mixed_lstm11_bn

I0425 10:06:42.668747 22523 solver.cpp:280] Solving mixed_lstm
I0425 10:06:42.668761 22523 solver.cpp:281] Learning Rate Policy: step
I0425 10:06:42.687485 22523 solver.cpp:338] Iteration 0, Testing net (#0)
I0425 10:07:34.893544 22523 solver.cpp:393] Test loss: 1.17466
I0425 10:07:34.894027 22523 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.822526
I0425 10:07:34.894048 22523 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.92
I0425 10:07:34.894062 22523 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.723
I0425 10:07:34.894073 22523 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.567
I0425 10:07:34.894085 22523 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.553
I0425 10:07:34.894098 22523 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.616
I0425 10:07:34.894109 22523 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.702
I0425 10:07:34.894120 22523 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.857
I0425 10:07:34.894132 22523 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.92
I0425 10:07:34.894145 22523 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.983
I0425 10:07:34.894155 22523 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.995
I0425 10:07:34.894167 22523 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 1
I0425 10:07:34.894179 22523 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 1
I0425 10:07:34.894191 22523 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 1
I0425 10:07:34.894204 22523 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 1
I0425 10:07:34.894215 22523 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 1
I0425 10:07:34.894227 22523 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 1
I0425 10:07:34.894238 22523 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 1
I0425 10:07:34.894249 22523 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 1
I0425 10:07:34.894260 22523 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 1
I0425 10:07:34.894273 22523 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 1
I0425 10:07:34.894284 22523 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0425 10:07:34.894294 22523 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0425 10:07:34.894306 22523 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.941001
I0425 10:07:34.894318 22523 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.951151
I0425 10:07:34.894335 22523 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 0.61019 (* 0.3 = 0.183057 loss)
I0425 10:07:34.894351 22523 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.200518 (* 0.3 = 0.0601554 loss)
I0425 10:07:34.894366 22523 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.377248 (* 0.0272727 = 0.0102886 loss)
I0425 10:07:34.894379 22523 solver.cpp:406]     Test net output #28: loss1/loss02 = 0.9168 (* 0.0272727 = 0.0250036 loss)
I0425 10:07:34.894393 22523 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.28908 (* 0.0272727 = 0.0351567 loss)
I0425 10:07:34.894407 22523 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.32121 (* 0.0272727 = 0.036033 loss)
I0425 10:07:34.894420 22523 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.13554 (* 0.0272727 = 0.0309692 loss)
I0425 10:07:34.894434 22523 solver.cpp:406]     Test net output #32: loss1/loss06 = 0.874274 (* 0.0272727 = 0.0238438 loss)
I0425 10:07:34.894448 22523 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.510629 (* 0.0272727 = 0.0139262 loss)
I0425 10:07:34.894462 22523 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.264151 (* 0.0272727 = 0.00720411 loss)
I0425 10:07:34.894475 22523 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.0765238 (* 0.0272727 = 0.00208701 loss)
I0425 10:07:34.894490 22523 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.0356554 (* 0.0272727 = 0.000972419 loss)
I0425 10:07:34.894505 22523 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.0239264 (* 0.0272727 = 0.000652538 loss)
I0425 10:07:34.894517 22523 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.0162795 (* 0.0272727 = 0.000443986 loss)
I0425 10:07:34.894531 22523 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.011529 (* 0.0272727 = 0.000314428 loss)
I0425 10:07:34.894564 22523 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.0073462 (* 0.0272727 = 0.000200351 loss)
I0425 10:07:34.894579 22523 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.0047215 (* 0.0272727 = 0.000128768 loss)
I0425 10:07:34.894593 22523 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.00209254 (* 0.0272727 = 5.70692e-05 loss)
I0425 10:07:34.894608 22523 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.000443415 (* 0.0272727 = 1.20931e-05 loss)
I0425 10:07:34.894621 22523 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.000239388 (* 0.0272727 = 6.52877e-06 loss)
I0425 10:07:34.894635 22523 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.000157837 (* 0.0272727 = 4.30464e-06 loss)
I0425 10:07:34.894649 22523 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.000128905 (* 0.0272727 = 3.5156e-06 loss)
I0425 10:07:34.894664 22523 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.000113549 (* 0.0272727 = 3.0968e-06 loss)
I0425 10:07:34.894676 22523 solver.cpp:406]     Test net output #48: loss1/loss22 = 9.77709e-05 (* 0.0272727 = 2.66648e-06 loss)
I0425 10:07:34.894688 22523 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.915257
I0425 10:07:34.894701 22523 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.964
I0425 10:07:34.894712 22523 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.918
I0425 10:07:34.894723 22523 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.799
I0425 10:07:34.894734 22523 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.679
I0425 10:07:34.894745 22523 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.703
I0425 10:07:34.894757 22523 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.763
I0425 10:07:34.894768 22523 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.896
I0425 10:07:34.894779 22523 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.936
I0425 10:07:34.894791 22523 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.984
I0425 10:07:34.894803 22523 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.994
I0425 10:07:34.894814 22523 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.999
I0425 10:07:34.894824 22523 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 1
I0425 10:07:34.894835 22523 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 1
I0425 10:07:34.894847 22523 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 1
I0425 10:07:34.894857 22523 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 1
I0425 10:07:34.894868 22523 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 1
I0425 10:07:34.894879 22523 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 1
I0425 10:07:34.894891 22523 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 1
I0425 10:07:34.894901 22523 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 1
I0425 10:07:34.894912 22523 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 1
I0425 10:07:34.894923 22523 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0425 10:07:34.894934 22523 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0425 10:07:34.894945 22523 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.972046
I0425 10:07:34.894956 22523 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.974253
I0425 10:07:34.894970 22523 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.330422 (* 0.3 = 0.0991267 loss)
I0425 10:07:34.894984 22523 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.107651 (* 0.3 = 0.0322954 loss)
I0425 10:07:34.894999 22523 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.193632 (* 0.0272727 = 0.00528088 loss)
I0425 10:07:34.895012 22523 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.37607 (* 0.0272727 = 0.0102564 loss)
I0425 10:07:34.895040 22523 solver.cpp:406]     Test net output #78: loss2/loss03 = 0.669993 (* 0.0272727 = 0.0182725 loss)
I0425 10:07:34.895056 22523 solver.cpp:406]     Test net output #79: loss2/loss04 = 0.907505 (* 0.0272727 = 0.0247501 loss)
I0425 10:07:34.895069 22523 solver.cpp:406]     Test net output #80: loss2/loss05 = 0.840621 (* 0.0272727 = 0.022926 loss)
I0425 10:07:34.895083 22523 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.666739 (* 0.0272727 = 0.0181838 loss)
I0425 10:07:34.895097 22523 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.361085 (* 0.0272727 = 0.00984777 loss)
I0425 10:07:34.895112 22523 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.193397 (* 0.0272727 = 0.00527445 loss)
I0425 10:07:34.895125 22523 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.0655005 (* 0.0272727 = 0.00178638 loss)
I0425 10:07:34.895139 22523 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.0268282 (* 0.0272727 = 0.000731679 loss)
I0425 10:07:34.895153 22523 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.0144892 (* 0.0272727 = 0.000395161 loss)
I0425 10:07:34.895166 22523 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.00944492 (* 0.0272727 = 0.000257589 loss)
I0425 10:07:34.895180 22523 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.00581651 (* 0.0272727 = 0.000158632 loss)
I0425 10:07:34.895195 22523 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.00368908 (* 0.0272727 = 0.000100611 loss)
I0425 10:07:34.895205 22523 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.00227243 (* 0.0272727 = 6.19754e-05 loss)
I0425 10:07:34.895215 22523 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.000740336 (* 0.0272727 = 2.0191e-05 loss)
I0425 10:07:34.895231 22523 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.000100104 (* 0.0272727 = 2.7301e-06 loss)
I0425 10:07:34.895246 22523 solver.cpp:406]     Test net output #93: loss2/loss18 = 5.50236e-05 (* 0.0272727 = 1.50064e-06 loss)
I0425 10:07:34.895262 22523 solver.cpp:406]     Test net output #94: loss2/loss19 = 3.4113e-05 (* 0.0272727 = 9.30355e-07 loss)
I0425 10:07:34.895275 22523 solver.cpp:406]     Test net output #95: loss2/loss20 = 2.50519e-05 (* 0.0272727 = 6.83233e-07 loss)
I0425 10:07:34.895289 22523 solver.cpp:406]     Test net output #96: loss2/loss21 = 2.2039e-05 (* 0.0272727 = 6.01064e-07 loss)
I0425 10:07:34.895303 22523 solver.cpp:406]     Test net output #97: loss2/loss22 = 2.09788e-05 (* 0.0272727 = 5.7215e-07 loss)
I0425 10:07:34.895315 22523 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.944211
I0425 10:07:34.895326 22523 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.967
I0425 10:07:34.895337 22523 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.952
I0425 10:07:34.895364 22523 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.954
I0425 10:07:34.895380 22523 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.946
I0425 10:07:34.895391 22523 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.941
I0425 10:07:34.895402 22523 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.914
I0425 10:07:34.895413 22523 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.942
I0425 10:07:34.895424 22523 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.975
I0425 10:07:34.895436 22523 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.985
I0425 10:07:34.895447 22523 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.995
I0425 10:07:34.895458 22523 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.999
I0425 10:07:34.895469 22523 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.999
I0425 10:07:34.895480 22523 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.999
I0425 10:07:34.895500 22523 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.999
I0425 10:07:34.895522 22523 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 1
I0425 10:07:34.895544 22523 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 1
I0425 10:07:34.895571 22523 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 1
I0425 10:07:34.895584 22523 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 1
I0425 10:07:34.895596 22523 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 1
I0425 10:07:34.895606 22523 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 1
I0425 10:07:34.895617 22523 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0425 10:07:34.895628 22523 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0425 10:07:34.895639 22523 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.980864
I0425 10:07:34.895654 22523 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.978543
I0425 10:07:34.895680 22523 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.239992 (* 1 = 0.239992 loss)
I0425 10:07:34.895702 22523 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.0812259 (* 1 = 0.0812259 loss)
I0425 10:07:34.895717 22523 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.164585 (* 0.0909091 = 0.0149623 loss)
I0425 10:07:34.895731 22523 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.23969 (* 0.0909091 = 0.02179 loss)
I0425 10:07:34.895745 22523 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.212758 (* 0.0909091 = 0.0193417 loss)
I0425 10:07:34.895759 22523 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.255293 (* 0.0909091 = 0.0232084 loss)
I0425 10:07:34.895772 22523 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.274379 (* 0.0909091 = 0.0249436 loss)
I0425 10:07:34.895787 22523 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.299142 (* 0.0909091 = 0.0271947 loss)
I0425 10:07:34.895802 22523 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.22632 (* 0.0909091 = 0.0205745 loss)
I0425 10:07:34.895817 22523 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.111592 (* 0.0909091 = 0.0101447 loss)
I0425 10:07:34.895833 22523 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.0531159 (* 0.0909091 = 0.00482872 loss)
I0425 10:07:34.895845 22523 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.0247186 (* 0.0909091 = 0.00224715 loss)
I0425 10:07:34.895859 22523 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.0161298 (* 0.0909091 = 0.00146634 loss)
I0425 10:07:34.895874 22523 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.00943379 (* 0.0909091 = 0.000857618 loss)
I0425 10:07:34.895887 22523 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.00646112 (* 0.0909091 = 0.000587375 loss)
I0425 10:07:34.895900 22523 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.0048245 (* 0.0909091 = 0.000438591 loss)
I0425 10:07:34.895915 22523 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.00359742 (* 0.0909091 = 0.000327038 loss)
I0425 10:07:34.895928 22523 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.00188496 (* 0.0909091 = 0.00017136 loss)
I0425 10:07:34.895942 22523 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.000649557 (* 0.0909091 = 5.90506e-05 loss)
I0425 10:07:34.895956 22523 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.0002564 (* 0.0909091 = 2.33091e-05 loss)
I0425 10:07:34.895972 22523 solver.cpp:406]     Test net output #143: loss3/loss19 = 7.79816e-05 (* 0.0909091 = 7.08923e-06 loss)
I0425 10:07:34.895985 22523 solver.cpp:406]     Test net output #144: loss3/loss20 = 4.36389e-05 (* 0.0909091 = 3.96717e-06 loss)
I0425 10:07:34.895999 22523 solver.cpp:406]     Test net output #145: loss3/loss21 = 2.14958e-05 (* 0.0909091 = 1.95417e-06 loss)
I0425 10:07:34.896013 22523 solver.cpp:406]     Test net output #146: loss3/loss22 = 1.49871e-05 (* 0.0909091 = 1.36246e-06 loss)
I0425 10:07:34.896025 22523 solver.cpp:406]     Test net output #147: total_accuracy = 0.859
I0425 10:07:34.896037 22523 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.807
I0425 10:07:34.896049 22523 solver.cpp:406]     Test net output #149: total_confidence = 0.818409
I0425 10:07:34.896070 22523 solver.cpp:406]     Test net output #150: total_confidence_nor_rec = 0.692034
I0425 10:07:34.896085 22523 solver.cpp:338] Iteration 0, Testing net (#1)
I0425 10:08:27.183346 22523 solver.cpp:393] Test loss: 2.39797
I0425 10:08:27.183483 22523 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.746234
I0425 10:08:27.183503 22523 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.854
I0425 10:08:27.183516 22523 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.688
I0425 10:08:27.183528 22523 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.504
I0425 10:08:27.183540 22523 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.505
I0425 10:08:27.183552 22523 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.546
I0425 10:08:27.183564 22523 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.632
I0425 10:08:27.183576 22523 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.744
I0425 10:08:27.183588 22523 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.828
I0425 10:08:27.183599 22523 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.893
I0425 10:08:27.183610 22523 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.905
I0425 10:08:27.183622 22523 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.909
I0425 10:08:27.183634 22523 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.924
I0425 10:08:27.183645 22523 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.941
I0425 10:08:27.183656 22523 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 0.95
I0425 10:08:27.183668 22523 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 0.965
I0425 10:08:27.183679 22523 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 0.971
I0425 10:08:27.183691 22523 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 0.991
I0425 10:08:27.183702 22523 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 0.993
I0425 10:08:27.183714 22523 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 0.995
I0425 10:08:27.183727 22523 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 0.998
I0425 10:08:27.183737 22523 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0425 10:08:27.183748 22523 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0425 10:08:27.183760 22523 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.888864
I0425 10:08:27.183771 22523 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.889193
I0425 10:08:27.183789 22523 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 0.876657 (* 0.3 = 0.262997 loss)
I0425 10:08:27.183804 22523 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.388629 (* 0.3 = 0.116589 loss)
I0425 10:08:27.183817 22523 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.613018 (* 0.0272727 = 0.0167187 loss)
I0425 10:08:27.183831 22523 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.04516 (* 0.0272727 = 0.0285043 loss)
I0425 10:08:27.183845 22523 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.4868 (* 0.0272727 = 0.0405491 loss)
I0425 10:08:27.183858 22523 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.49407 (* 0.0272727 = 0.0407474 loss)
I0425 10:08:27.183872 22523 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.39119 (* 0.0272727 = 0.0379416 loss)
I0425 10:08:27.183886 22523 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.17627 (* 0.0272727 = 0.0320802 loss)
I0425 10:08:27.183899 22523 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.867874 (* 0.0272727 = 0.0236693 loss)
I0425 10:08:27.183913 22523 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.593474 (* 0.0272727 = 0.0161857 loss)
I0425 10:08:27.183928 22523 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.391683 (* 0.0272727 = 0.0106823 loss)
I0425 10:08:27.183941 22523 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.371884 (* 0.0272727 = 0.0101423 loss)
I0425 10:08:27.183955 22523 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.377967 (* 0.0272727 = 0.0103082 loss)
I0425 10:08:27.183969 22523 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.347726 (* 0.0272727 = 0.00948343 loss)
I0425 10:08:27.184000 22523 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.284706 (* 0.0272727 = 0.00776472 loss)
I0425 10:08:27.184015 22523 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.253096 (* 0.0272727 = 0.00690263 loss)
I0425 10:08:27.184029 22523 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.192985 (* 0.0272727 = 0.00526323 loss)
I0425 10:08:27.184043 22523 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.170524 (* 0.0272727 = 0.00465065 loss)
I0425 10:08:27.184057 22523 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.0639627 (* 0.0272727 = 0.00174444 loss)
I0425 10:08:27.184072 22523 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.0512757 (* 0.0272727 = 0.00139843 loss)
I0425 10:08:27.184085 22523 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.04031 (* 0.0272727 = 0.00109936 loss)
I0425 10:08:27.184100 22523 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.0183857 (* 0.0272727 = 0.000501429 loss)
I0425 10:08:27.184114 22523 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.00237974 (* 0.0272727 = 6.49021e-05 loss)
I0425 10:08:27.184128 22523 solver.cpp:406]     Test net output #48: loss1/loss22 = 0.00202593 (* 0.0272727 = 5.52526e-05 loss)
I0425 10:08:27.184140 22523 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.843323
I0425 10:08:27.184152 22523 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.934
I0425 10:08:27.184164 22523 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.862
I0425 10:08:27.184175 22523 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.707
I0425 10:08:27.184185 22523 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.609
I0425 10:08:27.184196 22523 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.632
I0425 10:08:27.184213 22523 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.691
I0425 10:08:27.184224 22523 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.772
I0425 10:08:27.184236 22523 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.837
I0425 10:08:27.184247 22523 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.892
I0425 10:08:27.184258 22523 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.906
I0425 10:08:27.184269 22523 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.914
I0425 10:08:27.184280 22523 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.924
I0425 10:08:27.184291 22523 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.941
I0425 10:08:27.184303 22523 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.95
I0425 10:08:27.184314 22523 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.965
I0425 10:08:27.184325 22523 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 0.971
I0425 10:08:27.184336 22523 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 0.991
I0425 10:08:27.184347 22523 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 0.993
I0425 10:08:27.184358 22523 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 0.995
I0425 10:08:27.184370 22523 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 0.998
I0425 10:08:27.184381 22523 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0425 10:08:27.184391 22523 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0425 10:08:27.184402 22523 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.925455
I0425 10:08:27.184413 22523 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.92915
I0425 10:08:27.184427 22523 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.605154 (* 0.3 = 0.181546 loss)
I0425 10:08:27.184440 22523 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.276254 (* 0.3 = 0.0828763 loss)
I0425 10:08:27.184455 22523 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.342769 (* 0.0272727 = 0.00934824 loss)
I0425 10:08:27.184468 22523 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.531655 (* 0.0272727 = 0.0144997 loss)
I0425 10:08:27.184494 22523 solver.cpp:406]     Test net output #78: loss2/loss03 = 0.946422 (* 0.0272727 = 0.0258115 loss)
I0425 10:08:27.184509 22523 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.13822 (* 0.0272727 = 0.0310423 loss)
I0425 10:08:27.184522 22523 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.10903 (* 0.0272727 = 0.0302464 loss)
I0425 10:08:27.184536 22523 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.962378 (* 0.0272727 = 0.0262467 loss)
I0425 10:08:27.184550 22523 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.752566 (* 0.0272727 = 0.0205245 loss)
I0425 10:08:27.184563 22523 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.533582 (* 0.0272727 = 0.0145522 loss)
I0425 10:08:27.184577 22523 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.370309 (* 0.0272727 = 0.0100993 loss)
I0425 10:08:27.184592 22523 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.355532 (* 0.0272727 = 0.00969633 loss)
I0425 10:08:27.184604 22523 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.372072 (* 0.0272727 = 0.0101474 loss)
I0425 10:08:27.184618 22523 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.337484 (* 0.0272727 = 0.00920412 loss)
I0425 10:08:27.184633 22523 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.270242 (* 0.0272727 = 0.00737024 loss)
I0425 10:08:27.184645 22523 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.241962 (* 0.0272727 = 0.00659897 loss)
I0425 10:08:27.184659 22523 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.180052 (* 0.0272727 = 0.00491052 loss)
I0425 10:08:27.184674 22523 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.167668 (* 0.0272727 = 0.00457276 loss)
I0425 10:08:27.184686 22523 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.0588128 (* 0.0272727 = 0.00160399 loss)
I0425 10:08:27.184701 22523 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.0462162 (* 0.0272727 = 0.00126044 loss)
I0425 10:08:27.184715 22523 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.0364376 (* 0.0272727 = 0.000993752 loss)
I0425 10:08:27.184728 22523 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.0145262 (* 0.0272727 = 0.000396169 loss)
I0425 10:08:27.184742 22523 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.0016134 (* 0.0272727 = 4.40018e-05 loss)
I0425 10:08:27.184756 22523 solver.cpp:406]     Test net output #97: loss2/loss22 = 0.00143096 (* 0.0272727 = 3.90261e-05 loss)
I0425 10:08:27.184768 22523 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.882331
I0425 10:08:27.184779 22523 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.941
I0425 10:08:27.184790 22523 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.927
I0425 10:08:27.184801 22523 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.912
I0425 10:08:27.184813 22523 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.891
I0425 10:08:27.184824 22523 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.87
I0425 10:08:27.184835 22523 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.838
I0425 10:08:27.184847 22523 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.837
I0425 10:08:27.184859 22523 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.866
I0425 10:08:27.184870 22523 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.907
I0425 10:08:27.184880 22523 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.914
I0425 10:08:27.184891 22523 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.918
I0425 10:08:27.184902 22523 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.925
I0425 10:08:27.184913 22523 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.944
I0425 10:08:27.184924 22523 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.95
I0425 10:08:27.184936 22523 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.963
I0425 10:08:27.184947 22523 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 0.971
I0425 10:08:27.184967 22523 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 0.991
I0425 10:08:27.184980 22523 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 0.993
I0425 10:08:27.184991 22523 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 0.995
I0425 10:08:27.185000 22523 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 0.998
I0425 10:08:27.185008 22523 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0425 10:08:27.185019 22523 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0425 10:08:27.185030 22523 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.939364
I0425 10:08:27.185041 22523 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.945869
I0425 10:08:27.185055 22523 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.463197 (* 1 = 0.463197 loss)
I0425 10:08:27.185068 22523 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.222134 (* 1 = 0.222134 loss)
I0425 10:08:27.185082 22523 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.280876 (* 0.0909091 = 0.0255342 loss)
I0425 10:08:27.185096 22523 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.318469 (* 0.0909091 = 0.0289517 loss)
I0425 10:08:27.185109 22523 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.38046 (* 0.0909091 = 0.0345873 loss)
I0425 10:08:27.185122 22523 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.459046 (* 0.0909091 = 0.0417315 loss)
I0425 10:08:27.185137 22523 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.529175 (* 0.0909091 = 0.0481069 loss)
I0425 10:08:27.185149 22523 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.609533 (* 0.0909091 = 0.0554121 loss)
I0425 10:08:27.185163 22523 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.593512 (* 0.0909091 = 0.0539556 loss)
I0425 10:08:27.185176 22523 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.439948 (* 0.0909091 = 0.0399953 loss)
I0425 10:08:27.185189 22523 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.329349 (* 0.0909091 = 0.0299408 loss)
I0425 10:08:27.185204 22523 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.309108 (* 0.0909091 = 0.0281007 loss)
I0425 10:08:27.185216 22523 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.320984 (* 0.0909091 = 0.0291803 loss)
I0425 10:08:27.185230 22523 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.292492 (* 0.0909091 = 0.0265902 loss)
I0425 10:08:27.185243 22523 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.227713 (* 0.0909091 = 0.0207012 loss)
I0425 10:08:27.185261 22523 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.217242 (* 0.0909091 = 0.0197493 loss)
I0425 10:08:27.185274 22523 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.151064 (* 0.0909091 = 0.0137331 loss)
I0425 10:08:27.185288 22523 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.133447 (* 0.0909091 = 0.0121316 loss)
I0425 10:08:27.185302 22523 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.0551888 (* 0.0909091 = 0.00501717 loss)
I0425 10:08:27.185315 22523 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.0421647 (* 0.0909091 = 0.00383315 loss)
I0425 10:08:27.185328 22523 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.0433082 (* 0.0909091 = 0.00393711 loss)
I0425 10:08:27.185343 22523 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.019265 (* 0.0909091 = 0.00175136 loss)
I0425 10:08:27.185356 22523 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.000167117 (* 0.0909091 = 1.51925e-05 loss)
I0425 10:08:27.185370 22523 solver.cpp:406]     Test net output #146: loss3/loss22 = 9.56673e-05 (* 0.0909091 = 8.69702e-06 loss)
I0425 10:08:27.185382 22523 solver.cpp:406]     Test net output #147: total_accuracy = 0.727
I0425 10:08:27.185394 22523 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.676
I0425 10:08:27.185405 22523 solver.cpp:406]     Test net output #149: total_confidence = 0.704211
I0425 10:08:27.185425 22523 solver.cpp:406]     Test net output #150: total_confidence_nor_rec = 0.603197
I0425 10:08:27.957003 22523 solver.cpp:229] Iteration 0, loss = 2.10843
I0425 10:08:27.957063 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.64
I0425 10:08:27.957082 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0425 10:08:27.957094 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0425 10:08:27.957108 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 10:08:27.957119 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.625
I0425 10:08:27.957131 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 10:08:27.957144 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0425 10:08:27.957155 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 10:08:27.957167 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 10:08:27.957178 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 10:08:27.957190 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 10:08:27.957202 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 10:08:27.957214 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 10:08:27.957226 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 10:08:27.957238 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 10:08:27.957249 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 10:08:27.957262 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 10:08:27.957273 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 10:08:27.957284 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 10:08:27.957295 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 10:08:27.957314 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 10:08:27.957324 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 10:08:27.957336 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 10:08:27.957347 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.897727
I0425 10:08:27.957358 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.76
I0425 10:08:27.957384 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.28746 (* 0.3 = 0.386238 loss)
I0425 10:08:27.957399 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.397692 (* 0.3 = 0.119308 loss)
I0425 10:08:27.957413 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.05492 (* 0.0272727 = 0.0287707 loss)
I0425 10:08:27.957427 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.14317 (* 0.0272727 = 0.0311773 loss)
I0425 10:08:27.957442 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.92378 (* 0.0272727 = 0.0524668 loss)
I0425 10:08:27.957455 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.38261 (* 0.0272727 = 0.0377076 loss)
I0425 10:08:27.957468 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.9945 (* 0.0272727 = 0.0543953 loss)
I0425 10:08:27.957482 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.27491 (* 0.0272727 = 0.0347704 loss)
I0425 10:08:27.957496 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.839306 (* 0.0272727 = 0.0228902 loss)
I0425 10:08:27.957510 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.414382 (* 0.0272727 = 0.0113013 loss)
I0425 10:08:27.957525 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.379938 (* 0.0272727 = 0.010362 loss)
I0425 10:08:27.957540 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.45707 (* 0.0272727 = 0.0124655 loss)
I0425 10:08:27.957553 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.60101 (* 0.0272727 = 0.0163912 loss)
I0425 10:08:27.957595 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.500027 (* 0.0272727 = 0.0136371 loss)
I0425 10:08:27.957612 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.134707 (* 0.0272727 = 0.00367383 loss)
I0425 10:08:27.957625 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0563791 (* 0.0272727 = 0.00153761 loss)
I0425 10:08:27.957640 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0678173 (* 0.0272727 = 0.00184956 loss)
I0425 10:08:27.957655 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.014346 (* 0.0272727 = 0.000391253 loss)
I0425 10:08:27.957669 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00450749 (* 0.0272727 = 0.000122932 loss)
I0425 10:08:27.957690 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00246896 (* 0.0272727 = 6.73353e-05 loss)
I0425 10:08:27.957705 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00296268 (* 0.0272727 = 8.08003e-05 loss)
I0425 10:08:27.957718 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00122359 (* 0.0272727 = 3.33705e-05 loss)
I0425 10:08:27.957741 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00179281 (* 0.0272727 = 4.88949e-05 loss)
I0425 10:08:27.957756 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00156822 (* 0.0272727 = 4.27696e-05 loss)
I0425 10:08:27.957767 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.72
I0425 10:08:27.957778 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 10:08:27.957790 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 10:08:27.957801 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 10:08:27.957818 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0425 10:08:27.957830 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.125
I0425 10:08:27.957842 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 10:08:27.957854 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0425 10:08:27.957864 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 10:08:27.957876 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 10:08:27.957887 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 10:08:27.957900 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 10:08:27.957911 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 10:08:27.957921 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 10:08:27.957933 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 10:08:27.957944 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 10:08:27.957962 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 10:08:27.957973 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 10:08:27.957985 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 10:08:27.957996 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 10:08:27.958008 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 10:08:27.958025 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 10:08:27.958037 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 10:08:27.958048 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.914773
I0425 10:08:27.958060 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.92
I0425 10:08:27.958075 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.824297 (* 0.3 = 0.247289 loss)
I0425 10:08:27.958088 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.261894 (* 0.3 = 0.0785682 loss)
I0425 10:08:27.958113 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.739855 (* 0.0272727 = 0.0201779 loss)
I0425 10:08:27.958128 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.698599 (* 0.0272727 = 0.0190527 loss)
I0425 10:08:27.958142 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.26624 (* 0.0272727 = 0.0345338 loss)
I0425 10:08:27.958156 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.71784 (* 0.0272727 = 0.0468503 loss)
I0425 10:08:27.958169 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.85043 (* 0.0272727 = 0.0504664 loss)
I0425 10:08:27.958184 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.06245 (* 0.0272727 = 0.0289759 loss)
I0425 10:08:27.958197 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.548956 (* 0.0272727 = 0.0149715 loss)
I0425 10:08:27.958211 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.40352 (* 0.0272727 = 0.0110051 loss)
I0425 10:08:27.958225 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.321373 (* 0.0272727 = 0.00876471 loss)
I0425 10:08:27.958238 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.402757 (* 0.0272727 = 0.0109843 loss)
I0425 10:08:27.958252 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.669407 (* 0.0272727 = 0.0182565 loss)
I0425 10:08:27.958266 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.480673 (* 0.0272727 = 0.0131093 loss)
I0425 10:08:27.958281 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0438267 (* 0.0272727 = 0.00119527 loss)
I0425 10:08:27.958294 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0665154 (* 0.0272727 = 0.00181406 loss)
I0425 10:08:27.958308 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0274431 (* 0.0272727 = 0.000748448 loss)
I0425 10:08:27.958323 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0128665 (* 0.0272727 = 0.000350905 loss)
I0425 10:08:27.958336 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00227719 (* 0.0272727 = 6.2105e-05 loss)
I0425 10:08:27.958350 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0016201 (* 0.0272727 = 4.41845e-05 loss)
I0425 10:08:27.958365 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000624436 (* 0.0272727 = 1.70301e-05 loss)
I0425 10:08:27.958379 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.0008539 (* 0.0272727 = 2.32882e-05 loss)
I0425 10:08:27.958394 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00109231 (* 0.0272727 = 2.97902e-05 loss)
I0425 10:08:27.958408 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000928352 (* 0.0272727 = 2.53187e-05 loss)
I0425 10:08:27.958420 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.92
I0425 10:08:27.958432 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 10:08:27.958443 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 10:08:27.958454 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 10:08:27.958466 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 10:08:27.958477 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0425 10:08:27.958488 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 10:08:27.958500 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0425 10:08:27.958511 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 10:08:27.958523 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 10:08:27.958534 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 10:08:27.958544 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 10:08:27.958555 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 10:08:27.958567 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 10:08:27.958587 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 10:08:27.958600 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 10:08:27.958611 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 10:08:27.958623 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 10:08:27.958634 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 10:08:27.958644 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 10:08:27.958657 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 10:08:27.958667 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 10:08:27.958678 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 10:08:27.958689 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.977273
I0425 10:08:27.958701 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.96
I0425 10:08:27.958714 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.274882 (* 1 = 0.274882 loss)
I0425 10:08:27.958729 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.0846797 (* 1 = 0.0846797 loss)
I0425 10:08:27.958742 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.075529 (* 0.0909091 = 0.00686628 loss)
I0425 10:08:27.958757 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.116551 (* 0.0909091 = 0.0105955 loss)
I0425 10:08:27.958770 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.142877 (* 0.0909091 = 0.0129888 loss)
I0425 10:08:27.958784 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.0348416 (* 0.0909091 = 0.00316742 loss)
I0425 10:08:27.958798 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.160187 (* 0.0909091 = 0.0145625 loss)
I0425 10:08:27.958812 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.385504 (* 0.0909091 = 0.0350459 loss)
I0425 10:08:27.958827 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.270293 (* 0.0909091 = 0.0245721 loss)
I0425 10:08:27.958840 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.265021 (* 0.0909091 = 0.0240928 loss)
I0425 10:08:27.958853 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.323459 (* 0.0909091 = 0.0294054 loss)
I0425 10:08:27.958873 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.39173 (* 0.0909091 = 0.0356118 loss)
I0425 10:08:27.958887 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.679812 (* 0.0909091 = 0.0618011 loss)
I0425 10:08:27.958897 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.429379 (* 0.0909091 = 0.0390344 loss)
I0425 10:08:27.958911 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0269379 (* 0.0909091 = 0.0024489 loss)
I0425 10:08:27.958925 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00846041 (* 0.0909091 = 0.000769128 loss)
I0425 10:08:27.958940 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00561148 (* 0.0909091 = 0.000510135 loss)
I0425 10:08:27.958955 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00245594 (* 0.0909091 = 0.000223267 loss)
I0425 10:08:27.958972 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00110154 (* 0.0909091 = 0.00010014 loss)
I0425 10:08:27.958986 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000249801 (* 0.0909091 = 2.27092e-05 loss)
I0425 10:08:27.959000 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 5.13585e-05 (* 0.0909091 = 4.66896e-06 loss)
I0425 10:08:27.959014 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.83743e-05 (* 0.0909091 = 1.67039e-06 loss)
I0425 10:08:27.959035 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 6.959e-06 (* 0.0909091 = 6.32636e-07 loss)
I0425 10:08:27.959049 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 2.66733e-06 (* 0.0909091 = 2.42484e-07 loss)
I0425 10:08:27.959071 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.875
I0425 10:08:27.959084 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.875
I0425 10:08:27.959095 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.747572
I0425 10:08:27.959107 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.548365
I0425 10:08:27.959133 22523 sgd_solver.cpp:106] Iteration 0, lr = 0.01
I0425 10:14:09.177728 22523 solver.cpp:229] Iteration 500, loss = 3.24199
I0425 10:14:09.177865 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.64
I0425 10:14:09.177886 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.5
I0425 10:14:09.177898 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 10:14:09.177911 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0425 10:14:09.177922 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 10:14:09.177934 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.25
I0425 10:14:09.177947 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 10:14:09.177958 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 10:14:09.177970 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 10:14:09.177983 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 10:14:09.177994 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 10:14:09.178009 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 10:14:09.178032 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 10:14:09.178052 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 10:14:09.178066 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 10:14:09.178077 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 10:14:09.178089 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 10:14:09.178102 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 10:14:09.178112 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 10:14:09.178124 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 10:14:09.178135 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 10:14:09.178146 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 10:14:09.178158 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 10:14:09.178170 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.863636
I0425 10:14:09.178182 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.78
I0425 10:14:09.178202 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.45279 (* 0.3 = 0.435837 loss)
I0425 10:14:09.178218 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.550346 (* 0.3 = 0.165104 loss)
I0425 10:14:09.178233 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.39403 (* 0.0272727 = 0.038019 loss)
I0425 10:14:09.178248 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.15176 (* 0.0272727 = 0.0314116 loss)
I0425 10:14:09.178262 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.95109 (* 0.0272727 = 0.0532116 loss)
I0425 10:14:09.178277 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.92726 (* 0.0272727 = 0.0525616 loss)
I0425 10:14:09.178292 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.89464 (* 0.0272727 = 0.0516721 loss)
I0425 10:14:09.178305 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.61585 (* 0.0272727 = 0.0440686 loss)
I0425 10:14:09.178319 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.69475 (* 0.0272727 = 0.0189477 loss)
I0425 10:14:09.178333 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.449452 (* 0.0272727 = 0.0122578 loss)
I0425 10:14:09.178347 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.382251 (* 0.0272727 = 0.010425 loss)
I0425 10:14:09.178362 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.377811 (* 0.0272727 = 0.0103039 loss)
I0425 10:14:09.178376 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.383646 (* 0.0272727 = 0.0104631 loss)
I0425 10:14:09.178391 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.512933 (* 0.0272727 = 0.0139891 loss)
I0425 10:14:09.178406 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.1728 (* 0.0272727 = 0.00471273 loss)
I0425 10:14:09.178438 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0970667 (* 0.0272727 = 0.00264727 loss)
I0425 10:14:09.178454 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0493169 (* 0.0272727 = 0.00134501 loss)
I0425 10:14:09.178468 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0273057 (* 0.0272727 = 0.000744701 loss)
I0425 10:14:09.178483 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00473895 (* 0.0272727 = 0.000129244 loss)
I0425 10:14:09.178498 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00307206 (* 0.0272727 = 8.37834e-05 loss)
I0425 10:14:09.178513 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00119676 (* 0.0272727 = 3.26388e-05 loss)
I0425 10:14:09.178526 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000889795 (* 0.0272727 = 2.42671e-05 loss)
I0425 10:14:09.178541 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000535581 (* 0.0272727 = 1.46068e-05 loss)
I0425 10:14:09.178555 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000932329 (* 0.0272727 = 2.54271e-05 loss)
I0425 10:14:09.178567 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.7
I0425 10:14:09.178580 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 10:14:09.178591 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0425 10:14:09.178602 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 10:14:09.178614 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0425 10:14:09.178625 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0425 10:14:09.178637 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0425 10:14:09.178654 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 10:14:09.178666 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 10:14:09.178678 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 10:14:09.178689 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 10:14:09.178700 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 10:14:09.178717 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 10:14:09.178728 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 10:14:09.178740 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 10:14:09.178750 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 10:14:09.178761 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 10:14:09.178772 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 10:14:09.178783 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 10:14:09.178794 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 10:14:09.178810 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 10:14:09.178822 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 10:14:09.178833 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 10:14:09.178843 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.903409
I0425 10:14:09.178855 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.84
I0425 10:14:09.178871 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.990631 (* 0.3 = 0.297189 loss)
I0425 10:14:09.178890 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.34106 (* 0.3 = 0.102318 loss)
I0425 10:14:09.178905 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.434251 (* 0.0272727 = 0.0118432 loss)
I0425 10:14:09.178920 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.604758 (* 0.0272727 = 0.0164934 loss)
I0425 10:14:09.178946 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.26115 (* 0.0272727 = 0.0343949 loss)
I0425 10:14:09.178961 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.84738 (* 0.0272727 = 0.050383 loss)
I0425 10:14:09.178974 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.45858 (* 0.0272727 = 0.0397793 loss)
I0425 10:14:09.178988 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.21676 (* 0.0272727 = 0.0331843 loss)
I0425 10:14:09.179003 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.501079 (* 0.0272727 = 0.0136658 loss)
I0425 10:14:09.179016 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.408043 (* 0.0272727 = 0.0111284 loss)
I0425 10:14:09.179030 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.5388 (* 0.0272727 = 0.0146946 loss)
I0425 10:14:09.179044 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.572802 (* 0.0272727 = 0.0156219 loss)
I0425 10:14:09.179059 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.573051 (* 0.0272727 = 0.0156287 loss)
I0425 10:14:09.179072 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.658427 (* 0.0272727 = 0.0179571 loss)
I0425 10:14:09.179087 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0281815 (* 0.0272727 = 0.000768587 loss)
I0425 10:14:09.179101 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.014665 (* 0.0272727 = 0.000399954 loss)
I0425 10:14:09.179116 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00996967 (* 0.0272727 = 0.0002719 loss)
I0425 10:14:09.179131 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00360698 (* 0.0272727 = 9.83722e-05 loss)
I0425 10:14:09.179144 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000769615 (* 0.0272727 = 2.09895e-05 loss)
I0425 10:14:09.179158 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000633502 (* 0.0272727 = 1.72773e-05 loss)
I0425 10:14:09.179172 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000660519 (* 0.0272727 = 1.80141e-05 loss)
I0425 10:14:09.179186 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000210428 (* 0.0272727 = 5.73894e-06 loss)
I0425 10:14:09.179201 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000591114 (* 0.0272727 = 1.61213e-05 loss)
I0425 10:14:09.179215 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000326732 (* 0.0272727 = 8.91087e-06 loss)
I0425 10:14:09.179227 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.88
I0425 10:14:09.179239 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 10:14:09.179253 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 10:14:09.179266 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0425 10:14:09.179278 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 10:14:09.179289 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0425 10:14:09.179301 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 10:14:09.179312 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 10:14:09.179324 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 10:14:09.179337 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0425 10:14:09.179347 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 10:14:09.179374 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 10:14:09.179386 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 10:14:09.179399 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 10:14:09.179409 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 10:14:09.179420 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 10:14:09.179431 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 10:14:09.179455 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 10:14:09.179468 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 10:14:09.179479 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 10:14:09.179491 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 10:14:09.179502 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 10:14:09.179519 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 10:14:09.179527 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.960227
I0425 10:14:09.179539 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.92
I0425 10:14:09.179553 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.503567 (* 1 = 0.503567 loss)
I0425 10:14:09.179566 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.174887 (* 1 = 0.174887 loss)
I0425 10:14:09.179581 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.085374 (* 0.0909091 = 0.00776127 loss)
I0425 10:14:09.179595 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.110104 (* 0.0909091 = 0.0100095 loss)
I0425 10:14:09.179610 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.328201 (* 0.0909091 = 0.0298364 loss)
I0425 10:14:09.179623 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.235767 (* 0.0909091 = 0.0214333 loss)
I0425 10:14:09.179636 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.275356 (* 0.0909091 = 0.0250323 loss)
I0425 10:14:09.179651 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.303215 (* 0.0909091 = 0.027565 loss)
I0425 10:14:09.179664 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.472122 (* 0.0909091 = 0.0429202 loss)
I0425 10:14:09.179677 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.254741 (* 0.0909091 = 0.0231583 loss)
I0425 10:14:09.179692 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.509596 (* 0.0909091 = 0.046327 loss)
I0425 10:14:09.179705 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.376955 (* 0.0909091 = 0.0342686 loss)
I0425 10:14:09.179718 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.408593 (* 0.0909091 = 0.0371448 loss)
I0425 10:14:09.179733 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.585239 (* 0.0909091 = 0.0532036 loss)
I0425 10:14:09.179746 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0894262 (* 0.0909091 = 0.00812965 loss)
I0425 10:14:09.179760 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0406443 (* 0.0909091 = 0.00369494 loss)
I0425 10:14:09.179774 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.033415 (* 0.0909091 = 0.00303772 loss)
I0425 10:14:09.179788 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0226734 (* 0.0909091 = 0.00206122 loss)
I0425 10:14:09.179802 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00969453 (* 0.0909091 = 0.000881321 loss)
I0425 10:14:09.179816 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00503945 (* 0.0909091 = 0.000458132 loss)
I0425 10:14:09.179829 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00110786 (* 0.0909091 = 0.000100715 loss)
I0425 10:14:09.179843 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000300201 (* 0.0909091 = 2.7291e-05 loss)
I0425 10:14:09.179857 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 6.0429e-05 (* 0.0909091 = 5.49354e-06 loss)
I0425 10:14:09.179872 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 4.9673e-05 (* 0.0909091 = 4.51573e-06 loss)
I0425 10:14:09.179883 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.875
I0425 10:14:09.179894 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 10:14:09.179906 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.560144
I0425 10:14:09.179927 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.456436
I0425 10:14:09.179946 22523 sgd_solver.cpp:106] Iteration 500, lr = 0.01
I0425 10:19:50.566380 22523 solver.cpp:229] Iteration 1000, loss = 3.24969
I0425 10:19:50.566507 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.489362
I0425 10:19:50.566527 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.5
I0425 10:19:50.566540 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 10:19:50.566552 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 10:19:50.566565 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.125
I0425 10:19:50.566576 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 10:19:50.566589 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 10:19:50.566601 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 10:19:50.566613 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 10:19:50.566625 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 10:19:50.566637 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 10:19:50.566649 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 10:19:50.566660 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 10:19:50.566673 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 10:19:50.566684 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 10:19:50.566696 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 10:19:50.566709 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 10:19:50.566720 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 10:19:50.566732 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 10:19:50.566743 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 10:19:50.566756 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 10:19:50.566766 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 10:19:50.566778 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 10:19:50.566790 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.818182
I0425 10:19:50.566802 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.744681
I0425 10:19:50.566819 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.63358 (* 0.3 = 0.490073 loss)
I0425 10:19:50.566834 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.648418 (* 0.3 = 0.194525 loss)
I0425 10:19:50.566849 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.41798 (* 0.0272727 = 0.0386722 loss)
I0425 10:19:50.566864 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.84796 (* 0.0272727 = 0.0503989 loss)
I0425 10:19:50.566877 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.36917 (* 0.0272727 = 0.0646136 loss)
I0425 10:19:50.566892 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.4494 (* 0.0272727 = 0.0668018 loss)
I0425 10:19:50.566906 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.17139 (* 0.0272727 = 0.0592196 loss)
I0425 10:19:50.566920 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.73088 (* 0.0272727 = 0.0472058 loss)
I0425 10:19:50.566936 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.726696 (* 0.0272727 = 0.019819 loss)
I0425 10:19:50.566949 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.554796 (* 0.0272727 = 0.0151308 loss)
I0425 10:19:50.566963 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.496301 (* 0.0272727 = 0.0135355 loss)
I0425 10:19:50.566978 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.384195 (* 0.0272727 = 0.010478 loss)
I0425 10:19:50.566992 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.47998 (* 0.0272727 = 0.0130904 loss)
I0425 10:19:50.567014 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.475406 (* 0.0272727 = 0.0129656 loss)
I0425 10:19:50.567045 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.466902 (* 0.0272727 = 0.0127337 loss)
I0425 10:19:50.567061 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.245999 (* 0.0272727 = 0.00670907 loss)
I0425 10:19:50.567080 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.180602 (* 0.0272727 = 0.0049255 loss)
I0425 10:19:50.567093 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0510163 (* 0.0272727 = 0.00139135 loss)
I0425 10:19:50.567108 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0151559 (* 0.0272727 = 0.000413343 loss)
I0425 10:19:50.567122 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0134094 (* 0.0272727 = 0.00036571 loss)
I0425 10:19:50.567137 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00732898 (* 0.0272727 = 0.000199881 loss)
I0425 10:19:50.567152 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00434373 (* 0.0272727 = 0.000118465 loss)
I0425 10:19:50.567165 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00511182 (* 0.0272727 = 0.000139413 loss)
I0425 10:19:50.567180 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00433449 (* 0.0272727 = 0.000118213 loss)
I0425 10:19:50.567193 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.723404
I0425 10:19:50.567208 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 10:19:50.567220 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 10:19:50.567232 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 10:19:50.567244 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0425 10:19:50.567255 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0425 10:19:50.567266 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 10:19:50.567278 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0425 10:19:50.567291 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 10:19:50.567301 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 10:19:50.567313 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 10:19:50.567325 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 10:19:50.567337 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 10:19:50.567359 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 10:19:50.567375 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 10:19:50.567387 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 10:19:50.567399 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 10:19:50.567409 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 10:19:50.567421 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 10:19:50.567432 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 10:19:50.567445 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 10:19:50.567456 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 10:19:50.567466 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 10:19:50.567478 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.852273
I0425 10:19:50.567489 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.787234
I0425 10:19:50.567503 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.15335 (* 0.3 = 0.346004 loss)
I0425 10:19:50.567520 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.576135 (* 0.3 = 0.172841 loss)
I0425 10:19:50.567536 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.04406 (* 0.0272727 = 0.0284743 loss)
I0425 10:19:50.567550 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.844987 (* 0.0272727 = 0.0230451 loss)
I0425 10:19:50.567577 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.67696 (* 0.0272727 = 0.0457353 loss)
I0425 10:19:50.567594 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.49693 (* 0.0272727 = 0.0408254 loss)
I0425 10:19:50.567607 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.26416 (* 0.0272727 = 0.0617497 loss)
I0425 10:19:50.567621 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.52726 (* 0.0272727 = 0.0416526 loss)
I0425 10:19:50.567636 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.05068 (* 0.0272727 = 0.0286548 loss)
I0425 10:19:50.567649 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.7825 (* 0.0272727 = 0.0213409 loss)
I0425 10:19:50.567663 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.793966 (* 0.0272727 = 0.0216536 loss)
I0425 10:19:50.567677 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.485818 (* 0.0272727 = 0.0132496 loss)
I0425 10:19:50.567692 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.533254 (* 0.0272727 = 0.0145433 loss)
I0425 10:19:50.567705 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.624828 (* 0.0272727 = 0.0170408 loss)
I0425 10:19:50.567719 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.513778 (* 0.0272727 = 0.0140121 loss)
I0425 10:19:50.567734 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.12558 (* 0.0272727 = 0.00342491 loss)
I0425 10:19:50.567747 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0985047 (* 0.0272727 = 0.00268649 loss)
I0425 10:19:50.567762 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0310753 (* 0.0272727 = 0.000847507 loss)
I0425 10:19:50.567776 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00543729 (* 0.0272727 = 0.00014829 loss)
I0425 10:19:50.567790 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00613205 (* 0.0272727 = 0.000167238 loss)
I0425 10:19:50.567805 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00423219 (* 0.0272727 = 0.000115423 loss)
I0425 10:19:50.567819 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00308128 (* 0.0272727 = 8.4035e-05 loss)
I0425 10:19:50.567833 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00276013 (* 0.0272727 = 7.52762e-05 loss)
I0425 10:19:50.567848 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.00123546 (* 0.0272727 = 3.36943e-05 loss)
I0425 10:19:50.567860 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.744681
I0425 10:19:50.567873 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 10:19:50.567884 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 10:19:50.567895 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0425 10:19:50.567908 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 10:19:50.567919 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0425 10:19:50.567934 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 10:19:50.567945 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 10:19:50.567957 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0425 10:19:50.567970 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0425 10:19:50.567992 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 10:19:50.568004 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 10:19:50.568017 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 10:19:50.568027 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 10:19:50.568039 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 10:19:50.568050 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 10:19:50.568069 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 10:19:50.568090 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 10:19:50.568104 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 10:19:50.568115 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 10:19:50.568126 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 10:19:50.568147 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 10:19:50.568158 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 10:19:50.568171 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.880682
I0425 10:19:50.568181 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.87234
I0425 10:19:50.568195 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.776332 (* 1 = 0.776332 loss)
I0425 10:19:50.568209 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.460418 (* 1 = 0.460418 loss)
I0425 10:19:50.568223 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.366067 (* 0.0909091 = 0.0332788 loss)
I0425 10:19:50.568238 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.369337 (* 0.0909091 = 0.0335761 loss)
I0425 10:19:50.568254 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.947142 (* 0.0909091 = 0.0861038 loss)
I0425 10:19:50.568269 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.545367 (* 0.0909091 = 0.0495788 loss)
I0425 10:19:50.568284 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.38482 (* 0.0909091 = 0.125893 loss)
I0425 10:19:50.568297 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.30964 (* 0.0909091 = 0.119058 loss)
I0425 10:19:50.568311 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.28335 (* 0.0909091 = 0.116669 loss)
I0425 10:19:50.568325 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.10256 (* 0.0909091 = 0.100233 loss)
I0425 10:19:50.568338 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.557513 (* 0.0909091 = 0.050683 loss)
I0425 10:19:50.568352 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.370586 (* 0.0909091 = 0.0336897 loss)
I0425 10:19:50.568367 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.411373 (* 0.0909091 = 0.0373976 loss)
I0425 10:19:50.568380 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.491852 (* 0.0909091 = 0.0447138 loss)
I0425 10:19:50.568394 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.394812 (* 0.0909091 = 0.035892 loss)
I0425 10:19:50.568408 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.149559 (* 0.0909091 = 0.0135963 loss)
I0425 10:19:50.568423 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.084995 (* 0.0909091 = 0.00772682 loss)
I0425 10:19:50.568436 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0375995 (* 0.0909091 = 0.00341813 loss)
I0425 10:19:50.568450 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0118182 (* 0.0909091 = 0.00107438 loss)
I0425 10:19:50.568464 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00380242 (* 0.0909091 = 0.000345674 loss)
I0425 10:19:50.568480 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00110348 (* 0.0909091 = 0.000100316 loss)
I0425 10:19:50.568493 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000271398 (* 0.0909091 = 2.46725e-05 loss)
I0425 10:19:50.568508 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 9.74732e-05 (* 0.0909091 = 8.8612e-06 loss)
I0425 10:19:50.568522 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 4.56849e-05 (* 0.0909091 = 4.15317e-06 loss)
I0425 10:19:50.568534 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 10:19:50.568547 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0425 10:19:50.568558 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.56383
I0425 10:19:50.568583 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.392776
I0425 10:19:50.568600 22523 sgd_solver.cpp:106] Iteration 1000, lr = 0.01
I0425 10:25:31.975706 22523 solver.cpp:229] Iteration 1500, loss = 3.18386
I0425 10:25:31.975836 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.568627
I0425 10:25:31.975857 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.5
I0425 10:25:31.975869 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0425 10:25:31.975881 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 10:25:31.975894 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 10:25:31.975905 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.25
I0425 10:25:31.975917 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 10:25:31.975929 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 10:25:31.975941 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0425 10:25:31.975953 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0425 10:25:31.975965 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 10:25:31.975976 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 10:25:31.975989 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 10:25:31.976001 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 10:25:31.976012 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 10:25:31.976024 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 10:25:31.976035 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 10:25:31.976047 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 10:25:31.976059 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 10:25:31.976070 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 10:25:31.976083 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 10:25:31.976094 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 10:25:31.976105 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 10:25:31.976117 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.857955
I0425 10:25:31.976130 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.72549
I0425 10:25:31.976146 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.66571 (* 0.3 = 0.499713 loss)
I0425 10:25:31.976161 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.548662 (* 0.3 = 0.164599 loss)
I0425 10:25:31.976176 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.59453 (* 0.0272727 = 0.0434873 loss)
I0425 10:25:31.976191 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.1933 (* 0.0272727 = 0.0598172 loss)
I0425 10:25:31.976207 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.59954 (* 0.0272727 = 0.0708966 loss)
I0425 10:25:31.976222 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.06073 (* 0.0272727 = 0.0562016 loss)
I0425 10:25:31.976243 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.65119 (* 0.0272727 = 0.0450325 loss)
I0425 10:25:31.976256 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.51585 (* 0.0272727 = 0.0413414 loss)
I0425 10:25:31.976270 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.28264 (* 0.0272727 = 0.0349811 loss)
I0425 10:25:31.976284 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.26522 (* 0.0272727 = 0.0345059 loss)
I0425 10:25:31.976305 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.848942 (* 0.0272727 = 0.023153 loss)
I0425 10:25:31.976320 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.748832 (* 0.0272727 = 0.0204227 loss)
I0425 10:25:31.976335 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0744501 (* 0.0272727 = 0.00203046 loss)
I0425 10:25:31.976348 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.049996 (* 0.0272727 = 0.00136353 loss)
I0425 10:25:31.976363 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0242967 (* 0.0272727 = 0.000662637 loss)
I0425 10:25:31.976395 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00907335 (* 0.0272727 = 0.000247455 loss)
I0425 10:25:31.976411 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00460398 (* 0.0272727 = 0.000125563 loss)
I0425 10:25:31.976425 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00341489 (* 0.0272727 = 9.31334e-05 loss)
I0425 10:25:31.976440 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000343886 (* 0.0272727 = 9.37871e-06 loss)
I0425 10:25:31.976454 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000213204 (* 0.0272727 = 5.81466e-06 loss)
I0425 10:25:31.976469 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00011849 (* 0.0272727 = 3.23154e-06 loss)
I0425 10:25:31.976482 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 6.73304e-05 (* 0.0272727 = 1.83628e-06 loss)
I0425 10:25:31.976497 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 4.47739e-05 (* 0.0272727 = 1.22111e-06 loss)
I0425 10:25:31.976511 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 4.83898e-05 (* 0.0272727 = 1.31972e-06 loss)
I0425 10:25:31.976523 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.607843
I0425 10:25:31.976536 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 10:25:31.976548 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0425 10:25:31.976560 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 10:25:31.976572 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.125
I0425 10:25:31.976583 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 10:25:31.976594 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0425 10:25:31.976606 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 10:25:31.976619 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 10:25:31.976629 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0425 10:25:31.976640 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 10:25:31.976652 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 10:25:31.976671 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 10:25:31.976683 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 10:25:31.976694 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 10:25:31.976706 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 10:25:31.976717 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 10:25:31.976733 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 10:25:31.976745 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 10:25:31.976757 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 10:25:31.976768 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 10:25:31.976779 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 10:25:31.976790 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 10:25:31.976801 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.869318
I0425 10:25:31.976814 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.843137
I0425 10:25:31.976827 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.35703 (* 0.3 = 0.407108 loss)
I0425 10:25:31.976842 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.475326 (* 0.3 = 0.142598 loss)
I0425 10:25:31.976861 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.66472 (* 0.0272727 = 0.0181287 loss)
I0425 10:25:31.976876 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.6131 (* 0.0272727 = 0.0439937 loss)
I0425 10:25:31.976902 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.29048 (* 0.0272727 = 0.0351949 loss)
I0425 10:25:31.976917 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.30024 (* 0.0272727 = 0.0627338 loss)
I0425 10:25:31.976932 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.63116 (* 0.0272727 = 0.0444862 loss)
I0425 10:25:31.976945 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.33427 (* 0.0272727 = 0.0363892 loss)
I0425 10:25:31.976959 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.41771 (* 0.0272727 = 0.0386649 loss)
I0425 10:25:31.976974 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.05782 (* 0.0272727 = 0.0288497 loss)
I0425 10:25:31.976986 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.750501 (* 0.0272727 = 0.0204682 loss)
I0425 10:25:31.977000 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.625429 (* 0.0272727 = 0.0170572 loss)
I0425 10:25:31.977015 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.195086 (* 0.0272727 = 0.00532053 loss)
I0425 10:25:31.977030 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0565987 (* 0.0272727 = 0.0015436 loss)
I0425 10:25:31.977044 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0355998 (* 0.0272727 = 0.000970903 loss)
I0425 10:25:31.977058 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0104125 (* 0.0272727 = 0.000283979 loss)
I0425 10:25:31.977072 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00230727 (* 0.0272727 = 6.29256e-05 loss)
I0425 10:25:31.977087 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00183605 (* 0.0272727 = 5.0074e-05 loss)
I0425 10:25:31.977105 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000296589 (* 0.0272727 = 8.08879e-06 loss)
I0425 10:25:31.977119 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 6.78798e-05 (* 0.0272727 = 1.85127e-06 loss)
I0425 10:25:31.977133 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 2.89775e-05 (* 0.0272727 = 7.90295e-07 loss)
I0425 10:25:31.977147 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 3.16519e-05 (* 0.0272727 = 8.63232e-07 loss)
I0425 10:25:31.977161 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.06392e-05 (* 0.0272727 = 5.62886e-07 loss)
I0425 10:25:31.977175 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 3.50734e-05 (* 0.0272727 = 9.56548e-07 loss)
I0425 10:25:31.977187 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.843137
I0425 10:25:31.977200 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 10:25:31.977210 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 10:25:31.977222 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 10:25:31.977233 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0425 10:25:31.977246 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 10:25:31.977259 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 10:25:31.977270 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 10:25:31.977283 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.625
I0425 10:25:31.977293 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 10:25:31.977305 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 10:25:31.977316 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 10:25:31.977327 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 10:25:31.977339 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 10:25:31.977350 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 10:25:31.977361 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 10:25:31.977372 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 10:25:31.977393 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 10:25:31.977406 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 10:25:31.977417 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 10:25:31.977428 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 10:25:31.977440 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 10:25:31.977452 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 10:25:31.977463 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.943182
I0425 10:25:31.977473 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.941176
I0425 10:25:31.977488 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.649474 (* 1 = 0.649474 loss)
I0425 10:25:31.977501 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.222311 (* 1 = 0.222311 loss)
I0425 10:25:31.977515 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.187321 (* 0.0909091 = 0.0170291 loss)
I0425 10:25:31.977530 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.180198 (* 0.0909091 = 0.0163816 loss)
I0425 10:25:31.977543 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.530571 (* 0.0909091 = 0.0482337 loss)
I0425 10:25:31.977557 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.06954 (* 0.0909091 = 0.0972309 loss)
I0425 10:25:31.977571 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.377518 (* 0.0909091 = 0.0343198 loss)
I0425 10:25:31.977586 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.619491 (* 0.0909091 = 0.0563174 loss)
I0425 10:25:31.977601 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.87824 (* 0.0909091 = 0.07984 loss)
I0425 10:25:31.977614 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.1023 (* 0.0909091 = 0.100209 loss)
I0425 10:25:31.977627 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.52686 (* 0.0909091 = 0.0478963 loss)
I0425 10:25:31.977643 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.445076 (* 0.0909091 = 0.0404614 loss)
I0425 10:25:31.977656 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.298969 (* 0.0909091 = 0.027179 loss)
I0425 10:25:31.977671 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.172512 (* 0.0909091 = 0.0156829 loss)
I0425 10:25:31.977689 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0852429 (* 0.0909091 = 0.00774936 loss)
I0425 10:25:31.977704 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0503754 (* 0.0909091 = 0.00457958 loss)
I0425 10:25:31.977717 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0346721 (* 0.0909091 = 0.00315201 loss)
I0425 10:25:31.977731 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0174408 (* 0.0909091 = 0.00158552 loss)
I0425 10:25:31.977746 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00924937 (* 0.0909091 = 0.000840851 loss)
I0425 10:25:31.977762 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00278436 (* 0.0909091 = 0.000253123 loss)
I0425 10:25:31.977777 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000793495 (* 0.0909091 = 7.21359e-05 loss)
I0425 10:25:31.977790 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000192504 (* 0.0909091 = 1.75003e-05 loss)
I0425 10:25:31.977804 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 5.30462e-05 (* 0.0909091 = 4.82238e-06 loss)
I0425 10:25:31.977818 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.94318e-05 (* 0.0909091 = 1.76653e-06 loss)
I0425 10:25:31.977830 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 10:25:31.977843 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 10:25:31.977864 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.494776
I0425 10:25:31.977877 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.377477
I0425 10:25:31.977892 22523 sgd_solver.cpp:106] Iteration 1500, lr = 0.01
I0425 10:31:13.393270 22523 solver.cpp:229] Iteration 2000, loss = 3.34898
I0425 10:31:13.393395 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.625
I0425 10:31:13.393421 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 10:31:13.393435 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 10:31:13.393446 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 10:31:13.393458 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 10:31:13.393471 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 10:31:13.393482 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 10:31:13.393499 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 10:31:13.393512 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0425 10:31:13.393523 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 10:31:13.393535 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 10:31:13.393548 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 10:31:13.393559 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 10:31:13.393579 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 10:31:13.393594 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 10:31:13.393605 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 10:31:13.393617 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 10:31:13.393637 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 10:31:13.393649 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 10:31:13.393661 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 10:31:13.393672 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 10:31:13.393683 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 10:31:13.393694 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 10:31:13.393707 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.857955
I0425 10:31:13.393718 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.791667
I0425 10:31:13.393735 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.56926 (* 0.3 = 0.470778 loss)
I0425 10:31:13.393750 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.574655 (* 0.3 = 0.172396 loss)
I0425 10:31:13.393765 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.961188 (* 0.0272727 = 0.0262142 loss)
I0425 10:31:13.393780 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.779 (* 0.0272727 = 0.0485183 loss)
I0425 10:31:13.393795 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.55472 (* 0.0272727 = 0.0696742 loss)
I0425 10:31:13.393810 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.23492 (* 0.0272727 = 0.0609525 loss)
I0425 10:31:13.393823 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.91925 (* 0.0272727 = 0.0523433 loss)
I0425 10:31:13.393837 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.98414 (* 0.0272727 = 0.054113 loss)
I0425 10:31:13.393851 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.613551 (* 0.0272727 = 0.0167332 loss)
I0425 10:31:13.393867 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.91245 (* 0.0272727 = 0.024885 loss)
I0425 10:31:13.393880 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.584628 (* 0.0272727 = 0.0159444 loss)
I0425 10:31:13.393894 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.496486 (* 0.0272727 = 0.0135405 loss)
I0425 10:31:13.393908 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.489319 (* 0.0272727 = 0.0133451 loss)
I0425 10:31:13.393925 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0950069 (* 0.0272727 = 0.0025911 loss)
I0425 10:31:13.393941 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0732353 (* 0.0272727 = 0.00199733 loss)
I0425 10:31:13.393970 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0367488 (* 0.0272727 = 0.00100224 loss)
I0425 10:31:13.393985 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0346779 (* 0.0272727 = 0.000945761 loss)
I0425 10:31:13.394006 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0123926 (* 0.0272727 = 0.000337979 loss)
I0425 10:31:13.394021 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00196728 (* 0.0272727 = 5.3653e-05 loss)
I0425 10:31:13.394052 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000840357 (* 0.0272727 = 2.29188e-05 loss)
I0425 10:31:13.394071 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000379649 (* 0.0272727 = 1.03541e-05 loss)
I0425 10:31:13.394088 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000203319 (* 0.0272727 = 5.54506e-06 loss)
I0425 10:31:13.394104 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000314548 (* 0.0272727 = 8.57858e-06 loss)
I0425 10:31:13.394117 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000191522 (* 0.0272727 = 5.22334e-06 loss)
I0425 10:31:13.394130 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.770833
I0425 10:31:13.394142 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 10:31:13.394155 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0425 10:31:13.394166 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0425 10:31:13.394177 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 10:31:13.394189 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 10:31:13.394201 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 10:31:13.394212 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 10:31:13.394223 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 10:31:13.394235 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 10:31:13.394246 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 10:31:13.394258 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 10:31:13.394269 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 10:31:13.394281 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 10:31:13.394292 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 10:31:13.394304 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 10:31:13.394315 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 10:31:13.394325 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 10:31:13.394336 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 10:31:13.394347 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 10:31:13.394359 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 10:31:13.394371 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 10:31:13.394381 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 10:31:13.394393 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.909091
I0425 10:31:13.394404 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.916667
I0425 10:31:13.394418 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.789632 (* 0.3 = 0.236889 loss)
I0425 10:31:13.394433 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.333172 (* 0.3 = 0.0999515 loss)
I0425 10:31:13.394448 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.72568 (* 0.0272727 = 0.0197913 loss)
I0425 10:31:13.394461 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.893029 (* 0.0272727 = 0.0243553 loss)
I0425 10:31:13.394487 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.14737 (* 0.0272727 = 0.0585645 loss)
I0425 10:31:13.394502 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.04746 (* 0.0272727 = 0.0558398 loss)
I0425 10:31:13.394516 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.77737 (* 0.0272727 = 0.0484739 loss)
I0425 10:31:13.394531 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.77391 (* 0.0272727 = 0.0483793 loss)
I0425 10:31:13.394544 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.515144 (* 0.0272727 = 0.0140494 loss)
I0425 10:31:13.394558 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.51015 (* 0.0272727 = 0.0139132 loss)
I0425 10:31:13.394572 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.424707 (* 0.0272727 = 0.0115829 loss)
I0425 10:31:13.394587 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.537101 (* 0.0272727 = 0.0146482 loss)
I0425 10:31:13.394600 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.451927 (* 0.0272727 = 0.0123253 loss)
I0425 10:31:13.394615 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0750616 (* 0.0272727 = 0.00204713 loss)
I0425 10:31:13.394629 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0961084 (* 0.0272727 = 0.00262114 loss)
I0425 10:31:13.394647 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0164377 (* 0.0272727 = 0.000448301 loss)
I0425 10:31:13.394662 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0129968 (* 0.0272727 = 0.000354459 loss)
I0425 10:31:13.394677 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00398543 (* 0.0272727 = 0.000108694 loss)
I0425 10:31:13.394691 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00273704 (* 0.0272727 = 7.46466e-05 loss)
I0425 10:31:13.394702 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000723992 (* 0.0272727 = 1.97452e-05 loss)
I0425 10:31:13.394711 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000546718 (* 0.0272727 = 1.49105e-05 loss)
I0425 10:31:13.394726 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000166797 (* 0.0272727 = 4.549e-06 loss)
I0425 10:31:13.394742 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000200435 (* 0.0272727 = 5.4664e-06 loss)
I0425 10:31:13.394755 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000239843 (* 0.0272727 = 6.54118e-06 loss)
I0425 10:31:13.394769 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.895833
I0425 10:31:13.394781 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 10:31:13.394793 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 10:31:13.394804 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 10:31:13.394816 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 10:31:13.394827 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 10:31:13.394839 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0425 10:31:13.394860 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 10:31:13.394870 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 10:31:13.394882 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 10:31:13.394893 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 10:31:13.394904 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 10:31:13.394915 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 10:31:13.394927 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 10:31:13.394938 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 10:31:13.394948 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 10:31:13.394970 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 10:31:13.394986 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 10:31:13.394997 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 10:31:13.395009 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 10:31:13.395020 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 10:31:13.395030 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 10:31:13.395042 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 10:31:13.395053 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.943182
I0425 10:31:13.395064 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.958333
I0425 10:31:13.395078 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.362544 (* 1 = 0.362544 loss)
I0425 10:31:13.395092 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.187327 (* 1 = 0.187327 loss)
I0425 10:31:13.395107 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.559248 (* 0.0909091 = 0.0508408 loss)
I0425 10:31:13.395120 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.584847 (* 0.0909091 = 0.0531679 loss)
I0425 10:31:13.395134 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.554686 (* 0.0909091 = 0.050426 loss)
I0425 10:31:13.395148 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.400487 (* 0.0909091 = 0.0364079 loss)
I0425 10:31:13.395162 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.681371 (* 0.0909091 = 0.0619428 loss)
I0425 10:31:13.395176 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.03254 (* 0.0909091 = 0.0938673 loss)
