57.199345 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 1.17574e-05 (* 0.0909091 = 1.06885e-06 loss)
I0430 14:40:57.199359 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 1.09528e-05 (* 0.0909091 = 9.95705e-07 loss)
I0430 14:40:57.199373 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 9.40296e-06 (* 0.0909091 = 8.54814e-07 loss)
I0430 14:40:57.199386 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 9.6265e-06 (* 0.0909091 = 8.75136e-07 loss)
I0430 14:40:57.199400 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.02673e-05 (* 0.0909091 = 9.33391e-07 loss)
I0430 14:40:57.199412 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 14:40:57.199424 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 14:40:57.199445 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.509899
I0430 14:40:57.199457 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.46545
I0430 14:40:57.199491 15443 sgd_solver.cpp:106] Iteration 500, lr = 0.001
I0430 14:43:59.918297 15443 solver.cpp:229] Iteration 1000, loss = 3.74582
I0430 14:43:59.918467 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.433333
I0430 14:43:59.918488 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 14:43:59.918501 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0430 14:43:59.918514 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0430 14:43:59.918526 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.125
I0430 14:43:59.918539 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0430 14:43:59.918550 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.875
I0430 14:43:59.918563 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 14:43:59.918576 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 14:43:59.918587 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 14:43:59.918599 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 14:43:59.918612 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 14:43:59.918623 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 14:43:59.918637 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 14:43:59.918648 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 14:43:59.918660 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0430 14:43:59.918673 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0430 14:43:59.918684 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 0.875
I0430 14:43:59.918696 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 0.875
I0430 14:43:59.918709 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 0.875
I0430 14:43:59.918720 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 14:43:59.918732 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 14:43:59.918745 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 14:43:59.918756 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.801136
I0430 14:43:59.918767 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.683333
I0430 14:43:59.918784 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.98125 (* 0.3 = 0.594375 loss)
I0430 14:43:59.918798 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.714367 (* 0.3 = 0.21431 loss)
I0430 14:43:59.918813 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.69514 (* 0.0272727 = 0.0189584 loss)
I0430 14:43:59.918828 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.9512 (* 0.0272727 = 0.0259418 loss)
I0430 14:43:59.918843 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.69012 (* 0.0272727 = 0.0460941 loss)
I0430 14:43:59.918856 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.94293 (* 0.0272727 = 0.0529891 loss)
I0430 14:43:59.918870 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.68244 (* 0.0272727 = 0.0458848 loss)
I0430 14:43:59.918884 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.897235 (* 0.0272727 = 0.02447 loss)
I0430 14:43:59.918898 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.83346 (* 0.0272727 = 0.0227307 loss)
I0430 14:43:59.918912 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.934342 (* 0.0272727 = 0.025482 loss)
I0430 14:43:59.918926 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.728367 (* 0.0272727 = 0.0198645 loss)
I0430 14:43:59.918941 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.713642 (* 0.0272727 = 0.019463 loss)
I0430 14:43:59.918954 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.770174 (* 0.0272727 = 0.0210047 loss)
I0430 14:43:59.918968 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.535582 (* 0.0272727 = 0.0146068 loss)
I0430 14:43:59.919003 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.683052 (* 0.0272727 = 0.0186287 loss)
I0430 14:43:59.919019 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.349775 (* 0.0272727 = 0.00953932 loss)
I0430 14:43:59.919034 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.938375 (* 0.0272727 = 0.025592 loss)
I0430 14:43:59.919047 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 1.07026 (* 0.0272727 = 0.0291889 loss)
I0430 14:43:59.919061 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.844328 (* 0.0272727 = 0.0230271 loss)
I0430 14:43:59.919075 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.967769 (* 0.0272727 = 0.0263937 loss)
I0430 14:43:59.919090 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 1.19944 (* 0.0272727 = 0.032712 loss)
I0430 14:43:59.919105 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00124584 (* 0.0272727 = 3.39773e-05 loss)
I0430 14:43:59.919119 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000259831 (* 0.0272727 = 7.0863e-06 loss)
I0430 14:43:59.919133 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 8.26979e-05 (* 0.0272727 = 2.2554e-06 loss)
I0430 14:43:59.919145 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.55
I0430 14:43:59.919158 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0430 14:43:59.919170 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 14:43:59.919183 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 14:43:59.919194 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 14:43:59.919206 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0430 14:43:59.919219 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.875
I0430 14:43:59.919230 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 14:43:59.919242 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 14:43:59.919255 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0430 14:43:59.919265 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 14:43:59.919277 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0430 14:43:59.919289 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 14:43:59.919301 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 14:43:59.919317 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 14:43:59.919328 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 14:43:59.919340 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0430 14:43:59.919353 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 0.875
I0430 14:43:59.919364 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 0.875
I0430 14:43:59.919376 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 0.875
I0430 14:43:59.919389 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 14:43:59.919399 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 14:43:59.919411 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 14:43:59.919423 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.829545
I0430 14:43:59.919435 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.733333
I0430 14:43:59.919450 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.54887 (* 0.3 = 0.46466 loss)
I0430 14:43:59.919463 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.595427 (* 0.3 = 0.178628 loss)
I0430 14:43:59.919504 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.182952 (* 0.0272727 = 0.0049896 loss)
I0430 14:43:59.919528 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.437317 (* 0.0272727 = 0.0119268 loss)
I0430 14:43:59.919559 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.26698 (* 0.0272727 = 0.034554 loss)
I0430 14:43:59.919574 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.52997 (* 0.0272727 = 0.0417265 loss)
I0430 14:43:59.919589 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.51375 (* 0.0272727 = 0.0412841 loss)
I0430 14:43:59.919602 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.764788 (* 0.0272727 = 0.0208578 loss)
I0430 14:43:59.919616 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.15344 (* 0.0272727 = 0.0314576 loss)
I0430 14:43:59.919631 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.858036 (* 0.0272727 = 0.023401 loss)
I0430 14:43:59.919644 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.596727 (* 0.0272727 = 0.0162744 loss)
I0430 14:43:59.919658 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.543198 (* 0.0272727 = 0.0148145 loss)
I0430 14:43:59.919672 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.879508 (* 0.0272727 = 0.0239866 loss)
I0430 14:43:59.919687 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.400871 (* 0.0272727 = 0.0109329 loss)
I0430 14:43:59.919699 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.682633 (* 0.0272727 = 0.0186173 loss)
I0430 14:43:59.919713 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.292028 (* 0.0272727 = 0.0079644 loss)
I0430 14:43:59.919728 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.953727 (* 0.0272727 = 0.0260107 loss)
I0430 14:43:59.919741 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.955961 (* 0.0272727 = 0.0260717 loss)
I0430 14:43:59.919755 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.496474 (* 0.0272727 = 0.0135402 loss)
I0430 14:43:59.919770 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.69153 (* 0.0272727 = 0.0188599 loss)
I0430 14:43:59.919780 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.836263 (* 0.0272727 = 0.0228072 loss)
I0430 14:43:59.919790 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.0015237 (* 0.0272727 = 4.15553e-05 loss)
I0430 14:43:59.919806 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000677198 (* 0.0272727 = 1.8469e-05 loss)
I0430 14:43:59.919819 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000236242 (* 0.0272727 = 6.44297e-06 loss)
I0430 14:43:59.919831 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.75
I0430 14:43:59.919844 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 14:43:59.919857 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0430 14:43:59.919867 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0430 14:43:59.919879 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 14:43:59.919891 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 14:43:59.919903 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0430 14:43:59.919914 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 14:43:59.919926 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 14:43:59.919937 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 14:43:59.919950 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 14:43:59.919961 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0430 14:43:59.919973 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 14:43:59.919986 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 14:43:59.919996 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 14:43:59.920008 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 14:43:59.920029 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0430 14:43:59.920043 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 0.875
I0430 14:43:59.920055 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 0.875
I0430 14:43:59.920066 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 0.875
I0430 14:43:59.920078 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 14:43:59.920090 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 14:43:59.920102 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 14:43:59.920114 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.903409
I0430 14:43:59.920125 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.8
I0430 14:43:59.920140 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.14095 (* 1 = 1.14095 loss)
I0430 14:43:59.920153 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.419929 (* 1 = 0.419929 loss)
I0430 14:43:59.920167 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0213574 (* 0.0909091 = 0.00194159 loss)
I0430 14:43:59.920182 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.150616 (* 0.0909091 = 0.0136924 loss)
I0430 14:43:59.920197 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.477731 (* 0.0909091 = 0.0434301 loss)
I0430 14:43:59.920210 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.867794 (* 0.0909091 = 0.0788903 loss)
I0430 14:43:59.920223 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.933524 (* 0.0909091 = 0.0848659 loss)
I0430 14:43:59.920238 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.721225 (* 0.0909091 = 0.0655659 loss)
I0430 14:43:59.920251 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.751467 (* 0.0909091 = 0.0683151 loss)
I0430 14:43:59.920265 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.955364 (* 0.0909091 = 0.0868513 loss)
I0430 14:43:59.920279 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.471465 (* 0.0909091 = 0.0428605 loss)
I0430 14:43:59.920294 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.480765 (* 0.0909091 = 0.0437059 loss)
I0430 14:43:59.920307 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.643468 (* 0.0909091 = 0.0584971 loss)
I0430 14:43:59.920321 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.472557 (* 0.0909091 = 0.0429597 loss)
I0430 14:43:59.920334 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.646488 (* 0.0909091 = 0.0587716 loss)
I0430 14:43:59.920348 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.405554 (* 0.0909091 = 0.0368685 loss)
I0430 14:43:59.920362 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.853098 (* 0.0909091 = 0.0775544 loss)
I0430 14:43:59.920380 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.963596 (* 0.0909091 = 0.0875997 loss)
I0430 14:43:59.920394 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.512762 (* 0.0909091 = 0.0466148 loss)
I0430 14:43:59.920408 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.513563 (* 0.0909091 = 0.0466876 loss)
I0430 14:43:59.920421 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.657041 (* 0.0909091 = 0.059731 loss)
I0430 14:43:59.920436 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00489651 (* 0.0909091 = 0.000445138 loss)
I0430 14:43:59.920450 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00242869 (* 0.0909091 = 0.00022079 loss)
I0430 14:43:59.920464 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000669789 (* 0.0909091 = 6.08899e-05 loss)
I0430 14:43:59.920476 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0430 14:43:59.920488 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0430 14:43:59.920511 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.57543
I0430 14:43:59.920527 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.511015
I0430 14:43:59.920541 15443 sgd_solver.cpp:106] Iteration 1000, lr = 0.001
I0430 14:46:57.596113 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.1349 > 30) by scale factor 0.995523
I0430 14:47:42.087499 15443 solver.cpp:229] Iteration 1500, loss = 3.82782
I0430 14:47:42.087647 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.38
I0430 14:47:42.087677 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 14:47:42.087698 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0430 14:47:42.087721 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.125
I0430 14:47:42.087743 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.625
I0430 14:47:42.087764 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 14:47:42.087785 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.25
I0430 14:47:42.087807 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 14:47:42.087829 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 14:47:42.087851 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0430 14:47:42.087872 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 14:47:42.087898 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 14:47:42.087920 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 14:47:42.087941 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 14:47:42.087963 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 14:47:42.087985 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 14:47:42.088006 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 14:47:42.088027 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 14:47:42.088048 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 14:47:42.088069 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 14:47:42.088090 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 14:47:42.088111 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 14:47:42.088132 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 14:47:42.088152 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.806818
I0430 14:47:42.088174 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.76
I0430 14:47:42.088202 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.61537 (* 0.3 = 0.484612 loss)
I0430 14:47:42.088230 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.52988 (* 0.3 = 0.158964 loss)
I0430 14:47:42.088258 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.537762 (* 0.0272727 = 0.0146662 loss)
I0430 14:47:42.088284 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.61428 (* 0.0272727 = 0.0440257 loss)
I0430 14:47:42.088310 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.11312 (* 0.0272727 = 0.0576304 loss)
I0430 14:47:42.088340 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.44709 (* 0.0272727 = 0.039466 loss)
I0430 14:47:42.088366 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.9681 (* 0.0272727 = 0.0536755 loss)
I0430 14:47:42.088393 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.95529 (* 0.0272727 = 0.0533262 loss)
I0430 14:47:42.088419 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.37024 (* 0.0272727 = 0.0373702 loss)
I0430 14:47:42.088445 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.02563 (* 0.0272727 = 0.0279718 loss)
I0430 14:47:42.088474 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.000552961 (* 0.0272727 = 1.50808e-05 loss)
I0430 14:47:42.088500 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 2.87842e-05 (* 0.0272727 = 7.85022e-07 loss)
I0430 14:47:42.088529 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 4.69394e-06 (* 0.0272727 = 1.28016e-07 loss)
I0430 14:47:42.088562 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 2.3842e-06 (* 0.0272727 = 6.50237e-08 loss)
I0430 14:47:42.088589 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 1.2964e-06 (* 0.0272727 = 3.53565e-08 loss)
I0430 14:47:42.088642 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 1.63913e-07 (* 0.0272727 = 4.47035e-09 loss)
I0430 14:47:42.088676 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 1.49012e-07 (* 0.0272727 = 4.06395e-09 loss)
I0430 14:47:42.088703 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 7.45058e-08 (* 0.0272727 = 2.03198e-09 loss)
I0430 14:47:42.088732 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0 (* 0.0272727 = 0 loss)
I0430 14:47:42.088757 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0 (* 0.0272727 = 0 loss)
I0430 14:47:42.088783 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0 (* 0.0272727 = 0 loss)
I0430 14:47:42.088809 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0 (* 0.0272727 = 0 loss)
I0430 14:47:42.088835 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0 (* 0.0272727 = 0 loss)
I0430 14:47:42.088861 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0 (* 0.0272727 = 0 loss)
I0430 14:47:42.088884 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.6
I0430 14:47:42.088907 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 14:47:42.088928 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 14:47:42.088949 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 1
I0430 14:47:42.088973 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0430 14:47:42.088990 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.25
I0430 14:47:42.089010 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 14:47:42.089031 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 1
I0430 14:47:42.089053 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 14:47:42.089076 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0430 14:47:42.089097 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 14:47:42.089118 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 14:47:42.089140 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 14:47:42.089161 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 14:47:42.089184 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 14:47:42.089205 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 14:47:42.089226 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 14:47:42.089247 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 14:47:42.089268 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 14:47:42.089290 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 14:47:42.089311 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 14:47:42.089332 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 14:47:42.089354 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 14:47:42.089380 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.875
I0430 14:47:42.089402 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.86
I0430 14:47:42.089428 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.10515 (* 0.3 = 0.331545 loss)
I0430 14:47:42.089454 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.353809 (* 0.3 = 0.106143 loss)
I0430 14:47:42.089481 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.362471 (* 0.0272727 = 0.00988557 loss)
I0430 14:47:42.089509 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.869034 (* 0.0272727 = 0.0237009 loss)
I0430 14:47:42.089534 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.628231 (* 0.0272727 = 0.0171336 loss)
I0430 14:47:42.089578 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.56237 (* 0.0272727 = 0.0426102 loss)
I0430 14:47:42.089604 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.86574 (* 0.0272727 = 0.0508838 loss)
I0430 14:47:42.089632 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 2.58905 (* 0.0272727 = 0.0706105 loss)
I0430 14:47:42.089658 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.29719 (* 0.0272727 = 0.00810518 loss)
I0430 14:47:42.089685 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.742911 (* 0.0272727 = 0.0202612 loss)
I0430 14:47:42.089715 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.00102433 (* 0.0272727 = 2.79363e-05 loss)
I0430 14:47:42.089742 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 3.32904e-05 (* 0.0272727 = 9.0792e-07 loss)
I0430 14:47:42.089771 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.82693e-05 (* 0.0272727 = 4.98253e-07 loss)
I0430 14:47:42.089795 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 5.06642e-06 (* 0.0272727 = 1.38175e-07 loss)
I0430 14:47:42.089823 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 3.29317e-06 (* 0.0272727 = 8.98136e-08 loss)
I0430 14:47:42.089849 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 1.17719e-06 (* 0.0272727 = 3.21053e-08 loss)
I0430 14:47:42.089875 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 6.2585e-07 (* 0.0272727 = 1.70686e-08 loss)
I0430 14:47:42.089902 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 3.12925e-07 (* 0.0272727 = 8.53431e-09 loss)
I0430 14:47:42.089929 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 2.08616e-07 (* 0.0272727 = 5.68954e-09 loss)
I0430 14:47:42.089956 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 1.93715e-07 (* 0.0272727 = 5.28314e-09 loss)
I0430 14:47:42.089982 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 4.02332e-07 (* 0.0272727 = 1.09727e-08 loss)
I0430 14:47:42.090008 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 1.34111e-07 (* 0.0272727 = 3.65756e-09 loss)
I0430 14:47:42.090034 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 4.47035e-07 (* 0.0272727 = 1.21919e-08 loss)
I0430 14:47:42.090061 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 2.38419e-07 (* 0.0272727 = 6.50233e-09 loss)
I0430 14:47:42.090085 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.82
I0430 14:47:42.090106 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 14:47:42.090129 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 14:47:42.090152 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 14:47:42.090173 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0430 14:47:42.090194 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 14:47:42.090217 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.375
I0430 14:47:42.090240 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 14:47:42.090260 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 14:47:42.090281 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 14:47:42.090303 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 14:47:42.090327 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 14:47:42.090347 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 14:47:42.090368 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 14:47:42.090389 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 14:47:42.090411 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 14:47:42.090436 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 14:47:42.090458 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 14:47:42.090495 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 14:47:42.090518 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 14:47:42.090541 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 14:47:42.090565 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 14:47:42.090595 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 14:47:42.090620 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.943182
I0430 14:47:42.090642 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.94
I0430 14:47:42.090669 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.700481 (* 1 = 0.700481 loss)
I0430 14:47:42.090695 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.224368 (* 1 = 0.224368 loss)
I0430 14:47:42.090723 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.176037 (* 0.0909091 = 0.0160034 loss)
I0430 14:47:42.090749 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.37014 (* 0.0909091 = 0.0336491 loss)
I0430 14:47:42.090780 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.465561 (* 0.0909091 = 0.0423238 loss)
I0430 14:47:42.090806 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.2533 (* 0.0909091 = 0.0230273 loss)
I0430 14:47:42.090833 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.81445 (* 0.0909091 = 0.0740409 loss)
I0430 14:47:42.090859 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.91053 (* 0.0909091 = 0.173684 loss)
I0430 14:47:42.090886 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.616467 (* 0.0909091 = 0.0560425 loss)
I0430 14:47:42.090912 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.465447 (* 0.0909091 = 0.0423134 loss)
I0430 14:47:42.090939 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.00287801 (* 0.0909091 = 0.000261638 loss)
I0430 14:47:42.090965 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00121225 (* 0.0909091 = 0.000110204 loss)
I0430 14:47:42.090992 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.000126947 (* 0.0909091 = 1.15406e-05 loss)
I0430 14:47:42.091018 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 7.53737e-05 (* 0.0909091 = 6.85215e-06 loss)
I0430 14:47:42.091045 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 3.44319e-05 (* 0.0909091 = 3.13018e-06 loss)
I0430 14:47:42.091073 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 1.06992e-05 (* 0.0909091 = 9.72658e-07 loss)
I0430 14:47:42.091099 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 3.24846e-06 (* 0.0909091 = 2.95315e-07 loss)
I0430 14:47:42.091125 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 1.17719e-06 (* 0.0909091 = 1.07018e-07 loss)
I0430 14:47:42.091152 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 5.96047e-07 (* 0.0909091 = 5.41861e-08 loss)
I0430 14:47:42.091179 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 2.23517e-07 (* 0.0909091 = 2.03198e-08 loss)
I0430 14:47:42.091207 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 2.08616e-07 (* 0.0909091 = 1.89651e-08 loss)
I0430 14:47:42.091233 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.04308e-07 (* 0.0909091 = 9.48256e-09 loss)
I0430 14:47:42.091262 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 5.96047e-08 (* 0.0909091 = 5.4186e-09 loss)
I0430 14:47:42.091289 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 7.45058e-08 (* 0.0909091 = 6.77326e-09 loss)
I0430 14:47:42.091312 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 14:47:42.091336 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 14:47:42.091357 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.443008
I0430 14:47:42.091398 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.454806
I0430 14:47:42.091423 15443 sgd_solver.cpp:106] Iteration 1500, lr = 0.001
I0430 14:51:24.019585 15443 solver.cpp:229] Iteration 2000, loss = 3.80796
I0430 14:51:24.019738 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.38983
I0430 14:51:24.019758 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 14:51:24.019773 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0430 14:51:24.019784 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0430 14:51:24.019796 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0430 14:51:24.019809 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 14:51:24.019820 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0430 14:51:24.019832 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 14:51:24.019845 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 14:51:24.019856 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.625
I0430 14:51:24.019870 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0430 14:51:24.019881 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0430 14:51:24.019893 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.75
I0430 14:51:24.019906 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 14:51:24.019917 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 14:51:24.019929 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0430 14:51:24.019942 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 14:51:24.019953 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 14:51:24.019965 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 14:51:24.019978 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 14:51:24.019989 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 14:51:24.020000 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 14:51:24.020012 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 14:51:24.020023 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.738636
I0430 14:51:24.020035 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.677966
I0430 14:51:24.020053 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.31835 (* 0.3 = 0.695504 loss)
I0430 14:51:24.020068 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.06577 (* 0.3 = 0.319732 loss)
I0430 14:51:24.020083 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.39923 (* 0.0272727 = 0.0381608 loss)
I0430 14:51:24.020097 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.24088 (* 0.0272727 = 0.0611149 loss)
I0430 14:51:24.020112 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.38241 (* 0.0272727 = 0.0649749 loss)
I0430 14:51:24.020125 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.4432 (* 0.0272727 = 0.0666328 loss)
I0430 14:51:24.020139 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.13121 (* 0.0272727 = 0.0581239 loss)
I0430 14:51:24.020153 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.30447 (* 0.0272727 = 0.0628493 loss)
I0430 14:51:24.020166 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.81074 (* 0.0272727 = 0.0493839 loss)
I0430 14:51:24.020181 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.60644 (* 0.0272727 = 0.0438121 loss)
I0430 14:51:24.020195 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 1.50413 (* 0.0272727 = 0.0410218 loss)
I0430 14:51:24.020210 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.940349 (* 0.0272727 = 0.0256459 loss)
I0430 14:51:24.020223 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.871113 (* 0.0272727 = 0.0237576 loss)
I0430 14:51:24.020237 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.686378 (* 0.0272727 = 0.0187194 loss)
I0430 14:51:24.020272 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.453272 (* 0.0272727 = 0.012362 loss)
I0430 14:51:24.020287 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.421313 (* 0.0272727 = 0.0114903 loss)
I0430 14:51:24.020303 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.440181 (* 0.0272727 = 0.0120049 loss)
I0430 14:51:24.020319 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0855355 (* 0.0272727 = 0.00233279 loss)
I0430 14:51:24.020334 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.06461 (* 0.0272727 = 0.00176209 loss)
I0430 14:51:24.020349 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0295555 (* 0.0272727 = 0.000806059 loss)
I0430 14:51:24.020364 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.0190973 (* 0.0272727 = 0.000520836 loss)
I0430 14:51:24.020377 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.0070848 (* 0.0272727 = 0.000193222 loss)
I0430 14:51:24.020391 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00428325 (* 0.0272727 = 0.000116816 loss)
I0430 14:51:24.020406 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000756034 (* 0.0272727 = 2.06191e-05 loss)
I0430 14:51:24.020419 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.372881
I0430 14:51:24.020431 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 14:51:24.020443 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0430 14:51:24.020455 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0430 14:51:24.020467 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0430 14:51:24.020479 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.25
I0430 14:51:24.020491 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.375
I0430 14:51:24.020503 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 14:51:24.020515 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0430 14:51:24.020527 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.625
I0430 14:51:24.020539 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.625
I0430 14:51:24.020551 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.625
I0430 14:51:24.020563 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.625
I0430 14:51:24.020576 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.75
I0430 14:51:24.020586 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 14:51:24.020598 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 14:51:24.020611 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 14:51:24.020622 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 14:51:24.020633 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 14:51:24.020645 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 14:51:24.020658 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 14:51:24.020668 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 14:51:24.020680 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 14:51:24.020691 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.721591
I0430 14:51:24.020704 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.661017
I0430 14:51:24.020717 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 2.30694 (* 0.3 = 0.692082 loss)
I0430 14:51:24.020731 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.1045 (* 0.3 = 0.331351 loss)
I0430 14:51:24.020748 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.19603 (* 0.0272727 = 0.0326189 loss)
I0430 14:51:24.020763 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.94014 (* 0.0272727 = 0.0529129 loss)
I0430 14:51:24.020789 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.01374 (* 0.0272727 = 0.0549202 loss)
I0430 14:51:24.020804 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.04093 (* 0.0272727 = 0.0556618 loss)
I0430 14:51:24.020818 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.76129 (* 0.0272727 = 0.0480351 loss)
I0430 14:51:24.020833 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 2.46146 (* 0.0272727 = 0.0671307 loss)
I0430 14:51:24.020846 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 2.20989 (* 0.0272727 = 0.0602698 loss)
I0430 14:51:24.020861 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.93153 (* 0.0272727 = 0.0526781 loss)
I0430 14:51:24.020874 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.63585 (* 0.0272727 = 0.0446141 loss)
I0430 14:51:24.020889 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 1.13185 (* 0.0272727 = 0.0308688 loss)
I0430 14:51:24.020902 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.09303 (* 0.0272727 = 0.02981 loss)
I0430 14:51:24.020916 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 1.11776 (* 0.0272727 = 0.0304844 loss)
I0430 14:51:24.020930 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.530128 (* 0.0272727 = 0.014458 loss)
I0430 14:51:24.020944 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.525566 (* 0.0272727 = 0.0143336 loss)
I0430 14:51:24.020957 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.615637 (* 0.0272727 = 0.0167901 loss)
I0430 14:51:24.020972 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.169342 (* 0.0272727 = 0.00461841 loss)
I0430 14:51:24.020987 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0830546 (* 0.0272727 = 0.00226513 loss)
I0430 14:51:24.021000 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0473573 (* 0.0272727 = 0.00129156 loss)
I0430 14:51:24.021015 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0208224 (* 0.0272727 = 0.000567884 loss)
I0430 14:51:24.021029 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.0114714 (* 0.0272727 = 0.000312856 loss)
I0430 14:51:24.021044 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.0050078 (* 0.0272727 = 0.000136576 loss)
I0430 14:51:24.021057 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000793294 (* 0.0272727 = 2.16353e-05 loss)
I0430 14:51:24.021070 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.474576
I0430 14:51:24.021082 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 14:51:24.021095 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.5
I0430 14:51:24.021106 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.5
I0430 14:51:24.021118 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.375
I0430 14:51:24.021131 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.5
I0430 14:51:24.021142 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.25
I0430 14:51:24.021154 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0430 14:51:24.021165 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 14:51:24.021178 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0430 14:51:24.021189 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.625
I0430 14:51:24.021201 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0430 14:51:24.021214 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.625
I0430 14:51:24.021225 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.75
I0430 14:51:24.021237 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 14:51:24.021250 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 14:51:24.021261 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 14:51:24.021282 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 14:51:24.021296 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 14:51:24.021307 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 14:51:24.021319 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 14:51:24.021332 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 14:51:24.021343 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 14:51:24.021354 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.755682
I0430 14:51:24.021369 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.694915
I0430 14:51:24.021384 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.97018 (* 1 = 1.97018 loss)
I0430 14:51:24.021399 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.984303 (* 1 = 0.984303 loss)
I0430 14:51:24.021414 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 1.47782 (* 0.0909091 = 0.134347 loss)
I0430 14:51:24.021428 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 2.04044 (* 0.0909091 = 0.185494 loss)
I0430 14:51:24.021446 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 2.05063 (* 0.0909091 = 0.18642 loss)
I0430 14:51:24.021461 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 2.63349 (* 0.0909091 = 0.239408 loss)
I0430 14:51:24.021474 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.45332 (* 0.0909091 = 0.13212 loss)
I0430 14:51:24.021487 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 2.17104 (* 0.0909091 = 0.197367 loss)
I0430 14:51:24.021502 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.77708 (* 0.0909091 = 0.161553 loss)
I0430 14:51:24.021514 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.39187 (* 0.0909091 = 0.126533 loss)
I0430 14:51:24.021528 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 1.26678 (* 0.0909091 = 0.115162 loss)
I0430 14:51:24.021543 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 1.02547 (* 0.0909091 = 0.0932249 loss)
I0430 14:51:24.021556 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.898159 (* 0.0909091 = 0.0816508 loss)
I0430 14:51:24.021569 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.918571 (* 0.0909091 = 0.0835064 loss)
I0430 14:51:24.021584 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.543539 (* 0.0909091 = 0.0494126 loss)
I0430 14:51:24.021597 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.391678 (* 0.0909091 = 0.0356071 loss)
I0430 14:51:24.021611 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.445784 (* 0.0909091 = 0.0405258 loss)
I0430 14:51:24.021626 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0554589 (* 0.0909091 = 0.00504172 loss)
I0430 14:51:24.021641 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0317315 (* 0.0909091 = 0.00288468 loss)
I0430 14:51:24.021654 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0136085 (* 0.0909091 = 0.00123713 loss)
I0430 14:51:24.021667 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00809653 (* 0.0909091 = 0.000736048 loss)
I0430 14:51:24.021682 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00247566 (* 0.0909091 = 0.00022506 loss)
I0430 14:51:24.021695 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00107396 (* 0.0909091 = 9.76327e-05 loss)
I0430 14:51:24.021709 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000289014 (* 0.0909091 = 2.6274e-05 loss)
I0430 14:51:24.021721 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0
I0430 14:51:24.021733 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0
I0430 14:51:24.021744 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.151085
I0430 14:51:24.021766 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.140834
I0430 14:51:24.021781 15443 sgd_solver.cpp:106] Iteration 2000, lr = 0.001
I0430 14:54:51.970434 15443 solver.cpp:229] Iteration 2500, loss = 3.79071
I0430 14:54:51.970630 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.5
I0430 14:54:51.970651 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 14:54:51.970664 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0430 14:54:51.970677 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0430 14:54:51.970690 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0430 14:54:51.970701 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0430 14:54:51.970713 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.25
I0430 14:54:51.970726 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 14:54:51.970738 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 14:54:51.970751 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 14:54:51.970762 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 14:54:51.970775 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 14:54:51.970788 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 14:54:51.970799 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 14:54:51.970811 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 14:54:51.970824 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 14:54:51.970835 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 14:54:51.970847 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 14:54:51.970860 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 14:54:51.970870 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 14:54:51.970882 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 14:54:51.970895 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 14:54:51.970906 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 14:54:51.970917 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.829545
I0430 14:54:51.970929 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.8
I0430 14:54:51.970947 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.54769 (* 0.3 = 0.464307 loss)
I0430 14:54:51.970962 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.524513 (* 0.3 = 0.157354 loss)
I0430 14:54:51.970976 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.00328 (* 0.0272727 = 0.0273621 loss)
I0430 14:54:51.970990 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.731228 (* 0.0272727 = 0.0199426 loss)
I0430 14:54:51.971004 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.65431 (* 0.0272727 = 0.0451174 loss)
I0430 14:54:51.971019 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.85579 (* 0.0272727 = 0.0506125 loss)
I0430 14:54:51.971032 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.58919 (* 0.0272727 = 0.0433416 loss)
I0430 14:54:51.971046 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.98964 (* 0.0272727 = 0.0542628 loss)
I0430 14:54:51.971060 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.18659 (* 0.0272727 = 0.0323615 loss)
I0430 14:54:51.971074 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.40152 (* 0.0272727 = 0.0109505 loss)
I0430 14:54:51.971089 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.478536 (* 0.0272727 = 0.013051 loss)
I0430 14:54:51.971103 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.447814 (* 0.0272727 = 0.0122131 loss)
I0430 14:54:51.971118 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.188664 (* 0.0272727 = 0.00514538 loss)
I0430 14:54:51.971132 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0737085 (* 0.0272727 = 0.00201023 loss)
I0430 14:54:51.971148 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0307284 (* 0.0272727 = 0.000838049 loss)
I0430 14:54:51.971182 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0156197 (* 0.0272727 = 0.00042599 loss)
I0430 14:54:51.971199 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00464377 (* 0.0272727 = 0.000126648 loss)
I0430 14:54:51.971213 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0025064 (* 0.0272727 = 6.83563e-05 loss)
I0430 14:54:51.971227 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00105088 (* 0.0272727 = 2.86605e-05 loss)
I0430 14:54:51.971242 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000353179 (* 0.0272727 = 9.63216e-06 loss)
I0430 14:54:51.971256 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 8.16925e-05 (* 0.0272727 = 2.22798e-06 loss)
I0430 14:54:51.971271 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 1.8598e-05 (* 0.0272727 = 5.07219e-07 loss)
I0430 14:54:51.971285 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 1.02971e-05 (* 0.0272727 = 2.80831e-07 loss)
I0430 14:54:51.971300 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 2.04148e-06 (* 0.0272727 = 5.56766e-08 loss)
I0430 14:54:51.971315 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.58
I0430 14:54:51.971328 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.625
I0430 14:54:51.971341 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 14:54:51.971354 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 14:54:51.971365 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 14:54:51.971377 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 14:54:51.971390 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 14:54:51.971401 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 14:54:51.971415 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 14:54:51.971426 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 14:54:51.971438 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 14:54:51.971451 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 14:54:51.971462 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 14:54:51.971489 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 14:54:51.971506 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 14:54:51.971529 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 14:54:51.971547 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 14:54:51.971565 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 14:54:51.971581 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 14:54:51.971598 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 14:54:51.971616 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 14:54:51.971632 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 14:54:51.971654 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 14:54:51.971671 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.863636
I0430 14:54:51.971688 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.84
I0430 14:54:51.971710 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.39346 (* 0.3 = 0.418039 loss)
I0430 14:54:51.971730 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.440903 (* 0.3 = 0.132271 loss)
I0430 14:54:51.971752 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.36042 (* 0.0272727 = 0.0371024 loss)
I0430 14:54:51.971772 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.13182 (* 0.0272727 = 0.0308677 loss)
I0430 14:54:51.971809 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.3375 (* 0.0272727 = 0.0364772 loss)
I0430 14:54:51.971832 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.17483 (* 0.0272727 = 0.0320408 loss)
I0430 14:54:51.971853 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.42362 (* 0.0272727 = 0.038826 loss)
I0430 14:54:51.971874 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.66059 (* 0.0272727 = 0.0452889 loss)
I0430 14:54:51.971894 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.36594 (* 0.0272727 = 0.0372528 loss)
I0430 14:54:51.971915 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.583247 (* 0.0272727 = 0.0159067 loss)
I0430 14:54:51.971936 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.458576 (* 0.0272727 = 0.0125066 loss)
I0430 14:54:51.971957 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.319412 (* 0.0272727 = 0.00871123 loss)
I0430 14:54:51.971978 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.213043 (* 0.0272727 = 0.00581027 loss)
I0430 14:54:51.971999 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0599386 (* 0.0272727 = 0.00163469 loss)
I0430 14:54:51.972020 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0189075 (* 0.0272727 = 0.000515659 loss)
I0430 14:54:51.972041 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00970044 (* 0.0272727 = 0.000264557 loss)
I0430 14:54:51.972062 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00259598 (* 0.0272727 = 7.07994e-05 loss)
I0430 14:54:51.972082 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00115263 (* 0.0272727 = 3.14353e-05 loss)
I0430 14:54:51.972103 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000406351 (* 0.0272727 = 1.10823e-05 loss)
I0430 14:54:51.972124 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000202641 (* 0.0272727 = 5.52657e-06 loss)
I0430 14:54:51.972146 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 6.88698e-05 (* 0.0272727 = 1.87827e-06 loss)
I0430 14:54:51.972167 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 6.32118e-05 (* 0.0272727 = 1.72396e-06 loss)
I0430 14:54:51.972187 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.14148e-05 (* 0.0272727 = 5.8404e-07 loss)
I0430 14:54:51.972208 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.16234e-05 (* 0.0272727 = 3.17003e-07 loss)
I0430 14:54:51.972226 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.76
I0430 14:54:51.972244 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 14:54:51.972261 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 14:54:51.972278 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 14:54:51.972295 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 14:54:51.972313 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0430 14:54:51.972332 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 14:54:51.972348 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0430 14:54:51.972369 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 14:54:51.972386 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 14:54:51.972404 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 14:54:51.972421 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 14:54:51.972440 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 14:54:51.972455 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 14:54:51.972473 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 14:54:51.972491 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 14:54:51.972507 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 14:54:51.972537 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 14:54:51.972556 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 14:54:51.972574 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 14:54:51.972591 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 14:54:51.972609 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 14:54:51.972625 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 14:54:51.972642 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.926136
I0430 14:54:51.972659 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.9
I0430 14:54:51.972681 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.947144 (* 1 = 0.947144 loss)
I0430 14:54:51.972705 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.294982 (* 1 = 0.294982 loss)
I0430 14:54:51.972726 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.497765 (* 0.0909091 = 0.0452514 loss)
I0430 14:54:51.972748 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.347757 (* 0.0909091 = 0.0316143 loss)
I0430 14:54:51.972767 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.420979 (* 0.0909091 = 0.0382708 loss)
I0430 14:54:51.972789 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.484522 (* 0.0909091 = 0.0440474 loss)
I0430 14:54:51.972810 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.990055 (* 0.0909091 = 0.090005 loss)
I0430 14:54:51.972831 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.44818 (* 0.0909091 = 0.131653 loss)
I0430 14:54:51.972851 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.33673 (* 0.0909091 = 0.121521 loss)
I0430 14:54:51.972872 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.326919 (* 0.0909091 = 0.0297199 loss)
I0430 14:54:51.972892 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.51395 (* 0.0909091 = 0.0467227 loss)
I0430 14:54:51.972913 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.237239 (* 0.0909091 = 0.0215671 loss)
I0430 14:54:51.972934 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.214431 (* 0.0909091 = 0.0194938 loss)
I0430 14:54:51.972954 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.047027 (* 0.0909091 = 0.00427518 loss)
I0430 14:54:51.972975 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0121049 (* 0.0909091 = 0.00110044 loss)
I0430 14:54:51.972996 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00339967 (* 0.0909091 = 0.000309061 loss)
I0430 14:54:51.973017 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00225333 (* 0.0909091 = 0.000204849 loss)
I0430 14:54:51.973038 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00096567 (* 0.0909091 = 8.77882e-05 loss)
I0430 14:54:51.973059 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000894842 (* 0.0909091 = 8.13493e-05 loss)
I0430 14:54:51.973080 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000339253 (* 0.0909091 = 3.08411e-05 loss)
I0430 14:54:51.973100 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000270032 (* 0.0909091 = 2.45484e-05 loss)
I0430 14:54:51.973122 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000178957 (* 0.0909091 = 1.62688e-05 loss)
I0430 14:54:51.973143 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000148936 (* 0.0909091 = 1.35397e-05 loss)
I0430 14:54:51.973163 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 5.8292e-05 (* 0.0909091 = 5.29927e-06 loss)
I0430 14:54:51.973181 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0430 14:54:51.973198 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0430 14:54:51.973227 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.524356
I0430 14:54:51.973247 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.533491
I0430 14:54:51.973264 15443 sgd_solver.cpp:106] Iteration 2500, lr = 0.001
I0430 14:58:43.324324 15443 solver.cpp:229] Iteration 3000, loss = 3.72137
I0430 14:58:43.324492 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.613636
I0430 14:58:43.324517 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 14:58:43.324537 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 14:58:43.324554 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 14:58:43.324571 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 14:58:43.324589 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0430 14:58:43.324606 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 14:58:43.324625 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 1
I0430 14:58:43.324643 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0430 14:58:43.324661 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0430 14:58:43.324677 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 14:58:43.324694 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 14:58:43.324712 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 14:58:43.324728 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 14:58:43.324746 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 14:58:43.324764 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 14:58:43.324781 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 14:58:43.324797 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 14:58:43.324815 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 14:58:43.324831 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 14:58:43.324848 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 14:58:43.324865 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 14:58:43.324882 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 14:58:43.324899 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.897727
I0430 14:58:43.324916 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.886364
I0430 14:58:43.324939 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.27213 (* 0.3 = 0.381639 loss)
I0430 14:58:43.324961 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.341349 (* 0.3 = 0.102405 loss)
I0430 14:58:43.324982 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.653817 (* 0.0272727 = 0.0178314 loss)
I0430 14:58:43.325003 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.5603 (* 0.0272727 = 0.0425536 loss)
I0430 14:58:43.325024 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.09948 (* 0.0272727 = 0.0572586 loss)
I0430 14:58:43.325045 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.51026 (* 0.0272727 = 0.041189 loss)
I0430 14:58:43.325067 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.20155 (* 0.0272727 = 0.0327695 loss)
I0430 14:58:43.325088 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.631201 (* 0.0272727 = 0.0172146 loss)
I0430 14:58:43.325109 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.0127108 (* 0.0272727 = 0.000346659 loss)
I0430 14:58:43.325130 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.00010623 (* 0.0272727 = 2.89719e-06 loss)
I0430 14:58:43.325151 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 1.98186e-06 (* 0.0272727 = 5.40508e-08 loss)
I0430 14:58:43.325173 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 2.98023e-08 (* 0.0272727 = 8.12791e-10 loss)
I0430 14:58:43.325194 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325214 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325234 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325276 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325299 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325322 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325345 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325364 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325384 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325405 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325425 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325445 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.325464 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.75
I0430 14:58:43.325481 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 14:58:43.325501 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 14:58:43.325520 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0430 14:58:43.325537 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 14:58:43.325554 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 14:58:43.325572 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.875
I0430 14:58:43.325589 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0430 14:58:43.325606 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0430 14:58:43.325623 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0430 14:58:43.325640 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 14:58:43.325656 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 14:58:43.325675 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 14:58:43.325692 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 14:58:43.325709 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 14:58:43.325726 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 14:58:43.325743 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 14:58:43.325760 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 14:58:43.325778 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 14:58:43.325794 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 14:58:43.325812 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 14:58:43.325829 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 14:58:43.325845 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 14:58:43.325862 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.926136
I0430 14:58:43.325880 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.931818
I0430 14:58:43.325899 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.889777 (* 0.3 = 0.266933 loss)
I0430 14:58:43.325922 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.262034 (* 0.3 = 0.0786102 loss)
I0430 14:58:43.325942 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.398598 (* 0.0272727 = 0.0108708 loss)
I0430 14:58:43.325963 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.769047 (* 0.0272727 = 0.020974 loss)
I0430 14:58:43.325984 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.865593 (* 0.0272727 = 0.0236071 loss)
I0430 14:58:43.326004 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.49113 (* 0.0272727 = 0.0406672 loss)
I0430 14:58:43.326040 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.02461 (* 0.0272727 = 0.0279439 loss)
I0430 14:58:43.326061 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.391724 (* 0.0272727 = 0.0106834 loss)
I0430 14:58:43.326081 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.395431 (* 0.0272727 = 0.0107845 loss)
I0430 14:58:43.326102 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 6.25468e-05 (* 0.0272727 = 1.70582e-06 loss)
I0430 14:58:43.326124 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.12359e-05 (* 0.0272727 = 3.06434e-07 loss)
I0430 14:58:43.326144 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 2.17558e-06 (* 0.0272727 = 5.93341e-08 loss)
I0430 14:58:43.326166 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 3.66573e-06 (* 0.0272727 = 9.99744e-08 loss)
I0430 14:58:43.326186 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 7.4506e-07 (* 0.0272727 = 2.03198e-08 loss)
I0430 14:58:43.326207 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 3.42727e-07 (* 0.0272727 = 9.3471e-09 loss)
I0430 14:58:43.326230 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 1.93715e-07 (* 0.0272727 = 5.28314e-09 loss)
I0430 14:58:43.326251 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 2.98023e-08 (* 0.0272727 = 8.12791e-10 loss)
I0430 14:58:43.326270 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 2.98023e-08 (* 0.0272727 = 8.12791e-10 loss)
I0430 14:58:43.326292 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 1.49012e-08 (* 0.0272727 = 4.06395e-10 loss)
I0430 14:58:43.326313 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.326333 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 1.49012e-08 (* 0.0272727 = 4.06395e-10 loss)
I0430 14:58:43.326354 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0 (* 0.0272727 = 0 loss)
I0430 14:58:43.326377 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.98023e-08 (* 0.0272727 = 8.12791e-10 loss)
I0430 14:58:43.326398 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 4.47035e-08 (* 0.0272727 = 1.21919e-09 loss)
I0430 14:58:43.326416 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.931818
I0430 14:58:43.326433 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 14:58:43.326452 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 14:58:43.326468 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 14:58:43.326485 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 14:58:43.326501 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0430 14:58:43.326519 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0430 14:58:43.326536 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0430 14:58:43.326556 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0430 14:58:43.326573 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 14:58:43.326591 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 14:58:43.326607 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 14:58:43.326624 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 14:58:43.326642 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 14:58:43.326658 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 14:58:43.326675 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 14:58:43.326692 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 14:58:43.326709 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 14:58:43.326726 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 14:58:43.326756 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 14:58:43.326776 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 14:58:43.326792 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 14:58:43.326808 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 14:58:43.326825 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.982955
I0430 14:58:43.326843 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.954545
I0430 14:58:43.326865 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.260795 (* 1 = 0.260795 loss)
I0430 14:58:43.326886 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.0668199 (* 1 = 0.0668199 loss)
I0430 14:58:43.326908 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.513692 (* 0.0909091 = 0.0466993 loss)
I0430 14:58:43.326930 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.542733 (* 0.0909091 = 0.0493393 loss)
I0430 14:58:43.326949 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.508685 (* 0.0909091 = 0.0462441 loss)
I0430 14:58:43.326970 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.348896 (* 0.0909091 = 0.0317178 loss)
I0430 14:58:43.326992 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.1109 (* 0.0909091 = 0.0100818 loss)
I0430 14:58:43.327013 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.305214 (* 0.0909091 = 0.0277468 loss)
I0430 14:58:43.327033 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.00516251 (* 0.0909091 = 0.000469319 loss)
I0430 14:58:43.327054 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 5.80323e-05 (* 0.0909091 = 5.27567e-06 loss)
I0430 14:58:43.327075 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.000576776 (* 0.0909091 = 5.24342e-05 loss)
I0430 14:58:43.327090 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.000158923 (* 0.0909091 = 1.44475e-05 loss)
I0430 14:58:43.327111 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00037407 (* 0.0909091 = 3.40063e-05 loss)
I0430 14:58:43.327132 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.000158242 (* 0.0909091 = 1.43856e-05 loss)
I0430 14:58:43.327153 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 8.95712e-05 (* 0.0909091 = 8.14283e-06 loss)
I0430 14:58:43.327173 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 6.32114e-05 (* 0.0909091 = 5.74649e-06 loss)
I0430 14:58:43.327194 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 3.53132e-05 (* 0.0909091 = 3.21029e-06 loss)
I0430 14:58:43.327215 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 2.0148e-05 (* 0.0909091 = 1.83163e-06 loss)
I0430 14:58:43.327236 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 1.50213e-05 (* 0.0909091 = 1.36557e-06 loss)
I0430 14:58:43.327257 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 9.43279e-06 (* 0.0909091 = 8.57526e-07 loss)
I0430 14:58:43.327277 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 1.09826e-05 (* 0.0909091 = 9.98421e-07 loss)
I0430 14:58:43.327298 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 9.19435e-06 (* 0.0909091 = 8.3585e-07 loss)
I0430 14:58:43.327322 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.10273e-05 (* 0.0909091 = 1.00249e-06 loss)
I0430 14:58:43.327343 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 9.32847e-06 (* 0.0909091 = 8.48043e-07 loss)
I0430 14:58:43.327360 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.875
I0430 14:58:43.327378 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0430 14:58:43.327394 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.671863
I0430 14:58:43.327410 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.635889
I0430 14:58:43.327458 15443 sgd_solver.cpp:106] Iteration 3000, lr = 0.001
I0430 15:02:25.347329 15443 solver.cpp:229] Iteration 3500, loss = 3.71889
I0430 15:02:25.347497 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.376812
I0430 15:02:25.347528 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 15:02:25.347550 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0430 15:02:25.347573 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0430 15:02:25.347595 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0430 15:02:25.347618 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0430 15:02:25.347642 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.25
I0430 15:02:25.347667 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 15:02:25.347689 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 15:02:25.347712 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.625
I0430 15:02:25.347733 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 15:02:25.347755 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0430 15:02:25.347780 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.75
I0430 15:02:25.347803 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.75
I0430 15:02:25.347826 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.75
I0430 15:02:25.347847 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.75
I0430 15:02:25.347870 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0430 15:02:25.347892 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:02:25.347915 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:02:25.347934 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:02:25.347954 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:02:25.347973 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:02:25.347993 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:02:25.348016 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.744318
I0430 15:02:25.348039 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.637681
I0430 15:02:25.348068 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.852 (* 0.3 = 0.5556 loss)
I0430 15:02:25.348096 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.755421 (* 0.3 = 0.226626 loss)
I0430 15:02:25.348125 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.11807 (* 0.0272727 = 0.0304927 loss)
I0430 15:02:25.348152 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.02494 (* 0.0272727 = 0.0279529 loss)
I0430 15:02:25.348181 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.84427 (* 0.0272727 = 0.0502982 loss)
I0430 15:02:25.348206 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.18736 (* 0.0272727 = 0.0596552 loss)
I0430 15:02:25.348233 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.49526 (* 0.0272727 = 0.0407798 loss)
I0430 15:02:25.348260 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.9609 (* 0.0272727 = 0.053479 loss)
I0430 15:02:25.348287 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.56247 (* 0.0272727 = 0.0426127 loss)
I0430 15:02:25.348318 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.681836 (* 0.0272727 = 0.0185955 loss)
I0430 15:02:25.348346 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 1.13177 (* 0.0272727 = 0.0308664 loss)
I0430 15:02:25.348373 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.666861 (* 0.0272727 = 0.0181871 loss)
I0430 15:02:25.348400 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.685607 (* 0.0272727 = 0.0186984 loss)
I0430 15:02:25.348428 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.599974 (* 0.0272727 = 0.0163629 loss)
I0430 15:02:25.348481 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.785895 (* 0.0272727 = 0.0214335 loss)
I0430 15:02:25.348517 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.682069 (* 0.0272727 = 0.0186019 loss)
I0430 15:02:25.348547 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.776705 (* 0.0272727 = 0.0211829 loss)
I0430 15:02:25.348578 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.478525 (* 0.0272727 = 0.0130507 loss)
I0430 15:02:25.348606 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00346394 (* 0.0272727 = 9.4471e-05 loss)
I0430 15:02:25.348635 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000408403 (* 0.0272727 = 1.11383e-05 loss)
I0430 15:02:25.348662 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000140851 (* 0.0272727 = 3.8414e-06 loss)
I0430 15:02:25.348691 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 9.7633e-05 (* 0.0272727 = 2.66272e-06 loss)
I0430 15:02:25.348718 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 6.05057e-05 (* 0.0272727 = 1.65016e-06 loss)
I0430 15:02:25.348745 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000173384 (* 0.0272727 = 4.72867e-06 loss)
I0430 15:02:25.348769 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.449275
I0430 15:02:25.348793 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 15:02:25.348815 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 15:02:25.348837 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 15:02:25.348860 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 15:02:25.348882 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 15:02:25.348904 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 15:02:25.348927 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 15:02:25.348948 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 15:02:25.348969 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.625
I0430 15:02:25.348990 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0430 15:02:25.349014 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 15:02:25.349035 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 15:02:25.349057 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.75
I0430 15:02:25.349079 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.75
I0430 15:02:25.349102 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.75
I0430 15:02:25.349125 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0430 15:02:25.349148 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:02:25.349169 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:02:25.349192 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:02:25.349213 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:02:25.349236 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:02:25.349257 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:02:25.349279 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.778409
I0430 15:02:25.349301 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.637681
I0430 15:02:25.349328 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.64756 (* 0.3 = 0.494268 loss)
I0430 15:02:25.349355 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.682374 (* 0.3 = 0.204712 loss)
I0430 15:02:25.349386 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.840816 (* 0.0272727 = 0.0229313 loss)
I0430 15:02:25.349413 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.970395 (* 0.0272727 = 0.0264653 loss)
I0430 15:02:25.349458 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.45752 (* 0.0272727 = 0.0397504 loss)
I0430 15:02:25.349486 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.04109 (* 0.0272727 = 0.0556661 loss)
I0430 15:02:25.349514 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.57262 (* 0.0272727 = 0.0428896 loss)
I0430 15:02:25.349540 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.56315 (* 0.0272727 = 0.0426313 loss)
I0430 15:02:25.349572 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.27817 (* 0.0272727 = 0.0348591 loss)
I0430 15:02:25.349601 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.745328 (* 0.0272727 = 0.0203271 loss)
I0430 15:02:25.349628 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.10704 (* 0.0272727 = 0.030192 loss)
I0430 15:02:25.349654 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.644296 (* 0.0272727 = 0.0175717 loss)
I0430 15:02:25.349680 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.549932 (* 0.0272727 = 0.0149981 loss)
I0430 15:02:25.349706 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.540288 (* 0.0272727 = 0.0147351 loss)
I0430 15:02:25.349733 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.790893 (* 0.0272727 = 0.0215698 loss)
I0430 15:02:25.349761 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.606733 (* 0.0272727 = 0.0165473 loss)
I0430 15:02:25.349787 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.662113 (* 0.0272727 = 0.0180576 loss)
I0430 15:02:25.349814 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.44936 (* 0.0272727 = 0.0122553 loss)
I0430 15:02:25.349840 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0058079 (* 0.0272727 = 0.000158397 loss)
I0430 15:02:25.349869 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00130684 (* 0.0272727 = 3.5641e-05 loss)
I0430 15:02:25.349896 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000864942 (* 0.0272727 = 2.35893e-05 loss)
I0430 15:02:25.349923 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000538914 (* 0.0272727 = 1.46977e-05 loss)
I0430 15:02:25.349951 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000315118 (* 0.0272727 = 8.59413e-06 loss)
I0430 15:02:25.349978 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000134844 (* 0.0272727 = 3.67756e-06 loss)
I0430 15:02:25.350002 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.652174
I0430 15:02:25.350024 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 15:02:25.350046 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0430 15:02:25.350069 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 15:02:25.350090 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0430 15:02:25.350112 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0430 15:02:25.350134 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 15:02:25.350157 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 15:02:25.350179 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 15:02:25.350201 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.625
I0430 15:02:25.350224 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 15:02:25.350245 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0430 15:02:25.350267 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 15:02:25.350288 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.75
I0430 15:02:25.350311 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 15:02:25.350332 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 15:02:25.350369 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0430 15:02:25.350394 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:02:25.350419 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:02:25.350442 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:02:25.350464 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:02:25.350486 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:02:25.350507 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:02:25.350530 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.863636
I0430 15:02:25.350553 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.826087
I0430 15:02:25.350579 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.14674 (* 1 = 1.14674 loss)
I0430 15:02:25.350610 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.457497 (* 1 = 0.457497 loss)
I0430 15:02:25.350637 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.575737 (* 0.0909091 = 0.0523398 loss)
I0430 15:02:25.350666 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.431857 (* 0.0909091 = 0.0392597 loss)
I0430 15:02:25.350692 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.09752 (* 0.0909091 = 0.0997749 loss)
I0430 15:02:25.350718 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.808933 (* 0.0909091 = 0.0735393 loss)
I0430 15:02:25.350745 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.815009 (* 0.0909091 = 0.0740917 loss)
I0430 15:02:25.350772 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.951676 (* 0.0909091 = 0.086516 loss)
I0430 15:02:25.350797 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.751276 (* 0.0909091 = 0.0682978 loss)
I0430 15:02:25.350826 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.563477 (* 0.0909091 = 0.0512252 loss)
I0430 15:02:25.350848 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.833316 (* 0.0909091 = 0.075756 loss)
I0430 15:02:25.350878 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.494706 (* 0.0909091 = 0.0449732 loss)
I0430 15:02:25.350905 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.589131 (* 0.0909091 = 0.0535574 loss)
I0430 15:02:25.350932 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.448812 (* 0.0909091 = 0.0408011 loss)
I0430 15:02:25.350958 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.698781 (* 0.0909091 = 0.0635255 loss)
I0430 15:02:25.350986 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.555918 (* 0.0909091 = 0.050538 loss)
I0430 15:02:25.351013 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.424612 (* 0.0909091 = 0.0386011 loss)
I0430 15:02:25.351039 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.297571 (* 0.0909091 = 0.0270519 loss)
I0430 15:02:25.351066 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.010334 (* 0.0909091 = 0.000939456 loss)
I0430 15:02:25.351094 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00269012 (* 0.0909091 = 0.000244557 loss)
I0430 15:02:25.351119 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00104828 (* 0.0909091 = 9.52979e-05 loss)
I0430 15:02:25.351147 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00059955 (* 0.0909091 = 5.45046e-05 loss)
I0430 15:02:25.351174 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000320901 (* 0.0909091 = 2.91728e-05 loss)
I0430 15:02:25.351200 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000135068 (* 0.0909091 = 1.22789e-05 loss)
I0430 15:02:25.351223 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0430 15:02:25.351246 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 15:02:25.351285 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.384449
I0430 15:02:25.351310 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.366263
I0430 15:02:25.351333 15443 sgd_solver.cpp:106] Iteration 3500, lr = 0.001
I0430 15:06:08.316037 15443 solver.cpp:229] Iteration 4000, loss = 3.75943
I0430 15:06:08.316212 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.454545
I0430 15:06:08.316233 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.375
I0430 15:06:08.316247 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 15:06:08.316259 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 15:06:08.316272 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.625
I0430 15:06:08.316284 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0430 15:06:08.316296 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0430 15:06:08.316309 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0430 15:06:08.316324 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 15:06:08.316337 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 15:06:08.316349 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 15:06:08.316361 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 15:06:08.316375 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 15:06:08.316386 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 15:06:08.316398 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 15:06:08.316411 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 15:06:08.316421 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 15:06:08.316434 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:06:08.316445 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:06:08.316457 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:06:08.316469 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:06:08.316481 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:06:08.316493 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:06:08.316504 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.829545
I0430 15:06:08.316516 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.636364
I0430 15:06:08.316534 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.77103 (* 0.3 = 0.53131 loss)
I0430 15:06:08.316548 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.551585 (* 0.3 = 0.165475 loss)
I0430 15:06:08.316563 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 2.16399 (* 0.0272727 = 0.0590179 loss)
I0430 15:06:08.316577 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.29524 (* 0.0272727 = 0.0353248 loss)
I0430 15:06:08.316591 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.76303 (* 0.0272727 = 0.0480825 loss)
I0430 15:06:08.316606 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.65407 (* 0.0272727 = 0.045111 loss)
I0430 15:06:08.316619 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.33361 (* 0.0272727 = 0.0363713 loss)
I0430 15:06:08.316633 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.928709 (* 0.0272727 = 0.0253284 loss)
I0430 15:06:08.316648 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.46673 (* 0.0272727 = 0.012729 loss)
I0430 15:06:08.316663 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.445262 (* 0.0272727 = 0.0121435 loss)
I0430 15:06:08.316676 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.41811 (* 0.0272727 = 0.011403 loss)
I0430 15:06:08.316691 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.194769 (* 0.0272727 = 0.00531188 loss)
I0430 15:06:08.316705 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.348498 (* 0.0272727 = 0.00950449 loss)
I0430 15:06:08.316720 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.216083 (* 0.0272727 = 0.00589317 loss)
I0430 15:06:08.316754 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0455564 (* 0.0272727 = 0.00124245 loss)
I0430 15:06:08.316771 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0187885 (* 0.0272727 = 0.000512415 loss)
I0430 15:06:08.316786 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00603991 (* 0.0272727 = 0.000164725 loss)
I0430 15:06:08.316799 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00383578 (* 0.0272727 = 0.000104612 loss)
I0430 15:06:08.316813 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00433441 (* 0.0272727 = 0.000118211 loss)
I0430 15:06:08.316828 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00192988 (* 0.0272727 = 5.26331e-05 loss)
I0430 15:06:08.316843 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00259408 (* 0.0272727 = 7.07475e-05 loss)
I0430 15:06:08.316856 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00353322 (* 0.0272727 = 9.63605e-05 loss)
I0430 15:06:08.316870 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00298262 (* 0.0272727 = 8.13442e-05 loss)
I0430 15:06:08.316884 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00285261 (* 0.0272727 = 7.77984e-05 loss)
I0430 15:06:08.316897 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.545455
I0430 15:06:08.316910 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.5
I0430 15:06:08.316921 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0430 15:06:08.316933 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0430 15:06:08.316946 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 15:06:08.316957 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 15:06:08.316969 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 15:06:08.316982 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0430 15:06:08.316993 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 15:06:08.317005 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 15:06:08.317018 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 15:06:08.317029 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 15:06:08.317041 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 15:06:08.317052 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 15:06:08.317065 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 15:06:08.317076 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 15:06:08.317088 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 15:06:08.317100 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:06:08.317111 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:06:08.317123 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:06:08.317134 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:06:08.317147 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:06:08.317158 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:06:08.317169 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.846591
I0430 15:06:08.317181 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.659091
I0430 15:06:08.317195 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.85534 (* 0.3 = 0.556602 loss)
I0430 15:06:08.317209 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.611916 (* 0.3 = 0.183575 loss)
I0430 15:06:08.317227 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 2.20205 (* 0.0272727 = 0.060056 loss)
I0430 15:06:08.317242 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 2.03057 (* 0.0272727 = 0.0553793 loss)
I0430 15:06:08.317267 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.39604 (* 0.0272727 = 0.0380739 loss)
I0430 15:06:08.317283 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.77024 (* 0.0272727 = 0.0482793 loss)
I0430 15:06:08.317297 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.69954 (* 0.0272727 = 0.0463512 loss)
I0430 15:06:08.317312 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.02248 (* 0.0272727 = 0.0278859 loss)
I0430 15:06:08.317325 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.4567 (* 0.0272727 = 0.0124555 loss)
I0430 15:06:08.317340 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.443842 (* 0.0272727 = 0.0121048 loss)
I0430 15:06:08.317353 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.440674 (* 0.0272727 = 0.0120184 loss)
I0430 15:06:08.317371 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.285554 (* 0.0272727 = 0.00778785 loss)
I0430 15:06:08.317386 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.417276 (* 0.0272727 = 0.0113802 loss)
I0430 15:06:08.317400 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.304952 (* 0.0272727 = 0.00831689 loss)
I0430 15:06:08.317414 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.154255 (* 0.0272727 = 0.00420695 loss)
I0430 15:06:08.317430 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0837535 (* 0.0272727 = 0.00228419 loss)
I0430 15:06:08.317443 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0630259 (* 0.0272727 = 0.00171889 loss)
I0430 15:06:08.317457 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0256225 (* 0.0272727 = 0.000698796 loss)
I0430 15:06:08.317471 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00116121 (* 0.0272727 = 3.16692e-05 loss)
I0430 15:06:08.317487 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000551873 (* 0.0272727 = 1.50511e-05 loss)
I0430 15:06:08.317500 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000801091 (* 0.0272727 = 2.18479e-05 loss)
I0430 15:06:08.317515 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000168934 (* 0.0272727 = 4.6073e-06 loss)
I0430 15:06:08.317530 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 7.42797e-05 (* 0.0272727 = 2.02581e-06 loss)
I0430 15:06:08.317544 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000102275 (* 0.0272727 = 2.78933e-06 loss)
I0430 15:06:08.317558 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.613636
I0430 15:06:08.317569 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.625
