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I0327 12:46:12.071159 21344 solver.cpp:280] Solving mixed_lstm
I0327 12:46:12.071171 21344 solver.cpp:281] Learning Rate Policy: fixed
I0327 12:46:12.088115 21344 solver.cpp:338] Iteration 0, Testing net (#0)
I0327 12:46:43.353196 21344 solver.cpp:393] Test loss: 256.606
I0327 12:46:43.353467 21344 solver.cpp:406] Test net output #0: loss1/accuracy01 = 0.002
I0327 12:46:43.353493 21344 solver.cpp:406] Test net output #1: loss1/accuracy02 = 0.008
I0327 12:46:43.353507 21344 solver.cpp:406] Test net output #2: loss1/accuracy03 = 0.018
I0327 12:46:43.353519 21344 solver.cpp:406] Test net output #3: loss1/accuracy04 = 0.037
I0327 12:46:43.353531 21344 solver.cpp:406] Test net output #4: loss1/accuracy05 = 0.049
I0327 12:46:43.353559 21344 solver.cpp:406] Test net output #5: loss1/accuracy06 = 0.136
I0327 12:46:43.353574 21344 solver.cpp:406] Test net output #6: loss1/accuracy07 = 0.302
I0327 12:46:43.353585 21344 solver.cpp:406] Test net output #7: loss1/accuracy08 = 0.34
I0327 12:46:43.353597 21344 solver.cpp:406] Test net output #8: loss1/accuracy09 = 0.357
I0327 12:46:43.353610 21344 solver.cpp:406] Test net output #9: loss1/accuracy10 = 0.356
I0327 12:46:43.353621 21344 solver.cpp:406] Test net output #10: loss1/accuracy11 = 0.357
I0327 12:46:43.353632 21344 solver.cpp:406] Test net output #11: loss1/accuracy12 = 0.357
I0327 12:46:43.353644 21344 solver.cpp:406] Test net output #12: loss1/accuracy13 = 0.357
I0327 12:46:43.353655 21344 solver.cpp:406] Test net output #13: loss1/accuracy14 = 0.593
I0327 12:46:43.353667 21344 solver.cpp:406] Test net output #14: loss1/accuracy15 = 0.358
I0327 12:46:43.353678 21344 solver.cpp:406] Test net output #15: loss1/accuracy16 = 0.356
I0327 12:46:43.353689 21344 solver.cpp:406] Test net output #16: loss1/accuracy17 = 0.357
I0327 12:46:43.353701 21344 solver.cpp:406] Test net output #17: loss1/accuracy18 = 0.59
I0327 12:46:43.353713 21344 solver.cpp:406] Test net output #18: loss1/accuracy19 = 0.409
I0327 12:46:43.353724 21344 solver.cpp:406] Test net output #19: loss1/accuracy20 = 0.357
I0327 12:46:43.353736 21344 solver.cpp:406] Test net output #20: loss1/accuracy21 = 0.357
I0327 12:46:43.353747 21344 solver.cpp:406] Test net output #21: loss1/accuracy22 = 0.357
I0327 12:46:43.353763 21344 solver.cpp:406] Test net output #22: loss1/loss01 = 59.4289 (* 0.0272727 = 1.62079 loss)
I0327 12:46:43.353778 21344 solver.cpp:406] Test net output #23: loss1/loss02 = 59.1669 (* 0.0272727 = 1.61364 loss)
I0327 12:46:43.353792 21344 solver.cpp:406] Test net output #24: loss1/loss03 = 58.9049 (* 0.0272727 = 1.6065 loss)
I0327 12:46:43.353807 21344 solver.cpp:406] Test net output #25: loss1/loss04 = 58.2935 (* 0.0272727 = 1.58982 loss)
I0327 12:46:43.353821 21344 solver.cpp:406] Test net output #26: loss1/loss05 = 59.4289 (* 0.0272727 = 1.62079 loss)
I0327 12:46:43.353834 21344 solver.cpp:406] Test net output #27: loss1/loss06 = 59.5162 (* 0.0272727 = 1.62317 loss)
I0327 12:46:43.353848 21344 solver.cpp:406] Test net output #28: loss1/loss07 = 59.4289 (* 0.0272727 = 1.62079 loss)
I0327 12:46:43.353863 21344 solver.cpp:406] Test net output #29: loss1/loss08 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.353876 21344 solver.cpp:406] Test net output #30: loss1/loss09 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.353899 21344 solver.cpp:406] Test net output #31: loss1/loss10 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.353916 21344 solver.cpp:406] Test net output #32: loss1/loss11 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.353930 21344 solver.cpp:406] Test net output #33: loss1/loss12 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.353943 21344 solver.cpp:406] Test net output #34: loss1/loss13 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.353957 21344 solver.cpp:406] Test net output #35: loss1/loss14 = 38.9049 (* 0.0272727 = 1.06104 loss)
I0327 12:46:43.353971 21344 solver.cpp:406] Test net output #36: loss1/loss15 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.353986 21344 solver.cpp:406] Test net output #37: loss1/loss16 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.354002 21344 solver.cpp:406] Test net output #38: loss1/loss17 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.354032 21344 solver.cpp:406] Test net output #39: loss1/loss18 = 39.3416 (* 0.0272727 = 1.07295 loss)
I0327 12:46:43.354046 21344 solver.cpp:406] Test net output #40: loss1/loss19 = 55.0621 (* 0.0272727 = 1.50169 loss)
I0327 12:46:43.354060 21344 solver.cpp:406] Test net output #41: loss1/loss20 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.354074 21344 solver.cpp:406] Test net output #42: loss1/loss21 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.354089 21344 solver.cpp:406] Test net output #43: loss1/loss22 = 59.6036 (* 0.0272727 = 1.62555 loss)
I0327 12:46:43.354100 21344 solver.cpp:406] Test net output #44: loss2/accuracy01 = 0
I0327 12:46:43.354112 21344 solver.cpp:406] Test net output #45: loss2/accuracy02 = 0.004
I0327 12:46:43.354125 21344 solver.cpp:406] Test net output #46: loss2/accuracy03 = 0.02
I0327 12:46:43.354135 21344 solver.cpp:406] Test net output #47: loss2/accuracy04 = 0.009
I0327 12:46:43.354147 21344 solver.cpp:406] Test net output #48: loss2/accuracy05 = 0.012
I0327 12:46:43.354158 21344 solver.cpp:406] Test net output #49: loss2/accuracy06 = 0.045
I0327 12:46:43.354171 21344 solver.cpp:406] Test net output #50: loss2/accuracy07 = 0.088
I0327 12:46:43.354182 21344 solver.cpp:406] Test net output #51: loss2/accuracy08 = 0.1
I0327 12:46:43.354193 21344 solver.cpp:406] Test net output #52: loss2/accuracy09 = 0.102
I0327 12:46:43.354207 21344 solver.cpp:406] Test net output #53: loss2/accuracy10 = 0.102
I0327 12:46:43.354218 21344 solver.cpp:406] Test net output #54: loss2/accuracy11 = 0.099
I0327 12:46:43.354229 21344 solver.cpp:406] Test net output #55: loss2/accuracy12 = 0.097
I0327 12:46:43.354241 21344 solver.cpp:406] Test net output #56: loss2/accuracy13 = 0.117
I0327 12:46:43.354252 21344 solver.cpp:406] Test net output #57: loss2/accuracy14 = 0.103
I0327 12:46:43.354264 21344 solver.cpp:406] Test net output #58: loss2/accuracy15 = 0.146
I0327 12:46:43.354275 21344 solver.cpp:406] Test net output #59: loss2/accuracy16 = 0.103
I0327 12:46:43.354287 21344 solver.cpp:406] Test net output #60: loss2/accuracy17 = 0.101
I0327 12:46:43.354298 21344 solver.cpp:406] Test net output #61: loss2/accuracy18 = 0.103
I0327 12:46:43.354310 21344 solver.cpp:406] Test net output #62: loss2/accuracy19 = 0.104
I0327 12:46:43.354321 21344 solver.cpp:406] Test net output #63: loss2/accuracy20 = 0.278
I0327 12:46:43.354332 21344 solver.cpp:406] Test net output #64: loss2/accuracy21 = 0.101
I0327 12:46:43.354344 21344 solver.cpp:406] Test net output #65: loss2/accuracy22 = 0.182
I0327 12:46:43.354357 21344 solver.cpp:406] Test net output #66: loss2/loss01 = 80.9427 (* 0.0272727 = 2.20753 loss)
I0327 12:46:43.354372 21344 solver.cpp:406] Test net output #67: loss2/loss02 = 80.5933 (* 0.0272727 = 2.198 loss)
I0327 12:46:43.354387 21344 solver.cpp:406] Test net output #68: loss2/loss03 = 79.3706 (* 0.0272727 = 2.16465 loss)
I0327 12:46:43.354399 21344 solver.cpp:406] Test net output #69: loss2/loss04 = 80.6806 (* 0.0272727 = 2.20038 loss)
I0327 12:46:43.354413 21344 solver.cpp:406] Test net output #70: loss2/loss05 = 80.8553 (* 0.0272727 = 2.20515 loss)
I0327 12:46:43.354428 21344 solver.cpp:406] Test net output #71: loss2/loss06 = 79.8073 (* 0.0272727 = 2.17656 loss)
I0327 12:46:43.354441 21344 solver.cpp:406] Test net output #72: loss2/loss07 = 80.6806 (* 0.0272727 = 2.20038 loss)
I0327 12:46:43.354454 21344 solver.cpp:406] Test net output #73: loss2/loss08 = 80.8553 (* 0.0272727 = 2.20515 loss)
I0327 12:46:43.354468 21344 solver.cpp:406] Test net output #74: loss2/loss09 = 80.8553 (* 0.0272727 = 2.20515 loss)
I0327 12:46:43.354485 21344 solver.cpp:406] Test net output #75: loss2/loss10 = 80.8553 (* 0.0272727 = 2.20515 loss)
I0327 12:46:43.354501 21344 solver.cpp:406] Test net output #76: loss2/loss11 = 80.9427 (* 0.0272727 = 2.20753 loss)
I0327 12:46:43.354514 21344 solver.cpp:406] Test net output #77: loss2/loss12 = 80.9427 (* 0.0272727 = 2.20753 loss)
I0327 12:46:43.354539 21344 solver.cpp:406] Test net output #78: loss2/loss13 = 79.72 (* 0.0272727 = 2.17418 loss)
I0327 12:46:43.354554 21344 solver.cpp:406] Test net output #79: loss2/loss14 = 80.9427 (* 0.0272727 = 2.20753 loss)
I0327 12:46:43.354568 21344 solver.cpp:406] Test net output #80: loss2/loss15 = 76.8379 (* 0.0272727 = 2.09558 loss)
I0327 12:46:43.354583 21344 solver.cpp:406] Test net output #81: loss2/loss16 = 80.9427 (* 0.0272727 = 2.20753 loss)
I0327 12:46:43.354598 21344 solver.cpp:406] Test net output #82: loss2/loss17 = 80.9427 (* 0.0272727 = 2.20753 loss)
I0327 12:46:43.354611 21344 solver.cpp:406] Test net output #83: loss2/loss18 = 80.9427 (* 0.0272727 = 2.20753 loss)
I0327 12:46:43.354624 21344 solver.cpp:406] Test net output #84: loss2/loss19 = 80.8553 (* 0.0272727 = 2.20515 loss)
I0327 12:46:43.354638 21344 solver.cpp:406] Test net output #85: loss2/loss20 = 65.4842 (* 0.0272727 = 1.78593 loss)
I0327 12:46:43.354652 21344 solver.cpp:406] Test net output #86: loss2/loss21 = 80.9427 (* 0.0272727 = 2.20753 loss)
I0327 12:46:43.354666 21344 solver.cpp:406] Test net output #87: loss2/loss22 = 73.6938 (* 0.0272727 = 2.00983 loss)
I0327 12:46:43.354678 21344 solver.cpp:406] Test net output #88: loss3/accuracy01 = 0
I0327 12:46:43.354691 21344 solver.cpp:406] Test net output #89: loss3/accuracy02 = 0.01
I0327 12:46:43.354701 21344 solver.cpp:406] Test net output #90: loss3/accuracy03 = 0.043
I0327 12:46:43.354713 21344 solver.cpp:406] Test net output #91: loss3/accuracy04 = 0.09
I0327 12:46:43.354724 21344 solver.cpp:406] Test net output #92: loss3/accuracy05 = 0.212
I0327 12:46:43.354735 21344 solver.cpp:406] Test net output #93: loss3/accuracy06 = 0.5
I0327 12:46:43.354748 21344 solver.cpp:406] Test net output #94: loss3/accuracy07 = 0.888
I0327 12:46:43.354758 21344 solver.cpp:406] Test net output #95: loss3/accuracy08 = 0.963
I0327 12:46:43.354769 21344 solver.cpp:406] Test net output #96: loss3/accuracy09 = 0.991
I0327 12:46:43.354780 21344 solver.cpp:406] Test net output #97: loss3/accuracy10 = 0.994
I0327 12:46:43.354792 21344 solver.cpp:406] Test net output #98: loss3/accuracy11 = 0.995
I0327 12:46:43.354804 21344 solver.cpp:406] Test net output #99: loss3/accuracy12 = 0.996
I0327 12:46:43.354815 21344 solver.cpp:406] Test net output #100: loss3/accuracy13 = 0.999
I0327 12:46:43.354826 21344 solver.cpp:406] Test net output #101: loss3/accuracy14 = 0.995
I0327 12:46:43.354837 21344 solver.cpp:406] Test net output #102: loss3/accuracy15 = 0.996
I0327 12:46:43.354848 21344 solver.cpp:406] Test net output #103: loss3/accuracy16 = 0.996
I0327 12:46:43.354859 21344 solver.cpp:406] Test net output #104: loss3/accuracy17 = 0.995
I0327 12:46:43.354871 21344 solver.cpp:406] Test net output #105: loss3/accuracy18 = 0.996
I0327 12:46:43.354882 21344 solver.cpp:406] Test net output #106: loss3/accuracy19 = 0.995
I0327 12:46:43.354893 21344 solver.cpp:406] Test net output #107: loss3/accuracy20 = 0.996
I0327 12:46:43.354904 21344 solver.cpp:406] Test net output #108: loss3/accuracy21 = 0.995
I0327 12:46:43.354915 21344 solver.cpp:406] Test net output #109: loss3/accuracy22 = 0.995
I0327 12:46:43.354929 21344 solver.cpp:406] Test net output #110: loss3/loss01 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.354943 21344 solver.cpp:406] Test net output #111: loss3/loss02 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.354957 21344 solver.cpp:406] Test net output #112: loss3/loss03 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.354971 21344 solver.cpp:406] Test net output #113: loss3/loss04 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.354985 21344 solver.cpp:406] Test net output #114: loss3/loss05 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355000 21344 solver.cpp:406] Test net output #115: loss3/loss06 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355012 21344 solver.cpp:406] Test net output #116: loss3/loss07 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355036 21344 solver.cpp:406] Test net output #117: loss3/loss08 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355054 21344 solver.cpp:406] Test net output #118: loss3/loss09 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355068 21344 solver.cpp:406] Test net output #119: loss3/loss10 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355082 21344 solver.cpp:406] Test net output #120: loss3/loss11 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355096 21344 solver.cpp:406] Test net output #121: loss3/loss12 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355110 21344 solver.cpp:406] Test net output #122: loss3/loss13 = 86.9037 (* 0.0909091 = 7.90034 loss)
I0327 12:46:43.355124 21344 solver.cpp:406] Test net output #123: loss3/loss14 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355139 21344 solver.cpp:406] Test net output #124: loss3/loss15 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355151 21344 solver.cpp:406] Test net output #125: loss3/loss16 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355165 21344 solver.cpp:406] Test net output #126: loss3/loss17 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355180 21344 solver.cpp:406] Test net output #127: loss3/loss18 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355193 21344 solver.cpp:406] Test net output #128: loss3/loss19 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355206 21344 solver.cpp:406] Test net output #129: loss3/loss20 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355221 21344 solver.cpp:406] Test net output #130: loss3/loss21 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355234 21344 solver.cpp:406] Test net output #131: loss3/loss22 = 87.253 (* 0.0909091 = 7.9321 loss)
I0327 12:46:43.355245 21344 solver.cpp:406] Test net output #132: total_accuracy = 0
I0327 12:46:43.355257 21344 solver.cpp:406] Test net output #133: total_confidence = nan
I0327 12:46:43.627306 21344 solver.cpp:229] Iteration 0, loss = 15.0301
I0327 12:46:43.627372 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 12:46:43.627388 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 12:46:43.627401 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 12:46:43.627413 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 12:46:43.627424 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 12:46:43.627436 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0
I0327 12:46:43.627447 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0
I0327 12:46:43.627460 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0
I0327 12:46:43.627470 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0
I0327 12:46:43.627482 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 0
I0327 12:46:43.627493 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 0
I0327 12:46:43.627506 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 0
I0327 12:46:43.627516 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 0
I0327 12:46:43.627528 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 0
I0327 12:46:43.627540 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 0
I0327 12:46:43.627552 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 0
I0327 12:46:43.627564 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 0
I0327 12:46:43.627575 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 0
I0327 12:46:43.627588 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 0
I0327 12:46:43.627599 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 0
I0327 12:46:43.627611 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 0
I0327 12:46:43.627622 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 0
I0327 12:46:43.627670 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 5.68464 (* 0.0272727 = 0.155036 loss)
I0327 12:46:43.627686 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 5.1454 (* 0.0272727 = 0.140329 loss)
I0327 12:46:43.627701 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 4.55451 (* 0.0272727 = 0.124214 loss)
I0327 12:46:43.627714 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 5.74952 (* 0.0272727 = 0.156805 loss)
I0327 12:46:43.627728 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 5.16768 (* 0.0272727 = 0.140937 loss)
I0327 12:46:43.627743 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 5.41278 (* 0.0272727 = 0.147621 loss)
I0327 12:46:43.627756 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 5.6255 (* 0.0272727 = 0.153423 loss)
I0327 12:46:43.627769 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 5.76147 (* 0.0272727 = 0.157131 loss)
I0327 12:46:43.627784 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 4.91994 (* 0.0272727 = 0.13418 loss)
I0327 12:46:43.627797 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 5.3027 (* 0.0272727 = 0.144619 loss)
I0327 12:46:43.627811 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 5.22684 (* 0.0272727 = 0.14255 loss)
I0327 12:46:43.627825 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 6.47574 (* 0.0272727 = 0.176611 loss)
I0327 12:46:43.627838 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 4.98783 (* 0.0272727 = 0.136032 loss)
I0327 12:46:43.627852 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 4.8267 (* 0.0272727 = 0.131637 loss)
I0327 12:46:43.627866 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 6.01012 (* 0.0272727 = 0.163912 loss)
I0327 12:46:43.627879 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 6.17248 (* 0.0272727 = 0.16834 loss)
I0327 12:46:43.627893 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 4.96322 (* 0.0272727 = 0.135361 loss)
I0327 12:46:43.627907 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 4.12554 (* 0.0272727 = 0.112515 loss)
I0327 12:46:43.627921 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 4.01114 (* 0.0272727 = 0.109395 loss)
I0327 12:46:43.627935 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 4.68914 (* 0.0272727 = 0.127886 loss)
I0327 12:46:43.627948 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 5.68715 (* 0.0272727 = 0.155104 loss)
I0327 12:46:43.627962 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 5.71582 (* 0.0272727 = 0.155886 loss)
I0327 12:46:43.627974 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0
I0327 12:46:43.627986 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 12:46:43.627998 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 12:46:43.628010 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 12:46:43.628021 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 12:46:43.628033 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.125
I0327 12:46:43.628046 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0
I0327 12:46:43.628057 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0
I0327 12:46:43.628068 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0
I0327 12:46:43.628079 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 0
I0327 12:46:43.628090 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 0
I0327 12:46:43.628103 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 0
I0327 12:46:43.628113 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 0
I0327 12:46:43.628125 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 0
I0327 12:46:43.628140 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 0.125
I0327 12:46:43.628152 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 0
I0327 12:46:43.628175 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 0
I0327 12:46:43.628188 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 0
I0327 12:46:43.628199 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 0
I0327 12:46:43.628211 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 0
I0327 12:46:43.628222 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 0
I0327 12:46:43.628233 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 0
I0327 12:46:43.628247 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 4.21906 (* 0.0272727 = 0.115065 loss)
I0327 12:46:43.628262 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 4.78986 (* 0.0272727 = 0.130632 loss)
I0327 12:46:43.628275 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 4.35242 (* 0.0272727 = 0.118702 loss)
I0327 12:46:43.628289 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 5.43624 (* 0.0272727 = 0.148261 loss)
I0327 12:46:43.628304 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 5.53578 (* 0.0272727 = 0.150976 loss)
I0327 12:46:43.628319 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 4.61398 (* 0.0272727 = 0.125836 loss)
I0327 12:46:43.628334 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 5.11739 (* 0.0272727 = 0.139565 loss)
I0327 12:46:43.628348 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 5.0158 (* 0.0272727 = 0.136794 loss)
I0327 12:46:43.628361 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 5.48356 (* 0.0272727 = 0.149552 loss)
I0327 12:46:43.628376 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 5.10073 (* 0.0272727 = 0.139111 loss)
I0327 12:46:43.628389 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 6.06451 (* 0.0272727 = 0.165396 loss)
I0327 12:46:43.628403 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 5.07872 (* 0.0272727 = 0.13851 loss)
I0327 12:46:43.628417 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 4.66932 (* 0.0272727 = 0.127345 loss)
I0327 12:46:43.628430 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 5.25376 (* 0.0272727 = 0.143284 loss)
I0327 12:46:43.628444 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 4.00221 (* 0.0272727 = 0.109151 loss)
I0327 12:46:43.628458 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 4.52516 (* 0.0272727 = 0.123413 loss)
I0327 12:46:43.628473 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 5.95518 (* 0.0272727 = 0.162414 loss)
I0327 12:46:43.628486 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 5.16423 (* 0.0272727 = 0.140843 loss)
I0327 12:46:43.628499 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 5.56261 (* 0.0272727 = 0.151707 loss)
I0327 12:46:43.628514 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 4.43949 (* 0.0272727 = 0.121077 loss)
I0327 12:46:43.628526 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 5.61986 (* 0.0272727 = 0.153269 loss)
I0327 12:46:43.628540 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 4.01051 (* 0.0272727 = 0.109377 loss)
I0327 12:46:43.628552 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.125
I0327 12:46:43.628564 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 12:46:43.628576 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 12:46:43.628587 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 12:46:43.628599 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0
I0327 12:46:43.628610 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0
I0327 12:46:43.628623 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0
I0327 12:46:43.628633 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0
I0327 12:46:43.628645 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0
I0327 12:46:43.628669 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 0
I0327 12:46:43.628680 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 0
I0327 12:46:43.628692 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 0
I0327 12:46:43.628705 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 0
I0327 12:46:43.628715 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 0
I0327 12:46:43.628726 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 0
I0327 12:46:43.628738 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 0
I0327 12:46:43.628749 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 0
I0327 12:46:43.628761 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 0
I0327 12:46:43.628772 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 0
I0327 12:46:43.628783 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 0
I0327 12:46:43.628794 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 0
I0327 12:46:43.628805 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 0
I0327 12:46:43.628819 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 4.45217 (* 0.0909091 = 0.404743 loss)
I0327 12:46:43.628834 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 4.53398 (* 0.0909091 = 0.41218 loss)
I0327 12:46:43.628847 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 4.90274 (* 0.0909091 = 0.445704 loss)
I0327 12:46:43.628861 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 4.06555 (* 0.0909091 = 0.369595 loss)
I0327 12:46:43.628875 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 4.36368 (* 0.0909091 = 0.396698 loss)
I0327 12:46:43.628890 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 4.51967 (* 0.0909091 = 0.410879 loss)
I0327 12:46:43.628903 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 4.14595 (* 0.0909091 = 0.376904 loss)
I0327 12:46:43.628917 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 4.13856 (* 0.0909091 = 0.376233 loss)
I0327 12:46:43.628931 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 4.48967 (* 0.0909091 = 0.408151 loss)
I0327 12:46:43.628944 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 4.79787 (* 0.0909091 = 0.43617 loss)
I0327 12:46:43.628958 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 4.61206 (* 0.0909091 = 0.419278 loss)
I0327 12:46:43.628973 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 4.2766 (* 0.0909091 = 0.388782 loss)
I0327 12:46:43.628985 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 4.15767 (* 0.0909091 = 0.37797 loss)
I0327 12:46:43.628999 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 4.27407 (* 0.0909091 = 0.388552 loss)
I0327 12:46:43.629014 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 4.90893 (* 0.0909091 = 0.446267 loss)
I0327 12:46:43.629027 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 4.83672 (* 0.0909091 = 0.439702 loss)
I0327 12:46:43.629041 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 4.58095 (* 0.0909091 = 0.41645 loss)
I0327 12:46:43.629055 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 4.07921 (* 0.0909091 = 0.370837 loss)
I0327 12:46:43.629068 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 4.34711 (* 0.0909091 = 0.395192 loss)
I0327 12:46:43.629082 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 4.67036 (* 0.0909091 = 0.424578 loss)
I0327 12:46:43.629096 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 3.93824 (* 0.0909091 = 0.358022 loss)
I0327 12:46:43.629111 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 4.37128 (* 0.0909091 = 0.397389 loss)
I0327 12:46:43.629122 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 12:46:43.629134 21344 solver.cpp:245] Train net output #133: total_confidence = 1.76147e-27
I0327 12:46:43.629163 21344 sgd_solver.cpp:106] Iteration 0, lr = 0.01
I0327 12:48:31.543236 21344 solver.cpp:229] Iteration 500, loss = 3.90858
I0327 12:48:31.543367 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 12:48:31.543386 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 12:48:31.543400 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 12:48:31.543411 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 12:48:31.543422 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 12:48:31.543434 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.125
I0327 12:48:31.543447 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.25
I0327 12:48:31.543459 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 12:48:31.543470 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 12:48:31.543483 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 12:48:31.543493 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 12:48:31.543505 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 12:48:31.543516 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 12:48:31.543529 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 12:48:31.543540 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 12:48:31.543551 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 12:48:31.543562 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 12:48:31.543575 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 12:48:31.543586 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 12:48:31.543597 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 12:48:31.543609 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 12:48:31.543620 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 12:48:31.543637 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 4.47394 (* 0.0272727 = 0.122016 loss)
I0327 12:48:31.543651 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 4.5008 (* 0.0272727 = 0.122749 loss)
I0327 12:48:31.543666 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 4.69894 (* 0.0272727 = 0.128153 loss)
I0327 12:48:31.543680 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 4.93062 (* 0.0272727 = 0.134471 loss)
I0327 12:48:31.543694 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 5.29747 (* 0.0272727 = 0.144476 loss)
I0327 12:48:31.543709 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 4.19861 (* 0.0272727 = 0.114508 loss)
I0327 12:48:31.543721 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 3.63418 (* 0.0272727 = 0.0991139 loss)
I0327 12:48:31.543735 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 2.21025 (* 0.0272727 = 0.0602795 loss)
I0327 12:48:31.543750 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0367138 (* 0.0272727 = 0.00100129 loss)
I0327 12:48:31.543763 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0107152 (* 0.0272727 = 0.000292232 loss)
I0327 12:48:31.543778 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00212181 (* 0.0272727 = 5.78677e-05 loss)
I0327 12:48:31.543792 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00190636 (* 0.0272727 = 5.19917e-05 loss)
I0327 12:48:31.543807 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00215329 (* 0.0272727 = 5.87261e-05 loss)
I0327 12:48:31.543822 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00224281 (* 0.0272727 = 6.11675e-05 loss)
I0327 12:48:31.543835 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00291212 (* 0.0272727 = 7.94214e-05 loss)
I0327 12:48:31.543849 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00294394 (* 0.0272727 = 8.02892e-05 loss)
I0327 12:48:31.543862 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00360372 (* 0.0272727 = 9.82834e-05 loss)
I0327 12:48:31.543895 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00289147 (* 0.0272727 = 7.88583e-05 loss)
I0327 12:48:31.543910 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00232657 (* 0.0272727 = 6.34518e-05 loss)
I0327 12:48:31.543925 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00171768 (* 0.0272727 = 4.68458e-05 loss)
I0327 12:48:31.543938 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00345615 (* 0.0272727 = 9.42586e-05 loss)
I0327 12:48:31.543952 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00484534 (* 0.0272727 = 0.000132146 loss)
I0327 12:48:31.543964 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 12:48:31.543977 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 12:48:31.543988 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 12:48:31.544000 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 12:48:31.544011 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 12:48:31.544023 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.125
I0327 12:48:31.544035 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 12:48:31.544047 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.75
I0327 12:48:31.544062 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 12:48:31.544075 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 12:48:31.544085 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 12:48:31.544096 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 12:48:31.544108 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 12:48:31.544119 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 12:48:31.544131 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 12:48:31.544142 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 12:48:31.544153 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 12:48:31.544165 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 12:48:31.544176 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 12:48:31.544188 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 12:48:31.544198 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 12:48:31.544210 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 12:48:31.544224 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.944 (* 0.0272727 = 0.107564 loss)
I0327 12:48:31.544239 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 5.23305 (* 0.0272727 = 0.14272 loss)
I0327 12:48:31.544252 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 4.44548 (* 0.0272727 = 0.12124 loss)
I0327 12:48:31.544262 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.82866 (* 0.0272727 = 0.104418 loss)
I0327 12:48:31.544277 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 4.39554 (* 0.0272727 = 0.119878 loss)
I0327 12:48:31.544291 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 4.23636 (* 0.0272727 = 0.115537 loss)
I0327 12:48:31.544306 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 3.37183 (* 0.0272727 = 0.0919589 loss)
I0327 12:48:31.544319 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 2.09208 (* 0.0272727 = 0.0570566 loss)
I0327 12:48:31.544333 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0456227 (* 0.0272727 = 0.00124425 loss)
I0327 12:48:31.544348 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0188958 (* 0.0272727 = 0.000515339 loss)
I0327 12:48:31.544361 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00233449 (* 0.0272727 = 6.3668e-05 loss)
I0327 12:48:31.544379 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00220827 (* 0.0272727 = 6.02256e-05 loss)
I0327 12:48:31.544405 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00528225 (* 0.0272727 = 0.000144061 loss)
I0327 12:48:31.544420 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00371456 (* 0.0272727 = 0.000101306 loss)
I0327 12:48:31.544435 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00222608 (* 0.0272727 = 6.07113e-05 loss)
I0327 12:48:31.544450 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00241781 (* 0.0272727 = 6.59403e-05 loss)
I0327 12:48:31.544463 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00112357 (* 0.0272727 = 3.06428e-05 loss)
I0327 12:48:31.544477 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00168255 (* 0.0272727 = 4.58876e-05 loss)
I0327 12:48:31.544492 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00241282 (* 0.0272727 = 6.58041e-05 loss)
I0327 12:48:31.544505 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00182452 (* 0.0272727 = 4.97596e-05 loss)
I0327 12:48:31.544519 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00155533 (* 0.0272727 = 4.24181e-05 loss)
I0327 12:48:31.544533 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00330143 (* 0.0272727 = 9.0039e-05 loss)
I0327 12:48:31.544545 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0
I0327 12:48:31.544558 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 12:48:31.544569 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 12:48:31.544581 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 12:48:31.544592 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 12:48:31.544605 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.125
I0327 12:48:31.544615 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.375
I0327 12:48:31.544627 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.75
I0327 12:48:31.544639 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 12:48:31.544651 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 12:48:31.544662 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 12:48:31.544673 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 12:48:31.544684 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 12:48:31.544697 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 12:48:31.544708 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 12:48:31.544718 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 12:48:31.544730 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 12:48:31.544741 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 12:48:31.544752 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 12:48:31.544764 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 12:48:31.544775 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 12:48:31.544786 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 12:48:31.544800 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.76921 (* 0.0909091 = 0.342656 loss)
I0327 12:48:31.544813 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.7835 (* 0.0909091 = 0.343955 loss)
I0327 12:48:31.544827 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.59673 (* 0.0909091 = 0.326976 loss)
I0327 12:48:31.544842 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.80982 (* 0.0909091 = 0.346348 loss)
I0327 12:48:31.544855 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.6592 (* 0.0909091 = 0.332655 loss)
I0327 12:48:31.544869 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 3.53691 (* 0.0909091 = 0.321537 loss)
I0327 12:48:31.544893 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 3.24749 (* 0.0909091 = 0.295227 loss)
I0327 12:48:31.544909 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.67399 (* 0.0909091 = 0.152181 loss)
I0327 12:48:31.544922 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.023946 (* 0.0909091 = 0.0021769 loss)
I0327 12:48:31.544936 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0113547 (* 0.0909091 = 0.00103224 loss)
I0327 12:48:31.544951 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000469777 (* 0.0909091 = 4.2707e-05 loss)
I0327 12:48:31.544965 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000338399 (* 0.0909091 = 3.07635e-05 loss)
I0327 12:48:31.544980 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000529401 (* 0.0909091 = 4.81273e-05 loss)
I0327 12:48:31.544994 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.00033494 (* 0.0909091 = 3.04491e-05 loss)
I0327 12:48:31.545008 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.00035398 (* 0.0909091 = 3.218e-05 loss)
I0327 12:48:31.545022 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000445541 (* 0.0909091 = 4.05038e-05 loss)
I0327 12:48:31.545037 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000374069 (* 0.0909091 = 3.40062e-05 loss)
I0327 12:48:31.545052 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000473763 (* 0.0909091 = 4.30693e-05 loss)
I0327 12:48:31.545065 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000370532 (* 0.0909091 = 3.36847e-05 loss)
I0327 12:48:31.545079 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000392133 (* 0.0909091 = 3.56485e-05 loss)
I0327 12:48:31.545094 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000425709 (* 0.0909091 = 3.87009e-05 loss)
I0327 12:48:31.545110 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000293673 (* 0.0909091 = 2.66975e-05 loss)
I0327 12:48:31.545123 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 12:48:31.545135 21344 solver.cpp:245] Train net output #133: total_confidence = 2.0007e-05
I0327 12:48:31.545147 21344 sgd_solver.cpp:106] Iteration 500, lr = 0.01
I0327 12:50:19.381640 21344 solver.cpp:229] Iteration 1000, loss = 3.50695
I0327 12:50:19.381791 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 12:50:19.381810 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 12:50:19.381824 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 12:50:19.381835 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 12:50:19.381847 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 12:50:19.381858 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 12:50:19.381871 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 12:50:19.381883 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 12:50:19.381894 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 12:50:19.381906 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 12:50:19.381917 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 12:50:19.381929 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 12:50:19.381940 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 12:50:19.381953 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 12:50:19.381963 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 12:50:19.381975 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 12:50:19.381986 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 12:50:19.382001 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 12:50:19.382012 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 12:50:19.382025 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 12:50:19.382035 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 12:50:19.382047 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 12:50:19.382064 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 4.25361 (* 0.0272727 = 0.116007 loss)
I0327 12:50:19.382079 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.02768 (* 0.0272727 = 0.0825731 loss)
I0327 12:50:19.382094 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.58902 (* 0.0272727 = 0.0978823 loss)
I0327 12:50:19.382107 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.56867 (* 0.0272727 = 0.0973274 loss)
I0327 12:50:19.382122 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 4.24477 (* 0.0272727 = 0.115767 loss)
I0327 12:50:19.382135 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.64072 (* 0.0272727 = 0.0720196 loss)
I0327 12:50:19.382149 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.38793 (* 0.0272727 = 0.0651253 loss)
I0327 12:50:19.382164 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.210826 (* 0.0272727 = 0.0057498 loss)
I0327 12:50:19.382179 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0520047 (* 0.0272727 = 0.00141831 loss)
I0327 12:50:19.382192 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0328391 (* 0.0272727 = 0.000895611 loss)
I0327 12:50:19.382206 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00657769 (* 0.0272727 = 0.000179391 loss)
I0327 12:50:19.382220 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00254217 (* 0.0272727 = 6.93319e-05 loss)
I0327 12:50:19.382235 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00558509 (* 0.0272727 = 0.000152321 loss)
I0327 12:50:19.382248 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00597209 (* 0.0272727 = 0.000162875 loss)
I0327 12:50:19.382262 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00279138 (* 0.0272727 = 7.61285e-05 loss)
I0327 12:50:19.382277 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00398152 (* 0.0272727 = 0.000108587 loss)
I0327 12:50:19.382290 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00755032 (* 0.0272727 = 0.000205918 loss)
I0327 12:50:19.382318 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.0172695 (* 0.0272727 = 0.000470986 loss)
I0327 12:50:19.382334 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00337795 (* 0.0272727 = 9.21258e-05 loss)
I0327 12:50:19.382362 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00217044 (* 0.0272727 = 5.91937e-05 loss)
I0327 12:50:19.382380 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00836068 (* 0.0272727 = 0.000228019 loss)
I0327 12:50:19.382395 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00448312 (* 0.0272727 = 0.000122267 loss)
I0327 12:50:19.382407 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 12:50:19.382419 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 12:50:19.382431 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 12:50:19.382441 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 12:50:19.382452 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 12:50:19.382464 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 12:50:19.382475 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 12:50:19.382488 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 12:50:19.382499 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 12:50:19.382510 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 12:50:19.382521 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 12:50:19.382532 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 12:50:19.382544 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 12:50:19.382555 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 12:50:19.382566 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 12:50:19.382577 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 12:50:19.382588 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 12:50:19.382599 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 12:50:19.382611 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 12:50:19.382623 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 12:50:19.382632 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 12:50:19.382638 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 12:50:19.382652 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.69376 (* 0.0272727 = 0.0734662 loss)
I0327 12:50:19.382666 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.47722 (* 0.0272727 = 0.0948334 loss)
I0327 12:50:19.382680 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 4.08788 (* 0.0272727 = 0.111488 loss)
I0327 12:50:19.382694 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 4.27301 (* 0.0272727 = 0.116537 loss)
I0327 12:50:19.382709 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.81694 (* 0.0272727 = 0.104098 loss)
I0327 12:50:19.382722 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.98108 (* 0.0272727 = 0.0813021 loss)
I0327 12:50:19.382736 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 3.07572 (* 0.0272727 = 0.0838832 loss)
I0327 12:50:19.382750 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0809223 (* 0.0272727 = 0.00220697 loss)
I0327 12:50:19.382764 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.065965 (* 0.0272727 = 0.00179905 loss)
I0327 12:50:19.382778 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0126344 (* 0.0272727 = 0.000344574 loss)
I0327 12:50:19.382792 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00116792 (* 0.0272727 = 3.18523e-05 loss)
I0327 12:50:19.382810 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00259976 (* 0.0272727 = 7.09026e-05 loss)
I0327 12:50:19.382838 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00463104 (* 0.0272727 = 0.000126301 loss)
I0327 12:50:19.382853 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00107162 (* 0.0272727 = 2.92259e-05 loss)
I0327 12:50:19.382868 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00217358 (* 0.0272727 = 5.92795e-05 loss)
I0327 12:50:19.382881 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00263626 (* 0.0272727 = 7.18981e-05 loss)
I0327 12:50:19.382895 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000595484 (* 0.0272727 = 1.62405e-05 loss)
I0327 12:50:19.382910 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00174797 (* 0.0272727 = 4.76718e-05 loss)
I0327 12:50:19.382923 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00231636 (* 0.0272727 = 6.31733e-05 loss)
I0327 12:50:19.382937 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00658956 (* 0.0272727 = 0.000179715 loss)
I0327 12:50:19.382951 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00130017 (* 0.0272727 = 3.54593e-05 loss)
I0327 12:50:19.382966 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00132161 (* 0.0272727 = 3.60438e-05 loss)
I0327 12:50:19.382977 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0
I0327 12:50:19.382988 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.375
I0327 12:50:19.383000 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 12:50:19.383013 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 12:50:19.383023 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0
I0327 12:50:19.383034 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.5
I0327 12:50:19.383049 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.5
I0327 12:50:19.383061 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 12:50:19.383072 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 12:50:19.383085 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 12:50:19.383095 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 12:50:19.383106 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 12:50:19.383117 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 12:50:19.383128 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 12:50:19.383141 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 12:50:19.383152 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 12:50:19.383162 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 12:50:19.383174 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 12:50:19.383185 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 12:50:19.383196 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 12:50:19.383208 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 12:50:19.383219 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 12:50:19.383239 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.0248 (* 0.0909091 = 0.274982 loss)
I0327 12:50:19.383261 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.06841 (* 0.0909091 = 0.278946 loss)
I0327 12:50:19.383276 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.40723 (* 0.0909091 = 0.309748 loss)
I0327 12:50:19.383291 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.43294 (* 0.0909091 = 0.312086 loss)
I0327 12:50:19.383304 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.90354 (* 0.0909091 = 0.354868 loss)
I0327 12:50:19.383317 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.37401 (* 0.0909091 = 0.215819 loss)
I0327 12:50:19.383343 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 2.57219 (* 0.0909091 = 0.233835 loss)
I0327 12:50:19.383358 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.206626 (* 0.0909091 = 0.0187842 loss)
I0327 12:50:19.383373 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0542129 (* 0.0909091 = 0.00492845 loss)
I0327 12:50:19.383386 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0169058 (* 0.0909091 = 0.00153689 loss)
I0327 12:50:19.383400 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000245261 (* 0.0909091 = 2.22964e-05 loss)
I0327 12:50:19.383414 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000325769 (* 0.0909091 = 2.96154e-05 loss)
I0327 12:50:19.383429 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.0003626 (* 0.0909091 = 3.29636e-05 loss)
I0327 12:50:19.383442 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000479851 (* 0.0909091 = 4.36228e-05 loss)
I0327 12:50:19.383456 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000260266 (* 0.0909091 = 2.36606e-05 loss)
I0327 12:50:19.383471 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000236507 (* 0.0909091 = 2.15006e-05 loss)
I0327 12:50:19.383486 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000258127 (* 0.0909091 = 2.34661e-05 loss)
I0327 12:50:19.383499 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000615899 (* 0.0909091 = 5.59908e-05 loss)
I0327 12:50:19.383513 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000495585 (* 0.0909091 = 4.50532e-05 loss)
I0327 12:50:19.383527 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000328216 (* 0.0909091 = 2.98378e-05 loss)
I0327 12:50:19.383541 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000291243 (* 0.0909091 = 2.64767e-05 loss)
I0327 12:50:19.383555 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000307237 (* 0.0909091 = 2.79306e-05 loss)
I0327 12:50:19.383568 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 12:50:19.383579 21344 solver.cpp:245] Train net output #133: total_confidence = 5.13961e-05
I0327 12:50:19.383590 21344 sgd_solver.cpp:106] Iteration 1000, lr = 0.01
I0327 12:52:07.232967 21344 solver.cpp:229] Iteration 1500, loss = 3.37241
I0327 12:52:07.233185 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 12:52:07.233212 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 12:52:07.233227 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 12:52:07.233239 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 12:52:07.233253 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 12:52:07.233264 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.5
I0327 12:52:07.233276 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.75
I0327 12:52:07.233289 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 12:52:07.233302 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 12:52:07.233314 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 12:52:07.233326 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 12:52:07.233340 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 12:52:07.233352 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 12:52:07.233364 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 12:52:07.233376 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 12:52:07.233388 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 12:52:07.233400 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 12:52:07.233412 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 12:52:07.233424 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 12:52:07.233436 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 12:52:07.233448 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 12:52:07.233460 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 12:52:07.233479 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 4.45789 (* 0.0272727 = 0.121579 loss)
I0327 12:52:07.233494 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.9518 (* 0.0272727 = 0.107776 loss)
I0327 12:52:07.233510 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 4.64912 (* 0.0272727 = 0.126794 loss)
I0327 12:52:07.233523 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 5.29387 (* 0.0272727 = 0.144378 loss)
I0327 12:52:07.233551 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 5.03537 (* 0.0272727 = 0.137328 loss)
I0327 12:52:07.233568 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.2156 (* 0.0272727 = 0.0604254 loss)
I0327 12:52:07.233583 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.34738 (* 0.0272727 = 0.0367467 loss)
I0327 12:52:07.233597 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.435965 (* 0.0272727 = 0.01189 loss)
I0327 12:52:07.233613 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0332361 (* 0.0272727 = 0.000906438 loss)
I0327 12:52:07.233628 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00626612 (* 0.0272727 = 0.000170894 loss)
I0327 12:52:07.233642 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000574337 (* 0.0272727 = 1.56637e-05 loss)
I0327 12:52:07.233657 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000285592 (* 0.0272727 = 7.78887e-06 loss)
I0327 12:52:07.233672 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000966135 (* 0.0272727 = 2.63491e-05 loss)
I0327 12:52:07.233686 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000873922 (* 0.0272727 = 2.38342e-05 loss)
I0327 12:52:07.233701 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000523732 (* 0.0272727 = 1.42836e-05 loss)
I0327 12:52:07.233716 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00177308 (* 0.0272727 = 4.83568e-05 loss)
I0327 12:52:07.233731 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000368851 (* 0.0272727 = 1.00596e-05 loss)
I0327 12:52:07.233765 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000740837 (* 0.0272727 = 2.02047e-05 loss)
I0327 12:52:07.233782 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000768718 (* 0.0272727 = 2.0965e-05 loss)
I0327 12:52:07.233795 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000583582 (* 0.0272727 = 1.59159e-05 loss)
I0327 12:52:07.233810 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000505741 (* 0.0272727 = 1.37929e-05 loss)
I0327 12:52:07.233824 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00421253 (* 0.0272727 = 0.000114887 loss)
I0327 12:52:07.233837 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0
I0327 12:52:07.233850 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 12:52:07.233862 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 12:52:07.233875 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 12:52:07.233886 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 12:52:07.233898 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 12:52:07.233911 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.75
I0327 12:52:07.233922 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 12:52:07.233934 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 12:52:07.233947 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 12:52:07.233958 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 12:52:07.233970 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 12:52:07.233981 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 12:52:07.233996 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 12:52:07.234009 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 12:52:07.234020 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 12:52:07.234032 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 12:52:07.234043 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 12:52:07.234055 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 12:52:07.234067 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 12:52:07.234079 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 12:52:07.234091 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 12:52:07.234104 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 5.54598 (* 0.0272727 = 0.151254 loss)
I0327 12:52:07.234119 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 4.33609 (* 0.0272727 = 0.118257 loss)
I0327 12:52:07.234133 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 4.64431 (* 0.0272727 = 0.126663 loss)
I0327 12:52:07.234148 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 5.03972 (* 0.0272727 = 0.137447 loss)
I0327 12:52:07.234163 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 4.55441 (* 0.0272727 = 0.124211 loss)
I0327 12:52:07.234176 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.42615 (* 0.0272727 = 0.0661676 loss)
I0327 12:52:07.234194 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.72244 (* 0.0272727 = 0.0197029 loss)
I0327 12:52:07.234208 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.680219 (* 0.0272727 = 0.0185514 loss)
I0327 12:52:07.234223 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0666117 (* 0.0272727 = 0.00181668 loss)
I0327 12:52:07.234238 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0288703 (* 0.0272727 = 0.000787371 loss)
I0327 12:52:07.234252 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.0032752 (* 0.0272727 = 8.93238e-05 loss)
I0327 12:52:07.234278 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00939663 (* 0.0272727 = 0.000256272 loss)
I0327 12:52:07.234293 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00989826 (* 0.0272727 = 0.000269953 loss)
I0327 12:52:07.234308 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00327559 (* 0.0272727 = 8.93343e-05 loss)
I0327 12:52:07.234323 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.0076352 (* 0.0272727 = 0.000208233 loss)
I0327 12:52:07.234338 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00489671 (* 0.0272727 = 0.000133547 loss)
I0327 12:52:07.234352 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00328957 (* 0.0272727 = 8.97156e-05 loss)
I0327 12:52:07.234366 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.0056836 (* 0.0272727 = 0.000155007 loss)
I0327 12:52:07.234381 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.0092391 (* 0.0272727 = 0.000251975 loss)
I0327 12:52:07.234396 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00527483 (* 0.0272727 = 0.000143859 loss)
I0327 12:52:07.234411 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.0120252 (* 0.0272727 = 0.000327959 loss)
I0327 12:52:07.234426 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00355952 (* 0.0272727 = 9.70779e-05 loss)
I0327 12:52:07.234438 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0
I0327 12:52:07.234452 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 12:52:07.234463 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 12:52:07.234475 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 12:52:07.234488 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0
I0327 12:52:07.234499 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 12:52:07.234511 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 12:52:07.234524 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 12:52:07.234535 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 12:52:07.234547 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 12:52:07.234560 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 12:52:07.234570 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 12:52:07.234582 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 12:52:07.234594 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 12:52:07.234606 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 12:52:07.234617 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 12:52:07.234629 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 12:52:07.234642 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 12:52:07.234653 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 12:52:07.234665 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 12:52:07.234678 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 12:52:07.234688 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 12:52:07.234704 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 4.62053 (* 0.0909091 = 0.420048 loss)
I0327 12:52:07.234717 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.83703 (* 0.0909091 = 0.348821 loss)
I0327 12:52:07.234731 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 4.57988 (* 0.0909091 = 0.416353 loss)
I0327 12:52:07.234746 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 4.18939 (* 0.0909091 = 0.380854 loss)
I0327 12:52:07.234760 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 4.45138 (* 0.0909091 = 0.404671 loss)
I0327 12:52:07.234774 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.44494 (* 0.0909091 = 0.222267 loss)
I0327 12:52:07.234798 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.11218 (* 0.0909091 = 0.101108 loss)
I0327 12:52:07.234813 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.651304 (* 0.0909091 = 0.0592094 loss)
I0327 12:52:07.234828 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0236113 (* 0.0909091 = 0.00214648 loss)
I0327 12:52:07.234843 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00736285 (* 0.0909091 = 0.00066935 loss)
I0327 12:52:07.234858 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000191173 (* 0.0909091 = 1.73794e-05 loss)
I0327 12:52:07.234872 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000258755 (* 0.0909091 = 2.35231e-05 loss)
I0327 12:52:07.234887 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000339354 (* 0.0909091 = 3.08504e-05 loss)
I0327 12:52:07.234902 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000292898 (* 0.0909091 = 2.66271e-05 loss)
I0327 12:52:07.234913 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000350322 (* 0.0909091 = 3.18474e-05 loss)
I0327 12:52:07.234931 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000279621 (* 0.0909091 = 2.54201e-05 loss)
I0327 12:52:07.234946 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000222202 (* 0.0909091 = 2.02002e-05 loss)
I0327 12:52:07.234961 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.00026447 (* 0.0909091 = 2.40428e-05 loss)
I0327 12:52:07.234974 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000299953 (* 0.0909091 = 2.72684e-05 loss)
I0327 12:52:07.234989 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000207913 (* 0.0909091 = 1.89012e-05 loss)
I0327 12:52:07.235003 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000326543 (* 0.0909091 = 2.96857e-05 loss)
I0327 12:52:07.235018 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000302666 (* 0.0909091 = 2.75151e-05 loss)
I0327 12:52:07.235030 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 12:52:07.235044 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000330197
I0327 12:52:07.235059 21344 sgd_solver.cpp:106] Iteration 1500, lr = 0.01
I0327 12:53:55.091136 21344 solver.cpp:229] Iteration 2000, loss = 3.32151
I0327 12:53:55.091253 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 12:53:55.091272 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 12:53:55.091286 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 12:53:55.091298 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 12:53:55.091310 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 12:53:55.091322 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 12:53:55.091336 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 12:53:55.091348 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 12:53:55.091361 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 12:53:55.091372 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 12:53:55.091383 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 12:53:55.091395 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 12:53:55.091408 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 12:53:55.091418 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 12:53:55.091430 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 12:53:55.091442 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 12:53:55.091454 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 12:53:55.091465 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 12:53:55.091477 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 12:53:55.091490 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 12:53:55.091500 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 12:53:55.091512 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 12:53:55.091528 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.57693 (* 0.0272727 = 0.0975528 loss)
I0327 12:53:55.091543 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.75483 (* 0.0272727 = 0.102404 loss)
I0327 12:53:55.091558 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 4.10737 (* 0.0272727 = 0.112019 loss)
I0327 12:53:55.091572 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 4.18606 (* 0.0272727 = 0.114165 loss)
I0327 12:53:55.091586 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.95109 (* 0.0272727 = 0.107757 loss)
I0327 12:53:55.091600 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 3.68607 (* 0.0272727 = 0.100529 loss)
I0327 12:53:55.091614 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.77178 (* 0.0272727 = 0.0483213 loss)
I0327 12:53:55.091627 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.506239 (* 0.0272727 = 0.0138065 loss)
I0327 12:53:55.091642 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0752232 (* 0.0272727 = 0.00205154 loss)
I0327 12:53:55.091656 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0478075 (* 0.0272727 = 0.00130384 loss)
I0327 12:53:55.091671 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00634946 (* 0.0272727 = 0.000173167 loss)
I0327 12:53:55.091684 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00481317 (* 0.0272727 = 0.000131268 loss)
I0327 12:53:55.091698 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00239557 (* 0.0272727 = 6.53338e-05 loss)
I0327 12:53:55.091712 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00407622 (* 0.0272727 = 0.00011117 loss)
I0327 12:53:55.091727 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00568437 (* 0.0272727 = 0.000155028 loss)
I0327 12:53:55.091742 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00641085 (* 0.0272727 = 0.000174841 loss)
I0327 12:53:55.091755 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00783443 (* 0.0272727 = 0.000213666 loss)
I0327 12:53:55.091787 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.0203663 (* 0.0272727 = 0.000555443 loss)
I0327 12:53:55.091804 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.0030008 (* 0.0272727 = 8.184e-05 loss)
I0327 12:53:55.091819 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.0082354 (* 0.0272727 = 0.000224602 loss)
I0327 12:53:55.091832 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00632207 (* 0.0272727 = 0.00017242 loss)
I0327 12:53:55.091846 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00696639 (* 0.0272727 = 0.000189993 loss)
I0327 12:53:55.091858 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0
I0327 12:53:55.091871 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 12:53:55.091883 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 12:53:55.091894 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 12:53:55.091907 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 12:53:55.091917 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.125
I0327 12:53:55.091929 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 12:53:55.091941 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 12:53:55.091953 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 12:53:55.091964 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 12:53:55.091976 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 12:53:55.091987 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 12:53:55.092002 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 12:53:55.092015 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 12:53:55.092026 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 12:53:55.092037 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 12:53:55.092048 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 12:53:55.092061 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 12:53:55.092072 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 12:53:55.092082 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 12:53:55.092094 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 12:53:55.092103 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 12:53:55.092111 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.62051 (* 0.0272727 = 0.0987413 loss)
I0327 12:53:55.092121 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.54421 (* 0.0272727 = 0.0966603 loss)
I0327 12:53:55.092135 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 4.28893 (* 0.0272727 = 0.116971 loss)
I0327 12:53:55.092149 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.87374 (* 0.0272727 = 0.105647 loss)
I0327 12:53:55.092164 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.89525 (* 0.0272727 = 0.106234 loss)
I0327 12:53:55.092177 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.67744 (* 0.0272727 = 0.100294 loss)
I0327 12:53:55.092191 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.98291 (* 0.0272727 = 0.0540792 loss)
I0327 12:53:55.092206 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.797843 (* 0.0272727 = 0.0217593 loss)
I0327 12:53:55.092221 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0219833 (* 0.0272727 = 0.000599544 loss)
I0327 12:53:55.092234 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00816226 (* 0.0272727 = 0.000222607 loss)
I0327 12:53:55.092248 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00185897 (* 0.0272727 = 5.06991e-05 loss)
I0327 12:53:55.092262 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00208361 (* 0.0272727 = 5.68258e-05 loss)
I0327 12:53:55.092291 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00446882 (* 0.0272727 = 0.000121877 loss)
I0327 12:53:55.092308 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00221752 (* 0.0272727 = 6.04777e-05 loss)
I0327 12:53:55.092321 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00775633 (* 0.0272727 = 0.000211536 loss)
I0327 12:53:55.092335 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00431648 (* 0.0272727 = 0.000117722 loss)
I0327 12:53:55.092350 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000851279 (* 0.0272727 = 2.32167e-05 loss)
I0327 12:53:55.092363 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00156717 (* 0.0272727 = 4.27411e-05 loss)
I0327 12:53:55.092378 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.0036172 (* 0.0272727 = 9.8651e-05 loss)
I0327 12:53:55.092392 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000851591 (* 0.0272727 = 2.32252e-05 loss)
I0327 12:53:55.092406 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000883576 (* 0.0272727 = 2.40975e-05 loss)
I0327 12:53:55.092420 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.0030298 (* 0.0272727 = 8.26308e-05 loss)
I0327 12:53:55.092433 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.125
I0327 12:53:55.092445 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 12:53:55.092458 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 12:53:55.092469 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 12:53:55.092481 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 12:53:55.092494 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 12:53:55.092504 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 12:53:55.092516 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 12:53:55.092531 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 12:53:55.092553 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 12:53:55.092573 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 12:53:55.092586 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 12:53:55.092598 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 12:53:55.092609 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 12:53:55.092622 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 12:53:55.092633 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 12:53:55.092644 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 12:53:55.092655 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 12:53:55.092667 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 12:53:55.092679 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 12:53:55.092690 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 12:53:55.092701 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 12:53:55.092715 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.34428 (* 0.0909091 = 0.304025 loss)
I0327 12:53:55.092730 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.29341 (* 0.0909091 = 0.299401 loss)
I0327 12:53:55.092743 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.59695 (* 0.0909091 = 0.326996 loss)
I0327 12:53:55.092757 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.75138 (* 0.0909091 = 0.341034 loss)
I0327 12:53:55.092772 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.20627 (* 0.0909091 = 0.291479 loss)
I0327 12:53:55.092784 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 3.14938 (* 0.0909091 = 0.286307 loss)
I0327 12:53:55.092810 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.46882 (* 0.0909091 = 0.133529 loss)
I0327 12:53:55.092825 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.704465 (* 0.0909091 = 0.0640423 loss)
I0327 12:53:55.092839 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.051237 (* 0.0909091 = 0.00465791 loss)
I0327 12:53:55.092854 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0230871 (* 0.0909091 = 0.00209883 loss)
I0327 12:53:55.092867 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.0022422 (* 0.0909091 = 0.000203837 loss)
I0327 12:53:55.092882 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.00239424 (* 0.0909091 = 0.000217658 loss)
I0327 12:53:55.092896 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.00354162 (* 0.0909091 = 0.000321965 loss)
I0327 12:53:55.092911 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.00166194 (* 0.0909091 = 0.000151086 loss)
I0327 12:53:55.092924 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.00176161 (* 0.0909091 = 0.000160146 loss)
I0327 12:53:55.092938 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.00215574 (* 0.0909091 = 0.000195977 loss)
I0327 12:53:55.092952 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.00295385 (* 0.0909091 = 0.000268532 loss)
I0327 12:53:55.092967 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.00207206 (* 0.0909091 = 0.000188369 loss)
I0327 12:53:55.092980 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.00273071 (* 0.0909091 = 0.000248247 loss)
I0327 12:53:55.092994 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.0023072 (* 0.0909091 = 0.000209746 loss)
I0327 12:53:55.093008 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.00166511 (* 0.0909091 = 0.000151374 loss)
I0327 12:53:55.093021 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.00218901 (* 0.0909091 = 0.000199001 loss)
I0327 12:53:55.093034 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 12:53:55.093049 21344 solver.cpp:245] Train net output #133: total_confidence = 1.35545e-05
I0327 12:53:55.093061 21344 sgd_solver.cpp:106] Iteration 2000, lr = 0.01
I0327 12:55:42.896509 21344 solver.cpp:229] Iteration 2500, loss = 3.29051
I0327 12:55:42.896662 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 12:55:42.896682 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 12:55:42.896695 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 12:55:42.896708 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 12:55:42.896719 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 12:55:42.896731 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 12:55:42.896744 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 12:55:42.896756 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 12:55:42.896769 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 12:55:42.896780 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 12:55:42.896792 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 12:55:42.896805 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 12:55:42.896816 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 12:55:42.896827 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 12:55:42.896839 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 12:55:42.896850 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 12:55:42.896862 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 12:55:42.896874 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 12:55:42.896886 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 12:55:42.896898 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 12:55:42.896910 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 12:55:42.896921 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 12:55:42.896939 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.97465 (* 0.0272727 = 0.1084 loss)
I0327 12:55:42.896953 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 4.79516 (* 0.0272727 = 0.130777 loss)
I0327 12:55:42.896968 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 4.47242 (* 0.0272727 = 0.121975 loss)
I0327 12:55:42.896982 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.91597 (* 0.0272727 = 0.106799 loss)
I0327 12:55:42.896999 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 4.05815 (* 0.0272727 = 0.110677 loss)
I0327 12:55:42.897014 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 3.59534 (* 0.0272727 = 0.0980548 loss)
I0327 12:55:42.897028 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.40886 (* 0.0272727 = 0.0656961 loss)
I0327 12:55:42.897042 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.51853 (* 0.0272727 = 0.0414146 loss)
I0327 12:55:42.897058 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0665093 (* 0.0272727 = 0.00181389 loss)
I0327 12:55:42.897071 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0299062 (* 0.0272727 = 0.000815623 loss)
I0327 12:55:42.897086 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00312483 (* 0.0272727 = 8.52226e-05 loss)
I0327 12:55:42.897101 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00497226 (* 0.0272727 = 0.000135607 loss)
I0327 12:55:42.897115 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00463851 (* 0.0272727 = 0.000126505 loss)
I0327 12:55:42.897130 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00274441 (* 0.0272727 = 7.48475e-05 loss)
I0327 12:55:42.897145 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00791398 (* 0.0272727 = 0.000215836 loss)
I0327 12:55:42.897158 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.0036065 (* 0.0272727 = 9.83591e-05 loss)
I0327 12:55:42.897173 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00269869 (* 0.0272727 = 7.36006e-05 loss)
I0327 12:55:42.897200 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00177227 (* 0.0272727 = 4.83346e-05 loss)
I0327 12:55:42.897217 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00579979 (* 0.0272727 = 0.000158176 loss)
I0327 12:55:42.897231 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00334803 (* 0.0272727 = 9.131e-05 loss)
I0327 12:55:42.897245 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00239803 (* 0.0272727 = 6.54007e-05 loss)
I0327 12:55:42.897259 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00524121 (* 0.0272727 = 0.000142942 loss)
I0327 12:55:42.897272 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 12:55:42.897284 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 12:55:42.897296 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 12:55:42.897308 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 12:55:42.897320 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 12:55:42.897332 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 12:55:42.897344 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 12:55:42.897356 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.75
I0327 12:55:42.897368 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 12:55:42.897380 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 12:55:42.897392 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 12:55:42.897403 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 12:55:42.897414 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 12:55:42.897426 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 12:55:42.897439 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 12:55:42.897449 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 12:55:42.897460 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 12:55:42.897472 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 12:55:42.897485 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 12:55:42.897495 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 12:55:42.897507 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 12:55:42.897518 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 12:55:42.897532 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.74358 (* 0.0272727 = 0.102098 loss)
I0327 12:55:42.897562 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 4.35127 (* 0.0272727 = 0.118671 loss)
I0327 12:55:42.897578 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 4.0213 (* 0.0272727 = 0.109672 loss)
I0327 12:55:42.897593 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.63756 (* 0.0272727 = 0.0992062 loss)
I0327 12:55:42.897608 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.50989 (* 0.0272727 = 0.0957243 loss)
I0327 12:55:42.897621 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.3349 (* 0.0272727 = 0.0909519 loss)
I0327 12:55:42.897635 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.8162 (* 0.0272727 = 0.0495326 loss)
I0327 12:55:42.897650 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 1.37353 (* 0.0272727 = 0.0374598 loss)
I0327 12:55:42.897663 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0730229 (* 0.0272727 = 0.00199153 loss)
I0327 12:55:42.897680 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.036075 (* 0.0272727 = 0.000983863 loss)
I0327 12:55:42.897696 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00511213 (* 0.0272727 = 0.000139422 loss)
I0327 12:55:42.897711 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00173149 (* 0.0272727 = 4.72225e-05 loss)
I0327 12:55:42.897737 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00333363 (* 0.0272727 = 9.09172e-05 loss)
I0327 12:55:42.897753 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00790719 (* 0.0272727 = 0.000215651 loss)
I0327 12:55:42.897766 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00583085 (* 0.0272727 = 0.000159023 loss)
I0327 12:55:42.897780 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00257564 (* 0.0272727 = 7.02448e-05 loss)
I0327 12:55:42.897794 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00451892 (* 0.0272727 = 0.000123243 loss)
I0327 12:55:42.897809 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.0045582 (* 0.0272727 = 0.000124314 loss)
I0327 12:55:42.897824 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00556453 (* 0.0272727 = 0.00015176 loss)
I0327 12:55:42.897837 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.010843 (* 0.0272727 = 0.000295718 loss)
I0327 12:55:42.897851 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00538728 (* 0.0272727 = 0.000146926 loss)
I0327 12:55:42.897866 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00369391 (* 0.0272727 = 0.000100743 loss)
I0327 12:55:42.897878 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0
I0327 12:55:42.897891 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 12:55:42.897902 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 12:55:42.897913 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 12:55:42.897927 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 12:55:42.897938 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 12:55:42.897950 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 12:55:42.897961 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.75
I0327 12:55:42.897974 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 12:55:42.897984 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 12:55:42.897996 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 12:55:42.898007 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 12:55:42.898020 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 12:55:42.898030 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 12:55:42.898044 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 12:55:42.898056 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 12:55:42.898068 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 12:55:42.898080 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 12:55:42.898092 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 12:55:42.898102 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 12:55:42.898114 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 12:55:42.898125 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 12:55:42.898139 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.91079 (* 0.0909091 = 0.355526 loss)
I0327 12:55:42.898154 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 4.49466 (* 0.0909091 = 0.408606 loss)
I0327 12:55:42.898169 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.79945 (* 0.0909091 = 0.345404 loss)
I0327 12:55:42.898182 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.14691 (* 0.0909091 = 0.286083 loss)
I0327 12:55:42.898196 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.20925 (* 0.0909091 = 0.29175 loss)
I0327 12:55:42.898211 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.88504 (* 0.0909091 = 0.262276 loss)
I0327 12:55:42.898236 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 2.15065 (* 0.0909091 = 0.195514 loss)
I0327 12:55:42.898250 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.35258 (* 0.0909091 = 0.122962 loss)
I0327 12:55:42.898264 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0319761 (* 0.0909091 = 0.00290692 loss)
I0327 12:55:42.898278 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.022118 (* 0.0909091 = 0.00201073 loss)
I0327 12:55:42.898293 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000392085 (* 0.0909091 = 3.56441e-05 loss)
I0327 12:55:42.898308 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000334552 (* 0.0909091 = 3.04138e-05 loss)
I0327 12:55:42.898322 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000384189 (* 0.0909091 = 3.49263e-05 loss)
I0327 12:55:42.898336 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000432266 (* 0.0909091 = 3.92969e-05 loss)
I0327 12:55:42.898350 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000306437 (* 0.0909091 = 2.78579e-05 loss)
I0327 12:55:42.898365 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000437646 (* 0.0909091 = 3.9786e-05 loss)
I0327 12:55:42.898378 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000443729 (* 0.0909091 = 4.0339e-05 loss)
I0327 12:55:42.898392 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000291632 (* 0.0909091 = 2.6512e-05 loss)
I0327 12:55:42.898406 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000388532 (* 0.0909091 = 3.53211e-05 loss)
I0327 12:55:42.898422 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000409222 (* 0.0909091 = 3.7202e-05 loss)
I0327 12:55:42.898435 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000356961 (* 0.0909091 = 3.2451e-05 loss)
I0327 12:55:42.898448 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000410942 (* 0.0909091 = 3.73583e-05 loss)
I0327 12:55:42.898461 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 12:55:42.898473 21344 solver.cpp:245] Train net output #133: total_confidence = 2.79369e-05
I0327 12:55:42.898484 21344 sgd_solver.cpp:106] Iteration 2500, lr = 0.01
I0327 12:57:30.778131 21344 solver.cpp:229] Iteration 3000, loss = 3.22583
I0327 12:57:30.778247 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.5
I0327 12:57:30.778267 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 12:57:30.778280 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 12:57:30.778292 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 12:57:30.778304 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 12:57:30.778316 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 12:57:30.778327 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.75
I0327 12:57:30.778339 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 12:57:30.778350 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 12:57:30.778362 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 12:57:30.778374 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 12:57:30.778385 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 12:57:30.778396 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 12:57:30.778409 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 12:57:30.778419 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 12:57:30.778431 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 12:57:30.778444 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 12:57:30.778455 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 12:57:30.778466 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 12:57:30.778477 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 12:57:30.778488 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 12:57:30.778501 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 12:57:30.778517 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.61433 (* 0.0272727 = 0.0712998 loss)
I0327 12:57:30.778530 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.85047 (* 0.0272727 = 0.0777401 loss)
I0327 12:57:30.778545 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.68742 (* 0.0272727 = 0.100566 loss)
I0327 12:57:30.778559 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.3718 (* 0.0272727 = 0.0919581 loss)
I0327 12:57:30.778573 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.54902 (* 0.0272727 = 0.0967915 loss)
I0327 12:57:30.778587 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 3.17546 (* 0.0272727 = 0.0866036 loss)
I0327 12:57:30.778601 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.83201 (* 0.0272727 = 0.0499639 loss)
I0327 12:57:30.778615 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.116938 (* 0.0272727 = 0.00318921 loss)
I0327 12:57:30.778630 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.031104 (* 0.0272727 = 0.00084829 loss)
I0327 12:57:30.778643 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00483645 (* 0.0272727 = 0.000131903 loss)
I0327 12:57:30.778658 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00170768 (* 0.0272727 = 4.65732e-05 loss)
I0327 12:57:30.778672 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00148811 (* 0.0272727 = 4.05849e-05 loss)
I0327 12:57:30.778687 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00212895 (* 0.0272727 = 5.80623e-05 loss)
I0327 12:57:30.778700 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00589777 (* 0.0272727 = 0.000160848 loss)
I0327 12:57:30.778714 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000599694 (* 0.0272727 = 1.63553e-05 loss)
I0327 12:57:30.778728 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00246247 (* 0.0272727 = 6.71582e-05 loss)
I0327 12:57:30.778743 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00117762 (* 0.0272727 = 3.2117e-05 loss)
I0327 12:57:30.778774 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00101436 (* 0.0272727 = 2.76644e-05 loss)
I0327 12:57:30.778789 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00470485 (* 0.0272727 = 0.000128314 loss)
I0327 12:57:30.778802 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000469087 (* 0.0272727 = 1.27933e-05 loss)
I0327 12:57:30.778817 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000846789 (* 0.0272727 = 2.30942e-05 loss)
I0327 12:57:30.778831 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00342603 (* 0.0272727 = 9.34373e-05 loss)
I0327 12:57:30.778843 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.625
I0327 12:57:30.778856 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.25
I0327 12:57:30.778867 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 12:57:30.778878 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 12:57:30.778889 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 12:57:30.778901 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 12:57:30.778913 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.75
I0327 12:57:30.778924 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 12:57:30.778936 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 12:57:30.778947 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 12:57:30.778959 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 12:57:30.778970 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 12:57:30.778981 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 12:57:30.778995 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 12:57:30.779007 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 12:57:30.779018 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 12:57:30.779031 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 12:57:30.779042 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 12:57:30.779052 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 12:57:30.779063 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 12:57:30.779075 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 12:57:30.779086 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 12:57:30.779100 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.14774 (* 0.0272727 = 0.0585748 loss)
I0327 12:57:30.779114 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.06754 (* 0.0272727 = 0.0836602 loss)
I0327 12:57:30.779127 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.62706 (* 0.0272727 = 0.0989197 loss)
I0327 12:57:30.779141 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.57548 (* 0.0272727 = 0.097513 loss)
I0327 12:57:30.779155 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.99508 (* 0.0272727 = 0.108957 loss)
I0327 12:57:30.779170 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.82497 (* 0.0272727 = 0.0770447 loss)
I0327 12:57:30.779182 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.13128 (* 0.0272727 = 0.0308531 loss)
I0327 12:57:30.779196 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0596861 (* 0.0272727 = 0.0016278 loss)
I0327 12:57:30.779211 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0219689 (* 0.0272727 = 0.000599151 loss)
I0327 12:57:30.779224 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0130723 (* 0.0272727 = 0.000356518 loss)
I0327 12:57:30.779238 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00185313 (* 0.0272727 = 5.05398e-05 loss)
I0327 12:57:30.779266 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000924628 (* 0.0272727 = 2.52171e-05 loss)
I0327 12:57:30.779284 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00143317 (* 0.0272727 = 3.90866e-05 loss)
I0327 12:57:30.779294 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.0013278 (* 0.0272727 = 3.62126e-05 loss)
I0327 12:57:30.779304 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00111997 (* 0.0272727 = 3.05446e-05 loss)
I0327 12:57:30.779317 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00183482 (* 0.0272727 = 5.00406e-05 loss)
I0327 12:57:30.779331 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00219387 (* 0.0272727 = 5.98329e-05 loss)
I0327 12:57:30.779345 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00171946 (* 0.0272727 = 4.68944e-05 loss)
I0327 12:57:30.779359 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00108373 (* 0.0272727 = 2.95562e-05 loss)
I0327 12:57:30.779373 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000575418 (* 0.0272727 = 1.56932e-05 loss)
I0327 12:57:30.779387 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00150868 (* 0.0272727 = 4.11457e-05 loss)
I0327 12:57:30.779400 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00138148 (* 0.0272727 = 3.76767e-05 loss)
I0327 12:57:30.779412 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.25
I0327 12:57:30.779425 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 12:57:30.779436 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 12:57:30.779448 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 12:57:30.779459 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0