I0425 10:31:13.395190 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.345385 (* 0.0909091 = 0.0313986 loss)
I0425 10:31:13.395203 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.513575 (* 0.0909091 = 0.0466887 loss)
I0425 10:31:13.395217 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.355934 (* 0.0909091 = 0.0323576 loss)
I0425 10:31:13.395231 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.293943 (* 0.0909091 = 0.0267221 loss)
I0425 10:31:13.395246 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.432639 (* 0.0909091 = 0.0393308 loss)
I0425 10:31:13.395261 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.0223981 (* 0.0909091 = 0.0020362 loss)
I0425 10:31:13.395274 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00852191 (* 0.0909091 = 0.00077472 loss)
I0425 10:31:13.395288 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.003644 (* 0.0909091 = 0.000331273 loss)
I0425 10:31:13.395303 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00168184 (* 0.0909091 = 0.000152894 loss)
I0425 10:31:13.395316 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00078903 (* 0.0909091 = 7.173e-05 loss)
I0425 10:31:13.395331 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000635505 (* 0.0909091 = 5.77732e-05 loss)
I0425 10:31:13.395345 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000213343 (* 0.0909091 = 1.93948e-05 loss)
I0425 10:31:13.395380 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000157994 (* 0.0909091 = 1.43631e-05 loss)
I0425 10:31:13.395395 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 3.32166e-05 (* 0.0909091 = 3.01969e-06 loss)
I0425 10:31:13.395411 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 2.09224e-05 (* 0.0909091 = 1.90204e-06 loss)
I0425 10:31:13.395424 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 7.58481e-06 (* 0.0909091 = 6.89528e-07 loss)
I0425 10:31:13.395437 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 10:31:13.395448 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0425 10:31:13.395472 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.436546
I0425 10:31:13.395485 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.355099
I0425 10:31:13.395499 22523 sgd_solver.cpp:106] Iteration 2000, lr = 0.01
I0425 10:36:54.845994 22523 solver.cpp:229] Iteration 2500, loss = 3.33967
I0425 10:36:54.846149 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.55102
I0425 10:36:54.846169 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 1
I0425 10:36:54.846184 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 10:36:54.846196 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0425 10:36:54.846211 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 10:36:54.846223 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 10:36:54.846236 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 10:36:54.846247 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 10:36:54.846261 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 10:36:54.846271 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0425 10:36:54.846283 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 10:36:54.846295 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 10:36:54.846307 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 10:36:54.846319 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 10:36:54.846331 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 10:36:54.846343 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 10:36:54.846354 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 10:36:54.846367 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 10:36:54.846379 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 10:36:54.846391 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 10:36:54.846402 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 10:36:54.846415 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 10:36:54.846426 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 10:36:54.846437 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.852273
I0425 10:36:54.846449 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.795918
I0425 10:36:54.846467 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.51264 (* 0.3 = 0.453791 loss)
I0425 10:36:54.846482 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.512736 (* 0.3 = 0.153821 loss)
I0425 10:36:54.846496 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.453406 (* 0.0272727 = 0.0123656 loss)
I0425 10:36:54.846511 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.70769 (* 0.0272727 = 0.0465733 loss)
I0425 10:36:54.846525 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.71622 (* 0.0272727 = 0.046806 loss)
I0425 10:36:54.846539 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.97249 (* 0.0272727 = 0.0537951 loss)
I0425 10:36:54.846554 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.80822 (* 0.0272727 = 0.049315 loss)
I0425 10:36:54.846567 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.43699 (* 0.0272727 = 0.0391907 loss)
I0425 10:36:54.846581 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.06594 (* 0.0272727 = 0.029071 loss)
I0425 10:36:54.846595 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.744767 (* 0.0272727 = 0.0203118 loss)
I0425 10:36:54.846609 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.565247 (* 0.0272727 = 0.0154158 loss)
I0425 10:36:54.846622 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.455885 (* 0.0272727 = 0.0124332 loss)
I0425 10:36:54.846637 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.698641 (* 0.0272727 = 0.0190538 loss)
I0425 10:36:54.846659 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0350206 (* 0.0272727 = 0.000955108 loss)
I0425 10:36:54.846674 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0651148 (* 0.0272727 = 0.00177586 loss)
I0425 10:36:54.846707 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0271516 (* 0.0272727 = 0.000740499 loss)
I0425 10:36:54.846731 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0207141 (* 0.0272727 = 0.00056493 loss)
I0425 10:36:54.846746 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00601888 (* 0.0272727 = 0.000164151 loss)
I0425 10:36:54.846760 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00192875 (* 0.0272727 = 5.26024e-05 loss)
I0425 10:36:54.846774 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0012175 (* 0.0272727 = 3.32044e-05 loss)
I0425 10:36:54.846789 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00109434 (* 0.0272727 = 2.98457e-05 loss)
I0425 10:36:54.846804 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000318078 (* 0.0272727 = 8.67485e-06 loss)
I0425 10:36:54.846818 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000288951 (* 0.0272727 = 7.8805e-06 loss)
I0425 10:36:54.846832 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000261155 (* 0.0272727 = 7.12241e-06 loss)
I0425 10:36:54.846845 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.714286
I0425 10:36:54.846858 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 10:36:54.846870 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 1
I0425 10:36:54.846881 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0425 10:36:54.846894 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 10:36:54.846904 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 10:36:54.846916 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 10:36:54.846927 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0425 10:36:54.846940 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 10:36:54.846951 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0425 10:36:54.846963 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 10:36:54.846976 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 10:36:54.846987 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 10:36:54.846998 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 10:36:54.847010 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 10:36:54.847021 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 10:36:54.847033 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 10:36:54.847044 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 10:36:54.847053 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 10:36:54.847060 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 10:36:54.847071 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 10:36:54.847082 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 10:36:54.847095 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 10:36:54.847105 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.903409
I0425 10:36:54.847117 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.938776
I0425 10:36:54.847131 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.02811 (* 0.3 = 0.308434 loss)
I0425 10:36:54.847146 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.357537 (* 0.3 = 0.107261 loss)
I0425 10:36:54.847172 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.230149 (* 0.0272727 = 0.00627679 loss)
I0425 10:36:54.847188 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.234821 (* 0.0272727 = 0.0064042 loss)
I0425 10:36:54.847214 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.52276 (* 0.0272727 = 0.0415297 loss)
I0425 10:36:54.847234 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 0.929783 (* 0.0272727 = 0.0253577 loss)
I0425 10:36:54.847250 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.69739 (* 0.0272727 = 0.0462925 loss)
I0425 10:36:54.847266 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.746564 (* 0.0272727 = 0.0203608 loss)
I0425 10:36:54.847280 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.912734 (* 0.0272727 = 0.0248927 loss)
I0425 10:36:54.847295 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.03751 (* 0.0272727 = 0.0282958 loss)
I0425 10:36:54.847308 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.482756 (* 0.0272727 = 0.0131661 loss)
I0425 10:36:54.847323 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.523905 (* 0.0272727 = 0.0142883 loss)
I0425 10:36:54.847337 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.5579 (* 0.0272727 = 0.0152155 loss)
I0425 10:36:54.847364 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0487558 (* 0.0272727 = 0.0013297 loss)
I0425 10:36:54.847383 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0260219 (* 0.0272727 = 0.000709688 loss)
I0425 10:36:54.847398 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0138858 (* 0.0272727 = 0.000378704 loss)
I0425 10:36:54.847411 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0138948 (* 0.0272727 = 0.00037895 loss)
I0425 10:36:54.847426 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00254754 (* 0.0272727 = 6.94783e-05 loss)
I0425 10:36:54.847440 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000174081 (* 0.0272727 = 4.74767e-06 loss)
I0425 10:36:54.847455 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 6.18231e-05 (* 0.0272727 = 1.68609e-06 loss)
I0425 10:36:54.847468 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 1.6705e-05 (* 0.0272727 = 4.55592e-07 loss)
I0425 10:36:54.847483 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 9.65617e-06 (* 0.0272727 = 2.6335e-07 loss)
I0425 10:36:54.847496 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 3.15907e-06 (* 0.0272727 = 8.61564e-08 loss)
I0425 10:36:54.847512 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 8.82176e-06 (* 0.0272727 = 2.40593e-07 loss)
I0425 10:36:54.847523 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.755102
I0425 10:36:54.847535 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 10:36:54.847548 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 10:36:54.847558 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 10:36:54.847569 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 10:36:54.847580 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 10:36:54.847592 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 10:36:54.847604 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0425 10:36:54.847615 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0425 10:36:54.847627 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 10:36:54.847638 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 10:36:54.847651 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 10:36:54.847662 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 10:36:54.847673 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 10:36:54.847686 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 10:36:54.847697 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 10:36:54.847707 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 10:36:54.847730 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 10:36:54.847743 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 10:36:54.847755 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 10:36:54.847767 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 10:36:54.847779 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 10:36:54.847790 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 10:36:54.847801 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.914773
I0425 10:36:54.847813 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.918367
I0425 10:36:54.847827 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.618957 (* 1 = 0.618957 loss)
I0425 10:36:54.847841 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.225191 (* 1 = 0.225191 loss)
I0425 10:36:54.847856 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.116694 (* 0.0909091 = 0.0106085 loss)
I0425 10:36:54.847870 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0866959 (* 0.0909091 = 0.00788145 loss)
I0425 10:36:54.847885 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.344021 (* 0.0909091 = 0.0312747 loss)
I0425 10:36:54.847899 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.286129 (* 0.0909091 = 0.0260118 loss)
I0425 10:36:54.847913 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.514344 (* 0.0909091 = 0.0467586 loss)
I0425 10:36:54.847928 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.47751 (* 0.0909091 = 0.04341 loss)
I0425 10:36:54.847941 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.954015 (* 0.0909091 = 0.0867286 loss)
I0425 10:36:54.847955 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.900825 (* 0.0909091 = 0.0818932 loss)
I0425 10:36:54.847970 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.365608 (* 0.0909091 = 0.0332371 loss)
I0425 10:36:54.847983 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.383764 (* 0.0909091 = 0.0348877 loss)
I0425 10:36:54.847997 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.730337 (* 0.0909091 = 0.0663942 loss)
I0425 10:36:54.848012 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.0136229 (* 0.0909091 = 0.00123845 loss)
I0425 10:36:54.848026 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00739347 (* 0.0909091 = 0.000672133 loss)
I0425 10:36:54.848040 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00376968 (* 0.0909091 = 0.000342698 loss)
I0425 10:36:54.848055 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00232863 (* 0.0909091 = 0.000211694 loss)
I0425 10:36:54.848069 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00144377 (* 0.0909091 = 0.000131252 loss)
I0425 10:36:54.848083 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000850432 (* 0.0909091 = 7.7312e-05 loss)
I0425 10:36:54.848098 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000512472 (* 0.0909091 = 4.65883e-05 loss)
I0425 10:36:54.848112 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000308806 (* 0.0909091 = 2.80733e-05 loss)
I0425 10:36:54.848126 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00015039 (* 0.0909091 = 1.36719e-05 loss)
I0425 10:36:54.848140 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 4.11991e-05 (* 0.0909091 = 3.74538e-06 loss)
I0425 10:36:54.848155 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.30471e-05 (* 0.0909091 = 3.00428e-06 loss)
I0425 10:36:54.848168 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 10:36:54.848181 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 10:36:54.848201 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.514854
I0425 10:36:54.848218 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.440403
I0425 10:36:54.848234 22523 sgd_solver.cpp:106] Iteration 2500, lr = 0.01
I0425 10:42:36.246461 22523 solver.cpp:229] Iteration 3000, loss = 3.19838
I0425 10:42:36.246589 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.604651
I0425 10:42:36.246610 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 10:42:36.246623 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 10:42:36.246636 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 10:42:36.246649 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 10:42:36.246660 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0425 10:42:36.246672 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0425 10:42:36.246685 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 10:42:36.246696 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 10:42:36.246708 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 10:42:36.246721 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 10:42:36.246732 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 10:42:36.246752 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 10:42:36.246769 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 10:42:36.246781 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 10:42:36.246793 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 10:42:36.246809 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 10:42:36.246820 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 10:42:36.246831 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 10:42:36.246842 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 10:42:36.246853 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 10:42:36.246870 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 10:42:36.246882 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 10:42:36.246893 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.897727
I0425 10:42:36.246906 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.813953
I0425 10:42:36.246922 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.3526 (* 0.3 = 0.405779 loss)
I0425 10:42:36.246938 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.347478 (* 0.3 = 0.104243 loss)
I0425 10:42:36.246953 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.512981 (* 0.0272727 = 0.0139904 loss)
I0425 10:42:36.246968 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.35834 (* 0.0272727 = 0.0370456 loss)
I0425 10:42:36.246981 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.10321 (* 0.0272727 = 0.0573604 loss)
I0425 10:42:36.246995 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.58516 (* 0.0272727 = 0.0432317 loss)
I0425 10:42:36.247009 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.29898 (* 0.0272727 = 0.0354267 loss)
I0425 10:42:36.247023 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.33494 (* 0.0272727 = 0.0636803 loss)
I0425 10:42:36.247038 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.294699 (* 0.0272727 = 0.00803725 loss)
I0425 10:42:36.247053 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.384449 (* 0.0272727 = 0.010485 loss)
I0425 10:42:36.247068 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.00766299 (* 0.0272727 = 0.000208991 loss)
I0425 10:42:36.247082 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00338765 (* 0.0272727 = 9.23905e-05 loss)
I0425 10:42:36.247104 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00405266 (* 0.0272727 = 0.000110527 loss)
I0425 10:42:36.247123 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00230626 (* 0.0272727 = 6.28979e-05 loss)
I0425 10:42:36.247165 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00110664 (* 0.0272727 = 3.01811e-05 loss)
I0425 10:42:36.247181 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.000376599 (* 0.0272727 = 1.02709e-05 loss)
I0425 10:42:36.247195 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.000538408 (* 0.0272727 = 1.46839e-05 loss)
I0425 10:42:36.247215 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000220569 (* 0.0272727 = 6.01552e-06 loss)
I0425 10:42:36.247228 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 4.53398e-05 (* 0.0272727 = 1.23654e-06 loss)
I0425 10:42:36.247242 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 2.00128e-05 (* 0.0272727 = 5.45803e-07 loss)
I0425 10:42:36.247256 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 1.46036e-05 (* 0.0272727 = 3.98281e-07 loss)
I0425 10:42:36.247270 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 6.86954e-06 (* 0.0272727 = 1.87351e-07 loss)
I0425 10:42:36.247285 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 9.06011e-06 (* 0.0272727 = 2.47094e-07 loss)
I0425 10:42:36.247299 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.19213e-05 (* 0.0272727 = 3.25126e-07 loss)
I0425 10:42:36.247311 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.72093
I0425 10:42:36.247324 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 10:42:36.247335 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 1
I0425 10:42:36.247346 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0425 10:42:36.247375 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0425 10:42:36.247386 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 10:42:36.247398 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 10:42:36.247411 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 10:42:36.247421 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 10:42:36.247433 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 10:42:36.247445 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 10:42:36.247457 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 10:42:36.247467 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 10:42:36.247478 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 10:42:36.247490 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 10:42:36.247501 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 10:42:36.247512 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 10:42:36.247524 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 10:42:36.247534 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 10:42:36.247546 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 10:42:36.247557 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 10:42:36.247568 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 10:42:36.247580 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 10:42:36.247591 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.920455
I0425 10:42:36.247606 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.930233
I0425 10:42:36.247619 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.997215 (* 0.3 = 0.299165 loss)
I0425 10:42:36.247634 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.266314 (* 0.3 = 0.0798941 loss)
I0425 10:42:36.247649 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.325015 (* 0.0272727 = 0.00886404 loss)
I0425 10:42:36.247664 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.544371 (* 0.0272727 = 0.0148465 loss)
I0425 10:42:36.247691 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.68591 (* 0.0272727 = 0.0459795 loss)
I0425 10:42:36.247706 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.35881 (* 0.0272727 = 0.0370584 loss)
I0425 10:42:36.247720 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.0364 (* 0.0272727 = 0.0282654 loss)
I0425 10:42:36.247735 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.71353 (* 0.0272727 = 0.0467325 loss)
I0425 10:42:36.247748 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.551704 (* 0.0272727 = 0.0150465 loss)
I0425 10:42:36.247763 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.810683 (* 0.0272727 = 0.0221095 loss)
I0425 10:42:36.247777 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.011967 (* 0.0272727 = 0.000326372 loss)
I0425 10:42:36.247792 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00590376 (* 0.0272727 = 0.000161012 loss)
I0425 10:42:36.247807 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00235448 (* 0.0272727 = 6.42132e-05 loss)
I0425 10:42:36.247820 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00104952 (* 0.0272727 = 2.86232e-05 loss)
I0425 10:42:36.247834 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00079242 (* 0.0272727 = 2.16114e-05 loss)
I0425 10:42:36.247849 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000220525 (* 0.0272727 = 6.01431e-06 loss)
I0425 10:42:36.247864 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 7.97763e-05 (* 0.0272727 = 2.17572e-06 loss)
I0425 10:42:36.247877 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 3.71902e-05 (* 0.0272727 = 1.01428e-06 loss)
I0425 10:42:36.247891 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 6.55662e-06 (* 0.0272727 = 1.78817e-07 loss)
I0425 10:42:36.247906 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 5.57314e-06 (* 0.0272727 = 1.51995e-07 loss)
I0425 10:42:36.247920 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 8.04664e-07 (* 0.0272727 = 2.19454e-08 loss)
I0425 10:42:36.247934 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 8.49367e-07 (* 0.0272727 = 2.31646e-08 loss)
I0425 10:42:36.247949 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 9.8348e-07 (* 0.0272727 = 2.68222e-08 loss)
I0425 10:42:36.247963 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.59443e-06 (* 0.0272727 = 4.34846e-08 loss)
I0425 10:42:36.247975 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.883721
I0425 10:42:36.247987 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 10:42:36.247999 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 10:42:36.248011 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0425 10:42:36.248023 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0425 10:42:36.248034 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 10:42:36.248045 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 10:42:36.248057 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 10:42:36.248069 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 10:42:36.248080 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 10:42:36.248091 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 10:42:36.248103 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 10:42:36.248114 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 10:42:36.248126 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 10:42:36.248136 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 10:42:36.248148 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 10:42:36.248169 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 10:42:36.248183 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 10:42:36.248194 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 10:42:36.248206 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 10:42:36.248214 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 10:42:36.248221 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 10:42:36.248234 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 10:42:36.248248 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.971591
I0425 10:42:36.248271 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.953488
I0425 10:42:36.248286 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.464765 (* 1 = 0.464765 loss)
I0425 10:42:36.248301 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.119192 (* 1 = 0.119192 loss)
I0425 10:42:36.248316 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0826032 (* 0.0909091 = 0.00750938 loss)
I0425 10:42:36.248329 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.271317 (* 0.0909091 = 0.0246652 loss)
I0425 10:42:36.248343 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.628675 (* 0.0909091 = 0.0571523 loss)
I0425 10:42:36.248358 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.619992 (* 0.0909091 = 0.056363 loss)
I0425 10:42:36.248371 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.874941 (* 0.0909091 = 0.0795401 loss)
I0425 10:42:36.248385 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.983894 (* 0.0909091 = 0.0894449 loss)
I0425 10:42:36.248399 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.224471 (* 0.0909091 = 0.0204065 loss)
I0425 10:42:36.248414 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.54473 (* 0.0909091 = 0.0495209 loss)
I0425 10:42:36.248427 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.00820126 (* 0.0909091 = 0.000745569 loss)
I0425 10:42:36.248441 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00177456 (* 0.0909091 = 0.000161323 loss)
I0425 10:42:36.248456 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00521019 (* 0.0909091 = 0.000473654 loss)
I0425 10:42:36.248471 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00415648 (* 0.0909091 = 0.000377862 loss)
I0425 10:42:36.248484 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00449752 (* 0.0909091 = 0.000408866 loss)
I0425 10:42:36.248498 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00191858 (* 0.0909091 = 0.000174416 loss)
I0425 10:42:36.248512 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00196776 (* 0.0909091 = 0.000178888 loss)
I0425 10:42:36.248527 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00146938 (* 0.0909091 = 0.00013358 loss)
I0425 10:42:36.248540 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00152325 (* 0.0909091 = 0.000138477 loss)
I0425 10:42:36.248554 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00102624 (* 0.0909091 = 9.32944e-05 loss)
I0425 10:42:36.248569 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000409775 (* 0.0909091 = 3.72522e-05 loss)
I0425 10:42:36.248584 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000431096 (* 0.0909091 = 3.91906e-05 loss)
I0425 10:42:36.248597 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000107968 (* 0.0909091 = 9.81523e-06 loss)
I0425 10:42:36.248611 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 8.62822e-05 (* 0.0909091 = 7.84383e-06 loss)
I0425 10:42:36.248623 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.875
I0425 10:42:36.248636 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0425 10:42:36.248661 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.69962
I0425 10:42:36.248674 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.371191
I0425 10:42:36.248688 22523 sgd_solver.cpp:106] Iteration 3000, lr = 0.01
I0425 10:48:17.541756 22523 solver.cpp:229] Iteration 3500, loss = 3.29102
I0425 10:48:17.541893 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.630435
I0425 10:48:17.541913 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 1
I0425 10:48:17.541926 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0425 10:48:17.541939 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 10:48:17.541951 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.75
I0425 10:48:17.541962 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 10:48:17.541975 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 10:48:17.541987 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 10:48:17.541999 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 10:48:17.542011 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 10:48:17.542022 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 10:48:17.542034 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 10:48:17.542047 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 10:48:17.542058 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 10:48:17.542070 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 10:48:17.542088 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 10:48:17.542099 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 10:48:17.542111 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 10:48:17.542124 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 10:48:17.542135 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 10:48:17.542155 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 10:48:17.542166 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 10:48:17.542177 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 10:48:17.542189 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.886364
I0425 10:48:17.542203 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.891304
I0425 10:48:17.542222 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.13728 (* 0.3 = 0.341186 loss)
I0425 10:48:17.542237 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.347003 (* 0.3 = 0.104101 loss)
I0425 10:48:17.542253 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.347988 (* 0.0272727 = 0.00949058 loss)
I0425 10:48:17.542268 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.9285 (* 0.0272727 = 0.0253227 loss)
I0425 10:48:17.542281 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.95487 (* 0.0272727 = 0.0533147 loss)
I0425 10:48:17.542295 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.36832 (* 0.0272727 = 0.0373179 loss)
I0425 10:48:17.542309 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.32108 (* 0.0272727 = 0.0360294 loss)
I0425 10:48:17.542323 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.36638 (* 0.0272727 = 0.0372649 loss)
I0425 10:48:17.542338 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.689507 (* 0.0272727 = 0.0188047 loss)
I0425 10:48:17.542353 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.236235 (* 0.0272727 = 0.00644278 loss)
I0425 10:48:17.542367 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0478274 (* 0.0272727 = 0.00130438 loss)
I0425 10:48:17.542382 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00830191 (* 0.0272727 = 0.000226416 loss)
I0425 10:48:17.542397 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00597267 (* 0.0272727 = 0.000162891 loss)
I0425 10:48:17.542410 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00405092 (* 0.0272727 = 0.00011048 loss)
I0425 10:48:17.542425 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00296887 (* 0.0272727 = 8.09691e-05 loss)
I0425 10:48:17.542465 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00243811 (* 0.0272727 = 6.64938e-05 loss)
I0425 10:48:17.542481 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.000813899 (* 0.0272727 = 2.21973e-05 loss)
I0425 10:48:17.542503 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00042759 (* 0.0272727 = 1.16615e-05 loss)
I0425 10:48:17.542517 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000194224 (* 0.0272727 = 5.29701e-06 loss)
I0425 10:48:17.542532 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000170005 (* 0.0272727 = 4.63649e-06 loss)
I0425 10:48:17.542546 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 4.27092e-05 (* 0.0272727 = 1.16479e-06 loss)
I0425 10:48:17.542560 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 3.53264e-05 (* 0.0272727 = 9.63447e-07 loss)
I0425 10:48:17.542574 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 5.51708e-05 (* 0.0272727 = 1.50466e-06 loss)
I0425 10:48:17.542588 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 2.2293e-05 (* 0.0272727 = 6.07992e-07 loss)
I0425 10:48:17.542601 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.804348
I0425 10:48:17.542613 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 10:48:17.542625 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0425 10:48:17.542637 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 10:48:17.542649 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.75
I0425 10:48:17.542660 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 10:48:17.542671 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0425 10:48:17.542683 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 10:48:17.542695 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 10:48:17.542706 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 10:48:17.542717 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 10:48:17.542728 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 10:48:17.542739 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 10:48:17.542750 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 10:48:17.542762 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 10:48:17.542773 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 10:48:17.542783 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 10:48:17.542795 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 10:48:17.542806 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 10:48:17.542817 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 10:48:17.542829 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 10:48:17.542840 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 10:48:17.542850 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 10:48:17.542862 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.9375
I0425 10:48:17.542873 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.934783
I0425 10:48:17.542887 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.76549 (* 0.3 = 0.229647 loss)
I0425 10:48:17.542906 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.240297 (* 0.3 = 0.072089 loss)
I0425 10:48:17.542922 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.318065 (* 0.0272727 = 0.00867451 loss)
I0425 10:48:17.542937 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.935774 (* 0.0272727 = 0.0255211 loss)
I0425 10:48:17.542961 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.60285 (* 0.0272727 = 0.0437141 loss)
I0425 10:48:17.542976 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.14065 (* 0.0272727 = 0.0311085 loss)
I0425 10:48:17.542990 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.916154 (* 0.0272727 = 0.024986 loss)
I0425 10:48:17.543005 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.00614 (* 0.0272727 = 0.0274401 loss)
I0425 10:48:17.543020 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.98999 (* 0.0272727 = 0.0269997 loss)
I0425 10:48:17.543033 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.366031 (* 0.0272727 = 0.00998267 loss)
I0425 10:48:17.543047 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.00980704 (* 0.0272727 = 0.000267465 loss)
I0425 10:48:17.543061 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00287767 (* 0.0272727 = 7.8482e-05 loss)
I0425 10:48:17.543076 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00264921 (* 0.0272727 = 7.22512e-05 loss)
I0425 10:48:17.543089 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00138403 (* 0.0272727 = 3.77462e-05 loss)
I0425 10:48:17.543103 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.000492374 (* 0.0272727 = 1.34284e-05 loss)
I0425 10:48:17.543118 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000271293 (* 0.0272727 = 7.39889e-06 loss)
I0425 10:48:17.543131 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 9.72602e-05 (* 0.0272727 = 2.65255e-06 loss)
I0425 10:48:17.543145 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 5.10934e-05 (* 0.0272727 = 1.39346e-06 loss)
I0425 10:48:17.543159 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 1.78073e-05 (* 0.0272727 = 4.85653e-07 loss)
I0425 10:48:17.543174 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 5.55823e-06 (* 0.0272727 = 1.51588e-07 loss)
I0425 10:48:17.543189 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 1.1921e-06 (* 0.0272727 = 3.25117e-08 loss)
I0425 10:48:17.543202 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 1.34111e-06 (* 0.0272727 = 3.65757e-08 loss)
I0425 10:48:17.543216 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 1.1921e-06 (* 0.0272727 = 3.25117e-08 loss)
I0425 10:48:17.543231 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 6.85455e-07 (* 0.0272727 = 1.86942e-08 loss)
I0425 10:48:17.543243 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.847826
I0425 10:48:17.543258 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 10:48:17.543270 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 10:48:17.543282 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0425 10:48:17.543293 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 10:48:17.543305 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 10:48:17.543316 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 10:48:17.543329 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 10:48:17.543340 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 10:48:17.543365 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 10:48:17.543380 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 10:48:17.543390 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 10:48:17.543402 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 10:48:17.543413 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 10:48:17.543426 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 10:48:17.543437 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 10:48:17.543459 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 10:48:17.543473 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 10:48:17.543484 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 10:48:17.543496 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 10:48:17.543503 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 10:48:17.543511 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 10:48:17.543519 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 10:48:17.543530 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.960227
I0425 10:48:17.543542 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.956522
I0425 10:48:17.543556 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.622215 (* 1 = 0.622215 loss)
I0425 10:48:17.543570 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.170852 (* 1 = 0.170852 loss)
I0425 10:48:17.543584 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0485551 (* 0.0909091 = 0.0044141 loss)
I0425 10:48:17.543598 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.160579 (* 0.0909091 = 0.0145981 loss)
I0425 10:48:17.543612 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.814322 (* 0.0909091 = 0.0740293 loss)
I0425 10:48:17.543627 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.541351 (* 0.0909091 = 0.0492137 loss)
I0425 10:48:17.543640 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.507696 (* 0.0909091 = 0.0461541 loss)
I0425 10:48:17.543654 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.622297 (* 0.0909091 = 0.0565724 loss)
I0425 10:48:17.543668 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.06682 (* 0.0909091 = 0.096984 loss)
I0425 10:48:17.543681 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.244203 (* 0.0909091 = 0.0222003 loss)
I0425 10:48:17.543695 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.00591238 (* 0.0909091 = 0.000537489 loss)
I0425 10:48:17.543709 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00161842 (* 0.0909091 = 0.000147129 loss)
I0425 10:48:17.543723 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00463488 (* 0.0909091 = 0.000421352 loss)
I0425 10:48:17.543737 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00245896 (* 0.0909091 = 0.000223542 loss)
I0425 10:48:17.543751 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00171662 (* 0.0909091 = 0.000156056 loss)
I0425 10:48:17.543764 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00100368 (* 0.0909091 = 9.12439e-05 loss)
I0425 10:48:17.543778 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000714945 (* 0.0909091 = 6.4995e-05 loss)
I0425 10:48:17.543793 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000667059 (* 0.0909091 = 6.06417e-05 loss)
I0425 10:48:17.543807 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000463671 (* 0.0909091 = 4.21519e-05 loss)
I0425 10:48:17.543820 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000419312 (* 0.0909091 = 3.81193e-05 loss)
I0425 10:48:17.543834 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000192367 (* 0.0909091 = 1.74879e-05 loss)
I0425 10:48:17.543848 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 8.32856e-05 (* 0.0909091 = 7.57141e-06 loss)
I0425 10:48:17.543862 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 3.68677e-05 (* 0.0909091 = 3.35161e-06 loss)
I0425 10:48:17.543876 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.73156e-05 (* 0.0909091 = 1.57415e-06 loss)
I0425 10:48:17.543895 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 10:48:17.543907 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 10:48:17.543928 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.55697
I0425 10:48:17.543951 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.410428
I0425 10:48:17.543967 22523 sgd_solver.cpp:106] Iteration 3500, lr = 0.01
I0425 10:53:58.851306 22523 solver.cpp:229] Iteration 4000, loss = 3.2544
I0425 10:53:58.851459 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.681818
I0425 10:53:58.851481 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0425 10:53:58.851495 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 10:53:58.851506 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0425 10:53:58.851518 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 10:53:58.851531 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 10:53:58.851542 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.875
I0425 10:53:58.851554 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 10:53:58.851567 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 10:53:58.851578 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 10:53:58.851589 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 10:53:58.851603 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 10:53:58.851613 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 10:53:58.851625 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 10:53:58.851636 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 10:53:58.851649 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 10:53:58.851660 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 10:53:58.851671 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 10:53:58.851683 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 10:53:58.851694 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 10:53:58.851706 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 10:53:58.851717 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 10:53:58.851735 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 10:53:58.851747 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.903409
I0425 10:53:58.851758 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.886364
I0425 10:53:58.851775 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.16311 (* 0.3 = 0.348932 loss)
I0425 10:53:58.851797 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.363819 (* 0.3 = 0.109146 loss)
I0425 10:53:58.851812 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.627964 (* 0.0272727 = 0.0171263 loss)
I0425 10:53:58.851826 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.14484 (* 0.0272727 = 0.0312228 loss)
I0425 10:53:58.851840 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.76248 (* 0.0272727 = 0.0480675 loss)
I0425 10:53:58.851855 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.87002 (* 0.0272727 = 0.0510006 loss)
I0425 10:53:58.851869 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.37484 (* 0.0272727 = 0.0374956 loss)
I0425 10:53:58.851883 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.975337 (* 0.0272727 = 0.0266001 loss)
I0425 10:53:58.851897 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.18284 (* 0.0272727 = 0.0322592 loss)
I0425 10:53:58.851917 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.349181 (* 0.0272727 = 0.00952313 loss)
I0425 10:53:58.851932 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.325801 (* 0.0272727 = 0.00888548 loss)
I0425 10:53:58.851945 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.132579 (* 0.0272727 = 0.00361579 loss)
I0425 10:53:58.851959 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0223231 (* 0.0272727 = 0.000608812 loss)
I0425 10:53:58.851979 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00584604 (* 0.0272727 = 0.000159438 loss)
I0425 10:53:58.852012 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00342874 (* 0.0272727 = 9.35111e-05 loss)
I0425 10:53:58.852028 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00285075 (* 0.0272727 = 7.77478e-05 loss)
I0425 10:53:58.852042 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00103569 (* 0.0272727 = 2.8246e-05 loss)
I0425 10:53:58.852056 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00100959 (* 0.0272727 = 2.75344e-05 loss)
I0425 10:53:58.852072 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000253588 (* 0.0272727 = 6.91603e-06 loss)
I0425 10:53:58.852085 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000110525 (* 0.0272727 = 3.01432e-06 loss)
I0425 10:53:58.852099 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 5.45105e-05 (* 0.0272727 = 1.48665e-06 loss)
I0425 10:53:58.852113 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.15037e-05 (* 0.0272727 = 5.86465e-07 loss)
I0425 10:53:58.852128 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 3.64296e-05 (* 0.0272727 = 9.93535e-07 loss)
I0425 10:53:58.852143 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.94172e-05 (* 0.0272727 = 5.2956e-07 loss)
I0425 10:53:58.852154 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.636364
I0425 10:53:58.852166 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 10:53:58.852179 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0425 10:53:58.852190 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 10:53:58.852201 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.75
I0425 10:53:58.852210 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 10:53:58.852222 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 10:53:58.852234 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 10:53:58.852246 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 10:53:58.852257 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 10:53:58.852268 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 10:53:58.852286 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 10:53:58.852298 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 10:53:58.852309 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 10:53:58.852320 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 10:53:58.852331 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 10:53:58.852349 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 10:53:58.852360 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 10:53:58.852371 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 10:53:58.852382 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 10:53:58.852393 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 10:53:58.852404 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 10:53:58.852416 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 10:53:58.852427 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.897727
I0425 10:53:58.852438 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.954545
I0425 10:53:58.852452 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.802334 (* 0.3 = 0.2407 loss)
I0425 10:53:58.852466 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.237313 (* 0.3 = 0.0711938 loss)
I0425 10:53:58.852484 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.70282 (* 0.0272727 = 0.0191678 loss)
I0425 10:53:58.852499 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.957576 (* 0.0272727 = 0.0261157 loss)
I0425 10:53:58.852524 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.05981 (* 0.0272727 = 0.028904 loss)
I0425 10:53:58.852540 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.36324 (* 0.0272727 = 0.0371794 loss)
I0425 10:53:58.852553 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.14042 (* 0.0272727 = 0.0311023 loss)
I0425 10:53:58.852567 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.848799 (* 0.0272727 = 0.0231491 loss)
I0425 10:53:58.852581 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.868008 (* 0.0272727 = 0.023673 loss)
I0425 10:53:58.852596 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.0473525 (* 0.0272727 = 0.00129143 loss)
I0425 10:53:58.852610 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0109726 (* 0.0272727 = 0.000299253 loss)
I0425 10:53:58.852624 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00189563 (* 0.0272727 = 5.16989e-05 loss)
I0425 10:53:58.852638 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00217479 (* 0.0272727 = 5.93125e-05 loss)
I0425 10:53:58.852651 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00160183 (* 0.0272727 = 4.36862e-05 loss)
I0425 10:53:58.852665 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.000852295 (* 0.0272727 = 2.32444e-05 loss)
I0425 10:53:58.852679 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000355868 (* 0.0272727 = 9.7055e-06 loss)
I0425 10:53:58.852694 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000128874 (* 0.0272727 = 3.51476e-06 loss)
I0425 10:53:58.852707 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000115406 (* 0.0272727 = 3.14743e-06 loss)
I0425 10:53:58.852721 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 2.23679e-05 (* 0.0272727 = 6.10034e-07 loss)
I0425 10:53:58.852736 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 8.15107e-06 (* 0.0272727 = 2.22302e-07 loss)
I0425 10:53:58.852751 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 8.09153e-06 (* 0.0272727 = 2.20678e-07 loss)
I0425 10:53:58.852764 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 4.06806e-06 (* 0.0272727 = 1.10947e-07 loss)
I0425 10:53:58.852779 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 1.11614e-05 (* 0.0272727 = 3.04403e-07 loss)
I0425 10:53:58.852793 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 4.03827e-06 (* 0.0272727 = 1.10135e-07 loss)
I0425 10:53:58.852805 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.954545
I0425 10:53:58.852818 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 10:53:58.852829 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 10:53:58.852840 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 10:53:58.852851 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 10:53:58.852864 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 10:53:58.852874 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 1
I0425 10:53:58.852885 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 10:53:58.852897 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 10:53:58.852910 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 10:53:58.852921 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 10:53:58.852932 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 10:53:58.852943 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 10:53:58.852955 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 10:53:58.852967 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 10:53:58.852977 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 10:53:58.852988 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 10:53:58.853009 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 10:53:58.853021 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 10:53:58.853034 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 10:53:58.853044 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 10:53:58.853055 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 10:53:58.853067 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 10:53:58.853078 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.982955
I0425 10:53:58.853091 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 1
I0425 10:53:58.853104 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.123239 (* 1 = 0.123239 loss)
I0425 10:53:58.853118 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.0399505 (* 1 = 0.0399505 loss)
I0425 10:53:58.853133 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0479126 (* 0.0909091 = 0.00435569 loss)
I0425 10:53:58.853147 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0336837 (* 0.0909091 = 0.00306216 loss)
I0425 10:53:58.853162 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.0964089 (* 0.0909091 = 0.00876444 loss)
I0425 10:53:58.853175 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.0985014 (* 0.0909091 = 0.00895467 loss)
I0425 10:53:58.853189 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.263811 (* 0.0909091 = 0.0239828 loss)
I0425 10:53:58.853204 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.235189 (* 0.0909091 = 0.0213808 loss)
I0425 10:53:58.853217 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.300021 (* 0.0909091 = 0.0272746 loss)
I0425 10:53:58.853231 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.0324919 (* 0.0909091 = 0.00295381 loss)
I0425 10:53:58.853245 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.00865022 (* 0.0909091 = 0.000786383 loss)
I0425 10:53:58.853271 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00111419 (* 0.0909091 = 0.00010129 loss)
I0425 10:53:58.853286 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00705135 (* 0.0909091 = 0.000641032 loss)
I0425 10:53:58.853299 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00228967 (* 0.0909091 = 0.000208152 loss)
I0425 10:53:58.853313 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000698585 (* 0.0909091 = 6.35077e-05 loss)
I0425 10:53:58.853335 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000389324 (* 0.0909091 = 3.53931e-05 loss)
I0425 10:53:58.853349 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000228287 (* 0.0909091 = 2.07534e-05 loss)
I0425 10:53:58.853363 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000145877 (* 0.0909091 = 1.32616e-05 loss)
I0425 10:53:58.853379 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000128509 (* 0.0909091 = 1.16826e-05 loss)
I0425 10:53:58.853392 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 8.95119e-05 (* 0.0909091 = 8.13745e-06 loss)
I0425 10:53:58.853406 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 3.34199e-05 (* 0.0909091 = 3.03817e-06 loss)
I0425 10:53:58.853420 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.51851e-05 (* 0.0909091 = 1.38046e-06 loss)
I0425 10:53:58.853435 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 7.91275e-06 (* 0.0909091 = 7.19341e-07 loss)
I0425 10:53:58.853449 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.71364e-06 (* 0.0909091 = 1.55786e-07 loss)
I0425 10:53:58.853461 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 10:53:58.853473 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.875
I0425 10:53:58.853494 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.734003
I0425 10:53:58.853507 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.559204
I0425 10:53:58.853526 22523 sgd_solver.cpp:106] Iteration 4000, lr = 0.01
I0425 10:59:40.206821 22523 solver.cpp:229] Iteration 4500, loss = 3.29755
I0425 10:59:40.206972 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.58
I0425 10:59:40.206995 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.5
I0425 10:59:40.207007 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 10:59:40.207020 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 10:59:40.207031 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 10:59:40.207043 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.25
I0425 10:59:40.207056 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 10:59:40.207067 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 10:59:40.207079 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 10:59:40.207092 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 10:59:40.207111 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 10:59:40.207123 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 10:59:40.207135 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 10:59:40.207147 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 10:59:40.207165 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 10:59:40.207176 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 10:59:40.207188 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 10:59:40.207201 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 10:59:40.207214 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 10:59:40.207234 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 10:59:40.207245 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 10:59:40.207257 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 10:59:40.207268 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 10:59:40.207279 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.875
I0425 10:59:40.207298 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.76
I0425 10:59:40.207315 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.58538 (* 0.3 = 0.475614 loss)
I0425 10:59:40.207329 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.492187 (* 0.3 = 0.147656 loss)
I0425 10:59:40.207345 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.09326 (* 0.0272727 = 0.0298162 loss)
I0425 10:59:40.207372 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.56197 (* 0.0272727 = 0.042599 loss)
I0425 10:59:40.207388 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.74493 (* 0.0272727 = 0.047589 loss)
I0425 10:59:40.207402 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.86692 (* 0.0272727 = 0.050916 loss)
I0425 10:59:40.207417 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.86472 (* 0.0272727 = 0.050856 loss)
I0425 10:59:40.207430 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.4492 (* 0.0272727 = 0.0395237 loss)
I0425 10:59:40.207444 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.833557 (* 0.0272727 = 0.0227334 loss)
I0425 10:59:40.207459 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.288118 (* 0.0272727 = 0.00785776 loss)
I0425 10:59:40.207474 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.45659 (* 0.0272727 = 0.0124525 loss)
I0425 10:59:40.207487 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.53229 (* 0.0272727 = 0.014517 loss)
I0425 10:59:40.207501 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.515121 (* 0.0272727 = 0.0140488 loss)
I0425 10:59:40.207516 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.055517 (* 0.0272727 = 0.0015141 loss)
I0425 10:59:40.207530 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0178742 (* 0.0272727 = 0.000487479 loss)
I0425 10:59:40.207563 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0105912 (* 0.0272727 = 0.000288851 loss)
I0425 10:59:40.207579 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00758777 (* 0.0272727 = 0.000206939 loss)
I0425 10:59:40.207593 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00320136 (* 0.0272727 = 8.73097e-05 loss)
I0425 10:59:40.207607 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000749069 (* 0.0272727 = 2.04291e-05 loss)
I0425 10:59:40.207623 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000223317 (* 0.0272727 = 6.09046e-06 loss)
I0425 10:59:40.207636 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000182837 (* 0.0272727 = 4.98647e-06 loss)
I0425 10:59:40.207650 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000107844 (* 0.0272727 = 2.94121e-06 loss)
I0425 10:59:40.207664 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 7.14422e-05 (* 0.0272727 = 1.94842e-06 loss)
I0425 10:59:40.207684 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 4.64092e-05 (* 0.0272727 = 1.26571e-06 loss)
I0425 10:59:40.207696 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.68
I0425 10:59:40.207708 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 10:59:40.207720 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0425 10:59:40.207741 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 10:59:40.207752 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 10:59:40.207763 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 10:59:40.207775 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 10:59:40.207787 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 10:59:40.207798 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 10:59:40.207809 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 10:59:40.207820 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 10:59:40.207833 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 10:59:40.207844 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 10:59:40.207855 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 10:59:40.207866 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 10:59:40.207877 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 10:59:40.207888 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 10:59:40.207900 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 10:59:40.207911 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 10:59:40.207921 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 10:59:40.207932 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 10:59:40.207943 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 10:59:40.207954 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 10:59:40.207965 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.903409
I0425 10:59:40.207978 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.86
I0425 10:59:40.207994 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.04574 (* 0.3 = 0.313722 loss)
I0425 10:59:40.208009 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.326561 (* 0.3 = 0.0979684 loss)
I0425 10:59:40.208024 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.582099 (* 0.0272727 = 0.0158754 loss)
I0425 10:59:40.208037 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.01817 (* 0.0272727 = 0.0277682 loss)
I0425 10:59:40.208071 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.81055 (* 0.0272727 = 0.0493786 loss)
I0425 10:59:40.208086 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.2865 (* 0.0272727 = 0.0350862 loss)
I0425 10:59:40.208101 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.2995 (* 0.0272727 = 0.035441 loss)
I0425 10:59:40.208114 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.6216 (* 0.0272727 = 0.0442255 loss)
I0425 10:59:40.208130 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.965003 (* 0.0272727 = 0.0263183 loss)
I0425 10:59:40.208143 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.410595 (* 0.0272727 = 0.0111981 loss)
I0425 10:59:40.208158 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.491048 (* 0.0272727 = 0.0133922 loss)
I0425 10:59:40.208171 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.516889 (* 0.0272727 = 0.014097 loss)
I0425 10:59:40.208185 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.544975 (* 0.0272727 = 0.0148629 loss)
I0425 10:59:40.208199 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.047783 (* 0.0272727 = 0.00130317 loss)
I0425 10:59:40.208214 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0276498 (* 0.0272727 = 0.000754087 loss)
I0425 10:59:40.208227 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00944703 (* 0.0272727 = 0.000257646 loss)
I0425 10:59:40.208241 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00995714 (* 0.0272727 = 0.000271558 loss)
I0425 10:59:40.208258 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00529426 (* 0.0272727 = 0.000144389 loss)
I0425 10:59:40.208273 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000977537 (* 0.0272727 = 2.66601e-05 loss)
I0425 10:59:40.208287 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000530073 (* 0.0272727 = 1.44565e-05 loss)
I0425 10:59:40.208302 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000102254 (* 0.0272727 = 2.78876e-06 loss)
I0425 10:59:40.208315 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 7.81804e-05 (* 0.0272727 = 2.13219e-06 loss)
I0425 10:59:40.208329 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 5.32903e-05 (* 0.0272727 = 1.45337e-06 loss)
I0425 10:59:40.208343 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 4.79461e-05 (* 0.0272727 = 1.30762e-06 loss)
I0425 10:59:40.208355 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.84
I0425 10:59:40.208369 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 10:59:40.208379 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 10:59:40.208391 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 10:59:40.208402 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 10:59:40.208415 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0425 10:59:40.208425 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 10:59:40.208437 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0425 10:59:40.208448 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 10:59:40.208461 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 10:59:40.208472 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 10:59:40.208482 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 10:59:40.208494 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 10:59:40.208505 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 10:59:40.208516 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 10:59:40.208528 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 10:59:40.208539 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 10:59:40.208560 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 10:59:40.208573 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 10:59:40.208585 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 10:59:40.208595 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 10:59:40.208606 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 10:59:40.208618 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 10:59:40.208629 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.9375
I0425 10:59:40.208642 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.92
I0425 10:59:40.208654 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.570659 (* 1 = 0.570659 loss)
I0425 10:59:40.208668 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.225143 (* 1 = 0.225143 loss)
I0425 10:59:40.208683 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.168508 (* 0.0909091 = 0.0153189 loss)
I0425 10:59:40.208698 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.477686 (* 0.0909091 = 0.043426 loss)
I0425 10:59:40.208710 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.138574 (* 0.0909091 = 0.0125976 loss)
I0425 10:59:40.208724 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.317937 (* 0.0909091 = 0.0289034 loss)
I0425 10:59:40.208739 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.219874 (* 0.0909091 = 0.0199886 loss)
I0425 10:59:40.208752 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.708536 (* 0.0909091 = 0.0644123 loss)
I0425 10:59:40.208766 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.735193 (* 0.0909091 = 0.0668358 loss)
I0425 10:59:40.208781 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.449817 (* 0.0909091 = 0.0408925 loss)
I0425 10:59:40.208794 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.365894 (* 0.0909091 = 0.0332631 loss)
I0425 10:59:40.208808 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.458169 (* 0.0909091 = 0.0416518 loss)
I0425 10:59:40.208822 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.436931 (* 0.0909091 = 0.039721 loss)
I0425 10:59:40.208835 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.209845 (* 0.0909091 = 0.0190768 loss)
I0425 10:59:40.208849 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.163584 (* 0.0909091 = 0.0148713 loss)
I0425 10:59:40.208863 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0920798 (* 0.0909091 = 0.00837089 loss)
I0425 10:59:40.208878 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0593934 (* 0.0909091 = 0.0053994 loss)
I0425 10:59:40.208891 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0301431 (* 0.0909091 = 0.00274029 loss)
I0425 10:59:40.208905 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0303745 (* 0.0909091 = 0.00276132 loss)
I0425 10:59:40.208920 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0132337 (* 0.0909091 = 0.00120306 loss)
I0425 10:59:40.208933 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.0112746 (* 0.0909091 = 0.00102496 loss)
I0425 10:59:40.208947 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00353068 (* 0.0909091 = 0.000320971 loss)
I0425 10:59:40.208961 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00122014 (* 0.0909091 = 0.000110922 loss)
I0425 10:59:40.208976 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000358769 (* 0.0909091 = 3.26154e-05 loss)
I0425 10:59:40.208987 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 10:59:40.208999 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 10:59:40.209012 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.522281
I0425 10:59:40.209028 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.467971
I0425 10:59:40.209048 22523 sgd_solver.cpp:106] Iteration 4500, lr = 0.01
I0425 11:05:21.031108 22523 solver.cpp:338] Iteration 5000, Testing net (#0)
I0425 11:06:12.577870 22523 solver.cpp:393] Test loss: 1.6066
I0425 11:06:12.577989 22523 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.756205
I0425 11:06:12.578009 22523 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.867
I0425 11:06:12.578022 22523 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.664
I0425 11:06:12.578035 22523 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.517
I0425 11:06:12.578047 22523 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.523
I0425 11:06:12.578059 22523 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.576
I0425 11:06:12.578071 22523 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.679
I0425 11:06:12.578083 22523 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.812
I0425 11:06:12.578094 22523 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.92
I0425 11:06:12.578106 22523 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.982
I0425 11:06:12.578119 22523 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.994
I0425 11:06:12.578130 22523 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.996
I0425 11:06:12.578142 22523 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 1
I0425 11:06:12.578153 22523 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 1
I0425 11:06:12.578166 22523 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 1
I0425 11:06:12.578177 22523 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 1
I0425 11:06:12.578188 22523 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 1
I0425 11:06:12.578202 22523 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 1
I0425 11:06:12.578214 22523 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 1
I0425 11:06:12.578227 22523 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 1
I0425 11:06:12.578238 22523 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 1
I0425 11:06:12.578248 22523 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0425 11:06:12.578259 22523 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0425 11:06:12.578271 22523 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.919502
I0425 11:06:12.578284 22523 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.915909
I0425 11:06:12.578300 22523 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 0.827009 (* 0.3 = 0.248103 loss)
I0425 11:06:12.578315 22523 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.271184 (* 0.3 = 0.0813552 loss)
I0425 11:06:12.578330 22523 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.555891 (* 0.0272727 = 0.0151607 loss)
I0425 11:06:12.578343 22523 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.14845 (* 0.0272727 = 0.0313213 loss)
I0425 11:06:12.578357 22523 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.47164 (* 0.0272727 = 0.0401356 loss)
I0425 11:06:12.578371 22523 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.51674 (* 0.0272727 = 0.0413657 loss)
I0425 11:06:12.578384 22523 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.33112 (* 0.0272727 = 0.0363033 loss)
I0425 11:06:12.578399 22523 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.02042 (* 0.0272727 = 0.0278296 loss)
I0425 11:06:12.578413 22523 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.639568 (* 0.0272727 = 0.0174428 loss)
I0425 11:06:12.578428 22523 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.32658 (* 0.0272727 = 0.00890672 loss)
I0425 11:06:12.578441 22523 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.0912499 (* 0.0272727 = 0.00248863 loss)
I0425 11:06:12.578456 22523 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.0513619 (* 0.0272727 = 0.00140078 loss)
I0425 11:06:12.578470 22523 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.0329375 (* 0.0272727 = 0.000898295 loss)
I0425 11:06:12.578485 22523 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.0209564 (* 0.0272727 = 0.000571538 loss)
I0425 11:06:12.578498 22523 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.0152207 (* 0.0272727 = 0.00041511 loss)
I0425 11:06:12.578531 22523 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.0106767 (* 0.0272727 = 0.000291183 loss)
I0425 11:06:12.578552 22523 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.00664598 (* 0.0272727 = 0.000181254 loss)
I0425 11:06:12.578567 22523 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.00356115 (* 0.0272727 = 9.71223e-05 loss)
I0425 11:06:12.578580 22523 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.000797318 (* 0.0272727 = 2.1745e-05 loss)
I0425 11:06:12.578594 22523 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.00027758 (* 0.0272727 = 7.57036e-06 loss)
I0425 11:06:12.578608 22523 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.000128796 (* 0.0272727 = 3.51262e-06 loss)
I0425 11:06:12.578621 22523 solver.cpp:406]     Test net output #46: loss1/loss20 = 8.63586e-05 (* 0.0272727 = 2.35524e-06 loss)
I0425 11:06:12.578635 22523 solver.cpp:406]     Test net output #47: loss1/loss21 = 5.68045e-05 (* 0.0272727 = 1.54921e-06 loss)
I0425 11:06:12.578649 22523 solver.cpp:406]     Test net output #48: loss1/loss22 = 4.87635e-05 (* 0.0272727 = 1.32991e-06 loss)
I0425 11:06:12.578661 22523 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.884495
I0425 11:06:12.578673 22523 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.939
I0425 11:06:12.578685 22523 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.875
I0425 11:06:12.578696 22523 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.727
I0425 11:06:12.578707 22523 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.617
I0425 11:06:12.578719 22523 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.672
I0425 11:06:12.578730 22523 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.748
I0425 11:06:12.578742 22523 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.869
I0425 11:06:12.578753 22523 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.938
I0425 11:06:12.578764 22523 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.984
I0425 11:06:12.578775 22523 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.994
I0425 11:06:12.578788 22523 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.998
I0425 11:06:12.578799 22523 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.999
I0425 11:06:12.578809 22523 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 1
I0425 11:06:12.578821 22523 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 1
I0425 11:06:12.578832 22523 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 1
I0425 11:06:12.578843 22523 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 1
I0425 11:06:12.578855 22523 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 1
I0425 11:06:12.578866 22523 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 1
I0425 11:06:12.578876 22523 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 1
I0425 11:06:12.578886 22523 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 1
I0425 11:06:12.578898 22523 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0425 11:06:12.578909 22523 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0425 11:06:12.578920 22523 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.962819
I0425 11:06:12.578932 22523 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.959806
I0425 11:06:12.578944 22523 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.436629 (* 0.3 = 0.130989 loss)
I0425 11:06:12.578958 22523 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.14076 (* 0.3 = 0.0422279 loss)
I0425 11:06:12.578972 22523 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.299772 (* 0.0272727 = 0.00817559 loss)
I0425 11:06:12.578987 22523 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.528695 (* 0.0272727 = 0.014419 loss)
I0425 11:06:12.579015 22523 solver.cpp:406]     Test net output #78: loss2/loss03 = 0.884992 (* 0.0272727 = 0.0241361 loss)
I0425 11:06:12.579030 22523 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.06422 (* 0.0272727 = 0.0290242 loss)
I0425 11:06:12.579043 22523 solver.cpp:406]     Test net output #80: loss2/loss05 = 0.941313 (* 0.0272727 = 0.0256722 loss)
I0425 11:06:12.579057 22523 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.761388 (* 0.0272727 = 0.0207651 loss)
I0425 11:06:12.579071 22523 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.435875 (* 0.0272727 = 0.0118875 loss)
I0425 11:06:12.579085 22523 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.227935 (* 0.0272727 = 0.0062164 loss)
I0425 11:06:12.579099 22523 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.0778306 (* 0.0272727 = 0.00212265 loss)
I0425 11:06:12.579113 22523 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.0402589 (* 0.0272727 = 0.00109797 loss)
I0425 11:06:12.579128 22523 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.0275668 (* 0.0272727 = 0.000751821 loss)
I0425 11:06:12.579141 22523 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.0180454 (* 0.0272727 = 0.000492147 loss)
I0425 11:06:12.579155 22523 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.0128572 (* 0.0272727 = 0.000350652 loss)
I0425 11:06:12.579169 22523 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.0090979 (* 0.0272727 = 0.000248125 loss)
I0425 11:06:12.579183 22523 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.00585958 (* 0.0272727 = 0.000159807 loss)
I0425 11:06:12.579197 22523 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.00312403 (* 0.0272727 = 8.52007e-05 loss)
I0425 11:06:12.579212 22523 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.000815673 (* 0.0272727 = 2.22456e-05 loss)
I0425 11:06:12.579226 22523 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.000290952 (* 0.0272727 = 7.93506e-06 loss)
I0425 11:06:12.579239 22523 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.00013834 (* 0.0272727 = 3.77291e-06 loss)
I0425 11:06:12.579255 22523 solver.cpp:406]     Test net output #95: loss2/loss20 = 7.83019e-05 (* 0.0272727 = 2.13551e-06 loss)
I0425 11:06:12.579270 22523 solver.cpp:406]     Test net output #96: loss2/loss21 = 5.33788e-05 (* 0.0272727 = 1.45578e-06 loss)
I0425 11:06:12.579284 22523 solver.cpp:406]     Test net output #97: loss2/loss22 = 3.93826e-05 (* 0.0272727 = 1.07407e-06 loss)
I0425 11:06:12.579296 22523 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.918367
I0425 11:06:12.579308 22523 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.957
I0425 11:06:12.579320 22523 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.932
I0425 11:06:12.579331 22523 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.928
I0425 11:06:12.579342 22523 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.9
I0425 11:06:12.579370 22523 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.882
I0425 11:06:12.579385 22523 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.843
I0425 11:06:12.579396 22523 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.876
I0425 11:06:12.579407 22523 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.954
I0425 11:06:12.579418 22523 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.981
I0425 11:06:12.579430 22523 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.994
I0425 11:06:12.579442 22523 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.998
I0425 11:06:12.579453 22523 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.998
I0425 11:06:12.579460 22523 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.999
I0425 11:06:12.579468 22523 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.999
I0425 11:06:12.579483 22523 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.999
I0425 11:06:12.579494 22523 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 1
I0425 11:06:12.579517 22523 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 1
I0425 11:06:12.579530 22523 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 1
I0425 11:06:12.579546 22523 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 1
I0425 11:06:12.579557 22523 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 1
I0425 11:06:12.579568 22523 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0425 11:06:12.579579 22523 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0425 11:06:12.579591 22523 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.968682
I0425 11:06:12.579602 22523 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.965495
I0425 11:06:12.579617 22523 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.342194 (* 1 = 0.342194 loss)
I0425 11:06:12.579630 22523 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.125515 (* 1 = 0.125515 loss)
I0425 11:06:12.579644 22523 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.219236 (* 0.0909091 = 0.0199306 loss)
I0425 11:06:12.579658 22523 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.323514 (* 0.0909091 = 0.0294104 loss)
I0425 11:06:12.579671 22523 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.311928 (* 0.0909091 = 0.0283571 loss)
I0425 11:06:12.579685 22523 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.439514 (* 0.0909091 = 0.0399558 loss)
I0425 11:06:12.579699 22523 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.427638 (* 0.0909091 = 0.0388762 loss)
I0425 11:06:12.579717 22523 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.502571 (* 0.0909091 = 0.0456883 loss)
I0425 11:06:12.579730 22523 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.370479 (* 0.0909091 = 0.03368 loss)
I0425 11:06:12.579744 22523 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.155331 (* 0.0909091 = 0.014121 loss)
I0425 11:06:12.579758 22523 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.0657419 (* 0.0909091 = 0.00597654 loss)
I0425 11:06:12.579779 22523 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.0331221 (* 0.0909091 = 0.0030111 loss)
I0425 11:06:12.579793 22523 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.0235604 (* 0.0909091 = 0.00214185 loss)
I0425 11:06:12.579807 22523 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.015984 (* 0.0909091 = 0.00145309 loss)
I0425 11:06:12.579821 22523 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.0122692 (* 0.0909091 = 0.00111538 loss)
I0425 11:06:12.579835 22523 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.00867148 (* 0.0909091 = 0.000788316 loss)
I0425 11:06:12.579849 22523 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.00644092 (* 0.0909091 = 0.000585538 loss)
I0425 11:06:12.579862 22523 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.00386884 (* 0.0909091 = 0.000351713 loss)
I0425 11:06:12.579876 22523 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.0013461 (* 0.0909091 = 0.000122373 loss)
I0425 11:06:12.579890 22523 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.000838369 (* 0.0909091 = 7.62153e-05 loss)
I0425 11:06:12.579903 22523 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.000521028 (* 0.0909091 = 4.73662e-05 loss)
I0425 11:06:12.579917 22523 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.00024814 (* 0.0909091 = 2.25582e-05 loss)
I0425 11:06:12.579931 22523 solver.cpp:406]     Test net output #145: loss3/loss21 = 9.02011e-05 (* 0.0909091 = 8.2001e-06 loss)
I0425 11:06:12.579946 22523 solver.cpp:406]     Test net output #146: loss3/loss22 = 3.78246e-05 (* 0.0909091 = 3.4386e-06 loss)
I0425 11:06:12.579957 22523 solver.cpp:406]     Test net output #147: total_accuracy = 0.727
I0425 11:06:12.579968 22523 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.629
I0425 11:06:12.579979 22523 solver.cpp:406]     Test net output #149: total_confidence = 0.644202
I0425 11:06:12.579999 22523 solver.cpp:406]     Test net output #150: total_confidence_nor_rec = 0.508873
I0425 11:06:12.580013 22523 solver.cpp:338] Iteration 5000, Testing net (#1)
I0425 11:07:04.173466 22523 solver.cpp:393] Test loss: 2.86632
I0425 11:07:04.173619 22523 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.67259
I0425 11:07:04.173640 22523 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.802
I0425 11:07:04.173653 22523 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.627
I0425 11:07:04.173666 22523 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.455
I0425 11:07:04.173679 22523 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.468
I0425 11:07:04.173691 22523 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.511
I0425 11:07:04.173703 22523 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.569
I0425 11:07:04.173715 22523 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.719
I0425 11:07:04.173727 22523 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.821
I0425 11:07:04.173738 22523 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.9
I0425 11:07:04.173750 22523 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.901
I0425 11:07:04.173763 22523 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.91
I0425 11:07:04.173774 22523 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.925
I0425 11:07:04.173785 22523 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.943
I0425 11:07:04.173797 22523 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 0.952
I0425 11:07:04.173810 22523 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 0.965
I0425 11:07:04.173820 22523 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 0.97
I0425 11:07:04.173832 22523 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 0.99
I0425 11:07:04.173845 22523 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 0.992
I0425 11:07:04.173856 22523 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 0.994
I0425 11:07:04.173867 22523 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 0.998
I0425 11:07:04.173879 22523 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0425 11:07:04.173892 22523 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0425 11:07:04.173902 22523 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.866729
I0425 11:07:04.173914 22523 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.848128
I0425 11:07:04.173931 22523 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.10272 (* 0.3 = 0.330816 loss)
I0425 11:07:04.173946 22523 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.463167 (* 0.3 = 0.13895 loss)
I0425 11:07:04.173961 22523 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.78575 (* 0.0272727 = 0.0214296 loss)
I0425 11:07:04.173975 22523 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.23608 (* 0.0272727 = 0.0337112 loss)
I0425 11:07:04.173990 22523 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.63684 (* 0.0272727 = 0.044641 loss)
I0425 11:07:04.174003 22523 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.6936 (* 0.0272727 = 0.046189 loss)
I0425 11:07:04.174017 22523 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.54786 (* 0.0272727 = 0.0422143 loss)
I0425 11:07:04.174031 22523 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.3247 (* 0.0272727 = 0.0361282 loss)
I0425 11:07:04.174046 22523 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.984166 (* 0.0272727 = 0.0268409 loss)
I0425 11:07:04.174060 22523 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.667761 (* 0.0272727 = 0.0182117 loss)
I0425 11:07:04.174073 22523 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.411783 (* 0.0272727 = 0.0112304 loss)
I0425 11:07:04.174088 22523 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.380096 (* 0.0272727 = 0.0103662 loss)
I0425 11:07:04.174103 22523 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.363603 (* 0.0272727 = 0.00991644 loss)
I0425 11:07:04.174116 22523 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.331379 (* 0.0272727 = 0.0090376 loss)
I0425 11:07:04.174131 22523 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.267493 (* 0.0272727 = 0.00729525 loss)
I0425 11:07:04.174166 22523 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.24238 (* 0.0272727 = 0.00661035 loss)
I0425 11:07:04.174181 22523 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.185142 (* 0.0272727 = 0.00504932 loss)
I0425 11:07:04.174196 22523 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.162658 (* 0.0272727 = 0.00443613 loss)
I0425 11:07:04.174213 22523 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.0672186 (* 0.0272727 = 0.00183324 loss)
I0425 11:07:04.174227 22523 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.0574714 (* 0.0272727 = 0.0015674 loss)
I0425 11:07:04.174242 22523 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.0487262 (* 0.0272727 = 0.0013289 loss)
I0425 11:07:04.174257 22523 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.0188691 (* 0.0272727 = 0.000514611 loss)
I0425 11:07:04.174270 22523 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.000730347 (* 0.0272727 = 1.99185e-05 loss)
I0425 11:07:04.174285 22523 solver.cpp:406]     Test net output #48: loss1/loss22 = 0.000529622 (* 0.0272727 = 1.44442e-05 loss)
I0425 11:07:04.174298 22523 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.801994
I0425 11:07:04.174309 22523 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.9
I0425 11:07:04.174321 22523 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.833
I0425 11:07:04.174332 22523 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.647
I0425 11:07:04.174345 22523 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.595
I0425 11:07:04.174355 22523 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.587
I0425 11:07:04.174367 22523 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.648
I0425 11:07:04.174378 22523 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.768
I0425 11:07:04.174391 22523 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.833
I0425 11:07:04.174401 22523 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.902
I0425 11:07:04.174413 22523 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.904
I0425 11:07:04.174424 22523 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.916
I0425 11:07:04.174435 22523 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.924
I0425 11:07:04.174448 22523 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.938
I0425 11:07:04.174458 22523 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.95
I0425 11:07:04.174469 22523 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.964
I0425 11:07:04.174481 22523 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 0.97
I0425 11:07:04.174492 22523 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 0.99
I0425 11:07:04.174504 22523 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 0.992
I0425 11:07:04.174515 22523 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 0.994
I0425 11:07:04.174527 22523 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 0.998
I0425 11:07:04.174538 22523 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0425 11:07:04.174551 22523 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0425 11:07:04.174561 22523 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.910364
I0425 11:07:04.174573 22523 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.907319
I0425 11:07:04.174587 22523 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.729578 (* 0.3 = 0.218873 loss)
I0425 11:07:04.174602 22523 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.334099 (* 0.3 = 0.10023 loss)
I0425 11:07:04.174615 22523 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.476954 (* 0.0272727 = 0.0130078 loss)
I0425 11:07:04.174633 22523 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.659705 (* 0.0272727 = 0.017992 loss)
I0425 11:07:04.174659 22523 solver.cpp:406]     Test net output #78: loss2/loss03 = 1.11929 (* 0.0272727 = 0.030526 loss)
I0425 11:07:04.174674 22523 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.29889 (* 0.0272727 = 0.0354243 loss)
I0425 11:07:04.174687 22523 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.21477 (* 0.0272727 = 0.0331301 loss)
I0425 11:07:04.174700 22523 solver.cpp:406]     Test net output #81: loss2/loss06 = 1.05729 (* 0.0272727 = 0.0288352 loss)
I0425 11:07:04.174715 22523 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.804304 (* 0.0272727 = 0.0219356 loss)
I0425 11:07:04.174728 22523 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.578096 (* 0.0272727 = 0.0157662 loss)
I0425 11:07:04.174742 22523 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.381558 (* 0.0272727 = 0.0104061 loss)
I0425 11:07:04.174757 22523 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.372365 (* 0.0272727 = 0.0101554 loss)
I0425 11:07:04.174770 22523 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.360829 (* 0.0272727 = 0.00984079 loss)
I0425 11:07:04.174785 22523 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.322081 (* 0.0272727 = 0.00878404 loss)
I0425 11:07:04.174799 22523 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.268616 (* 0.0272727 = 0.0073259 loss)
I0425 11:07:04.174813 22523 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.240083 (* 0.0272727 = 0.00654771 loss)
I0425 11:07:04.174828 22523 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.179187 (* 0.0272727 = 0.00488693 loss)
I0425 11:07:04.174840 22523 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.16029 (* 0.0272727 = 0.00437156 loss)
I0425 11:07:04.174854 22523 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.0658702 (* 0.0272727 = 0.00179646 loss)
I0425 11:07:04.174868 22523 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.0533785 (* 0.0272727 = 0.00145578 loss)
I0425 11:07:04.174882 22523 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.0476926 (* 0.0272727 = 0.00130071 loss)
I0425 11:07:04.174896 22523 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.016022 (* 0.0272727 = 0.000436963 loss)
I0425 11:07:04.174911 22523 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.00126512 (* 0.0272727 = 3.45034e-05 loss)
I0425 11:07:04.174924 22523 solver.cpp:406]     Test net output #97: loss2/loss22 = 0.000902174 (* 0.0272727 = 2.46047e-05 loss)
I0425 11:07:04.174937 22523 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.847947
I0425 11:07:04.174949 22523 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.92
I0425 11:07:04.174962 22523 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.899
I0425 11:07:04.174973 22523 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.874
I0425 11:07:04.174984 22523 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.855
I0425 11:07:04.174995 22523 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.831
I0425 11:07:04.175011 22523 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.779
I0425 11:07:04.175019 22523 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.805
I0425 11:07:04.175030 22523 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.857