I0430 15:06:08.317581 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 15:06:08.317594 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 15:06:08.317605 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0430 15:06:08.317615 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 15:06:08.317631 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 15:06:08.317654 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 15:06:08.317670 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 15:06:08.317682 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 15:06:08.317694 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 15:06:08.317705 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 15:06:08.317718 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 15:06:08.317729 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 15:06:08.317740 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 15:06:08.317752 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 15:06:08.317775 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 15:06:08.317788 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:06:08.317800 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:06:08.317812 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:06:08.317823 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:06:08.317836 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:06:08.317847 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:06:08.317859 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.880682
I0430 15:06:08.317872 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.772727
I0430 15:06:08.317885 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.28668 (* 1 = 1.28668 loss)
I0430 15:06:08.317900 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.396387 (* 1 = 0.396387 loss)
I0430 15:06:08.317914 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 1.64588 (* 0.0909091 = 0.149625 loss)
I0430 15:06:08.317929 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 1.10157 (* 0.0909091 = 0.100143 loss)
I0430 15:06:08.317944 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.869806 (* 0.0909091 = 0.0790733 loss)
I0430 15:06:08.317957 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.18534 (* 0.0909091 = 0.107758 loss)
I0430 15:06:08.317971 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.62308 (* 0.0909091 = 0.0566437 loss)
I0430 15:06:08.317986 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.425463 (* 0.0909091 = 0.0386785 loss)
I0430 15:06:08.317999 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.422877 (* 0.0909091 = 0.0384434 loss)
I0430 15:06:08.318013 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.754902 (* 0.0909091 = 0.0686275 loss)
I0430 15:06:08.318027 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.648434 (* 0.0909091 = 0.0589485 loss)
I0430 15:06:08.318042 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0222216 (* 0.0909091 = 0.00202015 loss)
I0430 15:06:08.318056 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.455725 (* 0.0909091 = 0.0414296 loss)
I0430 15:06:08.318070 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.314313 (* 0.0909091 = 0.0285739 loss)
I0430 15:06:08.318084 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.151635 (* 0.0909091 = 0.013785 loss)
I0430 15:06:08.318099 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0510102 (* 0.0909091 = 0.00463729 loss)
I0430 15:06:08.318114 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00658 (* 0.0909091 = 0.000598182 loss)
I0430 15:06:08.318127 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00115343 (* 0.0909091 = 0.000104858 loss)
I0430 15:06:08.318141 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000292029 (* 0.0909091 = 2.65481e-05 loss)
I0430 15:06:08.318156 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00016908 (* 0.0909091 = 1.53709e-05 loss)
I0430 15:06:08.318171 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000165885 (* 0.0909091 = 1.50805e-05 loss)
I0430 15:06:08.318184 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000133457 (* 0.0909091 = 1.21325e-05 loss)
I0430 15:06:08.318199 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000117173 (* 0.0909091 = 1.06521e-05 loss)
I0430 15:06:08.318213 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000109676 (* 0.0909091 = 9.97051e-06 loss)
I0430 15:06:08.318225 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 15:06:08.318238 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 15:06:08.318259 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.354681
I0430 15:06:08.318276 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.355892
I0430 15:06:08.318289 15443 sgd_solver.cpp:106] Iteration 4000, lr = 0.001
I0430 15:06:51.094277 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 60.0448 > 30) by scale factor 0.499627
I0430 15:07:07.779942 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.3449 > 30) by scale factor 0.988633
I0430 15:07:16.628831 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 42.7739 > 30) by scale factor 0.701363
I0430 15:10:00.382439 15443 solver.cpp:229] Iteration 4500, loss = 3.66691
I0430 15:10:00.382557 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.384615
I0430 15:10:00.382582 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 15:10:00.382601 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 15:10:00.382619 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0430 15:10:00.382637 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0430 15:10:00.382654 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 15:10:00.382673 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0430 15:10:00.382689 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 15:10:00.382707 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 15:10:00.382725 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 15:10:00.382742 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 15:10:00.382761 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 15:10:00.382778 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 15:10:00.382796 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 15:10:00.382813 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 15:10:00.382830 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0430 15:10:00.382848 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0430 15:10:00.382866 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 0.875
I0430 15:10:00.382884 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 0.875
I0430 15:10:00.382900 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 0.875
I0430 15:10:00.382918 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:10:00.382936 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:10:00.382957 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:10:00.382975 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.772727
I0430 15:10:00.382992 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.630769
I0430 15:10:00.383015 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.67463 (* 0.3 = 0.802389 loss)
I0430 15:10:00.383038 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.99399 (* 0.3 = 0.298197 loss)
I0430 15:10:00.383059 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.873451 (* 0.0272727 = 0.0238214 loss)
I0430 15:10:00.383080 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.42695 (* 0.0272727 = 0.0389169 loss)
I0430 15:10:00.383101 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.26428 (* 0.0272727 = 0.061753 loss)
I0430 15:10:00.383122 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.1718 (* 0.0272727 = 0.059231 loss)
I0430 15:10:00.383143 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.72493 (* 0.0272727 = 0.0470434 loss)
I0430 15:10:00.383164 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.99978 (* 0.0272727 = 0.0545396 loss)
I0430 15:10:00.383184 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.37927 (* 0.0272727 = 0.0376165 loss)
I0430 15:10:00.383205 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.0705 (* 0.0272727 = 0.0291955 loss)
I0430 15:10:00.383225 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.769408 (* 0.0272727 = 0.0209838 loss)
I0430 15:10:00.383247 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.843898 (* 0.0272727 = 0.0230154 loss)
I0430 15:10:00.383268 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 1.09375 (* 0.0272727 = 0.0298296 loss)
I0430 15:10:00.383290 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 1.06866 (* 0.0272727 = 0.0291451 loss)
I0430 15:10:00.383332 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 1.11052 (* 0.0272727 = 0.0302868 loss)
I0430 15:10:00.383355 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 1.19763 (* 0.0272727 = 0.0326627 loss)
I0430 15:10:00.383379 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 1.29074 (* 0.0272727 = 0.0352019 loss)
I0430 15:10:00.383401 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 1.69128 (* 0.0272727 = 0.0461257 loss)
I0430 15:10:00.383422 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 1.47788 (* 0.0272727 = 0.0403057 loss)
I0430 15:10:00.383443 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 1.70572 (* 0.0272727 = 0.0465196 loss)
I0430 15:10:00.383481 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 1.9109 (* 0.0272727 = 0.0521154 loss)
I0430 15:10:00.383512 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.414e-06 (* 0.0272727 = 6.58364e-08 loss)
I0430 15:10:00.383538 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 1.2517e-06 (* 0.0272727 = 3.41373e-08 loss)
I0430 15:10:00.383555 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 4.32141e-06 (* 0.0272727 = 1.17857e-07 loss)
I0430 15:10:00.383569 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.338462
I0430 15:10:00.383580 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 15:10:00.383594 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0430 15:10:00.383605 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0430 15:10:00.383617 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0430 15:10:00.383630 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 15:10:00.383641 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.25
I0430 15:10:00.383653 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 15:10:00.383666 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0430 15:10:00.383677 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0430 15:10:00.383688 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 15:10:00.383700 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 15:10:00.383713 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 15:10:00.383725 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 15:10:00.383738 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 15:10:00.383749 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 15:10:00.383761 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0430 15:10:00.383774 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 0.875
I0430 15:10:00.383785 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 0.875
I0430 15:10:00.383796 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 0.875
I0430 15:10:00.383808 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:10:00.383821 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:10:00.383832 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:10:00.383843 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.755682
I0430 15:10:00.383855 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.630769
I0430 15:10:00.383869 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 2.89958 (* 0.3 = 0.869875 loss)
I0430 15:10:00.383883 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.08846 (* 0.3 = 0.326537 loss)
I0430 15:10:00.383898 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.2288 (* 0.0272727 = 0.0335129 loss)
I0430 15:10:00.383911 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.27936 (* 0.0272727 = 0.0348917 loss)
I0430 15:10:00.383939 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.74642 (* 0.0272727 = 0.0476297 loss)
I0430 15:10:00.383955 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.88057 (* 0.0272727 = 0.0512883 loss)
I0430 15:10:00.383970 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.65956 (* 0.0272727 = 0.0452608 loss)
I0430 15:10:00.383983 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 2.34084 (* 0.0272727 = 0.0638412 loss)
I0430 15:10:00.384002 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.54211 (* 0.0272727 = 0.0420575 loss)
I0430 15:10:00.384017 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.4084 (* 0.0272727 = 0.038411 loss)
I0430 15:10:00.384032 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.00134 (* 0.0272727 = 0.0273093 loss)
I0430 15:10:00.384045 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 1.12142 (* 0.0272727 = 0.0305842 loss)
I0430 15:10:00.384059 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.25428 (* 0.0272727 = 0.0342076 loss)
I0430 15:10:00.384073 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 1.29634 (* 0.0272727 = 0.0353547 loss)
I0430 15:10:00.384086 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 1.4527 (* 0.0272727 = 0.0396191 loss)
I0430 15:10:00.384100 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 1.36502 (* 0.0272727 = 0.0372277 loss)
I0430 15:10:00.384114 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 1.49662 (* 0.0272727 = 0.0408169 loss)
I0430 15:10:00.384129 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 1.64393 (* 0.0272727 = 0.0448345 loss)
I0430 15:10:00.384142 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 1.66763 (* 0.0272727 = 0.0454807 loss)
I0430 15:10:00.384156 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 1.55539 (* 0.0272727 = 0.0424198 loss)
I0430 15:10:00.384171 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 1.60297 (* 0.0272727 = 0.0437173 loss)
I0430 15:10:00.384186 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 6.78309e-05 (* 0.0272727 = 1.84993e-06 loss)
I0430 15:10:00.384199 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.51775e-05 (* 0.0272727 = 6.86658e-07 loss)
I0430 15:10:00.384213 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 6.51193e-06 (* 0.0272727 = 1.77598e-07 loss)
I0430 15:10:00.384225 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.569231
I0430 15:10:00.384238 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 15:10:00.384250 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 15:10:00.384263 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0430 15:10:00.384274 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 15:10:00.384285 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 15:10:00.384297 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.5
I0430 15:10:00.384310 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 15:10:00.384321 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 15:10:00.384333 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0430 15:10:00.384344 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 15:10:00.384356 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 15:10:00.384368 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 15:10:00.384379 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 15:10:00.384392 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 15:10:00.384403 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 15:10:00.384415 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0430 15:10:00.384440 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 0.875
I0430 15:10:00.384454 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 0.875
I0430 15:10:00.384466 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 0.875
I0430 15:10:00.384479 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:10:00.384490 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:10:00.384501 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:10:00.384513 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.840909
I0430 15:10:00.384526 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.676923
I0430 15:10:00.384539 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 2.30624 (* 1 = 2.30624 loss)
I0430 15:10:00.384553 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.85803 (* 1 = 0.85803 loss)
I0430 15:10:00.384568 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 1.4323 (* 0.0909091 = 0.130209 loss)
I0430 15:10:00.384582 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.40981 (* 0.0909091 = 0.0372554 loss)
I0430 15:10:00.384596 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.25708 (* 0.0909091 = 0.11428 loss)
I0430 15:10:00.384610 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.974649 (* 0.0909091 = 0.0886045 loss)
I0430 15:10:00.384625 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.02146 (* 0.0909091 = 0.0928603 loss)
I0430 15:10:00.384639 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.51684 (* 0.0909091 = 0.137894 loss)
I0430 15:10:00.384654 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.13301 (* 0.0909091 = 0.103001 loss)
I0430 15:10:00.384667 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.764724 (* 0.0909091 = 0.0695203 loss)
I0430 15:10:00.384681 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.803612 (* 0.0909091 = 0.0730556 loss)
I0430 15:10:00.384696 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.915253 (* 0.0909091 = 0.0832048 loss)
I0430 15:10:00.384709 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 1.05466 (* 0.0909091 = 0.095878 loss)
I0430 15:10:00.384724 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 1.03426 (* 0.0909091 = 0.0940239 loss)
I0430 15:10:00.384737 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 1.03803 (* 0.0909091 = 0.0943661 loss)
I0430 15:10:00.384752 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 1.06988 (* 0.0909091 = 0.0972616 loss)
I0430 15:10:00.384765 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 1.0772 (* 0.0909091 = 0.0979272 loss)
I0430 15:10:00.384779 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 1.21918 (* 0.0909091 = 0.110835 loss)
I0430 15:10:00.384793 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 1.10676 (* 0.0909091 = 0.100614 loss)
I0430 15:10:00.384807 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 1.11314 (* 0.0909091 = 0.101194 loss)
I0430 15:10:00.384821 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 1.21386 (* 0.0909091 = 0.110351 loss)
I0430 15:10:00.384835 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000145819 (* 0.0909091 = 1.32563e-05 loss)
I0430 15:10:00.384850 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 8.7777e-05 (* 0.0909091 = 7.97973e-06 loss)
I0430 15:10:00.384865 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 8.25152e-05 (* 0.0909091 = 7.50138e-06 loss)
I0430 15:10:00.384877 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 15:10:00.384888 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 15:10:00.384901 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.403349
I0430 15:10:00.384922 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.334249
I0430 15:10:00.384937 15443 sgd_solver.cpp:106] Iteration 4500, lr = 0.001
I0430 15:11:44.144258 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.0193 > 30) by scale factor 0.967141
I0430 15:13:51.687072 15443 solver.cpp:338] Iteration 5000, Testing net (#0)
I0430 15:15:50.579594 15443 solver.cpp:393] Test loss: 2.34018
I0430 15:15:50.579730 15443 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.634974
I0430 15:15:50.579753 15443 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.835
I0430 15:15:50.579766 15443 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.71
I0430 15:15:50.579778 15443 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.564
I0430 15:15:50.579790 15443 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.573
I0430 15:15:50.579802 15443 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.565
I0430 15:15:50.579814 15443 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.661
I0430 15:15:50.579825 15443 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.821
I0430 15:15:50.579838 15443 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.903
I0430 15:15:50.579849 15443 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.983
I0430 15:15:50.579861 15443 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.994
I0430 15:15:50.579872 15443 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.997
I0430 15:15:50.579885 15443 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.999
I0430 15:15:50.579896 15443 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 1
I0430 15:15:50.579907 15443 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 1
I0430 15:15:50.579919 15443 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 1
I0430 15:15:50.579931 15443 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 1
I0430 15:15:50.579942 15443 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 1
I0430 15:15:50.579953 15443 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 1
I0430 15:15:50.579964 15443 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 1
I0430 15:15:50.579975 15443 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 1
I0430 15:15:50.579988 15443 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0430 15:15:50.579998 15443 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0430 15:15:50.580009 15443 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.891229
I0430 15:15:50.580021 15443 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.861714
I0430 15:15:50.580036 15443 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.17151 (* 0.3 = 0.351453 loss)
I0430 15:15:50.580051 15443 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.354416 (* 0.3 = 0.106325 loss)
I0430 15:15:50.580065 15443 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.602147 (* 0.0272727 = 0.0164222 loss)
I0430 15:15:50.580080 15443 solver.cpp:406]     Test net output #28: loss1/loss02 = 0.990368 (* 0.0272727 = 0.02701 loss)
I0430 15:15:50.580093 15443 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.4028 (* 0.0272727 = 0.0382581 loss)
I0430 15:15:50.580106 15443 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.36905 (* 0.0272727 = 0.0373378 loss)
I0430 15:15:50.580121 15443 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.32869 (* 0.0272727 = 0.036237 loss)
I0430 15:15:50.580133 15443 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.02097 (* 0.0272727 = 0.0278448 loss)
I0430 15:15:50.580147 15443 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.57177 (* 0.0272727 = 0.0155937 loss)
I0430 15:15:50.580162 15443 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.293697 (* 0.0272727 = 0.00800993 loss)
I0430 15:15:50.580174 15443 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.0769722 (* 0.0272727 = 0.00209924 loss)
I0430 15:15:50.580188 15443 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.0343951 (* 0.0272727 = 0.000938048 loss)
I0430 15:15:50.580202 15443 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.013719 (* 0.0272727 = 0.000374156 loss)
I0430 15:15:50.580216 15443 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.00912074 (* 0.0272727 = 0.000248747 loss)
I0430 15:15:50.580230 15443 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.00593367 (* 0.0272727 = 0.000161827 loss)
I0430 15:15:50.580266 15443 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.00389587 (* 0.0272727 = 0.000106251 loss)
I0430 15:15:50.580282 15443 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.0024643 (* 0.0272727 = 6.72082e-05 loss)
I0430 15:15:50.580296 15443 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.00162897 (* 0.0272727 = 4.44263e-05 loss)
I0430 15:15:50.580310 15443 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.000946585 (* 0.0272727 = 2.5816e-05 loss)
I0430 15:15:50.580328 15443 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.000581907 (* 0.0272727 = 1.58702e-05 loss)
I0430 15:15:50.580343 15443 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.000422126 (* 0.0272727 = 1.15125e-05 loss)
I0430 15:15:50.580356 15443 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.000352227 (* 0.0272727 = 9.6062e-06 loss)
I0430 15:15:50.580370 15443 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.000302346 (* 0.0272727 = 8.2458e-06 loss)
I0430 15:15:50.580384 15443 solver.cpp:406]     Test net output #48: loss1/loss22 = 0.000255629 (* 0.0272727 = 6.97169e-06 loss)
I0430 15:15:50.580396 15443 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.738614
I0430 15:15:50.580409 15443 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.881
I0430 15:15:50.580420 15443 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.829
I0430 15:15:50.580430 15443 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.716
I0430 15:15:50.580442 15443 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.659
I0430 15:15:50.580453 15443 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.638
I0430 15:15:50.580466 15443 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.701
I0430 15:15:50.580476 15443 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.851
I0430 15:15:50.580487 15443 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.916
I0430 15:15:50.580499 15443 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.985
I0430 15:15:50.580507 15443 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.993
I0430 15:15:50.580514 15443 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.997
I0430 15:15:50.580521 15443 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.999
I0430 15:15:50.580533 15443 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.999
I0430 15:15:50.580544 15443 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 1
I0430 15:15:50.580555 15443 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 1
I0430 15:15:50.580566 15443 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 1
I0430 15:15:50.580577 15443 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 1
I0430 15:15:50.580588 15443 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 1
I0430 15:15:50.580600 15443 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 1
I0430 15:15:50.580610 15443 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 1
I0430 15:15:50.580621 15443 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0430 15:15:50.580632 15443 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0430 15:15:50.580642 15443 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.920865
I0430 15:15:50.580653 15443 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.903193
I0430 15:15:50.580667 15443 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.876732 (* 0.3 = 0.263019 loss)
I0430 15:15:50.580682 15443 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.270357 (* 0.3 = 0.081107 loss)
I0430 15:15:50.580694 15443 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.518813 (* 0.0272727 = 0.0141494 loss)
I0430 15:15:50.580708 15443 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.665379 (* 0.0272727 = 0.0181467 loss)
I0430 15:15:50.580734 15443 solver.cpp:406]     Test net output #78: loss2/loss03 = 1.0036 (* 0.0272727 = 0.0273709 loss)
I0430 15:15:50.580752 15443 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.1121 (* 0.0272727 = 0.0303301 loss)
I0430 15:15:50.580766 15443 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.12569 (* 0.0272727 = 0.0307006 loss)
I0430 15:15:50.580780 15443 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.890944 (* 0.0272727 = 0.0242985 loss)
I0430 15:15:50.580793 15443 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.50361 (* 0.0272727 = 0.0137348 loss)
I0430 15:15:50.580807 15443 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.278603 (* 0.0272727 = 0.00759826 loss)
I0430 15:15:50.580821 15443 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.0712345 (* 0.0272727 = 0.00194276 loss)
I0430 15:15:50.580835 15443 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.0315484 (* 0.0272727 = 0.000860411 loss)
I0430 15:15:50.580848 15443 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.0134482 (* 0.0272727 = 0.00036677 loss)
I0430 15:15:50.580862 15443 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.00840819 (* 0.0272727 = 0.000229314 loss)
I0430 15:15:50.580875 15443 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.00527099 (* 0.0272727 = 0.000143754 loss)
I0430 15:15:50.580889 15443 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.00335061 (* 0.0272727 = 9.13803e-05 loss)
I0430 15:15:50.580904 15443 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.00196152 (* 0.0272727 = 5.3496e-05 loss)
I0430 15:15:50.580916 15443 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.00110177 (* 0.0272727 = 3.00483e-05 loss)
I0430 15:15:50.580930 15443 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.000513995 (* 0.0272727 = 1.40181e-05 loss)
I0430 15:15:50.580945 15443 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.00025623 (* 0.0272727 = 6.98808e-06 loss)
I0430 15:15:50.580957 15443 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.000171956 (* 0.0272727 = 4.6897e-06 loss)
I0430 15:15:50.580971 15443 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.000112507 (* 0.0272727 = 3.06838e-06 loss)
I0430 15:15:50.580984 15443 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.000104234 (* 0.0272727 = 2.84275e-06 loss)
I0430 15:15:50.580997 15443 solver.cpp:406]     Test net output #97: loss2/loss22 = 7.63594e-05 (* 0.0272727 = 2.08253e-06 loss)
I0430 15:15:50.581009 15443 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.850921
I0430 15:15:50.581022 15443 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.898
I0430 15:15:50.581032 15443 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.878
I0430 15:15:50.581044 15443 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.83
I0430 15:15:50.581055 15443 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.838
I0430 15:15:50.581066 15443 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.83
I0430 15:15:50.581079 15443 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.857
I0430 15:15:50.581089 15443 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.884
I0430 15:15:50.581101 15443 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.941
I0430 15:15:50.581112 15443 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.983
I0430 15:15:50.581123 15443 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.993
I0430 15:15:50.581135 15443 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.997
I0430 15:15:50.581146 15443 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.999
I0430 15:15:50.581157 15443 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.999
I0430 15:15:50.581168 15443 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.999
I0430 15:15:50.581179 15443 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 1
I0430 15:15:50.581190 15443 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 1
I0430 15:15:50.581212 15443 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 1
I0430 15:15:50.581225 15443 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 1
I0430 15:15:50.581238 15443 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 1
I0430 15:15:50.581248 15443 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 1
I0430 15:15:50.581259 15443 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0430 15:15:50.581271 15443 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0430 15:15:50.581282 15443 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.952682
I0430 15:15:50.581295 15443 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.924405
I0430 15:15:50.581307 15443 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.589351 (* 1 = 0.589351 loss)
I0430 15:15:50.581321 15443 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.188677 (* 1 = 0.188677 loss)
I0430 15:15:50.581336 15443 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.421495 (* 0.0909091 = 0.0383178 loss)
I0430 15:15:50.581349 15443 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.517064 (* 0.0909091 = 0.0470058 loss)
I0430 15:15:50.581362 15443 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.665191 (* 0.0909091 = 0.0604719 loss)
I0430 15:15:50.581379 15443 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.622514 (* 0.0909091 = 0.0565922 loss)
I0430 15:15:50.581393 15443 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.665423 (* 0.0909091 = 0.060493 loss)
I0430 15:15:50.581406 15443 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.548369 (* 0.0909091 = 0.0498517 loss)
I0430 15:15:50.581419 15443 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.382015 (* 0.0909091 = 0.0347287 loss)
I0430 15:15:50.581432 15443 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.201716 (* 0.0909091 = 0.0183378 loss)
I0430 15:15:50.581446 15443 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.066762 (* 0.0909091 = 0.00606928 loss)
I0430 15:15:50.581459 15443 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.035929 (* 0.0909091 = 0.00326627 loss)
I0430 15:15:50.581472 15443 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.0163915 (* 0.0909091 = 0.00149014 loss)
I0430 15:15:50.581486 15443 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.0120617 (* 0.0909091 = 0.00109652 loss)
I0430 15:15:50.581501 15443 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.00785141 (* 0.0909091 = 0.000713765 loss)
I0430 15:15:50.581513 15443 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.00472316 (* 0.0909091 = 0.000429379 loss)
I0430 15:15:50.581527 15443 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.00263749 (* 0.0909091 = 0.000239771 loss)
I0430 15:15:50.581540 15443 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.00149442 (* 0.0909091 = 0.000135857 loss)
I0430 15:15:50.581554 15443 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.000606078 (* 0.0909091 = 5.5098e-05 loss)
I0430 15:15:50.581568 15443 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.000248813 (* 0.0909091 = 2.26194e-05 loss)
I0430 15:15:50.581581 15443 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.000131917 (* 0.0909091 = 1.19924e-05 loss)
I0430 15:15:50.581594 15443 solver.cpp:406]     Test net output #144: loss3/loss20 = 5.70821e-05 (* 0.0909091 = 5.18928e-06 loss)
I0430 15:15:50.581609 15443 solver.cpp:406]     Test net output #145: loss3/loss21 = 3.54222e-05 (* 0.0909091 = 3.2202e-06 loss)
I0430 15:15:50.581622 15443 solver.cpp:406]     Test net output #146: loss3/loss22 = 2.4917e-05 (* 0.0909091 = 2.26518e-06 loss)
I0430 15:15:50.581634 15443 solver.cpp:406]     Test net output #147: total_accuracy = 0.611
I0430 15:15:50.581645 15443 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.581
I0430 15:15:50.581656 15443 solver.cpp:406]     Test net output #149: total_confidence = 0.543636
I0430 15:15:50.581677 15443 solver.cpp:406]     Test net output #150: total_confidence_not_rec = 0.52445
I0430 15:15:50.581691 15443 solver.cpp:338] Iteration 5000, Testing net (#1)
I0430 15:17:58.263010 15443 solver.cpp:393] Test loss: 3.19561
I0430 15:17:58.263161 15443 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.597225
I0430 15:17:58.263190 15443 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.78
I0430 15:17:58.263212 15443 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.676
I0430 15:17:58.263234 15443 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.545
I0430 15:17:58.263257 15443 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.539
I0430 15:17:58.263278 15443 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.559
I0430 15:17:58.263298 15443 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.636
I0430 15:17:58.263324 15443 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.751
I0430 15:17:58.263346 15443 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.851
I0430 15:17:58.263368 15443 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.907
I0430 15:17:58.263388 15443 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.916
I0430 15:17:58.263409 15443 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.936
I0430 15:17:58.263430 15443 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.938
I0430 15:17:58.263453 15443 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.953
I0430 15:17:58.263496 15443 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 0.96
I0430 15:17:58.263523 15443 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 0.97
I0430 15:17:58.263545 15443 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 0.978
I0430 15:17:58.263566 15443 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 0.99
I0430 15:17:58.263588 15443 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 0.995
I0430 15:17:58.263609 15443 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 0.998
I0430 15:17:58.263631 15443 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 0.998
I0430 15:17:58.263650 15443 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 0.999
I0430 15:17:58.263672 15443 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0430 15:17:58.263694 15443 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.859275
I0430 15:17:58.263715 15443 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.820089
I0430 15:17:58.263742 15443 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.32696 (* 0.3 = 0.398089 loss)
I0430 15:17:58.263770 15443 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.47165 (* 0.3 = 0.141495 loss)
I0430 15:17:58.263795 15443 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.815035 (* 0.0272727 = 0.0222282 loss)
I0430 15:17:58.263821 15443 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.14072 (* 0.0272727 = 0.0311104 loss)
I0430 15:17:58.263847 15443 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.47704 (* 0.0272727 = 0.0402828 loss)
I0430 15:17:58.263873 15443 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.49561 (* 0.0272727 = 0.0407893 loss)
I0430 15:17:58.263900 15443 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.40005 (* 0.0272727 = 0.0381833 loss)
I0430 15:17:58.263923 15443 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.11048 (* 0.0272727 = 0.0302857 loss)
I0430 15:17:58.263949 15443 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.78378 (* 0.0272727 = 0.0213758 loss)
I0430 15:17:58.263974 15443 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.495332 (* 0.0272727 = 0.0135091 loss)
I0430 15:17:58.264000 15443 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.338002 (* 0.0272727 = 0.00921823 loss)
I0430 15:17:58.264027 15443 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.286217 (* 0.0272727 = 0.00780593 loss)
I0430 15:17:58.264051 15443 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.219056 (* 0.0272727 = 0.00597425 loss)
I0430 15:17:58.264077 15443 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.209717 (* 0.0272727 = 0.00571956 loss)
I0430 15:17:58.264130 15443 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.17017 (* 0.0272727 = 0.00464101 loss)
I0430 15:17:58.264158 15443 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.146518 (* 0.0272727 = 0.00399593 loss)
I0430 15:17:58.264190 15443 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.135272 (* 0.0272727 = 0.00368923 loss)
I0430 15:17:58.264217 15443 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.0882377 (* 0.0272727 = 0.00240648 loss)
I0430 15:17:58.264245 15443 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.0458246 (* 0.0272727 = 0.00124976 loss)
I0430 15:17:58.264273 15443 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.0257246 (* 0.0272727 = 0.000701579 loss)
I0430 15:17:58.264300 15443 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.0137175 (* 0.0272727 = 0.000374113 loss)
I0430 15:17:58.264328 15443 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.0125937 (* 0.0272727 = 0.000343465 loss)
I0430 15:17:58.264353 15443 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.00974283 (* 0.0272727 = 0.000265714 loss)
I0430 15:17:58.264384 15443 solver.cpp:406]     Test net output #48: loss1/loss22 = 2.19144e-05 (* 0.0272727 = 5.97665e-07 loss)
I0430 15:17:58.264407 15443 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.686872
I0430 15:17:58.264430 15443 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.839
I0430 15:17:58.264451 15443 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.784
I0430 15:17:58.264472 15443 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.686
I0430 15:17:58.264493 15443 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.606
I0430 15:17:58.264514 15443 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.621
I0430 15:17:58.264536 15443 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.686
I0430 15:17:58.264557 15443 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.784
I0430 15:17:58.264577 15443 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.853
I0430 15:17:58.264600 15443 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.903
I0430 15:17:58.264619 15443 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.924
I0430 15:17:58.264641 15443 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.939
I0430 15:17:58.264662 15443 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.94
I0430 15:17:58.264683 15443 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.954
I0430 15:17:58.264703 15443 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.963
I0430 15:17:58.264724 15443 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.971
I0430 15:17:58.264745 15443 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 0.978
I0430 15:17:58.264765 15443 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 0.99
I0430 15:17:58.264786 15443 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 0.995
I0430 15:17:58.264808 15443 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 0.998
I0430 15:17:58.264828 15443 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 0.998
I0430 15:17:58.264849 15443 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 0.999
I0430 15:17:58.264871 15443 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0430 15:17:58.264891 15443 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.886183
I0430 15:17:58.264912 15443 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.86289
I0430 15:17:58.264937 15443 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 1.06699 (* 0.3 = 0.320098 loss)
I0430 15:17:58.264963 15443 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.389985 (* 0.3 = 0.116996 loss)
I0430 15:17:58.264988 15443 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.6697 (* 0.0272727 = 0.0182646 loss)
I0430 15:17:58.265014 15443 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.808616 (* 0.0272727 = 0.0220532 loss)
I0430 15:17:58.265058 15443 solver.cpp:406]     Test net output #78: loss2/loss03 = 1.11425 (* 0.0272727 = 0.0303886 loss)
I0430 15:17:58.265084 15443 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.26093 (* 0.0272727 = 0.0343891 loss)
I0430 15:17:58.265110 15443 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.21432 (* 0.0272727 = 0.0331178 loss)
I0430 15:17:58.265135 15443 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.9755 (* 0.0272727 = 0.0266045 loss)
I0430 15:17:58.265159 15443 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.699259 (* 0.0272727 = 0.0190707 loss)
I0430 15:17:58.265185 15443 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.487706 (* 0.0272727 = 0.0133011 loss)
I0430 15:17:58.265210 15443 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.337392 (* 0.0272727 = 0.0092016 loss)
I0430 15:17:58.265240 15443 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.283476 (* 0.0272727 = 0.00773117 loss)
I0430 15:17:58.265266 15443 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.221385 (* 0.0272727 = 0.00603778 loss)
I0430 15:17:58.265291 15443 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.20389 (* 0.0272727 = 0.00556063 loss)
I0430 15:17:58.265316 15443 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.167701 (* 0.0272727 = 0.00457367 loss)
I0430 15:17:58.265341 15443 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.141342 (* 0.0272727 = 0.00385477 loss)
I0430 15:17:58.265365 15443 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.133283 (* 0.0272727 = 0.00363498 loss)
I0430 15:17:58.265390 15443 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.0918027 (* 0.0272727 = 0.00250371 loss)
I0430 15:17:58.265419 15443 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.0484734 (* 0.0272727 = 0.001322 loss)
I0430 15:17:58.265446 15443 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.0256059 (* 0.0272727 = 0.000698343 loss)
I0430 15:17:58.265471 15443 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.0139094 (* 0.0272727 = 0.000379348 loss)
I0430 15:17:58.265496 15443 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.0133303 (* 0.0272727 = 0.000363554 loss)
I0430 15:17:58.265522 15443 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.00807448 (* 0.0272727 = 0.000220213 loss)
I0430 15:17:58.265547 15443 solver.cpp:406]     Test net output #97: loss2/loss22 = 7.5747e-05 (* 0.0272727 = 2.06583e-06 loss)
I0430 15:17:58.265568 15443 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.786415
I0430 15:17:58.265589 15443 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.855
I0430 15:17:58.265610 15443 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.832
I0430 15:17:58.265630 15443 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.798
I0430 15:17:58.265650 15443 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.791
I0430 15:17:58.265671 15443 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.776
I0430 15:17:58.265692 15443 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.815
I0430 15:17:58.265713 15443 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.83
I0430 15:17:58.265733 15443 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.878
I0430 15:17:58.265754 15443 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.911
I0430 15:17:58.265775 15443 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.922
I0430 15:17:58.265794 15443 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.941
I0430 15:17:58.265815 15443 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.94
I0430 15:17:58.265836 15443 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.956
I0430 15:17:58.265857 15443 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.966
I0430 15:17:58.265877 15443 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.973
I0430 15:17:58.265897 15443 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 0.98
I0430 15:17:58.265934 15443 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 0.99
I0430 15:17:58.265957 15443 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 0.995
I0430 15:17:58.265979 15443 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 0.998
I0430 15:17:58.266000 15443 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 0.998
I0430 15:17:58.266021 15443 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 0.999
I0430 15:17:58.266041 15443 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0430 15:17:58.266060 15443 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.916819
I0430 15:17:58.266082 15443 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.891566
I0430 15:17:58.266106 15443 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.78641 (* 1 = 0.78641 loss)
I0430 15:17:58.266131 15443 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.302019 (* 1 = 0.302019 loss)
I0430 15:17:58.266157 15443 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.579186 (* 0.0909091 = 0.0526533 loss)
I0430 15:17:58.266182 15443 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.635074 (* 0.0909091 = 0.057734 loss)
I0430 15:17:58.266207 15443 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.728691 (* 0.0909091 = 0.0662446 loss)
I0430 15:17:58.266233 15443 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.745337 (* 0.0909091 = 0.0677579 loss)
I0430 15:17:58.266258 15443 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.798457 (* 0.0909091 = 0.072587 loss)
I0430 15:17:58.266288 15443 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.658203 (* 0.0909091 = 0.0598366 loss)
I0430 15:17:58.266314 15443 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.570369 (* 0.0909091 = 0.0518518 loss)
I0430 15:17:58.266340 15443 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.401231 (* 0.0909091 = 0.0364755 loss)
I0430 15:17:58.266365 15443 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.295147 (* 0.0909091 = 0.0268316 loss)
I0430 15:17:58.266391 15443 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.263034 (* 0.0909091 = 0.0239122 loss)
I0430 15:17:58.266415 15443 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.205344 (* 0.0909091 = 0.0186676 loss)
I0430 15:17:58.266440 15443 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.18916 (* 0.0909091 = 0.0171963 loss)
I0430 15:17:58.266466 15443 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.154744 (* 0.0909091 = 0.0140676 loss)
I0430 15:17:58.266497 15443 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.127376 (* 0.0909091 = 0.0115796 loss)
I0430 15:17:58.266521 15443 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.119539 (* 0.0909091 = 0.0108671 loss)
I0430 15:17:58.266547 15443 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.0725606 (* 0.0909091 = 0.00659642 loss)
I0430 15:17:58.266574 15443 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.0411278 (* 0.0909091 = 0.00373889 loss)
I0430 15:17:58.266598 15443 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.020414 (* 0.0909091 = 0.00185582 loss)
I0430 15:17:58.266625 15443 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.0113839 (* 0.0909091 = 0.0010349 loss)
I0430 15:17:58.266652 15443 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.0108735 (* 0.0909091 = 0.000988501 loss)
I0430 15:17:58.266677 15443 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.00661369 (* 0.0909091 = 0.000601245 loss)
I0430 15:17:58.266700 15443 solver.cpp:406]     Test net output #146: loss3/loss22 = 7.02608e-05 (* 0.0909091 = 6.38735e-06 loss)
I0430 15:17:58.266717 15443 solver.cpp:406]     Test net output #147: total_accuracy = 0.511
I0430 15:17:58.266736 15443 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.506
I0430 15:17:58.266754 15443 solver.cpp:406]     Test net output #149: total_confidence = 0.468388
I0430 15:17:58.266793 15443 solver.cpp:406]     Test net output #150: total_confidence_not_rec = 0.465508
I0430 15:17:58.645957 15443 solver.cpp:229] Iteration 5000, loss = 3.61654
I0430 15:17:58.646045 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.533333
I0430 15:17:58.646075 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 15:17:58.646096 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 15:17:58.646119 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 15:17:58.646142 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.75
I0430 15:17:58.646165 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 15:17:58.646188 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 15:17:58.646210 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 15:17:58.646232 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 15:17:58.646256 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0430 15:17:58.646277 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 15:17:58.646297 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 15:17:58.646322 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 15:17:58.646345 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 15:17:58.646368 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 15:17:58.646391 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 15:17:58.646412 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 15:17:58.646435 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:17:58.646458 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:17:58.646481 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:17:58.646502 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:17:58.646524 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:17:58.646546 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:17:58.646572 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.846591
I0430 15:17:58.646595 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.822222
I0430 15:17:58.646625 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.46042 (* 0.3 = 0.438127 loss)
I0430 15:17:58.646653 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.440553 (* 0.3 = 0.132166 loss)
I0430 15:17:58.646682 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.52312 (* 0.0272727 = 0.0142669 loss)
I0430 15:17:58.646713 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.55761 (* 0.0272727 = 0.0424802 loss)
I0430 15:17:58.646742 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.33071 (* 0.0272727 = 0.036292 loss)
I0430 15:17:58.646770 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.46787 (* 0.0272727 = 0.0400328 loss)
I0430 15:17:58.646798 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.76317 (* 0.0272727 = 0.0480864 loss)
I0430 15:17:58.646826 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.37607 (* 0.0272727 = 0.0375292 loss)
I0430 15:17:58.646852 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.903277 (* 0.0272727 = 0.0246348 loss)
I0430 15:17:58.646880 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.682677 (* 0.0272727 = 0.0186185 loss)
I0430 15:17:58.646908 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.052211 (* 0.0272727 = 0.00142394 loss)
I0430 15:17:58.646936 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00301113 (* 0.0272727 = 8.21218e-05 loss)
I0430 15:17:58.646963 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.000112475 (* 0.0272727 = 3.06749e-06 loss)
I0430 15:17:58.647035 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 1.68095e-05 (* 0.0272727 = 4.58442e-07 loss)
I0430 15:17:58.647068 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 7.15257e-07 (* 0.0272727 = 1.9507e-08 loss)
I0430 15:17:58.647097 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 2.83122e-07 (* 0.0272727 = 7.72152e-09 loss)
I0430 15:17:58.647125 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 5.96047e-08 (* 0.0272727 = 1.62558e-09 loss)
I0430 15:17:58.647152 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 8.9407e-08 (* 0.0272727 = 2.43837e-09 loss)
I0430 15:17:58.647181 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 4.47035e-08 (* 0.0272727 = 1.21919e-09 loss)
I0430 15:17:58.647208 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 2.98023e-08 (* 0.0272727 = 8.12791e-10 loss)
I0430 15:17:58.647238 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0 (* 0.0272727 = 0 loss)
I0430 15:17:58.647264 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0 (* 0.0272727 = 0 loss)
I0430 15:17:58.647291 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0 (* 0.0272727 = 0 loss)
I0430 15:17:58.647321 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0 (* 0.0272727 = 0 loss)
I0430 15:17:58.647346 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.688889
I0430 15:17:58.647369 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 15:17:58.647394 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 15:17:58.647418 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0430 15:17:58.647440 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 15:17:58.647462 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 15:17:58.647511 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0430 15:17:58.647536 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 15:17:58.647558 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 15:17:58.647581 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0430 15:17:58.647604 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 15:17:58.647630 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 15:17:58.647652 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 15:17:58.647675 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 15:17:58.647696 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 15:17:58.647719 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 15:17:58.647742 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 15:17:58.647768 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:17:58.647791 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:17:58.647814 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:17:58.647835 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:17:58.647858 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:17:58.647879 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:17:58.647902 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.903409
I0430 15:17:58.647925 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.888889
I0430 15:17:58.647951 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.991917 (* 0.3 = 0.297575 loss)
I0430 15:17:58.647979 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.307193 (* 0.3 = 0.092158 loss)
I0430 15:17:58.648006 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.70339 (* 0.0272727 = 0.0191834 loss)
I0430 15:17:58.648053 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.993224 (* 0.0272727 = 0.0270879 loss)
I0430 15:17:58.648082 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.01602 (* 0.0272727 = 0.0277097 loss)
I0430 15:17:58.648108 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.28351 (* 0.0272727 = 0.0350049 loss)
I0430 15:17:58.648136 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.25284 (* 0.0272727 = 0.0341684 loss)
I0430 15:17:58.648164 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.03143 (* 0.0272727 = 0.02813 loss)
I0430 15:17:58.648190 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.728831 (* 0.0272727 = 0.0198772 loss)
I0430 15:17:58.648216 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.376097 (* 0.0272727 = 0.0102572 loss)
I0430 15:17:58.648244 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0196846 (* 0.0272727 = 0.000536854 loss)
I0430 15:17:58.648270 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.0012526 (* 0.0272727 = 3.41618e-05 loss)
I0430 15:17:58.648298 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 3.15706e-05 (* 0.0272727 = 8.61016e-07 loss)
I0430 15:17:58.648325 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 5.4986e-06 (* 0.0272727 = 1.49962e-07 loss)
I0430 15:17:58.648351 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 1.80305e-06 (* 0.0272727 = 4.91741e-08 loss)
I0430 15:17:58.648380 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 5.96047e-07 (* 0.0272727 = 1.62558e-08 loss)
I0430 15:17:58.648406 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 4.47035e-07 (* 0.0272727 = 1.21919e-08 loss)
I0430 15:17:58.648433 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 1.63913e-07 (* 0.0272727 = 4.47035e-09 loss)
I0430 15:17:58.648460 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 4.32134e-07 (* 0.0272727 = 1.17855e-08 loss)
I0430 15:17:58.648488 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 9.68579e-07 (* 0.0272727 = 2.64158e-08 loss)
I0430 15:17:58.648514 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 3.27826e-07 (* 0.0272727 = 8.94071e-09 loss)
I0430 15:17:58.648541 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 3.57628e-07 (* 0.0272727 = 9.7535e-09 loss)
I0430 15:17:58.648568 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 1.04308e-07 (* 0.0272727 = 2.84477e-09 loss)
I0430 15:17:58.648596 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 4.47035e-08 (* 0.0272727 = 1.21919e-09 loss)
I0430 15:17:58.648619 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.8
I0430 15:17:58.648641 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 15:17:58.648668 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0430 15:17:58.648691 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 15:17:58.648713 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 15:17:58.648731 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0430 15:17:58.648751 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 15:17:58.648773 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 15:17:58.648795 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 15:17:58.648823 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 15:17:58.648845 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 15:17:58.648866 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 15:17:58.648890 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 15:17:58.648911 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 15:17:58.648949 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 15:17:58.648973 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 15:17:58.648995 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 15:17:58.649016 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:17:58.649039 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:17:58.649060 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:17:58.649083 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:17:58.649103 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:17:58.649126 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:17:58.649142 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.943182
I0430 15:17:58.649158 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.977778
I0430 15:17:58.649176 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.519827 (* 1 = 0.519827 loss)
I0430 15:17:58.649195 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.143736 (* 1 = 0.143736 loss)
I0430 15:17:58.649214 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.15155 (* 0.0909091 = 0.0137773 loss)
I0430 15:17:58.649232 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.229353 (* 0.0909091 = 0.0208503 loss)
I0430 15:17:58.649250 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.47409 (* 0.0909091 = 0.0430991 loss)
I0430 15:17:58.649268 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.258984 (* 0.0909091 = 0.023544 loss)
I0430 15:17:58.649286 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.71688 (* 0.0909091 = 0.065171 loss)
I0430 15:17:58.649305 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.653141 (* 0.0909091 = 0.0593764 loss)
I0430 15:17:58.649322 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.469426 (* 0.0909091 = 0.0426751 loss)
I0430 15:17:58.649341 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.245262 (* 0.0909091 = 0.0222965 loss)
I0430 15:17:58.649359 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.00177346 (* 0.0909091 = 0.000161224 loss)
I0430 15:17:58.649379 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.000462752 (* 0.0909091 = 4.20683e-05 loss)
I0430 15:17:58.649397 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 6.93306e-05 (* 0.0909091 = 6.30278e-06 loss)
I0430 15:17:58.649415 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 1.0431e-05 (* 0.0909091 = 9.48272e-07 loss)
I0430 15:17:58.649433 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 4.6343e-06 (* 0.0909091 = 4.213e-07 loss)
I0430 15:17:58.649452 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 1.93716e-06 (* 0.0909091 = 1.76105e-07 loss)
I0430 15:17:58.649471 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 6.70553e-07 (* 0.0909091 = 6.09594e-08 loss)
I0430 15:17:58.649488 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 4.02332e-07 (* 0.0909091 = 3.65756e-08 loss)
I0430 15:17:58.649507 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 1.07289e-06 (* 0.0909091 = 9.75352e-08 loss)
I0430 15:17:58.649524 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 6.55653e-07 (* 0.0909091 = 5.96048e-08 loss)
I0430 15:17:58.649543 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 8.34467e-07 (* 0.0909091 = 7.58607e-08 loss)
I0430 15:17:58.649560 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 5.0664e-07 (* 0.0909091 = 4.60582e-08 loss)
I0430 15:17:58.649579 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 4.61937e-07 (* 0.0909091 = 4.19943e-08 loss)
I0430 15:17:58.649596 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.27826e-07 (* 0.0909091 = 2.98024e-08 loss)
I0430 15:17:58.649633 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 15:17:58.649649 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 15:17:58.649663 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.445437
I0430 15:17:58.649677 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.469603
I0430 15:17:58.649693 15443 sgd_solver.cpp:106] Iteration 5000, lr = 0.001
I0430 15:21:50.676206 15443 solver.cpp:229] Iteration 5500, loss = 3.8965
I0430 15:21:50.676398 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.392857
I0430 15:21:50.676424 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 15:21:50.676443 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.875
I0430 15:21:50.676461 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 15:21:50.676481 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0430 15:21:50.676498 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0430 15:21:50.676515 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 15:21:50.676532 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 15:21:50.676550 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 15:21:50.676568 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 15:21:50.676585 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0430 15:21:50.676602 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 15:21:50.676620 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 15:21:50.676636 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 15:21:50.676654 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 15:21:50.676671 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 15:21:50.676688 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 15:21:50.676704 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:21:50.676722 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:21:50.676738 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:21:50.676755 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:21:50.676771 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:21:50.676789 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:21:50.676805 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.801136
I0430 15:21:50.676822 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.625
I0430 15:21:50.676844 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.12254 (* 0.3 = 0.636762 loss)
I0430 15:21:50.676865 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.705543 (* 0.3 = 0.211663 loss)
I0430 15:21:50.676887 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.882404 (* 0.0272727 = 0.0240656 loss)
I0430 15:21:50.676908 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.26807 (* 0.0272727 = 0.0345839 loss)
I0430 15:21:50.676929 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.36766 (* 0.0272727 = 0.0645726 loss)
I0430 15:21:50.676949 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.80844 (* 0.0272727 = 0.0493211 loss)
I0430 15:21:50.676970 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.81301 (* 0.0272727 = 0.0494458 loss)
I0430 15:21:50.676990 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.62548 (* 0.0272727 = 0.0443313 loss)
I0430 15:21:50.677011 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.08721 (* 0.0272727 = 0.0296511 loss)
I0430 15:21:50.677031 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.07363 (* 0.0272727 = 0.0292808 loss)
I0430 15:21:50.677052 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.865581 (* 0.0272727 = 0.0236068 loss)
I0430 15:21:50.677073 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 1.28064 (* 0.0272727 = 0.0349266 loss)
I0430 15:21:50.677093 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.782639 (* 0.0272727 = 0.0213447 loss)
I0430 15:21:50.677114 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 1.03587 (* 0.0272727 = 0.0282509 loss)
I0430 15:21:50.677158 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0049016 (* 0.0272727 = 0.00013368 loss)
I0430 15:21:50.677181 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00249157 (* 0.0272727 = 6.79518e-05 loss)
I0430 15:21:50.677203 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00156408 (* 0.0272727 = 4.26568e-05 loss)
I0430 15:21:50.677223 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00058479 (* 0.0272727 = 1.59488e-05 loss)
I0430 15:21:50.677244 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000378942 (* 0.0272727 = 1.03348e-05 loss)
I0430 15:21:50.677266 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000262934 (* 0.0272727 = 7.17092e-06 loss)
I0430 15:21:50.677286 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000366324 (* 0.0272727 = 9.99064e-06 loss)
I0430 15:21:50.677309 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000158678 (* 0.0272727 = 4.32757e-06 loss)
I0430 15:21:50.677332 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00017332 (* 0.0272727 = 4.72691e-06 loss)
I0430 15:21:50.677353 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 7.40699e-05 (* 0.0272727 = 2.02009e-06 loss)
I0430 15:21:50.677372 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.464286
I0430 15:21:50.677391 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 15:21:50.677412 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 15:21:50.677429 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0430 15:21:50.677446 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 15:21:50.677464 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 15:21:50.677481 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.375
I0430 15:21:50.677498 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 15:21:50.677515 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0430 15:21:50.677532 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0430 15:21:50.677549 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0430 15:21:50.677567 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 15:21:50.677584 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 15:21:50.677602 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 15:21:50.677618 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 15:21:50.677634 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 15:21:50.677651 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 15:21:50.677667 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:21:50.677685 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:21:50.677701 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:21:50.677718 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:21:50.677734 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:21:50.677752 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:21:50.677767 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.829545
I0430 15:21:50.677784 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.607143
I0430 15:21:50.677805 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.92812 (* 0.3 = 0.578435 loss)
I0430 15:21:50.677825 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.634206 (* 0.3 = 0.190262 loss)
I0430 15:21:50.677846 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.883423 (* 0.0272727 = 0.0240933 loss)
I0430 15:21:50.677867 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.65557 (* 0.0272727 = 0.0451519 loss)
I0430 15:21:50.677901 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.54501 (* 0.0272727 = 0.0421367 loss)
I0430 15:21:50.677922 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.39617 (* 0.0272727 = 0.0380774 loss)
I0430 15:21:50.677943 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.43031 (* 0.0272727 = 0.0390085 loss)
I0430 15:21:50.677963 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.67657 (* 0.0272727 = 0.0457247 loss)
I0430 15:21:50.677984 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.26468 (* 0.0272727 = 0.0344912 loss)
I0430 15:21:50.678005 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.12576 (* 0.0272727 = 0.0307025 loss)
I0430 15:21:50.678025 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.23017 (* 0.0272727 = 0.0335501 loss)
I0430 15:21:50.678045 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 1.36733 (* 0.0272727 = 0.0372907 loss)
I0430 15:21:50.678066 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.569337 (* 0.0272727 = 0.0155274 loss)
I0430 15:21:50.678086 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.642433 (* 0.0272727 = 0.0175209 loss)
I0430 15:21:50.678107 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.017311 (* 0.0272727 = 0.000472118 loss)
I0430 15:21:50.678128 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0151476 (* 0.0272727 = 0.000413117 loss)
I0430 15:21:50.678148 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0061051 (* 0.0272727 = 0.000166503 loss)
I0430 15:21:50.678169 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00162277 (* 0.0272727 = 4.42575e-05 loss)
I0430 15:21:50.678189 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00119232 (* 0.0272727 = 3.25179e-05 loss)
I0430 15:21:50.678211 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000767136 (* 0.0272727 = 2.09219e-05 loss)
I0430 15:21:50.678232 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000690979 (* 0.0272727 = 1.88449e-05 loss)
I0430 15:21:50.678254 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000347042 (* 0.0272727 = 9.46477e-06 loss)
I0430 15:21:50.678274 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000310932 (* 0.0272727 = 8.47997e-06 loss)
I0430 15:21:50.678295 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 6.42183e-05 (* 0.0272727 = 1.75141e-06 loss)
I0430 15:21:50.678313 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.553571
I0430 15:21:50.678330 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 15:21:50.678347 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 15:21:50.678366 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 15:21:50.678385 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0430 15:21:50.678401 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 15:21:50.678418 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 15:21:50.678436 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 15:21:50.678460 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.625
I0430 15:21:50.678478 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0430 15:21:50.678495 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.75
I0430 15:21:50.678513 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 15:21:50.678529 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 15:21:50.678546 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 15:21:50.678563 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 15:21:50.678580 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 15:21:50.678609 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 15:21:50.678627 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:21:50.678644 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:21:50.678661 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:21:50.678678 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:21:50.678694 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:21:50.678711 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:21:50.678728 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.857955
I0430 15:21:50.678745 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.732143
I0430 15:21:50.678766 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.54642 (* 1 = 1.54642 loss)
I0430 15:21:50.678786 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.509446 (* 1 = 0.509446 loss)
I0430 15:21:50.678805 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.519739 (* 0.0909091 = 0.047249 loss)
I0430 15:21:50.678827 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.874105 (* 0.0909091 = 0.0794641 loss)
I0430 15:21:50.678846 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.2792 (* 0.0909091 = 0.116291 loss)
I0430 15:21:50.678866 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.9381 (* 0.0909091 = 0.0852818 loss)
I0430 15:21:50.678886 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.818807 (* 0.0909091 = 0.074437 loss)
I0430 15:21:50.678907 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.22095 (* 0.0909091 = 0.110995 loss)
I0430 15:21:50.678927 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.04271 (* 0.0909091 = 0.0947916 loss)
I0430 15:21:50.678947 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.860832 (* 0.0909091 = 0.0782575 loss)
I0430 15:21:50.678967 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.892527 (* 0.0909091 = 0.0811388 loss)
I0430 15:21:50.678987 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 1.04287 (* 0.0909091 = 0.0948066 loss)
I0430 15:21:50.679008 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.551924 (* 0.0909091 = 0.0501749 loss)
I0430 15:21:50.679028 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.773882 (* 0.0909091 = 0.0703529 loss)
I0430 15:21:50.679049 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0157409 (* 0.0909091 = 0.00143099 loss)
I0430 15:21:50.679069 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00986931 (* 0.0909091 = 0.00089721 loss)
I0430 15:21:50.679090 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00635223 (* 0.0909091 = 0.000577475 loss)
I0430 15:21:50.679111 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00249742 (* 0.0909091 = 0.000227038 loss)
I0430 15:21:50.679131 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00181788 (* 0.0909091 = 0.000165262 loss)
I0430 15:21:50.679152 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00155968 (* 0.0909091 = 0.000141789 loss)
I0430 15:21:50.679172 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00148321 (* 0.0909091 = 0.000134837 loss)
I0430 15:21:50.679193 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000921558 (* 0.0909091 = 8.3778e-05 loss)
I0430 15:21:50.679213 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000712329 (* 0.0909091 = 6.47572e-05 loss)
I0430 15:21:50.679234 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000475449 (* 0.0909091 = 4.32227e-05 loss)
I0430 15:21:50.679251 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.25
I0430 15:21:50.679268 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.25
I0430 15:21:50.679297 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.299184
I0430 15:21:50.679316 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.300691
I0430 15:21:50.679334 15443 sgd_solver.cpp:106] Iteration 5500, lr = 0.001
I0430 15:25:42.672854 15443 solver.cpp:229] Iteration 6000, loss = 3.62512
I0430 15:25:42.673044 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.403226
I0430 15:25:42.673066 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 15:25:42.673079 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0430 15:25:42.673092 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0430 15:25:42.673105 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 15:25:42.673117 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0430 15:25:42.673130 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 15:25:42.673141 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0430 15:25:42.673153 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 15:25:42.673166 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 15:25:42.673178 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0430 15:25:42.673190 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0430 15:25:42.673202 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.75
I0430 15:25:42.673214 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 15:25:42.673228 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 15:25:42.673239 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0430 15:25:42.673251 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0430 15:25:42.673264 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:25:42.673276 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:25:42.673288 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:25:42.673300 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:25:42.673315 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:25:42.673327 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:25:42.673339 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.778409
I0430 15:25:42.673352 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.629032
I0430 15:25:42.673368 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.94299 (* 0.3 = 0.582897 loss)
I0430 15:25:42.673383 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.736344 (* 0.3 = 0.220903 loss)
I0430 15:25:42.673398 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.15654 (* 0.0272727 = 0.0315421 loss)
I0430 15:25:42.673413 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.08972 (* 0.0272727 = 0.0297196 loss)
I0430 15:25:42.673426 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.87301 (* 0.0272727 = 0.051082 loss)
I0430 15:25:42.673441 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.75138 (* 0.0272727 = 0.047765 loss)
I0430 15:25:42.673455 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.61777 (* 0.0272727 = 0.044121 loss)
I0430 15:25:42.673470 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.1134 (* 0.0272727 = 0.0303654 loss)
I0430 15:25:42.673483 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.00276 (* 0.0272727 = 0.0273479 loss)
I0430 15:25:42.673497 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.17434 (* 0.0272727 = 0.0320275 loss)
I0430 15:25:42.673511 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.724626 (* 0.0272727 = 0.0197625 loss)
I0430 15:25:42.673526 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.74594 (* 0.0272727 = 0.0203438 loss)
I0430 15:25:42.673540 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.719552 (* 0.0272727 = 0.0196241 loss)
I0430 15:25:42.673554 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.861342 (* 0.0272727 = 0.0234912 loss)
I0430 15:25:42.673588 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.706894 (* 0.0272727 = 0.0192789 loss)
I0430 15:25:42.673604 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.683725 (* 0.0272727 = 0.018647 loss)
I0430 15:25:42.673619 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.435632 (* 0.0272727 = 0.0118809 loss)
I0430 15:25:42.673632 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.620407 (* 0.0272727 = 0.0169202 loss)
I0430 15:25:42.673647 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0582398 (* 0.0272727 = 0.00158836 loss)
I0430 15:25:42.673662 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.03706 (* 0.0272727 = 0.00101073 loss)
I0430 15:25:42.673676 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.0151149 (* 0.0272727 = 0.000412225 loss)
I0430 15:25:42.673691 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00929637 (* 0.0272727 = 0.000253537 loss)
I0430 15:25:42.673705 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00238975 (* 0.0272727 = 6.51749e-05 loss)
I0430 15:25:42.673719 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000698622 (* 0.0272727 = 1.90533e-05 loss)
I0430 15:25:42.673732 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.451613
I0430 15:25:42.673745 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.375
I0430 15:25:42.673758 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 15:25:42.673769 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0430 15:25:42.673781 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 15:25:42.673792 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 15:25:42.673804 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 15:25:42.673817 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 15:25:42.673830 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 15:25:42.673840 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0430 15:25:42.673853 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0430 15:25:42.673864 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0430 15:25:42.673877 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.75
I0430 15:25:42.673888 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 15:25:42.673900 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 15:25:42.673913 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 15:25:42.673924 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0430 15:25:42.673936 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:25:42.673948 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:25:42.673959 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:25:42.673971 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:25:42.673984 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:25:42.673995 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:25:42.674006 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.801136
I0430 15:25:42.674018 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.596774
I0430 15:25:42.674032 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 2.09867 (* 0.3 = 0.6296 loss)
I0430 15:25:42.674047 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.780108 (* 0.3 = 0.234032 loss)
I0430 15:25:42.674065 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.84133 (* 0.0272727 = 0.050218 loss)
I0430 15:25:42.674079 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.656641 (* 0.0272727 = 0.0179084 loss)
I0430 15:25:42.674105 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.0561 (* 0.0272727 = 0.0560756 loss)
I0430 15:25:42.674120 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.91977 (* 0.0272727 = 0.0523574 loss)
I0430 15:25:42.674135 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.65249 (* 0.0272727 = 0.045068 loss)
I0430 15:25:42.674149 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.3125 (* 0.0272727 = 0.0357954 loss)
I0430 15:25:42.674163 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.80662 (* 0.0272727 = 0.0219987 loss)
I0430 15:25:42.674177 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.32948 (* 0.0272727 = 0.0362586 loss)
I0430 15:25:42.674191 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.764899 (* 0.0272727 = 0.0208609 loss)
I0430 15:25:42.674206 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.744466 (* 0.0272727 = 0.0203036 loss)
I0430 15:25:42.674219 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.00679 (* 0.0272727 = 0.027458 loss)
I0430 15:25:42.674233 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 1.20694 (* 0.0272727 = 0.0329167 loss)
I0430 15:25:42.674247 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.523471 (* 0.0272727 = 0.0142765 loss)
I0430 15:25:42.674262 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.618047 (* 0.0272727 = 0.0168558 loss)
I0430 15:25:42.674275 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.378619 (* 0.0272727 = 0.010326 loss)
I0430 15:25:42.674289 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.627388 (* 0.0272727 = 0.0171106 loss)
I0430 15:25:42.674304 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0730025 (* 0.0272727 = 0.00199098 loss)
I0430 15:25:42.674319 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0624879 (* 0.0272727 = 0.00170422 loss)
I0430 15:25:42.674332 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0299584 (* 0.0272727 = 0.000817047 loss)
I0430 15:25:42.674347 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.0208182 (* 0.0272727 = 0.000567768 loss)
I0430 15:25:42.674361 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00637056 (* 0.0272727 = 0.000173743 loss)
I0430 15:25:42.674379 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.00274524 (* 0.0272727 = 7.48702e-05 loss)
I0430 15:25:42.674392 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.645161
I0430 15:25:42.674406 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 15:25:42.674417 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0430 15:25:42.674428 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 15:25:42.674440 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0430 15:25:42.674453 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0430 15:25:42.674464 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 15:25:42.674476 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 15:25:42.674489 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 15:25:42.674500 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 15:25:42.674512 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.75
I0430 15:25:42.674525 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0430 15:25:42.674537 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.75
I0430 15:25:42.674549 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 15:25:42.674561 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 15:25:42.674572 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 15:25:42.674585 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0430 15:25:42.674609 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:25:42.674623 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:25:42.674635 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:25:42.674648 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:25:42.674659 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:25:42.674671 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:25:42.674684 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.869318
I0430 15:25:42.674695 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.741935
I0430 15:25:42.674710 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.35272 (* 1 = 1.35272 loss)
I0430 15:25:42.674723 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.521172 (* 1 = 0.521172 loss)
I0430 15:25:42.674738 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.661184 (* 0.0909091 = 0.0601077 loss)
I0430 15:25:42.674752 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.26158 (* 0.0909091 = 0.02378 loss)
I0430 15:25:42.674765 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.508626 (* 0.0909091 = 0.0462388 loss)
I0430 15:25:42.674780 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.23738 (* 0.0909091 = 0.112489 loss)
I0430 15:25:42.674794 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.604425 (* 0.0909091 = 0.0549477 loss)
I0430 15:25:42.674809 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.888143 (* 0.0909091 = 0.0807403 loss)
I0430 15:25:42.674823 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.639291 (* 0.0909091 = 0.0581174 loss)
I0430 15:25:42.674837 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.02459 (* 0.0909091 = 0.0931444 loss)
I0430 15:25:42.674851 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.643218 (* 0.0909091 = 0.0584744 loss)
I0430 15:25:42.674865 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.846713 (* 0.0909091 = 0.0769739 loss)
I0430 15:25:42.674878 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.820949 (* 0.0909091 = 0.0746317 loss)
I0430 15:25:42.674893 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 1.06265 (* 0.0909091 = 0.0966046 loss)
I0430 15:25:42.674907 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.615152 (* 0.0909091 = 0.0559229 loss)
I0430 15:25:42.674921 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.584799 (* 0.0909091 = 0.0531635 loss)
I0430 15:25:42.674935 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.32211 (* 0.0909091 = 0.0292828 loss)
I0430 15:25:42.674949 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.603521 (* 0.0909091 = 0.0548655 loss)
I0430 15:25:42.674964 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0584645 (* 0.0909091 = 0.00531496 loss)
I0430 15:25:42.674978 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0261604 (* 0.0909091 = 0.00237822 loss)
I0430 15:25:42.674993 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.0232147 (* 0.0909091 = 0.00211043 loss)
I0430 15:25:42.675006 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00949831 (* 0.0909091 = 0.000863483 loss)
I0430 15:25:42.675021 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.0028454 (* 0.0909091 = 0.000258673 loss)
I0430 15:25:42.675035 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000491162 (* 0.0909091 = 4.46511e-05 loss)
I0430 15:25:42.675047 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0430 15:25:42.675060 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 15:25:42.675071 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.328249
I0430 15:25:42.675094 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.387693
I0430 15:25:42.675113 15443 sgd_solver.cpp:106] Iteration 6000, lr = 0.001
I0430 15:26:51.258909 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.0078 > 30) by scale factor 0.999739
I0430 15:29:34.922966 15443 solver.cpp:229] Iteration 6500, loss = 3.74846
I0430 15:29:34.923123 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.490196
I0430 15:29:34.923146 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 15:29:34.923158 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0430 15:29:34.923171 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0430 15:29:34.923183 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0430 15:29:34.923197 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.125
I0430 15:29:34.923208 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0430 15:29:34.923220 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0430 15:29:34.923233 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 15:29:34.923245 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 15:29:34.923257 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 15:29:34.923269 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 15:29:34.923282 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 15:29:34.923295 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 15:29:34.923305 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 15:29:34.923321 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 15:29:34.923332 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 15:29:34.923344 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:29:34.923357 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:29:34.923367 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:29:34.923379 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:29:34.923391 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:29:34.923403 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:29:34.923414 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.846591
I0430 15:29:34.923426 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.72549
I0430 15:29:34.923442 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.56919 (* 0.3 = 0.470758 loss)
I0430 15:29:34.923457 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.523183 (* 0.3 = 0.156955 loss)
I0430 15:29:34.923499 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.676934 (* 0.0272727 = 0.0184618 loss)
I0430 15:29:34.923527 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.777888 (* 0.0272727 = 0.0212151 loss)
I0430 15:29:34.923548 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.20266 (* 0.0272727 = 0.0600726 loss)
I0430 15:29:34.923569 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.83754 (* 0.0272727 = 0.0501147 loss)