I0327 12:57:30.779470 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 12:57:30.779481 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 12:57:30.779492 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 12:57:30.779505 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 12:57:30.779515 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 12:57:30.779527 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 12:57:30.779538 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 12:57:30.779549 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 12:57:30.779561 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 12:57:30.779572 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 12:57:30.779582 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 12:57:30.779593 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 12:57:30.779604 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 12:57:30.779615 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 12:57:30.779626 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 12:57:30.779638 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 12:57:30.779649 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 12:57:30.779662 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.28349 (* 0.0909091 = 0.20759 loss)
I0327 12:57:30.779675 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.93501 (* 0.0909091 = 0.266819 loss)
I0327 12:57:30.779690 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.26092 (* 0.0909091 = 0.296447 loss)
I0327 12:57:30.779703 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.38097 (* 0.0909091 = 0.307361 loss)
I0327 12:57:30.779716 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.51769 (* 0.0909091 = 0.31979 loss)
I0327 12:57:30.779731 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.55042 (* 0.0909091 = 0.231856 loss)
I0327 12:57:30.779754 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.997974 (* 0.0909091 = 0.0907249 loss)
I0327 12:57:30.779769 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0770064 (* 0.0909091 = 0.00700058 loss)
I0327 12:57:30.779783 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0130896 (* 0.0909091 = 0.00118996 loss)
I0327 12:57:30.779798 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00604396 (* 0.0909091 = 0.000549451 loss)
I0327 12:57:30.779811 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000121159 (* 0.0909091 = 1.10145e-05 loss)
I0327 12:57:30.779825 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 9.44589e-05 (* 0.0909091 = 8.58718e-06 loss)
I0327 12:57:30.779839 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.00014887 (* 0.0909091 = 1.35336e-05 loss)
I0327 12:57:30.779853 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000144262 (* 0.0909091 = 1.31148e-05 loss)
I0327 12:57:30.779867 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000119763 (* 0.0909091 = 1.08875e-05 loss)
I0327 12:57:30.779881 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000110414 (* 0.0909091 = 1.00376e-05 loss)
I0327 12:57:30.779896 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000160058 (* 0.0909091 = 1.45507e-05 loss)
I0327 12:57:30.779909 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000114922 (* 0.0909091 = 1.04474e-05 loss)
I0327 12:57:30.779923 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000133485 (* 0.0909091 = 1.2135e-05 loss)
I0327 12:57:30.779937 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 9.76705e-05 (* 0.0909091 = 8.87913e-06 loss)
I0327 12:57:30.779950 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000129819 (* 0.0909091 = 1.18017e-05 loss)
I0327 12:57:30.779964 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000136572 (* 0.0909091 = 1.24157e-05 loss)
I0327 12:57:30.779976 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 12:57:30.779988 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000139215
I0327 12:57:30.779999 21344 sgd_solver.cpp:106] Iteration 3000, lr = 0.01
I0327 12:59:18.539057 21344 solver.cpp:229] Iteration 3500, loss = 3.19249
I0327 12:59:18.539204 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 12:59:18.539224 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 12:59:18.539237 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 12:59:18.539249 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 12:59:18.539261 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 12:59:18.539273 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 12:59:18.539285 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 12:59:18.539299 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 12:59:18.539310 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 12:59:18.539322 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 12:59:18.539335 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 12:59:18.539346 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 12:59:18.539357 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 12:59:18.539369 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 12:59:18.539381 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 12:59:18.539392 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 12:59:18.539403 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 12:59:18.539415 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 12:59:18.539427 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 12:59:18.539439 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 12:59:18.539450 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 12:59:18.539463 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 12:59:18.539479 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.44346 (* 0.0272727 = 0.0939125 loss)
I0327 12:59:18.539494 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 4.21077 (* 0.0272727 = 0.114839 loss)
I0327 12:59:18.539508 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.90873 (* 0.0272727 = 0.106602 loss)
I0327 12:59:18.539522 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.3698 (* 0.0272727 = 0.0919036 loss)
I0327 12:59:18.539536 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.48623 (* 0.0272727 = 0.095079 loss)
I0327 12:59:18.539551 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 3.43546 (* 0.0272727 = 0.0936944 loss)
I0327 12:59:18.539563 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.25221 (* 0.0272727 = 0.061424 loss)
I0327 12:59:18.539577 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.39291 (* 0.0272727 = 0.0379884 loss)
I0327 12:59:18.539592 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.946875 (* 0.0272727 = 0.0258239 loss)
I0327 12:59:18.539607 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0197835 (* 0.0272727 = 0.00053955 loss)
I0327 12:59:18.539621 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00313709 (* 0.0272727 = 8.55571e-05 loss)
I0327 12:59:18.539635 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00234501 (* 0.0272727 = 6.39549e-05 loss)
I0327 12:59:18.539650 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00151599 (* 0.0272727 = 4.13451e-05 loss)
I0327 12:59:18.539664 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00313386 (* 0.0272727 = 8.54688e-05 loss)
I0327 12:59:18.539680 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00216669 (* 0.0272727 = 5.90917e-05 loss)
I0327 12:59:18.539693 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00298166 (* 0.0272727 = 8.13179e-05 loss)
I0327 12:59:18.539708 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00241059 (* 0.0272727 = 6.57435e-05 loss)
I0327 12:59:18.539736 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00369566 (* 0.0272727 = 0.000100791 loss)
I0327 12:59:18.539752 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00272263 (* 0.0272727 = 7.42535e-05 loss)
I0327 12:59:18.539765 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.0015205 (* 0.0272727 = 4.14683e-05 loss)
I0327 12:59:18.539779 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00288363 (* 0.0272727 = 7.86443e-05 loss)
I0327 12:59:18.539793 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00305627 (* 0.0272727 = 8.33529e-05 loss)
I0327 12:59:18.539806 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0
I0327 12:59:18.539819 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 12:59:18.539830 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 12:59:18.539841 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 12:59:18.539852 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 12:59:18.539865 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 12:59:18.539876 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 12:59:18.539888 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.75
I0327 12:59:18.539901 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 12:59:18.539912 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 12:59:18.539923 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 12:59:18.539935 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 12:59:18.539947 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 12:59:18.539958 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 12:59:18.539969 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 12:59:18.539981 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 12:59:18.539995 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 12:59:18.540007 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 12:59:18.540019 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 12:59:18.540030 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 12:59:18.540042 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 12:59:18.540053 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 12:59:18.540067 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.35703 (* 0.0272727 = 0.0915554 loss)
I0327 12:59:18.540081 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 4.2103 (* 0.0272727 = 0.114826 loss)
I0327 12:59:18.540096 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.51561 (* 0.0272727 = 0.0958802 loss)
I0327 12:59:18.540109 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 4.15499 (* 0.0272727 = 0.113318 loss)
I0327 12:59:18.540123 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 4.07386 (* 0.0272727 = 0.111105 loss)
I0327 12:59:18.540138 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.48354 (* 0.0272727 = 0.0950057 loss)
I0327 12:59:18.540151 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 2.18294 (* 0.0272727 = 0.0595348 loss)
I0327 12:59:18.540165 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 1.67775 (* 0.0272727 = 0.0457568 loss)
I0327 12:59:18.540179 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.735149 (* 0.0272727 = 0.0200495 loss)
I0327 12:59:18.540194 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0435813 (* 0.0272727 = 0.00118858 loss)
I0327 12:59:18.540207 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.0025736 (* 0.0272727 = 7.01891e-05 loss)
I0327 12:59:18.540236 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00243438 (* 0.0272727 = 6.63922e-05 loss)
I0327 12:59:18.540251 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00332756 (* 0.0272727 = 9.07517e-05 loss)
I0327 12:59:18.540266 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.0211711 (* 0.0272727 = 0.000577394 loss)
I0327 12:59:18.540280 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00442567 (* 0.0272727 = 0.0001207 loss)
I0327 12:59:18.540295 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00666279 (* 0.0272727 = 0.000181712 loss)
I0327 12:59:18.540309 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00384771 (* 0.0272727 = 0.000104937 loss)
I0327 12:59:18.540323 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00250164 (* 0.0272727 = 6.82267e-05 loss)
I0327 12:59:18.540338 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00210083 (* 0.0272727 = 5.72955e-05 loss)
I0327 12:59:18.540352 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00289664 (* 0.0272727 = 7.89994e-05 loss)
I0327 12:59:18.540367 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00570008 (* 0.0272727 = 0.000155457 loss)
I0327 12:59:18.540381 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.002236 (* 0.0272727 = 6.09819e-05 loss)
I0327 12:59:18.540393 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.25
I0327 12:59:18.540405 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 12:59:18.540416 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 12:59:18.540427 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.25
I0327 12:59:18.540439 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 12:59:18.540452 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 12:59:18.540462 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 12:59:18.540474 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.75
I0327 12:59:18.540487 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 12:59:18.540498 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 12:59:18.540509 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 12:59:18.540520 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 12:59:18.540532 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 12:59:18.540544 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 12:59:18.540555 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 12:59:18.540566 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 12:59:18.540577 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 12:59:18.540590 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 12:59:18.540601 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 12:59:18.540611 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 12:59:18.540623 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 12:59:18.540634 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 12:59:18.540648 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.06061 (* 0.0909091 = 0.278237 loss)
I0327 12:59:18.540663 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.94749 (* 0.0909091 = 0.358863 loss)
I0327 12:59:18.540676 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.4969 (* 0.0909091 = 0.3179 loss)
I0327 12:59:18.540691 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.2387 (* 0.0909091 = 0.294427 loss)
I0327 12:59:18.540704 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.25296 (* 0.0909091 = 0.295723 loss)
I0327 12:59:18.540715 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 3.10186 (* 0.0909091 = 0.281987 loss)
I0327 12:59:18.540740 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.86168 (* 0.0909091 = 0.169244 loss)
I0327 12:59:18.540755 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.31596 (* 0.0909091 = 0.119633 loss)
I0327 12:59:18.540768 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.557776 (* 0.0909091 = 0.0507069 loss)
I0327 12:59:18.540782 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0172299 (* 0.0909091 = 0.00156636 loss)
I0327 12:59:18.540796 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000342146 (* 0.0909091 = 3.11042e-05 loss)
I0327 12:59:18.540810 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000213655 (* 0.0909091 = 1.94232e-05 loss)
I0327 12:59:18.540824 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.0002215 (* 0.0909091 = 2.01364e-05 loss)
I0327 12:59:18.540839 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000316614 (* 0.0909091 = 2.87831e-05 loss)
I0327 12:59:18.540853 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000228703 (* 0.0909091 = 2.07912e-05 loss)
I0327 12:59:18.540868 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000248547 (* 0.0909091 = 2.25952e-05 loss)
I0327 12:59:18.540881 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000293918 (* 0.0909091 = 2.67198e-05 loss)
I0327 12:59:18.540895 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000278086 (* 0.0909091 = 2.52806e-05 loss)
I0327 12:59:18.540910 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000262831 (* 0.0909091 = 2.38938e-05 loss)
I0327 12:59:18.540923 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000244087 (* 0.0909091 = 2.21897e-05 loss)
I0327 12:59:18.540937 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000248362 (* 0.0909091 = 2.25783e-05 loss)
I0327 12:59:18.540951 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000339208 (* 0.0909091 = 3.08371e-05 loss)
I0327 12:59:18.540963 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 12:59:18.540976 21344 solver.cpp:245] Train net output #133: total_confidence = 8.10943e-05
I0327 12:59:18.540987 21344 sgd_solver.cpp:106] Iteration 3500, lr = 0.01
I0327 13:01:06.256872 21344 solver.cpp:229] Iteration 4000, loss = 3.17806
I0327 13:01:06.257026 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 13:01:06.257046 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:01:06.257060 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 13:01:06.257071 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:01:06.257083 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 13:01:06.257096 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 13:01:06.257107 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 13:01:06.257119 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 13:01:06.257131 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:01:06.257143 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:01:06.257155 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:01:06.257167 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:01:06.257179 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:01:06.257191 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:01:06.257203 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:01:06.257215 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:01:06.257226 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:01:06.257238 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:01:06.257249 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:01:06.257261 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:01:06.257272 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:01:06.257284 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:01:06.257299 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.28474 (* 0.0272727 = 0.0895839 loss)
I0327 13:01:06.257315 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.25629 (* 0.0272727 = 0.088808 loss)
I0327 13:01:06.257329 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.36395 (* 0.0272727 = 0.0917441 loss)
I0327 13:01:06.257344 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.75742 (* 0.0272727 = 0.102475 loss)
I0327 13:01:06.257357 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.4328 (* 0.0272727 = 0.0936218 loss)
I0327 13:01:06.257371 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.86212 (* 0.0272727 = 0.0780577 loss)
I0327 13:01:06.257385 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.659287 (* 0.0272727 = 0.0179806 loss)
I0327 13:01:06.257400 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.183669 (* 0.0272727 = 0.00500915 loss)
I0327 13:01:06.257414 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0418214 (* 0.0272727 = 0.00114058 loss)
I0327 13:01:06.257428 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00631121 (* 0.0272727 = 0.000172124 loss)
I0327 13:01:06.257444 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00101837 (* 0.0272727 = 2.77738e-05 loss)
I0327 13:01:06.257459 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00069322 (* 0.0272727 = 1.8906e-05 loss)
I0327 13:01:06.257473 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000597837 (* 0.0272727 = 1.63046e-05 loss)
I0327 13:01:06.257488 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000761527 (* 0.0272727 = 2.07689e-05 loss)
I0327 13:01:06.257503 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.0011476 (* 0.0272727 = 3.1298e-05 loss)
I0327 13:01:06.257518 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00118123 (* 0.0272727 = 3.22154e-05 loss)
I0327 13:01:06.257531 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000467921 (* 0.0272727 = 1.27615e-05 loss)
I0327 13:01:06.257577 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000528544 (* 0.0272727 = 1.44148e-05 loss)
I0327 13:01:06.257593 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00167223 (* 0.0272727 = 4.56061e-05 loss)
I0327 13:01:06.257608 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000405327 (* 0.0272727 = 1.10544e-05 loss)
I0327 13:01:06.257622 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00034185 (* 0.0272727 = 9.32318e-06 loss)
I0327 13:01:06.257637 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00113411 (* 0.0272727 = 3.09304e-05 loss)
I0327 13:01:06.257649 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:01:06.257661 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:01:06.257673 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:01:06.257685 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:01:06.257697 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 13:01:06.257709 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 13:01:06.257720 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 13:01:06.257732 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:01:06.257745 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:01:06.257756 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:01:06.257767 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:01:06.257779 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:01:06.257791 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:01:06.257802 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:01:06.257813 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:01:06.257825 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:01:06.257836 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:01:06.257848 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:01:06.257859 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:01:06.257870 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:01:06.257882 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:01:06.257894 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:01:06.257907 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.42762 (* 0.0272727 = 0.0934806 loss)
I0327 13:01:06.257921 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.2827 (* 0.0272727 = 0.0895283 loss)
I0327 13:01:06.257936 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.2154 (* 0.0272727 = 0.0876928 loss)
I0327 13:01:06.257949 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.5537 (* 0.0272727 = 0.0969192 loss)
I0327 13:01:06.257963 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.52966 (* 0.0272727 = 0.0962635 loss)
I0327 13:01:06.257977 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.48831 (* 0.0272727 = 0.0951356 loss)
I0327 13:01:06.257994 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.723619 (* 0.0272727 = 0.0197351 loss)
I0327 13:01:06.258009 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.196107 (* 0.0272727 = 0.00534839 loss)
I0327 13:01:06.258023 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0404525 (* 0.0272727 = 0.00110325 loss)
I0327 13:01:06.258038 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0143688 (* 0.0272727 = 0.000391876 loss)
I0327 13:01:06.258052 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00283041 (* 0.0272727 = 7.7193e-05 loss)
I0327 13:01:06.258079 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00506621 (* 0.0272727 = 0.000138169 loss)
I0327 13:01:06.258093 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00514416 (* 0.0272727 = 0.000140295 loss)
I0327 13:01:06.258107 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00510222 (* 0.0272727 = 0.000139151 loss)
I0327 13:01:06.258121 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00767948 (* 0.0272727 = 0.00020944 loss)
I0327 13:01:06.258136 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00659715 (* 0.0272727 = 0.000179922 loss)
I0327 13:01:06.258150 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00108874 (* 0.0272727 = 2.9693e-05 loss)
I0327 13:01:06.258164 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00055951 (* 0.0272727 = 1.52594e-05 loss)
I0327 13:01:06.258178 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.0054204 (* 0.0272727 = 0.000147829 loss)
I0327 13:01:06.258193 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.0015281 (* 0.0272727 = 4.16754e-05 loss)
I0327 13:01:06.258208 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00278975 (* 0.0272727 = 7.60841e-05 loss)
I0327 13:01:06.258221 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00204279 (* 0.0272727 = 5.57124e-05 loss)
I0327 13:01:06.258234 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0
I0327 13:01:06.258245 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 13:01:06.258257 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:01:06.258270 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 13:01:06.258280 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 13:01:06.258292 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 13:01:06.258303 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.875
I0327 13:01:06.258316 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:01:06.258327 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:01:06.258338 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:01:06.258349 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:01:06.258360 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:01:06.258373 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:01:06.258383 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:01:06.258395 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:01:06.258406 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:01:06.258415 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:01:06.258422 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:01:06.258435 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:01:06.258446 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:01:06.258458 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:01:06.258469 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:01:06.258483 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.93912 (* 0.0909091 = 0.267193 loss)
I0327 13:01:06.258497 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.28377 (* 0.0909091 = 0.298524 loss)
I0327 13:01:06.258512 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.17025 (* 0.0909091 = 0.288204 loss)
I0327 13:01:06.258525 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.70214 (* 0.0909091 = 0.336559 loss)
I0327 13:01:06.258539 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.09944 (* 0.0909091 = 0.281767 loss)
I0327 13:01:06.258553 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.8181 (* 0.0909091 = 0.256191 loss)
I0327 13:01:06.258576 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.662376 (* 0.0909091 = 0.060216 loss)
I0327 13:01:06.258591 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.131959 (* 0.0909091 = 0.0119963 loss)
I0327 13:01:06.258605 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0476544 (* 0.0909091 = 0.00433222 loss)
I0327 13:01:06.258620 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00991928 (* 0.0909091 = 0.000901753 loss)
I0327 13:01:06.258635 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000187534 (* 0.0909091 = 1.70485e-05 loss)
I0327 13:01:06.258648 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000286789 (* 0.0909091 = 2.60717e-05 loss)
I0327 13:01:06.258662 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000221351 (* 0.0909091 = 2.01228e-05 loss)
I0327 13:01:06.258677 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000267045 (* 0.0909091 = 2.42768e-05 loss)
I0327 13:01:06.258690 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000255288 (* 0.0909091 = 2.3208e-05 loss)
I0327 13:01:06.258704 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000240423 (* 0.0909091 = 2.18566e-05 loss)
I0327 13:01:06.258718 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000233174 (* 0.0909091 = 2.11977e-05 loss)
I0327 13:01:06.258733 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000266159 (* 0.0909091 = 2.41963e-05 loss)
I0327 13:01:06.258746 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.00019046 (* 0.0909091 = 1.73146e-05 loss)
I0327 13:01:06.258760 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000311318 (* 0.0909091 = 2.83016e-05 loss)
I0327 13:01:06.258774 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000349096 (* 0.0909091 = 3.1736e-05 loss)
I0327 13:01:06.258788 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000211367 (* 0.0909091 = 1.92151e-05 loss)
I0327 13:01:06.258800 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:01:06.258811 21344 solver.cpp:245] Train net output #133: total_confidence = 1.50617e-05
I0327 13:01:06.258823 21344 sgd_solver.cpp:106] Iteration 4000, lr = 0.01
I0327 13:02:54.166479 21344 solver.cpp:229] Iteration 4500, loss = 3.13741
I0327 13:02:54.166606 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 13:02:54.166625 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:02:54.166638 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:02:54.166651 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 13:02:54.166662 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 13:02:54.166673 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 13:02:54.166685 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 13:02:54.166697 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:02:54.166709 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 13:02:54.166721 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 0.875
I0327 13:02:54.166733 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:02:54.166744 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:02:54.166756 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:02:54.166769 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:02:54.166779 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:02:54.166791 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:02:54.166802 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:02:54.166815 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:02:54.166826 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:02:54.166837 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:02:54.166848 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:02:54.166860 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:02:54.166877 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 4.28472 (* 0.0272727 = 0.116856 loss)
I0327 13:02:54.166892 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.50254 (* 0.0272727 = 0.0955237 loss)
I0327 13:02:54.166906 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 4.16483 (* 0.0272727 = 0.113586 loss)
I0327 13:02:54.166920 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.97911 (* 0.0272727 = 0.108521 loss)
I0327 13:02:54.166934 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.95279 (* 0.0272727 = 0.107803 loss)
I0327 13:02:54.166949 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.61208 (* 0.0272727 = 0.0712384 loss)
I0327 13:02:54.166962 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.00106 (* 0.0272727 = 0.0545744 loss)
I0327 13:02:54.166975 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.779984 (* 0.0272727 = 0.0212723 loss)
I0327 13:02:54.166992 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.721881 (* 0.0272727 = 0.0196877 loss)
I0327 13:02:54.167007 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.935556 (* 0.0272727 = 0.0255152 loss)
I0327 13:02:54.167022 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00156067 (* 0.0272727 = 4.25638e-05 loss)
I0327 13:02:54.167037 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00206065 (* 0.0272727 = 5.61995e-05 loss)
I0327 13:02:54.167052 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00163659 (* 0.0272727 = 4.46342e-05 loss)
I0327 13:02:54.167065 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00286204 (* 0.0272727 = 7.80557e-05 loss)
I0327 13:02:54.167079 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00107944 (* 0.0272727 = 2.94393e-05 loss)
I0327 13:02:54.167094 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00168267 (* 0.0272727 = 4.58909e-05 loss)
I0327 13:02:54.167107 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00201364 (* 0.0272727 = 5.49175e-05 loss)
I0327 13:02:54.167140 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00114744 (* 0.0272727 = 3.12938e-05 loss)
I0327 13:02:54.167155 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00199259 (* 0.0272727 = 5.43434e-05 loss)
I0327 13:02:54.167170 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00158845 (* 0.0272727 = 4.33214e-05 loss)
I0327 13:02:54.167183 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00209256 (* 0.0272727 = 5.70697e-05 loss)
I0327 13:02:54.167197 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00182692 (* 0.0272727 = 4.9825e-05 loss)
I0327 13:02:54.167209 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:02:54.167222 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.375
I0327 13:02:54.167234 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:02:54.167245 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:02:54.167256 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 13:02:54.167268 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:02:54.167280 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 13:02:54.167291 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:02:54.167304 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 13:02:54.167315 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 0.875
I0327 13:02:54.167327 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:02:54.167338 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:02:54.167351 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:02:54.167361 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:02:54.167372 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:02:54.167384 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:02:54.167395 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:02:54.167407 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:02:54.167418 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:02:54.167429 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:02:54.167440 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:02:54.167453 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:02:54.167465 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 4.21481 (* 0.0272727 = 0.114949 loss)
I0327 13:02:54.167479 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.72392 (* 0.0272727 = 0.0742886 loss)
I0327 13:02:54.167493 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 4.10057 (* 0.0272727 = 0.111834 loss)
I0327 13:02:54.167507 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.73271 (* 0.0272727 = 0.101801 loss)
I0327 13:02:54.167522 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 4.28115 (* 0.0272727 = 0.116759 loss)
I0327 13:02:54.167536 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.15684 (* 0.0272727 = 0.0860955 loss)
I0327 13:02:54.167549 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.94902 (* 0.0272727 = 0.0531552 loss)
I0327 13:02:54.167563 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 1.02206 (* 0.0272727 = 0.0278744 loss)
I0327 13:02:54.167577 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 1.26518 (* 0.0272727 = 0.034505 loss)
I0327 13:02:54.167592 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 1.11318 (* 0.0272727 = 0.0303593 loss)
I0327 13:02:54.167605 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000884742 (* 0.0272727 = 2.41293e-05 loss)
I0327 13:02:54.167634 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00224629 (* 0.0272727 = 6.12625e-05 loss)
I0327 13:02:54.167649 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00304856 (* 0.0272727 = 8.31425e-05 loss)
I0327 13:02:54.167665 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000791842 (* 0.0272727 = 2.15957e-05 loss)
I0327 13:02:54.167678 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00175599 (* 0.0272727 = 4.78906e-05 loss)
I0327 13:02:54.167692 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000738919 (* 0.0272727 = 2.01523e-05 loss)
I0327 13:02:54.167706 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00125974 (* 0.0272727 = 3.43564e-05 loss)
I0327 13:02:54.167721 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000929393 (* 0.0272727 = 2.53471e-05 loss)
I0327 13:02:54.167734 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00159745 (* 0.0272727 = 4.35668e-05 loss)
I0327 13:02:54.167748 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000984168 (* 0.0272727 = 2.68409e-05 loss)
I0327 13:02:54.167762 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000876157 (* 0.0272727 = 2.38952e-05 loss)
I0327 13:02:54.167776 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.002499 (* 0.0272727 = 6.81546e-05 loss)
I0327 13:02:54.167789 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0
I0327 13:02:54.167801 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.25
I0327 13:02:54.167814 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:02:54.167824 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 13:02:54.167835 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0
I0327 13:02:54.167846 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.5
I0327 13:02:54.167858 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.5
I0327 13:02:54.167870 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:02:54.167881 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 13:02:54.167892 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 0.875
I0327 13:02:54.167904 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:02:54.167915 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:02:54.167927 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:02:54.167938 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:02:54.167949 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:02:54.167961 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:02:54.167973 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:02:54.167984 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:02:54.167995 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:02:54.168006 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:02:54.168018 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:02:54.168030 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:02:54.168045 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.97898 (* 0.0909091 = 0.361726 loss)
I0327 13:02:54.168061 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.14984 (* 0.0909091 = 0.286349 loss)
I0327 13:02:54.168074 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 4.15962 (* 0.0909091 = 0.378147 loss)
I0327 13:02:54.168088 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.46982 (* 0.0909091 = 0.315438 loss)
I0327 13:02:54.168102 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.97484 (* 0.0909091 = 0.361349 loss)
I0327 13:02:54.168112 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.79873 (* 0.0909091 = 0.25443 loss)
I0327 13:02:54.168136 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.86303 (* 0.0909091 = 0.169366 loss)
I0327 13:02:54.168151 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.01324 (* 0.0909091 = 0.0921123 loss)
I0327 13:02:54.168165 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 1.01621 (* 0.0909091 = 0.0923825 loss)
I0327 13:02:54.168179 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 1.15502 (* 0.0909091 = 0.105002 loss)
I0327 13:02:54.168193 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000188548 (* 0.0909091 = 1.71407e-05 loss)
I0327 13:02:54.168208 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000237969 (* 0.0909091 = 2.16336e-05 loss)
I0327 13:02:54.168221 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.00023976 (* 0.0909091 = 2.17964e-05 loss)
I0327 13:02:54.168236 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000334238 (* 0.0909091 = 3.03853e-05 loss)
I0327 13:02:54.168251 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000182365 (* 0.0909091 = 1.65786e-05 loss)
I0327 13:02:54.168264 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000261214 (* 0.0909091 = 2.37467e-05 loss)
I0327 13:02:54.168278 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000250526 (* 0.0909091 = 2.27751e-05 loss)
I0327 13:02:54.168292 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000251275 (* 0.0909091 = 2.28432e-05 loss)
I0327 13:02:54.168305 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000210464 (* 0.0909091 = 1.91331e-05 loss)
I0327 13:02:54.168319 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000234857 (* 0.0909091 = 2.13506e-05 loss)
I0327 13:02:54.168334 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000329229 (* 0.0909091 = 2.99299e-05 loss)
I0327 13:02:54.168347 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000280349 (* 0.0909091 = 2.54862e-05 loss)
I0327 13:02:54.168359 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:02:54.168370 21344 solver.cpp:245] Train net output #133: total_confidence = 8.66659e-06
I0327 13:02:54.168383 21344 sgd_solver.cpp:106] Iteration 4500, lr = 0.01
I0327 13:04:41.803457 21344 solver.cpp:338] Iteration 5000, Testing net (#0)
I0327 13:05:12.803906 21344 solver.cpp:393] Test loss: 2.51615
I0327 13:05:12.804019 21344 solver.cpp:406] Test net output #0: loss1/accuracy01 = 0.298
I0327 13:05:12.804040 21344 solver.cpp:406] Test net output #1: loss1/accuracy02 = 0.095
I0327 13:05:12.804054 21344 solver.cpp:406] Test net output #2: loss1/accuracy03 = 0.101
I0327 13:05:12.804065 21344 solver.cpp:406] Test net output #3: loss1/accuracy04 = 0.129
I0327 13:05:12.804077 21344 solver.cpp:406] Test net output #4: loss1/accuracy05 = 0.243
I0327 13:05:12.804090 21344 solver.cpp:406] Test net output #5: loss1/accuracy06 = 0.513
I0327 13:05:12.804100 21344 solver.cpp:406] Test net output #6: loss1/accuracy07 = 0.896
I0327 13:05:12.804112 21344 solver.cpp:406] Test net output #7: loss1/accuracy08 = 0.972
I0327 13:05:12.804123 21344 solver.cpp:406] Test net output #8: loss1/accuracy09 = 0.995
I0327 13:05:12.804136 21344 solver.cpp:406] Test net output #9: loss1/accuracy10 = 0.998
I0327 13:05:12.804147 21344 solver.cpp:406] Test net output #10: loss1/accuracy11 = 1
I0327 13:05:12.804159 21344 solver.cpp:406] Test net output #11: loss1/accuracy12 = 1
I0327 13:05:12.804170 21344 solver.cpp:406] Test net output #12: loss1/accuracy13 = 1
I0327 13:05:12.804182 21344 solver.cpp:406] Test net output #13: loss1/accuracy14 = 1
I0327 13:05:12.804193 21344 solver.cpp:406] Test net output #14: loss1/accuracy15 = 1
I0327 13:05:12.804210 21344 solver.cpp:406] Test net output #15: loss1/accuracy16 = 1
I0327 13:05:12.804224 21344 solver.cpp:406] Test net output #16: loss1/accuracy17 = 1
I0327 13:05:12.804235 21344 solver.cpp:406] Test net output #17: loss1/accuracy18 = 1
I0327 13:05:12.804246 21344 solver.cpp:406] Test net output #18: loss1/accuracy19 = 1
I0327 13:05:12.804257 21344 solver.cpp:406] Test net output #19: loss1/accuracy20 = 1
I0327 13:05:12.804270 21344 solver.cpp:406] Test net output #20: loss1/accuracy21 = 1
I0327 13:05:12.804280 21344 solver.cpp:406] Test net output #21: loss1/accuracy22 = 1
I0327 13:05:12.804296 21344 solver.cpp:406] Test net output #22: loss1/loss01 = 2.729 (* 0.0272727 = 0.0744274 loss)
I0327 13:05:12.804309 21344 solver.cpp:406] Test net output #23: loss1/loss02 = 3.08709 (* 0.0272727 = 0.0841933 loss)
I0327 13:05:12.804323 21344 solver.cpp:406] Test net output #24: loss1/loss03 = 3.17215 (* 0.0272727 = 0.0865131 loss)
I0327 13:05:12.804337 21344 solver.cpp:406] Test net output #25: loss1/loss04 = 3.0672 (* 0.0272727 = 0.0836509 loss)
I0327 13:05:12.804352 21344 solver.cpp:406] Test net output #26: loss1/loss05 = 2.90011 (* 0.0272727 = 0.079094 loss)
I0327 13:05:12.804364 21344 solver.cpp:406] Test net output #27: loss1/loss06 = 1.97549 (* 0.0272727 = 0.0538771 loss)
I0327 13:05:12.804378 21344 solver.cpp:406] Test net output #28: loss1/loss07 = 0.725995 (* 0.0272727 = 0.0197999 loss)
I0327 13:05:12.804393 21344 solver.cpp:406] Test net output #29: loss1/loss08 = 0.223549 (* 0.0272727 = 0.0060968 loss)
I0327 13:05:12.804406 21344 solver.cpp:406] Test net output #30: loss1/loss09 = 0.0441595 (* 0.0272727 = 0.00120435 loss)
I0327 13:05:12.804421 21344 solver.cpp:406] Test net output #31: loss1/loss10 = 0.0212288 (* 0.0272727 = 0.000578967 loss)
I0327 13:05:12.804435 21344 solver.cpp:406] Test net output #32: loss1/loss11 = 0.00112421 (* 0.0272727 = 3.06603e-05 loss)
I0327 13:05:12.804450 21344 solver.cpp:406] Test net output #33: loss1/loss12 = 0.00106746 (* 0.0272727 = 2.91125e-05 loss)
I0327 13:05:12.804471 21344 solver.cpp:406] Test net output #34: loss1/loss13 = 0.00123596 (* 0.0272727 = 3.3708e-05 loss)
I0327 13:05:12.804484 21344 solver.cpp:406] Test net output #35: loss1/loss14 = 0.00112117 (* 0.0272727 = 3.05774e-05 loss)
I0327 13:05:12.804499 21344 solver.cpp:406] Test net output #36: loss1/loss15 = 0.00109958 (* 0.0272727 = 2.99884e-05 loss)
I0327 13:05:12.804513 21344 solver.cpp:406] Test net output #37: loss1/loss16 = 0.00132226 (* 0.0272727 = 3.60615e-05 loss)
I0327 13:05:12.804527 21344 solver.cpp:406] Test net output #38: loss1/loss17 = 0.0012943 (* 0.0272727 = 3.52992e-05 loss)
I0327 13:05:12.804560 21344 solver.cpp:406] Test net output #39: loss1/loss18 = 0.00118417 (* 0.0272727 = 3.22956e-05 loss)
I0327 13:05:12.804575 21344 solver.cpp:406] Test net output #40: loss1/loss19 = 0.00100196 (* 0.0272727 = 2.73261e-05 loss)
I0327 13:05:12.804589 21344 solver.cpp:406] Test net output #41: loss1/loss20 = 0.00108686 (* 0.0272727 = 2.96416e-05 loss)
I0327 13:05:12.804603 21344 solver.cpp:406] Test net output #42: loss1/loss21 = 0.000990167 (* 0.0272727 = 2.70045e-05 loss)
I0327 13:05:12.804617 21344 solver.cpp:406] Test net output #43: loss1/loss22 = 0.00103686 (* 0.0272727 = 2.82781e-05 loss)
I0327 13:05:12.804630 21344 solver.cpp:406] Test net output #44: loss2/accuracy01 = 0.278
I0327 13:05:12.804641 21344 solver.cpp:406] Test net output #45: loss2/accuracy02 = 0.108
I0327 13:05:12.804653 21344 solver.cpp:406] Test net output #46: loss2/accuracy03 = 0.091
I0327 13:05:12.804664 21344 solver.cpp:406] Test net output #47: loss2/accuracy04 = 0.14
I0327 13:05:12.804677 21344 solver.cpp:406] Test net output #48: loss2/accuracy05 = 0.246
I0327 13:05:12.804687 21344 solver.cpp:406] Test net output #49: loss2/accuracy06 = 0.516
I0327 13:05:12.804699 21344 solver.cpp:406] Test net output #50: loss2/accuracy07 = 0.897
I0327 13:05:12.804711 21344 solver.cpp:406] Test net output #51: loss2/accuracy08 = 0.972
I0327 13:05:12.804723 21344 solver.cpp:406] Test net output #52: loss2/accuracy09 = 0.995
I0327 13:05:12.804734 21344 solver.cpp:406] Test net output #53: loss2/accuracy10 = 0.998
I0327 13:05:12.804745 21344 solver.cpp:406] Test net output #54: loss2/accuracy11 = 1
I0327 13:05:12.804756 21344 solver.cpp:406] Test net output #55: loss2/accuracy12 = 1
I0327 13:05:12.804767 21344 solver.cpp:406] Test net output #56: loss2/accuracy13 = 1
I0327 13:05:12.804780 21344 solver.cpp:406] Test net output #57: loss2/accuracy14 = 1
I0327 13:05:12.804790 21344 solver.cpp:406] Test net output #58: loss2/accuracy15 = 1
I0327 13:05:12.804801 21344 solver.cpp:406] Test net output #59: loss2/accuracy16 = 1
I0327 13:05:12.804812 21344 solver.cpp:406] Test net output #60: loss2/accuracy17 = 1
I0327 13:05:12.804823 21344 solver.cpp:406] Test net output #61: loss2/accuracy18 = 1
I0327 13:05:12.804834 21344 solver.cpp:406] Test net output #62: loss2/accuracy19 = 1
I0327 13:05:12.804846 21344 solver.cpp:406] Test net output #63: loss2/accuracy20 = 1
I0327 13:05:12.804857 21344 solver.cpp:406] Test net output #64: loss2/accuracy21 = 1
I0327 13:05:12.804868 21344 solver.cpp:406] Test net output #65: loss2/accuracy22 = 1
I0327 13:05:12.804882 21344 solver.cpp:406] Test net output #66: loss2/loss01 = 2.51069 (* 0.0272727 = 0.0684735 loss)
I0327 13:05:12.804894 21344 solver.cpp:406] Test net output #67: loss2/loss02 = 2.91008 (* 0.0272727 = 0.0793658 loss)
I0327 13:05:12.804908 21344 solver.cpp:406] Test net output #68: loss2/loss03 = 3.04399 (* 0.0272727 = 0.083018 loss)
I0327 13:05:12.804922 21344 solver.cpp:406] Test net output #69: loss2/loss04 = 2.92304 (* 0.0272727 = 0.0797194 loss)
I0327 13:05:12.804935 21344 solver.cpp:406] Test net output #70: loss2/loss05 = 2.79087 (* 0.0272727 = 0.0761147 loss)
I0327 13:05:12.804949 21344 solver.cpp:406] Test net output #71: loss2/loss06 = 1.88467 (* 0.0272727 = 0.0514002 loss)
I0327 13:05:12.804962 21344 solver.cpp:406] Test net output #72: loss2/loss07 = 0.670684 (* 0.0272727 = 0.0182914 loss)
I0327 13:05:12.804976 21344 solver.cpp:406] Test net output #73: loss2/loss08 = 0.215558 (* 0.0272727 = 0.00587886 loss)
I0327 13:05:12.804994 21344 solver.cpp:406] Test net output #74: loss2/loss09 = 0.0403959 (* 0.0272727 = 0.00110171 loss)
I0327 13:05:12.805009 21344 solver.cpp:406] Test net output #75: loss2/loss10 = 0.0198054 (* 0.0272727 = 0.000540149 loss)
I0327 13:05:12.805023 21344 solver.cpp:406] Test net output #76: loss2/loss11 = 0.000731224 (* 0.0272727 = 1.99425e-05 loss)
I0327 13:05:12.805037 21344 solver.cpp:406] Test net output #77: loss2/loss12 = 0.00078926 (* 0.0272727 = 2.15253e-05 loss)
I0327 13:05:12.805061 21344 solver.cpp:406] Test net output #78: loss2/loss13 = 0.000659056 (* 0.0272727 = 1.79743e-05 loss)
I0327 13:05:12.805076 21344 solver.cpp:406] Test net output #79: loss2/loss14 = 0.000707006 (* 0.0272727 = 1.9282e-05 loss)
I0327 13:05:12.805090 21344 solver.cpp:406] Test net output #80: loss2/loss15 = 0.000670353 (* 0.0272727 = 1.82824e-05 loss)
I0327 13:05:12.805104 21344 solver.cpp:406] Test net output #81: loss2/loss16 = 0.000686329 (* 0.0272727 = 1.87181e-05 loss)
I0327 13:05:12.805119 21344 solver.cpp:406] Test net output #82: loss2/loss17 = 0.000793617 (* 0.0272727 = 2.16441e-05 loss)
I0327 13:05:12.805131 21344 solver.cpp:406] Test net output #83: loss2/loss18 = 0.000734819 (* 0.0272727 = 2.00405e-05 loss)
I0327 13:05:12.805145 21344 solver.cpp:406] Test net output #84: loss2/loss19 = 0.000795111 (* 0.0272727 = 2.16848e-05 loss)
I0327 13:05:12.805160 21344 solver.cpp:406] Test net output #85: loss2/loss20 = 0.000706819 (* 0.0272727 = 1.92769e-05 loss)
I0327 13:05:12.805172 21344 solver.cpp:406] Test net output #86: loss2/loss21 = 0.00091741 (* 0.0272727 = 2.50203e-05 loss)
I0327 13:05:12.805186 21344 solver.cpp:406] Test net output #87: loss2/loss22 = 0.000742784 (* 0.0272727 = 2.02577e-05 loss)
I0327 13:05:12.805198 21344 solver.cpp:406] Test net output #88: loss3/accuracy01 = 0.23
I0327 13:05:12.805210 21344 solver.cpp:406] Test net output #89: loss3/accuracy02 = 0.085
I0327 13:05:12.805222 21344 solver.cpp:406] Test net output #90: loss3/accuracy03 = 0.092
I0327 13:05:12.805233 21344 solver.cpp:406] Test net output #91: loss3/accuracy04 = 0.111
I0327 13:05:12.805244 21344 solver.cpp:406] Test net output #92: loss3/accuracy05 = 0.227
I0327 13:05:12.805256 21344 solver.cpp:406] Test net output #93: loss3/accuracy06 = 0.502
I0327 13:05:12.805268 21344 solver.cpp:406] Test net output #94: loss3/accuracy07 = 0.896
I0327 13:05:12.805279 21344 solver.cpp:406] Test net output #95: loss3/accuracy08 = 0.972
I0327 13:05:12.805286 21344 solver.cpp:406] Test net output #96: loss3/accuracy09 = 0.995
I0327 13:05:12.805294 21344 solver.cpp:406] Test net output #97: loss3/accuracy10 = 0.998
I0327 13:05:12.805306 21344 solver.cpp:406] Test net output #98: loss3/accuracy11 = 1
I0327 13:05:12.805317 21344 solver.cpp:406] Test net output #99: loss3/accuracy12 = 1
I0327 13:05:12.805330 21344 solver.cpp:406] Test net output #100: loss3/accuracy13 = 1
I0327 13:05:12.805341 21344 solver.cpp:406] Test net output #101: loss3/accuracy14 = 1
I0327 13:05:12.805358 21344 solver.cpp:406] Test net output #102: loss3/accuracy15 = 1
I0327 13:05:12.805371 21344 solver.cpp:406] Test net output #103: loss3/accuracy16 = 1
I0327 13:05:12.805382 21344 solver.cpp:406] Test net output #104: loss3/accuracy17 = 1
I0327 13:05:12.805393 21344 solver.cpp:406] Test net output #105: loss3/accuracy18 = 1
I0327 13:05:12.805404 21344 solver.cpp:406] Test net output #106: loss3/accuracy19 = 1
I0327 13:05:12.805415 21344 solver.cpp:406] Test net output #107: loss3/accuracy20 = 1
I0327 13:05:12.805426 21344 solver.cpp:406] Test net output #108: loss3/accuracy21 = 1
I0327 13:05:12.805436 21344 solver.cpp:406] Test net output #109: loss3/accuracy22 = 1
I0327 13:05:12.805450 21344 solver.cpp:406] Test net output #110: loss3/loss01 = 2.43294 (* 0.0909091 = 0.221176 loss)
I0327 13:05:12.805464 21344 solver.cpp:406] Test net output #111: loss3/loss02 = 2.93513 (* 0.0909091 = 0.26683 loss)
I0327 13:05:12.805477 21344 solver.cpp:406] Test net output #112: loss3/loss03 = 3.0786 (* 0.0909091 = 0.279873 loss)
I0327 13:05:12.805490 21344 solver.cpp:406] Test net output #113: loss3/loss04 = 3.03647 (* 0.0909091 = 0.276042 loss)
I0327 13:05:12.805505 21344 solver.cpp:406] Test net output #114: loss3/loss05 = 2.83567 (* 0.0909091 = 0.257788 loss)
I0327 13:05:12.805517 21344 solver.cpp:406] Test net output #115: loss3/loss06 = 1.87512 (* 0.0909091 = 0.170465 loss)
I0327 13:05:12.805553 21344 solver.cpp:406] Test net output #116: loss3/loss07 = 0.695374 (* 0.0909091 = 0.0632158 loss)
I0327 13:05:12.805570 21344 solver.cpp:406] Test net output #117: loss3/loss08 = 0.224267 (* 0.0909091 = 0.0203879 loss)
I0327 13:05:12.805584 21344 solver.cpp:406] Test net output #118: loss3/loss09 = 0.0459848 (* 0.0909091 = 0.00418043 loss)
I0327 13:05:12.805598 21344 solver.cpp:406] Test net output #119: loss3/loss10 = 0.0237412 (* 0.0909091 = 0.00215829 loss)
I0327 13:05:12.805611 21344 solver.cpp:406] Test net output #120: loss3/loss11 = 7.53839e-05 (* 0.0909091 = 6.85308e-06 loss)
I0327 13:05:12.805625 21344 solver.cpp:406] Test net output #121: loss3/loss12 = 7.26766e-05 (* 0.0909091 = 6.60697e-06 loss)
I0327 13:05:12.805639 21344 solver.cpp:406] Test net output #122: loss3/loss13 = 8.26042e-05 (* 0.0909091 = 7.50947e-06 loss)
I0327 13:05:12.805652 21344 solver.cpp:406] Test net output #123: loss3/loss14 = 8.09331e-05 (* 0.0909091 = 7.35755e-06 loss)
I0327 13:05:12.805666 21344 solver.cpp:406] Test net output #124: loss3/loss15 = 7.17971e-05 (* 0.0909091 = 6.52701e-06 loss)
I0327 13:05:12.805680 21344 solver.cpp:406] Test net output #125: loss3/loss16 = 7.73646e-05 (* 0.0909091 = 7.03314e-06 loss)
I0327 13:05:12.805693 21344 solver.cpp:406] Test net output #126: loss3/loss17 = 7.81954e-05 (* 0.0909091 = 7.10867e-06 loss)
I0327 13:05:12.805706 21344 solver.cpp:406] Test net output #127: loss3/loss18 = 7.23958e-05 (* 0.0909091 = 6.58143e-06 loss)
I0327 13:05:12.805721 21344 solver.cpp:406] Test net output #128: loss3/loss19 = 7.25853e-05 (* 0.0909091 = 6.59867e-06 loss)
I0327 13:05:12.805733 21344 solver.cpp:406] Test net output #129: loss3/loss20 = 8.68589e-05 (* 0.0909091 = 7.89627e-06 loss)
I0327 13:05:12.805747 21344 solver.cpp:406] Test net output #130: loss3/loss21 = 7.6509e-05 (* 0.0909091 = 6.95536e-06 loss)
I0327 13:05:12.805760 21344 solver.cpp:406] Test net output #131: loss3/loss22 = 7.42517e-05 (* 0.0909091 = 6.75015e-06 loss)
I0327 13:05:12.805773 21344 solver.cpp:406] Test net output #132: total_accuracy = 0
I0327 13:05:12.805783 21344 solver.cpp:406] Test net output #133: total_confidence = 0.000429127
I0327 13:05:12.917351 21344 solver.cpp:229] Iteration 5000, loss = 3.09521
I0327 13:05:12.917402 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 13:05:12.917419 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 13:05:12.917433 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:05:12.917444 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:05:12.917456 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 13:05:12.917469 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 13:05:12.917481 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.75
I0327 13:05:12.917493 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 13:05:12.917505 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:05:12.917517 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:05:12.917528 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:05:12.917554 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:05:12.917569 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:05:12.917582 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:05:12.917593 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:05:12.917604 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:05:12.917616 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:05:12.917628 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:05:12.917639 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:05:12.917672 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:05:12.917686 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:05:12.917698 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:05:12.917714 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.75415 (* 0.0272727 = 0.0751131 loss)
I0327 13:05:12.917729 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.13999 (* 0.0272727 = 0.085636 loss)
I0327 13:05:12.917743 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.69952 (* 0.0272727 = 0.100896 loss)
I0327 13:05:12.917757 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.84494 (* 0.0272727 = 0.0775893 loss)
I0327 13:05:12.917771 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.54967 (* 0.0272727 = 0.0968091 loss)
I0327 13:05:12.917785 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.64299 (* 0.0272727 = 0.0720816 loss)
I0327 13:05:12.917799 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.3522 (* 0.0272727 = 0.0368781 loss)
I0327 13:05:12.917814 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0750345 (* 0.0272727 = 0.00204639 loss)
I0327 13:05:12.917827 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.028068 (* 0.0272727 = 0.00076549 loss)
I0327 13:05:12.917841 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00780955 (* 0.0272727 = 0.000212988 loss)
I0327 13:05:12.917855 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00128865 (* 0.0272727 = 3.51449e-05 loss)
I0327 13:05:12.917870 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.0100744 (* 0.0272727 = 0.000274755 loss)
I0327 13:05:12.917883 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.0059974 (* 0.0272727 = 0.000163566 loss)
I0327 13:05:12.917897 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00241921 (* 0.0272727 = 6.59783e-05 loss)
I0327 13:05:12.917912 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00338069 (* 0.0272727 = 9.22006e-05 loss)
I0327 13:05:12.917927 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.0035085 (* 0.0272727 = 9.56863e-05 loss)
I0327 13:05:12.917940 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00619252 (* 0.0272727 = 0.000168887 loss)
I0327 13:05:12.917954 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.0171592 (* 0.0272727 = 0.000467979 loss)
I0327 13:05:12.917968 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.0040919 (* 0.0272727 = 0.000111597 loss)
I0327 13:05:12.917982 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00132275 (* 0.0272727 = 3.60751e-05 loss)
I0327 13:05:12.917996 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00166654 (* 0.0272727 = 4.5451e-05 loss)
I0327 13:05:12.918010 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00255575 (* 0.0272727 = 6.97022e-05 loss)
I0327 13:05:12.918022 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 13:05:12.918035 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.25
I0327 13:05:12.918046 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:05:12.918057 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:05:12.918069 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 13:05:12.918081 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 13:05:12.918095 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.75
I0327 13:05:12.918107 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:05:12.918119 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:05:12.918131 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:05:12.918143 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:05:12.918164 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:05:12.918177 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:05:12.918189 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:05:12.918200 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:05:12.918210 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:05:12.918222 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:05:12.918233 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:05:12.918246 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:05:12.918256 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:05:12.918267 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:05:12.918279 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:05:12.918293 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.3262 (* 0.0272727 = 0.0634418 loss)
I0327 13:05:12.918306 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.07777 (* 0.0272727 = 0.0839392 loss)
I0327 13:05:12.918320 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.36746 (* 0.0272727 = 0.0918398 loss)
I0327 13:05:12.918334 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.4115 (* 0.0272727 = 0.093041 loss)
I0327 13:05:12.918349 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.96867 (* 0.0272727 = 0.0809638 loss)
I0327 13:05:12.918361 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.22081 (* 0.0272727 = 0.0878404 loss)
I0327 13:05:12.918375 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.44793 (* 0.0272727 = 0.039489 loss)
I0327 13:05:12.918390 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.080962 (* 0.0272727 = 0.00220806 loss)
I0327 13:05:12.918407 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0277874 (* 0.0272727 = 0.000757838 loss)
I0327 13:05:12.918426 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0255014 (* 0.0272727 = 0.000695492 loss)
I0327 13:05:12.918439 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.0130408 (* 0.0272727 = 0.000355657 loss)
I0327 13:05:12.918453 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00497221 (* 0.0272727 = 0.000135606 loss)
I0327 13:05:12.918467 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00931755 (* 0.0272727 = 0.000254115 loss)
I0327 13:05:12.918481 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.0151466 (* 0.0272727 = 0.000413088 loss)
I0327 13:05:12.918495 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00726855 (* 0.0272727 = 0.000198233 loss)
I0327 13:05:12.918509 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00418462 (* 0.0272727 = 0.000114126 loss)
I0327 13:05:12.918522 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00399719 (* 0.0272727 = 0.000109014 loss)
I0327 13:05:12.918536 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.0107493 (* 0.0272727 = 0.000293163 loss)
I0327 13:05:12.918550 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00724869 (* 0.0272727 = 0.000197691 loss)
I0327 13:05:12.918563 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00727746 (* 0.0272727 = 0.000198476 loss)
I0327 13:05:12.918577 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.0102211 (* 0.0272727 = 0.000278759 loss)
I0327 13:05:12.918591 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.0143728 (* 0.0272727 = 0.000391985 loss)
I0327 13:05:12.918603 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0
I0327 13:05:12.918615 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:05:12.918627 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:05:12.918637 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:05:12.918660 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 13:05:12.918673 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 13:05:12.918685 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:05:12.918697 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:05:12.918709 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:05:12.918720 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:05:12.918731 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:05:12.918742 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:05:12.918753 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:05:12.918764 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:05:12.918776 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:05:12.918787 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:05:12.918798 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:05:12.918809 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:05:12.918820 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:05:12.918831 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:05:12.918843 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:05:12.918853 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:05:12.918866 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.7397 (* 0.0909091 = 0.249064 loss)
I0327 13:05:12.918880 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.16405 (* 0.0909091 = 0.287641 loss)
I0327 13:05:12.918895 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.43568 (* 0.0909091 = 0.312335 loss)
I0327 13:05:12.918907 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.59804 (* 0.0909091 = 0.236186 loss)
I0327 13:05:12.918921 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.00717 (* 0.0909091 = 0.273379 loss)
I0327 13:05:12.918934 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.46736 (* 0.0909091 = 0.224306 loss)
I0327 13:05:12.918948 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.44588 (* 0.0909091 = 0.131444 loss)
I0327 13:05:12.918962 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0510157 (* 0.0909091 = 0.00463779 loss)
I0327 13:05:12.918977 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0148611 (* 0.0909091 = 0.001351 loss)
I0327 13:05:12.918992 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00472478 (* 0.0909091 = 0.000429526 loss)
I0327 13:05:12.919005 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000442419 (* 0.0909091 = 4.02199e-05 loss)
I0327 13:05:12.919019 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000359011 (* 0.0909091 = 3.26374e-05 loss)
I0327 13:05:12.919034 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000608432 (* 0.0909091 = 5.5312e-05 loss)
I0327 13:05:12.919046 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000415166 (* 0.0909091 = 3.77424e-05 loss)
I0327 13:05:12.919060 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000523225 (* 0.0909091 = 4.7566e-05 loss)
I0327 13:05:12.919075 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000742401 (* 0.0909091 = 6.7491e-05 loss)
I0327 13:05:12.919087 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.00032491 (* 0.0909091 = 2.95372e-05 loss)
I0327 13:05:12.919101 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000501668 (* 0.0909091 = 4.56062e-05 loss)
I0327 13:05:12.919116 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000722887 (* 0.0909091 = 6.5717e-05 loss)
I0327 13:05:12.919142 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.00132116 (* 0.0909091 = 0.000120106 loss)
I0327 13:05:12.919157 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000593058 (* 0.0909091 = 5.39144e-05 loss)
I0327 13:05:12.919172 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000479452 (* 0.0909091 = 4.35865e-05 loss)
I0327 13:05:12.919183 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:05:12.919195 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000758416
I0327 13:05:12.919209 21344 sgd_solver.cpp:106] Iteration 5000, lr = 0.01
I0327 13:07:00.742449 21344 solver.cpp:229] Iteration 5500, loss = 3.0881
I0327 13:07:00.742583 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 13:07:00.742602 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 13:07:00.742615 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 13:07:00.742627 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 13:07:00.742640 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.375
I0327 13:07:00.742651 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.5
I0327 13:07:00.742663 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:07:00.742676 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 13:07:00.742687 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.75
I0327 13:07:00.742699 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 0.875
I0327 13:07:00.742712 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:07:00.742723 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:07:00.742735 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:07:00.742748 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:07:00.742759 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:07:00.742771 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:07:00.742784 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:07:00.742794 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:07:00.742806 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:07:00.742817 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:07:00.742830 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:07:00.742841 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:07:00.742857 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.2261 (* 0.0272727 = 0.0879847 loss)
I0327 13:07:00.742872 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.27836 (* 0.0272727 = 0.0894098 loss)
I0327 13:07:00.742887 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.47468 (* 0.0272727 = 0.0947639 loss)
I0327 13:07:00.742902 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.23653 (* 0.0272727 = 0.088269 loss)
I0327 13:07:00.742915 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.95648 (* 0.0272727 = 0.0806313 loss)
I0327 13:07:00.742929 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.09267 (* 0.0272727 = 0.0570727 loss)
I0327 13:07:00.742944 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.86417 (* 0.0272727 = 0.050841 loss)
I0327 13:07:00.742957 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.09727 (* 0.0272727 = 0.0299255 loss)
I0327 13:07:00.742971 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 1.69952 (* 0.0272727 = 0.0463507 loss)
I0327 13:07:00.742985 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 1.13399 (* 0.0272727 = 0.030927 loss)
I0327 13:07:00.743003 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00125639 (* 0.0272727 = 3.42651e-05 loss)
I0327 13:07:00.743018 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00365768 (* 0.0272727 = 9.97549e-05 loss)
I0327 13:07:00.743032 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00201492 (* 0.0272727 = 5.49524e-05 loss)
I0327 13:07:00.743047 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00199506 (* 0.0272727 = 5.44108e-05 loss)
I0327 13:07:00.743062 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.0015919 (* 0.0272727 = 4.34155e-05 loss)
I0327 13:07:00.743077 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00127471 (* 0.0272727 = 3.47649e-05 loss)
I0327 13:07:00.743090 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00335833 (* 0.0272727 = 9.15909e-05 loss)
I0327 13:07:00.743122 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00132302 (* 0.0272727 = 3.60825e-05 loss)
I0327 13:07:00.743139 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00162319 (* 0.0272727 = 4.42687e-05 loss)
I0327 13:07:00.743152 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00209445 (* 0.0272727 = 5.71213e-05 loss)
I0327 13:07:00.743166 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00169647 (* 0.0272727 = 4.62675e-05 loss)
I0327 13:07:00.743180 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.0020288 (* 0.0272727 = 5.5331e-05 loss)
I0327 13:07:00.743193 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:07:00.743206 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.25
I0327 13:07:00.743218 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:07:00.743229 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 13:07:00.743242 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.375
I0327 13:07:00.743253 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.625
I0327 13:07:00.743265 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:07:00.743278 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.75
I0327 13:07:00.743289 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.75
I0327 13:07:00.743301 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 0.875
I0327 13:07:00.743312 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:07:00.743324 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:07:00.743335 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:07:00.743346 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:07:00.743358 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:07:00.743369 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:07:00.743381 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:07:00.743392 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:07:00.743403 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:07:00.743414 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:07:00.743427 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:07:00.743438 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:07:00.743451 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.70901 (* 0.0272727 = 0.101155 loss)
I0327 13:07:00.743465 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.01743 (* 0.0272727 = 0.0822935 loss)
I0327 13:07:00.743479 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.51745 (* 0.0272727 = 0.0959305 loss)
I0327 13:07:00.743494 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.16417 (* 0.0272727 = 0.0862956 loss)
I0327 13:07:00.743507 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.68792 (* 0.0272727 = 0.0733069 loss)
I0327 13:07:00.743521 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.67158 (* 0.0272727 = 0.0455886 loss)
I0327 13:07:00.743535 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.89017 (* 0.0272727 = 0.05155 loss)
I0327 13:07:00.743549 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 1.14182 (* 0.0272727 = 0.0311406 loss)
I0327 13:07:00.743563 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 1.03128 (* 0.0272727 = 0.0281258 loss)
I0327 13:07:00.743577 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.670143 (* 0.0272727 = 0.0182766 loss)
I0327 13:07:00.743595 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00563942 (* 0.0272727 = 0.000153802 loss)
I0327 13:07:00.743621 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00692125 (* 0.0272727 = 0.000188761 loss)
I0327 13:07:00.743636 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.0057187 (* 0.0272727 = 0.000155964 loss)
I0327 13:07:00.743651 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00419535 (* 0.0272727 = 0.000114419 loss)
I0327 13:07:00.743665 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00167207 (* 0.0272727 = 4.5602e-05 loss)
I0327 13:07:00.743680 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00428177 (* 0.0272727 = 0.000116776 loss)
I0327 13:07:00.743695 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00320703 (* 0.0272727 = 8.74644e-05 loss)
I0327 13:07:00.743708 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00455327 (* 0.0272727 = 0.00012418 loss)
I0327 13:07:00.743722 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00304843 (* 0.0272727 = 8.3139e-05 loss)
I0327 13:07:00.743736 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00745543 (* 0.0272727 = 0.00020333 loss)
I0327 13:07:00.743751 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00619711 (* 0.0272727 = 0.000169012 loss)
I0327 13:07:00.743764 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.0127351 (* 0.0272727 = 0.00034732 loss)
I0327 13:07:00.743777 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.25
I0327 13:07:00.743789 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:07:00.743801 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:07:00.743813 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:07:00.743824 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.5
I0327 13:07:00.743835 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.5
I0327 13:07:00.743847 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:07:00.743859 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.75
I0327 13:07:00.743870 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.75
I0327 13:07:00.743882 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 0.875
I0327 13:07:00.743893 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:07:00.743906 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:07:00.743916 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:07:00.743928 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:07:00.743939 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:07:00.743952 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:07:00.743963 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:07:00.743974 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:07:00.743985 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:07:00.743996 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:07:00.744009 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:07:00.744019 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:07:00.744034 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.14315 (* 0.0909091 = 0.285741 loss)
I0327 13:07:00.744050 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.27299 (* 0.0909091 = 0.297545 loss)
I0327 13:07:00.744065 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.43902 (* 0.0909091 = 0.312638 loss)
I0327 13:07:00.744079 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.37157 (* 0.0909091 = 0.306506 loss)
I0327 13:07:00.744093 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.36635 (* 0.0909091 = 0.215123 loss)
I0327 13:07:00.744107 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.85195 (* 0.0909091 = 0.168359 loss)
I0327 13:07:00.744132 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.56683 (* 0.0909091 = 0.142439 loss)
I0327 13:07:00.744146 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.03733 (* 0.0909091 = 0.0943031 loss)
I0327 13:07:00.744160 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 1.35519 (* 0.0909091 = 0.123199 loss)
I0327 13:07:00.744175 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 1.06628 (* 0.0909091 = 0.0969342 loss)
I0327 13:07:00.744189 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.00089503 (* 0.0909091 = 8.13664e-05 loss)
I0327 13:07:00.744204 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000751017 (* 0.0909091 = 6.82743e-05 loss)
I0327 13:07:00.744218 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000645006 (* 0.0909091 = 5.86369e-05 loss)
I0327 13:07:00.744232 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000893801 (* 0.0909091 = 8.12546e-05 loss)
I0327 13:07:00.744247 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000604852 (* 0.0909091 = 5.49865e-05 loss)
I0327 13:07:00.744261 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000938738 (* 0.0909091 = 8.53398e-05 loss)
I0327 13:07:00.744276 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000958319 (* 0.0909091 = 8.71199e-05 loss)
I0327 13:07:00.744290 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000690401 (* 0.0909091 = 6.27637e-05 loss)
I0327 13:07:00.744304 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000679419 (* 0.0909091 = 6.17653e-05 loss)
I0327 13:07:00.744318 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000824553 (* 0.0909091 = 7.49594e-05 loss)
I0327 13:07:00.744333 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000922682 (* 0.0909091 = 8.38802e-05 loss)
I0327 13:07:00.744348 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000563234 (* 0.0909091 = 5.1203e-05 loss)
I0327 13:07:00.744359 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:07:00.744370 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000112848
I0327 13:07:00.744384 21344 sgd_solver.cpp:106] Iteration 5500, lr = 0.01
I0327 13:08:48.473244 21344 solver.cpp:229] Iteration 6000, loss = 3.063
I0327 13:08:48.473388 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 13:08:48.473408 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:08:48.473422 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:08:48.473433 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:08:48.473445 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 13:08:48.473458 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 13:08:48.473469 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 13:08:48.473481 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:08:48.473498 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 13:08:48.473520 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 0.875
I0327 13:08:48.473532 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:08:48.473559 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:08:48.473572 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:08:48.473584 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:08:48.473597 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:08:48.473608 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:08:48.473619 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:08:48.473631 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:08:48.473642 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:08:48.473654 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:08:48.473666 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:08:48.473677 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:08:48.473693 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 4.01938 (* 0.0272727 = 0.109619 loss)
I0327 13:08:48.473708 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.72006 (* 0.0272727 = 0.101456 loss)
I0327 13:08:48.473722 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.57936 (* 0.0272727 = 0.0976189 loss)
I0327 13:08:48.473737 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.47027 (* 0.0272727 = 0.0946437 loss)
I0327 13:08:48.473752 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.96256 (* 0.0272727 = 0.0807971 loss)
I0327 13:08:48.473765 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.52306 (* 0.0272727 = 0.0688107 loss)
I0327 13:08:48.473780 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.37519 (* 0.0272727 = 0.0647778 loss)
I0327 13:08:48.473794 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.430209 (* 0.0272727 = 0.011733 loss)
I0327 13:08:48.473809 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.502491 (* 0.0272727 = 0.0137043 loss)
I0327 13:08:48.473822 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.642286 (* 0.0272727 = 0.0175169 loss)
I0327 13:08:48.473837 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00203833 (* 0.0272727 = 5.55909e-05 loss)
I0327 13:08:48.473852 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000903128 (* 0.0272727 = 2.46308e-05 loss)
I0327 13:08:48.473866 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.0009226 (* 0.0272727 = 2.51618e-05 loss)
I0327 13:08:48.473881 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.003203 (* 0.0272727 = 8.73545e-05 loss)
I0327 13:08:48.473896 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000530272 (* 0.0272727 = 1.4462e-05 loss)
I0327 13:08:48.473911 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00225299 (* 0.0272727 = 6.14452e-05 loss)
I0327 13:08:48.473924 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000632389 (* 0.0272727 = 1.7247e-05 loss)
I0327 13:08:48.473958 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000369458 (* 0.0272727 = 1.00761e-05 loss)
I0327 13:08:48.473973 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00153433 (* 0.0272727 = 4.18454e-05 loss)
I0327 13:08:48.473987 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00124537 (* 0.0272727 = 3.39647e-05 loss)
I0327 13:08:48.474005 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000581473 (* 0.0272727 = 1.58584e-05 loss)
I0327 13:08:48.474020 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000946274 (* 0.0272727 = 2.58075e-05 loss)
I0327 13:08:48.474032 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:08:48.474045 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 13:08:48.474056 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:08:48.474068 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 13:08:48.474081 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:08:48.474092 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 13:08:48.474103 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 13:08:48.474115 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:08:48.474128 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 13:08:48.474139 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 0.875
I0327 13:08:48.474151 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:08:48.474164 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:08:48.474174 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:08:48.474186 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:08:48.474197 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:08:48.474210 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:08:48.474220 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:08:48.474232 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:08:48.474243 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:08:48.474254 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:08:48.474267 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:08:48.474278 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:08:48.474292 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.68019 (* 0.0272727 = 0.100369 loss)
I0327 13:08:48.474305 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.15157 (* 0.0272727 = 0.0859519 loss)
I0327 13:08:48.474320 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.64301 (* 0.0272727 = 0.0993548 loss)
I0327 13:08:48.474334 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.08569 (* 0.0272727 = 0.0841552 loss)
I0327 13:08:48.474349 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.66611 (* 0.0272727 = 0.072712 loss)
I0327 13:08:48.474362 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.04469 (* 0.0272727 = 0.083037 loss)
I0327 13:08:48.474376 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 2.65807 (* 0.0272727 = 0.0724929 loss)
I0327 13:08:48.474390 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.762923 (* 0.0272727 = 0.020807 loss)
I0327 13:08:48.474407 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.685946 (* 0.0272727 = 0.0187076 loss)
I0327 13:08:48.474422 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.800649 (* 0.0272727 = 0.0218359 loss)
I0327 13:08:48.474437 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000137806 (* 0.0272727 = 3.75836e-06 loss)
I0327 13:08:48.474463 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000386497 (* 0.0272727 = 1.05408e-05 loss)
I0327 13:08:48.474478 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00018425 (* 0.0272727 = 5.025e-06 loss)
I0327 13:08:48.474493 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000148259 (* 0.0272727 = 4.04342e-06 loss)
I0327 13:08:48.474509 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000253345 (* 0.0272727 = 6.90941e-06 loss)
I0327 13:08:48.474525 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00032074 (* 0.0272727 = 8.74746e-06 loss)
I0327 13:08:48.474539 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000276342 (* 0.0272727 = 7.53659e-06 loss)
I0327 13:08:48.474553 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000369035 (* 0.0272727 = 1.00646e-05 loss)
I0327 13:08:48.474568 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000318075 (* 0.0272727 = 8.67477e-06 loss)
I0327 13:08:48.474581 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000303884 (* 0.0272727 = 8.28775e-06 loss)
I0327 13:08:48.474596 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000304629 (* 0.0272727 = 8.30805e-06 loss)
I0327 13:08:48.474611 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000365797 (* 0.0272727 = 9.97627e-06 loss)
I0327 13:08:48.474622 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.25
I0327 13:08:48.474635 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:08:48.474647 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:08:48.474658 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.25
I0327 13:08:48.474670 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 13:08:48.474683 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:08:48.474694 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.5
I0327 13:08:48.474705 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:08:48.474717 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 13:08:48.474728 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 0.875
I0327 13:08:48.474740 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:08:48.474752 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:08:48.474763 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:08:48.474776 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:08:48.474786 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:08:48.474797 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:08:48.474808 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:08:48.474820 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:08:48.474831 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:08:48.474843 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:08:48.474853 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:08:48.474865 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:08:48.474879 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.3646 (* 0.0909091 = 0.305873 loss)
I0327 13:08:48.474892 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.21971 (* 0.0909091 = 0.292701 loss)
I0327 13:08:48.474906 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.35423 (* 0.0909091 = 0.30493 loss)
I0327 13:08:48.474920 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.77194 (* 0.0909091 = 0.251994 loss)
I0327 13:08:48.474933 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.47002 (* 0.0909091 = 0.224547 loss)
I0327 13:08:48.474957 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.51409 (* 0.0909091 = 0.228554 loss)
I0327 13:08:48.474973 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 2.59355 (* 0.0909091 = 0.235777 loss)
I0327 13:08:48.474987 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.567758 (* 0.0909091 = 0.0516144 loss)
I0327 13:08:48.475002 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.551475 (* 0.0909091 = 0.0501341 loss)
I0327 13:08:48.475015 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.605856 (* 0.0909091 = 0.0550778 loss)
I0327 13:08:48.475029 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.00030316 (* 0.0909091 = 2.756e-05 loss)
I0327 13:08:48.475046 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000289126 (* 0.0909091 = 2.62842e-05 loss)
I0327 13:08:48.475061 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000342874 (* 0.0909091 = 3.11703e-05 loss)
I0327 13:08:48.475076 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000278778 (* 0.0909091 = 2.53435e-05 loss)
I0327 13:08:48.475090 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000235474 (* 0.0909091 = 2.14068e-05 loss)
I0327 13:08:48.475105 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.00023434 (* 0.0909091 = 2.13036e-05 loss)
I0327 13:08:48.475118 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000289694 (* 0.0909091 = 2.63358e-05 loss)
I0327 13:08:48.475132 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000218793 (* 0.0909091 = 1.98903e-05 loss)
I0327 13:08:48.475147 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000339621 (* 0.0909091 = 3.08747e-05 loss)
I0327 13:08:48.475162 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000169042 (* 0.0909091 = 1.53674e-05 loss)
I0327 13:08:48.475175 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000230261 (* 0.0909091 = 2.09328e-05 loss)
I0327 13:08:48.475189 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000369406 (* 0.0909091 = 3.35823e-05 loss)
I0327 13:08:48.475201 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:08:48.475214 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000143455
I0327 13:08:48.475225 21344 sgd_solver.cpp:106] Iteration 6000, lr = 0.01
I0327 13:10:36.168670 21344 solver.cpp:229] Iteration 6500, loss = 3.04813
I0327 13:10:36.168792 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 13:10:36.168810 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:10:36.168823 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:10:36.168835 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:10:36.168848 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 13:10:36.168860 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 13:10:36.168872 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:10:36.168884 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 13:10:36.168897 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.75
I0327 13:10:36.168910 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 0.75
I0327 13:10:36.168921 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:10:36.168933 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:10:36.168944 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:10:36.168956 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:10:36.168967 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:10:36.168979 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:10:36.168993 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:10:36.169006 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:10:36.169018 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:10:36.169029 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:10:36.169041 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:10:36.169054 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:10:36.169071 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.6067 (* 0.0272727 = 0.0983644 loss)
I0327 13:10:36.169086 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.38563 (* 0.0272727 = 0.0923353 loss)
I0327 13:10:36.169100 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.88722 (* 0.0272727 = 0.106015 loss)
I0327 13:10:36.169114 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.09187 (* 0.0272727 = 0.0843238 loss)
I0327 13:10:36.169127 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.81447 (* 0.0272727 = 0.104031 loss)
I0327 13:10:36.169142 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.66312 (* 0.0272727 = 0.0726306 loss)
I0327 13:10:36.169155 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.05422 (* 0.0272727 = 0.0560242 loss)
I0327 13:10:36.169168 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.53578 (* 0.0272727 = 0.0418849 loss)
I0327 13:10:36.169183 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 1.70743 (* 0.0272727 = 0.0465662 loss)
I0327 13:10:36.169196 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 1.85986 (* 0.0272727 = 0.0507235 loss)
I0327 13:10:36.169211 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00346304 (* 0.0272727 = 9.44467e-05 loss)
I0327 13:10:36.169226 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00341401 (* 0.0272727 = 9.31095e-05 loss)
I0327 13:10:36.169240 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00225299 (* 0.0272727 = 6.14451e-05 loss)
I0327 13:10:36.169255 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00128854 (* 0.0272727 = 3.51421e-05 loss)
I0327 13:10:36.169270 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00222043 (* 0.0272727 = 6.05572e-05 loss)
I0327 13:10:36.169283 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00402971 (* 0.0272727 = 0.000109901 loss)
I0327 13:10:36.169297 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00328077 (* 0.0272727 = 8.94754e-05 loss)
I0327 13:10:36.169328 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00136917 (* 0.0272727 = 3.7341e-05 loss)
I0327 13:10:36.169344 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.0028367 (* 0.0272727 = 7.73645e-05 loss)
I0327 13:10:36.169359 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00104865 (* 0.0272727 = 2.85995e-05 loss)
I0327 13:10:36.169373 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00410549 (* 0.0272727 = 0.000111968 loss)
I0327 13:10:36.169387 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00233586 (* 0.0272727 = 6.37054e-05 loss)
I0327 13:10:36.169399 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:10:36.169412 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 13:10:36.169425 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:10:36.169435 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 13:10:36.169447 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 13:10:36.169459 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 13:10:36.169471 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:10:36.169483 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.75
I0327 13:10:36.169495 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.75
I0327 13:10:36.169507 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 0.75
I0327 13:10:36.169518 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:10:36.169530 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:10:36.169554 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:10:36.169569 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:10:36.169581 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:10:36.169592 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:10:36.169605 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:10:36.169616 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:10:36.169630 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:10:36.169637 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:10:36.169649 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:10:36.169661 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:10:36.169675 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.0503 (* 0.0272727 = 0.0831899 loss)
I0327 13:10:36.169689 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.37306 (* 0.0272727 = 0.0919925 loss)
I0327 13:10:36.169703 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.85721 (* 0.0272727 = 0.105197 loss)
I0327 13:10:36.169718 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.68829 (* 0.0272727 = 0.10059 loss)
I0327 13:10:36.169731 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 4.04024 (* 0.0272727 = 0.110188 loss)
I0327 13:10:36.169744 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.89582 (* 0.0272727 = 0.078977 loss)