I0425 11:07:04.175042 22523 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.91
I0425 11:07:04.175053 22523 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.907
I0425 11:07:04.175065 22523 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.916
I0425 11:07:04.175076 22523 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.925
I0425 11:07:04.175087 22523 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.943
I0425 11:07:04.175098 22523 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.947
I0425 11:07:04.175109 22523 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.962
I0425 11:07:04.175120 22523 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 0.971
I0425 11:07:04.175148 22523 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 0.989
I0425 11:07:04.175160 22523 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 0.992
I0425 11:07:04.175171 22523 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 0.994
I0425 11:07:04.175182 22523 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 0.998
I0425 11:07:04.175194 22523 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0425 11:07:04.175205 22523 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0425 11:07:04.175216 22523 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.920591
I0425 11:07:04.175227 22523 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.927846
I0425 11:07:04.175245 22523 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.575282 (* 1 = 0.575282 loss)
I0425 11:07:04.175261 22523 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.285767 (* 1 = 0.285767 loss)
I0425 11:07:04.175276 22523 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.373816 (* 0.0909091 = 0.0339833 loss)
I0425 11:07:04.175288 22523 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.44429 (* 0.0909091 = 0.04039 loss)
I0425 11:07:04.175302 22523 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.509976 (* 0.0909091 = 0.0463614 loss)
I0425 11:07:04.175317 22523 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.592726 (* 0.0909091 = 0.0538842 loss)
I0425 11:07:04.175330 22523 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.651603 (* 0.0909091 = 0.0592366 loss)
I0425 11:07:04.175343 22523 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.761598 (* 0.0909091 = 0.0692362 loss)
I0425 11:07:04.175372 22523 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.688231 (* 0.0909091 = 0.0625664 loss)
I0425 11:07:04.175387 22523 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.491827 (* 0.0909091 = 0.0447115 loss)
I0425 11:07:04.175401 22523 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.353589 (* 0.0909091 = 0.0321445 loss)
I0425 11:07:04.175415 22523 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.334898 (* 0.0909091 = 0.0304453 loss)
I0425 11:07:04.175429 22523 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.327391 (* 0.0909091 = 0.0297628 loss)
I0425 11:07:04.175443 22523 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.293012 (* 0.0909091 = 0.0266375 loss)
I0425 11:07:04.175457 22523 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.239076 (* 0.0909091 = 0.0217342 loss)
I0425 11:07:04.175470 22523 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.220442 (* 0.0909091 = 0.0200402 loss)
I0425 11:07:04.175484 22523 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.163802 (* 0.0909091 = 0.0148911 loss)
I0425 11:07:04.175498 22523 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.136409 (* 0.0909091 = 0.0124008 loss)
I0425 11:07:04.175513 22523 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.0627063 (* 0.0909091 = 0.00570057 loss)
I0425 11:07:04.175526 22523 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.0458492 (* 0.0909091 = 0.00416811 loss)
I0425 11:07:04.175540 22523 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.0433182 (* 0.0909091 = 0.00393802 loss)
I0425 11:07:04.175554 22523 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.0171505 (* 0.0909091 = 0.00155913 loss)
I0425 11:07:04.175568 22523 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.000347604 (* 0.0909091 = 3.16003e-05 loss)
I0425 11:07:04.175583 22523 solver.cpp:406]     Test net output #146: loss3/loss22 = 0.000109713 (* 0.0909091 = 9.97395e-06 loss)
I0425 11:07:04.175595 22523 solver.cpp:406]     Test net output #147: total_accuracy = 0.605
I0425 11:07:04.175607 22523 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.552
I0425 11:07:04.175618 22523 solver.cpp:406]     Test net output #149: total_confidence = 0.558094
I0425 11:07:04.175642 22523 solver.cpp:406]     Test net output #150: total_confidence_nor_rec = 0.442943
I0425 11:07:04.567821 22523 solver.cpp:229] Iteration 5000, loss = 3.2559
I0425 11:07:04.567914 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.416667
I0425 11:07:04.567934 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 11:07:04.567947 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 11:07:04.567960 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 11:07:04.567972 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 11:07:04.567984 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 11:07:04.567996 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0425 11:07:04.568008 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0425 11:07:04.568020 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 11:07:04.568032 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0425 11:07:04.568044 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0425 11:07:04.568056 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0425 11:07:04.568068 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 11:07:04.568080 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 11:07:04.568092 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 11:07:04.568104 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0425 11:07:04.568116 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 11:07:04.568128 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 11:07:04.568140 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 11:07:04.568150 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 11:07:04.568162 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 11:07:04.568173 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 11:07:04.568186 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 11:07:04.568197 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.795455
I0425 11:07:04.568208 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.65
I0425 11:07:04.568225 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.74669 (* 0.3 = 0.524007 loss)
I0425 11:07:04.568240 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.624618 (* 0.3 = 0.187385 loss)
I0425 11:07:04.568255 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.640023 (* 0.0272727 = 0.0174552 loss)
I0425 11:07:04.568270 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.28705 (* 0.0272727 = 0.0351014 loss)
I0425 11:07:04.568284 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.89581 (* 0.0272727 = 0.051704 loss)
I0425 11:07:04.568298 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.6817 (* 0.0272727 = 0.0731373 loss)
I0425 11:07:04.568315 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.18517 (* 0.0272727 = 0.0595956 loss)
I0425 11:07:04.568328 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.83966 (* 0.0272727 = 0.0501725 loss)
I0425 11:07:04.568342 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.50342 (* 0.0272727 = 0.0410022 loss)
I0425 11:07:04.568356 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.807965 (* 0.0272727 = 0.0220354 loss)
I0425 11:07:04.568370 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.846377 (* 0.0272727 = 0.023083 loss)
I0425 11:07:04.568384 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.754138 (* 0.0272727 = 0.0205674 loss)
I0425 11:07:04.568399 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 1.36468 (* 0.0272727 = 0.0372185 loss)
I0425 11:07:04.568452 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.564094 (* 0.0272727 = 0.0153844 loss)
I0425 11:07:04.568469 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.404419 (* 0.0272727 = 0.0110296 loss)
I0425 11:07:04.568483 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.367773 (* 0.0272727 = 0.0100302 loss)
I0425 11:07:04.568497 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.415955 (* 0.0272727 = 0.0113442 loss)
I0425 11:07:04.568513 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.124178 (* 0.0272727 = 0.00338667 loss)
I0425 11:07:04.568527 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0133196 (* 0.0272727 = 0.000363261 loss)
I0425 11:07:04.568542 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00361628 (* 0.0272727 = 9.86258e-05 loss)
I0425 11:07:04.568557 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00336 (* 0.0272727 = 9.16363e-05 loss)
I0425 11:07:04.568572 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00157644 (* 0.0272727 = 4.29939e-05 loss)
I0425 11:07:04.568586 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00187735 (* 0.0272727 = 5.12005e-05 loss)
I0425 11:07:04.568600 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00127776 (* 0.0272727 = 3.48479e-05 loss)
I0425 11:07:04.568614 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.516667
I0425 11:07:04.568626 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.625
I0425 11:07:04.568639 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0425 11:07:04.568650 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0425 11:07:04.568661 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0425 11:07:04.568673 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 11:07:04.568686 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.375
I0425 11:07:04.568697 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.375
I0425 11:07:04.568708 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 11:07:04.568720 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0425 11:07:04.568732 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 11:07:04.568743 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0425 11:07:04.568755 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 11:07:04.568768 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 11:07:04.568778 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 11:07:04.568790 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0425 11:07:04.568802 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 11:07:04.568814 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 11:07:04.568825 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 11:07:04.568836 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 11:07:04.568848 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 11:07:04.568859 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 11:07:04.568871 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 11:07:04.568882 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.823864
I0425 11:07:04.568895 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.816667
I0425 11:07:04.568908 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.53304 (* 0.3 = 0.459913 loss)
I0425 11:07:04.568923 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.568637 (* 0.3 = 0.170591 loss)
I0425 11:07:04.568948 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.14151 (* 0.0272727 = 0.031132 loss)
I0425 11:07:04.568969 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.36236 (* 0.0272727 = 0.0371552 loss)
I0425 11:07:04.568984 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.66875 (* 0.0272727 = 0.0455113 loss)
I0425 11:07:04.568997 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.14879 (* 0.0272727 = 0.0586034 loss)
I0425 11:07:04.569011 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.47619 (* 0.0272727 = 0.0675325 loss)
I0425 11:07:04.569025 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.81904 (* 0.0272727 = 0.0496101 loss)
I0425 11:07:04.569039 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.41262 (* 0.0272727 = 0.0385261 loss)
I0425 11:07:04.569054 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.895401 (* 0.0272727 = 0.02442 loss)
I0425 11:07:04.569067 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.745114 (* 0.0272727 = 0.0203213 loss)
I0425 11:07:04.569082 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.513932 (* 0.0272727 = 0.0140163 loss)
I0425 11:07:04.569095 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.06448 (* 0.0272727 = 0.0290313 loss)
I0425 11:07:04.569110 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.390664 (* 0.0272727 = 0.0106545 loss)
I0425 11:07:04.569124 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.395534 (* 0.0272727 = 0.0107873 loss)
I0425 11:07:04.569139 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.388896 (* 0.0272727 = 0.0106063 loss)
I0425 11:07:04.569152 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.484828 (* 0.0272727 = 0.0132226 loss)
I0425 11:07:04.569167 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.20172 (* 0.0272727 = 0.00550146 loss)
I0425 11:07:04.569181 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0303964 (* 0.0272727 = 0.000828991 loss)
I0425 11:07:04.569195 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0107332 (* 0.0272727 = 0.000292723 loss)
I0425 11:07:04.569211 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00546869 (* 0.0272727 = 0.000149146 loss)
I0425 11:07:04.569224 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00256543 (* 0.0272727 = 6.99662e-05 loss)
I0425 11:07:04.569238 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00232314 (* 0.0272727 = 6.33582e-05 loss)
I0425 11:07:04.569253 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.00127725 (* 0.0272727 = 3.48341e-05 loss)
I0425 11:07:04.569267 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.716667
I0425 11:07:04.569278 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 11:07:04.569289 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 11:07:04.569301 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 11:07:04.569314 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 11:07:04.569324 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0425 11:07:04.569336 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 11:07:04.569347 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 11:07:04.569360 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 11:07:04.569371 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0425 11:07:04.569383 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.75
I0425 11:07:04.569394 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0425 11:07:04.569406 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 11:07:04.569418 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 11:07:04.569437 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 11:07:04.569444 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0425 11:07:04.569453 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 11:07:04.569460 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 11:07:04.569473 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 11:07:04.569485 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 11:07:04.569497 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 11:07:04.569509 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 11:07:04.569520 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 11:07:04.569531 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.897727
I0425 11:07:04.569543 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.866667
I0425 11:07:04.569556 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.893486 (* 1 = 0.893486 loss)
I0425 11:07:04.569571 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.343015 (* 1 = 0.343015 loss)
I0425 11:07:04.569584 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.417232 (* 0.0909091 = 0.0379302 loss)
I0425 11:07:04.569598 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.156342 (* 0.0909091 = 0.0142129 loss)
I0425 11:07:04.569612 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.264193 (* 0.0909091 = 0.0240176 loss)
I0425 11:07:04.569627 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.281998 (* 0.0909091 = 0.0256361 loss)
I0425 11:07:04.569639 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.986211 (* 0.0909091 = 0.0896556 loss)
I0425 11:07:04.569653 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.693064 (* 0.0909091 = 0.0630058 loss)
I0425 11:07:04.569667 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.03546 (* 0.0909091 = 0.0941324 loss)
I0425 11:07:04.569680 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.932053 (* 0.0909091 = 0.0847321 loss)
I0425 11:07:04.569694 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.709561 (* 0.0909091 = 0.0645055 loss)
I0425 11:07:04.569708 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.468823 (* 0.0909091 = 0.0426203 loss)
I0425 11:07:04.569722 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.894352 (* 0.0909091 = 0.0813048 loss)
I0425 11:07:04.569736 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.425589 (* 0.0909091 = 0.0386899 loss)
I0425 11:07:04.569751 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.30049 (* 0.0909091 = 0.0273173 loss)
I0425 11:07:04.569763 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.357437 (* 0.0909091 = 0.0324943 loss)
I0425 11:07:04.569777 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.365624 (* 0.0909091 = 0.0332386 loss)
I0425 11:07:04.569792 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.175311 (* 0.0909091 = 0.0159374 loss)
I0425 11:07:04.569805 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0281496 (* 0.0909091 = 0.00255905 loss)
I0425 11:07:04.569819 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0113198 (* 0.0909091 = 0.00102907 loss)
I0425 11:07:04.569833 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00630927 (* 0.0909091 = 0.00057357 loss)
I0425 11:07:04.569847 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.0018009 (* 0.0909091 = 0.000163718 loss)
I0425 11:07:04.569861 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000213558 (* 0.0909091 = 1.94143e-05 loss)
I0425 11:07:04.569875 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000100477 (* 0.0909091 = 9.13432e-06 loss)
I0425 11:07:04.569897 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0425 11:07:04.569911 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0425 11:07:04.569922 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.309703
I0425 11:07:04.569933 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.235522
I0425 11:07:04.569948 22523 sgd_solver.cpp:106] Iteration 5000, lr = 0.01
I0425 11:12:45.823182 22523 solver.cpp:229] Iteration 5500, loss = 3.27387
I0425 11:12:45.823345 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.574468
I0425 11:12:45.823375 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 11:12:45.823397 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 11:12:45.823420 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 11:12:45.823441 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 11:12:45.823463 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0425 11:12:45.823485 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 11:12:45.823508 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 11:12:45.823529 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0425 11:12:45.823551 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 11:12:45.823573 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 11:12:45.823596 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 11:12:45.823617 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 11:12:45.823639 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 11:12:45.823665 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 11:12:45.823688 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 11:12:45.823710 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 11:12:45.823734 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 11:12:45.823755 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 11:12:45.823776 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 11:12:45.823799 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 11:12:45.823822 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 11:12:45.823843 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 11:12:45.823863 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.875
I0425 11:12:45.823886 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.808511
I0425 11:12:45.823917 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.37113 (* 0.3 = 0.41134 loss)
I0425 11:12:45.823945 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.420724 (* 0.3 = 0.126217 loss)
I0425 11:12:45.823973 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.994731 (* 0.0272727 = 0.027129 loss)
I0425 11:12:45.823999 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.54551 (* 0.0272727 = 0.0421504 loss)
I0425 11:12:45.824025 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.76519 (* 0.0272727 = 0.0481415 loss)
I0425 11:12:45.824054 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.1206 (* 0.0272727 = 0.0578346 loss)
I0425 11:12:45.824079 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.35036 (* 0.0272727 = 0.0368279 loss)
I0425 11:12:45.824108 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.53748 (* 0.0272727 = 0.0419313 loss)
I0425 11:12:45.824136 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.78458 (* 0.0272727 = 0.0213976 loss)
I0425 11:12:45.824162 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.01279 (* 0.0272727 = 0.0276215 loss)
I0425 11:12:45.824190 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0473248 (* 0.0272727 = 0.00129068 loss)
I0425 11:12:45.824223 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.0283373 (* 0.0272727 = 0.000772836 loss)
I0425 11:12:45.824251 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0229462 (* 0.0272727 = 0.000625806 loss)
I0425 11:12:45.824280 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0326982 (* 0.0272727 = 0.00089177 loss)
I0425 11:12:45.824306 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0199847 (* 0.0272727 = 0.000545036 loss)
I0425 11:12:45.824364 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.014792 (* 0.0272727 = 0.000403419 loss)
I0425 11:12:45.824393 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0114978 (* 0.0272727 = 0.000313576 loss)
I0425 11:12:45.824422 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00605498 (* 0.0272727 = 0.000165136 loss)
I0425 11:12:45.824450 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0019315 (* 0.0272727 = 5.26773e-05 loss)
I0425 11:12:45.824476 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000545579 (* 0.0272727 = 1.48794e-05 loss)
I0425 11:12:45.824503 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000438627 (* 0.0272727 = 1.19626e-05 loss)
I0425 11:12:45.824530 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000251269 (* 0.0272727 = 6.8528e-06 loss)
I0425 11:12:45.824556 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000195501 (* 0.0272727 = 5.33186e-06 loss)
I0425 11:12:45.824585 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000258351 (* 0.0272727 = 7.04594e-06 loss)
I0425 11:12:45.824611 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.744681
I0425 11:12:45.824635 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 11:12:45.824657 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 11:12:45.824681 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0425 11:12:45.824702 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0425 11:12:45.824725 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 11:12:45.824748 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 11:12:45.824769 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 11:12:45.824792 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 11:12:45.824815 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 11:12:45.824836 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 11:12:45.824858 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 11:12:45.824880 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 11:12:45.824901 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 11:12:45.824923 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 11:12:45.824945 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 11:12:45.824966 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 11:12:45.824988 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 11:12:45.825011 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 11:12:45.825032 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 11:12:45.825054 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 11:12:45.825076 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 11:12:45.825098 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 11:12:45.825119 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.931818
I0425 11:12:45.825141 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.957447
I0425 11:12:45.825167 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.703566 (* 0.3 = 0.21107 loss)
I0425 11:12:45.825196 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.207111 (* 0.3 = 0.0621333 loss)
I0425 11:12:45.825222 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.355089 (* 0.0272727 = 0.00968424 loss)
I0425 11:12:45.825254 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.696441 (* 0.0272727 = 0.0189938 loss)
I0425 11:12:45.825300 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.33975 (* 0.0272727 = 0.0365385 loss)
I0425 11:12:45.825330 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.62688 (* 0.0272727 = 0.0443695 loss)
I0425 11:12:45.825356 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.0096 (* 0.0272727 = 0.0275347 loss)
I0425 11:12:45.825384 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.06278 (* 0.0272727 = 0.0289849 loss)
I0425 11:12:45.825417 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.799689 (* 0.0272727 = 0.0218097 loss)
I0425 11:12:45.825446 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.612446 (* 0.0272727 = 0.0167031 loss)
I0425 11:12:45.825474 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0513296 (* 0.0272727 = 0.0013999 loss)
I0425 11:12:45.825501 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.0441048 (* 0.0272727 = 0.00120286 loss)
I0425 11:12:45.825527 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.0569078 (* 0.0272727 = 0.00155203 loss)
I0425 11:12:45.825556 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0482096 (* 0.0272727 = 0.00131481 loss)
I0425 11:12:45.825582 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0143685 (* 0.0272727 = 0.000391868 loss)
I0425 11:12:45.825606 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0222969 (* 0.0272727 = 0.000608098 loss)
I0425 11:12:45.825634 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0076779 (* 0.0272727 = 0.000209397 loss)
I0425 11:12:45.825661 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00334798 (* 0.0272727 = 9.13086e-05 loss)
I0425 11:12:45.825688 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000154424 (* 0.0272727 = 4.21156e-06 loss)
I0425 11:12:45.825716 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 6.43442e-05 (* 0.0272727 = 1.75484e-06 loss)
I0425 11:12:45.825742 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 8.16176e-05 (* 0.0272727 = 2.22593e-06 loss)
I0425 11:12:45.825768 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 4.84377e-05 (* 0.0272727 = 1.32103e-06 loss)
I0425 11:12:45.825794 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 4.33631e-06 (* 0.0272727 = 1.18263e-07 loss)
I0425 11:12:45.825824 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 2.50886e-05 (* 0.0272727 = 6.84234e-07 loss)
I0425 11:12:45.825845 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 1
I0425 11:12:45.825867 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 11:12:45.825891 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 11:12:45.825911 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 11:12:45.825932 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 11:12:45.825953 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 11:12:45.825976 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 1
I0425 11:12:45.825997 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 11:12:45.826020 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 11:12:45.826041 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 11:12:45.826063 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 11:12:45.826084 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 11:12:45.826107 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 11:12:45.826128 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 11:12:45.826150 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 11:12:45.826170 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 11:12:45.826195 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 11:12:45.826232 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 11:12:45.826256 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 11:12:45.826278 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 11:12:45.826302 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 11:12:45.826328 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 11:12:45.826349 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 11:12:45.826370 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.994318
I0425 11:12:45.826395 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 1
I0425 11:12:45.826421 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.0833271 (* 1 = 0.0833271 loss)
I0425 11:12:45.826447 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.032584 (* 1 = 0.032584 loss)
I0425 11:12:45.826480 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0282377 (* 0.0909091 = 0.00256707 loss)
I0425 11:12:45.826508 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0794623 (* 0.0909091 = 0.00722384 loss)
I0425 11:12:45.826536 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.16738 (* 0.0909091 = 0.0152164 loss)
I0425 11:12:45.826587 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.121536 (* 0.0909091 = 0.0110487 loss)
I0425 11:12:45.826614 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.458673 (* 0.0909091 = 0.0416975 loss)
I0425 11:12:45.826640 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.161791 (* 0.0909091 = 0.0147083 loss)
I0425 11:12:45.826668 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.47816 (* 0.0909091 = 0.0434691 loss)
I0425 11:12:45.826695 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.453424 (* 0.0909091 = 0.0412204 loss)
I0425 11:12:45.826721 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.01904 (* 0.0909091 = 0.0017309 loss)
I0425 11:12:45.826750 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0143378 (* 0.0909091 = 0.00130343 loss)
I0425 11:12:45.826776 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.0144108 (* 0.0909091 = 0.00131007 loss)
I0425 11:12:45.826802 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00599338 (* 0.0909091 = 0.000544853 loss)
I0425 11:12:45.826830 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00374186 (* 0.0909091 = 0.000340169 loss)
I0425 11:12:45.826856 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0016116 (* 0.0909091 = 0.000146509 loss)
I0425 11:12:45.826884 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000906466 (* 0.0909091 = 8.2406e-05 loss)
I0425 11:12:45.826911 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000579718 (* 0.0909091 = 5.27016e-05 loss)
I0425 11:12:45.826938 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000274558 (* 0.0909091 = 2.49598e-05 loss)
I0425 11:12:45.826967 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000198935 (* 0.0909091 = 1.8085e-05 loss)
I0425 11:12:45.826993 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000123863 (* 0.0909091 = 1.12602e-05 loss)
I0425 11:12:45.827020 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 7.20886e-05 (* 0.0909091 = 6.55351e-06 loss)
I0425 11:12:45.827046 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 2.71357e-05 (* 0.0909091 = 2.46688e-06 loss)
I0425 11:12:45.827074 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.76582e-05 (* 0.0909091 = 1.60529e-06 loss)
I0425 11:12:45.827097 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.875
I0425 11:12:45.827118 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0425 11:12:45.827155 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.669331
I0425 11:12:45.827179 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.359731
I0425 11:12:45.827204 22523 sgd_solver.cpp:106] Iteration 5500, lr = 0.01
I0425 11:18:27.151898 22523 solver.cpp:229] Iteration 6000, loss = 3.31348
I0425 11:18:27.152036 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.738095
I0425 11:18:27.152056 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 11:18:27.152070 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 11:18:27.152081 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.75
I0425 11:18:27.152093 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 11:18:27.152106 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.75
I0425 11:18:27.152117 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0425 11:18:27.152128 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 11:18:27.152140 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 11:18:27.152151 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 11:18:27.152163 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 11:18:27.152174 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 11:18:27.152186 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 11:18:27.152199 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 11:18:27.152212 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 11:18:27.152223 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 11:18:27.152236 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 11:18:27.152247 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 11:18:27.152259 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 11:18:27.152271 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 11:18:27.152281 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 11:18:27.152293 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 11:18:27.152308 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 11:18:27.152319 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.920455
I0425 11:18:27.152331 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.880952
I0425 11:18:27.152348 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 0.858301 (* 0.3 = 0.25749 loss)
I0425 11:18:27.152364 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.279336 (* 0.3 = 0.0838009 loss)
I0425 11:18:27.152377 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.631137 (* 0.0272727 = 0.0172128 loss)
I0425 11:18:27.152391 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.833839 (* 0.0272727 = 0.0227411 loss)
I0425 11:18:27.152406 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.23735 (* 0.0272727 = 0.0337458 loss)
I0425 11:18:27.152420 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.62697 (* 0.0272727 = 0.0443719 loss)
I0425 11:18:27.152434 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.27935 (* 0.0272727 = 0.0348912 loss)
I0425 11:18:27.152448 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.902203 (* 0.0272727 = 0.0246055 loss)
I0425 11:18:27.152462 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.575124 (* 0.0272727 = 0.0156852 loss)
I0425 11:18:27.152477 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.310012 (* 0.0272727 = 0.00845487 loss)
I0425 11:18:27.152492 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0708205 (* 0.0272727 = 0.00193147 loss)
I0425 11:18:27.152505 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.018635 (* 0.0272727 = 0.000508226 loss)
I0425 11:18:27.152525 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0141891 (* 0.0272727 = 0.000386974 loss)
I0425 11:18:27.152565 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0045907 (* 0.0272727 = 0.000125201 loss)
I0425 11:18:27.152600 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00700492 (* 0.0272727 = 0.000191043 loss)
I0425 11:18:27.152616 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00203357 (* 0.0272727 = 5.54611e-05 loss)
I0425 11:18:27.152631 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00135723 (* 0.0272727 = 3.70153e-05 loss)
I0425 11:18:27.152644 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000422698 (* 0.0272727 = 1.15281e-05 loss)
I0425 11:18:27.152658 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000220521 (* 0.0272727 = 6.0142e-06 loss)
I0425 11:18:27.152673 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000107746 (* 0.0272727 = 2.93852e-06 loss)
I0425 11:18:27.152688 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 2.64505e-05 (* 0.0272727 = 7.21378e-07 loss)
I0425 11:18:27.152701 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.28598e-05 (* 0.0272727 = 6.23449e-07 loss)
I0425 11:18:27.152714 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 1.79568e-05 (* 0.0272727 = 4.89731e-07 loss)
I0425 11:18:27.152729 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 6.30324e-06 (* 0.0272727 = 1.71907e-07 loss)
I0425 11:18:27.152740 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.833333
I0425 11:18:27.152753 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 11:18:27.152765 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 1
I0425 11:18:27.152776 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 11:18:27.152787 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 11:18:27.152801 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 11:18:27.152813 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.875
I0425 11:18:27.152824 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 11:18:27.152837 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 11:18:27.152856 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 11:18:27.152868 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 11:18:27.152879 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 11:18:27.152890 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 11:18:27.152902 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 11:18:27.152914 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 11:18:27.152921 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 11:18:27.152928 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 11:18:27.152941 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 11:18:27.152953 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 11:18:27.152964 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 11:18:27.152976 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 11:18:27.152987 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 11:18:27.153002 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 11:18:27.153013 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.931818
I0425 11:18:27.153024 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 1
I0425 11:18:27.153038 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.425893 (* 0.3 = 0.127768 loss)
I0425 11:18:27.153056 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.16963 (* 0.3 = 0.050889 loss)
I0425 11:18:27.153071 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.0862075 (* 0.0272727 = 0.00235111 loss)
I0425 11:18:27.153086 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.223942 (* 0.0272727 = 0.0061075 loss)
I0425 11:18:27.153112 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.784668 (* 0.0272727 = 0.0214 loss)
I0425 11:18:27.153127 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.28625 (* 0.0272727 = 0.0350795 loss)
I0425 11:18:27.153141 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.0956 (* 0.0272727 = 0.0298799 loss)
I0425 11:18:27.153154 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.859837 (* 0.0272727 = 0.0234501 loss)
I0425 11:18:27.153168 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.426617 (* 0.0272727 = 0.011635 loss)
I0425 11:18:27.153183 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.208407 (* 0.0272727 = 0.00568382 loss)
I0425 11:18:27.153198 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.11514 (* 0.0272727 = 0.00314017 loss)
I0425 11:18:27.153211 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.0761958 (* 0.0272727 = 0.00207807 loss)
I0425 11:18:27.153225 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.033245 (* 0.0272727 = 0.000906682 loss)
I0425 11:18:27.153239 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0223739 (* 0.0272727 = 0.000610197 loss)
I0425 11:18:27.153256 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00937263 (* 0.0272727 = 0.000255617 loss)
I0425 11:18:27.153271 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00778843 (* 0.0272727 = 0.000212412 loss)
I0425 11:18:27.153285 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00131675 (* 0.0272727 = 3.59113e-05 loss)
I0425 11:18:27.153298 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000518728 (* 0.0272727 = 1.41471e-05 loss)
I0425 11:18:27.153312 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00015371 (* 0.0272727 = 4.1921e-06 loss)
I0425 11:18:27.153326 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 4.1566e-05 (* 0.0272727 = 1.13362e-06 loss)
I0425 11:18:27.153340 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 1.44251e-05 (* 0.0272727 = 3.93411e-07 loss)
I0425 11:18:27.153354 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 6.07981e-06 (* 0.0272727 = 1.65813e-07 loss)
I0425 11:18:27.153368 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 3.3975e-06 (* 0.0272727 = 9.26592e-08 loss)
I0425 11:18:27.153383 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 3.74024e-06 (* 0.0272727 = 1.02007e-07 loss)
I0425 11:18:27.153394 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.857143
I0425 11:18:27.153406 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 11:18:27.153417 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 11:18:27.153429 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 11:18:27.153440 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 11:18:27.153452 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 11:18:27.153465 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 11:18:27.153475 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 11:18:27.153486 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 11:18:27.153498 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 11:18:27.153511 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 11:18:27.153522 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 11:18:27.153532 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 11:18:27.153543 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 11:18:27.153555 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 11:18:27.153566 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 11:18:27.153578 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 11:18:27.153599 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 11:18:27.153611 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 11:18:27.153623 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 11:18:27.153635 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 11:18:27.153645 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 11:18:27.153656 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 11:18:27.153667 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.948864
I0425 11:18:27.153679 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.97619
I0425 11:18:27.153693 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.322451 (* 1 = 0.322451 loss)
I0425 11:18:27.153707 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.121548 (* 1 = 0.121548 loss)
I0425 11:18:27.153722 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.00837296 (* 0.0909091 = 0.000761178 loss)
I0425 11:18:27.153735 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.025513 (* 0.0909091 = 0.00231936 loss)
I0425 11:18:27.153750 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.0432872 (* 0.0909091 = 0.0039352 loss)
I0425 11:18:27.153764 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.413132 (* 0.0909091 = 0.0375574 loss)
I0425 11:18:27.153779 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.462095 (* 0.0909091 = 0.0420086 loss)
I0425 11:18:27.153792 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.367296 (* 0.0909091 = 0.0333905 loss)
I0425 11:18:27.153806 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.427266 (* 0.0909091 = 0.0388424 loss)
I0425 11:18:27.153820 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.0661511 (* 0.0909091 = 0.00601374 loss)
I0425 11:18:27.153836 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.143174 (* 0.0909091 = 0.0130158 loss)
I0425 11:18:27.153849 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0297849 (* 0.0909091 = 0.00270772 loss)
I0425 11:18:27.153862 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00426516 (* 0.0909091 = 0.000387742 loss)
I0425 11:18:27.153877 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00332451 (* 0.0909091 = 0.000302228 loss)
I0425 11:18:27.153892 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00133814 (* 0.0909091 = 0.000121649 loss)
I0425 11:18:27.153905 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00073714 (* 0.0909091 = 6.70127e-05 loss)
I0425 11:18:27.153919 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000373733 (* 0.0909091 = 3.39758e-05 loss)
I0425 11:18:27.153934 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000204843 (* 0.0909091 = 1.86221e-05 loss)
I0425 11:18:27.153949 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000134243 (* 0.0909091 = 1.22039e-05 loss)
I0425 11:18:27.153962 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00010415 (* 0.0909091 = 9.46819e-06 loss)
I0425 11:18:27.153976 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 4.81374e-05 (* 0.0909091 = 4.37613e-06 loss)
I0425 11:18:27.153990 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.35903e-05 (* 0.0909091 = 1.23548e-06 loss)
I0425 11:18:27.154006 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 5.42408e-06 (* 0.0909091 = 4.93098e-07 loss)
I0425 11:18:27.154019 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.89245e-06 (* 0.0909091 = 1.72041e-07 loss)
I0425 11:18:27.154031 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 11:18:27.154043 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 11:18:27.154064 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.686103
I0425 11:18:27.154078 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.54058
I0425 11:18:27.154093 22523 sgd_solver.cpp:106] Iteration 6000, lr = 0.01
I0425 11:24:08.549916 22523 solver.cpp:229] Iteration 6500, loss = 3.16151
I0425 11:24:08.550092 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.695652
I0425 11:24:08.550123 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0425 11:24:08.550158 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 11:24:08.550180 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 1
I0425 11:24:08.550206 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 11:24:08.550230 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 11:24:08.550253 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 11:24:08.550278 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 11:24:08.550304 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 11:24:08.550326 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 11:24:08.550348 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 11:24:08.550370 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 11:24:08.550401 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 11:24:08.550421 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 11:24:08.550443 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 11:24:08.550472 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 11:24:08.550494 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 11:24:08.550515 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 11:24:08.550537 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 11:24:08.550560 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 11:24:08.550597 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 11:24:08.550624 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 11:24:08.550647 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 11:24:08.550669 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.909091
I0425 11:24:08.550699 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.869565
I0425 11:24:08.550729 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.12666 (* 0.3 = 0.337999 loss)
I0425 11:24:08.550756 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.344653 (* 0.3 = 0.103396 loss)
I0425 11:24:08.550784 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.47912 (* 0.0272727 = 0.0403397 loss)
I0425 11:24:08.550811 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.979261 (* 0.0272727 = 0.0267071 loss)
I0425 11:24:08.550837 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 0.860461 (* 0.0272727 = 0.0234671 loss)
I0425 11:24:08.550864 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.98776 (* 0.0272727 = 0.0542116 loss)
I0425 11:24:08.550894 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.33935 (* 0.0272727 = 0.0365277 loss)
I0425 11:24:08.550923 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.2882 (* 0.0272727 = 0.0351327 loss)
I0425 11:24:08.550950 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.395475 (* 0.0272727 = 0.0107857 loss)
I0425 11:24:08.550978 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.157453 (* 0.0272727 = 0.00429416 loss)
I0425 11:24:08.551005 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0810756 (* 0.0272727 = 0.00221115 loss)
I0425 11:24:08.551033 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.026668 (* 0.0272727 = 0.00072731 loss)
I0425 11:24:08.551060 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00360587 (* 0.0272727 = 9.8342e-05 loss)
I0425 11:24:08.551087 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00164714 (* 0.0272727 = 4.49219e-05 loss)
I0425 11:24:08.551123 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.000781268 (* 0.0272727 = 2.13073e-05 loss)
I0425 11:24:08.551170 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.000422624 (* 0.0272727 = 1.15261e-05 loss)
I0425 11:24:08.551200 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.000135059 (* 0.0272727 = 3.68344e-06 loss)
I0425 11:24:08.551228 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 8.21647e-05 (* 0.0272727 = 2.24086e-06 loss)
I0425 11:24:08.551259 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 3.66134e-05 (* 0.0272727 = 9.98548e-07 loss)
I0425 11:24:08.551287 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 2.08025e-05 (* 0.0272727 = 5.67341e-07 loss)
I0425 11:24:08.551314 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 5.40915e-06 (* 0.0272727 = 1.47522e-07 loss)
I0425 11:24:08.551343 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.36929e-06 (* 0.0272727 = 6.4617e-08 loss)
I0425 11:24:08.551391 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 1.35601e-06 (* 0.0272727 = 3.6982e-08 loss)
I0425 11:24:08.551420 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.07288e-06 (* 0.0272727 = 2.92605e-08 loss)
I0425 11:24:08.551445 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.76087
I0425 11:24:08.551467 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 11:24:08.551489 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 11:24:08.551512 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 11:24:08.551534 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0425 11:24:08.551556 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 11:24:08.551579 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0425 11:24:08.551601 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 11:24:08.551623 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 11:24:08.551645 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 11:24:08.551667 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 11:24:08.551688 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 11:24:08.551712 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 11:24:08.551733 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 11:24:08.551754 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 11:24:08.551775 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 11:24:08.551797 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 11:24:08.551820 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 11:24:08.551842 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 11:24:08.551864 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 11:24:08.551887 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 11:24:08.551909 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 11:24:08.551931 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 11:24:08.551952 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.914773
I0425 11:24:08.551975 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.913043
I0425 11:24:08.552001 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.12038 (* 0.3 = 0.336115 loss)
I0425 11:24:08.552029 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.353628 (* 0.3 = 0.106088 loss)
I0425 11:24:08.552057 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.211473 (* 0.0272727 = 0.00576745 loss)
I0425 11:24:08.552083 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.704012 (* 0.0272727 = 0.0192003 loss)
I0425 11:24:08.552129 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.941728 (* 0.0272727 = 0.0256835 loss)
I0425 11:24:08.552163 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.76122 (* 0.0272727 = 0.0480333 loss)
I0425 11:24:08.552191 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.883625 (* 0.0272727 = 0.0240989 loss)
I0425 11:24:08.552219 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.2108 (* 0.0272727 = 0.0330217 loss)
I0425 11:24:08.552248 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.695661 (* 0.0272727 = 0.0189726 loss)
I0425 11:24:08.552274 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.107494 (* 0.0272727 = 0.00293164 loss)
I0425 11:24:08.552300 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0819668 (* 0.0272727 = 0.00223546 loss)
I0425 11:24:08.552332 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.0411956 (* 0.0272727 = 0.00112352 loss)
I0425 11:24:08.552358 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.0339476 (* 0.0272727 = 0.000925844 loss)
I0425 11:24:08.552384 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0129606 (* 0.0272727 = 0.000353472 loss)
I0425 11:24:08.552412 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.013286 (* 0.0272727 = 0.000362347 loss)
I0425 11:24:08.552438 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00394226 (* 0.0272727 = 0.000107516 loss)
I0425 11:24:08.552464 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0020834 (* 0.0272727 = 5.68201e-05 loss)
I0425 11:24:08.552491 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00132358 (* 0.0272727 = 3.60977e-05 loss)
I0425 11:24:08.552518 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000379095 (* 0.0272727 = 1.0339e-05 loss)
I0425 11:24:08.552544 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000214409 (* 0.0272727 = 5.84753e-06 loss)
I0425 11:24:08.552577 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000276842 (* 0.0272727 = 7.55023e-06 loss)
I0425 11:24:08.552603 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000162901 (* 0.0272727 = 4.44276e-06 loss)
I0425 11:24:08.552639 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 5.10842e-05 (* 0.0272727 = 1.3932e-06 loss)
I0425 11:24:08.552665 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 5.15015e-05 (* 0.0272727 = 1.40459e-06 loss)
I0425 11:24:08.552687 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.913043
I0425 11:24:08.552711 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 11:24:08.552736 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 11:24:08.552754 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 11:24:08.552770 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0425 11:24:08.552794 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0425 11:24:08.552817 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 11:24:08.552839 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 11:24:08.552861 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 11:24:08.552882 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 11:24:08.552904 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 11:24:08.552925 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 11:24:08.552947 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 11:24:08.552968 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 11:24:08.552989 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 11:24:08.553011 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 11:24:08.553031 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 11:24:08.553069 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 11:24:08.553102 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 11:24:08.553122 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 11:24:08.553149 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 11:24:08.553174 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 11:24:08.553199 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 11:24:08.553220 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.971591
I0425 11:24:08.553242 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 1
I0425 11:24:08.553270 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.268345 (* 1 = 0.268345 loss)
I0425 11:24:08.553302 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.0834292 (* 1 = 0.0834292 loss)
I0425 11:24:08.553329 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.088984 (* 0.0909091 = 0.00808946 loss)
I0425 11:24:08.553360 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.440792 (* 0.0909091 = 0.040072 loss)
I0425 11:24:08.553390 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.103385 (* 0.0909091 = 0.00939867 loss)
I0425 11:24:08.553416 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.61419 (* 0.0909091 = 0.0558355 loss)
I0425 11:24:08.553442 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.635028 (* 0.0909091 = 0.0577299 loss)
I0425 11:24:08.553469 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.526162 (* 0.0909091 = 0.0478329 loss)
I0425 11:24:08.553496 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.420918 (* 0.0909091 = 0.0382652 loss)
I0425 11:24:08.553521 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.122543 (* 0.0909091 = 0.0111402 loss)
I0425 11:24:08.553549 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.0882194 (* 0.0909091 = 0.00801994 loss)
I0425 11:24:08.553575 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0313794 (* 0.0909091 = 0.00285267 loss)
I0425 11:24:08.553601 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.0240294 (* 0.0909091 = 0.00218449 loss)
I0425 11:24:08.553627 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00837075 (* 0.0909091 = 0.000760977 loss)
I0425 11:24:08.553654 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00578126 (* 0.0909091 = 0.00052557 loss)
I0425 11:24:08.553680 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00358421 (* 0.0909091 = 0.000325837 loss)
I0425 11:24:08.553706 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0013751 (* 0.0909091 = 0.000125009 loss)
I0425 11:24:08.553735 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00124315 (* 0.0909091 = 0.000113014 loss)
I0425 11:24:08.553761 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00156527 (* 0.0909091 = 0.000142298 loss)
I0425 11:24:08.553786 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000653137 (* 0.0909091 = 5.93761e-05 loss)
I0425 11:24:08.553813 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000643802 (* 0.0909091 = 5.85274e-05 loss)
I0425 11:24:08.553840 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000247461 (* 0.0909091 = 2.24964e-05 loss)
I0425 11:24:08.553866 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 5.72536e-05 (* 0.0909091 = 5.20487e-06 loss)
I0425 11:24:08.553891 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.72862e-05 (* 0.0909091 = 1.57147e-06 loss)
I0425 11:24:08.553915 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 11:24:08.553937 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0425 11:24:08.553975 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.57895
I0425 11:24:08.553999 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.472558
I0425 11:24:08.554023 22523 sgd_solver.cpp:106] Iteration 6500, lr = 0.01
I0425 11:29:49.798714 22523 solver.cpp:229] Iteration 7000, loss = 3.17744
I0425 11:29:49.798840 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.482759
I0425 11:29:49.798861 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0425 11:29:49.798874 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 11:29:49.798887 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0425 11:29:49.798898 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 11:29:49.798910 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0425 11:29:49.798923 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 11:29:49.798934 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 11:29:49.798946 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 11:29:49.798957 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0425 11:29:49.798969 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0425 11:29:49.798981 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0425 11:29:49.798993 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.75
I0425 11:29:49.799005 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.75
I0425 11:29:49.799016 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 11:29:49.799028 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 11:29:49.799041 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 11:29:49.799052 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 11:29:49.799064 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 11:29:49.799075 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 11:29:49.799088 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 11:29:49.799098 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 11:29:49.799118 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 11:29:49.799136 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.818182
I0425 11:29:49.799149 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.706897
I0425 11:29:49.799175 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.66406 (* 0.3 = 0.499218 loss)
I0425 11:29:49.799190 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.602046 (* 0.3 = 0.180614 loss)
I0425 11:29:49.799203 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.67774 (* 0.0272727 = 0.0457564 loss)
I0425 11:29:49.799217 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.15077 (* 0.0272727 = 0.0313847 loss)
I0425 11:29:49.799232 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.92242 (* 0.0272727 = 0.0524295 loss)
I0425 11:29:49.799245 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.65175 (* 0.0272727 = 0.0450478 loss)
I0425 11:29:49.799259 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.38479 (* 0.0272727 = 0.037767 loss)
I0425 11:29:49.799273 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.4944 (* 0.0272727 = 0.0407565 loss)
I0425 11:29:49.799288 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.741274 (* 0.0272727 = 0.0202166 loss)
I0425 11:29:49.799301 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.398686 (* 0.0272727 = 0.0108733 loss)
I0425 11:29:49.799315 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 1.0366 (* 0.0272727 = 0.028271 loss)
I0425 11:29:49.799329 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.907558 (* 0.0272727 = 0.0247516 loss)
I0425 11:29:49.799343 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.822991 (* 0.0272727 = 0.0224452 loss)
I0425 11:29:49.799373 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.988689 (* 0.0272727 = 0.0269642 loss)
I0425 11:29:49.799388 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.967544 (* 0.0272727 = 0.0263876 loss)
I0425 11:29:49.799422 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.741854 (* 0.0272727 = 0.0202324 loss)
I0425 11:29:49.799437 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0479092 (* 0.0272727 = 0.00130662 loss)
I0425 11:29:49.799451 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0391719 (* 0.0272727 = 0.00106833 loss)
I0425 11:29:49.799466 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0172852 (* 0.0272727 = 0.000471414 loss)
I0425 11:29:49.799480 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00167025 (* 0.0272727 = 4.55521e-05 loss)
I0425 11:29:49.799494 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00152031 (* 0.0272727 = 4.14631e-05 loss)
I0425 11:29:49.799509 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00090036 (* 0.0272727 = 2.45553e-05 loss)
I0425 11:29:49.799523 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000419491 (* 0.0272727 = 1.14407e-05 loss)
I0425 11:29:49.799537 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000287892 (* 0.0272727 = 7.85161e-06 loss)
I0425 11:29:49.799549 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.62069
I0425 11:29:49.799561 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 11:29:49.799573 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0425 11:29:49.799584 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0425 11:29:49.799597 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 11:29:49.799607 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0425 11:29:49.799619 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 11:29:49.799630 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0425 11:29:49.799643 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 11:29:49.799654 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0425 11:29:49.799665 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0425 11:29:49.799677 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 11:29:49.799688 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.75
I0425 11:29:49.799700 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.75
I0425 11:29:49.799711 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 11:29:49.799723 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 11:29:49.799734 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 11:29:49.799746 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 11:29:49.799760 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 11:29:49.799772 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 11:29:49.799787 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 11:29:49.799806 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 11:29:49.799819 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 11:29:49.799831 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.869318
I0425 11:29:49.799844 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.758621
I0425 11:29:49.799860 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.3369 (* 0.3 = 0.40107 loss)
I0425 11:29:49.799877 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.499371 (* 0.3 = 0.149811 loss)
I0425 11:29:49.799892 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.851263 (* 0.0272727 = 0.0232163 loss)
I0425 11:29:49.799906 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.826862 (* 0.0272727 = 0.0225508 loss)
I0425 11:29:49.799932 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.46824 (* 0.0272727 = 0.0400428 loss)
I0425 11:29:49.799947 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.627 (* 0.0272727 = 0.0443727 loss)
I0425 11:29:49.799962 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.06182 (* 0.0272727 = 0.0289588 loss)
I0425 11:29:49.799976 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.977916 (* 0.0272727 = 0.0266704 loss)
I0425 11:29:49.799990 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.593715 (* 0.0272727 = 0.0161922 loss)
I0425 11:29:49.800004 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.851148 (* 0.0272727 = 0.0232131 loss)
I0425 11:29:49.800019 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.815337 (* 0.0272727 = 0.0222365 loss)
I0425 11:29:49.800032 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.979786 (* 0.0272727 = 0.0267214 loss)
I0425 11:29:49.800046 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.777041 (* 0.0272727 = 0.021192 loss)
I0425 11:29:49.800060 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 1.06607 (* 0.0272727 = 0.0290746 loss)
I0425 11:29:49.800073 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.856487 (* 0.0272727 = 0.0233587 loss)
I0425 11:29:49.800088 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.833381 (* 0.0272727 = 0.0227286 loss)
I0425 11:29:49.800101 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.203016 (* 0.0272727 = 0.0055368 loss)
I0425 11:29:49.800115 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0823081 (* 0.0272727 = 0.00224477 loss)
I0425 11:29:49.800130 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0562264 (* 0.0272727 = 0.00153345 loss)
I0425 11:29:49.800144 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0130833 (* 0.0272727 = 0.000356818 loss)
I0425 11:29:49.800158 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00870657 (* 0.0272727 = 0.000237452 loss)
I0425 11:29:49.800173 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00534333 (* 0.0272727 = 0.000145727 loss)
I0425 11:29:49.800186 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00307628 (* 0.0272727 = 8.38986e-05 loss)
I0425 11:29:49.800200 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.0015914 (* 0.0272727 = 4.34018e-05 loss)
I0425 11:29:49.800212 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.655172
I0425 11:29:49.800225 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 11:29:49.800235 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 11:29:49.800247 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 11:29:49.800258 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 11:29:49.800271 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 11:29:49.800282 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 11:29:49.800293 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 11:29:49.800305 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 11:29:49.800318 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0425 11:29:49.800328 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 11:29:49.800340 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 11:29:49.800351 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.75
I0425 11:29:49.800364 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.75
I0425 11:29:49.800374 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 11:29:49.800386 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 11:29:49.800397 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 11:29:49.800418 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 11:29:49.800431 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 11:29:49.800442 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 11:29:49.800454 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 11:29:49.800465 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 11:29:49.800477 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 11:29:49.800488 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.857955
I0425 11:29:49.800500 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.758621
I0425 11:29:49.800514 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.13777 (* 1 = 1.13777 loss)
I0425 11:29:49.800529 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.503164 (* 1 = 0.503164 loss)
I0425 11:29:49.800542 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.485438 (* 0.0909091 = 0.0441307 loss)
I0425 11:29:49.800556 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.404436 (* 0.0909091 = 0.0367669 loss)
I0425 11:29:49.800570 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.641337 (* 0.0909091 = 0.0583034 loss)
I0425 11:29:49.800585 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.639078 (* 0.0909091 = 0.058098 loss)
I0425 11:29:49.800600 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.07286 (* 0.0909091 = 0.097533 loss)
I0425 11:29:49.800613 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.86488 (* 0.0909091 = 0.0786254 loss)
I0425 11:29:49.800627 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.394715 (* 0.0909091 = 0.0358832 loss)
I0425 11:29:49.800642 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.604641 (* 0.0909091 = 0.0549673 loss)
I0425 11:29:49.800655 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.767615 (* 0.0909091 = 0.0697832 loss)
I0425 11:29:49.800669 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.709554 (* 0.0909091 = 0.0645049 loss)
I0425 11:29:49.800683 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.698336 (* 0.0909091 = 0.0634851 loss)
I0425 11:29:49.800698 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.938152 (* 0.0909091 = 0.0852865 loss)
I0425 11:29:49.800712 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.723522 (* 0.0909091 = 0.0657748 loss)
I0425 11:29:49.800726 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.610118 (* 0.0909091 = 0.0554653 loss)
I0425 11:29:49.800740 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.24797 (* 0.0909091 = 0.0225428 loss)
I0425 11:29:49.800755 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.115688 (* 0.0909091 = 0.0105171 loss)
I0425 11:29:49.800768 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0674891 (* 0.0909091 = 0.00613537 loss)
I0425 11:29:49.800782 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0292354 (* 0.0909091 = 0.00265776 loss)
I0425 11:29:49.800796 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.0142058 (* 0.0909091 = 0.00129144 loss)
I0425 11:29:49.800811 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00257837 (* 0.0909091 = 0.000234397 loss)
I0425 11:29:49.800824 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00064243 (* 0.0909091 = 5.84028e-05 loss)
I0425 11:29:49.800839 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000142238 (* 0.0909091 = 1.29307e-05 loss)
I0425 11:29:49.800851 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 11:29:49.800863 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.75
I0425 11:29:49.800875 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.52278
I0425 11:29:49.800895 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.385598
I0425 11:29:49.800911 22523 sgd_solver.cpp:106] Iteration 7000, lr = 0.01
I0425 11:35:31.111706 22523 solver.cpp:229] Iteration 7500, loss = 3.14486
I0425 11:35:31.111800 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.681818
I0425 11:35:31.111820 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 11:35:31.111834 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 11:35:31.111845 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 11:35:31.111857 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.625
I0425 11:35:31.111868 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 11:35:31.111881 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 11:35:31.111896 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 11:35:31.111907 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 11:35:31.111919 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 11:35:31.111932 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 11:35:31.111943 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 11:35:31.111954 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 11:35:31.111966 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 11:35:31.111977 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 11:35:31.111989 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 11:35:31.112000 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 11:35:31.112012 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 11:35:31.112030 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 11:35:31.112041 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 11:35:31.112053 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 11:35:31.112064 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 11:35:31.112076 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 11:35:31.112093 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.892045
I0425 11:35:31.112105 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.931818
I0425 11:35:31.112123 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 0.963859 (* 0.3 = 0.289158 loss)
I0425 11:35:31.112138 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.31528 (* 0.3 = 0.0945839 loss)
I0425 11:35:31.112152 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.427166 (* 0.0272727 = 0.01165 loss)
I0425 11:35:31.112166 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.43563 (* 0.0272727 = 0.0391536 loss)
I0425 11:35:31.112180 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.43331 (* 0.0272727 = 0.0390902 loss)
I0425 11:35:31.112195 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.47494 (* 0.0272727 = 0.0402256 loss)
I0425 11:35:31.112208 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.66715 (* 0.0272727 = 0.0454676 loss)
I0425 11:35:31.112231 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.95138 (* 0.0272727 = 0.0259467 loss)
I0425 11:35:31.112244 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.91515 (* 0.0272727 = 0.0249586 loss)
I0425 11:35:31.112259 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.166911 (* 0.0272727 = 0.00455211 loss)
I0425 11:35:31.112273 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.376686 (* 0.0272727 = 0.0102733 loss)
I0425 11:35:31.112295 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.039499 (* 0.0272727 = 0.00107725 loss)
I0425 11:35:31.112310 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0331752 (* 0.0272727 = 0.000904778 loss)
I0425 11:35:31.112324 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0349083 (* 0.0272727 = 0.000952045 loss)
I0425 11:35:31.112357 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.016687 (* 0.0272727 = 0.000455099 loss)
I0425 11:35:31.112373 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0197358 (* 0.0272727 = 0.00053825 loss)
I0425 11:35:31.112387 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0110787 (* 0.0272727 = 0.000302147 loss)
I0425 11:35:31.112401 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0079175 (* 0.0272727 = 0.000215932 loss)
I0425 11:35:31.112416 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00363091 (* 0.0272727 = 9.90247e-05 loss)
I0425 11:35:31.112432 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000705396 (* 0.0272727 = 1.92381e-05 loss)
I0425 11:35:31.112445 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00132142 (* 0.0272727 = 3.60387e-05 loss)
I0425 11:35:31.112459 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000601922 (* 0.0272727 = 1.64161e-05 loss)
I0425 11:35:31.112481 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000731236 (* 0.0272727 = 1.99428e-05 loss)
I0425 11:35:31.112495 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000225948 (* 0.0272727 = 6.16222e-06 loss)
I0425 11:35:31.112507 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.863636
I0425 11:35:31.112519 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 11:35:31.112530 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0425 11:35:31.112547 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.875
I0425 11:35:31.112558 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 11:35:31.112571 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0425 11:35:31.112581 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 11:35:31.112593 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 11:35:31.112604 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 11:35:31.112615 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 11:35:31.112627 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 11:35:31.112638 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 11:35:31.112649 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 11:35:31.112660 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 11:35:31.112671 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 11:35:31.112684 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 11:35:31.112694 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 11:35:31.112705 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 11:35:31.112716 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 11:35:31.112727 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 11:35:31.112738 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 11:35:31.112749 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 11:35:31.112761 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 11:35:31.112771 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.965909
I0425 11:35:31.112787 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.977273
I0425 11:35:31.112802 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.424219 (* 0.3 = 0.127266 loss)
I0425 11:35:31.112817 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.123721 (* 0.3 = 0.0371164 loss)
I0425 11:35:31.112831 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.168774 (* 0.0272727 = 0.00460292 loss)
I0425 11:35:31.112845 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.403158 (* 0.0272727 = 0.0109952 loss)
I0425 11:35:31.112872 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.69421 (* 0.0272727 = 0.018933 loss)
I0425 11:35:31.112887 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.28825 (* 0.0272727 = 0.035134 loss)
I0425 11:35:31.112901 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.84786 (* 0.0272727 = 0.0503962 loss)
I0425 11:35:31.112915 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.700843 (* 0.0272727 = 0.0191139 loss)
I0425 11:35:31.112929 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.510744 (* 0.0272727 = 0.0139294 loss)
I0425 11:35:31.112946 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.217247 (* 0.0272727 = 0.00592492 loss)
I0425 11:35:31.112960 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.304149 (* 0.0272727 = 0.00829496 loss)
I0425 11:35:31.112974 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.08057 (* 0.0272727 = 0.00219737 loss)
I0425 11:35:31.112989 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.0127946 (* 0.0272727 = 0.000348944 loss)
I0425 11:35:31.113003 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00771042 (* 0.0272727 = 0.000210284 loss)
I0425 11:35:31.113018 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00352505 (* 0.0272727 = 9.61379e-05 loss)
I0425 11:35:31.113032 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00263021 (* 0.0272727 = 7.1733e-05 loss)
I0425 11:35:31.113046 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00180101 (* 0.0272727 = 4.91184e-05 loss)
I0425 11:35:31.113060 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00052503 (* 0.0272727 = 1.4319e-05 loss)
I0425 11:35:31.113075 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000179456 (* 0.0272727 = 4.89425e-06 loss)
I0425 11:35:31.113090 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 9.91091e-05 (* 0.0272727 = 2.70298e-06 loss)
I0425 11:35:31.113104 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000117441 (* 0.0272727 = 3.20293e-06 loss)
I0425 11:35:31.113118 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 4.04377e-05 (* 0.0272727 = 1.10285e-06 loss)
I0425 11:35:31.113132 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 5.80745e-05 (* 0.0272727 = 1.58385e-06 loss)
I0425 11:35:31.113147 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 3.97768e-05 (* 0.0272727 = 1.08482e-06 loss)
I0425 11:35:31.113159 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.977273
I0425 11:35:31.113171 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 11:35:31.113183 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 11:35:31.113194 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 11:35:31.113206 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 11:35:31.113217 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 11:35:31.113229 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 11:35:31.113240 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 11:35:31.113252 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 11:35:31.113263 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 11:35:31.113275 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 11:35:31.113286 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 11:35:31.113296 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 11:35:31.113308 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 11:35:31.113319 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 11:35:31.113330 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 11:35:31.113343 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 11:35:31.113364 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 11:35:31.113378 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 11:35:31.113389 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 11:35:31.113400 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 11:35:31.113411 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 11:35:31.113422 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 11:35:31.113435 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.988636
I0425 11:35:31.113446 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 1
I0425 11:35:31.113459 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.136668 (* 1 = 0.136668 loss)
I0425 11:35:31.113473 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.0436835 (* 1 = 0.0436835 loss)
I0425 11:35:31.113488 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0417291 (* 0.0909091 = 0.00379355 loss)
I0425 11:35:31.113502 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0403868 (* 0.0909091 = 0.00367152 loss)
I0425 11:35:31.113517 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.0289974 (* 0.0909091 = 0.00263613 loss)
I0425 11:35:31.113530 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.113009 (* 0.0909091 = 0.0102735 loss)
I0425 11:35:31.113544 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.244796 (* 0.0909091 = 0.0222542 loss)
I0425 11:35:31.113559 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.41887 (* 0.0909091 = 0.0380791 loss)
I0425 11:35:31.113572 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.266099 (* 0.0909091 = 0.0241908 loss)
I0425 11:35:31.113586 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.121596 (* 0.0909091 = 0.0110542 loss)
I0425 11:35:31.113600 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.144922 (* 0.0909091 = 0.0131747 loss)
I0425 11:35:31.113615 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0300495 (* 0.0909091 = 0.00273178 loss)
I0425 11:35:31.113628 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00342361 (* 0.0909091 = 0.000311237 loss)
I0425 11:35:31.113642 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00145225 (* 0.0909091 = 0.000132023 loss)
I0425 11:35:31.113656 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00126537 (* 0.0909091 = 0.000115034 loss)
I0425 11:35:31.113672 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000798955 (* 0.0909091 = 7.26323e-05 loss)
I0425 11:35:31.113685 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000677681 (* 0.0909091 = 6.16074e-05 loss)
I0425 11:35:31.113700 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000353121 (* 0.0909091 = 3.21019e-05 loss)
I0425 11:35:31.113714 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000152311 (* 0.0909091 = 1.38465e-05 loss)
I0425 11:35:31.113729 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000133168 (* 0.0909091 = 1.21062e-05 loss)
I0425 11:35:31.113742 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 6.98463e-05 (* 0.0909091 = 6.34967e-06 loss)
I0425 11:35:31.113756 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 4.20629e-05 (* 0.0909091 = 3.8239e-06 loss)
I0425 11:35:31.113771 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.70773e-05 (* 0.0909091 = 1.55248e-06 loss)
I0425 11:35:31.113785 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 5.14095e-06 (* 0.0909091 = 4.67359e-07 loss)
I0425 11:35:31.113797 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 11:35:31.113809 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 11:35:31.113834 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.674628
I0425 11:35:31.113845 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.565019
I0425 11:35:31.113860 22523 sgd_solver.cpp:106] Iteration 7500, lr = 0.01
I0425 11:40:23.542709 22523 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 30.68 > 30) by scale factor 0.977835
I0425 11:41:12.413755 22523 solver.cpp:229] Iteration 8000, loss = 3.2285
I0425 11:41:12.413878 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.487805
I0425 11:41:12.413898 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0425 11:41:12.413911 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 11:41:12.413923 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0425 11:41:12.413938 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 11:41:12.413950 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 11:41:12.413962 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 11:41:12.413975 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 11:41:12.413986 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 11:41:12.413998 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 11:41:12.414010 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 11:41:12.414021 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 11:41:12.414033 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 11:41:12.414046 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 11:41:12.414057 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 11:41:12.414068 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 11:41:12.414080 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 11:41:12.414093 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 11:41:12.414104 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 11:41:12.414115 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 11:41:12.414126 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 11:41:12.414139 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 11:41:12.414149 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 11:41:12.414160 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.829545
I0425 11:41:12.414172 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.682927
I0425 11:41:12.414189 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.70565 (* 0.3 = 0.511694 loss)
I0425 11:41:12.414204 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.584163 (* 0.3 = 0.175249 loss)
I0425 11:41:12.414225 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.658037 (* 0.0272727 = 0.0179465 loss)
I0425 11:41:12.414239 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.18513 (* 0.0272727 = 0.0323218 loss)
I0425 11:41:12.414253 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.62639 (* 0.0272727 = 0.0443561 loss)
I0425 11:41:12.414270 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.5481 (* 0.0272727 = 0.0422208 loss)
I0425 11:41:12.414293 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.76229 (* 0.0272727 = 0.0480624 loss)
I0425 11:41:12.414306 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.40407 (* 0.0272727 = 0.0382928 loss)
I0425 11:41:12.414321 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.630835 (* 0.0272727 = 0.0172046 loss)
I0425 11:41:12.414335 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.596189 (* 0.0272727 = 0.0162597 loss)
I0425 11:41:12.414350 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.623587 (* 0.0272727 = 0.0170069 loss)
I0425 11:41:12.414363 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.44373 (* 0.0272727 = 0.0121017 loss)
I0425 11:41:12.414377 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.626302 (* 0.0272727 = 0.017081 loss)
I0425 11:41:12.414391 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.72369 (* 0.0272727 = 0.019737 loss)
I0425 11:41:12.414423 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.84451 (* 0.0272727 = 0.0230321 loss)
I0425 11:41:12.414439 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.687196 (* 0.0272727 = 0.0187417 loss)
I0425 11:41:12.414454 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00302313 (* 0.0272727 = 8.24491e-05 loss)
I0425 11:41:12.414469 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00128053 (* 0.0272727 = 3.49235e-05 loss)
I0425 11:41:12.414482 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000207114 (* 0.0272727 = 5.64856e-06 loss)
I0425 11:41:12.414496 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 8.63142e-05 (* 0.0272727 = 2.35402e-06 loss)
I0425 11:41:12.414510 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 4.4148e-05 (* 0.0272727 = 1.20404e-06 loss)
I0425 11:41:12.414525 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.93055e-05 (* 0.0272727 = 7.99242e-07 loss)
I0425 11:41:12.414538 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 1.46929e-05 (* 0.0272727 = 4.00716e-07 loss)
I0425 11:41:12.414552 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.4976e-05 (* 0.0272727 = 4.08438e-07 loss)
I0425 11:41:12.414564 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.707317
I0425 11:41:12.414577 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 11:41:12.414588 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0425 11:41:12.414599 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0425 11:41:12.414610 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0425 11:41:12.414623 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 11:41:12.414633 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 11:41:12.414645 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 11:41:12.414656 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 11:41:12.414669 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 11:41:12.414680 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 11:41:12.414692 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 11:41:12.414703 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 11:41:12.414715 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 11:41:12.414726 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 11:41:12.414738 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 11:41:12.414749 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 11:41:12.414760 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 11:41:12.414772 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 11:41:12.414779 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 11:41:12.414788 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 11:41:12.414799 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 11:41:12.414810 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 11:41:12.414822 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.903409
I0425 11:41:12.414834 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.853659
I0425 11:41:12.414847 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.09055 (* 0.3 = 0.327164 loss)
I0425 11:41:12.414861 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.362363 (* 0.3 = 0.108709 loss)
I0425 11:41:12.414875 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.171701 (* 0.0272727 = 0.00468276 loss)
I0425 11:41:12.414890 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.183685 (* 0.0272727 = 0.00500959 loss)
I0425 11:41:12.414916 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.1344 (* 0.0272727 = 0.0309383 loss)
I0425 11:41:12.414930 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.09901 (* 0.0272727 = 0.0299729 loss)
I0425 11:41:12.414944 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.22402 (* 0.0272727 = 0.0333823 loss)
I0425 11:41:12.414958 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.719183 (* 0.0272727 = 0.0196141 loss)
I0425 11:41:12.414973 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.289025 (* 0.0272727 = 0.0078825 loss)
I0425 11:41:12.414989 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.480871 (* 0.0272727 = 0.0131147 loss)
I0425 11:41:12.415004 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.572918 (* 0.0272727 = 0.015625 loss)
I0425 11:41:12.415017 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.414776 (* 0.0272727 = 0.0113121 loss)
I0425 11:41:12.415031 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.750935 (* 0.0272727 = 0.02048 loss)
I0425 11:41:12.415045 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.933405 (* 0.0272727 = 0.0254565 loss)
I0425 11:41:12.415060 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 1.05988 (* 0.0272727 = 0.0289057 loss)
I0425 11:41:12.415072 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.781334 (* 0.0272727 = 0.0213091 loss)
I0425 11:41:12.415086 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000377655 (* 0.0272727 = 1.02997e-05 loss)
I0425 11:41:12.415101 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 6.26329e-05 (* 0.0272727 = 1.70817e-06 loss)
I0425 11:41:12.415114 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 8.31493e-06 (* 0.0272727 = 2.26771e-07 loss)
I0425 11:41:12.415128 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 1.77324e-06 (* 0.0272727 = 4.83611e-08 loss)
I0425 11:41:12.415143 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 1.90736e-06 (* 0.0272727 = 5.20188e-08 loss)
I0425 11:41:12.415156 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 7.30158e-07 (* 0.0272727 = 1.99134e-08 loss)
I0425 11:41:12.415170 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 4.91739e-07 (* 0.0272727 = 1.34111e-08 loss)
I0425 11:41:12.415184 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.63913e-07 (* 0.0272727 = 4.47035e-09 loss)
I0425 11:41:12.415195 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.804878
I0425 11:41:12.415207 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 11:41:12.415220 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 11:41:12.415230 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 11:41:12.415241 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 11:41:12.415253 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0425 11:41:12.415264 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 1