I0430 15:29:34.923590 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.36463 (* 0.0272727 = 0.0644899 loss)
I0430 15:29:34.923611 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.79068 (* 0.0272727 = 0.0488366 loss)
I0430 15:29:34.923632 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.959543 (* 0.0272727 = 0.0261693 loss)
I0430 15:29:34.923653 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.469189 (* 0.0272727 = 0.0127961 loss)
I0430 15:29:34.923674 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.414685 (* 0.0272727 = 0.0113096 loss)
I0430 15:29:34.923696 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.585736 (* 0.0272727 = 0.0159746 loss)
I0430 15:29:34.923717 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0665621 (* 0.0272727 = 0.00181533 loss)
I0430 15:29:34.923738 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0549243 (* 0.0272727 = 0.00149794 loss)
I0430 15:29:34.923784 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0303178 (* 0.0272727 = 0.00082685 loss)
I0430 15:29:34.923807 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0251946 (* 0.0272727 = 0.000687124 loss)
I0430 15:29:34.923830 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0211779 (* 0.0272727 = 0.00057758 loss)
I0430 15:29:34.923851 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0199211 (* 0.0272727 = 0.000543301 loss)
I0430 15:29:34.923871 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0107862 (* 0.0272727 = 0.000294169 loss)
I0430 15:29:34.923892 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0065672 (* 0.0272727 = 0.000179105 loss)
I0430 15:29:34.923913 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00391337 (* 0.0272727 = 0.000106728 loss)
I0430 15:29:34.923934 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00138896 (* 0.0272727 = 3.78806e-05 loss)
I0430 15:29:34.923955 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000807151 (* 0.0272727 = 2.20132e-05 loss)
I0430 15:29:34.923976 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 7.96646e-05 (* 0.0272727 = 2.17267e-06 loss)
I0430 15:29:34.923995 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.529412
I0430 15:29:34.924013 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.625
I0430 15:29:34.924031 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 15:29:34.924048 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0430 15:29:34.924067 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 15:29:34.924083 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.25
I0430 15:29:34.924100 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 15:29:34.924118 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 15:29:34.924139 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 15:29:34.924156 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 15:29:34.924175 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 15:29:34.924191 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 15:29:34.924208 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 15:29:34.924224 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 15:29:34.924242 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 15:29:34.924258 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 15:29:34.924275 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 15:29:34.924293 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:29:34.924309 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:29:34.924326 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:29:34.924343 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:29:34.924360 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:29:34.924381 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:29:34.924397 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.857955
I0430 15:29:34.924414 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.803922
I0430 15:29:34.924435 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.36397 (* 0.3 = 0.40919 loss)
I0430 15:29:34.924456 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.471151 (* 0.3 = 0.141345 loss)
I0430 15:29:34.924481 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.14919 (* 0.0272727 = 0.0313416 loss)
I0430 15:29:34.924504 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.742328 (* 0.0272727 = 0.0202453 loss)
I0430 15:29:34.924540 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.75878 (* 0.0272727 = 0.0479667 loss)
I0430 15:29:34.924561 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.52534 (* 0.0272727 = 0.0416003 loss)
I0430 15:29:34.924582 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.89129 (* 0.0272727 = 0.0515807 loss)
I0430 15:29:34.924602 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.57007 (* 0.0272727 = 0.0428202 loss)
I0430 15:29:34.924623 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.788981 (* 0.0272727 = 0.0215177 loss)
I0430 15:29:34.924644 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.593296 (* 0.0272727 = 0.0161808 loss)
I0430 15:29:34.924666 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.304109 (* 0.0272727 = 0.00829387 loss)
I0430 15:29:34.924687 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.322644 (* 0.0272727 = 0.00879939 loss)
I0430 15:29:34.924708 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.145705 (* 0.0272727 = 0.00397377 loss)
I0430 15:29:34.924729 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0762931 (* 0.0272727 = 0.00208072 loss)
I0430 15:29:34.924749 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0351061 (* 0.0272727 = 0.00095744 loss)
I0430 15:29:34.924770 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0267696 (* 0.0272727 = 0.000730081 loss)
I0430 15:29:34.924792 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0277931 (* 0.0272727 = 0.000757993 loss)
I0430 15:29:34.924813 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0102544 (* 0.0272727 = 0.000279667 loss)
I0430 15:29:34.924834 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00940305 (* 0.0272727 = 0.000256447 loss)
I0430 15:29:34.924854 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0104798 (* 0.0272727 = 0.000285813 loss)
I0430 15:29:34.924875 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0092532 (* 0.0272727 = 0.00025236 loss)
I0430 15:29:34.924896 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00397039 (* 0.0272727 = 0.000108283 loss)
I0430 15:29:34.924916 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00206097 (* 0.0272727 = 5.62083e-05 loss)
I0430 15:29:34.924937 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000649887 (* 0.0272727 = 1.77242e-05 loss)
I0430 15:29:34.924954 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.745098
I0430 15:29:34.924971 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 15:29:34.924989 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0430 15:29:34.925006 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0430 15:29:34.925022 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 15:29:34.925040 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 15:29:34.925056 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 15:29:34.925073 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 15:29:34.925091 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 15:29:34.925108 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 15:29:34.925125 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 15:29:34.925143 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 15:29:34.925158 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 15:29:34.925175 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 15:29:34.925196 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 15:29:34.925214 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 15:29:34.925230 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 15:29:34.925261 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:29:34.925279 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:29:34.925297 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:29:34.925313 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:29:34.925330 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:29:34.925348 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:29:34.925364 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.926136
I0430 15:29:34.925384 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.862745
I0430 15:29:34.925405 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.901268 (* 1 = 0.901268 loss)
I0430 15:29:34.925428 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.346155 (* 1 = 0.346155 loss)
I0430 15:29:34.925449 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.346385 (* 0.0909091 = 0.0314895 loss)
I0430 15:29:34.925470 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.152448 (* 0.0909091 = 0.013859 loss)
I0430 15:29:34.925492 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.933151 (* 0.0909091 = 0.0848319 loss)
I0430 15:29:34.925513 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.488397 (* 0.0909091 = 0.0443997 loss)
I0430 15:29:34.925534 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.546394 (* 0.0909091 = 0.0496722 loss)
I0430 15:29:34.925556 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.882862 (* 0.0909091 = 0.0802602 loss)
I0430 15:29:34.925576 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.534811 (* 0.0909091 = 0.0486192 loss)
I0430 15:29:34.925597 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.68303 (* 0.0909091 = 0.0620936 loss)
I0430 15:29:34.925618 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.323063 (* 0.0909091 = 0.0293693 loss)
I0430 15:29:34.925639 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.286695 (* 0.0909091 = 0.0260631 loss)
I0430 15:29:34.925659 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.0856163 (* 0.0909091 = 0.0077833 loss)
I0430 15:29:34.925680 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.0421185 (* 0.0909091 = 0.00382896 loss)
I0430 15:29:34.925701 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0337533 (* 0.0909091 = 0.00306848 loss)
I0430 15:29:34.925722 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0127291 (* 0.0909091 = 0.00115719 loss)
I0430 15:29:34.925743 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00850255 (* 0.0909091 = 0.000772959 loss)
I0430 15:29:34.925763 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0039001 (* 0.0909091 = 0.000354554 loss)
I0430 15:29:34.925784 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00317191 (* 0.0909091 = 0.000288355 loss)
I0430 15:29:34.925806 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000821322 (* 0.0909091 = 7.46657e-05 loss)
I0430 15:29:34.925827 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000398796 (* 0.0909091 = 3.62541e-05 loss)
I0430 15:29:34.925848 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000148771 (* 0.0909091 = 1.35247e-05 loss)
I0430 15:29:34.925868 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 2.93512e-05 (* 0.0909091 = 2.66829e-06 loss)
I0430 15:29:34.925889 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.37247e-05 (* 0.0909091 = 1.2477e-06 loss)
I0430 15:29:34.925905 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0430 15:29:34.925922 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0430 15:29:34.925952 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.598673
I0430 15:29:34.925971 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.53506
I0430 15:29:34.925989 15443 sgd_solver.cpp:106] Iteration 6500, lr = 0.001
I0430 15:33:26.970994 15443 solver.cpp:229] Iteration 7000, loss = 3.84187
I0430 15:33:26.971181 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.472727
I0430 15:33:26.971204 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 15:33:26.971216 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 15:33:26.971228 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0430 15:33:26.971240 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0430 15:33:26.971253 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0430 15:33:26.971266 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0430 15:33:26.971277 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 15:33:26.971289 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 15:33:26.971302 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 15:33:26.971317 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 15:33:26.971328 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 15:33:26.971340 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 15:33:26.971354 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 15:33:26.971365 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 15:33:26.971376 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0430 15:33:26.971388 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0430 15:33:26.971400 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 0.875
I0430 15:33:26.971412 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 0.875
I0430 15:33:26.971424 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:33:26.971436 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:33:26.971448 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:33:26.971459 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:33:26.971503 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.829545
I0430 15:33:26.971521 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.581818
I0430 15:33:26.971539 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.2102 (* 0.3 = 0.66306 loss)
I0430 15:33:26.971554 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.705618 (* 0.3 = 0.211685 loss)
I0430 15:33:26.971568 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.660766 (* 0.0272727 = 0.0180209 loss)
I0430 15:33:26.971582 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.72826 (* 0.0272727 = 0.0471343 loss)
I0430 15:33:26.971596 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.71299 (* 0.0272727 = 0.0467179 loss)
I0430 15:33:26.971609 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.29627 (* 0.0272727 = 0.0626256 loss)
I0430 15:33:26.971623 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.64561 (* 0.0272727 = 0.0448802 loss)
I0430 15:33:26.971637 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.872332 (* 0.0272727 = 0.0237909 loss)
I0430 15:33:26.971652 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.00321 (* 0.0272727 = 0.0273602 loss)
I0430 15:33:26.971665 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.3831 (* 0.0272727 = 0.0104482 loss)
I0430 15:33:26.971679 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.302024 (* 0.0272727 = 0.00823702 loss)
I0430 15:33:26.971693 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.307301 (* 0.0272727 = 0.00838093 loss)
I0430 15:33:26.971707 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.554928 (* 0.0272727 = 0.0151344 loss)
I0430 15:33:26.971721 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.355069 (* 0.0272727 = 0.00968371 loss)
I0430 15:33:26.971757 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.499182 (* 0.0272727 = 0.0136141 loss)
I0430 15:33:26.971772 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 1.23368 (* 0.0272727 = 0.0336459 loss)
I0430 15:33:26.971786 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.658295 (* 0.0272727 = 0.0179535 loss)
I0430 15:33:26.971801 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.687728 (* 0.0272727 = 0.0187562 loss)
I0430 15:33:26.971814 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.942513 (* 0.0272727 = 0.0257049 loss)
I0430 15:33:26.971827 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.956821 (* 0.0272727 = 0.0260951 loss)
I0430 15:33:26.971842 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00125978 (* 0.0272727 = 3.43577e-05 loss)
I0430 15:33:26.971855 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000108744 (* 0.0272727 = 2.96574e-06 loss)
I0430 15:33:26.971869 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 9.76064e-06 (* 0.0272727 = 2.66199e-07 loss)
I0430 15:33:26.971884 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.2219e-06 (* 0.0272727 = 3.33246e-08 loss)
I0430 15:33:26.971895 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.472727
I0430 15:33:26.971907 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.625
I0430 15:33:26.971918 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 15:33:26.971930 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 15:33:26.971942 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 15:33:26.971953 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0430 15:33:26.971966 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.875
I0430 15:33:26.971976 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 15:33:26.971988 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 15:33:26.972000 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 15:33:26.972012 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 15:33:26.972023 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 15:33:26.972035 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 15:33:26.972046 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 15:33:26.972059 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 15:33:26.972069 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 15:33:26.972081 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0430 15:33:26.972093 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 0.875
I0430 15:33:26.972105 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 0.875
I0430 15:33:26.972116 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:33:26.972127 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:33:26.972138 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:33:26.972149 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:33:26.972162 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.835227
I0430 15:33:26.972172 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.654545
I0430 15:33:26.972190 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.85893 (* 0.3 = 0.557678 loss)
I0430 15:33:26.972204 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.589304 (* 0.3 = 0.176791 loss)
I0430 15:33:26.972218 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.18345 (* 0.0272727 = 0.0322758 loss)
I0430 15:33:26.972232 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.30429 (* 0.0272727 = 0.0355714 loss)
I0430 15:33:26.972257 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.17283 (* 0.0272727 = 0.0319863 loss)
I0430 15:33:26.972272 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.43363 (* 0.0272727 = 0.0390989 loss)
I0430 15:33:26.972286 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.21597 (* 0.0272727 = 0.0604356 loss)
I0430 15:33:26.972301 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.692274 (* 0.0272727 = 0.0188802 loss)
I0430 15:33:26.972314 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.87706 (* 0.0272727 = 0.0239198 loss)
I0430 15:33:26.972328 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.426842 (* 0.0272727 = 0.0116412 loss)
I0430 15:33:26.972342 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.363397 (* 0.0272727 = 0.00991082 loss)
I0430 15:33:26.972355 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.341295 (* 0.0272727 = 0.00930805 loss)
I0430 15:33:26.972371 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.481222 (* 0.0272727 = 0.0131242 loss)
I0430 15:33:26.972386 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.342877 (* 0.0272727 = 0.00935119 loss)
I0430 15:33:26.972399 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.359633 (* 0.0272727 = 0.00980819 loss)
I0430 15:33:26.972414 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.854906 (* 0.0272727 = 0.0233156 loss)
I0430 15:33:26.972427 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.527654 (* 0.0272727 = 0.0143906 loss)
I0430 15:33:26.972441 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.470386 (* 0.0272727 = 0.0128287 loss)
I0430 15:33:26.972455 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.605872 (* 0.0272727 = 0.0165238 loss)
I0430 15:33:26.972468 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.697269 (* 0.0272727 = 0.0190164 loss)
I0430 15:33:26.972481 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0227185 (* 0.0272727 = 0.000619595 loss)
I0430 15:33:26.972496 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00698737 (* 0.0272727 = 0.000190565 loss)
I0430 15:33:26.972509 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00227735 (* 0.0272727 = 6.21095e-05 loss)
I0430 15:33:26.972523 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000206507 (* 0.0272727 = 5.63201e-06 loss)
I0430 15:33:26.972535 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.654545
I0430 15:33:26.972548 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 15:33:26.972559 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 15:33:26.972570 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 15:33:26.972582 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 15:33:26.972594 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 15:33:26.972605 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0430 15:33:26.972617 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 15:33:26.972625 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 15:33:26.972633 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 15:33:26.972640 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 15:33:26.972648 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 15:33:26.972656 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 15:33:26.972662 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 15:33:26.972671 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 15:33:26.972677 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 15:33:26.972694 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0430 15:33:26.972703 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 0.875
I0430 15:33:26.972712 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 0.875
I0430 15:33:26.972719 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:33:26.972726 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:33:26.972734 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:33:26.972741 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:33:26.972748 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.892045
I0430 15:33:26.972756 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.727273
I0430 15:33:26.972765 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.25476 (* 1 = 1.25476 loss)
I0430 15:33:26.972775 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.395661 (* 1 = 0.395661 loss)
I0430 15:33:26.972785 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.397168 (* 0.0909091 = 0.0361061 loss)
I0430 15:33:26.972795 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.470637 (* 0.0909091 = 0.0427852 loss)
I0430 15:33:26.972805 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.446758 (* 0.0909091 = 0.0406144 loss)
I0430 15:33:26.972813 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.51848 (* 0.0909091 = 0.0471346 loss)
I0430 15:33:26.972822 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.492653 (* 0.0909091 = 0.0447866 loss)
I0430 15:33:26.972832 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.36784 (* 0.0909091 = 0.03344 loss)
I0430 15:33:26.972841 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.523048 (* 0.0909091 = 0.0475498 loss)
I0430 15:33:26.972851 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.396176 (* 0.0909091 = 0.036016 loss)
I0430 15:33:26.972861 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.328881 (* 0.0909091 = 0.0298983 loss)
I0430 15:33:26.972869 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.22621 (* 0.0909091 = 0.0205646 loss)
I0430 15:33:26.972878 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.427011 (* 0.0909091 = 0.0388191 loss)
I0430 15:33:26.972888 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.402559 (* 0.0909091 = 0.0365963 loss)
I0430 15:33:26.972898 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.435212 (* 0.0909091 = 0.0395647 loss)
I0430 15:33:26.972906 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.674508 (* 0.0909091 = 0.0613189 loss)
I0430 15:33:26.972915 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.478984 (* 0.0909091 = 0.043544 loss)
I0430 15:33:26.972924 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.424847 (* 0.0909091 = 0.0386224 loss)
I0430 15:33:26.972934 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.49398 (* 0.0909091 = 0.0449073 loss)
I0430 15:33:26.972942 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.620602 (* 0.0909091 = 0.0564183 loss)
I0430 15:33:26.972952 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.0105061 (* 0.0909091 = 0.000955096 loss)
I0430 15:33:26.972961 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00846562 (* 0.0909091 = 0.000769602 loss)
I0430 15:33:26.972971 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00501071 (* 0.0909091 = 0.000455519 loss)
I0430 15:33:26.972980 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.00189095 (* 0.0909091 = 0.000171904 loss)
I0430 15:33:26.972988 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 15:33:26.972995 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 15:33:26.973011 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.394406
I0430 15:33:26.973021 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.399079
I0430 15:33:26.973029 15443 sgd_solver.cpp:106] Iteration 7000, lr = 0.001
I0430 15:37:19.126194 15443 solver.cpp:229] Iteration 7500, loss = 3.69947
I0430 15:37:19.126375 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.468085
I0430 15:37:19.126405 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 15:37:19.126427 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 15:37:19.126448 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.125
I0430 15:37:19.126471 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.625
I0430 15:37:19.126493 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 15:37:19.126513 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0430 15:37:19.126535 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 1
I0430 15:37:19.126557 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 15:37:19.126579 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 15:37:19.126598 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 15:37:19.126619 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 15:37:19.126642 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 15:37:19.126662 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 15:37:19.126682 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 15:37:19.126701 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 15:37:19.126723 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 15:37:19.126746 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:37:19.126770 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:37:19.126792 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:37:19.126814 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:37:19.126835 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:37:19.126857 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:37:19.126878 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.846591
I0430 15:37:19.126899 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.680851
I0430 15:37:19.126927 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.80242 (* 0.3 = 0.540725 loss)
I0430 15:37:19.126955 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.543306 (* 0.3 = 0.162992 loss)
I0430 15:37:19.126981 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.56079 (* 0.0272727 = 0.0425669 loss)
I0430 15:37:19.127007 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.34471 (* 0.0272727 = 0.036674 loss)
I0430 15:37:19.127032 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.45887 (* 0.0272727 = 0.06706 loss)
I0430 15:37:19.127058 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.52972 (* 0.0272727 = 0.0417195 loss)
I0430 15:37:19.127084 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.13832 (* 0.0272727 = 0.0583178 loss)
I0430 15:37:19.127110 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.739095 (* 0.0272727 = 0.0201571 loss)
I0430 15:37:19.127136 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.352528 (* 0.0272727 = 0.00961441 loss)
I0430 15:37:19.127162 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.349067 (* 0.0272727 = 0.00952001 loss)
I0430 15:37:19.127189 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.419344 (* 0.0272727 = 0.0114367 loss)
I0430 15:37:19.127216 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.338536 (* 0.0272727 = 0.00923281 loss)
I0430 15:37:19.127243 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.354884 (* 0.0272727 = 0.00967866 loss)
I0430 15:37:19.127269 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0959193 (* 0.0272727 = 0.00261598 loss)
I0430 15:37:19.127324 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.046974 (* 0.0272727 = 0.00128111 loss)
I0430 15:37:19.127353 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0182681 (* 0.0272727 = 0.000498221 loss)
I0430 15:37:19.127384 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0132163 (* 0.0272727 = 0.000360444 loss)
I0430 15:37:19.127413 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0104332 (* 0.0272727 = 0.000284542 loss)
I0430 15:37:19.127440 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0104845 (* 0.0272727 = 0.000285941 loss)
I0430 15:37:19.127482 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00570621 (* 0.0272727 = 0.000155624 loss)
I0430 15:37:19.127516 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00138862 (* 0.0272727 = 3.78714e-05 loss)
I0430 15:37:19.127542 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000432918 (* 0.0272727 = 1.18069e-05 loss)
I0430 15:37:19.127569 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00010943 (* 0.0272727 = 2.98444e-06 loss)
I0430 15:37:19.127595 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 3.28986e-05 (* 0.0272727 = 8.97234e-07 loss)
I0430 15:37:19.127619 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.553191
I0430 15:37:19.127640 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 15:37:19.127662 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0430 15:37:19.127684 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0430 15:37:19.127706 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0430 15:37:19.127727 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 15:37:19.127749 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0430 15:37:19.127773 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 1
I0430 15:37:19.127797 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 15:37:19.127821 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 15:37:19.127846 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 15:37:19.127864 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 15:37:19.127888 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 15:37:19.127912 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 15:37:19.127933 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 15:37:19.127954 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 15:37:19.127976 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 15:37:19.127998 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:37:19.128020 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:37:19.128041 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:37:19.128062 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:37:19.128083 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:37:19.128105 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:37:19.128125 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.869318
I0430 15:37:19.128147 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.744681
I0430 15:37:19.128175 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.59504 (* 0.3 = 0.478511 loss)
I0430 15:37:19.128201 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.482022 (* 0.3 = 0.144607 loss)
I0430 15:37:19.128228 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.757681 (* 0.0272727 = 0.020664 loss)
I0430 15:37:19.128255 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.8225 (* 0.0272727 = 0.0497047 loss)
I0430 15:37:19.128303 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.59803 (* 0.0272727 = 0.0435828 loss)
I0430 15:37:19.128330 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.97685 (* 0.0272727 = 0.0539141 loss)
I0430 15:37:19.128356 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.58933 (* 0.0272727 = 0.0433453 loss)
I0430 15:37:19.128387 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.79924 (* 0.0272727 = 0.0217974 loss)
I0430 15:37:19.128414 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.246224 (* 0.0272727 = 0.00671521 loss)
I0430 15:37:19.128445 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.349868 (* 0.0272727 = 0.00954186 loss)
I0430 15:37:19.128473 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.38231 (* 0.0272727 = 0.0104266 loss)
I0430 15:37:19.128500 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.318177 (* 0.0272727 = 0.00867754 loss)
I0430 15:37:19.128527 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.258723 (* 0.0272727 = 0.00705608 loss)
I0430 15:37:19.128554 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0321571 (* 0.0272727 = 0.000877013 loss)
I0430 15:37:19.128581 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0149639 (* 0.0272727 = 0.000408105 loss)
I0430 15:37:19.128607 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00545832 (* 0.0272727 = 0.000148863 loss)
I0430 15:37:19.128635 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00153033 (* 0.0272727 = 4.17362e-05 loss)
I0430 15:37:19.128660 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00031367 (* 0.0272727 = 8.55465e-06 loss)
I0430 15:37:19.128685 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000336196 (* 0.0272727 = 9.16898e-06 loss)
I0430 15:37:19.128712 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 8.419e-05 (* 0.0272727 = 2.29609e-06 loss)
I0430 15:37:19.128738 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 3.39122e-05 (* 0.0272727 = 9.24877e-07 loss)
I0430 15:37:19.128765 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 1.84639e-05 (* 0.0272727 = 5.03561e-07 loss)
I0430 15:37:19.128792 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 9.62652e-06 (* 0.0272727 = 2.62541e-07 loss)
I0430 15:37:19.128818 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.54973e-06 (* 0.0272727 = 4.22654e-08 loss)
I0430 15:37:19.128840 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.765957
I0430 15:37:19.128864 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 15:37:19.128885 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 15:37:19.128906 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 15:37:19.128927 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 15:37:19.128948 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 15:37:19.128970 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0430 15:37:19.128991 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0430 15:37:19.129014 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 15:37:19.129034 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 15:37:19.129055 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 15:37:19.129077 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 15:37:19.129098 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 15:37:19.129118 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 15:37:19.129140 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 15:37:19.129163 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 15:37:19.129199 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 15:37:19.129222 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:37:19.129245 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:37:19.129266 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:37:19.129288 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:37:19.129309 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:37:19.129331 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:37:19.129354 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.9375
I0430 15:37:19.129375 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.87234
I0430 15:37:19.129402 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.05785 (* 1 = 1.05785 loss)
I0430 15:37:19.129432 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.292372 (* 1 = 0.292372 loss)
I0430 15:37:19.129459 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.781257 (* 0.0909091 = 0.0710234 loss)
I0430 15:37:19.129490 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.892551 (* 0.0909091 = 0.081141 loss)
I0430 15:37:19.129516 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.930241 (* 0.0909091 = 0.0845674 loss)
I0430 15:37:19.129544 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.0986 (* 0.0909091 = 0.0998727 loss)
I0430 15:37:19.129570 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.85549 (* 0.0909091 = 0.168681 loss)
I0430 15:37:19.129596 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.40726 (* 0.0909091 = 0.0370237 loss)
I0430 15:37:19.129622 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.246039 (* 0.0909091 = 0.0223672 loss)
I0430 15:37:19.129649 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.380653 (* 0.0909091 = 0.0346048 loss)
I0430 15:37:19.129674 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.24928 (* 0.0909091 = 0.0226618 loss)
I0430 15:37:19.129700 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.306622 (* 0.0909091 = 0.0278748 loss)
I0430 15:37:19.129727 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.225664 (* 0.0909091 = 0.0205149 loss)
I0430 15:37:19.129753 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.0471113 (* 0.0909091 = 0.00428285 loss)
I0430 15:37:19.129779 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0163376 (* 0.0909091 = 0.00148524 loss)
I0430 15:37:19.129806 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00605706 (* 0.0909091 = 0.000550642 loss)
I0430 15:37:19.129832 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00135712 (* 0.0909091 = 0.000123374 loss)
I0430 15:37:19.129858 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000635667 (* 0.0909091 = 5.77879e-05 loss)
I0430 15:37:19.129884 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000250513 (* 0.0909091 = 2.27739e-05 loss)
I0430 15:37:19.129911 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000122189 (* 0.0909091 = 1.11081e-05 loss)
I0430 15:37:19.129937 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 6.54405e-05 (* 0.0909091 = 5.94913e-06 loss)
I0430 15:37:19.129963 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 2.47905e-05 (* 0.0909091 = 2.25368e-06 loss)
I0430 15:37:19.129989 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.19066e-05 (* 0.0909091 = 1.08242e-06 loss)
I0430 15:37:19.130017 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.08457e-06 (* 0.0909091 = 2.80416e-07 loss)
I0430 15:37:19.130039 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0430 15:37:19.130060 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 15:37:19.130097 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.394798
I0430 15:37:19.130120 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.433764
I0430 15:37:19.130142 15443 sgd_solver.cpp:106] Iteration 7500, lr = 0.001
I0430 15:41:11.452646 15443 solver.cpp:229] Iteration 8000, loss = 3.80904
I0430 15:41:11.452824 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.375
I0430 15:41:11.452853 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 15:41:11.452875 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 15:41:11.452898 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0430 15:41:11.452919 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 15:41:11.452941 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.25
I0430 15:41:11.452963 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.875
I0430 15:41:11.452987 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 15:41:11.453009 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 15:41:11.453032 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 15:41:11.453053 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 15:41:11.453074 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 15:41:11.453100 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 15:41:11.453124 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 15:41:11.453146 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 15:41:11.453168 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 15:41:11.453191 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0430 15:41:11.453212 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 0.875
I0430 15:41:11.453234 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:41:11.453255 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:41:11.453277 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:41:11.453299 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:41:11.453325 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:41:11.453346 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.789773
I0430 15:41:11.453368 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.642857
I0430 15:41:11.453397 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.84698 (* 0.3 = 0.554094 loss)
I0430 15:41:11.453424 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.637751 (* 0.3 = 0.191325 loss)
I0430 15:41:11.453452 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.12889 (* 0.0272727 = 0.0307879 loss)
I0430 15:41:11.453480 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.94135 (* 0.0272727 = 0.0529458 loss)
I0430 15:41:11.453505 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.97365 (* 0.0272727 = 0.0538269 loss)
I0430 15:41:11.453532 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.55402 (* 0.0272727 = 0.0423825 loss)
I0430 15:41:11.453562 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.66288 (* 0.0272727 = 0.0453512 loss)
I0430 15:41:11.453588 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.731117 (* 0.0272727 = 0.0199395 loss)
I0430 15:41:11.453615 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.959056 (* 0.0272727 = 0.0261561 loss)
I0430 15:41:11.453642 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.52265 (* 0.0272727 = 0.0142541 loss)
I0430 15:41:11.453668 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.410241 (* 0.0272727 = 0.0111884 loss)
I0430 15:41:11.453694 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.678462 (* 0.0272727 = 0.0185035 loss)
I0430 15:41:11.453722 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.294092 (* 0.0272727 = 0.00802068 loss)
I0430 15:41:11.453748 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.898249 (* 0.0272727 = 0.0244977 loss)
I0430 15:41:11.453800 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.289501 (* 0.0272727 = 0.00789549 loss)
I0430 15:41:11.453837 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.288129 (* 0.0272727 = 0.00785807 loss)
I0430 15:41:11.453867 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.246692 (* 0.0272727 = 0.00672797 loss)
I0430 15:41:11.453896 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.410244 (* 0.0272727 = 0.0111885 loss)
I0430 15:41:11.453923 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.99918 (* 0.0272727 = 0.0272504 loss)
I0430 15:41:11.453951 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 2.57805e-05 (* 0.0272727 = 7.03106e-07 loss)
I0430 15:41:11.453979 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 1.50655e-05 (* 0.0272727 = 4.10878e-07 loss)
I0430 15:41:11.454005 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 5.45391e-06 (* 0.0272727 = 1.48743e-07 loss)
I0430 15:41:11.454031 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 5.21546e-06 (* 0.0272727 = 1.4224e-07 loss)
I0430 15:41:11.454058 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 3.15907e-06 (* 0.0272727 = 8.61563e-08 loss)
I0430 15:41:11.454082 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.571429
I0430 15:41:11.454105 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0430 15:41:11.454126 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 15:41:11.454149 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.25
I0430 15:41:11.454171 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 15:41:11.454192 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 15:41:11.454216 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 15:41:11.454236 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 15:41:11.454258 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 15:41:11.454280 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 15:41:11.454301 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 15:41:11.454324 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 15:41:11.454345 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 15:41:11.454370 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 15:41:11.454392 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 15:41:11.454414 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 15:41:11.454437 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0430 15:41:11.454458 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 0.875
I0430 15:41:11.454480 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:41:11.454502 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:41:11.454524 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:41:11.454545 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:41:11.454566 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:41:11.454586 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.857955
I0430 15:41:11.454608 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.803571
I0430 15:41:11.454634 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.52046 (* 0.3 = 0.456139 loss)
I0430 15:41:11.454663 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.522634 (* 0.3 = 0.15679 loss)
I0430 15:41:11.454687 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.504988 (* 0.0272727 = 0.0137724 loss)
I0430 15:41:11.454715 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.88757 (* 0.0272727 = 0.0514792 loss)
I0430 15:41:11.454758 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.8784 (* 0.0272727 = 0.0512291 loss)
I0430 15:41:11.454787 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.0474 (* 0.0272727 = 0.0285655 loss)
I0430 15:41:11.454813 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.942352 (* 0.0272727 = 0.0257005 loss)
I0430 15:41:11.454838 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.902608 (* 0.0272727 = 0.0246166 loss)
I0430 15:41:11.454864 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.930758 (* 0.0272727 = 0.0253843 loss)
I0430 15:41:11.454895 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.49607 (* 0.0272727 = 0.0135292 loss)
I0430 15:41:11.454921 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.499583 (* 0.0272727 = 0.013625 loss)
I0430 15:41:11.454948 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.559251 (* 0.0272727 = 0.0152523 loss)
I0430 15:41:11.454974 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.3747 (* 0.0272727 = 0.0102191 loss)
I0430 15:41:11.455000 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.618164 (* 0.0272727 = 0.016859 loss)
I0430 15:41:11.455027 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.311917 (* 0.0272727 = 0.00850683 loss)
I0430 15:41:11.455054 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.269417 (* 0.0272727 = 0.00734774 loss)
I0430 15:41:11.455078 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.302547 (* 0.0272727 = 0.00825129 loss)
I0430 15:41:11.455106 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.313789 (* 0.0272727 = 0.00855788 loss)
I0430 15:41:11.455132 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.936702 (* 0.0272727 = 0.0255464 loss)
I0430 15:41:11.455157 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000383321 (* 0.0272727 = 1.04542e-05 loss)
I0430 15:41:11.455184 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000266784 (* 0.0272727 = 7.27592e-06 loss)
I0430 15:41:11.455210 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000199263 (* 0.0272727 = 5.43445e-06 loss)
I0430 15:41:11.455235 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000111753 (* 0.0272727 = 3.0478e-06 loss)
I0430 15:41:11.455262 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 9.45882e-05 (* 0.0272727 = 2.57968e-06 loss)
I0430 15:41:11.455286 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.625
I0430 15:41:11.455308 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 15:41:11.455329 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 15:41:11.455351 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0430 15:41:11.455373 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 15:41:11.455394 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 15:41:11.455432 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0430 15:41:11.455461 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 15:41:11.455482 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 15:41:11.455503 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 15:41:11.455525 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 15:41:11.455548 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 15:41:11.455569 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 15:41:11.455590 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 15:41:11.455611 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 15:41:11.455632 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 15:41:11.455672 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0430 15:41:11.455695 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 0.875
I0430 15:41:11.455718 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:41:11.455739 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:41:11.455760 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:41:11.455782 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:41:11.455803 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:41:11.455824 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.863636
I0430 15:41:11.455847 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.875
I0430 15:41:11.455873 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.19227 (* 1 = 1.19227 loss)
I0430 15:41:11.455899 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.417578 (* 1 = 0.417578 loss)
I0430 15:41:11.455920 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.587257 (* 0.0909091 = 0.053387 loss)
I0430 15:41:11.455952 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.927452 (* 0.0909091 = 0.0843138 loss)
I0430 15:41:11.455978 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.799316 (* 0.0909091 = 0.0726651 loss)
I0430 15:41:11.456001 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.946446 (* 0.0909091 = 0.0860405 loss)
I0430 15:41:11.456023 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.850072 (* 0.0909091 = 0.0772793 loss)
I0430 15:41:11.456049 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.552501 (* 0.0909091 = 0.0502273 loss)
I0430 15:41:11.456078 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.624643 (* 0.0909091 = 0.0567857 loss)
I0430 15:41:11.456105 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.354189 (* 0.0909091 = 0.032199 loss)
I0430 15:41:11.456133 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.436018 (* 0.0909091 = 0.039638 loss)
I0430 15:41:11.456163 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.473537 (* 0.0909091 = 0.0430488 loss)
I0430 15:41:11.456190 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.257925 (* 0.0909091 = 0.0234477 loss)
I0430 15:41:11.456218 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.604635 (* 0.0909091 = 0.0549668 loss)
I0430 15:41:11.456243 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.331012 (* 0.0909091 = 0.030092 loss)
I0430 15:41:11.456270 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.221688 (* 0.0909091 = 0.0201535 loss)
I0430 15:41:11.456296 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.298424 (* 0.0909091 = 0.0271294 loss)
I0430 15:41:11.456322 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.272092 (* 0.0909091 = 0.0247356 loss)
I0430 15:41:11.456348 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.740988 (* 0.0909091 = 0.0673626 loss)
I0430 15:41:11.456375 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000788028 (* 0.0909091 = 7.16389e-05 loss)
I0430 15:41:11.456401 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000297282 (* 0.0909091 = 2.70256e-05 loss)
I0430 15:41:11.456429 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.0001546 (* 0.0909091 = 1.40545e-05 loss)
I0430 15:41:11.456455 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000110072 (* 0.0909091 = 1.00065e-05 loss)
I0430 15:41:11.456485 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 5.5476e-05 (* 0.0909091 = 5.04327e-06 loss)
I0430 15:41:11.456509 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.25
I0430 15:41:11.456532 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.25
I0430 15:41:11.456569 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.280264
I0430 15:41:11.456593 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.266302
I0430 15:41:11.456615 15443 sgd_solver.cpp:106] Iteration 8000, lr = 0.001
I0430 15:44:31.563652 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 47.1435 > 30) by scale factor 0.636355
I0430 15:45:03.551497 15443 solver.cpp:229] Iteration 8500, loss = 3.61845
I0430 15:45:03.551654 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.477612
I0430 15:45:03.551684 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 1
I0430 15:45:03.551707 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 15:45:03.551729 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0430 15:45:03.551751 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 15:45:03.551774 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 15:45:03.551796 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0430 15:45:03.551820 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0430 15:45:03.551841 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 15:45:03.551864 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 15:45:03.551885 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0430 15:45:03.551905 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 15:45:03.551928 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.75
I0430 15:45:03.551954 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 15:45:03.551976 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 15:45:03.552000 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0430 15:45:03.552021 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0430 15:45:03.552044 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:45:03.552067 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:45:03.552089 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:45:03.552110 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:45:03.552132 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:45:03.552155 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:45:03.552175 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.784091
I0430 15:45:03.552197 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.671642
I0430 15:45:03.552225 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.7994 (* 0.3 = 0.539819 loss)
I0430 15:45:03.552253 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.725077 (* 0.3 = 0.217523 loss)
I0430 15:45:03.552281 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.256788 (* 0.0272727 = 0.0070033 loss)
I0430 15:45:03.552309 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.14416 (* 0.0272727 = 0.0312044 loss)
I0430 15:45:03.552340 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.53482 (* 0.0272727 = 0.0418588 loss)
I0430 15:45:03.552366 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.04582 (* 0.0272727 = 0.0557951 loss)
I0430 15:45:03.552393 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.72475 (* 0.0272727 = 0.0470386 loss)
I0430 15:45:03.552420 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.45626 (* 0.0272727 = 0.0397163 loss)
I0430 15:45:03.552446 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.56499 (* 0.0272727 = 0.0426816 loss)
I0430 15:45:03.552474 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.828292 (* 0.0272727 = 0.0225898 loss)
I0430 15:45:03.552500 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.697989 (* 0.0272727 = 0.0190361 loss)
I0430 15:45:03.552527 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 1.03946 (* 0.0272727 = 0.028349 loss)
I0430 15:45:03.552553 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 1.27494 (* 0.0272727 = 0.034771 loss)
I0430 15:45:03.552580 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.565777 (* 0.0272727 = 0.0154303 loss)
I0430 15:45:03.552634 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.565632 (* 0.0272727 = 0.0154263 loss)
I0430 15:45:03.552667 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.438221 (* 0.0272727 = 0.0119515 loss)
I0430 15:45:03.552695 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.58735 (* 0.0272727 = 0.0160186 loss)
I0430 15:45:03.552726 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.909613 (* 0.0272727 = 0.0248076 loss)
I0430 15:45:03.552755 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00296266 (* 0.0272727 = 8.07998e-05 loss)
I0430 15:45:03.552783 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000436526 (* 0.0272727 = 1.19053e-05 loss)
I0430 15:45:03.552810 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000126316 (* 0.0272727 = 3.44499e-06 loss)
I0430 15:45:03.552839 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.05353e-05 (* 0.0272727 = 5.60055e-07 loss)
I0430 15:45:03.552866 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 3.3379e-06 (* 0.0272727 = 9.10336e-08 loss)
I0430 15:45:03.552893 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.93715e-07 (* 0.0272727 = 5.28314e-09 loss)
I0430 15:45:03.552917 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.597015
I0430 15:45:03.552939 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0430 15:45:03.552963 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 15:45:03.552985 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0430 15:45:03.553007 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 15:45:03.553030 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 15:45:03.553051 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.375
I0430 15:45:03.553072 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 15:45:03.553094 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0430 15:45:03.553117 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0430 15:45:03.553138 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0430 15:45:03.553160 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0430 15:45:03.553182 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.75
I0430 15:45:03.553203 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 15:45:03.553225 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 15:45:03.553247 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 15:45:03.553269 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0430 15:45:03.553292 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:45:03.553313 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:45:03.553334 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:45:03.553356 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:45:03.553382 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:45:03.553406 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:45:03.553427 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.806818
I0430 15:45:03.553449 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.80597
I0430 15:45:03.553475 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.43154 (* 0.3 = 0.429461 loss)
I0430 15:45:03.553503 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.619757 (* 0.3 = 0.185927 loss)
I0430 15:45:03.553529 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.178198 (* 0.0272727 = 0.00485993 loss)
I0430 15:45:03.553556 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.761014 (* 0.0272727 = 0.0207549 loss)
I0430 15:45:03.553602 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.29341 (* 0.0272727 = 0.0352748 loss)
I0430 15:45:03.553632 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.54433 (* 0.0272727 = 0.0421181 loss)
I0430 15:45:03.553658 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.56808 (* 0.0272727 = 0.0427658 loss)
I0430 15:45:03.553684 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.36123 (* 0.0272727 = 0.0371244 loss)
I0430 15:45:03.553714 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.21291 (* 0.0272727 = 0.0330795 loss)
I0430 15:45:03.553741 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.17224 (* 0.0272727 = 0.0319702 loss)
I0430 15:45:03.553768 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.75885 (* 0.0272727 = 0.0206959 loss)
I0430 15:45:03.553797 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.929169 (* 0.0272727 = 0.025341 loss)
I0430 15:45:03.553823 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.52061 (* 0.0272727 = 0.0414713 loss)
I0430 15:45:03.553849 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.632286 (* 0.0272727 = 0.0172442 loss)
I0430 15:45:03.553875 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.56265 (* 0.0272727 = 0.015345 loss)
I0430 15:45:03.553902 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.373805 (* 0.0272727 = 0.0101947 loss)
I0430 15:45:03.553927 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.370943 (* 0.0272727 = 0.0101166 loss)
I0430 15:45:03.553953 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.684375 (* 0.0272727 = 0.0186648 loss)
I0430 15:45:03.553980 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0128434 (* 0.0272727 = 0.000350275 loss)
I0430 15:45:03.554005 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00240105 (* 0.0272727 = 6.54831e-05 loss)
I0430 15:45:03.554033 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000780446 (* 0.0272727 = 2.12849e-05 loss)
I0430 15:45:03.554059 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000526528 (* 0.0272727 = 1.43599e-05 loss)
I0430 15:45:03.554085 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 8.73907e-05 (* 0.0272727 = 2.38338e-06 loss)
I0430 15:45:03.554112 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 4.12441e-05 (* 0.0272727 = 1.12484e-06 loss)
I0430 15:45:03.554136 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.791045
I0430 15:45:03.554157 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 15:45:03.554178 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0430 15:45:03.554200 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0430 15:45:03.554221 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0430 15:45:03.554242 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 15:45:03.554265 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.5
I0430 15:45:03.554288 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.5
I0430 15:45:03.554309 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 15:45:03.554332 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 15:45:03.554353 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 15:45:03.554376 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 15:45:03.554396 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 15:45:03.554421 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 15:45:03.554445 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 15:45:03.554466 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 15:45:03.554488 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0430 15:45:03.554528 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:45:03.554551 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:45:03.554572 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:45:03.554595 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:45:03.554616 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:45:03.554637 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:45:03.554659 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.909091
I0430 15:45:03.554682 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.895522
I0430 15:45:03.554708 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.820049 (* 1 = 0.820049 loss)
I0430 15:45:03.554734 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.331452 (* 1 = 0.331452 loss)
I0430 15:45:03.554766 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0936699 (* 0.0909091 = 0.00851545 loss)
I0430 15:45:03.554795 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.123255 (* 0.0909091 = 0.011205 loss)
I0430 15:45:03.554823 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.0582415 (* 0.0909091 = 0.00529469 loss)
I0430 15:45:03.554852 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.326473 (* 0.0909091 = 0.0296793 loss)
I0430 15:45:03.554877 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.693784 (* 0.0909091 = 0.0630713 loss)
I0430 15:45:03.554905 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.832349 (* 0.0909091 = 0.0756681 loss)
I0430 15:45:03.554926 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.38577 (* 0.0909091 = 0.125979 loss)
I0430 15:45:03.554957 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.581465 (* 0.0909091 = 0.0528605 loss)
I0430 15:45:03.554985 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.467197 (* 0.0909091 = 0.0424725 loss)
I0430 15:45:03.555011 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.580539 (* 0.0909091 = 0.0527763 loss)
I0430 15:45:03.555037 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 1.18801 (* 0.0909091 = 0.108001 loss)
I0430 15:45:03.555063 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.399047 (* 0.0909091 = 0.036277 loss)
I0430 15:45:03.555090 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.553824 (* 0.0909091 = 0.0503476 loss)
I0430 15:45:03.555116 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.407362 (* 0.0909091 = 0.0370329 loss)
I0430 15:45:03.555143 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.589827 (* 0.0909091 = 0.0536207 loss)
I0430 15:45:03.555169 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.773249 (* 0.0909091 = 0.0702953 loss)
I0430 15:45:03.555197 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00302336 (* 0.0909091 = 0.00027485 loss)
I0430 15:45:03.555222 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00115684 (* 0.0909091 = 0.000105167 loss)
I0430 15:45:03.555249 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000349994 (* 0.0909091 = 3.18176e-05 loss)
I0430 15:45:03.555274 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000172053 (* 0.0909091 = 1.56411e-05 loss)
I0430 15:45:03.555300 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.3367e-05 (* 0.0909091 = 1.21518e-06 loss)
I0430 15:45:03.555328 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.28151e-06 (* 0.0909091 = 1.16501e-07 loss)
I0430 15:45:03.555351 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 15:45:03.555372 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 15:45:03.555411 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.377183
I0430 15:45:03.555434 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.258792
I0430 15:45:03.555457 15443 sgd_solver.cpp:106] Iteration 8500, lr = 0.001
I0430 15:48:14.149716 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 39.7418 > 30) by scale factor 0.754872
I0430 15:48:55.433557 15443 solver.cpp:229] Iteration 9000, loss = 3.73224
I0430 15:48:55.433723 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.326087
I0430 15:48:55.433749 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 15:48:55.433769 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 15:48:55.433787 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0430 15:48:55.433805 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0430 15:48:55.433823 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0430 15:48:55.433840 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0430 15:48:55.433858 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 15:48:55.433876 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 15:48:55.433895 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0430 15:48:55.433913 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 15:48:55.433930 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 15:48:55.433948 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 15:48:55.433964 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 15:48:55.433980 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 15:48:55.433997 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 15:48:55.434015 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 15:48:55.434031 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:48:55.434048 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:48:55.434065 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:48:55.434082 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:48:55.434099 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:48:55.434115 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:48:55.434134 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.801136
I0430 15:48:55.434150 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.673913
I0430 15:48:55.434173 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.10269 (* 0.3 = 0.630808 loss)
I0430 15:48:55.434195 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.640493 (* 0.3 = 0.192148 loss)
I0430 15:48:55.434216 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.875445 (* 0.0272727 = 0.0238758 loss)
I0430 15:48:55.434237 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.02075 (* 0.0272727 = 0.0278385 loss)
I0430 15:48:55.434257 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.40297 (* 0.0272727 = 0.0382627 loss)
I0430 15:48:55.434278 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.88012 (* 0.0272727 = 0.0512761 loss)
I0430 15:48:55.434298 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.60568 (* 0.0272727 = 0.071064 loss)
I0430 15:48:55.434319 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.8301 (* 0.0272727 = 0.0499119 loss)
I0430 15:48:55.434339 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.58715 (* 0.0272727 = 0.043286 loss)
I0430 15:48:55.434360 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.12622 (* 0.0272727 = 0.0307152 loss)
I0430 15:48:55.434386 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0538998 (* 0.0272727 = 0.00146999 loss)
I0430 15:48:55.434407 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00956184 (* 0.0272727 = 0.000260777 loss)
I0430 15:48:55.434428 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.000594333 (* 0.0272727 = 1.62091e-05 loss)
I0430 15:48:55.434449 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.000479311 (* 0.0272727 = 1.30721e-05 loss)
I0430 15:48:55.434494 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.000323377 (* 0.0272727 = 8.81937e-06 loss)
I0430 15:48:55.434516 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.000487212 (* 0.0272727 = 1.32876e-05 loss)
I0430 15:48:55.434537 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.000284778 (* 0.0272727 = 7.76668e-06 loss)
I0430 15:48:55.434558 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000174726 (* 0.0272727 = 4.76526e-06 loss)
I0430 15:48:55.434579 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000259072 (* 0.0272727 = 7.06559e-06 loss)
I0430 15:48:55.434602 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000120385 (* 0.0272727 = 3.28322e-06 loss)
I0430 15:48:55.434623 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000339626 (* 0.0272727 = 9.26254e-06 loss)
I0430 15:48:55.434643 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00055578 (* 0.0272727 = 1.51576e-05 loss)
I0430 15:48:55.434664 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000298857 (* 0.0272727 = 8.15065e-06 loss)
I0430 15:48:55.434684 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000413378 (* 0.0272727 = 1.12739e-05 loss)
I0430 15:48:55.434702 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.543478
I0430 15:48:55.434723 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.5
I0430 15:48:55.434741 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 15:48:55.434758 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.875
I0430 15:48:55.434777 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 15:48:55.434792 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 15:48:55.434810 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 15:48:55.434828 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 15:48:55.434844 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0430 15:48:55.434861 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0430 15:48:55.434878 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 15:48:55.434895 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 15:48:55.434911 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 15:48:55.434928 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 15:48:55.434947 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 15:48:55.434963 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 15:48:55.434980 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 15:48:55.434996 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:48:55.435014 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:48:55.435030 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:48:55.435050 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:48:55.435067 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:48:55.435084 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:48:55.435101 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.852273
I0430 15:48:55.435118 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.73913
I0430 15:48:55.435138 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.82695 (* 0.3 = 0.548086 loss)
I0430 15:48:55.435159 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.556585 (* 0.3 = 0.166976 loss)
I0430 15:48:55.435180 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.16072 (* 0.0272727 = 0.031656 loss)
I0430 15:48:55.435201 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.729526 (* 0.0272727 = 0.0198962 loss)
I0430 15:48:55.435236 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.741443 (* 0.0272727 = 0.0202212 loss)
I0430 15:48:55.435259 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.39247 (* 0.0272727 = 0.0379764 loss)
I0430 15:48:55.435281 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.31162 (* 0.0272727 = 0.0630441 loss)
I0430 15:48:55.435300 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.76253 (* 0.0272727 = 0.048069 loss)
I0430 15:48:55.435322 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.90476 (* 0.0272727 = 0.051948 loss)
I0430 15:48:55.435343 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 2.1354 (* 0.0272727 = 0.0582381 loss)
I0430 15:48:55.435364 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0451795 (* 0.0272727 = 0.00123217 loss)
I0430 15:48:55.435384 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00666842 (* 0.0272727 = 0.000181866 loss)
I0430 15:48:55.435405 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.000713454 (* 0.0272727 = 1.94578e-05 loss)
I0430 15:48:55.435441 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.000307393 (* 0.0272727 = 8.38343e-06 loss)
I0430 15:48:55.435464 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.000120164 (* 0.0272727 = 3.27719e-06 loss)
I0430 15:48:55.435489 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000116567 (* 0.0272727 = 3.1791e-06 loss)
I0430 15:48:55.435511 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 6.28682e-05 (* 0.0272727 = 1.71459e-06 loss)
I0430 15:48:55.435533 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 3.75321e-05 (* 0.0272727 = 1.0236e-06 loss)
I0430 15:48:55.435552 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 2.93208e-05 (* 0.0272727 = 7.9966e-07 loss)
I0430 15:48:55.435573 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 2.09973e-05 (* 0.0272727 = 5.72655e-07 loss)
I0430 15:48:55.435593 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 4.27583e-05 (* 0.0272727 = 1.16614e-06 loss)
I0430 15:48:55.435614 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 4.90863e-05 (* 0.0272727 = 1.33872e-06 loss)
I0430 15:48:55.435636 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 6.53415e-05 (* 0.0272727 = 1.78204e-06 loss)
I0430 15:48:55.435655 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 6.98529e-05 (* 0.0272727 = 1.90508e-06 loss)
I0430 15:48:55.435672 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.695652
I0430 15:48:55.435690 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 15:48:55.435708 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 15:48:55.435725 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0430 15:48:55.435744 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0430 15:48:55.435760 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 15:48:55.435781 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 15:48:55.435798 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 15:48:55.435816 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 15:48:55.435832 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 15:48:55.435849 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 15:48:55.435866 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 15:48:55.435883 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 15:48:55.435900 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 15:48:55.435917 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 15:48:55.435935 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 15:48:55.435966 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 15:48:55.435983 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:48:55.436002 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:48:55.436019 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:48:55.436036 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:48:55.436053 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:48:55.436069 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:48:55.436086 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.909091
I0430 15:48:55.436103 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.804348
I0430 15:48:55.436123 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.38556 (* 1 = 1.38556 loss)
I0430 15:48:55.436144 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.395093 (* 1 = 0.395093 loss)
I0430 15:48:55.436166 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.716795 (* 0.0909091 = 0.0651632 loss)
I0430 15:48:55.436185 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.371878 (* 0.0909091 = 0.0338071 loss)
I0430 15:48:55.436206 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.441878 (* 0.0909091 = 0.0401708 loss)
I0430 15:48:55.436226 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.03488 (* 0.0909091 = 0.09408 loss)
I0430 15:48:55.436247 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.33274 (* 0.0909091 = 0.121158 loss)
I0430 15:48:55.436267 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.27512 (* 0.0909091 = 0.11592 loss)
I0430 15:48:55.436288 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.29718 (* 0.0909091 = 0.117925 loss)
I0430 15:48:55.436308 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.26009 (* 0.0909091 = 0.114554 loss)
I0430 15:48:55.436328 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.0556376 (* 0.0909091 = 0.00505796 loss)
I0430 15:48:55.436349 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00753638 (* 0.0909091 = 0.000685126 loss)
I0430 15:48:55.436370 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00160068 (* 0.0909091 = 0.000145517 loss)
I0430 15:48:55.436391 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.000744615 (* 0.0909091 = 6.76923e-05 loss)
I0430 15:48:55.436413 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000585481 (* 0.0909091 = 5.32256e-05 loss)
I0430 15:48:55.436434 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000397892 (* 0.0909091 = 3.6172e-05 loss)
I0430 15:48:55.436453 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00034144 (* 0.0909091 = 3.104e-05 loss)
I0430 15:48:55.436475 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000281412 (* 0.0909091 = 2.55829e-05 loss)
I0430 15:48:55.436499 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000228778 (* 0.0909091 = 2.0798e-05 loss)
I0430 15:48:55.436520 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000155759 (* 0.0909091 = 1.41599e-05 loss)
I0430 15:48:55.436542 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000163668 (* 0.0909091 = 1.4879e-05 loss)
I0430 15:48:55.436563 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000144495 (* 0.0909091 = 1.31359e-05 loss)
I0430 15:48:55.436583 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000142884 (* 0.0909091 = 1.29895e-05 loss)
I0430 15:48:55.436604 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000150412 (* 0.0909091 = 1.36738e-05 loss)
I0430 15:48:55.436622 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 15:48:55.436640 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 15:48:55.436669 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.439997
I0430 15:48:55.436687 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.392485
I0430 15:48:55.436705 15443 sgd_solver.cpp:106] Iteration 9000, lr = 0.001
I0430 15:52:47.502504 15443 solver.cpp:229] Iteration 9500, loss = 3.61823
I0430 15:52:47.502728 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.385965
I0430 15:52:47.502760 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 15:52:47.502784 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 15:52:47.502806 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0430 15:52:47.502828 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0430 15:52:47.502851 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0430 15:52:47.502871 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0430 15:52:47.502893 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 15:52:47.502917 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 15:52:47.502939 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 15:52:47.502960 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 15:52:47.502981 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 15:52:47.503005 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 15:52:47.503029 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 15:52:47.503052 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 15:52:47.503075 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 15:52:47.503098 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 15:52:47.503120 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:52:47.503142 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:52:47.503165 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:52:47.503187 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:52:47.503209 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:52:47.503232 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:52:47.503253 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.778409
I0430 15:52:47.503276 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.719298
I0430 15:52:47.503305 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.81121 (* 0.3 = 0.543362 loss)
I0430 15:52:47.503337 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.684191 (* 0.3 = 0.205257 loss)
I0430 15:52:47.503366 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.697308 (* 0.0272727 = 0.0190175 loss)
I0430 15:52:47.503393 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.975087 (* 0.0272727 = 0.0265933 loss)
I0430 15:52:47.503422 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.6939 (* 0.0272727 = 0.0461973 loss)
I0430 15:52:47.503448 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.07202 (* 0.0272727 = 0.0565096 loss)
I0430 15:52:47.503497 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.12953 (* 0.0272727 = 0.058078 loss)
I0430 15:52:47.503530 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.45594 (* 0.0272727 = 0.0397075 loss)
I0430 15:52:47.503557 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.42141 (* 0.0272727 = 0.0387658 loss)
I0430 15:52:47.503584 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.667327 (* 0.0272727 = 0.0181998 loss)
I0430 15:52:47.503613 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.357058 (* 0.0272727 = 0.00973795 loss)
I0430 15:52:47.503639 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.295226 (* 0.0272727 = 0.00805163 loss)
I0430 15:52:47.503669 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.466832 (* 0.0272727 = 0.0127318 loss)
I0430 15:52:47.503695 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.396453 (* 0.0272727 = 0.0108124 loss)
I0430 15:52:47.503756 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.435791 (* 0.0272727 = 0.0118852 loss)
I0430 15:52:47.503787 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.472925 (* 0.0272727 = 0.012898 loss)
I0430 15:52:47.503816 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0500709 (* 0.0272727 = 0.00136557 loss)
I0430 15:52:47.503845 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0335442 (* 0.0272727 = 0.000914841 loss)
I0430 15:52:47.503873 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0142842 (* 0.0272727 = 0.000389568 loss)
I0430 15:52:47.503901 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0118638 (* 0.0272727 = 0.000323559 loss)
I0430 15:52:47.503927 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00391372 (* 0.0272727 = 0.000106738 loss)
I0430 15:52:47.503954 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00111459 (* 0.0272727 = 3.03978e-05 loss)
I0430 15:52:47.503983 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000191843 (* 0.0272727 = 5.23207e-06 loss)
I0430 15:52:47.504010 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 5.68534e-05 (* 0.0272727 = 1.55055e-06 loss)
I0430 15:52:47.504034 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.508772
I0430 15:52:47.504056 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0430 15:52:47.504079 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 15:52:47.504102 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 15:52:47.504125 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0430 15:52:47.504148 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0430 15:52:47.504170 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.25
I0430 15:52:47.504191 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 15:52:47.504215 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 15:52:47.504237 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0430 15:52:47.504258 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 15:52:47.504281 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 15:52:47.504303 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 15:52:47.504325 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 15:52:47.504348 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 15:52:47.504374 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 15:52:47.504398 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 15:52:47.504420 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:52:47.504441 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:52:47.504463 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:52:47.504487 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:52:47.504508 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:52:47.504530 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:52:47.504552 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.829545
I0430 15:52:47.504575 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.754386
I0430 15:52:47.504602 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.5398 (* 0.3 = 0.461939 loss)
I0430 15:52:47.504629 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.573602 (* 0.3 = 0.172081 loss)
I0430 15:52:47.504657 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.0966796 (* 0.0272727 = 0.00263672 loss)
I0430 15:52:47.504684 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.520129 (* 0.0272727 = 0.0141853 loss)
I0430 15:52:47.504729 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.70296 (* 0.0272727 = 0.0464444 loss)
I0430 15:52:47.504757 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.72373 (* 0.0272727 = 0.0470108 loss)
I0430 15:52:47.504786 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.84036 (* 0.0272727 = 0.0501916 loss)
I0430 15:52:47.504813 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.6235 (* 0.0272727 = 0.0442773 loss)
I0430 15:52:47.504842 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.22805 (* 0.0272727 = 0.0334923 loss)
I0430 15:52:47.504866 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.589139 (* 0.0272727 = 0.0160674 loss)
I0430 15:52:47.504892 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.33517 (* 0.0272727 = 0.00914099 loss)
I0430 15:52:47.504920 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.452505 (* 0.0272727 = 0.012341 loss)
I0430 15:52:47.504945 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.372389 (* 0.0272727 = 0.0101561 loss)
I0430 15:52:47.504971 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.252351 (* 0.0272727 = 0.0068823 loss)
I0430 15:52:47.504997 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.473056 (* 0.0272727 = 0.0129015 loss)
I0430 15:52:47.505023 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.555944 (* 0.0272727 = 0.0151621 loss)
I0430 15:52:47.505049 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0426138 (* 0.0272727 = 0.00116219 loss)
I0430 15:52:47.505076 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0126939 (* 0.0272727 = 0.000346197 loss)
I0430 15:52:47.505103 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00651897 (* 0.0272727 = 0.00017779 loss)
I0430 15:52:47.505128 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00358479 (* 0.0272727 = 9.77669e-05 loss)
I0430 15:52:47.505156 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00273373 (* 0.0272727 = 7.45562e-05 loss)
I0430 15:52:47.505182 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000277847 (* 0.0272727 = 7.57764e-06 loss)
I0430 15:52:47.505210 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.0003929 (* 0.0272727 = 1.07155e-05 loss)
I0430 15:52:47.505236 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 6.60433e-05 (* 0.0272727 = 1.80118e-06 loss)
I0430 15:52:47.505260 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.701754
I0430 15:52:47.505282 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 15:52:47.505303 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 15:52:47.505326 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 15:52:47.505348 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 15:52:47.505369 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0430 15:52:47.505391 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 15:52:47.505412 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0430 15:52:47.505437 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 15:52:47.505460 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 15:52:47.505482 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 15:52:47.505504 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 15:52:47.505524 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 15:52:47.505547 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 15:52:47.505569 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 15:52:47.505590 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 15:52:47.505626 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 15:52:47.505651 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:52:47.505673 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:52:47.505694 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:52:47.505717 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:52:47.505738 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:52:47.505759 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:52:47.505781 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.892045
I0430 15:52:47.505805 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.807018
I0430 15:52:47.505831 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.072 (* 1 = 1.072 loss)
I0430 15:52:47.505867 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.389339 (* 1 = 0.389339 loss)
I0430 15:52:47.505890 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.135449 (* 0.0909091 = 0.0123136 loss)
I0430 15:52:47.505918 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.481876 (* 0.0909091 = 0.0438069 loss)
I0430 15:52:47.505946 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.684021 (* 0.0909091 = 0.0621838 loss)
I0430 15:52:47.505972 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.11915 (* 0.0909091 = 0.101741 loss)
I0430 15:52:47.505998 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.956218 (* 0.0909091 = 0.0869289 loss)
I0430 15:52:47.506026 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.06501 (* 0.0909091 = 0.0968193 loss)
I0430 15:52:47.506052 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.1261 (* 0.0909091 = 0.102373 loss)
I0430 15:52:47.506078 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.42787 (* 0.0909091 = 0.0388973 loss)
I0430 15:52:47.506104 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.200601 (* 0.0909091 = 0.0182365 loss)
I0430 15:52:47.506130 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.267356 (* 0.0909091 = 0.0243051 loss)
I0430 15:52:47.506156 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.329818 (* 0.0909091 = 0.0299834 loss)
I0430 15:52:47.506182 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.342012 (* 0.0909091 = 0.031092 loss)
I0430 15:52:47.506208 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.345895 (* 0.0909091 = 0.031445 loss)
I0430 15:52:47.506234 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.495059 (* 0.0909091 = 0.0450053 loss)
I0430 15:52:47.506263 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.020554 (* 0.0909091 = 0.00186855 loss)
I0430 15:52:47.506289 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00914053 (* 0.0909091 = 0.000830957 loss)