I0327 13:10:36.169759 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.78107 (* 0.0272727 = 0.0485747 loss)
I0327 13:10:36.169772 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 1.69198 (* 0.0272727 = 0.0461448 loss)
I0327 13:10:36.169786 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 2.06529 (* 0.0272727 = 0.056326 loss)
I0327 13:10:36.169800 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 2.31628 (* 0.0272727 = 0.0631712 loss)
I0327 13:10:36.169817 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00259832 (* 0.0272727 = 7.08632e-05 loss)
I0327 13:10:36.169832 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00420847 (* 0.0272727 = 0.000114777 loss)
I0327 13:10:36.169859 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00313324 (* 0.0272727 = 8.5452e-05 loss)
I0327 13:10:36.169875 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.0022289 (* 0.0272727 = 6.07883e-05 loss)
I0327 13:10:36.169889 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00220098 (* 0.0272727 = 6.00268e-05 loss)
I0327 13:10:36.169903 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00325064 (* 0.0272727 = 8.86537e-05 loss)
I0327 13:10:36.169917 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00594015 (* 0.0272727 = 0.000162004 loss)
I0327 13:10:36.169931 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00268149 (* 0.0272727 = 7.31314e-05 loss)
I0327 13:10:36.169945 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00141249 (* 0.0272727 = 3.85224e-05 loss)
I0327 13:10:36.169960 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00194988 (* 0.0272727 = 5.31787e-05 loss)
I0327 13:10:36.169975 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.0105378 (* 0.0272727 = 0.000287394 loss)
I0327 13:10:36.169988 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00326486 (* 0.0272727 = 8.90416e-05 loss)
I0327 13:10:36.170001 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.125
I0327 13:10:36.170013 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:10:36.170025 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:10:36.170037 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 13:10:36.170052 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0
I0327 13:10:36.170063 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:10:36.170075 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:10:36.170086 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.75
I0327 13:10:36.170099 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.75
I0327 13:10:36.170110 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 0.75
I0327 13:10:36.170121 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:10:36.170132 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:10:36.170145 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:10:36.170156 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:10:36.170167 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:10:36.170178 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:10:36.170191 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:10:36.170202 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:10:36.170213 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:10:36.170224 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:10:36.170236 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:10:36.170248 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:10:36.170261 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.28137 (* 0.0909091 = 0.298306 loss)
I0327 13:10:36.170275 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.15632 (* 0.0909091 = 0.286938 loss)
I0327 13:10:36.170289 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.78735 (* 0.0909091 = 0.344304 loss)
I0327 13:10:36.170303 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.2675 (* 0.0909091 = 0.297045 loss)
I0327 13:10:36.170316 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 4.0553 (* 0.0909091 = 0.368664 loss)
I0327 13:10:36.170331 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.46554 (* 0.0909091 = 0.22414 loss)
I0327 13:10:36.170354 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.65923 (* 0.0909091 = 0.150839 loss)
I0327 13:10:36.170369 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.43484 (* 0.0909091 = 0.13044 loss)
I0327 13:10:36.170384 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 1.71599 (* 0.0909091 = 0.155999 loss)
I0327 13:10:36.170397 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 1.79845 (* 0.0909091 = 0.163495 loss)
I0327 13:10:36.170413 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.00210287 (* 0.0909091 = 0.00019117 loss)
I0327 13:10:36.170426 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.00320716 (* 0.0909091 = 0.00029156 loss)
I0327 13:10:36.170440 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.00155128 (* 0.0909091 = 0.000141026 loss)
I0327 13:10:36.170454 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.00157423 (* 0.0909091 = 0.000143112 loss)
I0327 13:10:36.170469 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.00304313 (* 0.0909091 = 0.000276648 loss)
I0327 13:10:36.170482 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.00211179 (* 0.0909091 = 0.000191981 loss)
I0327 13:10:36.170496 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.00190656 (* 0.0909091 = 0.000173324 loss)
I0327 13:10:36.170511 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.00243389 (* 0.0909091 = 0.000221263 loss)
I0327 13:10:36.170524 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.00262903 (* 0.0909091 = 0.000239002 loss)
I0327 13:10:36.170539 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.00209001 (* 0.0909091 = 0.000190001 loss)
I0327 13:10:36.170553 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.00151194 (* 0.0909091 = 0.000137449 loss)
I0327 13:10:36.170567 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.0018381 (* 0.0909091 = 0.0001671 loss)
I0327 13:10:36.170579 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:10:36.170591 21344 solver.cpp:245] Train net output #133: total_confidence = 5.28329e-05
I0327 13:10:36.170604 21344 sgd_solver.cpp:106] Iteration 6500, lr = 0.01
I0327 13:12:24.016520 21344 solver.cpp:229] Iteration 7000, loss = 3.00803
I0327 13:12:24.016669 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 13:12:24.016688 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:12:24.016701 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:12:24.016712 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 13:12:24.016724 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 13:12:24.016736 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0
I0327 13:12:24.016747 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:12:24.016760 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:12:24.016772 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:12:24.016784 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:12:24.016795 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:12:24.016808 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:12:24.016819 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:12:24.016830 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:12:24.016842 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:12:24.016855 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:12:24.016866 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:12:24.016886 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:12:24.016899 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:12:24.016921 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:12:24.016935 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:12:24.016947 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:12:24.016964 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.45788 (* 0.0272727 = 0.0943059 loss)
I0327 13:12:24.016978 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 4.45406 (* 0.0272727 = 0.121474 loss)
I0327 13:12:24.016995 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.70666 (* 0.0272727 = 0.101091 loss)
I0327 13:12:24.017010 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.85476 (* 0.0272727 = 0.10513 loss)
I0327 13:12:24.017024 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.82772 (* 0.0272727 = 0.104392 loss)
I0327 13:12:24.017037 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 4.15642 (* 0.0272727 = 0.113357 loss)
I0327 13:12:24.017051 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.30851 (* 0.0272727 = 0.0629593 loss)
I0327 13:12:24.017066 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.939984 (* 0.0272727 = 0.0256359 loss)
I0327 13:12:24.017079 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0723675 (* 0.0272727 = 0.00197366 loss)
I0327 13:12:24.017094 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.02945 (* 0.0272727 = 0.000803181 loss)
I0327 13:12:24.017109 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00325826 (* 0.0272727 = 8.88616e-05 loss)
I0327 13:12:24.017123 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00529479 (* 0.0272727 = 0.000144403 loss)
I0327 13:12:24.017138 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00623187 (* 0.0272727 = 0.00016996 loss)
I0327 13:12:24.017151 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00313057 (* 0.0272727 = 8.53793e-05 loss)
I0327 13:12:24.017173 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00331047 (* 0.0272727 = 9.02856e-05 loss)
I0327 13:12:24.017186 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00411925 (* 0.0272727 = 0.000112343 loss)
I0327 13:12:24.017200 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00546875 (* 0.0272727 = 0.000149148 loss)
I0327 13:12:24.017227 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00735585 (* 0.0272727 = 0.000200614 loss)
I0327 13:12:24.017243 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.0036461 (* 0.0272727 = 9.94391e-05 loss)
I0327 13:12:24.017258 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00394529 (* 0.0272727 = 0.000107599 loss)
I0327 13:12:24.017276 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00607605 (* 0.0272727 = 0.00016571 loss)
I0327 13:12:24.017290 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00748746 (* 0.0272727 = 0.000204203 loss)
I0327 13:12:24.017303 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0
I0327 13:12:24.017315 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:12:24.017326 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:12:24.017338 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:12:24.017349 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 13:12:24.017361 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0
I0327 13:12:24.017372 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:12:24.017385 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:12:24.017396 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:12:24.017408 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:12:24.017419 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:12:24.017431 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:12:24.017442 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:12:24.017453 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:12:24.017465 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:12:24.017477 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:12:24.017488 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:12:24.017499 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:12:24.017510 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:12:24.017523 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:12:24.017534 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:12:24.017559 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:12:24.017575 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.76808 (* 0.0272727 = 0.102766 loss)
I0327 13:12:24.017588 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 4.02197 (* 0.0272727 = 0.10969 loss)
I0327 13:12:24.017602 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.66382 (* 0.0272727 = 0.0999225 loss)
I0327 13:12:24.017616 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.71673 (* 0.0272727 = 0.101365 loss)
I0327 13:12:24.017629 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 4.31764 (* 0.0272727 = 0.117754 loss)
I0327 13:12:24.017643 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 4.47896 (* 0.0272727 = 0.122153 loss)
I0327 13:12:24.017657 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 2.15737 (* 0.0272727 = 0.0588373 loss)
I0327 13:12:24.017673 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.782407 (* 0.0272727 = 0.0213384 loss)
I0327 13:12:24.017689 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0750128 (* 0.0272727 = 0.0020458 loss)
I0327 13:12:24.017704 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.013571 (* 0.0272727 = 0.00037012 loss)
I0327 13:12:24.017719 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00157051 (* 0.0272727 = 4.2832e-05 loss)
I0327 13:12:24.017734 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00198264 (* 0.0272727 = 5.4072e-05 loss)
I0327 13:12:24.017760 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000756469 (* 0.0272727 = 2.0631e-05 loss)
I0327 13:12:24.017774 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000706914 (* 0.0272727 = 1.92795e-05 loss)
I0327 13:12:24.017789 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00185238 (* 0.0272727 = 5.05195e-05 loss)
I0327 13:12:24.017803 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000589573 (* 0.0272727 = 1.60793e-05 loss)
I0327 13:12:24.017817 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000855751 (* 0.0272727 = 2.33387e-05 loss)
I0327 13:12:24.017832 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00262217 (* 0.0272727 = 7.15137e-05 loss)
I0327 13:12:24.017845 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000548968 (* 0.0272727 = 1.49718e-05 loss)
I0327 13:12:24.017859 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000701692 (* 0.0272727 = 1.9137e-05 loss)
I0327 13:12:24.017874 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00136509 (* 0.0272727 = 3.72297e-05 loss)
I0327 13:12:24.017887 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000599953 (* 0.0272727 = 1.63623e-05 loss)
I0327 13:12:24.017899 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.125
I0327 13:12:24.017911 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 13:12:24.017923 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:12:24.017935 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 13:12:24.017946 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0
I0327 13:12:24.017957 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0
I0327 13:12:24.017968 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:12:24.017981 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:12:24.017992 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:12:24.018003 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:12:24.018014 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:12:24.018026 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:12:24.018038 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:12:24.018052 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:12:24.018064 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:12:24.018075 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:12:24.018087 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:12:24.018100 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:12:24.018107 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:12:24.018115 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:12:24.018129 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:12:24.018141 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:12:24.018154 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.35183 (* 0.0909091 = 0.304712 loss)
I0327 13:12:24.018173 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 4.03868 (* 0.0909091 = 0.367153 loss)
I0327 13:12:24.018188 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.3733 (* 0.0909091 = 0.306664 loss)
I0327 13:12:24.018206 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.77781 (* 0.0909091 = 0.343437 loss)
I0327 13:12:24.018221 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.80868 (* 0.0909091 = 0.346244 loss)
I0327 13:12:24.018234 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 3.99559 (* 0.0909091 = 0.363236 loss)
I0327 13:12:24.018260 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 2.09251 (* 0.0909091 = 0.190228 loss)
I0327 13:12:24.018275 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.50075 (* 0.0909091 = 0.0455227 loss)
I0327 13:12:24.018288 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.184272 (* 0.0909091 = 0.016752 loss)
I0327 13:12:24.018302 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.075354 (* 0.0909091 = 0.00685037 loss)
I0327 13:12:24.018316 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.00215649 (* 0.0909091 = 0.000196045 loss)
I0327 13:12:24.018331 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.00159895 (* 0.0909091 = 0.000145359 loss)
I0327 13:12:24.018344 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.00175194 (* 0.0909091 = 0.000159267 loss)
I0327 13:12:24.018358 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.00224756 (* 0.0909091 = 0.000204324 loss)
I0327 13:12:24.018373 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.00223686 (* 0.0909091 = 0.000203351 loss)
I0327 13:12:24.018386 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.00194066 (* 0.0909091 = 0.000176424 loss)
I0327 13:12:24.018400 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.00223643 (* 0.0909091 = 0.000203312 loss)
I0327 13:12:24.018414 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.00163592 (* 0.0909091 = 0.00014872 loss)
I0327 13:12:24.018427 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.00196637 (* 0.0909091 = 0.000178761 loss)
I0327 13:12:24.018441 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.00190727 (* 0.0909091 = 0.000173388 loss)
I0327 13:12:24.018455 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.00228345 (* 0.0909091 = 0.000207586 loss)
I0327 13:12:24.018470 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.00196137 (* 0.0909091 = 0.000178307 loss)
I0327 13:12:24.018481 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:12:24.018492 21344 solver.cpp:245] Train net output #133: total_confidence = 7.88918e-06
I0327 13:12:24.018504 21344 sgd_solver.cpp:106] Iteration 7000, lr = 0.01
I0327 13:14:11.798400 21344 solver.cpp:229] Iteration 7500, loss = 3.00917
I0327 13:14:11.798564 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 13:14:11.798586 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:14:11.798599 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:14:11.798611 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:14:11.798624 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 13:14:11.798636 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 13:14:11.798650 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 13:14:11.798661 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 13:14:11.798673 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:14:11.798688 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:14:11.798702 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:14:11.798713 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:14:11.798725 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:14:11.798738 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:14:11.798749 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:14:11.798761 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:14:11.798774 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:14:11.798785 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:14:11.798797 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:14:11.798810 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:14:11.798821 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:14:11.798832 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:14:11.798849 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.36983 (* 0.0272727 = 0.0919045 loss)
I0327 13:14:11.798864 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.05949 (* 0.0272727 = 0.0834407 loss)
I0327 13:14:11.798879 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 4.17254 (* 0.0272727 = 0.113797 loss)
I0327 13:14:11.798893 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.82812 (* 0.0272727 = 0.0771306 loss)
I0327 13:14:11.798907 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.27627 (* 0.0272727 = 0.0893529 loss)
I0327 13:14:11.798921 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.9864 (* 0.0272727 = 0.0814473 loss)
I0327 13:14:11.798935 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.45322 (* 0.0272727 = 0.0669059 loss)
I0327 13:14:11.798950 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.101707 (* 0.0272727 = 0.00277383 loss)
I0327 13:14:11.798965 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0124621 (* 0.0272727 = 0.000339875 loss)
I0327 13:14:11.798979 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00702104 (* 0.0272727 = 0.000191483 loss)
I0327 13:14:11.798995 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00035588 (* 0.0272727 = 9.70581e-06 loss)
I0327 13:14:11.799008 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000502227 (* 0.0272727 = 1.36971e-05 loss)
I0327 13:14:11.799023 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000596347 (* 0.0272727 = 1.6264e-05 loss)
I0327 13:14:11.799037 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00039775 (* 0.0272727 = 1.08477e-05 loss)
I0327 13:14:11.799052 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00033497 (* 0.0272727 = 9.13555e-06 loss)
I0327 13:14:11.799067 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000628751 (* 0.0272727 = 1.71477e-05 loss)
I0327 13:14:11.799080 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000466541 (* 0.0272727 = 1.27239e-05 loss)
I0327 13:14:11.799115 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000421859 (* 0.0272727 = 1.15052e-05 loss)
I0327 13:14:11.799134 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000841586 (* 0.0272727 = 2.29523e-05 loss)
I0327 13:14:11.799147 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000540103 (* 0.0272727 = 1.47301e-05 loss)
I0327 13:14:11.799161 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00067345 (* 0.0272727 = 1.83668e-05 loss)
I0327 13:14:11.799175 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00061269 (* 0.0272727 = 1.67097e-05 loss)
I0327 13:14:11.799188 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:14:11.799201 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.25
I0327 13:14:11.799212 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:14:11.799223 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:14:11.799234 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 13:14:11.799247 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 13:14:11.799258 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 13:14:11.799270 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:14:11.799283 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:14:11.799293 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:14:11.799305 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:14:11.799316 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:14:11.799329 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:14:11.799340 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:14:11.799350 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:14:11.799362 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:14:11.799373 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:14:11.799384 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:14:11.799396 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:14:11.799407 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:14:11.799418 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:14:11.799430 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:14:11.799443 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.91576 (* 0.0272727 = 0.106793 loss)
I0327 13:14:11.799458 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.00101 (* 0.0272727 = 0.0818457 loss)
I0327 13:14:11.799473 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.92244 (* 0.0272727 = 0.106976 loss)
I0327 13:14:11.799485 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.14628 (* 0.0272727 = 0.0858077 loss)
I0327 13:14:11.799499 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.37862 (* 0.0272727 = 0.0921442 loss)
I0327 13:14:11.799513 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.41995 (* 0.0272727 = 0.0659987 loss)
I0327 13:14:11.799527 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 2.39629 (* 0.0272727 = 0.0653535 loss)
I0327 13:14:11.799542 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.087695 (* 0.0272727 = 0.00239168 loss)
I0327 13:14:11.799556 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0178261 (* 0.0272727 = 0.000486168 loss)
I0327 13:14:11.799571 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0058005 (* 0.0272727 = 0.000158196 loss)
I0327 13:14:11.799584 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000681343 (* 0.0272727 = 1.85821e-05 loss)
I0327 13:14:11.799610 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00306652 (* 0.0272727 = 8.36324e-05 loss)
I0327 13:14:11.799625 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00152871 (* 0.0272727 = 4.16921e-05 loss)
I0327 13:14:11.799639 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000828348 (* 0.0272727 = 2.25913e-05 loss)
I0327 13:14:11.799654 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00156974 (* 0.0272727 = 4.2811e-05 loss)
I0327 13:14:11.799669 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00210575 (* 0.0272727 = 5.74294e-05 loss)
I0327 13:14:11.799682 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000865708 (* 0.0272727 = 2.36102e-05 loss)
I0327 13:14:11.799696 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000934833 (* 0.0272727 = 2.54954e-05 loss)
I0327 13:14:11.799710 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000582904 (* 0.0272727 = 1.58974e-05 loss)
I0327 13:14:11.799724 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00448833 (* 0.0272727 = 0.000122409 loss)
I0327 13:14:11.799742 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000983311 (* 0.0272727 = 2.68176e-05 loss)
I0327 13:14:11.799757 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00140544 (* 0.0272727 = 3.83302e-05 loss)
I0327 13:14:11.799769 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0
I0327 13:14:11.799782 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:14:11.799793 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 13:14:11.799805 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 13:14:11.799816 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 13:14:11.799829 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 13:14:11.799839 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.5
I0327 13:14:11.799851 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:14:11.799862 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:14:11.799873 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:14:11.799885 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:14:11.799896 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:14:11.799908 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:14:11.799919 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:14:11.799931 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:14:11.799942 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:14:11.799953 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:14:11.799965 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:14:11.799976 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:14:11.799988 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:14:11.799999 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:14:11.800010 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:14:11.800024 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.6974 (* 0.0909091 = 0.336127 loss)
I0327 13:14:11.800037 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.25519 (* 0.0909091 = 0.295926 loss)
I0327 13:14:11.800051 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.65048 (* 0.0909091 = 0.331862 loss)
I0327 13:14:11.800065 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.90651 (* 0.0909091 = 0.264228 loss)
I0327 13:14:11.800079 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.40272 (* 0.0909091 = 0.309339 loss)
I0327 13:14:11.800092 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.90681 (* 0.0909091 = 0.264256 loss)
I0327 13:14:11.800117 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 2.48192 (* 0.0909091 = 0.225629 loss)
I0327 13:14:11.800133 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0355646 (* 0.0909091 = 0.00323315 loss)
I0327 13:14:11.800149 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00943913 (* 0.0909091 = 0.000858102 loss)
I0327 13:14:11.800165 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00447221 (* 0.0909091 = 0.000406565 loss)
I0327 13:14:11.800179 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000264679 (* 0.0909091 = 2.40617e-05 loss)
I0327 13:14:11.800194 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000382367 (* 0.0909091 = 3.47606e-05 loss)
I0327 13:14:11.800209 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000368802 (* 0.0909091 = 3.35275e-05 loss)
I0327 13:14:11.800222 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000432904 (* 0.0909091 = 3.93549e-05 loss)
I0327 13:14:11.800236 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000280475 (* 0.0909091 = 2.54977e-05 loss)
I0327 13:14:11.800251 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000300025 (* 0.0909091 = 2.7275e-05 loss)
I0327 13:14:11.800264 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000320006 (* 0.0909091 = 2.90914e-05 loss)
I0327 13:14:11.800278 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000301852 (* 0.0909091 = 2.74411e-05 loss)
I0327 13:14:11.800292 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000306895 (* 0.0909091 = 2.78995e-05 loss)
I0327 13:14:11.800303 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000286319 (* 0.0909091 = 2.6029e-05 loss)
I0327 13:14:11.800312 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.00026479 (* 0.0909091 = 2.40718e-05 loss)
I0327 13:14:11.800326 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000298358 (* 0.0909091 = 2.71234e-05 loss)
I0327 13:14:11.800339 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:14:11.800350 21344 solver.cpp:245] Train net output #133: total_confidence = 1.2738e-05
I0327 13:14:11.800364 21344 sgd_solver.cpp:106] Iteration 7500, lr = 0.01
I0327 13:15:59.501698 21344 solver.cpp:229] Iteration 8000, loss = 2.9626
I0327 13:15:59.501824 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 13:15:59.501844 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:15:59.501857 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:15:59.501868 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 13:15:59.501880 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.5
I0327 13:15:59.501893 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 13:15:59.501905 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:15:59.501917 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.625
I0327 13:15:59.501929 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:15:59.501941 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:15:59.501952 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:15:59.501965 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:15:59.501976 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:15:59.501987 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:15:59.502002 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:15:59.502013 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:15:59.502025 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:15:59.502037 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:15:59.502048 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:15:59.502059 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:15:59.502071 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:15:59.502082 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:15:59.502099 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.2 (* 0.0272727 = 0.0872727 loss)
I0327 13:15:59.502115 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.40688 (* 0.0272727 = 0.092915 loss)
I0327 13:15:59.502128 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.3142 (* 0.0272727 = 0.0903872 loss)
I0327 13:15:59.502142 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.98707 (* 0.0272727 = 0.0814655 loss)
I0327 13:15:59.502156 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.5296 (* 0.0272727 = 0.068989 loss)
I0327 13:15:59.502171 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.55308 (* 0.0272727 = 0.0696295 loss)
I0327 13:15:59.502184 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.0702 (* 0.0272727 = 0.0291872 loss)
I0327 13:15:59.502198 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.31716 (* 0.0272727 = 0.0359226 loss)
I0327 13:15:59.502213 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0580067 (* 0.0272727 = 0.001582 loss)
I0327 13:15:59.502226 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.010492 (* 0.0272727 = 0.000286144 loss)
I0327 13:15:59.502241 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00060395 (* 0.0272727 = 1.64714e-05 loss)
I0327 13:15:59.502255 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000335345 (* 0.0272727 = 9.14577e-06 loss)
I0327 13:15:59.502269 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00104325 (* 0.0272727 = 2.84522e-05 loss)
I0327 13:15:59.502284 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00041991 (* 0.0272727 = 1.14521e-05 loss)
I0327 13:15:59.502298 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000163857 (* 0.0272727 = 4.46882e-06 loss)
I0327 13:15:59.502312 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000839414 (* 0.0272727 = 2.28931e-05 loss)
I0327 13:15:59.502326 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00103025 (* 0.0272727 = 2.80977e-05 loss)
I0327 13:15:59.502357 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00055113 (* 0.0272727 = 1.50308e-05 loss)
I0327 13:15:59.502373 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000508672 (* 0.0272727 = 1.38729e-05 loss)
I0327 13:15:59.502388 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00019016 (* 0.0272727 = 5.18619e-06 loss)
I0327 13:15:59.502401 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000437609 (* 0.0272727 = 1.19348e-05 loss)
I0327 13:15:59.502415 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000400364 (* 0.0272727 = 1.0919e-05 loss)
I0327 13:15:59.502429 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.375
I0327 13:15:59.502440 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 13:15:59.502452 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:15:59.502465 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 13:15:59.502475 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.375
I0327 13:15:59.502487 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:15:59.502499 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:15:59.502511 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.625
I0327 13:15:59.502523 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:15:59.502534 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:15:59.502545 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:15:59.502557 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:15:59.502568 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:15:59.502580 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:15:59.502591 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:15:59.502602 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:15:59.502614 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:15:59.502625 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:15:59.502636 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:15:59.502648 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:15:59.502660 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:15:59.502672 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:15:59.502682 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.62645 (* 0.0272727 = 0.0716303 loss)
I0327 13:15:59.502697 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.29527 (* 0.0272727 = 0.0898709 loss)
I0327 13:15:59.502712 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.57205 (* 0.0272727 = 0.0974196 loss)
I0327 13:15:59.502725 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.97141 (* 0.0272727 = 0.0810384 loss)
I0327 13:15:59.502739 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.99819 (* 0.0272727 = 0.0817688 loss)
I0327 13:15:59.502753 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.03614 (* 0.0272727 = 0.0555312 loss)
I0327 13:15:59.502768 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.12381 (* 0.0272727 = 0.0306495 loss)
I0327 13:15:59.502781 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 1.50142 (* 0.0272727 = 0.0409478 loss)
I0327 13:15:59.502795 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0984372 (* 0.0272727 = 0.00268465 loss)
I0327 13:15:59.502810 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.021894 (* 0.0272727 = 0.000597109 loss)
I0327 13:15:59.502823 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00681194 (* 0.0272727 = 0.00018578 loss)
I0327 13:15:59.502852 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00220393 (* 0.0272727 = 6.01072e-05 loss)
I0327 13:15:59.502868 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00175545 (* 0.0272727 = 4.78759e-05 loss)
I0327 13:15:59.502882 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00171909 (* 0.0272727 = 4.68844e-05 loss)
I0327 13:15:59.502897 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00216933 (* 0.0272727 = 5.91634e-05 loss)
I0327 13:15:59.502910 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00171785 (* 0.0272727 = 4.68506e-05 loss)
I0327 13:15:59.502924 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00634818 (* 0.0272727 = 0.000173132 loss)
I0327 13:15:59.502938 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.0042154 (* 0.0272727 = 0.000114966 loss)
I0327 13:15:59.502953 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00506086 (* 0.0272727 = 0.000138023 loss)
I0327 13:15:59.502966 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00600878 (* 0.0272727 = 0.000163876 loss)
I0327 13:15:59.502980 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00400268 (* 0.0272727 = 0.000109164 loss)
I0327 13:15:59.502995 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00287317 (* 0.0272727 = 7.83591e-05 loss)
I0327 13:15:59.503006 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.375
I0327 13:15:59.503018 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 13:15:59.503031 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:15:59.503043 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:15:59.503057 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 13:15:59.503068 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:15:59.503079 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:15:59.503092 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.625
I0327 13:15:59.503103 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:15:59.503113 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:15:59.503125 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:15:59.503136 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:15:59.503147 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:15:59.503159 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:15:59.503170 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:15:59.503181 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:15:59.503193 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:15:59.503204 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:15:59.503216 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:15:59.503227 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:15:59.503239 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:15:59.503250 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:15:59.503264 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.40537 (* 0.0909091 = 0.21867 loss)
I0327 13:15:59.503278 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.07477 (* 0.0909091 = 0.279525 loss)
I0327 13:15:59.503293 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.08952 (* 0.0909091 = 0.280866 loss)
I0327 13:15:59.503306 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.76849 (* 0.0909091 = 0.251681 loss)
I0327 13:15:59.503320 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.66367 (* 0.0909091 = 0.242152 loss)
I0327 13:15:59.503334 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.39076 (* 0.0909091 = 0.217342 loss)
I0327 13:15:59.503358 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.45448 (* 0.0909091 = 0.132225 loss)
I0327 13:15:59.503373 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.4333 (* 0.0909091 = 0.1303 loss)
I0327 13:15:59.503387 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0782209 (* 0.0909091 = 0.00711099 loss)
I0327 13:15:59.503401 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0184846 (* 0.0909091 = 0.00168042 loss)
I0327 13:15:59.503415 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000256597 (* 0.0909091 = 2.3327e-05 loss)
I0327 13:15:59.503430 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000260605 (* 0.0909091 = 2.36914e-05 loss)
I0327 13:15:59.503445 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000226915 (* 0.0909091 = 2.06286e-05 loss)
I0327 13:15:59.503459 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000330862 (* 0.0909091 = 3.00784e-05 loss)
I0327 13:15:59.503473 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000314136 (* 0.0909091 = 2.85578e-05 loss)
I0327 13:15:59.503487 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000258559 (* 0.0909091 = 2.35053e-05 loss)
I0327 13:15:59.503501 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000239655 (* 0.0909091 = 2.17868e-05 loss)
I0327 13:15:59.503515 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000260576 (* 0.0909091 = 2.36887e-05 loss)
I0327 13:15:59.503530 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000276715 (* 0.0909091 = 2.51559e-05 loss)
I0327 13:15:59.503545 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000201839 (* 0.0909091 = 1.8349e-05 loss)
I0327 13:15:59.503558 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000235932 (* 0.0909091 = 2.14484e-05 loss)
I0327 13:15:59.503572 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000267115 (* 0.0909091 = 2.42832e-05 loss)
I0327 13:15:59.503585 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:15:59.503597 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000555632
I0327 13:15:59.503609 21344 sgd_solver.cpp:106] Iteration 8000, lr = 0.01
I0327 13:17:47.988759 21344 solver.cpp:229] Iteration 8500, loss = 2.96114
I0327 13:17:47.988929 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 13:17:47.988950 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:17:47.988963 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 13:17:47.988976 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 13:17:47.988987 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 13:17:47.989002 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 13:17:47.989014 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 13:17:47.989027 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:17:47.989039 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 13:17:47.989053 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:17:47.989063 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:17:47.989075 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:17:47.989086 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:17:47.989099 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:17:47.989110 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:17:47.989121 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:17:47.989133 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:17:47.989145 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:17:47.989156 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:17:47.989168 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:17:47.989179 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:17:47.989192 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:17:47.989207 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.28561 (* 0.0272727 = 0.0896075 loss)
I0327 13:17:47.989223 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.77646 (* 0.0272727 = 0.102994 loss)
I0327 13:17:47.989236 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.65026 (* 0.0272727 = 0.0995524 loss)
I0327 13:17:47.989250 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.75427 (* 0.0272727 = 0.102389 loss)
I0327 13:17:47.989264 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 4.20232 (* 0.0272727 = 0.114609 loss)
I0327 13:17:47.989279 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.64007 (* 0.0272727 = 0.0720018 loss)
I0327 13:17:47.989292 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.446614 (* 0.0272727 = 0.0121804 loss)
I0327 13:17:47.989306 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.658225 (* 0.0272727 = 0.0179516 loss)
I0327 13:17:47.989320 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.614895 (* 0.0272727 = 0.0167699 loss)
I0327 13:17:47.989336 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0719653 (* 0.0272727 = 0.00196269 loss)
I0327 13:17:47.989351 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00293431 (* 0.0272727 = 8.00266e-05 loss)
I0327 13:17:47.989365 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000894456 (* 0.0272727 = 2.43943e-05 loss)
I0327 13:17:47.989379 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00139224 (* 0.0272727 = 3.79702e-05 loss)
I0327 13:17:47.989394 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.0014869 (* 0.0272727 = 4.05517e-05 loss)
I0327 13:17:47.989408 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00184582 (* 0.0272727 = 5.03406e-05 loss)
I0327 13:17:47.989423 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00266303 (* 0.0272727 = 7.2628e-05 loss)
I0327 13:17:47.989436 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00172552 (* 0.0272727 = 4.70597e-05 loss)
I0327 13:17:47.989464 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000739587 (* 0.0272727 = 2.01706e-05 loss)
I0327 13:17:47.989480 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00146764 (* 0.0272727 = 4.00267e-05 loss)
I0327 13:17:47.989498 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00278096 (* 0.0272727 = 7.58444e-05 loss)
I0327 13:17:47.989513 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00219936 (* 0.0272727 = 5.99826e-05 loss)
I0327 13:17:47.989527 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00144267 (* 0.0272727 = 3.93455e-05 loss)
I0327 13:17:47.989552 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:17:47.989567 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:17:47.989579 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:17:47.989590 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 13:17:47.989603 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 13:17:47.989614 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 13:17:47.989625 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 13:17:47.989637 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:17:47.989650 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 13:17:47.989661 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:17:47.989673 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:17:47.989684 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:17:47.989696 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:17:47.989707 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:17:47.989718 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:17:47.989729 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:17:47.989742 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:17:47.989753 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:17:47.989763 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:17:47.989775 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:17:47.989786 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:17:47.989799 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:17:47.989811 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.49715 (* 0.0272727 = 0.0953768 loss)
I0327 13:17:47.989825 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.68211 (* 0.0272727 = 0.100421 loss)
I0327 13:17:47.989840 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.73273 (* 0.0272727 = 0.101802 loss)
I0327 13:17:47.989855 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.97417 (* 0.0272727 = 0.108386 loss)
I0327 13:17:47.989868 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 4.46979 (* 0.0272727 = 0.121903 loss)
I0327 13:17:47.989881 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.17338 (* 0.0272727 = 0.0865466 loss)
I0327 13:17:47.989895 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.661313 (* 0.0272727 = 0.0180358 loss)
I0327 13:17:47.989909 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.576491 (* 0.0272727 = 0.0157225 loss)
I0327 13:17:47.989923 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.639776 (* 0.0272727 = 0.0174484 loss)
I0327 13:17:47.989943 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0152686 (* 0.0272727 = 0.000416416 loss)
I0327 13:17:47.989956 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000524852 (* 0.0272727 = 1.43141e-05 loss)
I0327 13:17:47.989984 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000361857 (* 0.0272727 = 9.86882e-06 loss)
I0327 13:17:47.989998 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000459113 (* 0.0272727 = 1.25213e-05 loss)
I0327 13:17:47.990012 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000526396 (* 0.0272727 = 1.43563e-05 loss)
I0327 13:17:47.990026 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000540065 (* 0.0272727 = 1.4729e-05 loss)
I0327 13:17:47.990043 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000403885 (* 0.0272727 = 1.1015e-05 loss)
I0327 13:17:47.990058 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000418817 (* 0.0272727 = 1.14223e-05 loss)
I0327 13:17:47.990073 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000279494 (* 0.0272727 = 7.62256e-06 loss)
I0327 13:17:47.990087 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000704126 (* 0.0272727 = 1.92034e-05 loss)
I0327 13:17:47.990103 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000472449 (* 0.0272727 = 1.2885e-05 loss)
I0327 13:17:47.990116 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000318359 (* 0.0272727 = 8.68252e-06 loss)
I0327 13:17:47.990130 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000445853 (* 0.0272727 = 1.21596e-05 loss)
I0327 13:17:47.990142 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.125
I0327 13:17:47.990154 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:17:47.990166 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 13:17:47.990178 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:17:47.990190 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0
I0327 13:17:47.990201 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 13:17:47.990213 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.875
I0327 13:17:47.990224 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:17:47.990236 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 13:17:47.990247 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:17:47.990259 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:17:47.990270 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:17:47.990278 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:17:47.990286 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:17:47.990298 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:17:47.990309 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:17:47.990321 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:17:47.990334 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:17:47.990345 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:17:47.990355 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:17:47.990367 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:17:47.990378 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:17:47.990391 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.2581 (* 0.0909091 = 0.296191 loss)
I0327 13:17:47.990406 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.44537 (* 0.0909091 = 0.313215 loss)
I0327 13:17:47.990419 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.435 (* 0.0909091 = 0.312273 loss)
I0327 13:17:47.990433 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.4147 (* 0.0909091 = 0.310427 loss)
I0327 13:17:47.990447 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.81033 (* 0.0909091 = 0.346394 loss)
I0327 13:17:47.990471 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.94949 (* 0.0909091 = 0.268136 loss)
I0327 13:17:47.990486 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.539927 (* 0.0909091 = 0.0490843 loss)
I0327 13:17:47.990500 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.503212 (* 0.0909091 = 0.0457465 loss)
I0327 13:17:47.990514 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.59311 (* 0.0909091 = 0.0539191 loss)
I0327 13:17:47.990528 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0168417 (* 0.0909091 = 0.00153106 loss)
I0327 13:17:47.990542 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000228999 (* 0.0909091 = 2.08181e-05 loss)
I0327 13:17:47.990556 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000192343 (* 0.0909091 = 1.74857e-05 loss)
I0327 13:17:47.990571 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.00016544 (* 0.0909091 = 1.504e-05 loss)
I0327 13:17:47.990584 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000178803 (* 0.0909091 = 1.62548e-05 loss)
I0327 13:17:47.990599 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000203818 (* 0.0909091 = 1.85289e-05 loss)
I0327 13:17:47.990613 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.00020999 (* 0.0909091 = 1.909e-05 loss)
I0327 13:17:47.990628 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000238154 (* 0.0909091 = 2.16504e-05 loss)
I0327 13:17:47.990641 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000184664 (* 0.0909091 = 1.67876e-05 loss)
I0327 13:17:47.990655 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000214799 (* 0.0909091 = 1.95272e-05 loss)
I0327 13:17:47.990669 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000188584 (* 0.0909091 = 1.7144e-05 loss)
I0327 13:17:47.990682 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000203595 (* 0.0909091 = 1.85087e-05 loss)
I0327 13:17:47.990696 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000230441 (* 0.0909091 = 2.09492e-05 loss)
I0327 13:17:47.990708 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:17:47.990720 21344 solver.cpp:245] Train net output #133: total_confidence = 4.80088e-05
I0327 13:17:47.990733 21344 sgd_solver.cpp:106] Iteration 8500, lr = 0.01
I0327 13:19:36.285869 21344 solver.cpp:229] Iteration 9000, loss = 2.89351
I0327 13:19:36.285996 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.25
I0327 13:19:36.286015 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:19:36.286028 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.25
I0327 13:19:36.286041 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 13:19:36.286052 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.375
I0327 13:19:36.286064 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.75
I0327 13:19:36.286077 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 13:19:36.286088 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:19:36.286100 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 13:19:36.286113 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 0.875
I0327 13:19:36.286124 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:19:36.286136 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:19:36.286149 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:19:36.286159 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:19:36.286171 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:19:36.286183 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:19:36.286195 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:19:36.286206 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:19:36.286218 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:19:36.286229 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:19:36.286242 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:19:36.286253 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:19:36.286269 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.49084 (* 0.0272727 = 0.0952047 loss)
I0327 13:19:36.286284 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.85424 (* 0.0272727 = 0.0778429 loss)
I0327 13:19:36.286298 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.38289 (* 0.0272727 = 0.0922606 loss)
I0327 13:19:36.286312 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.77343 (* 0.0272727 = 0.102912 loss)
I0327 13:19:36.286326 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.77399 (* 0.0272727 = 0.0756542 loss)
I0327 13:19:36.286340 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.46531 (* 0.0272727 = 0.0399629 loss)
I0327 13:19:36.286353 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.870973 (* 0.0272727 = 0.0237538 loss)
I0327 13:19:36.286368 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.643987 (* 0.0272727 = 0.0175633 loss)
I0327 13:19:36.286382 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.624721 (* 0.0272727 = 0.0170378 loss)
I0327 13:19:36.286396 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.817046 (* 0.0272727 = 0.0222831 loss)
I0327 13:19:36.286412 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000528744 (* 0.0272727 = 1.44203e-05 loss)
I0327 13:19:36.286427 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000875596 (* 0.0272727 = 2.38799e-05 loss)
I0327 13:19:36.286440 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000372054 (* 0.0272727 = 1.01469e-05 loss)
I0327 13:19:36.286454 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000733252 (* 0.0272727 = 1.99978e-05 loss)
I0327 13:19:36.286469 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000272746 (* 0.0272727 = 7.43853e-06 loss)
I0327 13:19:36.286484 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00155906 (* 0.0272727 = 4.25198e-05 loss)
I0327 13:19:36.286497 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000640227 (* 0.0272727 = 1.74607e-05 loss)
I0327 13:19:36.286528 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000784664 (* 0.0272727 = 2.13999e-05 loss)
I0327 13:19:36.286545 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000889735 (* 0.0272727 = 2.42655e-05 loss)
I0327 13:19:36.286558 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000979171 (* 0.0272727 = 2.67047e-05 loss)
I0327 13:19:36.286572 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000966434 (* 0.0272727 = 2.63573e-05 loss)
I0327 13:19:36.286586 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00123464 (* 0.0272727 = 3.3672e-05 loss)
I0327 13:19:36.286598 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0
I0327 13:19:36.286612 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:19:36.286623 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:19:36.286634 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:19:36.286646 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:19:36.286659 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.75
I0327 13:19:36.286669 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 13:19:36.286681 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:19:36.286694 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 13:19:36.286705 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 0.875
I0327 13:19:36.286717 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:19:36.286728 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:19:36.286741 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:19:36.286751 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:19:36.286763 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:19:36.286774 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:19:36.286787 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:19:36.286798 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:19:36.286808 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:19:36.286820 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:19:36.286831 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:19:36.286844 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:19:36.286857 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.19319 (* 0.0272727 = 0.087087 loss)
I0327 13:19:36.286871 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.09949 (* 0.0272727 = 0.0845316 loss)
I0327 13:19:36.286885 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.21067 (* 0.0272727 = 0.0875637 loss)
I0327 13:19:36.286900 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.51676 (* 0.0272727 = 0.0959117 loss)
I0327 13:19:36.286913 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.01192 (* 0.0272727 = 0.0821432 loss)
I0327 13:19:36.286927 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.42339 (* 0.0272727 = 0.0388198 loss)
I0327 13:19:36.286942 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.10204 (* 0.0272727 = 0.0300558 loss)
I0327 13:19:36.286957 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.334343 (* 0.0272727 = 0.00911846 loss)
I0327 13:19:36.286972 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.720372 (* 0.0272727 = 0.0196465 loss)
I0327 13:19:36.286985 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.649215 (* 0.0272727 = 0.0177059 loss)
I0327 13:19:36.287004 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000845895 (* 0.0272727 = 2.30699e-05 loss)
I0327 13:19:36.287030 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00104665 (* 0.0272727 = 2.85451e-05 loss)
I0327 13:19:36.287045 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000997937 (* 0.0272727 = 2.72165e-05 loss)
I0327 13:19:36.287060 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00073422 (* 0.0272727 = 2.00242e-05 loss)
I0327 13:19:36.287075 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00108267 (* 0.0272727 = 2.95274e-05 loss)
I0327 13:19:36.287088 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000359518 (* 0.0272727 = 9.80504e-06 loss)
I0327 13:19:36.287102 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00153578 (* 0.0272727 = 4.18848e-05 loss)
I0327 13:19:36.287117 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.0011214 (* 0.0272727 = 3.05836e-05 loss)
I0327 13:19:36.287132 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000396209 (* 0.0272727 = 1.08057e-05 loss)
I0327 13:19:36.287145 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000814289 (* 0.0272727 = 2.22079e-05 loss)
I0327 13:19:36.287159 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00117654 (* 0.0272727 = 3.20875e-05 loss)
I0327 13:19:36.287173 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00136973 (* 0.0272727 = 3.73562e-05 loss)
I0327 13:19:36.287185 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0
I0327 13:19:36.287199 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:19:36.287210 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 13:19:36.287219 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:19:36.287226 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 13:19:36.287238 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.75
I0327 13:19:36.287250 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.875
I0327 13:19:36.287262 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:19:36.287273 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 13:19:36.287286 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 0.875
I0327 13:19:36.287297 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:19:36.287308 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:19:36.287320 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:19:36.287333 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:19:36.287343 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:19:36.287354 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:19:36.287366 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:19:36.287377 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:19:36.287389 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:19:36.287400 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:19:36.287411 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:19:36.287422 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:19:36.287436 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.9748 (* 0.0909091 = 0.270437 loss)
I0327 13:19:36.287451 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.07171 (* 0.0909091 = 0.279246 loss)
I0327 13:19:36.287463 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.06843 (* 0.0909091 = 0.278948 loss)
I0327 13:19:36.287477 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.59028 (* 0.0909091 = 0.326389 loss)
I0327 13:19:36.287492 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.80586 (* 0.0909091 = 0.255078 loss)
I0327 13:19:36.287515 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.32811 (* 0.0909091 = 0.120737 loss)
I0327 13:19:36.287530 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.691466 (* 0.0909091 = 0.0628605 loss)
I0327 13:19:36.287544 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.384465 (* 0.0909091 = 0.0349514 loss)
I0327 13:19:36.287559 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.558781 (* 0.0909091 = 0.0507983 loss)
I0327 13:19:36.287572 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.485003 (* 0.0909091 = 0.0440912 loss)
I0327 13:19:36.287587 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000153237 (* 0.0909091 = 1.39306e-05 loss)
I0327 13:19:36.287601 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000188188 (* 0.0909091 = 1.7108e-05 loss)
I0327 13:19:36.287616 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000238959 (* 0.0909091 = 2.17236e-05 loss)
I0327 13:19:36.287629 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000195046 (* 0.0909091 = 1.77315e-05 loss)
I0327 13:19:36.287643 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000207521 (* 0.0909091 = 1.88655e-05 loss)
I0327 13:19:36.287657 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000159005 (* 0.0909091 = 1.4455e-05 loss)
I0327 13:19:36.287672 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000213286 (* 0.0909091 = 1.93896e-05 loss)
I0327 13:19:36.287685 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000213135 (* 0.0909091 = 1.93759e-05 loss)
I0327 13:19:36.287699 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000178471 (* 0.0909091 = 1.62246e-05 loss)
I0327 13:19:36.287714 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000195229 (* 0.0909091 = 1.77481e-05 loss)
I0327 13:19:36.287727 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000180051 (* 0.0909091 = 1.63683e-05 loss)
I0327 13:19:36.287741 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000201351 (* 0.0909091 = 1.83046e-05 loss)
I0327 13:19:36.287753 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:19:36.287765 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00055763
I0327 13:19:36.287777 21344 sgd_solver.cpp:106] Iteration 9000, lr = 0.01
I0327 13:21:24.122683 21344 solver.cpp:229] Iteration 9500, loss = 2.89062
I0327 13:21:24.122922 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.25
I0327 13:21:24.122947 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:21:24.122961 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 13:21:24.122973 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:21:24.122992 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 13:21:24.123005 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 13:21:24.123018 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 13:21:24.123030 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:21:24.123044 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:21:24.123055 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:21:24.123067 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:21:24.123080 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:21:24.123091 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:21:24.123103 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:21:24.123116 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:21:24.123127 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:21:24.123139 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:21:24.123152 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:21:24.123163 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:21:24.123177 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:21:24.123188 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:21:24.123200 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:21:24.123217 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.54662 (* 0.0272727 = 0.0694534 loss)
I0327 13:21:24.123234 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.00914 (* 0.0272727 = 0.0820675 loss)
I0327 13:21:24.123247 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.08197 (* 0.0272727 = 0.0840539 loss)
I0327 13:21:24.123262 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.25324 (* 0.0272727 = 0.0887248 loss)
I0327 13:21:24.123276 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.00667 (* 0.0272727 = 0.082 loss)
I0327 13:21:24.123291 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.67622 (* 0.0272727 = 0.0729879 loss)
I0327 13:21:24.123304 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.14978 (* 0.0272727 = 0.0586304 loss)
I0327 13:21:24.123318 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.660333 (* 0.0272727 = 0.0180091 loss)
I0327 13:21:24.123342 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0386639 (* 0.0272727 = 0.00105447 loss)
I0327 13:21:24.123358 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0068292 (* 0.0272727 = 0.000186251 loss)
I0327 13:21:24.123373 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00209744 (* 0.0272727 = 5.72029e-05 loss)
I0327 13:21:24.123389 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000952943 (* 0.0272727 = 2.59894e-05 loss)
I0327 13:21:24.123402 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000791841 (* 0.0272727 = 2.15957e-05 loss)
I0327 13:21:24.123416 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000730998 (* 0.0272727 = 1.99363e-05 loss)
I0327 13:21:24.123431 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00116255 (* 0.0272727 = 3.17058e-05 loss)
I0327 13:21:24.123445 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00108261 (* 0.0272727 = 2.95258e-05 loss)
I0327 13:21:24.123461 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00284669 (* 0.0272727 = 7.76369e-05 loss)
I0327 13:21:24.123492 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00318524 (* 0.0272727 = 8.68701e-05 loss)
I0327 13:21:24.123507 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000899941 (* 0.0272727 = 2.45439e-05 loss)
I0327 13:21:24.123522 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00168223 (* 0.0272727 = 4.58789e-05 loss)
I0327 13:21:24.123535 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00502717 (* 0.0272727 = 0.000137105 loss)
I0327 13:21:24.123550 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00225216 (* 0.0272727 = 6.14226e-05 loss)
I0327 13:21:24.123563 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.5
I0327 13:21:24.123576 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:21:24.123589 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:21:24.123600 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:21:24.123611 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:21:24.123625 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:21:24.123636 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 13:21:24.123648 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:21:24.123661 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:21:24.123672 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:21:24.123684 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:21:24.123697 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:21:24.123708 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:21:24.123720 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:21:24.123733 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:21:24.123744 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:21:24.123755 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:21:24.123766 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:21:24.123778 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:21:24.123790 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:21:24.123802 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:21:24.123813 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:21:24.123827 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.88821 (* 0.0272727 = 0.0514966 loss)
I0327 13:21:24.123842 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.23858 (* 0.0272727 = 0.0883248 loss)
I0327 13:21:24.123855 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.10356 (* 0.0272727 = 0.0846424 loss)
I0327 13:21:24.123870 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.31874 (* 0.0272727 = 0.0905111 loss)
I0327 13:21:24.123884 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.81234 (* 0.0272727 = 0.0767003 loss)
I0327 13:21:24.123898 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.81881 (* 0.0272727 = 0.0768766 loss)
I0327 13:21:24.123914 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 2.31155 (* 0.0272727 = 0.0630423 loss)
I0327 13:21:24.123934 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.522069 (* 0.0272727 = 0.0142382 loss)
I0327 13:21:24.123949 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0721382 (* 0.0272727 = 0.00196741 loss)
I0327 13:21:24.123958 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00916865 (* 0.0272727 = 0.000250054 loss)
I0327 13:21:24.123968 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000318202 (* 0.0272727 = 8.67824e-06 loss)
I0327 13:21:24.123996 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000296426 (* 0.0272727 = 8.08433e-06 loss)
I0327 13:21:24.124011 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000419269 (* 0.0272727 = 1.14346e-05 loss)
I0327 13:21:24.124025 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000393319 (* 0.0272727 = 1.07269e-05 loss)
I0327 13:21:24.124042 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000457847 (* 0.0272727 = 1.24867e-05 loss)
I0327 13:21:24.124058 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00032479 (* 0.0272727 = 8.8579e-06 loss)
I0327 13:21:24.124073 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000359603 (* 0.0272727 = 9.80736e-06 loss)
I0327 13:21:24.124088 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000348609 (* 0.0272727 = 9.50753e-06 loss)
I0327 13:21:24.124101 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000292437 (* 0.0272727 = 7.97556e-06 loss)
I0327 13:21:24.124116 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000302323 (* 0.0272727 = 8.24517e-06 loss)
I0327 13:21:24.124136 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000185112 (* 0.0272727 = 5.0485e-06 loss)
I0327 13:21:24.124152 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000464405 (* 0.0272727 = 1.26656e-05 loss)
I0327 13:21:24.124166 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.25
I0327 13:21:24.124177 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.25
I0327 13:21:24.124191 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:21:24.124202 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:21:24.124214 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 13:21:24.124227 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:21:24.124238 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.5
I0327 13:21:24.124249 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:21:24.124261 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:21:24.124274 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:21:24.124285 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:21:24.124296 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:21:24.124308 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:21:24.124320 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:21:24.124331 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:21:24.124343 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:21:24.124356 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:21:24.124367 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:21:24.124379 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:21:24.124392 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:21:24.124402 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:21:24.124414 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:21:24.124428 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.06386 (* 0.0909091 = 0.187623 loss)
I0327 13:21:24.124441 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.7417 (* 0.0909091 = 0.249246 loss)
I0327 13:21:24.124455 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.01654 (* 0.0909091 = 0.274231 loss)
I0327 13:21:24.124469 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.24496 (* 0.0909091 = 0.294996 loss)
I0327 13:21:24.124485 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.56613 (* 0.0909091 = 0.233285 loss)
I0327 13:21:24.124510 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.55473 (* 0.0909091 = 0.232248 loss)
I0327 13:21:24.124526 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 2.44523 (* 0.0909091 = 0.222294 loss)
I0327 13:21:24.124539 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.443019 (* 0.0909091 = 0.0402744 loss)
I0327 13:21:24.124553 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0328916 (* 0.0909091 = 0.00299015 loss)
I0327 13:21:24.124567 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00959882 (* 0.0909091 = 0.00087262 loss)
I0327 13:21:24.124583 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000113984 (* 0.0909091 = 1.03622e-05 loss)
I0327 13:21:24.124598 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000137302 (* 0.0909091 = 1.2482e-05 loss)
I0327 13:21:24.124611 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000134424 (* 0.0909091 = 1.22204e-05 loss)
I0327 13:21:24.124625 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000140246 (* 0.0909091 = 1.27496e-05 loss)
I0327 13:21:24.124640 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000126839 (* 0.0909091 = 1.15308e-05 loss)
I0327 13:21:24.124655 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000143645 (* 0.0909091 = 1.30587e-05 loss)
I0327 13:21:24.124670 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000129165 (* 0.0909091 = 1.17423e-05 loss)
I0327 13:21:24.124683 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.00015182 (* 0.0909091 = 1.38018e-05 loss)
I0327 13:21:24.124698 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.00015227 (* 0.0909091 = 1.38427e-05 loss)
I0327 13:21:24.124713 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000136476 (* 0.0909091 = 1.24069e-05 loss)
I0327 13:21:24.124727 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000191585 (* 0.0909091 = 1.74169e-05 loss)
I0327 13:21:24.124742 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000147659 (* 0.0909091 = 1.34235e-05 loss)
I0327 13:21:24.124753 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:21:24.124765 21344 solver.cpp:245] Train net output #133: total_confidence = 8.67072e-05
I0327 13:21:24.124779 21344 sgd_solver.cpp:106] Iteration 9500, lr = 0.01
I0327 13:23:11.878332 21344 solver.cpp:338] Iteration 10000, Testing net (#0)
I0327 13:23:42.874440 21344 solver.cpp:393] Test loss: 2.38051
I0327 13:23:42.874547 21344 solver.cpp:406] Test net output #0: loss1/accuracy01 = 0.312
I0327 13:23:42.874565 21344 solver.cpp:406] Test net output #1: loss1/accuracy02 = 0.131
I0327 13:23:42.874578 21344 solver.cpp:406] Test net output #2: loss1/accuracy03 = 0.113
I0327 13:23:42.874590 21344 solver.cpp:406] Test net output #3: loss1/accuracy04 = 0.141
I0327 13:23:42.874603 21344 solver.cpp:406] Test net output #4: loss1/accuracy05 = 0.234
I0327 13:23:42.874615 21344 solver.cpp:406] Test net output #5: loss1/accuracy06 = 0.501
I0327 13:23:42.874629 21344 solver.cpp:406] Test net output #6: loss1/accuracy07 = 0.892
I0327 13:23:42.874640 21344 solver.cpp:406] Test net output #7: loss1/accuracy08 = 0.97
I0327 13:23:42.874652 21344 solver.cpp:406] Test net output #8: loss1/accuracy09 = 0.995
I0327 13:23:42.874665 21344 solver.cpp:406] Test net output #9: loss1/accuracy10 = 0.998
I0327 13:23:42.874676 21344 solver.cpp:406] Test net output #10: loss1/accuracy11 = 1
I0327 13:23:42.874688 21344 solver.cpp:406] Test net output #11: loss1/accuracy12 = 1
I0327 13:23:42.874701 21344 solver.cpp:406] Test net output #12: loss1/accuracy13 = 1
I0327 13:23:42.874711 21344 solver.cpp:406] Test net output #13: loss1/accuracy14 = 1
I0327 13:23:42.874723 21344 solver.cpp:406] Test net output #14: loss1/accuracy15 = 1
I0327 13:23:42.874734 21344 solver.cpp:406] Test net output #15: loss1/accuracy16 = 1
I0327 13:23:42.874745 21344 solver.cpp:406] Test net output #16: loss1/accuracy17 = 1
I0327 13:23:42.874758 21344 solver.cpp:406] Test net output #17: loss1/accuracy18 = 1
I0327 13:23:42.874768 21344 solver.cpp:406] Test net output #18: loss1/accuracy19 = 1
I0327 13:23:42.874780 21344 solver.cpp:406] Test net output #19: loss1/accuracy20 = 1
I0327 13:23:42.874791 21344 solver.cpp:406] Test net output #20: loss1/accuracy21 = 1
I0327 13:23:42.874804 21344 solver.cpp:406] Test net output #21: loss1/accuracy22 = 1
I0327 13:23:42.874819 21344 solver.cpp:406] Test net output #22: loss1/loss01 = 2.45021 (* 0.0272727 = 0.0668239 loss)
I0327 13:23:42.874833 21344 solver.cpp:406] Test net output #23: loss1/loss02 = 2.80035 (* 0.0272727 = 0.0763731 loss)
I0327 13:23:42.874848 21344 solver.cpp:406] Test net output #24: loss1/loss03 = 2.94484 (* 0.0272727 = 0.0803138 loss)
I0327 13:23:42.874862 21344 solver.cpp:406] Test net output #25: loss1/loss04 = 2.8809 (* 0.0272727 = 0.07857 loss)
I0327 13:23:42.874876 21344 solver.cpp:406] Test net output #26: loss1/loss05 = 2.7647 (* 0.0272727 = 0.0754009 loss)
I0327 13:23:42.874891 21344 solver.cpp:406] Test net output #27: loss1/loss06 = 1.87586 (* 0.0272727 = 0.0511597 loss)
I0327 13:23:42.874904 21344 solver.cpp:406] Test net output #28: loss1/loss07 = 0.597768 (* 0.0272727 = 0.0163028 loss)
I0327 13:23:42.874918 21344 solver.cpp:406] Test net output #29: loss1/loss08 = 0.226958 (* 0.0272727 = 0.00618975 loss)
I0327 13:23:42.874933 21344 solver.cpp:406] Test net output #30: loss1/loss09 = 0.038961 (* 0.0272727 = 0.00106257 loss)
I0327 13:23:42.874948 21344 solver.cpp:406] Test net output #31: loss1/loss10 = 0.0176988 (* 0.0272727 = 0.000482695 loss)
I0327 13:23:42.874961 21344 solver.cpp:406] Test net output #32: loss1/loss11 = 0.00114515 (* 0.0272727 = 3.12314e-05 loss)
I0327 13:23:42.874975 21344 solver.cpp:406] Test net output #33: loss1/loss12 = 0.00119706 (* 0.0272727 = 3.26471e-05 loss)
I0327 13:23:42.874992 21344 solver.cpp:406] Test net output #34: loss1/loss13 = 0.00115281 (* 0.0272727 = 3.14404e-05 loss)
I0327 13:23:42.875007 21344 solver.cpp:406] Test net output #35: loss1/loss14 = 0.00104622 (* 0.0272727 = 2.85332e-05 loss)
I0327 13:23:42.875021 21344 solver.cpp:406] Test net output #36: loss1/loss15 = 0.000985538 (* 0.0272727 = 2.68783e-05 loss)
I0327 13:23:42.875036 21344 solver.cpp:406] Test net output #37: loss1/loss16 = 0.00115722 (* 0.0272727 = 3.15605e-05 loss)
I0327 13:23:42.875049 21344 solver.cpp:406] Test net output #38: loss1/loss17 = 0.00118904 (* 0.0272727 = 3.24283e-05 loss)
I0327 13:23:42.875082 21344 solver.cpp:406] Test net output #39: loss1/loss18 = 0.0011583 (* 0.0272727 = 3.15901e-05 loss)
I0327 13:23:42.875097 21344 solver.cpp:406] Test net output #40: loss1/loss19 = 0.000904245 (* 0.0272727 = 2.46612e-05 loss)
I0327 13:23:42.875111 21344 solver.cpp:406] Test net output #41: loss1/loss20 = 0.00101908 (* 0.0272727 = 2.7793e-05 loss)
I0327 13:23:42.875125 21344 solver.cpp:406] Test net output #42: loss1/loss21 = 0.000864371 (* 0.0272727 = 2.35738e-05 loss)
I0327 13:23:42.875139 21344 solver.cpp:406] Test net output #43: loss1/loss22 = 0.000980583 (* 0.0272727 = 2.67432e-05 loss)
I0327 13:23:42.875151 21344 solver.cpp:406] Test net output #44: loss2/accuracy01 = 0.31
I0327 13:23:42.875164 21344 solver.cpp:406] Test net output #45: loss2/accuracy02 = 0.127
I0327 13:23:42.875175 21344 solver.cpp:406] Test net output #46: loss2/accuracy03 = 0.109
I0327 13:23:42.875187 21344 solver.cpp:406] Test net output #47: loss2/accuracy04 = 0.148
I0327 13:23:42.875200 21344 solver.cpp:406] Test net output #48: loss2/accuracy05 = 0.237
I0327 13:23:42.875211 21344 solver.cpp:406] Test net output #49: loss2/accuracy06 = 0.504
I0327 13:23:42.875222 21344 solver.cpp:406] Test net output #50: loss2/accuracy07 = 0.892
I0327 13:23:42.875234 21344 solver.cpp:406] Test net output #51: loss2/accuracy08 = 0.97
I0327 13:23:42.875246 21344 solver.cpp:406] Test net output #52: loss2/accuracy09 = 0.995
I0327 13:23:42.875257 21344 solver.cpp:406] Test net output #53: loss2/accuracy10 = 0.998
I0327 13:23:42.875268 21344 solver.cpp:406] Test net output #54: loss2/accuracy11 = 1
I0327 13:23:42.875280 21344 solver.cpp:406] Test net output #55: loss2/accuracy12 = 1
I0327 13:23:42.875291 21344 solver.cpp:406] Test net output #56: loss2/accuracy13 = 1
I0327 13:23:42.875303 21344 solver.cpp:406] Test net output #57: loss2/accuracy14 = 1
I0327 13:23:42.875313 21344 solver.cpp:406] Test net output #58: loss2/accuracy15 = 1
I0327 13:23:42.875325 21344 solver.cpp:406] Test net output #59: loss2/accuracy16 = 1
I0327 13:23:42.875336 21344 solver.cpp:406] Test net output #60: loss2/accuracy17 = 1
I0327 13:23:42.875347 21344 solver.cpp:406] Test net output #61: loss2/accuracy18 = 1
I0327 13:23:42.875358 21344 solver.cpp:406] Test net output #62: loss2/accuracy19 = 1
I0327 13:23:42.875370 21344 solver.cpp:406] Test net output #63: loss2/accuracy20 = 1
I0327 13:23:42.875381 21344 solver.cpp:406] Test net output #64: loss2/accuracy21 = 1
I0327 13:23:42.875392 21344 solver.cpp:406] Test net output #65: loss2/accuracy22 = 1
I0327 13:23:42.875406 21344 solver.cpp:406] Test net output #66: loss2/loss01 = 2.47543 (* 0.0272727 = 0.0675118 loss)
I0327 13:23:42.875419 21344 solver.cpp:406] Test net output #67: loss2/loss02 = 2.78996 (* 0.0272727 = 0.0760897 loss)
I0327 13:23:42.875433 21344 solver.cpp:406] Test net output #68: loss2/loss03 = 2.94036 (* 0.0272727 = 0.0801916 loss)
I0327 13:23:42.875447 21344 solver.cpp:406] Test net output #69: loss2/loss04 = 2.89017 (* 0.0272727 = 0.0788227 loss)
I0327 13:23:42.875463 21344 solver.cpp:406] Test net output #70: loss2/loss05 = 2.75133 (* 0.0272727 = 0.0750363 loss)
I0327 13:23:42.875473 21344 solver.cpp:406] Test net output #71: loss2/loss06 = 1.87092 (* 0.0272727 = 0.0510251 loss)
I0327 13:23:42.875486 21344 solver.cpp:406] Test net output #72: loss2/loss07 = 0.615003 (* 0.0272727 = 0.0167728 loss)
I0327 13:23:42.875500 21344 solver.cpp:406] Test net output #73: loss2/loss08 = 0.223184 (* 0.0272727 = 0.00608685 loss)
I0327 13:23:42.875514 21344 solver.cpp:406] Test net output #74: loss2/loss09 = 0.0387181 (* 0.0272727 = 0.00105595 loss)
I0327 13:23:42.875529 21344 solver.cpp:406] Test net output #75: loss2/loss10 = 0.0182172 (* 0.0272727 = 0.000496833 loss)
I0327 13:23:42.875542 21344 solver.cpp:406] Test net output #76: loss2/loss11 = 0.000490839 (* 0.0272727 = 1.33865e-05 loss)
I0327 13:23:42.875556 21344 solver.cpp:406] Test net output #77: loss2/loss12 = 0.000374084 (* 0.0272727 = 1.02023e-05 loss)
I0327 13:23:42.875584 21344 solver.cpp:406] Test net output #78: loss2/loss13 = 0.00040518 (* 0.0272727 = 1.10504e-05 loss)
I0327 13:23:42.875600 21344 solver.cpp:406] Test net output #79: loss2/loss14 = 0.000322892 (* 0.0272727 = 8.80614e-06 loss)
I0327 13:23:42.875613 21344 solver.cpp:406] Test net output #80: loss2/loss15 = 0.000410175 (* 0.0272727 = 1.11866e-05 loss)
I0327 13:23:42.875627 21344 solver.cpp:406] Test net output #81: loss2/loss16 = 0.000354986 (* 0.0272727 = 9.68144e-06 loss)
I0327 13:23:42.875641 21344 solver.cpp:406] Test net output #82: loss2/loss17 = 0.000429287 (* 0.0272727 = 1.17078e-05 loss)
I0327 13:23:42.875654 21344 solver.cpp:406] Test net output #83: loss2/loss18 = 0.000388088 (* 0.0272727 = 1.05842e-05 loss)
I0327 13:23:42.875669 21344 solver.cpp:406] Test net output #84: loss2/loss19 = 0.000366389 (* 0.0272727 = 9.99244e-06 loss)
I0327 13:23:42.875682 21344 solver.cpp:406] Test net output #85: loss2/loss20 = 0.000395202 (* 0.0272727 = 1.07782e-05 loss)
I0327 13:23:42.875696 21344 solver.cpp:406] Test net output #86: loss2/loss21 = 0.000445298 (* 0.0272727 = 1.21445e-05 loss)
I0327 13:23:42.875710 21344 solver.cpp:406] Test net output #87: loss2/loss22 = 0.000426076 (* 0.0272727 = 1.16202e-05 loss)
I0327 13:23:42.875721 21344 solver.cpp:406] Test net output #88: loss3/accuracy01 = 0.232
I0327 13:23:42.875735 21344 solver.cpp:406] Test net output #89: loss3/accuracy02 = 0.122
I0327 13:23:42.875746 21344 solver.cpp:406] Test net output #90: loss3/accuracy03 = 0.095
I0327 13:23:42.875757 21344 solver.cpp:406] Test net output #91: loss3/accuracy04 = 0.114
I0327 13:23:42.875769 21344 solver.cpp:406] Test net output #92: loss3/accuracy05 = 0.213
I0327 13:23:42.875780 21344 solver.cpp:406] Test net output #93: loss3/accuracy06 = 0.509
I0327 13:23:42.875792 21344 solver.cpp:406] Test net output #94: loss3/accuracy07 = 0.892
I0327 13:23:42.875803 21344 solver.cpp:406] Test net output #95: loss3/accuracy08 = 0.97
I0327 13:23:42.875815 21344 solver.cpp:406] Test net output #96: loss3/accuracy09 = 0.995
I0327 13:23:42.875826 21344 solver.cpp:406] Test net output #97: loss3/accuracy10 = 0.998
I0327 13:23:42.875838 21344 solver.cpp:406] Test net output #98: loss3/accuracy11 = 1
I0327 13:23:42.875849 21344 solver.cpp:406] Test net output #99: loss3/accuracy12 = 1
I0327 13:23:42.875859 21344 solver.cpp:406] Test net output #100: loss3/accuracy13 = 1
I0327 13:23:42.875870 21344 solver.cpp:406] Test net output #101: loss3/accuracy14 = 1
I0327 13:23:42.875881 21344 solver.cpp:406] Test net output #102: loss3/accuracy15 = 1
I0327 13:23:42.875892 21344 solver.cpp:406] Test net output #103: loss3/accuracy16 = 1
I0327 13:23:42.875903 21344 solver.cpp:406] Test net output #104: loss3/accuracy17 = 1
I0327 13:23:42.875915 21344 solver.cpp:406] Test net output #105: loss3/accuracy18 = 1
I0327 13:23:42.875926 21344 solver.cpp:406] Test net output #106: loss3/accuracy19 = 1
I0327 13:23:42.875936 21344 solver.cpp:406] Test net output #107: loss3/accuracy20 = 1
I0327 13:23:42.875947 21344 solver.cpp:406] Test net output #108: loss3/accuracy21 = 1
I0327 13:23:42.875958 21344 solver.cpp:406] Test net output #109: loss3/accuracy22 = 1
I0327 13:23:42.875972 21344 solver.cpp:406] Test net output #110: loss3/loss01 = 2.26668 (* 0.0909091 = 0.206062 loss)
I0327 13:23:42.875987 21344 solver.cpp:406] Test net output #111: loss3/loss02 = 2.69376 (* 0.0909091 = 0.244887 loss)
I0327 13:23:42.875999 21344 solver.cpp:406] Test net output #112: loss3/loss03 = 2.92596 (* 0.0909091 = 0.265996 loss)
I0327 13:23:42.876013 21344 solver.cpp:406] Test net output #113: loss3/loss04 = 2.89635 (* 0.0909091 = 0.263304 loss)
I0327 13:23:42.876026 21344 solver.cpp:406] Test net output #114: loss3/loss05 = 2.74007 (* 0.0909091 = 0.249097 loss)
I0327 13:23:42.876040 21344 solver.cpp:406] Test net output #115: loss3/loss06 = 1.81578 (* 0.0909091 = 0.165071 loss)
I0327 13:23:42.876066 21344 solver.cpp:406] Test net output #116: loss3/loss07 = 0.600843 (* 0.0909091 = 0.0546221 loss)
I0327 13:23:42.876081 21344 solver.cpp:406] Test net output #117: loss3/loss08 = 0.213541 (* 0.0909091 = 0.0194128 loss)
I0327 13:23:42.876096 21344 solver.cpp:406] Test net output #118: loss3/loss09 = 0.0405805 (* 0.0909091 = 0.00368913 loss)
I0327 13:23:42.876109 21344 solver.cpp:406] Test net output #119: loss3/loss10 = 0.0216361 (* 0.0909091 = 0.00196692 loss)
I0327 13:23:42.876123 21344 solver.cpp:406] Test net output #120: loss3/loss11 = 0.000123227 (* 0.0909091 = 1.12025e-05 loss)
I0327 13:23:42.876137 21344 solver.cpp:406] Test net output #121: loss3/loss12 = 0.000154343 (* 0.0909091 = 1.40312e-05 loss)
I0327 13:23:42.876152 21344 solver.cpp:406] Test net output #122: loss3/loss13 = 0.000129556 (* 0.0909091 = 1.17778e-05 loss)
I0327 13:23:42.876165 21344 solver.cpp:406] Test net output #123: loss3/loss14 = 0.0001461 (* 0.0909091 = 1.32818e-05 loss)
I0327 13:23:42.876179 21344 solver.cpp:406] Test net output #124: loss3/loss15 = 0.000154306 (* 0.0909091 = 1.40278e-05 loss)
I0327 13:23:42.876194 21344 solver.cpp:406] Test net output #125: loss3/loss16 = 0.000152023 (* 0.0909091 = 1.38203e-05 loss)
I0327 13:23:42.876207 21344 solver.cpp:406] Test net output #126: loss3/loss17 = 0.00012316 (* 0.0909091 = 1.11963e-05 loss)
I0327 13:23:42.876220 21344 solver.cpp:406] Test net output #127: loss3/loss18 = 0.000133358 (* 0.0909091 = 1.21235e-05 loss)
I0327 13:23:42.876235 21344 solver.cpp:406] Test net output #128: loss3/loss19 = 0.000112536 (* 0.0909091 = 1.02305e-05 loss)
I0327 13:23:42.876248 21344 solver.cpp:406] Test net output #129: loss3/loss20 = 0.000146073 (* 0.0909091 = 1.32793e-05 loss)
I0327 13:23:42.876262 21344 solver.cpp:406] Test net output #130: loss3/loss21 = 0.000148882 (* 0.0909091 = 1.35348e-05 loss)
I0327 13:23:42.876276 21344 solver.cpp:406] Test net output #131: loss3/loss22 = 0.000152907 (* 0.0909091 = 1.39006e-05 loss)
I0327 13:23:42.876288 21344 solver.cpp:406] Test net output #132: total_accuracy = 0
I0327 13:23:42.876299 21344 solver.cpp:406] Test net output #133: total_confidence = 0.000686382
I0327 13:23:42.987792 21344 solver.cpp:229] Iteration 10000, loss = 2.87619
I0327 13:23:42.987833 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 13:23:42.987849 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 13:23:42.987862 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 13:23:42.987875 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:23:42.987887 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 13:23:42.987900 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 13:23:42.987913 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:23:42.987926 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:23:42.987937 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 13:23:42.987949 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:23:42.987962 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:23:42.987973 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:23:42.987984 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:23:42.987996 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:23:42.988008 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:23:42.988020 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:23:42.988034 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:23:42.988047 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:23:42.988059 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:23:42.988091 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:23:42.988106 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:23:42.988117 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:23:42.988137 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.2019 (* 0.0272727 = 0.0873244 loss)
I0327 13:23:42.988152 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.10446 (* 0.0272727 = 0.0846672 loss)
I0327 13:23:42.988167 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.94562 (* 0.0272727 = 0.080335 loss)
I0327 13:23:42.988180 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.25635 (* 0.0272727 = 0.0888097 loss)
I0327 13:23:42.988193 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.72015 (* 0.0272727 = 0.0741858 loss)
I0327 13:23:42.988207 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 3.07569 (* 0.0272727 = 0.0838825 loss)
I0327 13:23:42.988221 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.30783 (* 0.0272727 = 0.0356682 loss)
I0327 13:23:42.988235 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.581875 (* 0.0272727 = 0.0158693 loss)
I0327 13:23:42.988250 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.442101 (* 0.0272727 = 0.0120573 loss)
I0327 13:23:42.988265 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0717134 (* 0.0272727 = 0.00195582 loss)
I0327 13:23:42.988278 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00261423 (* 0.0272727 = 7.12971e-05 loss)
I0327 13:23:42.988292 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00136481 (* 0.0272727 = 3.72222e-05 loss)
I0327 13:23:42.988307 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00231753 (* 0.0272727 = 6.32053e-05 loss)
I0327 13:23:42.988322 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00185755 (* 0.0272727 = 5.06606e-05 loss)
I0327 13:23:42.988335 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00408382 (* 0.0272727 = 0.000111377 loss)
I0327 13:23:42.988349 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00118472 (* 0.0272727 = 3.23105e-05 loss)
I0327 13:23:42.988363 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00192768 (* 0.0272727 = 5.25732e-05 loss)
I0327 13:23:42.988378 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00137796 (* 0.0272727 = 3.75808e-05 loss)
I0327 13:23:42.988391 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00172397 (* 0.0272727 = 4.70175e-05 loss)
I0327 13:23:42.988405 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00213949 (* 0.0272727 = 5.83496e-05 loss)