I0425 11:41:12.415277 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 11:41:12.415287 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 11:41:12.415299 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 11:41:12.415310 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 11:41:12.415339 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 11:41:12.415354 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 11:41:12.415366 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 11:41:12.415379 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 11:41:12.415390 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 11:41:12.415413 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 11:41:12.415426 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 11:41:12.415438 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 11:41:12.415449 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 11:41:12.415462 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 11:41:12.415472 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 11:41:12.415484 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 11:41:12.415495 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.954545
I0425 11:41:12.415508 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.829268
I0425 11:41:12.415521 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.815622 (* 1 = 0.815622 loss)
I0425 11:41:12.415535 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.192985 (* 1 = 0.192985 loss)
I0425 11:41:12.415549 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0211376 (* 0.0909091 = 0.0019216 loss)
I0425 11:41:12.415565 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.00367676 (* 0.0909091 = 0.000334251 loss)
I0425 11:41:12.415578 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.208188 (* 0.0909091 = 0.0189262 loss)
I0425 11:41:12.415592 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.251988 (* 0.0909091 = 0.022908 loss)
I0425 11:41:12.415606 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.145949 (* 0.0909091 = 0.0132681 loss)
I0425 11:41:12.415619 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.115432 (* 0.0909091 = 0.0104938 loss)
I0425 11:41:12.415633 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.287401 (* 0.0909091 = 0.0261274 loss)
I0425 11:41:12.415647 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.326495 (* 0.0909091 = 0.0296814 loss)
I0425 11:41:12.415662 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.661524 (* 0.0909091 = 0.0601386 loss)
I0425 11:41:12.415675 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.448003 (* 0.0909091 = 0.0407275 loss)
I0425 11:41:12.415689 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.434705 (* 0.0909091 = 0.0395186 loss)
I0425 11:41:12.415702 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.466557 (* 0.0909091 = 0.0424143 loss)
I0425 11:41:12.415716 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.571797 (* 0.0909091 = 0.0519815 loss)
I0425 11:41:12.415729 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.475947 (* 0.0909091 = 0.0432679 loss)
I0425 11:41:12.415745 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00702464 (* 0.0909091 = 0.000638603 loss)
I0425 11:41:12.415758 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00324664 (* 0.0909091 = 0.00029515 loss)
I0425 11:41:12.415772 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000933577 (* 0.0909091 = 8.48707e-05 loss)
I0425 11:41:12.415786 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000401128 (* 0.0909091 = 3.64662e-05 loss)
I0425 11:41:12.415801 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000226805 (* 0.0909091 = 2.06187e-05 loss)
I0425 11:41:12.415814 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000113526 (* 0.0909091 = 1.03205e-05 loss)
I0425 11:41:12.415828 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 2.20545e-05 (* 0.0909091 = 2.00496e-06 loss)
I0425 11:41:12.415843 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 5.46877e-06 (* 0.0909091 = 4.97161e-07 loss)
I0425 11:41:12.415855 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.875
I0425 11:41:12.415868 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.75
I0425 11:41:12.415889 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.700141
I0425 11:41:12.415901 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.707156
I0425 11:41:12.415916 22523 sgd_solver.cpp:106] Iteration 8000, lr = 0.01
I0425 11:46:53.591812 22523 solver.cpp:229] Iteration 8500, loss = 3.07631
I0425 11:46:53.591998 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.608696
I0425 11:46:53.592020 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.5
I0425 11:46:53.592033 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.875
I0425 11:46:53.592046 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 11:46:53.592058 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 11:46:53.592070 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0425 11:46:53.592082 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 11:46:53.592094 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 11:46:53.592108 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 11:46:53.592119 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 11:46:53.592131 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 11:46:53.592144 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 11:46:53.592155 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 11:46:53.592167 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 11:46:53.592180 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 11:46:53.592191 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 11:46:53.592206 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 11:46:53.592218 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 11:46:53.592231 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 11:46:53.592242 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 11:46:53.592253 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 11:46:53.592265 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 11:46:53.592278 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 11:46:53.592289 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.875
I0425 11:46:53.592301 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.804348
I0425 11:46:53.592319 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.32829 (* 0.3 = 0.398487 loss)
I0425 11:46:53.592334 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.434417 (* 0.3 = 0.130325 loss)
I0425 11:46:53.592350 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.64479 (* 0.0272727 = 0.0448578 loss)
I0425 11:46:53.592363 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.07785 (* 0.0272727 = 0.0293958 loss)
I0425 11:46:53.592377 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.62206 (* 0.0272727 = 0.0442381 loss)
I0425 11:46:53.592391 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.28104 (* 0.0272727 = 0.0349375 loss)
I0425 11:46:53.592406 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.13644 (* 0.0272727 = 0.0309939 loss)
I0425 11:46:53.592419 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.34479 (* 0.0272727 = 0.036676 loss)
I0425 11:46:53.592433 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.594184 (* 0.0272727 = 0.016205 loss)
I0425 11:46:53.592447 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.4225 (* 0.0272727 = 0.0115227 loss)
I0425 11:46:53.592463 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.475882 (* 0.0272727 = 0.0129786 loss)
I0425 11:46:53.592476 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.384332 (* 0.0272727 = 0.0104818 loss)
I0425 11:46:53.592490 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.524516 (* 0.0272727 = 0.014305 loss)
I0425 11:46:53.592505 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.058823 (* 0.0272727 = 0.00160426 loss)
I0425 11:46:53.592520 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0455361 (* 0.0272727 = 0.00124189 loss)
I0425 11:46:53.592555 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0361837 (* 0.0272727 = 0.000986829 loss)
I0425 11:46:53.592572 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0181794 (* 0.0272727 = 0.000495802 loss)
I0425 11:46:53.592586 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00797412 (* 0.0272727 = 0.000217476 loss)
I0425 11:46:53.592608 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00624448 (* 0.0272727 = 0.000170304 loss)
I0425 11:46:53.592623 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00287144 (* 0.0272727 = 7.83121e-05 loss)
I0425 11:46:53.592638 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00162192 (* 0.0272727 = 4.42342e-05 loss)
I0425 11:46:53.592651 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000391019 (* 0.0272727 = 1.06642e-05 loss)
I0425 11:46:53.592670 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000887103 (* 0.0272727 = 2.41937e-05 loss)
I0425 11:46:53.592684 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000284614 (* 0.0272727 = 7.76221e-06 loss)
I0425 11:46:53.592697 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.76087
I0425 11:46:53.592710 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 11:46:53.592722 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 11:46:53.592735 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 11:46:53.592746 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 11:46:53.592758 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 11:46:53.592770 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 11:46:53.592782 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 11:46:53.592794 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 11:46:53.592806 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 11:46:53.592818 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 11:46:53.592830 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 11:46:53.592842 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 11:46:53.592854 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 11:46:53.592865 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 11:46:53.592877 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 11:46:53.592888 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 11:46:53.592900 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 11:46:53.592912 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 11:46:53.592923 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 11:46:53.592936 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 11:46:53.592947 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 11:46:53.592958 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 11:46:53.592970 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.903409
I0425 11:46:53.592983 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.891304
I0425 11:46:53.592996 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.821324 (* 0.3 = 0.246397 loss)
I0425 11:46:53.593017 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.290102 (* 0.3 = 0.0870305 loss)
I0425 11:46:53.593032 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.22641 (* 0.0272727 = 0.0334475 loss)
I0425 11:46:53.593046 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.687575 (* 0.0272727 = 0.0187521 loss)
I0425 11:46:53.593072 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.793983 (* 0.0272727 = 0.0216541 loss)
I0425 11:46:53.593088 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.15656 (* 0.0272727 = 0.0315424 loss)
I0425 11:46:53.593102 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.1208 (* 0.0272727 = 0.0305673 loss)
I0425 11:46:53.593116 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.32731 (* 0.0272727 = 0.0361992 loss)
I0425 11:46:53.593130 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.506609 (* 0.0272727 = 0.0138166 loss)
I0425 11:46:53.593145 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.357603 (* 0.0272727 = 0.00975281 loss)
I0425 11:46:53.593159 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.564837 (* 0.0272727 = 0.0154046 loss)
I0425 11:46:53.593173 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.258094 (* 0.0272727 = 0.00703893 loss)
I0425 11:46:53.593188 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.517765 (* 0.0272727 = 0.0141209 loss)
I0425 11:46:53.593202 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.071177 (* 0.0272727 = 0.00194119 loss)
I0425 11:46:53.593217 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0246697 (* 0.0272727 = 0.00067281 loss)
I0425 11:46:53.593232 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00934684 (* 0.0272727 = 0.000254914 loss)
I0425 11:46:53.593246 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00633 (* 0.0272727 = 0.000172636 loss)
I0425 11:46:53.593263 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00460753 (* 0.0272727 = 0.00012566 loss)
I0425 11:46:53.593278 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000583286 (* 0.0272727 = 1.59078e-05 loss)
I0425 11:46:53.593292 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000687006 (* 0.0272727 = 1.87365e-05 loss)
I0425 11:46:53.593307 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000149204 (* 0.0272727 = 4.06921e-06 loss)
I0425 11:46:53.593322 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00013093 (* 0.0272727 = 3.57083e-06 loss)
I0425 11:46:53.593336 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000164251 (* 0.0272727 = 4.47959e-06 loss)
I0425 11:46:53.593350 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 5.50197e-05 (* 0.0272727 = 1.50054e-06 loss)
I0425 11:46:53.593364 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.869565
I0425 11:46:53.593376 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 11:46:53.593389 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 11:46:53.593400 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 11:46:53.593412 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 11:46:53.593423 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0425 11:46:53.593436 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 11:46:53.593447 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0425 11:46:53.593459 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 11:46:53.593472 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 11:46:53.593483 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 11:46:53.593495 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 11:46:53.593508 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 11:46:53.593515 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 11:46:53.593523 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 11:46:53.593536 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 11:46:53.593552 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 11:46:53.593575 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 11:46:53.593587 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 11:46:53.593600 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 11:46:53.593611 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 11:46:53.593631 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 11:46:53.593642 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 11:46:53.593654 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.965909
I0425 11:46:53.593667 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.934783
I0425 11:46:53.593688 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.373058 (* 1 = 0.373058 loss)
I0425 11:46:53.593703 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.107532 (* 1 = 0.107532 loss)
I0425 11:46:53.593716 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.324699 (* 0.0909091 = 0.0295181 loss)
I0425 11:46:53.593730 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.199935 (* 0.0909091 = 0.0181759 loss)
I0425 11:46:53.593744 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.126674 (* 0.0909091 = 0.0115158 loss)
I0425 11:46:53.593760 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.086523 (* 0.0909091 = 0.00786573 loss)
I0425 11:46:53.593775 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.193118 (* 0.0909091 = 0.0175562 loss)
I0425 11:46:53.593788 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.677191 (* 0.0909091 = 0.0615628 loss)
I0425 11:46:53.593802 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.235579 (* 0.0909091 = 0.0214163 loss)
I0425 11:46:53.593816 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.22527 (* 0.0909091 = 0.0204791 loss)
I0425 11:46:53.593830 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.36078 (* 0.0909091 = 0.0327981 loss)
I0425 11:46:53.593852 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.248055 (* 0.0909091 = 0.0225505 loss)
I0425 11:46:53.593866 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.537361 (* 0.0909091 = 0.048851 loss)
I0425 11:46:53.593880 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.044475 (* 0.0909091 = 0.00404318 loss)
I0425 11:46:53.593895 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0123051 (* 0.0909091 = 0.00111865 loss)
I0425 11:46:53.593909 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00600407 (* 0.0909091 = 0.000545825 loss)
I0425 11:46:53.593924 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00286547 (* 0.0909091 = 0.000260497 loss)
I0425 11:46:53.593937 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00186132 (* 0.0909091 = 0.000169211 loss)
I0425 11:46:53.593951 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00181019 (* 0.0909091 = 0.000164563 loss)
I0425 11:46:53.593966 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000603704 (* 0.0909091 = 5.48822e-05 loss)
I0425 11:46:53.593979 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000502962 (* 0.0909091 = 4.57238e-05 loss)
I0425 11:46:53.593994 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000329214 (* 0.0909091 = 2.99285e-05 loss)
I0425 11:46:53.594008 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000122236 (* 0.0909091 = 1.11123e-05 loss)
I0425 11:46:53.594023 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 5.45162e-05 (* 0.0909091 = 4.95602e-06 loss)
I0425 11:46:53.594036 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 11:46:53.594048 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 11:46:53.594075 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.629441
I0425 11:46:53.594089 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.423382
I0425 11:46:53.594105 22523 sgd_solver.cpp:106] Iteration 8500, lr = 0.01
I0425 11:52:34.817062 22523 solver.cpp:229] Iteration 9000, loss = 3.19371
I0425 11:52:34.817222 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.368421
I0425 11:52:34.817244 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0425 11:52:34.817256 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 11:52:34.817268 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 11:52:34.817281 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.125
I0425 11:52:34.817292 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.25
I0425 11:52:34.817304 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0425 11:52:34.817317 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 11:52:34.817328 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 11:52:34.817340 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 11:52:34.817351 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 11:52:34.817363 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 11:52:34.817374 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 11:52:34.817386 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 11:52:34.817399 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 11:52:34.817409 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 11:52:34.817421 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 11:52:34.817432 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 11:52:34.817445 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 11:52:34.817456 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 11:52:34.817467 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 11:52:34.817478 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 11:52:34.817489 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 11:52:34.817502 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.835227
I0425 11:52:34.817513 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.763158
I0425 11:52:34.817530 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.88037 (* 0.3 = 0.564112 loss)
I0425 11:52:34.817545 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.509736 (* 0.3 = 0.152921 loss)
I0425 11:52:34.817560 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.12413 (* 0.0272727 = 0.0306581 loss)
I0425 11:52:34.817574 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.01683 (* 0.0272727 = 0.0550045 loss)
I0425 11:52:34.817589 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.41091 (* 0.0272727 = 0.0657521 loss)
I0425 11:52:34.817602 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.75543 (* 0.0272727 = 0.0751482 loss)
I0425 11:52:34.817616 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.2065 (* 0.0272727 = 0.0601773 loss)
I0425 11:52:34.817631 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.846667 (* 0.0272727 = 0.0230909 loss)
I0425 11:52:34.817644 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.802138 (* 0.0272727 = 0.0218765 loss)
I0425 11:52:34.817658 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.655848 (* 0.0272727 = 0.0178868 loss)
I0425 11:52:34.817673 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.015358 (* 0.0272727 = 0.000418854 loss)
I0425 11:52:34.817687 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00734701 (* 0.0272727 = 0.000200373 loss)
I0425 11:52:34.817703 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00478911 (* 0.0272727 = 0.000130612 loss)
I0425 11:52:34.817716 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00277754 (* 0.0272727 = 7.5751e-05 loss)
I0425 11:52:34.817749 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00191177 (* 0.0272727 = 5.21392e-05 loss)
I0425 11:52:34.817765 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00222408 (* 0.0272727 = 6.06566e-05 loss)
I0425 11:52:34.817780 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0018259 (* 0.0272727 = 4.97973e-05 loss)
I0425 11:52:34.817795 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000563027 (* 0.0272727 = 1.53553e-05 loss)
I0425 11:52:34.817808 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000174488 (* 0.0272727 = 4.75877e-06 loss)
I0425 11:52:34.817822 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 6.92621e-05 (* 0.0272727 = 1.88897e-06 loss)
I0425 11:52:34.817837 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 4.0841e-05 (* 0.0272727 = 1.11385e-06 loss)
I0425 11:52:34.817852 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.1399e-05 (* 0.0272727 = 5.83609e-07 loss)
I0425 11:52:34.817867 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 7.00362e-06 (* 0.0272727 = 1.91008e-07 loss)
I0425 11:52:34.817880 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 7.12285e-06 (* 0.0272727 = 1.94259e-07 loss)
I0425 11:52:34.817893 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.736842
I0425 11:52:34.817904 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 11:52:34.817916 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 11:52:34.817929 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 11:52:34.817940 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0425 11:52:34.817951 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0425 11:52:34.817963 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 11:52:34.817975 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 11:52:34.817986 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 11:52:34.817998 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 11:52:34.818009 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 11:52:34.818022 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 11:52:34.818032 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 11:52:34.818043 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 11:52:34.818055 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 11:52:34.818066 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 11:52:34.818078 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 11:52:34.818089 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 11:52:34.818099 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 11:52:34.818111 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 11:52:34.818122 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 11:52:34.818133 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 11:52:34.818145 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 11:52:34.818156 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.926136
I0425 11:52:34.818167 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.868421
I0425 11:52:34.818181 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.878985 (* 0.3 = 0.263695 loss)
I0425 11:52:34.818195 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.256014 (* 0.3 = 0.0768043 loss)
I0425 11:52:34.818214 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.18008 (* 0.0272727 = 0.00491128 loss)
I0425 11:52:34.818229 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.914392 (* 0.0272727 = 0.024938 loss)
I0425 11:52:34.818255 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.765474 (* 0.0272727 = 0.0208766 loss)
I0425 11:52:34.818271 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.4814 (* 0.0272727 = 0.0676744 loss)
I0425 11:52:34.818285 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.67497 (* 0.0272727 = 0.0729538 loss)
I0425 11:52:34.818300 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.854993 (* 0.0272727 = 0.023318 loss)
I0425 11:52:34.818313 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.295727 (* 0.0272727 = 0.00806529 loss)
I0425 11:52:34.818327 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.64349 (* 0.0272727 = 0.0175497 loss)
I0425 11:52:34.818342 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0154281 (* 0.0272727 = 0.000420766 loss)
I0425 11:52:34.818356 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00580888 (* 0.0272727 = 0.000158424 loss)
I0425 11:52:34.818370 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00153527 (* 0.0272727 = 4.1871e-05 loss)
I0425 11:52:34.818385 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00154929 (* 0.0272727 = 4.22534e-05 loss)
I0425 11:52:34.818399 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.000713921 (* 0.0272727 = 1.94706e-05 loss)
I0425 11:52:34.818413 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000430244 (* 0.0272727 = 1.17339e-05 loss)
I0425 11:52:34.818428 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00023142 (* 0.0272727 = 6.31145e-06 loss)
I0425 11:52:34.818442 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 6.91105e-05 (* 0.0272727 = 1.88483e-06 loss)
I0425 11:52:34.818456 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 2.26805e-05 (* 0.0272727 = 6.1856e-07 loss)
I0425 11:52:34.818470 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 1.93431e-05 (* 0.0272727 = 5.27539e-07 loss)
I0425 11:52:34.818485 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 3.15908e-06 (* 0.0272727 = 8.61568e-08 loss)
I0425 11:52:34.818500 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 1.77325e-06 (* 0.0272727 = 4.83614e-08 loss)
I0425 11:52:34.818513 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 8.34467e-07 (* 0.0272727 = 2.27582e-08 loss)
I0425 11:52:34.818527 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.08779e-06 (* 0.0272727 = 2.9667e-08 loss)
I0425 11:52:34.818541 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.894737
I0425 11:52:34.818552 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 11:52:34.818563 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 11:52:34.818575 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 11:52:34.818586 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0425 11:52:34.818598 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0425 11:52:34.818610 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 11:52:34.818621 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0425 11:52:34.818634 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 11:52:34.818645 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 11:52:34.818656 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 11:52:34.818667 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 11:52:34.818680 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 11:52:34.818691 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 11:52:34.818701 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 11:52:34.818712 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 11:52:34.818734 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 11:52:34.818747 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 11:52:34.818758 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 11:52:34.818770 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 11:52:34.818781 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 11:52:34.818792 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 11:52:34.818804 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 11:52:34.818815 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.965909
I0425 11:52:34.818827 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.921053
I0425 11:52:34.818841 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.42624 (* 1 = 0.42624 loss)
I0425 11:52:34.818856 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.141741 (* 1 = 0.141741 loss)
I0425 11:52:34.818869 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.175118 (* 0.0909091 = 0.0159198 loss)
I0425 11:52:34.818884 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.571865 (* 0.0909091 = 0.0519877 loss)
I0425 11:52:34.818898 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.466731 (* 0.0909091 = 0.0424301 loss)
I0425 11:52:34.818912 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.28514 (* 0.0909091 = 0.116831 loss)
I0425 11:52:34.818927 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.187516 (* 0.0909091 = 0.0170469 loss)
I0425 11:52:34.818940 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.203895 (* 0.0909091 = 0.0185359 loss)
I0425 11:52:34.818954 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.158453 (* 0.0909091 = 0.0144048 loss)
I0425 11:52:34.818969 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.231792 (* 0.0909091 = 0.021072 loss)
I0425 11:52:34.818982 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.0464629 (* 0.0909091 = 0.0042239 loss)
I0425 11:52:34.818997 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0177981 (* 0.0909091 = 0.00161801 loss)
I0425 11:52:34.819011 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.0221401 (* 0.0909091 = 0.00201274 loss)
I0425 11:52:34.819026 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.0183029 (* 0.0909091 = 0.0016639 loss)
I0425 11:52:34.819041 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0143994 (* 0.0909091 = 0.00130904 loss)
I0425 11:52:34.819056 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.01619 (* 0.0909091 = 0.00147182 loss)
I0425 11:52:34.819069 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00984008 (* 0.0909091 = 0.000894553 loss)
I0425 11:52:34.819083 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0049765 (* 0.0909091 = 0.000452409 loss)
I0425 11:52:34.819097 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00155227 (* 0.0909091 = 0.000141115 loss)
I0425 11:52:34.819111 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00122902 (* 0.0909091 = 0.000111729 loss)
I0425 11:52:34.819125 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000879711 (* 0.0909091 = 7.99737e-05 loss)
I0425 11:52:34.819140 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00119845 (* 0.0909091 = 0.00010895 loss)
I0425 11:52:34.819154 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.0002855 (* 0.0909091 = 2.59546e-05 loss)
I0425 11:52:34.819169 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000126188 (* 0.0909091 = 1.14716e-05 loss)
I0425 11:52:34.819181 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 11:52:34.819192 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 11:52:34.819214 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.625321
I0425 11:52:34.819227 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.551076
I0425 11:52:34.819242 22523 sgd_solver.cpp:106] Iteration 9000, lr = 0.01
I0425 11:58:16.116564 22523 solver.cpp:229] Iteration 9500, loss = 3.14958
I0425 11:58:16.116669 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.711111
I0425 11:58:16.116690 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0425 11:58:16.116703 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 11:58:16.116715 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 11:58:16.116727 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.125
I0425 11:58:16.116739 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 11:58:16.116750 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 11:58:16.116762 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 11:58:16.116775 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 11:58:16.116786 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 11:58:16.116797 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 11:58:16.116809 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 11:58:16.116825 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 11:58:16.116837 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 11:58:16.116848 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 11:58:16.116859 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 11:58:16.116871 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 11:58:16.116883 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 11:58:16.116894 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 11:58:16.116907 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 11:58:16.116919 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 11:58:16.116930 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 11:58:16.116942 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 11:58:16.116955 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.909091
I0425 11:58:16.116966 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.8
I0425 11:58:16.116983 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.15682 (* 0.3 = 0.347045 loss)
I0425 11:58:16.116998 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.348879 (* 0.3 = 0.104664 loss)
I0425 11:58:16.117012 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.74141 (* 0.0272727 = 0.0474929 loss)
I0425 11:58:16.117027 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.16105 (* 0.0272727 = 0.0316649 loss)
I0425 11:58:16.117040 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.34236 (* 0.0272727 = 0.0638826 loss)
I0425 11:58:16.117054 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.3279 (* 0.0272727 = 0.0634882 loss)
I0425 11:58:16.117069 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.673 (* 0.0272727 = 0.0456272 loss)
I0425 11:58:16.117084 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.22847 (* 0.0272727 = 0.0335037 loss)
I0425 11:58:16.117097 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.672877 (* 0.0272727 = 0.0183512 loss)
I0425 11:58:16.117111 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.190145 (* 0.0272727 = 0.00518576 loss)
I0425 11:58:16.117126 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0367259 (* 0.0272727 = 0.00100162 loss)
I0425 11:58:16.117141 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.0227904 (* 0.0272727 = 0.000621557 loss)
I0425 11:58:16.117156 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0319842 (* 0.0272727 = 0.000872295 loss)
I0425 11:58:16.117169 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0154939 (* 0.0272727 = 0.00042256 loss)
I0425 11:58:16.117183 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.008014 (* 0.0272727 = 0.000218564 loss)
I0425 11:58:16.117215 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00925032 (* 0.0272727 = 0.000252281 loss)
I0425 11:58:16.117230 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00647285 (* 0.0272727 = 0.000176532 loss)
I0425 11:58:16.117245 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00666347 (* 0.0272727 = 0.000181731 loss)
I0425 11:58:16.117259 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00135767 (* 0.0272727 = 3.70273e-05 loss)
I0425 11:58:16.117274 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000787145 (* 0.0272727 = 2.14676e-05 loss)
I0425 11:58:16.117290 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00038428 (* 0.0272727 = 1.04804e-05 loss)
I0425 11:58:16.117303 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00027218 (* 0.0272727 = 7.4231e-06 loss)
I0425 11:58:16.117317 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00024543 (* 0.0272727 = 6.69355e-06 loss)
I0425 11:58:16.117331 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000227986 (* 0.0272727 = 6.21779e-06 loss)
I0425 11:58:16.117344 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.755556
I0425 11:58:16.117357 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 11:58:16.117367 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 11:58:16.117379 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0425 11:58:16.117396 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0425 11:58:16.117408 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 11:58:16.117420 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.875
I0425 11:58:16.117431 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 11:58:16.117451 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 11:58:16.117463 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 11:58:16.117475 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 11:58:16.117486 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 11:58:16.117496 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 11:58:16.117507 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 11:58:16.117519 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 11:58:16.117530 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 11:58:16.117542 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 11:58:16.117552 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 11:58:16.117564 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 11:58:16.117575 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 11:58:16.117591 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 11:58:16.117602 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 11:58:16.117614 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 11:58:16.117625 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.920455
I0425 11:58:16.117637 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.888889
I0425 11:58:16.117650 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.891026 (* 0.3 = 0.267308 loss)
I0425 11:58:16.117666 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.301797 (* 0.3 = 0.0905391 loss)
I0425 11:58:16.117684 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.06364 (* 0.0272727 = 0.0290084 loss)
I0425 11:58:16.117698 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.772726 (* 0.0272727 = 0.0210743 loss)
I0425 11:58:16.117723 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.30409 (* 0.0272727 = 0.035566 loss)
I0425 11:58:16.117738 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.15991 (* 0.0272727 = 0.0589067 loss)
I0425 11:58:16.117753 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.51955 (* 0.0272727 = 0.0414422 loss)
I0425 11:58:16.117766 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.715967 (* 0.0272727 = 0.0195264 loss)
I0425 11:58:16.117780 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.57602 (* 0.0272727 = 0.0157096 loss)
I0425 11:58:16.117794 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.334977 (* 0.0272727 = 0.00913575 loss)
I0425 11:58:16.117808 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0998563 (* 0.0272727 = 0.00272335 loss)
I0425 11:58:16.117823 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.0593304 (* 0.0272727 = 0.0016181 loss)
I0425 11:58:16.117837 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.0373738 (* 0.0272727 = 0.00101929 loss)
I0425 11:58:16.117851 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0392262 (* 0.0272727 = 0.00106981 loss)
I0425 11:58:16.117869 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00476609 (* 0.0272727 = 0.000129984 loss)
I0425 11:58:16.117884 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00252214 (* 0.0272727 = 6.87857e-05 loss)
I0425 11:58:16.117899 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0014327 (* 0.0272727 = 3.90736e-05 loss)
I0425 11:58:16.117913 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00119768 (* 0.0272727 = 3.2664e-05 loss)
I0425 11:58:16.117928 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00056134 (* 0.0272727 = 1.53093e-05 loss)
I0425 11:58:16.117943 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000126587 (* 0.0272727 = 3.45238e-06 loss)
I0425 11:58:16.117959 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 5.14285e-05 (* 0.0272727 = 1.4026e-06 loss)
I0425 11:58:16.117972 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 2.41709e-05 (* 0.0272727 = 6.59206e-07 loss)
I0425 11:58:16.117987 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.08177e-05 (* 0.0272727 = 5.67754e-07 loss)
I0425 11:58:16.118001 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 9.23887e-06 (* 0.0272727 = 2.51969e-07 loss)
I0425 11:58:16.118015 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.955556
I0425 11:58:16.118026 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 11:58:16.118037 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 11:58:16.118049 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 11:58:16.118060 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0425 11:58:16.118072 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0425 11:58:16.118083 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 1
I0425 11:58:16.118095 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0425 11:58:16.118106 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 11:58:16.118118 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 11:58:16.118129 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 11:58:16.118140 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 11:58:16.118152 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 11:58:16.118163 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 11:58:16.118175 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 11:58:16.118185 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 11:58:16.118197 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 11:58:16.118219 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 11:58:16.118232 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 11:58:16.118244 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 11:58:16.118255 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 11:58:16.118268 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 11:58:16.118278 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 11:58:16.118289 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.988636
I0425 11:58:16.118301 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 1
I0425 11:58:16.118315 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.244285 (* 1 = 0.244285 loss)
I0425 11:58:16.118329 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.0672968 (* 1 = 0.0672968 loss)
I0425 11:58:16.118343 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.868918 (* 0.0909091 = 0.0789925 loss)
I0425 11:58:16.118356 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.249275 (* 0.0909091 = 0.0226614 loss)
I0425 11:58:16.118371 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.237139 (* 0.0909091 = 0.0215581 loss)
I0425 11:58:16.118384 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.718381 (* 0.0909091 = 0.0653073 loss)
I0425 11:58:16.118398 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.421445 (* 0.0909091 = 0.0383132 loss)
I0425 11:58:16.118412 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.418574 (* 0.0909091 = 0.0380522 loss)
I0425 11:58:16.118427 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.208585 (* 0.0909091 = 0.0189623 loss)
I0425 11:58:16.118440 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.13808 (* 0.0909091 = 0.0125527 loss)
I0425 11:58:16.118454 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.0123816 (* 0.0909091 = 0.0011256 loss)
I0425 11:58:16.118468 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00351136 (* 0.0909091 = 0.000319214 loss)
I0425 11:58:16.118482 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00346769 (* 0.0909091 = 0.000315244 loss)
I0425 11:58:16.118497 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00195072 (* 0.0909091 = 0.000177338 loss)
I0425 11:58:16.118511 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000938944 (* 0.0909091 = 8.53586e-05 loss)
I0425 11:58:16.118525 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000584185 (* 0.0909091 = 5.31077e-05 loss)
I0425 11:58:16.118541 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00026772 (* 0.0909091 = 2.43382e-05 loss)
I0425 11:58:16.118554 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000172376 (* 0.0909091 = 1.56706e-05 loss)
I0425 11:58:16.118568 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 5.15109e-05 (* 0.0909091 = 4.68281e-06 loss)
I0425 11:58:16.118582 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 5.10132e-05 (* 0.0909091 = 4.63756e-06 loss)
I0425 11:58:16.118597 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 3.50135e-05 (* 0.0909091 = 3.18305e-06 loss)
I0425 11:58:16.118612 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.80164e-05 (* 0.0909091 = 1.63785e-06 loss)
I0425 11:58:16.118625 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 6.79505e-06 (* 0.0909091 = 6.17732e-07 loss)
I0425 11:58:16.118640 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.2815e-06 (* 0.0909091 = 1.165e-07 loss)
I0425 11:58:16.118652 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 11:58:16.118664 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0425 11:58:16.118685 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.70095
I0425 11:58:16.118698 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.369621
I0425 11:58:16.118715 22523 sgd_solver.cpp:106] Iteration 9500, lr = 0.01
I0425 12:03:56.950549 22523 solver.cpp:456] Snapshotting to binary proto file /mnt/snapshots/mixed_lstm10_bn_iter_10000.caffemodel
I0425 12:03:57.669136 22523 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /mnt/snapshots/mixed_lstm10_bn_iter_10000.solverstate
I0425 12:03:58.003831 22523 solver.cpp:338] Iteration 10000, Testing net (#0)
I0425 12:04:49.654670 22523 solver.cpp:393] Test loss: 1.47625
I0425 12:04:49.654788 22523 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.768785
I0425 12:04:49.654806 22523 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.888
I0425 12:04:49.654819 22523 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.687
I0425 12:04:49.654831 22523 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.508
I0425 12:04:49.654844 22523 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.512
I0425 12:04:49.654856 22523 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.597
I0425 12:04:49.654868 22523 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.672
I0425 12:04:49.654881 22523 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.827
I0425 12:04:49.654893 22523 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.919
I0425 12:04:49.654906 22523 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.983
I0425 12:04:49.654917 22523 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.994
I0425 12:04:49.654929 22523 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.998
I0425 12:04:49.654942 22523 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.999
I0425 12:04:49.654953 22523 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.999
I0425 12:04:49.654965 22523 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 1
I0425 12:04:49.654978 22523 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 1
I0425 12:04:49.654989 22523 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 1
I0425 12:04:49.655010 22523 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 1
I0425 12:04:49.655021 22523 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 1
I0425 12:04:49.655033 22523 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 1
I0425 12:04:49.655045 22523 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 1
I0425 12:04:49.655066 22523 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0425 12:04:49.655077 22523 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0425 12:04:49.655088 22523 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.924775
I0425 12:04:49.655100 22523 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.926826
I0425 12:04:49.655118 22523 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 0.775221 (* 0.3 = 0.232566 loss)
I0425 12:04:49.655133 22523 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.248959 (* 0.3 = 0.0746876 loss)
I0425 12:04:49.655148 22523 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.490815 (* 0.0272727 = 0.0133859 loss)
I0425 12:04:49.655163 22523 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.10652 (* 0.0272727 = 0.0301777 loss)
I0425 12:04:49.655176 22523 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.44869 (* 0.0272727 = 0.0395097 loss)
I0425 12:04:49.655191 22523 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.46959 (* 0.0272727 = 0.0400797 loss)
I0425 12:04:49.655208 22523 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.28637 (* 0.0272727 = 0.0350829 loss)
I0425 12:04:49.655223 22523 solver.cpp:406]     Test net output #32: loss1/loss06 = 0.993339 (* 0.0272727 = 0.0270911 loss)
I0425 12:04:49.655237 22523 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.593548 (* 0.0272727 = 0.0161877 loss)
I0425 12:04:49.655252 22523 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.285546 (* 0.0272727 = 0.00778762 loss)
I0425 12:04:49.655267 22523 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.0654881 (* 0.0272727 = 0.00178604 loss)
I0425 12:04:49.655282 22523 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.0304204 (* 0.0272727 = 0.000829649 loss)
I0425 12:04:49.655297 22523 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.018252 (* 0.0272727 = 0.000497781 loss)
I0425 12:04:49.655311 22523 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.0122222 (* 0.0272727 = 0.000333333 loss)
I0425 12:04:49.655325 22523 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.00939786 (* 0.0272727 = 0.000256305 loss)
I0425 12:04:49.655382 22523 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.00652634 (* 0.0272727 = 0.000177991 loss)
I0425 12:04:49.655398 22523 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.00484498 (* 0.0272727 = 0.000132136 loss)
I0425 12:04:49.655421 22523 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.00274248 (* 0.0272727 = 7.47948e-05 loss)
I0425 12:04:49.655436 22523 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.00095269 (* 0.0272727 = 2.59824e-05 loss)
I0425 12:04:49.655450 22523 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.000494065 (* 0.0272727 = 1.34745e-05 loss)
I0425 12:04:49.655464 22523 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.000232738 (* 0.0272727 = 6.3474e-06 loss)
I0425 12:04:49.655479 22523 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.000140502 (* 0.0272727 = 3.83186e-06 loss)
I0425 12:04:49.655493 22523 solver.cpp:406]     Test net output #47: loss1/loss21 = 9.86916e-05 (* 0.0272727 = 2.69159e-06 loss)
I0425 12:04:49.655508 22523 solver.cpp:406]     Test net output #48: loss1/loss22 = 7.0533e-05 (* 0.0272727 = 1.92363e-06 loss)
I0425 12:04:49.655519 22523 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.88664
I0425 12:04:49.655532 22523 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.948
I0425 12:04:49.655544 22523 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.879
I0425 12:04:49.655555 22523 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.732
I0425 12:04:49.655566 22523 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.634
I0425 12:04:49.655583 22523 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.658
I0425 12:04:49.655594 22523 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.729
I0425 12:04:49.655606 22523 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.874
I0425 12:04:49.655617 22523 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.927
I0425 12:04:49.655628 22523 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.982
I0425 12:04:49.655645 22523 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.994
I0425 12:04:49.655658 22523 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.998
I0425 12:04:49.655668 22523 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.999
I0425 12:04:49.655680 22523 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.999
I0425 12:04:49.655691 22523 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.999
I0425 12:04:49.655704 22523 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.999
I0425 12:04:49.655714 22523 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 1
I0425 12:04:49.655725 22523 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 1
I0425 12:04:49.655737 22523 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 1
I0425 12:04:49.655748 22523 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 1
I0425 12:04:49.655760 22523 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 1
I0425 12:04:49.655771 22523 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0425 12:04:49.655781 22523 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0425 12:04:49.655792 22523 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.963636
I0425 12:04:49.655808 22523 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.96656
I0425 12:04:49.655822 22523 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.423509 (* 0.3 = 0.127053 loss)
I0425 12:04:49.655836 22523 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.132753 (* 0.3 = 0.0398259 loss)
I0425 12:04:49.655850 22523 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.248761 (* 0.0272727 = 0.0067844 loss)
I0425 12:04:49.655865 22523 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.485063 (* 0.0272727 = 0.013229 loss)
I0425 12:04:49.655891 22523 solver.cpp:406]     Test net output #78: loss2/loss03 = 0.8668 (* 0.0272727 = 0.02364 loss)
I0425 12:04:49.655907 22523 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.04194 (* 0.0272727 = 0.0284165 loss)
I0425 12:04:49.655921 22523 solver.cpp:406]     Test net output #80: loss2/loss05 = 0.951057 (* 0.0272727 = 0.0259379 loss)
I0425 12:04:49.655936 22523 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.736646 (* 0.0272727 = 0.0200904 loss)
I0425 12:04:49.655948 22523 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.421088 (* 0.0272727 = 0.0114842 loss)
I0425 12:04:49.655963 22523 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.209087 (* 0.0272727 = 0.00570238 loss)
I0425 12:04:49.655977 22523 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.0644503 (* 0.0272727 = 0.00175774 loss)
I0425 12:04:49.655992 22523 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.0259016 (* 0.0272727 = 0.000706406 loss)
I0425 12:04:49.656005 22523 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.0122043 (* 0.0272727 = 0.000332845 loss)
I0425 12:04:49.656019 22523 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.00812975 (* 0.0272727 = 0.000221721 loss)
I0425 12:04:49.656033 22523 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.0062762 (* 0.0272727 = 0.000171169 loss)
I0425 12:04:49.656047 22523 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.00488162 (* 0.0272727 = 0.000133135 loss)
I0425 12:04:49.656061 22523 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.00366243 (* 0.0272727 = 9.98843e-05 loss)
I0425 12:04:49.656075 22523 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.00161634 (* 0.0272727 = 4.40819e-05 loss)
I0425 12:04:49.656090 22523 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.000358198 (* 0.0272727 = 9.76904e-06 loss)
I0425 12:04:49.656103 22523 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.000136531 (* 0.0272727 = 3.72357e-06 loss)
I0425 12:04:49.656117 22523 solver.cpp:406]     Test net output #94: loss2/loss19 = 5.827e-05 (* 0.0272727 = 1.58918e-06 loss)
I0425 12:04:49.656131 22523 solver.cpp:406]     Test net output #95: loss2/loss20 = 3.26003e-05 (* 0.0272727 = 8.89098e-07 loss)
I0425 12:04:49.656146 22523 solver.cpp:406]     Test net output #96: loss2/loss21 = 2.21726e-05 (* 0.0272727 = 6.04706e-07 loss)
I0425 12:04:49.656160 22523 solver.cpp:406]     Test net output #97: loss2/loss22 = 1.62822e-05 (* 0.0272727 = 4.44059e-07 loss)
I0425 12:04:49.656172 22523 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.926277
I0425 12:04:49.656184 22523 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.955
I0425 12:04:49.656196 22523 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.938
I0425 12:04:49.656208 22523 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.93
I0425 12:04:49.656219 22523 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.914
I0425 12:04:49.656230 22523 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.894
I0425 12:04:49.656241 22523 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.862
I0425 12:04:49.656255 22523 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.911
I0425 12:04:49.656267 22523 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.955
I0425 12:04:49.656280 22523 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.981
I0425 12:04:49.656291 22523 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.996
I0425 12:04:49.656302 22523 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.999
I0425 12:04:49.656313 22523 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.999
I0425 12:04:49.656324 22523 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.999
I0425 12:04:49.656337 22523 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.999
I0425 12:04:49.656347 22523 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.999
I0425 12:04:49.656358 22523 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 1
I0425 12:04:49.656379 22523 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 1
I0425 12:04:49.656393 22523 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 1
I0425 12:04:49.656404 22523 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 1
I0425 12:04:49.656415 22523 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 1
I0425 12:04:49.656426 22523 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0425 12:04:49.656437 22523 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0425 12:04:49.656448 22523 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.974545
I0425 12:04:49.656461 22523 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.972453
I0425 12:04:49.656473 22523 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.306811 (* 1 = 0.306811 loss)
I0425 12:04:49.656488 22523 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.103134 (* 1 = 0.103134 loss)
I0425 12:04:49.656502 22523 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.197118 (* 0.0909091 = 0.0179198 loss)
I0425 12:04:49.656512 22523 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.292826 (* 0.0909091 = 0.0266205 loss)
I0425 12:04:49.656522 22523 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.297415 (* 0.0909091 = 0.0270377 loss)
I0425 12:04:49.656536 22523 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.363274 (* 0.0909091 = 0.0330249 loss)
I0425 12:04:49.656550 22523 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.419176 (* 0.0909091 = 0.0381069 loss)
I0425 12:04:49.656564 22523 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.444433 (* 0.0909091 = 0.040403 loss)
I0425 12:04:49.656577 22523 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.313774 (* 0.0909091 = 0.0285249 loss)
I0425 12:04:49.656591 22523 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.162465 (* 0.0909091 = 0.0147695 loss)
I0425 12:04:49.656605 22523 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.0644751 (* 0.0909091 = 0.00586137 loss)
I0425 12:04:49.656620 22523 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.0280745 (* 0.0909091 = 0.00255223 loss)
I0425 12:04:49.656632 22523 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.0190221 (* 0.0909091 = 0.00172928 loss)
I0425 12:04:49.656646 22523 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.0118532 (* 0.0909091 = 0.00107757 loss)
I0425 12:04:49.656661 22523 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.00910418 (* 0.0909091 = 0.000827653 loss)
I0425 12:04:49.656683 22523 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.00652823 (* 0.0909091 = 0.000593476 loss)
I0425 12:04:49.656697 22523 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.00489937 (* 0.0909091 = 0.000445398 loss)
I0425 12:04:49.656710 22523 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.00243641 (* 0.0909091 = 0.000221492 loss)
I0425 12:04:49.656723 22523 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.000863013 (* 0.0909091 = 7.84558e-05 loss)
I0425 12:04:49.656738 22523 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.000677801 (* 0.0909091 = 6.16182e-05 loss)
I0425 12:04:49.656750 22523 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.000482835 (* 0.0909091 = 4.38941e-05 loss)
I0425 12:04:49.656764 22523 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.000368893 (* 0.0909091 = 3.35358e-05 loss)
I0425 12:04:49.656777 22523 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.000174286 (* 0.0909091 = 1.58442e-05 loss)
I0425 12:04:49.656791 22523 solver.cpp:406]     Test net output #146: loss3/loss22 = 9.42158e-05 (* 0.0909091 = 8.56507e-06 loss)
I0425 12:04:49.656803 22523 solver.cpp:406]     Test net output #147: total_accuracy = 0.79
I0425 12:04:49.656815 22523 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.685
I0425 12:04:49.656826 22523 solver.cpp:406]     Test net output #149: total_confidence = 0.745846
I0425 12:04:49.656847 22523 solver.cpp:406]     Test net output #150: total_confidence_nor_rec = 0.549417
I0425 12:04:49.656865 22523 solver.cpp:338] Iteration 10000, Testing net (#1)
I0425 12:05:41.261382 22523 solver.cpp:393] Test loss: 2.63426
I0425 12:05:41.261536 22523 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.700349
I0425 12:05:41.261559 22523 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.825
I0425 12:05:41.261572 22523 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.632
I0425 12:05:41.261585 22523 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.472
I0425 12:05:41.261597 22523 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.476
I0425 12:05:41.261610 22523 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.549
I0425 12:05:41.261628 22523 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.58
I0425 12:05:41.261641 22523 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.722
I0425 12:05:41.261653 22523 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.824
I0425 12:05:41.261665 22523 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.903
I0425 12:05:41.261687 22523 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.907
I0425 12:05:41.261699 22523 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.911
I0425 12:05:41.261711 22523 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.927
I0425 12:05:41.261724 22523 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.942
I0425 12:05:41.261736 22523 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 0.953
I0425 12:05:41.261749 22523 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 0.966
I0425 12:05:41.261761 22523 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 0.972
I0425 12:05:41.261775 22523 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 0.992
I0425 12:05:41.261786 22523 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 0.994
I0425 12:05:41.261798 22523 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 0.996
I0425 12:05:41.261811 22523 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 0.998
I0425 12:05:41.261822 22523 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0425 12:05:41.261834 22523 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0425 12:05:41.261847 22523 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.874955
I0425 12:05:41.261858 22523 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.86587
I0425 12:05:41.261885 22523 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.03083 (* 0.3 = 0.309248 loss)
I0425 12:05:41.261900 22523 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.4304 (* 0.3 = 0.12912 loss)
I0425 12:05:41.261915 22523 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.706348 (* 0.0272727 = 0.019264 loss)
I0425 12:05:41.261929 22523 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.20604 (* 0.0272727 = 0.0328919 loss)
I0425 12:05:41.261952 22523 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.59969 (* 0.0272727 = 0.0436279 loss)
I0425 12:05:41.261967 22523 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.63707 (* 0.0272727 = 0.0446474 loss)
I0425 12:05:41.261981 22523 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.46691 (* 0.0272727 = 0.0400066 loss)
I0425 12:05:41.261996 22523 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.29989 (* 0.0272727 = 0.0354516 loss)
I0425 12:05:41.262011 22523 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.951154 (* 0.0272727 = 0.0259406 loss)
I0425 12:05:41.262024 22523 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.620157 (* 0.0272727 = 0.0169134 loss)
I0425 12:05:41.262039 22523 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.383139 (* 0.0272727 = 0.0104492 loss)
I0425 12:05:41.262054 22523 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.341053 (* 0.0272727 = 0.00930144 loss)
I0425 12:05:41.262068 22523 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.336817 (* 0.0272727 = 0.00918592 loss)
I0425 12:05:41.262084 22523 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.312326 (* 0.0272727 = 0.00851797 loss)
I0425 12:05:41.262117 22523 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.240956 (* 0.0272727 = 0.00657153 loss)
I0425 12:05:41.262132 22523 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.212296 (* 0.0272727 = 0.00578988 loss)
I0425 12:05:41.262147 22523 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.159786 (* 0.0272727 = 0.00435781 loss)
I0425 12:05:41.262161 22523 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.144515 (* 0.0272727 = 0.00394131 loss)
I0425 12:05:41.262176 22523 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.0552585 (* 0.0272727 = 0.00150705 loss)
I0425 12:05:41.262190 22523 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.0406491 (* 0.0272727 = 0.00110861 loss)
I0425 12:05:41.262208 22523 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.0301618 (* 0.0272727 = 0.000822595 loss)
I0425 12:05:41.262223 22523 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.0178508 (* 0.0272727 = 0.00048684 loss)
I0425 12:05:41.262238 22523 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.00107461 (* 0.0272727 = 2.93077e-05 loss)
I0425 12:05:41.262253 22523 solver.cpp:406]     Test net output #48: loss1/loss22 = 0.000700323 (* 0.0272727 = 1.90997e-05 loss)
I0425 12:05:41.262269 22523 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.811492
I0425 12:05:41.262281 22523 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.918
I0425 12:05:41.262293 22523 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.832
I0425 12:05:41.262305 22523 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.653
I0425 12:05:41.262316 22523 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.587
I0425 12:05:41.262328 22523 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.608
I0425 12:05:41.262341 22523 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.66
I0425 12:05:41.262351 22523 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.758
I0425 12:05:41.262363 22523 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.826
I0425 12:05:41.262383 22523 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.898
I0425 12:05:41.262395 22523 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.908
I0425 12:05:41.262406 22523 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.918
I0425 12:05:41.262418 22523 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.926
I0425 12:05:41.262430 22523 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.944
I0425 12:05:41.262444 22523 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.95
I0425 12:05:41.262452 22523 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.964
I0425 12:05:41.262465 22523 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 0.972
I0425 12:05:41.262475 22523 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 0.992
I0425 12:05:41.262487 22523 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 0.994
I0425 12:05:41.262500 22523 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 0.996
I0425 12:05:41.262511 22523 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 0.998
I0425 12:05:41.262522 22523 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0425 12:05:41.262534 22523 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0425 12:05:41.262547 22523 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.916228
I0425 12:05:41.262557 22523 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.9131
I0425 12:05:41.262572 22523 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.698277 (* 0.3 = 0.209483 loss)
I0425 12:05:41.262584 22523 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.304415 (* 0.3 = 0.0913246 loss)
I0425 12:05:41.262599 22523 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.395733 (* 0.0272727 = 0.0107927 loss)
I0425 12:05:41.262614 22523 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.596945 (* 0.0272727 = 0.0162803 loss)
I0425 12:05:41.262639 22523 solver.cpp:406]     Test net output #78: loss2/loss03 = 1.06435 (* 0.0272727 = 0.0290278 loss)
I0425 12:05:41.262655 22523 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.26008 (* 0.0272727 = 0.0343657 loss)
I0425 12:05:41.262668 22523 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.18656 (* 0.0272727 = 0.0323607 loss)
I0425 12:05:41.262681 22523 solver.cpp:406]     Test net output #81: loss2/loss06 = 1.01446 (* 0.0272727 = 0.0276671 loss)
I0425 12:05:41.262696 22523 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.778709 (* 0.0272727 = 0.0212375 loss)
I0425 12:05:41.262709 22523 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.550006 (* 0.0272727 = 0.0150002 loss)
I0425 12:05:41.262723 22523 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.357993 (* 0.0272727 = 0.00976345 loss)
I0425 12:05:41.262737 22523 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.329819 (* 0.0272727 = 0.00899505 loss)
I0425 12:05:41.262751 22523 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.324596 (* 0.0272727 = 0.00885261 loss)
I0425 12:05:41.262765 22523 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.299244 (* 0.0272727 = 0.00816121 loss)
I0425 12:05:41.262779 22523 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.233371 (* 0.0272727 = 0.00636467 loss)
I0425 12:05:41.262794 22523 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.209719 (* 0.0272727 = 0.0057196 loss)
I0425 12:05:41.262807 22523 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.154325 (* 0.0272727 = 0.00420886 loss)
I0425 12:05:41.262828 22523 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.141746 (* 0.0272727 = 0.00386581 loss)
I0425 12:05:41.262841 22523 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.0495661 (* 0.0272727 = 0.0013518 loss)
I0425 12:05:41.262856 22523 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.0362619 (* 0.0272727 = 0.00098896 loss)
I0425 12:05:41.262869 22523 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.0289015 (* 0.0272727 = 0.000788222 loss)
I0425 12:05:41.262890 22523 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.0159938 (* 0.0272727 = 0.000436196 loss)
I0425 12:05:41.262904 22523 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.000555651 (* 0.0272727 = 1.51541e-05 loss)
I0425 12:05:41.262918 22523 solver.cpp:406]     Test net output #97: loss2/loss22 = 0.000392202 (* 0.0272727 = 1.06964e-05 loss)
I0425 12:05:41.262930 22523 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.867305
I0425 12:05:41.262943 22523 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.928
I0425 12:05:41.262953 22523 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.913
I0425 12:05:41.262964 22523 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.893
I0425 12:05:41.262976 22523 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.86
I0425 12:05:41.262987 22523 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.846
I0425 12:05:41.263000 22523 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.796
I0425 12:05:41.263010 22523 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.826
I0425 12:05:41.263022 22523 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.862
I0425 12:05:41.263033 22523 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.913
I0425 12:05:41.263044 22523 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.913
I0425 12:05:41.263056 22523 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.918
I0425 12:05:41.263067 22523 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.931
I0425 12:05:41.263078 22523 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.946
I0425 12:05:41.263090 22523 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.951
I0425 12:05:41.263101 22523 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.964
I0425 12:05:41.263113 22523 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 0.971
I0425 12:05:41.263134 22523 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 0.991
I0425 12:05:41.263147 22523 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 0.994
I0425 12:05:41.263159 22523 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 0.996
I0425 12:05:41.263171 22523 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 0.998
I0425 12:05:41.263182 22523 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0425 12:05:41.263193 22523 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0425 12:05:41.263205 22523 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.933864
I0425 12:05:41.263216 22523 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.935604
I0425 12:05:41.263231 22523 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.524098 (* 1 = 0.524098 loss)
I0425 12:05:41.263243 22523 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.247826 (* 1 = 0.247826 loss)
I0425 12:05:41.263260 22523 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.330966 (* 0.0909091 = 0.0300878 loss)
I0425 12:05:41.263275 22523 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.357582 (* 0.0909091 = 0.0325075 loss)
I0425 12:05:41.263289 22523 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.440841 (* 0.0909091 = 0.0400764 loss)
I0425 12:05:41.263303 22523 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.55831 (* 0.0909091 = 0.0507555 loss)
I0425 12:05:41.263332 22523 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.606182 (* 0.0909091 = 0.0551074 loss)
I0425 12:05:41.263348 22523 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.697156 (* 0.0909091 = 0.0633778 loss)
I0425 12:05:41.263362 22523 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.637439 (* 0.0909091 = 0.057949 loss)
I0425 12:05:41.263376 22523 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.459259 (* 0.0909091 = 0.0417508 loss)
I0425 12:05:41.263391 22523 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.325485 (* 0.0909091 = 0.0295895 loss)
I0425 12:05:41.263403 22523 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.310666 (* 0.0909091 = 0.0282423 loss)
I0425 12:05:41.263417 22523 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.306196 (* 0.0909091 = 0.027836 loss)
I0425 12:05:41.263432 22523 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.28082 (* 0.0909091 = 0.0255291 loss)
I0425 12:05:41.263444 22523 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.222496 (* 0.0909091 = 0.0202269 loss)
I0425 12:05:41.263458 22523 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.194911 (* 0.0909091 = 0.0177192 loss)
I0425 12:05:41.263473 22523 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.14335 (* 0.0909091 = 0.0130318 loss)
I0425 12:05:41.263486 22523 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.121314 (* 0.0909091 = 0.0110285 loss)
I0425 12:05:41.263500 22523 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.0476576 (* 0.0909091 = 0.00433251 loss)
I0425 12:05:41.263514 22523 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.0316846 (* 0.0909091 = 0.00288042 loss)
I0425 12:05:41.263528 22523 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.0259514 (* 0.0909091 = 0.00235922 loss)
I0425 12:05:41.263542 22523 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.0172669 (* 0.0909091 = 0.00156972 loss)
I0425 12:05:41.263556 22523 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.00102664 (* 0.0909091 = 9.33311e-05 loss)
I0425 12:05:41.263571 22523 solver.cpp:406]     Test net output #146: loss3/loss22 = 0.000234677 (* 0.0909091 = 2.13342e-05 loss)
I0425 12:05:41.263582 22523 solver.cpp:406]     Test net output #147: total_accuracy = 0.675
I0425 12:05:41.263594 22523 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.589
I0425 12:05:41.263607 22523 solver.cpp:406]     Test net output #149: total_confidence = 0.648002
I0425 12:05:41.263629 22523 solver.cpp:406]     Test net output #150: total_confidence_nor_rec = 0.484556
I0425 12:05:41.654481 22523 solver.cpp:229] Iteration 10000, loss = 3.08777
I0425 12:05:41.654534 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.690476
I0425 12:05:41.654551 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0425 12:05:41.654564 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0425 12:05:41.654577 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.75
I0425 12:05:41.654588 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 12:05:41.654602 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.875
I0425 12:05:41.654613 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0425 12:05:41.654625 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 12:05:41.654638 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 12:05:41.654654 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 12:05:41.654665 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 12:05:41.654677 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 12:05:41.654690 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 12:05:41.654700 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 12:05:41.654712 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 12:05:41.654728 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 12:05:41.654748 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 12:05:41.654762 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 12:05:41.654773 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 12:05:41.654789 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 12:05:41.654801 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 12:05:41.654813 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 12:05:41.654824 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 12:05:41.654844 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.920455
I0425 12:05:41.654857 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.880952
I0425 12:05:41.654873 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.01853 (* 0.3 = 0.305558 loss)
I0425 12:05:41.654888 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.261126 (* 0.3 = 0.0783377 loss)
I0425 12:05:41.654907 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.810576 (* 0.0272727 = 0.0221066 loss)
I0425 12:05:41.654922 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.00318 (* 0.0272727 = 0.0546322 loss)
I0425 12:05:41.654935 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.06261 (* 0.0272727 = 0.0289803 loss)
I0425 12:05:41.654949 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.64981 (* 0.0272727 = 0.044995 loss)
I0425 12:05:41.654963 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 0.886848 (* 0.0272727 = 0.0241868 loss)
I0425 12:05:41.654978 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.910223 (* 0.0272727 = 0.0248243 loss)
I0425 12:05:41.654992 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.812586 (* 0.0272727 = 0.0221614 loss)
I0425 12:05:41.655007 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.0562249 (* 0.0272727 = 0.00153341 loss)
I0425 12:05:41.655021 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.00938339 (* 0.0272727 = 0.000255911 loss)
I0425 12:05:41.655036 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00760259 (* 0.0272727 = 0.000207343 loss)
I0425 12:05:41.655050 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.020489 (* 0.0272727 = 0.000558791 loss)
I0425 12:05:41.655088 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00754422 (* 0.0272727 = 0.000205752 loss)
I0425 12:05:41.655104 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00799445 (* 0.0272727 = 0.00021803 loss)
I0425 12:05:41.655119 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00273568 (* 0.0272727 = 7.46093e-05 loss)
I0425 12:05:41.655133 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00382316 (* 0.0272727 = 0.000104268 loss)
I0425 12:05:41.655148 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00102422 (* 0.0272727 = 2.79333e-05 loss)
I0425 12:05:41.655163 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000433874 (* 0.0272727 = 1.18329e-05 loss)
I0425 12:05:41.655177 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00027316 (* 0.0272727 = 7.44981e-06 loss)
I0425 12:05:41.655191 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 9.82447e-05 (* 0.0272727 = 2.6794e-06 loss)
I0425 12:05:41.655205 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000215122 (* 0.0272727 = 5.86697e-06 loss)
I0425 12:05:41.655220 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 7.53673e-05 (* 0.0272727 = 2.05547e-06 loss)
I0425 12:05:41.655235 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000121269 (* 0.0272727 = 3.30735e-06 loss)
I0425 12:05:41.655246 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.785714
I0425 12:05:41.655258 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 12:05:41.655270 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0425 12:05:41.655282 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 12:05:41.655294 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 12:05:41.655305 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0425 12:05:41.655318 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 12:05:41.655329 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0425 12:05:41.655340 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 12:05:41.655366 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 12:05:41.655380 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 12:05:41.655392 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 12:05:41.655403 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 12:05:41.655416 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 12:05:41.655426 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 12:05:41.655438 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 12:05:41.655449 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 12:05:41.655460 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 12:05:41.655472 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 12:05:41.655483 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 12:05:41.655494 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 12:05:41.655505 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 12:05:41.655517 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 12:05:41.655529 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.943182
I0425 12:05:41.655540 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.952381
I0425 12:05:41.655555 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.839953 (* 0.3 = 0.251986 loss)
I0425 12:05:41.655568 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.211925 (* 0.3 = 0.0635775 loss)
I0425 12:05:41.655596 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.363489 (* 0.0272727 = 0.00991333 loss)
I0425 12:05:41.655611 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.38828 (* 0.0272727 = 0.0378622 loss)
I0425 12:05:41.655627 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.15091 (* 0.0272727 = 0.0313883 loss)
I0425 12:05:41.655639 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.18545 (* 0.0272727 = 0.0323305 loss)
I0425 12:05:41.655653 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.649284 (* 0.0272727 = 0.0177078 loss)
I0425 12:05:41.655668 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.793419 (* 0.0272727 = 0.0216387 loss)
I0425 12:05:41.655681 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.6921 (* 0.0272727 = 0.0188754 loss)
I0425 12:05:41.655699 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.020688 (* 0.0272727 = 0.000564217 loss)
I0425 12:05:41.655714 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.00243642 (* 0.0272727 = 6.64479e-05 loss)
I0425 12:05:41.655727 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00144304 (* 0.0272727 = 3.93556e-05 loss)
I0425 12:05:41.655741 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00182836 (* 0.0272727 = 4.98643e-05 loss)
I0425 12:05:41.655755 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00186064 (* 0.0272727 = 5.07449e-05 loss)
I0425 12:05:41.655769 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.000549825 (* 0.0272727 = 1.49952e-05 loss)
I0425 12:05:41.655783 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000467997 (* 0.0272727 = 1.27636e-05 loss)
I0425 12:05:41.655797 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000383291 (* 0.0272727 = 1.04534e-05 loss)
I0425 12:05:41.655812 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000217094 (* 0.0272727 = 5.92074e-06 loss)
I0425 12:05:41.655825 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 9.1543e-05 (* 0.0272727 = 2.49663e-06 loss)
I0425 12:05:41.655839 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 2.57291e-05 (* 0.0272727 = 7.01702e-07 loss)
I0425 12:05:41.655853 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 9.46244e-06 (* 0.0272727 = 2.58067e-07 loss)
I0425 12:05:41.655867 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 4.88765e-06 (* 0.0272727 = 1.333e-07 loss)
I0425 12:05:41.655882 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.414e-06 (* 0.0272727 = 6.58364e-08 loss)
I0425 12:05:41.655896 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 8.34467e-07 (* 0.0272727 = 2.27582e-08 loss)
I0425 12:05:41.655908 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.857143
I0425 12:05:41.655920 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 12:05:41.655932 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 12:05:41.655946 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 12:05:41.655958 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 12:05:41.655969 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 12:05:41.655982 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 12:05:41.655992 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 12:05:41.656004 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 12:05:41.656015 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 12:05:41.656026 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 12:05:41.656038 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 12:05:41.656049 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 12:05:41.656060 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 12:05:41.656082 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 12:05:41.656095 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 12:05:41.656107 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 12:05:41.656119 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 12:05:41.656131 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 12:05:41.656141 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 12:05:41.656152 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 12:05:41.656164 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 12:05:41.656175 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 12:05:41.656186 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.965909
I0425 12:05:41.656198 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.952381
I0425 12:05:41.656213 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.723317 (* 1 = 0.723317 loss)
I0425 12:05:41.656226 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.175299 (* 1 = 0.175299 loss)
I0425 12:05:41.656240 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.058823 (* 0.0909091 = 0.00534754 loss)
I0425 12:05:41.656255 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.835133 (* 0.0909091 = 0.0759212 loss)
I0425 12:05:41.656270 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.659776 (* 0.0909091 = 0.0599796 loss)
I0425 12:05:41.656283 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.410473 (* 0.0909091 = 0.0373158 loss)
I0425 12:05:41.656297 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.420167 (* 0.0909091 = 0.038197 loss)
I0425 12:05:41.656311 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.40665 (* 0.0909091 = 0.0369682 loss)
I0425 12:05:41.656324 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.322559 (* 0.0909091 = 0.0293235 loss)
I0425 12:05:41.656338 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.0171714 (* 0.0909091 = 0.00156104 loss)
I0425 12:05:41.656353 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.000341504 (* 0.0909091 = 3.10458e-05 loss)
I0425 12:05:41.656368 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00022779 (* 0.0909091 = 2.07081e-05 loss)
I0425 12:05:41.656381 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.000666212 (* 0.0909091 = 6.05647e-05 loss)
I0425 12:05:41.656395 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00042737 (* 0.0909091 = 3.88519e-05 loss)
I0425 12:05:41.656409 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000307438 (* 0.0909091 = 2.79489e-05 loss)
I0425 12:05:41.656424 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000156309 (* 0.0909091 = 1.42099e-05 loss)
I0425 12:05:41.656437 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 7.45273e-05 (* 0.0909091 = 6.77521e-06 loss)
I0425 12:05:41.656456 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 7.00418e-05 (* 0.0909091 = 6.36744e-06 loss)
I0425 12:05:41.656471 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 3.43186e-05 (* 0.0909091 = 3.11987e-06 loss)
I0425 12:05:41.656486 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 2.62417e-05 (* 0.0909091 = 2.38561e-06 loss)
I0425 12:05:41.656512 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 2.53774e-05 (* 0.0909091 = 2.30704e-06 loss)
I0425 12:05:41.656527 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.30536e-05 (* 0.0909091 = 1.18669e-06 loss)
I0425 12:05:41.656541 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 6.82478e-06 (* 0.0909091 = 6.20435e-07 loss)
I0425 12:05:41.656556 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 2.27988e-06 (* 0.0909091 = 2.07262e-07 loss)
I0425 12:05:41.656579 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.875
I0425 12:05:41.656592 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.75
I0425 12:05:41.656605 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.683013
I0425 12:05:41.656616 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.609421
I0425 12:05:41.656630 22523 sgd_solver.cpp:106] Iteration 10000, lr = 0.01
I0425 12:11:23.040144 22523 solver.cpp:229] Iteration 10500, loss = 3.19441
I0425 12:11:23.040309 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.542373
I0425 12:11:23.040330 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0425 12:11:23.040343 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0425 12:11:23.040356 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 12:11:23.040376 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 12:11:23.040388 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 12:11:23.040401 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 12:11:23.040413 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 12:11:23.040426 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0425 12:11:23.040438 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0425 12:11:23.040451 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0425 12:11:23.040462 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 12:11:23.040474 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 12:11:23.040487 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 12:11:23.040499 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 12:11:23.040511 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0425 12:11:23.040524 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0425 12:11:23.040535 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 0.875
I0425 12:11:23.040547 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 0.875
I0425 12:11:23.040560 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 12:11:23.040571 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 12:11:23.040585 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 12:11:23.040596 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 12:11:23.040608 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.840909
I0425 12:11:23.040621 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.694915
I0425 12:11:23.040638 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.56763 (* 0.3 = 0.47029 loss)
I0425 12:11:23.040653 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.538631 (* 0.3 = 0.161589 loss)
I0425 12:11:23.040668 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.08315 (* 0.0272727 = 0.0295405 loss)
I0425 12:11:23.040683 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.95817 (* 0.0272727 = 0.0261319 loss)
I0425 12:11:23.040698 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.30408 (* 0.0272727 = 0.0628386 loss)
I0425 12:11:23.040712 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.70404 (* 0.0272727 = 0.0464737 loss)
I0425 12:11:23.040727 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.5467 (* 0.0272727 = 0.0421829 loss)
I0425 12:11:23.040741 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.349 (* 0.0272727 = 0.0367909 loss)
I0425 12:11:23.040755 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.841009 (* 0.0272727 = 0.0229366 loss)
I0425 12:11:23.040771 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.06251 (* 0.0272727 = 0.0289777 loss)
I0425 12:11:23.040784 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.598307 (* 0.0272727 = 0.0163175 loss)
I0425 12:11:23.040799 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.750927 (* 0.0272727 = 0.0204798 loss)
I0425 12:11:23.040813 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.38497 (* 0.0272727 = 0.0104992 loss)
I0425 12:11:23.040837 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.386806 (* 0.0272727 = 0.0105493 loss)
I0425 12:11:23.040863 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.592762 (* 0.0272727 = 0.0161662 loss)
I0425 12:11:23.040894 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.27067 (* 0.0272727 = 0.00738192 loss)
I0425 12:11:23.040913 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.60825 (* 0.0272727 = 0.0165886 loss)
I0425 12:11:23.040933 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.703545 (* 0.0272727 = 0.0191876 loss)
I0425 12:11:23.040948 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.880704 (* 0.0272727 = 0.0240192 loss)
I0425 12:11:23.040962 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.805342 (* 0.0272727 = 0.0219639 loss)
I0425 12:11:23.040977 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00386103 (* 0.0272727 = 0.000105301 loss)
I0425 12:11:23.040992 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00173389 (* 0.0272727 = 4.72879e-05 loss)
I0425 12:11:23.041007 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000715201 (* 0.0272727 = 1.95055e-05 loss)
I0425 12:11:23.041025 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00117961 (* 0.0272727 = 3.21713e-05 loss)
I0425 12:11:23.041038 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.610169
I0425 12:11:23.041050 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 12:11:23.041062 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 1
I0425 12:11:23.041074 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 12:11:23.041085 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0425 12:11:23.041105 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0425 12:11:23.041117 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0425 12:11:23.041128 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 12:11:23.041141 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 12:11:23.041160 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0425 12:11:23.041172 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0425 12:11:23.041183 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 12:11:23.041195 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 12:11:23.041208 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 12:11:23.041218 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 12:11:23.041230 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0425 12:11:23.041241 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0425 12:11:23.041254 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 0.875
I0425 12:11:23.041265 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 0.875
I0425 12:11:23.041276 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 12:11:23.041287 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 12:11:23.041299 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 12:11:23.041311 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 12:11:23.041322 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.869318
I0425 12:11:23.041334 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.728814
I0425 12:11:23.041348 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.33498 (* 0.3 = 0.400493 loss)
I0425 12:11:23.041363 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.461451 (* 0.3 = 0.138435 loss)
I0425 12:11:23.041376 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.513968 (* 0.0272727 = 0.0140173 loss)
I0425 12:11:23.041390 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.383085 (* 0.0272727 = 0.0104478 loss)
I0425 12:11:23.041416 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.20422 (* 0.0272727 = 0.0328424 loss)
I0425 12:11:23.041431 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.81489 (* 0.0272727 = 0.049497 loss)
I0425 12:11:23.041445 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.38672 (* 0.0272727 = 0.0378197 loss)
I0425 12:11:23.041458 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.40804 (* 0.0272727 = 0.0384011 loss)
I0425 12:11:23.041472 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.834504 (* 0.0272727 = 0.0227592 loss)