I0430 15:52:47.506315 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00510858 (* 0.0909091 = 0.000464417 loss)
I0430 15:52:47.506343 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00326427 (* 0.0909091 = 0.000296751 loss)
I0430 15:52:47.506369 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00148339 (* 0.0909091 = 0.000134854 loss)
I0430 15:52:47.506394 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00116105 (* 0.0909091 = 0.00010555 loss)
I0430 15:52:47.506422 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000670017 (* 0.0909091 = 6.09106e-05 loss)
I0430 15:52:47.506448 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000194049 (* 0.0909091 = 1.76408e-05 loss)
I0430 15:52:47.506474 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 15:52:47.506497 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 15:52:47.506536 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.331881
I0430 15:52:47.506561 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.173679
I0430 15:52:47.506583 15443 sgd_solver.cpp:106] Iteration 9500, lr = 0.001
I0430 15:53:23.155012 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.8729 > 30) by scale factor 0.971726
I0430 15:54:02.134588 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 46.9264 > 30) by scale factor 0.639299
I0430 15:55:15.377645 15443 solver.cpp:456] Snapshotting to binary proto file /mnt/snapshots/mixed_lstm14_bn_iter_10000.caffemodel
I0430 15:55:19.064538 15443 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /mnt/snapshots/mixed_lstm14_bn_iter_10000.solverstate
I0430 15:55:20.222025 15443 solver.cpp:338] Iteration 10000, Testing net (#0)
I0430 15:56:01.525923 15443 solver.cpp:393] Test loss: 2.3258
I0430 15:56:01.526036 15443 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.635637
I0430 15:56:01.526053 15443 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.846
I0430 15:56:01.526067 15443 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.711
I0430 15:56:01.526078 15443 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.571
I0430 15:56:01.526089 15443 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.587
I0430 15:56:01.526103 15443 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.553
I0430 15:56:01.526114 15443 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.665
I0430 15:56:01.526125 15443 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.825
I0430 15:56:01.526137 15443 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.901
I0430 15:56:01.526149 15443 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.983
I0430 15:56:01.526160 15443 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.994
I0430 15:56:01.526172 15443 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.997
I0430 15:56:01.526183 15443 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 1
I0430 15:56:01.526196 15443 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 1
I0430 15:56:01.526206 15443 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 1
I0430 15:56:01.526217 15443 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 1
I0430 15:56:01.526229 15443 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 1
I0430 15:56:01.526240 15443 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 1
I0430 15:56:01.526252 15443 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 1
I0430 15:56:01.526262 15443 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 1
I0430 15:56:01.526273 15443 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 1
I0430 15:56:01.526284 15443 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0430 15:56:01.526296 15443 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0430 15:56:01.526307 15443 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.892548
I0430 15:56:01.526321 15443 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.859342
I0430 15:56:01.526337 15443 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.16902 (* 0.3 = 0.350706 loss)
I0430 15:56:01.526352 15443 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.353016 (* 0.3 = 0.105905 loss)
I0430 15:56:01.526366 15443 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.581037 (* 0.0272727 = 0.0158465 loss)
I0430 15:56:01.526381 15443 solver.cpp:406]     Test net output #28: loss1/loss02 = 0.990851 (* 0.0272727 = 0.0270232 loss)
I0430 15:56:01.526393 15443 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.41271 (* 0.0272727 = 0.0385286 loss)
I0430 15:56:01.526407 15443 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.36621 (* 0.0272727 = 0.0372604 loss)
I0430 15:56:01.526420 15443 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.35847 (* 0.0272727 = 0.0370492 loss)
I0430 15:56:01.526433 15443 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.02094 (* 0.0272727 = 0.0278438 loss)
I0430 15:56:01.526448 15443 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.570978 (* 0.0272727 = 0.0155721 loss)
I0430 15:56:01.526461 15443 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.294662 (* 0.0272727 = 0.00803623 loss)
I0430 15:56:01.526474 15443 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.0775073 (* 0.0272727 = 0.00211384 loss)
I0430 15:56:01.526489 15443 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.0331173 (* 0.0272727 = 0.0009032 loss)
I0430 15:56:01.526504 15443 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.0124608 (* 0.0272727 = 0.000339841 loss)
I0430 15:56:01.526517 15443 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.0073339 (* 0.0272727 = 0.000200016 loss)
I0430 15:56:01.526530 15443 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.00468161 (* 0.0272727 = 0.00012768 loss)
I0430 15:56:01.526564 15443 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.00293609 (* 0.0272727 = 8.00751e-05 loss)
I0430 15:56:01.526581 15443 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.00181675 (* 0.0272727 = 4.95478e-05 loss)
I0430 15:56:01.526593 15443 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.00111064 (* 0.0272727 = 3.02902e-05 loss)
I0430 15:56:01.526607 15443 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.000749478 (* 0.0272727 = 2.04403e-05 loss)
I0430 15:56:01.526621 15443 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.000468343 (* 0.0272727 = 1.2773e-05 loss)
I0430 15:56:01.526635 15443 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.00037623 (* 0.0272727 = 1.02608e-05 loss)
I0430 15:56:01.526648 15443 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.000265677 (* 0.0272727 = 7.24575e-06 loss)
I0430 15:56:01.526662 15443 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.000230687 (* 0.0272727 = 6.29148e-06 loss)
I0430 15:56:01.526675 15443 solver.cpp:406]     Test net output #48: loss1/loss22 = 0.000195535 (* 0.0272727 = 5.33277e-06 loss)
I0430 15:56:01.526687 15443 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.735227
I0430 15:56:01.526700 15443 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.883
I0430 15:56:01.526710 15443 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.827
I0430 15:56:01.526722 15443 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.706
I0430 15:56:01.526733 15443 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.666
I0430 15:56:01.526746 15443 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.628
I0430 15:56:01.526757 15443 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.7
I0430 15:56:01.526768 15443 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.852
I0430 15:56:01.526779 15443 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.91
I0430 15:56:01.526790 15443 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.984
I0430 15:56:01.526803 15443 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.993
I0430 15:56:01.526813 15443 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.998
I0430 15:56:01.526825 15443 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.999
I0430 15:56:01.526836 15443 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 1
I0430 15:56:01.526847 15443 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 1
I0430 15:56:01.526859 15443 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 1
I0430 15:56:01.526870 15443 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 1
I0430 15:56:01.526880 15443 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 1
I0430 15:56:01.526891 15443 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 1
I0430 15:56:01.526902 15443 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 1
I0430 15:56:01.526913 15443 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 1
I0430 15:56:01.526924 15443 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0430 15:56:01.526935 15443 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0430 15:56:01.526947 15443 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.921229
I0430 15:56:01.526957 15443 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.902621
I0430 15:56:01.526971 15443 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.88091 (* 0.3 = 0.264273 loss)
I0430 15:56:01.526984 15443 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.269077 (* 0.3 = 0.080723 loss)
I0430 15:56:01.526998 15443 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.49709 (* 0.0272727 = 0.013557 loss)
I0430 15:56:01.527011 15443 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.665332 (* 0.0272727 = 0.0181454 loss)
I0430 15:56:01.527035 15443 solver.cpp:406]     Test net output #78: loss2/loss03 = 0.998094 (* 0.0272727 = 0.0272207 loss)
I0430 15:56:01.527053 15443 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.1088 (* 0.0272727 = 0.03024 loss)
I0430 15:56:01.527068 15443 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.13663 (* 0.0272727 = 0.030999 loss)
I0430 15:56:01.527081 15443 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.897602 (* 0.0272727 = 0.02448 loss)
I0430 15:56:01.527096 15443 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.491872 (* 0.0272727 = 0.0134147 loss)
I0430 15:56:01.527109 15443 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.273217 (* 0.0272727 = 0.00745137 loss)
I0430 15:56:01.527118 15443 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.0718603 (* 0.0272727 = 0.00195983 loss)
I0430 15:56:01.527128 15443 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.0318129 (* 0.0272727 = 0.000867624 loss)
I0430 15:56:01.527143 15443 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.0138888 (* 0.0272727 = 0.000378785 loss)
I0430 15:56:01.527156 15443 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.00789502 (* 0.0272727 = 0.000215319 loss)
I0430 15:56:01.527170 15443 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.00476476 (* 0.0272727 = 0.000129948 loss)
I0430 15:56:01.527184 15443 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.00281319 (* 0.0272727 = 7.67233e-05 loss)
I0430 15:56:01.527197 15443 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.00151759 (* 0.0272727 = 4.13888e-05 loss)
I0430 15:56:01.527211 15443 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.000736217 (* 0.0272727 = 2.00786e-05 loss)
I0430 15:56:01.527225 15443 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.000361529 (* 0.0272727 = 9.85988e-06 loss)
I0430 15:56:01.527237 15443 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.000202994 (* 0.0272727 = 5.53619e-06 loss)
I0430 15:56:01.527251 15443 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.000154567 (* 0.0272727 = 4.21545e-06 loss)
I0430 15:56:01.527264 15443 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.000128208 (* 0.0272727 = 3.49658e-06 loss)
I0430 15:56:01.527278 15443 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.00012109 (* 0.0272727 = 3.30244e-06 loss)
I0430 15:56:01.527292 15443 solver.cpp:406]     Test net output #97: loss2/loss22 = 9.39096e-05 (* 0.0272727 = 2.56117e-06 loss)
I0430 15:56:01.527303 15443 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.849301
I0430 15:56:01.527314 15443 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.9
I0430 15:56:01.527326 15443 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.871
I0430 15:56:01.527338 15443 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.829
I0430 15:56:01.527348 15443 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.836
I0430 15:56:01.527359 15443 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.831
I0430 15:56:01.527372 15443 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.846
I0430 15:56:01.527384 15443 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.889
I0430 15:56:01.527395 15443 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.941
I0430 15:56:01.527406 15443 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.98
I0430 15:56:01.527417 15443 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.993
I0430 15:56:01.527428 15443 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.998
I0430 15:56:01.527441 15443 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.998
I0430 15:56:01.527451 15443 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.999
I0430 15:56:01.527462 15443 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.999
I0430 15:56:01.527488 15443 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 1
I0430 15:56:01.527500 15443 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 1
I0430 15:56:01.527523 15443 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 1
I0430 15:56:01.527535 15443 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 1
I0430 15:56:01.527547 15443 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 1
I0430 15:56:01.527559 15443 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 1
I0430 15:56:01.527570 15443 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0430 15:56:01.527580 15443 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0430 15:56:01.527591 15443 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.952364
I0430 15:56:01.527602 15443 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.926036
I0430 15:56:01.527616 15443 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.583413 (* 1 = 0.583413 loss)
I0430 15:56:01.527629 15443 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.185884 (* 1 = 0.185884 loss)
I0430 15:56:01.527643 15443 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.413378 (* 0.0909091 = 0.0375798 loss)
I0430 15:56:01.527657 15443 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.516931 (* 0.0909091 = 0.0469937 loss)
I0430 15:56:01.527670 15443 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.674732 (* 0.0909091 = 0.0613393 loss)
I0430 15:56:01.527683 15443 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.615631 (* 0.0909091 = 0.0559665 loss)
I0430 15:56:01.527696 15443 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.650981 (* 0.0909091 = 0.0591801 loss)
I0430 15:56:01.527710 15443 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.549636 (* 0.0909091 = 0.0499669 loss)
I0430 15:56:01.527724 15443 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.361557 (* 0.0909091 = 0.0328688 loss)
I0430 15:56:01.527736 15443 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.193721 (* 0.0909091 = 0.017611 loss)
I0430 15:56:01.527750 15443 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.0691073 (* 0.0909091 = 0.00628248 loss)
I0430 15:56:01.527763 15443 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.0377099 (* 0.0909091 = 0.00342817 loss)
I0430 15:56:01.527776 15443 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.0155443 (* 0.0909091 = 0.00141311 loss)
I0430 15:56:01.527791 15443 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.00965614 (* 0.0909091 = 0.000877831 loss)
I0430 15:56:01.527803 15443 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.00573053 (* 0.0909091 = 0.000520958 loss)
I0430 15:56:01.527817 15443 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.00327347 (* 0.0909091 = 0.000297588 loss)
I0430 15:56:01.527830 15443 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.00164805 (* 0.0909091 = 0.000149823 loss)
I0430 15:56:01.527844 15443 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.000699825 (* 0.0909091 = 6.36204e-05 loss)
I0430 15:56:01.527858 15443 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.000308756 (* 0.0909091 = 2.80687e-05 loss)
I0430 15:56:01.527871 15443 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.000143476 (* 0.0909091 = 1.30433e-05 loss)
I0430 15:56:01.527884 15443 solver.cpp:406]     Test net output #143: loss3/loss19 = 8.62983e-05 (* 0.0909091 = 7.8453e-06 loss)
I0430 15:56:01.527899 15443 solver.cpp:406]     Test net output #144: loss3/loss20 = 4.62907e-05 (* 0.0909091 = 4.20824e-06 loss)
I0430 15:56:01.527911 15443 solver.cpp:406]     Test net output #145: loss3/loss21 = 3.17133e-05 (* 0.0909091 = 2.88303e-06 loss)
I0430 15:56:01.527925 15443 solver.cpp:406]     Test net output #146: loss3/loss22 = 2.62154e-05 (* 0.0909091 = 2.38322e-06 loss)
I0430 15:56:01.527936 15443 solver.cpp:406]     Test net output #147: total_accuracy = 0.591
I0430 15:56:01.527948 15443 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.569
I0430 15:56:01.527959 15443 solver.cpp:406]     Test net output #149: total_confidence = 0.541168
I0430 15:56:01.527981 15443 solver.cpp:406]     Test net output #150: total_confidence_not_rec = 0.517791
I0430 15:56:01.527994 15443 solver.cpp:338] Iteration 10000, Testing net (#1)
I0430 15:56:42.658488 15443 solver.cpp:393] Test loss: 3.19793
I0430 15:56:42.658643 15443 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.590673
I0430 15:56:42.658663 15443 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.79
I0430 15:56:42.658675 15443 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.66
I0430 15:56:42.658687 15443 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.549
I0430 15:56:42.658699 15443 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.544
I0430 15:56:42.658710 15443 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.55
I0430 15:56:42.658722 15443 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.642
I0430 15:56:42.658735 15443 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.756
I0430 15:56:42.658746 15443 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.852
I0430 15:56:42.658757 15443 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.906
I0430 15:56:42.658769 15443 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.926
I0430 15:56:42.658782 15443 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.937
I0430 15:56:42.658792 15443 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.94
I0430 15:56:42.658804 15443 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.952
I0430 15:56:42.658815 15443 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 0.959
I0430 15:56:42.658828 15443 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 0.968
I0430 15:56:42.658839 15443 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 0.978
I0430 15:56:42.658851 15443 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 0.99
I0430 15:56:42.658862 15443 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 0.995
I0430 15:56:42.658874 15443 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 0.998
I0430 15:56:42.658885 15443 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 0.998
I0430 15:56:42.658897 15443 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 0.999
I0430 15:56:42.658908 15443 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0430 15:56:42.658921 15443 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.857684
I0430 15:56:42.658931 15443 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.819499
I0430 15:56:42.658947 15443 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.3373 (* 0.3 = 0.401189 loss)
I0430 15:56:42.658962 15443 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.473293 (* 0.3 = 0.141988 loss)
I0430 15:56:42.658977 15443 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.800795 (* 0.0272727 = 0.0218399 loss)
I0430 15:56:42.658998 15443 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.15737 (* 0.0272727 = 0.0315646 loss)
I0430 15:56:42.659013 15443 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.49655 (* 0.0272727 = 0.0408151 loss)
I0430 15:56:42.659025 15443 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.50181 (* 0.0272727 = 0.0409585 loss)
I0430 15:56:42.659039 15443 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.42621 (* 0.0272727 = 0.0388967 loss)
I0430 15:56:42.659052 15443 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.10619 (* 0.0272727 = 0.0301687 loss)
I0430 15:56:42.659065 15443 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.787513 (* 0.0272727 = 0.0214776 loss)
I0430 15:56:42.659102 15443 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.493254 (* 0.0272727 = 0.0134524 loss)
I0430 15:56:42.659121 15443 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.333799 (* 0.0272727 = 0.00910362 loss)
I0430 15:56:42.659135 15443 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.27221 (* 0.0272727 = 0.00742392 loss)
I0430 15:56:42.659148 15443 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.214502 (* 0.0272727 = 0.00585005 loss)
I0430 15:56:42.659162 15443 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.206033 (* 0.0272727 = 0.00561908 loss)
I0430 15:56:42.659195 15443 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.173475 (* 0.0272727 = 0.00473114 loss)
I0430 15:56:42.659210 15443 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.149217 (* 0.0272727 = 0.00406954 loss)
I0430 15:56:42.659224 15443 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.138693 (* 0.0272727 = 0.00378254 loss)
I0430 15:56:42.659238 15443 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.0878215 (* 0.0272727 = 0.00239513 loss)
I0430 15:56:42.659252 15443 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.043656 (* 0.0272727 = 0.00119062 loss)
I0430 15:56:42.659266 15443 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.0244657 (* 0.0272727 = 0.000667246 loss)
I0430 15:56:42.659279 15443 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.0125699 (* 0.0272727 = 0.000342816 loss)
I0430 15:56:42.659293 15443 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.0116891 (* 0.0272727 = 0.000318792 loss)
I0430 15:56:42.659307 15443 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.0103033 (* 0.0272727 = 0.000280998 loss)
I0430 15:56:42.659324 15443 solver.cpp:406]     Test net output #48: loss1/loss22 = 4.58739e-05 (* 0.0272727 = 1.25111e-06 loss)
I0430 15:56:42.659337 15443 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.681626
I0430 15:56:42.659348 15443 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.834
I0430 15:56:42.659360 15443 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.777
I0430 15:56:42.659371 15443 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.687
I0430 15:56:42.659382 15443 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.613
I0430 15:56:42.659394 15443 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.61
I0430 15:56:42.659405 15443 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.69
I0430 15:56:42.659416 15443 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.789
I0430 15:56:42.659427 15443 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.853
I0430 15:56:42.659438 15443 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.905
I0430 15:56:42.659449 15443 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.928
I0430 15:56:42.659461 15443 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.943
I0430 15:56:42.659487 15443 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.942
I0430 15:56:42.659499 15443 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.952
I0430 15:56:42.659512 15443 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.96
I0430 15:56:42.659524 15443 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.969
I0430 15:56:42.659536 15443 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 0.978
I0430 15:56:42.659548 15443 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 0.99
I0430 15:56:42.659559 15443 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 0.995
I0430 15:56:42.659570 15443 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 0.998
I0430 15:56:42.659582 15443 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 0.998
I0430 15:56:42.659595 15443 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 0.999
I0430 15:56:42.659605 15443 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0430 15:56:42.659616 15443 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.885047
I0430 15:56:42.659628 15443 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.863022
I0430 15:56:42.659642 15443 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 1.07455 (* 0.3 = 0.322365 loss)
I0430 15:56:42.659659 15443 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.388281 (* 0.3 = 0.116484 loss)
I0430 15:56:42.659673 15443 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.665621 (* 0.0272727 = 0.0181533 loss)
I0430 15:56:42.659687 15443 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.823481 (* 0.0272727 = 0.0224586 loss)
I0430 15:56:42.659713 15443 solver.cpp:406]     Test net output #78: loss2/loss03 = 1.1032 (* 0.0272727 = 0.0300872 loss)
I0430 15:56:42.659729 15443 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.25785 (* 0.0272727 = 0.0343049 loss)
I0430 15:56:42.659741 15443 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.22271 (* 0.0272727 = 0.0333466 loss)
I0430 15:56:42.659755 15443 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.978768 (* 0.0272727 = 0.0266937 loss)
I0430 15:56:42.659768 15443 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.692086 (* 0.0272727 = 0.0188751 loss)
I0430 15:56:42.659781 15443 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.484762 (* 0.0272727 = 0.0132208 loss)
I0430 15:56:42.659795 15443 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.335361 (* 0.0272727 = 0.00914622 loss)
I0430 15:56:42.659808 15443 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.274654 (* 0.0272727 = 0.00749058 loss)
I0430 15:56:42.659822 15443 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.216255 (* 0.0272727 = 0.00589787 loss)
I0430 15:56:42.659835 15443 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.202943 (* 0.0272727 = 0.0055348 loss)
I0430 15:56:42.659849 15443 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.173019 (* 0.0272727 = 0.00471869 loss)
I0430 15:56:42.659862 15443 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.147351 (* 0.0272727 = 0.00401867 loss)
I0430 15:56:42.659876 15443 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.139119 (* 0.0272727 = 0.00379415 loss)
I0430 15:56:42.659889 15443 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.0933198 (* 0.0272727 = 0.00254509 loss)
I0430 15:56:42.659903 15443 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.0478231 (* 0.0272727 = 0.00130427 loss)
I0430 15:56:42.659915 15443 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.0249602 (* 0.0272727 = 0.000680732 loss)
I0430 15:56:42.659929 15443 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.0137786 (* 0.0272727 = 0.000375781 loss)
I0430 15:56:42.659945 15443 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.0131676 (* 0.0272727 = 0.000359116 loss)
I0430 15:56:42.659968 15443 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.00848008 (* 0.0272727 = 0.000231275 loss)
I0430 15:56:42.659994 15443 solver.cpp:406]     Test net output #97: loss2/loss22 = 5.12809e-05 (* 0.0272727 = 1.39857e-06 loss)
I0430 15:56:42.660009 15443 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.78574
I0430 15:56:42.660020 15443 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.859
I0430 15:56:42.660032 15443 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.833
I0430 15:56:42.660043 15443 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.792
I0430 15:56:42.660054 15443 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.793
I0430 15:56:42.660065 15443 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.772
I0430 15:56:42.660076 15443 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.808
I0430 15:56:42.660087 15443 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.827
I0430 15:56:42.660099 15443 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.878
I0430 15:56:42.660110 15443 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.915
I0430 15:56:42.660121 15443 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.926
I0430 15:56:42.660133 15443 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.941
I0430 15:56:42.660145 15443 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.941
I0430 15:56:42.660156 15443 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.955
I0430 15:56:42.660166 15443 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.964
I0430 15:56:42.660177 15443 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.973
I0430 15:56:42.660188 15443 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 0.979
I0430 15:56:42.660212 15443 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 0.99
I0430 15:56:42.660225 15443 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 0.995
I0430 15:56:42.660236 15443 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 0.998
I0430 15:56:42.660248 15443 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 0.998
I0430 15:56:42.660259 15443 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 0.999
I0430 15:56:42.660271 15443 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0430 15:56:42.660282 15443 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.917182
I0430 15:56:42.660293 15443 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.891566
I0430 15:56:42.660307 15443 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.783224 (* 1 = 0.783224 loss)
I0430 15:56:42.660321 15443 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.300268 (* 1 = 0.300268 loss)
I0430 15:56:42.660334 15443 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.584378 (* 0.0909091 = 0.0531253 loss)
I0430 15:56:42.660348 15443 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.645182 (* 0.0909091 = 0.0586529 loss)
I0430 15:56:42.660362 15443 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.721489 (* 0.0909091 = 0.0655899 loss)
I0430 15:56:42.660378 15443 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.755104 (* 0.0909091 = 0.0686458 loss)
I0430 15:56:42.660392 15443 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.798566 (* 0.0909091 = 0.0725969 loss)
I0430 15:56:42.660405 15443 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.655941 (* 0.0909091 = 0.059631 loss)
I0430 15:56:42.660418 15443 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.575565 (* 0.0909091 = 0.0523241 loss)
I0430 15:56:42.660431 15443 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.400542 (* 0.0909091 = 0.0364129 loss)
I0430 15:56:42.660444 15443 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.29493 (* 0.0909091 = 0.0268118 loss)
I0430 15:56:42.660459 15443 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.255281 (* 0.0909091 = 0.0232074 loss)
I0430 15:56:42.660471 15443 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.199863 (* 0.0909091 = 0.0181694 loss)
I0430 15:56:42.660485 15443 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.186615 (* 0.0909091 = 0.016965 loss)
I0430 15:56:42.660498 15443 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.158949 (* 0.0909091 = 0.0144499 loss)
I0430 15:56:42.660511 15443 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.130888 (* 0.0909091 = 0.0118989 loss)
I0430 15:56:42.660524 15443 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.121841 (* 0.0909091 = 0.0110764 loss)
I0430 15:56:42.660538 15443 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.0737945 (* 0.0909091 = 0.00670859 loss)
I0430 15:56:42.660552 15443 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.0410538 (* 0.0909091 = 0.00373217 loss)
I0430 15:56:42.660565 15443 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.01939 (* 0.0909091 = 0.00176273 loss)
I0430 15:56:42.660578 15443 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.0104091 (* 0.0909091 = 0.000946283 loss)
I0430 15:56:42.660593 15443 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.0100683 (* 0.0909091 = 0.000915304 loss)
I0430 15:56:42.660606 15443 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.00654461 (* 0.0909091 = 0.000594964 loss)
I0430 15:56:42.660619 15443 solver.cpp:406]     Test net output #146: loss3/loss22 = 0.00010255 (* 0.0909091 = 9.3227e-06 loss)
I0430 15:56:42.660630 15443 solver.cpp:406]     Test net output #147: total_accuracy = 0.519
I0430 15:56:42.660642 15443 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.502
I0430 15:56:42.660653 15443 solver.cpp:406]     Test net output #149: total_confidence = 0.471937
I0430 15:56:42.660676 15443 solver.cpp:406]     Test net output #150: total_confidence_not_rec = 0.458286
I0430 15:56:42.842408 15443 solver.cpp:229] Iteration 10000, loss = 3.70528
I0430 15:56:42.842473 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.454545
I0430 15:56:42.842490 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 15:56:42.842504 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 15:56:42.842515 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0430 15:56:42.842527 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 15:56:42.842541 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0430 15:56:42.842555 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0430 15:56:42.842566 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0430 15:56:42.842577 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 15:56:42.842591 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 15:56:42.842602 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 15:56:42.842614 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0430 15:56:42.842627 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 15:56:42.842638 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 15:56:42.842650 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 15:56:42.842661 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 15:56:42.842674 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 15:56:42.842685 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:56:42.842696 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:56:42.842707 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:56:42.842720 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:56:42.842730 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:56:42.842742 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:56:42.842753 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.801136
I0430 15:56:42.842766 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.727273
I0430 15:56:42.842782 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.88793 (* 0.3 = 0.56638 loss)
I0430 15:56:42.842797 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.813465 (* 0.3 = 0.244039 loss)
I0430 15:56:42.842810 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 2.58952 (* 0.0272727 = 0.0706234 loss)
I0430 15:56:42.842824 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.53712 (* 0.0272727 = 0.0691942 loss)
I0430 15:56:42.842839 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.80864 (* 0.0272727 = 0.0493265 loss)
I0430 15:56:42.842852 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.87891 (* 0.0272727 = 0.0512429 loss)
I0430 15:56:42.842866 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.43981 (* 0.0272727 = 0.0392675 loss)
I0430 15:56:42.842880 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.52017 (* 0.0272727 = 0.0414591 loss)
I0430 15:56:42.842893 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.399 (* 0.0272727 = 0.0381546 loss)
I0430 15:56:42.842907 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.967871 (* 0.0272727 = 0.0263965 loss)
I0430 15:56:42.842921 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.560229 (* 0.0272727 = 0.015279 loss)
I0430 15:56:42.842934 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.890352 (* 0.0272727 = 0.0242823 loss)
I0430 15:56:42.842983 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.785818 (* 0.0272727 = 0.0214314 loss)
I0430 15:56:42.842998 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.445054 (* 0.0272727 = 0.0121378 loss)
I0430 15:56:42.843014 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.161953 (* 0.0272727 = 0.00441689 loss)
I0430 15:56:42.843027 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.114752 (* 0.0272727 = 0.0031296 loss)
I0430 15:56:42.843041 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0215919 (* 0.0272727 = 0.00058887 loss)
I0430 15:56:42.843055 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00391595 (* 0.0272727 = 0.000106799 loss)
I0430 15:56:42.843070 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000859993 (* 0.0272727 = 2.34543e-05 loss)
I0430 15:56:42.843085 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000371652 (* 0.0272727 = 1.0136e-05 loss)
I0430 15:56:42.843098 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000149147 (* 0.0272727 = 4.06765e-06 loss)
I0430 15:56:42.843112 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000156886 (* 0.0272727 = 4.27872e-06 loss)
I0430 15:56:42.843127 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 4.67833e-05 (* 0.0272727 = 1.27591e-06 loss)
I0430 15:56:42.843142 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.32478e-05 (* 0.0272727 = 3.61304e-07 loss)
I0430 15:56:42.843153 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.490909
I0430 15:56:42.843165 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.5
I0430 15:56:42.843178 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.375
I0430 15:56:42.843189 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0430 15:56:42.843200 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 15:56:42.843216 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0430 15:56:42.843227 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 15:56:42.843240 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 15:56:42.843250 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0430 15:56:42.843262 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 15:56:42.843274 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0430 15:56:42.843286 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0430 15:56:42.843297 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 15:56:42.843309 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 15:56:42.843322 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 15:56:42.843333 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 15:56:42.843343 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 15:56:42.843355 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:56:42.843366 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:56:42.843379 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:56:42.843389 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:56:42.843400 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:56:42.843411 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:56:42.843422 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.823864
I0430 15:56:42.843435 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.8
I0430 15:56:42.843448 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.66422 (* 0.3 = 0.499265 loss)
I0430 15:56:42.843462 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.66482 (* 0.3 = 0.199446 loss)
I0430 15:56:42.843511 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.41852 (* 0.0272727 = 0.038687 loss)
I0430 15:56:42.843526 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 2.81417 (* 0.0272727 = 0.0767501 loss)
I0430 15:56:42.843540 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.0323 (* 0.0272727 = 0.0554263 loss)
I0430 15:56:42.843554 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.82073 (* 0.0272727 = 0.0496563 loss)
I0430 15:56:42.843569 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.29225 (* 0.0272727 = 0.0352431 loss)
I0430 15:56:42.843582 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.52624 (* 0.0272727 = 0.0416248 loss)
I0430 15:56:42.843598 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.19276 (* 0.0272727 = 0.0325297 loss)
I0430 15:56:42.843613 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.916988 (* 0.0272727 = 0.0250088 loss)
I0430 15:56:42.843627 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.566391 (* 0.0272727 = 0.015447 loss)
I0430 15:56:42.843641 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.852305 (* 0.0272727 = 0.0232447 loss)
I0430 15:56:42.843654 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.731657 (* 0.0272727 = 0.0199543 loss)
I0430 15:56:42.843668 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.583997 (* 0.0272727 = 0.0159272 loss)
I0430 15:56:42.843683 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.175236 (* 0.0272727 = 0.00477916 loss)
I0430 15:56:42.843696 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0171975 (* 0.0272727 = 0.000469024 loss)
I0430 15:56:42.843710 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00612079 (* 0.0272727 = 0.000166931 loss)
I0430 15:56:42.843724 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000381349 (* 0.0272727 = 1.04004e-05 loss)
I0430 15:56:42.843739 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000114902 (* 0.0272727 = 3.13369e-06 loss)
I0430 15:56:42.843753 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 9.83539e-05 (* 0.0272727 = 2.68238e-06 loss)
I0430 15:56:42.843767 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 3.32108e-05 (* 0.0272727 = 9.05749e-07 loss)
I0430 15:56:42.843781 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 6.79e-05 (* 0.0272727 = 1.85182e-06 loss)
I0430 15:56:42.843796 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 4.54119e-05 (* 0.0272727 = 1.23851e-06 loss)
I0430 15:56:42.843809 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.39184e-05 (* 0.0272727 = 3.79593e-07 loss)
I0430 15:56:42.843822 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.636364
I0430 15:56:42.843833 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 15:56:42.843845 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.375
I0430 15:56:42.843858 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 15:56:42.843869 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 15:56:42.843880 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 15:56:42.843893 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 15:56:42.843904 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 15:56:42.843915 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 15:56:42.843927 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 15:56:42.843940 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 15:56:42.843950 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0430 15:56:42.843962 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 15:56:42.843973 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 15:56:42.843997 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 15:56:42.844009 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 15:56:42.844022 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 15:56:42.844033 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:56:42.844044 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:56:42.844056 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:56:42.844069 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:56:42.844079 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:56:42.844091 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:56:42.844102 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.863636
I0430 15:56:42.844115 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.836364
I0430 15:56:42.844127 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.24764 (* 1 = 1.24764 loss)
I0430 15:56:42.844141 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.583723 (* 1 = 0.583723 loss)
I0430 15:56:42.844156 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 2.44014 (* 0.0909091 = 0.221831 loss)
I0430 15:56:42.844169 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 2.77291 (* 0.0909091 = 0.252083 loss)
I0430 15:56:42.844183 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 2.06668 (* 0.0909091 = 0.18788 loss)
I0430 15:56:42.844197 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.02716 (* 0.0909091 = 0.0933783 loss)
I0430 15:56:42.844210 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.816427 (* 0.0909091 = 0.0742207 loss)
I0430 15:56:42.844224 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.1635 (* 0.0909091 = 0.105772 loss)
I0430 15:56:42.844238 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.797607 (* 0.0909091 = 0.0725097 loss)
I0430 15:56:42.844252 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.566904 (* 0.0909091 = 0.0515367 loss)
I0430 15:56:42.844270 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.558031 (* 0.0909091 = 0.0507301 loss)
I0430 15:56:42.844285 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.625433 (* 0.0909091 = 0.0568575 loss)
I0430 15:56:42.844298 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.570615 (* 0.0909091 = 0.0518741 loss)
I0430 15:56:42.844311 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.307941 (* 0.0909091 = 0.0279946 loss)
I0430 15:56:42.844326 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0499091 (* 0.0909091 = 0.00453719 loss)
I0430 15:56:42.844341 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.2055 (* 0.0909091 = 0.0186819 loss)
I0430 15:56:42.844353 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0596069 (* 0.0909091 = 0.00541881 loss)
I0430 15:56:42.844367 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00594494 (* 0.0909091 = 0.00054045 loss)
I0430 15:56:42.844382 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00278412 (* 0.0909091 = 0.000253102 loss)
I0430 15:56:42.844395 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000594003 (* 0.0909091 = 5.40003e-05 loss)
I0430 15:56:42.844409 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000123607 (* 0.0909091 = 1.1237e-05 loss)
I0430 15:56:42.844424 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 2.45879e-05 (* 0.0909091 = 2.23526e-06 loss)
I0430 15:56:42.844437 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.19808e-05 (* 0.0909091 = 1.08917e-06 loss)
I0430 15:56:42.844450 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.77005e-06 (* 0.0909091 = 3.42731e-07 loss)
I0430 15:56:42.844473 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.25
I0430 15:56:42.844486 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 15:56:42.844498 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.459927
I0430 15:56:42.844509 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.470452
I0430 15:56:42.844522 15443 sgd_solver.cpp:106] Iteration 10000, lr = 0.001
I0430 15:58:59.877534 15443 solver.cpp:229] Iteration 10500, loss = 3.65666
I0430 15:58:59.877692 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.47619
I0430 15:58:59.877713 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 15:58:59.877727 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 15:58:59.877738 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0430 15:58:59.877750 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 15:58:59.877763 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0430 15:58:59.877774 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0430 15:58:59.877786 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 15:58:59.877797 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 15:58:59.877810 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 15:58:59.877821 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 15:58:59.877833 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 15:58:59.877846 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 15:58:59.877857 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 15:58:59.877868 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 15:58:59.877881 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 15:58:59.877892 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 15:58:59.877904 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 15:58:59.877915 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 15:58:59.877928 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 15:58:59.877938 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 15:58:59.877950 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 15:58:59.877962 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 15:58:59.877974 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.840909
I0430 15:58:59.877985 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.714286
I0430 15:58:59.878005 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.64032 (* 0.3 = 0.492095 loss)
I0430 15:58:59.878021 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.496794 (* 0.3 = 0.149038 loss)
I0430 15:58:59.878036 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.70922 (* 0.0272727 = 0.0193424 loss)
I0430 15:58:59.878049 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.97463 (* 0.0272727 = 0.0538535 loss)
I0430 15:58:59.878063 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.90849 (* 0.0272727 = 0.0520498 loss)
I0430 15:58:59.878077 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.32917 (* 0.0272727 = 0.03625 loss)
I0430 15:58:59.878090 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.16406 (* 0.0272727 = 0.031747 loss)
I0430 15:58:59.878104 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.771279 (* 0.0272727 = 0.0210349 loss)
I0430 15:58:59.878118 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.720064 (* 0.0272727 = 0.0196381 loss)
I0430 15:58:59.878131 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.451651 (* 0.0272727 = 0.0123177 loss)
I0430 15:58:59.878145 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.298223 (* 0.0272727 = 0.00813335 loss)
I0430 15:58:59.878159 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.173615 (* 0.0272727 = 0.00473495 loss)
I0430 15:58:59.878173 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.231642 (* 0.0272727 = 0.00631751 loss)
I0430 15:58:59.878187 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.171881 (* 0.0272727 = 0.00468767 loss)
I0430 15:58:59.878201 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0764842 (* 0.0272727 = 0.00208593 loss)
I0430 15:58:59.878235 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0265 (* 0.0272727 = 0.000722726 loss)
I0430 15:58:59.878252 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.010042 (* 0.0272727 = 0.000273874 loss)
I0430 15:58:59.878265 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00272288 (* 0.0272727 = 7.42603e-05 loss)
I0430 15:58:59.878279 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00100745 (* 0.0272727 = 2.74759e-05 loss)
I0430 15:58:59.878294 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000185251 (* 0.0272727 = 5.0523e-06 loss)
I0430 15:58:59.878309 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000160682 (* 0.0272727 = 4.38224e-06 loss)
I0430 15:58:59.878325 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000164627 (* 0.0272727 = 4.48983e-06 loss)
I0430 15:58:59.878340 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000116283 (* 0.0272727 = 3.17135e-06 loss)
I0430 15:58:59.878353 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000118737 (* 0.0272727 = 3.23827e-06 loss)
I0430 15:58:59.878366 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.571429
I0430 15:58:59.878378 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0430 15:58:59.878389 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 15:58:59.878401 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0430 15:58:59.878413 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 15:58:59.878425 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0430 15:58:59.878437 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0430 15:58:59.878448 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 15:58:59.878460 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 15:58:59.878471 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 15:58:59.878484 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 15:58:59.878495 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 15:58:59.878506 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 15:58:59.878517 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 15:58:59.878528 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 15:58:59.878540 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 15:58:59.878551 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 15:58:59.878563 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 15:58:59.878574 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 15:58:59.878585 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 15:58:59.878597 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 15:58:59.878608 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 15:58:59.878619 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 15:58:59.878631 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.875
I0430 15:58:59.878643 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.857143
I0430 15:58:59.878657 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.30309 (* 0.3 = 0.390926 loss)
I0430 15:58:59.878670 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.389342 (* 0.3 = 0.116803 loss)
I0430 15:58:59.878684 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.379656 (* 0.0272727 = 0.0103543 loss)
I0430 15:58:59.878703 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.40871 (* 0.0272727 = 0.0384193 loss)
I0430 15:58:59.878729 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.88748 (* 0.0272727 = 0.0514768 loss)
I0430 15:58:59.878744 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.33613 (* 0.0272727 = 0.0364398 loss)
I0430 15:58:59.878762 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.18248 (* 0.0272727 = 0.0322494 loss)
I0430 15:58:59.878777 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.627636 (* 0.0272727 = 0.0171173 loss)
I0430 15:58:59.878790 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.705015 (* 0.0272727 = 0.0192277 loss)
I0430 15:58:59.878804 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.476274 (* 0.0272727 = 0.0129893 loss)
I0430 15:58:59.878818 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.286264 (* 0.0272727 = 0.00780721 loss)
I0430 15:58:59.878832 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.340676 (* 0.0272727 = 0.00929116 loss)
I0430 15:58:59.878846 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.175098 (* 0.0272727 = 0.00477539 loss)
I0430 15:58:59.878860 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.114056 (* 0.0272727 = 0.00311063 loss)
I0430 15:58:59.878875 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0684968 (* 0.0272727 = 0.00186809 loss)
I0430 15:58:59.878888 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0210343 (* 0.0272727 = 0.000573664 loss)
I0430 15:58:59.878902 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0131932 (* 0.0272727 = 0.000359815 loss)
I0430 15:58:59.878916 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00461276 (* 0.0272727 = 0.000125802 loss)
I0430 15:58:59.878929 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00227119 (* 0.0272727 = 6.19414e-05 loss)
I0430 15:58:59.878943 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0015172 (* 0.0272727 = 4.13782e-05 loss)
I0430 15:58:59.878957 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00184323 (* 0.0272727 = 5.02699e-05 loss)
I0430 15:58:59.878973 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000779564 (* 0.0272727 = 2.12608e-05 loss)
I0430 15:58:59.878986 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00146312 (* 0.0272727 = 3.99032e-05 loss)
I0430 15:58:59.879000 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.00120829 (* 0.0272727 = 3.29533e-05 loss)
I0430 15:58:59.879012 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.666667
I0430 15:58:59.879024 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 15:58:59.879035 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.625
I0430 15:58:59.879047 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.375
I0430 15:58:59.879060 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 15:58:59.879070 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0430 15:58:59.879082 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 15:58:59.879093 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 15:58:59.879106 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 15:58:59.879117 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 15:58:59.879128 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 15:58:59.879139 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 15:58:59.879151 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 15:58:59.879163 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 15:58:59.879174 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 15:58:59.879185 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 15:58:59.879196 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 15:58:59.879218 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 15:58:59.879231 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 15:58:59.879243 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 15:58:59.879254 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 15:58:59.879266 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 15:58:59.879277 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 15:58:59.879288 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.909091
I0430 15:58:59.879300 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.904762
I0430 15:58:59.879314 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.890291 (* 1 = 0.890291 loss)
I0430 15:58:59.879328 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.255985 (* 1 = 0.255985 loss)
I0430 15:58:59.879343 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.282535 (* 0.0909091 = 0.025685 loss)
I0430 15:58:59.879355 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 1.14205 (* 0.0909091 = 0.103823 loss)
I0430 15:58:59.879372 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.7292 (* 0.0909091 = 0.1572 loss)
I0430 15:58:59.879386 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.637662 (* 0.0909091 = 0.0579693 loss)
I0430 15:58:59.879400 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.258447 (* 0.0909091 = 0.0234952 loss)
I0430 15:58:59.879413 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.467354 (* 0.0909091 = 0.0424867 loss)
I0430 15:58:59.879427 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.980361 (* 0.0909091 = 0.0891238 loss)
I0430 15:58:59.879441 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.62675 (* 0.0909091 = 0.0569773 loss)
I0430 15:58:59.879454 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.0798045 (* 0.0909091 = 0.00725496 loss)
I0430 15:58:59.879482 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0927714 (* 0.0909091 = 0.00843377 loss)
I0430 15:58:59.879498 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.239922 (* 0.0909091 = 0.0218111 loss)
I0430 15:58:59.879511 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.126183 (* 0.0909091 = 0.0114712 loss)
I0430 15:58:59.879525 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0386697 (* 0.0909091 = 0.00351543 loss)
I0430 15:58:59.879539 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00362423 (* 0.0909091 = 0.000329476 loss)
I0430 15:58:59.879554 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00114309 (* 0.0909091 = 0.000103917 loss)
I0430 15:58:59.879567 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000183232 (* 0.0909091 = 1.66574e-05 loss)
I0430 15:58:59.879581 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000115125 (* 0.0909091 = 1.04659e-05 loss)
I0430 15:58:59.879595 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 3.6868e-05 (* 0.0909091 = 3.35164e-06 loss)
I0430 15:58:59.879608 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 2.47669e-05 (* 0.0909091 = 2.25154e-06 loss)
I0430 15:58:59.879622 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.50657e-05 (* 0.0909091 = 1.36961e-06 loss)
I0430 15:58:59.879637 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.24579e-05 (* 0.0909091 = 1.13254e-06 loss)
I0430 15:58:59.879650 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 9.552e-06 (* 0.0909091 = 8.68364e-07 loss)
I0430 15:58:59.879662 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 15:58:59.879674 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 15:58:59.879698 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.368022
I0430 15:58:59.879710 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.395719
I0430 15:58:59.879724 15443 sgd_solver.cpp:106] Iteration 10500, lr = 0.001
I0430 16:01:16.828362 15443 solver.cpp:229] Iteration 11000, loss = 3.65696
I0430 16:01:16.828537 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.415385
I0430 16:01:16.828558 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 16:01:16.828572 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 16:01:16.828584 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 16:01:16.828596 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0430 16:01:16.828608 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 16:01:16.828620 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0430 16:01:16.828632 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0430 16:01:16.828644 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 16:01:16.828656 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:01:16.828667 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:01:16.828680 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0430 16:01:16.828691 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:01:16.828703 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.75
I0430 16:01:16.828716 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.75
I0430 16:01:16.828728 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0430 16:01:16.828742 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0430 16:01:16.828753 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:01:16.828765 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:01:16.828776 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:01:16.828788 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:01:16.828800 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:01:16.828811 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:01:16.828824 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.784091
I0430 16:01:16.828836 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.676923
I0430 16:01:16.828852 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.92782 (* 0.3 = 0.578346 loss)
I0430 16:01:16.828867 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.751574 (* 0.3 = 0.225472 loss)
I0430 16:01:16.828882 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.843308 (* 0.0272727 = 0.0229993 loss)
I0430 16:01:16.828896 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.39166 (* 0.0272727 = 0.0379544 loss)
I0430 16:01:16.828912 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.89617 (* 0.0272727 = 0.0789865 loss)
I0430 16:01:16.828925 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.96068 (* 0.0272727 = 0.0534732 loss)
I0430 16:01:16.828946 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.93377 (* 0.0272727 = 0.0527391 loss)
I0430 16:01:16.828969 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.33443 (* 0.0272727 = 0.0363937 loss)
I0430 16:01:16.828984 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.33482 (* 0.0272727 = 0.0364042 loss)
I0430 16:01:16.828999 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.789932 (* 0.0272727 = 0.0215436 loss)
I0430 16:01:16.829013 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.613068 (* 0.0272727 = 0.01672 loss)
I0430 16:01:16.829027 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.927776 (* 0.0272727 = 0.025303 loss)
I0430 16:01:16.829041 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.83863 (* 0.0272727 = 0.0228717 loss)
I0430 16:01:16.829056 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.422728 (* 0.0272727 = 0.011529 loss)
I0430 16:01:16.829090 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.754693 (* 0.0272727 = 0.0205825 loss)
I0430 16:01:16.829107 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 1.23129 (* 0.0272727 = 0.0335807 loss)
I0430 16:01:16.829120 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.394578 (* 0.0272727 = 0.0107612 loss)
I0430 16:01:16.829134 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.371494 (* 0.0272727 = 0.0101317 loss)
I0430 16:01:16.829149 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0236757 (* 0.0272727 = 0.000645701 loss)
I0430 16:01:16.829162 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0142298 (* 0.0272727 = 0.000388086 loss)
I0430 16:01:16.829176 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00540869 (* 0.0272727 = 0.00014751 loss)
I0430 16:01:16.829191 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000920094 (* 0.0272727 = 2.50935e-05 loss)
I0430 16:01:16.829205 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000536913 (* 0.0272727 = 1.46431e-05 loss)
I0430 16:01:16.829219 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000360903 (* 0.0272727 = 9.8428e-06 loss)
I0430 16:01:16.829231 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.476923
I0430 16:01:16.829244 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:01:16.829255 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 16:01:16.829267 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0430 16:01:16.829278 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 16:01:16.829290 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0430 16:01:16.829303 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 16:01:16.829318 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.5
I0430 16:01:16.829329 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 16:01:16.829339 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0430 16:01:16.829346 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0430 16:01:16.829358 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0430 16:01:16.829370 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.75
I0430 16:01:16.829382 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.75
I0430 16:01:16.829394 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.75
I0430 16:01:16.829406 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 16:01:16.829422 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0430 16:01:16.829447 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:01:16.829463 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:01:16.829474 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:01:16.829485 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:01:16.829498 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:01:16.829509 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:01:16.829519 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.806818
I0430 16:01:16.829531 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.738462
I0430 16:01:16.829550 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.63956 (* 0.3 = 0.491867 loss)
I0430 16:01:16.829565 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.62129 (* 0.3 = 0.186387 loss)
I0430 16:01:16.829579 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.412664 (* 0.0272727 = 0.0112545 loss)
I0430 16:01:16.829593 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.51584 (* 0.0272727 = 0.0413411 loss)
I0430 16:01:16.829619 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.90134 (* 0.0272727 = 0.0518548 loss)
I0430 16:01:16.829634 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.704 (* 0.0272727 = 0.0464727 loss)
I0430 16:01:16.829649 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.05866 (* 0.0272727 = 0.0561452 loss)
I0430 16:01:16.829663 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.00678 (* 0.0272727 = 0.0274575 loss)
I0430 16:01:16.829676 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.54706 (* 0.0272727 = 0.0421925 loss)
I0430 16:01:16.829690 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.792619 (* 0.0272727 = 0.0216169 loss)
I0430 16:01:16.829704 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.722737 (* 0.0272727 = 0.019711 loss)
I0430 16:01:16.829720 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.664293 (* 0.0272727 = 0.0181171 loss)
I0430 16:01:16.829732 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.859639 (* 0.0272727 = 0.0234447 loss)
I0430 16:01:16.829746 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.750653 (* 0.0272727 = 0.0204723 loss)
I0430 16:01:16.829761 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.817744 (* 0.0272727 = 0.0223021 loss)
I0430 16:01:16.829774 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 1.3101 (* 0.0272727 = 0.0357301 loss)
I0430 16:01:16.829787 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.36966 (* 0.0272727 = 0.0100816 loss)
I0430 16:01:16.829802 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.309074 (* 0.0272727 = 0.00842928 loss)
I0430 16:01:16.829815 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00993251 (* 0.0272727 = 0.000270887 loss)
I0430 16:01:16.829829 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00373515 (* 0.0272727 = 0.000101868 loss)
I0430 16:01:16.829843 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00115663 (* 0.0272727 = 3.15444e-05 loss)
I0430 16:01:16.829856 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000279895 (* 0.0272727 = 7.6335e-06 loss)
I0430 16:01:16.829870 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 5.29522e-05 (* 0.0272727 = 1.44415e-06 loss)
I0430 16:01:16.829885 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 2.05205e-05 (* 0.0272727 = 5.59651e-07 loss)
I0430 16:01:16.829897 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.615385
I0430 16:01:16.829910 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 16:01:16.829921 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.625
I0430 16:01:16.829932 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 16:01:16.829944 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 16:01:16.829957 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 16:01:16.829968 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 16:01:16.829979 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 16:01:16.829991 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 16:01:16.830003 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0430 16:01:16.830014 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:01:16.830026 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0430 16:01:16.830037 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.75
I0430 16:01:16.830049 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 16:01:16.830060 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.75
I0430 16:01:16.830072 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:01:16.830093 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:01:16.830106 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:01:16.830118 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:01:16.830130 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:01:16.830142 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:01:16.830152 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:01:16.830164 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:01:16.830175 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.852273
I0430 16:01:16.830188 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.876923
I0430 16:01:16.830201 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.23045 (* 1 = 1.23045 loss)
I0430 16:01:16.830215 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.467895 (* 1 = 0.467895 loss)
I0430 16:01:16.830229 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.556135 (* 0.0909091 = 0.0505577 loss)
I0430 16:01:16.830243 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.933777 (* 0.0909091 = 0.0848888 loss)
I0430 16:01:16.830256 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.09435 (* 0.0909091 = 0.0994864 loss)
I0430 16:01:16.830271 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.362161 (* 0.0909091 = 0.0329237 loss)
I0430 16:01:16.830284 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.840432 (* 0.0909091 = 0.0764029 loss)
I0430 16:01:16.830297 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.747853 (* 0.0909091 = 0.0679867 loss)
I0430 16:01:16.830312 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.542373 (* 0.0909091 = 0.0493067 loss)
I0430 16:01:16.830325 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.76341 (* 0.0909091 = 0.0694009 loss)
I0430 16:01:16.830339 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.48706 (* 0.0909091 = 0.0442781 loss)
I0430 16:01:16.830353 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.721619 (* 0.0909091 = 0.0656017 loss)
I0430 16:01:16.830369 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.6003 (* 0.0909091 = 0.0545728 loss)
I0430 16:01:16.830384 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.5622 (* 0.0909091 = 0.0511091 loss)
I0430 16:01:16.830397 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.496191 (* 0.0909091 = 0.0451082 loss)
I0430 16:01:16.830411 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 1.1047 (* 0.0909091 = 0.100427 loss)
I0430 16:01:16.830425 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.103519 (* 0.0909091 = 0.00941085 loss)
I0430 16:01:16.830440 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.104562 (* 0.0909091 = 0.00950561 loss)
I0430 16:01:16.830453 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.070816 (* 0.0909091 = 0.00643782 loss)
I0430 16:01:16.830467 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0129807 (* 0.0909091 = 0.00118006 loss)
I0430 16:01:16.830482 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00205032 (* 0.0909091 = 0.000186393 loss)
I0430 16:01:16.830495 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000376405 (* 0.0909091 = 3.42186e-05 loss)
I0430 16:01:16.830510 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000119271 (* 0.0909091 = 1.08428e-05 loss)
I0430 16:01:16.830524 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.19065e-05 (* 0.0909091 = 1.08241e-06 loss)
I0430 16:01:16.830536 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 16:01:16.830548 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 16:01:16.830569 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.350124
I0430 16:01:16.830582 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.479965
I0430 16:01:16.830600 15443 sgd_solver.cpp:106] Iteration 11000, lr = 0.001
I0430 16:03:33.764289 15443 solver.cpp:229] Iteration 11500, loss = 3.61426
I0430 16:03:33.764456 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.491525
I0430 16:03:33.764477 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 16:03:33.764490 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0430 16:03:33.764503 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.75
I0430 16:03:33.764515 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.75
I0430 16:03:33.764528 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0430 16:03:33.764539 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 16:03:33.764550 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 16:03:33.764562 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 16:03:33.764575 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 16:03:33.764585 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0430 16:03:33.764597 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0430 16:03:33.764610 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:03:33.764621 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 16:03:33.764633 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 16:03:33.764645 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:03:33.764657 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:03:33.764669 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:03:33.764680 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:03:33.764693 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:03:33.764703 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:03:33.764715 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:03:33.764726 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:03:33.764739 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.829545
I0430 16:03:33.764750 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.661017
I0430 16:03:33.764766 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.78974 (* 0.3 = 0.536921 loss)
I0430 16:03:33.764781 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.608593 (* 0.3 = 0.182578 loss)
I0430 16:03:33.764796 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.993135 (* 0.0272727 = 0.0270855 loss)
I0430 16:03:33.764811 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.60823 (* 0.0272727 = 0.0438609 loss)
I0430 16:03:33.764824 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 0.800402 (* 0.0272727 = 0.0218291 loss)
I0430 16:03:33.764838 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.47004 (* 0.0272727 = 0.0400919 loss)
I0430 16:03:33.764853 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.31962 (* 0.0272727 = 0.0359896 loss)
I0430 16:03:33.764866 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.40385 (* 0.0272727 = 0.0382868 loss)
I0430 16:03:33.764880 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.0956 (* 0.0272727 = 0.0298801 loss)
I0430 16:03:33.764894 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.913914 (* 0.0272727 = 0.0249249 loss)
I0430 16:03:33.764909 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.939277 (* 0.0272727 = 0.0256167 loss)
I0430 16:03:33.764922 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 1.11164 (* 0.0272727 = 0.0303174 loss)
I0430 16:03:33.764935 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 1.00322 (* 0.0272727 = 0.0273604 loss)
I0430 16:03:33.764950 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.512131 (* 0.0272727 = 0.0139672 loss)
I0430 16:03:33.764983 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.59162 (* 0.0272727 = 0.0161351 loss)
I0430 16:03:33.764998 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.680825 (* 0.0272727 = 0.0185679 loss)
I0430 16:03:33.765013 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0052717 (* 0.0272727 = 0.000143774 loss)
I0430 16:03:33.765028 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00196995 (* 0.0272727 = 5.37259e-05 loss)
I0430 16:03:33.765043 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00103375 (* 0.0272727 = 2.8193e-05 loss)
I0430 16:03:33.765056 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000585981 (* 0.0272727 = 1.59813e-05 loss)
I0430 16:03:33.765070 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00150089 (* 0.0272727 = 4.09333e-05 loss)
I0430 16:03:33.765084 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00113423 (* 0.0272727 = 3.09335e-05 loss)
I0430 16:03:33.765099 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00121973 (* 0.0272727 = 3.32653e-05 loss)
I0430 16:03:33.765112 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00165069 (* 0.0272727 = 4.50189e-05 loss)
I0430 16:03:33.765125 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.610169
I0430 16:03:33.765136 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:03:33.765148 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 16:03:33.765161 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0430 16:03:33.765172 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 16:03:33.765184 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 16:03:33.765197 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0430 16:03:33.765208 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 16:03:33.765219 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 16:03:33.765231 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0430 16:03:33.765244 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0430 16:03:33.765254 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0430 16:03:33.765266 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:03:33.765278 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 16:03:33.765292 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 16:03:33.765305 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:03:33.765318 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:03:33.765331 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:03:33.765342 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:03:33.765354 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:03:33.765365 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:03:33.765377 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:03:33.765388 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:03:33.765399 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.857955
I0430 16:03:33.765411 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.779661
I0430 16:03:33.765425 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.43539 (* 0.3 = 0.430617 loss)
I0430 16:03:33.765439 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.531679 (* 0.3 = 0.159504 loss)
I0430 16:03:33.765453 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.349065 (* 0.0272727 = 0.00951994 loss)
I0430 16:03:33.765472 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.482102 (* 0.0272727 = 0.0131482 loss)
I0430 16:03:33.765498 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.678207 (* 0.0272727 = 0.0184965 loss)
I0430 16:03:33.765514 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.60189 (* 0.0272727 = 0.0436879 loss)
I0430 16:03:33.765528 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.17354 (* 0.0272727 = 0.0320056 loss)
I0430 16:03:33.765542 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.10144 (* 0.0272727 = 0.0300392 loss)
I0430 16:03:33.765555 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.846507 (* 0.0272727 = 0.0230865 loss)
I0430 16:03:33.765569 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.896418 (* 0.0272727 = 0.0244478 loss)
I0430 16:03:33.765583 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.02509 (* 0.0272727 = 0.0279571 loss)
I0430 16:03:33.765597 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.985787 (* 0.0272727 = 0.0268851 loss)
I0430 16:03:33.765611 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.716744 (* 0.0272727 = 0.0195476 loss)
I0430 16:03:33.765625 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.384501 (* 0.0272727 = 0.0104864 loss)
I0430 16:03:33.765638 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.661824 (* 0.0272727 = 0.0180498 loss)
I0430 16:03:33.765652 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.639922 (* 0.0272727 = 0.0174524 loss)
I0430 16:03:33.765666 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00899636 (* 0.0272727 = 0.000245355 loss)
I0430 16:03:33.765679 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00197038 (* 0.0272727 = 5.37376e-05 loss)
I0430 16:03:33.765693 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000354127 (* 0.0272727 = 9.658e-06 loss)
I0430 16:03:33.765707 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000110491 (* 0.0272727 = 3.01339e-06 loss)
I0430 16:03:33.765722 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 3.32544e-05 (* 0.0272727 = 9.06938e-07 loss)
I0430 16:03:33.765735 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 7.56995e-06 (* 0.0272727 = 2.06453e-07 loss)
I0430 16:03:33.765748 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.78654e-06 (* 0.0272727 = 7.59967e-08 loss)
I0430 16:03:33.765763 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.1623e-06 (* 0.0272727 = 3.1699e-08 loss)
I0430 16:03:33.765775 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.745763
I0430 16:03:33.765787 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:03:33.765799 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0430 16:03:33.765810 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0430 16:03:33.765822 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 16:03:33.765835 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0430 16:03:33.765846 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 16:03:33.765857 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 16:03:33.765869 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 16:03:33.765882 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0430 16:03:33.765893 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.75
I0430 16:03:33.765904 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 16:03:33.765915 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 16:03:33.765928 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 16:03:33.765939 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 16:03:33.765950 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:03:33.765971 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:03:33.765985 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:03:33.765996 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:03:33.766008 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:03:33.766019 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:03:33.766031 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:03:33.766042 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:03:33.766053 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.914773
I0430 16:03:33.766065 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.847458
I0430 16:03:33.766078 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.847761 (* 1 = 0.847761 loss)
I0430 16:03:33.766093 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.306756 (* 1 = 0.306756 loss)
I0430 16:03:33.766108 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0308955 (* 0.0909091 = 0.00280869 loss)
I0430 16:03:33.766121 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0337271 (* 0.0909091 = 0.0030661 loss)
I0430 16:03:33.766135 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.119963 (* 0.0909091 = 0.0109057 loss)
I0430 16:03:33.766149 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.541719 (* 0.0909091 = 0.0492472 loss)
I0430 16:03:33.766162 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.201186 (* 0.0909091 = 0.0182896 loss)
I0430 16:03:33.766176 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.730663 (* 0.0909091 = 0.0664239 loss)
I0430 16:03:33.766191 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.575822 (* 0.0909091 = 0.0523475 loss)
I0430 16:03:33.766204 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.04826 (* 0.0909091 = 0.095296 loss)
I0430 16:03:33.766217 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.859003 (* 0.0909091 = 0.0780911 loss)
I0430 16:03:33.766232 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.906112 (* 0.0909091 = 0.0823738 loss)
I0430 16:03:33.766244 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.605188 (* 0.0909091 = 0.0550171 loss)
I0430 16:03:33.766258 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.428634 (* 0.0909091 = 0.0389667 loss)
I0430 16:03:33.766271 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.402796 (* 0.0909091 = 0.0366179 loss)
I0430 16:03:33.766285 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.397172 (* 0.0909091 = 0.0361066 loss)
I0430 16:03:33.766299 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00913929 (* 0.0909091 = 0.000830844 loss)
I0430 16:03:33.766314 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00235823 (* 0.0909091 = 0.000214385 loss)
I0430 16:03:33.766327 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000746843 (* 0.0909091 = 6.78948e-05 loss)
I0430 16:03:33.766341 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000355655 (* 0.0909091 = 3.23322e-05 loss)
I0430 16:03:33.766355 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000159851 (* 0.0909091 = 1.45319e-05 loss)
I0430 16:03:33.766372 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 6.7407e-05 (* 0.0909091 = 6.12791e-06 loss)
I0430 16:03:33.766386 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.66898e-05 (* 0.0909091 = 1.51726e-06 loss)
I0430 16:03:33.766401 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.62102e-06 (* 0.0909091 = 3.29183e-07 loss)
I0430 16:03:33.766412 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0430 16:03:33.766424 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.75
I0430 16:03:33.766445 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.580078
I0430 16:03:33.766458 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.603886
I0430 16:03:33.766470 15443 sgd_solver.cpp:106] Iteration 11500, lr = 0.001
I0430 16:05:50.909764 15443 solver.cpp:229] Iteration 12000, loss = 3.69655
I0430 16:05:50.910025 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.395833
I0430 16:05:50.910048 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 16:05:50.910060 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 16:05:50.910073 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 16:05:50.910085 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 16:05:50.910097 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.75
I0430 16:05:50.910109 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0430 16:05:50.910120 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0430 16:05:50.910132 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 16:05:50.910145 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:05:50.910156 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:05:50.910168 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:05:50.910181 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:05:50.910192 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:05:50.910203 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:05:50.910215 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:05:50.910226 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:05:50.910238 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:05:50.910250 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:05:50.910262 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:05:50.910274 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:05:50.910285 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:05:50.910297 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:05:50.910310 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.823864
I0430 16:05:50.910325 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.708333
I0430 16:05:50.910341 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.90112 (* 0.3 = 0.570336 loss)
I0430 16:05:50.910356 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.562414 (* 0.3 = 0.168724 loss)
I0430 16:05:50.910370 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.8845 (* 0.0272727 = 0.0241227 loss)
I0430 16:05:50.910384 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.88655 (* 0.0272727 = 0.0514515 loss)
I0430 16:05:50.910398 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.34642 (* 0.0272727 = 0.0639931 loss)
I0430 16:05:50.910413 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.73966 (* 0.0272727 = 0.0474451 loss)
I0430 16:05:50.910426 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 0.973605 (* 0.0272727 = 0.0265529 loss)
I0430 16:05:50.910439 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.45163 (* 0.0272727 = 0.0668627 loss)
I0430 16:05:50.910454 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.537782 (* 0.0272727 = 0.0146668 loss)
I0430 16:05:50.910467 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.257769 (* 0.0272727 = 0.00703007 loss)
I0430 16:05:50.910482 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.492654 (* 0.0272727 = 0.013436 loss)
I0430 16:05:50.910496 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.21231 (* 0.0272727 = 0.00579028 loss)
I0430 16:05:50.910511 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.371663 (* 0.0272727 = 0.0101363 loss)
I0430 16:05:50.910524 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.269271 (* 0.0272727 = 0.00734375 loss)
I0430 16:05:50.910552 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0196743 (* 0.0272727 = 0.000536572 loss)
I0430 16:05:50.910568 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00509849 (* 0.0272727 = 0.00013905 loss)
I0430 16:05:50.910583 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.000670871 (* 0.0272727 = 1.82965e-05 loss)
I0430 16:05:50.910596 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000147677 (* 0.0272727 = 4.02755e-06 loss)
I0430 16:05:50.910610 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 2.83435e-05 (* 0.0272727 = 7.73005e-07 loss)
I0430 16:05:50.910625 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 5.01207e-05 (* 0.0272727 = 1.36693e-06 loss)
I0430 16:05:50.910640 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 1.99688e-05 (* 0.0272727 = 5.44604e-07 loss)
I0430 16:05:50.910652 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 3.17057e-05 (* 0.0272727 = 8.64701e-07 loss)
I0430 16:05:50.910666 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 1.66306e-05 (* 0.0272727 = 4.53563e-07 loss)
I0430 16:05:50.910681 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 3.33156e-05 (* 0.0272727 = 9.08608e-07 loss)
I0430 16:05:50.910696 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.583333
I0430 16:05:50.910725 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0430 16:05:50.910737 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 16:05:50.910750 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0430 16:05:50.910761 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 16:05:50.910773 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 16:05:50.910785 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 16:05:50.910796 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0430 16:05:50.910807 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0430 16:05:50.910820 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:05:50.910830 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:05:50.910842 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:05:50.910854 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:05:50.910866 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:05:50.910876 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:05:50.910888 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:05:50.910899 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:05:50.910910 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:05:50.910923 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:05:50.910933 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:05:50.910944 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:05:50.910956 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:05:50.910967 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:05:50.910979 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.857955