I0327 13:23:42.988420 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000979596 (* 0.0272727 = 2.67163e-05 loss)
I0327 13:23:42.988433 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00138128 (* 0.0272727 = 3.76712e-05 loss)
I0327 13:23:42.988445 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.375
I0327 13:23:42.988457 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:23:42.988469 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:23:42.988481 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 13:23:42.988493 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 13:23:42.988505 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.125
I0327 13:23:42.988517 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:23:42.988528 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:23:42.988540 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 13:23:42.988553 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:23:42.988564 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:23:42.988586 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:23:42.988600 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:23:42.988611 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:23:42.988622 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:23:42.988634 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:23:42.988646 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:23:42.988657 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:23:42.988668 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:23:42.988679 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:23:42.988692 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:23:42.988703 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:23:42.988718 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.62476 (* 0.0272727 = 0.0715843 loss)
I0327 13:23:42.988731 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.30951 (* 0.0272727 = 0.0902594 loss)
I0327 13:23:42.988745 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.3336 (* 0.0272727 = 0.0909164 loss)
I0327 13:23:42.988759 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.24099 (* 0.0272727 = 0.0883907 loss)
I0327 13:23:42.988773 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.18065 (* 0.0272727 = 0.0867451 loss)
I0327 13:23:42.988787 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.48893 (* 0.0272727 = 0.0951527 loss)
I0327 13:23:42.988801 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.61842 (* 0.0272727 = 0.0441388 loss)
I0327 13:23:42.988816 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.594845 (* 0.0272727 = 0.0162231 loss)
I0327 13:23:42.988829 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.54751 (* 0.0272727 = 0.0149321 loss)
I0327 13:23:42.988843 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0349853 (* 0.0272727 = 0.000954144 loss)
I0327 13:23:42.988857 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000438208 (* 0.0272727 = 1.19511e-05 loss)
I0327 13:23:42.988872 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000301931 (* 0.0272727 = 8.23447e-06 loss)
I0327 13:23:42.988885 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00093302 (* 0.0272727 = 2.5446e-05 loss)
I0327 13:23:42.988899 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000891441 (* 0.0272727 = 2.4312e-05 loss)
I0327 13:23:42.988914 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000363443 (* 0.0272727 = 9.91208e-06 loss)
I0327 13:23:42.988927 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000355446 (* 0.0272727 = 9.69399e-06 loss)
I0327 13:23:42.988941 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00108456 (* 0.0272727 = 2.9579e-05 loss)
I0327 13:23:42.988955 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000300582 (* 0.0272727 = 8.19769e-06 loss)
I0327 13:23:42.988970 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00165616 (* 0.0272727 = 4.5168e-05 loss)
I0327 13:23:42.988983 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00180001 (* 0.0272727 = 4.90911e-05 loss)
I0327 13:23:42.988997 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00106156 (* 0.0272727 = 2.89516e-05 loss)
I0327 13:23:42.989012 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00026233 (* 0.0272727 = 7.15444e-06 loss)
I0327 13:23:42.989024 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.375
I0327 13:23:42.989037 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:23:42.989048 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 13:23:42.989070 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.5
I0327 13:23:42.989086 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 13:23:42.989099 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 13:23:42.989111 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:23:42.989122 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:23:42.989135 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 13:23:42.989146 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:23:42.989158 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:23:42.989169 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:23:42.989184 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:23:42.989195 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:23:42.989207 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:23:42.989218 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:23:42.989230 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:23:42.989241 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:23:42.989253 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:23:42.989264 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:23:42.989275 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:23:42.989287 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:23:42.989301 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.40435 (* 0.0909091 = 0.218577 loss)
I0327 13:23:42.989315 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.19962 (* 0.0909091 = 0.290875 loss)
I0327 13:23:42.989328 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.96414 (* 0.0909091 = 0.269468 loss)
I0327 13:23:42.989342 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.55514 (* 0.0909091 = 0.232286 loss)
I0327 13:23:42.989356 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.62225 (* 0.0909091 = 0.238387 loss)
I0327 13:23:42.989369 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.908 (* 0.0909091 = 0.264363 loss)
I0327 13:23:42.989383 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.18822 (* 0.0909091 = 0.10802 loss)
I0327 13:23:42.989398 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.535699 (* 0.0909091 = 0.0486999 loss)
I0327 13:23:42.989410 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.499041 (* 0.0909091 = 0.0453673 loss)
I0327 13:23:42.989425 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.021399 (* 0.0909091 = 0.00194536 loss)
I0327 13:23:42.989439 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000200776 (* 0.0909091 = 1.82524e-05 loss)
I0327 13:23:42.989454 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000184551 (* 0.0909091 = 1.67774e-05 loss)
I0327 13:23:42.989466 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000124854 (* 0.0909091 = 1.13504e-05 loss)
I0327 13:23:42.989480 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000134771 (* 0.0909091 = 1.22519e-05 loss)
I0327 13:23:42.989495 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000165613 (* 0.0909091 = 1.50557e-05 loss)
I0327 13:23:42.989508 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000183897 (* 0.0909091 = 1.67179e-05 loss)
I0327 13:23:42.989521 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000133901 (* 0.0909091 = 1.21728e-05 loss)
I0327 13:23:42.989536 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000169149 (* 0.0909091 = 1.53772e-05 loss)
I0327 13:23:42.989565 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.00014759 (* 0.0909091 = 1.34173e-05 loss)
I0327 13:23:42.989593 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000180341 (* 0.0909091 = 1.63946e-05 loss)
I0327 13:23:42.989608 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000221273 (* 0.0909091 = 2.01157e-05 loss)
I0327 13:23:42.989621 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000121006 (* 0.0909091 = 1.10006e-05 loss)
I0327 13:23:42.989634 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:23:42.989645 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000112937
I0327 13:23:42.989658 21344 sgd_solver.cpp:106] Iteration 10000, lr = 0.01
I0327 13:25:30.729514 21344 solver.cpp:229] Iteration 10500, loss = 2.9033
I0327 13:25:30.729701 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 13:25:30.729722 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 13:25:30.729735 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.25
I0327 13:25:30.729748 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 13:25:30.729759 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 13:25:30.729771 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.125
I0327 13:25:30.729784 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 13:25:30.729795 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.625
I0327 13:25:30.729809 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 13:25:30.729820 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 0.875
I0327 13:25:30.729832 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:25:30.729845 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:25:30.729856 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:25:30.729867 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:25:30.729879 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:25:30.729892 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:25:30.729903 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:25:30.729914 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:25:30.729926 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:25:30.729938 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:25:30.729950 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:25:30.729962 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:25:30.729989 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.87003 (* 0.0272727 = 0.0782736 loss)
I0327 13:25:30.730006 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.40508 (* 0.0272727 = 0.0928658 loss)
I0327 13:25:30.730021 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.94408 (* 0.0272727 = 0.0802931 loss)
I0327 13:25:30.730036 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 4.15453 (* 0.0272727 = 0.113305 loss)
I0327 13:25:30.730049 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.77976 (* 0.0272727 = 0.103084 loss)
I0327 13:25:30.730063 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 4.21557 (* 0.0272727 = 0.11497 loss)
I0327 13:25:30.730077 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.15592 (* 0.0272727 = 0.0587978 loss)
I0327 13:25:30.730093 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.90883 (* 0.0272727 = 0.052059 loss)
I0327 13:25:30.730105 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.556744 (* 0.0272727 = 0.0151839 loss)
I0327 13:25:30.730119 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.563802 (* 0.0272727 = 0.0153764 loss)
I0327 13:25:30.730135 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00129485 (* 0.0272727 = 3.53142e-05 loss)
I0327 13:25:30.730149 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00145952 (* 0.0272727 = 3.98051e-05 loss)
I0327 13:25:30.730165 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00232076 (* 0.0272727 = 6.32934e-05 loss)
I0327 13:25:30.730178 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00218394 (* 0.0272727 = 5.95621e-05 loss)
I0327 13:25:30.730193 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00260049 (* 0.0272727 = 7.09226e-05 loss)
I0327 13:25:30.730207 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00283218 (* 0.0272727 = 7.72414e-05 loss)
I0327 13:25:30.730221 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00209276 (* 0.0272727 = 5.70753e-05 loss)
I0327 13:25:30.730257 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00110053 (* 0.0272727 = 3.00145e-05 loss)
I0327 13:25:30.730273 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00301761 (* 0.0272727 = 8.22984e-05 loss)
I0327 13:25:30.730286 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00319151 (* 0.0272727 = 8.70411e-05 loss)
I0327 13:25:30.730300 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00218958 (* 0.0272727 = 5.97159e-05 loss)
I0327 13:25:30.730314 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00238752 (* 0.0272727 = 6.51142e-05 loss)
I0327 13:25:30.730327 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 13:25:30.730340 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:25:30.730351 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:25:30.730363 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:25:30.730376 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0
I0327 13:25:30.730386 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.125
I0327 13:25:30.730398 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 13:25:30.730412 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.625
I0327 13:25:30.730423 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 13:25:30.730432 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 0.875
I0327 13:25:30.730439 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:25:30.730451 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:25:30.730463 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:25:30.730475 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:25:30.730486 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:25:30.730505 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:25:30.730517 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:25:30.730530 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:25:30.730541 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:25:30.730552 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:25:30.730564 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:25:30.730576 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:25:30.730589 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.7681 (* 0.0272727 = 0.0754935 loss)
I0327 13:25:30.730603 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 4.04725 (* 0.0272727 = 0.11038 loss)
I0327 13:25:30.730618 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.93704 (* 0.0272727 = 0.0801011 loss)
I0327 13:25:30.730631 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.81414 (* 0.0272727 = 0.104022 loss)
I0327 13:25:30.730645 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.85206 (* 0.0272727 = 0.105056 loss)
I0327 13:25:30.730659 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.70407 (* 0.0272727 = 0.10102 loss)
I0327 13:25:30.730674 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.94569 (* 0.0272727 = 0.0530644 loss)
I0327 13:25:30.730686 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 1.75076 (* 0.0272727 = 0.0477479 loss)
I0327 13:25:30.730700 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.421663 (* 0.0272727 = 0.0114999 loss)
I0327 13:25:30.730717 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.72574 (* 0.0272727 = 0.0197929 loss)
I0327 13:25:30.730733 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00178083 (* 0.0272727 = 4.85681e-05 loss)
I0327 13:25:30.730759 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00137393 (* 0.0272727 = 3.74708e-05 loss)
I0327 13:25:30.730774 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00124257 (* 0.0272727 = 3.38884e-05 loss)
I0327 13:25:30.730788 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00187566 (* 0.0272727 = 5.11543e-05 loss)
I0327 13:25:30.730803 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00109513 (* 0.0272727 = 2.98671e-05 loss)
I0327 13:25:30.730818 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00241178 (* 0.0272727 = 6.57757e-05 loss)
I0327 13:25:30.730831 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00135395 (* 0.0272727 = 3.69259e-05 loss)
I0327 13:25:30.730845 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00179703 (* 0.0272727 = 4.90099e-05 loss)
I0327 13:25:30.730860 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00111922 (* 0.0272727 = 3.05242e-05 loss)
I0327 13:25:30.730875 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00275745 (* 0.0272727 = 7.52032e-05 loss)
I0327 13:25:30.730888 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00126222 (* 0.0272727 = 3.44242e-05 loss)
I0327 13:25:30.730902 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.0017824 (* 0.0272727 = 4.8611e-05 loss)
I0327 13:25:30.730916 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.125
I0327 13:25:30.730929 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:25:30.730952 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.375
I0327 13:25:30.730967 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 13:25:30.730980 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0
I0327 13:25:30.730993 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.125
I0327 13:25:30.731005 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.5
I0327 13:25:30.731017 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.625
I0327 13:25:30.731029 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 13:25:30.731043 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 0.875
I0327 13:25:30.731055 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:25:30.731067 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:25:30.731079 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:25:30.731091 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:25:30.731103 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:25:30.731114 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:25:30.731127 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:25:30.731138 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:25:30.731149 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:25:30.731161 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:25:30.731173 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:25:30.731185 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:25:30.731199 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.87417 (* 0.0909091 = 0.261288 loss)
I0327 13:25:30.731214 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.8605 (* 0.0909091 = 0.350955 loss)
I0327 13:25:30.731227 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.77931 (* 0.0909091 = 0.252665 loss)
I0327 13:25:30.731241 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 4.15157 (* 0.0909091 = 0.377416 loss)
I0327 13:25:30.731256 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.58251 (* 0.0909091 = 0.325683 loss)
I0327 13:25:30.731269 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 3.89607 (* 0.0909091 = 0.354188 loss)
I0327 13:25:30.731294 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 2.29148 (* 0.0909091 = 0.208316 loss)
I0327 13:25:30.731310 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.99525 (* 0.0909091 = 0.181387 loss)
I0327 13:25:30.731324 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.419408 (* 0.0909091 = 0.038128 loss)
I0327 13:25:30.731338 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.646621 (* 0.0909091 = 0.0587838 loss)
I0327 13:25:30.731353 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000380915 (* 0.0909091 = 3.46287e-05 loss)
I0327 13:25:30.731367 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000434001 (* 0.0909091 = 3.94546e-05 loss)
I0327 13:25:30.731381 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000370655 (* 0.0909091 = 3.36959e-05 loss)
I0327 13:25:30.731395 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000360448 (* 0.0909091 = 3.2768e-05 loss)
I0327 13:25:30.731410 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000375264 (* 0.0909091 = 3.41149e-05 loss)
I0327 13:25:30.731425 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000380289 (* 0.0909091 = 3.45718e-05 loss)
I0327 13:25:30.731438 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000325311 (* 0.0909091 = 2.95737e-05 loss)
I0327 13:25:30.731452 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000392693 (* 0.0909091 = 3.56994e-05 loss)
I0327 13:25:30.731467 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000391371 (* 0.0909091 = 3.55792e-05 loss)
I0327 13:25:30.731482 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000319389 (* 0.0909091 = 2.90354e-05 loss)
I0327 13:25:30.731495 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000457426 (* 0.0909091 = 4.15841e-05 loss)
I0327 13:25:30.731509 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000300643 (* 0.0909091 = 2.73312e-05 loss)
I0327 13:25:30.731521 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:25:30.731534 21344 solver.cpp:245] Train net output #133: total_confidence = 1.65649e-05
I0327 13:25:30.731545 21344 sgd_solver.cpp:106] Iteration 10500, lr = 0.01
I0327 13:27:18.538147 21344 solver.cpp:229] Iteration 11000, loss = 2.87102
I0327 13:27:18.538331 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 13:27:18.538352 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:27:18.538365 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 13:27:18.538378 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 13:27:18.538390 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 13:27:18.538403 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 13:27:18.538414 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 13:27:18.538426 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 13:27:18.538439 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:27:18.538450 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:27:18.538461 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:27:18.538473 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:27:18.538486 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:27:18.538496 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:27:18.538508 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:27:18.538521 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:27:18.538532 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:27:18.538544 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:27:18.538555 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:27:18.538568 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:27:18.538579 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:27:18.538591 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:27:18.538609 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.9702 (* 0.0272727 = 0.0810056 loss)
I0327 13:27:18.538624 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.69979 (* 0.0272727 = 0.100903 loss)
I0327 13:27:18.538638 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.52361 (* 0.0272727 = 0.0960984 loss)
I0327 13:27:18.538652 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 4.00476 (* 0.0272727 = 0.109221 loss)
I0327 13:27:18.538666 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.1269 (* 0.0272727 = 0.0852791 loss)
I0327 13:27:18.538681 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.88557 (* 0.0272727 = 0.0786975 loss)
I0327 13:27:18.538694 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.646133 (* 0.0272727 = 0.0176218 loss)
I0327 13:27:18.538709 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0846533 (* 0.0272727 = 0.00230873 loss)
I0327 13:27:18.538723 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0269619 (* 0.0272727 = 0.000735325 loss)
I0327 13:27:18.538738 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0118837 (* 0.0272727 = 0.0003241 loss)
I0327 13:27:18.538753 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000835556 (* 0.0272727 = 2.27879e-05 loss)
I0327 13:27:18.538768 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00202398 (* 0.0272727 = 5.51995e-05 loss)
I0327 13:27:18.538781 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000388585 (* 0.0272727 = 1.05978e-05 loss)
I0327 13:27:18.538796 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00143819 (* 0.0272727 = 3.92233e-05 loss)
I0327 13:27:18.538810 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000827279 (* 0.0272727 = 2.25622e-05 loss)
I0327 13:27:18.538825 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000640009 (* 0.0272727 = 1.74548e-05 loss)
I0327 13:27:18.538838 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000707748 (* 0.0272727 = 1.93022e-05 loss)
I0327 13:27:18.538866 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000768568 (* 0.0272727 = 2.0961e-05 loss)
I0327 13:27:18.538882 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00127282 (* 0.0272727 = 3.47134e-05 loss)
I0327 13:27:18.538897 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000706808 (* 0.0272727 = 1.92766e-05 loss)
I0327 13:27:18.538911 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000651616 (* 0.0272727 = 1.77713e-05 loss)
I0327 13:27:18.538925 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00153138 (* 0.0272727 = 4.1765e-05 loss)
I0327 13:27:18.538938 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:27:18.538951 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:27:18.538964 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.25
I0327 13:27:18.538975 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:27:18.538987 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 13:27:18.539002 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:27:18.539016 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 13:27:18.539027 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:27:18.539039 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:27:18.539052 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:27:18.539063 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:27:18.539075 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:27:18.539088 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:27:18.539099 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:27:18.539110 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:27:18.539122 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:27:18.539134 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:27:18.539145 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:27:18.539157 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:27:18.539168 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:27:18.539180 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:27:18.539192 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:27:18.539206 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.65055 (* 0.0272727 = 0.0722878 loss)
I0327 13:27:18.539221 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.70946 (* 0.0272727 = 0.101167 loss)
I0327 13:27:18.539237 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.32033 (* 0.0272727 = 0.0905543 loss)
I0327 13:27:18.539252 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.54035 (* 0.0272727 = 0.096555 loss)
I0327 13:27:18.539266 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.82001 (* 0.0272727 = 0.0769094 loss)
I0327 13:27:18.539280 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.49854 (* 0.0272727 = 0.068142 loss)
I0327 13:27:18.539295 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.697841 (* 0.0272727 = 0.019032 loss)
I0327 13:27:18.539309 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.147193 (* 0.0272727 = 0.00401435 loss)
I0327 13:27:18.539324 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0293281 (* 0.0272727 = 0.000799858 loss)
I0327 13:27:18.539338 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0235881 (* 0.0272727 = 0.000643311 loss)
I0327 13:27:18.539357 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000154952 (* 0.0272727 = 4.22597e-06 loss)
I0327 13:27:18.539384 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000254386 (* 0.0272727 = 6.93779e-06 loss)
I0327 13:27:18.539399 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000282134 (* 0.0272727 = 7.69456e-06 loss)
I0327 13:27:18.539414 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000414658 (* 0.0272727 = 1.13089e-05 loss)
I0327 13:27:18.539428 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000159828 (* 0.0272727 = 4.35894e-06 loss)
I0327 13:27:18.539443 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000161728 (* 0.0272727 = 4.41078e-06 loss)
I0327 13:27:18.539458 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000403488 (* 0.0272727 = 1.10042e-05 loss)
I0327 13:27:18.539471 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00036341 (* 0.0272727 = 9.91117e-06 loss)
I0327 13:27:18.539485 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000222088 (* 0.0272727 = 6.05695e-06 loss)
I0327 13:27:18.539500 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000186395 (* 0.0272727 = 5.08349e-06 loss)
I0327 13:27:18.539515 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00031361 (* 0.0272727 = 8.55299e-06 loss)
I0327 13:27:18.539528 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000177207 (* 0.0272727 = 4.83291e-06 loss)
I0327 13:27:18.539541 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.125
I0327 13:27:18.539554 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:27:18.539566 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 13:27:18.539578 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 13:27:18.539589 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 13:27:18.539602 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:27:18.539613 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.875
I0327 13:27:18.539625 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:27:18.539638 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:27:18.539649 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:27:18.539660 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:27:18.539671 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:27:18.539683 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:27:18.539695 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:27:18.539707 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:27:18.539718 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:27:18.539731 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:27:18.539742 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:27:18.539753 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:27:18.539765 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:27:18.539777 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:27:18.539788 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:27:18.539803 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.3807 (* 0.0909091 = 0.216428 loss)
I0327 13:27:18.539816 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.6252 (* 0.0909091 = 0.329564 loss)
I0327 13:27:18.539831 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.0645 (* 0.0909091 = 0.278591 loss)
I0327 13:27:18.539845 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.59534 (* 0.0909091 = 0.326849 loss)
I0327 13:27:18.539860 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.54041 (* 0.0909091 = 0.230947 loss)
I0327 13:27:18.539875 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.21816 (* 0.0909091 = 0.201651 loss)
I0327 13:27:18.539901 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.668619 (* 0.0909091 = 0.0607836 loss)
I0327 13:27:18.539916 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.117688 (* 0.0909091 = 0.0106989 loss)
I0327 13:27:18.539930 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0297075 (* 0.0909091 = 0.00270068 loss)
I0327 13:27:18.539944 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0126466 (* 0.0909091 = 0.00114969 loss)
I0327 13:27:18.539959 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000435949 (* 0.0909091 = 3.96317e-05 loss)
I0327 13:27:18.539973 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000448359 (* 0.0909091 = 4.07599e-05 loss)
I0327 13:27:18.539988 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000570761 (* 0.0909091 = 5.18874e-05 loss)
I0327 13:27:18.540002 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000416587 (* 0.0909091 = 3.78715e-05 loss)
I0327 13:27:18.540016 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000440906 (* 0.0909091 = 4.00824e-05 loss)
I0327 13:27:18.540031 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000423236 (* 0.0909091 = 3.8476e-05 loss)
I0327 13:27:18.540048 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000410653 (* 0.0909091 = 3.73321e-05 loss)
I0327 13:27:18.540065 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000390604 (* 0.0909091 = 3.55095e-05 loss)
I0327 13:27:18.540078 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000494193 (* 0.0909091 = 4.49267e-05 loss)
I0327 13:27:18.540093 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.00042771 (* 0.0909091 = 3.88827e-05 loss)
I0327 13:27:18.540107 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000417389 (* 0.0909091 = 3.79444e-05 loss)
I0327 13:27:18.540122 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000392633 (* 0.0909091 = 3.56939e-05 loss)
I0327 13:27:18.540134 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:27:18.540146 21344 solver.cpp:245] Train net output #133: total_confidence = 6.93416e-05
I0327 13:27:18.540159 21344 sgd_solver.cpp:106] Iteration 11000, lr = 0.01
I0327 13:29:06.410200 21344 solver.cpp:229] Iteration 11500, loss = 2.87181
I0327 13:29:06.410327 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.25
I0327 13:29:06.410347 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:29:06.410359 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:29:06.410372 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 13:29:06.410383 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 13:29:06.410395 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 13:29:06.410408 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 13:29:06.410419 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 13:29:06.410431 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:29:06.410442 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:29:06.410454 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:29:06.410465 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:29:06.410476 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:29:06.410488 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:29:06.410500 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:29:06.410511 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:29:06.410522 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:29:06.410534 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:29:06.410545 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:29:06.410557 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:29:06.410568 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:29:06.410580 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:29:06.410596 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.49305 (* 0.0272727 = 0.0679923 loss)
I0327 13:29:06.410611 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.66807 (* 0.0272727 = 0.100038 loss)
I0327 13:29:06.410625 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.28933 (* 0.0272727 = 0.0897089 loss)
I0327 13:29:06.410640 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.7062 (* 0.0272727 = 0.0738056 loss)
I0327 13:29:06.410655 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.52258 (* 0.0272727 = 0.0687976 loss)
I0327 13:29:06.410667 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.89315 (* 0.0272727 = 0.078904 loss)
I0327 13:29:06.410681 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.65597 (* 0.0272727 = 0.0178901 loss)
I0327 13:29:06.410696 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.148765 (* 0.0272727 = 0.00405724 loss)
I0327 13:29:06.410711 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0133974 (* 0.0272727 = 0.000365382 loss)
I0327 13:29:06.410725 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00785946 (* 0.0272727 = 0.000214349 loss)
I0327 13:29:06.410740 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000351267 (* 0.0272727 = 9.58e-06 loss)
I0327 13:29:06.410755 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000205296 (* 0.0272727 = 5.59899e-06 loss)
I0327 13:29:06.410769 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000233095 (* 0.0272727 = 6.35714e-06 loss)
I0327 13:29:06.410784 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000217898 (* 0.0272727 = 5.94266e-06 loss)
I0327 13:29:06.410797 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000201262 (* 0.0272727 = 5.48896e-06 loss)
I0327 13:29:06.410811 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000233455 (* 0.0272727 = 6.36696e-06 loss)
I0327 13:29:06.410825 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000430799 (* 0.0272727 = 1.17491e-05 loss)
I0327 13:29:06.410856 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000337291 (* 0.0272727 = 9.19886e-06 loss)
I0327 13:29:06.410872 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000247087 (* 0.0272727 = 6.73873e-06 loss)
I0327 13:29:06.410887 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000132198 (* 0.0272727 = 3.60539e-06 loss)
I0327 13:29:06.410900 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000362362 (* 0.0272727 = 9.8826e-06 loss)
I0327 13:29:06.410914 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000323588 (* 0.0272727 = 8.82514e-06 loss)
I0327 13:29:06.410926 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 13:29:06.410939 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:29:06.410950 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:29:06.410962 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.25
I0327 13:29:06.410974 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:29:06.410986 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:29:06.411000 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 13:29:06.411013 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:29:06.411025 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:29:06.411036 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:29:06.411047 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:29:06.411058 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:29:06.411070 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:29:06.411082 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:29:06.411092 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:29:06.411104 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:29:06.411115 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:29:06.411126 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:29:06.411137 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:29:06.411149 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:29:06.411160 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:29:06.411171 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:29:06.411185 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.0245 (* 0.0272727 = 0.0552137 loss)
I0327 13:29:06.411200 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.48136 (* 0.0272727 = 0.0949461 loss)
I0327 13:29:06.411213 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.19312 (* 0.0272727 = 0.087085 loss)
I0327 13:29:06.411226 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.98432 (* 0.0272727 = 0.0813907 loss)
I0327 13:29:06.411242 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.28514 (* 0.0272727 = 0.0623219 loss)
I0327 13:29:06.411254 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.25246 (* 0.0272727 = 0.0614307 loss)
I0327 13:29:06.411268 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.609194 (* 0.0272727 = 0.0166144 loss)
I0327 13:29:06.411283 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.062196 (* 0.0272727 = 0.00169626 loss)
I0327 13:29:06.411298 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0157656 (* 0.0272727 = 0.000429971 loss)
I0327 13:29:06.411311 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00699484 (* 0.0272727 = 0.000190768 loss)
I0327 13:29:06.411325 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000188791 (* 0.0272727 = 5.14885e-06 loss)
I0327 13:29:06.411355 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000167446 (* 0.0272727 = 4.56671e-06 loss)
I0327 13:29:06.411370 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000231915 (* 0.0272727 = 6.32495e-06 loss)
I0327 13:29:06.411386 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000126343 (* 0.0272727 = 3.44573e-06 loss)
I0327 13:29:06.411399 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000148745 (* 0.0272727 = 4.05669e-06 loss)
I0327 13:29:06.411413 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000134986 (* 0.0272727 = 3.68144e-06 loss)
I0327 13:29:06.411427 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000103496 (* 0.0272727 = 2.82262e-06 loss)
I0327 13:29:06.411442 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000240977 (* 0.0272727 = 6.57211e-06 loss)
I0327 13:29:06.411455 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000272599 (* 0.0272727 = 7.43453e-06 loss)
I0327 13:29:06.411468 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 8.69886e-05 (* 0.0272727 = 2.37242e-06 loss)
I0327 13:29:06.411484 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000123937 (* 0.0272727 = 3.38011e-06 loss)
I0327 13:29:06.411497 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000210352 (* 0.0272727 = 5.73687e-06 loss)
I0327 13:29:06.411506 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.5
I0327 13:29:06.411520 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 13:29:06.411531 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:29:06.411542 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.25
I0327 13:29:06.411555 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 13:29:06.411566 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 13:29:06.411577 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 13:29:06.411588 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:29:06.411600 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:29:06.411612 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:29:06.411623 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:29:06.411634 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:29:06.411645 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:29:06.411658 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:29:06.411669 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:29:06.411679 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:29:06.411691 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:29:06.411702 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:29:06.411713 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:29:06.411725 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:29:06.411736 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:29:06.411747 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:29:06.411761 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.68443 (* 0.0909091 = 0.15313 loss)
I0327 13:29:06.411774 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.37616 (* 0.0909091 = 0.306924 loss)
I0327 13:29:06.411788 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.19383 (* 0.0909091 = 0.290348 loss)
I0327 13:29:06.411803 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.85158 (* 0.0909091 = 0.259235 loss)
I0327 13:29:06.411816 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.22893 (* 0.0909091 = 0.20263 loss)
I0327 13:29:06.411830 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.39358 (* 0.0909091 = 0.217598 loss)
I0327 13:29:06.411854 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.734856 (* 0.0909091 = 0.0668051 loss)
I0327 13:29:06.411870 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.131668 (* 0.0909091 = 0.0119698 loss)
I0327 13:29:06.411883 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.015655 (* 0.0909091 = 0.00142318 loss)
I0327 13:29:06.411897 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00443159 (* 0.0909091 = 0.000402872 loss)
I0327 13:29:06.411911 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 7.70368e-05 (* 0.0909091 = 7.00335e-06 loss)
I0327 13:29:06.411926 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 8.75355e-05 (* 0.0909091 = 7.95777e-06 loss)
I0327 13:29:06.411941 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000107195 (* 0.0909091 = 9.74501e-06 loss)
I0327 13:29:06.411953 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 9.59562e-05 (* 0.0909091 = 8.72329e-06 loss)
I0327 13:29:06.411967 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 6.8072e-05 (* 0.0909091 = 6.18837e-06 loss)
I0327 13:29:06.411981 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 7.14178e-05 (* 0.0909091 = 6.49253e-06 loss)
I0327 13:29:06.411995 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 7.10754e-05 (* 0.0909091 = 6.4614e-06 loss)
I0327 13:29:06.412009 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 7.4823e-05 (* 0.0909091 = 6.80209e-06 loss)
I0327 13:29:06.412024 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 9.3475e-05 (* 0.0909091 = 8.49773e-06 loss)
I0327 13:29:06.412037 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 7.48602e-05 (* 0.0909091 = 6.80547e-06 loss)
I0327 13:29:06.412055 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 9.32581e-05 (* 0.0909091 = 8.47801e-06 loss)
I0327 13:29:06.412070 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000105189 (* 0.0909091 = 9.5626e-06 loss)
I0327 13:29:06.412081 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:29:06.412093 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000530846
I0327 13:29:06.412106 21344 sgd_solver.cpp:106] Iteration 11500, lr = 0.01
I0327 13:30:54.346276 21344 solver.cpp:229] Iteration 12000, loss = 2.81083
I0327 13:30:54.346443 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.25
I0327 13:30:54.346465 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:30:54.346478 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.25
I0327 13:30:54.346490 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.5
I0327 13:30:54.346503 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 13:30:54.346515 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.5
I0327 13:30:54.346526 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 13:30:54.346539 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.625
I0327 13:30:54.346552 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 13:30:54.346565 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:30:54.346576 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:30:54.346587 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:30:54.346599 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:30:54.346611 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:30:54.346622 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:30:54.346634 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:30:54.346647 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:30:54.346658 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:30:54.346669 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:30:54.346681 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:30:54.346693 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:30:54.346704 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:30:54.346720 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.11842 (* 0.0272727 = 0.0850477 loss)
I0327 13:30:54.346735 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.4064 (* 0.0272727 = 0.0929018 loss)
I0327 13:30:54.346750 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.70501 (* 0.0272727 = 0.0737731 loss)
I0327 13:30:54.346763 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.84577 (* 0.0272727 = 0.077612 loss)
I0327 13:30:54.346777 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.08163 (* 0.0272727 = 0.0840445 loss)
I0327 13:30:54.346791 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.03238 (* 0.0272727 = 0.0554286 loss)
I0327 13:30:54.346806 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.82323 (* 0.0272727 = 0.0769971 loss)
I0327 13:30:54.346819 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.98292 (* 0.0272727 = 0.0540796 loss)
I0327 13:30:54.346833 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.530591 (* 0.0272727 = 0.0144707 loss)
I0327 13:30:54.346848 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0117894 (* 0.0272727 = 0.000321529 loss)
I0327 13:30:54.346863 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000488114 (* 0.0272727 = 1.33122e-05 loss)
I0327 13:30:54.346878 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000364166 (* 0.0272727 = 9.93181e-06 loss)
I0327 13:30:54.346891 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000228615 (* 0.0272727 = 6.23497e-06 loss)
I0327 13:30:54.346905 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000243511 (* 0.0272727 = 6.6412e-06 loss)
I0327 13:30:54.346920 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000217852 (* 0.0272727 = 5.94142e-06 loss)
I0327 13:30:54.346935 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000385495 (* 0.0272727 = 1.05135e-05 loss)
I0327 13:30:54.346948 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000416944 (* 0.0272727 = 1.13712e-05 loss)
I0327 13:30:54.346977 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000183999 (* 0.0272727 = 5.01816e-06 loss)
I0327 13:30:54.346992 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000400178 (* 0.0272727 = 1.09139e-05 loss)
I0327 13:30:54.347007 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000283489 (* 0.0272727 = 7.73152e-06 loss)
I0327 13:30:54.347020 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000207645 (* 0.0272727 = 5.66305e-06 loss)
I0327 13:30:54.347034 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000218723 (* 0.0272727 = 5.96516e-06 loss)
I0327 13:30:54.347046 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:30:54.347059 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:30:54.347071 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:30:54.347082 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.5
I0327 13:30:54.347097 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:30:54.347110 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:30:54.347122 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 13:30:54.347134 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.625
I0327 13:30:54.347146 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 13:30:54.347158 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:30:54.347169 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:30:54.347180 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:30:54.347193 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:30:54.347208 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:30:54.347219 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:30:54.347231 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:30:54.347242 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:30:54.347254 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:30:54.347265 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:30:54.347277 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:30:54.347288 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:30:54.347300 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:30:54.347313 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.78492 (* 0.0272727 = 0.0759523 loss)
I0327 13:30:54.347327 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.3438 (* 0.0272727 = 0.0911946 loss)
I0327 13:30:54.347342 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.10866 (* 0.0272727 = 0.0847817 loss)
I0327 13:30:54.347355 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.48661 (* 0.0272727 = 0.0678166 loss)
I0327 13:30:54.347369 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.73508 (* 0.0272727 = 0.074593 loss)
I0327 13:30:54.347383 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.22533 (* 0.0272727 = 0.0606908 loss)
I0327 13:30:54.347396 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 3.15387 (* 0.0272727 = 0.0860147 loss)
I0327 13:30:54.347410 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 2.24075 (* 0.0272727 = 0.0611114 loss)
I0327 13:30:54.347424 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.705329 (* 0.0272727 = 0.0192363 loss)
I0327 13:30:54.347437 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00707691 (* 0.0272727 = 0.000193007 loss)
I0327 13:30:54.347451 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000716744 (* 0.0272727 = 1.95476e-05 loss)
I0327 13:30:54.347476 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000393637 (* 0.0272727 = 1.07356e-05 loss)
I0327 13:30:54.347492 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000136423 (* 0.0272727 = 3.72064e-06 loss)
I0327 13:30:54.347512 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000413985 (* 0.0272727 = 1.12905e-05 loss)
I0327 13:30:54.347540 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000369237 (* 0.0272727 = 1.00701e-05 loss)
I0327 13:30:54.347566 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000122564 (* 0.0272727 = 3.34266e-06 loss)
I0327 13:30:54.347582 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000189822 (* 0.0272727 = 5.17697e-06 loss)
I0327 13:30:54.347596 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00022228 (* 0.0272727 = 6.06218e-06 loss)
I0327 13:30:54.347610 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000391387 (* 0.0272727 = 1.06742e-05 loss)
I0327 13:30:54.347625 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000223701 (* 0.0272727 = 6.10094e-06 loss)
I0327 13:30:54.347640 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000278658 (* 0.0272727 = 7.59977e-06 loss)
I0327 13:30:54.347653 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000288672 (* 0.0272727 = 7.87288e-06 loss)
I0327 13:30:54.347666 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.375
I0327 13:30:54.347677 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:30:54.347689 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 13:30:54.347702 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.375
I0327 13:30:54.347710 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 13:30:54.347718 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:30:54.347730 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.5
I0327 13:30:54.347743 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.625
I0327 13:30:54.347754 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 13:30:54.347765 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:30:54.347776 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:30:54.347789 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:30:54.347800 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:30:54.347811 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:30:54.347822 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:30:54.347834 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:30:54.347846 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:30:54.347857 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:30:54.347868 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:30:54.347879 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:30:54.347890 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:30:54.347903 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:30:54.347919 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.72808 (* 0.0909091 = 0.248008 loss)
I0327 13:30:54.347934 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.96059 (* 0.0909091 = 0.269145 loss)
I0327 13:30:54.347947 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.83438 (* 0.0909091 = 0.257671 loss)
I0327 13:30:54.347961 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.76709 (* 0.0909091 = 0.251553 loss)
I0327 13:30:54.347975 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.85401 (* 0.0909091 = 0.259455 loss)
I0327 13:30:54.348000 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.24875 (* 0.0909091 = 0.204432 loss)
I0327 13:30:54.348014 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 2.69432 (* 0.0909091 = 0.244938 loss)
I0327 13:30:54.348028 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 2.40658 (* 0.0909091 = 0.21878 loss)
I0327 13:30:54.348042 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.923821 (* 0.0909091 = 0.0839837 loss)
I0327 13:30:54.348057 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00357683 (* 0.0909091 = 0.000325166 loss)
I0327 13:30:54.348070 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000218967 (* 0.0909091 = 1.9906e-05 loss)
I0327 13:30:54.348084 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000185488 (* 0.0909091 = 1.68626e-05 loss)
I0327 13:30:54.348098 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000224967 (* 0.0909091 = 2.04515e-05 loss)
I0327 13:30:54.348112 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000179587 (* 0.0909091 = 1.63261e-05 loss)
I0327 13:30:54.348126 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000192797 (* 0.0909091 = 1.7527e-05 loss)
I0327 13:30:54.348140 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000184794 (* 0.0909091 = 1.67995e-05 loss)
I0327 13:30:54.348156 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000172475 (* 0.0909091 = 1.56796e-05 loss)
I0327 13:30:54.348171 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000204357 (* 0.0909091 = 1.85779e-05 loss)
I0327 13:30:54.348186 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000200007 (* 0.0909091 = 1.81825e-05 loss)
I0327 13:30:54.348199 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000177115 (* 0.0909091 = 1.61014e-05 loss)
I0327 13:30:54.348212 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000201828 (* 0.0909091 = 1.8348e-05 loss)
I0327 13:30:54.348227 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000227253 (* 0.0909091 = 2.06593e-05 loss)
I0327 13:30:54.348239 21344 solver.cpp:245] Train net output #132: total_accuracy = 0.125
I0327 13:30:54.348250 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000636635
I0327 13:30:54.348268 21344 sgd_solver.cpp:106] Iteration 12000, lr = 0.01
I0327 13:32:42.128144 21344 solver.cpp:229] Iteration 12500, loss = 2.80798
I0327 13:32:42.128255 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.5
I0327 13:32:42.128274 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:32:42.128288 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:32:42.128299 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 13:32:42.128311 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 13:32:42.128324 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.625
I0327 13:32:42.128336 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 13:32:42.128347 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 13:32:42.128360 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:32:42.128372 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:32:42.128383 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:32:42.128396 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:32:42.128407 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:32:42.128418 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:32:42.128429 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:32:42.128442 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:32:42.128453 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:32:42.128464 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:32:42.128475 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:32:42.128487 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:32:42.128499 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:32:42.128510 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:32:42.128526 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.05751 (* 0.0272727 = 0.056114 loss)
I0327 13:32:42.128540 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.72028 (* 0.0272727 = 0.101462 loss)
I0327 13:32:42.128554 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.30847 (* 0.0272727 = 0.0902309 loss)
I0327 13:32:42.128568 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.96206 (* 0.0272727 = 0.0807835 loss)
I0327 13:32:42.128582 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.25989 (* 0.0272727 = 0.088906 loss)
I0327 13:32:42.128597 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.74766 (* 0.0272727 = 0.0476634 loss)
I0327 13:32:42.128610 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.454014 (* 0.0272727 = 0.0123822 loss)
I0327 13:32:42.128624 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0187629 (* 0.0272727 = 0.000511714 loss)
I0327 13:32:42.128638 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.00399972 (* 0.0272727 = 0.000109083 loss)
I0327 13:32:42.128653 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00127791 (* 0.0272727 = 3.4852e-05 loss)
I0327 13:32:42.128666 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00017528 (* 0.0272727 = 4.78035e-06 loss)
I0327 13:32:42.128681 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000294916 (* 0.0272727 = 8.04318e-06 loss)
I0327 13:32:42.128695 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000234748 (* 0.0272727 = 6.40222e-06 loss)
I0327 13:32:42.128710 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000313296 (* 0.0272727 = 8.54445e-06 loss)
I0327 13:32:42.128723 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000322803 (* 0.0272727 = 8.80372e-06 loss)
I0327 13:32:42.128737 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000571877 (* 0.0272727 = 1.55966e-05 loss)
I0327 13:32:42.128751 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 9.52742e-05 (* 0.0272727 = 2.59839e-06 loss)
I0327 13:32:42.128782 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00018385 (* 0.0272727 = 5.0141e-06 loss)
I0327 13:32:42.128798 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000168447 (* 0.0272727 = 4.594e-06 loss)
I0327 13:32:42.128811 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000132825 (* 0.0272727 = 3.62249e-06 loss)
I0327 13:32:42.128825 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 9.85579e-05 (* 0.0272727 = 2.68794e-06 loss)
I0327 13:32:42.128839 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000152619 (* 0.0272727 = 4.16234e-06 loss)
I0327 13:32:42.128851 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.5
I0327 13:32:42.128865 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 13:32:42.128876 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:32:42.128888 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.25
I0327 13:32:42.128901 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:32:42.128912 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.5
I0327 13:32:42.128924 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 1
I0327 13:32:42.128936 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:32:42.128947 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:32:42.128959 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:32:42.128970 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:32:42.128981 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:32:42.128996 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:32:42.129007 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:32:42.129019 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:32:42.129030 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:32:42.129041 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:32:42.129052 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:32:42.129065 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:32:42.129076 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:32:42.129087 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:32:42.129096 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:32:42.129104 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.03579 (* 0.0272727 = 0.0555215 loss)
I0327 13:32:42.129119 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.48389 (* 0.0272727 = 0.0950153 loss)
I0327 13:32:42.129133 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.15412 (* 0.0272727 = 0.0860215 loss)
I0327 13:32:42.129148 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.10769 (* 0.0272727 = 0.084755 loss)
I0327 13:32:42.129161 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.49079 (* 0.0272727 = 0.0952033 loss)
I0327 13:32:42.129175 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.12209 (* 0.0272727 = 0.0578753 loss)
I0327 13:32:42.129189 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.422786 (* 0.0272727 = 0.0115305 loss)
I0327 13:32:42.129204 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0462438 (* 0.0272727 = 0.00126119 loss)
I0327 13:32:42.129217 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0107296 (* 0.0272727 = 0.000292625 loss)
I0327 13:32:42.129231 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00263322 (* 0.0272727 = 7.18151e-05 loss)
I0327 13:32:42.129245 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000175752 (* 0.0272727 = 4.79322e-06 loss)
I0327 13:32:42.129273 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000271391 (* 0.0272727 = 7.40157e-06 loss)
I0327 13:32:42.129289 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00043999 (* 0.0272727 = 1.19997e-05 loss)
I0327 13:32:42.129303 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.0010474 (* 0.0272727 = 2.85654e-05 loss)
I0327 13:32:42.129317 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.0004189 (* 0.0272727 = 1.14246e-05 loss)
I0327 13:32:42.129331 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000250745 (* 0.0272727 = 6.83851e-06 loss)
I0327 13:32:42.129345 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000159398 (* 0.0272727 = 4.34721e-06 loss)
I0327 13:32:42.129359 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000347259 (* 0.0272727 = 9.47069e-06 loss)
I0327 13:32:42.129374 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000114098 (* 0.0272727 = 3.11176e-06 loss)
I0327 13:32:42.129387 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000322125 (* 0.0272727 = 8.78523e-06 loss)
I0327 13:32:42.129401 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000221993 (* 0.0272727 = 6.05435e-06 loss)
I0327 13:32:42.129415 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000228431 (* 0.0272727 = 6.22994e-06 loss)
I0327 13:32:42.129427 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.625
I0327 13:32:42.129439 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:32:42.129451 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.375
I0327 13:32:42.129463 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.25
I0327 13:32:42.129475 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 13:32:42.129487 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.625
I0327 13:32:42.129498 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.875
I0327 13:32:42.129510 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:32:42.129521 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:32:42.129533 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:32:42.129559 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:32:42.129573 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:32:42.129585 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:32:42.129596 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:32:42.129608 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:32:42.129619 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:32:42.129631 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:32:42.129642 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:32:42.129653 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:32:42.129664 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:32:42.129676 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:32:42.129688 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:32:42.129701 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.30891 (* 0.0909091 = 0.118992 loss)
I0327 13:32:42.129714 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.0933 (* 0.0909091 = 0.281209 loss)
I0327 13:32:42.129729 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.83759 (* 0.0909091 = 0.257963 loss)
I0327 13:32:42.129742 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.91162 (* 0.0909091 = 0.264693 loss)
I0327 13:32:42.129756 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.33724 (* 0.0909091 = 0.303385 loss)
I0327 13:32:42.129781 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.4241 (* 0.0909091 = 0.129463 loss)
I0327 13:32:42.129796 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.551226 (* 0.0909091 = 0.0501114 loss)
I0327 13:32:42.129811 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0299592 (* 0.0909091 = 0.00272356 loss)
I0327 13:32:42.129824 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00882906 (* 0.0909091 = 0.000802642 loss)
I0327 13:32:42.129838 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00473137 (* 0.0909091 = 0.000430124 loss)
I0327 13:32:42.129853 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 7.87213e-05 (* 0.0909091 = 7.15648e-06 loss)
I0327 13:32:42.129866 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 8.14036e-05 (* 0.0909091 = 7.40033e-06 loss)
I0327 13:32:42.129881 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 8.95564e-05 (* 0.0909091 = 8.14149e-06 loss)
I0327 13:32:42.129895 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 9.46971e-05 (* 0.0909091 = 8.60883e-06 loss)
I0327 13:32:42.129909 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 8.56059e-05 (* 0.0909091 = 7.78236e-06 loss)
I0327 13:32:42.129923 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 8.28418e-05 (* 0.0909091 = 7.53107e-06 loss)
I0327 13:32:42.129937 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 8.03159e-05 (* 0.0909091 = 7.30145e-06 loss)
I0327 13:32:42.129951 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 7.14336e-05 (* 0.0909091 = 6.49396e-06 loss)
I0327 13:32:42.129966 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 8.1419e-05 (* 0.0909091 = 7.40173e-06 loss)
I0327 13:32:42.129979 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 8.28346e-05 (* 0.0909091 = 7.53042e-06 loss)
I0327 13:32:42.129994 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 8.82376e-05 (* 0.0909091 = 8.0216e-06 loss)
I0327 13:32:42.130008 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 8.75515e-05 (* 0.0909091 = 7.95923e-06 loss)
I0327 13:32:42.130020 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:32:42.130031 21344 solver.cpp:245] Train net output #133: total_confidence = 9.04926e-05
I0327 13:32:42.130048 21344 sgd_solver.cpp:106] Iteration 12500, lr = 0.01
I0327 13:34:29.860158 21344 solver.cpp:229] Iteration 13000, loss = 2.80828
I0327 13:34:29.860309 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.25
I0327 13:34:29.860330 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:34:29.860343 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 13:34:29.860355 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.375
I0327 13:34:29.860368 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.5
I0327 13:34:29.860379 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.5
I0327 13:34:29.860391 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 13:34:29.860404 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:34:29.860415 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:34:29.860427 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:34:29.860440 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:34:29.860450 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:34:29.860462 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:34:29.860474 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:34:29.860486 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:34:29.860497 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:34:29.860508 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:34:29.860520 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:34:29.860532 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:34:29.860543 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:34:29.860555 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:34:29.860566 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:34:29.860582 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.38971 (* 0.0272727 = 0.0651738 loss)
I0327 13:34:29.860597 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.26878 (* 0.0272727 = 0.0891486 loss)
I0327 13:34:29.860611 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.62291 (* 0.0272727 = 0.071534 loss)
I0327 13:34:29.860625 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.73491 (* 0.0272727 = 0.0745884 loss)
I0327 13:34:29.860640 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 1.82241 (* 0.0272727 = 0.0497021 loss)
I0327 13:34:29.860653 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.96279 (* 0.0272727 = 0.0535306 loss)
I0327 13:34:29.860667 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.15458 (* 0.0272727 = 0.0587612 loss)
I0327 13:34:29.860682 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.364512 (* 0.0272727 = 0.00994125 loss)
I0327 13:34:29.860697 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0136941 (* 0.0272727 = 0.000373476 loss)
I0327 13:34:29.860710 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00614214 (* 0.0272727 = 0.000167513 loss)
I0327 13:34:29.860725 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000122899 (* 0.0272727 = 3.35179e-06 loss)
I0327 13:34:29.860739 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000203331 (* 0.0272727 = 5.54538e-06 loss)
I0327 13:34:29.860754 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00019237 (* 0.0272727 = 5.24645e-06 loss)
I0327 13:34:29.860767 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00015768 (* 0.0272727 = 4.30036e-06 loss)
I0327 13:34:29.860781 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000204989 (* 0.0272727 = 5.5906e-06 loss)
I0327 13:34:29.860795 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000156965 (* 0.0272727 = 4.28087e-06 loss)
I0327 13:34:29.860810 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000133347 (* 0.0272727 = 3.63672e-06 loss)
I0327 13:34:29.860837 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000157809 (* 0.0272727 = 4.30388e-06 loss)
I0327 13:34:29.860853 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000243953 (* 0.0272727 = 6.65328e-06 loss)
I0327 13:34:29.860885 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000168064 (* 0.0272727 = 4.58357e-06 loss)
I0327 13:34:29.860901 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000415257 (* 0.0272727 = 1.13252e-05 loss)
I0327 13:34:29.860915 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000347464 (* 0.0272727 = 9.4763e-06 loss)
I0327 13:34:29.860927 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 13:34:29.860939 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:34:29.860951 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.375
I0327 13:34:29.860963 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 13:34:29.860975 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:34:29.860987 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:34:29.861001 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 13:34:29.861013 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:34:29.861026 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:34:29.861037 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:34:29.861048 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:34:29.861059 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:34:29.861071 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:34:29.861083 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:34:29.861093 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:34:29.861105 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:34:29.861116 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:34:29.861127 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:34:29.861138 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:34:29.861150 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:34:29.861161 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:34:29.861173 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:34:29.861186 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.15049 (* 0.0272727 = 0.0586497 loss)
I0327 13:34:29.861201 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.31932 (* 0.0272727 = 0.090527 loss)
I0327 13:34:29.861214 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.37389 (* 0.0272727 = 0.0647425 loss)
I0327 13:34:29.861227 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.84611 (* 0.0272727 = 0.0776211 loss)
I0327 13:34:29.861241 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.24949 (* 0.0272727 = 0.0613498 loss)
I0327 13:34:29.861255 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.12252 (* 0.0272727 = 0.0578869 loss)
I0327 13:34:29.861268 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 2.44894 (* 0.0272727 = 0.0667894 loss)
I0327 13:34:29.861282 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.528778 (* 0.0272727 = 0.0144212 loss)
I0327 13:34:29.861296 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.00869116 (* 0.0272727 = 0.000237032 loss)
I0327 13:34:29.861310 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00263392 (* 0.0272727 = 7.18341e-05 loss)
I0327 13:34:29.861327 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 6.58613e-05 (* 0.0272727 = 1.79622e-06 loss)
I0327 13:34:29.861353 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000177835 (* 0.0272727 = 4.85005e-06 loss)
I0327 13:34:29.861369 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000286185 (* 0.0272727 = 7.80504e-06 loss)
I0327 13:34:29.861383 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000282914 (* 0.0272727 = 7.71582e-06 loss)
I0327 13:34:29.861397 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000119643 (* 0.0272727 = 3.26298e-06 loss)
I0327 13:34:29.861412 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 4.63829e-05 (* 0.0272727 = 1.26499e-06 loss)
I0327 13:34:29.861428 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000106346 (* 0.0272727 = 2.90035e-06 loss)
I0327 13:34:29.861443 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000168512 (* 0.0272727 = 4.59578e-06 loss)
I0327 13:34:29.861457 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 7.4793e-05 (* 0.0272727 = 2.03981e-06 loss)
I0327 13:34:29.861471 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000110982 (* 0.0272727 = 3.02679e-06 loss)
I0327 13:34:29.861485 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 6.92596e-05 (* 0.0272727 = 1.8889e-06 loss)
I0327 13:34:29.861500 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000154772 (* 0.0272727 = 4.22105e-06 loss)
I0327 13:34:29.861511 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.375
I0327 13:34:29.861523 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 13:34:29.861536 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.375
I0327 13:34:29.861562 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.375
I0327 13:34:29.861574 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 13:34:29.861587 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:34:29.861598 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.5
I0327 13:34:29.861609 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:34:29.861621 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:34:29.861632 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:34:29.861644 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:34:29.861654 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:34:29.861666 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:34:29.861677 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:34:29.861688 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:34:29.861701 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:34:29.861711 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:34:29.861722 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:34:29.861733 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:34:29.861745 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:34:29.861757 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:34:29.861768 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:34:29.861781 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.53908 (* 0.0909091 = 0.139917 loss)
I0327 13:34:29.861794 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.20929 (* 0.0909091 = 0.291754 loss)
I0327 13:34:29.861809 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.29936 (* 0.0909091 = 0.209033 loss)
I0327 13:34:29.861822 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.53982 (* 0.0909091 = 0.230893 loss)
I0327 13:34:29.861835 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.26611 (* 0.0909091 = 0.20601 loss)
I0327 13:34:29.861860 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.06029 (* 0.0909091 = 0.187299 loss)
I0327 13:34:29.861876 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 2.35443 (* 0.0909091 = 0.214039 loss)
I0327 13:34:29.861889 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.511495 (* 0.0909091 = 0.0464996 loss)
I0327 13:34:29.861903 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00625295 (* 0.0909091 = 0.00056845 loss)
I0327 13:34:29.861917 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00295558 (* 0.0909091 = 0.000268689 loss)
I0327 13:34:29.861932 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 6.2946e-05 (* 0.0909091 = 5.72236e-06 loss)
I0327 13:34:29.861945 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000105368 (* 0.0909091 = 9.5789e-06 loss)
I0327 13:34:29.861959 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 9.10761e-05 (* 0.0909091 = 8.27964e-06 loss)
I0327 13:34:29.861974 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000111688 (* 0.0909091 = 1.01535e-05 loss)
I0327 13:34:29.861987 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 9.7461e-05 (* 0.0909091 = 8.8601e-06 loss)
I0327 13:34:29.862002 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 9.84016e-05 (* 0.0909091 = 8.9456e-06 loss)
I0327 13:34:29.862016 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 8.18659e-05 (* 0.0909091 = 7.44235e-06 loss)
I0327 13:34:29.862030 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 8.01065e-05 (* 0.0909091 = 7.28241e-06 loss)
I0327 13:34:29.862047 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 8.96824e-05 (* 0.0909091 = 8.15294e-06 loss)
I0327 13:34:29.862061 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 9.13661e-05 (* 0.0909091 = 8.30601e-06 loss)
I0327 13:34:29.862076 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000120928 (* 0.0909091 = 1.09934e-05 loss)
I0327 13:34:29.862089 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 9.95402e-05 (* 0.0909091 = 9.04911e-06 loss)
I0327 13:34:29.862102 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:34:29.862113 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00256634
I0327 13:34:29.862125 21344 sgd_solver.cpp:106] Iteration 13000, lr = 0.01
I0327 13:36:17.617172 21344 solver.cpp:229] Iteration 13500, loss = 2.78226
I0327 13:36:17.617321 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.375
I0327 13:36:17.617341 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:36:17.617353 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:36:17.617365 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.375
I0327 13:36:17.617377 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.5
I0327 13:36:17.617389 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 13:36:17.617400 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:36:17.617413 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:36:17.617425 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:36:17.617437 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:36:17.617449 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:36:17.617460 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:36:17.617471 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:36:17.617483 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:36:17.617496 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:36:17.617506 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:36:17.617518 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:36:17.617530 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:36:17.617554 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:36:17.617568 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:36:17.617580 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:36:17.617591 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:36:17.617609 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.29567 (* 0.0272727 = 0.0626091 loss)
I0327 13:36:17.617622 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.09413 (* 0.0272727 = 0.0843852 loss)
I0327 13:36:17.617637 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.51151 (* 0.0272727 = 0.0957684 loss)
I0327 13:36:17.617651 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.75979 (* 0.0272727 = 0.075267 loss)
I0327 13:36:17.617666 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.36382 (* 0.0272727 = 0.0644677 loss)
I0327 13:36:17.617679 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.81879 (* 0.0272727 = 0.0768762 loss)
I0327 13:36:17.617693 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.71203 (* 0.0272727 = 0.0466918 loss)
I0327 13:36:17.617707 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.523539 (* 0.0272727 = 0.0142783 loss)
I0327 13:36:17.617722 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.121186 (* 0.0272727 = 0.00330507 loss)
I0327 13:36:17.617736 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0690465 (* 0.0272727 = 0.00188309 loss)
I0327 13:36:17.617750 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000371176 (* 0.0272727 = 1.0123e-05 loss)
I0327 13:36:17.617765 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00047469 (* 0.0272727 = 1.29461e-05 loss)
I0327 13:36:17.617779 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000699747 (* 0.0272727 = 1.9084e-05 loss)
I0327 13:36:17.617794 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000550624 (* 0.0272727 = 1.5017e-05 loss)
I0327 13:36:17.617808 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000350128 (* 0.0272727 = 9.54894e-06 loss)
I0327 13:36:17.617823 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000538459 (* 0.0272727 = 1.46852e-05 loss)
I0327 13:36:17.617837 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000730471 (* 0.0272727 = 1.99219e-05 loss)
I0327 13:36:17.617866 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00021315 (* 0.0272727 = 5.81318e-06 loss)
I0327 13:36:17.617882 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000355611 (* 0.0272727 = 9.69847e-06 loss)
I0327 13:36:17.617895 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000623389 (* 0.0272727 = 1.70015e-05 loss)
I0327 13:36:17.617909 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000123044 (* 0.0272727 = 3.35574e-06 loss)
I0327 13:36:17.617928 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000538055 (* 0.0272727 = 1.46742e-05 loss)
I0327 13:36:17.617941 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.5
I0327 13:36:17.617954 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.25
I0327 13:36:17.617966 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:36:17.617979 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.25
I0327 13:36:17.617992 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.375
I0327 13:36:17.618005 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:36:17.618016 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:36:17.618029 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:36:17.618041 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:36:17.618052 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:36:17.618064 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:36:17.618075 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:36:17.618086 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:36:17.618098 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:36:17.618109 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:36:17.618120 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:36:17.618132 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:36:17.618144 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:36:17.618155 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:36:17.618166 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:36:17.618177 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:36:17.618188 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:36:17.618202 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.97853 (* 0.0272727 = 0.05396 loss)
I0327 13:36:17.618216 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.99794 (* 0.0272727 = 0.0817621 loss)
I0327 13:36:17.618230 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.20416 (* 0.0272727 = 0.0873863 loss)
I0327 13:36:17.618245 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.8095 (* 0.0272727 = 0.0766228 loss)
I0327 13:36:17.618259 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.52723 (* 0.0272727 = 0.0689245 loss)
I0327 13:36:17.618273 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.56923 (* 0.0272727 = 0.0700698 loss)
I0327 13:36:17.618288 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.47709 (* 0.0272727 = 0.0402844 loss)
I0327 13:36:17.618301 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.53005 (* 0.0272727 = 0.0144559 loss)
I0327 13:36:17.618315 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.123081 (* 0.0272727 = 0.00335677 loss)
I0327 13:36:17.618330 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0340428 (* 0.0272727 = 0.000928439 loss)
I0327 13:36:17.618350 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00126824 (* 0.0272727 = 3.45885e-05 loss)
I0327 13:36:17.618376 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000447884 (* 0.0272727 = 1.2215e-05 loss)
I0327 13:36:17.618391 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000861739 (* 0.0272727 = 2.3502e-05 loss)
I0327 13:36:17.618405 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000411572 (* 0.0272727 = 1.12247e-05 loss)
I0327 13:36:17.618419 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000358447 (* 0.0272727 = 9.77583e-06 loss)
I0327 13:36:17.618433 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000496685 (* 0.0272727 = 1.3546e-05 loss)
I0327 13:36:17.618443 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000402863 (* 0.0272727 = 1.09872e-05 loss)
I0327 13:36:17.618458 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.0012818 (* 0.0272727 = 3.49583e-05 loss)
I0327 13:36:17.618474 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000422827 (* 0.0272727 = 1.15317e-05 loss)
I0327 13:36:17.618487 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000500004 (* 0.0272727 = 1.36365e-05 loss)
I0327 13:36:17.618501 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00024724 (* 0.0272727 = 6.74292e-06 loss)
I0327 13:36:17.618515 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000998298 (* 0.0272727 = 2.72263e-05 loss)
I0327 13:36:17.618527 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.5
I0327 13:36:17.618541 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:36:17.618552 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:36:17.618564 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:36:17.618577 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.5
I0327 13:36:17.618587 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:36:17.618599 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:36:17.618612 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:36:17.618623 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:36:17.618634 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:36:17.618646 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:36:17.618657 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:36:17.618669 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:36:17.618680 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:36:17.618691 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:36:17.618702 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:36:17.618715 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:36:17.618726 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:36:17.618736 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:36:17.618747 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:36:17.618759 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:36:17.618770 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:36:17.618784 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.72012 (* 0.0909091 = 0.156375 loss)
I0327 13:36:17.618798 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.96707 (* 0.0909091 = 0.269734 loss)
I0327 13:36:17.618811 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.74261 (* 0.0909091 = 0.340237 loss)
I0327 13:36:17.618825 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.97033 (* 0.0909091 = 0.27003 loss)
I0327 13:36:17.618839 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.47896 (* 0.0909091 = 0.22536 loss)
I0327 13:36:17.618863 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.65156 (* 0.0909091 = 0.241051 loss)
I0327 13:36:17.618878 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.53351 (* 0.0909091 = 0.13941 loss)
I0327 13:36:17.618891 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.514992 (* 0.0909091 = 0.0468175 loss)
I0327 13:36:17.618906 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.120035 (* 0.0909091 = 0.0109123 loss)
I0327 13:36:17.618921 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0376088 (* 0.0909091 = 0.00341899 loss)
I0327 13:36:17.618934 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000359199 (* 0.0909091 = 3.26545e-05 loss)
I0327 13:36:17.618948 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000188884 (* 0.0909091 = 1.71712e-05 loss)
I0327 13:36:17.618963 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000325675 (* 0.0909091 = 2.96069e-05 loss)
I0327 13:36:17.618976 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000341068 (* 0.0909091 = 3.10061e-05 loss)
I0327 13:36:17.618990 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000242033 (* 0.0909091 = 2.2003e-05 loss)
I0327 13:36:17.619004 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000226336 (* 0.0909091 = 2.0576e-05 loss)
I0327 13:36:17.619019 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000215208 (* 0.0909091 = 1.95643e-05 loss)
I0327 13:36:17.619032 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000359127 (* 0.0909091 = 3.26479e-05 loss)
I0327 13:36:17.619050 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000355424 (* 0.0909091 = 3.23113e-05 loss)
I0327 13:36:17.619065 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000232779 (* 0.0909091 = 2.11617e-05 loss)
I0327 13:36:17.619078 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000233226 (* 0.0909091 = 2.12024e-05 loss)
I0327 13:36:17.619092 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.00026633 (* 0.0909091 = 2.42118e-05 loss)
I0327 13:36:17.619104 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:36:17.619115 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000570221
I0327 13:36:17.619128 21344 sgd_solver.cpp:106] Iteration 13500, lr = 0.01
I0327 13:38:05.403362 21344 solver.cpp:229] Iteration 14000, loss = 2.78852
I0327 13:38:05.403502 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 13:38:05.403524 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:38:05.403537 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:38:05.403549 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:38:05.403561 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 13:38:05.403573 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 13:38:05.403586 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:38:05.403599 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:38:05.403610 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:38:05.403623 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:38:05.403635 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:38:05.403646 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:38:05.403658 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:38:05.403671 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:38:05.403682 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:38:05.403695 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:38:05.403707 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:38:05.403719 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:38:05.403730 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:38:05.403743 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:38:05.403754 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:38:05.403765 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:38:05.403782 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.47887 (* 0.0272727 = 0.0948782 loss)
I0327 13:38:05.403797 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.8506 (* 0.0272727 = 0.0777436 loss)
I0327 13:38:05.403812 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.30889 (* 0.0272727 = 0.0902424 loss)
I0327 13:38:05.403826 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.74392 (* 0.0272727 = 0.102107 loss)
I0327 13:38:05.403841 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.62918 (* 0.0272727 = 0.0717048 loss)
I0327 13:38:05.403854 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.97178 (* 0.0272727 = 0.0537758 loss)
I0327 13:38:05.403869 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.55942 (* 0.0272727 = 0.0425296 loss)
I0327 13:38:05.403883 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.891726 (* 0.0272727 = 0.0243198 loss)
I0327 13:38:05.403898 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0280083 (* 0.0272727 = 0.000763863 loss)
I0327 13:38:05.403913 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0075163 (* 0.0272727 = 0.00020499 loss)
I0327 13:38:05.403928 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000298411 (* 0.0272727 = 8.13848e-06 loss)
I0327 13:38:05.403942 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000234952 (* 0.0272727 = 6.40778e-06 loss)