I0425 12:11:23.041486 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.891544 (* 0.0272727 = 0.0243148 loss)
I0425 12:11:23.041501 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.720228 (* 0.0272727 = 0.0196426 loss)
I0425 12:11:23.041514 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 1.02586 (* 0.0272727 = 0.0279781 loss)
I0425 12:11:23.041528 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.388219 (* 0.0272727 = 0.0105878 loss)
I0425 12:11:23.041543 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.459845 (* 0.0272727 = 0.0125412 loss)
I0425 12:11:23.041556 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.497919 (* 0.0272727 = 0.0135796 loss)
I0425 12:11:23.041570 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.317728 (* 0.0272727 = 0.0086653 loss)
I0425 12:11:23.041584 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.646352 (* 0.0272727 = 0.0176278 loss)
I0425 12:11:23.041599 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.53949 (* 0.0272727 = 0.0147134 loss)
I0425 12:11:23.041612 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.828867 (* 0.0272727 = 0.0226055 loss)
I0425 12:11:23.041626 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.780895 (* 0.0272727 = 0.0212971 loss)
I0425 12:11:23.041640 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00350325 (* 0.0272727 = 9.55433e-05 loss)
I0425 12:11:23.041654 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00236584 (* 0.0272727 = 6.4523e-05 loss)
I0425 12:11:23.041668 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00140945 (* 0.0272727 = 3.84394e-05 loss)
I0425 12:11:23.041683 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000616167 (* 0.0272727 = 1.68046e-05 loss)
I0425 12:11:23.041695 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.728814
I0425 12:11:23.041707 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 12:11:23.041719 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 12:11:23.041730 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 12:11:23.041743 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 12:11:23.041754 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 12:11:23.041766 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 1
I0425 12:11:23.041777 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 12:11:23.041790 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0425 12:11:23.041801 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0425 12:11:23.041812 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.75
I0425 12:11:23.041824 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 12:11:23.041836 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 12:11:23.041847 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 12:11:23.041859 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 12:11:23.041872 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0425 12:11:23.041893 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0425 12:11:23.041908 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 0.875
I0425 12:11:23.041918 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 0.875
I0425 12:11:23.041930 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 12:11:23.041942 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 12:11:23.041954 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 12:11:23.041968 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 12:11:23.041980 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.909091
I0425 12:11:23.041992 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.813559
I0425 12:11:23.042006 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.950346 (* 1 = 0.950346 loss)
I0425 12:11:23.042021 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.340625 (* 1 = 0.340625 loss)
I0425 12:11:23.042034 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.5188 (* 0.0909091 = 0.0471637 loss)
I0425 12:11:23.042049 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0812907 (* 0.0909091 = 0.00739006 loss)
I0425 12:11:23.042067 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.150316 (* 0.0909091 = 0.0136651 loss)
I0425 12:11:23.042081 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.766139 (* 0.0909091 = 0.069649 loss)
I0425 12:11:23.042095 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.825353 (* 0.0909091 = 0.0750321 loss)
I0425 12:11:23.042109 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.38265 (* 0.0909091 = 0.0347864 loss)
I0425 12:11:23.042124 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.697124 (* 0.0909091 = 0.0633749 loss)
I0425 12:11:23.042138 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.808527 (* 0.0909091 = 0.0735024 loss)
I0425 12:11:23.042152 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.476888 (* 0.0909091 = 0.0433534 loss)
I0425 12:11:23.042166 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.769771 (* 0.0909091 = 0.0699792 loss)
I0425 12:11:23.042181 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.393652 (* 0.0909091 = 0.0357865 loss)
I0425 12:11:23.042194 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.389423 (* 0.0909091 = 0.035402 loss)
I0425 12:11:23.042208 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.488323 (* 0.0909091 = 0.044393 loss)
I0425 12:11:23.042223 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.320908 (* 0.0909091 = 0.0291734 loss)
I0425 12:11:23.042237 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.549143 (* 0.0909091 = 0.0499221 loss)
I0425 12:11:23.042251 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.59003 (* 0.0909091 = 0.0536391 loss)
I0425 12:11:23.042265 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.776556 (* 0.0909091 = 0.070596 loss)
I0425 12:11:23.042279 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.618415 (* 0.0909091 = 0.0562195 loss)
I0425 12:11:23.042289 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00540043 (* 0.0909091 = 0.000490948 loss)
I0425 12:11:23.042306 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00236821 (* 0.0909091 = 0.000215292 loss)
I0425 12:11:23.042321 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000337452 (* 0.0909091 = 3.06774e-05 loss)
I0425 12:11:23.042335 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000102749 (* 0.0909091 = 9.34086e-06 loss)
I0425 12:11:23.042347 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 12:11:23.042359 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 12:11:23.042382 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.501055
I0425 12:11:23.042394 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.447291
I0425 12:11:23.042409 22523 sgd_solver.cpp:106] Iteration 10500, lr = 0.01
I0425 12:17:04.356801 22523 solver.cpp:229] Iteration 11000, loss = 3.19586
I0425 12:17:04.356930 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.482143
I0425 12:17:04.356951 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0425 12:17:04.356966 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0425 12:17:04.356978 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 12:17:04.356991 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 12:17:04.357002 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.25
I0425 12:17:04.357015 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 12:17:04.357028 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 12:17:04.357041 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 12:17:04.357054 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0425 12:17:04.357066 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 12:17:04.357079 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 12:17:04.357096 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 12:17:04.357110 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 12:17:04.357121 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 12:17:04.357134 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 12:17:04.357146 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 12:17:04.357166 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 12:17:04.357178 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 12:17:04.357189 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 12:17:04.357204 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 12:17:04.357216 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 12:17:04.357228 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 12:17:04.357240 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.823864
I0425 12:17:04.357254 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.732143
I0425 12:17:04.357271 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.65438 (* 0.3 = 0.496314 loss)
I0425 12:17:04.357286 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.591553 (* 0.3 = 0.177466 loss)
I0425 12:17:04.357302 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.880377 (* 0.0272727 = 0.0240103 loss)
I0425 12:17:04.357316 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.889984 (* 0.0272727 = 0.0242723 loss)
I0425 12:17:04.357332 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.76417 (* 0.0272727 = 0.0481138 loss)
I0425 12:17:04.357347 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.78137 (* 0.0272727 = 0.0485828 loss)
I0425 12:17:04.357360 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.29805 (* 0.0272727 = 0.062674 loss)
I0425 12:17:04.357374 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.38695 (* 0.0272727 = 0.0378259 loss)
I0425 12:17:04.357388 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.22964 (* 0.0272727 = 0.0335356 loss)
I0425 12:17:04.357403 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.04271 (* 0.0272727 = 0.0284376 loss)
I0425 12:17:04.357417 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.787051 (* 0.0272727 = 0.021465 loss)
I0425 12:17:04.357432 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.52615 (* 0.0272727 = 0.0143495 loss)
I0425 12:17:04.357446 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.392756 (* 0.0272727 = 0.0107115 loss)
I0425 12:17:04.357461 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.368476 (* 0.0272727 = 0.0100493 loss)
I0425 12:17:04.357493 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.424816 (* 0.0272727 = 0.0115859 loss)
I0425 12:17:04.357511 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.330601 (* 0.0272727 = 0.00901638 loss)
I0425 12:17:04.357524 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0840905 (* 0.0272727 = 0.00229338 loss)
I0425 12:17:04.357539 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0302851 (* 0.0272727 = 0.000825957 loss)
I0425 12:17:04.357554 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0102353 (* 0.0272727 = 0.000279146 loss)
I0425 12:17:04.357569 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00231895 (* 0.0272727 = 6.32441e-05 loss)
I0425 12:17:04.357583 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00245766 (* 0.0272727 = 6.70271e-05 loss)
I0425 12:17:04.357597 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00085505 (* 0.0272727 = 2.33195e-05 loss)
I0425 12:17:04.357612 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000267719 (* 0.0272727 = 7.30144e-06 loss)
I0425 12:17:04.357626 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000141631 (* 0.0272727 = 3.86266e-06 loss)
I0425 12:17:04.357640 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.660714
I0425 12:17:04.357651 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 12:17:04.357663 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0425 12:17:04.357676 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 12:17:04.357686 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 12:17:04.357698 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 12:17:04.357710 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 12:17:04.357722 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.5
I0425 12:17:04.357733 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 12:17:04.357744 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0425 12:17:04.357756 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 12:17:04.357767 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 12:17:04.357779 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 12:17:04.357790 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 12:17:04.357802 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 12:17:04.357813 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 12:17:04.357825 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 12:17:04.357836 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 12:17:04.357847 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 12:17:04.357858 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 12:17:04.357870 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 12:17:04.357882 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 12:17:04.357892 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 12:17:04.357904 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.880682
I0425 12:17:04.357916 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.803571
I0425 12:17:04.357930 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.1996 (* 0.3 = 0.359881 loss)
I0425 12:17:04.357949 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.409524 (* 0.3 = 0.122857 loss)
I0425 12:17:04.357964 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.263778 (* 0.0272727 = 0.00719395 loss)
I0425 12:17:04.357978 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.416298 (* 0.0272727 = 0.0113536 loss)
I0425 12:17:04.358005 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.81032 (* 0.0272727 = 0.0220996 loss)
I0425 12:17:04.358019 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.56463 (* 0.0272727 = 0.0426716 loss)
I0425 12:17:04.358033 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.71543 (* 0.0272727 = 0.0467845 loss)
I0425 12:17:04.358048 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.19181 (* 0.0272727 = 0.032504 loss)
I0425 12:17:04.358062 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.37995 (* 0.0272727 = 0.0376349 loss)
I0425 12:17:04.358075 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.943147 (* 0.0272727 = 0.0257222 loss)
I0425 12:17:04.358089 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.08011 (* 0.0272727 = 0.0294575 loss)
I0425 12:17:04.358103 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.477462 (* 0.0272727 = 0.0130217 loss)
I0425 12:17:04.358117 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.295516 (* 0.0272727 = 0.00805954 loss)
I0425 12:17:04.358131 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.430866 (* 0.0272727 = 0.0117509 loss)
I0425 12:17:04.358145 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.446894 (* 0.0272727 = 0.012188 loss)
I0425 12:17:04.358160 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.404269 (* 0.0272727 = 0.0110255 loss)
I0425 12:17:04.358175 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00660028 (* 0.0272727 = 0.000180008 loss)
I0425 12:17:04.358189 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00340462 (* 0.0272727 = 9.28533e-05 loss)
I0425 12:17:04.358203 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00132391 (* 0.0272727 = 3.61065e-05 loss)
I0425 12:17:04.358217 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000340068 (* 0.0272727 = 9.27459e-06 loss)
I0425 12:17:04.358232 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000103869 (* 0.0272727 = 2.83279e-06 loss)
I0425 12:17:04.358247 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 8.1903e-05 (* 0.0272727 = 2.23372e-06 loss)
I0425 12:17:04.358263 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 3.0281e-05 (* 0.0272727 = 8.25846e-07 loss)
I0425 12:17:04.358283 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 9.40283e-06 (* 0.0272727 = 2.56441e-07 loss)
I0425 12:17:04.358295 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.75
I0425 12:17:04.358309 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 12:17:04.358319 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 12:17:04.358331 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 12:17:04.358350 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 12:17:04.358361 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 12:17:04.358372 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 12:17:04.358383 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 12:17:04.358395 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0425 12:17:04.358407 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0425 12:17:04.358417 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 12:17:04.358433 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 12:17:04.358443 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 12:17:04.358454 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 12:17:04.358466 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 12:17:04.358477 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 12:17:04.358500 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 12:17:04.358512 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 12:17:04.358523 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 12:17:04.358535 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 12:17:04.358546 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 12:17:04.358557 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 12:17:04.358568 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 12:17:04.358579 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.920455
I0425 12:17:04.358592 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.839286
I0425 12:17:04.358605 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.89739 (* 1 = 0.89739 loss)
I0425 12:17:04.358619 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.293417 (* 1 = 0.293417 loss)
I0425 12:17:04.358634 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0858169 (* 0.0909091 = 0.00780153 loss)
I0425 12:17:04.358649 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.30653 (* 0.0909091 = 0.0278664 loss)
I0425 12:17:04.358662 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.529182 (* 0.0909091 = 0.0481074 loss)
I0425 12:17:04.358676 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.764749 (* 0.0909091 = 0.0695227 loss)
I0425 12:17:04.358690 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.704929 (* 0.0909091 = 0.0640845 loss)
I0425 12:17:04.358703 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.772743 (* 0.0909091 = 0.0702493 loss)
I0425 12:17:04.358717 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.818063 (* 0.0909091 = 0.0743694 loss)
I0425 12:17:04.358731 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.791517 (* 0.0909091 = 0.0719561 loss)
I0425 12:17:04.358746 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.918689 (* 0.0909091 = 0.0835172 loss)
I0425 12:17:04.358759 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.348538 (* 0.0909091 = 0.0316852 loss)
I0425 12:17:04.358773 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.315624 (* 0.0909091 = 0.0286931 loss)
I0425 12:17:04.358788 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.416484 (* 0.0909091 = 0.0378622 loss)
I0425 12:17:04.358800 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.457909 (* 0.0909091 = 0.0416281 loss)
I0425 12:17:04.358814 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.458496 (* 0.0909091 = 0.0416814 loss)
I0425 12:17:04.358829 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0185224 (* 0.0909091 = 0.00168385 loss)
I0425 12:17:04.358844 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00914613 (* 0.0909091 = 0.000831466 loss)
I0425 12:17:04.358857 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0037099 (* 0.0909091 = 0.000337264 loss)
I0425 12:17:04.358871 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0028231 (* 0.0909091 = 0.000256646 loss)
I0425 12:17:04.358886 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00149426 (* 0.0909091 = 0.000135841 loss)
I0425 12:17:04.358899 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00072437 (* 0.0909091 = 6.58518e-05 loss)
I0425 12:17:04.358914 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000210388 (* 0.0909091 = 1.91262e-05 loss)
I0425 12:17:04.358928 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 8.41803e-05 (* 0.0909091 = 7.65276e-06 loss)
I0425 12:17:04.358940 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 12:17:04.358953 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.75
I0425 12:17:04.358974 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.601522
I0425 12:17:04.358988 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.482836
I0425 12:17:04.359007 22523 sgd_solver.cpp:106] Iteration 11000, lr = 0.01
I0425 12:22:45.750010 22523 solver.cpp:229] Iteration 11500, loss = 3.08166
I0425 12:22:45.750146 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.777778
I0425 12:22:45.750167 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 1
I0425 12:22:45.750181 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 12:22:45.750193 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0425 12:22:45.750210 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 12:22:45.750222 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 12:22:45.750234 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 12:22:45.750247 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 12:22:45.750259 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 12:22:45.750272 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 12:22:45.750283 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 12:22:45.750295 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 12:22:45.750308 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 12:22:45.750319 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 12:22:45.750331 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 12:22:45.750342 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 12:22:45.750355 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 12:22:45.750367 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 12:22:45.750380 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 12:22:45.750391 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 12:22:45.750403 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 12:22:45.750414 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 12:22:45.750427 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 12:22:45.750437 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.931818
I0425 12:22:45.750450 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.888889
I0425 12:22:45.750466 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 0.843027 (* 0.3 = 0.252908 loss)
I0425 12:22:45.750483 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.245713 (* 0.3 = 0.0737138 loss)
I0425 12:22:45.750497 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.295997 (* 0.0272727 = 0.00807264 loss)
I0425 12:22:45.750512 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.17433 (* 0.0272727 = 0.0593 loss)
I0425 12:22:45.750526 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.31164 (* 0.0272727 = 0.0357721 loss)
I0425 12:22:45.750540 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.422 (* 0.0272727 = 0.0387818 loss)
I0425 12:22:45.750555 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.41227 (* 0.0272727 = 0.0385165 loss)
I0425 12:22:45.750569 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.56095 (* 0.0272727 = 0.0425714 loss)
I0425 12:22:45.750583 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.936077 (* 0.0272727 = 0.0255294 loss)
I0425 12:22:45.750598 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.0658499 (* 0.0272727 = 0.00179591 loss)
I0425 12:22:45.750613 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.160873 (* 0.0272727 = 0.00438744 loss)
I0425 12:22:45.750627 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.0561861 (* 0.0272727 = 0.00153235 loss)
I0425 12:22:45.750643 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.019378 (* 0.0272727 = 0.000528491 loss)
I0425 12:22:45.750658 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00993751 (* 0.0272727 = 0.000271023 loss)
I0425 12:22:45.750672 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0029266 (* 0.0272727 = 7.98163e-05 loss)
I0425 12:22:45.750705 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00289661 (* 0.0272727 = 7.89985e-05 loss)
I0425 12:22:45.750720 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00168231 (* 0.0272727 = 4.58812e-05 loss)
I0425 12:22:45.750735 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000310916 (* 0.0272727 = 8.47953e-06 loss)
I0425 12:22:45.750751 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000124042 (* 0.0272727 = 3.38296e-06 loss)
I0425 12:22:45.750764 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 5.88536e-05 (* 0.0272727 = 1.6051e-06 loss)
I0425 12:22:45.750779 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 6.46465e-05 (* 0.0272727 = 1.76309e-06 loss)
I0425 12:22:45.750794 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.88803e-05 (* 0.0272727 = 7.87645e-07 loss)
I0425 12:22:45.750808 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 8.34476e-06 (* 0.0272727 = 2.27584e-07 loss)
I0425 12:22:45.750823 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.23533e-05 (* 0.0272727 = 3.36909e-07 loss)
I0425 12:22:45.750835 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.955556
I0425 12:22:45.750849 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 12:22:45.750859 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0425 12:22:45.750871 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 12:22:45.750882 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.75
I0425 12:22:45.750895 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 12:22:45.750906 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 12:22:45.750917 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0425 12:22:45.750928 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 12:22:45.750941 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 12:22:45.750952 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 12:22:45.750962 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 12:22:45.750973 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 12:22:45.750985 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 12:22:45.750996 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 12:22:45.751008 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 12:22:45.751019 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 12:22:45.751030 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 12:22:45.751041 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 12:22:45.751052 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 12:22:45.751065 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 12:22:45.751075 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 12:22:45.751086 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 12:22:45.751098 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.977273
I0425 12:22:45.751109 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 1
I0425 12:22:45.751123 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.262347 (* 0.3 = 0.078704 loss)
I0425 12:22:45.751137 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.0830422 (* 0.3 = 0.0249127 loss)
I0425 12:22:45.751152 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.0716105 (* 0.0272727 = 0.00195301 loss)
I0425 12:22:45.751170 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.50471 (* 0.0272727 = 0.0137648 loss)
I0425 12:22:45.751197 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.664559 (* 0.0272727 = 0.0181243 loss)
I0425 12:22:45.751212 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 0.793021 (* 0.0272727 = 0.0216279 loss)
I0425 12:22:45.751227 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.879361 (* 0.0272727 = 0.0239826 loss)
I0425 12:22:45.751241 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.697795 (* 0.0272727 = 0.0190308 loss)
I0425 12:22:45.751257 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.715535 (* 0.0272727 = 0.0195146 loss)
I0425 12:22:45.751272 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.194439 (* 0.0272727 = 0.00530289 loss)
I0425 12:22:45.751287 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0365667 (* 0.0272727 = 0.000997273 loss)
I0425 12:22:45.751302 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00812463 (* 0.0272727 = 0.000221581 loss)
I0425 12:22:45.751315 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00515758 (* 0.0272727 = 0.000140661 loss)
I0425 12:22:45.751329 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00304548 (* 0.0272727 = 8.30587e-05 loss)
I0425 12:22:45.751343 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.000675153 (* 0.0272727 = 1.84133e-05 loss)
I0425 12:22:45.751375 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000787504 (* 0.0272727 = 2.14774e-05 loss)
I0425 12:22:45.751391 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000290462 (* 0.0272727 = 7.9217e-06 loss)
I0425 12:22:45.751406 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 9.58753e-05 (* 0.0272727 = 2.61478e-06 loss)
I0425 12:22:45.751420 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 4.70799e-05 (* 0.0272727 = 1.284e-06 loss)
I0425 12:22:45.751435 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 2.53418e-05 (* 0.0272727 = 6.91139e-07 loss)
I0425 12:22:45.751449 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 2.68223e-06 (* 0.0272727 = 7.31518e-08 loss)
I0425 12:22:45.751463 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 1.01328e-06 (* 0.0272727 = 2.76349e-08 loss)
I0425 12:22:45.751478 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 7.59961e-07 (* 0.0272727 = 2.07262e-08 loss)
I0425 12:22:45.751488 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 2.23517e-07 (* 0.0272727 = 6.09593e-09 loss)
I0425 12:22:45.751502 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 1
I0425 12:22:45.751514 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 12:22:45.751526 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 12:22:45.751538 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 12:22:45.751549 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 12:22:45.751560 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 1
I0425 12:22:45.751571 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 12:22:45.751582 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 12:22:45.751595 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 12:22:45.751605 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 12:22:45.751616 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 12:22:45.751628 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 12:22:45.751639 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 12:22:45.751651 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 12:22:45.751662 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 12:22:45.751672 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 12:22:45.751683 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 12:22:45.751708 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 12:22:45.751720 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 12:22:45.751731 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 12:22:45.751744 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 12:22:45.751754 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 12:22:45.751765 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 12:22:45.751777 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.994318
I0425 12:22:45.751788 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 1
I0425 12:22:45.751802 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.0499511 (* 1 = 0.0499511 loss)
I0425 12:22:45.751816 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.0250174 (* 1 = 0.0250174 loss)
I0425 12:22:45.751830 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0164961 (* 0.0909091 = 0.00149965 loss)
I0425 12:22:45.751845 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.043058 (* 0.0909091 = 0.00391436 loss)
I0425 12:22:45.751859 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.0207686 (* 0.0909091 = 0.00188806 loss)
I0425 12:22:45.751873 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.0300229 (* 0.0909091 = 0.00272935 loss)
I0425 12:22:45.751888 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.187495 (* 0.0909091 = 0.017045 loss)
I0425 12:22:45.751901 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.361452 (* 0.0909091 = 0.0328593 loss)
I0425 12:22:45.751914 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.165923 (* 0.0909091 = 0.0150839 loss)
I0425 12:22:45.751929 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.0707928 (* 0.0909091 = 0.00643571 loss)
I0425 12:22:45.751942 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.0477329 (* 0.0909091 = 0.00433936 loss)
I0425 12:22:45.751957 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00596799 (* 0.0909091 = 0.000542545 loss)
I0425 12:22:45.751971 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00425572 (* 0.0909091 = 0.000386884 loss)
I0425 12:22:45.751984 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00145304 (* 0.0909091 = 0.000132095 loss)
I0425 12:22:45.751998 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00091983 (* 0.0909091 = 8.36209e-05 loss)
I0425 12:22:45.752012 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000713127 (* 0.0909091 = 6.48298e-05 loss)
I0425 12:22:45.752027 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000439017 (* 0.0909091 = 3.99107e-05 loss)
I0425 12:22:45.752040 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000281684 (* 0.0909091 = 2.56076e-05 loss)
I0425 12:22:45.752054 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000324758 (* 0.0909091 = 2.95234e-05 loss)
I0425 12:22:45.752068 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000239678 (* 0.0909091 = 2.17889e-05 loss)
I0425 12:22:45.752082 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000138015 (* 0.0909091 = 1.25468e-05 loss)
I0425 12:22:45.752096 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 6.63446e-05 (* 0.0909091 = 6.03133e-06 loss)
I0425 12:22:45.752110 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 2.83438e-05 (* 0.0909091 = 2.57671e-06 loss)
I0425 12:22:45.752125 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.02075e-05 (* 0.0909091 = 9.27958e-07 loss)
I0425 12:22:45.752136 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.875
I0425 12:22:45.752148 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.75
I0425 12:22:45.752169 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.763087
I0425 12:22:45.752183 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.547916
I0425 12:22:45.752198 22523 sgd_solver.cpp:106] Iteration 11500, lr = 0.01
I0425 12:28:27.158880 22523 solver.cpp:229] Iteration 12000, loss = 3.13438
I0425 12:28:27.159032 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.537037
I0425 12:28:27.159054 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.5
I0425 12:28:27.159067 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 12:28:27.159080 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 12:28:27.159092 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 12:28:27.159106 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 12:28:27.159117 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 12:28:27.159131 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 12:28:27.159143 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0425 12:28:27.159155 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 12:28:27.159168 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 12:28:27.159181 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 12:28:27.159193 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 12:28:27.159205 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 12:28:27.159227 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 12:28:27.159240 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0425 12:28:27.159252 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0425 12:28:27.159265 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 12:28:27.159276 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 12:28:27.159297 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 12:28:27.159309 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 12:28:27.159322 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 12:28:27.159333 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 12:28:27.159345 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.840909
I0425 12:28:27.159373 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.722222
I0425 12:28:27.159391 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.46324 (* 0.3 = 0.438971 loss)
I0425 12:28:27.159407 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.474396 (* 0.3 = 0.142319 loss)
I0425 12:28:27.159422 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.47932 (* 0.0272727 = 0.0403451 loss)
I0425 12:28:27.159437 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.62303 (* 0.0272727 = 0.0442646 loss)
I0425 12:28:27.159452 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.93831 (* 0.0272727 = 0.0528629 loss)
I0425 12:28:27.159466 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.6405 (* 0.0272727 = 0.0447409 loss)
I0425 12:28:27.159482 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.88192 (* 0.0272727 = 0.0513252 loss)
I0425 12:28:27.159499 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.14527 (* 0.0272727 = 0.0312346 loss)
I0425 12:28:27.159514 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.882168 (* 0.0272727 = 0.0240591 loss)
I0425 12:28:27.159529 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.663173 (* 0.0272727 = 0.0180865 loss)
I0425 12:28:27.159543 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.369955 (* 0.0272727 = 0.0100897 loss)
I0425 12:28:27.159559 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.344165 (* 0.0272727 = 0.00938632 loss)
I0425 12:28:27.159572 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.626065 (* 0.0272727 = 0.0170745 loss)
I0425 12:28:27.159587 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.359747 (* 0.0272727 = 0.00981129 loss)
I0425 12:28:27.159621 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.403567 (* 0.0272727 = 0.0110064 loss)
I0425 12:28:27.159636 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.556617 (* 0.0272727 = 0.0151805 loss)
I0425 12:28:27.159651 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.592438 (* 0.0272727 = 0.0161574 loss)
I0425 12:28:27.159664 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.687079 (* 0.0272727 = 0.0187385 loss)
I0425 12:28:27.159680 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00506137 (* 0.0272727 = 0.000138037 loss)
I0425 12:28:27.159694 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00334949 (* 0.0272727 = 9.13498e-05 loss)
I0425 12:28:27.159709 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00120457 (* 0.0272727 = 3.2852e-05 loss)
I0425 12:28:27.159723 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000988291 (* 0.0272727 = 2.69534e-05 loss)
I0425 12:28:27.159739 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000314099 (* 0.0272727 = 8.56635e-06 loss)
I0425 12:28:27.159752 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000334617 (* 0.0272727 = 9.12591e-06 loss)
I0425 12:28:27.159765 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.62963
I0425 12:28:27.159777 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 12:28:27.159790 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 12:28:27.159801 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 12:28:27.159812 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0425 12:28:27.159824 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0425 12:28:27.159837 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 12:28:27.159847 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0425 12:28:27.159859 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 12:28:27.159870 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 12:28:27.159883 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 12:28:27.159893 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 12:28:27.159905 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 12:28:27.159916 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 12:28:27.159930 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 12:28:27.159943 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0425 12:28:27.159955 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0425 12:28:27.159966 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 12:28:27.159978 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 12:28:27.159989 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 12:28:27.160001 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 12:28:27.160012 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 12:28:27.160023 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 12:28:27.160035 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.875
I0425 12:28:27.160048 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.833333
I0425 12:28:27.160061 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.0811 (* 0.3 = 0.324329 loss)
I0425 12:28:27.160075 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.358973 (* 0.3 = 0.107692 loss)
I0425 12:28:27.160090 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.362415 (* 0.0272727 = 0.00988405 loss)
I0425 12:28:27.160104 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.58084 (* 0.0272727 = 0.043114 loss)
I0425 12:28:27.160130 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.43812 (* 0.0272727 = 0.0392215 loss)
I0425 12:28:27.160146 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.86513 (* 0.0272727 = 0.0508673 loss)
I0425 12:28:27.160161 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.6947 (* 0.0272727 = 0.046219 loss)
I0425 12:28:27.160174 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.76147 (* 0.0272727 = 0.0480402 loss)
I0425 12:28:27.160187 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.888156 (* 0.0272727 = 0.0242224 loss)
I0425 12:28:27.160202 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.475239 (* 0.0272727 = 0.0129611 loss)
I0425 12:28:27.160215 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.413324 (* 0.0272727 = 0.0112725 loss)
I0425 12:28:27.160229 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.250259 (* 0.0272727 = 0.00682524 loss)
I0425 12:28:27.160243 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.752533 (* 0.0272727 = 0.0205236 loss)
I0425 12:28:27.160259 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.306465 (* 0.0272727 = 0.00835814 loss)
I0425 12:28:27.160272 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.501739 (* 0.0272727 = 0.0136838 loss)
I0425 12:28:27.160286 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.441175 (* 0.0272727 = 0.012032 loss)
I0425 12:28:27.160300 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.494822 (* 0.0272727 = 0.0134952 loss)
I0425 12:28:27.160315 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.527198 (* 0.0272727 = 0.0143781 loss)
I0425 12:28:27.160328 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.012959 (* 0.0272727 = 0.000353428 loss)
I0425 12:28:27.160342 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00431651 (* 0.0272727 = 0.000117723 loss)
I0425 12:28:27.160356 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00166298 (* 0.0272727 = 4.53539e-05 loss)
I0425 12:28:27.160370 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000745166 (* 0.0272727 = 2.03227e-05 loss)
I0425 12:28:27.160384 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000249858 (* 0.0272727 = 6.81432e-06 loss)
I0425 12:28:27.160398 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000171843 (* 0.0272727 = 4.68663e-06 loss)
I0425 12:28:27.160410 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.87037
I0425 12:28:27.160423 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 12:28:27.160434 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 12:28:27.160445 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 12:28:27.160457 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 12:28:27.160470 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0425 12:28:27.160480 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 12:28:27.160492 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0425 12:28:27.160504 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 12:28:27.160516 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 12:28:27.160527 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 12:28:27.160539 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 12:28:27.160555 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 12:28:27.160563 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 12:28:27.160572 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 12:28:27.160583 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0425 12:28:27.160605 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0425 12:28:27.160621 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 12:28:27.160632 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 12:28:27.160645 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 12:28:27.160655 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 12:28:27.160666 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 12:28:27.160678 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 12:28:27.160689 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.954545
I0425 12:28:27.160701 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.907407
I0425 12:28:27.160714 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.465315 (* 1 = 0.465315 loss)
I0425 12:28:27.160728 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.158715 (* 1 = 0.158715 loss)
I0425 12:28:27.160742 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0416117 (* 0.0909091 = 0.00378288 loss)
I0425 12:28:27.160756 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0388703 (* 0.0909091 = 0.00353366 loss)
I0425 12:28:27.160770 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.028129 (* 0.0909091 = 0.00255718 loss)
I0425 12:28:27.160784 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.138644 (* 0.0909091 = 0.012604 loss)
I0425 12:28:27.160797 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.842023 (* 0.0909091 = 0.0765476 loss)
I0425 12:28:27.160811 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.554019 (* 0.0909091 = 0.0503654 loss)
I0425 12:28:27.160825 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.226164 (* 0.0909091 = 0.0205604 loss)
I0425 12:28:27.160838 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.61739 (* 0.0909091 = 0.0561263 loss)
I0425 12:28:27.160852 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.127911 (* 0.0909091 = 0.0116283 loss)
I0425 12:28:27.160866 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.258724 (* 0.0909091 = 0.0235204 loss)
I0425 12:28:27.160879 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.68568 (* 0.0909091 = 0.0623345 loss)
I0425 12:28:27.160893 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.237155 (* 0.0909091 = 0.0215595 loss)
I0425 12:28:27.160907 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.350828 (* 0.0909091 = 0.0318935 loss)
I0425 12:28:27.160922 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.36228 (* 0.0909091 = 0.0329345 loss)
I0425 12:28:27.160934 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.248862 (* 0.0909091 = 0.0226238 loss)
I0425 12:28:27.160948 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.429946 (* 0.0909091 = 0.039086 loss)
I0425 12:28:27.160962 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0281367 (* 0.0909091 = 0.00255788 loss)
I0425 12:28:27.160976 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0111879 (* 0.0909091 = 0.00101708 loss)
I0425 12:28:27.160994 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00361988 (* 0.0909091 = 0.00032908 loss)
I0425 12:28:27.161007 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.0011496 (* 0.0909091 = 0.000104509 loss)
I0425 12:28:27.161021 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000138569 (* 0.0909091 = 1.25971e-05 loss)
I0425 12:28:27.161036 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.24727e-05 (* 0.0909091 = 1.13388e-06 loss)
I0425 12:28:27.161047 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 12:28:27.161059 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 12:28:27.161082 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.673957
I0425 12:28:27.161094 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.468472
I0425 12:28:27.161109 22523 sgd_solver.cpp:106] Iteration 12000, lr = 0.01
I0425 12:34:08.569085 22523 solver.cpp:229] Iteration 12500, loss = 3.01546
I0425 12:34:08.569228 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.526316
I0425 12:34:08.569249 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 12:34:08.569263 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.25
I0425 12:34:08.569277 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.875
I0425 12:34:08.569288 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 12:34:08.569301 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 12:34:08.569314 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0425 12:34:08.569325 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 12:34:08.569339 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 12:34:08.569351 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 12:34:08.569363 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 12:34:08.569375 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 12:34:08.569388 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 12:34:08.569401 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 12:34:08.569413 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 12:34:08.569424 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 12:34:08.569437 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 12:34:08.569448 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 12:34:08.569469 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 12:34:08.569480 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 12:34:08.569492 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 12:34:08.569504 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 12:34:08.569524 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 12:34:08.569535 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.869318
I0425 12:34:08.569547 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.789474
I0425 12:34:08.569566 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.43655 (* 0.3 = 0.430964 loss)
I0425 12:34:08.569581 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.423704 (* 0.3 = 0.127111 loss)
I0425 12:34:08.569596 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.671131 (* 0.0272727 = 0.0183036 loss)
I0425 12:34:08.569610 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.70214 (* 0.0272727 = 0.0464221 loss)
I0425 12:34:08.569624 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.02568 (* 0.0272727 = 0.0279732 loss)
I0425 12:34:08.569639 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.7466 (* 0.0272727 = 0.0476347 loss)
I0425 12:34:08.569653 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.39859 (* 0.0272727 = 0.0381432 loss)
I0425 12:34:08.569667 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.716111 (* 0.0272727 = 0.0195303 loss)
I0425 12:34:08.569684 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.335829 (* 0.0272727 = 0.00915896 loss)
I0425 12:34:08.569699 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.501182 (* 0.0272727 = 0.0136686 loss)
I0425 12:34:08.569712 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.31786 (* 0.0272727 = 0.00866891 loss)
I0425 12:34:08.569727 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.354662 (* 0.0272727 = 0.00967259 loss)
I0425 12:34:08.569741 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.448112 (* 0.0272727 = 0.0122212 loss)
I0425 12:34:08.569756 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0741195 (* 0.0272727 = 0.00202144 loss)
I0425 12:34:08.569788 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0233482 (* 0.0272727 = 0.00063677 loss)
I0425 12:34:08.569804 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00863108 (* 0.0272727 = 0.000235393 loss)
I0425 12:34:08.569819 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00578503 (* 0.0272727 = 0.000157774 loss)
I0425 12:34:08.569833 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00214684 (* 0.0272727 = 5.85501e-05 loss)
I0425 12:34:08.569849 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000437242 (* 0.0272727 = 1.19248e-05 loss)
I0425 12:34:08.569862 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000518627 (* 0.0272727 = 1.41444e-05 loss)
I0425 12:34:08.569877 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000139627 (* 0.0272727 = 3.80802e-06 loss)
I0425 12:34:08.569891 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 8.20984e-05 (* 0.0272727 = 2.23905e-06 loss)
I0425 12:34:08.569913 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 3.82189e-05 (* 0.0272727 = 1.04233e-06 loss)
I0425 12:34:08.569927 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 3.1989e-05 (* 0.0272727 = 8.72427e-07 loss)
I0425 12:34:08.569941 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.5
I0425 12:34:08.569952 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 12:34:08.569963 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0425 12:34:08.569975 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 12:34:08.569986 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0425 12:34:08.569998 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.75
I0425 12:34:08.570009 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 12:34:08.570021 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 12:34:08.570032 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 12:34:08.570044 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 12:34:08.570056 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 12:34:08.570068 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 12:34:08.570080 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 12:34:08.570091 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 12:34:08.570101 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 12:34:08.570116 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 12:34:08.570128 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 12:34:08.570139 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 12:34:08.570150 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 12:34:08.570161 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 12:34:08.570173 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 12:34:08.570184 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 12:34:08.570195 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 12:34:08.570211 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.869318
I0425 12:34:08.570225 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.763158
I0425 12:34:08.570238 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.5174 (* 0.3 = 0.455221 loss)
I0425 12:34:08.570252 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.431521 (* 0.3 = 0.129456 loss)
I0425 12:34:08.570271 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.700163 (* 0.0272727 = 0.0190953 loss)
I0425 12:34:08.570284 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 2.0467 (* 0.0272727 = 0.055819 loss)
I0425 12:34:08.570310 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.04586 (* 0.0272727 = 0.0285233 loss)
I0425 12:34:08.570325 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.47751 (* 0.0272727 = 0.0402957 loss)
I0425 12:34:08.570339 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.06258 (* 0.0272727 = 0.0289795 loss)
I0425 12:34:08.570353 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.567184 (* 0.0272727 = 0.0154687 loss)
I0425 12:34:08.570368 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.42937 (* 0.0272727 = 0.0117101 loss)
I0425 12:34:08.570381 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.603886 (* 0.0272727 = 0.0164696 loss)
I0425 12:34:08.570395 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.484685 (* 0.0272727 = 0.0132187 loss)
I0425 12:34:08.570410 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.481299 (* 0.0272727 = 0.0131263 loss)
I0425 12:34:08.570423 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.856864 (* 0.0272727 = 0.023369 loss)
I0425 12:34:08.570437 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00463732 (* 0.0272727 = 0.000126472 loss)
I0425 12:34:08.570451 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00445281 (* 0.0272727 = 0.00012144 loss)
I0425 12:34:08.570472 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0027386 (* 0.0272727 = 7.4689e-05 loss)
I0425 12:34:08.570502 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000723651 (* 0.0272727 = 1.97359e-05 loss)
I0425 12:34:08.570521 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000771848 (* 0.0272727 = 2.10504e-05 loss)
I0425 12:34:08.570536 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000183682 (* 0.0272727 = 5.00952e-06 loss)
I0425 12:34:08.570550 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 3.03781e-05 (* 0.0272727 = 8.28495e-07 loss)
I0425 12:34:08.570564 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 1.20702e-05 (* 0.0272727 = 3.29187e-07 loss)
I0425 12:34:08.570580 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 5.79661e-06 (* 0.0272727 = 1.58089e-07 loss)
I0425 12:34:08.570593 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.4736e-06 (* 0.0272727 = 6.74619e-08 loss)
I0425 12:34:08.570607 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 8.04664e-07 (* 0.0272727 = 2.19454e-08 loss)
I0425 12:34:08.570619 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.763158
I0425 12:34:08.570631 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 12:34:08.570643 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 12:34:08.570654 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 12:34:08.570667 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0425 12:34:08.570678 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 12:34:08.570690 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 12:34:08.570701 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 12:34:08.570713 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 12:34:08.570725 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 12:34:08.570736 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 12:34:08.570747 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 12:34:08.570760 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 12:34:08.570770 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 12:34:08.570782 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 12:34:08.570793 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 12:34:08.570816 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 12:34:08.570830 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 12:34:08.570842 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 12:34:08.570852 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 12:34:08.570864 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 12:34:08.570875 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 12:34:08.570888 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 12:34:08.570899 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.9375
I0425 12:34:08.570911 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.842105
I0425 12:34:08.570925 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.686722 (* 1 = 0.686722 loss)
I0425 12:34:08.570940 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.191295 (* 1 = 0.191295 loss)
I0425 12:34:08.570953 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.34375 (* 0.0909091 = 0.03125 loss)
I0425 12:34:08.570968 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.337757 (* 0.0909091 = 0.0307052 loss)
I0425 12:34:08.570981 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.432177 (* 0.0909091 = 0.0392888 loss)
I0425 12:34:08.570996 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.818295 (* 0.0909091 = 0.0743904 loss)
I0425 12:34:08.571010 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.213707 (* 0.0909091 = 0.0194279 loss)
I0425 12:34:08.571024 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.253326 (* 0.0909091 = 0.0230297 loss)
I0425 12:34:08.571038 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.259295 (* 0.0909091 = 0.0235723 loss)
I0425 12:34:08.571053 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.432095 (* 0.0909091 = 0.0392813 loss)
I0425 12:34:08.571066 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.260735 (* 0.0909091 = 0.0237032 loss)
I0425 12:34:08.571080 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.22048 (* 0.0909091 = 0.0200437 loss)
I0425 12:34:08.571094 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.430647 (* 0.0909091 = 0.0391498 loss)
I0425 12:34:08.571108 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.0829322 (* 0.0909091 = 0.00753929 loss)
I0425 12:34:08.571122 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0448844 (* 0.0909091 = 0.0040804 loss)
I0425 12:34:08.571136 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.023957 (* 0.0909091 = 0.00217791 loss)
I0425 12:34:08.571151 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0182949 (* 0.0909091 = 0.00166318 loss)
I0425 12:34:08.571164 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00755773 (* 0.0909091 = 0.000687066 loss)
I0425 12:34:08.571178 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00534893 (* 0.0909091 = 0.000486266 loss)
I0425 12:34:08.571192 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00364802 (* 0.0909091 = 0.000331638 loss)
I0425 12:34:08.571207 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00233565 (* 0.0909091 = 0.000212332 loss)
I0425 12:34:08.571220 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00160864 (* 0.0909091 = 0.00014624 loss)
I0425 12:34:08.571235 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000156264 (* 0.0909091 = 1.42058e-05 loss)
I0425 12:34:08.571254 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 6.21076e-05 (* 0.0909091 = 5.64614e-06 loss)
I0425 12:34:08.571264 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.875
I0425 12:34:08.571271 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.75
I0425 12:34:08.571295 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.705202
I0425 12:34:08.571307 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.663638
I0425 12:34:08.571322 22523 sgd_solver.cpp:106] Iteration 12500, lr = 0.01
I0425 12:39:49.911934 22523 solver.cpp:229] Iteration 13000, loss = 3.05475
I0425 12:39:49.912072 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.617021
I0425 12:39:49.912093 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0425 12:39:49.912107 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 12:39:49.912120 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0425 12:39:49.912132 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 12:39:49.912144 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0425 12:39:49.912158 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 12:39:49.912170 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 12:39:49.912183 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 12:39:49.912195 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 12:39:49.912210 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 12:39:49.912222 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 12:39:49.912235 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 12:39:49.912247 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 12:39:49.912259 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 12:39:49.912271 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 12:39:49.912283 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 12:39:49.912295 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 12:39:49.912307 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 12:39:49.912319 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 12:39:49.912331 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 12:39:49.912343 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 12:39:49.912355 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 12:39:49.912367 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.875
I0425 12:39:49.912379 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.808511
I0425 12:39:49.912396 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.24622 (* 0.3 = 0.373865 loss)
I0425 12:39:49.912412 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.435692 (* 0.3 = 0.130708 loss)
I0425 12:39:49.912427 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.739306 (* 0.0272727 = 0.0201629 loss)
I0425 12:39:49.912442 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.37588 (* 0.0272727 = 0.0375241 loss)
I0425 12:39:49.912457 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.71401 (* 0.0272727 = 0.0467459 loss)
I0425 12:39:49.912472 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.91482 (* 0.0272727 = 0.0522224 loss)
I0425 12:39:49.912487 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.29586 (* 0.0272727 = 0.0353416 loss)
I0425 12:39:49.912500 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.3762 (* 0.0272727 = 0.0648054 loss)
I0425 12:39:49.912514 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.27478 (* 0.0272727 = 0.0347667 loss)
I0425 12:39:49.912528 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.453186 (* 0.0272727 = 0.0123596 loss)
I0425 12:39:49.912544 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.203404 (* 0.0272727 = 0.00554739 loss)
I0425 12:39:49.912559 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.0695254 (* 0.0272727 = 0.00189615 loss)
I0425 12:39:49.912574 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0360109 (* 0.0272727 = 0.000982116 loss)
I0425 12:39:49.912587 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0304433 (* 0.0272727 = 0.000830271 loss)
I0425 12:39:49.912602 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0180499 (* 0.0272727 = 0.000492269 loss)
I0425 12:39:49.912634 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00648316 (* 0.0272727 = 0.000176814 loss)
I0425 12:39:49.912650 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.004459 (* 0.0272727 = 0.000121609 loss)
I0425 12:39:49.912665 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00282126 (* 0.0272727 = 7.69434e-05 loss)
I0425 12:39:49.912684 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00128828 (* 0.0272727 = 3.5135e-05 loss)
I0425 12:39:49.912699 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000644558 (* 0.0272727 = 1.75789e-05 loss)
I0425 12:39:49.912714 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000254855 (* 0.0272727 = 6.95059e-06 loss)
I0425 12:39:49.912729 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000148719 (* 0.0272727 = 4.05596e-06 loss)
I0425 12:39:49.912746 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 9.15153e-05 (* 0.0272727 = 2.49587e-06 loss)
I0425 12:39:49.912761 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 4.12271e-05 (* 0.0272727 = 1.12438e-06 loss)
I0425 12:39:49.912773 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.808511
I0425 12:39:49.912786 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 12:39:49.912797 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0425 12:39:49.912809 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.875
I0425 12:39:49.912820 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0425 12:39:49.912832 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 12:39:49.912844 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 12:39:49.912855 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 12:39:49.912868 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 12:39:49.912878 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 12:39:49.912890 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 12:39:49.912901 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 12:39:49.912914 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 12:39:49.912925 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 12:39:49.912935 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 12:39:49.912946 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 12:39:49.912957 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 12:39:49.912968 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 12:39:49.912979 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 12:39:49.912992 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 12:39:49.913002 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 12:39:49.913013 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 12:39:49.913025 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 12:39:49.913036 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.943182
I0425 12:39:49.913048 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.957447
I0425 12:39:49.913063 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.682063 (* 0.3 = 0.204619 loss)
I0425 12:39:49.913076 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.234852 (* 0.3 = 0.0704556 loss)
I0425 12:39:49.913094 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.47396 (* 0.0272727 = 0.0129262 loss)
I0425 12:39:49.913110 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.729597 (* 0.0272727 = 0.0198981 loss)
I0425 12:39:49.913135 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.924527 (* 0.0272727 = 0.0252144 loss)
I0425 12:39:49.913151 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.92548 (* 0.0272727 = 0.052513 loss)
I0425 12:39:49.913164 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.46374 (* 0.0272727 = 0.0399202 loss)
I0425 12:39:49.913178 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.982242 (* 0.0272727 = 0.0267884 loss)
I0425 12:39:49.913192 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.738729 (* 0.0272727 = 0.0201472 loss)
I0425 12:39:49.913206 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.0705998 (* 0.0272727 = 0.00192545 loss)
I0425 12:39:49.913221 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0194675 (* 0.0272727 = 0.000530933 loss)
I0425 12:39:49.913235 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00443939 (* 0.0272727 = 0.000121074 loss)
I0425 12:39:49.913251 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00461015 (* 0.0272727 = 0.000125731 loss)
I0425 12:39:49.913266 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00191484 (* 0.0272727 = 5.22228e-05 loss)
I0425 12:39:49.913281 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00198751 (* 0.0272727 = 5.42047e-05 loss)
I0425 12:39:49.913295 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00151418 (* 0.0272727 = 4.12957e-05 loss)
I0425 12:39:49.913310 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000600058 (* 0.0272727 = 1.63652e-05 loss)
I0425 12:39:49.913323 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000545093 (* 0.0272727 = 1.48662e-05 loss)
I0425 12:39:49.913337 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000109168 (* 0.0272727 = 2.9773e-06 loss)
I0425 12:39:49.913352 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 2.52884e-05 (* 0.0272727 = 6.89684e-07 loss)
I0425 12:39:49.913367 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 8.46397e-06 (* 0.0272727 = 2.30836e-07 loss)
I0425 12:39:49.913380 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 3.84452e-06 (* 0.0272727 = 1.04851e-07 loss)
I0425 12:39:49.913395 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 5.60292e-06 (* 0.0272727 = 1.52807e-07 loss)
I0425 12:39:49.913409 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.68384e-06 (* 0.0272727 = 4.59228e-08 loss)
I0425 12:39:49.913421 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.893617
I0425 12:39:49.913434 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 12:39:49.913445 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 12:39:49.913456 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 12:39:49.913468 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0425 12:39:49.913480 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 12:39:49.913492 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 1
I0425 12:39:49.913503 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0425 12:39:49.913514 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 12:39:49.913527 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 12:39:49.913537 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 12:39:49.913548 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 12:39:49.913560 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 12:39:49.913571 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 12:39:49.913583 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 12:39:49.913594 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 12:39:49.913605 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 12:39:49.913627 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 12:39:49.913640 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 12:39:49.913651 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 12:39:49.913663 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 12:39:49.913674 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 12:39:49.913686 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 12:39:49.913697 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.965909
I0425 12:39:49.913709 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.978723
I0425 12:39:49.913723 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.380249 (* 1 = 0.380249 loss)
I0425 12:39:49.913738 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.123009 (* 1 = 0.123009 loss)
I0425 12:39:49.913753 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0902554 (* 0.0909091 = 0.00820504 loss)
I0425 12:39:49.913766 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.225651 (* 0.0909091 = 0.0205137 loss)
I0425 12:39:49.913781 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.199488 (* 0.0909091 = 0.0181353 loss)
I0425 12:39:49.913795 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.21378 (* 0.0909091 = 0.110343 loss)
I0425 12:39:49.913813 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.28517 (* 0.0909091 = 0.0259246 loss)
I0425 12:39:49.913828 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.259883 (* 0.0909091 = 0.0236257 loss)
I0425 12:39:49.913842 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.837804 (* 0.0909091 = 0.076164 loss)
I0425 12:39:49.913856 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.0446316 (* 0.0909091 = 0.00405742 loss)
I0425 12:39:49.913871 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.0342987 (* 0.0909091 = 0.00311806 loss)
I0425 12:39:49.913884 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00603837 (* 0.0909091 = 0.000548942 loss)
I0425 12:39:49.913897 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00475584 (* 0.0909091 = 0.000432349 loss)
I0425 12:39:49.913911 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.0022232 (* 0.0909091 = 0.000202109 loss)
I0425 12:39:49.913925 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000820659 (* 0.0909091 = 7.46054e-05 loss)
I0425 12:39:49.913939 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000643577 (* 0.0909091 = 5.8507e-05 loss)
I0425 12:39:49.913954 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00035069 (* 0.0909091 = 3.18809e-05 loss)
I0425 12:39:49.913967 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000332932 (* 0.0909091 = 3.02665e-05 loss)
I0425 12:39:49.913981 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00018519 (* 0.0909091 = 1.68355e-05 loss)
I0425 12:39:49.913995 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000187574 (* 0.0909091 = 1.70521e-05 loss)
I0425 12:39:49.914008 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000138399 (* 0.0909091 = 1.25817e-05 loss)
I0425 12:39:49.914022 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000128891 (* 0.0909091 = 1.17174e-05 loss)
I0425 12:39:49.914036 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 5.32299e-05 (* 0.0909091 = 4.83908e-06 loss)
I0425 12:39:49.914050 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.72708e-05 (* 0.0909091 = 1.57007e-06 loss)
I0425 12:39:49.914062 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 12:39:49.914074 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 12:39:49.914096 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.513311
I0425 12:39:49.914109 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.416636
I0425 12:39:49.914124 22523 sgd_solver.cpp:106] Iteration 13000, lr = 0.01
I0425 12:45:31.265033 22523 solver.cpp:229] Iteration 13500, loss = 3.09215
I0425 12:45:31.265169 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.58
I0425 12:45:31.265190 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0425 12:45:31.265208 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.25
I0425 12:45:31.265220 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 12:45:31.265233 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 12:45:31.265245 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 12:45:31.265259 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 12:45:31.265270 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 12:45:31.265283 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 12:45:31.265295 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 12:45:31.265308 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 12:45:31.265321 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 12:45:31.265332 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 12:45:31.265344 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 12:45:31.265357 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 12:45:31.265368 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 12:45:31.265380 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 12:45:31.265391 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 12:45:31.265411 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 12:45:31.265422 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 12:45:31.265434 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 12:45:31.265446 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 12:45:31.265465 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 12:45:31.265477 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.869318
I0425 12:45:31.265489 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.84
I0425 12:45:31.265506 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.5705 (* 0.3 = 0.471149 loss)
I0425 12:45:31.265522 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.485558 (* 0.3 = 0.145667 loss)
I0425 12:45:31.265537 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.29521 (* 0.0272727 = 0.035324 loss)
I0425 12:45:31.265552 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.45717 (* 0.0272727 = 0.0670137 loss)
I0425 12:45:31.265566 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.77905 (* 0.0272727 = 0.0485195 loss)
I0425 12:45:31.265581 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.87558 (* 0.0272727 = 0.0511522 loss)
I0425 12:45:31.265595 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.72031 (* 0.0272727 = 0.0469177 loss)
I0425 12:45:31.265609 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.91251 (* 0.0272727 = 0.0521593 loss)
I0425 12:45:31.265624 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.24389 (* 0.0272727 = 0.0339243 loss)
I0425 12:45:31.265638 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.056235 (* 0.0272727 = 0.00153368 loss)
I0425 12:45:31.265653 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0097688 (* 0.0272727 = 0.000266422 loss)
I0425 12:45:31.265667 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00314875 (* 0.0272727 = 8.58751e-05 loss)
I0425 12:45:31.265682 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00367143 (* 0.0272727 = 0.00010013 loss)
I0425 12:45:31.265697 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0024616 (* 0.0272727 = 6.71345e-05 loss)
I0425 12:45:31.265712 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00202589 (* 0.0272727 = 5.52515e-05 loss)
I0425 12:45:31.265743 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0016145 (* 0.0272727 = 4.40319e-05 loss)
I0425 12:45:31.265759 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00202957 (* 0.0272727 = 5.53518e-05 loss)
I0425 12:45:31.265774 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000409055 (* 0.0272727 = 1.1156e-05 loss)
I0425 12:45:31.265789 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000120806 (* 0.0272727 = 3.2947e-06 loss)
I0425 12:45:31.265805 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000128954 (* 0.0272727 = 3.51694e-06 loss)
I0425 12:45:31.265818 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 5.6823e-05 (* 0.0272727 = 1.54972e-06 loss)
I0425 12:45:31.265832 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 2.96105e-05 (* 0.0272727 = 8.07558e-07 loss)
I0425 12:45:31.265856 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 2.99162e-05 (* 0.0272727 = 8.15896e-07 loss)
I0425 12:45:31.265871 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.55125e-05 (* 0.0272727 = 4.23068e-07 loss)
I0425 12:45:31.265882 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.56
I0425 12:45:31.265894 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 12:45:31.265905 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 12:45:31.265925 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0425 12:45:31.265938 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 12:45:31.265949 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 12:45:31.265961 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 12:45:31.265972 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 12:45:31.265985 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 12:45:31.265995 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 12:45:31.266006 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 12:45:31.266018 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 12:45:31.266029 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 12:45:31.266041 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 12:45:31.266052 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 12:45:31.266063 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 12:45:31.266074 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 12:45:31.266085 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 12:45:31.266096 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 12:45:31.266108 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 12:45:31.266119 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 12:45:31.266130 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 12:45:31.266141 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 12:45:31.266152 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.863636
I0425 12:45:31.266165 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.84
I0425 12:45:31.266181 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.72331 (* 0.3 = 0.516994 loss)
I0425 12:45:31.266196 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.527355 (* 0.3 = 0.158207 loss)
I0425 12:45:31.266211 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.355843 (* 0.0272727 = 0.0097048 loss)
I0425 12:45:31.266225 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.76627 (* 0.0272727 = 0.0481709 loss)
I0425 12:45:31.266252 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.32037 (* 0.0272727 = 0.0632828 loss)
I0425 12:45:31.266268 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.1801 (* 0.0272727 = 0.0321847 loss)
I0425 12:45:31.266283 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.8283 (* 0.0272727 = 0.0498627 loss)
I0425 12:45:31.266296 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.12679 (* 0.0272727 = 0.0307306 loss)
I0425 12:45:31.266310 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.08174 (* 0.0272727 = 0.0295019 loss)
I0425 12:45:31.266324 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.0538671 (* 0.0272727 = 0.0014691 loss)
I0425 12:45:31.266338 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.000720398 (* 0.0272727 = 1.96472e-05 loss)
I0425 12:45:31.266352 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.000386149 (* 0.0272727 = 1.05313e-05 loss)
I0425 12:45:31.266367 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00203486 (* 0.0272727 = 5.54961e-05 loss)
I0425 12:45:31.266381 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.000780905 (* 0.0272727 = 2.12974e-05 loss)
I0425 12:45:31.266396 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.000871291 (* 0.0272727 = 2.37625e-05 loss)
I0425 12:45:31.266409 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000385549 (* 0.0272727 = 1.0515e-05 loss)
I0425 12:45:31.266422 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000235946 (* 0.0272727 = 6.43488e-06 loss)
I0425 12:45:31.266438 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 6.20465e-05 (* 0.0272727 = 1.69218e-06 loss)
I0425 12:45:31.266450 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 2.66444e-05 (* 0.0272727 = 7.26667e-07 loss)
I0425 12:45:31.266465 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 2.58102e-05 (* 0.0272727 = 7.03915e-07 loss)
I0425 12:45:31.266479 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 3.27827e-06 (* 0.0272727 = 8.94074e-08 loss)
I0425 12:45:31.266494 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 6.18405e-06 (* 0.0272727 = 1.68656e-07 loss)
I0425 12:45:31.266507 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 2.07127e-06 (* 0.0272727 = 5.64892e-08 loss)
I0425 12:45:31.266522 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 9.23874e-07 (* 0.0272727 = 2.51966e-08 loss)
I0425 12:45:31.266535 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.8
I0425 12:45:31.266547 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 12:45:31.266558 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 12:45:31.266571 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.625
I0425 12:45:31.266582 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0425 12:45:31.266593 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 12:45:31.266605 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 12:45:31.266618 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 12:45:31.266628 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 12:45:31.266640 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 12:45:31.266651 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 12:45:31.266662 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 12:45:31.266674 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 12:45:31.266685 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 12:45:31.266697 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 12:45:31.266708 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 12:45:31.266721 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 12:45:31.266741 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 12:45:31.266754 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 12:45:31.266767 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 12:45:31.266777 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 12:45:31.266789 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 12:45:31.266800 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 12:45:31.266811 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.931818
I0425 12:45:31.266824 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.9
I0425 12:45:31.266837 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.20392 (* 1 = 1.20392 loss)
I0425 12:45:31.266851 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.38985 (* 1 = 0.38985 loss)
I0425 12:45:31.266865 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.071281 (* 0.0909091 = 0.00648009 loss)
I0425 12:45:31.266880 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 1.08957 (* 0.0909091 = 0.0990521 loss)
I0425 12:45:31.266894 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 2.76456 (* 0.0909091 = 0.251323 loss)
I0425 12:45:31.266908 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.39836 (* 0.0909091 = 0.127124 loss)
I0425 12:45:31.266922 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.53966 (* 0.0909091 = 0.139969 loss)
I0425 12:45:31.266937 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.585408 (* 0.0909091 = 0.0532189 loss)
I0425 12:45:31.266950 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.661223 (* 0.0909091 = 0.0601112 loss)
I0425 12:45:31.266964 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.0134343 (* 0.0909091 = 0.0012213 loss)
I0425 12:45:31.266978 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.00394371 (* 0.0909091 = 0.000358519 loss)
I0425 12:45:31.266993 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.000906264 (* 0.0909091 = 8.23876e-05 loss)
I0425 12:45:31.267007 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.000707414 (* 0.0909091 = 6.43104e-05 loss)
I0425 12:45:31.267021 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00035584 (* 0.0909091 = 3.23491e-05 loss)
I0425 12:45:31.267035 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000180856 (* 0.0909091 = 1.64415e-05 loss)
I0425 12:45:31.267050 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000124387 (* 0.0909091 = 1.13079e-05 loss)
I0425 12:45:31.267063 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 6.52641e-05 (* 0.0909091 = 5.9331e-06 loss)
I0425 12:45:31.267077 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 3.78802e-05 (* 0.0909091 = 3.44366e-06 loss)
I0425 12:45:31.267091 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 2.70022e-05 (* 0.0909091 = 2.45475e-06 loss)
I0425 12:45:31.267105 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 2.31575e-05 (* 0.0909091 = 2.10523e-06 loss)
I0425 12:45:31.267119 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 2.53484e-05 (* 0.0909091 = 2.3044e-06 loss)
I0425 12:45:31.267133 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 1.54829e-05 (* 0.0909091 = 1.40754e-06 loss)
I0425 12:45:31.267148 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 7.55507e-06 (* 0.0909091 = 6.86824e-07 loss)
I0425 12:45:31.267163 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 1.89246e-06 (* 0.0909091 = 1.72042e-07 loss)
I0425 12:45:31.267175 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0425 12:45:31.267187 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0425 12:45:31.267204 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.734748
I0425 12:45:31.267212 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.429598
I0425 12:45:31.267233 22523 sgd_solver.cpp:106] Iteration 13500, lr = 0.01
I0425 12:51:12.559489 22523 solver.cpp:229] Iteration 14000, loss = 2.96695
I0425 12:51:12.559643 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.630435
I0425 12:51:12.559664 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 12:51:12.559685 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.25
I0425 12:51:12.559698 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 12:51:12.559710 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 12:51:12.559722 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.5
I0425 12:51:12.559742 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 12:51:12.559754 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 12:51:12.559767 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 12:51:12.559779 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 12:51:12.559792 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 12:51:12.559804 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 12:51:12.559816 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 12:51:12.559828 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 12:51:12.559840 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 12:51:12.559852 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 12:51:12.559864 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 12:51:12.559876 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 12:51:12.559888 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 12:51:12.559900 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 12:51:12.559912 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 12:51:12.559924 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 12:51:12.559937 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 12:51:12.559948 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.897727
I0425 12:51:12.559960 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.804348
I0425 12:51:12.559978 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.54349 (* 0.3 = 0.463048 loss)
I0425 12:51:12.559993 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.429918 (* 0.3 = 0.128975 loss)
I0425 12:51:12.560008 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.551805 (* 0.0272727 = 0.0150492 loss)
I0425 12:51:12.560022 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.57394 (* 0.0272727 = 0.0701983 loss)
I0425 12:51:12.560036 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.55979 (* 0.0272727 = 0.0425398 loss)
I0425 12:51:12.560051 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.13851 (* 0.0272727 = 0.0583231 loss)
I0425 12:51:12.560065 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.2401 (* 0.0272727 = 0.0610936 loss)
I0425 12:51:12.560080 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.984302 (* 0.0272727 = 0.0268446 loss)
I0425 12:51:12.560094 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.09889 (* 0.0272727 = 0.0299698 loss)
I0425 12:51:12.560109 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.133238 (* 0.0272727 = 0.00363376 loss)
I0425 12:51:12.560124 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0101206 (* 0.0272727 = 0.000276016 loss)
I0425 12:51:12.560139 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00902867 (* 0.0272727 = 0.000246236 loss)
I0425 12:51:12.560154 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00852316 (* 0.0272727 = 0.00023245 loss)
I0425 12:51:12.560168 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00498284 (* 0.0272727 = 0.000135896 loss)
I0425 12:51:12.560204 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00270355 (* 0.0272727 = 7.37331e-05 loss)
I0425 12:51:12.560225 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00236367 (* 0.0272727 = 6.44637e-05 loss)
I0425 12:51:12.560240 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00163836 (* 0.0272727 = 4.46825e-05 loss)
I0425 12:51:12.560255 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000936928 (* 0.0272727 = 2.55526e-05 loss)
I0425 12:51:12.560269 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00100264 (* 0.0272727 = 2.73447e-05 loss)
I0425 12:51:12.560284 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000274408 (* 0.0272727 = 7.48386e-06 loss)
I0425 12:51:12.560299 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 8.93462e-05 (* 0.0272727 = 2.43671e-06 loss)
I0425 12:51:12.560314 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 9.82943e-05 (* 0.0272727 = 2.68075e-06 loss)
I0425 12:51:12.560328 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 7.7628e-05 (* 0.0272727 = 2.11713e-06 loss)
I0425 12:51:12.560343 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 5.93501e-05 (* 0.0272727 = 1.61864e-06 loss)
I0425 12:51:12.560355 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.73913
I0425 12:51:12.560367 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 12:51:12.560379 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0425 12:51:12.560390 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 12:51:12.560402 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0425 12:51:12.560415 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 12:51:12.560426 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 12:51:12.560437 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0425 12:51:12.560449 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 12:51:12.560461 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 12:51:12.560472 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 12:51:12.560483 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 12:51:12.560494 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 12:51:12.560505 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 12:51:12.560518 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 12:51:12.560528 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 12:51:12.560539 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 12:51:12.560550 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 12:51:12.560561 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 12:51:12.560573 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 12:51:12.560585 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 12:51:12.560595 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 12:51:12.560607 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 12:51:12.560618 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.931818
I0425 12:51:12.560631 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.913043
I0425 12:51:12.560648 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.16648 (* 0.3 = 0.349944 loss)
I0425 12:51:12.560663 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.32076 (* 0.3 = 0.0962279 loss)
I0425 12:51:12.560678 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.199244 (* 0.0272727 = 0.00543393 loss)
I0425 12:51:12.560693 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 2.44807 (* 0.0272727 = 0.0667656 loss)
I0425 12:51:12.560719 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.13655 (* 0.0272727 = 0.0309969 loss)
I0425 12:51:12.560734 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.23058 (* 0.0272727 = 0.0608341 loss)
I0425 12:51:12.560747 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.03967 (* 0.0272727 = 0.0556274 loss)
I0425 12:51:12.560761 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.0192 (* 0.0272727 = 0.0277962 loss)
I0425 12:51:12.560775 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.937762 (* 0.0272727 = 0.0255753 loss)
I0425 12:51:12.560791 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.0249825 (* 0.0272727 = 0.000681341 loss)
I0425 12:51:12.560804 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.00464997 (* 0.0272727 = 0.000126817 loss)
I0425 12:51:12.560818 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00252063 (* 0.0272727 = 6.87444e-05 loss)
I0425 12:51:12.560832 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00359276 (* 0.0272727 = 9.79844e-05 loss)
I0425 12:51:12.560847 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00170695 (* 0.0272727 = 4.65531e-05 loss)
I0425 12:51:12.560860 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00113341 (* 0.0272727 = 3.09112e-05 loss)