I0430 16:05:50.910990 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.729167
I0430 16:05:50.911005 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.57001 (* 0.3 = 0.471002 loss)
I0430 16:05:50.911018 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.504381 (* 0.3 = 0.151314 loss)
I0430 16:05:50.911039 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.312015 (* 0.0272727 = 0.00850949 loss)
I0430 16:05:50.911054 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.31083 (* 0.0272727 = 0.03575 loss)
I0430 16:05:50.911080 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.61562 (* 0.0272727 = 0.0440623 loss)
I0430 16:05:50.911095 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.81429 (* 0.0272727 = 0.0494807 loss)
I0430 16:05:50.911109 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.52351 (* 0.0272727 = 0.0415503 loss)
I0430 16:05:50.911123 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.66462 (* 0.0272727 = 0.0453987 loss)
I0430 16:05:50.911136 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.433905 (* 0.0272727 = 0.0118338 loss)
I0430 16:05:50.911150 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.271936 (* 0.0272727 = 0.00741644 loss)
I0430 16:05:50.911164 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.373826 (* 0.0272727 = 0.0101953 loss)
I0430 16:05:50.911178 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.351968 (* 0.0272727 = 0.00959912 loss)
I0430 16:05:50.911192 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.273184 (* 0.0272727 = 0.00745048 loss)
I0430 16:05:50.911206 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.327271 (* 0.0272727 = 0.00892556 loss)
I0430 16:05:50.911219 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.089162 (* 0.0272727 = 0.00243169 loss)
I0430 16:05:50.911234 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0283254 (* 0.0272727 = 0.000772511 loss)
I0430 16:05:50.911247 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00795396 (* 0.0272727 = 0.000216926 loss)
I0430 16:05:50.911262 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000624412 (* 0.0272727 = 1.70294e-05 loss)
I0430 16:05:50.911275 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000300147 (* 0.0272727 = 8.18584e-06 loss)
I0430 16:05:50.911288 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 5.09049e-05 (* 0.0272727 = 1.38832e-06 loss)
I0430 16:05:50.911303 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 6.20873e-05 (* 0.0272727 = 1.69329e-06 loss)
I0430 16:05:50.911316 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 5.79437e-05 (* 0.0272727 = 1.58028e-06 loss)
I0430 16:05:50.911329 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.74346e-05 (* 0.0272727 = 7.48216e-07 loss)
I0430 16:05:50.911344 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 3.53575e-05 (* 0.0272727 = 9.64296e-07 loss)
I0430 16:05:50.911355 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.625
I0430 16:05:50.911370 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:05:50.911381 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 16:05:50.911392 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 16:05:50.911404 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0430 16:05:50.911415 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 16:05:50.911427 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 16:05:50.911438 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 16:05:50.911450 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0430 16:05:50.911461 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:05:50.911487 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 16:05:50.911500 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 16:05:50.911511 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 16:05:50.911523 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:05:50.911535 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:05:50.911546 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:05:50.911569 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:05:50.911582 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:05:50.911594 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:05:50.911605 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:05:50.911617 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:05:50.911628 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:05:50.911640 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:05:50.911651 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.892045
I0430 16:05:50.911664 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.875
I0430 16:05:50.911677 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.17253 (* 1 = 1.17253 loss)
I0430 16:05:50.911690 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.354446 (* 1 = 0.354446 loss)
I0430 16:05:50.911705 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0646614 (* 0.0909091 = 0.00587831 loss)
I0430 16:05:50.911720 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.79142 (* 0.0909091 = 0.0719472 loss)
I0430 16:05:50.911733 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.05602 (* 0.0909091 = 0.0960015 loss)
I0430 16:05:50.911746 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.866369 (* 0.0909091 = 0.0787608 loss)
I0430 16:05:50.911761 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.591777 (* 0.0909091 = 0.0537979 loss)
I0430 16:05:50.911773 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.42856 (* 0.0909091 = 0.129869 loss)
I0430 16:05:50.911787 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.489895 (* 0.0909091 = 0.0445359 loss)
I0430 16:05:50.911800 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.183673 (* 0.0909091 = 0.0166975 loss)
I0430 16:05:50.911814 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.504252 (* 0.0909091 = 0.0458411 loss)
I0430 16:05:50.911828 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.265305 (* 0.0909091 = 0.0241186 loss)
I0430 16:05:50.911841 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.445757 (* 0.0909091 = 0.0405234 loss)
I0430 16:05:50.911855 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.321296 (* 0.0909091 = 0.0292087 loss)
I0430 16:05:50.911870 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0233228 (* 0.0909091 = 0.00212025 loss)
I0430 16:05:50.911882 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00493266 (* 0.0909091 = 0.000448424 loss)
I0430 16:05:50.911896 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00151412 (* 0.0909091 = 0.000137647 loss)
I0430 16:05:50.911911 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00162974 (* 0.0909091 = 0.000148158 loss)
I0430 16:05:50.911924 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000668313 (* 0.0909091 = 6.07557e-05 loss)
I0430 16:05:50.911938 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000430879 (* 0.0909091 = 3.91708e-05 loss)
I0430 16:05:50.911952 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00065393 (* 0.0909091 = 5.94482e-05 loss)
I0430 16:05:50.911965 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000434642 (* 0.0909091 = 3.95129e-05 loss)
I0430 16:05:50.911979 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000326699 (* 0.0909091 = 2.96999e-05 loss)
I0430 16:05:50.911993 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 6.70055e-05 (* 0.0909091 = 6.09141e-06 loss)
I0430 16:05:50.912005 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.25
I0430 16:05:50.912017 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.125
I0430 16:05:50.912039 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.257778
I0430 16:05:50.912051 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.238868
I0430 16:05:50.912065 15443 sgd_solver.cpp:106] Iteration 12000, lr = 0.001
I0430 16:07:29.600847 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.8071 > 30) by scale factor 0.973801
I0430 16:08:07.951339 15443 solver.cpp:229] Iteration 12500, loss = 3.76566
I0430 16:08:07.951503 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.583333
I0430 16:08:07.951522 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 16:08:07.951536 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 16:08:07.951548 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 16:08:07.951560 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 16:08:07.951573 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.75
I0430 16:08:07.951586 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.875
I0430 16:08:07.951598 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 16:08:07.951619 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0430 16:08:07.951643 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0430 16:08:07.951665 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 16:08:07.951688 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 16:08:07.951710 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 16:08:07.951733 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:08:07.951756 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:08:07.951779 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:08:07.951803 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:08:07.951827 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:08:07.951850 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:08:07.951874 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:08:07.951896 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:08:07.951918 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:08:07.951941 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:08:07.951963 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.880682
I0430 16:08:07.951987 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.8125
I0430 16:08:07.952011 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.39711 (* 0.3 = 0.419133 loss)
I0430 16:08:07.952041 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.405178 (* 0.3 = 0.121553 loss)
I0430 16:08:07.952070 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.906859 (* 0.0272727 = 0.0247325 loss)
I0430 16:08:07.952100 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.96044 (* 0.0272727 = 0.0261938 loss)
I0430 16:08:07.952128 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.25314 (* 0.0272727 = 0.0614493 loss)
I0430 16:08:07.952157 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.2886 (* 0.0272727 = 0.0351436 loss)
I0430 16:08:07.952184 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 0.879476 (* 0.0272727 = 0.0239857 loss)
I0430 16:08:07.952213 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.856666 (* 0.0272727 = 0.0233636 loss)
I0430 16:08:07.952240 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.45093 (* 0.0272727 = 0.0395708 loss)
I0430 16:08:07.952270 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.0713425 (* 0.0272727 = 0.00194571 loss)
I0430 16:08:07.952299 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.00202393 (* 0.0272727 = 5.51981e-05 loss)
I0430 16:08:07.952330 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.000268605 (* 0.0272727 = 7.32558e-06 loss)
I0430 16:08:07.952347 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 8.48759e-05 (* 0.0272727 = 2.3148e-06 loss)
I0430 16:08:07.952363 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 3.82965e-06 (* 0.0272727 = 1.04445e-07 loss)
I0430 16:08:07.952400 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 2.96536e-06 (* 0.0272727 = 8.08736e-08 loss)
I0430 16:08:07.952415 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 1.49012e-06 (* 0.0272727 = 4.06398e-08 loss)
I0430 16:08:07.952430 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 3.57628e-07 (* 0.0272727 = 9.7535e-09 loss)
I0430 16:08:07.952443 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 2.98023e-08 (* 0.0272727 = 8.12791e-10 loss)
I0430 16:08:07.952458 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0 (* 0.0272727 = 0 loss)
I0430 16:08:07.952472 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0 (* 0.0272727 = 0 loss)
I0430 16:08:07.952486 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 1.49012e-08 (* 0.0272727 = 4.06395e-10 loss)
I0430 16:08:07.952502 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0 (* 0.0272727 = 0 loss)
I0430 16:08:07.952519 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0 (* 0.0272727 = 0 loss)
I0430 16:08:07.952533 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0 (* 0.0272727 = 0 loss)
I0430 16:08:07.952545 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.666667
I0430 16:08:07.952558 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:08:07.952569 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 16:08:07.952581 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 16:08:07.952594 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0430 16:08:07.952605 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 1
I0430 16:08:07.952616 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0430 16:08:07.952627 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 16:08:07.952639 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0430 16:08:07.952651 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0430 16:08:07.952662 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 16:08:07.952673 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 16:08:07.952685 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 16:08:07.952697 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:08:07.952708 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:08:07.952719 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:08:07.952730 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:08:07.952742 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:08:07.952754 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:08:07.952764 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:08:07.952776 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:08:07.952787 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:08:07.952798 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:08:07.952811 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.897727
I0430 16:08:07.952822 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.9375
I0430 16:08:07.952836 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.926254 (* 0.3 = 0.277876 loss)
I0430 16:08:07.952849 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.278066 (* 0.3 = 0.0834198 loss)
I0430 16:08:07.952863 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.685914 (* 0.0272727 = 0.0187067 loss)
I0430 16:08:07.952877 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.737974 (* 0.0272727 = 0.0201266 loss)
I0430 16:08:07.952891 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.01544 (* 0.0272727 = 0.0276939 loss)
I0430 16:08:07.952918 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.41229 (* 0.0272727 = 0.0385169 loss)
I0430 16:08:07.952932 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.56648 (* 0.0272727 = 0.0154495 loss)
I0430 16:08:07.952944 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.762964 (* 0.0272727 = 0.0208081 loss)
I0430 16:08:07.952958 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.997852 (* 0.0272727 = 0.0272142 loss)
I0430 16:08:07.952972 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.039492 (* 0.0272727 = 0.00107705 loss)
I0430 16:08:07.952987 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.00075194 (* 0.0272727 = 2.05075e-05 loss)
I0430 16:08:07.953001 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.000256088 (* 0.0272727 = 6.98421e-06 loss)
I0430 16:08:07.953016 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 3.21134e-05 (* 0.0272727 = 8.7582e-07 loss)
I0430 16:08:07.953029 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 1.73752e-05 (* 0.0272727 = 4.7387e-07 loss)
I0430 16:08:07.953042 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 9.16433e-06 (* 0.0272727 = 2.49936e-07 loss)
I0430 16:08:07.953057 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 4.6045e-06 (* 0.0272727 = 1.25577e-07 loss)
I0430 16:08:07.953070 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 3.17397e-06 (* 0.0272727 = 8.65628e-08 loss)
I0430 16:08:07.953083 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 8.04664e-07 (* 0.0272727 = 2.19454e-08 loss)
I0430 16:08:07.953097 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 3.87431e-07 (* 0.0272727 = 1.05663e-08 loss)
I0430 16:08:07.953110 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 2.38419e-07 (* 0.0272727 = 6.50233e-09 loss)
I0430 16:08:07.953125 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 3.87431e-07 (* 0.0272727 = 1.05663e-08 loss)
I0430 16:08:07.953137 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 5.0664e-07 (* 0.0272727 = 1.38175e-08 loss)
I0430 16:08:07.953151 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 3.57628e-07 (* 0.0272727 = 9.7535e-09 loss)
I0430 16:08:07.953166 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.1921e-06 (* 0.0272727 = 3.25118e-08 loss)
I0430 16:08:07.953177 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.916667
I0430 16:08:07.953189 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 16:08:07.953200 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 16:08:07.953212 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0430 16:08:07.953223 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 16:08:07.953235 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0430 16:08:07.953246 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 1
I0430 16:08:07.953258 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 16:08:07.953269 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0430 16:08:07.953280 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 16:08:07.953291 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 16:08:07.953304 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:08:07.953315 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 16:08:07.953325 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:08:07.953337 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:08:07.953348 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:08:07.953359 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:08:07.953383 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:08:07.953397 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:08:07.953408 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:08:07.953420 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:08:07.953431 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:08:07.953443 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:08:07.953454 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.971591
I0430 16:08:07.953466 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.979167
I0430 16:08:07.953480 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.436861 (* 1 = 0.436861 loss)
I0430 16:08:07.953495 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.141494 (* 1 = 0.141494 loss)
I0430 16:08:07.953508 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.30234 (* 0.0909091 = 0.0274855 loss)
I0430 16:08:07.953522 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.310207 (* 0.0909091 = 0.0282007 loss)
I0430 16:08:07.953536 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.163779 (* 0.0909091 = 0.014889 loss)
I0430 16:08:07.953550 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.285724 (* 0.0909091 = 0.0259749 loss)
I0430 16:08:07.953567 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.210728 (* 0.0909091 = 0.019157 loss)
I0430 16:08:07.953583 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.135825 (* 0.0909091 = 0.0123477 loss)
I0430 16:08:07.953595 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.33246 (* 0.0909091 = 0.121133 loss)
I0430 16:08:07.953609 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.0570286 (* 0.0909091 = 0.00518442 loss)
I0430 16:08:07.953624 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.00808181 (* 0.0909091 = 0.00073471 loss)
I0430 16:08:07.953637 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.000717745 (* 0.0909091 = 6.52496e-05 loss)
I0430 16:08:07.953651 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.000227991 (* 0.0909091 = 2.07265e-05 loss)
I0430 16:08:07.953665 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 4.40735e-05 (* 0.0909091 = 4.00668e-06 loss)
I0430 16:08:07.953678 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 3.74635e-05 (* 0.0909091 = 3.40578e-06 loss)
I0430 16:08:07.953692 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 1.49313e-05 (* 0.0909091 = 1.35739e-06 loss)
I0430 16:08:07.953707 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 1.13698e-05 (* 0.0909091 = 1.03362e-06 loss)
I0430 16:08:07.953722 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 7.46556e-06 (* 0.0909091 = 6.78688e-07 loss)
I0430 16:08:07.953734 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 3.29317e-06 (* 0.0909091 = 2.99379e-07 loss)
I0430 16:08:07.953748 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 2.3693e-06 (* 0.0909091 = 2.15391e-07 loss)
I0430 16:08:07.953763 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 3.20378e-06 (* 0.0909091 = 2.91253e-07 loss)
I0430 16:08:07.953776 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 2.20539e-06 (* 0.0909091 = 2.0049e-07 loss)
I0430 16:08:07.953790 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 9.68578e-07 (* 0.0909091 = 8.80525e-08 loss)
I0430 16:08:07.953804 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 8.34467e-07 (* 0.0909091 = 7.58606e-08 loss)
I0430 16:08:07.953816 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0430 16:08:07.953829 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0430 16:08:07.953840 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.506507
I0430 16:08:07.953860 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.483702
I0430 16:08:07.953874 15443 sgd_solver.cpp:106] Iteration 12500, lr = 0.001
I0430 16:10:24.948482 15443 solver.cpp:229] Iteration 13000, loss = 3.63056
I0430 16:10:24.948652 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.54902
I0430 16:10:24.948673 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 16:10:24.948685 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0430 16:10:24.948698 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0430 16:10:24.948710 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 16:10:24.948722 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0430 16:10:24.948734 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 16:10:24.948745 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 16:10:24.948758 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 16:10:24.948770 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:10:24.948781 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:10:24.948793 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:10:24.948806 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 16:10:24.948817 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:10:24.948828 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:10:24.948840 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:10:24.948853 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:10:24.948863 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:10:24.948875 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:10:24.948886 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:10:24.948899 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:10:24.948910 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:10:24.948922 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:10:24.948933 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.863636
I0430 16:10:24.948946 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.745098
I0430 16:10:24.948962 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.42722 (* 0.3 = 0.728165 loss)
I0430 16:10:24.948976 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.71691 (* 0.3 = 0.215073 loss)
I0430 16:10:24.948992 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.598918 (* 0.0272727 = 0.0163341 loss)
I0430 16:10:24.949005 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.89249 (* 0.0272727 = 0.0516133 loss)
I0430 16:10:24.949019 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.20726 (* 0.0272727 = 0.0329253 loss)
I0430 16:10:24.949033 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.68625 (* 0.0272727 = 0.0732614 loss)
I0430 16:10:24.949046 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.59386 (* 0.0272727 = 0.0434689 loss)
I0430 16:10:24.949060 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.6565 (* 0.0272727 = 0.0451772 loss)
I0430 16:10:24.949074 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.41626 (* 0.0272727 = 0.0386254 loss)
I0430 16:10:24.949087 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.02232 (* 0.0272727 = 0.0278814 loss)
I0430 16:10:24.949101 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 2.04498 (* 0.0272727 = 0.0557722 loss)
I0430 16:10:24.949115 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 1.73577 (* 0.0272727 = 0.0473391 loss)
I0430 16:10:24.949127 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 2.03992 (* 0.0272727 = 0.0556342 loss)
I0430 16:10:24.949141 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 4.02332e-07 (* 0.0272727 = 1.09727e-08 loss)
I0430 16:10:24.949177 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 4.02332e-07 (* 0.0272727 = 1.09727e-08 loss)
I0430 16:10:24.949193 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 4.91739e-07 (* 0.0272727 = 1.34111e-08 loss)
I0430 16:10:24.949206 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 5.81147e-07 (* 0.0272727 = 1.58495e-08 loss)
I0430 16:10:24.949220 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 4.02332e-07 (* 0.0272727 = 1.09727e-08 loss)
I0430 16:10:24.949234 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 1.04308e-07 (* 0.0272727 = 2.84477e-09 loss)
I0430 16:10:24.949247 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 1.49012e-08 (* 0.0272727 = 4.06395e-10 loss)
I0430 16:10:24.949261 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 1.49012e-08 (* 0.0272727 = 4.06395e-10 loss)
I0430 16:10:24.949276 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0 (* 0.0272727 = 0 loss)
I0430 16:10:24.949290 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0 (* 0.0272727 = 0 loss)
I0430 16:10:24.949303 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0 (* 0.0272727 = 0 loss)
I0430 16:10:24.949318 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.588235
I0430 16:10:24.949331 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:10:24.949342 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 16:10:24.949354 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 16:10:24.949367 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 16:10:24.949378 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 16:10:24.949388 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0430 16:10:24.949400 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 16:10:24.949412 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 16:10:24.949424 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:10:24.949434 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:10:24.949446 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:10:24.949458 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 16:10:24.949470 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:10:24.949481 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:10:24.949492 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:10:24.949504 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:10:24.949515 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:10:24.949527 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:10:24.949537 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:10:24.949543 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:10:24.949555 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:10:24.949568 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:10:24.949579 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.863636
I0430 16:10:24.949590 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.745098
I0430 16:10:24.949604 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.83949 (* 0.3 = 0.551848 loss)
I0430 16:10:24.949617 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.573257 (* 0.3 = 0.171977 loss)
I0430 16:10:24.949631 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.355441 (* 0.0272727 = 0.00969384 loss)
I0430 16:10:24.949645 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.55034 (* 0.0272727 = 0.0422819 loss)
I0430 16:10:24.949676 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.0671 (* 0.0272727 = 0.0291027 loss)
I0430 16:10:24.949692 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.39189 (* 0.0272727 = 0.0379607 loss)
I0430 16:10:24.949705 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.54012 (* 0.0272727 = 0.0420032 loss)
I0430 16:10:24.949718 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.11276 (* 0.0272727 = 0.0303481 loss)
I0430 16:10:24.949733 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.10569 (* 0.0272727 = 0.0301552 loss)
I0430 16:10:24.949746 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.788321 (* 0.0272727 = 0.0214997 loss)
I0430 16:10:24.949760 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.21245 (* 0.0272727 = 0.0330669 loss)
I0430 16:10:24.949774 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.929337 (* 0.0272727 = 0.0253455 loss)
I0430 16:10:24.949787 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.08914 (* 0.0272727 = 0.0297039 loss)
I0430 16:10:24.949801 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.000874833 (* 0.0272727 = 2.38591e-05 loss)
I0430 16:10:24.949815 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.000249967 (* 0.0272727 = 6.81727e-06 loss)
I0430 16:10:24.949829 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000136798 (* 0.0272727 = 3.73086e-06 loss)
I0430 16:10:24.949843 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 8.25694e-05 (* 0.0272727 = 2.25189e-06 loss)
I0430 16:10:24.949856 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 6.97639e-05 (* 0.0272727 = 1.90265e-06 loss)
I0430 16:10:24.949870 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 3.82046e-05 (* 0.0272727 = 1.04194e-06 loss)
I0430 16:10:24.949884 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 2.65491e-05 (* 0.0272727 = 7.24067e-07 loss)
I0430 16:10:24.949898 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 7.58491e-06 (* 0.0272727 = 2.06861e-07 loss)
I0430 16:10:24.949911 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 8.04686e-06 (* 0.0272727 = 2.1946e-07 loss)
I0430 16:10:24.949925 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 9.2389e-06 (* 0.0272727 = 2.5197e-07 loss)
I0430 16:10:24.949939 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 2.62263e-06 (* 0.0272727 = 7.15261e-08 loss)
I0430 16:10:24.949951 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.764706
I0430 16:10:24.949964 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 16:10:24.949975 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 16:10:24.949986 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 16:10:24.949998 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 16:10:24.950009 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0430 16:10:24.950021 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 16:10:24.950033 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 16:10:24.950044 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:10:24.950057 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:10:24.950068 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:10:24.950079 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 16:10:24.950091 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 16:10:24.950103 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:10:24.950114 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:10:24.950125 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:10:24.950136 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:10:24.950157 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:10:24.950170 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:10:24.950182 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:10:24.950194 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:10:24.950206 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:10:24.950217 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:10:24.950228 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.931818
I0430 16:10:24.950240 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.823529
I0430 16:10:24.950253 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.18 (* 1 = 1.18 loss)
I0430 16:10:24.950268 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.344004 (* 1 = 0.344004 loss)
I0430 16:10:24.950281 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.325884 (* 0.0909091 = 0.0296259 loss)
I0430 16:10:24.950295 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.778106 (* 0.0909091 = 0.0707369 loss)
I0430 16:10:24.950309 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.574598 (* 0.0909091 = 0.0522362 loss)
I0430 16:10:24.950322 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.676848 (* 0.0909091 = 0.0615316 loss)
I0430 16:10:24.950336 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.746878 (* 0.0909091 = 0.067898 loss)
I0430 16:10:24.950350 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.854804 (* 0.0909091 = 0.0777094 loss)
I0430 16:10:24.950366 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.697815 (* 0.0909091 = 0.0634377 loss)
I0430 16:10:24.950381 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.629149 (* 0.0909091 = 0.0571954 loss)
I0430 16:10:24.950394 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 1.03287 (* 0.0909091 = 0.0938971 loss)
I0430 16:10:24.950408 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.858931 (* 0.0909091 = 0.0780846 loss)
I0430 16:10:24.950422 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 1.09255 (* 0.0909091 = 0.0993228 loss)
I0430 16:10:24.950435 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00102165 (* 0.0909091 = 9.28771e-05 loss)
I0430 16:10:24.950450 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00077085 (* 0.0909091 = 7.00773e-05 loss)
I0430 16:10:24.950464 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00080761 (* 0.0909091 = 7.34191e-05 loss)
I0430 16:10:24.950479 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000673089 (* 0.0909091 = 6.11899e-05 loss)
I0430 16:10:24.950492 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000517269 (* 0.0909091 = 4.70244e-05 loss)
I0430 16:10:24.950505 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000354507 (* 0.0909091 = 3.22279e-05 loss)
I0430 16:10:24.950520 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000257376 (* 0.0909091 = 2.33978e-05 loss)
I0430 16:10:24.950533 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000228837 (* 0.0909091 = 2.08034e-05 loss)
I0430 16:10:24.950547 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000200335 (* 0.0909091 = 1.82123e-05 loss)
I0430 16:10:24.950562 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000183806 (* 0.0909091 = 1.67097e-05 loss)
I0430 16:10:24.950574 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000202387 (* 0.0909091 = 1.83988e-05 loss)
I0430 16:10:24.950587 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0430 16:10:24.950598 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.75
I0430 16:10:24.950619 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.495744
I0430 16:10:24.950633 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.384281
I0430 16:10:24.950645 15443 sgd_solver.cpp:106] Iteration 13000, lr = 0.001
I0430 16:12:41.840829 15443 solver.cpp:229] Iteration 13500, loss = 3.5792
I0430 16:12:41.840996 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.45098
I0430 16:12:41.841017 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 16:12:41.841030 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 16:12:41.841043 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0430 16:12:41.841055 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0430 16:12:41.841068 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 16:12:41.841079 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 16:12:41.841090 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 16:12:41.841102 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0430 16:12:41.841114 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:12:41.841126 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:12:41.841138 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:12:41.841150 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 16:12:41.841161 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 16:12:41.841173 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 16:12:41.841186 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0430 16:12:41.841197 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:12:41.841209 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:12:41.841220 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:12:41.841233 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:12:41.841243 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:12:41.841255 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:12:41.841266 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:12:41.841279 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.823864
I0430 16:12:41.841290 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.686275
I0430 16:12:41.841306 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.83056 (* 0.3 = 0.549167 loss)
I0430 16:12:41.841323 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.575264 (* 0.3 = 0.172579 loss)
I0430 16:12:41.841338 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.0756 (* 0.0272727 = 0.0293346 loss)
I0430 16:12:41.841353 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.37517 (* 0.0272727 = 0.0375047 loss)
I0430 16:12:41.841367 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.82927 (* 0.0272727 = 0.0498892 loss)
I0430 16:12:41.841382 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.77481 (* 0.0272727 = 0.0484038 loss)
I0430 16:12:41.841395 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.62406 (* 0.0272727 = 0.0442926 loss)
I0430 16:12:41.841409 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.941816 (* 0.0272727 = 0.0256859 loss)
I0430 16:12:41.841423 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.782108 (* 0.0272727 = 0.0213302 loss)
I0430 16:12:41.841439 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.229231 (* 0.0272727 = 0.00625176 loss)
I0430 16:12:41.841452 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.465352 (* 0.0272727 = 0.0126914 loss)
I0430 16:12:41.841466 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.358061 (* 0.0272727 = 0.00976529 loss)
I0430 16:12:41.841480 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.344714 (* 0.0272727 = 0.00940129 loss)
I0430 16:12:41.841495 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.220022 (* 0.0272727 = 0.00600061 loss)
I0430 16:12:41.841529 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.680939 (* 0.0272727 = 0.0185711 loss)
I0430 16:12:41.841545 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.246592 (* 0.0272727 = 0.00672523 loss)
I0430 16:12:41.841559 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.629002 (* 0.0272727 = 0.0171546 loss)
I0430 16:12:41.841574 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0134332 (* 0.0272727 = 0.000366361 loss)
I0430 16:12:41.841588 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00405831 (* 0.0272727 = 0.000110681 loss)
I0430 16:12:41.841603 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00173864 (* 0.0272727 = 4.74173e-05 loss)
I0430 16:12:41.841616 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00110589 (* 0.0272727 = 3.01607e-05 loss)
I0430 16:12:41.841630 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000155564 (* 0.0272727 = 4.24266e-06 loss)
I0430 16:12:41.841645 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 4.69508e-05 (* 0.0272727 = 1.28047e-06 loss)
I0430 16:12:41.841658 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.60346e-05 (* 0.0272727 = 4.37306e-07 loss)
I0430 16:12:41.841670 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.588235
I0430 16:12:41.841682 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 16:12:41.841694 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 16:12:41.841706 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 16:12:41.841717 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 16:12:41.841729 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0430 16:12:41.841742 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0430 16:12:41.841753 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0430 16:12:41.841764 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0430 16:12:41.841776 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:12:41.841789 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:12:41.841800 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:12:41.841809 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:12:41.841817 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 16:12:41.841837 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 16:12:41.841858 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 16:12:41.841879 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:12:41.841897 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:12:41.841909 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:12:41.841922 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:12:41.841933 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:12:41.841943 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:12:41.841955 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:12:41.841966 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.869318
I0430 16:12:41.841977 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.803922
I0430 16:12:41.841992 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.5043 (* 0.3 = 0.45129 loss)
I0430 16:12:41.842010 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.469659 (* 0.3 = 0.140898 loss)
I0430 16:12:41.842025 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.842963 (* 0.0272727 = 0.0229899 loss)
I0430 16:12:41.842038 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.519784 (* 0.0272727 = 0.0141759 loss)
I0430 16:12:41.842066 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.892398 (* 0.0272727 = 0.0243381 loss)
I0430 16:12:41.842082 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.72222 (* 0.0272727 = 0.0469695 loss)
I0430 16:12:41.842095 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.76492 (* 0.0272727 = 0.0481342 loss)
I0430 16:12:41.842108 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.702501 (* 0.0272727 = 0.0191591 loss)
I0430 16:12:41.842123 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.521097 (* 0.0272727 = 0.0142117 loss)
I0430 16:12:41.842138 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.247988 (* 0.0272727 = 0.00676331 loss)
I0430 16:12:41.842151 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.478517 (* 0.0272727 = 0.0130505 loss)
I0430 16:12:41.842165 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.31748 (* 0.0272727 = 0.00865854 loss)
I0430 16:12:41.842178 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.313663 (* 0.0272727 = 0.00855444 loss)
I0430 16:12:41.842192 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.248888 (* 0.0272727 = 0.00678786 loss)
I0430 16:12:41.842206 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.693725 (* 0.0272727 = 0.0189198 loss)
I0430 16:12:41.842221 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.327514 (* 0.0272727 = 0.00893221 loss)
I0430 16:12:41.842234 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.497674 (* 0.0272727 = 0.0135729 loss)
I0430 16:12:41.842248 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0112887 (* 0.0272727 = 0.000307875 loss)
I0430 16:12:41.842262 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00514357 (* 0.0272727 = 0.000140279 loss)
I0430 16:12:41.842275 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00108319 (* 0.0272727 = 2.95415e-05 loss)
I0430 16:12:41.842289 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000202558 (* 0.0272727 = 5.52431e-06 loss)
I0430 16:12:41.842303 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 3.79354e-05 (* 0.0272727 = 1.0346e-06 loss)
I0430 16:12:41.842316 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 1.16679e-05 (* 0.0272727 = 3.18215e-07 loss)
I0430 16:12:41.842330 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 8.88138e-06 (* 0.0272727 = 2.42219e-07 loss)
I0430 16:12:41.842342 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.745098
I0430 16:12:41.842355 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:12:41.842368 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 16:12:41.842381 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 16:12:41.842392 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 16:12:41.842404 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 16:12:41.842417 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 16:12:41.842427 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0430 16:12:41.842439 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:12:41.842450 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:12:41.842463 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:12:41.842473 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 16:12:41.842485 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 16:12:41.842496 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 16:12:41.842507 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:12:41.842519 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 16:12:41.842540 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:12:41.842553 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:12:41.842566 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:12:41.842577 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:12:41.842588 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:12:41.842599 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:12:41.842612 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:12:41.842622 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.914773
I0430 16:12:41.842634 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.862745
I0430 16:12:41.842648 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.23016 (* 1 = 1.23016 loss)
I0430 16:12:41.842661 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.402229 (* 1 = 0.402229 loss)
I0430 16:12:41.842676 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.233912 (* 0.0909091 = 0.0212648 loss)
I0430 16:12:41.842690 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.489489 (* 0.0909091 = 0.044499 loss)
I0430 16:12:41.842705 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.36981 (* 0.0909091 = 0.124529 loss)
I0430 16:12:41.842717 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.61485 (* 0.0909091 = 0.146805 loss)
I0430 16:12:41.842731 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.86864 (* 0.0909091 = 0.169876 loss)
I0430 16:12:41.842744 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.680022 (* 0.0909091 = 0.0618202 loss)
I0430 16:12:41.842758 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.137152 (* 0.0909091 = 0.0124683 loss)
I0430 16:12:41.842772 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.214142 (* 0.0909091 = 0.0194675 loss)
I0430 16:12:41.842785 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.488938 (* 0.0909091 = 0.0444489 loss)
I0430 16:12:41.842798 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.336342 (* 0.0909091 = 0.0305765 loss)
I0430 16:12:41.842813 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.296667 (* 0.0909091 = 0.0269697 loss)
I0430 16:12:41.842825 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.110119 (* 0.0909091 = 0.0100108 loss)
I0430 16:12:41.842839 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.436043 (* 0.0909091 = 0.0396403 loss)
I0430 16:12:41.842852 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.11074 (* 0.0909091 = 0.0100673 loss)
I0430 16:12:41.842866 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.501748 (* 0.0909091 = 0.0456134 loss)
I0430 16:12:41.842880 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00438383 (* 0.0909091 = 0.00039853 loss)
I0430 16:12:41.842895 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00423688 (* 0.0909091 = 0.000385171 loss)
I0430 16:12:41.842908 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00196443 (* 0.0909091 = 0.000178584 loss)
I0430 16:12:41.842922 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00106887 (* 0.0909091 = 9.71699e-05 loss)
I0430 16:12:41.842936 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000339927 (* 0.0909091 = 3.09025e-05 loss)
I0430 16:12:41.842950 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.0002391 (* 0.0909091 = 2.17364e-05 loss)
I0430 16:12:41.842963 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000106056 (* 0.0909091 = 9.64147e-06 loss)
I0430 16:12:41.842977 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 16:12:41.842988 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 16:12:41.843008 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.424107
I0430 16:12:41.843021 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.477769
I0430 16:12:41.843034 15443 sgd_solver.cpp:106] Iteration 13500, lr = 0.001
I0430 16:14:35.362535 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 37.1191 > 30) by scale factor 0.808209
I0430 16:14:35.919958 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.079 > 30) by scale factor 0.965281
I0430 16:14:58.837294 15443 solver.cpp:229] Iteration 14000, loss = 3.70748
I0430 16:14:58.837375 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.5
I0430 16:14:58.837393 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 16:14:58.837406 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.875
I0430 16:14:58.837420 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 16:14:58.837432 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.625
I0430 16:14:58.837445 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0430 16:14:58.837456 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 16:14:58.837467 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 16:14:58.837481 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 16:14:58.837491 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 16:14:58.837503 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:14:58.837515 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:14:58.837527 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:14:58.837539 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:14:58.837551 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:14:58.837563 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:14:58.837576 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:14:58.837589 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:14:58.837601 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:14:58.837612 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:14:58.837625 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:14:58.837636 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:14:58.837649 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:14:58.837661 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.835227
I0430 16:14:58.837672 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.659091
I0430 16:14:58.837689 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.94216 (* 0.3 = 0.582647 loss)
I0430 16:14:58.837703 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.634954 (* 0.3 = 0.190486 loss)
I0430 16:14:58.837718 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.08566 (* 0.0272727 = 0.0296088 loss)
I0430 16:14:58.837733 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.892228 (* 0.0272727 = 0.0243335 loss)
I0430 16:14:58.837748 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.90166 (* 0.0272727 = 0.0518634 loss)
I0430 16:14:58.837761 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.53049 (* 0.0272727 = 0.0417405 loss)
I0430 16:14:58.837775 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.6266 (* 0.0272727 = 0.0443618 loss)
I0430 16:14:58.837790 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.15141 (* 0.0272727 = 0.031402 loss)
I0430 16:14:58.837805 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.03293 (* 0.0272727 = 0.0281709 loss)
I0430 16:14:58.837818 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.884866 (* 0.0272727 = 0.0241327 loss)
I0430 16:14:58.837832 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.751031 (* 0.0272727 = 0.0204827 loss)
I0430 16:14:58.837846 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.504371 (* 0.0272727 = 0.0137556 loss)
I0430 16:14:58.837860 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.446325 (* 0.0272727 = 0.0121725 loss)
I0430 16:14:58.837916 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.448074 (* 0.0272727 = 0.0122202 loss)
I0430 16:14:58.837932 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.110412 (* 0.0272727 = 0.00301124 loss)
I0430 16:14:58.837947 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0521296 (* 0.0272727 = 0.00142172 loss)
I0430 16:14:58.837961 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0277287 (* 0.0272727 = 0.000756237 loss)
I0430 16:14:58.837975 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0105078 (* 0.0272727 = 0.000286576 loss)
I0430 16:14:58.837990 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00417088 (* 0.0272727 = 0.000113751 loss)
I0430 16:14:58.838003 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00192038 (* 0.0272727 = 5.23741e-05 loss)
I0430 16:14:58.838017 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00196906 (* 0.0272727 = 5.37015e-05 loss)
I0430 16:14:58.838032 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00102796 (* 0.0272727 = 2.80352e-05 loss)
I0430 16:14:58.838045 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00110032 (* 0.0272727 = 3.00088e-05 loss)
I0430 16:14:58.838059 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000564301 (* 0.0272727 = 1.539e-05 loss)
I0430 16:14:58.838071 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.5
I0430 16:14:58.838083 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 16:14:58.838094 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 16:14:58.838106 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 16:14:58.838119 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 16:14:58.838130 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 16:14:58.838141 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 16:14:58.838153 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 16:14:58.838165 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 16:14:58.838176 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:14:58.838192 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:14:58.838206 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:14:58.838217 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:14:58.838228 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:14:58.838239 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:14:58.838251 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:14:58.838263 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:14:58.838274 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:14:58.838285 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:14:58.838296 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:14:58.838309 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:14:58.838320 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:14:58.838331 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:14:58.838342 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.846591
I0430 16:14:58.838354 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.636364
I0430 16:14:58.838368 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.86025 (* 0.3 = 0.558074 loss)
I0430 16:14:58.838382 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.571729 (* 0.3 = 0.171519 loss)
I0430 16:14:58.838408 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.13597 (* 0.0272727 = 0.030981 loss)
I0430 16:14:58.838426 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.553032 (* 0.0272727 = 0.0150827 loss)
I0430 16:14:58.838436 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.58728 (* 0.0272727 = 0.0432895 loss)
I0430 16:14:58.838446 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.12382 (* 0.0272727 = 0.0306497 loss)
I0430 16:14:58.838460 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.49202 (* 0.0272727 = 0.0406915 loss)
I0430 16:14:58.838475 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.25298 (* 0.0272727 = 0.0341721 loss)
I0430 16:14:58.838488 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.08611 (* 0.0272727 = 0.0296213 loss)
I0430 16:14:58.838502 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.861981 (* 0.0272727 = 0.0235086 loss)
I0430 16:14:58.838516 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.5874 (* 0.0272727 = 0.01602 loss)
I0430 16:14:58.838531 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.472195 (* 0.0272727 = 0.012878 loss)
I0430 16:14:58.838543 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.403863 (* 0.0272727 = 0.0110144 loss)
I0430 16:14:58.838557 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.413182 (* 0.0272727 = 0.0112686 loss)
I0430 16:14:58.838572 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0936882 (* 0.0272727 = 0.00255513 loss)
I0430 16:14:58.838585 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0530381 (* 0.0272727 = 0.00144649 loss)
I0430 16:14:58.838598 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0367542 (* 0.0272727 = 0.00100239 loss)
I0430 16:14:58.838613 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0261016 (* 0.0272727 = 0.000711863 loss)
I0430 16:14:58.838629 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0122781 (* 0.0272727 = 0.000334858 loss)
I0430 16:14:58.838642 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.007873 (* 0.0272727 = 0.000214718 loss)
I0430 16:14:58.838656 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00476092 (* 0.0272727 = 0.000129843 loss)
I0430 16:14:58.838670 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00317931 (* 0.0272727 = 8.67086e-05 loss)
I0430 16:14:58.838683 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00499869 (* 0.0272727 = 0.000136328 loss)
I0430 16:14:58.838697 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000546756 (* 0.0272727 = 1.49115e-05 loss)
I0430 16:14:58.838709 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.636364
I0430 16:14:58.838721 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 16:14:58.838732 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 16:14:58.838743 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 16:14:58.838755 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0430 16:14:58.838767 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 16:14:58.838778 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 16:14:58.838788 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 16:14:58.838800 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 16:14:58.838811 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0430 16:14:58.838824 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:14:58.838835 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 16:14:58.838846 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 16:14:58.838857 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:14:58.838879 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:14:58.838892 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:14:58.838903 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:14:58.838914 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:14:58.838927 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:14:58.838938 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:14:58.838949 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:14:58.838960 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:14:58.838973 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:14:58.838984 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.886364
I0430 16:14:58.838996 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.704545
I0430 16:14:58.839010 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.69857 (* 1 = 1.69857 loss)
I0430 16:14:58.839023 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.511335 (* 1 = 0.511335 loss)
I0430 16:14:58.839037 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.996989 (* 0.0909091 = 0.0906353 loss)
I0430 16:14:58.839051 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.441783 (* 0.0909091 = 0.0401621 loss)
I0430 16:14:58.839066 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.15883 (* 0.0909091 = 0.105349 loss)
I0430 16:14:58.839078 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.19652 (* 0.0909091 = 0.108774 loss)
I0430 16:14:58.839092 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.43068 (* 0.0909091 = 0.130062 loss)
I0430 16:14:58.839105 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.972493 (* 0.0909091 = 0.0884085 loss)
I0430 16:14:58.839119 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.1874 (* 0.0909091 = 0.107945 loss)
I0430 16:14:58.839133 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.826739 (* 0.0909091 = 0.0751581 loss)
I0430 16:14:58.839146 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.685576 (* 0.0909091 = 0.0623251 loss)
I0430 16:14:58.839160 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.425178 (* 0.0909091 = 0.0386526 loss)
I0430 16:14:58.839174 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.315086 (* 0.0909091 = 0.0286442 loss)
I0430 16:14:58.839186 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.400794 (* 0.0909091 = 0.0364358 loss)
I0430 16:14:58.839200 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.042823 (* 0.0909091 = 0.003893 loss)
I0430 16:14:58.839215 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0197701 (* 0.0909091 = 0.00179728 loss)
I0430 16:14:58.839228 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00720644 (* 0.0909091 = 0.000655131 loss)
I0430 16:14:58.839247 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00416803 (* 0.0909091 = 0.000378912 loss)
I0430 16:14:58.839262 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0021362 (* 0.0909091 = 0.0001942 loss)
I0430 16:14:58.839289 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00123752 (* 0.0909091 = 0.000112502 loss)
I0430 16:14:58.839308 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00110001 (* 0.0909091 = 0.000100001 loss)
I0430 16:14:58.839323 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000830196 (* 0.0909091 = 7.54724e-05 loss)
I0430 16:14:58.839336 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000625909 (* 0.0909091 = 5.69009e-05 loss)
I0430 16:14:58.839350 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000193867 (* 0.0909091 = 1.76243e-05 loss)
I0430 16:14:58.839373 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0430 16:14:58.839386 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 16:14:58.839398 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.479499
I0430 16:14:58.839409 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.506014
I0430 16:14:58.839422 15443 sgd_solver.cpp:106] Iteration 14000, lr = 0.001
I0430 16:15:29.848376 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 32.784 > 30) by scale factor 0.91508
I0430 16:17:15.833968 15443 solver.cpp:229] Iteration 14500, loss = 3.71331
I0430 16:17:15.834159 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.530612
I0430 16:17:15.834183 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 16:17:15.834197 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 16:17:15.834209 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 16:17:15.834221 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 16:17:15.834233 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 16:17:15.834245 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0430 16:17:15.834257 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 16:17:15.834270 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 16:17:15.834281 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:17:15.834293 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:17:15.834306 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:17:15.834321 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:17:15.834332 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 16:17:15.834345 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:17:15.834357 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:17:15.834368 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:17:15.834381 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:17:15.834393 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:17:15.834404 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:17:15.834415 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:17:15.834427 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:17:15.834439 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:17:15.834452 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.852273
I0430 16:17:15.834473 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.755102
I0430 16:17:15.834501 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.70734 (* 0.3 = 0.512201 loss)
I0430 16:17:15.834527 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.537561 (* 0.3 = 0.161268 loss)
I0430 16:17:15.834550 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.891073 (* 0.0272727 = 0.024302 loss)
I0430 16:17:15.834574 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.63237 (* 0.0272727 = 0.0445192 loss)
I0430 16:17:15.834599 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.68 (* 0.0272727 = 0.0458183 loss)
I0430 16:17:15.834625 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.7561 (* 0.0272727 = 0.0478937 loss)
I0430 16:17:15.834640 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.48508 (* 0.0272727 = 0.0405021 loss)
I0430 16:17:15.834655 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.91528 (* 0.0272727 = 0.0249622 loss)
I0430 16:17:15.834668 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.679655 (* 0.0272727 = 0.018536 loss)
I0430 16:17:15.834682 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.430038 (* 0.0272727 = 0.0117283 loss)
I0430 16:17:15.834697 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.703189 (* 0.0272727 = 0.0191779 loss)
I0430 16:17:15.834710 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.342625 (* 0.0272727 = 0.00934431 loss)
I0430 16:17:15.834725 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.478026 (* 0.0272727 = 0.0130371 loss)
I0430 16:17:15.834739 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.495247 (* 0.0272727 = 0.0135067 loss)
I0430 16:17:15.834777 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.412871 (* 0.0272727 = 0.0112601 loss)
I0430 16:17:15.834794 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0220051 (* 0.0272727 = 0.000600139 loss)
I0430 16:17:15.834807 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00840147 (* 0.0272727 = 0.000229131 loss)
I0430 16:17:15.834821 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00493781 (* 0.0272727 = 0.000134668 loss)
I0430 16:17:15.834836 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00327329 (* 0.0272727 = 8.92714e-05 loss)
I0430 16:17:15.834849 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00251254 (* 0.0272727 = 6.85239e-05 loss)
I0430 16:17:15.834863 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00168812 (* 0.0272727 = 4.60398e-05 loss)
I0430 16:17:15.834877 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000784502 (* 0.0272727 = 2.13955e-05 loss)
I0430 16:17:15.834892 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000236884 (* 0.0272727 = 6.46049e-06 loss)
I0430 16:17:15.834905 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 8.17969e-05 (* 0.0272727 = 2.23082e-06 loss)
I0430 16:17:15.834918 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.612245
I0430 16:17:15.834929 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 16:17:15.834941 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 16:17:15.834954 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0430 16:17:15.834964 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 16:17:15.834975 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 16:17:15.834988 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 16:17:15.835000 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 16:17:15.835011 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 16:17:15.835022 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:17:15.835034 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:17:15.835046 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:17:15.835057 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:17:15.835065 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 16:17:15.835073 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:17:15.835085 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:17:15.835100 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:17:15.835122 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:17:15.835146 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:17:15.835180 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:17:15.835202 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:17:15.835216 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:17:15.835227 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:17:15.835238 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.875
I0430 16:17:15.835250 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.836735
I0430 16:17:15.835264 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.3071 (* 0.3 = 0.392129 loss)
I0430 16:17:15.835279 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.419084 (* 0.3 = 0.125725 loss)
I0430 16:17:15.835294 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.782063 (* 0.0272727 = 0.021329 loss)
I0430 16:17:15.835307 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.09828 (* 0.0272727 = 0.029953 loss)
I0430 16:17:15.835335 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.01367 (* 0.0272727 = 0.0276454 loss)
I0430 16:17:15.835350 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.10676 (* 0.0272727 = 0.0301843 loss)
I0430 16:17:15.835367 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.42598 (* 0.0272727 = 0.0388903 loss)
I0430 16:17:15.835381 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.19117 (* 0.0272727 = 0.0324864 loss)
I0430 16:17:15.835396 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.711472 (* 0.0272727 = 0.0194038 loss)
I0430 16:17:15.835410 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.346207 (* 0.0272727 = 0.00944201 loss)
I0430 16:17:15.835424 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.586949 (* 0.0272727 = 0.0160077 loss)
I0430 16:17:15.835438 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.30231 (* 0.0272727 = 0.00824482 loss)
I0430 16:17:15.835453 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.486252 (* 0.0272727 = 0.0132614 loss)
I0430 16:17:15.835480 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.462006 (* 0.0272727 = 0.0126002 loss)
I0430 16:17:15.835496 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.420139 (* 0.0272727 = 0.0114583 loss)
I0430 16:17:15.835511 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0366296 (* 0.0272727 = 0.000998989 loss)
I0430 16:17:15.835525 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0209542 (* 0.0272727 = 0.000571479 loss)
I0430 16:17:15.835538 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.016567 (* 0.0272727 = 0.000451828 loss)
I0430 16:17:15.835552 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00775518 (* 0.0272727 = 0.000211505 loss)
I0430 16:17:15.835566 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00319631 (* 0.0272727 = 8.7172e-05 loss)
I0430 16:17:15.835580 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00142838 (* 0.0272727 = 3.89558e-05 loss)
I0430 16:17:15.835594 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00134881 (* 0.0272727 = 3.67858e-05 loss)
I0430 16:17:15.835608 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000562895 (* 0.0272727 = 1.53517e-05 loss)
I0430 16:17:15.835621 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 7.15381e-05 (* 0.0272727 = 1.95104e-06 loss)
I0430 16:17:15.835633 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.734694
I0430 16:17:15.835645 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 16:17:15.835657 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 16:17:15.835669 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 16:17:15.835680 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 16:17:15.835691 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 16:17:15.835703 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 16:17:15.835716 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 16:17:15.835726 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:17:15.835738 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:17:15.835749 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:17:15.835762 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 16:17:15.835772 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 16:17:15.835783 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 16:17:15.835794 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:17:15.835806 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:17:15.835829 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:17:15.835842 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:17:15.835855 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:17:15.835866 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:17:15.835877 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:17:15.835888 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:17:15.835901 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:17:15.835911 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.920455
I0430 16:17:15.835923 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.836735
I0430 16:17:15.835937 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.07639 (* 1 = 1.07639 loss)
I0430 16:17:15.835950 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.319135 (* 1 = 0.319135 loss)
I0430 16:17:15.835964 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.557955 (* 0.0909091 = 0.0507231 loss)
I0430 16:17:15.835978 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.674403 (* 0.0909091 = 0.0613094 loss)
I0430 16:17:15.835991 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.720536 (* 0.0909091 = 0.0655033 loss)
I0430 16:17:15.836005 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.534309 (* 0.0909091 = 0.0485735 loss)
I0430 16:17:15.836019 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.05543 (* 0.0909091 = 0.0959479 loss)
I0430 16:17:15.836032 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.28129 (* 0.0909091 = 0.116481 loss)
I0430 16:17:15.836046 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.61977 (* 0.0909091 = 0.0563428 loss)
I0430 16:17:15.836061 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.2864 (* 0.0909091 = 0.0260364 loss)
I0430 16:17:15.836073 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.730558 (* 0.0909091 = 0.0664144 loss)
I0430 16:17:15.836087 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.396325 (* 0.0909091 = 0.0360296 loss)
I0430 16:17:15.836102 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.615519 (* 0.0909091 = 0.0559563 loss)
I0430 16:17:15.836114 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.397922 (* 0.0909091 = 0.0361748 loss)
I0430 16:17:15.836128 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.475474 (* 0.0909091 = 0.0432249 loss)
I0430 16:17:15.836141 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0138226 (* 0.0909091 = 0.0012566 loss)
I0430 16:17:15.836155 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00104841 (* 0.0909091 = 9.53103e-05 loss)
I0430 16:17:15.836170 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000480315 (* 0.0909091 = 4.3665e-05 loss)
I0430 16:17:15.836184 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000557875 (* 0.0909091 = 5.07159e-05 loss)
I0430 16:17:15.836199 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000256092 (* 0.0909091 = 2.32811e-05 loss)
I0430 16:17:15.836215 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000157149 (* 0.0909091 = 1.42863e-05 loss)
I0430 16:17:15.836230 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000133917 (* 0.0909091 = 1.21743e-05 loss)
I0430 16:17:15.836244 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 9.63217e-05 (* 0.0909091 = 8.75652e-06 loss)
I0430 16:17:15.836258 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 7.13337e-05 (* 0.0909091 = 6.48488e-06 loss)
I0430 16:17:15.836271 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0430 16:17:15.836282 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0430 16:17:15.836303 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.372044
I0430 16:17:15.836316 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.429915
I0430 16:17:15.836328 15443 sgd_solver.cpp:106] Iteration 14500, lr = 0.001
I0430 16:17:23.009541 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 34.9143 > 30) by scale factor 0.859246
I0430 16:19:32.594528 15443 solver.cpp:338] Iteration 15000, Testing net (#0)
I0430 16:20:13.490738 15443 solver.cpp:393] Test loss: 2.31174
I0430 16:20:13.490890 15443 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.637096
I0430 16:20:13.490911 15443 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.85
I0430 16:20:13.490924 15443 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.72
I0430 16:20:13.490936 15443 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.576
I0430 16:20:13.490948 15443 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.573
I0430 16:20:13.490959 15443 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.556
I0430 16:20:13.490972 15443 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.669
I0430 16:20:13.490983 15443 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.813
I0430 16:20:13.490994 15443 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.898
I0430 16:20:13.491005 15443 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.983
I0430 16:20:13.491017 15443 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.994
I0430 16:20:13.491029 15443 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.997
I0430 16:20:13.491040 15443 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.999
I0430 16:20:13.491051 15443 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 1
I0430 16:20:13.491062 15443 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 1
I0430 16:20:13.491073 15443 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 1
I0430 16:20:13.491085 15443 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 1
I0430 16:20:13.491096 15443 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 1
I0430 16:20:13.491107 15443 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 1
I0430 16:20:13.491118 15443 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 1
I0430 16:20:13.491129 15443 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 1
I0430 16:20:13.491142 15443 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0430 16:20:13.491153 15443 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0430 16:20:13.491164 15443 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.893001
I0430 16:20:13.491175 15443 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.862959
I0430 16:20:13.491191 15443 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.16654 (* 0.3 = 0.349962 loss)
I0430 16:20:13.491206 15443 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.352524 (* 0.3 = 0.105757 loss)
I0430 16:20:13.491220 15443 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.57707 (* 0.0272727 = 0.0157383 loss)
I0430 16:20:13.491233 15443 solver.cpp:406]     Test net output #28: loss1/loss02 = 0.988097 (* 0.0272727 = 0.0269481 loss)
I0430 16:20:13.491247 15443 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.38917 (* 0.0272727 = 0.0378866 loss)
I0430 16:20:13.491261 15443 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.36815 (* 0.0272727 = 0.0373133 loss)
I0430 16:20:13.491274 15443 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.3444 (* 0.0272727 = 0.0366654 loss)
I0430 16:20:13.491287 15443 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.01898 (* 0.0272727 = 0.0277904 loss)
I0430 16:20:13.491300 15443 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.576318 (* 0.0272727 = 0.0157178 loss)
I0430 16:20:13.491317 15443 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.300419 (* 0.0272727 = 0.00819325 loss)
I0430 16:20:13.491331 15443 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.0783467 (* 0.0272727 = 0.00213673 loss)
I0430 16:20:13.491345 15443 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.033004 (* 0.0272727 = 0.000900109 loss)
I0430 16:20:13.491361 15443 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.0144523 (* 0.0272727 = 0.000394154 loss)
I0430 16:20:13.491375 15443 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.00893894 (* 0.0272727 = 0.000243789 loss)
I0430 16:20:13.491389 15443 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.00614785 (* 0.0272727 = 0.000167669 loss)
I0430 16:20:13.491423 15443 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.00385937 (* 0.0272727 = 0.000105255 loss)
I0430 16:20:13.491438 15443 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.00248812 (* 0.0272727 = 6.78578e-05 loss)
I0430 16:20:13.491452 15443 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.00152938 (* 0.0272727 = 4.17104e-05 loss)
I0430 16:20:13.491479 15443 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.0010036 (* 0.0272727 = 2.7371e-05 loss)
I0430 16:20:13.491495 15443 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.000669227 (* 0.0272727 = 1.82517e-05 loss)
I0430 16:20:13.491509 15443 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.000530841 (* 0.0272727 = 1.44775e-05 loss)
I0430 16:20:13.491523 15443 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.000439814 (* 0.0272727 = 1.19949e-05 loss)
I0430 16:20:13.491536 15443 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.000377546 (* 0.0272727 = 1.02967e-05 loss)
I0430 16:20:13.491549 15443 solver.cpp:406]     Test net output #48: loss1/loss22 = 0.000337808 (* 0.0272727 = 9.21296e-06 loss)
I0430 16:20:13.491561 15443 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.738115
I0430 16:20:13.491574 15443 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.882
I0430 16:20:13.491585 15443 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.831
I0430 16:20:13.491595 15443 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.728
I0430 16:20:13.491606 15443 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.649
I0430 16:20:13.491618 15443 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.644
I0430 16:20:13.491629 15443 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.71
I0430 16:20:13.491641 15443 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.845
I0430 16:20:13.491652 15443 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.913
I0430 16:20:13.491662 15443 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.983
I0430 16:20:13.491674 15443 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.994
I0430 16:20:13.491685 15443 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.998
I0430 16:20:13.491696 15443 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.999
I0430 16:20:13.491708 15443 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.999
I0430 16:20:13.491719 15443 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 1
I0430 16:20:13.491729 15443 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 1
I0430 16:20:13.491740 15443 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 1
I0430 16:20:13.491751 15443 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 1
I0430 16:20:13.491762 15443 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 1
I0430 16:20:13.491772 15443 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 1
I0430 16:20:13.491783 15443 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 1
I0430 16:20:13.491794 15443 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0430 16:20:13.491806 15443 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0430 16:20:13.491816 15443 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.921092
I0430 16:20:13.491827 15443 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.905399
I0430 16:20:13.491842 15443 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.873241 (* 0.3 = 0.261972 loss)
I0430 16:20:13.491854 15443 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.269415 (* 0.3 = 0.0808247 loss)
I0430 16:20:13.491868 15443 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.505428 (* 0.0272727 = 0.0137844 loss)
I0430 16:20:13.491881 15443 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.665347 (* 0.0272727 = 0.0181458 loss)
I0430 16:20:13.491909 15443 solver.cpp:406]     Test net output #78: loss2/loss03 = 0.990997 (* 0.0272727 = 0.0270272 loss)
I0430 16:20:13.491925 15443 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.11384 (* 0.0272727 = 0.0303776 loss)
I0430 16:20:13.491938 15443 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.13842 (* 0.0272727 = 0.0310479 loss)
I0430 16:20:13.491952 15443 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.887869 (* 0.0272727 = 0.0242146 loss)
I0430 16:20:13.491967 15443 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.496807 (* 0.0272727 = 0.0135493 loss)
I0430 16:20:13.491981 15443 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.271317 (* 0.0272727 = 0.00739955 loss)
I0430 16:20:13.491997 15443 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.0730923 (* 0.0272727 = 0.00199343 loss)
I0430 16:20:13.492010 15443 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.0329949 (* 0.0272727 = 0.000899862 loss)
I0430 16:20:13.492024 15443 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.0150452 (* 0.0272727 = 0.000410323 loss)
I0430 16:20:13.492038 15443 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.00917852 (* 0.0272727 = 0.000250323 loss)
I0430 16:20:13.492051 15443 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.00600894 (* 0.0272727 = 0.00016388 loss)
I0430 16:20:13.492064 15443 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.00382909 (* 0.0272727 = 0.00010443 loss)
I0430 16:20:13.492079 15443 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.0022021 (* 0.0272727 = 6.00573e-05 loss)
I0430 16:20:13.492091 15443 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.00115751 (* 0.0272727 = 3.15685e-05 loss)
I0430 16:20:13.492105 15443 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.00050927 (* 0.0272727 = 1.38892e-05 loss)
I0430 16:20:13.492118 15443 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.000271765 (* 0.0272727 = 7.41178e-06 loss)
I0430 16:20:13.492131 15443 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.000196322 (* 0.0272727 = 5.35423e-06 loss)
I0430 16:20:13.492144 15443 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.000150155 (* 0.0272727 = 4.09515e-06 loss)
I0430 16:20:13.492157 15443 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.000141235 (* 0.0272727 = 3.85186e-06 loss)
I0430 16:20:13.492171 15443 solver.cpp:406]     Test net output #97: loss2/loss22 = 0.000112593 (* 0.0272727 = 3.07072e-06 loss)
I0430 16:20:13.492182 15443 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.850162
I0430 16:20:13.492193 15443 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.9
I0430 16:20:13.492204 15443 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.874
I0430 16:20:13.492215 15443 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.834
I0430 16:20:13.492226 15443 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.832
I0430 16:20:13.492238 15443 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.831
I0430 16:20:13.492249 15443 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.857
I0430 16:20:13.492259 15443 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.893
I0430 16:20:13.492270 15443 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.943
I0430 16:20:13.492281 15443 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.983
I0430 16:20:13.492292 15443 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.992
I0430 16:20:13.492303 15443 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.998
I0430 16:20:13.492314 15443 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.999
I0430 16:20:13.492326 15443 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.999
I0430 16:20:13.492336 15443 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.999
I0430 16:20:13.492347 15443 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 1
I0430 16:20:13.492358 15443 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 1
I0430 16:20:13.492383 15443 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 1
I0430 16:20:13.492395 15443 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 1
I0430 16:20:13.492406 15443 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 1
I0430 16:20:13.492418 15443 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 1
I0430 16:20:13.492429 15443 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0430 16:20:13.492439 15443 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0430 16:20:13.492450 15443 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.953455
I0430 16:20:13.492461 15443 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.925259
I0430 16:20:13.492475 15443 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.580015 (* 1 = 0.580015 loss)
I0430 16:20:13.492488 15443 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.184664 (* 1 = 0.184664 loss)
I0430 16:20:13.492501 15443 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.408 (* 0.0909091 = 0.0370909 loss)
I0430 16:20:13.492514 15443 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.509898 (* 0.0909091 = 0.0463544 loss)
I0430 16:20:13.492527 15443 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.656089 (* 0.0909091 = 0.0596445 loss)
I0430 16:20:13.492542 15443 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.603999 (* 0.0909091 = 0.054909 loss)
I0430 16:20:13.492554 15443 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.660394 (* 0.0909091 = 0.0600358 loss)
I0430 16:20:13.492568 15443 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.528315 (* 0.0909091 = 0.0480286 loss)
I0430 16:20:13.492580 15443 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.357087 (* 0.0909091 = 0.0324625 loss)
I0430 16:20:13.492594 15443 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.195411 (* 0.0909091 = 0.0177647 loss)
I0430 16:20:13.492607 15443 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.0645271 (* 0.0909091 = 0.0058661 loss)
I0430 16:20:13.492621 15443 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.0333775 (* 0.0909091 = 0.00303432 loss)
I0430 16:20:13.492635 15443 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.0143239 (* 0.0909091 = 0.00130217 loss)
I0430 16:20:13.492648 15443 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.00976188 (* 0.0909091 = 0.000887443 loss)
I0430 16:20:13.492661 15443 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.00641939 (* 0.0909091 = 0.000583581 loss)
I0430 16:20:13.492674 15443 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.00362161 (* 0.0909091 = 0.000329237 loss)
I0430 16:20:13.492688 15443 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.0019131 (* 0.0909091 = 0.000173918 loss)
I0430 16:20:13.492702 15443 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.000968893 (* 0.0909091 = 8.80812e-05 loss)
I0430 16:20:13.492715 15443 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.000395066 (* 0.0909091 = 3.5915e-05 loss)
I0430 16:20:13.492728 15443 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.000216596 (* 0.0909091 = 1.96905e-05 loss)
I0430 16:20:13.492741 15443 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.000156624 (* 0.0909091 = 1.42386e-05 loss)
I0430 16:20:13.492754 15443 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.000102159 (* 0.0909091 = 9.28716e-06 loss)
I0430 16:20:13.492769 15443 solver.cpp:406]     Test net output #145: loss3/loss21 = 7.95252e-05 (* 0.0909091 = 7.22956e-06 loss)
I0430 16:20:13.492781 15443 solver.cpp:406]     Test net output #146: loss3/loss22 = 6.89089e-05 (* 0.0909091 = 6.26445e-06 loss)
I0430 16:20:13.492792 15443 solver.cpp:406]     Test net output #147: total_accuracy = 0.616
I0430 16:20:13.492805 15443 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.577
I0430 16:20:13.492815 15443 solver.cpp:406]     Test net output #149: total_confidence = 0.544777
I0430 16:20:13.492836 15443 solver.cpp:406]     Test net output #150: total_confidence_not_rec = 0.517389
I0430 16:20:13.492851 15443 solver.cpp:338] Iteration 15000, Testing net (#1)
I0430 16:20:54.379060 15443 solver.cpp:393] Test loss: 3.14952
I0430 16:20:54.379206 15443 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.595637
I0430 16:20:54.379235 15443 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.789
I0430 16:20:54.379256 15443 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.675
I0430 16:20:54.379274 15443 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.541
I0430 16:20:54.379294 15443 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.536
I0430 16:20:54.379320 15443 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.553
I0430 16:20:54.379343 15443 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.649