I0327 13:38:05.403956 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00035221 (* 0.0272727 = 9.60573e-06 loss)
I0327 13:38:05.403971 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000373395 (* 0.0272727 = 1.01835e-05 loss)
I0327 13:38:05.403986 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00051902 (* 0.0272727 = 1.41551e-05 loss)
I0327 13:38:05.404005 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000442875 (* 0.0272727 = 1.20784e-05 loss)
I0327 13:38:05.404019 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000191633 (* 0.0272727 = 5.22637e-06 loss)
I0327 13:38:05.404050 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000213148 (* 0.0272727 = 5.81313e-06 loss)
I0327 13:38:05.404067 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000226425 (* 0.0272727 = 6.17523e-06 loss)
I0327 13:38:05.404080 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000408371 (* 0.0272727 = 1.11374e-05 loss)
I0327 13:38:05.404094 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000249477 (* 0.0272727 = 6.80392e-06 loss)
I0327 13:38:05.404109 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000429737 (* 0.0272727 = 1.17201e-05 loss)
I0327 13:38:05.404121 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0
I0327 13:38:05.404134 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:38:05.404145 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:38:05.404157 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:38:05.404168 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.5
I0327 13:38:05.404181 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:38:05.404193 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:38:05.404206 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:38:05.404217 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:38:05.404228 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:38:05.404240 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:38:05.404253 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:38:05.404264 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:38:05.404275 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:38:05.404287 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:38:05.404299 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:38:05.404310 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:38:05.404321 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:38:05.404333 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:38:05.404345 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:38:05.404356 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:38:05.404368 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:38:05.404382 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.68017 (* 0.0272727 = 0.100368 loss)
I0327 13:38:05.404397 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.88689 (* 0.0272727 = 0.0787333 loss)
I0327 13:38:05.404410 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.03293 (* 0.0272727 = 0.0827162 loss)
I0327 13:38:05.404424 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.55949 (* 0.0272727 = 0.097077 loss)
I0327 13:38:05.404438 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.2783 (* 0.0272727 = 0.0621354 loss)
I0327 13:38:05.404451 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.11678 (* 0.0272727 = 0.0577305 loss)
I0327 13:38:05.404466 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.24048 (* 0.0272727 = 0.0338313 loss)
I0327 13:38:05.404480 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.745608 (* 0.0272727 = 0.0203348 loss)
I0327 13:38:05.404495 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0541043 (* 0.0272727 = 0.00147557 loss)
I0327 13:38:05.404508 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0136536 (* 0.0272727 = 0.000372372 loss)
I0327 13:38:05.404526 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000959546 (* 0.0272727 = 2.61694e-05 loss)
I0327 13:38:05.404551 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00058299 (* 0.0272727 = 1.58997e-05 loss)
I0327 13:38:05.404567 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000531162 (* 0.0272727 = 1.44862e-05 loss)
I0327 13:38:05.404582 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000643033 (* 0.0272727 = 1.75373e-05 loss)
I0327 13:38:05.404595 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000838522 (* 0.0272727 = 2.28688e-05 loss)
I0327 13:38:05.404610 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000688392 (* 0.0272727 = 1.87743e-05 loss)
I0327 13:38:05.404624 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000380382 (* 0.0272727 = 1.0374e-05 loss)
I0327 13:38:05.404639 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000443804 (* 0.0272727 = 1.21037e-05 loss)
I0327 13:38:05.404652 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000674941 (* 0.0272727 = 1.84075e-05 loss)
I0327 13:38:05.404666 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000795223 (* 0.0272727 = 2.16879e-05 loss)
I0327 13:38:05.404681 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00068025 (* 0.0272727 = 1.85523e-05 loss)
I0327 13:38:05.404695 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000520615 (* 0.0272727 = 1.41986e-05 loss)
I0327 13:38:05.404707 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.125
I0327 13:38:05.404719 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 13:38:05.404731 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 13:38:05.404743 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 13:38:05.404754 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.5
I0327 13:38:05.404767 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:38:05.404778 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:38:05.404789 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:38:05.404800 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:38:05.404813 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:38:05.404824 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:38:05.404834 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:38:05.404846 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:38:05.404857 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:38:05.404868 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:38:05.404881 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:38:05.404891 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:38:05.404903 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:38:05.404914 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:38:05.404925 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:38:05.404937 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:38:05.404948 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:38:05.404963 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.95326 (* 0.0909091 = 0.268478 loss)
I0327 13:38:05.404976 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.91414 (* 0.0909091 = 0.264922 loss)
I0327 13:38:05.404990 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.07709 (* 0.0909091 = 0.279735 loss)
I0327 13:38:05.405004 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.61057 (* 0.0909091 = 0.328234 loss)
I0327 13:38:05.405019 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.15239 (* 0.0909091 = 0.195672 loss)
I0327 13:38:05.405037 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.06928 (* 0.0909091 = 0.188117 loss)
I0327 13:38:05.405055 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.22502 (* 0.0909091 = 0.111365 loss)
I0327 13:38:05.405069 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.654805 (* 0.0909091 = 0.0595277 loss)
I0327 13:38:05.405083 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0913219 (* 0.0909091 = 0.00830199 loss)
I0327 13:38:05.405097 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0257534 (* 0.0909091 = 0.00234122 loss)
I0327 13:38:05.405112 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000537183 (* 0.0909091 = 4.88348e-05 loss)
I0327 13:38:05.405125 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000592985 (* 0.0909091 = 5.39078e-05 loss)
I0327 13:38:05.405139 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000543524 (* 0.0909091 = 4.94112e-05 loss)
I0327 13:38:05.405153 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000498671 (* 0.0909091 = 4.53338e-05 loss)
I0327 13:38:05.405167 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000533478 (* 0.0909091 = 4.8498e-05 loss)
I0327 13:38:05.405181 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000483904 (* 0.0909091 = 4.39913e-05 loss)
I0327 13:38:05.405195 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000726191 (* 0.0909091 = 6.60173e-05 loss)
I0327 13:38:05.405210 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000510721 (* 0.0909091 = 4.64292e-05 loss)
I0327 13:38:05.405223 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000524034 (* 0.0909091 = 4.76395e-05 loss)
I0327 13:38:05.405237 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.00052241 (* 0.0909091 = 4.74918e-05 loss)
I0327 13:38:05.405252 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000459526 (* 0.0909091 = 4.17751e-05 loss)
I0327 13:38:05.405267 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000581329 (* 0.0909091 = 5.28481e-05 loss)
I0327 13:38:05.405278 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:38:05.405289 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000248387
I0327 13:38:05.405303 21344 sgd_solver.cpp:106] Iteration 14000, lr = 0.01
I0327 13:39:53.324316 21344 solver.cpp:229] Iteration 14500, loss = 2.76681
I0327 13:39:53.324458 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.625
I0327 13:39:53.324481 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:39:53.324493 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:39:53.324506 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 13:39:53.324517 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 13:39:53.324529 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 13:39:53.324542 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:39:53.324553 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:39:53.324565 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 13:39:53.324578 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 0.875
I0327 13:39:53.324589 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:39:53.324601 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:39:53.324612 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:39:53.324625 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:39:53.324635 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:39:53.324647 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:39:53.324659 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:39:53.324671 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:39:53.324682 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:39:53.324694 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:39:53.324705 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:39:53.324717 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:39:53.324733 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.98267 (* 0.0272727 = 0.0540727 loss)
I0327 13:39:53.324748 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.18887 (* 0.0272727 = 0.0869692 loss)
I0327 13:39:53.324762 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.65277 (* 0.0272727 = 0.0996209 loss)
I0327 13:39:53.324776 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.1325 (* 0.0272727 = 0.0854317 loss)
I0327 13:39:53.324790 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.29635 (* 0.0272727 = 0.0899004 loss)
I0327 13:39:53.324805 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 3.00352 (* 0.0272727 = 0.0819143 loss)
I0327 13:39:53.324817 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.52122 (* 0.0272727 = 0.0414877 loss)
I0327 13:39:53.324831 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.483624 (* 0.0272727 = 0.0131897 loss)
I0327 13:39:53.324846 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.440637 (* 0.0272727 = 0.0120174 loss)
I0327 13:39:53.324861 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.533713 (* 0.0272727 = 0.0145558 loss)
I0327 13:39:53.324874 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00101095 (* 0.0272727 = 2.75713e-05 loss)
I0327 13:39:53.324889 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00187141 (* 0.0272727 = 5.10386e-05 loss)
I0327 13:39:53.324903 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.0068516 (* 0.0272727 = 0.000186862 loss)
I0327 13:39:53.324918 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00260003 (* 0.0272727 = 7.091e-05 loss)
I0327 13:39:53.324933 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00210263 (* 0.0272727 = 5.73446e-05 loss)
I0327 13:39:53.324946 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00307438 (* 0.0272727 = 8.38468e-05 loss)
I0327 13:39:53.324961 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00299423 (* 0.0272727 = 8.16607e-05 loss)
I0327 13:39:53.324988 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00116149 (* 0.0272727 = 3.16769e-05 loss)
I0327 13:39:53.325007 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00215325 (* 0.0272727 = 5.87251e-05 loss)
I0327 13:39:53.325021 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00150531 (* 0.0272727 = 4.10538e-05 loss)
I0327 13:39:53.325036 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00230907 (* 0.0272727 = 6.29748e-05 loss)
I0327 13:39:53.325049 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00351501 (* 0.0272727 = 9.58638e-05 loss)
I0327 13:39:53.325062 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.625
I0327 13:39:53.325074 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:39:53.325085 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:39:53.325098 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:39:53.325109 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.375
I0327 13:39:53.325121 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 13:39:53.325132 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:39:53.325145 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:39:53.325156 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 13:39:53.325168 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 0.875
I0327 13:39:53.325179 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:39:53.325191 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:39:53.325202 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:39:53.325213 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:39:53.325224 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:39:53.325237 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:39:53.325248 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:39:53.325258 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:39:53.325269 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:39:53.325280 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:39:53.325291 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:39:53.325304 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:39:53.325314 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.90215 (* 0.0272727 = 0.0518768 loss)
I0327 13:39:53.325323 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.35679 (* 0.0272727 = 0.0915489 loss)
I0327 13:39:53.325337 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.12805 (* 0.0272727 = 0.0853103 loss)
I0327 13:39:53.325351 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.31498 (* 0.0272727 = 0.0904085 loss)
I0327 13:39:53.325366 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.605 (* 0.0272727 = 0.0710454 loss)
I0327 13:39:53.325379 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.24461 (* 0.0272727 = 0.0884894 loss)
I0327 13:39:53.325393 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.78626 (* 0.0272727 = 0.0487161 loss)
I0327 13:39:53.325407 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.639773 (* 0.0272727 = 0.0174483 loss)
I0327 13:39:53.325420 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.53135 (* 0.0272727 = 0.0144914 loss)
I0327 13:39:53.325434 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.694693 (* 0.0272727 = 0.0189462 loss)
I0327 13:39:53.325449 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00042289 (* 0.0272727 = 1.15334e-05 loss)
I0327 13:39:53.325477 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000627649 (* 0.0272727 = 1.71177e-05 loss)
I0327 13:39:53.325494 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000938941 (* 0.0272727 = 2.56075e-05 loss)
I0327 13:39:53.325508 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00038741 (* 0.0272727 = 1.05657e-05 loss)
I0327 13:39:53.325523 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00030928 (* 0.0272727 = 8.43491e-06 loss)
I0327 13:39:53.325548 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000230488 (* 0.0272727 = 6.28602e-06 loss)
I0327 13:39:53.325567 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000557629 (* 0.0272727 = 1.52081e-05 loss)
I0327 13:39:53.325580 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000638887 (* 0.0272727 = 1.74242e-05 loss)
I0327 13:39:53.325594 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000699768 (* 0.0272727 = 1.90846e-05 loss)
I0327 13:39:53.325608 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000566933 (* 0.0272727 = 1.54618e-05 loss)
I0327 13:39:53.325623 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000384155 (* 0.0272727 = 1.0477e-05 loss)
I0327 13:39:53.325636 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000547302 (* 0.0272727 = 1.49264e-05 loss)
I0327 13:39:53.325649 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.75
I0327 13:39:53.325660 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 13:39:53.325671 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:39:53.325682 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0
I0327 13:39:53.325695 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 13:39:53.325706 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 13:39:53.325717 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:39:53.325728 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:39:53.325741 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 13:39:53.325752 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 0.875
I0327 13:39:53.325762 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:39:53.325773 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:39:53.325785 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:39:53.325796 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:39:53.325808 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:39:53.325819 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:39:53.325829 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:39:53.325841 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:39:53.325852 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:39:53.325863 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:39:53.325875 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:39:53.325886 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:39:53.325899 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.34414 (* 0.0909091 = 0.122195 loss)
I0327 13:39:53.325913 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.07022 (* 0.0909091 = 0.279111 loss)
I0327 13:39:53.325927 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.62101 (* 0.0909091 = 0.329183 loss)
I0327 13:39:53.325940 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.11453 (* 0.0909091 = 0.283139 loss)
I0327 13:39:53.325954 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.66296 (* 0.0909091 = 0.242087 loss)
I0327 13:39:53.325980 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.89379 (* 0.0909091 = 0.263072 loss)
I0327 13:39:53.325995 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.53323 (* 0.0909091 = 0.139384 loss)
I0327 13:39:53.326009 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.440756 (* 0.0909091 = 0.0400687 loss)
I0327 13:39:53.326023 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.412996 (* 0.0909091 = 0.0375451 loss)
I0327 13:39:53.326037 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.463326 (* 0.0909091 = 0.0421206 loss)
I0327 13:39:53.326056 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000270174 (* 0.0909091 = 2.45613e-05 loss)
I0327 13:39:53.326069 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.00028088 (* 0.0909091 = 2.55345e-05 loss)
I0327 13:39:53.326084 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000510604 (* 0.0909091 = 4.64185e-05 loss)
I0327 13:39:53.326098 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000401712 (* 0.0909091 = 3.65193e-05 loss)
I0327 13:39:53.326112 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000426885 (* 0.0909091 = 3.88078e-05 loss)
I0327 13:39:53.326128 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000285838 (* 0.0909091 = 2.59853e-05 loss)
I0327 13:39:53.326141 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000248804 (* 0.0909091 = 2.26186e-05 loss)
I0327 13:39:53.326155 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.00046607 (* 0.0909091 = 4.237e-05 loss)
I0327 13:39:53.326169 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000443181 (* 0.0909091 = 4.02892e-05 loss)
I0327 13:39:53.326184 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000422785 (* 0.0909091 = 3.8435e-05 loss)
I0327 13:39:53.326198 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000345611 (* 0.0909091 = 3.14192e-05 loss)
I0327 13:39:53.326211 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000282211 (* 0.0909091 = 2.56556e-05 loss)
I0327 13:39:53.326225 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:39:53.326236 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00021582
I0327 13:39:53.326248 21344 sgd_solver.cpp:106] Iteration 14500, lr = 0.01
I0327 13:41:40.947301 21344 solver.cpp:338] Iteration 15000, Testing net (#0)
I0327 13:42:11.949666 21344 solver.cpp:393] Test loss: 2.19642
I0327 13:42:11.949815 21344 solver.cpp:406] Test net output #0: loss1/accuracy01 = 0.504
I0327 13:42:11.949846 21344 solver.cpp:406] Test net output #1: loss1/accuracy02 = 0.158
I0327 13:42:11.949861 21344 solver.cpp:406] Test net output #2: loss1/accuracy03 = 0.164
I0327 13:42:11.949872 21344 solver.cpp:406] Test net output #3: loss1/accuracy04 = 0.187
I0327 13:42:11.949885 21344 solver.cpp:406] Test net output #4: loss1/accuracy05 = 0.251
I0327 13:42:11.949898 21344 solver.cpp:406] Test net output #5: loss1/accuracy06 = 0.503
I0327 13:42:11.949908 21344 solver.cpp:406] Test net output #6: loss1/accuracy07 = 0.895
I0327 13:42:11.949921 21344 solver.cpp:406] Test net output #7: loss1/accuracy08 = 0.969
I0327 13:42:11.949933 21344 solver.cpp:406] Test net output #8: loss1/accuracy09 = 0.995
I0327 13:42:11.949944 21344 solver.cpp:406] Test net output #9: loss1/accuracy10 = 0.998
I0327 13:42:11.949955 21344 solver.cpp:406] Test net output #10: loss1/accuracy11 = 1
I0327 13:42:11.949967 21344 solver.cpp:406] Test net output #11: loss1/accuracy12 = 1
I0327 13:42:11.949980 21344 solver.cpp:406] Test net output #12: loss1/accuracy13 = 1
I0327 13:42:11.949993 21344 solver.cpp:406] Test net output #13: loss1/accuracy14 = 1
I0327 13:42:11.950006 21344 solver.cpp:406] Test net output #14: loss1/accuracy15 = 1
I0327 13:42:11.950017 21344 solver.cpp:406] Test net output #15: loss1/accuracy16 = 1
I0327 13:42:11.950028 21344 solver.cpp:406] Test net output #16: loss1/accuracy17 = 1
I0327 13:42:11.950040 21344 solver.cpp:406] Test net output #17: loss1/accuracy18 = 1
I0327 13:42:11.950052 21344 solver.cpp:406] Test net output #18: loss1/accuracy19 = 1
I0327 13:42:11.950062 21344 solver.cpp:406] Test net output #19: loss1/accuracy20 = 1
I0327 13:42:11.950075 21344 solver.cpp:406] Test net output #20: loss1/accuracy21 = 1
I0327 13:42:11.950086 21344 solver.cpp:406] Test net output #21: loss1/accuracy22 = 1
I0327 13:42:11.950103 21344 solver.cpp:406] Test net output #22: loss1/loss01 = 1.87191 (* 0.0272727 = 0.0510522 loss)
I0327 13:42:11.950117 21344 solver.cpp:406] Test net output #23: loss1/loss02 = 2.63544 (* 0.0272727 = 0.0718757 loss)
I0327 13:42:11.950131 21344 solver.cpp:406] Test net output #24: loss1/loss03 = 2.73185 (* 0.0272727 = 0.074505 loss)
I0327 13:42:11.950145 21344 solver.cpp:406] Test net output #25: loss1/loss04 = 2.69624 (* 0.0272727 = 0.0735338 loss)
I0327 13:42:11.950158 21344 solver.cpp:406] Test net output #26: loss1/loss05 = 2.56566 (* 0.0272727 = 0.0699727 loss)
I0327 13:42:11.950171 21344 solver.cpp:406] Test net output #27: loss1/loss06 = 1.74966 (* 0.0272727 = 0.0477181 loss)
I0327 13:42:11.950186 21344 solver.cpp:406] Test net output #28: loss1/loss07 = 0.611665 (* 0.0272727 = 0.0166818 loss)
I0327 13:42:11.950199 21344 solver.cpp:406] Test net output #29: loss1/loss08 = 0.214454 (* 0.0272727 = 0.00584873 loss)
I0327 13:42:11.950213 21344 solver.cpp:406] Test net output #30: loss1/loss09 = 0.0414711 (* 0.0272727 = 0.00113103 loss)
I0327 13:42:11.950227 21344 solver.cpp:406] Test net output #31: loss1/loss10 = 0.0178535 (* 0.0272727 = 0.000486915 loss)
I0327 13:42:11.950242 21344 solver.cpp:406] Test net output #32: loss1/loss11 = 0.000150324 (* 0.0272727 = 4.09974e-06 loss)
I0327 13:42:11.950255 21344 solver.cpp:406] Test net output #33: loss1/loss12 = 0.000196083 (* 0.0272727 = 5.34773e-06 loss)
I0327 13:42:11.950269 21344 solver.cpp:406] Test net output #34: loss1/loss13 = 0.000129358 (* 0.0272727 = 3.52795e-06 loss)
I0327 13:42:11.950284 21344 solver.cpp:406] Test net output #35: loss1/loss14 = 0.000160415 (* 0.0272727 = 4.37495e-06 loss)
I0327 13:42:11.950297 21344 solver.cpp:406] Test net output #36: loss1/loss15 = 0.000146672 (* 0.0272727 = 4.00014e-06 loss)
I0327 13:42:11.950320 21344 solver.cpp:406] Test net output #37: loss1/loss16 = 0.000172033 (* 0.0272727 = 4.6918e-06 loss)
I0327 13:42:11.950347 21344 solver.cpp:406] Test net output #38: loss1/loss17 = 0.000137874 (* 0.0272727 = 3.76021e-06 loss)
I0327 13:42:11.950381 21344 solver.cpp:406] Test net output #39: loss1/loss18 = 0.000160305 (* 0.0272727 = 4.37195e-06 loss)
I0327 13:42:11.950395 21344 solver.cpp:406] Test net output #40: loss1/loss19 = 0.000124393 (* 0.0272727 = 3.39253e-06 loss)
I0327 13:42:11.950409 21344 solver.cpp:406] Test net output #41: loss1/loss20 = 0.000128285 (* 0.0272727 = 3.49868e-06 loss)
I0327 13:42:11.950423 21344 solver.cpp:406] Test net output #42: loss1/loss21 = 0.000139348 (* 0.0272727 = 3.8004e-06 loss)
I0327 13:42:11.950438 21344 solver.cpp:406] Test net output #43: loss1/loss22 = 0.000171503 (* 0.0272727 = 4.67736e-06 loss)
I0327 13:42:11.950449 21344 solver.cpp:406] Test net output #44: loss2/accuracy01 = 0.526
I0327 13:42:11.950461 21344 solver.cpp:406] Test net output #45: loss2/accuracy02 = 0.162
I0327 13:42:11.950474 21344 solver.cpp:406] Test net output #46: loss2/accuracy03 = 0.148
I0327 13:42:11.950485 21344 solver.cpp:406] Test net output #47: loss2/accuracy04 = 0.192
I0327 13:42:11.950496 21344 solver.cpp:406] Test net output #48: loss2/accuracy05 = 0.268
I0327 13:42:11.950508 21344 solver.cpp:406] Test net output #49: loss2/accuracy06 = 0.516
I0327 13:42:11.950520 21344 solver.cpp:406] Test net output #50: loss2/accuracy07 = 0.895
I0327 13:42:11.950531 21344 solver.cpp:406] Test net output #51: loss2/accuracy08 = 0.969
I0327 13:42:11.950543 21344 solver.cpp:406] Test net output #52: loss2/accuracy09 = 0.995
I0327 13:42:11.950554 21344 solver.cpp:406] Test net output #53: loss2/accuracy10 = 0.998
I0327 13:42:11.950570 21344 solver.cpp:406] Test net output #54: loss2/accuracy11 = 1
I0327 13:42:11.950582 21344 solver.cpp:406] Test net output #55: loss2/accuracy12 = 1
I0327 13:42:11.950593 21344 solver.cpp:406] Test net output #56: loss2/accuracy13 = 1
I0327 13:42:11.950604 21344 solver.cpp:406] Test net output #57: loss2/accuracy14 = 1
I0327 13:42:11.950615 21344 solver.cpp:406] Test net output #58: loss2/accuracy15 = 1
I0327 13:42:11.950626 21344 solver.cpp:406] Test net output #59: loss2/accuracy16 = 1
I0327 13:42:11.950639 21344 solver.cpp:406] Test net output #60: loss2/accuracy17 = 1
I0327 13:42:11.950645 21344 solver.cpp:406] Test net output #61: loss2/accuracy18 = 1
I0327 13:42:11.950652 21344 solver.cpp:406] Test net output #62: loss2/accuracy19 = 1
I0327 13:42:11.950664 21344 solver.cpp:406] Test net output #63: loss2/accuracy20 = 1
I0327 13:42:11.950676 21344 solver.cpp:406] Test net output #64: loss2/accuracy21 = 1
I0327 13:42:11.950687 21344 solver.cpp:406] Test net output #65: loss2/accuracy22 = 1
I0327 13:42:11.950701 21344 solver.cpp:406] Test net output #66: loss2/loss01 = 1.84603 (* 0.0272727 = 0.0503462 loss)
I0327 13:42:11.950716 21344 solver.cpp:406] Test net output #67: loss2/loss02 = 2.65535 (* 0.0272727 = 0.0724188 loss)
I0327 13:42:11.950728 21344 solver.cpp:406] Test net output #68: loss2/loss03 = 2.74451 (* 0.0272727 = 0.0748503 loss)
I0327 13:42:11.950742 21344 solver.cpp:406] Test net output #69: loss2/loss04 = 2.68009 (* 0.0272727 = 0.0730933 loss)
I0327 13:42:11.950755 21344 solver.cpp:406] Test net output #70: loss2/loss05 = 2.55573 (* 0.0272727 = 0.0697018 loss)
I0327 13:42:11.950768 21344 solver.cpp:406] Test net output #71: loss2/loss06 = 1.66501 (* 0.0272727 = 0.0454094 loss)
I0327 13:42:11.950783 21344 solver.cpp:406] Test net output #72: loss2/loss07 = 0.542146 (* 0.0272727 = 0.0147858 loss)
I0327 13:42:11.950796 21344 solver.cpp:406] Test net output #73: loss2/loss08 = 0.218141 (* 0.0272727 = 0.0059493 loss)
I0327 13:42:11.950810 21344 solver.cpp:406] Test net output #74: loss2/loss09 = 0.0456984 (* 0.0272727 = 0.00124632 loss)
I0327 13:42:11.950824 21344 solver.cpp:406] Test net output #75: loss2/loss10 = 0.023659 (* 0.0272727 = 0.000645247 loss)
I0327 13:42:11.950839 21344 solver.cpp:406] Test net output #76: loss2/loss11 = 0.000395519 (* 0.0272727 = 1.07869e-05 loss)
I0327 13:42:11.950852 21344 solver.cpp:406] Test net output #77: loss2/loss12 = 0.000360678 (* 0.0272727 = 9.83666e-06 loss)
I0327 13:42:11.950877 21344 solver.cpp:406] Test net output #78: loss2/loss13 = 0.000306976 (* 0.0272727 = 8.37208e-06 loss)
I0327 13:42:11.950892 21344 solver.cpp:406] Test net output #79: loss2/loss14 = 0.00029397 (* 0.0272727 = 8.01736e-06 loss)
I0327 13:42:11.950906 21344 solver.cpp:406] Test net output #80: loss2/loss15 = 0.00036707 (* 0.0272727 = 1.0011e-05 loss)
I0327 13:42:11.950922 21344 solver.cpp:406] Test net output #81: loss2/loss16 = 0.000352768 (* 0.0272727 = 9.62093e-06 loss)
I0327 13:42:11.950938 21344 solver.cpp:406] Test net output #82: loss2/loss17 = 0.000363443 (* 0.0272727 = 9.91207e-06 loss)
I0327 13:42:11.950953 21344 solver.cpp:406] Test net output #83: loss2/loss18 = 0.00030045 (* 0.0272727 = 8.19408e-06 loss)
I0327 13:42:11.950965 21344 solver.cpp:406] Test net output #84: loss2/loss19 = 0.000356124 (* 0.0272727 = 9.71248e-06 loss)
I0327 13:42:11.950979 21344 solver.cpp:406] Test net output #85: loss2/loss20 = 0.000322125 (* 0.0272727 = 8.78524e-06 loss)
I0327 13:42:11.950994 21344 solver.cpp:406] Test net output #86: loss2/loss21 = 0.00035904 (* 0.0272727 = 9.79201e-06 loss)
I0327 13:42:11.951006 21344 solver.cpp:406] Test net output #87: loss2/loss22 = 0.000373929 (* 0.0272727 = 1.01981e-05 loss)
I0327 13:42:11.951019 21344 solver.cpp:406] Test net output #88: loss3/accuracy01 = 0.471
I0327 13:42:11.951030 21344 solver.cpp:406] Test net output #89: loss3/accuracy02 = 0.15
I0327 13:42:11.951045 21344 solver.cpp:406] Test net output #90: loss3/accuracy03 = 0.153
I0327 13:42:11.951057 21344 solver.cpp:406] Test net output #91: loss3/accuracy04 = 0.193
I0327 13:42:11.951069 21344 solver.cpp:406] Test net output #92: loss3/accuracy05 = 0.289
I0327 13:42:11.951081 21344 solver.cpp:406] Test net output #93: loss3/accuracy06 = 0.522
I0327 13:42:11.951092 21344 solver.cpp:406] Test net output #94: loss3/accuracy07 = 0.894
I0327 13:42:11.951102 21344 solver.cpp:406] Test net output #95: loss3/accuracy08 = 0.969
I0327 13:42:11.951113 21344 solver.cpp:406] Test net output #96: loss3/accuracy09 = 0.995
I0327 13:42:11.951124 21344 solver.cpp:406] Test net output #97: loss3/accuracy10 = 0.998
I0327 13:42:11.951135 21344 solver.cpp:406] Test net output #98: loss3/accuracy11 = 1
I0327 13:42:11.951146 21344 solver.cpp:406] Test net output #99: loss3/accuracy12 = 1
I0327 13:42:11.951159 21344 solver.cpp:406] Test net output #100: loss3/accuracy13 = 1
I0327 13:42:11.951169 21344 solver.cpp:406] Test net output #101: loss3/accuracy14 = 1
I0327 13:42:11.951180 21344 solver.cpp:406] Test net output #102: loss3/accuracy15 = 1
I0327 13:42:11.951191 21344 solver.cpp:406] Test net output #103: loss3/accuracy16 = 1
I0327 13:42:11.951202 21344 solver.cpp:406] Test net output #104: loss3/accuracy17 = 1
I0327 13:42:11.951213 21344 solver.cpp:406] Test net output #105: loss3/accuracy18 = 1
I0327 13:42:11.951225 21344 solver.cpp:406] Test net output #106: loss3/accuracy19 = 1
I0327 13:42:11.951236 21344 solver.cpp:406] Test net output #107: loss3/accuracy20 = 1
I0327 13:42:11.951246 21344 solver.cpp:406] Test net output #108: loss3/accuracy21 = 1
I0327 13:42:11.951257 21344 solver.cpp:406] Test net output #109: loss3/accuracy22 = 1
I0327 13:42:11.951270 21344 solver.cpp:406] Test net output #110: loss3/loss01 = 1.88179 (* 0.0909091 = 0.171072 loss)
I0327 13:42:11.951284 21344 solver.cpp:406] Test net output #111: loss3/loss02 = 2.68507 (* 0.0909091 = 0.244098 loss)
I0327 13:42:11.951298 21344 solver.cpp:406] Test net output #112: loss3/loss03 = 2.7728 (* 0.0909091 = 0.252073 loss)
I0327 13:42:11.951311 21344 solver.cpp:406] Test net output #113: loss3/loss04 = 2.69694 (* 0.0909091 = 0.245176 loss)
I0327 13:42:11.951325 21344 solver.cpp:406] Test net output #114: loss3/loss05 = 2.51814 (* 0.0909091 = 0.228922 loss)
I0327 13:42:11.951339 21344 solver.cpp:406] Test net output #115: loss3/loss06 = 1.67098 (* 0.0909091 = 0.151907 loss)
I0327 13:42:11.951364 21344 solver.cpp:406] Test net output #116: loss3/loss07 = 0.560404 (* 0.0909091 = 0.0509458 loss)
I0327 13:42:11.951378 21344 solver.cpp:406] Test net output #117: loss3/loss08 = 0.256286 (* 0.0909091 = 0.0232988 loss)
I0327 13:42:11.951392 21344 solver.cpp:406] Test net output #118: loss3/loss09 = 0.0535895 (* 0.0909091 = 0.00487177 loss)
I0327 13:42:11.951406 21344 solver.cpp:406] Test net output #119: loss3/loss10 = 0.0283712 (* 0.0909091 = 0.0025792 loss)
I0327 13:42:11.951421 21344 solver.cpp:406] Test net output #120: loss3/loss11 = 4.61352e-05 (* 0.0909091 = 4.19411e-06 loss)
I0327 13:42:11.951434 21344 solver.cpp:406] Test net output #121: loss3/loss12 = 5.37207e-05 (* 0.0909091 = 4.8837e-06 loss)
I0327 13:42:11.951447 21344 solver.cpp:406] Test net output #122: loss3/loss13 = 5.95732e-05 (* 0.0909091 = 5.41575e-06 loss)
I0327 13:42:11.951494 21344 solver.cpp:406] Test net output #123: loss3/loss14 = 5.59876e-05 (* 0.0909091 = 5.08978e-06 loss)
I0327 13:42:11.951509 21344 solver.cpp:406] Test net output #124: loss3/loss15 = 5.23517e-05 (* 0.0909091 = 4.75925e-06 loss)
I0327 13:42:11.951524 21344 solver.cpp:406] Test net output #125: loss3/loss16 = 4.81611e-05 (* 0.0909091 = 4.37828e-06 loss)
I0327 13:42:11.951537 21344 solver.cpp:406] Test net output #126: loss3/loss17 = 5.00921e-05 (* 0.0909091 = 4.55383e-06 loss)
I0327 13:42:11.951550 21344 solver.cpp:406] Test net output #127: loss3/loss18 = 5.99147e-05 (* 0.0909091 = 5.44679e-06 loss)
I0327 13:42:11.951565 21344 solver.cpp:406] Test net output #128: loss3/loss19 = 4.72103e-05 (* 0.0909091 = 4.29184e-06 loss)
I0327 13:42:11.951578 21344 solver.cpp:406] Test net output #129: loss3/loss20 = 5.56817e-05 (* 0.0909091 = 5.06197e-06 loss)
I0327 13:42:11.951591 21344 solver.cpp:406] Test net output #130: loss3/loss21 = 5.21409e-05 (* 0.0909091 = 4.74008e-06 loss)
I0327 13:42:11.951606 21344 solver.cpp:406] Test net output #131: loss3/loss22 = 4.61948e-05 (* 0.0909091 = 4.19952e-06 loss)
I0327 13:42:11.951617 21344 solver.cpp:406] Test net output #132: total_accuracy = 0.001
I0327 13:42:11.951628 21344 solver.cpp:406] Test net output #133: total_confidence = 0.00178901
I0327 13:42:12.062741 21344 solver.cpp:229] Iteration 15000, loss = 2.7479
I0327 13:42:12.062795 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.375
I0327 13:42:12.062813 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:42:12.062825 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 13:42:12.062837 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.625
I0327 13:42:12.062849 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.75
I0327 13:42:12.062861 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.75
I0327 13:42:12.062873 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:42:12.062885 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 13:42:12.062897 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:42:12.062909 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:42:12.062922 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:42:12.062933 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:42:12.062948 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:42:12.062961 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:42:12.062973 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:42:12.062985 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:42:12.062997 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:42:12.063009 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:42:12.063020 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:42:12.063056 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:42:12.063071 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:42:12.063082 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:42:12.063100 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.5243 (* 0.0272727 = 0.0688446 loss)
I0327 13:42:12.063117 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.86045 (* 0.0272727 = 0.0780121 loss)
I0327 13:42:12.063132 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.8532 (* 0.0272727 = 0.0778146 loss)
I0327 13:42:12.063145 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.30696 (* 0.0272727 = 0.062917 loss)
I0327 13:42:12.063159 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 1.56652 (* 0.0272727 = 0.0427233 loss)
I0327 13:42:12.063174 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.47069 (* 0.0272727 = 0.0401098 loss)
I0327 13:42:12.063187 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.20653 (* 0.0272727 = 0.0329053 loss)
I0327 13:42:12.063201 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.28118 (* 0.0272727 = 0.0349413 loss)
I0327 13:42:12.063220 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0143061 (* 0.0272727 = 0.000390166 loss)
I0327 13:42:12.063243 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00498629 (* 0.0272727 = 0.00013599 loss)
I0327 13:42:12.063258 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000228643 (* 0.0272727 = 6.23573e-06 loss)
I0327 13:42:12.063273 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000187118 (* 0.0272727 = 5.10321e-06 loss)
I0327 13:42:12.063287 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000221054 (* 0.0272727 = 6.02875e-06 loss)
I0327 13:42:12.063302 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000244485 (* 0.0272727 = 6.66776e-06 loss)
I0327 13:42:12.063316 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00011734 (* 0.0272727 = 3.20019e-06 loss)
I0327 13:42:12.063330 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000299418 (* 0.0272727 = 8.16595e-06 loss)
I0327 13:42:12.063344 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000155134 (* 0.0272727 = 4.23092e-06 loss)
I0327 13:42:12.063359 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000207984 (* 0.0272727 = 5.67229e-06 loss)
I0327 13:42:12.063372 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 6.94284e-05 (* 0.0272727 = 1.8935e-06 loss)
I0327 13:42:12.063386 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000112263 (* 0.0272727 = 3.06171e-06 loss)
I0327 13:42:12.063401 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000221309 (* 0.0272727 = 6.03571e-06 loss)
I0327 13:42:12.063415 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000141552 (* 0.0272727 = 3.86051e-06 loss)
I0327 13:42:12.063427 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 13:42:12.063439 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.25
I0327 13:42:12.063452 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:42:12.063462 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.5
I0327 13:42:12.063474 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.5
I0327 13:42:12.063487 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:42:12.063498 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:42:12.063510 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.75
I0327 13:42:12.063522 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:42:12.063534 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:42:12.063544 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:42:12.063567 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:42:12.063581 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:42:12.063592 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:42:12.063604 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:42:12.063616 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:42:12.063627 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:42:12.063638 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:42:12.063650 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:42:12.063662 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:42:12.063673 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:42:12.063685 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:42:12.063699 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.07887 (* 0.0272727 = 0.0566965 loss)
I0327 13:42:12.063714 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.496 (* 0.0272727 = 0.0680726 loss)
I0327 13:42:12.063727 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.90322 (* 0.0272727 = 0.0791788 loss)
I0327 13:42:12.063741 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.37337 (* 0.0272727 = 0.0647284 loss)
I0327 13:42:12.063755 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.01357 (* 0.0272727 = 0.0549156 loss)
I0327 13:42:12.063769 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.96098 (* 0.0272727 = 0.0534813 loss)
I0327 13:42:12.063783 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.41645 (* 0.0272727 = 0.0386305 loss)
I0327 13:42:12.063797 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 1.42606 (* 0.0272727 = 0.0388925 loss)
I0327 13:42:12.063812 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0182835 (* 0.0272727 = 0.00049864 loss)
I0327 13:42:12.063825 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00474456 (* 0.0272727 = 0.000129397 loss)
I0327 13:42:12.063839 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000105095 (* 0.0272727 = 2.86622e-06 loss)
I0327 13:42:12.063853 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 8.39325e-05 (* 0.0272727 = 2.28907e-06 loss)
I0327 13:42:12.063868 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000165653 (* 0.0272727 = 4.51782e-06 loss)
I0327 13:42:12.063881 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000113325 (* 0.0272727 = 3.09069e-06 loss)
I0327 13:42:12.063895 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000115679 (* 0.0272727 = 3.15489e-06 loss)
I0327 13:42:12.063910 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000156553 (* 0.0272727 = 4.26963e-06 loss)
I0327 13:42:12.063923 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 9.71745e-05 (* 0.0272727 = 2.65021e-06 loss)
I0327 13:42:12.063937 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000134268 (* 0.0272727 = 3.66185e-06 loss)
I0327 13:42:12.063951 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000126418 (* 0.0272727 = 3.44777e-06 loss)
I0327 13:42:12.063966 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000208872 (* 0.0272727 = 5.69651e-06 loss)
I0327 13:42:12.063979 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000119083 (* 0.0272727 = 3.24773e-06 loss)
I0327 13:42:12.063995 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000143656 (* 0.0272727 = 3.9179e-06 loss)
I0327 13:42:12.064009 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.75
I0327 13:42:12.064021 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:42:12.064033 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 13:42:12.064055 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.375
I0327 13:42:12.064069 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 13:42:12.064080 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:42:12.064092 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:42:12.064105 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.75
I0327 13:42:12.064116 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:42:12.064127 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:42:12.064138 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:42:12.064153 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:42:12.064165 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:42:12.064177 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:42:12.064188 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:42:12.064199 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:42:12.064211 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:42:12.064223 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:42:12.064234 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:42:12.064245 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:42:12.064257 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:42:12.064268 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:42:12.064282 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.59038 (* 0.0909091 = 0.14458 loss)
I0327 13:42:12.064296 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.67046 (* 0.0909091 = 0.242769 loss)
I0327 13:42:12.064311 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.10484 (* 0.0909091 = 0.282258 loss)
I0327 13:42:12.064324 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.36734 (* 0.0909091 = 0.215213 loss)
I0327 13:42:12.064338 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 1.89253 (* 0.0909091 = 0.172049 loss)
I0327 13:42:12.064352 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.66898 (* 0.0909091 = 0.151725 loss)
I0327 13:42:12.064365 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.25891 (* 0.0909091 = 0.114446 loss)
I0327 13:42:12.064379 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.05489 (* 0.0909091 = 0.0958994 loss)
I0327 13:42:12.064393 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0160238 (* 0.0909091 = 0.00145671 loss)
I0327 13:42:12.064409 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00604909 (* 0.0909091 = 0.000549917 loss)
I0327 13:42:12.064422 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000157305 (* 0.0909091 = 1.43005e-05 loss)
I0327 13:42:12.064436 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000139786 (* 0.0909091 = 1.27078e-05 loss)
I0327 13:42:12.064450 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000142165 (* 0.0909091 = 1.29241e-05 loss)
I0327 13:42:12.064465 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000219182 (* 0.0909091 = 1.99257e-05 loss)
I0327 13:42:12.064478 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000237844 (* 0.0909091 = 2.16222e-05 loss)
I0327 13:42:12.064497 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000182753 (* 0.0909091 = 1.66139e-05 loss)
I0327 13:42:12.064512 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000158959 (* 0.0909091 = 1.44508e-05 loss)
I0327 13:42:12.064525 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000171555 (* 0.0909091 = 1.55959e-05 loss)
I0327 13:42:12.064549 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000201817 (* 0.0909091 = 1.8347e-05 loss)
I0327 13:42:12.064565 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000174803 (* 0.0909091 = 1.58912e-05 loss)
I0327 13:42:12.064579 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000187451 (* 0.0909091 = 1.7041e-05 loss)
I0327 13:42:12.064594 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000204689 (* 0.0909091 = 1.86081e-05 loss)
I0327 13:42:12.064604 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:42:12.064616 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000470584
I0327 13:42:12.064630 21344 sgd_solver.cpp:106] Iteration 15000, lr = 0.01
I0327 13:44:00.001790 21344 solver.cpp:229] Iteration 15500, loss = 2.73598
I0327 13:44:00.001899 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.375
I0327 13:44:00.001917 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:44:00.001930 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:44:00.001942 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 13:44:00.001955 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.5
I0327 13:44:00.001966 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 13:44:00.001978 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.75
I0327 13:44:00.001991 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 13:44:00.002002 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:44:00.002014 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:44:00.002025 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:44:00.002038 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:44:00.002048 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:44:00.002060 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:44:00.002073 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:44:00.002087 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:44:00.002099 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:44:00.002111 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:44:00.002123 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:44:00.002135 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:44:00.002146 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:44:00.002158 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:44:00.002174 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.39835 (* 0.0272727 = 0.0654095 loss)
I0327 13:44:00.002189 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.96882 (* 0.0272727 = 0.0809679 loss)
I0327 13:44:00.002203 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.54148 (* 0.0272727 = 0.096586 loss)
I0327 13:44:00.002218 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.21295 (* 0.0272727 = 0.087626 loss)
I0327 13:44:00.002233 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.72392 (* 0.0272727 = 0.0742888 loss)
I0327 13:44:00.002246 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.55621 (* 0.0272727 = 0.0697148 loss)
I0327 13:44:00.002259 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.13043 (* 0.0272727 = 0.0308299 loss)
I0327 13:44:00.002274 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0476531 (* 0.0272727 = 0.00129963 loss)
I0327 13:44:00.002288 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.00620348 (* 0.0272727 = 0.000169186 loss)
I0327 13:44:00.002303 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00553044 (* 0.0272727 = 0.00015083 loss)
I0327 13:44:00.002317 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000134966 (* 0.0272727 = 3.68088e-06 loss)
I0327 13:44:00.002331 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00020837 (* 0.0272727 = 5.68283e-06 loss)
I0327 13:44:00.002346 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 9.93902e-05 (* 0.0272727 = 2.71064e-06 loss)
I0327 13:44:00.002360 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000115692 (* 0.0272727 = 3.15525e-06 loss)
I0327 13:44:00.002374 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000182895 (* 0.0272727 = 4.98804e-06 loss)
I0327 13:44:00.002388 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000102922 (* 0.0272727 = 2.80695e-06 loss)
I0327 13:44:00.002403 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000111268 (* 0.0272727 = 3.03459e-06 loss)
I0327 13:44:00.002434 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 8.63668e-05 (* 0.0272727 = 2.35546e-06 loss)
I0327 13:44:00.002449 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00141514 (* 0.0272727 = 3.85947e-05 loss)
I0327 13:44:00.002463 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000123048 (* 0.0272727 = 3.35586e-06 loss)
I0327 13:44:00.002477 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000280966 (* 0.0272727 = 7.6627e-06 loss)
I0327 13:44:00.002491 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000487192 (* 0.0272727 = 1.32871e-05 loss)
I0327 13:44:00.002504 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.5
I0327 13:44:00.002516 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.25
I0327 13:44:00.002528 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:44:00.002540 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 13:44:00.002552 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.375
I0327 13:44:00.002564 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.5
I0327 13:44:00.002576 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.75
I0327 13:44:00.002588 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:44:00.002600 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:44:00.002611 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:44:00.002624 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:44:00.002635 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:44:00.002646 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:44:00.002657 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:44:00.002668 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:44:00.002681 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:44:00.002691 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:44:00.002703 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:44:00.002714 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:44:00.002725 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:44:00.002737 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:44:00.002748 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:44:00.002763 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.67653 (* 0.0272727 = 0.0457235 loss)
I0327 13:44:00.002776 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.41643 (* 0.0272727 = 0.0659028 loss)
I0327 13:44:00.002790 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.09821 (* 0.0272727 = 0.0844967 loss)
I0327 13:44:00.002804 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.89404 (* 0.0272727 = 0.0789284 loss)
I0327 13:44:00.002818 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.76687 (* 0.0272727 = 0.0754601 loss)
I0327 13:44:00.002831 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.07518 (* 0.0272727 = 0.0565957 loss)
I0327 13:44:00.002846 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.15607 (* 0.0272727 = 0.0315292 loss)
I0327 13:44:00.002859 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0286903 (* 0.0272727 = 0.000782462 loss)
I0327 13:44:00.002871 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.00906118 (* 0.0272727 = 0.000247123 loss)
I0327 13:44:00.002879 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00571084 (* 0.0272727 = 0.00015575 loss)
I0327 13:44:00.002894 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000130067 (* 0.0272727 = 3.54729e-06 loss)
I0327 13:44:00.002919 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000328566 (* 0.0272727 = 8.9609e-06 loss)
I0327 13:44:00.002934 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000197719 (* 0.0272727 = 5.39234e-06 loss)
I0327 13:44:00.002948 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000213695 (* 0.0272727 = 5.82805e-06 loss)
I0327 13:44:00.002962 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000106567 (* 0.0272727 = 2.90638e-06 loss)
I0327 13:44:00.002976 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000119834 (* 0.0272727 = 3.26819e-06 loss)
I0327 13:44:00.002990 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00020599 (* 0.0272727 = 5.61791e-06 loss)
I0327 13:44:00.003005 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000111081 (* 0.0272727 = 3.02949e-06 loss)
I0327 13:44:00.003018 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00030297 (* 0.0272727 = 8.26281e-06 loss)
I0327 13:44:00.003032 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000115305 (* 0.0272727 = 3.14468e-06 loss)
I0327 13:44:00.003047 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 9.64577e-05 (* 0.0272727 = 2.63066e-06 loss)
I0327 13:44:00.003060 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000119723 (* 0.0272727 = 3.26518e-06 loss)
I0327 13:44:00.003073 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.625
I0327 13:44:00.003085 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:44:00.003098 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:44:00.003108 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.25
I0327 13:44:00.003120 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 13:44:00.003136 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 13:44:00.003149 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 13:44:00.003160 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:44:00.003171 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:44:00.003183 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:44:00.003195 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:44:00.003206 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:44:00.003217 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:44:00.003228 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:44:00.003240 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:44:00.003252 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:44:00.003262 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:44:00.003274 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:44:00.003285 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:44:00.003296 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:44:00.003309 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:44:00.003319 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:44:00.003334 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.262 (* 0.0909091 = 0.114727 loss)
I0327 13:44:00.003346 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.66779 (* 0.0909091 = 0.242527 loss)
I0327 13:44:00.003360 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.16526 (* 0.0909091 = 0.287751 loss)
I0327 13:44:00.003373 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.94842 (* 0.0909091 = 0.268038 loss)
I0327 13:44:00.003387 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.54011 (* 0.0909091 = 0.230919 loss)
I0327 13:44:00.003412 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.34255 (* 0.0909091 = 0.212959 loss)
I0327 13:44:00.003427 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.20911 (* 0.0909091 = 0.109919 loss)
I0327 13:44:00.003442 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0172607 (* 0.0909091 = 0.00156916 loss)
I0327 13:44:00.003455 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00526433 (* 0.0909091 = 0.000478575 loss)
I0327 13:44:00.003469 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00229277 (* 0.0909091 = 0.000208434 loss)
I0327 13:44:00.003484 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000232512 (* 0.0909091 = 2.11374e-05 loss)
I0327 13:44:00.003499 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000179481 (* 0.0909091 = 1.63165e-05 loss)
I0327 13:44:00.003512 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000204633 (* 0.0909091 = 1.8603e-05 loss)
I0327 13:44:00.003527 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000153844 (* 0.0909091 = 1.39858e-05 loss)
I0327 13:44:00.003541 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000245021 (* 0.0909091 = 2.22746e-05 loss)
I0327 13:44:00.003556 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000162587 (* 0.0909091 = 1.47806e-05 loss)
I0327 13:44:00.003569 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000183099 (* 0.0909091 = 1.66454e-05 loss)
I0327 13:44:00.003583 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000166881 (* 0.0909091 = 1.5171e-05 loss)
I0327 13:44:00.003597 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000245611 (* 0.0909091 = 2.23283e-05 loss)
I0327 13:44:00.003612 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000182882 (* 0.0909091 = 1.66256e-05 loss)
I0327 13:44:00.003625 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000153294 (* 0.0909091 = 1.39358e-05 loss)
I0327 13:44:00.003639 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000233036 (* 0.0909091 = 2.11851e-05 loss)
I0327 13:44:00.003651 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:44:00.003662 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000644672
I0327 13:44:00.003675 21344 sgd_solver.cpp:106] Iteration 15500, lr = 0.01
I0327 13:45:47.773134 21344 solver.cpp:229] Iteration 16000, loss = 2.72474
I0327 13:45:47.773277 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 13:45:47.773298 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:45:47.773309 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:45:47.773320 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:45:47.773334 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.375
I0327 13:45:47.773345 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.625
I0327 13:45:47.773357 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.75
I0327 13:45:47.773370 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 13:45:47.773381 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:45:47.773393 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:45:47.773406 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:45:47.773416 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:45:47.773427 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:45:47.773439 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:45:47.773452 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:45:47.773463 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:45:47.773473 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:45:47.773491 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:45:47.773504 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:45:47.773516 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:45:47.773527 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:45:47.773551 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:45:47.773581 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.35673 (* 0.0272727 = 0.0915472 loss)
I0327 13:45:47.773597 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.5564 (* 0.0272727 = 0.0969928 loss)
I0327 13:45:47.773612 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.29873 (* 0.0272727 = 0.0899655 loss)
I0327 13:45:47.773627 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.02338 (* 0.0272727 = 0.0824558 loss)
I0327 13:45:47.773640 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.60666 (* 0.0272727 = 0.0710908 loss)
I0327 13:45:47.773654 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.9338 (* 0.0272727 = 0.05274 loss)
I0327 13:45:47.773668 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.985629 (* 0.0272727 = 0.0268808 loss)
I0327 13:45:47.773682 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.34141 (* 0.0272727 = 0.036584 loss)
I0327 13:45:47.773696 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.147567 (* 0.0272727 = 0.00402456 loss)
I0327 13:45:47.773710 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0683712 (* 0.0272727 = 0.00186467 loss)
I0327 13:45:47.773725 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000662057 (* 0.0272727 = 1.80561e-05 loss)
I0327 13:45:47.773738 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000528936 (* 0.0272727 = 1.44255e-05 loss)
I0327 13:45:47.773752 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000230601 (* 0.0272727 = 6.28911e-06 loss)
I0327 13:45:47.773766 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00189041 (* 0.0272727 = 5.15565e-05 loss)
I0327 13:45:47.773782 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000493856 (* 0.0272727 = 1.34688e-05 loss)
I0327 13:45:47.773795 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000520017 (* 0.0272727 = 1.41823e-05 loss)
I0327 13:45:47.773809 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000690437 (* 0.0272727 = 1.88301e-05 loss)
I0327 13:45:47.773841 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000368563 (* 0.0272727 = 1.00517e-05 loss)
I0327 13:45:47.773857 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000724616 (* 0.0272727 = 1.97622e-05 loss)
I0327 13:45:47.773877 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00104339 (* 0.0272727 = 2.8456e-05 loss)
I0327 13:45:47.773895 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00047574 (* 0.0272727 = 1.29747e-05 loss)
I0327 13:45:47.773919 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00042537 (* 0.0272727 = 1.1601e-05 loss)
I0327 13:45:47.773932 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:45:47.773946 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 13:45:47.773957 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:45:47.773968 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 13:45:47.773980 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.375
I0327 13:45:47.773995 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.625
I0327 13:45:47.774008 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.75
I0327 13:45:47.774019 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.75
I0327 13:45:47.774030 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:45:47.774042 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:45:47.774054 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:45:47.774065 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:45:47.774075 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:45:47.774086 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:45:47.774098 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:45:47.774109 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:45:47.774121 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:45:47.774132 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:45:47.774142 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:45:47.774154 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:45:47.774165 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:45:47.774176 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:45:47.774190 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.62701 (* 0.0272727 = 0.0989184 loss)
I0327 13:45:47.774204 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.22589 (* 0.0272727 = 0.0879788 loss)
I0327 13:45:47.774217 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.97051 (* 0.0272727 = 0.0810139 loss)
I0327 13:45:47.774231 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.09507 (* 0.0272727 = 0.0844111 loss)
I0327 13:45:47.774245 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.66933 (* 0.0272727 = 0.0728 loss)
I0327 13:45:47.774260 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.57742 (* 0.0272727 = 0.0430206 loss)
I0327 13:45:47.774272 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.828766 (* 0.0272727 = 0.0226027 loss)
I0327 13:45:47.774286 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 1.09631 (* 0.0272727 = 0.0298993 loss)
I0327 13:45:47.774304 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0340029 (* 0.0272727 = 0.000927353 loss)
I0327 13:45:47.774319 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0106478 (* 0.0272727 = 0.000290394 loss)
I0327 13:45:47.774333 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.0001886 (* 0.0272727 = 5.14364e-06 loss)
I0327 13:45:47.774359 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000197644 (* 0.0272727 = 5.39029e-06 loss)
I0327 13:45:47.774374 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000303203 (* 0.0272727 = 8.26916e-06 loss)
I0327 13:45:47.774389 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 8.41634e-05 (* 0.0272727 = 2.29536e-06 loss)
I0327 13:45:47.774404 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000271799 (* 0.0272727 = 7.4127e-06 loss)
I0327 13:45:47.774417 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00014067 (* 0.0272727 = 3.83646e-06 loss)
I0327 13:45:47.774431 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000169119 (* 0.0272727 = 4.61233e-06 loss)
I0327 13:45:47.774446 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000143781 (* 0.0272727 = 3.92129e-06 loss)
I0327 13:45:47.774459 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000209133 (* 0.0272727 = 5.70364e-06 loss)
I0327 13:45:47.774473 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 6.71424e-05 (* 0.0272727 = 1.83116e-06 loss)
I0327 13:45:47.774487 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000150109 (* 0.0272727 = 4.09389e-06 loss)
I0327 13:45:47.774502 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000166643 (* 0.0272727 = 4.5448e-06 loss)
I0327 13:45:47.774513 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0
I0327 13:45:47.774525 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 13:45:47.774536 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:45:47.774547 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.25
I0327 13:45:47.774559 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 13:45:47.774570 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.625
I0327 13:45:47.774581 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 13:45:47.774592 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.75
I0327 13:45:47.774605 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:45:47.774616 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:45:47.774626 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:45:47.774637 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:45:47.774649 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:45:47.774660 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:45:47.774672 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:45:47.774682 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:45:47.774694 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:45:47.774705 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:45:47.774716 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:45:47.774727 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:45:47.774739 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:45:47.774749 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:45:47.774762 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 3.72351 (* 0.0909091 = 0.338501 loss)
I0327 13:45:47.774776 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.94736 (* 0.0909091 = 0.267942 loss)
I0327 13:45:47.774791 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.1959 (* 0.0909091 = 0.290536 loss)
I0327 13:45:47.774803 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.06695 (* 0.0909091 = 0.278814 loss)
I0327 13:45:47.774817 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 1.97985 (* 0.0909091 = 0.179986 loss)
I0327 13:45:47.774832 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.62346 (* 0.0909091 = 0.147587 loss)
I0327 13:45:47.774854 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.873927 (* 0.0909091 = 0.079448 loss)
I0327 13:45:47.774869 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.04755 (* 0.0909091 = 0.0952319 loss)
I0327 13:45:47.774883 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0805231 (* 0.0909091 = 0.00732028 loss)
I0327 13:45:47.774898 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0274551 (* 0.0909091 = 0.00249592 loss)
I0327 13:45:47.774911 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000246967 (* 0.0909091 = 2.24515e-05 loss)
I0327 13:45:47.774925 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000215442 (* 0.0909091 = 1.95856e-05 loss)
I0327 13:45:47.774940 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000267668 (* 0.0909091 = 2.43335e-05 loss)
I0327 13:45:47.774953 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000269752 (* 0.0909091 = 2.45229e-05 loss)
I0327 13:45:47.774967 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.00017135 (* 0.0909091 = 1.55772e-05 loss)
I0327 13:45:47.774977 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000304886 (* 0.0909091 = 2.77169e-05 loss)
I0327 13:45:47.774992 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000262615 (* 0.0909091 = 2.38741e-05 loss)
I0327 13:45:47.775007 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000225909 (* 0.0909091 = 2.05372e-05 loss)
I0327 13:45:47.775027 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000268737 (* 0.0909091 = 2.44306e-05 loss)
I0327 13:45:47.775044 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000200417 (* 0.0909091 = 1.82198e-05 loss)
I0327 13:45:47.775075 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000237905 (* 0.0909091 = 2.16277e-05 loss)
I0327 13:45:47.775091 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000220481 (* 0.0909091 = 2.00437e-05 loss)
I0327 13:45:47.775104 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:45:47.775125 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00068003
I0327 13:45:47.775147 21344 sgd_solver.cpp:106] Iteration 16000, lr = 0.01
I0327 13:47:35.576812 21344 solver.cpp:229] Iteration 16500, loss = 2.68612
I0327 13:47:35.577029 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.5
I0327 13:47:35.577061 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 13:47:35.577085 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.25
I0327 13:47:35.577105 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 13:47:35.577127 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 13:47:35.577149 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 13:47:35.577170 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:47:35.577193 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:47:35.577214 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:47:35.577236 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:47:35.577258 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:47:35.577280 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:47:35.577301 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:47:35.577325 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:47:35.577349 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:47:35.577371 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:47:35.577394 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:47:35.577415 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:47:35.577437 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:47:35.577460 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:47:35.577481 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:47:35.577502 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:47:35.577533 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.39512 (* 0.0272727 = 0.0380487 loss)
I0327 13:47:35.577589 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.09236 (* 0.0272727 = 0.0843372 loss)
I0327 13:47:35.577617 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.88196 (* 0.0272727 = 0.0785989 loss)
I0327 13:47:35.577643 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.30262 (* 0.0272727 = 0.0900715 loss)
I0327 13:47:35.577671 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.38773 (* 0.0272727 = 0.0651198 loss)
I0327 13:47:35.577699 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.87692 (* 0.0272727 = 0.0784613 loss)
I0327 13:47:35.577726 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.60286 (* 0.0272727 = 0.0437144 loss)
I0327 13:47:35.577754 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.184262 (* 0.0272727 = 0.00502533 loss)
I0327 13:47:35.577780 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0240096 (* 0.0272727 = 0.000654807 loss)
I0327 13:47:35.577807 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00279339 (* 0.0272727 = 7.61834e-05 loss)
I0327 13:47:35.577836 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 3.72631e-05 (* 0.0272727 = 1.01627e-06 loss)
I0327 13:47:35.577862 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 8.60085e-05 (* 0.0272727 = 2.34569e-06 loss)
I0327 13:47:35.577889 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 4.2046e-05 (* 0.0272727 = 1.14671e-06 loss)
I0327 13:47:35.577916 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 2.67335e-05 (* 0.0272727 = 7.29094e-07 loss)
I0327 13:47:35.577944 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 4.47207e-05 (* 0.0272727 = 1.21966e-06 loss)
I0327 13:47:35.577971 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 2.73591e-05 (* 0.0272727 = 7.46158e-07 loss)
I0327 13:47:35.578003 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 4.61662e-05 (* 0.0272727 = 1.25908e-06 loss)
I0327 13:47:35.578054 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 2.92073e-05 (* 0.0272727 = 7.96561e-07 loss)
I0327 13:47:35.578084 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 2.60477e-05 (* 0.0272727 = 7.10392e-07 loss)
I0327 13:47:35.578114 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 4.51752e-05 (* 0.0272727 = 1.23205e-06 loss)
I0327 13:47:35.578145 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 6.46183e-05 (* 0.0272727 = 1.76232e-06 loss)
I0327 13:47:35.578173 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 4.47051e-05 (* 0.0272727 = 1.21923e-06 loss)
I0327 13:47:35.578197 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.625
I0327 13:47:35.578219 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:47:35.578241 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:47:35.578263 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:47:35.578285 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:47:35.578308 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:47:35.578330 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:47:35.578353 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:47:35.578375 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:47:35.578397 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:47:35.578419 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:47:35.578441 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:47:35.578464 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:47:35.578485 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:47:35.578507 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:47:35.578529 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:47:35.578550 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:47:35.578572 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:47:35.578593 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:47:35.578614 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:47:35.578637 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:47:35.578660 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:47:35.578685 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.52833 (* 0.0272727 = 0.0416818 loss)
I0327 13:47:35.578712 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.87683 (* 0.0272727 = 0.078459 loss)
I0327 13:47:35.578739 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.11757 (* 0.0272727 = 0.0850246 loss)
I0327 13:47:35.578768 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.52914 (* 0.0272727 = 0.0962494 loss)
I0327 13:47:35.578794 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.65928 (* 0.0272727 = 0.0725257 loss)
I0327 13:47:35.578819 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.65744 (* 0.0272727 = 0.0724756 loss)
I0327 13:47:35.578845 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 2.20845 (* 0.0272727 = 0.0602303 loss)
I0327 13:47:35.578871 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0705645 (* 0.0272727 = 0.00192449 loss)
I0327 13:47:35.578897 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0283347 (* 0.0272727 = 0.000772763 loss)
I0327 13:47:35.578924 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0029012 (* 0.0272727 = 7.91236e-05 loss)
I0327 13:47:35.578951 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00012749 (* 0.0272727 = 3.477e-06 loss)
I0327 13:47:35.578997 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00010392 (* 0.0272727 = 2.83417e-06 loss)
I0327 13:47:35.579025 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000113015 (* 0.0272727 = 3.08223e-06 loss)
I0327 13:47:35.579057 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000156429 (* 0.0272727 = 4.26625e-06 loss)
I0327 13:47:35.579087 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000313882 (* 0.0272727 = 8.56042e-06 loss)
I0327 13:47:35.579113 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000110956 (* 0.0272727 = 3.02607e-06 loss)
I0327 13:47:35.579140 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 7.44696e-05 (* 0.0272727 = 2.03099e-06 loss)
I0327 13:47:35.579167 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 7.5304e-05 (* 0.0272727 = 2.05375e-06 loss)
I0327 13:47:35.579195 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 9.20052e-05 (* 0.0272727 = 2.50923e-06 loss)
I0327 13:47:35.579221 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 7.59439e-05 (* 0.0272727 = 2.0712e-06 loss)
I0327 13:47:35.579248 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 3.28881e-05 (* 0.0272727 = 8.96948e-07 loss)
I0327 13:47:35.579274 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000114036 (* 0.0272727 = 3.11008e-06 loss)
I0327 13:47:35.579298 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.75
I0327 13:47:35.579319 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:47:35.579340 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:47:35.579361 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:47:35.579383 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 13:47:35.579404 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:47:35.579426 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:47:35.579447 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:47:35.579469 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:47:35.579490 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:47:35.579511 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:47:35.579533 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:47:35.579555 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:47:35.579576 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:47:35.579596 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:47:35.579618 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:47:35.579639 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:47:35.579660 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:47:35.579682 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:47:35.579704 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:47:35.579725 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:47:35.579746 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:47:35.579772 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.19136 (* 0.0909091 = 0.108305 loss)
I0327 13:47:35.579798 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.96563 (* 0.0909091 = 0.269603 loss)
I0327 13:47:35.579824 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.93349 (* 0.0909091 = 0.266681 loss)
I0327 13:47:35.579850 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.16574 (* 0.0909091 = 0.287794 loss)
I0327 13:47:35.579876 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.7372 (* 0.0909091 = 0.248837 loss)
I0327 13:47:35.579919 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.54054 (* 0.0909091 = 0.230958 loss)
I0327 13:47:35.579947 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.70648 (* 0.0909091 = 0.155135 loss)
I0327 13:47:35.579973 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.230447 (* 0.0909091 = 0.0209497 loss)
I0327 13:47:35.580000 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0536131 (* 0.0909091 = 0.00487392 loss)
I0327 13:47:35.580026 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0145906 (* 0.0909091 = 0.00132641 loss)
I0327 13:47:35.580052 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000150677 (* 0.0909091 = 1.36979e-05 loss)
I0327 13:47:35.580078 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.0001517 (* 0.0909091 = 1.37909e-05 loss)
I0327 13:47:35.580111 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000114373 (* 0.0909091 = 1.03976e-05 loss)
I0327 13:47:35.580138 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000165722 (* 0.0909091 = 1.50656e-05 loss)
I0327 13:47:35.580165 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000165701 (* 0.0909091 = 1.50638e-05 loss)
I0327 13:47:35.580193 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000175129 (* 0.0909091 = 1.59208e-05 loss)
I0327 13:47:35.580219 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000138778 (* 0.0909091 = 1.26162e-05 loss)
I0327 13:47:35.580247 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000119445 (* 0.0909091 = 1.08586e-05 loss)
I0327 13:47:35.580282 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000168297 (* 0.0909091 = 1.52997e-05 loss)
I0327 13:47:35.580312 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000144134 (* 0.0909091 = 1.31031e-05 loss)
I0327 13:47:35.580340 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000128774 (* 0.0909091 = 1.17067e-05 loss)
I0327 13:47:35.580365 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000129281 (* 0.0909091 = 1.17528e-05 loss)
I0327 13:47:35.580389 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:47:35.580410 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000691999
I0327 13:47:35.580431 21344 sgd_solver.cpp:106] Iteration 16500, lr = 0.01
I0327 13:49:23.494496 21344 solver.cpp:229] Iteration 17000, loss = 2.68192
I0327 13:49:23.494674 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.375
I0327 13:49:23.494695 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:49:23.494709 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 13:49:23.494720 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.5
I0327 13:49:23.494732 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.375
I0327 13:49:23.494750 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.625
I0327 13:49:23.494762 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 13:49:23.494774 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:49:23.494786 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:49:23.494797 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:49:23.494809 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:49:23.494822 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:49:23.494832 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:49:23.494844 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:49:23.494856 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:49:23.494868 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:49:23.494879 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:49:23.494899 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:49:23.494910 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:49:23.494921 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:49:23.494933 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:49:23.494945 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:49:23.494967 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.34494 (* 0.0272727 = 0.063953 loss)
I0327 13:49:23.494982 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.82161 (* 0.0272727 = 0.0769529 loss)
I0327 13:49:23.494997 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.84048 (* 0.0272727 = 0.0774678 loss)
I0327 13:49:23.495010 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.50238 (* 0.0272727 = 0.0682467 loss)
I0327 13:49:23.495024 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.0448 (* 0.0272727 = 0.0557672 loss)
I0327 13:49:23.495046 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.58838 (* 0.0272727 = 0.0433196 loss)
I0327 13:49:23.495060 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.649891 (* 0.0272727 = 0.0177243 loss)
I0327 13:49:23.495074 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.370235 (* 0.0272727 = 0.0100973 loss)
I0327 13:49:23.495097 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.205406 (* 0.0272727 = 0.00560197 loss)
I0327 13:49:23.495115 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.13443 (* 0.0272727 = 0.00366626 loss)
I0327 13:49:23.495129 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00416288 (* 0.0272727 = 0.000113533 loss)
I0327 13:49:23.495143 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.0085909 (* 0.0272727 = 0.000234297 loss)
I0327 13:49:23.495167 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00695728 (* 0.0272727 = 0.000189744 loss)
I0327 13:49:23.495189 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00787535 (* 0.0272727 = 0.000214782 loss)
I0327 13:49:23.495228 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.0057814 (* 0.0272727 = 0.000157675 loss)
I0327 13:49:23.495257 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.0111118 (* 0.0272727 = 0.000303049 loss)
I0327 13:49:23.495302 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00426174 (* 0.0272727 = 0.000116229 loss)
I0327 13:49:23.495343 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00862271 (* 0.0272727 = 0.000235165 loss)
I0327 13:49:23.495359 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.0086664 (* 0.0272727 = 0.000236356 loss)
I0327 13:49:23.495374 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00516728 (* 0.0272727 = 0.000140926 loss)
I0327 13:49:23.495388 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00648902 (* 0.0272727 = 0.000176973 loss)
I0327 13:49:23.495404 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00175307 (* 0.0272727 = 4.78109e-05 loss)
I0327 13:49:23.495415 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.375
I0327 13:49:23.495429 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:49:23.495440 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:49:23.495451 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.5
I0327 13:49:23.495466 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:49:23.495478 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.75
I0327 13:49:23.495491 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 13:49:23.495502 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:49:23.495514 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:49:23.495525 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:49:23.495537 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:49:23.495548 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:49:23.495559 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:49:23.495570 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:49:23.495581 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:49:23.495592 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:49:23.495604 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:49:23.495615 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:49:23.495626 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:49:23.495637 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:49:23.495648 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:49:23.495661 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:49:23.495673 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.27099 (* 0.0272727 = 0.061936 loss)
I0327 13:49:23.495687 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.38096 (* 0.0272727 = 0.0922081 loss)
I0327 13:49:23.495702 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.22515 (* 0.0272727 = 0.0879588 loss)
I0327 13:49:23.495717 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.56286 (* 0.0272727 = 0.0698961 loss)
I0327 13:49:23.495730 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.4801 (* 0.0272727 = 0.0676391 loss)
I0327 13:49:23.495743 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.49042 (* 0.0272727 = 0.0406477 loss)
I0327 13:49:23.495759 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.573091 (* 0.0272727 = 0.0156298 loss)
I0327 13:49:23.495772 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.300234 (* 0.0272727 = 0.00818821 loss)
I0327 13:49:23.495786 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.103987 (* 0.0272727 = 0.00283602 loss)
I0327 13:49:23.495801 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0449973 (* 0.0272727 = 0.0012272 loss)
I0327 13:49:23.495815 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000940126 (* 0.0272727 = 2.56398e-05 loss)
I0327 13:49:23.495841 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00307592 (* 0.0272727 = 8.38886e-05 loss)
I0327 13:49:23.495858 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00162364 (* 0.0272727 = 4.42811e-05 loss)
I0327 13:49:23.495872 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00109355 (* 0.0272727 = 2.9824e-05 loss)
I0327 13:49:23.495893 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000894689 (* 0.0272727 = 2.44006e-05 loss)
I0327 13:49:23.495906 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00090903 (* 0.0272727 = 2.47917e-05 loss)
I0327 13:49:23.495920 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000515639 (* 0.0272727 = 1.40629e-05 loss)
I0327 13:49:23.495934 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000590338 (* 0.0272727 = 1.61001e-05 loss)
I0327 13:49:23.495949 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000573172 (* 0.0272727 = 1.5632e-05 loss)
I0327 13:49:23.495970 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00222105 (* 0.0272727 = 6.05741e-05 loss)
I0327 13:49:23.495985 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000978542 (* 0.0272727 = 2.66875e-05 loss)
I0327 13:49:23.495998 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00356498 (* 0.0272727 = 9.72267e-05 loss)
I0327 13:49:23.496011 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.375
I0327 13:49:23.496022 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.375
I0327 13:49:23.496034 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.25
I0327 13:49:23.496047 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.25
I0327 13:49:23.496059 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 13:49:23.496071 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.875
I0327 13:49:23.496083 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 13:49:23.496093 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:49:23.496114 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:49:23.496125 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:49:23.496136 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:49:23.496147 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:49:23.496162 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:49:23.496173 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:49:23.496186 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:49:23.496196 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:49:23.496207 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:49:23.496218 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:49:23.496229 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:49:23.496240 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:49:23.496253 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:49:23.496263 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:49:23.496276 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.81995 (* 0.0909091 = 0.16545 loss)
I0327 13:49:23.496290 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.53993 (* 0.0909091 = 0.230903 loss)
I0327 13:49:23.496304 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.1686 (* 0.0909091 = 0.288055 loss)
I0327 13:49:23.496317 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.90981 (* 0.0909091 = 0.264528 loss)
I0327 13:49:23.496331 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.24651 (* 0.0909091 = 0.204228 loss)
I0327 13:49:23.496345 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.25399 (* 0.0909091 = 0.113999 loss)
I0327 13:49:23.496371 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.719914 (* 0.0909091 = 0.0654467 loss)
I0327 13:49:23.496386 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.259135 (* 0.0909091 = 0.0235577 loss)
I0327 13:49:23.496399 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.100086 (* 0.0909091 = 0.00909874 loss)
I0327 13:49:23.496413 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0417042 (* 0.0909091 = 0.00379129 loss)
I0327 13:49:23.496428 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.00145036 (* 0.0909091 = 0.000131851 loss)
I0327 13:49:23.496443 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000898964 (* 0.0909091 = 8.1724e-05 loss)
I0327 13:49:23.496456 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.00131547 (* 0.0909091 = 0.000119588 loss)
I0327 13:49:23.496470 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000965254 (* 0.0909091 = 8.77504e-05 loss)
I0327 13:49:23.496484 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.00100795 (* 0.0909091 = 9.16318e-05 loss)
I0327 13:49:23.496498 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.00101299 (* 0.0909091 = 9.20903e-05 loss)
I0327 13:49:23.496515 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.00140868 (* 0.0909091 = 0.000128062 loss)
I0327 13:49:23.496529 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000792402 (* 0.0909091 = 7.20365e-05 loss)
I0327 13:49:23.496544 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.0008928 (* 0.0909091 = 8.11636e-05 loss)
I0327 13:49:23.496558 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.00162617 (* 0.0909091 = 0.000147834 loss)
I0327 13:49:23.496572 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.0011032 (* 0.0909091 = 0.000100291 loss)
I0327 13:49:23.496587 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000877029 (* 0.0909091 = 7.97299e-05 loss)
I0327 13:49:23.496598 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:49:23.496610 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000716852
I0327 13:49:23.496623 21344 sgd_solver.cpp:106] Iteration 17000, lr = 0.01
I0327 13:51:11.408695 21344 solver.cpp:229] Iteration 17500, loss = 2.66249
I0327 13:51:11.408807 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.5