I0425 12:51:12.560875 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000483134 (* 0.0272727 = 1.31764e-05 loss)
I0425 12:51:12.560889 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000216504 (* 0.0272727 = 5.90467e-06 loss)
I0425 12:51:12.560904 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 7.50349e-05 (* 0.0272727 = 2.04641e-06 loss)
I0425 12:51:12.560919 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000157385 (* 0.0272727 = 4.29233e-06 loss)
I0425 12:51:12.560932 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 5.1356e-05 (* 0.0272727 = 1.40062e-06 loss)
I0425 12:51:12.560946 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 1.3769e-05 (* 0.0272727 = 3.75518e-07 loss)
I0425 12:51:12.560961 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 1.40672e-05 (* 0.0272727 = 3.8365e-07 loss)
I0425 12:51:12.560976 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 5.78173e-06 (* 0.0272727 = 1.57683e-07 loss)
I0425 12:51:12.560989 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 9.00054e-06 (* 0.0272727 = 2.45469e-07 loss)
I0425 12:51:12.561002 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.869565
I0425 12:51:12.561014 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 12:51:12.561025 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 12:51:12.561038 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 12:51:12.561049 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0425 12:51:12.561060 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 12:51:12.561072 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 12:51:12.561084 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 12:51:12.561096 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 12:51:12.561107 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 12:51:12.561118 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 12:51:12.561131 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 12:51:12.561141 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 12:51:12.561152 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 12:51:12.561164 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 12:51:12.561175 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 12:51:12.561187 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 12:51:12.561209 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 12:51:12.561223 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 12:51:12.561234 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 12:51:12.561245 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 12:51:12.561260 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 12:51:12.561272 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 12:51:12.561285 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.965909
I0425 12:51:12.561296 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.956522
I0425 12:51:12.561311 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.626915 (* 1 = 0.626915 loss)
I0425 12:51:12.561324 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.168263 (* 1 = 0.168263 loss)
I0425 12:51:12.561343 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0275062 (* 0.0909091 = 0.00250057 loss)
I0425 12:51:12.561358 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.155646 (* 0.0909091 = 0.0141496 loss)
I0425 12:51:12.561372 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.0296562 (* 0.0909091 = 0.00269602 loss)
I0425 12:51:12.561388 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.586377 (* 0.0909091 = 0.053307 loss)
I0425 12:51:12.561400 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.873116 (* 0.0909091 = 0.0793741 loss)
I0425 12:51:12.561414 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.811314 (* 0.0909091 = 0.0737558 loss)
I0425 12:51:12.561434 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.885503 (* 0.0909091 = 0.0805003 loss)
I0425 12:51:12.561447 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.00526601 (* 0.0909091 = 0.000478728 loss)
I0425 12:51:12.561461 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.000285374 (* 0.0909091 = 2.59431e-05 loss)
I0425 12:51:12.561475 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.000164205 (* 0.0909091 = 1.49277e-05 loss)
I0425 12:51:12.561489 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00099758 (* 0.0909091 = 9.0689e-05 loss)
I0425 12:51:12.561508 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.000550787 (* 0.0909091 = 5.00715e-05 loss)
I0425 12:51:12.561522 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000434765 (* 0.0909091 = 3.9524e-05 loss)
I0425 12:51:12.561535 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000232605 (* 0.0909091 = 2.11459e-05 loss)
I0425 12:51:12.561549 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000123179 (* 0.0909091 = 1.11981e-05 loss)
I0425 12:51:12.561563 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 8.30978e-05 (* 0.0909091 = 7.55435e-06 loss)
I0425 12:51:12.561578 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 5.71443e-05 (* 0.0909091 = 5.19493e-06 loss)
I0425 12:51:12.561591 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 4.61153e-05 (* 0.0909091 = 4.1923e-06 loss)
I0425 12:51:12.561605 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 3.8358e-05 (* 0.0909091 = 3.48709e-06 loss)
I0425 12:51:12.561619 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 3.37381e-05 (* 0.0909091 = 3.0671e-06 loss)
I0425 12:51:12.561633 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 1.22192e-05 (* 0.0909091 = 1.11084e-06 loss)
I0425 12:51:12.561648 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 4.6343e-06 (* 0.0909091 = 4.213e-07 loss)
I0425 12:51:12.561661 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 12:51:12.561672 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 12:51:12.561698 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.664857
I0425 12:51:12.561712 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.451256
I0425 12:51:12.561727 22523 sgd_solver.cpp:106] Iteration 14000, lr = 0.01
I0425 12:56:53.953660 22523 solver.cpp:229] Iteration 14500, loss = 3.05462
I0425 12:56:53.953814 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.575
I0425 12:56:53.953836 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 12:56:53.953850 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 12:56:53.953862 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0425 12:56:53.953874 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 12:56:53.953886 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0425 12:56:53.953899 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 12:56:53.953912 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 12:56:53.953923 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0425 12:56:53.953938 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 12:56:53.953949 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 12:56:53.953961 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 12:56:53.953974 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 12:56:53.953985 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 12:56:53.953999 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 12:56:53.954010 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 12:56:53.954021 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 12:56:53.954033 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 12:56:53.954046 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 12:56:53.954057 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 12:56:53.954069 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 12:56:53.954082 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 12:56:53.954093 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 12:56:53.954105 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.875
I0425 12:56:53.954118 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.825
I0425 12:56:53.954135 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.47132 (* 0.3 = 0.441396 loss)
I0425 12:56:53.954150 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.476328 (* 0.3 = 0.142898 loss)
I0425 12:56:53.954166 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.394606 (* 0.0272727 = 0.010762 loss)
I0425 12:56:53.954181 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.29837 (* 0.0272727 = 0.03541 loss)
I0425 12:56:53.954195 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.94898 (* 0.0272727 = 0.0804267 loss)
I0425 12:56:53.954213 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.93942 (* 0.0272727 = 0.0528933 loss)
I0425 12:56:53.954227 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.28408 (* 0.0272727 = 0.0350202 loss)
I0425 12:56:53.954242 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.51294 (* 0.0272727 = 0.041262 loss)
I0425 12:56:53.954257 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.872989 (* 0.0272727 = 0.0238088 loss)
I0425 12:56:53.954272 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.571273 (* 0.0272727 = 0.0155802 loss)
I0425 12:56:53.954287 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0423718 (* 0.0272727 = 0.00115559 loss)
I0425 12:56:53.954301 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.0163717 (* 0.0272727 = 0.000446502 loss)
I0425 12:56:53.954315 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0126504 (* 0.0272727 = 0.000345011 loss)
I0425 12:56:53.954330 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00501734 (* 0.0272727 = 0.000136837 loss)
I0425 12:56:53.954345 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00225936 (* 0.0272727 = 6.1619e-05 loss)
I0425 12:56:53.954378 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0035325 (* 0.0272727 = 9.63409e-05 loss)
I0425 12:56:53.954396 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00109305 (* 0.0272727 = 2.98104e-05 loss)
I0425 12:56:53.954409 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00109317 (* 0.0272727 = 2.98138e-05 loss)
I0425 12:56:53.954424 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000319846 (* 0.0272727 = 8.72308e-06 loss)
I0425 12:56:53.954439 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000225119 (* 0.0272727 = 6.13962e-06 loss)
I0425 12:56:53.954453 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 5.54397e-05 (* 0.0272727 = 1.51199e-06 loss)
I0425 12:56:53.954468 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 3.84576e-05 (* 0.0272727 = 1.04884e-06 loss)
I0425 12:56:53.954483 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 1.35159e-05 (* 0.0272727 = 3.68615e-07 loss)
I0425 12:56:53.954498 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 1.13699e-05 (* 0.0272727 = 3.10088e-07 loss)
I0425 12:56:53.954510 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.575
I0425 12:56:53.954522 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 12:56:53.954535 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0425 12:56:53.954546 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0425 12:56:53.954558 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 12:56:53.954569 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 12:56:53.954581 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 12:56:53.954593 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 12:56:53.954604 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 12:56:53.954617 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 12:56:53.954628 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 12:56:53.954639 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 12:56:53.954651 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 12:56:53.954663 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 12:56:53.954674 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 12:56:53.954685 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 12:56:53.954696 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 12:56:53.954707 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 12:56:53.954718 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 12:56:53.954730 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 12:56:53.954741 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 12:56:53.954752 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 12:56:53.954763 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 12:56:53.954776 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.886364
I0425 12:56:53.954787 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.825
I0425 12:56:53.954800 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.25634 (* 0.3 = 0.376901 loss)
I0425 12:56:53.954815 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.372494 (* 0.3 = 0.111748 loss)
I0425 12:56:53.954833 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.145703 (* 0.0272727 = 0.00397373 loss)
I0425 12:56:53.954849 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.00811 (* 0.0272727 = 0.0274938 loss)
I0425 12:56:53.954874 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.56419 (* 0.0272727 = 0.0699325 loss)
I0425 12:56:53.954890 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.5075 (* 0.0272727 = 0.0411136 loss)
I0425 12:56:53.954903 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.9115 (* 0.0272727 = 0.0521317 loss)
I0425 12:56:53.954917 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.10462 (* 0.0272727 = 0.0301261 loss)
I0425 12:56:53.954931 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.796041 (* 0.0272727 = 0.0217102 loss)
I0425 12:56:53.954946 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.880032 (* 0.0272727 = 0.0240009 loss)
I0425 12:56:53.954960 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.106038 (* 0.0272727 = 0.00289195 loss)
I0425 12:56:53.954974 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.0279216 (* 0.0272727 = 0.000761499 loss)
I0425 12:56:53.954988 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00440279 (* 0.0272727 = 0.000120076 loss)
I0425 12:56:53.955003 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00393441 (* 0.0272727 = 0.000107302 loss)
I0425 12:56:53.955018 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.000994064 (* 0.0272727 = 2.71108e-05 loss)
I0425 12:56:53.955031 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000521282 (* 0.0272727 = 1.42168e-05 loss)
I0425 12:56:53.955045 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000485726 (* 0.0272727 = 1.32471e-05 loss)
I0425 12:56:53.955060 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000215062 (* 0.0272727 = 5.86533e-06 loss)
I0425 12:56:53.955073 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 5.52664e-05 (* 0.0272727 = 1.50726e-06 loss)
I0425 12:56:53.955087 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 2.68089e-05 (* 0.0272727 = 7.31153e-07 loss)
I0425 12:56:53.955102 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 1.00883e-05 (* 0.0272727 = 2.75136e-07 loss)
I0425 12:56:53.955116 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 3.44219e-06 (* 0.0272727 = 9.38779e-08 loss)
I0425 12:56:53.955130 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 9.08972e-07 (* 0.0272727 = 2.47902e-08 loss)
I0425 12:56:53.955145 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 5.21541e-07 (* 0.0272727 = 1.42238e-08 loss)
I0425 12:56:53.955157 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.75
I0425 12:56:53.955169 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 12:56:53.955180 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 12:56:53.955193 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0425 12:56:53.955204 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 12:56:53.955215 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 12:56:53.955227 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 12:56:53.955238 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 12:56:53.955252 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.625
I0425 12:56:53.955265 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 12:56:53.955277 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 12:56:53.955288 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 12:56:53.955299 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 12:56:53.955312 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 12:56:53.955322 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 12:56:53.955333 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 12:56:53.955345 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 12:56:53.955384 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 12:56:53.955397 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 12:56:53.955410 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 12:56:53.955421 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 12:56:53.955432 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 12:56:53.955445 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 12:56:53.955456 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.914773
I0425 12:56:53.955468 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.9
I0425 12:56:53.955482 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.821041 (* 1 = 0.821041 loss)
I0425 12:56:53.955497 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.297806 (* 1 = 0.297806 loss)
I0425 12:56:53.955512 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0296934 (* 0.0909091 = 0.0026994 loss)
I0425 12:56:53.955525 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.303645 (* 0.0909091 = 0.0276041 loss)
I0425 12:56:53.955539 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 1.27201 (* 0.0909091 = 0.115637 loss)
I0425 12:56:53.955554 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.709635 (* 0.0909091 = 0.0645123 loss)
I0425 12:56:53.955567 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.78336 (* 0.0909091 = 0.0712145 loss)
I0425 12:56:53.955581 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.788687 (* 0.0909091 = 0.0716988 loss)
I0425 12:56:53.955595 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.647073 (* 0.0909091 = 0.0588248 loss)
I0425 12:56:53.955610 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.635095 (* 0.0909091 = 0.0577359 loss)
I0425 12:56:53.955623 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.133921 (* 0.0909091 = 0.0121746 loss)
I0425 12:56:53.955638 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0278037 (* 0.0909091 = 0.00252761 loss)
I0425 12:56:53.955652 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.0173439 (* 0.0909091 = 0.00157672 loss)
I0425 12:56:53.955667 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.0103664 (* 0.0909091 = 0.0009424 loss)
I0425 12:56:53.955682 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00489823 (* 0.0909091 = 0.000445294 loss)
I0425 12:56:53.955695 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00280795 (* 0.0909091 = 0.000255268 loss)
I0425 12:56:53.955709 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00240467 (* 0.0909091 = 0.000218606 loss)
I0425 12:56:53.955724 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00171765 (* 0.0909091 = 0.00015615 loss)
I0425 12:56:53.955739 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000963741 (* 0.0909091 = 8.76128e-05 loss)
I0425 12:56:53.955752 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00060677 (* 0.0909091 = 5.51609e-05 loss)
I0425 12:56:53.955762 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000357746 (* 0.0909091 = 3.25224e-05 loss)
I0425 12:56:53.955772 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000258614 (* 0.0909091 = 2.35104e-05 loss)
I0425 12:56:53.955786 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000110474 (* 0.0909091 = 1.00431e-05 loss)
I0425 12:56:53.955801 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.10702e-05 (* 0.0909091 = 2.82456e-06 loss)
I0425 12:56:53.955813 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0425 12:56:53.955826 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0425 12:56:53.955848 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.557251
I0425 12:56:53.955862 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.532522
I0425 12:56:53.955880 22523 sgd_solver.cpp:106] Iteration 14500, lr = 0.01
I0425 13:02:06.212210 22523 sgd_solver.cpp:92] Gradient clipping: scaling down gradients (L2 norm 46.7986 > 30) by scale factor 0.641045
I0425 13:02:34.900357 22523 solver.cpp:338] Iteration 15000, Testing net (#0)
I0425 13:03:26.439437 22523 solver.cpp:393] Test loss: 1.53709
I0425 13:03:26.439561 22523 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.744025
I0425 13:03:26.439580 22523 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.879
I0425 13:03:26.439594 22523 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.672
I0425 13:03:26.439607 22523 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.534
I0425 13:03:26.439620 22523 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.506
I0425 13:03:26.439632 22523 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.588
I0425 13:03:26.439645 22523 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.67
I0425 13:03:26.439656 22523 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.804
I0425 13:03:26.439669 22523 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.914
I0425 13:03:26.439682 22523 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.981
I0425 13:03:26.439694 22523 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.994
I0425 13:03:26.439707 22523 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.999
I0425 13:03:26.439718 22523 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.999
I0425 13:03:26.439730 22523 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.999
I0425 13:03:26.439743 22523 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 1
I0425 13:03:26.439754 22523 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 1
I0425 13:03:26.439766 22523 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 1
I0425 13:03:26.439779 22523 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 1
I0425 13:03:26.439790 22523 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 1
I0425 13:03:26.439802 22523 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 1
I0425 13:03:26.439813 22523 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 1
I0425 13:03:26.439826 22523 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0425 13:03:26.439837 22523 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0425 13:03:26.439848 22523 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.919728
I0425 13:03:26.439862 22523 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.916103
I0425 13:03:26.439878 22523 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 0.85125 (* 0.3 = 0.255375 loss)
I0425 13:03:26.439894 22523 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.26647 (* 0.3 = 0.0799409 loss)
I0425 13:03:26.439908 22523 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.509566 (* 0.0272727 = 0.0138972 loss)
I0425 13:03:26.439924 22523 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.08951 (* 0.0272727 = 0.029714 loss)
I0425 13:03:26.439937 22523 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.42948 (* 0.0272727 = 0.0389859 loss)
I0425 13:03:26.439952 22523 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.48426 (* 0.0272727 = 0.0404798 loss)
I0425 13:03:26.439966 22523 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.28146 (* 0.0272727 = 0.0349488 loss)
I0425 13:03:26.439980 22523 solver.cpp:406]     Test net output #32: loss1/loss06 = 0.969239 (* 0.0272727 = 0.0264338 loss)
I0425 13:03:26.439995 22523 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.580005 (* 0.0272727 = 0.0158183 loss)
I0425 13:03:26.440009 22523 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.303177 (* 0.0272727 = 0.00826847 loss)
I0425 13:03:26.440023 22523 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.0855596 (* 0.0272727 = 0.00233344 loss)
I0425 13:03:26.440038 22523 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.0340021 (* 0.0272727 = 0.000927331 loss)
I0425 13:03:26.440053 22523 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.0168168 (* 0.0272727 = 0.000458641 loss)
I0425 13:03:26.440068 22523 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.0101703 (* 0.0272727 = 0.000277372 loss)
I0425 13:03:26.440083 22523 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.00733002 (* 0.0272727 = 0.00019991 loss)
I0425 13:03:26.440116 22523 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.00487551 (* 0.0272727 = 0.000132969 loss)
I0425 13:03:26.440132 22523 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.00375365 (* 0.0272727 = 0.000102372 loss)
I0425 13:03:26.440147 22523 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.00177192 (* 0.0272727 = 4.83251e-05 loss)
I0425 13:03:26.440162 22523 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.000709636 (* 0.0272727 = 1.93537e-05 loss)
I0425 13:03:26.440177 22523 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.000369912 (* 0.0272727 = 1.00885e-05 loss)
I0425 13:03:26.440191 22523 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.000171336 (* 0.0272727 = 4.67279e-06 loss)
I0425 13:03:26.440208 22523 solver.cpp:406]     Test net output #46: loss1/loss20 = 9.69515e-05 (* 0.0272727 = 2.64413e-06 loss)
I0425 13:03:26.440223 22523 solver.cpp:406]     Test net output #47: loss1/loss21 = 5.12442e-05 (* 0.0272727 = 1.39757e-06 loss)
I0425 13:03:26.440246 22523 solver.cpp:406]     Test net output #48: loss1/loss22 = 3.05602e-05 (* 0.0272727 = 8.3346e-07 loss)
I0425 13:03:26.440263 22523 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.884829
I0425 13:03:26.440275 22523 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.946
I0425 13:03:26.440287 22523 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.879
I0425 13:03:26.440299 22523 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.752
I0425 13:03:26.440310 22523 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.631
I0425 13:03:26.440326 22523 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.651
I0425 13:03:26.440337 22523 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.745
I0425 13:03:26.440348 22523 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.88
I0425 13:03:26.440361 22523 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.926
I0425 13:03:26.440371 22523 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.98
I0425 13:03:26.440383 22523 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.993
I0425 13:03:26.440395 22523 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.998
I0425 13:03:26.440407 22523 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.999
I0425 13:03:26.440418 22523 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.999
I0425 13:03:26.440429 22523 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.999
I0425 13:03:26.440441 22523 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 1
I0425 13:03:26.440453 22523 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 1
I0425 13:03:26.440464 22523 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 1
I0425 13:03:26.440474 22523 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 1
I0425 13:03:26.440486 22523 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 1
I0425 13:03:26.440497 22523 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 1
I0425 13:03:26.440508 22523 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0425 13:03:26.440520 22523 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0425 13:03:26.440531 22523 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.959455
I0425 13:03:26.440541 22523 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.962639
I0425 13:03:26.440556 22523 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.431345 (* 0.3 = 0.129403 loss)
I0425 13:03:26.440569 22523 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.14481 (* 0.3 = 0.0434431 loss)
I0425 13:03:26.440583 22523 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.264182 (* 0.0272727 = 0.00720496 loss)
I0425 13:03:26.440598 22523 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.497769 (* 0.0272727 = 0.0135755 loss)
I0425 13:03:26.440623 22523 solver.cpp:406]     Test net output #78: loss2/loss03 = 0.827161 (* 0.0272727 = 0.0225589 loss)
I0425 13:03:26.440639 22523 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.05861 (* 0.0272727 = 0.0288713 loss)
I0425 13:03:26.440652 22523 solver.cpp:406]     Test net output #80: loss2/loss05 = 0.963342 (* 0.0272727 = 0.026273 loss)
I0425 13:03:26.440665 22523 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.755661 (* 0.0272727 = 0.0206089 loss)
I0425 13:03:26.440680 22523 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.434694 (* 0.0272727 = 0.0118553 loss)
I0425 13:03:26.440693 22523 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.233869 (* 0.0272727 = 0.00637825 loss)
I0425 13:03:26.440707 22523 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.0774089 (* 0.0272727 = 0.00211115 loss)
I0425 13:03:26.440721 22523 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.0344433 (* 0.0272727 = 0.000939363 loss)
I0425 13:03:26.440735 22523 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.0180318 (* 0.0272727 = 0.000491776 loss)
I0425 13:03:26.440749 22523 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.0113805 (* 0.0272727 = 0.000310378 loss)
I0425 13:03:26.440763 22523 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.00761668 (* 0.0272727 = 0.000207728 loss)
I0425 13:03:26.440778 22523 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.00551422 (* 0.0272727 = 0.000150388 loss)
I0425 13:03:26.440791 22523 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.00386871 (* 0.0272727 = 0.00010551 loss)
I0425 13:03:26.440805 22523 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.00202235 (* 0.0272727 = 5.5155e-05 loss)
I0425 13:03:26.440819 22523 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.000700229 (* 0.0272727 = 1.90972e-05 loss)
I0425 13:03:26.440834 22523 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.000322236 (* 0.0272727 = 8.78826e-06 loss)
I0425 13:03:26.440847 22523 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.000130372 (* 0.0272727 = 3.55559e-06 loss)
I0425 13:03:26.440861 22523 solver.cpp:406]     Test net output #95: loss2/loss20 = 6.22659e-05 (* 0.0272727 = 1.69816e-06 loss)
I0425 13:03:26.440876 22523 solver.cpp:406]     Test net output #96: loss2/loss21 = 4.14388e-05 (* 0.0272727 = 1.13015e-06 loss)
I0425 13:03:26.440889 22523 solver.cpp:406]     Test net output #97: loss2/loss22 = 2.06476e-05 (* 0.0272727 = 5.63116e-07 loss)
I0425 13:03:26.440902 22523 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.919756
I0425 13:03:26.440913 22523 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.953
I0425 13:03:26.440927 22523 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.93
I0425 13:03:26.440937 22523 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.931
I0425 13:03:26.440949 22523 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.905
I0425 13:03:26.440960 22523 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.902
I0425 13:03:26.440973 22523 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.889
I0425 13:03:26.440984 22523 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.918
I0425 13:03:26.440994 22523 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.957
I0425 13:03:26.441005 22523 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.981
I0425 13:03:26.441017 22523 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.994
I0425 13:03:26.441028 22523 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.998
I0425 13:03:26.441040 22523 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.999
I0425 13:03:26.441051 22523 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.999
I0425 13:03:26.441062 22523 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.999
I0425 13:03:26.441074 22523 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.999
I0425 13:03:26.441085 22523 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 1
I0425 13:03:26.441107 22523 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 1
I0425 13:03:26.441119 22523 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 1
I0425 13:03:26.441131 22523 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 1
I0425 13:03:26.441143 22523 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 1
I0425 13:03:26.441154 22523 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0425 13:03:26.441165 22523 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0425 13:03:26.441176 22523 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.972955
I0425 13:03:26.441189 22523 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.971404
I0425 13:03:26.441202 22523 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.327676 (* 1 = 0.327676 loss)
I0425 13:03:26.441216 22523 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.109819 (* 1 = 0.109819 loss)
I0425 13:03:26.441229 22523 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.204162 (* 0.0909091 = 0.0185602 loss)
I0425 13:03:26.441243 22523 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.326225 (* 0.0909091 = 0.0296568 loss)
I0425 13:03:26.441260 22523 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.306638 (* 0.0909091 = 0.0278762 loss)
I0425 13:03:26.441274 22523 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.382483 (* 0.0909091 = 0.0347712 loss)
I0425 13:03:26.441288 22523 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.388336 (* 0.0909091 = 0.0353032 loss)
I0425 13:03:26.441305 22523 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.41449 (* 0.0909091 = 0.0376809 loss)
I0425 13:03:26.441319 22523 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.294186 (* 0.0909091 = 0.0267442 loss)
I0425 13:03:26.441334 22523 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.151036 (* 0.0909091 = 0.0137305 loss)
I0425 13:03:26.441347 22523 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.0628351 (* 0.0909091 = 0.00571228 loss)
I0425 13:03:26.441361 22523 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.0256188 (* 0.0909091 = 0.00232899 loss)
I0425 13:03:26.441375 22523 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.0154491 (* 0.0909091 = 0.00140447 loss)
I0425 13:03:26.441390 22523 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.00994234 (* 0.0909091 = 0.000903849 loss)
I0425 13:03:26.441403 22523 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.00717898 (* 0.0909091 = 0.000652634 loss)
I0425 13:03:26.441417 22523 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.00574725 (* 0.0909091 = 0.000522477 loss)
I0425 13:03:26.441431 22523 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.00453183 (* 0.0909091 = 0.000411985 loss)
I0425 13:03:26.441445 22523 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.00216664 (* 0.0909091 = 0.000196967 loss)
I0425 13:03:26.441459 22523 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.000717549 (* 0.0909091 = 6.52317e-05 loss)
I0425 13:03:26.441473 22523 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.000466841 (* 0.0909091 = 4.24401e-05 loss)
I0425 13:03:26.441488 22523 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.000359761 (* 0.0909091 = 3.27056e-05 loss)
I0425 13:03:26.441501 22523 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.000272499 (* 0.0909091 = 2.47726e-05 loss)
I0425 13:03:26.441512 22523 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.000113827 (* 0.0909091 = 1.03479e-05 loss)
I0425 13:03:26.441521 22523 solver.cpp:406]     Test net output #146: loss3/loss22 = 3.60497e-05 (* 0.0909091 = 3.27724e-06 loss)
I0425 13:03:26.441534 22523 solver.cpp:406]     Test net output #147: total_accuracy = 0.785
I0425 13:03:26.441545 22523 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.705
I0425 13:03:26.441566 22523 solver.cpp:406]     Test net output #149: total_confidence = 0.762435
I0425 13:03:26.441579 22523 solver.cpp:406]     Test net output #150: total_confidence_nor_rec = 0.603414
I0425 13:03:26.441594 22523 solver.cpp:338] Iteration 15000, Testing net (#1)
I0425 13:04:18.053591 22523 solver.cpp:393] Test loss: 2.67589
I0425 13:04:18.053791 22523 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.677389
I0425 13:04:18.053822 22523 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.818
I0425 13:04:18.053834 22523 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.629
I0425 13:04:18.053848 22523 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.483
I0425 13:04:18.053860 22523 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.476
I0425 13:04:18.053881 22523 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.546
I0425 13:04:18.053894 22523 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.609
I0425 13:04:18.053906 22523 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.698
I0425 13:04:18.053920 22523 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.824
I0425 13:04:18.053931 22523 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.901
I0425 13:04:18.053944 22523 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.904
I0425 13:04:18.053957 22523 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.915
I0425 13:04:18.053969 22523 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.924
I0425 13:04:18.053982 22523 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.938
I0425 13:04:18.053994 22523 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 0.951
I0425 13:04:18.054013 22523 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 0.964
I0425 13:04:18.054025 22523 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 0.97
I0425 13:04:18.054038 22523 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 0.99
I0425 13:04:18.054049 22523 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 0.992
I0425 13:04:18.054061 22523 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 0.994
I0425 13:04:18.054074 22523 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 0.998
I0425 13:04:18.054085 22523 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0425 13:04:18.054097 22523 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0425 13:04:18.054111 22523 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.870819
I0425 13:04:18.054122 22523 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.860137
I0425 13:04:18.054141 22523 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.07471 (* 0.3 = 0.322414 loss)
I0425 13:04:18.054155 22523 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.441408 (* 0.3 = 0.132422 loss)
I0425 13:04:18.054170 22523 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.704379 (* 0.0272727 = 0.0192103 loss)
I0425 13:04:18.054185 22523 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.19959 (* 0.0272727 = 0.0327162 loss)
I0425 13:04:18.054203 22523 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.59523 (* 0.0272727 = 0.0435062 loss)
I0425 13:04:18.054217 22523 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.64717 (* 0.0272727 = 0.0449228 loss)
I0425 13:04:18.054232 22523 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.48559 (* 0.0272727 = 0.0405161 loss)
I0425 13:04:18.054246 22523 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.25749 (* 0.0272727 = 0.0342951 loss)
I0425 13:04:18.054260 22523 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.96807 (* 0.0272727 = 0.0264019 loss)
I0425 13:04:18.054275 22523 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.625462 (* 0.0272727 = 0.0170581 loss)
I0425 13:04:18.054289 22523 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.378416 (* 0.0272727 = 0.0103204 loss)
I0425 13:04:18.054303 22523 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.348242 (* 0.0272727 = 0.0094975 loss)
I0425 13:04:18.054318 22523 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.324199 (* 0.0272727 = 0.00884178 loss)
I0425 13:04:18.054332 22523 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.294603 (* 0.0272727 = 0.00803464 loss)
I0425 13:04:18.054360 22523 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.235035 (* 0.0272727 = 0.00641006 loss)
I0425 13:04:18.054376 22523 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.212487 (* 0.0272727 = 0.00579511 loss)
I0425 13:04:18.054390 22523 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.164056 (* 0.0272727 = 0.00447425 loss)
I0425 13:04:18.054404 22523 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.155286 (* 0.0272727 = 0.00423507 loss)
I0425 13:04:18.054419 22523 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.0670209 (* 0.0272727 = 0.00182784 loss)
I0425 13:04:18.054435 22523 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.0537765 (* 0.0272727 = 0.00146663 loss)
I0425 13:04:18.054448 22523 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.0474134 (* 0.0272727 = 0.00129309 loss)
I0425 13:04:18.054462 22523 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.0191631 (* 0.0272727 = 0.000522631 loss)
I0425 13:04:18.054477 22523 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.000673694 (* 0.0272727 = 1.83735e-05 loss)
I0425 13:04:18.054491 22523 solver.cpp:406]     Test net output #48: loss1/loss22 = 0.000372726 (* 0.0272727 = 1.01653e-05 loss)
I0425 13:04:18.054504 22523 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.813319
I0425 13:04:18.054515 22523 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.902
I0425 13:04:18.054527 22523 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.832
I0425 13:04:18.054539 22523 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.675
I0425 13:04:18.054550 22523 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.591
I0425 13:04:18.054563 22523 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.606
I0425 13:04:18.054574 22523 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.666
I0425 13:04:18.054594 22523 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.779
I0425 13:04:18.054605 22523 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.834
I0425 13:04:18.054616 22523 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.899
I0425 13:04:18.054627 22523 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.909
I0425 13:04:18.054639 22523 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.919
I0425 13:04:18.054658 22523 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.928
I0425 13:04:18.054671 22523 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.939
I0425 13:04:18.054682 22523 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.948
I0425 13:04:18.054692 22523 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.964
I0425 13:04:18.054703 22523 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 0.97
I0425 13:04:18.054715 22523 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 0.99
I0425 13:04:18.054726 22523 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 0.992
I0425 13:04:18.054738 22523 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 0.994
I0425 13:04:18.054749 22523 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 0.998
I0425 13:04:18.054761 22523 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0425 13:04:18.054772 22523 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0425 13:04:18.054783 22523 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.913364
I0425 13:04:18.054798 22523 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.913727
I0425 13:04:18.054813 22523 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.687838 (* 0.3 = 0.206351 loss)
I0425 13:04:18.054827 22523 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.311589 (* 0.3 = 0.0934768 loss)
I0425 13:04:18.054842 22523 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.43313 (* 0.0272727 = 0.0118126 loss)
I0425 13:04:18.054855 22523 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.612059 (* 0.0272727 = 0.0166925 loss)
I0425 13:04:18.054880 22523 solver.cpp:406]     Test net output #78: loss2/loss03 = 1.06034 (* 0.0272727 = 0.0289182 loss)
I0425 13:04:18.054895 22523 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.25584 (* 0.0272727 = 0.0342503 loss)
I0425 13:04:18.054909 22523 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.1907 (* 0.0272727 = 0.0324736 loss)
I0425 13:04:18.054924 22523 solver.cpp:406]     Test net output #81: loss2/loss06 = 1.0372 (* 0.0272727 = 0.0282871 loss)
I0425 13:04:18.054936 22523 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.780637 (* 0.0272727 = 0.0212901 loss)
I0425 13:04:18.054951 22523 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.556834 (* 0.0272727 = 0.0151864 loss)
I0425 13:04:18.054965 22523 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.357931 (* 0.0272727 = 0.00976175 loss)
I0425 13:04:18.054980 22523 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.334717 (* 0.0272727 = 0.00912865 loss)
I0425 13:04:18.054992 22523 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.318861 (* 0.0272727 = 0.00869622 loss)
I0425 13:04:18.055006 22523 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.287559 (* 0.0272727 = 0.00784252 loss)
I0425 13:04:18.055021 22523 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.238223 (* 0.0272727 = 0.00649698 loss)
I0425 13:04:18.055034 22523 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.209769 (* 0.0272727 = 0.00572097 loss)
I0425 13:04:18.055048 22523 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.160861 (* 0.0272727 = 0.00438713 loss)
I0425 13:04:18.055063 22523 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.144474 (* 0.0272727 = 0.0039402 loss)
I0425 13:04:18.055076 22523 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.0592801 (* 0.0272727 = 0.00161673 loss)
I0425 13:04:18.055090 22523 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.0447156 (* 0.0272727 = 0.00121952 loss)
I0425 13:04:18.055104 22523 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.0365088 (* 0.0272727 = 0.000995695 loss)
I0425 13:04:18.055119 22523 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.0150265 (* 0.0272727 = 0.000409814 loss)
I0425 13:04:18.055132 22523 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.00110906 (* 0.0272727 = 3.02471e-05 loss)
I0425 13:04:18.055150 22523 solver.cpp:406]     Test net output #97: loss2/loss22 = 0.000561893 (* 0.0272727 = 1.53244e-05 loss)
I0425 13:04:18.055161 22523 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.862576
I0425 13:04:18.055173 22523 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.924
I0425 13:04:18.055184 22523 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.914
I0425 13:04:18.055196 22523 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.879
I0425 13:04:18.055207 22523 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.859
I0425 13:04:18.055218 22523 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.847
I0425 13:04:18.055229 22523 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.802
I0425 13:04:18.055240 22523 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.829
I0425 13:04:18.055255 22523 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.859
I0425 13:04:18.055268 22523 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.906
I0425 13:04:18.055279 22523 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.918
I0425 13:04:18.055290 22523 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.92
I0425 13:04:18.055301 22523 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.932
I0425 13:04:18.055312 22523 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.943
I0425 13:04:18.055325 22523 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.952
I0425 13:04:18.055335 22523 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.964
I0425 13:04:18.055346 22523 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 0.97
I0425 13:04:18.055384 22523 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 0.989
I0425 13:04:18.055398 22523 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 0.991
I0425 13:04:18.055410 22523 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 0.995
I0425 13:04:18.055421 22523 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 0.998
I0425 13:04:18.055433 22523 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0425 13:04:18.055444 22523 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0425 13:04:18.055455 22523 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.932909
I0425 13:04:18.055467 22523 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.936293
I0425 13:04:18.055481 22523 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.539762 (* 1 = 0.539762 loss)
I0425 13:04:18.055495 22523 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.250803 (* 1 = 0.250803 loss)
I0425 13:04:18.055508 22523 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.332699 (* 0.0909091 = 0.0302454 loss)
I0425 13:04:18.055521 22523 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.381317 (* 0.0909091 = 0.0346652 loss)
I0425 13:04:18.055536 22523 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.491579 (* 0.0909091 = 0.044689 loss)
I0425 13:04:18.055549 22523 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.567492 (* 0.0909091 = 0.0515902 loss)
I0425 13:04:18.055562 22523 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.622051 (* 0.0909091 = 0.0565501 loss)
I0425 13:04:18.055577 22523 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.697144 (* 0.0909091 = 0.0633767 loss)
I0425 13:04:18.055589 22523 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.622452 (* 0.0909091 = 0.0565865 loss)
I0425 13:04:18.055603 22523 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.448272 (* 0.0909091 = 0.040752 loss)
I0425 13:04:18.055618 22523 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.32611 (* 0.0909091 = 0.0296464 loss)
I0425 13:04:18.055631 22523 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.292887 (* 0.0909091 = 0.0266261 loss)
I0425 13:04:18.055645 22523 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.298036 (* 0.0909091 = 0.0270942 loss)
I0425 13:04:18.055660 22523 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.264918 (* 0.0909091 = 0.0240835 loss)
I0425 13:04:18.055673 22523 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.213726 (* 0.0909091 = 0.0194297 loss)
I0425 13:04:18.055687 22523 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.195945 (* 0.0909091 = 0.0178132 loss)
I0425 13:04:18.055701 22523 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.14004 (* 0.0909091 = 0.0127309 loss)
I0425 13:04:18.055714 22523 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.126149 (* 0.0909091 = 0.0114681 loss)
I0425 13:04:18.055728 22523 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.0540343 (* 0.0909091 = 0.00491221 loss)
I0425 13:04:18.055742 22523 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.0355429 (* 0.0909091 = 0.00323117 loss)
I0425 13:04:18.055755 22523 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.0331656 (* 0.0909091 = 0.00301506 loss)
I0425 13:04:18.055769 22523 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.0158786 (* 0.0909091 = 0.00144351 loss)
I0425 13:04:18.055783 22523 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.00154928 (* 0.0909091 = 0.000140844 loss)
I0425 13:04:18.055797 22523 solver.cpp:406]     Test net output #146: loss3/loss22 = 0.000249136 (* 0.0909091 = 2.26487e-05 loss)
I0425 13:04:18.055809 22523 solver.cpp:406]     Test net output #147: total_accuracy = 0.649
I0425 13:04:18.055820 22523 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.579
I0425 13:04:18.055832 22523 solver.cpp:406]     Test net output #149: total_confidence = 0.645714
I0425 13:04:18.055857 22523 solver.cpp:406]     Test net output #150: total_confidence_nor_rec = 0.520323
I0425 13:04:18.448051 22523 solver.cpp:229] Iteration 15000, loss = 3.11564
I0425 13:04:18.448115 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.571429
I0425 13:04:18.448134 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 1
I0425 13:04:18.448148 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.875
I0425 13:04:18.448160 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 13:04:18.448173 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 13:04:18.448186 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.125
I0425 13:04:18.448199 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 13:04:18.448211 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 13:04:18.448225 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0425 13:04:18.448236 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 13:04:18.448249 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 13:04:18.448261 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 13:04:18.448274 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 13:04:18.448287 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 13:04:18.448300 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 13:04:18.448312 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 13:04:18.448324 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 13:04:18.448343 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 13:04:18.448361 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 13:04:18.448374 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 13:04:18.448386 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 13:04:18.448400 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 13:04:18.448415 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 13:04:18.448428 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.840909
I0425 13:04:18.448441 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.785714
I0425 13:04:18.448467 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.24845 (* 0.3 = 0.374536 loss)
I0425 13:04:18.448482 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.452183 (* 0.3 = 0.135655 loss)
I0425 13:04:18.448498 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.346925 (* 0.0272727 = 0.00946159 loss)
I0425 13:04:18.448513 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.08119 (* 0.0272727 = 0.0294871 loss)
I0425 13:04:18.448529 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.75163 (* 0.0272727 = 0.0477716 loss)
I0425 13:04:18.448542 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.1822 (* 0.0272727 = 0.032242 loss)
I0425 13:04:18.448557 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.57338 (* 0.0272727 = 0.0701831 loss)
I0425 13:04:18.448572 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.55851 (* 0.0272727 = 0.0425048 loss)
I0425 13:04:18.448586 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.16499 (* 0.0272727 = 0.0317725 loss)
I0425 13:04:18.448601 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.716509 (* 0.0272727 = 0.0195411 loss)
I0425 13:04:18.448616 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.483247 (* 0.0272727 = 0.0131795 loss)
I0425 13:04:18.448631 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.424047 (* 0.0272727 = 0.0115649 loss)
I0425 13:04:18.448645 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.406102 (* 0.0272727 = 0.0110755 loss)
I0425 13:04:18.448695 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.418999 (* 0.0272727 = 0.0114272 loss)
I0425 13:04:18.448712 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0322362 (* 0.0272727 = 0.00087917 loss)
I0425 13:04:18.448727 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00948699 (* 0.0272727 = 0.000258736 loss)
I0425 13:04:18.448742 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00203937 (* 0.0272727 = 5.56191e-05 loss)
I0425 13:04:18.448756 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000815247 (* 0.0272727 = 2.2234e-05 loss)
I0425 13:04:18.448771 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000510094 (* 0.0272727 = 1.39117e-05 loss)
I0425 13:04:18.448786 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00021347 (* 0.0272727 = 5.8219e-06 loss)
I0425 13:04:18.448801 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 3.5899e-05 (* 0.0272727 = 9.79065e-07 loss)
I0425 13:04:18.448828 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 1.34113e-05 (* 0.0272727 = 3.65761e-07 loss)
I0425 13:04:18.448843 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 7.80829e-06 (* 0.0272727 = 2.12953e-07 loss)
I0425 13:04:18.448858 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 5.8562e-06 (* 0.0272727 = 1.59715e-07 loss)
I0425 13:04:18.448870 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.607143
I0425 13:04:18.448892 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 13:04:18.448904 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0425 13:04:18.448916 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 13:04:18.448928 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0425 13:04:18.448940 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.25
I0425 13:04:18.448952 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 13:04:18.448964 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.5
I0425 13:04:18.448976 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0425 13:04:18.448992 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 13:04:18.449003 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 13:04:18.449015 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 13:04:18.449028 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 13:04:18.449039 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 13:04:18.449051 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 13:04:18.449062 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 13:04:18.449074 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 13:04:18.449085 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 13:04:18.449097 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 13:04:18.449108 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 13:04:18.449120 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 13:04:18.449131 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 13:04:18.449143 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 13:04:18.449156 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.857955
I0425 13:04:18.449167 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.892857
I0425 13:04:18.449182 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.25338 (* 0.3 = 0.376015 loss)
I0425 13:04:18.449196 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.453648 (* 0.3 = 0.136094 loss)
I0425 13:04:18.449223 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.538362 (* 0.0272727 = 0.0146826 loss)
I0425 13:04:18.449237 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.725798 (* 0.0272727 = 0.0197945 loss)
I0425 13:04:18.449252 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.56511 (* 0.0272727 = 0.0426848 loss)
I0425 13:04:18.449266 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.28319 (* 0.0272727 = 0.034996 loss)
I0425 13:04:18.449280 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.85665 (* 0.0272727 = 0.0506358 loss)
I0425 13:04:18.449295 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.970658 (* 0.0272727 = 0.0264725 loss)
I0425 13:04:18.449308 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.00809 (* 0.0272727 = 0.0274933 loss)
I0425 13:04:18.449322 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.640686 (* 0.0272727 = 0.0174732 loss)
I0425 13:04:18.449337 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.483252 (* 0.0272727 = 0.0131796 loss)
I0425 13:04:18.449350 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.454851 (* 0.0272727 = 0.012405 loss)
I0425 13:04:18.449364 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.485553 (* 0.0272727 = 0.0132424 loss)
I0425 13:04:18.449379 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.360714 (* 0.0272727 = 0.00983766 loss)
I0425 13:04:18.449393 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.110495 (* 0.0272727 = 0.0030135 loss)
I0425 13:04:18.449407 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0641899 (* 0.0272727 = 0.00175063 loss)
I0425 13:04:18.449421 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0323365 (* 0.0272727 = 0.000881904 loss)
I0425 13:04:18.449435 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0210478 (* 0.0272727 = 0.00057403 loss)
I0425 13:04:18.449450 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0057507 (* 0.0272727 = 0.000156837 loss)
I0425 13:04:18.449470 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00287521 (* 0.0272727 = 7.84149e-05 loss)
I0425 13:04:18.449484 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0020737 (* 0.0272727 = 5.65555e-05 loss)
I0425 13:04:18.449498 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000719468 (* 0.0272727 = 1.96218e-05 loss)
I0425 13:04:18.449512 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000106186 (* 0.0272727 = 2.89598e-06 loss)
I0425 13:04:18.449527 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000190903 (* 0.0272727 = 5.20644e-06 loss)
I0425 13:04:18.449540 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.75
I0425 13:04:18.449553 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 13:04:18.449564 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 13:04:18.449575 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 13:04:18.449587 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 13:04:18.449599 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 13:04:18.449611 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 13:04:18.449623 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0425 13:04:18.449635 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 13:04:18.449647 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 13:04:18.449659 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 13:04:18.449671 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 13:04:18.449682 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 13:04:18.449694 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 13:04:18.449717 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 13:04:18.449730 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 13:04:18.449743 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 13:04:18.449754 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 13:04:18.449766 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 13:04:18.449777 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 13:04:18.449790 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 13:04:18.449800 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 13:04:18.449812 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 13:04:18.449823 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.903409
I0425 13:04:18.449836 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.910714
I0425 13:04:18.449851 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.704333 (* 1 = 0.704333 loss)
I0425 13:04:18.449864 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.277288 (* 1 = 0.277288 loss)
I0425 13:04:18.449879 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.120645 (* 0.0909091 = 0.0109678 loss)
I0425 13:04:18.449893 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0361501 (* 0.0909091 = 0.00328637 loss)
I0425 13:04:18.449908 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.520257 (* 0.0909091 = 0.0472961 loss)
I0425 13:04:18.449923 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.29914 (* 0.0909091 = 0.0271946 loss)
I0425 13:04:18.449935 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.877531 (* 0.0909091 = 0.0797755 loss)
I0425 13:04:18.449950 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.6442 (* 0.0909091 = 0.0585637 loss)
I0425 13:04:18.449965 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.720924 (* 0.0909091 = 0.0655385 loss)
I0425 13:04:18.449978 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.478847 (* 0.0909091 = 0.0435315 loss)
I0425 13:04:18.449992 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.37183 (* 0.0909091 = 0.0338027 loss)
I0425 13:04:18.450006 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.367971 (* 0.0909091 = 0.0334519 loss)
I0425 13:04:18.450021 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.360314 (* 0.0909091 = 0.0327558 loss)
I0425 13:04:18.450037 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.3188 (* 0.0909091 = 0.0289818 loss)
I0425 13:04:18.450052 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.114537 (* 0.0909091 = 0.0104124 loss)
I0425 13:04:18.450067 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0438241 (* 0.0909091 = 0.00398401 loss)
I0425 13:04:18.450080 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0269306 (* 0.0909091 = 0.00244824 loss)
I0425 13:04:18.450095 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00838207 (* 0.0909091 = 0.000762006 loss)
I0425 13:04:18.450109 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00461667 (* 0.0909091 = 0.000419697 loss)
I0425 13:04:18.450124 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00277889 (* 0.0909091 = 0.000252626 loss)
I0425 13:04:18.450139 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00149896 (* 0.0909091 = 0.000136269 loss)
I0425 13:04:18.450153 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000632759 (* 0.0909091 = 5.75236e-05 loss)
I0425 13:04:18.450167 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000221224 (* 0.0909091 = 2.01113e-05 loss)
I0425 13:04:18.450182 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 2.7509e-05 (* 0.0909091 = 2.50082e-06 loss)
I0425 13:04:18.450201 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.375
I0425 13:04:18.450211 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 13:04:18.450225 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.385211
I0425 13:04:18.450238 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.313942
I0425 13:04:18.450253 22523 sgd_solver.cpp:106] Iteration 15000, lr = 0.01
I0425 13:09:59.791797 22523 solver.cpp:229] Iteration 15500, loss = 3.11596
I0425 13:09:59.791929 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.625
I0425 13:09:59.791949 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 13:09:59.791961 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0425 13:09:59.791975 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.5
I0425 13:09:59.791986 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.75
I0425 13:09:59.791999 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0425 13:09:59.792011 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0425 13:09:59.792024 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 13:09:59.792037 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0425 13:09:59.792048 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 13:09:59.792062 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 13:09:59.792073 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 13:09:59.792085 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 13:09:59.792098 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 13:09:59.792110 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 13:09:59.792122 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0425 13:09:59.792135 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 13:09:59.792146 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 13:09:59.792158 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 13:09:59.792171 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 13:09:59.792182 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 13:09:59.792194 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 13:09:59.792209 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 13:09:59.792222 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.875
I0425 13:09:59.792233 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.791667
I0425 13:09:59.792251 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.1738 (* 0.3 = 0.352139 loss)
I0425 13:09:59.792268 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.381612 (* 0.3 = 0.114484 loss)
I0425 13:09:59.792282 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.482175 (* 0.0272727 = 0.0131502 loss)
I0425 13:09:59.792297 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.14038 (* 0.0272727 = 0.0311013 loss)
I0425 13:09:59.792312 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.6371 (* 0.0272727 = 0.0446483 loss)
I0425 13:09:59.792326 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.0387 (* 0.0272727 = 0.0283282 loss)
I0425 13:09:59.792340 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.09756 (* 0.0272727 = 0.0299335 loss)
I0425 13:09:59.792356 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.777333 (* 0.0272727 = 0.0212 loss)
I0425 13:09:59.792369 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.668485 (* 0.0272727 = 0.0182314 loss)
I0425 13:09:59.792383 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.570186 (* 0.0272727 = 0.0155505 loss)
I0425 13:09:59.792398 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.289674 (* 0.0272727 = 0.0079002 loss)
I0425 13:09:59.792413 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.401584 (* 0.0272727 = 0.0109523 loss)
I0425 13:09:59.792428 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.374167 (* 0.0272727 = 0.0102046 loss)
I0425 13:09:59.792443 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.331034 (* 0.0272727 = 0.00902821 loss)
I0425 13:09:59.792474 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.420252 (* 0.0272727 = 0.0114614 loss)
I0425 13:09:59.792490 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.576769 (* 0.0272727 = 0.0157301 loss)
I0425 13:09:59.792505 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.621379 (* 0.0272727 = 0.0169467 loss)
I0425 13:09:59.792520 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0763699 (* 0.0272727 = 0.00208281 loss)
I0425 13:09:59.792533 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.035847 (* 0.0272727 = 0.000977645 loss)
I0425 13:09:59.792548 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0111517 (* 0.0272727 = 0.000304138 loss)
I0425 13:09:59.792563 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00391605 (* 0.0272727 = 0.000106801 loss)
I0425 13:09:59.792577 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00222734 (* 0.0272727 = 6.07457e-05 loss)
I0425 13:09:59.792592 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00177633 (* 0.0272727 = 4.84453e-05 loss)
I0425 13:09:59.792606 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00115214 (* 0.0272727 = 3.1422e-05 loss)
I0425 13:09:59.792618 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.5625
I0425 13:09:59.792630 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 13:09:59.792642 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0425 13:09:59.792654 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0425 13:09:59.792665 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 13:09:59.792676 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0425 13:09:59.792688 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0425 13:09:59.792701 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.75
I0425 13:09:59.792711 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 13:09:59.792723 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 13:09:59.792734 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 13:09:59.792747 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 13:09:59.792757 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 13:09:59.792769 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 13:09:59.792780 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 13:09:59.792791 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0425 13:09:59.792804 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 13:09:59.792814 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 13:09:59.792826 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 13:09:59.792837 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 13:09:59.792850 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 13:09:59.792860 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 13:09:59.792871 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 13:09:59.792882 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.852273
I0425 13:09:59.792894 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.8125
I0425 13:09:59.792908 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.33802 (* 0.3 = 0.401407 loss)
I0425 13:09:59.792922 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.438317 (* 0.3 = 0.131495 loss)
I0425 13:09:59.792937 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.539311 (* 0.0272727 = 0.0147085 loss)
I0425 13:09:59.792953 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.980507 (* 0.0272727 = 0.0267411 loss)
I0425 13:09:59.792979 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.08427 (* 0.0272727 = 0.0568438 loss)
I0425 13:09:59.792995 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.003 (* 0.0272727 = 0.0273545 loss)
I0425 13:09:59.793009 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.45303 (* 0.0272727 = 0.039628 loss)
I0425 13:09:59.793023 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.58196 (* 0.0272727 = 0.0431444 loss)
I0425 13:09:59.793036 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.800853 (* 0.0272727 = 0.0218415 loss)
I0425 13:09:59.793051 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.719918 (* 0.0272727 = 0.0196341 loss)
I0425 13:09:59.793064 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.197929 (* 0.0272727 = 0.00539806 loss)
I0425 13:09:59.793081 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.459521 (* 0.0272727 = 0.0125324 loss)
I0425 13:09:59.793094 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.404958 (* 0.0272727 = 0.0110443 loss)
I0425 13:09:59.793109 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.456956 (* 0.0272727 = 0.0124624 loss)
I0425 13:09:59.793123 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.481296 (* 0.0272727 = 0.0131263 loss)
I0425 13:09:59.793136 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.640353 (* 0.0272727 = 0.0174642 loss)
I0425 13:09:59.793153 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.58 (* 0.0272727 = 0.0158182 loss)
I0425 13:09:59.793166 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0133093 (* 0.0272727 = 0.000362981 loss)
I0425 13:09:59.793180 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0152333 (* 0.0272727 = 0.000415454 loss)
I0425 13:09:59.793195 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00398779 (* 0.0272727 = 0.000108758 loss)
I0425 13:09:59.793210 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00165532 (* 0.0272727 = 4.5145e-05 loss)
I0425 13:09:59.793223 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00146957 (* 0.0272727 = 4.00791e-05 loss)
I0425 13:09:59.793237 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00209029 (* 0.0272727 = 5.70079e-05 loss)
I0425 13:09:59.793254 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000222385 (* 0.0272727 = 6.06506e-06 loss)
I0425 13:09:59.793267 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.791667
I0425 13:09:59.793279 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 13:09:59.793292 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 13:09:59.793303 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 13:09:59.793314 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 13:09:59.793325 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 13:09:59.793337 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 13:09:59.793349 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 13:09:59.793360 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.875
I0425 13:09:59.793371 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 13:09:59.793383 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 13:09:59.793395 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 13:09:59.793406 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 13:09:59.793418 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 13:09:59.793429 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 13:09:59.793442 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0425 13:09:59.793453 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 13:09:59.793475 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 13:09:59.793489 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 13:09:59.793500 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 13:09:59.793512 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 13:09:59.793524 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 13:09:59.793535 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 13:09:59.793546 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.943182
I0425 13:09:59.793558 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.895833
I0425 13:09:59.793572 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.609453 (* 1 = 0.609453 loss)
I0425 13:09:59.793596 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.172109 (* 1 = 0.172109 loss)
I0425 13:09:59.793609 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0812248 (* 0.0909091 = 0.00738408 loss)
I0425 13:09:59.793623 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.334752 (* 0.0909091 = 0.030432 loss)
I0425 13:09:59.793637 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.64167 (* 0.0909091 = 0.0583337 loss)
I0425 13:09:59.793658 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.03667 (* 0.0909091 = 0.00333364 loss)
I0425 13:09:59.793671 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.406407 (* 0.0909091 = 0.0369461 loss)
I0425 13:09:59.793685 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.257044 (* 0.0909091 = 0.0233676 loss)
I0425 13:09:59.793699 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.595081 (* 0.0909091 = 0.0540982 loss)
I0425 13:09:59.793714 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.378174 (* 0.0909091 = 0.0343794 loss)
I0425 13:09:59.793726 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.225348 (* 0.0909091 = 0.0204862 loss)
I0425 13:09:59.793740 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.385372 (* 0.0909091 = 0.0350338 loss)
I0425 13:09:59.793754 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.295841 (* 0.0909091 = 0.0268946 loss)
I0425 13:09:59.793768 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.288528 (* 0.0909091 = 0.0262298 loss)
I0425 13:09:59.793782 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.40968 (* 0.0909091 = 0.0372437 loss)
I0425 13:09:59.793797 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.488585 (* 0.0909091 = 0.0444169 loss)
I0425 13:09:59.793810 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.420161 (* 0.0909091 = 0.0381965 loss)
I0425 13:09:59.793824 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0476482 (* 0.0909091 = 0.00433165 loss)
I0425 13:09:59.793838 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00622486 (* 0.0909091 = 0.000565896 loss)
I0425 13:09:59.793853 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00398867 (* 0.0909091 = 0.000362606 loss)
I0425 13:09:59.793866 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.0015134 (* 0.0909091 = 0.000137582 loss)
I0425 13:09:59.793881 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000739729 (* 0.0909091 = 6.72481e-05 loss)
I0425 13:09:59.793895 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00021718 (* 0.0909091 = 1.97436e-05 loss)
I0425 13:09:59.793910 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.89241e-05 (* 0.0909091 = 3.53856e-06 loss)
I0425 13:09:59.793923 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 13:09:59.793934 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0425 13:09:59.793956 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.661104
I0425 13:09:59.793977 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.540794
I0425 13:09:59.793992 22523 sgd_solver.cpp:106] Iteration 15500, lr = 0.01
I0425 13:15:41.108134 22523 solver.cpp:229] Iteration 16000, loss = 3.29372
I0425 13:15:41.108258 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.642857
I0425 13:15:41.108278 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0425 13:15:41.108292 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0425 13:15:41.108304 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 13:15:41.108317 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 13:15:41.108330 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 13:15:41.108343 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 13:15:41.108355 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.875
I0425 13:15:41.108367 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 13:15:41.108379 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 13:15:41.108392 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 13:15:41.108404 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 13:15:41.108417 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 13:15:41.108429 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 13:15:41.108441 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 13:15:41.108453 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 13:15:41.108465 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 13:15:41.108477 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 13:15:41.108489 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 13:15:41.108501 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 13:15:41.108513 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 13:15:41.108525 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 13:15:41.108536 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 13:15:41.108548 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.897727
I0425 13:15:41.108561 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.738095
I0425 13:15:41.108578 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.42845 (* 0.3 = 0.428536 loss)
I0425 13:15:41.108593 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.422633 (* 0.3 = 0.12679 loss)
I0425 13:15:41.108608 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.971377 (* 0.0272727 = 0.0264921 loss)
I0425 13:15:41.108623 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.94252 (* 0.0272727 = 0.0529779 loss)
I0425 13:15:41.108639 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.20104 (* 0.0272727 = 0.0600284 loss)
I0425 13:15:41.108654 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.04373 (* 0.0272727 = 0.0557382 loss)
I0425 13:15:41.108669 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.44576 (* 0.0272727 = 0.0667025 loss)
I0425 13:15:41.108682 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.819812 (* 0.0272727 = 0.0223585 loss)
I0425 13:15:41.108697 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.693082 (* 0.0272727 = 0.0189022 loss)
I0425 13:15:41.108711 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.556314 (* 0.0272727 = 0.0151722 loss)
I0425 13:15:41.108726 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.00784975 (* 0.0272727 = 0.000214084 loss)
I0425 13:15:41.108741 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.00469429 (* 0.0272727 = 0.000128026 loss)
I0425 13:15:41.108755 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.00231695 (* 0.0272727 = 6.31895e-05 loss)
I0425 13:15:41.108770 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.00143014 (* 0.0272727 = 3.90037e-05 loss)
I0425 13:15:41.108803 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.000827369 (* 0.0272727 = 2.25646e-05 loss)
I0425 13:15:41.108819 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.000334028 (* 0.0272727 = 9.10986e-06 loss)
I0425 13:15:41.108834 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00021971 (* 0.0272727 = 5.9921e-06 loss)
I0425 13:15:41.108847 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.000110421 (* 0.0272727 = 3.01148e-06 loss)
I0425 13:15:41.108862 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 5.78436e-05 (* 0.0272727 = 1.57755e-06 loss)
I0425 13:15:41.108877 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 3.3067e-05 (* 0.0272727 = 9.01827e-07 loss)
I0425 13:15:41.108891 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 1.02968e-05 (* 0.0272727 = 2.80822e-07 loss)
I0425 13:15:41.108906 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 7.80834e-06 (* 0.0272727 = 2.12955e-07 loss)
I0425 13:15:41.108921 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 3.75511e-06 (* 0.0272727 = 1.02412e-07 loss)
I0425 13:15:41.108935 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 4.00844e-06 (* 0.0272727 = 1.09321e-07 loss)
I0425 13:15:41.108947 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.666667
I0425 13:15:41.108959 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 13:15:41.108971 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.375
I0425 13:15:41.108983 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 13:15:41.108995 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0425 13:15:41.109006 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0425 13:15:41.109019 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 13:15:41.109030 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 13:15:41.109041 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 13:15:41.109053 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 13:15:41.109064 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 13:15:41.109076 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 13:15:41.109087 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 13:15:41.109098 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 13:15:41.109110 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 13:15:41.109122 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 13:15:41.109133 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 13:15:41.109143 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 13:15:41.109155 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 13:15:41.109166 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 13:15:41.109179 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 13:15:41.109189 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 13:15:41.109205 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 13:15:41.109216 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.903409
I0425 13:15:41.109228 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.857143
I0425 13:15:41.109243 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.1646 (* 0.3 = 0.34938 loss)
I0425 13:15:41.109257 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.322046 (* 0.3 = 0.0966137 loss)
I0425 13:15:41.109274 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.23058 (* 0.0272727 = 0.0335614 loss)
I0425 13:15:41.109290 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.91502 (* 0.0272727 = 0.0522279 loss)
I0425 13:15:41.109316 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.06259 (* 0.0272727 = 0.0289796 loss)
I0425 13:15:41.109331 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.63931 (* 0.0272727 = 0.0447084 loss)
I0425 13:15:41.109345 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.82079 (* 0.0272727 = 0.0496579 loss)
I0425 13:15:41.109359 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.908246 (* 0.0272727 = 0.0247703 loss)
I0425 13:15:41.109374 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.457407 (* 0.0272727 = 0.0124747 loss)
I0425 13:15:41.109387 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.379089 (* 0.0272727 = 0.0103388 loss)
I0425 13:15:41.109401 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.00718075 (* 0.0272727 = 0.000195839 loss)
I0425 13:15:41.109416 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00338596 (* 0.0272727 = 9.23443e-05 loss)
I0425 13:15:41.109431 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00387668 (* 0.0272727 = 0.000105728 loss)
I0425 13:15:41.109444 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00184421 (* 0.0272727 = 5.02966e-05 loss)
I0425 13:15:41.109458 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00100528 (* 0.0272727 = 2.74168e-05 loss)
I0425 13:15:41.109473 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.000688016 (* 0.0272727 = 1.87641e-05 loss)
I0425 13:15:41.109488 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000153119 (* 0.0272727 = 4.17599e-06 loss)
I0425 13:15:41.109501 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 9.12037e-05 (* 0.0272727 = 2.48737e-06 loss)
I0425 13:15:41.109516 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 3.26647e-05 (* 0.0272727 = 8.90855e-07 loss)
I0425 13:15:41.109526 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 1.66449e-05 (* 0.0272727 = 4.53951e-07 loss)
I0425 13:15:41.109544 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 7.56989e-06 (* 0.0272727 = 2.06452e-07 loss)
I0425 13:15:41.109558 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 8.46405e-06 (* 0.0272727 = 2.30838e-07 loss)
I0425 13:15:41.109572 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 1.2815e-06 (* 0.0272727 = 3.49501e-08 loss)
I0425 13:15:41.109587 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.2964e-06 (* 0.0272727 = 3.53565e-08 loss)
I0425 13:15:41.109599 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.857143
I0425 13:15:41.109611 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 13:15:41.109622 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 13:15:41.109634 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 13:15:41.109645 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 13:15:41.109658 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 13:15:41.109668 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 13:15:41.109680 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0425 13:15:41.109691 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 13:15:41.109702 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 13:15:41.109714 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 13:15:41.109725 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 13:15:41.109736 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 13:15:41.109747 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 13:15:41.109758 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 13:15:41.109769 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 13:15:41.109791 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 13:15:41.109804 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 13:15:41.109815 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 13:15:41.109827 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 13:15:41.109838 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 13:15:41.109849 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 13:15:41.109861 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 13:15:41.109872 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.960227
I0425 13:15:41.109884 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.97619
I0425 13:15:41.109899 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.418885 (* 1 = 0.418885 loss)
I0425 13:15:41.109912 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.120113 (* 1 = 0.120113 loss)
I0425 13:15:41.109926 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.248953 (* 0.0909091 = 0.0226321 loss)
I0425 13:15:41.109941 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.692687 (* 0.0909091 = 0.0629716 loss)
I0425 13:15:41.109954 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.578522 (* 0.0909091 = 0.052593 loss)
I0425 13:15:41.109968 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.406648 (* 0.0909091 = 0.036968 loss)
I0425 13:15:41.109982 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.547965 (* 0.0909091 = 0.049815 loss)
I0425 13:15:41.109995 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.371841 (* 0.0909091 = 0.0338037 loss)
I0425 13:15:41.110009 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.110104 (* 0.0909091 = 0.0100094 loss)
I0425 13:15:41.110023 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.116683 (* 0.0909091 = 0.0106075 loss)
I0425 13:15:41.110038 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.00101825 (* 0.0909091 = 9.25686e-05 loss)
I0425 13:15:41.110051 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00143805 (* 0.0909091 = 0.000130732 loss)
I0425 13:15:41.110065 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00198548 (* 0.0909091 = 0.000180498 loss)
I0425 13:15:41.110080 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00118548 (* 0.0909091 = 0.000107771 loss)
I0425 13:15:41.110095 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000691365 (* 0.0909091 = 6.28514e-05 loss)
I0425 13:15:41.110108 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000548425 (* 0.0909091 = 4.98569e-05 loss)
I0425 13:15:41.110122 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000386976 (* 0.0909091 = 3.51797e-05 loss)
I0425 13:15:41.110136 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000284978 (* 0.0909091 = 2.59071e-05 loss)
I0425 13:15:41.110151 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000210864 (* 0.0909091 = 1.91694e-05 loss)
I0425 13:15:41.110164 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000157477 (* 0.0909091 = 1.43161e-05 loss)
I0425 13:15:41.110178 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000128169 (* 0.0909091 = 1.16517e-05 loss)
I0425 13:15:41.110193 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000134323 (* 0.0909091 = 1.22112e-05 loss)
I0425 13:15:41.110208 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 7.18456e-05 (* 0.0909091 = 6.53142e-06 loss)
I0425 13:15:41.110221 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.11299e-05 (* 0.0909091 = 2.82999e-06 loss)
I0425 13:15:41.110234 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0425 13:15:41.110246 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 13:15:41.110270 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.462219
I0425 13:15:41.110285 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.362417
I0425 13:15:41.110299 22523 sgd_solver.cpp:106] Iteration 16000, lr = 0.01
I0425 13:21:22.493744 22523 solver.cpp:229] Iteration 16500, loss = 3.16791
I0425 13:21:22.493883 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.477273
I0425 13:21:22.493904 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.375
I0425 13:21:22.493917 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0425 13:21:22.493929 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0425 13:21:22.493942 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 13:21:22.493955 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 13:21:22.493968 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0425 13:21:22.493981 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0425 13:21:22.493994 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 13:21:22.494006 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 13:21:22.494019 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 13:21:22.494030 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 13:21:22.494042 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 13:21:22.494055 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 13:21:22.494073 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 13:21:22.494086 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 13:21:22.494097 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 13:21:22.494108 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 13:21:22.494127 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 13:21:22.494139 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 13:21:22.494151 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 13:21:22.494163 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 13:21:22.494175 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 13:21:22.494187 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.846591
I0425 13:21:22.494202 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.727273
I0425 13:21:22.494220 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.87576 (* 0.3 = 0.562727 loss)
I0425 13:21:22.494236 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.56128 (* 0.3 = 0.168384 loss)
I0425 13:21:22.494251 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 2.49627 (* 0.0272727 = 0.06808 loss)
I0425 13:21:22.494266 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 0.803123 (* 0.0272727 = 0.0219033 loss)
I0425 13:21:22.494282 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.20455 (* 0.0272727 = 0.0328514 loss)
I0425 13:21:22.494297 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.01197 (* 0.0272727 = 0.0548718 loss)
I0425 13:21:22.494312 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 3.01028 (* 0.0272727 = 0.0820985 loss)
I0425 13:21:22.494325 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.16509 (* 0.0272727 = 0.0317751 loss)
I0425 13:21:22.494339 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.11119 (* 0.0272727 = 0.0303053 loss)
I0425 13:21:22.494354 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.212986 (* 0.0272727 = 0.00580871 loss)
I0425 13:21:22.494369 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.0273774 (* 0.0272727 = 0.000746656 loss)
I0425 13:21:22.494384 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.0146927 (* 0.0272727 = 0.000400711 loss)
I0425 13:21:22.494398 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0221982 (* 0.0272727 = 0.000605405 loss)
I0425 13:21:22.494412 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0160678 (* 0.0272727 = 0.000438213 loss)
I0425 13:21:22.494427 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.00842034 (* 0.0272727 = 0.000229646 loss)
I0425 13:21:22.494459 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00739674 (* 0.0272727 = 0.000201729 loss)
I0425 13:21:22.494475 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00330141 (* 0.0272727 = 9.00386e-05 loss)
I0425 13:21:22.494490 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00314766 (* 0.0272727 = 8.58453e-05 loss)
I0425 13:21:22.494504 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00172575 (* 0.0272727 = 4.70659e-05 loss)
I0425 13:21:22.494519 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00115426 (* 0.0272727 = 3.14798e-05 loss)
I0425 13:21:22.494534 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000578899 (* 0.0272727 = 1.57882e-05 loss)
I0425 13:21:22.494549 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000509443 (* 0.0272727 = 1.38939e-05 loss)
I0425 13:21:22.494562 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00031831 (* 0.0272727 = 8.68118e-06 loss)
I0425 13:21:22.494577 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000187786 (* 0.0272727 = 5.12144e-06 loss)
I0425 13:21:22.494590 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.727273
I0425 13:21:22.494601 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.625
I0425 13:21:22.494613 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 1
I0425 13:21:22.494626 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 13:21:22.494637 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.75
I0425 13:21:22.494648 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 13:21:22.494660 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 13:21:22.494673 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 13:21:22.494683 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.875