I0430 16:20:54.379364 15443 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.763
I0430 16:20:54.379384 15443 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.856
I0430 16:20:54.379405 15443 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.905
I0430 16:20:54.379427 15443 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.922
I0430 16:20:54.379449 15443 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.932
I0430 16:20:54.379490 15443 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.939
I0430 16:20:54.379513 15443 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.956
I0430 16:20:54.379537 15443 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 0.963
I0430 16:20:54.379560 15443 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 0.967
I0430 16:20:54.379582 15443 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 0.977
I0430 16:20:54.379604 15443 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 0.99
I0430 16:20:54.379626 15443 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 0.995
I0430 16:20:54.379647 15443 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 0.998
I0430 16:20:54.379667 15443 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 0.998
I0430 16:20:54.379696 15443 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 0.999
I0430 16:20:54.379716 15443 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0430 16:20:54.379737 15443 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.858956
I0430 16:20:54.379758 15443 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.822903
I0430 16:20:54.379786 15443 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.32043 (* 0.3 = 0.396129 loss)
I0430 16:20:54.379812 15443 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.467396 (* 0.3 = 0.140219 loss)
I0430 16:20:54.379838 15443 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.792019 (* 0.0272727 = 0.0216005 loss)
I0430 16:20:54.379863 15443 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.13404 (* 0.0272727 = 0.0309283 loss)
I0430 16:20:54.379889 15443 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.46923 (* 0.0272727 = 0.04007 loss)
I0430 16:20:54.379914 15443 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.49461 (* 0.0272727 = 0.0407622 loss)
I0430 16:20:54.379940 15443 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.37821 (* 0.0272727 = 0.0375876 loss)
I0430 16:20:54.379964 15443 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.09592 (* 0.0272727 = 0.0298887 loss)
I0430 16:20:54.379988 15443 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.773968 (* 0.0272727 = 0.0211082 loss)
I0430 16:20:54.380013 15443 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.492541 (* 0.0272727 = 0.0134329 loss)
I0430 16:20:54.380040 15443 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.340611 (* 0.0272727 = 0.0092894 loss)
I0430 16:20:54.380065 15443 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.274882 (* 0.0272727 = 0.00749679 loss)
I0430 16:20:54.380090 15443 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.213295 (* 0.0272727 = 0.00581715 loss)
I0430 16:20:54.380116 15443 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.210339 (* 0.0272727 = 0.00573651 loss)
I0430 16:20:54.380167 15443 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.174706 (* 0.0272727 = 0.00476472 loss)
I0430 16:20:54.380195 15443 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.144742 (* 0.0272727 = 0.00394752 loss)
I0430 16:20:54.380220 15443 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.135373 (* 0.0272727 = 0.00369198 loss)
I0430 16:20:54.380247 15443 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.0907345 (* 0.0272727 = 0.00247458 loss)
I0430 16:20:54.380273 15443 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.0441327 (* 0.0272727 = 0.00120362 loss)
I0430 16:20:54.380300 15443 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.02666 (* 0.0272727 = 0.00072709 loss)
I0430 16:20:54.380326 15443 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.0136762 (* 0.0272727 = 0.000372987 loss)
I0430 16:20:54.380350 15443 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.0128926 (* 0.0272727 = 0.000351616 loss)
I0430 16:20:54.380380 15443 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.0104609 (* 0.0272727 = 0.000285297 loss)
I0430 16:20:54.380409 15443 solver.cpp:406]     Test net output #48: loss1/loss22 = 3.74658e-05 (* 0.0272727 = 1.0218e-06 loss)
I0430 16:20:54.380432 15443 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.689351
I0430 16:20:54.380448 15443 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.829
I0430 16:20:54.380472 15443 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.779
I0430 16:20:54.380494 15443 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.691
I0430 16:20:54.380517 15443 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.618
I0430 16:20:54.380537 15443 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.634
I0430 16:20:54.380556 15443 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.697
I0430 16:20:54.380578 15443 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.787
I0430 16:20:54.380599 15443 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.843
I0430 16:20:54.380620 15443 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.898
I0430 16:20:54.380641 15443 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.926
I0430 16:20:54.380663 15443 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.943
I0430 16:20:54.380683 15443 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.941
I0430 16:20:54.380704 15443 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.953
I0430 16:20:54.380731 15443 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.962
I0430 16:20:54.380753 15443 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.97
I0430 16:20:54.380772 15443 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 0.977
I0430 16:20:54.380795 15443 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 0.99
I0430 16:20:54.380815 15443 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 0.995
I0430 16:20:54.380836 15443 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 0.998
I0430 16:20:54.380857 15443 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 0.998
I0430 16:20:54.380878 15443 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 0.999
I0430 16:20:54.380897 15443 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0430 16:20:54.380918 15443 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.88782
I0430 16:20:54.380939 15443 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.868597
I0430 16:20:54.380964 15443 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 1.04309 (* 0.3 = 0.312926 loss)
I0430 16:20:54.380990 15443 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.379928 (* 0.3 = 0.113978 loss)
I0430 16:20:54.381016 15443 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.658743 (* 0.0272727 = 0.0179657 loss)
I0430 16:20:54.381042 15443 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.801951 (* 0.0272727 = 0.0218714 loss)
I0430 16:20:54.381085 15443 solver.cpp:406]     Test net output #78: loss2/loss03 = 1.10221 (* 0.0272727 = 0.0300604 loss)
I0430 16:20:54.381113 15443 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.24274 (* 0.0272727 = 0.0338928 loss)
I0430 16:20:54.381137 15443 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.1721 (* 0.0272727 = 0.0319664 loss)
I0430 16:20:54.381163 15443 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.954927 (* 0.0272727 = 0.0260435 loss)
I0430 16:20:54.381188 15443 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.681773 (* 0.0272727 = 0.0185938 loss)
I0430 16:20:54.381213 15443 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.487068 (* 0.0272727 = 0.0132837 loss)
I0430 16:20:54.381238 15443 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.341831 (* 0.0272727 = 0.00932265 loss)
I0430 16:20:54.381265 15443 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.27576 (* 0.0272727 = 0.00752073 loss)
I0430 16:20:54.381290 15443 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.217402 (* 0.0272727 = 0.00592915 loss)
I0430 16:20:54.381316 15443 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.206432 (* 0.0272727 = 0.00562997 loss)
I0430 16:20:54.381341 15443 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.1744 (* 0.0272727 = 0.00475635 loss)
I0430 16:20:54.381366 15443 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.144184 (* 0.0272727 = 0.00393229 loss)
I0430 16:20:54.381389 15443 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.135951 (* 0.0272727 = 0.00370775 loss)
I0430 16:20:54.381415 15443 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.0941084 (* 0.0272727 = 0.00256659 loss)
I0430 16:20:54.381445 15443 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.0486505 (* 0.0272727 = 0.00132683 loss)
I0430 16:20:54.381469 15443 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.0249154 (* 0.0272727 = 0.00067951 loss)
I0430 16:20:54.381494 15443 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.0136426 (* 0.0272727 = 0.000372071 loss)
I0430 16:20:54.381520 15443 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.0133093 (* 0.0272727 = 0.00036298 loss)
I0430 16:20:54.381544 15443 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.00735594 (* 0.0272727 = 0.000200616 loss)
I0430 16:20:54.381570 15443 solver.cpp:406]     Test net output #97: loss2/loss22 = 9.56856e-05 (* 0.0272727 = 2.60961e-06 loss)
I0430 16:20:54.381592 15443 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.788302
I0430 16:20:54.381614 15443 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.861
I0430 16:20:54.381634 15443 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.827
I0430 16:20:54.381654 15443 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.812
I0430 16:20:54.381675 15443 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.796
I0430 16:20:54.381696 15443 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.786
I0430 16:20:54.381716 15443 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.828
I0430 16:20:54.381736 15443 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.825
I0430 16:20:54.381758 15443 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.873
I0430 16:20:54.381783 15443 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.912
I0430 16:20:54.381804 15443 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.922
I0430 16:20:54.381825 15443 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.94
I0430 16:20:54.381846 15443 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.941
I0430 16:20:54.381865 15443 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.957
I0430 16:20:54.381886 15443 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.964
I0430 16:20:54.381906 15443 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.973
I0430 16:20:54.381927 15443 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 0.978
I0430 16:20:54.381963 15443 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 0.99
I0430 16:20:54.381986 15443 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 0.995
I0430 16:20:54.382007 15443 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 0.998
I0430 16:20:54.382026 15443 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 0.998
I0430 16:20:54.382046 15443 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 0.999
I0430 16:20:54.382068 15443 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0430 16:20:54.382088 15443 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.917273
I0430 16:20:54.382108 15443 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.894465
I0430 16:20:54.382133 15443 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.772464 (* 1 = 0.772464 loss)
I0430 16:20:54.382159 15443 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.297482 (* 1 = 0.297482 loss)
I0430 16:20:54.382184 15443 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.573875 (* 0.0909091 = 0.0521704 loss)
I0430 16:20:54.382210 15443 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.61535 (* 0.0909091 = 0.0559409 loss)
I0430 16:20:54.382235 15443 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.714904 (* 0.0909091 = 0.0649912 loss)
I0430 16:20:54.382259 15443 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.736726 (* 0.0909091 = 0.0669751 loss)
I0430 16:20:54.382283 15443 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.749801 (* 0.0909091 = 0.0681637 loss)
I0430 16:20:54.382308 15443 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.64165 (* 0.0909091 = 0.0583318 loss)
I0430 16:20:54.382333 15443 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.56115 (* 0.0909091 = 0.0510137 loss)
I0430 16:20:54.382357 15443 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.404153 (* 0.0909091 = 0.0367412 loss)
I0430 16:20:54.382382 15443 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.311215 (* 0.0909091 = 0.0282922 loss)
I0430 16:20:54.382408 15443 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.260942 (* 0.0909091 = 0.023722 loss)
I0430 16:20:54.382432 15443 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.202941 (* 0.0909091 = 0.0184492 loss)
I0430 16:20:54.382457 15443 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.188634 (* 0.0909091 = 0.0171485 loss)
I0430 16:20:54.382485 15443 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.162975 (* 0.0909091 = 0.0148159 loss)
I0430 16:20:54.382511 15443 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.129277 (* 0.0909091 = 0.0117524 loss)
I0430 16:20:54.382535 15443 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.120078 (* 0.0909091 = 0.0109162 loss)
I0430 16:20:54.382560 15443 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.0770295 (* 0.0909091 = 0.00700268 loss)
I0430 16:20:54.382586 15443 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.0431558 (* 0.0909091 = 0.00392325 loss)
I0430 16:20:54.382611 15443 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.019991 (* 0.0909091 = 0.00181736 loss)
I0430 16:20:54.382637 15443 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.0113818 (* 0.0909091 = 0.0010347 loss)
I0430 16:20:54.382663 15443 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.0106981 (* 0.0909091 = 0.000972551 loss)
I0430 16:20:54.382688 15443 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.00672681 (* 0.0909091 = 0.000611528 loss)
I0430 16:20:54.382714 15443 solver.cpp:406]     Test net output #146: loss3/loss22 = 0.000118592 (* 0.0909091 = 1.07811e-05 loss)
I0430 16:20:54.382735 15443 solver.cpp:406]     Test net output #147: total_accuracy = 0.514
I0430 16:20:54.382756 15443 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.515
I0430 16:20:54.382776 15443 solver.cpp:406]     Test net output #149: total_confidence = 0.472529
I0430 16:20:54.382812 15443 solver.cpp:406]     Test net output #150: total_confidence_not_rec = 0.45119
I0430 16:20:54.563714 15443 solver.cpp:229] Iteration 15000, loss = 3.6514
I0430 16:20:54.563792 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.574468
I0430 16:20:54.563819 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 16:20:54.563840 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 16:20:54.563860 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 16:20:54.563882 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 16:20:54.563904 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.75
I0430 16:20:54.563926 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0430 16:20:54.563948 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 16:20:54.563971 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 16:20:54.563993 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:20:54.564016 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:20:54.564043 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 16:20:54.564065 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 16:20:54.564087 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:20:54.564113 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:20:54.564136 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:20:54.564157 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:20:54.564182 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:20:54.564204 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:20:54.564225 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:20:54.564249 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:20:54.564270 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:20:54.564291 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:20:54.564313 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.880682
I0430 16:20:54.564337 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.744681
I0430 16:20:54.564365 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.64877 (* 0.3 = 0.494631 loss)
I0430 16:20:54.564393 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.482111 (* 0.3 = 0.144633 loss)
I0430 16:20:54.564420 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.604967 (* 0.0272727 = 0.0164991 loss)
I0430 16:20:54.564446 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.759294 (* 0.0272727 = 0.020708 loss)
I0430 16:20:54.564474 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.63776 (* 0.0272727 = 0.0719388 loss)
I0430 16:20:54.564501 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.87898 (* 0.0272727 = 0.0512449 loss)
I0430 16:20:54.564527 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.02212 (* 0.0272727 = 0.0278759 loss)
I0430 16:20:54.564553 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.48926 (* 0.0272727 = 0.0406162 loss)
I0430 16:20:54.564580 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.830166 (* 0.0272727 = 0.0226409 loss)
I0430 16:20:54.564611 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.542356 (* 0.0272727 = 0.0147915 loss)
I0430 16:20:54.564640 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.200781 (* 0.0272727 = 0.00547586 loss)
I0430 16:20:54.564667 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.469468 (* 0.0272727 = 0.0128037 loss)
I0430 16:20:54.564694 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.000523084 (* 0.0272727 = 1.42659e-05 loss)
I0430 16:20:54.564764 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 8.52748e-05 (* 0.0272727 = 2.32568e-06 loss)
I0430 16:20:54.564795 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 4.48046e-05 (* 0.0272727 = 1.22194e-06 loss)
I0430 16:20:54.564822 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 2.39475e-05 (* 0.0272727 = 6.53114e-07 loss)
I0430 16:20:54.564851 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 1.28453e-05 (* 0.0272727 = 3.50326e-07 loss)
I0430 16:20:54.564878 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 1.02821e-05 (* 0.0272727 = 2.80422e-07 loss)
I0430 16:20:54.564904 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 7.83824e-06 (* 0.0272727 = 2.1377e-07 loss)
I0430 16:20:54.564932 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 2.56302e-06 (* 0.0272727 = 6.99007e-08 loss)
I0430 16:20:54.564961 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 2.02657e-06 (* 0.0272727 = 5.52702e-08 loss)
I0430 16:20:54.564990 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 1.14739e-06 (* 0.0272727 = 3.12926e-08 loss)
I0430 16:20:54.565021 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 3.68064e-06 (* 0.0272727 = 1.00381e-07 loss)
I0430 16:20:54.565048 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.83285e-06 (* 0.0272727 = 4.99869e-08 loss)
I0430 16:20:54.565071 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.553191
I0430 16:20:54.565100 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 16:20:54.565124 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 16:20:54.565146 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 16:20:54.565168 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 16:20:54.565191 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0430 16:20:54.565213 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.375
I0430 16:20:54.565234 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0430 16:20:54.565258 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 16:20:54.565279 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:20:54.565301 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:20:54.565323 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 16:20:54.565346 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 16:20:54.565366 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:20:54.565389 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:20:54.565412 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:20:54.565433 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:20:54.565454 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:20:54.565476 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:20:54.565498 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:20:54.565521 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:20:54.565543 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:20:54.565564 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:20:54.565587 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.869318
I0430 16:20:54.565609 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.829787
I0430 16:20:54.565634 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.40288 (* 0.3 = 0.420864 loss)
I0430 16:20:54.565666 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.442329 (* 0.3 = 0.132699 loss)
I0430 16:20:54.565711 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.77814 (* 0.0272727 = 0.021222 loss)
I0430 16:20:54.565740 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.665567 (* 0.0272727 = 0.0181518 loss)
I0430 16:20:54.565768 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.11646 (* 0.0272727 = 0.0577216 loss)
I0430 16:20:54.565794 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.50147 (* 0.0272727 = 0.0409492 loss)
I0430 16:20:54.565820 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.916082 (* 0.0272727 = 0.024984 loss)
I0430 16:20:54.565846 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.38324 (* 0.0272727 = 0.0377247 loss)
I0430 16:20:54.565874 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.478992 (* 0.0272727 = 0.0130634 loss)
I0430 16:20:54.565901 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.413437 (* 0.0272727 = 0.0112756 loss)
I0430 16:20:54.565927 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.311395 (* 0.0272727 = 0.00849259 loss)
I0430 16:20:54.565954 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.44452 (* 0.0272727 = 0.0121233 loss)
I0430 16:20:54.565980 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.018496 (* 0.0272727 = 0.000504437 loss)
I0430 16:20:54.566004 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.022157 (* 0.0272727 = 0.00060428 loss)
I0430 16:20:54.566030 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00554729 (* 0.0272727 = 0.00015129 loss)
I0430 16:20:54.566058 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00291324 (* 0.0272727 = 7.94519e-05 loss)
I0430 16:20:54.566084 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00169516 (* 0.0272727 = 4.62317e-05 loss)
I0430 16:20:54.566110 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000907457 (* 0.0272727 = 2.47488e-05 loss)
I0430 16:20:54.566141 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000443178 (* 0.0272727 = 1.20867e-05 loss)
I0430 16:20:54.566169 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000274445 (* 0.0272727 = 7.48487e-06 loss)
I0430 16:20:54.566196 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000171779 (* 0.0272727 = 4.68489e-06 loss)
I0430 16:20:54.566223 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 2.43434e-05 (* 0.0272727 = 6.63911e-07 loss)
I0430 16:20:54.566249 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 3.30626e-05 (* 0.0272727 = 9.01707e-07 loss)
I0430 16:20:54.566275 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.65414e-05 (* 0.0272727 = 4.51128e-07 loss)
I0430 16:20:54.566299 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.680851
I0430 16:20:54.566320 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:20:54.566341 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 16:20:54.566364 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 16:20:54.566387 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 16:20:54.566408 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 16:20:54.566431 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 16:20:54.566454 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 16:20:54.566475 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:20:54.566498 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 16:20:54.566519 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:20:54.566540 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:20:54.566561 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 16:20:54.566582 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:20:54.566620 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:20:54.566644 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:20:54.566665 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:20:54.566686 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:20:54.566711 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:20:54.566733 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:20:54.566754 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:20:54.566777 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:20:54.566798 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:20:54.566819 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.903409
I0430 16:20:54.566841 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.893617
I0430 16:20:54.566867 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.03489 (* 1 = 1.03489 loss)
I0430 16:20:54.566893 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.336262 (* 1 = 0.336262 loss)
I0430 16:20:54.566920 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.158769 (* 0.0909091 = 0.0144335 loss)
I0430 16:20:54.566948 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.539015 (* 0.0909091 = 0.0490013 loss)
I0430 16:20:54.566973 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.49356 (* 0.0909091 = 0.135778 loss)
I0430 16:20:54.566999 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.626773 (* 0.0909091 = 0.0569794 loss)
I0430 16:20:54.567026 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.82397 (* 0.0909091 = 0.0749064 loss)
I0430 16:20:54.567052 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.768985 (* 0.0909091 = 0.0699077 loss)
I0430 16:20:54.567077 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.781333 (* 0.0909091 = 0.0710303 loss)
I0430 16:20:54.567104 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.475814 (* 0.0909091 = 0.0432558 loss)
I0430 16:20:54.567131 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.0948828 (* 0.0909091 = 0.00862571 loss)
I0430 16:20:54.567157 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.381709 (* 0.0909091 = 0.0347008 loss)
I0430 16:20:54.567188 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00155806 (* 0.0909091 = 0.000141642 loss)
I0430 16:20:54.567217 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.000754966 (* 0.0909091 = 6.86332e-05 loss)
I0430 16:20:54.567244 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000291384 (* 0.0909091 = 2.64895e-05 loss)
I0430 16:20:54.567270 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000109989 (* 0.0909091 = 9.99896e-06 loss)
I0430 16:20:54.567297 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 4.94046e-05 (* 0.0909091 = 4.49133e-06 loss)
I0430 16:20:54.567323 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 1.33518e-05 (* 0.0909091 = 1.2138e-06 loss)
I0430 16:20:54.567350 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 1.36499e-05 (* 0.0909091 = 1.2409e-06 loss)
I0430 16:20:54.567378 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 4.57469e-06 (* 0.0909091 = 4.15881e-07 loss)
I0430 16:20:54.567404 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 2.22028e-06 (* 0.0909091 = 2.01844e-07 loss)
I0430 16:20:54.567430 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 8.04664e-07 (* 0.0909091 = 7.31512e-08 loss)
I0430 16:20:54.567457 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 6.40751e-07 (* 0.0909091 = 5.82501e-08 loss)
I0430 16:20:54.567504 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 4.61936e-07 (* 0.0909091 = 4.19942e-08 loss)
I0430 16:20:54.567546 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 16:20:54.567571 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 16:20:54.567594 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.394119
I0430 16:20:54.567615 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.408941
I0430 16:20:54.567632 15443 sgd_solver.cpp:106] Iteration 15000, lr = 0.001
I0430 16:23:11.592226 15443 solver.cpp:229] Iteration 15500, loss = 3.74013
I0430 16:23:11.592447 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.275362
I0430 16:23:11.592468 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 16:23:11.592481 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 16:23:11.592494 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0430 16:23:11.592505 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0
I0430 16:23:11.592517 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 16:23:11.592530 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0430 16:23:11.592541 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0430 16:23:11.592552 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 16:23:11.592564 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 16:23:11.592576 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.625
I0430 16:23:11.592588 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0430 16:23:11.592600 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:23:11.592612 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.75
I0430 16:23:11.592623 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.75
I0430 16:23:11.592636 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.75
I0430 16:23:11.592648 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.75
I0430 16:23:11.592659 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:23:11.592671 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:23:11.592682 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:23:11.592694 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:23:11.592706 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:23:11.592717 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:23:11.592730 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.698864
I0430 16:23:11.592741 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.521739
I0430 16:23:11.592757 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.43534 (* 0.3 = 0.730601 loss)
I0430 16:23:11.592772 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.04379 (* 0.3 = 0.313138 loss)
I0430 16:23:11.592790 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 2.11311 (* 0.0272727 = 0.0576302 loss)
I0430 16:23:11.592818 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.61736 (* 0.0272727 = 0.0441097 loss)
I0430 16:23:11.592845 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.09882 (* 0.0272727 = 0.0572407 loss)
I0430 16:23:11.592870 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.24301 (* 0.0272727 = 0.0611729 loss)
I0430 16:23:11.592895 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.9293 (* 0.0272727 = 0.0526173 loss)
I0430 16:23:11.592921 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.73706 (* 0.0272727 = 0.0473743 loss)
I0430 16:23:11.592941 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.97695 (* 0.0272727 = 0.0539169 loss)
I0430 16:23:11.592955 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.17918 (* 0.0272727 = 0.0321595 loss)
I0430 16:23:11.592969 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 1.49179 (* 0.0272727 = 0.0406852 loss)
I0430 16:23:11.592983 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 1.62951 (* 0.0272727 = 0.0444411 loss)
I0430 16:23:11.592996 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.69773 (* 0.0272727 = 0.019029 loss)
I0430 16:23:11.593010 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.693826 (* 0.0272727 = 0.0189225 loss)
I0430 16:23:11.593042 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.920275 (* 0.0272727 = 0.0250984 loss)
I0430 16:23:11.593058 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.848983 (* 0.0272727 = 0.0231541 loss)
I0430 16:23:11.593072 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.950598 (* 0.0272727 = 0.0259254 loss)
I0430 16:23:11.593086 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 1.13631 (* 0.0272727 = 0.0309902 loss)
I0430 16:23:11.593101 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0628577 (* 0.0272727 = 0.0017143 loss)
I0430 16:23:11.593116 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0311471 (* 0.0272727 = 0.000849467 loss)
I0430 16:23:11.593129 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.0179176 (* 0.0272727 = 0.000488663 loss)
I0430 16:23:11.593143 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00767801 (* 0.0272727 = 0.0002094 loss)
I0430 16:23:11.593158 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00472975 (* 0.0272727 = 0.000128993 loss)
I0430 16:23:11.593170 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00379142 (* 0.0272727 = 0.000103402 loss)
I0430 16:23:11.593183 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.434783
I0430 16:23:11.593195 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.5
I0430 16:23:11.593206 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 16:23:11.593219 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0430 16:23:11.593230 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0430 16:23:11.593241 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.25
I0430 16:23:11.593253 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 16:23:11.593264 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 16:23:11.593276 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 16:23:11.593287 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0430 16:23:11.593299 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.625
I0430 16:23:11.593313 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:23:11.593327 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.75
I0430 16:23:11.593338 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.75
I0430 16:23:11.593349 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.75
I0430 16:23:11.593361 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.75
I0430 16:23:11.593372 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.75
I0430 16:23:11.593384 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:23:11.593395 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:23:11.593407 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:23:11.593418 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:23:11.593430 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:23:11.593441 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:23:11.593456 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.75
I0430 16:23:11.593469 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.710145
I0430 16:23:11.593483 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.88929 (* 0.3 = 0.566787 loss)
I0430 16:23:11.593497 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.840835 (* 0.3 = 0.252251 loss)
I0430 16:23:11.593513 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.63683 (* 0.0272727 = 0.0446408 loss)
I0430 16:23:11.593523 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.09582 (* 0.0272727 = 0.029886 loss)
I0430 16:23:11.593549 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.45557 (* 0.0272727 = 0.0396973 loss)
I0430 16:23:11.593564 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.90612 (* 0.0272727 = 0.051985 loss)
I0430 16:23:11.593577 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.79294 (* 0.0272727 = 0.0488983 loss)
I0430 16:23:11.593590 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.34219 (* 0.0272727 = 0.0366051 loss)
I0430 16:23:11.593605 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.53465 (* 0.0272727 = 0.0418542 loss)
I0430 16:23:11.593617 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.915101 (* 0.0272727 = 0.0249573 loss)
I0430 16:23:11.593631 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.09141 (* 0.0272727 = 0.0297656 loss)
I0430 16:23:11.593644 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 1.1209 (* 0.0272727 = 0.0305699 loss)
I0430 16:23:11.593658 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.705872 (* 0.0272727 = 0.0192511 loss)
I0430 16:23:11.593672 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.869258 (* 0.0272727 = 0.023707 loss)
I0430 16:23:11.593685 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.718248 (* 0.0272727 = 0.0195886 loss)
I0430 16:23:11.593698 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.815135 (* 0.0272727 = 0.022231 loss)
I0430 16:23:11.593713 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.805402 (* 0.0272727 = 0.0219655 loss)
I0430 16:23:11.593725 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.997544 (* 0.0272727 = 0.0272057 loss)
I0430 16:23:11.593739 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0705137 (* 0.0272727 = 0.0019231 loss)
I0430 16:23:11.593753 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0420886 (* 0.0272727 = 0.00114787 loss)
I0430 16:23:11.593766 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0146808 (* 0.0272727 = 0.000400384 loss)
I0430 16:23:11.593780 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00401827 (* 0.0272727 = 0.000109589 loss)
I0430 16:23:11.593794 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.0011372 (* 0.0272727 = 3.10146e-05 loss)
I0430 16:23:11.593807 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.00110834 (* 0.0272727 = 3.02275e-05 loss)
I0430 16:23:11.593819 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.536232
I0430 16:23:11.593832 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.5
I0430 16:23:11.593842 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 16:23:11.593853 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 16:23:11.593865 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.5
I0430 16:23:11.593876 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 16:23:11.593888 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.5
I0430 16:23:11.593899 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.375
I0430 16:23:11.593910 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:23:11.593922 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0430 16:23:11.593933 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.625
I0430 16:23:11.593945 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 16:23:11.593956 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.75
I0430 16:23:11.593967 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.75
I0430 16:23:11.593978 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 16:23:11.593991 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.75
I0430 16:23:11.594002 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.75
I0430 16:23:11.594023 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:23:11.594036 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:23:11.594048 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:23:11.594059 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:23:11.594071 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:23:11.594082 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:23:11.594094 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.789773
I0430 16:23:11.594105 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.73913
I0430 16:23:11.594120 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.68757 (* 1 = 1.68757 loss)
I0430 16:23:11.594132 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.767685 (* 1 = 0.767685 loss)
I0430 16:23:11.594147 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 1.93742 (* 0.0909091 = 0.176129 loss)
I0430 16:23:11.594161 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.631 (* 0.0909091 = 0.0573636 loss)
I0430 16:23:11.594174 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.12119 (* 0.0909091 = 0.101926 loss)
I0430 16:23:11.594188 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.50658 (* 0.0909091 = 0.136962 loss)
I0430 16:23:11.594202 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.004 (* 0.0909091 = 0.0912729 loss)
I0430 16:23:11.594215 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.40564 (* 0.0909091 = 0.127786 loss)
I0430 16:23:11.594228 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.78226 (* 0.0909091 = 0.162024 loss)
I0430 16:23:11.594243 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.867816 (* 0.0909091 = 0.0788924 loss)
I0430 16:23:11.594255 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.97289 (* 0.0909091 = 0.0884445 loss)
I0430 16:23:11.594269 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.962322 (* 0.0909091 = 0.0874838 loss)
I0430 16:23:11.594282 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.595593 (* 0.0909091 = 0.0541448 loss)
I0430 16:23:11.594295 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.78773 (* 0.0909091 = 0.0716118 loss)
I0430 16:23:11.594310 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.743779 (* 0.0909091 = 0.0676162 loss)
I0430 16:23:11.594322 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.526946 (* 0.0909091 = 0.0479041 loss)
I0430 16:23:11.594336 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.751991 (* 0.0909091 = 0.0683629 loss)
I0430 16:23:11.594350 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.836151 (* 0.0909091 = 0.0760138 loss)
I0430 16:23:11.594367 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0296409 (* 0.0909091 = 0.00269463 loss)
I0430 16:23:11.594380 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0104986 (* 0.0909091 = 0.00095442 loss)
I0430 16:23:11.594394 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00336168 (* 0.0909091 = 0.000305607 loss)
I0430 16:23:11.594408 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00170387 (* 0.0909091 = 0.000154897 loss)
I0430 16:23:11.594422 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000319218 (* 0.0909091 = 2.90198e-05 loss)
I0430 16:23:11.594436 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000114291 (* 0.0909091 = 1.03901e-05 loss)
I0430 16:23:11.594449 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.25
I0430 16:23:11.594460 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.25
I0430 16:23:11.594470 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.193123
I0430 16:23:11.594492 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.173004
I0430 16:23:11.594511 15443 sgd_solver.cpp:106] Iteration 15500, lr = 0.001
I0430 16:25:28.813055 15443 solver.cpp:229] Iteration 16000, loss = 3.6464
I0430 16:25:28.813240 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.560976
I0430 16:25:28.813261 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 1
I0430 16:25:28.813273 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.875
I0430 16:25:28.813285 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0430 16:25:28.813297 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.625
I0430 16:25:28.813308 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.75
I0430 16:25:28.813320 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 16:25:28.813333 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 16:25:28.813344 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 16:25:28.813356 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:25:28.813367 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 16:25:28.813380 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 16:25:28.813390 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 16:25:28.813402 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:25:28.813415 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:25:28.813426 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:25:28.813437 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:25:28.813449 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:25:28.813460 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:25:28.813472 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:25:28.813483 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:25:28.813494 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:25:28.813506 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:25:28.813518 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.880682
I0430 16:25:28.813529 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.853659
I0430 16:25:28.813546 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.21227 (* 0.3 = 0.36368 loss)
I0430 16:25:28.813560 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.340995 (* 0.3 = 0.102299 loss)
I0430 16:25:28.813578 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.0992729 (* 0.0272727 = 0.00270744 loss)
I0430 16:25:28.813594 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.810105 (* 0.0272727 = 0.0220938 loss)
I0430 16:25:28.813608 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.06027 (* 0.0272727 = 0.0289163 loss)
I0430 16:25:28.813622 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.40996 (* 0.0272727 = 0.0384535 loss)
I0430 16:25:28.813637 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 0.990155 (* 0.0272727 = 0.0270042 loss)
I0430 16:25:28.813650 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.52854 (* 0.0272727 = 0.0416874 loss)
I0430 16:25:28.813663 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.02933 (* 0.0272727 = 0.0280725 loss)
I0430 16:25:28.813678 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.508983 (* 0.0272727 = 0.0138814 loss)
I0430 16:25:28.813691 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.46715 (* 0.0272727 = 0.0127405 loss)
I0430 16:25:28.813706 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00455683 (* 0.0272727 = 0.000124277 loss)
I0430 16:25:28.813720 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00200666 (* 0.0272727 = 5.4727e-05 loss)
I0430 16:25:28.813735 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00136664 (* 0.0272727 = 3.7272e-05 loss)
I0430 16:25:28.813772 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.000343542 (* 0.0272727 = 9.36933e-06 loss)
I0430 16:25:28.813787 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 8.15425e-05 (* 0.0272727 = 2.22389e-06 loss)
I0430 16:25:28.813802 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 2.96642e-05 (* 0.0272727 = 8.09022e-07 loss)
I0430 16:25:28.813815 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 2.3842e-06 (* 0.0272727 = 6.50236e-08 loss)
I0430 16:25:28.813830 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 2.99516e-06 (* 0.0272727 = 8.16861e-08 loss)
I0430 16:25:28.813844 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 1.57953e-06 (* 0.0272727 = 4.3078e-08 loss)
I0430 16:25:28.813858 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 1.89246e-06 (* 0.0272727 = 5.16125e-08 loss)
I0430 16:25:28.813873 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.53322e-06 (* 0.0272727 = 6.90879e-08 loss)
I0430 16:25:28.813886 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 6.28844e-06 (* 0.0272727 = 1.71503e-07 loss)
I0430 16:25:28.813900 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.95207e-06 (* 0.0272727 = 5.32382e-08 loss)
I0430 16:25:28.813912 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.585366
I0430 16:25:28.813925 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:25:28.813936 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 16:25:28.813948 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 16:25:28.813961 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 16:25:28.813972 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0430 16:25:28.813984 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 16:25:28.813997 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 16:25:28.814007 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 16:25:28.814020 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:25:28.814031 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 16:25:28.814040 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 16:25:28.814049 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 16:25:28.814060 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:25:28.814071 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:25:28.814084 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:25:28.814095 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:25:28.814106 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:25:28.814117 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:25:28.814129 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:25:28.814141 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:25:28.814152 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:25:28.814167 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:25:28.814178 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.886364
I0430 16:25:28.814190 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.804878
I0430 16:25:28.814204 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.46046 (* 0.3 = 0.438137 loss)
I0430 16:25:28.814218 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.398364 (* 0.3 = 0.119509 loss)
I0430 16:25:28.814232 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.729118 (* 0.0272727 = 0.019885 loss)
I0430 16:25:28.814246 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.45558 (* 0.0272727 = 0.0396977 loss)
I0430 16:25:28.814271 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.21539 (* 0.0272727 = 0.0331471 loss)
I0430 16:25:28.814286 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.14542 (* 0.0272727 = 0.0312386 loss)
I0430 16:25:28.814301 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.908955 (* 0.0272727 = 0.0247897 loss)
I0430 16:25:28.814314 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.66361 (* 0.0272727 = 0.0453712 loss)
I0430 16:25:28.814327 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.16769 (* 0.0272727 = 0.0318461 loss)
I0430 16:25:28.814342 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.403184 (* 0.0272727 = 0.0109959 loss)
I0430 16:25:28.814355 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.401511 (* 0.0272727 = 0.0109503 loss)
I0430 16:25:28.814369 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.0360877 (* 0.0272727 = 0.000984211 loss)
I0430 16:25:28.814383 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.0289165 (* 0.0272727 = 0.000788633 loss)
I0430 16:25:28.814398 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00224294 (* 0.0272727 = 6.1171e-05 loss)
I0430 16:25:28.814411 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.000636965 (* 0.0272727 = 1.73718e-05 loss)
I0430 16:25:28.814425 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000239428 (* 0.0272727 = 6.52985e-06 loss)
I0430 16:25:28.814438 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 2.06993e-05 (* 0.0272727 = 5.64526e-07 loss)
I0430 16:25:28.814452 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 5.12607e-06 (* 0.0272727 = 1.39802e-07 loss)
I0430 16:25:28.814466 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 6.70553e-07 (* 0.0272727 = 1.82878e-08 loss)
I0430 16:25:28.814479 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 3.8743e-07 (* 0.0272727 = 1.05663e-08 loss)
I0430 16:25:28.814493 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 1.63913e-07 (* 0.0272727 = 4.47035e-09 loss)
I0430 16:25:28.814507 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 2.68221e-07 (* 0.0272727 = 7.31512e-09 loss)
I0430 16:25:28.814522 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.5332e-07 (* 0.0272727 = 6.90872e-09 loss)
I0430 16:25:28.814535 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 2.38419e-07 (* 0.0272727 = 6.50233e-09 loss)
I0430 16:25:28.814548 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.756098
I0430 16:25:28.814559 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:25:28.814571 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 16:25:28.814582 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0430 16:25:28.814594 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 16:25:28.814605 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0430 16:25:28.814617 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 16:25:28.814632 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 16:25:28.814645 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:25:28.814656 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:25:28.814667 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 16:25:28.814679 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:25:28.814690 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 16:25:28.814702 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:25:28.814713 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:25:28.814725 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:25:28.814746 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:25:28.814759 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:25:28.814771 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:25:28.814782 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:25:28.814795 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:25:28.814805 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:25:28.814817 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:25:28.814828 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.931818
I0430 16:25:28.814841 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.926829
I0430 16:25:28.814854 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.824264 (* 1 = 0.824264 loss)
I0430 16:25:28.814868 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.246883 (* 1 = 0.246883 loss)
I0430 16:25:28.814882 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.232093 (* 0.0909091 = 0.0210994 loss)
I0430 16:25:28.814896 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.697132 (* 0.0909091 = 0.0633757 loss)
I0430 16:25:28.814909 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.447582 (* 0.0909091 = 0.0406893 loss)
I0430 16:25:28.814924 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.742834 (* 0.0909091 = 0.0675304 loss)
I0430 16:25:28.814937 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.416427 (* 0.0909091 = 0.037857 loss)
I0430 16:25:28.814950 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.00981 (* 0.0909091 = 0.0918011 loss)
I0430 16:25:28.814965 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.3637 (* 0.0909091 = 0.0330636 loss)
I0430 16:25:28.814978 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.263585 (* 0.0909091 = 0.0239623 loss)
I0430 16:25:28.814991 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.277853 (* 0.0909091 = 0.0252594 loss)
I0430 16:25:28.815006 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0446254 (* 0.0909091 = 0.00405686 loss)
I0430 16:25:28.815019 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.0178864 (* 0.0909091 = 0.00162604 loss)
I0430 16:25:28.815033 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00438398 (* 0.0909091 = 0.000398543 loss)
I0430 16:25:28.815047 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00137522 (* 0.0909091 = 0.00012502 loss)
I0430 16:25:28.815060 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000327214 (* 0.0909091 = 2.97467e-05 loss)
I0430 16:25:28.815074 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000152256 (* 0.0909091 = 1.38415e-05 loss)
I0430 16:25:28.815088 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 5.00268e-05 (* 0.0909091 = 4.54789e-06 loss)
I0430 16:25:28.815102 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 3.7076e-05 (* 0.0909091 = 3.37055e-06 loss)
I0430 16:25:28.815115 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 2.42598e-05 (* 0.0909091 = 2.20544e-06 loss)
I0430 16:25:28.815129 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 2.119e-05 (* 0.0909091 = 1.92636e-06 loss)
I0430 16:25:28.815143 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.62128e-05 (* 0.0909091 = 1.47389e-06 loss)
I0430 16:25:28.815157 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.18913e-05 (* 0.0909091 = 1.08103e-06 loss)
I0430 16:25:28.815171 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.16231e-05 (* 0.0909091 = 1.05665e-06 loss)
I0430 16:25:28.815182 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 16:25:28.815194 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 16:25:28.815218 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.535802
I0430 16:25:28.815232 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.437113
I0430 16:25:28.815244 15443 sgd_solver.cpp:106] Iteration 16000, lr = 0.001
I0430 16:27:45.797958 15443 solver.cpp:229] Iteration 16500, loss = 3.70552
I0430 16:27:45.798075 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.290323
I0430 16:27:45.798095 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 16:27:45.798110 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 16:27:45.798122 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0430 16:27:45.798138 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0430 16:27:45.798151 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 16:27:45.798162 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0430 16:27:45.798174 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 16:27:45.798185 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 16:27:45.798197 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 16:27:45.798209 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0430 16:27:45.798220 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0430 16:27:45.798233 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:27:45.798244 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.75
I0430 16:27:45.798256 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.75
I0430 16:27:45.798267 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.75
I0430 16:27:45.798280 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.75
I0430 16:27:45.798291 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:27:45.798303 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:27:45.798315 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:27:45.798326 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:27:45.798337 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:27:45.798348 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:27:45.798360 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.744318
I0430 16:27:45.798372 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.612903
I0430 16:27:45.798388 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.09604 (* 0.3 = 0.628813 loss)
I0430 16:27:45.798403 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.758944 (* 0.3 = 0.227683 loss)
I0430 16:27:45.798418 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.13638 (* 0.0272727 = 0.0309921 loss)
I0430 16:27:45.798431 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.1333 (* 0.0272727 = 0.0309081 loss)
I0430 16:27:45.798452 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.91534 (* 0.0272727 = 0.0522366 loss)
I0430 16:27:45.798473 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.55057 (* 0.0272727 = 0.0695611 loss)
I0430 16:27:45.798487 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.20427 (* 0.0272727 = 0.0328438 loss)
I0430 16:27:45.798501 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.31211 (* 0.0272727 = 0.0357847 loss)
I0430 16:27:45.798514 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.0144 (* 0.0272727 = 0.0276654 loss)
I0430 16:27:45.798528 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.854539 (* 0.0272727 = 0.0233056 loss)
I0430 16:27:45.798542 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.820533 (* 0.0272727 = 0.0223782 loss)
I0430 16:27:45.798555 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.651192 (* 0.0272727 = 0.0177598 loss)
I0430 16:27:45.798573 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.786998 (* 0.0272727 = 0.0214636 loss)
I0430 16:27:45.798586 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.86603 (* 0.0272727 = 0.023619 loss)
I0430 16:27:45.798620 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.699625 (* 0.0272727 = 0.0190807 loss)
I0430 16:27:45.798635 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.957455 (* 0.0272727 = 0.0261124 loss)
I0430 16:27:45.798650 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 1.04389 (* 0.0272727 = 0.0284698 loss)
I0430 16:27:45.798663 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 1.01592 (* 0.0272727 = 0.027707 loss)
I0430 16:27:45.798677 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0238904 (* 0.0272727 = 0.000651557 loss)
I0430 16:27:45.798691 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00569936 (* 0.0272727 = 0.000155437 loss)
I0430 16:27:45.798707 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00283316 (* 0.0272727 = 7.72681e-05 loss)
I0430 16:27:45.798719 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00139631 (* 0.0272727 = 3.80811e-05 loss)
I0430 16:27:45.798733 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00172124 (* 0.0272727 = 4.69429e-05 loss)
I0430 16:27:45.798748 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000701736 (* 0.0272727 = 1.91382e-05 loss)
I0430 16:27:45.798759 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.451613
I0430 16:27:45.798771 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 16:27:45.798784 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0430 16:27:45.798795 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.875
I0430 16:27:45.798806 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 16:27:45.798818 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 16:27:45.798830 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 16:27:45.798841 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 16:27:45.798853 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 16:27:45.798864 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0430 16:27:45.798876 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0430 16:27:45.798887 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0430 16:27:45.798899 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.75
I0430 16:27:45.798912 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.75
I0430 16:27:45.798919 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.75
I0430 16:27:45.798926 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.75
I0430 16:27:45.798934 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.75
I0430 16:27:45.798946 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:27:45.798959 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:27:45.798969 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:27:45.798981 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:27:45.798992 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:27:45.799011 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:27:45.799028 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.801136
I0430 16:27:45.799041 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.693548
I0430 16:27:45.799057 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 2.03776 (* 0.3 = 0.611328 loss)
I0430 16:27:45.799077 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.727558 (* 0.3 = 0.218267 loss)
I0430 16:27:45.799093 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.956595 (* 0.0272727 = 0.0260889 loss)
I0430 16:27:45.799106 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.65194 (* 0.0272727 = 0.0450529 loss)
I0430 16:27:45.799134 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.885439 (* 0.0272727 = 0.0241483 loss)
I0430 16:27:45.799149 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.12577 (* 0.0272727 = 0.0579756 loss)
I0430 16:27:45.799162 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.59017 (* 0.0272727 = 0.0433682 loss)
I0430 16:27:45.799180 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.59998 (* 0.0272727 = 0.0436358 loss)
I0430 16:27:45.799195 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.957232 (* 0.0272727 = 0.0261063 loss)
I0430 16:27:45.799208 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.75654 (* 0.0272727 = 0.0206329 loss)
I0430 16:27:45.799222 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.687586 (* 0.0272727 = 0.0187524 loss)
I0430 16:27:45.799237 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.626533 (* 0.0272727 = 0.0170873 loss)
I0430 16:27:45.799249 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.826417 (* 0.0272727 = 0.0225386 loss)
I0430 16:27:45.799263 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 1.03714 (* 0.0272727 = 0.0282857 loss)
I0430 16:27:45.799276 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.575806 (* 0.0272727 = 0.0157038 loss)
I0430 16:27:45.799290 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.941681 (* 0.0272727 = 0.0256822 loss)
I0430 16:27:45.799304 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.935704 (* 0.0272727 = 0.0255192 loss)
I0430 16:27:45.799317 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 1.03746 (* 0.0272727 = 0.0282942 loss)
I0430 16:27:45.799331 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0098394 (* 0.0272727 = 0.000268347 loss)
I0430 16:27:45.799345 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00174846 (* 0.0272727 = 4.76853e-05 loss)
I0430 16:27:45.799360 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000931382 (* 0.0272727 = 2.54013e-05 loss)
I0430 16:27:45.799372 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000245378 (* 0.0272727 = 6.69213e-06 loss)
I0430 16:27:45.799386 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 7.92797e-05 (* 0.0272727 = 2.16217e-06 loss)
I0430 16:27:45.799401 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.61385e-05 (* 0.0272727 = 4.4014e-07 loss)
I0430 16:27:45.799412 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.5
I0430 16:27:45.799423 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 16:27:45.799435 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 16:27:45.799446 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 16:27:45.799458 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.5
I0430 16:27:45.799489 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 16:27:45.799502 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 16:27:45.799515 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 16:27:45.799526 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 16:27:45.799538 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0430 16:27:45.799549 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:27:45.799561 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0430 16:27:45.799573 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.75
I0430 16:27:45.799584 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.75
I0430 16:27:45.799597 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.75
I0430 16:27:45.799609 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.75
I0430 16:27:45.799621 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.75
I0430 16:27:45.799645 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:27:45.799659 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:27:45.799670 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:27:45.799681 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:27:45.799693 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:27:45.799705 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:27:45.799715 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.823864
I0430 16:27:45.799727 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.774194
I0430 16:27:45.799741 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.63406 (* 1 = 1.63406 loss)
I0430 16:27:45.799756 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.586377 (* 1 = 0.586377 loss)
I0430 16:27:45.799769 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.382251 (* 0.0909091 = 0.0347501 loss)
I0430 16:27:45.799783 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.894461 (* 0.0909091 = 0.0813146 loss)
I0430 16:27:45.799798 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.2897 (* 0.0909091 = 0.117246 loss)
I0430 16:27:45.799811 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.97454 (* 0.0909091 = 0.179504 loss)
I0430 16:27:45.799824 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.24035 (* 0.0909091 = 0.11276 loss)
I0430 16:27:45.799839 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.933787 (* 0.0909091 = 0.0848897 loss)
I0430 16:27:45.799852 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.613353 (* 0.0909091 = 0.0557593 loss)
I0430 16:27:45.799866 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.66961 (* 0.0909091 = 0.0608736 loss)
I0430 16:27:45.799880 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.578214 (* 0.0909091 = 0.0525649 loss)
I0430 16:27:45.799893 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.456587 (* 0.0909091 = 0.0415079 loss)
I0430 16:27:45.799906 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.767782 (* 0.0909091 = 0.0697984 loss)
I0430 16:27:45.799921 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.979085 (* 0.0909091 = 0.0890078 loss)
I0430 16:27:45.799934 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.676157 (* 0.0909091 = 0.0614688 loss)
I0430 16:27:45.799947 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.721654 (* 0.0909091 = 0.0656049 loss)
I0430 16:27:45.799962 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.687881 (* 0.0909091 = 0.0625346 loss)
I0430 16:27:45.799974 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.738213 (* 0.0909091 = 0.0671102 loss)
I0430 16:27:45.799988 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0160817 (* 0.0909091 = 0.00146197 loss)
I0430 16:27:45.800003 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00546945 (* 0.0909091 = 0.000497223 loss)
I0430 16:27:45.800016 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00250124 (* 0.0909091 = 0.000227386 loss)
I0430 16:27:45.800030 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000901986 (* 0.0909091 = 8.19987e-05 loss)
I0430 16:27:45.800045 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000206468 (* 0.0909091 = 1.87699e-05 loss)
I0430 16:27:45.800058 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 5.93509e-05 (* 0.0909091 = 5.39554e-06 loss)
I0430 16:27:45.800071 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 16:27:45.800081 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.25
I0430 16:27:45.800103 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.295031
I0430 16:27:45.800117 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.249823
I0430 16:27:45.800129 15443 sgd_solver.cpp:106] Iteration 16500, lr = 0.001
I0430 16:30:02.693073 15443 solver.cpp:229] Iteration 17000, loss = 3.63587
I0430 16:30:02.693236 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.516129
I0430 16:30:02.693258 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 16:30:02.693270 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0430 16:30:02.693282 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0430 16:30:02.693295 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 16:30:02.693306 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.75
I0430 16:30:02.693321 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0430 16:30:02.693333 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0430 16:30:02.693346 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0430 16:30:02.693358 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 16:30:02.693370 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0430 16:30:02.693382 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:30:02.693394 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:30:02.693405 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 16:30:02.693418 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 16:30:02.693429 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0430 16:30:02.693441 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0430 16:30:02.693452 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 0.875
I0430 16:30:02.693464 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:30:02.693476 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:30:02.693488 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:30:02.693500 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:30:02.693512 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:30:02.693523 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.829545
I0430 16:30:02.693536 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.709677
I0430 16:30:02.693550 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.64533 (* 0.3 = 0.493599 loss)
I0430 16:30:02.693565 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.592236 (* 0.3 = 0.177671 loss)
I0430 16:30:02.693579 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.641076 (* 0.0272727 = 0.0174839 loss)
I0430 16:30:02.693593 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.03766 (* 0.0272727 = 0.0282998 loss)
I0430 16:30:02.693608 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.25678 (* 0.0272727 = 0.0342758 loss)
I0430 16:30:02.693621 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.54273 (* 0.0272727 = 0.0420744 loss)
I0430 16:30:02.693635 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 0.985617 (* 0.0272727 = 0.0268805 loss)
I0430 16:30:02.693650 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.920818 (* 0.0272727 = 0.0251132 loss)
I0430 16:30:02.693663 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.95036 (* 0.0272727 = 0.0531916 loss)
I0430 16:30:02.693677 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.479355 (* 0.0272727 = 0.0130733 loss)
I0430 16:30:02.693691 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.812105 (* 0.0272727 = 0.0221483 loss)
I0430 16:30:02.693704 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.893246 (* 0.0272727 = 0.0243613 loss)
I0430 16:30:02.693718 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.779569 (* 0.0272727 = 0.021261 loss)
I0430 16:30:02.693732 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.319573 (* 0.0272727 = 0.00871562 loss)
I0430 16:30:02.693768 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.53655 (* 0.0272727 = 0.0146332 loss)
I0430 16:30:02.693783 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.398792 (* 0.0272727 = 0.0108762 loss)
I0430 16:30:02.693796 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.339932 (* 0.0272727 = 0.00927087 loss)
I0430 16:30:02.693810 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.431697 (* 0.0272727 = 0.0117735 loss)
I0430 16:30:02.693825 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.519466 (* 0.0272727 = 0.0141672 loss)
I0430 16:30:02.693838 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00932963 (* 0.0272727 = 0.000254444 loss)
I0430 16:30:02.693852 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00306804 (* 0.0272727 = 8.36738e-05 loss)
I0430 16:30:02.693866 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000124349 (* 0.0272727 = 3.39133e-06 loss)
I0430 16:30:02.693881 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 9.10479e-06 (* 0.0272727 = 2.48312e-07 loss)
I0430 16:30:02.693894 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 3.57628e-07 (* 0.0272727 = 9.7535e-09 loss)
I0430 16:30:02.693907 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.516129
I0430 16:30:02.693918 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:30:02.693930 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 16:30:02.693941 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 16:30:02.693953 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 16:30:02.693965 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 16:30:02.693976 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 16:30:02.693989 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 16:30:02.694000 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 16:30:02.694011 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0430 16:30:02.694023 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0430 16:30:02.694034 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0430 16:30:02.694046 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:30:02.694057 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 16:30:02.694068 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 16:30:02.694080 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 16:30:02.694092 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0430 16:30:02.694103 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 0.875
I0430 16:30:02.694114 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:30:02.694126 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:30:02.694138 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:30:02.694149 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:30:02.694160 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:30:02.694174 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.829545
I0430 16:30:02.694185 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.725806
I0430 16:30:02.694200 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.47762 (* 0.3 = 0.443285 loss)
I0430 16:30:02.694212 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.533829 (* 0.3 = 0.160149 loss)
I0430 16:30:02.694226 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.420102 (* 0.0272727 = 0.0114573 loss)
I0430 16:30:02.694241 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.831491 (* 0.0272727 = 0.022677 loss)
I0430 16:30:02.694270 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.989405 (* 0.0272727 = 0.0269838 loss)
I0430 16:30:02.694285 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.30939 (* 0.0272727 = 0.0357107 loss)
I0430 16:30:02.694299 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.959376 (* 0.0272727 = 0.0261648 loss)
I0430 16:30:02.694314 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.93386 (* 0.0272727 = 0.0254689 loss)
I0430 16:30:02.694327 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.32665 (* 0.0272727 = 0.0361815 loss)
I0430 16:30:02.694340 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.420176 (* 0.0272727 = 0.0114593 loss)
I0430 16:30:02.694355 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.893056 (* 0.0272727 = 0.0243561 loss)
I0430 16:30:02.694371 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.762547 (* 0.0272727 = 0.0207967 loss)
I0430 16:30:02.694386 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.850747 (* 0.0272727 = 0.0232022 loss)
I0430 16:30:02.694399 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.351977 (* 0.0272727 = 0.00959937 loss)
I0430 16:30:02.694413 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.386431 (* 0.0272727 = 0.010539 loss)
I0430 16:30:02.694427 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.392224 (* 0.0272727 = 0.010697 loss)
I0430 16:30:02.694442 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.2583 (* 0.0272727 = 0.00704454 loss)
I0430 16:30:02.694454 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.372204 (* 0.0272727 = 0.010151 loss)
I0430 16:30:02.694468 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.50377 (* 0.0272727 = 0.0137392 loss)
I0430 16:30:02.694481 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0340152 (* 0.0272727 = 0.000927686 loss)
I0430 16:30:02.694495 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0132993 (* 0.0272727 = 0.000362707 loss)
I0430 16:30:02.694509 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00446481 (* 0.0272727 = 0.000121768 loss)
I0430 16:30:02.694522 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.0010391 (* 0.0272727 = 2.8339e-05 loss)
I0430 16:30:02.694536 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000123368 (* 0.0272727 = 3.36458e-06 loss)
I0430 16:30:02.694548 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.677419
I0430 16:30:02.694561 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 16:30:02.694571 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 16:30:02.694582 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0430 16:30:02.694594 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 16:30:02.694605 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 16:30:02.694617 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0430 16:30:02.694628 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 16:30:02.694639 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:30:02.694651 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:30:02.694664 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.75
I0430 16:30:02.694674 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 16:30:02.694685 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 16:30:02.694697 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 16:30:02.694708 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:30:02.694720 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 16:30:02.694741 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0430 16:30:02.694754 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 0.875
I0430 16:30:02.694766 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:30:02.694778 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:30:02.694789 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:30:02.694802 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:30:02.694813 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:30:02.694824 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.886364
I0430 16:30:02.694836 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.822581
I0430 16:30:02.694850 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.04358 (* 1 = 1.04358 loss)
I0430 16:30:02.694864 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.384073 (* 1 = 0.384073 loss)
I0430 16:30:02.694878 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.469897 (* 0.0909091 = 0.0427179 loss)
I0430 16:30:02.694891 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.411773 (* 0.0909091 = 0.0374339 loss)
I0430 16:30:02.694905 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.00803882 (* 0.0909091 = 0.000730802 loss)
I0430 16:30:02.694919 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.540663 (* 0.0909091 = 0.0491512 loss)
I0430 16:30:02.694933 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.62426 (* 0.0909091 = 0.0567509 loss)
I0430 16:30:02.694947 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.358174 (* 0.0909091 = 0.0325613 loss)
I0430 16:30:02.694960 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.592183 (* 0.0909091 = 0.0538348 loss)
I0430 16:30:02.694974 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.278823 (* 0.0909091 = 0.0253476 loss)
I0430 16:30:02.694988 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.500382 (* 0.0909091 = 0.0454893 loss)
I0430 16:30:02.695001 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.867131 (* 0.0909091 = 0.0788301 loss)
I0430 16:30:02.695014 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 1.07965 (* 0.0909091 = 0.09815 loss)
I0430 16:30:02.695029 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.380649 (* 0.0909091 = 0.0346045 loss)
I0430 16:30:02.695041 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.297649 (* 0.0909091 = 0.027059 loss)
I0430 16:30:02.695055 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.220617 (* 0.0909091 = 0.0200561 loss)
I0430 16:30:02.695068 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.270443 (* 0.0909091 = 0.0245857 loss)
I0430 16:30:02.695082 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.326767 (* 0.0909091 = 0.0297061 loss)
I0430 16:30:02.695096 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.44671 (* 0.0909091 = 0.04061 loss)
I0430 16:30:02.695109 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00803891 (* 0.0909091 = 0.00073081 loss)
I0430 16:30:02.695123 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00480847 (* 0.0909091 = 0.000437134 loss)
I0430 16:30:02.695137 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00152221 (* 0.0909091 = 0.000138383 loss)
I0430 16:30:02.695152 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000307989 (* 0.0909091 = 2.7999e-05 loss)
I0430 16:30:02.695164 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 4.30717e-05 (* 0.0909091 = 3.91561e-06 loss)
I0430 16:30:02.695178 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 16:30:02.695188 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 16:30:02.695209 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.546935
I0430 16:30:02.695222 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.588134
I0430 16:30:02.695235 15443 sgd_solver.cpp:106] Iteration 17000, lr = 0.001
I0430 16:32:19.892537 15443 solver.cpp:229] Iteration 17500, loss = 3.64418
I0430 16:32:19.892683 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.302632
I0430 16:32:19.892702 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 16:32:19.892715 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0430 16:32:19.892729 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 16:32:19.892740 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 16:32:19.892751 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0430 16:32:19.892763 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.25
I0430 16:32:19.892776 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0430 16:32:19.892787 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0430 16:32:19.892799 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.625
I0430 16:32:19.892812 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.625
I0430 16:32:19.892822 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.625
I0430 16:32:19.892834 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.625
I0430 16:32:19.892846 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.75
I0430 16:32:19.892858 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.625
I0430 16:32:19.892869 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.625
I0430 16:32:19.892881 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.75
I0430 16:32:19.892892 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 0.875
I0430 16:32:19.892904 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:32:19.892916 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:32:19.892927 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:32:19.892940 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:32:19.892951 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:32:19.892961 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.676136
I0430 16:32:19.892982 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.565789
I0430 16:32:19.893002 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.23551 (* 0.3 = 0.670653 loss)
I0430 16:32:19.893018 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.09768 (* 0.3 = 0.329304 loss)
I0430 16:32:19.893031 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.77078 (* 0.0272727 = 0.048294 loss)
I0430 16:32:19.893045 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.50639 (* 0.0272727 = 0.0683561 loss)
I0430 16:32:19.893059 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.6749 (* 0.0272727 = 0.0456791 loss)
I0430 16:32:19.893074 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.86833 (* 0.0272727 = 0.0509544 loss)
I0430 16:32:19.893086 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.49095 (* 0.0272727 = 0.0406622 loss)
I0430 16:32:19.893100 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.34352 (* 0.0272727 = 0.0639142 loss)
I0430 16:32:19.893113 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.4596 (* 0.0272727 = 0.0398072 loss)
I0430 16:32:19.893126 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.714429 (* 0.0272727 = 0.0194844 loss)
I0430 16:32:19.893141 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 1.37353 (* 0.0272727 = 0.0374599 loss)
I0430 16:32:19.893154 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.967508 (* 0.0272727 = 0.0263866 loss)
I0430 16:32:19.893167 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 1.01674 (* 0.0272727 = 0.0277292 loss)
I0430 16:32:19.893182 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 1.85446 (* 0.0272727 = 0.0505762 loss)
I0430 16:32:19.893213 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.884645 (* 0.0272727 = 0.0241267 loss)
I0430 16:32:19.893227 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 1.46284 (* 0.0272727 = 0.0398956 loss)
I0430 16:32:19.893240 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.897945 (* 0.0272727 = 0.0244894 loss)
I0430 16:32:19.893254 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.973897 (* 0.0272727 = 0.0265608 loss)
I0430 16:32:19.893268 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.743453 (* 0.0272727 = 0.020276 loss)
I0430 16:32:19.893282 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.123759 (* 0.0272727 = 0.00337525 loss)
I0430 16:32:19.893296 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.0409283 (* 0.0272727 = 0.00111623 loss)
I0430 16:32:19.893309 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.0311669 (* 0.0272727 = 0.000850005 loss)
I0430 16:32:19.893327 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00792125 (* 0.0272727 = 0.000216034 loss)
I0430 16:32:19.893342 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00123259 (* 0.0272727 = 3.36162e-05 loss)
I0430 16:32:19.893352 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.368421
I0430 16:32:19.893364 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 16:32:19.893376 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0430 16:32:19.893388 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0430 16:32:19.893399 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 16:32:19.893410 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 16:32:19.893422 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.375
I0430 16:32:19.893434 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.375
I0430 16:32:19.893445 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0430 16:32:19.893456 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.625
I0430 16:32:19.893467 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.625
I0430 16:32:19.893479 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.625
I0430 16:32:19.893491 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.625
I0430 16:32:19.893502 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.75
I0430 16:32:19.893513 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.625
I0430 16:32:19.893525 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.625
I0430 16:32:19.893537 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0430 16:32:19.893548 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 0.875
I0430 16:32:19.893559 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:32:19.893571 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:32:19.893582 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:32:19.893594 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:32:19.893605 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:32:19.893616 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.710227
I0430 16:32:19.893627 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.605263
I0430 16:32:19.893641 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 2.14445 (* 0.3 = 0.643334 loss)
I0430 16:32:19.893656 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.03407 (* 0.3 = 0.310222 loss)
I0430 16:32:19.893668 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.33067 (* 0.0272727 = 0.0362909 loss)
I0430 16:32:19.893682 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.82866 (* 0.0272727 = 0.0498727 loss)
I0430 16:32:19.893710 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.80791 (* 0.0272727 = 0.0493066 loss)
I0430 16:32:19.893726 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.8892 (* 0.0272727 = 0.0515235 loss)
I0430 16:32:19.893739 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.50538 (* 0.0272727 = 0.0410558 loss)
I0430 16:32:19.893754 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.93128 (* 0.0272727 = 0.0526714 loss)
I0430 16:32:19.893766 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.54615 (* 0.0272727 = 0.0421676 loss)
I0430 16:32:19.893780 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.04644 (* 0.0272727 = 0.0285393 loss)
I0430 16:32:19.893793 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.25327 (* 0.0272727 = 0.0341801 loss)
I0430 16:32:19.893806 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 1.10028 (* 0.0272727 = 0.0300077 loss)
I0430 16:32:19.893820 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.12878 (* 0.0272727 = 0.0307849 loss)
I0430 16:32:19.893832 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 1.40697 (* 0.0272727 = 0.0383719 loss)
I0430 16:32:19.893846 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.903916 (* 0.0272727 = 0.0246523 loss)
I0430 16:32:19.893859 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 1.61531 (* 0.0272727 = 0.0440538 loss)
I0430 16:32:19.893872 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 1.27128 (* 0.0272727 = 0.0346712 loss)
I0430 16:32:19.893885 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.653867 (* 0.0272727 = 0.0178327 loss)
I0430 16:32:19.893899 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.695921 (* 0.0272727 = 0.0189797 loss)
I0430 16:32:19.893913 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.102684 (* 0.0272727 = 0.00280048 loss)
I0430 16:32:19.893926 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0397161 (* 0.0272727 = 0.00108317 loss)
I0430 16:32:19.893940 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.0236383 (* 0.0272727 = 0.00064468 loss)
I0430 16:32:19.893954 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.0101259 (* 0.0272727 = 0.00027616 loss)
I0430 16:32:19.893967 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.00246372 (* 0.0272727 = 6.71924e-05 loss)
I0430 16:32:19.893980 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.421053
I0430 16:32:19.893991 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 16:32:19.894002 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 16:32:19.894014 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.5