I0327 13:51:11.408828 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 13:51:11.408840 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:51:11.408852 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:51:11.408864 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0
I0327 13:51:11.408876 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.75
I0327 13:51:11.408888 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 13:51:11.408900 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:51:11.408913 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 13:51:11.408926 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 0.875
I0327 13:51:11.408937 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:51:11.408948 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:51:11.408960 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:51:11.408972 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:51:11.408983 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:51:11.408998 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:51:11.409009 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:51:11.409021 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:51:11.409034 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:51:11.409044 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:51:11.409055 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:51:11.409067 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:51:11.409083 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.83468 (* 0.0272727 = 0.0500366 loss)
I0327 13:51:11.409097 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.5274 (* 0.0272727 = 0.0689291 loss)
I0327 13:51:11.409111 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.53906 (* 0.0272727 = 0.0692472 loss)
I0327 13:51:11.409126 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.0156 (* 0.0272727 = 0.0822437 loss)
I0327 13:51:11.409139 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.79891 (* 0.0272727 = 0.103607 loss)
I0327 13:51:11.409153 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.37318 (* 0.0272727 = 0.0374505 loss)
I0327 13:51:11.409168 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.17455 (* 0.0272727 = 0.0593058 loss)
I0327 13:51:11.409183 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.437127 (* 0.0272727 = 0.0119216 loss)
I0327 13:51:11.409196 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.483319 (* 0.0272727 = 0.0131814 loss)
I0327 13:51:11.409210 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.846897 (* 0.0272727 = 0.0230972 loss)
I0327 13:51:11.409225 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000233643 (* 0.0272727 = 6.37209e-06 loss)
I0327 13:51:11.409240 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000327906 (* 0.0272727 = 8.94289e-06 loss)
I0327 13:51:11.409255 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000193043 (* 0.0272727 = 5.2648e-06 loss)
I0327 13:51:11.409268 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000166805 (* 0.0272727 = 4.54924e-06 loss)
I0327 13:51:11.409282 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000233906 (* 0.0272727 = 6.37925e-06 loss)
I0327 13:51:11.409296 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000281903 (* 0.0272727 = 7.68828e-06 loss)
I0327 13:51:11.409310 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000280746 (* 0.0272727 = 7.65671e-06 loss)
I0327 13:51:11.409342 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000411516 (* 0.0272727 = 1.12232e-05 loss)
I0327 13:51:11.409358 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000548751 (* 0.0272727 = 1.49659e-05 loss)
I0327 13:51:11.409371 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000189749 (* 0.0272727 = 5.17497e-06 loss)
I0327 13:51:11.409385 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000104957 (* 0.0272727 = 2.86247e-06 loss)
I0327 13:51:11.409399 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000233066 (* 0.0272727 = 6.35635e-06 loss)
I0327 13:51:11.409411 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.625
I0327 13:51:11.409423 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:51:11.409435 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:51:11.409446 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:51:11.409458 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 13:51:11.409471 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:51:11.409482 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 13:51:11.409493 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:51:11.409504 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 13:51:11.409517 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 0.875
I0327 13:51:11.409528 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:51:11.409554 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:51:11.409570 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:51:11.409582 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:51:11.409593 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:51:11.409605 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:51:11.409615 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:51:11.409626 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:51:11.409637 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:51:11.409649 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:51:11.409660 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:51:11.409672 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:51:11.409685 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.03113 (* 0.0272727 = 0.0553945 loss)
I0327 13:51:11.409699 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.15544 (* 0.0272727 = 0.0860575 loss)
I0327 13:51:11.409713 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.01874 (* 0.0272727 = 0.0823291 loss)
I0327 13:51:11.409726 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.16419 (* 0.0272727 = 0.0862961 loss)
I0327 13:51:11.409741 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.46514 (* 0.0272727 = 0.0945039 loss)
I0327 13:51:11.409754 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.82982 (* 0.0272727 = 0.0499042 loss)
I0327 13:51:11.409768 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.65257 (* 0.0272727 = 0.0450701 loss)
I0327 13:51:11.409782 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.32189 (* 0.0272727 = 0.00877881 loss)
I0327 13:51:11.409796 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.454016 (* 0.0272727 = 0.0123823 loss)
I0327 13:51:11.409811 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.625885 (* 0.0272727 = 0.0170696 loss)
I0327 13:51:11.409827 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000145713 (* 0.0272727 = 3.974e-06 loss)
I0327 13:51:11.409855 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000181293 (* 0.0272727 = 4.94435e-06 loss)
I0327 13:51:11.409870 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000183022 (* 0.0272727 = 4.99151e-06 loss)
I0327 13:51:11.409885 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000172715 (* 0.0272727 = 4.71041e-06 loss)
I0327 13:51:11.409899 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 7.53393e-05 (* 0.0272727 = 2.05471e-06 loss)
I0327 13:51:11.409914 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000134968 (* 0.0272727 = 3.68095e-06 loss)
I0327 13:51:11.409927 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000160273 (* 0.0272727 = 4.37109e-06 loss)
I0327 13:51:11.409941 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000223785 (* 0.0272727 = 6.10322e-06 loss)
I0327 13:51:11.409955 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000138804 (* 0.0272727 = 3.78555e-06 loss)
I0327 13:51:11.409970 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000155311 (* 0.0272727 = 4.23574e-06 loss)
I0327 13:51:11.409981 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000184421 (* 0.0272727 = 5.02967e-06 loss)
I0327 13:51:11.409996 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 9.76268e-05 (* 0.0272727 = 2.66255e-06 loss)
I0327 13:51:11.410007 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.375
I0327 13:51:11.410020 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.375
I0327 13:51:11.410032 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.375
I0327 13:51:11.410046 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:51:11.410058 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 13:51:11.410070 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.5
I0327 13:51:11.410081 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.5
I0327 13:51:11.410094 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:51:11.410105 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 13:51:11.410116 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 0.875
I0327 13:51:11.410128 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:51:11.410140 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:51:11.410151 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:51:11.410161 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:51:11.410173 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:51:11.410184 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:51:11.410195 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:51:11.410207 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:51:11.410218 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:51:11.410228 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:51:11.410239 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:51:11.410250 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:51:11.410264 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.60326 (* 0.0909091 = 0.145751 loss)
I0327 13:51:11.410277 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.69441 (* 0.0909091 = 0.244947 loss)
I0327 13:51:11.410291 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.28101 (* 0.0909091 = 0.207365 loss)
I0327 13:51:11.410305 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.26471 (* 0.0909091 = 0.296792 loss)
I0327 13:51:11.410318 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.42517 (* 0.0909091 = 0.311379 loss)
I0327 13:51:11.410342 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.56699 (* 0.0909091 = 0.142454 loss)
I0327 13:51:11.410357 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 2.0846 (* 0.0909091 = 0.189509 loss)
I0327 13:51:11.410372 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.354433 (* 0.0909091 = 0.0322212 loss)
I0327 13:51:11.410385 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.445359 (* 0.0909091 = 0.0404872 loss)
I0327 13:51:11.410399 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.586467 (* 0.0909091 = 0.0533151 loss)
I0327 13:51:11.410413 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 8.8657e-05 (* 0.0909091 = 8.05973e-06 loss)
I0327 13:51:11.410428 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 9.81578e-05 (* 0.0909091 = 8.92344e-06 loss)
I0327 13:51:11.410440 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000108496 (* 0.0909091 = 9.86324e-06 loss)
I0327 13:51:11.410454 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000105777 (* 0.0909091 = 9.61609e-06 loss)
I0327 13:51:11.410468 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000103962 (* 0.0909091 = 9.4511e-06 loss)
I0327 13:51:11.410482 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 9.45738e-05 (* 0.0909091 = 8.59762e-06 loss)
I0327 13:51:11.410496 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000102364 (* 0.0909091 = 9.30578e-06 loss)
I0327 13:51:11.410511 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000128358 (* 0.0909091 = 1.16689e-05 loss)
I0327 13:51:11.410524 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 9.23165e-05 (* 0.0909091 = 8.39241e-06 loss)
I0327 13:51:11.410538 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 9.61324e-05 (* 0.0909091 = 8.73931e-06 loss)
I0327 13:51:11.410552 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000116106 (* 0.0909091 = 1.05551e-05 loss)
I0327 13:51:11.410567 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 9.34624e-05 (* 0.0909091 = 8.49658e-06 loss)
I0327 13:51:11.410578 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:51:11.410589 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00246034
I0327 13:51:11.410601 21344 sgd_solver.cpp:106] Iteration 17500, lr = 0.01
I0327 13:52:59.679381 21344 solver.cpp:229] Iteration 18000, loss = 2.67964
I0327 13:52:59.679525 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0
I0327 13:52:59.679546 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 13:52:59.679569 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.25
I0327 13:52:59.679585 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:52:59.679597 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.375
I0327 13:52:59.679610 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.5
I0327 13:52:59.679622 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 13:52:59.679635 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 13:52:59.679646 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:52:59.679658 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:52:59.679669 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:52:59.679682 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:52:59.679693 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:52:59.679704 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:52:59.679716 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:52:59.679728 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:52:59.679739 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:52:59.679757 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:52:59.679775 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:52:59.679788 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:52:59.679800 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:52:59.679811 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:52:59.679826 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.11202 (* 0.0272727 = 0.0848734 loss)
I0327 13:52:59.679841 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.26493 (* 0.0272727 = 0.0890436 loss)
I0327 13:52:59.679855 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.71264 (* 0.0272727 = 0.0739811 loss)
I0327 13:52:59.679869 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.06159 (* 0.0272727 = 0.083498 loss)
I0327 13:52:59.679884 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.47441 (* 0.0272727 = 0.067484 loss)
I0327 13:52:59.679898 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.1369 (* 0.0272727 = 0.0582791 loss)
I0327 13:52:59.679913 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.09467 (* 0.0272727 = 0.0298547 loss)
I0327 13:52:59.679927 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0661555 (* 0.0272727 = 0.00180424 loss)
I0327 13:52:59.679941 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.00744993 (* 0.0272727 = 0.00020318 loss)
I0327 13:52:59.679960 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00401114 (* 0.0272727 = 0.000109395 loss)
I0327 13:52:59.679982 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000227222 (* 0.0272727 = 6.19696e-06 loss)
I0327 13:52:59.680001 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000199814 (* 0.0272727 = 5.44946e-06 loss)
I0327 13:52:59.680016 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000364553 (* 0.0272727 = 9.94235e-06 loss)
I0327 13:52:59.680037 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000148496 (* 0.0272727 = 4.0499e-06 loss)
I0327 13:52:59.680068 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000207898 (* 0.0272727 = 5.66996e-06 loss)
I0327 13:52:59.680099 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000369025 (* 0.0272727 = 1.00643e-05 loss)
I0327 13:52:59.680127 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000137285 (* 0.0272727 = 3.74414e-06 loss)
I0327 13:52:59.680163 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 8.21266e-05 (* 0.0272727 = 2.23982e-06 loss)
I0327 13:52:59.680181 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00046313 (* 0.0272727 = 1.26308e-05 loss)
I0327 13:52:59.680194 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000261697 (* 0.0272727 = 7.13719e-06 loss)
I0327 13:52:59.680208 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000151835 (* 0.0272727 = 4.14095e-06 loss)
I0327 13:52:59.680222 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000359047 (* 0.0272727 = 9.79219e-06 loss)
I0327 13:52:59.680235 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.125
I0327 13:52:59.680248 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:52:59.680259 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:52:59.680270 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.25
I0327 13:52:59.680282 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.5
I0327 13:52:59.680294 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.5
I0327 13:52:59.680306 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 13:52:59.680318 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:52:59.680330 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:52:59.680342 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:52:59.680353 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:52:59.680366 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:52:59.680377 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:52:59.680388 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:52:59.680399 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:52:59.680411 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:52:59.680423 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:52:59.680434 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:52:59.680445 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:52:59.680456 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:52:59.680469 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:52:59.680480 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:52:59.680493 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.9326 (* 0.0272727 = 0.0799799 loss)
I0327 13:52:59.680507 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.40979 (* 0.0272727 = 0.0929943 loss)
I0327 13:52:59.680521 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.22466 (* 0.0272727 = 0.0879454 loss)
I0327 13:52:59.680536 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.63721 (* 0.0272727 = 0.0991965 loss)
I0327 13:52:59.680553 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.8895 (* 0.0272727 = 0.0788046 loss)
I0327 13:52:59.680567 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.21519 (* 0.0272727 = 0.0604143 loss)
I0327 13:52:59.680582 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.08782 (* 0.0272727 = 0.0296679 loss)
I0327 13:52:59.680596 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0668747 (* 0.0272727 = 0.00182386 loss)
I0327 13:52:59.680606 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0191596 (* 0.0272727 = 0.000522535 loss)
I0327 13:52:59.680621 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00722366 (* 0.0272727 = 0.000197009 loss)
I0327 13:52:59.680635 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00546675 (* 0.0272727 = 0.000149093 loss)
I0327 13:52:59.680660 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00139503 (* 0.0272727 = 3.80463e-05 loss)
I0327 13:52:59.680675 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00382727 (* 0.0272727 = 0.00010438 loss)
I0327 13:52:59.680690 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00169205 (* 0.0272727 = 4.61467e-05 loss)
I0327 13:52:59.680703 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00187917 (* 0.0272727 = 5.12501e-05 loss)
I0327 13:52:59.680717 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00361664 (* 0.0272727 = 9.86355e-05 loss)
I0327 13:52:59.680732 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.0013756 (* 0.0272727 = 3.75165e-05 loss)
I0327 13:52:59.680747 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00142356 (* 0.0272727 = 3.88245e-05 loss)
I0327 13:52:59.680760 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00190754 (* 0.0272727 = 5.20239e-05 loss)
I0327 13:52:59.680773 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00106212 (* 0.0272727 = 2.89668e-05 loss)
I0327 13:52:59.680788 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00164872 (* 0.0272727 = 4.49651e-05 loss)
I0327 13:52:59.680801 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00177482 (* 0.0272727 = 4.84042e-05 loss)
I0327 13:52:59.680814 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.25
I0327 13:52:59.680825 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:52:59.680837 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.25
I0327 13:52:59.680850 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:52:59.680861 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.625
I0327 13:52:59.680872 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.5
I0327 13:52:59.680884 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.875
I0327 13:52:59.680896 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:52:59.680907 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:52:59.680918 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:52:59.680930 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:52:59.680941 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:52:59.680953 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:52:59.680964 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:52:59.680976 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:52:59.680987 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:52:59.680999 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:52:59.681010 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:52:59.681021 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:52:59.681032 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:52:59.681046 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:52:59.681058 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:52:59.681072 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.54846 (* 0.0909091 = 0.231678 loss)
I0327 13:52:59.681087 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.77092 (* 0.0909091 = 0.251902 loss)
I0327 13:52:59.681100 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.94267 (* 0.0909091 = 0.267516 loss)
I0327 13:52:59.681114 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.16146 (* 0.0909091 = 0.287405 loss)
I0327 13:52:59.681128 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.06259 (* 0.0909091 = 0.187509 loss)
I0327 13:52:59.681152 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.82265 (* 0.0909091 = 0.165695 loss)
I0327 13:52:59.681169 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.741988 (* 0.0909091 = 0.0674535 loss)
I0327 13:52:59.681182 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0238172 (* 0.0909091 = 0.0021652 loss)
I0327 13:52:59.681196 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00530338 (* 0.0909091 = 0.000482126 loss)
I0327 13:52:59.681210 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00260942 (* 0.0909091 = 0.00023722 loss)
I0327 13:52:59.681224 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000125469 (* 0.0909091 = 1.14062e-05 loss)
I0327 13:52:59.681238 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000167509 (* 0.0909091 = 1.5228e-05 loss)
I0327 13:52:59.681253 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 9.86505e-05 (* 0.0909091 = 8.96823e-06 loss)
I0327 13:52:59.681267 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000141878 (* 0.0909091 = 1.2898e-05 loss)
I0327 13:52:59.681282 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000138749 (* 0.0909091 = 1.26135e-05 loss)
I0327 13:52:59.681295 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000136862 (* 0.0909091 = 1.2442e-05 loss)
I0327 13:52:59.681309 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.00011305 (* 0.0909091 = 1.02773e-05 loss)
I0327 13:52:59.681324 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 9.87723e-05 (* 0.0909091 = 8.9793e-06 loss)
I0327 13:52:59.681337 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000111352 (* 0.0909091 = 1.01229e-05 loss)
I0327 13:52:59.681351 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000122941 (* 0.0909091 = 1.11764e-05 loss)
I0327 13:52:59.681365 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000130287 (* 0.0909091 = 1.18443e-05 loss)
I0327 13:52:59.681378 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000138898 (* 0.0909091 = 1.26271e-05 loss)
I0327 13:52:59.681391 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:52:59.681402 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00115384
I0327 13:52:59.681414 21344 sgd_solver.cpp:106] Iteration 18000, lr = 0.01
I0327 13:54:47.399299 21344 solver.cpp:229] Iteration 18500, loss = 2.64639
I0327 13:54:47.399421 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.5
I0327 13:54:47.399441 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:54:47.399453 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:54:47.399466 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 13:54:47.399477 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 13:54:47.399490 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 13:54:47.399502 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:54:47.399514 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 13:54:47.399526 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:54:47.399538 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:54:47.399549 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:54:47.399560 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:54:47.399572 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:54:47.399583 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:54:47.399595 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:54:47.399606 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:54:47.399618 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:54:47.399631 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:54:47.399641 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:54:47.399653 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:54:47.399664 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:54:47.399677 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:54:47.399691 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.75502 (* 0.0272727 = 0.0478642 loss)
I0327 13:54:47.399706 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.1421 (* 0.0272727 = 0.0856936 loss)
I0327 13:54:47.399720 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.30868 (* 0.0272727 = 0.0902367 loss)
I0327 13:54:47.399734 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.35827 (* 0.0272727 = 0.0915893 loss)
I0327 13:54:47.399749 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.71736 (* 0.0272727 = 0.0741098 loss)
I0327 13:54:47.399762 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.24713 (* 0.0272727 = 0.0612853 loss)
I0327 13:54:47.399776 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.09818 (* 0.0272727 = 0.0299504 loss)
I0327 13:54:47.399791 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0681258 (* 0.0272727 = 0.00185798 loss)
I0327 13:54:47.399804 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0100922 (* 0.0272727 = 0.000275243 loss)
I0327 13:54:47.399818 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00647977 (* 0.0272727 = 0.000176721 loss)
I0327 13:54:47.399833 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 6.59131e-05 (* 0.0272727 = 1.79763e-06 loss)
I0327 13:54:47.399847 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000135444 (* 0.0272727 = 3.69393e-06 loss)
I0327 13:54:47.399860 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000153192 (* 0.0272727 = 4.17796e-06 loss)
I0327 13:54:47.399874 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000187593 (* 0.0272727 = 5.11616e-06 loss)
I0327 13:54:47.399888 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000247645 (* 0.0272727 = 6.75396e-06 loss)
I0327 13:54:47.399902 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000110556 (* 0.0272727 = 3.01518e-06 loss)
I0327 13:54:47.399916 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 8.69312e-05 (* 0.0272727 = 2.37085e-06 loss)
I0327 13:54:47.399946 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 7.06991e-05 (* 0.0272727 = 1.92816e-06 loss)
I0327 13:54:47.399962 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000174891 (* 0.0272727 = 4.76974e-06 loss)
I0327 13:54:47.399976 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000149154 (* 0.0272727 = 4.06783e-06 loss)
I0327 13:54:47.399992 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 6.07357e-05 (* 0.0272727 = 1.65643e-06 loss)
I0327 13:54:47.400007 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 8.59267e-05 (* 0.0272727 = 2.34346e-06 loss)
I0327 13:54:47.400020 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.5
I0327 13:54:47.400032 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:54:47.400044 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 13:54:47.400055 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 13:54:47.400068 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:54:47.400079 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 13:54:47.400091 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.75
I0327 13:54:47.400104 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 13:54:47.400115 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:54:47.400126 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:54:47.400137 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:54:47.400148 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:54:47.400161 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:54:47.400172 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:54:47.400183 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:54:47.400194 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:54:47.400207 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:54:47.400218 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:54:47.400228 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:54:47.400240 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:54:47.400251 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:54:47.400264 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:54:47.400277 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.48552 (* 0.0272727 = 0.0405141 loss)
I0327 13:54:47.400290 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.65497 (* 0.0272727 = 0.0724082 loss)
I0327 13:54:47.400305 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.57947 (* 0.0272727 = 0.0976219 loss)
I0327 13:54:47.400318 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.13492 (* 0.0272727 = 0.0854978 loss)
I0327 13:54:47.400332 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.29118 (* 0.0272727 = 0.0897593 loss)
I0327 13:54:47.400346 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.22952 (* 0.0272727 = 0.0608052 loss)
I0327 13:54:47.400362 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.25402 (* 0.0272727 = 0.0342005 loss)
I0327 13:54:47.400374 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.055253 (* 0.0272727 = 0.0015069 loss)
I0327 13:54:47.400388 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.00710375 (* 0.0272727 = 0.000193739 loss)
I0327 13:54:47.400398 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00171843 (* 0.0272727 = 4.68662e-05 loss)
I0327 13:54:47.400413 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 8.70565e-05 (* 0.0272727 = 2.37427e-06 loss)
I0327 13:54:47.400441 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 9.48181e-05 (* 0.0272727 = 2.58595e-06 loss)
I0327 13:54:47.400457 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 7.5676e-05 (* 0.0272727 = 2.06389e-06 loss)
I0327 13:54:47.400471 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000126297 (* 0.0272727 = 3.44447e-06 loss)
I0327 13:54:47.400485 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000154782 (* 0.0272727 = 4.22132e-06 loss)
I0327 13:54:47.400499 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 2.9499e-05 (* 0.0272727 = 8.04519e-07 loss)
I0327 13:54:47.400513 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 4.29774e-05 (* 0.0272727 = 1.17211e-06 loss)
I0327 13:54:47.400527 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 4.88433e-05 (* 0.0272727 = 1.33209e-06 loss)
I0327 13:54:47.400542 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 4.04732e-05 (* 0.0272727 = 1.10381e-06 loss)
I0327 13:54:47.400555 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 4.05331e-05 (* 0.0272727 = 1.10545e-06 loss)
I0327 13:54:47.400569 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 6.76424e-05 (* 0.0272727 = 1.84479e-06 loss)
I0327 13:54:47.400583 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000100171 (* 0.0272727 = 2.73194e-06 loss)
I0327 13:54:47.400594 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.625
I0327 13:54:47.400607 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.5
I0327 13:54:47.400619 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 13:54:47.400630 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:54:47.400642 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 13:54:47.400655 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 13:54:47.400665 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 13:54:47.400677 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 13:54:47.400689 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:54:47.400701 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:54:47.400712 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:54:47.400722 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:54:47.400734 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:54:47.400745 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:54:47.400758 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:54:47.400768 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:54:47.400780 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:54:47.400791 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:54:47.400802 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:54:47.400813 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:54:47.400825 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:54:47.400836 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:54:47.400849 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.4225 (* 0.0909091 = 0.129318 loss)
I0327 13:54:47.400863 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.08716 (* 0.0909091 = 0.189742 loss)
I0327 13:54:47.400877 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.21206 (* 0.0909091 = 0.292005 loss)
I0327 13:54:47.400892 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.86019 (* 0.0909091 = 0.260018 loss)
I0327 13:54:47.400905 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.76741 (* 0.0909091 = 0.251582 loss)
I0327 13:54:47.400929 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.02142 (* 0.0909091 = 0.183765 loss)
I0327 13:54:47.400944 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.06701 (* 0.0909091 = 0.0970006 loss)
I0327 13:54:47.400959 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0262948 (* 0.0909091 = 0.00239044 loss)
I0327 13:54:47.400974 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00402643 (* 0.0909091 = 0.00036604 loss)
I0327 13:54:47.400987 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00199225 (* 0.0909091 = 0.000181113 loss)
I0327 13:54:47.401001 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 4.25601e-05 (* 0.0909091 = 3.8691e-06 loss)
I0327 13:54:47.401015 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 4.22614e-05 (* 0.0909091 = 3.84195e-06 loss)
I0327 13:54:47.401029 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 4.40052e-05 (* 0.0909091 = 4.00047e-06 loss)
I0327 13:54:47.401046 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 4.35583e-05 (* 0.0909091 = 3.95984e-06 loss)
I0327 13:54:47.401062 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 4.12634e-05 (* 0.0909091 = 3.75122e-06 loss)
I0327 13:54:47.401075 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 3.39163e-05 (* 0.0909091 = 3.0833e-06 loss)
I0327 13:54:47.401089 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 4.42882e-05 (* 0.0909091 = 4.0262e-06 loss)
I0327 13:54:47.401103 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 3.94302e-05 (* 0.0909091 = 3.58456e-06 loss)
I0327 13:54:47.401118 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 4.26045e-05 (* 0.0909091 = 3.87314e-06 loss)
I0327 13:54:47.401131 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 4.61069e-05 (* 0.0909091 = 4.19153e-06 loss)
I0327 13:54:47.401145 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 4.52269e-05 (* 0.0909091 = 4.11154e-06 loss)
I0327 13:54:47.401160 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 3.7925e-05 (* 0.0909091 = 3.44773e-06 loss)
I0327 13:54:47.401171 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:54:47.401182 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00314658
I0327 13:54:47.401195 21344 sgd_solver.cpp:106] Iteration 18500, lr = 0.01
I0327 13:56:35.120676 21344 solver.cpp:229] Iteration 19000, loss = 2.64078
I0327 13:56:35.120847 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.375
I0327 13:56:35.120867 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.375
I0327 13:56:35.120880 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:56:35.120893 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 13:56:35.120905 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.375
I0327 13:56:35.120918 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.5
I0327 13:56:35.120929 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:56:35.120940 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 13:56:35.120952 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:56:35.120965 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:56:35.120976 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:56:35.120987 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:56:35.121002 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:56:35.121016 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:56:35.121029 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:56:35.121042 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:56:35.121053 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:56:35.121065 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:56:35.121076 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:56:35.121088 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:56:35.121099 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:56:35.121111 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:56:35.121126 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.63962 (* 0.0272727 = 0.0719896 loss)
I0327 13:56:35.121141 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.146 (* 0.0272727 = 0.0858 loss)
I0327 13:56:35.121155 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.55097 (* 0.0272727 = 0.0968447 loss)
I0327 13:56:35.121170 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.92679 (* 0.0272727 = 0.0798217 loss)
I0327 13:56:35.121183 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.91578 (* 0.0272727 = 0.0795212 loss)
I0327 13:56:35.121198 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.10834 (* 0.0272727 = 0.0575001 loss)
I0327 13:56:35.121212 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.41241 (* 0.0272727 = 0.0385203 loss)
I0327 13:56:35.121225 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.602174 (* 0.0272727 = 0.0164229 loss)
I0327 13:56:35.121239 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.108633 (* 0.0272727 = 0.00296271 loss)
I0327 13:56:35.121254 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0272227 (* 0.0272727 = 0.000742437 loss)
I0327 13:56:35.121269 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000324322 (* 0.0272727 = 8.84515e-06 loss)
I0327 13:56:35.121284 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000482728 (* 0.0272727 = 1.31653e-05 loss)
I0327 13:56:35.121299 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000642709 (* 0.0272727 = 1.75284e-05 loss)
I0327 13:56:35.121312 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00046613 (* 0.0272727 = 1.27126e-05 loss)
I0327 13:56:35.121326 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000434824 (* 0.0272727 = 1.18588e-05 loss)
I0327 13:56:35.121340 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000159597 (* 0.0272727 = 4.35265e-06 loss)
I0327 13:56:35.121354 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000611796 (* 0.0272727 = 1.66853e-05 loss)
I0327 13:56:35.121382 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000247017 (* 0.0272727 = 6.73682e-06 loss)
I0327 13:56:35.121397 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00054841 (* 0.0272727 = 1.49566e-05 loss)
I0327 13:56:35.121412 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000275736 (* 0.0272727 = 7.52007e-06 loss)
I0327 13:56:35.121439 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000305945 (* 0.0272727 = 8.34396e-06 loss)
I0327 13:56:35.121455 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00075568 (* 0.0272727 = 2.06094e-05 loss)
I0327 13:56:35.121469 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.5
I0327 13:56:35.121480 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 13:56:35.121492 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 13:56:35.121505 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.375
I0327 13:56:35.121516 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.375
I0327 13:56:35.121528 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.625
I0327 13:56:35.121556 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:56:35.121570 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:56:35.121583 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:56:35.121594 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:56:35.121606 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:56:35.121618 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:56:35.121629 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:56:35.121640 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:56:35.121651 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:56:35.121664 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:56:35.121675 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:56:35.121686 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:56:35.121697 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:56:35.121708 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:56:35.121721 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:56:35.121731 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:56:35.121745 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.15122 (* 0.0272727 = 0.0586697 loss)
I0327 13:56:35.121772 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.26053 (* 0.0272727 = 0.0889237 loss)
I0327 13:56:35.121799 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.29377 (* 0.0272727 = 0.08983 loss)
I0327 13:56:35.121826 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.69494 (* 0.0272727 = 0.0734982 loss)
I0327 13:56:35.121851 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.84068 (* 0.0272727 = 0.077473 loss)
I0327 13:56:35.121881 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.84111 (* 0.0272727 = 0.0502121 loss)
I0327 13:56:35.121903 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.57566 (* 0.0272727 = 0.0429726 loss)
I0327 13:56:35.121927 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.56305 (* 0.0272727 = 0.0153559 loss)
I0327 13:56:35.121949 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.115885 (* 0.0272727 = 0.0031605 loss)
I0327 13:56:35.121973 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0333445 (* 0.0272727 = 0.000909396 loss)
I0327 13:56:35.121999 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000942027 (* 0.0272727 = 2.56916e-05 loss)
I0327 13:56:35.122046 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000236086 (* 0.0272727 = 6.43872e-06 loss)
I0327 13:56:35.122078 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00099923 (* 0.0272727 = 2.72517e-05 loss)
I0327 13:56:35.122105 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000142619 (* 0.0272727 = 3.88961e-06 loss)
I0327 13:56:35.122133 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000476948 (* 0.0272727 = 1.30077e-05 loss)
I0327 13:56:35.122158 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000947919 (* 0.0272727 = 2.58523e-05 loss)
I0327 13:56:35.122185 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000374245 (* 0.0272727 = 1.02067e-05 loss)
I0327 13:56:35.122205 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000876208 (* 0.0272727 = 2.38966e-05 loss)
I0327 13:56:35.122233 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000417904 (* 0.0272727 = 1.13974e-05 loss)
I0327 13:56:35.122261 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000454664 (* 0.0272727 = 1.23999e-05 loss)
I0327 13:56:35.122287 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000460438 (* 0.0272727 = 1.25574e-05 loss)
I0327 13:56:35.122313 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000362642 (* 0.0272727 = 9.89025e-06 loss)
I0327 13:56:35.122334 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.375
I0327 13:56:35.122357 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.25
I0327 13:56:35.122380 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.25
I0327 13:56:35.122401 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.25
I0327 13:56:35.122423 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 13:56:35.122445 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.625
I0327 13:56:35.122467 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 13:56:35.122489 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:56:35.122511 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:56:35.122534 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:56:35.122556 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:56:35.122577 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:56:35.122601 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:56:35.122622 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:56:35.122643 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:56:35.122665 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:56:35.122686 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:56:35.122707 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:56:35.122727 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:56:35.122747 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:56:35.122768 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:56:35.122788 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:56:35.122817 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.2798 (* 0.0909091 = 0.207254 loss)
I0327 13:56:35.122843 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.87567 (* 0.0909091 = 0.261424 loss)
I0327 13:56:35.122869 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.08149 (* 0.0909091 = 0.280136 loss)
I0327 13:56:35.122894 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.80206 (* 0.0909091 = 0.254732 loss)
I0327 13:56:35.122925 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.02615 (* 0.0909091 = 0.275104 loss)
I0327 13:56:35.122967 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.948 (* 0.0909091 = 0.177091 loss)
I0327 13:56:35.122992 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.34852 (* 0.0909091 = 0.122592 loss)
I0327 13:56:35.123014 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.468303 (* 0.0909091 = 0.042573 loss)
I0327 13:56:35.123040 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.185527 (* 0.0909091 = 0.0168661 loss)
I0327 13:56:35.123064 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.034608 (* 0.0909091 = 0.00314618 loss)
I0327 13:56:35.123080 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 6.98848e-05 (* 0.0909091 = 6.35316e-06 loss)
I0327 13:56:35.123096 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000113026 (* 0.0909091 = 1.02751e-05 loss)
I0327 13:56:35.123111 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000102428 (* 0.0909091 = 9.31163e-06 loss)
I0327 13:56:35.123126 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 8.35502e-05 (* 0.0909091 = 7.59547e-06 loss)
I0327 13:56:35.123141 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 8.59424e-05 (* 0.0909091 = 7.81294e-06 loss)
I0327 13:56:35.123154 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 8.02493e-05 (* 0.0909091 = 7.29539e-06 loss)
I0327 13:56:35.123168 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 8.21874e-05 (* 0.0909091 = 7.47158e-06 loss)
I0327 13:56:35.123183 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 8.56667e-05 (* 0.0909091 = 7.78788e-06 loss)
I0327 13:56:35.123196 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 9.5005e-05 (* 0.0909091 = 8.63682e-06 loss)
I0327 13:56:35.123211 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 8.74189e-05 (* 0.0909091 = 7.94717e-06 loss)
I0327 13:56:35.123225 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000112405 (* 0.0909091 = 1.02187e-05 loss)
I0327 13:56:35.123239 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000101965 (* 0.0909091 = 9.26953e-06 loss)
I0327 13:56:35.123252 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:56:35.123263 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00466071
I0327 13:56:35.123276 21344 sgd_solver.cpp:106] Iteration 19000, lr = 0.01
I0327 13:58:23.284157 21344 solver.cpp:229] Iteration 19500, loss = 2.62943
I0327 13:58:23.284307 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.375
I0327 13:58:23.284327 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 13:58:23.284339 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 13:58:23.284351 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 13:58:23.284363 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 13:58:23.284376 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.5
I0327 13:58:23.284389 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 13:58:23.284400 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 13:58:23.284412 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 13:58:23.284425 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 13:58:23.284442 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 13:58:23.284456 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 13:58:23.284467 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 13:58:23.284478 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 13:58:23.284490 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 13:58:23.284502 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 13:58:23.284513 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 13:58:23.284525 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 13:58:23.284538 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 13:58:23.284548 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 13:58:23.284560 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 13:58:23.284571 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 13:58:23.284587 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.79711 (* 0.0272727 = 0.0762848 loss)
I0327 13:58:23.284602 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.25195 (* 0.0272727 = 0.0886896 loss)
I0327 13:58:23.284616 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.99487 (* 0.0272727 = 0.0816783 loss)
I0327 13:58:23.284634 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.06587 (* 0.0272727 = 0.0836147 loss)
I0327 13:58:23.284649 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 1.97717 (* 0.0272727 = 0.0539228 loss)
I0327 13:58:23.284663 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.82777 (* 0.0272727 = 0.0498482 loss)
I0327 13:58:23.284677 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.7227 (* 0.0272727 = 0.0469826 loss)
I0327 13:58:23.284692 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.388659 (* 0.0272727 = 0.0105998 loss)
I0327 13:58:23.284706 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0108463 (* 0.0272727 = 0.000295809 loss)
I0327 13:58:23.284720 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00181101 (* 0.0272727 = 4.93911e-05 loss)
I0327 13:58:23.284735 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000124311 (* 0.0272727 = 3.39031e-06 loss)
I0327 13:58:23.284754 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000126434 (* 0.0272727 = 3.4482e-06 loss)
I0327 13:58:23.284785 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 5.7487e-05 (* 0.0272727 = 1.56783e-06 loss)
I0327 13:58:23.284816 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000101061 (* 0.0272727 = 2.75621e-06 loss)
I0327 13:58:23.284847 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 8.79419e-05 (* 0.0272727 = 2.39842e-06 loss)
I0327 13:58:23.284874 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 6.13222e-05 (* 0.0272727 = 1.67242e-06 loss)
I0327 13:58:23.284893 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000105075 (* 0.0272727 = 2.86567e-06 loss)
I0327 13:58:23.284921 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 5.94092e-05 (* 0.0272727 = 1.62025e-06 loss)
I0327 13:58:23.284936 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 7.68449e-05 (* 0.0272727 = 2.09577e-06 loss)
I0327 13:58:23.284950 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 5.7889e-05 (* 0.0272727 = 1.57879e-06 loss)
I0327 13:58:23.284965 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 4.96533e-05 (* 0.0272727 = 1.35418e-06 loss)
I0327 13:58:23.284978 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 4.75066e-05 (* 0.0272727 = 1.29563e-06 loss)
I0327 13:58:23.284994 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.375
I0327 13:58:23.285007 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 13:58:23.285019 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.25
I0327 13:58:23.285032 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 13:58:23.285043 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 13:58:23.285054 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.5
I0327 13:58:23.285066 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 13:58:23.285079 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 13:58:23.285090 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 13:58:23.285101 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 13:58:23.285112 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 13:58:23.285123 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 13:58:23.285135 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 13:58:23.285146 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 13:58:23.285158 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 13:58:23.285169 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 13:58:23.285181 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 13:58:23.285192 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 13:58:23.285202 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 13:58:23.285214 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 13:58:23.285225 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 13:58:23.285236 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 13:58:23.285250 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.80615 (* 0.0272727 = 0.0765314 loss)
I0327 13:58:23.285264 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.219 (* 0.0272727 = 0.087791 loss)
I0327 13:58:23.285277 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.80714 (* 0.0272727 = 0.0765584 loss)
I0327 13:58:23.285291 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.31334 (* 0.0272727 = 0.0903637 loss)
I0327 13:58:23.285305 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.06878 (* 0.0272727 = 0.0564213 loss)
I0327 13:58:23.285320 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.79178 (* 0.0272727 = 0.0488666 loss)
I0327 13:58:23.285336 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.15947 (* 0.0272727 = 0.0316218 loss)
I0327 13:58:23.285351 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.569858 (* 0.0272727 = 0.0155416 loss)
I0327 13:58:23.285365 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.00792751 (* 0.0272727 = 0.000216205 loss)
I0327 13:58:23.285378 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00160483 (* 0.0272727 = 4.37681e-05 loss)
I0327 13:58:23.285393 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000103547 (* 0.0272727 = 2.82402e-06 loss)
I0327 13:58:23.285418 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 8.12456e-05 (* 0.0272727 = 2.21579e-06 loss)
I0327 13:58:23.285434 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000165185 (* 0.0272727 = 4.50504e-06 loss)
I0327 13:58:23.285447 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000106415 (* 0.0272727 = 2.90222e-06 loss)
I0327 13:58:23.285461 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 6.52609e-05 (* 0.0272727 = 1.77984e-06 loss)
I0327 13:58:23.285475 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000167686 (* 0.0272727 = 4.57325e-06 loss)
I0327 13:58:23.285490 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000132198 (* 0.0272727 = 3.60541e-06 loss)
I0327 13:58:23.285503 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00036747 (* 0.0272727 = 1.00219e-05 loss)
I0327 13:58:23.285517 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000113511 (* 0.0272727 = 3.09576e-06 loss)
I0327 13:58:23.285531 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 9.43996e-05 (* 0.0272727 = 2.57454e-06 loss)
I0327 13:58:23.285564 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.0002258 (* 0.0272727 = 6.15819e-06 loss)
I0327 13:58:23.285579 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 9.69851e-05 (* 0.0272727 = 2.64505e-06 loss)
I0327 13:58:23.285593 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.25
I0327 13:58:23.285604 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 13:58:23.285616 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.375
I0327 13:58:23.285629 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 13:58:23.285640 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 13:58:23.285651 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.5
I0327 13:58:23.285670 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 13:58:23.285692 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 13:58:23.285708 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 13:58:23.285720 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 13:58:23.285732 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 13:58:23.285742 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 13:58:23.285754 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 13:58:23.285765 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 13:58:23.285776 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 13:58:23.285789 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 13:58:23.285799 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 13:58:23.285811 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 13:58:23.285823 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 13:58:23.285835 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 13:58:23.285846 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 13:58:23.285857 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 13:58:23.285871 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.51606 (* 0.0909091 = 0.228732 loss)
I0327 13:58:23.285886 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.71238 (* 0.0909091 = 0.24658 loss)
I0327 13:58:23.285900 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.4565 (* 0.0909091 = 0.223318 loss)
I0327 13:58:23.285914 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.32076 (* 0.0909091 = 0.301888 loss)
I0327 13:58:23.285928 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 1.87802 (* 0.0909091 = 0.170729 loss)
I0327 13:58:23.285955 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.77975 (* 0.0909091 = 0.161795 loss)
I0327 13:58:23.285970 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.950977 (* 0.0909091 = 0.0864525 loss)
I0327 13:58:23.285984 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.469794 (* 0.0909091 = 0.0427085 loss)
I0327 13:58:23.285998 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0125131 (* 0.0909091 = 0.00113755 loss)
I0327 13:58:23.286012 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00271159 (* 0.0909091 = 0.000246508 loss)
I0327 13:58:23.286026 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 5.21954e-05 (* 0.0909091 = 4.74503e-06 loss)
I0327 13:58:23.286043 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 8.79049e-05 (* 0.0909091 = 7.99136e-06 loss)
I0327 13:58:23.286058 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 5.7604e-05 (* 0.0909091 = 5.23672e-06 loss)
I0327 13:58:23.286073 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 7.37686e-05 (* 0.0909091 = 6.70624e-06 loss)
I0327 13:58:23.286087 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 7.30819e-05 (* 0.0909091 = 6.64381e-06 loss)
I0327 13:58:23.286101 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 7.0652e-05 (* 0.0909091 = 6.42291e-06 loss)
I0327 13:58:23.286116 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 5.452e-05 (* 0.0909091 = 4.95637e-06 loss)
I0327 13:58:23.286130 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 6.60329e-05 (* 0.0909091 = 6.00299e-06 loss)
I0327 13:58:23.286144 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 5.33795e-05 (* 0.0909091 = 4.85268e-06 loss)
I0327 13:58:23.286159 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 6.64577e-05 (* 0.0909091 = 6.04161e-06 loss)
I0327 13:58:23.286173 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 7.27688e-05 (* 0.0909091 = 6.61534e-06 loss)
I0327 13:58:23.286187 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 7.67038e-05 (* 0.0909091 = 6.97307e-06 loss)
I0327 13:58:23.286200 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 13:58:23.286211 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000647507
I0327 13:58:23.286223 21344 sgd_solver.cpp:106] Iteration 19500, lr = 0.01
I0327 14:00:11.277266 21344 solver.cpp:338] Iteration 20000, Testing net (#0)
I0327 14:00:42.515892 21344 solver.cpp:393] Test loss: 2.1399
I0327 14:00:42.516005 21344 solver.cpp:406] Test net output #0: loss1/accuracy01 = 0.334
I0327 14:00:42.516024 21344 solver.cpp:406] Test net output #1: loss1/accuracy02 = 0.202
I0327 14:00:42.516037 21344 solver.cpp:406] Test net output #2: loss1/accuracy03 = 0.196
I0327 14:00:42.516049 21344 solver.cpp:406] Test net output #3: loss1/accuracy04 = 0.198
I0327 14:00:42.516062 21344 solver.cpp:406] Test net output #4: loss1/accuracy05 = 0.287
I0327 14:00:42.516073 21344 solver.cpp:406] Test net output #5: loss1/accuracy06 = 0.533
I0327 14:00:42.516084 21344 solver.cpp:406] Test net output #6: loss1/accuracy07 = 0.892
I0327 14:00:42.516096 21344 solver.cpp:406] Test net output #7: loss1/accuracy08 = 0.971
I0327 14:00:42.516108 21344 solver.cpp:406] Test net output #8: loss1/accuracy09 = 0.995
I0327 14:00:42.516120 21344 solver.cpp:406] Test net output #9: loss1/accuracy10 = 0.998
I0327 14:00:42.516132 21344 solver.cpp:406] Test net output #10: loss1/accuracy11 = 1
I0327 14:00:42.516144 21344 solver.cpp:406] Test net output #11: loss1/accuracy12 = 1
I0327 14:00:42.516155 21344 solver.cpp:406] Test net output #12: loss1/accuracy13 = 1
I0327 14:00:42.516166 21344 solver.cpp:406] Test net output #13: loss1/accuracy14 = 1
I0327 14:00:42.516177 21344 solver.cpp:406] Test net output #14: loss1/accuracy15 = 1
I0327 14:00:42.516190 21344 solver.cpp:406] Test net output #15: loss1/accuracy16 = 1
I0327 14:00:42.516201 21344 solver.cpp:406] Test net output #16: loss1/accuracy17 = 1
I0327 14:00:42.516211 21344 solver.cpp:406] Test net output #17: loss1/accuracy18 = 1
I0327 14:00:42.516223 21344 solver.cpp:406] Test net output #18: loss1/accuracy19 = 1
I0327 14:00:42.516234 21344 solver.cpp:406] Test net output #19: loss1/accuracy20 = 1
I0327 14:00:42.516245 21344 solver.cpp:406] Test net output #20: loss1/accuracy21 = 1
I0327 14:00:42.516257 21344 solver.cpp:406] Test net output #21: loss1/accuracy22 = 1
I0327 14:00:42.516273 21344 solver.cpp:406] Test net output #22: loss1/loss01 = 2.33994 (* 0.0272727 = 0.0638166 loss)
I0327 14:00:42.516286 21344 solver.cpp:406] Test net output #23: loss1/loss02 = 2.71243 (* 0.0272727 = 0.0739755 loss)
I0327 14:00:42.516300 21344 solver.cpp:406] Test net output #24: loss1/loss03 = 2.73478 (* 0.0272727 = 0.074585 loss)
I0327 14:00:42.516314 21344 solver.cpp:406] Test net output #25: loss1/loss04 = 2.71821 (* 0.0272727 = 0.074133 loss)
I0327 14:00:42.516327 21344 solver.cpp:406] Test net output #26: loss1/loss05 = 2.57047 (* 0.0272727 = 0.0701037 loss)
I0327 14:00:42.516340 21344 solver.cpp:406] Test net output #27: loss1/loss06 = 1.69267 (* 0.0272727 = 0.0461638 loss)
I0327 14:00:42.516355 21344 solver.cpp:406] Test net output #28: loss1/loss07 = 0.669451 (* 0.0272727 = 0.0182577 loss)
I0327 14:00:42.516368 21344 solver.cpp:406] Test net output #29: loss1/loss08 = 0.205151 (* 0.0272727 = 0.00559504 loss)
I0327 14:00:42.516381 21344 solver.cpp:406] Test net output #30: loss1/loss09 = 0.0455807 (* 0.0272727 = 0.00124311 loss)
I0327 14:00:42.516396 21344 solver.cpp:406] Test net output #31: loss1/loss10 = 0.0201621 (* 0.0272727 = 0.000549876 loss)
I0327 14:00:42.516410 21344 solver.cpp:406] Test net output #32: loss1/loss11 = 0.00041675 (* 0.0272727 = 1.13659e-05 loss)
I0327 14:00:42.516424 21344 solver.cpp:406] Test net output #33: loss1/loss12 = 0.000465576 (* 0.0272727 = 1.26975e-05 loss)
I0327 14:00:42.516438 21344 solver.cpp:406] Test net output #34: loss1/loss13 = 0.000509294 (* 0.0272727 = 1.38898e-05 loss)
I0327 14:00:42.516451 21344 solver.cpp:406] Test net output #35: loss1/loss14 = 0.000483326 (* 0.0272727 = 1.31816e-05 loss)
I0327 14:00:42.516466 21344 solver.cpp:406] Test net output #36: loss1/loss15 = 0.000527545 (* 0.0272727 = 1.43876e-05 loss)
I0327 14:00:42.516479 21344 solver.cpp:406] Test net output #37: loss1/loss16 = 0.000482522 (* 0.0272727 = 1.31597e-05 loss)
I0327 14:00:42.516494 21344 solver.cpp:406] Test net output #38: loss1/loss17 = 0.000547437 (* 0.0272727 = 1.49301e-05 loss)
I0327 14:00:42.516527 21344 solver.cpp:406] Test net output #39: loss1/loss18 = 0.000447424 (* 0.0272727 = 1.22025e-05 loss)
I0327 14:00:42.516542 21344 solver.cpp:406] Test net output #40: loss1/loss19 = 0.00043174 (* 0.0272727 = 1.17747e-05 loss)
I0327 14:00:42.516556 21344 solver.cpp:406] Test net output #41: loss1/loss20 = 0.00042038 (* 0.0272727 = 1.14649e-05 loss)
I0327 14:00:42.516571 21344 solver.cpp:406] Test net output #42: loss1/loss21 = 0.000533918 (* 0.0272727 = 1.45614e-05 loss)
I0327 14:00:42.516584 21344 solver.cpp:406] Test net output #43: loss1/loss22 = 0.000503512 (* 0.0272727 = 1.37321e-05 loss)
I0327 14:00:42.516597 21344 solver.cpp:406] Test net output #44: loss2/accuracy01 = 0.554
I0327 14:00:42.516609 21344 solver.cpp:406] Test net output #45: loss2/accuracy02 = 0.24
I0327 14:00:42.516620 21344 solver.cpp:406] Test net output #46: loss2/accuracy03 = 0.196
I0327 14:00:42.516633 21344 solver.cpp:406] Test net output #47: loss2/accuracy04 = 0.223
I0327 14:00:42.516644 21344 solver.cpp:406] Test net output #48: loss2/accuracy05 = 0.294
I0327 14:00:42.516655 21344 solver.cpp:406] Test net output #49: loss2/accuracy06 = 0.542
I0327 14:00:42.516666 21344 solver.cpp:406] Test net output #50: loss2/accuracy07 = 0.892
I0327 14:00:42.516679 21344 solver.cpp:406] Test net output #51: loss2/accuracy08 = 0.971
I0327 14:00:42.516690 21344 solver.cpp:406] Test net output #52: loss2/accuracy09 = 0.995
I0327 14:00:42.516701 21344 solver.cpp:406] Test net output #53: loss2/accuracy10 = 0.998
I0327 14:00:42.516712 21344 solver.cpp:406] Test net output #54: loss2/accuracy11 = 1
I0327 14:00:42.516723 21344 solver.cpp:406] Test net output #55: loss2/accuracy12 = 1
I0327 14:00:42.516736 21344 solver.cpp:406] Test net output #56: loss2/accuracy13 = 1
I0327 14:00:42.516746 21344 solver.cpp:406] Test net output #57: loss2/accuracy14 = 1
I0327 14:00:42.516757 21344 solver.cpp:406] Test net output #58: loss2/accuracy15 = 1
I0327 14:00:42.516768 21344 solver.cpp:406] Test net output #59: loss2/accuracy16 = 1
I0327 14:00:42.516779 21344 solver.cpp:406] Test net output #60: loss2/accuracy17 = 1
I0327 14:00:42.516790 21344 solver.cpp:406] Test net output #61: loss2/accuracy18 = 1
I0327 14:00:42.516801 21344 solver.cpp:406] Test net output #62: loss2/accuracy19 = 1
I0327 14:00:42.516813 21344 solver.cpp:406] Test net output #63: loss2/accuracy20 = 1
I0327 14:00:42.516824 21344 solver.cpp:406] Test net output #64: loss2/accuracy21 = 1
I0327 14:00:42.516834 21344 solver.cpp:406] Test net output #65: loss2/accuracy22 = 1
I0327 14:00:42.516849 21344 solver.cpp:406] Test net output #66: loss2/loss01 = 1.85596 (* 0.0272727 = 0.0506171 loss)
I0327 14:00:42.516862 21344 solver.cpp:406] Test net output #67: loss2/loss02 = 2.5794 (* 0.0272727 = 0.0703474 loss)
I0327 14:00:42.516875 21344 solver.cpp:406] Test net output #68: loss2/loss03 = 2.67397 (* 0.0272727 = 0.0729265 loss)
I0327 14:00:42.516890 21344 solver.cpp:406] Test net output #69: loss2/loss04 = 2.64583 (* 0.0272727 = 0.0721591 loss)
I0327 14:00:42.516903 21344 solver.cpp:406] Test net output #70: loss2/loss05 = 2.51427 (* 0.0272727 = 0.0685709 loss)
I0327 14:00:42.516916 21344 solver.cpp:406] Test net output #71: loss2/loss06 = 1.61635 (* 0.0272727 = 0.0440823 loss)
I0327 14:00:42.516929 21344 solver.cpp:406] Test net output #72: loss2/loss07 = 0.627496 (* 0.0272727 = 0.0171135 loss)
I0327 14:00:42.516943 21344 solver.cpp:406] Test net output #73: loss2/loss08 = 0.197887 (* 0.0272727 = 0.00539692 loss)
I0327 14:00:42.516957 21344 solver.cpp:406] Test net output #74: loss2/loss09 = 0.0413561 (* 0.0272727 = 0.00112789 loss)
I0327 14:00:42.516970 21344 solver.cpp:406] Test net output #75: loss2/loss10 = 0.0222891 (* 0.0272727 = 0.000607884 loss)
I0327 14:00:42.516984 21344 solver.cpp:406] Test net output #76: loss2/loss11 = 0.000451037 (* 0.0272727 = 1.2301e-05 loss)
I0327 14:00:42.517002 21344 solver.cpp:406] Test net output #77: loss2/loss12 = 0.000367869 (* 0.0272727 = 1.00328e-05 loss)
I0327 14:00:42.517027 21344 solver.cpp:406] Test net output #78: loss2/loss13 = 0.000337117 (* 0.0272727 = 9.1941e-06 loss)
I0327 14:00:42.517042 21344 solver.cpp:406] Test net output #79: loss2/loss14 = 0.00035923 (* 0.0272727 = 9.79718e-06 loss)
I0327 14:00:42.517056 21344 solver.cpp:406] Test net output #80: loss2/loss15 = 0.000438662 (* 0.0272727 = 1.19635e-05 loss)
I0327 14:00:42.517071 21344 solver.cpp:406] Test net output #81: loss2/loss16 = 0.000345243 (* 0.0272727 = 9.41571e-06 loss)
I0327 14:00:42.517084 21344 solver.cpp:406] Test net output #82: loss2/loss17 = 0.000405949 (* 0.0272727 = 1.10713e-05 loss)
I0327 14:00:42.517098 21344 solver.cpp:406] Test net output #83: loss2/loss18 = 0.000401585 (* 0.0272727 = 1.09523e-05 loss)
I0327 14:00:42.517112 21344 solver.cpp:406] Test net output #84: loss2/loss19 = 0.000342342 (* 0.0272727 = 9.3366e-06 loss)
I0327 14:00:42.517127 21344 solver.cpp:406] Test net output #85: loss2/loss20 = 0.00037511 (* 0.0272727 = 1.02303e-05 loss)
I0327 14:00:42.517140 21344 solver.cpp:406] Test net output #86: loss2/loss21 = 0.000424313 (* 0.0272727 = 1.15722e-05 loss)
I0327 14:00:42.517153 21344 solver.cpp:406] Test net output #87: loss2/loss22 = 0.000420774 (* 0.0272727 = 1.14757e-05 loss)
I0327 14:00:42.517166 21344 solver.cpp:406] Test net output #88: loss3/accuracy01 = 0.557
I0327 14:00:42.517177 21344 solver.cpp:406] Test net output #89: loss3/accuracy02 = 0.274
I0327 14:00:42.517189 21344 solver.cpp:406] Test net output #90: loss3/accuracy03 = 0.213
I0327 14:00:42.517200 21344 solver.cpp:406] Test net output #91: loss3/accuracy04 = 0.216
I0327 14:00:42.517211 21344 solver.cpp:406] Test net output #92: loss3/accuracy05 = 0.289
I0327 14:00:42.517222 21344 solver.cpp:406] Test net output #93: loss3/accuracy06 = 0.552
I0327 14:00:42.517235 21344 solver.cpp:406] Test net output #94: loss3/accuracy07 = 0.89
I0327 14:00:42.517246 21344 solver.cpp:406] Test net output #95: loss3/accuracy08 = 0.971
I0327 14:00:42.517257 21344 solver.cpp:406] Test net output #96: loss3/accuracy09 = 0.995
I0327 14:00:42.517268 21344 solver.cpp:406] Test net output #97: loss3/accuracy10 = 0.998
I0327 14:00:42.517279 21344 solver.cpp:406] Test net output #98: loss3/accuracy11 = 1
I0327 14:00:42.517290 21344 solver.cpp:406] Test net output #99: loss3/accuracy12 = 1
I0327 14:00:42.517302 21344 solver.cpp:406] Test net output #100: loss3/accuracy13 = 1
I0327 14:00:42.517313 21344 solver.cpp:406] Test net output #101: loss3/accuracy14 = 1
I0327 14:00:42.517323 21344 solver.cpp:406] Test net output #102: loss3/accuracy15 = 1
I0327 14:00:42.517334 21344 solver.cpp:406] Test net output #103: loss3/accuracy16 = 1
I0327 14:00:42.517345 21344 solver.cpp:406] Test net output #104: loss3/accuracy17 = 1
I0327 14:00:42.517356 21344 solver.cpp:406] Test net output #105: loss3/accuracy18 = 1
I0327 14:00:42.517367 21344 solver.cpp:406] Test net output #106: loss3/accuracy19 = 1
I0327 14:00:42.517379 21344 solver.cpp:406] Test net output #107: loss3/accuracy20 = 1
I0327 14:00:42.517390 21344 solver.cpp:406] Test net output #108: loss3/accuracy21 = 1
I0327 14:00:42.517400 21344 solver.cpp:406] Test net output #109: loss3/accuracy22 = 1
I0327 14:00:42.517415 21344 solver.cpp:406] Test net output #110: loss3/loss01 = 1.7403 (* 0.0909091 = 0.158209 loss)
I0327 14:00:42.517427 21344 solver.cpp:406] Test net output #111: loss3/loss02 = 2.40787 (* 0.0909091 = 0.218897 loss)
I0327 14:00:42.517441 21344 solver.cpp:406] Test net output #112: loss3/loss03 = 2.62136 (* 0.0909091 = 0.238305 loss)
I0327 14:00:42.517454 21344 solver.cpp:406] Test net output #113: loss3/loss04 = 2.66025 (* 0.0909091 = 0.241841 loss)
I0327 14:00:42.517468 21344 solver.cpp:406] Test net output #114: loss3/loss05 = 2.53292 (* 0.0909091 = 0.230265 loss)
I0327 14:00:42.517482 21344 solver.cpp:406] Test net output #115: loss3/loss06 = 1.56611 (* 0.0909091 = 0.142374 loss)
I0327 14:00:42.517506 21344 solver.cpp:406] Test net output #116: loss3/loss07 = 0.598087 (* 0.0909091 = 0.0543715 loss)
I0327 14:00:42.517521 21344 solver.cpp:406] Test net output #117: loss3/loss08 = 0.190009 (* 0.0909091 = 0.0172736 loss)
I0327 14:00:42.517535 21344 solver.cpp:406] Test net output #118: loss3/loss09 = 0.0453543 (* 0.0909091 = 0.00412312 loss)
I0327 14:00:42.517567 21344 solver.cpp:406] Test net output #119: loss3/loss10 = 0.0258703 (* 0.0909091 = 0.00235185 loss)
I0327 14:00:42.517582 21344 solver.cpp:406] Test net output #120: loss3/loss11 = 0.000200383 (* 0.0909091 = 1.82166e-05 loss)
I0327 14:00:42.517596 21344 solver.cpp:406] Test net output #121: loss3/loss12 = 0.000260139 (* 0.0909091 = 2.3649e-05 loss)
I0327 14:00:42.517609 21344 solver.cpp:406] Test net output #122: loss3/loss13 = 0.000219063 (* 0.0909091 = 1.99148e-05 loss)
I0327 14:00:42.517622 21344 solver.cpp:406] Test net output #123: loss3/loss14 = 0.000217088 (* 0.0909091 = 1.97352e-05 loss)
I0327 14:00:42.517637 21344 solver.cpp:406] Test net output #124: loss3/loss15 = 0.000210053 (* 0.0909091 = 1.90957e-05 loss)
I0327 14:00:42.517650 21344 solver.cpp:406] Test net output #125: loss3/loss16 = 0.000230233 (* 0.0909091 = 2.09303e-05 loss)
I0327 14:00:42.517663 21344 solver.cpp:406] Test net output #126: loss3/loss17 = 0.00022241 (* 0.0909091 = 2.02191e-05 loss)
I0327 14:00:42.517676 21344 solver.cpp:406] Test net output #127: loss3/loss18 = 0.000206114 (* 0.0909091 = 1.87377e-05 loss)
I0327 14:00:42.517690 21344 solver.cpp:406] Test net output #128: loss3/loss19 = 0.000179209 (* 0.0909091 = 1.62917e-05 loss)
I0327 14:00:42.517704 21344 solver.cpp:406] Test net output #129: loss3/loss20 = 0.000215026 (* 0.0909091 = 1.95478e-05 loss)
I0327 14:00:42.517717 21344 solver.cpp:406] Test net output #130: loss3/loss21 = 0.000216333 (* 0.0909091 = 1.96667e-05 loss)
I0327 14:00:42.517730 21344 solver.cpp:406] Test net output #131: loss3/loss22 = 0.000181497 (* 0.0909091 = 1.64997e-05 loss)
I0327 14:00:42.517742 21344 solver.cpp:406] Test net output #132: total_accuracy = 0.002
I0327 14:00:42.517753 21344 solver.cpp:406] Test net output #133: total_confidence = 0.00402119
I0327 14:00:42.628387 21344 solver.cpp:229] Iteration 20000, loss = 2.59294
I0327 14:00:42.628423 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.375
I0327 14:00:42.628439 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 14:00:42.628453 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 14:00:42.628463 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 14:00:42.628475 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 14:00:42.628487 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 14:00:42.628499 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 14:00:42.628511 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 14:00:42.628523 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 14:00:42.628535 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:00:42.628546 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:00:42.628558 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:00:42.628569 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:00:42.628581 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:00:42.628592 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:00:42.628603 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:00:42.628614 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:00:42.628626 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:00:42.628638 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:00:42.628665 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:00:42.628679 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:00:42.628690 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:00:42.628705 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.02147 (* 0.0272727 = 0.0551311 loss)
I0327 14:00:42.628720 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.8083 (* 0.0272727 = 0.0765901 loss)
I0327 14:00:42.628734 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.18963 (* 0.0272727 = 0.0869899 loss)
I0327 14:00:42.628748 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.31065 (* 0.0272727 = 0.0902905 loss)
I0327 14:00:42.628762 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.03663 (* 0.0272727 = 0.0828173 loss)
I0327 14:00:42.628777 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.84456 (* 0.0272727 = 0.0775789 loss)
I0327 14:00:42.628790 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.617078 (* 0.0272727 = 0.0168294 loss)
I0327 14:00:42.628804 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.718371 (* 0.0272727 = 0.0195919 loss)
I0327 14:00:42.628818 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.616567 (* 0.0272727 = 0.0168155 loss)
I0327 14:00:42.628832 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.022192 (* 0.0272727 = 0.000605236 loss)
I0327 14:00:42.628847 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000773852 (* 0.0272727 = 2.11051e-05 loss)
I0327 14:00:42.628861 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000551609 (* 0.0272727 = 1.50439e-05 loss)
I0327 14:00:42.628875 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000526549 (* 0.0272727 = 1.43604e-05 loss)
I0327 14:00:42.628890 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000423209 (* 0.0272727 = 1.15421e-05 loss)
I0327 14:00:42.628903 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00035554 (* 0.0272727 = 9.69655e-06 loss)
I0327 14:00:42.628918 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000373223 (* 0.0272727 = 1.01788e-05 loss)
I0327 14:00:42.628932 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000319929 (* 0.0272727 = 8.72533e-06 loss)
I0327 14:00:42.628949 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000285838 (* 0.0272727 = 7.7956e-06 loss)
I0327 14:00:42.628964 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00040981 (* 0.0272727 = 1.11766e-05 loss)
I0327 14:00:42.628978 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000513867 (* 0.0272727 = 1.40146e-05 loss)
I0327 14:00:42.628993 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000412888 (* 0.0272727 = 1.12606e-05 loss)
I0327 14:00:42.629006 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00090184 (* 0.0272727 = 2.45956e-05 loss)
I0327 14:00:42.629019 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.625
I0327 14:00:42.629031 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.375
I0327 14:00:42.629043 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.25
I0327 14:00:42.629057 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 14:00:42.629070 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 14:00:42.629081 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 14:00:42.629092 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 14:00:42.629104 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 14:00:42.629117 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 14:00:42.629128 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:00:42.629139 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:00:42.629163 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:00:42.629175 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:00:42.629186 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:00:42.629199 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:00:42.629209 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:00:42.629220 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:00:42.629232 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:00:42.629243 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:00:42.629254 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:00:42.629266 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:00:42.629277 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:00:42.629292 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.6593 (* 0.0272727 = 0.0452536 loss)
I0327 14:00:42.629305 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.30639 (* 0.0272727 = 0.0629016 loss)
I0327 14:00:42.629320 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.75546 (* 0.0272727 = 0.0751489 loss)
I0327 14:00:42.629334 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.40275 (* 0.0272727 = 0.0928023 loss)
I0327 14:00:42.629348 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.16641 (* 0.0272727 = 0.0863566 loss)
I0327 14:00:42.629362 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.80808 (* 0.0272727 = 0.0765839 loss)
I0327 14:00:42.629376 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.545443 (* 0.0272727 = 0.0148757 loss)
I0327 14:00:42.629390 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.544323 (* 0.0272727 = 0.0148452 loss)
I0327 14:00:42.629405 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.675075 (* 0.0272727 = 0.0184111 loss)
I0327 14:00:42.629418 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00487935 (* 0.0272727 = 0.000133073 loss)
I0327 14:00:42.629432 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000299411 (* 0.0272727 = 8.16575e-06 loss)
I0327 14:00:42.629446 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 7.87026e-05 (* 0.0272727 = 2.14643e-06 loss)
I0327 14:00:42.629461 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000212274 (* 0.0272727 = 5.78928e-06 loss)
I0327 14:00:42.629474 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000196676 (* 0.0272727 = 5.3639e-06 loss)
I0327 14:00:42.629488 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000196165 (* 0.0272727 = 5.34996e-06 loss)
I0327 14:00:42.629503 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000183244 (* 0.0272727 = 4.99755e-06 loss)
I0327 14:00:42.629516 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000206412 (* 0.0272727 = 5.62943e-06 loss)
I0327 14:00:42.629530 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000170313 (* 0.0272727 = 4.64489e-06 loss)
I0327 14:00:42.629564 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000143432 (* 0.0272727 = 3.91179e-06 loss)
I0327 14:00:42.629580 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000144208 (* 0.0272727 = 3.93293e-06 loss)
I0327 14:00:42.629593 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000194833 (* 0.0272727 = 5.31363e-06 loss)
I0327 14:00:42.629607 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000256688 (* 0.0272727 = 7.00058e-06 loss)
I0327 14:00:42.629619 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.625
I0327 14:00:42.629631 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.25
I0327 14:00:42.629643 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 14:00:42.629667 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 14:00:42.629679 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 14:00:42.629691 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 14:00:42.629703 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 1
I0327 14:00:42.629714 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 14:00:42.629725 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 14:00:42.629737 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:00:42.629748 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:00:42.629760 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:00:42.629770 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:00:42.629782 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:00:42.629793 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:00:42.629804 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:00:42.629817 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:00:42.629827 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:00:42.629838 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:00:42.629850 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:00:42.629861 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:00:42.629873 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:00:42.629885 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.66726 (* 0.0909091 = 0.151569 loss)
I0327 14:00:42.629899 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.31497 (* 0.0909091 = 0.210452 loss)
I0327 14:00:42.629914 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.8948 (* 0.0909091 = 0.263164 loss)
I0327 14:00:42.629927 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.9634 (* 0.0909091 = 0.2694 loss)
I0327 14:00:42.629941 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.04144 (* 0.0909091 = 0.276494 loss)
I0327 14:00:42.629956 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.47273 (* 0.0909091 = 0.224794 loss)
I0327 14:00:42.629969 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.417611 (* 0.0909091 = 0.0379647 loss)
I0327 14:00:42.629983 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.547329 (* 0.0909091 = 0.0497572 loss)
I0327 14:00:42.630000 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.635546 (* 0.0909091 = 0.0577769 loss)
I0327 14:00:42.630015 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00542317 (* 0.0909091 = 0.000493015 loss)
I0327 14:00:42.630029 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000111738 (* 0.0909091 = 1.0158e-05 loss)
I0327 14:00:42.630043 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 7.12215e-05 (* 0.0909091 = 6.47468e-06 loss)
I0327 14:00:42.630058 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 8.38648e-05 (* 0.0909091 = 7.62407e-06 loss)
I0327 14:00:42.630071 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 7.01047e-05 (* 0.0909091 = 6.37316e-06 loss)
I0327 14:00:42.630085 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 5.96171e-05 (* 0.0909091 = 5.41974e-06 loss)
I0327 14:00:42.630101 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 6.53983e-05 (* 0.0909091 = 5.9453e-06 loss)
I0327 14:00:42.630115 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 9.44728e-05 (* 0.0909091 = 8.58844e-06 loss)
I0327 14:00:42.630130 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 6.45647e-05 (* 0.0909091 = 5.86952e-06 loss)
I0327 14:00:42.630153 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 5.43265e-05 (* 0.0909091 = 4.93877e-06 loss)
I0327 14:00:42.630169 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 6.3418e-05 (* 0.0909091 = 5.76527e-06 loss)
I0327 14:00:42.630183 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 9.30984e-05 (* 0.0909091 = 8.46349e-06 loss)
I0327 14:00:42.630198 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 8.21692e-05 (* 0.0909091 = 7.46993e-06 loss)
I0327 14:00:42.630209 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:00:42.630221 21344 solver.cpp:245] Train net output #133: total_confidence = 0.0009274
I0327 14:00:42.630234 21344 sgd_solver.cpp:106] Iteration 20000, lr = 0.01
I0327 14:02:30.719386 21344 solver.cpp:229] Iteration 20500, loss = 2.59883
I0327 14:02:30.719568 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.5
I0327 14:02:30.719589 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 14:02:30.719604 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 14:02:30.719615 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 14:02:30.719627 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 14:02:30.719640 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 14:02:30.719651 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.625
I0327 14:02:30.719665 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 14:02:30.719676 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 14:02:30.719688 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:02:30.719701 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:02:30.719712 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:02:30.719724 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:02:30.719738 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:02:30.719749 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:02:30.719761 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:02:30.719774 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:02:30.719785 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:02:30.719797 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:02:30.719808 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:02:30.719820 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:02:30.719832 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:02:30.719849 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.70647 (* 0.0272727 = 0.0465402 loss)
I0327 14:02:30.719866 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.37376 (* 0.0272727 = 0.0920116 loss)
I0327 14:02:30.719879 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.21635 (* 0.0272727 = 0.0877187 loss)
I0327 14:02:30.719893 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.43216 (* 0.0272727 = 0.0936044 loss)
I0327 14:02:30.719907 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.75754 (* 0.0272727 = 0.0752055 loss)
I0327 14:02:30.719923 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.62451 (* 0.0272727 = 0.0715775 loss)
I0327 14:02:30.719936 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.23003 (* 0.0272727 = 0.0335462 loss)
I0327 14:02:30.719950 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.422414 (* 0.0272727 = 0.0115204 loss)
I0327 14:02:30.719964 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.43986 (* 0.0272727 = 0.0119962 loss)
I0327 14:02:30.719980 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0079292 (* 0.0272727 = 0.000216251 loss)
I0327 14:02:30.719997 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000136455 (* 0.0272727 = 3.7215e-06 loss)
I0327 14:02:30.720012 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000123858 (* 0.0272727 = 3.37794e-06 loss)
I0327 14:02:30.720027 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000531781 (* 0.0272727 = 1.45031e-05 loss)
I0327 14:02:30.720041 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000140419 (* 0.0272727 = 3.8296e-06 loss)
I0327 14:02:30.720057 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000267714 (* 0.0272727 = 7.30128e-06 loss)
I0327 14:02:30.720070 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 6.1429e-05 (* 0.0272727 = 1.67534e-06 loss)
I0327 14:02:30.720084 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000139074 (* 0.0272727 = 3.79293e-06 loss)
I0327 14:02:30.720113 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 6.57562e-05 (* 0.0272727 = 1.79335e-06 loss)
I0327 14:02:30.720134 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 9.10819e-05 (* 0.0272727 = 2.48405e-06 loss)
I0327 14:02:30.720149 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000151921 (* 0.0272727 = 4.1433e-06 loss)
I0327 14:02:30.720163 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 8.04107e-05 (* 0.0272727 = 2.19302e-06 loss)
I0327 14:02:30.720177 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000118185 (* 0.0272727 = 3.22321e-06 loss)
I0327 14:02:30.720191 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.375
I0327 14:02:30.720203 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 14:02:30.720216 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.375
I0327 14:02:30.720227 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.375
I0327 14:02:30.720238 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 14:02:30.720250 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 14:02:30.720263 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 14:02:30.720274 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 14:02:30.720285 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 14:02:30.720298 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:02:30.720309 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:02:30.720320 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:02:30.720331 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:02:30.720342 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:02:30.720353 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:02:30.720366 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:02:30.720376 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:02:30.720387 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:02:30.720398 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:02:30.720410 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:02:30.720422 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:02:30.720432 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:02:30.720446 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.62843 (* 0.0272727 = 0.0444116 loss)
I0327 14:02:30.720460 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.26679 (* 0.0272727 = 0.0890943 loss)
I0327 14:02:30.720474 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.71544 (* 0.0272727 = 0.0740575 loss)
I0327 14:02:30.720489 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.14866 (* 0.0272727 = 0.0858725 loss)
I0327 14:02:30.720502 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.33851 (* 0.0272727 = 0.0910502 loss)
I0327 14:02:30.720516 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.53158 (* 0.0272727 = 0.0690431 loss)
I0327 14:02:30.720530 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.66965 (* 0.0272727 = 0.045536 loss)
I0327 14:02:30.720544 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.441902 (* 0.0272727 = 0.0120519 loss)
I0327 14:02:30.720559 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.487369 (* 0.0272727 = 0.0132919 loss)
I0327 14:02:30.720577 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0100713 (* 0.0272727 = 0.000274671 loss)
I0327 14:02:30.720592 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 9.45419e-05 (* 0.0272727 = 2.57842e-06 loss)
I0327 14:02:30.720618 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 5.54285e-05 (* 0.0272727 = 1.51169e-06 loss)
I0327 14:02:30.720633 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 3.64049e-05 (* 0.0272727 = 9.92862e-07 loss)
I0327 14:02:30.720646 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 9.0715e-05 (* 0.0272727 = 2.47405e-06 loss)
I0327 14:02:30.720661 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 3.14575e-05 (* 0.0272727 = 8.57933e-07 loss)
I0327 14:02:30.720675 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 7.56615e-05 (* 0.0272727 = 2.0635e-06 loss)
I0327 14:02:30.720690 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 5.31494e-05 (* 0.0272727 = 1.44953e-06 loss)
I0327 14:02:30.720703 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000131531 (* 0.0272727 = 3.58721e-06 loss)
I0327 14:02:30.720718 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000164704 (* 0.0272727 = 4.49193e-06 loss)
I0327 14:02:30.720732 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000104943 (* 0.0272727 = 2.86209e-06 loss)
I0327 14:02:30.720746 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000174679 (* 0.0272727 = 4.76396e-06 loss)
I0327 14:02:30.720760 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 4.19133e-05 (* 0.0272727 = 1.14309e-06 loss)
I0327 14:02:30.720772 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.5
I0327 14:02:30.720785 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.375
I0327 14:02:30.720796 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.25
I0327 14:02:30.720808 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 14:02:30.720819 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 14:02:30.720831 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 14:02:30.720842 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 14:02:30.720854 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 14:02:30.720865 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 14:02:30.720876 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:02:30.720887 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:02:30.720899 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:02:30.720911 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:02:30.720921 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:02:30.720933 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:02:30.720944 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:02:30.720955 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:02:30.720968 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:02:30.720978 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:02:30.720989 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:02:30.721001 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:02:30.721012 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:02:30.721025 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.34115 (* 0.0909091 = 0.121923 loss)