I0425 13:21:22.494695 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 13:21:22.494706 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 13:21:22.494717 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 13:21:22.494729 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 13:21:22.494740 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 13:21:22.494751 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 13:21:22.494762 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 13:21:22.494773 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 13:21:22.494784 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 13:21:22.494796 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 13:21:22.494807 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 13:21:22.494818 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 13:21:22.494829 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 13:21:22.494842 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 13:21:22.494853 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.903409
I0425 13:21:22.494865 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.909091
I0425 13:21:22.494879 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.06398 (* 0.3 = 0.319195 loss)
I0425 13:21:22.494896 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.349028 (* 0.3 = 0.104708 loss)
I0425 13:21:22.494911 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.95973 (* 0.0272727 = 0.0534471 loss)
I0425 13:21:22.494926 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.252841 (* 0.0272727 = 0.00689567 loss)
I0425 13:21:22.494951 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.814584 (* 0.0272727 = 0.0222159 loss)
I0425 13:21:22.494967 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.00076 (* 0.0272727 = 0.0272935 loss)
I0425 13:21:22.494982 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.74108 (* 0.0272727 = 0.0747566 loss)
I0425 13:21:22.494995 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.645158 (* 0.0272727 = 0.0175952 loss)
I0425 13:21:22.495009 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.707783 (* 0.0272727 = 0.0193032 loss)
I0425 13:21:22.495024 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.228336 (* 0.0272727 = 0.00622734 loss)
I0425 13:21:22.495038 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.0149786 (* 0.0272727 = 0.000408507 loss)
I0425 13:21:22.495054 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.00365788 (* 0.0272727 = 9.97602e-05 loss)
I0425 13:21:22.495069 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.00330799 (* 0.0272727 = 9.0218e-05 loss)
I0425 13:21:22.495082 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00150655 (* 0.0272727 = 4.10877e-05 loss)
I0425 13:21:22.495096 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00175635 (* 0.0272727 = 4.79006e-05 loss)
I0425 13:21:22.495111 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00042088 (* 0.0272727 = 1.14785e-05 loss)
I0425 13:21:22.495126 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000305324 (* 0.0272727 = 8.32702e-06 loss)
I0425 13:21:22.495139 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000287646 (* 0.0272727 = 7.84488e-06 loss)
I0425 13:21:22.495153 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000168069 (* 0.0272727 = 4.58369e-06 loss)
I0425 13:21:22.495167 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 4.61967e-05 (* 0.0272727 = 1.25991e-06 loss)
I0425 13:21:22.495182 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 2.42454e-05 (* 0.0272727 = 6.61237e-07 loss)
I0425 13:21:22.495196 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 1.4857e-05 (* 0.0272727 = 4.05191e-07 loss)
I0425 13:21:22.495210 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 1.17126e-05 (* 0.0272727 = 3.19435e-07 loss)
I0425 13:21:22.495225 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.17723e-05 (* 0.0272727 = 3.21062e-07 loss)
I0425 13:21:22.495234 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.886364
I0425 13:21:22.495247 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0425 13:21:22.495262 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 13:21:22.495275 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 13:21:22.495293 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 13:21:22.495306 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 13:21:22.495316 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 13:21:22.495328 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0425 13:21:22.495339 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 13:21:22.495367 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 13:21:22.495381 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 13:21:22.495393 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 13:21:22.495404 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 13:21:22.495415 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 13:21:22.495427 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 13:21:22.495439 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 13:21:22.495458 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 13:21:22.495481 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 13:21:22.495494 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 13:21:22.495513 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 13:21:22.495524 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 13:21:22.495537 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 13:21:22.495548 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 13:21:22.495559 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.954545
I0425 13:21:22.495571 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.954545
I0425 13:21:22.495585 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.541476 (* 1 = 0.541476 loss)
I0425 13:21:22.495599 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.172265 (* 1 = 0.172265 loss)
I0425 13:21:22.495614 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 1.19006 (* 0.0909091 = 0.108187 loss)
I0425 13:21:22.495627 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0714841 (* 0.0909091 = 0.00649856 loss)
I0425 13:21:22.495641 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.300106 (* 0.0909091 = 0.0272824 loss)
I0425 13:21:22.495656 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.300897 (* 0.0909091 = 0.0273543 loss)
I0425 13:21:22.495671 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.40742 (* 0.0909091 = 0.127948 loss)
I0425 13:21:22.495684 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.611187 (* 0.0909091 = 0.0555624 loss)
I0425 13:21:22.495698 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.175051 (* 0.0909091 = 0.0159137 loss)
I0425 13:21:22.495712 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.0414114 (* 0.0909091 = 0.00376467 loss)
I0425 13:21:22.495726 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.00652075 (* 0.0909091 = 0.000592795 loss)
I0425 13:21:22.495739 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.00135197 (* 0.0909091 = 0.000122907 loss)
I0425 13:21:22.495754 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00138323 (* 0.0909091 = 0.000125748 loss)
I0425 13:21:22.495767 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.000856816 (* 0.0909091 = 7.78923e-05 loss)
I0425 13:21:22.495781 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.000618384 (* 0.0909091 = 5.62167e-05 loss)
I0425 13:21:22.495795 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000433077 (* 0.0909091 = 3.93706e-05 loss)
I0425 13:21:22.495808 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000375857 (* 0.0909091 = 3.41688e-05 loss)
I0425 13:21:22.495822 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000292846 (* 0.0909091 = 2.66224e-05 loss)
I0425 13:21:22.495836 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000336381 (* 0.0909091 = 3.05801e-05 loss)
I0425 13:21:22.495851 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000277007 (* 0.0909091 = 2.51825e-05 loss)
I0425 13:21:22.495864 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000289431 (* 0.0909091 = 2.63119e-05 loss)
I0425 13:21:22.495878 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000259115 (* 0.0909091 = 2.35559e-05 loss)
I0425 13:21:22.495893 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00013017 (* 0.0909091 = 1.18336e-05 loss)
I0425 13:21:22.495908 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 5.67365e-05 (* 0.0909091 = 5.15786e-06 loss)
I0425 13:21:22.495919 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0425 13:21:22.495931 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0425 13:21:22.495956 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.517761
I0425 13:21:22.495970 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.403021
I0425 13:21:22.495985 22523 sgd_solver.cpp:106] Iteration 16500, lr = 0.01
I0425 13:27:03.855590 22523 solver.cpp:229] Iteration 17000, loss = 3.21044
I0425 13:27:03.855729 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.421053
I0425 13:27:03.855751 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.5
I0425 13:27:03.855764 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 13:27:03.855777 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 13:27:03.855789 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.125
I0425 13:27:03.855803 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.25
I0425 13:27:03.855814 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0425 13:27:03.855828 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 13:27:03.855840 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0425 13:27:03.855854 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 13:27:03.855865 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 13:27:03.855877 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 13:27:03.855890 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 13:27:03.855903 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 13:27:03.855916 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 13:27:03.855927 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0425 13:27:03.855940 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 13:27:03.855952 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 13:27:03.855964 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 13:27:03.855976 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 13:27:03.855988 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 13:27:03.856000 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 13:27:03.856012 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 13:27:03.856024 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.795455
I0425 13:27:03.856037 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.596491
I0425 13:27:03.856055 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.9656 (* 0.3 = 0.589679 loss)
I0425 13:27:03.856071 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.72586 (* 0.3 = 0.217758 loss)
I0425 13:27:03.856086 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.42875 (* 0.0272727 = 0.0389659 loss)
I0425 13:27:03.856109 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.51306 (* 0.0272727 = 0.0412652 loss)
I0425 13:27:03.856124 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.07499 (* 0.0272727 = 0.0565907 loss)
I0425 13:27:03.856139 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.8799 (* 0.0272727 = 0.0785427 loss)
I0425 13:27:03.856161 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.36672 (* 0.0272727 = 0.064547 loss)
I0425 13:27:03.856175 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.60942 (* 0.0272727 = 0.0438932 loss)
I0425 13:27:03.856189 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.60657 (* 0.0272727 = 0.0438155 loss)
I0425 13:27:03.856209 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.48524 (* 0.0272727 = 0.0405067 loss)
I0425 13:27:03.856223 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.302045 (* 0.0272727 = 0.0082376 loss)
I0425 13:27:03.856238 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.365933 (* 0.0272727 = 0.00997999 loss)
I0425 13:27:03.856253 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.457599 (* 0.0272727 = 0.01248 loss)
I0425 13:27:03.856268 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.384448 (* 0.0272727 = 0.0104849 loss)
I0425 13:27:03.856302 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.496101 (* 0.0272727 = 0.01353 loss)
I0425 13:27:03.856317 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.594284 (* 0.0272727 = 0.0162077 loss)
I0425 13:27:03.856331 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.563319 (* 0.0272727 = 0.0153632 loss)
I0425 13:27:03.856348 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.117412 (* 0.0272727 = 0.00320215 loss)
I0425 13:27:03.856361 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0553965 (* 0.0272727 = 0.00151081 loss)
I0425 13:27:03.856377 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0238796 (* 0.0272727 = 0.000651261 loss)
I0425 13:27:03.856392 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.0169485 (* 0.0272727 = 0.000462232 loss)
I0425 13:27:03.856406 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00716745 (* 0.0272727 = 0.000195476 loss)
I0425 13:27:03.856421 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00483263 (* 0.0272727 = 0.000131799 loss)
I0425 13:27:03.856436 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00263221 (* 0.0272727 = 7.17875e-05 loss)
I0425 13:27:03.856449 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.54386
I0425 13:27:03.856462 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 13:27:03.856473 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0425 13:27:03.856485 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0425 13:27:03.856498 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0425 13:27:03.856511 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 13:27:03.856523 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0425 13:27:03.856535 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 13:27:03.856547 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 13:27:03.856559 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 13:27:03.856571 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 13:27:03.856583 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 13:27:03.856595 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 13:27:03.856614 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 13:27:03.856626 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 13:27:03.856638 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0425 13:27:03.856649 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 13:27:03.856662 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 13:27:03.856673 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 13:27:03.856684 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 13:27:03.856703 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 13:27:03.856714 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 13:27:03.856726 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 13:27:03.856741 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.852273
I0425 13:27:03.856755 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.719298
I0425 13:27:03.856770 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.66795 (* 0.3 = 0.500386 loss)
I0425 13:27:03.856783 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.594443 (* 0.3 = 0.178333 loss)
I0425 13:27:03.856797 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.922624 (* 0.0272727 = 0.0251625 loss)
I0425 13:27:03.856812 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.61418 (* 0.0272727 = 0.044023 loss)
I0425 13:27:03.856837 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.52301 (* 0.0272727 = 0.0415366 loss)
I0425 13:27:03.856853 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.01345 (* 0.0272727 = 0.0549124 loss)
I0425 13:27:03.856866 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.16308 (* 0.0272727 = 0.0589931 loss)
I0425 13:27:03.856880 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.52138 (* 0.0272727 = 0.0414923 loss)
I0425 13:27:03.856894 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 2.03891 (* 0.0272727 = 0.0556068 loss)
I0425 13:27:03.856909 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.35673 (* 0.0272727 = 0.0370016 loss)
I0425 13:27:03.856923 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.283461 (* 0.0272727 = 0.00773075 loss)
I0425 13:27:03.856938 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.626773 (* 0.0272727 = 0.0170938 loss)
I0425 13:27:03.856952 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.452022 (* 0.0272727 = 0.0123279 loss)
I0425 13:27:03.856966 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.388665 (* 0.0272727 = 0.0105999 loss)
I0425 13:27:03.856981 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.743964 (* 0.0272727 = 0.0202899 loss)
I0425 13:27:03.856994 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.552902 (* 0.0272727 = 0.0150791 loss)
I0425 13:27:03.857014 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.671661 (* 0.0272727 = 0.018318 loss)
I0425 13:27:03.857028 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0180587 (* 0.0272727 = 0.000492509 loss)
I0425 13:27:03.857043 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0010822 (* 0.0272727 = 2.95145e-05 loss)
I0425 13:27:03.857059 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0017643 (* 0.0272727 = 4.81174e-05 loss)
I0425 13:27:03.857077 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000262029 (* 0.0272727 = 7.14624e-06 loss)
I0425 13:27:03.857091 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00031462 (* 0.0272727 = 8.58055e-06 loss)
I0425 13:27:03.857106 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 7.35367e-05 (* 0.0272727 = 2.00555e-06 loss)
I0425 13:27:03.857121 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 8.06573e-05 (* 0.0272727 = 2.19974e-06 loss)
I0425 13:27:03.857133 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.666667
I0425 13:27:03.857146 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0425 13:27:03.857157 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.625
I0425 13:27:03.857169 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 13:27:03.857182 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0425 13:27:03.857193 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 13:27:03.857205 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0425 13:27:03.857218 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 13:27:03.857229 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0425 13:27:03.857240 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 13:27:03.857255 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 13:27:03.857269 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 13:27:03.857280 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 13:27:03.857291 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 13:27:03.857303 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 13:27:03.857314 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0425 13:27:03.857336 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 13:27:03.857350 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 13:27:03.857362 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 13:27:03.857374 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 13:27:03.857386 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 13:27:03.857398 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 13:27:03.857409 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 13:27:03.857420 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.886364
I0425 13:27:03.857434 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.789474
I0425 13:27:03.857447 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.21023 (* 1 = 1.21023 loss)
I0425 13:27:03.857461 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.438143 (* 1 = 0.438143 loss)
I0425 13:27:03.857477 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.745486 (* 0.0909091 = 0.0677714 loss)
I0425 13:27:03.857491 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 1.26891 (* 0.0909091 = 0.115355 loss)
I0425 13:27:03.857506 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.77687 (* 0.0909091 = 0.0706245 loss)
I0425 13:27:03.857519 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.50695 (* 0.0909091 = 0.136996 loss)
I0425 13:27:03.857533 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.597577 (* 0.0909091 = 0.0543252 loss)
I0425 13:27:03.857547 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.934722 (* 0.0909091 = 0.0849747 loss)
I0425 13:27:03.857561 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.50004 (* 0.0909091 = 0.136368 loss)
I0425 13:27:03.857576 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.18898 (* 0.0909091 = 0.108089 loss)
I0425 13:27:03.857590 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.258633 (* 0.0909091 = 0.0235121 loss)
I0425 13:27:03.857604 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.462692 (* 0.0909091 = 0.0420629 loss)
I0425 13:27:03.857619 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.420537 (* 0.0909091 = 0.0382307 loss)
I0425 13:27:03.857632 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.349381 (* 0.0909091 = 0.0317619 loss)
I0425 13:27:03.857645 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.508344 (* 0.0909091 = 0.0462131 loss)
I0425 13:27:03.857661 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.468118 (* 0.0909091 = 0.0425562 loss)
I0425 13:27:03.857674 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.422484 (* 0.0909091 = 0.0384076 loss)
I0425 13:27:03.857688 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0449122 (* 0.0909091 = 0.00408293 loss)
I0425 13:27:03.857702 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00911728 (* 0.0909091 = 0.000828844 loss)
I0425 13:27:03.857717 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00779119 (* 0.0909091 = 0.00070829 loss)
I0425 13:27:03.857731 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00613112 (* 0.0909091 = 0.000557375 loss)
I0425 13:27:03.857746 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.0044176 (* 0.0909091 = 0.0004016 loss)
I0425 13:27:03.857760 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00255055 (* 0.0909091 = 0.000231869 loss)
I0425 13:27:03.857774 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.00110424 (* 0.0909091 = 0.000100386 loss)
I0425 13:27:03.857787 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 13:27:03.857803 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0425 13:27:03.857825 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.422342
I0425 13:27:03.857839 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.22159
I0425 13:27:03.857854 22523 sgd_solver.cpp:106] Iteration 17000, lr = 0.01
I0425 13:32:45.245761 22523 solver.cpp:229] Iteration 17500, loss = 3.12756
I0425 13:32:45.245895 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.290323
I0425 13:32:45.245916 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.5
I0425 13:32:45.245930 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.75
I0425 13:32:45.245942 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.125
I0425 13:32:45.245955 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 13:32:45.245967 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.625
I0425 13:32:45.245980 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.25
I0425 13:32:45.245992 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 13:32:45.246004 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.5
I0425 13:32:45.246017 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.625
I0425 13:32:45.246029 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.625
I0425 13:32:45.246042 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0425 13:32:45.246054 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 13:32:45.246067 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 13:32:45.246079 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 13:32:45.246091 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0425 13:32:45.246104 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 13:32:45.246115 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 13:32:45.246127 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 13:32:45.246140 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 13:32:45.246151 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 13:32:45.246170 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 13:32:45.246181 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 13:32:45.246193 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.732955
I0425 13:32:45.246208 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.612903
I0425 13:32:45.246235 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.21997 (* 0.3 = 0.665992 loss)
I0425 13:32:45.246251 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.856022 (* 0.3 = 0.256807 loss)
I0425 13:32:45.246268 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.37147 (* 0.0272727 = 0.0374038 loss)
I0425 13:32:45.246281 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.18204 (* 0.0272727 = 0.0322374 loss)
I0425 13:32:45.246296 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.85179 (* 0.0272727 = 0.0777761 loss)
I0425 13:32:45.246310 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.14678 (* 0.0272727 = 0.0585484 loss)
I0425 13:32:45.246325 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.21548 (* 0.0272727 = 0.0331495 loss)
I0425 13:32:45.246340 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.8446 (* 0.0272727 = 0.0775801 loss)
I0425 13:32:45.246353 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.7707 (* 0.0272727 = 0.0482918 loss)
I0425 13:32:45.246367 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.50913 (* 0.0272727 = 0.0411581 loss)
I0425 13:32:45.246382 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 1.48288 (* 0.0272727 = 0.0404422 loss)
I0425 13:32:45.246397 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 1.31461 (* 0.0272727 = 0.0358531 loss)
I0425 13:32:45.246410 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 1.08209 (* 0.0272727 = 0.0295115 loss)
I0425 13:32:45.246424 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.384489 (* 0.0272727 = 0.0104861 loss)
I0425 13:32:45.246456 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.518885 (* 0.0272727 = 0.0141514 loss)
I0425 13:32:45.246471 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.618331 (* 0.0272727 = 0.0168636 loss)
I0425 13:32:45.246486 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.461335 (* 0.0272727 = 0.0125819 loss)
I0425 13:32:45.246500 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0636274 (* 0.0272727 = 0.00173529 loss)
I0425 13:32:45.246515 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0213842 (* 0.0272727 = 0.000583206 loss)
I0425 13:32:45.246529 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00997038 (* 0.0272727 = 0.000271919 loss)
I0425 13:32:45.246544 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.0095128 (* 0.0272727 = 0.00025944 loss)
I0425 13:32:45.246558 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00257221 (* 0.0272727 = 7.01511e-05 loss)
I0425 13:32:45.246573 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00209248 (* 0.0272727 = 5.70676e-05 loss)
I0425 13:32:45.246587 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00140813 (* 0.0272727 = 3.84034e-05 loss)
I0425 13:32:45.246600 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.483871
I0425 13:32:45.246613 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.625
I0425 13:32:45.246624 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 13:32:45.246635 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 13:32:45.246647 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0425 13:32:45.246659 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.5
I0425 13:32:45.246670 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.25
I0425 13:32:45.246682 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 13:32:45.246695 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.5
I0425 13:32:45.246706 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.625
I0425 13:32:45.246717 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.625
I0425 13:32:45.246728 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0425 13:32:45.246740 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 13:32:45.246752 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 13:32:45.246763 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 13:32:45.246775 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0425 13:32:45.246786 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 13:32:45.246798 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 13:32:45.246809 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 13:32:45.246820 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 13:32:45.246831 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 13:32:45.246842 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 13:32:45.246855 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 13:32:45.246866 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.801136
I0425 13:32:45.246878 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.709677
I0425 13:32:45.246892 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.69154 (* 0.3 = 0.507462 loss)
I0425 13:32:45.246909 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.680907 (* 0.3 = 0.204272 loss)
I0425 13:32:45.246924 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.40053 (* 0.0272727 = 0.0381962 loss)
I0425 13:32:45.246939 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.866491 (* 0.0272727 = 0.0236316 loss)
I0425 13:32:45.246965 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.47711 (* 0.0272727 = 0.0402849 loss)
I0425 13:32:45.246980 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.77594 (* 0.0272727 = 0.0484347 loss)
I0425 13:32:45.246994 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.36202 (* 0.0272727 = 0.0371459 loss)
I0425 13:32:45.247009 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 2.30953 (* 0.0272727 = 0.0629873 loss)
I0425 13:32:45.247022 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.13533 (* 0.0272727 = 0.0309635 loss)
I0425 13:32:45.247036 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.413 (* 0.0272727 = 0.0385365 loss)
I0425 13:32:45.247051 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.18144 (* 0.0272727 = 0.032221 loss)
I0425 13:32:45.247063 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 1.15471 (* 0.0272727 = 0.0314921 loss)
I0425 13:32:45.247077 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.30041 (* 0.0272727 = 0.0354657 loss)
I0425 13:32:45.247092 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.557678 (* 0.0272727 = 0.0152094 loss)
I0425 13:32:45.247105 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.422195 (* 0.0272727 = 0.0115144 loss)
I0425 13:32:45.247119 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.665452 (* 0.0272727 = 0.0181487 loss)
I0425 13:32:45.247133 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.357667 (* 0.0272727 = 0.00975455 loss)
I0425 13:32:45.247148 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0360663 (* 0.0272727 = 0.000983625 loss)
I0425 13:32:45.247161 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0176577 (* 0.0272727 = 0.000481574 loss)
I0425 13:32:45.247175 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0215528 (* 0.0272727 = 0.000587803 loss)
I0425 13:32:45.247186 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000970422 (* 0.0272727 = 2.64661e-05 loss)
I0425 13:32:45.247200 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00084058 (* 0.0272727 = 2.29249e-05 loss)
I0425 13:32:45.247215 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000820219 (* 0.0272727 = 2.23696e-05 loss)
I0425 13:32:45.247228 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000448639 (* 0.0272727 = 1.22356e-05 loss)
I0425 13:32:45.247241 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.645161
I0425 13:32:45.247256 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 13:32:45.247268 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 13:32:45.247279 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0425 13:32:45.247292 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0425 13:32:45.247303 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 13:32:45.247313 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.5
I0425 13:32:45.247325 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 13:32:45.247336 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.5
I0425 13:32:45.247359 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.625
I0425 13:32:45.247375 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.75
I0425 13:32:45.247387 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.75
I0425 13:32:45.247398 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 13:32:45.247411 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 13:32:45.247421 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 13:32:45.247432 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0425 13:32:45.247457 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 13:32:45.247469 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 13:32:45.247480 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 13:32:45.247491 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 13:32:45.247503 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 13:32:45.247514 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 13:32:45.247526 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 13:32:45.247537 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.857955
I0425 13:32:45.247550 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.822581
I0425 13:32:45.247563 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.07355 (* 1 = 1.07355 loss)
I0425 13:32:45.247576 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.449593 (* 1 = 0.449593 loss)
I0425 13:32:45.247591 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.612331 (* 0.0909091 = 0.0556665 loss)
I0425 13:32:45.247604 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.239941 (* 0.0909091 = 0.0218128 loss)
I0425 13:32:45.247618 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.573998 (* 0.0909091 = 0.0521816 loss)
I0425 13:32:45.247632 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.493395 (* 0.0909091 = 0.0448541 loss)
I0425 13:32:45.247645 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.07634 (* 0.0909091 = 0.0978495 loss)
I0425 13:32:45.247659 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.42457 (* 0.0909091 = 0.129506 loss)
I0425 13:32:45.247673 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.02212 (* 0.0909091 = 0.0929203 loss)
I0425 13:32:45.247687 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.13476 (* 0.0909091 = 0.10316 loss)
I0425 13:32:45.247701 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 1.07896 (* 0.0909091 = 0.0980877 loss)
I0425 13:32:45.247715 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 1.05474 (* 0.0909091 = 0.0958853 loss)
I0425 13:32:45.247730 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 1.01798 (* 0.0909091 = 0.0925441 loss)
I0425 13:32:45.247742 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.46478 (* 0.0909091 = 0.0422528 loss)
I0425 13:32:45.247756 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.368182 (* 0.0909091 = 0.0334711 loss)
I0425 13:32:45.247771 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.554129 (* 0.0909091 = 0.0503754 loss)
I0425 13:32:45.247784 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.441234 (* 0.0909091 = 0.0401121 loss)
I0425 13:32:45.247803 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0279663 (* 0.0909091 = 0.00254239 loss)
I0425 13:32:45.247817 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0104301 (* 0.0909091 = 0.000948195 loss)
I0425 13:32:45.247831 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00654719 (* 0.0909091 = 0.000595199 loss)
I0425 13:32:45.247845 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00369309 (* 0.0909091 = 0.000335735 loss)
I0425 13:32:45.247864 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00209207 (* 0.0909091 = 0.000190188 loss)
I0425 13:32:45.247879 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000664203 (* 0.0909091 = 6.03821e-05 loss)
I0425 13:32:45.247894 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.00012621 (* 0.0909091 = 1.14736e-05 loss)
I0425 13:32:45.247905 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0425 13:32:45.247917 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0425 13:32:45.247938 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.380139
I0425 13:32:45.247951 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.318278
I0425 13:32:45.247969 22523 sgd_solver.cpp:106] Iteration 17500, lr = 0.01
I0425 13:38:26.554471 22523 solver.cpp:229] Iteration 18000, loss = 3.04545
I0425 13:38:26.554606 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.492308
I0425 13:38:26.554628 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0425 13:38:26.554641 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 13:38:26.554654 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 13:38:26.554667 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 13:38:26.554679 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.25
I0425 13:38:26.554692 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 13:38:26.554705 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 1
I0425 13:38:26.554718 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0425 13:38:26.554731 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0425 13:38:26.554744 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0425 13:38:26.554757 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.75
I0425 13:38:26.554770 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.75
I0425 13:38:26.554783 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.75
I0425 13:38:26.554796 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 13:38:26.554816 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0425 13:38:26.554828 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0425 13:38:26.554841 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 13:38:26.554852 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 13:38:26.554864 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 13:38:26.554884 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 13:38:26.554896 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 13:38:26.554908 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 13:38:26.554920 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.801136
I0425 13:38:26.554934 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.676923
I0425 13:38:26.554951 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.78659 (* 0.3 = 0.535978 loss)
I0425 13:38:26.554966 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.718952 (* 0.3 = 0.215685 loss)
I0425 13:38:26.554982 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.1396 (* 0.0272727 = 0.0310801 loss)
I0425 13:38:26.554996 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.41149 (* 0.0272727 = 0.0384952 loss)
I0425 13:38:26.555011 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.41112 (* 0.0272727 = 0.0657577 loss)
I0425 13:38:26.555027 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.30017 (* 0.0272727 = 0.0627319 loss)
I0425 13:38:26.555040 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 3.12983 (* 0.0272727 = 0.0853589 loss)
I0425 13:38:26.555055 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.37843 (* 0.0272727 = 0.0375936 loss)
I0425 13:38:26.555069 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.574854 (* 0.0272727 = 0.0156778 loss)
I0425 13:38:26.555084 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.811857 (* 0.0272727 = 0.0221415 loss)
I0425 13:38:26.555099 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.953621 (* 0.0272727 = 0.0260078 loss)
I0425 13:38:26.555114 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.658587 (* 0.0272727 = 0.0179615 loss)
I0425 13:38:26.555130 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.66968 (* 0.0272727 = 0.018264 loss)
I0425 13:38:26.555143 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 1.23542 (* 0.0272727 = 0.0336933 loss)
I0425 13:38:26.555176 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 1.30978 (* 0.0272727 = 0.0357214 loss)
I0425 13:38:26.555192 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 1.1298 (* 0.0272727 = 0.0308126 loss)
I0425 13:38:26.555217 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.980029 (* 0.0272727 = 0.0267281 loss)
I0425 13:38:26.555232 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 1.06391 (* 0.0272727 = 0.0290159 loss)
I0425 13:38:26.555246 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0396864 (* 0.0272727 = 0.00108236 loss)
I0425 13:38:26.555261 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0255979 (* 0.0272727 = 0.000698124 loss)
I0425 13:38:26.555279 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00778794 (* 0.0272727 = 0.000212398 loss)
I0425 13:38:26.555294 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00576334 (* 0.0272727 = 0.000157182 loss)
I0425 13:38:26.555308 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00198509 (* 0.0272727 = 5.41388e-05 loss)
I0425 13:38:26.555322 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00132333 (* 0.0272727 = 3.60909e-05 loss)
I0425 13:38:26.555335 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.6
I0425 13:38:26.555347 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 13:38:26.555378 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0425 13:38:26.555392 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0425 13:38:26.555402 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0425 13:38:26.555414 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.25
I0425 13:38:26.555426 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.375
I0425 13:38:26.555438 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 13:38:26.555450 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 13:38:26.555462 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0425 13:38:26.555474 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 13:38:26.555485 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.75
I0425 13:38:26.555497 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.75
I0425 13:38:26.555510 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.75
I0425 13:38:26.555521 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 13:38:26.555532 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0425 13:38:26.555544 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0425 13:38:26.555557 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 13:38:26.555568 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 13:38:26.555579 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 13:38:26.555590 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 13:38:26.555603 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 13:38:26.555616 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 13:38:26.555629 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.840909
I0425 13:38:26.555641 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.815385
I0425 13:38:26.555655 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.26428 (* 0.3 = 0.379285 loss)
I0425 13:38:26.555670 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.508539 (* 0.3 = 0.152562 loss)
I0425 13:38:26.555685 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.601154 (* 0.0272727 = 0.0163951 loss)
I0425 13:38:26.555698 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.498716 (* 0.0272727 = 0.0136013 loss)
I0425 13:38:26.555726 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.54 (* 0.0272727 = 0.0420001 loss)
I0425 13:38:26.555742 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.68323 (* 0.0272727 = 0.0459062 loss)
I0425 13:38:26.555755 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.06375 (* 0.0272727 = 0.0562841 loss)
I0425 13:38:26.555770 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 2.13765 (* 0.0272727 = 0.0582996 loss)
I0425 13:38:26.555784 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.722932 (* 0.0272727 = 0.0197163 loss)
I0425 13:38:26.555799 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.859749 (* 0.0272727 = 0.0234477 loss)
I0425 13:38:26.555812 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.791787 (* 0.0272727 = 0.0215942 loss)
I0425 13:38:26.555826 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.668196 (* 0.0272727 = 0.0182235 loss)
I0425 13:38:26.555841 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.803021 (* 0.0272727 = 0.0219006 loss)
I0425 13:38:26.555855 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 1.06889 (* 0.0272727 = 0.0291514 loss)
I0425 13:38:26.555869 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 1.04155 (* 0.0272727 = 0.0284058 loss)
I0425 13:38:26.555883 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.904856 (* 0.0272727 = 0.0246779 loss)
I0425 13:38:26.555897 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.637379 (* 0.0272727 = 0.0173831 loss)
I0425 13:38:26.555912 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.794557 (* 0.0272727 = 0.0216697 loss)
I0425 13:38:26.555927 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0168686 (* 0.0272727 = 0.000460052 loss)
I0425 13:38:26.555940 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00704704 (* 0.0272727 = 0.000192192 loss)
I0425 13:38:26.555955 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.0047394 (* 0.0272727 = 0.000129256 loss)
I0425 13:38:26.555969 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.0024592 (* 0.0272727 = 6.70692e-05 loss)
I0425 13:38:26.555984 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000996521 (* 0.0272727 = 2.71779e-05 loss)
I0425 13:38:26.555999 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.00058141 (* 0.0272727 = 1.58566e-05 loss)
I0425 13:38:26.556011 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.769231
I0425 13:38:26.556023 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 13:38:26.556035 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 13:38:26.556046 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 13:38:26.556058 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 13:38:26.556069 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 13:38:26.556082 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 13:38:26.556094 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 13:38:26.556107 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0425 13:38:26.556118 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.75
I0425 13:38:26.556130 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 13:38:26.556141 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 13:38:26.556156 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.75
I0425 13:38:26.556167 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.75
I0425 13:38:26.556180 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 13:38:26.556190 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0425 13:38:26.556202 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0425 13:38:26.556226 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 13:38:26.556238 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 13:38:26.556253 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 13:38:26.556265 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 13:38:26.556277 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 13:38:26.556289 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 13:38:26.556300 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.909091
I0425 13:38:26.556313 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.907692
I0425 13:38:26.556326 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.853065 (* 1 = 0.853065 loss)
I0425 13:38:26.556340 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.323376 (* 1 = 0.323376 loss)
I0425 13:38:26.556355 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.260802 (* 0.0909091 = 0.0237092 loss)
I0425 13:38:26.556370 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.249845 (* 0.0909091 = 0.0227132 loss)
I0425 13:38:26.556383 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.380742 (* 0.0909091 = 0.0346129 loss)
I0425 13:38:26.556397 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.514833 (* 0.0909091 = 0.046803 loss)
I0425 13:38:26.556411 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.939624 (* 0.0909091 = 0.0854204 loss)
I0425 13:38:26.556422 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.501876 (* 0.0909091 = 0.0456251 loss)
I0425 13:38:26.556432 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.642708 (* 0.0909091 = 0.058428 loss)
I0425 13:38:26.556445 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.955395 (* 0.0909091 = 0.0868541 loss)
I0425 13:38:26.556459 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.905283 (* 0.0909091 = 0.0822984 loss)
I0425 13:38:26.556473 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.412064 (* 0.0909091 = 0.0374603 loss)
I0425 13:38:26.556488 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.475898 (* 0.0909091 = 0.0432635 loss)
I0425 13:38:26.556501 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.876967 (* 0.0909091 = 0.0797243 loss)
I0425 13:38:26.556515 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 1.07847 (* 0.0909091 = 0.0980423 loss)
I0425 13:38:26.556529 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.672419 (* 0.0909091 = 0.061129 loss)
I0425 13:38:26.556542 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.471672 (* 0.0909091 = 0.0428793 loss)
I0425 13:38:26.556556 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.600521 (* 0.0909091 = 0.0545928 loss)
I0425 13:38:26.556571 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0175041 (* 0.0909091 = 0.00159128 loss)
I0425 13:38:26.556584 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0147399 (* 0.0909091 = 0.00133999 loss)
I0425 13:38:26.556598 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00771243 (* 0.0909091 = 0.00070113 loss)
I0425 13:38:26.556612 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.0029253 (* 0.0909091 = 0.000265936 loss)
I0425 13:38:26.556627 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00169316 (* 0.0909091 = 0.000153924 loss)
I0425 13:38:26.556641 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.0001496 (* 0.0909091 = 1.36e-05 loss)
I0425 13:38:26.556653 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 13:38:26.556668 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 13:38:26.556690 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.541295
I0425 13:38:26.556704 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.336203
I0425 13:38:26.556718 22523 sgd_solver.cpp:106] Iteration 18000, lr = 0.01
I0425 13:44:07.917062 22523 solver.cpp:229] Iteration 18500, loss = 3.07818
I0425 13:44:07.917160 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.467742
I0425 13:44:07.917179 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0425 13:44:07.917193 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.125
I0425 13:44:07.917207 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0425 13:44:07.917218 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.375
I0425 13:44:07.917232 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 13:44:07.917243 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 13:44:07.917256 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.375
I0425 13:44:07.917269 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0425 13:44:07.917281 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.75
I0425 13:44:07.917294 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0425 13:44:07.917305 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 13:44:07.917318 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 13:44:07.917330 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 13:44:07.917342 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 13:44:07.917354 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 13:44:07.917366 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 13:44:07.917378 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 13:44:07.917390 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 13:44:07.917402 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 13:44:07.917413 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 13:44:07.917425 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 13:44:07.917436 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 13:44:07.917448 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.801136
I0425 13:44:07.917460 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.741935
I0425 13:44:07.917477 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.67358 (* 0.3 = 0.502074 loss)
I0425 13:44:07.917492 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.644438 (* 0.3 = 0.193332 loss)
I0425 13:44:07.917508 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.20442 (* 0.0272727 = 0.0328478 loss)
I0425 13:44:07.917523 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.00531 (* 0.0272727 = 0.0546903 loss)
I0425 13:44:07.917537 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.24565 (* 0.0272727 = 0.0339724 loss)
I0425 13:44:07.917552 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.75549 (* 0.0272727 = 0.0478771 loss)
I0425 13:44:07.917567 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.22483 (* 0.0272727 = 0.0606771 loss)
I0425 13:44:07.917580 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.09079 (* 0.0272727 = 0.0570215 loss)
I0425 13:44:07.917594 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 2.12063 (* 0.0272727 = 0.0578353 loss)
I0425 13:44:07.917609 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.13791 (* 0.0272727 = 0.0310339 loss)
I0425 13:44:07.917623 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.576121 (* 0.0272727 = 0.0157124 loss)
I0425 13:44:07.917637 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.623129 (* 0.0272727 = 0.0169944 loss)
I0425 13:44:07.917652 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.563222 (* 0.0272727 = 0.0153606 loss)
I0425 13:44:07.917666 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.486928 (* 0.0272727 = 0.0132799 loss)
I0425 13:44:07.917697 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.699736 (* 0.0272727 = 0.0190837 loss)
I0425 13:44:07.917713 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0974413 (* 0.0272727 = 0.00265749 loss)
I0425 13:44:07.917728 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0307395 (* 0.0272727 = 0.000838349 loss)
I0425 13:44:07.917742 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0122266 (* 0.0272727 = 0.000333453 loss)
I0425 13:44:07.917757 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00626548 (* 0.0272727 = 0.000170877 loss)
I0425 13:44:07.917771 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00181499 (* 0.0272727 = 4.94997e-05 loss)
I0425 13:44:07.917786 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00095361 (* 0.0272727 = 2.60075e-05 loss)
I0425 13:44:07.917800 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000263477 (* 0.0272727 = 7.18573e-06 loss)
I0425 13:44:07.917815 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000374791 (* 0.0272727 = 1.02216e-05 loss)
I0425 13:44:07.917829 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00028912 (* 0.0272727 = 7.8851e-06 loss)
I0425 13:44:07.917842 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.612903
I0425 13:44:07.917855 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 13:44:07.917866 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0425 13:44:07.917877 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 13:44:07.917889 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.625
I0425 13:44:07.917901 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 13:44:07.917912 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.25
I0425 13:44:07.917924 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.25
I0425 13:44:07.917935 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0425 13:44:07.917950 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.75
I0425 13:44:07.917963 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0425 13:44:07.917974 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 13:44:07.917985 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 13:44:07.917997 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 13:44:07.918009 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 13:44:07.918020 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 13:44:07.918031 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 13:44:07.918042 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 13:44:07.918054 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 13:44:07.918066 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 13:44:07.918076 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 13:44:07.918088 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 13:44:07.918099 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 13:44:07.918112 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.846591
I0425 13:44:07.918123 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.83871
I0425 13:44:07.918138 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.14872 (* 0.3 = 0.344616 loss)
I0425 13:44:07.918150 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.473671 (* 0.3 = 0.142101 loss)
I0425 13:44:07.918164 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.663775 (* 0.0272727 = 0.018103 loss)
I0425 13:44:07.918179 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.992117 (* 0.0272727 = 0.0270577 loss)
I0425 13:44:07.918207 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.892658 (* 0.0272727 = 0.0243452 loss)
I0425 13:44:07.918223 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.27891 (* 0.0272727 = 0.0348793 loss)
I0425 13:44:07.918237 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.59892 (* 0.0272727 = 0.0436069 loss)
I0425 13:44:07.918251 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 2.08982 (* 0.0272727 = 0.0569952 loss)
I0425 13:44:07.918264 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.29298 (* 0.0272727 = 0.0352631 loss)
I0425 13:44:07.918278 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.56187 (* 0.0272727 = 0.0425964 loss)
I0425 13:44:07.918292 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.699939 (* 0.0272727 = 0.0190893 loss)
I0425 13:44:07.918306 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.508988 (* 0.0272727 = 0.0138815 loss)
I0425 13:44:07.918320 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.798623 (* 0.0272727 = 0.0217806 loss)
I0425 13:44:07.918335 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.364502 (* 0.0272727 = 0.00994097 loss)
I0425 13:44:07.918349 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 1.02741 (* 0.0272727 = 0.0280202 loss)
I0425 13:44:07.918364 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00756181 (* 0.0272727 = 0.000206231 loss)
I0425 13:44:07.918377 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00274078 (* 0.0272727 = 7.47486e-05 loss)
I0425 13:44:07.918391 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00155118 (* 0.0272727 = 4.23048e-05 loss)
I0425 13:44:07.918406 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000628321 (* 0.0272727 = 1.7136e-05 loss)
I0425 13:44:07.918421 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00037509 (* 0.0272727 = 1.02297e-05 loss)
I0425 13:44:07.918434 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000196824 (* 0.0272727 = 5.36791e-06 loss)
I0425 13:44:07.918448 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 8.88347e-05 (* 0.0272727 = 2.42276e-06 loss)
I0425 13:44:07.918463 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 8.56736e-05 (* 0.0272727 = 2.33655e-06 loss)
I0425 13:44:07.918478 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 2.19503e-05 (* 0.0272727 = 5.98645e-07 loss)
I0425 13:44:07.918489 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.822581
I0425 13:44:07.918501 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 13:44:07.918514 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 13:44:07.918525 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 13:44:07.918537 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 13:44:07.918548 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 13:44:07.918560 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 13:44:07.918571 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.375
I0425 13:44:07.918583 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0425 13:44:07.918596 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 13:44:07.918606 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 13:44:07.918618 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 13:44:07.918629 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 13:44:07.918642 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 13:44:07.918653 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 13:44:07.918664 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 13:44:07.918685 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 13:44:07.918699 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 13:44:07.918710 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 13:44:07.918721 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 13:44:07.918733 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 13:44:07.918745 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 13:44:07.918756 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 13:44:07.918768 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.914773
I0425 13:44:07.918781 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.935484
I0425 13:44:07.918793 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.539621 (* 1 = 0.539621 loss)
I0425 13:44:07.918807 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.259453 (* 1 = 0.259453 loss)
I0425 13:44:07.918823 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.107486 (* 0.0909091 = 0.00977144 loss)
I0425 13:44:07.918836 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.16671 (* 0.0909091 = 0.0151554 loss)
I0425 13:44:07.918850 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.35226 (* 0.0909091 = 0.0320236 loss)
I0425 13:44:07.918864 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.331346 (* 0.0909091 = 0.0301224 loss)
I0425 13:44:07.918879 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.438777 (* 0.0909091 = 0.0398888 loss)
I0425 13:44:07.918889 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.568211 (* 0.0909091 = 0.0516555 loss)
I0425 13:44:07.918897 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.05776 (* 0.0909091 = 0.0961602 loss)
I0425 13:44:07.918912 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.53773 (* 0.0909091 = 0.0488845 loss)
I0425 13:44:07.918926 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.455022 (* 0.0909091 = 0.0413657 loss)
I0425 13:44:07.918941 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.441544 (* 0.0909091 = 0.0401403 loss)
I0425 13:44:07.918953 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.66023 (* 0.0909091 = 0.0600209 loss)
I0425 13:44:07.918967 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.47434 (* 0.0909091 = 0.0431218 loss)
I0425 13:44:07.918982 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.707633 (* 0.0909091 = 0.0643303 loss)
I0425 13:44:07.918998 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0450264 (* 0.0909091 = 0.00409331 loss)
I0425 13:44:07.919013 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0251889 (* 0.0909091 = 0.0022899 loss)
I0425 13:44:07.919026 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00910946 (* 0.0909091 = 0.000828133 loss)
I0425 13:44:07.919040 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00491309 (* 0.0909091 = 0.000446645 loss)
I0425 13:44:07.919054 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00255116 (* 0.0909091 = 0.000231923 loss)
I0425 13:44:07.919069 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00201597 (* 0.0909091 = 0.00018327 loss)
I0425 13:44:07.919082 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00108683 (* 0.0909091 = 9.88023e-05 loss)
I0425 13:44:07.919096 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000521924 (* 0.0909091 = 4.74477e-05 loss)
I0425 13:44:07.919111 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 4.63689e-05 (* 0.0909091 = 4.21536e-06 loss)
I0425 13:44:07.919122 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0425 13:44:07.919134 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.25
I0425 13:44:07.919157 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.376691
I0425 13:44:07.919169 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.220297
I0425 13:44:07.919183 22523 sgd_solver.cpp:106] Iteration 18500, lr = 0.01
I0425 13:49:49.327728 22523 solver.cpp:229] Iteration 19000, loss = 3.1844
I0425 13:49:49.327873 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.40678
I0425 13:49:49.327894 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 13:49:49.327908 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0425 13:49:49.327921 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 13:49:49.327934 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 13:49:49.327946 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 13:49:49.327960 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0425 13:49:49.327972 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 13:49:49.327986 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0425 13:49:49.327998 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 13:49:49.328011 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.75
I0425 13:49:49.328023 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 13:49:49.328035 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 13:49:49.328048 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 13:49:49.328061 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 13:49:49.328073 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0425 13:49:49.328086 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 0.875
I0425 13:49:49.328099 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 13:49:49.328119 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 13:49:49.328130 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 13:49:49.328142 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 13:49:49.328155 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 13:49:49.328166 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 13:49:49.328186 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.795455
I0425 13:49:49.328200 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.644068
I0425 13:49:49.328219 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.00958 (* 0.3 = 0.602874 loss)
I0425 13:49:49.328235 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.712749 (* 0.3 = 0.213825 loss)
I0425 13:49:49.328250 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.715559 (* 0.0272727 = 0.0195152 loss)
I0425 13:49:49.328265 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.94221 (* 0.0272727 = 0.0529693 loss)
I0425 13:49:49.328280 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.06702 (* 0.0272727 = 0.0563734 loss)
I0425 13:49:49.328294 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.06703 (* 0.0272727 = 0.0563736 loss)
I0425 13:49:49.328310 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.65603 (* 0.0272727 = 0.0724371 loss)
I0425 13:49:49.328323 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.0328 (* 0.0272727 = 0.05544 loss)
I0425 13:49:49.328338 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.51304 (* 0.0272727 = 0.0412646 loss)
I0425 13:49:49.328352 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.714674 (* 0.0272727 = 0.0194911 loss)
I0425 13:49:49.328367 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.388604 (* 0.0272727 = 0.0105983 loss)
I0425 13:49:49.328382 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.6532 (* 0.0272727 = 0.0178145 loss)
I0425 13:49:49.328397 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.403367 (* 0.0272727 = 0.0110009 loss)
I0425 13:49:49.328411 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.304633 (* 0.0272727 = 0.00830818 loss)
I0425 13:49:49.328444 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.305394 (* 0.0272727 = 0.00832893 loss)
I0425 13:49:49.328460 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.539471 (* 0.0272727 = 0.0147128 loss)
I0425 13:49:49.328475 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.603229 (* 0.0272727 = 0.0164517 loss)
I0425 13:49:49.328490 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.495365 (* 0.0272727 = 0.0135099 loss)
I0425 13:49:49.328505 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00187161 (* 0.0272727 = 5.10438e-05 loss)
I0425 13:49:49.328521 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00111558 (* 0.0272727 = 3.04249e-05 loss)
I0425 13:49:49.328536 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000366756 (* 0.0272727 = 1.00024e-05 loss)
I0425 13:49:49.328554 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000232501 (* 0.0272727 = 6.34093e-06 loss)
I0425 13:49:49.328568 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000139144 (* 0.0272727 = 3.79485e-06 loss)
I0425 13:49:49.328583 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 3.82171e-05 (* 0.0272727 = 1.04228e-06 loss)
I0425 13:49:49.328595 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.525424
I0425 13:49:49.328614 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 13:49:49.328626 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 13:49:49.328637 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.5
I0425 13:49:49.328649 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0425 13:49:49.328661 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0425 13:49:49.328673 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0425 13:49:49.328685 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 13:49:49.328696 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 13:49:49.328708 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 13:49:49.328721 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.75
I0425 13:49:49.328732 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 13:49:49.328742 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 13:49:49.328754 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 13:49:49.328766 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 13:49:49.328778 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0425 13:49:49.328789 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 0.875
I0425 13:49:49.328800 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 13:49:49.328811 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 13:49:49.328824 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 13:49:49.328835 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 13:49:49.328845 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 13:49:49.328857 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 13:49:49.328868 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.840909
I0425 13:49:49.328881 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.711864
I0425 13:49:49.328897 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.62947 (* 0.3 = 0.488841 loss)
I0425 13:49:49.328912 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.558131 (* 0.3 = 0.167439 loss)
I0425 13:49:49.328927 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.07045 (* 0.0272727 = 0.0291941 loss)
I0425 13:49:49.328941 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.35405 (* 0.0272727 = 0.0369287 loss)
I0425 13:49:49.328968 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.28761 (* 0.0272727 = 0.0351167 loss)
I0425 13:49:49.328982 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.14289 (* 0.0272727 = 0.0584425 loss)
I0425 13:49:49.328996 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.49391 (* 0.0272727 = 0.0680157 loss)
I0425 13:49:49.329010 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 2.05662 (* 0.0272727 = 0.0560896 loss)
I0425 13:49:49.329025 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.2778 (* 0.0272727 = 0.0348492 loss)
I0425 13:49:49.329038 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.801185 (* 0.0272727 = 0.0218505 loss)
I0425 13:49:49.329052 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.337033 (* 0.0272727 = 0.00919181 loss)
I0425 13:49:49.329067 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.615813 (* 0.0272727 = 0.0167949 loss)
I0425 13:49:49.329082 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.406691 (* 0.0272727 = 0.0110916 loss)
I0425 13:49:49.329097 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.304124 (* 0.0272727 = 0.00829429 loss)
I0425 13:49:49.329110 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.434197 (* 0.0272727 = 0.0118417 loss)
I0425 13:49:49.329125 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.616068 (* 0.0272727 = 0.0168018 loss)
I0425 13:49:49.329139 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.529939 (* 0.0272727 = 0.0144529 loss)
I0425 13:49:49.329154 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.321061 (* 0.0272727 = 0.0087562 loss)
I0425 13:49:49.329169 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0124103 (* 0.0272727 = 0.000338464 loss)
I0425 13:49:49.329182 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00571114 (* 0.0272727 = 0.000155758 loss)
I0425 13:49:49.329197 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00511449 (* 0.0272727 = 0.000139486 loss)
I0425 13:49:49.329211 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00265556 (* 0.0272727 = 7.24242e-05 loss)
I0425 13:49:49.329226 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000946432 (* 0.0272727 = 2.58118e-05 loss)
I0425 13:49:49.329241 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000961497 (* 0.0272727 = 2.62226e-05 loss)
I0425 13:49:49.329255 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.627119
I0425 13:49:49.329268 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 13:49:49.329279 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 13:49:49.329291 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 13:49:49.329303 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0425 13:49:49.329314 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.625
I0425 13:49:49.329326 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.625
I0425 13:49:49.329339 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.75
I0425 13:49:49.329349 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0425 13:49:49.329361 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 13:49:49.329373 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.75
I0425 13:49:49.329385 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 13:49:49.329396 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 13:49:49.329407 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 13:49:49.329419 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 13:49:49.329430 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0425 13:49:49.329453 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 0.875
I0425 13:49:49.329466 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 13:49:49.329478 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 13:49:49.329489 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 13:49:49.329501 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 13:49:49.329514 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 13:49:49.329524 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 13:49:49.329536 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.875
I0425 13:49:49.329548 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.79661
I0425 13:49:49.329563 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 1.41658 (* 1 = 1.41658 loss)
I0425 13:49:49.329577 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.490018 (* 1 = 0.490018 loss)
I0425 13:49:49.329592 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 1.19607 (* 0.0909091 = 0.108734 loss)
I0425 13:49:49.329607 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.910457 (* 0.0909091 = 0.0827688 loss)
I0425 13:49:49.329620 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.373776 (* 0.0909091 = 0.0339796 loss)
I0425 13:49:49.329634 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.37284 (* 0.0909091 = 0.124803 loss)
I0425 13:49:49.329649 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.31527 (* 0.0909091 = 0.11957 loss)
I0425 13:49:49.329663 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 1.29 (* 0.0909091 = 0.117272 loss)
I0425 13:49:49.329677 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.17253 (* 0.0909091 = 0.106594 loss)
I0425 13:49:49.329691 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.6161 (* 0.0909091 = 0.0560091 loss)
I0425 13:49:49.329705 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.465734 (* 0.0909091 = 0.0423395 loss)
I0425 13:49:49.329720 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.578111 (* 0.0909091 = 0.0525555 loss)
I0425 13:49:49.329735 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.362855 (* 0.0909091 = 0.0329868 loss)
I0425 13:49:49.329748 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.280526 (* 0.0909091 = 0.0255024 loss)
I0425 13:49:49.329762 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.354931 (* 0.0909091 = 0.0322665 loss)
I0425 13:49:49.329777 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.558084 (* 0.0909091 = 0.0507349 loss)
I0425 13:49:49.329792 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.451204 (* 0.0909091 = 0.0410186 loss)
I0425 13:49:49.329802 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.345899 (* 0.0909091 = 0.0314454 loss)
I0425 13:49:49.329816 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0117741 (* 0.0909091 = 0.00107037 loss)
I0425 13:49:49.329830 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00645263 (* 0.0909091 = 0.000586603 loss)
I0425 13:49:49.329844 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.0024103 (* 0.0909091 = 0.000219119 loss)
I0425 13:49:49.329859 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00198179 (* 0.0909091 = 0.000180163 loss)
I0425 13:49:49.329874 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000531283 (* 0.0909091 = 4.82985e-05 loss)
I0425 13:49:49.329887 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 5.69207e-05 (* 0.0909091 = 5.17461e-06 loss)
I0425 13:49:49.329900 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0425 13:49:49.329911 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0425 13:49:49.329933 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.471216
I0425 13:49:49.329951 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.314898
I0425 13:49:49.329964 22523 sgd_solver.cpp:106] Iteration 19000, lr = 0.01
I0425 13:55:30.687749 22523 solver.cpp:229] Iteration 19500, loss = 3.05134
I0425 13:55:30.687892 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.4375
I0425 13:55:30.687914 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0425 13:55:30.687928 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0425 13:55:30.687942 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 13:55:30.687955 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 13:55:30.687968 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.25
I0425 13:55:30.687980 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 13:55:30.687994 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 13:55:30.688006 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 13:55:30.688019 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 13:55:30.688032 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 13:55:30.688045 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 13:55:30.688057 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 13:55:30.688069 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 13:55:30.688082 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 13:55:30.688094 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 13:55:30.688107 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 13:55:30.688119 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 13:55:30.688133 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 13:55:30.688144 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 13:55:30.688158 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 13:55:30.688169 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 13:55:30.688189 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 13:55:30.688205 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.829545
I0425 13:55:30.688217 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.6875
I0425 13:55:30.688235 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.91097 (* 0.3 = 0.573292 loss)
I0425 13:55:30.688251 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.605014 (* 0.3 = 0.181504 loss)
I0425 13:55:30.688273 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.17739 (* 0.0272727 = 0.0321108 loss)
I0425 13:55:30.688288 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.34892 (* 0.0272727 = 0.0640614 loss)
I0425 13:55:30.688302 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.6807 (* 0.0272727 = 0.0731101 loss)
I0425 13:55:30.688318 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.24153 (* 0.0272727 = 0.0611327 loss)
I0425 13:55:30.688331 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.16876 (* 0.0272727 = 0.059148 loss)
I0425 13:55:30.688346 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.498 (* 0.0272727 = 0.0408547 loss)
I0425 13:55:30.688360 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.03334 (* 0.0272727 = 0.028182 loss)
I0425 13:55:30.688375 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.194645 (* 0.0272727 = 0.00530851 loss)
I0425 13:55:30.688390 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.218346 (* 0.0272727 = 0.00595488 loss)
I0425 13:55:30.688405 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.121584 (* 0.0272727 = 0.00331594 loss)
I0425 13:55:30.688421 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0480841 (* 0.0272727 = 0.00131138 loss)
I0425 13:55:30.688436 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0347525 (* 0.0272727 = 0.000947795 loss)
I0425 13:55:30.688449 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0141079 (* 0.0272727 = 0.000384762 loss)
I0425 13:55:30.688483 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0137899 (* 0.0272727 = 0.000376088 loss)
I0425 13:55:30.688499 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00875439 (* 0.0272727 = 0.000238756 loss)
I0425 13:55:30.688513 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00689434 (* 0.0272727 = 0.000188027 loss)
I0425 13:55:30.688529 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00510196 (* 0.0272727 = 0.000139144 loss)
I0425 13:55:30.688544 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00218986 (* 0.0272727 = 5.97234e-05 loss)
I0425 13:55:30.688558 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00078652 (* 0.0272727 = 2.14505e-05 loss)
I0425 13:55:30.688573 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000697689 (* 0.0272727 = 1.90279e-05 loss)
I0425 13:55:30.688587 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000340874 (* 0.0272727 = 9.29658e-06 loss)
I0425 13:55:30.688602 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000153233 (* 0.0272727 = 4.17909e-06 loss)
I0425 13:55:30.688616 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.729167
I0425 13:55:30.688628 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 13:55:30.688648 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.75
I0425 13:55:30.688660 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.25
I0425 13:55:30.688673 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.5
I0425 13:55:30.688684 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 13:55:30.688696 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 13:55:30.688710 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 13:55:30.688722 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 13:55:30.688735 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 13:55:30.688745 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 13:55:30.688756 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 13:55:30.688768 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 13:55:30.688779 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 13:55:30.688791 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 13:55:30.688802 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 13:55:30.688813 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 13:55:30.688825 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 13:55:30.688837 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 13:55:30.688848 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 13:55:30.688859 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 13:55:30.688871 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 13:55:30.688882 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 13:55:30.688894 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.909091
I0425 13:55:30.688910 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.916667
I0425 13:55:30.688925 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 0.76247 (* 0.3 = 0.228741 loss)
I0425 13:55:30.688940 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.255498 (* 0.3 = 0.0766493 loss)
I0425 13:55:30.688954 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.286522 (* 0.0272727 = 0.00781424 loss)
I0425 13:55:30.688968 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.700255 (* 0.0272727 = 0.0190979 loss)
I0425 13:55:30.688994 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.19739 (* 0.0272727 = 0.0599288 loss)
I0425 13:55:30.689009 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.80403 (* 0.0272727 = 0.0492009 loss)
I0425 13:55:30.689023 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.29708 (* 0.0272727 = 0.0353748 loss)
I0425 13:55:30.689038 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.924506 (* 0.0272727 = 0.0252138 loss)
I0425 13:55:30.689052 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.339227 (* 0.0272727 = 0.00925165 loss)
I0425 13:55:30.689067 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.134135 (* 0.0272727 = 0.00365821 loss)
I0425 13:55:30.689081 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.156779 (* 0.0272727 = 0.00427578 loss)
I0425 13:55:30.689095 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.0904781 (* 0.0272727 = 0.00246758 loss)
I0425 13:55:30.689110 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.0233717 (* 0.0272727 = 0.000637409 loss)
I0425 13:55:30.689123 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0135076 (* 0.0272727 = 0.00036839 loss)
I0425 13:55:30.689138 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.0027148 (* 0.0272727 = 7.40399e-05 loss)
I0425 13:55:30.689152 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0017419 (* 0.0272727 = 4.75063e-05 loss)
I0425 13:55:30.689167 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.000697186 (* 0.0272727 = 1.90142e-05 loss)
I0425 13:55:30.689180 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000454971 (* 0.0272727 = 1.24083e-05 loss)
I0425 13:55:30.689195 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.000171821 (* 0.0272727 = 4.68604e-06 loss)
I0425 13:55:30.689209 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000186359 (* 0.0272727 = 5.08253e-06 loss)
I0425 13:55:30.689224 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 5.42775e-05 (* 0.0272727 = 1.48029e-06 loss)
I0425 13:55:30.689239 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 2.89925e-05 (* 0.0272727 = 7.90704e-07 loss)
I0425 13:55:30.689255 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 1.81209e-05 (* 0.0272727 = 4.94207e-07 loss)
I0425 13:55:30.689270 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.30838e-05 (* 0.0272727 = 3.5683e-07 loss)
I0425 13:55:30.689291 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.895833
I0425 13:55:30.689303 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 13:55:30.689316 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 13:55:30.689327 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 13:55:30.689339 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.625
I0425 13:55:30.689357 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 13:55:30.689369 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 13:55:30.689381 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 1
I0425 13:55:30.689393 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 13:55:30.689404 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 13:55:30.689416 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 13:55:30.689427 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 13:55:30.689440 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 13:55:30.689450 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 13:55:30.689461 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 13:55:30.689472 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 13:55:30.689484 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 13:55:30.689507 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 13:55:30.689519 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 13:55:30.689532 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 13:55:30.689543 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 13:55:30.689554 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 13:55:30.689566 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 13:55:30.689587 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.960227
I0425 13:55:30.689599 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.958333
I0425 13:55:30.689613 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.389942 (* 1 = 0.389942 loss)
I0425 13:55:30.689627 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.152404 (* 1 = 0.152404 loss)
I0425 13:55:30.689648 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0665776 (* 0.0909091 = 0.00605251 loss)
I0425 13:55:30.689662 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0370638 (* 0.0909091 = 0.00336944 loss)
I0425 13:55:30.689677 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.743441 (* 0.0909091 = 0.0675855 loss)
I0425 13:55:30.689692 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 1.10763 (* 0.0909091 = 0.100694 loss)
I0425 13:55:30.689705 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.462722 (* 0.0909091 = 0.0420657 loss)
I0425 13:55:30.689719 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.370034 (* 0.0909091 = 0.0336395 loss)
I0425 13:55:30.689734 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.206489 (* 0.0909091 = 0.0187717 loss)
I0425 13:55:30.689749 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.134461 (* 0.0909091 = 0.0122238 loss)
I0425 13:55:30.689762 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.151918 (* 0.0909091 = 0.0138108 loss)
I0425 13:55:30.689777 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0498782 (* 0.0909091 = 0.00453438 loss)
I0425 13:55:30.689791 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.0109101 (* 0.0909091 = 0.000991829 loss)
I0425 13:55:30.689805 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00550323 (* 0.0909091 = 0.000500294 loss)
I0425 13:55:30.689820 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00277993 (* 0.0909091 = 0.000252721 loss)
I0425 13:55:30.689833 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.000973167 (* 0.0909091 = 8.84697e-05 loss)
I0425 13:55:30.689847 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.000645778 (* 0.0909091 = 5.87071e-05 loss)
I0425 13:55:30.689862 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.000400812 (* 0.0909091 = 3.64374e-05 loss)
I0425 13:55:30.689875 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000488907 (* 0.0909091 = 4.44461e-05 loss)
I0425 13:55:30.689890 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000265416 (* 0.0909091 = 2.41287e-05 loss)
I0425 13:55:30.689904 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000248269 (* 0.0909091 = 2.25699e-05 loss)
I0425 13:55:30.689918 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.000208189 (* 0.0909091 = 1.89262e-05 loss)
I0425 13:55:30.689934 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000109113 (* 0.0909091 = 9.91933e-06 loss)
I0425 13:55:30.689947 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.82786e-05 (* 0.0909091 = 3.47987e-06 loss)
I0425 13:55:30.689963 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.75
I0425 13:55:30.689976 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0425 13:55:30.689999 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.434239
I0425 13:55:30.690012 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.349212
I0425 13:55:30.690028 22523 sgd_solver.cpp:106] Iteration 19500, lr = 0.01
I0425 14:01:11.616948 22523 solver.cpp:456] Snapshotting to binary proto file /mnt/snapshots/mixed_lstm10_bn_iter_20000.caffemodel
I0425 14:01:12.265326 22523 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /mnt/snapshots/mixed_lstm10_bn_iter_20000.solverstate
I0425 14:01:12.584873 22523 solver.cpp:338] Iteration 20000, Testing net (#0)
I0425 14:02:04.247581 22523 solver.cpp:393] Test loss: 1.45594
I0425 14:02:04.247705 22523 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.775009
I0425 14:02:04.247725 22523 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.878
I0425 14:02:04.247738 22523 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.701
I0425 14:02:04.247751 22523 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.552
I0425 14:02:04.247764 22523 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.528
I0425 14:02:04.247776 22523 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.603
I0425 14:02:04.247789 22523 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.677
I0425 14:02:04.247802 22523 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.847
I0425 14:02:04.247813 22523 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.92
I0425 14:02:04.247826 22523 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.984
I0425 14:02:04.247838 22523 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.995
I0425 14:02:04.247851 22523 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.997
I0425 14:02:04.247864 22523 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.999
I0425 14:02:04.247875 22523 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.999
I0425 14:02:04.247887 22523 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 0.999
I0425 14:02:04.247900 22523 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 0.999
I0425 14:02:04.247911 22523 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 1
I0425 14:02:04.247931 22523 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 1
I0425 14:02:04.247942 22523 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 1
I0425 14:02:04.247953 22523 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 1
I0425 14:02:04.247966 22523 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 1
I0425 14:02:04.247977 22523 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0425 14:02:04.247995 22523 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0425 14:02:04.248008 22523 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.930456
I0425 14:02:04.248019 22523 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.930982
I0425 14:02:04.248037 22523 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 0.777635 (* 0.3 = 0.23329 loss)
I0425 14:02:04.248052 22523 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.237776 (* 0.3 = 0.0713329 loss)
I0425 14:02:04.248067 22523 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.487687 (* 0.0272727 = 0.0133006 loss)
I0425 14:02:04.248082 22523 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.03026 (* 0.0272727 = 0.0280981 loss)
I0425 14:02:04.248096 22523 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.39811 (* 0.0272727 = 0.0381304 loss)
I0425 14:02:04.248111 22523 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.44 (* 0.0272727 = 0.0392727 loss)
I0425 14:02:04.248126 22523 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.21944 (* 0.0272727 = 0.0332575 loss)
I0425 14:02:04.248139 22523 solver.cpp:406]     Test net output #32: loss1/loss06 = 0.957512 (* 0.0272727 = 0.026114 loss)
I0425 14:02:04.248153 22523 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.537717 (* 0.0272727 = 0.014665 loss)
I0425 14:02:04.248168 22523 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.281445 (* 0.0272727 = 0.00767577 loss)
I0425 14:02:04.248183 22523 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.0699431 (* 0.0272727 = 0.00190754 loss)
I0425 14:02:04.248200 22523 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.0328229 (* 0.0272727 = 0.000895169 loss)
I0425 14:02:04.248216 22523 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.0205477 (* 0.0272727 = 0.000560393 loss)
I0425 14:02:04.248230 22523 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.0129443 (* 0.0272727 = 0.000353026 loss)
I0425 14:02:04.248245 22523 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.00979594 (* 0.0272727 = 0.000267162 loss)
I0425 14:02:04.248288 22523 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.00781158 (* 0.0272727 = 0.000213043 loss)
I0425 14:02:04.248304 22523 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.0059437 (* 0.0272727 = 0.000162101 loss)
I0425 14:02:04.248328 22523 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.00315494 (* 0.0272727 = 8.60439e-05 loss)
I0425 14:02:04.248342 22523 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.00126007 (* 0.0272727 = 3.43655e-05 loss)
I0425 14:02:04.248358 22523 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.000770402 (* 0.0272727 = 2.1011e-05 loss)
I0425 14:02:04.248373 22523 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.000409651 (* 0.0272727 = 1.11723e-05 loss)
I0425 14:02:04.248386 22523 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.000228145 (* 0.0272727 = 6.22214e-06 loss)
I0425 14:02:04.248401 22523 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.000121729 (* 0.0272727 = 3.31987e-06 loss)
I0425 14:02:04.248415 22523 solver.cpp:406]     Test net output #48: loss1/loss22 = 6.75964e-05 (* 0.0272727 = 1.84354e-06 loss)
I0425 14:02:04.248427 22523 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.894575
I0425 14:02:04.248440 22523 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.949
I0425 14:02:04.248451 22523 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.889
I0425 14:02:04.248463 22523 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.755
I0425 14:02:04.248474 22523 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.655
I0425 14:02:04.248486 22523 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.673
I0425 14:02:04.248498 22523 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.732