I0430 16:32:19.894026 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0430 16:32:19.894037 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 16:32:19.894047 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.5
I0430 16:32:19.894059 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0430 16:32:19.894070 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 16:32:19.894083 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.625
I0430 16:32:19.894093 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.75
I0430 16:32:19.894105 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:32:19.894116 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.625
I0430 16:32:19.894127 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 16:32:19.894139 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.75
I0430 16:32:19.894150 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.75
I0430 16:32:19.894161 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0430 16:32:19.894182 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 0.875
I0430 16:32:19.894196 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:32:19.894207 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:32:19.894218 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:32:19.894229 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:32:19.894240 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:32:19.894253 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.744318
I0430 16:32:19.894263 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.671053
I0430 16:32:19.894280 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.73153 (* 1 = 1.73153 loss)
I0430 16:32:19.894290 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.839544 (* 1 = 0.839544 loss)
I0430 16:32:19.894305 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 1.11035 (* 0.0909091 = 0.100941 loss)
I0430 16:32:19.894318 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 1.64296 (* 0.0909091 = 0.14936 loss)
I0430 16:32:19.894332 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.07962 (* 0.0909091 = 0.0981477 loss)
I0430 16:32:19.894345 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.18677 (* 0.0909091 = 0.107888 loss)
I0430 16:32:19.894366 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.968429 (* 0.0909091 = 0.088039 loss)
I0430 16:32:19.894387 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.65902 (* 0.0909091 = 0.15082 loss)
I0430 16:32:19.894402 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.08709 (* 0.0909091 = 0.0988264 loss)
I0430 16:32:19.894421 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.869009 (* 0.0909091 = 0.0790009 loss)
I0430 16:32:19.894438 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.960324 (* 0.0909091 = 0.0873022 loss)
I0430 16:32:19.894453 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.766363 (* 0.0909091 = 0.0696694 loss)
I0430 16:32:19.894465 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.592621 (* 0.0909091 = 0.0538746 loss)
I0430 16:32:19.894479 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 1.31882 (* 0.0909091 = 0.119893 loss)
I0430 16:32:19.894492 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.740535 (* 0.0909091 = 0.0673214 loss)
I0430 16:32:19.894505 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.891006 (* 0.0909091 = 0.0810005 loss)
I0430 16:32:19.894518 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.758694 (* 0.0909091 = 0.0689721 loss)
I0430 16:32:19.894532 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.541129 (* 0.0909091 = 0.0491936 loss)
I0430 16:32:19.894546 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.322315 (* 0.0909091 = 0.0293014 loss)
I0430 16:32:19.894559 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.106191 (* 0.0909091 = 0.00965372 loss)
I0430 16:32:19.894572 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.088217 (* 0.0909091 = 0.00801973 loss)
I0430 16:32:19.894587 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.0826596 (* 0.0909091 = 0.00751451 loss)
I0430 16:32:19.894599 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.0327345 (* 0.0909091 = 0.00297587 loss)
I0430 16:32:19.894613 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.00397146 (* 0.0909091 = 0.000361042 loss)
I0430 16:32:19.894624 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 16:32:19.894635 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 16:32:19.894646 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.281911
I0430 16:32:19.894670 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.320244
I0430 16:32:19.894683 15443 sgd_solver.cpp:106] Iteration 17500, lr = 0.001
I0430 16:33:36.935142 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.9646 > 30) by scale factor 0.968847
I0430 16:34:36.884011 15443 solver.cpp:229] Iteration 18000, loss = 3.72678
I0430 16:34:36.884124 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.571429
I0430 16:34:36.884155 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 16:34:36.884179 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 16:34:36.884202 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0430 16:34:36.884224 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.625
I0430 16:34:36.884246 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.75
I0430 16:34:36.884269 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 16:34:36.884294 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 16:34:36.884315 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0430 16:34:36.884340 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0430 16:34:36.884361 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 16:34:36.884382 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 16:34:36.884402 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 16:34:36.884423 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:34:36.884444 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:34:36.884465 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:34:36.884487 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:34:36.884516 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:34:36.884538 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:34:36.884559 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:34:36.884582 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:34:36.884606 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:34:36.884629 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:34:36.884652 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.880682
I0430 16:34:36.884675 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.690476
I0430 16:34:36.884702 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.77503 (* 0.3 = 0.532509 loss)
I0430 16:34:36.884730 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.496336 (* 0.3 = 0.148901 loss)
I0430 16:34:36.884759 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.2354 (* 0.0272727 = 0.0336927 loss)
I0430 16:34:36.884789 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.34626 (* 0.0272727 = 0.0367162 loss)
I0430 16:34:36.884817 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.10598 (* 0.0272727 = 0.0301631 loss)
I0430 16:34:36.884845 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.45062 (* 0.0272727 = 0.0395623 loss)
I0430 16:34:36.884872 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 0.918044 (* 0.0272727 = 0.0250376 loss)
I0430 16:34:36.884898 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.80057 (* 0.0272727 = 0.0491064 loss)
I0430 16:34:36.884925 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.82842 (* 0.0272727 = 0.0498659 loss)
I0430 16:34:36.884950 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.11224 (* 0.0272727 = 0.0303337 loss)
I0430 16:34:36.884977 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.110512 (* 0.0272727 = 0.00301396 loss)
I0430 16:34:36.885006 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.0306662 (* 0.0272727 = 0.00083635 loss)
I0430 16:34:36.885032 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00246323 (* 0.0272727 = 6.7179e-05 loss)
I0430 16:34:36.885061 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00083781 (* 0.0272727 = 2.28494e-05 loss)
I0430 16:34:36.885087 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.000733485 (* 0.0272727 = 2.00041e-05 loss)
I0430 16:34:36.885138 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.000309898 (* 0.0272727 = 8.45177e-06 loss)
I0430 16:34:36.885167 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.000131707 (* 0.0272727 = 3.59202e-06 loss)
I0430 16:34:36.885196 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000133227 (* 0.0272727 = 3.63346e-06 loss)
I0430 16:34:36.885223 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 1.53937e-05 (* 0.0272727 = 4.19828e-07 loss)
I0430 16:34:36.885249 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 6.64608e-06 (* 0.0272727 = 1.81257e-07 loss)
I0430 16:34:36.885277 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 4.99198e-06 (* 0.0272727 = 1.36145e-07 loss)
I0430 16:34:36.885303 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 6.55668e-06 (* 0.0272727 = 1.78819e-07 loss)
I0430 16:34:36.885331 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 2.95046e-06 (* 0.0272727 = 8.04672e-08 loss)
I0430 16:34:36.885359 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 9.22416e-06 (* 0.0272727 = 2.51568e-07 loss)
I0430 16:34:36.885382 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.619048
I0430 16:34:36.885406 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:34:36.885428 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 16:34:36.885452 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0430 16:34:36.885474 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 16:34:36.885495 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.875
I0430 16:34:36.885517 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 16:34:36.885540 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 16:34:36.885566 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 16:34:36.885589 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0430 16:34:36.885610 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 16:34:36.885632 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 16:34:36.885656 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 16:34:36.885679 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:34:36.885700 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:34:36.885722 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:34:36.885743 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:34:36.885764 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:34:36.885787 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:34:36.885807 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:34:36.885828 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:34:36.885851 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:34:36.885872 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:34:36.885892 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.886364
I0430 16:34:36.885915 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.738095
I0430 16:34:36.885941 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.31431 (* 0.3 = 0.394293 loss)
I0430 16:34:36.885967 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.386894 (* 0.3 = 0.116068 loss)
I0430 16:34:36.885993 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.459458 (* 0.0272727 = 0.0125307 loss)
I0430 16:34:36.886020 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.934552 (* 0.0272727 = 0.0254878 loss)
I0430 16:34:36.886064 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.759561 (* 0.0272727 = 0.0207153 loss)
I0430 16:34:36.886091 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.43996 (* 0.0272727 = 0.0392715 loss)
I0430 16:34:36.886118 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.77525 (* 0.0272727 = 0.0211432 loss)
I0430 16:34:36.886144 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.59026 (* 0.0272727 = 0.0433708 loss)
I0430 16:34:36.886169 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.34301 (* 0.0272727 = 0.0366275 loss)
I0430 16:34:36.886195 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.890491 (* 0.0272727 = 0.0242861 loss)
I0430 16:34:36.886221 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.262423 (* 0.0272727 = 0.00715698 loss)
I0430 16:34:36.886247 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.102535 (* 0.0272727 = 0.00279642 loss)
I0430 16:34:36.886275 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.0236384 (* 0.0272727 = 0.000644683 loss)
I0430 16:34:36.886302 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00932796 (* 0.0272727 = 0.000254399 loss)
I0430 16:34:36.886330 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0118176 (* 0.0272727 = 0.000322299 loss)
I0430 16:34:36.886358 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00490099 (* 0.0272727 = 0.000133663 loss)
I0430 16:34:36.886385 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00263059 (* 0.0272727 = 7.17433e-05 loss)
I0430 16:34:36.886411 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00152298 (* 0.0272727 = 4.15359e-05 loss)
I0430 16:34:36.886440 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00201405 (* 0.0272727 = 5.49286e-05 loss)
I0430 16:34:36.886466 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00098457 (* 0.0272727 = 2.68519e-05 loss)
I0430 16:34:36.886492 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000442376 (* 0.0272727 = 1.20648e-05 loss)
I0430 16:34:36.886518 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000258197 (* 0.0272727 = 7.04175e-06 loss)
I0430 16:34:36.886545 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000211208 (* 0.0272727 = 5.76023e-06 loss)
I0430 16:34:36.886569 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000140215 (* 0.0272727 = 3.82405e-06 loss)
I0430 16:34:36.886589 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.761905
I0430 16:34:36.886615 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 16:34:36.886637 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 16:34:36.886659 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 16:34:36.886682 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 16:34:36.886706 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 16:34:36.886729 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 16:34:36.886751 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0430 16:34:36.886773 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 16:34:36.886793 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 16:34:36.886814 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 16:34:36.886837 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:34:36.886858 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 16:34:36.886878 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:34:36.886899 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:34:36.886920 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:34:36.886958 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:34:36.886982 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:34:36.887004 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:34:36.887027 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:34:36.887048 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:34:36.887068 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:34:36.887089 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:34:36.887112 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.926136
I0430 16:34:36.887133 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.857143
I0430 16:34:36.887159 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.766814 (* 1 = 0.766814 loss)
I0430 16:34:36.887186 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.226565 (* 1 = 0.226565 loss)
I0430 16:34:36.887212 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.606162 (* 0.0909091 = 0.0551057 loss)
I0430 16:34:36.887238 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.428289 (* 0.0909091 = 0.0389354 loss)
I0430 16:34:36.887264 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.493548 (* 0.0909091 = 0.044868 loss)
I0430 16:34:36.887291 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.4759 (* 0.0909091 = 0.0432636 loss)
I0430 16:34:36.887318 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.661536 (* 0.0909091 = 0.0601397 loss)
I0430 16:34:36.887344 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.16269 (* 0.0909091 = 0.105699 loss)
I0430 16:34:36.887370 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.973925 (* 0.0909091 = 0.0885386 loss)
I0430 16:34:36.887398 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.716342 (* 0.0909091 = 0.065122 loss)
I0430 16:34:36.887423 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.112295 (* 0.0909091 = 0.0102087 loss)
I0430 16:34:36.887449 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0208494 (* 0.0909091 = 0.0018954 loss)
I0430 16:34:36.887497 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.0015116 (* 0.0909091 = 0.000137418 loss)
I0430 16:34:36.887528 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00049896 (* 0.0909091 = 4.536e-05 loss)
I0430 16:34:36.887554 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000330742 (* 0.0909091 = 3.00675e-05 loss)
I0430 16:34:36.887583 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000184677 (* 0.0909091 = 1.67888e-05 loss)
I0430 16:34:36.887609 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 7.4271e-05 (* 0.0909091 = 6.75191e-06 loss)
I0430 16:34:36.887635 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 3.90219e-05 (* 0.0909091 = 3.54744e-06 loss)
I0430 16:34:36.887668 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 1.65856e-05 (* 0.0909091 = 1.50778e-06 loss)
I0430 16:34:36.887696 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 8.34482e-06 (* 0.0909091 = 7.5862e-07 loss)
I0430 16:34:36.887722 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 2.90575e-06 (* 0.0909091 = 2.64159e-07 loss)
I0430 16:34:36.887748 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.05798e-06 (* 0.0909091 = 9.61805e-08 loss)
I0430 16:34:36.887779 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 4.02332e-07 (* 0.0909091 = 3.65756e-08 loss)
I0430 16:34:36.887805 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.42727e-07 (* 0.0909091 = 3.1157e-08 loss)
I0430 16:34:36.887827 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 16:34:36.887850 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 16:34:36.887889 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.480904
I0430 16:34:36.887913 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.507659
I0430 16:34:36.887935 15443 sgd_solver.cpp:106] Iteration 18000, lr = 0.001
I0430 16:36:03.176672 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 49.1279 > 30) by scale factor 0.610651
I0430 16:36:53.897325 15443 solver.cpp:229] Iteration 18500, loss = 3.67406
I0430 16:36:53.897481 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.362319
I0430 16:36:53.897502 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 16:36:53.897516 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 16:36:53.897527 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0430 16:36:53.897539 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0430 16:36:53.897552 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 16:36:53.897562 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0430 16:36:53.897574 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 16:36:53.897586 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 16:36:53.897598 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0430 16:36:53.897609 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0430 16:36:53.897621 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0430 16:36:53.897634 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.75
I0430 16:36:53.897644 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.75
I0430 16:36:53.897656 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.75
I0430 16:36:53.897668 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.75
I0430 16:36:53.897680 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.75
I0430 16:36:53.897691 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 0.875
I0430 16:36:53.897703 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:36:53.897716 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:36:53.897727 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:36:53.897738 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:36:53.897750 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:36:53.897761 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.738636
I0430 16:36:53.897773 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.73913
I0430 16:36:53.897789 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.97227 (* 0.3 = 0.591681 loss)
I0430 16:36:53.897804 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.811559 (* 0.3 = 0.243468 loss)
I0430 16:36:53.897817 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.721524 (* 0.0272727 = 0.0196779 loss)
I0430 16:36:53.897832 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.69348 (* 0.0272727 = 0.0461857 loss)
I0430 16:36:53.897846 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.94712 (* 0.0272727 = 0.0531034 loss)
I0430 16:36:53.897861 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.67717 (* 0.0272727 = 0.0457411 loss)
I0430 16:36:53.897873 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.92455 (* 0.0272727 = 0.0524878 loss)
I0430 16:36:53.897887 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.18344 (* 0.0272727 = 0.0322757 loss)
I0430 16:36:53.897902 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.978118 (* 0.0272727 = 0.026676 loss)
I0430 16:36:53.897915 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.776819 (* 0.0272727 = 0.021186 loss)
I0430 16:36:53.897929 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.804042 (* 0.0272727 = 0.0219284 loss)
I0430 16:36:53.897943 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.652516 (* 0.0272727 = 0.0177959 loss)
I0430 16:36:53.897956 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.806131 (* 0.0272727 = 0.0219854 loss)
I0430 16:36:53.897970 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.700682 (* 0.0272727 = 0.0191095 loss)
I0430 16:36:53.898005 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.752319 (* 0.0272727 = 0.0205178 loss)
I0430 16:36:53.898021 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.771221 (* 0.0272727 = 0.0210333 loss)
I0430 16:36:53.898036 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.8338 (* 0.0272727 = 0.02274 loss)
I0430 16:36:53.898049 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 1.05223 (* 0.0272727 = 0.0286971 loss)
I0430 16:36:53.898062 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.650284 (* 0.0272727 = 0.017735 loss)
I0430 16:36:53.898077 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0581139 (* 0.0272727 = 0.00158492 loss)
I0430 16:36:53.898092 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.0148926 (* 0.0272727 = 0.000406163 loss)
I0430 16:36:53.898105 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00498498 (* 0.0272727 = 0.000135954 loss)
I0430 16:36:53.898119 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000339393 (* 0.0272727 = 9.25616e-06 loss)
I0430 16:36:53.898133 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 4.5896e-06 (* 0.0272727 = 1.25171e-07 loss)
I0430 16:36:53.898145 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.449275
I0430 16:36:53.898157 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 16:36:53.898169 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 16:36:53.898181 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 16:36:53.898193 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 16:36:53.898205 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.125
I0430 16:36:53.898216 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.875
I0430 16:36:53.898228 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 16:36:53.898239 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 16:36:53.898252 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:36:53.898263 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0430 16:36:53.898274 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0430 16:36:53.898286 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.75
I0430 16:36:53.898298 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.75
I0430 16:36:53.898309 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.75
I0430 16:36:53.898324 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.75
I0430 16:36:53.898336 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.75
I0430 16:36:53.898347 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 0.875
I0430 16:36:53.898360 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:36:53.898371 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:36:53.898382 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:36:53.898393 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:36:53.898406 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:36:53.898416 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.767045
I0430 16:36:53.898428 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.681159
I0430 16:36:53.898442 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.84245 (* 0.3 = 0.552736 loss)
I0430 16:36:53.898457 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.766918 (* 0.3 = 0.230075 loss)
I0430 16:36:53.898469 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.974234 (* 0.0272727 = 0.02657 loss)
I0430 16:36:53.898483 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.00771 (* 0.0272727 = 0.0274829 loss)
I0430 16:36:53.898515 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.46147 (* 0.0272727 = 0.0398582 loss)
I0430 16:36:53.898543 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.86516 (* 0.0272727 = 0.0508681 loss)
I0430 16:36:53.898569 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.92427 (* 0.0272727 = 0.05248 loss)
I0430 16:36:53.898584 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.05303 (* 0.0272727 = 0.028719 loss)
I0430 16:36:53.898610 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.2501 (* 0.0272727 = 0.0340936 loss)
I0430 16:36:53.898632 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.75518 (* 0.0272727 = 0.0205958 loss)
I0430 16:36:53.898646 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.802064 (* 0.0272727 = 0.0218745 loss)
I0430 16:36:53.898660 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.857919 (* 0.0272727 = 0.0233978 loss)
I0430 16:36:53.898674 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.869764 (* 0.0272727 = 0.0237208 loss)
I0430 16:36:53.898687 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.715522 (* 0.0272727 = 0.0195142 loss)
I0430 16:36:53.898701 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.86914 (* 0.0272727 = 0.0237038 loss)
I0430 16:36:53.898715 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 1.00421 (* 0.0272727 = 0.0273875 loss)
I0430 16:36:53.898728 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.9886 (* 0.0272727 = 0.0269618 loss)
I0430 16:36:53.898741 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 1.06027 (* 0.0272727 = 0.0289164 loss)
I0430 16:36:53.898756 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.741253 (* 0.0272727 = 0.020216 loss)
I0430 16:36:53.898768 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0191806 (* 0.0272727 = 0.000523108 loss)
I0430 16:36:53.898782 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0108007 (* 0.0272727 = 0.000294564 loss)
I0430 16:36:53.898797 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00811307 (* 0.0272727 = 0.000221266 loss)
I0430 16:36:53.898810 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00487338 (* 0.0272727 = 0.00013291 loss)
I0430 16:36:53.898824 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000820173 (* 0.0272727 = 2.23683e-05 loss)
I0430 16:36:53.898836 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.536232
I0430 16:36:53.898849 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:36:53.898859 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 16:36:53.898870 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0430 16:36:53.898882 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0430 16:36:53.898893 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.5
I0430 16:36:53.898905 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 16:36:53.898916 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 16:36:53.898928 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.625
I0430 16:36:53.898939 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0430 16:36:53.898950 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.75
I0430 16:36:53.898962 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0430 16:36:53.898973 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.75
I0430 16:36:53.898984 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.75
I0430 16:36:53.898996 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.75
I0430 16:36:53.899008 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.75
I0430 16:36:53.899019 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.75
I0430 16:36:53.899055 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 0.875
I0430 16:36:53.899075 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:36:53.899086 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:36:53.899097 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:36:53.899109 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:36:53.899121 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:36:53.899132 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.8125
I0430 16:36:53.899143 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.652174
I0430 16:36:53.899157 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.52435 (* 1 = 1.52435 loss)
I0430 16:36:53.899170 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.617622 (* 1 = 0.617622 loss)
I0430 16:36:53.899184 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.288989 (* 0.0909091 = 0.0262717 loss)
I0430 16:36:53.899199 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.901445 (* 0.0909091 = 0.0819495 loss)
I0430 16:36:53.899212 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.775671 (* 0.0909091 = 0.0705155 loss)
I0430 16:36:53.899226 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.927353 (* 0.0909091 = 0.0843048 loss)
I0430 16:36:53.899240 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.08687 (* 0.0909091 = 0.0988065 loss)
I0430 16:36:53.899253 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.754727 (* 0.0909091 = 0.0686115 loss)
I0430 16:36:53.899266 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.735448 (* 0.0909091 = 0.0668589 loss)
I0430 16:36:53.899281 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.694015 (* 0.0909091 = 0.0630923 loss)
I0430 16:36:53.899294 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.779608 (* 0.0909091 = 0.0708735 loss)
I0430 16:36:53.899307 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.945083 (* 0.0909091 = 0.0859166 loss)
I0430 16:36:53.899320 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.762225 (* 0.0909091 = 0.0692932 loss)
I0430 16:36:53.899334 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.718788 (* 0.0909091 = 0.0653444 loss)
I0430 16:36:53.899348 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.741294 (* 0.0909091 = 0.0673904 loss)
I0430 16:36:53.899361 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.888181 (* 0.0909091 = 0.0807438 loss)
I0430 16:36:53.899379 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.925931 (* 0.0909091 = 0.0841755 loss)
I0430 16:36:53.899392 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 1.12036 (* 0.0909091 = 0.101851 loss)
I0430 16:36:53.899405 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.757184 (* 0.0909091 = 0.0688349 loss)
I0430 16:36:53.899420 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00861708 (* 0.0909091 = 0.000783371 loss)
I0430 16:36:53.899435 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00512236 (* 0.0909091 = 0.000465669 loss)
I0430 16:36:53.899448 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00328047 (* 0.0909091 = 0.000298224 loss)
I0430 16:36:53.899461 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00177183 (* 0.0909091 = 0.000161075 loss)
I0430 16:36:53.899492 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000572194 (* 0.0909091 = 5.20177e-05 loss)
I0430 16:36:53.899504 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 16:36:53.899516 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.25
I0430 16:36:53.899544 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.360291
I0430 16:36:53.899557 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.329999
I0430 16:36:53.899570 15443 sgd_solver.cpp:106] Iteration 18500, lr = 0.001
I0430 16:39:10.873841 15443 solver.cpp:229] Iteration 19000, loss = 3.58621
I0430 16:39:10.873986 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.574074
I0430 16:39:10.874014 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 16:39:10.874037 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 16:39:10.874058 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0430 16:39:10.874080 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.125
I0430 16:39:10.874101 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.75
I0430 16:39:10.874124 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0430 16:39:10.874145 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 16:39:10.874166 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 16:39:10.874188 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0430 16:39:10.874209 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:39:10.874230 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 16:39:10.874253 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 16:39:10.874276 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:39:10.874296 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:39:10.874323 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:39:10.874346 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:39:10.874366 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:39:10.874388 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:39:10.874409 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:39:10.874430 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:39:10.874451 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:39:10.874475 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:39:10.874495 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.857955
I0430 16:39:10.874516 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.925926
I0430 16:39:10.874544 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.09601 (* 0.3 = 0.328802 loss)
I0430 16:39:10.874570 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.360776 (* 0.3 = 0.108233 loss)
I0430 16:39:10.874598 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.162307 (* 0.0272727 = 0.00442656 loss)
I0430 16:39:10.874626 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.20079 (* 0.0272727 = 0.0327489 loss)
I0430 16:39:10.874652 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.11021 (* 0.0272727 = 0.0302783 loss)
I0430 16:39:10.874680 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.63694 (* 0.0272727 = 0.0446438 loss)
I0430 16:39:10.874704 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 0.857049 (* 0.0272727 = 0.0233741 loss)
I0430 16:39:10.874732 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.19261 (* 0.0272727 = 0.0325257 loss)
I0430 16:39:10.874758 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.909488 (* 0.0272727 = 0.0248042 loss)
I0430 16:39:10.874784 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.656374 (* 0.0272727 = 0.0179011 loss)
I0430 16:39:10.874810 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0474375 (* 0.0272727 = 0.00129375 loss)
I0430 16:39:10.874837 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.227247 (* 0.0272727 = 0.00619766 loss)
I0430 16:39:10.874863 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0105527 (* 0.0272727 = 0.000287801 loss)
I0430 16:39:10.874891 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.000767514 (* 0.0272727 = 2.09322e-05 loss)
I0430 16:39:10.874940 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.000312835 (* 0.0272727 = 8.53185e-06 loss)
I0430 16:39:10.874969 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 7.37375e-05 (* 0.0272727 = 2.01102e-06 loss)
I0430 16:39:10.875001 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 1.73014e-05 (* 0.0272727 = 4.71857e-07 loss)
I0430 16:39:10.875033 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 1.93715e-07 (* 0.0272727 = 5.28314e-09 loss)
I0430 16:39:10.875061 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 1.04308e-07 (* 0.0272727 = 2.84477e-09 loss)
I0430 16:39:10.875089 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 1.49012e-08 (* 0.0272727 = 4.06395e-10 loss)
I0430 16:39:10.875118 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0 (* 0.0272727 = 0 loss)
I0430 16:39:10.875144 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 8.9407e-08 (* 0.0272727 = 2.43837e-09 loss)
I0430 16:39:10.875172 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 2.98023e-08 (* 0.0272727 = 8.12791e-10 loss)
I0430 16:39:10.875198 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 8.9407e-08 (* 0.0272727 = 2.43837e-09 loss)
I0430 16:39:10.875221 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.685185
I0430 16:39:10.875243 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:39:10.875265 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 16:39:10.875288 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0430 16:39:10.875310 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 16:39:10.875331 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0430 16:39:10.875354 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 16:39:10.875380 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.375
I0430 16:39:10.875402 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0430 16:39:10.875424 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0430 16:39:10.875445 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 16:39:10.875481 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 16:39:10.875509 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 16:39:10.875532 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:39:10.875555 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:39:10.875576 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:39:10.875598 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:39:10.875619 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:39:10.875641 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:39:10.875663 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:39:10.875684 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:39:10.875705 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:39:10.875726 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:39:10.875747 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.897727
I0430 16:39:10.875771 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.962963
I0430 16:39:10.875797 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.830536 (* 0.3 = 0.249161 loss)
I0430 16:39:10.875824 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.27351 (* 0.3 = 0.0820531 loss)
I0430 16:39:10.875852 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.139434 (* 0.0272727 = 0.00380276 loss)
I0430 16:39:10.875879 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.707459 (* 0.0272727 = 0.0192943 loss)
I0430 16:39:10.875924 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.55427 (* 0.0272727 = 0.0151165 loss)
I0430 16:39:10.875953 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.30115 (* 0.0272727 = 0.035486 loss)
I0430 16:39:10.875982 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.835848 (* 0.0272727 = 0.0227959 loss)
I0430 16:39:10.876008 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.90954 (* 0.0272727 = 0.0248056 loss)
I0430 16:39:10.876034 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.08962 (* 0.0272727 = 0.0297168 loss)
I0430 16:39:10.876063 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.21241 (* 0.0272727 = 0.0330656 loss)
I0430 16:39:10.876090 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0390982 (* 0.0272727 = 0.00106632 loss)
I0430 16:39:10.876117 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.111168 (* 0.0272727 = 0.00303185 loss)
I0430 16:39:10.876143 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.0268542 (* 0.0272727 = 0.000732388 loss)
I0430 16:39:10.876168 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0111958 (* 0.0272727 = 0.000305339 loss)
I0430 16:39:10.876194 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00117599 (* 0.0272727 = 3.20725e-05 loss)
I0430 16:39:10.876221 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000566747 (* 0.0272727 = 1.54567e-05 loss)
I0430 16:39:10.876247 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00010317 (* 0.0272727 = 2.81373e-06 loss)
I0430 16:39:10.876273 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 9.80523e-06 (* 0.0272727 = 2.67415e-07 loss)
I0430 16:39:10.876301 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 1.49012e-06 (* 0.0272727 = 4.06397e-08 loss)
I0430 16:39:10.876327 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 1.46032e-06 (* 0.0272727 = 3.98269e-08 loss)
I0430 16:39:10.876351 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 4.76838e-07 (* 0.0272727 = 1.30047e-08 loss)
I0430 16:39:10.876379 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 2.98023e-08 (* 0.0272727 = 8.12791e-10 loss)
I0430 16:39:10.876405 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.83122e-07 (* 0.0272727 = 7.72152e-09 loss)
I0430 16:39:10.876435 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 3.12925e-07 (* 0.0272727 = 8.53431e-09 loss)
I0430 16:39:10.876457 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.851852
I0430 16:39:10.876478 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:39:10.876499 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0430 16:39:10.876519 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0430 16:39:10.876541 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 16:39:10.876562 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0430 16:39:10.876584 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 16:39:10.876605 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.5
I0430 16:39:10.876627 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 16:39:10.876647 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 16:39:10.876668 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:39:10.876688 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:39:10.876710 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 16:39:10.876731 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:39:10.876752 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:39:10.876773 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:39:10.876796 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:39:10.876832 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:39:10.876855 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:39:10.876878 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:39:10.876898 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:39:10.876917 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:39:10.876938 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:39:10.876960 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.943182
I0430 16:39:10.876982 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.981481
I0430 16:39:10.877007 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.330986 (* 1 = 0.330986 loss)
I0430 16:39:10.877034 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.115894 (* 1 = 0.115894 loss)
I0430 16:39:10.877060 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0162467 (* 0.0909091 = 0.00147697 loss)
I0430 16:39:10.877086 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.202131 (* 0.0909091 = 0.0183756 loss)
I0430 16:39:10.877117 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.0380187 (* 0.0909091 = 0.00345624 loss)
I0430 16:39:10.877145 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.273512 (* 0.0909091 = 0.0248648 loss)
I0430 16:39:10.877171 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.212989 (* 0.0909091 = 0.0193626 loss)
I0430 16:39:10.877197 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.430899 (* 0.0909091 = 0.0391727 loss)
I0430 16:39:10.877223 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.815431 (* 0.0909091 = 0.0741301 loss)
I0430 16:39:10.877248 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.670155 (* 0.0909091 = 0.0609232 loss)
I0430 16:39:10.877274 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.0326191 (* 0.0909091 = 0.00296537 loss)
I0430 16:39:10.877300 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.137375 (* 0.0909091 = 0.0124887 loss)
I0430 16:39:10.877326 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00983922 (* 0.0909091 = 0.000894474 loss)
I0430 16:39:10.877354 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00323004 (* 0.0909091 = 0.00029364 loss)
I0430 16:39:10.877382 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000845459 (* 0.0909091 = 7.68599e-05 loss)
I0430 16:39:10.877414 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000387076 (* 0.0909091 = 3.51887e-05 loss)
I0430 16:39:10.877442 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000212085 (* 0.0909091 = 1.92805e-05 loss)
I0430 16:39:10.877473 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 4.42038e-05 (* 0.0909091 = 4.01853e-06 loss)
I0430 16:39:10.877501 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 1.0908e-05 (* 0.0909091 = 9.91638e-07 loss)
I0430 16:39:10.877526 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 7.76372e-06 (* 0.0909091 = 7.05793e-07 loss)
I0430 16:39:10.877553 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 3.74024e-06 (* 0.0909091 = 3.40022e-07 loss)
I0430 16:39:10.877578 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.04308e-06 (* 0.0909091 = 9.48259e-08 loss)
I0430 16:39:10.877604 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 5.0664e-07 (* 0.0909091 = 4.60582e-08 loss)
I0430 16:39:10.877630 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.63913e-07 (* 0.0909091 = 1.49012e-08 loss)
I0430 16:39:10.877652 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 16:39:10.877673 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 16:39:10.877712 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.494054
I0430 16:39:10.877735 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.434662
I0430 16:39:10.877758 15443 sgd_solver.cpp:106] Iteration 19000, lr = 0.001
I0430 16:41:27.805430 15443 solver.cpp:229] Iteration 19500, loss = 3.67102
I0430 16:41:27.805613 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.513514
I0430 16:41:27.805634 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0430 16:41:27.805646 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 16:41:27.805660 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0430 16:41:27.805671 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.625
I0430 16:41:27.805682 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.75
I0430 16:41:27.805694 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.875
I0430 16:41:27.805706 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0430 16:41:27.805717 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 16:41:27.805729 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:41:27.805742 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:41:27.805753 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 16:41:27.805765 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 16:41:27.805776 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:41:27.805788 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:41:27.805800 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:41:27.805811 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:41:27.805824 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:41:27.805835 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:41:27.805846 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:41:27.805857 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:41:27.805869 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:41:27.805881 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:41:27.805892 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.886364
I0430 16:41:27.805904 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.756757
I0430 16:41:27.805920 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.52233 (* 0.3 = 0.456698 loss)
I0430 16:41:27.805934 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.355032 (* 0.3 = 0.10651 loss)
I0430 16:41:27.805949 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.890559 (* 0.0272727 = 0.024288 loss)
I0430 16:41:27.805963 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.925941 (* 0.0272727 = 0.0252529 loss)
I0430 16:41:27.805977 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.42689 (* 0.0272727 = 0.0389152 loss)
I0430 16:41:27.805991 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 0.967833 (* 0.0272727 = 0.0263954 loss)
I0430 16:41:27.806005 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 0.977254 (* 0.0272727 = 0.0266524 loss)
I0430 16:41:27.806020 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.437776 (* 0.0272727 = 0.0119393 loss)
I0430 16:41:27.806033 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.38682 (* 0.0272727 = 0.0105496 loss)
I0430 16:41:27.806046 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.504757 (* 0.0272727 = 0.0137661 loss)
I0430 16:41:27.806061 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.843077 (* 0.0272727 = 0.022993 loss)
I0430 16:41:27.806074 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.212443 (* 0.0272727 = 0.0057939 loss)
I0430 16:41:27.806089 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00457028 (* 0.0272727 = 0.000124644 loss)
I0430 16:41:27.806103 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00158959 (* 0.0272727 = 4.33524e-05 loss)
I0430 16:41:27.806138 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.000551609 (* 0.0272727 = 1.50439e-05 loss)
I0430 16:41:27.806152 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.000388492 (* 0.0272727 = 1.05952e-05 loss)
I0430 16:41:27.806166 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.000238882 (* 0.0272727 = 6.51497e-06 loss)
I0430 16:41:27.806180 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000142119 (* 0.0272727 = 3.87598e-06 loss)
I0430 16:41:27.806193 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000284516 (* 0.0272727 = 7.75952e-06 loss)
I0430 16:41:27.806207 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 7.78526e-05 (* 0.0272727 = 2.12325e-06 loss)
I0430 16:41:27.806221 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000125478 (* 0.0272727 = 3.42213e-06 loss)
I0430 16:41:27.806234 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 6.09605e-05 (* 0.0272727 = 1.66256e-06 loss)
I0430 16:41:27.806248 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 6.35544e-05 (* 0.0272727 = 1.7333e-06 loss)
I0430 16:41:27.806262 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 2.23983e-05 (* 0.0272727 = 6.10863e-07 loss)
I0430 16:41:27.806274 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.540541
I0430 16:41:27.806287 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 16:41:27.806298 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 16:41:27.806309 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0430 16:41:27.806324 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 16:41:27.806336 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0430 16:41:27.806349 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.875
I0430 16:41:27.806360 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 16:41:27.806371 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 16:41:27.806382 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:41:27.806394 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:41:27.806406 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 16:41:27.806417 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 16:41:27.806428 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:41:27.806439 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:41:27.806450 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:41:27.806463 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:41:27.806473 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:41:27.806484 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:41:27.806496 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:41:27.806507 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:41:27.806519 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:41:27.806529 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:41:27.806540 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.886364
I0430 16:41:27.806552 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.756757
I0430 16:41:27.806566 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.34444 (* 0.3 = 0.403332 loss)
I0430 16:41:27.806579 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.349023 (* 0.3 = 0.104707 loss)
I0430 16:41:27.806593 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.99307 (* 0.0272727 = 0.0270837 loss)
I0430 16:41:27.806607 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.54564 (* 0.0272727 = 0.0148811 loss)
I0430 16:41:27.806638 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.16721 (* 0.0272727 = 0.031833 loss)
I0430 16:41:27.806653 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.00655 (* 0.0272727 = 0.0274515 loss)
I0430 16:41:27.806666 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.19447 (* 0.0272727 = 0.0325764 loss)
I0430 16:41:27.806681 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.512851 (* 0.0272727 = 0.0139869 loss)
I0430 16:41:27.806690 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.371846 (* 0.0272727 = 0.0101413 loss)
I0430 16:41:27.806700 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.467115 (* 0.0272727 = 0.0127395 loss)
I0430 16:41:27.806715 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.851471 (* 0.0272727 = 0.0232219 loss)
I0430 16:41:27.806728 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.5155 (* 0.0272727 = 0.0140591 loss)
I0430 16:41:27.806742 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00719706 (* 0.0272727 = 0.000196283 loss)
I0430 16:41:27.806756 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00494961 (* 0.0272727 = 0.000134989 loss)
I0430 16:41:27.806769 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00266231 (* 0.0272727 = 7.26083e-05 loss)
I0430 16:41:27.806783 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00172184 (* 0.0272727 = 4.69592e-05 loss)
I0430 16:41:27.806797 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00174988 (* 0.0272727 = 4.77241e-05 loss)
I0430 16:41:27.806810 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000880431 (* 0.0272727 = 2.40118e-05 loss)
I0430 16:41:27.806824 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000316957 (* 0.0272727 = 8.64427e-06 loss)
I0430 16:41:27.806838 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000326035 (* 0.0272727 = 8.89185e-06 loss)
I0430 16:41:27.806850 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000263976 (* 0.0272727 = 7.19934e-06 loss)
I0430 16:41:27.806864 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000290436 (* 0.0272727 = 7.92099e-06 loss)
I0430 16:41:27.806877 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000368794 (* 0.0272727 = 1.0058e-05 loss)
I0430 16:41:27.806891 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000364425 (* 0.0272727 = 9.93887e-06 loss)
I0430 16:41:27.806902 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.702703
I0430 16:41:27.806915 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 16:41:27.806926 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 16:41:27.806937 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 16:41:27.806948 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 16:41:27.806959 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0430 16:41:27.806972 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 1
I0430 16:41:27.806982 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0430 16:41:27.806993 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:41:27.807005 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:41:27.807016 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 16:41:27.807027 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:41:27.807039 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 16:41:27.807049 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:41:27.807060 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:41:27.807071 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:41:27.807092 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:41:27.807106 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:41:27.807116 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:41:27.807127 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:41:27.807139 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:41:27.807150 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:41:27.807162 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:41:27.807173 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.931818
I0430 16:41:27.807184 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.810811
I0430 16:41:27.807199 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.908535 (* 1 = 0.908535 loss)
I0430 16:41:27.807212 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.211659 (* 1 = 0.211659 loss)
I0430 16:41:27.807226 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.723402 (* 0.0909091 = 0.0657638 loss)
I0430 16:41:27.807240 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.411739 (* 0.0909091 = 0.0374308 loss)
I0430 16:41:27.807253 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.965974 (* 0.0909091 = 0.0878158 loss)
I0430 16:41:27.807266 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.668222 (* 0.0909091 = 0.0607475 loss)
I0430 16:41:27.807281 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.0536505 (* 0.0909091 = 0.00487731 loss)
I0430 16:41:27.807294 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.0230516 (* 0.0909091 = 0.0020956 loss)
I0430 16:41:27.807308 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.0777757 (* 0.0909091 = 0.00707052 loss)
I0430 16:41:27.807322 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.350779 (* 0.0909091 = 0.031889 loss)
I0430 16:41:27.807335 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.511029 (* 0.0909091 = 0.0464572 loss)
I0430 16:41:27.807349 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0460974 (* 0.0909091 = 0.00419067 loss)
I0430 16:41:27.807365 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00433536 (* 0.0909091 = 0.000394123 loss)
I0430 16:41:27.807379 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00248699 (* 0.0909091 = 0.00022609 loss)
I0430 16:41:27.807394 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00182921 (* 0.0909091 = 0.000166292 loss)
I0430 16:41:27.807407 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00119046 (* 0.0909091 = 0.000108224 loss)
I0430 16:41:27.807421 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000950536 (* 0.0909091 = 8.64124e-05 loss)
I0430 16:41:27.807435 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000783482 (* 0.0909091 = 7.12256e-05 loss)
I0430 16:41:27.807448 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000426912 (* 0.0909091 = 3.88102e-05 loss)
I0430 16:41:27.807462 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000255447 (* 0.0909091 = 2.32225e-05 loss)
I0430 16:41:27.807492 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000209885 (* 0.0909091 = 1.90804e-05 loss)
I0430 16:41:27.807505 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000152911 (* 0.0909091 = 1.3901e-05 loss)
I0430 16:41:27.807519 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000110697 (* 0.0909091 = 1.00633e-05 loss)
I0430 16:41:27.807533 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 8.32128e-05 (* 0.0909091 = 7.5648e-06 loss)
I0430 16:41:27.807544 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0430 16:41:27.807556 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 16:41:27.807579 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.385515
I0430 16:41:27.807590 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.464559
I0430 16:41:27.807603 15443 sgd_solver.cpp:106] Iteration 19500, lr = 0.001
I0430 16:42:26.453346 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 32.9507 > 30) by scale factor 0.91045
I0430 16:43:44.574877 15443 solver.cpp:456] Snapshotting to binary proto file /mnt/snapshots/mixed_lstm14_bn_iter_20000.caffemodel
I0430 16:43:53.446482 15443 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /mnt/snapshots/mixed_lstm14_bn_iter_20000.solverstate
I0430 16:43:57.046567 15443 solver.cpp:338] Iteration 20000, Testing net (#0)
I0430 16:44:37.852020 15443 solver.cpp:393] Test loss: 2.336
I0430 16:44:37.852151 15443 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.638281
I0430 16:44:37.852171 15443 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.843
I0430 16:44:37.852185 15443 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.716
I0430 16:44:37.852196 15443 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.57
I0430 16:44:37.852208 15443 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.576
I0430 16:44:37.852219 15443 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.572
I0430 16:44:37.852231 15443 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.681
I0430 16:44:37.852242 15443 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.809
I0430 16:44:37.852253 15443 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.894
I0430 16:44:37.852265 15443 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.983
I0430 16:44:37.852277 15443 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.994
I0430 16:44:37.852288 15443 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.998
I0430 16:44:37.852299 15443 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.999
I0430 16:44:37.852313 15443 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.999
I0430 16:44:37.852325 15443 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 1
I0430 16:44:37.852337 15443 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 1
I0430 16:44:37.852349 15443 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 1
I0430 16:44:37.852360 15443 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 1
I0430 16:44:37.852371 15443 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 1
I0430 16:44:37.852382 15443 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 1
I0430 16:44:37.852393 15443 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 1
I0430 16:44:37.852406 15443 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0430 16:44:37.852416 15443 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0430 16:44:37.852427 15443 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.891593
I0430 16:44:37.852439 15443 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.863632
I0430 16:44:37.852455 15443 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.1624 (* 0.3 = 0.34872 loss)
I0430 16:44:37.852469 15443 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.355905 (* 0.3 = 0.106771 loss)
I0430 16:44:37.852484 15443 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.592212 (* 0.0272727 = 0.0161512 loss)
I0430 16:44:37.852497 15443 solver.cpp:406]     Test net output #28: loss1/loss02 = 0.99735 (* 0.0272727 = 0.0272005 loss)
I0430 16:44:37.852511 15443 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.41325 (* 0.0272727 = 0.0385432 loss)
I0430 16:44:37.852524 15443 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.36912 (* 0.0272727 = 0.0373396 loss)
I0430 16:44:37.852546 15443 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.32158 (* 0.0272727 = 0.036043 loss)
I0430 16:44:37.852560 15443 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.01685 (* 0.0272727 = 0.0277323 loss)
I0430 16:44:37.852573 15443 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.585564 (* 0.0272727 = 0.0159699 loss)
I0430 16:44:37.852587 15443 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.303679 (* 0.0272727 = 0.00828214 loss)
I0430 16:44:37.852602 15443 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.0931388 (* 0.0272727 = 0.00254015 loss)
I0430 16:44:37.852615 15443 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.0348995 (* 0.0272727 = 0.000951804 loss)
I0430 16:44:37.852628 15443 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.0146865 (* 0.0272727 = 0.000400541 loss)
I0430 16:44:37.852646 15443 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.00940922 (* 0.0272727 = 0.000256615 loss)
I0430 16:44:37.852668 15443 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.00639762 (* 0.0272727 = 0.000174481 loss)
I0430 16:44:37.852702 15443 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.00397434 (* 0.0272727 = 0.000108391 loss)
I0430 16:44:37.852718 15443 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.00253513 (* 0.0272727 = 6.914e-05 loss)
I0430 16:44:37.852732 15443 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.00153331 (* 0.0272727 = 4.18176e-05 loss)
I0430 16:44:37.852746 15443 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.000940312 (* 0.0272727 = 2.56449e-05 loss)
I0430 16:44:37.852761 15443 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.000503219 (* 0.0272727 = 1.37242e-05 loss)
I0430 16:44:37.852773 15443 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.000310622 (* 0.0272727 = 8.4715e-06 loss)
I0430 16:44:37.852787 15443 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.000203373 (* 0.0272727 = 5.54655e-06 loss)
I0430 16:44:37.852802 15443 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.000139761 (* 0.0272727 = 3.81166e-06 loss)
I0430 16:44:37.852814 15443 solver.cpp:406]     Test net output #48: loss1/loss22 = 0.000108657 (* 0.0272727 = 2.96336e-06 loss)
I0430 16:44:37.852826 15443 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.738716
I0430 16:44:37.852838 15443 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.875
I0430 16:44:37.852849 15443 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.829
I0430 16:44:37.852860 15443 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.725
I0430 16:44:37.852872 15443 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.659
I0430 16:44:37.852883 15443 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.634
I0430 16:44:37.852895 15443 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.707
I0430 16:44:37.852906 15443 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.852
I0430 16:44:37.852917 15443 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.906
I0430 16:44:37.852928 15443 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.981
I0430 16:44:37.852941 15443 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.994
I0430 16:44:37.852952 15443 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.998
I0430 16:44:37.852963 15443 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.999
I0430 16:44:37.852974 15443 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.999
I0430 16:44:37.852985 15443 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 1
I0430 16:44:37.852996 15443 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 1
I0430 16:44:37.853008 15443 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 1
I0430 16:44:37.853018 15443 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 1
I0430 16:44:37.853029 15443 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 1
I0430 16:44:37.853040 15443 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 1
I0430 16:44:37.853051 15443 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 1
I0430 16:44:37.853062 15443 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0430 16:44:37.853073 15443 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0430 16:44:37.853085 15443 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.920637
I0430 16:44:37.853096 15443 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.901552
I0430 16:44:37.853109 15443 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.876162 (* 0.3 = 0.262849 loss)
I0430 16:44:37.853122 15443 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.27215 (* 0.3 = 0.0816449 loss)
I0430 16:44:37.853137 15443 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.5159 (* 0.0272727 = 0.01407 loss)
I0430 16:44:37.853149 15443 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.664668 (* 0.0272727 = 0.0181273 loss)
I0430 16:44:37.853178 15443 solver.cpp:406]     Test net output #78: loss2/loss03 = 1.00389 (* 0.0272727 = 0.0273788 loss)
I0430 16:44:37.853190 15443 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.11158 (* 0.0272727 = 0.0303157 loss)
I0430 16:44:37.853199 15443 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.13046 (* 0.0272727 = 0.0308308 loss)
I0430 16:44:37.853209 15443 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.891955 (* 0.0272727 = 0.0243261 loss)
I0430 16:44:37.853222 15443 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.498047 (* 0.0272727 = 0.0135831 loss)
I0430 16:44:37.853237 15443 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.281914 (* 0.0272727 = 0.00768856 loss)
I0430 16:44:37.853250 15443 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.0911213 (* 0.0272727 = 0.00248513 loss)
I0430 16:44:37.853265 15443 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.0341427 (* 0.0272727 = 0.000931165 loss)
I0430 16:44:37.853284 15443 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.0139341 (* 0.0272727 = 0.000380021 loss)
I0430 16:44:37.853303 15443 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.00838577 (* 0.0272727 = 0.000228703 loss)
I0430 16:44:37.853317 15443 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.00537615 (* 0.0272727 = 0.000146622 loss)
I0430 16:44:37.853330 15443 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.00326607 (* 0.0272727 = 8.90747e-05 loss)
I0430 16:44:37.853344 15443 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.00192272 (* 0.0272727 = 5.24379e-05 loss)
I0430 16:44:37.853358 15443 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.00103013 (* 0.0272727 = 2.80945e-05 loss)
I0430 16:44:37.853375 15443 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.000466211 (* 0.0272727 = 1.27148e-05 loss)
I0430 16:44:37.853389 15443 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.000224102 (* 0.0272727 = 6.11188e-06 loss)
I0430 16:44:37.853402 15443 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.000134348 (* 0.0272727 = 3.66404e-06 loss)
I0430 16:44:37.853415 15443 solver.cpp:406]     Test net output #95: loss2/loss20 = 8.84072e-05 (* 0.0272727 = 2.41111e-06 loss)
I0430 16:44:37.853428 15443 solver.cpp:406]     Test net output #96: loss2/loss21 = 7.63515e-05 (* 0.0272727 = 2.08231e-06 loss)
I0430 16:44:37.853441 15443 solver.cpp:406]     Test net output #97: loss2/loss22 = 6.18383e-05 (* 0.0272727 = 1.6865e-06 loss)
I0430 16:44:37.853453 15443 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.846155
I0430 16:44:37.853464 15443 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.892
I0430 16:44:37.853476 15443 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.884
I0430 16:44:37.853487 15443 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.826
I0430 16:44:37.853497 15443 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.836
I0430 16:44:37.853508 15443 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.82
I0430 16:44:37.853520 15443 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.862
I0430 16:44:37.853531 15443 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.878
I0430 16:44:37.853543 15443 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.941
I0430 16:44:37.853554 15443 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.98
I0430 16:44:37.853564 15443 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.991
I0430 16:44:37.853575 15443 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.998
I0430 16:44:37.853586 15443 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.999
I0430 16:44:37.853597 15443 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.999
I0430 16:44:37.853608 15443 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.999
I0430 16:44:37.853620 15443 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 1
I0430 16:44:37.853631 15443 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 1
I0430 16:44:37.853653 15443 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 1
I0430 16:44:37.853667 15443 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 1
I0430 16:44:37.853677 15443 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 1
I0430 16:44:37.853688 15443 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 1
I0430 16:44:37.853699 15443 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0430 16:44:37.853710 15443 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0430 16:44:37.853721 15443 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.950592
I0430 16:44:37.853732 15443 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.926443
I0430 16:44:37.853745 15443 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.58604 (* 1 = 0.58604 loss)
I0430 16:44:37.853759 15443 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.190562 (* 1 = 0.190562 loss)
I0430 16:44:37.853772 15443 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.416596 (* 0.0909091 = 0.0378723 loss)
I0430 16:44:37.853786 15443 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.514612 (* 0.0909091 = 0.0467829 loss)
I0430 16:44:37.853799 15443 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.669356 (* 0.0909091 = 0.0608505 loss)
I0430 16:44:37.853812 15443 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.612573 (* 0.0909091 = 0.0556885 loss)
I0430 16:44:37.853826 15443 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.650265 (* 0.0909091 = 0.059115 loss)
I0430 16:44:37.853839 15443 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.54513 (* 0.0909091 = 0.0495573 loss)
I0430 16:44:37.853852 15443 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.377912 (* 0.0909091 = 0.0343556 loss)
I0430 16:44:37.853865 15443 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.198183 (* 0.0909091 = 0.0180166 loss)
I0430 16:44:37.853878 15443 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.0786095 (* 0.0909091 = 0.00714632 loss)
I0430 16:44:37.853893 15443 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.0385075 (* 0.0909091 = 0.00350068 loss)
I0430 16:44:37.853905 15443 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.0164907 (* 0.0909091 = 0.00149916 loss)
I0430 16:44:37.853919 15443 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.0111749 (* 0.0909091 = 0.0010159 loss)
I0430 16:44:37.853932 15443 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.00716974 (* 0.0909091 = 0.000651795 loss)
I0430 16:44:37.853946 15443 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.00420519 (* 0.0909091 = 0.00038229 loss)
I0430 16:44:37.853960 15443 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.00231925 (* 0.0909091 = 0.000210841 loss)
I0430 16:44:37.853972 15443 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.00120437 (* 0.0909091 = 0.000109488 loss)
I0430 16:44:37.853986 15443 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.000540383 (* 0.0909091 = 4.91257e-05 loss)
I0430 16:44:37.854001 15443 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.000249301 (* 0.0909091 = 2.26638e-05 loss)
I0430 16:44:37.854013 15443 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.00015935 (* 0.0909091 = 1.44864e-05 loss)
I0430 16:44:37.854027 15443 solver.cpp:406]     Test net output #144: loss3/loss20 = 7.57025e-05 (* 0.0909091 = 6.88204e-06 loss)
I0430 16:44:37.854039 15443 solver.cpp:406]     Test net output #145: loss3/loss21 = 4.59118e-05 (* 0.0909091 = 4.1738e-06 loss)
I0430 16:44:37.854053 15443 solver.cpp:406]     Test net output #146: loss3/loss22 = 3.44741e-05 (* 0.0909091 = 3.13401e-06 loss)
I0430 16:44:37.854064 15443 solver.cpp:406]     Test net output #147: total_accuracy = 0.601
I0430 16:44:37.854076 15443 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.576
I0430 16:44:37.854087 15443 solver.cpp:406]     Test net output #149: total_confidence = 0.542057
I0430 16:44:37.854109 15443 solver.cpp:406]     Test net output #150: total_confidence_not_rec = 0.517883
I0430 16:44:37.854121 15443 solver.cpp:338] Iteration 20000, Testing net (#1)
I0430 16:45:18.941287 15443 solver.cpp:393] Test loss: 3.11848
I0430 16:45:18.941433 15443 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.596494
I0430 16:45:18.941460 15443 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.792
I0430 16:45:18.941483 15443 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.676
I0430 16:45:18.941505 15443 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.547
I0430 16:45:18.941526 15443 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.545
I0430 16:45:18.941546 15443 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.56
I0430 16:45:18.941567 15443 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.644
I0430 16:45:18.941587 15443 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.752
I0430 16:45:18.941608 15443 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.85
I0430 16:45:18.941628 15443 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.908
I0430 16:45:18.941649 15443 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.921
I0430 16:45:18.941671 15443 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.936
I0430 16:45:18.941696 15443 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.937
I0430 16:45:18.941718 15443 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.952
I0430 16:45:18.941738 15443 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 0.961
I0430 16:45:18.941761 15443 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 0.965
I0430 16:45:18.941792 15443 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 0.978
I0430 16:45:18.941814 15443 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 0.99
I0430 16:45:18.941836 15443 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 0.995
I0430 16:45:18.941857 15443 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 0.998
I0430 16:45:18.941879 15443 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 0.998
I0430 16:45:18.941901 15443 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 0.999
I0430 16:45:18.941923 15443 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0430 16:45:18.941944 15443 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.85832
I0430 16:45:18.941965 15443 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.82946
I0430 16:45:18.941993 15443 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.29903 (* 0.3 = 0.389709 loss)
I0430 16:45:18.942019 15443 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.46927 (* 0.3 = 0.140781 loss)
I0430 16:45:18.942046 15443 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.778169 (* 0.0272727 = 0.0212228 loss)
I0430 16:45:18.942072 15443 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.11241 (* 0.0272727 = 0.0303385 loss)
I0430 16:45:18.942098 15443 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.44769 (* 0.0272727 = 0.0394824 loss)
I0430 16:45:18.942124 15443 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.47288 (* 0.0272727 = 0.0401693 loss)
I0430 16:45:18.942148 15443 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.41044 (* 0.0272727 = 0.0384665 loss)
I0430 16:45:18.942174 15443 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.1173 (* 0.0272727 = 0.0304717 loss)
I0430 16:45:18.942199 15443 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.787888 (* 0.0272727 = 0.0214879 loss)
I0430 16:45:18.942225 15443 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.482248 (* 0.0272727 = 0.0131522 loss)
I0430 16:45:18.942251 15443 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.327684 (* 0.0272727 = 0.00893685 loss)
I0430 16:45:18.942277 15443 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.277165 (* 0.0272727 = 0.00755904 loss)
I0430 16:45:18.942302 15443 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.213914 (* 0.0272727 = 0.00583402 loss)
I0430 16:45:18.942333 15443 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.206755 (* 0.0272727 = 0.00563878 loss)
I0430 16:45:18.942385 15443 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.176322 (* 0.0272727 = 0.00480879 loss)
I0430 16:45:18.942414 15443 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.157322 (* 0.0272727 = 0.00429059 loss)
I0430 16:45:18.942446 15443 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.149295 (* 0.0272727 = 0.00407167 loss)
I0430 16:45:18.942476 15443 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.0910477 (* 0.0272727 = 0.00248312 loss)
I0430 16:45:18.942513 15443 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.0444722 (* 0.0272727 = 0.00121288 loss)
I0430 16:45:18.942541 15443 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.0254422 (* 0.0272727 = 0.000693878 loss)
I0430 16:45:18.942566 15443 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.0134651 (* 0.0272727 = 0.000367229 loss)
I0430 16:45:18.942594 15443 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.012627 (* 0.0272727 = 0.000344373 loss)
I0430 16:45:18.942620 15443 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.0105618 (* 0.0272727 = 0.00028805 loss)
I0430 16:45:18.942646 15443 solver.cpp:406]     Test net output #48: loss1/loss22 = 2.87161e-05 (* 0.0272727 = 7.83166e-07 loss)
I0430 16:45:18.942667 15443 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.69416
I0430 16:45:18.942688 15443 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.842
I0430 16:45:18.942710 15443 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.781
I0430 16:45:18.942731 15443 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.705
I0430 16:45:18.942754 15443 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.624
I0430 16:45:18.942775 15443 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.615
I0430 16:45:18.942796 15443 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.679
I0430 16:45:18.942817 15443 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.787
I0430 16:45:18.942837 15443 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.846
I0430 16:45:18.942859 15443 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.904
I0430 16:45:18.942880 15443 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.923
I0430 16:45:18.942900 15443 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.939
I0430 16:45:18.942924 15443 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.942
I0430 16:45:18.942945 15443 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.953
I0430 16:45:18.942965 15443 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.96
I0430 16:45:18.942986 15443 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.967
I0430 16:45:18.943006 15443 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 0.978
I0430 16:45:18.943027 15443 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 0.99
I0430 16:45:18.943048 15443 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 0.995
I0430 16:45:18.943068 15443 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 0.998
I0430 16:45:18.943089 15443 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 0.998
I0430 16:45:18.943110 15443 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 0.999
I0430 16:45:18.943132 15443 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0430 16:45:18.943152 15443 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.887592
I0430 16:45:18.943173 15443 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.869176
I0430 16:45:18.943199 15443 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 1.03634 (* 0.3 = 0.310902 loss)
I0430 16:45:18.943224 15443 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.383934 (* 0.3 = 0.11518 loss)
I0430 16:45:18.943249 15443 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.632029 (* 0.0272727 = 0.0172372 loss)
I0430 16:45:18.943275 15443 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.801587 (* 0.0272727 = 0.0218615 loss)
I0430 16:45:18.943320 15443 solver.cpp:406]     Test net output #78: loss2/loss03 = 1.06741 (* 0.0272727 = 0.0291113 loss)
I0430 16:45:18.943347 15443 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.23898 (* 0.0272727 = 0.0337903 loss)
I0430 16:45:18.943377 15443 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.22695 (* 0.0272727 = 0.0334624 loss)
I0430 16:45:18.943404 15443 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.9955 (* 0.0272727 = 0.02715 loss)
I0430 16:45:18.943429 15443 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.696104 (* 0.0272727 = 0.0189847 loss)
I0430 16:45:18.943455 15443 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.47905 (* 0.0272727 = 0.013065 loss)
I0430 16:45:18.943500 15443 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.331539 (* 0.0272727 = 0.00904196 loss)
I0430 16:45:18.943528 15443 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.279001 (* 0.0272727 = 0.00760913 loss)
I0430 16:45:18.943554 15443 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.219126 (* 0.0272727 = 0.00597617 loss)
I0430 16:45:18.943579 15443 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.203079 (* 0.0272727 = 0.00553851 loss)
I0430 16:45:18.943605 15443 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.175182 (* 0.0272727 = 0.00477769 loss)
I0430 16:45:18.943630 15443 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.152583 (* 0.0272727 = 0.00416136 loss)
I0430 16:45:18.943656 15443 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.146087 (* 0.0272727 = 0.00398419 loss)
I0430 16:45:18.943681 15443 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.0966387 (* 0.0272727 = 0.0026356 loss)
I0430 16:45:18.943706 15443 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.049656 (* 0.0272727 = 0.00135425 loss)
I0430 16:45:18.943732 15443 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.0236201 (* 0.0272727 = 0.000644183 loss)
I0430 16:45:18.943758 15443 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.0129567 (* 0.0272727 = 0.000353363 loss)
I0430 16:45:18.943783 15443 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.0123345 (* 0.0272727 = 0.000336395 loss)
I0430 16:45:18.943809 15443 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.00772679 (* 0.0272727 = 0.000210731 loss)
I0430 16:45:18.943833 15443 solver.cpp:406]     Test net output #97: loss2/loss22 = 7.89498e-05 (* 0.0272727 = 2.15318e-06 loss)
I0430 16:45:18.943856 15443 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.792057
I0430 16:45:18.943876 15443 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.86
I0430 16:45:18.943897 15443 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.823
I0430 16:45:18.943918 15443 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.828
I0430 16:45:18.943939 15443 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.794
I0430 16:45:18.943960 15443 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.771
I0430 16:45:18.943981 15443 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.82
I0430 16:45:18.944001 15443 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.828
I0430 16:45:18.944021 15443 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.877
I0430 16:45:18.944041 15443 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.912
I0430 16:45:18.944062 15443 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.926
I0430 16:45:18.944083 15443 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.939
I0430 16:45:18.944103 15443 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.94
I0430 16:45:18.944124 15443 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.954
I0430 16:45:18.944145 15443 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.963
I0430 16:45:18.944165 15443 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.972
I0430 16:45:18.944185 15443 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 0.979
I0430 16:45:18.944223 15443 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 0.99
I0430 16:45:18.944245 15443 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 0.996
I0430 16:45:18.944267 15443 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 0.998
I0430 16:45:18.944288 15443 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 0.998
I0430 16:45:18.944308 15443 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 0.999
I0430 16:45:18.944329 15443 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0430 16:45:18.944349 15443 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.916955
I0430 16:45:18.944370 15443 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.900681
I0430 16:45:18.944394 15443 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.749809 (* 1 = 0.749809 loss)
I0430 16:45:18.944423 15443 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.296508 (* 1 = 0.296508 loss)
I0430 16:45:18.944449 15443 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.542666 (* 0.0909091 = 0.0493332 loss)
I0430 16:45:18.944475 15443 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.611403 (* 0.0909091 = 0.0555821 loss)
I0430 16:45:18.944499 15443 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.646155 (* 0.0909091 = 0.0587413 loss)
I0430 16:45:18.944528 15443 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.733856 (* 0.0909091 = 0.0667142 loss)
I0430 16:45:18.944555 15443 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.800558 (* 0.0909091 = 0.072778 loss)
I0430 16:45:18.944581 15443 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.675554 (* 0.0909091 = 0.061414 loss)
I0430 16:45:18.944607 15443 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.5692 (* 0.0909091 = 0.0517454 loss)
I0430 16:45:18.944633 15443 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.394707 (* 0.0909091 = 0.0358825 loss)
I0430 16:45:18.944656 15443 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.297623 (* 0.0909091 = 0.0270566 loss)
I0430 16:45:18.944681 15443 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.263539 (* 0.0909091 = 0.0239581 loss)
I0430 16:45:18.944706 15443 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.205256 (* 0.0909091 = 0.0186596 loss)
I0430 16:45:18.944730 15443 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.186089 (* 0.0909091 = 0.0169171 loss)
I0430 16:45:18.944756 15443 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.160106 (* 0.0909091 = 0.0145551 loss)
I0430 16:45:18.944783 15443 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.136212 (* 0.0909091 = 0.0123829 loss)
I0430 16:45:18.944803 15443 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.129973 (* 0.0909091 = 0.0118158 loss)
I0430 16:45:18.944831 15443 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.0779458 (* 0.0909091 = 0.00708598 loss)
I0430 16:45:18.944867 15443 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.0432625 (* 0.0909091 = 0.00393296 loss)
I0430 16:45:18.944900 15443 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.0203725 (* 0.0909091 = 0.00185204 loss)
I0430 16:45:18.944927 15443 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.0112453 (* 0.0909091 = 0.0010223 loss)
I0430 16:45:18.944954 15443 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.0103392 (* 0.0909091 = 0.000939925 loss)
I0430 16:45:18.944978 15443 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.00669406 (* 0.0909091 = 0.000608551 loss)
I0430 16:45:18.945003 15443 solver.cpp:406]     Test net output #146: loss3/loss22 = 9.40952e-05 (* 0.0909091 = 8.55411e-06 loss)
I0430 16:45:18.945026 15443 solver.cpp:406]     Test net output #147: total_accuracy = 0.514
I0430 16:45:18.945046 15443 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.508
I0430 16:45:18.945066 15443 solver.cpp:406]     Test net output #149: total_confidence = 0.476212
I0430 16:45:18.945103 15443 solver.cpp:406]     Test net output #150: total_confidence_not_rec = 0.462266
I0430 16:45:19.124050 15443 solver.cpp:229] Iteration 20000, loss = 3.71068
I0430 16:45:19.124125 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.409091
I0430 16:45:19.124153 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 16:45:19.124174 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 16:45:19.124197 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0430 16:45:19.124218 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.625
I0430 16:45:19.124238 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 16:45:19.124260 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 16:45:19.124282 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 16:45:19.124305 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 16:45:19.124326 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:45:19.124348 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 16:45:19.124369 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0430 16:45:19.124390 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0430 16:45:19.124413 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:45:19.124434 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:45:19.124457 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:45:19.124480 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:45:19.124502 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:45:19.124527 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:45:19.124550 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:45:19.124572 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:45:19.124593 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:45:19.124614 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:45:19.124637 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.840909
I0430 16:45:19.124660 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.659091
I0430 16:45:19.124687 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.92223 (* 0.3 = 0.57667 loss)
I0430 16:45:19.124714 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.549866 (* 0.3 = 0.16496 loss)
I0430 16:45:19.124742 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.848254 (* 0.0272727 = 0.0231342 loss)