I0327 14:02:30.721040 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.61758 (* 0.0909091 = 0.237962 loss)
I0327 14:02:30.721057 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.82025 (* 0.0909091 = 0.256387 loss)
I0327 14:02:30.721071 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.85351 (* 0.0909091 = 0.25941 loss)
I0327 14:02:30.721086 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.74169 (* 0.0909091 = 0.249244 loss)
I0327 14:02:30.721109 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.54658 (* 0.0909091 = 0.231507 loss)
I0327 14:02:30.721124 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.23468 (* 0.0909091 = 0.112244 loss)
I0327 14:02:30.721138 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.395049 (* 0.0909091 = 0.0359135 loss)
I0327 14:02:30.721153 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.574826 (* 0.0909091 = 0.0522569 loss)
I0327 14:02:30.721166 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0271303 (* 0.0909091 = 0.00246639 loss)
I0327 14:02:30.721181 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000134524 (* 0.0909091 = 1.22294e-05 loss)
I0327 14:02:30.721195 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000133049 (* 0.0909091 = 1.20954e-05 loss)
I0327 14:02:30.721210 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 9.76696e-05 (* 0.0909091 = 8.87905e-06 loss)
I0327 14:02:30.721223 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000145457 (* 0.0909091 = 1.32234e-05 loss)
I0327 14:02:30.721238 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000116286 (* 0.0909091 = 1.05714e-05 loss)
I0327 14:02:30.721252 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000151446 (* 0.0909091 = 1.37679e-05 loss)
I0327 14:02:30.721266 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000117389 (* 0.0909091 = 1.06717e-05 loss)
I0327 14:02:30.721281 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000112041 (* 0.0909091 = 1.01855e-05 loss)
I0327 14:02:30.721295 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000103185 (* 0.0909091 = 9.38047e-06 loss)
I0327 14:02:30.721309 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.00013992 (* 0.0909091 = 1.272e-05 loss)
I0327 14:02:30.721323 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000167848 (* 0.0909091 = 1.52589e-05 loss)
I0327 14:02:30.721338 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000123046 (* 0.0909091 = 1.1186e-05 loss)
I0327 14:02:30.721350 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:02:30.721361 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00185571
I0327 14:02:30.721374 21344 sgd_solver.cpp:106] Iteration 20500, lr = 0.01
I0327 14:04:19.094635 21344 solver.cpp:229] Iteration 21000, loss = 2.57035
I0327 14:04:19.094764 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.25
I0327 14:04:19.094784 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 14:04:19.094797 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 14:04:19.094808 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 14:04:19.094820 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 14:04:19.094832 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.5
I0327 14:04:19.094844 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.75
I0327 14:04:19.094856 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 14:04:19.094868 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 14:04:19.094880 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:04:19.094892 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:04:19.094903 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:04:19.094915 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:04:19.094928 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:04:19.094938 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:04:19.094951 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:04:19.094962 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:04:19.094974 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:04:19.094985 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:04:19.095000 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:04:19.095012 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:04:19.095024 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:04:19.095041 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.10124 (* 0.0272727 = 0.0573066 loss)
I0327 14:04:19.095055 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.56448 (* 0.0272727 = 0.097213 loss)
I0327 14:04:19.095070 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.66959 (* 0.0272727 = 0.072807 loss)
I0327 14:04:19.095084 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.4673 (* 0.0272727 = 0.0945627 loss)
I0327 14:04:19.095098 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.77321 (* 0.0272727 = 0.075633 loss)
I0327 14:04:19.095113 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.22963 (* 0.0272727 = 0.0608081 loss)
I0327 14:04:19.095126 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.49197 (* 0.0272727 = 0.04069 loss)
I0327 14:04:19.095140 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.09096 (* 0.0272727 = 0.0297535 loss)
I0327 14:04:19.095155 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.517671 (* 0.0272727 = 0.0141183 loss)
I0327 14:04:19.095168 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0215792 (* 0.0272727 = 0.000588524 loss)
I0327 14:04:19.095183 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000102445 (* 0.0272727 = 2.79396e-06 loss)
I0327 14:04:19.095198 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000115266 (* 0.0272727 = 3.14362e-06 loss)
I0327 14:04:19.095212 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 6.65766e-05 (* 0.0272727 = 1.81572e-06 loss)
I0327 14:04:19.095227 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00013401 (* 0.0272727 = 3.65482e-06 loss)
I0327 14:04:19.095240 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000119633 (* 0.0272727 = 3.26271e-06 loss)
I0327 14:04:19.095254 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000117781 (* 0.0272727 = 3.21222e-06 loss)
I0327 14:04:19.095268 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 5.1039e-05 (* 0.0272727 = 1.39197e-06 loss)
I0327 14:04:19.095301 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000186254 (* 0.0272727 = 5.07964e-06 loss)
I0327 14:04:19.095317 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 9.14944e-05 (* 0.0272727 = 2.4953e-06 loss)
I0327 14:04:19.095331 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000209419 (* 0.0272727 = 5.71141e-06 loss)
I0327 14:04:19.095345 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 8.82182e-05 (* 0.0272727 = 2.40595e-06 loss)
I0327 14:04:19.095360 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000158653 (* 0.0272727 = 4.32691e-06 loss)
I0327 14:04:19.095371 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.375
I0327 14:04:19.095384 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 14:04:19.095396 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.25
I0327 14:04:19.095408 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0
I0327 14:04:19.095420 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.5
I0327 14:04:19.095432 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.625
I0327 14:04:19.095444 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 14:04:19.095455 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.75
I0327 14:04:19.095463 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 14:04:19.095475 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:04:19.095487 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:04:19.095499 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:04:19.095510 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:04:19.095521 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:04:19.095533 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:04:19.095544 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:04:19.095556 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:04:19.095567 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:04:19.095578 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:04:19.095590 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:04:19.095602 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:04:19.095613 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:04:19.095628 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.01868 (* 0.0272727 = 0.0550548 loss)
I0327 14:04:19.095641 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.37129 (* 0.0272727 = 0.0919442 loss)
I0327 14:04:19.095655 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.45205 (* 0.0272727 = 0.0668741 loss)
I0327 14:04:19.095669 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.44769 (* 0.0272727 = 0.0940279 loss)
I0327 14:04:19.095682 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.1556 (* 0.0272727 = 0.0587891 loss)
I0327 14:04:19.095696 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.76338 (* 0.0272727 = 0.0480921 loss)
I0327 14:04:19.095710 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.276 (* 0.0272727 = 0.0348 loss)
I0327 14:04:19.095724 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.970722 (* 0.0272727 = 0.0264742 loss)
I0327 14:04:19.095738 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.471667 (* 0.0272727 = 0.0128636 loss)
I0327 14:04:19.095753 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0611583 (* 0.0272727 = 0.00166795 loss)
I0327 14:04:19.095767 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00374408 (* 0.0272727 = 0.000102111 loss)
I0327 14:04:19.095795 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00409301 (* 0.0272727 = 0.000111628 loss)
I0327 14:04:19.095811 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00460533 (* 0.0272727 = 0.0001256 loss)
I0327 14:04:19.095826 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00188928 (* 0.0272727 = 5.15259e-05 loss)
I0327 14:04:19.095840 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00561062 (* 0.0272727 = 0.000153017 loss)
I0327 14:04:19.095854 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00318326 (* 0.0272727 = 8.68161e-05 loss)
I0327 14:04:19.095868 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00430081 (* 0.0272727 = 0.000117295 loss)
I0327 14:04:19.095882 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00493494 (* 0.0272727 = 0.000134589 loss)
I0327 14:04:19.095897 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.0043321 (* 0.0272727 = 0.000118148 loss)
I0327 14:04:19.095911 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00325173 (* 0.0272727 = 8.86836e-05 loss)
I0327 14:04:19.095926 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00232662 (* 0.0272727 = 6.34533e-05 loss)
I0327 14:04:19.095939 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00159786 (* 0.0272727 = 4.35781e-05 loss)
I0327 14:04:19.095952 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.375
I0327 14:04:19.095964 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 14:04:19.095975 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 14:04:19.095988 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 14:04:19.095999 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 14:04:19.096010 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 14:04:19.096021 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 14:04:19.096034 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.75
I0327 14:04:19.096047 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 14:04:19.096060 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:04:19.096071 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:04:19.096082 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:04:19.096094 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:04:19.096106 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:04:19.096117 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:04:19.096128 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:04:19.096139 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:04:19.096151 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:04:19.096163 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:04:19.096174 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:04:19.096185 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:04:19.096197 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:04:19.096210 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.63853 (* 0.0909091 = 0.148957 loss)
I0327 14:04:19.096225 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.29892 (* 0.0909091 = 0.299902 loss)
I0327 14:04:19.096240 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.31137 (* 0.0909091 = 0.210124 loss)
I0327 14:04:19.096253 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.05071 (* 0.0909091 = 0.277337 loss)
I0327 14:04:19.096267 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.23225 (* 0.0909091 = 0.202932 loss)
I0327 14:04:19.096282 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.89404 (* 0.0909091 = 0.172185 loss)
I0327 14:04:19.096305 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.27922 (* 0.0909091 = 0.116293 loss)
I0327 14:04:19.096320 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.865347 (* 0.0909091 = 0.0786679 loss)
I0327 14:04:19.096335 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.509131 (* 0.0909091 = 0.0462847 loss)
I0327 14:04:19.096349 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0160765 (* 0.0909091 = 0.0014615 loss)
I0327 14:04:19.096364 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000172037 (* 0.0909091 = 1.56397e-05 loss)
I0327 14:04:19.096379 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000140235 (* 0.0909091 = 1.27487e-05 loss)
I0327 14:04:19.096392 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.00018372 (* 0.0909091 = 1.67018e-05 loss)
I0327 14:04:19.096407 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000190849 (* 0.0909091 = 1.735e-05 loss)
I0327 14:04:19.096421 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000115785 (* 0.0909091 = 1.05259e-05 loss)
I0327 14:04:19.096436 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000212133 (* 0.0909091 = 1.92848e-05 loss)
I0327 14:04:19.096451 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000161845 (* 0.0909091 = 1.47132e-05 loss)
I0327 14:04:19.096464 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000203531 (* 0.0909091 = 1.85028e-05 loss)
I0327 14:04:19.096478 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000157921 (* 0.0909091 = 1.43565e-05 loss)
I0327 14:04:19.096493 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000182392 (* 0.0909091 = 1.65811e-05 loss)
I0327 14:04:19.096506 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000202691 (* 0.0909091 = 1.84264e-05 loss)
I0327 14:04:19.096520 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000167119 (* 0.0909091 = 1.51926e-05 loss)
I0327 14:04:19.096532 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:04:19.096544 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00257538
I0327 14:04:19.096556 21344 sgd_solver.cpp:106] Iteration 21000, lr = 0.01
I0327 14:06:07.582128 21344 solver.cpp:229] Iteration 21500, loss = 2.57818
I0327 14:06:07.582375 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 14:06:07.582406 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 14:06:07.582430 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 14:06:07.582442 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 14:06:07.582455 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 14:06:07.582468 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.125
I0327 14:06:07.582486 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 14:06:07.582499 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 14:06:07.582510 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 14:06:07.582523 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:06:07.582545 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:06:07.582557 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:06:07.582569 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:06:07.582581 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:06:07.582593 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:06:07.582609 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:06:07.582622 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:06:07.582633 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:06:07.582645 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:06:07.582658 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:06:07.582669 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:06:07.582681 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:06:07.582700 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.55285 (* 0.0272727 = 0.0696232 loss)
I0327 14:06:07.582715 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.56127 (* 0.0272727 = 0.0971256 loss)
I0327 14:06:07.582729 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.15036 (* 0.0272727 = 0.0859189 loss)
I0327 14:06:07.582743 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.77198 (* 0.0272727 = 0.0755994 loss)
I0327 14:06:07.582757 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.84349 (* 0.0272727 = 0.0775497 loss)
I0327 14:06:07.582772 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 3.02264 (* 0.0272727 = 0.0824356 loss)
I0327 14:06:07.582787 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.37176 (* 0.0272727 = 0.0646842 loss)
I0327 14:06:07.582800 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.04782 (* 0.0272727 = 0.028577 loss)
I0327 14:06:07.582814 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.714195 (* 0.0272727 = 0.0194781 loss)
I0327 14:06:07.582830 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0191642 (* 0.0272727 = 0.000522659 loss)
I0327 14:06:07.582845 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000397083 (* 0.0272727 = 1.08295e-05 loss)
I0327 14:06:07.582859 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000434994 (* 0.0272727 = 1.18635e-05 loss)
I0327 14:06:07.582875 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000433064 (* 0.0272727 = 1.18108e-05 loss)
I0327 14:06:07.582888 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000484057 (* 0.0272727 = 1.32016e-05 loss)
I0327 14:06:07.582903 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000704846 (* 0.0272727 = 1.92231e-05 loss)
I0327 14:06:07.582918 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000526415 (* 0.0272727 = 1.43568e-05 loss)
I0327 14:06:07.582932 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000985052 (* 0.0272727 = 2.6865e-05 loss)
I0327 14:06:07.582962 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000587273 (* 0.0272727 = 1.60165e-05 loss)
I0327 14:06:07.582978 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000466157 (* 0.0272727 = 1.27134e-05 loss)
I0327 14:06:07.582995 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000939003 (* 0.0272727 = 2.56092e-05 loss)
I0327 14:06:07.583010 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000830874 (* 0.0272727 = 2.26602e-05 loss)
I0327 14:06:07.583025 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000571886 (* 0.0272727 = 1.55969e-05 loss)
I0327 14:06:07.583046 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 14:06:07.583058 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 14:06:07.583071 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.375
I0327 14:06:07.583086 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 14:06:07.583106 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 14:06:07.583118 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.125
I0327 14:06:07.583132 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.5
I0327 14:06:07.583143 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.75
I0327 14:06:07.583156 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 14:06:07.583168 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:06:07.583180 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:06:07.583191 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:06:07.583204 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:06:07.583215 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:06:07.583233 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:06:07.583245 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:06:07.583257 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:06:07.583268 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:06:07.583288 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:06:07.583300 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:06:07.583312 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:06:07.583323 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:06:07.583338 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.88771 (* 0.0272727 = 0.0787557 loss)
I0327 14:06:07.583353 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.66555 (* 0.0272727 = 0.0999696 loss)
I0327 14:06:07.583370 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.39906 (* 0.0272727 = 0.0927015 loss)
I0327 14:06:07.583385 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.76803 (* 0.0272727 = 0.0754918 loss)
I0327 14:06:07.583400 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.44343 (* 0.0272727 = 0.066639 loss)
I0327 14:06:07.583415 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.79137 (* 0.0272727 = 0.0761283 loss)
I0327 14:06:07.583428 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 2.0369 (* 0.0272727 = 0.0555517 loss)
I0327 14:06:07.583442 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.802375 (* 0.0272727 = 0.0218829 loss)
I0327 14:06:07.583456 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.693013 (* 0.0272727 = 0.0189004 loss)
I0327 14:06:07.583472 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0373449 (* 0.0272727 = 0.0010185 loss)
I0327 14:06:07.583485 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000927823 (* 0.0272727 = 2.53043e-05 loss)
I0327 14:06:07.583511 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000380275 (* 0.0272727 = 1.03711e-05 loss)
I0327 14:06:07.583528 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000646882 (* 0.0272727 = 1.76422e-05 loss)
I0327 14:06:07.583541 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000182645 (* 0.0272727 = 4.98122e-06 loss)
I0327 14:06:07.583556 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000321667 (* 0.0272727 = 8.77274e-06 loss)
I0327 14:06:07.583570 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00101553 (* 0.0272727 = 2.76962e-05 loss)
I0327 14:06:07.583585 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000553461 (* 0.0272727 = 1.50944e-05 loss)
I0327 14:06:07.583600 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00069889 (* 0.0272727 = 1.90606e-05 loss)
I0327 14:06:07.583614 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.0009769 (* 0.0272727 = 2.66427e-05 loss)
I0327 14:06:07.583628 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000693999 (* 0.0272727 = 1.89273e-05 loss)
I0327 14:06:07.583643 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000879902 (* 0.0272727 = 2.39973e-05 loss)
I0327 14:06:07.583657 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00087106 (* 0.0272727 = 2.37562e-05 loss)
I0327 14:06:07.583670 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.625
I0327 14:06:07.583683 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.25
I0327 14:06:07.583695 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 14:06:07.583708 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.375
I0327 14:06:07.583720 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 14:06:07.583732 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 14:06:07.583744 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.5
I0327 14:06:07.583755 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.75
I0327 14:06:07.583767 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 14:06:07.583780 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:06:07.583791 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:06:07.583803 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:06:07.583816 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:06:07.583827 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:06:07.583839 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:06:07.583852 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:06:07.583863 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:06:07.583875 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:06:07.583887 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:06:07.583899 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:06:07.583911 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:06:07.583923 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:06:07.583937 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.82716 (* 0.0909091 = 0.257014 loss)
I0327 14:06:07.583951 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.81376 (* 0.0909091 = 0.255797 loss)
I0327 14:06:07.583966 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.14112 (* 0.0909091 = 0.285557 loss)
I0327 14:06:07.583979 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.32619 (* 0.0909091 = 0.211472 loss)
I0327 14:06:07.583993 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.47821 (* 0.0909091 = 0.225292 loss)
I0327 14:06:07.584018 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.6974 (* 0.0909091 = 0.245218 loss)
I0327 14:06:07.584045 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.96313 (* 0.0909091 = 0.178466 loss)
I0327 14:06:07.584056 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.95106 (* 0.0909091 = 0.08646 loss)
I0327 14:06:07.584071 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.586868 (* 0.0909091 = 0.0533516 loss)
I0327 14:06:07.584085 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0228755 (* 0.0909091 = 0.00207959 loss)
I0327 14:06:07.584105 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000169406 (* 0.0909091 = 1.54005e-05 loss)
I0327 14:06:07.584118 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000189854 (* 0.0909091 = 1.72594e-05 loss)
I0327 14:06:07.584132 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000184622 (* 0.0909091 = 1.67838e-05 loss)
I0327 14:06:07.584146 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000164005 (* 0.0909091 = 1.49096e-05 loss)
I0327 14:06:07.584161 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000168921 (* 0.0909091 = 1.53565e-05 loss)
I0327 14:06:07.584175 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.00014204 (* 0.0909091 = 1.29127e-05 loss)
I0327 14:06:07.584189 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000151396 (* 0.0909091 = 1.37633e-05 loss)
I0327 14:06:07.584204 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000158204 (* 0.0909091 = 1.43822e-05 loss)
I0327 14:06:07.584218 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000154605 (* 0.0909091 = 1.4055e-05 loss)
I0327 14:06:07.584233 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000182055 (* 0.0909091 = 1.65505e-05 loss)
I0327 14:06:07.584247 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000174806 (* 0.0909091 = 1.58915e-05 loss)
I0327 14:06:07.584261 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000146988 (* 0.0909091 = 1.33626e-05 loss)
I0327 14:06:07.584273 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:06:07.584285 21344 solver.cpp:245] Train net output #133: total_confidence = 0.0013521
I0327 14:06:07.584298 21344 sgd_solver.cpp:106] Iteration 21500, lr = 0.01
I0327 14:07:56.638605 21344 solver.cpp:229] Iteration 22000, loss = 2.53138
I0327 14:07:56.638743 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.5
I0327 14:07:56.638762 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 14:07:56.638784 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 14:07:56.638797 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.375
I0327 14:07:56.638808 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.5
I0327 14:07:56.638820 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.5
I0327 14:07:56.638831 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 14:07:56.638844 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 14:07:56.638855 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:07:56.638867 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:07:56.638880 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:07:56.638898 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:07:56.638911 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:07:56.638922 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:07:56.638933 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:07:56.638945 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:07:56.638965 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:07:56.638978 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:07:56.638991 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:07:56.639003 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:07:56.639015 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:07:56.639027 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:07:56.639052 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.36571 (* 0.0272727 = 0.0645194 loss)
I0327 14:07:56.639067 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.05227 (* 0.0272727 = 0.0832438 loss)
I0327 14:07:56.639081 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.46874 (* 0.0272727 = 0.094602 loss)
I0327 14:07:56.639104 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.36476 (* 0.0272727 = 0.0644935 loss)
I0327 14:07:56.639118 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 1.86804 (* 0.0272727 = 0.0509464 loss)
I0327 14:07:56.639132 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.86439 (* 0.0272727 = 0.050847 loss)
I0327 14:07:56.639147 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.866682 (* 0.0272727 = 0.0236368 loss)
I0327 14:07:56.639161 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0681982 (* 0.0272727 = 0.00185995 loss)
I0327 14:07:56.639175 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0162487 (* 0.0272727 = 0.000443145 loss)
I0327 14:07:56.639189 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00716776 (* 0.0272727 = 0.000195484 loss)
I0327 14:07:56.639204 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00141328 (* 0.0272727 = 3.85441e-05 loss)
I0327 14:07:56.639219 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00251826 (* 0.0272727 = 6.86798e-05 loss)
I0327 14:07:56.639232 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000927305 (* 0.0272727 = 2.52901e-05 loss)
I0327 14:07:56.639246 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.0017687 (* 0.0272727 = 4.82374e-05 loss)
I0327 14:07:56.639261 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00201624 (* 0.0272727 = 5.49884e-05 loss)
I0327 14:07:56.639276 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00154954 (* 0.0272727 = 4.22602e-05 loss)
I0327 14:07:56.639291 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00208436 (* 0.0272727 = 5.68462e-05 loss)
I0327 14:07:56.639323 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00180729 (* 0.0272727 = 4.92897e-05 loss)
I0327 14:07:56.639339 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00232079 (* 0.0272727 = 6.32942e-05 loss)
I0327 14:07:56.639354 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00178115 (* 0.0272727 = 4.85769e-05 loss)
I0327 14:07:56.639369 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000898628 (* 0.0272727 = 2.4508e-05 loss)
I0327 14:07:56.639382 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00141725 (* 0.0272727 = 3.86523e-05 loss)
I0327 14:07:56.639395 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.375
I0327 14:07:56.639407 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.25
I0327 14:07:56.639420 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.25
I0327 14:07:56.639431 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.5
I0327 14:07:56.639443 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.625
I0327 14:07:56.639456 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.5
I0327 14:07:56.639467 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 14:07:56.639478 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 14:07:56.639490 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:07:56.639503 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:07:56.639513 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:07:56.639525 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:07:56.639538 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:07:56.639549 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:07:56.639560 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:07:56.639571 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:07:56.639583 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:07:56.639595 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:07:56.639606 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:07:56.639617 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:07:56.639628 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:07:56.639641 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:07:56.639654 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.45366 (* 0.0272727 = 0.0669181 loss)
I0327 14:07:56.639668 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.96435 (* 0.0272727 = 0.080846 loss)
I0327 14:07:56.639683 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.63669 (* 0.0272727 = 0.0719097 loss)
I0327 14:07:56.639695 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 1.96701 (* 0.0272727 = 0.0536458 loss)
I0327 14:07:56.639709 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 1.44173 (* 0.0272727 = 0.03932 loss)
I0327 14:07:56.639724 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.54857 (* 0.0272727 = 0.0422337 loss)
I0327 14:07:56.639737 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.621873 (* 0.0272727 = 0.0169602 loss)
I0327 14:07:56.639755 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0932583 (* 0.0272727 = 0.00254341 loss)
I0327 14:07:56.639770 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0332446 (* 0.0272727 = 0.000906672 loss)
I0327 14:07:56.639785 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00323781 (* 0.0272727 = 8.83038e-05 loss)
I0327 14:07:56.639798 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.00265135 (* 0.0272727 = 7.23097e-05 loss)
I0327 14:07:56.639824 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00123885 (* 0.0272727 = 3.37868e-05 loss)
I0327 14:07:56.639840 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.00153797 (* 0.0272727 = 4.19446e-05 loss)
I0327 14:07:56.639854 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.00145818 (* 0.0272727 = 3.97685e-05 loss)
I0327 14:07:56.639868 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.0015999 (* 0.0272727 = 4.36336e-05 loss)
I0327 14:07:56.639883 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00134922 (* 0.0272727 = 3.67969e-05 loss)
I0327 14:07:56.639897 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00149678 (* 0.0272727 = 4.08213e-05 loss)
I0327 14:07:56.639911 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.00160658 (* 0.0272727 = 4.38157e-05 loss)
I0327 14:07:56.639925 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.00102881 (* 0.0272727 = 2.80584e-05 loss)
I0327 14:07:56.639940 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.00129808 (* 0.0272727 = 3.54021e-05 loss)
I0327 14:07:56.639953 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.0018911 (* 0.0272727 = 5.15756e-05 loss)
I0327 14:07:56.639967 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00095345 (* 0.0272727 = 2.60032e-05 loss)
I0327 14:07:56.639979 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.375
I0327 14:07:56.639992 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 14:07:56.640004 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.25
I0327 14:07:56.640017 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.375
I0327 14:07:56.640028 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.5
I0327 14:07:56.640050 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.5
I0327 14:07:56.640063 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 14:07:56.640074 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 14:07:56.640086 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:07:56.640105 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:07:56.640116 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:07:56.640127 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:07:56.640139 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:07:56.640151 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:07:56.640162 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:07:56.640173 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:07:56.640185 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:07:56.640197 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:07:56.640208 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:07:56.640219 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:07:56.640231 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:07:56.640242 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:07:56.640256 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.26674 (* 0.0909091 = 0.206067 loss)
I0327 14:07:56.640270 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.57885 (* 0.0909091 = 0.234441 loss)
I0327 14:07:56.640285 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.57169 (* 0.0909091 = 0.23379 loss)
I0327 14:07:56.640298 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.21787 (* 0.0909091 = 0.201625 loss)
I0327 14:07:56.640312 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 1.5338 (* 0.0909091 = 0.139436 loss)
I0327 14:07:56.640326 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.88204 (* 0.0909091 = 0.171095 loss)
I0327 14:07:56.640352 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.810993 (* 0.0909091 = 0.0737267 loss)
I0327 14:07:56.640367 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.120723 (* 0.0909091 = 0.0109748 loss)
I0327 14:07:56.640382 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.02315 (* 0.0909091 = 0.00210455 loss)
I0327 14:07:56.640395 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00282445 (* 0.0909091 = 0.000256768 loss)
I0327 14:07:56.640410 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000177728 (* 0.0909091 = 1.61571e-05 loss)
I0327 14:07:56.640424 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000212854 (* 0.0909091 = 1.93503e-05 loss)
I0327 14:07:56.640439 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000193363 (* 0.0909091 = 1.75784e-05 loss)
I0327 14:07:56.640452 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000217645 (* 0.0909091 = 1.9786e-05 loss)
I0327 14:07:56.640466 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000187534 (* 0.0909091 = 1.70485e-05 loss)
I0327 14:07:56.640481 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000208305 (* 0.0909091 = 1.89368e-05 loss)
I0327 14:07:56.640496 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000179351 (* 0.0909091 = 1.63046e-05 loss)
I0327 14:07:56.640509 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000174737 (* 0.0909091 = 1.58851e-05 loss)
I0327 14:07:56.640523 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000170623 (* 0.0909091 = 1.55111e-05 loss)
I0327 14:07:56.640537 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000187899 (* 0.0909091 = 1.70817e-05 loss)
I0327 14:07:56.640552 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.00020639 (* 0.0909091 = 1.87627e-05 loss)
I0327 14:07:56.640566 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000199513 (* 0.0909091 = 1.81375e-05 loss)
I0327 14:07:56.640578 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:07:56.640589 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00553458
I0327 14:07:56.640602 21344 sgd_solver.cpp:106] Iteration 22000, lr = 0.01
I0327 14:09:45.640401 21344 solver.cpp:229] Iteration 22500, loss = 2.49254
I0327 14:09:45.640615 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.25
I0327 14:09:45.640636 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 14:09:45.640650 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.25
I0327 14:09:45.640661 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 14:09:45.640673 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.625
I0327 14:09:45.640686 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.625
I0327 14:09:45.640698 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 1
I0327 14:09:45.640710 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 14:09:45.640722 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:09:45.640734 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:09:45.640746 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:09:45.640758 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:09:45.640770 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:09:45.640782 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:09:45.640794 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:09:45.640806 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:09:45.640818 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:09:45.640830 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:09:45.640841 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:09:45.640853 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:09:45.640866 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:09:45.640877 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:09:45.640894 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.15943 (* 0.0272727 = 0.0588935 loss)
I0327 14:09:45.640909 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.10466 (* 0.0272727 = 0.0846724 loss)
I0327 14:09:45.640931 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.7669 (* 0.0272727 = 0.0754608 loss)
I0327 14:09:45.640947 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.31453 (* 0.0272727 = 0.0903963 loss)
I0327 14:09:45.640961 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 1.943 (* 0.0272727 = 0.0529909 loss)
I0327 14:09:45.640975 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.63165 (* 0.0272727 = 0.0444997 loss)
I0327 14:09:45.640993 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.227553 (* 0.0272727 = 0.00620599 loss)
I0327 14:09:45.641008 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0284701 (* 0.0272727 = 0.000776458 loss)
I0327 14:09:45.641023 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.00673501 (* 0.0272727 = 0.000183682 loss)
I0327 14:09:45.641038 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00235512 (* 0.0272727 = 6.42305e-05 loss)
I0327 14:09:45.641052 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00229146 (* 0.0272727 = 6.24945e-05 loss)
I0327 14:09:45.641067 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000372684 (* 0.0272727 = 1.01641e-05 loss)
I0327 14:09:45.641082 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00479313 (* 0.0272727 = 0.000130722 loss)
I0327 14:09:45.641096 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00257095 (* 0.0272727 = 7.01169e-05 loss)
I0327 14:09:45.641110 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00109225 (* 0.0272727 = 2.97886e-05 loss)
I0327 14:09:45.641125 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00126148 (* 0.0272727 = 3.4404e-05 loss)
I0327 14:09:45.641139 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00132137 (* 0.0272727 = 3.60375e-05 loss)
I0327 14:09:45.641167 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00191732 (* 0.0272727 = 5.22906e-05 loss)
I0327 14:09:45.641183 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00184006 (* 0.0272727 = 5.01834e-05 loss)
I0327 14:09:45.641197 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00183888 (* 0.0272727 = 5.01513e-05 loss)
I0327 14:09:45.641211 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00164415 (* 0.0272727 = 4.48405e-05 loss)
I0327 14:09:45.641227 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00111006 (* 0.0272727 = 3.02745e-05 loss)
I0327 14:09:45.641238 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.75
I0327 14:09:45.641252 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 14:09:45.641263 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.25
I0327 14:09:45.641275 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.25
I0327 14:09:45.641288 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.625
I0327 14:09:45.641299 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.75
I0327 14:09:45.641311 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 14:09:45.641324 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 14:09:45.641336 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:09:45.641347 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:09:45.641360 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:09:45.641371 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:09:45.641382 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:09:45.641394 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:09:45.641407 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:09:45.641418 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:09:45.641429 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:09:45.641441 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:09:45.641453 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:09:45.641464 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:09:45.641476 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:09:45.641487 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:09:45.641501 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.53095 (* 0.0272727 = 0.0417531 loss)
I0327 14:09:45.641515 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.57117 (* 0.0272727 = 0.0973954 loss)
I0327 14:09:45.641530 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.97778 (* 0.0272727 = 0.0812121 loss)
I0327 14:09:45.641561 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.63271 (* 0.0272727 = 0.099074 loss)
I0327 14:09:45.641577 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 1.80518 (* 0.0272727 = 0.0492322 loss)
I0327 14:09:45.641592 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.53574 (* 0.0272727 = 0.0418839 loss)
I0327 14:09:45.641605 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.504629 (* 0.0272727 = 0.0137626 loss)
I0327 14:09:45.641623 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0691677 (* 0.0272727 = 0.00188639 loss)
I0327 14:09:45.641639 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0102117 (* 0.0272727 = 0.0002785 loss)
I0327 14:09:45.641654 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00166494 (* 0.0272727 = 4.54075e-05 loss)
I0327 14:09:45.641669 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 5.44919e-05 (* 0.0272727 = 1.48614e-06 loss)
I0327 14:09:45.641695 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 4.93254e-05 (* 0.0272727 = 1.34524e-06 loss)
I0327 14:09:45.641711 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 4.77848e-05 (* 0.0272727 = 1.30322e-06 loss)
I0327 14:09:45.641726 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 7.49543e-05 (* 0.0272727 = 2.04421e-06 loss)
I0327 14:09:45.641741 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 6.82122e-05 (* 0.0272727 = 1.86033e-06 loss)
I0327 14:09:45.641755 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000153485 (* 0.0272727 = 4.18595e-06 loss)
I0327 14:09:45.641769 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 8.22164e-05 (* 0.0272727 = 2.24227e-06 loss)
I0327 14:09:45.641783 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 7.5332e-05 (* 0.0272727 = 2.05451e-06 loss)
I0327 14:09:45.641798 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 7.74788e-05 (* 0.0272727 = 2.11306e-06 loss)
I0327 14:09:45.641811 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 5.59604e-05 (* 0.0272727 = 1.52619e-06 loss)
I0327 14:09:45.641826 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 7.65076e-05 (* 0.0272727 = 2.08657e-06 loss)
I0327 14:09:45.641840 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 5.20675e-05 (* 0.0272727 = 1.42002e-06 loss)
I0327 14:09:45.641852 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.5
I0327 14:09:45.641865 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 14:09:45.641877 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.25
I0327 14:09:45.641890 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.25
I0327 14:09:45.641901 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.625
I0327 14:09:45.641913 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.75
I0327 14:09:45.641926 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 1
I0327 14:09:45.641937 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 14:09:45.641949 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:09:45.641960 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:09:45.641973 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:09:45.641983 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:09:45.641995 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:09:45.642007 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:09:45.642019 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:09:45.642030 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:09:45.642045 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:09:45.642057 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:09:45.642068 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:09:45.642081 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:09:45.642092 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:09:45.642104 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:09:45.642118 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.56755 (* 0.0909091 = 0.142505 loss)
I0327 14:09:45.642132 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.04264 (* 0.0909091 = 0.276604 loss)
I0327 14:09:45.642146 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.6875 (* 0.0909091 = 0.244319 loss)
I0327 14:09:45.642161 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.19442 (* 0.0909091 = 0.290402 loss)
I0327 14:09:45.642175 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 1.84231 (* 0.0909091 = 0.167483 loss)
I0327 14:09:45.642200 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.07183 (* 0.0909091 = 0.0974395 loss)
I0327 14:09:45.642215 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.414413 (* 0.0909091 = 0.0376739 loss)
I0327 14:09:45.642230 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.064025 (* 0.0909091 = 0.00582046 loss)
I0327 14:09:45.642244 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00802189 (* 0.0909091 = 0.000729263 loss)
I0327 14:09:45.642258 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00383822 (* 0.0909091 = 0.000348929 loss)
I0327 14:09:45.642273 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000196485 (* 0.0909091 = 1.78622e-05 loss)
I0327 14:09:45.642287 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000210564 (* 0.0909091 = 1.91422e-05 loss)
I0327 14:09:45.642302 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000258603 (* 0.0909091 = 2.35094e-05 loss)
I0327 14:09:45.642316 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000252808 (* 0.0909091 = 2.29826e-05 loss)
I0327 14:09:45.642330 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000201781 (* 0.0909091 = 1.83437e-05 loss)
I0327 14:09:45.642345 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000232173 (* 0.0909091 = 2.11067e-05 loss)
I0327 14:09:45.642359 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000185789 (* 0.0909091 = 1.68899e-05 loss)
I0327 14:09:45.642374 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000202157 (* 0.0909091 = 1.83779e-05 loss)
I0327 14:09:45.642388 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000175705 (* 0.0909091 = 1.59732e-05 loss)
I0327 14:09:45.642403 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000217648 (* 0.0909091 = 1.97862e-05 loss)
I0327 14:09:45.642417 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000155325 (* 0.0909091 = 1.41204e-05 loss)
I0327 14:09:45.642431 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000172454 (* 0.0909091 = 1.56777e-05 loss)
I0327 14:09:45.642444 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:09:45.642455 21344 solver.cpp:245] Train net output #133: total_confidence = 0.0038611
I0327 14:09:45.642469 21344 sgd_solver.cpp:106] Iteration 22500, lr = 0.01
I0327 14:11:34.024080 21344 solver.cpp:229] Iteration 23000, loss = 2.51829
I0327 14:11:34.024263 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.25
I0327 14:11:34.024284 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 14:11:34.024297 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 14:11:34.024310 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 14:11:34.024323 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 14:11:34.024335 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 14:11:34.024348 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.5
I0327 14:11:34.024359 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.75
I0327 14:11:34.024372 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:11:34.024384 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:11:34.024396 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:11:34.024408 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:11:34.024420 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:11:34.024432 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:11:34.024444 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:11:34.024456 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:11:34.024468 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:11:34.024480 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:11:34.024492 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:11:34.024504 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:11:34.024516 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:11:34.024528 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:11:34.024545 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 3.02421 (* 0.0272727 = 0.0824784 loss)
I0327 14:11:34.024561 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 4.37247 (* 0.0272727 = 0.119249 loss)
I0327 14:11:34.024575 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.69404 (* 0.0272727 = 0.100746 loss)
I0327 14:11:34.024590 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.33754 (* 0.0272727 = 0.0910239 loss)
I0327 14:11:34.024605 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 3.14677 (* 0.0272727 = 0.085821 loss)
I0327 14:11:34.024618 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.7965 (* 0.0272727 = 0.0762682 loss)
I0327 14:11:34.024632 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 2.22771 (* 0.0272727 = 0.0607558 loss)
I0327 14:11:34.024646 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 1.19704 (* 0.0272727 = 0.0326467 loss)
I0327 14:11:34.024662 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0904552 (* 0.0272727 = 0.00246696 loss)
I0327 14:11:34.024677 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0202223 (* 0.0272727 = 0.000551518 loss)
I0327 14:11:34.024691 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000670607 (* 0.0272727 = 1.82893e-05 loss)
I0327 14:11:34.024706 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00132312 (* 0.0272727 = 3.60851e-05 loss)
I0327 14:11:34.024720 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00145359 (* 0.0272727 = 3.96435e-05 loss)
I0327 14:11:34.024735 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00121803 (* 0.0272727 = 3.3219e-05 loss)
I0327 14:11:34.024749 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00181853 (* 0.0272727 = 4.95964e-05 loss)
I0327 14:11:34.024765 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00260905 (* 0.0272727 = 7.11559e-05 loss)
I0327 14:11:34.024780 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00166618 (* 0.0272727 = 4.54412e-05 loss)
I0327 14:11:34.024806 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00150302 (* 0.0272727 = 4.09915e-05 loss)
I0327 14:11:34.024822 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.0028168 (* 0.0272727 = 7.68219e-05 loss)
I0327 14:11:34.024837 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00158046 (* 0.0272727 = 4.31035e-05 loss)
I0327 14:11:34.024855 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00279416 (* 0.0272727 = 7.62044e-05 loss)
I0327 14:11:34.024871 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00159752 (* 0.0272727 = 4.35688e-05 loss)
I0327 14:11:34.024884 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.5
I0327 14:11:34.024898 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 14:11:34.024909 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 14:11:34.024920 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 14:11:34.024932 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 14:11:34.024945 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 14:11:34.024957 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.625
I0327 14:11:34.024969 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.75
I0327 14:11:34.024981 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:11:34.024996 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:11:34.025008 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:11:34.025020 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:11:34.025032 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:11:34.025044 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:11:34.025056 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:11:34.025068 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:11:34.025079 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:11:34.025091 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:11:34.025102 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:11:34.025115 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:11:34.025126 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:11:34.025137 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:11:34.025151 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.83008 (* 0.0272727 = 0.077184 loss)
I0327 14:11:34.025166 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.98988 (* 0.0272727 = 0.108815 loss)
I0327 14:11:34.025180 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.52607 (* 0.0272727 = 0.0961656 loss)
I0327 14:11:34.025194 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.45479 (* 0.0272727 = 0.0942215 loss)
I0327 14:11:34.025208 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 3.00009 (* 0.0272727 = 0.0818207 loss)
I0327 14:11:34.025223 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.6659 (* 0.0272727 = 0.0727065 loss)
I0327 14:11:34.025238 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 2.0825 (* 0.0272727 = 0.0567954 loss)
I0327 14:11:34.025251 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 1.69849 (* 0.0272727 = 0.0463224 loss)
I0327 14:11:34.025265 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.114694 (* 0.0272727 = 0.003128 loss)
I0327 14:11:34.025284 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0297062 (* 0.0272727 = 0.00081017 loss)
I0327 14:11:34.025298 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000607217 (* 0.0272727 = 1.65605e-05 loss)
I0327 14:11:34.025323 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.00160792 (* 0.0272727 = 4.38523e-05 loss)
I0327 14:11:34.025339 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000987579 (* 0.0272727 = 2.6934e-05 loss)
I0327 14:11:34.025354 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000960296 (* 0.0272727 = 2.61899e-05 loss)
I0327 14:11:34.025368 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.0016796 (* 0.0272727 = 4.58074e-05 loss)
I0327 14:11:34.025383 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.00168135 (* 0.0272727 = 4.58549e-05 loss)
I0327 14:11:34.025398 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.00115761 (* 0.0272727 = 3.15712e-05 loss)
I0327 14:11:34.025413 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000581727 (* 0.0272727 = 1.58653e-05 loss)
I0327 14:11:34.025426 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000594828 (* 0.0272727 = 1.62226e-05 loss)
I0327 14:11:34.025440 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000747559 (* 0.0272727 = 2.0388e-05 loss)
I0327 14:11:34.025455 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00110262 (* 0.0272727 = 3.00715e-05 loss)
I0327 14:11:34.025470 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.00125361 (* 0.0272727 = 3.41895e-05 loss)
I0327 14:11:34.025482 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.5
I0327 14:11:34.025496 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 14:11:34.025507 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 14:11:34.025519 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.375
I0327 14:11:34.025532 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 14:11:34.025563 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.5
I0327 14:11:34.025578 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.625
I0327 14:11:34.025590 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.75
I0327 14:11:34.025602 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:11:34.025614 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:11:34.025626 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:11:34.025637 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:11:34.025650 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:11:34.025661 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:11:34.025674 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:11:34.025686 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:11:34.025696 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:11:34.025702 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:11:34.025715 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:11:34.025727 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:11:34.025739 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:11:34.025751 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:11:34.025765 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.14401 (* 0.0909091 = 0.19491 loss)
I0327 14:11:34.025779 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.45733 (* 0.0909091 = 0.314303 loss)
I0327 14:11:34.025794 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 3.24655 (* 0.0909091 = 0.295141 loss)
I0327 14:11:34.025807 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.94412 (* 0.0909091 = 0.267647 loss)
I0327 14:11:34.025821 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 3.04664 (* 0.0909091 = 0.276967 loss)
I0327 14:11:34.025836 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.13979 (* 0.0909091 = 0.194526 loss)
I0327 14:11:34.025862 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.87334 (* 0.0909091 = 0.170304 loss)
I0327 14:11:34.025877 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 1.41073 (* 0.0909091 = 0.128248 loss)
I0327 14:11:34.025892 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.16209 (* 0.0909091 = 0.0147354 loss)
I0327 14:11:34.025907 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0473641 (* 0.0909091 = 0.00430583 loss)
I0327 14:11:34.025920 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000200745 (* 0.0909091 = 1.82495e-05 loss)
I0327 14:11:34.025935 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000206284 (* 0.0909091 = 1.87531e-05 loss)
I0327 14:11:34.025949 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000164121 (* 0.0909091 = 1.49201e-05 loss)
I0327 14:11:34.025964 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.00012725 (* 0.0909091 = 1.15682e-05 loss)
I0327 14:11:34.025979 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 9.32019e-05 (* 0.0909091 = 8.4729e-06 loss)
I0327 14:11:34.025993 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000143007 (* 0.0909091 = 1.30006e-05 loss)
I0327 14:11:34.026007 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000197044 (* 0.0909091 = 1.79131e-05 loss)
I0327 14:11:34.026021 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000128271 (* 0.0909091 = 1.1661e-05 loss)
I0327 14:11:34.026036 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000159328 (* 0.0909091 = 1.44844e-05 loss)
I0327 14:11:34.026053 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000127697 (* 0.0909091 = 1.16088e-05 loss)
I0327 14:11:34.026067 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000136196 (* 0.0909091 = 1.23815e-05 loss)
I0327 14:11:34.026082 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000211035 (* 0.0909091 = 1.9185e-05 loss)
I0327 14:11:34.026094 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:11:34.026105 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00201328
I0327 14:11:34.026118 21344 sgd_solver.cpp:106] Iteration 23000, lr = 0.01
I0327 14:13:22.498188 21344 solver.cpp:229] Iteration 23500, loss = 2.47864
I0327 14:13:22.498356 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.125
I0327 14:13:22.498378 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 14:13:22.498391 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.375
I0327 14:13:22.498404 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 14:13:22.498416 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 14:13:22.498430 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 14:13:22.498441 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 14:13:22.498453 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 14:13:22.498466 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:13:22.498478 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:13:22.498489 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:13:22.498502 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:13:22.498513 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:13:22.498525 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:13:22.498538 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:13:22.498549 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:13:22.498561 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:13:22.498574 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:13:22.498586 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:13:22.498599 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:13:22.498610 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:13:22.498621 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:13:22.498638 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.88544 (* 0.0272727 = 0.0786937 loss)
I0327 14:13:22.498654 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.85464 (* 0.0272727 = 0.0778537 loss)
I0327 14:13:22.498669 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.52072 (* 0.0272727 = 0.068747 loss)
I0327 14:13:22.498684 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.26792 (* 0.0272727 = 0.0891252 loss)
I0327 14:13:22.498698 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.85167 (* 0.0272727 = 0.0777729 loss)
I0327 14:13:22.498713 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.96566 (* 0.0272727 = 0.0808816 loss)
I0327 14:13:22.498726 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.552987 (* 0.0272727 = 0.0150815 loss)
I0327 14:13:22.498741 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.155018 (* 0.0272727 = 0.00422777 loss)
I0327 14:13:22.498756 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0324266 (* 0.0272727 = 0.000884363 loss)
I0327 14:13:22.498770 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0129484 (* 0.0272727 = 0.000353137 loss)
I0327 14:13:22.498785 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00635206 (* 0.0272727 = 0.000173238 loss)
I0327 14:13:22.498800 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00320675 (* 0.0272727 = 8.74568e-05 loss)
I0327 14:13:22.498814 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000958524 (* 0.0272727 = 2.61416e-05 loss)
I0327 14:13:22.498829 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00160384 (* 0.0272727 = 4.3741e-05 loss)
I0327 14:13:22.498844 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.00319728 (* 0.0272727 = 8.71985e-05 loss)
I0327 14:13:22.498858 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.0024788 (* 0.0272727 = 6.76036e-05 loss)
I0327 14:13:22.498873 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00139671 (* 0.0272727 = 3.80922e-05 loss)
I0327 14:13:22.498908 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00367029 (* 0.0272727 = 0.000100099 loss)
I0327 14:13:22.498924 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.00146028 (* 0.0272727 = 3.98257e-05 loss)
I0327 14:13:22.498937 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00129161 (* 0.0272727 = 3.52258e-05 loss)
I0327 14:13:22.498952 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00101671 (* 0.0272727 = 2.77284e-05 loss)
I0327 14:13:22.498966 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00245906 (* 0.0272727 = 6.70653e-05 loss)
I0327 14:13:22.498978 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0
I0327 14:13:22.498994 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 14:13:22.499006 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.5
I0327 14:13:22.499019 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 14:13:22.499032 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 14:13:22.499043 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 14:13:22.499055 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 14:13:22.499068 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 14:13:22.499080 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:13:22.499092 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:13:22.499104 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:13:22.499115 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:13:22.499127 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:13:22.499140 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:13:22.499152 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:13:22.499164 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:13:22.499176 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:13:22.499188 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:13:22.499199 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:13:22.499212 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:13:22.499223 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:13:22.499234 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:13:22.499248 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 3.05973 (* 0.0272727 = 0.0834471 loss)
I0327 14:13:22.499264 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.18486 (* 0.0272727 = 0.0868598 loss)
I0327 14:13:22.499279 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.48132 (* 0.0272727 = 0.0676724 loss)
I0327 14:13:22.499292 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.11374 (* 0.0272727 = 0.0849201 loss)
I0327 14:13:22.499306 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.70488 (* 0.0272727 = 0.0737694 loss)
I0327 14:13:22.499321 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 3.1154 (* 0.0272727 = 0.0849653 loss)
I0327 14:13:22.499336 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.462366 (* 0.0272727 = 0.01261 loss)
I0327 14:13:22.499351 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0873227 (* 0.0272727 = 0.00238153 loss)
I0327 14:13:22.499367 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0155153 (* 0.0272727 = 0.000423145 loss)
I0327 14:13:22.499383 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0094942 (* 0.0272727 = 0.000258933 loss)
I0327 14:13:22.499398 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000205893 (* 0.0272727 = 5.61525e-06 loss)
I0327 14:13:22.499423 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000344176 (* 0.0272727 = 9.38661e-06 loss)
I0327 14:13:22.499439 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000285837 (* 0.0272727 = 7.79556e-06 loss)
I0327 14:13:22.499454 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000340293 (* 0.0272727 = 9.28072e-06 loss)
I0327 14:13:22.499469 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000148383 (* 0.0272727 = 4.04681e-06 loss)
I0327 14:13:22.499483 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000293942 (* 0.0272727 = 8.01661e-06 loss)
I0327 14:13:22.499497 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000463235 (* 0.0272727 = 1.26337e-05 loss)
I0327 14:13:22.499511 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000168839 (* 0.0272727 = 4.60471e-06 loss)
I0327 14:13:22.499526 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000328804 (* 0.0272727 = 8.96737e-06 loss)
I0327 14:13:22.499541 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000226251 (* 0.0272727 = 6.17048e-06 loss)
I0327 14:13:22.499554 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000511469 (* 0.0272727 = 1.39492e-05 loss)
I0327 14:13:22.499569 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000205041 (* 0.0272727 = 5.59204e-06 loss)
I0327 14:13:22.499582 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.375
I0327 14:13:22.499594 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.25
I0327 14:13:22.499606 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.375
I0327 14:13:22.499617 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 14:13:22.499630 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 14:13:22.499641 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 14:13:22.499653 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.875
I0327 14:13:22.499666 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 14:13:22.499678 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:13:22.499689 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:13:22.499701 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:13:22.499712 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:13:22.499724 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:13:22.499737 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:13:22.499748 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:13:22.499759 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:13:22.499771 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:13:22.499783 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:13:22.499795 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:13:22.499806 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:13:22.499819 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:13:22.499830 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:13:22.499845 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.4185 (* 0.0909091 = 0.219864 loss)
I0327 14:13:22.499858 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.74109 (* 0.0909091 = 0.24919 loss)
I0327 14:13:22.499872 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.23956 (* 0.0909091 = 0.203596 loss)
I0327 14:13:22.499886 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 3.16292 (* 0.0909091 = 0.287538 loss)
I0327 14:13:22.499900 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.7642 (* 0.0909091 = 0.251291 loss)
I0327 14:13:22.499924 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.45405 (* 0.0909091 = 0.223096 loss)
I0327 14:13:22.499940 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.403863 (* 0.0909091 = 0.0367148 loss)
I0327 14:13:22.499954 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.184812 (* 0.0909091 = 0.0168011 loss)
I0327 14:13:22.499969 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0135346 (* 0.0909091 = 0.00123042 loss)
I0327 14:13:22.499984 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00380241 (* 0.0909091 = 0.000345674 loss)
I0327 14:13:22.499997 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000135982 (* 0.0909091 = 1.2362e-05 loss)
I0327 14:13:22.500012 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 9.21962e-05 (* 0.0909091 = 8.38147e-06 loss)
I0327 14:13:22.500026 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000109659 (* 0.0909091 = 9.96898e-06 loss)
I0327 14:13:22.500043 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 9.74716e-05 (* 0.0909091 = 8.86106e-06 loss)
I0327 14:13:22.500059 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000119259 (* 0.0909091 = 1.08418e-05 loss)
I0327 14:13:22.500074 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000105709 (* 0.0909091 = 9.60994e-06 loss)
I0327 14:13:22.500088 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 8.19827e-05 (* 0.0909091 = 7.45298e-06 loss)
I0327 14:13:22.500102 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000102835 (* 0.0909091 = 9.34859e-06 loss)
I0327 14:13:22.500118 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000102714 (* 0.0909091 = 9.33765e-06 loss)
I0327 14:13:22.500133 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000114445 (* 0.0909091 = 1.04041e-05 loss)
I0327 14:13:22.500146 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000105808 (* 0.0909091 = 9.61894e-06 loss)
I0327 14:13:22.500161 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000115348 (* 0.0909091 = 1.04862e-05 loss)
I0327 14:13:22.500174 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:13:22.500185 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00263211
I0327 14:13:22.500198 21344 sgd_solver.cpp:106] Iteration 23500, lr = 0.01
I0327 14:15:10.805251 21344 solver.cpp:229] Iteration 24000, loss = 2.46838
I0327 14:15:10.805415 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.875
I0327 14:15:10.805438 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 14:15:10.805450 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 14:15:10.805462 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.5
I0327 14:15:10.805474 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 14:15:10.805486 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.875
I0327 14:15:10.805498 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.75
I0327 14:15:10.805510 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 14:15:10.805522 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:15:10.805534 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:15:10.805558 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:15:10.805572 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:15:10.805583 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:15:10.805594 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:15:10.805606 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:15:10.805619 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:15:10.805629 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:15:10.805641 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:15:10.805652 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:15:10.805665 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:15:10.805675 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:15:10.805687 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:15:10.805703 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 0.744001 (* 0.0272727 = 0.0202909 loss)
I0327 14:15:10.805718 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.36447 (* 0.0272727 = 0.0917583 loss)
I0327 14:15:10.805732 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.74454 (* 0.0272727 = 0.074851 loss)
I0327 14:15:10.805747 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.2921 (* 0.0272727 = 0.0625119 loss)
I0327 14:15:10.805760 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.23764 (* 0.0272727 = 0.0610266 loss)
I0327 14:15:10.805774 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 0.941604 (* 0.0272727 = 0.0256801 loss)
I0327 14:15:10.805788 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.876657 (* 0.0272727 = 0.0239088 loss)
I0327 14:15:10.805802 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.114526 (* 0.0272727 = 0.00312343 loss)
I0327 14:15:10.805817 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0146845 (* 0.0272727 = 0.000400488 loss)
I0327 14:15:10.805831 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00395376 (* 0.0272727 = 0.00010783 loss)
I0327 14:15:10.805846 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00013437 (* 0.0272727 = 3.66463e-06 loss)
I0327 14:15:10.805860 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 7.99483e-05 (* 0.0272727 = 2.18041e-06 loss)
I0327 14:15:10.805874 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000284013 (* 0.0272727 = 7.74581e-06 loss)
I0327 14:15:10.805889 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000326417 (* 0.0272727 = 8.90228e-06 loss)
I0327 14:15:10.805903 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000142471 (* 0.0272727 = 3.88558e-06 loss)
I0327 14:15:10.805917 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000135422 (* 0.0272727 = 3.69332e-06 loss)
I0327 14:15:10.805932 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 8.58311e-05 (* 0.0272727 = 2.34085e-06 loss)
I0327 14:15:10.805964 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00012937 (* 0.0272727 = 3.52829e-06 loss)
I0327 14:15:10.805980 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000193899 (* 0.0272727 = 5.28814e-06 loss)
I0327 14:15:10.805997 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000346507 (* 0.0272727 = 9.45018e-06 loss)
I0327 14:15:10.806012 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000114384 (* 0.0272727 = 3.11955e-06 loss)
I0327 14:15:10.806026 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 9.80173e-05 (* 0.0272727 = 2.6732e-06 loss)
I0327 14:15:10.806038 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.75
I0327 14:15:10.806051 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 14:15:10.806063 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.25
I0327 14:15:10.806076 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.375
I0327 14:15:10.806087 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.25
I0327 14:15:10.806099 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.75
I0327 14:15:10.806110 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.75
I0327 14:15:10.806123 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 14:15:10.806133 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:15:10.806145 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:15:10.806156 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:15:10.806169 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:15:10.806180 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:15:10.806190 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:15:10.806202 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:15:10.806213 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:15:10.806224 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:15:10.806236 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:15:10.806247 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:15:10.806258 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:15:10.806270 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:15:10.806282 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:15:10.806295 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 0.833926 (* 0.0272727 = 0.0227434 loss)
I0327 14:15:10.806309 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.8789 (* 0.0272727 = 0.0785154 loss)
I0327 14:15:10.806324 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.28798 (* 0.0272727 = 0.0623993 loss)
I0327 14:15:10.806337 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.52308 (* 0.0272727 = 0.0688114 loss)
I0327 14:15:10.806351 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.36288 (* 0.0272727 = 0.0644421 loss)
I0327 14:15:10.806366 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.02198 (* 0.0272727 = 0.0278722 loss)
I0327 14:15:10.806380 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.09364 (* 0.0272727 = 0.0298265 loss)
I0327 14:15:10.806394 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.102805 (* 0.0272727 = 0.00280379 loss)
I0327 14:15:10.806408 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0173498 (* 0.0272727 = 0.000473177 loss)
I0327 14:15:10.806426 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0084493 (* 0.0272727 = 0.000230435 loss)
I0327 14:15:10.806440 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000126246 (* 0.0272727 = 3.44306e-06 loss)
I0327 14:15:10.806465 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 9.36114e-05 (* 0.0272727 = 2.55304e-06 loss)
I0327 14:15:10.806481 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000349008 (* 0.0272727 = 9.51839e-06 loss)
I0327 14:15:10.806495 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 7.54029e-05 (* 0.0272727 = 2.05644e-06 loss)
I0327 14:15:10.806509 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00010331 (* 0.0272727 = 2.81754e-06 loss)
I0327 14:15:10.806524 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000108394 (* 0.0272727 = 2.95619e-06 loss)
I0327 14:15:10.806538 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000567252 (* 0.0272727 = 1.54705e-05 loss)
I0327 14:15:10.806552 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000210116 (* 0.0272727 = 5.73042e-06 loss)
I0327 14:15:10.806566 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000208217 (* 0.0272727 = 5.67865e-06 loss)
I0327 14:15:10.806581 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 7.86971e-05 (* 0.0272727 = 2.14628e-06 loss)
I0327 14:15:10.806594 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 8.19859e-05 (* 0.0272727 = 2.23598e-06 loss)
I0327 14:15:10.806608 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000154282 (* 0.0272727 = 4.20769e-06 loss)
I0327 14:15:10.806622 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.875
I0327 14:15:10.806633 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.125
I0327 14:15:10.806645 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.375
I0327 14:15:10.806656 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.5
I0327 14:15:10.806668 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 14:15:10.806680 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.875
I0327 14:15:10.806691 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 14:15:10.806704 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 14:15:10.806715 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:15:10.806725 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:15:10.806736 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:15:10.806748 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:15:10.806759 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:15:10.806771 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:15:10.806782 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:15:10.806793 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:15:10.806805 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:15:10.806816 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:15:10.806828 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:15:10.806838 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:15:10.806850 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:15:10.806861 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:15:10.806875 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 0.599877 (* 0.0909091 = 0.0545343 loss)
I0327 14:15:10.806890 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.28416 (* 0.0909091 = 0.29856 loss)
I0327 14:15:10.806903 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.3612 (* 0.0909091 = 0.214655 loss)
I0327 14:15:10.806916 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 1.91364 (* 0.0909091 = 0.173967 loss)
I0327 14:15:10.806931 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.2378 (* 0.0909091 = 0.203436 loss)
I0327 14:15:10.806953 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 0.665275 (* 0.0909091 = 0.0604796 loss)
I0327 14:15:10.806968 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.10045 (* 0.0909091 = 0.100041 loss)
I0327 14:15:10.806982 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0373259 (* 0.0909091 = 0.00339327 loss)
I0327 14:15:10.806996 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00444986 (* 0.0909091 = 0.000404532 loss)
I0327 14:15:10.807010 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00281712 (* 0.0909091 = 0.000256102 loss)
I0327 14:15:10.807025 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 8.18879e-05 (* 0.0909091 = 7.44436e-06 loss)
I0327 14:15:10.807039 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 6.49753e-05 (* 0.0909091 = 5.90685e-06 loss)
I0327 14:15:10.807056 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000127064 (* 0.0909091 = 1.15512e-05 loss)
I0327 14:15:10.807070 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000106226 (* 0.0909091 = 9.65694e-06 loss)
I0327 14:15:10.807085 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 8.60448e-05 (* 0.0909091 = 7.82225e-06 loss)
I0327 14:15:10.807103 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 8.77952e-05 (* 0.0909091 = 7.98138e-06 loss)
I0327 14:15:10.807117 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 9.17463e-05 (* 0.0909091 = 8.34057e-06 loss)
I0327 14:15:10.807132 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000104553 (* 0.0909091 = 9.50478e-06 loss)
I0327 14:15:10.807147 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 9.39257e-05 (* 0.0909091 = 8.5387e-06 loss)
I0327 14:15:10.807160 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000111408 (* 0.0909091 = 1.0128e-05 loss)
I0327 14:15:10.807173 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000106069 (* 0.0909091 = 9.64262e-06 loss)
I0327 14:15:10.807188 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000113708 (* 0.0909091 = 1.03371e-05 loss)
I0327 14:15:10.807200 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:15:10.807211 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00561889
I0327 14:15:10.807224 21344 sgd_solver.cpp:106] Iteration 24000, lr = 0.01
I0327 14:16:59.112181 21344 solver.cpp:229] Iteration 24500, loss = 2.48738
I0327 14:16:59.112336 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.375
I0327 14:16:59.112356 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 14:16:59.112370 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 14:16:59.112382 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 14:16:59.112395 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.25
I0327 14:16:59.112407 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 14:16:59.112419 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.75
I0327 14:16:59.112432 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 14:16:59.112443 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 0.875
I0327 14:16:59.112455 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:16:59.112468 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:16:59.112479 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:16:59.112490 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:16:59.112503 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:16:59.112514 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:16:59.112526 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:16:59.112537 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:16:59.112550 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:16:59.112561 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:16:59.112573 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:16:59.112584 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:16:59.112596 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:16:59.112612 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.79829 (* 0.0272727 = 0.0490442 loss)
I0327 14:16:59.112627 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.51252 (* 0.0272727 = 0.0685232 loss)
I0327 14:16:59.112640 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.94867 (* 0.0272727 = 0.0804183 loss)
I0327 14:16:59.112655 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.19924 (* 0.0272727 = 0.087252 loss)
I0327 14:16:59.112668 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.38818 (* 0.0272727 = 0.0651321 loss)
I0327 14:16:59.112682 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.96136 (* 0.0272727 = 0.0534917 loss)
I0327 14:16:59.112696 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.21562 (* 0.0272727 = 0.0331532 loss)
I0327 14:16:59.112710 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.193716 (* 0.0272727 = 0.00528315 loss)
I0327 14:16:59.112725 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.362558 (* 0.0272727 = 0.00988794 loss)
I0327 14:16:59.112738 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00938002 (* 0.0272727 = 0.000255819 loss)
I0327 14:16:59.112753 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 3.94599e-05 (* 0.0272727 = 1.07618e-06 loss)
I0327 14:16:59.112767 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 6.11924e-05 (* 0.0272727 = 1.66888e-06 loss)
I0327 14:16:59.112782 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 6.02495e-05 (* 0.0272727 = 1.64317e-06 loss)
I0327 14:16:59.112797 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 8.22291e-05 (* 0.0272727 = 2.24261e-06 loss)
I0327 14:16:59.112810 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 5.04663e-05 (* 0.0272727 = 1.37635e-06 loss)
I0327 14:16:59.112824 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 8.69064e-05 (* 0.0272727 = 2.37018e-06 loss)
I0327 14:16:59.112838 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 4.74627e-05 (* 0.0272727 = 1.29444e-06 loss)
I0327 14:16:59.112865 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 6.48482e-05 (* 0.0272727 = 1.76859e-06 loss)
I0327 14:16:59.112881 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 7.06015e-05 (* 0.0272727 = 1.9255e-06 loss)
I0327 14:16:59.112895 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 4.17854e-05 (* 0.0272727 = 1.1396e-06 loss)
I0327 14:16:59.112910 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 4.23069e-05 (* 0.0272727 = 1.15383e-06 loss)
I0327 14:16:59.112923 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 5.41028e-05 (* 0.0272727 = 1.47553e-06 loss)
I0327 14:16:59.112936 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 14:16:59.112949 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 14:16:59.112962 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 14:16:59.112972 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.375
I0327 14:16:59.112984 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.5
I0327 14:16:59.113000 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.5
I0327 14:16:59.113013 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.75
I0327 14:16:59.113024 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 14:16:59.113036 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 0.875
I0327 14:16:59.113047 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:16:59.113059 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:16:59.113071 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:16:59.113082 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:16:59.113090 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:16:59.113098 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:16:59.113109 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:16:59.113121 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:16:59.113133 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:16:59.113144 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:16:59.113155 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:16:59.113168 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:16:59.113178 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:16:59.113193 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.77163 (* 0.0272727 = 0.0483173 loss)
I0327 14:16:59.113206 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.65801 (* 0.0272727 = 0.0724913 loss)
I0327 14:16:59.113220 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.93453 (* 0.0272727 = 0.0800326 loss)
I0327 14:16:59.113234 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.39396 (* 0.0272727 = 0.0652897 loss)
I0327 14:16:59.113248 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.47391 (* 0.0272727 = 0.0674702 loss)
I0327 14:16:59.113261 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.72831 (* 0.0272727 = 0.0471358 loss)
I0327 14:16:59.113276 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.2007 (* 0.0272727 = 0.0327464 loss)
I0327 14:16:59.113289 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.432398 (* 0.0272727 = 0.0117927 loss)
I0327 14:16:59.113303 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.503387 (* 0.0272727 = 0.0137287 loss)
I0327 14:16:59.113317 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0151887 (* 0.0272727 = 0.000414238 loss)
I0327 14:16:59.113332 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000675221 (* 0.0272727 = 1.84151e-05 loss)
I0327 14:16:59.113360 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000737401 (* 0.0272727 = 2.01109e-05 loss)
I0327 14:16:59.113375 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000403264 (* 0.0272727 = 1.09981e-05 loss)
I0327 14:16:59.113390 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000405127 (* 0.0272727 = 1.10489e-05 loss)
I0327 14:16:59.113404 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00050335 (* 0.0272727 = 1.37277e-05 loss)
I0327 14:16:59.113418 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000245455 (* 0.0272727 = 6.69423e-06 loss)
I0327 14:16:59.113432 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000558359 (* 0.0272727 = 1.5228e-05 loss)
I0327 14:16:59.113445 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000617274 (* 0.0272727 = 1.68347e-05 loss)
I0327 14:16:59.113461 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000977972 (* 0.0272727 = 2.6672e-05 loss)
I0327 14:16:59.113474 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000309729 (* 0.0272727 = 8.44715e-06 loss)
I0327 14:16:59.113487 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.00176827 (* 0.0272727 = 4.82255e-05 loss)