I0425 14:02:04.248509 22523 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.887
I0425 14:02:04.248520 22523 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.928
I0425 14:02:04.248533 22523 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.984
I0425 14:02:04.248543 22523 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.994
I0425 14:02:04.248555 22523 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.998
I0425 14:02:04.248566 22523 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.999
I0425 14:02:04.248577 22523 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.999
I0425 14:02:04.248589 22523 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.999
I0425 14:02:04.248600 22523 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.999
I0425 14:02:04.248612 22523 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 1
I0425 14:02:04.248623 22523 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 1
I0425 14:02:04.248634 22523 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 1
I0425 14:02:04.248646 22523 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 1
I0425 14:02:04.248656 22523 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 1
I0425 14:02:04.248667 22523 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0425 14:02:04.248679 22523 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0425 14:02:04.248690 22523 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.966227
I0425 14:02:04.248702 22523 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.96665
I0425 14:02:04.248718 22523 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.433914 (* 0.3 = 0.130174 loss)
I0425 14:02:04.248733 22523 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.131984 (* 0.3 = 0.0395952 loss)
I0425 14:02:04.248749 22523 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.277909 (* 0.0272727 = 0.00757933 loss)
I0425 14:02:04.248762 22523 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.476957 (* 0.0272727 = 0.0130079 loss)
I0425 14:02:04.248787 22523 solver.cpp:406]     Test net output #78: loss2/loss03 = 0.782693 (* 0.0272727 = 0.0213462 loss)
I0425 14:02:04.248806 22523 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.01408 (* 0.0272727 = 0.0276568 loss)
I0425 14:02:04.248821 22523 solver.cpp:406]     Test net output #80: loss2/loss05 = 0.912204 (* 0.0272727 = 0.0248783 loss)
I0425 14:02:04.248834 22523 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.713196 (* 0.0272727 = 0.0194508 loss)
I0425 14:02:04.248848 22523 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.392274 (* 0.0272727 = 0.0106984 loss)
I0425 14:02:04.248863 22523 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.217805 (* 0.0272727 = 0.00594013 loss)
I0425 14:02:04.248878 22523 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.0593711 (* 0.0272727 = 0.00161921 loss)
I0425 14:02:04.248891 22523 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.0289166 (* 0.0272727 = 0.000788634 loss)
I0425 14:02:04.248905 22523 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.0121632 (* 0.0272727 = 0.000331724 loss)
I0425 14:02:04.248919 22523 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.00677671 (* 0.0272727 = 0.000184819 loss)
I0425 14:02:04.248934 22523 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.00493593 (* 0.0272727 = 0.000134616 loss)
I0425 14:02:04.248947 22523 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.0040642 (* 0.0272727 = 0.000110842 loss)
I0425 14:02:04.248962 22523 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.00320519 (* 0.0272727 = 8.74142e-05 loss)
I0425 14:02:04.248976 22523 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.00153557 (* 0.0272727 = 4.18792e-05 loss)
I0425 14:02:04.248991 22523 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.000361925 (* 0.0272727 = 9.87068e-06 loss)
I0425 14:02:04.249004 22523 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.000193693 (* 0.0272727 = 5.28255e-06 loss)
I0425 14:02:04.249018 22523 solver.cpp:406]     Test net output #94: loss2/loss19 = 6.90858e-05 (* 0.0272727 = 1.88416e-06 loss)
I0425 14:02:04.249032 22523 solver.cpp:406]     Test net output #95: loss2/loss20 = 3.19305e-05 (* 0.0272727 = 8.70832e-07 loss)
I0425 14:02:04.249047 22523 solver.cpp:406]     Test net output #96: loss2/loss21 = 1.55178e-05 (* 0.0272727 = 4.23213e-07 loss)
I0425 14:02:04.249061 22523 solver.cpp:406]     Test net output #97: loss2/loss22 = 7.50338e-06 (* 0.0272727 = 2.04638e-07 loss)
I0425 14:02:04.249073 22523 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.931608
I0425 14:02:04.249085 22523 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.963
I0425 14:02:04.249097 22523 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.941
I0425 14:02:04.249109 22523 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.934
I0425 14:02:04.249120 22523 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.92
I0425 14:02:04.249132 22523 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.88
I0425 14:02:04.249143 22523 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.866
I0425 14:02:04.249155 22523 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.926
I0425 14:02:04.249166 22523 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.971
I0425 14:02:04.249177 22523 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.983
I0425 14:02:04.249189 22523 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.994
I0425 14:02:04.249200 22523 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.999
I0425 14:02:04.249212 22523 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 1
I0425 14:02:04.249223 22523 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 1
I0425 14:02:04.249234 22523 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 1
I0425 14:02:04.249245 22523 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 1
I0425 14:02:04.249259 22523 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 1
I0425 14:02:04.249281 22523 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 1
I0425 14:02:04.249294 22523 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 1
I0425 14:02:04.249305 22523 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 1
I0425 14:02:04.249316 22523 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 1
I0425 14:02:04.249327 22523 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0425 14:02:04.249338 22523 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0425 14:02:04.249351 22523 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.976182
I0425 14:02:04.249361 22523 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.971549
I0425 14:02:04.249375 22523 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.315129 (* 1 = 0.315129 loss)
I0425 14:02:04.249389 22523 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.102581 (* 1 = 0.102581 loss)
I0425 14:02:04.249403 22523 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.210237 (* 0.0909091 = 0.0191125 loss)
I0425 14:02:04.249418 22523 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.316895 (* 0.0909091 = 0.0288087 loss)
I0425 14:02:04.249431 22523 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.30805 (* 0.0909091 = 0.0280045 loss)
I0425 14:02:04.249445 22523 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.368365 (* 0.0909091 = 0.0334877 loss)
I0425 14:02:04.249455 22523 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.404549 (* 0.0909091 = 0.0367771 loss)
I0425 14:02:04.249464 22523 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.417057 (* 0.0909091 = 0.0379143 loss)
I0425 14:02:04.249480 22523 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.243043 (* 0.0909091 = 0.0220948 loss)
I0425 14:02:04.249493 22523 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.105708 (* 0.0909091 = 0.00960981 loss)
I0425 14:02:04.249507 22523 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.0537252 (* 0.0909091 = 0.00488411 loss)
I0425 14:02:04.249521 22523 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.0231341 (* 0.0909091 = 0.0021031 loss)
I0425 14:02:04.249536 22523 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.00920281 (* 0.0909091 = 0.000836619 loss)
I0425 14:02:04.249549 22523 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.00477906 (* 0.0909091 = 0.00043446 loss)
I0425 14:02:04.249563 22523 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.00333843 (* 0.0909091 = 0.000303494 loss)
I0425 14:02:04.249577 22523 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.00278543 (* 0.0909091 = 0.000253221 loss)
I0425 14:02:04.249591 22523 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.0018694 (* 0.0909091 = 0.000169945 loss)
I0425 14:02:04.249605 22523 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.000724679 (* 0.0909091 = 6.58799e-05 loss)
I0425 14:02:04.249619 22523 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.000229278 (* 0.0909091 = 2.08435e-05 loss)
I0425 14:02:04.249632 22523 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.000162727 (* 0.0909091 = 1.47934e-05 loss)
I0425 14:02:04.249646 22523 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.000142966 (* 0.0909091 = 1.29969e-05 loss)
I0425 14:02:04.249660 22523 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.000121002 (* 0.0909091 = 1.10002e-05 loss)
I0425 14:02:04.249675 22523 solver.cpp:406]     Test net output #145: loss3/loss21 = 6.81888e-05 (* 0.0909091 = 6.19898e-06 loss)
I0425 14:02:04.249688 22523 solver.cpp:406]     Test net output #146: loss3/loss22 = 1.81191e-05 (* 0.0909091 = 1.64719e-06 loss)
I0425 14:02:04.249701 22523 solver.cpp:406]     Test net output #147: total_accuracy = 0.819
I0425 14:02:04.249711 22523 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.707
I0425 14:02:04.249723 22523 solver.cpp:406]     Test net output #149: total_confidence = 0.764548
I0425 14:02:04.249744 22523 solver.cpp:406]     Test net output #150: total_confidence_nor_rec = 0.622476
I0425 14:02:04.249758 22523 solver.cpp:338] Iteration 20000, Testing net (#1)
I0425 14:02:55.861536 22523 solver.cpp:393] Test loss: 2.61214
I0425 14:02:55.861691 22523 solver.cpp:406]     Test net output #0: loss1/accuracy = 0.694141
I0425 14:02:55.861713 22523 solver.cpp:406]     Test net output #1: loss1/accuracy01 = 0.829
I0425 14:02:55.861727 22523 solver.cpp:406]     Test net output #2: loss1/accuracy02 = 0.665
I0425 14:02:55.861740 22523 solver.cpp:406]     Test net output #3: loss1/accuracy03 = 0.474
I0425 14:02:55.861752 22523 solver.cpp:406]     Test net output #4: loss1/accuracy04 = 0.499
I0425 14:02:55.861765 22523 solver.cpp:406]     Test net output #5: loss1/accuracy05 = 0.537
I0425 14:02:55.861778 22523 solver.cpp:406]     Test net output #6: loss1/accuracy06 = 0.605
I0425 14:02:55.861789 22523 solver.cpp:406]     Test net output #7: loss1/accuracy07 = 0.736
I0425 14:02:55.861801 22523 solver.cpp:406]     Test net output #8: loss1/accuracy08 = 0.829
I0425 14:02:55.861822 22523 solver.cpp:406]     Test net output #9: loss1/accuracy09 = 0.897
I0425 14:02:55.861835 22523 solver.cpp:406]     Test net output #10: loss1/accuracy10 = 0.908
I0425 14:02:55.861847 22523 solver.cpp:406]     Test net output #11: loss1/accuracy11 = 0.915
I0425 14:02:55.861860 22523 solver.cpp:406]     Test net output #12: loss1/accuracy12 = 0.921
I0425 14:02:55.861881 22523 solver.cpp:406]     Test net output #13: loss1/accuracy13 = 0.939
I0425 14:02:55.861892 22523 solver.cpp:406]     Test net output #14: loss1/accuracy14 = 0.95
I0425 14:02:55.861904 22523 solver.cpp:406]     Test net output #15: loss1/accuracy15 = 0.962
I0425 14:02:55.861917 22523 solver.cpp:406]     Test net output #16: loss1/accuracy16 = 0.97
I0425 14:02:55.861929 22523 solver.cpp:406]     Test net output #17: loss1/accuracy17 = 0.992
I0425 14:02:55.861942 22523 solver.cpp:406]     Test net output #18: loss1/accuracy18 = 0.994
I0425 14:02:55.861954 22523 solver.cpp:406]     Test net output #19: loss1/accuracy19 = 0.996
I0425 14:02:55.861966 22523 solver.cpp:406]     Test net output #20: loss1/accuracy20 = 0.998
I0425 14:02:55.861986 22523 solver.cpp:406]     Test net output #21: loss1/accuracy21 = 1
I0425 14:02:55.861999 22523 solver.cpp:406]     Test net output #22: loss1/accuracy22 = 1
I0425 14:02:55.862010 22523 solver.cpp:406]     Test net output #23: loss1/accuracy_incl_empty = 0.877684
I0425 14:02:55.862022 22523 solver.cpp:406]     Test net output #24: loss1/accuracy_top3 = 0.87212
I0425 14:02:55.862046 22523 solver.cpp:406]     Test net output #25: loss1/cross_entropy_loss = 1.00622 (* 0.3 = 0.301865 loss)
I0425 14:02:55.862061 22523 solver.cpp:406]     Test net output #26: loss1/cross_entropy_loss_incl_empty = 0.412927 (* 0.3 = 0.123878 loss)
I0425 14:02:55.862076 22523 solver.cpp:406]     Test net output #27: loss1/loss01 = 0.687794 (* 0.0272727 = 0.018758 loss)
I0425 14:02:55.862090 22523 solver.cpp:406]     Test net output #28: loss1/loss02 = 1.14855 (* 0.0272727 = 0.0313242 loss)
I0425 14:02:55.862104 22523 solver.cpp:406]     Test net output #29: loss1/loss03 = 1.56449 (* 0.0272727 = 0.042668 loss)
I0425 14:02:55.862119 22523 solver.cpp:406]     Test net output #30: loss1/loss04 = 1.56721 (* 0.0272727 = 0.0427422 loss)
I0425 14:02:55.862133 22523 solver.cpp:406]     Test net output #31: loss1/loss05 = 1.43979 (* 0.0272727 = 0.0392669 loss)
I0425 14:02:55.862148 22523 solver.cpp:406]     Test net output #32: loss1/loss06 = 1.23269 (* 0.0272727 = 0.0336189 loss)
I0425 14:02:55.862161 22523 solver.cpp:406]     Test net output #33: loss1/loss07 = 0.90444 (* 0.0272727 = 0.0246665 loss)
I0425 14:02:55.862175 22523 solver.cpp:406]     Test net output #34: loss1/loss08 = 0.600198 (* 0.0272727 = 0.016369 loss)
I0425 14:02:55.862190 22523 solver.cpp:406]     Test net output #35: loss1/loss09 = 0.384105 (* 0.0272727 = 0.0104756 loss)
I0425 14:02:55.862208 22523 solver.cpp:406]     Test net output #36: loss1/loss10 = 0.343849 (* 0.0272727 = 0.0093777 loss)
I0425 14:02:55.862223 22523 solver.cpp:406]     Test net output #37: loss1/loss11 = 0.327929 (* 0.0272727 = 0.00894352 loss)
I0425 14:02:55.862238 22523 solver.cpp:406]     Test net output #38: loss1/loss12 = 0.311029 (* 0.0272727 = 0.00848262 loss)
I0425 14:02:55.862273 22523 solver.cpp:406]     Test net output #39: loss1/loss13 = 0.241838 (* 0.0272727 = 0.00659557 loss)
I0425 14:02:55.862288 22523 solver.cpp:406]     Test net output #40: loss1/loss14 = 0.217568 (* 0.0272727 = 0.00593366 loss)
I0425 14:02:55.862303 22523 solver.cpp:406]     Test net output #41: loss1/loss15 = 0.170102 (* 0.0272727 = 0.00463914 loss)
I0425 14:02:55.862318 22523 solver.cpp:406]     Test net output #42: loss1/loss16 = 0.146737 (* 0.0272727 = 0.00400192 loss)
I0425 14:02:55.862332 22523 solver.cpp:406]     Test net output #43: loss1/loss17 = 0.055119 (* 0.0272727 = 0.00150324 loss)
I0425 14:02:55.862347 22523 solver.cpp:406]     Test net output #44: loss1/loss18 = 0.0396739 (* 0.0272727 = 0.00108202 loss)
I0425 14:02:55.862362 22523 solver.cpp:406]     Test net output #45: loss1/loss19 = 0.029457 (* 0.0272727 = 0.000803373 loss)
I0425 14:02:55.862376 22523 solver.cpp:406]     Test net output #46: loss1/loss20 = 0.0169947 (* 0.0272727 = 0.000463492 loss)
I0425 14:02:55.862391 22523 solver.cpp:406]     Test net output #47: loss1/loss21 = 0.00117227 (* 0.0272727 = 3.19709e-05 loss)
I0425 14:02:55.862406 22523 solver.cpp:406]     Test net output #48: loss1/loss22 = 0.000556993 (* 0.0272727 = 1.51907e-05 loss)
I0425 14:02:55.862418 22523 solver.cpp:406]     Test net output #49: loss2/accuracy = 0.814229
I0425 14:02:55.862431 22523 solver.cpp:406]     Test net output #50: loss2/accuracy01 = 0.909
I0425 14:02:55.862442 22523 solver.cpp:406]     Test net output #51: loss2/accuracy02 = 0.846
I0425 14:02:55.862453 22523 solver.cpp:406]     Test net output #52: loss2/accuracy03 = 0.692
I0425 14:02:55.862465 22523 solver.cpp:406]     Test net output #53: loss2/accuracy04 = 0.595
I0425 14:02:55.862478 22523 solver.cpp:406]     Test net output #54: loss2/accuracy05 = 0.612
I0425 14:02:55.862488 22523 solver.cpp:406]     Test net output #55: loss2/accuracy06 = 0.671
I0425 14:02:55.862499 22523 solver.cpp:406]     Test net output #56: loss2/accuracy07 = 0.771
I0425 14:02:55.862511 22523 solver.cpp:406]     Test net output #57: loss2/accuracy08 = 0.833
I0425 14:02:55.862522 22523 solver.cpp:406]     Test net output #58: loss2/accuracy09 = 0.899
I0425 14:02:55.862534 22523 solver.cpp:406]     Test net output #59: loss2/accuracy10 = 0.907
I0425 14:02:55.862545 22523 solver.cpp:406]     Test net output #60: loss2/accuracy11 = 0.913
I0425 14:02:55.862557 22523 solver.cpp:406]     Test net output #61: loss2/accuracy12 = 0.921
I0425 14:02:55.862568 22523 solver.cpp:406]     Test net output #62: loss2/accuracy13 = 0.94
I0425 14:02:55.862581 22523 solver.cpp:406]     Test net output #63: loss2/accuracy14 = 0.947
I0425 14:02:55.862591 22523 solver.cpp:406]     Test net output #64: loss2/accuracy15 = 0.964
I0425 14:02:55.862602 22523 solver.cpp:406]     Test net output #65: loss2/accuracy16 = 0.97
I0425 14:02:55.862614 22523 solver.cpp:406]     Test net output #66: loss2/accuracy17 = 0.992
I0425 14:02:55.862627 22523 solver.cpp:406]     Test net output #67: loss2/accuracy18 = 0.994
I0425 14:02:55.862637 22523 solver.cpp:406]     Test net output #68: loss2/accuracy19 = 0.996
I0425 14:02:55.862649 22523 solver.cpp:406]     Test net output #69: loss2/accuracy20 = 0.998
I0425 14:02:55.862660 22523 solver.cpp:406]     Test net output #70: loss2/accuracy21 = 1
I0425 14:02:55.862671 22523 solver.cpp:406]     Test net output #71: loss2/accuracy22 = 1
I0425 14:02:55.862684 22523 solver.cpp:406]     Test net output #72: loss2/accuracy_incl_empty = 0.916409
I0425 14:02:55.862699 22523 solver.cpp:406]     Test net output #73: loss2/accuracy_top3 = 0.917755
I0425 14:02:55.862714 22523 solver.cpp:406]     Test net output #74: loss2/cross_entropy_loss = 0.68577 (* 0.3 = 0.205731 loss)
I0425 14:02:55.862727 22523 solver.cpp:406]     Test net output #75: loss2/cross_entropy_loss_incl_empty = 0.302677 (* 0.3 = 0.090803 loss)
I0425 14:02:55.862742 22523 solver.cpp:406]     Test net output #76: loss2/loss01 = 0.406676 (* 0.0272727 = 0.0110912 loss)
I0425 14:02:55.862756 22523 solver.cpp:406]     Test net output #77: loss2/loss02 = 0.573987 (* 0.0272727 = 0.0156542 loss)
I0425 14:02:55.862782 22523 solver.cpp:406]     Test net output #78: loss2/loss03 = 1.01697 (* 0.0272727 = 0.0277356 loss)
I0425 14:02:55.862797 22523 solver.cpp:406]     Test net output #79: loss2/loss04 = 1.1952 (* 0.0272727 = 0.0325963 loss)
I0425 14:02:55.862810 22523 solver.cpp:406]     Test net output #80: loss2/loss05 = 1.15111 (* 0.0272727 = 0.031394 loss)
I0425 14:02:55.862833 22523 solver.cpp:406]     Test net output #81: loss2/loss06 = 0.983471 (* 0.0272727 = 0.0268219 loss)
I0425 14:02:55.862846 22523 solver.cpp:406]     Test net output #82: loss2/loss07 = 0.766946 (* 0.0272727 = 0.0209167 loss)
I0425 14:02:55.862860 22523 solver.cpp:406]     Test net output #83: loss2/loss08 = 0.544017 (* 0.0272727 = 0.0148368 loss)
I0425 14:02:55.862870 22523 solver.cpp:406]     Test net output #84: loss2/loss09 = 0.366095 (* 0.0272727 = 0.0099844 loss)
I0425 14:02:55.862889 22523 solver.cpp:406]     Test net output #85: loss2/loss10 = 0.334374 (* 0.0272727 = 0.00911928 loss)
I0425 14:02:55.862903 22523 solver.cpp:406]     Test net output #86: loss2/loss11 = 0.318652 (* 0.0272727 = 0.0086905 loss)
I0425 14:02:55.862918 22523 solver.cpp:406]     Test net output #87: loss2/loss12 = 0.299246 (* 0.0272727 = 0.00816126 loss)
I0425 14:02:55.862932 22523 solver.cpp:406]     Test net output #88: loss2/loss13 = 0.233864 (* 0.0272727 = 0.00637811 loss)
I0425 14:02:55.862946 22523 solver.cpp:406]     Test net output #89: loss2/loss14 = 0.205023 (* 0.0272727 = 0.00559153 loss)
I0425 14:02:55.862960 22523 solver.cpp:406]     Test net output #90: loss2/loss15 = 0.157105 (* 0.0272727 = 0.00428467 loss)
I0425 14:02:55.862974 22523 solver.cpp:406]     Test net output #91: loss2/loss16 = 0.139208 (* 0.0272727 = 0.00379659 loss)
I0425 14:02:55.862988 22523 solver.cpp:406]     Test net output #92: loss2/loss17 = 0.0493391 (* 0.0272727 = 0.00134561 loss)
I0425 14:02:55.863008 22523 solver.cpp:406]     Test net output #93: loss2/loss18 = 0.035755 (* 0.0272727 = 0.000975138 loss)
I0425 14:02:55.863021 22523 solver.cpp:406]     Test net output #94: loss2/loss19 = 0.0272942 (* 0.0272727 = 0.000744386 loss)
I0425 14:02:55.863034 22523 solver.cpp:406]     Test net output #95: loss2/loss20 = 0.0150746 (* 0.0272727 = 0.000411127 loss)
I0425 14:02:55.863049 22523 solver.cpp:406]     Test net output #96: loss2/loss21 = 0.000976185 (* 0.0272727 = 2.66232e-05 loss)
I0425 14:02:55.863062 22523 solver.cpp:406]     Test net output #97: loss2/loss22 = 0.000460933 (* 0.0272727 = 1.25709e-05 loss)
I0425 14:02:55.863075 22523 solver.cpp:406]     Test net output #98: loss3/accuracy = 0.863851
I0425 14:02:55.863092 22523 solver.cpp:406]     Test net output #99: loss3/accuracy01 = 0.92
I0425 14:02:55.863104 22523 solver.cpp:406]     Test net output #100: loss3/accuracy02 = 0.91
I0425 14:02:55.863116 22523 solver.cpp:406]     Test net output #101: loss3/accuracy03 = 0.9
I0425 14:02:55.863129 22523 solver.cpp:406]     Test net output #102: loss3/accuracy04 = 0.864
I0425 14:02:55.863150 22523 solver.cpp:406]     Test net output #103: loss3/accuracy05 = 0.824
I0425 14:02:55.863165 22523 solver.cpp:406]     Test net output #104: loss3/accuracy06 = 0.78
I0425 14:02:55.863180 22523 solver.cpp:406]     Test net output #105: loss3/accuracy07 = 0.82
I0425 14:02:55.863191 22523 solver.cpp:406]     Test net output #106: loss3/accuracy08 = 0.869
I0425 14:02:55.863204 22523 solver.cpp:406]     Test net output #107: loss3/accuracy09 = 0.912
I0425 14:02:55.863214 22523 solver.cpp:406]     Test net output #108: loss3/accuracy10 = 0.915
I0425 14:02:55.863225 22523 solver.cpp:406]     Test net output #109: loss3/accuracy11 = 0.921
I0425 14:02:55.863242 22523 solver.cpp:406]     Test net output #110: loss3/accuracy12 = 0.926
I0425 14:02:55.863256 22523 solver.cpp:406]     Test net output #111: loss3/accuracy13 = 0.938
I0425 14:02:55.863268 22523 solver.cpp:406]     Test net output #112: loss3/accuracy14 = 0.947
I0425 14:02:55.863279 22523 solver.cpp:406]     Test net output #113: loss3/accuracy15 = 0.963
I0425 14:02:55.863291 22523 solver.cpp:406]     Test net output #114: loss3/accuracy16 = 0.97
I0425 14:02:55.863313 22523 solver.cpp:406]     Test net output #115: loss3/accuracy17 = 0.991
I0425 14:02:55.863327 22523 solver.cpp:406]     Test net output #116: loss3/accuracy18 = 0.993
I0425 14:02:55.863337 22523 solver.cpp:406]     Test net output #117: loss3/accuracy19 = 0.996
I0425 14:02:55.863361 22523 solver.cpp:406]     Test net output #118: loss3/accuracy20 = 0.998
I0425 14:02:55.863376 22523 solver.cpp:406]     Test net output #119: loss3/accuracy21 = 1
I0425 14:02:55.863387 22523 solver.cpp:406]     Test net output #120: loss3/accuracy22 = 1
I0425 14:02:55.863399 22523 solver.cpp:406]     Test net output #121: loss3/accuracy_incl_empty = 0.929818
I0425 14:02:55.863410 22523 solver.cpp:406]     Test net output #122: loss3/accuracy_top3 = 0.938974
I0425 14:02:55.863423 22523 solver.cpp:406]     Test net output #123: loss3/cross_entropy_loss = 0.522821 (* 1 = 0.522821 loss)
I0425 14:02:55.863437 22523 solver.cpp:406]     Test net output #124: loss3/cross_entropy_loss_incl_empty = 0.252787 (* 1 = 0.252787 loss)
I0425 14:02:55.863452 22523 solver.cpp:406]     Test net output #125: loss3/loss01 = 0.342191 (* 0.0909091 = 0.0311083 loss)
I0425 14:02:55.863466 22523 solver.cpp:406]     Test net output #126: loss3/loss02 = 0.375919 (* 0.0909091 = 0.0341745 loss)
I0425 14:02:55.863479 22523 solver.cpp:406]     Test net output #127: loss3/loss03 = 0.427196 (* 0.0909091 = 0.038836 loss)
I0425 14:02:55.863494 22523 solver.cpp:406]     Test net output #128: loss3/loss04 = 0.523911 (* 0.0909091 = 0.0476283 loss)
I0425 14:02:55.863507 22523 solver.cpp:406]     Test net output #129: loss3/loss05 = 0.669641 (* 0.0909091 = 0.0608765 loss)
I0425 14:02:55.863522 22523 solver.cpp:406]     Test net output #130: loss3/loss06 = 0.746244 (* 0.0909091 = 0.0678404 loss)
I0425 14:02:55.863535 22523 solver.cpp:406]     Test net output #131: loss3/loss07 = 0.639539 (* 0.0909091 = 0.0581399 loss)
I0425 14:02:55.863549 22523 solver.cpp:406]     Test net output #132: loss3/loss08 = 0.448731 (* 0.0909091 = 0.0407937 loss)
I0425 14:02:55.863562 22523 solver.cpp:406]     Test net output #133: loss3/loss09 = 0.331683 (* 0.0909091 = 0.030153 loss)
I0425 14:02:55.863576 22523 solver.cpp:406]     Test net output #134: loss3/loss10 = 0.315001 (* 0.0909091 = 0.0286365 loss)
I0425 14:02:55.863590 22523 solver.cpp:406]     Test net output #135: loss3/loss11 = 0.296149 (* 0.0909091 = 0.0269226 loss)
I0425 14:02:55.863605 22523 solver.cpp:406]     Test net output #136: loss3/loss12 = 0.273333 (* 0.0909091 = 0.0248484 loss)
I0425 14:02:55.863618 22523 solver.cpp:406]     Test net output #137: loss3/loss13 = 0.213729 (* 0.0909091 = 0.0194299 loss)
I0425 14:02:55.863632 22523 solver.cpp:406]     Test net output #138: loss3/loss14 = 0.191369 (* 0.0909091 = 0.0173972 loss)
I0425 14:02:55.863646 22523 solver.cpp:406]     Test net output #139: loss3/loss15 = 0.142937 (* 0.0909091 = 0.0129943 loss)
I0425 14:02:55.863661 22523 solver.cpp:406]     Test net output #140: loss3/loss16 = 0.12583 (* 0.0909091 = 0.0114391 loss)
I0425 14:02:55.863674 22523 solver.cpp:406]     Test net output #141: loss3/loss17 = 0.0453958 (* 0.0909091 = 0.00412689 loss)
I0425 14:02:55.863688 22523 solver.cpp:406]     Test net output #142: loss3/loss18 = 0.0303521 (* 0.0909091 = 0.00275928 loss)
I0425 14:02:55.863701 22523 solver.cpp:406]     Test net output #143: loss3/loss19 = 0.0260478 (* 0.0909091 = 0.00236798 loss)
I0425 14:02:55.863716 22523 solver.cpp:406]     Test net output #144: loss3/loss20 = 0.0146164 (* 0.0909091 = 0.00132877 loss)
I0425 14:02:55.863730 22523 solver.cpp:406]     Test net output #145: loss3/loss21 = 0.00124179 (* 0.0909091 = 0.00011289 loss)
I0425 14:02:55.863749 22523 solver.cpp:406]     Test net output #146: loss3/loss22 = 0.0001312 (* 0.0909091 = 1.19273e-05 loss)
I0425 14:02:55.863761 22523 solver.cpp:406]     Test net output #147: total_accuracy = 0.659
I0425 14:02:55.863773 22523 solver.cpp:406]     Test net output #148: total_accuracy_not_rec = 0.575
I0425 14:02:55.863785 22523 solver.cpp:406]     Test net output #149: total_confidence = 0.644884
I0425 14:02:55.863807 22523 solver.cpp:406]     Test net output #150: total_confidence_nor_rec = 0.529727
I0425 14:02:56.254529 22523 solver.cpp:229] Iteration 20000, loss = 2.99501
I0425 14:02:56.254586 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.448276
I0425 14:02:56.254604 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0425 14:02:56.254617 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.25
I0425 14:02:56.254629 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.375
I0425 14:02:56.254642 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.5
I0425 14:02:56.254654 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 14:02:56.254667 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.375
I0425 14:02:56.254679 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.375
I0425 14:02:56.254691 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 1
I0425 14:02:56.254703 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 14:02:56.254715 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 14:02:56.254727 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 14:02:56.254739 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 14:02:56.254751 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 14:02:56.254767 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 14:02:56.254781 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0425 14:02:56.254793 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 14:02:56.254804 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 14:02:56.254817 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 14:02:56.254828 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 14:02:56.254840 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 14:02:56.254858 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 14:02:56.254868 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 14:02:56.254883 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.8125
I0425 14:02:56.254894 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.706897
I0425 14:02:56.254920 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.70476 (* 0.3 = 0.511428 loss)
I0425 14:02:56.254935 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.587527 (* 0.3 = 0.176258 loss)
I0425 14:02:56.254950 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.971553 (* 0.0272727 = 0.0264969 loss)
I0425 14:02:56.254963 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.03318 (* 0.0272727 = 0.0554503 loss)
I0425 14:02:56.254978 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.79531 (* 0.0272727 = 0.0489631 loss)
I0425 14:02:56.254992 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.6226 (* 0.0272727 = 0.0442528 loss)
I0425 14:02:56.255007 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.33673 (* 0.0272727 = 0.063729 loss)
I0425 14:02:56.255022 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 2.13202 (* 0.0272727 = 0.0581459 loss)
I0425 14:02:56.255035 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 2.50563 (* 0.0272727 = 0.0683353 loss)
I0425 14:02:56.255049 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.305839 (* 0.0272727 = 0.00834108 loss)
I0425 14:02:56.255064 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.704406 (* 0.0272727 = 0.0192111 loss)
I0425 14:02:56.255079 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.325284 (* 0.0272727 = 0.00887138 loss)
I0425 14:02:56.255094 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.214268 (* 0.0272727 = 0.00584368 loss)
I0425 14:02:56.255134 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.254361 (* 0.0272727 = 0.00693711 loss)
I0425 14:02:56.255151 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.581888 (* 0.0272727 = 0.0158697 loss)
I0425 14:02:56.255165 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.448755 (* 0.0272727 = 0.0122388 loss)
I0425 14:02:56.255179 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.421819 (* 0.0272727 = 0.0115042 loss)
I0425 14:02:56.255194 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0364176 (* 0.0272727 = 0.000993208 loss)
I0425 14:02:56.255209 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00135041 (* 0.0272727 = 3.68294e-05 loss)
I0425 14:02:56.255224 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00126587 (* 0.0272727 = 3.45238e-05 loss)
I0425 14:02:56.255239 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000337344 (* 0.0272727 = 9.20029e-06 loss)
I0425 14:02:56.255254 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000195594 (* 0.0272727 = 5.33439e-06 loss)
I0425 14:02:56.255267 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 8.0554e-05 (* 0.0272727 = 2.19693e-06 loss)
I0425 14:02:56.255281 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 3.34552e-05 (* 0.0272727 = 9.12416e-07 loss)
I0425 14:02:56.255295 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.62069
I0425 14:02:56.255306 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 14:02:56.255321 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.875
I0425 14:02:56.255332 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 14:02:56.255343 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.75
I0425 14:02:56.255370 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0425 14:02:56.255383 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.5
I0425 14:02:56.255401 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 14:02:56.255414 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 14:02:56.255425 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 14:02:56.255436 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 14:02:56.255448 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 14:02:56.255460 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 14:02:56.255472 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 14:02:56.255483 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 14:02:56.255496 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0425 14:02:56.255507 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 14:02:56.255518 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 14:02:56.255529 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 14:02:56.255542 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 14:02:56.255553 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 14:02:56.255563 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 14:02:56.255575 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 14:02:56.255587 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.869318
I0425 14:02:56.255599 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.844828
I0425 14:02:56.255612 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.28594 (* 0.3 = 0.385781 loss)
I0425 14:02:56.255627 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.445693 (* 0.3 = 0.133708 loss)
I0425 14:02:56.255655 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.18268 (* 0.0272727 = 0.0322549 loss)
I0425 14:02:56.255669 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.11292 (* 0.0272727 = 0.0303523 loss)
I0425 14:02:56.255683 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.06932 (* 0.0272727 = 0.0291634 loss)
I0425 14:02:56.255697 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.54674 (* 0.0272727 = 0.0421838 loss)
I0425 14:02:56.255712 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.94645 (* 0.0272727 = 0.0530849 loss)
I0425 14:02:56.255727 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.70595 (* 0.0272727 = 0.046526 loss)
I0425 14:02:56.255740 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 2.24253 (* 0.0272727 = 0.0611599 loss)
I0425 14:02:56.255754 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.326718 (* 0.0272727 = 0.00891049 loss)
I0425 14:02:56.255769 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.545157 (* 0.0272727 = 0.0148679 loss)
I0425 14:02:56.255784 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.448849 (* 0.0272727 = 0.0122413 loss)
I0425 14:02:56.255797 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.323165 (* 0.0272727 = 0.00881359 loss)
I0425 14:02:56.255811 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.372321 (* 0.0272727 = 0.0101542 loss)
I0425 14:02:56.255830 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.515933 (* 0.0272727 = 0.0140709 loss)
I0425 14:02:56.255843 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.486757 (* 0.0272727 = 0.0132752 loss)
I0425 14:02:56.255857 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.488252 (* 0.0272727 = 0.013316 loss)
I0425 14:02:56.255872 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.0238664 (* 0.0272727 = 0.000650902 loss)
I0425 14:02:56.255887 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00531729 (* 0.0272727 = 0.000145017 loss)
I0425 14:02:56.255900 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00746779 (* 0.0272727 = 0.000203667 loss)
I0425 14:02:56.255914 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00153311 (* 0.0272727 = 4.1812e-05 loss)
I0425 14:02:56.255931 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000998123 (* 0.0272727 = 2.72215e-05 loss)
I0425 14:02:56.255946 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000888577 (* 0.0272727 = 2.42339e-05 loss)
I0425 14:02:56.255960 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000229673 (* 0.0272727 = 6.26382e-06 loss)
I0425 14:02:56.255973 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.793103
I0425 14:02:56.255985 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 14:02:56.255998 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.75
I0425 14:02:56.256011 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 14:02:56.256021 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 14:02:56.256033 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 14:02:56.256045 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 14:02:56.256057 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 14:02:56.256068 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 14:02:56.256079 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 14:02:56.256091 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 14:02:56.256103 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 14:02:56.256114 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 14:02:56.256126 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 14:02:56.256157 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 14:02:56.256171 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0425 14:02:56.256182 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 14:02:56.256194 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 14:02:56.256214 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 14:02:56.256225 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 14:02:56.256237 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 14:02:56.256248 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 14:02:56.256260 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 14:02:56.256271 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.920455
I0425 14:02:56.256283 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.913793
I0425 14:02:56.256297 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.68179 (* 1 = 0.68179 loss)
I0425 14:02:56.256311 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.249337 (* 1 = 0.249337 loss)
I0425 14:02:56.256326 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.480883 (* 0.0909091 = 0.0437167 loss)
I0425 14:02:56.256340 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.410349 (* 0.0909091 = 0.0373044 loss)
I0425 14:02:56.256355 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.427457 (* 0.0909091 = 0.0388597 loss)
I0425 14:02:56.256368 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.286863 (* 0.0909091 = 0.0260784 loss)
I0425 14:02:56.256382 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 1.00983 (* 0.0909091 = 0.0918028 loss)
I0425 14:02:56.256397 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.453175 (* 0.0909091 = 0.0411977 loss)
I0425 14:02:56.256410 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.00471 (* 0.0909091 = 0.0913375 loss)
I0425 14:02:56.256424 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.176772 (* 0.0909091 = 0.0160702 loss)
I0425 14:02:56.256438 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.470747 (* 0.0909091 = 0.0427952 loss)
I0425 14:02:56.256453 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.400736 (* 0.0909091 = 0.0364305 loss)
I0425 14:02:56.256475 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.269579 (* 0.0909091 = 0.0245072 loss)
I0425 14:02:56.256489 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.293225 (* 0.0909091 = 0.0266568 loss)
I0425 14:02:56.256503 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.377841 (* 0.0909091 = 0.0343492 loss)
I0425 14:02:56.256517 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.387599 (* 0.0909091 = 0.0352362 loss)
I0425 14:02:56.256537 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.341054 (* 0.0909091 = 0.0310049 loss)
I0425 14:02:56.256551 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.130756 (* 0.0909091 = 0.0118869 loss)
I0425 14:02:56.256566 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.0143734 (* 0.0909091 = 0.00130667 loss)
I0425 14:02:56.256579 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00373106 (* 0.0909091 = 0.000339187 loss)
I0425 14:02:56.256593 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00316031 (* 0.0909091 = 0.000287301 loss)
I0425 14:02:56.256608 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00211355 (* 0.0909091 = 0.00019214 loss)
I0425 14:02:56.256621 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00100189 (* 0.0909091 = 9.10808e-05 loss)
I0425 14:02:56.256636 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 8.24204e-05 (* 0.0909091 = 7.49277e-06 loss)
I0425 14:02:56.256659 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 14:02:56.256672 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.625
I0425 14:02:56.256685 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.54781
I0425 14:02:56.256696 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.350919
I0425 14:02:56.256711 22523 sgd_solver.cpp:106] Iteration 20000, lr = 0.01
I0425 14:08:37.644294 22523 solver.cpp:229] Iteration 20500, loss = 2.95585
I0425 14:08:37.644426 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.65
I0425 14:08:37.644446 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.875
I0425 14:08:37.644460 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.625
I0425 14:08:37.644472 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 14:08:37.644485 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.75
I0425 14:08:37.644498 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.75
I0425 14:08:37.644510 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.75
I0425 14:08:37.644522 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 1
I0425 14:08:37.644536 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.875
I0425 14:08:37.644548 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 14:08:37.644561 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 14:08:37.644573 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 14:08:37.644585 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 14:08:37.644598 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 14:08:37.644609 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 14:08:37.644620 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 14:08:37.644634 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 14:08:37.644644 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 14:08:37.644657 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 14:08:37.644670 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 14:08:37.644681 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 14:08:37.644692 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 14:08:37.644704 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 14:08:37.644716 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.903409
I0425 14:08:37.644728 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.75
I0425 14:08:37.644745 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.45546 (* 0.3 = 0.436639 loss)
I0425 14:08:37.644760 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.384912 (* 0.3 = 0.115474 loss)
I0425 14:08:37.644775 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 0.742563 (* 0.0272727 = 0.0202517 loss)
I0425 14:08:37.644789 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.94582 (* 0.0272727 = 0.0530677 loss)
I0425 14:08:37.644804 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 2.37013 (* 0.0272727 = 0.0646398 loss)
I0425 14:08:37.644819 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 0.862898 (* 0.0272727 = 0.0235336 loss)
I0425 14:08:37.644834 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.37284 (* 0.0272727 = 0.037441 loss)
I0425 14:08:37.644850 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 0.824631 (* 0.0272727 = 0.0224899 loss)
I0425 14:08:37.644863 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.23441 (* 0.0272727 = 0.006393 loss)
I0425 14:08:37.644878 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 0.312209 (* 0.0272727 = 0.00851479 loss)
I0425 14:08:37.644892 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.467471 (* 0.0272727 = 0.0127492 loss)
I0425 14:08:37.644914 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.441066 (* 0.0272727 = 0.0120291 loss)
I0425 14:08:37.644929 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.147826 (* 0.0272727 = 0.00403163 loss)
I0425 14:08:37.644943 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0420235 (* 0.0272727 = 0.0011461 loss)
I0425 14:08:37.644958 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0198498 (* 0.0272727 = 0.000541358 loss)
I0425 14:08:37.644990 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.00928754 (* 0.0272727 = 0.000253296 loss)
I0425 14:08:37.645006 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.00387412 (* 0.0272727 = 0.000105658 loss)
I0425 14:08:37.645020 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00142098 (* 0.0272727 = 3.8754e-05 loss)
I0425 14:08:37.645035 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.000586205 (* 0.0272727 = 1.59874e-05 loss)
I0425 14:08:37.645050 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.000718762 (* 0.0272727 = 1.96026e-05 loss)
I0425 14:08:37.645064 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.000203998 (* 0.0272727 = 5.56358e-06 loss)
I0425 14:08:37.645078 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000155227 (* 0.0272727 = 4.23347e-06 loss)
I0425 14:08:37.645092 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 6.86787e-05 (* 0.0272727 = 1.87306e-06 loss)
I0425 14:08:37.645107 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 3.96551e-05 (* 0.0272727 = 1.0815e-06 loss)
I0425 14:08:37.645119 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.75
I0425 14:08:37.645131 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 1
I0425 14:08:37.645143 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0425 14:08:37.645155 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.75
I0425 14:08:37.645166 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.875
I0425 14:08:37.645179 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 14:08:37.645190 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.875
I0425 14:08:37.645205 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.875
I0425 14:08:37.645217 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 1
I0425 14:08:37.645229 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 14:08:37.645241 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 14:08:37.645252 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 14:08:37.645264 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 14:08:37.645275 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 14:08:37.645287 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 14:08:37.645298 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 14:08:37.645309 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 14:08:37.645320 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 14:08:37.645333 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 14:08:37.645342 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 14:08:37.645354 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 14:08:37.645365 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 14:08:37.645377 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 14:08:37.645388 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.926136
I0425 14:08:37.645403 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.95
I0425 14:08:37.645417 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.01028 (* 0.3 = 0.303083 loss)
I0425 14:08:37.645431 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.304845 (* 0.3 = 0.0914535 loss)
I0425 14:08:37.645449 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.380961 (* 0.0272727 = 0.0103899 loss)
I0425 14:08:37.645463 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 2.09221 (* 0.0272727 = 0.0570603 loss)
I0425 14:08:37.645489 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 0.891204 (* 0.0272727 = 0.0243056 loss)
I0425 14:08:37.645504 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 0.975289 (* 0.0272727 = 0.0265988 loss)
I0425 14:08:37.645519 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.881608 (* 0.0272727 = 0.0240438 loss)
I0425 14:08:37.645532 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 0.67094 (* 0.0272727 = 0.0182984 loss)
I0425 14:08:37.645547 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.497143 (* 0.0272727 = 0.0135584 loss)
I0425 14:08:37.645561 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 0.216222 (* 0.0272727 = 0.00589698 loss)
I0425 14:08:37.645583 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.488481 (* 0.0272727 = 0.0133222 loss)
I0425 14:08:37.645597 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.522326 (* 0.0272727 = 0.0142453 loss)
I0425 14:08:37.645612 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.0412177 (* 0.0272727 = 0.00112412 loss)
I0425 14:08:37.645625 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.0152247 (* 0.0272727 = 0.000415218 loss)
I0425 14:08:37.645640 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00511931 (* 0.0272727 = 0.000139617 loss)
I0425 14:08:37.645656 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00202345 (* 0.0272727 = 5.51851e-05 loss)
I0425 14:08:37.645670 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00169807 (* 0.0272727 = 4.63111e-05 loss)
I0425 14:08:37.645684 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.000562322 (* 0.0272727 = 1.5336e-05 loss)
I0425 14:08:37.645699 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00025962 (* 0.0272727 = 7.08055e-06 loss)
I0425 14:08:37.645714 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000228495 (* 0.0272727 = 6.23168e-06 loss)
I0425 14:08:37.645727 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000316718 (* 0.0272727 = 8.63777e-06 loss)
I0425 14:08:37.645741 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00012532 (* 0.0272727 = 3.41782e-06 loss)
I0425 14:08:37.645756 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 8.63359e-05 (* 0.0272727 = 2.35462e-06 loss)
I0425 14:08:37.645766 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 3.59243e-05 (* 0.0272727 = 9.79752e-07 loss)
I0425 14:08:37.645788 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.875
I0425 14:08:37.645800 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 1
I0425 14:08:37.645812 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.875
I0425 14:08:37.645823 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 14:08:37.645835 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 14:08:37.645848 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 14:08:37.645859 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 14:08:37.645871 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 14:08:37.645882 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 1
I0425 14:08:37.645895 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.875
I0425 14:08:37.645905 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 14:08:37.645917 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 14:08:37.645928 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 14:08:37.645939 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 14:08:37.645951 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 14:08:37.645961 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 14:08:37.645973 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 14:08:37.645995 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 14:08:37.646008 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 14:08:37.646019 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 14:08:37.646031 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 14:08:37.646042 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 14:08:37.646054 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 14:08:37.646064 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.954545
I0425 14:08:37.646076 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.95
I0425 14:08:37.646090 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.526542 (* 1 = 0.526542 loss)
I0425 14:08:37.646105 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.170553 (* 1 = 0.170553 loss)
I0425 14:08:37.646118 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.0992946 (* 0.0909091 = 0.00902678 loss)
I0425 14:08:37.646132 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 1.01151 (* 0.0909091 = 0.0919554 loss)
I0425 14:08:37.646147 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.530073 (* 0.0909091 = 0.0481884 loss)
I0425 14:08:37.646160 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.0847896 (* 0.0909091 = 0.00770815 loss)
I0425 14:08:37.646174 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.238508 (* 0.0909091 = 0.0216826 loss)
I0425 14:08:37.646188 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.216205 (* 0.0909091 = 0.019655 loss)
I0425 14:08:37.646201 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.336894 (* 0.0909091 = 0.0306267 loss)
I0425 14:08:37.646215 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.113397 (* 0.0909091 = 0.0103088 loss)
I0425 14:08:37.646229 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.319667 (* 0.0909091 = 0.0290606 loss)
I0425 14:08:37.646244 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.399911 (* 0.0909091 = 0.0363555 loss)
I0425 14:08:37.646260 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.1247 (* 0.0909091 = 0.0113364 loss)
I0425 14:08:37.646278 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.0427805 (* 0.0909091 = 0.00388913 loss)
I0425 14:08:37.646292 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.0318386 (* 0.0909091 = 0.00289442 loss)
I0425 14:08:37.646306 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0135999 (* 0.0909091 = 0.00123635 loss)
I0425 14:08:37.646320 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00709225 (* 0.0909091 = 0.00064475 loss)
I0425 14:08:37.646334 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0028629 (* 0.0909091 = 0.000260264 loss)
I0425 14:08:37.646348 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.000764145 (* 0.0909091 = 6.94677e-05 loss)
I0425 14:08:37.646363 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.000551963 (* 0.0909091 = 5.01785e-05 loss)
I0425 14:08:37.646376 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.000299385 (* 0.0909091 = 2.72168e-05 loss)
I0425 14:08:37.646390 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.00030425 (* 0.0909091 = 2.76591e-05 loss)
I0425 14:08:37.646414 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.000143438 (* 0.0909091 = 1.30398e-05 loss)
I0425 14:08:37.646428 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 3.77021e-05 (* 0.0909091 = 3.42747e-06 loss)
I0425 14:08:37.646440 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 14:08:37.646452 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.75
I0425 14:08:37.646474 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.563487
I0425 14:08:37.646487 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.639209
I0425 14:08:37.646505 22523 sgd_solver.cpp:106] Iteration 20500, lr = 0.01
I0425 14:14:18.974727 22523 solver.cpp:229] Iteration 21000, loss = 3.18448
I0425 14:14:18.974903 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.54
I0425 14:14:18.974925 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.625
I0425 14:14:18.974938 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.375
I0425 14:14:18.974951 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0
I0425 14:14:18.974963 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 14:14:18.974977 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 14:14:18.974989 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 14:14:18.975003 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.75
I0425 14:14:18.975014 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.75
I0425 14:14:18.975026 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 1
I0425 14:14:18.975039 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 1
I0425 14:14:18.975051 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 1
I0425 14:14:18.975064 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 1
I0425 14:14:18.975081 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 1
I0425 14:14:18.975093 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 14:14:18.975106 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 14:14:18.975117 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 14:14:18.975129 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 14:14:18.975149 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 14:14:18.975162 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 14:14:18.975173 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 14:14:18.975185 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 14:14:18.975198 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 14:14:18.975213 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.852273
I0425 14:14:18.975224 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.7
I0425 14:14:18.975242 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.88766 (* 0.3 = 0.566297 loss)
I0425 14:14:18.975258 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.656132 (* 0.3 = 0.19684 loss)
I0425 14:14:18.975273 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.81701 (* 0.0272727 = 0.0495549 loss)
I0425 14:14:18.975287 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.68535 (* 0.0272727 = 0.0732368 loss)
I0425 14:14:18.975302 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 3.16635 (* 0.0272727 = 0.086355 loss)
I0425 14:14:18.975317 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.73266 (* 0.0272727 = 0.074527 loss)
I0425 14:14:18.975332 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 2.13696 (* 0.0272727 = 0.0582807 loss)
I0425 14:14:18.975347 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.13957 (* 0.0272727 = 0.0310792 loss)
I0425 14:14:18.975386 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 0.717311 (* 0.0272727 = 0.019563 loss)
I0425 14:14:18.975402 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.08177 (* 0.0272727 = 0.0295027 loss)
I0425 14:14:18.975416 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.391805 (* 0.0272727 = 0.0106856 loss)
I0425 14:14:18.975430 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.192589 (* 0.0272727 = 0.00525241 loss)
I0425 14:14:18.975455 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.0961511 (* 0.0272727 = 0.0026223 loss)
I0425 14:14:18.975469 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.0479747 (* 0.0272727 = 0.0013084 loss)
I0425 14:14:18.975484 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.0253289 (* 0.0272727 = 0.000690788 loss)
I0425 14:14:18.975525 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0176743 (* 0.0272727 = 0.000482027 loss)
I0425 14:14:18.975541 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0190825 (* 0.0272727 = 0.000520433 loss)
I0425 14:14:18.975556 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.00598704 (* 0.0272727 = 0.000163283 loss)
I0425 14:14:18.975579 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.00542276 (* 0.0272727 = 0.000147893 loss)
I0425 14:14:18.975594 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.005136 (* 0.0272727 = 0.000140073 loss)
I0425 14:14:18.975608 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00146138 (* 0.0272727 = 3.98559e-05 loss)
I0425 14:14:18.975622 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000348856 (* 0.0272727 = 9.51424e-06 loss)
I0425 14:14:18.975637 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.000173965 (* 0.0272727 = 4.7445e-06 loss)
I0425 14:14:18.975651 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.000151503 (* 0.0272727 = 4.13191e-06 loss)
I0425 14:14:18.975664 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.52
I0425 14:14:18.975677 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.75
I0425 14:14:18.975687 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.5
I0425 14:14:18.975699 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0425 14:14:18.975711 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0425 14:14:18.975723 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.25
I0425 14:14:18.975734 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.625
I0425 14:14:18.975746 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.625
I0425 14:14:18.975759 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.75
I0425 14:14:18.975769 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 1
I0425 14:14:18.975780 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 1
I0425 14:14:18.975792 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 1
I0425 14:14:18.975803 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 1
I0425 14:14:18.975814 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 1
I0425 14:14:18.975826 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 14:14:18.975837 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 14:14:18.975848 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 14:14:18.975859 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 14:14:18.975870 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 14:14:18.975883 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 14:14:18.975893 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 14:14:18.975905 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 14:14:18.975916 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 14:14:18.975934 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.840909
I0425 14:14:18.975945 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.84
I0425 14:14:18.975960 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.42469 (* 0.3 = 0.427408 loss)
I0425 14:14:18.975973 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.514038 (* 0.3 = 0.154211 loss)
I0425 14:14:18.975987 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.902134 (* 0.0272727 = 0.0246037 loss)
I0425 14:14:18.976001 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 1.94454 (* 0.0272727 = 0.053033 loss)
I0425 14:14:18.976027 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 2.16165 (* 0.0272727 = 0.058954 loss)
I0425 14:14:18.976042 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 1.85906 (* 0.0272727 = 0.0507017 loss)
I0425 14:14:18.976057 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 2.23141 (* 0.0272727 = 0.0608567 loss)
I0425 14:14:18.976070 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.31231 (* 0.0272727 = 0.0357902 loss)
I0425 14:14:18.976084 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 0.951401 (* 0.0272727 = 0.0259473 loss)
I0425 14:14:18.976099 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.05113 (* 0.0272727 = 0.0286671 loss)
I0425 14:14:18.976112 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.155986 (* 0.0272727 = 0.00425416 loss)
I0425 14:14:18.976126 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.12598 (* 0.0272727 = 0.00343581 loss)
I0425 14:14:18.976140 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.0394203 (* 0.0272727 = 0.0010751 loss)
I0425 14:14:18.976155 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.00898939 (* 0.0272727 = 0.000245165 loss)
I0425 14:14:18.976168 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.00821493 (* 0.0272727 = 0.000224044 loss)
I0425 14:14:18.976182 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.00359007 (* 0.0272727 = 9.7911e-05 loss)
I0425 14:14:18.976197 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.00357486 (* 0.0272727 = 9.74961e-05 loss)
I0425 14:14:18.976210 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00368761 (* 0.0272727 = 0.000100571 loss)
I0425 14:14:18.976224 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00135426 (* 0.0272727 = 3.69343e-05 loss)
I0425 14:14:18.976238 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.000784683 (* 0.0272727 = 2.14004e-05 loss)
I0425 14:14:18.976255 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.000261328 (* 0.0272727 = 7.12712e-06 loss)
I0425 14:14:18.976270 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 3.56557e-05 (* 0.0272727 = 9.72429e-07 loss)
I0425 14:14:18.976284 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 3.29728e-05 (* 0.0272727 = 8.99258e-07 loss)
I0425 14:14:18.976300 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 1.41867e-05 (* 0.0272727 = 3.86909e-07 loss)
I0425 14:14:18.976311 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.76
I0425 14:14:18.976325 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.75
I0425 14:14:18.976332 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 0.625
I0425 14:14:18.976341 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.75
I0425 14:14:18.976354 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.75
I0425 14:14:18.976366 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 14:14:18.976378 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.75
I0425 14:14:18.976389 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.875
I0425 14:14:18.976408 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.75
I0425 14:14:18.976419 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 14:14:18.976430 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 1
I0425 14:14:18.976441 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 1
I0425 14:14:18.976452 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 1
I0425 14:14:18.976471 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 1
I0425 14:14:18.976482 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 14:14:18.976495 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 14:14:18.976505 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 14:14:18.976526 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 14:14:18.976539 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 14:14:18.976552 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 14:14:18.976562 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 14:14:18.976573 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 14:14:18.976585 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 14:14:18.976596 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.931818
I0425 14:14:18.976608 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.84
I0425 14:14:18.976621 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.793057 (* 1 = 0.793057 loss)
I0425 14:14:18.976634 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.272586 (* 1 = 0.272586 loss)
I0425 14:14:18.976649 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.531716 (* 0.0909091 = 0.0483378 loss)
I0425 14:14:18.976663 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.876396 (* 0.0909091 = 0.0796724 loss)
I0425 14:14:18.976676 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.99437 (* 0.0909091 = 0.0903973 loss)
I0425 14:14:18.976691 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.858501 (* 0.0909091 = 0.0780456 loss)
I0425 14:14:18.976704 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.774078 (* 0.0909091 = 0.0703708 loss)
I0425 14:14:18.976718 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.785287 (* 0.0909091 = 0.0713898 loss)
I0425 14:14:18.976732 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.549472 (* 0.0909091 = 0.049952 loss)
I0425 14:14:18.976745 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 0.742589 (* 0.0909091 = 0.0675081 loss)
I0425 14:14:18.976759 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.145963 (* 0.0909091 = 0.0132693 loss)
I0425 14:14:18.976773 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.0438706 (* 0.0909091 = 0.00398823 loss)
I0425 14:14:18.976788 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.00682462 (* 0.0909091 = 0.00062042 loss)
I0425 14:14:18.976801 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.00291597 (* 0.0909091 = 0.000265088 loss)
I0425 14:14:18.976814 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.00186881 (* 0.0909091 = 0.000169892 loss)
I0425 14:14:18.976829 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.00141616 (* 0.0909091 = 0.000128742 loss)
I0425 14:14:18.976842 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.00155276 (* 0.0909091 = 0.00014116 loss)
I0425 14:14:18.976856 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.00169425 (* 0.0909091 = 0.000154022 loss)
I0425 14:14:18.976871 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00129811 (* 0.0909091 = 0.00011801 loss)
I0425 14:14:18.976884 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00159428 (* 0.0909091 = 0.000144935 loss)
I0425 14:14:18.976897 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.00171897 (* 0.0909091 = 0.00015627 loss)
I0425 14:14:18.976912 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.0020629 (* 0.0909091 = 0.000187536 loss)
I0425 14:14:18.976927 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00179534 (* 0.0909091 = 0.000163212 loss)
I0425 14:14:18.976940 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000507139 (* 0.0909091 = 4.61036e-05 loss)
I0425 14:14:18.976953 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.625
I0425 14:14:18.976964 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.5
I0425 14:14:18.976979 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.446232
I0425 14:14:18.977001 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.275461
I0425 14:14:18.977016 22523 sgd_solver.cpp:106] Iteration 21000, lr = 0.01
I0425 14:20:00.377557 22523 solver.cpp:229] Iteration 21500, loss = 3.13562
I0425 14:20:00.377687 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.449275
I0425 14:20:00.377707 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0425 14:20:00.377728 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.25
I0425 14:20:00.377740 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.625
I0425 14:20:00.377753 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 14:20:00.377766 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.25
I0425 14:20:00.377786 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.625
I0425 14:20:00.377799 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.5
I0425 14:20:00.377811 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.5
I0425 14:20:00.377823 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.5
I0425 14:20:00.377835 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.625
I0425 14:20:00.377848 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.625
I0425 14:20:00.377861 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.75
I0425 14:20:00.377873 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.75
I0425 14:20:00.377887 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 0.875
I0425 14:20:00.377899 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 0.875
I0425 14:20:00.377912 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 14:20:00.377924 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 14:20:00.377936 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 14:20:00.377948 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 14:20:00.377961 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 14:20:00.377972 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 14:20:00.377985 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 14:20:00.377996 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.767045
I0425 14:20:00.378010 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.608696
I0425 14:20:00.378027 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 2.04447 (* 0.3 = 0.613342 loss)
I0425 14:20:00.378042 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.930361 (* 0.3 = 0.279108 loss)
I0425 14:20:00.378057 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.30795 (* 0.0272727 = 0.0356714 loss)
I0425 14:20:00.378072 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 2.44199 (* 0.0272727 = 0.0665998 loss)
I0425 14:20:00.378087 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.27295 (* 0.0272727 = 0.0347169 loss)
I0425 14:20:00.378101 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 2.81255 (* 0.0272727 = 0.0767059 loss)
I0425 14:20:00.378115 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.69032 (* 0.0272727 = 0.0460997 loss)
I0425 14:20:00.378130 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.89434 (* 0.0272727 = 0.0516638 loss)
I0425 14:20:00.378145 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.54403 (* 0.0272727 = 0.04211 loss)
I0425 14:20:00.378160 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.79095 (* 0.0272727 = 0.0488441 loss)
I0425 14:20:00.378175 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 1.48729 (* 0.0272727 = 0.0405625 loss)
I0425 14:20:00.378190 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 1.63071 (* 0.0272727 = 0.044474 loss)
I0425 14:20:00.378207 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 1.09428 (* 0.0272727 = 0.029844 loss)
I0425 14:20:00.378221 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 1.48262 (* 0.0272727 = 0.0404352 loss)
I0425 14:20:00.378253 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 1.00344 (* 0.0272727 = 0.0273666 loss)
I0425 14:20:00.378269 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.567826 (* 0.0272727 = 0.0154862 loss)
I0425 14:20:00.378283 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.731158 (* 0.0272727 = 0.0199407 loss)
I0425 14:20:00.378298 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.0555467 (* 0.0272727 = 0.00151491 loss)
I0425 14:20:00.378314 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0613677 (* 0.0272727 = 0.00167366 loss)
I0425 14:20:00.378329 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.0284183 (* 0.0272727 = 0.000775045 loss)
I0425 14:20:00.378343 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.0126121 (* 0.0272727 = 0.000343967 loss)
I0425 14:20:00.378357 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.00405681 (* 0.0272727 = 0.00011064 loss)
I0425 14:20:00.378372 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00130839 (* 0.0272727 = 3.56834e-05 loss)
I0425 14:20:00.378387 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 0.00091171 (* 0.0272727 = 2.48648e-05 loss)
I0425 14:20:00.378399 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.463768
I0425 14:20:00.378412 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 14:20:00.378423 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0425 14:20:00.378435 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.625
I0425 14:20:00.378448 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.25
I0425 14:20:00.378458 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.375
I0425 14:20:00.378470 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.25
I0425 14:20:00.378482 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.375
I0425 14:20:00.378494 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0425 14:20:00.378506 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.625
I0425 14:20:00.378517 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.625
I0425 14:20:00.378530 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.625
I0425 14:20:00.378540 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.75
I0425 14:20:00.378552 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.75
I0425 14:20:00.378564 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 0.875
I0425 14:20:00.378576 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 0.875
I0425 14:20:00.378587 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 14:20:00.378599 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 14:20:00.378610 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 14:20:00.378623 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 14:20:00.378633 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 14:20:00.378644 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 14:20:00.378655 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 14:20:00.378667 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.772727
I0425 14:20:00.378679 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.724638
I0425 14:20:00.378693 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.75932 (* 0.3 = 0.527797 loss)
I0425 14:20:00.378712 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.761214 (* 0.3 = 0.228364 loss)
I0425 14:20:00.378726 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 1.05464 (* 0.0272727 = 0.0287629 loss)
I0425 14:20:00.378741 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.895955 (* 0.0272727 = 0.0244351 loss)
I0425 14:20:00.378767 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.38382 (* 0.0272727 = 0.0377406 loss)
I0425 14:20:00.378782 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.5307 (* 0.0272727 = 0.0690191 loss)
I0425 14:20:00.378795 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 1.84801 (* 0.0272727 = 0.0504003 loss)
I0425 14:20:00.378810 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.82095 (* 0.0272727 = 0.0496624 loss)
I0425 14:20:00.378824 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.63003 (* 0.0272727 = 0.0444553 loss)
I0425 14:20:00.378839 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.42339 (* 0.0272727 = 0.0388197 loss)
I0425 14:20:00.378851 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 1.47197 (* 0.0272727 = 0.0401447 loss)
I0425 14:20:00.378866 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 1.44156 (* 0.0272727 = 0.0393153 loss)
I0425 14:20:00.378880 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 1.47103 (* 0.0272727 = 0.0401191 loss)
I0425 14:20:00.378895 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 1.43287 (* 0.0272727 = 0.0390782 loss)
I0425 14:20:00.378908 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 1.0542 (* 0.0272727 = 0.0287508 loss)
I0425 14:20:00.378922 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.367933 (* 0.0272727 = 0.0100345 loss)
I0425 14:20:00.378937 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.619043 (* 0.0272727 = 0.016883 loss)
I0425 14:20:00.378950 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.110554 (* 0.0272727 = 0.0030151 loss)
I0425 14:20:00.378964 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.0620367 (* 0.0272727 = 0.00169191 loss)
I0425 14:20:00.378978 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.0286375 (* 0.0272727 = 0.000781024 loss)
I0425 14:20:00.379001 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00678976 (* 0.0272727 = 0.000185175 loss)
I0425 14:20:00.379015 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.00619459 (* 0.0272727 = 0.000168943 loss)
I0425 14:20:00.379029 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.00328837 (* 0.0272727 = 8.96828e-05 loss)
I0425 14:20:00.379043 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 0.000874106 (* 0.0272727 = 2.38393e-05 loss)
I0425 14:20:00.379062 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.695652
I0425 14:20:00.379075 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 14:20:00.379086 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 14:20:00.379097 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 1
I0425 14:20:00.379109 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 0.875
I0425 14:20:00.379120 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.75
I0425 14:20:00.379132 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 14:20:00.379144 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0425 14:20:00.379155 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.625
I0425 14:20:00.379168 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 0.5
I0425 14:20:00.379175 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.5
I0425 14:20:00.379184 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.625
I0425 14:20:00.379191 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.75
I0425 14:20:00.379204 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.75
I0425 14:20:00.379215 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 0.875
I0425 14:20:00.379226 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 0.875
I0425 14:20:00.379238 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 14:20:00.379262 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 14:20:00.379276 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 14:20:00.379287 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 14:20:00.379298 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 14:20:00.379309 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 14:20:00.379322 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 14:20:00.379333 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.857955
I0425 14:20:00.379344 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.855072
I0425 14:20:00.379374 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.904749 (* 1 = 0.904749 loss)
I0425 14:20:00.379389 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.439434 (* 1 = 0.439434 loss)
I0425 14:20:00.379402 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.43034 (* 0.0909091 = 0.0391218 loss)
I0425 14:20:00.379417 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.16917 (* 0.0909091 = 0.0153791 loss)
I0425 14:20:00.379431 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.187395 (* 0.0909091 = 0.0170359 loss)
I0425 14:20:00.379446 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.492096 (* 0.0909091 = 0.044736 loss)
I0425 14:20:00.379459 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.451228 (* 0.0909091 = 0.0410207 loss)
I0425 14:20:00.379473 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.933361 (* 0.0909091 = 0.084851 loss)
I0425 14:20:00.379487 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 1.06755 (* 0.0909091 = 0.0970499 loss)
I0425 14:20:00.379501 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.22646 (* 0.0909091 = 0.111496 loss)
I0425 14:20:00.379515 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 1.38039 (* 0.0909091 = 0.12549 loss)
I0425 14:20:00.379529 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 1.26397 (* 0.0909091 = 0.114906 loss)
I0425 14:20:00.379542 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 1.34477 (* 0.0909091 = 0.122252 loss)
I0425 14:20:00.379556 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 1.30647 (* 0.0909091 = 0.11877 loss)
I0425 14:20:00.379570 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.913082 (* 0.0909091 = 0.0830074 loss)
I0425 14:20:00.379585 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.34883 (* 0.0909091 = 0.0317118 loss)
I0425 14:20:00.379597 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.54179 (* 0.0909091 = 0.0492536 loss)
I0425 14:20:00.379612 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.0901875 (* 0.0909091 = 0.00819886 loss)
I0425 14:20:00.379626 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.067486 (* 0.0909091 = 0.00613509 loss)
I0425 14:20:00.379640 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.0402649 (* 0.0909091 = 0.00366045 loss)
I0425 14:20:00.379654 22523 solver.cpp:245]     Train net output #143: loss3/loss19 = 0.0206437 (* 0.0909091 = 0.0018767 loss)
I0425 14:20:00.379668 22523 solver.cpp:245]     Train net output #144: loss3/loss20 = 0.0092561 (* 0.0909091 = 0.000841464 loss)
I0425 14:20:00.379683 22523 solver.cpp:245]     Train net output #145: loss3/loss21 = 0.00343693 (* 0.0909091 = 0.000312448 loss)
I0425 14:20:00.379696 22523 solver.cpp:245]     Train net output #146: loss3/loss22 = 0.000287863 (* 0.0909091 = 2.61694e-05 loss)
I0425 14:20:00.379709 22523 solver.cpp:245]     Train net output #147: total_accuracy = 0.5
I0425 14:20:00.379721 22523 solver.cpp:245]     Train net output #148: total_accuracy_not_rec = 0.375
I0425 14:20:00.379734 22523 solver.cpp:245]     Train net output #149: total_confidence = 0.403609
I0425 14:20:00.379760 22523 solver.cpp:245]     Train net output #150: total_confidence_nor_rec = 0.216558
I0425 14:20:00.379777 22523 sgd_solver.cpp:106] Iteration 21500, lr = 0.01
I0425 14:25:41.735908 22523 solver.cpp:229] Iteration 22000, loss = 2.9961
I0425 14:25:41.736047 22523 solver.cpp:245]     Train net output #0: loss1/accuracy = 0.5
I0425 14:25:41.736068 22523 solver.cpp:245]     Train net output #1: loss1/accuracy01 = 0.75
I0425 14:25:41.736080 22523 solver.cpp:245]     Train net output #2: loss1/accuracy02 = 0.5
I0425 14:25:41.736093 22523 solver.cpp:245]     Train net output #3: loss1/accuracy03 = 0.25
I0425 14:25:41.736105 22523 solver.cpp:245]     Train net output #4: loss1/accuracy04 = 0.25
I0425 14:25:41.736119 22523 solver.cpp:245]     Train net output #5: loss1/accuracy05 = 0.375
I0425 14:25:41.736135 22523 solver.cpp:245]     Train net output #6: loss1/accuracy06 = 0.5
I0425 14:25:41.736147 22523 solver.cpp:245]     Train net output #7: loss1/accuracy07 = 0.625
I0425 14:25:41.736160 22523 solver.cpp:245]     Train net output #8: loss1/accuracy08 = 0.625
I0425 14:25:41.736173 22523 solver.cpp:245]     Train net output #9: loss1/accuracy09 = 0.875
I0425 14:25:41.736186 22523 solver.cpp:245]     Train net output #10: loss1/accuracy10 = 0.875
I0425 14:25:41.736198 22523 solver.cpp:245]     Train net output #11: loss1/accuracy11 = 0.875
I0425 14:25:41.736212 22523 solver.cpp:245]     Train net output #12: loss1/accuracy12 = 0.875
I0425 14:25:41.736223 22523 solver.cpp:245]     Train net output #13: loss1/accuracy13 = 0.875
I0425 14:25:41.736244 22523 solver.cpp:245]     Train net output #14: loss1/accuracy14 = 1
I0425 14:25:41.736256 22523 solver.cpp:245]     Train net output #15: loss1/accuracy15 = 1
I0425 14:25:41.736268 22523 solver.cpp:245]     Train net output #16: loss1/accuracy16 = 1
I0425 14:25:41.736280 22523 solver.cpp:245]     Train net output #17: loss1/accuracy17 = 1
I0425 14:25:41.736301 22523 solver.cpp:245]     Train net output #18: loss1/accuracy18 = 1
I0425 14:25:41.736313 22523 solver.cpp:245]     Train net output #19: loss1/accuracy19 = 1
I0425 14:25:41.736325 22523 solver.cpp:245]     Train net output #20: loss1/accuracy20 = 1
I0425 14:25:41.736337 22523 solver.cpp:245]     Train net output #21: loss1/accuracy21 = 1
I0425 14:25:41.736349 22523 solver.cpp:245]     Train net output #22: loss1/accuracy22 = 1
I0425 14:25:41.736361 22523 solver.cpp:245]     Train net output #23: loss1/accuracy_incl_empty = 0.829545
I0425 14:25:41.736373 22523 solver.cpp:245]     Train net output #24: loss1/accuracy_top3 = 0.759259
I0425 14:25:41.736390 22523 solver.cpp:245]     Train net output #25: loss1/cross_entropy_loss = 1.52618 (* 0.3 = 0.457855 loss)
I0425 14:25:41.736407 22523 solver.cpp:245]     Train net output #26: loss1/cross_entropy_loss_incl_empty = 0.548385 (* 0.3 = 0.164515 loss)
I0425 14:25:41.736421 22523 solver.cpp:245]     Train net output #27: loss1/loss01 = 1.1551 (* 0.0272727 = 0.0315027 loss)
I0425 14:25:41.736435 22523 solver.cpp:245]     Train net output #28: loss1/loss02 = 1.60168 (* 0.0272727 = 0.0436822 loss)
I0425 14:25:41.736449 22523 solver.cpp:245]     Train net output #29: loss1/loss03 = 1.43283 (* 0.0272727 = 0.0390773 loss)
I0425 14:25:41.736464 22523 solver.cpp:245]     Train net output #30: loss1/loss04 = 1.87738 (* 0.0272727 = 0.0512012 loss)
I0425 14:25:41.736479 22523 solver.cpp:245]     Train net output #31: loss1/loss05 = 1.58594 (* 0.0272727 = 0.043253 loss)
I0425 14:25:41.736493 22523 solver.cpp:245]     Train net output #32: loss1/loss06 = 1.16 (* 0.0272727 = 0.0316363 loss)
I0425 14:25:41.736507 22523 solver.cpp:245]     Train net output #33: loss1/loss07 = 1.11867 (* 0.0272727 = 0.0305091 loss)
I0425 14:25:41.736522 22523 solver.cpp:245]     Train net output #34: loss1/loss08 = 1.50093 (* 0.0272727 = 0.0409343 loss)
I0425 14:25:41.736536 22523 solver.cpp:245]     Train net output #35: loss1/loss09 = 0.567262 (* 0.0272727 = 0.0154708 loss)
I0425 14:25:41.736552 22523 solver.cpp:245]     Train net output #36: loss1/loss10 = 0.411993 (* 0.0272727 = 0.0112362 loss)
I0425 14:25:41.736565 22523 solver.cpp:245]     Train net output #37: loss1/loss11 = 0.398857 (* 0.0272727 = 0.0108779 loss)
I0425 14:25:41.736589 22523 solver.cpp:245]     Train net output #38: loss1/loss12 = 0.626229 (* 0.0272727 = 0.017079 loss)
I0425 14:25:41.736603 22523 solver.cpp:245]     Train net output #39: loss1/loss13 = 0.274286 (* 0.0272727 = 0.00748052 loss)
I0425 14:25:41.736635 22523 solver.cpp:245]     Train net output #40: loss1/loss14 = 0.0750226 (* 0.0272727 = 0.00204607 loss)
I0425 14:25:41.736659 22523 solver.cpp:245]     Train net output #41: loss1/loss15 = 0.0333662 (* 0.0272727 = 0.000909987 loss)
I0425 14:25:41.736672 22523 solver.cpp:245]     Train net output #42: loss1/loss16 = 0.021564 (* 0.0272727 = 0.000588109 loss)
I0425 14:25:41.736687 22523 solver.cpp:245]     Train net output #43: loss1/loss17 = 0.0268562 (* 0.0272727 = 0.000732441 loss)
I0425 14:25:41.736701 22523 solver.cpp:245]     Train net output #44: loss1/loss18 = 0.00626974 (* 0.0272727 = 0.000170993 loss)
I0425 14:25:41.736716 22523 solver.cpp:245]     Train net output #45: loss1/loss19 = 0.00229809 (* 0.0272727 = 6.26751e-05 loss)
I0425 14:25:41.736731 22523 solver.cpp:245]     Train net output #46: loss1/loss20 = 0.000288609 (* 0.0272727 = 7.87114e-06 loss)
I0425 14:25:41.736745 22523 solver.cpp:245]     Train net output #47: loss1/loss21 = 0.00011906 (* 0.0272727 = 3.24709e-06 loss)
I0425 14:25:41.736759 22523 solver.cpp:245]     Train net output #48: loss1/loss22 = 2.83289e-05 (* 0.0272727 = 7.72607e-07 loss)
I0425 14:25:41.736773 22523 solver.cpp:245]     Train net output #49: loss2/accuracy = 0.648148
I0425 14:25:41.736784 22523 solver.cpp:245]     Train net output #50: loss2/accuracy01 = 0.875
I0425 14:25:41.736796 22523 solver.cpp:245]     Train net output #51: loss2/accuracy02 = 0.625
I0425 14:25:41.736809 22523 solver.cpp:245]     Train net output #52: loss2/accuracy03 = 0.375
I0425 14:25:41.736819 22523 solver.cpp:245]     Train net output #53: loss2/accuracy04 = 0.375
I0425 14:25:41.736831 22523 solver.cpp:245]     Train net output #54: loss2/accuracy05 = 0.625
I0425 14:25:41.736843 22523 solver.cpp:245]     Train net output #55: loss2/accuracy06 = 0.75
I0425 14:25:41.736855 22523 solver.cpp:245]     Train net output #56: loss2/accuracy07 = 0.5
I0425 14:25:41.736867 22523 solver.cpp:245]     Train net output #57: loss2/accuracy08 = 0.625
I0425 14:25:41.736878 22523 solver.cpp:245]     Train net output #58: loss2/accuracy09 = 0.875
I0425 14:25:41.736891 22523 solver.cpp:245]     Train net output #59: loss2/accuracy10 = 0.875
I0425 14:25:41.736901 22523 solver.cpp:245]     Train net output #60: loss2/accuracy11 = 0.875
I0425 14:25:41.736913 22523 solver.cpp:245]     Train net output #61: loss2/accuracy12 = 0.875
I0425 14:25:41.736925 22523 solver.cpp:245]     Train net output #62: loss2/accuracy13 = 0.875
I0425 14:25:41.736937 22523 solver.cpp:245]     Train net output #63: loss2/accuracy14 = 1
I0425 14:25:41.736948 22523 solver.cpp:245]     Train net output #64: loss2/accuracy15 = 1
I0425 14:25:41.736963 22523 solver.cpp:245]     Train net output #65: loss2/accuracy16 = 1
I0425 14:25:41.736974 22523 solver.cpp:245]     Train net output #66: loss2/accuracy17 = 1
I0425 14:25:41.736986 22523 solver.cpp:245]     Train net output #67: loss2/accuracy18 = 1
I0425 14:25:41.736997 22523 solver.cpp:245]     Train net output #68: loss2/accuracy19 = 1
I0425 14:25:41.737009 22523 solver.cpp:245]     Train net output #69: loss2/accuracy20 = 1
I0425 14:25:41.737020 22523 solver.cpp:245]     Train net output #70: loss2/accuracy21 = 1
I0425 14:25:41.737031 22523 solver.cpp:245]     Train net output #71: loss2/accuracy22 = 1
I0425 14:25:41.737042 22523 solver.cpp:245]     Train net output #72: loss2/accuracy_incl_empty = 0.886364
I0425 14:25:41.737053 22523 solver.cpp:245]     Train net output #73: loss2/accuracy_top3 = 0.888889
I0425 14:25:41.737067 22523 solver.cpp:245]     Train net output #74: loss2/cross_entropy_loss = 1.22525 (* 0.3 = 0.367575 loss)
I0425 14:25:41.737082 22523 solver.cpp:245]     Train net output #75: loss2/cross_entropy_loss_incl_empty = 0.409756 (* 0.3 = 0.122927 loss)
I0425 14:25:41.737097 22523 solver.cpp:245]     Train net output #76: loss2/loss01 = 0.565635 (* 0.0272727 = 0.0154264 loss)
I0425 14:25:41.737110 22523 solver.cpp:245]     Train net output #77: loss2/loss02 = 0.980367 (* 0.0272727 = 0.0267373 loss)
I0425 14:25:41.737136 22523 solver.cpp:245]     Train net output #78: loss2/loss03 = 1.82892 (* 0.0272727 = 0.0498795 loss)
I0425 14:25:41.737151 22523 solver.cpp:245]     Train net output #79: loss2/loss04 = 2.09854 (* 0.0272727 = 0.0572329 loss)
I0425 14:25:41.737165 22523 solver.cpp:245]     Train net output #80: loss2/loss05 = 0.865605 (* 0.0272727 = 0.0236074 loss)
I0425 14:25:41.737182 22523 solver.cpp:245]     Train net output #81: loss2/loss06 = 1.00865 (* 0.0272727 = 0.0275087 loss)
I0425 14:25:41.737197 22523 solver.cpp:245]     Train net output #82: loss2/loss07 = 1.30606 (* 0.0272727 = 0.0356199 loss)
I0425 14:25:41.737211 22523 solver.cpp:245]     Train net output #83: loss2/loss08 = 1.20738 (* 0.0272727 = 0.0329285 loss)
I0425 14:25:41.737226 22523 solver.cpp:245]     Train net output #84: loss2/loss09 = 0.512126 (* 0.0272727 = 0.0139671 loss)
I0425 14:25:41.737239 22523 solver.cpp:245]     Train net output #85: loss2/loss10 = 0.31788 (* 0.0272727 = 0.00866945 loss)
I0425 14:25:41.737253 22523 solver.cpp:245]     Train net output #86: loss2/loss11 = 0.410586 (* 0.0272727 = 0.0111978 loss)
I0425 14:25:41.737267 22523 solver.cpp:245]     Train net output #87: loss2/loss12 = 0.751208 (* 0.0272727 = 0.0204875 loss)
I0425 14:25:41.737282 22523 solver.cpp:245]     Train net output #88: loss2/loss13 = 0.337832 (* 0.0272727 = 0.00921359 loss)
I0425 14:25:41.737295 22523 solver.cpp:245]     Train net output #89: loss2/loss14 = 0.0329552 (* 0.0272727 = 0.000898779 loss)
I0425 14:25:41.737309 22523 solver.cpp:245]     Train net output #90: loss2/loss15 = 0.0120589 (* 0.0272727 = 0.00032888 loss)
I0425 14:25:41.737323 22523 solver.cpp:245]     Train net output #91: loss2/loss16 = 0.00703029 (* 0.0272727 = 0.000191735 loss)
I0425 14:25:41.737337 22523 solver.cpp:245]     Train net output #92: loss2/loss17 = 0.00536045 (* 0.0272727 = 0.000146194 loss)
I0425 14:25:41.737352 22523 solver.cpp:245]     Train net output #93: loss2/loss18 = 0.00129154 (* 0.0272727 = 3.52239e-05 loss)
I0425 14:25:41.737366 22523 solver.cpp:245]     Train net output #94: loss2/loss19 = 0.00036584 (* 0.0272727 = 9.97747e-06 loss)
I0425 14:25:41.737380 22523 solver.cpp:245]     Train net output #95: loss2/loss20 = 0.000260735 (* 0.0272727 = 7.11096e-06 loss)
I0425 14:25:41.737395 22523 solver.cpp:245]     Train net output #96: loss2/loss21 = 0.000121846 (* 0.0272727 = 3.32308e-06 loss)
I0425 14:25:41.737409 22523 solver.cpp:245]     Train net output #97: loss2/loss22 = 4.36233e-05 (* 0.0272727 = 1.18973e-06 loss)
I0425 14:25:41.737421 22523 solver.cpp:245]     Train net output #98: loss3/accuracy = 0.740741
I0425 14:25:41.737433 22523 solver.cpp:245]     Train net output #99: loss3/accuracy01 = 0.875
I0425 14:25:41.737445 22523 solver.cpp:245]     Train net output #100: loss3/accuracy02 = 1
I0425 14:25:41.737457 22523 solver.cpp:245]     Train net output #101: loss3/accuracy03 = 0.875
I0425 14:25:41.737468 22523 solver.cpp:245]     Train net output #102: loss3/accuracy04 = 1
I0425 14:25:41.737479 22523 solver.cpp:245]     Train net output #103: loss3/accuracy05 = 0.875
I0425 14:25:41.737491 22523 solver.cpp:245]     Train net output #104: loss3/accuracy06 = 0.875
I0425 14:25:41.737503 22523 solver.cpp:245]     Train net output #105: loss3/accuracy07 = 0.625
I0425 14:25:41.737514 22523 solver.cpp:245]     Train net output #106: loss3/accuracy08 = 0.625
I0425 14:25:41.737525 22523 solver.cpp:245]     Train net output #107: loss3/accuracy09 = 1
I0425 14:25:41.737537 22523 solver.cpp:245]     Train net output #108: loss3/accuracy10 = 0.875
I0425 14:25:41.737548 22523 solver.cpp:245]     Train net output #109: loss3/accuracy11 = 0.875
I0425 14:25:41.737560 22523 solver.cpp:245]     Train net output #110: loss3/accuracy12 = 0.875
I0425 14:25:41.737571 22523 solver.cpp:245]     Train net output #111: loss3/accuracy13 = 0.875
I0425 14:25:41.737588 22523 solver.cpp:245]     Train net output #112: loss3/accuracy14 = 1
I0425 14:25:41.737599 22523 solver.cpp:245]     Train net output #113: loss3/accuracy15 = 1
I0425 14:25:41.737622 22523 solver.cpp:245]     Train net output #114: loss3/accuracy16 = 1
I0425 14:25:41.737634 22523 solver.cpp:245]     Train net output #115: loss3/accuracy17 = 1
I0425 14:25:41.737643 22523 solver.cpp:245]     Train net output #116: loss3/accuracy18 = 1
I0425 14:25:41.737650 22523 solver.cpp:245]     Train net output #117: loss3/accuracy19 = 1
I0425 14:25:41.737673 22523 solver.cpp:245]     Train net output #118: loss3/accuracy20 = 1
I0425 14:25:41.737685 22523 solver.cpp:245]     Train net output #119: loss3/accuracy21 = 1
I0425 14:25:41.737697 22523 solver.cpp:245]     Train net output #120: loss3/accuracy22 = 1
I0425 14:25:41.737709 22523 solver.cpp:245]     Train net output #121: loss3/accuracy_incl_empty = 0.914773
I0425 14:25:41.737720 22523 solver.cpp:245]     Train net output #122: loss3/accuracy_top3 = 0.888889
I0425 14:25:41.737735 22523 solver.cpp:245]     Train net output #123: loss3/cross_entropy_loss = 0.766021 (* 1 = 0.766021 loss)
I0425 14:25:41.737749 22523 solver.cpp:245]     Train net output #124: loss3/cross_entropy_loss_incl_empty = 0.261523 (* 1 = 0.261523 loss)
I0425 14:25:41.737763 22523 solver.cpp:245]     Train net output #125: loss3/loss01 = 0.322974 (* 0.0909091 = 0.0293612 loss)
I0425 14:25:41.737777 22523 solver.cpp:245]     Train net output #126: loss3/loss02 = 0.0676578 (* 0.0909091 = 0.00615071 loss)
I0425 14:25:41.737797 22523 solver.cpp:245]     Train net output #127: loss3/loss03 = 0.291194 (* 0.0909091 = 0.0264722 loss)
I0425 14:25:41.737812 22523 solver.cpp:245]     Train net output #128: loss3/loss04 = 0.162135 (* 0.0909091 = 0.0147396 loss)
I0425 14:25:41.737825 22523 solver.cpp:245]     Train net output #129: loss3/loss05 = 0.520505 (* 0.0909091 = 0.0473186 loss)
I0425 14:25:41.737839 22523 solver.cpp:245]     Train net output #130: loss3/loss06 = 0.503854 (* 0.0909091 = 0.0458049 loss)
I0425 14:25:41.737854 22523 solver.cpp:245]     Train net output #131: loss3/loss07 = 0.937504 (* 0.0909091 = 0.0852276 loss)
I0425 14:25:41.737867 22523 solver.cpp:245]     Train net output #132: loss3/loss08 = 1.25143 (* 0.0909091 = 0.113767 loss)
I0425 14:25:41.737881 22523 solver.cpp:245]     Train net output #133: loss3/loss09 = 0.19523 (* 0.0909091 = 0.0177482 loss)
I0425 14:25:41.737895 22523 solver.cpp:245]     Train net output #134: loss3/loss10 = 0.279519 (* 0.0909091 = 0.0254108 loss)
I0425 14:25:41.737908 22523 solver.cpp:245]     Train net output #135: loss3/loss11 = 0.302493 (* 0.0909091 = 0.0274993 loss)
I0425 14:25:41.737922 22523 solver.cpp:245]     Train net output #136: loss3/loss12 = 0.514732 (* 0.0909091 = 0.0467939 loss)
I0425 14:25:41.737936 22523 solver.cpp:245]     Train net output #137: loss3/loss13 = 0.372113 (* 0.0909091 = 0.0338285 loss)
I0425 14:25:41.737951 22523 solver.cpp:245]     Train net output #138: loss3/loss14 = 0.0444504 (* 0.0909091 = 0.00404094 loss)
I0425 14:25:41.737964 22523 solver.cpp:245]     Train net output #139: loss3/loss15 = 0.0236984 (* 0.0909091 = 0.0021544 loss)
I0425 14:25:41.737978 22523 solver.cpp:245]     Train net output #140: loss3/loss16 = 0.017706 (* 0.0909091 = 0.00160964 loss)
I0425 14:25:41.737993 22523 solver.cpp:245]     Train net output #141: loss3/loss17 = 0.00950913 (* 0.0909091 = 0.000864466 loss)
I0425 14:25:41.738009 22523 solver.cpp:245]     Train net output #142: loss3/loss18 = 0.00536644 (* 0.0909091 = 0.0004878