I0430 16:45:19.124768 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.76356 (* 0.0272727 = 0.0480972 loss)
I0430 16:45:19.124795 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.63941 (* 0.0272727 = 0.0719838 loss)
I0430 16:45:19.124821 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.67488 (* 0.0272727 = 0.0456784 loss)
I0430 16:45:19.124848 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.55951 (* 0.0272727 = 0.042532 loss)
I0430 16:45:19.124873 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.16339 (* 0.0272727 = 0.0317287 loss)
I0430 16:45:19.124899 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.527976 (* 0.0272727 = 0.0143993 loss)
I0430 16:45:19.124927 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.581732 (* 0.0272727 = 0.0158654 loss)
I0430 16:45:19.124953 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.472796 (* 0.0272727 = 0.0128944 loss)
I0430 16:45:19.124980 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.217692 (* 0.0272727 = 0.00593705 loss)
I0430 16:45:19.125007 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.127019 (* 0.0272727 = 0.00346415 loss)
I0430 16:45:19.125075 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0714825 (* 0.0272727 = 0.00194952 loss)
I0430 16:45:19.125105 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0350059 (* 0.0272727 = 0.000954706 loss)
I0430 16:45:19.125133 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00955908 (* 0.0272727 = 0.000260702 loss)
I0430 16:45:19.125159 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00459419 (* 0.0272727 = 0.000125296 loss)
I0430 16:45:19.125187 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000691338 (* 0.0272727 = 1.88547e-05 loss)
I0430 16:45:19.125214 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000693422 (* 0.0272727 = 1.89115e-05 loss)
I0430 16:45:19.125244 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000229672 (* 0.0272727 = 6.26378e-06 loss)
I0430 16:45:19.125274 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 8.37648e-05 (* 0.0272727 = 2.28449e-06 loss)
I0430 16:45:19.125303 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.84121e-05 (* 0.0272727 = 7.74876e-07 loss)
I0430 16:45:19.125332 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 4.24157e-05 (* 0.0272727 = 1.15679e-06 loss)
I0430 16:45:19.125368 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 3.20638e-05 (* 0.0272727 = 8.74467e-07 loss)
I0430 16:45:19.125399 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.522727
I0430 16:45:19.125424 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:45:19.125447 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 16:45:19.125469 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0430 16:45:19.125491 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 16:45:19.125514 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 16:45:19.125536 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 16:45:19.125558 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0430 16:45:19.125586 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 16:45:19.125609 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:45:19.125630 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0430 16:45:19.125653 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0430 16:45:19.125675 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0430 16:45:19.125695 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:45:19.125717 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:45:19.125740 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:45:19.125761 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:45:19.125782 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:45:19.125803 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:45:19.125823 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:45:19.125844 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:45:19.125866 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:45:19.125887 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:45:19.125908 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.863636
I0430 16:45:19.125931 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.727273
I0430 16:45:19.125957 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.53597 (* 0.3 = 0.46079 loss)
I0430 16:45:19.125984 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.47113 (* 0.3 = 0.141339 loss)
I0430 16:45:19.126029 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.539935 (* 0.0272727 = 0.0147255 loss)
I0430 16:45:19.126060 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.45214 (* 0.0272727 = 0.0396038 loss)
I0430 16:45:19.126086 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.04797 (* 0.0272727 = 0.0558537 loss)
I0430 16:45:19.126112 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.32915 (* 0.0272727 = 0.0362497 loss)
I0430 16:45:19.126139 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.30509 (* 0.0272727 = 0.0355933 loss)
I0430 16:45:19.126164 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.43032 (* 0.0272727 = 0.0390086 loss)
I0430 16:45:19.126191 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.485566 (* 0.0272727 = 0.0132427 loss)
I0430 16:45:19.126217 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.653194 (* 0.0272727 = 0.0178144 loss)
I0430 16:45:19.126243 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.500383 (* 0.0272727 = 0.0136468 loss)
I0430 16:45:19.126269 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.147675 (* 0.0272727 = 0.00402751 loss)
I0430 16:45:19.126297 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.104781 (* 0.0272727 = 0.00285767 loss)
I0430 16:45:19.126322 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0717 (* 0.0272727 = 0.00195545 loss)
I0430 16:45:19.126348 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0442884 (* 0.0272727 = 0.00120787 loss)
I0430 16:45:19.126377 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0195695 (* 0.0272727 = 0.000533712 loss)
I0430 16:45:19.126404 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0132067 (* 0.0272727 = 0.000360182 loss)
I0430 16:45:19.126428 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00562264 (* 0.0272727 = 0.000153345 loss)
I0430 16:45:19.126456 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00260099 (* 0.0272727 = 7.0936e-05 loss)
I0430 16:45:19.126482 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00119497 (* 0.0272727 = 3.25901e-05 loss)
I0430 16:45:19.126508 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000975936 (* 0.0272727 = 2.66164e-05 loss)
I0430 16:45:19.126533 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000745202 (* 0.0272727 = 2.03237e-05 loss)
I0430 16:45:19.126561 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000371177 (* 0.0272727 = 1.0123e-05 loss)
I0430 16:45:19.126586 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000249662 (* 0.0272727 = 6.80897e-06 loss)
I0430 16:45:19.126608 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.659091
I0430 16:45:19.126636 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:45:19.126657 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.375
I0430 16:45:19.126678 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.5
I0430 16:45:19.126700 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 16:45:19.126723 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 16:45:19.126742 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0430 16:45:19.126763 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 16:45:19.126785 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:45:19.126807 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:45:19.126827 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:45:19.126849 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:45:19.126871 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0430 16:45:19.126893 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:45:19.126929 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:45:19.126953 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:45:19.126974 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:45:19.126996 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:45:19.127015 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:45:19.127038 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:45:19.127059 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:45:19.127079 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:45:19.127100 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:45:19.127122 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.897727
I0430 16:45:19.127145 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.772727
I0430 16:45:19.127169 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.29233 (* 1 = 1.29233 loss)
I0430 16:45:19.127195 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.383044 (* 1 = 0.383044 loss)
I0430 16:45:19.127221 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.22853 (* 0.0909091 = 0.0207755 loss)
I0430 16:45:19.127248 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 1.48412 (* 0.0909091 = 0.13492 loss)
I0430 16:45:19.127274 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.76846 (* 0.0909091 = 0.160769 loss)
I0430 16:45:19.127301 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.840034 (* 0.0909091 = 0.0763667 loss)
I0430 16:45:19.127327 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.2847 (* 0.0909091 = 0.116791 loss)
I0430 16:45:19.127352 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.791372 (* 0.0909091 = 0.0719429 loss)
I0430 16:45:19.127378 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.431842 (* 0.0909091 = 0.0392583 loss)
I0430 16:45:19.127403 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.467165 (* 0.0909091 = 0.0424695 loss)
I0430 16:45:19.127429 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.427408 (* 0.0909091 = 0.0388553 loss)
I0430 16:45:19.127455 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.262343 (* 0.0909091 = 0.0238494 loss)
I0430 16:45:19.127501 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.15927 (* 0.0909091 = 0.0144791 loss)
I0430 16:45:19.127529 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.0742103 (* 0.0909091 = 0.00674639 loss)
I0430 16:45:19.127557 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0234111 (* 0.0909091 = 0.00212828 loss)
I0430 16:45:19.127583 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00622283 (* 0.0909091 = 0.000565712 loss)
I0430 16:45:19.127609 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0021396 (* 0.0909091 = 0.000194509 loss)
I0430 16:45:19.127635 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000645839 (* 0.0909091 = 5.87127e-05 loss)
I0430 16:45:19.127662 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000261401 (* 0.0909091 = 2.37638e-05 loss)
I0430 16:45:19.127693 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00012021 (* 0.0909091 = 1.09282e-05 loss)
I0430 16:45:19.127720 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 5.90115e-05 (* 0.0909091 = 5.36468e-06 loss)
I0430 16:45:19.127748 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 2.97528e-05 (* 0.0909091 = 2.7048e-06 loss)
I0430 16:45:19.127774 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.75544e-05 (* 0.0909091 = 1.59585e-06 loss)
I0430 16:45:19.127800 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 5.76681e-06 (* 0.0909091 = 5.24255e-07 loss)
I0430 16:45:19.127845 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.25
I0430 16:45:19.127869 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 16:45:19.127892 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.352372
I0430 16:45:19.127912 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.361762
I0430 16:45:19.127933 15443 sgd_solver.cpp:106] Iteration 20000, lr = 0.001
I0430 16:45:46.822196 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.1742 > 30) by scale factor 0.994228
I0430 16:47:36.052430 15443 solver.cpp:229] Iteration 20500, loss = 3.56483
I0430 16:47:36.052603 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.535714
I0430 16:47:36.052624 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 16:47:36.052637 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.875
I0430 16:47:36.052649 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.75
I0430 16:47:36.052661 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 16:47:36.052673 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0430 16:47:36.052685 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0430 16:47:36.052696 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 16:47:36.052708 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 16:47:36.052719 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:47:36.052731 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:47:36.052743 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:47:36.052754 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:47:36.052767 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:47:36.052778 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:47:36.052790 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:47:36.052803 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:47:36.052814 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:47:36.052825 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:47:36.052836 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:47:36.052848 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:47:36.052860 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:47:36.052875 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:47:36.052888 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.840909
I0430 16:47:36.052901 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.839286
I0430 16:47:36.052916 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.32072 (* 0.3 = 0.396217 loss)
I0430 16:47:36.052930 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.483471 (* 0.3 = 0.145041 loss)
I0430 16:47:36.052944 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.4389 (* 0.0272727 = 0.01197 loss)
I0430 16:47:36.052959 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.490657 (* 0.0272727 = 0.0133816 loss)
I0430 16:47:36.052973 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 0.76839 (* 0.0272727 = 0.0209561 loss)
I0430 16:47:36.052986 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.74344 (* 0.0272727 = 0.0475483 loss)
I0430 16:47:36.053000 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.17289 (* 0.0272727 = 0.0592606 loss)
I0430 16:47:36.053019 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.26648 (* 0.0272727 = 0.061813 loss)
I0430 16:47:36.053032 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.1526 (* 0.0272727 = 0.0314346 loss)
I0430 16:47:36.053045 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.2288 (* 0.0272727 = 0.0335127 loss)
I0430 16:47:36.053059 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.37549 (* 0.0272727 = 0.0102406 loss)
I0430 16:47:36.053073 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.38281 (* 0.0272727 = 0.0104403 loss)
I0430 16:47:36.053086 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.372054 (* 0.0272727 = 0.0101469 loss)
I0430 16:47:36.053100 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.409871 (* 0.0272727 = 0.0111783 loss)
I0430 16:47:36.053114 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0174683 (* 0.0272727 = 0.000476409 loss)
I0430 16:47:36.053149 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0109818 (* 0.0272727 = 0.000299504 loss)
I0430 16:47:36.053165 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00508253 (* 0.0272727 = 0.000138614 loss)
I0430 16:47:36.053179 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000356518 (* 0.0272727 = 9.72322e-06 loss)
I0430 16:47:36.053194 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000203937 (* 0.0272727 = 5.56193e-06 loss)
I0430 16:47:36.053207 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 3.7042e-05 (* 0.0272727 = 1.01024e-06 loss)
I0430 16:47:36.053220 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 1.66904e-05 (* 0.0272727 = 4.55192e-07 loss)
I0430 16:47:36.053234 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.78007e-05 (* 0.0272727 = 7.58202e-07 loss)
I0430 16:47:36.053248 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 1.96859e-05 (* 0.0272727 = 5.36889e-07 loss)
I0430 16:47:36.053262 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 5.57313e-06 (* 0.0272727 = 1.51994e-07 loss)
I0430 16:47:36.053274 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.607143
I0430 16:47:36.053287 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:47:36.053298 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 16:47:36.053309 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0430 16:47:36.053324 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 16:47:36.053335 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 16:47:36.053347 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0430 16:47:36.053359 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 16:47:36.053371 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0430 16:47:36.053382 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:47:36.053395 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:47:36.053406 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:47:36.053418 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:47:36.053429 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0430 16:47:36.053442 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:47:36.053450 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:47:36.053462 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:47:36.053473 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:47:36.053485 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:47:36.053496 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:47:36.053508 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:47:36.053519 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:47:36.053530 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:47:36.053545 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.863636
I0430 16:47:36.053557 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.892857
I0430 16:47:36.053571 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.07248 (* 0.3 = 0.321744 loss)
I0430 16:47:36.053586 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.382335 (* 0.3 = 0.1147 loss)
I0430 16:47:36.053602 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.331977 (* 0.0272727 = 0.00905391 loss)
I0430 16:47:36.053622 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.673108 (* 0.0272727 = 0.0183575 loss)
I0430 16:47:36.053648 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.740625 (* 0.0272727 = 0.0201989 loss)
I0430 16:47:36.053663 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.39821 (* 0.0272727 = 0.038133 loss)
I0430 16:47:36.053678 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.38907 (* 0.0272727 = 0.0378836 loss)
I0430 16:47:36.053691 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.1532 (* 0.0272727 = 0.031451 loss)
I0430 16:47:36.053704 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.930624 (* 0.0272727 = 0.0253807 loss)
I0430 16:47:36.053719 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.1411 (* 0.0272727 = 0.031121 loss)
I0430 16:47:36.053732 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.362944 (* 0.0272727 = 0.00989849 loss)
I0430 16:47:36.053746 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.328893 (* 0.0272727 = 0.00896981 loss)
I0430 16:47:36.053760 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.288734 (* 0.0272727 = 0.00787456 loss)
I0430 16:47:36.053773 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.411163 (* 0.0272727 = 0.0112135 loss)
I0430 16:47:36.053787 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0142147 (* 0.0272727 = 0.000387673 loss)
I0430 16:47:36.053802 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0061049 (* 0.0272727 = 0.000166497 loss)
I0430 16:47:36.053814 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00212804 (* 0.0272727 = 5.80374e-05 loss)
I0430 16:47:36.053828 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 5.44457e-05 (* 0.0272727 = 1.48488e-06 loss)
I0430 16:47:36.053841 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 5.37059e-05 (* 0.0272727 = 1.46471e-06 loss)
I0430 16:47:36.053855 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 2.59973e-05 (* 0.0272727 = 7.09019e-07 loss)
I0430 16:47:36.053869 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 3.14376e-05 (* 0.0272727 = 8.5739e-07 loss)
I0430 16:47:36.053882 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 5.67043e-05 (* 0.0272727 = 1.54648e-06 loss)
I0430 16:47:36.053895 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 8.20053e-05 (* 0.0272727 = 2.23651e-06 loss)
I0430 16:47:36.053910 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 4.07463e-05 (* 0.0272727 = 1.11126e-06 loss)
I0430 16:47:36.053920 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.875
I0430 16:47:36.053932 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:47:36.053944 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0430 16:47:36.053956 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 16:47:36.053967 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 16:47:36.053978 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 16:47:36.053990 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 1
I0430 16:47:36.054002 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 16:47:36.054013 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0430 16:47:36.054024 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:47:36.054036 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 16:47:36.054047 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:47:36.054059 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 16:47:36.054069 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0430 16:47:36.054081 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:47:36.054092 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:47:36.054113 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:47:36.054126 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:47:36.054138 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:47:36.054149 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:47:36.054160 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:47:36.054172 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:47:36.054183 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:47:36.054195 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.954545
I0430 16:47:36.054206 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 1
I0430 16:47:36.054220 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.431158 (* 1 = 0.431158 loss)
I0430 16:47:36.054234 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.166797 (* 1 = 0.166797 loss)
I0430 16:47:36.054249 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0493382 (* 0.0909091 = 0.00448529 loss)
I0430 16:47:36.054262 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0450038 (* 0.0909091 = 0.00409126 loss)
I0430 16:47:36.054276 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.186223 (* 0.0909091 = 0.0169294 loss)
I0430 16:47:36.054289 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.857765 (* 0.0909091 = 0.0779787 loss)
I0430 16:47:36.054303 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.0732 (* 0.0909091 = 0.0975636 loss)
I0430 16:47:36.054317 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.236906 (* 0.0909091 = 0.0215369 loss)
I0430 16:47:36.054330 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.443168 (* 0.0909091 = 0.040288 loss)
I0430 16:47:36.054343 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.00678 (* 0.0909091 = 0.0915256 loss)
I0430 16:47:36.054358 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.226215 (* 0.0909091 = 0.020565 loss)
I0430 16:47:36.054374 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0826825 (* 0.0909091 = 0.00751659 loss)
I0430 16:47:36.054388 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.0967322 (* 0.0909091 = 0.00879383 loss)
I0430 16:47:36.054402 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.292269 (* 0.0909091 = 0.0265699 loss)
I0430 16:47:36.054416 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0213156 (* 0.0909091 = 0.00193779 loss)
I0430 16:47:36.054430 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00809029 (* 0.0909091 = 0.000735481 loss)
I0430 16:47:36.054445 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00201914 (* 0.0909091 = 0.000183558 loss)
I0430 16:47:36.054458 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000313745 (* 0.0909091 = 2.85222e-05 loss)
I0430 16:47:36.054472 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000137993 (* 0.0909091 = 1.25448e-05 loss)
I0430 16:47:36.054486 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 5.11493e-05 (* 0.0909091 = 4.64994e-06 loss)
I0430 16:47:36.054499 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 2.24428e-05 (* 0.0909091 = 2.04026e-06 loss)
I0430 16:47:36.054512 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 5.72215e-06 (* 0.0909091 = 5.20195e-07 loss)
I0430 16:47:36.054527 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.38581e-06 (* 0.0909091 = 1.25983e-07 loss)
I0430 16:47:36.054540 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 2.08616e-07 (* 0.0909091 = 1.89651e-08 loss)
I0430 16:47:36.054553 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 16:47:36.054563 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 16:47:36.054585 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.466303
I0430 16:47:36.054599 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.478255
I0430 16:47:36.054611 15443 sgd_solver.cpp:106] Iteration 20500, lr = 0.001
I0430 16:49:53.010582 15443 solver.cpp:229] Iteration 21000, loss = 3.69881
I0430 16:49:53.010763 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.490566
I0430 16:49:53.010784 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 16:49:53.010797 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0430 16:49:53.010809 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0430 16:49:53.010821 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.125
I0430 16:49:53.010833 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0430 16:49:53.010845 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0430 16:49:53.010857 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0430 16:49:53.010869 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 16:49:53.010880 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0430 16:49:53.010893 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:49:53.010905 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:49:53.010916 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:49:53.010928 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 16:49:53.010941 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 16:49:53.010951 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:49:53.010963 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:49:53.010974 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:49:53.010987 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:49:53.010998 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:49:53.011009 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:49:53.011020 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:49:53.011032 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:49:53.011044 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.846591
I0430 16:49:53.011055 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.849057
I0430 16:49:53.011073 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.56231 (* 0.3 = 0.468694 loss)
I0430 16:49:53.011087 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.495284 (* 0.3 = 0.148585 loss)
I0430 16:49:53.011102 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.42953 (* 0.0272727 = 0.0117145 loss)
I0430 16:49:53.011116 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.986257 (* 0.0272727 = 0.0268979 loss)
I0430 16:49:53.011132 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.89612 (* 0.0272727 = 0.0517123 loss)
I0430 16:49:53.011145 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.82986 (* 0.0272727 = 0.0499052 loss)
I0430 16:49:53.011159 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.6116 (* 0.0272727 = 0.0439526 loss)
I0430 16:49:53.011173 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.703957 (* 0.0272727 = 0.0191988 loss)
I0430 16:49:53.011186 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.21786 (* 0.0272727 = 0.0332145 loss)
I0430 16:49:53.011200 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.367071 (* 0.0272727 = 0.010011 loss)
I0430 16:49:53.011215 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.271011 (* 0.0272727 = 0.00739121 loss)
I0430 16:49:53.011229 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.515279 (* 0.0272727 = 0.0140531 loss)
I0430 16:49:53.011243 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.455734 (* 0.0272727 = 0.0124291 loss)
I0430 16:49:53.011257 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.404897 (* 0.0272727 = 0.0110427 loss)
I0430 16:49:53.011292 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.319749 (* 0.0272727 = 0.00872041 loss)
I0430 16:49:53.011308 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.379297 (* 0.0272727 = 0.0103445 loss)
I0430 16:49:53.011325 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0192553 (* 0.0272727 = 0.000525145 loss)
I0430 16:49:53.011340 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0106434 (* 0.0272727 = 0.000290274 loss)
I0430 16:49:53.011354 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00446698 (* 0.0272727 = 0.000121827 loss)
I0430 16:49:53.011368 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00335222 (* 0.0272727 = 9.14241e-05 loss)
I0430 16:49:53.011381 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00138713 (* 0.0272727 = 3.78307e-05 loss)
I0430 16:49:53.011395 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.0018379 (* 0.0272727 = 5.01244e-05 loss)
I0430 16:49:53.011410 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000300566 (* 0.0272727 = 8.19727e-06 loss)
I0430 16:49:53.011425 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 3.87559e-05 (* 0.0272727 = 1.05698e-06 loss)
I0430 16:49:53.011436 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.566038
I0430 16:49:53.011448 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:49:53.011461 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 16:49:53.011487 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.875
I0430 16:49:53.011499 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 16:49:53.011512 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.25
I0430 16:49:53.011523 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.875
I0430 16:49:53.011535 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0430 16:49:53.011546 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 16:49:53.011559 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:49:53.011570 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:49:53.011581 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:49:53.011593 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:49:53.011605 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 16:49:53.011615 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 16:49:53.011627 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:49:53.011638 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:49:53.011651 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:49:53.011662 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:49:53.011673 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:49:53.011684 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:49:53.011695 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:49:53.011708 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:49:53.011718 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.863636
I0430 16:49:53.011730 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.754717
I0430 16:49:53.011744 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.33975 (* 0.3 = 0.401926 loss)
I0430 16:49:53.011759 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.422501 (* 0.3 = 0.12675 loss)
I0430 16:49:53.011775 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.395351 (* 0.0272727 = 0.0107823 loss)
I0430 16:49:53.011790 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.754169 (* 0.0272727 = 0.0205683 loss)
I0430 16:49:53.011817 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.885593 (* 0.0272727 = 0.0241525 loss)
I0430 16:49:53.011832 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.21555 (* 0.0272727 = 0.0331515 loss)
I0430 16:49:53.011847 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.56455 (* 0.0272727 = 0.0426697 loss)
I0430 16:49:53.011860 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.680358 (* 0.0272727 = 0.0185552 loss)
I0430 16:49:53.011874 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.73471 (* 0.0272727 = 0.0200375 loss)
I0430 16:49:53.011888 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.33676 (* 0.0272727 = 0.00918436 loss)
I0430 16:49:53.011903 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.370571 (* 0.0272727 = 0.0101065 loss)
I0430 16:49:53.011917 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.59339 (* 0.0272727 = 0.0161834 loss)
I0430 16:49:53.011931 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.361805 (* 0.0272727 = 0.00986742 loss)
I0430 16:49:53.011945 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.457956 (* 0.0272727 = 0.0124897 loss)
I0430 16:49:53.011955 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.604183 (* 0.0272727 = 0.0164777 loss)
I0430 16:49:53.011970 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.549927 (* 0.0272727 = 0.014998 loss)
I0430 16:49:53.011984 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0226548 (* 0.0272727 = 0.000617859 loss)
I0430 16:49:53.011998 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00736246 (* 0.0272727 = 0.000200794 loss)
I0430 16:49:53.012012 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00139803 (* 0.0272727 = 3.81282e-05 loss)
I0430 16:49:53.012027 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000437711 (* 0.0272727 = 1.19376e-05 loss)
I0430 16:49:53.012039 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000227277 (* 0.0272727 = 6.19846e-06 loss)
I0430 16:49:53.012053 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000188209 (* 0.0272727 = 5.13297e-06 loss)
I0430 16:49:53.012068 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 3.71466e-05 (* 0.0272727 = 1.01309e-06 loss)
I0430 16:49:53.012080 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.33372e-05 (* 0.0272727 = 3.63742e-07 loss)
I0430 16:49:53.012092 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.716981
I0430 16:49:53.012104 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0430 16:49:53.012115 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 16:49:53.012127 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 16:49:53.012138 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 16:49:53.012150 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 16:49:53.012161 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 1
I0430 16:49:53.012172 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 16:49:53.012183 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:49:53.012195 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:49:53.012207 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:49:53.012218 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 16:49:53.012229 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 16:49:53.012240 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 16:49:53.012251 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 16:49:53.012264 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:49:53.012284 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:49:53.012297 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:49:53.012310 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:49:53.012320 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:49:53.012332 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:49:53.012343 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:49:53.012354 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:49:53.012368 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.914773
I0430 16:49:53.012380 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.849057
I0430 16:49:53.012394 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.910629 (* 1 = 0.910629 loss)
I0430 16:49:53.012408 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.277768 (* 1 = 0.277768 loss)
I0430 16:49:53.012423 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.437394 (* 0.0909091 = 0.0397631 loss)
I0430 16:49:53.012436 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.399839 (* 0.0909091 = 0.036349 loss)
I0430 16:49:53.012450 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.402828 (* 0.0909091 = 0.0366207 loss)
I0430 16:49:53.012465 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.540703 (* 0.0909091 = 0.0491548 loss)
I0430 16:49:53.012477 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.834977 (* 0.0909091 = 0.075907 loss)
I0430 16:49:53.012491 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.142976 (* 0.0909091 = 0.0129978 loss)
I0430 16:49:53.012506 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.465625 (* 0.0909091 = 0.0423296 loss)
I0430 16:49:53.012519 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.257217 (* 0.0909091 = 0.0233833 loss)
I0430 16:49:53.012532 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.242847 (* 0.0909091 = 0.022077 loss)
I0430 16:49:53.012547 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.604955 (* 0.0909091 = 0.0549959 loss)
I0430 16:49:53.012559 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.38739 (* 0.0909091 = 0.0352173 loss)
I0430 16:49:53.012573 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.327488 (* 0.0909091 = 0.0297717 loss)
I0430 16:49:53.012586 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.283841 (* 0.0909091 = 0.0258037 loss)
I0430 16:49:53.012600 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.278523 (* 0.0909091 = 0.0253203 loss)
I0430 16:49:53.012614 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00741597 (* 0.0909091 = 0.000674179 loss)
I0430 16:49:53.012629 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00216398 (* 0.0909091 = 0.000196726 loss)
I0430 16:49:53.012642 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0011557 (* 0.0909091 = 0.000105063 loss)
I0430 16:49:53.012656 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000600341 (* 0.0909091 = 5.45765e-05 loss)
I0430 16:49:53.012670 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000568236 (* 0.0909091 = 5.16578e-05 loss)
I0430 16:49:53.012683 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000313075 (* 0.0909091 = 2.84613e-05 loss)
I0430 16:49:53.012697 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000158908 (* 0.0909091 = 1.44462e-05 loss)
I0430 16:49:53.012712 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 2.71451e-05 (* 0.0909091 = 2.46774e-06 loss)
I0430 16:49:53.012723 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0430 16:49:53.012734 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0430 16:49:53.012756 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.549923
I0430 16:49:53.012769 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.592648
I0430 16:49:53.012783 15443 sgd_solver.cpp:106] Iteration 21000, lr = 0.001
I0430 16:52:09.944110 15443 solver.cpp:229] Iteration 21500, loss = 3.6552
I0430 16:52:09.944281 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.4
I0430 16:52:09.944301 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 16:52:09.944317 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0430 16:52:09.944329 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0430 16:52:09.944342 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0430 16:52:09.944355 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.125
I0430 16:52:09.944366 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0430 16:52:09.944377 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0430 16:52:09.944389 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0430 16:52:09.944401 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:52:09.944413 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0430 16:52:09.944425 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:52:09.944437 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:52:09.944448 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 16:52:09.944460 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0430 16:52:09.944473 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0430 16:52:09.944484 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:52:09.944495 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:52:09.944507 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:52:09.944519 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:52:09.944530 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:52:09.944541 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:52:09.944553 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:52:09.944564 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.772727
I0430 16:52:09.944576 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.666667
I0430 16:52:09.944592 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.02763 (* 0.3 = 0.608288 loss)
I0430 16:52:09.944607 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.78434 (* 0.3 = 0.235302 loss)
I0430 16:52:09.944622 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.62751 (* 0.0272727 = 0.0443865 loss)
I0430 16:52:09.944635 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.97766 (* 0.0272727 = 0.0539362 loss)
I0430 16:52:09.944648 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.24814 (* 0.0272727 = 0.0613128 loss)
I0430 16:52:09.944663 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.908 (* 0.0272727 = 0.0520364 loss)
I0430 16:52:09.944676 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.23396 (* 0.0272727 = 0.0609261 loss)
I0430 16:52:09.944690 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.17752 (* 0.0272727 = 0.059387 loss)
I0430 16:52:09.944703 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.17538 (* 0.0272727 = 0.0320559 loss)
I0430 16:52:09.944717 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.401023 (* 0.0272727 = 0.010937 loss)
I0430 16:52:09.944731 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.357833 (* 0.0272727 = 0.00975909 loss)
I0430 16:52:09.944746 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.176839 (* 0.0272727 = 0.00482289 loss)
I0430 16:52:09.944759 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.44751 (* 0.0272727 = 0.0122048 loss)
I0430 16:52:09.944773 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.440371 (* 0.0272727 = 0.0120101 loss)
I0430 16:52:09.944808 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.366986 (* 0.0272727 = 0.0100087 loss)
I0430 16:52:09.944824 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.271401 (* 0.0272727 = 0.00740183 loss)
I0430 16:52:09.944839 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.62828 (* 0.0272727 = 0.0171349 loss)
I0430 16:52:09.944852 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.153511 (* 0.0272727 = 0.00418668 loss)
I0430 16:52:09.944866 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0325043 (* 0.0272727 = 0.000886482 loss)
I0430 16:52:09.944880 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0065118 (* 0.0272727 = 0.000177595 loss)
I0430 16:52:09.944895 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00166187 (* 0.0272727 = 4.53237e-05 loss)
I0430 16:52:09.944910 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00194525 (* 0.0272727 = 5.30523e-05 loss)
I0430 16:52:09.944922 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.0025265 (* 0.0272727 = 6.89044e-05 loss)
I0430 16:52:09.944936 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00115159 (* 0.0272727 = 3.1407e-05 loss)
I0430 16:52:09.944948 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.45
I0430 16:52:09.944960 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0430 16:52:09.944972 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0430 16:52:09.944984 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.25
I0430 16:52:09.944996 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0430 16:52:09.945008 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 16:52:09.945016 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.125
I0430 16:52:09.945024 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.5
I0430 16:52:09.945031 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0430 16:52:09.945044 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:52:09.945056 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:52:09.945067 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:52:09.945080 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:52:09.945091 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 16:52:09.945102 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0430 16:52:09.945114 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0430 16:52:09.945125 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:52:09.945137 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:52:09.945148 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:52:09.945160 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:52:09.945171 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:52:09.945183 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:52:09.945194 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:52:09.945205 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.795455
I0430 16:52:09.945216 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.733333
I0430 16:52:09.945230 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.91791 (* 0.3 = 0.575372 loss)
I0430 16:52:09.945245 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.72631 (* 0.3 = 0.217893 loss)
I0430 16:52:09.945257 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.31416 (* 0.0272727 = 0.0358406 loss)
I0430 16:52:09.945271 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 2.40604 (* 0.0272727 = 0.0656194 loss)
I0430 16:52:09.945300 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.92919 (* 0.0272727 = 0.0526144 loss)
I0430 16:52:09.945317 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.71385 (* 0.0272727 = 0.0467412 loss)
I0430 16:52:09.945330 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.75497 (* 0.0272727 = 0.0478629 loss)
I0430 16:52:09.945343 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 2.30063 (* 0.0272727 = 0.0627443 loss)
I0430 16:52:09.945358 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.32225 (* 0.0272727 = 0.0360613 loss)
I0430 16:52:09.945374 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.221878 (* 0.0272727 = 0.00605122 loss)
I0430 16:52:09.945387 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.439035 (* 0.0272727 = 0.0119737 loss)
I0430 16:52:09.945402 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.316676 (* 0.0272727 = 0.00863662 loss)
I0430 16:52:09.945416 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.450381 (* 0.0272727 = 0.0122831 loss)
I0430 16:52:09.945430 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.328444 (* 0.0272727 = 0.00895756 loss)
I0430 16:52:09.945443 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.398852 (* 0.0272727 = 0.0108778 loss)
I0430 16:52:09.945457 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.352312 (* 0.0272727 = 0.00960852 loss)
I0430 16:52:09.945472 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.579623 (* 0.0272727 = 0.0158079 loss)
I0430 16:52:09.945484 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.169499 (* 0.0272727 = 0.00462271 loss)
I0430 16:52:09.945498 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0667417 (* 0.0272727 = 0.00182023 loss)
I0430 16:52:09.945513 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0274358 (* 0.0272727 = 0.000748249 loss)
I0430 16:52:09.945526 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0117631 (* 0.0272727 = 0.000320812 loss)
I0430 16:52:09.945539 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00404095 (* 0.0272727 = 0.000110208 loss)
I0430 16:52:09.945554 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000508608 (* 0.0272727 = 1.38711e-05 loss)
I0430 16:52:09.945567 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000296293 (* 0.0272727 = 8.08073e-06 loss)
I0430 16:52:09.945580 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.683333
I0430 16:52:09.945591 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0430 16:52:09.945603 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 16:52:09.945614 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0430 16:52:09.945626 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 16:52:09.945638 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 16:52:09.945649 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.5
I0430 16:52:09.945662 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 16:52:09.945672 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0430 16:52:09.945684 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:52:09.945696 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 16:52:09.945708 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0430 16:52:09.945719 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 16:52:09.945730 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 16:52:09.945741 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0430 16:52:09.945752 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0430 16:52:09.945765 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:52:09.945787 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:52:09.945801 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:52:09.945812 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:52:09.945823 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:52:09.945835 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:52:09.945847 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:52:09.945858 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.892045
I0430 16:52:09.945870 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.816667
I0430 16:52:09.945883 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.15982 (* 1 = 1.15982 loss)
I0430 16:52:09.945897 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.42934 (* 1 = 0.42934 loss)
I0430 16:52:09.945911 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 1.11272 (* 0.0909091 = 0.101156 loss)
I0430 16:52:09.945925 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.953863 (* 0.0909091 = 0.0867148 loss)
I0430 16:52:09.945940 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.628143 (* 0.0909091 = 0.0571039 loss)
I0430 16:52:09.945953 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.898435 (* 0.0909091 = 0.0816759 loss)
I0430 16:52:09.945967 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.981817 (* 0.0909091 = 0.0892561 loss)
I0430 16:52:09.945981 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.85222 (* 0.0909091 = 0.168383 loss)
I0430 16:52:09.945994 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.872675 (* 0.0909091 = 0.0793341 loss)
I0430 16:52:09.946008 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.134828 (* 0.0909091 = 0.0122571 loss)
I0430 16:52:09.946022 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.367314 (* 0.0909091 = 0.0333921 loss)
I0430 16:52:09.946035 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.17283 (* 0.0909091 = 0.0157118 loss)
I0430 16:52:09.946048 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.496114 (* 0.0909091 = 0.0451013 loss)
I0430 16:52:09.946063 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.352937 (* 0.0909091 = 0.0320852 loss)
I0430 16:52:09.946075 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.390087 (* 0.0909091 = 0.0354624 loss)
I0430 16:52:09.946089 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.272816 (* 0.0909091 = 0.0248015 loss)
I0430 16:52:09.946104 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.58923 (* 0.0909091 = 0.0535664 loss)
I0430 16:52:09.946117 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.044266 (* 0.0909091 = 0.00402418 loss)
I0430 16:52:09.946131 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0157897 (* 0.0909091 = 0.00143543 loss)
I0430 16:52:09.946146 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00844 (* 0.0909091 = 0.000767273 loss)
I0430 16:52:09.946158 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00178282 (* 0.0909091 = 0.000162075 loss)
I0430 16:52:09.946172 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000532846 (* 0.0909091 = 4.84406e-05 loss)
I0430 16:52:09.946187 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000191353 (* 0.0909091 = 1.73958e-05 loss)
I0430 16:52:09.946200 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 5.08596e-05 (* 0.0909091 = 4.6236e-06 loss)
I0430 16:52:09.946213 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.25
I0430 16:52:09.946224 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.125
I0430 16:52:09.946236 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.216883
I0430 16:52:09.946257 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.187777
I0430 16:52:09.946272 15443 sgd_solver.cpp:106] Iteration 21500, lr = 0.001
I0430 16:52:45.842980 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 31.567 > 30) by scale factor 0.95036
I0430 16:54:26.838058 15443 solver.cpp:229] Iteration 22000, loss = 3.60076
I0430 16:54:26.838168 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.325301
I0430 16:54:26.838187 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0430 16:54:26.838201 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0430 16:54:26.838213 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0430 16:54:26.838224 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0430 16:54:26.838237 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 16:54:26.838248 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0430 16:54:26.838259 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0430 16:54:26.838271 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 16:54:26.838282 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.5
I0430 16:54:26.838294 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.625
I0430 16:54:26.838305 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0430 16:54:26.838317 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.75
I0430 16:54:26.838330 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.625
I0430 16:54:26.838341 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.5
I0430 16:54:26.838352 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.625
I0430 16:54:26.838364 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.625
I0430 16:54:26.838377 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 0.875
I0430 16:54:26.838388 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 0.875
I0430 16:54:26.838400 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 0.875
I0430 16:54:26.838413 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 0.875
I0430 16:54:26.838424 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:54:26.838435 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:54:26.838448 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.676136
I0430 16:54:26.838459 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.481928
I0430 16:54:26.838474 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.1992 (* 0.3 = 0.65976 loss)
I0430 16:54:26.838490 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 1.07496 (* 0.3 = 0.322489 loss)
I0430 16:54:26.838503 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.1038 (* 0.0272727 = 0.0301037 loss)
I0430 16:54:26.838517 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.42941 (* 0.0272727 = 0.038984 loss)
I0430 16:54:26.838531 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.02805 (* 0.0272727 = 0.0553104 loss)
I0430 16:54:26.838547 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.59401 (* 0.0272727 = 0.0434731 loss)
I0430 16:54:26.838562 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.5396 (* 0.0272727 = 0.0419892 loss)
I0430 16:54:26.838575 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.0613 (* 0.0272727 = 0.0289446 loss)
I0430 16:54:26.838589 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.17589 (* 0.0272727 = 0.0320697 loss)
I0430 16:54:26.838603 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.18628 (* 0.0272727 = 0.0323532 loss)
I0430 16:54:26.838616 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 1.38307 (* 0.0272727 = 0.0377201 loss)
I0430 16:54:26.838630 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 1.16562 (* 0.0272727 = 0.0317895 loss)
I0430 16:54:26.838644 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 1.48081 (* 0.0272727 = 0.0403857 loss)
I0430 16:54:26.838656 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 1.25528 (* 0.0272727 = 0.0342349 loss)
I0430 16:54:26.838690 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 1.33518 (* 0.0272727 = 0.0364141 loss)
I0430 16:54:26.838706 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 1.53119 (* 0.0272727 = 0.0417598 loss)
I0430 16:54:26.838719 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 1.63111 (* 0.0272727 = 0.0444849 loss)
I0430 16:54:26.838733 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 1.44208 (* 0.0272727 = 0.0393296 loss)
I0430 16:54:26.838747 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.531466 (* 0.0272727 = 0.0144945 loss)
I0430 16:54:26.838760 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.594142 (* 0.0272727 = 0.0162039 loss)
I0430 16:54:26.838779 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.363426 (* 0.0272727 = 0.00991163 loss)
I0430 16:54:26.838793 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.335377 (* 0.0272727 = 0.00914664 loss)
I0430 16:54:26.838807 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00156778 (* 0.0272727 = 4.27577e-05 loss)
I0430 16:54:26.838821 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 9.29983e-05 (* 0.0272727 = 2.53632e-06 loss)
I0430 16:54:26.838834 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.313253
I0430 16:54:26.838845 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.375
I0430 16:54:26.838857 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0430 16:54:26.838870 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0430 16:54:26.838881 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0430 16:54:26.838892 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 16:54:26.838904 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 16:54:26.838915 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 16:54:26.838927 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0430 16:54:26.838938 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.5
I0430 16:54:26.838950 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.5
I0430 16:54:26.838961 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0430 16:54:26.838973 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.75
I0430 16:54:26.838984 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.5
I0430 16:54:26.838996 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.5
I0430 16:54:26.839010 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.5
I0430 16:54:26.839021 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.625
I0430 16:54:26.839032 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 0.875
I0430 16:54:26.839045 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 0.875
I0430 16:54:26.839056 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 0.875
I0430 16:54:26.839067 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 0.875
I0430 16:54:26.839079 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:54:26.839090 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:54:26.839102 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.676136
I0430 16:54:26.839114 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.53012
I0430 16:54:26.839128 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 2.2582 (* 0.3 = 0.677459 loss)
I0430 16:54:26.839141 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 1.08931 (* 0.3 = 0.326794 loss)
I0430 16:54:26.839155 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.4631 (* 0.0272727 = 0.0399027 loss)
I0430 16:54:26.839169 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.08029 (* 0.0272727 = 0.0294625 loss)
I0430 16:54:26.839193 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.97689 (* 0.0272727 = 0.0539151 loss)
I0430 16:54:26.839208 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.81851 (* 0.0272727 = 0.0495958 loss)
I0430 16:54:26.839222 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.42845 (* 0.0272727 = 0.0389578 loss)
I0430 16:54:26.839236 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.08565 (* 0.0272727 = 0.0296086 loss)
I0430 16:54:26.839249 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.26142 (* 0.0272727 = 0.0344023 loss)
I0430 16:54:26.839262 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.27147 (* 0.0272727 = 0.0346763 loss)
I0430 16:54:26.839277 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.42282 (* 0.0272727 = 0.0388041 loss)
I0430 16:54:26.839289 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 1.38338 (* 0.0272727 = 0.0377284 loss)
I0430 16:54:26.839303 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.42875 (* 0.0272727 = 0.0389658 loss)
I0430 16:54:26.839315 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 1.44496 (* 0.0272727 = 0.0394081 loss)
I0430 16:54:26.839329 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 1.52608 (* 0.0272727 = 0.0416203 loss)
I0430 16:54:26.839342 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 1.65019 (* 0.0272727 = 0.0450053 loss)
I0430 16:54:26.839355 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 1.60566 (* 0.0272727 = 0.0437909 loss)
I0430 16:54:26.839370 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 1.38408 (* 0.0272727 = 0.0377477 loss)
I0430 16:54:26.839382 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.513962 (* 0.0272727 = 0.0140171 loss)
I0430 16:54:26.839395 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.546928 (* 0.0272727 = 0.0149162 loss)
I0430 16:54:26.839408 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.375813 (* 0.0272727 = 0.0102494 loss)
I0430 16:54:26.839422 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.370988 (* 0.0272727 = 0.0101179 loss)
I0430 16:54:26.839437 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.0280419 (* 0.0272727 = 0.00076478 loss)
I0430 16:54:26.839449 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.0205235 (* 0.0272727 = 0.000559731 loss)
I0430 16:54:26.839462 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.46988
I0430 16:54:26.839488 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.625
I0430 16:54:26.839501 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0430 16:54:26.839512 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0430 16:54:26.839524 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0430 16:54:26.839536 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0430 16:54:26.839547 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 16:54:26.839560 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 16:54:26.839570 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.625
I0430 16:54:26.839582 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.5
I0430 16:54:26.839596 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.5
I0430 16:54:26.839608 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0430 16:54:26.839620 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.75
I0430 16:54:26.839632 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.5
I0430 16:54:26.839643 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.5
I0430 16:54:26.839655 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.5
I0430 16:54:26.839666 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.625
I0430 16:54:26.839689 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 0.875
I0430 16:54:26.839702 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 0.875
I0430 16:54:26.839715 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 0.875
I0430 16:54:26.839726 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 0.875
I0430 16:54:26.839738 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:54:26.839750 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:54:26.839761 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.75
I0430 16:54:26.839772 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.626506
I0430 16:54:26.839787 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.88881 (* 1 = 1.88881 loss)
I0430 16:54:26.839800 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.914682 (* 1 = 0.914682 loss)
I0430 16:54:26.839814 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 1.06887 (* 0.0909091 = 0.0971703 loss)
I0430 16:54:26.839833 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.729046 (* 0.0909091 = 0.0662769 loss)
I0430 16:54:26.839846 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.27234 (* 0.0909091 = 0.115667 loss)
I0430 16:54:26.839860 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.12064 (* 0.0909091 = 0.101876 loss)
I0430 16:54:26.839884 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.14615 (* 0.0909091 = 0.104195 loss)
I0430 16:54:26.839906 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.842627 (* 0.0909091 = 0.0766024 loss)
I0430 16:54:26.839920 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.04541 (* 0.0909091 = 0.0950369 loss)
I0430 16:54:26.839934 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.06002 (* 0.0909091 = 0.0963652 loss)
I0430 16:54:26.839948 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 1.35174 (* 0.0909091 = 0.122886 loss)
I0430 16:54:26.839962 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 1.26714 (* 0.0909091 = 0.115194 loss)
I0430 16:54:26.839975 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 1.27112 (* 0.0909091 = 0.115557 loss)
I0430 16:54:26.839989 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 1.11192 (* 0.0909091 = 0.101083 loss)
I0430 16:54:26.840003 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 1.4665 (* 0.0909091 = 0.133318 loss)
I0430 16:54:26.840015 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 1.51815 (* 0.0909091 = 0.138014 loss)
I0430 16:54:26.840029 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 1.5707 (* 0.0909091 = 0.142791 loss)
I0430 16:54:26.840042 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 1.38493 (* 0.0909091 = 0.125903 loss)
I0430 16:54:26.840056 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.492817 (* 0.0909091 = 0.0448015 loss)
I0430 16:54:26.840070 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.527107 (* 0.0909091 = 0.0479188 loss)
I0430 16:54:26.840083 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.396007 (* 0.0909091 = 0.0360006 loss)
I0430 16:54:26.840097 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.442944 (* 0.0909091 = 0.0402676 loss)
I0430 16:54:26.840111 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.0159924 (* 0.0909091 = 0.00145386 loss)
I0430 16:54:26.840126 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.00464862 (* 0.0909091 = 0.000422602 loss)
I0430 16:54:26.840137 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0430 16:54:26.840148 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0430 16:54:26.840160 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.474276
I0430 16:54:26.840183 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.48585
I0430 16:54:26.840198 15443 sgd_solver.cpp:106] Iteration 22000, lr = 0.001
I0430 16:56:19.204946 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 34.3764 > 30) by scale factor 0.872691
I0430 16:56:43.807881 15443 solver.cpp:229] Iteration 22500, loss = 3.66754
I0430 16:56:43.807960 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.471698
I0430 16:56:43.807978 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 1
I0430 16:56:43.807992 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 16:56:43.808007 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0430 16:56:43.808019 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0430 16:56:43.808032 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0430 16:56:43.808043 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 16:56:43.808054 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0430 16:56:43.808066 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0430 16:56:43.808078 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:56:43.808089 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:56:43.808101 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:56:43.808114 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:56:43.808125 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0430 16:56:43.808136 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:56:43.808148 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:56:43.808159 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:56:43.808171 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:56:43.808183 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:56:43.808195 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:56:43.808207 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:56:43.808218 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:56:43.808229 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:56:43.808240 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.835227
I0430 16:56:43.808253 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.716981
I0430 16:56:43.808269 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.61352 (* 0.3 = 0.484057 loss)
I0430 16:56:43.808284 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.524305 (* 0.3 = 0.157291 loss)
I0430 16:56:43.808298 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.328012 (* 0.0272727 = 0.00894579 loss)
I0430 16:56:43.808312 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.13794 (* 0.0272727 = 0.0310348 loss)
I0430 16:56:43.808326 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.0081 (* 0.0272727 = 0.0547663 loss)
I0430 16:56:43.808339 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.52822 (* 0.0272727 = 0.0416789 loss)
I0430 16:56:43.808353 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.59798 (* 0.0272727 = 0.0435812 loss)
I0430 16:56:43.808367 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.63708 (* 0.0272727 = 0.0446477 loss)
I0430 16:56:43.808380 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.1018 (* 0.0272727 = 0.0300492 loss)
I0430 16:56:43.808393 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.16286 (* 0.0272727 = 0.0317143 loss)
I0430 16:56:43.808406 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.424758 (* 0.0272727 = 0.0115843 loss)
I0430 16:56:43.808420 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.372746 (* 0.0272727 = 0.0101658 loss)
I0430 16:56:43.808434 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.463036 (* 0.0272727 = 0.0126282 loss)
I0430 16:56:43.808449 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.51431 (* 0.0272727 = 0.0140266 loss)
I0430 16:56:43.808502 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0886034 (* 0.0272727 = 0.00241646 loss)
I0430 16:56:43.808518 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0242174 (* 0.0272727 = 0.000660473 loss)
I0430 16:56:43.808532 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0105801 (* 0.0272727 = 0.000288549 loss)
I0430 16:56:43.808547 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00320358 (* 0.0272727 = 8.73704e-05 loss)
I0430 16:56:43.808560 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00256208 (* 0.0272727 = 6.98748e-05 loss)
I0430 16:56:43.808574 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000471003 (* 0.0272727 = 1.28455e-05 loss)
I0430 16:56:43.808588 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000209291 (* 0.0272727 = 5.70792e-06 loss)
I0430 16:56:43.808603 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000253719 (* 0.0272727 = 6.9196e-06 loss)
I0430 16:56:43.808619 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00027458 (* 0.0272727 = 7.48853e-06 loss)
I0430 16:56:43.808634 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000116059 (* 0.0272727 = 3.16526e-06 loss)
I0430 16:56:43.808645 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.566038
I0430 16:56:43.808657 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:56:43.808670 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0430 16:56:43.808681 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.25
I0430 16:56:43.808692 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0430 16:56:43.808704 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0430 16:56:43.808715 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0430 16:56:43.808727 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0430 16:56:43.808738 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0430 16:56:43.808750 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:56:43.808761 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:56:43.808773 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:56:43.808784 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:56:43.808796 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 16:56:43.808807 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:56:43.808820 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:56:43.808831 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:56:43.808842 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:56:43.808854 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:56:43.808868 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:56:43.808876 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:56:43.808883 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:56:43.808890 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:56:43.808902 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.840909
I0430 16:56:43.808914 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.792453
I0430 16:56:43.808928 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.38383 (* 0.3 = 0.415149 loss)
I0430 16:56:43.808941 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.513085 (* 0.3 = 0.153925 loss)
I0430 16:56:43.808955 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.340045 (* 0.0272727 = 0.00927396 loss)
I0430 16:56:43.808981 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.743428 (* 0.0272727 = 0.0202753 loss)
I0430 16:56:43.808996 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.03137 (* 0.0272727 = 0.0554011 loss)
I0430 16:56:43.809010 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.40689 (* 0.0272727 = 0.0383697 loss)
I0430 16:56:43.809025 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.33108 (* 0.0272727 = 0.0363022 loss)
I0430 16:56:43.809037 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.5807 (* 0.0272727 = 0.0431099 loss)
I0430 16:56:43.809052 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.84883 (* 0.0272727 = 0.0231499 loss)
I0430 16:56:43.809067 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.08356 (* 0.0272727 = 0.0295518 loss)
I0430 16:56:43.809082 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.315919 (* 0.0272727 = 0.00861598 loss)
I0430 16:56:43.809095 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.484012 (* 0.0272727 = 0.0132003 loss)
I0430 16:56:43.809108 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.491998 (* 0.0272727 = 0.0134181 loss)
I0430 16:56:43.809123 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.5938 (* 0.0272727 = 0.0161945 loss)
I0430 16:56:43.809135 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.234335 (* 0.0272727 = 0.00639096 loss)
I0430 16:56:43.809149 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.183959 (* 0.0272727 = 0.00501705 loss)
I0430 16:56:43.809164 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.108112 (* 0.0272727 = 0.0029485 loss)
I0430 16:56:43.809177 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0753912 (* 0.0272727 = 0.00205612 loss)
I0430 16:56:43.809190 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0113467 (* 0.0272727 = 0.000309456 loss)
I0430 16:56:43.809204 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00622247 (* 0.0272727 = 0.000169704 loss)
I0430 16:56:43.809218 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00382582 (* 0.0272727 = 0.000104341 loss)
I0430 16:56:43.809231 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000869684 (* 0.0272727 = 2.37187e-05 loss)
I0430 16:56:43.809245 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00018777 (* 0.0272727 = 5.121e-06 loss)
I0430 16:56:43.809259 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.09825e-05 (* 0.0272727 = 2.99523e-07 loss)
I0430 16:56:43.809272 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.830189
I0430 16:56:43.809283 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:56:43.809294 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0430 16:56:43.809305 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0430 16:56:43.809317 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0430 16:56:43.809329 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0430 16:56:43.809340 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.5
I0430 16:56:43.809352 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0430 16:56:43.809363 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:56:43.809376 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0430 16:56:43.809386 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0430 16:56:43.809398 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:56:43.809409 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 16:56:43.809422 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 16:56:43.809433 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:56:43.809444 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:56:43.809466 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:56:43.809479 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:56:43.809490 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:56:43.809502 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:56:43.809514 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:56:43.809525 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:56:43.809536 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:56:43.809548 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.931818
I0430 16:56:43.809561 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.924528
I0430 16:56:43.809573 15443 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.629915 (* 1 = 0.629915 loss)
I0430 16:56:43.809588 15443 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.246845 (* 1 = 0.246845 loss)
I0430 16:56:43.809602 15443 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0227186 (* 0.0909091 = 0.00206532 loss)
I0430 16:56:43.809617 15443 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0954579 (* 0.0909091 = 0.00867799 loss)
I0430 16:56:43.809630 15443 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.804344 (* 0.0909091 = 0.0731222 loss)
I0430 16:56:43.809644 15443 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.00236 (* 0.0909091 = 0.0911233 loss)
I0430 16:56:43.809658 15443 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.846244 (* 0.0909091 = 0.0769313 loss)
I0430 16:56:43.809674 15443 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.717335 (* 0.0909091 = 0.0652122 loss)
I0430 16:56:43.809689 15443 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.623016 (* 0.0909091 = 0.0566378 loss)
I0430 16:56:43.809702 15443 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.73326 (* 0.0909091 = 0.06666 loss)
I0430 16:56:43.809715 15443 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.378422 (* 0.0909091 = 0.034402 loss)
I0430 16:56:43.809730 15443 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.177429 (* 0.0909091 = 0.0161299 loss)
I0430 16:56:43.809743 15443 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.0580558 (* 0.0909091 = 0.0052778 loss)
I0430 16:56:43.809756 15443 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.508483 (* 0.0909091 = 0.0462258 loss)
I0430 16:56:43.809769 15443 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.262101 (* 0.0909091 = 0.0238274 loss)
I0430 16:56:43.809783 15443 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.18287 (* 0.0909091 = 0.0166246 loss)
I0430 16:56:43.809798 15443 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0342513 (* 0.0909091 = 0.00311375 loss)
I0430 16:56:43.809811 15443 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00563868 (* 0.0909091 = 0.000512607 loss)
I0430 16:56:43.809824 15443 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00207224 (* 0.0909091 = 0.000188386 loss)
I0430 16:56:43.809839 15443 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000446225 (* 0.0909091 = 4.05659e-05 loss)
I0430 16:56:43.809852 15443 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000434996 (* 0.0909091 = 3.95451e-05 loss)
I0430 16:56:43.809865 15443 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000136882 (* 0.0909091 = 1.24438e-05 loss)
I0430 16:56:43.809880 15443 solver.cpp:245]     Train net output #145: loss3/loss21 = 7.53927e-05 (* 0.0909091 = 6.85388e-06 loss)
I0430 16:56:43.809893 15443 solver.cpp:245]     Train net output #146: loss3/loss22 = 2.53122e-05 (* 0.0909091 = 2.30111e-06 loss)
I0430 16:56:43.809905 15443 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0430 16:56:43.809916 15443 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0430 16:56:43.809937 15443 solver.cpp:245]     Train net output #149: total_confidence = 0.335207
I0430 16:56:43.809950 15443 solver.cpp:245]     Train net output #150: total_confidence_not_rec = 0.359083
I0430 16:56:43.809963 15443 sgd_solver.cpp:106] Iteration 22500, lr = 0.001
I0430 16:57:50.217910 15443 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 32.513 > 30) by scale factor 0.922707
I0430 16:59:00.771482 15443 solver.cpp:229] Iteration 23000, loss = 3.63249
I0430 16:59:00.771601 15443 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.659091
I0430 16:59:00.771620 15443 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0430 16:59:00.771633 15443 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0430 16:59:00.771646 15443 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.875
I0430 16:59:00.771658 15443 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.75
I0430 16:59:00.771673 15443 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0430 16:59:00.771687 15443 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0430 16:59:00.771698 15443 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0430 16:59:00.771710 15443 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0430 16:59:00.771723 15443 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0430 16:59:00.771734 15443 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0430 16:59:00.771746 15443 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0430 16:59:00.771757 15443 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0430 16:59:00.771770 15443 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0430 16:59:00.771781 15443 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0430 16:59:00.771793 15443 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0430 16:59:00.771805 15443 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0430 16:59:00.771816 15443 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0430 16:59:00.771828 15443 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0430 16:59:00.771841 15443 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0430 16:59:00.771852 15443 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0430 16:59:00.771864 15443 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0430 16:59:00.771875 15443 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0430 16:59:00.771888 15443 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.886364
I0430 16:59:00.771899 15443 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.886364
I0430 16:59:00.771915 15443 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.12291 (* 0.3 = 0.336872 loss)
I0430 16:59:00.771930 15443 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.341913 (* 0.3 = 0.102574 loss)
I0430 16:59:00.771944 15443 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.463823 (* 0.0272727 = 0.0126497 loss)
I0430 16:59:00.771960 15443 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.20882 (* 0.0272727 = 0.0329679 loss)
I0430 16:59:00.771973 15443 solver.cpp:245]     Train net output #29: loss1/loss03 = 0.577676 (* 0.0272727 = 0.0157548 loss)
I0430 16:59:00.771987 15443 solver.cpp:245]     Train net output #30: loss1/loss04 = 0.690791 (* 0.0272727 = 0.0188398 loss)
I0430 16:59:00.772001 15443 solver.cpp:245]     Train net output #31: loss1/loss05 = 0.68781 (* 0.0272727 = 0.0187585 loss)
I0430 16:59:00.772016 15443 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.15753 (* 0.0272727 = 0.031569 loss)
I0430 16:59:00.772029 15443 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.342376 (* 0.0272727 = 0.00933752 loss)
I0430 16:59:00.772043 15443 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.34414 (* 0.0272727 = 0.00938565 loss)
I0430 16:59:00.772058 15443 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.309672 (* 0.0272727 = 0.0084456 loss)
I0430 16:59:00.772071 15443 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.320599 (* 0.0272727 = 0.00874361 loss)
I0430 16:59:00.772085 15443 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.297268 (* 0.0272727 = 0.00810731 loss)
I0430 16:59:00.772109 15443 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.397601 (* 0.0272727 = 0.0108437 loss)
I0430 16:59:00.772153 15443 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.444294 (* 0.0272727 = 0.0121171 loss)
I0430 16:59:00.772171 15443 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0152888 (* 0.0272727 = 0.000416968 loss)
I0430 16:59:00.772184 15443 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00859124 (* 0.0272727 = 0.000234307 loss)
I0430 16:59:00.772198 15443 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00159313 (* 0.0272727 = 4.34489e-05 loss)
I0430 16:59:00.772212 15443 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000705958 (* 0.0272727 = 1.92534e-05 loss)
I0430 16:59:00.772227 15443 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000284063 (* 0.0272727 = 7.74717e-06 loss)
I0430 16:59:00.772240 15443 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000105858 (* 0.0272727 = 2.88704e-06 loss)
I0430 16:59:00.772254 15443 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000180307 (* 0.0272727 = 4.91747e-06 loss)
I0430 16:59:00.772269 15443 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000106932 (* 0.0272727 = 2.91632e-06 loss)
I0430 16:59:00.772282 15443 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000203178 (* 0.0272727 = 5.54123e-06 loss)
I0430 16:59:00.772294 15443 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.659091
I0430 16:59:00.772306 15443 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0430 16:59:00.772318 15443 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0430 16:59:00.772330 15443 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 1
I0430 16:59:00.772341 15443 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.875
I0430 16:59:00.772353 15443 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0430 16:59:00.772366 15443 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0430 16:59:00.772377 15443 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0430 16:59:00.772388 15443 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0430 16:59:00.772399 15443 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0430 16:59:00.772411 15443 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0430 16:59:00.772423 15443 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0430 16:59:00.772435 15443 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0430 16:59:00.772446 15443 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0430 16:59:00.772457 15443 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0430 16:59:00.772469 15443 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0430 16:59:00.772480 15443 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0430 16:59:00.772492 15443 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0430 16:59:00.772503 15443 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0430 16:59:00.772514 15443 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0430 16:59:00.772526 15443 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0430 16:59:00.772537 15443 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0430 16:59:00.772549 15443 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0430 16:59:00.772560 15443 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.897727
I0430 16:59:00.772572 15443 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.909091
I0430 16:59:00.772583 15443 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.923314 (* 0.3 = 0.276994 loss)
I0430 16:59:00.772593 15443 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.316597 (* 0.3 = 0.0949792 loss)
I0430 16:59:00.772608 15443 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.436849 (* 0.0272727 = 0.0119141 loss)
I0430 16:59:00.772625 15443 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.10379 (* 0.0272727 = 0.0301034 loss)
I0430 16:59:00.772651 15443 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.191666 (* 0.0272727 = 0.00522726 loss)
I0430 16:59:00.772667 15443 solver.cpp:245]     Train net output #79: loss2/loss04 = 0.5307 (* 0.0272727 = 0.0144736 loss)
I0430 16:59:00.772681 15443 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.00479 (* 0.0272727 = 0.0274033 loss)
I0430 16:59:00.772694 15443 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.937184 (* 0.0272727 = 0.0255596 loss)
I0430 16:59:00.772708 15443 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.410267 (* 0.0272727 = 0.0111891 loss)
I0430 16:59:00.772725 15443 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.330154 (* 0.0272727 = 0.00900419 loss)
I0430 16:59:00.772740 15443 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.272161 (* 0.0272727 = 0.00742258 loss)
I0430 16:59:00.772754 15443 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.281955 (* 0.0272727 = 0.00768968 loss)
I0430 16:59:00.772768 15443 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.440531 (* 0.0272727 = 0.0120145 loss)
I0430 16:59:00.772783 15443 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.298077 (* 0.0272727 = 0.00812936 loss)
I0430 16:59:00.772796 15443 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.404852 (* 0.0272727 = 0.0110414 loss)
I0430 16:59:00.772810 15443 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0411173 (* 0.0272727 = 0.00112138 loss)
I0430 16:59:00.772825 15443 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00837459 (* 0.0272727 = 0.000228398 loss)
I0430 16:59:00.772838 15443 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00116788 (* 0.0272727 = 3.18512e-05 loss)
I0430 16:59:00.772852 15443 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000210582 (* 0.0272727 = 5.74314e-06 loss)
I0430 16:59:00.772866 15443 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00010934 (* 0.0272727 = 2.98201e-06 loss)
I0430 16:59:00.772879 15443 solver.cpp:245]     Train net output #94: loss2/loss19 = 6.06475e-05 (* 0.0272727 = 1.65402e-06 loss)
I0430 16:59:00.772892 15443 solver.cpp:245]     Train net output #95: loss2/loss20 = 4.52034e-05 (* 0.0272727 = 1.23282e-06 loss)
I0430 16:59:00.772907 15443 solver.cpp:245]     Train net output #96: loss2/loss21 = 1.03716e-05 (* 0.0272727 = 2.82863e-07 loss)
I0430 16:59:00.772920 15443 solver.cpp:245]     Train net output #97: loss2/loss22 = 4.48533e-06 (* 0.0272727 = 1.22327e-07 loss)
I0430 16:59:00.772933 15443 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.795455
I0430 16:59:00.772944 15443 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0430 16:59:00.772955 15443 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0430 16:59:00.772966 15443 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0430 16:59:00.772979 15443 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0430 16:59:00.772989 15443 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0430 16:59:00.773001 15443 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0430 16:59:00.773012 15443 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0430 16:59:00.773025 15443 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0430 16:59:00.773036 15443 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0430 16:59:00.773047 15443 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0430 16:59:00.773059 15443 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0430 16:59:00.773071 15443 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0430 16:59:00.773082 15443 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0430 16:59:00.773093 15443 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0430 16:59:00.773105 15443 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0430 16:59:00.773128 15443 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0430 16:59:00.773140 15443 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0430 16:59:00.773152 15443 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0430 16:59:00.773164 15443 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0430 16:59:00.773175 15443 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0430 16:59:00.773186 15443 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0430 16:59:00.773198 15443 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0430 16:59:00.773210 15443 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.943182
I0430 16:59:00.773221 15443 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.977273
I0430 16:59:00.773236 1