I0327 14:16:59.113502 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000315866 (* 0.0272727 = 8.61453e-06 loss)
I0327 14:16:59.113514 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.5
I0327 14:16:59.113526 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 14:16:59.113549 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 14:16:59.113564 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.125
I0327 14:16:59.113576 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 14:16:59.113589 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 14:16:59.113600 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 14:16:59.113611 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 14:16:59.113623 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 0.875
I0327 14:16:59.113634 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:16:59.113646 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:16:59.113657 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:16:59.113669 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:16:59.113680 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:16:59.113692 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:16:59.113703 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:16:59.113714 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:16:59.113725 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:16:59.113736 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:16:59.113749 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:16:59.113759 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:16:59.113771 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:16:59.113785 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.73784 (* 0.0909091 = 0.157986 loss)
I0327 14:16:59.113798 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.68342 (* 0.0909091 = 0.243947 loss)
I0327 14:16:59.113812 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.91026 (* 0.0909091 = 0.264569 loss)
I0327 14:16:59.113826 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.5772 (* 0.0909091 = 0.23429 loss)
I0327 14:16:59.113840 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.21481 (* 0.0909091 = 0.201347 loss)
I0327 14:16:59.113867 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.54293 (* 0.0909091 = 0.140266 loss)
I0327 14:16:59.113883 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.98097 (* 0.0909091 = 0.0891791 loss)
I0327 14:16:59.113896 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.287356 (* 0.0909091 = 0.0261233 loss)
I0327 14:16:59.113910 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.418977 (* 0.0909091 = 0.0380888 loss)
I0327 14:16:59.113924 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00962642 (* 0.0909091 = 0.000875129 loss)
I0327 14:16:59.113939 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000110226 (* 0.0909091 = 1.00206e-05 loss)
I0327 14:16:59.113952 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 8.11902e-05 (* 0.0909091 = 7.38093e-06 loss)
I0327 14:16:59.113966 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 8.192e-05 (* 0.0909091 = 7.44727e-06 loss)
I0327 14:16:59.113981 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000103018 (* 0.0909091 = 9.36531e-06 loss)
I0327 14:16:59.113994 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 7.36257e-05 (* 0.0909091 = 6.69325e-06 loss)
I0327 14:16:59.114009 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 8.19647e-05 (* 0.0909091 = 7.45134e-06 loss)
I0327 14:16:59.114023 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 8.82178e-05 (* 0.0909091 = 8.0198e-06 loss)
I0327 14:16:59.114037 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000105044 (* 0.0909091 = 9.54944e-06 loss)
I0327 14:16:59.114055 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 9.78018e-05 (* 0.0909091 = 8.89107e-06 loss)
I0327 14:16:59.114069 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 9.78448e-05 (* 0.0909091 = 8.89498e-06 loss)
I0327 14:16:59.114084 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 9.57141e-05 (* 0.0909091 = 8.70128e-06 loss)
I0327 14:16:59.114097 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 7.7016e-05 (* 0.0909091 = 7.00146e-06 loss)
I0327 14:16:59.114109 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:16:59.114121 21344 solver.cpp:245] Train net output #133: total_confidence = 0.0013402
I0327 14:16:59.114133 21344 sgd_solver.cpp:106] Iteration 24500, lr = 0.01
I0327 14:18:47.405966 21344 solver.cpp:338] Iteration 25000, Testing net (#0)
I0327 14:19:18.664235 21344 solver.cpp:393] Test loss: 2.08724
I0327 14:19:18.664321 21344 solver.cpp:406] Test net output #0: loss1/accuracy01 = 0.482
I0327 14:19:18.664340 21344 solver.cpp:406] Test net output #1: loss1/accuracy02 = 0.251
I0327 14:19:18.664351 21344 solver.cpp:406] Test net output #2: loss1/accuracy03 = 0.25
I0327 14:19:18.664365 21344 solver.cpp:406] Test net output #3: loss1/accuracy04 = 0.225
I0327 14:19:18.664376 21344 solver.cpp:406] Test net output #4: loss1/accuracy05 = 0.252
I0327 14:19:18.664387 21344 solver.cpp:406] Test net output #5: loss1/accuracy06 = 0.522
I0327 14:19:18.664398 21344 solver.cpp:406] Test net output #6: loss1/accuracy07 = 0.892
I0327 14:19:18.664410 21344 solver.cpp:406] Test net output #7: loss1/accuracy08 = 0.969
I0327 14:19:18.664422 21344 solver.cpp:406] Test net output #8: loss1/accuracy09 = 0.995
I0327 14:19:18.664436 21344 solver.cpp:406] Test net output #9: loss1/accuracy10 = 0.998
I0327 14:19:18.664448 21344 solver.cpp:406] Test net output #10: loss1/accuracy11 = 1
I0327 14:19:18.664460 21344 solver.cpp:406] Test net output #11: loss1/accuracy12 = 1
I0327 14:19:18.664472 21344 solver.cpp:406] Test net output #12: loss1/accuracy13 = 1
I0327 14:19:18.664484 21344 solver.cpp:406] Test net output #13: loss1/accuracy14 = 1
I0327 14:19:18.664494 21344 solver.cpp:406] Test net output #14: loss1/accuracy15 = 1
I0327 14:19:18.664506 21344 solver.cpp:406] Test net output #15: loss1/accuracy16 = 1
I0327 14:19:18.664517 21344 solver.cpp:406] Test net output #16: loss1/accuracy17 = 1
I0327 14:19:18.664528 21344 solver.cpp:406] Test net output #17: loss1/accuracy18 = 1
I0327 14:19:18.664540 21344 solver.cpp:406] Test net output #18: loss1/accuracy19 = 1
I0327 14:19:18.664551 21344 solver.cpp:406] Test net output #19: loss1/accuracy20 = 1
I0327 14:19:18.664562 21344 solver.cpp:406] Test net output #20: loss1/accuracy21 = 1
I0327 14:19:18.664573 21344 solver.cpp:406] Test net output #21: loss1/accuracy22 = 1
I0327 14:19:18.664589 21344 solver.cpp:406] Test net output #22: loss1/loss01 = 1.80568 (* 0.0272727 = 0.0492458 loss)
I0327 14:19:18.664604 21344 solver.cpp:406] Test net output #23: loss1/loss02 = 2.45192 (* 0.0272727 = 0.0668705 loss)
I0327 14:19:18.664618 21344 solver.cpp:406] Test net output #24: loss1/loss03 = 2.50689 (* 0.0272727 = 0.0683699 loss)
I0327 14:19:18.664631 21344 solver.cpp:406] Test net output #25: loss1/loss04 = 2.58306 (* 0.0272727 = 0.0704472 loss)
I0327 14:19:18.664645 21344 solver.cpp:406] Test net output #26: loss1/loss05 = 2.79652 (* 0.0272727 = 0.0762688 loss)
I0327 14:19:18.664659 21344 solver.cpp:406] Test net output #27: loss1/loss06 = 1.73664 (* 0.0272727 = 0.0473629 loss)
I0327 14:19:18.664671 21344 solver.cpp:406] Test net output #28: loss1/loss07 = 0.622061 (* 0.0272727 = 0.0169653 loss)
I0327 14:19:18.664685 21344 solver.cpp:406] Test net output #29: loss1/loss08 = 0.239719 (* 0.0272727 = 0.0065378 loss)
I0327 14:19:18.664700 21344 solver.cpp:406] Test net output #30: loss1/loss09 = 0.049266 (* 0.0272727 = 0.00134362 loss)
I0327 14:19:18.664713 21344 solver.cpp:406] Test net output #31: loss1/loss10 = 0.0247208 (* 0.0272727 = 0.000674203 loss)
I0327 14:19:18.664727 21344 solver.cpp:406] Test net output #32: loss1/loss11 = 4.3523e-05 (* 0.0272727 = 1.18699e-06 loss)
I0327 14:19:18.664741 21344 solver.cpp:406] Test net output #33: loss1/loss12 = 4.38744e-05 (* 0.0272727 = 1.19658e-06 loss)
I0327 14:19:18.664755 21344 solver.cpp:406] Test net output #34: loss1/loss13 = 3.58745e-05 (* 0.0272727 = 9.78396e-07 loss)
I0327 14:19:18.664768 21344 solver.cpp:406] Test net output #35: loss1/loss14 = 3.66461e-05 (* 0.0272727 = 9.99438e-07 loss)
I0327 14:19:18.664783 21344 solver.cpp:406] Test net output #36: loss1/loss15 = 4.42558e-05 (* 0.0272727 = 1.20698e-06 loss)
I0327 14:19:18.664796 21344 solver.cpp:406] Test net output #37: loss1/loss16 = 4.23911e-05 (* 0.0272727 = 1.15612e-06 loss)
I0327 14:19:18.664810 21344 solver.cpp:406] Test net output #38: loss1/loss17 = 3.9696e-05 (* 0.0272727 = 1.08262e-06 loss)
I0327 14:19:18.664844 21344 solver.cpp:406] Test net output #39: loss1/loss18 = 3.79676e-05 (* 0.0272727 = 1.03548e-06 loss)
I0327 14:19:18.664860 21344 solver.cpp:406] Test net output #40: loss1/loss19 = 3.26479e-05 (* 0.0272727 = 8.90398e-07 loss)
I0327 14:19:18.664873 21344 solver.cpp:406] Test net output #41: loss1/loss20 = 3.84735e-05 (* 0.0272727 = 1.04928e-06 loss)
I0327 14:19:18.664886 21344 solver.cpp:406] Test net output #42: loss1/loss21 = 3.64971e-05 (* 0.0272727 = 9.95376e-07 loss)
I0327 14:19:18.664903 21344 solver.cpp:406] Test net output #43: loss1/loss22 = 4.2706e-05 (* 0.0272727 = 1.16471e-06 loss)
I0327 14:19:18.664916 21344 solver.cpp:406] Test net output #44: loss2/accuracy01 = 0.612
I0327 14:19:18.664928 21344 solver.cpp:406] Test net output #45: loss2/accuracy02 = 0.286
I0327 14:19:18.664940 21344 solver.cpp:406] Test net output #46: loss2/accuracy03 = 0.232
I0327 14:19:18.664952 21344 solver.cpp:406] Test net output #47: loss2/accuracy04 = 0.237
I0327 14:19:18.664963 21344 solver.cpp:406] Test net output #48: loss2/accuracy05 = 0.264
I0327 14:19:18.664974 21344 solver.cpp:406] Test net output #49: loss2/accuracy06 = 0.552
I0327 14:19:18.664985 21344 solver.cpp:406] Test net output #50: loss2/accuracy07 = 0.892
I0327 14:19:18.664997 21344 solver.cpp:406] Test net output #51: loss2/accuracy08 = 0.969
I0327 14:19:18.665009 21344 solver.cpp:406] Test net output #52: loss2/accuracy09 = 0.995
I0327 14:19:18.665019 21344 solver.cpp:406] Test net output #53: loss2/accuracy10 = 0.998
I0327 14:19:18.665027 21344 solver.cpp:406] Test net output #54: loss2/accuracy11 = 1
I0327 14:19:18.665040 21344 solver.cpp:406] Test net output #55: loss2/accuracy12 = 1
I0327 14:19:18.665050 21344 solver.cpp:406] Test net output #56: loss2/accuracy13 = 1
I0327 14:19:18.665061 21344 solver.cpp:406] Test net output #57: loss2/accuracy14 = 1
I0327 14:19:18.665072 21344 solver.cpp:406] Test net output #58: loss2/accuracy15 = 1
I0327 14:19:18.665084 21344 solver.cpp:406] Test net output #59: loss2/accuracy16 = 1
I0327 14:19:18.665096 21344 solver.cpp:406] Test net output #60: loss2/accuracy17 = 1
I0327 14:19:18.665107 21344 solver.cpp:406] Test net output #61: loss2/accuracy18 = 1
I0327 14:19:18.665117 21344 solver.cpp:406] Test net output #62: loss2/accuracy19 = 1
I0327 14:19:18.665128 21344 solver.cpp:406] Test net output #63: loss2/accuracy20 = 1
I0327 14:19:18.665139 21344 solver.cpp:406] Test net output #64: loss2/accuracy21 = 1
I0327 14:19:18.665150 21344 solver.cpp:406] Test net output #65: loss2/accuracy22 = 1
I0327 14:19:18.665163 21344 solver.cpp:406] Test net output #66: loss2/loss01 = 1.63004 (* 0.0272727 = 0.0444557 loss)
I0327 14:19:18.665176 21344 solver.cpp:406] Test net output #67: loss2/loss02 = 2.45412 (* 0.0272727 = 0.0669307 loss)
I0327 14:19:18.665190 21344 solver.cpp:406] Test net output #68: loss2/loss03 = 2.56321 (* 0.0272727 = 0.0699056 loss)
I0327 14:19:18.665204 21344 solver.cpp:406] Test net output #69: loss2/loss04 = 2.63895 (* 0.0272727 = 0.0719714 loss)
I0327 14:19:18.665217 21344 solver.cpp:406] Test net output #70: loss2/loss05 = 2.69589 (* 0.0272727 = 0.0735242 loss)
I0327 14:19:18.665230 21344 solver.cpp:406] Test net output #71: loss2/loss06 = 1.57159 (* 0.0272727 = 0.0428614 loss)
I0327 14:19:18.665243 21344 solver.cpp:406] Test net output #72: loss2/loss07 = 0.503922 (* 0.0272727 = 0.0137433 loss)
I0327 14:19:18.665257 21344 solver.cpp:406] Test net output #73: loss2/loss08 = 0.21627 (* 0.0272727 = 0.00589828 loss)
I0327 14:19:18.665271 21344 solver.cpp:406] Test net output #74: loss2/loss09 = 0.0485414 (* 0.0272727 = 0.00132386 loss)
I0327 14:19:18.665284 21344 solver.cpp:406] Test net output #75: loss2/loss10 = 0.0235078 (* 0.0272727 = 0.000641123 loss)
I0327 14:19:18.665298 21344 solver.cpp:406] Test net output #76: loss2/loss11 = 0.000137109 (* 0.0272727 = 3.73935e-06 loss)
I0327 14:19:18.665312 21344 solver.cpp:406] Test net output #77: loss2/loss12 = 0.000129944 (* 0.0272727 = 3.54393e-06 loss)
I0327 14:19:18.665338 21344 solver.cpp:406] Test net output #78: loss2/loss13 = 0.000136871 (* 0.0272727 = 3.73284e-06 loss)
I0327 14:19:18.665352 21344 solver.cpp:406] Test net output #79: loss2/loss14 = 0.000111499 (* 0.0272727 = 3.04087e-06 loss)
I0327 14:19:18.665365 21344 solver.cpp:406] Test net output #80: loss2/loss15 = 0.000145956 (* 0.0272727 = 3.98063e-06 loss)
I0327 14:19:18.665380 21344 solver.cpp:406] Test net output #81: loss2/loss16 = 0.000154138 (* 0.0272727 = 4.20378e-06 loss)
I0327 14:19:18.665393 21344 solver.cpp:406] Test net output #82: loss2/loss17 = 0.00012439 (* 0.0272727 = 3.39245e-06 loss)
I0327 14:19:18.665406 21344 solver.cpp:406] Test net output #83: loss2/loss18 = 0.000133399 (* 0.0272727 = 3.63814e-06 loss)
I0327 14:19:18.665421 21344 solver.cpp:406] Test net output #84: loss2/loss19 = 0.000130074 (* 0.0272727 = 3.54747e-06 loss)
I0327 14:19:18.665434 21344 solver.cpp:406] Test net output #85: loss2/loss20 = 0.000140249 (* 0.0272727 = 3.82498e-06 loss)
I0327 14:19:18.665447 21344 solver.cpp:406] Test net output #86: loss2/loss21 = 0.000116271 (* 0.0272727 = 3.17101e-06 loss)
I0327 14:19:18.665460 21344 solver.cpp:406] Test net output #87: loss2/loss22 = 0.00014193 (* 0.0272727 = 3.87083e-06 loss)
I0327 14:19:18.665472 21344 solver.cpp:406] Test net output #88: loss3/accuracy01 = 0.543
I0327 14:19:18.665487 21344 solver.cpp:406] Test net output #89: loss3/accuracy02 = 0.304
I0327 14:19:18.665499 21344 solver.cpp:406] Test net output #90: loss3/accuracy03 = 0.263
I0327 14:19:18.665510 21344 solver.cpp:406] Test net output #91: loss3/accuracy04 = 0.262
I0327 14:19:18.665523 21344 solver.cpp:406] Test net output #92: loss3/accuracy05 = 0.312
I0327 14:19:18.665534 21344 solver.cpp:406] Test net output #93: loss3/accuracy06 = 0.583
I0327 14:19:18.665560 21344 solver.cpp:406] Test net output #94: loss3/accuracy07 = 0.893
I0327 14:19:18.665573 21344 solver.cpp:406] Test net output #95: loss3/accuracy08 = 0.969
I0327 14:19:18.665585 21344 solver.cpp:406] Test net output #96: loss3/accuracy09 = 0.995
I0327 14:19:18.665596 21344 solver.cpp:406] Test net output #97: loss3/accuracy10 = 0.998
I0327 14:19:18.665607 21344 solver.cpp:406] Test net output #98: loss3/accuracy11 = 1
I0327 14:19:18.665618 21344 solver.cpp:406] Test net output #99: loss3/accuracy12 = 1
I0327 14:19:18.665629 21344 solver.cpp:406] Test net output #100: loss3/accuracy13 = 1
I0327 14:19:18.665640 21344 solver.cpp:406] Test net output #101: loss3/accuracy14 = 1
I0327 14:19:18.665652 21344 solver.cpp:406] Test net output #102: loss3/accuracy15 = 1
I0327 14:19:18.665663 21344 solver.cpp:406] Test net output #103: loss3/accuracy16 = 1
I0327 14:19:18.665674 21344 solver.cpp:406] Test net output #104: loss3/accuracy17 = 1
I0327 14:19:18.665685 21344 solver.cpp:406] Test net output #105: loss3/accuracy18 = 1
I0327 14:19:18.665696 21344 solver.cpp:406] Test net output #106: loss3/accuracy19 = 1
I0327 14:19:18.665707 21344 solver.cpp:406] Test net output #107: loss3/accuracy20 = 1
I0327 14:19:18.665719 21344 solver.cpp:406] Test net output #108: loss3/accuracy21 = 1
I0327 14:19:18.665729 21344 solver.cpp:406] Test net output #109: loss3/accuracy22 = 1
I0327 14:19:18.665743 21344 solver.cpp:406] Test net output #110: loss3/loss01 = 1.69125 (* 0.0909091 = 0.15375 loss)
I0327 14:19:18.665756 21344 solver.cpp:406] Test net output #111: loss3/loss02 = 2.33569 (* 0.0909091 = 0.212335 loss)
I0327 14:19:18.665771 21344 solver.cpp:406] Test net output #112: loss3/loss03 = 2.58721 (* 0.0909091 = 0.235201 loss)
I0327 14:19:18.665783 21344 solver.cpp:406] Test net output #113: loss3/loss04 = 2.67185 (* 0.0909091 = 0.242896 loss)
I0327 14:19:18.665796 21344 solver.cpp:406] Test net output #114: loss3/loss05 = 2.61498 (* 0.0909091 = 0.237725 loss)
I0327 14:19:18.665809 21344 solver.cpp:406] Test net output #115: loss3/loss06 = 1.53602 (* 0.0909091 = 0.139638 loss)
I0327 14:19:18.665835 21344 solver.cpp:406] Test net output #116: loss3/loss07 = 0.479315 (* 0.0909091 = 0.0435741 loss)
I0327 14:19:18.665850 21344 solver.cpp:406] Test net output #117: loss3/loss08 = 0.209989 (* 0.0909091 = 0.0190899 loss)
I0327 14:19:18.665864 21344 solver.cpp:406] Test net output #118: loss3/loss09 = 0.0538638 (* 0.0909091 = 0.00489671 loss)
I0327 14:19:18.665879 21344 solver.cpp:406] Test net output #119: loss3/loss10 = 0.0277896 (* 0.0909091 = 0.00252633 loss)
I0327 14:19:18.665891 21344 solver.cpp:406] Test net output #120: loss3/loss11 = 0.000181311 (* 0.0909091 = 1.64828e-05 loss)
I0327 14:19:18.665905 21344 solver.cpp:406] Test net output #121: loss3/loss12 = 0.000224149 (* 0.0909091 = 2.03772e-05 loss)
I0327 14:19:18.665920 21344 solver.cpp:406] Test net output #122: loss3/loss13 = 0.000224761 (* 0.0909091 = 2.04328e-05 loss)
I0327 14:19:18.665932 21344 solver.cpp:406] Test net output #123: loss3/loss14 = 0.000188146 (* 0.0909091 = 1.71042e-05 loss)
I0327 14:19:18.665948 21344 solver.cpp:406] Test net output #124: loss3/loss15 = 0.000205054 (* 0.0909091 = 1.86412e-05 loss)
I0327 14:19:18.665963 21344 solver.cpp:406] Test net output #125: loss3/loss16 = 0.000199709 (* 0.0909091 = 1.81553e-05 loss)
I0327 14:19:18.665977 21344 solver.cpp:406] Test net output #126: loss3/loss17 = 0.000188191 (* 0.0909091 = 1.71083e-05 loss)
I0327 14:19:18.665992 21344 solver.cpp:406] Test net output #127: loss3/loss18 = 0.000191626 (* 0.0909091 = 1.74205e-05 loss)
I0327 14:19:18.666005 21344 solver.cpp:406] Test net output #128: loss3/loss19 = 0.000162315 (* 0.0909091 = 1.47559e-05 loss)
I0327 14:19:18.666018 21344 solver.cpp:406] Test net output #129: loss3/loss20 = 0.000190823 (* 0.0909091 = 1.73476e-05 loss)
I0327 14:19:18.666033 21344 solver.cpp:406] Test net output #130: loss3/loss21 = 0.000188225 (* 0.0909091 = 1.71114e-05 loss)
I0327 14:19:18.666045 21344 solver.cpp:406] Test net output #131: loss3/loss22 = 0.000154318 (* 0.0909091 = 1.40289e-05 loss)
I0327 14:19:18.666057 21344 solver.cpp:406] Test net output #132: total_accuracy = 0.004
I0327 14:19:18.666069 21344 solver.cpp:406] Test net output #133: total_confidence = 0.00870709
I0327 14:19:18.776504 21344 solver.cpp:229] Iteration 25000, loss = 2.46874
I0327 14:19:18.776545 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.75
I0327 14:19:18.776561 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 14:19:18.776573 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.375
I0327 14:19:18.776585 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.125
I0327 14:19:18.776597 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 14:19:18.776609 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.125
I0327 14:19:18.776621 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.75
I0327 14:19:18.776633 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 14:19:18.776645 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:19:18.776657 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:19:18.776669 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:19:18.776679 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:19:18.776691 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:19:18.776702 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:19:18.776713 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:19:18.776726 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:19:18.776736 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:19:18.776748 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:19:18.776759 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:19:18.776787 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:19:18.776801 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:19:18.776813 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:19:18.776829 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.48181 (* 0.0272727 = 0.040413 loss)
I0327 14:19:18.776844 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.74405 (* 0.0272727 = 0.0748377 loss)
I0327 14:19:18.776857 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.55421 (* 0.0272727 = 0.0696604 loss)
I0327 14:19:18.776871 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.04577 (* 0.0272727 = 0.0830664 loss)
I0327 14:19:18.776885 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.42043 (* 0.0272727 = 0.0660116 loss)
I0327 14:19:18.776898 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.0079 (* 0.0272727 = 0.054761 loss)
I0327 14:19:18.776912 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.33426 (* 0.0272727 = 0.036389 loss)
I0327 14:19:18.776926 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.674217 (* 0.0272727 = 0.0183877 loss)
I0327 14:19:18.776940 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.00720049 (* 0.0272727 = 0.000196377 loss)
I0327 14:19:18.776954 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00576841 (* 0.0272727 = 0.00015732 loss)
I0327 14:19:18.776968 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 9.03632e-05 (* 0.0272727 = 2.46445e-06 loss)
I0327 14:19:18.776983 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 8.56376e-05 (* 0.0272727 = 2.33557e-06 loss)
I0327 14:19:18.776996 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 4.43184e-05 (* 0.0272727 = 1.20868e-06 loss)
I0327 14:19:18.777010 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 7.64271e-05 (* 0.0272727 = 2.08437e-06 loss)
I0327 14:19:18.777025 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 7.22059e-05 (* 0.0272727 = 1.96925e-06 loss)
I0327 14:19:18.777040 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000106445 (* 0.0272727 = 2.90303e-06 loss)
I0327 14:19:18.777053 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 6.69193e-05 (* 0.0272727 = 1.82507e-06 loss)
I0327 14:19:18.777067 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 6.28201e-05 (* 0.0272727 = 1.71328e-06 loss)
I0327 14:19:18.777084 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 5.83052e-05 (* 0.0272727 = 1.59014e-06 loss)
I0327 14:19:18.777098 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 9.17743e-05 (* 0.0272727 = 2.50294e-06 loss)
I0327 14:19:18.777112 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 5.03987e-05 (* 0.0272727 = 1.37451e-06 loss)
I0327 14:19:18.777127 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000166269 (* 0.0272727 = 4.53462e-06 loss)
I0327 14:19:18.777138 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 14:19:18.777150 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.25
I0327 14:19:18.777163 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.375
I0327 14:19:18.777173 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 14:19:18.777185 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 14:19:18.777197 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 14:19:18.777209 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 14:19:18.777220 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 14:19:18.777231 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:19:18.777242 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:19:18.777253 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:19:18.777276 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:19:18.777287 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:19:18.777299 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:19:18.777310 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:19:18.777321 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:19:18.777333 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:19:18.777343 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:19:18.777354 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:19:18.777361 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:19:18.777369 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:19:18.777381 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:19:18.777395 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.07259 (* 0.0272727 = 0.0565251 loss)
I0327 14:19:18.777408 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.6602 (* 0.0272727 = 0.0725509 loss)
I0327 14:19:18.777422 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.45891 (* 0.0272727 = 0.0670611 loss)
I0327 14:19:18.777436 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 3.06378 (* 0.0272727 = 0.0835577 loss)
I0327 14:19:18.777449 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.72921 (* 0.0272727 = 0.0744329 loss)
I0327 14:19:18.777463 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.26385 (* 0.0272727 = 0.0617413 loss)
I0327 14:19:18.777477 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.846358 (* 0.0272727 = 0.0230825 loss)
I0327 14:19:18.777492 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.701606 (* 0.0272727 = 0.0191347 loss)
I0327 14:19:18.777505 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0427901 (* 0.0272727 = 0.001167 loss)
I0327 14:19:18.777519 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0145918 (* 0.0272727 = 0.000397959 loss)
I0327 14:19:18.777532 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000334998 (* 0.0272727 = 9.13631e-06 loss)
I0327 14:19:18.777565 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000605081 (* 0.0272727 = 1.65022e-05 loss)
I0327 14:19:18.777581 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000806947 (* 0.0272727 = 2.20077e-05 loss)
I0327 14:19:18.777595 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000345231 (* 0.0272727 = 9.4154e-06 loss)
I0327 14:19:18.777609 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.00093144 (* 0.0272727 = 2.54029e-05 loss)
I0327 14:19:18.777623 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000360457 (* 0.0272727 = 9.83065e-06 loss)
I0327 14:19:18.777637 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000506588 (* 0.0272727 = 1.3816e-05 loss)
I0327 14:19:18.777652 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000458009 (* 0.0272727 = 1.24912e-05 loss)
I0327 14:19:18.777665 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000709444 (* 0.0272727 = 1.93485e-05 loss)
I0327 14:19:18.777678 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000779683 (* 0.0272727 = 2.12641e-05 loss)
I0327 14:19:18.777693 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000514648 (* 0.0272727 = 1.40359e-05 loss)
I0327 14:19:18.777705 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000902805 (* 0.0272727 = 2.4622e-05 loss)
I0327 14:19:18.777717 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.5
I0327 14:19:18.777729 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.5
I0327 14:19:18.777741 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.375
I0327 14:19:18.777765 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.375
I0327 14:19:18.777778 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.125
I0327 14:19:18.777791 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 14:19:18.777802 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 14:19:18.777813 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 14:19:18.777824 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:19:18.777837 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:19:18.777848 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:19:18.777858 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:19:18.777869 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:19:18.777880 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:19:18.777891 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:19:18.777904 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:19:18.777915 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:19:18.777925 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:19:18.777936 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:19:18.777947 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:19:18.777958 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:19:18.777969 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:19:18.777982 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.29006 (* 0.0909091 = 0.117278 loss)
I0327 14:19:18.777997 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.2686 (* 0.0909091 = 0.206237 loss)
I0327 14:19:18.778009 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.05048 (* 0.0909091 = 0.186407 loss)
I0327 14:19:18.778023 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.6676 (* 0.0909091 = 0.242509 loss)
I0327 14:19:18.778036 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.61792 (* 0.0909091 = 0.237992 loss)
I0327 14:19:18.778050 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.02089 (* 0.0909091 = 0.183717 loss)
I0327 14:19:18.778064 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 1.03577 (* 0.0909091 = 0.0941609 loss)
I0327 14:19:18.778077 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.822614 (* 0.0909091 = 0.0747831 loss)
I0327 14:19:18.778091 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.0137973 (* 0.0909091 = 0.0012543 loss)
I0327 14:19:18.778105 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00366038 (* 0.0909091 = 0.000332762 loss)
I0327 14:19:18.778118 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000153633 (* 0.0909091 = 1.39666e-05 loss)
I0327 14:19:18.778136 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000180397 (* 0.0909091 = 1.63997e-05 loss)
I0327 14:19:18.778149 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000130654 (* 0.0909091 = 1.18777e-05 loss)
I0327 14:19:18.778163 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.00011321 (* 0.0909091 = 1.02918e-05 loss)
I0327 14:19:18.778177 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000184642 (* 0.0909091 = 1.67856e-05 loss)
I0327 14:19:18.778190 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000158549 (* 0.0909091 = 1.44135e-05 loss)
I0327 14:19:18.778204 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000138163 (* 0.0909091 = 1.25603e-05 loss)
I0327 14:19:18.778218 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.00012033 (* 0.0909091 = 1.09391e-05 loss)
I0327 14:19:18.778242 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.000127928 (* 0.0909091 = 1.16298e-05 loss)
I0327 14:19:18.778257 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000130687 (* 0.0909091 = 1.18806e-05 loss)
I0327 14:19:18.778271 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 0.000184723 (* 0.0909091 = 1.6793e-05 loss)
I0327 14:19:18.778285 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 0.000127226 (* 0.0909091 = 1.1566e-05 loss)
I0327 14:19:18.778297 21344 solver.cpp:245] Train net output #132: total_accuracy = 0.125
I0327 14:19:18.778308 21344 solver.cpp:245] Train net output #133: total_confidence = 0.000476407
I0327 14:19:18.778321 21344 sgd_solver.cpp:106] Iteration 25000, lr = 0.01
I0327 14:21:07.229307 21344 solver.cpp:229] Iteration 25500, loss = 2.4247
I0327 14:21:07.229451 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.25
I0327 14:21:07.229471 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 14:21:07.229485 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.25
I0327 14:21:07.229497 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 14:21:07.229509 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.5
I0327 14:21:07.229522 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.625
I0327 14:21:07.229534 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 1
I0327 14:21:07.229547 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 14:21:07.229558 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:21:07.229583 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:21:07.229596 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:21:07.229609 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:21:07.229620 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:21:07.229632 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:21:07.229645 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:21:07.229656 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:21:07.229667 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:21:07.229679 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:21:07.229691 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:21:07.229703 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:21:07.229715 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:21:07.229727 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:21:07.229743 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.70713 (* 0.0272727 = 0.0738307 loss)
I0327 14:21:07.229758 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.33034 (* 0.0272727 = 0.0908275 loss)
I0327 14:21:07.229773 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.17685 (* 0.0272727 = 0.0866414 loss)
I0327 14:21:07.229787 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.88566 (* 0.0272727 = 0.0786999 loss)
I0327 14:21:07.229801 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.21489 (* 0.0272727 = 0.060406 loss)
I0327 14:21:07.229815 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.35721 (* 0.0272727 = 0.0370148 loss)
I0327 14:21:07.229830 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.331135 (* 0.0272727 = 0.00903095 loss)
I0327 14:21:07.229845 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0618246 (* 0.0272727 = 0.00168613 loss)
I0327 14:21:07.229858 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.00966211 (* 0.0272727 = 0.000263512 loss)
I0327 14:21:07.229873 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00210104 (* 0.0272727 = 5.73012e-05 loss)
I0327 14:21:07.229887 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000353976 (* 0.0272727 = 9.65389e-06 loss)
I0327 14:21:07.229902 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000509843 (* 0.0272727 = 1.39048e-05 loss)
I0327 14:21:07.229917 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000191711 (* 0.0272727 = 5.22848e-06 loss)
I0327 14:21:07.229930 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000105338 (* 0.0272727 = 2.87286e-06 loss)
I0327 14:21:07.229945 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000164267 (* 0.0272727 = 4.48001e-06 loss)
I0327 14:21:07.229959 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000102055 (* 0.0272727 = 2.78333e-06 loss)
I0327 14:21:07.229974 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000242816 (* 0.0272727 = 6.62225e-06 loss)
I0327 14:21:07.230008 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000361119 (* 0.0272727 = 9.84869e-06 loss)
I0327 14:21:07.230023 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000457923 (* 0.0272727 = 1.24888e-05 loss)
I0327 14:21:07.230037 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00019892 (* 0.0272727 = 5.4251e-06 loss)
I0327 14:21:07.230052 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 9.29818e-05 (* 0.0272727 = 2.53587e-06 loss)
I0327 14:21:07.230067 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.000330335 (* 0.0272727 = 9.00915e-06 loss)
I0327 14:21:07.230078 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 14:21:07.230092 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 14:21:07.230103 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 14:21:07.230114 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.625
I0327 14:21:07.230126 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.625
I0327 14:21:07.230139 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.625
I0327 14:21:07.230150 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 1
I0327 14:21:07.230162 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 14:21:07.230175 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:21:07.230185 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:21:07.230196 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:21:07.230208 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:21:07.230219 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:21:07.230226 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:21:07.230239 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:21:07.230252 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:21:07.230262 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:21:07.230274 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:21:07.230285 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:21:07.230296 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:21:07.230309 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:21:07.230319 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:21:07.230334 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.47229 (* 0.0272727 = 0.067426 loss)
I0327 14:21:07.230347 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 3.26755 (* 0.0272727 = 0.0891151 loss)
I0327 14:21:07.230362 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.09024 (* 0.0272727 = 0.0842793 loss)
I0327 14:21:07.230376 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.25588 (* 0.0272727 = 0.061524 loss)
I0327 14:21:07.230391 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.27821 (* 0.0272727 = 0.0621331 loss)
I0327 14:21:07.230404 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.66641 (* 0.0272727 = 0.0454477 loss)
I0327 14:21:07.230418 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.38924 (* 0.0272727 = 0.0106156 loss)
I0327 14:21:07.230432 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0442067 (* 0.0272727 = 0.00120564 loss)
I0327 14:21:07.230446 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.00514157 (* 0.0272727 = 0.000140225 loss)
I0327 14:21:07.230464 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00299363 (* 0.0272727 = 8.16444e-05 loss)
I0327 14:21:07.230479 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000359372 (* 0.0272727 = 9.80107e-06 loss)
I0327 14:21:07.230505 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000138988 (* 0.0272727 = 3.79057e-06 loss)
I0327 14:21:07.230520 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000225917 (* 0.0272727 = 6.16139e-06 loss)
I0327 14:21:07.230535 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000172772 (* 0.0272727 = 4.71196e-06 loss)
I0327 14:21:07.230548 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000187195 (* 0.0272727 = 5.10533e-06 loss)
I0327 14:21:07.230562 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000184723 (* 0.0272727 = 5.03789e-06 loss)
I0327 14:21:07.230576 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 8.59787e-05 (* 0.0272727 = 2.34487e-06 loss)
I0327 14:21:07.230590 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000315578 (* 0.0272727 = 8.60668e-06 loss)
I0327 14:21:07.230605 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000128757 (* 0.0272727 = 3.51156e-06 loss)
I0327 14:21:07.230619 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000199978 (* 0.0272727 = 5.45395e-06 loss)
I0327 14:21:07.230633 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000152927 (* 0.0272727 = 4.17074e-06 loss)
I0327 14:21:07.230648 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000140375 (* 0.0272727 = 3.8284e-06 loss)
I0327 14:21:07.230659 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.25
I0327 14:21:07.230672 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0
I0327 14:21:07.230684 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 14:21:07.230695 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.375
I0327 14:21:07.230707 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 14:21:07.230718 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.75
I0327 14:21:07.230731 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 1
I0327 14:21:07.230741 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 14:21:07.230753 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:21:07.230764 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:21:07.230777 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:21:07.230787 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:21:07.230799 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:21:07.230810 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:21:07.230823 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:21:07.230834 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:21:07.230845 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:21:07.230857 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:21:07.230868 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:21:07.230880 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:21:07.230891 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:21:07.230902 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:21:07.230916 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.02981 (* 0.0909091 = 0.184528 loss)
I0327 14:21:07.230931 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 3.47167 (* 0.0909091 = 0.315607 loss)
I0327 14:21:07.230944 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.72524 (* 0.0909091 = 0.247749 loss)
I0327 14:21:07.230958 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.67043 (* 0.0909091 = 0.242767 loss)
I0327 14:21:07.230973 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.10604 (* 0.0909091 = 0.191459 loss)
I0327 14:21:07.230996 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.0432 (* 0.0909091 = 0.0948365 loss)
I0327 14:21:07.231012 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.282637 (* 0.0909091 = 0.0256943 loss)
I0327 14:21:07.231026 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0538023 (* 0.0909091 = 0.00489112 loss)
I0327 14:21:07.231043 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00551699 (* 0.0909091 = 0.000501544 loss)
I0327 14:21:07.231058 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00294984 (* 0.0909091 = 0.000268167 loss)
I0327 14:21:07.231072 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000106634 (* 0.0909091 = 9.69404e-06 loss)
I0327 14:21:07.231087 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.000127821 (* 0.0909091 = 1.16201e-05 loss)
I0327 14:21:07.231101 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000141875 (* 0.0909091 = 1.28977e-05 loss)
I0327 14:21:07.231115 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 0.000126558 (* 0.0909091 = 1.15053e-05 loss)
I0327 14:21:07.231129 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000108973 (* 0.0909091 = 9.90665e-06 loss)
I0327 14:21:07.231144 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000121374 (* 0.0909091 = 1.1034e-05 loss)
I0327 14:21:07.231158 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 0.000112666 (* 0.0909091 = 1.02424e-05 loss)
I0327 14:21:07.231173 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.00011326 (* 0.0909091 = 1.02964e-05 loss)
I0327 14:21:07.231187 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 0.00011588 (* 0.0909091 = 1.05345e-05 loss)
I0327 14:21:07.231201 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 0.000126582 (* 0.0909091 = 1.15075e-05 loss)
I0327 14:21:07.231215 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 9.08575e-05 (* 0.0909091 = 8.25977e-06 loss)
I0327 14:21:07.231230 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 9.78707e-05 (* 0.0909091 = 8.89734e-06 loss)
I0327 14:21:07.231241 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:21:07.231253 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00116113
I0327 14:21:07.231266 21344 sgd_solver.cpp:106] Iteration 25500, lr = 0.01
I0327 14:22:55.535845 21344 solver.cpp:229] Iteration 26000, loss = 2.41636
I0327 14:22:55.536013 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.5
I0327 14:22:55.536033 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 14:22:55.536046 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 14:22:55.536059 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.5
I0327 14:22:55.536072 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.5
I0327 14:22:55.536083 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 14:22:55.536097 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 14:22:55.536108 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 14:22:55.536120 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:22:55.536133 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:22:55.536144 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:22:55.536157 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:22:55.536169 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:22:55.536181 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:22:55.536192 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:22:55.536204 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:22:55.536216 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:22:55.536228 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:22:55.536240 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:22:55.536252 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:22:55.536263 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:22:55.536275 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:22:55.536291 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.5648 (* 0.0272727 = 0.0426764 loss)
I0327 14:22:55.536308 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.76909 (* 0.0272727 = 0.0755208 loss)
I0327 14:22:55.536321 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.62244 (* 0.0272727 = 0.071521 loss)
I0327 14:22:55.536335 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 1.82521 (* 0.0272727 = 0.0497786 loss)
I0327 14:22:55.536350 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.10585 (* 0.0272727 = 0.0574324 loss)
I0327 14:22:55.536363 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.31764 (* 0.0272727 = 0.0632085 loss)
I0327 14:22:55.536377 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.970001 (* 0.0272727 = 0.0264546 loss)
I0327 14:22:55.536391 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0367633 (* 0.0272727 = 0.00100263 loss)
I0327 14:22:55.536406 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.00477021 (* 0.0272727 = 0.000130097 loss)
I0327 14:22:55.536420 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00182269 (* 0.0272727 = 4.97096e-05 loss)
I0327 14:22:55.536434 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 4.52968e-05 (* 0.0272727 = 1.23537e-06 loss)
I0327 14:22:55.536449 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 8.99469e-05 (* 0.0272727 = 2.4531e-06 loss)
I0327 14:22:55.536463 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000129542 (* 0.0272727 = 3.53297e-06 loss)
I0327 14:22:55.536478 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.000180329 (* 0.0272727 = 4.91807e-06 loss)
I0327 14:22:55.536491 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000161326 (* 0.0272727 = 4.39981e-06 loss)
I0327 14:22:55.536505 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.000140795 (* 0.0272727 = 3.83988e-06 loss)
I0327 14:22:55.536520 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 5.85105e-05 (* 0.0272727 = 1.59574e-06 loss)
I0327 14:22:55.536559 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 8.79942e-05 (* 0.0272727 = 2.39984e-06 loss)
I0327 14:22:55.536576 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 3.57584e-05 (* 0.0272727 = 9.75229e-07 loss)
I0327 14:22:55.536591 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 5.06414e-05 (* 0.0272727 = 1.38113e-06 loss)
I0327 14:22:55.536605 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.000133751 (* 0.0272727 = 3.64775e-06 loss)
I0327 14:22:55.536619 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 9.03538e-05 (* 0.0272727 = 2.46419e-06 loss)
I0327 14:22:55.536631 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.375
I0327 14:22:55.536643 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 14:22:55.536655 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.25
I0327 14:22:55.536666 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.375
I0327 14:22:55.536679 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.375
I0327 14:22:55.536690 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.5
I0327 14:22:55.536702 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 14:22:55.536713 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 14:22:55.536725 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:22:55.536736 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:22:55.536748 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:22:55.536759 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:22:55.536770 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:22:55.536782 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:22:55.536793 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:22:55.536805 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:22:55.536816 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:22:55.536828 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:22:55.536839 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:22:55.536849 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:22:55.536861 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:22:55.536872 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:22:55.536885 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.81988 (* 0.0272727 = 0.049633 loss)
I0327 14:22:55.536901 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.41048 (* 0.0272727 = 0.0657403 loss)
I0327 14:22:55.536916 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.99458 (* 0.0272727 = 0.0816705 loss)
I0327 14:22:55.536931 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.12497 (* 0.0272727 = 0.0579538 loss)
I0327 14:22:55.536944 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 1.77073 (* 0.0272727 = 0.0482926 loss)
I0327 14:22:55.536957 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.78096 (* 0.0272727 = 0.0485717 loss)
I0327 14:22:55.536972 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 1.00087 (* 0.0272727 = 0.0272964 loss)
I0327 14:22:55.536985 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0429421 (* 0.0272727 = 0.00117115 loss)
I0327 14:22:55.537004 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0109232 (* 0.0272727 = 0.000297905 loss)
I0327 14:22:55.537019 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.00184778 (* 0.0272727 = 5.0394e-05 loss)
I0327 14:22:55.537034 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 1.79265e-05 (* 0.0272727 = 4.88904e-07 loss)
I0327 14:22:55.537058 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 2.80448e-05 (* 0.0272727 = 7.64857e-07 loss)
I0327 14:22:55.537075 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 1.69727e-05 (* 0.0272727 = 4.62892e-07 loss)
I0327 14:22:55.537088 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 4.46251e-05 (* 0.0272727 = 1.21705e-06 loss)
I0327 14:22:55.537102 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 2.44091e-05 (* 0.0272727 = 6.65703e-07 loss)
I0327 14:22:55.537117 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 1.31878e-05 (* 0.0272727 = 3.59666e-07 loss)
I0327 14:22:55.537130 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 2.43937e-05 (* 0.0272727 = 6.65282e-07 loss)
I0327 14:22:55.537144 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 1.68088e-05 (* 0.0272727 = 4.58421e-07 loss)
I0327 14:22:55.537158 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 2.62863e-05 (* 0.0272727 = 7.169e-07 loss)
I0327 14:22:55.537173 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 2.25758e-05 (* 0.0272727 = 6.15703e-07 loss)
I0327 14:22:55.537186 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 3.56602e-05 (* 0.0272727 = 9.7255e-07 loss)
I0327 14:22:55.537200 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 2.36341e-05 (* 0.0272727 = 6.44566e-07 loss)
I0327 14:22:55.537212 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.5
I0327 14:22:55.537225 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.25
I0327 14:22:55.537236 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 14:22:55.537248 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.375
I0327 14:22:55.537261 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.625
I0327 14:22:55.537272 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 14:22:55.537284 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.875
I0327 14:22:55.537295 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 14:22:55.537307 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:22:55.537318 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:22:55.537329 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:22:55.537340 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:22:55.537351 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:22:55.537364 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:22:55.537374 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:22:55.537385 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:22:55.537396 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:22:55.537408 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:22:55.537420 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:22:55.537431 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:22:55.537442 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:22:55.537453 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:22:55.537467 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.47327 (* 0.0909091 = 0.133934 loss)
I0327 14:22:55.537480 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 1.96023 (* 0.0909091 = 0.178202 loss)
I0327 14:22:55.537494 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.82474 (* 0.0909091 = 0.256795 loss)
I0327 14:22:55.537508 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 1.91691 (* 0.0909091 = 0.174265 loss)
I0327 14:22:55.537523 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 1.61793 (* 0.0909091 = 0.147084 loss)
I0327 14:22:55.537564 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 1.9178 (* 0.0909091 = 0.174345 loss)
I0327 14:22:55.537580 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.727998 (* 0.0909091 = 0.0661817 loss)
I0327 14:22:55.537595 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0469809 (* 0.0909091 = 0.00427099 loss)
I0327 14:22:55.537608 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00997504 (* 0.0909091 = 0.000906822 loss)
I0327 14:22:55.537622 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00248677 (* 0.0909091 = 0.00022607 loss)
I0327 14:22:55.537636 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 6.46854e-05 (* 0.0909091 = 5.88049e-06 loss)
I0327 14:22:55.537652 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 8.30316e-05 (* 0.0909091 = 7.54832e-06 loss)
I0327 14:22:55.537665 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 9.25633e-05 (* 0.0909091 = 8.41484e-06 loss)
I0327 14:22:55.537679 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 6.39758e-05 (* 0.0909091 = 5.81598e-06 loss)
I0327 14:22:55.537693 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 6.31942e-05 (* 0.0909091 = 5.74493e-06 loss)
I0327 14:22:55.537708 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 7.51994e-05 (* 0.0909091 = 6.83631e-06 loss)
I0327 14:22:55.537722 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 5.42068e-05 (* 0.0909091 = 4.92789e-06 loss)
I0327 14:22:55.537736 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 6.08163e-05 (* 0.0909091 = 5.52875e-06 loss)
I0327 14:22:55.537750 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 4.56896e-05 (* 0.0909091 = 4.1536e-06 loss)
I0327 14:22:55.537765 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 8.01786e-05 (* 0.0909091 = 7.28897e-06 loss)
I0327 14:22:55.537778 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 6.63531e-05 (* 0.0909091 = 6.0321e-06 loss)
I0327 14:22:55.537792 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 7.32237e-05 (* 0.0909091 = 6.6567e-06 loss)
I0327 14:22:55.537804 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:22:55.537817 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00302173
I0327 14:22:55.537829 21344 sgd_solver.cpp:106] Iteration 26000, lr = 0.01
I0327 14:24:43.894778 21344 solver.cpp:229] Iteration 26500, loss = 2.43568
I0327 14:24:43.894947 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.75
I0327 14:24:43.894968 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.125
I0327 14:24:43.894981 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.375
I0327 14:24:43.894996 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0
I0327 14:24:43.895009 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 14:24:43.895020 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.75
I0327 14:24:43.895032 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 14:24:43.895045 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 14:24:43.895056 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:24:43.895068 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:24:43.895081 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:24:43.895093 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:24:43.895104 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:24:43.895117 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:24:43.895128 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:24:43.895139 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:24:43.895150 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:24:43.895162 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:24:43.895174 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:24:43.895185 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:24:43.895200 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:24:43.895222 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:24:43.895254 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.07641 (* 0.0272727 = 0.0293566 loss)
I0327 14:24:43.895280 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.82082 (* 0.0272727 = 0.0769315 loss)
I0327 14:24:43.895304 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 2.04991 (* 0.0272727 = 0.0559066 loss)
I0327 14:24:43.895330 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.80167 (* 0.0272727 = 0.0764091 loss)
I0327 14:24:43.895359 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.66835 (* 0.0272727 = 0.0727731 loss)
I0327 14:24:43.895385 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.4389 (* 0.0272727 = 0.0392426 loss)
I0327 14:24:43.895400 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.796142 (* 0.0272727 = 0.021713 loss)
I0327 14:24:43.895414 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0245291 (* 0.0272727 = 0.000668975 loss)
I0327 14:24:43.895428 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.00524537 (* 0.0272727 = 0.000143056 loss)
I0327 14:24:43.895442 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00164224 (* 0.0272727 = 4.47882e-05 loss)
I0327 14:24:43.895457 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.000108553 (* 0.0272727 = 2.96053e-06 loss)
I0327 14:24:43.895472 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.000155954 (* 0.0272727 = 4.25328e-06 loss)
I0327 14:24:43.895486 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000268524 (* 0.0272727 = 7.32338e-06 loss)
I0327 14:24:43.895500 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 8.76778e-05 (* 0.0272727 = 2.39121e-06 loss)
I0327 14:24:43.895515 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 9.86208e-05 (* 0.0272727 = 2.68966e-06 loss)
I0327 14:24:43.895529 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 9.01204e-05 (* 0.0272727 = 2.45783e-06 loss)
I0327 14:24:43.895545 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.000108241 (* 0.0272727 = 2.95204e-06 loss)
I0327 14:24:43.895573 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.000124647 (* 0.0272727 = 3.39947e-06 loss)
I0327 14:24:43.895588 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 4.43424e-05 (* 0.0272727 = 1.20934e-06 loss)
I0327 14:24:43.895602 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.000276329 (* 0.0272727 = 7.53624e-06 loss)
I0327 14:24:43.895617 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 9.99004e-05 (* 0.0272727 = 2.72456e-06 loss)
I0327 14:24:43.895632 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 3.74627e-05 (* 0.0272727 = 1.02171e-06 loss)
I0327 14:24:43.895643 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.75
I0327 14:24:43.895656 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.25
I0327 14:24:43.895668 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.125
I0327 14:24:43.895679 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.125
I0327 14:24:43.895691 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.125
I0327 14:24:43.895704 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.5
I0327 14:24:43.895715 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 14:24:43.895727 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 14:24:43.895740 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:24:43.895756 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:24:43.895776 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:24:43.895788 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:24:43.895800 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:24:43.895812 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:24:43.895823 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:24:43.895834 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:24:43.895846 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:24:43.895858 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:24:43.895869 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:24:43.895880 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:24:43.895891 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:24:43.895903 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:24:43.895917 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.02118 (* 0.0272727 = 0.0278503 loss)
I0327 14:24:43.895931 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.53361 (* 0.0272727 = 0.0690985 loss)
I0327 14:24:43.895946 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.68621 (* 0.0272727 = 0.0732603 loss)
I0327 14:24:43.895963 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.51717 (* 0.0272727 = 0.0686501 loss)
I0327 14:24:43.895977 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.86953 (* 0.0272727 = 0.0782599 loss)
I0327 14:24:43.895992 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 1.30891 (* 0.0272727 = 0.0356975 loss)
I0327 14:24:43.896005 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.739244 (* 0.0272727 = 0.0201612 loss)
I0327 14:24:43.896019 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0372046 (* 0.0272727 = 0.00101467 loss)
I0327 14:24:43.896034 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.00422677 (* 0.0272727 = 0.000115276 loss)
I0327 14:24:43.896050 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.000746251 (* 0.0272727 = 2.03523e-05 loss)
I0327 14:24:43.896065 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 5.17943e-05 (* 0.0272727 = 1.41257e-06 loss)
I0327 14:24:43.896092 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 5.45595e-05 (* 0.0272727 = 1.48799e-06 loss)
I0327 14:24:43.896107 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 2.31429e-05 (* 0.0272727 = 6.31169e-07 loss)
I0327 14:24:43.896121 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 2.20402e-05 (* 0.0272727 = 6.01096e-07 loss)
I0327 14:24:43.896136 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 1.86567e-05 (* 0.0272727 = 5.08819e-07 loss)
I0327 14:24:43.896150 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 6.53761e-05 (* 0.0272727 = 1.78299e-06 loss)
I0327 14:24:43.896164 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 5.27628e-05 (* 0.0272727 = 1.43899e-06 loss)
I0327 14:24:43.896178 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 3.9498e-05 (* 0.0272727 = 1.07722e-06 loss)
I0327 14:24:43.896191 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 2.32165e-05 (* 0.0272727 = 6.33178e-07 loss)
I0327 14:24:43.896206 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 4.96184e-05 (* 0.0272727 = 1.35323e-06 loss)
I0327 14:24:43.896219 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 9.3758e-05 (* 0.0272727 = 2.55704e-06 loss)
I0327 14:24:43.896234 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 2.58844e-05 (* 0.0272727 = 7.05938e-07 loss)
I0327 14:24:43.896245 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.875
I0327 14:24:43.896258 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.5
I0327 14:24:43.896270 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.625
I0327 14:24:43.896281 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.375
I0327 14:24:43.896293 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 14:24:43.896304 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.875
I0327 14:24:43.896317 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 14:24:43.896327 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 14:24:43.896339 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:24:43.896350 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:24:43.896361 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:24:43.896373 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:24:43.896384 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:24:43.896396 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:24:43.896407 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:24:43.896419 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:24:43.896430 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:24:43.896441 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:24:43.896452 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:24:43.896463 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:24:43.896476 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:24:43.896487 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:24:43.896500 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 0.703618 (* 0.0909091 = 0.0639653 loss)
I0327 14:24:43.896514 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 1.92067 (* 0.0909091 = 0.174606 loss)
I0327 14:24:43.896529 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 1.74485 (* 0.0909091 = 0.158623 loss)
I0327 14:24:43.896543 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.2212 (* 0.0909091 = 0.201927 loss)
I0327 14:24:43.896556 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.46223 (* 0.0909091 = 0.223839 loss)
I0327 14:24:43.896582 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 0.870109 (* 0.0909091 = 0.0791008 loss)
I0327 14:24:43.896597 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.722505 (* 0.0909091 = 0.0656823 loss)
I0327 14:24:43.896611 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0246521 (* 0.0909091 = 0.0022411 loss)
I0327 14:24:43.896625 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00510663 (* 0.0909091 = 0.000464239 loss)
I0327 14:24:43.896639 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00133359 (* 0.0909091 = 0.000121236 loss)
I0327 14:24:43.896654 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 1.32027e-05 (* 0.0909091 = 1.20024e-06 loss)
I0327 14:24:43.896668 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 1.59297e-05 (* 0.0909091 = 1.44816e-06 loss)
I0327 14:24:43.896682 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 1.60937e-05 (* 0.0909091 = 1.46306e-06 loss)
I0327 14:24:43.896697 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 1.68985e-05 (* 0.0909091 = 1.53623e-06 loss)
I0327 14:24:43.896709 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 1.93723e-05 (* 0.0909091 = 1.76112e-06 loss)
I0327 14:24:43.896723 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 1.99386e-05 (* 0.0909091 = 1.8126e-06 loss)
I0327 14:24:43.896738 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 1.42161e-05 (* 0.0909091 = 1.29237e-06 loss)
I0327 14:24:43.896751 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 1.41565e-05 (* 0.0909091 = 1.28695e-06 loss)
I0327 14:24:43.896765 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 1.50058e-05 (* 0.0909091 = 1.36417e-06 loss)
I0327 14:24:43.896780 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 1.30835e-05 (* 0.0909091 = 1.18941e-06 loss)
I0327 14:24:43.896792 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 1.50207e-05 (* 0.0909091 = 1.36552e-06 loss)
I0327 14:24:43.896806 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 1.2696e-05 (* 0.0909091 = 1.15418e-06 loss)
I0327 14:24:43.896818 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:24:43.896831 21344 solver.cpp:245] Train net output #133: total_confidence = 0.0200405
I0327 14:24:43.896843 21344 sgd_solver.cpp:106] Iteration 26500, lr = 0.01
I0327 14:26:32.150620 21344 solver.cpp:229] Iteration 27000, loss = 2.37812
I0327 14:26:32.150782 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.375
I0327 14:26:32.150804 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0
I0327 14:26:32.150816 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.25
I0327 14:26:32.150830 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 14:26:32.150841 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.125
I0327 14:26:32.150853 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.25
I0327 14:26:32.150864 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.75
I0327 14:26:32.150876 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 14:26:32.150889 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:26:32.150902 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:26:32.150913 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:26:32.150925 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:26:32.150936 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:26:32.150949 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:26:32.150960 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:26:32.150972 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:26:32.150984 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:26:32.150997 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:26:32.151010 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:26:32.151021 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:26:32.151033 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:26:32.151046 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:26:32.151062 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 2.91315 (* 0.0272727 = 0.0794495 loss)
I0327 14:26:32.151077 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 3.39223 (* 0.0272727 = 0.0925154 loss)
I0327 14:26:32.151092 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.29132 (* 0.0272727 = 0.0897633 loss)
I0327 14:26:32.151105 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 2.58661 (* 0.0272727 = 0.070544 loss)
I0327 14:26:32.151119 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.61416 (* 0.0272727 = 0.0712952 loss)
I0327 14:26:32.151134 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.50498 (* 0.0272727 = 0.0683177 loss)
I0327 14:26:32.151147 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 1.0322 (* 0.0272727 = 0.0281508 loss)
I0327 14:26:32.151161 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.471682 (* 0.0272727 = 0.0128641 loss)
I0327 14:26:32.151176 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.0375955 (* 0.0272727 = 0.00102533 loss)
I0327 14:26:32.151190 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.0164111 (* 0.0272727 = 0.000447575 loss)
I0327 14:26:32.151206 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 0.00121762 (* 0.0272727 = 3.32078e-05 loss)
I0327 14:26:32.151219 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 0.00110503 (* 0.0272727 = 3.01371e-05 loss)
I0327 14:26:32.151233 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.00213708 (* 0.0272727 = 5.82841e-05 loss)
I0327 14:26:32.151247 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 0.00479503 (* 0.0272727 = 0.000130773 loss)
I0327 14:26:32.151262 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 0.000890223 (* 0.0272727 = 2.42788e-05 loss)
I0327 14:26:32.151276 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 0.00140841 (* 0.0272727 = 3.84111e-05 loss)
I0327 14:26:32.151290 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 0.00131372 (* 0.0272727 = 3.58288e-05 loss)
I0327 14:26:32.151324 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 0.00259844 (* 0.0272727 = 7.08665e-05 loss)
I0327 14:26:32.151340 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 0.000969239 (* 0.0272727 = 2.64338e-05 loss)
I0327 14:26:32.151355 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 0.00153254 (* 0.0272727 = 4.17965e-05 loss)
I0327 14:26:32.151372 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 0.00239432 (* 0.0272727 = 6.52998e-05 loss)
I0327 14:26:32.151399 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 0.00114633 (* 0.0272727 = 3.12636e-05 loss)
I0327 14:26:32.151423 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.25
I0327 14:26:32.151439 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0.125
I0327 14:26:32.151451 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0.25
I0327 14:26:32.151463 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.375
I0327 14:26:32.151475 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.375
I0327 14:26:32.151489 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.375
I0327 14:26:32.151500 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.75
I0327 14:26:32.151512 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 0.875
I0327 14:26:32.151525 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:26:32.151536 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:26:32.151548 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:26:32.151561 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:26:32.151572 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:26:32.151583 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:26:32.151595 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:26:32.151607 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:26:32.151618 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:26:32.151630 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:26:32.151641 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:26:32.151654 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:26:32.151665 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:26:32.151677 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:26:32.151690 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 2.41005 (* 0.0272727 = 0.0657287 loss)
I0327 14:26:32.151705 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.72476 (* 0.0272727 = 0.0743115 loss)
I0327 14:26:32.151720 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 2.81157 (* 0.0272727 = 0.0766792 loss)
I0327 14:26:32.151733 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.40589 (* 0.0272727 = 0.0656151 loss)
I0327 14:26:32.151747 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 2.54245 (* 0.0272727 = 0.0693395 loss)
I0327 14:26:32.151762 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.23012 (* 0.0272727 = 0.0608215 loss)
I0327 14:26:32.151775 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.718382 (* 0.0272727 = 0.0195922 loss)
I0327 14:26:32.151793 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.646072 (* 0.0272727 = 0.0176201 loss)
I0327 14:26:32.151808 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.00526265 (* 0.0272727 = 0.000143527 loss)
I0327 14:26:32.151823 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0022028 (* 0.0272727 = 6.00764e-05 loss)
I0327 14:26:32.151837 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 5.21865e-05 (* 0.0272727 = 1.42327e-06 loss)
I0327 14:26:32.151865 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000110139 (* 0.0272727 = 3.00379e-06 loss)
I0327 14:26:32.151880 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 3.54511e-05 (* 0.0272727 = 9.66847e-07 loss)
I0327 14:26:32.151895 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 9.05414e-05 (* 0.0272727 = 2.46931e-06 loss)
I0327 14:26:32.151909 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000106585 (* 0.0272727 = 2.90686e-06 loss)
I0327 14:26:32.151924 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 7.92087e-05 (* 0.0272727 = 2.16024e-06 loss)
I0327 14:26:32.151938 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000100341 (* 0.0272727 = 2.73657e-06 loss)
I0327 14:26:32.151953 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 7.37987e-05 (* 0.0272727 = 2.01269e-06 loss)
I0327 14:26:32.151968 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 8.24549e-05 (* 0.0272727 = 2.24877e-06 loss)
I0327 14:26:32.151983 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 4.24106e-05 (* 0.0272727 = 1.15665e-06 loss)
I0327 14:26:32.151996 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 6.81319e-05 (* 0.0272727 = 1.85814e-06 loss)
I0327 14:26:32.152011 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000100602 (* 0.0272727 = 2.74368e-06 loss)
I0327 14:26:32.152024 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.5
I0327 14:26:32.152036 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.375
I0327 14:26:32.152052 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0.125
I0327 14:26:32.152065 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.25
I0327 14:26:32.152076 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.375
I0327 14:26:32.152088 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.25
I0327 14:26:32.152101 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.75
I0327 14:26:32.152112 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 0.875
I0327 14:26:32.152123 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:26:32.152135 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:26:32.152146 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:26:32.152158 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:26:32.152169 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:26:32.152181 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:26:32.152192 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:26:32.152204 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:26:32.152216 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:26:32.152227 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:26:32.152240 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:26:32.152251 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:26:32.152262 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:26:32.152273 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:26:32.152288 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 2.07525 (* 0.0909091 = 0.188659 loss)
I0327 14:26:32.152302 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 2.12949 (* 0.0909091 = 0.19359 loss)
I0327 14:26:32.152317 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.64103 (* 0.0909091 = 0.240094 loss)
I0327 14:26:32.152330 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.72284 (* 0.0909091 = 0.247531 loss)
I0327 14:26:32.152344 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.16777 (* 0.0909091 = 0.19707 loss)
I0327 14:26:32.152372 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.15725 (* 0.0909091 = 0.196114 loss)
I0327 14:26:32.152389 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.704467 (* 0.0909091 = 0.0640424 loss)
I0327 14:26:32.152402 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.54251 (* 0.0909091 = 0.0493191 loss)
I0327 14:26:32.152416 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00783952 (* 0.0909091 = 0.000712684 loss)
I0327 14:26:32.152431 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.00269319 (* 0.0909091 = 0.000244835 loss)
I0327 14:26:32.152446 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 8.86359e-05 (* 0.0909091 = 8.05781e-06 loss)
I0327 14:26:32.152459 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 9.8508e-05 (* 0.0909091 = 8.95527e-06 loss)
I0327 14:26:32.152473 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000143441 (* 0.0909091 = 1.30401e-05 loss)
I0327 14:26:32.152487 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 7.4948e-05 (* 0.0909091 = 6.81345e-06 loss)
I0327 14:26:32.152501 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000104151 (* 0.0909091 = 9.46829e-06 loss)
I0327 14:26:32.152516 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 8.13671e-05 (* 0.0909091 = 7.39701e-06 loss)
I0327 14:26:32.152529 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 8.74438e-05 (* 0.0909091 = 7.94944e-06 loss)
I0327 14:26:32.152544 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 0.000118908 (* 0.0909091 = 1.08098e-05 loss)
I0327 14:26:32.152559 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 8.34536e-05 (* 0.0909091 = 7.58669e-06 loss)
I0327 14:26:32.152572 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 9.60387e-05 (* 0.0909091 = 8.73079e-06 loss)
I0327 14:26:32.152586 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 9.57704e-05 (* 0.0909091 = 8.7064e-06 loss)
I0327 14:26:32.152601 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 9.09616e-05 (* 0.0909091 = 8.26924e-06 loss)
I0327 14:26:32.152613 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:26:32.152624 21344 solver.cpp:245] Train net output #133: total_confidence = 0.00253394
I0327 14:26:32.152637 21344 sgd_solver.cpp:106] Iteration 27000, lr = 0.01
I0327 14:28:20.411342 21344 solver.cpp:229] Iteration 27500, loss = 2.37042
I0327 14:28:20.411525 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.5
I0327 14:28:20.411545 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.375
I0327 14:28:20.411558 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0
I0327 14:28:20.411571 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 14:28:20.411583 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.375
I0327 14:28:20.411595 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.375
I0327 14:28:20.411607 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 14:28:20.411619 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 1
I0327 14:28:20.411631 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:28:20.411643 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:28:20.411655 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:28:20.411667 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:28:20.411679 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:28:20.411690 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:28:20.411701 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:28:20.411713 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:28:20.411725 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:28:20.411736 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:28:20.411748 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:28:20.411759 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:28:20.411772 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:28:20.411782 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:28:20.411798 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.69631 (* 0.0272727 = 0.0462631 loss)
I0327 14:28:20.411813 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.17176 (* 0.0272727 = 0.0592298 loss)
I0327 14:28:20.411828 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.02526 (* 0.0272727 = 0.082507 loss)
I0327 14:28:20.411842 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.29563 (* 0.0272727 = 0.0898809 loss)
I0327 14:28:20.411855 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 2.08237 (* 0.0272727 = 0.0567919 loss)
I0327 14:28:20.411870 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 2.32821 (* 0.0272727 = 0.0634967 loss)
I0327 14:28:20.411883 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.507427 (* 0.0272727 = 0.0138389 loss)
I0327 14:28:20.411898 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.0300475 (* 0.0272727 = 0.000819478 loss)
I0327 14:28:20.411912 21344 solver.cpp:245] Train net output #30: loss1/loss09 = 0.00848167 (* 0.0272727 = 0.000231318 loss)
I0327 14:28:20.411926 21344 solver.cpp:245] Train net output #31: loss1/loss10 = 0.00174422 (* 0.0272727 = 4.75697e-05 loss)
I0327 14:28:20.411942 21344 solver.cpp:245] Train net output #32: loss1/loss11 = 2.17414e-05 (* 0.0272727 = 5.92947e-07 loss)
I0327 14:28:20.411955 21344 solver.cpp:245] Train net output #33: loss1/loss12 = 9.15868e-05 (* 0.0272727 = 2.49782e-06 loss)
I0327 14:28:20.411969 21344 solver.cpp:245] Train net output #34: loss1/loss13 = 0.000101423 (* 0.0272727 = 2.76608e-06 loss)
I0327 14:28:20.411983 21344 solver.cpp:245] Train net output #35: loss1/loss14 = 3.9812e-05 (* 0.0272727 = 1.08578e-06 loss)
I0327 14:28:20.412001 21344 solver.cpp:245] Train net output #36: loss1/loss15 = 4.52582e-05 (* 0.0272727 = 1.23431e-06 loss)
I0327 14:28:20.412016 21344 solver.cpp:245] Train net output #37: loss1/loss16 = 6.06397e-05 (* 0.0272727 = 1.65381e-06 loss)
I0327 14:28:20.412030 21344 solver.cpp:245] Train net output #38: loss1/loss17 = 4.36131e-05 (* 0.0272727 = 1.18945e-06 loss)
I0327 14:28:20.412061 21344 solver.cpp:245] Train net output #39: loss1/loss18 = 4.7934e-05 (* 0.0272727 = 1.30729e-06 loss)
I0327 14:28:20.412078 21344 solver.cpp:245] Train net output #40: loss1/loss19 = 4.5519e-05 (* 0.0272727 = 1.24143e-06 loss)
I0327 14:28:20.412092 21344 solver.cpp:245] Train net output #41: loss1/loss20 = 3.727e-05 (* 0.0272727 = 1.01645e-06 loss)
I0327 14:28:20.412107 21344 solver.cpp:245] Train net output #42: loss1/loss21 = 7.43455e-05 (* 0.0272727 = 2.02761e-06 loss)
I0327 14:28:20.412122 21344 solver.cpp:245] Train net output #43: loss1/loss22 = 5.9186e-05 (* 0.0272727 = 1.61416e-06 loss)
I0327 14:28:20.412133 21344 solver.cpp:245] Train net output #44: loss2/accuracy01 = 0.5
I0327 14:28:20.412147 21344 solver.cpp:245] Train net output #45: loss2/accuracy02 = 0
I0327 14:28:20.412158 21344 solver.cpp:245] Train net output #46: loss2/accuracy03 = 0
I0327 14:28:20.412169 21344 solver.cpp:245] Train net output #47: loss2/accuracy04 = 0.375
I0327 14:28:20.412181 21344 solver.cpp:245] Train net output #48: loss2/accuracy05 = 0.5
I0327 14:28:20.412194 21344 solver.cpp:245] Train net output #49: loss2/accuracy06 = 0.25
I0327 14:28:20.412205 21344 solver.cpp:245] Train net output #50: loss2/accuracy07 = 0.875
I0327 14:28:20.412217 21344 solver.cpp:245] Train net output #51: loss2/accuracy08 = 1
I0327 14:28:20.412230 21344 solver.cpp:245] Train net output #52: loss2/accuracy09 = 1
I0327 14:28:20.412240 21344 solver.cpp:245] Train net output #53: loss2/accuracy10 = 1
I0327 14:28:20.412251 21344 solver.cpp:245] Train net output #54: loss2/accuracy11 = 1
I0327 14:28:20.412263 21344 solver.cpp:245] Train net output #55: loss2/accuracy12 = 1
I0327 14:28:20.412276 21344 solver.cpp:245] Train net output #56: loss2/accuracy13 = 1
I0327 14:28:20.412286 21344 solver.cpp:245] Train net output #57: loss2/accuracy14 = 1
I0327 14:28:20.412298 21344 solver.cpp:245] Train net output #58: loss2/accuracy15 = 1
I0327 14:28:20.412310 21344 solver.cpp:245] Train net output #59: loss2/accuracy16 = 1
I0327 14:28:20.412322 21344 solver.cpp:245] Train net output #60: loss2/accuracy17 = 1
I0327 14:28:20.412333 21344 solver.cpp:245] Train net output #61: loss2/accuracy18 = 1
I0327 14:28:20.412344 21344 solver.cpp:245] Train net output #62: loss2/accuracy19 = 1
I0327 14:28:20.412355 21344 solver.cpp:245] Train net output #63: loss2/accuracy20 = 1
I0327 14:28:20.412367 21344 solver.cpp:245] Train net output #64: loss2/accuracy21 = 1
I0327 14:28:20.412379 21344 solver.cpp:245] Train net output #65: loss2/accuracy22 = 1
I0327 14:28:20.412392 21344 solver.cpp:245] Train net output #66: loss2/loss01 = 1.56412 (* 0.0272727 = 0.0426578 loss)
I0327 14:28:20.412406 21344 solver.cpp:245] Train net output #67: loss2/loss02 = 2.41752 (* 0.0272727 = 0.0659325 loss)
I0327 14:28:20.412420 21344 solver.cpp:245] Train net output #68: loss2/loss03 = 3.05172 (* 0.0272727 = 0.0832288 loss)
I0327 14:28:20.412434 21344 solver.cpp:245] Train net output #69: loss2/loss04 = 2.72753 (* 0.0272727 = 0.0743873 loss)
I0327 14:28:20.412448 21344 solver.cpp:245] Train net output #70: loss2/loss05 = 1.86125 (* 0.0272727 = 0.0507613 loss)
I0327 14:28:20.412462 21344 solver.cpp:245] Train net output #71: loss2/loss06 = 2.31529 (* 0.0272727 = 0.0631443 loss)
I0327 14:28:20.412477 21344 solver.cpp:245] Train net output #72: loss2/loss07 = 0.555671 (* 0.0272727 = 0.0151547 loss)
I0327 14:28:20.412490 21344 solver.cpp:245] Train net output #73: loss2/loss08 = 0.0631192 (* 0.0272727 = 0.00172143 loss)
I0327 14:28:20.412505 21344 solver.cpp:245] Train net output #74: loss2/loss09 = 0.0153546 (* 0.0272727 = 0.000418762 loss)
I0327 14:28:20.412519 21344 solver.cpp:245] Train net output #75: loss2/loss10 = 0.0057204 (* 0.0272727 = 0.000156011 loss)
I0327 14:28:20.412538 21344 solver.cpp:245] Train net output #76: loss2/loss11 = 0.000280285 (* 0.0272727 = 7.64413e-06 loss)
I0327 14:28:20.412564 21344 solver.cpp:245] Train net output #77: loss2/loss12 = 0.000352547 (* 0.0272727 = 9.61492e-06 loss)
I0327 14:28:20.412580 21344 solver.cpp:245] Train net output #78: loss2/loss13 = 0.000313081 (* 0.0272727 = 8.53856e-06 loss)
I0327 14:28:20.412595 21344 solver.cpp:245] Train net output #79: loss2/loss14 = 0.000142332 (* 0.0272727 = 3.88178e-06 loss)
I0327 14:28:20.412608 21344 solver.cpp:245] Train net output #80: loss2/loss15 = 0.000527631 (* 0.0272727 = 1.43899e-05 loss)
I0327 14:28:20.412622 21344 solver.cpp:245] Train net output #81: loss2/loss16 = 0.000137165 (* 0.0272727 = 3.74087e-06 loss)
I0327 14:28:20.412636 21344 solver.cpp:245] Train net output #82: loss2/loss17 = 0.000462162 (* 0.0272727 = 1.26044e-05 loss)
I0327 14:28:20.412649 21344 solver.cpp:245] Train net output #83: loss2/loss18 = 0.000511235 (* 0.0272727 = 1.39428e-05 loss)
I0327 14:28:20.412664 21344 solver.cpp:245] Train net output #84: loss2/loss19 = 0.000206628 (* 0.0272727 = 5.6353e-06 loss)
I0327 14:28:20.412678 21344 solver.cpp:245] Train net output #85: loss2/loss20 = 0.000240644 (* 0.0272727 = 6.56302e-06 loss)
I0327 14:28:20.412693 21344 solver.cpp:245] Train net output #86: loss2/loss21 = 0.000325434 (* 0.0272727 = 8.87547e-06 loss)
I0327 14:28:20.412706 21344 solver.cpp:245] Train net output #87: loss2/loss22 = 0.000318537 (* 0.0272727 = 8.68736e-06 loss)
I0327 14:28:20.412719 21344 solver.cpp:245] Train net output #88: loss3/accuracy01 = 0.75
I0327 14:28:20.412730 21344 solver.cpp:245] Train net output #89: loss3/accuracy02 = 0.375
I0327 14:28:20.412742 21344 solver.cpp:245] Train net output #90: loss3/accuracy03 = 0
I0327 14:28:20.412753 21344 solver.cpp:245] Train net output #91: loss3/accuracy04 = 0.5
I0327 14:28:20.412765 21344 solver.cpp:245] Train net output #92: loss3/accuracy05 = 0.25
I0327 14:28:20.412777 21344 solver.cpp:245] Train net output #93: loss3/accuracy06 = 0.375
I0327 14:28:20.412788 21344 solver.cpp:245] Train net output #94: loss3/accuracy07 = 0.875
I0327 14:28:20.412801 21344 solver.cpp:245] Train net output #95: loss3/accuracy08 = 1
I0327 14:28:20.412812 21344 solver.cpp:245] Train net output #96: loss3/accuracy09 = 1
I0327 14:28:20.412823 21344 solver.cpp:245] Train net output #97: loss3/accuracy10 = 1
I0327 14:28:20.412834 21344 solver.cpp:245] Train net output #98: loss3/accuracy11 = 1
I0327 14:28:20.412845 21344 solver.cpp:245] Train net output #99: loss3/accuracy12 = 1
I0327 14:28:20.412858 21344 solver.cpp:245] Train net output #100: loss3/accuracy13 = 1
I0327 14:28:20.412868 21344 solver.cpp:245] Train net output #101: loss3/accuracy14 = 1
I0327 14:28:20.412880 21344 solver.cpp:245] Train net output #102: loss3/accuracy15 = 1
I0327 14:28:20.412891 21344 solver.cpp:245] Train net output #103: loss3/accuracy16 = 1
I0327 14:28:20.412904 21344 solver.cpp:245] Train net output #104: loss3/accuracy17 = 1
I0327 14:28:20.412914 21344 solver.cpp:245] Train net output #105: loss3/accuracy18 = 1
I0327 14:28:20.412925 21344 solver.cpp:245] Train net output #106: loss3/accuracy19 = 1
I0327 14:28:20.412936 21344 solver.cpp:245] Train net output #107: loss3/accuracy20 = 1
I0327 14:28:20.412947 21344 solver.cpp:245] Train net output #108: loss3/accuracy21 = 1
I0327 14:28:20.412960 21344 solver.cpp:245] Train net output #109: loss3/accuracy22 = 1
I0327 14:28:20.412973 21344 solver.cpp:245] Train net output #110: loss3/loss01 = 1.00648 (* 0.0909091 = 0.091498 loss)
I0327 14:28:20.412987 21344 solver.cpp:245] Train net output #111: loss3/loss02 = 1.87389 (* 0.0909091 = 0.170354 loss)
I0327 14:28:20.413002 21344 solver.cpp:245] Train net output #112: loss3/loss03 = 2.68191 (* 0.0909091 = 0.24381 loss)
I0327 14:28:20.413015 21344 solver.cpp:245] Train net output #113: loss3/loss04 = 2.2445 (* 0.0909091 = 0.204045 loss)
I0327 14:28:20.413029 21344 solver.cpp:245] Train net output #114: loss3/loss05 = 2.21574 (* 0.0909091 = 0.201431 loss)
I0327 14:28:20.413058 21344 solver.cpp:245] Train net output #115: loss3/loss06 = 2.18504 (* 0.0909091 = 0.19864 loss)
I0327 14:28:20.413074 21344 solver.cpp:245] Train net output #116: loss3/loss07 = 0.38037 (* 0.0909091 = 0.0345791 loss)
I0327 14:28:20.413087 21344 solver.cpp:245] Train net output #117: loss3/loss08 = 0.0175006 (* 0.0909091 = 0.00159096 loss)
I0327 14:28:20.413102 21344 solver.cpp:245] Train net output #118: loss3/loss09 = 0.00418773 (* 0.0909091 = 0.000380703 loss)
I0327 14:28:20.413116 21344 solver.cpp:245] Train net output #119: loss3/loss10 = 0.0014082 (* 0.0909091 = 0.000128018 loss)
I0327 14:28:20.413130 21344 solver.cpp:245] Train net output #120: loss3/loss11 = 0.000126498 (* 0.0909091 = 1.14999e-05 loss)
I0327 14:28:20.413146 21344 solver.cpp:245] Train net output #121: loss3/loss12 = 0.00015199 (* 0.0909091 = 1.38173e-05 loss)
I0327 14:28:20.413159 21344 solver.cpp:245] Train net output #122: loss3/loss13 = 0.000123037 (* 0.0909091 = 1.11852e-05 loss)
I0327 14:28:20.413173 21344 solver.cpp:245] Train net output #123: loss3/loss14 = 8.98724e-05 (* 0.0909091 = 8.17022e-06 loss)
I0327 14:28:20.413183 21344 solver.cpp:245] Train net output #124: loss3/loss15 = 0.000106019 (* 0.0909091 = 9.63812e-06 loss)
I0327 14:28:20.413193 21344 solver.cpp:245] Train net output #125: loss3/loss16 = 0.000107762 (* 0.0909091 = 9.79652e-06 loss)
I0327 14:28:20.413208 21344 solver.cpp:245] Train net output #126: loss3/loss17 = 6.372e-05 (* 0.0909091 = 5.79273e-06 loss)
I0327 14:28:20.413223 21344 solver.cpp:245] Train net output #127: loss3/loss18 = 7.80476e-05 (* 0.0909091 = 7.09523e-06 loss)
I0327 14:28:20.413236 21344 solver.cpp:245] Train net output #128: loss3/loss19 = 7.51303e-05 (* 0.0909091 = 6.83003e-06 loss)
I0327 14:28:20.413250 21344 solver.cpp:245] Train net output #129: loss3/loss20 = 7.55188e-05 (* 0.0909091 = 6.86535e-06 loss)
I0327 14:28:20.413264 21344 solver.cpp:245] Train net output #130: loss3/loss21 = 8.33529e-05 (* 0.0909091 = 7.57754e-06 loss)
I0327 14:28:20.413278 21344 solver.cpp:245] Train net output #131: loss3/loss22 = 9.20666e-05 (* 0.0909091 = 8.36969e-06 loss)
I0327 14:28:20.413290 21344 solver.cpp:245] Train net output #132: total_accuracy = 0
I0327 14:28:20.413302 21344 solver.cpp:245] Train net output #133: total_confidence = 0.0328967
I0327 14:28:20.413314 21344 sgd_solver.cpp:106] Iteration 27500, lr = 0.01
I0327 14:30:08.731334 21344 solver.cpp:229] Iteration 28000, loss = 2.35233
I0327 14:30:08.731519 21344 solver.cpp:245] Train net output #0: loss1/accuracy01 = 0.375
I0327 14:30:08.731540 21344 solver.cpp:245] Train net output #1: loss1/accuracy02 = 0.25
I0327 14:30:08.731554 21344 solver.cpp:245] Train net output #2: loss1/accuracy03 = 0.125
I0327 14:30:08.731565 21344 solver.cpp:245] Train net output #3: loss1/accuracy04 = 0.25
I0327 14:30:08.731576 21344 solver.cpp:245] Train net output #4: loss1/accuracy05 = 0.375
I0327 14:30:08.731588 21344 solver.cpp:245] Train net output #5: loss1/accuracy06 = 0.625
I0327 14:30:08.731601 21344 solver.cpp:245] Train net output #6: loss1/accuracy07 = 0.875
I0327 14:30:08.731613 21344 solver.cpp:245] Train net output #7: loss1/accuracy08 = 0.875
I0327 14:30:08.731626 21344 solver.cpp:245] Train net output #8: loss1/accuracy09 = 1
I0327 14:30:08.731637 21344 solver.cpp:245] Train net output #9: loss1/accuracy10 = 1
I0327 14:30:08.731648 21344 solver.cpp:245] Train net output #10: loss1/accuracy11 = 1
I0327 14:30:08.731660 21344 solver.cpp:245] Train net output #11: loss1/accuracy12 = 1
I0327 14:30:08.731672 21344 solver.cpp:245] Train net output #12: loss1/accuracy13 = 1
I0327 14:30:08.731683 21344 solver.cpp:245] Train net output #13: loss1/accuracy14 = 1
I0327 14:30:08.731694 21344 solver.cpp:245] Train net output #14: loss1/accuracy15 = 1
I0327 14:30:08.731706 21344 solver.cpp:245] Train net output #15: loss1/accuracy16 = 1
I0327 14:30:08.731717 21344 solver.cpp:245] Train net output #16: loss1/accuracy17 = 1
I0327 14:30:08.731729 21344 solver.cpp:245] Train net output #17: loss1/accuracy18 = 1
I0327 14:30:08.731740 21344 solver.cpp:245] Train net output #18: loss1/accuracy19 = 1
I0327 14:30:08.731752 21344 solver.cpp:245] Train net output #19: loss1/accuracy20 = 1
I0327 14:30:08.731763 21344 solver.cpp:245] Train net output #20: loss1/accuracy21 = 1
I0327 14:30:08.731775 21344 solver.cpp:245] Train net output #21: loss1/accuracy22 = 1
I0327 14:30:08.731791 21344 solver.cpp:245] Train net output #22: loss1/loss01 = 1.9655 (* 0.0272727 = 0.0536045 loss)
I0327 14:30:08.731806 21344 solver.cpp:245] Train net output #23: loss1/loss02 = 2.88463 (* 0.0272727 = 0.0786718 loss)
I0327 14:30:08.731819 21344 solver.cpp:245] Train net output #24: loss1/loss03 = 3.07615 (* 0.0272727 = 0.083895 loss)
I0327 14:30:08.731833 21344 solver.cpp:245] Train net output #25: loss1/loss04 = 3.25196 (* 0.0272727 = 0.0886899 loss)
I0327 14:30:08.731848 21344 solver.cpp:245] Train net output #26: loss1/loss05 = 1.90994 (* 0.0272727 = 0.0520892 loss)
I0327 14:30:08.731861 21344 solver.cpp:245] Train net output #27: loss1/loss06 = 1.38553 (* 0.0272727 = 0.0377872 loss)
I0327 14:30:08.731875 21344 solver.cpp:245] Train net output #28: loss1/loss07 = 0.923126 (* 0.0272727 = 0.0251762 loss)
I0327 14:30:08.731889 21344 solver.cpp:245] Train net output #29: loss1/loss08 = 0.564068 (* 0.0272727 = 0.0153837 loss)
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